Nexus supplier transparency and supply network accessibility: effects on buyer ESG risk exposure

Abstract

Purpose

This paper investigates the role of nexus supplier transparency, which involves the collective information disclosure to the public by second-tier nexus suppliers, as an alternative mechanism for mitigating buyer environmental, social and governance (ESG) risk exposure. We also examine buyer supply network accessibility as a moderating factor that facilitates collecting detailed information and undertaking corrective actions accordingly.

Design/methodology/approach

We collected a sample of 428 focal buyer firms and their supply networks up to third-tier suppliers. Data were obtained from Bloomberg and RepRisk databases. We identified critical nexus suppliers using data envelopment analysis (DEA) and tested hypotheses using regression analysis.

Findings

The results show that the benefits of nexus supplier transparency, such as reducing buyer ESG risk exposure, differ depending on the type of nexus supplier disclosing information and buyer supply network accessibility. Informational nexus supplier transparency was found to be beneficial. However, the results revealed the double-edged sword of monopolistic nexus supplier transparency, which benefits buyers with higher levels of accessibility but increases risk exposure for buyers with lower accessibility.

Originality/value

This study demonstrates that the transparency of critical second-tier suppliers mitigates buyer ESG risk exposure by providing information about lower tiers in the supply network. Challenging the notion of the focal buyer as the main orchestrator of supply chain initiatives, our alternative perspective opens a new avenue for risk management in multi-tier supply chains.

Citation

Diego, J. and Montes-Sancho, M.J. (2024), "Nexus supplier transparency and supply network accessibility: effects on buyer ESG risk exposure", International Journal of Operations & Production Management, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/IJOPM-12-2023-0972

1. Introduction

Supply chain transparency has become a central concern, with stronger disclosure requirements coming into place, such as the EU’s new Corporate Sustainability Reporting Directive (CSRD). Transparency is often associated with reduced risk exposure and improved efficiency (Sodhi and Tang, 2019). In the sustainability context, supply network transparency requires the collective disclosure of environmental and social information by focal firms, strategic suppliers, and lower-tier suppliers (Gualandris et al., 2021). Empirical evidence indicates that the further upstream the supplier is positioned in the network, the lower their transparency. This exposes focal buyers to higher risk as lower-tier suppliers manage sustainability passively (Gualandris et al., 2021; Villena and Gioia, 2018). Still, buyer monitoring efforts, collaboration programs, and supplier development are usually limited to the supply base (Beske and Seuring, 2014; Foerstl et al., 2015; Pagell and Wu, 2009), but transparency in the upstream supply chain requires visibility beyond first-tier suppliers (Sodhi and Tang, 2019).

A supply network is costly and complex to manage (Sharma et al., 2020), making sustainable supply chain management programs difficult and often prohibitive for a single focal buyer. For companies like Kellogg’s and Nestlé, for example, traditional management practices have proven inefficient in their multi-tier supply networks, where workers at palm oil plantations have been exposed to human rights violations (Amnesty International, 2016). Most sustainable supply chain management frameworks conceive the focal buyer as the central orchestrator and lower-tier suppliers (i.e. those beyond first-tier suppliers) as a homogeneous group to be managed (Kähkönen et al., 2023; Tachizawa and Wong, 2014; Villena and Gioia, 2018). This approach does not acknowledge the differences in supplier structural embeddedness. Indeed, not all lower-tier suppliers are equally important. Some have privileged structural positions, making them critical (Yan et al., 2015) and better equipped than the focal buyer for developing, assessing, and verifying sustainability practices in a multi-tier supply network.

Second-tier suppliers (i.e. sub-suppliers) sit at the top of the lower-tier network (Hofstetter, 2018) and have a gatekeeping role for deeper levels of the supply network. They can switch to new suppliers without focal buyers necessarily knowing about the new third-tier suppliers (Kim and Davis, 2016). For Intel, for example, the transparency of sub-suppliers (i.e. smelters and refiners) was critical to gaining visibility of conflict minerals that can potentially finance armed groups and contribute to human rights abuses (Intel, 2015; Shotts and Melvin, 2015). Given their role in smelting various ores into metals, these sub-suppliers were crucial for the buyer to obtain information about lower tiers of the mineral supply chain, such as mines. Smelters and refiners shaped the upstream supply network by deciding which mines, countries, and intermediaries to contract, while Intel’s first-tier suppliers did not fully understand these lower-tier networks (Shotts and Melvin, 2015).

This study examines the underexplored role of nexus supplier transparency for buyer risk management. Nexus suppliers are defined as critical suppliers in the multi-tier supply network that can substantially impact the focal buyer firm (Yan et al., 2015). Their criticality stems from their unique structural embeddedness in the buyer supply network, for example, being at the network core or having a diverse supply base. Unlike strategic suppliers, nexus suppliers are critical not necessarily for their internal attributes but because of their relationships with other suppliers within the supply network (Shao et al., 2018; Yan et al., 2015). Nexus suppliers in lower tiers are of particular interest for supply chain risk management, as they are often overlooked by the focal buyer (Choi and Linton, 2011; Yan et al., 2015). We focus on nexus suppliers in the second tier, who can be a relevant source of information and risk mitigation despite not having a direct tie with the focal buyer (Choi et al., 2015; Villena and Gioia, 2018; Yan et al., 2015), as the Intel case illustrates. By studying these less visible critical sub-suppliers and their transparency efforts, we aim to answer the following research question: How does nexus supplier transparency influence buyer ESG risk exposure? Given sub-supplier heterogeneity, we distinguish between three types of nexus suppliers: informational, monopolistic, and operational. For nexus suppliers of the same type, nexus supplier transparency refers to their aggregated disclosure of sustainability information to stakeholders.

Building on the theory of the nexus supplier (Yan et al., 2015) and institutional theory (Oliver, 1990, 1991), we argue that disclosure of sustainability information by nexus suppliers helps the buyer firm mitigate ESG risk exposure for several reasons. First, nexus supplier transparency allows the buyer to gain visibility into the core and peripheral areas of the multi-tier supply network where risk is higher due to opacity and difficult access (Marttinen and Kähkönen, 2022; Sodhi and Tang, 2019). Second, relying on nexus supplier information disclosure relaxes the burden of transaction cost and complex coordination that the focal buyer would otherwise incur to access similar information through direct collaboration with strategic suppliers. Third, nexus suppliers may adopt different sustainable supply chain governance approaches according to their structural embeddedness (Vurro et al., 2009). Sustainability information disclosure by nexus suppliers helps the buyer understand each nexus supplier’s sustainable supply chain governance approach and use this knowledge to prevent ESG risk exposure. For example, buyers can detect areas where nexus suppliers passively manage sustainability with transactional governance (Vurro et al., 2009) and intervene to mitigate adverse environmental and social outcomes in lower tiers.

We also investigate the structural characteristics of supply networks that facilitate a focal buyer gathering information from multiple tiers. In particular, we examine buyer supply network accessibility as a moderating factor in the relationship between nexus supplier transparency and buyer ESG risk exposure. Defined in terms of speed and extent, buyer supply network accessibility entails the focal company having several alternative and short paths to access information at different nodes of its supply network (Bellamy et al., 2014). We argue that with enhanced accessibility, the buyer can obtain accurate real-time information, increase bargaining power with sub-suppliers, and have alternative ways of taking corrective actions when needed. For example, in Intel’s case, not only was sub-supplier transparency necessary, but creating a system with multiple players at different tiers allowed access to detailed information and traceability of purchased products to mitigate the risk of conflict minerals (Intel, 2022).

We tested our arguments on a sample of 428 focal buyer firms and their multi-tier supply networks up to third-tier suppliers. The buyer companies belong to the manufacturing sector, have headquarters in the United States, and are listed on the NYSE and NASDAQ stock exchanges. We performed an OLS regression analysis for the year 2019, before COVID-19, to avoid external disruptions influencing buyer ESG risk exposure. To identify the different nexus suppliers in each focal buyer’s supply network, we employed data envelopment analysis (DEA). The RepRisk’s risk index (RRI) served to operationalize buyer ESG risk exposure, and Bloomberg was the source of data about the supply network structure and the firms’ ESG information disclosure.

2. Literature review

2.1 Critical suppliers in the supply network: nexus suppliers

The supply network structure is complex, but certain suppliers play a critical role in focal buyer performance and risk (Choi and Krause, 2006; Yan et al., 2015). To date, researchers have mainly focused on first-tier suppliers (Dong et al., 2020; Lu and Shang, 2017; Palit et al., 2022), but there is growing interest in the role of lower-tier suppliers (Gualandris et al., 2021; Kähkönen et al., 2023; Villena and Gioia, 2018; Wang et al., 2021). Among lower-tier suppliers, nexus suppliers are considered particularly salient in managing the upstream supply chain (Choi et al., 2015; Sancha et al., 2019; Yan et al., 2015).

