Developing a partner selection framework for digital agricultural science and technology innovation alliances

Selecting appropriate collaborators represents a critical factor in ensuring optimal innovation outcomes for Digital Agricultural Science and Technology (DAST) alliances. The partner identification process for DAST innovation coalitions constitutes a multi-criteria decision analysis (MCDA) challenge characterized by temporal dynamics and information ambiguity. This study proposes a time-weighted orthogonal projection approach within dynamic intuitionistic fuzzy environments to address this challenge. This approach fully considers decision-making rules and resource complementarity: First, it allocates weights by integrating the time dimension with multi-objective optimization model construction, fully absorbing decision information at different stages through time weight solutions to reduce uncertainty in multi-stage information collection; Second, it employs fuzzy set theory and orthogonal projection method to evaluate members and candidate partners. Based on this theoretical foundation, this paper proposes a field model incorporating resource complementarity for selecting optimal partners. This novel field model can assist agricultural science and technology innovation alliances in implementing collaborative innovation practices, while simultaneously providing decision-making guidance for optimizing dynamic partner selection models to build long-term stable partnership relationships. Practical validation through China’s 5G Agricultural Digitalization Alliance case study demonstrates the methodology’s practicality and operational efficacy.