# Risky business or strategic advantage? The varying effects of vertical coopetition on firm risk

Wenbin Sun, Rahul Govind, Mahabubur Rahman

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## To cite this version:

Wenbin Sun, Rahul Govind, Mahabubur Rahman. Risky business or strategic advantage? The varying effects of vertical coopetition on firm risk. Industrial Marketing Management, 2025, 129, pp.50 - 65. 10.1016/j.indmarman.2025.07.002. hal-05224431

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Submitted on 26 Aug 2025

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# Risky business or strategic advantage? The varying effects of vertical coopetition on firm risk

Wenbin Sun<sup>a</sup>, Rahul Govind<sup>b, *</sup>, Mahabubur Rahman<sup>c</sup>

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## ARTICLE INFO

**Keywords:**

Vertical coopetition

Firm performance

Relationship length

## ABSTRACT

This study explores the under-researched area of vertical coopetition in business-to-business markets. Drawing on the resource-based view of the firm and signaling theory, we develop a conceptual model linking vertical coopetition to a supplier firm's systematic risk (SR) and idiosyncratic risk (IR) profile and incorporating coopetition-specific attributes as the boundary conditions. Using a dataset of over 20,000 observations from more than 4000 firms spanning 29 years, employing a novel measure of vertical coopetition and a robust analytical method, we document that vertical coopetition with customers reduces a firm's SR. Additionally, we uncover an inverted U-shaped relationship between vertical coopetition and IR, suggesting that moderate levels of coopetition heighten firm-specific risks due to competitive tensions, while higher levels mitigate risk through improved resource coordination. We also identify that the length of the coopetitive relationship amplifies the risk-reducing effects on SR. In contrast, competition intensity within the relationship increases SR but has a non-monotonic effect on IR. The support for the results is further validated with several additional measures of the key variables, ensuring the robustness of our results. These insights contribute to the theoretical understanding of vertical coopetition and offer practical implications for B2B managers in strategic risk management, emphasizing the importance of balancing cooperation and competition to achieve long-term stability and competitive advantage.

## 1. Introduction

As industries become increasingly interdependent, firms are often compelled to collaborate with competitors across their value chains. This strategic balancing, known as coopetition, has become especially relevant in business-to-business (B2B) markets where firms need to balance the dual imperatives of cooperation and competition. Coopetition can take two primary forms: horizontal, where firms at the same value chain level engage one another, and vertical, where interaction occurs across different stages of the chain, such as between suppliers and buyers (Bengtsson & Kock, 2000; Gernsheimer et al., 2021). While horizontal coopetition often supports cost-sharing, innovation, and knowledge transfer, it also raises risks such as opportunism and competitive tension (Bouncken et al., 2016; Yami & Nemeh, 2014). Similarly, vertical coopetition offers efficiency and value creation benefits but can increase dependency, expose firms to power asymmetries, and raise the risk of intellectual property leakage (Bengtsson & Kock, 2000). Although both forms involve simultaneous cooperation and

competition, horizontal coopetition emphasizes collaboration among rivals, while vertical coopetition centers on competitive threats within cooperative buyer-supplier ties. While we retain the term “coopetition” for consistency, vertical cases often reflect asymmetric dependencies where competition dominates. Our focus is thus on the competitive tensions embedded in structurally cooperative relationships.

Although vertical coopetition is increasingly prevalent and strategically important, it remains significantly underexplored in literature. Only 14% of coopetition research focuses on vertical relationships between firms (Bengtsson & Raza-Ullah, 2016; Rajala & Tidström, 2021; see Web Appendix, Table A1). The distinct risk-reward dynamics discussed above, such as the tension between value creation and exposure to dependency and opportunism, highlight the need to examine how vertical coopetition shapes firm-level outcomes.

In practice, the distinction between vertical and horizontal forms of coopetition is often blurred. Some supplier-buyer relationships also involve competition in downstream markets, creating dual cooperative and competitive roles. For example, Samsung supplies components to

<footnote><sup>a</sup> Helzberg School of Management, Rockhurst University, 1100 Rockhurst Road, Kansas City, MO, USA</footnote>

<footnote><sup>b</sup> School of Marketing, University of New South Wales, Sydney, Australia</footnote>

<footnote><sup>c</sup> Department of Marketing, Rennes School of Business, Rennes, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail addresses: wenbin.sun@rockhurst.edu (W. Sun), r.govind@unsw.edu.au (R. Govind), Mahabubur.rahman@rennes-sb.com (M. Rahman).</footnote>
Apple while also competing with it in the smartphone market. In contrast, Pfizer and Merck's joint R&D activity lacks a supplier-buyer link and instead reflects horizontal coopetition between firms at the same stage of the value chain. Similarly, Tesla sources batteries from LG, even as both firms compete in energy storage solutions, reflecting a vertical relationship that also includes product-market rivalry.

While prior research links coopetition to innovation (Ritala & Hurmelinna-Laukkanen, 2013), financial performance (Luo et al., 2006), and managerial capability (Bengtsson et al., 2016), it also highlights risks such as resource strain, knowledge leakage, and erosion of capabilities (Gnyawali & Park, 2011; Rajala & Tidström, 2021; Ritala & Tidström, 2014). Vertical coopetition compounds these risks by adding task, process, and relational conflict (Rajala & Tidström, 2021). Despite the importance of risk to firm stability and stakeholder decision-making, it has received limited attention in coopetition research (Tidström, 2014). This oversight is particularly important in B2B contexts, where exposure to both systemic and firm-specific risks can shape long-term viability.

To address this gap in both theory and evidence, we develop a conceptual model grounded in the resource-based view (RBV) and signaling theory, linking vertical coopetition to a supplier firm's risk profile. Although prior work recognizes the role of contingencies in shaping coopetition outcomes (Chai et al., 2020; Crick et al., 2024), relationship duration and competition intensity have received little empirical attention, despite their theoretical relevance (Gnyawali et al., 2006). Our inquiry is guided by two questions: (1) Does vertical coopetition affect a supplier firm's risk profile? (2) How do relational attributes, particularly relationship duration and competition intensity, shape this effect? We propose that vertical coopetition reduces systematic risk (SR) through strategic coordination. Its relationship with idiosyncratic risk (IR), however, follows an inverted U-shape due to competing pressures of learning and rivalry. We further argue that longer relationships strengthen both effects, while competition intensity moderates them by amplifying exposure to SR and dampening the influence on firm IR. We test our proposed model using a panel dataset of more than 20,000 observations from over 4000 firms across 29 years, applying multiple methods and risk measures to ensure robustness.

This study makes four contributions to the literature on coopetition and firm risk. First, it offers a risk-based perspective on vertical coopetition by showing distinct effects on systematic and firm IR (Jo & Na, 2012). Second, it introduces a scalable measure of vertical coopetition based on observable ties. Third, it identifies internal contingencies that condition coopetition's risk effects, advancing relational governance theory. Fourth, it contributes to the RBV by illustrating how firms navigate the tension between cooperation and competition to create durable resource advantages (Chai et al., 2020; Van den Broek et al., 2018). These insights inform how B2B firms can structure supplier relationships to manage risk and achieve long-term strategic resilience.

## 2. Theory and hypotheses

We draw on two well-established theories to develop our hypotheses: the RBV and signaling theory. RBV emphasizes access to heterogeneous resources as a source of competitive advantage (Barney, 1991; Peteraf, 1993), suggesting that a firm's success depends on its ability to acquire, configure, and deploy resources in response to external conditions (Barney, 1991). It is important to note that in vertical coopetition, the cooperation is structurally mandated by supplier-buyer ties, but the competitive dimension often arises from strategic encroachment or overlapping business domains. Thus, while our terminology retains 'coopetition' for consistency with prior literature, our analytical emphasis remains on the risks arising from this embedded competition. Signaling theory (Spence, 1978) explains decision-making under information asymmetry between insiders (firms) and outsiders (financial stakeholders). Insiders send observable signals, defined as actions conveying information, about unobservable attributes, thereby reducing

information asymmetry (Bergh & Gibbons, 2011). These signals communicate key insights into a firm's attributes, intentions, and likely outcomes (Bergh & Gibbons, 2011).

Drawing on RBV and signaling theory, we theorize how SR and IR of a supplier firm are affected in a vertical coopetitive customer alliance. SR arises from macroeconomic and industry-wide factors and may be reduced through access to complementary resources and stable demand. IR, shaped by firm-specific strategies and operational uncertainties, may follow an inverted U-shape as initial tensions give way to improved coordination and lower risk.

The RBV provides a foundation for understanding how firms in vertical coopetition acquire, configure, and deploy internal and external resources for competitive advantage (Barney, 1991). Coopetition can enhance a firm's resource base and help manage competitive risks, particularly in uncertain environments. However, beyond the internal strategic logic emphasized by RBV, investor responses may also be shaped by how coopetitive relationships are perceived externally. Signaling theory complements RBV by explaining how observable firm actions, such as coopetition with key customers, convey information about strategic capabilities, resilience, or risk exposure to outside stakeholders, especially in financial markets.

In our context, vertical coopetition may serve as a signal of either strategic competence or potential instability, depending on its intensity. High levels of coopetition can signal that a firm possesses strong coordination capabilities and maturity to manage complex interdependencies, traits likely to reduce perceived risk (Zacharia et al., 2019). Moderate levels, by contrast, may send ambiguous signals, suggesting misalignment, limited experience, or unproven strategy (Bengtsson & Raza-Ullah, 2016). Low levels may indicate risk aversion or strategic conservatism (Opper et al., 2017). This targeted application of signaling theory highlights its relevance to our framework, where investor interpretations of coopetition intensity directly influence perceptions of firm-specific risk. This logic aligns with prior work in finance and strategy, where firm behavior under uncertainty serves as a cue for external stakeholders evaluating future performance and stability (Bergh & Gibbons, 2011; Spence, 1978).

### 2.1. Vertical coopetition and systematic risk

Vertical coopetition, collaboration between firms at different stages of the value chain, offers an effective means to mitigate SR. While vertical coopetition can mitigate SR by strengthening customer relationships and reducing market volatility, these benefits are contingent on a firm's ability to manage dependency risks and power asymmetries (Gulati & Sytch, 2007). If firms fail to align strategic interests with powerful customers, coopetition may instead increase their exposure to SR due to weakened bargaining power and reduced control over pricing dynamics (Ma et al., 2020). We argue that vertical coopetition enhances a supplier firm's resources, capabilities, and resilience. Specifically, vertical coopetition enables a supplier firm to access complementary resources from the partner, strengthening its ability to navigate volatility and uncertainty in the macroenvironment, the source of systemic risk. We identify three pathways through which vertical coopetition mitigates SR:

#### 2.1.1. Complementarity of tangible resources

Firms need tangible resources such as financial and physical assets to maintain competitiveness (Srivastava et al., 2001), and limited access to these resources can increase risk and threaten survival (Esteve-Pérez & Manez-Castillejo, 2008). In vertical coopetition, firms in different value chain segments can access heterogeneous markets for tangible resources, enabling the development of a differentiated resource base (Bahar et al., 2022). This allows for the exchange and combination of complementary resources, helping to diversify risk. For example, Apple's supplier relationships with firms like Foxconn reflect vertical coopetition that enhances production efficiency while managing
competitive pressure. Acharya (2009) shows that value chain integration reduces market-wide risk by dispersing it across interconnected activities, supporting the RBV logic of resilience through resource complementarity.

### 2.1.2. Complementarity of intellectual resources

Owing to their distinct positions in the value chain, partnering firms in vertical coopetition develop and possess a differentiated intellectual knowledge base through interactions with diverse actors in their individual networks. (Bahar et al., 2022; Srivastava et al., 2001). Sharing complimentary knowledge and information in vertical coopetition provides firms with richer insights into market trends, enhancing their responsiveness to external pressures (Lacoste, 2012; Li, 2002). Pfizer and Merck KGaA's selective sharing of noncritical, precompetitive information in coopetitive research accelerates development, reduces uncertainties, and enhances forecasting capabilities, a key resource under the RBV, that in turn minimizes SR (Grant, 1996).

