Each research domain carries the burden of examining the effects of common method variance (CMV) on published research within the domain. To focus on this concern in the context of the theory of planned behavior (TPB), this research empirically compares several methods of detecting the presence of and estimating the level of CMV in the TPB domain. These methods include various implementations of the marker variable technique and versions of the multitrait-multimethod (MTMM) technique. The results show that the marker variable technique provides estimates of CMV and CMV-corrected correlations and paths that are consistent with those produced using the other methods. Next, one implementation of the marker variable technique method is implemented post hoc on a large data set of published TPB studies. This analysis provides strong confirmatory evidence that the effects of CMV do not alter the substantive inferences of study results in prior research. Overall, these findings support putting to rest concerns about the adverse influence of CMV in the TPB domain.
AjzenI. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179–211.
2.
AjzenI. (2001). Nature and operations of attitudes. Annual Review of Psychology, 52, 27–58.
3.
AlgeB. J.BallingerB. A.TangiralaS.OakleyJ. L. (2006). Information privacy in organizations: Empowering creative and extrarole performance. Journal of Applied Psychology, 91, 221–232.
4.
BagozziR. P.WarshawP. R. (1990). Trying to consume. Journal of Consumer Research, 17, 127–140.
5.
BagozziR. P.WongN.AbeS.BergamiM. (2000). Cultural and situational contingencies and the theory of reasoned action: Application to fast food restaurant consumption. Journal of Consumer Psychology, 9, 97–106.
6.
BagozziR. P.YiY. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16, 74–94.
7.
BaumeisterR. F.VohsK. D.FunderD. C. (2007). Psychology as the science of self-reports and finger movements: Whatever happened to actual behavior?Perspectives on Psychological Science, 2, 396–403.
8.
BeckerJ. U.GreveG.AlbersS. (2009). The impact of technological and organizational implementation of CRM on customer acquisition, maintenance, and retention. International Journal of Research in Marketing, 26, 207–215.
9.
BergkvistL.RossiterJ. (2007). The predictive validity of multiple-item versus single-item measures of the same constructs. Journal of Marketing Research, 44, 175–184.
10.
BobkoP.RothP. L.BusterM. A. (2007). The usefulness of unit weights in creating composite scores: A literature review, application to content validity, and meta-analysis. Organizational Research Methods, 10, 689–709.
11.
BollenK. A. (1989). A new incremental fit index for general structural equation models. Sociological Methods and Research, 17, 303–316.
12.
BrannickM. T.SpectorP. E. (1990). Estimation problems in the block-diagonal model of the multitrait-multimethod matrix. Applied Psychological Measurement, 14, 325–339.
13.
CampbellD. T.FiskeD. W. (1959). Convergent and discriminant validity by the multitrait-multimethod matrix. Psychological Bulletin, 56, 81–105.
14.
ChangS.van WitteloostuijnA.EdenL. (2010). From the editors: Common method variance in international business research. Journal of International Business Studies, 41, 178–184.
15.
CohenJ. B.FishbeinM.AhtolaO. T. (1972). The nature and uses of expectancy-value models in consumer attitude research. Journal of Marketing Research, 9, 456–460.
16.
ConnerM.ArmitageC. J. (1998). Extending the theory of planned behavior: A review and avenues for further research. Journal of Applied Social Psychology, 28, 1429–1464.
17.
CoteJ. A.BuckleyM. R. (1987). Estimating trait, method, and error variance: Generalizing across 70 construct validation studies. Journal of Marketing Research, 24, 315–318.
18.
CramptonS. M.WagnerJ. A. (1994). Percept-percept inflation in microorganizational research: An investigation of prevalence and effect. Journal of Applied Psychology, 79, 67–76.
19.
DavisF. D.BagozziR. P.WarshawP. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35, 982–1003.
20.
DotyD. H.GlickW. H. (1998). Common methods bias: Does common methods variance really bias results?Organizational Research Methods, 1, 374–406.
21.
ElliottM. A.ArmitageC. J.BaughanC. J. (2003). Drivers’ compliance with speed limits: An application of the theory of planned behavior. Journal of Applied Psychology, 88, 964–972.
22.
FishbeinM.AjzenI. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley.
23.
FornellC.LarckerD. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18, 382–388.
24.
FrazierG. L.MaltzE.AntiaK. D.RindfleischA. (2009). Distributor sharing of strategic information with suppliers. Journal of Marketing, 73, 3–43.
25.
FreseM.ZapfD. (1988). Methodological issues in the study of work stress: Objective vs subjective measurement of work stress and the question of longitudinal studies. In CooperC. L.PayneR. (Eds.), Causes, coping and consequences of stress at work (pp. 375–411). Oxford, UK: John Wiley & Sons.
