Unlocking LinkedIn adoption for higher education students: findings from PLS-SEM and NCA

Purpose LinkedIn offers multiple benefits to higher education students to build their careers. However, there is a lack of understanding of the factors which are significant and necessary to increase its adoption among such students. Having this granular understanding enables developers to design the platform more effectively. This study aims to investigate the factors affecting the adoption of LinkedIn for higher education students. Design/methodology/approach Using the partial least squares structural equation modeling (PLS-SEM) and necessary condition analysis (NCA), the authors analyzed the results from a self-administered survey of 120 higher education students with a model integrating subjective norm with variables from the value-based adoption model (VAM). Findings Subjective norm and perceived usefulness were found to be significant factors for increasing LinkedIn adoption. However, perceived enjoyment remained a necessary condition for higher education students to adopt LinkedIn. Originality/value This study contributes to the technology adoption and higher education literature on using social networking services for higher education marketing. To the best of the authors’ knowledge, this research is also among the first to examine the necessary factors affecting the adoption of LinkedIn among higher education students using the NCA. It further proposes a practical multi-stakeholder three-stage blueprint to increase LinkedIn adoption.