case paper
Supply Chain Integrity: A Key to Sustainable Supply Chain Management Vincent E. Castillo, Diane A. Mollenkopf, John E. Bell, and Hamparsum Bozdogan The University of Tennessee
A s stakeholders continue to increasingly hold firms accountable for environmental and social performance in their supply chains, the impor-tance of understanding how firms can be more sustainable becomes more prescient. Based on the underlying premise of stakeholder the- ory that business and ethics decisions are intertwined, the current research introduces the concept of supply chain integrity (SCI) to explore how the interdependence of business and ethics decisions can lead to improvements in sustainable supply chain management (SSCM) practices. Exploratory analysis employing secondary data sources in an elastic net (EN) logistic regression provides support for the proposed construct, by providing preliminary empirical evidence that SCI, measured through two subdimensions of structural and moral SCI, can be linked to firm sus- tainability. The research contributes to the supply chain management literature by: (1) introducing the concept of SCI; (2) performing an exploratory econometric analysis to provide initial validity of the SCI construct; and (3) providing a research agenda to guide further research on the concept of SCI and its role in SSCM.
Keywords: supply chain management; sustainability; social responsibility; strategy; integrity
INTRODUCTION
Sustainable supply chain management (SSCM) has become a focus for business practitioners and supply chain researchers. Issues of climate change, geopolitics, labor conditions in emerg- ing economies, and pressure from stakeholders and supply chain partners all play a role in shifting corporate focus toward the tri- ple bottom line (TBL), the simultaneous achievement of environ- mental, social, and financial performance (Elkington 1998, 2004; Orlitzky et al. 2003; Carter and Rogers 2008; Golicic and Smith 2013; Waller et al. 2015). The focus on SSCM is particularly important as the millennial generation comes of age. Millennials are value-driven consumers, expecting good corporate citizenship from the companies with whom they interact (Solomon 2014). Not only is the millennial generation the largest in history, but this group of consumers is still growing worldwide. Millennials also seem to wield significant influence over other generations’ attitudes and consumption patterns (Solomon 2014), suggesting that value-based consumption attitudes and behaviors will become increasingly widespread. As market pressures continue to evolve, supply chain managers will increasingly assume responsibility for improving their firms’ SSCM performance, due to the boundary spanning nature of their roles across functional and organizational boundaries (Carter and Jennings 2004).
As sustainability gained traction in the supply chain literature, initial attention focused on whether sustainability leads to enhanced firm (financial) performance (see, e.g., Porter and van der Linde 1995; Kleindorfer et al. 2005; Pagell and Wu 2009; Reuter et al. 2010; Golicic and Smith 2013; Thornton et al. 2013). More recently, SSCM researchers have noted that the question of whether sustainability pays is no longer the most
salient; rather, it has been surpassed by the question of how firms can be more environmentally sustainable and socially responsible (Pagell et al. 2013). Current SSCM thought emphasizes TBL performance as an outcome of various sustainability activities that span four facets of the firm—strategy, risk management, organizational culture, and transparency (Carter and Rogers 2008; Carter and Easton 2011). But, while much supply chain research has focused on the TBL as an outcome since it was first introduced (Elkington 1998), less emphasis has been placed on the four facets related to sustainability as introduced in Carter and colleagues’ SSCM framework with respect to how they relate to each other in enabling SSCM across the economic, social, and environmental arenas.
Such understanding is important as the value-based millennial consumers become increasingly influential in the marketplace. Firms will need to develop strategy, risk management, trans- parency, and organizational cultures that readily demonstrate integrity-laden approaches to supply chain management. While integrity is most commonly thought of as an individual-level con- struct relating to one’s virtue and character (Paine 1994; Audi and Murphy 2006; Brown 2006; Palanski and Yammarino 2007; Maak 2008; Maurer 2009), the notion of firm-level corporate integrity is well established in the business ethics literature (Gowans 1984; Guerrette 1986; Solomon 1992b; Paine 1994; Koehn 2005; Audi and Murphy 2006; Brown 2006; Maak 2008; Becker 2009; Mau- rer 2009; Li et al. 2016). Corporate integrity provides a foundation for value-based decisions that companies make. Interestingly, Maak (2008, 361) introduced the notion of supply chain integrity (SCI) as a “major challenge for the corporation” to ensure its own corporate integrity when dealing with external organizations. How- ever, the SCI construct has yet to be conceptually developed or introduced to the supply chain literature.
Therefore, the purpose of this study is to develop the logic and definition of SCI as a construct useful to SSCM researchers and practitioners. We develop the SCI construct within the framework provided by Carter and Rogers (2008), as a means to further understand how firms can be more sustainable through
Corresponding author: John E. Bell, Department of Marketing & Supply Chain Manage- ment, The University of Tennessee, 310 Stokely Management Cen- ter, Knoxville, TN 37996-0530, USA; E-mail: bell@utk.edu
Journal of Business Logistics, 2018, 39(1): 38–56 doi: 10.1111/jbl.12176 © Council of Supply Chain Management Professionals
their supply chain management practices. Following the concep- tual development of SCI, we conduct an exploratory analysis as a first step toward construct validation, trying to assess the con- ceptual merit of the proposed construct. In essence, the explora- tory analysis provides preliminary empirical evidence for answering the research question as to whether SCI can help dif- ferentiate between sustainable and nonsustainable firms, a ques- tion that is important as firms continue to consider how to be more sustainable. Specifically, we use secondary data sources to explore whether the nascent SCI construct can differentiate between organizations and their designations as sustainable com- panies (or not). We conduct a set of analyses across manufactur- ers and retailers as a means of assessing the robustness of the proposed construct across supply chain echelons, thus setting the stage for future research to more fully develop the SCI concept and a more explicit framework for supply chain managers to build the sustainable supply chains increasingly required by cus- tomers and other stakeholders.
