Abstract Many studies have attempted to investigate the potential benefits of open innovation. However, the long-term effects of openness have yet to be demonstrated, even if few researchers hypothesized that high openness could increase firms’ dynamic capabilities and hence their resilience in the face of adversities, such economic downturns. Hence, this article attempts to investigate this dynamic relationship between openness and firm performance with particular considerations addressing the recent financial crisis in 2008. Based upon the UK Community Innovation Survey (CIS) panel data collected between 2006 and 2012, this study finds evidence that supports the positive influence of openness on long-term firm performance. The results show that (i) increasing a firm's openness is an effective way of enhancing its dynamic capability and hence its resilience, and (ii) of all the various configurations of openness, the collaboration with partners outside the firm's value chain and international partners has the highest impact on turnover recovery, as they will increase the chances of acquiring newer knowledge, which in turn will help firms to identify new opportunities to achieve sustainable growth. The findings of this article have some practical implications for managers and policymakers. 1. Introduction In recent years, a substantial number of firms, from large to small, have been attracted to the application of open innovation (OI) (Van de Vrande et al., 2009, Chesbrough and Brunswicker, 2013, Spithoven et al., 2013). Since Chesbrough first defined the paradigm in 2003, OI has, as a result, become an important research topic in the field of innovation management (Dahlander and Gann, 2010, West et al., 2014). The majority of early OI studies have focused on illustrative examples of OI adoption and implementation, while recently the focus has been on understanding OI benefits and characteristics by analyzing large-scale data sets (Schroll and Mild, 2012, Podmetina et al., 2014). Researchers have followed the phenomenon closely (Dahlander and Gann, 2010) and started to evaluate the impact of OI expanding the level of analysis, from that of the firm to that of R&D projects (Du et al., 2014) and public sector level (Lee et al., 2012). Through exploring and exploiting external knowledge and collaborating with various innovation partners, a firm embracing OI may enjoy many benefits, such as the access to necessary complementary assets, the maximization of the return of investment from intellectual property (IP), the acceleration of innovation processes, the attraction of new customers, and establishment of new technology standards (West and Gallagher, 2006, Dahlander and Gann, 2010, Savitskaya et al., 2010), and these benefits are not confined to a firm level. As noted by Roper et al. (2013), the level of openness in the firm’s industry can even stimulate the firm’s openness, which suggests the positive externality of an open approach in innovation. Recognizing these virtues, it is highly probable that firms actively adopt open strategy to leverage external knowledge sources or internalize the externality in a benign environment and in periods of available slack (O'Brien, 2003). However, it is difficult to predict whether firms are still willing to pursue open strategy during an economic downturn. On the one hand, due to resource constraints and high uncertainty, it is not easy for firms to strongly engage in various innovations in a turbulent environment, but on the other hand, it is also true that OI enables firms to adapt to a dynamic environment (Du et al., 2014) and to achieve a sustainable growth (Chesbrough, 2003). In fact, open strategy can be the source of firms dynamic capabilities (Teece, 2007), which may be even more important when turbulent environments require substantial changes to organizational routines. The recent global financial crisis, generated by the failure of the US sub-prime market, is an example of a turbulent context that would require firms to reconfigure their innovation strategies for their survival. The adoption of an open approach has been suggested as a way of coping with such a strong external perturbation. The case studies of Chesbrough and Garman (2009) and Di Minin et al. (2010) illustrated how firms can seize future opportunities for revitalizing innovation capabilities by releasing non-core knowledge and establishing new partnerships, and a longitudinal analysis are needed to reinforce and further investigate their initial observations. To meet this need, this study aims to explore the longitudinal impact of openness, its linkage to dynamic capability, and innovation resilience of firms using data from the three waves of the UK Community Innovation Survey (CIS) encompassing the period of the recent economic downturn. This article focuses on manufacturing firms, as it is assumed that innovation patterns may be different for service firms. Manufacturing firms have been prioritized as the impact of OI should be more evident as these firms typically benefit from external information sources, while service firms are less likely to engage in formal R&D activities and hence depend upon a more limited range of innovation sources (Mol and Birkinshaw, 2009). The remainder of this article comprises five sections. The first describes the theoretical background and develops hypotheses focused on the relationship between openness proxies and firm performance. The data and method are then described in Section 3 and the results and discussion follow thereafter. The article concludes with the implications and limitations. 2. Theoretical backgrounds and hypotheses 2.1 Openness, changing routines, and dynamic capabilities Dynamic capability refers to the firm's capacity to reconfigure resources in response to changes in the environment (Teece and Pisano, 1994, Teece et al., 1997). Incremental innovation occurs at all times, and firms might need dynamic capabilities even in a buoyant period to accelerate innovation and adapt to small changes in the environment. However, qualified posture may be more favored in a prosperous environment. As noted by Teece (2007), a firm’s success will contribute to the creation of an organizational routine. A routine can be defined as a smooth sequence of coordinated behavior (Nelson and Winter, 1982), grammars of action (Pentland and Rueter, 1994) or a patterned sequence of learned behavior (Cohen and Bacdayan, 1994), and a well-established routine enables firms to avoid risks by following these verified procedures (Cyert and March, 1963, Argote and Greve, 2007). Firms can reduce the number of further deliberate choices