Oct 2026· Journal of Enterprise Information Management· 0 citations· 26 references
Technology Adoption and User BehaviourAI and HR Technologies
Abstract
Artificial intelligence (AI) is transforming human resource management (HRM), yet the cognitive and psychological factors associated with human resource (HR) professionals’ intentions to use AI remain incompletely understood. Drawing on the Technology Acceptance Model (TAM) and appraisal perspectives, this study examines the relative associations of Cognitive Evaluation, AI-Use Anxiety, AI-Related Job Insecurity, organizational AI use across HR functions, and AI Knowledge with behavioral intention to use AI. It also explores whether AI Knowledge moderates these relationships.
Survey data were collected from 285 HR professionals. Confirmatory factor analyses assessed the measurement structure of the study constructs, followed by hierarchical moderated regression analyses examining their unique associations with behavioral intention and the exploratory moderating role of AI Knowledge.
Cognitive Evaluation, reflecting perceived usefulness and ease of use, showed the strongest unique association with behavioral intention to use AI. AI-Use Anxiety demonstrated a smaller negative association, whereas AI-Related Job Insecurity, AI Use Across HR Functions, and AI Knowledge did not explain significant unique variance once the competing predictors were considered simultaneously. The comprehensive model explained 66.8% of the variance in behavioral intention. The exploratory interaction analyses provided no clear evidence that AI Knowledge systematically moderated the relationships examined.
Organizations implementing AI in HR should address both employees’ evaluations of the technology and apprehension surrounding its use. Implementation strategies should demonstrate the practical usefulness and usability of AI through role-specific examples, hands-on training, and opportunities to experiment with AI tools in realistic HR contexts. Organizations should also recognize AI-use anxiety as a distinct potential barrier by providing accessible support, low-risk opportunities for familiarization, and clear communication throughout implementation. These strategies may help create conditions that support employees’ willingness to use AI.
This study extends research on AI use intentions by examining cognitive, emotional, knowledge-related, and organizational-exposure factors simultaneously rather than in isolation. The findings show that HR professionals’ intentions to use AI are most strongly associated with evaluations of its usefulness and usability, while AI-use anxiety represents a smaller but distinct negative correlate. By distinguishing AI-use anxiety from AI-related job insecurity and individual AI knowledge from organizational AI exposure, the study provides a more differentiated account of the factors associated with HR professionals’ intentions to use AI.
This paper investigates developing real-world perception for active agents, proposes Gibson Environment for this purpose, and showcases a set of perceptual tasks learned therein.
F. Xia, Amir Zamir, Zhi-Yang He et al.· 2018 IEEE/CVF Conference on...· 1,069 citations· ⚡112
GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations...
Xiaotian Zhang, Chun-yan Li, Yi Zong et al.· arXiv.org· 216 citations· ⚡17
An algorithm is proposed which leverages disentangled semantic factors to generate adversarial perturbation by altering controlled semantic attributes to fool the learner towards various "adversarial" targets.
Haonan Qiu, Chaowei Xiao, Lei Yang et al.· arXiv.org· 208 citations· ⚡17
This work proposes a HALO that directly maximizes the utility of generations instead of maximizing the log-likelihood of preferences, and suggests that the best loss depends on the inductive biases most appropriate for a given setting, an oft-overlooked consideration.
This work investigates the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection and finds that the MTEs are biased, significantly favoring White-associated names in 85% of cases and female-associated names in only 11.1% of cases.
This work shows that orders of magnitude enhancement in performance could be obtained by a combination of hardware improvements and tight quantum-HPC integration and introduces high-performance architectures for quantum-probabilistic computing with custom-designed accelerators to tackle today's industry-scale classical...
Masoud Mohseni, Artur Scherer, K. Johnson et al.· arXiv.org· 121 citations· ⚡9
Related blog posts
MIT News · Artificial Intelligence· news.mit.eduSep 30, 2026
With $2.1 million funding from Google.org, the open-source Public Transit Intelligence Hub will unify public transit monitoring, operations, and passenger communication.