Socially Responsible Firms Outsource Less

Abstract

Implementing corporate social responsibility (CSR) in supply chains is not a trivial task. In fact, many firms in recent years have publicly proclaimed that in order to keep their CSR commitments, they had to reduce reliance on external suppliers by vertically integrating their operations. Our aim in this article is to examine whether there is truly a relationship between a firm’s CSR performance and its level of vertical integration. Drawing on a multi-industry sample of 2,715 firm-year observations, and after addressing endogeneity concerns, we demonstrate that firms with higher CSR performance tend to vertically integrate more (or, outsource less). We also demonstrate that this tendency is weaker for firms that have higher degrees of asset specificity or international diversification. Our core conclusion is that CSR performance and outsourcing are at odds, but firms can reconcile this tension by deepening their collaborations with suppliers.

Keywords

outsourcing supply-chain CSR sustainable supply chain vertical integration

Outsourcing offers numerous economic benefits to global firms (Contractor et al., 2010). At the same time, it also presents them with an onerous challenge to implement what is called supply-chain responsibility (Maloni & Brown, 2006; Spence & Bourlakis, 2009), value chain responsibility (Phillips & Caldwell, 2005), upstream corporate social responsibility (Schrempf-Stirling & Palazzo, 2016), logistic social responsibility (Carter & Jennings, 2002), and sustainable supply-chain management (Seuring & Müller, 2008). These designations have a common argument at their core: a firm’s responsibility toward society and the environment (corporate social responsibility or CSR) is not limited within the traditionally defined boundaries of a firm; indeed, it must encompass the firm’s complete supply chain (Matten & Crane, 2005; McGahan, 2019; Schrempf, 2012). Correspondingly, because contemporary firms are often part of a supply chain, their CSR performance is contingent upon their ability to implement CSR throughout their supply chains.

Implementing supply-chain CSR is, however, riddled with myriad challenges (Lund-Thomsen & Lindgreen, 2014; Short et al., 2016), which primarily emanate from the complex and multitiered nature of supply chains (Wilhelm et al., 2016). In some cases, supply chains are not just complex but they are intractable and obscure. For example, in analyzing the reports submitted to the Securities and Exchange Commission by 1,300 firms on whether their products contain “conflict minerals,” Kim and Davis (2016) found that 80% of firms could not even determine the country of origin of their products, and just 1% could confidently certify themselves as conflict-free. Similarly, in a survey of 1,700 global firms, only 19% reported confidence in their knowledge of the actions of their supply-chain partners (The Sustainability Consortium, 2016). Worse yet, many firms do not even know who exactly their suppliers are because of the rampant unauthorized subcontracting in many industries (Choi et al., 2001; Egels-Zandén, 2017a). Even when supply chains are tractable and suppliers are easy to identify, implementation of supply-chain CSR can still be a challenging task due to misalignment between a focal firm and its suppliers about the relevance of CSR itself (Jamali et al., 2017; Wijen, 2014), discrepancies in their respective intentions for implementing CSR (Soundararajan et al., 2019), differences in CSR-related regulations across jurisdictional boundaries (Hörisch et al., 2017), and the limited ability of a firm to influence on-the-ground actions of its suppliers (Christmann & Taylor, 2006). Given such multifaceted challenges, undercompliance and noncompliance with a focal firm’s CSR stipulations are not uncommon phenomena in contemporary supply chains (Soundararajan & Brown, 2016; Soundararajan et al., 2018).

Confronted with these challenges, firms take a wide variety of steps to implement supply-chain CSR that are already analyzed and surveyed in previous studies (Yawar & Seuring, 2017). Rather recently, a growing number of firms have been proclaiming that their commitments to CSR have led them to vertically integrate their operations. For example, Symrise, a major producer of flavors and fragrances, claims that to be able to work directly with vanilla bean producers in Madagascar to implement sustainability practices, the company has been vertically integrating its operations (Symrise, 2017). Similarly, Ferrero, a leading confectionery manufacturer, announces that to meet an ambitious CSR target of 100% sustainability in its raw materials, it had acquired Oltan Group, which was formerly its major supplier of hazelnuts (Kittilaksanawong & Curcuraci, 2017). Taylor Guitars, a well-recognized manufacturer of acoustic and electric guitars, credits its purchase of an ebony sawmill in Cameroon to its commitment to ensuring that all its products are free of endangered wood species (McDonnell, 2019). IKEA ascribes its purchase of a large area of forestland in Romania to its commitment to implement sustainable forest management practices at the origin of its products’ supply chains (Chaudhuri, 2015). While these anecdotes suggest that concerns for CSR performance may lead firms to vertically integrate as Van Buren and colleagues (2021) recognize in their conceptual analysis of human trafficking in global supply chains, it is also possible that these anecdotes are misleading corporate announcements, simply meant to give a positive CSR-spin to an otherwise motivated vertical integration (VI). Moreover, even if these corporate claims were proven true, one could conjecture that the positive relationship between CSR performance and VI might only occur within the idiosyncrasies of particular firms or industries. Our motivation in this article is to dispel these ambiguities by conducting a panel study of a large, multi-industry sample of firms. Our primary research question is as follows: “Is there a relationship between CSR performance and the level of vertical integration?”

We integrate core tenets of transaction cost economics (TCE) with literature in supply-chain CSR to theorize the relationship between CSR performance and VI. According to TCE, a firm pursues VI when the total cost of performing an activity in-house is likely to be lower than the cost of the activity done by external suppliers. Thus, costs and risks associated with monitoring its suppliers are integral to the calculus that a firm uses to determine the extent to which it vertically integrates or outsources its activities (Acquier et al., 2017; Shelanski & Klein, 1995). Using this as a core basis for our analytical framework, we consider the costs and risks of supplier monitoring as primary factors to explain the relationship between CSR performance and VI. We introduce two moderating variables, namely, the firm’s asset specificity and the level of international diversification, which are central to TCE. Asset specificity denotes the extent to which a firm engages with suppliers (Williamson, 1975), which can engender mutual trust and collaboration and reduces the need for supplier monitoring (Bird & Soundararajan, 2020). In contrast, international diversification, which denotes a firm’s expansion beyond its domestic market (Kang, 2013), may impede a firm’s ability to develop trust-based relationships with suppliers because such firms typically source from a much wider set of suppliers (Kotabe & Mudambi, 2009). Mutual trust and collaborations may not be easy to develop with a large number of suppliers and hence may necessitate a firm to emphasize supplier monitoring. The two moderating variables, therefore, allow us to produce variation in a firm’s need to monitor suppliers’ actions vis-à-vis its ability to develop collaborative partnerships with them. As such, in answering our primary research question, “is there a relationship between CSR performance and the level of VI,” we also address how this relationship would change for firms with different levels of (a) asset specificity and (b) international diversification.

