Complementary Methods to Mitigate the Misinterpretation of Results Due to Collinearity in International Business Research

Autores

DOI:

https://doi.org/10.18568/internext.v17i1.681

Palavras-chave:

Palavras-chave: Modelagem de equações estruturais. Multicolinearidade. Negócios internacionais.

Resumo

Objetivos do estudo: evidenciar lacuna metodológica nos trabalhos empíricos da área de Negócios Internacionais que utilizam modelos estruturais, e prescrever métodos complementares para mitigar o problema da colinearidade

Método: empregou-se uma simulação para evidenciar os efeitos da colinearidade em relação à importância e significância dos preditores, e apresentados métodos voltados ao controle do efeito indesejado da colinearidade

Principais resultados: proposição de métodos complementares que incluem o agrupamento das variáveis latentes que apresentam multicolinearidade em modelos de segunda ordem, e a utilização da medida de importância relativa dos preditores

Contribuições teóricas e metodológicas: a contribuição se dá frente à prescricao de técnicas oferecidas à comunidade acadêmica para a realização de pesquisas empíricas, que foram alcançadas pelo presente estudo.

Relevância e originalidade: a partir das lacunas apontadas na produção cientifica recente do campo de conhecimento dos Negócios Internacionais, são elencadas medidas para mitigar a questao da colinearidade

Contribuições sociais e para a gestão: dentre as principais implicações gerenciais e sociais alcançadas por meio dos achados aqui apresentados, promove-se o desenvolvimento de pesquisas empíricas robustas, relevantes e confiáveis

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Biografia do Autor

Diogenes de Souza Bido, Universidade Presbiteriana Mackenzie - M, São Paulo, (Brasil)

Dr. Diogenes de Souza Bido is Professor and Researcher of the postgraduate program in Business Administration at Universidade Presbiteriana Mackenzie. His main interests are related to the following themes: methodological aspects of research in administration, quantitative methods and organizational behavior. 

Antonio Carlos de Oliveira Barroso, Instituto de Pesquisas Energéticas e Nucleares – IPEN, São Paulo, (Brasil)

Dr. Antonio Carlos de Oliveira Barroso is Professor and Researcher in the postgraduate studies program at the Instituto de Pesquisas Energéticas e Nucleares. His research interests include Knowledge management applications for the R&D management, with emphasis on: Knowledge gaps; Social networks; Organizational culture; Technological and graph-bibliometric roadmap models; human factors; and socio-technical modeling of complex systems.

Eric David Cohen, Universidade Estadual de Campinas - UNICAMP, Campinas, São Paulo, (Brasil)

Dr. Eric David Cohen is professor of the graduate program in Business Administration at State University of Campinas. His research interests revolve around International Marketing, Business Strategy, and Marketing research.

Referências

Academic Journal Guide (2021), Available: https://charteredabs.org/academic-journal-guide-2021/ (Accessed 20 July 2021)

Atinc, G., Simmering, M. and Kroll, M. (2011), Control Variable Use and Reporting in Macro and Micro-Management Research. Organizational Research Methods Vol. 15, 57–74. https://doi.org/10.1177/1094428110397773

Bansal, H. (2013), Investigating the measures of relative importance in marketing research. International Journal of Market Research Vol. 55, 675–695.

Beaujean, A. (2014), Latent Variable Modeling Using R: a step-by-step guide. New York: Routledge – Taylor & Francis Group.

Carlson, K. and Wu, J. (2011), The Illusion of Statistical Control: Control Variable Practice in Management Research. Organizational Research Methods Vol. 15, 413–435.

Chin, W., Thatcher, J., Wright, R. and Steel, D. (2013), Controlling for common method variance in PLS analysis: the measured latent marker variable approach. In: H. Abdi, W. Chin, V. Vinzi, G. Russolillo, L. Trinchera (Eds.), New Perspectives in Partial Least Squares and Related Methods. New York: Springer. 231–239.

Cohen, J. (1988), Statistical Power Analysis for the Behavioral Sciences. 2nd ed. New York: Psychology Press.

