Design Science Research Methods - User contributions [en]

Research Methods:About

2026-04-12T13:12:36Z

MichiGau: /* Operational Editor */

<templatestyles src="Template:Styles.css" />__NOTOC__''Research Process Wiki''

Scientific methods are core for any scientific discipline. By defining a standardized set of research activities, they enable researchers to build scientific knowledge. They explain how to make valid observations, how to interpret results, and how to generalize those results. Furthermore, scientific methods allow other researchers to test pre-existing theories and prior findings independently.

In this research process wiki we are going to make research methods more accessible for colleagues, and thus further foster diversity and quality of our research. It will also allow further development of existing and emergent research methods and processes. We believe that collective improvement can lead to complete and accurate research method descriptions and also supports on one hand further development, as well as the acceptance by other researchers of methods and the underlying processes. Furthermore, this wiki will make it easier for researchers to understand and conduct various research methods in IS. This collaborative platform provides an overview of well established, state of the art, and common grounded research methods. In turn, this will safeguard the quality and improve the contribution of findings in research projects.

This site is built on user contributions. Please share your knowledge and contribute! This site is maintained as an ongoing project at the Karlsruhe Institute of Technology and the University of Liechtenstein. All textual content of Research Process Wiki is licensed under the [https://creativecommons.org/licenses/by-sa/3.0/at/ Creative Commons Attribution/Share-Alike License](CC BY-SA) and the [https://www.gnu.org/licenses/fdl-1.3.html GNU Free Documentation License](GFDL).

A short description of how to use and collaborate on the wiki can be found on the [[HowTo|how to page]].
== Editorial Board ==

{| class="wikitable" style="border: none #ffffff; background-color: #ffffff; margin-left: auto; margin-right: auto;" border="0" cellpadding="5"
|-
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|-
| style="text-align: center; width: 204.812px;" | Jan vom Brocke
| style="text-align: center; width: 253.812px;" | Alexander Maedche
|-
| style="text-align: center; width: 204.812px;" | Co-Editor
| style="text-align: center; width: 253.812px;" | Co-Editor 
|-
| style="text-align: center; width: 204.812px;" | [//uni.li University of Liechtenstein]
| style="text-align: center; width: 253.812px;" | [https://www.kit.edu/ Karlsruhe Institute of Technology]
|-
| style="text-align: center; width: 204.812px;" | 
| style="text-align: center; width: 253.812px;" |
 
|}

== Operational Editor ==

{| class="wikitable" style="border: none #ffffff; background-color: #ffffff; margin-left: auto; margin-right: auto;" border="0" cellpadding="5"
|-
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|-
| style="text-align: center;" | Michael Gau
|-
| style="text-align: center;" | Senior Scientist
|-
| style="text-align: center;" | [//uni.li University of Liechtenstein]
[https://www.kit.edu/ Karlsruhe Institute of Technology]
|-
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|}

== Acknowledgments ==
THANK YOU!! to the individuals who have contributed to pages or added new pages to this wiki and made this knowledge base even more valuable.

File:EDSR.png

2026-04-12T13:11:41Z

MichiGau: MichiGau uploaded a new version of File:EDSR.png

Focus Groups according to Tremblay et al

2024-06-01T15:32:28Z

MichiGau: /* Further Readings */

== Process description ==
[[File:Focus groups.png|thumb]]
The following activities describe the use of focus group methods to evaluate and refine design artifacts in the IS field. This method is adapted from traditional focus group techniques for use in design research projects. This method describes two types of focus groups: exploratory focus groups (EFG), which are used for the design and refinement of an artifact; and confirmatory focus groups (CFG), which are used for the confirmatory proof of an artifact’s utility in the field. The primary challenge is the structuring of focus groups so participants can collectively use an information systems artifact in order to provide feedback. This method describes one potential approach, in which participants collectively decide on an outcome both with and without the artifact, in order to compare decision-making strategies<ref name="FocusGroup">Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.</ref>.

== Formulate Research Problem ==
=== Description ===
[[File:Focus group types.png|thumb]]
In order to effectively define the content and focus groups, the research goals must be clearly identified. Design researchers seek to design an artifact, incrementally improve the design, and evaluate its utility and efficacy. These are two complementary, yet different, research goals. The figure illustrates the positioning of the two types of focus groups—exploratory and confirmatory—in the design research process. As discussed more fully in Hevner
[2007], two forms of artifact evaluation are performed in a design research project—the evaluation of the artifact to refine its design in the design science build/evaluate cycle and the field testing of the released artifact in the application environment. We discuss the similarities and differences between exploratory focus groups and confirmatory focus groups as follows<ref name="FocusGroup">Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.</ref>.

===Examples===

===Further Readings===
Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.

== Identify Sample Frame ==
=== Description ===
Three decisions are made in this step:
# Number of each type of focus group to run
# The desired number of participants in each group
# What type of participant to recruit

==== Number of focus groups ====
Deciding how many focus groups to run can prove to be quite challenging. The literature states that focus groups should continue until nothing new is learned <ref>Krueger, R.A. and M.A. Casey (2000) Focus Groups: A Practical Guide for Applied Research, 3rd edition, Thousand
Oaks, CA: Sage Publications.</ref> , yet deciding ―nothing new is being learned is a difficult and somewhat arbitrary task. This is especially challenging in design research. When conducting an EFG, the designers will find that there is always room for improvement of an artifact and certainly a fair amount of
subjectivity in interpreting when the design of an artifact is indeed complete. There is a point where we choose to satisfy in order to move forward. For CFG, the decision that enough evidence of utility has been collected is somewhat subjective. Additionally, there is a need to balance available people and resources since focus groups can be expensive both in terms of time and money (most participants receive some sort of compensation), and expert participants may be difficult to find. In our experience, at the minimum, one pilot focus group, two EFGs, and at least two CFGs should be run. The pilot is informal (one could use students) and is used to understand timing issues and any kinks in the questioning route. A design researcher should allow for at least two design cycles and enough contrast for field test analysis. Since the unit of analysis is the focus group, it would be difficult to make a compelling argument for the utility of the designed artifact with just one CFG. In the example we outline in the later part of this manuscript, we used two CFGs <ref>Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.</ref> .

=== Number of participants ===
Selecting group size has several considerations. It may seem simpler (and less expensive) to run fewer, larger focus groups since it takes fewer focus groups to hear from the same number of participants. Yet this could lower "sample size" since there are fewer groups to compare. Additionally, the dynamics of smaller versus larger groups are different; smaller groups require greater participation from each member, larger groups can lead to "social loafing"<ref>Morgan, D.L. (1988) Focus Groups as Qualitative Research. Newbury Park, CA: Sage Publications.</ref>. suggests a lower boundary of four participants and an upper boundary of twelve participants. Depending on the approach taken to demonstrate the artifact to the group (for example, whether each individual uses the artifact, versus if a moderator demonstrates it), large focus groups (more than six) could be tricky in design research since the subject matter is more complex than traditional focus group topics<ref name="FocusGroup">Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.</ref>.

=== Examples ===

=== Further Readings ===
Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.

== Recruit Participants ==
=== Description ===
The identification of focus group participants is not a random selection, but rather is based on characteristics of the participants in relation to the artifact that is being discussed. A diversity of participants will potentially produce more creative ideas (and perhaps more conflict depending on topic), but segregation of participants based on skills and knowledge may provide more in-depth tradeoffs in values and success measures. In fact, research shows that bringing together groups which are too diverse in relationship to the topic of interests could result in data of insufficient depth<ref>Bloor, M., et al. (2001) Focus Groups in Social Research, London.</ref>.

For design research, the participants should be from a population familiar with the application environment for which the artifact is designed so they can adequately inform the refinement and evaluation of the artifact. Care should be taken that the participant groups are from a similar pool for both EFGs and CFGs, so that CFGs are in fact confirming a final design. Though the authors have never attempted this, an interesting approach is to use the same
groups of participants twice, once to evaluate and once to confirm.

Research is mixed on whether to use pre-existing groups, though for design topics this may be advantageous since the participants have problem solved together and the focus group may approximate a realistic environment. Interaction among participants is one of the most important aspects of focus groups. For example, a group consisting of all technical experts may be very different than an expert/non-expert group<ref>Stewart, D.W., P.N. Shamdasani, and D.W. Rook (2007) Focus Groups: Theory and Practice, 2nd edition, vol. 20, Newbury Park, CA: Sage Publications.</ref>. A design researcher must consider membership of the focus groups and how it aligns with the research objective early in the participant selection process. For example, if the artifact is a software requirement methodology, the group membership may consist predominately of requirements analysis experts. If the artifact is a decision aid tool, a design researcher may purposely mix different skill sets: such as systems analysis, business analysis, and context experts in order to include different aspects of the aid in the conversation.

Design researchers should strive to recruit participants that are familiar with the application environment and would be potential users of the proposed artifact. Unfortunately, in many cases such individuals are not easy to find, so plenty of time and effort should be allotted for this task. For instance, it might be possible to conduct the focus group in the evening (most participants will likely work) and offer dinner. Another good approach is to conduct the focus group at a place where the potential participants work, again enticing them with lunch or breakfast. Phone calls and e-mails should be placed at least a month before the focus groups are planned. A few days before the focus groups the participants should be reminded. Researchers should plan for a few participants to not show up, so if the goal is six people, invite eight. However, care should be taken if the no-shows upset the diversity of the focus group. For example, if both medical doctors invited are no-shows, then the group may be left with no doctors.<ref name="FocusGroup">Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.</ref>

=== Examples ===

=== Further Readings ===
Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.

== Identify Moderator ==
=== Description ===
Due to the open-ended nature of focus groups, moderation can be complex, especially in social research. Several skills are important when moderating a focus group. Krueger et al. [2000] find the following skills to be highly important:
* Respect for participants, allowing all participants the opportunity to express their views
* The ability to communicate clearly, both orally and in writing
* The ability to listen and the self-discipline to control personal views
* A friendly manner and a sense of humor, and (5) the ability to involve all participants in the conversation
For design research, the moderator not only needs to have these skills, but also a clear understanding of various aspects of the designed artifact. The moderator should be familiar with the artifact and be comfortable presenting it to focus group participants.<ref name="FocusGroup">Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.</ref>

In some cases, the moderator may be one of the artifact designers. In this case, the moderator has to be very careful not to introduce any personal bias in the presentation of the artifact (we tend to be proud of our work), particularly when conducting an EFG. It may be possible to enlist a second observer to guard against the encroachment of personal views (at least during the initial groups). This is an excellent time to receive good suggestions for improvement of the design and the designer has to be receptive to criticism and suggestions given by the participants; being careful to justify or defend the design work only in appropriate ways that do not discourage discussion and feedback.<ref name="FocusGroup">Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.</ref>

=== Examples ===

=== Further Readings ===
Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.

== Develop and Pre-Test a Questioning Route ==
=== Description ===
The questioning route is the agenda for the focus group. In the questioning route you are setting the direction for a group discussion and it should closely align with your research objectives. There should be no more than twelve questions for a two-hour session. Two general principals outlined by Stewart et al. [2007, p. 61] are to order the questions from the most general to the more specific and to order the topics by the relative importance to the research agenda. Thus, the topics to be discussed are ordered by importance, and within those topics, the questions are ordered from general to specific.<ref>Stewart, D.W., P.N. Shamdasani, and D.W. Rook (2007) Focus Groups: Theory and Practice, 2nd edition, vol. 20, Newbury Park, CA: Sage Publications.</ref>

For a designed artifact, this means beginning with an explanation of the motivation behind the design of this artifact, followed by a broad explanation of different scenarios on where and how the artifact could be utilized, a description of the details of the design of the artifact, training on its use, and finishing with a task where focus group participants are asked to utilize and evaluate the artifact.

For an EFG, the "rolling interview guide" is an excellent approach. With a rolling interview guide, a script is created for the first EFG but is modified for the next EFG, based on the outcome of the previous EFG. One of the advantages of this approach is that it allows for information to unfold over time as you discover more about how people would understand and use the artifact. However, it is imperative that no revisions are made to the interview guide in the CFGs, since continuous change would make comparisons across the focus groups difficult, compromising rigorous interpretation of the results.

A promising evaluation approach in design research focus groups (both EFGs and CFGs) is to create a manipulation within the focus group. Participants can be asked to collectively complete a task without the artifact and then again with the artifact. The ensuing discussion should revolve around how the artifact was used and how the completion of the task was altered by its use.<ref name="FocusGroup">Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.</ref>

=== Examples ===

=== Further Readings ===
Stewart, D.W., P.N. Shamdasani, and D.W. Rook (2007) Focus Groups: Theory and Practice, 2nd edition, vol. 20, Newbury Park, CA: Sage Publications.

Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.

== Conduct the Focus Group ==
=== Description ===
Focus group sessions should be fun and stimulating for the participants and moderator. The moderator usually greets the participants as they enter and may ask them to fill out demographic information and informed consent forms (e.g., IRB forms). The participants are generally seated in a U-shape arrangement to encourage collaboration and allow space for the moderator to demonstrate the artifact. Seating arrangements are also very important. A good approach is to get to know the participants before the questioning route begins. Greeting them when they arrive is a good first step. The most assertive and expert participant should be seated next to the moderator and the least talkative directly across from the moderator. One potential risk is that an assertive participant could manipulate the conversation and the moderator will have to discreetly refocus the group.<ref name="FocusGroup">Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.</ref>

Depending on your research protocols, focus groups may be video and/or audio taped. Generally, the participants are told they are being recorded and most institutional review boards require written consent. It is also a good idea to have an observer. The observer will not participate in the focus group, rather will take careful notes, noting in particular any strong reactions, the participants’ facial expression and general tone of any exchange between
participants or between the participant and the moderator. Time management is also important when conducting a focus group. A moderator should be able to recognize when all possible issues for a topic have been covered and move on to the next topic. Pilot focus groups can help anticipate and manage the timing of focus groups.<ref name="FocusGroup">Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.</ref>

=== Examples ===

=== Further Readings ===
Krueger, R.A. and M.A. Casey (2000) Focus Groups: A Practical Guide for Applied Research, 3rd edition, Thousand Oaks, CA: Sage Publications.

Stewart, D.W., P.N. Shamdasani, and D.W. Rook (2007) Focus Groups: Theory and Practice, 2nd edition, vol. 20, Newbury Park, CA: Sage Publications.

Bloor, M., et al. (2001) Focus Groups in Social Research, London.

Morgan, D.L. (1988) Focus Groups as Qualitative Research. Newbury Park, CA: Sage Publications.

Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.

== Analyze and Interpret Data ==
=== Description ===
The two design research goals for using focus groups are the incremental improvement of the design of the artifact and the demonstration of the utility of the design. For this reason, we have suggested the different focus group types of EFG and CFG. While the objectives of the two group types are very different, the methods of analyzing the focus group data from both EFG and CFG can be similar. The interpretation of the focus group discussions has many of the same challenges in demonstrating rigor that all qualitative research encounters share. Several techniques that are used for qualitative data analysis can be considered, carefully selecting those techniques that emphasize the reliability and replicability of the observations and results.<ref name="FocusGroup">Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.</ref>

One possible approach is template analysis. Template analysis normally starts with at least a few predefined codes which help guide analysis. The first step in template analysis is to create an initial template by exploring the focus group transcripts, academic literature, the researchers’ own experiences, anecdotal and informal evidence, and other exploratory research. The contents of the discussions are also examined for the meanings and implications for the research questions. Analysts will look for common themes and variations within the transcripts that would provide rich descriptions of the participants’ reactions to design features.<ref name="FocusGroup">Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.</ref>
In template analysis, the initial template is applied in order to analyze the text, but is revised between each EFG
session. Once the final template is created after the final EFG, it is used to code the CFG sessions.<ref name="FocusGroup">Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.</ref>

=== Examples ===

=== Further Readings ===
Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.

== Report Results ==
=== Description ===
King suggests that qualitative results can be reported by creating an account structured around the main themes identified; drawing illustrative examples from each transcript as required<ref>King, N. (1998) "Template Analysis", in Symon, G. and C. Cassell (eds.) Qualitative Methods and Analysis in
Organizational Research, London: Sage Publications.</ref>. A similar approach can be taken when reporting focus group results. Short quotes are used to aid in the specific points of interpretation and longer passages of quotation are used to give a flavor of the original discussions. Summary tables can be very helpful,
displaying both evidence and counter-evidence of the utility of the artifact by focus group. Rich descriptions can further corroborate results by using quotes from the focus group participants.

=== Examples ===

=== Further Readings ===
Miles, M.B. and A M. Huberman (1994) Qualitative Data Analysis: An Expanded Sourcebook, 2nd edition, Thousand Oaks: Sage Publications

King, N. (1998) "Template Analysis", in Symon, G. and C. Cassell (eds.) Qualitative Methods and Analysis in Organizational Research, London: Sage Publications.

Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.

==References==

[[Category:Focus Groups Research]]

Focus Groups according to Tremblay et al

2024-06-01T15:32:15Z

MichiGau: /* Further Readings */

== Process description ==
[[File:Focus groups.png|thumb]]
The following activities describe the use of focus group methods to evaluate and refine design artifacts in the IS field. This method is adapted from traditional focus group techniques for use in design research projects. This method describes two types of focus groups: exploratory focus groups (EFG), which are used for the design and refinement of an artifact; and confirmatory focus groups (CFG), which are used for the confirmatory proof of an artifact’s utility in the field. The primary challenge is the structuring of focus groups so participants can collectively use an information systems artifact in order to provide feedback. This method describes one potential approach, in which participants collectively decide on an outcome both with and without the artifact, in order to compare decision-making strategies<ref name="FocusGroup">Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.</ref>.

== Formulate Research Problem ==
=== Description ===
[[File:Focus group types.png|thumb]]
In order to effectively define the content and focus groups, the research goals must be clearly identified. Design researchers seek to design an artifact, incrementally improve the design, and evaluate its utility and efficacy. These are two complementary, yet different, research goals. The figure illustrates the positioning of the two types of focus groups—exploratory and confirmatory—in the design research process. As discussed more fully in Hevner
[2007], two forms of artifact evaluation are performed in a design research project—the evaluation of the artifact to refine its design in the design science build/evaluate cycle and the field testing of the released artifact in the application environment. We discuss the similarities and differences between exploratory focus groups and confirmatory focus groups as follows<ref name="FocusGroup">Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.</ref>.

===Examples===

===Further Readings===
Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.

== Identify Sample Frame ==
=== Description ===
Three decisions are made in this step:
# Number of each type of focus group to run
# The desired number of participants in each group
# What type of participant to recruit

==== Number of focus groups ====
Deciding how many focus groups to run can prove to be quite challenging. The literature states that focus groups should continue until nothing new is learned <ref>Krueger, R.A. and M.A. Casey (2000) Focus Groups: A Practical Guide for Applied Research, 3rd edition, Thousand
Oaks, CA: Sage Publications.</ref> , yet deciding ―nothing new is being learned is a difficult and somewhat arbitrary task. This is especially challenging in design research. When conducting an EFG, the designers will find that there is always room for improvement of an artifact and certainly a fair amount of
subjectivity in interpreting when the design of an artifact is indeed complete. There is a point where we choose to satisfy in order to move forward. For CFG, the decision that enough evidence of utility has been collected is somewhat subjective. Additionally, there is a need to balance available people and resources since focus groups can be expensive both in terms of time and money (most participants receive some sort of compensation), and expert participants may be difficult to find. In our experience, at the minimum, one pilot focus group, two EFGs, and at least two CFGs should be run. The pilot is informal (one could use students) and is used to understand timing issues and any kinks in the questioning route. A design researcher should allow for at least two design cycles and enough contrast for field test analysis. Since the unit of analysis is the focus group, it would be difficult to make a compelling argument for the utility of the designed artifact with just one CFG. In the example we outline in the later part of this manuscript, we used two CFGs <ref>Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.</ref> .

=== Number of participants ===
Selecting group size has several considerations. It may seem simpler (and less expensive) to run fewer, larger focus groups since it takes fewer focus groups to hear from the same number of participants. Yet this could lower "sample size" since there are fewer groups to compare. Additionally, the dynamics of smaller versus larger groups are different; smaller groups require greater participation from each member, larger groups can lead to "social loafing"<ref>Morgan, D.L. (1988) Focus Groups as Qualitative Research. Newbury Park, CA: Sage Publications.</ref>. suggests a lower boundary of four participants and an upper boundary of twelve participants. Depending on the approach taken to demonstrate the artifact to the group (for example, whether each individual uses the artifact, versus if a moderator demonstrates it), large focus groups (more than six) could be tricky in design research since the subject matter is more complex than traditional focus group topics<ref name="FocusGroup">Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.</ref>.

=== Examples ===

=== Further Readings ===
Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.

== Recruit Participants ==
=== Description ===
The identification of focus group participants is not a random selection, but rather is based on characteristics of the participants in relation to the artifact that is being discussed. A diversity of participants will potentially produce more creative ideas (and perhaps more conflict depending on topic), but segregation of participants based on skills and knowledge may provide more in-depth tradeoffs in values and success measures. In fact, research shows that bringing together groups which are too diverse in relationship to the topic of interests could result in data of insufficient depth<ref>Bloor, M., et al. (2001) Focus Groups in Social Research, London.</ref>.

For design research, the participants should be from a population familiar with the application environment for which the artifact is designed so they can adequately inform the refinement and evaluation of the artifact. Care should be taken that the participant groups are from a similar pool for both EFGs and CFGs, so that CFGs are in fact confirming a final design. Though the authors have never attempted this, an interesting approach is to use the same
groups of participants twice, once to evaluate and once to confirm.

Research is mixed on whether to use pre-existing groups, though for design topics this may be advantageous since the participants have problem solved together and the focus group may approximate a realistic environment. Interaction among participants is one of the most important aspects of focus groups. For example, a group consisting of all technical experts may be very different than an expert/non-expert group<ref>Stewart, D.W., P.N. Shamdasani, and D.W. Rook (2007) Focus Groups: Theory and Practice, 2nd edition, vol. 20, Newbury Park, CA: Sage Publications.</ref>. A design researcher must consider membership of the focus groups and how it aligns with the research objective early in the participant selection process. For example, if the artifact is a software requirement methodology, the group membership may consist predominately of requirements analysis experts. If the artifact is a decision aid tool, a design researcher may purposely mix different skill sets: such as systems analysis, business analysis, and context experts in order to include different aspects of the aid in the conversation.

Design researchers should strive to recruit participants that are familiar with the application environment and would be potential users of the proposed artifact. Unfortunately, in many cases such individuals are not easy to find, so plenty of time and effort should be allotted for this task. For instance, it might be possible to conduct the focus group in the evening (most participants will likely work) and offer dinner. Another good approach is to conduct the focus group at a place where the potential participants work, again enticing them with lunch or breakfast. Phone calls and e-mails should be placed at least a month before the focus groups are planned. A few days before the focus groups the participants should be reminded. Researchers should plan for a few participants to not show up, so if the goal is six people, invite eight. However, care should be taken if the no-shows upset the diversity of the focus group. For example, if both medical doctors invited are no-shows, then the group may be left with no doctors.<ref name="FocusGroup">Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.</ref>

=== Examples ===

=== Further Readings ===
Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.

== Identify Moderator ==
=== Description ===
Due to the open-ended nature of focus groups, moderation can be complex, especially in social research. Several skills are important when moderating a focus group. Krueger et al. [2000] find the following skills to be highly important:
* Respect for participants, allowing all participants the opportunity to express their views
* The ability to communicate clearly, both orally and in writing
* The ability to listen and the self-discipline to control personal views
* A friendly manner and a sense of humor, and (5) the ability to involve all participants in the conversation
For design research, the moderator not only needs to have these skills, but also a clear understanding of various aspects of the designed artifact. The moderator should be familiar with the artifact and be comfortable presenting it to focus group participants.<ref name="FocusGroup">Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.</ref>

In some cases, the moderator may be one of the artifact designers. In this case, the moderator has to be very careful not to introduce any personal bias in the presentation of the artifact (we tend to be proud of our work), particularly when conducting an EFG. It may be possible to enlist a second observer to guard against the encroachment of personal views (at least during the initial groups). This is an excellent time to receive good suggestions for improvement of the design and the designer has to be receptive to criticism and suggestions given by the participants; being careful to justify or defend the design work only in appropriate ways that do not discourage discussion and feedback.<ref name="FocusGroup">Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.</ref>

=== Examples ===

=== Further Readings ===
Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.

== Develop and Pre-Test a Questioning Route ==
=== Description ===
The questioning route is the agenda for the focus group. In the questioning route you are setting the direction for a group discussion and it should closely align with your research objectives. There should be no more than twelve questions for a two-hour session. Two general principals outlined by Stewart et al. [2007, p. 61] are to order the questions from the most general to the more specific and to order the topics by the relative importance to the research agenda. Thus, the topics to be discussed are ordered by importance, and within those topics, the questions are ordered from general to specific.<ref>Stewart, D.W., P.N. Shamdasani, and D.W. Rook (2007) Focus Groups: Theory and Practice, 2nd edition, vol. 20, Newbury Park, CA: Sage Publications.</ref>

For a designed artifact, this means beginning with an explanation of the motivation behind the design of this artifact, followed by a broad explanation of different scenarios on where and how the artifact could be utilized, a description of the details of the design of the artifact, training on its use, and finishing with a task where focus group participants are asked to utilize and evaluate the artifact.

For an EFG, the "rolling interview guide" is an excellent approach. With a rolling interview guide, a script is created for the first EFG but is modified for the next EFG, based on the outcome of the previous EFG. One of the advantages of this approach is that it allows for information to unfold over time as you discover more about how people would understand and use the artifact. However, it is imperative that no revisions are made to the interview guide in the CFGs, since continuous change would make comparisons across the focus groups difficult, compromising rigorous interpretation of the results.

A promising evaluation approach in design research focus groups (both EFGs and CFGs) is to create a manipulation within the focus group. Participants can be asked to collectively complete a task without the artifact and then again with the artifact. The ensuing discussion should revolve around how the artifact was used and how the completion of the task was altered by its use.<ref name="FocusGroup">Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.</ref>

=== Examples ===

=== Further Readings ===
Stewart, D.W., P.N. Shamdasani, and D.W. Rook (2007) Focus Groups: Theory and Practice, 2nd edition, vol. 20, Newbury Park, CA: Sage Publications.
Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.

== Conduct the Focus Group ==
=== Description ===
Focus group sessions should be fun and stimulating for the participants and moderator. The moderator usually greets the participants as they enter and may ask them to fill out demographic information and informed consent forms (e.g., IRB forms). The participants are generally seated in a U-shape arrangement to encourage collaboration and allow space for the moderator to demonstrate the artifact. Seating arrangements are also very important. A good approach is to get to know the participants before the questioning route begins. Greeting them when they arrive is a good first step. The most assertive and expert participant should be seated next to the moderator and the least talkative directly across from the moderator. One potential risk is that an assertive participant could manipulate the conversation and the moderator will have to discreetly refocus the group.<ref name="FocusGroup">Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.</ref>

Depending on your research protocols, focus groups may be video and/or audio taped. Generally, the participants are told they are being recorded and most institutional review boards require written consent. It is also a good idea to have an observer. The observer will not participate in the focus group, rather will take careful notes, noting in particular any strong reactions, the participants’ facial expression and general tone of any exchange between
participants or between the participant and the moderator. Time management is also important when conducting a focus group. A moderator should be able to recognize when all possible issues for a topic have been covered and move on to the next topic. Pilot focus groups can help anticipate and manage the timing of focus groups.<ref name="FocusGroup">Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.</ref>

=== Examples ===

=== Further Readings ===
Krueger, R.A. and M.A. Casey (2000) Focus Groups: A Practical Guide for Applied Research, 3rd edition, Thousand Oaks, CA: Sage Publications.

Stewart, D.W., P.N. Shamdasani, and D.W. Rook (2007) Focus Groups: Theory and Practice, 2nd edition, vol. 20, Newbury Park, CA: Sage Publications.

Bloor, M., et al. (2001) Focus Groups in Social Research, London.

Morgan, D.L. (1988) Focus Groups as Qualitative Research. Newbury Park, CA: Sage Publications.

Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.

== Analyze and Interpret Data ==
=== Description ===
The two design research goals for using focus groups are the incremental improvement of the design of the artifact and the demonstration of the utility of the design. For this reason, we have suggested the different focus group types of EFG and CFG. While the objectives of the two group types are very different, the methods of analyzing the focus group data from both EFG and CFG can be similar. The interpretation of the focus group discussions has many of the same challenges in demonstrating rigor that all qualitative research encounters share. Several techniques that are used for qualitative data analysis can be considered, carefully selecting those techniques that emphasize the reliability and replicability of the observations and results.<ref name="FocusGroup">Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.</ref>

One possible approach is template analysis. Template analysis normally starts with at least a few predefined codes which help guide analysis. The first step in template analysis is to create an initial template by exploring the focus group transcripts, academic literature, the researchers’ own experiences, anecdotal and informal evidence, and other exploratory research. The contents of the discussions are also examined for the meanings and implications for the research questions. Analysts will look for common themes and variations within the transcripts that would provide rich descriptions of the participants’ reactions to design features.<ref name="FocusGroup">Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.</ref>
In template analysis, the initial template is applied in order to analyze the text, but is revised between each EFG
session. Once the final template is created after the final EFG, it is used to code the CFG sessions.<ref name="FocusGroup">Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.</ref>

=== Examples ===

=== Further Readings ===
Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.

== Report Results ==
=== Description ===
King suggests that qualitative results can be reported by creating an account structured around the main themes identified; drawing illustrative examples from each transcript as required<ref>King, N. (1998) "Template Analysis", in Symon, G. and C. Cassell (eds.) Qualitative Methods and Analysis in
Organizational Research, London: Sage Publications.</ref>. A similar approach can be taken when reporting focus group results. Short quotes are used to aid in the specific points of interpretation and longer passages of quotation are used to give a flavor of the original discussions. Summary tables can be very helpful,
displaying both evidence and counter-evidence of the utility of the artifact by focus group. Rich descriptions can further corroborate results by using quotes from the focus group participants.

=== Examples ===

=== Further Readings ===
Miles, M.B. and A M. Huberman (1994) Qualitative Data Analysis: An Expanded Sourcebook, 2nd edition, Thousand Oaks: Sage Publications

King, N. (1998) "Template Analysis", in Symon, G. and C. Cassell (eds.) Qualitative Methods and Analysis in Organizational Research, London: Sage Publications.

Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.

==References==

[[Category:Focus Groups Research]]

Focus Groups according to Tremblay et al

2024-06-01T15:31:56Z

MichiGau: /* Further Readings */

== Process description ==
[[File:Focus groups.png|thumb]]
The following activities describe the use of focus group methods to evaluate and refine design artifacts in the IS field. This method is adapted from traditional focus group techniques for use in design research projects. This method describes two types of focus groups: exploratory focus groups (EFG), which are used for the design and refinement of an artifact; and confirmatory focus groups (CFG), which are used for the confirmatory proof of an artifact’s utility in the field. The primary challenge is the structuring of focus groups so participants can collectively use an information systems artifact in order to provide feedback. This method describes one potential approach, in which participants collectively decide on an outcome both with and without the artifact, in order to compare decision-making strategies<ref name="FocusGroup">Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.</ref>.

== Formulate Research Problem ==
=== Description ===
[[File:Focus group types.png|thumb]]
In order to effectively define the content and focus groups, the research goals must be clearly identified. Design researchers seek to design an artifact, incrementally improve the design, and evaluate its utility and efficacy. These are two complementary, yet different, research goals. The figure illustrates the positioning of the two types of focus groups—exploratory and confirmatory—in the design research process. As discussed more fully in Hevner
[2007], two forms of artifact evaluation are performed in a design research project—the evaluation of the artifact to refine its design in the design science build/evaluate cycle and the field testing of the released artifact in the application environment. We discuss the similarities and differences between exploratory focus groups and confirmatory focus groups as follows<ref name="FocusGroup">Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.</ref>.

===Examples===

===Further Readings===
Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.

== Identify Sample Frame ==
=== Description ===
Three decisions are made in this step:
# Number of each type of focus group to run
# The desired number of participants in each group
# What type of participant to recruit

==== Number of focus groups ====
Deciding how many focus groups to run can prove to be quite challenging. The literature states that focus groups should continue until nothing new is learned <ref>Krueger, R.A. and M.A. Casey (2000) Focus Groups: A Practical Guide for Applied Research, 3rd edition, Thousand
Oaks, CA: Sage Publications.</ref> , yet deciding ―nothing new is being learned is a difficult and somewhat arbitrary task. This is especially challenging in design research. When conducting an EFG, the designers will find that there is always room for improvement of an artifact and certainly a fair amount of
subjectivity in interpreting when the design of an artifact is indeed complete. There is a point where we choose to satisfy in order to move forward. For CFG, the decision that enough evidence of utility has been collected is somewhat subjective. Additionally, there is a need to balance available people and resources since focus groups can be expensive both in terms of time and money (most participants receive some sort of compensation), and expert participants may be difficult to find. In our experience, at the minimum, one pilot focus group, two EFGs, and at least two CFGs should be run. The pilot is informal (one could use students) and is used to understand timing issues and any kinks in the questioning route. A design researcher should allow for at least two design cycles and enough contrast for field test analysis. Since the unit of analysis is the focus group, it would be difficult to make a compelling argument for the utility of the designed artifact with just one CFG. In the example we outline in the later part of this manuscript, we used two CFGs <ref>Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.</ref> .

=== Number of participants ===
Selecting group size has several considerations. It may seem simpler (and less expensive) to run fewer, larger focus groups since it takes fewer focus groups to hear from the same number of participants. Yet this could lower "sample size" since there are fewer groups to compare. Additionally, the dynamics of smaller versus larger groups are different; smaller groups require greater participation from each member, larger groups can lead to "social loafing"<ref>Morgan, D.L. (1988) Focus Groups as Qualitative Research. Newbury Park, CA: Sage Publications.</ref>. suggests a lower boundary of four participants and an upper boundary of twelve participants. Depending on the approach taken to demonstrate the artifact to the group (for example, whether each individual uses the artifact, versus if a moderator demonstrates it), large focus groups (more than six) could be tricky in design research since the subject matter is more complex than traditional focus group topics<ref name="FocusGroup">Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.</ref>.

=== Examples ===

=== Further Readings ===
Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.

== Recruit Participants ==
=== Description ===
The identification of focus group participants is not a random selection, but rather is based on characteristics of the participants in relation to the artifact that is being discussed. A diversity of participants will potentially produce more creative ideas (and perhaps more conflict depending on topic), but segregation of participants based on skills and knowledge may provide more in-depth tradeoffs in values and success measures. In fact, research shows that bringing together groups which are too diverse in relationship to the topic of interests could result in data of insufficient depth<ref>Bloor, M., et al. (2001) Focus Groups in Social Research, London.</ref>.

For design research, the participants should be from a population familiar with the application environment for which the artifact is designed so they can adequately inform the refinement and evaluation of the artifact. Care should be taken that the participant groups are from a similar pool for both EFGs and CFGs, so that CFGs are in fact confirming a final design. Though the authors have never attempted this, an interesting approach is to use the same
groups of participants twice, once to evaluate and once to confirm.

Research is mixed on whether to use pre-existing groups, though for design topics this may be advantageous since the participants have problem solved together and the focus group may approximate a realistic environment. Interaction among participants is one of the most important aspects of focus groups. For example, a group consisting of all technical experts may be very different than an expert/non-expert group<ref>Stewart, D.W., P.N. Shamdasani, and D.W. Rook (2007) Focus Groups: Theory and Practice, 2nd edition, vol. 20, Newbury Park, CA: Sage Publications.</ref>. A design researcher must consider membership of the focus groups and how it aligns with the research objective early in the participant selection process. For example, if the artifact is a software requirement methodology, the group membership may consist predominately of requirements analysis experts. If the artifact is a decision aid tool, a design researcher may purposely mix different skill sets: such as systems analysis, business analysis, and context experts in order to include different aspects of the aid in the conversation.

Design researchers should strive to recruit participants that are familiar with the application environment and would be potential users of the proposed artifact. Unfortunately, in many cases such individuals are not easy to find, so plenty of time and effort should be allotted for this task. For instance, it might be possible to conduct the focus group in the evening (most participants will likely work) and offer dinner. Another good approach is to conduct the focus group at a place where the potential participants work, again enticing them with lunch or breakfast. Phone calls and e-mails should be placed at least a month before the focus groups are planned. A few days before the focus groups the participants should be reminded. Researchers should plan for a few participants to not show up, so if the goal is six people, invite eight. However, care should be taken if the no-shows upset the diversity of the focus group. For example, if both medical doctors invited are no-shows, then the group may be left with no doctors.<ref name="FocusGroup">Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.</ref>

=== Examples ===

=== Further Readings ===
Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.

== Identify Moderator ==
=== Description ===
Due to the open-ended nature of focus groups, moderation can be complex, especially in social research. Several skills are important when moderating a focus group. Krueger et al. [2000] find the following skills to be highly important:
* Respect for participants, allowing all participants the opportunity to express their views
* The ability to communicate clearly, both orally and in writing
* The ability to listen and the self-discipline to control personal views
* A friendly manner and a sense of humor, and (5) the ability to involve all participants in the conversation
For design research, the moderator not only needs to have these skills, but also a clear understanding of various aspects of the designed artifact. The moderator should be familiar with the artifact and be comfortable presenting it to focus group participants.<ref name="FocusGroup">Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.</ref>

In some cases, the moderator may be one of the artifact designers. In this case, the moderator has to be very careful not to introduce any personal bias in the presentation of the artifact (we tend to be proud of our work), particularly when conducting an EFG. It may be possible to enlist a second observer to guard against the encroachment of personal views (at least during the initial groups). This is an excellent time to receive good suggestions for improvement of the design and the designer has to be receptive to criticism and suggestions given by the participants; being careful to justify or defend the design work only in appropriate ways that do not discourage discussion and feedback.<ref name="FocusGroup">Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.</ref>

=== Examples ===

=== Further Readings ===
Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.