This research effort makes three contributions to the logistics and supply chain literature. First, the introduction of SCI adds depth to the SSCM framework (Carter and Rogers 2008; Carter and Easton 2011) by bringing concepts found in the business ethics literature into the SSCM literature base to explore the interdependence between business and ethics decisions as related to sustainability (Maak 2008). Using a stakeholder the- ory perspective, we conceptually develop the SCI construct within the framework of SSCM presented by Carter and Rogers (2008). Second, we perform an exploratory analysis using EN logistic regression evaluated with the Information Complexity (ICOMP) criterion, to empirically assess the merit of the pro- posed SCI construct by examining how well the proposed con- struct can differentiate between firms with respect to sustainability (Zou and Hastie 2005; Bozdogan and Pamukcu 2016). Finally, an agenda is developed to guide scholars in pur- suing future research within the SSCM domain that can help firms to understand how being sustainable is affected by differ- ent contexts and mechanisms.
The article is structured as follows. A review of the literature on sustainability thought in supply chain management research lays the groundwork for developing the new concept of SCI. The methodology section contains an explanation of the use of sec- ondary data in a logistic regression to explore whether the pro- posed SCI construct can differentiate between firms with regard to sustainability. Finally, the findings are discussed and a research agenda to guide future inquiry is presented.
SUSTAINABILITY THOUGHT IN SUPPLY CHAIN RESEARCH
Scholarly interest in sustainability began to emerge based on the seminal work of Bowen (1953) and Carroll (1979, 1991), who refined the notion of corporate social responsibility (CSR). CSR focuses on the pyramid of corporate responsibilities: the eco- nomic, legal, ethical, and philanthropic activities of the firm (Carter and Jennings 2004). In its original conceptualization, the economic and legal responsibilities of a firm were considered mandatory, while ethical and philanthropic responsibilities were considered to be of lower importance (Richter 2010).
Supply chain scholars continue to adapt CSR into sustainabil- ity discussions, often using the terms “CSR” and “sustainability” interchangeably. Yet, in the supply chain literature, the concept of sustainability invokes a TBL approach toward performance, based on the work of Elkington (1998, 2004). SSCM, as defined by Carter and Rogers (2008), is “the strategic, transparent inte- gration and achievement of an organization’s social, environmen- tal, and economic goals in the systemic coordination of key interorganizational business processes for improving the long- term economic performance of the individual company and its supply chains” (p. 368). Such SSCM theorizing shifts the con- versation from social/environmental “responsibility” to one of business strategy by which a firm can prosper over the long term (Carter and Easton 2011).
Of particular relevance to the current research is the TBL logic found in the SSCM framework (Carter and Rogers 2008; Carter and Easton 2011). The TBL becomes the focal lens for supply chain managers by which to make decisions that affect both the “natural environment and society, but which also result in long- term economic benefits and competitive advantage for the firm” (Carter and Rogers 2008, 365). Within the SSCM framework, four supporting facets of SSCM are identified: risk management, transparency, strategy, and corporate culture. Each of these four facets is actively addressed within the supply chain literature and increasingly being tied to TBL performance. Yet the underlying role of these four facets within the SSCM framework remains underexplored.
SSCM research has also consistently been linked to stake- holder theory as a rationale for why firms should care about environmental or social sustainability (Carter and Jennings 2002; Sarkis et al. 2010; Hofer et al. 2012; Swanson and Smith 2013). Stakeholder theory has diverged into three distinct approaches— descriptive, normative, and instrumental (Brenner 1992; Donald- son and Preston 1995; Jones and Wicks 1999). However, Freeman (1999) takes exception to this divergence, arguing that a divorce has resulted between business and ethics in both research and practice. He articulates that the original focus on stakeholders vis-�a-vis shareholders was explicitly meant to imply a value-laden approach to management (Freeman 1994, 1999). Thus, the discourse of business should not be separated from the discourse of ethics. Indeed, the original stakeholder approach was built on instrumental premises that inherently include descriptive and normative considerations, clearly suggesting the interrelated nature of business and ethics (Freeman 1984).
The focus of stakeholder theory is built primarily on the notion that consequences count, which is important when consid- ering the evolution of SSCM to include a TBL approach. Stake- holder theory posits that firms need to consider the larger group of stakeholders in their decision-making frameworks. Not only do a firm’s actions have consequences for stakeholders, but also stakeholders’ actions can have consequences for the firm. Those consequences are extended beyond the economic realm to also include environmental and social consequences. This becomes especially important when considering the challenges of manag- ing global supply chains. Stakeholder theory is becoming increasingly sensitized to the complexities and uncertainties involved in managing multinational networks due to several fac- tors: the liberalization of markets and political institutions, the emergence of environmentalism and social values, and the
Supply Chain Integrity 39
dramatic growth in information technology capabilities and adop- tion worldwide (Freeman et al. 2007). Additionally, the complex- ity of global operations raises the awareness levels of firms’ responsibilities embedded in their value creation processes (Jen- sen and Sandstr€om 2011).
SUPPLY CHAIN INTEGRITY
We extend the concept of corporate integrity, which has previ- ously been introduced in the business ethics literature (e.g., Gowans 1984; Guerrette 1986; Solomon 1992b; Paine 1994; Koehn 2005; Audi and Murphy 2006; Brown 2006; Maak 2008; Becker 2009; Maurer 2009; Li et al. 2016), to the supply chain context to provide a research foundation for SCM scholars, as well as a foundation for a business framework that will more fully enable managers and their firms to realize their social, envi- ronmental, and economic objectives.
The concept of corporate integrity
The corporate integrity concept refers to a firm’s awareness of and commitment to high ethical principles and business practices (Paine 1994; Brown 2005; Marsh 2009). Integrity is often identi- fied as one of the most important or desirable moral traits in an organization (Audi and Murphy 2006; Stevens 2012; Cox et al. 2013; Dodd and Dodd 2014). Businesses typically adopt the term to guide their corporate philosophies of ethical practice. For example, Walmart’s statement of ethics emphasizes the impor- tance of an individual’s “moral integrity” (Walmart 2008). How- ever, Walmart does not clearly define “moral integrity” and assumes employees maintain a common understanding of the concept. This is not a fault per se, as the general prevalence and recognition of the word results in shared understanding of the meaning of “integrity” in industry.
