Development of a comprehensive model for management efficiency in agricultural enterprises amid digital transformation

The study is devoted to assessing the effectiveness of enterprise innovation management in the context of digital transformation. The relevance of this research is determined by the need to develop a comprehensive methodology that enables diagnosing the level of companies' innovation activity and identifying priority directions for its advancement. The methodological framework includes a vector model for evaluating innovation management, indicator normalization, as well as correlation, cluster, and regression analyses. The empirical basis of the study comprises data from 24 Chinese companies operating in the agricultural sector. For each enterprise, the level of management efficiency was determined across three dimensions: innovation performance, innovation potential, and financial support of digital innovation processes. The key findings indicate that innovation potential (0.794) and financial support of innovation processes (0.683) exert the strongest influence on the formation of the innovation management vector, whereas innovation performance demonstrates a comparatively weaker effect (0.334). Hierarchical clustering made it possible to distinguish four groups of companies by their level of innovation activity: low, moderate, significant, and high, which provides opportunities for comprehensive diagnostics and benchmarking. The developed regression models confirm the reliability of the assessments and enable forecasting changes in innovation performance depending on variations in the three key indicators. The proposed methodology can be practically applied for evaluating enterprise innovation management, determining priority development pathways, and designing strategies for digital transformation.