Science–industry knowledge transfer has long been considered important to explain innovation in the food industry, however, it has also highlighted challenges raised between actors [1
]. Knowledge transfer has been found to improve growth trajectories through the network settings promoted by knowledge transfer projects [2
], nonetheless trust and language are identified as key obstacles that hamper the transfer of scientific insights to firms in the food sector [3
]. Scholars and practitioners posit a strong focus on economic gains that firms can potentially attain from knowledge transfer activities with either science-academic partners or other firms [4
]. Mechanisms such as economic contracts [5
], social exchange [6
], autonomy [7
], engaged scholarship [8
], and learning [9
] are seen as useful tools that can improve knowledge transfer and increase competitive advantage.
A recognition of the importance of science to industry knowledge transfer in this context led to the motivation to investigate the generative mechanisms for science–industry knowledge transfer in the food industry, especially to Small and Medium Sized Enterprises (SMEs) in the UK. The food industry in the UK it is composed of over 6000 SMEs which are not always able to directly access the latest innovation and technology. Whilst there is a plethora of studies investigating the motivations for firms to engage in knowledge transfer with scientists, there is a paucity of inductive studies that look for fine-grained explanations of why this process happens from science to industry. Science to industry knowledge transfer is increasingly important for universities and other research organisations to demonstrate the value and relevance of their research. Whereas there is evidence that scientists are increasingly engaging in commercial activities [10
], what remains unknown is whether or not they are driven by the same for-profit mechanism as in the traditional private sector. Thus, the main research questions addressing this gap in the food industry are: (1) what are the key generative mechanisms within science–industry knowledge transfer? and (2) what are (if any) the implications of these mechanisms to policy?
To understand the generative mechanisms of science–industry knowledge transfer, this study proposes an inventive analytical framework, at both macro and micro levels (structure, agency and interaction). This framework builds on previous studies that focus on either macro level relationships (i.e., [11
]) or micro level relationships (i.e., [14
]). This process enables us to move from a limited understanding of knowledge transfer as a singular functional perspective to a cognitive understanding of knowledge transfer as situated in organisational structures, individual motivations and their interactions. Furthermore, the explanation is based on a grounded approach that outlines the dominant mechanisms that cause the transfer of new scientific insights to SMEs, using different theoretical lenses for each mechanism.
We apply this framework to three collaborative knowledge transfer projects in the food industry. These were specifically selected to examine how knowledge is transferred between the largest food research institute in the UK and food manufacturer SMEs, via in-depth interviews, observations and document analysis. Drawing on our analysis, we offer the conceptualisation of three dominant generative mechanisms for each level of abstraction. The macro or situational mechanisms include nonpecuniary incentives, reputation and opportunity whilst the micro or action-formation mechanisms include instrumental rationality, self-interest and strategic calculation. The interactive or transformation mechanisms are aggregation, learning and adaptive self-regulation. These insights contribute to a deeper understanding of scientists’ environmental constraints and opportunities to collaborate with industry and their individual motivations that are seemed to be beyond economics and extrinsic rewards to include nonpecuniary, social and personal aspects related to intrinsic motivation. We conclude with the implications that this mechanismic perspective of knowledge transfer has for policies.
2. Literature Review
The literature identifies interdependent streams of science–industry knowledge transfer, and it includes those focused on motivations and assessing the psychological side of relationships; those focused on the process itself, roles, its structure and various stages; those focused on the economical and performance impact of knowledge transfer; and those focused on relational aspects such as trust, learning, networks and social exchange.
From a motivational perspective, academic capitalism [16
] and entrepreneurial universities [17
] describe academics as promoters of commercialisation, emphasising the for-profit motive of the entrepreneurial scientist. Whilst there is evidence that scientists are increasingly engaging in commercial activities, such as patenting, spin-off company formations [15
] and licensing [18
], what remains unknown is whether they are driven by the same for-profit mechanism as in the private sector. Other motivators for academic–industry knowledge transfer have been around the reputation of scientific peers and the availability of institutional technology transfer support [19
Citations, prizes and other similar forms of peer recognition have typically been recognised as the predominant extrinsic rewards for academic career advancement or increased salaries and as the main motivators to engage with industry. However, D’Este and Perkmann’s [15
] study on the entrepreneurial university and on the motivations for academic–industry engagement, concludes that most academics engage with industry to further their research rather than to commercialise their knowledge. Similarly, Göktepe-Hulten and Mahagaonkar’s [20
] and Lam’s [21
] studies of the motivations for scientists to pursue commercial activities report that reputation is a stronger incentive than financial rewards. Furthermore, Iorio et al. [22
] suggest that the “mission” is a key motivation to pursue knowledge transfer activities, where the academic scientist advances the societal role of universities.
