Internationalizing the Coopetition Construct: Quadratic Effects on Financial Performance Under Different Degrees of Export Intensity and an Export Geographical Scope

Abstract

Although coopetition (simultaneous cooperation and competition) is likely to enhance financial performance if effectively managed, earlier investigations have overlooked the complexities of this relationship. Most notably, understanding the impact of moderating factors can help unpack the complexity of the association between coopetition and performance. Therefore, grounded in resource-based theory and the relational view, this study focuses on the quadratic relationship between coopetition and financial performance under different degrees of export intensity and export geographical scope. Using survey responses from 101 wine producers in New Zealand, the empirical results show that coopetition has a nonlinear (inverted U-shaped) relationship with financial performance. Furthermore, export intensity and export geographical scope positively moderate this quadratic association. As such, for underresourced firms with overseas market potential, decision makers should consider the merits of combining the benefits of coopetition with those from an internationalized business model. This arrangement can help them navigate these potentially paradoxical forces, assuming they engage with trustworthy and complementary rivals in coopetition partnerships.

Keywords

coopetition financial performance internationalization resource-based theory relational view

Coopetition is a “paradoxical relationship between two or more actors, regardless of whether they are involved in horizontal or vertical relationships, simultaneously in cooperative and competitive interactions” (Bengtsson and Kock 2014, p. 180). Since coopetition strategies are intended to equip underresourced companies with new tangible and intangible assets, as well as different opportunities, it is not surprising that a substantial amount of research has been published on the positive relationship between these activities (or, alternatively, similar notions like interfirm cooperation or alliances) and measures of company performance (see Estrada and Dong 2020; Le Roy and Czakon 2016; Rindfleisch and Moorman 2003; Ritala 2012).

The relationship between coopetition and financial performance has sometimes been conceptualized (and evaluated) in investigations in rather simplistic terms, such as being linear and lacking moderating factors, leading to calls for further research (see Crick 2019; Czakon et al. 2020; Shu, Jin, and Zhou 2017). This research is important because it is unclear how decision makers can best navigate the interplay between cooperation and competition so that they can collaborate with their rivals to enhance their performance, but not to the extent that these interfirm partnerships become counterproductive. Consequently, there is a need for studies to explore the complexities of this relationship to assist companies in effectively managing the paradoxical forces that surround coopetition activities (see Mattsson and Tidstrom 2015; Park, Srivastava, and Gnyawali 2014).

Table 1 summarizes key studies regarding how certain internationalizing firms cooperate with their competitors to enhance performance within their overseas markets. The current study focuses on coopetition practices among small, underresourced exporting firms, whereas other researchers have examined how businesses in formal alliances with multinational corporations engage in these strategies to create value for their customers, develop efficient and effective supply chains, and expand their product lines (Bello, Katsikeas, and Robson 2010; Crick and Crick 2020; Luo 2004; Luo and Tung 2007). Much of the earlier coopetition research has focused on domestic arenas, which is a somewhat limited perspective given that the decision to internationalize is a fundamental consideration for many owner-managers and can expand the size of their market even though these strategies are often resource intensive (Ahi et al. 2017; Crick and Crick 2016; Hagen, Zuchella, and Ghauri 2019; Lu and Beamish 2001; Nemkova et al. 2015; Yang and Gabrielson 2018). In fact, it is widely recognized that internationalized firms behave differently from those in predominately domestic markets, for example, adapting their offerings to varied cultures, languages, and traditions (Calof and Beamish 1995; Cadogan et al. 2012; Hitt et al. 2006). Prior experiences may result in decision makers not following a forward-moving internationalization path and instead entering and exiting overseas markets (Crick, Crick, and Chaudhry 2020) or amending their modes of market entry (Elia, Larsen, and Piscitello 2019). However, if managed effectively, the mutual benefits of sharing resources and capabilities potentially reduce costs and the level of rivalry between firms in a coopetition relationship (Felzensztein, Deans, and Dana 2019; Felzensztein et al. 2014).

Table 1.

Overview of Literature on International Aspects of Coopetition.

Source	Publication	Paper Type	Research Design	Country Context(s)	Description and Key Findings
Chetty and Wilson (2003)	International Business Review	Empirical	Mixed methods	New Zealand	Interfirm cooperation can occur in numerous respects, such as with supply chain partners and between competitors. By cooperating with rivals, entrepreneurs can learn new ways to compete within their sectors and enter export markets.
Luo (2004)	Journal of International Management	Conceptual	Not applicable	Not applicable	Multinational corporations can engage in coopetition activities with various organizations. This form of international-level coopetition can involve working with public sector organizations.
Luo (2005)	Journal of World Business	Conceptual	Not applicable	Not applicable	Multinational corporations can have varying degrees of competitiveness and cooperation between their departmental functions, as well as among their subsidiaries spread across the world. The competitiveness depends on how resources are allocated to these divisions of such large companies.
Freeman, Edwards, and Schroder (2006)	Journal of International Marketing	Empirical	Qualitative	Australia	Smaller-sized companies have various resource constraints that limit the performance potential of their assets. By engaging in certain alliances, small businesses can obtain vital resources and capabilities that allow them to thrive in international markets.
Luo (2007)	Journal of World Business	Conceptual	Not applicable	Not applicable	When firms engage in coopetition on a global stage, their willingness to cooperate and/or compete with their rivals can be influenced by factors within the firm, as well as at an environmental level.
Luo and Tung (2007)	Journal of International Business Studies	Conceptual	Not applicable	Not applicable	Coopetition among globally oriented firms can help decision makers ensure that their firms survive and grow in international markets. Coopetition is an organization-wide mindset in which collaborating with competitors is perceived as an important activity. That is, coopetition-oriented beliefs involve decision makers assuming that it is beneficial to collaborate with rivals in various capacities.
Bello, Katsikeas, and Robson (2010)	Journal of Marketing	Empirical	Quantitative	United Kingdom	In the context of large-scale international-level alliances, cooperative and competitive forces can be fostered via the notion of accommodation. This is where the rival firms manage their self-serving needs, which amplify aggressive behaviors that can affect their success in international markets.
Kock, Nisuls, and Soderqvist (2010)	Competitiveness Review	Empirical	Qualitative	Finland	Coopetition-based relationships are heterogeneous and vary depending on the cooperative and competitive forces at play. By collaborating with industry rivals, companies can learn new ways to compete within their markets to secure opportunities that would not exist under individualistic business models. Coopetition helps smaller-sized enterprises improve their performance in international markets.
Gnyawali and Park (2011)	Research Policy	Empirical	Quantitative	Japan and South Korea	Coopetition is driven by firm-level and industry-level factors that motivate the managers of large (internationally oriented) corporations to collaborate with their competitors. Certain organizational capabilities can enhance (moderate) the link between coopetition strategies and their performance outcomes.
Felzensztein et al. (2014)	Journal of Business Research	Empirical	Quantitative	Australia, New Zealand, Chile, and Argentina	Interfirm cooperation was found to be highly effective for branding regional-level clusters and improving the individual performance of the rival firms involved. By engaging in these activities, companies can market their products and services domestically and internationally to a greater volume of customers than if they competed under individualistic business models.
Shu, Jin, and Zhou (2017)	Journal of International Marketing	Empirical	Quantitative	China	Coopetition yields higher levels of international joint venture performance. However, this link is positively moderated by high foreign equity share, low partner cultural compatibility, high technological turbulence, and/or high market growth. Coopetition negatively affects international joint venture performance under low levels of foreign equity share, technological turbulence, and/or market growth, or at high levels of partner cultural compatibility.
Vanyushyn et al. (2018)	Review of Managerial Science	Empirical	Quantitative	Sweden	Internationalization, accompanied by coopetition, can help organizations foster their innovative capabilities to secure higher levels of performance. This effect depends on the magnitude of adjustments that owner-managers make to their business models.
Felzensztein Deans, and Dana (2019)	Journal of Small Business Management	Empirical	Qualitative	Argentina, Chile, and New Zealand	While collaborating with competitors can lead to higher levels of company performance in export markets (as well as in domestic markets), it is more effective for small businesses to have access to an industry-level governing body that helps them gain access to customers and supply chain partners in their international markets. Therefore, coopetition-oriented behaviors, alongside industry support, can enhance firms’ abilities to reach foreign markets and improve their performance in an international arena.
Ryan et al. (2019)	International Marketing Review	Empirical	Qualitative	Republic of Ireland	Smaller organizations engage in horizontal networks to enter and operate within their international markets. By engaging in these forms of international-level coopetition-oriented behaviors, small firms can acquire information about their customers and supply chain partners, as well as other types of competitive intelligence. If firms obtain this information from their competitors, they can quickly enter international markets and increase their performance.
Crick and Crick (2020)	Industrial Marketing Management	Conceptual	Not applicable	Not applicable	The COVID-19 pandemic has led to many organizations (small and large) engaging in coopetition. These coopetition strategies include rival pharmaceutical corporations (from different countries) collaborating to expedite the process of developing a vaccine. Despite these benefits of coopetition strategies (including those in an international arena), these interfirm relationships can be challenging to manage because of the fragile nature of the coopetition paradox.