Business ethics researchers have explored various levels of analysis in their development of the corporate integrity construct. At the individual level of analysis, Audi and Murphy (2006) argue that the multitude of interpretations of individual integrity can be distilled into having either an integrational sense of the term or one that relates to virtue ethics. Brown (2006) connects the individual and firm levels of analysis by exploring whether individual leaders can have personal integrity in a corporation that does not. He explains the link between levels by concluding, “leaders have integrity only to the degree that they participate in and promote corporate integrity” (Brown 2006, 17). Other researchers have also built upon the individual as the unit of analysis to examine firm-level or corporate integrity (Paine 1994; Maak 2008; Maurer 2009). Because firms can be considered cor- porate citizens of communities with the ability to manage poli- cies, actions, and ethical concerns of stakeholders, integrity can also reside at the firm level of analysis with both an internal and external orientation (Donaldson 1982; Velasquez and Goodpaster 1983; French 1984, 1995; Maak 2008).
While multiple business ethics definitions of corporate integ- rity exist, they generally align with one of two connotations: structural integrity and moral integrity (Solomon 1992a; Koehn 2005; Audi and Murphy 2006; Maak 2008; Becker 2009; Maurer
2009). Structural integrity refers to the completeness or consis- tency sense of the term “integrity.” However, to define corporate integrity only in this manner is insufficient (McFall 1987; Koehn 2005; Bauman 2013). Consider, for example, if a manager at Volkswagen who was involved in the 2015 vehicle emissions scandal was acting in accordance with prevalent organizational norms, that manager could be deemed to have integrity under a consistency-based definition of the term. Of course, it is not rea- sonable to accept that this individual acted morally with respect to the company’s stakeholder groups if that person actively facil- itated the company’s deceit in circumventing emissions stan- dards. This exemplifies the need for a moral dimension of corporate integrity as well. The second dimension, moral integ- rity, refers to either a specific set of moral virtues or moral virtue in general (Audi and Murphy 2006).
Over the past 15 years, the predominant stream of business ethics research has built upon this dualistic definition of corpo- rate integrity. Several authors (e.g., Palanski and Yammarino 2007; Bauman 2013) provide excellent discussions of the philo- sophical and theoretical origins of corporate integrity. Synthesiz- ing the reviews of these authors with the stream of corporate integrity research allows for theoretical development of the con- cept of SCI.
The concept of SCI
Much of the research that has been conducted with regard to cor- porate integrity has focused on an intrafirm orientation. For example, Paine (1994) compares the effectiveness of integrity- based strategies over compliance-based strategies for firms to encourage ethical behavior of individuals within the organization. Other researchers, however, call for firms to consider themselves as part of a society, rather than private and separate from society (Brown 2006; Richter 2010). Maak (2008) explicitly argues for an interfirm extension of corporate integrity, on the basis that activities in the supply chain influence a firm’s corporate integ- rity. In other words, a company’s SCI, or its cognizance of and dedication to maintaining high ethical principles in its supply chain activities, is paramount to having corporate integrity. How- ever, while Maak (2008) invokes the concept in the ethics litera- ture, SCI has yet to be explored or examined within the supply chain domain. We therefore propose SCI as a firm-level, inter- firm-oriented concept. Positioning SCI as a firm-level concept within the broader context of an interfirm domain is consistent with the conceptualization of corporate integrity as a firm-level concept and also replicates the externally focused approach taken by those who research the concept of supply chain orientation within the supply chain literature (Mentzer et al. 2001).
Adapting established concepts from the business ethics litera- ture to the supply chain context, a synthesized definition of SCI is presented as the dedication to maintaining integrity in supply chain activities and the recognition of the systemic and strategic implications of maintaining integrity in supply chain processes and flows. Thus, SCI is characterized by both structural and moral dimensions that underlie supply chain activities. The Structural SCI of a firm is its development of a series of sustain- able supply chain practices while seeking like-minded supply chain partners and being able to act in accordance with stated
40 V. E. Castillo et al.
responsibility objectives. Structural SCI also relates to sustain- ability decisions that can be considered “good” business deci- sions, that is, decisions that promote TBL performance. However, SCI also requires attentiveness to ethics and the effects on stakeholders, including communities in which a firm operates, which is referred to as Moral SCI. In other words, SCI requires both structural and moral components, which is consistent with extant understandings of corporate integrity (Audi and Murphy 2006; Maak 2008).
Structural dimension of SCI
Structural SCI refers to the unity of character that a firm creates by selecting a combination of socially and environmentally responsible sourcing, production, and logistics practices, where a lapse in any one of the components degrades the others (Audi and Murphy 2006). If the supply chain practices are selected strategically such that they align with corporate strategy and allow the firm to pursue economic performance in sustainable ways or achieve other benefits such as mitigating supply-side risk (Zsidisin et al. 2004; Bell et al. 2012), then a gestalt of sus- tainable performance may be achieved. Structural SCI is also concerned with a firm’s ability to communicate to stakeholders how consistent its supply chain actions are with its stated sus- tainability objectives, which essentially equates to transparency (Koehn 2005; New 2010; Carter and Easton 2011). Thus, in the context of the SSCM framework, Structural SCI essentially encapsulates the strategy, risk management, and transparency facets. The following exemplar of Structural SCI in practice helps clarify the integrity-based concepts being conveyed.
A perennially recognized leader in supply chain operations, Intel has gone to great lengths to increase social and environ- mental performance efforts by certifying its supply chain as “conflict-free,” a testament to removing any possibility of sour- cing tin, tungsten, tantalum, or gold (3TG) from mines that support conflict in the Democratic Republic of Congo (MH&L 2016; Scott 2016). In its annual CSR Report, Intel outlines five components that are most critical to its overall corporate responsibility mission: Caring for our People, Caring for our Planet, Inspiring the Next Generation, Supply Chain Responsi- bility, and Respecting Human Rights (Intel 2015). Focusing on this gestalt of areas has resulted in Intel being able to achieve certification of certain 3TG smelters as conflict-free, while elim- inating supply chain problems such as unethical labor issues (e.g., underage or underpaid workers), and undereducated work- force of their suppliers. The gestalt or the firm’s unity of char- acter would be degraded by the absence or failure of any one of the subareas. That is, the unity of character is a mechanism that enables Intel to achieve superior sustainability in its supply chain.
Intel also displays a strong resolution to report the results of its responsibility initiatives whether the results are favorable or unfavorable to its reputation. In its most recent sustainability report, Intel outlines quantitative goals for each of the five aspects of its responsibility mission and explains where it missed goals and how the company intends to fix its approach to ensure the goal will be met in the future (Intel 2015). By making this information publically available, Intel is taking measures to make
transparent its consistency between its stated actions in its sus- tainability reports and its actual actions.
