The influence of inbound open innovation on ambidexterity performance: Does it pay to source knowledge from supply chain stakeholders?

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The influence of inbound open innovation on ambidexterity performance: Does it pay to source knowledge from supply chain stakeholders? ⁎

Lorenzo Arditoa, , Antonio Messeni Petruzzellib, Luca Dezic, Sylvaine Castellanod a

Department of Engineering, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, - 00128 Rome, Italy Department of Mechanics, Mathematics and Management, Politecnico di Bari, Viale Japigia 182/B, - 70126 Bari, Italy c Department of Business and Quantitative Studies, Università degli Studi di Napoli Parthenope, Via Generale Parisi 13, - 80132 Naples, Italy d Management & Strategy Research Department, PSB Paris School of Business, 59 rue Nationale, - 75013 Paris, France b

A R T I C LE I N FO

A B S T R A C T

Keywords: Innovation ambidexterity Supply chain stakeholders Radical innovation and incremental innovation Exploration and exploitation Inbound open innovation External knowledge sourcing

Extant research has neglected an in-depth examination of the relationship between external knowledge sourcing and the ability of firms to balance radical and incremental innovation activities (i.e., innovation ambidexterity). Therefore, the present paper seeks to reveal the effects of knowledge sourcing activities directed toward three relevant supply chain stakeholders (i.e., suppliers, customers, and competitors) on innovation ambidexterity. Based on a sample of 5897 firms that participated in the Italian Innovation Survey (IIS) (2008–2010), we reveal that sourcing knowledge from suppliers, customers, and competitors has a positive influence on innovation ambidexterity, hence confirming our hypotheses. Specifically, suppliers represent the most relevant knowledge source, followed by customers and, then, competitors. These results expand the literature discussing the relationship between inbound open innovation and ambidexterity performance, which falls short of a clear understanding of whether and the extent to which sourcing knowledge from supply chain stakeholders facilitates achieving innovation ambidexterity.

1. Introduction In today's economic landscape, the ability of firms to innovate is crucial for survival (e.g., Kostopoulos, Papalexandris, Papachroni, & Ioannou, 2011); notably, however, the innovation outcomes of firms are not all equal. There is a very well-established distinction between radical and incremental innovations. The former refer to products (goods or services) that are new, at least with respect to a firm's current offerings; the latter reflect minor changes to existing products (Jansen, Van Den Bosch, & Volberda, 2006). Although radical innovations are considered the most beneficial for achieving a sustainable competitive advantage in the long run (Song & Thieme, 2009; Sorescu, Chandy, & Prabhu, 2003), incremental innovations are also needed to compete in the short run (Connor, 1999; O'Reilly & Tushman, 2013). This fact means that firms should pursue an innovation strategy that focuses on both radical and incremental innovation processes, which we refer to as innovation ambidexterity (Dunlap, Parente, Geleilate, & Marion, 2016; Lin, McDonough, Lin, & Lin, 2013). Organizational learning activities aimed at generating new knowledge are crucial for pursuing such an innovation strategy. Nonetheless,

due to rapid changes in technology and the globalization of markets, firms can no longer internally develop all the knowledge required to be ambidextrous (Chesbrough, 2003; Chesbrough & Bogers, 2014). This issue is exacerbated by the fact that explorative learning activities, which sustain radical innovations, compete for the same scarce resources devoted to conducting exploitative learning activities, which sustain incremental innovations (Benner & Tushman, 2015; March, 1991). Following the principles of the open innovation paradigm (Chesbrough, 2003), the purposive inflow of knowledge from external sources (e.g., research entities, firms, customers, and documents) has become a popular approach to complementing internal knowledge creation efforts and managing the tensions in learning and innovation (Carayannis, Del Giudice, Della Peruta, & Sindakis, 2017; Parida, Westerberg, & Frishammar, 2012). Consequently, a wealth of research has investigated the advantages/disadvantages of external knowledge sourcing. For instance, the concept of external search breadth (i.e., sourcing knowledge from many sources) has been widely discussed, revealing that, up to a certain level, broad searches benefit innovation performance (e.g., Ardito & Messeni Petruzzelli, 2017; Laursen & Salter,

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Corresponding author. E-mail addresses: [email protected] (L. Ardito), [email protected] (A. Messeni Petruzzelli), [email protected] (L. Dezi), [email protected] (S. Castellano). https://doi.org/10.1016/j.jbusres.2018.12.043 Received 9 April 2018; Received in revised form 12 December 2018; Accepted 14 December 2018 0148-2963/ © 2018 Elsevier Inc. All rights reserved.

Please cite this article as: Ardito, L., Journal of Business Research, https://doi.org/10.1016/j.jbusres.2018.12.043