Cohen, J., Cohen, P., West, S. and Aiken, L. (2003). Applied multiple regression/correlation analysis for the behavioral sciences. 3rd ed. New Jersey: Lawrence Erlbaum Associates, Publishers.

Conway, J. and Huffcutt, A. (2003), A Review and Evaluation of Exploratory Factor Analysis Practices in Organizational Research. Organizational Research Methods Vol. 6, 147–168.

Conway, J. and Lance, C. (2010), What reviewers should expect from authors regarding common method bias in organizational research. Journal of Business and Psychology Vol. 25, 325–334.

Diamantopoulos, A., Riefler, P. and Rith, K. (2008), Advancing formative measurement models. Journal of Business Research Vol. 61, 1203-1218.

Fabrigar, L., Wegener, D., Maccallum, R. and Strahan, E. (1999), Evaluating the use of exploratory factor analysis in psychological research. Psychological methods Vol. 4, 272.

Falk, R. and Miller, N. (1992), A Primer for Soft Modeling. Ohio: The University of Akron Press.

Grewal, R., Cote, J. and Baumgartner, H. (2004), Multicollinearity and measurement error in structural equation models: implications for theory testing. Marketing Science Vol. 23, 519-529.

Groemping, U (2006) Relative Importance for Linear Regression in R: The Package relaimpo. Journal of Statistics Software Vol. 17, 1–27.

Groemping, U. (2020), Package ‘relaimpo’: Relative importance of regressors in linear models (R package version 2.2-3) [software]. Available at: <https://cran.r-project.org/web/packages/relaimpo/relaimpo.pdf>. (Accessed 20 November 2020).

Groemping, U. (2021), relaimpo: Relative Importance of Regressors. Access: <http://prof.beuth-hochschule.de/groemping/relaimpo/> on 24/04/2021.

Gujarati, D. (2003), Basic econometrics. 4th ed. New York: McGraw-Hill/Irwin.

Hair Jr., J., Black, W., Babin, B. and Anderson, R. (2010), Multivariate Data Analysis. 7th ed. Upper Side River, NJ: Prentice Hall.

Hair Jr., J., Hult, G., Ringle, C. and Sarstedt, M. (2016), A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). 2nd ed. Thousand Oaks, CA: Sage Publications, Inc.

Henseler, J., Hubona, G., Ray, P. (2016), Using PLS Path Modeling in New Technology Research: Updated Guidelines. Industrial Management & Data Systems Vol. 116, 2–20.

Henseler, J., Ringle, C. and Sinkovics, R. (2009), The use of partial least squares path modeling in International Business. Advances in International Business Vol. 20, 277-319.

Hult, G.; Hair Jr., J.; Proksch, D.; Sarstedt, M.; Pinkwart, A. and Ringle, C. (2018), Addressing Endogeneity in International Business Applications of Partial Least Squares Structural Equation Modeling, Journal of International Business, Vol. 26, No. 3, 1–21

Johnson, J. (2000), A heuristic method for estimating the relative weight of predictor variables in multiple regression. Multivariate Behavioral Research Vol. 35, 1-19. DOI: 10.1207/S15327906MBR3501_1

Johnson, J. and Lebreton, J. (2004), History and use of relative importance indices in organizational research. Organizational Research Methods Vol. 7, 238–257.

Krasikova, D.; LeBreton, J.; Tonidandel, S. (2011). Estimating the Relative Importance of Variables in Multiple Regression Models. In International Review of Industrial and Organizational Psychology Vol. 26, 119-141, John Wiley and Sons Ltd. https://doi.org/10.1002/9781119992592.ch4

Kennedy, P. (1998), A guide to econometrics. 4th ed. Malden: Blackwell Publishing Ltd.

Kock, N. (2015) Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration Vol. 11, 1–10.

Nimon, K. and Oswald, F. (2013), Understanding the Results of Multiple Linear Regression: Beyond Standardized Regression Coefficients. Organizational Research Methods Vol. 16, 650–674.

