Peculiarities of industry-academia knowledge relations

Industry-academia relations and related knowledge exchange processes have been of special importance in the academic literature of learning and innovation in general, as well as of RIS in specifically.⁷⁹ For several decades, technology policy has aimed to link industry and academia more closely and to intensify interaction and cooperation between the two worlds.⁸⁰ Several objectives and factors are linked to intensified industry-academia relations: 1) the economic exploitation of public investments in higher education through spillovers on the private sector, 2) the increase of firms’ competitiveness and innovativeness, especially in the knowledge-based economy and science-based industries, respectively, by gaining access to scientific knowledge and other kinds of resources, as well as 3) public budget constraints that require universities and non-university R&D institutions to seek alternative external sources of income resulting in increased research activities financed by the private sector (Polt et al., 2001).⁸¹ Polt et al. (2001) have developed a heuristic model, which distinguishes three influencing determinants of industry-academia relations (see Figure 8):

1) The structure and performance of the private sector (e.g. size, industry and R&D intensity) and the scientific institutions (e.g. types of research institutions such as universities, technical universities and joint industry-university labs, as well as research focus) represent the supply and demand of knowledge, respectively. Affected by existing incentive structures and obstables, the coherence of demand and supply structures determine the scope and form of industry-academia linkages.

2) Framework conditions for industry-academia relations that comprise the legal and regulatory environment, institutional incentives and barriers, regulations of talent mobility and training, as well as public support programmes, intermediary organizations and structures either promote or harm industry-academia linkages. Especially the effects and mechanisms of specific public support programmes and intermediary

79 However, it is also acknowledged that science only is one source of firms’ innovation active ties. Among others, internal knowledge (e.g. personnel and R&D activities) and market relations (e.g. clients, suppliers, start-ups and customers) also are important sources of novel knowledge in the complex and multi-dimensional model of innovation (Polt et al., 2001; Kujath & Schmidt, 2010; Gilsing et al., 2011).

80 Governments worldwide support industry-academia knowledge interaction through R&D subsidies, among others (Hewitt-Dundas, 2013). For instance, the German national government has supported formal univer-sity-industry cooperation under the label of so-called Verbundforschung since 1984 (Schmoch, 1999).

81 The concept of the entrepreneurial university linked to the necessity of universities to embody a more en-trepreneurial role in the learning economy also underlines this aspect (Etzkowitz & Leyesdorff, 1997;

Bercovitz & Feldman, 2006).

structures have been subject of extensive research (e.g. Bozeman, 2000; Agrawal, 2001; Polt et al., 2009).⁸²

3) Performance indicators measure the scope and the various dimensions of knowledge interaction between for industry and academia (e.g. informal, formal and HR links).

Figure 8: Determinants of industry-academia relations

Source: Polt et al. (2001, p. 251)

Overall, the literature has stressed various significant barriers and obstacles impeding industry-academia knowledge relations and the sharing of knowledge (e.g. Ponds et al.,

82 The objectives of instruments and organizations promoting industry-science relations can be summarized as follows: 1) information services about the supply and demand of knowledge and other resources of in-dustry and academia, 2) initiation of informal and formal interaction, and 3) financial support of formalized cooperation projects, talent exchange and spin-off activities (Kröcher, 2005). For example, public support programmes provide financial incentives to businesses and scientific entities to overcome existing barriers in order to foster cross-institutional knowledge exchange. Through the government funding in particular SMEs are able to leverage their R&D funding (Polt et al., 2009; Perkmann et al., 2011).

2007; Boschma & Frenken, 2010).⁸³ Already in the description of institutional proximity in Chapter 2.2.5, I have underlined several fundamental institutional dissimilarities reflected in a lack of shared visions, diverging goals and motivations, varying nature of products, different incentive structures, as well as unequal work practices that hinder the successful creation and realization of industry-academia relationships. Subsequently, the main barriers of productive industry-academia knowledge interaction that have been identified in the literature are summarized.

Firstly, the objectives and the nature of end-products of businesses and academia typically differ from each other. Firms aim to develop concrete applications and technologies in terms of marketable products and services, but also innovative approaches to problem solving. In contrast, academic work usually is complex, ambiguous, and abstract. Scientists aim to develop new knowledge about scientific concepts, models, empirical findings and methodological techniques, which can be disseminated in the academic community.