Nexus suppliers are critical suppliers with a privileged position in the supply network, which can strongly impact the performance of focal buyers (Yan et al., 2015). While strategic suppliers are in the first tier, nexus suppliers can be in any tier. Of particular interest for risk management are those hidden critical lower-tier suppliers with no direct tie to the focal buyer (Choi and Linton, 2011; Sancha et al., 2019; Yan et al., 2015). Due to their unique ties with other organizations in lower tiers, nexus suppliers can influence buyer operational performance, risk, and innovation (Yan et al., 2015). Researchers identify three main types of nexus suppliers critical to buyer performance and risk: informational, operational, and monopolistic (Yan et al., 2015). Informational nexus suppliers have access to unique and valuable information from their highly diverse ego-network; operational nexus suppliers enjoy a central position through which most of the supply network flow passes; and monopolistic nexus suppliers possess low substitutability and high market concentration (Shao et al., 2018; Yan et al., 2015).

Regarding relational dynamics, not all nexus suppliers are expected to have the same relationship with the focal buyer (Yan et al., 2015). Given their position in the core of the supply network, operational nexus suppliers have a balanced relationship characterized by mutual dependence with the focal buyer. However, for monopolistic nexus suppliers, the focal buyer is just one of several buyers as this supplier has a high market concentration. Monopolistic nexus suppliers thus have a low dependence on the focal buyer, while the focal buyer is highly dependent on them for operational continuity. Therefore, this highly interdependence asymmetry favors monopolistic nexus suppliers. Informational nexus suppliers and focal buyers have reciprocally low dependence. As these critical suppliers are in peripheral areas of the supply network, the value of informational nexus suppliers lies in their role as environmental sensors.

Given all these differences, it is expected that the impact of each type of nexus supplier on the focal buyer’s performance also varies in terms of supply cost, risk, responsiveness, and innovation (Yan et al., 2015).

To illustrate the different structural characteristics of the three categories of nexus suppliers, Figure 1 shows the supply network of IVERIC bio Inc., an American company that manufactures biological products (NAICS 3254). The left graph shows three second-tier nexus suppliers, one from each category. The operational nexus supplier in the graph is Pfizer Inc, also in the pharma industry, lying in the core of the network with a high degree and eigenvector centralities. The monopolistic nexus supplier is the Japanese company Fujitsu Ltd., well known for its technological developments in healthcare. This critical supplier enjoys high betweenness centrality, serving as a bridge between other supplier companies positioned on its left and right sides in the graph. The informational nexus supplier DCC PLC, an international sales, marketing, and support group, is close to the periphery of the supply network, with less centrality than the other two nexus suppliers. Third-tier supplier industries are plotted in different colors in the graph on the right. This graph exhibits multiple colors among DCC PLC’s direct suppliers, which span 17 industries. This evidences that DCC PLC is connected to a diverse group of suppliers, a distinctive characteristic of informational nexus suppliers (Shao et al., 2018).

2.2 Sustainable supply chain management and governance approaches

Sustainable supply chain management refers to the managerial decisions and behaviors that ensure a company’s supply chain is environmentally and socially responsible while meeting the business’s needs (Pagell and Wu, 2009). For example, Villena and Gioia (2018) developed a theoretical model to manage a sustainable supply network, beginning with a commitment to sustainability and relying on three main strategic processes: assessment of practices, risk and opportunity management, and building sustainability capabilities. Other models include processes such as evaluation and verification and the participation of stakeholders (Gualandris et al., 2015). Buyer firms can rely on direct practices to manage lower tiers, including monitoring and collaboration with suppliers, and indirect practices, such as monitoring suppliers through cooperation with third parties (Tachizawa and Wong, 2014; Marttinen and Kähkönen, 2022). However, buyers can also rely on public sustainability disclosures made by suppliers to stakeholders as they contain relevant information to prevent disruptions and improve performance (Gualandris et al., 2021; Villena and Dhanorkar, 2020). The increasing societal demands for supply chain transparency go beyond the focal buyer, adding pressure for supplier ESG disclosure (Dahlmann and Roehrich, 2019; Villena and Dhanorkar, 2020). Nonetheless, based on a recent study (Kähkönen et al., 2023), it is clear that buyer use of publicly disclosed supplier information remains an opportunity not fully explored in sustainable supply chain management practices and strategies.

Scholars have proposed different governance mechanisms for sustainable supply chain management beyond the expected collaboration among supply network partners. For example, building on institutional theory (Oliver, 1990), Vurro et al. (2009) proposed that structural characteristics of the supply network, such as centrality and density, explain the type of sustainable supply chain governance in place. Other factors such as power, stakeholder pressure, knowledge resources, distance, and dependency have also been identified as determinants of the effectiveness of a focal firm’s approach to multi-tier sustainable supply chain management (Tachizawa and Wong, 2014, 2015).

Our theoretical framework builds on Vurro et al.’s (2009) framework to propose new arguments explaining the governance mechanism each type of nexus supplier is more likely to adopt in their own supply network and the implications for buyer ESG risk exposure. Vurro’s framework helped us identify supply chain governance approaches for each nexus supplier category according to the nexus supplier centrality and the density of the nexus supplier’s upstream network. This enabled us to explain how nexus supplier transparency can provide buyers with information to detect flaws in these nexus supplier governance approaches and gain visibility in the upstream network for better risk management.

2.3 Nexus supplier transparency

Researchers consider transparency as a multidimensional concept. For example, Egels-Zandén et al. (2015) propose traceability, sustainability conditions at suppliers, and buyer firms’ purchasing practices as core dimensions of supply chain transparency. Morgan et al. (2018) identify visibility and traceability as the two primary dimensions. In a broader sense, some scholars consider supply network transparency mainly as disclosing information regarding upstream operations and the products sold to the public (Sodhi and Tang, 2019). More recently, Gualandris et al. (2021) defined supply chain transparency as the aggregated ESG information that a focal firm and its supply chain partners collectively disclose to the public. This study focuses on nexus supplier transparency rather than broad supply chain transparency.

Building on the definition of supply chain transparency (Gualandris et al., 2021) and the conceptualization of nexus suppliers (Yan et al., 2015), we define nexus supplier transparency as the collective disclosure of environmental, social, and governance information made public to stakeholders by nexus suppliers of the same type. We, therefore, distinguish between informational, operational, and monopolistic nexus supplier transparency as three different aggregated disclosures of sustainability information. We make this distinction as the aggregated information of each collective provides unique information according to their structural position in the supply network, governance approaches, and relational dynamics with focal buyers (Vurro et al., 2009; Yan et al., 2015).

2.4 Buyer ESG risk exposure

The literature on sustainable supply chain management acknowledges that lower-tier suppliers are especially risky because they address environmental, social, and governance issues passively (Villena and Gioia, 2018). In addition to well-known market and operational supply risks in the literature on purchasing and supply chain management (Zsidisin, 2003), buyer exposure to ESG risks damages the focal buyer’s reputation and disrupts normal operations in the supply network. For example, the Rana Plaza accident generated negative media and disrupted supply networks for several companies, including J.C. Penney and Benetton (Kazmin, 2015). Indeed, there is evidence that the supply network structure influences how issues in lower tiers cascade downstream, exposing focal buyers to risks (Wang et al., 2021). In addition, sub-supplier practices, particularly by suppliers with high market concentration, can be easily associated with focal buyers, either benefiting or damaging their reputation. Stakeholders are also relevant actors when it comes to risk exposure. For example, Nike and Nestlé faced negative media, primarily due to stakeholder involvement. NGOs like Greenpeace raised awareness, and consumers used social media to demand the firms address human rights and deforestation issues in their supply networks (Grimm et al., 2016; Hartmann and Moeller, 2014).

In line with Schoenherr et al.’s (2023) conceptualization of risk exposure, we define buyer ESG risk exposure as the degree of negative media received by a focal buyer firm regarding ESG violations. Negative media alters the public perception of a company, damaging its reputation with negative consequences for buyer performance. ESG risk exposure may vary among companies depending on the frequency at which the company appears in news articles, media coverage, and the severity of the issues being exposed.

2.5 Supply network accessibility

Supply network accessibility refers to the breadth of opportunities a particular company has to access information from other network partners in multiple tiers (Bellamy et al., 2014). Shorter paths imply shorter geodesic distances, meaning fewer steps between the buyer and suppliers in the upstream network. This reduces the number of intermediaries and constraints, simplifying the collection of lower-tier information (Bellamy et al., 2020). Therefore, buyer firms with increased supply network accessibility have faster access to information and fewer chances of information distortion (Bellamy et al., 2014).

Accessibility within supply chains is recognized as a pivotal factor influencing innovation, risk management, and operational performance. Park et al. (2018) identified accessibility as part of a focal firm’s supply network strategy, with focal firms having more incentives to develop accessibility when non-redundant information is available in their networks. In this regard, Bellamy et al. (2014) found network accessibility to enhance firm innovation. Accessibility is also crucial for risk management, a complex task requiring contrasting heterogeneous sources of information, sometimes contradictory, in lower-tier suppliers (Zsidisin, 2003). Christopher and Lee (2004) emphasize the critical role of accessibility and information accuracy among supply chain partners for risk mitigation. Enhanced accessibility also provides supply chain agility (Nazempour et al., 2018). To improve accessibility, buyers can develop shorter and alternative paths to the upstream supply network, especially with lower-tier suppliers who have a privileged structural position (Bellamy et al., 2014; Yan et al., 2015). For example, LG Electronics (LGE) established an alternative shorter path to its second-tier nexus supplier, Taiwan Semiconductors, circumventing existing first-tier suppliers (Choi and Linton, 2011).