### 2.1.3. Complementarity of relational resources

Firms in vertical coopetitions build and sustain relationships with their context-specific external network partners, enabling them to develop a differentiated stock of relational resources due to their distinct roles in the value chain (Bahar et al., 2022; Srivastava et al., 2001). The co-deployment of complementary relational resources enables coopetitive partners to build and strengthen their market positions vis-à-vis various business-critical stakeholders, leading to better access to market-based assets required to forecast and prepare for macro-environmental shocks. RBV also suggests that pooling resources in joint initiatives, such as R&D or shared supply chains, creates a buffer against external shocks (Barney, 2001). For instance, collaborations between Apple and Samsung, Boeing and Airbus demonstrate how resource pooling enhances adaptability to industry disruptions, allowing for swift and efficient response to market shifts and reducing SR (Gnyawali & Ryan Charleton, 2018).

Thus, by exchanging and combining tangible, intellectual and relational resources, vertical coopetition enables a supplier firm to attain and heighten the heterogeneity of its resource base, hence bolstering its resilience. These resources provide firms with a robust foundation to better withstand market-wide fluctuations, thereby enhancing adaptability and reducing SR. Therefore, we hypothesize:

**H1.** Vertical coopetition with customers reduces a supplier firms' systematic risk.

This and all following proposed hypotheses are presented in the theoretical framework (Fig. 1).

### 2.2. Vertical coopetition and firm idiosyncratic risk

Unlike SR, IR is firm-specific and influenced by internal factors such as management orientations and strategic actions (Bansal & Clelland, 2004; Luo & Bhattacharya, 2009). In the context of vertical coopetition, IR is particularly sensitive to the structural intensity of customer-competitor overlap. When only a few customer relationships involve competitive overlap (i.e., low vertical coopetition), firms face limited exposure to tension. At moderate levels, however, exposure rises, while capabilities to manage coopetition may still be underdeveloped. At high levels, firms are likely to have developed routines, internal levers, and coordination mechanisms that reduce IR. This dynamic reinforces the inverted U-shaped pattern we hypothesize. We argue that while vertical coopetition initially (at a low to moderate level) heightens a supplier firm's IR due to competitive tensions, it eventually (at a high level) reduces this risk as firms develop resource-sharing capabilities and enhance relationship stability. While proponents of the RBV highlight the importance of a unique combination of resources garnered from various internal and external sources, advocates of signaling theory argue that a firm's ability to efficiently marshal and deploy these resources serves as a positive signal to external stakeholders (Barney, 1991). As firms become adept at managing coopetitive dynamics, they

<figure><img src="image_4.png" /><figcaption>**Fig. 1.** The theoretical framework of vertical coopetition and firm risks.</figcaption></figure>
leverage these critical resources to mitigate IR over time. We propose that vertical coopetition affect a supplier firm's IR in three main stages:

### 2.2.1. Initial increase due to competitive tensions

At a low level of coopetition, the propensity to compete outweighs the propensity to cooperate, thereby making it difficult to exchange and combine complimentary resources of the partnering firms (Mariani & Belitski, 2023). At a lower level of coopetitive intensity, the cost of maintaining the coopetitive relationship outstrips the benefits. Consequently, a lower level of coopetition between the partnering firms sends a negative signal to the focal firm's financial stakeholders, thereby adversely impacting the firms' IR, as competition within the partnership can create an impression of instability or conflict among investors (Bansal & Clelland, 2004). That is, due to the paradoxical nature of relationships encompassing both cooperation and competition, at low levels of coopetition, partnering firms may exhibit opportunistic behavior and prioritize private gains over collective gains (Mariani & Belitski, 2023; Rajala & Tidström, 2021). Such opportunistic behavior is predicted to engender conflict and tensions, leading to increase in supplier firm's cost and risks (Chai et al., 2020; Rajala & Tidström, 2021). For instance, Pfizer and Merck's cancer research partnership illustrates how such alliances might raise concerns among investors about competitive tensions interfering with collaboration. However, as the alliance demonstrates clear benefits and establishes stable resource-sharing practices, this risk perception may lessen (Rajala & Tidström, 2021).

### 2.2.2. Risk reduction through experience and competency development

With experience, partnering firms develop coopetition capability defined as the ability to manage tension between the partnering firms to optimize collective benefits (Bengtsson et al., 2016). Specifically, the partnering firms become more competent in managing coopetitive tensions, thereby increasing the commitment of the firms to benefit more from each other's complimentary resource base (Rai et al., 2023). Such heightened inclination to reap rewards though the amalgamation of resources decreases the supplier firm's IR. RBV holds that long-term alliances enhance a firm's strategic asset base. As an example, Oracle and Microsoft's coopetition on cloud services initially encountered investor skepticism; however, as they improved resource-sharing and interoperability, investor concerns eased, highlighting the value of developing coopetitive expertise through accumulated experience (Dorn et al., 2016).

### 2.2.3. Stabilization as the relationship matures

At a high level of coopetitive relationships, firms achieve a stable balance of cooperation and competition (Child et al., 2019). Trust may contribute to and result from a stable relationship, which in turn promotes greater informational transparency between partnering firms (Gulati, 1995). Increase of informational transparency positively contributes to the firms' capacity to identify each other's sources of relative competitive edge stemming from their individual, heterogeneous resource base (Zacharia et al., 2019). In essence, a high level of coopetition facilitates the dynamic combination and (re)configuration of resources to optimize the mutual benefits. Further, such a high level of coopetitive relationship serves a positive signal to the focal firm's external resource-supplying partners such as the investors thereby reducing the supplier firm's IR. In the auto industry, for example, Tesla and Panasonic initially faced tension as partners, but their coopetition stabilized over time as they expanded their networks, resulting in a reduction of perceived risk (Rajala & Tidström, 2021).

In essence, we propose that vertical coopetition follows an inverted U-shaped relationship with supplier firm IR. At low levels of coopetition, firms face limited competitive tension and coordination costs, keeping IR low. At high levels, firms are more likely to have developed coopetition capabilities, such as governance mechanisms, trust, and strategic alignment, that help reduce uncertainty and stabilize performance.

However, moderate levels of coopetition may create ambiguity and unresolved conflict, elevating perceived risk among investors. These firms may appear “stuck in the middle,” sending mixed signals about their strategy and capability. Hence, we put forward the following hypothesis.

**H2.** Vertical coopetition exhibits an inverted U-shaped relationship with supplier firm IR, such that firm IR is higher at moderate levels of vertical coopetition than at low or high levels (displays an inverted U-shaped relationship).

### 2.3. Coopetition relationship length and systematic risk

We next explore how the length of a coopetitive relationship impacts a firm's ability to manage SR. We argue that extended coopetitive relationships strengthen a supplier firm's resource combination and configuration capabilities, resilience, and adaptability, which are essential for reducing SR. RBV emphasizes that competitive advantage and effective risk management improve through the development and utilization of unique resources, including relational resources built over long-term partnerships (Barney, 1991; Peteraf, 1993). We identify three mechanisms through which coopetition duration reduces SR.

#### 2.3.1. Accumulated experience in managing coopetitive dynamics

Extended engagement in coopetition provides firms with the skills required to efficiently balance cooperation and competition, enhancing resilience to external shocks (Cygler et al., 2018). From an RBV perspective, this experience evolves into a strategic resource, enabling firms to adapt more effectively to SR by leveraging lessons learned from previous interactions (Hernández-Espallardo et al., 2011).

#### 2.3.2. Relationship stability and strategic resource exchange

Long-standing coopetitive relationships often indicate that firms have reached a stable, mutually beneficial dynamic, fostering trust and consistent resource flows (Greven et al., 2022). That is, this stability facilitates the identification and acquisition of various types of resources (i.e., informational resources) from the partnering firm which enables them to predict potential sources of shocks emanating from the firm's macro-environment (Gnyawali & Ryan Charleton, 2018). This, in turn, reduces SR by ensuring predictable exchanges and aligns with RBV's emphasis on relational assets as competitive advantages for managing market risks (Balaji, 2015; Gölgeci et al., 2021; Luo, 2006).

#### 2.3.3. Increased flexibility in strategic execution

Over time, extended relationships foster strategic flexibility, enabling the supplier firms to coordinate better in responding to market disruptions. As a critical asset as per RBV, this flexibility enhances adaptive capability to strategize in keeping with the external market dynamics, thereby reducing SR by improving responses to macroeconomic fluctuations (Gölgeci et al., 2021).

In sum, by enhancing coopetitive experience, establishing stability, and increasing flexibility, longer relationships equip the supplier firms with resources and capabilities for effective SR management. Consequently, extended coopetition improves a firm's adaptability to external pressures, reducing SR. We therefore propose the following hypotheses.

**H3a.** Coopetition relationship length is negatively related to a supplier firm's systematic risk.

**H3b.** The negative effect of vertical coopetition on a supplier firm's systematic risk is higher when firms maintain longer-term coopetitive relationships.

### 2.4. Coopetition relationship length and IR

We next assess how coopetition relationship length impacts IR, a critical factor for investors evaluating firm stability and profitability.
Though the lens of RBV and signaling theory, we argue that longer re-
lationships serve as strategic assets that engender stability, reduce un-
certainty, and signal reliability to external resource-supplying
stakeholders. Such enduring partnerships are valuable resources,
fostering resilience and sustained cooperation, both of which contribute
to competitive advantage (Barney, 1991; Todeva & Knoke, 2005). We
identify three key pathways through which relationship length reduces
IR:

2.4.1. Signal of effective cooperation and stability

Longer coopetitive relationships serve as a positive signal to the firms' business-critical stakeholders that firms have successfully navigated cooperation over time and developed coopetition capability, providing investors with reassurance about stability and dependability. From an RBV perspective, such longevity serves as an intangible asset, demonstrating the ability to sustain beneficial alliances, which lowers perceived risk and attracts positive investor valuations (Todeva & Knoke, 2005).

2.4.2. *Indicator of competitive skill and risk management*

The duration of these relationships reflects a firm's skill in balancing collaboration and competition - a capability closely tied to reduced IR. According to RBV, maintaining long-term alliances showcases strong risk management capability, appealing to investors who value strategic adaptability in dynamic markets (Hsu et al., 2016; Hung & Chou, 2013).

2.4.3. Amplifier of coopetition's risk-reducing benefits

Extended relationships allow firms to refine cooperative efforts,
resolve conflicts effectively, and build more reliable partnerships,
increasing investor confidence. This aligns with RBV's emphasis on
relational resources as stabilizing factors that enhance operational effi-
ciency, which, in turn, mitigate risk (Yeh et al., 2018).

Thus, coopetition relationship length signals effective cooperation, competitive skill, and enhanced risk mitigation, all of which lowers IR. In sum, relational longevity serves as a strategic resource that stabilizes operations and enhances investor appeal. Hence, we posit:

**H4a.** Coopetition relationship length is negatively related to a supplier firm's IR.

**H4b.** The negative effect of vertical coopetition on a supplier firm's IR is higher when firms maintain longer-term coopetitive relationships.

2.5. Competition intensity and systematic risk

We next examine how competition intensity within vertical coope-
tition which we have previously defined as the degree of product-market
overlap between a supplier and its customer, affects SR. In contrast to
traditional competition, which centers solely on rivalry, coopetition
requires firms to navigate simultaneous collaboration and competition,
introducing relational complexity and potential instability. Drawing on
the RBV, we argue that competition intensity affects SR through two
distinct routes: it directly raises risk by discouraging resource sharing
and cooperative alignment, and it may also alter the marginal effect of
vertical coopetition on SR.

2.5.1. Direct effect of competition intensity on systematic risk

High competition intensity increases tension within coopetitive re-
lationships, leading to strategic behavior that weakens the cooperative
foundation. When partnering firms compete in overlapping markets,
they are less likely to share resources or align long-term objectives. This
dynamic increases relational opportunism, reduces joint adaptability,
and heightens SR exposure (Gnyawali & Ryan Charleton, 2018). From
an RBV perspective, the benefits of coopetition depend largely on access
to complementary resources. However, when rivalry is intense part-
nering firms are less inclined to co-deploy tangible, intellectual, or
relational assets due to concerns about appropriation (Chai et al., 2019).

