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The rapid evolution of artificial intelligence (AI) is revolutionizing organizational operations and altering competitive landscapes. This study examines the influence of organizational resources on AI adoption in recruitment, focusing on their role in achieving competitive advantage through effective implementation.

This research utilizes a cross-sectional quantitative approach, applying partial least squares structural equation modeling (PLS-SEM) to data from 290 human resource (HR) professionals. It is grounded in the resource-based view (RBV) and dynamic capability framework (DCF).

The results reveal that HR competencies and open innovation significantly influence dynamic capabilities, which are essential for AI integration, supported by financial support and information technology (IT) infrastructure. These capabilities enable effective AI adoption, leading to a competitive advantage.

The cross-sectional data in this study captures the current landscape of AI adoption in recruitment, providing a snapshot of the present scenario in a rapidly evolving technological environment.

This study offers HR professionals and managers strategic guidance on effectively integrating AI into recruitment processes. By enhancing HR competencies, fostering collaboration and ensuring sufficient financial and infrastructural support, organizations can navigate AI adoption challenges and secure a competitive advantage in a rapidly evolving technological landscape.

The adoption of AI in recruitment can reduce biases, enhance diversity and improve fairness through standardized assessments. However, as AI technologies evolve, continuous human oversight is essential to ensure ethical use and to modify AI systems as needed, further reducing biases and addressing societal concerns in AI-driven recruitment processes.

This research introduces a novel framework that underscores the importance of integrating human expertise with advanced technological tools to ensure successful AI implementation. A key contribution is that HR professionals not only facilitate AI integration but also ensure accuracy, accountability and configure the most suitable AI tools for recruitment by collaborating with AI developers to meet the specific needs of the organization.

Generative Artificial Intelligence (Gen-AI) has provided new opportunities and challenges in using educational environments for students’ interaction and knowledge acquisition. Based on the expectation–confirmation theory, this paper aims to investigate the effect of different constructs associated with Gen-AI on engagement, satisfaction and word-of-mouth.

We collected data from 508 students in the UK using Qualtrics, a prominent online data collection platform. The conceptual framework was analysed through structural equation modelling.

The findings show that Gen-AI expectation formation and Gen-AI quality help to boost Gen-AI engagement. Further, we found that active engagement positively affects Gen-AI satisfaction and positive word of mouth. The mediating role of Gen-AI expectation confirmation between engagement and the two outcomes, satisfaction and positive word of mouth, was also confirmed. The moderating role of cognitive processing in the relationship between Gen-AI quality and engagement was found.

This paper extends the Expectation-Confirmation Theory on how Gen-AI can enhance students’ engagement and satisfaction. Suggestions for future research are derived to advance beyond the confines of the current study and to capture the development in the use of AI in education.

The purpose of this paper is to introduce the artificial intelligence (AI) Citizenship Framework, a model that equips teachers and school library professionals with the tools to develop AI literacy and citizenship in students. As AI becomes increasingly prevalent, it is essential to prepare students for an AI-driven future. The framework aims to foster foundational knowledge of AI, critical thinking and ethical decision-making, empowering students to engage responsibly with AI technologies. By providing a structured approach to AI literacy, the framework helps educators integrate AI concepts into their lessons, ensuring students develop the skills needed to navigate and contribute to an AI-driven society.

This paper presents a theoretical framework, developed from the author’s experience as an information and digital literacy coach and teacher librarian across Asia, the Middle East and Europe. The AI Citizenship Framework was created without following specific empirical methodologies, drawing instead on practical insights and educational needs observed in diverse contexts. It outlines a scope and sequence for integrating AI literacy into school curricula. The framework’s components build on existing pedagogical practices while emphasising critical, ethical and responsible AI engagement. By providing a structure for AI education, it serves as a practical resource for school librarians and educators.