Studies have also proposed that individuals invest time, energy and effort into knowledge transfer to create collaborative networks and cooperative relationships [12
]. Although a variety of motivations for academics to engage with industry have been identified, studies agree that there are both intrinsic and extrinsic motivations. Intrinsic motivations include reputation and research support, whilst extrinsic commercialisation-maximising motivations include increased income.
There is a recognition that knowledge transfer is a process with various stages of transfer and the factors that correlate to the difficulty of transfer activities. For example, Böcher [11
] looks at scientific knowledge transfer as the connection between research (R), integration (I), and utilisation (U), the RIU model. Within this RIU-model, scientific knowledge is produced in the science system (research), and science-based problem solutions are utilised within practice by political actors (utilisation). The key mechanism in this model is integration, which is the step that connects the science sphere to utilisation. The Triple Helix model [23
] also goes beyond dyadic relationships and offers insights into the dynamics of the relationship among research, industry and government. One limitation of these models is that they focus at the macro level activity and tend not to include an individual level of analysis.
Studies which propose that academics are motivated by monetary profit, suggest that researchers use patents to increase their income, and pursue relationships with firms to access equipment or exploit other research-related opportunities [24
]. Taking a transaction cost economics perspective, Katz and Martin [5
] suggest that academic–industry collaborations can be prolonged by economic commitments, which create a ‘locked-in condition’ between partners, therefore ensuring that the cooperation is continued and endured. Another economic lens used to view knowledge transfer is the prisoner’s dilemma of collective action (game theory) which suggests that information asymmetry and independent strategies within firms that are transferring knowledge can cause conflicting interests in learning, which could lead to the end of the collaboration [9
Engaged scholarship also affects knowledge transfer relationships and knowledge is more likely to be adopted when the stakeholders have been involved in the process of knowledge creation [8
]. It is important that collaborative work between research and practice produces knowledge that is more penetrating and insightful than when researchers work alone. A perception misalignment between SME entrepreneurs and academics can hinder innovation and lead to the failure of many knowledge transfer initiatives [25
]. This perspective offers a traditional view of science–industry knowledge transfer activities, with economic-type gains to businesses and reputational-type gains to researchers.
From a relational perspective, Adler and Kwon [26
] work on social capital and suggest that informal social ties are superior conduits for knowledge sharing. Furthermore, the social capital dimensions of networks—structure, cognition and relation—affect the transfer of knowledge. Inkpen and Tsang [27
] examine how organisations acquire knowledge depending on their positions within networks and conclude that organisations should build and use their social capital proactively for efficient knowledge transfer. This view is also shared by Yli-Renko et al. [28
], who suggest that knowledge transfer is facilitated by the intensive social interactions of various actors.
Another widely cited theory that explains knowledge transfer is absorptive capacity [29
], which implies that knowledge transfer is only successful if the receiver of the information has prior related knowledge in order to recognise the value of what they are receiving and to be able to assimilate it effectively. Thus, knowledge stickiness is a major barrier to knowledge transfer when the recipient lacks absorptive capacity, which affects the execution and implementation of the transfer.
Social exchange theory refers to situations where rewards or punishments are provided in recurring interactions. Muthusamy and White [6
] found that social exchanges such as reciprocal commitment, trust and mutual influence are positively related to knowledge transfer. Whereas economists assume that firms’ behaviours towards knowledge transfer are motivated by self-interest, social exchange theorists believe that knowledge transfer can be motivated by a broad array of interests and that self-interest and group interests can coexist [30
]. The knowledge flow between scientific networks and industry, and its relation to human resource issues, identifies two perspectives on knowledge transfer: cognition and competencies versus careers and incentives [31
Liyanage et al. [32
] propose a process model of knowledge transfer using the theory of communication and theory of translation. They argue that knowledge transfer is facilitated by collaboration (theory of communication) and transformation of knowledge into a usable form (theory of translation). This concurs with Holden and Von Kortzfleisch [33
] who argue that the perceived utility of knowledge from the receiver determines the effective translation and quality of the knowledge transferred. They used translation theory as an applicable analogy to explore the nature of knowledge transfer and go a step further to explain that the process is only successful if the source understands their own knowledge and if they understand what it means to the receiver. Thus, this translation involves the interpretation of the same knowledge in a different manner or context in order to be accessible and absorbed.