We exploit multiple data sets to execute this study empirically. The firm sample was drawn from U.S.-based firms listed in the S&P 500 index. CSR performance was operationalized as firm performance on environmental, social, and governance (ESG) issues, measurement of which was drawn from a well-regarded Thomson Reuters ASSET4 database. Global Compustat was used to extract input data for computing VI, asset specificity, international diversification, and control variables. Overall, 2,715 firm-year observations were included in our analyses. To foreshadow our results, we find a positive relationship between a firm’s CSR performance and the level of VI. We also find that this relationship is weaker for firms that have higher levels of either asset specificity or international diversification.

This article advances business and society literature as follows. Our primary contributions are in the area of supply-chain CSR. We empirically demonstrate that even though VI may seem to be an “extreme solution” to the challenges of implementing supply-chain CSR (Van Buren et al., 2021, p. 359), it is a widely adopted approach among firms with high CSR performance. Also, we lend empirical support to previous proposals that monitoring and trust-building are not mutually exclusive approaches (Andersen & Skjoett-Larsen, 2009). Rather, they can co-occur and allow firms to continue with similar levels of CSR performance and outsourcing by deepening engagement with their suppliers and developing trust-based relationships. As such, our results provide a multiwin prescription such that enhanced engagement with suppliers can help firms achieve high CSR performance without having to reduce the level of their outsourced activities. Conversely, failing to build trust-based relationships with suppliers may require firms to reduce the level of outsourced activities to maintain high CSR performance. The novelty of the article, therefore, is an empirical demonstration of a dynamic relationship between three core variables: CSR performance, VI/outsourcing, and trust and mutual dependence between a firm and its suppliers. The article also contributes to the literature at the intersection of CSR and strategic management. By showing that CSR can influence a firm’s fundamental strategic decisions, such as VI and outsourcing, the article bolsters the observation that a firm’s strategic choices and CSR performance are closely related (Panwar et al., 2016). The article also responds to the call for improving methodological sophistication in business and society research (Crane et al., 2017), specifically by addressing endogeneity concerns.

The rest of this article is organized as follows. Below, we first outline how outsourcing, which is a common strategy for contemporary firms, presents them with unique CSR challenges. At the end of this section, we speculate that firms might find these challenges so onerous that they might decide to reduce outsourcing and, instead, increase the level of VI. Subsequently, we consider this speculation from a TCE viewpoint in the “Hypotheses” section, where we propose a core hypothesis and two moderation effect hypotheses. We then proceed to describe our empirical analysis in the “Methods and Results” section, which is followed by a discussion wherein we describe the theoretical implications of the article, highlight its limitations, and outline a future research agenda. The article concludes with a brief summary note.

Literature Review

Outsourcing is a strategic alternative to insourcing. It essentially denotes a vertical disintegration strategy, wherein a firm transfers to an external party the activities and processes that it determines not to conduct internally (Gilley & Rasheed, 2000). Although originally viewed as a tactical procurement plan, outsourcing has now become a competitive strategy for contemporary firms (Contractor et al., 2010). Its appeal is so high that it has become a mega-trend in many industries, even those—such as automobiles—that were once considered “the textbook example of [vertical] integration” (Jacobides, 2005, p. 465). Supply-chain management literature shows that outsourcing can help firms access cutting-edge technologies (Li et al., 2008), skilled workforce (Lewin et al., 2009), high-end services (Bunyaratavej et al., 2007), the newest software applications (Verwaal et al., 2009), and superior product designs (Ettlie & Sethuraman, 2002)—all at a lower cost than the costs involved in developing such capabilities in-house (Gunasekaran et al., 2015). In addition, outsourcing allows firms to better leverage their core competencies (Prahalad & Hamel, 1990) and develop distributed production-based business models to gain competitive advantage (Hätönen & Eriksson, 2009). Such multifaceted advantages have made outsourcing a ubiquitous phenomenon (Lahiri, 2016).

Outsourcing also presents firms with multiple challenges that are mainly related to supplier selection, coordination, and relationship management (Holcomb & Hitt, 2007; Metters & Verma, 2008). A more profound challenge, however, and the one that is of interest to us in this article, is that outsourcing subjects a firm to the forces of global production networks (Egels-Zandén, 2017b; Lund-Thomsen, 2013). These networks comprise not only the firms’ suppliers but also other entities such as labor organizations, trade associations, government agencies, nongovernmental organizations, and consumers, each with their own social and political interests (Barrientos, 2013; Levy, 2008). As a result, a firm, even though it may have adopted outsourcing for simple economic reasons, quickly finds itself surrounded by a complex set of social and political stakes to which it must respond. Whether it is the unsafe working conditions in manufacturing plants of Asian suppliers of major apparel and footwear brands (Reinecke & Donaghey, 2015), unfair prices paid to coffee growers in South America by large coffee companies (Kolk, 2005), use of child labor in Africa by major electronic corporations (Cuthbertson, 2016), or ecological crises in the tropics attributed to the destructive practices of commodity suppliers of well-known consumer brands (Dauvergne, 2017), it is ultimately the outsourcing firms that are held responsible for all such upstream wrongdoings (Schrempf-Stirling & Palazzo, 2016).

Firms adopt a variety of initiatives to ensure that their supply chains are governed responsibly and remain free of any wrongdoing. The most popular among these initiatives is the adoption of CSR standards (Potoski & Prakash, 2005; Waddock, 2008) which can either be industry-wide (Mena & Waeger, 2014) or specific to individual firms (Perez-Batres et al., 2012). The use of standards has become so common that there are often multiple, competing standards within the same industry (Husted et al., 2016; Reinecke et al., 2012). While standards help a firm communicate CSR goals to its suppliers, their effective implementation remains a critical challenge (Pagell & Shevchenko, 2014; Soundararajan & Brown, 2016). Are these challenges so profound or so costly to address that they might compel firms to reduce their dependence on suppliers as Symrise, IKEA, and others proclaim? Do firms that emphasize CSR performance end up reducing their levels of outsourcing and instead increasing the levels of VI? Below we explore these issues in the “Hypotheses” section.

Hypotheses

CSR implementation in supply chains can be considered a transaction between a focal firm and its suppliers to whom the firm stipulates general guidelines and specific expectations related to CSR (Cruz & Wakolbinger, 2008). This transaction, however, often takes place within an opaque field (Wijen, 2014), because firms have limited access to on-the-ground information about suppliers’ actions and commitments toward CSR and the steps they take—or fail to take—to implement it (Letizia & Hendrikse, 2016). This limited access gives rise to information asymmetries, which would increase the likelihood of opportunistic behavior or moral hazard (Husted, 2007). To avoid these possibilities, a focal firm would initiate monitoring mechanisms that allow oversight of CSR implementation in its supply chain (Alchian & Demsetz, 1972; O’Connell et al., 2005).

