business and economic literature review on the industries

What makes industry–university collaboration succeed? A systematic review of the literature

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• Open access
• Published: 12 September 2018
• Volume 89 , pages 221–250, ( 2019 )

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• Robert Rybnicek   ORCID: orcid.org/0000-0001-5863-8179 1 &
• Roland Königsgruber 2  

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Industry–university collaborations (IUCs) have received increased attention in management practice and research. The need for innovation in today’s business environment and the ambition of policymakers to commercialize academic knowledge intensify this trend. However, although research has devoted considerable effort to finding the determinants of success for interfirm collaboration, much less is known about IUCs. This article presents the results of a systematic review of the literature on the collaboration between industry and universities. We perform an extensive analysis of research published on industry–university collaboration projects with the objective of distilling factors that influence the success of such collaborations. We propose a novel conceptual model, which synthesizes our empirical results, and use it to organize and categorize influencing factors and their interrelationship within the collaboration process. Based on our review of existing literature, we identify an agenda for future research in this domain.

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1 Introduction

Collaboration between firms has been an increasingly important issue for some decades and researchers have devoted considerable effort to finding the determinants of their success (e.g. Hillebrand and Biemans 2003 ; Parkhe 1993 ). A different form of collaboration with presumably different success factors is the one between industry and universities. These actors pursue different objectives and face different constraints. This contrast potentially enhances the value of collaboration but it is also a source of complications. Results from the literature on interfirm collaboration do not necessarily carry over to this setting. In this paper, we investigate the success factors of collaboration between industry and universities.

Industry–university collaborations (IUCs) have a long tradition in several countries worldwide (Ankrah and AL-Tabbaa 2015 ) and universities play a crucial role in achieving economic growth in today’s knowledge-based societies (Pinheiro et al. 2015a ). The ambition of policymakers and universities to develop ‘third missions’ in addition to the two traditional core missions of research and teaching, and to commercialize academic knowledge, for instance through continuing education programmes, patenting, technology transfer offices, science parks or incubators has intensified the relevance of such collaborations (Marhl and Pausits 2011 ; Perkmann et al. 2013 ).

There are many reasons for IUCs: companies profit from highly qualified human resources such as researchers or students (Myoken 2013 ); they gain access to technology and knowledge (Barnes et al. 2002 ); and they can use expensive research infrastructure (Ankrah and AL-Tabbaa 2015 ). According to some estimates, up to 10 per cent of new products or processes are based on the contribution of academic research (Bekkers and Bodas Freitas 2008 ). Universities, in return, benefit from additional funding provided, from access to industry equipment or from licensing or patenting income (Barnes et al. 2002 ). In fact, collaboration with industry has become an inevitable part of university funding and the funds from international organizations and business enterprises for R&D in the higher education sector nowadays represent a ‘significant source’ in many countries (OECD 2015 ).

In light of these effects and financial relevance, it is important to ensure a successful management of IUCs to realize the advantages on both sides. While the number of research articles has increased in recent years, there is not yet a systematic overview of success factors that emerge from a detailed analysis of individual studies. Most studies do not directly address this question. Case studies, for example, generally only refer to individual lessons learned. Recent reviews summarizing the literature have mostly focused on other issues: for example, Perkmann et al. ( 2013 ) investigate how academic engagement differs from commercialization (in the sense of the exploitation of patented inventions), Schofield’s ( 2013 ) systematic review of the literature is dedicated to success factors in the emerging market context, and while Ankrah and AL-Tabbaa ( 2015 ) also briefly discuss success factors, they focus primarily on organizational forms of IUCs, motivations for IUCs as well as their formation and operationalization. Therefore, our article aims to fill this gap by identifying relevant success factors, establishing a model for organizing them and providing practical recommendations and suggestions for future research. The strong focus on the core question ‘What factors influence the success of a collaboration between industry and universities? ’ distinguishes our analysis from other reviews and articles, which mostly examine this question merely as a subtopic. Our approach allows a more thorough analysis of factors that facilitate or inhibit the IUCs’ success, which goes beyond existing reviews such as those mentioned above. Our review adds to a better understanding of IUC success factors not only by summarizing the evidence but also by developing a conceptual model on the basis of a rigorous and transparent methodological approach, including the most recent research up to the year 2017.

The theoretical contribution of this article therefore consists of a novel conceptual model that we derive from our review of the literature and which we use to organize success factors and their interrelationship within the collaboration process. Our conceptual model hence synthesizes the current state of research regarding our research question. We draw on this model to discuss implications for management practice and propose a research agenda for further investigations. Based on the investigated literature we derive recommendations for the practical implementation of IUCs and furthermore identify a number of important gaps in the current literature. In particular, current literature provides some indication that a variety of parameters interfere with the factors we identify. The scale of the institutions, the phase of the collaboration project, the (scientific) discipline, or the organizational level (e.g., leaders, staff members) are potential moderators, which have not yet been investigated in depth. Our article is of interest to university members and corporate managers engaged in IUCs, to researchers in the field of R&D management or higher education, and to policymakers.

The remainder of this article is structured as follows: In the next section, we summarize the method used for the selection and review of the literature. We explain our search strategy and illustrate the procedure. Following this, we present our results as we first describe our conceptual model and go on to report the synthesis of our findings. In the discussion, we then deliberate on implications of our study for practice and research.

2 Methodology

To answer our research question, a systematic review of the literature was conducted. We followed the principles of Denyer and Tranfield ( 2009 ) and Tranfield et al. ( 2003 ) who emphasize core principles that apply to systematic reviews of literature in the field of management and organization. Those principles allow the design of a replicable investigation and minimize any bias caused by the subjective assessment of different researchers (Tranfield et al. 2003 ).

The iterative review process consisted of a number of stages and is summarized in Fig.  1 . Following Tranfield et al.’s ( 2003 ) principles, the process consisted of three main steps: (1) the locating of studies; (2) the study selection and evaluation; and (3) the analysis and synthesis.

Overview of the systematic review process

2.1 Locating of studies and study selection

In the first step , the procedure started with a database search in EBSCO Business Source Premier, which includes 14,914 peer-reviewed journals (EBSCO 2016 ), and was performed in March 2016 and March 2018. The time frame for the database search was determined as 2000–2017 because there has been an increased interest in university cooperation over the last two decades (e.g., Ankrah and AL-Tabbaa 2015 ; Barnes et al. 2002 ; Perkmann et al. 2013 ) and a number of current concepts and strategies, such as the entrepreneurial university (Clark 2001 ; Etzkowitz 2003 ), the Triple Helix (Etzkowitz and Leydesdorff 2000 ), the mode 2 of knowledge production (Gibbons et al. 2002 ), and new public management (Leisytë and Kizniene 2006 ), stress the importance for collaboration and the need for third-party funding.

The search included only peer-reviewed papers published in English. Search terms applied for the database search were ‘universit* cooperat*’; ‘universit* collaborat*’; ‘universit* allianc*’; ‘inter-universit*’; ‘universit* ventur*’ and ‘universit* partner*’. These terms are on the one hand broad enough to capture the most suitable articles and on the other sufficiently expressive to remove less suitable articles. Although some researchers use the terms ‘university’ and ‘higher education’ as a synonym, we decided to differentiate clearly between these two terms and searched solely for universities, as determined by our research question. Higher education systems differ greatly between countries and over time, which provokes essential problems in terms of comparability (OECD 2017 ). For instance, the OECD, which has long-standing institutional experience regarding international educational statistics, states that comparison issues remain a quintessential characteristic of the data. To illustrate, the OECD’s ( 2017 ) report on the state of higher education devotes six entire pages to the challenges resulting from data acquisition and different definitions across countries. In order to ensure comparability, we therefore refrained from using a broader and ambiguous term but acknowledge that this may result in the exclusion of potentially relevant studies. The database search identified 461 papers. After eliminating duplicates, 405 papers remained. For administration of all articles, the reference management program Citavi 5 was used.

In the second step , we selected and evaluated the literature regarding our research question. As suggested by Tranfield et al. ( 2003 ), all of the 405 papers were evaluated by two reviewers who independently extracted data from these studies. Three research assistants were employed in this project to carry out these tasks. The assistants hold master-level diplomas and at the time of the study were employed in junior research positions at the university with which one of the authors is affiliated. They have research experience and engaged in methodological discussions with the authors. The iterative process of analysis and discussion was deliberately designed to achieve high inter-rater agreement. The definition of the terms, the guidelines for coding, and specifications for interpretation were elaborated and decided by the authors. Decisions on adaptations were the sole responsibility of the authors. The findings and interpretation of the reviewers were compared to minimize errors, resolve differences and produce a more robust data set (Tranfield et al. 2003 ). We developed a set of five exclusion criteria to allow the assessment of each study. Articles not meeting these criteria were excluded. First, we excluded articles from the review in the rare case of the document not being accessible. The second exclusion criterion concerned the scientific approach of the papers—we excluded, for example, book reviews or any kind of non-scientific articles. Using the third and fourth exclusion criteria, we eliminated articles that slipped into our search results but did not address both universities (criteria 3) and industry (criteria 4). The fifth exclusion criterion was whether a paper explicitly addressed factors that influence the success of a collaboration. As recommended by Boaz and Ashby ( 2003 ) and Pawson ( 2006 ) we decided to include a wide range of studies in our examination in order to capture anything that was new to our understanding of those factors. To ensure this, the exclusion of articles and the in-depth analysis were designed as an iterative process. Hence, at this step some articles remained in the investigation but were excluded later when the in-depth analysis revealed that these articles ultimately did not deal with success factors. For the assessment and evaluation of articles, the above-mentioned guidance for interpretation was applied. In the end, the exclusion process resulted in a total of 103 academic papers.

2.2 Analysis and synthesis

In the third step , the research assistants used data extraction sheets independently from one another in order to analyse all articles identified. These data extraction sheets were implemented in Microsoft Access 2016 to allow a high degree of transparency and to minimize input data errors. The research assistants continually compared their results; in case of disagreement, the opinion of an additional reviewer was decisive. Each data extraction sheet consisted of the following elements: bibliographic data, research aim, method, type of collaboration, country, university, results, limitations and factors that influence the success of an IUC.

The aim of the present study was to determine factors influencing the success of an IUC. In that regard, we faced a trade-off between imposing a homogeneous definition of success, which would improve comparability but potentially violate the ‘fit’ between the aim of the original study and its method (a key quality criterion of research identified, e.g., by Boaz and Ashby 2003 ), or taking the original papers’ definition of success to uphold that fit. We only examined papers that explicitly considered factors that facilitate or inhibit the IUCs’ success as part of our inclusion and exclusion criteria. We hence opted for the option to keep the original papers’ definition of success to the detriment of imposing a homogeneous definition.

We followed an iterative process to derive those factors. In this process, deductive and inductive methods were combined. We started with factors facilitating or impeding IUCs that have been suggested by other authors, e.g. Ankrah and AL-Tabbaa ( 2015 ), who provide a short list of factors. We also drew on related literature that refers to other sorts of collaboration—for instance, Mattessich et al. ( 2004 ), who investigated collaborations formed by human service, government, and other non-profit agencies. We used this initial list of factors and iteratively adjusted it, adopting, adding, and eliminating individual factors in a process of refinement involving continual discussions between the authors and the research assistants working on the data. In the process of the analysis, a guidance to interpretation was developed, which was used to determine whether a paper’s content could be subsumed under a particular factor. This led to a modification of some factors and to the addition and deletion of others. As suggested by Denyer and Tranfield ( 2009 ), we then cross-tabulated the investigated studies to identify key issues and to see whether there is one single foundation or whether there are contending or complementary findings. To be specific, we first counted how often a factor was mentioned. Then all relevant text passages were copied in a single spreadsheet table, which we used to examine whether certain subjects were mentioned repeatedly. In our quantitative analyses we investigated regularities and discrepancies and in our qualitative analyses we analysed and explored the studies for analogous or different meanings (Denyer and Tranfield 2009 ).

Subsequently, we synthesized our findings and inductively developed a conceptual model. Categories developed in Perkmann et al. ( 2013 ) were taken as a starting point. We classified factors identified in the process above into individual categories and adapted the category system to fit our research question and the factors identified. During the development of this model, we noticed that authors repeatedly mentioned a number of circumstances as potentially affecting the relation between the factors we had identified and the success of IUCs. We call these circumstances ‘moderators’. In a final step, we added our implications for managerial practice and completed the model with the identified moderators that might interfere with our success factors. More information is provided in Sect.  4.2 .

