How is artificial intelligence reshaping the HR function and with what implications? A qualitative exploration in the service sector

Modern organizations are implementing artificial intelligence (AI) applications to improve human resource management practices through increased decision transparency and improved efficiency outcomes. Decision flexibility, employee acceptance issues, and the risk of bias create significant challenges in AI adoption in HR. This study examines the implementation of AI in the HR function to evaluate its impact on operational efficiency and employee well-being, using a comprehensive theoretical framework integrating Task-Technology Fit, Sociotechnical Systems, Job Demands-Resources, and Explainable AI. The study implemented a mixed-methods design involving explanatory sequential elements to analyze the effectiveness of AI systems and employee perceptions. Quantitative analysis revealed that AI technology improves HR efficiency by up to 45% despite posing challenges in decision-making flexibility. Qualitative exploration identified differences in AI acceptance across organizational levels, with 80% of employees indicating limited understanding of AI decision mechanisms and 60% concerned about potential bias. The study findings suggest that optimal AI implementation requires a collaborative human-AI model that combines algorithmic analysis with human oversight, especially for complex scenarios that require contextual interpretation. This study highlights the importance of algorithmic transparency and targeted communication strategies to increase technology adoption. Organizations should develop comprehensive Explainable AI mechanisms that effectively communicate decision rationales in accessible language, while simultaneously implementing monitoring protocols to evaluate and address potential patterns of bias. This research contributes to the understanding of specific factors that influence successful AI implementation in the HR context, emphasizing that technology optimization and human-centered design represent complementary priorities.

Artificial Intelligence (AI) is poised to aid in implementing the United Nations Sustainable Development Goals (SDGs), especially in Low- and Middle-Income Countries (LMICs) where developmental challenges are most evident. Yet the ethical, logistical, and governance challenges of applying AI in these settings remain underexplored. This research examines the factors that enable the scalable and ethically responsible deployment of AI in education, healthcare, and clean energy, contributing to SDG achievement. It also examines the changing nature of accountability, assurance and transparency in the sustainability reporting systems with the adoption of AI. Data collection was based on inductive exploratory design whereby semi-structured interviews were conducted on 37 actors (AI developers, policymakers, and community representatives) in five countries (India, Kenya, Bangladesh, Ghana and the Philippines). Thematic analysis identified four major themes influencing AI adoption: ethical concerns (e.g., bias, transparency), adoption barriers (e.g., infrastructure gaps, digital illiteracy), success enablers (e.g., institutional capacity, community involvement), and governance bottlenecks (e.g., lack of oversight, policy fragmentation). The findings, interpreted through accountability and assurance theory, show how ethical design, institutional readiness, and governance quality jointly determine the reliability and legitimacy of AI-driven sustainability data. Based on these insights, the paper develops a conceptual model for context-sensitive and ethically sound AI systems. The framework serves as a diagnostic and prescriptive tool for inclusive policy design, stakeholder engagement, and capacity-building. By extending accounting theory into the digital domain, it conceptualizes AI as both a technological and ethical actor within sustainability accounting systems, reinforcing transparency, comparability, and stakeholder trust.

This study examines the integration of Artificial Intelligence (AI) into Decision Support Systems (DSS) for Strategic Human Resource Management (SHRM) through a systematic literature review (SLR). The research investigates how AI technologies enhance various HR functions, such as recruitment, training, performance evaluation, and talent management. The findings indicate that AI significantly improves operational efficiency, decision-making accuracy, and the overall speed of HR processes. However, challenges such as ethical concerns, data privacy issues, and potential algorithmic bias in decision-making are highlighted. The paper aims to provide a conceptual framework for the effective application of AI in SHRM and identifies key trends, challenges, and opportunities in AI adoption within HR functions. By synthesizing existing research, this study offers valuable insights into optimizing the use of AI in DSS for SHRM, emphasizing the importance of aligning AI implementation with ethical principles and organizational goals. The research advances theoretical and practical understanding of AI's role in HRM and encourages further exploration of its impact across various organizational contexts.

