Abstract
Introducing students to research-based technologies can positively impact their learning and future entrepreneurial efforts in technology commercialisation. This paper investigates how students in entrepreneurship education learn when evaluating the commercial potential of research-based technologies. We perform an in-depth qualitative study of five student teams in a venture creation programme during a week-long educational activity where the objective was to evaluate the commercial potential of research-based technologies developed at the European Organisation for Nuclear Research (CERN). The present paper expands upon previous conceptions of an authentic, self-guided, and experiential learning process. It contributes to an understanding of students’ learning through imitation as an example of how students in entrepreneurship education can become sufficiently positioned to progressively learn, develop, and contribute in the process of commercialising research-based technologies.
Keywords
Introduction
Authentic entrepreneurial experiences can prepare students by providing them with essential skills for success in different work situations and industries (Aadland and Aaboen, 2020; Alsos et al., 2023). In higher education, entrepreneurship education has been extensively researched in recent decades (Ajeel et al., 2025), with growing attention being given to how involving students in the commercialisation of research-based technologies can support both students’ learning and future entrepreneurial actions (Barr et al., 2009; Bell and Bell, 2016; Lackéus and Williams Middleton, 2015; Lundqvist and Williams-Middleton, 2024). By working with research-based technologies as potential venture ideas, students gain exposure to the uncertainties and challenges of early-stage commercialisation (Battaglia et al., 2022; Meyer et al., 2011). Simultaneously, students can also support researchers in the commercialisation of research-based technologies by discovering additional entrepreneurial opportunities and helping to initiate start-up activities that facilitate technology transfer and new venture creation (Boh et al., 2016; Giones et al., 2022; Hayter et al., 2017).
Research-based technologies are often developed without a specific customer or market in mind (Danneels, 2007; Vohora et al., 2004). Recognising the fit between technological capabilities and market needs is an essential activity in the early phases of commercialisation (Andries et al., 2021; Clarysse et al., 2011; Gruber et al., 2013), and ‘market search’ activities are needed (Danneels et al., 2024). Such activities are often attributed to experienced entrepreneurs who are already relatively well-positioned through industry experience and professional networks (Brown et al., 2022). However, students do not (yet) possess the experience and professional networks – or in many cases, the technological expertise – that are needed to fully understand the capabilities of a research-based technology (Beyhan and Findik, 2018; Fayolle et al., 2021; Haneberg and Aaboen, 2020; Voudouris et al., 2011). Still, previous studies have shown that students can develop and learn by conducting market search activities around research-based technologies, and that such processes may even lead to the creation of new technology-based ventures (Kaspersen and Aaboen, 2021; Lundqvist and Williams-Middleton, 2024; Meyer et al., 2011).
However, what remains less well understood is how students in entrepreneurship education become sufficiently positioned to progressively learn, develop, and contribute to the process of conducting market search activities based on research-based technologies. This process includes, for instance, identifying and evaluating the relevance and attractiveness of potential markets by considering feasible technology–market combinations (Danneels et al., 2024). In this paper, we therefore conceptualise students’ market search as a learning process in which they evaluate the commercial potential of research-based technologies while developing and applying knowledge related to technologies and markets, and the connections between them in the early stages of commercialisation. Our research question is: How do students in entrepreneurship education learn when evaluating the commercial potential of research-based technologies?
To address the research question, this paper investigates students’ learning processes in a venture creation programme (VCP). A VCP is an action-based entrepreneurship education programme that promotes students’ learning through involvement in the actions, experiences, and reflections of the entrepreneurial process (Haneberg and Aadland, 2020; Rasmussen and Sørheim, 2006). VCP students are involved in authentic entrepreneurial processes by starting new ventures as part of their education (Lackéus and Williams Middleton, 2015; Sørheim et al., 2021). We perform an in-depth qualitative study of five student teams during a week-long educational activity where the objective was to evaluate the commercial potential of research-based technologies for eventual further commercialisation by the students (cf. Kaspersen and Aaboen, 2021).
The findings show that students’ learning is self-guided and experiential, grounded in imitation behaviours and social learning processes (Bandura, 1977). Through observing and enacting the practices of relevant market actors, students become progressively better positioned to engage in the evaluation of the commercialisation of research-based technologies. The paper thus extends previous research (Blankesteijn et al., 2020; Giones et al., 2022; Hall, 2021) by identifying imitation as a key mechanism through which students enhance both the authenticity and the developmental value of experiential entrepreneurship education. In doing so, it contributes to research on entrepreneurship education by explaining how students become positioned to progressively learn, develop, and contribute within market search processes based on research-based technologies. The next section presents the conceptual frame of reference, followed by the research methods and empirical findings. The paper concludes with a discussion of the implications and limitations, and avenues for future research.
