Savior or saboteur? Big data and company employees innovative behavior: a socio-technical systems theory perspective

Purpose This research integrates a knowledge-based view with socio-technical systems (STS) theory to examine how organizational big data predictive analytics (BDPA) influence employee innovative behavior through a multilevel theoretical framework. The study aims to theorize the cross-level impact of organizational BDPA adoption on individual innovative behavior and identify the underlying mechanisms through which team knowledge-sharing behavior and knowledge process capability mediate this relationship. Design/methodology/approach The research involves an empirical study that collected data from 48 firms, which included 100 teams and 561 company employees. The study uses a multi-level model to analyze the data and examine the relationships between organizational BDPA, company employees’ innovative behavior and the mediating effects of team knowledge-sharing behavior and knowledge process capability. Findings (a) There is a significant cross-level main effect, indicating that organizational BDPA has a noteworthy impact on company employees’ innovative behavior. (b) Knowledge sharing behavior and knowledge process capability serve as bridging mechanisms between BDPA and employee innovative behavior. Originality/value This study makes three key theoretical contributions: first, it advances STS theory by identifying dual knowledge-based mediating mechanisms that explain how organizational technological capabilities translate into individual innovative behavior. Second, it extends STS theory from horizontal to vertical socio-technical interactions through a validated multilevel framework spanning organizational, team and individual levels. Third, it demonstrates the contextual applicability of STS principles in China’s unique institutional environment, revealing amplified conditions for team-based knowledge processes in emerging economies.