Title: Personalised learning systems: drivers of employees' behavioural intention

Authors: Sandra Schlagheck; Gerhard Schewe

Addresses: Center for Management, School of Business and Economics, University of Münster, Germany ' Center for Management, School of Business and Economics, University of Münster, Germany

Abstract: Knowledge management is essential for achieving and maintaining competitive advantage. This can be fostered by learning activities. Due to personalisation, learning materials can be tailored to the learners' needs and, thus, improve effectiveness and efficiency. To successfully implement such systems, users' acceptance is crucial. However, which factors affect the intention to use personalised learning systems remains unclear. By applying the unified theory of acceptance and use of technology, we explore factors influencing the intention to use them. Using a quantitative cross-sectional survey, 331 German employees from various industries and positions are asked. A structural equation model with maximum likelihood estimation is chosen for the analysis. Three potential moderators (gender, age, and experience) are examined based on multi-group analyses. Our results suggest that behavioural intention is mainly driven by the expected performance and the anticipated pleasure of using the system. Performance expectancy fully mediates the influence of trustworthiness on intention.

Keywords: behavioural intention; corporate learning; employees; knowledge management; moderation analysis; personalised learning systems; PLS; structural equation model; SEM; technology acceptance; trustworthiness; UTAUT2.

DOI: 10.1504/IJWET.2023.133621

International Journal of Web Engineering and Technology, 2023 Vol.18 No.3, pp.238 - 272

Received: 31 Dec 2022
Accepted: 24 May 2023
Published online: 25 Sep 2023 *

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