技术并购情境下专利价值评估研究OA

In the current landscape of intensified technological competition,technology-driven mergers and acquisitions(M&A)have emerged as a key strategy for firms to acquire core technologies and enhance innovation.Accurately assessing the synergistic value of target patents post-merger is crucial for improving innovation output,preventing asset loss,and avoiding good-will impairment.Traditional patent valuation methods are insufficient in quantifying the synergis-tic premium from M&A integration and often overlook contextual drivers specific to post-merger scenarios.This study addresses this gap by focusing on the post-merger technology integration phase,aiming to construct an effective framework for quantifying the integrated value of patents during this process. Guided by the value chain theory,we first identify core patents from the target's portfolio that are highly related to the acquirer's technology chain.A novel two-dimensional evaluation system is then developed,integrating"fundamental patent attributes"and"technology M&A at-tributes".This system incorporates contextual indicators—technical compatibility,complementa-rity,and similarity—to systematically capture a patent's synergistic potential in the integration process.To handle the complex,nonlinear relationships among these indicators and limited sample data,Grey Relational Analysis(GRA)is applied to determine initial weights,which are further optimized using a Backpropagation(BP)neural network,resulting in a robust assessment model. The model is validated through a case study of Midea Group's acquisition of Wuxi Little Swan.Applying the model to 316 screened patents,the assessed overall technological value of Little Swan's portfolio is 803 million CNY.Compared to the actual transaction price of 807 mil-lion CNY,the error rate is only 0.49%,well within the common industry tolerance threshold of 5%.This result confirms the model's accuracy in quantifying post-merger patent integration value and underscores the importance of technology fusion indicators. Subsequently,this research contributes to theory by introducing a post-merger integration perspective and a contextualized evaluation system,addressing the gap in synergistic value quan-tification.Methodologically,the successful integration of GRA for initial weighting with BP neu-ral networks for non-linear offers a new tool for valuation under conditions of nonlinearity and small samples.Practically,the study delivers a structured,operable tool for managers and ana-lysts.It aids in post-merger intellectual property audit,integration planning,synergy realization tracking,and informed resource allocation,thereby ultimately enhancing the success rate and in-novation yield of technology-centric M&A activities.