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Changing the way we study change: advocating longitudinal research in MIS

Published:14 May 2014Publication History
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Abstract

Recently, scholars in various disciplines have called for the use of longitudinal research designs to test and build theory. Their argument is that key phenomena in virtually every theory change over time. As such, cross-sectional research designs used to test and extend theories do not provide insights that help understand the nature of temporal relationships between variables that are central to theory. Evidence is emerging that in some cases the strength and the direction of the relationship between variables found using longitudinal data is quite different relative to that found using cross-sectional data. The view expressed in this paper is that longitudinal research brings with it both new opportunities and challenges for information systems (IS) researchers. Opportunities will come in the form of explicitly incorporating time in testing and applying IS theories to cast new light on prior research that has been predominantly based on cross-sectional designs. At the same time, challenges will come in the form of proposing hypotheses on interrelationships between variables over time, and using newer data analytic techniques that are better suited to analyzing longitudinal data. We provide illustrations that highlight both advantages and challenges associated with longitudinal research in the field of IS.

References

  1. Agarwal, R., Sambamurthy, V., and Stair, R. M. 2000. "Research Report: The Evolving Relationship between General and Specific Computer Self-Efficacy -- An Empirical Assessment," Information Systems Research, (11:4), pp. 418--430. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Bandura, A. Self-Efficacy: The Exercise of Control, W. H. Freeman, New York, 1997.Google ScholarGoogle Scholar
  3. Bandura, A. 1977. "Self-Efficacy: Towards a Unifying Theory of Behavioral Change", Psychological Review, pp. 191--215.Google ScholarGoogle ScholarCross RefCross Ref
  4. Bliese, P. D., and Ployhart, R. E. 2002. "Growth Modeling Using Random Coefficients Models: Model Building, Testing and Illustrations", Organizational Research Methods, (5: 4), pp. 362--387.Google ScholarGoogle Scholar
  5. Chua, L. S., Chen, D., and Wong, A. F. L. 1999. "Computer Anxiety and its Correlates: A Meta-Analysis," Computers in Human Behavior (15), pp. 609--623.Google ScholarGoogle ScholarCross RefCross Ref
  6. Collins, L.M. 2006. Analysis of longitudinal data: The integration of theoretical model, temporal design and statistical model. Annual Review of Psychology, 57, 505--528.Google ScholarGoogle ScholarCross RefCross Ref
  7. Compeau D., and Higgins, C. A. 1995. "Computer Self-Efficacy: Development of a Measure and Initial Test," MIS Quarterly (19:2), pp. 189--21 Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Compeau D., Higgins, C. A., and Huff, S. 1999. "Social Cognitive Theory and Individual Reactions to Computing Technology: A Longitudinal Study," MIS Quarterly (23:2), pp. 145--158. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Duncan T. E., Duncan, S. C., and Strycker, L. A., 2006. An Introduction to Latent Variable Growth Curve Modeling: Concepts, Issues and Applications, Second Edition, Mahwah, NJ, Lawrence Erlbaum Associates Publishers.Google ScholarGoogle Scholar
  10. Fuller, M., Hardin, A., and Davison, R. (2007), Efficacy in Technology-Mediated Distributed Teams, Journal of Management Information Systems, Winter 2006--7, 23(3) 221--247. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Gefen, D., Rigdon, E. E., and Straub, D. 2011. "An Update and Extension of SEM Guidelines for Administrative and Social Science Research," MIS Quarterly (35:2), Editor's Comments. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. George, J. M., and Jones, G. R. 2000. "The Role of Time in Theory and Theory Building", Journal of Management, (26:4), pp. 657--684.Google ScholarGoogle Scholar