Recent literature also highlights the implications of accessibility for environmental disclosure and sustainability. Increased accessibility enables focal firms to gather knowledge about environmental systems and best practices in lower tiers of the supply network, enabling buyers to improve environmental disclosure practices (Bellamy et al., 2020). Accessibility is at the heart of risk management for multinational companies such as Apple, which prohibited suppliers from operating, recruiting, or sourcing materials in regions where Apple or collaborators could not access information on suppliers’ compliance with sustainability standards (Apple Inc, 2023).

3. Hypotheses development

This section examines the relationship between nexus supplier transparency and buyer ESG risk exposure. Nexus supplier transparency entails unique information as each type of nexus supplier differs in terms of structural embeddedness, expected governance of lower tiers, and the degree of interdependence with the focal buyer. We outline the distinct mechanisms involved in how the aggregated disclosure of sustainability information from each of the three types of nexus suppliers influences buyer ESG risk exposure. Understanding these differences will enable buyers to leverage nexus supplier transparency for effective risk management. We also provide several arguments demonstrating that buyer supply network accessibility has a moderating effect on the relationship between nexus supplier transparency and buyer ESG risk exposure.

3.1 Informational nexus supplier transparency and buyer ESG risk exposure

Information disclosure from informational nexus suppliers can help buyers mitigate ESG risk exposure for three main reasons. First, informational nexus suppliers act as “environmental sensors” for the focal buyer, thanks to the diversity of firms in their ego-network (Yan et al., 2015). Closely working with a diverse set of suppliers, informational nexus suppliers gain a broad and deep understanding of the various factors impacting the supply chain, including emerging practices, shifts in stakeholder demands, new pressures for sustainability commitments, and changing market conditions. Their disclosed information also provides buyers visibility into where access is difficult, “zooming in” on peripheral parts of the supply network where these nexus suppliers are located (Yan et al., 2015).

Second, informational nexus supplier transparency warns buyers about risks from transactional governance adopted by these nexus suppliers. Informational nexus suppliers have low centrality given their limited ties within the buyer supply network, while their own ego-network is low density, composed of diverse and otherwise disconnected parties (Burt, 1993; Yan et al., 2015). From an institutional theory perspective, these two conditions lower and constrain their influence within the network, leading informational nexus suppliers to adopt a transactional governance approach to their own supply networks (Oliver, 1991; Vurro et al., 2009, p. 613). Such a transactional governance approach by informational nexus suppliers exposes the buyer to higher ESG risks due to a tendency for a short-term orientation and a lack of incentives for profoundly integrating sustainability practices and robust monitoring in lower tiers (Drake and Schlachter, 2008; Vurro et al., 2009). Consequently, informational nexus supplier transparency enables buyers to detect early signals of laissez-faire in their governance of upstream sub-tier networks, allowing buyers to take corrective actions to mitigate ESG risks.

Third, given the low interdependence in relational dynamics between the focal buyer and informational nexus suppliers (Yan et al., 2015), their relationship is likely to be transactional, making it difficult and costly to align incentives or collaborate. Therefore, informational nexus supplier transparency provides the buyer with information that is otherwise hard to obtain. Such information enables the focal buyer to allocate resources more efficiently to risk management practices at lower tiers, such as aligning incentives through direct collaboration (Kähkönen et al., 2023; Tachizawa and Wong, 2014) when deviant behaviors are detected in the nexus supplier disclosure.

Overall, informational nexus supplier transparency can be critical for focal buyers effectively managing risk, as it allows them to anticipate and be prepared for potential disruptions and challenges that may arise in peripheral parts of the supply network. By leveraging the insights and knowledge of their informational nexus suppliers, buyers can make more informed decisions about navigating and mitigating the risk exposure in their supply chain. Based on the mechanisms discussed, we propose the following hypothesis:

H1.

Informational nexus supplier transparency decreases the focal buyer firm’s ESG risk exposure.

3.2 Monopolistic nexus supplier transparency and buyer ESG risk exposure

To guarantee supply continuity, focal buyer firms rely heavily on monopolistic nexus suppliers (Yan et al., 2015). Monopolistic nexus supplier disclosure is expected to reduce buyer ESG risk exposure for three main reasons. First, these nexus suppliers usually go unnoticed by the focal buyer, as the buyer does not purchase directly from them, and in some cases, the purchased volume is low. However, identifying monopolistic nexus suppliers is critical for the buyer firm, given their low substitutability and the interest and impact these large corporations can generate on several stakeholders (Magill et al., 2015; Yan et al., 2015). Once identified, buyers can rely on the information these suppliers disclose to detect early signals of non-compliance related to sustainability issues and adopt risk mitigation actions. On the contrary, if information from monopolistic nexus suppliers is opaque and the media becomes aware of ESG violations, the buyer will have no time to take any action while being easily associated with the non-compliant supplier. This mechanism was demonstrated by the disaster at Rana Plaza, a hub in the garment industrial network where poor labor conditions went unnoticed for several years. Following the building’s sudden collapse, over 30 indirectly linked buyer companies, including Benetton, were exposed in the media and held responsible for the disaster (Clean Clothes Campaign, 2019).

Second, monopolistic nexus suppliers are central to the industrial network, connecting several buyers with suppliers for a specific product or service (Yan et al., 2015). However, their own supply network has a lower density in relative terms, and consequently, their first-tier suppliers lack bargaining power. Due to their market power, monopolistic nexus suppliers can impose their sustainability practices and norms, adopting a dictatorial governance model on their own supply network, according to institutional theory (Vurro et al., 2009). Given their dictatorial governance approach, sustainability practices in their information disclosure will likely be enforced among lower tiers in their supply network. Monopolistic nexus supplier transparency enables buyers to understand existing practices and leverage these suppliers’ influential power to spread sustainability practices in lower tiers. By delegating this responsibility, buyers can concentrate on other risk management activities.

Third, asymmetric relational dynamics exist between a focal buyer and monopolistic nexus suppliers, as buyers are highly dependent on this type of nexus supplier (Yan et al., 2015). Also, the high market power held by monopolistic nexus suppliers may hinder the buyer firm’s ability to cascade its own practices and principles to these nexus suppliers and their lower tiers (Marttinen and Kähkönen, 2022). Therefore, monopolistic nexus suppliers are more likely to have a dictatorial role in their relationship with a focal buyer (Vurro et al., 2009). The power position held by monopolistic nexus suppliers makes other suppliers reluctant to follow any instructions from the focal buyer or directly share information about their upstream operations unless the buyer creates an incentive to do so (Touboulic et al., 2014). Consequently, information disclosure from monopolistic nexus suppliers gives the buyer access to information about certain parts of the supply network that would otherwise be costly to acquire and require complex negotiations. Hence, monopolistic nexus supplier transparency makes complexity more manageable, allowing the focal buyer to enter into negotiations when a significant misalignment with buyer principles is detected in the disclosed information.

Given the mechanisms discussed above, we propose the following hypothesis:

H2.

Monopolistic nexus supplier transparency decreases the focal buyer firm’s ESG risk exposure.

3.3 Operational nexus supplier transparency and buyer ESG risk exposure

Operational nexus suppliers occupy a central position in the supply network, having several ties to lower-tier suppliers, similar to a contract manufacturer (Yan et al., 2015). We expect information disclosure from operational nexus suppliers to positively contribute to buyer risk management, reducing buyer ESG risk exposure in three main ways. First, operational nexus supplier transparency gives the focal buyer feedback on how its sustainability principles and practices cascade to the core of its upstream network. Such disclosure provides visibility into operations at lower tiers that directly influence the operational performance of the buyer (Yan et al., 2015). Based on the disclosed information, the buyer can execute corrective responses to any misalignment detected at the core of the supply network, where buyers have a higher bargaining power.

Second, operational nexus suppliers have high centrality, acting as gatekeepers or pivots in the buyer supply network (Kim et al., 2011; Yan et al., 2015), which is also dense at the core. From an institutional theory perspective, these structural conditions favor operational nexus suppliers developing a participative governance approach with their own supply network, as they enjoy a brokering position while receiving strong influence from other suppliers at the network core (Frenkel and Scott, 2002; Vurro et al., 2009). Participative governance also opens channels for collaboration, negotiation, and mutual compromise among parties (Frenkel and Scott, 2002; Vurro et al., 2009). Therefore, buyers can use these nexus suppliers as network exchange brokers to diffuse sustainability practices in the core areas of the supply network (Saunders et al., 2019). Information disclosure from operational nexus suppliers informs the focal buyer about misalignments in sustainability practices, indicating when to resort to these network mechanisms to take corrective actions.

Third, given the mutual interdependence in relational dynamics between focal buyers and operational nexus suppliers (Yan et al., 2015), both parties have incentives to collaborate and align their sustainability management approaches. Shared incentives for collaboration and aligned goals serve buyers in negotiating sustainability practices but also for influencing and controlling information disclosed by operational nexus suppliers to a certain extent. Consequently, buyers can prevent unnecessary ESG risk exposure to controversial information while enjoying the legitimacy benefits of transparency as, in the eyes of the stakeholders, sustainability information is disclosed by a sub-supplier.