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rather than choose one or the other”. With these words, he underlined that firms cannot survive if they focus on incremental (radical) innovations and neglect radical (incremental) innovations (see also Jansen et al., 2006; Lin et al., 2013). Notably, radical innovations help firms remain competitive in the long term, but they are riskier and, compared to incremental innovations, do not provide firms with the short-term profits that they require to survive in the present (O'Connor & Rice, 2013; Song & Thieme, 2009). Therefore, balancing radical and incremental innovation efforts (i.e., innovation ambidexterity) (Lin et al., 2013) allows firms to compete in current and future market conditions, hence improving the likelihood of survival (Connor, 1999; O'Reilly & Tushman, 2013). The balance between radical and incremental innovation activities involves generating explorative and exploitative knowledge, respectively. However, the development of explorative and exploitative knowledge requires different learning models that compete for the same scarce organizational resources, ultimately leading to tensions and tradeoffs (Benner & Tushman, 2015; March, 1991). Additionally, due to the information asymmetries caused by high degrees of product complexity, technological turbulence, and market dynamism (e.g., Bolisani & Bratianu, 2017), it has become extremely complex for firms to “go it alone” in knowledge generation (Chesbrough, 2003; Chesbrough & Bogers, 2014). As a solution to these issues, firms may favor purposive search for an inflow of knowledge originating beyond their boundaries (i.e., external knowledge sourcing). This approach has been defined as an inbound open innovation model (Natalicchio, Ardito, Savino, & Albino, 2017; Scuotto, Del Giudice, Bresciani, & Meissner, 2017) to complement and renew firms' internal knowledge base, with the ultimate aim of improving firms' innovation performance (Laursen & Salter, 2006; Savino, Messeni Petruzzelli, & Albino, 2017; Vrontis, Thrassou, Santoro, & Papa, 2017). Firms might direct their external search strategies toward different knowledge sources, in which each provides knowledge of a different nature (e.g., technological knowledge or information on markets and competition) and may support the explorative and exploitative learning activities needed to achieve innovation ambidexterity (Benner & Tushman, 2003; Tranekjer & Søndergaard, 2013). In this context, identifying the knowledge to be combined with firms' existing knowledge base requires firms to deliberately search for and tap into promising knowledge sources (Köhler, Sofka, & Grimpe, 2012). Nevertheless, previous studies “offer mixed results and do not enable reaching a clear conclusion about which external knowledge sources are more relevant in order to reach different innovation outputs”, such as radical and incremental innovations (Cruz-González et al., 2015, p. 76). We seek to address this gap by revealing which specific knowledge sources help firms attain innovation ambidexterity.

2006). Other studies have concentrated on the effects of some specific knowledge sources (e.g., customers and universities) on a firm's innovation capabilities. However, this research has provided contrasting results and failed to provide clear evidence on which knowledge sources help reconcile the tensions between explorative and exploitative learning activities (Abdel Aziz & Rizkallah, 2015; CruzGonzález, López-Sáez, & Navas-López, 2015; Spanjol, Mühlmeier, & Tomczak, 2012). In turn, an in-depth understanding of the relationship between external knowledge sourcing and innovation ambidexterity has yet to be obtained. Based on the foregoing discussion, we aim to delve into the actual knowledge sources that are relevant to improving the innovativeness of firms in terms of the joint development of radical and incremental innovations. Relying on stakeholder theory (Donaldson & Preston, 1995) and the supply chain management literature (Lii & Kuo, 2016), we focus on knowledge sourcing activities directed toward suppliers, customers, and competitors. This distinction helps disentangle the effects of backward, forward, and horizontal knowledge sourcing activities, which are all considered relevant for firms and may affect the radicalness of innovative outputs (e.g., Love, Roper, & Bryson, 2011; Roper & Arvanitis, 2012). Furthermore, suppliers, customers, and competitors may be considered relevant knowledge sources because they are directly related to a firm's market activities. Nonetheless, empirical analysis concerning their specific influence on ambidexterity performance has been neglected. Moreover, the relative importance of each supply chain source, from a knowledge search perspective, is far from established and has been found to change depending on the radicalness of the innovative outputs (Cruz-González et al., 2015; Laursen & Salter, 2006). Ultimately, our research questions are as follows: (i) does sourcing knowledge from suppliers, customers, and competitors affect innovation ambidexterity? and (ii) what is the relative importance of each supply chain stakeholder in external knowledge sourcing activities? We answer these questions based on a sample of 5897 firms that participated in the Italian Innovation Survey (IIS) (2008–2010), which has already been validated as a suitable data source for conducting research in the domains of innovation and knowledge management (e.g., Ardito & Messeni Petruzzelli, 2017). The results of our analyses reveal that the knowledge of suppliers, customers, and competitors is needed to improve innovation ambidexterity. However, suppliers represent the most relevant knowledge source, followed by customers and, then, competitors. Overall, based on stakeholder theory as well as the open innovation and supply chain management literature, we argue that a firm's search for external knowledge should not only be defined based on attributes such as breadth and depth. Indeed, we highlight that the potential value of each specific knowledge source must be assessed to better design knowledge search and inbound open innovation strategies. Specifically, we reveal whether and the extent to which sourcing knowledge from these three supply chain stakeholders facilitates achieving innovation ambidexterity. In this way, based on our research questions, we advance the discussion about which knowledge sources may help reconcile the tensions between radical and incremental innovation activities one step forward. This paper proceeds as follows. Section 2 presents the theoretical background and hypotheses. Section 3 describes the data collection process and the econometric approach. Section 4 shows the results. Finally, Section 5 discusses the key findings, implications, and future research directions.