However, monitoring is a complex issue in the context of supply-chain CSR. Foremost, it has a significant cost implication because monitoring often entails adding new entities such as third-party certification bodies and auditors (Busse, 2016; Montiel et al., 2012; Pedersen & Andersen, 2006). These new entities add costs for a firm not just due to the fees they charge (Dogui et al., 2014), but also because they require a firm to develop an elaborate infrastructure so it can screen suppliers, craft contracts, improve surveillance, develop compliance mechanisms, and introduce rigorous reporting (Acquier et al., 2017; Fortanier et al., 2011). Because a firm would then impose these requirements on suppliers, its pool of eligible suppliers will shrink as potential suppliers might not be able to meet CSR standards.

Even after developing such extensive infrastructure and incurring significant monitoring costs, a firm rarely has complete, accurate, and verifiable information about its suppliers’ actions (Heras-Saizarbitoria & Boiral, 2013) for two reasons: a large number of suppliers typically remain unaudited (Egels-Zandén, 2017a; Heide et al., 2007) and audit reports are not usually that reliable. For example, Short et al. (2016) highlight that audit reports are influenced by factors such as the gender composition of the auditing team members, their levels of training, prior auditing experience, prior experience with the firm being audited, and, more strikingly, whether the payments were made by a focal firm or a supplier. The reliability of audit reports remains so questionable that monitoring the monitors in itself has become a critical problem in supply-chain CSR management (O’Rourke, 2003). In such situations, frictions and disputes between a focal firm and its suppliers frequently emerge, giving rise to transactional inefficiencies due to haggling, renegotiation, and adaptation (Gibbons, 2005; Williamson, 1975), and, in some cases, due to arbitration and litigation (Ketokivi & Mahoney, 2016).

In summary, supply-chain CSR constitutes a complex transaction that involves significant costs and uncertain outcomes. Firms that are able to achieve high CSR performance might find it necessary to restrict the extent of their outsourcing so they can not only reduce monitoring costs but also ensure that their entire value chain is socially responsible. Where possible, such firms would consider increasing their levels of VI, which would enable them to have tighter control over their operations and leverage their internal hierarchies to coordinate the myriad actions that supply-chain CSR requires. Therefore, we propose the following hypothesis:

Hypothesis 1 (H1): The higher a firm’s CSR performance, the higher its level of VI.

While firms with high CSR performance would increase their levels of VI to avert the challenges of implementing supply-chain CSR, they also may seek to overcome these challenges by developing strategic partnerships with their suppliers. The importance of such partnerships in implementing supply-chain CSR is extensively emphasized in supply-chain CSR literature (Gold et al., 2010; Pagell & Wu, 2009). While different studies take a somewhat different approach to analyze these partnerships, there is a common understanding that in developing these partnerships, firms first identify suitable suppliers and then engage with them in setting joint goals, adopting mutually acceptable CSR standards, training their personnel, and developing logistical processes (Foerstl et al., 2010; Joshi & Stump, 1999; Lim & Phillips, 2008; Lu et al., 2012). This deep level of engagement with suppliers increases the level of asset specificity in the relationship such that both parties—a firm and its suppliers—can obtain higher value within the relationship than outside it (Thauer, 2014).

Through the investment in specific assets, a firm can deepen social relations across its supply chain, promote information exchange and commitment to mutual problem-solving, and facilitate adaptation for sustained and effective exchange (Elfenbein & Zenger, 2014; Poppo & Zenger, 2002). As supply-chain partners accumulate a common history, they codevelop relational capital with each other that builds mutual trust (Kale et al., 2000), cooperative routines (Chassang, 2010), and an overall expectation of a continued relationship which further reinforces mutual trust (Brahm & Tarzijan, 2016).

Thus, trust breeds trust, and hence, asset specificity over time becomes a source of collaborative advantage for firms. Firms that emphasize CSR performance would be able to leverage this advantage to implement CSR within their supply chains. Therefore, we predict that as firms’ asset specificity increases, they would rely less on monitoring, and more on collaboration with suppliers, which would improve their odds to effectively implement supply-chain CSR without having to increase their levels of VI. Therefore, we advance our second hypothesis:

Hypothesis 2 (H2): The positive relationship between CSR performance and the level of VI would be weaker for firms with higher asset specificity.

A firm can develop relational capital only with a limited number of suppliers. The challenges to developing relational capital are most evident in the case of internationally diversified firms, which sell their products or services into multiple markets that are often institutionally and culturally distinct from one another (Marano et al., 2016). This market multiplicity complicates a firm’s task environment, compelling it to find efficient ways to reach its dispersed customers (Lafontaine & Slade, 2007). Efficiency concerns lead a firm to alter its supply-chain configurations so that it can source material and products from multiple, specialized suppliers to cater to the needs of its multiple distinct market segments (Buckley & Ghauri, 2004; Levina & Su, 2008). Therefore, internationally diversified firms source from a greater number of geographically dispersed specialized suppliers, which allow a firm to reap economies-of-specialization benefits (Kotabe & Mudambi, 2009; Mauri & de Figueiredo, 2012).

Geographically dispersed suppliers, however, complicate the implementation of supply-chain CSR in multiple ways. First, they restrict a firm’s ability to use a one-size-fits-all approach in developing its supply-chain CSR policies and procedures, as institutional, cultural, and normative expectations of propriety vary across countries (Hamann et al., 2005; Jamali, 2010; Matten & Moon, 2008). In some cases, a firm can localize its CSR policies and tools through minor adaptation, but at times, geographically dispersed suppliers may expose a firm to completely new CSR issues that may be challenging to handle (Hillman & Keim, 2001; Zyglidopoulos, 2002). Firms located in the West, for example, would struggle to effectively deal with indigenous communities when they source from suppliers located in the Chilean Andes, or issues of caste-based discrimination when they source from suppliers located in India. Developing a comprehensive understanding of such localized issues may require hiring specialized staff or consulting firms that would entail significant resources. In addition, when a firm’s suppliers are geographically spread out, a firm becomes more visible to international media and organizations, which subject the firm to a stricter scrutiny for its CSR-related actions (Rehbein et al., 2004; Shiu & Yang, 2017; Symeou et al., 2018). As a result, the firm faces greater pressure to effectively implement supply-chain CSR and communicate its actions to a much wider range of audiences than a firm that has a presence in less diversified markets.