by confining their organizational behavior to well-developed channels (Nelson and Winter, 1982). Thus, by following the verified routine firms can stably enjoy the outcome of innovation while minimizing uncertainties, which can be considered as an effective strategic posture in a benign environment. Yet, it is hard to deny that dynamic capabilities will play a more vital role in a turbulent environment (Teece et al., 1997). In a turbulent and uncertain environment firms will be at risk of suffering, if they stick only to a tested and tried routine. A closed system may enable firms to harness the existing routine intensively, but they will lose their capability to engage in a new routine (Teece and Pisano, 1994, Teece et al., 1997). Hence, firms occasionally have to set up a new routine to adapt themselves to a changing environment, and increasing openness can be a good way of establishing dynamic capability. In fact, OI cannot be separated from firm strategy (Chesbrough and Appleyard, 2007), in that firms adopt OI to achieve corporate renewal and gain competitive edge (Vanhaverbeke and Cloodt, 2014). Searching and integrating both external and internal knowledge is one of the fundamental ways to harness and develop dynamic capabilities (Nonaka and Takeuchi, 1995, Teece, 2007). Because interaction with external knowledge and partners across firm boundaries triggers resource allocation and the reconfiguration of strategic posture, increasing openness provides a variety of new innovation routes to a firm (Ahn et al., 2015). In this regard, increasing or establishing an open strategy for innovation is an organizational routine change that should lead to the enhancement of a firm’s dynamic capability that innately assumes a certain level of change and strategic adaptation (Teece, 1996). 2.2. Increasing openness in a downturn Firms often change their strategic directions to adapt to a changing environment (Lawrence and Lorsch, 1967), and this can be more critical in particularly dramatic and uncertain circumstances, such as an economic downturn. The literature has showed that increasing openness is associated with the development of dynamic capabilities. For example, Almirall and Casadesus-Masanell (2010) suggested that a high level of openness can lead to better performance in a dynamic environment. Cruz-González et al. (2015) found that the effect of search depth—i.e., the extent to which firms draw intensively from different search channels or sources of innovative ideas (Laursen and Salter, 2006)—is positively significant in more dynamic and turbulent contexts. However, these fragmented findings may not be sufficient to answer whether the benefit of openness could be particularly valuable in to respond to exogenous economic shocks. An economic downturn, such as a recent global financial crisis, is an immense, uncontrollable external change, and given the situation, firms needed to display strong adaptive behavior to insure their survival. From the perspective of dynamic capability, a sustainable growth can be achieved by firms that will effectively and adequately steer their strategic directions. As shown in Figure 1, organizational routine change can be interpreted as a process of finding a new optimal equilibrium. In a downturn, firms can increase openness to cope with external challenges, strengthen in-house R&D, or simply cut employee and innovation investment under an austerity plan. These three scenarios are examples of a firm’s routine change for its organizational adaptation. Figure 1. View largeDownload slide Types of possible routine changes. Figure 1. View largeDownload slide Types of possible routine changes. Cutting the innovation budget may be one of the easiest ways of coping with an economic crisis, and in fact, during the recent economic recession, many firms severely reduced their investment in innovation (OECD, 2009, Filippetti and Archibugi, 2011, Paunov, 2012). However, firms are able to better cope with an economic downturn by rather expanding innovation activities instead (Archibugi et al., 2013). Archibugi et al. (2013) showed that most firms reduced innovation input, but highly innovative incumbents and fast-growing new entrants instead increased their innovation investment during the recent crisis, which eventually helped them to overcome the challenges of operating in a slow economy. Their results also suggested that both a strong internal R&D and the search into new market are significant predictors of an increase of innovation investment during the downturn (Archibugi et al., 2013). As noted by Chesbrough and Garman (2009), increasing openness can also be an effective approach for coping with an economic crisis. During the crisis, firms actively engaged in collaboration with new external partners displayed an increased organizational flexibility while preserving innovation capabilities for future growth (Chesbrough and Garman, 2009). For example, Fiat's core R&D organization, Centro Ricerche Fiat (CRF), did not simply reduce its internal R&D costs during an economic downturn (1993–2003) (Di Minin et al., 2010). Rather, CRF selected what knowledge to keep private and what to expose and then shared non-core technologies with external partners to generate additional income. CRF also established long-term strategic partnerships with customers and new partners to diversify the exploitation of their complementary assets (Di Minin et al., 2010). Such open approaches helped Fiat to avoid substantial reduction of R&D and innovation capability that would have impacted negatively the firm in a long term (Di Minin et al., 2010). As an open approach increases organizational flexibility by adding new innovation routes (Mortara et al., 2011, Vanhaverbeke and Cloodt, 2014, Vanhaverbeke and Roijakkers, 2014, Ahn et al., 2015, Ahn et al., 2017), firms can move onto a new equilibrium point that is more robust against external turbulence. Moreover, as openness enables firms to keep necessary innovation resources at arm’s length (Lichtenthaler and Lichtenthaler, 2009), this knowledge retention will prevent firms from losing innovation capability and better recover once an economic downturn finally ends (Chesbrough and Garman, 2009, Di Minin et al., 2010). However, while the various benefits of open approaches for innovation (e.g., accessing complementary assets) have been identified and proposed as enhancers of firms' resilience, the linkage between open strategy and resilience is still substantially unproven. 