Measuring efficiency of university-industry Ph.D. projects using best worst method

Area of Science:

• Collaboration between academia and industry in Ph.D. research.
• Project management and evaluation methodologies.

Background:

• Collaborative Ph.D. projects are vital for university-industry partnerships.
• Existing success metrics focus on outputs, neglecting crucial inputs.
• A comprehensive evaluation requires considering both resource investment and outcomes.

Purpose of the Study:

• To develop a novel efficiency metric for collaborative Ph.D. projects.
• To integrate both project inputs and outputs into a unified evaluation framework.
• To compare efficiency scores derived from candidate and supervisor perspectives.

Main Methods:

• Utilized multi-criteria decision-making (MCDM) techniques.
• Employed the Best Worst Method (BWM) to determine input and output weights.
• Collected data from 51 Ph.D. candidates and their supervisors at Eindhoven University of Technology.

Main Results:

• Identified significant differences in efficiency scores based on candidate versus supervisor weight perspectives.
• Efficiency scores diverged from traditional success scores, indicating potential implications for project assessment.
• Sensitivity analysis highlighted key contributing inputs and outputs to project efficiency.

Conclusions:

• Efficiency, encompassing both inputs and outputs, offers a more holistic evaluation of collaborative Ph.D. projects.
• Discrepancies in perceived importance of inputs/outputs between candidates and supervisors necessitate careful consideration.
• The BWM method provides a robust approach for quantifying efficiency in academic-industry collaborations.