Purpose Predictive HR algorithms now govern much of how employees learn, receive feedback and develop professionally. What remains poorly understood is what these systems do to motivation itself. This paper introduces Neural Hijacking as a construct describing how algorithmically engineered feedback cycles can co-opt employees’ intrinsic motivational systems, producing surface-level engagement at the expense of autonomy, deep learning and psychological sustainability over time. Design/methodology/approach This conceptual study develops a dual-pathway framework by drawing on three theoretical bodies. Self-Determination Theory (SDT) (Deci and Ryan, 1985; Ryan and Deci, 2000) specifies the psychological needs that algorithmic design can either support or undermine. Nudge Theory (Thaler and Sunstein, 2008; Sunstein, 2016) explains how choice architecture steers behavior without explicit mandate. Research on dopaminergic reward processing (Schultz et al., 1997; Volkow et al., 2011) grounds the claim that frequent micro-rewards can shift motivational orientation away from intrinsic engagement and toward externally cued compliance. Findings The framework identifies two distinct algorithmic pathways. The empowerment pathway, characterized by ethical design, personalized development and growth-oriented feedback, supports autonomy, competence and relatedness as defined by SDT (Ryan and Deci, 2000), and is associated with deep learning and long-term adaptability. The neural hijacking pathway, driven by engagement-metric optimization, opaque algorithms and gamified reward cycles (Deterding et al., 2011; Mekler et al., 2017), erodes intrinsic motivation, creates dependency on algorithmic cues and raises burnout risk over time. Five proposition sets are developed linking antecedents, mechanisms and outcomes across both pathways. Originality/value This paper addresses a specific gap in the HR technology literature: the absence of a theorized mechanism explaining why identical algorithmic systems produce divergent motivational outcomes across implementation contexts. By connecting AI design choices to psychological processes and workforce sustainability, the framework gives researchers a structured empirical agenda and gives practitioners a diagnostic tool for evaluating whether their HR systems are building or slowly depleting workforce capacity. In practical terms, the framework gives HR practitioners a diagnostic question they currently lack: not whether their AI system is engaging employees, but whether it is engaging them in ways that build or deplete the motivational foundations that sustained workforce performance requires.
S. et al. (Thu,) studied this question.