Notes: This list provides a brief overview of how coopetition has been explored in an international arena. Some of these investigations addressed concepts that are similar to coopetition, such as interfirm cooperation and alliances. Nonetheless, they demonstrate pertinent issues surrounding the internationalization of the coopetition construct.

Thus, grounded in two complementary perspectives, namely, resource-based theory and the relational view (Barney 2018; Dyer, Singh, and Hesterly 2018; Lavie 2006), the current investigation aims to evaluate the quadratic relationship between coopetition and financial performance under different degrees of export intensity and export geographical scope. By achieving this research objective, it makes two contributions to the international marketing literature. First, the underlying mechanisms of the coopetition–performance relationship are examined through the potential for quadratic and moderating effects (Czakon et al. 2020; Hoffmann et al. 2018; responding to Shu, Jin, and Zhou 2017). These complexities are designed to challenge certain earlier assumptions pertaining to coopetition having a linear (positive) link with financial performance (Bengtsson and Kock 2000; Hannah and Eisenhardt 2018; Ritala 2012). Second, new insights emerge on how organizations can combine the benefits of collaborating with their competitors with those from an internationalized business model (measured via export intensity and export geographical scope) to illustrate how these companies can manage the interplay between cooperation and competition across overseas markets (following Chetty and Wilson 2003; Luo 2007; Ryan et al. 2019).

To make these two contributions, this article proceeds as follows. First, the underpinning theories are outlined to develop the components of the conceptual framework. Second, the methodology, involving a survey of 101 wine producers in New Zealand, is described. Third, the key results are presented, revealing that coopetition has a quadratic link with financial performance but is positively moderated by export intensity and export geographical scope. Fourth, these results are discussed in relation to the existing international marketing literature. Finally, some actionable practitioner implications and recommendations are presented, along with several limitations and avenues for future research.

Theory and Hypotheses

Facets of Resource-Based Theory

Resource-based theory focuses on the relationship between organizational resources/capabilities and company performance (Barney 1991; Hunt and Morgan 1995; Nason and Wiklund 2018). Barney (1991) notes that the value, rarity, inimitability, and nonsubstitutability (VRIN) framework can be utilized to assess the extent to which firms’ tangible and intangible assets lead to sustainable competitive advantages. A sustainable competitive advantage is seen when businesses have outperformed their competitors over an extended period, while managing the volatility of their markets (Oliver 1997). In the current study, financial performance served as the outcome variable (following Morgan, Vorhies, and Mason 2009; Vorhies and Morgan 2005) to investigate the benefits and drawbacks of collaborating with competitors in an international arena.

This theoretical lens has largely focused on internal firm issues, namely, how leveraging organizational resources and capabilities can boost performance (Barney 1991; Hunt and Morgan 1995). Researchers nonetheless recognize that environmental-level contingencies can affect company performance (see Barney 2001; Cadogan, Kuivalainen, and Sundqvist 2009; Priem and Butler 2001; Schilke 2014). In the context of coopetition, resource-based theory to some extent helps explain the mutual benefits that organizations obtain by collaborating rather than competing with their industry rivals to reduce competitive rivalry and overcome resource disadvantages (see, e.g., Bengtsson, Raza-Ullah, and Vanyushyn 2016; Bouncken et al. 2015; Felzensztein and Deans 2013; Gnyawali and Charleton 2018; Ritala, Golnam, and Wegmann 2014). To illustrate, many firms are underresourced in terms of lacking funds for promotions, vital equipment, tools, information, and skilled employees that allow them to innovate and deliver value to their customers. It follows that relational factors are often highly important in cooperative interactions between two or more organizations (Hunt and Derozier 2004); hence, Barney (2018) recommended that resource-based theory should account for a stakeholder perspective. By integrating resource-based theory with the relational view (see Dyer and Singh 1998; Dyer, Singh, and Hesterly 2018; Lavie 2006), researchers can explore how businesses have the potential to combine their own assets with those from their coopetition partners. At the same time, decision makers must carefully manage the coopetition paradox to minimize the possibility of conflict, power imbalances, and opportunistic behaviors (following Raza-Ullah, Bengtsson, and Kock 2014; Tidstrom 2014).

The notion of being underresourced does not just apply to smaller firms (as per Felzensztein, Gimmon, and Deans 2018; Granata et al. 2018; Rusko 2011); it has implications outside of the scope of this study for larger corporations that cooperate with certain rivals to address a lack of key assets (Crick and Crick 2020; Gnyawali and Park 2011; Luo 2004). Large-scale coopetition is prominent within the airline sector, which includes strategic alliances (like Star Alliance and SkyTeam). These alliances consist of rival carriers that collaborate in some areas, such as offering frequent flyer membership benefits, but compete in other ways, not the least of which is being financially responsible for their own profits (Lazzarini 2007).

