Unlocking the Path to AI Adoption: Antecedents to Behavioral Intentions in Utilizing AI for Effective Job (Re)Design

The rapid advancements in artificial intelligence (AI) have impacted various industries, including human resources (HR). This thesis aims to explore the role of AI in HR and its potential implications on organizations and employees. A comprehensive literature review was conducted to identify the various applications of AI in HR, such as recruitment, employee engagement, performance management, and training and development. The study also analyzed the potential benefits and risks associated with the integration of AI in HR, including issues related to bias, privacy, and job displacement. The findings of this study suggest that AI can enhance HR practices by improving efficiency, accuracy, and objectivity. However, the risks associated with AI adoption must be carefully considered and managed to ensure ethical and responsible use. This study provides insights into the current state of AI in HR and its future potential, offering recommendations for organizations and policymakers to maximize the benefits and minimize the risks of AI integration in the HR function. The use of artificial intelligence (AI) in human resources (HR) has become increasingly popular in recent years. AI has the potential to transform HR practices by enabling organizations to automate routine tasks, make more data-driven decisions, and improve the employee experience. However, the use of AI in HR also raises important ethical and legal considerations, such as algorithmic bias and data privacy. This thesis aims to explore the role of AI in HR and its impact on various HR functions, including recruitment and selection, employee engagement, performance management, and training and development. The study also examines the potential risks and challenges of using AI in HR and identifies strategies to mitigate these risks. The research methodology employed in this study is a mixed-methods approach, combining both qualitative and quantitative research methods. The qualitative component involves a literature review and case studies of organizations that have implemented AI in HR. The quantitative component involves a survey of HR professionals to understand their perceptions of AI in HR and their readiness to adopt AI in their organizations. The findings of this study reveal that AI has significant potential to improve HR practices, particularly in recruitment and selection, where it can reduce bias and improve the accuracy and efficiency of the hiring process. AI can also improve employee engagement by providing personalized experiences and feedback, and enhance performance management by enabling real-time monitoring and feedback. In training and development, AI can provide personalized learning experiences that meet the unique needs and preferences of individual employees. However, the study also reveals that the use of AI in HR raises important ethical and legal considerations that must be addressed. Algorithmic bias, data privacy, and the potential for job displacement are some of the key risks and challenges associated with the use of AI in HR. To mitigate these risks, organizations must adopt a proactive approach that involves regular monitoring and evaluation of AI systems, transparency in decision-making processes, and ongoing training and development for HR professionals. The study also identifies several critical success factors for the successful implementation of AI in HR, including strong leadership support, a clear understanding of business objectives, collaboration between HR and IT professionals, and a focus on employee engagement and well- being. Overall, this thesis contributes to the growing body of knowledge on the role of AI in HR and its implications for organizations and HR professionals. By identifying the potential benefits, risks, and challenges of using AI in HR, and providing strategies to mitigate these risks, this study aims to inform organizational decision-making and help HR professionals prepare for the future of work..

