Although existing strategic information management (SIM) frameworks and systems exist as described by Galliers (1985), Myburgh (2002), and Galliers, Leidner and Simeonova (2020), the commercialisation dynamics system principles of SIM in the context of an entrepreneurial university presented a research gap. An investigation of the commercialisation dynamics was required in order to develop a SIM system to facilitate the commercialisation process. Universities play an important role in equipping students with skills and knowledge conducive for commercialisation of innovation (CoI). Commercialisation of innovation is part of an overarching research agenda in the information age (Datta, Reed & Jessup 2013).

The information age has given value to higher education institutions (HEIs) as the core source of knowledge and innovation development (Overton-de Klerk 2016). The information age introduced the Fourth Industrial Revolution (4IR), which has given rise to the demand for HEIs to produce new knowledge and innovation such as cloud systems, additive manufacturing, machine learning, and artificial intelligence for ‘smart ecosystems’ and ‘smart innovation’ (Kruger & Steyn 2020; Mhlanga 2021). Universities, be it private, public, entrepreneurial, comprehensive, technology or other focused universities, are regarded as HEIs that play a significant role within societies by teaching and educating a variety of students, generating new knowledge, and sharing knowledge (Eletter, Refae & Kaba 2020; Matli & Ngoepe 2021; Perkmann et al. 2013).

Universities are generally viewed as one of the pillars for generating new knowledge, producing skilled personnel and citizenry, and stimulating innovation. Bansi (2016), and Overton-de Klerk (2016) identified innovation as one of the desired outcomes of government funding and businesses’ investment for higher education, specifically on research and development (R&D). Research and development, innovation, commercialisation and entrepreneurship serve as proxy for a country’s economic growth (Mhlanga 2021; Shane 2012; Sibanda 2008). The role universities play in developing and nurturing students’ entrepreneurial skills varies. Some universities have knowledge management project offices, commercialisation units and strategic information management systems, while others do not intend on commercialising innovations, transferring technology, and managing information of strategic importance for commercialisation of innovation (Ismail, Majid & Omar 2011; Mabunda & Du Plessis 2022; Morland, Scott & Thomspon 2021).

The identified gap presented a research opportunity focused on commercialisation dynamics as the unit of analysis. To fill the gap, it was necessary to develop a SIM system to guide innovators through the complexities of commercialisation dynamics. This paper is focused on addressing the research question: ‘What are the commercialisation dynamics system principles and support units of entrepreneurial universities?’ The paper begins with literature review on universities creating an entrepreneurial environment, and the commercialisation stages and steps in entrepreneurial activities, followed by the research methodology, findings, analysis, discussion, conclusion and recommendation.

This section describes the qualitative research component of a larger mixed method complex action research (AR) strategy. The larger study consisted of quantitative research and qualitative research, and the qualitative research (Khumalo & Du Plessis 2023), had two components, namely business case study (BCS) content analysis and an in-depth face-to-face interview. This paper presents the interview component of the AR strategy, illustrated in Figure 1, cycle 1.

Figure 1 illustrates this study’s AR strategy consisting of three cycles of diagnosing, planning action, taking action and evaluating action which ultimately produced a SIM system for commercialisation dynamics of entrepreneurial universities (Khumalo 2022). This paper relays findings from AR cycle 1 of data collected and analysed through descriptive content analysis for sensemaking as described by Saunders et al. (2016). According to Saunders et al. (2016:186–187), AR is research with purpose, process, participation, knowledge and implications. The researcher’s interpretation was that an AR strategy referred to the involvement of practitioners in research and a collaborative equal partnership between various research participants, whether they were internal or external participants. In this study, the researcher was part of the organisation within which the research and change were taking place. The AR purpose was to develop a SIM system that would act as a safety net to safeguard innovators while they were walking on the tightrope of CoI complexity. The AR unit of analysis was commercialisation dynamics which included relevant principles, stages, synergies, capabilities, competencies, skills, pitfalls, benefits, best practices, processes and activities for commercialisation of business ideas and innovations. By analysing commercialisation dynamics, the researcher would then be able to develop a SIM system.

The AR strategy was best suited for this research taking into consideration the practical application of a signification framework’s intervention keys. A retroductive approach was chosen for this study because it combined well with an AR strategy of which the findings had to be generalisable to support innovators across all departments of an entrepreneurial university. This paper presents the qualitative research carried out through an in-depth interview which was conducted with UJ entrepreneurship and commercialisation units. These were UJ Project Management Office, UJ Commercialisation & Technology Transfer Office, UJ Centre for Entrepreneurship, UJ Izindaba Zokudla, UJ Process, Energy & Environment Technology Station (UJ-PEETS), and UJ Technopreneurship Centre.