2.3 Descriptive data

The research yielded 103 suitable articles, which corresponded to our inclusion criteria. Of these, 56 included quantitative and 47 qualitative analyses, 26 of which were case studies. Furthermore, 12 literature reviews were analysed. (We note that there were also papers with mixed methodology, which were assigned to more than one category.) The high number of case studies could be an indication that research regarding IUCs is still explorative to a great extent.

The analysed papers were published in 64 different journals; about two-thirds of the papers were published in journals with an impact factor (IF). In Appendix an overview of the journals, the number of articles published in these journals and their impact factors is provided. The key publication outlets are Research Policy (9 articles), R&D Management (5), International Journal of Innovation Management (4) and Journal of Product Innovation Management (3), Management Decision (3), Small Business Economics (3) , Technology Analysis & Strategic Management (3), Technovation (3). The scope of the journals indicates that our research topic has been studied from at least three different perspectives. The first perspective refers to research administration and research policy, the second perspective is aimed at the innovation process and innovation management and the third perspective focuses on management and business.

As depicted in Fig.  2 , most articles are from authors or co-authors from the United States (24), followed by the United Kingdom (18), Italy (11) and Spain (8). A total of 26 articles have authors from more than one country and in 60 per cent at least one author from Europe contributed to the paper. Authors were assigned to countries by their affiliation.

Country of authors

Figure  3 shows the number of publications per year. The number of papers has increased over the last decade and has remained stable at a high level since 2015, which indicates the rising relevance of the investigated topic in recent years.

Number of publications per year

3.1 Conceptual model

Based on the insights gained from the review, we derived a conceptual model, which we depict in Fig.  4 . During the development of our model, we had to make some fundamental decisions to reduce complexity. The main idea that guided these decisions was to relate the factors to the collaboration process. Some factors might address different facets within this process and, consequently, their assignment is not absolutely clear. In those cases we closely analysed which of the identified issues discussed in the literature are predominantly relevant in terms of our research question. The conceptual model contains three distinct elements:

Conceptual model

Generic collaboration: According to Bryson et al. ( 2006 ) a cross-sector collaboration is defined as the linking of organizations in two or more sectors to achieve jointly an outcome that could not be achieved otherwise. Hence, this simplified presentation comprises the organizations (industry partner and university partner), the linking between those organizations and the results.

Factors (see the coloured headings in Fig.  4 ): We organized the factors identified around this process and derived four distinct, overarching categories. Firstly, there are the institutional factors, which refer to the participating institutions; secondly, there are the relationship factors, which refer to the linking between those partners; thirdly, we have the output factors, which refer to the desired results of the collaboration; and fourthly, there are the framework factors, which refer to environmental aspects. These findings are presented in the Sects. 3.2 , 3.3 , 3.4 and 3.5 below.

Moderators (see the ellipses in Fig.  4 ): These represent circumstances of IUCs that might have an impact on the way individual factors affect the collaboration. The literature provides various hints that the phase of a collaboration process, the scale of the partners, the organizational level or the (scientific) discipline might interfere with our factors, but as yet this has not been investigated. We hence consider them to be particularly promising avenues for future research and discuss them in Sect.  4.2 under the heading ‘Implications for future research’.

For illustrative purposes, we have also included one core recommendation for managerial practice stemming from each category of factors in Fig.  4 . We describe the recommendations in more detail in Sect.  4.1 .

Overall, Fig.  4 provides an overview of the current state of research, visualizes relationships between the identified factors and a generic collaboration process, allows the identification of future research activities and depicts issues for the practical implementation of successful IUCs. In the subsequent sections, we synthesize the main results of our study.

3.2 Institutional factors

Resources play an important role in the successful implementation of IUCs. In general, the quality and the utility of a collaboration is strongly dependent on the resources a partner can offer. The need for certain kinds of resources limits the number of potential partners considerably (Ferru 2010 ). In our analysis, we identified finance, time, staff and equipment as critical resources. For successful collaboration, the discussed issues range from the provision of resources, to the accuracy of planning and the commitment of partners (e.g., Arvanitis et al. 2008 ; Schofield 2013 ). Potential barriers are, for example, the different understanding of time issues (Goduscheit and Knudsen 2015 ), the short-term orientation of industry research (Muscio and Vallanti 2014 ) or unrealistic expectations of the partners (Barnes et al. 2002 ). Good timing is also critical for successful IUCs, however, while industry requests constant availability, universities have restrictions in the context of their semester structure (Wu 2017 ). Furthermore, to facilitate a successful collaboration, access to highly qualified human resources (Myoken 2013 ), to infrastructure such as libraries and lab space (Boardman and Bozeman 2015 ) or to technical equipment (Arvanitis et al. 2008 ) is important. However, such expensive infrastructure is often obtained for internal use. A potential way to gain mutual access to it are shared-use equipment arrangements (Bychkova 2016 ).

A further institutional factor is related to structure . In this regard, the different background of companies and universities is especially challenging. For example, the bureaucracy, the complex structure and the inflexibility of universities (Schofield 2013 ) can hinder the success of IUCs because universities’ rigid framework is opposed to the flat hierarchy of company management (Boardman and Bozeman 2015 ; Schofield 2013 ). Bureaucratic organizations and unclear responsibilities are therefore major barriers to a successful partnership. For a fruitful project, for instance, researchers have to feel responsible for it (Franco and Haase 2015 ). Further issues refer to the lack of administrative support (Franco and Haase 2015 ) or to decision-making differences (Reeve and Gallacher 2005 ). Project management can help here to enhance the coordination and communication between partners (Rajalo and Vadi 2017 ). Positive effects are also reported for the adoption of formal rules (Muscio and Vallanti 2014 ) or when responsibilities and roles are clarified right from the beginning of a partnership (Barnes et al. 2002 ). The development of mechanisms and processes, including the roles in the teams and a mutual terminology, can improve the collaboration (Canhoto et al. 2016 ).

There is also some evidence that willingness to change is another salient success factor. That means, for instance, adapting to different circumstances and cultures (Logar et al. 2001 ), being open to listening (Ryan 2007 , 2009 ) and managing corporate changes (Barnes et al. 2002 ). The ability of partners to learn about and understand one another is essential for a successful collaboration (Hadjimanolis 2006 ). However, partners need to have time for this learning process in order to find the best way to collaborate (Canhoto et al. 2016 ). Both companies and universities will benefit when they work together closely and use each other’s experience and feedback for further improvements (Ryan 2009 ).

3.3 Relationship factors

The impact of communication has been extensively addressed in the investigated literature. With respect to communication, the frequency of communication is vital to create a shared understanding (Hong et al. 2010 ; Lee 2011 ). Good personal relationships are the basis to enabling vital linkages between companies and universities (Barnes et al. 2002 ; Collier et al. 2011 ). In this regard, contacts and actions should not only include management level but must be on the operational level too (Wu 2017 ). This includes regular interaction, continuous feedback, mutual exchange of information and updating partners about incidents or new activities. Furthermore, communication through a variety of channels, such as e-mails, regular meetings or face-to-face communication is advantageous (Clauss and Kesting 2017 ; Hong et al. 2010 ), although partners need to select those channels carefully since the lack of efficient communication channels is also considered a main barrier in partnerships (Guan et al. 2005 ). A reciprocal communication (regularly, timely, adequately and accurately) is also beneficial to establish positive expectations about the future behaviour of partners, particularly when the partnership is new (Bstieler et al. 2017 ). Another requirement for a successful collaboration is to find an appropriate ‘language’ suitable for both partners, because IUCs are often affected by the use of different dictions and styles in the academic and business environments (Baba et al. 2010 ; Gawel 2014 ).

A large proportion of studies discusses the importance of commitment . Commitment refers to the questions of how much a person identifies with the collaboration and its goals, how loyal this person is to this collaboration and whether they are willing to put sufficient effort into it (based on Porter and Smith 1970 as cited in Mowday et al. 1979 ). The existence of a mutual commitment supports industry–university partnerships (Attia 2015 ), and the commitment of the top management in particular is a crucial factor in that regard (Ankrah and AL-Tabbaa 2015 ) because partners (and their leaders) will not share resources when they are not committed to a collaboration. There is also evidence that the attitude to IUCs affects commitment. For example, researchers are more likely to be committed to a collaboration with industry partners in cases where they have a positive attitude towards collaboration (Sellenthin 2011 ).

Many authors consider trust as another important relationship factor in fostering a collaboration between industry and universities (e.g., Attia 2015 ; Canhoto et al. 2016 ). Mistrust, in turn, influences the information flow and can lead to a departure from the original focus of a collaboration project (Barnes et al. 2002 ). Therefore, partners need to spend sufficient time on establishing mutual trust (Gawel 2014 ). Past experiences in working together, historical experiences in collaborating, or undertaking smaller projects in order to maintain personal contacts at the beginning of a new partnership can facilitate trust (Barnes et al. 2002 ). There is also evidence that trust can be maintained and reinforced by adopting similar operating and decision-making styles (Bstieler et al. 2017 ). Ambiguous experiences regarding the relationship between trust and the type of communication are reported by Canhoto et al. ( 2016 ). While some interviewees stated that face-to-face communication is still essential for trust building, others indicated that they do not need to meet in person to establish trust. Additionally, the leadership in IUCs can set an example and send positive signals for building trust (Barnes et al. 2002 ). Furthermore, strong ties between partners, a good reputation and contractual safeguards to reduce uncertainty usually have positive effects on trust (Hemmert et al. 2014 ). However, the intensive use of contractual safeguards can also weaken each other’s trust in cases where there are already strong ties between partners (Hemmert et al. 2014 ). The role of trust might also vary with regard to the IUCs’ quality. In excellent or promising collaborations, partners experience trust as a ‘glue’ or supportive factor, while in modest collaborations the lack of trust is often mentioned by partners as negatively affecting the collaboration project (Rajalo and Vadi 2017 ).

From our analysis, it is evident that culture plays a crucial role in IUCs. Culture refers to the mutual understanding within an organization about how members should perceive, think and feel about problems and challenges (Schein 2004 ). Partners have to handle the cultural gap between industry and universities carefully and must achieve a balance between each partner’s requirements and priorities (Barnes et al. 2002 ). In this context, it is important to acknowledge that each organization or department has its own terminology and mode of operation and partners must identify these discrepancies and must establish a common language early in the project (Canhoto et al. 2016 ). Even trivial issues like meetings can be challenging when the participating persons have different ideas about the procedures or consequences of those meetings (Starbuck 2001 ). An entirely different stream of research refers to national differences—for instance, different trust-building measures, different time frames or different interpretations of contracts (e.g., Hemmert et al. 2014 ).

3.4 Output factors

A factor that has received much scholarly attention is objectives . Objectives refer to the strategy, visions, goals, plans or expected outcomes of a collaboration. One of the most discussed subjects is the compatibility of goals. A lack of compatibility can endanger the achievement of desired outcomes (Henderson et al. 2006 ). For example, universities wish to publish findings whereas companies seek to withhold them from competitors (Newberg and Dunn 2002 ). Similar results are provided by Lai and Lu ( 2016 ) who state that universities and companies are looking for different outcomes and it is hence important to understand the other’s interests and to create a win–win situation in which the benefits are correctly balanced. It appears to be essential that the partners establish a shared understanding of the objectives, agree upon achievable project goals and develop an exact strategy plan throughout the whole collaboration (Hong et al. 2010 ). A proper partner selection process ahead of a collaboration, in order to find the right partner, is advisable. In this regard, it is necessary to be sure of one’s own needs and requirements. Only then can the search for an adequate partner with concordant interests and goals begin (Arvanitis et al. 2008 ). Appropriate search strategies can help to find partners that fit and that match each other’s expectations. Barnes et al. ( 2002 ) recommend a partner evaluation method with specific criteria. Furthermore, partners often have unrealistic expectations regarding the outcome of a collaboration or they have a different sense of urgency (Attia 2015 ). This lack of understanding of each other’s work practices can lead to doubts about the priorities of both partners (Attia 2015 ).