This erosion of collaborative engagement impairs firms' ability to co-
ordinate responses to external shocks and weakens resilience to mac-
roeconomic volatility (Ariño et al., 2008). Thus:

**H5a.** Competition intensity is positively related to a supplier firm's systematic risk.

2.5.2. Moderating effect of competition intensity on the coopetition-systematic risk relationship

While high competition intensity typically increases SR, it can also
reshape how coopetition affects that risk. Specifically, in highly
competitive environments, coopetition may serve as a stabilizing
mechanism by introducing structural and systematic coordination and
goal-driven interactions between firms. These dynamics can mitigate the
volatility typically associated with competitive exposure. In such set-
tings, coopetitive arrangements are more likely to incorporate formal
governance mechanisms, boundary-spanning roles, or information-
sharing routines that help firms navigate interdependence despite ri-
valry (Park et al., 2014). These coordination structures enable firms to
manage external shocks more effectively, reducing SR even when
competition is intense. Thus, competition intensity may enhance the
risk-reducing effect of vertical coopetition under the right conditions.
We therefore hypothesize:

**H5b.** The negative effect of vertical coopetition on a supplier firm's systematic risk is stronger when firms face higher competition intensity.

2.6. Competition intensity and firm IR

We next discuss how competition intensity within vertical coopetition influences IR.

2.6.1. Direct effect of competition intensity on IR

Investor evaluations of IR are inherently forward-looking and sen-
sitive to both uncertainty and growth potential. Competition intensity
introduces strategic complexity to coopetition, potentially heightening
firm-specific risks due to opportunism, while also encouraging firms to
differentiate strategically in ways that reduce uncertainty. Drawing on
the RBV and signaling theory, we argue that competition intensity can
exert both risk-enhancing and risk-reducing effects on IR. On one hand,
increased rivalry may destabilize cooperation by creating incentives for
opportunistic behavior. On the other hand, competitive pressure may
compel firms to develop distinctive capabilities that signal strength and
reduce investor-perceived risk. We identify two mechanisms through
which competition intensity directly affects IR:

a) *Increase in IR through opportunistic behavior:* High competition intensity within coopetition increases the risk of conflict as firms prioritize private gains over joint outcomes. This can lead to information asymmetries and reduced trust, weakening collaborative alignment and increasing IR (Gnyawali & Ryan Charleton, 2018). From an RBV perspective, heightened rivalry disrupts cooperative resource flows and deters shared investment, limiting the benefits of coopetition and raising uncertainty for investors (Raza-Ullah et al., 2014).

b) *Reduction in IR through strategic differentiation:* Conversely, competitive pressure may encourage firms to sharpen their strategic focus, enhancing differentiation in products or capabilities (McEvily & Marcus, 2005). These firm-specific investments not only support performance but also signal growth potential to investors, thereby reducing IR (Cracolici & Nijkamp, 2009; Grahovac & Miller, 2009).

These contrasting mechanisms give rise to two competing
hypotheses:

**H6a(1).** Competition intensity is positively related to supplier firm's IR.
**H6a(2).** Competition intensity is negatively related to supplier firm's IR.

### 2.6.2. Moderating role of competition intensity

Beyond its direct effects, competition intensity may also shape how vertical coopetition influences IR. Specifically, when competition intensity is high, it may weaken the risk-reducing benefits of coopetition by increasing relational instability and misalignment of strategic goals of the partners. Effective coopetition depends on a careful balance of collaboration and competition; excessive rivalry may erode trust, discourage resource sharing, and hinder the joint routines that typically reduce IR (Park et al., 2014). From an RBV perspective, excessive competition undermines the development and deployment of relational and intellectual resources that help manage firm-specific uncertainty (Hoffmann et al., 2018). As a result, the ability of coopetition to lower IR may be diminished under high competitive intensity. We therefore hypothesize:

**H6b.** The negative effect of vertical coopetition on a supplier firm's IR is higher when firms face higher competition intensity.

## 3. Data sources, operationalizations, and estimation

The study employs robust data collection method drawing from multiple well-documented databases such as WRDS Linking Suite, Compustat, Hoberg-Phillips Data Library, and S&P Business Segments. The data sources for each of the variables used in our analyses are provided in Table 1. We outline the contributions of each database below, detailing their role in measuring our focal variables. This multi-source archival data collection strategy eliminates common method bias and enhances data reliability, as evidenced by prior research (Schniederjans & Khalajhedayati, 2021). This approach has strengths in its wide industry coverage, enhancing external validity. Notably, most previous studies on coopetition relied on survey-based perceptual data (soft data), which are prone to managerial bias. In contrast, the present research utilized actual company data (hard data). The current study worked with multi-year data spanning from 1992 to 2020, addressing potential time-related bias. The dataset includes 21,707 observations from 4155 firms for the SR and 23,280 observations from 4488 firms for the IR model. Descriptive statistics and correlations for the variables used in our analyses are provided in Table 2.

### 3.1. Firm systematic and idiosyncratic risk

The systematic and IR data are obtained from stock price data collected from the Center for Research in Security Prices (CRSP). Following McAlister et al. (2007), we measure SR using the beta coefficient (β) from the Capital Asset Pricing Model (CAPM), which captures the sensitivity of a firm's stock returns to overall market fluctuations. A lower beta indicates greater resilience to market-wide shocks, while a higher beta suggests greater vulnerability. SR is quantified by estimating the sensitivity of a firm's stock return to the overall market return in three-year rolling windows of monthly data. Specifically, we estimate the following regression model:

$$R_{it} = \alpha_i + \beta_i R_{mt} + \varepsilon_{it} \quad (1)$$

where:

$R_{it}$ represents the monthly stock return for firm i at time t,

$R_{mt}$ denotes the market rate of return (the ex post return for all NYSE firms) at time t,

$\alpha_i$ is the intercept, capturing firm-specific returns independent of market movements,

$\beta_i$ is the SR coefficient, measuring the covariation between the firm's stock return and the market return,

$\varepsilon_{it}$ represents the error term, reflecting unsystematic risk.

The $\beta_i$ coefficient is the primary focus, as it indicates the extent to

<caption>**Table 1**</caption>

The measurement method and the data source for constructs used in the analyses.

<table><thead><tr><th>Constructs</th><th>Measurement method</th><th>Data Source(s)</th></tr></thead><tbody><tr><td>Firm SR</td><td>Sensitivity of a firm's stock return to the overall market return, based on CAPM methodology with three-year rolling windows of monthly data.</td><td>Center for Research in Security Prices (CRSP)</td></tr><tr><td>Firm IR</td><td>Variation in stock return attributable to firm-specific factors, estimated using the Fama-French Four Factor model.</td><td>Center for Research in Security Prices (CRSP)</td></tr><tr><td>Vertical coopetition</td><td>Ratio of customers with overlapping product offerings to total customers, combining cooperation and competition metrics.</td><td>WRDS Linking Suite</td></tr><tr><td>Coopetition relationship length</td><td>Average years of a firm's coopetition relationships</td><td>WRDS Linking Suite</td></tr><tr><td>Competition intensity</td><td>Product market fluidity scores, which indicates the overlap between a firm's product offerings to its competitors</td><td>Hoberg-Phillips Data Library</td></tr><tr><td>Firm size gap</td><td>The ratio of a firm's asset size on the average asset size of its coopetition firms</td><td>Compustat, WRDS Linking Suite</td></tr><tr><td>Firm age gap</td><td>The ratio of a firm's age on the average age of its coopetition firms</td><td>Compustat, WRDS Linking Suite</td></tr><tr><td>Firm size</td><td>Firm total asset size (log-transformed)</td><td>Compustat</td></tr><tr><td>Firm age</td><td>Number of years a firm is publicly listed (log-transformed)</td><td>Compustat</td></tr><tr><td>Product diversification</td><td>Entropy score of a firm's sales across business segments</td><td>S&P Business Segment Database</td></tr><tr><td>Vertical integration</td><td>The text analysis on the product description from the 10 K reports regarding the up- and down-stream value chain</td><td>Hoberg-Phillips Data Library</td></tr><tr><td>Firm leverage</td><td>The ratio between firm long-term debt and total assets</td><td>Compustat</td></tr><tr><td>SG&A</td><td>The Selling, General, and Administrative expense, scaled by firm sales</td><td>Compustat</td></tr><tr><td>Environmental dynamism</td><td>The coefficient of variation on the industry sales over five-year moving windows.</td><td>Compustat</td></tr><tr><td>Environmental competition</td><td>The reversed Herfindahl-Hirschman index (1-HHI)</td><td>Compustat</td></tr></tbody></table>

which a firm's stock return moves in tandem with the overall market. A higher $\beta_i$ suggests greater exposure to market-wide risks, while a lower $\beta_i$ implies less sensitivity to such risks, and thus becomes the measure of SR. We measure IR using the residual variance from the Fama-French Four-Factor model, which isolates firm-specific fluctuations from market-driven effects. This approach allows us to capture how vertical coopetition influences risk that is unique to each firm, independent of external economic forces:

$$R_{it} - R_{ft} = \alpha_i + \beta_{i1} (R_{mt} - R_{ft}) + \beta_{i2} SMB_t + \beta_{i3} HML_t + \beta_{i4} WML_t + \varepsilon_{it} \quad (2)$$