Intel is one of many possible examples of a sustainable com- pany. While the specific sustainable supply chain activities that Intel has chosen are important to increasing its social and envi- ronmental performance, what is more important is the combina- tion of the behaviors across the five components of its corporate responsibility mission. That combination, or unity of character (Audi and Murphy 2006), reflects a strategic decision to mitigate risk associated with the supply of 3TG minerals and make trans- parent the results, which are all elements of Structural SCI and help to explain how Intel achieves its sustainability performance. This suggests Structural SCI acts as a mechanism linking the strategy, risk management, and transparency facets of SSCM (Carter and Rogers 2008).
Moral dimension of SCI
A firm with a global supply chain that remains receptive to the needs of its immediate community and its culture, and also to the needs of the communities in which its suppliers and cus- tomers are located, may recognize opportunities to empower and develop the workforce of its suppliers and customers, thus strengthening its supply chain (Pagell et al. 2010). That is, a firm that shows compassion and receptivity to the regions and com- munities affected by its supply chain operations has the potential to form strong relationships with those communities, and cus- tomers or suppliers within those communities (Brown 2006). Furthermore, a firm that maintains self-awareness and impartial judgment in understanding how its supply chain operations affect those communities, whether positively or negatively, allows it to more clearly see chances to change how it does business in those areas (Koehn 2005). Last, a firm’s level of commitment to serv- ing the needs of the communities it affects is critical to a firm’s SCI and sustainable performance (Maak 2008). Thus, when firms demonstrate a corporate culture that maintains values and ethics in their supply chain operations (Carter and Rogers 2008; Carter and Easton 2011), they are implementing the Moral dimension of SCI.
Consider, as an example, Taylor Guitars, an American manu- facturer of high-end acoustic guitars, as one such firm that has demonstrated compassion and receptivity toward its supplier communities. Taylor recognized industry-wide ebony sourcing practices in Africa had caused depressed economic conditions in local areas and damaged ebony forests, harming the ebony sup- ply altogether for the guitar industry (Taylor 2012). To ensure long-term supply availability, the company chose to support the communities providing the valuable ebony resource. Partnering with Madinter Trade S.L., an American manufacturer of guitar parts, Taylor purchased an ebony mill in Cameroon and estab- lished more sustainable harvesting practices while creating liv- able wages for employees of the mill. Taylor Guitars also demonstrates introspection and objectivity in assessing how its operations affect ebony forests in Africa, exemplifying the com- pany’s impartial self-awareness of the consequences of its activi- ties on different communities. Taylor’s subsequent commitment to serving the appropriate needs of those communi- ties is demonstrated through its investments in the lumber mill.
Supply Chain Integrity 41
The Moral SCI behaviors of compassion and receptivity, self- awareness and impartial judgment, and commitment suggest a means for increased firm sustainability. In other words, Moral SCI forms a mechanism through which firms create responsible corporate citizenship behavior and thus develop more sustainable supply chains.
To summarize our conceptual development, and in parallel with the introduction of corporate integrity within the business ethics literature, SCI emphasizes that “good” business decisions and “ethical” decisions need not be divorced from each other. In fact, when conceptualized within the SSCM framework, SCI sup- ports the argument that firms can make good business decisions in responsible and sustainable ways (Carter and Rogers 2008; Carter and Easton 2011). The two dimensions of SCI suggest how the four SSCM facets of strategy—risk management, trans- parency, and organizational culture—are interdependent upon each other in creating sustainable supply chain outcomes (as measured by the TBL). Structural SCI is directly concerned with strategy, risk management, and transparency, where supply chain processes and activities spanning these three areas create a unity of character for the firm. Moral SCI is concerned with value- laden approaches to management and an organizational culture that promotes ethical decision making, which also supports the firm’s strategic, transparency, and risk management activities.
Thus far, the SCI construct has been defined, with two sug- gested dimensions along structural and moral lines. As with any construct, the ultimate aim is to understand the role of SCI rela- tive to other constructs. This is a normal part of developing, and ultimately testing, theory. But before that can happen, the SCI construct needs to be validated. Before engaging in scale devel- opment efforts, we performed an exploratory analysis to assess at a high level the credibility of the underlying ideas, to provide initial validation of the SCI construct along its two proposed dimensions.
EXPLORATORY ANALYSIS OF THE SCI CONSTRUCT
The exploratory approach was warranted to assess the merit of the proposed SCI construct before further theorization about the con- struct and its relationship to other constructs. We turned to sec- ondary data sources that enabled the creation of proxy variables for the two dimensions of SCI, as well as a categorical variable identifying a firm as either “sustainable” or “nonsustainable.” Essentially, we asked whether SCI could differentiate between firms that are deemed sustainable and those that are not considered sustainable. To explore SCI’s ability to differentiate between the two categories of firms (sustainable, or not), we set up an econo- metric analysis that would allow us to regress the SCI dimensions against the firm type. Lack of discernment between firms would suggest that the construct as currently defined has little merit; on the contrary, if SCI can differentiate between firms, such an out- come would support the underlying rationale for the construct, as defined within the SSCM framework.
Rabinovich and Cheon (2011) assert that secondary data anal- yses can be used to support the nascent development of concepts and relationships. Such an approach is particularly appropriate “in the early stages of the study of a phenomenon, when neither theory nor knowledge about correlates of the phenomenon is
well developed” (Menard 1995, 42), and has been described as suitable when theoretical grounding is lacking or nascent (Menard 1995; Agresti and Finlay 1997; Hosmer and Lemeshow 2000). As such, an EN logistic regression using the ICOMP model evaluation criterion is employed with secondary data, as a first step in the ongoing development of the SCI construct.