SMEs working with university research centres tend to rely on relational trust and self-interest [34
]. Studies have considered knowledge transfer from an individual’s trust perspective and the importance of boundary spanning individuals to build trust with other organisations [35
]. Both interpersonal and interorganisational trust are considered key drivers for knowledge exchange performance, with the former affecting institutionalisation, and the latter associated with lowered costs of negotiation and conflict.
Autonomy and trust are considered important mechanisms for knowledge transfer [7
]. Where individual autonomy is impractical, organisations can minimise the effects of low autonomy by fostering institutional and interpersonal (benevolence-based) trust. The mechanisms of trust have either assumed a self-interest angle [34
], a cooperative competency perspective [13
] or a relational trust angle [8
]. These studies investigate how trust, both interpersonal and interorganisational, affects knowledge transfer and consequently organisational performance. However, these studies do not explain the generative mechanisms of trust.
Overall, there are four broad perspectives of mechanisms. The first has a strong focus on motivations such as rewards, peer recognition, enhanced reputation and access to knowledge and resources. The second type of mechanism focusses on economic commitments to ensure collaboration and increased competitive advantage. The third offers an emphasis on the process of knowledge transfer whilst the fourth type emphasises a relational and social capital dimension. Most of these studies position these mechanisms from either a macro or micro perspective but never from both angles, nor do they consider interaction mechanisms.
This study inductively investigates the generative mechanisms for scientific knowledge transfer, which are broadly related to the four types of mechanisms observed in the current literature. It applies an analytical framework that looks at structural mechanisms by looking at rules, norms and resources; agency mechanisms by looking at motivations and beliefs; interaction mechanisms by looking at actions and interactions. This analysis differs from current literature as each mechanism is inductively reasoned from rich data whilst previous studies have concluded their mechanisms from a predetermined correlation with other variables.
Despite the voluminous literature on knowledge transfer, studies have not yet investigated generative mechanisms. So far it has been difficult to provide in-depth insight as to why knowledge transfer occurs. This study aims to advance prior research by offering an alternative perspective, with alternative ontological and epistemological assumptions about cause and effect, grounding the knowledge transfer literature in a robust framework based on a process-oriented view. Through an inductive study with scientists and food sector SMEs, this study investigates the generative mechanisms for knowledge transfer and whether or not they have an impact on this type of relationship.
3. Materials and Methods
The methodology used in this study was process-tracing [36
], with abductive reasoning [37
] as the main analytical method. Process-tracing is a within-case method of analysis and a key technique for capturing the presence or absence of generative mechanisms [36
]. It goes beyond the identification of correlations between independent variables and outcomes, with the ambition to trace underlying generative mechanisms that involve interpretation, contextualisation and abstraction by analytically or temporally ordering the empirical data.
This study applied explaining-outcome process-tracing to build a theoretical explanation from the empirical evidence. The goal was to trace the generative mechanisms that explain the knowledge that is transferred from science to industry in the food sector. By investigating different knowledge transfer projects and utilising a multiple framework, empirical data from 52 in-depth interviews with scientists and directors of food SMEs, 17 supporting project documents, and 16 observations were reviewed and analysed. Systematic combining through abductive reasoning was then used as the analytical method to abstract the generative mechanisms and match corresponding theoretical explanations. Abductive reasoning develops a narrative from empirical data, where the evidence leads to the formation of the most plausible explanation, followed by the abstraction of mechanisms which are then matched to a suitable theoretical framework. Process-tracing is well placed to move theory beyond either/or debates to empirical applications in which both agents and structures matter. It moves us away from correlational arguments and as-if styles of reasoning toward theories that capture and explain the world as it really works. Process-tracing also offers the ability to make connections between different theories.