Artificial intelligence (AI) is transforming the mental health care environment. AI tools are increasingly accessed by clients and service users. Mental health professionals must be prepared not only to use AI but also to have conversations about it when delivering care. Despite the potential for AI to enable more efficient and reliable and higher-quality care delivery, there is a persistent gap among mental health professionals in the adoption of AI. A needs assessment was conducted among mental health professionals to (1) understand the learning needs of the workforce and their attitudes toward AI and (2) inform the development of AI education curricula and knowledge translation products. A qualitative descriptive approach was taken to explore the needs of mental health professionals regarding their adoption of AI through semistructured interviews. To reach maximum variation sampling, mental health professionals (eg, psychiatrists, mental health nurses, educators, scientists, and social workers) in various settings across Ontario (eg, urban and rural, public and private sector, and clinical and research) were recruited. A total of 20 individuals were recruited. Participants included practitioners (9/20, 45% social workers and 1/20, 5% mental health nurses), educator scientists (5/20, 25% with dual roles as professors/lecturers and researchers), and practitioner scientists (3/20, 15% with dual roles as researchers and psychiatrists and 2/20, 10% with dual roles as researchers and mental health nurses). Four major themes emerged: (1) fostering practice change and building self-efficacy to integrate AI into patient care; (2) promoting system-level change to accelerate the adoption of AI in mental health; (3) addressing the importance of organizational readiness as a catalyst for AI adoption; and (4) ensuring that mental health professionals have the education, knowledge, and skills to harness AI in optimizing patient care. AI technologies are starting to emerge in mental health care. Although many digital tools, web-based services, and mobile apps are designed using AI algorithms, mental health professionals have generally been slower in the adoption of AI. As indicated by this study's findings, the implications are 3-fold. At the individual level, digital professionals must see the value in digitally compassionate tools that retain a humanistic approach to care. For mental health professionals, resistance toward AI adoption must be acknowledged through educational initiatives to raise awareness about the relevance, practicality, and benefits of AI. At the organizational level, digital professionals and leaders must collaborate on governance and funding structures to promote employee buy-in. At the societal level, digital and mental health professionals should collaborate in the creation of formal AI training programs specific to mental health to address knowledge gaps. This study promotes the design of relevant and sustainable education programs to support the adoption of AI within the mental health care sphere.

Purpose: The aim of the study was to investigate the ethical issues in artificial intelligence adoption in African higher education institutions. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Ethical concerns surrounding the adoption of Artificial Intelligence (AI) in African higher education institutions reveal key findings from recent studies. These include issues of data privacy, transparency in AI algorithms, and ethical implications in decision-making processes. Universities in Africa implementing AI technologies must navigate challenges such as ensuring ethical data handling and maintaining transparency in AI systems. Unique Contribution to Theory, Practice and Policy: Ethical frameworks in AI adoption, critical theory & ethics of care may be used to anchor future studies on the ethical issues in artificial intelligence adoption in African higher education institutions. Embed ethics education into AI courses and training programs across African universities. Equip students, educators, and AI developers with the knowledge and skills to identify, analyze, and address ethical dilemmas associated with AI technologies. Develop and enforce clear, context-specific AI ethics policies and regulatory frameworks at national and institutional levels. These policies should outline guidelines for data management, algorithmic transparency, and ethical review processes.

The adoption of artificial intelligence (AI) in the public sector is catalyzing global digital transformation. In Indonesia, despite the policy push for digitization, AI implementation faces unique challenges influenced by the dynamics of institutional pressures. This study aims to uncover the influence of coercive, mimetic, and normative pressures on AI adoption in Jabar Digital Services (JDS) and identify barriers and opportunities that arise in the local context. Using a qualitative approach with a case study design, data were obtained through semi-structured interviews, focus group discussions, and analysis of official documents. Thematic analysis was conducted to identify key patterns that reflect institutional dynamics in AI implementation. The findings of this study reveal that the adoption of artificial intelligence (AI) in Jabar Digital Services (JDS) is driven by a complex synergy between three types of institutional pressures. Coercive pressure, reflected through political pressures and leadership role, is considered to strongly encourage AI adoption. On the other hand, mimetic pressure encourages JDS to adopt best practices from developed countries from the perspective of frameworks, case studies, and international AI maturity models. Meanwhile, normative pressure arising from the expectations of the five pillars of pentahelix stakeholders including academia, private sector, government, community, and media increases the urgency of AI adoption. The synergy between coercive, mimetic, and normative pressures not only drives digital transformation and contextual approaches in JDS. This research extends the understanding of institutional isomorphism theory in developing countries and provides strategic recommendations for policymakers to integrate global practices with contextual customization to achieve effective digital transformation.