Thus, we argue that as a firm’s international diversification increases, its need to implement CSR increases but its ability to codevelop relational capital with suppliers decreases due to an increase in the number of suppliers. In the absence of trust-based relationships with suppliers, diversified firms would tend to rely on monitoring mechanisms, which, as discussed in H1, will be riddled with significant cost implications and uncertain results. Therefore, we predict that firms, which emphasize CSR would be even more likely to increase VI if they are internationally diversified. Accordingly, we advance our third hypothesis as follows:

Hypothesis 3 (H3): The positive relationship between CSR performance and the level of VI would be stronger for more internationally diversified firms.

Methods and Results

Sample and Data Sources

We aggregated three separate data sets to execute this study empirically. The first data set, Mergent Online©, was used to draw an initial sample of U.S.-headquartered global firms listed in the S&P 500 Index. We restricted this sample by excluding firms from financial services, insurance, real estate trust, and media sectors because VI and outsourcing in these sectors cannot be concretely identified and measured (Jacobides & Hitt, 2005; Morris et al., 2016). Our final sample consisted of a panel of 278 firms representing a wide range of industries (Table 1). The data were collected for the period 2002 to 2014, yielding 2,715 total firm-year observations.

Table 1.

Industry Composition of Study Sample.

SIC codes	Industry	No. of firms
0100–1999	Mining, construction	23
2000–2390	Food, textiles, apparel	20
2391–2780	Forest products, paper, publishing	6
2781–2890	Chemicals, pharmaceuticals	26
2891–3199	Refining, rubber, plastic	7
3200–3569	Containers, steel, heavy manufacturing	17
3570–3990	Computers, autos, aerospace	66
3991–4731	Transportation	13
4732–4991	Telephone, utilities	32
4992–5990	Wholesale, retail	43
6800–8051	Hotel, entertainment	25
	Total	278

Financial and market data—which we used to compute the dependent variable (VI), moderating variables (firms’ asset specificity and international diversification), and control variables (outlined later)—were retrieved from Global Compustat. We used Thomson Reuters ASSET4 ESG scores to measure CSR performance.¹

Variable Definition

CSR performance

CSR performance can be measured in multiple ways. We used annual ESG scores from ASSET4 to construct a composite CSR index. This data set has been used extensively to measure CSR performance in a number of previous studies. In this data set, the environmental performance score is based on resource-use reduction (e.g., water or energy usage), emissions reduction, and product innovation; the social performance score is based on community contributions, health and safety concerns, employee training and development, human rights, and product responsibility; and the governance score is based on board structure and functions, compensation policy, protection of shareholder rights, vision and strategy, among others (see Note 1). Consistent with previous studies (e.g., Cheng et al., 2014; Shahzad & Sharfman, 2017), ESG scores were given equal weight in computing the composite CSR performance index.

VI

VI is a complex construct that is conceptualized and measured both qualitatively and quantitatively. We chose to adopt a quantitative measure that is better suited for cross-industry samples (Mauri & de Figueiredo, 2012). Specifically, we identified the firm’s value-added to sales ratio (VA/S), one of the most commonly used quantitative measures of VI (e.g., Barney et al., 1992; Lajili et al., 2007; H.-L. Li & Tang, 2010), and one especially suitable for firms that engage in multiple cross-sector transactions (which is typically the case for S&P 500 firms). Our choice of VA/S as a measure of VI is also appropriate because it does not view VI (or outsourcing) as a binary construct but rather considers it as a continuum that represents the relative level of VI versus outsourcing (Lieberman & Dhawan, 2005).

A firm’s value-added is computed as the difference between its sales revenue and the purchasing costs paid to external suppliers of products or services (Tucker & Wilder, 1977). Sales include the value-added created by the focal firm and all of its upstream suppliers, whereas purchases represent the aggregated value-added created by all upstream suppliers. Following previous studies (e.g., Chen, 2017; Hutzschenreuter & Groene, 2009; Lieberman & Dhawan, 2005), we included the sum of the following items in our calculation of the value-added: depreciation and amortization, interest expense, labor and related expense, pension and retirement expense, incentive compensation expense, income taxes, net income, and rental expense. VA/S ratios have one main limitation: they may be distorted by changes in profitability, or due to taxes and depreciation. To correct for such distortions, we followed Hutzschenreuter and Groene (2009) and calculated the VA/S ratio as follows:

\frac{VA}{S} = \frac{[VA - (net income + income taxes)]}{[sales - (net income + income taxes)]}

Asset specificity

Following Berrone et al. (2013), asset specificity was measured as the natural logarithm of the ratio of the book value of a buying firm’s property, plant, and equipment to the number of employees. This ratio is considered a reliable measure of asset specificity because investments in property, plant, and equipment are usually difficult to re-deploy and are less likely to be valuable for other firms if the company needs to liquidate assets (De Vita et al., 2011).

International diversification

International diversification refers to a firm’s expansion beyond its domestic market into other regions or countries (Kang, 2013). Specifically, we measure this variable using the ratio of nondomestic sales to total sales to capture a firm’s level of exposure to international markets (e.g., Alessandri et al., 2018; Hitt et al., 2006; Hult et al., 2008).

Control variables

We controlled for the following firm-level variables that previous literature has identified as antecedents to VI. Financial slack, measured as a ratio of account receivables to sales, was included as a control variable because firms with higher financial slack tend to be more vertically integrated (Schilling & Steensma, 2002). Firm size, measured as the natural logarithm of the firm’s assets, was included as a control variable because larger firms tend to be more integrated (Villalonga & McGahan, 2005). We also included as an additional control variable a firm’s degree of business diversification because it influences a firm’s level of VI as well (Brahm & Tarzijan, 2013). Business diversification was measured as the logarithm of the number of business segments in which a firm operates (Hutzschenreuter & Groene, 2009).

In addition to firm-level variables, we also included industry-fixed effects and year-fixed effects to control for the industry and period-specific variations in the levels of VI.

Analyses

All analyses were conducted using STATA SE 12 software. Table 2 depicts descriptive statistics and pairwise correlations among study variables.

Table 2.

Descriptive Statistics and Pairwise Correlations.

	VI	CSR	AS	INTL	Slack	Size	Business diversification
Vertical Integration (VI)	1
CSR performance	.1209**	1
Asset Specificity (AS)	.3894**	.1388**	1
International diversification (INTL)	−.1513**	.236**	−.1548**	1
Financial slack	−.0425**	.0691**	.0489**	.1762**	1
Firm size	.2391**	.5383**	.3984**	.1001**	.1662**	1
Business diversification	.1141**	.2645**	.0416**	.1197**	.1401**	.3635**	1
Mean	0.711	0.648	9.308	0.322	0.153	9.159	2.151
Standard Deviation	0.256	0.222	1.597	0.253	0.159	1.354	0.755

Note. N = 2,715. CSR = corporate social responsibility.