== Develop and Pre-Test a Questioning Route ==
=== Description ===
The questioning route is the agenda for the focus group. In the questioning route you are setting the direction for a group discussion and it should closely align with your research objectives. There should be no more than twelve questions for a two-hour session. Two general principals outlined by Stewart et al. [2007, p. 61] are to order the questions from the most general to the more specific and to order the topics by the relative importance to the research agenda. Thus, the topics to be discussed are ordered by importance, and within those topics, the questions are ordered from general to specific.<ref>Stewart, D.W., P.N. Shamdasani, and D.W. Rook (2007) Focus Groups: Theory and Practice, 2nd edition, vol. 20, Newbury Park, CA: Sage Publications.</ref>

For a designed artifact, this means beginning with an explanation of the motivation behind the design of this artifact, followed by a broad explanation of different scenarios on where and how the artifact could be utilized, a description of the details of the design of the artifact, training on its use, and finishing with a task where focus group participants are asked to utilize and evaluate the artifact.

For an EFG, the "rolling interview guide" is an excellent approach. With a rolling interview guide, a script is created for the first EFG but is modified for the next EFG, based on the outcome of the previous EFG. One of the advantages of this approach is that it allows for information to unfold over time as you discover more about how people would understand and use the artifact. However, it is imperative that no revisions are made to the interview guide in the CFGs, since continuous change would make comparisons across the focus groups difficult, compromising rigorous interpretation of the results.

A promising evaluation approach in design research focus groups (both EFGs and CFGs) is to create a manipulation within the focus group. Participants can be asked to collectively complete a task without the artifact and then again with the artifact. The ensuing discussion should revolve around how the artifact was used and how the completion of the task was altered by its use.<ref name="FocusGroup">Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.</ref>

=== Examples ===

=== Further Readings ===
Stewart, D.W., P.N. Shamdasani, and D.W. Rook (2007) Focus Groups: Theory and Practice, 2nd edition, vol. 20, Newbury Park, CA: Sage Publications.
Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.

== Conduct the Focus Group ==
=== Description ===
Focus group sessions should be fun and stimulating for the participants and moderator. The moderator usually greets the participants as they enter and may ask them to fill out demographic information and informed consent forms (e.g., IRB forms). The participants are generally seated in a U-shape arrangement to encourage collaboration and allow space for the moderator to demonstrate the artifact. Seating arrangements are also very important. A good approach is to get to know the participants before the questioning route begins. Greeting them when they arrive is a good first step. The most assertive and expert participant should be seated next to the moderator and the least talkative directly across from the moderator. One potential risk is that an assertive participant could manipulate the conversation and the moderator will have to discreetly refocus the group.<ref name="FocusGroup">Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.</ref>

Depending on your research protocols, focus groups may be video and/or audio taped. Generally, the participants are told they are being recorded and most institutional review boards require written consent. It is also a good idea to have an observer. The observer will not participate in the focus group, rather will take careful notes, noting in particular any strong reactions, the participants’ facial expression and general tone of any exchange between
participants or between the participant and the moderator. Time management is also important when conducting a focus group. A moderator should be able to recognize when all possible issues for a topic have been covered and move on to the next topic. Pilot focus groups can help anticipate and manage the timing of focus groups.<ref name="FocusGroup">Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.</ref>

=== Examples ===

=== Further Readings ===
Krueger, R.A. and M.A. Casey (2000) Focus Groups: A Practical Guide for Applied Research, 3rd edition, Thousand Oaks, CA: Sage Publications.
Stewart, D.W., P.N. Shamdasani, and D.W. Rook (2007) Focus Groups: Theory and Practice, 2nd edition, vol. 20, Newbury Park, CA: Sage Publications.
Bloor, M., et al. (2001) Focus Groups in Social Research, London.
Morgan, D.L. (1988) Focus Groups as Qualitative Research. Newbury Park, CA: Sage Publications.
Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.

== Analyze and Interpret Data ==
=== Description ===
The two design research goals for using focus groups are the incremental improvement of the design of the artifact and the demonstration of the utility of the design. For this reason, we have suggested the different focus group types of EFG and CFG. While the objectives of the two group types are very different, the methods of analyzing the focus group data from both EFG and CFG can be similar. The interpretation of the focus group discussions has many of the same challenges in demonstrating rigor that all qualitative research encounters share. Several techniques that are used for qualitative data analysis can be considered, carefully selecting those techniques that emphasize the reliability and replicability of the observations and results.<ref name="FocusGroup">Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.</ref>

One possible approach is template analysis. Template analysis normally starts with at least a few predefined codes which help guide analysis. The first step in template analysis is to create an initial template by exploring the focus group transcripts, academic literature, the researchers’ own experiences, anecdotal and informal evidence, and other exploratory research. The contents of the discussions are also examined for the meanings and implications for the research questions. Analysts will look for common themes and variations within the transcripts that would provide rich descriptions of the participants’ reactions to design features.<ref name="FocusGroup">Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.</ref>
In template analysis, the initial template is applied in order to analyze the text, but is revised between each EFG
session. Once the final template is created after the final EFG, it is used to code the CFG sessions.<ref name="FocusGroup">Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.</ref>

=== Examples ===

=== Further Readings ===
Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.

== Report Results ==
=== Description ===
King suggests that qualitative results can be reported by creating an account structured around the main themes identified; drawing illustrative examples from each transcript as required<ref>King, N. (1998) "Template Analysis", in Symon, G. and C. Cassell (eds.) Qualitative Methods and Analysis in
Organizational Research, London: Sage Publications.</ref>. A similar approach can be taken when reporting focus group results. Short quotes are used to aid in the specific points of interpretation and longer passages of quotation are used to give a flavor of the original discussions. Summary tables can be very helpful,
displaying both evidence and counter-evidence of the utility of the artifact by focus group. Rich descriptions can further corroborate results by using quotes from the focus group participants.

=== Examples ===

=== Further Readings ===
Miles, M.B. and A M. Huberman (1994) Qualitative Data Analysis: An Expanded Sourcebook, 2nd edition, Thousand Oaks: Sage Publications

King, N. (1998) "Template Analysis", in Symon, G. and C. Cassell (eds.) Qualitative Methods and Analysis in Organizational Research, London: Sage Publications.

Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.

==References==

[[Category:Focus Groups Research]]

Focus Groups according to Tremblay et al

2024-06-01T15:23:39Z

MichiGau: /* Number of focus groups */

== Process description ==
[[File:Focus groups.png|thumb]]
The following activities describe the use of focus group methods to evaluate and refine design artifacts in the IS field. This method is adapted from traditional focus group techniques for use in design research projects. This method describes two types of focus groups: exploratory focus groups (EFG), which are used for the design and refinement of an artifact; and confirmatory focus groups (CFG), which are used for the confirmatory proof of an artifact’s utility in the field. The primary challenge is the structuring of focus groups so participants can collectively use an information systems artifact in order to provide feedback. This method describes one potential approach, in which participants collectively decide on an outcome both with and without the artifact, in order to compare decision-making strategies<ref name="FocusGroup">Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.</ref>.

== Formulate Research Problem ==
=== Description ===
[[File:Focus group types.png|thumb]]
In order to effectively define the content and focus groups, the research goals must be clearly identified. Design researchers seek to design an artifact, incrementally improve the design, and evaluate its utility and efficacy. These are two complementary, yet different, research goals. The figure illustrates the positioning of the two types of focus groups—exploratory and confirmatory—in the design research process. As discussed more fully in Hevner
[2007], two forms of artifact evaluation are performed in a design research project—the evaluation of the artifact to refine its design in the design science build/evaluate cycle and the field testing of the released artifact in the application environment. We discuss the similarities and differences between exploratory focus groups and confirmatory focus groups as follows<ref name="FocusGroup">Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.</ref>.

===Examples===

===Further Readings===
Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.

== Identify Sample Frame ==
=== Description ===
Three decisions are made in this step:
# Number of each type of focus group to run
# The desired number of participants in each group
# What type of participant to recruit

==== Number of focus groups ====
Deciding how many focus groups to run can prove to be quite challenging. The literature states that focus groups should continue until nothing new is learned <ref>Krueger, R.A. and M.A. Casey (2000) Focus Groups: A Practical Guide for Applied Research, 3rd edition, Thousand
Oaks, CA: Sage Publications.</ref> , yet deciding ―nothing new is being learned is a difficult and somewhat arbitrary task. This is especially challenging in design research. When conducting an EFG, the designers will find that there is always room for improvement of an artifact and certainly a fair amount of
subjectivity in interpreting when the design of an artifact is indeed complete. There is a point where we choose to satisfy in order to move forward. For CFG, the decision that enough evidence of utility has been collected is somewhat subjective. Additionally, there is a need to balance available people and resources since focus groups can be expensive both in terms of time and money (most participants receive some sort of compensation), and expert participants may be difficult to find. In our experience, at the minimum, one pilot focus group, two EFGs, and at least two CFGs should be run. The pilot is informal (one could use students) and is used to understand timing issues and any kinks in the questioning route. A design researcher should allow for at least two design cycles and enough contrast for field test analysis. Since the unit of analysis is the focus group, it would be difficult to make a compelling argument for the utility of the designed artifact with just one CFG. In the example we outline in the later part of this manuscript, we used two CFGs <ref>Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.</ref> .

=== Number of participants ===
Selecting group size has several considerations. It may seem simpler (and less expensive) to run fewer, larger focus groups since it takes fewer focus groups to hear from the same number of participants. Yet this could lower "sample size" since there are fewer groups to compare. Additionally, the dynamics of smaller versus larger groups are different; smaller groups require greater participation from each member, larger groups can lead to "social loafing"<ref>Morgan, D.L. (1988) Focus Groups as Qualitative Research. Newbury Park, CA: Sage Publications.</ref>. suggests a lower boundary of four participants and an upper boundary of twelve participants. Depending on the approach taken to demonstrate the artifact to the group (for example, whether each individual uses the artifact, versus if a moderator demonstrates it), large focus groups (more than six) could be tricky in design research since the subject matter is more complex than traditional focus group topics<ref name="FocusGroup">Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.</ref>.

=== Examples ===

=== Further Readings ===
Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.

== Recruit Participants ==
=== Description ===
The identification of focus group participants is not a random selection, but rather is based on characteristics of the participants in relation to the artifact that is being discussed. A diversity of participants will potentially produce more creative ideas (and perhaps more conflict depending on topic), but segregation of participants based on skills and knowledge may provide more in-depth tradeoffs in values and success measures. In fact, research shows that bringing together groups which are too diverse in relationship to the topic of interests could result in data of insufficient depth<ref>Bloor, M., et al. (2001) Focus Groups in Social Research, London.</ref>.

For design research, the participants should be from a population familiar with the application environment for which the artifact is designed so they can adequately inform the refinement and evaluation of the artifact. Care should be taken that the participant groups are from a similar pool for both EFGs and CFGs, so that CFGs are in fact confirming a final design. Though the authors have never attempted this, an interesting approach is to use the same
groups of participants twice, once to evaluate and once to confirm.

Research is mixed on whether to use pre-existing groups, though for design topics this may be advantageous since the participants have problem solved together and the focus group may approximate a realistic environment. Interaction among participants is one of the most important aspects of focus groups. For example, a group consisting of all technical experts may be very different than an expert/non-expert group<ref>Stewart, D.W., P.N. Shamdasani, and D.W. Rook (2007) Focus Groups: Theory and Practice, 2nd edition, vol. 20, Newbury Park, CA: Sage Publications.</ref>. A design researcher must consider membership of the focus groups and how it aligns with the research objective early in the participant selection process. For example, if the artifact is a software requirement methodology, the group membership may consist predominately of requirements analysis experts. If the artifact is a decision aid tool, a design researcher may purposely mix different skill sets: such as systems analysis, business analysis, and context experts in order to include different aspects of the aid in the conversation.

Design researchers should strive to recruit participants that are familiar with the application environment and would be potential users of the proposed artifact. Unfortunately, in many cases such individuals are not easy to find, so plenty of time and effort should be allotted for this task. For instance, it might be possible to conduct the focus group in the evening (most participants will likely work) and offer dinner. Another good approach is to conduct the focus group at a place where the potential participants work, again enticing them with lunch or breakfast. Phone calls and e-mails should be placed at least a month before the focus groups are planned. A few days before the focus groups the participants should be reminded. Researchers should plan for a few participants to not show up, so if the goal is six people, invite eight. However, care should be taken if the no-shows upset the diversity of the focus group. For example, if both medical doctors invited are no-shows, then the group may be left with no doctors.<ref name="FocusGroup">Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.</ref>

=== Examples ===

=== Further Readings ===
Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.

== Identify Moderator ==
=== Description ===
Due to the open-ended nature of focus groups, moderation can be complex, especially in social research. Several skills are important when moderating a focus group. Krueger et al. [2000] find the following skills to be highly important:
* Respect for participants, allowing all participants the opportunity to express their views
* The ability to communicate clearly, both orally and in writing
* The ability to listen and the self-discipline to control personal views
* A friendly manner and a sense of humor, and (5) the ability to involve all participants in the conversation
For design research, the moderator not only needs to have these skills, but also a clear understanding of various aspects of the designed artifact. The moderator should be familiar with the artifact and be comfortable presenting it to focus group participants.<ref name="FocusGroup">Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.</ref>

In some cases, the moderator may be one of the artifact designers. In this case, the moderator has to be very careful not to introduce any personal bias in the presentation of the artifact (we tend to be proud of our work), particularly when conducting an EFG. It may be possible to enlist a second observer to guard against the encroachment of personal views (at least during the initial groups). This is an excellent time to receive good suggestions for improvement of the design and the designer has to be receptive to criticism and suggestions given by the participants; being careful to justify or defend the design work only in appropriate ways that do not discourage discussion and feedback.<ref name="FocusGroup">Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.</ref>

=== Examples ===

=== Further Readings ===
Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.

== Develop and Pre-Test a Questioning Route ==
=== Description ===
The questioning route is the agenda for the focus group. In the questioning route you are setting the direction for a group discussion and it should closely align with your research objectives. There should be no more than twelve questions for a two-hour session. Two general principals outlined by Stewart et al. [2007, p. 61] are to order the questions from the most general to the more specific and to order the topics by the relative importance to the research agenda. Thus, the topics to be discussed are ordered by importance, and within those topics, the questions are ordered from general to specific.<ref>Stewart, D.W., P.N. Shamdasani, and D.W. Rook (2007) Focus Groups: Theory and Practice, 2nd edition, vol. 20, Newbury Park, CA: Sage Publications.</ref>

For a designed artifact, this means beginning with an explanation of the motivation behind the design of this artifact, followed by a broad explanation of different scenarios on where and how the artifact could be utilized, a description of the details of the design of the artifact, training on its use, and finishing with a task where focus group participants are asked to utilize and evaluate the artifact.

For an EFG, the "rolling interview guide" is an excellent approach. With a rolling interview guide, a script is created for the first EFG but is modified for the next EFG, based on the outcome of the previous EFG. One of the advantages of this approach is that it allows for information to unfold over time as you discover more about how people would understand and use the artifact. However, it is imperative that no revisions are made to the interview guide in the CFGs, since continuous change would make comparisons across the focus groups difficult, compromising rigorous interpretation of the results.

A promising evaluation approach in design research focus groups (both EFGs and CFGs) is to create a manipulation within the focus group. Participants can be asked to collectively complete a task without the artifact and then again with the artifact. The ensuing discussion should revolve around how the artifact was used and how the completion of the task was altered by its use.<ref name="FocusGroup">Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.</ref>

=== Examples ===

=== Further Readings ===
Stewart, D.W., P.N. Shamdasani, and D.W. Rook (2007) Focus Groups: Theory and Practice, 2nd edition, vol. 20, Newbury Park, CA: Sage Publications.
Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.

== Conduct the Focus Group ==
=== Description ===
Focus group sessions should be fun and stimulating for the participants and moderator. The moderator usually greets the participants as they enter and may ask them to fill out demographic information and informed consent forms (e.g., IRB forms). The participants are generally seated in a U-shape arrangement to encourage collaboration and allow space for the moderator to demonstrate the artifact. Seating arrangements are also very important. A good approach is to get to know the participants before the questioning route begins. Greeting them when they arrive is a good first step. The most assertive and expert participant should be seated next to the moderator and the least talkative directly across from the moderator. One potential risk is that an assertive participant could manipulate the conversation and the moderator will have to discreetly refocus the group.<ref name="FocusGroup">Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.</ref>

Depending on your research protocols, focus groups may be video and/or audio taped. Generally, the participants are told they are being recorded and most institutional review boards require written consent. It is also a good idea to have an observer. The observer will not participate in the focus group, rather will take careful notes, noting in particular any strong reactions, the participants’ facial expression and general tone of any exchange between
participants or between the participant and the moderator. Time management is also important when conducting a focus group. A moderator should be able to recognize when all possible issues for a topic have been covered and move on to the next topic. Pilot focus groups can help anticipate and manage the timing of focus groups.<ref name="FocusGroup">Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.</ref>

=== Examples ===

=== Further Readings ===
Krueger, R.A. and M.A. Casey (2000) Focus Groups: A Practical Guide for Applied Research, 3rd edition, Thousand Oaks, CA: Sage Publications.
Stewart, D.W., P.N. Shamdasani, and D.W. Rook (2007) Focus Groups: Theory and Practice, 2nd edition, vol. 20, Newbury Park, CA: Sage Publications.
Bloor, M., et al. (2001) Focus Groups in Social Research, London.
Morgan, D.L. (1988) Focus Groups as Qualitative Research. Newbury Park, CA: Sage Publications.
Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.

== Analyze and Interpret Data ==
=== Description ===
The two design research goals for using focus groups are the incremental improvement of the design of the artifact and the demonstration of the utility of the design. For this reason, we have suggested the different focus group types of EFG and CFG. While the objectives of the two group types are very different, the methods of analyzing the focus group data from both EFG and CFG can be similar. The interpretation of the focus group discussions has many of the same challenges in demonstrating rigor that all qualitative research encounters share. Several techniques that are used for qualitative data analysis can be considered, carefully selecting those techniques that emphasize the reliability and replicability of the observations and results.<ref name="FocusGroup">Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.</ref>

One possible approach is template analysis. Template analysis normally starts with at least a few predefined codes which help guide analysis. The first step in template analysis is to create an initial template by exploring the focus group transcripts, academic literature, the researchers’ own experiences, anecdotal and informal evidence, and other exploratory research. The contents of the discussions are also examined for the meanings and implications for the research questions. Analysts will look for common themes and variations within the transcripts that would provide rich descriptions of the participants’ reactions to design features.<ref name="FocusGroup">Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.</ref>
In template analysis, the initial template is applied in order to analyze the text, but is revised between each EFG
session. Once the final template is created after the final EFG, it is used to code the CFG sessions.<ref name="FocusGroup">Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.</ref>

=== Examples ===

=== Further Readings ===
Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.

== Report Results ==
=== Description ===
King suggests that qualitative results can be reported by creating an account structured around the main themes identified; drawing illustrative examples from each transcript as required<ref>King, N. (1998) "Template Analysis", in Symon, G. and C. Cassell (eds.) Qualitative Methods and Analysis in
Organizational Research, London: Sage Publications.</ref>. A similar approach can be taken when reporting focus group results. Short quotes are used to aid in the specific points of interpretation and longer passages of quotation are used to give a flavor of the original discussions. Summary tables can be very helpful,
displaying both evidence and counter-evidence of the utility of the artifact by focus group. Rich descriptions can further corroborate results by using quotes from the focus group participants.

=== Examples ===

=== Further Readings ===
Miles, M.B. and A M. Huberman (1994) Qualitative Data Analysis: An Expanded Sourcebook, 2nd edition, Thousand Oaks: Sage Publications
Tremblay, Monica & Hevner, Alan & Berndt, Donald & Chatterjee, Samir. (2010). The Use of Focus Groups in Design Science Research. 10.1007/978-1-4419-5653-8_10.

==References==

[[Category:Focus Groups Research]]

Case Study according to Ebneyamini

2024-05-29T14:49:57Z

MichiGau: /* Further Readings */

==Process description==
Case research, also called case study, is a method of intensively studying a phenomenon over time within its natural setting in one or a few sites. Multiple methods of data collection, such as interviews, observations, prerecorded documents, and secondary data, may be employed and inferences about the phenomenon of interest tend to be rich, detailed, and contextualized. Case research can be employed in a positivist manner for the purpose of theory testing or in an interpretive manner for theory building. This method is more popular in business research than in other social science disciplines.

There are different ideas about what a case study is. If we try to find a common denominator that case study researchers might agree on, it would be something along the following lines:
''The case study should have a “case” which is the object of study. The case should be a complex functioning unit, be investigated in its natural context with a multitude of methods, and be contemporary.''
Nevertheless, the case study researchers mentioned above emphasize different features. Stake points out that crucial to case study research are not the methods of investigation, but that the object of study is a case:
''“As a form of research, the case study is defined by the interest in individual cases, not by the methods of inquiry used.”''<ref name="ebneyamini">Ebneyamini, Shiva & Moghadam, Mohammad. (2018). Toward Developing a Framework for Conducting Case Study Research. International Journal of Qualitative Methods. 17. 160940691881795. 10.1177/1609406918817954.</ref>
Other researchers, such as Yin (1994), place more emphasis on the method and the techniques that constitute a case study (Johansson, 2003)<ref name="ebneyamini">Ebneyamini, Shiva & Moghadam, Mohammad. (2018). Toward Developing a Framework for Conducting Case Study Research. International Journal of Qualitative Methods. 17. 160940691881795. 10.1177/1609406918817954.</ref>.

==Purpose of Case Study Research==
===Description===
Researchers have different ideas, but many agree on what is the purpose of doing research using a case study research strategy. We try to provide an overview of those here. First, we have to decide whether a case study can help us to achieve our research goal or not. There are three factors that determine the best research methodology: The types of questions to be answered, the extent of control over behavioral events, and the degree of focus on contemporary as opposed to historical events.

Case study research is appropriate to be used for researches with “how” and “why” questions. Yin (1994) also presented at least four applications for a case study<ref>Yin, R. K. (1994). Case study research: Design and methods. Beverly Hills, CA: Sage.</ref>
* To explain complex causal links in real-life interventions
* To describe the real-life context in which the intervention has occurred
* To describe the intervention itself
* To explore those situations in which the intervention being evaluated has no clear set of outcomes.

Meredith (1998) noted that there are three outstanding strengths of case study research<ref>Meredith, J. (1998). Building operations management theory through case and field research. Journal of Operations Management, 16, 441–454</ref>:
* The phenomenon can be studied in its natural setting and meaningful, relevant theory generated from the understanding gained through observing actual practice
* The case method allows the questions of why, what, and how, to be answered with a relatively full understanding of the nature and complexity of the complete phenomenon
* The case method lends itself to early, exploratory investigations where variables are still unknown and the phenomenon not at all understood

Voss and his colleagues (2002) have mentioned that the purpose of the case study is<ref>Voss, C., Tsikriktsis, N., & Frohlich, M. (2002). Case research in
operations management. International journal of operations & production management, 22, 195–219.</ref>:
* Exploration to uncover areas for research and theory development
* Theory building to identify/describe key variables, identifying linkages between variables and identifying “why” these relationships exist
* Theory testing to test the theories developed in the previous stages or predict future outcomes
* Theory extension/refinement to better structure the theories in the light of the observed results

Dul and Hak (2008) noted that most of the researchers consider case study research as a useful research strategy (A) when the topic is broad and highly complex, (B) when there is not a lot of theory available, and (C) when “context” is very important. So they consider using case study for theory building, theory testing, hypothesis building, hypothesis testing, and description.<ref>Dul, J., & Hak, T. (2008). Case study methodology in business research. Abingdon, England: Routledge</ref>.

===Examples===
Theory oriented, practice oriented

===Further Readings===
Ebneyamini, Shiva & Moghadam, Mohammad. (2018). Toward Developing a Framework for Conducting Case Study Research. International Journal of Qualitative Methods. 17. 160940691881795. 10.1177/1609406918817954.

Benbasat, I., Goldstein, D. K., and Mead, M. (1987). "The Case Research Strategy in Studies of Information Systems," MIS Quarterly (11:3), 369-386.

Bhattacherjee, Anol, "Social Science Research: Principles, Methods, and Practices" (2012). Textbooks Collection. 3. https://scholarcommons.usf.edu/oa_textbooks/3.

Yin, R. K. (2002), Case Study Research: Design and Methods. Thousand Oaks, CA: Sage Publications.
==Reasons to Use Case Study Research==
===Description===
Choosing a research strategy or methodology depends on three things<ref>Yin, R. K. (2014). Case study research: Design and methods. Los Angeles, CA: Sage</ref>:
# Research question
# The extent of control the researcher has over actual behavioral events
# The degree of focus on contemporary as opposed to historical events
Case study research is generally thought of as being useful to answer “how and why?” questions, and where in-depth research is needed using a holistic lens. It does not require control over actual behavioral events. Meredith suggests four strong points for case study research<ref>Meredith, J. (1998). Building operations management theory through case and field research. Journal of Operations Management, 16, 441–454.</ref>:
* The phenomenon can be studied in its natural setting and meaningful
* Relevant theory generated from the understanding gained through observing actual practice
* The case method allows the questions of why, what, and how to be answered with a relatively full understanding of the nature and complexity of the compete for phenomenon
* The case method lends itself to early, exploratory investigations where the variables are still unknown and phenomenon not at all understood

Handfield and Melnyk (1998)<ref>Handfield, R., and Melnyk, S. 1998. “The Scientific Theory-Building Process: A Primer Using the Case of TQM,” Journal of Operations Management (16), pp. 321–339. (https://doi.org/10.1016/S0272-6963(98)00017-5).</ref> have identified four purposes for case study research: exploration, theory building, theory testing, and theory extension/refinement. According to Dul and Hak, most of the authors consider case study research as a useful research strategy when the topic is broad and highly complex, when there is not a lot of theory available, and when “context” is very important<ref>Dul, J., & Hak, T. (2008). Case study methodology in business research. Abingdon, England: Routledge</ref>. They suggest that the case study can be appropriate for building theory, testing theory, building hypothesis, testing hypothesis, and descriptive study. In our analysis of 15 latest articles published in three important journals of management of technology, 14 of the 15 of them were theory-oriented and one practice-oriented study<ref name="ebneyamini">Ebneyamini, Shiva & Moghadam, Mohammad. (2018). Toward Developing a Framework for Conducting Case Study Research. International Journal of Qualitative Methods. 17. 160940691881795. 10.1177/1609406918817954.</ref>.

===Examples===
Theory extension/refinement, theory building, hypothesis testing
===Further Readings===
Ebneyamini, Shiva & Moghadam, Mohammad. (2018). Toward Developing a Framework for Conducting Case Study Research. International Journal of Qualitative Methods. 17. 160940691881795. 10.1177/1609406918817954.

Benbasat, I., Goldstein, D. K., and Mead, M. (1987). "The Case Research Strategy in Studies of Information Systems," MIS Quarterly (11:3), 369-386.

Bhattacherjee, Anol, "Social Science Research: Principles, Methods, and Practices" (2012). Textbooks Collection. 3. https://scholarcommons.usf.edu/oa_textbooks/3.

Yin, R. K. (2014). Case study research: Design and methods. Los Angeles, CA: Sage

Yin, R. K. (2002), Case Study Research: Design and Methods. Thousand Oaks, CA: Sage Publications.

Handfield, R., and Melnyk, S. 1998. “The Scientific Theory-Building Process: A Primer Using the Case of TQM,” Journal of Operations Management (16), pp. 321–339. (https://doi.org/10.1016/S0272-6963(98)00017-5)

==Types of Case Study Research==
===Description===
In terms of defining types of case study research, researchers title them differently. Yin has identified some specific types of case study research<ref>Yin, R. K., & Campbell, D. T. (1989). Case study research: Design and methods. University of California: Sage</ref>:
* Exploratory
* Explanatory
* Eescriptive

Stake identified three others:
* Intrinsic
* Instrumental
* Collective

Or according to Zainal, other categories include interpretive and evaluative case studies<ref>Zainal, Z. (2007). Case study as a research method. Journal Kemanusiaan, 9, 1–6</ref>. Dul and Hak categorize the case study research as the single case study and comparative case study<ref>Dul, J., & Hak, T. (2008). Case study methodology in business research. Abingdon, England: Routledge.</ref>. All in all, Ebneyamini et al. believe that in using the case study research as a methodology we have two types: single case study or multiple case study which follows a replication logic.

===Examples===
Comparative case study, longitudinal case study, [[Single Case Study|single case study]]
===Further Readings===
Ebneyamini, Shiva & Moghadam, Mohammad. (2018). Toward Developing a Framework for Conducting Case Study Research. International Journal of Qualitative Methods. 17. 160940691881795. 10.1177/1609406918817954.

Benbasat, I., Goldstein, D. K., and Mead, M. (1987). "The Case Research Strategy in Studies of Information Systems," MIS Quarterly (11:3), 369-386.

Bhattacherjee, Anol, "Social Science Research: Principles, Methods, and Practices" (2012). Textbooks Collection. 3. https://scholarcommons.usf.edu/oa_textbooks/3.

Yin, R. K. (2002), Case Study Research: Design and Methods. Thousand Oaks, CA: Sage Publications.
==Methods of Gathering Data==
===Description===
There are recommendations proposed by researchers about data collection that if it’s done well, it will help the generalization and viability of the case study research:
Yin suggests three principles of data collection for case studies: use multiple sources of data, create a case study database, and maintain a chain of evidence<ref>Yin, R. K. (2014). Case study research: Design and methods. Los Angeles, CA: Sage.</ref>. Stake states that essential parts of a data-gathering plan are definition of the case, list of research questions, identification of helpers, data sources, allocation of time, expenses, and intended reporting. Dul and Hak suggest a stepwise procedure for the development of valid and reliable procedures for measurement (data collection): formulate a precise definition of the concept; determine the object of measurement; identify the location of the object of measurement; specify how evidence of the value of the variable will be extracted from the object of measurement; specify how sources of evidence will be identified, selected, and accessed; specify how evidence will be recorded; specify how data will be categorized; and lastly write a measurement protocol.

As for data sources, Yin suggests documentation, archival records, interview, direct observation, participant observation, and physical artifacts. As for data collection instruments, Stake suggests the use of observation, interview, and document review in qualitative case study research. Merriam suggests conducting effective interviews, being a careful observer, mining data from documents as techniques and procedures that researchers need in order to become effective users of the collection tools. Dul and Hak also propose a qualitative interview, using archives, questionnaire, and observation<ref>Dul, J., & Hak, T. (2008). Case study methodology in business research. Abingdon, England: Routledge</ref>. Some recommendations mentioned by researchers while conducting an interview is that the starting question after the preliminaries should invite the interviewee to tell the story of their experience of whatever the research is about. The starting point is a question that is almost content free. This is your warranty that the answers came from the respondent and did not arise simply because your questions created a self-fulfilling prophecy. Be aware that the data collection in case study research can be both quantitative and qualitative.

===Examples===
Structured interview, semistructured interview, in-depth interview, archival data, meetings, informal meetings, observation

===Further Readings===
Ebneyamini, Shiva & Moghadam, Mohammad. (2018). Toward Developing a Framework for Conducting Case Study Research. International Journal of Qualitative Methods. 17. 160940691881795. 10.1177/1609406918817954.

Benbasat, I., Goldstein, D. K., and Mead, M. (1987). "The Case Research Strategy in Studies of Information Systems," MIS Quarterly (11:3), 369-386.

Bhattacherjee, Anol, "Social Science Research: Principles, Methods, and Practices" (2012). Textbooks Collection. 3. https://scholarcommons.usf.edu/oa_textbooks/3.

Dul, J., & Hak, T. (2008). Case study methodology in business research. Abingdon, England: Routledge

Yin, R. K. (2002), Case Study Research: Design and Methods. Thousand Oaks, CA: Sage Publications.

==Data Analysis==
===Description===
Data analysis consists of examining, categorizing, tabulating, or otherwise recombining the evidence to address the initial propositions of a study. The analysis of the case study is one of the least developed aspects of the case study methodology.
The researcher needs to rely on experience and the literature to present the evidence in various ways, using various interpretations.
Case study researchers have different interpretations of the stage, for example, Miles and Huberman describe the phases of data analysis as data reduction, data display, and conclusion drawing and verification. Yin identifies three ways for this stage: pattern matching, explanation building, and time series. He also mentions the use of logic models and cross-case synthesis. None of these come with any formulas, although statistical calculations can be part of them.
Eisenhardt suggests two stage for data analysis: the analysis within case data and searching for cross-case patterns. Stake identifies three ways: correspondence and patterns, categorical aggregation, or direct interpretation, and naturalistic generalization. Merriam mentions six analytic
strategies such as ethnographic analysis, narrative analysis, phenomenological analysis, constant comparative method, content analysis, and analytic induction. Dul and Hak also mention pattern matching and visual inspection as ways of analyzing data.

===Examples===
Within-case analysis, cross-case analysis, compare the narratives of informants, pattern matching, writing individual case histories by using archival data, using repertory grid method, data reduction (open coding), visual inspection
===Further Readings===
Ebneyamini, Shiva & Moghadam, Mohammad. (2018). Toward Developing a Framework for Conducting Case Study Research. International Journal of Qualitative Methods. 17. 160940691881795. 10.1177/1609406918817954.

Benbasat, I., Goldstein, D. K., and Mead, M. (1987). "The Case Research Strategy in Studies of Information Systems," MIS Quarterly (11:3), 369-386.

Bhattacherjee, Anol, "Social Science Research: Principles, Methods, and Practices" (2012). Textbooks Collection. 3. https://scholarcommons.usf.edu/oa_textbooks/3.

Yin, R. K. (2002), Case Study Research: Design and Methods. Thousand Oaks, CA: Sage Publications.

==References==

[[Category:Case Study Research]]

Case Study according to Ebneyamini

2024-05-29T14:49:36Z

MichiGau: /* Methods of Gathering Data */

==Process description==
Case research, also called case study, is a method of intensively studying a phenomenon over time within its natural setting in one or a few sites. Multiple methods of data collection, such as interviews, observations, prerecorded documents, and secondary data, may be employed and inferences about the phenomenon of interest tend to be rich, detailed, and contextualized. Case research can be employed in a positivist manner for the purpose of theory testing or in an interpretive manner for theory building. This method is more popular in business research than in other social science disciplines.

There are different ideas about what a case study is. If we try to find a common denominator that case study researchers might agree on, it would be something along the following lines:
''The case study should have a “case” which is the object of study. The case should be a complex functioning unit, be investigated in its natural context with a multitude of methods, and be contemporary.''
Nevertheless, the case study researchers mentioned above emphasize different features. Stake points out that crucial to case study research are not the methods of investigation, but that the object of study is a case:
''“As a form of research, the case study is defined by the interest in individual cases, not by the methods of inquiry used.”''<ref name="ebneyamini">Ebneyamini, Shiva & Moghadam, Mohammad. (2018). Toward Developing a Framework for Conducting Case Study Research. International Journal of Qualitative Methods. 17. 160940691881795. 10.1177/1609406918817954.</ref>
Other researchers, such as Yin (1994), place more emphasis on the method and the techniques that constitute a case study (Johansson, 2003)<ref name="ebneyamini">Ebneyamini, Shiva & Moghadam, Mohammad. (2018). Toward Developing a Framework for Conducting Case Study Research. International Journal of Qualitative Methods. 17. 160940691881795. 10.1177/1609406918817954.</ref>.

==Purpose of Case Study Research==
===Description===
Researchers have different ideas, but many agree on what is the purpose of doing research using a case study research strategy. We try to provide an overview of those here. First, we have to decide whether a case study can help us to achieve our research goal or not. There are three factors that determine the best research methodology: The types of questions to be answered, the extent of control over behavioral events, and the degree of focus on contemporary as opposed to historical events.

Case study research is appropriate to be used for researches with “how” and “why” questions. Yin (1994) also presented at least four applications for a case study<ref>Yin, R. K. (1994). Case study research: Design and methods. Beverly Hills, CA: Sage.</ref>
* To explain complex causal links in real-life interventions
* To describe the real-life context in which the intervention has occurred
* To describe the intervention itself
* To explore those situations in which the intervention being evaluated has no clear set of outcomes.

Meredith (1998) noted that there are three outstanding strengths of case study research<ref>Meredith, J. (1998). Building operations management theory through case and field research. Journal of Operations Management, 16, 441–454</ref>:
* The phenomenon can be studied in its natural setting and meaningful, relevant theory generated from the understanding gained through observing actual practice
* The case method allows the questions of why, what, and how, to be answered with a relatively full understanding of the nature and complexity of the complete phenomenon
* The case method lends itself to early, exploratory investigations where variables are still unknown and the phenomenon not at all understood

Voss and his colleagues (2002) have mentioned that the purpose of the case study is<ref>Voss, C., Tsikriktsis, N., & Frohlich, M. (2002). Case research in
operations management. International journal of operations & production management, 22, 195–219.</ref>:
* Exploration to uncover areas for research and theory development
* Theory building to identify/describe key variables, identifying linkages between variables and identifying “why” these relationships exist
* Theory testing to test the theories developed in the previous stages or predict future outcomes
* Theory extension/refinement to better structure the theories in the light of the observed results

Dul and Hak (2008) noted that most of the researchers consider case study research as a useful research strategy (A) when the topic is broad and highly complex, (B) when there is not a lot of theory available, and (C) when “context” is very important. So they consider using case study for theory building, theory testing, hypothesis building, hypothesis testing, and description.<ref>Dul, J., & Hak, T. (2008). Case study methodology in business research. Abingdon, England: Routledge</ref>.

