Unlocking the value of artificial intelligence in human resource management through AI capability framework

How could fit between polychronicity and multitasking shape employees' self-leadership? The moderating role of AI-empowered task processing

As AI becomes increasingly integrated into the workplace, understanding how prevailing multitasking practices interact with AI support to foster employee self-leadership is essential for enhancing organizational effectiveness. This study elucidates how the fit between multitasking and polychronicity among employees in organizations can synergistically influence their self-leadership within the context of AI empowerment. This study conducts two time-lagged survey studies using polynomial regression analysis, block variable analysis, and response surface methodology based on the "Fit Between Individuals, Tasks and Technology" (FITT) framework and the JD-R theoretical model. Study 1 examined the polychronicity-multitasking fit based on data collected from 116 employees at two time points in an AI company in China. Study 2 tested the mediating and moderating effect based on data of 188 employees from two other AI companies in China at three time points. The results show that congruence between polychronicity and multitasking predicts greater employee self-leadership compared to incongruence, and the higher the degree of congruence, the stronger the self-leadership. For incongruence, the "high-low" state promotes self-leadership better than the "low-high" state. We also reveal the mediating role of thriving at work and the moderating role of AI-empowered task processing between polychronicity-multitasking fit and self-leadership. For well-matched employees, AI serves as a facilitator of task processing, thereby enhancing employee self-leadership; whereas for mismatched ones, AI acts as an additional task burden or as a catalyst that exacerbates the existing imbalance, which impedes the motivation for self-leadership. These findings advance the understanding of self-leadership in multitasking contexts and provide valuable insights for organizations implementing AI tools. This study underscores the critical importance of aligning employees' work preferences with task demands to fully leverage the potential of AI empowerment.

When Healthcare Professionals Use AI: Exploring Work Well-Being Through Psychological Needs Satisfaction and Job Complexity

This study examines how the use of artificial intelligence (AI) by healthcare professionals affects their work well-being through the satisfaction of basic psychological needs, framed within Self-Determination Theory. Data from 280 healthcare professionals across various departments in Chinese hospitals were collected, and the hierarchical regression and regression were analyzed to assess the relationship between the use of AI, psychological needs satisfaction (autonomy, competence, and relatedness), and their work well-being. The results reveal that the use of AI enhances work well-being indirectly by increasing the satisfaction of these psychological needs. Additionally, job complexity serves as a boundary condition that moderates the relationship between the use of AI and work well-being. Specifically, job complexity weakens the relationship between the use of AI and the satisfaction of autonomy and competence, while having no significant effect on the relationship between the use of AI and the satisfaction of relatedness. These findings suggest that the impact of the use of AI on healthcare professionals' well-being is contingent on job complexity. This study highlights that promoting healthcare professionals' well-being at work in the context of AI adoption requires not only technological implementation but also ongoing adaptation to meet their evolving psychological needs. These insights provide a theoretical foundation and practical guidance for integrating AI into healthcare to support the well-being of healthcare professionals.

Leadership for AI Transformation in Health Care Organization: Scoping Review

BACKGROUND

The leaders of health care organizations are grappling with rising expenses and surging demands for health services. In response, they are increasingly embracing artificial intelligence (AI) technologies to improve patient care delivery, alleviate operational burdens, and efficiently improve health care safety and quality.

OBJECTIVE

In this paper, we map the current literature and synthesize insights on the role of leadership in driving AI transformation within health care organizations.

METHODS

We conducted a comprehensive search across several databases, including MEDLINE (via Ovid), PsycINFO (via Ovid), CINAHL (via EBSCO), Business Source Premier (via EBSCO), and Canadian Business & Current Affairs (via ProQuest), spanning articles published from 2015 to June 2023 discussing AI transformation within the health care sector. Specifically, we focused on empirical studies with a particular emphasis on leadership. We used an inductive, thematic analysis approach to qualitatively map the evidence. The findings were reported in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Reviews) guidelines.