Conceptual Framework

Grounded in the two previously described theoretical lenses (Barney 2018; Dyer, Singh, and Hesterly 2018; Lavie 2006), the current study’s conceptual framework (Figure 1) included three research hypotheses and three main control paths.¹ First, coopetition was expected to have a quadratic relationship with financial performance (Crick and Crick 2021; Luo, Rindfleisch, and Tse 2007). Second, export intensity and export geographical scope were anticipated to positively moderate this nonlinear (inverted U-shaped) association (Chetty and Wilson 2003; Felzensztein, Deans, and Dana 2019). Third, the outcome variable (namely, financial performance) was controlled for firm size, firm age, and competitive intensity, as additional factors that might explain its variance (Crick and Crick 2020; Leonidou et al. 2013; Vorhies, Orr, and Bush 2011). Fourth, several procedural control variables were modeled to test these complex paths, namely, the nonlinear (inverted U-shaped) and interaction effects (Cadogan, Kuivalainen, and Sundqvist 2009).

Figure 1.

Conceptual framework.

The Quadratic Association Between Coopetition and Financial Performance

Decision makers engage in coopetition to equip their firms with new assets (resources/capabilities) and opportunities that they could not obtain if they operated under an individualistic business model (Ritala 2012; Rusko 2011). For example, guided by an infusion of resource-based theory and the relational view, Crick and Crick (2020) provided illustrations regarding how organizations from different sectors and countries have been implementing coopetition strategies in response to the COVID-19 pandemic. They argued that because of the economic and health-related shocks that the novel coronavirus has caused, various firms (small and large) have found that it is beneficial to work with rivals to access vital equipment and information, as well as sharing certain risks and costs that might make the difference between surviving and failing in these volatile circumstances. Although motives for engaging in coopetition activities vary, the underlying aim is to obtain mutually beneficial outcomes rather than employing an individualistic business model (linking with Bengtsson and Kock 2014; Felzensztein, Deans, and Dana 2019; Hannah and Eisenhardt 2018). Nonetheless, the management of coopetition relationships is likely to be complex because of competitive forces that might limit the performance outcomes of these interfirm partnerships (Mattsson and Tidstrom 2015; Park, Srivastava, and Gnyawali 2014).

Consequently, particular studies indicate that it can be potentially dangerous for decision makers to engage in “too little” or “too much” coopetition (see Crick 2019; Luo, Rindfleisch, and Tse 2007). For instance, “cooperation with competitors needs to be carefully considered and judiciously executed because an over-reliance on highly intensive competitor alliances may be just as harmful as under-using such alliances. Excessive cooperation [with rivals] may lead to free-riding and opportunistic exploitation, a potential loss of proprietary, technological, and marketing capabilities, and a possible dulling of a firm’s incentives to stay customer-focused” (Luo, Rindfleisch, and Tse 2007, p. 81). With “too little” coopetition, decision makers may experience difficulties operating within their markets, because resource constraints might prevent them from creating value for their customers (Gnyawali and Charleton 2018). Even so, engaging in “excessive” coopetition could result in several problematic side effects, such as experiencing tensions (conflict, power imbalances, and opportunistic behaviors), losing critical intellectual property, and having their points of difference diluted (Crick and Crick 2021; Ritala and Hurmelinna-Laukkanen 2013; Tidstrom 2014). It follows that the coopetition paradox is a somewhat fragile notion, and it is often difficult for companies to judge how to best navigate these activities (Czakon and Czernek 2016; Czakon et al. 2020; Hoffmann et al. 2018). Consequently, it is likely that:

H₁: A quadratic relationship exists between coopetition and financial performance.

The Moderating Role of Export Intensity

In an international context, the export intensity (or ratio) refers to the extent to which organizations export their products (Cadogan, Kuivalainen, and Sundqvist 2009). Various investigations have highlighted the benefits of exporting, as opposed to concentrating on domestic markets. These advantages include developing a first-mover status and having access to new opportunities, like a larger portfolio of customers and supply chain network members (see Bell, Crick, and Young 1998; Hagen, Zuchella, and Ghauri 2019; Knight and Cavusgil 2004; Kuivalainen, Sundqvist, and Servais 2007; McDougall and Oviatt 2000). In fact, some domestic markets are small, and if firms in these markets do not export, they limit their scalability by being able to reach only a finite number of customers (Crick and Crick 2016; Kahiya 2020; Ojala and Tyrvainen 2007). Exporting can help decision makers achieve their short-term and long-term goals, including yielding higher levels of sales and lowering their operating costs, leading to increased profits (Cavusgil and Zou 1994; Sousa, Ruzo, and Losado 2010; Spence and Crick 2006). Hence, through exporting (including working with network partners like investors and government support providers in addition to intermediaries such as agents and distributors), firms can improve their performance (Crick and Crick 2020; Katsikeas, Leonidou, and Morgan 2000; Leonidou, Katsikeas, and Samiee 2002; Spence and Crick 2001; Sraha et al. 2020).

With regard to coopetition, both resource-based theory and the relational view highlight the importance of managing high-quality interfirm partnerships to leverage resources and capabilities effectively and foster amicable professional networks (Dyer, Singh, and Hesterly 2018; Lavie 2006). If firms are competing for predominately domestic markets, the coopetition paradox may be challenging to manage (Crick and Crick 2021; Felzensztein, Deans, and Dana 2019). As an illustration, if firms sell their products to domestic establishments (e.g., restaurants, hotels, bars), the number of customers is likely to be limited. This domestically focused strategy might amplify the competitiveness that underpins coopetition activities—potentially outweighing the degree of collaboration. However, by internationalizing (via higher levels of export intensity), firms can increase their presence in other countries, which might reduce the degree of rivalry that would exist if they were to compete mostly on a domestic basis (building upon Kock, Nisuls, and Soderqvist 2010). Put another way, these new opportunities can overcome some of the challenges of coopetition strategies, not least of which is the risk of interfirm tensions (extending the work of Luo, Rindfleisch, and Tse 2007; Raza-Ullah, Bengtsson, and Kock 2014). Consequently, it is expected that:

H₂: Export intensity positively moderates the quadratic relationship between coopetition and financial performance.

The Moderating Role of Export Geographical Scope

Different ways exist to measure firms’ degree of internationalization (Crick and Crick 2014). In addition to export intensity, export geographical scope is a different metric that describes the degree to which companies take a concentrated versus spreading (diversified) approach to their exporting practices; this would likely be assessed through the number of countries that firms export their products to (Piercy 1981; Tallman and Li 1996). Export intensity and export geographical scope do not always go hand in hand. As an example, although firms’ internationalization processes vary, a business might export almost all of its products (a high export ratio) but focus only on one or a small number of international markets, perhaps because of cultural and/or economic similarities (Katsikeas and Leonidou 1996; Piercy 1981). Moreover, international marketing scholars have had a long-lasting debate about whether it is preferable to concentrate on a single (or a few) export market(s) or diversify (spread) to numerous countries as respective firms’ geographic expansion increases (see D’Angelo et al. 2013; Piercy 1981). Each of these strategies has benefits and drawbacks, including the huge risk if one market fails and the difficulties in adapting to different cultures, regimes, economies, and languages depending on firms’ capabilities (Piercy 1981; Piercy, Kaleka, and Katsikeas 1998).