2.2. Supply chain stakeholders as knowledge sources As argued in the previous section, innovation ambidexterity is rooted in different types of knowledge (e.g., explorative and exploitative) that are usually difficult for a single company to generate. Hence, firms must compensate through external knowledge sourcing activities. These usually involve different types of knowledge sources, often categorized as supply chain sources (i.e., suppliers, customers, and competitors), non-supply chain sources (e.g., research organizations and governmental organizations), and other/specialized sources (e.g., conferences, fairs, exhibitions, standards, and regulations) (e.g., Golicic & Smith, 2013; Laursen & Salter, 2006; Love et al., 2011). Among these categories, supply chain sources are increasingly of interest to innovation and supply chain management scholars. Several reasons may explain this phenomenon. First, suppliers, customers, and competitors reflect firms' most relevant stakeholders (Cruz-González et al., 2015; De Luca & AtuaheneGima, 2007; OECD, 2010; Ren, Eisingerich, & Tsai, 2015; Tödtling, Lehner, & Kaufmann, 2009). Suppliers and customers are considered

2. Theory and hypotheses 2.1. Innovation ambidexterity and external knowledge sourcing As Connor (1999, p. 1156) argued two decades ago, “companies live in the present and the future, with the need to satisfy current customers and to anticipate the future of markets… The successful managers will be those who select wisely the balance between now and the future 2

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direct stakeholders since focal firms depend on them for much of their business. In turn, the knowledge of suppliers and customers is valuable for further designing focal firms' short-term and long-term innovation strategies (Soosay, Hyland, & Ferrer, 2008). For instance, HewlettPackard, 3M, Boeing, Hitachi, and Black and Decker have widely recognized that sourcing knowledge backward and forward in the supply chain may benefit current and future innovation performance, in addition to operational and financial performance (Phillips, Lamming, Bessant, & Noke, 2006; Spekman, Kamauff, & Myhr, 1998). Competitors are also counted as supply chain stakeholders (Golicic & Smith, 2013) given their capacity to affect firms' choices and relationships with other stakeholders (Donaldson & Preston, 1995; Friedman & Miles, 2002). In particular, the knowledge owned by competitors may intimate the direction of future innovation activities, thus affecting focal firms' innovation strategies. This phenomenon echoes the view that organizations that are relevant to firms' environment and influence firms' choices can be considered stakeholders even though they do not have a specific stake in the focal firms themselves like suppliers and customers do (Tseng, Lim, & Wong, 2015). In addition to this conjecture, due to the need to complement firms' internal knowledge base, there is a growing tendency to rely on coopetitive activities to develop innovations (Ghobadi & D'Ambra, 2012), further suggesting the role of competitors as stakeholders. Second, although the advantages of supply chain interactions in terms of risk sharing, access to complementary resources, reduced logistics costs, and productivity enhancement cannot be neglected, a more recent rationale for explaining the value of being involved in such interactions involves capitalizing on opportunities for organizational learning and knowledge creation (e.g., Lii & Kuo, 2016; Un, CuervoCazurra, & Asakawa, 2010; Yang, 2013). Indeed, supply chain interactions allow the forward, backward, and horizontal integration of information among suppliers, customers, and competitors (Phillips et al., 2006; Roper & Arvanitis, 2012). In this way, firms can tap into knowledge to be used across their innovation value chain. Moreover, this knowledge is of multiple types. For example, suppliers possess operational knowledge about processes and specific product/technology components, while customers are a valuable source of market knowledge (Tether, 2002) similar to competitors, which may also provide relevant technological knowledge (Cruz-González et al., 2015; De Luca & Atuahene-Gima, 2007; Tödtling et al., 2009). Such diverse external knowledge may favor both explorative and exploitative learning possibilities, thus constituting the basis for designing radical and/or incremental innovation activities (Lii & Kuo, 2016; Ren et al., 2015). Based on the foregoing discussion, we analyze in depth the potential effects of the purposive inflow of knowledge of suppliers, customers, and competitors for achieving innovation ambidexterity.

et al., 2017) while reducing the risks of falling into core rigidities and competency traps (Leonard-Barton, 1992; Levinthal & March, 1993). Consequently, firms that consider suppliers' knowledge as a relevant resource are likely to be more able to cope with complex problems, diversify R&D activities, and recognize novel technological opportunities (Clauß, 2012; Li & Tang, 2010; Song & Thieme, 2009). The implication is that explorative learning activities are fostered; thus, firms will develop radical innovations more easily (Song & Di Benedetto, 2008; Zhao, Cavusgil, & Cavusgil, 2014). At the same time, suppliers might provide firms with knowledge that tailors the production system to the requirements of focal firms, with the aim of ensuring that the objectives of the NPD process are met by adapting to technological standards or investing in worker training. In this case, the focus of external knowledge sourcing from suppliers falls on the development of better system interfaces, NPD project and design specification improvements (Perols, planning, Zimmermann, & Kortmann, 2013), which all lead to incremental changes in current products. In line with this view, some previous studies have revealed that suppliers can become particularly relevant to augment firms' process variability and help them refine some product components according to market needs (Das, Narasimhan, & Talluri, 2006; Lii & Kuo, 2016). For instance, Toyota is very well known for considering suppliers as an important knowledge source in its attempt to improve its product lines and reduce costs and the time to market (Wang et al., 2016). Overall, it appears that firms that devote efforts to sourcing knowledge from suppliers may gain advantages in developing both radical and incremental innovations. In addition, the advantages in terms of, for example, quality improvement and cost reductions that underlie incremental innovations may save resources for conducting radical innovation activities (Luzzini et al., 2015; Un et al., 2010), ultimately relaxing the internal tensions that exist when firms try to be ambidextrous. Therefore, we expect the following: Hypothesis 1. Sourcing knowledge from suppliers will have a positive influence on innovation ambidexterity. 2.4. Sourcing knowledge from customers Customers' needs dynamically change over time, thus stimulating the continuous adaptation of value creation activities based on these evolving needs (Chen, Yang, Dou, & Wang, 2018; Sirmon, Hitt, & Ireland, 2007). Such stimuli are useful since firms can sense market requirements, hence making customers an important source of knowledge. Specifically, if firms search for knowledge among their customers, then they are in a better position to detect business opportunities that are not being explored or avenues for minor improvements to current businesses (Ganotakis & Love, 2012). In other words, customers' knowledge may trigger both radical and incremental innovation activities. Indeed, in many cases, customers have been revealed as valuable knowledge sources since their demands may anticipate new trends (Köhler et al., 2012; Lukas, Menon, & Bell, 2002; von Hippel, 1988), thus providing new ideas for more radical products. For instance, Cohen, Nelson, and Walsh (2002) found that 90% of their sample firms initiated novel NPD projects owing to the acquisition of customers' knowledge. This finding is in line with the studies by Love et al. (2011) and Arora, Cohen, and Walsh (2016), who showed that firms that have links to customers have a greater extent of new service and technological ideas than firms that do not account for customers' knowledge. Some explanations lie in the fact that customers' knowledge provides “outside-the-box” thinking, in that it is not subject to the path-dependent nature of internal knowledge creation processes (Yli-Renko & Janakiraman, 2008). Furthermore, closer attention to customers may uncover latent or unexpressed needs (Zhou, Yim, & Tse, 2005). Thus, firms may formulate ideas that are evidently different from current