RESULTS

A comprehensive review of 2813 unique abstracts led to the retrieval of 97 full-text articles, with 22 included for detailed assessment. Our literature mapping reveals that successful AI integration within healthcare organizations requires leadership engagement across technological, strategic, operational, and organizational domains. Leaders must demonstrate a blend of technical expertise, adaptive strategies, and strong interpersonal skills to navigate the dynamic healthcare landscape shaped by complex regulatory, technological, and organizational factors.

CONCLUSIONS

In conclusion, leading AI transformation in healthcare requires a multidimensional approach, with leadership across technological, strategic, operational, and organizational domains. Organizations should implement a comprehensive leadership development strategy, including targeted training and cross-functional collaboration, to equip leaders with the skills needed for AI integration. Additionally, when upskilling or recruiting AI talent, priority should be given to individuals with a strong mix of technical expertise, adaptive capacity, and interpersonal acumen, enabling them to navigate the unique complexities of the healthcare environment.

Taming the terminological tempest in invasion science

Standardised terminology in science is important for clarity of interpretation and communication. In invasion science - a dynamic and rapidly evolving discipline - the proliferation of technical terminology has lacked a standardised framework for its development. The result is a convoluted and inconsistent usage of terminology, with various discrepancies in descriptions of damage and interventions. A standardised framework is therefore needed for a clear, universally applicable, and consistent terminology to promote more effective communication across researchers, stakeholders, and policymakers. Inconsistencies in terminology stem from the exponential increase in scientific publications on the patterns and processes of biological invasions authored by experts from various disciplines and countries since the 1990s, as well as publications by legislators and policymakers focusing on practical applications, regulations, and management of resources. Aligning and standardising terminology across stakeholders remains a challenge in invasion science. Here, we review and evaluate the multiple terms used in invasion science (e.g. 'non-native', 'alien', 'invasive' or 'invader', 'exotic', 'non-indigenous', 'naturalised', 'pest') to propose a more simplified and standardised terminology. The streamlined framework we propose and translate into 28 other languages is based on the terms (i) 'non-native', denoting species transported beyond their natural biogeographic range, (ii) 'established non-native', i.e. those non-native species that have established self-sustaining populations in their new location(s) in the wild, and (iii) 'invasive non-native' - populations of established non-native species that have recently spread or are spreading rapidly in their invaded range actively or passively with or without human mediation. We also highlight the importance of conceptualising 'spread' for classifying invasiveness and 'impact' for management. Finally, we propose a protocol for classifying populations based on (i) dispersal mechanism, (ii) species origin, (iii) population status, and (iv) impact. Collectively and without introducing new terminology, the framework that we present aims to facilitate effective communication and collaboration in invasion science and management of non-native species.

Optimizing Nursing Productivity: Exploring the Role of Artificial Intelligence, Technology Integration, Competencies, and Leadership

Background

In the rapidly evolving healthcare management landscape, technology integration and artificial intelligence utilization play pivotal roles in shaping employee productivity. This research investigates these dynamics within Riyadh Province, Kingdom of Saudi Arabia, focusing on the relationships between technology integration, the use of artificial intelligence in nursing profession, nursing workforce competencies, technological leadership, and employee productivity.

Methods

A quantitative approach was employed, involving 329 nurses from five hospitals in Riyadh Province. Partial Least Squares Structural Equation Modeling facilitated comprehensive analysis of direct and indirect relationships among variables.

Results

Findings reveal that technology integration significantly enhances nursing productivity, while the use of artificial intelligence initially presents disruptions before yielding productivity gains. Nursing workforce competencies mediate these relationships, emphasizing the critical role of workforce readiness in harnessing technology's benefits. Surprisingly, technological leadership did not significantly moderate these effects.

Conclusions

This research offers vital insights for healthcare organizations, advocating strategic technology integration and workforce development. It underscores the significance of nursing competencies in navigating technological transformations and affirms the enduring importance of leadership in guiding these changes. As healthcare evolves, these findings provide guidance for optimizing technology and artificial intelligence to enhance employee productivity and patient care.