As organizations navigate the complexities of the digital age, the role of Human Resources (HR) is evolving to meet the demands of a digital workforce. This review explores the integration of Artificial Intelligence (AI) and Machine Learning (ML) in HR practices to enhance efficiency, effectiveness, and employee satisfaction in the digital era. AI and ML technologies offer HR departments the opportunity to streamline operations, improve decision-making processes, and enhance employee experiences. By leveraging AI and ML, HR professionals can automate routine tasks such as recruitment, onboarding, training, and performance evaluation, allowing them to focus on more strategic initiatives that drive organizational success. One of the key advantages of integrating AI and ML in HR practices is the ability to personalize employee experiences. These technologies can analyze large volumes of data to identify patterns and trends, enabling HR professionals to tailor programs and policies to meet the unique needs of individual employees. This personalization can lead to higher levels of employee engagement, satisfaction, and retention. Furthermore, AI and ML can help HR departments make more informed decisions by providing data-driven insights. These technologies can analyze employee data to identify areas for improvement, predict future trends, and develop strategies to address challenges proactively. By leveraging these insights, HR professionals can make strategic decisions that align with the organization's goals and objectives. However, integrating AI and ML in HR practices also presents challenges, such as data privacy concerns, ethical considerations, and the need for upskilling HR professionals to use these technologies effectively. Organizations must address these challenges to realize the full potential of AI and ML in HR practices. In conclusion, integrating AI and ML in HR practices offers organizations the opportunity to enhance efficiency, effectiveness, and employee satisfaction in the digital age. By leveraging these technologies, HR departments can streamline operations, personalize employee experiences, and make more informed decisions that drive organizational success. As organizations increasingly turn to digital solutions, the role of artificial intelligence (AI) and machine learning (ML) in Human Resources becomes pivotal. This paper will focus on how AI and ML are being integrated into HR functions such as recruitment, onboarding, and employee engagement. It will also discuss the ethical implications and the challenges of maintaining human touch in an increasingly automated workplace. Case studies of companies leading in digital HR practices will be highlighted to provide real-world insights. Keywords: Digital Force, HR Practices, AI, Machine Learning, Future.

IntroductionThis correlational study investigates the psychological and contextual factors associated with the adoption of artificial intelligence (AI) technologies among Italian high school students. Building on the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), the study extends the model by incorporating Problematic Internet Use (PIU) and Attitudes Toward AI (ATAI) to better account for habitual AI use and behavioural intentions.MethodA sample of 933 students (Mage = 16.20, SDage = 1.29, 54.98% female) completed a survey assessing key UTAUT2 dimensions, psychological traits, and usage patterns of AI tools in educational contexts. Confirmatory factor analysis (CFA) was used to evaluate the functioning of the adapted UTAUT2. Multiple regression was used to investigate factors predicting habit formation and behavioural intention related to AI use.ResultsConfirmatory factor analysis supported the structural validity of the adapted UTAUT2 model. Multiple regression analyses revealed that Performance Expectancy, Social Influence, Hedonic Motivation, and Schoolwork-related AI use were significant predictors of both habit and behavioural intention. PIU showed a robust association with habitual use, suggesting a spillover effect from compulsive Internet behavior to AI engagement. ATAI was associated only with behavioural intention, indicating its role in initial adoption rather than sustained use. Demographic and contextual factors (e.g., school type, citizenship) showed additional effects.DiscussionThese findings contribute to a more comprehensive understanding of adolescent AI engagement by highlighting the role of compulsive tendencies and motivational beliefs. The study underscores the importance of designing inclusive, age-appropriate interventions to promote balanced and informed AI use in educational settings.

This study investigates the determinants of artificial intelligence (AI) tool adoption among professional designers using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) as the guiding theoretical framework. As AI increasingly reshapes creative industries, understanding the factors that drive designers’ intention to adopt and actual use of AI tools has become both theoretically and managerially relevant. Data were collected through a cross-sectional survey administered to 170 professional designers, including graphic, UI/UX, and product designers. The proposed research model was tested using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results show that performance expectancy, effort expectancy, social influence, hedonic motivation, and price value have significant positive effects on designers’ behavioral intention to adopt AI tools. In contrast, facilitating conditions do not significantly influence behavioral intention but exert a strong positive effect on actual use behavior. Habit and behavioral intention also significantly predict use behavior, with habit emerging as the strongest determinant of sustained usage. The model explains a substantial proportion of variance in both behavioral intention and use behavior, confirming the strong predictive power of the UTAUT2 model in a creative professional context. This study contributes to the growing literature on AI adoption by extending UTAUT2 to the design industry and highlighting the joint role of utilitarian, hedonic, and habitual factors. Managerially, the findings provide actionable insights for AI tool developers and design organizations seeking to foster effective and sustained AI adoption. Keywords: Artificial Intelligence, UTAUT2, Technology Adoption, Designers, Use Behavior