While no empirical data was collected for this theoretical paper, the AI Citizenship Framework offers a structured approach for school librarians and educators to introduce and develop AI literacy. It has the potential to influence AI education by fostering critical and ethical awareness among students, empowering them to participate responsibly in an AI-driven world. The framework’s practical application can be expanded beyond school librarians to include classroom teachers, offering a comprehensive model adaptable to various educational settings. Its real-world implementation could enhance students’ readiness to engage with AI technologies, providing long-term benefits for both educational institutions and the broader society.

One limitation of the AI Citizenship Framework is that it has not yet been empirically validated. Future research could focus on testing its practical effectiveness in real-world settings, offering insights that may inform refinements and adaptations to better support school librarians and educators in fostering AI literacy and AI citizenship.

The practical implication of the AI Citizenship Framework is its application in educational settings to equip students with AI literacy and responsible citizenship skills. School library professionals and teachers can use the framework to integrate AI concepts into curricula, fostering critical thinking, ethical understanding and informed decision-making about AI technologies. The framework provides ready-to-use curriculum plans, enabling educators to prepare students for an AI-driven world. Its adaptability also allows classroom teachers to lead AI literacy initiatives, making it a versatile tool for embedding AI education across subjects and promoting responsible use and engagement with AI technologies in real-world contexts.

The originality and value of the AI Citizenship Framework lie in its approach to integrate AI literacy into educational contexts, specifically tailored for teacher librarians and school librarians. To the best of the authors’ knowledge, it is the first framework that comprehensively addresses the need for AI literacy from an ethical, critical and societal perspective, while also promoting active participation and leadership in AI governance. The framework equips educators with practical tools and curriculum plans, fostering responsible AI use and engagement. Its adaptable structure ensures it can be implemented by classroom teachers as well, adding significant value to AI education across disciplines and age groups.

This study aims to investigate academic integrity considerations of Generative Pre-Trained Transformer (ChatGPT) usage in assessment activities among the finalist student teachers at the University of Dar es Salaam. The study builds upon other previous studies on the topic of artificial intelligence (AI) tools. However, it is unique in terms of its context and the focus on integrity.

A questionnaire was used to collect data from 383 finalist student teachers, and other 30 students were involved in focus group discussions (FGDs). Data for this study were analysed using IBM-SPSS Statistics Versions 25 to obtain descriptive statistics for the quantitative part and thematic analysis was used along with QDA Miner Lite for the qualitative part to generate themes.

Student teachers consider ChatGPT as a transformative AI technology to ease the accomplishment of their assessment tasks. However, a large percentage of them did not pay much attention to academic integrity while using the AI tool. About 199 (52%) out of 383 respondents said ChatGPT is a useful tool in generating answers for their assessment tasks compared to 192 (50.1%) of respondents who had negative opinions.

Given the sensitivity of the topic on academic integrity some participants were hesitant to share information until when the researchers clarified the purpose of the study. Participants were told that the information they would provide was purely for academic purpose. Also, this study was conducted when students’ regular classes were in progress, such that it was challenging to set universal time to conduct FGDs where all intended participants could attend. The researcher was flexible enough to find hours which were conducive for participants to participate in FGDs.

For practitioners, given the fact that ChatGPT is a relatively new AI tool, training for raising awareness of its use should be conducted. Besides, specific academic integrity guidelines for its ethical use should be formulated and implemented. Likewise, instructors should set questions that prompt students original thinking which the AI tool cannot easily give contextualized solutions.

The university where the AI tool is used is an open system that is within the society, and the impacts of AI technologies are inevitable in social domains. The society, therefore, should be informed about the emerging AI tools with their associated potentials and shortcomings to safeguard the norms and interests of the society.

The study provides a comprehensive overview of student teachers’ ChatGPT use and its implication on academic integrity. Given the novelty nature of ChatGPT use in education, course instructors and students may benefit from insights into responsible utilization of this AI technology without compromising integrity and quality of education.

This study explores the factors influencing artificial intelligence (AI)-driven decision-making proficiency (AIDP) among management students, focusing on foundational AI knowledge, data literacy, problem-solving, ethical considerations and collaboration skills. The research examines how these competencies enhance self-efficacy and engagement, with curriculum design, industry exposure and faculty support as moderating factors. This study aims to provide actionable insights for educational strategies that prepare students for AI-driven business environments.