Data collection
Data were collected from three sources to operationalize depen- dent, independent, and control variables. First, the MSCI KLD 400 Social Index (referred to henceforth as the Social Index) is an investment portfolio of the top 400 large-, mid-, and small- cap companies in the United States with positive social responsi- bility and environmental sustainability characteristics, excluding any company that has negative social or environmental impacts. This was used to operationalize the dependent variable related to social and environmental performance. Second, the independent variables were operationalized through use of the MSCI KLD ESG STATS database (referred to henceforth as KLD ESG), which consists of over 60 types of events or corporate behaviors across three pillars—environmental, social, and governance (ESG). This database has been used in previous research on CSR (e.g., Waddock and Graves 1997; Bird et al. 2007) and repre- sents one of the most favorable means to quantify corporate social and environmental sustainability events and corporate behaviors for academic research (Waddock 2003; Mattingly and Berman 2006; Kim et al. 2012). The KLD ESG database also has the highest granularity of measures relative to similar data- bases, thus providing the ability to amalgamate proxies into a suitable measure of SCI. Both the Social Index and KLD ESG database are managed and provided by the investment research firm Morgan-Stanley Capital International. Finally, Compustat was used to collect demographic information about the compa- nies in the analysis to operationalize several control variables.
Dependent variable: measuring social and environmental sustainability performance To explore the salience of the SCI concept, a dependent variable that could demonstrate differences in companies based on the dimensions of SCI was needed. Consistent with previous research (e.g., McWilliams and Siegel 2000; Rodriguez-Domin- gues et al. 2009; Zhang et al. 2013), the Social Index was used to differentiate between companies considered to be highly socially responsible and environmentally sustainable (SRES) from those that are comparatively less so. The measure used for the dependent variable is binary with the company being classi- fied as either a SRES or non-SRES company. The total number of companies on the Social Index was first filtered based on availability in the KLD ESG database for the years 2010–12, resulting in a total of 1,060 firm-year observations of SRES com- panies. This list of companies was further reduced by North American Industry Classification System (NAICS) classification. We selected companies with manufacturing classifications 31–33, wholesaler classification 42, and retailer classifications 44–45. Narrowing the analysis by these three sets of classifications had two purposes: (1) to focus on SCI of goods-related firms rather than service-related firms; and (2) to be able to discern differ- ences between three levels of the supply chain: manufacturer,
42 V. E. Castillo et al.
wholesaler, and retailer. After filtering based on NAICS classifi- cation, 474 manufacturing, 28 wholesaler, and 70 retail firm-year observations remained. Because the small number of wholesalers on the Social Index would result in a sample size less than the advised size for logistic regression (Peng et al. 2002), the final analysis was focused on estimating manufacturer and retailer models only.
Independent variables: measuring SCI The KLD ESG ratings consist of paired items within each of the ESG pillars. Each paired item has both a “strength” and “con- cern” indicator. KLD rates a company as having a strength or a concern in each item of the ESG pillars with a single point (+1). If the company was not rated in that area or if nothing occurred during the fiscal year to be considered a strength or concern for the company, then a score of zero was assigned. For example, if a company had an event such as an overseas factory collapse in its supply chain, that would be considered a concern under the “Supply Chain Labor Standards” measurement item and the company would receive a single point for that event in the “con- cerns” column for that year. Data for the years 2010–12 were collected when the number and availability of ESG measurement items were broadest before KLD modified its ESG measurement items in 2013. Twenty-two indicators from the KLD ESG data- base were chosen as proxies for the dimensions of SCI (seven for Structural SCI and 15 for Moral SCI). The seven proxies for Structural SCI were selected based on their congruence with the completeness or wholeness connotation of integrity in the supply chain. That is, any measure that indicates a firm’s deliberate selection of certain virtues, its propensity to seek supply chain partners with comparably high integrity, or demonstration of con- sistency in stated and achieved sustainability goals was selected. Similarly, the 15 indicators of Moral SCI were selected based on congruence with the moral virtue connotation of integrity. Indica- tors of a firm’s compassion and receptivity to communities, self- awareness and impartial judgment of the consequences of its operations, and commitment to sustainability were chosen. The specific indicators chosen and the SCI dimension they proxy are provided in Appendix A1.
Data were then collected on companies examined in the KLD ESG data set but not listed on the Social Index, resulting in 6,796 total firm-year observations of “non-SRES” companies. The list of 6,796 firm-year observations was then reduced by selecting manufacturing NAICS classifications 31–33 and 44–45 and matching each SRES firm that was listed in both the Social Index and KLD ESG database with two non-SRES firms (i.e., only listed in the KLD ESG database). The manufacturer model has a total sample size of N = 1,414 firm-year observations, comprised of 940 non-SRES and 474 SRES firms. The retailer model has a total sample size of N = 212, comprised of 141 non-SRES firms and 71 SRES firms. While there is a variety of guidance on sample sizes for logistic regression, both the manu- facturer and retailer models meet prescribed sample size guideli- nes of 100 observations at a minimum (Peng et al. 2002) and 10 observations per variable (Hosmer and Lemeshow 2000).
Adopting techniques from previous research, three measures each of Structural SCI (SSCI) and Moral SCI (MSCI) are gleaned from this data set (Waddock and Graves 1997; Bird et al. 2007; Boulouta 2013). The first is an aggregate score (SSCIAGG and
MSCIAGG), which is the difference between the total number of strengths and the total number of concerns in a given area for each year. The limitation to this measure is that an aggregate score of zero could be reflective of either a company which had an equal number of strengths and concerns in a given year or a company that simply was not rated on that dimension. Simply using the aggregate scores would discard valuable information. Thus, two additional measures were employed: total strengths (SSCISTR and MSCISTR) and total concerns (SSCICON and MSCICON). Both are summations of the relevant strengths or concerns in a given year. In addition to preventing the loss of useful information, these two sets of measures enhance explanatory power and increase potential for insights that can be made.
Control variables The control variables included are company size, as measured by total assets and total number of employees. Both items were col- lected from the Compustat database for the 2010–12 timeframe to match the independent and dependent variables. A control variable for industry was introduced as well but this variable was not significant in either the manufacturer or retailer model and therefore is not presented here.
EN logistic regression
The central premise of our exploratory analysis is that firms scor- ing highly on Structural SCI and Moral SCI measures are more likely to be considered SRES companies with comparatively high social and environmental performance, as indicated by inclusion on the Social Index. This analysis is intended to determine whether Structural SCI and Moral SCI indicators can be used to differentiate between those firms that appear on the Social Index from those that do not. Logistic regression, which is used to determine the probability that an independent variable predicts one of two outcomes of a dependent variable, is useful to predict group membership given certain characteristics, without more stringent constraints as in related methods (Menard 1995; Hos- mer and Lemeshow 2000; Hair et al. 2010). Logistic regression was therefore selected because it does not require an assumption of normality in data or equality of covariance matrices (Vogt et al. 2014).
