METHODOLOGICAL AND PRACTICAL ADVANTAGES OF PLSSEM IN MANAGEMENT RESEARCH: THE CASE OF RAKIJA

Slobodan Adžić; Doloris  Bešić-Vukašinović; Ivana  Savić

doi:10.59267/ekoPolj2601217A

Authors

Slobodan Adžić Academy of Hospitality, Tourism, and Wellness, Tošin Bunar 179đ, 11070 Belgrade, Serbia https://orcid.org/0000-0002-8827-5492
Doloris Bešić-Vukašinović Academy of Technical and Art Applied Studies, Textile School for Design, Technology and Management, Starine Novaka 24, 11000 Belgrade, Serbia https://orcid.org/0000-0001-6763-9747
Ivana Savić niversity of Adriatik, Faculty of Management Herceg Novi, Zemunska 143 street, 85348 Meljine, Montenegro https://orcid.org/0009-0006-3458-0009

DOI:

https://doi.org/10.59267/ekoPolj2601217A

Keywords:

Structural equation modeling, PLS-SEM, CB-SEM, SmartPLS, Agricultural products, Rakija

Abstract

Structural equation modeling (SEM) is widely applied inconsumer behavior research; however, the methodologicalchoice between covariance-based SEM (CB-SEM) andpartial least squares SEM (PLS-SEM) remains contestedin applied research contexts. This study compares theperformance of CB-SEM and PLS-SEM using an identicaltheoretical model previously employed to analyze culturalfactors influencing consumer purchasing behavior forrakija, a culturally embedded agricultural product. Surveydata were reanalyzed using SmartPLS, focusing onmeasurement model evaluation and the estimation of directand indirect structural effects. The results show that PLSSEM yields lower path coefficient estimates but higherexplained variance (R²) compared to CB-SEM, indicatingmore conservative effect estimation alongside strongerexplanatory performance. These findings suggest thatPLS-SEM represents a methodologically appropriate andpractically advantageous approach for modeling consumerbehavior in culturally specific agricultural markets.The study contributes to the applied SEM literature byproviding empirical guidance on method selection inagricultural economics research.

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References

Adžić, S. (2025). Exploring cultural influences on national alcoholic beverage purchases: A full latent variable structural equation model. International Journal of Wine Business Research, 37(3), 445–475. https://doi.org/10.1108/IJWBR-05-2024-0029

Adžić, S., Pavlović, M., Milunović, M., Pavlović, Đ., Savić Tot, T., & Radanov, P. (2024). The impact of rakija cultural heritage and rakija marketing on the consumer purchasing decisions. Economics of Agriculture, 71(3), 923–941. https://doi.org/10.59267/ekoPolj2403923A

Calvo-Porral, C., Rivaroli, S., & Orosa-Gonzalez, J. (2020). How consumer involvement influences beer flavour preferences. International Journal of Wine Business Research, 32(4), 537–554. Emerald Insight. https://doi.org/10.1108/IJWBR-10-2019-0054

Gelbrich, K., Müller, S., & Westjohn, S. (2023). Cross-Cultural Consumer Behavior. In Cross-Cultural Consumer Behavior. Edward Elgar Publishing. https://www.elgaronline.com/monobook/book/9781803923192/9781803923192.xml

Gul, G. F., Gul, G. N., Gul, G. R., Zhenxing, G., Gul, G. W., & Nawaz, T. M. (2021). The Ties That Bind: Do Brand Attachment and Brand Passion Translate Into Consumer Purchase Intention? Journal of Management and Business Administration. Central Europe, 29(1), 14–38.

Hair, J. F., Babin, B. J., Ringle, C. M., Sarstedt, M., & Becker, J.-M. (2025). Covariance-based structural equation modeling (CB-SEM): A SmartPLS 4 software tutorial. Journal of Marketing Analytics. https://doi.org/10.1057/s41270-025-00414-6

Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). SAGE Publications.

Hair, J. F., Ringle, C. M., & Sarstedt, M. (2012). Partial Least Squares: The Better Approach to Structural Equation Modeling? Long Range Planning, Analytical Approaches to Strategic Management: Partial Least Squares Modeling in Strategy Research, 45(5), 312–319. https://doi.org/10.1016/j.lrp.2012.09.011

Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203

Hair, J. F., Sarstedt, M., Ringle, C. M., & Gudergan, S. P. (2024). Advanced Issues in Partial Least Squares Structural Equation Modeling. SAGE Publications, Incorporated.