2.3. Heterogeneity of openness As noted by Cruz-González et al. (2015), this article assumes that openness in innovation might encompass very diverse approaches (i.e., it is heterogeneous). This suggests that Laursen and Salter's (2006) openness proxies, the breadth of the external search, the depth of the external search, and innovation collaboration may have different characteristics (Cruz-González et al., 2015). According to the definition of Laursen and Salter (2006), “search breadth” indicates the number of different external information sources, while “search depth” refers to the number of different external information sources upon which firms intensively depend. These definitions suggest that a key factor distinguishing these two concepts is intensity (Cruz-González et al., 2015). As absorptive capacity is required to understand and digest new external knowledge (Cohen and Levinthal, 1990, Spithoven et al., 2011, West and Bogers, 2014), access to various external information sources cannot simply be equated to the intensive utilization of external knowledge. The understanding of newly imported knowledge will demand substantial efforts for internalization in addition to search activity. Further, since a firm's existing knowledge is a basic reference point for understanding new external knowledge, the boundary of search will have a certain limit. The expansion of the search reaches a peak with an optimal “cognitive distance” and then inevitably decreases (Wuyts et al., 2005, Cruz-González et al., 2015). Firms might navigate far from their current knowledge domain, but this alone cannot enable firms to effectively leverage external knowledge. Intensive focusing and repetitive access would be necessary to introduce newness for an organizational routine change, but the concept of search breadth does not reflect this aspect. Consequently, high search breadth is apt to be a shallow search, which fits with superficial knowledge exploitation (Cruz-González et al., 2015). In contrast, search depth indicates intensive (i.e., repetitive) access to particular external information sources; thus it implies a substantial accumulation of specific knowledge (Cruz-González et al., 2015). The repetitive access enhances the understanding of a knowledge acquiring firm, who thereby might learn from even more cognitively distant knowledge that could not be obtained from a shallow search (Hsieh and Tidd, 2012). Therefore, search depth relates to the exploration leading to in-depth knowledge of new and distant information (Cruz-González et al., 2015). Collaboration constitutes a different openness dimension from search breadth and depth. Laursen and Salter (2006) treated collaboration as an auxiliary variable that confirms the effects of openness on innovation performance, but collaboration is innately different from a knowledge search. External information search (both breadth and depth) is a one-directional knowledge transfer (i.e., outside-in or in-bound). Therefore, it assumes the internal use of external knowledge (i.e., knowledge integration). However, collaboration is a more complicated process involving mutual interactions or even an organizational resource swap. Therefore, it is a coupled process involving a two-directional (i.e., inside-out and outside-in) knowledge transfer (Enkel et al., 2009, Ahn et al., 2013). This ambi-directional interaction distinguishes collaboration from external search activities. A firm can retain required knowledge or skills at an arm’s length by establishing a collaborative relationship (Lichtenthaler and Lichtenthaler, 2009). Accordingly, successful collaboration is a complex process requiring an additional capacity (e.g., connective or transformative capacity) on top of absorptive capacity (Zahra and George, 2002, Lichtenthaler and Lichtenthaler, 2009). Recognizing these aspects, innovation collaboration can be considered to be a higher degree of openness compared to the one-directional knowledge search. However, as in the case of search breadth and depth, the achievable level of collaboration may not be the same for all the types of collaboration partner. For instance, it may be easier to collaborate with a partner located inside a firm’s value chain (e.g., suppliers, customers, enterprise groups), since these actors share innovation processes in the value chain. However, by being bounded to a value chain that enforces knowledge and goal sharing, a firm might not find it easy to import substantially new knowledge. In contrast, it may be harder to collaborate with a partner located outside a value chain (e.g., universities and private research institutes) or an international partner. It is because of the time- and resource-consuming trust-building process (Narula, 2004, Oakey, 2013), or different cultures, regulations, or technology standards (Hottenrott and Lopes-Bento, 2014). However, collaboration with this type of partners can increase the chances of acquiring newer knowledge, in the sense that universities or research institutes usually conduct more scientific or long-term R&D projects, and the interaction with partners distributed across the globe enables firm to access location-specific knowledge (Hagedoorn, 2002). 2.4. Research hypotheses The early case studies and subsequent empirical analyses on OI have revealed the significant impact of this open approach on firms' innovation performance (Dahlander and Gann, 2010, Schroll and Mild, 2012, Podmetina et al., 2014), and we argue that an open strategy can contribute to the enhancement of a firm’s financial performance over time even during a downturn. The mechanism behind this reasoning is that an open strategy increases firms' managerial flexibility and provides new innovation opportunities by expanding knowledge stocks and network boundaries (Chesbrough et al., 2006). As such, a high level of openness will enhance a firm’s dynamic capability by helping its resource reconfiguration, which consequently enables it to cope with turbulent environment, such as an economic crisis. To benefit from an open strategy, firms have to increase the degree of openness that will be attained by their innovation activities, such as external searches and collaborations. Firms taking more extrovert approaches can better cope with an economic crisis by successfully establishing dynamic capability necessary for a routine change. As openness offers managerial flexibility (Ahn et al., 2015, Ahn et al., 2017), firms will be able to diversify their innovation routes without solely depending upon resource-consuming internal knowledge creation. This new routine and knowledge retention achieved by openness will enable firms to save resources on their innovation while preserving capacity to innovate after a downturn (Chesbrough and Garman, 2009, Archibugi et al., 