University vs. industry: how does the type of organization shape expectations, problems, and solutions in collaborative research?

Abstract

While research on university–industry collaboration has significantly increased in recent years, there is still little knowledge about collaboration problems and their solutions for this particular form of joint research. Drawing from existing insight on research collaboration and systematization of university and non-academic collaboration, we aim to detect differences regarding what expectations, problems, and solutions that university and industry members deem relevant for university–industry collaboration. We conducted three focus group discussions with lead collaborators in Germany. With the help of Qualitative Content Analysis, we developed an accurate and theoretically informed and empirically differentiated ranking that catalogues their expectations and perceptions of problems and solutions. Our results reveal more differences than commonalities. Concerning the expectations with respect to how collaboration should work, for example, researchers from universities focus on the exchange of knowledge and education. Industry members, in contrast, primarily expect innovative growth of knowledge and consequent economically relevant problem solving. Overall, it appears that the type of organization acts as a distinct perceptual filter: while university members follow a logic of “thinking in persons,” industry members adhere to a logic of “thinking in structures.”

Introduction

Currently, most research endeavors are of collaborative nature (Bozeman and Boardman, 2014; Bozeman and Youtie, 2017; Baker et al., 2017), which presents researchers with challenges that they would not be able to overcome working alone (Falk-Krzesinski et al., 2011). As a result, research has begun to focus on the problems that arise from collaborative work and possible ways to solve them (e.g., Youtie and Bozeman, 2014; Bozeman et al., 2016; Hückstädt, 2022; Meißner et al., 2022). The situation is complicated by the fact that to an ever growing degree, research collaborations (RCs) do not only include academic institutions but also non-academic organizations, especially from the business sector (Teirlinck and Spithoven, 2015; Tijssen and Wong, 2016). Consequently, there is a growing amount of studies on this type of collaboration, i.e., university-industry collaboration (UIC; e.g., Ankrah and AL-Tabbaa, 2015; Mascarenhas et al., 2018; Yegros-Yegros et al., 2016; Azagra-Caro et al., 2010; Tijssen et al., 2009). However, while research has tackled collaboration problems and potential solutions at a general level, there is still little knowledge about this issue with respect to UIC in specific. We address this research gap in this article.

Previous research on UIC

There are several reviews of the scientific research on UIC which group existing studies into clusters, trends, or key aspects. Each of those reviews takes a specific angle on UIC and consequently on previous research. We discuss three of those reviews which we consider most appropriate to locate our research within this field of study. The review by Skute et al. (2019), for example, grouped the literature on UIC along its perspective taken: (1) “Ecosystem perspective” (p. 926), which investigates the relationship between academia, industry, and government, (2) “Social relations perspective” (p. 927), which focuses on the interaction channels that are used by the collaboration partners, (3) “Academic entrepreneurship perspective” (p. 928), which examines the transition of universities from research organizations to entrepreneurship, and (4) “Distance perspective,” which focuses on the distance and complementarity of the collaboration partners involved (p. 929). Furthermore, the authors state that research can synthesize the field of research into three levels, which they describe as the directions of future research: the individual, organizational, and institutional level. In terms of this systematization, our study can be located at the individual level and the social relations perspective. And indeed, the authors identify the question of how different characteristics and motivations of academics and partners from the industry shape successful collaboration as one topic that needs more scientific attention.

Two other reviews looked at previous research with a more thematic lens. An earlier one by Ankrah and AL-Tabbaa (2015) identified the five key aspects of research on UIC: (1) “organizational forms of UIC” (p. 390), (2) “motivations for UIC” (p. 390), (3) “UIC formation” and operationalization (p. 394), (4) “factors that facilitate or inhibit the operation of UIC” (p. 395), and (5) “outcomes of UIC” (p. 395). A more recent review by Bastos et al. (2021) systematized the research into eight trends, among which the topics of motivations, barriers, and benefits are also emphasized as one trend. For example, studies that investigate what prevents academics from collaborating or encourages them to do so can be included in this trend (e.g., Ho et al., 2016). Research results show that collaboration is becoming more common, as can be seen from the increasing number of authors per publication. However, collaboration is made difficult by the dispute over authorship (e.g., Lazebnik et al., 2023; Savchenko and Rosenfeld, 2023; Laudel, 2002). Another example are studies that investigate the reasons why members from the industry collaborate with academics. A number of reasons can be identified here, such as access to resources, expertise, and funding as well as increased international visibility. It is also apparent that larger companies and those that are already involved in research are particularly motivated to collaborate with academia (e.g., Cugmas et al., 2020; Ryan et al., 2008).

