Drawing on matching theory, this study examines how configurations of artificial intelligence (AI) capability orientations – automation and smartness – within buyer–supplier dyads influence buyer firm resilience.
We employ Bidirectional Encoder Representations from Transformers (BERT), a deep learning approach, to measure firms' AI automation orientation and AI smartness orientation based on annual report disclosures. Using a sample of 3,279 buyer–supplier dyads from Chinese A-share listed firms between 2018 and 2022, we test our hypotheses with ordinary least squares (OLS) regression models.
The results show that complementarity-based configurations, specifically buyer automation combined with supplier smartness and buyer smartness combined with supplier automation, significantly enhance buyer resilience. A compatibility-based configuration in which both buyer and supplier emphasise automation also strengthens resilience. In contrast, the matching between buyer smartness and supplier smartness is negatively associated with firm resilience.
This research distinguishes between AI automation and smartness orientation and demonstrates how their configurations across buyer–supplier dyads generate compatibility, complementarity, or friction. By moving beyond firm-centred and monolithic views of AI, the study offers a novel framework for understanding when dyadic AI alignment enhances resilience and when it undermines it, providing actionable insights for AI-enabled supply chain management in disruption-prone environments.