2013). This resilience power will make firms better cope with a downturn and recover their performance after a downturn. Hence: H1: Increasing openness during the downturn will positively impact a firm’s financial performance recovery after the downturn. As an important dynamic capability, increasing openness may help firms to adequately reconfigure their strategic posture during and after a downturn. However, the effect of knowledge search breadth, depth, and collaboration may not be the same because the degree of newness that each type of openness will bring will be different. Recognizing this heterogeneity, we presume the level of openness increases from search breadth to depth, value chain, and outside value chain/international collaboration. Search depth may be a higher degree of openness than search breadth because of the repetition of access (Cruz-González et al., 2015), and collaboration may be higher openness than search due to its involvement in ambi-directional interaction. Similarly, collaboration with universities/research institutes (non-value chain partner) or international partners may provide a high level of openness because universities/research institutes conduct longer-term, science-based R&D and by means of international collaboration firms can realize cross-fertilization of technology using location-specific knowledge. New knowledge imported via higher-ranked openness will be used for firms to identify new technological opportunities and achieve new growth momentum. Hence: H2: Higher forms of openness during a downturn will lead to higher financial performance recovery after the downturn. 3. Data and method 3.1 Data To see the longitudinal effects of openness, this article analyzed the UK version of CIS data sets1 that were based on the Organisation for Economic Co-operation and Development's (OECD) Oslo Manual (OECD, 1997). The data sets include CIS 6, 7, and 8 that were collected at three different time points. CIS 6, 7, and 8 have a similar question structure that enables us to stack data. We aggregated the CIS 6, 7, and 8 data and then selected firms that participated in all three CISs using list-wise deletion. Consequently, 1440 observations of 480 manufacturing firms were selected for the analysis. To see the longitudinal effects of openness, difference terms were made by measuring changes between CIS 8 and 7 or CIS 7 and 6. CIS 6 was collected during the period between 2006 and 2008, i.e., before the crisis. CIS 7 was collected during the financial crisis period between 2008 and 2010, while CIS 8 was collected after the crisis between 2010 and 2012. As such, by measuring differences between CIS data (see Figure 2), this article attempts to investigate how strategic routine changes during the crisis (compared to before the crisis) affected the power of resilience of firms after the crisis (compared to during the crisis). As shown in Figure 1, a firm may choose one of the possible routine changes during the crisis, and this study aims to analyze the consequence of its selections: open strategy, closed approach (strengthen internal R&D), or implementing an austerity plan (employment cut). Figure 2. View largeDownload slide Research model. Figure 2. View largeDownload slide Research model. 3.2 Variables As shown in Figure 2, the key variables of this article are openness, closed approach (internal R&D), and employment cut (austerity plan). Detailed measurements of each variable are shown below, and overall illustrative and descriptive statistics are shown in Tables 1 and 2, respectively. Table 1. Variable illustration Variable name Meaning Measurement Tech level The level of technology (four levels): high, medium-high, medium-low, and low Two-digit SIC based on OECD classification Firm size The size of the company Natural logarithm of the employment number in CIS 8 Government Whether a firm received financial support from the UK or European Union government 0: not received, 1: received ΔInternal R&D The differences of internal R&D investment between CIS 7 and CIS 6 (unit: 1000 GBP) Internal R&D of CIS 7—Internal R&D of CIS 6 ΔS&T employee The differences of S&T employee portion between CIS 7 and CIS 6 The proportion of S&T employees of CIS 7−the proportion of S&T employees of CIS 6 ΔEmployee cut The decrease of employment of CIS 7 compared to that of CIS 6 Employment of CIS 6−employment of CIS 7 (negative coding) ΔSearch breadth The differences of the number of external information source utilization between CIS 7 and CIS 6 Breadth of information source of CIS 7−breadth of information source of CIS 6 ΔSearch depth The differences of the number of external information source highly used between CIS 7 and CIS 6 Depth of information source of CIS 7−depth of information source of CIS 6 ΔCollaboration The differences of the number of collaboration partners between CIS 7 and CIS 6 Breadth of collaboration of CIS 7−breadth of collaboration of CIS 6 Δ Value chain collaboration The differences of the number of value chain collaboration between CIS 7 and CIS 6 Vertical collaboration of CIS 7−vertical collaboration of CIS 6 ΔOutside value chain collaboration The differences of the number of non-value chain collaboration between CIS 7 and CIS 6 Horizontal collaboration of CIS 7−horizontal collaboration of CIS 6 ΔInternational collaboration The differences of the number of international collaboration between CIS 7 and CIS 6 International collaboration of CIS 7−international collaboration of CIS 6 ΔTurnover The differences of turnover between CIS 8 and CIS 7 (unit: 1000 GBP) Turnover of CIS 8−turnover of CIS 7 Variable name Meaning Measurement Tech level The level of technology (four levels): high, medium-high, medium-low, and low Two-digit SIC based on OECD classification Firm size The size of the company Natural logarithm of the employment number in CIS 8 Government Whether a firm received financial support from the UK or European Union government 0: not received, 1: received ΔInternal R&D The differences of internal R&D investment between CIS 7 and CIS 6 (unit: 1000 GBP) Internal R&D of CIS 7—Internal R&D of CIS 6 ΔS&T employee The differences of S&T employee portion between CIS 7 and CIS 6 The proportion of S&T employees of CIS 7−the proportion of S&T employees of CIS 6 ΔEmployee cut The decrease of employment of CIS 7 compared to that of CIS 6 Employment of CIS 6−employment of CIS 7 (negative coding) ΔSearch breadth The differences of the number of external information source utilization between CIS 7 and CIS 6 Breadth of information source of CIS 7−breadth of information source of CIS 6 ΔSearch depth The differences of the number of external