2.3. Sourcing knowledge from suppliers Since suppliers are directly involved in new product development (NPD) projects, firms often benefit from better and faster access to information on processes and product components (Dunlap, Parente, & Geleilate, 2017; Yan & Azadegan, 2017; Yang, 2013). Accordingly, firms that direct their knowledge search strategies toward suppliers are thought to improve their innovation performance. In detail, the contribution of suppliers may include the provision of innovative components and product/process technologies (Luzzini, Amann, Caniato, Essig, & Ronchi, 2015; Walter, 2003). Such components/technologies are usually not available within the boundaries of firms because they are a complementary part of their businesses, not the core element. In other words, suppliers' components/technologies represent the outcomes of innovation activities that firms could not conduct internally due to resource constraints (von Hippel, 1988; Wang, Li, & Chang, 2016). Therefore, they represent knowledge components that expand firms' knowledge along distant trajectories and, in turn, enhance recombination opportunities (Fleming, 2001; Savino 3

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managers reporting that a relevant part of the market information that they accessed was due to their interactions with competitors and that such information was important because competitors attempt to deliver value to the same customers (see also Wu, 2014). Finally, competitors have been revealed to be a source of technological knowledge, especially when they generate a breakthrough. Indeed, a breakthrough by a competitor will compel firms to engage in exploratory learning activities to compensate for their gap and ultimately boost ideas that go beyond the breakthrough itself (McGahan & Silverman, 2006). This phenomenon is also facilitated by the fact that the similarity in the language and processes of competing firms can reduce information asymmetries and causal ambiguity in knowledge assimilation, thus improving the ability of firms to spot and build on the exploratory knowledge generated by competing organizations (Ritala & Hurmelinna-Laukkanen, 2009). Ultimately, we contend that both radical and incremental innovation activities may be the result of sourcing knowledge from competitors. Therefore, we propose the following:

offerings (Chang & Taylor, 2006; Morgan, Obal, & Anokhin, 2018). In addition, knowledge about customers' needs helps in understanding whether the market is ready to accept an innovation that departs from current norms and values, thus reducing the risks underlying radical innovations (Morgan et al., 2018; Vrontis, Bresciani, & Giacosa, 2016). Naturally, sourcing knowledge from customers does not always lead to radical innovation activities. Notably, customers' knowledge also includes complaints, and firms work to reduce these complaints by incorporating minor changes that aim to provide the market with improved versions of existing products (Fredberg & Piller, 2011; Singh & Power, 2009; Tranekjer & Søndergaard, 2013). Furthermore, customers may also “serve as effective testing outlets”, hence helping to refine earlier versions of launched products (Morgan et al., 2018). Finally, the expressed needs of customers may be useful for product customization. That is, by sourcing information about customers' desires, firms innovate by delivering products that can be slightly modified according to such needs. One example of this phenomenon is Dell, which embedded the knowledge that it obtained from customers into its existing systems and developed marginal variants of its products to satisfy the various desires of its customers (Rungtusanatham, Salvador, Forza, & Choi, 2003). In summary, firms that devote attention to customers' knowledge may have the possibility pf engaging in explorative and exploitative learning activities that favor innovation ambidexterity. Stated more formally, we propose the following:

Hypothesis 3. Sourcing knowledge from competitors will have a positive effect on innovation ambidexterity. 3. Methods and data 3.1. Data collection Our data source for testing the hypotheses is the IIS for the period 2008–2010. This survey is the Italian version of the European Community Innovation Survey (CIS) and is conducted by the Italian statistical office (ISTAT). Similar to the CIS, the main goal of the IIS is to investigate the status of the innovation activities of Italian firms. In addition to specific questions related to the outputs of firms' innovation activities, the IIS provides firm-level information about topics including the operating market, knowledge sourcing activities, and collaboration strategies. Therefore, the survey allowed us to have complete information regarding the antecedents and outputs of innovation activities. Through the IIS, we were able to observe and analyze 5897 firms, which constituted our final sample. The choice to use a national innovation survey, specifically with regard to the IIS (Ardito, Besson, Petruzzelli, & Gregori, 2018; Ardito & Messeni Petruzzelli, 2017), is consistent with previous studies (e.g., Blindenbach-Driessen & van den Ende, 2014; Laursen & Salter, 2006). Notably, innovation surveys such as the IIS are considered reliable because the related questions follow the definitions and descriptions provided by the Oslo Manual (OECD, 2005), thus reducing the potential misalignment between the measurement of variables and theories of innovation. Furthermore, such surveys have been subject to extensive pilot tests and have been proposed to firms frequently in recent years; therefore, issues of interpretability, reliability, and validity are limited (Arundel & Smith, 2013; Laursen & Salter, 2006). Finally, it has been proven that common method bias does not represent a key concern in these surveys (Mairesse & Mohnen, 2010). Nevertheless, we conducted principal component analysis, which confirms that common method bias is not a relevant issue since the first factor accounts for only approximately 6% of the total variance explained (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003).

