Division of Human Resource Management

Purpose This study examines how the adoption of artificial intelligence (AI) is reshaping the HR function in large organisations. Addressing the predominance of normative accounts and limited empirical evidence in the AI–HRM literature, it explores how AI influences HR roles, the strategic positioning of the HR function, employee well-being and the professional future of HR specialists. Design/methodology/approach The authors used a multi-case qualitative research design to examine AI adoption and impacts in the HR function, studying five large financial services organisations in France and Canada with similar AI–HRM maturity. Data were collected through semi-structured interviews with HR analytics leaders overseeing AI–HR applications, HR professionals and line managers. Findings The study highlights certain trends and identifies two trajectories underpinning the AI–HR interaction: an “AIHR” model, prioritising automation and cost-minimisation through AI, and potentially compromising employee well-being and deprofessionalising the HR function; and an “HAIR” model, placing greater emphasis on maintaining a human-centric, well-being-oriented approach, one in which “shepherding” of AI applications by HR specialists helps to entrench HR professionalism and to minimise perversities. Practical implications The choice between an AIHR or HAIR model requires periodic assessment of HR sub-functions and evolving HR roles. Under an HAIR model, closer collaboration between AI developers and HR professionals is essential to reduce opacity and mistrust, requiring the development of HR “fusion skills” and the implementation of a formal AI–HRM strategy encompassing governance, data, training, communication and stakeholder engagement. Originality/value The fast-paced rise of AI in HRM is reshaping the activities and roles of the HR function, with significant implications for organisational performance, employee well-being and the future of the HR profession. Despite growing interest and limited empirical research on the AI–HR interaction, this study explores how AI is being implemented in the HR function and its impacts on HR roles and the tensions they entail.

The study examines the role of trade unions ivn organizational decision-making, focusing on the University of Dodoma. It highlights that trade unions represent worker interests and serve as critical stakeholders in discussions about organizational change. Active unions lead to higher employee satisfaction, lower turnover rates, increased productivity, and better working conditions. The research design used was a case study, with a sample size of 100 respondents from the University of Dodoma's three colleges. Data was analyzed using descriptive, regression, and correlation methods.

HRMARS - The turnover in the manufacturing sector has remained high for the past several years and often contributes to the loss of highly skilled workers in the workforce. The study aimed to investigate the moderating effect of Perceived Organisational Support (POS) on the relationship between employee engagement and turnover intention among employees. This research involved 151 respondents from several manufacturing companies located in Southern, Malaysia. The sample was selected through convenient and snowball sampling methods. The Gallup Employee Engagement Survey, Survey of Perceived Organisational Support (SPOS), and Turnover Intention Scale were used to measure the variables in the current research. The moderation model was tested by using the regression analysis. Overall, there was a moderate level of employee engagement and turnover intention among manufacturing employees. The findings show a moderate and negative influence of employee engagement on turnover intention. Further analysis found that there was a significant moderating effect of POS on the relationship between the two variables. The study highlights the importance of employee engagement and POS in a manufacturing sector in order to retain optimum human resources within the organisation, thereby improving the organisational efficiency.

Abstract Consumptive use of freshwater is of concern in many estuarine ecosystems, and various frameworks have been used to prescribe environmental flows to benefit native species. However, few of these frameworks explicitly examine the potential trade‐offs between socioeconomic and conservation‐oriented values. This is exemplified in California, USA, where freshwater management has been an area of focus and controversy. Operations of numerous reservoirs and water diversion facilities distributed throughout the state, while critical for economic and public health benefits, have contributed to the decline of many native species. The endangered delta smelt ( Hypomesus transpacificus ) is endemic to the Sacramento‐San Joaquin Delta, the heart of California's complex water conveyance system. To aid recovery of delta smelt, fall‐timed freshwater pulse flows were implemented, which require water to be either released from reservoirs, or made unavailable to export for consumptive uses. Previous research has indicated that the effectiveness of the current pulse flow action could be improved by reconsidering the timing and magnitude; however, uncertainties in the predicted fish response to flow pulses may hinder decision‐making about flow management. Using a water resource planning model, different iterations of an individual‐based life cycle model, and decision analysis tools, we assessed the importance of sources of uncertainty to hypothetical flow management decisions, including uncertainty surrounding the predicted responses in delta smelt population growth rates, and variability of decision‐maker's values. We found both the choice of which (if any) flow action to take for delta smelt, and the expected value of further research, depended on how decision‐makers weight the delta smelt and water supply objectives. There was expected value of information (VOI) only if a decision‐maker weighted the delta smelt objective ≥0.59, and within this range, research to improve estimates of changes in delta smelt prey items related to flow actions could be prioritized over other sources of uncertainty to improve outcomes of decision‐making. Our study demonstrates how uncertainty, even if large, may not be equally relevant to different decision‐makers (e.g., with different agency missions), and how VOI analysis can be used to guide management in an overallocated water system such as California.