Whether the research outcome has got a commercial value often is not of importance (Cyert & Goodman, 1997). Consequently, a firm’s motivation typically diverges from the interests and incentive schemes of universities and other research institutions. Whereas businesses seek to commercialize exclusive technological knowledge in terms of new innovative producs and processes as quickly as possible in order to create economic profits and competitve advantages, universities aim to contribute to the public knowledge domain and increase their academic reputation via the dissemination of obtained research results in publications (Gilsing et al., 2011). Furthermore, firms usually operate in a short-term deadlines based environment, as they develop and produce new products and services in response to market forces. In contrast, academic work often is not bound to specific deadlines and much time can pass between project initiation and product development (Cyert & Goodman, 1997).

Secondly, cognitive distances in terms of different or unrelated knowledge bases and the lack of absorptive capacity also is detrimental to effective communication and, in turn, knowledge sharing. In this respect, the knowledge provided by scientific institutions has to be of value to firms and has to meet their specialized knowledge demands, respectively.

Thus, too general and theoretical scientific knowledge, which lacks sufficient specificity and

83 Generally, it can be distinguished in four types of obstacles harming interaction between firms and scientific institutions: 1) barriers of not knowing (e.g. information deficits about knowledge, availability of resources and contact persons), 2) barriers of not being able (e.g. lack of absorptive capacity, as well as financial, or-ganizational and methodological resources), 3) barriers of not wanting (e.g. prejudices, high costs, status quo thinking and other priorities), and 4) barriers of not being allowed (e.g. lack of flexibility due to rigid or-ganizational structures and decision-making processes) (Saxony Economic Development Corporation, 2008; Polt et al., 2009).

can easily obtained from other sources, may impede knowledge relations between firms and academia (Polt et al., 2001; Gilsing et al., 2011).⁸⁴

Thirdly, a lack of personal contacts to similar, complementary and diverse knowledge sources harms the probability of access to new relevant knowledge and ideas. Personal links, based on mutual experiences, are often reliable sources for industry-academia knowledge relations. A large variety of such ties increases the accessibility of appropriate external knowledge (Battistella et al., 2016). Closely linked, a lack of trust is an additional major reason collaborations fail in general. Trust between involved actors entails several dimensions, as outlined in Chapter 2.2.2.⁸⁵ Also in industry-academia relations, trust is fundamental to create openness for the sharing of tacit and confidential knowledge (Nooteboom, 2002).

Fourthly, the risk of unintended knowledge spillover is considered another major obstacle that inhibits firms from engaging in knowledge transfer with academia. This risk tends to be even greater than in collaborative activities with (competing) firms. Knowledge interaction with research institutions and universities does not lead to direct free-riding as such, but leaked information and knowledge to academia are very likely to be disseminated publicly, for instance, within scientific networks (Gilsing et al., 2011).⁸⁶

Fifthly, a lack of or ineffectively performing knowledge transfer services and instruments provided by specialized intermediary organizations are identified as additional obstacles of successful knowledge interaction between firms and academia (Saxony Economic Development Corporation, 2008; Polt et al., 2009).⁸⁷ As an illustration, Schmoch (1999) and Fritsch et al. (2007) have found that German university technology transfer offices (TTO) predominantly do not meet the expectations in improving knowledge exchange between universities and industry. Instead of being actively engaged in university-industry

84 However, distinct scientific areas appear to converge well with specific industries and economic sectors.

Applied and engineering-related research is strongly integrated in various engineering-related industries, but also basic research is strongly applied in industries characterized by a prevalence of basic research, for example, in pharmaceuticals and chemicals (Gilsing et al., 2011).

85 Accordingly, trust refers to the belief towards the partner’s honest motivations and fair actions (i.e. accuracy of information and willingness to share required information), as well as in the partners’ capabilities to meet the defined obligations.

86 As a consequence, confidentiality and defined regulations regarding intellectual property ownership often are mandatory in such inter-organizational cooperation (Van Looy et al., 2003).

