The Impact of Social Influence on the Online Behavior of Moroccan Consumers


Authors : Abderrazak Hormi; Bouchra Ouarraoui; Naoual Benaini

Volume/Issue : Volume 9 - 2024, Issue 12 - December

Google Scholar : https://tinyurl.com/yrapf47z

Scribd : https://tinyurl.com/fm6eu2vw

DOI : https://doi.org/10.5281/zenodo.14651390

Abstract : This study aims primarily to develop a model based on structural equation modeling to explain the impact of social influence on online shopping behavior in Morocco. Referring to the literature review we generated four research hypotheses explaining the effect of social influence on online shopping behavior and we introduced in addition to social influence and online shopping, an intermediate variable which is the purchase intention and a moderating variable which is the user experience. Secondly, this model is tested by the interim of an online survey of a sample size of 211 Moroccan respondents. The result of this study manages to explain more than 77.3% of the variation of the online purchase variable, and the application of the model on another random sample would allow to explain about 72.10% of the information on online purchase. The study proposes to the marketing manager’s elements to take into consideration for the elaboration of a strategy adapted to the context of the e-commerce market in order to provide an ethical response to the needs of the Moroccan consumer.

Keywords : Online Shopping, Social Influence, user Experience, Purchases Intention, Structural Equation Modelling.

References :

  1. Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179‑211.
  2. Ajzen, I., & Manstead, AS (2007). Changing health-related behaviours: An approach based on the theory of planned behaviour. In The scope of social psychology (pp. 55‑76). Psychology Press.
  3. Ajzen, I. (2012). Attitudes and persuasion.
  4. D’Astous, A., Daghfous, N., Balloffet, P., & Boulaire, (2018). Consumer behaviour (5 Edition).
  5. Boomsma, A. (1985). Nonconvergence, improper solutions, and starting values ​​in LISREL maximum likelihood estimation. Psychometrika, 50(2), 229-242..
  6. Cialdini, RB, Reno, RR, & Kallgren, CA (1990). A focus theory of normative conduct: Recycling the concept of norms to reduce littering in public places. Journal of personality and social psychology, 58(6), 1015.
  7. Cialdini, R. B. (2009). Influence: The Psychology of Persuasion. Harper Business.
  8. Davis, F.D. (1989). Technology acceptance model: TAM. Al-Suqri, MN, Al-Aufi, AS: Information Seeking Behavior and Technology Adoption, 205-219.
  9. Ding, L., Velicer, WF, & Harlow, LL (1995). Effects of estimation methods, number of indicators per factor, and improper solutions on structural equation modeling fit indices. Structural Equation Modeling: A Multidisciplinary Journal, 2(2), 119‑143.
  10. Fishbein, M., & Ajzen, I. (1977). Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research. Philosophy and Rhetoric, 10(2), 130-132.
  11. Henningsen, DD, & Henningsen, MLM (2003). Examining social influence in information-sharing contexts. Small Group Research, 34(4), 391-412.
  12. Hundleby, JD (1968). Reviews: Nunnally, Jum. Psychometric Theory. New York: McGraw-Hill, 1967. 640+ xiii pp. $12.95. American Educational Research Journal, 5(3), 431-433.
  13. Karahanna, E., Straub, DW, & Chervany, NL (1999). Information technology adoption across time: A cross-sectional comparison of pre-adoption and post-adoption beliefs. MIS quarterly, 183‑213.
  14. Kosmopoulos, C. (2002). Berthelot J.-M. (dir), 2001, Epistemology of social sciences, Paris, PUF, 593 p. Cybergeo: European Journal of Geography. https://journals.openedition.org/cybergeo/875
  15. Lee, S.-Y., Poon, W.-Y., & Bentler, PM (1990). A three-stage estimation procedure for structural equation models with polytomous variables. Psychometrika, 55(1), 45‑51.
  16. Luk, JW, Wang, J., & Simons-Morton, BG (2012). The co-occurrence of substance use and bullying behaviors among US adolescents: Understanding demographic characteristics and social influences. Journal of adolescence, 35(5), 1351-1360.
  17. Malhotra, NK, & McCort, JD (2001). A cross-cultural comparison of behavioral intention models-Theoretical consideration and an empirical investigation. International Marketing Review.
  18. Morwitz, VG, Steckel, JH, & Gupta, A. (2007). When do purchase intentions predict sales? International Journal of Forecasting, 23(3), 347-364.
  19. Park, C. W., & Lessig, V. P. (1977). Students' attitudes toward reference groups: A cross-cultural approach. Journal of Consumer Research, 4(2), 102-111
  20. Pearl, J. (2012). The causal foundations of structural equation modelling. California Univ Los Angeles Dept of Computer Science.
  21. Raykov, T., & Marcoulides, GA (2006). On multilevel model reliability estimation from the perspective of structural equation modeling. Structural Equation Modeling, 13(1), 130-141.
  22. Taylor, S., & Todd, P. (1995). Assessing IT usage: The role of prior experience. MIS quarterly, 561-570.
  23. Trusov, M., Bodapati, AV, & Bucklin, RE (2010). Determining influential users in internet social networks. Journal of marketing research, 47(4), 643-658.
  24. Venkatesh, V., Morris, MG, & Ackerman, PL (2000). A longitudinal field investigation of gender differences in individual technology adoption decision-making processes. Organizational behavior and human decision processes, 83(1), 33‑60.
  25. Venkatesh, V., Morris, MG, Davis, GB, & Davis, FD (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 425-478.
  26. Zheng, L., Plaisent, M., Zuccaro, C., & Bernard, P. (2019). Introduction to Structural Equation Modeling: AMOS in Management Research. PUQ.

This study aims primarily to develop a model based on structural equation modeling to explain the impact of social influence on online shopping behavior in Morocco. Referring to the literature review we generated four research hypotheses explaining the effect of social influence on online shopping behavior and we introduced in addition to social influence and online shopping, an intermediate variable which is the purchase intention and a moderating variable which is the user experience. Secondly, this model is tested by the interim of an online survey of a sample size of 211 Moroccan respondents. The result of this study manages to explain more than 77.3% of the variation of the online purchase variable, and the application of the model on another random sample would allow to explain about 72.10% of the information on online purchase. The study proposes to the marketing manager’s elements to take into consideration for the elaboration of a strategy adapted to the context of the e-commerce market in order to provide an ethical response to the needs of the Moroccan consumer.

Keywords : Online Shopping, Social Influence, user Experience, Purchases Intention, Structural Equation Modelling.

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