Hypothesis 2. Sourcing knowledge from customers will have a positive effect on innovation ambidexterity. 2.5. Sourcing knowledge from competitors Monitoring the competition is a reasonable tactic for understanding and simultaneously responding to the weaknesses and strengths of present and potential competitors (Ganotakis & Love, 2012). In turn, this tactic allows firms to access the knowledge of competitors, which can be used for developing new and improved products (Love et al., 2011). Although competitors' knowledge is considered important, the conventional wisdom is that such knowledge leads to incremental innovations. Two reasons explain this view. First, firms that focus on competitors are found to be more devoted to imitation activities (Katila & Chen, 2008). They deflect their attention from other innovation strategies and use competitors' knowledge to refine existing products and to make them more attractive, disregarding radical innovation activities (Cheng & Krumwiede, 2012; Zhou, Brown, Dev, & Agarwal, 2007). Second, it is argued that firms can gain only a limited extent of actual new knowledge from their competitors since they operate in the same market (Köhler et al., 2012; Lukas et al., 2002); in fact, this limitation influences the expansion of firms' knowledge base such that exploratory learning activities are hindered (Un et al., 2010). However, some scholars have found that an emphasis on customers' knowledge can still produce discontinuities within firms' existing product portfolios, i.e., competitors may still provide valuable hints that trigger the development of innovations that, at least, are new to the firm (Baker & Sinkula, 2007; Lukas et al., 2002). Indeed, monitoring competitors may not only push firms to catch up with competing organizations but also stimulate programs aimed at surpassing competitors' innovations (Han, Kim, & Srivastava, 1998) by engaging in radical innovation activities. According to Hughes, Hugo, and Blatt (1996) and Hipp (2000), firms that utilize competitors as an external source of knowledge are able to launch novel services. Furthermore, since competitors and innovating firms operate in the same market, competitors might result in an indirect source of market information and trends, hence complementing firms' mechanisms for uncovering new product ideas (Baker & Sinkula, 2007). This phenomenon recalls the study by Ingram, and Roberts, and Peter W. (2000), who interviewed several

3.2. Variables To compute the dependent variable (Innovation ambidexterity), we relied on the study by Blindenbach-Driessen and van den Ende (2014). First, for each firm, we identified the share of turnover from incremental (i.e., incremental innovation performance), new-to-the-firm, and new-to-the-market products. Then, we summed the share of turnover from new-to-the-world and new-to-the-market products. This measure reflects the share of turnover from radical innovations since it recalls innovation activities that are completely new at either the firm 4

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or the market level (i.e., radical innovation performance). Finally, Innovation ambidexterity is operationalized as incremental innovation performance times radical innovation performance (see also Ardito et al., 2018). A multiplicative scale is preferred over a sum because it makes it possible to capture whether the two performance measures are in balance (Cao, Gedajlovic, & Zhang, 2009; Gibson & Birkinshaw, 2004). Indeed, the maximum ambidexterity performance is reached when exploratory performance and exploitative performance are equal (0.5), i.e., in perfect balance. Innovation ambidexterity tends toward zero as exploratory performance outperforms exploitative performance, and vice versa. The first independent variable (Suppliers) is measured as follows. We checked whether a firm has declared that it relies on knowledge originating from suppliers and whether it perceives that this knowledge sourcing activity is of moderate or relevant importance. In this case, we set the value of Suppliers as one, otherwise zero. The operationalization of the second (Customers) and third (Competitors) independent variables followed the same procedure but checked whether customers and competitors, respectively, are knowledge sources of moderate or relevant importance. This approach has been used extensively by previous studies that have employed data available in national innovation surveys (e.g., Ardito & Messeni Petruzzelli, 2017; Laursen & Salter, 2006) or ad hoc questionnaires (e.g., Cruz-González et al., 2015; Santoro, Vrontis, & Pastore, 2017). To improve the reliability of our analyses, we included several control variables. First, we counted the number of other knowledge sources that firms consider important (Other sources) (e.g., Laursen & Salter, 2006). Second, to control for firm size (Firm size), we included the natural logarithm of firms' turnover in 2008 (Klingebiel & Rammer, 2014). Third, we included a dummy variable set to one if firms have ongoing innovation activities (Ongoing innovation). Fourth, another dummy variable (Collaboration) was added to assess whether firms engage in formal collaboration activities (value of one) (Gulati, 1998). Fifth, we controlled for the origins of subsidies that firms receive by using two dummy variables, which are not mutually exclusive. The first is equal to one if firms have received regional or national subsidies (Local subsidies); the second is equal to one if firms have received foreign subsidies (Foreign subsidies) (Czarnitzki, Hanel, & Rosa, 2011). Sixth, we controlled for whether a firm is a member of a group (Group) (value of one) (Grimpe & Sofka, 2009). Seventh, we included a set of dummy variables considering the extent to which firms' employees have a university degree (Dummy degree) (Ardito & Messeni Petruzzelli, 2017). Eighth, another set of dummy variables was added to control for sector-specific effects (Dummy sector). Finally, the IIS reports sampling weights, which let us take into account potential sample biases and nonresponses (e.g., Riillo, 2017). Indeed, each firm in the sample is representative of the group of similar firms in the whole population. Therefore, we included such weights in the regression (Weights).

