Investigating working adults' intentions to participate in microlearning using the decomposed theory of planned behaviour

Abstract

AbstractThe current work validated the decomposed theory of planned behaviour (DTPB) with working adults to assess its ability to predict intentions to participate in microlearning and also identify the significant factors that drive microlearning usage decisions. We found that positive attitudes towards microlearning (Attitude), stronger beliefs in others' opinions regarding microlearning use (SN), and stronger perceptions about one's capability to engage in microlearning (PBC), are associated with stronger intentions to participate in microlearning. All decomposed constructs were found to be significant predictors of the respective factors, except superior influence (SI) and resource facilitating conditions. We discuss potential targeted interventions focused on what works best to encourage microlearning adoption. For instance, while reporting superiors have no significant influence over one's microlearning use decisions, the opinions of peers and colleagues positively influence microlearning use. Hence, focusing on embedding community‐related aspects into a microlearning design may effectively encourage the use of microlearning. Practitioner notesWhat is already known about this topic The decomposed theory of planned behaviour (DTPB) is reported to be suited for studying e‐Learning adoption due to its robustness and high explanatory power. The DTPB allows for a deeper and more detailed explanation of behavioural intentions compared to other models. However, the DTPB has not been explored for learning innovation such as microlearning. What this paper adds The DTPB was found to be theoretically sufficient for predicting intentions to participate in microlearning. Attitudes towards microlearning was found as the strongest determinant of intentions, followed by one's beliefs in others' opinions regarding microlearning use, and lastly, perceptions about one's capability to engage in microlearning. Contrary to expectations, only the influence of peers in one's social circle was significantly predictive of subjective norm, while the influence of those in positions of authority in the workplace (e.g., one's reporting manager or superior) was not. Additionally, while self‐efficacy and technology‐related facilitating conditions (e.g., technical compatibility, technical support) were significant predictors of perceived behavioural control, the availability of resources required to perform microlearning was not. Implications for practice and/or policy Factors identified as strongly influential for behavioural intentions may aid in facilitating and accelerating microlearning adoption in the workforce.