An EN algorithm for the logistic regression was chosen to select the best model in each of six analyses based on ICOMP criterion (Bozdogan and Pamukcu 2016). EN is a regularized regression technique used for variable selection that minimizes overfitting of models (Zou and Hastie 2005). We adopt the EN method for the logistic regression because it penalizes the entry of a variable into the model as well as the magnitude of each parameter, thus increasing the chance that the parameters obtained in the final model reflect global and not just local opti- mal weights (Atanasov et al. 2017). While EN has not been used to date in logistics and supply chain management research, it has been used in related disciplines such as marketing (e.g., Rutz et al. 2011) and strategic management (e.g., Atanasov et al. 2017).
ICOMP is a model selection criterion that accounts for the complexity of a system being modeled, where complexity “is a measure of the degree of interdependency between the whole system and a simple enumerative composition of its subsystems
Supply Chain Integrity 43
or parts” (Bozdogan 2000, 72). More succinctly, ICOMP is a measure of the ability of a model to maximize explanatory power with the fewest number of parameters (Akman and Hallam 2010). This means ICOMP simultaneously measures model fit and individual parameters and is superior to only assessing R- square because it accounts for the interdependencies of the parameter estimates as well as the dependencies of the model residuals (Bozdogan 2000, 2004; Akman and Hallam 2010; Boz- dogan and Pamukcu 2016). Furthermore, p-value as a measure of the significance of an individual parameter in an estimation model only summarizes the data through testing and does not account for the probability that an effect is there in the first place (Nuzzo 2014; Wasserstein and Lazar 2016). Complementary measures to p-values include examining effect sizes and using methods that emphasize estimation over testing, which includes ICOMP (Bozdogan 2004; Wasserstein and Lazar 2016). A more detailed discussion of EN as a regression technique and ICOMP as a measure of model fit is provided in Appendix A2.
RESULTS
The results of the six analyses (three each for manufacturers and retailers) using the Structural SCI and Moral SCI aggregate scores, total strengths scores, and total concerns scores as inde- pendent variables are presented in Table 1a,b. For the six analy- ses, the best model was selected based on the two measures of firm size, Structural SCI, and Moral SCI. In all but the retailer concerns analysis, the estimation model that includes Structural SCI and Moral SCI were the best fitting models, as indicated by ICOMP, implying that Structural and Moral SCI were important predictors of whether or not a sustainable firm can be differenti- ated from a nonsustainable firm. Multicollinearity was assessed in each of the six analyses by calculating variance inflation factors (VIFs) for each predictor and controlled for by using ICOMP to find the best estimation model. Multicollinearity of the Structural and Moral SCI predictors in all six manufacturer and retailer anal- yses was not an issue, as all VIFs were below the recommended value of 2.5 for logistic regression (Allison 2001). However, the VIFs of the two control measures of firm size in the retailer anal- yses (but not in the manufacturer analyses) exceeded the 2.5 rec- ommended value. This is accounted for by using the ICOMP criterion, which penalizes the presence of multicollinearity through the complexity measure of the inverse-Fisher information matrix (Bozdogan 2000, 2004). This implies that a final estima- tion model controls for and minimizes the impact of multi- collinearity; therefore, it was not an issue in these analyses.
Manufacturer analyses
The aggregate analysis for manufacturers reveals that both Struc- tural SCI and Moral SCI can differentiate between SRES and non-SRES firms, as indicated by inclusion in the final model. The odds ratios indicate that for every one-unit increase in Struc- tural SCI, a firm is 1.10 times more likely to be considered a SRES company, whereas a one-unit increase in Moral SCI means a company is 2.50 times more likely to be considered SRES. The strengths analysis is consistent with the aggregate analysis in that both types of SCI are predictors of a firm’s inclusion on
the Social Index. A one-unit increase in a Structural SCI strength means that a company is 1.17 times more likely to be considered a sustainable company and a Moral SCI strength means that a company is 1.44 times more likely. Finally, the concerns analysis shows that both Structural SCI and Moral SCI can differentiate between SRES and non-SRES firms. The negative sign on the coefficient implies that a one-unit increase in Structural SCI con- cerns makes a company less likely (0.76 times) to be considered SRES. Consistently, a one-unit increase in Moral SCI concerns means that a company is less likely (0.07 times) to be considered a SRES company.
Firm size as measured by number of employees and total assets was controlled for in each of the analyses. All three models indicated that as manufacturing companies add one unit (1,000) of employees, the odds increase that the firm is more likely to be considered a company listed in the Social Index. These findings are consistent with previous research arguing that larger firms are more visible and thus subject to greater scrutiny, and therefore are more likely to be adopters of sus- tainability practice (Brammer and Millington 2006; Chiu and Sharfman 2011; Marano and Kostova 2016). This is con- founded, however, by the total assets measure, which indicates an opposing effect on the odds of being sustainable. That is, all three analyses show that as manufacturers have more assets, they are less likely to be considered sustainable. This control variable suggests an interesting tension between measures of firm size.
Retailer analyses The aggregate analysis for retailers indicates that Structural and Moral SCI can differentiate between firms that are SRES or non-SRES, as measured by inclusion on the Social Index. A one-unit increase in Structural SCI raises the odds that a retai- ler is considered SRES by 1.59 times. A one-unit increase in Moral SCI raises the odds that a retailer is considered SRES by 3.15 times. Similar to the results of the aggregate analysis, the strengths analysis for retailers also shows both forms of SCI are predictors of being SRES. A one-unit increase in Structural SCI means that a retailer is 2.11 times more likely to be considered SRES whereas a one-unit increase in Moral SCI implies a firm is 1.97 times more likely. In the concerns analysis, Structural SCI was dropped because as a variable selection method, the EN regression demonstrated that the best estimation model as measured by ICOMP was one that only included Moral SCI. A Moral SCI concern, that is, the occur- rence of a negative event, is a significant predictor of a retai- ler’s sustainability performance, as a one-unit increase means a firm is 0.11 times less likely to be considered to be a SRES firm.