The latter perspective suggests that by spreading to different export markets, firms can diversify their risk between these countries (Lu and Beamish 2004; Oliveira et al. 2018). Put another way, higher levels of export geographical scope can potentially help organizations minimize the “liability of foreignness” and avoid losing large amounts of income (and reputational factors) if one of these countries becomes unprofitable as a result of an unforeseen political event, a natural disaster, changing consumer preferences, higher taxes, or loss of contracts with agents and distributors (Olmos and Diez-Vial 2015; Piercy 1981). The benefit of export geographical scope is linked to coopetition in that, as mentioned, firms might suffer if they restrict their operations to predominately domestic markets because of the finite number of customers (Crick and Crick 2016; Ojala and Tyrvainen 2007). Yet, if companies increase their export geographical scope, they may obtain more opportunities (via new customers and supply chains) that would not exist in these domestic arenas (Piercy 1981; Piercy, Kaleka, and Katsikeas 1998). Thus, by obtaining a larger presence beyond their domestic market, firms might reduce the degree of rivalry with their domestic competitors (like those within a certain cluster) to counterbalance the harmful effects of coopetition, such as losing vital knowledge (expanding on Bouncken et al. 2018; Czakon and Czernek 2016; Ritala and Hurmelinna-Laukkanen 2013). Export geographical scope could therefore be another form of internationalization (other than export intensity) that stabilizes the risks of coopetition. Consequently:

H₃: Export geographical scope positively moderates the quadratic relationship between coopetition and financial performance.

Control Variables

The outcome variable (namely, financial performance) was controlled for firm size, firm age, and competitive intensity. First, larger businesses are more likely to outperform smaller rivals because larger firms possess a greater volume of assets (Barney 1991; Hunt and Morgan 1995). Thus, firm size was modeled as a control variable. Second, organizations can succeed within their markets in various ways other than through size and scale (Barney 2001; Nason and Wiklund 2018). For example, older companies can potentially leverage their heritage and experience within their markets in ways that younger firms might struggle to achieve (Vorhies, Orr, and Bush 2011). Accordingly, firm age featured as a control path. Third, as mentioned earlier, the competitive business environment is a prominent issue within the chosen theoretical lenses, as market-level forces can help or hinder company performance (see Leonidou et al. 2013; Priem and Butler 2001; Schilke 2014). For this reason, the outcome variable was controlled for competitive intensity. Fourth, several procedural controls were added to the model for statistical purposes, to evaluate the nonlinear (inverted U-shaped) and interaction effects. The methodology follows in the next section.

Methods

Population of Interest

An ideal empirical context for coopetition-based research is a sector that is both highly cooperative and highly competitive (as per Bengtsson and Raza-Ullah 2016; Crick and Crick 2020; Hannah and Eisenhardt 2018). For instance, earlier coopetition-oriented work has been conducted in high-tech firms (Gnyawali and Park 2011), automotive manufacturers (Akpinar and Vincze 2016), tourism services (Czakon and Czernek 2016), agricultural growers (Felzensztein and Deans 2013), and alcohol producers (Mathias et al. 2018). Authors have typically selected one industry (in one country) to assess the propensity of coopetition strategies (see Rusko 2011; Ryan et al. 2019). The current study features the New Zealand wine industry as the empirical context, as it hosts these collaborative and rivalrous forces (Felzensztein et al. 2014).²

In fact, wine producers (around the world) are well-known for engaging in several forms of coopetition, such as informal resource and capability-sharing activities, as well as engaging in alliances through contractually binding agreements (Felzensztein et al. 2014; Granata et al. 2018). Moreover, wine producers are typically involved in varying degrees of internationalization, especially exporting, but also including other product-market strategies, such as selling their produce to foreign customers in a wine tourism capacity (Crick and Crick 2015; Felzensztein, Deans, and Dana 2019). Although wine is a consumer good, producers are often involved in augmented operations like cafes, restaurants, and gift shops in addition to core wine sales, which may be undertaken via distributors in business-to-business relationships (Crick and Crick 2015). Yet, in light of the international theme of the current investigation, the research team focused on the core product sold, namely, bottled wine. Later, some profiling variables are presented to show how the final sample comprised wine producers located throughout New Zealand that were involved in different degrees of coopetition and internationalization.

Data Collection

The research team accessed a sampling frame of 726 New Zealand wine producers with their contact details.³ The New Zealand wine industry (as well as its equivalent in certain other countries) consists of mostly smaller businesses (Crick and Crick 2015; Granata et al. 2018). Coopetition is normally studied from the perspective of owner-managers, as these individuals typically possess the authority to decide on the magnitude of their coopetition activities (following Bengtsson and Raza-Ullah 2016; Felzensztein and Deans 2013). Therefore, owner-managers served as the key informants.

The research team developed an electronic survey (through Qualtrics) that contained operationalizations of the key constructs. This survey was pretested with expert academics (n = 10) and practitioners (n = 15) to check that the questions were formatted in a clear and understandable manner (Reynolds and Diamantopoulos 1998). In the latter case, when the survey was discussed with practitioners (decision makers within the New Zealand wine sector, including those in the sector’s governing body and former owner-managers), the core focus was to ensure that the operationalizations were correctly adapted to the population of interest (Hulland, Baumgartner, and Smith 2018). For instance, as presented later, the selected measurement scale for the coopetition construct was originally used in the context of technologically oriented firms in Germany (see Bouncken et al. 2018; Bouncken and Kraus 2013). Due to various industry-level and national-level factors, differences between this setting and the New Zealand wine industry were expected. Thus, care was taken to assess the appropriateness of these measurement scales to reduce the chance of misunderstanding by the key informants when the survey was administered (following Katsikeas, Samiee, and Theodosiou 2006). This assessment extended to clarifying that the core international-level variables (export intensity and export geographical scope) were relevant to these organizations; no problems were encountered in this regard.

Since the pretesting stage did not reveal any concerns, the pilot study was launched (n = 20). During the pilot study (sent to actual owner-managers of New Zealand wine producers via a random sample), the descriptive statistics of the measurement items were checked, and the likely response rate was gauged (Hunt, Sparkman, and Wilcox 1982). No changes were made to the survey, and subsequently the data for the core study were collected (n = 81). Owing to the fact that the operationalizations for the pilot study were the same as those utilized in the core study, these two data sets were combined (as per Morgan and Hunt 1994). This sample did not include the data from the pretesting stage but consisted of responses from owner-managers across the New Zealand wine industry (20 plus 81). The final sample (n = 101) accounted for a 13.91% response rate, which was deemed acceptable on the basis of similar values reported within the international marketing literature (Nemkova et al. 2015; Theodosiou and Katsikea 2013). Moreover, the statistical data were checked for nonresponse bias with t-tests for each latent variable, which displayed nonsignificant differences (following Armstrong and Overton 1977).⁴ As described later, several procedural and statistical assessments were used to ensure that the final sample size was sufficient to test the research hypotheses and control paths.