information source highly used between CIS 7 and CIS 6 Depth of information source of CIS 7−depth of information source of CIS 6 ΔCollaboration The differences of the number of collaboration partners between CIS 7 and CIS 6 Breadth of collaboration of CIS 7−breadth of collaboration of CIS 6 Δ Value chain collaboration The differences of the number of value chain collaboration between CIS 7 and CIS 6 Vertical collaboration of CIS 7−vertical collaboration of CIS 6 ΔOutside value chain collaboration The differences of the number of non-value chain collaboration between CIS 7 and CIS 6 Horizontal collaboration of CIS 7−horizontal collaboration of CIS 6 ΔInternational collaboration The differences of the number of international collaboration between CIS 7 and CIS 6 International collaboration of CIS 7−international collaboration of CIS 6 ΔTurnover The differences of turnover between CIS 8 and CIS 7 (unit: 1000 GBP) Turnover of CIS 8−turnover of CIS 7 Table 2. Descriptive statistics Variable name Mean Minimum Maximum Sample number Tech level 2.25 1 4 480 Firm size 5.70 2.30 8.70 480 Government 0.18 0 1 397 ΔInternal R&D (1000 GBP) 546.12 −29,640.00 119,555.00 253 ΔS&T employee 1.2688 −70.00 78.00 398 ΔEmployee cut 24.3167 −1528.00 1449.00 480 ΔSearch breadth 0.8116 −10.00 10.00 207 ΔSearch depth 0.0188 −7.00 5.00 213 ΔCollaboration −0.0696 −7.00 7.00 345 ΔValue chain collaboration 0.0000 −3.00 3.00 317 ΔOutside value chain collaboration −0.0357 −4.00 4.00 308 ΔInternational collaboration 0.2742 −7.00 10.00 310 ΔTurnover (1000 GBP) 11,502.95 −224,505.00 1,293,123.00 480 Variable name Mean Minimum Maximum Sample number Tech level 2.25 1 4 480 Firm size 5.70 2.30 8.70 480 Government 0.18 0 1 397 ΔInternal R&D (1000 GBP) 546.12 −29,640.00 119,555.00 253 ΔS&T employee 1.2688 −70.00 78.00 398 ΔEmployee cut 24.3167 −1528.00 1449.00 480 ΔSearch breadth 0.8116 −10.00 10.00 207 ΔSearch depth 0.0188 −7.00 5.00 213 ΔCollaboration −0.0696 −7.00 7.00 345 ΔValue chain collaboration 0.0000 −3.00 3.00 317 ΔOutside value chain collaboration −0.0357 −4.00 4.00 308 ΔInternational collaboration 0.2742 −7.00 10.00 310 ΔTurnover (1000 GBP) 11,502.95 −224,505.00 1,293,123.00 480 3.2.1 Openness: breadth, depth, and collaboration Laursen and Salter's (2006) seminal paper suggested a way of measuring openness by counting the number of information sources (breadth) and the degree of their importance (depth); the effectiveness of this measure has been verified in many studies (Schweitzer et al., 2011, Roper et al., 2013). Using and expanding on Laursen and Salter's (2006) concepts, “search breadth” indicates how widely firms explore external information.2 All the external information source variables in the raw data were transformed into binary variables (0: not used, 1: used) and then added up to indicate 12 levels (0: none of the information sources used to 11: 11 different information sources used). “Search depth” refers to how intensively firms use external information sources. The respondents’ answers assessing the importance of external information sources were transformed into binary variables (0: not, low or medium-level importance, 1: high importance) and then added up to make a “depth of search” variable (i.e., 0–11 according to the number of information sources significantly used). “Collaboration” refers to how actively firms cooperate with external partners.3 This variable refers to the formal engagement of the company with external partners. A firm gets a score of “0” when it does not collaborate at all and “7” when it collaborates with all types of partners. In a similar way, we created a “value chain collaboration” (with suppliers, customers, and enterprise groups), an “outside value chain collaboration” (with competitors, R&D institutes, universities, and public institutes), and an “international collaboration” (partners outside UK). After creating all the openness measures, difference terms were made by subtracting openness in CIS 6 from openness in CIS 7 to identify firms’ strategic innovation routine change (see Figure 2 and Table 1). 3.2.2 Closed strategy: internal R&D To measure the closed approach, we made a variable indicating a change of internal R&D investment and a change in the portion of employees who have a degree or higher qualification (e.g., BA/BSc, MA, PhD, PGCE) in science and technology (S&T). These variables attempt to measure whether a firm increased internal R&D investment during the financial crisis by increasing either internal R&D financing or employees. 3.2.3 Austerity plan4: employment cut To identify an austerity plan, we created a variable indicating a change of employment between CIS 6 and CIS 7. However, as the variable name “employment cut” indicates, negative coding was adopted by subtracting the employment number of CIS 7 from that of CIS 6 (see Table 1). Thus, a positive value of this variable indicates an employment reduction (job cut) during the crisis, i.e., an austerity plan, while a negative value indicates an increase in employment. 3.2.4 Dependent variable: turnover change As for dependent variables, an objective measure of general financial performance, the firm’s turnover, was employed. This choice was made, as we think that the dependent variable typically employed in other OI studies (e.g., the innovation performance measured for instance from the launch of new products) is too closely linked with an innovation input (e.g., internal R&D or increasing openness). However, this may be misleading because it underestimates the financial and cognitive costs involved in an open strategy (Cruz-González et al., 2015) and would neglect its possible delayed effects (Ahn et al., 2013). Yet, in fact, external knowledge is not free (Cruz-González et al., 2015). To benefit from external knowledge, firms have to build substantial absorptive capacity to assimilate it with internal knowledge, thereby making it digestible and understandable (Cohen and Levinthal, 1990, Salter et al., 2014, Cruz-González et al., 2015). In this respect, to ascertain whether openness eventually brings benefits to a firm, it is necessary to investigate a firm's final output variable reflecting costs, organizational efforts and the possible delayed effects of open strategy. Due to their reliability and easy accessibility as publicly announced data, turnover data were obtained from IDBR (the Inter-Departmental Business Register) database using firm reference numbers in the UK CIS data. Turnover change was calculated by subtracting the turnover value for CIS 7 from that of CIS 8 to capture a firm’s resilience after the downturn. 