Girne American University

The electronic word of mouth (eWOM) has emerged as a communication tool that significantly influences consumers’ attitudes and purchasing behavior in the online market. Research indicates that the effect of eWOM sources, such as (strong ties, weak ties, and influencers) varies in terms of perceived value components (price, quality, emotional, and social value) and purchase intention, particularly with regard to gender. This study, which is based on the SOR framework; examines the role of eWOM as a stimulus affecting student responses and considers the mediating role of perceived value components and the moderate effect of gender. A sample of 901 students from Westbank universities was analyzed using Smart PLS software. The findings reveal that strong ties and influencer eWOM are positively associated with perceived value components and purchase intention, while weak tie eWOM does not directly correlate with purchase intention. Mediation analyses show that perceived quality and social value act as mediators of purchase intent towards eWOM sources, while emotional value specifically mediates strong relationships and influencers. Notably, price value exerts only a mediating effect on purchase intention when communicated through influencers, highlighting the unique role of the influencer in shaping price perceptions and its broad impact on all components of perceived value. Gender differences were observed in students’ responses to eWOM content; particularly in terms of price, quality, and emotional appeal but not in terms of social factors. The outcomes of this study underscore the significance of considering both the source of the message and the characteristics of the audience when formulating targeted marketing strategies.

Small- and medium-sized enterprises (SMEs) increasingly rely on digital technologies to sustain innovation, yet limited empirical evidence explains how business intelligence capabilities translate into superior innovation outcomes, particularly in emerging economy contexts. Addressing this gap, this study examines the direct and indirect effects of business intelligence capabilities on innovation performance by unpacking the mediating role of knowledge management capability and the moderating role of data-driven decision making within an integrated Resource-Based View and Knowledge-Based View framework. Conceptually, the study advances prior research by clarifying the complementary roles of these theoretical perspectives: the Resource-Based View explains what strategic digital resources firms possess, the Knowledge-Based View explains how these resources are transformed into organizational knowledge through knowledge management capability, and data-driven decision making explains when these capabilities are effectively converted into innovation outcomes. Data were collected through a survey of 316 owners and senior managers of small- and medium-sized hotels operating in Amman, Jordan, and analyzed using partial least squares structural equation modeling (PLS-SEM) as the primary analytical technique. The results indicate that business intelligence capabilities exert a significant positive effect on innovation performance, with this relationship largely transmitted through knowledge management capability, demonstrating that the value of business intelligence lies in its integration into organizational knowledge processes rather than in data availability alone. Moreover, data-driven decision making strengthens the relationship between business intelligence capabilities and innovation performance, functioning as an execution-level capability that enhances the conversion of digital and knowledge-based resources into innovation outcomes. To further validate the robustness of the findings, a post-hoc moderated mediation analysis using Hayes’ PROCESS macro version 4.2 was conducted as a confirmatory analysis. By conceptualizing business intelligence, knowledge management, and data-driven decision making as an interconnected socio-technical capability system, this study advances digital innovation theory and offers actionable insights for SME managers seeking to orchestrate capabilities for innovation under resource constraints.

Smart city programs are increasingly used by governments to manage public services through digital and automated systems, and this development is closely linked with questions of administrative power, fairness, and accountability. Existing public law doctrines in common law systems were shaped for human decision-making and have not been fully adapted to administrative action carried out through algorithmic systems, which creates a clear gap in current legal thinking. The research is motivated by the growing use of algorithmic systems as effective decision-makers in areas such as utilities, mobility, welfare, and digital identity, especially in contexts marked by limited state capacity and prolonged emergency conditions. The study aims to examine how judicial review should respond to algorithmic administrative systems so that legality, fairness, and accountability remain protected while legitimate administrative goals are still met. Methodologically, the article adopts a doctrinal and normative legal research design based on structured analysis of public law doctrine, relevant judicial and administrative materials, and governance instruments on automated decision-making. The focus is on developing a doctrinal framework that treats algorithmic systems as legally reviewable decision infrastructures rather than neutral technical tools. The study highlights the importance of reviewing not only final automated outcomes but also earlier design choices, including data use, system objectives, and oversight mechanisms. The research is important because it offers courts and lawmakers clearer legal tools to assess algorithmic administration, with particular attention to settings where oversight is weak and emergency powers risk becoming normalized, increasing the danger of opacity, discrimination, and unchecked security repurposing.

Artificial Intelligence capabilities make the organisational innovation process more critical and sustainable, especially in SMEs. This research explored the influence of AI capabilities on organisational innovation within Malaysian SMEs and the role of transformational leadership as a mediator for the above effects, while considering the moderating role of digital organisational culture. The questionnaire was distributed electronically via Google Forms to a study sample of (900) SMEs in Kuala Lumpur, Selangor, and Johor Bahru, targeting owners and managers. Two weeks after distribution, (565) questionnaires were received; however, (215) questionnaires were excluded because the respondents were neither managers nor owners. A total of (350) questionnaires were valid for analysis. Using SMART-PLS software v.4.1.1.6 (PLS-SEM analysis) in analysing data, the study found that AI capability has a positive impact on organisational innovation and a positive impact on transformational leadership. Moreover, transformational leadership has a positive impact on organisational innovation, and transformational leadership mediates the relationship between AI capability and organisational innovation. Furthermore, the study found that digital organisational culture does not moderate the relationship between AI capability and transformational leadership. Digital organisational culture moderates the relationship between AI capability and organisational innovation; also, digital organisational culture moderates the relationship between transformational leadership and organisational innovation.

This study investigates the nonlinear and quantile-dependent effects of four macroeconomic uncertainty measures [financial market uncertainty (VIX), economic policy uncertainty (EPU), climate policy uncertainty (CPU), and geopolitical risk (GPR)] on novel broad-based green growth index in the U.S. from January 1990 to December 2021. Employing cross-quantilogram and quantile-on-quantile correlation techniques, we find that the correlation and dependence structure between uncertainty and green growth varies significantly across quantiles and time horizons. Financial market uncertainty negatively affects green growth at lower quantiles but positively influences it at median to higher quantiles, while higher VIX quantiles exert a uniformly negative impact. EPU, except at its lowest quantile, consistently undermines green growth at the median and higher quantiles. CPU displays a short-run negative influence on median to higher green growth quantiles, but in the long run, median to higher CPU quantiles become positively associated with mid-level green growth. GPR initially supports green growth at lower quantiles but becomes increasingly detrimental across higher quantiles and over longer horizons. These findings underscore the inadequacy of one-size-fits-all strategies and highlight the need for heterogeneous, uncertainty-specific policy interventions to sustain and enhance green growth trajectories.