The effects of artificial intelligence on human resource activities and the roles of the human resource triad: opportunities and challenges

Introduction

This study analyzes the existing academic literature to identify the effects of artificial intelligence (AI) on human resource (HR) activities, highlighting both opportunities and associated challenges, and on the roles of employees, line managers, and HR professionals, collectively referred to as the HR triad.

Methods

We employed the scoping review method to capture and synthesize relevant academic literature in the AI-human resource management (HRM) field, examining 27 years of research (43 peer-reviewed articles are included).

Results

Based on the results, we propose an integrative framework that outlines the five primary effects of AI on HR activities: task automation, optimized HR data use, augmentation of human capabilities, work context redesign, and transformation of the social and relational aspects of work. We also detail the opportunities and challenges associated with each of these effects and the changes in the roles of the HR triad.

Discussion

This research contributes to the ongoing debate on AI-augmented HRM by discussing the theoretical contributions and managerial implications of our findings, along with avenues for future research. By considering the most recent studies on the topic, this scoping review sheds light on the effects of AI on the roles of the HR triad, enabling these key stakeholders to better prepare for this technological change. The findings can inform future academic research, organizations using or considering the application of AI in HRM, and policymakers. This is particularly timely, given the growing adoption of AI in HRM activities.

Development of big data assisted effective enterprise resource planning framework for smart human resource management

The planning of human resources and the management of enterprises consider the organization's size, the amount of effort put into operations, and the level of productivity. Inefficient allocation of resources in organizations due to skill-task misalignment lowers production and operational efficiency. This study addresses organizations' poor resource allocation and use, which reduces productivity and the efficiency of operations, and inefficiency may adversely impact company production and finances. This research aims to develop and assess a Placement-Assisted Resource Management Scheme (PRMS) to improve resource allocation and usage and businesses' operational efficiency and productivity. PRMS uses expertise, business requirements, and processes that are driven by data to match resources with activities that align with their capabilities and require them to perform promptly. The proposed system PRMS outperforms existing approaches on various performance metrics at two distinct levels of operations and operating levels, with a success rate of 0.9328% and 0.9302%, minimal swapping ratios of 12.052% and 11.658%, smaller resource mitigation ratios of 4.098% and 4.815%, mean decision times of 5.414s and 4.976s, and data analysis counts of 6387 and 6335 Success and data analysis increase by 9.98% and 8.2%, respectively, with the proposed strategy. This technique cuts the switching ratio, resource mitigation, and decision time by 6.52%, 13.84%, and 8.49%. The study concluded that PRMS is a solid, productivity-focused corporate improvement method that optimizes the allocation of resources and meets business needs.

Evaluating human resources management literacy: A performance analysis of ChatGPT and bard

This study presents a comprehensive analysis comparing the literacy levels of two Generative Artificial Intelligence (GAI) tools, ChatGPT and Bard, using a dataset of 134 questions from the Human Resources (HR) domain. The generated responses are evaluated for accuracy, relevance, and clarity. We find that ChatGPT outperforms Bard in overall accuracy (84.3% vs. 82.8%). This difference in performance suggests that ChatGPT could serve as a robotic advisor in transactional HR roles. In contrast, Bard may possess additional safeguards against misuse in the HR function, making it less capable of generating responses to certain types of questions. Statistical tests reveal that although the two systems differ in their mean accuracy, relevance, and clarity of the responses, the observed differences are not always statistically significant, implying that both tools may be more complementary than competitive. The Pearson correlation coefficients further support this by showing weak to non-existent relationships in performance metrics between the two tools. Confirmation queries don't improve ChatGPT or Bard's response accuracy. The study thus contributes to emerging research on the utility of GAI tools in Human Resources Management and suggests that involving certified HR professionals in the design phase could enhance underlying language model performance.