===Examples===
Theory oriented, practice oriented

===Further Readings===
Ebneyamini, Shiva & Moghadam, Mohammad. (2018). Toward Developing a Framework for Conducting Case Study Research. International Journal of Qualitative Methods. 17. 160940691881795. 10.1177/1609406918817954.

Benbasat, I., Goldstein, D. K., and Mead, M. (1987). "The Case Research Strategy in Studies of Information Systems," MIS Quarterly (11:3), 369-386.

Bhattacherjee, Anol, "Social Science Research: Principles, Methods, and Practices" (2012). Textbooks Collection. 3. https://scholarcommons.usf.edu/oa_textbooks/3.

Yin, R. K. (2002), Case Study Research: Design and Methods. Thousand Oaks, CA: Sage Publications.
==Reasons to Use Case Study Research==
===Description===
Choosing a research strategy or methodology depends on three things<ref>Yin, R. K. (2014). Case study research: Design and methods. Los Angeles, CA: Sage</ref>:
# Research question
# The extent of control the researcher has over actual behavioral events
# The degree of focus on contemporary as opposed to historical events
Case study research is generally thought of as being useful to answer “how and why?” questions, and where in-depth research is needed using a holistic lens. It does not require control over actual behavioral events. Meredith suggests four strong points for case study research<ref>Meredith, J. (1998). Building operations management theory through case and field research. Journal of Operations Management, 16, 441–454.</ref>:
* The phenomenon can be studied in its natural setting and meaningful
* Relevant theory generated from the understanding gained through observing actual practice
* The case method allows the questions of why, what, and how to be answered with a relatively full understanding of the nature and complexity of the compete for phenomenon
* The case method lends itself to early, exploratory investigations where the variables are still unknown and phenomenon not at all understood

Handfield and Melnyk (1998)<ref>Handfield, R., and Melnyk, S. 1998. “The Scientific Theory-Building Process: A Primer Using the Case of TQM,” Journal of Operations Management (16), pp. 321–339. (https://doi.org/10.1016/S0272-6963(98)00017-5).</ref> have identified four purposes for case study research: exploration, theory building, theory testing, and theory extension/refinement. According to Dul and Hak, most of the authors consider case study research as a useful research strategy when the topic is broad and highly complex, when there is not a lot of theory available, and when “context” is very important<ref>Dul, J., & Hak, T. (2008). Case study methodology in business research. Abingdon, England: Routledge</ref>. They suggest that the case study can be appropriate for building theory, testing theory, building hypothesis, testing hypothesis, and descriptive study. In our analysis of 15 latest articles published in three important journals of management of technology, 14 of the 15 of them were theory-oriented and one practice-oriented study<ref name="ebneyamini">Ebneyamini, Shiva & Moghadam, Mohammad. (2018). Toward Developing a Framework for Conducting Case Study Research. International Journal of Qualitative Methods. 17. 160940691881795. 10.1177/1609406918817954.</ref>.

===Examples===
Theory extension/refinement, theory building, hypothesis testing
===Further Readings===
Ebneyamini, Shiva & Moghadam, Mohammad. (2018). Toward Developing a Framework for Conducting Case Study Research. International Journal of Qualitative Methods. 17. 160940691881795. 10.1177/1609406918817954.

Benbasat, I., Goldstein, D. K., and Mead, M. (1987). "The Case Research Strategy in Studies of Information Systems," MIS Quarterly (11:3), 369-386.

Bhattacherjee, Anol, "Social Science Research: Principles, Methods, and Practices" (2012). Textbooks Collection. 3. https://scholarcommons.usf.edu/oa_textbooks/3.

Yin, R. K. (2014). Case study research: Design and methods. Los Angeles, CA: Sage

Yin, R. K. (2002), Case Study Research: Design and Methods. Thousand Oaks, CA: Sage Publications.

Handfield, R., and Melnyk, S. 1998. “The Scientific Theory-Building Process: A Primer Using the Case of TQM,” Journal of Operations Management (16), pp. 321–339. (https://doi.org/10.1016/S0272-6963(98)00017-5)

==Types of Case Study Research==
===Description===
In terms of defining types of case study research, researchers title them differently. Yin has identified some specific types of case study research<ref>Yin, R. K., & Campbell, D. T. (1989). Case study research: Design and methods. University of California: Sage</ref>:
* Exploratory
* Explanatory
* Eescriptive

Stake identified three others:
* Intrinsic
* Instrumental
* Collective

Or according to Zainal, other categories include interpretive and evaluative case studies<ref>Zainal, Z. (2007). Case study as a research method. Journal Kemanusiaan, 9, 1–6</ref>. Dul and Hak categorize the case study research as the single case study and comparative case study<ref>Dul, J., & Hak, T. (2008). Case study methodology in business research. Abingdon, England: Routledge.</ref>. All in all, Ebneyamini et al. believe that in using the case study research as a methodology we have two types: single case study or multiple case study which follows a replication logic.

===Examples===
Comparative case study, longitudinal case study, [[Single Case Study|single case study]]
===Further Readings===
Ebneyamini, Shiva & Moghadam, Mohammad. (2018). Toward Developing a Framework for Conducting Case Study Research. International Journal of Qualitative Methods. 17. 160940691881795. 10.1177/1609406918817954.

Benbasat, I., Goldstein, D. K., and Mead, M. (1987). "The Case Research Strategy in Studies of Information Systems," MIS Quarterly (11:3), 369-386.

Bhattacherjee, Anol, "Social Science Research: Principles, Methods, and Practices" (2012). Textbooks Collection. 3. https://scholarcommons.usf.edu/oa_textbooks/3.

Yin, R. K. (2002), Case Study Research: Design and Methods. Thousand Oaks, CA: Sage Publications.
==Methods of Gathering Data==
===Description===
There are recommendations proposed by researchers about data collection that if it’s done well, it will help the generalization and viability of the case study research:
Yin suggests three principles of data collection for case studies: use multiple sources of data, create a case study database, and maintain a chain of evidence<ref>Yin, R. K. (2014). Case study research: Design and methods. Los Angeles, CA: Sage.</ref>. Stake states that essential parts of a data-gathering plan are definition of the case, list of research questions, identification of helpers, data sources, allocation of time, expenses, and intended reporting. Dul and Hak suggest a stepwise procedure for the development of valid and reliable procedures for measurement (data collection): formulate a precise definition of the concept; determine the object of measurement; identify the location of the object of measurement; specify how evidence of the value of the variable will be extracted from the object of measurement; specify how sources of evidence will be identified, selected, and accessed; specify how evidence will be recorded; specify how data will be categorized; and lastly write a measurement protocol.

As for data sources, Yin suggests documentation, archival records, interview, direct observation, participant observation, and physical artifacts. As for data collection instruments, Stake suggests the use of observation, interview, and document review in qualitative case study research. Merriam suggests conducting effective interviews, being a careful observer, mining data from documents as techniques and procedures that researchers need in order to become effective users of the collection tools. Dul and Hak also propose a qualitative interview, using archives, questionnaire, and observation<ref>Dul, J., & Hak, T. (2008). Case study methodology in business research. Abingdon, England: Routledge</ref>. Some recommendations mentioned by researchers while conducting an interview is that the starting question after the preliminaries should invite the interviewee to tell the story of their experience of whatever the research is about. The starting point is a question that is almost content free. This is your warranty that the answers came from the respondent and did not arise simply because your questions created a self-fulfilling prophecy. Be aware that the data collection in case study research can be both quantitative and qualitative.

===Examples===
Structured interview, semistructured interview, in-depth interview, archival data, meetings, informal meetings, observation

===Further Readings===
Ebneyamini, Shiva & Moghadam, Mohammad. (2018). Toward Developing a Framework for Conducting Case Study Research. International Journal of Qualitative Methods. 17. 160940691881795. 10.1177/1609406918817954.

Benbasat, I., Goldstein, D. K., and Mead, M. (1987). "The Case Research Strategy in Studies of Information Systems," MIS Quarterly (11:3), 369-386.

Bhattacherjee, Anol, "Social Science Research: Principles, Methods, and Practices" (2012). Textbooks Collection. 3. https://scholarcommons.usf.edu/oa_textbooks/3.

Yin, R. K. (2002), Case Study Research: Design and Methods. Thousand Oaks, CA: Sage Publications.

==Data Analysis==
===Description===
Data analysis consists of examining, categorizing, tabulating, or otherwise recombining the evidence to address the initial propositions of a study. The analysis of the case study is one of the least developed aspects of the case study methodology.
The researcher needs to rely on experience and the literature to present the evidence in various ways, using various interpretations.
Case study researchers have different interpretations of the stage, for example, Miles and Huberman describe the phases of data analysis as data reduction, data display, and conclusion drawing and verification. Yin identifies three ways for this stage: pattern matching, explanation building, and time series. He also mentions the use of logic models and cross-case synthesis. None of these come with any formulas, although statistical calculations can be part of them.
Eisenhardt suggests two stage for data analysis: the analysis within case data and searching for cross-case patterns. Stake identifies three ways: correspondence and patterns, categorical aggregation, or direct interpretation, and naturalistic generalization. Merriam mentions six analytic
strategies such as ethnographic analysis, narrative analysis, phenomenological analysis, constant comparative method, content analysis, and analytic induction. Dul and Hak also mention pattern matching and visual inspection as ways of analyzing data.

===Examples===
Within-case analysis, cross-case analysis, compare the narratives of informants, pattern matching, writing individual case histories by using archival data, using repertory grid method, data reduction (open coding), visual inspection
===Further Readings===
Ebneyamini, Shiva & Moghadam, Mohammad. (2018). Toward Developing a Framework for Conducting Case Study Research. International Journal of Qualitative Methods. 17. 160940691881795. 10.1177/1609406918817954.

Benbasat, I., Goldstein, D. K., and Mead, M. (1987). "The Case Research Strategy in Studies of Information Systems," MIS Quarterly (11:3), 369-386.

Bhattacherjee, Anol, "Social Science Research: Principles, Methods, and Practices" (2012). Textbooks Collection. 3. https://scholarcommons.usf.edu/oa_textbooks/3.

Yin, R. K. (2002), Case Study Research: Design and Methods. Thousand Oaks, CA: Sage Publications.

==References==

[[Category:Case Study Research]]

Case Study according to Ebneyamini

2024-05-29T14:44:29Z

MichiGau: /* Further Readings */

==Process description==
Case research, also called case study, is a method of intensively studying a phenomenon over time within its natural setting in one or a few sites. Multiple methods of data collection, such as interviews, observations, prerecorded documents, and secondary data, may be employed and inferences about the phenomenon of interest tend to be rich, detailed, and contextualized. Case research can be employed in a positivist manner for the purpose of theory testing or in an interpretive manner for theory building. This method is more popular in business research than in other social science disciplines.

There are different ideas about what a case study is. If we try to find a common denominator that case study researchers might agree on, it would be something along the following lines:
''The case study should have a “case” which is the object of study. The case should be a complex functioning unit, be investigated in its natural context with a multitude of methods, and be contemporary.''
Nevertheless, the case study researchers mentioned above emphasize different features. Stake points out that crucial to case study research are not the methods of investigation, but that the object of study is a case:
''“As a form of research, the case study is defined by the interest in individual cases, not by the methods of inquiry used.”''<ref name="ebneyamini">Ebneyamini, Shiva & Moghadam, Mohammad. (2018). Toward Developing a Framework for Conducting Case Study Research. International Journal of Qualitative Methods. 17. 160940691881795. 10.1177/1609406918817954.</ref>
Other researchers, such as Yin (1994), place more emphasis on the method and the techniques that constitute a case study (Johansson, 2003)<ref name="ebneyamini">Ebneyamini, Shiva & Moghadam, Mohammad. (2018). Toward Developing a Framework for Conducting Case Study Research. International Journal of Qualitative Methods. 17. 160940691881795. 10.1177/1609406918817954.</ref>.

==Purpose of Case Study Research==
===Description===
Researchers have different ideas, but many agree on what is the purpose of doing research using a case study research strategy. We try to provide an overview of those here. First, we have to decide whether a case study can help us to achieve our research goal or not. There are three factors that determine the best research methodology: The types of questions to be answered, the extent of control over behavioral events, and the degree of focus on contemporary as opposed to historical events.

Case study research is appropriate to be used for researches with “how” and “why” questions. Yin (1994) also presented at least four applications for a case study<ref>Yin, R. K. (1994). Case study research: Design and methods. Beverly Hills, CA: Sage.</ref>
* To explain complex causal links in real-life interventions
* To describe the real-life context in which the intervention has occurred
* To describe the intervention itself
* To explore those situations in which the intervention being evaluated has no clear set of outcomes.

Meredith (1998) noted that there are three outstanding strengths of case study research<ref>Meredith, J. (1998). Building operations management theory through case and field research. Journal of Operations Management, 16, 441–454</ref>:
* The phenomenon can be studied in its natural setting and meaningful, relevant theory generated from the understanding gained through observing actual practice
* The case method allows the questions of why, what, and how, to be answered with a relatively full understanding of the nature and complexity of the complete phenomenon
* The case method lends itself to early, exploratory investigations where variables are still unknown and the phenomenon not at all understood

Voss and his colleagues (2002) have mentioned that the purpose of the case study is<ref>Voss, C., Tsikriktsis, N., & Frohlich, M. (2002). Case research in
operations management. International journal of operations & production management, 22, 195–219.</ref>:
* Exploration to uncover areas for research and theory development
* Theory building to identify/describe key variables, identifying linkages between variables and identifying “why” these relationships exist
* Theory testing to test the theories developed in the previous stages or predict future outcomes
* Theory extension/refinement to better structure the theories in the light of the observed results

Dul and Hak (2008) noted that most of the researchers consider case study research as a useful research strategy (A) when the topic is broad and highly complex, (B) when there is not a lot of theory available, and (C) when “context” is very important. So they consider using case study for theory building, theory testing, hypothesis building, hypothesis testing, and description.<ref>Dul, J., & Hak, T. (2008). Case study methodology in business research. Abingdon, England: Routledge</ref>.

===Examples===
Theory oriented, practice oriented

===Further Readings===
Ebneyamini, Shiva & Moghadam, Mohammad. (2018). Toward Developing a Framework for Conducting Case Study Research. International Journal of Qualitative Methods. 17. 160940691881795. 10.1177/1609406918817954.

Benbasat, I., Goldstein, D. K., and Mead, M. (1987). "The Case Research Strategy in Studies of Information Systems," MIS Quarterly (11:3), 369-386.

Bhattacherjee, Anol, "Social Science Research: Principles, Methods, and Practices" (2012). Textbooks Collection. 3. https://scholarcommons.usf.edu/oa_textbooks/3.

Yin, R. K. (2002), Case Study Research: Design and Methods. Thousand Oaks, CA: Sage Publications.
==Reasons to Use Case Study Research==
===Description===
Choosing a research strategy or methodology depends on three things<ref>Yin, R. K. (2014). Case study research: Design and methods. Los Angeles, CA: Sage</ref>:
# Research question
# The extent of control the researcher has over actual behavioral events
# The degree of focus on contemporary as opposed to historical events
Case study research is generally thought of as being useful to answer “how and why?” questions, and where in-depth research is needed using a holistic lens. It does not require control over actual behavioral events. Meredith suggests four strong points for case study research<ref>Meredith, J. (1998). Building operations management theory through case and field research. Journal of Operations Management, 16, 441–454.</ref>:
* The phenomenon can be studied in its natural setting and meaningful
* Relevant theory generated from the understanding gained through observing actual practice
* The case method allows the questions of why, what, and how to be answered with a relatively full understanding of the nature and complexity of the compete for phenomenon
* The case method lends itself to early, exploratory investigations where the variables are still unknown and phenomenon not at all understood

Handfield and Melnyk (1998)<ref>Handfield, R., and Melnyk, S. 1998. “The Scientific Theory-Building Process: A Primer Using the Case of TQM,” Journal of Operations Management (16), pp. 321–339. (https://doi.org/10.1016/S0272-6963(98)00017-5).</ref> have identified four purposes for case study research: exploration, theory building, theory testing, and theory extension/refinement. According to Dul and Hak, most of the authors consider case study research as a useful research strategy when the topic is broad and highly complex, when there is not a lot of theory available, and when “context” is very important<ref>Dul, J., & Hak, T. (2008). Case study methodology in business research. Abingdon, England: Routledge</ref>. They suggest that the case study can be appropriate for building theory, testing theory, building hypothesis, testing hypothesis, and descriptive study. In our analysis of 15 latest articles published in three important journals of management of technology, 14 of the 15 of them were theory-oriented and one practice-oriented study<ref name="ebneyamini">Ebneyamini, Shiva & Moghadam, Mohammad. (2018). Toward Developing a Framework for Conducting Case Study Research. International Journal of Qualitative Methods. 17. 160940691881795. 10.1177/1609406918817954.</ref>.

===Examples===
Theory extension/refinement, theory building, hypothesis testing
===Further Readings===
Ebneyamini, Shiva & Moghadam, Mohammad. (2018). Toward Developing a Framework for Conducting Case Study Research. International Journal of Qualitative Methods. 17. 160940691881795. 10.1177/1609406918817954.

Benbasat, I., Goldstein, D. K., and Mead, M. (1987). "The Case Research Strategy in Studies of Information Systems," MIS Quarterly (11:3), 369-386.

Bhattacherjee, Anol, "Social Science Research: Principles, Methods, and Practices" (2012). Textbooks Collection. 3. https://scholarcommons.usf.edu/oa_textbooks/3.

Yin, R. K. (2014). Case study research: Design and methods. Los Angeles, CA: Sage

Yin, R. K. (2002), Case Study Research: Design and Methods. Thousand Oaks, CA: Sage Publications.

Handfield, R., and Melnyk, S. 1998. “The Scientific Theory-Building Process: A Primer Using the Case of TQM,” Journal of Operations Management (16), pp. 321–339. (https://doi.org/10.1016/S0272-6963(98)00017-5)

==Types of Case Study Research==
===Description===
In terms of defining types of case study research, researchers title them differently. Yin has identified some specific types of case study research<ref>Yin, R. K., & Campbell, D. T. (1989). Case study research: Design and methods. University of California: Sage</ref>:
* Exploratory
* Explanatory
* Eescriptive

Stake identified three others:
* Intrinsic
* Instrumental
* Collective

Or according to Zainal, other categories include interpretive and evaluative case studies<ref>Zainal, Z. (2007). Case study as a research method. Journal Kemanusiaan, 9, 1–6</ref>. Dul and Hak categorize the case study research as the single case study and comparative case study<ref>Dul, J., & Hak, T. (2008). Case study methodology in business research. Abingdon, England: Routledge.</ref>. All in all, Ebneyamini et al. believe that in using the case study research as a methodology we have two types: single case study or multiple case study which follows a replication logic.

===Examples===
Comparative case study, longitudinal case study, [[Single Case Study|single case study]]
===Further Readings===
Ebneyamini, Shiva & Moghadam, Mohammad. (2018). Toward Developing a Framework for Conducting Case Study Research. International Journal of Qualitative Methods. 17. 160940691881795. 10.1177/1609406918817954.

Benbasat, I., Goldstein, D. K., and Mead, M. (1987). "The Case Research Strategy in Studies of Information Systems," MIS Quarterly (11:3), 369-386.

Bhattacherjee, Anol, "Social Science Research: Principles, Methods, and Practices" (2012). Textbooks Collection. 3. https://scholarcommons.usf.edu/oa_textbooks/3.

Yin, R. K. (2002), Case Study Research: Design and Methods. Thousand Oaks, CA: Sage Publications.
==Methods of Gathering Data==
===Description===
There are recommendations proposed by researchers about data collection that if it’s done well, it will help the generalization and viability of the case study research:
Yin suggests three principles of data collection for case studies: use multiple sources of data, create a case study database, and maintain a chain of evidence<ref>Yin, R. K. (2014). Case study research: Design and methods. Los Angeles, CA: Sage.</ref>. Stake states that essential parts of a data-gathering plan are definition of the case, list of research questions, identification of helpers, data sources, allocation of time, expenses, and intended reporting. Dul and Hak suggest a stepwise procedure for the development of valid and reliable procedures for measurement (data collection): formulate a precise definition of the concept; determine the object of measurement; identify the location of the object of measurement; specify how evidence of the value of the variable will be extracted from the object of measurement; specify how sources of evidence will be identified, selected, and accessed; specify how evidence will be recorded; specify how data will be categorized; and lastly write a measurement protocol.

As for data sources, Yin suggests documentation, archival records, interview, direct observation, participant observation, and physical artifacts. As for data collection instruments, Stake suggests the use of observation, interview, and document review in qualitative case study research. Merriam suggests conducting effective interviews, being a careful observer, mining data from documents as techniques and procedures that researchers need in order to become effective users of the collection tools. Dul and Hak also propose a qualitative interview, using archives, questionnaire, and observation. Some recommendations mentioned by researchers while conducting an interview is that the starting question after the preliminaries should invite the interviewee to tell the story of their experience of whatever the research is about (Perry, 1998). The starting point is a question that is almost content free. This is your warranty that the answers came from the respondent and did not arise simply because your questions created a self-fulfilling prophecy. Be aware that the data collection in case study research can be both quantitative and qualitative.

===Examples===
Structured interview, semistructured interview, in-depth interview, archival data, meetings, informal meetings, observation

===Further Readings===
Ebneyamini, Shiva & Moghadam, Mohammad. (2018). Toward Developing a Framework for Conducting Case Study Research. International Journal of Qualitative Methods. 17. 160940691881795. 10.1177/1609406918817954.

Benbasat, I., Goldstein, D. K., and Mead, M. (1987). "The Case Research Strategy in Studies of Information Systems," MIS Quarterly (11:3), 369-386.

Bhattacherjee, Anol, "Social Science Research: Principles, Methods, and Practices" (2012). Textbooks Collection. 3. https://scholarcommons.usf.edu/oa_textbooks/3.

Yin, R. K. (2002), Case Study Research: Design and Methods. Thousand Oaks, CA: Sage Publications.
==Data Analysis==
===Description===
Data analysis consists of examining, categorizing, tabulating, or otherwise recombining the evidence to address the initial propositions of a study. The analysis of the case study is one of the least developed aspects of the case study methodology.
The researcher needs to rely on experience and the literature to present the evidence in various ways, using various interpretations.
Case study researchers have different interpretations of the stage, for example, Miles and Huberman describe the phases of data analysis as data reduction, data display, and conclusion drawing and verification. Yin identifies three ways for this stage: pattern matching, explanation building, and time series. He also mentions the use of logic models and cross-case synthesis. None of these come with any formulas, although statistical calculations can be part of them.
Eisenhardt suggests two stage for data analysis: the analysis within case data and searching for cross-case patterns. Stake identifies three ways: correspondence and patterns, categorical aggregation, or direct interpretation, and naturalistic generalization. Merriam mentions six analytic
strategies such as ethnographic analysis, narrative analysis, phenomenological analysis, constant comparative method, content analysis, and analytic induction. Dul and Hak also mention pattern matching and visual inspection as ways of analyzing data.

===Examples===
Within-case analysis, cross-case analysis, compare the narratives of informants, pattern matching, writing individual case histories by using archival data, using repertory grid method, data reduction (open coding), visual inspection
===Further Readings===
Ebneyamini, Shiva & Moghadam, Mohammad. (2018). Toward Developing a Framework for Conducting Case Study Research. International Journal of Qualitative Methods. 17. 160940691881795. 10.1177/1609406918817954.

Benbasat, I., Goldstein, D. K., and Mead, M. (1987). "The Case Research Strategy in Studies of Information Systems," MIS Quarterly (11:3), 369-386.

Bhattacherjee, Anol, "Social Science Research: Principles, Methods, and Practices" (2012). Textbooks Collection. 3. https://scholarcommons.usf.edu/oa_textbooks/3.

Yin, R. K. (2002), Case Study Research: Design and Methods. Thousand Oaks, CA: Sage Publications.

==References==

[[Category:Case Study Research]]

Case Study according to Ebneyamini

2024-05-29T14:43:50Z

MichiGau: /* Description */

==Process description==
Case research, also called case study, is a method of intensively studying a phenomenon over time within its natural setting in one or a few sites. Multiple methods of data collection, such as interviews, observations, prerecorded documents, and secondary data, may be employed and inferences about the phenomenon of interest tend to be rich, detailed, and contextualized. Case research can be employed in a positivist manner for the purpose of theory testing or in an interpretive manner for theory building. This method is more popular in business research than in other social science disciplines.

There are different ideas about what a case study is. If we try to find a common denominator that case study researchers might agree on, it would be something along the following lines:
''The case study should have a “case” which is the object of study. The case should be a complex functioning unit, be investigated in its natural context with a multitude of methods, and be contemporary.''
Nevertheless, the case study researchers mentioned above emphasize different features. Stake points out that crucial to case study research are not the methods of investigation, but that the object of study is a case:
''“As a form of research, the case study is defined by the interest in individual cases, not by the methods of inquiry used.”''<ref name="ebneyamini">Ebneyamini, Shiva & Moghadam, Mohammad. (2018). Toward Developing a Framework for Conducting Case Study Research. International Journal of Qualitative Methods. 17. 160940691881795. 10.1177/1609406918817954.</ref>
Other researchers, such as Yin (1994), place more emphasis on the method and the techniques that constitute a case study (Johansson, 2003)<ref name="ebneyamini">Ebneyamini, Shiva & Moghadam, Mohammad. (2018). Toward Developing a Framework for Conducting Case Study Research. International Journal of Qualitative Methods. 17. 160940691881795. 10.1177/1609406918817954.</ref>.

==Purpose of Case Study Research==
===Description===
Researchers have different ideas, but many agree on what is the purpose of doing research using a case study research strategy. We try to provide an overview of those here. First, we have to decide whether a case study can help us to achieve our research goal or not. There are three factors that determine the best research methodology: The types of questions to be answered, the extent of control over behavioral events, and the degree of focus on contemporary as opposed to historical events.

Case study research is appropriate to be used for researches with “how” and “why” questions. Yin (1994) also presented at least four applications for a case study<ref>Yin, R. K. (1994). Case study research: Design and methods. Beverly Hills, CA: Sage.</ref>
* To explain complex causal links in real-life interventions
* To describe the real-life context in which the intervention has occurred
* To describe the intervention itself
* To explore those situations in which the intervention being evaluated has no clear set of outcomes.

Meredith (1998) noted that there are three outstanding strengths of case study research<ref>Meredith, J. (1998). Building operations management theory through case and field research. Journal of Operations Management, 16, 441–454</ref>:
* The phenomenon can be studied in its natural setting and meaningful, relevant theory generated from the understanding gained through observing actual practice
* The case method allows the questions of why, what, and how, to be answered with a relatively full understanding of the nature and complexity of the complete phenomenon
* The case method lends itself to early, exploratory investigations where variables are still unknown and the phenomenon not at all understood

Voss and his colleagues (2002) have mentioned that the purpose of the case study is<ref>Voss, C., Tsikriktsis, N., & Frohlich, M. (2002). Case research in
operations management. International journal of operations & production management, 22, 195–219.</ref>:
* Exploration to uncover areas for research and theory development
* Theory building to identify/describe key variables, identifying linkages between variables and identifying “why” these relationships exist
* Theory testing to test the theories developed in the previous stages or predict future outcomes
* Theory extension/refinement to better structure the theories in the light of the observed results

Dul and Hak (2008) noted that most of the researchers consider case study research as a useful research strategy (A) when the topic is broad and highly complex, (B) when there is not a lot of theory available, and (C) when “context” is very important. So they consider using case study for theory building, theory testing, hypothesis building, hypothesis testing, and description.<ref>Dul, J., & Hak, T. (2008). Case study methodology in business research. Abingdon, England: Routledge</ref>.

===Examples===
Theory oriented, practice oriented

===Further Readings===
Ebneyamini, Shiva & Moghadam, Mohammad. (2018). Toward Developing a Framework for Conducting Case Study Research. International Journal of Qualitative Methods. 17. 160940691881795. 10.1177/1609406918817954.

Benbasat, I., Goldstein, D. K., and Mead, M. (1987). "The Case Research Strategy in Studies of Information Systems," MIS Quarterly (11:3), 369-386.

Bhattacherjee, Anol, "Social Science Research: Principles, Methods, and Practices" (2012). Textbooks Collection. 3. https://scholarcommons.usf.edu/oa_textbooks/3.

Yin, R. K. (2002), Case Study Research: Design and Methods. Thousand Oaks, CA: Sage Publications.
==Reasons to Use Case Study Research==
===Description===
Choosing a research strategy or methodology depends on three things<ref>Yin, R. K. (2014). Case study research: Design and methods. Los Angeles, CA: Sage</ref>:
# Research question
# The extent of control the researcher has over actual behavioral events
# The degree of focus on contemporary as opposed to historical events
Case study research is generally thought of as being useful to answer “how and why?” questions, and where in-depth research is needed using a holistic lens. It does not require control over actual behavioral events. Meredith suggests four strong points for case study research<ref>Meredith, J. (1998). Building operations management theory through case and field research. Journal of Operations Management, 16, 441–454.</ref>:
* The phenomenon can be studied in its natural setting and meaningful
* Relevant theory generated from the understanding gained through observing actual practice
* The case method allows the questions of why, what, and how to be answered with a relatively full understanding of the nature and complexity of the compete for phenomenon
* The case method lends itself to early, exploratory investigations where the variables are still unknown and phenomenon not at all understood

Handfield and Melnyk (1998)<ref>Handfield, R., and Melnyk, S. 1998. “The Scientific Theory-Building Process: A Primer Using the Case of TQM,” Journal of Operations Management (16), pp. 321–339. (https://doi.org/10.1016/S0272-6963(98)00017-5).</ref> have identified four purposes for case study research: exploration, theory building, theory testing, and theory extension/refinement. According to Dul and Hak, most of the authors consider case study research as a useful research strategy when the topic is broad and highly complex, when there is not a lot of theory available, and when “context” is very important<ref>Dul, J., & Hak, T. (2008). Case study methodology in business research. Abingdon, England: Routledge</ref>. They suggest that the case study can be appropriate for building theory, testing theory, building hypothesis, testing hypothesis, and descriptive study. In our analysis of 15 latest articles published in three important journals of management of technology, 14 of the 15 of them were theory-oriented and one practice-oriented study<ref name="ebneyamini">Ebneyamini, Shiva & Moghadam, Mohammad. (2018). Toward Developing a Framework for Conducting Case Study Research. International Journal of Qualitative Methods. 17. 160940691881795. 10.1177/1609406918817954.</ref>.

===Examples===
Theory extension/refinement, theory building, hypothesis testing
===Further Readings===
Ebneyamini, Shiva & Moghadam, Mohammad. (2018). Toward Developing a Framework for Conducting Case Study Research. International Journal of Qualitative Methods. 17. 160940691881795. 10.1177/1609406918817954.

Benbasat, I., Goldstein, D. K., and Mead, M. (1987). "The Case Research Strategy in Studies of Information Systems," MIS Quarterly (11:3), 369-386.

Bhattacherjee, Anol, "Social Science Research: Principles, Methods, and Practices" (2012). Textbooks Collection. 3. https://scholarcommons.usf.edu/oa_textbooks/3.

Yin, R. K. (2014). Case study research: Design and methods. Los Angeles, CA: Sage

Yin, R. K. (2002), Case Study Research: Design and Methods. Thousand Oaks, CA: Sage Publications.
==Types of Case Study Research==
===Description===
In terms of defining types of case study research, researchers title them differently. Yin has identified some specific types of case study research<ref>Yin, R. K., & Campbell, D. T. (1989). Case study research: Design and methods. University of California: Sage</ref>:
* Exploratory
* Explanatory
* Eescriptive

Stake identified three others:
* Intrinsic
* Instrumental
* Collective

Or according to Zainal, other categories include interpretive and evaluative case studies<ref>Zainal, Z. (2007). Case study as a research method. Journal Kemanusiaan, 9, 1–6</ref>. Dul and Hak categorize the case study research as the single case study and comparative case study<ref>Dul, J., & Hak, T. (2008). Case study methodology in business research. Abingdon, England: Routledge.</ref>. All in all, Ebneyamini et al. believe that in using the case study research as a methodology we have two types: single case study or multiple case study which follows a replication logic.

===Examples===
Comparative case study, longitudinal case study, [[Single Case Study|single case study]]
===Further Readings===
Ebneyamini, Shiva & Moghadam, Mohammad. (2018). Toward Developing a Framework for Conducting Case Study Research. International Journal of Qualitative Methods. 17. 160940691881795. 10.1177/1609406918817954.

Benbasat, I., Goldstein, D. K., and Mead, M. (1987). "The Case Research Strategy in Studies of Information Systems," MIS Quarterly (11:3), 369-386.

Bhattacherjee, Anol, "Social Science Research: Principles, Methods, and Practices" (2012). Textbooks Collection. 3. https://scholarcommons.usf.edu/oa_textbooks/3.

Yin, R. K. (2002), Case Study Research: Design and Methods. Thousand Oaks, CA: Sage Publications.
==Methods of Gathering Data==
===Description===
There are recommendations proposed by researchers about data collection that if it’s done well, it will help the generalization and viability of the case study research:
Yin suggests three principles of data collection for case studies: use multiple sources of data, create a case study database, and maintain a chain of evidence<ref>Yin, R. K. (2014). Case study research: Design and methods. Los Angeles, CA: Sage.</ref>. Stake states that essential parts of a data-gathering plan are definition of the case, list of research questions, identification of helpers, data sources, allocation of time, expenses, and intended reporting. Dul and Hak suggest a stepwise procedure for the development of valid and reliable procedures for measurement (data collection): formulate a precise definition of the concept; determine the object of measurement; identify the location of the object of measurement; specify how evidence of the value of the variable will be extracted from the object of measurement; specify how sources of evidence will be identified, selected, and accessed; specify how evidence will be recorded; specify how data will be categorized; and lastly write a measurement protocol.

As for data sources, Yin suggests documentation, archival records, interview, direct observation, participant observation, and physical artifacts. As for data collection instruments, Stake suggests the use of observation, interview, and document review in qualitative case study research. Merriam suggests conducting effective interviews, being a careful observer, mining data from documents as techniques and procedures that researchers need in order to become effective users of the collection tools. Dul and Hak also propose a qualitative interview, using archives, questionnaire, and observation. Some recommendations mentioned by researchers while conducting an interview is that the starting question after the preliminaries should invite the interviewee to tell the story of their experience of whatever the research is about (Perry, 1998). The starting point is a question that is almost content free. This is your warranty that the answers came from the respondent and did not arise simply because your questions created a self-fulfilling prophecy. Be aware that the data collection in case study research can be both quantitative and qualitative.

===Examples===
Structured interview, semistructured interview, in-depth interview, archival data, meetings, informal meetings, observation

===Further Readings===
Ebneyamini, Shiva & Moghadam, Mohammad. (2018). Toward Developing a Framework for Conducting Case Study Research. International Journal of Qualitative Methods. 17. 160940691881795. 10.1177/1609406918817954.

Benbasat, I., Goldstein, D. K., and Mead, M. (1987). "The Case Research Strategy in Studies of Information Systems," MIS Quarterly (11:3), 369-386.

Bhattacherjee, Anol, "Social Science Research: Principles, Methods, and Practices" (2012). Textbooks Collection. 3. https://scholarcommons.usf.edu/oa_textbooks/3.

Yin, R. K. (2002), Case Study Research: Design and Methods. Thousand Oaks, CA: Sage Publications.
==Data Analysis==
===Description===
Data analysis consists of examining, categorizing, tabulating, or otherwise recombining the evidence to address the initial propositions of a study. The analysis of the case study is one of the least developed aspects of the case study methodology.
The researcher needs to rely on experience and the literature to present the evidence in various ways, using various interpretations.
Case study researchers have different interpretations of the stage, for example, Miles and Huberman describe the phases of data analysis as data reduction, data display, and conclusion drawing and verification. Yin identifies three ways for this stage: pattern matching, explanation building, and time series. He also mentions the use of logic models and cross-case synthesis. None of these come with any formulas, although statistical calculations can be part of them.
Eisenhardt suggests two stage for data analysis: the analysis within case data and searching for cross-case patterns. Stake identifies three ways: correspondence and patterns, categorical aggregation, or direct interpretation, and naturalistic generalization. Merriam mentions six analytic
strategies such as ethnographic analysis, narrative analysis, phenomenological analysis, constant comparative method, content analysis, and analytic induction. Dul and Hak also mention pattern matching and visual inspection as ways of analyzing data.

===Examples===
Within-case analysis, cross-case analysis, compare the narratives of informants, pattern matching, writing individual case histories by using archival data, using repertory grid method, data reduction (open coding), visual inspection
===Further Readings===
Ebneyamini, Shiva & Moghadam, Mohammad. (2018). Toward Developing a Framework for Conducting Case Study Research. International Journal of Qualitative Methods. 17. 160940691881795. 10.1177/1609406918817954.

