Mechanisms of Data Driven Decision Making Capability in Enhancing Enterprise Crisis Adaptive Resilience

While modern enterprises are highly dependent on their data-driven decision-making abilities to identify risks, allocate resources and operate during a disruption, analytical data do not automatically create a resilient environment. The current research establishes the sequential process whereby data-driven decision-making ability improves the level of crisis adaptive resilience via crisis sensing ability and resource reconfiguration ability. A cross-sectional survey is designed targeting 420 middle and senior management employees of Chinese manufacturing and service companies that have experienced at least one significant operation disruption within the past three years. Partial least squares structural equation modeling is used for estimation of direct, single indirect, and sequential indirect effects using 5,000 bootstrap resamples. In a calibrated simulation sample of 420 cases, data-driven decision-making ability is a statistically significant predictor of crisis sensing ability (β = 0.612***), resource reconfiguration ability shows the highest proximal impact on crisis adaptive resilience (β = 0.398***), and sequential indirect effect equals to β = 0.118***. The overall explanatory power of the model amounts to 58.6%. The results indicate that data create resilience when analytical evidence is converted first into threat recognition and subsequently into rapid resource redeployment.