Frame of reference – learning through market search
Entrepreneurship is a process of continuous learning in which experiences of challenging, real-world situations can significantly shape how learning unfolds (Cope, 2003; Minniti and Bygrave, 2001). Given the purpose and research question of this paper, and its focus on the market search activity of evaluating the commercial potential of research-based technologies, this section first outlines market search as part of the entrepreneurial process and then develops a conceptual frame of reference for understanding students’ learning in that context.
Market search starting from research-based technologies involves linking technological knowledge to possible products and/or services that address market demand (Gruber et al., 2008; Reinsberger et al., 2026). Because a technology may have multiple possible applications, it often gives rise to a broad range of potential offerings and market opportunities (Chattopadhyay et al., 2025; Danneels, 2007). However, the research-based technologies are often far from ready for market launch, and a scientific advancement through research may not automatically translate to solutions that address a need in the market (Brown et al., 2022). With all the options comes the challenge of identifying which potential uses of a technology can be meaningfully matched with a sufficiently validated market demand (Andries et al., 2021). Previous research has pointed to the importance of interacting with potential customers or users as early as possible in the entrepreneurial process (Blank and Eckhardt, 2024; Klofsten et al., 2020; Kruachottikul et al., 2023). Such interaction may involve physical prototypes (Goldsby et al., 2014) or more conceptual ‘opportunity prototypes’ (Baron and Ensley, 2006; Grégoire et al., 2010) that help to elicit feedback on potential value propositions.
However, a recurring challenge is knowing where and how to start searching (Fiet, 2007). Students are not likely to know a good starting point for their market search a priori, meaning that one of their challenges is to identify a starting point – who to contact – and then how to engage in meaningful conversations to fruitfully inform their market search process. Giones et al. (2022) highlighted that students who work on identifying commercial opportunities for research-based technologies had to identify potential customers that the researchers were not aware of or familiar with, and had to engage in dialogues with them. Blankesteijn et al. (2020) emphasised that students should translate technological capabilities to market needs, and Hall (2021) found that universities support students in identifying and interacting with potential customers. However, previous research leaves open the questions of how students were able to do this and how support in the process is provided – for instance, by the university. We know from previous research that an individual’s ability to identify potential customers or users improves with, for instance, their own experience (Fiet, 2007), support from a technology transfer office (Mosey and Wright, 2007), or social networks (Elfring and Hulsink, 2003). Less experienced individuals – such as students – may lack the prior knowledge and networks, and may also struggle to develop the social ties needed to develop networks for the commercialisation process. Thus, a remaining route is how students can be supported so that they learn how to identify customers and users.
Action-based entrepreneurship education (ABEE) offers one way of supporting students in their entrepreneurial learning through student-driven and experiential approaches (Politis et al., 2019; Rasmussen and Sørheim, 2006). Through ABEE, students learn entrepreneurship by experiencing entrepreneurship (Politis et al., 2025), which is enabled by support from a wider network of actors supporting the students’ process (Haneberg et al., 2022; Lackéus and Williams Middleton, 2015). Some, but not all, ABEE programmes expose students to research-based technologies as a basis for learning through commercialisation activities (e.g., Giones et al., 2022; Kaspersen and Aaboen, 2021; Lundqvist and Williams-Middleton, 2024). Actors in the entrepreneurial ecosystem at universities, such as technology transfer offices (TTOs), may support or take part in ABEE efforts as they share an interest in the commercialisation of research-based technologies (Bolzani et al., 2020; Lundqvist and Williams-Middleton, 2024). Students in ABEE learn from the knowledge and practices of such stakeholders in the ecosystem through their entrepreneurial process, which means that the learning environment and the learning process are different from traditional classroom approaches by offering a learning environment for experimentation and external collaborations (Haneberg et al., 2022; Hasche and Linton, 2021; Linton and Xu, 2021).
Students’ learning in ABEE has been the subject of several studies, including how students learn by doing (Rasmussen and Sørheim, 2006), through emotional exposure and situated learning in contexts similar to those of entrepreneurs (Pittaway and Cope, 2007), by transforming into entrepreneurs (Lackéus and Williams Middleton, 2015), through authentic student ventures (Neck and Corbett, 2018), and from each others’ entrepreneurial practices (Haneberg and Aadland, 2020). Aadland and Aaboen (2020) argue that students in ‘authentic’ ABEE are professionals – they learn by doing what entrepreneurs do. However, Hägg and Kurczewska (2020) emphasise that students in entrepreneurship education are ‘emerging adults’ who are starting to experiment, make decisions, and interact with their environment, but still have limited previous experience to build their learning and action upon. Thus, although students are self-guided in the entrepreneurial process as emerging adults, they still require some kind of guidance (Hägg and Kurczewska, 2020). Recent research has emphasised how carefully designed pedagogical interventions – i.e., reflection exercises, entrepreneurial challenges, pitching, and not least observing others and learning with others – in ABEE facilitate students’ learning (Haneberg and Aadland, 2020; Neergaard et al., 2021; Politis et al., 2025).