Firm size in terms of total assets and number of employees was controlled for in each model, the results of which are reported in Table 1b. Both measures of firm size were selected for the final model in each analysis, implying they have signif- icance to the prediction of the dependent variable. However, the number of employees variable is not significant as mea- sured by p-value, which is the largest among all variables in any of the analyses. This might be explained by the fact that p-values tend to be inflated when there is strong correlation with another variable in the model (Wasserstein and Lazar
44 V. E. Castillo et al.
T ab
le 1:
E N
lo gi st ic
re gr es si on
re su lt s. (a ) M an uf ac tu re rs . (b ) R et ai le rs
(a )
(b )
B S E
t- S ta t
S ig .
O d d s ra ti o
B S E
t- S ta t
S ig .
O d d s ra ti o
A gg re ga te
m ea su re s of
S S C I an
d M S C I
A gg re ga te
m ea su re s of
S S C I an
d M S C I
T ot al
as se ts
�0 .1 8
0. 09
�2 .0 3
0. 04
0. 84
T ot al
as se ts
0. 87
0. 24
3. 60
0. 00
2. 39
N o.
em pl oy ee s
0. 77
0. 09
8. 81
0. 00
2. 16
N o.
em pl oy ee s
�0 .2 1
0. 23
�0 .9 0
0. 37
0. 81
S S C I A
G G
0. 09
0. 11
0. 89
0. 37
1. 10
S S C I A
G G
0. 46
0. 27
1. 71
0. 09
1. 59
M S C I A
G G
0. 92
0. 14
6. 48
0. 00
2. 50
M S C I A
G G
1. 15
0. 37
3. 11
0. 00
3. 15
C on st an t
�0 .5 1
0. 55
�0 .9 4
0. 35
0. 60
C on st an t
�7 .1 6
1. 37
�5 .2 4
0. 00
0. 00
M od el
in fo rm
at io n:
IC O M P = 1, 36 0. 2;
m od el
ch i- sq ua re d = 50 8,
p < .0 01 ; N
= 1, 41 4,
df = 1, 40 9
M od el
in fo rm
at io n : IC O M P = 19 6. 66 ; m od el
ch i- sq ua re d = 81 .8 , p < .0 01 ;
N = 21 2,
df = 20 7
T ot al
st re n gt h s as
m ea su re s of
S S C I an
d M S C I
T ot al
st re n gt h s as
m ea su re s of
S S C I an
d M S C I
T ot al
as se ts
�0 .1 4
0. 09
�1 .6 0
0. 11
0. 87
T ot al
as se ts
0. 78
0. 24
3. 29
0. 00
2. 18
N o.
em pl oy ee s
0. 76
0. 09
8. 84
0. 00
2. 15
N o.
em pl oy ee s
�0 .1 5
0. 23
�0 .6 4
0. 52
0. 86
S S C I S T R
0. 16
0. 18
0. 91
0. 36
1. 17
S S C I S T R
0. 75
0. 40
1. 87
0. 06
2. 11
M S C I S T R
0. 37
0. 14
2. 60
0. 01
1. 44
M S C I S T R
0. 68
0. 40
1. 71
0. 09
1. 97
C on st an t
�0 .7 5
0. 55
�1 .3 5
0. 18
0. 47
C on st an t
�6 .7 8
1. 36
�4 .9 9
0. 00
0. 00
M od el
in fo rm
at io n:
IC O M P = 1, 42 3. 3;
m od el
ch i- sq ua re d = 46 5,
p < .0 01 ; N
= 1, 41 4,
df = 1, 40 9
M od el
in fo rm
at io n : IC O M P = 19 8. 85 ; m od el
ch i- sq ua re d = 76 .3 , p < .0 01 ;
N = 21 2,
df = 20 7
T ot al
co n ce rn s as
m ea su re s of
S S C I an
d M S C I
T ot al
co n ce rn s as
m ea su re s of
S S C I an
d M S C I
T ot al
as se ts
0. 18
0. 09
2. 05
0. 04
1. 20
T ot al
as se ts
1. 07
0. 25
4. 18
0. 00
2. 90
N o.
em pl oy ee s
0. 69
0. 09
7. 87
0. 00
2. 00
N o.
em pl oy ee s
�0 .0 8
0. 25
�0 .3 3
0. 74
0. 93
S S C I C
O N
�0 .2 7
0. 14
�1 .8 6
0. 06
0. 76
S S C I C
O N
– –
– –
– M S C I C
O N
�2 .6 3
0. 31
�8 .4 9
0. 00
0. 07
M S C I C
O N
�2 .2 2
0. 79
�2 .8 3
0. 01
0. 11
C on st an t
�2 .6 7
0. 56
�4 .7 4
0. 00
0. 07
C on st an t
�8 .8 0
1. 49
�5 .9 0
0. 00
0. 00
M od el
in fo rm
at io n:
IC O M P = 1, 33 1. 2;
m od el
ch i- sq ua re d = 54 6,
p < .0 01 ; N
= 1, 41 4,
df = 1, 40 9
M od el
in fo rm
at io n : IC O M P = 21 3. 02 ; m od el
ch i- sq ua re d = 75 .3 , p < .0 01 ;
N = 21 2,
df = 20 8
Supply Chain Integrity 45
2016); in this case, the two measures of firm size are corre- lated with each other, thus explaining why there is a high p- value. Comparing this with the results of the manufacturer analyses, firm size does have an effect of the odds of a firm being sustainable or not, but the effects depending on firm size are the opposite of that of manufacturers. That is, as retail firms add one unit (1,000) of employees, they are less likely to be sustainable but as they add more assets, they are more likely to be SRES, as indicated by the sign on the coefficient for each variable. These results are discussed further in the ensuing section.
DISCUSSION
The results of the six analyses provide initial credibility for the role of the SCI construct in the SSCM framework. The proxy measures employed were able to differentiate between firms considered to be sustainable from those that are not, which was evaluated across two echelons of the supply chain. Thus, the six analyses for manufacturers and retailers have in com- mon the general finding that having SCI means that a firm is more likely to be considered a sustainable company. This study was part of the SCI construct development process and pro- vides initial empirical support for the idea that that firms with Structural SCI and Moral SCI are more likely to be sustain- able. We have also suggested that the reason for the increased probability of SCI-laden firms being sustainable is that SCI links the strategy, risk management, transparency, and organi- zational culture facets of SSCM. Further construct development is needed, however, to expatiate exactly how SCI links those four facets.