Following the above mentioned reviews, our research adds to an existing line of research that investigates collaboration problems (mostly called “barriers”; e.g., Kleiner-Schaefer and Schaefer, 2022) and ways to solve them (mostly called “success factors”; e.g., Barnes et al., 2002). And like our study, there are previous ones that connect collaboration problems and potential solutions (e.g., Bruneel et al., 2010). However, there are no studies with a comparative approach on collaboration problems and solutions. This is different with respect to motivations: The review by Ankrah and AL-Tabbaa (2015) demonstrates that while universities and industry share some motivations, there is also a huge amount of differences that depend on the type of organization. For example, both types of organizations share the motivation to respond to government initiatives through entering an UIC. Differences become apparent, for example, with respect to what each type of organization expects from their partners: While universities aim to gain access to funding for their research, the industry seeks to commercialize technologies which were developed in academia.

Expectations, problems, and solutions in collaborative research

What Ankrah and AL-Tabbaa (2015) call “motivations” could also be described as “expectations” towards RC. Following Sacco (2020) and Meißner et al. (2022), the expectations of researchers are the origin of collaboration problems: If researchers’ expectations with respect to how collaboration should function are not met, specific problems may occur. Therefore, it seems plausible that the type of organization (i.e., university vs. industry) not only matters when it comes to the expectations towards RC but also concerning the perception of collaboration problems and ideas to solve them. Against this backdrop, our aim is to answer the question how the type of organization shapes expectations, problems, and solutions in UIC.

To this end, we rely on recent studies by Meißner et al. (2022) and Hückstädt (2022) that propose a theoretically and empirically informed catalogue of seven typical problems that researchers perceive as relevant. All of these problems are characterized by the fact that they are based on specific expectations (see Table 1). This catalogue will serve as a basis for our empirical study, with the aim to further specify these problems and expectations with respect to UIC.

While this list may serve a useful basis to study collaboration problems at a general level, it is a rather generic one. It may therefore overlook essential differences, for example, between disciplines and status groups. Especially important in this context, the list addresses RCs in general and offers no knowledge concerning the differences between the two types of organizations included in UIC, i.e., which problems and expectations are more important for them than others. Therefore, we aim to answer the following research questions:

RQ1: Which differences between researchers from universities and researchers from the industry can be detected with respect to the expectations towards RC?

RQ2: Which differences between researchers from universities and researchers from the industry can be detected with respect to the perception of problems in RC?

Meißner et al. (2022) also proposed a catalogue of twelve solutions which can be used to address the collaboration problems listed above. These solutions are grouped along three central targets. Table 2 presents an overview of these solutions including their descriptions.

There is also no empirical knowledge on how researchers from universities and from the industry differ with respect to which solutions they perceive as appropriate means to address collaboration problems. Our third research question thus reads:

RQ3: Which differences between researchers from universities and researchers from the industry can be detected with respect to the perception of solutions to collaboration problems?

We can summarize that there are individual studies on expectations, problems, or solutions regarding UIC from the perspective of academia or industry. However, there has not yet been a study that looks at everything in a comprehensive and comparative way. By answering the three research questions, we can therefore build on previous research and fill a research gap. Centrally, we argue that the specific type of organization—university versus industry—serves as a distinct perceptual filter, shaping how collaboration is experienced. Specifically, our analysis will suggest that while university members follow a logic of “thinking in persons,” industry members adhere to a logic of “thinking in structures.” This conclusion is based on a systematic comparison of the expectations, perceived problems, and preferred solutions of university and industry actors derived from comparative focus group discussions. Put differently, our central argument follows a dialectical approach: We contrast the thesis of academic logic (“thinking in persons”) with the counter-thesis of industrial logic (“thinking in structures”).