where $R_{it}$ and $R_{ft}$ are firm return and risk-free rate for period t. $R_{mt}$ is the market return. SMB is the size factor (small vs. large), HML is the book-to-market factor (high vs. low), and WML is the momentum factor (winner vs. loser). The unexplained portion in the $R^2$ represents the stock variation attributed to firm-specific ingredients and thus becomes the measure of IR. Due to the bounded nature of $R^2$, we conduct a logistic transformation $Risk_{it} = \ln(\frac{1-R^2_{it}}{R^2_{it}})$ to obtain the final measure (Luo & Bhattacharya, 2009). It should be noted that these financial measures provide a consistent and quantifiable approach to assessing risk across firms, although they do not isolate the precise internal mechanisms through which coopetition influences risk.
<table><caption>Table 2<br>Variable mean, standard deviations, and correlations.</caption><thead><tr><th></th><th>Mean</th><th>SD</th><th>V1</th><th>V2</th><th>V3</th><th>V4</th><th>V5</th><th>V6</th><th>V7</th><th>V8</th><th>V9</th><th>V10</th><th>V11</th><th>V12</th><th>V13</th><th>V14</th></tr></thead><tbody><tr><td>System Risk</td><td>V1</td><td>1.08</td><td>1.06</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Idiosyncratic Risk</td><td>V2</td><td>5.14</td><td>3.24</td><td>-0.16<sup>***</sup></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Coopetition</td><td>V3</td><td>0.85</td><td>0.23</td><td>-0.04<sup>***</sup></td><td>0.02<sup>***</sup></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Coop Relation Length</td><td>V4</td><td>6.05</td><td>4.47</td><td>-0.07<sup>***</sup></td><td>-0.12<sup>***</sup></td><td>0.01</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Coop Comp. Intensity</td><td>V5</td><td>0.09</td><td>0.06</td><td>0.08<sup>***</sup></td><td>-0.12<sup>***</sup></td><td>0.03<sup>***</sup></td><td>-0.05<sup>***</sup></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Coop Size Gap</td><td>V6</td><td>0.76</td><td>0.13</td><td>0.04<sup>***</sup></td><td>-0.27<sup>***</sup></td><td>-0.04<sup>***</sup></td><td>0.08<sup>***</sup></td><td>0.24<sup>***</sup></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Coop Age Gap</td><td>V7</td><td>0.80</td><td>1.07</td><td>-0.02<sup>***</sup></td><td>-0.01<sup>*</sup></td><td>-0.06<sup>***</sup></td><td>0.03<sup>***</sup></td><td>0.06<sup>***</sup></td><td>0.03<sup>***</sup></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Firm Size</td><td>V8</td><td>12.53</td><td>2.12</td><td>0.04<sup>***</sup></td><td>-0.41<sup>***</sup></td><td>0.26<sup>***</sup></td><td>0.12<sup>***</sup></td><td>0.06<sup>***</sup></td><td>0.24<sup>***</sup></td><td>0.75<sup>***</sup></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Firm Age</td><td>V9</td><td>2.65</td><td>0.74</td><td>-0.09<sup>***</sup></td><td>-0.12<sup>***</sup></td><td>0.00</td><td>-0.10<sup>***</sup></td><td>0.19<sup>***</sup></td><td>0.19<sup>***</sup></td><td>0.19<sup>***</sup></td><td>0.19<sup>***</sup></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Product Diversification</td><td>V10</td><td>0.99</td><td>0.35</td><td>-0.03<sup>***</sup></td><td>-0.06<sup>***</sup></td><td>0.05<sup>***</sup></td><td>0.14<sup>***</sup></td><td>0.15<sup>***</sup></td><td>0.07<sup>***</sup></td><td>0.07<sup>***</sup></td><td>0.03<sup>***</sup></td><td>0.39<sup>***</sup></td><td>0.34<sup>***</sup></td><td></td><td></td><td></td></tr><tr><td>Vertical Integration</td><td>V11</td><td>0.01</td><td>0.01</td><td>0.01<sup>*</sup></td><td>-0.06<sup>***</sup></td><td>0.02<sup>***</sup></td><td>0.16<sup>***</sup></td><td>0.04<sup>***</sup></td><td>0.03<sup>***</sup></td><td>0.01<sup>*</sup></td><td>0.33<sup>***</sup></td><td>-0.23<sup>***</sup></td><td>0.19<sup>***</sup></td><td>0.19<sup>***</sup></td><td>0.08<sup>***</sup></td><td></td></tr><tr><td>Firm Leverage</td><td>V12</td><td>0.18</td><td>0.20</td><td>-0.01<sup>*</sup></td><td>0.00</td><td>-0.08<sup>***</sup></td><td>0.07<sup>***</sup></td><td>0.03<sup>***</sup></td><td>0.01<sup>*</sup></td><td>-0.07<sup>***</sup></td><td>-0.03<sup>***</sup></td><td>-0.13<sup>***</sup></td><td>-0.09<sup>***</sup></td><td>-0.11<sup>***</sup></td><td>-0.14<sup>***</sup></td><td></td></tr><tr><td>SG&A Expenditure</td><td>V13</td><td>0.34</td><td>0.65</td><td>0.05<sup>***</sup></td><td>0.05<sup>***</sup></td><td>0.03<sup>***</sup></td><td>-0.11<sup>***</sup></td><td>-0.01<sup>*</sup></td><td>-0.09<sup>***</sup></td><td>-0.17<sup>***</sup></td><td>-0.02<sup>***</sup></td><td>-0.16<sup>***</sup></td><td>-0.02<sup>***</sup></td><td>-0.06<sup>***</sup></td><td>0.00</td><td></td></tr><tr><td>Env. Dynamism</td><td>V14</td><td>0.18</td><td>0.13</td><td>0.03<sup>***</sup></td><td>0.05<sup>***</sup></td><td>0.02<sup>***</sup></td><td>-0.14<sup>***</sup></td><td>0.00</td><td>-0.09<sup>***</sup></td><td>-0.01<sup>*</sup></td><td>-0.12<sup>***</sup></td><td>-0.07<sup>***</sup></td><td>-0.07<sup>***</sup></td><td>-0.16<sup>***</sup></td><td>-0.11<sup>***</sup></td><td>0.01</td><td></td></tr><tr><td>Env. Competition</td><td>V15</td><td>0.79</td><td>0.17</td><td>0.03<sup>***</sup></td><td>0.00</td><td>-0.10<sup>***</sup></td><td>-0.13<sup>***</sup></td><td>0.09<sup>***</sup></td><td>0.12<sup>***</sup></td><td>-0.01<sup>*</sup></td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.07<sup>***</sup></td></tr></tbody><tfoot><tr><td colspan="20">* p < 0.1.<br>** p < 0.05.<br>*** p < 0.01.</td></tr></tfoot></table>

## 3.2. Vertical coopetition

Empirical research on vertical coopetition is sparse. This study introduces an innovative approach to fill this gap. The first step involves identifying firms engaged in coopetition with their customers. WRDS Linking Suite offers valuable data on supplier-buyer connections, sourced from the firm's annual reports. These reports obligate firms to disclose customers that significantly influence their business performance (Dai et al., 2022; Schniederjans & Khalajhedayati, 2021), thereby capturing the cooperative aspect of the vertical relationship.

To identify the customers who compete with the firm, we utilize data from the Hoberg-Phillips Data Library, which offers inter-firm competition metrics based on product market fluidity scores (Hoberg et al., 2014; Kini et al., 2024). We specifically use the firm-to-firm product market similarity scores from the TNIC3, an advanced dataset in the Hoberg-Phillips Data Library (Hoberg et al., 2014). These scores are based on cosine similarity between product descriptions extracted from firms' 10-K filings and quantify the degree of product-market overlap between any two firms. Unlike the aggregate firm-level fluidity scores, which capture market dynamism, these dyadic scores reflect actual competitive proximity. For our measure of vertical coopetition, we identify whether a supplier's disclosed customer also competes with it in the product market (i.e., has a similarity score > 0). We then compute the proportion of the supplier's customers that meet this criterion. This approach ensures that our measure captures both structural cooperation (via supply links) and competitive overlap (via product-market similarity). Product market fluidity surpasses standard industry classifications like SIC and NAICS by analyzing textual changes in annual 10-K product descriptions to capture how firms compete across sectors (Hoberg et al., 2014). In practice, each firm's product text is mapped to a universal vocabulary, and a cosine similarity measure compares this to an aggregate change vector, indicating how closely the firm's offerings align with evolving competitor shifts. This dynamic measure uncovers threats from direct rivals, new entrants, and adjacent industries by tracking actual product overlaps rather than relying on static concentration ratios. Because it draws on granular text rather than rigid codes, fluidity provides a forward-looking lens on how competition develops. This measure of firm competition is widely used in the literature (Do et al., 2022; Kini et al., 2024).

To measure vertical coopetition, we use a ratio: the number of a firm's disclosed key customers that overlap in product offerings (i.e., compete with the firm) divided by the total number of disclosed key customers. These customer disclosures, available through Capital IQ and WRDS Linking Suite, typically include customers that account for a significant portion of firm revenue, and are mandated in 10-K filings when they exceed certain materiality thresholds. While we acknowledge that this subset does not represent the firm's complete customer base, prior studies have used disclosed key customers as a valid proxy for major supplier-customer relationships (e.g., Dai et al., 2022; Schniederjans & Khalajhedayati, 2021). These customers are more likely to engage in recurrent transactions, carry greater strategic weight, and reflect a level of interaction that can involve coordination, adaptation, and information exchange, characteristics that align with resource-based interpretations of cooperative ties in a B2B context. Overall, this comprehensive measurement framework provides a detailed understanding of vertical coopetition. It incorporates both cooperation and competition metrics, aligned closely with real-world business complexities, to offer a fuller depiction of dyadic relationship. We measure coopetition by the ratio between the number of a firm's customers that have overlap with its own business (competition) and its total customer numbers:

$$ \text{Vertical Coopetition}_{it} = \frac{N_{\text{Customers with competition.it}}}{N_{\text{All Key Customers.it}}} \qquad (3) $$

Essentially, this measurement serves as a comprehensive gauge of a firm's coopetition across its entire business landscape. However, a
complete understanding of coopetition also necessitates an examination of other facets, including the duration of coopetition relationships and the intensity of competition. These elements, discussed in later sections, add further depth to our analyses, contributing to a robust understanding of the complexities involved in coopetitive interactions. Our measure of vertical coopetition captures the proportion of a firm's customers that also offer overlapping products, indicating the extent to which a supplier simultaneously collaborates and competes with its customers. While this measure reflects structural coopetition across the value chain, we acknowledge that in practice, the boundary between vertical and horizontal relationships can be fluid, and competition may arise in indirect or adjacent markets. Our focus on supplier-customer ties with overlapping product offerings ensures that both cooperation and competitive tension are present, consistent with our conceptual definition of vertical coopetition.

It is also important to acknowledge that while this metric effectively quantifies the prevalence of vertical coopetition, it does not capture the specific scope of collaboration, such as whether the coopetitive relationship is based on core product competition (e.g., mobile phones) or complementary components (e.g., display panels). We lay the groundwork for future research to explore ideas that could incorporate the strategic scope of cooperation in addition to its intensity.

### 3.3. Coopetition relationship length and competition intensity

The above measure of vertical coopetition reflects only the scope of firms' engagement in coopetition. However, coopetition has different aspects, with the length of the relationship being one of the most important. Coopetition length indicates the time since the firms' business relationship was formed, representing the duration of cooperation. To measure coopetition length, we calculate the number of years that firms have had relationships with their customers in the WRDS Supplier-Buyer links. A firm may have multiple customers, each with a different relationship length. Therefore, we average the relationship lengths for all the competing customers of a firm to obtain the measure of coopetition length. Competition intensity is another key aspect of coopetition. It is measured using the firm competition (fluidity) scores from the Hoberg-Phillips Data Library (Hoberg et al., 2014). Since a firm has multiple customers, we average the competition intensity scores of all its competing customers, resulting in a measure of overall competition intensity in the firm's coopetition.

In the three measures discussed above, vertical coopetition reflects the overall intensity (scope) of a firm's engagement in coopetition, coopetition length captures the varying aspect of cooperation, and competition intensity focuses on the varying levels of competition. Together, these factors represent the essential traits of coopetition, allowing for an enriched investigation of their effects on firm outcomes, such as firm risks in the current study.

### 3.4. Control variables

In examining coopetition, it's crucial to account for demographic disparities among firms. Specifically, this study incorporates the differences in size and age between a focal firm and its coopetition partners. Firm size is gauged by employee numbers (Darnall et al., 2010). We calculate the average size of a firm's coopetition partners and determine the size gap by dividing the firm's size by this average. We measure a firm's age by the number of years it has been publicly listed (Wies & Moorman, 2015). The age differential is calculated by dividing the firm's age by the average age of its coopetition partners. Beyond these inter-firm variables, several theoretically relevant firm-specific internal and industry-specific external control variables merit consideration. We control for a firm's own size and age, using log-transformed metrics. Product diversification is controlled by gauging the entropy score of a firm's sales across business segments (Becerra et al., 2020). Given that vertical coopetition impacts value chain configuration, we account for

the degree of vertical integration, using data from the Hoberg-Phillips Data Library as in prior studies (e.g., Bonaime et al., 2018). We also incorporate the debt level, which is measured as firm leverage, calculated by scaling long-term debt by total assets. Marketing spending is also included, represented by SG&A expenditures scaled by firm sales. We also incorporated industry-related covariate to control for their effects. Environmental dynamism is accounted for, measured by the fluctuation (coefficient of variation) in industry sales over rolling five-year periods (Haushalter et al., 2007). We modelled industry competition, represented by the reversed Herfindahl-Hirschman Index (1-HHI) (Testoni, 2022). To control for temporal and industry effects, we use time and industry dummies. Time dummies account for business cycles within the dataset's timeframe, while industry dummies, identified by 2-digit SIC codes, control for time invariant industry heterogeneities. These control variables - firm size, age, product diversification, vertical integration, debt level, marketing spending, environmental dynamism, industry competition, and time/industry dummies - are widely supported in literature as essential to understanding inter-firm relationships and firm performance.

