This chapter presents the research strategy, research approach, and research methods of the dissertation. In addition, the processes and methods for data gathering are presented.
3.1. Research approach
According to Gummesson (2000), conducting science is a continuing search; it can be seen as a generation of theories, models, concepts, and categories. According to Crotty (1998), the basic elements of any research process can be divided to four different parts that are related to epistemology, theoretical perspective, methodology, and methods. Crotty (1998) further states that epistemology can be considered as the theory of knowledge embedded in the theoretical perspective and thereby in the methodology. According to Crook and Garratt (2005), a research paradigm can be considered as a conceptual framework that provides a model for adoption of particular coherent traditions of scientific research. Sparkes (1992) argues that the term paradigm refers to the different research frameworks or perspectives, including contrasting form of values, assumptions, and beliefs. According to Crook and Garratt (2005), these perspectives and frameworks deal with methodological, ontological, and epistemological considerations shaping the nature and conduct of research.
From the theoretical perspective, Crotty (1998) states that theoretical perspective of research refers to the philosophical stance informing the methodology and, thus, provides a context for the process and grounding its logic and criteria. According to Gummesson (2000), the subject of research paradigms is usually discussed in terms of an antithesis between two schools of philosophy: the positivistic, traditional natural science school and the humanistic school. In order to avoid confusion, the latter can be simply referred to as hermeneutics (Gummesson, 2000). Hirsjärvi et al. (2007) argue that, among the social sciences, there exists an antithesis between the phenomenological/hermeneutic and positivistic research approaches. Kasanen et al. (1991) explain that the focus of the research in business economics is related to a collision between the traditional positivistic research and its alternatives. Even though both hermeneutic and positivistic research seek objectivity, there exist differences in the level of objectivity achieved by these paradigms. The focus of research in the positivistic paradigm relies on explanation and description, trying to maintain a high level of objectivity. For the research related to the positivistic paradigm, the utilization of quantitative research methods and mathematical and statistical analyzation techniques are characteristic of the approach. The utilization of different statistical and mathematics tools and methods creates a distinction between facts and value judgements. In comparison, for the hermeneutic paradigm, it characteristically focuses on the understanding and interpretation, as well as generalizations, of research findings. In the hermeneutic paradigm, the distinction between facts and value judgement can be less clear. In other words, compared with the positivistic paradigm, the focus is not on achieving pure objectivity, but also on accepting the subjectivity as a part of the research findings in order to understand the holistic nature of the research objectives. According to Gummesson (2000), in
hermeneutics, when forming a preunderstanding for the research strategy and research objectives, researchers usually accept the influence of both personal experience and science.
In addition to the theoretical perspective of conducting research and the antithesis between research paradigms, Crotty (1998) argues that research methodology is related to the strategy of the research, the plan of different actions during the research, the selection and use of particular research methods, and linking the selection of the methods to the outcomes. As a part of the methodological choices, the research approach can typically be categorized as qualitative research and quantitative research. Where quantitative research usually refers to positivism, the approach of qualitative research is usually related to hermeneutics. Hirsjärvi et al. (2007) state that the idea of utilizing qualitative research is in understanding and describing real-life phenomena, including the idea of the varieties of reality and the real world.
Referring to the selection of research methods, Crotty (1998) indicates that research methods can be considered as the techniques or procedures used to gather and analyze data related to some research question and hypothesis. The understanding of the most applicable way of conducting the research and searching for this understanding and its supporting facts should provide guidance for the selection of the best research method. As a part of the qualitative research approach, there has been a recognition of the importance of bringing empirical level clarity and increased rigor to theory building by utilizing case studies. Gummesson (2000) argue that case studies, as a part of the qualitative research approach, provide powerful tools for researchers in management and business subjects that can be utilized to generate in-depth understanding of the explored phenomena and mechanisms. As an alternative for more positivistic statistical and survey-based research, operations management scholars have also embraced the utilization of qualitative and case study research (Barratt et al., 2011). As such, using case studies as a research approach has become more accepted as a scientific tool in management research (Gummesson, 2000), and a number of articles have demonstrated how to apply case studies among the different academic disciplines (e.g., Barratt et al., 2011;
Bitektine, 2008: Eisenhardt and Graebner, 2007; Stuart et al., 2002; Voss et al., 2002; Yin, 1994).