Table 1 Descriptive statistics.

Innovation ambidexterity Suppliers Customers Competitors Other sources Firm size Ongoing innovation Collaboration Local subsidies Foreign subsidies Group Weights

Mean

S.D.

Min

Max

0.036 0.283 0.191 0.132 1.014 15.996 0.220 0.119 0.110 0.028 0.360 27.482

0.074 0.451 0.393 0.339 1.647 2.932 0.420 0.315 0.313 0.164 0.480 77.581

0 0 0 0 0 0 0 0 0 0 0 0.001

0.250 1 1 1 7 23.450 1 1 1 1 1 1768.31

N = 5897.

threshold, thus limiting issues of multicollinearity (Cohen, Cohen, West, & Aiken, 2013). Table 3 presents the results of the Tobit regression. Model 1 includes the control variables, whereas Models 2, 3, and 4 are partial models that display the effect of each independent variable on innovation ambidexterity. Model 5 is the full model; it incorporates all the variables and confirms the results of Models 2–4. Specifically, Model 1 reveals that innovation ambidexterity is supported by the adoption of other knowledge sources (β = 0.047, p < .001), firm size (β = 0.007, p < .001), ongoing innovation activities (β = 0.084, p < .001), and local subsidies (β = 0.048, p < .001). Foreign subsidies have the opposite effect (β = −0.034, p < .05). Model 2 supports Hypothesis 1, in that the coefficient estimate of Suppliers is positive and significant (β = 0.113, p < .001). Similarly, Model 3 reveals that sourcing knowledge from customers positively affects innovation ambidexterity, supporting Hypothesis 2 (β = 0.094, p < .001). In line with Hypothesis 3, Model 4 suggests that knowledge originating from competitors is useful for jointly developing radical and incremental innovations. We also scrutinized the differences in the marginal effects of each variable, revealing that suppliers represent the most impactful knowledge source, followed by customers and, then, competitors. Indeed, the coefficient estimate of Suppliers is significantly higher than that of Customers (F = 5.30, p < .05) and Competitors (F = 43.28, p < .01), and the coefficient estimate of Customers is significantly higher than that of Suppliers (F = 12.69, p < .01). 5. Discussion and conclusions 5.1. Main findings This study elucidates the relationship between external knowledge sourcing – as a means of implementing inbound open innovation strategies – and ambidexterity performance. In detail, we examine the contribution of sourcing knowledge from three different supply chain stakeholders (i.e., suppliers, customers, and competitors) to innovation ambidexterity, that is, the ability of firms to develop radical and incremental innovations simultaneously (Dunlap et al., 2016; Lin et al., 2013). Based on a sample of 5897 firms that participated in the IIS (2008–2010), according to the proposed hypotheses, our findings indicate that all three knowledge sources lead firms to achieve innovation ambidexterity, although the magnitude of their effects is not the same. In detail, sourcing knowledge from suppliers allows firms to enhance their radical innovation capabilities owing to the possibility of tapping into novel product/process technologies and components that expand recombination opportunities and limit competency traps (Clauß, 2012; Li & Tang, 2010; Song & Thieme, 2009). At the same time, suppliers' knowledge may favor incremental innovation activities since such knowledge is helpful in improving production processes and/ or refining some product components over time (Das et al., 2006; Lii &

3.3. Model specification The dependent variable of this study is double bounded and assumes values between 0 and 0.25. Therefore, it falls into the category of limited dependent variables (Long, 1997). In this case, linear regressions (e.g., ordinary least squares) lead to incorrect parameter estimations, making them less than ideal (Wiersema & Bowen, 2009; Wooldridge, 2012). Therefore, following previous studies (e.g., Ardito et al., 2018; Banerjee & Cole, 2010), we corrected this issue by adopting a Tobit regression, which, indeed, is the econometric technique that is considered the most suitable for managing limited dependent variables (Wooldridge, 2012). 4. Results Table 1 shows the descriptive statistics, while Table 2 presents the pairwise correlations. The correlation values are all below the 0.70 5

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Table 2 Pairwise correlations.

1- Innovation ambidexterity 2-Suppliers 3-Customers 4-Competitors 5-Other sources 6-Firm size 7-Ongoing innovation 8-Collaboration 9-Local subsidies 10-Foreign subsidies 11-Group 12-Weights

1

2

3

4

5

6

7

8

9

10

11

12

1 0.462⁎ 0.441⁎ 0.361⁎ 0.512⁎ 0.210⁎ 0.419⁎ 0.339⁎ 0.303⁎ 0.150⁎ 0.199⁎ −0.056⁎

1 0.516⁎ 0.409⁎ 0.628⁎ 0.184⁎ 0.531⁎ 0.337⁎ 0.335⁎ 0.151⁎ 0.177⁎ −0.061⁎

1 0.508⁎ 0.570⁎ 0.190⁎ 0.445⁎ 0.362⁎ 0.304⁎ 0.173⁎ 0.223⁎ −0.0515⁎

1 0.533⁎ 0.161⁎ 0.354⁎ 0.317⁎ 0.241⁎ 0.161⁎ 0.191⁎ −0.058⁎

1 0.267⁎ 0.595⁎ 0.527⁎ 0.418⁎ 0.288⁎ 0.298⁎ −0.110⁎

1 0.231⁎ 0.227⁎ 0.142⁎ 0.134⁎ 0.429⁎ −0.190⁎

1 0.448⁎ 0.374⁎ 0.230⁎ 0.256⁎ −0.091⁎

1 0.362⁎ 0.311⁎ 0.251⁎ −0.083⁎

1 0.283⁎ 0.143⁎ −0.063⁎

1 0.135⁎ −0.042⁎

1 −0.172⁎

1

N = 5897. ⁎ p < .05. Table 3 Results of the Tobit regression (with robust s.e. in parentheses). Model 1 Suppliers Customers Competitors Other sources Firm size Ongoing innovation Collaboration Local subsidies Foreign subsidies Group Weights Dummy degree Dummy sector Constant F statistic Log pseudolikelihood