### 3.5. Empirical models and methods

While we develop our hypotheses based on theoretical arguments and prior literature, our empirical tests establish associations consistent with the proposed relationships, it is important to acknowledge that they do not directly measure the underlying mechanisms. The Firm SR and the Firm IR models are specified as:

$$ 
\begin{aligned}
\text{Firm Systematic Risk}_{it+1} / \text{Firm Idiosyncratic Risk}_{it+1} \\
= \beta_0 + \beta_1 \text{Coopetition}_{it} + \beta_2 \text{Coopetition}_{it}^2 + \beta_3 \text{Coopetition}_{it} \\
\times \text{Relationship Length}_{it} + \beta_4 \text{Coopetition}_{it} \\
\times \text{Competition Intensity}_{it} + \beta_5 \text{Relationship Length}_{it} \\
+ \beta_6 \text{Competition Intensity}_{it} + \beta_7 \text{Coopetition Size Gap}_{it} \\
+ \beta_8 \text{Coopetition Age Gap}_{it} + \beta_9 \text{Firm Size}_{it} + \beta_{10} \text{Firm Age}_{it} \\
+ \beta_{11} \text{Product Diversification}_{it} + \beta_{12} \text{Vertical Integration}_{it} \\
+ \beta_{13} \text{Firm Leverage}_{it} + \beta_{14} \text{SG&A}_{it} \\
+ \beta_{15} \text{Environmental Dynamism}_{it} \\
+ \beta_{16} \text{Industry Competition}_{it} + \text{Time Dummy Variables} \\
+ \text{Industry Dummy Variables} + \varepsilon_{it}
\end{aligned}
\quad (4) $$

Where *i* denotes firms, *j* means industries, *t* is time. We use *t* + 1 for the measure of SR and IR. This lagged dependent variable serves two key purposes. First, this mitigates concerns of reverse causality, a main source of endogeneity, thus enhancing causal inference (Ataman et al., 2010). Second, this considers the time delay associated with strategies like coopetition. Firms require time to implement cooperative strategies and adapt to competitive circumstances. Incorporating this lagged term captures these delayed effects, aligning the empirical model more closely with real business dynamics.

Estimating the model presents challenges. While the use of panel data enhances the precision of estimators (Hsiao, 2022), it raises issues of autocorrelation. When the same firm is observed over multiple years, error terms may be correlated, leading to biased standard errors. Additionally, the model includes a large pool of firms, potentially leading to heteroscedasticity. This occurs when the variances in risk are disproportionately influenced by clusters of firms sharing similar traits along unspecified dimensions. To mitigate these issues, we employ two robust methodologies commonly used in similar research contexts (Jindal & Slotegraaf, 2024). These approaches account for correlated error terms and heteroscedasticity, ensuring more reliable and generalizable results. First, we use Newey and West's (1987) robust regression model:

$$ X \hat{\Omega} X = X' \hat{\Omega}_0 X + \frac{n}{n-k} \sum_{l=1}^{m} \left( 1 - \frac{l}{m+1} \right) \sum_{t=l+1}^{n} \hat{e}_t \hat{e}_{t-l} (x_t' x_{t-l} + x_{t-l}' x_t) \quad (5) $$
Where $x_t$ represents the row of matrix X that is observed at time $t$, $m$ is the first-order autocorrelation, $n$ is the number of observations, and $k$ the number of predictors. This approach yields standard errors consistent with both heteroscedasticity and autocorrelation (HAC), meeting the standards established by extensive existing research utilizing panel data (Kurt & Hulland, 2013). To validate the robustness of these Newey-West-based estimators, we employ an alternative approach i.e., the White robust method. This allows for heteroscedasticity-consistent estimates. Concurrently, we cluster the standard errors by firm to maintain HAC consistency (Tambe et al., 2012).

$$ \widehat{Var}(\hat{\beta}) = (X'\Omega X)^{-1} X'\Omega\hat{\mu}\hat{\mu}'\Omega X(X'\Omega X)^{-1} \quad (6) $$

In addition, we also utilize additional validation methods to check the reliability of the results, which we discuss in the results section. This multi-pronged approach ensures a higher degree of confidence in the accuracy and reliability of our estimates.

## 4. Analysis, results and discussion

The results of the analysis are presented in Table 3, focusing on firm SR, and Table 4, addressing firm IR. We employ an incremental approach to model-building. Initially, we run models with only control

variables, followed by the inclusion of main effects. The final models incorporate robust regression techniques, specifically Newey-West and White Robust methods with clustering. Throughout this incremental process, variable consistency remains stable, and variance inflation factors (VIF) for all variables remain below five, confirming that multicollinearity does not compromise the models.

Before assessing the focal variables and hypotheses, we first review the control variables. For SR, firm size correlates positively, likely due to increased exposure to economic factors as firms expand (Low, 2009), while firm age correlates negatively, indicating improved risk management with experience (Gomez-Mejia et al., 2010). Product diversification reduces SR by spreading exposure across industries, whereas vertical integration increases it by internalizing value chain risks (Lafontaine & Slade, 2007). Industry dynamism also correlates positively, reflecting macro-environmental volatility.

For IR, firm size and age show similar patterns, with leverage positively correlated reflecting investor concerns over high debt (Luo & Bhattacharya, 2009). Environmental dynamism and industry competition positively influence investor favorability, signaling market appeal and fostering valued competitive skills (Giroud & Mueller, 2010; Karuna, 2007). This analysis deepens understanding of the drivers of firm risk.

Hypothesis H1 posits that vertical coopetition reduces a firm's SR.

<table><caption>**Table 3**
The relationship between coopetition and firm systematic risk.</caption><thead><tr><th rowspan="2"></th><th colspan="2">Controls</th><th colspan="2">Newey-West Robust Regression</th><th colspan="2">White Robust with Clustering</th><th colspan="2">Driscoll-Kraay Robust Regression</th><th colspan="2">Two Stage Least Square</th></tr><tr><th>Coef./Sig (t)</th><th>Main Effects Coef./Sig (t)</th><th>Coef./Sig (t)</th><th>Coef./Sig (t)</th><th>Coef./Sig (t)</th><th>Coef./Sig (t)</th><th>Coef./Sig (t)</th><th>Coef./Sig (t)</th><th>Coef./Sig (t)</th><th>Coef./Sig (t)</th></tr></thead><tbody><tr><td>Coopetition (Coop)</td><td></td><td>-0.037***<br>(-4.59)</td><td>-0.049***<br>(-3.48)</td><td></td><td>-0.049***<br>(-3.00)</td><td></td><td>-0.049***<br>(-2.84)</td><td></td><td>-0.078**<br>(-2.37)</td><td></td></tr><tr><td>Coopetition<sup>2</sup></td><td></td><td></td><td>-0.014<br>(-1.03)</td><td></td><td>-0.014<br>(-0.89)</td><td></td><td>-0.014<br>(-1.15)</td><td></td><td>-0.033<br>(-1.13)</td><td></td></tr><tr><td>Coop x CL</td><td></td><td></td><td>-0.015**<br>(-2.14)</td><td></td><td>-0.015*<br>(-1.89)</td><td></td><td>-0.015*<br>(-1.86)</td><td></td><td>-0.014*<br>(-1.81)</td><td></td></tr><tr><td>Coop x CCI</td><td></td><td></td><td>-0.023***<br>(-2.70)</td><td></td><td>-0.023**<br>(-2.19)</td><td></td><td>-0.023**<br>(-2.13)</td><td></td><td>-0.021**<br>(-2.38)</td><td></td></tr><tr><td>Coop Length (CL)</td><td></td><td>-0.050***<br>(-6.33)</td><td>-0.049***<br>(-6.31)</td><td></td><td>-0.049***<br>(-4.67)</td><td></td><td>-0.049***<br>(-8.24)</td><td></td><td>-0.049***<br>(-6.07)</td><td></td></tr><tr><td>Coop Comp Intensity (CCI)</td><td></td><td></td><td>0.053***<br>(5.43)</td><td>0.056***<br>(5.88)</td><td></td><td>0.056***<br>(4.03)</td><td></td><td>0.056***<br>(6.43)</td><td></td><td>0.062***<br>(6.64)</td></tr><tr><td>Coop Size Gap (CZG)</td><td>-0.001<br>(-0.05)</td><td>-0.021<br>(-1.54)</td><td>-0.019<br>(-1.45)</td><td></td><td>-0.019<br>(-1.15)</td><td></td><td>-0.019<br>(-1.55)</td><td></td><td>-0.032**<br>(-2.32)</td><td></td></tr><tr><td>Coop Age Gap (CAG)</td><td>0.021***<br>(2.59)</td><td>0.006<br>(0.77)</td><td>0.005<br>(0.67)</td><td></td><td>0.005<br>(0.55)</td><td></td><td>0.005<br>(0.74)</td><td></td><td>-0.002<br>(-0.23)</td><td></td></tr><tr><td>Firm Size</td><td>0.119***<br>(8.02)</td><td>0.130***<br>(8.70)</td><td>0.130***<br>(8.70)</td><td></td><td>0.130***<br>(6.80)</td><td></td><td>0.130***<br>(4.31)</td><td></td><td>0.136***<br>(8.71)</td><td></td></tr><tr><td>Firm Age</td><td>-0.117***<br>(-12.16)</td><td>-0.086***<br>(-8.33)</td><td>-0.086***<br>(-8.30)</td><td></td><td>-0.086***<br>(-6.28)</td><td></td><td>-0.086***<br>(-5.29)</td><td></td><td>-0.086***<br>(-8.11)</td><td></td></tr><tr><td>Product Diversification</td><td>-0.026***<br>(-3.63)</td><td>-0.021***<br>(-2.95)</td><td>-0.021***<br>(-2.90)</td><td></td><td>-0.021**<br>(-2.24)</td><td></td><td>-0.021**<br>(-2.36)</td><td></td><td>-0.020***<br>(-2.75)</td><td></td></tr><tr><td>Vertical Integration</td><td>0.020**<br>(2.29)</td><td>0.020**<br>(2.31)</td><td>0.020**<br>(2.29)</td><td></td><td>0.020*<br>(1.76)</td><td></td><td>0.020<br>(1.63)</td><td></td><td>0.020**<br>(2.16)</td><td></td></tr><tr><td>Firm Leverage</td><td>0.012<br>(1.31)</td><td>0.013<br>(1.44)</td><td>0.013<br>(1.39)</td><td></td><td>0.013<br>(1.15)</td><td></td><td>0.013<br>(0.86)</td><td></td><td>0.031***<br>(3.17)</td><td></td></tr><tr><td>SG&A Expenditure</td><td>0.039***<br>(4.29)</td><td>0.039***<br>(4.23)</td><td>0.038***<br>(4.20)</td><td></td><td>0.038***<br>(3.85)</td><td></td><td>0.038**<br>(2.47)</td><td></td><td>0.037***<br>(3.60)</td><td></td></tr><tr><td>Environmental Dynamism</td><td>0.025***<br>(2.86)</td><td>0.023***<br>(2.65)</td><td>0.022***<br>(2.59)</td><td></td><td>0.022**<br>(2.24)</td><td></td><td>0.022<br>(1.38)</td><td></td><td>0.018**<br>(2.00)</td><td></td></tr><tr><td>Environmental Competition</td><td>0.011<br>(1.16)</td><td>0.007<br>(0.72)</td><td>0.006<br>(0.68)</td><td></td><td>0.006<br>(0.51)</td><td></td><td>0.006<br>(0.75)</td><td></td><td>0.005<br>(0.58)</td><td></td></tr><tr><td>Time Dummies</td><td>Included</td><td>Included</td><td>Included</td><td></td><td>Included</td><td></td><td>Included</td><td></td><td>Included</td><td></td></tr><tr><td>Industry Dummies</td><td>Included</td><td>Included</td><td>Included</td><td></td><td>Included</td><td></td><td>Included</td><td></td><td>Included</td><td></td></tr><tr><td>R-squared</td><td>0.065</td><td>0.070</td><td>0.071</td><td></td><td>0.071</td><td></td><td>0.071</td><td></td><td>0.081</td><td></td></tr><tr><td>Partial F</td><td>-</td><td>29.49***</td><td>4.25***</td><td></td><td>4.25***</td><td></td><td>7.35***</td><td></td><td>11.28**<br>Chi<sup>2</sup> = 5.83</td><td></td></tr><tr><td>Sargan-Hansen Test</td><td>-</td><td>-</td><td>-</td><td></td><td>-</td><td></td><td>-</td><td></td><td>p > 0.05</td><td></td></tr><tr><td># of Observations</td><td>21,707</td><td>21,707</td><td>21,707</td><td></td><td>21,707</td><td></td><td>21,707</td><td></td><td>15,885</td><td></td></tr></tbody></table>