From the above arguments, we propose the following hypothesis:

H3.

Operational nexus supplier transparency decreases the focal buyer firm’s ESG risk exposure.

3.4 Supply network accessibility of focal buyers as a moderating factor

Mobilizing resources embedded in the supply network, such as gathering detailed information from nexus suppliers, depends on buyer accessibility (Lin, 1999; Pena-López and Sánchez-Santos, 2017). For example, LG Electronics improved accessibility by establishing new contracts to create an alternative path to its second-tier nexus supplier, Taiwan Semiconductors. With fewer intermediaries and shorter distance to the lower tiers, LGE was able to more effectively acquire and leverage information embedded in its supply network. Taiwan Semiconductors served as a source of information from multiple industries, enabling LGE to detect early signals of market movements and save billions of dollars in costs (Choi and Linton, 2011; Yan et al., 2015). Accessibility thus provides the buyer with the necessary structural position and agility to gather the information required for risk management from relevant nexus suppliers, even when such suppliers are located in peripheral areas (Bellamy et al., 2020; Christopher and Lee, 2004; Nazempour et al., 2018). Buyer firms with enhanced accessibility can efficiently collect non-redundant information in a multi-tier supply network (Park et al., 2018) and integrate the collected information into decision-making (Krijestorac et al., 2021). Therefore, accessibility can be a factor that further potentiates gathering and integrating heterogeneous information from different nexus supplier disclosures for ESG risk management.

Regardless of the type of nexus supplier, transparency initiatives yield public records of information. However, these records are aggregated based on a wealth of detailed information that is collected but not ultimately disclosed to the public (Chen et al., 2015). By developing supply network accessibility, the buyer increases the number of ways to collect such detailed information about sustainability practices in the supply network (Bellamy et al., 2020). In other words, with enhanced accessibility, the focal firm can delve below the surface of aggregated disclosure. Obtaining more detailed information from nexus supplier transparency, beyond what is publicly disclosed, improves supply network visibility and risk assessment, further reducing buyer ESG risk exposure (Sodhi and Tang, 2019).

Supply network accessibility further reduces buyer ESG risk exposure through different mechanisms depending on the type of nexus supplier disclosing the information. For instance, buyers can benefit even more from informational nexus supplier transparency, as enhanced network accessibility may facilitate buyer access to information in peripheral parts of the supply network (Bellamy et al., 2020). Also, with several alternative paths connecting focal buyers with such peripheral areas, informational nexus suppliers may feel more pressured to listen to focal buyer demands and either compromise or acquiesce to them (Oliver, 1991). Therefore, accessibility helps buyers align incentives with informational nexus suppliers, overcoming the limitations of their transactional relational dynamics (Yan et al., 2015) and incentivizing them to share quality information (Villena and Dhanorkar, 2020). Consequently, buyer risk prevention actions based on informational nexus supplier transparency become more effective with higher accessibility.

Supply network accessibility also increases the chances of negotiating with monopolistic nexus suppliers, balancing asymmetric relational dynamics (Yan et al., 2015). Monopolistic nexus suppliers become more embedded in the buyer supply network when several short and alternative paths connect them to the focal buyer. Buyers can leverage those nodes in common to negotiate with monopolistic nexus suppliers. From an institutional perspective, when confronted by multiple network partners, monopolistic nexus suppliers are more likely to compromise by balancing, accommodating, or negotiating their demands and expectations (Oliver, 1991). Therefore, enhanced supply network accessibility further improves the effectiveness of focal buyer risk management actions based on monopolistic nexus supplier transparency.

Accessing detailed information from operational nexus supplier transparency is relatively straightforward due to balanced relational dynamics with the focal buyer (Yan et al., 2015). Still, buyer supply network accessibility can further increase the speed and accuracy of accessing detailed information in the core of the supply network, a critical area for flow continuity. Accessibility thus facilitates constant feedback with operational nexus suppliers, speeding up and reducing the cost of developing and implementing sustainability strategies in lower tiers. Therefore, with enhanced supply network accessibility, operational nexus supplier transparency further reduces the focal firm’s ESG risk exposure.

From the above discussion, we propose the following hypotheses:

H4a.

With enhanced focal buyer supply network accessibility, informational nexus supplier transparency further reduces the focal buyer firm’s ESG risk exposure.

H4b.

With enhanced focal buyer supply network accessibility, monopolistic nexus supplier transparency further reduces the focal buyer firm’s ESG risk exposure.

H4c.

With enhanced focal buyer supply network accessibility, operational nexus supplier transparency further reduces the focal buyer firm’s ESG risk exposure.

4. Methodology

4.1 Data and sample selection

To test our hypotheses, we gathered ESG risk exposure, supplier information disclosure, and supply network data from different sources. Buyer ESG risk exposure was obtained from the RepRisk database for the year 2019. The supply network structure was obtained from Bloomberg SPLC, and the information disclosure score from the Bloomberg ESG database, both of which are widely used in our field (e.g. Gualandris et al., 2021; Bellamy et al., 2020).

The final sample is composed of 428 focal buyer firms and their corresponding supply networks up to third-tier suppliers, comprising 187,564 unique buyer-supplier relationships and 43,621 unique suppliers across all tiers. All the selected buyers are in the manufacturing sector, have headquarters in the United States, and are listed on the NYSE and NASDAQ stock exchanges. We selected focal buyers with headquarters in the US as they all are subject to similar institutional environments, which improves the comparability among observations in our sample and reduces the possibility of confounding factors driving our results. The sample selection process followed is presented below in Figure 2.

4.2 Procedure to identify nexus suppliers from critical suppliers

We used data envelopment analysis (DEA) and followed the procedure proposed by Shao et al. (2018) to identify the nexus suppliers of each focal buyer firm in a two-step process.

The first step was to build a nexus supplier index based on a DEA input-oriented optimization model with variable returns to scale. The optimization model used to maximize had a fractional form where the supplier’s eigenvector, betweenness, and degree centrality metrics were in the numerator, while the farness distance (inverse of closeness) of the supplier to other members of the supply network was in the denominator (for more details on network metrics, see Wasserman and Faust, 1994). The evaluated units (i.e. decision-making units, as they are known in DEA analysis) were the suppliers of each focal buyer firm. DEA provided the efficient frontier for each supply network, where all suppliers (i.e. first, second, and third tiers) were ranked according to their nexus supplier index. The result of this first step was the identification of the top 50 critical suppliers in each tier, from first to third-tier level, in descending order according to their nexus supplier index. We used the package deaR in RStudio for this first step.

In the second step, we followed Shao et al.’s (2018) decision rules and classified the top 50 critical suppliers in each tier into three categories following the nexus supplier framework: operational, monopolistic, and informational nexus suppliers (Yan et al., 2015). A critical supplier was classified as an informational nexus supplier if the number of unique industries among its suppliers was in the top 10 percentile among the top 50 nexus suppliers in the same tier. Similarly, a monopolistic nexus supplier had a betweenness centrality among the top 10 percentile, and an operational nexus supplier had a degree, betweenness, and eigenvector centrality in the top 20 percentile among the top 50 nexus suppliers in its own tier (Shao et al., 2018). The output of the second step was a list of critical suppliers that are also nexus suppliers in a buyer supply network and their respective classifications. Given our focus on the transparency of critical sub-suppliers, we examined nexus supplier transparency for the second-tier nexus suppliers obtained in this second step.

4.3 Measures

Table 1 provides measurement details of the variables used in the empirical analysis to test the proposed hypotheses and relevant references.

4.3.1 Dependent variable

RepRisk was our source for capturing buyer ESG risk exposure, which covers over 225,000 companies worldwide. This data provider combines human and artificial intelligence and machine learning to monitor and flag the material ESG risks and breaches of international standards by companies (RepRisk, 2023). The RepRisk dataset has been used in previous literature to estimate a firm’s real ESG impact (Li and Wu, 2020).

The variable Buyer ESG risk exposure was constructed by averaging RepRisk Index (RRI) values for a focal buyer firm in 2019, as a company may have different scores throughout the year that reflect changes in risk exposure. The RRI Index is a proprietary score calculated by RepRisk based on 28 ESG issues with which a focal firm may be involved or associated. The calculation accounts for the novelty and severity of issues and the frequency and reach of the media agencies reporting them. The algorithm screens around 100,000 public sources daily, including online and printed media, social media, government bodies, and regulators, among other sources (RepRisk, 2023).

4.3.2 Independent and moderating variables

The independent variables included in the analysis were ESG transparency NS (Informational), ESG transparency NS (Monopolistic), and ESG transparency NS (Operational). These transparency variables were calculated for each focal buyer supply network as the average Bloomberg ESG disclosure scores of all second-tier nexus suppliers of the same type. For example, if a company had four informational nexus suppliers in the second tier, its ESG transparency NS (informational) variable was the average of Bloomberg ESG disclosure scores of these four nexus suppliers. Therefore, each transparency variable captured the aggregated disclosure of nexus suppliers of the same type, in line with our definition of nexus supplier transparency.

