Digital Connectivity and Human Capital Development: Understanding the Distraction Paradox in Higher Education Using Structural Equation Modelling

Abstract

Abstract Digital technologies have rapidly transformed teaching and learning in higher education in recent years, reshaping the learning environment alongside factors that influence technology acceptance and users' digital competence. Grounded in the Technology Acceptance Model (TAM), this quantitative study examined the relationships among Perceived Ease of Use (PEOU), Perceived Usefulness (PU), Use Behaviour (UB), Behavioural Intention (BI), and Distraction from Digital Devices (DD), along with the single moderating effect of age. Data were collected via survey from 261 students across South Korean universities and analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM) in SmartPLS 4. Results showed that perceived usefulness had a significant positive effect on behavioural intention, while perceived ease of use had a significant positive effect on both perceived usefulness and use behaviour. Use behaviour was also significantly and positively related to behavioural intention. Therefore, behavioural intention and usage behaviour significantly predicted digital distraction. However, age group was not statistically significant on behavioural intention and did not moderate the relationship between use behaviour and behavioural intention positively. Among the devices assessed, smartphones emerged as the most distracting to students. These findings underscore the importance of user perception in driving technology adoption and highlight the limited role of age in shaping behavioural intentions within this context. This study contributes to the TAM literature by clarifying the pathways linking technology acceptance constructs to digital distraction, and it offers practical implications for educational institutions seeking to promote effective technology integration while reducing digital distractions and strengthening students' digital skills in learning environments. Keywords: Technology Acceptance Model, Perceived Ease of Use, Perceived Usefulness, Behavioural Intention, Digital Distraction, PLS-SEM