ObjectiveThe integration of Artificial Intelligence (AI) in healthcare offers opportunities to transform patient care, improve efficiency, and support clinical decision-making. Yet, its implementation is hindered by technical, organizational, and collaborative challenges. This study explores the key success factors for sustainable AI adoption from the perspectives of healthcare professionals (HCPs) and AI experts, with the aim of identifying alignments and gaps that influence collaboration and sustainability.MethodsGuided by the NASSS framework, semi-structured interviews were conducted with four HCPs and five AI experts with experience related to AI technologies in the healthcare sector. Thematic analysis was applied to examine stakeholder perspectives, focusing on gaps and alignments between stakeholders and identifying common patterns.ResultsTransparency leading to trust was the most emphasized factor by both AI experts and HCPs. Alignments were also found in recognizing the importance of interorganizational cooperation, demand-side value, scope of use, usability, responsibility and redefining the roles. Gaps included challenges in cooperation with HCPs, misunderstandings between stakeholders and the need for interdisciplinary experts, insufficient training, concerns over data quality and privacy, and limited attention to usability. Based on the found patterns, a framework is proposed to strengthen collaboration between AI experts and HCPs, enabling effective and sustainable AI implementation in healthcare.ConclusionThis study extends the NASSS framework to a dual-stakeholder context, offering insights into the alignments and gaps between HCPs and AI experts. The proposed framework supports improved collaboration, guiding healthcare professionals, AI developers, and managers toward effective, sustainable AI implementation and fostering mutual understanding for successful adoption.

The rapid evolution of artificial intelligence (AI) has profoundly reconfigured the contemporary workplace, redefining the interactions among human employees, AI systems, and organizational processes. Yet, most research adopts a tool-centric view, overlooking how AI’s emergence as an alternative working agent reshapes managerial attention and employee welfare. Drawing on the attention-based view (ABV) and a dual-agent model, we theorize that AI adoption activates two opposing mechanisms: a human attention gain mechanism, where collaboration needs heightened focus on employees and increased employee-related corporate social responsibility (ECSR), and an AI attention shift mechanism, where deep AI embedding redirects attention toward AI, suppressing ECSR. Using panel data from 2575 Chinese listed firms (2013–2023), we find an inverted U-shaped relationship between AI adoption and ECSR. Moreover, industry AI substitution risk sharpens and left-shifts this curve, while top management team (TMT) functional diversity and employee stock ownership flattens and right-shifts it. These findings advance research on AI adoption, managerial attention, and employee-focused CSR by illuminating how attention allocation in dual-agent contexts shapes ethical and strategic outcomes, offering actionable insights for balancing human–AI integration with sustained employee welfare.

Artificial intelligence (AI) and big data are technologies with the potential to transform transfusion medicine (TM). This survey explored the scope of AI and big data use in TM across the Eastern Mediterranean and North Africa region. A survey was distributed among transfusion professionals to explore current use, perceived benefits and barriers to adopting AI and big data. Fifty respondents participated; the majority worked in national/regional transfusion services, and 58% worked in academic institutions. Only 24% reported using AI in daily TM practice, primarily for administrative tasks, education and research. Clinical applications were mainly in blood donor recruitment and management. Most used generative AI tools (92%) and were self-taught. Big data were employed in 36% of respondents' institutions, most often for inventory forecasting and optimizing blood product utilization. Most institutions used data based on laboratory information systems (89%), donor databases (72%) and electronic healthcare/patient records (67%). The main challenges and concerns regarding AI adoption were the lack of regulatory guidance, limited expertise, insufficient clinical validation of AI tools, implementation cost and ethical and privacy concerns. In terms of big data, the key barriers were insufficient expertise in data management and a lack of infrastructure for data storage. AI and big data adoption in TM within the region remains limited. Major barriers include regulatory gaps, lack of expertise, cost constraints and infrastructure limitations. Strategic investment in regulatory frameworks, targeted training and technical resources is essential to facilitate safe and effective integration into transfusion practice.