Correlations significant at p < .05.

Prior to testing our hypotheses, we first conducted a modified Wald test to test for group-wise heteroscedasticity. Results suggested that standard errors may provide biased estimates. To address this concern, we used panel-corrected standard errors clustered by the firm for all our analyses. In addition, we tested for autocorrelation using the Wooldridge (2010) test and ruled out first-order autocorrelation among variables (p < .001). Hypotheses were tested using ordinary least squares (OLS) regression. Results are presented in Table 3.

Table 3.

OLS Regression Results.

	Dependent variable = VI
	Model 1	Model 2
Main variables
CSR performance (CSR)	0.1310*** (0.0268)	0.8332*** (0.1528)
Asset specificity (AS)	0.0528*** (0.0069)	0.0908*** (0.0129)
International diversification (INTL)	−0.0300(0.0297)	0.2145** (0.0880)
CSR × AS		−0.0636*** (0.0139)
CSR × INTL		−0.3769*** (0.1013)
Controls
Financial slack	−0.0696*** (0.0190)	−0.0722*** (0.0185)
Firm size	0.0083* (0.0047)	0.0111** (0.0048)
Business diversification	0.0229*** (−0.0079)	0.0269*** (0.0082)
Constant	0.0480(0.0861)	0.3963*** (0.1485)
Industry-fixed effects	Yes	Yes
Year-fixed effects	Yes	Yes
No. of observations (N)	2,715	2,715
Wald χ²	2,306.34***	2,327.74***
R ²	.2699	.2802

Note. Standard errors in parentheses. OLS = ordinary least squares; VI = vertical integration; CSR = corporate social responsibility.

p < .10. **p < .05. ***p < .01.

Model 1 contains CSR performance, asset specificity, international diversification, industry- and year-fixed effects, and firm-level control variables. Model 2 is the full model, which also includes interaction effect variables. Model 2 shows that the coefficient of CSR performance is positive and significant (β = 0.1205, p < .01), providing support for H1 and suggesting that higher CSR performance is associated with higher levels of VI.

We assessed the economic effect of CSR on VI, which is graphically represented in Figure 1. It can be seen in the figure that one standard deviation increase in CSR performance (i.e., the mean plus one standard deviation) is associated with a 3% increase in the level of VI. In a similar fashion, the effect of one standard deviation decrease in CSR performance (i.e., the mean minus one standard deviation) is associated with a 3% decrease in the level of VI.

Figure 1.

Economic effect of corporate social responsibility on vertical integration.

In testing for interaction effects, the coefficient of the interaction between CSR and asset specificity (CSR × AS) in Model 2 is negative and significant (β = −0.0636, p < .01), which supports H2, and suggests that the positive relationship between CSR performance and VI becomes weaker for firms with higher levels of asset specificity. The coefficient of the interaction between CSR performance and international diversification (CSR × INTL) is also negative and significant (β = −0.3769, p < .01). Thus, H3 was not supported and we found that the positive relationship between CSR performance and VI becomes weaker for firms that are more internationally diversified.

The graphs in Figures 2 and 3 provide visual supplements to the above results. Figure 2 shows that the slope of the line illustrating the relationship between CSR performance and VI trends downward for firms with higher levels of asset specificity. Similarly, Figure 3 shows the relationship between CSR performance and VI for firms with higher levels of international diversification as a downward-trending slope.

Figure 2.

The moderating effect of asset specificity on the level of vertical integration.

Figure 3.

The moderating effect of international diversification on the level of vertical integration.

Endogeneity Tests

To strengthen the validity of our results, we tested for endogeneity, which can occur due to the following three main reasons: measurement error, omitted variables, and reverse causality (Wooldridge, 2010). We tested measurement error and omitted variable bias using instrumental variables analysis and reverse causality using simultaneous equations regression as follows.

Instrumental variables analysis

OLS regression analyses may produce biased estimates due to measurement errors and/or omitted variables bias (Hamilton & Nickerson, 2003), both leading to endogeneity concerns. For instance, CSR performance may be affected by unobservable variables, which may also affect the level of VI. To test whether endogeneity is a concern in this study, we conducted instrumental variables analysis by using two separate instruments for measuring CSR performance that we expect to be related to a firm’s CSR performance but not to a firm’s level of VI.

We deployed two instruments for deducing robust inference (Garcia-Castro et al., 2010). Our first instrument for measuring CSR performance is lagged CSR performance (LCSR) which essentially denotes a firm’s CSR performance in the previous year. The choice of this variable is based on the understanding that while LCSR is expected to be related to current CSR performance, it should not affect current levels of VI (Boulouta & Pitelis, 2014; Zhao & Murrell, 2016).

Our second instrument for measuring CSR performance is the average CSR performance for each industry-year pair, excluding the contribution of the focal firm (INDCSR). The choice of this variable is based on previous studies which show that CSR performance is determined by industry characteristics and hence industry average is a predictor of a firm’s CSR performance (Cheng et al., 2014), but cannot be assumed to be related to the firm’s level of VI as our measure purposively excludes the focal firm’s contribution to the industry average.

We conducted two tests to assess whether our instruments satisfy the conditions of relevance and exogeneity. The F statistic of Kleibergen–Paap rk Wald is higher than the critical value of 12.20, suggesting that instruments are relevant. The overidentification test (Hansen J Statistic, robust under heteroscedasticity) assesses whether instruments comply with the exogeneity condition. The value obtained for the p value of the Hansen test (p = .1935) indicates that our instruments are exogenous. These results are reported in the lower section of Table 4.

Table 4.

Instrumental Variables Regression Results.

