Human-AI collaboration (HAC) has attracted growing attention in operations and supply chain management due to its potential to enhance decision-making and organisational performance. Despite this promise, automation preferences remain uneven across organisations. This study examines preferences for full, partial, and no automation using a discrete choice experiment. Data were collected from 398 managers, generating 1592 choice sets and 4776 alternative-level observations. Alternative-specific conditional logit results show that automation preferences are primarily shaped by a trade-off between performance gains and workforce concerns. Operational efficiency and accuracy significantly increase the likelihood of selecting automation, whereas job insecurity exerts a strong negative influence. Anthropomorphism generates positive but context-dependent utility, while social interaction and investment cost are not statistically significant in the overall model. Managers prefer collaborative human-AI arrangements, choosing partial automation in 55.7% of scenarios and selecting no automation in only 3.4% of choice scenarios, indicating that the central decision concerns the degree of automation rather than its adoption. Further subgroup analyses reveal context-specific patterns across gender, sector, and organisational size, indicating subgroup-specific associations between socio-technical attributes and automation preferences. By identifying subgroup-specific patterns in the utility of AI attributes, this study extends socio-technical systems and contingency perspectives on technology adoption and highlights the need for context-sensitive automation strategies that balance efficiency gains with workforce security.
Digital transformation and artificial intelligence are reshaping human resource management through algorithmic recruitment, automated evaluation, predictive analytics, and generative tools. While these systems improve efficiency, they also intensify technostress through opacity, surveillance, reduced control, and digitally intensified work arrangements. Responding to a fragmented literature spanning both AI-enabled and broader digital work systems, this study provides a B-SLR that maps technostress in digital transformation while identifying AI-specific stressors as a key emerging stream for HRM research. Drawing on 194 Scopus-indexed articles, we combine performance analysis and science mapping to identify publication trends, dominant themes, and intellectual structures. The review shows how AI-specific stressors extend established technostress mechanisms and shift attention toward transparency, control, fairness, and responsible HRM design. We conclude with actionable implications for researchers and practitioners aiming to design more transparent, responsible, and sustainable digital and AI-enabled HRM systems.
Virtual immersive learning platforms (VILPs), integrating immersive technologies, gamified design, and learning tools, enable meaningful engagement in learning while also creating possibilities for compulsive engagement. Drawing on a sociotechnical affordance perspective, this study examines how configurations of affordances are associated with meaningful and compulsive engagement and how these pathways vary across demographic conditions. Using a mixed-methods design, we first uncover the core affordances of VILPs and organize them within a sociotechnical perspective. We then employ a two-stage fuzzy-set qualitative comparative analysis (fsQCA) to examine affordance configurations associated with different engagement outcomes and the role of demographic conditions. The findings show that meaningful and compulsive engagement are associated with distinct combinations of affordances and these configurational patterns vary across demographic conditions. These findings advance understanding of VILP engagement by revealing the configurational pathways associated with meaningful and compulsive engagement and offering practical insights into designing more inclusive and responsible learning platforms.
Anthropomorphism is increasingly incorporated into AI agent design to enhance human–AI interaction. However, prior research has largely emphasized its benefits, leaving unclear how anthropomorphic AI shapes moral behavior when AI actively participates in decision-making. Across six studies (N = 1680), including a real-time human–AI interaction experiment, we show that highly (vs. lowly) anthropomorphized AI increases individuals’ tendency to engage in unethical behavior. We identify a responsibility-attribution process underlying this effect: AI anthropomorphism increases perceived AI moral agency, which shifts perceived responsibility for decision outcomes from the self toward AI and, in turn, increases unethical behavior. Further, this effect is attenuated when personal responsibility is made salient or when AI service psychological ownership is high. These findings reveal a potential dark side of AI anthropomorphism by showing how humanlike AI design reshapes moral agency perceptions and responsibility attribution, with implications for the responsible design and use of AI agents.
This study examines LLM-based AI chatbots as social interaction partners. It identifies young adults’ support-seeking motivations and models their associations with two interaction processes (Perceived Authenticity and Interaction Intensity) and two psychological outcomes (Resilience and AI Dependence). In Study 1, we developed the Motivation Scale for Generative AI (MS-GAI) through theory-driven item generation and interviews and validated it with a survey of Korean young adults (N = 300). Exploratory and confirmatory factor analyses supported an eight-factor, 40-item structure grounded in four social-support domains and supplemented by context-specific motivations. In Study 2, structural equation modeling showed that Emotional Support and Self-Enhancement were positively associated with both Perceived Authenticity and Interaction Intensity. Objective Advice Seeking was positively associated with Perceived Authenticity, whereas Learning Assistance, Daily Problem-Solving, and Entertainment/Mood Diversion were positively associated with Interaction Intensity. Perceived Authenticity was also positively associated with Interaction Intensity. Both interaction processes were positively related to self-reported Resilience and AI Dependence, with Interaction Intensity showing the stronger association with AI Dependence. These findings identify distinct but overlapping interaction pathways through which support-seeking motivations are associated with adaptive and maladaptive outcomes. The study extends understanding of social support in human–AI interaction by providing a validated motivation measure and showing how shared interaction processes relate to both short-term perceived recovery and dependence. The findings are consistent with the Paradoxes of Technology perspective and highlight the need for emotionally supportive yet balanced design and policy safeguards.