<footnote>* <0.1, ** <0.05, *** <0.01; All variance inflation factors are lower than 5.</footnote>
<table><caption>**Table 4**
The relationship between coopetition and firm idiosyncratic risk.</caption><thead><tr><th rowspan="2"></th><th colspan="2">Controls</th><th colspan="2">Main Effects</th><th colspan="2">Newey-West<br>Robust Regression</th><th colspan="2">White Robust with Clustering</th><th colspan="2">Driscoll-Kraay Robust Regression</th><th colspan="2">Two Stage Least Square</th></tr><tr><th>Coef./Sig<br>(t)</th><th>Coef./Sig<br>(t)</th><th>Coef./Sig<br>(t)</th><th>Coef./Sig<br>(t)</th><th>Coef./Sig<br>(t)</th><th>Coef./Sig<br>(t)</th><th>Coef./Sig<br>(t)</th><th>Coef./Sig<br>(t)</th><th>Coef./Sig<br>(t)</th><th>Coef./Sig<br>(t)</th><th>Coef./Sig<br>(t)</th><th>Coef./Sig<br>(t)</th></tr></thead><tbody><tr><td>Coopetition (Coop)</td><td></td><td>0.011<br>(1.53)</td><td></td><td>-0.030**<br>(-2.11)</td><td></td><td>-0.030<br>(-1.55)</td><td></td><td>-0.030**<br>(-2.30)</td><td></td><td>-0.030**<br>(-2.30)</td><td></td><td>-0.050<br>(-1.52)</td></tr><tr><td>Coopetition<sup>2</sup></td><td></td><td></td><td></td><td>-0.048***<br>(-2.99)</td><td></td><td>-0.048**<br>(-2.11)</td><td></td><td>-0.048***<br>(-2.65)</td><td></td><td>-0.048***<br>(-2.65)</td><td></td><td>-0.071**<br>(-2.36)</td></tr><tr><td>Coop x CL</td><td></td><td></td><td></td><td>-0.003<br>(-0.51)</td><td></td><td>-0.003<br>(-0.36)</td><td></td><td>-0.003<br>(-0.57)</td><td></td><td>-0.003<br>(-0.57)</td><td></td><td>-0.005<br>(-0.59)</td></tr><tr><td>Coop x CCI</td><td></td><td></td><td></td><td>0.023***<br>(3.04)</td><td></td><td>0.023**<br>(2.00)</td><td></td><td>0.023***<br>(4.11)</td><td></td><td>0.023***<br>(4.11)</td><td></td><td>0.019**<br>(2.15)</td></tr><tr><td>Coop Length (CL)</td><td></td><td></td><td>-0.002<br>(-0.19)</td><td></td><td>-0.002<br>(-0.21)</td><td></td><td>-0.002<br>(-0.13)</td><td></td><td>-0.002<br>(-0.29)</td><td></td><td>0.006<br>(0.71)</td><td></td></tr><tr><td>Coop Comp Intensity (CCI)</td><td></td><td></td><td>-0.045***<br>(-4.34)</td><td></td><td>-0.047***<br>(-4.80)</td><td></td><td>-0.047***<br>(-2.75)</td><td></td><td>-0.047***<br>(-5.39)</td><td></td><td>-0.047***<br>(-4.18)</td><td></td></tr><tr><td>Coop Size Gap (CZG)</td><td>0.014<br>(-0.05)</td><td>0.029**<br>(2.48)</td><td></td><td>0.028**<br>(2.40)</td><td></td><td>0.028<br>(1.52)</td><td></td><td>0.028***<br>(3.18)</td><td></td><td>0.028***<br>(3.18)</td><td></td><td>0.023<br>(1.56)</td></tr><tr><td>Coop Age Gap (CAG)</td><td>0.001<br>(2.59)</td><td>0.005<br>(0.63)</td><td></td><td>0.004<br>(0.58)</td><td></td><td>0.004<br>(0.41)</td><td></td><td>0.004<br>(0.56)</td><td></td><td>0.004<br>(0.56)</td><td></td><td>0.016*<br>(1.67)</td></tr><tr><td>Firm Size</td><td>-0.464***<br>(8.02)</td><td>-0.468***<br>(-35.55)</td><td></td><td>-0.468***<br>(-35.61)</td><td></td><td>-0.468***<br>(-21.05)</td><td></td><td>-0.468***<br>(-27.92)</td><td></td><td>-0.468***<br>(-27.92)</td><td></td><td>-0.425***<br>(-27.01)</td></tr><tr><td>Firm Age</td><td>0.063***<br>(-12.16)</td><td>0.055***<br>(5.95)</td><td></td><td>0.056***<br>(6.07)</td><td></td><td>0.056***<br>(3.73)</td><td></td><td>0.056***<br>(5.52)</td><td></td><td>0.056***<br>(5.52)</td><td></td><td>0.052***<br>(5.05)</td></tr><tr><td>Product Diversification</td><td>0.011<br>(-3.63)</td><td>0.008<br>(0.97)</td><td></td><td>0.008<br>(0.93)</td><td></td><td>0.008<br>(0.54)</td><td></td><td>0.008<br>(0.90)</td><td></td><td>0.008<br>(0.90)</td><td></td><td>-0.002<br>(-0.26)</td></tr><tr><td>Vertical Integration</td><td>-0.017**<br>(2.29)</td><td>-0.015*<br>(-1.71)</td><td></td><td>-0.014*<br>(-1.65)</td><td></td><td>-0.014<br>(-0.89)</td><td></td><td>-0.014<br>(-1.43)</td><td></td><td>-0.014<br>(-1.43)</td><td></td><td>-0.014<br>(-1.43)</td></tr><tr><td>Firm Leverage</td><td>0.118***<br>(1.31)</td><td>0.116***<br>(14.36)</td><td></td><td>0.116***<br>(14.35)</td><td></td><td>0.116***<br>(8.61)</td><td></td><td>0.116***<br>(18.58)</td><td></td><td>0.116***<br>(18.58)</td><td></td><td>0.110***<br>(12.06)</td></tr><tr><td>SG&A Expenditure</td><td>-0.007<br>(4.29)</td><td>-0.008<br>(-1.38)</td><td></td><td>-0.007<br>(-1.30)</td><td></td><td>-0.007<br>(-1.12)</td><td></td><td>-0.007<br>(-0.89)</td><td></td><td>-0.007<br>(-0.89)</td><td></td><td>-0.008<br>(-1.26)</td></tr><tr><td>Environmental Dynamism</td><td>-0.038***<br>(2.86)</td><td>-0.038***<br>(-6.23)</td><td></td><td>-0.038***<br>(-6.30)</td><td></td><td>-0.038***<br>(-4.97)</td><td></td><td>-0.038***<br>(-5.41)</td><td></td><td>-0.038***<br>(-5.41)</td><td></td><td>-0.031***<br>(-4.43)</td></tr><tr><td>Environmental Competition</td><td>-0.014**<br>(1.16)</td><td>-0.013*<br>(-1.94)</td><td></td><td>-0.012*<br>(-1.76)</td><td></td><td>-0.012<br>(-1.00)</td><td></td><td>-0.012*<br>(-1.85)</td><td></td><td>-0.012*<br>(-1.85)</td><td></td><td>-0.004<br>(-0.61)</td></tr><tr><td>Time Dummies</td><td>Included</td><td>Included</td><td></td><td>Included</td><td></td><td>Included</td><td></td><td>Included</td><td></td><td>Included</td><td></td><td>Included</td></tr><tr><td>Industry Dummies</td><td>Included</td><td>Included</td><td></td><td>Included</td><td></td><td>Included</td><td></td><td>Included</td><td></td><td>Included</td><td></td><td>Included</td></tr><tr><td>R-squared</td><td>0.300</td><td>0.302</td><td></td><td>0.303</td><td></td><td>0.303</td><td></td><td>0.303</td><td></td><td>0.303</td><td></td><td>0.304</td></tr><tr><td>Partial F</td><td>-</td><td>7.21***</td><td></td><td>7.15***</td><td></td><td>3.73**</td><td></td><td>9.91***</td><td></td><td>5.02**<br>Chi<sup>2</sup> = 6.89</td><td></td><td>p > 0.05</td></tr><tr><td>Sargan-Hansen Test<br># of Observations</td><td>23,280</td><td>23,280</td><td></td><td>23,280</td><td></td><td>23,280</td><td></td><td>23,280</td><td></td><td>13,504</td><td></td><td></td></tr></tbody></table>

Our results indicate that vertical coopetition is negatively associated with SR, ($\beta$ = -0.049, $p$ < 0.01). This finding aligns with the theoretical expectation that vertical coopetition can enhance resilience by leveraging both cooperation and competition. Additionally, the square term of coopetition is not statistically significant, indicating a linear rather than a curvilinear relation between vertical coopetition and SR reflects a firm's vulnerability to macro-environmental factors, and our results indicate that vertical coopetition acts as a buffer against this risk. This protective effect likely stems from the dual elements of cooperation and competition inherent in vertical coopetition. Through cooperation, firms appear better equipped to navigate challenges in the broader business landscape. While coopetition theory traditionally highlights the benefits of collaboration among horizontally aligned firms, our findings extend this understanding by demonstrating that vertical relationships with customers can also mitigate a supplier's risks, even in competitive contexts. This result emphasizes that vertical coopetition – typically less studied – can significantly reduce SR, highlighting the comparable value of vertical and horizontal relationships in managing macro-environmental vulnerabilities.

Our second hypothesis (H2) suggests an inverted U-shaped relationship between coopetition and firm IR. The data support this hypothesis, with both the linear term of coopetition ($\beta$ = -0.030, $p$ < 0.05) and its squared term ($\beta$ = -0.048, $p$ < 0.01) displaying statistical significance. This pattern, in which the squared term is negative and statistically significant, indicates an inverted U-shaped relationship

between vertical coopetition and IR. This implies that IR initially rises with increases in coopetition, peaks at moderate levels, and declines at higher levels. This suggests that IR is greatest when firms are partially engaged in coopetition but have not yet developed the governance mechanisms or relational capabilities to manage its tensions. From a signaling perspective, this intermediate zone also generates the greatest investor uncertainty, as it lacks the clarity of a low-coopetition strategy or the demonstrated capability implied by high coopetition. As a result, IR may peak at moderate levels because the signals being sent are the most difficult for external stakeholders to interpret with confidence. At low levels, competitive overlap is limited, reducing exposure to conflict; at high levels, firms are more likely to have established coordination routines, trust, and safeguards that mitigate risk. The beta coefficients thus imply that coopetition has a greater effect on reducing IR when it is either low or high, and the greatest effect on increasing IR when coopetition is moderate, thus reflecting a zone of strategic ambiguity that elevates investor-perceived firm-specific risk. This dynamic aligns with Farrell and Klemperer's (2007) notion of a "stuck-in-the-middle" position, where firms face the disadvantages of both strategies without securing the benefits of either. In the context of vertical coopetition, moderate engagement may signal indecision or instability, while high engagement reflects strategic maturity and risk-managed collaboration.

Hypotheses H3a and H4a focus on the influence of coopetition length on firm risks. The data support that the length of coopetition is negatively correlated with SR ($\beta$ = -0.049, $p$ < 0.01), but not with IR. Thus,

<footnote>* <0.1, ** <0.05, *** <0.01; All variance inflation factors are lower than 5.</footnote>
H3a is supported, while H4a is not. Coopetition length enhances the stability of dyadic relationships, reducing SR, a finding consistent with Gnyawali and Park (2011), who highlight collaboration's role in strengthening relationship health. Our study extends this understanding by showing that stable, long-term coopetitive relationships buffer firms against external uncertainties, aligning with research that suggests such relationships improve market intelligence and risk forecasting (Ryals, 2006). However, the lack of impact on IR warrants further exploration; while long-term relationships may indicate stability, they also bring higher switching costs (Aydin & Özer, 2005) and may be perceived by investors as reducing a firm's adaptability, leading to mixed perceptions of extended coopetition.

