How artificial intelligence is changing organizations: Portuguese CEOs’ perspective

The adoption of Artificial Intelligence (AI) in human resource management (HRM) and the acceptance of digital HR systems by employees in higher education institutions have become important factors in the management of the workforce in these institutions. This study aims to present a systematic literature review on the available research on the adoption of AI in HRM in higher education institutions and the factors that influence employee acceptance of digital HR systems. The available literature on the adoption of AI in HRM in higher education institutions was identified using the PRISMA approach, and relevant literature was identified from Scopus, Web of Science, Google Scholar, and Africa Journals Online (AJOL) databases, with the publication dates ranging from 2019 to 2026. The literature review indicates that the adoption of AI in HRM in higher education institutions is mainly in administrative functions, not in strategic functions, and that the adoption is influenced by technological, organisational, and environmental factors, while the barriers to adoption include infrastructural, cost, skill, and ethical factors. Employee acceptance is influenced by perceived usefulness, ease of use, trust, and organisational support, but resistance is driven by fear, ethics, and threats to professional autonomy. These findings are discussed using the Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT), and the Diffusion of Innovation (DOI) theory, which offer a comprehensive understanding of the technology and socio-organisational factors that influence AI adoption in HE HRM. In the final section, recommendations are offered for policy, practice, and further research, which highlight the importance of strategic alignment, capacity building, ethics, and contextual approaches to AI adoption and employee engagement with HR technology.

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.

Artificial Intelligence (AI) transforms Education by enhancing personalized learning, automating administrative tasks, and improving student engagement. Despite its growing presence, AI adoption among university students in Malaysia remains limited. This study investigates the level of AI application usage, identifies key factors influencing adoption, and examines the relationship between these factors and actual AI usage. Using the Technology Acceptance Model (TAM), the study focuses on perceived usefulness (PU), perceived ease of use (PEOU), and technological skills as primary determinants of AI adoption. A quantitative research approach was employed, with data collected through a structured survey from 115 undergraduate students at a university in Kedah, Malaysia. After data screening, 105 valid responses were analyzed using descriptive statistics, correlation analysis, and multiple regression analysis. The findings reveal that AI-powered chatbots and academic support tools are the most frequently used applications due to their perceived usefulness and ease of use. However, AI adoption remains moderate, primarily due to limited technological skills and concerns about AI complexity and ethical implications. The study highlights the need for AI literacy programs, improved user-friendly AI designs, and clear ethical policies on AI-assisted learning. Universities should integrate AI training into curricula, and developers should ensure AI accessibility for students with varying technical proficiencies. Future research should explore cultural attitudes, ethical concerns, and trust in AI within educational settings. By addressing these challenges, institutions can enhance AI adoption, ultimately improving student learning outcomes and academic experiences.

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 ...

The recent rise of artificial intelligence (AI) has sparked interest among scholars in various fields, particularly public relations (PR) and strategic communication (SC). As the digital landscape evolves and competition intensifies, integrating AI technology into PR and SC practices becomes increasingly essential. This study aims to identify factors influencing AI adoption in PR and SC agencies using the technology–organization–environment (TOE) framework. Structural equation modeling was employed to analyze the relationships between influencing factors—client pressure, competitive pressure, compatibility, complexity, relative advantage, top management support, and organizational readiness—and the intention to adopt AI. A survey of 277 PR and SC agencies across the Arab world was conducted for data collection. The analysis indicated that client pressure, competitive pressure, relative advantage, and organizational readiness positively impact AI adoption, while complexity negatively affects it. Additionally, relative advantage mediates the relationship between competitive pressure and AI adoption, and organizational readiness mediates between compatibility and AI adoption, as well as between top management support and AI adoption. The study discusses the theoretical contributions and practical implications of these findings.

Artificial Intelligence (AI) is increasingly recognized as a key driver of economic growth, innovation, and enhanced efficiency across public services. In a Muslim-majority country like Malaysia, where the religion is recognized as the state's official religion, the widespread adoption of AI should take into consideration ethical and religious compatibility alongside technological and organizational readiness. Despite its transformative potential, AI adoption in public services remains constrained by concerns related to Shariah compliance, trust, and governance. This study introduces a conceptual model, TOEM (Technology–Organization–Environment–Maqasid), to guide AI adoption in Malaysia's public service landscape. The model is grounded in the Technology–Organization–Environment (TOE) framework and Diffusion of Innovation (DOI) theory, and uniquely integrates Maqasid Shariah principles as a moderating factor to ensure alignment with Islamic ethical foundations. The development of the TOEM model is informed by a comprehensive review of existing technology adoption theories, Islamic ethical frameworks, and preliminary fieldwork involving public agencies in Selangor. Findings highlight key barriers such as limited infrastructure, lack of expertise, and ethical concerns surrounding AI deployment. By aligning AI strategies with both conventional determinants and Islamic principles, TOEM provides a structured pathway for responsible and inclusive AI integration. Without such a model, Malaysia risks hindering AI adoption, thereby missing out on its economic and societal benefits. This model offers policymakers and practitioners a culturally coherent and values-driven framework to harness AI's full potential, mitigating adoption risks while maintaining public trust and adherence to national and religious values.

