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The Problem of Common Method Variance in IS Research

The Problem of Common Method Variance in IS Research

Amy B. Woszczynski, Michael E. Whitman
Copyright: © 2004 |Pages: 13
ISBN13: 9781591401445|ISBN10: 1591401445|EISBN13: 9781591401452
DOI: 10.4018/978-1-59140-144-5.ch005
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MLA

Woszczynski, Amy B., and Michael E. Whitman. "The Problem of Common Method Variance in IS Research." The Handbook of Information Systems Research, edited by Michael Whitman and Amy Woszczynski, IGI Global, 2004, pp. 66-78. https://doi.org/10.4018/978-1-59140-144-5.ch005

APA

Woszczynski, A. B. & Whitman, M. E. (2004). The Problem of Common Method Variance in IS Research. In M. Whitman & A. Woszczynski (Eds.), The Handbook of Information Systems Research (pp. 66-78). IGI Global. https://doi.org/10.4018/978-1-59140-144-5.ch005

Chicago

Woszczynski, Amy B., and Michael E. Whitman. "The Problem of Common Method Variance in IS Research." In The Handbook of Information Systems Research, edited by Michael Whitman and Amy Woszczynski, 66-78. Hershey, PA: IGI Global, 2004. https://doi.org/10.4018/978-1-59140-144-5.ch005

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Abstract

Many IS researchers obtain data through the use of self-reports. However, self-reports have inherent problems and limitations, most notably the problem of common method variance. Common method variance can cause researchers to find a significant effect, when in fact, the true effect is due to the method employed. In this chapter, we examined published research in leading information systems (IS) journals to determine if common method variance is a potential problem in IS research and how IS researchers have attempted to overcome problems with method bias. We analyzed 116 research articles that used a survey approach as the predominant method in MIS Quarterly, Information Systems Research, and Journal of Management Information Systems. The results indicate that only a minority of IS researchers have reported on common method variance. We recommend that IS researchers undertake techniques to minimize the effects of common method variance, including using multiple types of respondents, longitudinal designs, and confirmatory factor analysis that explicitly models method effects.

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