IS Research Methods: Interdisciplinary NAture, Qualitative Research, and Innovative Data Collection
IS Research Methods 1IS Research Methods: Interdisciplinary Nature, Qualitative Research and Innovative Data Collection Edgardo Donovan ITM 699 – Dr. Wenli Wang Module 4 – Case Analysis Monday, March 8, 2010
IS Research Methods 2 IS Research Methods: Interdisciplinary Nature, Qualitative Research and Innovative Data Collection The use of electronic information systems is a recent human phenomenon that in thelast 50 years has dramatically changed the way humans socialize, communicate, conductbusiness, study, and make decisions regarding any information-intensive affairs. In a similarfashion Information Systems as an academic discipline has emerged from being a small highlyconstrained area of study into a an ever expansive body of knowledge that draws upon a widerange of disciplines such as technology, psychology, economics, sociology, mathematics,linguistics, semiotics that encompass very different research traditions (Mingers 240). Despite the controversy among some academics as to whether it is moreadvantageous for the study of Information Systems to isolate itself from these other fields, it ishighly unlikely that it will happen. As demonstrated by the design of automated Internetinformation retrieval agents, even the most technical/scientific endeavors must take intoconsideration how to best design technological processes that have the best chance of achievinghigh usability levels of human and computer interaction. Different paradigms each focusattention on different aspects of the situation and so multimethod research is necessary to dealeffectively with the full richness of the real world (Mingers 243). Different trends in research topics and philosophical perspective have led to a widerdiversity in research methods in information systems research (Dube 598). Phenomena exist thatare not easily observable nor easily discernable: human nature, cognitive styles, personalitytraits, factors internal to the person, user friendliness, technical deficiency in a system,ergonomics, intra-organizational power, and the distribution of power within the design of an
IS Research Methods 3information system (Lee 37). Over the years this has led many researchers to increasingly rely oncase research methodology not only as an Information Systems didactic but as a researchmethod as well. Case methodology is useful when a phenomenon is broad and complex andwhen a holistic in-depth investigation is needed, and when a phenomenon cannot be studiesoutside the context in which it occurs (Dube 598). Figure 1. Principles for Interpretive Research (Klein 72) Despite the wide range of disciplines Information Systems intersects with it isstill very important that researcher attempt to maintain the highest scientific standards of rigor aspossible in making controlled observations, making controlled deductions, and allowing forreplicable theories of a universal nature. This is fraught with challenges given the wide body ofacademic disciplines that cross-pollinate with Information Systems and requires a highlycontextualized research portfolio. At the same time the researcher will have to maintain an
IS Research Methods 4opening to take into account multiple explanations of similar phenomena as they attempt toprovide new knowledge in the form of a theory that is abstract and generalizable. The utilization of Internet agents for data collection has huge potential not only forautomating business tasks but for academic research as well. Research access to commercialwebsites may be manual using a standard web browser or automated using automated datacollection agents. These approaches have different effects on websites. Researchers usingmanual access tend to make a limited number of page requests because manual access is costlyto perform. Researchers using automated access methods can request large numbers of pages at alow cost. Therefore, website administrators tend to view manual access and automated accessvery differently (Allen 3). Internet data gathering does offer potentially large data sets withrepeated observation of individual choices and action. In addition, the automated data collectionholds promise for greatly reduced cost per observation. Significant challenges remain indeveloping appropriate sampling techniques integrating data from heterogeneous sources in avariety of formats, constructing generalizable processes and understanding legal constraints(Bapna 116). Early information agents were used by search engines to find and index web pageson the world wide web as well by spammers to cull email addresses published on the Internet.These tools have progressed in recent years into much more sophisticated processes asprogrammers were able to design qualifiers to enable very specific results to be returned.However, much of today’s useful web information is stored within password protected web siteswhich automated agents cannot legally access. Furthermore, even the most sophisticated of
IS Research Methods 5applications will return results that require “cleaning up” through a manual or semi-automatedmethod at best which can be extremely costly depending on the amount of information retrieved. The use of electronic information systems is a recent human phenomenon that in thelast 50 years has dramatically changed the way humans socialize, communicate, conductbusiness, study, and make decisions regarding any information-intensive affairs. Research resultswill continue to be richer and more reliable if different research methods, preferably fromdifferent (existing) paradigms are routinely combined together (Mingers 240).
IS Research Methods 6 BibliographyAllen, Gove N.; Burk, Dan L.; Davis, Gordon B. (2006). Academic data collection in electronicenvironments: defining acceptable use of internet resources. MIS Quarterly, 30(3), September,p599-610.Bapna, R., Goes, P., Gopal, R., and Marsden, J., (2006). Moving from data-constrained to data-enabled research: experiences and challenges in collecting, validating and analyzing large-scale e-commerce data. Statistical Science 21(2), p.116-130.Dubé, Line; Paré, Guy. (2003). Rigor in information systems positivist case research: currentpractices, trends, and recommendations. MIS Quarterly, 27(4), December, p597-635.Klein, HK, (1999). A set of principles for conducting and evaluating interpretive field studies ininformation systems. MIS Quarterly, 23(1), March, pp. 67-93.Lee, A. S. (1989). A scientific methodology for mis case studies. MIS Quarterly (13:1), March,pp. 33-50.Mingers, J. (2001). Combining IS research methods: towards a pluralist methodology.Information Systems Research (12:3), September, pp. 240-259.