Mohammad Alhur

Type of the article: Research Article AbstractIn an era of persistent economic uncertainty and supply chain disruptions, organizational resilience has emerged as a critical determinant of long-term sustainability, particularly in rapidly evolving industries such as food manufacturing. Emotional intelligence (EI) has been identified as a key driver of resilience, enabling individuals and organizations to better adapt, recover, and grow in the face of challenges. This study aims to investigate the impact of EI on organizational resilience and to assess the moderating role of transformational leadership in this relationship within the context of Jordan’s yogurt industry. A quantitative research design was employed, and data were collected in 2024 from 768 employees working across three leading yogurt manufacturing companies in Jordan. Structural Equation Modeling (SEM) using Smart PLS was applied to analyze the relationships among the variables. The results demonstrate that all four dimensions of EI, self-awareness, self-regulation, social awareness, and relationship management, exert a statistically significant positive effect on organizational resilience (p < 0.001). Furthermore, transformational leadership was found to moderate these relationships significantly, enhancing the strength of EI’s influence on resilience outcomes. These findings offer robust empirical support for the integration of EI and leadership development in resilience-building strategies. The study provides sector-specific insights that contribute to the theoretical advancement of resilience frameworks and offers actionable guidance for organizational leaders seeking to enhance adaptability in dynamic and uncertain environments.

The examination of the impact of Generative AI (GenAI) on higher education, especially from the viewpoint of students, is gaining significance. Although prior research has underscored GenAI's potential advantages in higher education, there exists a discernible research gap concerning the determinants that affect its adoption. In the present study, we aim to enhance our comprehension of the factors influencing the willingness of higher education students to adopt GenAI tools. To achieve this, we have developed an extended Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model incorporating specific GenAI constructs. Our research methodology entailed the selection of a diverse sample of 374 students through random sampling. We then analyzed their data using Structural Equation Modeling (SEM) to gain insights into the complex relationships between various variables. The study found that students are more likely to use GenAI tools when they view them as supplemental resource and effort expectancy. It also revealed that perceived costs negatively impact adoption intentions, highlighting that financial factors are a significant barrier. Interestingly, Factors like information accuracy and hedonic motivation did not significantly affect students' adoption intentions. This study offers key insights for eLearning practitioners on integrating Generative AI (GenAI) tools into educational settings. It emphasizes the significance of resource perception and effort expectancy, demonstrating GenAI's potential to personalize learning experiences. eLearning platforms can utilize GenAI to enhance active learning through engaging methods and streamline course development. Addressing cost barriers is crucial for equitable access and inclusivity. A gradual approach to integration aligned with learning objectives is recommended, along with fostering critical engagement with GenAI tools to enhance digital literacy. Lastly, the study is constrained by its specific context, potential biases in self-reported data, a narrow focus on factors influencing students' intent to use GenAI tools and a cross-sectional design. Future research should encompass a broader range of factors, employ objective measures, and integrate observational data. Longitudinal studies or experimental designs could offer more comprehensive insights into how students' perceptions and intentions develop, thus promoting a more inclusive educational environment for all students.

This study investigates the uptake of emerging technologies—generative AI, agentic AI, and blockchain—by start‐up companies in Dubai, a rapidly emerging innovation hub in the MENA region. Based on the innovation diffusion theory (IDT) and the technology–organization–environment (TOE) framework, the study investigates how technology, organizational, and environmental determinants influence technology adoption by start‐ups. Specifically, it takes into account trialability, observability, relative advantage, and compatibility (technological context); stakeholder dynamics, innovation capability, and organizational resources (organizational context); and competition intensity and regulatory environment (environmental context). Citing recent criticisms of IDT, the study removes complexity as a determinant considering the growing user‐centric design of technologies like GenAI. Using a mixed‐methods design, the study collects data through interviews and surveys from start‐up founders and technology leads. The outcomes should unveil the way start‐ups view and imbed disruptive technologies and equip policymakers, entrepreneurs, and strategists for innovations with lessons to inform real‐world applications. Placing the study in Dubai′s vibrant setting, this study provides theoretical and empirical contributions to technology diffusion in the emerging economies, where the flexibility of regulation, preparedness of infrastructure, and competitive stress all come together to create innovation trajectories.