The variable Buyer accessibility captured the supply network accessibility of the focal firm as a moderator factor. It was calculated based on an information centrality metric (Stephenson and Zelen, 1989; Wasserman and Faust, 1994). Following Bellamy et al. (2014), we used the harmonic mean of path lengths ending at the focal buyer node. For the calculation, we employed the R package SNA. In simple terms, this metric assesses the average distance or number of steps required to reach the focal buyer node from other nodes in the supply network.

4.3.3 Control variables

We included relevant controls obtained from the risk management and operations management literature (see Table 1). For focal firms, the variables included were ESG transparency, Profitability, Leverage, Size, Age, and Diversification. For suppliers, we controlled for the total number and geographical dispersion of all first-tier suppliers linked to each focal buyer company included in the analysis, using the variables First-tier suppliers (total) and First-tier suppliers (dispersion).

4.4 Model specification

We tested our hypotheses using the OLS regression specification with robust standard errors, given that the assumption of homoskedasticity was not met. To avoid reverse causality, we lagged all regressors and control variables for one year (i.e. 2018) with respect to buyer ESG risk exposure (i.e. 2019). We also included relevant control variables to prevent an omitted variable bias.

The estimated full model, including the proposed moderating effects, is formulated below (Eq. 1):

(1)Buyer ESG risk exposure=α+β1ESG Transparency NS(Informational)+β2ESG Transparency NS(Monopolistic)+β3ESG Transparency NS(Operational)+β4 Buyer accessibility+β5ESG Transparency NS(Informational)*Buyer accessibility+β6ESG Transparency NS(Monopolistic)*Buyer accessibility+β7ESG Transparency NS(Operational)*Buyer accessibility+Controls+ε

The left-hand side variable is Buyer ESG risk exposure. As main explanatory variables, we included the transparency variables accounting for the aggregated disclosure of sustainability information among sub-suppliers that are nexus suppliers of the same type. We also included the interaction terms with Buyer accessibility.

5. Empirical analysis

5.1 Regression results

The correlation matrix and descriptive statistics of the variables used in the analysis are presented in the Supplementary material (Table A1).

Table 2 displays the regression results on buyer ESG risk exposure. Model 1, the base model, only contains control variables. Model 2 includes the independent transparency variables for each type of nexus supplier and buyer supply network accessibility. In Model 3, we added the interaction terms between nexus supplier transparency and buyer accessibility, which correspond to the full model formulated in Equation (1).

The regression results in Models 2 and 3 show that informational nexus supplier transparency negatively affects buyer ESG risk exposure (β = −0.2427, p < 0.05, in the full model). This supports Hypothesis 1, implying that the public information disclosure of informational nexus suppliers has a significant effect by decreasing the ESG risk exposure of the focal firm. In the case of monopolistic and operational nexus suppliers’ disclosure, the coefficients were insignificant. Therefore, our results do not support Hypotheses 2 and 3.

Regarding the supply network accessibility of the focal buyer firm, the results show that buyer accessibility has a direct negative effect on buyer ESG risk exposure in Model 2 (β = −7.5884, p < 0.05). When analyzing the interaction terms in Model 3, the results suggested no further reductions in buyer ESG risk exposure for either informational or operational nexus supplier transparency. Therefore, the findings did not support Hypotheses H4a and H4c. However, the interaction of monopolistic nexus supplier transparency with buyer supply network accessibility was negatively significant (β = −1.1288, p < 0.05). This supports Hypothesis 4b, showing that combined with enhanced buyer supply network accessibility, monopolistic nexus supplier transparency further reduces buyer ESG risk exposure.

We further explored this finding by examining the conditional marginal effects of this interaction on buyer ESG risk exposure, illustrated in Figure 3. The Y-axis captures the expected buyer ESG risk exposure for each unit of increase of monopolistic nexus supplier transparency in the X-axis, conditional on low and high values of buyer supply network accessibility (i.e. one standard deviation below and above the mean, respectively). More specifically, when buyer accessibility is below the mean, a positive slope is observed in the graph. This indicates that each unit of increase of monopolistic supplier transparency increases buyer ESG risk exposure (β = 0.3062, p < 0.05), keeping all other covariates at means. However, we observe the opposite when buyer accessibility is high. The negative slope in the graph indicates that, on average, each unit of increase of monopolistic nexus supplier transparency contributes to a decrease in buyer risk exposure (β = −0.2318, p < 0.1), keeping other covariates at means.

The cross-over among these conditional marginal effects in Figure 3 illustrates a disordinal interaction effect where the relationship between monopolistic nexus supplier transparency and Buyer ESG risk exposure changes direction depending on the level of buyer supply network accessibility. In other words, the same level of monopolistic nexus supplier transparency may increase buyer ESG risk exposure when buyer supply network accessibility is low and, inversely, decrease buyer ESG risk when buyer accessibility is high.

5.2 Addressing potential endogeneity concerns

Nexus supplier transparency variables can be correlated with unobserved factors, such as supplier development or monitoring, that also explain buyer ESG risk exposure. To address this endogeneity concern, we employed an instrumental variable approach (Wooldridge, 2012). Following extant research, we built our instruments by relying on industry peer information, which, as informed by institutional theory, is expected to generate relevant instruments that satisfy the exclusion restriction (Palit et al., 2022). Specifically, we instrumented the three nexus supplier transparency variables and their interactions with buyer supply network accessibility.

The procedure to build our instruments required three main steps. First, we obtained other firms in the same industry (i.e. peer buyers) for our sample of focal buyers from Bloomberg. Second, for each peer buyer, we matched their supply network and identified their nexus suppliers (i.e. peer nexus suppliers), following the procedure from Shao et al. (2018). Third, nexus supplier transparency variables were instrumented through the average value of peer nexus suppliers’ ESG disclosure score from Bloomberg, the average of peer nexus suppliers’ ESG scores from Refinitiv Eikon, and the average peer nexus suppliers’ country voice and accountability from the World Governance Indicators dataset (The World Bank, 2024).

Building on DiMaggio and Powell (1983), we argue that similar institutional and mimetic pressures impact nexus suppliers of buyers in the same industry. Consequently, nexus supplier transparency variables are likely associated with industry trends shared among peer nexus suppliers, satisfying the relevance condition of our instruments. In addition, because our instrumental variables are operationalized based on industry peers, we expect these instruments to be only indirectly related to our dependent variable, with no direct causality, thus satisfying the exclusion restriction (Palit et al., 2022).

5.3 Regression results with endogeneity corrections

The results of the instrumental variable regressions are presented in Table 3. We used a two-step model with an efficient generalized method of moments (GMM) specification in Stata. In the first stage, several instruments significantly explained the instrumented variables (p < 0.10), confirming their relevance as instrumental variables. The overidentification test, assessed by the Hansen J statistic, found no evidence against the null hypothesis of instrument validity and uncorrelation with the error term (p = 0.2057). Results of an endogeneity test based on the C statistic suggest that there is no prominent endogeneity problem in our initial OLS model, as we did not have sufficient evidence to reject the null hypothesis that our regressors can be treated as exogenous (p = 0.5844). Finally, the Sanderson-Windmeijer test confirmed the correct identification of each endogenous regressor (p < 0.001).

The results with endogeneity corrections are aligned with those obtained in the initial OLS regression. The variable reflecting the informational nexus supplier transparency is significant and negatively related to buyer ESG risk exposure (β = −0.7605, p < 0.10), supporting Hypothesis 1. Regarding the interaction terms, monopolistic nexus supplier transparency and buyer supply network accessibility remained negative and significantly related to buyer ESG risk exposure as in the initial regression (β = −3.3680, p < 0.10), supporting Hypothesis 4b.

In conclusion, we did not find strong evidence that endogeneity is an issue in our analysis (i.e. endogeneity test p = 0.5844). However, we report the results of the instrumental variables regression as additional evidence of the validity and consistency of our findings.

5.4 Robustness tests

We performed several robustness analyses using different dependent variables, extending the study period and including additional control variables.

The RRI index used to operationalize the dependent variable covers a broad range of 28 incident categories. It includes incidents that may not be directly related to the supply chain, which may reduce the explanatory power of the analysis. In the Supplementary material (Table A2), we present the results for an alternative dependent variable, which only entails risk incidents explicitly classified by RepRisk as cross-cutting supply chain incidents. The obtained results are in line with prior findings.

We also examined the consistency of our results by extending the study period to 2016–2019. The results shown in the Supplementary material (Table A3) are the same as the results obtained in the initial OLS model. This analysis was made assuming that no significant systematic changes occurred in our sample of supply networks in this period concerning which companies were nexus suppliers. This stability assumption was made based on a social-ecological perspective of supply network resilience (Wieland, 2021). According to this view, in a period of relative economic and political stability, such as 2016–2019, as opposed to more recent years with the irruption of the pandemic, supply networks in our sample were expected to remain in a conservation phase of their adaptive cycle. Therefore, slowly moving variables, like the average structural configuration of our sample networks, were expected to remain mostly stable.

