Evaluating firms' R&D performance using best worst method. (February 2018)

• Record Type: Journal Article
• Authors: Salimi, Negin; Rezaei, Jafar
• Is Part Of: Evaluation and program planning. Volume 66(2018)
• Page Start: 147
• Page End: 155
• Publication Date: 2018-02
• Subjects: R&D performance -- R&D measures -- Best worst method (BWM) -- Small-to-medium-sized enterprises (SMEs); Health planning -- Periodicals; Medical care -- Evaluation -- Periodicals; 362.1068
• DOI: 10.1016/j.evalprogplan.2017.10.002
• Languages: English
• ISSNs: 0149-7189

Abstract

Highlights: A multi-criteria framework for firm's R&D performance evaluation is proposed. Best Worst Method (BWM) is used to identify the weights (importance) of R&D measures. R&D performance of 50 high-tech Dutch SMEs is measured using the proposed methodology. Assigning weights to different R&D measures results in different ranking of the firms. The proposed methodology allow SMEs to improve their R&D performance. Abstract: Since research and development (R&D) is the most critical determinant of the productivity, growth and competitive advantage of firms, measuring R&D performance has become the core of attention of R&D managers, and an extensive body of literature has examined and identified different R&D measurements and determinants of R&D performance. However, measuring R&D performance and assigning the same level of importance to different R&D measures, which is the common approach in existing studies, can oversimplify the R&D measuring process, which may result in misinterpretation of the performance and consequently fallacy R&D strategies. The aim of this study is to measure R&D performance taking into account the different levels of importance of R&D measures, using a multi-criteria decision-making method called Best Worst Method (BWM) to identify the weights (importance) of R&D measures and measure the R&D performance of 50 high-tech SMEs in the Netherlands using the data gathered in a survey among SMEs and from R&D experts. The results show how assigning different weights to different R&D measures (in contrast to simple mean) results in a different ranking of the firms and allow R&D managers to formulate more effective strategies to improve their firm's R&D performance by applying knowledge regarding the importance of different R&D measures.