Taxonomy development

Process description

Taxonomy Development (Redrawn from Nickerson et al.)

This process goes beyond Bailey’s three-level indicator model 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. 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.

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.

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.

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.

Approach Selection

Description

After these steps the researcher can begin with either an empirical approach or a conceptual approach. The choice of which approach to use depends on the availability of data about objects under study and the knowledge of the researcher about the domain of interest. There are for possibilities to decide what approach has to be selected:

1. If little data are available but the researcher has significant understanding of the domain, then starting with the conceptual-to-empirical approach would be advised.
2. On the other hand, if the researcher has little understanding of the domain but significant data about the objects is available, then starting with the empirical-to-conceptual approach is appropriate.
3. If the researcher has both significant knowledge of the domain and significant data available about the objects, then the researcher will have to use individual judgment to decide which approach is best.
4. In the fourth case, where the researcher has little knowledge of the domain and little data available, the researcher should investigate the domain of interest more before attempting to develop a taxonomy for it.

In subsequent iteration the researcher may choose to use a different approach in order to view the taxonomy from a different perspective and possibly gain new insight about the taxonomy.

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.

Approach A: Identify (new) subset of objects

Description

In the empirical-to-conceptual approach, the researcher identifies a subset of objects that he/she wishes to classify. These objects are likely to be the ones with which the researcher is most familiar or that are most easily accessible, possibly through a review of the literature.

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.

Approach A: Identify common characteristics and group objects

Description

The characteristics must be logical consequences of the meta-characteristic. Thus, the researcher starts with the meta-characteristic and identifies characteristics of the objects that follow from the meta-characteristic. The characteristics must, however, discriminate among the objects; a characteristic that has the same value for all or nearly all objects is of no use in the taxonomy even if it does follow from the meta-characteristic. The knowledge and intuition of the researcher or other experts will be needed to identify the characteristics.

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.

Approach A: Group characteristics into dimensions to create (revise) taxonomy

Description

Once a set of characteristics has been identified, they can be grouped formally using statistical techniques or informally using a manual or graphical process. The resulting groups form the initial dimensions of the taxonomy. This grouping involves creating ‘conceptual labels’ for sets of related characteristics, that is, for the dimensions. Each dimension contains characteristics that are mutually exclusive and collectively exhaustive. Examined empirical cases can be used to revise the new characteristics and dimensions to determine their usefulness in classifying objects.

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.

Approach B: Conceptualize (new) characteristics and dimensions of objects

Description

In the conceptual-to-empirical approach, the researcher begins by conceptualizing the dimensions of the taxonomy without examining actual objects. This process is based on the researcher’s notions about how objects are similar and how they are dissimilar. Since this is a deductive process, researcher uses his/her knowledge of existing foundations, experience, and judgment to deduce what he/she thinks will be relevant dimensions.

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.

Approach B: Examine objects for these characteristics and dimensions

Description

Each dimension contains characteristics that must be logical consequences of the meta-characteristic. Thus, a test of the appropriateness of a dimension is whether its characteristics follow from the meta-characteristic. In the process, the researcher may propose dimensions that are not appropriate and thus can be eliminated. The researcher then examines objects for these dimensions and characteristics.

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.

Approach B: Create (revise) taxonomy

Description

Are there objects that have each of the characteristics in each dimension? If not, then the dimension may not be appropriate. As before, each dimension must contain characteristics that are mutually exclusive and collectively exhaustive. Examined empirical cases can be used to revise the new characteristics and dimensions to determine their usefulness in classifying objects.

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 met?

Description

At the end of either of these steps, the researcher asks if the ending conditions have been met with the current version of the taxonomy. Both objective and subjective conditions must be checked. The objective and subjective conditions need be met to end the process. If they are not met another iteration needs to be done.

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.