Does AI-Driven Technostress Promote or Hinder Employees' Artificial Intelligence Adoption Intention? A Moderated Mediation Model of Affective Reactions and Technical Self-Efficacy

PURPOSE

The increasing integration of Artificial Intelligence (AI) within enterprises is generates significant technostress among employees, potentially influencing their intention to adopt AI. However, existing research on the psychological effects of this phenomenon remains inconclusive. Drawing on the Affective Events Theory (AET) and the Challenge-Hindrance Stressor Framework (CHSF), the current study aims to explore the "black box" between challenge and hindrance technology stressors and employees' intention to adopt AI, as well as the boundary conditions of this mediation relationship.

METHODS

The study employs a quantitative approach and utilizes three-wave data. Data were collected through the snowball sampling technique and a structured questionnaire survey. The sample comprises employees from 11 distinct organizations located in Guangdong Province, China. We received 301 valid questionnaires, representing an overall response rate of 75%. The theoretical model was tested through confirmatory factor analysis and regression analyses using Mplus and the Process macro for SPSS.

RESULTS

The results indicate that positive affect mediates the positive relationship between challenge technology stressors and AI adoption intention, whereas AI anxiety mediates the negative relationship between hindrance technology stressors and AI adoption intention. Furthermore, the results reveal that technical self-efficacy moderates the effects of challenge and hindrance technology stressors on affective reactions and the indirect effects of challenge and hindrance technology stressors on AI adoption intention through positive affect and AI anxiety, respectively.

CONCLUSION

Overall, our study suggests that AI-driven challenge technology stressors positively impact AI adoption intention through the cultivation of positive affect, while hindrance technology stressors impede AI adoption intention by triggering AI anxiety. Additionally, technical self-efficacy emerges as a crucial moderator in shaping these relationships. This research has the potential to make a meaningful contribution to the literature on AI adoption intention, deepening our holistic understanding of the influential mechanisms involved. Furthermore, the study affirms the applicability and relevance of Affective Events Theory (AET) and the Challenge-Hindrance Stressor Framework (CHSF). In practical terms, the research provides actionable insights for organizations to effectively manage employees' AI adoption intention.

Revisiting the role of HR in the age of AI: bringing humans and machines closer together in the workplace

The functions of human resource management (HRM) have changed radically in the past 20 years due to market and technological forces, becoming more cross-functional and data-driven. In the age of AI, the role of HRM professionals in organizations continues to evolve. Artificial intelligence (AI) is transforming many HRM functions and practices throughout organizations creating system and process efficiencies, performing advanced data analysis, and contributing to the value creation process of the organization. A growing body of evidence highlights the benefits AI brings to the field of HRM. Despite the increased interest in AI-HRM scholarship, focus on human-AI interaction at work and AI-based technologies for HRM is limited and fragmented. Moreover, the lack of human considerations in HRM tech design and deployment can hamper AI digital transformation efforts. This paper provides a contemporary and forward-looking perspective to the strategic and human-centric role HRM plays within organizations as AI becomes more integrated in the workplace. Spanning three distinct phases of AI-HRM integration (technocratic, integrated, and fully-embedded), it examines the technical, human, and ethical challenges at each phase and provides suggestions on how to overcome them using a human-centric approach. Our paper highlights the importance of the evolving role of HRM in the AI-driven organization and provides a roadmap on how to bring humans and machines closer together in the workplace.

