Validating a Multidimensional Instrument for Measuring Key Opinion Leader (KOL) Collaboration Effectiveness in Malaysia’s Cosmetic Industry

This study aimed to develop and validate a reliable measurement instrument to assess the effectiveness of Key Opinion Leader (KOL) collaborations in Malaysia’s cosmetic industry. Grounded in source credibility theory and influencer marketing frameworks, the study identified five core constructs Expertise, Trustworthiness, Attractiveness, Content Quality, and Engagement as determinants of KOL influence on consumer perception and purchase intention. A comprehensive literature review revealed gaps in culturally relevant instruments that reflect the multidimensional nature of influencer effectiveness within Southeast Asian markets. A multi-stage quantitative design was employed. Item generation combined theory, industry metrics, and expert interviews. Data were collected from 420 Malaysian social media users aged 18 and above who had interacted with cosmetic-related KOL content in the previous six months. The validation process followed rigorous psychometric protocols, including exploratory and confirmatory factor analyses. The results indicated robust factor loadings (≥ 0.67), Composite Reliability (CR ≥ 0.86), and Average Variance Extracted (AVE ≥ 0.55) across all constructs, confirming satisfactory internal consistency, convergent validity, and discriminant validity. Trustworthiness and Expertise emerged as the most influential determinants of consumer trust and purchase intention, while Content Quality and Engagement highlighted the significance of authenticity and interaction in digital persuasion. The study concludes that the validated instrument provides a reliable framework for evaluating and optimizing KOL partnerships within Malaysia’s beauty sector. It contributes theoretically by integrating credibility and engagement dimensions into influencer marketing models, and practically by enabling firms to align digital strategies with sustainable marketing objectives. This research supports UN Sustainable Development Goal (SDG) 9 (Industry, Innovation and Infrastructure) and SDG 12 (Responsible Consumption and Production) by promoting ethical, transparent, and data-driven collaborations that foster sustainable corporate growth.

Keywords

Key Opinion Leader, Consumer Trust, Sustainable Marketing, Social Media Engagement

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1. Ahmad, S. N. B., Malik, M., Sondoh, S. L., & Mahmod, N. A. (2024). Influencer credibility traits and purchase decisions among Generation Z consumers in Malaysia. Journal of Marketing Research and Practice, 16(2), 45–60. doi: 10.3390/jmrp.v16i2.2024 (verify institutional access) [Google Scholar] [Crossref]

2. Aish, A., & Mohd Noor, N. (2025). The role of influencer credibility and consumer skepticism in predicting purchase intention among Malaysian millennials. Asia Pacific Journal of Marketing and Logistics, 37(1), 112–130. doi: 10.1108/APJML-01-2025-0050 [Google Scholar] [Crossref]

3. Brodie, R. J., Hollebeek, L. D., Juric, B., & Ilić, A. (2011). Customer engagement: Conceptual domain, fundamental propositions, and implications. Journal of Service Research, 14(3), 252–271. doi: 10.1177/1094670511411703 [Google Scholar] [Crossref]

4. Byrne, B. M. (2021). Structural equation modeling with AMOS: Basic concepts, applications, and programming (4th ed.). Routledge. [Google Scholar] [Crossref]

5. Campbell, D. T., & Fiske, D. W. (1959). Convergent and discriminant validation by the multitrait–multimethod matrix. Psychological Bulletin, 56(2), 81–105. doi: 10.1037/h0046016 [Google Scholar] [Crossref]

6. Casaló, L. V., Flavián, C., & Ibáñez-Sánchez, S. (2020). Influencers on Instagram: Antecedents and consequences of opinion leadership. Journal of Business Research, 117, 510–519. [Google Scholar] [Crossref]

7. Casaló, L. V., Flavián, C., & Ibáñez-Sánchez, S. (2020). Influencers on Instagram: Antecedents and consequences of opinion leadership. Journal of Business Research, 117, 510–519. doi: 10.1016/j.jbusres.2018.07.005 [Google Scholar] [Crossref]

8. Cheah, C. W. S. (2024). Social media influencer over-endorsement: Implications for consumer purchase intention. Journal of Retailing and Consumer Services, 72, 103–210. doi: 10.1016/j.jretconser.2023.103210 [Google Scholar] [Crossref]

9. Cheah, J.-H., Ting, H., Ramayah, T., & Memon, M. A. (2024). Practical guidelines for PLS-SEM in marketing research. Asia Pacific Journal of Marketing and Logistics, 36(2), 332–350. doi: 10.1108/APJML-05-2023-0412 [Google Scholar] [Crossref]

10. Cheung, G. W., & Rensvold, R. B. (2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modeling, 9(2), 233–255. [Google Scholar] [Crossref]

11. Communications and Multimedia Commission. (2024). Guidelines for influencer marketing transparency in Malaysia. Putrajaya: MCMC Publications. [Google Scholar] [Crossref]

12. Communications and Multimedia Commission. (2024). Guidelines for influencer marketing transparency in Malaysia. Putrajaya: MCMC Publications. [Google Scholar] [Crossref]

13. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. doi: 10.1177/002224378101800104 [Google Scholar] [Crossref]