  13. Hancock, G. R., Kuo, W., and Lawrence, F. R. 2001. "An Illustration of Second-Order Latent Growth Models," Structural Equation Modeling (8:3), pp. 470--489.Google ScholarGoogle ScholarCross RefCross Ref
  14. Hancock, G. R., and Lawrence, F. R. 2006. Using Latent Growth Models to Evaluate Longitudinal Change. G. R. Hancock & R. O. Mueller (Eds.), Structural Equation Modeling: A Second Course. Greenwood, CT: Information Age Publishing, Inc.Google ScholarGoogle Scholar
  15. Hardin, A. and Looney, C. (2012) "Myopic Loss Aversion: Demystifying the Key Factors Influencing Decision Problem Framing" Organizational Behavior and Human Decision Processes, 117(2), 311--331.Google ScholarGoogle ScholarCross RefCross Ref
  16. Hoffman, L., and Stawski, R. L. 2009. "Persons as Contexts: Evaluating Between-Person and Within-Person Effects in Longitudinal Analysis". Research in Human Development, (6: 2--3), pp. 97--120.Google ScholarGoogle Scholar
  17. Kher, H. V, Serva, M. A., Davidson, S. and Monk, E. 2009. "Leveraging Latent Growth Models to Better Understand MIS Theory: A Primer". Proceedings of the Special Interest Group on Management Information Systems, 47th Annual Conference on Computer Personnel Research, Limerick, Ireland, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Kraemer, H. C., Yesavage, J. A., Taylor, J. L., and Kupfer, D. 2000. "How can we Learn about Developmental Processes from Cross-Sectional Studies, or can we"? American Journal of Psychiatry (157), pp. 163--171.Google ScholarGoogle ScholarCross RefCross Ref
  19. Looney, C. and Hardin A. (2009) "Decision Support for Retirement Portfolio Management: Overcoming Myopic Loss Aversion via Technology Design. Management Science, 55(10) 1688--1703. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Marakas, G. M., Yi, M. Y., and Johnson, R. D. 1998. "The Multilevel and Multifaceted Character of Computer Self-Efficacy: Toward Clarification of the Construct and an Integrative Framework for Research," Information Systems Research (9:2), pp. 126--163. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Marakas, G. M., Johnson, R. D., and Clay, P. F. 2007. "The Evolving Nature of the Computer Self-Efficacy Construct: An Empirical Investigation of Measurement Construction, Validity, Reliability, and Stability Over Time," Journal of the Association for Information Systems (8:1), pp. 16--46.Google ScholarGoogle ScholarCross RefCross Ref
  22. Maxwell, S. E., & Cole, D. A. 2007. Bias in cross-sectional analyses of longitudinal mediation. Psychological Methods, 12, 23--44.Google ScholarGoogle ScholarCross RefCross Ref
  23. McArdle, J.J. 1988. Dynamic but structural equation modeling of repeated measures data. In J.R. Nesselroade & R.B. Cattell (Eds.), The Handbook of Multivariate Experimental Psychology, Volume 2. New York, Plenum Press, 561--614.Google ScholarGoogle Scholar
  24. Mitchell, T. R., and James, L. R. 2001. "Building Better Theory: Time and Specification of When Things Happen", Academy of Management Review, (26:4), pp. 530--547.Google ScholarGoogle Scholar
  25. Otondo, R. F., Barnett, T., Kellermanns, F. W., Pearson, A. W., and Pearson, R. A. 2009. "Assessing Information Technology Usage over Time with Growth Modeling and Hierarchical Linear Modeling: A Tutorial," Communications of the Association for Information Systems (25), pp. 607--640.Google ScholarGoogle Scholar
  26. Pavlou, P. A., Zheng, E., and Gu, B. 2010. "Latent Growth Modeling in IS Research: Basic Tenets, Illustration, and Practical Guidelines," Proceedings of the Thirty First International Conference on Information Systems, St. Louis, 2010.Google ScholarGoogle Scholar
