Post-merger high technology R&D human resources optimization through the De Novo perspective

In high technology firms, merger and acquisition (M&A) already has become the major strategy for enriching product portfolios, entering new markets, and thus enhancing the core competences in research and development (R&D). R&D human resource (HR) is the most critical factor to develop the competitive advantage and the post-merger R&D performance of knowledge-based labor intensive high technology firms, in general, and the IC design houses and IC design service companies in particular. However, little literature has discussed how the post-merger R&D human resources should be optimized so as to achieve the best R&D performance and thus, final success of the merger. Meanwhile, traditional literature on R&D resource optimization has focused mainly on optimizng existing resources, which is not realistic in the real world where external R&D human resources can be leveraged. Thus, this research will develop an analytic framework to best utilize the post-merger R&D human resources and achieve the best performance by optimizing internal resources and leveraging external resources by using the De Novo programming proposed by Professor Milan Zeleny. An empirical study of optimizing the post-combination R&D human resources in a merger of an integrated circuit (IC) design service company by a professional semiconductor foundry company is given as an illustration for the analytic procedures. The results demonstrate that the de Novo programming can best optimize the post-merger R&D human resources in merger and can be applied to other M&A cases.