Transforming learning & development
: the impact of artificial intelligence and automation on employee motivation to learn

  • Tanja Reitgruber (Student)

Student thesis: Master's Thesis

Abstract

Technological advancements have transformed employee learning and development (L&D) from one-size-fits-all approaches to personalized initiatives. Given AI’s potential to learn and adapt to individuals’ demands, researchers and practitioners have started investigating AI applications in L&D. However, whether employees prefer AI-guided learning and whether it actually drives motivation to learn remains an empirical question. Thus, this research aims to investigate the impact of AI-guided L&D compared to simple automation-based L&D on employee motivation to learn, drawing on the Self-determination Theory (SDT) and the Unified Theory of Acceptance and Use of Technology (UTAUT). The proposed model was tested using a PLS-SEM analysis with 144 participants in an experimental survey. The results revealed thatAI in L&D increases motivation to learn more than simple automation. However, this effect is fully mediated by the increase in perceived competence due to AI, emphasizing the importance of providing customized trainings tailored to employees’ learning styles and skills, along with consistent feedback, to foster perceived competence. Furthermore, the study demonstrates that motivation to learn significantly predicts individuals’ behavioural intention to use a L&D system. Specifically, AI-guided L&D, promoting competence, generates higher motivation to learn, leading to increased use intentions. Thus, the study highlights that AI in employee L&D drives autonomous motivation through self-determination surpassing simple automation-based approaches. These findings provide valuable implications for organizations and practitioners seeking to foster employee motivation and technology acceptance through new L&D solutions, suggesting that investing in AI could be beneficial.
Date of Award27 Jun 2023
Original languageEnglish
Awarding Institution
  • Universidade Católica Portuguesa
SupervisorFilipa de Almeida (Supervisor)

Keywords

  • Artificial intelligence
  • Simple automation
  • Learning & development
  • Employee motivation
  • SDT
  • Technology acceptance
  • UTAUT

Designation

  • Mestrado em Gestão e Administração de Empresas

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