26.
GreeneW. H. (2000). Econometric analysis. Upper Saddle River, NJ: Prentice-Hall, Inc.
27.
HuL.BentlerP. M. (1999). Cutoff criteria for fit indexes in covariance analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6, 1–55.
LanceC. E.DawsonB.BirkelbachD.HoffmanB. J. (2010). Method effects, measurement error, and substantive conclusions. Organizational Research Methods, 13, 407–420.
30.
LindellM. K.WhitneyD. J. (2001). Accounting for common method variance in cross-sectional research designs. Journal of Applied Psychology, 86, 114–121.
31.
LongJ. S. (1997). Regression models for categorical and limited dependent variables. Thousand Oaks, CA: Sage.
32.
LumpkinJ. R.DardenW. R. (1982). Relating television preference viewing to shopping orientations, life styles, and demographics: The examination of perceptual and preference dimensions of television programming. Journal of Advertising, 11, 56–67.
33.
MalhotraN. K.KimS. S.PatilA. (2006). Accounting for common method variance in IS research: Reanalysis of past studies using a marker-variable technique. Management Science, 52, 1865–1883.
34.
MathwickC.WiertzC.De RuyterK. (2008). Social capital production in a virtual P3 community. Journal of Consumer Research, 34, 832–849.
35.
MoormanC.MatulichE. (1993). A model of consumers’ preventive health behaviors: The role of health motivation and health ability. Journal of Consumer Research, 20, 208–228.
36.
O’GuinnT. C.FaberR. J. (1989). Compulsive buying: A phenomenological exploration. Journal of Consumer Research, 16, 147–57.
37.
PodsakoffP. M.MacKenzieS. B.LeeJ.-Y.PodsakoffN. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88, 879–903.
38.
PodsakoffP. M.MacKenzieS. B.PodsakoffN. P. (2012). Sources of method bias in social science research and recommendations on how to control it. Annual Review of Psychology, 63, 539–569.
39.
RajuP. S. (1980). Optimum stimulation level: Its relationship to personality, demographics, and exploratory behavior. Journal of Consumer Research, 7, 272–282.
40.
RichardsonH. A.SimmeringM. J.SturmanM. C. (2009). A tale of three perspectives: Examining post hoc statistical techniques for detection and correction of common method variance. Organizational Research Methods, 12, 762–800.
41.
RindfleischA.MalterA. J.GanesanS.MoormanC. (2008). Cross-sectional versus longitudinal survey research: Concepts, findings, and guidelines. Journal of Marketing Research, 45, 261–279.
42.
SharmaR.YettonP.CrawfordJ. (2009). Estimating the effect of common method variance: The method–method pair technique with an illustration from TAM research. MIS Quarterly, 33, 473–490.
43.
SheppardB. H.HartwickJ.WarshawP. R. (1988). The theory of reasoned action: A meta-analysis of past research with recommendations for modifications and future research. Journal of Consumer Research, 15, 325–343.
44.
SiemsenE.RothA.OliveiraP. (2010). Common method bias in regression models with linear, quadratic, and interaction effects. Organizational Research Methods, 13, 456–476.
45.
SpearmanC. (1904). The proof and measurement of association between two things. American Journal of Psychology, 15, 72–101.
46.
SpectorP. E. (1987). Method variance as an artifact in self-reported affect and perceptions at work: Myth or significant problem?Journal of Applied Psychology, 72, 438–443.
47.
SpectorP. E. (2006). Method variance in organizational research: Truth or urban legend?Organizational Research Methods, 9, 221–232.
48.
SpectorP. E.BrannickM. T. (2010). Common method issues: An introduction to the feature topic in organizational research methods. Organizational Research Methods, 13, 403–406.
49.
WeijtersB.CabooterE.SchillewaertN. (2010). The effect of rating scale format on response styles: The number of response categories and response category labels. International Journal of Research in Marketing, 27, 236–247.
50.
WellsW. D.TigertD. J. (1971). Activities, interests and opinions. Journal of Advertising Research, 11, 27–35.
51.
WilkinsonL., The APA Task Force on Statistical Inference (1999). Statistical methods in psychology journals: Guidelines and explanations. American Psychologist, 54, 594–604.
52.
WilliamsL. J.CoteJ. A.BuckleyM. R. (1989). Lack of method variance in self-reported affect and perceptions at work: Reality or artifact?Journal of Applied Psychology, 74, 462–468.
53.
WilliamsL. J.HartmanN.CavazotteF. (2010). Method variance and marker variables: A review and comprehensive CFA marker technique. Organizational Research Methods, 13, 477–514.