Benbasat, I., Goldstein, D. K., and Mead, M. (1987). "The Case Research Strategy in Studies of Information Systems," MIS Quarterly (11:3), 369-386.

Bhattacherjee, Anol, "Social Science Research: Principles, Methods, and Practices" (2012). Textbooks Collection. 3. https://scholarcommons.usf.edu/oa_textbooks/3.

Yin, R. K. (2002), Case Study Research: Design and Methods. Thousand Oaks, CA: Sage Publications.

==References==

[[Category:Case Study Research]]

Case Study according to Ebneyamini

2024-05-29T14:36:20Z

MichiGau: /* Process description */

==Process description==
Case research, also called case study, is a method of intensively studying a phenomenon over time within its natural setting in one or a few sites. Multiple methods of data collection, such as interviews, observations, prerecorded documents, and secondary data, may be employed and inferences about the phenomenon of interest tend to be rich, detailed, and contextualized. Case research can be employed in a positivist manner for the purpose of theory testing or in an interpretive manner for theory building. This method is more popular in business research than in other social science disciplines.

There are different ideas about what a case study is. If we try to find a common denominator that case study researchers might agree on, it would be something along the following lines:
''The case study should have a “case” which is the object of study. The case should be a complex functioning unit, be investigated in its natural context with a multitude of methods, and be contemporary.''
Nevertheless, the case study researchers mentioned above emphasize different features. Stake points out that crucial to case study research are not the methods of investigation, but that the object of study is a case:
''“As a form of research, the case study is defined by the interest in individual cases, not by the methods of inquiry used.”''<ref name="ebneyamini">Ebneyamini, Shiva & Moghadam, Mohammad. (2018). Toward Developing a Framework for Conducting Case Study Research. International Journal of Qualitative Methods. 17. 160940691881795. 10.1177/1609406918817954.</ref>
Other researchers, such as Yin (1994), place more emphasis on the method and the techniques that constitute a case study (Johansson, 2003)<ref name="ebneyamini">Ebneyamini, Shiva & Moghadam, Mohammad. (2018). Toward Developing a Framework for Conducting Case Study Research. International Journal of Qualitative Methods. 17. 160940691881795. 10.1177/1609406918817954.</ref>.

==Purpose of Case Study Research==
===Description===
Researchers have different ideas, but many agree on what is the purpose of doing research using a case study research strategy. We try to provide an overview of those here. First, we have to decide whether a case study can help us to achieve our research goal or not. There are three factors that determine the best research methodology: The types of questions to be answered, the extent of control over behavioral events, and the degree of focus on contemporary as opposed to historical events.

Case study research is appropriate to be used for researches with “how” and “why” questions. Yin (1994) also presented at least four applications for a case study<ref>Yin, R. K. (1994). Case study research: Design and methods. Beverly Hills, CA: Sage.</ref>
* To explain complex causal links in real-life interventions
* To describe the real-life context in which the intervention has occurred
* To describe the intervention itself
* To explore those situations in which the intervention being evaluated has no clear set of outcomes.

Meredith (1998) noted that there are three outstanding strengths of case study research<ref>Meredith, J. (1998). Building operations management theory through case and field research. Journal of Operations Management, 16, 441–454</ref>:
* The phenomenon can be studied in its natural setting and meaningful, relevant theory generated from the understanding gained through observing actual practice
* The case method allows the questions of why, what, and how, to be answered with a relatively full understanding of the nature and complexity of the complete phenomenon
* The case method lends itself to early, exploratory investigations where variables are still unknown and the phenomenon not at all understood

Voss and his colleagues (2002) have mentioned that the purpose of the case study is<ref>Voss, C., Tsikriktsis, N., & Frohlich, M. (2002). Case research in
operations management. International journal of operations & production management, 22, 195–219.</ref>:
* Exploration to uncover areas for research and theory development
* Theory building to identify/describe key variables, identifying linkages between variables and identifying “why” these relationships exist
* Theory testing to test the theories developed in the previous stages or predict future outcomes
* Theory extension/refinement to better structure the theories in the light of the observed results

Dul and Hak (2008) noted that most of the researchers consider case study research as a useful research strategy (A) when the topic is broad and highly complex, (B) when there is not a lot of theory available, and (C) when “context” is very important. So they consider using case study for theory building, theory testing, hypothesis building, hypothesis testing, and description.<ref>Dul, J., & Hak, T. (2008). Case study methodology in business research. Abingdon, England: Routledge</ref>.

===Examples===
Theory oriented, practice oriented

===Further Readings===
Ebneyamini, Shiva & Moghadam, Mohammad. (2018). Toward Developing a Framework for Conducting Case Study Research. International Journal of Qualitative Methods. 17. 160940691881795. 10.1177/1609406918817954.

Benbasat, I., Goldstein, D. K., and Mead, M. (1987). "The Case Research Strategy in Studies of Information Systems," MIS Quarterly (11:3), 369-386.

Bhattacherjee, Anol, "Social Science Research: Principles, Methods, and Practices" (2012). Textbooks Collection. 3. https://scholarcommons.usf.edu/oa_textbooks/3.

Yin, R. K. (2002), Case Study Research: Design and Methods. Thousand Oaks, CA: Sage Publications.
==Reasons to Use Case Study Research==
===Description===
Choosing a research strategy or methodology depends on three things<ref>Yin, R. K. (2014). Case study research: Design and methods. Los Angeles, CA: Sage</ref>:
# Research question
# The extent of control the researcher has over actual behavioral events
# The degree of focus on contemporary as opposed to historical events
Case study research is generally thought of as being useful to answer “how and why?” questions, and where in-depth research is needed using a holistic lens. It does not require control over actual behavioral events. Meredith suggests four strong points for case study research<ref>Meredith, J. (1998). Building operations management theory through case and field research. Journal of Operations Management, 16, 441–454.</ref>:
* The phenomenon can be studied in its natural setting and meaningful
* Relevant theory generated from the understanding gained through observing actual practice
* The case method allows the questions of why, what, and how to be answered with a relatively full understanding of the nature and complexity of the compete for phenomenon
* The case method lends itself to early, exploratory investigations where the variables are still unknown and phenomenon not at all understood

Handfield and Melnyk (1998) have identified four purposes for case study research: exploration, theory building, theory testing, and theory extension/refinement. According to Dul and Hak, most of the authors consider case study research as a useful research strategy when the topic is broad and highly complex, when there is not a lot of theory available, and when “context” is very important<ref>Dul, J., & Hak, T. (2008). Case study methodology in business research. Abingdon, England: Routledge</ref>. They suggest that the case study can be appropriate for building theory, testing theory, building hypothesis, testing hypothesis, and descriptive study. In our analysis of 15 latest articles published in three important journals of management of technology, 14 of the 15 of them were theory-oriented and one practice-oriented study<ref name="ebneyamini">Ebneyamini, Shiva & Moghadam, Mohammad. (2018). Toward Developing a Framework for Conducting Case Study Research. International Journal of Qualitative Methods. 17. 160940691881795. 10.1177/1609406918817954.</ref>.

===Examples===
Theory extension/refinement, theory building, hypothesis testing
===Further Readings===
Ebneyamini, Shiva & Moghadam, Mohammad. (2018). Toward Developing a Framework for Conducting Case Study Research. International Journal of Qualitative Methods. 17. 160940691881795. 10.1177/1609406918817954.

Benbasat, I., Goldstein, D. K., and Mead, M. (1987). "The Case Research Strategy in Studies of Information Systems," MIS Quarterly (11:3), 369-386.

Bhattacherjee, Anol, "Social Science Research: Principles, Methods, and Practices" (2012). Textbooks Collection. 3. https://scholarcommons.usf.edu/oa_textbooks/3.

Yin, R. K. (2014). Case study research: Design and methods. Los Angeles, CA: Sage

Yin, R. K. (2002), Case Study Research: Design and Methods. Thousand Oaks, CA: Sage Publications.
==Types of Case Study Research==
===Description===
In terms of defining types of case study research, researchers title them differently. Yin has identified some specific types of case study research<ref>Yin, R. K., & Campbell, D. T. (1989). Case study research: Design and methods. University of California: Sage</ref>:
* Exploratory
* Explanatory
* Eescriptive

Stake identified three others:
* Intrinsic
* Instrumental
* Collective

Or according to Zainal, other categories include interpretive and evaluative case studies<ref>Zainal, Z. (2007). Case study as a research method. Journal Kemanusiaan, 9, 1–6</ref>. Dul and Hak categorize the case study research as the single case study and comparative case study<ref>Dul, J., & Hak, T. (2008). Case study methodology in business research. Abingdon, England: Routledge.</ref>. All in all, Ebneyamini et al. believe that in using the case study research as a methodology we have two types: single case study or multiple case study which follows a replication logic.

===Examples===
Comparative case study, longitudinal case study, [[Single Case Study|single case study]]
===Further Readings===
Ebneyamini, Shiva & Moghadam, Mohammad. (2018). Toward Developing a Framework for Conducting Case Study Research. International Journal of Qualitative Methods. 17. 160940691881795. 10.1177/1609406918817954.

Benbasat, I., Goldstein, D. K., and Mead, M. (1987). "The Case Research Strategy in Studies of Information Systems," MIS Quarterly (11:3), 369-386.

Bhattacherjee, Anol, "Social Science Research: Principles, Methods, and Practices" (2012). Textbooks Collection. 3. https://scholarcommons.usf.edu/oa_textbooks/3.

Yin, R. K. (2002), Case Study Research: Design and Methods. Thousand Oaks, CA: Sage Publications.
==Methods of Gathering Data==
===Description===
There are recommendations proposed by researchers about data collection that if it’s done well, it will help the generalization and viability of the case study research:
Yin suggests three principles of data collection for case studies: use multiple sources of data, create a case study database, and maintain a chain of evidence<ref>Yin, R. K. (2014). Case study research: Design and methods. Los Angeles, CA: Sage.</ref>. Stake states that essential parts of a data-gathering plan are definition of the case, list of research questions, identification of helpers, data sources, allocation of time, expenses, and intended reporting. Dul and Hak suggest a stepwise procedure for the development of valid and reliable procedures for measurement (data collection): formulate a precise definition of the concept; determine the object of measurement; identify the location of the object of measurement; specify how evidence of the value of the variable will be extracted from the object of measurement; specify how sources of evidence will be identified, selected, and accessed; specify how evidence will be recorded; specify how data will be categorized; and lastly write a measurement protocol.

As for data sources, Yin suggests documentation, archival records, interview, direct observation, participant observation, and physical artifacts. As for data collection instruments, Stake suggests the use of observation, interview, and document review in qualitative case study research. Merriam suggests conducting effective interviews, being a careful observer, mining data from documents as techniques and procedures that researchers need in order to become effective users of the collection tools. Dul and Hak also propose a qualitative interview, using archives, questionnaire, and observation. Some recommendations mentioned by researchers while conducting an interview is that the starting question after the preliminaries should invite the interviewee to tell the story of their experience of whatever the research is about (Perry, 1998). The starting point is a question that is almost content free. This is your warranty that the answers came from the respondent and did not arise simply because your questions created a self-fulfilling prophecy. Be aware that the data collection in case study research can be both quantitative and qualitative.

===Examples===
Structured interview, semistructured interview, in-depth interview, archival data, meetings, informal meetings, observation

===Further Readings===
Ebneyamini, Shiva & Moghadam, Mohammad. (2018). Toward Developing a Framework for Conducting Case Study Research. International Journal of Qualitative Methods. 17. 160940691881795. 10.1177/1609406918817954.

Benbasat, I., Goldstein, D. K., and Mead, M. (1987). "The Case Research Strategy in Studies of Information Systems," MIS Quarterly (11:3), 369-386.

Bhattacherjee, Anol, "Social Science Research: Principles, Methods, and Practices" (2012). Textbooks Collection. 3. https://scholarcommons.usf.edu/oa_textbooks/3.

Yin, R. K. (2002), Case Study Research: Design and Methods. Thousand Oaks, CA: Sage Publications.
==Data Analysis==
===Description===
Data analysis consists of examining, categorizing, tabulating, or otherwise recombining the evidence to address the initial propositions of a study. The analysis of the case study is one of the least developed aspects of the case study methodology.
The researcher needs to rely on experience and the literature to present the evidence in various ways, using various interpretations.
Case study researchers have different interpretations of the stage, for example, Miles and Huberman describe the phases of data analysis as data reduction, data display, and conclusion drawing and verification. Yin identifies three ways for this stage: pattern matching, explanation building, and time series. He also mentions the use of logic models and cross-case synthesis. None of these come with any formulas, although statistical calculations can be part of them.
Eisenhardt suggests two stage for data analysis: the analysis within case data and searching for cross-case patterns. Stake identifies three ways: correspondence and patterns, categorical aggregation, or direct interpretation, and naturalistic generalization. Merriam mentions six analytic
strategies such as ethnographic analysis, narrative analysis, phenomenological analysis, constant comparative method, content analysis, and analytic induction. Dul and Hak also mention pattern matching and visual inspection as ways of analyzing data.

===Examples===
Within-case analysis, cross-case analysis, compare the narratives of informants, pattern matching, writing individual case histories by using archival data, using repertory grid method, data reduction (open coding), visual inspection
===Further Readings===
Ebneyamini, Shiva & Moghadam, Mohammad. (2018). Toward Developing a Framework for Conducting Case Study Research. International Journal of Qualitative Methods. 17. 160940691881795. 10.1177/1609406918817954.

Benbasat, I., Goldstein, D. K., and Mead, M. (1987). "The Case Research Strategy in Studies of Information Systems," MIS Quarterly (11:3), 369-386.

Bhattacherjee, Anol, "Social Science Research: Principles, Methods, and Practices" (2012). Textbooks Collection. 3. https://scholarcommons.usf.edu/oa_textbooks/3.

Yin, R. K. (2002), Case Study Research: Design and Methods. Thousand Oaks, CA: Sage Publications.

==References==

[[Category:Case Study Research]]

Echeloned DSR according to Tuuanen et al.

2024-05-29T08:47:44Z

MichiGau: MichiGau moved page Echeloned DSR according to Tuuanen et al. to Echeloned DSR according to Tuuanen et al

#REDIRECT [[Echeloned DSR according to Tuuanen et al]]

Echeloned DSR according to Tuuanen et al

2024-05-29T08:47:44Z

MichiGau: MichiGau moved page Echeloned DSR according to Tuuanen et al. to Echeloned DSR according to Tuuanen et al

== Introduction ==
Given the complexity of DSR projects, Tuunanen et. al. (2024) <ref>Tuuanen T., Winter. R., vom Brocke J. (2023), Dealing with Complexity in Design Science Research: Using Design Echelons to Support Planning, Conducting, and Communicating Design Knowledge Contributions, in: Management Information Systems Quarterly (MISQ), 47</ref> propose a rethinking of DSR based on the theory of hierarchical, multi-level systems. This involves decomposing a complex DSR project into specifically defined, self-contained intermediate units - which we refer to as "echelons", following the theory of hierarchical systems (Mesarovic et al., 1970). In an echelon-oriented approach, we decompose a (larger) problem into a hierarchy of logical sub-problems. We create solutions to such subproblems, which serve as intermediate results that can be developed, validated and communicated independently. In combination, such intermediate results contribute to the overall solution.

[[File:EDSR.png|300px|thumb|eDSR]]

Echelons are essentially organizational units that the DSR researcher is free to choose according to his or her understanding and choice of how to decompose a problem. To further conceptualize the echeloned DSR (eDSR) methodology, we distinguish five types of design echelons. One form of type formation is to differentiate design echelons as they combine specific analysis/design and validation activities related to a specific intermediate state of the artifact:
*Problem analysis—contributing the problem statement
*Objectives and requirements definition—contributing design requirements
*Design and development—contributing a projectable solution design
*Demonstration—contributing an illustrative instance of the artifact (in an artificial or natural context)
*Evaluation—contributing the contextualized artifact in use.

== Problem analysis ==
=== Description ===
The overarching problem addressed by the research is the limited ability of large, decentralised organizations to achieve enterprise-wide, long-term 'global' benefits (e.g., leveraging synergies through shared software solutions, limiting the complexity of the IT application landscape) because local decision-makers instead focus on project-, unit- or function-specific, short-term 'local' benefits. One symptom of this overarching problem is that enterprise-wide coordination approaches, such as enterprise architecture management, appear to have reached impact limits due to a lack of institutionalisation by most local decision-makers. The limited impact of coordination interventions can be explained by local decision-makers perceived social legitimacy, efficiency, organizational grounding, and trust in the interventions. Thus, an effective design solution would include design principles for coordination interventions that effectively improve local decision-makers perceptions of the legitimacy, efficiency, organizational grounding, and/or perceived trustworthiness of the interventions.

=== Examples ===
Research objective: Identify effective guidance for enterprise-wide IS coordination in settings with high complexity and high local autonomy
Common methods:
* Literature review
* Review practitioner initiatives
* Expert interview
* Focus group
* Surveys

=== Further Readings ===
Tuuanen T., Winter. R., vom Brocke J. (2023), Dealing with Complexity in Design Science Research: Using Design Echelons to Support Planning, Conducting, and Communicating Design Knowledge Contributions, in: Management Information Systems Quarterly (MISQ), 47

== Objectives and requirements definition ==
=== Description ===
As the empirically based design goals are quite different, in the research we use as an example, it was found that solution design should first focus on a specific goal, such as better demonstrating the effectiveness of the intervention or improving the social legitimacy of enterprise-wide coordination. By testing the effectiveness of different novel interventions, it should be possible to generalize effective intervention design to design principles (linking generalized design requirements to generalized design features).

=== Examples ===
Increase effectivity of coordination interventions

Generalize artifacts to generic coordination guidance.

Common methods:
* Logical reasoning
* Benchmarking
* Expert interview
* Focus group

=== Further Readings ===
Tuuanen T., Winter. R., vom Brocke J. (2023), Dealing with Complexity in Design Science Research: Using Design Echelons to Support Planning, Conducting, and Communicating Design Knowledge Contributions, in: Management Information Systems Quarterly (MISQ), 47

== Design and development ==
=== Description ===
For each proposed set of design requirements, the analysis of existing research and the state of practice would lead to different sets of solution components, which would then be integrated and tailored to the problem at hand. For example, to demonstrate the efficiency of coordination ("what's in it for me?"), either the costing and charging of technical debt to non-compliant change projects or the provision of cost-reducing project support (e.g. by supporting architects or reducing the cost of shared software solutions) could be used as foundations. A possible basis for improving the social legitimacy of enterprise-wide coordination ("Why should I comply?") could be communication measures (e.g., showcasing successful solution sharing) or engineered social pressure (e.g., by making non-compliant projects transparent across the organization). At a later stage, the generalization of effective design features and design requirements allows the formulation of design principles.

=== Examples ===
Intervention type 1: Charge projects for technical debt (internalization of effects)

Intervention type 2: Use a label to create transparency about incompliant decision-making

Intervention type 3: Use other social clues to create transparency

Consolidate design requirements and design features to design principles

=== Further Readings ===
Tuuanen T., Winter. R., vom Brocke J. (2023), Dealing with Complexity in Design Science Research: Using Design Echelons to Support Planning, Conducting, and Communicating Design Knowledge Contributions, in: Management Information Systems Quarterly (MISQ), 47

== Demonstration ==
=== Description ===
All of the above solution strategies need to be contextualized to demonstrate their potential to solve the design problem. For example, to demonstrate the ability of social interventions to effectively increase the compliance of decentralized decisions in change projects, labels must be developed together with the case organization to ensure understanding and acceptance. Such labels make transparent which projects (or business units) are more or less compliant and thus create or reduce technical debt for the entire organization (Schilling et al., 2019). An alternative PoC for this projectable design is to create "relationship manager guilds" where know-your-customer data quality issues can be discussed and a common sense of compliance can be institutionalized.

=== Examples ===
Re-calculate historical projects
Compliance label prototype

Show that successful coordination artifacts instantiate design principles

=== Further Readings ===
Tuuanen T., Winter. R., vom Brocke J. (2023), Dealing with Complexity in Design Science Research: Using Design Echelons to Support Planning, Conducting, and Communicating Design Knowledge Contributions, in: Management Information Systems Quarterly (MISQ), 47

== Evaluation ==
=== Description ===
Promising concepts need to be applied with real people who have a real stake in the outcome. In the project used as an example, two companies were involved in field tests (indifferent business units) and a pilot study, respectively.

=== Examples ===
Case study: Resistance and escalation prevents sustained effectiveness of intervention

Field test: Compliance label rollout in Business Unit

A
Pilot study in company: Digital KYC data quality nudges

Focus group: Design principles development

=== Further Readings ===
Tuuanen T., Winter. R., vom Brocke J. (2023), Dealing with Complexity in Design Science Research: Using Design Echelons to Support Planning, Conducting, and Communicating Design Knowledge Contributions, in: Management Information Systems Quarterly (MISQ), 47

== References ==
[[Category:Design Science Research]]

Main Page

2024-05-21T07:27:20Z

MichiGau:

<templatestyles src="Template:Styles.css" />

<div id="audiences" class="mainpage_row">
<div class="mainpage_box">
<h3>Welcome to the Design Science Research Process Wiki</h3>
<div id="mainpage-admins" class="items" title="System administrators">
This site provides researchers with descriptions and guidance on Design Science Research (DSR) processes widely used in information systems (IS). Click on a linked process name below to find details about the process. Please feel free to add to the information already present, correct errors, or suggest additional methods or processes for the list.

This site is built on user contributions. Please share your knowledge and contribute!

In order to do so, you need to request an invitation from one of the [[Research Methods:About|Editors]].</div></div></div>

<div id="audiences" class="mainpage_row">
<div class="mainpage_box">
<h3>List of DSR processes</h3>
<div id="mainpage-admins" class="items" title="System administrators">[[Action Design Research according to Sein et al.]]

[[Design Science Research according to Kuechler & Vaishnavi]]

[[Design Science Research according to Peffers et al.]]

[[Echeloned DSR according to Tuuanen et al.]]
</div></div></div>
<div id="audiences" class="mainpage_row">
<div class="mainpage_box">
<h3>List of DSR support processes</h3>
<div id="mainpage-admins" class="items" title="System administrators">[[Literature Review Research]]

[[Case Study Research]]

[[Taxonomy Development Research]]

[[Focus Groups Research]]</div></div></div>

EDSR- A Methodology Using Design Echelons

2024-05-21T07:27:04Z

MichiGau: MichiGau moved page EDSR- A Methodology Using Design Echelons to Echeloned DSR according to Tuuanen et al.

#REDIRECT [[Echeloned DSR according to Tuuanen et al.]]

Echeloned DSR according to Tuuanen et al

2024-05-21T07:27:04Z

MichiGau: MichiGau moved page EDSR- A Methodology Using Design Echelons to Echeloned DSR according to Tuuanen et al.

Echeloned DSR according to Tuuanen et al

2024-05-21T07:23:13Z

MichiGau: /* Description */

Echeloned DSR according to Tuuanen et al

2024-05-21T07:19:15Z

MichiGau: /* Examples */

== Introduction ==
Given the complexity of DSR projects, Tuunanen et. al. (2024) <ref>Tuuanen T., Winter. R., vom Brocke J. (2023), Dealing with Complexity in Design Science Research: Using Design Echelons to Support Planning, Conducting, and Communicating Design Knowledge Contributions, in: Management Information Systems Quarterly (MISQ), 47</ref> propose a rethinking of DSR based on the theory of hierarchical, multi-level systems. This involves decomposing a complex DSR project into specifically defined, self-contained intermediate units - which we refer to as "echelons", following the theory of hierarchical systems (Mesarovic et al., 1970). In an echelon-oriented approach, we decompose a (larger) problem into a hierarchy of logical sub-problems. We create solutions to such subproblems, which serve as intermediate results that can be developed, validated and communicated independently. In combination, such intermediate results contribute to the overall solution.

[[File:EDSR.png|300px|thumb|eDSR]]

Echelons are essentially organizational units that the DSR researcher is free to choose according to his or her understanding and choice of how to decompose a problem. To further conceptualize the echeloned DSR (eDSR) methodology, we distinguish five types of design echelons. One form of type formation is to differentiate design echelons as they combine specific analysis/design and validation activities related to a specific intermediate state of the artifact:
*Problem analysis—contributing the problem statement
*Objectives and requirements definition—contributing design requirements
*Design and development—contributing a projectable solution design
*Demonstration—contributing an illustrative instance of the artifact (in an artificial or natural context)
*Evaluation—contributing the contextualized artifact in use.

== Problem analysis ==
=== Description ===
The overarching problem addressed by the research used as an example in Figure 2 is the limited ability of large, decentralised organizations to achieve enterprise-wide, long-term 'global' benefits (e.g., leveraging synergies through shared software solutions, limiting the complexity of the IT application landscape) because local decision-makers instead focus on project-, unit- or function-specific, short-term 'local' benefits. One symptom of this overarching problem is that enterprise-wide coordination approaches, such as enterprise architecture management, appear to have reached impact limits due to a lack of institutionalisation by most local decision-makers. The limited impact of coordination interventions can be explained by local decision-makers perceived social legitimacy, efficiency, organizational grounding, and trust in the interventions. Thus, an effective design solution would include design principles for coordination interventions that effectively improve local decision-makers perceptions of the legitimacy, efficiency, organizational grounding, and/or perceived trustworthiness of the interventions.

=== Examples ===
Research objective: Identify effective guidance for enterprise-wide IS coordination in settings with high complexity and high local autonomy
Common methods:
* Literature review
* Review practitioner initiatives
* Expert interview
* Focus group
* Surveys

=== Further Readings ===
Tuuanen T., Winter. R., vom Brocke J. (2023), Dealing with Complexity in Design Science Research: Using Design Echelons to Support Planning, Conducting, and Communicating Design Knowledge Contributions, in: Management Information Systems Quarterly (MISQ), 47

== Objectives and requirements definition ==
=== Description ===
As the empirically based design goals are quite different, in the research we use as an example, it was found that solution design should first focus on a specific goal, such as better demonstrating the effectiveness of the intervention or improving the social legitimacy of enterprise-wide coordination. By testing the effectiveness of different novel interventions, it should be possible to generalize effective intervention design to design principles (linking generalized design requirements to generalized design features).

=== Examples ===
Increase effectivity of coordination interventions

Generalize artifacts to generic coordination guidance.

Common methods:
* Logical reasoning
* Benchmarking
* Expert interview
* Focus group

=== Further Readings ===
Tuuanen T., Winter. R., vom Brocke J. (2023), Dealing with Complexity in Design Science Research: Using Design Echelons to Support Planning, Conducting, and Communicating Design Knowledge Contributions, in: Management Information Systems Quarterly (MISQ), 47

== Design and development ==
=== Description ===
For each proposed set of design requirements, the analysis of existing research and the state of practice would lead to different sets of solution components, which would then be integrated and tailored to the problem at hand. For example, to demonstrate the efficiency of coordination ("what's in it for me?"), either the costing and charging of technical debt to non-compliant change projects or the provision of cost-reducing project support (e.g. by supporting architects or reducing the cost of shared software solutions) could be used as foundations. A possible basis for improving the social legitimacy of enterprise-wide coordination ("Why should I comply?") could be communication measures (e.g., showcasing successful solution sharing) or engineered social pressure (e.g., by making non-compliant projects transparent across the organization). At a later stage, the generalization of effective design features and design requirements allows the formulation of design principles.

=== Examples ===
Intervention type 1: Charge projects for technical debt (internalization of effects)

Intervention type 2: Use a label to create transparency about incompliant decision-making

Intervention type 3: Use other social clues to create transparency

Consolidate design requirements and design features to design principles

=== Further Readings ===
Tuuanen T., Winter. R., vom Brocke J. (2023), Dealing with Complexity in Design Science Research: Using Design Echelons to Support Planning, Conducting, and Communicating Design Knowledge Contributions, in: Management Information Systems Quarterly (MISQ), 47

== Demonstration ==
=== Description ===
All of the above solution strategies need to be contextualized to demonstrate their potential to solve the design problem. For example, to demonstrate the ability of social interventions to effectively increase the compliance of decentralized decisions in change projects, labels must be developed together with the case organization to ensure understanding and acceptance. Such labels make transparent which projects (or business units) are more or less compliant and thus create or reduce technical debt for the entire organization (Schilling et al., 2019). An alternative PoC for this projectable design is to create "relationship manager guilds" where know-your-customer data quality issues can be discussed and a common sense of compliance can be institutionalized.

=== Examples ===
Re-calculate historical projects
Compliance label prototype

Show that successful coordination artifacts instantiate design principles

=== Further Readings ===
Tuuanen T., Winter. R., vom Brocke J. (2023), Dealing with Complexity in Design Science Research: Using Design Echelons to Support Planning, Conducting, and Communicating Design Knowledge Contributions, in: Management Information Systems Quarterly (MISQ), 47

== Evaluation ==
=== Description ===
Promising concepts need to be applied with real people who have a real stake in the outcome. In the project used as an example, two companies were involved in field tests (indifferent business units) and a pilot study, respectively.

=== Examples ===
Case study: Resistance and escalation prevents sustained effectiveness of intervention

Field test: Compliance label rollout in Business Unit

A
Pilot study in company: Digital KYC data quality nudges

Focus group: Design principles development

=== Further Readings ===
Tuuanen T., Winter. R., vom Brocke J. (2023), Dealing with Complexity in Design Science Research: Using Design Echelons to Support Planning, Conducting, and Communicating Design Knowledge Contributions, in: Management Information Systems Quarterly (MISQ), 47

== References ==
[[Category:Design Science Research]]

Echeloned DSR according to Tuuanen et al

2024-05-21T07:18:28Z

MichiGau: /* Examples */

== Introduction ==
Given the complexity of DSR projects, Tuunanen et. al. (2024) <ref>Tuuanen T., Winter. R., vom Brocke J. (2023), Dealing with Complexity in Design Science Research: Using Design Echelons to Support Planning, Conducting, and Communicating Design Knowledge Contributions, in: Management Information Systems Quarterly (MISQ), 47</ref> propose a rethinking of DSR based on the theory of hierarchical, multi-level systems. This involves decomposing a complex DSR project into specifically defined, self-contained intermediate units - which we refer to as "echelons", following the theory of hierarchical systems (Mesarovic et al., 1970). In an echelon-oriented approach, we decompose a (larger) problem into a hierarchy of logical sub-problems. We create solutions to such subproblems, which serve as intermediate results that can be developed, validated and communicated independently. In combination, such intermediate results contribute to the overall solution.

[[File:EDSR.png|300px|thumb|eDSR]]

Echelons are essentially organizational units that the DSR researcher is free to choose according to his or her understanding and choice of how to decompose a problem. To further conceptualize the echeloned DSR (eDSR) methodology, we distinguish five types of design echelons. One form of type formation is to differentiate design echelons as they combine specific analysis/design and validation activities related to a specific intermediate state of the artifact:
*Problem analysis—contributing the problem statement
*Objectives and requirements definition—contributing design requirements
*Design and development—contributing a projectable solution design
*Demonstration—contributing an illustrative instance of the artifact (in an artificial or natural context)
*Evaluation—contributing the contextualized artifact in use.

== Problem analysis ==
=== Description ===
The overarching problem addressed by the research used as an example in Figure 2 is the limited ability of large, decentralised organizations to achieve enterprise-wide, long-term 'global' benefits (e.g., leveraging synergies through shared software solutions, limiting the complexity of the IT application landscape) because local decision-makers instead focus on project-, unit- or function-specific, short-term 'local' benefits. One symptom of this overarching problem is that enterprise-wide coordination approaches, such as enterprise architecture management, appear to have reached impact limits due to a lack of institutionalisation by most local decision-makers. The limited impact of coordination interventions can be explained by local decision-makers perceived social legitimacy, efficiency, organizational grounding, and trust in the interventions. Thus, an effective design solution would include design principles for coordination interventions that effectively improve local decision-makers perceptions of the legitimacy, efficiency, organizational grounding, and/or perceived trustworthiness of the interventions.

=== Examples ===
Research objective: Identify effective guidance for enterprise-wide IS coordination in settings with high complexity and high local autonomy
Common methods:
* Literature review
* Review practitioner initiatives
* Expert interview
* Focus group
* Surveys

=== Further Readings ===
Tuuanen T., Winter. R., vom Brocke J. (2023), Dealing with Complexity in Design Science Research: Using Design Echelons to Support Planning, Conducting, and Communicating Design Knowledge Contributions, in: Management Information Systems Quarterly (MISQ), 47

== Objectives and requirements definition ==
=== Description ===
As the empirically based design goals are quite different, in the research we use as an example, it was found that solution design should first focus on a specific goal, such as better demonstrating the effectiveness of the intervention or improving the social legitimacy of enterprise-wide coordination. By testing the effectiveness of different novel interventions, it should be possible to generalize effective intervention design to design principles (linking generalized design requirements to generalized design features).

=== Examples ===
Increase effectivity of coordination interventions

Generalize artifacts to generic coordination guidance.

Common methods:
* Logical reasoning
* Benchmarking
* Expert interview
* Focus group

=== Further Readings ===
Tuuanen T., Winter. R., vom Brocke J. (2023), Dealing with Complexity in Design Science Research: Using Design Echelons to Support Planning, Conducting, and Communicating Design Knowledge Contributions, in: Management Information Systems Quarterly (MISQ), 47

== Design and development ==
=== Description ===
For each proposed set of design requirements, the analysis of existing research and the state of practice would lead to different sets of solution components, which would then be integrated and tailored to the problem at hand. For example, to demonstrate the efficiency of coordination ("what's in it for me?"), either the costing and charging of technical debt to non-compliant change projects or the provision of cost-reducing project support (e.g. by supporting architects or reducing the cost of shared software solutions) could be used as foundations. A possible basis for improving the social legitimacy of enterprise-wide coordination ("Why should I comply?") could be communication measures (e.g., showcasing successful solution sharing) or engineered social pressure (e.g., by making non-compliant projects transparent across the organization). At a later stage, the generalization of effective design features and design requirements allows the formulation of design principles.

=== Examples ===
Intervention type 1: Charge projects for technical debt (internalization of effects)

Intervention type 2: Use a label to create transparency about incompliant decision-making

Intervention type 3: Use other social clues to create transparency

Consolidate design requirements and design features to design principles

=== Further Readings ===
Tuuanen T., Winter. R., vom Brocke J. (2023), Dealing with Complexity in Design Science Research: Using Design Echelons to Support Planning, Conducting, and Communicating Design Knowledge Contributions, in: Management Information Systems Quarterly (MISQ), 47

== Demonstration ==
=== Description ===
All of the above solution strategies need to be contextualized to demonstrate their potential to solve the design problem. For example, to demonstrate the ability of social interventions to effectively increase the compliance of decentralized decisions in change projects, labels must be developed together with the case organization to ensure understanding and acceptance. Such labels make transparent which projects (or business units) are more or less compliant and thus create or reduce technical debt for the entire organization (Schilling et al., 2019). An alternative PoC for this projectable design is to create "relationship manager guilds" where know-your-customer data quality issues can be discussed and a common sense of compliance can be institutionalized.

=== Examples ===
Re-calculate historical projects
Compliance label prototype

Show that successful coordination artifacts instantiate design principles

=== Further Readings ===
Tuuanen T., Winter. R., vom Brocke J. (2023), Dealing with Complexity in Design Science Research: Using Design Echelons to Support Planning, Conducting, and Communicating Design Knowledge Contributions, in: Management Information Systems Quarterly (MISQ), 47

== Evaluation ==
=== Description ===
Promising concepts need to be applied with real people who have a real stake in the outcome. In the project used as an example, two companies were involved in field tests (indifferent business units) and a pilot study, respectively.

=== Examples ===
Case study: Resistance and escalation prevents sustained effectiveness of intervention
Field test: Compliance label rollout in Business Unit A
Pilot study in company: Digital KYC data quality nudges
Focus group: Design principles development

=== Further Readings ===
Tuuanen T., Winter. R., vom Brocke J. (2023), Dealing with Complexity in Design Science Research: Using Design Echelons to Support Planning, Conducting, and Communicating Design Knowledge Contributions, in: Management Information Systems Quarterly (MISQ), 47

== References ==
[[Category:Design Science Research]]

Echeloned DSR according to Tuuanen et al

2024-05-21T07:17:34Z

MichiGau: /* Examples */

== Introduction ==
Given the complexity of DSR projects, Tuunanen et. al. (2024) <ref>Tuuanen T., Winter. R., vom Brocke J. (2023), Dealing with Complexity in Design Science Research: Using Design Echelons to Support Planning, Conducting, and Communicating Design Knowledge Contributions, in: Management Information Systems Quarterly (MISQ), 47</ref> propose a rethinking of DSR based on the theory of hierarchical, multi-level systems. This involves decomposing a complex DSR project into specifically defined, self-contained intermediate units - which we refer to as "echelons", following the theory of hierarchical systems (Mesarovic et al., 1970). In an echelon-oriented approach, we decompose a (larger) problem into a hierarchy of logical sub-problems. We create solutions to such subproblems, which serve as intermediate results that can be developed, validated and communicated independently. In combination, such intermediate results contribute to the overall solution.