Hypotheses H3b and H4b, explore a moderating effect of coopetition length. The data support this only for SR ($\beta$ = -0.015, *p* < 0.05). Fig. 2 shows that longer-term relationships enhance coopetition's ability to reduce SR, highlighting the cumulative benefits of resource sharing, process coordination, and goal alignment. This finding extends existing research by demonstrating that the duration of coopetition relationships strengthens SR mitigation, suggesting that risk-reducing advantages intensify over time. However, it diverges from past expectations regarding IR, indicating that coopetition length may not significantly influence firm-specific risks, potentially due to investors' perceptions of reduced adaptability in extended alliances. It is important to acknowledge that our models do not explicitly test the managerial mechanisms underlying this effect, such as the gradual development of trust or enhanced coordination capabilities over time.

Hypotheses H5a and H6a examine the influence of competition intensity within vertical coopetition on firm risks, revealing distinct effects. For SR, competition intensity is positively correlated ($\beta$ = 0.056, *p* < 0.01), support H5a. Conversely, for IR, a negative correlation is observed ($\beta$ = -0.047, *p* < 0.01), thus supporting H6a(1). SR reflects a firm's past and present responses to external uncertainties, while IR captures investors' forward-looking assessments. The differing effects of competition intensity on these risks provide detailed and multi-layered insights into the coopetition landscape: while competition within coopetition heightens susceptibility to broad external factors, it also signals to investors a drive to cultivate unique capabilities, positioning firms favorably to address competitive challenges. Investigating further into the role of competition intensity as a moderator, different effects emerge. For SR, competition intensity exerts a negative moderating effect ($\beta$ = -0.023, *p* < 0.01) as posited in H5b. In contrast, for IR, its role is positively moderating ($\beta$ = 0.023, *p* < 0.01), in line with H6b. Figs. 3 and 4 depict these effects. This finding extends existing literature by

<figure><img src="image_5.png" /><figcaption>Fig. 2. Moderating effect of coopetition relation length on the relationship between vertical coopetition and firm systematic risk.</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Fig. 3. Moderating effect of competition intensity on the relationship between vertical coopetition and firm systematic risk.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>Fig. 4. Moderating effect of competition intensity on the relationship between vertical coopetition and firm idiosyncratic risk.</figcaption></figure>

showing that competition intensity can simultaneously increase SR while reducing IR, emphasizing the dual nature of competition's influence on firm risk.

When firms compete intensely with their partners, coopetition mitigates SR more effectively, suggesting that collaboration can counter competition's adverse effects on external vulnerabilities. For IR, lower competitive intensity within coopetition reduces risk more effectively, indicating that reduced competition strengthens coopetition's focus on firm-specific advantages. This finding extends existing research by showing that coopetition's risk-mitigating benefits vary with competitive intensity, highlighting how collaboration can adapt to different competitive contexts to address distinct types of risk.

## 4.1. Robustness analyses and alternative measures

### 4.1.1. Using additional methodologies

To test robustness, we extended our analysis beyond the Newey-West method by using white robust regression with clustering and Driscoll-Kraay robust regression for HAC standard errors. Results, shown in later columns of Tables 2 and 3, consistently support our hypothesized
relationships across analytical methods.

To address endogeneity concerns, we employed a robust two-stage least squares (2SLS) approach, utilizing lagged variables as instrumental variables i.e., lagged coopetition (number ratio), relationship length, competition intensity, and customer metrics. These are exogenous to current firm performance, ensuring the reliability of our causal estimates. In the first stage, we regressed coopetition on these lagged IVs to generate predicted values free from reverse causality and omitted variable bias. In the second stage, these predicted values replaced actual coopetition in the main regression, isolating the causal impact of product market fluidity and coopetition on firm risk. Diagnostic tests, including the Hansen J test (p > 0.05) and a first-stage F-statistic of 0.55, validated the exogeneity and relevance of our instruments. These results, consistent with prior findings, demonstrate both the strength and robustness of our estimation, confirming the credibility of our causal inferences.

### 4.1.2. Using additional measures of risk

Recognizing that both SR and IRs stem from market factors, we incorporate cash flow volatility (CFV) as an additional financial risk measure. CFV, (from Compustat), captures operational income fluctuations and serves as a retrospective financial risk indicator. We computed the five-year cash flow coefficient of variation using methods aligned

<figure><img src="image_8.png" /><figcaption>Fig. 5. Moderating effect of competition intensity on the relationship between vertical coopetition and cash flow volatility.</figcaption></figure>

<table><caption>**Table 5**
The relationship between coopetition and firm cash flow volatility.</caption><thead><tr><th rowspan="2"></th><th colspan="2">Controls</th><th colspan="2">Main Effects</th><th colspan="2">Newey-West<br>Robust Regression</th><th colspan="2">White Robust with Clustering</th><th colspan="2">Driscoll-Kraay Robust Regression</th><th colspan="2">Two Stage Least Square</th></tr><tr><th>Coef./Sig<br>(t)</th><th>Coef./Sig<br>(t)</th><th>Coef./Sig<br>(t)</th><th>Coef./Sig<br>(t)</th><th>Coef./Sig<br>(t)</th><th>Coef./Sig<br>(t)</th><th>Coef./Sig<br>(t)</th><th>Coef./Sig<br>(t)</th><th>Coef./Sig<br>(t)</th><th>Coef./Sig<br>(t)</th><th>Coef./Sig<br>(t)</th><th>Coef./Sig<br>(t)</th></tr></thead><tbody><tr><td>Coopetition (Coop)</td><td></td><td>-0.011*</td><td>-0.036***</td><td>-0.036***</td><td>-0.036**</td><td>-0.036**</td><td>-0.036***</td><td>-0.036***</td><td>-0.036***</td><td>-0.036***</td><td>-0.052**</td><td>-0.052**</td></tr><tr><td>Coopetition<sup>2</sup></td><td></td><td>(-1.83)</td><td>(-3.32)</td><td>(-2.53)</td><td>(-2.53)</td><td>(-2.53)</td><td>(-3.23)</td><td>(-3.23)</td><td>(-3.23)</td><td>(-3.23)</td><td>(-2.52)</td><td>(-2.52)</td></tr><tr><td>Coop x CL</td><td></td><td></td><td>-0.029***</td><td>-0.029***</td><td>-0.029***</td><td>-0.029***</td><td>-0.029***</td><td>-0.029***</td><td>-0.029***</td><td>-0.029***</td><td>-0.046***</td><td>-0.046***</td></tr><tr><td>Coop x CCI</td><td></td><td></td><td>(-2.89)</td><td>(-2.24)</td><td>(-2.24)</td><td>(-2.24)</td><td>(-2.89)</td><td>(-2.58)</td><td>(-2.58)</td><td>(-2.58)</td><td>(-2.64)</td><td>(-2.64)</td></tr><tr><td>Coop Length (CL)</td><td></td><td></td><td>-0.004</td><td>-0.004</td><td>-0.004</td><td>-0.004</td><td>-0.004</td><td>-0.004</td><td>-0.004</td><td>-0.004</td><td>-0.010*</td><td>-0.010*</td></tr><tr><td>Coop Comp Intensity (CCI)</td><td></td><td></td><td>(-0.89)</td><td>(-0.60)</td><td>(-0.60)</td><td>(-0.60)</td><td>(-0.89)</td><td>(-0.78)</td><td>(-0.78)</td><td>(-0.78)</td><td>(-1.66)</td><td>(-1.66)</td></tr><tr><td>Coop Size Gap (CZG)</td><td>-0.034***</td><td>-0.068***</td><td>-0.066***</td><td>-0.066***</td><td>-0.066***</td><td>-0.066***</td><td>-0.066***</td><td>-0.066***</td><td>-0.066***</td><td>-0.066***</td><td>-0.086***</td><td>-0.086***</td></tr><tr><td>Coop Age Gap (CAG)</td><td>(-3.30)</td><td>(-6.47)</td><td>(-6.33)</td><td>(-6.33)</td><td>(-4.55)</td><td>(-4.55)</td><td>(-3.30)</td><td>(-5.53)</td><td>(-5.53)</td><td>(-5.53)</td><td>(-8.39)</td><td>(-8.39)</td></tr><tr><td>Firm Size</td><td>-0.444***</td><td>-0.422***</td><td>-0.422***</td><td>-0.422***</td><td>-0.422***</td><td>-0.422***</td><td>-0.422***</td><td>-0.422***</td><td>-0.422***</td><td>-0.422***</td><td>-0.414***</td><td>-0.414***</td></tr><tr><td>Firm Age</td><td>(-38.82)</td><td>(-36.72)</td><td>(-36.92)</td><td>(-25.17)</td><td>(-25.17)</td><td>(-25.17)</td><td>(-38.82)</td><td>(-27.05)</td><td>(-27.05)</td><td>(-27.05)</td><td>(-30.99)</td><td>(-30.99)</td></tr><tr><td>Product Diversification</td><td>(-20.56)</td><td>(-14.01)</td><td>(-14.07)</td><td>(-9.43)</td><td>(-9.43)</td><td>(-9.43)</td><td>(-20.56)</td><td>(-13.19)</td><td>(-13.19)</td><td>(-13.19)</td><td>(-9.08)</td><td>(-9.08)</td></tr><tr><td>Vertical Integration</td><td>(-0.020***</td><td>-0.015**</td><td>-0.014**</td><td>-0.014</td><td>-0.014</td><td>-0.014</td><td>(-3.48)</td><td>(-2.70)</td><td>(-2.70)</td><td>(-2.70)</td><td>-0.020***</td><td>-0.020***</td></tr><tr><td>Firm Leverage</td><td>(-0.027***</td><td>-0.024***</td><td>-0.025***</td><td>-0.025***</td><td>-0.025***</td><td>-0.025***</td><td>(-3.78)</td><td>(-4.68)</td><td>(-4.68)</td><td>(-4.68)</td><td>-0.025***</td><td>-0.025***</td></tr><tr><td>SG&A Expenditure</td><td>(-6.82)</td><td>(-6.77)</td><td>(-6.83)</td><td>(-4.64)</td><td>(-4.64)</td><td>(-4.64)</td><td>(-3.48)</td><td>(-4.68)</td><td>(-4.68)</td><td>(-4.68)</td><td>(-4.76)</td><td>(-4.76)</td></tr><tr><td>Environmental Dynamism</td><td>(10.75)</td><td>(10.63)</td><td>(10.61)</td><td>(9.16)</td><td>(9.16)</td><td>(9.16)</td><td>(10.75)</td><td>(7.45)</td><td>(7.45)</td><td>(7.45)</td><td>(7.07)</td><td>(7.07)</td></tr><tr><td>Environmental Competition</td><td>(4.33)</td><td>(3.94)</td><td>(3.79)</td><td>(2.98)</td><td>(2.98)</td><td>(2.98)</td><td>(4.33)</td><td>(2.40)</td><td>(2.40)</td><td>(2.40)</td><td>(4.19)</td><td>(4.19)</td></tr><tr><td>Time Dummies</td><td>(0.63)</td><td>(0.07)</td><td>(-0.01)</td><td>(-0.01)</td><td>(-0.01)</td><td>(-0.01)</td><td>(0.63)</td><td>(-0.01)</td><td>(-0.01)</td><td>(-0.01)</td><td>(-1.06)</td><td>(-1.06)</td></tr><tr><td>Industry Dummies</td><td>Included</td><td>Included</td><td>Included</td><td>Included</td><td>Included</td><td>Included</td><td>Included</td><td>Included</td><td>Included</td><td>Included</td><td>Included</td><td>Included</td></tr><tr><td>R-squared</td><td>0.448</td><td>0.458</td><td>0.460</td><td>0.460</td><td>0.460</td><td>0.460</td><td>0.448</td><td>0.460</td><td>0.460</td><td>0.460</td><td>0.464</td><td>0.464</td></tr><tr><td>Partial F</td><td></td><td>91.03***</td><td>14.99***</td><td>8.46***</td><td>8.46***</td><td>8.46***</td><td></td><td>37.95***</td><td>37.95***</td><td>37.95***</td><td>49.39***</td><td>49.39***</td></tr><tr><td>Sargan-Hansen Test</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>Chi<sup>2</sup> = 12.20</td><td>Chi<sup>2</sup> = 12.20</td></tr><tr><td># of Observations</td><td>21,707</td><td>21,707</td><td>21,707</td><td>21,707</td><td>21,707</td><td>21,707</td><td>21,707</td><td>21,707</td><td>21,707</td><td>21,707</td><td>p > 0.05</td><td>p > 0.05</td></tr></tbody></table>

<footnote>* <0.1, ** <0.05, *** <0.01; All variance inflation factors are lower than 5.</footnote>
with our prior risk analyses. Results are detailed in Table 5 and Fig. 5 and provide a broader view of vertical coopetition's risk impact by incorporating multiple measures.