  27. Petter, S., Straub, D., and Rai, A. 2007. "Specifying Formative Constructs in Information Systems Research," MIS Quarterly (31:4), pp. 623--656. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Pitariu, A. H., and Ployhart, R. E. 2010. "Explaining Change: Theorizing and Testing Dynamic Mediated Longitudinal Relationships", Journal of Management, (36:2), pp. 405--429.Google ScholarGoogle Scholar
  29. Ployhart, R. E., and Vandenberg, R. J. 2010. "Longitudinal Research: The Theory, Design, and Analysis of Change," Journal of Management (36:1), pp. 91--120.Google ScholarGoogle Scholar
  30. Preacher, K. J., Curran, P. J., and Bauer, D. J. 2006. "Computational Tools for Probing Interactions in Multiple Linear Regression, Multilevel Modeling, and Latent Curve Analysis," Journal of Educational and Behavioral Statistics (31:3), pp. 437--448.Google ScholarGoogle ScholarCross RefCross Ref
  31. Preacher, K. J., Wichman, A. L., MacCallum, R. C., and Briggs, N. E. 2008. Latent Growth Curve Modeling, Sage Publications.Google ScholarGoogle Scholar
  32. Qureshi, I., Wang, Y., Compeau, D., and Meister, D. 2008. "Capturing the dynamics of adoption through Latent Curve Modeling," DIGIT 2008 Proceedings.Google ScholarGoogle Scholar
  33. Raudenbush, W. and Bryk, S. 2002. Hierarchical Linear Models: Applications and Data Analysis Methods, Thousand Oaks, CA: Sage Publications.Google ScholarGoogle Scholar
  34. Rogosa, D., Brandt, D., and Zimowski, M. 1982. "A Growth Curve Approach to the Measurement of Change," Psychological Bulletin (92:3), pp. 726--748.Google ScholarGoogle ScholarCross RefCross Ref
  35. Saunders, C. 2007. "Perspectives on Time", MIS Quarterly, (31:4), pp. iii-xi. Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Serva, M. A., Kher, H. V., and Laurenceau, J. P. 2011. "Using Latent Growth Modeling to Understand Longitudinal Effects in MIS Theory: A Primer," Communications of the Association for Information Systems (28:1), pp. 213--233.Google ScholarGoogle Scholar
  37. Singer, J. D. and Willett, J. B. 2003. Applied Longitudinal Data Analysis, New York, Oxford University Press.Google ScholarGoogle Scholar
  38. Tallon, Paul P. and Pinsonneault, Alain. 2011. "Competing Perspectives on the Link Between Strategic Information Technology Alignment and Organizational Agility: Insights from a Mediation Model," MIS Quarterly, (35:2) pp.463--486. Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. Thatcher, J. B. and Perrewe, P. L. 2002. "An Empirical Examination of Individual Traits as Antecedents to Computer Anxiety and Computer Self-Efficacy," MIS Quarterly (26:4), pp. 381--396. Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. Thatcher, J. B., Zimmer, C. J., Gundlach, M. J., Harrison McKnight, D. 2008. "Internal and External Dimensions of Computer Self-Efficacy: An Empirical Examination". IEEE Transactions on Engineering Management, (55:4), pp. 628--643.Google ScholarGoogle ScholarCross RefCross Ref
  41. Torkzadeh, G. and Van Dyke, T. P. 2002. "Effects of Training in Internet Self-Efficacy and Computer Use Attitudes," Computers in Human Behavior, (18), pp. 479--494.Google ScholarGoogle ScholarCross RefCross Ref
  42. Venkatesh, V., Morris, M. G., Davis, G. B., and Davis, F. D. 2003. "User Acceptance of Information Technology: Toward a Unified View," MIS Quarterly (27:3), pp. 425--478. Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. Whetten, D. A. 1989. What Constitutes a Theoretical Contribution?, Academy of Management Review, (14:4), pp. 490--495).Google ScholarGoogle Scholar
  44. Wright, T. P., 1936. Factors affecting the cost of airplanes. Journal of Aeronautical Sciences, 3, 122--128.Google ScholarGoogle ScholarCross RefCross Ref

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