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This study aims to investigate the interplay between a proactive attitude towards disruptions – supply chain disruption orientation – and supply chain resilience, increasing our understanding of their influence on reducing the impact of supply chain disruptions within the B2B context.

As unexpected disruptions are closely related to a dynamic and changing perception of the environment, this research is framed under the dynamic capabilities lens, consistent with existing resilience literature. The authors used partial least squares-path modeling (PLS-PM) to empirically test the proposed research model using survey data from 216 firms.

Results show that a proactive approach to disruptions alone is insufficient in mitigating their negative impact. Instead, a firm’s disruption orientation plays a crucial role in boosting its resilience, which acts as a mediator, reducing the impact of disruptions.

This paper sheds light on the mechanisms by which firms can mitigate the effects of supply chain disruptions and offers insights into how certain capabilities are needed so that firms’ attitudes can effectively impact firm performance. This research thus suggests that dynamic capabilities, traditionally perceived as being enabled by other elements, act themselves as enablers. Consequently, they have the potential to translate strategic orientation or attitudes into tangible effects on performance, enriching our understanding of how firms combine their internal attitudes and capabilities to achieve sustained competitive advantage.

To determine how to best deploy the Triple-A supply chain (SC) capabilities (AAA-agility, adaptability and alignment) to improve competitive advantage (CA) by identifying the Triple-A SC model with the highest CA predictive capability.

Assessment of in-sample and out-of-sample predictive capacity of Triple-A-CA models (considering AAA as individual constructs) to find which has the highest CA predictive capacity. BIC, BIC-Akaike weights and PLSpredict are used in a multi-country, multi-informant, multi-sector 304 plant sample.

Greater direct relationship model (DRM) in-sample and out-of-sample CA predictive capacity suggests DRM's greater likelihood of achieving a higher CA predictive capacity than mediated relationship model (MRM). So, DRM can be considered a benchmark for research/practice and the Triple-A SC capabilities as independent levers of performance/CA.

DRM emerges as a reference for analysing how to trigger the three Triple-A SC levers for better performance/CA predictive capacity. Therefore, MRM proposals should be compared to DRM to determine whether their performance is significantly better considering the study's aim.

Results with our sample justify how managers can suitably deploy the Triple-A SC capabilities to improve CA by implementing AAA as independent levers. Single capability deployment does not require levels to be reached in others.

First research considering Triple-A SC capability deployment to better improve performance/CA focusing on model's predictive capability (essential for decision-making), further highlighting the lack of theory and contrasted models for Lee's Triple-A framework.