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The rapid advancement of Artificial Intelligence (AI) in the business sector has led to a new era of digital transformation. AI is transforming processes, functions, and practices throughout organizations creating system and process efficiencies, performing advanced data analysis, and contributing to the value creation process of the organization. However, the implementation and adoption of AI systems in the organization is not without challenges, ranging from technical issues to human-related barriers, leading to failed AI transformation efforts or lower than expected gains. We argue that while engineers and data scientists excel in handling AI and data-related tasks, they often lack insights into the nuanced human aspects critical for organizational AI success. Thus, Human Resource Management (HRM) emerges as a crucial facilitator, ensuring AI implementation and adoption are aligned with human values and organizational goals. This paper explores the critical role of HRM in harmonizing AI's technological capabilities with human-centric needs within organizations while achieving business objectives. Our positioning paper delves into HRM's multifaceted potential to contribute toward AI organizational success, including enabling digital transformation, humanizing AI usage decisions, providing strategic foresight regarding AI, and facilitating AI adoption by addressing concerns related to fears, ethics, and employee well-being. It reviews key considerations and best practices for operationalizing human-centric AI through culture, leadership, knowledge, policies, and tools. By focusing on what HRM can realistically achieve today, we emphasize its role in reshaping roles, advancing skill sets, and curating workplace dynamics to accommodate human-centric AI implementation. This repositioning involves an active HRM role in ensuring that the aspirations, rights, and needs of individuals are integral to the economic, social, and environmental policies within the organization. This study not only fills a critical gap in existing research but also provides a roadmap for organizations seeking to improve AI implementation and adoption and humanizing their digital transformation journey.

With an escalating level of disruption and complexity, the success of crisis management depends more on the symbolic support of the leaders to new technologies such as artificial intelligence (AI). The aim of the research is to investigate why AI symbolization by leaders that is conceptualized as the observable encouragement and endorsement of AI in depth actions and artifacts is important in crisis response within organizations. Drawing on the contingency theory, we investigate what mechanism connect AI-symbolizing connect leaders to improved crisis management. Specifically, we test a serial mediation model where AI knowledge sharing (AIKS) and AI self-efficacy (AISE) explain the pathway from leaders’ AI symbolization to crisis management effectiveness. Data were collected from 343 executives in Jordan’s private airline sector as well as analyzed using PLS-SEM. The findings reveal that leaders’ AI symbolization significantly enhances AIKS and AISE, which in turn strengthen crisis management capabilities. In practice, the findings indicate that leaders’ visible advocacy for AI fosters an organizational climate that strengthens technological confidence and communication, particularly in high-stakes contexts. Moreover, the study addresses gaps at the intersection of leadership, artificial intelligence, and crisis management, offering a timely lens for rethinking organizational preparedness in the digital era.

The popularity of artificial intelligence (AI) has highlighted the necessity of K-12 AI education, particularly at the elementary level. However, the lack of a comprehensive and age-appropriate AI curriculum integrated into school subjects, along with the abstract and complex nature of AI concepts, exacerbates student inequalities. Researchers addressed this by developing an AI curriculum using tangible computing tools and assessed its effectiveness in improving students' AI knowledge, perception, and behavioral intention. The study involved 60 elementary students from the US Midwest. The effectiveness of the curriculum and the students’ learning experiences were investigated. The results demonstrated the success of the curriculum among all students, with improved AI knowledge, perception, and behavioral intention after using tangible computing tools. Four themes about learning experiences were identified: (1) Augmentation of cognitive learning gains, (2) Augmentation of affective attributes, (3) Advantages of utilizing tangible computing tools for AI education, and (4) Obstacles encountered in the process of learning AI. The practical and theoretical contributions and implications of this study are discussed.

Artificial intelligence (AI) has the potential to support self-regulated learning (SRL) because of its strong anthropomorphic characteristics. However, most studies of AI in education have focused on cognitive outcomes in higher education, and little research has examined how psychological needs affect SRL with AI in the K–12 setting. SRL is a self-directed process driven by psychological factors that can be explained by the three basic needs of self-determination theory (SDT), i.e., autonomy, competence, and relatedness. This study fills a research gap by examining the moderating effects of need satisfaction and gender in predicting SRL among Grade 9 students. The results indicate that girls perceive more need support than boys. In predicting SRL, satisfaction of the need for autonomy and competence is moderated by both gender and AI knowledge, whereas satisfaction of the need for relatedness is moderated by gender only. Particularly among girls, the effects of autonomy and competence more strongly predict SRL when AI knowledge is low. These findings confirm the gender differences in need satisfaction when predicting SRL with a chatbot. The findings have implications for both teacher instruction and the design and development of intelligent learning environments.

