The AI–Gamification Integrated HR Control (AGIHC) Model: A Conceptual Framework for Employee Selection and Placement in Indonesia - Sony Putra; Wilda Fasim; Basri; Etty Sri Wahyuni (Journal of Deep Learning, Computer Vision, and Digital Image Processing, 2026)
Purpose – Selection and placement in many organizations remain subjective and inefficient, while gamification’s engagement potential is rarely applied to hiring. This study explains why an integrated approach is needed and proposes the AI–Gamification Integrated HR Control (AGIHC) Model, which couples AI as an analytic engine with gamification as an engagement interface to optimize the HR control system in an emerging-market (Indonesian) context.Methods – A Design Science Research approach was combined with a structured narrative synthesis of Scopus- and Sinta-indexed literature (2020–2026). The model was developed as a designed artifact, with a mixed-methods validation roadmap specified for the next phase.Findings – Based on the synthesized literature rather than on primary measurement in this study, individual primary studies report reductions in time-to-hire and cost-per-hire on the order of one-third; this figure is taken from those cited studies and is not an average computed in the present synthesis. Only 7.3% of gamification studies address recruitment versus 85.4% targeting existing employees. The resulting AGIHC Model specifies three layers: an Input Layer (AI screening plus gamified assessment), a Process Layer (AI matching plus gamified onboarding), and an Output Layer (AI analytics plus a gamified dashboard). Research implications – As a conceptual contribution, the propositions await empirical testing, and the single-country Indonesian framing bounds generalizability. Deployment also depends on data governance, candidate consent, and the explainability of AI scoring.Originality – The study bridges two previously parallel literatures in an integrated AI–gamification HR control framework and provides testable propositions for engagement-driven, fairness-aware selection and placement.