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Bloomberg Supply Chain Analysis: A Data Source for Investigating the Nature, Size, and Structure of Interorganizational Relationships
Ace Beorchia and T. Russell Crook
Abstract
Research involving interorganizational relationships (IORs) has grown at an impressive rate. Several datasets have been used to understand the nature and performance implications of these relationships. Given the importance of such relationships, we describe a relatively new dataset, Bloomberg SPLC, which contains data regarding the percentage of costs and revenues attributed to suppliers and customers, as well as allows researchers to construct a comprehensive dataset of IORs of buyer–supplier networks. Because of this, Bloomberg SPLC data can be used to uncover new and exciting theoretical and empirical implications. This chapter provides background information about this dataset, guidance on how it can be leveraged, and new theoretical terrain that can be charted to better understand IORs.
Keywords: Interorganizational relationships; alliances; supply chain; networks; buyer–supplier agreements; partnerships
Introduction
Over the last several decades, research involving interorganizational relationships (IORs) has grown at an impressive rate (cf. Connelly, Crook, Combs, Ketchen, & Aguinis, 2018; Parmigiani & Rivera-Santos, 2011). Research in this area has shown that firms operate in complex environments, and their fates are intertwined with those of other firms (e.g., Munyon, Jenkins, Crook, Edwards, & Harvey, 2019; Zimmermann & Foerstl, 2014). There are a variety of types of IORs, including strategic alliances (e.g., Ahuja, Lampert, & Tandon, 2008; Wassmer, 2010), joint ventures (e.g., Brouthers & Hennart, 2007), buyer–supplier agreements (e.g., Holcomb & Hitt, 2007; McCutcheon & Stuart, 2000), franchising (e.g., Combs & Ketchen, 2003), cross-sector partnerships (e.g., Selsky & Parker, 2005), networks (e.g., Hoang & Antoncic, 2003; Provan, Fish, & Sydow, 2007), interlocking directorates (e.g., Burt, 1980; Mizruchi, 1996), and coalitions (e.g.,Man Zhang & Greve, 2019; Polzer, Mannix, & Neale, 1998). Such relationships are generally considered “strategically important, cooperative relationships between a focal organization and one or more other organizations to share or exchange resources with the goal of improved performance” (Parmigiani & Rivera-Santos, 2011, p. 1109).
While research supports the notion that firms' fates are linked with other firms (Parmigiani & Rivera-Santos, 2011), IOR data can be difficult to procure. There are several reasons for this. First, firms may prefer to keep such relationships private in order to maintain a competitive advantage (Handfield, 2012). Second, few regulations require that firms disclose their IORs. In fact, only public firms are required – at least in the United States – to disclose a relationship when a customer is responsible for at least 10% of a firm's revenue on an annual basis (Davenport, 2011). Of course, this list of customers is relatively small for most firms – containing no, one, or a just few key relationships. Due of the lack of available data on IOR linkages, relatively few data sources are available that allow researchers to study such relationships among firms, such as SDC Platinum (e.g., Kim, Howard, Cox Pahnke, & Boeker, 2016), ReCap (e.g., Mindruta, Moeen, & Agarwal, 2016), and ISS (formerly RiskMetrics) (Sauerwald, Lin, & Peng, 2016).
Although these datasets can provide valuable insights into IORs – mainly strategic alliances, networks, and board interlocks – data sources quantifying the size of IORs have been largely unavailable. Considering the magnitude of IORs has important theoretical implications. It is well known that relationship type (e.g., alliance, network, joint venture) is an important factor of IORs (Barringer & Harrison, 2000). However, it is also known that each relationship and type that firms engage in can have varying levels of magnitude (Golicic, Foggin, & Mentzer, 2003) that impact how and to what extent a firm interacts with other firms. For example, although a firm may have multiple suppliers and customers, it is likely to allocate more resources to suppliers and customers to which the central firm is more exposed (Golicic et al., 2003). Up to now, researchers have had limited access to data regarding IOR magnitude – especially from secondary data sources.
In this chapter, we describe a relatively new data source that has been largely unexplored by management scholars (Kim & Davis, 2016) – the Bloomberg Supply Chain Analysis (hereafter Bloomberg SPLC) – and how it may be used by researchers interested in IORs. 1 With data dating back to 2006, the SPLC function in Bloomberg allows users to search a focal firm and view a list of its suppliers, customers, and competitors. Specifically, it offers data regarding the percentage of costs and revenues attributed to suppliers and customers. This allows researchers to create a comprehensive dataset of IORs to construct entire buyer–supplier networks. When paired with other datasets such as Compustat and Factiva, among others, Bloomberg SPLC data have the potential to yield new insights into the nature of IORs. In particular, we believe the use of Bloomberg SPLC data will allow researchers to more effectively theorize about the implications of the magnitude of IORs and use Bloomberg SPLC data to test and expand theoretical perspectives, mainly transaction cost economics (TCE), resource-based (relational) view, resource dependence theory (RDT), stakeholder theory, institutional theory, and social network theory.