There is also a lot of evidence that an effective knowledge and technology transfer is important for a successful collaboration (Philbin 2010 ). An intense transfer can foster innovation performance (MingJi and Ping 2014 ), improve the technology novelty (Guan et al. 2005 ) or enhance product development (George et al. 2002 ). This holds true particularly for knowledge-intensive business services (Fernandes and Ferreira 2013 ). But there are significant barriers to knowledge transfer—for example, differences in the knowledge base (Hong et al. 2010 ), cultural factors (de Medeiros et al. 2012 ) or limited knowledge transfer experience (Schofield 2013 ). Further aspects that can facilitate or impede transfer activities are the nature of the knowledge and technology (Ankrah and AL-Tabbaa 2015 ) and the explicitness of the knowledge (Santoro and Bierly 2006 ; Xu et al. 2014 ). As knowledge and technology transfer is also a question of motivation and strategy (Flores et al. 2009 ), policies and appropriate incentives can foster transfer activities in such collaborations (Schofield 2013 ). Interestingly, universities and companies might have different roles in knowledge transfers. While university scientists often initiate knowledge transfer, companies take on more managerial roles afterwards (Goel et al. 2017 ). However, as stated by Goel et al. ( 2017 ), a technology transfer system that solely relies on this allocation of roles may not be sustainable.

3.5 Framework factors

According to the literature, the environment can also have an impact on IUCs. It refers, for instance, to governmental support, legal restrictions or the market environment. The government is an influential power that can either enhance or harm collaboration (Kozlinska 2012 ). On the one hand, tax incentives (Bodas Freitas et al. 2013a ), public funding (e.g., Flores et al. 2009 ; Piva and Rossi-Lamastra 2013 ) or the governmental network (Rampersad 2015 ) can facilitate IUCs. On the other hand, legal restrictions and regulations (Arvanitis et al. 2008 ; Attia 2015 ; Hadjimanolis 2006 ) or the lack of regional support structures (Şerbănică 2011 ) can have a negative impact on collaboration. Generally, governmental support is often necessary to establish a collaboration between universities and industry work (e.g., Collier et al. 2011 ; de Medeiros et al. 2012 ; Hemmert et al. 2014 ; Muscio and Vallanti 2014 ; Myoken 2013 ; Newberg and Dunn 2002 ; Schofield 2013 ; Sohal 2013 ). Further environmental success factors refer, for instance, to the market potential of the research results (Ankrah and AL-Tabbaa 2015 ; Barnes et al. 2002 ; Guan et al. 2005 ; Hadjimanolis 2006 ) or to market uncertainties (Hemmert et al. 2014 ).

The next two factors we identified within our literature review refer to the legal aspects of IUC collaborations and are about contracts and intellectual property rights (IPRs). Contracts detailing the arrangement, roles and responsibilities reduce the possibility of later disputes (Ankrah and AL-Tabbaa 2015 ; Barnes et al. 2002 ; Lee 2011 ; Ryan 2009 ), can help to establish trust (Hemmert et al. 2014 ) and are necessary to verify whether objectives have been met by the partners (Xu et al. 2014 ). Formal agreements are especially advisable in complex collaboration projects (Starbuck 2001 ) or to ensure mutual access to expensive infrastructure (Bychkova 2016 ). Confidentiality and non-disclosure agreements play an important role in IUC projects and the setting up of proper agreements is an important task for the participating partners (Attia 2015 ; Bruneel et al. 2010 ; Perkmann and Salter 2012 ; Perkmann and Schildt 2015 ; Rampersad 2015 ). With respect to patents or other IPRs, problems and conflicts can arise regarding the project ownership or royalty payments (Arvanitis et al. 2008 ; Attia 2015 ; Bodas Freitas et al. 2013a ; Bruneel et al. 2010 ; Guan et al. 2005 ; Muscio 2013 ; Perkmann et al. 2011 ; Piva and Rossi-Lamastra 2013 ; Schofield 2013 ).

Finally, there is evidence that geographical distance is another relevant success factor. A suitable geographical distance enhances the access to highly qualified facilities and human resources (Myoken 2013 ) and makes the collaboration between industry and university partners more likely (D’Este et al. 2012 ). Even today, face-to-face interaction is preferred to other forms of communication and can therefore be a motivation for engaging in a collaboration with close geographical proximity (Indarti and Wahid 2013 ). However, there is evidence that the importance of geographical proximity diminishes when there are employee-driven relations (e.g., the graduation of employees or managers from a certain university) between the university and the company (Drejer and Ostergaard 2017 ). Despite these results, Drejer and Ostergaard ( 2017 ) conclude that to a certain extent geographical proximity matters for IUCs regardless of these relationships or the university’s quality and ranking position. Interestingly, in this context we have also to distinguish between different kinds of knowledge: companies collaborating with universities for consulting issues searched for partners in the same region, while those collaborating for R&D or technical advice searched for partners outside their region (Isabel Maria et al. 2014 ). Although most evidence is in favour of a short distance between collaborating partners, there are also results to the contrary, which indicate that successful IUCs tend to occur particularly between partners who are geographically at some distance from one another (Petruzzelli 2011 ).

4 Discussion and implications

4.1 implications for practitioners.

In this section, we discuss and summarize recommendations for management practice derived from the investigated literature, which are of importance for those parties who are or will be engaged in IUCs or who are responsible for their implementation. In order to derive these key recommendations, we proceeded as follows: We started by establishing an extensive table where we extracted the practical recommendations from individual articles. To do this, we excerpted or paraphrased recommendations from a close reading of each article. Subsequently, we categorized these recommendations according to our conceptual model and assigned them individually to a factor we had identified. Finally, we iteratively clustered the recommendations to determine superordinate recommendations. In this final step, we identified one overarching recommendation per category of factors, which we included in the depiction of the conceptual model in Fig.  4 .

Before proposing one main advice for each category, we note that the initial decisions of a collaboration are whether is it reasonable to collaborate and if so with whom. With the prospect of all the possible advantages of an IUC, the temptation to enter into an overhasty collaboration without sufficient consideration of the above-mentioned issues is very high. Universities’ third missions virtually force them into an exchange with industry and, in turn, the prospect of a ‘scientific touch’ might be seductive to some companies. However, the wrong partner could even increase costs if the partners’ objectives do not align, if there are controversial views on certain aspects or if problems occur in working together (Banal-Estañol et al. 2013 ). Collaboration for collaboration’s sake or because of internal or external pressure should be avoided at all cost. Below, we identify one key aspect per category of factors that appears essential to us based on the review of the literature presented in this article.

Regarding the institutional factors, we advise flexibility . That means, for instance, to be flexible regarding one’s own priorities as the partner might have others (Poston and Richardson 2011 ), to adopt formal rules where necessary and to compromise where appropriate (Muscio and Vallanti 2014 ), to be open-minded and to seize chances (Barnes et al. 2002 ; Ryan 2007 ). It is important to understand and accept cultural differences and to not impose one’s own conventions and approaches on the partner (Barnes et al. 2002 ; Starbuck 2001 ). It has been shown to be beneficial to create collective goals and to share the same visions and interests (Hong and Su 2013 ). Altogether, management and management processes need to be flexible enough to cope with instability and change (Barnes et al. 2002 ) as well as with the diverse interests of the partners.

Regarding the relationship between both partners, we advise paying attention to honesty . That means, treating the partner fairly, communicating openly and honestly, and informing partners of current developments immediately in order to foster trust (Barnes et al. 2002 ). We further recommend commitment to promises made (Sellenthin 2011 ) and transparency and honesty regarding goals, IPR policies or knowledge transfer (Bstieler et al. 2015 ; Santoro and Bierly 2006 ). Trust and reputation play an important role when partners share one of their most valuable assets—their knowledge. Therefore, trust and trust building is an important issue (Bstieler et al. 2015 ; Hemmert et al. 2014 ) and honesty is a fundamental basis of this.

Regarding the output factors, our advice is for clarity . That means, for example, having clear aims, planning as realistically as possible, agreeing on responsibilities, specifying the extent of the contribution of each partner and defining roles right at the beginning (Barnes et al. 2002 ; Franco and Haase 2015 ). Articles we reviewed also recommend being clear about expectations (Barnes et al. 2002 ) regarding IPR policies (Starbuck 2001 ), ownership and patent earnings (Barnes et al. 2002 ; Bruneel et al. 2010 ) or about the exploitation of project results (Newberg and Dunn 2002 ). In this context it is essential to take enough time to understand the partner’s interests, to ask questions if necessary, to discuss purposes and visions and eventually to negotiate these (Borgia et al. 2011 ; Ryan 2009 ). Concrete agreements and contractual safeguards might help in this regard (Hemmert et al. 2014 ). Summing up, while the different background of the two partners may lead to temptations to remain vague in the conception of the IUC, in the long term a collaboration is more likely to succeed if the main points are clarified between the partners.

Regarding the framework factors we particularly advise partners raising awareness of current economic, legal, political or social developments. These developments have a great impact on collaboration and therefore should be neither underestimated nor neglected. This implies keeping up to date with them and being aware of their influence on companies and universities—for example, observing and exploiting opportunities for public funding (Flores et al. 2009 ; Piva and Rossi-Lamastra 2013 ) or watching out for the possibility of (tax) incentives for IUCs (Bodas Freitas et al. 2013b ; Myoken 2013 ). Furthermore, it includes monitoring changes in the market environment (Hadjimanolis 2006 ) and being aware of corporate instability (Barnes et al. 2002 ). It also means on a more general level, analysing the wealth, the innovation intensity or the employment market of a region (Berbegal-Mirabent et al. 2015 ). To sum up, for the establishment of a successful IUC it is advisable to study the environment in which the collaboration is to take place, to be aware of current developments or future changes and to monitor environmental influences.

4.2 Implications for future research

Our review of the literature shows that the study of factors influencing the success of an IUC requires further attention. During the analysis, some more aspects that might interfere with our factors were identified but their actual effect is unclear. We call these aspects moderators as they potentially influence the impact of our factors on the probabilities of a collaboration project being successful. We repeatedly noted that authors mention these aspects in their research articles but do not study them in great depth. Researchers thus recognized their importance but apparently did not investigate them as they were only recognized ex post facto . Future research will be necessary to examine these issues.

4.2.1 Different phases

Some studies indicate that the importance of the investigated factors varies over the course of an IUC. During the formation of a collaboration, for example, regular meetings and frequent communication (Hong et al. 2010 ), clearly defined responsibilities (Barnes et al. 2002 ) and/or a considerable time commitment to share ideas (Poston and Richardson 2011 ) are necessary. As the collaboration progresses other aspects may become important, e.g., flexibility and the ability to learn and understand one another (Hadjimanolis 2006 ) and/or the existence of mutual trust (Attia 2015 ). And, again, in long-term collaborations there will possibly be other factors that are particularly relevant.

Previous studies remain vague about these deliberations and refer more to personal experiences or lessons learned. Nevertheless, statements like ‘especially at the beginning’ point toward differences regarding the phases of a collaboration. While conducting our review, this idea was reinforced and there are logical reasons for an approach that additionally considers different phases. Challenges regarding the partner evaluation or the accurate estimation of costs and revenues occur naturally at the beginning of the collaboration, while delays and postponements or the acquiring of highly qualified human resources might be important during the actual process of collaboration.

There already exist some more recent studies that investigate selected factors across different phases like Plewa et al. ( 2013a ) or Plewa et al. ( 2013b ) with regard to communication and trust. However, for most success factors it remains unclear to what extent their impact varies over the course of time. Therefore, future research should investigate the relationship between different factors and different phases of a collaboration project, because the likelihood of a successful collaboration will increase when the participating partners know what they should keep their eye on during the project.

4.2.2 Different scales

The existing literature does not really discuss differences regarding the scale of companies or universities, although there is some relevant research that focuses specifically on small and medium-sized enterprises (SMEs) (e.g. Collier et al. 2011 , Karlson and Callagher 2012 and Goduscheit and Knudsen 2015 ).

We can say that SMEs in general have different requirements and possibilities to those of large-scale firms and therefore it might also be that for collaboration with SMEs other factors are more relevant than those for a collaboration with larger companies. When reviewing the literature this assumption became even more evident. SMEs often do not come into contact with researchers and have problems gaining access to information and knowledge from universities (Howells et al. 2012 ). They may find the search and scanning costs for an appropriate partner too high (Howells et al. 2012 ), a problem that most certainly will not occur for financially strong and well-known (international) players. Also, corporate instability is more often a concern for SMEs since they are particularly vulnerable to closure, takeover or changes in business strategy (Barnes et al. 2002 ). Hence, when collaborating with SMEs it might be more important to watch out for environmental changes. Further important issues for a collaboration with SMEs refer to the motives for partner selection (Karlson and Callagher 2012 ), to the organizational culture (e.g., levels of formality or risk perception; Collier et al. 2011 ) or to values and time horizons (Hadjimanolis 2006 ). Furthermore, SMEs are known to participate avidly in EU calls that provide funding for IUCs (Piva and Rossi-Lamastra 2013 ), which suggests that governmental funding plays a special role in such collaboration projects.

