None of the research strategies are considered to be unconditionally superior or inferior to the others -- they all have qualities that make them preferable for some purposes and research problems.
This mode of research may be appropriate for formal sciences as exemplified by mathematics and parts of computer science. But the concern in this project is practical enterprise modeling and the study of real world projects. A logical theoretical approach would not draw upon the benefits of empirical work. Also, according to Hirschheim et al. (1995:195, italics added),
"data modeling is first and foremost a social and organizational activity and very little, if anything (except consulting folklore) is known how data modeling is exercised in practice and what its impacts are on organizations, their information systems management, and business operations."Enterprise modeling and data modeling are closely related activities (as will be discussed in chapter 3), and although the quite pessimistic assessment cited above must be taken with a pinch of salt, lack of knowledge about actual practice is assumed to be a valid claim within enterprise modeling as well.
To be able to propose fruitful hypotheses, one must have a well developed
understanding of the research area. In addition, in order to gain statistically
reliable results, the number of samples must be large (for survey studies,
in the range 40 and up, according to Galtung, 1967). Both these requirements
suggest looking for other research instruments: A well developed understanding
of enterprise modeling practice is not widely available (at least not according
to Hirschheim et al., loc. cit.), and the access to directly comparable
projects is limited. There have also been some critique of adoption of
the scientific method as an approach to research on social systems, at
least unmodified. See e.g. (Guba and Lincoln, 1994) for a discussion.
|One perspective on quantitative research is as counting. Correlation between variables are estimated using statistical devices. However, in order for counting to be meaningful, one must also know that the variables counted are meaningful in the given setting. Hence, quantitative research requires well developed understanding of a domain in advance in order to judge if variables are meaningful.|
Through close contact with the research field in question for a prolonged
period of time, the researcher develops a profound understanding and (as
claimed for grounded theory studies) becomes able to formulate a conceptually
rich theory explaining the phenomenon under investigation. Contact with
the field of research may be based on interviews, observations, or analysis
of documents and other artifacts. In addition, literature studies are performed
to the extent required to develop sensitivity in observation and interpretation.
|A qualitative research approach can be used to develop the understanding required for evaluating if a variable is relevant or not to a given problem situation. Compared to the perspective on quantitative research as counting, qualitative research can be seen as proposing which variables to count.|
This approach to validation of results is interesting, but somewhat
questioned by more traditional scientists. Walsham (1995:77) points out
the problems of being perceived to have a personal stake in the researched
project, and reporting on one's own role within the project as particularly
challenging problems of participatory action research.
|A possible adaptation of an action research strategy would be to investigate a few initial enterprise modeling projects, propose a method for enterprise modeling, and apply this in another full-scale project. The principal problems of this approach would be to trace (hopefully) successful outcomes back to the use of the method and to know what would be the situation without the method (as there is no control project).|
Non-interpretive studies focus on describing the life world that is investigated. Observations are not necessarily analyzed and made sense of by the researcher, but instead left for the reader to interpret. Miles and Huberman (1994:8) associate ethnographic methods with this leaning towards descriptions.
Interpretive studies acknowledge the importance of the analysis performed by the researcher to the meaning attributed to observations. The researcher sets out create an account of the empirical observations, consisting of descriptive as well as analytical passages. The intention is to provide the reader with a sense of the "real world".
The grounded theory approach, being the prime example of a theory building approach, is a relatively well defined and comprehensive research method that seeks to develop conceptually rich theories grounded in observations from empirical studies. The approach (as presented by Strauss and Corbin, 1990) is based on a number of rather rigorous procedures and techniques, and adhering strictly to these is claimed to provide valid theories (ibid.:27).
The research approach followed here is in accordance with the interpretive scheme outlined above 51; observations are analyzed to make sense of enterprise modeling practice; a number of principles are proposed as lessons learned from the analysis of the observations (meeting RO1), and finally, a framework for enterprise modeling is developed (meeting RO2). The choice of research approach is also consistent with Walsham's (1995:74) discussion of interpretive case studies as the preferred strategy to answering "how?"-questions like the main research question in section 1.2.2.
Loose research design: The researcher is assumed to approach the empirical studies with few expectations about what to find. The research questions are allowed to emerge from observations made in the enterprise modeling projects. Miles and Huberman (1994:16) refer to this as a loose research design.
The researcher as an observer: The role of the researcher is to observe and not to purposely manipulate the projects through active participation. Although project participants must be made aware of the presence and intentions of the researcher, substantial contributions to project work is not made.
The researcher's sensitivity: The researcher's sensitivity towards observed events and situations, and the interpretation of these, will be developed by literature studies and analyses of observations. Hence, frequent alternations between literature studies and analysis of observations are considered preferable.
A note on the decision to have a loose research design: There is ample supply of warnings against following this guideline, e.g., that studies tend to take extensive amounts of time and resources, data are sampled too broadly and coarsely, and cross-case comparability is jeopardized (Miles and Huberman, 1994:17). On the other hand, a loose design may be the most pragmatically feasible when studying phenomena that are not under the control of the researcher (e.g., when the availability of projects to study is highly uncertain and unpredictable, as was the case in this research project).
The purpose of adhering to the above guidelines was to reach a practical understanding of enterprise modeling practice, enabling the provision of convincing arguments in favor of features of the proposed framework. Particularities of the chosen research process are discussed in more detail in chapter 10 when evaluating both the outcome and the conduct of the research project.
The most influential empirical study is the one referred to as the main
study. At that point in time, the research focus was on modeling as a way
to support human sense-making and communication. The first three projects
were not focused to the same degree, as data were sampled in a more broad
and coarse way.