Purpose This study explores how organizational artificial intelligence (AI) adoption influences employee voice behavior through psychological mechanisms. Drawing on conservation of resources theory, we investigate job engagement as a mediator and leader-member exchange (LMX) quality as a moderator in this relationship. Design/methodology/approach We employed a 3-wave time-lagged design with 327 employees from South Korean organizations. Data were collected at six-week intervals: organizational AI adoption and LMX (T1), job engagement (T2) and voice behavior (T3). Moderated mediation analysis tested our hypothesized model. Findings Organizational AI adoption positively relates to voice behavior through enhanced job engagement. This indirect effect is strengthened by high-quality LMX relationships. Employees experiencing high LMX show stronger positive relationships between AI adoption and job engagement, subsequently exhibiting more voice behavior. The moderated mediation index confirms that LMX quality significantly influences how AI adoption affects voice behavior through engagement. Originality/value This study advances understanding of AI's human-side implications beyond productivity metrics. By revealing how technological and relational resources interact to influence discretionary behaviors, we contribute to literature on technology adoption, employee engagement and leadership. The findings demonstrate that AI adoption can enhance rather than diminish employee voice when supported by appropriate leadership relationships.

Over the recent years, firms have showed an increased demand for leveraging the performance potential of Artificial Intelligence (AI) technologies. However, the general applicability of AI to many settings raises important managerial questions about whether aspects such as potential for in-house versus external AI development, the presence of complementary technological resources (e.g. access to cloud computing), or even the specific use of AI (e.g. product/service development, operational management) would guide firm-level decisions underlying AI adoption. While recent research has provided some insight on worker-level decisions about using AI, knowledge about firm-level AI adoption, as well as heterogeneities in adoption, remains scant. To address this empirical gap, this preliminary paper provides one of the first comprehensive assessments of firm-level determinants of AI adoption while accounting for the heterogeneous sources, applications, and complementary technological resources of AI in organizations. Specifically, this paper reports the first results of a novel firm-level AI adoption survey deployed to a representative sample of South Korean firms from 2017 and 2018. Econometric assessments identified that firm size and use of existing intangible assets are important for AI adoption. However, while these characteristics are significant for AI adoption regardless of whether the technology was produced in-house or obtained from a vendor, analyses suggest that there may be heterogeneities on the determinants of AI adoption depending on the operational application of AI. Furthermore, AI adoption also ensues in tandem with the adoption of other digital technologies (e.g. big data, cloud computing, and the Internet of Things) and with contemporaneous firm reorganization.

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.

This study investigates the adoption of artificial intelligence (AI) among students in learning enhancement within open, distance, and digital education (ODDE) higher institutions, a context where AI holds huge potential to personalise learning experiences, bridge geographical barriers, and enhance engagement. Given the fragmented research landscape and the unique challenges faced by ODDE students, this study aims to examine the complex relationships between perceived usefulness, perceived ease of use, learners’ autonomy, and AI adoption, with learning engagement acting as a mediator. Employing a comprehensive survey instrument comprising 23 observable variables adapted from established scales, data were collected from 297 valid responses obtained through purposive sampling. Structural equation modelling (SEM) was utilised via SmartPLS software to analyse the data and test the proposed hypotheses. The results generally support the proposed model, revealing that learners' autonomy and perceived usefulness positively influence AI adoption, with learning engagement playing a crucial mediating role. However, perceived ease of use did not directly impact AI adoption. These findings highlight the importance of strategic implementation of AI, with an emphasis on creating user-friendly interfaces and fostering learner autonomy. Future research could explore longitudinal dynamics, contextual barriers, and specific design features to enhance AI adoption further. This study offers valuable theoretical implications by integrating the Technology Acceptance Model (TAM) with constructivist learning principles, while also providing actionable managerial insights for higher education institutions aiming to optimise AI integration for improved learning outcomes and organisational competitiveness. By focusing on perceptual and behavioural factors, institutions can foster sustainable and impactful AI adoption in the digital education landscape.

