Underlying theoretical assumptions for this QCA are based on the innovation mode approach and regional innovation models such as regional innovation systems and relational approaches, and on earlier empirical findings of innovation research and our own findings from previous analyses of the same interview material. This is necessary practice for QCA as its causal claims rely on interpretation, which is based on triangulation with substantive empirical and theoretical knowledge (Rutten, 2020a). A core element of QCA is to analyze possible necessary and/or sufficient conditions for a specific outcome in order to reveal causal complexity. This paper aims to analyze innovativeness, which we define as “the implementation of a new or significantly improved product (good or service), or process, a new marketing method, or a new organizational method in business practices, workplace organization or external relations” (OECD 2005, p. 46). Selection of conditions expected to explain the outcome are guided by theory and former case knowledge, constituting an iterative process of model-building (Amenta & Poulsen, 1994; Greckhamer et al., 2018). Thus, for model-building, different ways of innovating are identified and described in the following, introducing the four condition variables expected to explain high innovation performance: learning-by-science, learning-by-doing, learning-by-using, and learning-by-interacting. The configurational rationale of conditions is explained as follows:
Learning in the STI Mode
According to Jensen et al. (2007), different processes of idea-finding and innovation processes exist: STI and DUI mode of innovation. Closely related to the knowledge base approach (Asheim & Gertler, 2005; Manniche, 2012), both leading to innovation performance.
The STI mode relies on production and exploitation of scientific knowledge usually codified and based on know-what and know-why. This analytical knowledge is usually developed by searching and researching (Manniche, 2012) at universities, by R&D departments, or in cooperation with research institutions (Johnson et al., 2002). Traditional innovation research often used patent or R&D investment data to measure learning-by-science (Grillitsch et al., 2019). However, current research shows there are further mechanisms used to integrate scientific knowledge into innovation processes, like seeking analytical knowledge through trade magazines or scientific journals, training employees or integrating academics, up to R&D collaboration with research organizations (Alhusen & Bennat, 2020). Thus, learning-by-searching is not only tied to internal R&D departments, high-tech sectors or larger firms. It is also used by small and medium-sized firms. Rather, a firm’s absorptive capacity to learn from scientific knowledge and to innovate through an STI mode seems to be in the foreground. However, the STI mode of innovation has been generally associated with production of radical innovations (Nunes & Lopes, 2015).
Learning in the DUI Mode
In contrast, innovations in the DUI mode are based on the application of mostly tacit and synthetic knowledge with a focus on know-how and know-who (Jensen et al., 2007; Johnson et al., 2002). Learning is more informal and conducted through doing, using, and interacting. However, the definition and operationalization of the core learning mechanism of doing, using, and interacting are inconclusive. Jensen et al. (2007) proposed a holistic concept of the DUI mode, explaining that learning-by-doing and learning-by-using both “involve interaction between people and departments” (Jensen et al., 2007, p. 684). Nevertheless, most quantitative studies aim to measure DUI innovativeness based on a firm’s internal or (more commonly) external interactions (Apanasovich, 2016), using indicators of either learning-by-doing, learning-by-using, and learning-by-interacting as representative for the DUI mode of innovation (see for overview Alhusen et al., 2019; González-Pernía et al., 2015; Parrilli & Heras, 2016). Nevertheless, the learning mechanisms of DUI differ in many aspects (e.g., actors involved, firm-internal and firm-external processes, and usefulness at different stages of innovation processes).
Therefore, it is worth breaking the DUI mode into its core learning mechanisms, according to the detailed definition of what constitutes each learning facet suggested by Alhusen et al. (2019):
Learning-by-doing is defined by learning from experienced workers as well as organizational structures fostering employee involvement in innovation processes (Arrow, 1962; Thompson, 2010). However, not only formal organizational structures but also informal institutions like openness to learn from trial-and-error or an innovation-friendly culture influence learning-by-doing (Bennat, 2020). It is strongly associated with firm-internal interacting (i.e., knowledge creation and sharing mechanisms inside a firm). Firm-internal interacting is therefore conceptually close to learning-by-doing but is sometimes considered a separate learning process in the literature (Apanasovich, 2016). However, we conflate these two mechanisms in order to emphasize the differentiation between firm-internal and -external learning.
Learning-by-using is defined as learning from customers or final users of a product or service who report the experience of using the product or service (Rosenberg, 1982), or who approach a firm to invent a product or service aligned with their specific needs (Alhusen et al., 2019). Such feedback provides the basis for knowledge accumulation and innovation opportunities from outside the firm. Firms use this learning mechanism to modify or re-design existing products/services or to develop new ones (Alhusen et al., 2019; Rosenberg, 1982). Thus, integrating users can vary across a spectrum from “just stating an idea” to “active involvement in the innovation process and cooperation.”
Learning-by-interacting is the product of firms’ external interactions with suppliers, competitors, firms from other sectors, consultancies, or industrial associations (Alhusen et al., 2019; Apanasovich, 2016; Johnson, 2010). Thus, external interaction captures all external, non-science-based actors who are not customers. This interaction includes informal and formal exchange of ideas and cooperation in innovation processes.
Innovation outputs of the DUI mode are often new customer-specific products or incremental in nature due to cost reductions or quality improvements (von Hippel, 2005).