Mechanisms are analytical constructs that draw useful connections between social instances [38
]. Generative mechanisms are unobservable; we do not observe causality but make inferences about it, hypotheses about them generate observable and testable implications. Mechanisms cannot establish causality but they allow explanatory accounts by first utilising historical or causal narratives and then abstracting the mechanisms. Hedstrom and Ylikoski [39
] (p. 51), defined mechanisms as “consisting of entities (with their properties) and the activities that these entities engage in, either by themselves or in concert with other entities. These activities bring about change, and the type of change brought about depends on the properties of the entities and how the entities are organised spatially and temporally.”
A mechanismic explanation advocates that there is no mechanism that operates solely at the macro level. In other words, there are no macro-level entities that possess the capacity to act or the capability of producing outcomes, hence the importance of looking at individual actions. However, that is not to say that macro-level explanations are not important. They are very relevant to establish correlations between macro-variables and are a useful shorthand, however they need further explanation at the micro-level. A mechanismic explanation takes the position that a macro phenomenon such as knowledge transfer in a science–industry setting must ultimately be grounded in explanatory mechanisms that involve individual actions and interactions.
Mechanism-based explanations aim to provide a plausible account of the generative mechanisms that are necessary to explain how, under certain contextual conditions, an observed phenomenon has emerged. This perspective aims to identify the generative mechanisms that allow us to explain with some confidence “how” and “why” something happened rather than merely observing that something happened [40
There were three projects that were analysed in depth. These were collaborative projects that occurred between the largest publicly funded food research institute in the UK and food manufacturer SMEs. Although the SMEs involved had first-hand access to the projects’ findings, there were no royalties involved, and once the projects were completed, findings could be accessed by other food manufacturers. The projects were selected from rigorous selection criteria that included purpose, variety, evidence, industry presence and accessibility, as summarised in Table 1
From a pool of 11 projects, the chosen ones met the criteria more closely. The goal was to trace the generative mechanisms that explain knowledge that transferred from this institute to food SMEs. One project was health driven (BACCHUS), one safety driven (SUSSLE) and one regulation driven (NIS), as summarised in Table 2
Primary data for the research were gathered through two rounds of semi-structured interviews, each lasting between 40 and 90 min, with key stakeholders from the research institute involved in the projects. Participants from food SMEs were also interviewed to confirm that knowledge was transferred and utilised within their organisations. Stakeholders outside the project that were involved in the wider environment, including government, policy and funding bodies, were also interviewed, as summarised in Table 3
. The interview data were triangulated between interviews with different participants, and the various documents and the observations examined. In total, there were 52 semi-structured interviews that formed the primary dataset.
Documents from a wide range of sources were used as evidence. These included official documents from the food research institute such as project contracts and terms of agreements. Other documents from mass-media outputs such as magazines, newspapers, internet resources and archived documents were also accessed. Data sources such as official websites, background documents and publicly available reports, interviews and articles were also used to extend the findings. The documents added context, and gave further information that could be utilised during the face-to-face interviews, provided contact detail information, and confirmation that the industry and government are utilising the knowledge from the research institute, including their results and findings. The documents also gave an indication of the scope of reach that their projects had, not only to the immediate stakeholders involved but also to the wider industry.
Observations were conducted in 16 events, including networking events, workshops, seminars, and sector-specific conferences. The purpose of attending these events was twofold. Firstly, the events provided current information regarding challenges affecting the food industry in general. Secondly, they provided a platform to network with various actors and established contacts that were later used for interviews or obtaining access to documents and reports. They also provided an opportunity to discuss issues around innovation and knowledge transfer with the most prominent figures in the sector. Observations focused on problem-solving discussions and talks, where the interaction and opinions of different actors could be captured. They attempted to record information about (a) what are the challenges occurring in the food industry during the period of this study, including backgrounds, processes and outcomes; (b) how are solutions proposed for current challenges; (c) which processes facilitate and inhibit innovation and knowledge transfer; (d) what are the characteristics of different actors and an understanding of power dynamics (e.g., funders × research institute × food SMEs; complex or simple, ambiguous × clear).
