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

Authors:

Andrew Schwarz,

Tracey Rizzuto,

Colleen Carraher-Wolverton,

José L. Roldán,

Ramón Barrera-BarreraAuthors Info & Claims

ACM SIGMIS Database: the DATABASE for Advances in Information Systems, Volume 48, Issue 1

Pages 93 - 119

https://doi.org/10.1145/3051473.3051479

Published: 08 February 2017 Publication History

Abstract

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.

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Index Terms

Examining the Impact and Detection of the "Urban Legend" of Common Method Bias
1. General and reference
  1. Document types
    1. Reference works
2. Mathematics of computing
  1. Probability and statistics

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Published In

cover image ACM SIGMIS Database: the DATABASE for Advances in Information Systems

ACM SIGMIS Database: the DATABASE for Advances in Information Systems Volume 48, Issue 1

February 2017

113 pages

ISSN:0095-0033

EISSN:1532-0936

DOI:10.1145/3051473

Editors:
Stacie Petter
Baylor University, Waco, TX
,
Tom Stafford
Louisiana Tech University, Ruston, LA
,
Xihui "Paul" Zhang
Northern Alabama University, Florence, AL

Issue’s Table of Contents

Copyright © 2017 ACM.

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Published: 08 February 2017

Published in SIGMIS Volume 48, Issue 1

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Cited By

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https://doi.org/10.1177/00131644241311851
Shahzad MChen SIqbal TLi Z(2025)Sustainable futures: leveraging green intellectual capital, HRM, innovation and leadershipJournal of Intellectual Capital10.1108/JIC-03-2024-0091Online publication date: 10-Mar-2025
https://doi.org/10.1108/JIC-03-2024-0091
Loh KTan C(2025)Agility and internal integration as a catalyst to the organizational recovery in SMEs: the mediating role of resilienceBenchmarking: An International Journal10.1108/BIJ-03-2022-0185Online publication date: 20-Feb-2025
https://doi.org/10.1108/BIJ-03-2022-0185
Ju CWang S(2025)Determinants of consumer intention to use autonomous delivery vehicles: based on the planned behavior theory and normative activation modelHumanities and Social Sciences Communications10.1057/s41599-025-04662-w12:1Online publication date: 7-Mar-2025
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