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Purpose: This study aims to assess suppliers' learning from knowledge transfers with a global truck manufacturer, considering both source and supplier capacity, and the cultural proximity between the parties. Design/methodology/approach: A case study was conducted between two factories, one in Brazil and one in Germany. This study adopted a mixed-method sequential explanatory approach, which involves a quantitative phase followed by a qualitative one to provide a better understanding of the studied phenomenon. Quantitative data were collected from the automaker's suppliers in both countries and analyzed using factor and inferential analyses. Qualitative data were obtained from the automaker's purchasing executives, and from the company's suppliers in both countries. Content analysis was used to analyze data. Findings: Results suggest that both the source's disseminative capacity and suppliers' absorptive capacity had a positive effect on suppliers' learning during knowledge transfers. The study also found out that cultural proximity among parties positively moderates the relationship between suppliers' absorptive capacity and their learning. However, cultural proximity does not moderate the relationship between a source's disseminative capacity and supplier learning. Practical implications: This study's findings are important to foster knowledge transfers by developing absorptive and disseminative capabilities in the automakers industry, in which the implementation of interorganizational learning is quite challenging due to the large number of strategic providers. Originality/value: This study contributes to theoretical and conceptual consolidation of knowledge transfer, which includes cultural proximity among parties and the source's and supplier's disseminative and absorptive capacities, respectively. This study constructs and validates a model of knowledge transfer using a large automaker with a worldwide presence.

Big data analytics capabilities (BDACs) are strategic capabilities that expedite decision-making processes, empowering organizations to mitigate the impacts of supply chain disruptions. These capabilities enhance the ability of companies to be more proactive in detecting and predicting disruptive events, increasing their resilience. This study analyzed the effects BDACs have on firms' reaction time and the effects companies' reaction time has on their resilience. The research model was assessed with 263 responses from a survey with professionals of auto-parts companies in Brazil. Data were analyzed with the Partial-Least-Squares—Structural Equation Modeling method. Cluster analysis techniques were also applied. This study found that BDACs reduce reaction time, which, in turn, improves firms' resilience. We also observed greater effects in first-tier and in companies with longer Industry 4.0 journeys, opening further perspectives to investigate the complex mediations of digital readiness, reaction time, and organizational resilience performance of firms and supply chains. Our research builds upon the dynamic capabilities theory and identifies BDACs as dynamic capabilities with the potential to enhance resilience by reducing data, analytical, and decision latencies, which are recognized as core elements of the reaction time concept, which is particularly crucial during disruptive supply chain events. [ABSTRACT FROM AUTHOR]