Sustainable E-Commerce in MENA: An SOR Analysis of Gen Z Purchase Intentions through Open-Source LLM (DeepSeek) Interactions, Trust, Familiarity, and Privacy Concerns, Dr Ahmad Shaheen, Dr Fandi Omeish, Dr Dina AlGhamdi, Dr Abdullah Khataan, Dr Abdelrehim Awad
Dr Ahmad Yahya Moustafa Shaheen*
Assistant Professor of Marketing and Business Administration
Arab Academy for Science, Technology and Maritime Transport
Egypt
ORCID: 0009-0002-5677-1990
Dr Fandi Omeish
Assistant Professor, E-Marketing and Social Media Department
Princess Sumaya University for Technology
Jordan
ORCID: 0000-0002-8042-3622
Dr Dina S. AlGhamdi
Assistant Professor of Marketing
Marketing Department, Applied College, Taibah University
Saudi Arabia
ORCID: 0009-0009-6669-0458
Dr Abdullah Khataan
Assistant Professor of Marketing and Business Administration
Marketing Department, College of Management and Technology
Arab Academy for Science, Technology and Maritime Transport
Egypt
Dr Abdelrehim Awad
Assistant Professor of Business Administration
Department of Business Administration, College of Business, University of Bisha
Saudi Arabia
ORCID: 0009-0005-3649-430X
Type of Paper: Research
Purpose: To model how an open-source LLM recommender's interaction quality (personalization and responsiveness) affects Gen Z online purchase decisions through trust, AI familiarity, and privacy concerns using the Stimulus-Organism-Response framework.
Design/Methodology/Approach: Survey data from 570 Gen Z online shoppers in Egypt, Saudi Arabia, Jordan, and Qatar were analyzed using PLS-SEM.
Findings: Interaction quality significantly improved trust (β = 0.2585) and AI familiarity (β = 0.2943). Contrary to some expectations but aligning with the study's hypothesis, interaction quality also significantly increased privacy concerns (β = 0.5499). Purchase intention was positively influenced by trust (β = 0.2848) and, counter-intuitively, privacy concerns (β = 0.3583). AI familiarity had no direct impact on purchase intention (β = 0.0477). The model explained 47% of the variance in purchase intention (R²=0.47) and demonstrated medium predictive power.
Originality/Value: This study presents the first Middle Eastern cross-national test comparing trust-privacy dynamics related to an open-source LLM recommender across four diverse economies. Research limitations/implications: Results apply SOR theory to open-source LLMs, highlighting trust as a key positive mediator and suggesting high interaction quality increases, rather than neutralizes, privacy concerns among Gen Z in this context. The positive link between privacy concern and purchase intention warrants further investigation.
Practical Implications: Retailers can leverage open-source AI but must balance interaction quality optimization with managing heightened privacy concerns. Transparency and user control mechanisms appear crucial to drive purchase intentions.
Keywords: Open-Source Large Language Models, E-Commerce Interaction Quality, Trust, and Privacy, Stimulus-Organism-Response Model, Generation Z Consumer Behavior, Middle East Online Shopping.
Citation:Shaheen, A. Y. M., Omeish, F., AlGhamdi, D. S., Khataan, A., and Awad, A. (2026). Sustainable E-Commerce in MENA: An SOR Analysis of Gen Z Purchase Intentions through Open-Source LLM (DeepSeek) Interactions, Trust, Familiarity, and Privacy Concerns. World Journal of Entrepreneurship, Management and Sustainable Development, Vol. 22, Nos 1-2, pp. xx-xx.