Analyses reveal an inverted U-shaped relationship between vertical coopetition and CFV, with coopetition length negatively related to CFV, indicating that prolonged cooperation reduces operational revenue volatility. In contrast, competition intensity correlates positively with CFV, consistent with research linking competitive pressures to cash flow volatility (Tang & Yan, 2010). Notably, higher competition intensity enhances coopetition's risk-mitigating effects, improved cooperative skills strengthen collaborative capabilities, particularly when opportunistic risks are elevated. These findings advance and deepen our understanding of the balance between cooperation and competition in coopetitive relationships.

To further check the sensitivity of our analysis, we examined the key thesis of the current treatise using three additional risk measures: ROA volatility, profit margin volatility, and default risk. ROA volatility and profit margin volatility are calculated as the coefficient of variation over five-year intervals, reflecting fluctuations in a firm's operational revenues rather than market-driven financial risks. These measures provide insights into the stability of a firm's core financial performance, distinct from market-based systematic and IRs. The results support that coopetition affects these variables in a manner similar to cash flow volatility (CFV), further highlighting its stabilizing effect on operational metrics.

Default risk, measured using bond ratings from the Mergent Fixed Investment Securities Database (FISD) available on WRDS, reflects debtholders' evaluations of a firm's bankruptcy risk, providing a complementary perspective to traditional market risks (Amiraslani et al., 2023). Table 6 reveals that coopetition, especially in longer relationships, lowers default risk, suggesting that coopetition not only mitigates market-exposed risks but also enhances financial stability as perceived by creditors. These findings highlight coopetition's broad, stabilizing impact on various dimensions of firm risk.

Across the multiple measures of risk examined – systematic, idiosyncratic, and various operational metrics including cash flow volatility, ROA volatility, profit margin volatility, and default risk – the findings remain stable and mutually reinforcing. The patterns hold under diverse estimation techniques (Newey-West, White robust, Driscoll-Kraay), with

<caption>**Table 6**</caption>

Additional studies: coopetition's effects on ROA volatility, profit margin volatility, and default risk.

<table><thead><tr><th rowspan="2"></th><th>ROA Volatility</th><th>Profit Margin Volatility</th><th>Default Risk</th></tr><tr><th>Coef./Sig (t)</th><th>Coef./Sig (t)</th><th>Coef./Sig (t)</th></tr></thead><tbody><tr><td>Coopetition (Coop)</td><td>-0.053***<br>(-4.54)</td><td>-0.051***<br>(-4.29)</td><td>-0.049**<br>(-1.98)</td></tr><tr><td>Coopetition<sup>2</sup></td><td>-0.039***<br>(-3.53)</td><td>-0.030***<br>(-2.87)</td><td>-0.035<br>(-1.47)</td></tr><tr><td>Coop x CL</td><td>-0.016***<br>(-2.77)</td><td>-0.013***<br>(-2.58)</td><td>0.006<br>(0.39)</td></tr><tr><td>Coop x CCI</td><td>-0.022***<br>(-3.57)</td><td>-0.029***<br>(-4.11)</td><td>-0.020<br>(-1.48)</td></tr><tr><td>Coop Length (CL)</td><td>-0.077***<br>(-10.34)</td><td>-0.093***<br>(-14.42)</td><td>-0.070***<br>(-4.01)</td></tr><tr><td>Coop Comp Intensity (CCI)</td><td>0.096***<br>(13.89)</td><td>0.123***<br>(15.26)</td><td>0.014<br>(0.91)</td></tr><tr><td>Coop Size Gap (CZG)</td><td>-0.057***<br>(-5.23)</td><td>-0.041***<br>(-3.29)</td><td>-0.086***<br>(-3.84)</td></tr><tr><td>Coop Age Gap (CAG)</td><td>0.027***<br>(3.84)</td><td>0.010<br>(1.47)</td><td>0.020<br>(1.54)</td></tr><tr><td>Control Variables</td><td>Included</td><td>Included</td><td>Included</td></tr><tr><td>Time Dummies</td><td>Included</td><td>Included</td><td>Included</td></tr><tr><td>Industry Dummies</td><td>Included</td><td>Included</td><td>Included</td></tr><tr><td>R-squared</td><td>0.374</td><td>0.345</td><td>0.646</td></tr><tr><td># of Observations</td><td>23,522</td><td>23,522</td><td>2920</td></tr></tbody></table>

VIFs consistently below five and instrumental variable approaches mitigating endogeneity concerns. Moreover, the relationships persist across both market-based and operational risk measures, demonstrating that the influence of vertical coopetition, relationship length, and competition intensity is not limited to a single type of risk exposure. This consistency, supported even using alternative methodologies and additional controls, highlights the reliability and breadth of our conclusions (Gnyawali & Park, 2011; Ryals, 2006; Tang & Yan, 2010).

## 5. Implications

Our research provides multiple implications toward theory as well as practice in coopetition literature by analyzing over 20,000 observations from more than 4000 firms over 29 years, enabling rigorous hypothesis testing. It deepens the understanding of vertical coopetition and provides actionable insights for stakeholders and its findings call for both theoretical frameworks and practical strategies to manage cooperative dynamics and enhance financial stability and market resilience. We discuss these in detail next.

### 5.1. Implications for theory

First, our research advances the coopetition literature by offering new insights into vertical coopetition, an area less studied than horizontal coopetition, and linking it to firm risk measures such as SR, IR, cash flow volatility, ROA, and default risk. Notably, our findings show that systematic and IRs impact shareholders by influencing cash flow and profit volatility, while default risk holds particular importance for debtholders, reflecting concerns over a firm's financial stability. This holistic view highlights vertical coopetition's varied implications across different stakeholder groups.

Second, our finding of an inverted U-shaped relationship between vertical coopetition and IR has important theoretical implications. It suggests that risk does not decline linearly with greater coopetition, but instead peaks at moderate levels. This reflects a zone of strategic ambiguity, in which firms may be experimenting with coopetition but lack the capabilities or alignment needed to manage its inherent tensions. This state may also send inconsistent signals to external stakeholders, including investors, alliance partners, and customers, who may perceive such firms as lacking strategic clarity. Future research could examine how different stakeholder audiences interpret coopetition intensity and how these perceptions influence strategic and financial outcomes.

Third, our findings refine the traditional RBV by examining vertical coopetition. While RBV typically emphasizes the acquisition of heterogeneous resources to outperform rivals, often viewing competition as external to the firm, coopetition introduces a more complex reality: firms must simultaneously collaborate and compete, requiring resource coordination amid relational tension. By integrating this duality, our study expands the RBV and shows how vertical coopetition complicates resource deployment. The inverted U-shaped pattern suggests an “all or nothing” dynamic: firms gain the most when they either fully commit to coopetition, developing trust, governance, and coordination routines, or avoid it altogether. In contrast, firms at moderate levels may experience instability and risk without the structures to manage them. This contributes to coopetition theory by highlighting that value creation is contingent upon threshold capabilities and strategic coherence. Moreover, our findings suggest that coopetition evolves: firms initially face challenges, but as experience builds, they refine their ability to manage dual goals. This progression highlights the temporal and structural conditions needed to convert coopetition into sustained advantage, extending the RBV’s dynamic capabilities lens.

Fourth, our study also contributes to signaling theory. We show that vertical coopetition between a supplier and its customers may signal either stability or risk, depending on its intensity. A low to moderate level of coopetition may signal uncertainty or lack of strategic clarity, leading to higher IR, while a high level may signal coordination

<footnote>* <0.1, ** <0.05, *** <0.01; All variance inflation factors are lower than 5.</footnote>
capability and confidence, resulting in lower IR. This contributes to signaling theory by demonstrating that the same strategic action (copetition) may be interpreted positively or negatively depending on its degree of execution, thus highlighting the importance of signal intensity in shaping external stakeholder perceptions.

## 5.2. Implications for practice

B2B managers today must navigate the complexities of customer-competitors, making an in-depth understanding of vertical coopetition essential for risk minimization. Our findings highlight three strategic priorities for B2B managers. First, managers must conduct a comprehensive risk assessment by carefully identifying primary risk sources, distinguishing between market-driven SR and firm specific IR. While coopetition generally lowers SR, partial or sporadic engagement with vertical partners may hinder effective resource-sharing, raising IR by weakening the collaborative benefits. B2B managers should commit to clear risk-mitigation objectives, aligning coopetitive engagement strategies with environmental and investor-driven pressures. Second, managers should emphasize relationship longevity, as long-term coopetitive relationships with key vertical partners yield risk-reduction benefits by enabling deeper resource integration and improved information flows that mitigate market volatility. Prioritizing long-term coopetition aligns with strategic risk mitigation, as well-established partnerships better withstand external shocks. The insights suggest that managers should consider relationship continuity a central factor in risk management strategies and, where possible, incentivize vertical partners toward lasting collaboration. Third, managers should re-evaluate competition's role within coopetition by recognizing that competitive dynamics with customer bases may potentially enhance market-based skills, positioning, and investor confidence, thereby reducing IR and driving differentiation through improved stakeholder trust, making competition intensity a strategic lever for resilience.

Beyond risk assessment, relationship longevity, and competition intensity, our research shows that coopetition offers a holistic approach to balancing stakeholder interests by addressing both systematic and IRs, as evidenced by its inverted U-shaped impact on IR, and calls for B2B managers to integrate strategic adjustments and dedicated expertise in managing complex vertical relationships in converging markets where customers increasingly become competitors.

# 6. Limitations and future research

By examining key factors such as relationship duration and competition intensity, this study enhances our understanding of vertical coopetition and highlights several avenues for future research. For instance, while our measurement approach effectively captures vertical coopetition, it does not differentiate between strategic motivations, e.g. Apple's component supply collaboration with Samsung despite their rivalry, which calls for refined, qualitative measures to explore the functional role of collaboration. This also points to the need to account for power dynamics that may influence resource coordination and benefit distribution in ways not observable through archival data.

Moreover, although our study primarily draws on the RBV, our findings suggest that investor responses to vertical coopetition may be shaped by signaling effects. These relationships may signal either resource synergies or heightened risk exposure, highlighting the importance of integrating signaling theory to better understand market perceptions. Future research could examine how different stakeholder groups, such as investors, alliance partners, and customers, interpret varying levels of coopetition and how these interpretations influence firm outcomes. Our finding of an inverted U-shaped relationship between coopetition and firm IR suggests that stakeholder perceptions may not track coopetition intensity in a linear way. Moderate engagement may create ambiguity or signal strategic inconsistency, while high levels may indicate maturity and coordination capability. Experimental or

qualitative studies could explore how such signals are decoded across different contexts, industries, or institutional environments.

Additionally, sectoral variation may affect how coopetition strategies are structured and perceived. A single model may not universally apply, particularly when comparing manufacturing to service sectors. Future work could incorporate time-series designs and industry-specific analyses while also examining how external factors, such as technology, regulation, culture, digital transformation, and climate-related risks, shape coopetition strategies and competitive advantage (Plouffe et al., 2023).

Finally, our reliance on secondary data limits our ability to directly observe the mechanisms linking vertical coopetition to risk outcomes. Future research could therefore benefit from qualitative, case-based, or experimental approaches to better capture resource-sharing, strategic coordination, and inter-firm dynamics in action.

## CRediT authorship contribution statement

**Wenbin Sun:** Writing – review & editing, Writing – original draft, Supervision, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Rahul Govind:** Writing – review & editing, Writing – original draft, Validation, Supervision, Resources, Investigation, Conceptualization. **Mahabubur Rahman:** Writing – review & editing, Writing – original draft, Conceptualization.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.indmarman.2025.07.002.

## Data availability

Data will be made available on request.

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