The financial power, the bargaining strength, the potential endurance and the possibility to handle setbacks adequately might vary between companies of different scale. It would appear that, reported success factors, such as the financing of a collaboration project, culture and trust, the mutual use of resources like equipment, infrastructure or the exchange of qualified staff, are also influenced by the scale of a company and might differ between SMEs and larger companies. Future investigation in this regard is required to allow further conclusions to be reached. This topic is becoming increasingly important because today IUCs are not only a matter for international firms but also concern enterprises of all sizes, beginning with start-ups and other entrepreneurial activities with high innovation potential.

4.2.3 Different organizational levels

We also found some evidence that the affiliation to different organizational levels might interfere with some of our factors. Some aspects are more important for the leadership or management of an institution, while others primarily concern researchers or staff members. We note that most IUCs are operationalized at the level of individual academic departments rather than the level of a school or the entire university. This often necessitates the existence of individual ‘champions’ who help bridge the gap between different organizational levels within the university and between the university and its industrial partner (e.g., Santoro and Chakrabarti 2002 ). The role of such champions in fostering trust between partners appears to be particularly important in settings where there is little experience with IUC (Hemmert et al. 2014 ).

Leaders, for example, can foster trust, conduct an honest communication and have a strong role model effect (Barnes et al. 2002 ). The commitment and support of leaders is known to facilitate or impede collaboration on all levels (Ankrah and AL-Tabbaa 2015 ; Bergner et al. 2010 ). Many tasks are often the responsibility of leaders and managers: for instance, the distribution of resources or goal setting or the establishment of incentives or rules to motivate members to share their knowledge (Schofield 2013 ). Staff members, however, are responsible for maintaining frequent communication via multiple communication channels (e.g., e-mail, telephone, meetings) between the workforce (Hong et al. 2010 ), or have to find a mutual language between academic and business staff (Baba et al. 2010 ; Gawel 2014 ). Further aspects refer to the attitude and commitment of staff members towards collaboration (Sellenthin 2011 ) or interpersonal links and networks (Collier et al. 2011 ). And some other aspects might be considered on an institutional level—for instance, cultural differences between organizations (Barnes et al. 2002 ; Starbuck 2001 ).

Altogether, the investigated literature supports the consideration that some of our factors interfere with the organizational level of individuals; but these studies do not investigate this question specifically and remain rather speculative on this point. It seems worth taking a closer look at these differences, because for a successful IUC individuals at all levels have to contribute. Hence, it will be advantageous to have a better understanding of which factors are relevant for leaders and managers and which for staff members, such as experts, researchers and administrators. The literature on power in organizations (Pfeffer 1981 ), and in particular the role of champions in using their power to highlight the value of collaboration (Santoro and Chakrabarti 2002 ), may provide a relevant theoretical basis. Footnote 1

4.2.4 Different disciplines

The last potential moderator refers to (scientific) disciplines. There are good reasons to suggest that scientific disciplines might moderate the relevance of some of our identified factors.

Scientific disciplines have different conventions and cultures, they use different methods and instruments and some of them have a strong focus on applied research or are more open to the needs of industry. The investigated articles do not examine this question, but, for example, with respect to academic R&D collaboration projects Niedergassel and Leker ( 2011 ) conclude in their study that other scientific disciplines could have yielded different results. In that vein, Cummings and Kiesler ( 2007 ) state in their investigation regarding multi-university collaborations that they cannot guarantee the generalizability of their results when it comes to other disciplines.

If these assumptions hold true for academic collaborations, it is reasonable that the scientific field might also impact IUCs. For instance, trust-building measures between industry and university partners might be more important for a project involving humanists than economists, or access to research equipment and infrastructure might be particularly relevant for natural scientists. Hence, we assume that the (scientific) discipline is a potential moderator for our success factors and future research should investigate its specific role in that regard.

Finally, it is interesting to note that the articles we reviewed hardly ever addressed the subject of risk management, a formal process of ‘coordinated activities to direct and control an organization with regard to risk’ (International Organization for Standardization 2018 ). This is all the more surprising as today risk management should be an indispensable part of the governance and leadership of an organization and should be included in all activities associated with an organization and its stakeholders (International Organization for Standardization 2018 ). In other cross-sector collaborations risk management is already considered an important topic and has prompted ample research on it. In public–private partnerships (PPPs), for example, some authors even assume that PPPs have more and a higher degree of risks than other projects because they involve many stakeholders, implicate complex project arrangements, may have special rules regarding financing, documentation and taxation, or lack in experienced partners (e.g., Carbonara et al. 2015 ; Grimsey and Lewis 2002 ; Wang et al. 2018 ). The situation in IUCs seems to be similar but while the PPP literature offers a huge number of articles investigating risks and risk management in such projects, the literature on IUCs rather ignores this aspect. This may either be because there is currently no application of risk management or because there is no research about it, or—most likely—both. It is hard to tell why practitioners as well as researchers disregard this topic in such a way and we can only hypothesize the reasons as to why this is. Possibly, it is due to the good faith in universities and their reputation as reliable partners, but given the resources invested in such collaborations and the difficulties involved in bringing them to successful conclusion, both the financial and non-financial risks as well as the procedures put in place to manage them appear a subject worthy of investigation.

5 Conclusion

IUCs are increasingly important and it is in the interests of governments, policymakers, researchers and practitioners that such collaborations are successfully implemented. While the advantages and potential of these collaborations are well recognized, there are at the same time numerous hindrances and challenges to be met, which can lead to failure. In this review article, we identify factors that influence the success of an IUC and derive recommendations from the literature for a successful realization and implementation of such a partnership. These insights will help to establish fruitful collaborations between these two very different types of organizations.

We draw on the insights gained from reviewing the literature to identify potential issues that are hinted at in the works reviewed but not researched in depth. These gaps provide potential for future research. In particular, we identified four moderators that appear to interfere with influencing factors we derived from the literature. The actual impact of these moderators is as yet unclear. Future research should investigate whether and how the phase of a collaboration project, the scale of the partners, the organizational level or the scientific discipline interact with the main factors of influence. We also discuss implications for practitioners and propose a main recommendation for each category of our conceptual model. For the institutional factors we advise flexibility, for the relationship factors, honesty, for the output factors we advise clarity and for the framework factors, awareness. The conceptual model that we have proposed should serve as a useful framework for discussions among practitioners as well as for researchers.

Methodological choices always result from a weighing of advantages and drawbacks. To conclude, we briefly discuss limitations of our study that result from these choices. First, the process of literature selection involves certain limitations. Some relevant articles might be excluded due to the formulated definitions of the exclusion criteria, others remain undiscovered due to the selection of our search terms (for example, we did not search for ‘higher education’) and still others were not included because of the determined time frame. Second, although we conducted a systematic review of literature to minimize any bias and to ensure the replicability of the investigation, a certain degree of professional judgment cannot be eliminated within a review of social science literature (Denyer and Tranfield 2009 ). The development of our conceptual model, the definition and categorization of the factors or the assessment of the importance of certain factors is subject to fundamental decisions. Although, our decisions were guided by methodological considerations and recommendations of previous research or by the synopsis of our quantitative and qualitative syntheses of the reviewed papers, we still have to acknowledge that other judges might have drawn different conclusions or might have rated some aspects differently (inter-rater reliability). Finally, we only selected papers that explicitly considered the success of IUC, many of which did not address the reason the IUC was entered into originally. Following from this choice, we accepted the original papers’ definition of success in the context of IUCs rather than imposing a homogeneous definition of success across studies. While this ensures upholding the fit of the original studies’ aims and methods (a key quality criterion of research identified, e.g., by Boaz and Ashby 2003 ), it does not address any selection bias stemming from the fact that certain factors may influence both the propensity to start an IUC and its subsequent success.

We thank the reviewer for pointing out this literature.

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Rybnicek, R., Königsgruber, R. What makes industry–university collaboration succeed? A systematic review of the literature. J Bus Econ 89 , 221–250 (2019). https://doi.org/10.1007/s11573-018-0916-6

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Please note you do not have access to teaching notes, a literature review exploring the role of technology in business survival during the covid-19 lockdowns.

International Journal of Organizational Analysis

ISSN : 1934-8835

Article publication date: 18 May 2021

Issue publication date: 23 November 2022

The Covid-19 pandemic has affected every aspect of human life. Even though the pandemic length was not too long, a huge volume of research relating to Covid-19 has been published in different contexts. This paper aims to review the literature investigating the impact of Covid −19 on businesses generally and explore studies examining the technology role of business survival during the Covid-19 lockdowns specifically.

Design/methodology/approach

This study implemented the concept of a systematic review approach to review the literature that has been conducted in the business field during the Covid-19 crisis in general. Additionally, it looks into the research examining the role of technology in business survival in the Covid-19 crisis specifically. All studies were conducted in 2020. A total of 53 studies were identified and categorised into different themes. The research methods, theories and locations have also been analysed.

It was found that Covid-19 pandemic has affected all business sectors in several ways. Technology adoption has a critical role for business survival during the Covid-19 crises especially with small businesses. Very limited research has been conducted on the adoption of different technologies during the Covid-19 lockdowns.

Originality/value

This study presents the most frequent themes and topics that have been explored in the literature during the Covid-19 crisis in the business field. It highlights the methods used in addition to the theories and research locations present in this literature. Finally, it proposes the possible implications of this literature review.

• Literature review

Abed, S.S. (2022), "A literature review exploring the role of technology in business survival during the Covid-19 lockdowns", International Journal of Organizational Analysis , Vol. 30 No. 5, pp. 1045-1062. https://doi.org/10.1108/IJOA-11-2020-2501

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Economic effects of the COVID-19 pandemic on entrepreneurship and small businesses

Maksim belitski.

1 University of Reading, Henley Business School, Reading, UK

2 ICD Business School, IGS-Groupe, Paris, France

Christina Guenther

3 WHU, Otto Beisheim School of Management, Vallendar, Germany

Alexander S. Kritikos

4 German Institute for Economic Research (DIW Berlin), Berlin, Germany

5 University of Potsdam, Potsdam, Germany

6 IZA, Bonn, Germany

7 IAB, Nuremberg, Germany

8 Montpellier Business School, Montpellier, France

9 Erasmus School of Economics, Rotterdam, Netherlands

The existential threat to small businesses, based on their crucial role in the economy, is behind the plethora of scholarly studies in 2020, the first year of the COVID-19 pandemic. Examining the 15 contributions of the special issue on the “Economic effects of the COVID-19 pandemic on entrepreneurship and small businesses,” the paper comprises four parts: a systematic review of the literature on the effect on entrepreneurship and small businesses; a discussion of four literature strands based on this review; an overview of the contributions in this special issue; and some ideas for post-pandemic economic research.

Plain English Summary

Responding to COVID-19 involves not just shielding small business jobs, supporting entrepreneurship, and raising government debt but also creating productive entrepreneurship and resilient location-specific entrepreneurial ecosystems. The COVID-19 pandemic is an unprecedented challenge for small businesses that also brings new market opportunities. The papers in this special issue of Small Business Economics Journal aim to shed light on the economic effects of the COVID-19 pandemic by looking at the macro- and microeconomic effects on entrepreneurship and small businesses as well as the role of financial support policies and well-being in both developed and developing countries. Future research should focus on the role of digitization and financial mechanisms supporting small businesses during crises.

Introduction

Epidemics and pandemics do not just come and go, they impact the economy and society. For example, the epidemic in the early 1830s, when France (and other parts of central Europe) was hit hard by cholera with hospitals overwhelmed by patients whose ailments doctors could not explain (O'Sullivan, 2021 ). While the epidemic wiped out at least 3% of Parisians in the first month, it would contribute to an industrial revolution in France. It also increased political instability and social disparity, with the city’s poor being hit hardest by the pandemic, while the wealthier used their savings and resources to relocate from pandemic-impacted cities and reduce their interactions with the community (Economist, 2021 ).

