Sustainable E-Commerce in MENA: An SOR Analysis of Gen Z Purchase Intentions through Open-Source LLM (DeepSeek) Interactions, Trust, Familiarity, and Privacy Concerns, Ahmad Shaheen, Fandi Omeish, Dina AlGhamdi, Abdullah Khataan, Abdelrehim Awad
Ahmad Yahya Moustafa Shaheen*
Assistant Professor of Marketing and Business Administration
Marketing Department, College of Management and Technology
Arab Academy for Science, Technology and Maritime Transport, Alexandria
Egypt
ORCID: 0009-0002-5677-1990
Fandi Omeish
Assistant Professor, E-Marketing and Social Media Department
Princess Sumaya University for Technology, Amman
Jordan
ORCID: 0000-0002-8042-3622
Dina S. AlGhamdi
Assistant Professor, Marketing Department, Applied College
Taibah University
Saudi Arabia
ORCID: 0009-0009-6669-0458
Abdullah Khataan
Assistant Professor of Marketing and Business Administration
Marketing Department, College of Management and Technology
Arab Academy for Science, Technology and Maritime Transport, Alexandria
Egypt
ORCID: 0009-0004-6279-1685
Abdelrehim Awad
Assistant Professor of Business Administration
Department of Business Administration, College of Business
University of Bisha, Bisha 61922
Saudi Arabia
ORCID: 0009-0005-3649-430X
Type of Paper: Research
Received: 11 December 2025 / Revised: 19 January 2026 / Accepted: 30 January 2026 / Published: 12 February 2026
DOI: 10.47556/J.WJEMSD.22.1-2.2026.8
Purpose: This study models how open-source large language model (LLM) recommender interaction quality (personalisation and responsiveness) affects Gen Z online purchase decisions through trust, AI familiarity, and privacy concerns using the Stimulus-Organism-Response (SOR) framework.
Design/Methodology/Approach: We have conducted a Partial Least Squares Structural Equation Modeling (PLS-SEM) analysis of 570 Gen Z online shoppers from Egypt, Saudi Arabia, Jordan, and Qatar.
Findings: Interaction quality improved trust (β=0.2585) and AI familiarity (β=0.2943) while increasing privacy concerns (β=0.5499). Purchase intention was positively influenced by trust (β=0.2848) and privacy concerns (β=0.3583). AI familiarity showed no direct impact (β=0.0477). Model explained 47% variance in purchase intention.
Originality/Value: Few studies examined trust-privacy dynamics for open-source LLM recommenders across four economies.
Research Implications: SOR theory application reveals trust mediates positively while high interaction quality increases privacy concerns among Gen Z.
Practical Implications: Retailers should optimise interaction quality while implementing transparency and user control mechanisms to address heightened privacy concerns.
Keywords: Open-Source LLMs; E-Commerce Interaction Quality; Trust-Privacy; SOR Model; Gen Z; Middle East Shopping.
Citation: Shaheen, A., 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 (WJEMSD), Vol. 22, Nos 1-2, pp. 133-150.