Materials and methods

As no systematic analyses of UIC that comprise all three aspects of expectations (RQ1), problems (RQ2), and solutions for self-governance (RQ3) of RC participants and their different perception by university or industry members exist, this calls for an exploratory procedure. Thus, we operated qualitatively and conducted focus group discussions. In our study, we followed a deductive-inductive approach with the aim to test established analytical categories that we borrow from Meißner et al. (2022) as well as to iteratively develop new categories that allow for an improved in-depth insight into RC, especially into UIC. We chose this qualitative design to ensure applicability to the complex dynamics of UIC. Focus groups were particularly useful for capturing the subjective interpretation of expectations, problems, and solutions rather than just their frequency. By adjusting the theoretical framework via a deductive-inductive approach, we could uncover how identical behaviors are interpreted through divergent organizational logics—a nuance that purely quantitative methods likely miss. The deductive categories were based on the seven problems and twelve solutions derived from the Meißner et al. (2022) study exploring a first framework of research collaboration problems and how to address them from a self-governance perspective. The inductive categories were then developed based on the focus group discussions according to the Qualitative Content Analysis (QCA) method (Kuckartz, 2019) until saturation occurred.

The study included three focus group discussions with ten participants: eight academic researchers employed as professors, engineers, or graduated research assistants at universities or organizations for applied research and two industry members from five RCs in Germany. The participants are experts in applied mechanics, gas technology, thermodynamics, robotics, plant sciences, biochemistry, tourism, and alternative fuel development. One of the industry members is a lawyer on management level in a small-scale tourism company and the other a project coordinator for industry-university collaboration in a large-scale automotive company. They were recruited via email in the period from January 10 to February 23, 2021. The average participant is male, middle-aged, and highly educated. In choosing these participants for the focus group discussions, we considered the extent of interdisciplinarity and the degrees of network complexity (team sizes and constellations). To obtain data relevant for UIC, most of the participants are members of heterogeneous RCs that partner with large-scale companies as well as small- and medium-sized enterprises (SMEs). Only two participants are members of a homogenous RC (i.e., university-only). The ten academic researchers and industry members occupy different roles in the UIC: speakers (n = 4), principal investigators (n = 3), and managing coordinators (n = 3). They were divided into focus groups according to their role (i.e., the groups included both university and industry members). This separation allowed for three focus group discussions that enabled the participants to discuss the research questions together from the perspective of their role. Furthermore, through this composition we were able to guarantee largely open and non-hierarchical discussions.

Two authors of this paper moderated the semi-structured focus group discussions in a semi-directive manner. Due to the ongoing COVID-19 pandemic, all discussions were held online on March 23 and 29 and on April 1, 2021. Beforehand, all participants were asked to sign written declarations of consent and information on data protection. The discussion questions were premised on the research questions. After some opening questions, the respondents were asked to report on their expectations concerning collaboration in research. After that, the seven problems in RC (Meißner et al., 2022) were explained to the participants and they were asked to rank them according to perceived relevance. Moreover, they were encouraged to report on additional problems to expand the existing catalogue of problems. Additionally, the utility of previously identified solutions in the context of RC (Meißner et al., 2022) was discussed and the catalogue of solutions was augmented. In the last step, we asked the participants to relate the enhanced list of problems and solutions to each other and determine who is responsible for initiating solutions as well as when. All these questions supported an advanced examination of UIC as further inquiries reiterated differences between university and industry that participants detected.

Based on 295 min of discussion material, which was transcribed into 96 pages of written text, we analyzed our data using the five steps of Kuckartz’s (2019) Qualitative Content Analysis. This qualitative approach allows for a detailed coding and subsequent categorization of the transcribed data to consolidate relevant information regarding our research questions. As this study follows a deductive-inductive approach, we developed both concept-driven (deductive) and data-driven (inductive) categories to evaluate our material. To achieve an in-depth understanding of the focus group discussions and relevant insights into UIC, we (1) intensively read and studied the text as well as wrote summaries of their content, (2) formed main categories that corresponded to the questions asked in the focus group discussions, (3) consensually coded the data according to the main categories, (4) compiled text passages of the main categories and inductively formed subcategories based on this compilation and coded all data according to the main categories as well as subcategories (Kuckartz, 2019). The entire coding process was conducted with the software MAXQDA and allowed us to apply the categories deduced from the previous Meißner et al. (2022) study as well as to detect new categories that significantly enrich our understanding of expectations (RQ1), problems (RQ2) and solutions (RQ3) which participants of UIC deem relevant. In contrast to quantitative content analysis in which coding units are defined in advance, Kuckartz’s QCA allowed for a definition of coding units by the coding process. Therefore, the analysis was carried out on a circular basis to accurately code and categorize all relevant data in repeated coding processes. The main coder independently completed a first round of coding, which then was surveyed by another author of this article. Upon agreement of the coded elements, the remaining circular coding was carried out by the main coder. To code the transcribed text, it was split into precisely defined parts of material covering content relevant for the research questions and either subsequently assigned an established category or designated a new category. Saturation during analysis was ensured through reading the entirety of the transcribed text repeatedly and either assigning its elements to previously established deductive categories or establishing new inductive categories. In sum, 97 elements of the text were coded. In a final step of the QCA process, the analyzed data is (5) reported and documented in this publication to retain our findings (Kuckartz, 2019).