The selection and utilization of a single case study or multiple case studies as a research method should usually be done based on the research problem to be explored. A single case study can be considered as an appropriate research method under the circumstances where an investigator or researcher has the opportunity to observe and analyze a previously inaccessible phenomenon (Yin, 2009). Voss et al. (2002) note the dilemma of choosing the correct number of cases, and they suggest that the fewer the number of cases creates possibilities for deeper observation. In contrast, the utilization of multiple cases can provide more robust and reliable data that can be applied in research and data triangulation in order to avoid possible observation biases (Barratt et al., 2011; Eisenhardt and Graebner, 2007).
As university-industry collaboration is a multi-level phenomenon involving different stakeholders with different organizational cultures, and with different aims and goals for the collaboration, an empirical qualitative research approach was selected for this dissertation in order to search for an in-depth understanding of the role of performance measurement in the university collaborations. As it searches for an in-depth understanding of the operational
level performance measurement in university-industry collaboration, this study can be considered as hermeneutic. As a research method, a single case study and multiple case studies were utilized.
3.2 Data gathering
As this study focuses on the operational level performance measurement in industry collaborations, the data for this dissertation were gathered from different university-industry collaboration projects in Finland. The scientific disciplines that the university participants represented were mainly related to industrial management, economics, and engineering.
Even though both qualitative and quantitative methods for data gathering can be utilized in case studies, and case studies can be considered as empirical research where contextually rich data are derived from real-life settings (Barratt et al., 2011), the utilization of qualitative research methods usually dominates the process of data collection (Gummesson, 2000).
When utilizing case studies as a research method, there typically exist several different ways for data collection: interviews that can be either structured or semi-structured, research observations during the research projects, and some archival sources (e.g., organizations reports and statistics) (Barratt et al., 2011). Instead of using one specific method for data gathering, the utilization of different methods and multiple sources for data gathering increases the reliability of the data and analyzed results. The gathering and utilization of data from different sources also provides possibilities for data triangulation (e.g., Choi and Hong, 2002).
The empirical data for this dissertation was gathered from five different cases of university-industry collaboration activities in Finland.
➢ The data for the first publication was gathered from two single-case studies that explored the implementation practices and the challenges of performance measurement in university-industry collaboration. The phenomenon was explored in two university-industry SME innovation networks in Finland. The data were gathered through individual interviews, workshop observations, and a survey conducted for the participating organizations.
➢ The data for the second publication were gathered from interviews with twelve university project managers, representing three different universities in Finland. The interview participants were chosen by how actively they had been part of managing and measurement of these projects, which were funded by different external sources.
In addition, two financier representatives from the European Regional Development Funds of Finland and two members of the Finnish Funding Agency for Technology and Innovation were also interviewed. Semi-structured interviews were conducted with the same themes and factors as were used with the university project managers to ensure comparability with the evaluation processes employed and the challenges that were included.
➢ The data for the third publication to support the understanding of the development of innovation and collaboration activities between Universities of Applied Sciences and industry were gathered through two different questionnaires, workshop observations, and semi-structured interviews from different regions in Finland.
➢ The empirical results for the fourth publication were gathered from the research and development project where a performance measurement system for the university-public organization collaboration was collaboratively designed. The data were gathered from interviews with the management team (including both public organization and university members), workshop observations, and a survey that was arranged for participants from public organizations.
➢ The empirical data for the fifth publication were gathered from two longitudinal Finnish case studies from European regional development activities established between university research units and private and public sector organizations operating in the same regional area. The data from two different research projects were gathered from the individual and group interviews, surveys, workshop observations, field notes, memos, and drawings.
While it might be possible to conduct research by utilizing a single specific research method, for example, observation (Gersick, 1988), the utilization of multiple methods in data gathering from different sources enables data triangulation (Barratt et al., 2011; Choi and Hong, 2002). Utilizing multiple methods and sources for data gathering increases the reliability of the data and research findings and produces stronger constructs and propositions (Eisenhardt, 1989; Voss et al., 2002). The utilization of multiple methods and sources for data gathering also reduces possible biases related to data gathering.
In addition to data gathering, a major part of the research strategy was related to data analyzation (Barratt et al., 2011; Eisenhardt, 1989; Stuart et al., 2002; Yin, 1994). According to Glaser and Strauss (1967), the data analyzation needs to occur simultaneously with the data gathering. Achieving the overlap between the data gathering and analyzation makes it possible for the researcher to capture and interpret the reality that the gathered data represent (McCutcheon and Meredith, 1993). The data gathering and analyzation is summarized Table 1.
Table 1. Data gathering and analyzation
Publication I Publication II Publication III Publication IV Publication V Title Performance
Interviewer Part of the data analyzation