Rule (2006, p. 2) suggests that learning through authentic contexts entails that “the activity involves real-world problems that mimic the work of professionals in the discipline with presentation of findings to audiences beyond the classroom”. Learning through mimicking the work of professionals has its roots in the social learning theory (cf. Bandura, 1977) and resembles a ‘modelling’ learning process, such as how a child can learn from a parent by imitating behaviours to gain autonomy in their own development gradually. Within the focus of this paper, students’ initial reliance on, for instance, stakeholders in ABEE (market experts, technology experts, experienced entrepreneurs, etc.) or university faculty members (Haneberg et al., 2022; Zozimo et al., 2017) allows students to mimic their behavioural patterns in authentic contexts. This process requires the replication or reproduction of specific patterns of behaviour by observing others through participative interaction with them (Holcomb et al., 2009; Seet et al., 2018). Social learning theory also suggests that the likelihood of modelling others increases if students identify with the person with whom they are interacting (cf. Bandura, 1977). Experiencing support from someone or considering someone important based on, for instance, status or knowledge, fosters identification. Thus, we conceptualise that students’ learning when engaging in market search is a process of social learning in an authentic entrepreneurial context where interactions with other students, faculty, and stakeholders are important for the process.
Methodology
Research design
To address the research question, we conducted a qualitative, exploratory case study of five student teams enrolled in a VCP. The empirical setting was the NTNU–CERN Screening Week in 2022, which is a 1-week intensive module embedded within a five-module feasibility analysis course in the VCP’s first semester. The unit of analysis is the teams’ learning process as each team works with a technology to develop entrepreneurial ideas for future start-ups.
We conducted focus-group interviews with five student teams at multiple points – before, during, and after the NTNU–CERN Screening Week. The data were analysed inductively to support transparency and rigour in developing new theoretical insights (Gioia et al., 2013). We followed a stepwise research design (Gioia et al., 2013) to articulate the defined research question in relation to the existing literature, thus supporting the theoretical conceptualisation.
Our primary data source consisted of focus-group interview transcripts, which captured students’ accounts of their experiences during the NTNU–CERN Screening Week. As data collection progressed, we followed the research protocol while also incorporating emerging questions when students raised new insights about their team processes. To enrich and contextualise the interview data, we also drew on observations and students’ final reports. These additional sources helped us to develop a more nuanced understanding of the students’ learning processes.
Through iterative analysis, we identified and refined a set of themes and aggregate dimensions, which were then discussed in relation to the literature and the empirical patterns observed during the Screening Week. We also compared the five teams’ processes across the week to examine similarities and differences in how learning developed over time. The following subsections describe the research context, data collection, data analysis, and methodological reflections, including the limitations of the study.
Research context – CERN as a provider of research-based technologies
The NTNU–CERN Screening Week is an annual activity that has been embedded in a feasibility analysis course organised by the NTNU School of Entrepreneurship (cf. Sørheim et al., 2021) in collaboration with CERN’s Knowledge Transfer Group since 2008. During the first semester of the programme, students complete five feasibility analyses focused on market research and evaluation of technology-based ideas originating from sources such as the university, business partners, technology transfer offices, and external entrepreneurs. The NTNU–CERN Screening Week is one of the feasibility analyses where the technologies developed by CERN’s researchers are analysed. All the feasibility analyses are graded in groups, with weightings of 60% on the students’ reports averaged across all five modules and 40% on students’ individual reflections 1 . In 2022, the NTNU–CERN Screening Week was the fourth feasibility analysis (module) that students participated in during the semester.
The purpose of Screening Week is two-fold. First, it exposes students to complex research-based technologies, and challenges them to evaluate their commercial potential and identify possible venture opportunities. Second, it also promotes further entrepreneurship activities at CERN by involving students in the assessment of possible applications beyond the research laboratory. The week also creates an authentic entrepreneurial learning environment, grounded in CERN’s technologies. Because it is the VCP students’ only feasibility analysis conducted abroad in Switzerland, students often face greater uncertainty and pressure than in many of their other feasibility modules.