The Configurational Model of High Innovativeness
Since the seminal paper of Jensen et al. (2007), the main tenet of the literature on innovation modes is that a combination of both modes leads to higher rates of innovation output (Apanasovich et al., 2016, 2017; Chen et al., 2011; Fitjar & Rodríguez-Pose, 2013; Fu et al., 2013; González-Pernía et al., 2015; Jensen et al., 2007; Nunes & Lopes, 2015; Parrilli & Heras, 2016; Thomä, 2017). Also, the literature on innovation collaboration mentions that various partners may provide different types of knowledge, enhancing firms’ innovation potential (Bennat & Sternberg, 2020; Cooke, 2012; Strambach & Klement, 2012). Combining scientific and supply-chain synthetic knowledge thus fosters firm-level innovativeness, and different knowledge types are mostly regarded as complementary. However, Haus-Reve et al. (2019) criticize that those studies only focus on additive rather than multiplicative effects of combining STI and DUI. Their analysis of Norwegian firms revealed a negative interaction between scientific and supply-chain collaboration for product innovation, implying that they are substitutes rather than complements. These findings challenge the dominant tenet asserting the benefits of combining different knowledge types. Nevertheless, their analysis only includes collaborations with actors having different knowledge bases, influencing product innovation. It remains unclear whether a combination of DUI and STI learning mechanisms (which are more than collaborations as discussed in the former section) will also point in the same direction. Former cluster analyses report that the combination of innovation modes is connected with higher levels of innovation performance. However, its definitions, the indicators used (especially for DUI), and interpretations still differ. In sum, multiple ideas exist regarding what constitutes a combinatorial innovation mode.
There is a scarcity of studies that could answer the question: Which concrete learning mechanism contributes to high innovativeness? Again, it is worth differentiating between the learning mechanisms of DUI due to their substantial differences in actors involved and applied innovation micro-processes (Alhusen et al., 2019). However, as the original idea of the DUI mode is a holistic view of mechanisms, we expect a strong interdependence between learning-by-doing, learning-by-using, and learning-by-interacting. This expectation is also based on our previous studies of the analyzed SMEs, indicating that ideal types of innovation modes hardly exist in practice. This aligns with Isaksen and Karlsen (2010), who argued that innovation modes are not found in pure forms (Aslesen & Pettersen, 2017; Holtskog, 2017), but it is unclear whether this implies that “doing more of all” is a successful strategy for innovation in SMEs (Haus-Reve et al., 2019).
Based on these theoretical concepts and previous empirical research, our research framework posits that high innovativeness depends on four learning mechanisms (learning-by-searching, learning-by-doing, learning-by-using, and learning-by-interacting) implying the following general propositions:
Disparate configurations of conditions are equifinal in explaining high innovativeness.
The same condition can either foster or inhibit high innovativeness, depending on how it is configured with other conditions.
Innovation processes are therefore complex, while micro-processes seem to influence each other. Nevertheless, there exists a bias in theory and policy-making that neglects innovation developed through a DUI mode, which may partly be explained by the STI focus on innovation measurement (Jensen et al., 2007; Laestadius, 1998). This can be observed, for example, in the trend of technology transfer activities, the continuous improvement of R&D infrastructure, and political trials to connect DUI firms with STI partners to increase their innovation output (Cooke, 2014; Isaksen & Karlsen, 2010). Without an internal R&D department, learning-by-science is less likely to occur (Amara et al., 2008; Cohen & Levinthal, 1989). Therefore, the integration of STI into non-R&D firms is an important goal of current innovation policy, actively effecting innovation processes (e.g., BMBF, 2018). Furthermore, state-financed regional innovation consultancies can be important interacting partners in DUI mode innovation processes: giving advice for improving firm-internal innovation processes, establishing connections with other actors, counseling during funding applications and increasing firms’ visibility through hosting innovation awards and network events (Alhusen et al., 2019).
Hence, we assume that political authorities have been—and are increasingly—designing regional framework conditions. This assumption aligns with the literature on regional innovation systems (RIS) (Asheim et al., 2016): highlighting the role of regional policy in innovation processes, research from this field calls for tailor-made support strategies, recognizing the existing regional innovation structure (Martin et al., 2011) and its historical contingency (Asheim et al., 2011). Furthermore, the given R&D infrastructure, as well as regional financial incentives and subsidies, does differ between regions. This is also true for regional facilitators, competencies and networks. That means, being embedded in a specific region, firms’ locations may also influence innovation processes, strategies and finally, the applied bundle of learning mechanisms. But it is not geographical concentration alone that might explain regional innovation processes. Rather, its conceptual connection with social spaces manifested in institutions (Lenz & Glückler, 2020), networks and communities might complete the argument of regional innovation. According to the relational approach to economic geography, the focus on micro-level interactions of individuals as principal agents of knowledge creation highlights the connection of social and physical spaces (Bathelt & Glückler, 2018). From this relational perspective, location determines access to local and global knowledge. For example, at research centers, campuses, conference venues or cultural facilities, physical and social spaces become connected through the co-presence of individuals, allowing the exchange of tacit knowledge through face-to-face communication (Rutten, 2017). Thus, hosting those venues, a diverse economic and social-culture and further characteristics of social spaces like shared norms, values, routines and trust informally coordinate the mechanism of knowledge exchange. Clearly there are different approaches to explaining regional innovation. However, they all share knowledge exchange, and thus, innovation processes might differ between regions. Therefore, we assume these regional differences are also visible in configurations of conditions, ending in Proposition 3: