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.
- 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 ScholarDigital Library
- Bandura, A. Self-Efficacy: The Exercise of Control, W. H. Freeman, New York, 1997.Google Scholar
- Bandura, A. 1977. "Self-Efficacy: Towards a Unifying Theory of Behavioral Change", Psychological Review, pp. 191--215.Google ScholarCross Ref
- 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 Scholar
- 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 ScholarCross Ref
- 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 ScholarCross Ref
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 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 Scholar
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 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 Scholar
- 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 ScholarCross Ref
- 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 Scholar
- 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 ScholarCross Ref
- 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 Scholar
- 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 ScholarDigital Library
- 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 ScholarCross Ref
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 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 ScholarCross Ref
- Maxwell, S. E., & Cole, D. A. 2007. Bias in cross-sectional analyses of longitudinal mediation. Psychological Methods, 12, 23--44.Google ScholarCross Ref
- 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 Scholar
- 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 Scholar
- 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 Scholar
- 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 Scholar
- Petter, S., Straub, D., and Rai, A. 2007. "Specifying Formative Constructs in Information Systems Research," MIS Quarterly (31:4), pp. 623--656. Google ScholarDigital Library
- 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 Scholar
- 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 Scholar
- 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 ScholarCross Ref
- Preacher, K. J., Wichman, A. L., MacCallum, R. C., and Briggs, N. E. 2008. Latent Growth Curve Modeling, Sage Publications.Google Scholar
- Qureshi, I., Wang, Y., Compeau, D., and Meister, D. 2008. "Capturing the dynamics of adoption through Latent Curve Modeling," DIGIT 2008 Proceedings.Google Scholar
- Raudenbush, W. and Bryk, S. 2002. Hierarchical Linear Models: Applications and Data Analysis Methods, Thousand Oaks, CA: Sage Publications.Google Scholar
- 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 ScholarCross Ref
- Saunders, C. 2007. "Perspectives on Time", MIS Quarterly, (31:4), pp. iii-xi. Google ScholarDigital Library
- 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 Scholar
- Singer, J. D. and Willett, J. B. 2003. Applied Longitudinal Data Analysis, New York, Oxford University Press.Google Scholar
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 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 ScholarCross Ref
- 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 ScholarCross Ref
- 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 ScholarDigital Library
- Whetten, D. A. 1989. What Constitutes a Theoretical Contribution?, Academy of Management Review, (14:4), pp. 490--495).Google Scholar
- Wright, T. P., 1936. Factors affecting the cost of airplanes. Journal of Aeronautical Sciences, 3, 122--128.Google ScholarCross Ref
Index Terms
- Changing the way we study change: advocating longitudinal research in MIS
Recommendations
Robust statistic for the one-way MANOVA
The Wilks' Lambda Statistic (likelihood ratio test, LRT) is a commonly used tool for inference about the mean vectors of several multivariate normal populations. However, it is well known that the Wilks' Lambda statistic which is based on the classical ...
One-way ANOVA based on interval information
This paper deals with extending the one-way analysis of variance ANOVA to the case where the observed data are represented by closed intervals rather than real numbers. In this approach, first a notion of interval random variable is introduced. ...
Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysis
The machine learning community adopted the use of null hypothesis significance testing (NHST) in order to ensure the statistical validity of results. Many scientific fields however realized the shortcomings of frequentist reasoning and in the most ...
Comments