87 In the literature, primarily public and university-based TTO, regional development agencies, Chambers of Industry and Commerce (CIC), Industrial Liaison Offices, STPs, incubators and innovation centres are re-garded as intermediaries between the private sector and the scientific community (Youtie & Shapira, 2008;

Battistella et al., 2016). Additional instruments and services assisting in knowledge exchange processes between industry and academia are outlined in the next chapter and for the STPs examined in Berlin and Seville in Chapter 3.1.

technology and knowledge transfer processes (including patenting and licensing), most of them primarily focus on PR activities in practice. Also, TTO often lack the capabilities and experience in working with industry. In addition, the weak performance as active knowledge transfer intermediaries is primarily allocated to limited resources regarding TTO’s staff and budget in relation to the often very large scope and high quantity of research carried out at universities.⁸⁸ Also, the effectiveness of public programmes promoting industry-academia linkages appears to vary significantly. A programme’s design, implementation, management and underlying mechanisms to overcome specific barriers in distinct compositions of industry-academia linkages are identified as success criteria, which are specific to each public programme (Polt et al., 2001). Figure 9 illustrates the anticipated role of specialized third actors and settings such as TTO and public support programmes as facilitators of knowledge sharing processes between the private sector and scientific institutions, often installed within the framework of territorial innovation, technology and economic development policies.

Figure 9: Model of direct and indirect industry-academia knowledge relations

Source: Author (based on Saxony Economic Development Corporation, 2008)

88 Siegel et al. (2003c) have also underlined the need to improve the university TTO’s management (including the reward system) by enhancing the staff’s capabilities and adapting related university policies. In addition, Polt et al. (2009) have emphasized that the tasks and responsibilities of TTO in Germany have to be up-graded beyond a brokerage function.

Yet, despite fundamental discrepancies between businesses and scientific institutions in general, the probability of successful industry-academia relations also depends on additional criteria, which are linked to the specific characteristics of firms and industries, as well as scientific institutions (Polt et al., 2001).⁸⁹ Thereafter, various studies have found that start-ups, of which many are assumed to be academic spin-offs, and large firms with larger resources by trend are more likely to take advantage of interactive links to public research institutions (Cohen et al., 2002; Fontana et al., 2006; Polt et al., 2009). In terms of economic sectors and industries, Schartinger et al. (2002) have stressed that SME-dominated industries, as well as industries with high R&D intensity and high talent mobility show a higher probability to engage in knowledge interaction with academia. More specifically, especially R&D intensive manufacturing industries and technical sciences tend to realize direct research cooperation in particular, while service industries and social and economic sciences have the tendency to take advantage of talent mobility and training-related interactions more intensely. Furthermore, multiple scholars have underlined inter-industry differences in manufacturing industries regarding the impact of public research on industrial innovations. Thereafter, public research is an important source for new and innovative products in the pharmaceutical, petroleum, steel, machine tool, semiconductor and aerospace industries in particular. In terms of the impact of specific scientific fields, chemistry has a strong impact on R&D and new products in food, petroleum, metals and chemical industries, biology primarily in pharmaceuticals, and physics predominantly in the semiconductor industry. Computer sciences, engineering and mathematics are found to strongly contribute to R&D activities in a broad set of manufacturing and engineering-related industries, for example, automotive, aerospace and computer industries (Cohen et al., 2002; Mowery & Sampat, 2005; Polt et al., 2009; Gilsing et al., 2011).⁹⁰ In terms of university-industry ties in particular, university departments in natural sciences, technical sciences, agricultural science and economics tend to show higher levels of interaction with the private sector than those in medicine, social sciences or the humanities (Schartinger et al., 2002).

89 For the former, the Saxony Economic Development Corporation (2008) has distinguished four general types of SMEs’ readiness towards knowledge interaction with academia in general; 1) SMEs geared to-wards knowledge transfer, 2) SMEs interested in knowledge transfer, 3) SMEs not interested in knowledge transfer and 4) non-innovators.

90 The pharmaceutical and chemical industries are characterized by basic research and a strong dependency on scientific knowledge, for example, in biology, chemical engineering, chemistry and medical science. In this case, especially scientific publications, patent texts, academic spin-offs and consultancy by academic staff are important modes of knowledge transfer. In contrast, engineering-oriented industries, which are primarily characterized by applied knowledge, scientific partners only are part of a broader portfolio of ex-ternal knowledge sources, also including suppliers and customers. In this case, industry-academia knowledge exchange is primarily realized through joint R&D projects, participation in conferences, profes-sional networks and the hiring of PhD graduates (Gilsing et al., 2011).

2.3.3 Knowledge management in local and regional innovation systems