GENERATIONAL COHORT VARIATIONS IN E-COMMERCE BUYING INTENTIONS: A MULTI-GROUP STRUCTURAL INVARIANCE ANALYSIS (MICOM-MGA APPROACH)
DOI:
https://doi.org/10.69980/mth8qh23Keywords:
Electronic Commerce (E-Commerce), Technology Acceptance Model (TAM), Perceived Trust, Perceived Benefits, Multi-Group Analysis (PLS-MGA), Generational Cohort TheoryAbstract
Purpose: The research investigates the structural factors behind the intentions of online purchasing in emerging digital retail economies by integrating technology acceptance model (TAM) principles with the consumer trust and risk-benefit economic framework. Moreover, it considers the variations brought by structural drivers among different generations, especially comparing those of the digital natives (Youth) to those of adapted generations (Mature).
Methodology: An active e-commerce user data collection was carried out through quantitative cross-sectional research using a stratified random sampling strategy based on probability proportionate. Structural characteristics, specific indirect mediation pathways, and multi-group path differences were assessed using Variance-Based Structural Equation Modelling (PLS-SEM). In seeking operational measurement invariance across demographic strata, the authors used a three-step sequence of the Measurement Invariance of Composites (MICOM) methodology followed by a non-parametric version of Partial Least Squares Multi-Group Analysis (PLS-MGA).
Findings: The obtained empirical data verify that the basic structural ways determined cognitive trust and utility are applicable universally throughout visitor path or consumer life cycle; platform quality (PQ→PT) and platform usefulness (PU→PT) remain true existential conditions for both groups (p<0.05). But at the same time, critical generational differences emerge in terms of behavioral execution: older people are more value-oriented regarding the perceived benefits to buying intention (β=0.693, p_"MGA" =0.090), whereas younger people do not rely on benefits but on continuous trust flow (β=0.263). Lastly, specific indirect path estimates indicate that perceived benefits are an efficient mediator of the relationship between usefulness of the platform and purchase execution for both groups.
Originality/Value: This study contributes to the field of e-commerce research by departing from broad classification in consumer models. Through innovative application of multi-group analysis (MGA) and validation techniques, this research proves that generational socialization influences the manner of consumer evaluation of value and trust in online transactions. Moreover, the outcome of the research offers online retailers data-based solutions for their strategies to adjust their websites for each target group of consumers.
References
1. Arango-Botero, D., Valencia-Arias, A., Herrera-Restrepo, O., & Cruz, J. E. (2020). Factors shaping youth consumer behavior in digital environments. International Journal of Interdisciplinary Studies, 12(2), 85-102.
2. Choudhury, K. (2014). Service quality and customers’ behavioral intentions: A study of the Indian banking sector. International Journal of Bank Marketing, 32(1), 10–27. https://doi.org/10.1108/ijbm-02-2013-0009
3. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
4. Flavián, C., Guinalíu, M., & Gurrea, R. (2006). The role played by perceived usability, reputation and familiarity on website trust, and a website’s loyalty. Information & Management, 43(1), 1–14. https://doi.org/10.1016/j.im.2005.01.002
5. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50. https://doi.org/10.1177/002224378101800104
6. Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51–90. https://doi.org/10.2307/30036519
7. 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
8. Hair, J. F., Sarstedt, M., Ringle, C. M., & Gudergan, S. P. (2018). Advanced issues in partial least squares structural equation modeling (PLS-SEM). SAGE Publications.