The research adopts a hybrid methodology, integrating partial least squares structural equation modeling (PLS-SEM) with artificial neural networks (ANNs), using quantitative data collected from 526 management students across five Indian universities. The PLS-SEM model validates linear relationships, while ANN captures nonlinear complexities, complemented by sensitivity analyses for deeper insights.

The results highlight the pivotal roles of foundational AI knowledge, data literacy and problem-solving in fostering self-efficacy. Behavioral, cognitive, emotional and social engagement significantly influence AIDP. Moderation analysis underscores the importance of curriculum design and faculty support in enhancing the efficacy of these constructs. ANN sensitivity analysis identifies problem-solving and social engagement as the most critical predictors of self-efficacy and AIDP, respectively.

The study is limited to Indian central universities and may require contextual adaptation for global applications. Future research could explore longitudinal impacts of AIDP development in diverse educational and cultural settings.

The findings provide actionable insights for curriculum designers, policymakers and educators to integrate AI competencies into management education. Emphasis on experiential learning, ethical frameworks and interdisciplinary collaboration is critical for preparing students for AI-centric business landscapes.

By equipping future leaders with AI proficiency, this study contributes to societal readiness for technological disruptions, promoting sustainable and ethical decision-making in diverse business contexts.

To the author’s best knowledge, this study uniquely integrates PLS-SEM and ANN to analyze the interplay of competencies and engagement in shaping AIDP. It advances theoretical models by linking foundational learning theories with practical AI education strategies, offering a comprehensive framework for developing AI competencies in management students.

This study aims to deepen our understanding of how chatbots’ empathy influences humans–AI relationship in frontline service encounters. The authors investigate the underlying mechanisms, including perceived anthropomorphism, perceived intelligence and psychological empowerment, while also considering variations between different stages of the customer journey (before and after purchase).

Data collection was conducted through an online survey distributed among 301 customers who had experience using AI-based service chatbot in frontline service encounters in China. The hypotheses were examined through structural equation modeling and multi-group analysis.

The findings of this study revealed the positive impacts of emotional and cognitive empathy on humans–AI relationship through perceived anthropomorphism, perceived intelligence and psychological empowerment. Furthermore, this study verified the moderating effect of the customer journey stages, such that the impacts of anthropomorphism and intelligence on humans–AI relationship displayed more strength during the pre- and post-purchase phases, respectively.

This research offers practical implications for companies: recognize and enhance empathy dimensions in AI-based service chatbot to empower human–AI relationships; boost customer empowerment in human–AI interactions; and tailor anthropomorphic features in the pre-purchase stage and improve problem-solving capability in the post-purchase stage to enrich user experiences.

This study extends relationship marketing theory and human–AI interaction frameworks by investigating the underlying mechanisms of the effect of two-dimensional empathy on human–AI relationship. This study also enriches service design theories by revealing the moderating effect of customer journey stages.

This study aims to explore the role of ChatGPT literacy within the technology acceptance model (TAM) framework and its potential for learners of English as a foreign language (EFL), whereby ChatGPT is integrated into their informal digital learning of English activities.

Data from 543 Chinese EFL learners were collected using a cross-sectional quantitative method. The relationships between six factors, namely, ChatGPT literacy, perceived ease of use, perceived usefulness, attitude, behavioral intention and actual use, were conceptualized and tested based on the TAM framework. The conceptualized model was verified using partial least squares structural equation modeling.

The findings indicated that ChatGPT literacy is a significant predictor of perceived ease of use and perceived usefulness, which were the two core variables with a significant impact on attitude. Perceived ease of use positively influenced perceived usefulness, indicating the mediating role of perceived usefulness in this path. Attitude significantly and positively influenced behavioral intention, which positively predicted actual use. Moreover, ChatGPT literacy moderated the relationship between behavioral intention and actual use.

