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

Chi Yo Huang, Gwo Hshiung Tzeng

Research output: Chapter in Book/Report/Conference proceedingChapter

5 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationAdvances in Multiple Criteria Decision Making and Human Systems Management
Subtitle of host publicationKnowledge and Wisdom
PublisherIOS Press
Pages47-64
Number of pages18
ISBN (Print)9781586037482
Publication statusPublished - 2007 May 1

Fingerprint

Human resources
High technology
Mergers
Resources
Integrated circuits
Programming
High-technology firms
Service design
New markets
Product portfolio
Knowledge-based
Competitive advantage
Critical factors
Core competence
Labor
Semiconductors
Empirical study
Mergers and acquisitions

Keywords

  • De Novo programming
  • High technology
  • Human resource optimization
  • Merger and Acquisition (M&A)
  • R&D management
  • R&D resource optimization

ASJC Scopus subject areas

  • Business, Management and Accounting(all)

Cite this

Huang, C. Y., & Tzeng, G. H. (2007). Post-merger high technology R&D human resources optimization through the De Novo perspective. In Advances in Multiple Criteria Decision Making and Human Systems Management: Knowledge and Wisdom (pp. 47-64). IOS Press.

Post-merger high technology R&D human resources optimization through the De Novo perspective. / Huang, Chi Yo; Tzeng, Gwo Hshiung.

Advances in Multiple Criteria Decision Making and Human Systems Management: Knowledge and Wisdom. IOS Press, 2007. p. 47-64.

Research output: Chapter in Book/Report/Conference proceedingChapter

Huang, CY & Tzeng, GH 2007, Post-merger high technology R&D human resources optimization through the De Novo perspective. in Advances in Multiple Criteria Decision Making and Human Systems Management: Knowledge and Wisdom. IOS Press, pp. 47-64.
Huang CY, Tzeng GH. Post-merger high technology R&D human resources optimization through the De Novo perspective. In Advances in Multiple Criteria Decision Making and Human Systems Management: Knowledge and Wisdom. IOS Press. 2007. p. 47-64
Huang, Chi Yo ; Tzeng, Gwo Hshiung. / Post-merger high technology R&D human resources optimization through the De Novo perspective. Advances in Multiple Criteria Decision Making and Human Systems Management: Knowledge and Wisdom. IOS Press, 2007. pp. 47-64
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