	Model 3		Model 4
	First stage	Second stage; DV = VI	First stage	Second stage; DV = VI
Main variables
CSR performance		0.1629***(0.0449)		0.1564*** (0.0447)
LCSR (Instrument 1 for CSR: lagged CSR)	0.8402*** (0.0113)		0.8381*** (0.0113)
INDCSR (Instrument 2 for CSR: industry mean of CSR)	−0.0015* (0.0008)		0.0020(0.0073)
Asset specificity	−0.0011(0.0018)	0.0549*** (0.0090)	−0.0019(0.0018)	0.0521*** (0.0085)
International diversification	0.0274*** (0.0093)	−0.0214(0.0431)	0.0285*** (0.0096)	−0.0159(0.0442)
CSR × AS				−0.0773*** (0.0188)
LCSR × AS (Instrument 1 for CSR × AS)			−0.0114* (0.0054)
INDCSR × AS (Instrument 2 for CSR × AS)			0.0081(0.0104)
CSR × INTL				−0.4598*** (0.1749)
LCSR × INTL (Instrument 1 for CSR × INTL)			−0.0416(0.0365)
INDCSR × INTL (Instrument 2 for CSR × INTL)			−0.0532(0.0541)
Controls
Financial slack	−0.0198** (0.0091)	−0.0791*** (0.0196)	−0.0206** (0.0092)	−0.0805*** (0.0193)
Firm size	0.0122*** (0.0019)	0.0072(0.0061)	0.0129*** (0.0019)	0.0104* (0.0061)
Business diversification	0.0005(0.0028)	0.0203* (0.0119)	0.0008(0.0028)	0.0253** (0.0122)
Industry-fixed effects	Yes	Yes	Yes	Yes
Year-fixed effects	Yes	Yes	Yes	Yes
No. of obs. (N)	2,454	2,454	2,454	2,454
F test	1653.09***	16.97***	1070.19***	13.65***
(Centered) R²	.8094	.0988	.8099	.1156
Kleibergen–Paap rk LM statistic(Underidentification test)		491,355(p = .0000)		395,111(p = .0000)
Kleibergen–Paap rk Wald F statistic(Weak identification test)		2,769,516(Critical value 5% = 19.93)		661,756(Critical value 5% = 12.20)
Hansen J statistic		0.273		4.72
(Overidentification test)		(p = .6015)		(p = .1935)

Note. Standard errors in parentheses. DV = dependent variable; VI = vertical integration; CSR = corporate social responsibility; AS = asset specificity; INTL = international diversification.

p < .10. **p < .05. ***p < .01.

Following Bascle (2008), we used a two-stage least squares estimation. The first stage is meant to provide input for the second stage. Model 3 comprises only instruments, and Model 4 comprises interaction terms. We also tested whether our instrumental variables model is identified. The null hypothesis of the underidentification test (Kleibergen–Paap LM statistic) was rejected, indicating that the model is identified for our data. IV estimation results are presented in Table 4.

The information reported in Table 4 indicates that the instrumental variables results are consistent with the results obtained in our OLS specification. Specifically, Model 4 (the full model) shows that, after accounting for endogeneity, the coefficient for CSR performance is positive and significant (β = 0.1564, p < .01), bolstering support to Hypothesis 1. The interaction term CSR × AS is negative and significant (β = −0.0773, p < .01), bolstering support for H2. The interaction term CSR × INTL is negative and significant too (β = −0.4598, p < .01), bolstering rejection of H3.

We also examined whether the results obtained through two instrumental variables we used were sensitive to our choice of instruments. To do so, we followed Flammer and Kacperczyk (2016) and created yet another instrument for evaluating CSR performance using the location of a firm’s incorporation. Some U.S. states have enacted constituency statutes (CS) providing firms with a regulatory impetus to include social, environmental, and corporate governance objectives that go beyond financial objectives in their corporate policy (Bisconti, 2009). We extracted firm incorporation data from Global Compustat and used the list provided by Bisconti (2009) to measure the “constituency statute” as a dummy variable that equals “one” if the firm is incorporated in a state that passed a CS before the study period, and “zero” otherwise. We then replaced the previously-used instrument INDCSR with the new instrument CS.

We conducted relevance (weak identification test) and exogeneity (overidentification test) tests for this combination of instrumental variables, which showed positive results. We also found that the model is identified (underidentification test). Test results are reported toward the bottom of Table 5. Again, results bolster support for H1 and H2 and rejection for H3.

Table 5.

Regression Results With New Instrumental Variables.

	Model 5		Model 6
	First stage	Second stage; DV = VI	First stage	Second stage; DV = VI
Main variables
CSR performance		0.1629*** (0.0441)		0.1556*** (0.0434)
LCSR (Instrument 1 for CSR: lagged CSR)	0.8370*** (0.0112)		0.8326*** (0.0114)
CS (Instrument 2 for CSR: constituency statute dummy)	0.0093** (0.0040)		0.0106*** (0.0040)
Asset specificity	−0.0011(0.0018)	0.0545*** (0.0089)	−0.0015(0.0017)	0.0518*** (0.0084)
International diversification	0.0268*** (0.0094)	−0.0206(0.0426)	0.0271*** (0.0094)	−0.0147(0.0437)
CSR × AS				−0.0750***(0.0185)
LCSR × AS (Instrument 1 for CSR × AS)			−0.0135** (0.0054)
CS × AS (Instrument 2 for CSR × AS)			0.0050* (0.0027)
CSR × INTL				−0.4512***(0.1683)
LCSR × INTL (Instrument 1 for CSR × INTL)			−0.0414(0.0361)
CS × INTL (Instrument 2 for CSR × INTL)			−0.0118(0.0153)
Controls
Financial slack	−0.0202** (0.0085)	−0.0710*** (0.0202)	−0.0213** (0.0084)	−0.0723*** (0.0195)
Firm size	0.0125*** (0.0019)	0.0070(0.0061)	0.0134*** (0.0019)	0.0099* (0.0061)
Business diversification	0.0002(0.0028)	0.0204* (0.0120)	0.0005(0.0028)	0.0254** (0.0121)
Industry-fixed effects	Yes	Yes	Yes	Yes
Year-fixed effects	Yes	Yes	Yes	Yes
No. of obs. (N)	2,499	2,499	2,499	2,499
F test	1,589.67***	17.36***	1,041.42***	13.89***
(Centered) R²	.8070	.0988	.8078	.1151
Kleibergen–Paap rk LM statistic(Underidentification test)		504,471(p = .0000)		432,895(p = .0000)
Kleibergen–Paap rk Wald F statistic(Weak identification test)		2,834,782(Critical value 5% = 19.93)		787,842(Critical value 5% = 12.20)
Hansen J statistic(Overidentification test)		0.039(p = .8432)		0.396(p = .9411)

Note. Standard errors in parentheses. DV = dependent variable; VI = vertical integration; CSR = CSR performance; AS = asset specificity; INTL = international diversification.

p < .10. **p < .05. ***p < .01.

Reverse causality/directionality test

We used simultaneous equations regression to test for potential reverse causality. To do so, we estimated a system of two equations, one for each plausible causal direction. We instrumented VI using the average VA/S score for each industry-year pair, excluding the focal firm’s contribution. For CSR performance, we used average-industry CSR performance and lagged CSR, as we did in the instrumental variables analysis. We used three-stage least squares regression (3SLS) to produce consistent estimates (Wooldridge, 2010). Results are reported in Table 6.

Table 6.

Simultaneous Equations Regression Results.