NTNU–CERN Screening Week is distinctive in at least three ways. First, it relocates students from their usual study setting in Norway into an ‘authentic’ professional work environment at CERN in Geneva, Switzerland. Although they are typically at an early stage of entrepreneurial development at the start of the course, there are strong expectations from CERN partners regarding the students’ capacity to generate credible venture ideas (cf. Vogel, 2017) from CERN’s technologies. Second, students collaborate with international stakeholders, including scientists, engineers, and industrial and technology experts at CERN, with minimal to no guidance from NTNU faculty members during the week. This design pushes the student teams to learn through direct engagement: they evaluate the potential of technologies, build networks beyond their home university, and interact with diverse experts internationally. Third, CERN’s technologies are considered deep technologies characterised by high complexity, scientific novelty, and multiple potential application domains (Andersen and Åberg, 2017; Nilsen and Anelli, 2016). Many are at low technology readiness levels, which makes it challenging to identify feasible commercial applications within the available time. Given the 1-week duration and with limited technical domain knowledge, the five teams often struggle to develop a sufficient understanding of the commercial potential of the technologies. Figure 1 below introduces the overall structure of the Screening Week and how the interview procedure of this paper was conducted according to this structure, in 2022. NTNU–CERN screening week and interview procedure.
Expected learning outcomes
Students’ learning outcomes during the NTNU–CERN screening week, adapted from table 14.1 in Kaspersen and Aaboen (2021).
Data collection
Data were collected from a cohort of 39 students, grouped into five teams, in their first year at the NTNU School of Entrepreneurship, a VCP, in 2022. Although the students had not yet established their ventures at the time of data collection, they can still be considered student entrepreneurs – defined as those who create new ventures during entrepreneurship education programmes (Bergmann et al., 2016; Rasmussen et al., 2024). The students had strong motivation to engage in the new venture creation process, but had limited industrial and entrepreneurial experience and were eager to learn and develop during the programme (cf. Haneberg et al., 2022). During each feasibility analysis, students were grouped into multidisciplinary teams to evaluate potential ideas and develop new opportunities. They often worked with new group members in each module so they could get to know their classmates better. This process served as a foundation for future entrepreneurial activities throughout the remainder of their studies, including the selection of ideas and team members, and the development of entrepreneurial capabilities.
Student teams and technologies during NTNU–CERN screening week in 2022.
Interview procedures
We conducted two rounds of focus-group interviews with five groups of students: one before and one after the NTNU–CERN Screening Week. The first round of interviews was conducted between 11 and 14 October 2022, and the second round of interviews was conducted in mid-November 2022. A total of 10 interviews were conducted in a semi-structured format. Focus-group interviews enabled us to capture team-level reflections and interactions. For instance, the interviews aimed to understand how teams collectively learn from their experiences, develop ideas, and make decisions, rather than relying solely on individual recollections (Rabiee, 2004). This choice aligned with our interest in exploring the team’s entrepreneurial learning process under conditions of uncertainty and technological complexity.
One of the researchers conducted the interviews. We first discussed the interview procedures within the group, outlining the list of questions to be asked. Interview guides were informed by prior literature and structured around students’ expectations, learning processes, and perceived learning outcomes. Interviews lasted 45–60 minutes and generated approximately 70 pages of transcripts across the five teams. We supplemented interview data with field notes, observations, and students’ final reports to strengthen contextual understanding and support triangulation. During data collection, we iteratively refined the interview guide to follow up on emergent issues and support open discussion (Corbin and Strauss, 2015).
Ethical procedures
The paper adhered to ethical procedures for collecting personal data in Norway. We presented the research project before the first round of interviews to explain the research protocol and considerations of ethics and confidentiality. We then distributed the consent forms that were to be shared with all students participating in the interview, explaining the project and that their answers would only be used for research purposes. Students had the opportunity to ask questions and share any concerns about data collection. At the time, students were aware of the researchers’ role in the project and in the course’s teaching activities. The main author’s role was to conduct research and document the students’ process, with neither organising nor teaching activities in 2022, thereby facilitating students’ openness to the sharing of reflections, feedback, and insights during data collection. Access to students’ interviews, reports, and information regarding the identification of individuals was limited to the research group. In addition, we coded the students’ responses numerically and deleted all information that might reveal their identities, thus protecting their confidentiality.
Data analysis
We analysed the data inductively, following established procedures associated with grounded theorising and qualitative theory building (Corbin and Strauss, 2015; Eisenhardt and Graebner, 2007). All empirical materials were combined and imported into the NVivo software for data management and analysis. We developed a data structure moving from first-order codes (participants’ terms and meanings) to second-order themes (researcher-generated themes) and then to aggregate dimensions (Gehman et al., 2018; Gioia et al., 2013). Coding proceeded iteratively in several rounds.