Behavioral science confronts the issue of how people’s behaviors differ from what they intend to do. However, current models, such as the theory of planned behavior, are insufficient to account for contextual influences and interdisciplinary effects, especially in the case of modern social phenomena. The majority of studies concentrate on single domains (e.g., health and consumer behavior) and employ manual coding schemes, overlooking essential thematic relationships. This research highlights the necessity for integrative frameworks that attempt to analyze why intentions fail to be realized in complex settings such as climate change and digitalization. The primary objectives of this research are to identify and operate dominant and emerging thematic trends in intention–behavior literature in a time series from 1979 to 2025 and to analyze and investigate the effects of publication index status and citation patterns on scholarly impact. This study uses structural topic modeling (STM) alongside bibliometric analyses to identify themes and correlations in intention–behavior research. STM employs generalized linear models to include document‐level metadata, allowing for the discovery of related topics and the key factors influencing the development of the literature. Data collection was initially performed on February 20, 2025, through the Web of Science database, using studies that were identified following PRISMA guidelines, reviewed, and considered relevant. The initial records numbered 5350. Significant thematic trends were found to define, and key psychological mechanisms to explain the intention–behavior gap were identified. The study also found that the determinants of publication index status and citation trends play important roles in establishing the discipline’s fate and the impact of intention–behavior literature. Based on these findings, the study highlights how strong thematic links in intention–behavior research can inform cross‐domain interventions—such as integrating physical activity and organic food campaigns or leveraging sustainable tourism to promote ethical consumption—by targeting shared psychological drivers like health identity and self‐image. In future research, the intention–behavior gap should be investigated across different disciplines and contexts and with longitudinal and experimental designs to take advantage of the psychological and contextual factors that affect behavior.

Social work is a humanitarian profession that intersects with a variety of fields. It focuses on supporting individuals through the effective utilization of resources and the recognition of their potential. To practice effectively, social workers require substantial professional development, which includes extensive field training. This training is crucial as it allows them to apply theoretical knowledge in real-world settings while acquiring essential skills. Governments increasingly acknowledge the importance of field training in educating social workers. This study presents a unique contribution to the field of social work education by evaluating the impact of field training quality on student satisfaction among bachelor's students at the University of Jordan. To achieve this, a quantitative methodology was employed, surveying 137 bachelor students during the academic year of 2023/2024. The data were analyzed using Structural Equation Modeling (SEM). The findings highlight that factors such as assurance, tangibles, and responsiveness significantly influence student satisfaction with the field training program. Conversely, reliability and empathy did not significantly impact, suggesting a disconnect between what students expect and what is delivered. The structural model accounts for approximately one-third of the variation in satisfaction, underscoring the necessity to enhance service quality factors to improve the effectiveness of field training programs.

The COVID-19 pandemic has had a profound effect on the mental health and job satisfaction of healthcare workers (HCWs), contributing to heightened levels of stigma and anxiety within this population. Although there has been significant research focusing on various mental health outcomes for HCWs, there remains a lack of studies directly linking social stigma and interaction anxiety to job satisfaction. This study aims to address this gap by exploring the lived experiences of healthcare workers in Jordan, focusing on their encounters with social interaction anxiety and social stigma arising from the pandemic. To achieve this, we have developed a model based on the short social interaction anxiety and social stigma scales. Our research methodology entailed selecting a diverse sample of 198 healthcare workers in Jordanian hospitals through convenience sampling. We then analyzed their data using structural equation modeling (SEM) to gain insights into the complex relationships between various variables. The study's findings identified three critical dimensions of social stigma—fear, prejudice, and stereotype—along with social interaction anxiety, each of which negatively affects job satisfaction by 41%. These results highlight the considerable influence that stigma and social interaction anxiety have on the experiences of health workers in times of crisis.

This study empirically investigated the drivers of port competitiveness among low-, upper-, and high-income countries in the Middle East and North Africa region. It explored the effects of country-level competitiveness, logistic performance, and ease of doing business on port competitiveness for 17 countries from the region using a 14-item scale and covering the years 2010 to 2022. Port competitiveness indicators were analyzed using confirmatory factor analysis to determine the research constructs’ validity. Structural path analysis was deployed to verify hypotheses concerning effects between conceptualized variables. The findings demonstrate that in high-income countries, an increase in country competitiveness notably enhances port sustainability and competitiveness. Conversely, in low-income countries, higher country competitiveness appears to diminish port competitiveness. For countries with average income, the association is relatively neutral, exhibiting a slight positive trend. This study explains the specific drivers and interactions that improve port sustainability and competitiveness as countries move from low- to high-income levels of development.

This study explores the impact of digital banking channels on the operational excellence of Jordanian banks. It investigates the impact of social media, chatbots, digital wallets, and mobile applications on operational excellence in Jordanian banks, focusing on the moderating role of organizational culture. The study used a quantitative research method and a stratified random sampling technique to ensure a diverse sample of the five largest banks in Jordan, including managers and IT professionals responsible for managing digital banking operations. A total of 276 completed questionnaires were analyzed using structural equation modeling. The findings show that digital banking channels significantly contribute to operational excellence. Social media banking (β = 0.155, p < 0.05) and chatbots (β = 0.212, p < 0.01) positively impact operational excellence, while digital wallets (β = 0.301, p < 0.001) and mobile banking applications (β = 0.22, p < 0.01) also exhibit significant positive effects. The structural model explained 63% of the variance in operational excellence. Additionally, organizational culture was found to positively moderate the impact of social media banking (β = 0.20, p < 0.01), chatbots (β = 0.10, p < 0.05), and digital wallets (β = 0.16, p < 0.01) on operational excellence. However, the interaction between mobile applications and organizational culture was not significant (β = 0.078, p = 0.065).