[[File:EDSR.png|300px|thumb|eDSR]]

Echelons are essentially organizational units that the DSR researcher is free to choose according to his or her understanding and choice of how to decompose a problem. To further conceptualize the echeloned DSR (eDSR) methodology, we distinguish five types of design echelons. One form of type formation is to differentiate design echelons as they combine specific analysis/design and validation activities related to a specific intermediate state of the artifact:
*Problem analysis—contributing the problem statement
*Objectives and requirements definition—contributing design requirements
*Design and development—contributing a projectable solution design
*Demonstration—contributing an illustrative instance of the artifact (in an artificial or natural context)
*Evaluation—contributing the contextualized artifact in use.

== Problem analysis ==
=== Description ===
The overarching problem addressed by the research used as an example in Figure 2 is the limited ability of large, decentralised organizations to achieve enterprise-wide, long-term 'global' benefits (e.g., leveraging synergies through shared software solutions, limiting the complexity of the IT application landscape) because local decision-makers instead focus on project-, unit- or function-specific, short-term 'local' benefits. One symptom of this overarching problem is that enterprise-wide coordination approaches, such as enterprise architecture management, appear to have reached impact limits due to a lack of institutionalisation by most local decision-makers. The limited impact of coordination interventions can be explained by local decision-makers perceived social legitimacy, efficiency, organizational grounding, and trust in the interventions. Thus, an effective design solution would include design principles for coordination interventions that effectively improve local decision-makers perceptions of the legitimacy, efficiency, organizational grounding, and/or perceived trustworthiness of the interventions.

=== Examples ===
Research objective: Identify effective guidance for enterprise-wide IS coordination in settings with high complexity and high local autonomy
Common methods:
* Literature review
* Review practitioner initiatives
* Expert interview
* Focus group
* Surveys

=== Further Readings ===
Tuuanen T., Winter. R., vom Brocke J. (2023), Dealing with Complexity in Design Science Research: Using Design Echelons to Support Planning, Conducting, and Communicating Design Knowledge Contributions, in: Management Information Systems Quarterly (MISQ), 47

== Objectives and requirements definition ==
=== Description ===
As the empirically based design goals are quite different, in the research we use as an example, it was found that solution design should first focus on a specific goal, such as better demonstrating the effectiveness of the intervention or improving the social legitimacy of enterprise-wide coordination. By testing the effectiveness of different novel interventions, it should be possible to generalize effective intervention design to design principles (linking generalized design requirements to generalized design features).

=== Examples ===
Increase effectivity of coordination interventions

Generalize artifacts to generic coordination guidance.

Common methods:
* Logical reasoning
* Benchmarking
* Expert interview
* Focus group

=== Further Readings ===
Tuuanen T., Winter. R., vom Brocke J. (2023), Dealing with Complexity in Design Science Research: Using Design Echelons to Support Planning, Conducting, and Communicating Design Knowledge Contributions, in: Management Information Systems Quarterly (MISQ), 47

== Design and development ==
=== Description ===
For each proposed set of design requirements, the analysis of existing research and the state of practice would lead to different sets of solution components, which would then be integrated and tailored to the problem at hand. For example, to demonstrate the efficiency of coordination ("what's in it for me?"), either the costing and charging of technical debt to non-compliant change projects or the provision of cost-reducing project support (e.g. by supporting architects or reducing the cost of shared software solutions) could be used as foundations. A possible basis for improving the social legitimacy of enterprise-wide coordination ("Why should I comply?") could be communication measures (e.g., showcasing successful solution sharing) or engineered social pressure (e.g., by making non-compliant projects transparent across the organization). At a later stage, the generalization of effective design features and design requirements allows the formulation of design principles.

=== Examples ===
Intervention type 1: Charge projects for technical debt (internalization of effects)

Intervention type 2: Use a label to create transparency about incompliant decision-making

Intervention type 3: Use other social clues to create transparency

Consolidate design requirements and design features to design principles

=== Further Readings ===
Tuuanen T., Winter. R., vom Brocke J. (2023), Dealing with Complexity in Design Science Research: Using Design Echelons to Support Planning, Conducting, and Communicating Design Knowledge Contributions, in: Management Information Systems Quarterly (MISQ), 47

== Demonstration ==
=== Description ===
All of the above solution strategies need to be contextualized to demonstrate their potential to solve the design problem. For example, to demonstrate the ability of social interventions to effectively increase the compliance of decentralized decisions in change projects, labels must be developed together with the case organization to ensure understanding and acceptance. Such labels make transparent which projects (or business units) are more or less compliant and thus create or reduce technical debt for the entire organization (Schilling et al., 2019). An alternative PoC for this projectable design is to create "relationship manager guilds" where know-your-customer data quality issues can be discussed and a common sense of compliance can be institutionalized.

=== Examples ===
Re-calculate historical projects
Compliance label prototype
Show that successful coordination artifacts instantiate design principles

=== Further Readings ===
Tuuanen T., Winter. R., vom Brocke J. (2023), Dealing with Complexity in Design Science Research: Using Design Echelons to Support Planning, Conducting, and Communicating Design Knowledge Contributions, in: Management Information Systems Quarterly (MISQ), 47

== Evaluation ==
=== Description ===
Promising concepts need to be applied with real people who have a real stake in the outcome. In the project used as an example, two companies were involved in field tests (indifferent business units) and a pilot study, respectively.

=== Examples ===
Case study: Resistance and escalation prevents sustained effectiveness of intervention
Field test: Compliance label rollout in Business Unit A
Pilot study in company: Digital KYC data quality nudges
Focus group: Design principles development

=== Further Readings ===
Tuuanen T., Winter. R., vom Brocke J. (2023), Dealing with Complexity in Design Science Research: Using Design Echelons to Support Planning, Conducting, and Communicating Design Knowledge Contributions, in: Management Information Systems Quarterly (MISQ), 47

== References ==
[[Category:Design Science Research]]

Echeloned DSR according to Tuuanen et al

2024-05-21T07:12:10Z

MichiGau:

== Introduction ==
Given the complexity of DSR projects, Tuunanen et. al. (2024) <ref>Tuuanen T., Winter. R., vom Brocke J. (2023), Dealing with Complexity in Design Science Research: Using Design Echelons to Support Planning, Conducting, and Communicating Design Knowledge Contributions, in: Management Information Systems Quarterly (MISQ), 47</ref> propose a rethinking of DSR based on the theory of hierarchical, multi-level systems. This involves decomposing a complex DSR project into specifically defined, self-contained intermediate units - which we refer to as "echelons", following the theory of hierarchical systems (Mesarovic et al., 1970). In an echelon-oriented approach, we decompose a (larger) problem into a hierarchy of logical sub-problems. We create solutions to such subproblems, which serve as intermediate results that can be developed, validated and communicated independently. In combination, such intermediate results contribute to the overall solution.

[[File:EDSR.png|300px|thumb|eDSR]]

Echelons are essentially organizational units that the DSR researcher is free to choose according to his or her understanding and choice of how to decompose a problem. To further conceptualize the echeloned DSR (eDSR) methodology, we distinguish five types of design echelons. One form of type formation is to differentiate design echelons as they combine specific analysis/design and validation activities related to a specific intermediate state of the artifact:
*Problem analysis—contributing the problem statement
*Objectives and requirements definition—contributing design requirements
*Design and development—contributing a projectable solution design
*Demonstration—contributing an illustrative instance of the artifact (in an artificial or natural context)
*Evaluation—contributing the contextualized artifact in use.

== Problem analysis ==
=== Description ===
The overarching problem addressed by the research used as an example in Figure 2 is the limited ability of large, decentralised organizations to achieve enterprise-wide, long-term 'global' benefits (e.g., leveraging synergies through shared software solutions, limiting the complexity of the IT application landscape) because local decision-makers instead focus on project-, unit- or function-specific, short-term 'local' benefits. One symptom of this overarching problem is that enterprise-wide coordination approaches, such as enterprise architecture management, appear to have reached impact limits due to a lack of institutionalisation by most local decision-makers. The limited impact of coordination interventions can be explained by local decision-makers perceived social legitimacy, efficiency, organizational grounding, and trust in the interventions. Thus, an effective design solution would include design principles for coordination interventions that effectively improve local decision-makers perceptions of the legitimacy, efficiency, organizational grounding, and/or perceived trustworthiness of the interventions.

=== Examples ===
Research objective: Identify effective guidance for enterprise-wide IS coordination in settings with high complexity and high local autonomy
Common methods:
* Literature review
* Review practitioner initiatives
* Expert interview
* Focus group
* Surveys

=== Further Readings ===
Tuuanen T., Winter. R., vom Brocke J. (2023), Dealing with Complexity in Design Science Research: Using Design Echelons to Support Planning, Conducting, and Communicating Design Knowledge Contributions, in: Management Information Systems Quarterly (MISQ), 47

== Objectives and requirements definition ==
=== Description ===
As the empirically based design goals are quite different, in the research we use as an example, it was found that solution design should first focus on a specific goal, such as better demonstrating the effectiveness of the intervention or improving the social legitimacy of enterprise-wide coordination. By testing the effectiveness of different novel interventions, it should be possible to generalize effective intervention design to design principles (linking generalized design requirements to generalized design features).

=== Examples ===
Increase effectivity of coordination interventions

Generalize artifacts to generic coordination guidance.

Common methods:
* Logical reasoning
* Benchmarking
* Expert interview
* Focus group

=== Further Readings ===
Tuuanen T., Winter. R., vom Brocke J. (2023), Dealing with Complexity in Design Science Research: Using Design Echelons to Support Planning, Conducting, and Communicating Design Knowledge Contributions, in: Management Information Systems Quarterly (MISQ), 47

== Design and development ==
=== Description ===
For each proposed set of design requirements, the analysis of existing research and the state of practice would lead to different sets of solution components, which would then be integrated and tailored to the problem at hand. For example, to demonstrate the efficiency of coordination ("what's in it for me?"), either the costing and charging of technical debt to non-compliant change projects or the provision of cost-reducing project support (e.g. by supporting architects or reducing the cost of shared software solutions) could be used as foundations. A possible basis for improving the social legitimacy of enterprise-wide coordination ("Why should I comply?") could be communication measures (e.g., showcasing successful solution sharing) or engineered social pressure (e.g., by making non-compliant projects transparent across the organization). At a later stage, the generalization of effective design features and design requirements allows the formulation of design principles.

=== Examples ===
Intervention type 1: Charge projects for technical debt (internalization of effects)
Intervention type 2: Use a label to create transparency about incompliant decision-making
Intervention type 3: Use other social clues to create transparency
Consolidate design requirements and design features to design principles

=== Further Readings ===
Tuuanen T., Winter. R., vom Brocke J. (2023), Dealing with Complexity in Design Science Research: Using Design Echelons to Support Planning, Conducting, and Communicating Design Knowledge Contributions, in: Management Information Systems Quarterly (MISQ), 47

== Demonstration ==
=== Description ===
All of the above solution strategies need to be contextualized to demonstrate their potential to solve the design problem. For example, to demonstrate the ability of social interventions to effectively increase the compliance of decentralized decisions in change projects, labels must be developed together with the case organization to ensure understanding and acceptance. Such labels make transparent which projects (or business units) are more or less compliant and thus create or reduce technical debt for the entire organization (Schilling et al., 2019). An alternative PoC for this projectable design is to create "relationship manager guilds" where know-your-customer data quality issues can be discussed and a common sense of compliance can be institutionalized.

=== Examples ===
Re-calculate historical projects
Compliance label prototype
Show that successful coordination artifacts instantiate design principles

=== Further Readings ===
Tuuanen T., Winter. R., vom Brocke J. (2023), Dealing with Complexity in Design Science Research: Using Design Echelons to Support Planning, Conducting, and Communicating Design Knowledge Contributions, in: Management Information Systems Quarterly (MISQ), 47

== Evaluation ==
=== Description ===
Promising concepts need to be applied with real people who have a real stake in the outcome. In the project used as an example, two companies were involved in field tests (indifferent business units) and a pilot study, respectively.

=== Examples ===
Case study: Resistance and escalation prevents sustained effectiveness of intervention
Field test: Compliance label rollout in Business Unit A
Pilot study in company: Digital KYC data quality nudges
Focus group: Design principles development

=== Further Readings ===
Tuuanen T., Winter. R., vom Brocke J. (2023), Dealing with Complexity in Design Science Research: Using Design Echelons to Support Planning, Conducting, and Communicating Design Knowledge Contributions, in: Management Information Systems Quarterly (MISQ), 47

== References ==
[[Category:Design Science Research]]

Echeloned DSR according to Tuuanen et al

2024-05-21T07:11:49Z

MichiGau:

== Introduction ==
Given the complexity of DSR projects, Tuunanen et. al. (2024) <ref>Tuuanen T., Winter. R., vom Brocke J. (2023), Dealing with Complexity in Design Science Research: Using Design Echelons to Support Planning, Conducting, and Communicating Design Knowledge Contributions, in: Management Information Systems Quarterly (MISQ), 47</ref> propose a rethinking of DSR based on the theory of hierarchical, multi-level systems. This involves decomposing a complex DSR project into specifically defined, self-contained intermediate units - which we refer to as "echelons", following the theory of hierarchical systems (Mesarovic et al., 1970). In an echelon-oriented approach, we decompose a (larger) problem into a hierarchy of logical sub-problems. We create solutions to such subproblems, which serve as intermediate results that can be developed, validated and communicated independently. In combination, such intermediate results contribute to the overall solution.

Echelons are essentially organizational units that the DSR researcher is free to choose according to his or her understanding and choice of how to decompose a problem. To further conceptualize the echeloned DSR (eDSR) methodology, we distinguish five types of design echelons. One form of type formation is to differentiate design echelons as they combine specific analysis/design and validation activities related to a specific intermediate state of the artifact:
*Problem analysis—contributing the problem statement
*Objectives and requirements definition—contributing design requirements
*Design and development—contributing a projectable solution design
*Demonstration—contributing an illustrative instance of the artifact (in an artificial or natural context)
*Evaluation—contributing the contextualized artifact in use.
[[File:EDSR.png|300px|thumb|eDSR]]
== Problem analysis ==
=== Description ===
The overarching problem addressed by the research used as an example in Figure 2 is the limited ability of large, decentralised organizations to achieve enterprise-wide, long-term 'global' benefits (e.g., leveraging synergies through shared software solutions, limiting the complexity of the IT application landscape) because local decision-makers instead focus on project-, unit- or function-specific, short-term 'local' benefits. One symptom of this overarching problem is that enterprise-wide coordination approaches, such as enterprise architecture management, appear to have reached impact limits due to a lack of institutionalisation by most local decision-makers. The limited impact of coordination interventions can be explained by local decision-makers perceived social legitimacy, efficiency, organizational grounding, and trust in the interventions. Thus, an effective design solution would include design principles for coordination interventions that effectively improve local decision-makers perceptions of the legitimacy, efficiency, organizational grounding, and/or perceived trustworthiness of the interventions.

=== Examples ===
Research objective: Identify effective guidance for enterprise-wide IS coordination in settings with high complexity and high local autonomy
Common methods:
* Literature review
* Review practitioner initiatives
* Expert interview
* Focus group
* Surveys

=== Further Readings ===
Tuuanen T., Winter. R., vom Brocke J. (2023), Dealing with Complexity in Design Science Research: Using Design Echelons to Support Planning, Conducting, and Communicating Design Knowledge Contributions, in: Management Information Systems Quarterly (MISQ), 47

== Objectives and requirements definition ==
=== Description ===
As the empirically based design goals are quite different, in the research we use as an example, it was found that solution design should first focus on a specific goal, such as better demonstrating the effectiveness of the intervention or improving the social legitimacy of enterprise-wide coordination. By testing the effectiveness of different novel interventions, it should be possible to generalize effective intervention design to design principles (linking generalized design requirements to generalized design features).

=== Examples ===
Increase effectivity of coordination interventions

Generalize artifacts to generic coordination guidance.

Common methods:
* Logical reasoning
* Benchmarking
* Expert interview
* Focus group

=== Further Readings ===
Tuuanen T., Winter. R., vom Brocke J. (2023), Dealing with Complexity in Design Science Research: Using Design Echelons to Support Planning, Conducting, and Communicating Design Knowledge Contributions, in: Management Information Systems Quarterly (MISQ), 47

== Design and development ==
=== Description ===
For each proposed set of design requirements, the analysis of existing research and the state of practice would lead to different sets of solution components, which would then be integrated and tailored to the problem at hand. For example, to demonstrate the efficiency of coordination ("what's in it for me?"), either the costing and charging of technical debt to non-compliant change projects or the provision of cost-reducing project support (e.g. by supporting architects or reducing the cost of shared software solutions) could be used as foundations. A possible basis for improving the social legitimacy of enterprise-wide coordination ("Why should I comply?") could be communication measures (e.g., showcasing successful solution sharing) or engineered social pressure (e.g., by making non-compliant projects transparent across the organization). At a later stage, the generalization of effective design features and design requirements allows the formulation of design principles.

=== Examples ===
Intervention type 1: Charge projects for technical debt (internalization of effects)
Intervention type 2: Use a label to create transparency about incompliant decision-making
Intervention type 3: Use other social clues to create transparency
Consolidate design requirements and design features to design principles

=== Further Readings ===
Tuuanen T., Winter. R., vom Brocke J. (2023), Dealing with Complexity in Design Science Research: Using Design Echelons to Support Planning, Conducting, and Communicating Design Knowledge Contributions, in: Management Information Systems Quarterly (MISQ), 47

== Demonstration ==
=== Description ===
All of the above solution strategies need to be contextualized to demonstrate their potential to solve the design problem. For example, to demonstrate the ability of social interventions to effectively increase the compliance of decentralized decisions in change projects, labels must be developed together with the case organization to ensure understanding and acceptance. Such labels make transparent which projects (or business units) are more or less compliant and thus create or reduce technical debt for the entire organization (Schilling et al., 2019). An alternative PoC for this projectable design is to create "relationship manager guilds" where know-your-customer data quality issues can be discussed and a common sense of compliance can be institutionalized.

=== Examples ===
Re-calculate historical projects
Compliance label prototype
Show that successful coordination artifacts instantiate design principles

=== Further Readings ===
Tuuanen T., Winter. R., vom Brocke J. (2023), Dealing with Complexity in Design Science Research: Using Design Echelons to Support Planning, Conducting, and Communicating Design Knowledge Contributions, in: Management Information Systems Quarterly (MISQ), 47

== Evaluation ==
=== Description ===
Promising concepts need to be applied with real people who have a real stake in the outcome. In the project used as an example, two companies were involved in field tests (indifferent business units) and a pilot study, respectively.

=== Examples ===
Case study: Resistance and escalation prevents sustained effectiveness of intervention
Field test: Compliance label rollout in Business Unit A
Pilot study in company: Digital KYC data quality nudges
Focus group: Design principles development

=== Further Readings ===
Tuuanen T., Winter. R., vom Brocke J. (2023), Dealing with Complexity in Design Science Research: Using Design Echelons to Support Planning, Conducting, and Communicating Design Knowledge Contributions, in: Management Information Systems Quarterly (MISQ), 47

== References ==
[[Category:Design Science Research]]

File:EDSR.png

2024-05-21T07:11:40Z

MichiGau:

Echeloned DSR according to Tuuanen et al

2024-05-20T10:21:08Z

MichiGau:

== Introduction ==
Given the complexity of DSR projects, Tuunanen et. al. (2024) <ref>Tuuanen T., Winter. R., vom Brocke J. (2023), Dealing with Complexity in Design Science Research: Using Design Echelons to Support Planning, Conducting, and Communicating Design Knowledge Contributions, in: Management Information Systems Quarterly (MISQ), 47</ref> propose a rethinking of DSR based on the theory of hierarchical, multi-level systems. This involves decomposing a complex DSR project into specifically defined, self-contained intermediate units - which we refer to as "echelons", following the theory of hierarchical systems (Mesarovic et al., 1970). In an echelon-oriented approach, we decompose a (larger) problem into a hierarchy of logical sub-problems. We create solutions to such subproblems, which serve as intermediate results that can be developed, validated and communicated independently. In combination, such intermediate results contribute to the overall solution.

Echelons are essentially organizational units that the DSR researcher is free to choose according to his or her understanding and choice of how to decompose a problem. To further conceptualize the echeloned DSR (eDSR) methodology, we distinguish five types of design echelons. One form of type formation is to differentiate design echelons as they combine specific analysis/design and validation activities related to a specific intermediate state of the artifact:
*Problem analysis—contributing the problem statement
*Objectives and requirements definition—contributing design requirements
*Design and development—contributing a projectable solution design
*Demonstration—contributing an illustrative instance of the artifact (in an artificial or natural context)
*Evaluation—contributing the contextualized artifact in use.

== Problem analysis ==
=== Description ===
The overarching problem addressed by the research used as an example in Figure 2 is the limited ability of large, decentralised organizations to achieve enterprise-wide, long-term 'global' benefits (e.g., leveraging synergies through shared software solutions, limiting the complexity of the IT application landscape) because local decision-makers instead focus on project-, unit- or function-specific, short-term 'local' benefits. One symptom of this overarching problem is that enterprise-wide coordination approaches, such as enterprise architecture management, appear to have reached impact limits due to a lack of institutionalisation by most local decision-makers. The limited impact of coordination interventions can be explained by local decision-makers perceived social legitimacy, efficiency, organizational grounding, and trust in the interventions. Thus, an effective design solution would include design principles for coordination interventions that effectively improve local decision-makers perceptions of the legitimacy, efficiency, organizational grounding, and/or perceived trustworthiness of the interventions.

=== Examples ===
Research objective: Identify effective guidance for enterprise-wide IS coordination in settings with high complexity and high local autonomy
Common methods:
* Literature review
* Review practitioner initiatives
* Expert interview
* Focus group
* Surveys

=== Further Readings ===
Tuuanen T., Winter. R., vom Brocke J. (2023), Dealing with Complexity in Design Science Research: Using Design Echelons to Support Planning, Conducting, and Communicating Design Knowledge Contributions, in: Management Information Systems Quarterly (MISQ), 47

== Objectives and requirements definition ==
=== Description ===
As the empirically based design goals are quite different, in the research we use as an example, it was found that solution design should first focus on a specific goal, such as better demonstrating the effectiveness of the intervention or improving the social legitimacy of enterprise-wide coordination. By testing the effectiveness of different novel interventions, it should be possible to generalize effective intervention design to design principles (linking generalized design requirements to generalized design features).

=== Examples ===
Increase effectivity of coordination interventions

Generalize artifacts to generic coordination guidance.

Common methods:
* Logical reasoning
* Benchmarking
* Expert interview
* Focus group

=== Further Readings ===
Tuuanen T., Winter. R., vom Brocke J. (2023), Dealing with Complexity in Design Science Research: Using Design Echelons to Support Planning, Conducting, and Communicating Design Knowledge Contributions, in: Management Information Systems Quarterly (MISQ), 47

== Design and development ==
=== Description ===
For each proposed set of design requirements, the analysis of existing research and the state of practice would lead to different sets of solution components, which would then be integrated and tailored to the problem at hand. For example, to demonstrate the efficiency of coordination ("what's in it for me?"), either the costing and charging of technical debt to non-compliant change projects or the provision of cost-reducing project support (e.g. by supporting architects or reducing the cost of shared software solutions) could be used as foundations. A possible basis for improving the social legitimacy of enterprise-wide coordination ("Why should I comply?") could be communication measures (e.g., showcasing successful solution sharing) or engineered social pressure (e.g., by making non-compliant projects transparent across the organization). At a later stage, the generalization of effective design features and design requirements allows the formulation of design principles.

=== Examples ===
Intervention type 1: Charge projects for technical debt (internalization of effects)
Intervention type 2: Use a label to create transparency about incompliant decision-making
Intervention type 3: Use other social clues to create transparency
Consolidate design requirements and design features to design principles

=== Further Readings ===
Tuuanen T., Winter. R., vom Brocke J. (2023), Dealing with Complexity in Design Science Research: Using Design Echelons to Support Planning, Conducting, and Communicating Design Knowledge Contributions, in: Management Information Systems Quarterly (MISQ), 47

== Demonstration ==
=== Description ===
All of the above solution strategies need to be contextualized to demonstrate their potential to solve the design problem. For example, to demonstrate the ability of social interventions to effectively increase the compliance of decentralized decisions in change projects, labels must be developed together with the case organization to ensure understanding and acceptance. Such labels make transparent which projects (or business units) are more or less compliant and thus create or reduce technical debt for the entire organization (Schilling et al., 2019). An alternative PoC for this projectable design is to create "relationship manager guilds" where know-your-customer data quality issues can be discussed and a common sense of compliance can be institutionalized.

=== Examples ===
Re-calculate historical projects
Compliance label prototype
Show that successful coordination artifacts instantiate design principles

=== Further Readings ===
Tuuanen T., Winter. R., vom Brocke J. (2023), Dealing with Complexity in Design Science Research: Using Design Echelons to Support Planning, Conducting, and Communicating Design Knowledge Contributions, in: Management Information Systems Quarterly (MISQ), 47

== Evaluation ==
=== Description ===
Promising concepts need to be applied with real people who have a real stake in the outcome. In the project used as an example, two companies were involved in field tests (indifferent business units) and a pilot study, respectively.

=== Examples ===
Case study: Resistance and escalation prevents sustained effectiveness of intervention
Field test: Compliance label rollout in Business Unit A
Pilot study in company: Digital KYC data quality nudges
Focus group: Design principles development

=== Further Readings ===
Tuuanen T., Winter. R., vom Brocke J. (2023), Dealing with Complexity in Design Science Research: Using Design Echelons to Support Planning, Conducting, and Communicating Design Knowledge Contributions, in: Management Information Systems Quarterly (MISQ), 47

== References ==
[[Category:Design Science Research]]

Echeloned DSR according to Tuuanen et al

2024-05-20T08:51:41Z

MichiGau:

== Introduction ==
Given the complexity of DSR projects, Tuunanen et. al. (2024) <ref>Tuuanen T., Winter. R., vom Brocke J. (2023), Dealing with Complexity in Design Science Research: Using Design Echelons to Support Planning, Conducting, and Communicating Design Knowledge Contributions, in: Management Information Systems Quarterly (MISQ), 47</ref> propose a rethinking of DSR based on the theory of hierarchical, multi-level systems. This involves decomposing a complex DSR project into specifically defined, self-contained intermediate units - which we refer to as "echelons", following the theory of hierarchical systems (Mesarovic et al., 1970). In an echelon-oriented approach, we decompose a (larger) problem into a hierarchy of logical sub-problems. We create solutions to such subproblems, which serve as intermediate results that can be developed, validated and communicated independently. In combination, such intermediate results contribute to the overall solution.

Echelons are essentially organizational units that the DSR researcher is free to choose according to his or her understanding and choice of how to decompose a problem. To further conceptualize the echeloned DSR (eDSR) methodology, we distinguish five types of design echelons. One form of type formation is to differentiate design echelons as they combine specific analysis/design and validation activities related to a specific intermediate state of the artifact:
*Problem analysis—contributing the problem statement
*Objectives and requirements definition—contributing design requirements
*Design and development—contributing a projectable solution design
*Demonstration—contributing an illustrative instance of the artifact (in an artificial or natural context)
*Evaluation—contributing the contextualized artifact in use.

== Problem analysis ==
=== Description ===
The overarching problem addressed by the research used as an example in Figure 2 is the limited ability of large, decentralised organizations to achieve enterprise-wide, long-term 'global' benefits (e.g., leveraging synergies through shared software solutions, limiting the complexity of the IT application landscape) because local decision-makers instead focus on project-, unit- or function-specific, short-term 'local' benefits. One symptom of this overarching problem is that enterprise-wide coordination approaches, such as enterprise architecture management, appear to have reached impact limits due to a lack of institutionalisation by most local decision-makers. The limited impact of coordination interventions can be explained by local decision-makers perceived social legitimacy, efficiency, organizational grounding, and trust in the interventions. Thus, an effective design solution would include design principles for coordination interventions that effectively improve local decision-makers perceptions of the legitimacy, efficiency, organizational grounding, and/or perceived trustworthiness of the interventions.

=== Examples ===
Research objective: Identify effective guidance for enterprise-wide IS coordination in settings with high complexity and high local autonomy
Common methods:
* Literature review
* Review practitioner initiatives
* Expert interview
* Focus group
* Surveys

=== Further Readings ===
Tuuanen T., Winter. R., vom Brocke J. (2023), Dealing with Complexity in Design Science Research: Using Design Echelons to Support Planning, Conducting, and Communicating Design Knowledge Contributions, in: Management Information Systems Quarterly (MISQ), 47

== Objectives and requirements definition ==
=== Description ===
As the empirically based design goals are quite different, in the research we use as an example, it was found that solution design should first focus on a specific goal, such as better demonstrating the effectiveness of the intervention or improving the social legitimacy of enterprise-wide coordination. By testing the effectiveness of different novel interventions, it should be possible to generalize effective intervention design to design principles (linking generalized design requirements to generalized design features).

=== Examples ===
Increase effectivity of coordination interventions

Generalize artifacts to generic coordination guidance.

Common methods:
* Logical reasoning
* Benchmarking
* Expert interview
* Focus group

=== Further Readings ===
Tuuanen T., Winter. R., vom Brocke J. (2023), Dealing with Complexity in Design Science Research: Using Design Echelons to Support Planning, Conducting, and Communicating Design Knowledge Contributions, in: Management Information Systems Quarterly (MISQ), 47

== Design and development ==
=== Description ===
For each proposed set of design requirements, the analysis of existing research and the state of practice would lead to different sets of solution components, which would then be integrated and tailored to the problem at hand. For example, to demonstrate the efficiency of coordination ("what's in it for me?"), either the costing and charging of technical debt to non-compliant change projects or the provision of cost-reducing project support (e.g. by supporting architects or reducing the cost of shared software solutions) could be used as foundations. A possible basis for improving the social legitimacy of enterprise-wide coordination ("Why should I comply?") could be communication measures (e.g., showcasing successful solution sharing) or engineered social pressure (e.g., by making non-compliant projects transparent across the organization). At a later stage, the generalization of effective design features and design requirements allows the formulation of design principles.

=== Examples ===
Intervention type 1: Charge projects for technical debt (internalization of effects)
Intervention type 2: Use a label to create transparency about incompliant decision-making
Intervention type 3: Use other social clues to create transparency
Consolidate design requirements and design features to design principles

=== Further Readings ===
Tuuanen T., Winter. R., vom Brocke J. (2023), Dealing with Complexity in Design Science Research: Using Design Echelons to Support Planning, Conducting, and Communicating Design Knowledge Contributions, in: Management Information Systems Quarterly (MISQ), 47

== Demonstration ==
=== Description ===
All of the above solution strategies need to be contextualized to demonstrate their potential to solve the design problem. For example, to demonstrate the ability of social interventions to effectively increase the compliance of decentralized decisions in change projects, labels must be developed together with the case organization to ensure understanding and acceptance. Such labels make transparent which projects (or business units) are more or less compliant and thus create or reduce technical debt for the entire organization (Schilling et al., 2019). An alternative PoC for this projectable design is to create "relationship manager guilds" where know-your-customer data quality issues can be discussed and a common sense of compliance can be institutionalized.

=== Examples ===
Re-calculate historical projects
Compliance label prototype
Show that successful coordination artifacts instantiate design principles

=== Further Readings ===
Tuuanen T., Winter. R., vom Brocke J. (2023), Dealing with Complexity in Design Science Research: Using Design Echelons to Support Planning, Conducting, and Communicating Design Knowledge Contributions, in: Management Information Systems Quarterly (MISQ), 47

== Evaluation ==
=== Description ===
Promising concepts need to be applied with real people who have a real stake in the outcome. In the project used as an example, two companies were involved in field tests (indifferent business units) and a pilot study, respectively.

=== Examples ===
Case study: Resistance and escalation prevents sustained effectiveness of intervention
Field test: Compliance label rollout in Business Unit A
Pilot study in company: Digital KYC data quality nudges
Focus group: Design principles development

=== Further Readings ===
Tuuanen T., Winter. R., vom Brocke J. (2023), Dealing with Complexity in Design Science Research: Using Design Echelons to Support Planning, Conducting, and Communicating Design Knowledge Contributions, in: Management Information Systems Quarterly (MISQ), 47

== References ==
[[Category:Design Science Research]]

Echeloned DSR according to Tuuanen et al

2024-05-20T08:50:22Z

MichiGau:

== Introduction ==
Given the complexity of DSR projects, Tuunanen et. al. (2024) <ref>Tuuanen T., Winter. R., vom Brocke J. (2023), Dealing with Complexity in Design Science Research: Using Design Echelons to Support Planning, Conducting, and Communicating Design Knowledge Contributions, in: Management Information Systems Quarterly (MISQ), 47</ref> propose a rethinking of DSR based on the theory of hierarchical, multi-level systems. This involves decomposing a complex DSR project into specifically defined, self-contained intermediate units - which we refer to as "echelons", following the theory of hierarchical systems (Mesarovic et al., 1970). In an echelon-oriented approach, we decompose a (larger) problem into a hierarchy of logical sub-problems. We create solutions to such subproblems, which serve as intermediate results that can be developed, validated and communicated independently. In combination, such intermediate results contribute to the overall solution.

Echelons are essentially organizational units that the DSR researcher is free to choose according to his or her understanding and choice of how to decompose a problem. To further conceptualize the echeloned DSR (eDSR) methodology, we distinguish five types of design echelons. One form of type formation is to differentiate design echelons as they combine specific analysis/design and validation activities related to a specific intermediate state of the artifact:

*Problem analysis—contributing the problem statement
*Objectives and requirements definition—contributing design requirements
*Design and development—contributing a projectable solution design
*Demonstration—contributing an illustrative instance of the artifact (in an artificial or natural
context)
*Evaluation—contributing the contextualized artifact in use.

== Problem analysis ==
=== Description ===
The overarching problem addressed by the research used as an example in Figure 2 is the limited ability of large, decentralised organizations to achieve enterprise-wide, long-term 'global' benefits (e.g., leveraging synergies through shared software solutions, limiting the complexity of the IT application landscape) because local decision-makers instead focus on project-, unit- or function-specific, short-term 'local' benefits. One symptom of this overarching problem is that enterprise-wide coordination approaches, such as enterprise architecture management, appear to have reached impact limits due to a lack of institutionalisation by most local decision-makers. The limited impact of coordination interventions can be explained by local decision-makers perceived social legitimacy, efficiency, organizational grounding, and trust in the interventions. Thus, an effective design solution would include design principles for coordination interventions that effectively improve local decision-makers perceptions of the legitimacy, efficiency, organizational grounding, and/or perceived trustworthiness of the interventions.

=== Examples ===
Research objective: Identify effective guidance for enterprise-wide IS coordination in settings with high complexity and high local autonomy
Common methods:
* Literature review
* Review practitioner initiatives
* Expert interview
* Focus group
* Surveys

=== Further Readings ===
Tuuanen T., Winter. R., vom Brocke J. (2023), Dealing with Complexity in Design Science Research: Using Design Echelons to Support Planning, Conducting, and Communicating Design Knowledge Contributions, in: Management Information Systems Quarterly (MISQ), 47

== Objectives and requirements definition ==
=== Description ===
As the empirically based design goals are quite different, in the research we use as an example, it was found that solution design should first focus on a specific goal, such as better demonstrating the effectiveness of the intervention or improving the social legitimacy of enterprise-wide coordination. By testing the effectiveness of different novel interventions, it should be possible to generalize effective intervention design to design principles (linking generalized design requirements to generalized design features).

=== Examples ===
Increase effectivity of coordination interventions

Generalize artifacts to generic coordination guidance.

Common methods:
* Logical reasoning
* Benchmarking
* Expert interview
* Focus group

=== Further Readings ===
Tuuanen T., Winter. R., vom Brocke J. (2023), Dealing with Complexity in Design Science Research: Using Design Echelons to Support Planning, Conducting, and Communicating Design Knowledge Contributions, in: Management Information Systems Quarterly (MISQ), 47

== Design and development ==
=== Description ===
For each proposed set of design requirements, the analysis of existing research and the state of practice would lead to different sets of solution components, which would then be integrated and tailored to the problem at hand. For example, to demonstrate the efficiency of coordination ("what's in it for me?"), either the costing and charging of technical debt to non-compliant change projects or the provision of cost-reducing project support (e.g. by supporting architects or reducing the cost of shared software solutions) could be used as foundations. A possible basis for improving the social legitimacy of enterprise-wide coordination ("Why should I comply?") could be communication measures (e.g., showcasing successful solution sharing) or engineered social pressure (e.g., by making non-compliant projects transparent across the organization). At a later stage, the generalization of effective design features and design requirements allows the formulation of design principles.

=== Examples ===
Intervention type 1: Charge projects for technical debt (internalization of effects)
Intervention type 2: Use a label to create transparency about incompliant decision-making
Intervention type 3: Use other social clues to create transparency
Consolidate design requirements and design features to design principles

=== Further Readings ===
Tuuanen T., Winter. R., vom Brocke J. (2023), Dealing with Complexity in Design Science Research: Using Design Echelons to Support Planning, Conducting, and Communicating Design Knowledge Contributions, in: Management Information Systems Quarterly (MISQ), 47

== Demonstration ==
=== Description ===
All of the above solution strategies need to be contextualized to demonstrate their potential to solve the design problem. For example, to demonstrate the ability of social interventions to effectively increase the compliance of decentralized decisions in change projects, labels must be developed together with the case organization to ensure understanding and acceptance. Such labels make transparent which projects (or business units) are more or less compliant and thus create or reduce technical debt for the entire organization (Schilling et al., 2019). An alternative PoC for this projectable design is to create "relationship manager guilds" where know-your-customer data quality issues can be discussed and a common sense of compliance can be institutionalized.