The Spanish flu affected most of Europe and the USA in 1918. While it infected 500 million people—about a third of the world’s population at the time—it killed between 20 and 50 million people across four successive waves, including some 675,000 Americans (History.com, 2020 ). The enforcement of various restrictions varied across the cities and countries: the New York City Health Commissioner, for example, ordered businesses to open and close on staggered shifts to avoid overcrowding on the subway (History.com, 2020 ). In the USA and Europe, businesses were forced to shut down because so many employees were sick. Several authors demonstrate that the Spanish flu pandemic gave way to new businesses, with start-ups booming from 1919 in the middle of the pandemic onward (Beach et al., 2020 ; Karlsson et al., 2014 ).

The COVID-19 pandemic presents an unprecedented challenge in many ways. First, it threatens millions of people’s lives all over the world. It has already taken a death toll of almost four million people worldwide, as of the end of June 2021 (Worldometers, 2021 ). At the same time, the social distancing guidelines, taken to contain the virus, affected the service sector in particular, an area where physical proximity often matters and a sector that depends more on micro and small businesses than the manufacturing sector.

Therefore, COVID-19 directly affected self-employed individuals more than employed individuals (Kritikos et al., 2020 ) and small businesses more than large businesses, both in Europe and the USA (Digitally Driven, 2020 , 2021 ).

A survey conducted by NBER of more than 5800 small businesses in the USA found that 43% of small firms were expected to be closed by December 2020 (Bartik et al., 2020 ). Small firms in hospitality, retail, personal services, entertainment, and the arts were most affected (Bartik et al., 2020 ). A survey conducted by the Connected Commerce Council of more than 5016 European small and medium-sized businesses carried out in November–December 2020 found that practically all SMEs were affected, with an average 20% decrease in sales and a 16% decrease in customer base (Digitally Driven, 2021 ).

Barrero et al., ( 2020 : 17) demonstrate for the USA that, “temporary layoffs and furloughs account for 77% of gross staffing reductions in the first months of crises in the United States,” while the Financial Times ( 2020 ) reports that, “more than 3 m Americans filed for first-time unemployment benefits during a first week of May 2020, taking the number of applications for the first three months of the lockdown to 33.5 million. The number of working business owners in the United States plummeted from 15.0 million in February 2020 to 11.7 million two months later in April” (Fairlie, 2020 ). In the UK, the unemployment rate surged to its highest level since 2017 as the pandemic continued to affect jobs (Thomas, 2020 ). In the long term, the COVID-19 pandemic is expected to become a cleansing process and a large reallocation shock (Caballero and Hammour, 1991 ) for firms of different sizes and industries.

Governments throughout the world responded with support initiatives. In the USA, the largest program providing funds to small businesses is the Paycheck Protection Program (PPP) with a volume of $650 billion during the early stages of the pandemic (Bhutta et al., 2020 ). The Small Business Administration (SBA)–administered program provided loans to small businesses through banks, credit unions, and other financial institutions with the goal of keeping small businesses open and retaining employees on the payroll (Fairlie & Fossen, 2021 ). In the UK, the government implemented the Coronavirus Job Retention Scheme (CJRS) (popularly known as “the Furlough” scheme) for waged workers. The CJRS covers 80% of employee salaries up to a maximum of £2500 per month. More than 8.7 million jobs were furloughed at an estimated total cost of around £60 billion (Yue & Cowling, 2021 ). After initially ignoring the 4.6 million self-employed, the UK government announced the Self-Employment Income Support Scheme, which awarded grants of 70% of average monthly trading profits calculated from tax returns for 2018 and 2019. This scheme only applied to those self-employed who earned less than £50,000 in profit for the relevant period (Yue & Cowling, 2021 ). The measures supported by the German government intended to protect businesses and start-ups affected by the COVID-19 crisis include taxation support, state-supported short-time work compensation schemes, improved measures at guarantee banks, loans and special programs provided by KfW (Kreditanstalt für Wiederaufbau) (PWC, 2020 ), and an emergency aid that offers one-off lump sum payments to self-employed facing substantial revenue declines (Block et al., 2020 ).

In China, measures started in February 2020 when Chinese central bank unblocked extensions or renewals of loans to companies and announced a reduction in the banks’ mandatory reserve ratio. The government presented a package to support the digitalization of SMEs in the context of the crisis. A wide range of policy measures was announced for SMEs at the regional level in China, including deferred tax payments for SMEs, reducing rent costs, waiving administrative fees, subsidizing R&D costs for SMEs, social insurance subsidies, subsidies for training and purchasing teleworking services, and additional funding to spur SME loans (KPMG, 2020 ). The 2020 GEM report mentions that 54 national governments made emergency policy decisions and actions in response to the COVID-19 pandemic (GEM 2020 ). Unprecedented amounts of state aid were channeled into propping up economies around the globe.

Despite the deployment of administrative, fiscal, and monetary tools to counter the fall in employment and demand, it seemed unlikely that these measures will be enough to attain a full offset. The response to COVID-19 requires both top-down and bottom-up approaches, e.g., government and private initiatives to support productive entrepreneurs, instead of dying industries and failing firms.

The shock of the pandemic may further increase inequality in at least two ways: First, female owners of small businesses faced a 35% higher probability of experiencing income losses than their male counterparts with the gender gap among the self-employed being largely explained by the fact that women disproportionately work in industries that are more severely affected by the COVID-19 pandemic (Graeber et al., 2021 ). Second, the consequences of the COVID-19 pandemic may be more pronounced for minorities in developed (Fairlie and Fossen, 2021 ) and developing countries (Maliszewska et al., 2020 ; Pereira & Patel, 2021 ).

More efficient and productive incumbents are likely to grow, with new businesses and industries emerging. The new “Never-Meet-in-Person Era” will change industries, impacting large and small firms in certain industries, such as transport, hospitality, arts and entertainment, and personal services. The weight of hybrid firms, platform-based firms, and platform-matchmakers in the global economy will grow rapidly (Kenney & Zysman, 2020 ).

The emergence of digital technologies has significantly reduced the economic costs of data—search, storage, computation, transmission—and enabled new economic activities during the COVID-19 pandemic and a change in lifestyle. Since the start of the pandemic, small and large firms, able to create a platform-based ecosystem, have become a force of “creative destruction,” value creation, and value appropriation (Acs et al., 2021 ).

The big issue is how the shock and the resulting recession will affect firms, large and small, young and mature, family and non-family firms, community-embedded small firms, and platform-based blitz-scalers not only in the short term but also the mid- and long terms. Will this be different than for any other exogeneous shock?

The potential consequences for businesses may include but are not limited to closed premises, reduced operating hours, job cuts, supply chain disruptions, jeopardizing the R&D processes, cessation of operations, business model changes, loss of key customers, and restrictions on products/services.

News stories highlight the millions of layoffs triggered by the pandemic and lockdown (Barrero et al., 2020 ), while they also relate to examples of large-scale hiring. For example, on April 18, 2020, Walmart reported that it had hired 150,000 new employees, with plans to hire 50,000 more (Nassauer, 2020 ). Fidelity Investments and Fifth Third Bancorp have also been on “hiring sprees,” and hires through Zoom eliminated the worry to be spotted during a job interview lunch by current employers. Will this be the beginning of a new revolution toward large multinational corporate structure, away from micro and small businesses? Businesses may have had different experiences from responding to the previous recessions and other pandemics but can these lessons be useful for small and large firms to respond to COVID-19?

Therefore, the objective of this special issue is to examine the economic effects of the COVID-19 pandemic on entrepreneurship and small businesses as well as help to promote research and economic implications relevant to understanding the nature of the pandemic shock, consequences, and opportunities for SMEs and large firms in the short- and long-term perspectives more broadly.

The present introduction to the special issue is organized as follows. It consists of four parts: a systematic review of the literature on the effect of COVID-19 on entrepreneurship and small businesses; a discussion of four literature strands based on this overview; an overview of the contributions in this special issue; and some ideas about the post-pandemic economic research, organized according to four avenues.

Systematic literature review

We start our analysis by performing a “systematic literature review” (Tranfield et al., 2003 ). It is a reliable and efficient method of identifying and evaluating a sizeable literature volume and is widely used in business research (Verma & Gustafsson, 2020 ). The advantage of this method is that it allows for capturing all existing studies on the topic, to incorporate quantitative, qualitative, and mixed-method studies, as well as to identify the state of knowledge regarding theories, special entities, and fields of study.

Based on this systematic and comprehensive literature review, we investigate research gaps and identify areas that require further research using the Scopus and Web of Science database, taking our lead from prior systematic literature reviews of Rousseau et al. ( 2008 ) and Verma and Gustafsson ( 2020 ). Before moving to the systematic literature review on the effect of COVID-19 pandemic on small business and entrepreneurship, we wanted to find out whether there is prior research on the economic effects of historic pandemics, such as the Spanish flu. Therefore, we use the period of 50 years, which resulted in only 60 publications, related to the effect of Spanish flu on small businesses. Interestingly, most papers on the effect of the Spanish flu on small business were published during the COVID-19 pandemic (see Fig.  1 ). Researchers from the USA, UK, and Canada have led this field of research.

Timeline of publications on small business and the Spanish flu. Note: 2021 is an incomplete year since the research was done in May of that year

Our next, and main, step was to review the literature on the economic effects of COVID-19 on small businesses and entrepreneurship. We used the period from December 2019 to June 2021 because it corresponds to the pandemic period. We included all articles, data sets, early-access publications, and data studies in English, yielding 3607 published pieces. Once we applied the selection criteria, including only articles published in international peer-reviewed journals, in English and the area of study, the number of publications dropped to 1789. The distribution of articles by field of science is as follows: social sciences (29.3%), business management (22.6%), economics (12.9%), environmental sciences (10.9%), energy (8.8%), organizational studies (2.3%), arts and humanities (2.0%), psychology (1.9%), and other (9.30%).

In the third stage, we used the field of research exclusion criteria with the aim of retaining publications from relevant fields such as business economics, management, social sciences, and economics. Most of the publications come from the USA, China, and the UK (see Fig.  2 ).

The region of the publications on small business and COVID-19

We excluded the BIOSIS Citation Index, BIOSIS Previews, Medline, Zoological Record, and FSTA. This means that we just kept the Web of Science and Scopus databases, yielding to 285 papers. Based on the keywords, text, and abstracts from these 285 papers, we created the visualization network to identify the themes related to the impact of COVID-19 on small businesses using VOSviewer. Co-word analysis applies text-mining techniques to the papers’ titles, abstracts, keywords, and text. Co-word connections allow for identifying and combining multiple co-occurrences and keywords in the same paper, as well as determining the relationship between different keywords (Verma & Gustafsson, 2020 ).

The outcome of the systemic literature review resulted in a keywords network visualization that required (i) selecting the patterns of topics and (ii) clustering topics theories: digitization and open innovation, resilience and disaster, knowledge creation and learning (dynamic capabilities), including industry effects (e.g., healthcare, information technology, tourisms) (Fig.  3 ). The theories were identified by reading all the abstracts and keywords of the 285 papers. These four theories are further explained in the next section and will be matched to the papers that comprise this special issue. We note that a clear discrimination between these literatures is not always possible.

The keywords network visualization

Theories and contributions

Based on the systematic literature review, this section describes how four literatures can be used by scholars to better understand and explain the economic effects of the COVID-19 pandemic on small business across different countries, firm sizes, and the severity of the crisis. First, there is disaster theory literature, which focuses on the financial and physical resources enabling small firms to be more resilient during crises. A body of literature stresses the importance of community-based networks and the role of social capital in helping small businesses to respond to disasters (Bin & Edwards, 2009 ; Torres et al., 2019 ).

Torres et al. ( 2019 ) investigate small business owners’ response to natural disasters and catastrophes through the lens of resources and social capital, drawing a line between resilient small businesses that not only remain operating but also thrive after a disaster and those exiting. Evidence focusing on small businesses shows that they widely engage in disaster relief for their community (Bin & Edwards, 2009 ), clarifying that in addition to governments, entrepreneurs and small businesses also become active (Markman et al., 2019 ). Post-disaster business resilience is the product of many complex decisions resulting from the interaction of individuals, families, businesses, and communities (Marshall & Schrank, 2014 ).

