The Effect of Big Data HR and Personalized Learning on Career Development: Mediating Effect of Employee Engagement

Saiful Anuar; Jhonni Ardan Mardan; Faira Mardina Dzikra; Herispon

doi:10.37641/jimkes.v14i1.4674

Authors

Saiful Anuar Sekolah Tinggi Ilmu Ekonomi Riau, Indonesia
Jhonni Ardan Mardan Sekolah Tinggi Ilmu Ekonomi Riau, Indonesia
Faira Mardina Dzikra Sekolah Tinggi Ilmu Ekonomi Riau, Indonesia
Herispon Sekolah Tinggi Ilmu Ekonomi Persada Bunda, Indonesia

DOI:

https://doi.org/10.37641/jimkes.v14i1.4674

Keywords:

Big Data in HR Technology, Career Development, Employee Engagement, Fiverr, Personalized Learning Paths

Abstract

Digitalization has transformed career development in modern work environments. This study investigates the effect of big data in human resources technology and personalized learning paths on career development, with employee engagement as a mediating variable, using freelancers on the Fiverr platform as the research context. Employing a quantitative research design and Structural Equation Modeling with SmartPLS, data were collected from 100 respondents recruited through active Fiverr communities on social media. The results reveal that big data in human resources technology significantly influences career development directly and indirectly through employee engagement. Similarly, personalized learning paths indirectly enhance career development by fostering higher employee engagement, although the direct effect was not significant. These findings underscore the critical role of engagement in bridging innovative human resources practices and career outcomes. The study contributes to the existing body of knowledge by providing empirical evidence on the interconnectedness of advanced human resources technologies, engagement, and career growth in the digital era. Practical implications highlight the need for organizations to prioritize engagement-driven strategies and integrate advanced technologies to empower employees. Future research should explore these relationships in diverse cultural and industrial contexts to enhance the generalizability of the findings.

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