Abstract

This study aimed to identify patterns of sustainability engagement based on circular economy (CE) strategy implementation, CE-oriented collaborative relationships, and environmental, social, and governance (ESG) performance, as well as to investigate whether these dimensions predict corporate reputation. Data were collected through a survey of 235 upper-level managers in the Brazilian agribusiness sector. A two-step analytical approach was applied, with cluster analysis identifying groups exhibiting distinct patterns regarding sustainability engagement (Very Sustainable and Low-Sustainable), followed by logistic regression, which singled out six key predictors among 28 variables, namely avoiding non-sustainable materials, repurposing by-products, fostering a shared CE vision, adhering to ethical guidelines, ensuring financial transparency, and fair labor practices. The final model achieved 83.4% accuracy, underscoring how an integrated approach to sustainability enhances corporate reputation. Considering its theoretical contributions, this study extends the NRBV and RV theories by demonstrating that CE strategies, CE-oriented collaborative relationships, and ESG performance strengthen pollution prevention initiatives, sustainable product development efforts, and trust among partners, among other achievements, thereby enhancing firms' reputation and sustainable performance. Methodologically, the study integrates cluster analysis and predictive modeling to assess sustainability's impact on reputation. From a managerial perspective, findings emphasize that corporate reputation benefits from circularity, governance integrity, and stakeholder engagement. However, the cross-sectional design, industry-specific sample, and reliance on self-reported data limit generalizability. Future research should adopt longitudinal and cross-industry approaches, examining regulatory shifts, technological advances, and evolving stakeholder demands in the sustainability-reputation nexus while incorporating external data sources to assess variations across institutional and cultural settings.