	Model 7		Model 8
	DV = CSR performance	DV = VI	DV = CSR performance	DV = VI
Main variables
VI	0.0195(0.0208)		0.0201(0.0208)
INDVI (Instrument for VI: industry mean of VI)		0.6992*** (0.0334)		0.6902*** (0.0336)
CSR performance		0.1532*** (0.0287)		0.1459*** (0.0287)
LESG (Instrument 1 for CSR: lagged CSR)	0.8379*** (0.0108)		0.8359*** (0.0108)
INDESG (Instrument 1 for CSR: industry mean of CSR)	−0.0018(0.0032)		−0.0001(0.0087)
Asset specificity	−0.0020(0.0022)	0.0328*** (0.0044)	−0.0028(0.0021)	0.0307*** (0.0044)
International diversification	0.0268*** (0.0096)	−0.0241(0.0223)	0.0278*** (0.0097)	−0.0219(0.0222)
CSR × AS				−0.0721*** (0.0137)
LCSR × AS (Instrument 1 for CSR × AS)			−0.0104* (0.0061)
INDCSR × AS (Instrument 2 for CSR × AS)			0.0058(0.0112)
CSR × INTL				−0.3395*** (0.0835)
LCSR × INTL (Instrument 1 for CSR × INTL)			−0.0350(0.0367)
INDCSR × INTL (Instrument 2 for CSR × INTL)			−0.0580(0.0605)
Controls
Financial slack	−0.0184(0.0120)	−0.0782*** (0.0276)	−0.0190(0.0120)	−0.0787*** (0.0274)
Firm size	0.0119*** (0.0021)	0.0079(0.0051)	0.0125*** (0.0022)	0.0107** (0.0050)
Business diversification	−0.0002(0.0029)	0.0058(0.0068)	−0.0000(0.0030)	0.0096(0.0068)
Constant	0.5932*** (0.0265)	−0.3113*** (0.0581)	0.6036*** (0.0267)	−0.2937*** (0.0584)
Industry-fixed effects	Yes	Yes	Yes	Yes
Year-fixed effects	Yes	Yes	Yes	Yes
No. of obs. (N)	2,427	2,427	2,427	2,427
χ²	11,902.20***	1,453.67***	11,929.70***	1,519.19***
R ²	.8306	.3743	.8309	.3844

Note. Standard errors in parentheses. DV = dependent variable; CSR = corporate social responsibility; VI = vertical integration; AS = asset specificity; INTL = international diversification.

p < .10. **p < .05. ***p < .01.

Model 8 results (i.e., the full model) suggest that CSR performance is positively associated with higher levels of VI (β = 0.1459, p <.01); however, VI shows no significant association with CSR performance (β = .0201, p > .10). Hence, we find no evidence of reverse causality.

Overall, the foregoing analyses provide multiple evidence that H1 and H2 are supported, whereas H3 is not supported, and that our results are largely free from endogeneity concerns.

Discussion

We conducted this study to examine the relationship between CSR performance and the level of VI. Our analytical framework comprised a core hypothesis and two moderation effect hypotheses. The results of the three hypotheses shed new light on supply-chain CSR and strategy literature.

Our core hypothesis—predicting a positive relationship between CSR performance and the level of VI—was empirically supported. This finding adds a novel dimension to the supply-chain CSR literature where the need for supply-chain monitoring is already recognized as a critical issue (Klassen & Vereecke, 2012), but the implications of related monitoring costs have not been considered before. Our empirical findings—that firms with higher CSR performance tend to pursue higher levels of VI (or lower levels of outsourcing)—bolster our underlying arguments that supply-chain monitoring costs and risks can become so profound that firms which emphasize CSR performance may decide to increase the proportion of the activities they conduct in-house and decrease the proportion that they outsource. Thus, our findings not only bring to light a neglected phenomenon in the area of supply-chain CSR, but they also challenge the long-established notion in the strategy literature that a firm’s VI decisions are determined by economic criteria alone. Here, we show that a firm’s concern for broader societal issues, reflected in its CSR performance, is also an important determinant of VI. Previous literature has analyzed how CSR performance affects a firm’s strategic decisions (McWilliams et al., 2006), but most of these studies are limited to analyzing the effect of CSR either on a firm’s choice of competitive strategies (Panwar et al., 2016) or on the firm’s strategic orientation (e.g., Flammer & Bansal, 2017; Flammer & Kacperczyk, 2016; Maignan & Ferrell, 2004). We take this literature further by demonstrating that CSR affects one of the firm’s most fundamental strategic decisions, that is, the level of VI. Thus, the article integrates CSR and strategic management at a much deeper level and suggests that CSR concerns have grown so profound for contemporary firms that they can alter a firm’s business model in the most fundamental way.

Our second hypothesis—predicting a negative moderation effect of asset specificity on the relationship between CSR performance and the level of VI—was empirically supported too. This finding has multiple implications. First, it reinforces the importance of developing trust-based partnerships with suppliers, which has long been emphasized in supply-chain CSR literature (Gimenez & Tachizawa, 2012). Second, and more importantly, this finding brings forth the fact that monitoring and collaboration-based approaches are not mutually exclusive, but that they co-occur. In fact, the negative moderation effect of asset specificity on the CSR-VI relationship explains that by developing trust-based partnerships with some suppliers, firms can achieve high CSR performance without having to reduce their levels of outsourcing. In this sense, asset specificity allows a firm to balance its twin needs to achieve high CSR performance and reap the benefits of outsourcing. The more trust-based partnerships a firm can cultivate with its suppliers, the more it can continue to outsource without compromising its CSR performance. Third, while the negative moderation effect of asset specificity on the CSR-VI relationship is reconciliatory for CSR scholars, it is rather provocative for strategy scholars. Since Williamson’s seminal work in the 1970s, strategy scholars have predominantly viewed asset specificity as a source of opportunistic behavior by a firm’s suppliers. Their argument is that by heavily investing in fewer suppliers, a firm loses its bargaining power, which its suppliers exploit opportunistically to their own benefit (Grover & Malhotra, 2003). Our findings call into question this adversarial view of asset specificity because, as we argued and empirically demonstrated, it can actually offer firms a collaborative advantage. This collaborative view of asset specificity also resonates with more recent supply-chain literature which depicts supply chains as fundamentally collaborative arenas where supply-chain partners safeguard their collective interests (Bird & Soundararajan, 2020; Ketchen & Hult, 2007; Krause et al., 2007). Fourth, our results show that trust-building is a time-dependent phenomenon and hence our finding situates well with the emerging notion of historic CSR (Schrempf, 2012; Schrempf-Stirling et al., 2016), which explains how CSR manifests in a temporally integrated manner. In fact, our results in H2 would suggest that this temporal integration occurs not just within a firm as these authors argue, but it can also happen at the supply-chain level. Repeated CSR transactions among supply-chain partners may make CSR a routine, and hence shape future CSR practices in supply chains.

