Generative AI and digital transformation in organizational roadmaps: employee value perceptions and knowledge sharing

Purpose This study explains how employees translate the day-to-day value they derive from generative artificial intelligence (GAI) into employees' perceived insider status and knowledge sharing behavior. Design/methodology/approach Integrating the job demands–resources theory, we theorize four value perceptions of GAI, functional, epistemic, conditional and emotional, as distinct socio-cognitive cues from which employees infer perceived insider status. A cross-sectional survey design was employed using a structured questionnaire administered to employees across multiple industries where GAI is embedded in daily work practices. The final sample consisted of 298 valid responses, and the proposed relationships were tested using covariance-based structural equation modeling, including mediation and moderation analyses. Findings We found that emotional value is positively associated with perceived insider status, conditional value is negatively associated with perceived insider status, and functional and epistemic value show no direct associations. Perceived insider status predicts greater knowledge sharing, which in turn is linked to stronger commitment, more citizenship behavior, deeper identification and lower alienation. Knowledge of artificial intelligence strengthens the associations of functional and emotional value with perceived insider status, and weakens the association of conditional value with perceived insider status. Originality/value The findings re-orient technology value theorizing toward socioemotional inclusion, identify perceived insider status as a proximal mechanism connecting everyday GAI experiences to cooperative behavior, and specify knowledge of artificial intelligence as a capability amplifier that shapes how value cues are read as signals of membership. Practical guidance follows for embedding GAI in routine workflows, investing in literacy and designing for emotionally positive user experiences.