We first coded interview excerpts to capture the meanings that students attributed to key moments and decisions. In the first-order data analysis, we adhered to the close meanings shared by the students and developed as many codes as possible. We combined the interviews before and after the week for each team, then coded the students’ process inductively based on what they had shared. We particularly focused on how the students perceived the technologies and their approaches to evaluating the potential through learning and interactions with others. For instance, one student stated: “You throw it at a wall, and you see whether it sticks. So, pretty much calling people, I guess, is the best answer to going deeper and deeper into what they actually need”, which was a representative code from “Calling around to get a sense of direction on how the technology can fit into other markets”. We then compared and consolidated codes to reduce duplication and sharpen distinctions (Willig and Rogers, 2017), and we continued this process for each team.
As the research progressed, we investigated the similarities and differences among the five teams by examining all code labels and merging them into second-order themes. We relied on the potential dimensions in the learning outcomes (Table 1) as the starting point from which to examine how the students assessed the technologies and developed the teams’ ideas – for instance, how they dealt with pressure to become more confident, how they interacted with other experts to learn about the technologies, and how they collaborated in teams. We then iteratively revisited the evolving codebook and themes against the full dataset to check coherence, coverage, and consistency, including reviewing how frequently and in what contexts sub-themes appeared. Code development was conducted several times with the entire team involved.
The research group discussed the second-order themes and the stepwise process to understand the students’ learning process. We openly shared our thoughts and observations, and continued developing the second-order themes to strengthen the alignment of interpretations. For instance, when we discussed the student teams’ process, one researcher noted that students constantly sought out other researchers and market leaders to discuss their venture ideas. Another researcher pointed to the messy process that students encountered when technologies were very complex and difficult to commercialise. These discussions reached the conclusion that the students’ processes were iterative, and learning from interaction with other market experts was an important process when they had limited understanding.
Furthermore, we mapped the students’ learning process and saw how each team’s process could be translated into the potential outcomes highlighted in Table 1. We considered the development of venture ideas and reflected on how students could understand and assess the potential of technologies, as a main measurement for expected outcomes. The mapping of the entrepreneurial process started with what students perceived to be the potential of the technologies at the start, and how this perception changed over the course of the week in relation to what they had learnt. We mainly focused on the learning dynamics in relation to the students’ interaction with the other people they had contacted during the week. We also mapped how their ideas changed towards the end of the week, with inputs from the students’ reports. As the teams’ ideas were distinct, with no right or wrong answers, the entrepreneurial practice identified in the paper was a conceptualisation of how the students reached a new stage of idea development, starting from research-based technologies. We triangulated our findings with the students’ reports and our observations during the week. In short, a total of 22 first-order codes, eight second-order themes, and three aggregate dimensions were included in the data structure, as shown in Figure 2, below. Data structure.
Methodological reflections and limitations
Gioia et al. (2013) consider several aspects of rigour in qualitative research, such as transferability of the qualitative research and the potential to generalise findings to other contexts. In this paper, our findings were based on five student teams in a technology-based entrepreneurship education programme. The transferability aspect is reflected in the paper’s methodology as follows.
First, the research design followed rigid procedures. The main author first conducted two pilot studies during the NTNU–CERN Screening Week in 2021 in Norway and another engineering education course in Switzerland in 2021, before the actual data collection for this study in 2022. The pilot studies aimed to understand how different groups of students might respond to similar questions and whether our findings could be generalised to other students, with different technologies. This avoided the bias that the findings would only be applicable to a particular course – i.e., NTNU–CERN Screening Week – and that they would lack generalisability if the same process were to be followed in other similar entrepreneurship education courses.
Secondly, the use of data triangulation from multiple sources, such as interviews, students’ reports, presentations, reflections, and observations, could enrich the data analysis and findings. Furthermore, reports from other student groups from the previous cohort who attended the screening week were also included. Nevertheless, our interpretation and conceptualisation of the students’ learning process were mainly derived from interview transcripts. Although the paper has provided insights into the learning process, the paper has not sufficiently addressed the relationships between learning and new venture idea development. For instance, future research can examine whether the learning process enhances students’ ability to develop innovative ideas from research-based technologies.
Lastly, the research team’s close engagement with the empirical setting provided privileged access to the empirical data. However, students may have felt inclined to share their insights and feelings, or been hesitant to share any positive or negative feedback during the week, given their ‘student status’ or due to fears that their feedback might affect NTNU–CERN relationships. To avoid bias in data collection, we have clearly communicated to the students that their feedback would not affect their learning outcomes during the Screening Week or at the NTNU School of Entrepreneurship in general.
Findings
In this section, we present our findings related to the three dimensions of the students’ learning process, which were presented in Figure 2.