9. Hair, J. F., Sarstedt, M., Matthews, L. M., & Ringle, C. M. (2022). Partial least squares structural equation modeling (PLS-SEM) in business research. Journal of Business Research, 148, 370-380. https://doi.org/10.1016/j.jbusres.2022.04.054
10. 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. https://doi.org/10.1007/s11747-014-0403-8
11. Henseler, J., Ringle, C. M., & Sarstedt, M. (2016). Testing measurement invariance of composites using partial least squares. International Marketing Review, 33(3), 405–431. https://doi.org/10.1108/imr-09-2014-0304
12. Iqbal, M. S., Deari, M. I. A., & Jamil, S. A. (2018). Impact of self-service technology (SST) service quality on customer loyalty and behavioral intention in e-banking. International Journal of Electronic Business, 14(2), 128–149. https://doi.org/10.1504/ijeb.2018.093952
13. Kim, C., Galliers, R. D., Shin, N., Ryoo, J. H., & Kim, J. (2007). Factors influencing Internet shopping value and trust: A multi-group analysis. Information & Management, 44(8), 691-702. https://doi.org/10.1016/j.im.2007.07.001
14. Kim, D. J., Ferrin, D. L., & Rao, H. R. (2008). A trust-based consumer decision-making model in electronic commerce: The role of trust, perceived risk, and their antecedents. Decision Support Systems, 44(2), 544–564. https://doi.org/10.1016/j.dss.2007.07.001
15. Kim, H. W., Xu, Y., & Koh, J. (2003). A comparison of online trust-building mechanisms. Journal of Organizational Computing and Electronic Commerce, 13(3), 163-180. https://doi.org/10.1207/S15327744JOCE1303_1
16. Kim, J., & Park, J. (2004). An empirical validation of a consumer acceptance model for electronic commerce. Journal of Electronic Commerce Research, 5(1), 32-44.
17. Lohmöller, J. B. (1989). Latent variable path modeling with partial least squares. Physica-Verlag. https://doi.org/10.1007/978-3-642-52465-3
18. Malmendier, U., & Nagel, S. (2011). Depression babies: Do macroeconomic experiences affect risk taking? The Quarterly Journal of Economics, 126(1), 373–416. https://doi.org/10.1093/qje/qjq004
19. Mani, Z., & Chouk, I. (2017). Drivers of consumers’ resistance to smart products. Journal of Marketing Management, 33(1-2), 76–97. https://doi.org/10.1080/0267257x.2016.1245212
20. Mann, S. L. (2003). Generational cohort theory and consumer risk socialization: Analyzing long-term technological transformations in retail markets. Journal of Consumer Behaviour, 3(2), 170–188. https://doi.org/10.1002/cb.132
21. Matthews, L. M. (2017). Applying the MICOM procedure in PLS-SEM multi-group analysis. Journal of Applied Structural Equation Modeling, 1(1), 1-15. https://doi.org/10.47263/jasem.1(1)02
22. McCormick, H., Cartwright, J., Perry, P., & Duan, L. (2014). Fashion retailing — Past, present and future. Textile Progress, 46(3), 227–301. https://doi.org/10.1080/00405167.2014.973216
23. Mohan, G., Sivakumaran, B., & Sharma, P. (2013). Impact of store environment on impulse buying behavior. European Journal of Marketing, 47(10), 1711–1732. https://doi.org/10.1108/ejm-03-2011-0110
24. Oblinger, D., & Oblinger, J. (2005). Is it age or IT: First steps toward understanding the net generation. Educating the Net Generation, 2(1), 2-15.
25. Pavlou, P. A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 7(3), 101–134. https://doi.org/10.1080/10864415.2003.11044275
26. Ringle, C. M., Sarstedt, M., Sinkovics, N., & Sinkovics, R. R. (2023). A perspective on using partial least squares structural equation modelling in data articles. Data in Brief, 48, Article 109074. https://doi.org/10.1016/j.dib.2023.109074
27. Sarstedt, M., Henseler, J., & Ringle, C. M. (2011). Multigroup analysis in partial least squares structural equation modeling. In Measurement and Research Methods in International Marketing (pp. 195-219). Emerald Group Publishing. https://doi.org/10.1108/s1474-7979(2011)0000022012
28. Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J. H., Ting, H., Vaithilingam, S., & Ringle, C. M. (2019). Predictive model assessment in PLS-SEM. Journal of Business Research, 105, 321-329. https://doi.org/10.1016/j.jbusres.2019.08.016
29. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
30. Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178. https://doi.org/10.2307/41410412
31. Zeithaml, V. A. (1988). Consumer perceptions of price, quality, and value: A means-end model and synthesis of evidence. Journal of Marketing, 52(3), 2–22. https://doi.org/10.1177/002224398805200302
