Recruitment chatbot acceptance in a company: a mixed method study on human-centered technology acceptance model

Abstract

This study developed a Human-Centered Technology Acceptance Model (HC-TAM) for recruitment chatbots, integrating aspects of the traditional Technology Acceptance Model (TAM)(Davis in 1989) with a focus on human-centered factors such as transparency, personalization, efficiency, and ethical concerns, alongside the fundamental TAM constructs of perceived ease of use and perceived usefulness. The study shows that the intention to use technology is influenced by their perceptions of its usefulness and ease of use. By extending TAM to include human-centered considerations, this research aimed to capture the diverse factors that significantly influence users’ acceptance of chatbots in the recruitment process. A three-phase study has been carried out, each serving a distinct purpose. (a) Phase 1 focuses on defining primary themes through qualitative interviews with 10 participants, laying the foundation for subsequent research. (b)Building upon this foundation, Phase 2 engages 28 participants in a refined exploration of these themes, ending in a comprehensive landscape of user perspectives. (c) Finally, Phase 3 employs rigorous Structural Equation Modeling for theoretical framework examination, yielding critical constructs and hypotheses. Moreover, Phase 3 encompasses the thorough development of measurement instruments and extensive data collection, involving 146 participants through questionnaires, the study found that the acceptance of recruitment chatbots is significantly enhanced when these systems are designed to be transparent, provide personalized interactions, efficiently fulfill user needs, and address ethical concerns. These findings contribute to the broader understanding of technology acceptance in the context of recruitment, offering valuable insights for developers and designers to create chatbots that are not only technically advanced but also ethically sound, user-friendly, and effectively aligned with human needs and expectations in recruitment settings.