Nimon, K., Oswald, F. and Roberts, J. (2020), Package ‘yhat’: Interpreting Regression Effects (R package version 2.0-0) [software] Available: <https://cran.r-project.org/web/packages/yhat/yhat.pdf> (Accessed 20 November 2020).

Peterson, R. and Brown, S. (2005), On the use of beta coefficients in meta-analysis. Journal of Applied Psychology Vol. 90, 175-181. DOI: 10.1037/0021-9010.90.1.175

Podsakoff, P., Mackenzie, S., Lee, J.-Y. and Podsakoff, N. (2003), Common method biases in behavioral research: a critical review of the literature and recommended remedies. The Journal of Applied Psychology, Vol. 88, 879–903.

Podsakoff, P., Mackenzie, S. and Podsakoff, N. (2012), Sources of method bias in social science research and recommendations on how to control it. Annual Review of Psychology, Vol. 63, 539–69.

Podsakoff, P. and Organ, D. (1986), Self-reports in organizational research: problems and prospects. Journal of Management, Vol. 12, 531–544.

Richter, N., Sinkovics, R., Ringle, C., and Schlägel, C. (2016) A critical look at the use of SEM in international business research, International Business Review, Vol. 33 No. 3, 376-404. DOI 10.1108/IMR-04-2014-0148.

Ringle, C. and Sarstedt, M. (2020), Gain More Insight from Your PLS-SEM Results: The Importance-Performance Map Analysis (October 31, 2015). Industrial Management & Data Systems, Vol. 116, No. 9, 1865-1886, 2016, Available at SSRN: https://ssrn.com/abstract=2984821 (Accessed 20 July 2021)

Ringle, C., Wende, S. and Becker, J.-M. (2020), Software SmartPLS 3. Boenningstedt: SmartPLS GmbH. Access: http://www.smartpls.com (Accessed 20 November 2020).

Shackman, J. (2013), The Use of Partial Least Squares Path Modeling and Generalized Structured Component Analysis in International Business Research: A Literature Review, International Journal of Management, Vol. 30 No. 3 Part 1

SmartPLS v. 3 (2021), European Customer Satisfaction Index (ECSI) example. Data and project available at: https://www.smartpls.com/documentation/sample-projects/ecsi (Accessed 20 July 2021)

Spector, P. and Brannick, M. (2011), Methodological urban legends: The misuse of statistical control variables. Organizational Research Methods Vol. 14, 287–305. https://doi.org/10.1177/1094428110369842.

Tenenhaus, M., Esposito Vinzi, V., Chatelin, Y. and Lauro, C. (2005), PLS path modeling. Computational Statistics & Data Analysis Vol. 48, 159–205.

Wetzels, M., Odekerken-Schröder, G. and van Oppen, C. (2009), Using PLS path modeling for assessing hierarchical construct models: guidelines and empirical illustration. MIS Quarterly Vol. 33, 177–195.

White, G., Guldiken, O., Hemphill, T., He, W., Khoobdeh, M. (2016), Trends in International Strategic Management Research from 2000 to 2013: Text Mining and Bibliometric Analyses, Management International Review, Springer, vol. 56(1), 35-65, February, DOI: 10.1007/s11575-015-0260-9

Wilson, B. and Henseler, J. (2007), Modeling reflective higher-order constructs using three approaches with PLS path modeling: A Monte Carlo comparison. Australian and New Zealand Marketing Academy (ANZMAC) Conference. Proceedings... 791–800. Available: <http://doc.utwente.nl/91758/1/BWilson_2.pdf>.

Xin, J., Chen, S., Kwan, H., Chiu, R. and Yim, F. (2018), Work–Family Spillover and Crossover Effects of Sexual Harassment: The Moderating Role of Work–Home Segmentation Preference. Journal of Business Ethics Vol. 147, 619–629. https://doi.org/10.1007/s10551-015-2966-9

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Publicado

2022-01-01

Como Citar

Bido, D. de S., Barroso, A. C. de O., & Cohen, E. D. (2022). Complementary Methods to Mitigate the Misinterpretation of Results Due to Collinearity in International Business Research. Internext, 17(1), 105–127. https://doi.org/10.18568/internext.v17i1.681

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