Academic journal article JITTA : Journal of Information Technology Theory and Application

Case-Based Research in Information Systems: Gaps and Trends

Academic journal article JITTA : Journal of Information Technology Theory and Application

Case-Based Research in Information Systems: Gaps and Trends

Article excerpt


The purpose of this research is to identify gaps and trends in case-based research in the field of information systems (IS). We argue that case-based research is necessary in all sub-disciplines of the field, but that the use of case-based methodologies has been uneven, concentrated in some sub-fields, such as enterprise resource planning and knowledge management, and almost lacking in others, such as decision support systems and humancomputer interaction. The findings presented here should motivate researchers to augment their research with case studies in areas where it has been lacking. It should also help doctoral students and case researchers who have yet to specialize or who would like to broaden their research interests to identify promising topics for case-based study.

Keywords: case-based research, research methodology, latent semantic analysis, IS core, IS research agenda, IS research interests, IS research trends


The case study has long been a tool for the study of social and organizational phenomena. For information systems (IS), where these phenomena are particularly important, the virtues of case-based research have been widely recognized. Case-based research enables us to study complex social phenomena in their natural context (Yin 1994; Walsham 1995; Dubé and Paré 2003). It is particularly well-suited to the development of new theory (Glaser and Strauss 1967; Benbasat et al. 1987; Eisenhardt 1989), and it generates output that practitioners can easily understand and relate to (Siggelkow 2007; Gordon 2008).

In addition, case-based research provides an important complement to large-sample research. One of the most important areas of complementarity is research relevance (Benbasat and Zmud 1999; Robey and Markus 1998; Senn 1998; Truex 2001). Large-sample statistical studies all report some degree, often a large degree, of unexplained variance. A practitioner attempting to manipulate independent variables to achieve the outcome predicted by a statistical study is hindered by this unexplained variance and has little ability to assess whether the desired outcome is likely to be achieved in his or her context. Case studies provide the context these practitioners need to better understand what situations support or refute the statistical norm. Relevance is achieved when the practitioner identifies with the context of the case. The complementarity between case-based and large-sample statistical research is reciprocal-not only does case-based research complement statistical research, but the reverse is also true. For example, a practitioner who would like to emulate the protagonist of a case to produce a similar outcome will feel more confident if statistical studies support that outcome, even if his or her situation is somewhat different from that described in the case.1

Another significant area of complementarity between case-based and large-sample research is theory development (Benbasat et al. 1987; Carroll and Swatman 2000; Eisenhardt 1989; Yin 1994). Large-sample research is typically used for theory testing rather than theory development. Although statistical studies can uncover relationships to develop theory, they are hampered by the "curse of dimensionality" (Bellman 1961). The factors that can affect outcomes in a normal business environment are numerous, and accounting for all their interactions in a closed form model quickly becomes infeasible. In case-based research, however, any factor can be observed and its impact traced, in time and in state, to document its effect. As a result, case studies can suggest what constructs to examine in a large-sample study. They can also complement large sample studies by augmenting the factors identified in such studies, providing a more nuanced understanding than can be achieved from large-sample studies alone.

Knowing that case-based and large-sample statistical research methods are complementary, it is logical to analyze the extent to which they have addressed the same topics. …

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