Background on IOR Research
All firms cooperate with other organizations for their survival and growth (Scott, 1987). However, the relationships entered into vary both in why and how firms decide to cooperate (Oliver, 1990; Parmigiani & Rivera-Santos, 2011). Put differently, the intent as well as the structure of cooperation has historically been important to IOR researchers. In terms of motives, researchers suggest that firms enter into IORs in an effort to explore or exploit knowledge, tasks, functions, or activities (March, 1991). Parmigiani and Rivera-Santos (2011) refer to these motivations as “co-exploration” and “co-exploitation.”
Under the assumption that firms enter these relationships intentionally and consciously for a strategic purpose, several determinants of IORs also help to explain the motivation for cooperation. According to Oliver (1990), these motivations include necessity (i.e., to meet legal or regulatory requirements – Zald, 1978), asymmetry (i.e., to assert power, influence, or control over organizations with scarce resources – Crook & Combs, 2007), reciprocity (i.e., to pursue common goals or interests – Jamali & Keshishian, 2009), efficiency (i.e., to improve internal input/output ratio – Elston, MacCarthaigh, & Verhoest, 2018), stability (i.e., to reduce environmental uncertainty – Park & Mezias, 2005), legitimacy (i.e., to appear in agreement with prevailing norms, rules, beliefs, or expectations – Dacin, Oliver, & Roy, 2007), or some combination of these motivations.
Beyond these motivations, IORs can also yield benefits by helping firms gain access to resources, build economies of scale, share risks and costs, access new markets, develop product and services, learn, reduce time to market, become more flexible, lobby as a collective, and block competitors, among others (Barringer & Harrison, 2000). Taken together, the list of motivations and benefits of engaging in IORs is extensive. So, too, are the types of IORs. As noted earlier, firms engage in various IOR types, including strategic alliances, joint ventures, buyer–supplier agreements, franchising, cross-sector partnerships, networks, trade associations, interlocking directorates, and coalitions (e.g., Barringer & Harrison, 2000; Oliver, 1990).
Researchers interested in IORs have applied a variety of theoretical perspectives to understand motivations and benefits. From an economic standpoint, researchers have theorized that firms engage and cooperate with other organizations to become more efficient, reduce costs, acquire resources, and gain economies of scale (cf. Barringer & Harrison, 2000). In doing so, researchers have leveraged ideas from TCE (i.e., IORs used to increase efficiency), and resource-based view (RBV) (i.e., IORs used to acquire new resources), to name a few. From an organization theory standpoint, researchers have noted the importance of social structures and relationships in IORs such that firms engage in them to increase social standing, make connections, flex power, as well as reduce dependency and uncertainty. In doing so, researchers have built on ideas from RDT (i.e., to reduce uncertainty due to power and dependence), stakeholder theory (i.e., to reduce uncertainty from reputational concerns), institutional theory (i.e., to conform to socially constructed norms), and social network theory (i.e., to gain information and knowledge by creating or strengthening a relationship) (cf. Parmigiani & Rivera-Santos, 2011).
The topic of IORs remains a vibrant area of inquiry within strategic management and organizational theory research (e.g., Oliveira & Lumineau, 2019; Villena, Choi, & Revilla, 2019). Indeed, to obtain a relevant understanding of the most current IOR research and datasets being examined, we reviewed articles published from 2016 to 2018 in Strategic Management Journal. We flagged articles researching IORs, and identified the dataset(s) used in each study (Table 1). In this review, we discovered that all IOR types are not given equal attention in current research, an occurrence others have also noted (e.g., Parmigiani & Rivera-Santos, 2011). Specifically, recent studies have largely focused on interfirm alliances (e.g., Asgari, Singh, & Mitchell, 2017; Bakker, 2016; Blevins & Ragozzino, 2018) and networks (e.g., Jiang, Xia, Cannella, & Xiao, 2018; Kim et al., 2016; Sauerwald et al., 2016). Moreover, many studies rely on the same data sources in IOR research. For example, SDC Platinum and ReCap – as described earlier – are two of the most commonly used data sources in recent strategy research.
Table 1. Recently Used Interorganizational Relationship (IOR) Secondary Datasets in the Strategic Management Journal.
| Dataset | IOR Types | Citations | Complementary Datasets Utilized | Content |
| SDC Platinum | Alliances, networks | Blevins and Ragozzino (2018), Bos, Faems, and Noseleit (2017), Devarakonda and Reuer (2018), Ghosh et al. (2016), Howard, Withers, Carnes, and Hillman (2016), Jiang et al. (2018), and Kim et al. (2016) | BioScan Directory, Bloomberg Private Firm Directory, Bureau van Dijk, Center for Research on Securities Pricing (CRSP), Compustat, Execucomp Fortune's Most Admired Firms, Institutional Brokers' Estimate System (IBES), News archives, Patent Network Dataverse, Proxy statements, United States Patent and Trademark Office (USPTO), ReCap, U.S. Securities and Exchange Commission (SEC) | - Alliance information
- Mergers and acquisition data
- Joint venture and repurchase information
- Corporate governance data (e.g., shareholder rights plan adoptions, amendments, and expirations; dissident shareholder campaign data)
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| ReCap | Alliances, networks | Asgari et al. (2017), Jiang et al. (2018), Devarakonda and Reuer (2018), Kim et al. (2016), and Mindruta et al. (2016) | BioScan Directory, Factiva, SDC Platinum, European Patent Office (EPO), PATSTAT | - Pharmaceutical (biotech) alliance (i.e., R&D) data
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| Co... |