=== Examples ===
Re-calculate historical projects
Compliance label prototype
Show that successful coordination artifacts instantiate design principles

=== Further Readings ===
Tuuanen T., Winter. R., vom Brocke J. (2023), Dealing with Complexity in Design Science Research: Using Design Echelons to Support Planning, Conducting, and Communicating Design Knowledge Contributions, in: Management Information Systems Quarterly (MISQ), 47

== Evaluation ==
=== Description ===
Promising concepts need to be applied with real people who have a real stake in the outcome. In the project used as an example, two companies were involved in field tests (indifferent business units) and a pilot study, respectively.

=== Examples ===
Case study: Resistance and escalation prevents sustained effectiveness of intervention
Field test: Compliance label rollout in Business Unit A
Pilot study in company: Digital KYC data quality nudges
Focus group: Design principles development

=== Further Readings ===
Tuuanen T., Winter. R., vom Brocke J. (2023), Dealing with Complexity in Design Science Research: Using Design Echelons to Support Planning, Conducting, and Communicating Design Knowledge Contributions, in: Management Information Systems Quarterly (MISQ), 47

== References ==
[[Category:Design Science Research]]

A Methodology Using Design Echelons

2024-05-20T08:25:49Z

MichiGau: MichiGau moved page A Methodology Using Design Echelons to EDSR- A Methodology Using Design Echelons

#REDIRECT [[EDSR- A Methodology Using Design Echelons]]

Echeloned DSR according to Tuuanen et al

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MichiGau: MichiGau moved page A Methodology Using Design Echelons to EDSR- A Methodology Using Design Echelons

Echeloned DSR according to Tuuanen et al

2024-05-20T08:25:13Z

MichiGau:

Echeloned DSR according to Tuuanen et al

2024-05-20T08:19:44Z

MichiGau:

Echeloned DSR according to Tuuanen et al

2024-05-20T08:12:10Z

MichiGau: Created page with "==Activity Name== ===Description=== Provide an activity description. ===Examples=== Add some examples here. ===Further Readings=== Add some further readings here."

==Activity Name==
===Description===
Provide an activity description.

===Examples===
Add some examples here.

===Further Readings===
Add some further readings here.

HowTo

2023-05-29T18:54:13Z

MichiGau:

This HowTo provides guidance to create, edit, or delete process and activity descriptions on this Wiki.
== Basic Idea ==
Scientific methods are core for any scientific discipline. By defining a standardized set of research activities, they enable researchers to build scientific knowledge. They explain how to make valid observations, how to interpret results, and how to generalize those results. Furthermore, scientific methods allow other researchers to test pre-existing theories and prior findings independently <ref>Bhattacherjee, A. 2012. Social Science Research: Principles, Methods, and Practices, (Vol. Textbooks Collection. 3. http://scholarcommons.usf.edu/oa_textbooks/3).</ref> . The Information Systems (IS) field is characterized by a pluralism of scientific methods <ref>Mingers, J. 2001. “Combining IS Research Methods: Towards a Pluralist Methodology,” Information Systems Research (12:3), INFORMS, pp. 240–259. (https://doi.org/10.1287/isre.12.3.240.9709).</ref> . Many different research methods are leveraged by IS researchers to contribute to the IS body of knowledge. For example, prominent methods in IS are case study research, experimental research, and survey-based research. This diversity of research methods within the discipline is a positive source of strength <ref>Orlikowski, W. J., and Robey, D. 1991. “Information Technology and the Structuring of Organizations,” Information Systems Research (2:2), INFORMS, pp. 143–169. (https://doi.org/10.1287/isre.2.2.143).</ref> but also leads to some challenges in managing and applying them. Knowledge on research methods and the descriptions of the specific research activities are scattered in different books or other publications. Furthermore, research methods evolve and can have different levels of maturity, or even new branches emerge depending on the specific research approaches.

In this Wiki, together with the IS community, we aim to provide an overview of well-established, state-of-the-art, and common grounded research methods in the field of IS. In turn, this will safeguard the quality and improve the contribution of findings in research projects. We particularly believe that our research method descriptions and the tool will be used to explore research methods and improve method accuracy in IS research and beyond.
== Documentation Process ==
[[File:Meta-model.png|right|middle|thumb|Research Methods Meta-Model]]

In this Wiki we propose a research method documentation structure containing the following three main concepts: '''Method''', '''Process,''' and '''Activity'''. The underlying meta-model of this structure is illustrated in the figure: Research Methods Meta-Model.

Examples for such '''methods''' are literature review research <ref>Webster, Jane and Watson, Richard T.. 2002. "ANALYZING THE PAST TO PREPARE FOR THE FUTURE: WRITING A LITERATURE REVIEW," MIS Quarterly, (26: 2).</ref> , case study research <ref>Yin, R. K. 1981. “The Case Study as a Serious Research Strategy,” Knowledge (3:1), SAGE Publications, pp. 97–114. (https://doi.org/10.1177/107554708100300106).</ref> , or design science research <ref>Hevner, A. R., March, S. T., Park, J., and Ram, S. 2004. “Design Science in Information Systems Research,” MIS Quarterly (28:1), pp. 75–105. (https://doi.org/10.2307/25148625).</ref> .

A '''process''' is a set, mostly in a predefined order, of activities. Each method can contain one or more processes with different characterizations. For example, the different approaches in design science research (DSR) literature <ref>Kuechler, B., and Vaishnavi, V. 2008. “On Theory Development in Design Science Research: Anatomy of a Research Project,” European Journal of Information Systems (17:5), Taylor & Francis, pp. 489–504. (https://doi.org/10.1057/ejis.2008.40).</ref> <ref>Peffers, K., Tuunanen, T., Rothenberger, M. A., and Chatterjee, S. 2007. “A Design Science Research Methodology for Information Systems Research,” Journal of Management Information Systems (24:3), Taylor & Francis Ltd, pp. 45–77. (https://doi.org/10.2753/MIS0742-1222240302).</ref> <ref>Sein, M. K., Henfridsson, O., Purao, S., Rossi, M., and Lindgren, R. 2011. “Action Design Research,” MIS Quarterly (35:1), pp. 37–56. (https://doi.org/10.2307/23043488).</ref> . '''Activities''' describe what has to be done in a specific process e.g. ''definition of review scope'' as part of the literature review process <ref>vom Brocke, J., Simons, A., Niehaves, Bjoern, Niehaves, Bjorn, Reimer, K., Plattfaut, R., and Cleven, A. 2009. “RECONSTRUCTING THE GIANT: ON THE IMPORTANCE OF RIGOUR IN DOCUMENTING THE LITERATURE SEARCH PROCESS,” ECIS 2009 Proceedings (161. https://aisel.aisnet.org/ecis2009/161). (https://aisel.aisnet.org/ecis2009/161).</ref> or ''problem identification & motivation'' in the design science research process according to Peffers et. al <ref>Peffers, K., Tuunanen, T., Rothenberger, M. A., and Chatterjee, S. 2007. “A Design Science Research Methodology for Information Systems Research,” Journal of Management Information Systems (24:3), Taylor & Francis Ltd, pp. 45–77. (https://doi.org/10.2753/MIS0742-1222240302).</ref> .
== Create a new Research Process ==
[[File:New process.png|right|middle|thumb|New Process]]

Open the method where you want to create a new process and provide a process name in the text field. By clicking on the button 'New Process' a new wiki page will be generated. The new process page will contain some empty activity templates that can be filled out. If more activity templates are needed you can use the activity template by adding the following code<code><nowiki>{{subst:Activity}}</nowiki></code>
== Edit an Existing Research Process ==
[[File:Delete.png|right|middle|thumb|More edit options]]

You can '''edit''' an existing research process by logging in and then use 'Edit' in the top menu in order to edit the whole document or choose 'Edit' in a document section.

To '''rename''' an existing research process you need to select 'Move' from the 'More' menu at the top and provide a new name for the process description.

If you want to '''delete''' an existing research process you have to choose the 'Delete' item in the 'More' menu at the top.
== Create a new Research Method. ==
New research methods can be created by one of the administrators. Please get in contact with one of our [[Research Methods:About|operational editors]] if you need to create a new method.
== Invite Collaborators ==
As a department editor, you can invite as many process editors as you want. You only have to add the eMail address of the collaborator in the following list:[[Special:InviteSignup|Invited Editors]]
== More Help about MediaWiki ==
General help about the media wiki and its functionality can be found on the official documentation page of [https://www.mediawiki.org/wiki/Special:MyLanguage/Help:Contents MediaWiki].

How to add [https://www.mediawiki.org/wiki/Help:Cite citations] in MediaWiki
== Reference ==

File:Meta-model.png

2023-05-29T18:53:36Z

MichiGau: MichiGau uploaded a new version of File:Meta-model.png

Research Methods:About

2023-05-29T18:51:01Z

MichiGau: Created page with "<templatestyles src="Template:Styles.css" />__NOTOC__''Research Process Wiki'' Scientific methods are core for any scientific discipline. By defining a standardized set of research activities, they enable researchers to build scientific knowledge. They explain how to make valid observations, how to interpret results, and how to generalize those results. Furthermore, scientific methods allow other researchers to test pre-existing theories and prior findings independently..."

<templatestyles src="Template:Styles.css" />__NOTOC__''Research Process Wiki''

Scientific methods are core for any scientific discipline. By defining a standardized set of research activities, they enable researchers to build scientific knowledge. They explain how to make valid observations, how to interpret results, and how to generalize those results. Furthermore, scientific methods allow other researchers to test pre-existing theories and prior findings independently.

In this research process wiki we are going to make research methods more accessible for colleagues, and thus further foster diversity and quality of our research. It will also allow further development of existing and emergent research methods and processes. We believe that collective improvement can lead to complete and accurate research method descriptions and also supports on one hand further development, as well as the acceptance by other researchers of methods and the underlying processes. Furthermore, this wiki will make it easier for researchers to understand and conduct various research methods in IS. This collaborative platform provides an overview of well established, state of the art, and common grounded research methods. In turn, this will safeguard the quality and improve the contribution of findings in research projects.

This site is built on user contributions. Please share your knowledge and contribute! This site is maintained as an ongoing project at the Karlsruhe Institute of Technology and the University of Liechtenstein. All textual content of Research Process Wiki is licensed under the [https://creativecommons.org/licenses/by-sa/3.0/at/ Creative Commons Attribution/Share-Alike License](CC BY-SA) and the [https://www.gnu.org/licenses/fdl-1.3.html GNU Free Documentation License](GFDL).

A short description of how to use and collaborate on the wiki can be found on the [[HowTo|how to page]].
== Editorial Board ==

{| class="wikitable" style="border: none #ffffff; background-color: #ffffff; margin-left: auto; margin-right: auto;" border="0" cellpadding="5"
|-
| style="width: 204.812px;" | [[File:Jan.png|180px|center|middle|thumb]]
| style="width: 253.812px;" | [[File:Alex.png|180px|center|middle|thumb]]
|-
| style="text-align: center; width: 204.812px;" | Jan vom Brocke
| style="text-align: center; width: 253.812px;" | Alexander Maedche
|-
| style="text-align: center; width: 204.812px;" | Co-Editor
| style="text-align: center; width: 253.812px;" | Co-Editor 
|-
| style="text-align: center; width: 204.812px;" | [//uni.li University of Liechtenstein]
| style="text-align: center; width: 253.812px;" | [https://www.kit.edu/ Karlsruhe Institute of Technology]
|-
| style="text-align: center; width: 204.812px;" | 
| style="text-align: center; width: 253.812px;" |
 
|}

== Operational Editor ==

{| class="wikitable" style="border: none #ffffff; background-color: #ffffff; margin-left: auto; margin-right: auto;" border="0" cellpadding="5"
|-
| style="text-align: center;" | [[File:michi_gau.jpeg|180px|center|middle|thumb]]
|-
| style="text-align: center;" | Michael Gau
|-
| style="text-align: center;" | Research Software Engineer
|-
| style="text-align: center;" | [//uni.li University of Liechtenstein]
[https://www.kit.edu/ Karlsruhe Institute of Technology]
|-
| style="text-align: center;" | 
|}

== Acknowledgments ==
THANK YOU!! to the individuals who have contributed to pages or added new pages to this wiki and made this knowledge base even more valuable.

Focus Groups Research

2023-05-18T06:54:15Z

MichiGau:

List of existing processes:

{{#categorytree:{{PAGENAME}}|hideroot|mode=pages}}

--------

To create a new process model, use the following input box and provide a name for the new process.

<inputbox>
type=create
break=no
buttonlabel=New Process Description
default=Process Name
preload=Template:Process
preloadparams[]={{PAGENAME}}
</inputbox>

[[Category:Method]]

Taxonomy Development Research

2023-05-18T06:54:00Z

MichiGau:

Case Study Research

2023-05-18T06:53:43Z

MichiGau:

Literature Review Research

2023-05-18T06:53:28Z

MichiGau:

List of existing processes:

{{#categorytree:{{PAGENAME}}|hideroot|mode=pages}}

--------
To create a new process model, use the following input box and provide a name for the new process.

<inputbox>
type=create
break=no
buttonlabel=New Process Description
default=Process Name
preload=Template:Process
preloadparams[]={{PAGENAME}}
</inputbox>

[[Category:Method]]

Literature Review Research

2023-05-18T06:51:59Z

MichiGau:

List of existing processes:

{{#categorytree:{{PAGENAME}}|hideroot|mode=pages}}

--------
To create a new process model, use the following input box and provide a name for the new process.

<inputbox>
type=create
break=no
buttonlabel=New Process Description
default=process name
preload=Template:Process
preloadparams[]={{PAGENAME}}
</inputbox>

[[Category:Method]]

Test

2023-05-18T06:45:02Z

MichiGau:

List of existing processes:

{{#categorytree:{{PAGENAME}}|hideroot|mode=pages}}

{{subst:Activity}}

--------
To create a new process model use the following input box and provide a name for the new process.

<inputbox>
type=create
break=no
buttonlabel=New sub page
default=process name
preload=Template:Process
preloadparams[]={{PAGENAME}}
</inputbox>

[[Category:Method]]

HowTo

2023-05-14T08:50:29Z

MichiGau:

This HowTo provides guidance to create, edit, or delete process and activity descriptions on this Wiki.
== Basic Idea ==
Scientific methods are core for any scientific discipline. By defining a standardized set of research activities, they enable researchers to build scientific knowledge. They explain how to make valid observations, how to interpret results, and how to generalize those results. Furthermore, scientific methods allow other researchers to test pre-existing theories and prior findings independently <ref>Bhattacherjee, A. 2012. Social Science Research: Principles, Methods, and Practices, (Vol. Textbooks Collection. 3. http://scholarcommons.usf.edu/oa_textbooks/3).</ref> . The Information Systems (IS) field is characterized by a pluralism of scientific methods <ref>Mingers, J. 2001. “Combining IS Research Methods: Towards a Pluralist Methodology,” Information Systems Research (12:3), INFORMS, pp. 240–259. (https://doi.org/10.1287/isre.12.3.240.9709).</ref> . Many different research methods are leveraged by IS researchers to contribute to the IS body of knowledge. For example, prominent methods in IS are case study research, experimental research, and survey-based research. This diversity of research methods within the discipline is a positive source of strength <ref>Orlikowski, W. J., and Robey, D. 1991. “Information Technology and the Structuring of Organizations,” Information Systems Research (2:2), INFORMS, pp. 143–169. (https://doi.org/10.1287/isre.2.2.143).</ref> but also leads to some challenges in managing and applying them. Knowledge on research methods and the descriptions of the specific research activities are scattered in different books or other publications. Furthermore, research methods evolve and can have different levels of maturity, or even new branches emerge depending on the specific research approaches.

In this Wiki, together with the IS community, we aim to provide an overview of well-established, state-of-the-art, and common grounded research methods in the field of IS. In turn, this will safeguard the quality and improve the contribution of findings in research projects. We particularly believe that our research method descriptions and the tool will be used to explore research methods and improve method accuracy in IS research and beyond.
== Documentation Process ==
[[File:Meta-model.png|right|middle|thumb|Research Methods Meta-Model]]

In this Wiki we propose a research method documentation structure containing the following three main concepts: '''Method''', '''Process,''' and '''Activity'''. The underlying meta-model of this structure is illustrated in the figure: Research Methods Meta-Model

Examples for such '''methods''' are literature review research <ref>Webster, Jane and Watson, Richard T.. 2002. "ANALYZING THE PAST TO PREPARE FOR THE FUTURE: WRITING A LITERATURE REVIEW," MIS Quarterly, (26: 2).</ref> , case study research <ref>Yin, R. K. 1981. “The Case Study as a Serious Research Strategy,” Knowledge (3:1), SAGE Publications, pp. 97–114. (https://doi.org/10.1177/107554708100300106).</ref> , or design science research <ref>Hevner, A. R., March, S. T., Park, J., and Ram, S. 2004. “Design Science in Information Systems Research,” MIS Quarterly (28:1), pp. 75–105. (https://doi.org/10.2307/25148625).</ref> .

A '''process''' is a set, mostly in a predefined order, of activities. Each method can contain one or more processes with different characterizations. For example, the different approaches in design science research (DSR) literature <ref>Kuechler, B., and Vaishnavi, V. 2008. “On Theory Development in Design Science Research: Anatomy of a Research Project,” European Journal of Information Systems (17:5), Taylor & Francis, pp. 489–504. (https://doi.org/10.1057/ejis.2008.40).</ref> <ref>Peffers, K., Tuunanen, T., Rothenberger, M. A., and Chatterjee, S. 2007. “A Design Science Research Methodology for Information Systems Research,” Journal of Management Information Systems (24:3), Taylor & Francis Ltd, pp. 45–77. (https://doi.org/10.2753/MIS0742-1222240302).</ref> <ref>Sein, M. K., Henfridsson, O., Purao, S., Rossi, M., and Lindgren, R. 2011. “Action Design Research,” MIS Quarterly (35:1), pp. 37–56. (https://doi.org/10.2307/23043488).</ref> . '''Activities''' describe what has to be done in a specific process e.g. ''definition of review scope'' as part of the literature review process <ref>vom Brocke, J., Simons, A., Niehaves, Bjoern, Niehaves, Bjorn, Reimer, K., Plattfaut, R., and Cleven, A. 2009. “RECONSTRUCTING THE GIANT: ON THE IMPORTANCE OF RIGOUR IN DOCUMENTING THE LITERATURE SEARCH PROCESS,” ECIS 2009 Proceedings (161. https://aisel.aisnet.org/ecis2009/161). (https://aisel.aisnet.org/ecis2009/161).</ref> or ''problem identification & motivation'' in the design science research process according to Peffers et. al <ref>Peffers, K., Tuunanen, T., Rothenberger, M. A., and Chatterjee, S. 2007. “A Design Science Research Methodology for Information Systems Research,” Journal of Management Information Systems (24:3), Taylor & Francis Ltd, pp. 45–77. (https://doi.org/10.2753/MIS0742-1222240302).</ref> .
== Create a new Research Process ==
[[File:New process.png|right|middle|thumb|New Process]]

Open the method where you want to create a new process and provide a process name in the text field. By clicking on the button 'New Process' a new wiki page will be generated. The new process page will contain some empty activity templates that can be filled out. If more activity templates are needed you can use the activity template by adding the following code<code><nowiki>{{subst:Activity}}</nowiki></code>
== Edit an Existing Research Process ==
[[File:Delete.png|right|middle|thumb|More edit options]]

You can '''edit''' an existing research process by logging in and then use 'Edit' in the top menu in order to edit the whole document or choose 'Edit' in a document section.

To '''rename''' an existing research process you need to select 'Move' from the 'More' menu at the top and provide a new name for the process description.

If you want to '''delete''' an existing research process you have to choose the 'Delete' item in the 'More' menu at the top.
== Create a new Research Method. ==
New research methods can be created by one of the administrators. Please get in contact with one of our [[Research Methods:About|operational editors]] if you need to create a new method.
== Invite Collaborators ==
As a department editor, you can invite as many process editors as you want. You only have to add the eMail address of the collaborator in the following list:[[Special:InviteSignup|Invited Editors]]
== More Help about MediaWiki ==
General help about the media wiki and its functionality can be found on the official documentation page of [https://www.mediawiki.org/wiki/Special:MyLanguage/Help:Contents MediaWiki].

How to add [https://www.mediawiki.org/wiki/Help:Cite citations] in MediaWiki
== Reference ==

Design Science Research according to Kuechler & Vaishnavi

2023-03-09T14:07:53Z

MichiGau: /* Further Readings */

== Introduction ==
''"Two paradigms characterize much of the research in the Information Systems discipline: behavioral science and design science. The behavioral science paradigm seeks to develop and verify theories that explain or predict human or organizational behavior. The design-science paradigm seeks to extend the boundaries of human and organizational capabilities by creating new and innovative artifacts. Both paradigms are foundational to the IS discipline, positioned as it is at the confluence of people, organizations, and technology." ''(Hevner et. al.) <ref>Hevner AR, March ST, Park J and Ram S (2004) Design Science in Information Systems Research. MIS Quarterly 28(1), 75-105.</ref>





In many publications, essays, editorials or books describing the Design Science Research (DSR) method the authors aim to support researchers to conduct DSR projects for example by providing guidelines <ref>Hevner AR, March ST, Park J and Ram S (2004) Design Science in Information Systems Research. MIS Quarterly 28(1), 75-105.</ref> <ref>van der Merwe A., Gerber A., Smuts H. (2020) Guidelines for Conducting Design Science Research in Information Systems. In: Tait B., Kroeze J., Gruner S. (eds) ICT Education. SACLA 2019. Communications in Computer and Information Science, vol 1136. Springer, Cham. https://doi.org/10.1007/978-3-030-35629-3_11</ref> or how to extend on existing design knowledge <ref>vom Brocke, J., Winter, R., Hevner, A., Maedche, A. (2020), Accumulation and Evolution of Design Knowledge in Design Science Research – A Journey Through Time and Space, in: Journals of the Association for Information Systems (JAIS), 2020, forthcoming (ABDC_2016: A*; ABS: 4; ISI: 3.487; ISI: 2.109; VHB_3: A).</ref> and many more. Gregor and Hevner provide foundational guidance by describing their understanding and application of DSR concepts and providing guidance for researches on how to:
# Appreciate the levels of artifact abstractions that may be DSR contributions
# Identify appropriate ways of consuming and producing knowledge when you are preparing journal articles or other scholarly works
# Understand and position the knowledge contributions of your research projects
# Structure a DSR article so that it emphasizes significant contributions to the knowledge base
Their focal contribution is the DSR knowledge contribution framework with two dimensions based on the existing state of knowledge in both the problem and solution domains for the research opportunity under study. In addition, they propose a DSR communication schema with similarities to more conventional publication patterns, but which substitutes the description of the DSR artifact in place of a traditional results section. <ref>Gregor S and Hevner AR (2013) Positioning and Presenting Design Science Research for Maximum Impact. MIS Quarterly 37(2), 337-55.</ref>

Other researchers propose a set of core dimensions of a Design Science Research project that facilitates effective capture of the most relevant aspects of a DSR project to efficiently plan and communicate key considerations and conceptualizations of a DSR project. In this work, the authors represent six dimensions in the form of a DSR grid, a one-page visualization of the DSR project that is adjustable to the specific purpose for using the concept. <ref>vom Brocke, J., Maedche, A. (2019), The DSR Grid: Six Core Dimensions for Effectively Planning and Communicating Design Science Research Projects, in: Electronic Markets, Volume 29, Issue 3, pp 379–385 (ABDC: A; ABS: 2; ISI: 2.121; VHB: B).</ref>

Another important role in DSR is how to specify design theory so that it can be communicated, justified, and developed cumulatively. In the essay of Gregor and Jones they focus on the structural components or anatomy of design theories in IS <ref>Gregor S and Jones D (2007) The Anatomy of a Design Theory. Journal Of The Association For Information Systems 8(5), 312-335.</ref> . Baskerville and Pries-Heje focus on how design theories are explanatory. In their work, they demonstrate that design theories deliver functional explanations with a simple and elegant structure explaining generalized solution components by the related generalized requirements <ref>Baskerville, R.; Pries-Heje, J.: Explanatory Design Theory, in: Business & Information Systems Engineering, 2, 5, 2010, pp. 271-282.</ref> .

In the book, Design Science Research Cases <ref>Brocke, J. vom, Hevner, A., and Mädche, A. (eds.). 2020. Design Science Research. Cases, Progress in IS, Springer International Publishing. https://doi.org/10.1007/978-3-030-46781-4.</ref> several DSR cases are presented by experienced researchers in the field. It offers readers access to real-world DSR studies, together with the authors’ reflections on their research processes. The description of the cases supports researchers in addition to existing introductions to DSR methods and processes.

== Process description ==
[[File:Kuechler & Vaishnavi.png|thumb]]

This process describes the Design Science Research process according to Kuechler & Vaishnavi.This model is an adaptation of a computable design process model developed by Takeda, et al. (1990) <ref>Takeda, H., Veerkamp, P., Tomiyama, T., and Yoshikawam, H. (1990). “Modeling Design Processes.” AI Magazine Winter: 37–48.</ref> . Even though the different phases in a design process and a design science research process are similar, the activities carried out within these phases are considerably different. Also, what makes the design science research process model different from the corresponding design process model is the fact that contribution of new (and true) knowledge needs to be a key focus of design science research. <ref>Vaishnavi, Vijay & Kuechler, B. (2004). Design Science Research in Information Systems. Association for Information Systems.</ref>

== Awareness of Problem ==
=== Description ===
Describe the awareness of an interesting problem. Awareness can come from new developments in the industry or from other reference disciplines.

=== Examples ===
Expert interviews with eight employees of the case company revealed:
* Lack of process (model) understanding Difficulties in execution business processes
* Structured literature review on the concept “guidance” in IS research
Structured literature review on the concept “guidance” in IS research:
* Overview on existing research ([[Literature Review according to vom Brocke et al.]] or [[Literature Review according to Webster and Watson]])
* Taxonomy of guidance in IS research ([[Taxonomy development]])

=== Further Readings ===
Kuechler, W., & Vaishnavi, V. (2008). On theory development in design science research: Anatomy of a research project. European Journal of Information Systems, 17(5), 489–504. https://doi.org/10.1057/ejis.2008.40

Vaishnavi, V., Kuechler, W., and Petter, S. (Eds.) (2004/19). “Design Science Research in
Information Systems” January 20, 2004 (created in 2004 and updated until 2015 by Vaishnavi, V.
and Kuechler, W.); last updated (by Vaishnavi, V. and Petter, S.), June 30, 2019. URL:
http://www.desrist.org/design-research-in-information-systems/.

== Suggestion ==
=== Description ===
[[File:Suggestion.png|thumb|Suggestion]]

Synthesis of design principles based on empirical findings:
* Extraction of Meta-Requirements from existing literature.
* Derivation of Design Principles based on the Meta- Requirements.
* Selection of Design Decisions to implement the Design Principles.

=== Examples ===
=== Further Readings ===
Kuechler, W., & Vaishnavi, V. (2008). On theory development in design science research: Anatomy of a research project. European Journal of Information Systems, 17(5), 489–504. https://doi.org/10.1057/ejis.2008.40

Vaishnavi, V., Kuechler, W., and Petter, S. (Eds.) (2004/19). “Design Science Research in Information Systems” January 20, 2004 (created in 2004 and updated until 2015 by Vaishnavi, V. and Kuechler, W.); last updated (by Vaishnavi, V. and Petter, S.), June 30, 2019. URL: http://www.desrist.org/design-research-in-information-systems/.

Gregor, S., Chandra Kruse, L., & Seidel, S. (in press). The Anatomy of a Design Principle.Journal of the Association for Information Systems. Chandra Kruse, L. and Nickerson, J. V. (2018), Portraying Design Essence (January 2018). Paper presented at the 51st Hawaii International Conference in System Science (HICSS)

Gregor, S., Chandra Kruse, L., & Seidel, S. (in press). The Anatomy of a Design Principle. Journal of the Association for Information Systems.

== Development ==
=== Description ===
[[File:Development.png|thumb]]

Implement the suggested design. Different techniques can be used depending on the artifact to be constructed.

=== Examples ===
Instantiation of design principles as a prototype

=== Further Readings ===
Kuechler, W., & Vaishnavi, V. (2008). On theory development in design science research: Anatomy of a research project. European Journal of Information Systems, 17(5), 489–504. https://doi.org/10.1057/ejis.2008.40

Vaishnavi, V., Kuechler, W., and Petter, S. (Eds.) (2004/19). “Design Science Research in Information Systems” January 20, 2004 (created in 2004 and updated until 2015 by Vaishnavi, V. and Kuechler, W.); last updated (by Vaishnavi, V. and Petter, S.), June 30, 2019. URL: http://www.desrist.org/design-research-in-information-systems/.

== Evaluation ==
=== Description ===
[[File:Evaluation.png|thumb]]

Evaluate the artifact according to criteria that are made in the 'Awareness of Problem' activity.

=== Examples ===
Qualitative evaluation of the prototype by eg. focus group workshops in the case company:
* SWOT analysis
* Feedback by the participants

=== Further Readings ===
[[Evaluation Patterns|Evaluation Patterns for Design Science Research Artefacts]]

Kuechler, W., & Vaishnavi, V. (2008). On theory development in design science research: Anatomy of a research project. European Journal of Information Systems, 17(5), 489–504. https://doi.org/10.1057/ejis.2008.40

Vaishnavi, V., Kuechler, W., and Petter, S. (Eds.) (2004/19). “Design Science Research in
Information Systems” January 20, 2004 (created in 2004 and updated until 2015 by Vaishnavi, V.
and Kuechler, W.); last updated (by Vaishnavi, V. and Petter, S.), June 30, 2019. URL:
http://www.desrist.org/design-research-in-information-systems/.

== Conclusion ==
=== Description ===
Write up the results and explain the gained knowledge. Learnings can be described as 'firm' facts that can be:
* repeatably applied
* repeatably invoked
Or there are findings that may serve as the subject of further research.

=== Examples ===

=== Further Readings ===
Kuechler, W., & Vaishnavi, V. (2008). On theory development in design science research: Anatomy of a research project. European Journal of Information Systems, 17(5), 489–504. https://doi.org/10.1057/ejis.2008.40

Vaishnavi, V., Kuechler, W., and Petter, S. (Eds.) (2004/19). “Design Science Research in
Information Systems” January 20, 2004 (created in 2004 and updated until 2015 by Vaishnavi, V.
and Kuechler, W.); last updated (by Vaishnavi, V. and Petter, S.), June 30, 2019. URL:
http://www.desrist.org/design-research-in-information-systems/.

== References ==

[[Category:Design Science Research]]

Design Science Research according to Kuechler & Vaishnavi

2023-03-09T14:07:43Z

MichiGau: /* Further Readings */

== Introduction ==
''"Two paradigms characterize much of the research in the Information Systems discipline: behavioral science and design science. The behavioral science paradigm seeks to develop and verify theories that explain or predict human or organizational behavior. The design-science paradigm seeks to extend the boundaries of human and organizational capabilities by creating new and innovative artifacts. Both paradigms are foundational to the IS discipline, positioned as it is at the confluence of people, organizations, and technology." ''(Hevner et. al.) <ref>Hevner AR, March ST, Park J and Ram S (2004) Design Science in Information Systems Research. MIS Quarterly 28(1), 75-105.</ref>





In many publications, essays, editorials or books describing the Design Science Research (DSR) method the authors aim to support researchers to conduct DSR projects for example by providing guidelines <ref>Hevner AR, March ST, Park J and Ram S (2004) Design Science in Information Systems Research. MIS Quarterly 28(1), 75-105.</ref> <ref>van der Merwe A., Gerber A., Smuts H. (2020) Guidelines for Conducting Design Science Research in Information Systems. In: Tait B., Kroeze J., Gruner S. (eds) ICT Education. SACLA 2019. Communications in Computer and Information Science, vol 1136. Springer, Cham. https://doi.org/10.1007/978-3-030-35629-3_11</ref> or how to extend on existing design knowledge <ref>vom Brocke, J., Winter, R., Hevner, A., Maedche, A. (2020), Accumulation and Evolution of Design Knowledge in Design Science Research – A Journey Through Time and Space, in: Journals of the Association for Information Systems (JAIS), 2020, forthcoming (ABDC_2016: A*; ABS: 4; ISI: 3.487; ISI: 2.109; VHB_3: A).</ref> and many more. Gregor and Hevner provide foundational guidance by describing their understanding and application of DSR concepts and providing guidance for researches on how to:
# Appreciate the levels of artifact abstractions that may be DSR contributions
# Identify appropriate ways of consuming and producing knowledge when you are preparing journal articles or other scholarly works
# Understand and position the knowledge contributions of your research projects
# Structure a DSR article so that it emphasizes significant contributions to the knowledge base
Their focal contribution is the DSR knowledge contribution framework with two dimensions based on the existing state of knowledge in both the problem and solution domains for the research opportunity under study. In addition, they propose a DSR communication schema with similarities to more conventional publication patterns, but which substitutes the description of the DSR artifact in place of a traditional results section. <ref>Gregor S and Hevner AR (2013) Positioning and Presenting Design Science Research for Maximum Impact. MIS Quarterly 37(2), 337-55.</ref>

Other researchers propose a set of core dimensions of a Design Science Research project that facilitates effective capture of the most relevant aspects of a DSR project to efficiently plan and communicate key considerations and conceptualizations of a DSR project. In this work, the authors represent six dimensions in the form of a DSR grid, a one-page visualization of the DSR project that is adjustable to the specific purpose for using the concept. <ref>vom Brocke, J., Maedche, A. (2019), The DSR Grid: Six Core Dimensions for Effectively Planning and Communicating Design Science Research Projects, in: Electronic Markets, Volume 29, Issue 3, pp 379–385 (ABDC: A; ABS: 2; ISI: 2.121; VHB: B).</ref>

Another important role in DSR is how to specify design theory so that it can be communicated, justified, and developed cumulatively. In the essay of Gregor and Jones they focus on the structural components or anatomy of design theories in IS <ref>Gregor S and Jones D (2007) The Anatomy of a Design Theory. Journal Of The Association For Information Systems 8(5), 312-335.</ref> . Baskerville and Pries-Heje focus on how design theories are explanatory. In their work, they demonstrate that design theories deliver functional explanations with a simple and elegant structure explaining generalized solution components by the related generalized requirements <ref>Baskerville, R.; Pries-Heje, J.: Explanatory Design Theory, in: Business & Information Systems Engineering, 2, 5, 2010, pp. 271-282.</ref> .

In the book, Design Science Research Cases <ref>Brocke, J. vom, Hevner, A., and Mädche, A. (eds.). 2020. Design Science Research. Cases, Progress in IS, Springer International Publishing. https://doi.org/10.1007/978-3-030-46781-4.</ref> several DSR cases are presented by experienced researchers in the field. It offers readers access to real-world DSR studies, together with the authors’ reflections on their research processes. The description of the cases supports researchers in addition to existing introductions to DSR methods and processes.

== Process description ==
[[File:Kuechler & Vaishnavi.png|thumb]]

This process describes the Design Science Research process according to Kuechler & Vaishnavi.This model is an adaptation of a computable design process model developed by Takeda, et al. (1990) <ref>Takeda, H., Veerkamp, P., Tomiyama, T., and Yoshikawam, H. (1990). “Modeling Design Processes.” AI Magazine Winter: 37–48.</ref> . Even though the different phases in a design process and a design science research process are similar, the activities carried out within these phases are considerably different. Also, what makes the design science research process model different from the corresponding design process model is the fact that contribution of new (and true) knowledge needs to be a key focus of design science research. <ref>Vaishnavi, Vijay & Kuechler, B. (2004). Design Science Research in Information Systems. Association for Information Systems.</ref>

== Awareness of Problem ==
=== Description ===
Describe the awareness of an interesting problem. Awareness can come from new developments in the industry or from other reference disciplines.

=== Examples ===
Expert interviews with eight employees of the case company revealed:
* Lack of process (model) understanding Difficulties in execution business processes
* Structured literature review on the concept “guidance” in IS research
Structured literature review on the concept “guidance” in IS research:
* Overview on existing research ([[Literature Review according to vom Brocke et al.]] or [[Literature Review according to Webster and Watson]])
* Taxonomy of guidance in IS research ([[Taxonomy development]])

=== Further Readings ===
Kuechler, W., & Vaishnavi, V. (2008). On theory development in design science research: Anatomy of a research project. European Journal of Information Systems, 17(5), 489–504. https://doi.org/10.1057/ejis.2008.40

Vaishnavi, V., Kuechler, W., and Petter, S. (Eds.) (2004/19). “Design Science Research in
Information Systems” January 20, 2004 (created in 2004 and updated until 2015 by Vaishnavi, V.
and Kuechler, W.); last updated (by Vaishnavi, V. and Petter, S.), June 30, 2019. URL:
http://www.desrist.org/design-research-in-information-systems/.

== Suggestion ==
=== Description ===
[[File:Suggestion.png|thumb|Suggestion]]

Synthesis of design principles based on empirical findings:
* Extraction of Meta-Requirements from existing literature.
* Derivation of Design Principles based on the Meta- Requirements.
* Selection of Design Decisions to implement the Design Principles.

