In an era where social media influencers shape consumer attitudes and behaviors, the emergence of artificial intelligence (AI) influencers challenges traditional notions of authenticity and trust. This study explores whether AI influencers can cultivate parasocial relationships (PSRs) and credibility perceptions comparable to their human counterparts, and how these relationships translate into social capital within a culturally rich, collectivist setting. Drawing on Parasocial Interaction Theory, Source Credibility Theory, and Social Capital Theory, we examine TikTok users in Egypt and Jordan to determine how influencer type human or AI affects emotional engagement, perceived trustworthiness, and the formation of bonding and bridging social capital. Our findings reveal that AI influencers can indeed establish meaningful emotional bonds and credibility, sometimes outperforming human influencers in generating community cohesion and network expansion. Yet, these pathways differ, as human influencers rely more on narrative depth and cultural resonance to achieve similar outcomes. By integrating recently recommended studies and acknowledging alternative theoretical frameworks such as (S-O-R), we highlight the complexity and cultural specificity of influencer-follower interactions. This research not only advances our theoretical understanding of parasocial ties, credibility, and social capital but also provides actionable insights for marketers, brands, and platform developers. We conclude with methodological recommendations such as ensuring measurement invariance and conducting longitudinal, mixed-method research and emphasize the need to adapt influencer strategies to align with varying cultural orientations and rapidly evolving digital ecosystems.

Influencer marketing is a prominent strategy for engaging young consumers via social media. This study aimed to explore the effects of different dimensions of influencer value of content and influencer credibility on purchase intention among young Egyptian adult consumers. Employing a descriptive-explanatory design, survey data were collected from 633 social media users. Analysis via Structural Equation Modeling (SEM) revealed that all examined dimensions of influencer content value significantly and positively influence purchase intention, with entertainment value (r = 0.878 in correlation analysis, SEM estimate = 0.150) and functional value (r = 0.819 in correlation, SEM estimate = 0.118) demonstrating particularly strong contributions. Regarding influencer credibility, perceived trustworthiness (r = 0.918 in correlation, SEM estimate = 0.218) and perceived similarity (r = 0.923 in correlation, SEM estimate = 0.280) exhibited the most substantial positive impacts on purchase intention; perceived expertise and attractiveness also yielded significant, albeit comparatively moderate, effects. These findings underscore the critical, differentiated roles of specific content value and credibility facets in shaping consumer purchase decisions within the Egyptian youth market, offering actionable insights for optimizing influencer marketing strategies. AcknowledgmentThe authors are thankful to the Deanship of Graduate Studies and Scientific Research at University of Bisha for supporting this work through the Fast-Track Research Support Program.

Empirical research has revealed conflicting associations between dependent and independent variables, with few studies tackling the dynamics in developing economies. This study investigates the effects of carbon dioxide emissions, renewable energy consumption (REC), foreign direct investment (FDI), government green capital spending (GGCS), and economic growth (EG) in Morocco, employing the Keynesian framework of economic growth. An autoregressive distributed lag (ARDL) methodology was applied to assess both short- and long-term relationships among the model’s variables, using annual data from the World Development Indicators (WDI) database for the period 1993–2020. All ARDL variables were transformed into first differences to ensure stationarity. The bounds test confirmed a long-term equilibrium relationship between the dependent and independent variables. Diagnostic tests, including the White test, indicated no evidence of heteroscedasticity, and the Shapiro–Wilk test confirmed that residuals followed a normal distribution, validating model robustness. The model demonstrated overall stability across the study period with no structural breaks. The empirical findings suggest that both carbon dioxide emissions and renewable energy consumption exhibit positive trends, whereas GGCS demonstrates a significant short-run negative correlation with economic growth. However, the long-term coefficients were found to be statistically insignificant, suggesting that sustained policy effects may be attenuated by macroeconomic structural factors.

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.

This paper focuses on the role of Artificial Intelligence (AI) in revolutionizing digital marketing practices, particularly in creating customer-centric strategies that support sustainable business practices. It examines how AI tools, such as machine learning, chatbots, and data analytics, are used to personalize marketing efforts, improve customer engagement, and drive sustainable business growth. The study also analyzes the ethical considerations and challenges of using AI in digital marketing, particularly regarding data privacy and customer trust, and explores how businesses can maintain transparency while benefiting from AI technologies.

The relationship between public education spending and economic growth remains contested in developing economies, where quality, not just quantity, of investment may matter most. This study examines whether public education expenditure has driven economic growth in Egypt over 1980–2023, addressing the under-studied role of education quality and testing the robustness of the estimated relationship to structural breaks linked to major reforms. Using the Autoregressive Distributed Lag (ARDL) bounds testing approach together with Zivot–Andrews structural break unit root tests and CUSUM/CUSUMSQ parameter-stability tests, the study models short- and long-run effects of education expenditure on GDP growth, controlling for capital formation, labor participation, trade openness, and foreign investment, and incorporating quality proxies such as student–teacher ratios and completion rates. The bound F-statistic (6.23) exceeds the 5% upper-bound critical value of 3.83 (Narayan in 2005 on small-sample critical values), confirming long-run cointegration, and the error-correction coefficient (−0.835, p < 0.01) indicates rapid adjustment to equilibrium. Physical capital is the strongest driver of long-run growth, while education expenditure shows a weakly negative long-run association (significant at 10%), suggesting allocative inefficiency rather than absent returns; quality proxies (completion rate, student–teacher ratio) show a negative short-run and positive one-period-lagged effect, consistent with a delayed adjustment process. Stability tests confirm the long-run relationship is structurally invariant once short-run dynamics are accounted for. Thus, improving the efficiency and quality of education spending, rather than its volume, appears essential for Egypt to advance Vision 2030, SDG 4, and SDG 8.