3.2.5 Controls Three control variables, technology level, firm size, and government support, were adopted to enhance the explanation power of the analysis. Four technology levels, high, medium-high, medium-low, and low, were measured based on OECD classifications. A two-digit Standard Industrial Classification (SIC) was used to classify technology levels. Firm size was also controlled in that it is an important factor affecting the extent of openness (Van de Vrande et al., 2009, Drechsler and Natter, 2012, Spithoven et al., 2013, Vanhaverbeke and Cloodt, 2014). This variable was measured by taking the natural logarithm of total employee5 numbers of CIS 8 data. Government support was employed because it encourages firms’ networking and interactions (Rothwell and Dodgson, 1994, Kang and Park, 2012) even in an economic downturn (Hud and Hussinger, 2015). It was measured as a binary variable using data from the CIS 8 dataset. 3.3 Method Two statistical methods, cluster analysis and econometric regression, were employed to investigate the long-term impact of openness on firms' financial performance. First, a cluster analysis was conducted to classify firms’ strategic routine change. As such, three open strategy variables (search breadth change, search depth change, collaboration breadth change), two closed strategy variable (internal investment change, S&T employee portion change), and one austerity plan variable (employment cut) were used as criteria identifying firm groups that implemented different strategic routine change. Three to four groups were initially suggested by hierarchical clustering analysis (Ward method), and then K-means cluster analysis was applied to group the three identified clusters. Further, to breakdown the longitudinal effects of each variable on financial performance change, a regression analysis was conducted. Before the analysis, all strategic routine variables (open, closed, and austerity) were standardized, and Variance Inflation Factor (VIF) values were checked. However, it was indicated that there were no serious multicollinearity issues because all the VIF values were between 1.043 and 2.351. For lack of space, VIF values for the Model 5 were only reported here. For heteroscedasticity control purposes, robust standard errors were employed in evaluating the significance of the variables. To assess the explanation power of regression models, both R2 and adjusted R2 were reported.6 4. Results 4.1 Cluster analysis Cluster analysis identified three different firm groups. The mean values of all the variables and the number of the firms for each group are reported in Table 3 to show their innovation behavior pattern during the economic crisis. Table 3. Mean values of clusters Mean values Cluster 1 Cluster 2 Cluster 3 (N = 59) (N = 46) (N = 50) Open innovator Closed innovator Austerity planners Tech level 2.29 2.26 2.54 Firm size 591.63 394.35 564.96 ΔInternal R&D+(1000 GBP) −0.0706 0.678 −0.1850 ΔS&T employee+ 0.0422 0.1543 −0.2744 ΔEmployee cut (negative coding) −121.01 74.44 92.00 ΔSearch breadth + −0.0411 −0.0177 −0.2988 ΔSearch depth+ 0.1223 −0.2296 −0.4024 ΔCollaboration+ 0.1143 0.0806 −0.2627 ΔValue chain collaboration+ −0.0428 0.0893 −0.1342 ΔOutside value chain collaboration+ 0.2062 0.0120 −0.2567 ΔInternational collaboration+ 0.0638 −0.0273 −0.2184 ΔTurnover (1000 GBP) 20,421.94 18,123.39 7491.72 Mean values Cluster 1 Cluster 2 Cluster 3 (N = 59) (N = 46) (N = 50) Open innovator Closed innovator Austerity planners Tech level 2.29 2.26 2.54 Firm size 591.63 394.35 564.96 ΔInternal R&D+(1000 GBP) −0.0706 0.678 −0.1850 ΔS&T employee+ 0.0422 0.1543 −0.2744 ΔEmployee cut (negative coding) −121.01 74.44 92.00 ΔSearch breadth + −0.0411 −0.0177 −0.2988 ΔSearch depth+ 0.1223 −0.2296 −0.4024 ΔCollaboration+ 0.1143 0.0806 −0.2627 ΔValue chain collaboration+ −0.0428 0.0893 −0.1342 ΔOutside value chain collaboration+ 0.2062 0.0120 −0.2567 ΔInternational collaboration+ 0.0638 −0.0273 −0.2184 ΔTurnover (1000 GBP) 20,421.94 18,123.39 7491.72 Note: Total sample number = 155/+ standardized mean. Bold font emphasizes the characteristic of each cluster As shown in Table 3, the strategic changes of each cluster (i.e., open, closed strategy, and austerity plan variable) were different. The firms in Cluster 1 did not reduce employees while showing larger openness values. On the basis of this extrovert and out-looking routine change, we labeled them “Open Innovators.” The firms in Cluster 2 conducted an employment cut, but increased internal innovation capacity by increasing internal R&D investment and the number of scientific and technologically knowledgeable employees. Due to this introvert, in-house oriented strategic posture, they were labeled as “Closed Innovators.” Finally, the firms in Cluster 3 conducted the largest employment cut and reduced all the innovation activities, both open and closed. In this respect, they were labeled as an “Austerity planners.” To identify any differences in financial performance (i.e., ΔTurnover) among these groups, one-way analysis of variance was conducted. As shown in Table 3, the results showed that the turnover change of “Open Innovators” was the largest, and, as expected, that of “Austerity planners” was the smallest. To examine the group difference of “ΔTurnover” among “Open Innovator,” “Closed Innovator,” and “Austerity Planners,” a Bonferroni test was conducted. These post hoc analysis results showed that the “ΔTurnover” of “Open Innovators” and “Closed Innovators” was statistically different from that of “Austerity Planner” (both P-values for “Open Innovator—Austerity Planner” and “Closed Innovator—Austerity Planner” were smaller than 0.05). However, a significant difference between “Open Innovators” and “Closed Innovators” was not identified (P-value > 0.05). 