Finally, as noted by Yan et al. (2015), nexus supplier types are not mutually exclusive, making it possible that one nexus supplier spans more than one category. In our study, about 50% of the nexus suppliers were categorized in one category only, but we had some “frontier nexus suppliers” that spanned more than one category. We performed an additional robustness test with a control variable accounting for the degree of overlapping among nexus suppliers in each supply network. The regression results, presented in the Supplementary material (Table A4), are consistent with our main findings.

6. Discussion

Building on nexus supplier (Yan et al., 2015) and institutional (Oliver, 1990, 1991) theories, this study offers a nuanced understanding of the relationship between nexus supplier transparency and buyer ESG risk exposure. Nexus supplier transparency improves risk management by enhancing visibility in areas where access is difficult in the upstream supply network, helps buyers to leverage or detect flaws earlier in nexus suppliers’ sustainable supply chain governance (Vurro et al., 2009), and overcomes limitations of relational dynamics with nexus suppliers (Yan et al., 2015). By bringing together these three main mechanisms for the different types of nexus suppliers, this study contributes to the literature on transparency and risk management in multi-tier supply chains (Gualandris et al., 2021; Kähkönen et al., 2023; Tachizawa and Wong, 2014; Wilhelm et al., 2016).

However, our findings suggest that collective disclosure of sustainability information by different types of second-tier nexus suppliers does not always reduce buyer ESG risk exposure. Effects on buyer ESG risk exposure vary depending on the type of nexus supplier and moderating factors, such as buyer accessibility. Consequently, nexus supplier transparency may either help mitigate buyer ESG risk exposure, increase it, or have no significant impact. In line with Kim et al. (2022), these findings point to the need to expand current knowledge on lower-tier suppliers.

Our findings provide evidence that informational nexus supplier transparency allows buyers to reduce ESG risk exposure, reflecting their role as environmental sensors (Yan et al., 2015). In contrast, the nonsignificant interaction effect suggests that informational nexus supplier disclosure alone provides novel information and that buyer accessibility does not offer further risk mitigation. Therefore, for peripheral parts of the supply network where lower tiers are mostly heterogeneous, informational nexus supplier transparency offers the focal buyer an alternative to internal network mechanisms for gathering and assessing sustainability information.

The influence of monopolistic nexus supplier transparency on buyer ESG risk exposure must be considered jointly with buyer supply network accessibility. Our results indicate that high supply network accessibility (i.e. one standard deviation above the mean in our sample) is required for buyers to achieve reduced ESG risk exposure from monopolistic nexus supplier transparency. Buyer supply network accessibility increases the number of short paths connecting buyers with their suppliers (Bellamy et al., 2014; Stephenson and Zelen, 1989), making the buyer upstream network more representative of a monopolistic nexus supplier’s supply network. Enhanced accessibility grants buyers more bargaining power to undertake corrective actions if non-compliance is detected. From an institutional theory perspective (Oliver, 1991), when buyers have access through multiple paths, they can leverage multiple network constituents to negotiate with monopolistic nexus suppliers.

Buyers must always remain vigilant about monopolistic nexus supplier disclosure as it increases their risk exposure when supply network accessibility is low (i.e. one standard deviation below the mean in our sample). With few short paths to the monopolistic nexus supplier, buyers lack the network resources that confer bargaining power to negotiate or exercise pressures for change (DiMaggio and Powell, 1983; Kim et al., 2011), making corrective actions difficult when non-compliance is detected. Risk by association with non-compliance by these nexus suppliers is high because of their monopolistic position, which makes them salient to stakeholders and easy to link to the buyer (Koenig and Poncet, 2022; Magill et al., 2015; Petersen and Lemke, 2015).

We do not find evidence that operational nexus supplier transparency influences buyer ESG risk exposure. This may be because buyers constantly receive information from these suppliers due to their central position in the network, enabling buyers to take corrective actions as required. Also, the mutually dependent relationship with operational nexus suppliers provides reciprocity, facilitating information exchanges and coordination (Oliver, 1990). The buyer already has effective ways to access information on these suppliers, reducing the value of aggregated information disclosed to the public for risk mitigation. Consequently, buyers may benefit from exploring other practices like partnership programs (Petersen and Lemke, 2015) to unlock the potential of operational nexus supplier transparency for risk management.

6.1 Theoretical and managerial implications

Our empirical study on the relationship between nexus supplier transparency and buyer ESG risk exposure makes several contributions to the literature. First, we contribute to the literature on risk management and sustainability in multi-tier supply networks (Gualandris et al., 2015; Kähkönen et al., 2023; Pagell and Wu, 2009; Tachizawa and Wong, 2014; Villena and Gioia, 2018; Wilhelm et al., 2016) by offering an alternative mechanism for focal buyer firms to mitigate their ESG risk exposure from lower tiers. In contrast to the predominant emphasis on the focal buyer as the main orchestrator in multi-tier supply chain management, we draw attention to the relevance of other actors: second-tier nexus suppliers. Our empirical findings support the prominence of information disclosure for risk management from two types of nexus suppliers: informational and monopolistic. Still, in line with current frameworks, we demonstrate that monopolistic nexus supplier transparency also requires buyers to play an active role in developing supply network accessibility for effective risk mitigation.

Second, we contribute to transparency research (Gualandris et al., 2021; Sodhi and Tang, 2019) by extending the transparency definition to the context of nexus suppliers (i.e. nexus supplier transparency) as the collective disclosure of sustainability information to stakeholders by critical sub-suppliers of the same type, such as informational, operational, and monopolistic nexus suppliers. The significant effects found for two sub-groups of nexus suppliers (i.e. informational and monopolistic) on buyer ESG risk inform about the urgency of examining transparency across different sub-groups of suppliers and not only focusing on individual suppliers. Our study offers a “meso level” analysis of supply chain transparency that has not been extensively studied in extant research. We name it “meso level” because this level of analysis into subgroups of critical suppliers is positioned between the organizational-level transparency that examines individual suppliers (Sodhi and Tang, 2019; Villena and Dhanorkar, 2020) and the overall supply transparency that considers all and not only critical suppliers in the supply network (Gualandris et al., 2021).

Third, we also contribute to understanding the relationship between supply network structure and transparency (Gualandris et al., 2021) for risk management. Our empirical findings highlight that both the buyer firm’s structural embeddedness (i.e. its supply network accessibility) and the structural embeddedness of the sources of information disclosure, such as monopolistic nexus suppliers with high betweenness centrality, shape how transparency influences buyer ESG risk exposure.

Finally, we extend the nexus supplier theory (Yan et al., 2015) beyond pure operational mechanisms by exploring nexus supplier transparency, finding support for two of our hypotheses. We complement the nexus supplier theory with institutional theory (Oliver, 1990, 1991) to explain the expected governance approach of nexus suppliers toward lower tiers (Vurro et al., 2009). Knowing this, buyers can screen disclosed information more precisely to discover areas of higher risk in the supply network, particularly where lower tiers are managed passively (Drake and Schlachter, 2008). Indeed, our findings suggest that informational nexus supplier transparency positively contributes to this aim, mitigating buyer ESG risk exposure. In addition, institutional theory (Oliver, 1990, 1991) informs how buyers can overcome limitations in relational dynamics with nexus suppliers who have no incentive to collaborate. In this regard, our findings confirm that developing supply network accessibility is crucial to benefit from information disclosure from suppliers with high bargaining power, like monopolistic nexus suppliers.

We recommend managers begin by identifying critical suppliers besides the usual strategic suppliers, especially in the second tier, where they are often overlooked. To identify critical suppliers, managers must map their supply network and analyze not only internal attributes but also the network position of lower-tier suppliers. Once critical suppliers are identified, we advise buyers to monitor their public information disclosure to gain visibility into areas where access is difficult in the upstream supply chain and use this information to mitigate ESG-related risks. Our findings show that leveraging information disclosures from informational nexus suppliers, typically in peripheral areas of the supply chain, provides valuable insights for risk management due to their diverse supply base across various industries. However, relying on public disclosure of information by operational nexus suppliers, who are central to the material flow, may not be sufficient to reduce ESG risk. Instead, we suggest managers pursue internal collaborations and direct supplier development programs to gather more detailed information from operational nexus suppliers.

Another key factor is supply network accessibility, which depends on the number of intermediaries between a buying company and lower-tier suppliers and the number of alternative paths available. When accessibility is limited, managers must carefully monitor transparency at lower tiers, as they can increase ESG risk exposure. It is especially important to monitor nexus suppliers known for specific materials, as buyers can be easily associated with them when ESG violations occur at supplier facilities. Therefore, buying companies must establish robust mechanisms to prepare for and address any non-compliance. For companies with limited resources, intensive collaboration with stakeholders can enhance visibility into these suppliers while reducing the coordination burden (Gualandris et al., 2015). Another option is developing better network accessibility, which is a strategic decision requiring time and resources (Bellamy et al., 2020). A company can develop supply network accessibility by shortening the distance to lower tiers. For example, a buying company can establish alternative contractual agreements with some of the critical suppliers in the upstream supply network, as LGE did with Taiwan Semiconductors. Our findings indicate that companies require enhanced accessibility to benefit from monopolistic nexus supplier disclosures to reduce ESG risk exposure. When companies formulate the supply network strategy, it is important to consider this finding about the need for enhanced accessibility.