We did not find support for our third hypothesis, which predicted a positive moderation effect of international diversification on the relationship between CSR performance and the level of VI. Our prediction was in line with the existing literature on multinational CSR and hence this empirical finding is rather surprising. In deducing this hypothesis, we presupposed that diversified suppliers would enhance the need for monitoring, but perhaps internationally diversified firms find monitoring an unwieldy activity, especially when pursuing the multidomestic CSR strategy typical of internationally diversified firms. Rather than relying on hired expertise—internally or through external consultants—it seems that internationally diversified firms find more feasible to develop collaborative relationships with suppliers and rely on them to develop and implement contextually relevant CSR practices. Our speculation is aligned with emergent literature in operations and supply chain management which argues that supply chains are so complex that firms have no choice but to form relational ties with their suppliers and manage supply chains as social networks (Borgatti & Li, 2009; Lomi & Pattison, 2006). In fact, some scholars have argued that supply chains are complex adaptive systems that constantly learn from, and adapt to, their environments (Bode & Wagner, 2015; Choi & Krause, 2006). This would require a firm to engage heavily with its suppliers, who better understand their context and can provide firms with the necessary input for understanding its audience and their CSR-related expectations, thus allowing the firm to more effectively tailor its CSR policy to different cultural contexts.

Limitations and Future Research

The results of this study offer important insight into supply-chain CSR, but there remain a few limitations that future studies may overcome. First, while we tested for bidirectionality and found that the relationship in our data set is unidirectional (from CSR to VI; not from VI to CSR), it may simply be a reflection of the research design of this study. For example, we cannot negate the possibility of a virtuous circle such that high CSR performance necessitates higher levels of VI and then higher levels of VI lead to high CSR performance in the future. In fact, we took a preliminary step by conducting a time-lagged regression analysis and found that a 1-year time-lagged CSR performance is a predictor of VI. However, truly establishing a time-lagged relationship will require a cross-lagged panel analysis (Kearney, 2017), which will constitute a separate study altogether. We hope future research can provide a finer-grained understanding of the time-lagged nature of the relationship between CSR performance and VI. Second, it is also important to note that we propose two interwoven factors—monitoring costs and a true concern for responsible sourcing—as the key drivers of a positive relationship between CSR and the level of VI. But, our study design does not allow us to tease apart whether the relationship between CSR performance and VI is governed by cost consideration or outcome consideration or a combination of both. In other words, our study does not explain whether the firms with high CSR performance increase their levels of VI due to monitoring costs or inability to effectively implement CSR initiatives through suppliers or both. We call supply-chain CSR scholars to conduct follow-up studies to shed light on this ambiguity, ideally by focusing on specific corporations to conduct in-depth case studies (Goldstein & Newell, 2019). Third, due to data set limitations, we could not consider the location of suppliers as a variable in determining the CSR-VI relationship but would expect it to influence the CSR-VI relationship. A supplier located where there are stringent and effective social and environmental regulations will not pose the same level of threat to the implementation of supply-chain CSR as a supplier located where regulations are weak or poorly enforced. Similarly, a supplier located within a cluster of socially and environmentally progressive firms will be more inclined to implement supply-chain CSR initiatives relative to a supplier located where social and environmental issues are not important considerations (DeBoer et al., 2017). Therefore, future studies should consider suppliers’ locations while examining the relationship between CSR performance and VI. Fourth, we used a composite CSR index that bundles together a number of social and environmental issues that can, in fact, be characteristically different. For example, firms’ response to tackle human trafficking in its supply chain would be very different from making their supply chains deforestation-free, even though both issues share a commonality that they are inherently systemic in nature. Thus, while our study provides an overarching framework to examine the link between CSR and VI, future studies could examine this link separately for specific social and environmental problems. Finally, it is also worth noting that higher levels of VI are not a panacea for overcoming supply-chain CSR challenges. In fact, higher levels of VI create distinct problems for a firm because monitoring internal activities can also become a complex and tedious task; one which a firm might simply not perform on its own due to financial or technical limitations. Moreover, VI can lead to new societal problems such as corporate takeover of small businesses and displacement of disadvantaged communities. Future studies may consider such trade-offs.

Conclusion

We initiated this study to examine the effect of CSR performance on VI. We find that firms with high CSR performance tend to vertically integrate more and reduce the proportion of their outsourced activities. However, the extent to which they increase their level of VI (or, reduce outsourcing) depends on their ability to foster collaborative, trust-based relationships with their suppliers. Therefore, CSR and outsourcing do not manifest as an either-or scenario; instead, they can coexist as a dynamic triad comprising CSR performance, VI/outsourcing, and mutual trust and dependence among a firm and its suppliers.

While our study establishes that in their quest to be socially responsible, firms tend to vertically integrate their operations, this study is simply an important first step toward building a comprehensive understanding about the effect of CSR on firms’ decisions related to VI and outsourcing. Our hope is that this study will stimulate future research at the intersection of strategy and supply-chain CSR literature, where a dovetail can provide a “win-win” between firms’ societal obligations and their strategic priorities to leverage the expertise of external suppliers.

Footnotes

Acknowledgements

The authors sincerely thank Associate Editor, Dr. Judith Schrempf-Stirling, for her precise guidance through the review process and the three anonymous reviewers for their valuable feedback. The authors are also thankful to Eric Hansen and Martin Meznar for their insightful comments on the initial draft of this article.

Declaration of Conflicting Interests

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

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Maria Jose Murcia

Notes

Author Biographies

Maria Jose Murcia (PhD, University of British Columbia, Canada) is an assistant professor at IAE Business School and Facultad de Ciencias Empresariales, Universidad Austral, Argentina. She conducts research in the broad fields of corporate sustainability and strategy, which she has published in such journals as Business & Society, Journal of Business Ethics, and Management Learning, among others.

Rajat Panwar (PhD, Oregon State University; DBA, Grenoble Ecole de Management, France) is an associate professor in the Walker College of Business, Appalachian State University, Boone, NC, USA. He conducts firm and industry level research in broad fields of corporate social responsibility and corporate sustainability, which he has published in such journals as Business & Society, Business Strategy & Environment, Journal of Business Ethics, Journal of Public Affairs, and Organization & Environment, among others.

Jorge Tarzijan (PhD in Managerial Economics & Strategy, Kellogg Graduate School of Management, Northwestern University) is a full professor at the School of Management, Pontificia Universidad Católica de Chile, Santiago, Chile. He conducts research in corporate strategy, firm boundaries, and business models and has published several articles in Strategic Management Journal. He has also published in Harvard Business Review, Industrial and Corporate Change, Journal of Business Research, Journal of Economic Behavior and Organization, Journal of Supply Chain Management, and Long Range Planning, among others.

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