Performance measurement in university-industry collaborations

From a strategic perspective, the performance measurement of universities’ third missions and collaboration activities should focus on highlighting the dialogue inside universities and between their external stakeholders and society as a whole (Secundo et al., 2017). The growing interest in the collaboration activities between universities and other societal organizations has resulted in the demand for more comprehensive performance measurement processes for all participants (e.g., Secundo et al., 2010). Despite the increased focus on strategies and processes to develop the collaboration activities, universities seem to lack specific information and frameworks with which to evaluate the performance of their entrepreneurial activities (Wright et al., 2004), in particular the third mission activities of societal effectiveness (Secundo et al., 2017).

Since the collaboration activities in general have been increasing, a growing amount of research has been conducted focusing on the management and the role of performance measurement in collaboration activities and collaborative networks (e.g., Tsai, 2009). As university-industry collaborations can be considered as different types of networks among participating organizations, the performance management and measurement practices developed to support the management of networking activities among organizations could also be utilized in the context of university-industry collaborations. For example, some theoretical studies have focused on performance measurement in collaborative organizations and networks (Busi and Bititci, 2006; Varamäki et al., 2008).

According to Perkmann et al. (2011), contemporary university-industry collaborations differ from other types of research and development activities and alliances in several ways. The

outputs of these collaborations are often intangible and likely to be complex (Perkmann et al., 2011). In addition, authors further argue that benefits from these collaborative projects might be realized but only a long time after the projects are finished.

An increasing number of the contemporary university-industry collaboration activities are pursued in different types of research and development projects that form networks and ecosystems around the participating stakeholders. Even though these collaborative research and development projects between universities and industry organizations are unique by nature, there exist plenty of similarities that could be measured and evaluated by utilizing the same measures and frameworks. Albats et al. (2018) state that earlier attempts to address the issue of developing comprehensive and universal measures and indicators have recognized deficiencies in the currently utilized indicators. The authors further argue that the utilized indicators are mainly focused on the macro-level evaluation and are applied by financier delegates and governmental funding programs.

The previous literature on performance management and measurement in university-industry collaborations have presented tools and frameworks to support the performance measurement of these collaborations (Al-Ashaab et al., 2011; Albats et al., 2018; Iqbal et al., 2011; Mora-Valentin et al., 2004; Perkmann et al., 2011; Tijssen, 2012). These studies suggest that the performance measurement frameworks and tools to evaluate university-industry collaborations should include a balanced set of measures that pay attention to needs of all participating stakeholders. Generally, the previous studies have identified four stages of university-industry collaborations (input, in process, output, impact/outcome) that should be paid attention to in performance measurement (Rantala and Ukko, 2018):

- Input: participating organizations’ resources (time, money, and staff allocated to collaboration), and the capabilities and motivations of participants.

- In process: relevant research, high-quality research, and training and learning opportunities.

- Output: new technologies, services, and innovations, as well as new scientific knowledge, and skilled and trained persons.

- Impact/outcome: new ideas, new research and development plans, solution concepts, and human capital.

As university-industry collaborations can be considered different types of networks among participating organizations, Kaplan et al. (2010) indicate that understanding how to measure network-level performance can support the collaboration at the network level and enhance the participants’ understanding of how to create a joint strategy and insure commitment.

Thus, the performance measurement in university-industry collaborations should be designed, implemented, and made visible to all stakeholders to support evaluation and management of the collaboration activities. In other words, the designed and implemented performance measurement systems should pay attention to the aims and goals of the researchers, societal organizations, and financier delegates (in cases where the collaboration activities are receiving funding support from governmental funding programs/agencies).

An important part of the universities entrepreneurial activities is supplying other societal organizations with specialized knowledge, as well as acting as counterparts in innovation processes of organizations (Albats et al., 2018). The traditional performance measurement

theory suggests that performance measures at the organizational level for processes, teams, and individuals must be integrated and aligned and be used for reward and guidance purposes (Bourne et al., 2000; Ukko et al., 2008;). However, some challenges are apparent in the emerging networked, open-innovation environment, with the vague aim of its working processes and measurable outputs (e.g., Ulhoi, 2004). In addition to challenge to understanding the context of collaboration networks, where the participants’ actions and performances are measured, it is not obvious how such measurements should be done. The open-innovation environment creates even more challenges, where it is not evident who the creator or owner of the new knowledge and intellectual capital should be and who should be responsible for the measurement.

The literature further recognizes the importance of innovation and development activities and the management of knowledge, innovation capabilities, and intellectual capital for the organization’s future competitiveness. Adams et al. (2006) suggest that, although it is a difficult process, measuring and evaluating these elements are important in driving continuous improvement and creativity. However, the evaluation of innovation activities is usually divided into input, process, and output measures. The problem with this kind of measurement is that it is only suitable to certain types of innovation and collaboration activities. The type of evaluation seems to depend on the contextual factors and the type of innovation activity (Carayannis and Provance, 2008). Further, in the context of innovation-related collaboration activities between university and other societal organizations, focusing solely on measuring resources and outputs does not fully capture all the components of innovation capability. The measurement of innovation capabilities is an issue that has been given attention among academics during last decade. Saunila and Ukko (2012) have devised a conceptual framework for measuring innovation capability and its effects. They argue that simply knowing how many new innovative processes, actions, or products have been initiated is insufficient if there is no understanding about their connections to performance.

As a summary of the contemporary performance measurement activities in university-industry collaborations, the entrepreneurial and third mission activities of universities need an overall evaluation that pays attention to all participating stakeholders (this is displayed in blue in Figure 6). According to Secundo et al. (2017), the evaluation activities should go beyond the context-specific aspects, to pay attention to wider social and economic benefits, such as transformation of knowledge, the development of intangible assets behind the new venture process, and contributions to social, cultural, and economic development.

Figure 6. The framework of the performance measurement in university-industry collaboration.