Exploring Consumer-Robot interaction in the hospitality sector: Unpacking the reasons for adoption (or resistance) to artificial intelligence

Artificial intelligence (AI) is transforming industries, but its adoption in healthcare, especially radiology, remains contentious. This study evaluated the perceptions and attitudes of trainee and qualified radiologists towards the adoption of AI in practice. A cross-sectional survey using a paper-based questionnaire was completed by trainee and qualified radiologists. Survey questions covered AI knowledge, perceptions, attitudes, and AI training in the registrar programme on a 3-point Likert scale. A total of 100 participants completed the survey; 54% were aged 26-65 years and 61% were female, with none currently using AI in daily radiology practice. The majority (78%) of participants understood the basics and knew the role of AI in radiology. Most knew about AI from media reports (77%) and majority (95%) were never involved in AI training; only 3% of participants had no knowledge of AI at all. Participants agreed that AI could reliably detect pathological conditions (89%), reach reliable diagnosis (89%), improve daily work (78%), and 89% favoured AI practice; 89% believed that in the future, machine learning will not be independent of the radiologist. Participants were willing to learn (98%) and contribute towards advancing AI software (97%) and agreed that AI will improve the registrars' programme (97%), also noting that AI applications are as important as medical skills (87%). The findings suggest AI in radiology is in its infancy, with a need for educational programmes to upskill radiologists. Participants were positive about AI implementation in practice and in the registrar learning programme.

The current literature reveals a gap in understanding how rehabilitation professionals, such as physical and occupational therapists, perceive and prepare to implement artificial intelligence (AI) in their practices. Therefore, we conducted a cross-sectional observational study to assess the perceptions, knowledge, and willingness of rehabilitation healthcare providers to implement AI in practice. This study was conducted in Saudi Arabia, with data collected from 430 physical therapy professionals via an online SurveyMonkey questionnaire between January and March 2024. The survey assessed demographics, AI knowledge and skills, and perceived challenges. Data were analyzed using Statistical Package for the Social Science (SPSS 27) and DATAtab (version 2025), with frequencies, percentages, and nonparametric tests used to examine the relationships between the variables. The majority of respondents (80.9%) believed that AI would be integrated into physical therapy in future, with 78.6% seeing AI as significantly impacting their work. While 61.4% thought that AI would reduce workload and enhance productivity, only 30% expressed concerns about AI endangering their profession. A lack of formal AI training has commonly been reported, with social media platforms being respondents' primary source of AI knowledge. Despite these challenges, 85.1% expressed an eagerness to learn and use AI. Organizational preparedness was a significant barrier, with 45.6% of respondents reporting that their organizations lacked AI strategies. There were insignificant differences in the mean rank of AI perceptions or knowledge based on the gender, years of experience, and qualification degree of the respondents. The results demonstrated a strong interest in AI implementation in physical therapy. The majority of respondents expressed confidence in AI's future utility and readiness to incorporate it into their practice. However, challenges, such as a lack of formal training and organizational preparedness, were identified. Overall, the findings highlight AI's potential to revolutionize physical therapy while underscoring the necessity to address training and readiness to fully realize this potential.