Based on our three focus group discussions, we formed 14 main categories, derived from our research questions and further discussion questions as well as 131 subcategories. Since this paper concentrates on UIC, the following four categories are of particular relevance: (1) expectations from industry members, (2) expectations from university members, (3) perceived relevance of problems in RC as well as (4) solution approaches perceived as especially useful. For each of these main categories, we developed a range of subcategories whose frequency of mention (i.e., number of coded elements) we ranked to obtain the three most relevant subcategories for university as well as for industry members. See Table 3 for a description of the subcategories.

Results

We present our findings in three main parts: First, we discuss the expectations from industry and university members (RQ1), then we outline the problems perceived as most relevant in the context of UIC (RQ2), and lastly, we demonstrate the solutions perceived as most useful to solve these problems potentially arising in UIC (RQ3). There are more findings for the university members’ perception of RC since they constitute most of our focus group participants.

Expectations towards UIC

Expectations from industry members

Concerning expectations industry members have regarding UIC, one participating industry member reported the overarching goal of UIC as improving and enriching established ideas and applications that are already used in practice: “Anything the project adds to [a labeling they previously developed] can improve the status quo.” Next to this added value through research, the other industry member expected to find economically relevant solutions from collaboration with universities through selling outcomes developed during RC (Table 4).

Expectations from university members

Additionally, university members ranked the expectations of industry members according to their individual perception and corroborated the previously mentioned intention of finding economically relevant solutions and fund raising as the most significant factor in deciding to partake in UIC for their industry counterparts (8 coded elements).¹ One spoke of “financial pressure” the industry partner in UIC faces due to monetary involvement and the pursuit of market advantages. In addition, university members noticed that their industry partners often understand them as problem solvers and assume that university members can figure out all issues arising during UIC without industrial assistance (3 CE; Table 5). One speaker observed how they “showed what we can do in 2 years with two PhD candidates and a little bit of money and one of the company’s CEOs was a little disappointed that we did not solve the problem.” He goes on to explain that the industry side’s “expectation was very, very high and I [the speaker] believe that this was a little unworldly because the company believed that we could solve all problems and develop a product that they can use instantly.” Here, it is crucial to note that this perception of how industry members engage in UIC is based on university members’ experience. This is due to the constrained number of industry participants. Nevertheless, the data was coded and cited with the aim to highlight both university and industry members’ expectations, problems, and solutions about UIC that transcend organizational background to enable a more comprehensive awareness of them.

Further highlighting the university perspective, it becomes clear that when going into UIC, university members also have clear expectations of what to gain from this type of collaboration (Table 5). They primarily expect the possibility of exchange among scientists themselves as well as between university and industry members (5 CE). Interdisciplinarity resounded as a key word throughout the discussion. One participant phrased it as “peeking into someone else’s professional operations” to learn from them. They hope for an interchange of theory and ideas about how to apply the knowledge gained in RC in the future, an expectation that did not concern industry members at all. Moreover, they pragmatically understand UIC as a facilitator of education for junior researchers since the money allocated to their research endeavor enables them to employ PhD candidates with otherwise tight budgets (4 CE). However, the financial aspect is not the only factor playing into the expectation to be able to educate junior researchers. University members also hope that the participation of junior researchers in UIC allows them to establish contact with industry members for future collaborations, as one coordinator clarified: “[…] we work together with the industry for our junior researcher programs. Speed Dating, different other Career Talk series. There, we kind of try to introduce the junior researchers to the industry. So that they can build a network too.” Additionally, university members value a strict adherence to the work schedule with the expectation that all research collaborators execute their work packages according to previous agreement (4 CE). Apart from that, university members aim to be able to use the new expertise derived from UIC in lecture halls and see their theoretical progress translated into practice, a transfer likened to “cutting the Gordian knot” by one discussion participant (3 CE).

Both rankings reinforce the very organizational nature of university on the one hand, and industry on the other hand. They demonstrate that expectations from university and industry members differ in the sense that the industry side primarily expects financial gain from UIC while university members strive for knowledge production. Of course, universities are also interested in the financial dimension of UIC but more so to ensure that research is carried on by junior researchers.