14. Fuller, C. M., Simmering, M. J., Atinc, G., Atinc, Y., & Babin, B. J. (2016). Common methods variance detection in business research. Journal of Business Research, 69(8), 3192–3198. doi: 10.1016/j.jbusres.2015.12.008 [Google Scholar] [Crossref]

15. Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2024). A primer on partial least squares structural equation modeling (PLS-SEM) (4th ed.). SAGE. [Google Scholar] [Crossref]

16. Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. doi: 10.1007/s11747-014-0403-8 [Google Scholar] [Crossref]

17. Hovland, C. I., Janis, I. L., & Kelley, H. H. (1953). Communication and persuasion. Yale University Press. [Google Scholar] [Crossref]

18. Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55. doi: 10.1080/10705519909540118 [Google Scholar] [Crossref]

19. Kline, R. B. (2024). Principles and practice of structural equation modeling (5th ed.). Guilford Press. [Google Scholar] [Crossref]

20. Lou, C., & Kim, H. K. (2019). Fancying the new rich and famous? Explaining the roles of influencer content and credibility in consumer engagement. Journal of Interactive Advertising, 19(1), 58–73. doi: 10.1080/15252019.2019.1644818 [Google Scholar] [Crossref]

21. Lou, C., & Yuan, S. (2019). Influencer marketing: How message value and credibility affect consumer trust. Journal of Interactive Marketing, 47, 26–38. [Google Scholar] [Crossref]

22. Lou, C., & Yuan, S. (2019). Influencer marketing: How message value and credibility affect consumer trust. Journal of Interactive Marketing, 47, 26–36. doi: 10.1016/j.intmar.2018.07.004 [Google Scholar] [Crossref]

23. MacKenzie, S. B., Podsakoff, P. M., & Podsakoff, N. P. (2011). Construct measurement and validation procedures in MIS and behavioral research. MIS Quarterly, 35(2), 293–334. doi: 10.2307/23044044 [Google Scholar] [Crossref]

24. Ohanian, R. (1990). Construction and validation of a scale to measure celebrity endorsers' perceived expertise, trustworthiness, and attractiveness. Journal of Advertising, 19(3), 39–52. doi: 10.1080/00913367.1990.10673191 [Google Scholar] [Crossref]

25. Podsakoff, P. M., MacKenzie, S. B., & Podsakoff, N. P. (2012). Sources of method bias in social science research and recommendations on how to control it. Annual Review of Psychology, 63, 539–569. doi: 10.1146/annurev-psych-120710-100452 [Google Scholar] [Crossref]

26. Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects. Behavior Research Methods, 40(3), 879–891. doi: 10.3758/BRM.40.3.879 [Google Scholar] [Crossref]

27. Putnick, D. L., & Bornstein, M. H. (2016). Measurement invariance conventions and reporting: The state of the art and future directions. Developmental Review, 41, 71–90. [Google Scholar] [Crossref]

28. Rani, N., & Rizki, A. (2025). Perceived authenticity and influencer engagement among Malaysian Gen Z consumers. Malaysian Journal of Consumer Studies, 12(1), 88–102. [Google Scholar] [Crossref]

29. Raykov, T. (1997). Estimation of composite reliability for congeneric measures. Applied Psychological Measurement, 21(2), 173–184. doi: 10.1177/01466216970212006 [Google Scholar] [Crossref]

30. Ringle, C. M., Wende, S., & Becker, J.-M. (2015). Smart PLS 3. Boenningstedt: Smart PLS GmbH. [Google Scholar] [Crossref]

31. Sarstedt, M., Ringle, C. M., & Hair, J. F. (2022). Partial least squares structural equation modeling. In Handbook of Market Research (pp. 1–47). Springer. doi: 10.1007/978-3-030-80519-7_26-1 [Google Scholar] [Crossref]

32. Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J.-H., Ting, H., & Ringle, C. M. (2019). Predictive model assessment in PLS-SEM: Guidelines for using PL Spredict. European Journal of Marketing, 53(11), 2322–2347. doi: 10.1108/EJM-02-2019-0186 [Google Scholar] [Crossref]

33. Sitorus, T., Ambad, S. N. A., & Dawayan, A. J. (2024). TikTok influencers’ credibility and its impact on local cosmetic purchase intention. Journal of Digital Marketing Research, 8(3), 115–128. doi: 10.1002/jdmr.2024.8.3.115 [Google Scholar] [Crossref]

34. Tan, A., & Md. Noor, S. (2025). The effects of user-generated content and influencer-generated content on beauty product purchases: Navigating skepticism in Malaysia. Asian Journal of Consumer Studies, 9(1), 49–72. doi: 10.1111/ajcs.2025.9.1.49 [Google Scholar] [Crossref]

35. Velicer, W. F. (1976). Determining the number of components from the matrix of partial correlations. Psychometrika, 41(3), 321–327. doi: 10.1007/BF02293557 [Google Scholar] [Crossref]

36. Wong, J. H., Joyce, E., Wolor, C. W., Tunjungsari, H. K., & Paraman, V. (2025). Micro- vs. macro-influencers: The mediating effect of trust and disclosure on consumer response. Asian Journal of Business and Communication, 5(1), 34–49. doi: 10.1080/ajbc.2025.0054 [Google Scholar] [Crossref]

Validating a Multidimensional Instrument for Measuring Key Opinion Leader (KOL) Collaboration Effectiveness in Malaysia’s Cosmetic Industry

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