This study investigates how Entrepreneurial Orientation (EO), comprising of innovativeness, proactiveness, and risk-taking, influences the adoption of artificial intelligence (AI) within entrepreneurial firms. Using the Technology Acceptance Model (TAM) as a theoretical framework, data were collected from 324 entrepreneurs through an online survey, designed to capture their perspectives on AI adoption without priming participants toward specific AI technologies. The findings reveal that firms exhibiting higher levels of EO are significantly more likely to adopt AI. This research expands the application of EO and TAM in the context of AI adoption, providing critical insights for understanding how entrepreneurial firms approach emerging technologies. By laying a foundation for future research, this study encourages the exploration of AI adoption through qualitative and mixed-method approaches, offering a pathway for a deeper understanding of the strategic role AI plays in small businesses.

The adoption of artificial intelligence (AI) in the construction industry is shaped by a complex interplay of technological, organizational, regulatory, and market-driven factors. This study empirically examines the critical drivers of AI adoption in construction projects within the context of a developing economy. Using survey data and principal component analysis, 25 initial drivers were distilled into seven coherent components. These factors were subsequently mapped into four theoretical categories: technological, organizational, environmental policy and sustainability, and market & ecosystem drivers. This creates a conceptually grounded framework that bridges empirical evidence with theoretical understanding. Findings indicate that AI adoption extends beyond technology readiness, encompassing organizational priorities such as productivity, quality control, operational efficiency, regulatory & safety compliance, sustainability & environmental considerations, and market-driven imperatives. The study confirms that safety and sustainability emerge as distinct, but interrelated drivers, reflecting the growing role of policy and responsible practice in AI integration. The study contributes a multidimensional framework that offers both academic and practical insights for AI adoption strategies in construction. For practitioners, the findings highlight the importance of aligning technological investments, digital transformation initiatives, and operational workflows with organizational and market needs. For policymakers, the study underscores the value of governance frameworks, standards, and incentives that support safe, sustainable, and effective AI deployment.

The adoption of artificial intelligence (AI) systems is on the rise owing to their many benefits. This study conducted a bibliometric analysis to identify (1) how the literature on AI adoption has evolved over the past few years, (2) key themes associated with AI adoption in the literature, and (3) the gaps in the literature. To achieve these objectives, we utilised the Biblioshiny of R-package bibliometric analysis tool to analyse the AI adoption literature. A total of 91 articles were reviewed and analysed in this study. Four major themes were identified: AI, machine learning, the unified theory of acceptance and use of technology (UTAUT) model and the technology acceptance model (TAM). Using a content analysis of the identified themes, the study gained additional insight into the studies on AI adoption. Previous studies have been limited to specific industries and systems, and adoption theories like the UTAUT and TAM have also been utilised to a limited extent. Directions for future studies were provided.

This study examines the determinants of Artificial Intelligence (AI) adoption and its influence on employee engagement in Egyptian organizations, utilizing the Technology–Organization–Environment (TOE) framework. Based on survey data from 210 professionals across various industries and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM), the findings reveal that relative advantage, compatibility, and top management support significantly drive AI adoption. In contrast, complexity, security/privacy concerns, organizational readiness, competitive pressure, and external and government support do not show significant effects. Furthermore, AI adoption demonstrates a positive but modest impact on employee engagement, suggesting the presence of other contributing factors. These results reflect the unique socio-economic conditions in Egypt, where internal organizational factors outweigh external influences in shaping technology adoption. The findings have practical implications for organizations and policymakers, emphasizing the need to prioritize internal drivers—such as leadership commitment, perceived benefits, and system compatibility—when promoting AI adoption. Additionally, organizations should align AI initiatives with employee needs and workplace culture to enhance engagement outcomes.

English writing proficiency is vital for educational, professional and personal development. With the rapid growth of Artificial Intelligence (AI) in academia, understanding its impact on language learning is essential. Students' attitudes toward AI influence their motivation, engagement, and learning outcomes. This mixed-method research aimed to explore the attitudes of Pakistani English as a Second Language (ESL) undergraduates toward AI in improving writing skills. Perceived usefulness, ease of use, and behavioral intentions of ESL undergraduates toward AI are examined through the integration of Technology Acceptance Model (TAM) and Constructivist Learning Theory (CLT). This study also investigated the influence of motivation, engagement, and societal expectations on AI adoption for improving writing skills along with the constructivist learning strategies used by students. Quantitative data were collected from 215 students through a Google survey and semi-structured interviews were used to gather qualitative data from 10 students. Descriptive statistics and percentages were used to analyze quantitative data; however, qualitative data were analyzed through thematic analysis. The findings highlight that ESL undergraduates’ attitudes are strongly influenced by perceived usefulness and ease of use; however, attitudes and behavioral intentions received slightly lower scores. Furthermore, challenges related to over-dependency, ethical considerations, and subscription issues affect AI adoption. Motivation and societal expectations increase AI adoption; on the contrary, learner-centered and interactive approaches are needed to enhance engagement levels. Additionally, this study suggests the need for AI ethical guidelines, institutional support, and literacy training to fully benefit from AI tools in improving writing skills.