The sampling method for this study was non-probability sampling, that is, purposive. Purposive sampling is the deliberate and thoughtful selection of the right participants for the research project (Saunders, Lewis & Thornhill 2009). The data from the in-depth interview were analysed using the qualitative descriptive content analysis method to obtain a rich understanding from interpreting data using thematic coding techniques. The tools and features of ATLAS.ti qualitative data analysis software facilitated the process of thematic analysis with the commercialisation units. Data analysis was guided by the ATLAS.ti filter ‘commercialisation of innovation’. What this means is the words ‘commercialisation’ and ‘innovation’ had to be in proximity of four words of each other in any order and allowing truncation. The study utilised a signification framework to make sense of the data collected, as illustrated in Figure 2 and Figure 3.

Figure 2 illustrates the first step in the signification framework method of reducing the original transcripts from the verbatim interview transcripts to produce the combined coded transcripts per colour coded analytical category, that is, what is an entrepreneur (yellow), key success factors of entrepreneurship (green), entrepreneurship benefits and/or advantages and challenges and/or pitfalls (teal), core skills and competencies required of an entrepreneur (blue), institution equipped student with skills (grey), entrepreneurial skills (pink), innovator character traits (red) and best practices (jade and olive green). The identification of principles of commercialisation dynamics was linked to each of the coloured themes; for example, best practice for commercialising a business idea is linked to the theme of entrepreneurship benefits and/or advantages and challenges and/or pitfalls, as evidenced by the large teal component in Figure 3 and principles listed in Table 1 in the findings section. This process allowed for triangulation of data by comparing findings from interviews with entrepreneurs and support units in the second step of the signification framework illustrated in Figure 3.

Figure 2 and Figure 3 illustrate the steps in the signification framework method of relevance to this paper, whereas Khumalo (2022) describes three complete AR cycles, further demonstrating proof of concept, implementation and value.

This section presents the findings from the data collected through face-to-face interviews with 11 entrepreneurs and staff from UJ commercialisation units. This section begins with presenting the findings to these interview questions: What is a successful business idea? What are the best practices for commercialising a business idea or innovation? What are the key business principles to apply in testing the viability of an innovation, idea, or product? The findings are summarised in Table 1, followed by an outline of commercialisation dynamics associated with competencies, skills and knowledge required within institutions to produce capable innovators, entrepreneurs, and business experts.

Table 1 presents the research findings regarding CoI dynamics. A successful business idea must add value to the customer and the society and gain profit. Conducting proper feasibility and viability studies for a business idea, acquiring funding and protecting the IP are the best practices for commercialising a business idea. Applying design thinking, conducting viability and feasibility studies, market field test, and pilot or demo tests are key in testing the viability of a business idea. The dynamic system principles of CoI were derived by grouping the findings in Table 1, comparing findings with literature review findings, and comparing findings in thematic analysis, AR cycle 2.

The study identified dynamic system principles in seven categories:

The dynamic system principles form an integral part of the signification framework for the SIM system for commercialisation dynamics of entrepreneurial universities illustrated in Figure 4.

The findings presented from the qualitative component of the mixed method complex AR cycle 1 were added to the qualitative and quantitative findings in AR cycle 2 and AR cycle 3, which has led to the development of a SIM system, illustrated in Figure 4 (Khumalo 2022). The system will only be of value if certain signification framework elements are identified according to their significance to be able to develop an effective safe guiding intervention. The nexus of dynamic system principles and intervention benchmarks is described by Khumalo (2022). The signification framework elements are the different agents that form the system, and these agents possess the embedded knowledge, insight, experience, skills, processes, architecting infrastructure, and methodologies to aid student innovators to effectively have their ideas commercialised. In turn, the agents will assist in the process of ensuring that student innovators develop into well-established entrepreneurs who own their businesses and contribute to the wealth creation. The findings presented in this paper contributed to developing the signification framework which demonstrates the value of SIM summarised in the next section.

This study developed a SIM system through literature review and action research aimed at identifying commercialisation dynamics system principles to ensure the commercialisation of ideas, innovative products, and services. Universities must create an environment conducive for entrepreneurship to thrive if they wish to contribute to the economy through converting students’ inventive ideas into commercial products and services. This paper identified principles of relevance to the commercialisation of innovation through qualitative interviews conducted with an entrepreneurial university’s commercialisation units and entrepreneurs. The development of the SIM system was based on the insights collected from entrepreneurs’ business case studies, commercialisation units, and innovation experts. The SIM system incorporates dynamic system principles with intervention benchmarks and has intervention keys that will provide innovators with the safety net that will assist them in converting their ideas into commercialised products and services.

The study recommends commercialisation dynamics system principles that are fundamental to CoI; for instance, if funding is the major challenge most innovators come across, an intervention is required which means that entrepreneurial universities must create funding opportunities and models directly for viable and feasible innovations and ideas. The signification framework suggests how this can be achieved through continuous engagement with stakeholders who can provide funding and getting buy-in from them. Stakeholders mentioned by this study’s interview participants were the Technology Innovation Agency, Department of Science and Innovation, and the Technology Station Programme (cf. Table 1).

In conclusion, this study has the potential to guide HEIs and other institutions involved in entrepreneurship, innovation and commercialisation, in creating an entrepreneurial environment for their innovators. This study can guide students, entrepreneurs, inventors and innovators in terms of the process to follow to commercialise their business ideas, inventions and innovation.