Understanding self-directed learning behavior towards digital competence among business research students: SEM-neural analysis

Digital competence among business research students is heralded as a pragmatic expression of the quality of research output and effective collaboration. Self-Directed Learning (SDL) is a resourceful personal and professional development technique, yet there is minimal research on SDL for digital competence among business scholars. This study investigates the behavioral aspects of business research students to engage in the SDL mechanism for digital competence. A hypothesis-based research framework was outlined through Perceived Usefulness (PU), Facilitating Conditions (FC), Self-Directed Learning Readiness (SDLR), Personal Innovativeness (PI), Computer Self-Efficacy (CSE), and Behavioral Intention (BI). Data were collected through a quantitative survey and then analyzed by the novel multi-analytical approach, i.e., Partial Least Squares Structural Equation Modelling (PLS-SEM) to test hypotheses, Artificial Neural Network (ANN) to manage the non-linear associations in the model and to rank the predictors, and Importance Performance Map Analysis (IPMA) to assess the variables through importance and performance chart. Data analysis showed that all variables were significant predictors of SDL behavior where PI and CSE were prominent model antecedents. The study's contributions towards knowledge included the practical implications for boosting digital competence among young researchers, providing the in-depth analysis of antecedents of SDL behavior, and validation of multi-analytical tools in technology integration literature.