This study extends the TAM framework by incorporating ChatGPT literacy as a predictor and moderator between behavioral intention and actual use. Empirical evidence is further offered for including ChatGPT as a language-learning instrument with great potential for EFL learners in extramural EFL learner settings.

The pressing issues of climate change and environmental degradation call for a reevaluation of how we approach economic activities. Both leaders and corporations are now shifting their focus, toward adopting practices and embracing the concept of circular economy (CE). Within this context, the Food and Beverage (F&B) sector, which significantly contributes to greenhouse gas (GHG) emissions, holds the potential for undergoing transformations. This study aims to explore the role that Artificial Intelligence (AI) can play in facilitating the adoption of CE principles, within the F&B sector.

This research employs the Best Worst Method, a technique in multi-criteria decision-making. It focuses on identifying and ranking the challenges in implementing AI-driven CE in the F&B sector, with expert insights enhancing the ranking’s credibility and precision.

The study reveals and prioritizes barriers to AI-supported CE in the F&B sector and offers actionable insights. It also outlines strategies to overcome these barriers, providing a targeted roadmap for businesses seeking sustainable practices.

This research is socially significant as it supports the F&B industry’s shift to sustainable practices. It identifies key barriers and solutions, contributing to global climate change mitigation and sustainable development.

The research addresses a gap in literature at the intersection of AI and CE in the F&B sector. It introduces a system to rank challenges and strategies, offering distinct insights for academia and industry stakeholders.

Generative artificial intelligence (GenAI) can potentially improve supply chain management (SCM) processes across levels and verticals. However, despite its promise, the implementation of GenAI for SCM remains challenging, mainly due to the lack of knowledge regarding its key drivers. To address this gap, this study examines the factors driving GenAI implementation in an SCM environment and how these factors optimize SCM performance.

A thorough literature review was followed to identify the drivers. The resultant model from the drivers was validated using a quantitative study based on partial least squares structural equation modeling (PLS-SEM) that used responses from 315 expert respondents from the field of SCM.

The results confirmed the positive effect of performance expectancy, output quality and reliability, organizational innovativeness and management commitment to GenAI usage. Further, they showed that successful GenAI usage improved SCM performance through improved transparency, better decision-making, innovative design, robust development and responsiveness.

This study reports the potential drivers for the contemporary development of GenAI in SCM and highlights an action plan for GenAI’s optimal performance. The findings suggest that by increasing the rate of GenAI implementation, organizations can continuously improve their strategies and practices for better SCM performance.

This study establishes the first step toward empirically testing and validating a theoretical model for GenAI implementation and its effect on SCM performance.

This study investigates the influences of socioeconomic, familial and personal, cultural, organizational and technological factors on succession planning within family businesses (FBs). It aims to untangle the complex web these factors weave together, shedding light on their collective impact on the seamless leadership transition from generation to generation.

The theory of planned behavior (TPB) and systems theory (ST) were utilized in this study. The proposed framework is supported by a purposive quantitative design from 388 FBs in Jordan. The collected data were rigorously assessed using partial least squares-structural equation modeling and importance-performance map analysis.

Our results have shown that successor characteristics, such as age, gender, education and attitude toward the takeover, strongly affect effective succession planning. Other major factors include strong family relationships, the size of the business and modern digital integration. However, the religious belief variable did not seem to influence succession planning. The results conclude that technological savvy and online community mediating factors support smooth transitions.

Offering a treasure trove of insights, this study equips FB stakeholders with strategic keys to unlock the potential of digital and communal resources in succession planning. It champions a dual approach that venerates age-old family values while embracing the digital age, paving the way for transitions that are not just smooth but also forward-thinking and resilient.

This study harmonizes the TPB with ST to forge an innovative lens through which succession planning in FBs can be viewed. It underscores the burgeoning role of digital integration, communal networks and the potential of AI and GPTs in enriching traditional succession planning paradigms. Given that FBs are significant to the Jordanian economy, this area is under-researched as for many emerging nations.