Taxonomy development
Process description
This process goes beyond Bailey’s three-level indicator model[1] to combine the conceptualization/deduction and empiricism/induction strategies into a single method that encourages the researcher to use the strategies in an iterative manner to best reach a useful taxonomy. In addition, it includes specific ending conditions that test the taxonomy as it is being developed. This approach is consistent with the design science ‘generate/test cycle’ described by Hevner et al[2]. Finally, it adds the important concept of meta-characteristic that Bailey does not identify explicitly or implicitly.
Determine meta-characteristic
Description
Meta-characteristic: High-level interaction between the application user and the application.
The choice of the meta-characteristic should be based on the purpose of the taxonomy. The purpose of the taxonomy should, in turn, be based on the expected use of the taxonomy and thus could be defined by the eventual users of the taxonomy. The design process could involve first identifying the user(s) of the taxonomy who then specify the projected use of the taxonomy, either explicitly or implicitly. The choice of the meta-characteristic must be done carefully as it impacts critically the resulting taxonomy.[3]
Examples
If the researcher’s purpose is to distinguish platforms based on processing power, then the meta-characteristic is the hardware and software characteristics, such as CPU power, memory, and operating system efficiency that impact measures of power such as speed and capacity.
If the researcher’s purpose is to distinguish among computer platforms based on how users use them, then the meta-characteristic is the capability of the platform to interact with users, such as the maximum number of simultaneously running applications and the user interface.[3]
Further Readings
Nickerson R, Varshney U and Muntermann J (2013) A method for taxonomy development and its application in information systems, European Journal of Information Systems 22, 336.
Ending conditions
Description
Ending conditions are both objective and subjective.
A fundamental objective ending condition is that the taxonomy must satisfy our definition of a taxonomy, specifically that it consists of dimensions each with mutually exclusive and collectively exhaustive characteristics. The following list provides eight objective ending conditions:
- All objects or a representative sample of objects have been examined
- No object was merged with a similar object or split into multiple objects in the last iteration
- At least one object is classified under every characteristics of every dimension
- No new dimensions or characteristics were added in the last iteration
- No dimensions or characteristics were merged or split in the last iteration
- Every dimension is unique and not repeated (i.e., there is no dimension duplication)
- Every characteristic is unique within its dimension (i.e., there is no characteristic duplication within a dimension)
- Each cell (combination of characteristics) is unique and is not repeated (i.e., there is no cell duplication)
Subjective ending conditions also need to be examined:
- Concise
- Robust Comprehensive
- Extendible Explanatory
The researcher may wish to add more subjective conditions to these based on the researcher’s particular view. The researcher needs to be able to argue that all subjective conditions have been met before terminating the method.
Examples
Further Readings
Provide further readings for activity 2.
Activity X
Description
Describe the Activity X.
Examples
Provide some examples for activity X.
Further Readings
Provide further readings for activity X.
References
- ↑ BAILEY KD (1994) Typologies and Taxonomies – An Introduction to Classification Techniques. Sage, Thousand Oaks, CA.
- ↑ HEVNER AR, MARCH ST, PARK J and RAM S (2004) Design science in information systems research. MIS Quarterly 28(1), 75–105.
- ↑ 3.0 3.1 Nickerson R, Varshney U and Muntermann J (2013) A method for taxonomy development and its application in information systems, European Journal of Information Systems 22, 336.