6.2 Limitations and opportunities for future research

Our study sheds light on how buyer firms can mitigate ESG risk exposure by leveraging nexus supplier transparency. This approach opens a new avenue for risk management in multi-tier supply chains, complementing the well-known direct collaboration and monitoring practices for risk management in supply chain management (Gualandris et al., 2015; Kähkönen et al., 2023; Tachizawa and Wong, 2014; Villena and Gioia, 2018). In addition, future research could empirically examine relational dynamics between buyers and each nexus supplier type.

Another avenue for future research is investigating how nexus supplier transparency can influence buyer reputation (Petersen and Lemke, 2015) through stakeholder involvement in sustainable supply chain management (Gualandris et al., 2015; Tachizawa and Wong, 2014; Villena and Gioia, 2018). Stakeholders are increasingly holding buyers accountable beyond their organizational boundaries, across their entire supply network (Hartmann and Moeller, 2014). In theory, the reputation that nexus suppliers create through transparency efforts, especially when stakeholder involvement is high, can be transferred to buyers (Petersen and Lemke, 2015). However, nexus supplier transparency is a double-edged sword, revealing ESG violations in lower tiers that damage buyer reputation. Therefore, future studies could also investigate the dual role of nexus supplier transparency for risk management and reputation transfer.

Finally, we faced the same trade-off as other researchers conducting empirical studies with large supply networks (Gualandris et al., 2021), having to focus on a cross-section to study several supply networks in-depth, up to third-tier suppliers, given data availability. We also recommend that future research continue developing metrics and methods for identifying nexus suppliers to minimize overlap among different nexus supplier categories, not necessarily based on DEA and fixed percentiles proposed by Shao et al. (2018).

6.3 Conclusions

Supply chain transparency has gained significant importance, driven by stricter disclosure mandates like the EU’s new Corporate Sustainability Reporting Directive. Focal buyer firms have deployed efforts to monitor and control their suppliers. However, recent scandals due to sustainability incidents in lower tiers raise doubts about whether this is the most appropriate approach. In this study, we offer a novel perspective, drawing attention to nexus suppliers and how they play a prominent role in providing visibility into the multi-tier supply chain to mitigate risk for focal buyer firms. We no longer treat lower-tier suppliers as a homogeneous group but acknowledge differences in their structural embeddedness by demonstrating how information disclosure from different types of nexus suppliers helps to reduce buyer ESG risk exposure. Informational nexus supplier transparency is associated with reduced buyer ESG risk exposure. However, buyers must complement monopolistic nexus supplier transparency with resource-intensive mechanisms like supply network accessibility to mitigate ESG risk exposure.

Figures

Figure 1

Supply network of IVERIC bio Inc

Figure 2

Sample selection process

Figure 3

Conditional effects of monopolistic NS transparency and buyer supply network accessibility on buyer ESG risk exposure

Table 1

Variable descriptions

Variables	Operationalization	Reference
Buyer ESG risk exposure	Average of RepRisk Index (RRI) values of a buyer firm across the fiscal year 2019. Values range from 0 to 100; higher values indicate more company exposure to ESG issues	Adapted from Li and Wu (2020)
ESG transparency NS (Informational)	Average Bloomberg ESG disclosure scores for second-tier informational nexus suppliers of the focal buyer firm for the fiscal year 2018. Range from 0 to 100; higher values indicate that a larger amount of ESG data is publicly disclosed by this type of nexus suppliers	New, not empirically examined. Adapted from Gualandris et al. (2021) and from Yan et al. (2015)
ESG transparency NS (Monopolistic)	Average Bloomberg ESG disclosure scores for second-tier monopolistic nexus suppliers of the focal buyer firm for the fiscal year 2018. Range from 0 to 100; higher values indicate that a larger amount of ESG data is publicly disclosed by this type of nexus suppliers	New, not empirically examined. Adapted from Gualandris et al. (2021) and from Yan et al. (2015)
ESG transparency NS (Operational)	Average Bloomberg ESG disclosure scores for second-tier operational nexus suppliers of the focal buyer firm for the fiscal year 2018. Range from 0 to 100; higher values indicate that a larger amount of ESG data is publicly disclosed by this type of nexus suppliers	New, not empirically examined. Adapted from Gualandris et al. (2021) and from Yan et al. (2015)
Buyer accessibility	Information centrality metric for the focal buyer with respect to its supply network, calculated as the harmonic mean of the length of paths ending at the focal buyer. It has a minimum value of 0 and no maximum value. Higher values are obtained for focal buyers with more short paths connecting them with the other nodes in the supply network	Bellamy et al. (2014),Stephenson and Zelen (1989)
ESG transparency	Bloomberg ESG disclosure score for the focal buyer firm for the fiscal year 2018	Bellamy et al. (2020), Gualandris et al. (2021)
Profitability	Net income divided by total assets for the fiscal year 2018	Bellamy et al. (2020), Gualandris et al. (2021), Sharma et al. (2020)
Leverage	Total debt divided by total shareholder equity for the fiscal year 2018	Lu and Shang (2017), Montes-Sancho et al. (2022)
Size	Number of employees for the fiscal year 2018 (logarithm)	Bode and Wagner (2015), Sharma et al. (2020)
Age	Number of years since the company was founded as of fiscal year 2018	Bode and Wagner (2015), Montes-Sancho et al. (2022), Park et al. (2018), Sharma et al. (2020)
Diversification	Number of product industry segments in which the firm operates for the fiscal year 2018 (logarithm)	Lu and Shang (2017), Montes-Sancho et al. (2022)
First-tier suppliers (total)	Number of first-tier suppliers of the focal buyer firm for the fiscal year 2018 (logarithm)	Dong et al. (2020)
First-tier suppliers (dispersion)	An entropy measure based on the countries of the first-tier suppliers of the focal buyer firm for the fiscal year 2018	Adapted from Dong et al. (2020)

Source(s): Authors’ own creation

Table 2

Regression results on buyer ESG risk exposure

	Models without IV
	Model 1	Model 2	Model 3
ESG transparency NS		−0.1957*	−0.2427**
(Informational)		(0.117)	(0.122)
ESG transparency NS		0.0968	0.0372
(Monopolistic)		(0.081)	(0.078)
ESG transparency NS		0.0281	−0.0181
(Operational)		(0.080)	(0.109)
Buyer accessibility		−7.5884**	−5.2578
		(3.469)	(3.332)
ESG transparency NS			0.3370
(Informational) * Buyer accessibility			(0.419)
ESG transparency NS			−1.1288**
(Monopolistic) * Buyer accessibility			(0.458)
ESG transparency NS			0.1779
(Operational) * Buyer accessibility			(0.325)
ESG transparency	0.1377**	0.1276**	0.1156*
	(0.062)	(0.062)	(0.064)
Profitability	−0.8878	−0.5284	−0.5122
	(0.799)	(0.758)	(0.759)
Leverage	−0.0142**	−0.0152**	−0.0167**
	(0.007)	(0.007)	(0.006)
Size	0.7522**	0.7741**	0.7216**
	(0.358)	(0.349)	(0.348)
Age	0.0129	0.0137	0.0150
	(0.016)	(0.016)	(0.017)
Diversification	0.9675	0.8086	0.8294
	(0.650)	(0.667)	(0.641)
First-tier suppliers (total)	1.4860**	3.3274***	2.8706***
	(0.618)	(1.071)	(1.032)
First-tier suppliers (dispersion)	0.4210	0.0051	0.1569
	(0.758)	(0.770)	(0.756)
R squared	0.232	0.253	0.274
R squared (adjusted)	0.202	0.216	0.233

Note(s): Robust standard errors in parentheses ***p < 0.01, **p < 0.05, *p < 0.1. Industry sector dummies are included in the model but not reported in the output. Each observation in the model, 428 in total, corresponds to a buyer supply network up to third-tier suppliers

Source(s): Authors’ own creation

Table 3

Regression results on buyer ESG risk exposure with endogeneity corrections

	Models with IV
	Model 1	Model 2
ESG transparency NS	−0.8924*	−0.7605*
(Informational)	(0.489)	(0.455)
ESG transparency NS	1.1289*	0.3389
(Monopolistic)	(0.605)	(0.563)
ESG transparency NS	0.0071	−0.0957
(Operational)	(0.380)	(0.506)
Buyer accessibility	−4.3272	1.1640
	(4.979)	(6.119)
ESG transparency NS		0.4509
(Informational) * Buyer accessibility		(1.886)
ESG transparency NS		−3.3680*
(Monopolistic) * Buyer accessibility		(2.004)
ESG transparency NS		0.5887
(Operational) * Buyer accessibility		(1.657)
ESG transparency	0.0452	−0.0079
	(0.053)	(0.061)
Profitability	−1.5885*	−0.8858
	(0.866)	(0.859)
Leverage	−0.0211***	−0.0268***
	(0.007)	(0.007)
Size	0.2833	−0.1718
	(0.448)	(0.450)
Age	0.0308*	0.0249**
	(0.016)	(0.012)
Diversification	1.5366*	0.6331
	(0.848)	(0.732)
First-tier suppliers (total)	2.5439***	1.0767
	(0.959)	(1.165)
First-tier suppliers (dispersion)	−0.7828 (0.761)	0.3059 (0.895)

