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All Journal JURISMA: Jurnal Riset Bisnis & Manajemen Almana : Jurnal Manajemen dan Bisnis
Tatang Supriyadi
Universitas Komputer Indonesia
Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

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Optimasi Perekrutan Strategis Melalui Big Data dan Sistem Informasi Manajemen Tatang Supriyadi; Okta Five; Agus Riyanto; Suryatno Wiganepdo Soegoto; Trustorini Handayani
JURISMA : Jurnal Riset Bisnis & Manajemen Vol. 16 No. 1: April 2026
Publisher : Program Studi Manajemen, Fakultas Ekonomi dan Bisnis, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/jurisma.v16i1.19513

Abstract

Human resource management is reaching a tipping point in the digital age, as data-driven approaches are beginning to outperform traditional intuition-based hiring techniques. In order to create a framework for strategic decision-making in the hiring process, this study attempts to summarize the function of Big Data in Management Information Systems. In this study, 20 chosen publications from scholarly databases are subjected to a systematic literature review (SLR) using the PRISMA standard. According to the findings, hiring can now take on a predictive function with a person-job matching accuracy of up to 98.1% thanks to the integration of Big Data and AI in Management Information Systems. It has been demonstrated that using blockchain technology and cloud architecture increases operational effectiveness and data security. Big Data is a strategic tool that helps HR professionals become skilled data analysts by reconstructing their function. Technically, ethical governance to mitigate privacy threats and an integrated SIM infrastructure are necessary for execution. There is currently a gap in the literature about the impact of an algorithm-based recruitment approach on employee psychological elements, such as affective commitment and individual inventiveness, which has to be empirically tested in future research. Keywords: Big Data; Human Resource Management; Information System; Recruitment; Strategic Decision
Structural Model of AI-Based Feedback's Impact on Employee Commitment and Creativity to Enhance Organizational Innovation Mari Maryati; Tatang Supriyadi
Almana : Jurnal Manajemen dan Bisnis Vol. 10 No. 1 (2026): April
Publisher : Bandung: Prodi Manajemen FE Universitas Langlangbuana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36555/almana.v10i1.3018

Abstract

The increasing use of artificial intelligence (AI) in workplace feedback systems has reshaped how employees receive performance evaluations and guidance. While AI promises efficiency and objectivity, its influence on employees’ psychological engagement, creative behavior, and organizational innovation is not yet fully understood. This study aims to examine how AI-based feedback affects employees’ work commitment and creativity and how these factors, in turn, contribute to organizational innovation. A quantitative explanatory approach was employed, using survey data collected from employees who had experienced AI-supported feedback in their organizations. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings reveal that AI-based feedback positively influences both work commitment and employee creativity. In addition, work commitment and creativity each play a significant role in promoting organizational innovation and jointly mediate the relationship between AI-based feedback and innovation outcomes. These results suggest that AI-based feedback does not automatically lead to innovation; instead, its benefits emerge when the technology strengthens employees’ emotional attachment to their work and supports creative exploration. In conclusion, this study highlights the importance of designing AI-based feedback systems that are transparent, supportive, and development-oriented in order to fully realize their potential in fostering sustainable organizational innovation.
Co-Authors Agus Riyanto Mari Maryati Okta Five Suryatno Wiganepdo Soegoto Trustorini Handayani