Second, responses to crises and exogeneous shocks is at the heart of resilience theory. The origins of the resilience concept in the business literature go back to Staw et al. ( 1981 ) and Meyer ( 1982 ). Both authors draw upon variation–selection–retention mechanisms posited by evolutionary theory (Campbell 1965) and develop very different propositions regarding how organizations respond to external shocks. Staw et al. ( 1981 ) introduce a theory on how negatively framed situations lead to risk avoidance in the form of “threat-rigidity effects.” Meyer ( 1982 ) extends the resilience framework by studying hospital responses to an unexpected doctors’ strike or “environmental jolt,” contradicting the proposition by Staw et al. ( 1981 ) that an external threat automatically places an organization at risk.

Resilience takes place over time and is related to the recovery of individuals, businesses, communities, and institutions. Most studies consider post-disaster business resilience as a binary stage of open or closed businesses (Marshall & Schrank, 2014 ). By capturing measures and processes that contribute to small business resilience as a disaster response, Tugade and Fredrickson ( 2004 ) provide real world examples, while Torres et al. ( 2019 ) emphasize the role of community and support to entrepreneurs in a post-shock period.

Research on resilience and post-disaster management literature began to comment that there are few avenues to detect whether or not an entrepreneur had “resilience potential,” prior to demonstrating a resilient or non-resilient response (Linnenluecke et al., 2012 ). Furthermore, researchers argue that more attention should be devoted to the period of detecting a threat and activating firm’s response. Conceptualization of organizational resilience broadly fall in three categories: (1) resilience as an outcome, (2) resilience as a process, and (3) resilience capabilities (Bullough et al., 2014 ; Duchek, 2020 ).

In the post-COVID world, agile and resilient new businesses will be able to take advantage of their entrepreneurial orientation and find opportunities in the upheaval that the pandemic has caused globally (Zahra, 2020 ). In an environment characterized by high volatility and uncertainty, the importance of the firms’ dynamic capabilities (DC) to integrate resources in recognizing new opportunities is also further heightened (Battisti & Deakins, 2017 ). The role of DCs and the role of resilience (Bergami et. al, 2021 ; Bullough & Renko, 2013 ; Bullough et al., 2014 ) are differentiators between not just the survival and failure of small businesses and entrepreneurs and also the speed with which new ventures are able to learn, both determining their growth and survival in the long term (Zahra, 2020 ).

Third, there is a literature on the role of knowledge creation and absorptive capacity in addressing the negative effects of disasters and crises. Dynamic capabilities (DC) are the key concept underlying absorptive capacity as the antecedent organizational and strategic routines by which managers alter their resource base—acquire and shed resources, integrate them together, and recombine them—to generate new value-creating strategies (Eisenhardt & Martin, 2000 ; Grant, 1996 ). Teece et al., ( 1997 : 516) defines DCs as “the firm’s ability to integrate, build, and reconfigure internal and external competences to address rapidly changing environments. Dynamic capabilities thus reflect an organization’s ability to achieve new and innovative forms of competitive advantage given path dependencies and market positions.”

Managing uncertainty tends to be the new normal for many companies around the world (i.e., climate change, COVID-19), thus stressing the importance of creating competitive advantage and improving dynamic capabilities that are so important for small business (Arend, 2013 ) and that seem to be the only antidotes to uncertainty during the COVID-19 pandemic (Flammer & Ioannou, 2020 ).

The role of dynamic capabilities was brought forward by Priyono et al. ( 2020 ) in their analysis of how small businesses cope with environmental changes due to the COVID-19 pandemic by pursuing the business model transformation with the change in dynamic capabilities related to adaptation of digital technologies and digital skills.

Dynamic capabilities, which became even more relevant in the digital era (Li et al., 2016 ), enable small businesses to adopt digital tools more quickly and efficiently. This enables stronger response to the COVID-19 pandemic. For example, Audretsch and Belitski ( 2021 ) demonstrate how European small businesses adopt digital technologies and develop strategic, managerial, and digital skills to increase their efficiency.

The DC theory could be relevant in the response to the volatility, velocity, and criticality of COVID-19 effects (Obal & Gao, 2020 ), for instance, by redeploying salespeople to virtual rather than physical sales calls. The literature on dynamic capabilities could draw on prior research in times of high turbulence but is sparse and focuses mainly on financial crises. For example, Fainshmidt and Frazier ( 2017 ) and Makkonen et al. ( 2014 ) find a disconnect between pre-crisis settings and the types of DCs most useful during crisis.

Bartik et al. ( 2020 ) and Kuckertz et al. ( 2020 ) suggest how government initiatives help businesses cope with the COVID-19 pandemic. A further cluster of papers use information gathering surveys (e.g., Bartik et al., 2020 ; Fairlie, 2020 ; Kritikos, et al., 2020 ) and case studies (Kuckertz et al., 2020 ; Robinson & Kengatharan, 2020 ). There is a lack of research on the intersection of the pandemic and DCs.

It is important to understand the boundary conditions explaining whether DCs can benefit small businesses compared to larger firms. Prior research suggests the existence of a positive feedback loop that results in firms with the largest initial capability endowments generating more new capabilities. Taken together, despite prior research on DC in small businesses (Arend, 2013 ; Fainshmidt & Frazier, 2017 ), only a few studies deal with the role of firm size in determining DCs and in response to the COVID-19 shock.

The fourth strand of literature is related to digitization and the role of digital capabilities in adopting new business models, responding to uncertainty, and developing resilience. Behavior of rapidly growing small businesses depends on their business models (Hennart, 2014 ; Kuratko et al., 2020 ), and the role of digitally enabled firms and business models is important in times of volatility (Li et al., 2016 ; Vadana et al., 2019 ). The role of digital capabilities is expected to grow in importance for entrepreneurship and small business research and practice during and after the COVID-19 crisis. Digital capabilities will be able to change business models and introduce business model innovation (Clauss et al., 2019 ). In the entrepreneurship literature, entrepreneurial growth remains an oft-neglected topic of research, as only a few studies (Asemokha et al., 2019 ; Child et al., 2017 ) shed light on the dynamics of business models and growth or performance in entrepreneurship.

There is still a gap with respect to understanding which DCs need to be developed for firms to respond to opportunities of COVID-19, such as digitalization and business model change (Seetharaman, 2020 ).

Works on digitization in small businesses analyze the implementation of business intelligence as part of their efforts to increase competitiveness in a highly dynamic business environment. A better understanding of the adoption levels of innovation by small businesses is relevant due to the important contribution of small businesses to both employment generation and economic growth (Audretsch et al. 2021b ).

Studies commissioned by Google in the USA in 2020 and in Europe in 2021 demonstrate that the so-called Digital Safety Net has empowered millions of small businesses to shift resources, modify business plans, and continually evolve throughout the pandemic (Digitally Driven, 2021 ). The COVID-19 pandemic threatened small businesses globally, but their use of digital tools has acted as a “Digital Safety Net” and saved many of them (Digitally Driven, 2020 , 2021 ).

Papers in the present special issue

The papers in this special issue can be divided into four strands by the unit of analysis, policy implications, and the literature used. These strands can be connected to the four literatures distinguished in the previous section. The first strand reveals the macro-economic effects of Covid-19 on entrepreneurship, small businesses, and the role of digital technologies in changing work routines of entrepreneurs, which relates to the literature on disaster management and the role of digital tools and capabilities. The second strand touches upon the economic and socio-psychological impact of the COVID-19 pandemic on entrepreneurship building on resilience literature and literature on the role of dynamic capabilities, in addition raising the issues of inequality and the effects of COVID-19 in developing and developed economies. The third strand deals with financial support to small businesses and entrepreneurship, building on the literature that addresses the negative effects of disasters and crises as well as macroeconomic responses to shocks. Finally, the fourth strand discusses the effect of various policy and well-being issues for small businesses during COVID-19 drawing on resilience and disaster theory literature.

The first strand contains three papers. Addressing the macroeconomic effects of COVID-19 on the way of living and working, a study of Zhang et al. ( 2021 ) “Working from Home: Small Business Performance and the COVID-19 Pandemic” focuses on working from home as an opportunity rather than an activity that leads to frustration, loneliness, and worries about the future (Banerjee & Rai, 2020 ). In this paper, working from home appears to be an opportunity to improve small businesses’ performance in the COVID-19 crisis. The authors built a theoretical framework based on firm profit maximization using daily and weekly data to demonstrate that working from home impacts the industrial structure and peoples’ work behavior.

A study by Meurer et al. ( 2021 ) demonstrates how entrepreneurs can use alternative support sources of communication and business, such as online communities, raising the question of how support is created in such spaces. Drawing on an affordances perspective, the authors investigate how entrepreneurs interact with online communities and base their qualitative analysis on conversation data (76,365 posts) from an online community of entrepreneurs on Reddit during the COVID-19 pandemic. The findings draw out four affordances that online communities offer to entrepreneurs (resolving problems, reframing problems, reflecting on situations, refocusing thinking and efforts), resulting in a framework of entrepreneurial support creation in online communities.

Altogether these two papers demonstrate how small businesses and individual entrepreneurs can adjust to new business conditions by working from home, developing new business models, and seeking social support to leverage the negative impact of the COVID-19 pandemic.

The study of Pedauga et al. ( 2021 ), “Macroeconomic Lockdown and SMEs: The Impact of the COVID-19 Pandemic in Spain,” takes a macroeconomic perspective to empirically test the role of small business in the economy. The authors use a financial social accounting matrix to distinguish between the direct and indirect effects that are transferred from micro, small, medium, and large firms to the rest of the economy during the COVID-19 pandemic. The authors explore the sequence of reactions associated with shocks that arise from the COVID-19 lockdown to small businesses using a structural model for the Spanish economy and identifying the role of businesses of different sizes for the gross domestic product (GDP). Interestingly, small businesses “explain” 43% of the gross domestic product and two-thirds of the unemployment decline caused by the COVID-19 pandemic.

The second strand of studies in this special issue examines the economic and non-economic impact on small business performance of the COVID-19 pandemic. The study of Grözinger et al. ( 2021 ) on “The Power of Positivity: Organizational Psychological Capital and Firm Performance During Exogenous Crisis” investigates how psychological capital in businesses impacts performance and creative innovation through organizational citizenship behavior, solidarity, and cooperation. The authors use structural equation modelling and regression analysis on 379 small businesses to demonstrate that psychological capital positively influences creative innovation and thus performance during crises. This research contributes to the organizational behavior approach of the small business literature by showing that psychological resources of small businesses can strengthen performance in times of crisis and help to prepare for future shocks.

The study by Torrès et al. ( 2021a ), “Risk of Burnout in French Entrepreneurs During the COVID-19 Crisis,” discriminates between three sources of burnout: the threat of becoming ill, having to stay at home due to the lockdown, and having to file for bankruptcy due to the economic downturn. They use seven data sets of French entrepreneurs with a temporal comparison of averages and two data sets of French entrepreneurs with a cross-sectional analysis of individuals. They show that the risk of burnout increased during the pandemic, that all three factors play important roles, and that the financial threat is the dominant one. These findings call for the extension of entrepreneurial support systems beyond the financial by also involving an “entrepreneurship care” aspect, which includes telephone support, webinars, mental help facilities, and other support measures.

The study by Kalenkoski and Wulff Pabilonia ( 2021 ), called “Impacts of COVID-19 on the Self-employed,” uses monthly panel data from the Current Population Survey in the USA and examines the initial impacts of COVID-19 on the employment and hours of unincorporated self-employed workers. The authors find that effects become visible in March 2020 as voluntary social distancing started, peaked in April during the complete shutdown, and were slightly smaller in May. They conclude that self-employed married mothers were hit hardest and were even forced out of the labor force to care for children. Moreover, remote work and working in an essential industry mitigate some of the negative effects on employment and hours worked.

Pereira and Patel ( 2021 ) in their study, “Is the Impact of COVID-19 More Severe on Self-employed of Colour? Large Scale Evidence from Brazil,” complement prior research on self-employed from racial minority groups and use resilience theory to explain how minority self-employed in Brazil responded to the COVID-19 pandemic with lessons for other developing countries (e.g., Sri Lanka) (Robinson & Kengatharan, 2020 ). The paper extends the argument that minorities may face greater adversity from the COVID-19 pandemic in the USA and other developed countries (Buheji et al., 2020 ), while there is little evidence that minority self-employed in a developing country are also significantly affected in the context of the COVID-19 pandemic.