Perceived relevance of problems in RC

When asked to discuss the perceived relevance of problems in RC, our focus group participants had strong views about five particular problems: Commitment problem, management problem, communication problem, difference problem, and relationship problem.

Perceived relevance of problems by university members

Among the university members, the commitment problem was detected as the major issue that most notoriously complicates collaboration [15 coded elements (CE); Table 6]. One speaker with longstanding experiences with large-scale research collaborations in the energy sector noted a particular trend regarding the commitment made by industry partners in UIC: “[…] that they clearly commit sadly is something I detect as becoming more complicated in the last few years, especially with industry partners. Very often, they only participate in projects to find out how they work and only contribute half-heartedly.” He goes on to criticize that industry partners are inexperienced with research collaboration and become overwhelmed by the administrative demands of UIC.² Especially noteworthy is that the commitment problem was never mentioned as highly relevant by the industry members. Moreover, the university members reported the management problem as the second most common obstacle that prevents successful UIC (10 CE). For them, its high relevance is founded in the belief that a functioning management prevents all other problems from occurring. One focus group participant noted that “the manager’s position is the most important one of all […] because the management is responsible for preventing all other problems. If the manager does not intervene if needed, the entire project has a problem.” Additionally, reinforcing its significance, they discussed the perception of different management levels such as the leader as the key figure who must account for the success of the collaboration at large with the added risk of unequally distributing the funds as well as the management tasks on a coordinator’s basis. As a third problem that decides about successful or failed UIC, they frequently named the communication problem, particularly regarding communication between different roles and interdisciplinary communication (8 CE; Table 6): “If there’s problems with communication, the whole project is doomed to fail,” one discussant emphasized.

Perceived relevance of problems by industry members

Industry members, on the other hand, reported the difference problem as most symptomatic of UIC, which university members did also address with high relevance but in fewer instances [5 coded elements (CE)]. They focused on differences between university practice and industry approaches and concluded that university members primarily focus on getting their theory right while their industry counterparts set their eyes on the final product (Table 6). As one coordinator phrased it: “[…] the inherent difference between research facilities and industry is that the research facility scans the comprehensive literature, everything that is available, while an enterprise refers to what fits their own products or their own strategy.”³ A second problem with high relevance for industry members designates the relationship problem (3 CE). One of the industry members recognized a direct connection of the relationship problem and the previously evaluated difference problem and explicitly mentioned friction between two university and industry members. The other industry member even went as far as to predict that the relationship problem can result in the failure of UIC. Additionally, industry members elaborated on the communication problem as another highly relevant factor in determining the success or failure of UIC, especially regarding the leader (3 CE; Table 6).

This analysis demonstrates that university and industry members only share the perception of the communication problem as highly relevant. Most prominently, their views on the commitment problem diverge with the university side classifying it as the most relevant problem in UIC, whereas their industry counterparts do not mention it as highly relevant at all (Table 6).

Solutions perceived as especially useful

From our empirical insight, we were able to extract the most frequently mentioned solutions by university as well as industry members (Table 7).

Solutions perceived by university members

As far as university members are concerned, they perceived the selection of participants as the most useful solution to ensuring successful UIC [6 coded elements (CE)]. By proactively selecting who participates in the RC and confiding in trustworthy and familiar partners, university members aim to particularly prevent the commitment problem as well as the management problem as their two most frequent problems (see Table 6) with this solution approach, a solution barely accounted for as useful by industry members. As one speaker succinctly summarized: “The selection of participants is definitely crucial. When I have a good team that I am familiar with, it works, and I do not have to worry.” Targeting participants as well, university members classified the leadership personality as another highly useful solution and addressed the need for additional workshops that teach how to govern UIC to ensure integrative and competent leaders (5 CE). In the same vein of deliberate participant selection, a well-conceived selection of an UIC leader also counteracts the management problem, which they perceived as the second most pressing issue (see Table 6). Two other significant solutions for university members relate to the interaction and communication target. They rank both the accompanying evaluation (4 CE) and the communication space (4 CE) as valuable solutions that help keep the communication problem in check as third most important issue perceived by university members (see Table 6).