Various wine producers took part in this investigation, including different numbers of employees, export activities, years in the market, and annual sales. Survey responses were collected from across New Zealand, including the well-known wine regions, like Marlborough, Hawke’s Bay, and Central Otago in addition to smaller areas, such as the Wairarapa, Gisborne, and the Bay of Plenty. This meant that the statistical results were based on a national-level study rather than one part of the country. Appendix A displays information about the sampled organizations’ operations, their locations, and the respondents’ job titles.

Operationalizations

The constructs within the conceptual framework were operationalized as follows. First, coopetition (COOP) was measured with three items on a seven-point Likert scale ranging from 1 = “Very strongly disagree” to 7 = “Very strongly agree” (adapted from Bouncken and Kraus 2013; Bouncken et al. 2018). Coopetition has been operationalized in various ways throughout the wider marketing literature. These methods include single indicators, archival proxies, regression-based residual techniques, and multiplication tools (e.g., Bengtsson, Raza-Ullah, and Vanyushyn 2016; Ritala 2012; Shu, Jin, and Zhou 2017). The adopted scale captures the organizations’ propensity of coopetition activities, or the degree to which they collaborate with their competitors (recommended by Bengtsson and Raza-Ullah 2016). Second, performance can be measured in various capacities, because decision makers may have particular objectives not necessarily involving sustainable competitive advantages, such as survival at a time of crisis (see Crick and Crick 2020; Katsikeas et al. 2016). For instance, some decision makers evaluate their performance using financial metrics (see Bolton 2004; Vorhies, Orr, and Bush 2011). Financial performance (PERF) was measured using five items on a seven-point interval scale ranging from 1 = “Much worse than competitors” to 7 = “Much better than competitors” (adapted from Morgan, Vorhies, and Mason 2009; Vorhies and Morgan 2005). Third, export intensity (EXPORTS) was captured by firms’ export ratios (adapted from Cadogan, Kuivalainen, and Sundqvist 2009).

Fourth, export geographical scope (SCOPE) was measured as the number of export markets that these companies operate in (Oliveira et al. 2018; Tallman and Li 1996). Fifth, firm size (SIZE) was captured by using the total number of full-time and part-time employees (Peng and Luo 2000).⁵ Sixth, firm age (AGE) was operationalized as the number of years since the firm was established (Vorhies, Orr, and Bush 2011). Seventh, competitive intensity (COMP) was captured with five items on a seven-point Likert scale ranging from 1 = “Very strongly disagree” to 7 = “Very strongly agree” (adapted from Jaworski and Kohli 1993). Eighth, to test for endogeneity bias (as explained later), a measure for industry experience (INDS) was included in the survey as an instrumental variable. This construct was measured by using a sliding ratio scale regarding the number of years that the respondent has worked in the worldwide wine sector (Crick and Crick 2021). Ninth, an informant quality tool (INQ) was operationalized with three items using a seven-point Likert scale ranging from 1 = “Very strongly disagree” to 7 = “Very strongly agree” (see Hultman, Robson, and Katsikeas 2009; Katsikeas, Samiee, and Theodosiou 2006). The single-item measures were transformed through natural logarithms to lessen their variances (Henseler, Ringle, and Sarstedt 2016).

Data Analysis

The quantitative data were analyzed as follows. First, through SPSS 23, the characteristics of the sampled firms were examined (Churchill 1979). Second, using SPSS 23, an exploratory factor analysis was “run” through a principal components analysis extraction and a Varimax rotation (Peterson 2000). The items for coopetition, financial performance, competitive intensity, and informant quality loaded onto four factors (Table 2). The Kaiser–Meyer–Olkin test of sampling adequacy (.81) and Bartlett’s test of sphericity (χ² = 1,329.60; d.f. = 120; Sig. = .00) produced acceptable results, explaining 80.16% of the overall variance.

Table 2.

Exploratory Factor Analysis Model.

	Components
Items	Factor 1	Factor 2	Factor 3	Factor 4
COOP_1	.74	.41	−.12	−.01
COOP_2	.88	.35	−.04	−.06
COOP_3	.83	.35	−.14	−.09
PERF_1	.25	.87	−.05	.13
PERF_2	.20	.90	−.10	−.04
PERF_3	.22	.85	−.15	−.02
PERF_4	.32	.81	−.12	.03
PERF_5	.16	.87	−.09	.09
COMP_1	−.04	−.12	.84	−.03
COMP_2	−.01	−.08	.89	−.07
COMP_3	−.33	.09	.68	.08
COMP_4	−.10	−.16	.86	.03
COMP_5	.02	−.15	.84	−.04
INQ_1	−.04	.02	−.03	.96
INQ_2	−.03	.09	.03	.93
INQ_3	−.06	.02	−.04	.94

Notes: The items presented in bold were utilized to measure each latent variable within the four-factor solution.

Third, via LISREL 9.30, the measurement model was undertaken to refine the operationalizations (Cadogan, Kuivalainen, and Sundqvist 2009). Specifically, indicators were deemed to be of poor quality if they had low standardized factor loadings, high standardized error variances, nonsignificant t-values, and other problems pertaining to the modification indices (Steenkamp and Baumgartner 2000). Although data were collected for a relatively low sample size, this did not prevent the use of LISREL 9.30 for the confirmatory factor analysis model (see Moorman 1995). Specifically, this software package was employed only for measurement development purposes and was not used to test the elements of the conceptual framework. As described later, a hierarchical regression analysis, which was more appropriate for the sample size, was used for the model-testing stage (following Morgan, Vorhies, and Mason 2009; Oliveira et al. 2018; Olson, Slater, and Hult 2005). As displayed in Table 3, the final items were satisfactory, and the fit indices met the minimum thresholds (χ² = 85.06; d.f. = 73; χ²/d.f. = 1.17; Sig. = .16; root mean square error of approximation = .04; comparative fit index = .98; incremental fit index = .98; nonnormed fit index = .97; goodness-of-fit index = .90; standardized root mean square residual = .04).

Table 3.

Final Operationalizations.