This study aims to investigate the adoption of Artificial Intelligence (AI) by Micro, Small, and Medium Enterprises (MSMEs) in Indonesia, integrating the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT). The research examines explicitly how adoption determinants influence Behavioral Intention (BI) and how BI, in turn, drives business performance across key functional areas—marketing, human resources, finance, and operations. A quantitative research design was employed using a cross-sectional survey of 460 MSME owners, managers, and employees from various sectors. Structural Equation Modeling–Partial Least Squares (SEM-PLS) with SmartPLS 4.0 was applied to test the proposed model. Constructs were adapted from established TAM–UTAUT scales and extended with business performance measures. The results confirm that Performance Expectancy, Effort Expectancy, and Facilitating Conditions significantly influence BI, whereas Social Influence does not significantly shape adoption intention. Moreover, BI exerts a significant positive effect on marketing, human resources, financial, and operational performance, and mediates the relationship between adoption determinants and business outcomes. This study extends the TAM–UTAUT framework by empirically linking AI adoption determinants to functional business performance in MSMEs, particularly in a developing economy. The findings highlight the critical role of BI as a mediating mechanism, underscoring that adoption decisions are driven more by perceived value and ease of use than by external social pressures.

Purpose: This study examines artificial intelligence (AI) literacy and its influence on the adoption of human-centered AI among Generation Z (those born between 1997 and 2012) in Pakistan pursuing education and career in Library and Information Science (LIS). Drawing on the Unified Theory of Acceptance and Use of Technology (UTAUT) and UNESCO’s AI Competency Framework, the study examines how AI literacy and awareness of human agency shape students’ behavioral intention and actual use of AI in academic research. Methodology: A quantitative survey design was employed, with convenience sampling. Data were collected from 680 MPhil and PhD LIS research students enrolled in seven universities. Descriptive and inferential statistical techniques, including correlation analysis, multiple regression, and moderation analysis, were used to test the proposed research model. Findings: The results indicate that performance expectancy, effort expectancy, and AI literacy have significant positive effects on students’ behavioral intention and academic performance with AI. In this study, AI literacy was conceptualized as a competency-based antecedent and was found to be the most effective predictor of AI adoption; it was not examined as an outcome of prior adoption. Social influence showed mixed effects: it sometimes discouraged adoption when ethical or academic concerns were present, while facilitating conditions had a limited impact. Human agency awareness negatively influenced adoption, reflecting students’ concerns about academic integrity, autonomy, and over-reliance on AI. Behavioral intention was the strongest determinant of actual AI usage. Age and gender moderated selected relationships, whereas qualification level and university type did not. Implications: The findings demonstrate that AI adoption among Gen Z LIS students is primarily literacy-driven and human-centered rather than infrastructure-driven. Universities and academic libraries must prioritize AI literacy education, ethical guidance, and human-centered AI training within LIS curricula to support responsible, effective, and sustainable AI integration in higher education.

Artificial Intelligence (AI) is revolutionizing the recruitment landscape, presenting both opportunities and challenges for Human Resources (HR) professionals. This abstract explores the evolving role of AI in modern recruitment and its implications for HR practitioners. AI technologies offer HR professionals a multitude of opportunities to enhance recruitment efficiency, streamline processes, and improve decision-making. From automated resume screening and candidate sourcing to predictive analytics for talent forecasting, AI empowers HR teams to optimize their workflow and allocate resources more effectively. Moreover, AI-driven chatbots and virtual assistants enhance candidate engagement by providing instant responses and personalized interactions, thereby elevating the overall candidate experience. However, the adoption of AI in recruitment also presents challenges that HR professionals must navigate skillfully. Ethical considerations surrounding data privacy, algorithmic bias, and fairness in candidate selection require careful scrutiny and proactive measures to mitigate potential risks. Furthermore, there is a pressing need for HR professionals to develop competencies in data analysis, algorithm management, and ethical AI usage to harness the full potential of these technologies effectively. The study is aimed to find out the opportunities and challenges faced by hr professional by employing AI tools, quantitative data has been collected through surveys using stratified sampling method and for analyzing the data chi-square, correlations and Anova tools has been used. Key Word: Artificial Intelligence (AI), recruitment, Human Resources (HR), opportunities, challenges, efficiency, decision-making, candidate engagement, ethical considerations, data privacy, algorithmic bias.