NVivo 12 software was used to assist in data management in terms of classification and organisation and subsequent qualitative content analysis. The evidence was organised into macro codes for each level of the framework, as shown in Table 4
Identifying the generative mechanisms for knowledge transfer on each project was an iterative process of constant matching of what was found, the broader context, theoretical constructs found in the literature, and the emerging contextualised explanation. Dubois and Gadde [37
] explain this process as systematic combining, a process based on abductive reasoning. The process of systematic combining leads to directing and redirecting the search for more sources of information, and possible explanatory theories to reconstruct the most acceptable causal explanation [37
]. The cornerstone of systematic combining is ‘matching’ which means “going back and forth between framework, data sources and analysis” [37
] (p. 556), This process differs from the mainstream positivist literature where the researcher begins from propositions [42
] or a ‘tight and prestructured’ framework [43
] or follows specific steps from ‘getting started’ to ‘reaching closure’ [44
]. An abductive approach seeks the most plausible explanation among several alternatives. Abductively derived explanations require support from deductively (theory) and inductively (empirical) sourced evidence. Thus, it can be problematic due to the subjective nature of choosing between plausible alternatives.
Another limitation of abductive reasoning is that it presupposes the existence of theoretical frameworks that can explain the suggested generative mechanisms. These mechanisms, although firmly based in generally accepted theories, could only be inferred, but not tested. The theoretical frameworks are supposed to guide the researcher in their approach, as in the analysis. One limitation is that either those theoretical frameworks are lacking, or they are ill-suited, leaving the researcher vulnerable to biases or forced to use an ill-adapted theory. When a theory does exist, it is often insufficiently specified and rarely tailored to the problem at hand. In this study, although an engagement in theory through systematic combining was a significant contribution, there were parts of theories that were used to explain the various mechanisms, and not a single theory was found to be all-encompassing.
The type of mechanism was classified into different levels: situational, action-formation and transformational, as explained in the analytical framework in Figure 1
. The choice of the predominant mechanisms for each project involved abductive reasoning, where the evidence led to the formation of the most plausible explanation. For example, when it became clear that projects were affected by the UK research impact agenda at the structure level, it was necessary to look for further evidence as to why scientists responded to this structural constraint in different ways.
The next analytical stage is to link the generative mechanisms from the empirical findings to the extant body of theory to find a suitable conceptual framework, through the iterative process of systematic combining [37
]. A close examination of macro and micro theories allowed for the explanation of the various mechanisms. For the situational mechanisms, there was an investigation into macro theories that explain structural constraints and opportunities, whilst for the transformational mechanisms there was an investigation into macro theories that explain individuals’ interactions such as decision-making type of theories. The action-formation mechanisms were explained by micro theories that ranged from rational to more behavioural types. These theories aided the most suitable explanation for each mechanism and included, among others, the macro theories of compliance and trust, and the micro theories of rational choice and the theory of reasoned action.
The multilevel nature of the model encouraged more rigorous thinking about how certain theories might apply to multiple levels of analysis and about the potential boundary conditions of a mechanism approach. This framework is based on the macro–micro–macro model of social action proposed by sociologist James Coleman and is referred to as Coleman’s boat (or bathtub) [45
]. Coleman’s framework has been widely used in sociology to explain social interactions. Transposed to an organisational setting, this framework can be substantially informative regarding the micro foundations of the phenomenon under study.
The analytical framework comprises of three main levels: structure, agency and interaction, as shown in Figure 1
. The structure level unveils the situational mechanism related to the structural side of the project. Elements of the system including norms, rules and resources help to identify the structural constraints and opportunity for action. The agency level unveils the action-formation mechanism related to agency and the explanation of the actors’ behaviours and choices on the basis of their motivations and beliefs. The actors are the research leaders and scientists from the research institute and also individuals from the food SMEs involved in each project. The third analytical level is the interaction, where the transformational mechanism is abstracted, relating to the explanation of an outcome which unfolds over time, on the basis of the interaction and actions of different individuals.
This analytical framework promotes going beyond analysing relationships between phenomena exclusively on the macro level (arrow 4). It identifies the “situational mechanisms by which social structures constrain individuals’ action and cultural environments shape their desires and beliefs (arrow 1), describes the action-formation mechanisms linking individuals’ desires, beliefs, etc., to their actions (arrow 2), and specifies the transformational mechanisms by which individuals, through their actions and interactions, generate various intended and unintended social outcomes (arrow 3)” [39
] (p. 58).