"It takes a village" to build an ethical workplace, and employees who witness unethical behavior in the workplace play a crucial role in alerting organizations to wrongdoing and preventing its recurrence. While prior research has primarily focused on personal and intraorganizational factors shaping observer responses to workplace unethical behavior, the potential influence of extraorganizational factors has been largely overlooked. Recognizing that employees often share workplace experiences with close others, such as their family members, we explore how the extent of sharing with family members influences employee responses to observed unethical behavior. Integrating the functional perspective of rumination and social sharing research, we theorize that the extent of sharing with family members increases observer reflective rumination about the unethical behavior and their tendency to engage in ethical voice at work. We found empirical evidence in two critical incident surveys (Studies 1 and 4), an experiment (Study 2), and a cross-lagged panel survey (Study 3). Finally, we discuss the theoretical and practical implications of this research. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Purpose AI and digital innovation (DI) enhance the operational efficiency of hotels while simultaneously fostering green innovation (GI). This dual benefit provides hotels with a competitive edge, enabling them to pursue more sustainable practices that align with the growing emphasis on environmental protection among consumers. This paper seeks to examine the influence of AI and DI on GI, sustainable performance (SP) and competitive advantage (CA). Design/methodology/approach Employing path modeling through the partial least squares structural equation modeling (PLS-SEM) technique to enhance understanding of the effects of artificial intelligence and digital innovation. An anonymous survey consisting of 34 questions was administered to 668 participants from various hotels in Vietnam. Findings The findings indicate that artificial intelligence positively influences both DI and GI, with DI serving as a mediator in the connection between AI and GI. Additionally, AI contributes positively to SP and CA, with digital and GI playing mediating roles in this relationship. Practical implications AI and digital innovation enhance green innovation and sustainability in Vietnamese hotels through smart data and energy management. Furthermore, it underscores the necessity of combining these two forms of innovation to effectively promote environmental protection and sustainable development within the hospitality sector. Originality/value This research highlights the significant impact of AI as a driving force for both digital and environmental innovation. A key finding of the study is that the advantages related to SP and the competitive edge derived from AI are primarily attained through initiatives in green and DI.

In the context of advancing financial inclusion in developing economies, e-wallets have emerged as essential enablers of user access and engagement. This study examines the factors influencing Generation Z’s intention to adopt e-wallets, a digitally native cohort likely to drive the future of financial technology adoption. Based on a structured survey of 480 Gen Z respondents from four major cities in Vietnam, the research utilizes Partial Least Squares Structural Equation Modeling (PLS-SEM) to assess the impact of key determinants, including mobile ease of use (MEOU), mobile usefulness (MU), privacy and security (PS), interface design (ID), customer service (CS), and preferential treatment (PF). The findings indicate that MEOU, MU, PS, and PF significantly influence Gen Z’s intention to use e-wallets. Moreover, ID and CS, which have received limited attention in prior models, also exhibit statistically significant effects, albeit of lesser magnitude. These results extend the Mobile Technology Acceptance Model by incorporating emerging experiential and service-related factors, providing both theoretical insights and practical recommendations for designing e-wallet platforms that meet the needs of digitally native users in emerging economies.

The study seeks to ascertain if there is any relationship between performance of state-owned enterprises (SOEs) and board characteristics. Three proxiesor characteristics of the board, including board independence, board gender diversity and board size as vices of corporate governance are used to find their effects on the performance. The performance of the sampled firm X was measured by ROA, an accounting-based measure. The study reviews literature on board characteristics linking it mainly to the Agency Theory underpinning the study. The quantitative research approach is used to find the relationship between independent and dependent variables with a case study as a design. The sample of the study was drawn from the selected organisation and its subsidiaries using stratified random sampling. The sample consists of members from directors and senior management. The data collected through questionnaires was entered into IBM SPSS software for analysis. SPSS, Pearson correlation analysis and multiple regression were conducted to examine the relationships between the variables under study. Both the correlation and regression results reveal a significant positive relationship between board independence, gender diversity and firm performance in Zimbabwean SOEs. On the contrary, a significant negative relationship was found between board size and firm performance. The study recommends establishing boards dominated by independent non-executive directors and embarking on gender streamlining to achieve improved performance. Similar studies that could be useful in covering a large number of companies are recommended.

Community-based smallholder goat producers’ participation in breeding programs is a means of improving the production and productivity of their goats, but nowadays it does not yield significant results in pastoral and agro-pastoral areas. This is due to many determining factors, that smallholder goat producers do not participate in community-based breeding programs (CBBPs). In Ethiopia, studies tried to identify the determining factors that influence smallholder goat producers’ participation in CBBP but did not address the problem in the study area. As a result, this research aimed to identify the determinants of community-based goat breeding programs in the study area. A semi-structured interview schedule, focus group discussions and key informant interviews were used to gather primary data. Primary data about the study was collected from 138 sample households. The study was conducted in the Bena-tsemay district of southern Ethiopia. A probit regression model was used to analyze determinants of smallholder goat producers’ participation in CBBP. In the probit regression among 13 identified independent variables, age, goat farming experience, access to credit and distance to training center influence CBBP participation at (p < .01). Education status and veterinary service problems influence CBBP participation at (p < .05) significance level. To strengthen CBBP, government lead investment in goat breeding is important.

Strategic product development is crucial for maintaining competitive advantage and promoting innovation in contemporary dynamic and customer-centric marketplaces. The Kano Model provides a comprehensive framework for categorizing product characteristics into basic, performance, and exciting categories, offering insights into the impact of different variables on consumer satisfaction. This methodology was implemented using the Nakano_model 4 dataset, obtained from the OTTO – Multi-Objective Recommender System on Kaggle, which includes extensive behavioral data from user sessions. Product characteristics were assessed for their influence on user engagement and satisfaction via the integration of Kano-based categorization and multi-objective optimization approaches. Analytical methods, including data pretreatment and feature significance ranking, were used to enhance the feature selection process. The execution of this hybrid architecture produced substantial outcomes, with the model attaining a 98% accuracy rate. These results emphasize the need of integrating Kano Model insights with recommended system methodologies to inform product innovation and customer-centric design. The model's efficacy underscores its capacity to improve decision-making about product feature prioritization and to maintain innovation in competitive business contexts.