The integration of Artificial Intelligence (AI) into Human Resource (HR) functions is increasingly transforming how organizations manage talent and design people-centric strategies. Advances in AI technologies have enabled organizations to improve efficiency, enhance decision-making, and strengthen workforce management across the talent lifecycle (Davenport &Ronanki, 2018). This study explores the application of AI in key HR functions such as recruitment and selection, onboarding, learning and development, performance management, employee engagement, and retention. By automating repetitive administrative tasks and leveraging predictive analytics, AI allows HR professionals to focus on strategic roles while offering more personalized and data-driven employee experiences (Bersin, 2019).However, the adoption of AI in HR presents several organizational and ethical challenges. Integrating AI tools with existing HR systems, managing employee resistance, and ensuring digital readiness remain significant implementation barriers (Marler & Boudreau, 2017). In addition, concerns related to data privacy, algorithmic transparency, and the risk of embedded biases in AI-driven decision-making have gained increasing scholarly attention (O’Neil, 2016; Raghavan et al., 2020). These concerns raise critical questions regarding fairness, accountability, and the long-term impact of AI on workforce diversity and inclusion.This study synthesizes insights from academic literature, industry reports, and organizational case evidence to provide a comprehensive understanding of AI-driven HR transformation. By examining both opportunities and challenges, the research offers valuable guidance for HR practitioners, business leaders, and policymakers. The study emphasizes the importance of balancing AI-enabled efficiency with human judgment, advocating for responsible and ethical AI adoption that supports transparent, inclusive, and sustainable talent management practices.

The rapid advancement of Artificial Intelligence (AI) is transforming the landscape of Human Resources (HR) in the United States, enabling HR professionals to shift from routine administrative tasks to strategic roles that support organizational agility and workforce preparedness for future challenges. This paper explores the transformative role played by AI in HR, with a particular focus on its potential to empower HR professionals to shift from routine administrative tasks to strategic roles that support organizational agility and workforce preparedness for future challenges. AI trends in HR are multifaceted, impacting various facets of HR functions. Automation of routine tasks frees HR professionals from repetitive administrative duties, allowing them to redirect their efforts toward strategic initiatives. AI is revolutionizing talent acquisition and selection by employing algorithms that analyze data from diverse sources to identify top talent, optimize recruitment processes, and predict candidate success. Performance management and employee development are entering a new era with AI tools providing personalized feedback, recommending learning paths, and identifying potential training needs. Employee engagement and well-being are being monitored through AI-powered sentiment analysis tools, ensuring a positive work environment. Workforce analytics and prediction powered by AI platforms analyze extensive datasets to predict hiring trends, workforce turnover, and potential skill gaps, thus informing strategic workforce planning. The adoption of AI in HR practices results in a significant shift from operational to strategic HR. As AI takes over routine tasks, HR professionals are liberated to focus on strategic initiatives such as workforce planning, talent management, and aligning organizational goals with long-term strategies. Data-driven decision-making becomes a hallmark of AI-integrated HR, providing real-time insights and predictive analytics that empower HR professionals to make informed decisions grounded in data. To future-proof the workforce, HR professionals must focus on developing AI-powered skillsets, including skills in data analysis, AI literacy, and effective human-AI collaboration. Addressing ethical considerations is crucial in the implementation of AI-powered HR solutions, with transparency, fairness, and accountability being imperative to protect employee privacy and build trust. Continuous learning and upskilling become non-negotiable commitments for both HR professionals and employees, ensuring they remain relevant and competitive in an environment characterized by the continuous evolution of AI and automation. Keywords: Human Resources; AI; USA; Innovation; HR Trends.