4.2 Econometric analysis: the dynamic effect of openness To investigate the impact of each group of variable, a hierarchical linear regression was employed. The results of five different models were reported in Table 4. The Model 1 included only control variables, and closed approach and austerity variables were added to the Model 2. The Model 3 included three openness variables (search breadth, search depth’, and collaboration) in addition to control variables. The Model 4 was basically the same as the Model 3, but three specific collaboration variables, collaboration inside value chain partners, outside value chain partners, and international partners, were added. However, “Δcollaboration” was dropped in the Model 4 to avoid possible multicollinearity. Finally, the Model 5 included all the variables except for “Δcollaboration.” This selection was made because “Δcollaboration” showed no statistical significant impact in Model 3, but outside-value chain and international collaboration variables showed their significant influence on performance in Model 4. As the results show, employment cut played a negative role in increasing turnover after the crisis, which is in line with the results of cluster analysis. Maintaining internal innovation capacity through an increase in internal R&D investment was important for turnover enhancement, and a high level of openness, such as collaboration with partners outside value chain or international partners, was positively associated with turnover increase after the financial crisis. Table 4. Econometric analysis results Variable Model 1 Model 2 Model 3 Model 4 Model 5 VIF (Model5) Tech level 0.094* 0.098 0.128* 0.131* 0.101 1.400 Firm size 0.147*** 0.158** 0.163** 0.156** 0.220*** 1.151 Government 0.063 0.057 0.064 0.047 −0.030 1.217 ΔInternal R&D 0.136** 0.396*** 1.164 ΔS&T employee 0.058 0.101 1.387 ΔEmployee cut −0.185*** −0.206*** 1.043 ΔSearch breadth −0.003 −0.013 −0.042 1.223 ΔSearch depth −0.042 −0.029 −0.025 1.111 ΔCollaboration −0.050 ΔValue chain collaboration 0.020 0.071 1.953 ΔOutside value chain collaboration 0.223*** 0.171** 1.880 ΔInternational collaboration 0.207** 0.212** 2.351 R2 0.035 0.109 0.045 0.094 0.310 Adjusted R2 0.028 0.086 0.015 0.054 0.256 Durbin–Watson’s d 2.009 1.964 1.999 2.027 1.985 N (sample number) List-wise deletion 397 240 195 193 152 Variable Model 1 Model 2 Model 3 Model 4 Model 5 VIF (Model5) Tech level 0.094* 0.098 0.128* 0.131* 0.101 1.400 Firm size 0.147*** 0.158** 0.163** 0.156** 0.220*** 1.151 Government 0.063 0.057 0.064 0.047 −0.030 1.217 ΔInternal R&D 0.136** 0.396*** 1.164 ΔS&T employee 0.058 0.101 1.387 ΔEmployee cut −0.185*** −0.206*** 1.043 ΔSearch breadth −0.003 −0.013 −0.042 1.223 ΔSearch depth −0.042 −0.029 −0.025 1.111 ΔCollaboration −0.050 ΔValue chain collaboration 0.020 0.071 1.953 ΔOutside value chain collaboration 0.223*** 0.171** 1.880 ΔInternational collaboration 0.207** 0.212** 2.351 R2 0.035 0.109 0.045 0.094 0.310 Adjusted R2 0.028 0.086 0.015 0.054 0.256 Durbin–Watson’s d 2.009 1.964 1.999 2.027 1.985 N (sample number) List-wise deletion 397 240 195 193 152 Note: Significance level: * P < 0.1, ** P < 0.05, *** P < 0.01 5. Discussion The recent global economic crisis has substantially affected firms' willingness to invest in innovation (Filippetti and Archibugi, 2011, Paunov, 2012). Many firms had to stop or postpone innovation projects, but firms “swimming against the stream” by aggressively investing in innovation can better cope with an economic crisis (Paunov, 2012: 303). Recent case studies (Chesbrough and Garman, 2009, Di Minin et al., 2010) showed that increasing openness can also be an effective way of coping with hardship, and the current article has investigated the generalizability of this finding using the UK CIS panel data. Our results confirmed that increasing openness can be an effective approach to enhancing firm performance over time, in particular in an economic downturn. As illustrated in Figure 1, firms have to change their routines to adapt themselves to the new environment dictated by the financial crisis. The cluster analysis results supported the idea that pursuing innovation (both open and closed) during the crisis enables firms to have resilience power high enough to achieve a sustainable growth in the long term. The sample firms were grouped into three categories—Closed Innovators who focused on internal R&D, Open Innovators who increased openness, and Austerity Planners who simply reduced employment and all the innovation activities. However, these different strategic choices brought dissimilar consequences to firms; the turnover enhancement of both the Open and Closed Innovators outweighed that of the Austerity Planners, which suggests the importance of resilience power during the crisis. In hard times, an austerity plan could be a tempting offer. Employment cuts and the reduction of internal and external innovation may save resources for the moment, so this retrenchment management style might help firms to endure hardship. However, the main problem of this strategic choice is that it makes firms lose their capability to innovate, thus difficult to adequately recover after the downturn. As noted by Archibugi et al. (2013), firms that maintained innovation investment better coped with the crisis, and our results showed that increasing openness can also be an important dynamic capability for a sustainable growth. Instead of an austerity plan, Open Innovators enhanced their openness via innovation collaboration and even increased their number of employees. Due to this bold approach, they were able to achieve a high turnover increase after the crisis. This is in line with what indicated by the case studies carried out by Chesbrough and Garman (2009) and Di Minin et al. (2010) that showed that open strategy can preserve innovation capability, and our econometric analysis results also support this interpretation. As shown in Table 4, employment cuts were negatively associated with turnover increase, suggesting that such cuts can harm firms’ resilience power. However, strategic changes associated with open strategies, such as innovation collaboration with partners outside value chain and across borders, contributed to the enhancement of turnover when the economic turmoil ends. This is attributed to the virtue of an open strategy enabling firms to access innovation resources at arm’s length without internalization (Chesbrough et al., 2014). Certainly, reducing innovation investment and cutting employment can save resources, but utilizing external knowledge can be an effective approach in a downturn when firms seek an alternative way of knowledge creation. By opening innovation process firms are able to broaden the boundary of firms’ innovation resources. As external knowledge and networks are newly included to the expanded boundary, partners’ complementary assets are retained around the firms. As such, without generating knowledge internally, firms are able to save resources while maintaining the access to the necessary knowledge, which in turn will help firms to preserver the power to innovate continuously. Further, external knowledge can contribute to the development of new innovation routes (Ahn et al., 2015). As shown in Figure 1, increasing openness can be recognized as an effective tool of moving onto a new routine, and in this process newly imported knowledge will play a vital role in helping firms to create innovation and pivot strategic directions. However, opening innovation process does not necessarily introduce a high level of newness to