Artificial intelligence (AI) technologies are rapidly changing communities and societies and have had a significant impact on the way of life of individuals. Yet, understanding the influence of AI adoptionamong people of different sociodemographic backgroundAsocio-demographic , especially in non-Western settings, remains limited. This study examines the knowledge of AI use and the prediction of AI adoption among residents in the Greater Kumasi area of Ghana. The study employed a cross-sectional survey of 407 residents using a multistage sampling technique. The study examined how socio-demographic factors (age, education, gender) influence the perceived usefulness of AI among residents in the Greater Kumasi area and further assessed how AI knowledge and confidence shaped the perceived ease of use of AI among the selected residents. Data was analysed both descriptively presented using frequency distribution and inferentially by way of a binary logistic regression, with a p-value < 0.05 considered statistically significant. Results show that most participants are aware of and use AI with high confidence. The logistic regression analysis showed that the level of education, age, knowledge and confidence in AI are determinants of AI adoption. Our findings reflect the literature on technology adoption pathways. These findings contribute to AI adoption literature in non-Western settings with implications for promoting AI education with targeted outreach programmes.

This study examines how employees' AI knowledge and understanding affects their auto-efficacy, behavioural intents, and ICT learning in Saudi Arabian software houses. The study seeks to understand how AI knowledge affects these outcomes and discover moderating and mediating factors. A method of quantitative analysis was used with 289 software firm employees. Data were acquired using a structured questionnaire using research-based scales. Data analysis using Partial Least Squares Structural Equation Modelling (PLS-SEM) examined complex construct interactions. AI knowledge significantly affects auto-efficacy, behavioural intention, and ICT learning. Behavioural intention mediates AI knowledge, auto-efficacy, and ICT learning. The impact of AI knowledge on self-efficacy is moderated by information source quality. These findings emphasise AI knowledge and context of AI learning. This study integrates AI-specific features into technology adoption models to improve theory. It highlights how targeted AI training programs and high[1]quality information sources may raise employee engagement with AI technology, serving as practical advice for companies aiming to improve their technology and workforce readiness

In the workplace, the application of artificial intelligence (AI) is becoming increasingly widespread, including in employee performance management where AI feedback is gaining importance. Some companies are also using AI to provide negative feedback to employees. Our research compares the impact of AI negative feedback and leader negative feedback on employees. In order to explore the impact of AI negative feedback on employees, we investigated how AI negative feedback impacts employee psychology and behavior and compared these effects to those of human leader negative feedback, within the framework of the feedback process model. To explore these differences, we conducted three experimental studies (n = 772) from two different regions (i.e., China and the United States). The results reveal that leader negative feedback induces greater feelings of shame in employees, leading to work withdrawal behaviors, compared to AI negative feedback. Conversely, AI negative feedback has a more detrimental effect on employees’ self-efficacy, leading to work withdrawal behaviors, compared to leader negative feedback. Furthermore, employees’ AI knowledge moderates the relationship between negative feedback sources and employee withdrawal behavior. Specifically, employees who perceive themselves as having limited AI knowledge are more likely to feel ashamed when receiving leader negative feedback than when receiving AI negative feedback. Conversely, employees who believe they are knowledgeable about AI are more likely to have their self-efficacy undermined by AI negative feedback than leader negative feedback. Our research contributes significantly to the literature on AI versus human feedback and the role of feedback sources, providing practical insights for organizations on optimizing AI usage in delivering negative feedback.

Nearly half of the respondents reported significant or even predominant use of Al tools for their marketing activities. [...]there was a strong consensus among respondents that generative Al will significantly improve their marketing activities, with almost two-thirds expecting a substantial improvement (see Figure 1). [...]Al helps to generate higher-quality ideas, a critical component for differentiating marketing strategies in a competitive landscape. * Realizing cost savings in content creation and personalization x In terms of cost-effectiveness, the perception of the value of generative Al is more nuanced. [...]these companies successfully foster a culture where sharing insights and best practices around generative Al is commonplace. By speeding up market research and content creation, improving the quality of analytical and market research tasks, text generation and ideation, and offering the potential for cost efficiencies, Al is proving to be a valuable asset in the marketing toolkit of experienced, knowledgeable users.

PurposeThis 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.Design/methodology/approachThe 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.FindingsThe 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.Research limitations/implicationsThe 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.Practical implicationsThe 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.Social implicationsBy 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.Originality/valueTo 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 investigates the relationship between AI ethics literacy and students’ self-rated learning competence using AI by developing a comprehensive framework of AI ethics literacy comprising knowledge, attitude, and competence dimensions. Data were collected from 482 college students through an online questionnaire and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Key findings reveal that: (1) AI ethics knowledge is primarily characterized by four ethical principles: fairness and inclusivity, privacy protection, human-centricity, and responsibility and accountability; (2) AI ethics knowledge positively influences both AI ethics attitude and competence; and (3) AI ethics attitude and competence significantly enhance students’ self-rated learning competence using AI. This research contributes a novel theoretical framework for understanding AI ethics literacy while providing practical insights for cultivating students’ self-rated learning competence using AI.