Solutions perceived by industry members

When asked about their solution preferences, industry members agreed with university members about the leadership personality. One industry member highlighted the personal dedication of the leader as the pivotal point for the success of UIC [2 coded elements (CE)]. For industry members, a deliberate leadership choice counteracts potential difference problems, which they listed as single most important issue in UIC (see Table 6). Going hand in hand with this, a suitable leadership personality can also remedy relationship problems, the second most important problem perceived by industry members (see Table 6). Additionally, industry members classified the overall target of interaction and communication as equally useful in combating problems in RC without specifying particular further subcategories pertaining to the overall category such as communication space or accompanying evaluation (2 CE). However, one PI connected the solution approach of interaction and communication to participation and the approach of motivating and appreciating other RC members: “[…] interaction and communication is what I can identify with. Communication is, as I said, a very important approach and tool to generate and maintain participation and appreciation.” This solution can neutralize both relationship and communication problems through open and honest communication and thereby eliminate the second most important problems according to industry members.

Discussion

Based on the assumption that successful collaborative research depends on the solving or prevention of collaboration problems (Youtie and Bozeman, 2014; Bozeman et al., 2016; Meißner et al., 2022), the present study sought to identify the expectations, problems, and solutions to these problems in the context of UIC. Specifically, we aimed to carve out the differences between academic and non-academic organizations through focus group discussions with researchers from UICs. Indeed, we found more differences than commonalities. First of all, concerning the expectations with respect to how collaboration should work and what should result from it, industry members lay a strong emphasis on gaining financial profit. Researchers from universities, in contrast, primarily expect the exchange of knowledge and education for junior researchers. Consequently, university and industry members also differ when it comes to the most exigent problems in collaboration. Researchers from universities report the commitment problem as the most important one and designate the industry members as the ones lacking commitment, thus impeding successful collaboration. For industry members, the difference problem is the most prominent one. This primarily results from their perceived priority of university members to focus on theory, while industry members prioritise practicality. Lastly, concerning the solutions to collaboration problems, university members’ primary recommendation is to select participants based on their proven ability to collaborate. For members of the industry, the most promising solution is an integrative and competent personality for leadership.

A particularly striking finding is the discrepancy in the perception of the commitment problem. This problem was ranked as the most critical issue by university members but was not highlighted by industry partners. This divergence may not necessarily reflect a lack of self-awareness from industry members, but rather a difference in how each side interprets the root cause of collaboration challenges. From the university perspective, which is often process-oriented and focused on continuous engagement, a lack of participation from industry partners is perceived as a failure of commitment. Conversely, industry members, who are typically results-oriented, may view the same disengagement not as a commitment issue, but as a symptom of the difference problem. Thus, they may perceive the academic activities as insufficiently practical or relevant to their commercial goals. In this light, what one partner labels a “commitment problem,” the other may experience and describe as a “difference problem,” highlighting a fundamental disconnect in collaborative expectations and priorities.

To deepen the interpretation of this discrepancy, it is helpful to look beyond the project level and apply the lens of “research duos” (Glebova, 2024). As Glebova (2024) argues, the collaborative essence of research lies in dyads, which form the elementary building blocks of wider networks. These “research duos”—for instance, a university PI and a specific industry project lead—are the locus where trust, communication patterns, and mutual expectations are negotiated. Consequently, the clash we observed is not just an abstract institutional conflict but a friction experienced within these specific dyads. In a duo where the academic partner expects continuous interpersonal exchange, a lack of constant feedback may be interpreted as a personal commitment problem. Conversely, the industry partner in that same duo may view the academic’s expectations as a difference problem. Thus, the divergent problem perceptions can be understood as emergent properties of misaligned expectations within specific research duos.

What can be concluded from these findings and what is their added value for research and practice? On the one hand, our findings have an epistemological value in that they reveal that the type of organization has substantial consequences with respect to what researchers expect from collaborations, what they regard as the most relevant problems, and what they recommend as solutions to these problems. This underlines that there is an urgent need to investigate UIC because universities no longer conduct research among themselves, but together with organizations from other sectors (e.g., Falk-Krzesinski et al., 2011; Teirlinck and Spithoven, 2015; Tijssen and Wong, 2016). In a nutshell, researchers from universities seem to focus on cooperation and working together, intellectually as well as personally. Collaboration members from the industry, in contrast, seem to mostly appreciate the organizational and structural aspects of collaborative research projects. To put it pointedly, academic researchers seem to think RCs in persons (i.e.,” thinking in persons”), non-academics in structures (i.e.,” thinking in structures”). Our study thus follows on from previous research that has shown that researchers from universities and industry approach research collaborations with very specific, different motivations (e.g., Ankrah and AL-Tabbaa, 2015; Cugmas et al., 2020; Ryan et al., 2008). Hence, our findings suggest a conceptual model connecting organizational background to self-governance: The type of organization (university vs. industry) shapes expectations, which in turn determines perceived problems (e.g., commitment vs. difference) and preferred solutions (e.g., selection of participants vs. leadership personality). Furthermore, the insights of our study add to the empirical knowledge within research on UIC insofar as our study is to the best of our knowledge, the first one that took a comparative perspective on the expectations, problems, as well as on the solutions in the context of UIC.