Hayduk, L. (2014). Seeing Perfectly Fitting Factor Models That Are Causally Misspecified: Understanding That Close-Fitting Models Can Be Worse. Educational and Psychological Measurement, 74(6), 905–926. https://doi.org/10.1177/0013164414527449

Hermida, R. (2015). The Problem of Allowing Correlated Errors in Structural Equation Modeling: Concerns and Considerations. Computational Methods in Social Sciences, 3, 1–17.

Hwang, H., Sarstedt, M., Cheah, J. H., & Ringle, C. M. (2020). A concept analysis of methodological research on composite-based structural equation modeling: Bridging PLSPM and GSCA. Behaviormetrika, 47(1), 219–241. https://doi.org/10.1007/s41237-019-00085-5

Kenny, D. A. (2018). SEM: Moderation. https://davidakenny.net/cm/moderation. htm

Kruger, M., & Viljoen, A. (2022). That old saying about wine and age: Identifying South African age-cohort preferences. International Journal of Wine Business Research, 34(4), 495–522. Emerald Insight. https://doi.org/10.1108/IJWBR-06-2021-0033

Majeed, M. U., Aftab, H., Arslan, A., & Shakeel, Z. (2024). Determining online consumer’s luxury purchase intention: The influence of antecedent factors and the moderating role of brand awareness, perceived risk, and web atmospherics. PLOS ONE, 19(2), e0295514. https://doi.org/10.1371/journal.pone.0295514

Nagarjuna, B., & Prasad, K. R. (2023). Impact of Cultural Elements on Brand Preferences An Explorative Research. European Journal of Business and Management, 15(10), 13–32.

Prendergast, G. P., Tsang, A. S. L., & Chan, C. N. W. (2010). The interactive influence of country of origin of brand and product involvement on purchase intention. Journal of Consumer Marketing, 27(2), 180–188. https://doi.org/10.1108/07363761011027277

Razak, N., Themba, O. S., & Sjahruddin, H. (2019). Brand awareness as predictors of repurchase intention: Brand attitude as a moderator. Advances in Social Sciences Research Journal, 6(2), 541–554. https://doi.org/10.14738/assrj.62.6264

Reinartz, W., Haenlein, M., & Henseler, J. (2009). An empirical comparison of the efficacy of covariance-based and variance-based SEM. International Journal of Research in Marketing, 26(4), 332–344. https://doi.org/10.1016/j.ijresmar.2009.08.001

Ringle, C. M., Wende, S., & Becker, J.-M. (2024). SmartPLS [Computer software]. Bönningstedt: SmartPLS. Retrieved from https://www.smartpls.com.

Shenoy, V., Aithal, P. S., & Raj, K. (2021). Exploring Convergent Validity for Select Employability Skill Constructs (SSRN Scholarly Paper No. 4079676). Social Science Research Network. https://doi.org/10.2139/ssrn.4079676

Spears, N., & Singh, S. (2004). Measuring Attitude Toward the Brand and Purchase Intentions. Journal of Current Issues and Research in Advertising, 26, 53–66. https://doi.org/10.1080/10641734.2004.10505164

Streukens, S., & Leroi-Werelds, S. (2016). Bootstrapping and PLS-SEM: A stepby-step guide to get more out of your bootstrap results. European Management Journal, European Management Research Using Partial Least Squares Structural Equation Modeling (PLS-SEM), 34(6), 618–632. https://doi.org/10.1016/j.emj.2016.06.003

UNESCO. (2022). UNESCO - Social practices and knowledge related to the preparation and use of the traditional plum spirit – šljivovica. https://ich.unesco.org/en/RL/social-practices-and-knowledge-related-to-the-preparation-and-use-ofthe-traditional-plum-spirit-ljivovica-01882

Wong, K. K.-K. (2013). Partial least squares structural equation modeling (PLSSEM) techniques using SmartPLS. Marketing Bulletin, 24(1), 1–32.

Xing, Y., & Jin, C.-H. (2023). The Impact of Cultural Values on Attitude Formation toward Cultural Products: Mediating Effects of Country Image. Sustainability, 15(14), Article 14. https://doi.org/10.3390/su151411172

Yap, M. H. T. & Nan Chen. (2017). Understanding young chinese wine consumers through innovation diffusion theory. Tourism & Hospitality Management, 23(1), 51–68. https://doi.org/10.20867/thm.23.1.3

Zhao, X., Lynch, J. G., Jr., & Chen, Q. (2010). Reconsidering Baron and Kenny: Myths and Truths about Mediation Analysis. Journal of Consumer Research, 37(2), 197–206. https://doi.org/10.1086/651257