=== Examples ===
=== Further Readings ===
Kuechler, W., & Vaishnavi, V. (2008). On theory development in design science research: Anatomy of a research project. European Journal of Information Systems, 17(5), 489–504. https://doi.org/10.1057/ejis.2008.40

Vaishnavi, V., Kuechler, W., and Petter, S. (Eds.) (2004/19). “Design Science Research in Information Systems” January 20, 2004 (created in 2004 and updated until 2015 by Vaishnavi, V. and Kuechler, W.); last updated (by Vaishnavi, V. and Petter, S.), June 30, 2019. URL: http://www.desrist.org/design-research-in-information-systems/.

Gregor, S., Chandra Kruse, L., & Seidel, S. (in press). The Anatomy of a Design Principle.Journal of the Association for Information Systems. Chandra Kruse, L. and Nickerson, J. V. (2018), Portraying Design Essence (January 2018). Paper presented at the 51st Hawaii International Conference in System Science (HICSS)

Gregor, S., Chandra Kruse, L., & Seidel, S. (in press). The Anatomy of a Design Principle. Journal of the Association for Information Systems.

== Development ==
=== Description ===
[[File:Development.png|thumb]]

Implement the suggested design. Different techniques can be used depending on the artifact to be constructed.

=== Examples ===
Instantiation of design principles as a prototype

=== Further Readings ===
Kuechler, W., & Vaishnavi, V. (2008). On theory development in design science research: Anatomy of a research project. European Journal of Information Systems, 17(5), 489–504. https://doi.org/10.1057/ejis.2008.40

Vaishnavi, V., Kuechler, W., and Petter, S. (Eds.) (2004/19). “Design Science Research in Information Systems” January 20, 2004 (created in 2004 and updated until 2015 by Vaishnavi, V. and Kuechler, W.); last updated (by Vaishnavi, V. and Petter, S.), June 30, 2019. URL: http://www.desrist.org/design-research-in-information-systems/.

== Evaluation ==
=== Description ===
[[File:Evaluation.png|thumb]]

Evaluate the artifact according to criteria that are made in the 'Awareness of Problem' activity.

=== Examples ===
Qualitative evaluation of the prototype by eg. focus group workshops in the case company:
* SWOT analysis
* Feedback by the participants

=== Further Readings ===
[[Evaluation Patterns|Evaluation Patterns for Design Science Research Artefacts]]

Kuechler, W., & Vaishnavi, V. (2008). On theory development in design science research: Anatomy of a research project. European Journal of Information Systems, 17(5), 489–504. https://doi.org/10.1057/ejis.2008.40

Vaishnavi, V., Kuechler, W., and Petter, S. (Eds.) (2004/19). “Design Science Research in
Information Systems” January 20, 2004 (created in 2004 and updated until 2015 by Vaishnavi, V.
and Kuechler, W.); last updated (by Vaishnavi, V. and Petter, S.), June 30, 2019. URL:
http://www.desrist.org/design-research-in-information-systems/.

== Conclusion ==
=== Description ===
Write up the results and explain the gained knowledge. Learnings can be described as 'firm' facts that can be:
* repeatably applied
* repeatably invoked
Or there are findings that may serve as the subject of further research.

=== Examples ===

=== Further Readings ===
Kuechler, B., & Vaishnavi, V. (2008). On theory development in design science research: Anatomy of a research project. European Journal of Information Systems, 17(5), 489–504. https://doi.org/10.1057/ejis.2008.40

Vaishnavi, V., Kuechler, W., and Petter, S. (Eds.) (2004/19). “Design Science Research in
Information Systems” January 20, 2004 (created in 2004 and updated until 2015 by Vaishnavi, V.
and Kuechler, W.); last updated (by Vaishnavi, V. and Petter, S.), June 30, 2019. URL:
http://www.desrist.org/design-research-in-information-systems/.

== References ==

[[Category:Design Science Research]]

Design Science Research according to Kuechler & Vaishnavi

2023-03-09T14:07:33Z

MichiGau: /* Further Readings */

== Introduction ==
''"Two paradigms characterize much of the research in the Information Systems discipline: behavioral science and design science. The behavioral science paradigm seeks to develop and verify theories that explain or predict human or organizational behavior. The design-science paradigm seeks to extend the boundaries of human and organizational capabilities by creating new and innovative artifacts. Both paradigms are foundational to the IS discipline, positioned as it is at the confluence of people, organizations, and technology." ''(Hevner et. al.) <ref>Hevner AR, March ST, Park J and Ram S (2004) Design Science in Information Systems Research. MIS Quarterly 28(1), 75-105.</ref>





In many publications, essays, editorials or books describing the Design Science Research (DSR) method the authors aim to support researchers to conduct DSR projects for example by providing guidelines <ref>Hevner AR, March ST, Park J and Ram S (2004) Design Science in Information Systems Research. MIS Quarterly 28(1), 75-105.</ref> <ref>van der Merwe A., Gerber A., Smuts H. (2020) Guidelines for Conducting Design Science Research in Information Systems. In: Tait B., Kroeze J., Gruner S. (eds) ICT Education. SACLA 2019. Communications in Computer and Information Science, vol 1136. Springer, Cham. https://doi.org/10.1007/978-3-030-35629-3_11</ref> or how to extend on existing design knowledge <ref>vom Brocke, J., Winter, R., Hevner, A., Maedche, A. (2020), Accumulation and Evolution of Design Knowledge in Design Science Research – A Journey Through Time and Space, in: Journals of the Association for Information Systems (JAIS), 2020, forthcoming (ABDC_2016: A*; ABS: 4; ISI: 3.487; ISI: 2.109; VHB_3: A).</ref> and many more. Gregor and Hevner provide foundational guidance by describing their understanding and application of DSR concepts and providing guidance for researches on how to:
# Appreciate the levels of artifact abstractions that may be DSR contributions
# Identify appropriate ways of consuming and producing knowledge when you are preparing journal articles or other scholarly works
# Understand and position the knowledge contributions of your research projects
# Structure a DSR article so that it emphasizes significant contributions to the knowledge base
Their focal contribution is the DSR knowledge contribution framework with two dimensions based on the existing state of knowledge in both the problem and solution domains for the research opportunity under study. In addition, they propose a DSR communication schema with similarities to more conventional publication patterns, but which substitutes the description of the DSR artifact in place of a traditional results section. <ref>Gregor S and Hevner AR (2013) Positioning and Presenting Design Science Research for Maximum Impact. MIS Quarterly 37(2), 337-55.</ref>

Other researchers propose a set of core dimensions of a Design Science Research project that facilitates effective capture of the most relevant aspects of a DSR project to efficiently plan and communicate key considerations and conceptualizations of a DSR project. In this work, the authors represent six dimensions in the form of a DSR grid, a one-page visualization of the DSR project that is adjustable to the specific purpose for using the concept. <ref>vom Brocke, J., Maedche, A. (2019), The DSR Grid: Six Core Dimensions for Effectively Planning and Communicating Design Science Research Projects, in: Electronic Markets, Volume 29, Issue 3, pp 379–385 (ABDC: A; ABS: 2; ISI: 2.121; VHB: B).</ref>

Another important role in DSR is how to specify design theory so that it can be communicated, justified, and developed cumulatively. In the essay of Gregor and Jones they focus on the structural components or anatomy of design theories in IS <ref>Gregor S and Jones D (2007) The Anatomy of a Design Theory. Journal Of The Association For Information Systems 8(5), 312-335.</ref> . Baskerville and Pries-Heje focus on how design theories are explanatory. In their work, they demonstrate that design theories deliver functional explanations with a simple and elegant structure explaining generalized solution components by the related generalized requirements <ref>Baskerville, R.; Pries-Heje, J.: Explanatory Design Theory, in: Business & Information Systems Engineering, 2, 5, 2010, pp. 271-282.</ref> .

In the book, Design Science Research Cases <ref>Brocke, J. vom, Hevner, A., and Mädche, A. (eds.). 2020. Design Science Research. Cases, Progress in IS, Springer International Publishing. https://doi.org/10.1007/978-3-030-46781-4.</ref> several DSR cases are presented by experienced researchers in the field. It offers readers access to real-world DSR studies, together with the authors’ reflections on their research processes. The description of the cases supports researchers in addition to existing introductions to DSR methods and processes.

== Process description ==
[[File:Kuechler & Vaishnavi.png|thumb]]

This process describes the Design Science Research process according to Kuechler & Vaishnavi.This model is an adaptation of a computable design process model developed by Takeda, et al. (1990) <ref>Takeda, H., Veerkamp, P., Tomiyama, T., and Yoshikawam, H. (1990). “Modeling Design Processes.” AI Magazine Winter: 37–48.</ref> . Even though the different phases in a design process and a design science research process are similar, the activities carried out within these phases are considerably different. Also, what makes the design science research process model different from the corresponding design process model is the fact that contribution of new (and true) knowledge needs to be a key focus of design science research. <ref>Vaishnavi, Vijay & Kuechler, B. (2004). Design Science Research in Information Systems. Association for Information Systems.</ref>

== Awareness of Problem ==
=== Description ===
Describe the awareness of an interesting problem. Awareness can come from new developments in the industry or from other reference disciplines.

=== Examples ===
Expert interviews with eight employees of the case company revealed:
* Lack of process (model) understanding Difficulties in execution business processes
* Structured literature review on the concept “guidance” in IS research
Structured literature review on the concept “guidance” in IS research:
* Overview on existing research ([[Literature Review according to vom Brocke et al.]] or [[Literature Review according to Webster and Watson]])
* Taxonomy of guidance in IS research ([[Taxonomy development]])

=== Further Readings ===
Kuechler, W., & Vaishnavi, V. (2008). On theory development in design science research: Anatomy of a research project. European Journal of Information Systems, 17(5), 489–504. https://doi.org/10.1057/ejis.2008.40

Vaishnavi, V., Kuechler, W., and Petter, S. (Eds.) (2004/19). “Design Science Research in
Information Systems” January 20, 2004 (created in 2004 and updated until 2015 by Vaishnavi, V.
and Kuechler, W.); last updated (by Vaishnavi, V. and Petter, S.), June 30, 2019. URL:
http://www.desrist.org/design-research-in-information-systems/.

== Suggestion ==
=== Description ===
[[File:Suggestion.png|thumb|Suggestion]]

Synthesis of design principles based on empirical findings:
* Extraction of Meta-Requirements from existing literature.
* Derivation of Design Principles based on the Meta- Requirements.
* Selection of Design Decisions to implement the Design Principles.

=== Examples ===
=== Further Readings ===
Kuechler, W., & Vaishnavi, V. (2008). On theory development in design science research: Anatomy of a research project. European Journal of Information Systems, 17(5), 489–504. https://doi.org/10.1057/ejis.2008.40

Vaishnavi, V., Kuechler, W., and Petter, S. (Eds.) (2004/19). “Design Science Research in Information Systems” January 20, 2004 (created in 2004 and updated until 2015 by Vaishnavi, V. and Kuechler, W.); last updated (by Vaishnavi, V. and Petter, S.), June 30, 2019. URL: http://www.desrist.org/design-research-in-information-systems/.

Gregor, S., Chandra Kruse, L., & Seidel, S. (in press). The Anatomy of a Design Principle.Journal of the Association for Information Systems. Chandra Kruse, L. and Nickerson, J. V. (2018), Portraying Design Essence (January 2018). Paper presented at the 51st Hawaii International Conference in System Science (HICSS)

Gregor, S., Chandra Kruse, L., & Seidel, S. (in press). The Anatomy of a Design Principle. Journal of the Association for Information Systems.

== Development ==
=== Description ===
[[File:Development.png|thumb]]

Implement the suggested design. Different techniques can be used depending on the artifact to be constructed.

=== Examples ===
Instantiation of design principles as a prototype

=== Further Readings ===
Kuechler, W., & Vaishnavi, V. (2008). On theory development in design science research: Anatomy of a research project. European Journal of Information Systems, 17(5), 489–504. https://doi.org/10.1057/ejis.2008.40

Vaishnavi, V., Kuechler, W., and Petter, S. (Eds.) (2004/19). “Design Science Research in Information Systems” January 20, 2004 (created in 2004 and updated until 2015 by Vaishnavi, V. and Kuechler, W.); last updated (by Vaishnavi, V. and Petter, S.), June 30, 2019. URL: http://www.desrist.org/design-research-in-information-systems/.

== Evaluation ==
=== Description ===
[[File:Evaluation.png|thumb]]

Evaluate the artifact according to criteria that are made in the 'Awareness of Problem' activity.

=== Examples ===
Qualitative evaluation of the prototype by eg. focus group workshops in the case company:
* SWOT analysis
* Feedback by the participants

=== Further Readings ===
[[Evaluation Patterns|Evaluation Patterns for Design Science Research Artefacts]]

Kuechler, B., & Vaishnavi, V. (2008). On theory development in design science research: Anatomy of a research project. European Journal of Information Systems, 17(5), 489–504. https://doi.org/10.1057/ejis.2008.40

Vaishnavi, V., Kuechler, W., and Petter, S. (Eds.) (2004/19). “Design Science Research in
Information Systems” January 20, 2004 (created in 2004 and updated until 2015 by Vaishnavi, V.
and Kuechler, W.); last updated (by Vaishnavi, V. and Petter, S.), June 30, 2019. URL:
http://www.desrist.org/design-research-in-information-systems/.

== Conclusion ==
=== Description ===
Write up the results and explain the gained knowledge. Learnings can be described as 'firm' facts that can be:
* repeatably applied
* repeatably invoked
Or there are findings that may serve as the subject of further research.

=== Examples ===

=== Further Readings ===
Kuechler, B., & Vaishnavi, V. (2008). On theory development in design science research: Anatomy of a research project. European Journal of Information Systems, 17(5), 489–504. https://doi.org/10.1057/ejis.2008.40

Vaishnavi, V., Kuechler, W., and Petter, S. (Eds.) (2004/19). “Design Science Research in
Information Systems” January 20, 2004 (created in 2004 and updated until 2015 by Vaishnavi, V.
and Kuechler, W.); last updated (by Vaishnavi, V. and Petter, S.), June 30, 2019. URL:
http://www.desrist.org/design-research-in-information-systems/.

== References ==

[[Category:Design Science Research]]

Design Science Research according to Kuechler & Vaishnavi

2023-03-09T14:07:22Z

MichiGau: /* Further Readings */

== Introduction ==
''"Two paradigms characterize much of the research in the Information Systems discipline: behavioral science and design science. The behavioral science paradigm seeks to develop and verify theories that explain or predict human or organizational behavior. The design-science paradigm seeks to extend the boundaries of human and organizational capabilities by creating new and innovative artifacts. Both paradigms are foundational to the IS discipline, positioned as it is at the confluence of people, organizations, and technology." ''(Hevner et. al.) <ref>Hevner AR, March ST, Park J and Ram S (2004) Design Science in Information Systems Research. MIS Quarterly 28(1), 75-105.</ref>





In many publications, essays, editorials or books describing the Design Science Research (DSR) method the authors aim to support researchers to conduct DSR projects for example by providing guidelines <ref>Hevner AR, March ST, Park J and Ram S (2004) Design Science in Information Systems Research. MIS Quarterly 28(1), 75-105.</ref> <ref>van der Merwe A., Gerber A., Smuts H. (2020) Guidelines for Conducting Design Science Research in Information Systems. In: Tait B., Kroeze J., Gruner S. (eds) ICT Education. SACLA 2019. Communications in Computer and Information Science, vol 1136. Springer, Cham. https://doi.org/10.1007/978-3-030-35629-3_11</ref> or how to extend on existing design knowledge <ref>vom Brocke, J., Winter, R., Hevner, A., Maedche, A. (2020), Accumulation and Evolution of Design Knowledge in Design Science Research – A Journey Through Time and Space, in: Journals of the Association for Information Systems (JAIS), 2020, forthcoming (ABDC_2016: A*; ABS: 4; ISI: 3.487; ISI: 2.109; VHB_3: A).</ref> and many more. Gregor and Hevner provide foundational guidance by describing their understanding and application of DSR concepts and providing guidance for researches on how to:
# Appreciate the levels of artifact abstractions that may be DSR contributions
# Identify appropriate ways of consuming and producing knowledge when you are preparing journal articles or other scholarly works
# Understand and position the knowledge contributions of your research projects
# Structure a DSR article so that it emphasizes significant contributions to the knowledge base
Their focal contribution is the DSR knowledge contribution framework with two dimensions based on the existing state of knowledge in both the problem and solution domains for the research opportunity under study. In addition, they propose a DSR communication schema with similarities to more conventional publication patterns, but which substitutes the description of the DSR artifact in place of a traditional results section. <ref>Gregor S and Hevner AR (2013) Positioning and Presenting Design Science Research for Maximum Impact. MIS Quarterly 37(2), 337-55.</ref>

Other researchers propose a set of core dimensions of a Design Science Research project that facilitates effective capture of the most relevant aspects of a DSR project to efficiently plan and communicate key considerations and conceptualizations of a DSR project. In this work, the authors represent six dimensions in the form of a DSR grid, a one-page visualization of the DSR project that is adjustable to the specific purpose for using the concept. <ref>vom Brocke, J., Maedche, A. (2019), The DSR Grid: Six Core Dimensions for Effectively Planning and Communicating Design Science Research Projects, in: Electronic Markets, Volume 29, Issue 3, pp 379–385 (ABDC: A; ABS: 2; ISI: 2.121; VHB: B).</ref>

Another important role in DSR is how to specify design theory so that it can be communicated, justified, and developed cumulatively. In the essay of Gregor and Jones they focus on the structural components or anatomy of design theories in IS <ref>Gregor S and Jones D (2007) The Anatomy of a Design Theory. Journal Of The Association For Information Systems 8(5), 312-335.</ref> . Baskerville and Pries-Heje focus on how design theories are explanatory. In their work, they demonstrate that design theories deliver functional explanations with a simple and elegant structure explaining generalized solution components by the related generalized requirements <ref>Baskerville, R.; Pries-Heje, J.: Explanatory Design Theory, in: Business & Information Systems Engineering, 2, 5, 2010, pp. 271-282.</ref> .

In the book, Design Science Research Cases <ref>Brocke, J. vom, Hevner, A., and Mädche, A. (eds.). 2020. Design Science Research. Cases, Progress in IS, Springer International Publishing. https://doi.org/10.1007/978-3-030-46781-4.</ref> several DSR cases are presented by experienced researchers in the field. It offers readers access to real-world DSR studies, together with the authors’ reflections on their research processes. The description of the cases supports researchers in addition to existing introductions to DSR methods and processes.

== Process description ==
[[File:Kuechler & Vaishnavi.png|thumb]]

This process describes the Design Science Research process according to Kuechler & Vaishnavi.This model is an adaptation of a computable design process model developed by Takeda, et al. (1990) <ref>Takeda, H., Veerkamp, P., Tomiyama, T., and Yoshikawam, H. (1990). “Modeling Design Processes.” AI Magazine Winter: 37–48.</ref> . Even though the different phases in a design process and a design science research process are similar, the activities carried out within these phases are considerably different. Also, what makes the design science research process model different from the corresponding design process model is the fact that contribution of new (and true) knowledge needs to be a key focus of design science research. <ref>Vaishnavi, Vijay & Kuechler, B. (2004). Design Science Research in Information Systems. Association for Information Systems.</ref>

== Awareness of Problem ==
=== Description ===
Describe the awareness of an interesting problem. Awareness can come from new developments in the industry or from other reference disciplines.

=== Examples ===
Expert interviews with eight employees of the case company revealed:
* Lack of process (model) understanding Difficulties in execution business processes
* Structured literature review on the concept “guidance” in IS research
Structured literature review on the concept “guidance” in IS research:
* Overview on existing research ([[Literature Review according to vom Brocke et al.]] or [[Literature Review according to Webster and Watson]])
* Taxonomy of guidance in IS research ([[Taxonomy development]])

=== Further Readings ===
Kuechler, W., & Vaishnavi, V. (2008). On theory development in design science research: Anatomy of a research project. European Journal of Information Systems, 17(5), 489–504. https://doi.org/10.1057/ejis.2008.40

Vaishnavi, V., Kuechler, W., and Petter, S. (Eds.) (2004/19). “Design Science Research in
Information Systems” January 20, 2004 (created in 2004 and updated until 2015 by Vaishnavi, V.
and Kuechler, W.); last updated (by Vaishnavi, V. and Petter, S.), June 30, 2019. URL:
http://www.desrist.org/design-research-in-information-systems/.

== Suggestion ==
=== Description ===
[[File:Suggestion.png|thumb|Suggestion]]

Synthesis of design principles based on empirical findings:
* Extraction of Meta-Requirements from existing literature.
* Derivation of Design Principles based on the Meta- Requirements.
* Selection of Design Decisions to implement the Design Principles.

=== Examples ===
=== Further Readings ===
Kuechler, W., & Vaishnavi, V. (2008). On theory development in design science research: Anatomy of a research project. European Journal of Information Systems, 17(5), 489–504. https://doi.org/10.1057/ejis.2008.40

Vaishnavi, V., Kuechler, W., and Petter, S. (Eds.) (2004/19). “Design Science Research in Information Systems” January 20, 2004 (created in 2004 and updated until 2015 by Vaishnavi, V. and Kuechler, W.); last updated (by Vaishnavi, V. and Petter, S.), June 30, 2019. URL: http://www.desrist.org/design-research-in-information-systems/.

Gregor, S., Chandra Kruse, L., & Seidel, S. (in press). The Anatomy of a Design Principle.Journal of the Association for Information Systems. Chandra Kruse, L. and Nickerson, J. V. (2018), Portraying Design Essence (January 2018). Paper presented at the 51st Hawaii International Conference in System Science (HICSS)

Gregor, S., Chandra Kruse, L., & Seidel, S. (in press). The Anatomy of a Design Principle. Journal of the Association for Information Systems.

== Development ==
=== Description ===
[[File:Development.png|thumb]]

Implement the suggested design. Different techniques can be used depending on the artifact to be constructed.

=== Examples ===
Instantiation of design principles as a prototype

=== Further Readings ===
Kuechler, B., & Vaishnavi, V. (2008). On theory development in design science research: Anatomy of a research project. European Journal of Information Systems, 17(5), 489–504. https://doi.org/10.1057/ejis.2008.40

Vaishnavi, V., Kuechler, W., and Petter, S. (Eds.) (2004/19). “Design Science Research in Information Systems” January 20, 2004 (created in 2004 and updated until 2015 by Vaishnavi, V. and Kuechler, W.); last updated (by Vaishnavi, V. and Petter, S.), June 30, 2019. URL: http://www.desrist.org/design-research-in-information-systems/.

== Evaluation ==
=== Description ===
[[File:Evaluation.png|thumb]]

Evaluate the artifact according to criteria that are made in the 'Awareness of Problem' activity.

=== Examples ===
Qualitative evaluation of the prototype by eg. focus group workshops in the case company:
* SWOT analysis
* Feedback by the participants

=== Further Readings ===
[[Evaluation Patterns|Evaluation Patterns for Design Science Research Artefacts]]

Kuechler, B., & Vaishnavi, V. (2008). On theory development in design science research: Anatomy of a research project. European Journal of Information Systems, 17(5), 489–504. https://doi.org/10.1057/ejis.2008.40

Vaishnavi, V., Kuechler, W., and Petter, S. (Eds.) (2004/19). “Design Science Research in
Information Systems” January 20, 2004 (created in 2004 and updated until 2015 by Vaishnavi, V.
and Kuechler, W.); last updated (by Vaishnavi, V. and Petter, S.), June 30, 2019. URL:
http://www.desrist.org/design-research-in-information-systems/.

== Conclusion ==
=== Description ===
Write up the results and explain the gained knowledge. Learnings can be described as 'firm' facts that can be:
* repeatably applied
* repeatably invoked
Or there are findings that may serve as the subject of further research.

=== Examples ===

=== Further Readings ===
Kuechler, B., & Vaishnavi, V. (2008). On theory development in design science research: Anatomy of a research project. European Journal of Information Systems, 17(5), 489–504. https://doi.org/10.1057/ejis.2008.40

Vaishnavi, V., Kuechler, W., and Petter, S. (Eds.) (2004/19). “Design Science Research in
Information Systems” January 20, 2004 (created in 2004 and updated until 2015 by Vaishnavi, V.
and Kuechler, W.); last updated (by Vaishnavi, V. and Petter, S.), June 30, 2019. URL:
http://www.desrist.org/design-research-in-information-systems/.

== References ==

[[Category:Design Science Research]]

Design Science Research according to Kuechler & Vaishnavi

2023-03-09T14:06:51Z

MichiGau: /* Further Readings */

== Introduction ==
''"Two paradigms characterize much of the research in the Information Systems discipline: behavioral science and design science. The behavioral science paradigm seeks to develop and verify theories that explain or predict human or organizational behavior. The design-science paradigm seeks to extend the boundaries of human and organizational capabilities by creating new and innovative artifacts. Both paradigms are foundational to the IS discipline, positioned as it is at the confluence of people, organizations, and technology." ''(Hevner et. al.) <ref>Hevner AR, March ST, Park J and Ram S (2004) Design Science in Information Systems Research. MIS Quarterly 28(1), 75-105.</ref>





In many publications, essays, editorials or books describing the Design Science Research (DSR) method the authors aim to support researchers to conduct DSR projects for example by providing guidelines <ref>Hevner AR, March ST, Park J and Ram S (2004) Design Science in Information Systems Research. MIS Quarterly 28(1), 75-105.</ref> <ref>van der Merwe A., Gerber A., Smuts H. (2020) Guidelines for Conducting Design Science Research in Information Systems. In: Tait B., Kroeze J., Gruner S. (eds) ICT Education. SACLA 2019. Communications in Computer and Information Science, vol 1136. Springer, Cham. https://doi.org/10.1007/978-3-030-35629-3_11</ref> or how to extend on existing design knowledge <ref>vom Brocke, J., Winter, R., Hevner, A., Maedche, A. (2020), Accumulation and Evolution of Design Knowledge in Design Science Research – A Journey Through Time and Space, in: Journals of the Association for Information Systems (JAIS), 2020, forthcoming (ABDC_2016: A*; ABS: 4; ISI: 3.487; ISI: 2.109; VHB_3: A).</ref> and many more. Gregor and Hevner provide foundational guidance by describing their understanding and application of DSR concepts and providing guidance for researches on how to:
# Appreciate the levels of artifact abstractions that may be DSR contributions
# Identify appropriate ways of consuming and producing knowledge when you are preparing journal articles or other scholarly works
# Understand and position the knowledge contributions of your research projects
# Structure a DSR article so that it emphasizes significant contributions to the knowledge base
Their focal contribution is the DSR knowledge contribution framework with two dimensions based on the existing state of knowledge in both the problem and solution domains for the research opportunity under study. In addition, they propose a DSR communication schema with similarities to more conventional publication patterns, but which substitutes the description of the DSR artifact in place of a traditional results section. <ref>Gregor S and Hevner AR (2013) Positioning and Presenting Design Science Research for Maximum Impact. MIS Quarterly 37(2), 337-55.</ref>

Other researchers propose a set of core dimensions of a Design Science Research project that facilitates effective capture of the most relevant aspects of a DSR project to efficiently plan and communicate key considerations and conceptualizations of a DSR project. In this work, the authors represent six dimensions in the form of a DSR grid, a one-page visualization of the DSR project that is adjustable to the specific purpose for using the concept. <ref>vom Brocke, J., Maedche, A. (2019), The DSR Grid: Six Core Dimensions for Effectively Planning and Communicating Design Science Research Projects, in: Electronic Markets, Volume 29, Issue 3, pp 379–385 (ABDC: A; ABS: 2; ISI: 2.121; VHB: B).</ref>

Another important role in DSR is how to specify design theory so that it can be communicated, justified, and developed cumulatively. In the essay of Gregor and Jones they focus on the structural components or anatomy of design theories in IS <ref>Gregor S and Jones D (2007) The Anatomy of a Design Theory. Journal Of The Association For Information Systems 8(5), 312-335.</ref> . Baskerville and Pries-Heje focus on how design theories are explanatory. In their work, they demonstrate that design theories deliver functional explanations with a simple and elegant structure explaining generalized solution components by the related generalized requirements <ref>Baskerville, R.; Pries-Heje, J.: Explanatory Design Theory, in: Business & Information Systems Engineering, 2, 5, 2010, pp. 271-282.</ref> .

In the book, Design Science Research Cases <ref>Brocke, J. vom, Hevner, A., and Mädche, A. (eds.). 2020. Design Science Research. Cases, Progress in IS, Springer International Publishing. https://doi.org/10.1007/978-3-030-46781-4.</ref> several DSR cases are presented by experienced researchers in the field. It offers readers access to real-world DSR studies, together with the authors’ reflections on their research processes. The description of the cases supports researchers in addition to existing introductions to DSR methods and processes.

== Process description ==
[[File:Kuechler & Vaishnavi.png|thumb]]

This process describes the Design Science Research process according to Kuechler & Vaishnavi.This model is an adaptation of a computable design process model developed by Takeda, et al. (1990) <ref>Takeda, H., Veerkamp, P., Tomiyama, T., and Yoshikawam, H. (1990). “Modeling Design Processes.” AI Magazine Winter: 37–48.</ref> . Even though the different phases in a design process and a design science research process are similar, the activities carried out within these phases are considerably different. Also, what makes the design science research process model different from the corresponding design process model is the fact that contribution of new (and true) knowledge needs to be a key focus of design science research. <ref>Vaishnavi, Vijay & Kuechler, B. (2004). Design Science Research in Information Systems. Association for Information Systems.</ref>

== Awareness of Problem ==
=== Description ===
Describe the awareness of an interesting problem. Awareness can come from new developments in the industry or from other reference disciplines.

=== Examples ===
Expert interviews with eight employees of the case company revealed:
* Lack of process (model) understanding Difficulties in execution business processes
* Structured literature review on the concept “guidance” in IS research
Structured literature review on the concept “guidance” in IS research:
* Overview on existing research ([[Literature Review according to vom Brocke et al.]] or [[Literature Review according to Webster and Watson]])
* Taxonomy of guidance in IS research ([[Taxonomy development]])

=== Further Readings ===
Kuechler, W., & Vaishnavi, V. (2008). On theory development in design science research: Anatomy of a research project. European Journal of Information Systems, 17(5), 489–504. https://doi.org/10.1057/ejis.2008.40

Vaishnavi, V., Kuechler, W., and Petter, S. (Eds.) (2004/19). “Design Science Research in
Information Systems” January 20, 2004 (created in 2004 and updated until 2015 by Vaishnavi, V.
and Kuechler, W.); last updated (by Vaishnavi, V. and Petter, S.), June 30, 2019. URL:
http://www.desrist.org/design-research-in-information-systems/.

== Suggestion ==
=== Description ===
[[File:Suggestion.png|thumb|Suggestion]]

Synthesis of design principles based on empirical findings:
* Extraction of Meta-Requirements from existing literature.
* Derivation of Design Principles based on the Meta- Requirements.
* Selection of Design Decisions to implement the Design Principles.

=== Examples ===
=== Further Readings ===
Kuechler, B., & Vaishnavi, V. (2008). On theory development in design science research: Anatomy of a research project. European Journal of Information Systems, 17(5), 489–504. https://doi.org/10.1057/ejis.2008.40

Vaishnavi, V., Kuechler, W., and Petter, S. (Eds.) (2004/19). “Design Science Research in Information Systems” January 20, 2004 (created in 2004 and updated until 2015 by Vaishnavi, V. and Kuechler, W.); last updated (by Vaishnavi, V. and Petter, S.), June 30, 2019. URL: http://www.desrist.org/design-research-in-information-systems/.

Gregor, S., Chandra Kruse, L., & Seidel, S. (in press). The Anatomy of a Design Principle.Journal of the Association for Information Systems. Chandra Kruse, L. and Nickerson, J. V. (2018), Portraying Design Essence (January 2018). Paper presented at the 51st Hawaii International Conference in System Science (HICSS)

Gregor, S., Chandra Kruse, L., & Seidel, S. (in press). The Anatomy of a Design Principle. Journal of the Association for Information Systems.

== Development ==
=== Description ===
[[File:Development.png|thumb]]

Implement the suggested design. Different techniques can be used depending on the artifact to be constructed.

=== Examples ===
Instantiation of design principles as a prototype

=== Further Readings ===
Kuechler, B., & Vaishnavi, V. (2008). On theory development in design science research: Anatomy of a research project. European Journal of Information Systems, 17(5), 489–504. https://doi.org/10.1057/ejis.2008.40

Vaishnavi, V., Kuechler, W., and Petter, S. (Eds.) (2004/19). “Design Science Research in Information Systems” January 20, 2004 (created in 2004 and updated until 2015 by Vaishnavi, V. and Kuechler, W.); last updated (by Vaishnavi, V. and Petter, S.), June 30, 2019. URL: http://www.desrist.org/design-research-in-information-systems/.

== Evaluation ==
=== Description ===
[[File:Evaluation.png|thumb]]

Evaluate the artifact according to criteria that are made in the 'Awareness of Problem' activity.

=== Examples ===
Qualitative evaluation of the prototype by eg. focus group workshops in the case company:
* SWOT analysis
* Feedback by the participants

=== Further Readings ===
[[Evaluation Patterns|Evaluation Patterns for Design Science Research Artefacts]]

Kuechler, B., & Vaishnavi, V. (2008). On theory development in design science research: Anatomy of a research project. European Journal of Information Systems, 17(5), 489–504. https://doi.org/10.1057/ejis.2008.40

Vaishnavi, V., Kuechler, W., and Petter, S. (Eds.) (2004/19). “Design Science Research in
Information Systems” January 20, 2004 (created in 2004 and updated until 2015 by Vaishnavi, V.
and Kuechler, W.); last updated (by Vaishnavi, V. and Petter, S.), June 30, 2019. URL:
http://www.desrist.org/design-research-in-information-systems/.

== Conclusion ==
=== Description ===
Write up the results and explain the gained knowledge. Learnings can be described as 'firm' facts that can be:
* repeatably applied
* repeatably invoked
Or there are findings that may serve as the subject of further research.

=== Examples ===

=== Further Readings ===
Kuechler, B., & Vaishnavi, V. (2008). On theory development in design science research: Anatomy of a research project. European Journal of Information Systems, 17(5), 489–504. https://doi.org/10.1057/ejis.2008.40

Vaishnavi, V., Kuechler, W., and Petter, S. (Eds.) (2004/19). “Design Science Research in
Information Systems” January 20, 2004 (created in 2004 and updated until 2015 by Vaishnavi, V.
and Kuechler, W.); last updated (by Vaishnavi, V. and Petter, S.), June 30, 2019. URL:
http://www.desrist.org/design-research-in-information-systems/.

== References ==

[[Category:Design Science Research]]

Design Science Research according to Kuechler & Vaishnavi

2023-03-09T14:02:09Z

MichiGau: /* Further Readings */

== Introduction ==
''"Two paradigms characterize much of the research in the Information Systems discipline: behavioral science and design science. The behavioral science paradigm seeks to develop and verify theories that explain or predict human or organizational behavior. The design-science paradigm seeks to extend the boundaries of human and organizational capabilities by creating new and innovative artifacts. Both paradigms are foundational to the IS discipline, positioned as it is at the confluence of people, organizations, and technology." ''(Hevner et. al.) <ref>Hevner AR, March ST, Park J and Ram S (2004) Design Science in Information Systems Research. MIS Quarterly 28(1), 75-105.</ref>





In many publications, essays, editorials or books describing the Design Science Research (DSR) method the authors aim to support researchers to conduct DSR projects for example by providing guidelines <ref>Hevner AR, March ST, Park J and Ram S (2004) Design Science in Information Systems Research. MIS Quarterly 28(1), 75-105.</ref> <ref>van der Merwe A., Gerber A., Smuts H. (2020) Guidelines for Conducting Design Science Research in Information Systems. In: Tait B., Kroeze J., Gruner S. (eds) ICT Education. SACLA 2019. Communications in Computer and Information Science, vol 1136. Springer, Cham. https://doi.org/10.1007/978-3-030-35629-3_11</ref> or how to extend on existing design knowledge <ref>vom Brocke, J., Winter, R., Hevner, A., Maedche, A. (2020), Accumulation and Evolution of Design Knowledge in Design Science Research – A Journey Through Time and Space, in: Journals of the Association for Information Systems (JAIS), 2020, forthcoming (ABDC_2016: A*; ABS: 4; ISI: 3.487; ISI: 2.109; VHB_3: A).</ref> and many more. Gregor and Hevner provide foundational guidance by describing their understanding and application of DSR concepts and providing guidance for researches on how to:
# Appreciate the levels of artifact abstractions that may be DSR contributions
# Identify appropriate ways of consuming and producing knowledge when you are preparing journal articles or other scholarly works
# Understand and position the knowledge contributions of your research projects
# Structure a DSR article so that it emphasizes significant contributions to the knowledge base
Their focal contribution is the DSR knowledge contribution framework with two dimensions based on the existing state of knowledge in both the problem and solution domains for the research opportunity under study. In addition, they propose a DSR communication schema with similarities to more conventional publication patterns, but which substitutes the description of the DSR artifact in place of a traditional results section. <ref>Gregor S and Hevner AR (2013) Positioning and Presenting Design Science Research for Maximum Impact. MIS Quarterly 37(2), 337-55.</ref>

Other researchers propose a set of core dimensions of a Design Science Research project that facilitates effective capture of the most relevant aspects of a DSR project to efficiently plan and communicate key considerations and conceptualizations of a DSR project. In this work, the authors represent six dimensions in the form of a DSR grid, a one-page visualization of the DSR project that is adjustable to the specific purpose for using the concept. <ref>vom Brocke, J., Maedche, A. (2019), The DSR Grid: Six Core Dimensions for Effectively Planning and Communicating Design Science Research Projects, in: Electronic Markets, Volume 29, Issue 3, pp 379–385 (ABDC: A; ABS: 2; ISI: 2.121; VHB: B).</ref>

Another important role in DSR is how to specify design theory so that it can be communicated, justified, and developed cumulatively. In the essay of Gregor and Jones they focus on the structural components or anatomy of design theories in IS <ref>Gregor S and Jones D (2007) The Anatomy of a Design Theory. Journal Of The Association For Information Systems 8(5), 312-335.</ref> . Baskerville and Pries-Heje focus on how design theories are explanatory. In their work, they demonstrate that design theories deliver functional explanations with a simple and elegant structure explaining generalized solution components by the related generalized requirements <ref>Baskerville, R.; Pries-Heje, J.: Explanatory Design Theory, in: Business & Information Systems Engineering, 2, 5, 2010, pp. 271-282.</ref> .

