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Evaluating project performance by removing external effects: Implications to the efficiency of research and development resource allocation

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  • Chia-Liang Hung
  • Pei-Jen Shiu

Abstract

Research and development (R&D) project performance is traditionally evaluated using cost-benefit analysis. However, external effects beyond managerial control often create advantageous or disadvantageous biases during evaluation. In this study, we used a three-stage data envelopment analysis method to evaluate R&D efficiency and Tobit regressions, removing the external effects of technology type, accumulative experience, international linkage, and group diversity. This study examined these external factors by using 39 academic projects constituting the Taiwan National Telecommunication Program. Technology type and accumulative experience enabled R&D projects to be advantageously implemented in the human resource department, whereas group diversity was disadvantageous and created superfluous repetition in human resources. International linkage was disadvantageous because of slack subsidies. After external effects were removed, most projects shifted from a state in which returns decreased to a state in which returns increased. Finally, we discussed the implications of these findings for project management and governmental subsidy policies that are implemented to evaluate performance and allocate resources.

Suggested Citation

  • Chia-Liang Hung & Pei-Jen Shiu, 2014. "Evaluating project performance by removing external effects: Implications to the efficiency of research and development resource allocation," Research Evaluation, Oxford University Press, vol. 23(4), pages 366-380.
    • Handle: RePEc:oup:rseval:v:23:y:2014:i:4:p:366-380.
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    File URL: http://hdl.handle.net/10.1093/reseval/rvu022
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    Cited by:

    1. Franc Mali & Toni Pustovrh & Rok Platinovšek & Luka Kronegger & Anuška Ferligoj, 2017. "The effects of funding and co-authorship on research performance in a small scientific community," Science and Public Policy, Oxford University Press, vol. 44(4), pages 486-496.
    2. Hung, Chia-Liang, 2017. "Social networks, technology ties, and gatekeeper functionality: Implications for the performance management of R&D projects," Research Policy, Elsevier, vol. 46(1), pages 305-315.

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