Whereas correlational analysis involves identifying antecedents regularly conjoined with outcomes, mechanismic analysis consists of specifying the ‘mechanism’ that underlies and generates empirical regularities or outcomes. These mechanisms explain why a phenomenon happened, and knowledge of their operation allows results to go beyond correlations or the relationship between variables.
In much of the literature there is a proliferation of macro level constructs which can be problematic because the micro mechanisms that influence knowledge transfer and its outcomes are seldom identified. By grounding the knowledge transfer debate in a framework that bridges macro and micro levels, this study contributes to the emerging body of literature on mechanismic explanations [90
]. The focus on generative mechanisms in multiple projects marks an advance over earlier methodologies and theorising. Rather than employing vague notions of correlations between variables, the theorisation has been based on generative knowledge transfer mechanisms. The combination of micro and macro perspectives complements and adds to the knowledge transfer literature by delving into a deeper ontological layer. In addition, each mechanism is explained based on further theoretical perspectives and how these influence practice. By unpacking the microfoundations, the interactions and the macro influence on each knowledge transfer project, this study provided an in-depth mechanismic explanation that adds to previous research.
At a macro level, structural conditions influenced each of the projects differently. Whilst incentive has been viewed in the literature as either carrot or stick [46
] and as a tangible resource [15
] predominantly from an economic angle (better pay, promotion, bonuses), this study positions nonpecuniary incentive as a generative mechanism driven by social norms and social cohesion. The implications of this view are that a focus on social relationships and interpersonal interactions are more important than financial rewards, which is an outlook shared with studies on how organisational climate affects subjective norms [93
]. Similarly, reputation is a generative mechanism which is directly related to the international reputation of the research institute. This mechanism has influenced the application of the project as a driving or enabling mechanism, which differs from current literature that sees reputation at an individual level as a motivation to engage in knowledge transfer activities [20
]. This view of reputation as an enabler has direct implications regarding the perceived importance of institutes or organisations as a whole rather than the reputation of individuals. Opportunity is a mechanism that was driven by a new national regulation that food manufactures had to comply to. This is not a mechanism explored in current literature, however, opportunistic elements such as having the right facilitating conditions [94
] has been seen as an opportunity to engage in knowledge transfer.
The micro generative mechanisms are closely linked to scientists’ intrinsic motivations to engage in knowledge transfer and often play a prominent role to drive knowledge transfer activities. It is argued that scientists’ entrepreneurial commitments are driven by rational and relational-type generative mechanisms which are rooted in individual’s motivations and beliefs and can be explained by different micro theories. Instrumental rationality is derived from the willingness to be nearer the market and to access technologies and products from practitioners, whilst self-interest comes from the motivation to have an impact on wider societal issues and strategic calculation comes from the exploitation of a commercial opportunity. This resonates with studies such as Lam’s [21
] “puzzle”, which refer to the satisfaction derived from puzzle-solving activities but also from contributing to the knowledge of society and from prosocial behaviours such as mission [22
]. Similarly, Ramos-Vielba et al. [95
] find that intrinsic motivations are important in their analysis of the motivations and barriers to scientific research groups’ cooperation with firms and government agencies in Spain. They derive three categories of motivations: advancing research, applying knowledge and accessing financial resources.
Transformational mechanisms show the interactions among individuals and have several implications. Aggregation meant that individuals had to adapt to each other’s styles of decision-making, which means persuasion skills and sensitivity to others’ value systems are important implications for management. Learning means a sense of belonging and group identity are important, whilst adaptive self-regulation means that autonomy and decentralised control facilitate knowledge transfer. The implications for these kinds of independent interactions are that communication channels should be transparent and well-defined roles and tasks help with clarity and effective execution.
There is a direct implication for organisational rules and policies. By understanding the generative mechanisms that drive knowledge transfer, it is possible to design organisational rules and policies that are more effective. Identifying these mechanisms not only provides evidence for policies, but also distinguishes generic factors from those that arise from unique projects. This, in turn, should lead to more informed contributions to academic–industry relationships and, arguably, to more effective support for knowledge transfer between scientists and industry.