Note(s): Clustered robust standard errors in parentheses ***p < 0.01, **p < 0.05, *p < 0.1. Industry sector dummies are included in the model but not reported in the output. Each observation in the model, 428 in total, corresponds to a buyer supply network up to third-tier suppliers

Source(s): Authors’ own creation

Table A1

Correlation matrix and descriptive statistics

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)
(1) Buyer ESG risk exposure	1.00
(2) ESG transparency NS (Informational)	−0.10**	1.00
(3) ESG transparency NS (Monopolistic)	−0.07	0.62***	1.00
(4) ESG transparency NS (Operational)	−0.01	0.68***	0.66***	1.00
(5) Buyer accessibility	0.27***	0.02	−0.12**	0.13***	1.00
(6) ESG transparency	0.35***	−0.04	−0.09*	0.04	0.48***	1.00
(7) Profitability	0.13***	0.00	−0.06	0.00	0.31***	0.24***	1.00
(8) Leverage	0.08	−0.06	−0.06	−0.01	0.29***	0.28***	0.02	1.00
(9) Size	0.34***	−0.08*	−0.17***	0.00	0.66***	0.55***	0.49***	0.33***	1.00
(10) Age	0.14***	−0.02	−0.07	−0.01	0.20***	0.22***	0.20***	−0.04	0.27***
(11) Diversification	0.22***	−0.11**	−0.17***	−0.02	0.33***	0.28***	0.26***	0.19***	0.40***
(12) First-tier suppliers (total)	0.37***	−0.04	−0.15***	0.06	0.89***	0.54***	0.25***	0.32***	0.67***
(13) First-tier suppliers (dispersion)	0.17***	−0.01	−0.03	0.07	0.34***	0.24***	−0.01	0.07	0.20***
Mean	4.24	57.39(a)	55.14(a)	57.96(a)	0.98(a)	35.48	−0.06	45.64	6.98
SD	7.66	5.09	6.55	5.4	0.24	7.56	0.32	52.79	1.95
(continued)

	(10)	(11)	(12)	(13)
(10) Age	1.00
(11) Diversification	0.16***	1.00
(12) First-tier suppliers (total)	0.19***	0.35***	1.00
(13) First-tier suppliers (dispersion)	0.01	0.08	0.48***	1.00
Mean	29.19	0.33	2.03	0.84
SD	25.22	0.54	0.94	0.53

Note(s): N = 428 * p < 0.10, **p < 0.05, ***p < 0.01

(a) Values of variables before applying the de-mean in the operationalization of the interaction terms

Source(s): Authors’ own creation

Table A2

Robustness analysis: Regression results on buyer ESG risk exposure restricted to supply chain incidents (SC incidents)

	Buyer ESG risk (SC incidents)
	Model 1	Model 2	Model 3
ESG transparency NS		−0.0032*	−0.0030*
(Informational)		(0.002)	(0.002)
ESG transparency NS		0.0015*	0.0008
(Monopolistic)		(0.001)	(0.001)
ESG transparency NS		0.0002	0.0004
(Operational)		(0.001)	(0.001)
Buyer accessibility		−0.0836	−0.0671
		(0.058)	(0.061)
ESG transparency NS			0.0087
(Informational) * Buyer accessibility			(0.010)
ESG transparency NS			−0.0151*
(Monopolistic) * Buyer accessibility			(0.008)
ESG transparency NS			0.0052
(Operational) * Buyer accessibility			(0.005)
ESG transparency	0.0018	0.0017	0.0015
	(0.001)	(0.001)	(0.001)
Profitability	−0.0027	0.0019	0.0028
	(0.009)	(0.007)	(0.007)
Leverage	−0.0001**	−0.0001**	−0.0001**
	(0.000)	(0.000)	(0.000)
Size	−0.0039	−0.0039	−0.0042
	(0.004)	(0.004)	(0.004)
Age	−0.0001	−0.0001	−0.0000
	(0.000)	(0.000)	(0.000)
Diversification	0.0101	0.0078	0.0090
	(0.012)	(0.012)	(0.012)
First-tier suppliers (total)	0.0137*	0.0348**	0.0281*
	(0.008)	(0.017)	(0.015)
First-tier suppliers (dispersion)	0.0142	0.0092	0.0104
	(0.010)	(0.012)	(0.012)
R-squared (Adj)	0.0273	0.0422	0.0507

Note(s): The dependent variable Buyer ESG Risk (SC incidents) encompasses the risk incidents explicitly classified as cross-cutting supply chain incidents. Following the RepRisk methodology, we calculated the incident value coefficient for each incident as one-third of the square root of the product of incident severity, novelty, and reach. We then calculated a weighted average of the incident value coefficient for each buyer for 2019 and used it as the dependent variable in this reported analysis. Robust standard errors in parentheses ***p < 0.01, **p < 0.05, *p < 0.1. Industry sector dummies are included in the model but not reported in the output. Each observation in the model, 428 in total, corresponds to a buyer supply network up to third-tier suppliers

Source(s): Authors’ own creation

Table A3

Robustness analysis: Regression results on buyer ESG risk exposure (2016–2019)

	Model 1	Model 2	Model 3
ESG transparency NS		−0.1436***	−0.1357***
(Informational)		(0.049)	(0.048)
ESG transparency NS		0.0445	0.0145
(Monopolistic)		(0.031)	(0.032)
ESG transparency NS		0.0594	0.0274
(Operational)		(0.040)	(0.050)
Buyer accessibility		−8.8271***	−7.5719***
		(1.975)	(1.858)
ESG transparency NS			0.2586
(Informational) * Buyer accessibility			(0.198)
ESG transparency NS			−0.5596***
(Monopolistic) * Buyer accessibility			(0.196)
ESG transparency NS			0.0747
(Operational) * Buyer accessibility			(0.177)
ESG transparency	0.1190***	0.1159***	0.1122***
	(0.034)	(0.034)	(0.034)
Profitability	0.0014**	0.0012**	0.0011**
	(0.001)	(0.001)	(0.001)
Leverage	0.0005	0.0004	0.0004
	(0.000)	(0.000)	(0.000)
Size	0.7918***	0.8354***	0.8225***
	(0.165)	(0.166)	(0.163)
Age	0.0120	0.0139	0.0162*
	(0.009)	(0.009)	(0.009)
Diversification	0.4874	0.6037	0.6335
	(0.407)	(0.414)	(0.421)
First-tier suppliers (total)	0.5317*	2.4516***	2.2502***
	(0.308)	(0.547)	(0.529)
First-tier suppliers (dispersion)	0.8047**	0.3124	0.3496
	(0.405)	(0.415)	(0.413)
Year fixed effects	Yes	Yes	Yes
Industry fixed effects	Yes	Yes	Yes
R-squared (Adj)	0.332	0.347	0.351

Note(s): Robust standard errors in parentheses ***p < 0.01, **p < 0.05, *p < 0.1. Industry sector dummies are included in the model but not reported in the output. Each observation in the model, 428 in total, corresponds to a buyer supply network up to third-tier suppliers

Source(s): Authors’ own creation

Table A4

Robustness analysis: Regression results with an additional control variable to account for nexus suppliers spanning more than one category

	Model 1	Model 2	Model 3
ESG transparency NS		−0.1961*	−0.2434**
(Informational)		(0.118)	(0.123)
ESG transparency NS		0.0956	0.0324
(Monopolistic)		(0.079)	(0.077)
ESG transparency NS		0.0302	−0.0112
(Operational)		(0.083)	(0.111)
Buyer accessibility		−7.5782**	−5.1689
		(3.458)	(3.298)
ESG transparency NS			0.3520
(Informational) * Buyer accessibility			(0.425)
ESG transparency NS			−1.1531**
(Monopolistic) * Buyer accessibility			(0.477)
ESG transparency NS			0.1861
(Operational) * Buyer accessibility			(0.325)
ESG transparency	0.1398**	0.1286**	0.1184*
	(0.061)	(0.061)	(0.063)
Profitability	−0.8987	−0.5337	−0.5264
	(0.793)	(0.751)	(0.750)
Leverage	−0.0142**	−0.0151**	−0.0167**
	(0.007)	(0.007)	(0.006)
Size	0.7572**	0.7762**	0.7274**
	(0.354)	(0.347)	(0.346)
Age	0.0125	0.0136	0.0145
	(0.016)	(0.016)	(0.017)
Diversification	0.9947	0.8205	0.8676
	(0.657)	(0.671)	(0.646)
First-tier suppliers (total)	1.4832**	3.3221***	2.8438***
	(0.616)	(1.062)	(1.016)
First-tier suppliers (dispersion)	0.3958	−0.0072	0.1217
	(0.763)	(0.779)	(0.764)
Overlap	0.6053	0.2968	0.8998
	(1.781)	(1.759)	(1.817)
Observations	428	428	428
R-squared	0.232	0.253	0.274

Note(s): Robust standard errors in parentheses ***p < 0.01, **p < 0.05, *p < 0.1. Industry sector dummies are included in the model but not reported in the output. Each observation in the model, 428 in total, corresponds to a buyer supply network up to third-tier suppliers

Source(s): Authors’ own creation