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Purpose This study aims to investigate the mechanisms through which knowledge integration (KI) depth enhances corporate crisis management (CCM) efficacy. Moving beyond a direct-effect model, it examines the mediating role of transactive memory systems (TMS) and the critical negative moderating effect of artificial intelligence (AI) counter-productivity on the KI–TMS relationship. Design/methodology/approach A sequential explanatory mixed-methods design was used. Study 1 used a quantitative cross-sectional survey of 492 knowledge workers to test the hypothesized relationships (H1–H3). Study 2 then undertook a qualitative, 14-month ethnographic multi-case study of four organizations, collecting data from 62 interviews, 42 observations and 215 documents to explicate the underlying “mechanism-evidence” chain. This design allows the qualitative findings to explain and contextualize the initial quantitative results. Findings The quantitative results confirm the hypothesized model: KI depth positively influences CCM efficacy (H1), TMS mediates this relationship (H2) and AI counter-productivity negatively moderates the KI–TMS link (H3). The qualitative analysis elucidates how this occurs, revealing that TMS functions as a three-stage sequential mediator (specialization, credibility and coordination) to translate KI into action. It further specifies that AI counter-productivity disrupts this precise pathway: information overload undermines specialization, relational alienation erodes credibility and cognitive inertia impairs coordination. The integrated findings highlight that this disruption is not inevitable but contingent on organizational design. Originality/value First, it specifies the sequential socio-cognitive mechanism by which AI counter-productivity disrupts knowledge processes, moving beyond generic stressor models. Second, it questions assumptions of technological determinism by illustrating a shift from “technological fix” to “organizational immunity,” suggesting resilience can stem from human-centric governance that preemptively neutralizes technological disruptions. Third, it proposes the functional equivalence of social capital, as evidence indicates rich relational networks can compensate for a lack of technological advancement in enabling effective TMS, offering a “low-tech resilience” pathway. Fourth, it reconceptualizes TMS as a dynamic, enactable capability, a “living protocol” activated under crisis, providing a micro-foundation for understanding knowledge mobilization under duress. Collectively, these contributions offer a process-based theory of how KI translates into CCM efficacy through mechanisms that are vulnerable to, yet defensible against, technological disruption.
Liu et al. (Sat,) studied this question.