Optimizing Ecosystem Partner Selection Decisions for Platform Enterprises: An Embedded Innovation Demand-Driven Framework

The rapid emergence of the platform economy has accelerated the practice of embedded innovation, with ecosystem partner selection serving as a critical first step in platform enterprises’ innovation collaborations and playing a key role in enhancing innovation efficiency and outcomes. Based on the theory of embedded innovation, this study identifies the core innovation demands of platform enterprises at distinct stages. It then employs QFD to quantify decision indicator weights for ecosystem partner selection. By integrating Prospect Theory with Field Theory, this study develops both a decision evaluation model and an optimization model to achieve the optimal screening of ecosystem partners. Specifically, this study contributes in the following ways: (1) It constructs an embedded innovation direction selection model to uncover the evolving innovation demands at each stage. Within the QFD framework, we map these demands onto selection evaluation indicators, assess their importance via the maximum entropy principle, and determine indicator weights through a correlation matrix. (2) It proposes a Prospect Theory-based TOPSIS evaluation model, incorporating decision-makers’ psychological preferences to mitigate bias arising from singular or excessive risk attitudes. This model ranks potential partners according to their closeness to an ideal solution. Finally, (3) it designs a Field Theory-based optimization model that accounts for the platform enterprise’s perspective, partner-matching rationality, and continuity of interaction. This model emphasizes the complementarity and synergy of innovation resources to enhance cooperation fit and strategic alignment between the platform and its partners. Finally, this study conducts an empirical analysis on platform enterprise XM and validates the model’s feasibility and stability through sensitivity testing and comparative analyses. This study enriches the understanding of ecosystem partner selection within platform ecosystems by advancing methods for quantifying partner demands and refining the selection of evaluation indicators. It also deepens the depiction of non-rational characteristics in behavioral decision-making and elucidates the mechanisms underlying the ongoing interactions between platform enterprises and their ecosystem partners. These theoretical contributions not only extend the scope of research on platform ecosystems and embedded innovation but also provide feasible approaches for platform enterprises to improve partner governance and foster collaborative innovation in dynamic and complex environments. Ultimately, the findings offer strong support for enhancing innovation performance and building sustainable competitive advantages.