firms. As shown in Table 4, information search and collaboration with value chain partners were not significantly associated with turnover change, which validates our assumption of heterogeneity in openness. This article distinguished search breadth and depth due to their different intensities, cognitive distances, and degree of introducing new information. Similarly, for innovation collaboration, a distinction was made between partnering within outside the value chain and international collaborators according to the degree of new ideas these collaborations would introduce. Recognizing this heterogeneity, the results showed that the increase of high-level openness—collaboration with outside value chain and international partners—was positively associated with turnover recovery. As noted by Almirall and Casadesus-Masanell (2010), a high level of openness can lead to better performance in a dynamic environment, and our results suggest the importance of a high level of newness as a source of dynamic capability creation. Utilizing external knowledge can be an effective approach in a downturn, but this does not necessarily mean that all types of open approaches help firms to establish the dynamic capability for strategic adaptation. Since firms should deviate from the current routines, substantial driving force would be necessary for firms to arrive at a new equilibrium. In this process, newness acquired by fresher and more divergent knowledge will play an important role in establishing the necessary driving force for a routine change. A high level of openness, such as collaborating with a partner who is substantially differently configured, can be an effective way of introducing high newness to firms. Expanded knowledge stock and strong engagement with new partners will trigger internal resource allocation and increase organizational flexibility in firms, which will help them to reconfigure or change their strategic posture. This can be particularly important in a downturn, in the sense that new knowledge and networks imported via higher-ranked openness will provide firms with a complementary innovation route that an austerity plan may not offer, which in turn will help firms to identify new opportunities to survive and achieve sustainable growth. 6. Implications and limitations Recently, more attention has been given to open strategy for innovation, and many studies have revealed various benefits from opening firm boundaries (West et al., 2014). Among these benefits, the fact that an open strategy can be an effective way of coping with an economic downturn is noteworthy. However, to the best of our knowledge, few attempts have been made to examine the longitudinal effects of openness on firm performance during a downturn. The present research recognized the different characteristics in openness dimensions and investigated the impact of strategic change during the recent crisis on financial performance recovery after the downturn. Based on empirical evidence from the UK CIS panel data, the findings reported in this article add to our knowledge, showing that open approaches can contribute to the establishment of the necessary dynamic capability for a strategic adaptation in a slow economy. The results provide some practical implications. Managers in firms should realize that the benefits of open approaches are valid in a longitudinal context. Roper et al. (2013) showed the positive externality of openness, and this article confirmed that the virtue of increasing openness is still valid even in hard times. When faced with a turbulent external shock, such as economic turmoil, it may be easier for top management to reduce innovation inputs. However, our results suggest that increasing a high-level openness can be an effective approach for the adaptation to the turbulent environment and preserving resilience. For sustainable growth, it is important for firms to keep their growth momentum. Establishing relevant dynamic capabilities plays a vital role in achieving a new leap forward. Acknowledging this positive role of openness, policymakers have to develop a policy promoting firms' openness to help those facing difficulties during an economic recession. Though there are potential benefits offered by this research, it cannot be denied that the article suffers from research limitations. Despite of the various benefits of out-bound OI, its longitudinal effect could not be explored in this article. As noted by Chesbrough and Garman (2009), out-bound OI, such as IP licensing-out or spin-offs, may positively affect firm performance in a downturn. However, because some variables associated with out-bound OI did not consistently appear in the CIS data, they could not be included in the analysis. For a similar reason, the boundary of the analysis was limited because the main purpose of the CIS is in investigating general innovation activities. Panel data sets were created by integrating different waves of surveys. However, as there were some changes in questionnaires, only limited variables were employed for the analysis. Future studies and data set presenting broader variables and more consistent longitudinal data will enable us to obtain more in-depth understanding about OI phenomena. Acknowledgments The authors wish to thank the UK Data Archive for access to the UK Innovation Survey. The authors sincerely thank Henry Chesbrough, Christopher Tucci, and two anonymous reviewers who kindly offered invaluable comments and suggestions. The early version of this article was presented at the 1st World Open Innovation Conference, Napa, US, in December 2014. Footnotes 1 Department for Business, Innovation and Skills and Office for National Statistics, UK Innovation Survey, 1996–2013: Secure Data Access [computer file], Colchester, Essex; UK Data Archive [distributor], July 2013, SN:6699 2 Through firms, suppliers, customers, other firms, consultants, universities, public research institutes, conferences, industry association, technical standards, and journals. 3 With enterprise groups, suppliers, customers, competitors, R&D institutes, universities, and public institutes (governments, etc.). 4 If a firm reduced its internal R&D investment or S&T employee portion, those variables can also be used to identify the firm’s austerity plan. 5 To increase data quality, the number of employees was also obtained from the IDBR database. 6 As the regression is not time-series analysis, autocorrelation issues may not occur. 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Industrial and Corporate Change – Oxford University Press
Published: Feb 1, 2018
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