Evaluating the research and development performance using multi-criteria decision-making: a case study
- Typ:Master's thesis
Even though research and development (R&D) activities are viewed as a determinant for productivity, growth, and competitive advantage, they also lead to high risks and costs. Therefore, companies have made measuring the R&D performance a main concern. Several researchers have proposed various R&D performance measures and measurement systems (PMS) to help firms with this matter. Despite the various approaches, they have not explicitly differentiated the importance level of the different criteria. However, managers are unlikely to value every criterion equally. With this in mind, two researchers have addressed this shortcoming and have proposed weighting techniques through multi-criteria decision-making (MCDM) methods. Nevertheless, they have not yet illustrated its applicability at a company's detailed level. As a result, they have not proven that managers value the indicators and perspectives differently in their companies and have not demonstrated that organizations can possess enough data to evaluate the R&D performance using various indicators. Therefore, the applicability of using MCDM to evaluate the R&D performance of a company is not yet confirmed. Our study aims at addressing this shortcoming by designing and implementing such an R&D PMS in a company. We deemed Siemens Energy AG a suitable candidate for our study due to its high investments in R&D activities. The R&D PMS was designed at project level and captured R&D indicators from the balanced scorecard perspectives and from the entire R&D stages. Our study found that managers value the indicators and perspectives differently and have enough data to assess the R&D PMS using multiple indicators. This finding confirmed the applicability of using MCDM in Siemens Energy AG. Another important aspect that we tackled in our study is the validity of the requirements drawn by Brown and Svenson in companies (Brown, M. G., & Svenson, R. A. (1988). Measuring R&D Productivity. Research-Technology Management, 31(4), 11-15). We thereby revisited their requirements and proposed additional ones for MCDM specifically.