Performance measurement in university–industry innovation networks: implementation practices and challenges of industrial organisations

Figures & data

Table 1. Summary of the data collection.

Data collection method	Respondents in each phase of data collection	Number of participants	Data analysis methods
Individual interviews	1–2 members of each participating organisation (CEOs, sales managers, development managers)	50 persons	Qualitative content analysis, cross-analysis
Workshop observation	Participating organisations:	Workshops involving 15–45 participants	Qualitative content analysis, cross-analysis
	10 workshops in Case A
	3 workshops in Case B	Workshops involving 8–15 participants
	Data based on observations, discussions, and feedback during the workshops
Individual interviews	Interviews arranged after the project to explore the evaluation and effects of the collaborative actions	10 from Case A	Qualitative content analysis, cross-analysis
		5 from Case B
	1–2 members of each participating organisation (CEOs, sales managers, development managers)
Survey	Survey arranged after the projects to explore the evaluation of the networks	10 from Case A	Quantitative analysis
		5 from Case B

Table 2. Summary of the coding process.

Coding	Data utilised	Aim of the coding	Main results
First round	Individual interviews/discussions:	To arrange patterns related to implementation challenges and practices of performance measurement in university–industry innovation networks from the perspective of industrial organisations	• Implementation practices and challenges related to: • the context of performance measurement • the process of performance measurement • the content of performance measurement
	1–2 members of the each participating organisation (CEOs, sales managers, development managers)
Second round	Individual interviews/discussions:	To arrange generic patterns related to the current implementation of input, in-process, and output measures in university–industry innovation networks	• Practices and challenges related to the implementation of: • input measures • in-process measures • output measures
	1–2 members of each participating organisation (CEOs, sales managers, development managers)
	Workshop observations:
	10 workshops in Case A
	3 workshops in Case B
Third round	Workshop observations:	To arrange patterns related to performance measurement practices and challenges to the success of university–industry innovation networks	• Practices and challenges related to the implementation of: • outcome/impact measures • collaboration success measures
	10 workshops in Case A	To arrange patterns related to performance measurement practices and challenges to outcome and impact measures
	3 workshops in Case B
	Interviews arranged after the project to explore the
	evaluation and effects of the collaborative actions
	1–2 members of each participating organisation (CEOs, sales managers, development managers)
Additional data analyses	Survey conducted after the projects to explore the evaluation of the networks	To confirm the findings and patterns arranged in the previous rounds of coding	• Implementation practices and challenges related to the context, process, and content of performance measurement • Practices and challenges related to the implementation of input, in-process, output, and outcome/impact measures

Table 3. Summary of performance measurement practices and challenges of industrial SMEs.

	Industrial SME’s views on implementation of performance measurement practices	Challenges related to the implementation of performance measurement
Societal-level performance measurement of university–industry innovation networks	• Interest in the performance measurement of societal-level outputs of university–industry innovation networks.	• Industrial SMEs are not aware of the aims and goals of the funding programmes. From their point of view, they participate in individual research and development projects.
Network-level performance measurement of university–industry innovation networks	• Industrial SMEs recognise the importance of the performance measurement of network-level activities. The participating SMEs mainly shared the opinion that a PMS should be created to evaluate the performance of individual organisations and the entire network. • SMEs are mainly interested in measuring the innovation activities and advances gained in activities with other industrial organisations. Although the motivation for participation in innovation networks seem related to intellectual capital, the performance measurement activities seem to be strongly related to business.	• The purpose of the network level of PMS in university–industry collaboration activities did not seem obvious to the industrial SMEs. • The context of the performance measurement of the university–industry innovation networks seemed unclear for the participating SMEs, and they seemed to be confused about the conditions under which the measurement should take place. • Although the literature on the performance measurement of university-industry collaborations recognises the frameworks developed for such contexts, the industrial SMEs seemed to be unfamiliar with them and to lack the proper understanding and means to implement them.
Individual-level performance measurement of university–industry innovation networks	• Performance measurement of goals and tasks that were originally defined for the aims of collaboration. • The current focus and interest in performance measurement seemed to be evaluating process activities and process outputs and outcomes (e.g. new customer relationships, innovations, or strategic partnerships) with other industrial organisations. • Interest in the performance measurement of increased learning, increased social capital, and new innovations.	• Challenges in developing and implementing new tools and methods to evaluate or measure the results or effects that were secondary outcomes of the collaboration. • Industrial SMEs currently seemed to be unfamiliar with frameworks and tools for measuring and evaluating innovation capabilities, which caused the failure of implementation.