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Do Corporate Customers Prefer Socially Responsible Suppliers? An Instrumental Stakeholder Theory Perspective

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Abstract

This paper studies the way supplier firms’ corporate social responsibility (CSR) affects their likelihood of being selected as new suppliers. Using a large sample of US public firms with detailed supply chain and CSR data, we provide empirical evidence that corporate customers prefer socially responsible suppliers, and that the effect is more prominent when the supplier industry is more competitive, the customer’s own CSR performance is better, or the supplier and the customer have more similar CSR focuses. Our paper contributes to the literature of instrumental stakeholder theory (IST) by confirming corporate customer attraction as a desirable outcome of supplier CSR engagement. It complements the existing IST studies on customer responses by showing that CSR attracts not only final customers but also corporate customers. Moreover, by focusing on corporate customers’ revealed preferences for socially responsible suppliers, our paper also complements the stated-preference-based evidence in the literature of sustainable supply chain management. Our paper’s findings encourage supplier firm managers to invest in CSR to gain competitive advantages in the form of a higher likelihood of selection while simultaneously making positive contributions to society.

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Notes

  1. As one of the world’s largest wood consumers, IKEA gives particular attention to the sustainability performance of its wood suppliers. It requires wood origin documents from suppliers, conducts its own forest tracing, and aims to have its wood 100% FSC (Forestry Stewardship Council) certified or recycled by 2020.

  2. Out of the CSR crisis management context, proactive CSR could refer to voluntarily adopted actions exceeding regulatory requirements and the reactive CSR refers to actions strictly adhering to the minimum level required by regulations (or, more broadly, external stakeholders) (Cheong et al., 2017; Kim, 2017; Torugsa et al., 2012, 2013).

  3. Other empirically studied financial outcomes include (but are not limited to) risk [e.g., (Harjoto & Laksmana, 2018)], volatility [e.g., (Harjoto et al., 2017)], and financing cost [e.g., (El Ghoul et al., 2011; Francis et al., 2019)].

  4. See Ghodsypour and O’Brien (1998) and Weber et al. (1991) for a literature review on traditional supplier selection criteria.

  5. Studies in SSCM examine how internal and external factors affect the integration of sustainability into SCM, as well as how SSCM affects firm sustainability and economic performance [e.g., (Morali & Searcy, 2013; Paulraj et al., 2017; Wolf, 2011, 2014)]. See Khan et al. (2020) and Koberg and Longoni (2019) for a recent review on the topic.

  6. A closely related literature strand to SSCM is green supply chain management (GSCM), which has a narrower focus on environmental related issues in SCM. See Balon (2020) for a recent review on GSCM.

  7. Tachizawa and Wong (2014) provide a review on the framework of multitier sustainable supply chains.

  8. For example, Apple significantly increased its own CSR staff and began to publish annual supplier responsibility reports after repeated labor rights violations had been reported at Foxconn, one of Apple’s largest and oldest suppliers (Lee et al., 2016).

  9. Thomson Reuters ASSET4 and Sustainalytics are the two other databases used in academic research. According to Bouten et al. (2017), among a sample of 86 CSR-related publications or working papers, 73 used MSCI, 12 used Thomson Reuters ASSET4, and three used Sustainalytics.

  10. All data points are designed and measured by MSCI, not self-reported by firms. MSCI also provides data on firms’ corporate governance and involvement in controversial business (e.g., gambling). Following the literature [e.g., (Di Giuli & Kostovetsky, 2014; Jha & Cox, 2015)], we do not consider these aspects as firm CSR practices, because corporate governance is concerned largely with the way firms address principal-agent problems and involvement in controversial business is a fixed industry characteristic.

  11. For example, although Apple does not report Glu Mobile as its supplier, we still consider Glu Mobile one of Apple’s suppliers because Glu Mobile reports Apple as its customer (providing iOS apps to Apple).