Basic understanding to start conversations
Abstract understanding of the technologies
In the first learning phase, students had limited knowledge of technology, markets, and technology–market combinations to develop new ideas. The knowledge they accumulated in the beginning was mainly technological knowledge regarding technological limitations, capabilities, and specifications provided by external technological experts. The students initially worked to understand the technology at a basic and abstract level. However, as the technology is very complex and challenging to understand entirely, students found it hard to absorb all the information within a short period of time. The short timeline of the Screening Week was thus among the major challenges for the students’ ability to fully understand the technologies and their commercial potential.
To deal with such challenges, students identified and focused on the value propositions of the technologies. This means that the students did not learn and understand all the aspects of the technologies but tried to grasp the basic knowledge and characteristics of the technologies needed to develop future ideas that could be of value within different market segments. Through conversations with other researchers at the home university, the students built up their understanding by constantly finding people who possessed the knowledge that they themselves lacked. Due to the time pressure, students learned from the technology experts that they are already in contact with at Screening Week to a certain extent, allowing them to explain the technology and their specifications to other people. For instance, Team 5 outlined that “There is just so much knowledge we can learn from. However, we try to learn enough to explain to other customers in different markets.”
Abstract understanding of the market
As the students also lacked knowledge of how the technologies (of which they now know the basic characteristics) could serve potential customers, they also searched for and identified people who could help them to understand the technologies in different contexts, scenarios, and market segments. Students in Team 1 sought suggestions on the potential market segments through discussions with these market experts, resulting in advice such as: “This technology has applications in solar panels.” To compensate for the lack of knowledge, students identified and interacted with other people who were considered market experts – people who possess knowledge related to the commercial potential of technologies. These experts could, for example, be relevant researchers or market actors who provided insights into the students’ assessments and provided suggestions on future commercial ideas. Additionally, the students further explored different market segments based on the market experts’ opinions. Without a clear direction to investigate the market potential, students struggled to find the starting points of potential markets and people who were actors in those markets. For example, Team 2 indicated that: “We needed to find important leads, and when we could not find them, we were stuck. What could we do?”
Making decisions through experts’ feedback
Finding experts in the fields
After gaining a basic level of understanding from both technology and market perspectives, the students identified individuals who could help them with further insights into the future applications of technologies. As reflected by all teams, students from Team 5 said: “We focused on finding key people who knew the most about that market and just asked them all the things we needed to know.” Students presented their assumptions and ideas on future venture ideas to diverse actors, such as those whom they considered to be potential customers, collaborators, technology experts, or company market leaders, to gain new insights. To do this, students ‘wandered around’ through different channels and networks to interact and discuss with people in diverse fields. With their limited networks and prior knowledge, students searched broadly and depended to some degree on contingencies in their process, so it took time and effort for students to find relevant experts and identify potential customers. For example, Team 3 reflected: “I found a guy on LinkedIn who made tunnels, and we had a one-hour conversation where he explained how they built a tunnel from scratch.”
Getting experts’ insights and feedback
Students sought feedback and validation from the diverse actors in different markets on the ideas that they constructed to make evaluations and decisions related to the feasibility of the venture ideas. They also relied on external market feedback and continuously built that feedback into the venture ideas that they presented during the process. Market experts could also suggest directions for further discussions, making suggestions for the improvement of venture ideas, or simply explaining how feasible (or not) the commercial potential of these ideas was. The students therefore had to compile different insights and suggestions from a diverse group of market actors. As Team 4 explained: “You need to connect the dots between technology and how this can be transferred to our idea and business model. That can be very difficult.”
Given the students’ limited understanding of the technologies (see above), it took time for them to receive expert feedback and to make sense of the feedback. This may have resulted in the students asking open-ended questions, and not the critical, essential questions that could help them to progress into the development of new venture ideas. As Team 3 explained: “I think in the beginning, you just ask these open questions, and you do not get deep enough to get some value out of the questions.”
Reflections on feedback and commercial potential
After receiving feedback from diverse market actors, the students reflected upon the feedback to understand and further develop their venture ideas and also to make decisions about their abilities to bring forward new projects based on the ideas (as a potential outcome of the Screening Week is that the students themselves work further on commercialising the technologies). By reflecting upon their team’s resource capabilities and the knowledge they possessed, students could make decisions regarding people with whom they could collaborate to develop these ideas. These reflections helped them to adjust their strategic decision-making regarding with whom they would engage in future discussions and on which topics. Through constant reflections upon feedback, the students were able to narrow down their choices related to the potential venture ideas and identify people who could be crucial in providing insights into the development of those ideas. Team 4 elaborated upon their reflections on finding people to discuss with: “I would take some time to find the right person to talk to […] instead of trying to call as many people as possible […] the five most important phone calls might give us more relevant information than talking to 50 other people.”