s.e.

Model 2 0.113

⁎⁎⁎

s.e.

Model 3

s.e.

Model 4

(0.008) (0.008) ⁎⁎⁎

(0.002) (0.001) (0.008) (0.010) (0.009) (0.015) (0.008) (0.000)

(0.036)

0.033⁎⁎⁎ 0.006⁎⁎⁎ 0.059⁎⁎⁎ 0.015 0.039⁎⁎⁎ −0.023 −0.004 0.000 Included Included −0.251⁎⁎⁎ 52.31⁎⁎⁎ −581.13

Model 5 ⁎⁎⁎

0.094⁎⁎⁎ 0.047⁎⁎⁎ 0.007⁎⁎⁎ 0.084⁎⁎⁎ 0.013 0.048⁎⁎⁎ −0.034⁎ −0.009 0.000⁎ Included Included −0.249⁎⁎⁎ 45.93⁎⁎⁎ −708.03

s.e.

0.038⁎⁎⁎ 0.006⁎⁎⁎ 0.074⁎⁎⁎ 0.009 0.0433⁎⁎⁎ −0.027+ −0.012 0.000+ Included Included −0.249⁎⁎⁎ 49.11⁎⁎⁎ −622.75

(0.002) (0.001) (0.008) (0.009) (0.009) (0.014) (0.007) (0.000)

(0.035)

(0.002) (0.001) (0.008) (0.010) (0.009) (0.015) (0.008) (0.000)

(0.035)

0.050 0.042⁎⁎⁎ 0.007⁎⁎⁎ 0.082⁎⁎⁎ 0.011 0.047⁎⁎⁎ −0.033⁎ −0.010 0.000⁎ Included Included −0.248⁎⁎⁎ 45.69⁎⁎⁎ −688.36

(0.009) (0.002) (0.001) (0.008) (0.010) (0.009) (0.015) (0.008) (0.000)

(0.035)

0.097 0.068⁎⁎⁎ 0.020⁎⁎⁎ 0.026⁎⁎⁎ 0.006⁎⁎⁎ 0.055⁎⁎⁎ 0.011 0.037⁎⁎⁎ −0.020 −0.007 0.000 Included Included −0.252⁎⁎⁎ 53.05⁎⁎⁎ −526.24

s.e. (0.008) (0.008) (0.009) (0.002) (0.001) (0.008) (0.009) (0.009) (0.014) (0.007) (0.000)

(0.034)

N = 5897. + p < .10. ⁎ p < .05. ⁎⁎ p < .01. ⁎⁎⁎ p < .001.

5.2. Theoretical implications

Kuo, 2016). Similarly, customers' knowledge, on the one hand, benefits radical innovations because it stimulates “outside-the-box” thinking (Yli-Renko & Janakiraman, 2008) and helps firms anticipate new trends in the market and identify (latent) market needs (Cohen et al., 2002; Love et al., 2011); on the other hand, customers play a role in improving and customizing existing products through information about their complaints and special requirements (Morgan et al., 2018; Tranekjer & Søndergaard, 2013). Finally, competitors' knowledge may push firms to imitate them (Katila & Chen, 2008) and refine their products to cope with high competitive intensity, hence favoring incremental innovation activities (Cheng & Krumwiede, 2012; Zhou et al., 2007); on the other hand, to overcome competitors, firms can start from their knowledge to engage in radical innovation activities (Baker & Sinkula, 2007). Bearing these results in mind, we note that although all the knowledge sources examined are positively related to innovation ambidexterity, analysis of their marginal effects suggests that sourcing knowledge from suppliers is more important than sourcing knowledge from customers and competitors, with competitors being the least relevant knowledge source. This finding is consistent with the fact that, compared to competitors, suppliers and customers have more direct interactions with firms. Therefore, there are more opportunities to exploit suppliers' and customers' knowledge in both radical and incremental innovation activities. These results provide some relevant theoretical and managerial implications.