Using this approach, this research finds that a construction of relationships based around social norms, autonomy and relatedness are more optimal in science–industry knowledge transfer relationships than a focus on financial incentives or transaction cost theories [62
]. Scientists draw from social norms and act in an instrumental way to solve problems. Considering how organisational and national policies support or undermine the norms of self-interest and nonpecuniary incentives could offer more satisfactory knowledge transfer results. A reliance on solely improving access to funding is likely to be of limited effectiveness in increasing science–industry engagement, whereas an increased emphasis on tackling research compatibility may be more fruitful.
Reputation as a mechanism for knowledge transfer reinforces the idea that the process is highly dependent on the relevance and quality of research that scientists develop and also reflects the importance of trust in social interactions and the strong influence of the relationships that scientists establish within and outside organisational boundaries. One of the implications for reputation as a mechanism for knowledge transfer is that it carries a visible perceived status. In fact, reputation is part of the class of intangible assets identified as social approval assets, because they derive their value from favourable collective perceptions.
Public policy often seeks evidence-based research findings. Typically, researchers carry out experiments or surveys. Although these studies provide useful outcomes, they do not identify the mechanisms that explain the outcomes. Identifying the mechanisms, whilst also distinguishing generic factors, should lead to more informed contributions to public policy making and, arguably, to more effective support for knowledge transfer between scientists and industry. For example, the UK Research Councils assess researcher progress and performance. One of the items within their assessment relates to the research organisations’ achievements in knowledge exchange and commercialisation (KEC). KEC has a strong focus on direct financial impact through commercialisation and support in economic competitiveness. From the findings in this study, a strong focus on economic competitiveness might work against KEC activities unless nonpecuniary incentives such as relevance, morality and status are also included. Other implications for policy are related to strategies that enhance scientists’ autonomy, relatedness and competence, which would offer better outcomes. Thus, providing choices of projects with meaningful rationales for the application of their science could improve knowledge transfer. Another key aspect for autonomy is the minimisation of control. It can be argued that an organisational environment that focuses on applied science could enhance the perceived relatedness need. Furthermore, strategies to enhance competence could involve subject familiarity and exposure to industrial communities.
This study represents the first attempt to systematically analyse, from a mechanismic perspective, how knowledge is transferred from science to industry. This mechanismic view integrates a multilayered framework which offers direct implications for organisational policy. Whilst the traditional view of scientists as producers of scientific discoveries is outdated, there is a reluctance to see them as pure commercialisers, who pursue commercial activities mainly to obtain the much needed funding for research in an increasingly resource constrained environment. Evidence based on the interviews suggests that the position and motivations of scientists are not fully determined by their commercial orientations, but have nonpecuniary and socially related drivers which influence their efforts.
Drawing on social psychology theories, this study offers important insights into the social and personal mechanisms driving the knowledge transfer and the commercialisation behaviour of scientists. These mechanisms have been recognised by social psychologists as a pervasive and powerful driver of human action, but they are neglected in much of the existing research on academic entrepreneurship. This study suggests that a fuller explanation of scientists’ commercial behaviour will need to consider a broader mix of mechanisms that goes beyond economics and extrinsic rewards to include nonpecuniary, social and personal aspects related to intrinsic motivation.
Fostering an incentive that gives value to sharing behaviours is likely to increase the mutual social exchange relationships that are apparently important in driving knowledge transfer intentions. It can also be argued that providing a work environment characterised by high levels of organisational citizenship would support the formation of robust communities within research organisations, consequently supporting the social norms of sharing.
Knowledge transfer is a socially situated activity, therefore individuals’ motivations and beliefs (action-formation mechanisms), interactions (transformational mechanisms) and their environments (situational mechanisms) are important elements in understanding this process. The importance of the social context helps to explain why individuals get involved in knowledge transfer. Mechanisms such as nonpecuniary incentives, self-interest and strategic calculation show that individuals engage in knowledge transfer if there are social norms in place, if they are sending or acquiring knowledge from similarly reputable partners, and are operating in a culture that encourages sharing. It can be argued that these mechanisms help develop a sense of ownership whereby scientists feel a personal affinity to the knowledge transfer process effort and are committed to its success. Scientists’ personal interest in knowledge application also appears to strengthen a strong professional conviction to make their knowledge socially relevant. Therefore, an organisational environment conducive of transparency with a focus on scientists’ belief systems is more like to be successful than a focus simply on industry engagement.