The integration of Artificial Intelligence (AI) has been reshaping healthcare globally. However, the AI adoption in Jordan is met with cautious progress. AI has shown substantial potential to enhance healthcare services and foster Emotional Intelligence (EI), especially in advanced economies. Despite its proven effectiveness elsewhere, the Jordanian populace is reluctant to adopt AI in the healthcare sector, with predictions for hospitalizations, medical consultations, and treatment recommendations being sluggish to gain acceptance. This study investigates the combination of Emotional Intelligence and AI adoption in the healthcare system in Jordan, guided by the Unified Theory of Acceptance and Use of Technology (UTAUT) model. While UTAUT typically considers performance expectancy, effort expectancy, social influence, and facilitating conditions as key determinants of technology acceptance, this study argues that emotional intelligence, including self-regulated, self-awareness, motivation, empathy, and social skills, should be integrated as direct determinants of behavioural intention. In this study, a quantitative approach has been employed, whereby questionnaires were delivered through email and messaging apps to evaluate the impact of emotional intelligence on Jordanians’ willingness to adopt AI technology in the healthcare sector. The findings suggested that the UTAUT model should be further expanded to encompass emotional intelligence as its fifth construct, particularly in developing countries like Jordan, where user models for AI adoption are less explored. The implications of the study extend to healthcare planners and developers in Jordan, providing insights into factors, which influence the successful adoption of AI technologies among diverse user groups. This study has provided valuable recommendations for developers of AI-based healthcare systems, enabling them to align their assistance with the perceptions and behaviours of Middle Eastern users. By doing so, they can foster increased acceptance of AI-based healthcare systems in the Middle East and other developing regions to improve healthcare services.

PurposeThe 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.Design/methodology/approachThis 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).FindingsThe 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.Research limitations/implicationsThe 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.Practical implicationsThis 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.Social implicationsThe 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.Originality/valueThis 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.

Background: This research investigates the perceptions, benefits, and challenges of generative artificial intelligence (AI) tools among students of Technical and Vocational Education and Training (TVET). A sample of 200 students from various institutions in Malaysia including Technical and Vocational Education and Training (TVET) institutions and universities in the fields of engineering, information technology, business studies and hospitality were surveyed for this study. You were selected for this study due to your experience with generative AI in your academic and real-world learning experiences. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT), the study addresses whether performance expectancy, effort expectancy, social influence and facilitating conditions are key factors in students' intentions to adopt and use generative AI. The findings demonstrate the importance of generative AI in improving TVET education but also spotlight challenges to its mainstream implementation. The study then draws recommendations for educators and policymakers, on how to ensure informed and effective AI use in TVET settings based on these findings.Objectives: The purpose of this research was to investigate the factors affecting the implementation of Generative AI among TVET students using UTAUT model, its advantages and disadvantages, as well as the role of institutional support and ethical concerns such as plagiarism and data privacy.Methods: Quantitative survey approach applied to collect comprehensive data on the adoption of generative AI among TVET students. A total of 200 students across multiple disciplines, including engineering, IT, and hospitality participated in this study. The participants were selected based on their exposure on generative AI tools to ensure relevance in assessing adoption factors. Data collection was conducted through a structured questionnaire based on the UTAUT model, covering constructs key such as performance expectancy, effort expectancy, social influence, and facilitating conditions. The survey aimed to capture students’ perceptions, experiences, and challenges related to AI adoption in their fields of study. Additionally, regression technique was used to analysed the data and identify relationships between UTAUT constructs and adoption behaviour.Results: The findings of the study focus on the validation of the UTAUT constructs and the analysis of survey responses. The descriptive statistics (mean, standard deviation) and inferential statistics (correlation, regression analysis) were applied to understand the impact of various factors on AI adoption.Conclusions: This study highlights the potential of generative AI tools to change the landscape of TVET education. These tools can make the learning experience richer by improving the learning efficiency, enhancing creativity and improving problem-solving skills. Nevertheless, successful adoption is contingent on overcoming major obstacles, including technological literacy gaps, institutional support, and ethical considerations. The statistical analysis showed that performance expectancy and facilitating conditions were significant determinants of students' behavioral intention to adopt AI. It further stresses the importance of organized policies and training programs to promote responsible AI use. They can help facilitate an environment where generative AI can be maximized for TVET, ultimately ensuring that students are being equipped with essential digital skills necessary for a technology-driven future.