Promoting new venture ideas
Critical reasoning for in-depth insights
Further conversations with market experts allowed the students to gather more insights into the decision-making process of choosing one concept over others. If students received positive and strong interest from experts, they would consider investigating those venture ideas further. On the other hand, students were more likely to abandon the ideas to pursue another concept if they did not receive interest or lacked engagement from potential market actors. Team 1 described their reasoning approach: “We made a map on the board where we had the different markets and all the pros and cons.” By constantly mapping potential market interests to different venture ideas, students could analyse, make decisions, and initiate further discussion topics to gain more market feedback and insights. The feedback received by Team 1 – “We do not see anything like that […] there is a potential of bringing this technology into space” – is an example of the market interest that students received in their process. This feedback would motivate students to engage in further discussions with other market actors to gain more insights.
Imitation of experts’ language and behaviours
To spark interest and engagement from the market and to gain insights into venture ideas, the students first adapted to the style of language, discussions, and conversations, or topics that frequently occurred and were common across stakeholders in the market. Thus, by having many conversations that might not always result in in-depth insights (see above), students learned to steer conversations to include topics that interested potential customers and other stakeholders, extracting information related to their needs, suggestions, and interests more effectively. Examples of the more in-depth insights they could achieve include awareness of existing problems in the market, views on the adoption of new technology, and the potential for technology adoption in the future.
Over time, students built up their knowledge related to the patterns of thinking, discussion points, and frequently occurring topics among market stakeholders. This new knowledge enabled the students to identify the relevant people with whom they should engage in discussion, such as potential customers, collaborators, investors, and so on, consequently narrowing their market search efforts. By understanding thinking patterns, students could maintain the conversations, build trust with stakeholders, and understand potential customers’ interests and needs. Students learned how to maintain conversations with “confidence, like we are the experts in the fields and we know what we are talking about” (Team 2). Team 5 also explained their imitation process: “I think it is important to talk to many people so that you know the pattern of how they are thinking.” Team 4 shared similar reflections on the imitation process: “You can respond and ask questions back, and you can also predict which questions they will ask because you have talked to many of them, so you know how they are thinking.”
Gathering attention for new venture ideas
Constant imitation of different experts also allowed the students to gather insights and interests from the market to be able to “sell the ideas that we have in mind” (Team 3). Testing the market responses in this way served as an important learning mechanism for students to gain relevant insights into the development and to come closer to understanding the commercial potential of their venture ideas. By imitating experts and pretending to know more than they did, the students positioned themselves as experts in their respective fields throughout conversations with stakeholders. Such an approach allowed them to gain more trust from potential customers, sparking more interest in the markets and obtaining important market insights on the venture ideas. For example, Team 1 were able to identify potential customers with strong interests in future collaborations who eventually said that: “We want to test early versions of the product and be a pilot customer.” Through the imitation process, students gained sufficient market insights to co-develop venture ideas with the stakeholders they were interacting with, and could build their confidence and credibility in dialogues with relevant market stakeholders to the extent that the students were actually invited into the market, such as in the example above. Team 2 explained their imitation process to gain customer interest and build their credibility as follows: “If you are going to be a supplier to a big company, you have to make them trust your product by providing the credibility to be able to go forward with this project.” Students were able to do this by learning how to act like market actors through an imitation process so that they could eventually become market actors.
Discussions
The paper supports the findings of previous studies that students identify commercial opportunities for research-based technologies through the identification of potential customers, engaging in dialogues with them, and translating technological capabilities to meet market needs. Students are in some way supported in identifying and interacting with potential customers (cf. Blankesteijn et al., 2020; Giones et al., 2022; Hall, 2021). Moreover, the findings also contribute with insights about how – through an imitation process – students learn to understand who to contact, how to interact with them, and how to leverage those interactions to continue learning and to identify the commercial potential of research-based technologies. The findings also show that students are supported in the process, and that the function of this support is primarily to get students started with interacting with someone and with a diverse group of relevant and less relevant externals. After this, the students are left more on their own to learn how to identify the most relevant actors and how to engage in dialogues that provide in-depth insights, co-creation of venture ideas, and potential commitments for further collaboration.
Overall, the first two dimensions of students’ learning processes (see Figure 3 and findings section) reinforce existing knowledge from previous research, while the third dimension extends upon what we know from previous research by introducing imitation as a perspective on the learning process in entrepreneurship education. The first dimension, Basic understanding to start conversations, relates to how previous research has also described the interactive nature of students’ learning in the context of ABEE, exposing students to authentic challenges and positioning them in a situation where they must interact with external stakeholders (Haneberg and Aadland, 2020; Longva, 2021). Also, students’ initial efforts demonstrate that knowledge related to both technological and market domains is essential, including in our research context (cf. Andries et al., 2021; Clarysse et al., 2011). Students therefore initially learn by being informed about technological capabilities and potential markets through interactions with diverse stakeholders outside the classroom. To a large degree, the initial interactions are facilitated by the ABEE programme structure and network (cf. Haneberg et al., 2022; Lundqvist and Williams-Middleton, 2024). Interestingly, the findings show that although learning through these initial interactions only directly results in basic knowledge, the experiential and social learning from interacting in itself pays off in the longer term, as students learn how to imitate market actors and other stakeholders. Imitation learning mechanism.