In the book, Design Science Research Cases <ref>Brocke, J. vom, Hevner, A., and Mädche, A. (eds.). 2020. Design Science Research. Cases, Progress in IS, Springer International Publishing. https://doi.org/10.1007/978-3-030-46781-4.</ref> several DSR cases are presented by experienced researchers in the field. It offers readers access to real-world DSR studies, together with the authors’ reflections on their research processes. The description of the cases supports researchers in addition to existing introductions to DSR methods and processes.

== Process description ==
[[File:Kuechler & Vaishnavi.png|thumb]]

This process describes the Design Science Research process according to Kuechler & Vaishnavi.This model is an adaptation of a computable design process model developed by Takeda, et al. (1990) <ref>Takeda, H., Veerkamp, P., Tomiyama, T., and Yoshikawam, H. (1990). “Modeling Design Processes.” AI Magazine Winter: 37–48.</ref> . Even though the different phases in a design process and a design science research process are similar, the activities carried out within these phases are considerably different. Also, what makes the design science research process model different from the corresponding design process model is the fact that contribution of new (and true) knowledge needs to be a key focus of design science research. <ref>Vaishnavi, Vijay & Kuechler, B. (2004). Design Science Research in Information Systems. Association for Information Systems.</ref>

== Awareness of Problem ==
=== Description ===
Describe the awareness of an interesting problem. Awareness can come from new developments in the industry or from other reference disciplines.

=== Examples ===
Expert interviews with eight employees of the case company revealed:
* Lack of process (model) understanding Difficulties in execution business processes
* Structured literature review on the concept “guidance” in IS research
Structured literature review on the concept “guidance” in IS research:
* Overview on existing research ([[Literature Review according to vom Brocke et al.]] or [[Literature Review according to Webster and Watson]])
* Taxonomy of guidance in IS research ([[Taxonomy development]])

=== Further Readings ===
Kuechler, B., & Vaishnavi, V. (2008). On theory development in design science research: Anatomy of a research project. European Journal of Information Systems, 17(5), 489–504. https://doi.org/10.1057/ejis.2008.40

Vaishnavi, V., Kuechler, W., and Petter, S. (Eds.) (2004/19). “Design Science Research in
Information Systems” January 20, 2004 (created in 2004 and updated until 2015 by Vaishnavi, V.
and Kuechler, W.); last updated (by Vaishnavi, V. and Petter, S.), June 30, 2019. URL:
http://www.desrist.org/design-research-in-information-systems/.

== Suggestion ==
=== Description ===
[[File:Suggestion.png|thumb|Suggestion]]

Synthesis of design principles based on empirical findings:
* Extraction of Meta-Requirements from existing literature.
* Derivation of Design Principles based on the Meta- Requirements.
* Selection of Design Decisions to implement the Design Principles.

=== Examples ===
=== Further Readings ===
Kuechler, B., & Vaishnavi, V. (2008). On theory development in design science research: Anatomy of a research project. European Journal of Information Systems, 17(5), 489–504. https://doi.org/10.1057/ejis.2008.40

Vaishnavi, V., Kuechler, W., and Petter, S. (Eds.) (2004/19). “Design Science Research in Information Systems” January 20, 2004 (created in 2004 and updated until 2015 by Vaishnavi, V. and Kuechler, W.); last updated (by Vaishnavi, V. and Petter, S.), June 30, 2019. URL: http://www.desrist.org/design-research-in-information-systems/.

Gregor, S., Chandra Kruse, L., & Seidel, S. (in press). The Anatomy of a Design Principle.Journal of the Association for Information Systems. Chandra Kruse, L. and Nickerson, J. V. (2018), Portraying Design Essence (January 2018). Paper presented at the 51st Hawaii International Conference in System Science (HICSS)

Gregor, S., Chandra Kruse, L., & Seidel, S. (in press). The Anatomy of a Design Principle. Journal of the Association for Information Systems.

== Development ==
=== Description ===
[[File:Development.png|thumb]]

Implement the suggested design. Different techniques can be used depending on the artifact to be constructed.

=== Examples ===
Instantiation of design principles as a prototype

=== Further Readings ===
Kuechler, B., & Vaishnavi, V. (2008). On theory development in design science research: Anatomy of a research project. European Journal of Information Systems, 17(5), 489–504. https://doi.org/10.1057/ejis.2008.40

Vaishnavi, V., Kuechler, W., and Petter, S. (Eds.) (2004/19). “Design Science Research in Information Systems” January 20, 2004 (created in 2004 and updated until 2015 by Vaishnavi, V. and Kuechler, W.); last updated (by Vaishnavi, V. and Petter, S.), June 30, 2019. URL: http://www.desrist.org/design-research-in-information-systems/.

== Evaluation ==
=== Description ===
[[File:Evaluation.png|thumb]]

Evaluate the artifact according to criteria that are made in the 'Awareness of Problem' activity.

=== Examples ===
Qualitative evaluation of the prototype by eg. focus group workshops in the case company:
* SWOT analysis
* Feedback by the participants

=== Further Readings ===
[[Evaluation Patterns|Evaluation Patterns for Design Science Research Artefacts]]

Kuechler, B., & Vaishnavi, V. (2008). On theory development in design science research: Anatomy of a research project. European Journal of Information Systems, 17(5), 489–504. https://doi.org/10.1057/ejis.2008.40

Vaishnavi, V., Kuechler, W., and Petter, S. (Eds.) (2004/19). “Design Science Research in
Information Systems” January 20, 2004 (created in 2004 and updated until 2015 by Vaishnavi, V.
and Kuechler, W.); last updated (by Vaishnavi, V. and Petter, S.), June 30, 2019. URL:
http://www.desrist.org/design-research-in-information-systems/.

== Conclusion ==
=== Description ===
Write up the results and explain the gained knowledge. Learnings can be described as 'firm' facts that can be:
* repeatably applied
* repeatably invoked
Or there are findings that may serve as the subject of further research.

=== Examples ===

=== Further Readings ===
Kuechler, B., & Vaishnavi, V. (2008). On theory development in design science research: Anatomy of a research project. European Journal of Information Systems, 17(5), 489–504. https://doi.org/10.1057/ejis.2008.40

Vaishnavi, V., Kuechler, W., and Petter, S. (Eds.) (2004/19). “Design Science Research in
Information Systems” January 20, 2004 (created in 2004 and updated until 2015 by Vaishnavi, V.
and Kuechler, W.); last updated (by Vaishnavi, V. and Petter, S.), June 30, 2019. URL:
http://www.desrist.org/design-research-in-information-systems/.

== References ==

[[Category:Design Science Research]]

Design Science Research according to Kuechler & Vaishnavi

2023-03-09T14:01:47Z

MichiGau: /* Further Readings */

== Introduction ==
''"Two paradigms characterize much of the research in the Information Systems discipline: behavioral science and design science. The behavioral science paradigm seeks to develop and verify theories that explain or predict human or organizational behavior. The design-science paradigm seeks to extend the boundaries of human and organizational capabilities by creating new and innovative artifacts. Both paradigms are foundational to the IS discipline, positioned as it is at the confluence of people, organizations, and technology." ''(Hevner et. al.) <ref>Hevner AR, March ST, Park J and Ram S (2004) Design Science in Information Systems Research. MIS Quarterly 28(1), 75-105.</ref>





In many publications, essays, editorials or books describing the Design Science Research (DSR) method the authors aim to support researchers to conduct DSR projects for example by providing guidelines <ref>Hevner AR, March ST, Park J and Ram S (2004) Design Science in Information Systems Research. MIS Quarterly 28(1), 75-105.</ref> <ref>van der Merwe A., Gerber A., Smuts H. (2020) Guidelines for Conducting Design Science Research in Information Systems. In: Tait B., Kroeze J., Gruner S. (eds) ICT Education. SACLA 2019. Communications in Computer and Information Science, vol 1136. Springer, Cham. https://doi.org/10.1007/978-3-030-35629-3_11</ref> or how to extend on existing design knowledge <ref>vom Brocke, J., Winter, R., Hevner, A., Maedche, A. (2020), Accumulation and Evolution of Design Knowledge in Design Science Research – A Journey Through Time and Space, in: Journals of the Association for Information Systems (JAIS), 2020, forthcoming (ABDC_2016: A*; ABS: 4; ISI: 3.487; ISI: 2.109; VHB_3: A).</ref> and many more. Gregor and Hevner provide foundational guidance by describing their understanding and application of DSR concepts and providing guidance for researches on how to:
# Appreciate the levels of artifact abstractions that may be DSR contributions
# Identify appropriate ways of consuming and producing knowledge when you are preparing journal articles or other scholarly works
# Understand and position the knowledge contributions of your research projects
# Structure a DSR article so that it emphasizes significant contributions to the knowledge base
Their focal contribution is the DSR knowledge contribution framework with two dimensions based on the existing state of knowledge in both the problem and solution domains for the research opportunity under study. In addition, they propose a DSR communication schema with similarities to more conventional publication patterns, but which substitutes the description of the DSR artifact in place of a traditional results section. <ref>Gregor S and Hevner AR (2013) Positioning and Presenting Design Science Research for Maximum Impact. MIS Quarterly 37(2), 337-55.</ref>

Other researchers propose a set of core dimensions of a Design Science Research project that facilitates effective capture of the most relevant aspects of a DSR project to efficiently plan and communicate key considerations and conceptualizations of a DSR project. In this work, the authors represent six dimensions in the form of a DSR grid, a one-page visualization of the DSR project that is adjustable to the specific purpose for using the concept. <ref>vom Brocke, J., Maedche, A. (2019), The DSR Grid: Six Core Dimensions for Effectively Planning and Communicating Design Science Research Projects, in: Electronic Markets, Volume 29, Issue 3, pp 379–385 (ABDC: A; ABS: 2; ISI: 2.121; VHB: B).</ref>

Another important role in DSR is how to specify design theory so that it can be communicated, justified, and developed cumulatively. In the essay of Gregor and Jones they focus on the structural components or anatomy of design theories in IS <ref>Gregor S and Jones D (2007) The Anatomy of a Design Theory. Journal Of The Association For Information Systems 8(5), 312-335.</ref> . Baskerville and Pries-Heje focus on how design theories are explanatory. In their work, they demonstrate that design theories deliver functional explanations with a simple and elegant structure explaining generalized solution components by the related generalized requirements <ref>Baskerville, R.; Pries-Heje, J.: Explanatory Design Theory, in: Business & Information Systems Engineering, 2, 5, 2010, pp. 271-282.</ref> .

In the book, Design Science Research Cases <ref>Brocke, J. vom, Hevner, A., and Mädche, A. (eds.). 2020. Design Science Research. Cases, Progress in IS, Springer International Publishing. https://doi.org/10.1007/978-3-030-46781-4.</ref> several DSR cases are presented by experienced researchers in the field. It offers readers access to real-world DSR studies, together with the authors’ reflections on their research processes. The description of the cases supports researchers in addition to existing introductions to DSR methods and processes.

== Process description ==
[[File:Kuechler & Vaishnavi.png|thumb]]

This process describes the Design Science Research process according to Kuechler & Vaishnavi.This model is an adaptation of a computable design process model developed by Takeda, et al. (1990) <ref>Takeda, H., Veerkamp, P., Tomiyama, T., and Yoshikawam, H. (1990). “Modeling Design Processes.” AI Magazine Winter: 37–48.</ref> . Even though the different phases in a design process and a design science research process are similar, the activities carried out within these phases are considerably different. Also, what makes the design science research process model different from the corresponding design process model is the fact that contribution of new (and true) knowledge needs to be a key focus of design science research. <ref>Vaishnavi, Vijay & Kuechler, B. (2004). Design Science Research in Information Systems. Association for Information Systems.</ref>

== Awareness of Problem ==
=== Description ===
Describe the awareness of an interesting problem. Awareness can come from new developments in the industry or from other reference disciplines.

=== Examples ===
Expert interviews with eight employees of the case company revealed:
* Lack of process (model) understanding Difficulties in execution business processes
* Structured literature review on the concept “guidance” in IS research
Structured literature review on the concept “guidance” in IS research:
* Overview on existing research ([[Literature Review according to vom Brocke et al.]] or [[Literature Review according to Webster and Watson]])
* Taxonomy of guidance in IS research ([[Taxonomy development]])

=== Further Readings ===
Kuechler, B., & Vaishnavi, V. (2008). On theory development in design science research: Anatomy of a research project. European Journal of Information Systems, 17(5), 489–504. https://doi.org/10.1057/ejis.2008.40

Vaishnavi, V., Kuechler, W., and Petter, S. (Eds.) (2004/19). “Design Science Research in
Information Systems” January 20, 2004 (created in 2004 and updated until 2015 by Vaishnavi, V.
and Kuechler, W.); last updated (by Vaishnavi, V. and Petter, S.), June 30, 2019. URL:
http://www.desrist.org/design-research-in-information-systems/.

== Suggestion ==
=== Description ===
[[File:Suggestion.png|thumb|Suggestion]]

Synthesis of design principles based on empirical findings:
* Extraction of Meta-Requirements from existing literature.
* Derivation of Design Principles based on the Meta- Requirements.
* Selection of Design Decisions to implement the Design Principles.

=== Examples ===
=== Further Readings ===
Kuechler, B., & Vaishnavi, V. (2008). On theory development in design science research: Anatomy of a research project. European Journal of Information Systems, 17(5), 489–504. https://doi.org/10.1057/ejis.2008.40

Vaishnavi, V., Kuechler, W., and Petter, S. (Eds.) (2004/19). “Design Science Research in Information Systems” January 20, 2004 (created in 2004 and updated until 2015 by Vaishnavi, V. and Kuechler, W.); last updated (by Vaishnavi, V. and Petter, S.), June 30, 2019. URL: http://www.desrist.org/design-research-in-information-systems/.

Gregor, S., Chandra Kruse, L., & Seidel, S. (in press). The Anatomy of a Design Principle.Journal of the Association for Information Systems. Chandra Kruse, L. and Nickerson, J. V. (2018), Portraying Design Essence (January 2018). Paper presented at the 51st Hawaii International Conference in System Science (HICSS)

Gregor, S., Chandra Kruse, L., & Seidel, S. (in press). The Anatomy of a Design Principle. Journal of the Association for Information Systems.

== Development ==
=== Description ===
[[File:Development.png|thumb]]

Implement the suggested design. Different techniques can be used depending on the artifact to be constructed.

=== Examples ===
Instantiation of design principles as a prototype

=== Further Readings ===
Kuechler, B., & Vaishnavi, V. (2008). On theory development in design science research: Anatomy of a research project. European Journal of Information Systems, 17(5), 489–504. https://doi.org/10.1057/ejis.2008.40

Vaishnavi, V., Kuechler, W., and Petter, S. (Eds.) (2004/19). “Design Science Research in Information Systems” January 20, 2004 (created in 2004 and updated until 2015 by Vaishnavi, V. and Kuechler, W.); last updated (by Vaishnavi, V. and Petter, S.), June 30, 2019. URL: http://www.desrist.org/design-research-in-information-systems/.

== Evaluation ==
=== Description ===
[[File:Evaluation.png|thumb]]

Evaluate the artifact according to criteria that are made in the 'Awareness of Problem' activity.

=== Examples ===
Qualitative evaluation of the prototype by eg. focus group workshops in the case company:
* SWOT analysis
* Feedback by the participants

=== Further Readings ===
[[Evaluation Patterns|Evaluation Patterns for Design Science Research Artefacts]]

Kuechler, B., & Vaishnavi, V. (2008). On theory development in design science research: Anatomy of a research project. European Journal of Information Systems, 17(5), 489–504. https://doi.org/10.1057/ejis.2008.40

Vaishnavi, V., Kuechler, W., and Petter, S. (Eds.) (2004/19). “Design Science Research in
Information Systems” January 20, 2004 (created in 2004 and updated until 2015 by Vaishnavi, V.
and Kuechler, W.); last updated (by Vaishnavi, V. and Petter, S.), June 30, 2019. URL:
http://www.desrist.org/design-research-in-information-systems/.

== Conclusion ==
=== Description ===
Write up the results and explain the gained knowledge. Learnings can be described as 'firm' facts that can be:
* repeatably applied
* repeatably invoked
Or there are findings that may serve as the subject of further research.

=== Examples ===

=== Further Readings ===
Vaishnavi, V., Kuechler, W., and Petter, S. (Eds.) (2004/19). “Design Science Research in
Information Systems” January 20, 2004 (created in 2004 and updated until 2015 by Vaishnavi, V.
and Kuechler, W.); last updated (by Vaishnavi, V. and Petter, S.), June 30, 2019. URL:
http://www.desrist.org/design-research-in-information-systems/.

== References ==

[[Category:Design Science Research]]

Design Science Research according to Kuechler & Vaishnavi

2023-03-09T14:01:30Z

MichiGau: /* Further Readings */

== Introduction ==
''"Two paradigms characterize much of the research in the Information Systems discipline: behavioral science and design science. The behavioral science paradigm seeks to develop and verify theories that explain or predict human or organizational behavior. The design-science paradigm seeks to extend the boundaries of human and organizational capabilities by creating new and innovative artifacts. Both paradigms are foundational to the IS discipline, positioned as it is at the confluence of people, organizations, and technology." ''(Hevner et. al.) <ref>Hevner AR, March ST, Park J and Ram S (2004) Design Science in Information Systems Research. MIS Quarterly 28(1), 75-105.</ref>





In many publications, essays, editorials or books describing the Design Science Research (DSR) method the authors aim to support researchers to conduct DSR projects for example by providing guidelines <ref>Hevner AR, March ST, Park J and Ram S (2004) Design Science in Information Systems Research. MIS Quarterly 28(1), 75-105.</ref> <ref>van der Merwe A., Gerber A., Smuts H. (2020) Guidelines for Conducting Design Science Research in Information Systems. In: Tait B., Kroeze J., Gruner S. (eds) ICT Education. SACLA 2019. Communications in Computer and Information Science, vol 1136. Springer, Cham. https://doi.org/10.1007/978-3-030-35629-3_11</ref> or how to extend on existing design knowledge <ref>vom Brocke, J., Winter, R., Hevner, A., Maedche, A. (2020), Accumulation and Evolution of Design Knowledge in Design Science Research – A Journey Through Time and Space, in: Journals of the Association for Information Systems (JAIS), 2020, forthcoming (ABDC_2016: A*; ABS: 4; ISI: 3.487; ISI: 2.109; VHB_3: A).</ref> and many more. Gregor and Hevner provide foundational guidance by describing their understanding and application of DSR concepts and providing guidance for researches on how to:
# Appreciate the levels of artifact abstractions that may be DSR contributions
# Identify appropriate ways of consuming and producing knowledge when you are preparing journal articles or other scholarly works
# Understand and position the knowledge contributions of your research projects
# Structure a DSR article so that it emphasizes significant contributions to the knowledge base
Their focal contribution is the DSR knowledge contribution framework with two dimensions based on the existing state of knowledge in both the problem and solution domains for the research opportunity under study. In addition, they propose a DSR communication schema with similarities to more conventional publication patterns, but which substitutes the description of the DSR artifact in place of a traditional results section. <ref>Gregor S and Hevner AR (2013) Positioning and Presenting Design Science Research for Maximum Impact. MIS Quarterly 37(2), 337-55.</ref>

Other researchers propose a set of core dimensions of a Design Science Research project that facilitates effective capture of the most relevant aspects of a DSR project to efficiently plan and communicate key considerations and conceptualizations of a DSR project. In this work, the authors represent six dimensions in the form of a DSR grid, a one-page visualization of the DSR project that is adjustable to the specific purpose for using the concept. <ref>vom Brocke, J., Maedche, A. (2019), The DSR Grid: Six Core Dimensions for Effectively Planning and Communicating Design Science Research Projects, in: Electronic Markets, Volume 29, Issue 3, pp 379–385 (ABDC: A; ABS: 2; ISI: 2.121; VHB: B).</ref>

Another important role in DSR is how to specify design theory so that it can be communicated, justified, and developed cumulatively. In the essay of Gregor and Jones they focus on the structural components or anatomy of design theories in IS <ref>Gregor S and Jones D (2007) The Anatomy of a Design Theory. Journal Of The Association For Information Systems 8(5), 312-335.</ref> . Baskerville and Pries-Heje focus on how design theories are explanatory. In their work, they demonstrate that design theories deliver functional explanations with a simple and elegant structure explaining generalized solution components by the related generalized requirements <ref>Baskerville, R.; Pries-Heje, J.: Explanatory Design Theory, in: Business & Information Systems Engineering, 2, 5, 2010, pp. 271-282.</ref> .

In the book, Design Science Research Cases <ref>Brocke, J. vom, Hevner, A., and Mädche, A. (eds.). 2020. Design Science Research. Cases, Progress in IS, Springer International Publishing. https://doi.org/10.1007/978-3-030-46781-4.</ref> several DSR cases are presented by experienced researchers in the field. It offers readers access to real-world DSR studies, together with the authors’ reflections on their research processes. The description of the cases supports researchers in addition to existing introductions to DSR methods and processes.

== Process description ==
[[File:Kuechler & Vaishnavi.png|thumb]]

This process describes the Design Science Research process according to Kuechler & Vaishnavi.This model is an adaptation of a computable design process model developed by Takeda, et al. (1990) <ref>Takeda, H., Veerkamp, P., Tomiyama, T., and Yoshikawam, H. (1990). “Modeling Design Processes.” AI Magazine Winter: 37–48.</ref> . Even though the different phases in a design process and a design science research process are similar, the activities carried out within these phases are considerably different. Also, what makes the design science research process model different from the corresponding design process model is the fact that contribution of new (and true) knowledge needs to be a key focus of design science research. <ref>Vaishnavi, Vijay & Kuechler, B. (2004). Design Science Research in Information Systems. Association for Information Systems.</ref>

== Awareness of Problem ==
=== Description ===
Describe the awareness of an interesting problem. Awareness can come from new developments in the industry or from other reference disciplines.

=== Examples ===
Expert interviews with eight employees of the case company revealed:
* Lack of process (model) understanding Difficulties in execution business processes
* Structured literature review on the concept “guidance” in IS research
Structured literature review on the concept “guidance” in IS research:
* Overview on existing research ([[Literature Review according to vom Brocke et al.]] or [[Literature Review according to Webster and Watson]])
* Taxonomy of guidance in IS research ([[Taxonomy development]])

=== Further Readings ===
Kuechler, B., & Vaishnavi, V. (2008). On theory development in design science research: Anatomy of a research project. European Journal of Information Systems, 17(5), 489–504. https://doi.org/10.1057/ejis.2008.40

Vaishnavi, V., Kuechler, W., and Petter, S. (Eds.) (2004/19). “Design Science Research in
Information Systems” January 20, 2004 (created in 2004 and updated until 2015 by Vaishnavi, V.
and Kuechler, W.); last updated (by Vaishnavi, V. and Petter, S.), June 30, 2019. URL:
http://www.desrist.org/design-research-in-information-systems/.

== Suggestion ==
=== Description ===
[[File:Suggestion.png|thumb|Suggestion]]

Synthesis of design principles based on empirical findings:
* Extraction of Meta-Requirements from existing literature.
* Derivation of Design Principles based on the Meta- Requirements.
* Selection of Design Decisions to implement the Design Principles.

=== Examples ===
=== Further Readings ===
Kuechler, B., & Vaishnavi, V. (2008). On theory development in design science research: Anatomy of a research project. European Journal of Information Systems, 17(5), 489–504. https://doi.org/10.1057/ejis.2008.40

Vaishnavi, V., Kuechler, W., and Petter, S. (Eds.) (2004/19). “Design Science Research in Information Systems” January 20, 2004 (created in 2004 and updated until 2015 by Vaishnavi, V. and Kuechler, W.); last updated (by Vaishnavi, V. and Petter, S.), June 30, 2019. URL: http://www.desrist.org/design-research-in-information-systems/.

Gregor, S., Chandra Kruse, L., & Seidel, S. (in press). The Anatomy of a Design Principle.Journal of the Association for Information Systems. Chandra Kruse, L. and Nickerson, J. V. (2018), Portraying Design Essence (January 2018). Paper presented at the 51st Hawaii International Conference in System Science (HICSS)

Gregor, S., Chandra Kruse, L., & Seidel, S. (in press). The Anatomy of a Design Principle. Journal of the Association for Information Systems.

== Development ==
=== Description ===
[[File:Development.png|thumb]]

Implement the suggested design. Different techniques can be used depending on the artifact to be constructed.

=== Examples ===
Instantiation of design principles as a prototype

=== Further Readings ===
Kuechler, B., & Vaishnavi, V. (2008). On theory development in design science research: Anatomy of a research project. European Journal of Information Systems, 17(5), 489–504. https://doi.org/10.1057/ejis.2008.40

Vaishnavi, V., Kuechler, W., and Petter, S. (Eds.) (2004/19). “Design Science Research in Information Systems” January 20, 2004 (created in 2004 and updated until 2015 by Vaishnavi, V. and Kuechler, W.); last updated (by Vaishnavi, V. and Petter, S.), June 30, 2019. URL: http://www.desrist.org/design-research-in-information-systems/.

== Evaluation ==
=== Description ===
[[File:Evaluation.png|thumb]]

Evaluate the artifact according to criteria that are made in the 'Awareness of Problem' activity.

=== Examples ===
Qualitative evaluation of the prototype by eg. focus group workshops in the case company:
* SWOT analysis
* Feedback by the participants

=== Further Readings ===
[[Evaluation Patterns|Evaluation Patterns for Design Science Research Artefacts]]

Vaishnavi, V., Kuechler, W., and Petter, S. (Eds.) (2004/19). “Design Science Research in
Information Systems” January 20, 2004 (created in 2004 and updated until 2015 by Vaishnavi, V.
and Kuechler, W.); last updated (by Vaishnavi, V. and Petter, S.), June 30, 2019. URL:
http://www.desrist.org/design-research-in-information-systems/.

== Conclusion ==
=== Description ===
Write up the results and explain the gained knowledge. Learnings can be described as 'firm' facts that can be:
* repeatably applied
* repeatably invoked
Or there are findings that may serve as the subject of further research.

=== Examples ===

=== Further Readings ===
Vaishnavi, V., Kuechler, W., and Petter, S. (Eds.) (2004/19). “Design Science Research in
Information Systems” January 20, 2004 (created in 2004 and updated until 2015 by Vaishnavi, V.
and Kuechler, W.); last updated (by Vaishnavi, V. and Petter, S.), June 30, 2019. URL:
http://www.desrist.org/design-research-in-information-systems/.

== References ==

[[Category:Design Science Research]]

Design Science Research according to Kuechler & Vaishnavi

2023-03-09T14:01:19Z

MichiGau: /* Further Readings */

== Introduction ==
''"Two paradigms characterize much of the research in the Information Systems discipline: behavioral science and design science. The behavioral science paradigm seeks to develop and verify theories that explain or predict human or organizational behavior. The design-science paradigm seeks to extend the boundaries of human and organizational capabilities by creating new and innovative artifacts. Both paradigms are foundational to the IS discipline, positioned as it is at the confluence of people, organizations, and technology." ''(Hevner et. al.) <ref>Hevner AR, March ST, Park J and Ram S (2004) Design Science in Information Systems Research. MIS Quarterly 28(1), 75-105.</ref>





In many publications, essays, editorials or books describing the Design Science Research (DSR) method the authors aim to support researchers to conduct DSR projects for example by providing guidelines <ref>Hevner AR, March ST, Park J and Ram S (2004) Design Science in Information Systems Research. MIS Quarterly 28(1), 75-105.</ref> <ref>van der Merwe A., Gerber A., Smuts H. (2020) Guidelines for Conducting Design Science Research in Information Systems. In: Tait B., Kroeze J., Gruner S. (eds) ICT Education. SACLA 2019. Communications in Computer and Information Science, vol 1136. Springer, Cham. https://doi.org/10.1007/978-3-030-35629-3_11</ref> or how to extend on existing design knowledge <ref>vom Brocke, J., Winter, R., Hevner, A., Maedche, A. (2020), Accumulation and Evolution of Design Knowledge in Design Science Research – A Journey Through Time and Space, in: Journals of the Association for Information Systems (JAIS), 2020, forthcoming (ABDC_2016: A*; ABS: 4; ISI: 3.487; ISI: 2.109; VHB_3: A).</ref> and many more. Gregor and Hevner provide foundational guidance by describing their understanding and application of DSR concepts and providing guidance for researches on how to:
# Appreciate the levels of artifact abstractions that may be DSR contributions
# Identify appropriate ways of consuming and producing knowledge when you are preparing journal articles or other scholarly works
# Understand and position the knowledge contributions of your research projects
# Structure a DSR article so that it emphasizes significant contributions to the knowledge base
Their focal contribution is the DSR knowledge contribution framework with two dimensions based on the existing state of knowledge in both the problem and solution domains for the research opportunity under study. In addition, they propose a DSR communication schema with similarities to more conventional publication patterns, but which substitutes the description of the DSR artifact in place of a traditional results section. <ref>Gregor S and Hevner AR (2013) Positioning and Presenting Design Science Research for Maximum Impact. MIS Quarterly 37(2), 337-55.</ref>

Other researchers propose a set of core dimensions of a Design Science Research project that facilitates effective capture of the most relevant aspects of a DSR project to efficiently plan and communicate key considerations and conceptualizations of a DSR project. In this work, the authors represent six dimensions in the form of a DSR grid, a one-page visualization of the DSR project that is adjustable to the specific purpose for using the concept. <ref>vom Brocke, J., Maedche, A. (2019), The DSR Grid: Six Core Dimensions for Effectively Planning and Communicating Design Science Research Projects, in: Electronic Markets, Volume 29, Issue 3, pp 379–385 (ABDC: A; ABS: 2; ISI: 2.121; VHB: B).</ref>

Another important role in DSR is how to specify design theory so that it can be communicated, justified, and developed cumulatively. In the essay of Gregor and Jones they focus on the structural components or anatomy of design theories in IS <ref>Gregor S and Jones D (2007) The Anatomy of a Design Theory. Journal Of The Association For Information Systems 8(5), 312-335.</ref> . Baskerville and Pries-Heje focus on how design theories are explanatory. In their work, they demonstrate that design theories deliver functional explanations with a simple and elegant structure explaining generalized solution components by the related generalized requirements <ref>Baskerville, R.; Pries-Heje, J.: Explanatory Design Theory, in: Business & Information Systems Engineering, 2, 5, 2010, pp. 271-282.</ref> .

In the book, Design Science Research Cases <ref>Brocke, J. vom, Hevner, A., and Mädche, A. (eds.). 2020. Design Science Research. Cases, Progress in IS, Springer International Publishing. https://doi.org/10.1007/978-3-030-46781-4.</ref> several DSR cases are presented by experienced researchers in the field. It offers readers access to real-world DSR studies, together with the authors’ reflections on their research processes. The description of the cases supports researchers in addition to existing introductions to DSR methods and processes.

== Process description ==
[[File:Kuechler & Vaishnavi.png|thumb]]

This process describes the Design Science Research process according to Kuechler & Vaishnavi.This model is an adaptation of a computable design process model developed by Takeda, et al. (1990) <ref>Takeda, H., Veerkamp, P., Tomiyama, T., and Yoshikawam, H. (1990). “Modeling Design Processes.” AI Magazine Winter: 37–48.</ref> . Even though the different phases in a design process and a design science research process are similar, the activities carried out within these phases are considerably different. Also, what makes the design science research process model different from the corresponding design process model is the fact that contribution of new (and true) knowledge needs to be a key focus of design science research. <ref>Vaishnavi, Vijay & Kuechler, B. (2004). Design Science Research in Information Systems. Association for Information Systems.</ref>

== Awareness of Problem ==
=== Description ===
Describe the awareness of an interesting problem. Awareness can come from new developments in the industry or from other reference disciplines.

=== Examples ===
Expert interviews with eight employees of the case company revealed:
* Lack of process (model) understanding Difficulties in execution business processes
* Structured literature review on the concept “guidance” in IS research
Structured literature review on the concept “guidance” in IS research:
* Overview on existing research ([[Literature Review according to vom Brocke et al.]] or [[Literature Review according to Webster and Watson]])
* Taxonomy of guidance in IS research ([[Taxonomy development]])

=== Further Readings ===
Kuechler, B., & Vaishnavi, V. (2008). On theory development in design science research: Anatomy of a research project. European Journal of Information Systems, 17(5), 489–504. https://doi.org/10.1057/ejis.2008.40

Vaishnavi, V., Kuechler, W., and Petter, S. (Eds.) (2004/19). “Design Science Research in
Information Systems” January 20, 2004 (created in 2004 and updated until 2015 by Vaishnavi, V.
and Kuechler, W.); last updated (by Vaishnavi, V. and Petter, S.), June 30, 2019. URL:
http://www.desrist.org/design-research-in-information-systems/.

== Suggestion ==
=== Description ===
[[File:Suggestion.png|thumb|Suggestion]]

Synthesis of design principles based on empirical findings:
* Extraction of Meta-Requirements from existing literature.
* Derivation of Design Principles based on the Meta- Requirements.
* Selection of Design Decisions to implement the Design Principles.

=== Examples ===
=== Further Readings ===
Kuechler, B., & Vaishnavi, V. (2008). On theory development in design science research: Anatomy of a research project. European Journal of Information Systems, 17(5), 489–504. https://doi.org/10.1057/ejis.2008.40

Vaishnavi, V., Kuechler, W., and Petter, S. (Eds.) (2004/19). “Design Science Research in Information Systems” January 20, 2004 (created in 2004 and updated until 2015 by Vaishnavi, V. and Kuechler, W.); last updated (by Vaishnavi, V. and Petter, S.), June 30, 2019. URL: http://www.desrist.org/design-research-in-information-systems/.

Gregor, S., Chandra Kruse, L., & Seidel, S. (in press). The Anatomy of a Design Principle.Journal of the Association for Information Systems. Chandra Kruse, L. and Nickerson, J. V. (2018), Portraying Design Essence (January 2018). Paper presented at the 51st Hawaii International Conference in System Science (HICSS)

Gregor, S., Chandra Kruse, L., & Seidel, S. (in press). The Anatomy of a Design Principle. Journal of the Association for Information Systems.

== Development ==
=== Description ===
[[File:Development.png|thumb]]

Implement the suggested design. Different techniques can be used depending on the artifact to be constructed.

=== Examples ===
Instantiation of design principles as a prototype

=== Further Readings ===
Vaishnavi, V., Kuechler, W., and Petter, S. (Eds.) (2004/19). “Design Science Research in Information Systems” January 20, 2004 (created in 2004 and updated until 2015 by Vaishnavi, V. and Kuechler, W.); last updated (by Vaishnavi, V. and Petter, S.), June 30, 2019. URL: http://www.desrist.org/design-research-in-information-systems/.

== Evaluation ==
=== Description ===
[[File:Evaluation.png|thumb]]

Evaluate the artifact according to criteria that are made in the 'Awareness of Problem' activity.

=== Examples ===
Qualitative evaluation of the prototype by eg. focus group workshops in the case company:
* SWOT analysis
* Feedback by the participants

=== Further Readings ===
[[Evaluation Patterns|Evaluation Patterns for Design Science Research Artefacts]]

Vaishnavi, V., Kuechler, W., and Petter, S. (Eds.) (2004/19). “Design Science Research in
Information Systems” January 20, 2004 (created in 2004 and updated until 2015 by Vaishnavi, V.
and Kuechler, W.); last updated (by Vaishnavi, V. and Petter, S.), June 30, 2019. URL:
http://www.desrist.org/design-research-in-information-systems/.

== Conclusion ==
=== Description ===
Write up the results and explain the gained knowledge. Learnings can be described as 'firm' facts that can be:
* repeatably applied
* repeatably invoked
Or there are findings that may serve as the subject of further research.

=== Examples ===

=== Further Readings ===
Vaishnavi, V., Kuechler, W., and Petter, S. (Eds.) (2004/19). “Design Science Research in
Information Systems” January 20, 2004 (created in 2004 and updated until 2015 by Vaishnavi, V.
and Kuechler, W.); last updated (by Vaishnavi, V. and Petter, S.), June 30, 2019. URL:
http://www.desrist.org/design-research-in-information-systems/.

== References ==

[[Category:Design Science Research]]