  12. We use Apple as an example to illustrate the type of supply chain information contained in our database. In 2013, Apple had 86 suppliers and 39 customers. Some of its key suppliers include Analog Devices, a semiconductor company that supplies touchscreen controllers, and Micron Technology, a computer memory producer that supplies various memory modules. Many of Apple’s corporate customers are retailers, such as Bestbuy, Walmart, AT &T, etc.

  13. As SIC and NAICS industry classifications use predefined industry categories, they impose transitivity among members in the same industry category, while TNIC does not impose such transitivity.

  14. We again use Apple and its suppliers to illustrate the TNIC database. As mentioned above, in 2013, one of Apple’s key suppliers is Analog Device, a semiconductor company that supplies touchscreen controllers. Based on TNIC, Analog Device’s potential rivals in 2013 include Micrel, Freescale Semiconductor, and MACOM Technology Solutions among other firms that specialize in semiconductor devices and have high similarity in product offerings.

  15. We exclude the first years of customer firms in the Factset database.

  16. For a logit model \(log(\frac{y}{1-y})=\underset{m}{\sum }\beta _{m}x_{m}\), the odds ratio (\(\frac{y}{1-y}\)) increases by \(100\times (e^{\beta _{m}\Delta }-1)\)% for each \(\Delta\) unit increase in an explanatory variable \(x_{m}\)

  17. As overlapping director is a dummy variable, it is more appropriate to calculate how a change from zero to one changes the odds ratio. Yet, as a change from zero to one may involve several standard deviations, we choose to report the change in odds ratio per a one standard deviation increase in overlapping director for the ease of comparison.

  18. Of 680,987 actual and potential new suppliers in our sample, 294,519 (43%) suppliers have at least one other customer (in Factset Revere) with available CSR data in year t-2. Therefore, our sample size reduces by approximately half in instrumental variable estimations compared to the models in Table 2.

  19. When comparing economic magnitudes, it is important to note that the sample average selection rate is higher in Table 4 than in Table 2. For example, in the sample of all TNIC-3 rivals, the average selection rate is 2.77% in the original regression, but 4.15% in the instrumental variable regression. This is likely attributable to the fact that our sample of actual suppliers is, on average, larger than that of rival suppliers, and therefore, more likely to have other customers with lagged CSR data available (to be included in the instrumental variable sample).

  20. We do not include lagged customer CSR in the regression. The value of such a variable is the same for both actual suppliers and potential rival suppliers, so it should not affect customer’s selection.

  21. Jaffe (1986)’s measure has been widely used in the literature to measure the similarity between pairs of firms [e.g., (Bena & Li, 2014; Bereskin et al., 2018; Lee et al., 2019)].

  22. One may notice that the coefficient of CSR overlap is close to zero in most columns. This does not mean that CSR overlap has no effect on selection probability, because the sample mean of supplier CSR is positive (0.27, 0.35, 0.48 for all-rival, top-50, top-ten samples, respectively). Therefore, the average net effect of CSR overlap on selection likelihood (with sample average value of supplier CSR in the interaction term) is positive.

  23. As TNIC classifications do not impose transitivity, a firm’s HHI differs from the HHI of another firm in that firm’s TNIC industry.

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Acknowledgements

We gratefully acknowledge the NEOMA Business School Seed Money that has made this research possible. We are also grateful to four anonymous referees and the editor, Ming Jia, for their constructive comments. All errors are our own.

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Authors and Affiliations

  1. Department of Finance, NEOMA Business School, 59 Rue Pierre Taittinger, 51100, Reims, France

    Ran Tao & Hong Zhao

  2. Department of Finance, NEOMA Business School, 1 Rue du Maréchal Juin, 76130, Mont-Saint-Aignan, France

    Jian Wu

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  1. Ran Tao
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  2. Jian Wu
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  3. Hong Zhao
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Correspondence to Hong Zhao.

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Tao, R., Wu, J. & Zhao, H. Do Corporate Customers Prefer Socially Responsible Suppliers? An Instrumental Stakeholder Theory Perspective. J Bus Ethics 185, 689–712 (2023). https://doi.org/10.1007/s10551-022-05171-5

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