The third strand of studies brings together the role of financing for entrepreneurship and small businesses in crises and a variety of support tools. Studies in this part discuss the role of financial support and other government programs to respond to economic disruption. Various support policies were developed and provided by governments all over the world in response to address their small businesses’ financing needs. In a paper by Liu et al. ( 2021 ), “SMEs’ Line of Credit under the COVID-19: Evidence from China,” the Chinese SMEs’ financing responses to the outbreak of COVID-19 are examined. The study shows the supportive role of Chinese state-owned banks on small businesses’ lines of credit. These policy instruments can be broadly categorized into loan guarantees, direct lending to small businesses, grants and subsidies, and equity instruments. Interestingly, there are considerable differences in supporting small businesses’ financing policies between countries. For example, in the USA, European Union, the UK, and China and Russia, policies to support small businesses during the pandemic were a commonplace. Brazilian and Indian government provided little support to small business.

The study of Fairlie and Fossen ( 2021 ), “Did the Paycheck Protection Program and Economic Injury Disaster Loan Program Get Disbursed to Minority Communities in the Early Stages of COVID-19?,” examines the effect of the US federal government response to help small businesses—the Paycheck Protection Program (PPP) and the related Economic Injury Disaster Loans (EIDL). The program’s stated goal is helping disadvantaged groups. The authors provide the first detailed analysis of how the 2020 PPP and EIDL funds were disbursed across minority communities in the country. The authors find a positive relationship between PPP loan receipt per business and the minority share of the population or businesses, although funds flowed to minority communities later than to communities with lower minority shares. This study acknowledges the importance of financial support through PPP loans of minority communities as a share of the population. The important evidence is that the EIDL program, both in numbers per business and amounts per employee, was positively distributed to minority communities. This is the first study about how loans and advances from these programs were distributed between minority and non-minority communities.

Another study by Atkins et al. ( 2021 ), “Discrimination in Lending? Evidence from the Paycheck Protection Program,” adds to our understanding of the role of race in loans made through the Paycheck Protection Program (PPP). Expanding the paper of Fairlie and Fossen ( 2021 ), the authors argue that the historical record and PPP program design choices made it likely that many Black-owned businesses received smaller PPP loans than White-owned businesses: Black-owned businesses received loans that were approximately 50% smaller than observationally similar White-owned businesses. Interestingly, the effect is marginally smaller in areas with more bank competition and disappeared over time as changes to the PPP program were implemented allowing for entry by fintechs and other non-traditional lenders.

The study by Block et al ( 2021 ), “The Determinants of Bootstrap Financing in Crises: Evidence from Entrepreneurial Ventures in the COVID-19 Pandemic,” investigates the measures that entrepreneurial ventures undertake to preserve liquidity. The authors build on prior research on bootstrap financing as an important enabler for the growth of resource-constrained early-stage ventures. Their work fills the gap about the use of bootstrap financing during COVID-19, during which the preservation of liquidity is particularly salient. The determinants of bootstrap financing are embedded into a “necessity” human capital perspective and an “opportunity” cost perspective. The analyses are based on data of 17,046 German entrepreneurial ventures.

The study of Dörr et al. ( 2021 ), “Small Firms and the COVID-19 Insolvency Gap,” focuses on fiscal policy in rescuing companies short of liquidity from insolvency. The authors show that, in the first months of the crisis, the small businesses that are the backbone of Germany’s economy benefited from large and mainly indiscriminate aid measures. The authors estimate the extent to which the policy response induced an insolvency gap and analyze whether the gap is characterized by firms that were already struggling before the pandemic. They also examined whether this insolvency gap differs with respect to firm size and find that the gap was larger for smaller firms. The theoretical contribution of the paper is in translating Schumpeter’s theory of the cleansing effect in economic crises into an empirical assessment by estimating the size of a policy-induced insolvency gap using firm-specific credit rating data combined with information on insolvency filings.

The fourth strand of studies represents a variety of micro and macro public support and well-being programs aiming to mitigate the negative effects of the COVID-19 crises.

The Lastauskas ( 2021 ) study, called “Lockdown, Employment Adjustment, and Financial Frictions,” examines businesses’ employment adjustments after the imposition of stringent lockdown in March 2020. It uses monthly administrative data and takes value-added tax payment changes as a proxy for the demand shock. The main finding is that all businesses in the manufacturing sector reduced employment more if they had uncovered tax liabilities before the lockdown. Among small businesses, those in the real estate and the service sectors downsized more rapidly. While employment changes are rather modest, this early evidence points to the importance of addressing liquidity needs and specific pre-conditions among capital-intensive and services businesses to avoid employment losses.

The Belghitar et al. ( 2021 ) study, “When the rainy day is the worst hurricane ever: the effects of governmental policies on SMEs during COVID-19,” examines the impact of COVID-19 on 42,401 UK small businesses and how government intervention affected their capability to survive the pandemic. The results show that, without governmental mitigation schemes, 59% of UK small businesses report negative earnings and that their residual life is reduced from 164 to 139 days. This analysis demonstrates that government financial support may reduce the number of small businesses with negative earnings and allows extending the residual life for small businesses with negative earnings up to 194 days. Block et al. ( 2020 ), who analyze the first emergency aid program in Germany, find similar effects among German businesses hit by the crisis. Interestingly, the study of Belghitar et al. ( 2021 ) highlights that—in contrast to Block et al ( 2020 )—those industries that were worst hit by COVID-19 are not those that benefited the most from the government support scheme. The possible reason is that the government scheme does not differentiate between firms that do or do not deserve support.

Finally, the study of Braunerhjelm ( 2021 ) deals with macro-economic stabilization policies and discusses that targeting aggregate demand may not suffice to mitigate the comprehensive effects of the COVID-19 crisis. Entitled “Rethinking Stabilization Policies: Including Supply-side Effects and Entrepreneurial Processes,” it suggests that a more active role for fiscal policies is needed and presents a modified framework for stabilization policies, giving an extended role to supply-side measures and emphasizing policies that can promote entrepreneurial processes and knowledge upgrading efforts. Aligning policies at the micro- and macro-levels can be expected to counteract economic downturns more efficiently as the potential for long-term growth is enhanced. Such a redirection of stabilization policies is argued to strengthen the competitive standing of both firms and individuals.

Future research

There are many discussions and arguments proclaiming that nothing in business will be left unchanged: in the post-COVID world, there will be opportunities for entrepreneurs to embark on creating new products and services, with novel business models and business routines arising that are different from traditional ones (Janssen et al., 2021 ). Changes in (the perception of) well-being, the way of consuming, in the way of filtering out the resilient and the agile, the adoption of new digital technologies and learning skills, and much more will all contribute to something that some call the “new normal.” Below, we contribute to this discussion with respect to four dimensions of future research, all connected to the contents of this special issue, initially sparked by our discussions with authors and referees during the online paper-development-workshop organized by the University of Reading on November 20, 2020: caution is warranted as all suffer from a certain degree of speculation.

Long- and short-term economic effect of COVID-19

The results of several papers in this special issue demonstrate that investigating the long-term effects induced by the policy responses to COVID-19 on turnover, productivity, innovation, and entrepreneurship in developed countries is needed. However, future research may also want to demonstrate a wider economic, political, and societal challenge, including inequality and poverty, unemployment within poor countries, and the gap between rich and poor countries (Bartik et al., 2020 ; Robinson & Kengatharan, 2020 ).

Real wages in certain sectors may rise, such as tourism, hospitality, and restaurants, as the disease reduces the supply of workers, leaving survivors in a stronger bargaining position.

The macro- and microeconomic effects of the COVID-19 shock are different between small and large firms as well as between the self-employed and incorporated business. Smaller businesses are typically disadvantaged in their ability to capture the opportunities that crises have created. It is important to research further the role of local and national governments, public organizations, civil society, and other stakeholders in mitigating the effect of crises.

Forming partnerships between small and large firms, the role of open innovation and knowledge spillovers may emerge as an important conduit for entrepreneurship and for mitigating the effects of COVID-19. Particularly interesting is the dynamics of so-called science, technology, engineering, and mathematics (STEM)–related jobs in the long term.

Further insights are needed to understand economic and psychological drivers of innovation during crises. While previous research demonstrates that context matters (Audretsch et al., 2021a ; Welter, 2011 ; Welter et al., 2019 ), the context of a crisis is a compelling, yet understudied, one. Welter et al. ( 2019 ) outlines three recent and overlapping waves of contextualization in the entrepreneurship field and shows that the discussion has moved from challenging the Silicon Valley model by considering the why, what, and how of entrepreneurship (first wave) to considering more subjective elements in enactment of contexts (second wave), through broadening the domain of entrepreneurship research (third wave).

To quantify the effect of the COVID-19 lockdown on economic activity, it may be possible to consider the links between all three waves (Welter et al., 2019 ) at the idiosyncratic level and their aggregate impact. It is probably not just sectoral issues and those issues related to the labor market or economic growth that play a role, but also deeper mental issues (Torrès et al., 2021a ).

The use of digital technology, competencies, and robots

Digital skills trends seem to be interacting with the pandemic and its social, political, economic, environmental, and demographic tensions, combining to accelerate the reconfiguration of production and service systems. This reconfiguration of existing skills and adoption of digital skills not only affects employment trends, but also the way we work and experience our mental and physical health, perhaps even long after the crisis is over.

The role of digital technology has significantly increased under COVID-19. For instance, digital technologies affected the way firms do head-hunting during COVID-19 as well as how products and services are manufactured and delivered. During disease outbreaks—Ebola in 2014–2016 and COVID-19 in 2019, among others—the adoption of robot and digital tools accelerates, especially when the health impact is severe and associated with potential economic losses or economic crises.

Entrepreneurship in the post-pandemic world will further fuse with the digital economy. This will take the form of entrepreneurs increasingly selling products on digital platforms, using digital tools like TikTok for marketing and relying on platforms such as Kickstarter for funding. Moreover, we believe that entrepreneurs will further seek to use peers in online communities to develop opportunities, get assistance with problems, and find collaborators. The key implication is that, while entrepreneurs in the past have often physically worked side by side to develop their business locally, in the future such bounds will play a diminishing role. One can start a business in Ghana, work with a programmer in Indonesia, find a marketing specialist in Paris, secure funding over Kickstarter, and sell the product through a digital platform. In other words, COVID-19 fosters the transition of the entrepreneurial economy into a digital, disembodied economy. The next big technology to be adopted at large scale is likely to be 5G. The large-scale use of artificial intelligence is being pushed but may not be relevant until 2025 at the earliest. Quantum computing is also being pushed, but not likely to affect small businesses before 2030.

All small businesses must be prepared for the “new normal” of a digitally driven economy (Meurer et al., 2021 ). Many are well positioned, but others feel uncertain due to challenges accessing capital, tools, and training, as well as with measuring success. During the pandemic, so-called advanced small businesses invested more than twice as much money in digital tools than the so-called uncertain small businesses (Digitally Driven, 2021 ). The working environment changed fundamentally with the digitalization and flexibilization of work receiving a considerable boost. These changes probably make companies more resilient to future shocks.

Even though the self-employed initially were hit harder by the COVID-19 pandemic than larger firms in the USA and Europe (Digitally Driven, 2020 , 2021 ), there is reason to be optimistic because, for the millions of SMEs that still lack skills, technology, and resources, adopting digital tools is within reach with the right mindset, strategy, access to world-class digital technologies, and training. As the working world has become more flexible, it is likely that mixed forms of remote and physical working (especially in teams) will become accepted in the future. However, we also learned that remote work cannot sufficiently replace personal encounters in all cases. Therefore, we believe that society and the working world will learn to appreciate such personal encounters again and that these will be valued differently in the future. Future research may need to better understand the role personal encounters and skills, which, along with new technology, will be valued more in the future.

Financing for entrepreneurship

As witnessed by several contributions in the present special issue, there are many promising avenues for research regarding what drives the financing of entrepreneurial activity during and after the COVID-19 crisis. For example, we would expect that entrepreneurial motivation may play an important role, along with networks of venture capital and angel investors. A significant share of solo self-employed individuals start their businesses out of necessity (Block et al., 2015 ; Caliendo & Kritikos, 2019 ; De Vries et al., 2020 ; Zwan et al., 2016 ). As policymakers want more high-growth ventures to recover from the crisis, their interest in opportunity-driven entrepreneurs may grow. Human and social capital including networks for entrepreneurship may be important for sourcing entrepreneurial financing. Finally, research should also analyze performance effects and investigate whether and how various sources of finance, beyond bootstrapping during the COVID-19 crisis, may impact long-term entrepreneurial performance, survival, and high growth (Audretsch et al., 2021b ).