	Descriptive Statistics				Measurement Model
Items	Mean	Median	Standard Deviation	Variance	λx	t	θδ	t
COOP_2	4.79	5.00	1.17	1.37	.92	Fixed	.15	2.48
COOP_3	4.59	4.75	1.12	1.25	.93	11.68	.14	2.32
PERF_2	3.99	4.00	1.13	1.28	.96	Fixed	.07	2.12
PERF_3	4.09	4.00	.96	.92	.81	11.88	.34	6.24
PERF_4	4.08	4.00	1.07	1.14	.90	15.24	.19	4.81
COMP_1	4.30	4.30	1.36	1.85	.82	8.53	.33	5.06
COMP_2	4.49	4.49	1.36	1.85	.92	9.07	.16	2.55
COMP_4	4.86	5.00	1.19	1.42	.77	Fixed	.40	5.66
INQ_1	5.80	6.00	.99	.98	.96	Fixed	.08	2.63
INQ_2	5.69	5.00	1.09	1.19	.88	14.93	.22	5.53
INQ_3	5.92	6.00	.92	.85	.91	16.10	.18	4.90
SIZE	1.78	1.79	1.22	1.49	1.00	Fixed	.00	Fixed
AGE	1.51	1.61	.34	.12	1.00	Fixed	.00	Fixed
SCOPE	1.44	1.79	1.18	1.39	1.00	Fixed	.00	Fixed
EXPORTS	2.56	3.32	1.55	2.40	1.00	Fixed	.00	Fixed
INDS	3.99	4.00	1.25	1.56	1.00	Fixed	.00	Fixed

Notes: The critical t-value was 1.65 (5%, one-sided). For the measurement model, the research team recorded the standardized factor loadings (λx) and the standardized error variances (θδ).

Fourth, using SPSS 23, the research hypotheses and control paths were evaluated via a three-step hierarchical regression analysis (Morgan, Vorhies, and Mason 2009). This technique was deemed to be appropriate because the model had various independent moderators and control variables and one outcome variable (similar to Oliveira et al. 2018), as opposed to a chain model, which would have been suitably tested via structural equation modeling (as per Steenkamp and Baumgartner 2000). In terms of the quadratic and interaction effects, residual centering was employed to lessen the odds of multicollinearity (see Echambadi and Hess 2007). This approach included creating composite variables for the purified (final) multi-item measures (Ping 1995). Furthermore, the research team recorded the unstandardized regression coefficients (β), the t-values (and their significances), the model fit summaries, and their step-level changes (Olson, Slater, and Hult 2005). The hypothesized and control paths were assessed during the last stage, as variables were “nested” within the analysis (following Cadogan et al. 2012).

Sample Size Checks

The sample size of 101 observations is relatively small. Therefore, the following checks were undertaken to ensure that enough survey responses had been collected. First, smaller sample sizes have been presented in other work throughout the marketing literature. For instance, Moorman (1995) collected 92 usable responses to evaluate a more complex conceptual framework than the one presented in the current investigation. Second, as mentioned previously, when conducting the exploratory factor analysis model, the result of the Kaiser–Meyer–Olkin test of sampling adequacy (.81) exceeded the minimum threshold. This indicated that enough survey data had been collected for this aspect of the measurement development stage (Crick and Crick 2021). Third, using G*Power 3.1.9.2, an a priori test was undertaken (Faul et al. 2009). Before the hierarchical regression analysis, the research team expected that a large effect size would exist (.35), because the independent variables were expected to explain a considerable proportion of the variance of the outcome variable. The power (or error probability) was set at .95 (α = .05), with predictors representing the number of independent variables, including the various procedural controls. The results highlighted that 88 responses were needed to observe this effect size. Collectively, these tools confirmed that 101 responses constituted a satisfactory sample size.

Reliability and Validity

Reliability was checked through the Cronbach’s alpha coefficients (α) of the multi-item scales before and after they were purified (internal consistency), all of which exceeded the minimum benchmark of .70 (Churchill 1979). Face validity was evaluated by pretesting the survey with an expert panel of knowledgeable academics and practitioners, revealing no problems (Reynolds and Diamantopoulos 1998). Moreover, face validity was assessed with an informant quality tool (Hultman, Robson, and Katsikeas 2009; Katsikeas, Samiee, and Theodosiou 2006). Content validity was assured because the measurement scales were sourced from the extant literature (Bottger et al. 2017). Convergent validity was present, as the final operationalizations yielded composite reliabilities greater than .60 and average variance extracted values greater than .50 (Souchon et al. 2016). Discriminant validity was tested by comparing the squared phi matrix correlations (accessed from LISREL 9.30) against the average variance extracted values for each construct (Table 4). Since the largest squared phi matrix correlation (.32) was less than the lowest average variance extracted (.70), discriminant validity should be present (Fornell and Larcker 1981).

Table 4.

Major Robustness Checks.

Latent Variables	1	2	3	4	5	6	7	8	9
1. Coopetition	1.00
2. Financial performance	.32	1.00
3. Competitive intensity	.04	.05	1.00
4. Informant quality (marker variable)	.01	.00	.00	1.00
5. Firm size	.00	.07	.02	.02	1.00
6. Firm age	.04	.00	.04	.06	.03	1.00
7. Export geographical scope	.00	.04	.00	.00	.02	.00	1.00
8. Export intensity	.05	.06	.05	.04	.30	.00	.06	1.00
9. Industry experience (instrumental variable)	.06	.12	.00	.02	.02	.04	.06	.03	1.00
Cronbach’s alpha coefficient (α)	.92	.91	.88	.94	—	—	—	—	—
Composite reliability	.92	.92	.88	.94	—	—	—	—	—
Average variance extracted	.75	.80	.70	.84	—	—	—	—	—

Notes: The scale reliabilities for the single-item measures could not be calculated. However, because the scale reliabilities for the multi-item operationalizations exceeded the minimum thresholds, the single-item indicators were assumed to meet these benchmarks. Further, the values within this matrix represent the squared phi matrix correlations that were required to assess the degree of shared variance between these latent variables.

Common Method Variance

Various procedural and statistical tools are available to address common method variance (Chang, Van Witteloostujin, and Eden 2010). The electronic survey instrument was designed to be interactive, relatively short, and clear for the respondents to complete (Hulland, Baumgartner, and Smith 2018; Podsakoff et al. 2003). More formally, using SPSS 23, the marker variable technique was employed as follows (following Durand and Jacqueminet 2015). First, a construct that was theoretically unrelated to the other latent variables within the conceptual framework, namely, the informant quality tool, was selected (adapted from Hultman, Robson, and Katsikeas 2009; Katsikeas, Samiee, and Theodosiou 2006). Second, a bivariate correlation matrix was created, containing all latent variables. Third, a partial correlation matrix was established, with the same constructs, with the marker variable as a control. Fourth, the differences between the bivariate and partial correlation matrices were averaged. Since the mean difference was very small (r = .01), it is unlikely that the quantitative data were biased by a common method factor (see Lindell and Whitney 2001). Moreover, the informant quality tool yielded a considerable variance (.90), permitting its use as the marker variable.

Endogeneity Bias

Endogeneity bias was managed as follows. First, the conceptual framework was refined to test the association between coopetition and financial performance. Second, an instrumental variable, namely, industry experience, was selected (following Crick and Crick 2021) as a construct that is likely to be directly related to coopetition, but is indirectly related to financial performance. The reason is that if decision makers possess knowledge about how to succeed within their markets, they can use it to engage in more effective (performance-enhancing) coopetition strategies (see Bouncken and Kraus 2013; Estrada and Dong 2020). Third, through LISREL 9.30, two structural models were run, one examining the link between coopetition and financial performance and another that included the instrumental variable (Souchon et al. 2016). Fourth, the change statistics were calculated. Since the Δχ² (11.63), relative to the Δd.f. (4), was nonsignificant (less than the critical value of 13.28), the statistical data were unaffected by endogeneity bias (Sande and Ghosh 2018). The empirical results are as follows.