From a theoretical perspective, first, we contribute to the (inbound) open innovation literature, particularly with regard to knowledge search strategies (Natalicchio et al., 2017; Savino et al., 2017). We highlight whether and the extent to which three specific knowledge sources affect firms' innovativeness. Thus, we overcome the oversimplistic concept of external search breadth (Laursen & Salter, 2006), which neglects the possibility of clearly identifying which external knowledge source(s) firms should rely on to successfully innovate. Indeed, this concept underestimates the heterogeneity of external knowledge sourcing activities, especially because it does not make it possible to capture the relative value of each source. In other words, the novelty of this study lies in the fact that our focus is less on the extent to which firms should tap into diverse knowledge sources but more on their specific value. Second, we contribute to the ambidexterity literature (Cao et al., 2009; Lin et al., 2013) since, to the best of our knowledge, this study is one of the first attempts to empirically test the influence of (external) knowledge sourcing strategies on a balance measure that jointly assesses radical and incremental innovation performance. Specifically, previous studies have examined the effects of the three considered knowledge sources on either radical or incremental innovation performance, which has led to contrasting results because each knowledge source has been found to influence both types

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of innovation performance (e.g., Abdel Aziz & Rizkallah, 2015; CruzGonzález et al., 2015; Spanjol et al., 2012). We reconcile these contrasting results by showing that they are complementary and not substitutive to each other. Third, the supply chain management literature (Lii & Kuo, 2016) may further benefit from this study, in that we redirect attention from operational performance to innovation performance. This shift in focus is consistent with the emerging supply chain management research (Lee, Ooi, Chong, & Sohal, 2018), which aims to elucidate how to improve the design of supply chain management strategies focused on practices that will boost a firm's innovativeness.

production, IT, and logistics process innovations on ambidexterity performance. Business Process Management Journal, 24(5), 1271–1284. Ardito, L., & Messeni Petruzzelli, A. (2017). Breadth of external knowledge sourcing and product innovation: The moderating role of strategic human resource practices. European Management Journal, 35(2), 261–272. Arora, A., Cohen, W. M., & Walsh, J. P. (2016). The acquisition and commercialization of invention in American manufacturing: Incidence and impact. Research Policy, 45(6), 1113–1128. Arundel, A., & Smith, K. (2013). History of the community innovation survey. In F. Gault (Ed.). Handbook of innovation indicators and measurement (pp. 60–70). United Kingdom: Edward Elgar Publishing Limited. Cheltenham. Baker, W. E., & Sinkula, J. M. (2007). Does market orientation facilitate balanced innovation programs? An organizational learning perspective. Journal of Product Innovation Management, 24(4), 316–334. Banerjee, P. M., & Cole, B. M. (2010). Breadth-of-impact frontier: How firm-level decisions and selection environment dynamics generate boundary-spanning inventions. Technovation, 30(7–8), 411–419. Benner, M. J., & Tushman, M. L. (2003). Exploitation, exploration, and process management: The productivity dilemma revisited. Academy of Management Review, 28(2), 238–256. Benner, M. J., & Tushman, M. L. (2015). Reflections on the 2013 decade award—“exploitation, exploration, and process management: The productivity dilemma revisited” ten years later. Academy of Management Review, 40(4), 497–514. Blindenbach-Driessen, F., & van den Ende, J. (2014). The locus of innovation: The effect of a separate innovation unit on exploration, exploitation, and ambidexterity in manufacturing and service firms. Journal of Product Innovation Management, 31(5), 1089–1105. Bolisani, E., & Bratianu, C. (2017). Knowledge strategy planning: An integrated approach to manage uncertainty, turbulence, and dynamics. 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5.3. Managerial implications From a managerial perspective, we contend that management choices are not (only) between search breadth and depth. Rather, we provide specific directions for firms' external search decisions, in which these directions suggest the potential value of three distinct knowledge sources to managers. Relatedly, since the ultimate value of a knowledge source for the searching firm is, ex-ante, ambiguous, this research may help managers overcome this concern. Specifically, we advise managers that sourcing knowledge from suppliers, customers, and competitors is important for improving ambidexterity performance; however, not all these sources are of equal value in enhancing firms' innovativeness in terms of the joint development of radical and incremental innovations. Specifically, if firms do not have the resources to tap into all three examined knowledge sources, they should choose among them in the following order: first, suppliers; second, customers; and, third, competitors. In turn, our findings may further guide managers in implementing proper supply chain management practices by adding information regarding the relevance of the stakeholder organizations in a firm's supply chain from a knowledge search perspective. 5.4. Limitations and future research directions One limitation of this paper is the measures used. Adopting the IIS limits the operationalization of some variables. For instance, the extent of reliance on external knowledge sources can be captured only through dummy variables. Higher flexibility in defining variables, such as the use of absolute measures, would benefit future studies. Dummy variables also hinder the possibility of examining the substitutive/cumulative effects derived from the simultaneous use of diverse knowledge sources, which opens another interesting line of inquiry. Additionally, the IIS focuses on the Italian context and allows cross-sectional studies only. Therefore, our study can be refined by examining other national contexts, providing cross-country comparisons, and/or employing longitudinal data. Furthermore, the influence of external knowledge sourcing activities is contingent upon factors such as absorptive capacity, human resource practices, internal learning mechanisms, international diversification, and corporate governance (e.g., family vs. nonfamily ownership) (Ardito & Messeni Petruzzelli, 2017; Bresciani, Giacosa, Broccardo, & Culasso, 2016; Campanella, Del Giudice, Thrassou, & Vrontis, 2016; Ferraris, Bresciani, & Del Giudice, 2016; Yan & Azadegan, 2017). Therefore, future research may analyze whether these contingent factors exert the same or different moderating effects on each specific knowledge source. Finally, due to limited data, we could not delve into when (e.g., temporal domain separation) and/or how (e.g., interfunctional vs. specialized teams) the knowledge gained from suppliers, customers, and competitors is actually managed. Naturally, this issue requires further investigation. References Abdel Aziz, H. H., & Rizkallah, A. (2015). Effect of organizational factors on employees' generation of innovative ideas: Empirical study on the Egyptian software development industry. EuroMed Journal of Business, 10(2), 134–146. Ardito, L., Besson, E., Petruzzelli, A. M., & Gregori, G. L. (2018). The influence of

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