PurposeThis paper uses the Technology Acceptance Model (TAM) to assess the readiness of the HR function within the UK Civil Service (CSHR) to implement AI to support performance. Academic literature in relation to AI acceptance in HR functions is currently limited, so this paper aims to establish a better understanding of the current landscape and level of ambition in this area.Design/methodology/approachA quantitative research approach was adopted to determine likely behavioral intentions of workers in the human resource (HR)function if AI were implemented, by investigating key aspects of the TAM (the perceived usefulness of AI and the transparency of the CSHR in adopting AI).FindingsWhile the results suggest that the CSHR is not ready to harness AI opportunities, employees were personally ready, despite perceiving a lack of sufficient knowledge in this area. The paper identifies that more time needs to be spent on raising awareness and upskilling the HR function before the CS can be considered fully ready to harness these opportunities.Originality/valueThe penetration of artificial intelligence (AI) technologies into the global workforce brings transformative potential to the governance structures and use of digital platforms in public sector organizations. AI is likely to play a role in the operation of HR functions and influence how they might operate in the near future.

This paper presents a feasibility study on the integration of artificial intelligence (AI) into human resource (HR) practices within the manufacturing sector of China. With the rapid advancement of AI technologies, industries worldwide are exploring its potential applications to streamline operations and enhance efficiency. However, the adoption of AI in HR functions, particularly within manufacturing companies in China, remains relatively unexplored. This study aims to assess the feasibility of implementing AI-driven solutions in various HR processes such as recruitment, training, performance evaluation, and employee engagement. The research methodology involves a combination of qualitative and quantitative approaches. Primary data will be collected through surveys, interviews, and focus group discussions with HR professionals, managers, and employees from a diverse range of manufacturing companies across different regions in China. Additionally, secondary data from relevant literature, industry reports, and case studies will be analysed to gain insights into current trends, challenges, and best practices associated with AI adoption in HR. Key factors influencing the feasibility of AI integration will be examined, including technological readiness, organizational culture, regulatory environment, cost-benefit analysis, and potential socio-economic implications. The study will also explore the perceived benefits and concerns regarding the use of AI in HR practices, such as improved recruitment accuracy, enhanced employee productivity, data privacy concerns, and ethical considerations. Furthermore, the research will identify potential barriers and enablers to successful AI implementation and provide recommendations for policymakers, HR practitioners, and organizational leaders to navigate the challenges and leverage the opportunities presented by AI in the manufacturing sector. By shedding light on the feasibility and implications of AI adoption in HR practices, this study seeks to contribute to the ongoing discourse on the future of work and technological innovation in China's manufacturing industry.

The adoption of Artificial Intelligence (AI) in the private sector is increasingly transforming workplace practices, necessitating an understanding of the factors influencing its acceptance and usage among employees. This study explores the relationships between key determinants, performance expectancy, effort expectancy, social influence, and facilitating conditions and their impact on the actual use of AI, mediated by behavioral intention. Guided by the Unified Theory of Acceptance and Use of Technology (UTAUT), this study utilizes a quantitative approach to collect and analyze data from employees in Malaysia's private sector. The findings reveal that performance expectancy, effort expectancy, social influence, and facilitating conditions significantly influence behavioral intention, which, in turn, mediates their effects on actual AI usage. Facilitating conditions also directly affect actual use, highlighting the importance of organizational support in fostering adoption. The study provides critical insights for private-sector organizations, policymakers, and practitioners aiming to enhance AI acceptance and integration in workplace environments. These results contribute to the existing literature by offering a holistic understanding of the factors driving AI adoption and use, with practical implications for improving workforce readiness and technological implementation strategies.