Results

The bivariate correlations (and their descriptive statistics) showed the core linkages between the key constructs (Table 5). For the hierarchical regression analysis (Table 6), the pertinent results were as follows. First, coopetition yielded a quadratic and significant relationship with financial performance. Therefore, H₁ was supported. Second, export intensity and export geographical scope (respectively) positively and significantly moderated this nonlinear (inverted U-shaped) association. Thus, support existed for H₂ and H₃. These interaction effects are displayed in Appendix B, showing that these international-level factors influence the shape of the quadratic effect. Third, the main (nonprocedural) control variables all produced significant effects. Yet, firm size was positive, whereas firm age and competitive intensity were negative. Fourth, the independent variables explained 52% of the variance of the outcome variable (based on the adjusted R²). In addition, the ΔR², relative to the ΔF, were significant across all stages of the model. These statistical results are discussed, with some theoretical implications, in the following section.

Table 5.

Bivariate Correlations and Descriptive Statistics.

Latent Variables	1	2	3	4	5	6	7
1. Coopetition	1.00
2. Financial performance	.58**	1.00
3. Export geographical scope	−.01	.20*	1.00
4. Export intensity	.21*	.20*	.22*	1.00
5. Firm size	.05	.22*	.12	.53**	1.00
6. Firm age	.19^†	−.05	−.02	−.06	−.18^†	1.00
7. Competitive intensity	−.20*	−.25*	.02	.20*	.09	−.19^†	1.00
Mean	4.69	4.05	1.44	2.56	1.78	1.51	4.55
Median	4.89	4.00	1.79	3.32	1.79	1.60	4.55
Standard deviation	1.10	.97	1.18	1.55	1.22	.34	1.16
Variance	1.21	.94	1.40	2.42	1.50	.11	1.36

^† p < .10; *p < .05; **p < .01 (two-tailed tests).

Table 6.

Three-Step Hierarchical Regression Model.

	Step 1			Step 2			Step 3
	β	t	Sig.	β	t	Sig.	β	t	Sig.
Main Control Variables
Firm size	.19	2.43	.02	.17	2.25	.03	.18	2.67	.01
Firm age	−.19	−.66	.51	−.50	−2.06	.04	−.47	−2.06	.04
Competitive intensity	−.24	−2.95	.00	−.14	−2.09	.04	−.15	−2.28	.03
Procedural Control Variables
Coopetition				.39	4.25	.00	.30	3.19	.00
Export intensity				.35	1.88	.06	.29	1.43	.16
Export geographical scope				.30	1.05	.30	.33	1.23	.22
Export intensity × export intensity				.05	1.09	.28	.04	1.08	.28
Export geographical scope × Export geographical scope				−.04	−.77	.44	−.01	−.21	.84
Coopetition × Export intensity				−.08	−2.13	.04	−.06	−1.45	.15
Coopetition × Export geographical scope				−.03	−.49	.63	−.04	−.69	.50
Research Hypotheses
Coopetition × Coopetition (H₁)							−.05	−2.13	.04
Coopetition × Coopetition × Export intensity (H₂)							.03	1.85	.07
Coopetition × Coopetition × Export geographical scope (H₃)							.09	3.48	.00
Model Fit Summary
R²	.13			.49			.58
Adjusted R²	.10			.44			.52
Change Statistics
ΔR²	.13			.36			.09
ΔF	4.72			9.23			6.11
Sig.	.00			.00			.00

Notes: The critical t-value was 1.65 (5%, one-sided, because the paths were directional).

Discussion and Theoretical Implications

Prior studies have recognized that by working with industry rivals, organizations can be equipped with new assets and opportunities that they could not access if they operated on the basis of their own assets (Bouncken and Kraus 2013; Crick and Crick 2020; Rusko 2011). However, a need exists to unpack the coopetition–performance relationship by accounting for the potential nonlinear (inverted U-shaped) and moderating factors that might be at play (Czakon et al. 2020; Hoffmann et al. 2018). Consequently, grounded in resource-based theory and the relational view (Barney 2018; Lavie 2006), the current study examined the quadratic relationship between coopetition and financial performance under the moderating roles of export intensity and export geographical scope. The following key findings enhance the earlier literature.

First, coopetition yielded a quadratic relationship with financial performance. This result signifies that if internationalizing firms engage in too little or too much coopetition, they may see harmful effects on their performance (reinforcing Crick and Crick 2021; Luo, Rindfleisch, and Tse 2007). In other words, firms risk having their resource disadvantages exposed, so that they struggle to create value for their customers, or they may deplete their resources and capabilities by sharing excessive volumes of information, knowledge, or equipment with rivals (Gnyawali and Charleton 2018; Park, Srivastava, and Gnyawali 2014). Indeed, this quadratic relationship highlights that if firms behave in a cavalier manner in their coopetition strategies, they could experience negative outcomes that decrease their financial performance, such as tensions, loss of knowledge, and a diminished ability to develop a sustainable competitive advantage (Czakon and Czernek 2016; Ritala and Hurmelinna-Laukkanen 2013; Tidstrom 2014). Consequently, although coopetition can lead to positive outcomes, decision makers must be careful when navigating its paradoxical forces (Bengtsson, Raza-Ullah, and Vanyushyn 2016; Mattsson and Tidstrom 2015; Raza-Ullah, Bengtsson, and Kock 2014).

Second, export intensity and export geographical scope both positively moderated the nonlinear (inverted U-shaped) link between coopetition and financial performance. These international-level variables offer new insights regarding how firms can adapt their business models to maximize the value of their coopetition strategies. The result for export intensity highlights that if companies increase their activity in export markets (focusing their resources and capabilities away from customers within their home country), they have the potential to mitigate some of the dangers of coopetition. That is, if firms operate predominately on a domestic level, their performance outcomes may be limited because of the finite number of customers that can be reached (Crick and Crick 2016; Ojala and Tyrvainen 2007). By internationalizing, organizations can experience a range of performance-enhancing opportunities that a domestically oriented business model would not allow them to achieve (Knight and Cavusgil 2004; Kuivalainen, Sundqvist, and Servais 2007; McDougall and Oviatt 2000; Nemkova et al. 2015; Sousa, Ruzo, and Losado 2010). Likewise, with higher degrees of exporting, companies can cooperate with their competitors and decrease the underlying potential for domestic-level interfirm rivalry, because they are competing for a larger volume of customers internationally (building on Felzensztein and Deans 2013; Kock, Nisuls, and Soderqvist 2010; Vanyushyn et al. 2018).