Exploring the antecedents of AI adoption for effective HRM practices in the Indian pharmaceutical sector

Purpose: The aim of this research is to investigate the factors that facilitate the adoption of artificial intelligence (AI) in order to establish effective human resource management (HRM) practices within the Indian pharmaceutical sector. Design/methodology/approach: A model explaining the antecedents of AI adoption for building effective HRM practices in the Indian pharmaceutical sector is proposed in this study. The proposed model is based on task-technology fit theory. To test the model, a two-step procedure, known as partial least squares structural equational modeling (PLS-SEM), was used. To collect data, 160 HRM employees from pharmacy firms from pan India were approached. Only senior and specialized HRM positions were sought. Findings: An examination of the relevant literature reveals factors such as how prepared an organization is, how people perceive the benefits, and how technological readiness influences AI adoption. As a result, HR systems may become more efficient. The PLS-SEM data support all the mediation hypothesized by proving both full and partial mediation, demonstrating the accuracy of the proposed model. Originality: There has been little prior research on the topic; this study adds a great deal to our understanding of what motivates human resource departments to adopt AI in the pharmaceutical companies of India. Furthermore, AI-related recommendations are made available to HRM based on the results of a statistical analysis.

Investigating the impact of artificial intelligence on human resource functions in the health sector of China: A mediated moderation model

Artificial intelligence (AI) is rapidly transforming the way human resources (HR) functions are carried out in the health sector of China. This study aims to scrutinize the impact of artificial intelligence on the human resource functions operating in the healthcare sector through technological awareness, social media influence, and personal innovativeness. Additionally, this study examines the moderating role of perceived risk between technological awareness and human resources functions. An online questionnaire was administered to human resources professionals in the health sector of China to gather data from 363 respondents. Partial least squares structural equation modeling (PLS-SEM), a statistical procedure, is implemented to investigate the hypothesis of the projected model of artificial intelligence and human resource functions. The research findings reveal that artificial intelligence significantly influences human resource functions through technological awareness, social media influence, and personal innovativeness. Furthermore, perceived risk significantly moderates the relationship between technological awareness and human resource functions. The findings of this study have important implications for HR practitioners and policymakers in the health sectors of China, who can leverage artificial intelligence technologies to optimize and improve organizational performance. However, its adoption needs to be carefully planned and managed to reap the full benefits of this transformative technology.

First impressions of a financial AI assistant: differences between high trust and low trust users

Calibrating appropriate trust of non-expert users in artificial intelligence (AI) systems is a challenging yet crucial task. To align subjective levels of trust with the objective trustworthiness of a system, users need information about its strengths and weaknesses. The specific explanations that help individuals avoid over- or under-trust may vary depending on their initial perceptions of the system. In an online study, 127 participants watched a video of a financial AI assistant with varying degrees of decision agency. They generated 358 spontaneous text descriptions of the system and completed standard questionnaires from the Trust in Automation and Technology Acceptance literature (including perceived system competence, understandability, human-likeness, uncanniness, intention of developers, intention to use, and trust). Comparisons between a high trust and a low trust user group revealed significant differences in both open-ended and closed-ended answers. While high trust users characterized the AI assistant as more useful, competent, understandable, and humanlike, low trust users highlighted the system's uncanniness and potential dangers. Manipulating the AI assistant's agency had no influence on trust or intention to use. These findings are relevant for effective communication about AI and trust calibration of users who differ in their initial levels of trust.

Exploring the relationships between attitudes toward emission trading schemes, artificial intelligence, climate entrepreneurship, and sustainable performance

The research objective of this research paper is to examine the relationships between organizational commitment to emission trading schemes, artificial intelligence, and climate entrepreneurship, as well as their impact on sustainable performance, i.e., environmental and organizational performance in organizations. This study aims to identify the key drivers and barriers to the adoption of these factors and to understand how they influence environmental and organizational performance. The study utilizes a cause-and-effect design on the sampled size of 387 subjects and employs the Smart PLS version 4.0 statistical tool to estimate the interactions between all constructs in the structural equation modeling. The research questions aim to explore the key drivers and barriers to the adoption of these factors in organization and their impact on sustainable performance, i.e., environmental and organizational performance. The study hypothesizes that organizational commitment to emission trading schemes, AI, and climate entrepreneurship has a positive impact on sustainable performances such as environmental and organizational performance. The findings suggest that attitudes toward AI and emission trading schemes have a direct impact on climate entrepreneurship, environmental performance, and organizational performance. By considering these factors together, the study seeks to uncover the synergistic effects and potential interactions between them and sheds light on their combined influence on environmental and organizational performance. Organizations can enhance environmental and organizational performance by prioritizing their attitudes toward emission trading schemes, AI, and climate entrepreneurship through resource allocation, technology investment, and fostering a climate entrepreneurship mindset. The research concludes that businesses that demonstrate high organizational commitment, positive attitude toward the ETS, and a focus on climate entrepreneurship experiences improved environmental and organizational performance.

