<![CDATA[Digital transformation has become an increasingly vital strategic element for Small and Medium Enterprises (SMEs) in the agribusiness sector, which faces unique challenges in the adoption of advanced technologies. This study focuses on how agribusiness SMEs can effectively implement digital transformation strategies by considering cognitive models, routines, and organizational structures. Through a comprehensive literature review and case study approach, the research explores the internal dynamics influencing SMEs' readiness and success in the digital transformation process, particularly in leveraging technologies such as the Internet of Things (IoT), data analytics, and automation. The implementation of digital transformation involves not only the adoption of new technologies but also innovation in business models and updates in decision-making processes. The research highlights the importance of a flexible and proactive cognitive model, wherein SME managers must possess the capacity to recognize technological opportunities and innovate within their organizational frameworks. Furthermore, existing organizational routines need modification to support digitalization processes, ensuring that organizational structures are adaptive and responsive to market changes. This flexibility is crucial in enhancing innovation capabilities and operational efficiency among agribusiness SMEs. Key findings indicate that successful digital transformation in agribusiness SMEs relies on the strategic utilization of micro-foundations such as cognitive model adjustments, routine management, and organizational restructuring. By understanding management's role in overcoming organizational inertia and promoting internal collaboration and training, SMEs can integrate new technologies more effectively into their business processes. This research contributes significantly to enriching the theoretical framework of digital transformation in the agribusiness sector, with recommendations for further research focusing on quantitative and cross-sector explorations to broaden the generalizability of the findings.]]>
Copyrights © 2024