Next, export geographical scope was another international-level variable that positively influenced the performance outcomes of coopetition. This issue was conceptualized with slightly more hesitation than export intensity because of the long-lasting debate among the international marketing community about both firms’ internationalization processes and the advantages and disadvantages of a concentrated versus spreading exporting strategy (D’Angelo et al. 2013; Leonidou and Katsikeas 1996; Piercy 1981). Indeed, many issues influence owner-managers’ internationalization pathways, including their decisions to export to one or a few markets in comparison to diversified export markets (Olmos and Diez-Vial 2015; Piercy 1981). In the current study, the positive interaction effect of export geographical scope could potentially be explained by the view that as organizations export to more countries, they can market their products to new customers and access other benefits, such as improved supply chain networks (Olivera et al. 2018). Regarding coopetition, the interviews at the pretesting stage indicated that a diversified exporting strategy helps firms avoid some of the undesirable side effects of these activities by reducing the rivalrous behaviors that could exist on a domestic basis, assuming that trustworthy relationships are developed among firms with complementary product-market strategies. Thus, relationship-building is another internationalization consideration that can help stabilize the coopetition paradox if managed effectively (building on Bengtsson, Raza-Ullah, and Vanyushyn 2016; Mattsson and Tidstrom 2015; Park, Srivastava, and Gnyawali 2014).

Third, the main control variables produced significant results but explained a limited proportion of the variance of financial performance. Consequently, one can derive primarily theoretical value from discussion of the results for the hypothesized paths and their contributions to the international marketing literature. Fourth, examining the underlying theories is important when unpacking the complexities of the relationship between coopetition and financial performance. Put simply, while the resource-based view helps to explain how sharing resources and capabilities with competitors can affect their performance (Gnyawali and Charleton 2018; Hannah and Eisenhardt 2018), it overlooks the relational factors that are meaningful in coopetition strategies (Barney 2018; Hunt and Derozier 2004). Hence, by infusing resource-based theory with the relational view (Crick and Crick 2021; Lavie 2006), this study reveals stronger insights on the dynamics and performance consequences of coopetition. These findings include how these international-level variables (namely, export intensity and export geographical scope) could affect the quadratic effects that might otherwise occur with respect to performance. Some actionable practitioner implications and recommendations are as follows.

Practitioner Implications and Recommendations

The following implications and recommendations are offered for management teams. First, while coopetition may help businesses (small and large) to be more successful, these interfirm partnerships have potential limitations:

In situations with too little coopetition, companies might be constrained by the underresourced nature of their assets (resources and capabilities). Thus, decision makers might struggle to create value for their customers if they attempt to develop and execute their strategies without the assistance of certain competitors.

Too much coopetition can lead to several negative outcomes, such as interfirm tensions, lost knowledge, and weakened competitive advantages. This situation can be just as harmful as operating under an individualistic business model.

Decision makers must always remember that when their firms engage in coopetition activities, cooperation will exist alongside a certain degree of competitiveness. This situation can be challenging to navigate, so organizations must be able to exit their coopetition arrangements if these activities become damaging to their financial performance.

Second, despite these risks associated with coopetition, firms can harness certain factors to mitigate the chances of their financial performance suffering when they collaborate with competitors. In practice, these strategy-level mechanisms involve combining the advantages of internationalization with those from coopetition for firms with international market potential. These mechanisms are explained as follows:

By concentrating on domestic markets, firms are more likely to experience the negative outcomes of coopetition activities, because their rivals might behave more aggressively in the process of fighting for a small, finite number of customers. This effect is especially relevant in regional clusters, where supply might outweigh demand unless firms compete for different customers (e.g., in the current study, by offering particular varietals of wine).

Through higher levels of exporting (measured via an export ratio), companies can attempt to secure new opportunities, such as targeting different customers and acquiring stronger supply chains. In turn, this reduces the extent to which rivalry exists between competitors similar to their domestic context.

By exporting to a variety of countries (a diversified exporting strategy), organizations can deliver value to new customers and develop stronger supply chains. At the same time, a spread of export markets can help decision makers limit the risks of coopetition by lessening the rivalry among domestic competitors.

Third, the results from this investigation shed light on the merits of implementing coopetition strategies along with internationalization activities. Yet, certain potentially harmful side effects may arise:

Internationalization can be expensive, in terms of being a time-consuming process (e.g., developing a presence overseas) and typically requiring firms to leverage relatively large-scale resources and capabilities. Thus, firms must ensure that they can afford to internationalize and operate within their means, such as by pooling resources within coopetition partnerships.

Although diversifying to different export markets may be beneficial (in light of the previously mentioned links with coopetition), this strategy can be difficult to execute in practice because it may be challenging for certain firms to adapt their practices across various export markets as a result of cultural, religious, economic, and political factors. Depending on the nature of perceived opportunities, owner-managers should consider internationalizing to countries with a low psychic distance from their home country before adapting to countries with vast macro-level differences.

Although internationalization strategies (measured in terms of export intensity and export geographical scope) can mitigate the degree of rivalry in a domestic arena, they may lead to increased competitiveness in these new countries unless they are effectively managed. This need for effective management should influence not only the scale and scope of internationalization but also the extent to which decision makers invest their resources and capabilities to market their products, especially with regard to their choice of coopetition partners and the degree of partnership.

In short, these practitioner implications and recommendations are designed to help decision makers maximize the performance outcomes of coopetition activities, including how these international-level issues (export intensity and an export geographical scope) can reduce the drawbacks of these business-to-business marketing partnerships. Some limitations and avenues for future research are presented in the next section.

Limitations and Avenues for Future Research

Although this study has contributed to the literature, certain limitations provide avenues for future research. First, although an acceptable response rate was obtained (13.91%), the sample size was relatively small (n = 101). In future research, academics should try to collect larger volumes of empirical data to yield generalizable results. That said, from the various robustness checks, one can infer that the quantitative findings in this study are potentially transferable to the wider population. Second, on a related note, this investigation used single-source survey data to evaluate the elements of the conceptual framework. Although the constructs were operationalized using established measurement scales, future studies could employ a combination of survey-based and archival measures. For example, subject to gaining access, financial performance might be captured using secondary data (including longitudinal information). Third, the empirical context was chosen because it includes high degrees of cooperativeness and competitiveness, as well as varied internationalization activities. However, future research could collect data from different industries (and countries) to evaluate the extent to which these results vary across such settings. This research might extend to cross-national comparisons, starting with culturally and/or economically similar countries before proceeding to multicountry studies involving those with larger psychic distances. In closing, these limitations were not problematic; rather, they present interesting directions for future research.

Footnotes

Appendix A. Profile of the Final Sample

Table A3.

Managerial Titles of the Key Informants.

Managerial Titles	Frequency	%
Chief Executive Officer	17	16.8
Chief Operating Officer	3	3.0
Managing Director	33	32.7
Vineyard Director	5	5.0
Marketing Director	13	12.9
Sales Director	14	13.8
Finance Director	1	1.0
Logistics Director	1	1.0
Chief Wine-Maker	1	1.0
Wine-Maker	4	3.9
Other	9	8.9
TOTAL	101	100.0

Appendix B. Graphical Depictions of the Key Moderating Effects

Associate Editor

Michael Haenlein

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Notes

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