Gamification and Soft Skills Assessment in the Development of a Serious Game: Design and Feasibility Pilot Study

BACKGROUND

The advent of new technologies has had a profound impact on the labor market, transforming the way we work and interact with each other. With the rise of digital tools and platforms, gamification has emerged as a powerful technique for enhancing productivity and engagement in various fields, including human resource management. In particular, gamification has been found to be effective in developing and assessing soft skills, which play a critical role in determining the success of individuals, teams, and organizations.

OBJECTIVE

We present a serious game that identifies the most sought-after skills in the job market and offers feedback, and we provide a set of guidelines for the creation of serious games.

METHODS

We present the serious game Among the Office Criticality (AOC). The AOC game structure involves a set of sequence analysis techniques, which is known as process mining.

RESULTS

The pilot study findings indicate that the game is both engaging and beneficial to subjects, suggesting that the results align with current theoretical perspectives. Furthermore, the study suggests that the obtained data can be extended to the broader population.

CONCLUSIONS

This study illustrates a serious game structured according to the needs of the labor market and developed to put the user at the center, using evaluation techniques consistent with the literature, with the aim of constituting an interdisciplinary approach suitable for adequately assessing users and creating value for them.

Fuzzy comprehensive evaluation and quantitative weight analysis in structure management of human resources

This research delves into the application effects of Fuzzy Comprehensive Evaluation (FCE) and quantitative weight analysis in the structure management of human resources (SMHR) to optimize the structure management. The research begins by analyzing the existing problems in SMHR, such as incomplete performance feedback and error-prone outsourcing decisions. By leveraging human resource management (HRM) characteristics, the researchers construct the SMHR evaluation index system. The Analytical Hierarchy Process (AHP) is employed to establish a hierarchical human resource structure model to determine the relative weight of each HRM indicator. Subsequently, the FCE method is utilized to build an SMHR optimization model, which is then scrutinized and assessed by means of an example. The findings indicate that the consistency ratio (C.R.) values of the first and second-level evaluation factors of the constructed model are less than 0.1, thus passing the consistency test and demonstrating credibility. Ultimately, the research effectively grades SMHR in the enterprise through the analysis of HRM optimization. Accordingly, this research presents a set of optimization suggestions and measures, including the establishment of a professional HRM operation team, acceleration of the construction of a professional talent team, enhancement of the intelligent level of the HRM center, and transition towards digital sharing. These proposed measures can serve as valuable experimental references for the optimization and improvement of HRM structures in future enterprises.

Human Resources Analytics for Public Personnel Management: Concepts, Cases, and Caveats

The advancement of data technology such as machine learning and artificial intelligence has broadened the scope of human resources (HR) analytics, commonly referred to as “people analytics.” This field has seen significant growth in recent years as organizations increasingly rely on algorithm-based predictive tools for HR-related decision making. However, its application in the public sector is not yet fully understood. This study examined the concepts and practices of HR analytics through a thematic review, and proposed a five-step process (define, collect, analyze, share, and reflect) for implementation in the public sector—the process aims to assist with the integration of HR analytics in public personnel management practices. By analyzing cases in both the public and private sectors, this study identified key lessons for functional areas such as workforce planning, recruitment, HR development, and performance management. This research also identified the necessary conditions for introducing HR analytics in public organizations, including data management, staff capabilities, and acceptance, and discussed the potential challenges of privacy, integrity, algorithmic bias, and publicness.