Examining the Impact and Detection of the "Urban Legend" of Common Method Bias

Common Method Bias (CMB) represents one of the most frequently cited concerns among Information System (IS) and social science researchers. Despite the broad number of commentaries lamenting the importance of CMB, most empirical studies have relied upon Monte Carlo simulations, assuming that all of the sources of bias are homogenous in their impact. Comparatively analyzing field-based data, we address the following questions: (1) What is the impact of different sources of CMB on measurement and structural models? (2) Do the most commonly utilized approaches for detecting CMB produce similar estimates? Our results provide empirical evidence that the sources of CMB have differential impacts on measurement and structural models, and that many of the detection techniques commonly utilized within the IS field demonstrate inconsistent accuracy in discerning these differences.

[1]  Graham Turpin,et al.  The life events scale for students: Validation for use with British samples , 1996 .

[2]  Cheri Ostroff,et al.  Substantive and operational issues of response bias across levels of analysis: an example of climate-satisfaction relationships. , 2002, The Journal of applied psychology.

[3]  Bert Weijters,et al.  Commentary on “Common Method Bias in Marketing: Causes, Mechanisms, and Procedural Remedies” , 2012 .

[4]  Rajeev Sharma,et al.  Estimating the effect of common method variance: the method-method pair technique with an illustration from TAM research , 2009 .

[5]  M. Lindell,et al.  Accounting for common method variance in cross-sectional research designs. , 2001, The Journal of applied psychology.

[6]  Ritu Agarwal,et al.  A Conceptual and Operational Definition of Personal Innovativeness in the Domain of Information Technology , 1998, Inf. Syst. Res..

[7]  L. G. Rorer,et al.  THE GREAT RESPONSE-STYLE MYTH. , 1965, Psychological bulletin.

[8]  Ellen M. Hufnagel,et al.  User Response Data: The Potential for Errors and Biases , 1994, Inf. Syst. Res..

[9]  Vallabh Sambamurthy,et al.  Sources of Influence on Beliefs about Information Technolgoy Use: An Empirical Study of Knowledge Workers , 2003, MIS Q..

[10]  William P. Dunlap,et al.  Partialling Factor Scores Does Not Control Method Variance: A Reply to Podsakoff and Todor , 1986 .

[11]  D. Campbell,et al.  Convergent and discriminant validation by the multitrait-multimethod matrix. , 1959, Psychological bulletin.

[12]  J. Wagner,et al.  Percept-Percept Inflation in Microorganizational Research: An Investigation of Prevalence and Effect , 1994 .

[13]  PetterStacie,et al.  The Data Base for Advances in Information Systems , 2017 .

[14]  Larry J. Williams,et al.  An Alternative Approach to Method Effects by Using Latent-Variable Models: Applications in Organizational Behavior Research , 1994 .

[15]  Paul E. Spector,et al.  Common Method Issues: An Introduction to the Feature Topic in Organizational Research Methods , 2010 .

[16]  Scott B. MacKenzie,et al.  Common Method Bias in Marketing: Causes, Mechanisms, and Procedural Remedies , 2012 .

[17]  Detmar W. Straub,et al.  An Update and Extension to SEM Guidelines for Admnistrative and Social Science Research , 2011 .

[18]  Naresh K. Malhotra,et al.  Common Method Variance in IS Research: A Comparison of Alternative Approaches and a Reanalysis of Past Research , 2006, Manag. Sci..

[19]  Jack M. Feldman,et al.  Self-generated validity and other effects of measurement on belief, attitude, intention, and behavior. , 1988 .

[20]  Adam W. Meade,et al.  Assessing Common Methods Bias in Organizational Research , 2007 .

[21]  Ryan T. Wright,et al.  Assessing Common Method Bias: Problems with the ULMC Technique , 2012, MIS Q..

[22]  D. Watson,et al.  Development and validation of brief measures of positive and negative affect: the PANAS scales. , 1988, Journal of personality and social psychology.

[23]  H. Wold.  Systems Analysis by Partial Least Squares , 1983 .

[24]  J. Christopher Westland,et al.  Lower bounds on sample size in structural equation modeling , 2010, Electron. Commer. Res. Appl..

[25]  Scott B. MacKenzie,et al.  Common method biases in behavioral research: a critical review of the literature and recommended remedies. , 2003, The Journal of applied psychology.

[26]  Detmar W. Straub,et al.  Validation Guidelines for IS Positivist Research , 2004, Commun. Assoc. Inf. Syst..

[27]  Wynne W. Chin.  The partial least squares approach for structural equation modeling. , 1998 .

[28]  L. J. Williams,et al.  An Alternative Approach to Method Effects by Using Latent-Variable Models: Applications in Organizational Behavior Research , 1994 .

[29]  Ujwal Kayande,et al.  Commentary on “Common Method Bias in Marketing: Causes, Mechanisms, and Procedural Remedies” , 2012 .

[30]  A. Burton-Jones.  Minimizing Method Bias Through Programmatic Research , 2009 .

[31]  Straub,et al.  Editor's Comments: An Update and Extension to SEM Guidelines for Administrative and Social Science Research , 2011 .

[32]  Hans Baumgartner,et al.  Response Styles in Marketing Research: A Cross-National Investigation , 2001 .

[33]  D. Dwyer.  Changes in Teachers' Beliefs and Practices in Technology-Rich Classrooms , 1991 .