On the other hand, our findings provide practical guidance for researchers and other actors involved in UIC. Researchers can rely on this knowledge to better select and understand their partners and to improve their collaboration. For example, university members of a RC could pay more attention to the potential financial merit of their research (e.g., in form of patent applications or public relations), because this is an important goal for the industry. Furthermore, the results of our study can also be used by funding agencies and policy makers, for example, to revise existing criteria for the reception of funding. For funding agencies, this may imply moving beyond checking boxes for industry partners. Calls could require a specific “collaboration governance plan” that explicitly addresses how the consortium plans to bridge organizational logics. The consortia could in turn organize preparatory workshops to raise awareness among participants of the different logics. As previous research has shown, there is great potential for optimization, for example with regard to state funding of UIC (e.g., Ryan et al., 2008; Veletanlić and Sá, 2019). Furthermore, apart from specific measures taken, our findings show that all actors involved need to be sensitive to the different organizational background of researchers and the resulting implications. Thus, our findings may also enable collaboration to become increasingly successful in researchers’ everyday working life.

Of course, our study has a number of limitations that need to be discussed. Before we come to the conclusion, we would like to focus on what we consider to be the three most important limitations. First, our findings are based on three focus group discussions with German researchers, and only two out of ten participants were industry members. We made sure that the participants had different backgrounds in terms of disciplines, organizational affiliation, and socio-demographics. This means that the various types relevant to our research questions were covered. In addition, we made sure that the industry’s views were saturated adequately in each of the discussions by asking specific questions and follow-up questions. Nevertheless, our findings are based on a modest and asymmetric sample, and we cannot rule out the possibility that the views of academia carried disproportionate weight in our study, which would distort the comparison between the two types of organizations. Thus, to ensure full empirical as well as theoretical saturation, more studies, specifically ones with quantitative designs, are needed to examine the validity of our results on a broader basis and in other contexts (e.g., different countries and academic environments) and increase generalizability. Future research should also more explicitly consider the specific context of the German research landscape. For instance, cultural and institutional norms, such as Germany’s federally organized research funding structure—in contrast to more centralized systems in countries like France—or its traditionally hierarchical university systems compared to the flatter departmental structures common in the US, may shape collaboration dynamics in ways not fully captured by our study. Furthermore, it is crucial to note that this study was conducted during the COVID-19 pandemic. The “distance perspective” (Skute et al., 2019) was thus not only cognitive but physical. The lack of informal face-to-face interaction and informal exchange likely exacerbated the commitment problem perceived by university as well as industry members. It should therefore be emphasized that our study does not claim to be universally valid. However, our results offer a good starting point for formulating and testing specific hypotheses, improving deficiencies in terms of standard quality criteria (such as validity and reliability), and testing our findings on a broader basis. Second, our study was closely based on the findings by Meißner et al. (2022), that is, on a pre-existing catalogue of problems and solutions identified in the context of collaborative research. Consequently, there may be other collaboration problems and solutions to these problems we may have overlooked, for example, the problem to develop common goals and make joint decisions (Volk, 2021). Third, we focused on the top three expectations, problems, and solutions. Most certainly there are more differences to be found when looking at the whole picture of the different motivations, problems, and solutions. However, our study does not explain the empirically observed differences in expectations and perceptions of problems and solutions. This can now be addressed in further studies with different methodological designs, relying on specific theoretical approaches (e.g., self-governance, motivation theory) with the necessary explanatory potential.

To summarize, like other studies before ours have shown, studying the details that account for the success of UICs is a complex matter. Specifically, our findings reveal that focusing on the differences that emerge from researchers’ organizational backgrounds is pivotal in order to understand the cornerstones of this success. We hope that our study not only informs further research on this issue but may also guide researchers through their collaborative journey.

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