This study examines the impact of artificial intelligence (AI) adoption on economic growth and unemployment across G7 countries and India. As AI emerges as a transformative technology, there is a need to understand its effects on labour markets and develop appropriate policy frameworks. The research analyses historical patterns of automation and technological change to provide context for the current AI revolution. The study employs a mixed-methods approach, combining quantitative statistical analyses with qualitative assessments. Variables examined include GDP growth rates, R&D investment levels, AI adoption rates, and productivity gains. Descriptive statistics, correlation analyses, regression models, and cluster analyses were conducted to identify relationships between key variables. Results reveal a significant technology adoption gap between high-tech leaders and developing economies. A growth paradox was observed in developing tech economies, where rapid AI adoption did not necessarily translate to proportional economic gains. The research found complex relationships between technological advancement, unemployment rates, and investment levels across countries. Cluster analysis identified three distinct groups: Advanced Economies, Technology Leaders, and Developing Tech Economies. ANOVA and chi-square tests confirmed statistically significant differences between these clusters. Multiple regression analysis on unemployment rates provided insights into the factors influencing job displacement. Key findings include: 1) AI adoption shows potential to significantly boost GDP growth and productivity, though effects vary by country. 2) The relationship between technological progress and unemployment is nuanced, defying simplistic narratives. 3) Investment levels strongly correlate with technological advancement, but other factors also play important roles. 4) Leaders in AI adoption exhibit certain common characteristics, offering potential lessons for other ...

Real-World Surveillance of FDA-Cleared Artificial Intelligence Models: Rationale and Logistics.

Aim/Purpose: This paper aims to investigate and understand the intentions of management undergraduate students in Hangzhou, China, regarding the adoption of Artificial Intelligence (AI) technologies in their education. It addresses the need to explore the factors influencing AI adoption in the educational context and contribute to the ongoing discourse on technology integration in higher education. Background: The paper addresses the problem by conducting a comprehensive investigation into the perceptions of management undergraduate students in Hangzhou, China, regarding the adoption of AI in education. The study explores various factors, including Perceived Relative Advantage and Trialability, to shed light on the nuanced dynamics influencing AI technology adoption in the context of higher education. Methodology: The study employs a quantitative research approach, utilizing the Confirmatory Tetrad Analysis (CTA) and Partial Least Squares Structural Equation Modeling (PLS-SEM) methodologies. The research sample consists of management undergraduate students in Hangzhou, China, and the methods include data screening, principal component analysis, confirmatory tetrad analysis, and evaluation of the measurement and structural models. We used a random sampling method to distribute 420 online, self-administered questionnaires among management students aged 18 to 21 at universities in Hangzhou. Contribution: This paper explores how management students in Hangzhou, China, perceive the adoption of AI in education. It identifies factors that influence AI adoption intention. Furthermore, the study emphasizes the complex nature of technology adoption in the changing educational technology landscape. It offers a thorough comprehension of this process while challenging and expanding the existing literature by revealing the insignificant impacts of certain factors. This highlights the need for an approach to AI integration in education that is context-specific and culturally sensitive. Findings: The study highlights students’ positive attitudes toward integrating AI in educational settings. Perceived relative advantage and trialability were found to impact AI adoption intention significantly. AI adoption is influenced by social and cultural contexts rather than factors like compatibility, complexity, and observability. Peer influence, instructor guidance, and the university environment were identified as pivotal in shaping students’ attitudes toward AI technologies. Recommendations for Practitioners: To promote the use of AI among management students in Hangzhou, practitioners should highlight the benefits and the ease of testing these technologies. It is essential to create communication strategies tailored to the student’s needs, consider cultural differences, and utilize the influence of peers and instructors. Establishing a supportive environment within the university that encourages innovation through policies and regulations is vital. Additionally, it is recommended that students’ attitudes towards AI be monitored constantly, and strategies adjusted accordingly to keep up with the changing technological landscape. Recommendation for Researchers: Researchers should conduct cross-disciplinary and cross-cultural studies with qualitative and longitudinal research designs to understand factors affecting AI adoption in education. It is essential to investigate compatibility, complexity, observability, individual attitudes, prior experience, and the evolving role of peers and instructors. Impact on Society: The study’s insights into the positive attitudes of management students in Hangzhou, China, toward AI adoption in education have broader societal implications. It reflects a readiness for transformative educational experiences in a region known for technological advancements. However, the study also underscores the importance of cautious integration, considering associated risks like data privacy and biases to ensure equitable benefits and uphold educational values. Future Research: Future research should delve into AI adoption in various academic disciplines and regions, employing longitudinal designs and qualitative methods to understand cultural influences and the roles of peers and instructors. Investigating moderating factors influencing specific factors’ relationship with AI adoption intention is essential for a comprehensive understanding.