Following that, the dimension Making decisions through expert feedback represents how the learning conditions require students to process information from various stakeholders and independently make their decisions and reflections upon that feedback (cf. Aadland and Aaboen, 2020). Constant and iterative analysis of feedback steers the development of students’ venture ideas, and as students’ previous knowledge is limited, they co-develop their new venture ideas together with market stakeholders. Students thus rely upon external market insights from market stakeholders to make decisions related to the commercial potential of different ideas. This dimension also covers how students experience many different interactions (some of which are more fruitful than others) and is an example of how the Screening Week intensifies experiences and interactions as part of ABEE (Politis et al., 2025). Through this, the students get to test out and learn from different approaches, and ultimately, learn with whom they get the most fruitful interactions.
The third dimension, Promoting new venture ideas, is where the findings contribute with insights about how students use imitation as a way to learn who to contact and how to interact with them, as well as how to leverage those interactions to continue learning and identify the commercial potential of research-based technologies. The present paper strengthens the relevance of imitation of others through participative observation (Holcomb et al., 2009; Seet et al., 2018) and, thus, the relevance of social learning theory (cf. Bandura, 1977) for students who work on market search activities in entrepreneurship education. Although students are ‘emerging entrepreneurs’ and (as in Hägg and Kurczewska, 2020) ‘emerging adults’, they gradually become more self-guided in finding their role and position as potential entrepreneurs commercialising a research-based technology by imitating market actors, and thereby gradually become market actors in their conversations with stakeholders. This enables students to learn to act like entrepreneurs, which contributes to their further learning through imitation. Furthermore, the present paper also contributes by highlighting imitation as a process of experimenting with different stakeholders – starting to learn how to imitate and with whom to interact (dimensions Basic understanding to start conversations and Making decisions through expert feedback). The present paper thereby expands upon previous research (Blankesteijn et al., 2020; Giones et al., 2022; Hall, 2021) and adds an understanding of how students’ learning through becoming part of a (niche) market by imitation enables an enhanced form of experiential learning and authenticity in the ABEE programme. The present paper therefore contributes with an example of how students in entrepreneurship education can become sufficiently positioned to progressively learn, develop, and contribute in the process of performing market search activities starting from research-based technologies.
Conclusions, limitations, and future research
The present paper contributes to an understanding of how students learn when evaluating the commercial potential of research-based technologies as an iterative, self-driven, imitation process that facilitates progression, both in terms of learning and development of venture ideas through market search activities (cf. Danneels et al., 2024). By outlining their three dimensions of the learning process, the present paper finds that imitation is a critical learning enabler in this process. Students deliberately imitate the behaviours of more experienced market actors to gain market insights. Imitation practices allow students to position themselves as more knowledgeable individuals to spark interest and engagement from stakeholders. Besides imitation, two other dimensions of learning are also presented. For practice, the present paper suggests that entrepreneurship education can facilitate situations where students are allowed and encouraged to imitate as a pedagogical intervention to accelerate their learning (cf. Politis et al., 2025).
The empirical base for the present paper was derived from the 1-week NTNU–CERN Screening Week, which allowed us to conduct an in-depth analysis of the imitation mechanisms, but longer longitudinal data collection should be conducted in future research so that the potential further evolvement of imitation practices, and their potential impact on students’ entrepreneurial processes, can be studied. Identification is another important element of social learning theory that has not been the primary focus of the present paper, but given its importance for imitation (cf. Bandura, 1977), further research could focus on how students identify with certain stakeholders, and how they develop their own identities as entrepreneurs through identification and imitation.
Footnotes
Acknowledgements
We would like to acknowledge the constructive feedback provided by the research community at the SEFI Conference of Engineering Education at TU Dublin in 2023. Additionally, we are grateful for the constructive feedback from our colleagues at the Engage - Centre for Engaged Education through Entrepreneurship in providing feedback to improve the manuscript.
Ethical considerations
This article received ethical approval from the Norwegian Research Information Repository (Sikt) (Reference number: 166971).
Consent to participate
Consent was obtained from all the participants involved in the article.
Consent for publication
All participants provided consent for the data to be used in the article and associated publications.
Funding
The authors received no financial support for the research, authorship, and/or publication of this article.
Declaration of conflicting interests
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Data Availability Statement
Data will be available upon request.