Financial support policies are important for supporting small businesses and individual entrepreneurs with the mechanisms and the extent of such support being substantially different between OECD and non-OECD countries. Thus, understanding the causes and consequences of SME financing policies in the COVID-19 era would be intriguing and pivotal for both academic researchers and policymakers. Future research could also examine whether and how the institutional and development stage heterogeneities shape the policy differences related to stakeholders, unit of financing, and form of financing (e.g., grants, loans, equity). In that sense, the pandemic is a natural experiment.

A criticism of the financial support programs is that often there was no data collected on applications for loans that were denied (Fairlie & Fossen, 2021 ). This is an important piece of information that should be collected for future research on public support to small businesses and entrepreneurs to gauge demand and unmet need for these loans, in particular by minority businesses in developed and developing countries. As in the case of the USA, PPP and EIDL funds were allocated to support businesses, and it is crucial to track who receives funding and how it helps small businesses to become more resilient and grow during the crisis.

During the first phase of the pandemic, massive government support slowed firm exits. However, it may be argued that the resources were not spent efficiently and that public support mechanisms slowed down industrial dynamics. Hence, an important challenge for the post-pandemic world is to revitalize entry rates and stimulate technology adaption while also encouraging the adoption of new business models that restore productivity and growth beyond pre-crisis levels. In this context, research in industrial dynamics may help to contribute to the existing long-run challenges faced by modern societies such as digitization, decarbonization, and sustained prosperity.

Looking ahead, government and policymakers may want to design financial policy interventions that dampen the impacts of the pandemic on small businesses. Future research should focus on direct policies, like zero-interest loans, subsidies, and grants. According to Liu et al. ( 2021 ), in this special issue, the measures should target subgroups, firms that heavily rely on supply chains, and small businesses without stable bank relationships.

Understanding the effects of the interplay between liquidity support, on the one hand, and temporary adjustments to insolvency regimes, on the other, will provide an important lesson from the COVID-19 crisis. Further research may focus on the interplay of these two instruments as it is assumed that they may discourage struggling firms from exiting the market.

Non-economic effects of the COVID-19

An increasing number of studies in the entrepreneurship literature analyzes to what degree entrepreneurs’ mental health influences their activities. Further studies about the perception of burnout or general mental health issues, with a focus on experiences during the COVID-19 pandemic across more countries, industries, and fields, could expand what we know about the response of entrepreneurs during crises and how negative effects (e.g., burnout) could be leveraged.

COVID-19 put a large strain on entrepreneurs, who experienced an unprecedent shock to their businesses (Torrès et al., 2021b ) Without being able to meet physically with investors and clients, some entrepreneurs had to scale down their businesses; others closed their business, and solo entrepreneurs were left more isolated than before. The COVID-19 pandemic has likely been detrimental to the mental health of entrepreneurs. The pandemic forced entrepreneurs to reflect on the importance of their mental health and to actively seek and establish coping techniques. Some entrepreneurs experiencing failure may decide that entrepreneurship is not for them, but we expect that those who continue their entrepreneurial career found ways to cope with high stress levels. For instance, such entrepreneurs will use “time boxing” to become more productive, meditate regularly, or use digital tools to connect with peers. These entrepreneurs will likely also focus more on balancing their working and private lives by creating a working situation that suits their social needs. In that sense, some of the entrepreneurs who suffered during the pandemic may come back mentally stronger and more resilient.

The lockdown likely led to frustration, loneliness, and worries about the future (Kritikos et al., 2020 ), which are also risk factors for mental illnesses (Banerjee & Rai, 2020 ). Future research can focus on the impact of lockdowns and quarantine on small businesses as well as on the link between lockdowns, psychological effects (Brooks et al., 2020 ), and entrepreneurship (Shepherd, 2020 ). Results of future investigations could inspire entrepreneurs to search for novel, more sustainable, and more social forms of entrepreneurship, better understanding failures and successes of small businesses. This knowledge, which is often informal and tacit, represents a source of wealth for dealing with new forms of crisis (both health related and economic).

Protecting and supporting the health of small businesses and entrepreneurs during and after the COVID-19 pandemic is essential because they have a special role in the aftermath of crisis and in the anticipated post-pandemic boom. This aftermath may be predominantly dematerialized with a virtual mode of working and new norms of working from home. The climate and the green agenda would be a priority. A large part of business services would be contactless. Entrepreneurs’ health—both physical and mental—would be acknowledged and recognized as vital, both by the entrepreneurs themselves and by the policy makers.

However, given the length of school closures and the considerable reduction in the availability of childcare centers, the gender gap in entrepreneurship, which was identified at the beginning of this crisis, may widen in the post-pandemic period (Seebauer et al., 2021 ).

In general, economic inequality between and within nations is likely to also increase the likelihood of contracting the coronavirus and dying from it. Developing nations with weak healthcare systems and an inability to practice social distancing also account for the unequal impact. For people of low socio-economic status and economically disadvantaged people in developed countries, COVID-19 also poses higher risks of living in overcrowded accommodations increasing risk of illness (Patel et al., 2020 ). Racial and ethnic minorities experience higher death rates from COVID-19, which has also unequally affected urban residents and foreign migrants around the world. With the closure of schools, nurseries, and other childcare facilities for all but children of essential workers (Blundell et al., 2020 ), parents were typically left with the sole responsibility for caring for their children, including education, which particularly affected the survival of the self-employed. How these growing inequalities affect business dynamics will become an entire field of scholarly research and, hopefully, of compensating policy interventions.

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Contributor Information

Maksim Belitski, Email: [email protected] .

Christina Guenther, Email: [email protected] .

Alexander S. Kritikos, Email: ed.wid@sokitirka .

Roy Thurik, Email: ln.rue.ese@kiruht .

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‘It Is Desolate’: China’s Glut of Unused Car Factories

Manufacturers like BYD, Tesla and Li Auto are cutting prices to move their electric cars. For gasoline-powered vehicles, the surplus of factories is even worse.

By Keith Bradsher

Reporting from Chongqing, China

On the outskirts of Chongqing, western China’s largest city, sits a huge symbol of the country’s glut of car factories. It’s a complex of gray buildings, nearly a square mile in size. The thousands of employees who used to work there have moved on. Its crimson loading docks are closed.

The facility, a former assembly plant and engine factory, had been a joint venture of a Chinese company and Hyundai, the South Korean giant. The complex opened in 2017 with robots and other equipment to make gasoline-powered cars. Hyundai sold the campus late last year for a fraction of the $1.1 billion it took to build and equip it. Unmown grass at the site has already grown knee high.

“It was all highly automated, but now, it is desolate,” said Zhou Zhehui, 24, who works for a rival Chinese automaker, Chang’an, and whose apartment looks down on the former Hyundai complex.

China has more than 100 factories with the capacity to build close to 40 million internal combustion engine cars a year. That is roughly twice as many as people in China want to buy, and sales of these cars are dropping fast as electric vehicles become more popular.

Last month, for the first time, sales of battery-electric and plug-in gasoline-electric hybrid cars together surpassed those of gasoline-powered cars in China’s 35 largest cities.

Dozens of gasoline-powered vehicle factories are barely running or have already been mothballed.

The country’s auto industry is near the start of an E.V. transition that is expected to last years and eventually claim many of those factories. How China manages that long change will influence its future economic growth, since the auto sector is so big and could transform its work force.

The stakes are great for the rest of the world, too.

China, the world’s largest car market, became the largest exporter last year, having passed Japan and Germany. China’s auto sales abroad are exploding.

Three-quarters of China’s exported cars are gasoline-powered models that the domestic market no longer needs, said Bill Russo, an electric car consultant in Shanghai. Those exports threaten to flatten producers elsewhere.

At the same time, China’s electric vehicle companies are still investing heavily in new factories. BYD and other automakers are expected to introduce more electric models at the opening of the Beijing auto show on Thursday.

Electric car sales in China are still growing. But the pace of growth has halved since last summer, as consumer spending has faltered in China because of a housing market crisis.

“There is a slowdown trend, especially for pure electric vehicles,” said Cui Dongshu, secretary general of the China Passenger Car Association.

China also has overcapacity in electric vehicle manufacturing, although less than for gasoline-powered cars. Price cutting for electric vehicles is common. Li Auto, a fast-growing Chinese manufacturer, reduced its prices on Monday. Tesla did the same a day earlier, and on Tuesday reported a large decline in profits during the first three months of this year. BYD, the industry leader in China , made price cuts in February. Volkswagen and General Motors have also lowered E.V. prices in China this year.

Automakers with factories close to China’s coast are exporting gasoline-powered cars. But many of the endangered factories are in cities deep inside the country, like Chongqing, where high transport costs to the coast make it too expensive to export.

Almost all of China’s electric cars are assembled at newly built factories, which qualify for subsidies from municipal governments and state-directed banks. It’s cheaper for automakers to build new factories than to convert existing ones. The result has been enormous overcapacity.

“The Chinese auto industry is experiencing a revolution,” said John Zeng, the director of Asia forecasting at GlobalData Automotive. “The old internal combustion capacity is dying.”

Sales of gasoline-powered cars plummeted to 17.7 million last year from 28.3 million in 2017, the year that Hyundai opened its Chongqing complex. That drop is equivalent to the entire European Union car market last year, or all of the United States’ annual car and light truck production.

Hyundai’s sales in China have plunged 69 percent since 2017. The company put the factory up for sale last summer, but no other automaker wanted it. Hyundai ended up selling the land, the buildings and much of the equipment back to a municipal development company in Chongqing for just $224 million, or 20 cents on the dollar.

The municipal company said this year, while seeking insurance on the site, that it did not have a new tenant.

Other multinational automakers have reduced output in China. Ford Motor has three factories in Chongqing that have been running at a tiny fraction of their capacity for the past five years .

Hyundai is one of the very few automakers, mostly foreign, that have halted production entirely at some locations, although the company still has three factories in China.

“There doesn’t seem to be a concerted effort to shut down excess capacity, but more of a shift from foreign owned to Chinese owned,” said Michael Dunne, a former president of General Motors Indonesia.

The longstanding benchmark is that car factories should run at 80 percent of capacity, or more, to be efficient and make money. But with new electric car factories opening and few older factories closing, capacity utilization across the entire industry fell to 65 percent in the first three months of this year from 75 percent last year and 80 percent or more before the Covid-19 pandemic, according to China’s National Bureau of Statistics.

Without a big burst of exports last year, the industry would have operated even further below full capacity.

Chinese manufacturers, many of them partly or entirely owned by city governments, have been reluctant to reduce output and cut jobs. Chang’an, a state-owned carmaker, has a factory just a 20-minute walk down pink-bougainvillea-lined lanes from the former Hyundai complex. The factory’s many acres of parking were completely full of unsold cars on Sunday.

Cities that are particularly dependent on gasoline-powered car production, like Chongqing, face a jobs dilemma. Assembling electric vehicles requires considerably fewer workers than making gasoline-powered cars, because E.V.s have much fewer components.

Workers with strong technical backgrounds, particularly in robotics , can easily and quickly find jobs if they’re laid off, autoworkers in Chongqing said in interviews. But semiskilled workers — including those who are older and have not taken training courses to develop their abilities — are now finding it more difficult to obtain work.

Mr. Zhou said that when he applied for his job at Chang’an, “it was a fierce competition.”

Still, it is extremely hard to find unemployed former Hyundai workers in Chongqing these days, even in the neighborhood of the former factory.

Most factory workers in China are migrants who grew up in rural areas and have few connections to the communities where gasoline-powered cars have been built. So they can easily move to other cities or industries when they lose jobs.

Yet a tinge of gloom hangs over the car industry in Chongqing, as demand slows and less skilled workers have fewer opportunities to earn overtime pay. Hyundai’s signage is still visible in many places at its former factory, but a large shadow on the front gate shows where an optimistic slogan used to hang: “New Thinking, New Possibilities.”

Li You contributed research.

Keith Bradsher is the Beijing bureau chief for The Times. He previously served as bureau chief in Shanghai, Hong Kong and Detroit and as a Washington correspondent. He has lived and reported in mainland China through the pandemic. More about Keith Bradsher

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