An intriguing result for both types of companies emerges from the results of concerns analyses. Of the aggregate, strengths, and concerns models, the largest coefficient magnitudes for Structural and Moral SCI for either type of company are in the concerns analyses. This suggests that when events occur in supply chains that can be considered ethical violations, there is potentially a greater negative impact of not having SCI, compared to the ben- efits that can be gained from having it. Thus, firms are more likely to be punished for not having SCI than they are to be rewarded or recognized for having SCI. Therefore, SCI may be a means of reputational risk mitigation (Lemke and Petersen 2013; Petersen and Lemke 2015), and should be explored with such a perspective in mind.
The differences between effect sizes of Structural and Moral SCI across company types also present interesting results. While the results show that both Structural SCI and Moral SCI are important to discerning between sustainable and nonsustainable companies, that retailers mostly have lar- ger coefficient magnitudes suggests that SCI may have a greater impact on SSCM for retailers than for manufacturers. That is, there appears to be a contextual influence of supply chain echelon on the impact of SCI. This might be because retailers are closer to end consumers and stakeholders who are concerned with the consumer marketplace and thus tend to receive the lion’s share of media scrutiny following adverse events or discovery of less sustainable processes in the supply chain (Schmidt et al. 2017). As a result of this
greater supply and reputational risk, retailers may have more leverage in promoting SCI activities (Brockhaus et al. 2013), such as community impact, labor relations, human rights sup- port (or violations against), and social engagement in supply chain partners. Therefore, retailers with greater SCI may be more likely to be seen as sustainable companies than those without it.
For manufacturers, the story seems to be more nuanced. While both Structural and Moral SCI are important, Moral SCI has a greater impact than Structural SCI on the odds of whether a company is sustainable or not. A possible explanation is that compared to Structural SCI, the attributes encompassed within Moral SCI are more reflective of an internal organizational cul- ture that considers welfare of others, which has been shown to increase sustainability performance (Carter and Jennings 2004; Carter and Rogers 2008; Thornton et al. 2013; Griffis et al. 2014). Additionally, the stakeholder-oriented actions that firms take which benefit supplier communities or the environment, for example, potentially originate from managers within the firm and are more likely to be highlighted (Ehrgott et al. 2011, 2013). This is consistent with the idea that SCI spans facets of SSCM—in this case, organizational culture, strategy, and risk management. The finding that SCI affects companies differ- ently depending on the supply chain echelon is limited by the nature of the secondary data used in this study. Thus, as the SCI construct continues to be refined, more research will be needed to examine how or why the echelon has an impact on how companies develop SCI and use it as a means of increas- ing SSCM.
The inclusion of firm size as a predictor was intended to address the possibility of a so-called halo effect, in which larger firms may be perceived as being more sustainable because of the fact they are more likely to be in the press. We found that for manufacturers, larger firms in terms of the number of employees were certainly more likely to be considered sustainable. However, this contrasts with measuring firm size as total assets, which shows an opposite effect. Manufacturers with more total assets were less likely to be considered sustainable. This suggests that as manufacturing com- panies acquire more assets to increase capacity to fill growing demand, they are potentially growing their carbon footprint or increasing the possibility of having adverse events occur in the supply chain as well. The opposite situation was found in the retai- ler analyses. For retailers, larger firms as measured by the number of employees were less likely to be sustainable; yet, as retailers acquire total assets, they are more likely to be sustainable. This might be explained as a function of retailer susceptibility to con- sumer-oriented stakeholder scrutiny and a need to invest in more sustainable technologies sooner than their upstream supply chain partners (Schmidt et al. 2017). The theoretical tension between measures of firm size should also be considered as the SCI con- struct continues to be developed.
Limitations
The interpretation of these results should be considered with limi- tations in mind. First, the econometric analysis performed is an exploratory one based on initial theoretical development, previous literature, and use of proxies for the dimensions of SCI (the items used are listed in Appendix A1). While the method employed
46 V. E. Castillo et al.
was performed with rigor and relevance in mind (Mentzer 2008; Goldsby and Zinn 2016), future research should refine the SCI construct more deeply and collect primary data to broaden the impact of the findings, as well as to clearly position SCI vis-�a-vis related concepts such as corporate values and culture.
Second, while the data used to operationalize the independent variables in the exploratory analysis were sourced from the KLD ESG database and the dependent variable was sourced from the Social Index, both sources are now owned and managed by Mor- gan-Stanley Capital International (MSCI), which acquired Kin- der, Lydenberg, Domini & Co., Inc. (KLD) in 2010. The two databases are linked in that MSCI uses results from the KLD ESG database to select the companies that comprise the Social Index. To mitigate possible common-method variance resulting from this linkage, we matched each company on the Social Index with two companies that are not; however, the use of these data- bases may still limit generalizability of our findings. Further- more, the KLD ESG database does not include companies that produce alcohol, tobacco, firearms, gambling services, and nuclear power; thus, these types of firms were not included in the exploratory analysis.
Third, future research needs to overcome limitations related to the range of the SCI construct. We have positioned SCI as a firm- level construct, but there are surely micro level dimensions of SCI that need to be explored. The data themselves are collected at the firm level and do not include micro level data on individual behavior; yet, a firm’s actions are ultimately executed by individ- uals through their own actions. Integrity has been studied in the psychology and ethics literatures at an individual level. As sup- ply chain researchers increasingly address the individual micro- level behaviors underlying firm-level constructs, research on SCI will also need to be expanded to address the individual-level cognition, behaviors, and motivations that drive organization actions with respect to SCI. Such an approach may also help tease out the factors giving rise to each of the independent vari- ables included in the current research.
Last, the analysis was limited in scope to U.S. manufacturers and retailers over a period of three years. Future research should explore SCI in other types of firms, including raw material sup- pliers, component suppliers, and wholesalers as the construct is refined and tested for robustness. Future research should also address the SCI phenomenon across other cultures beyond the U.S. business community and over longer periods of time to explore how SCI evolves. This is particularly important in light of the current impact of the millennial generation and ever-chan- ging dynamics within consumer markets.
Research agenda
Given the introductory approach to the concept of SCI in this research, avenues for future research are warranted. Table 2 pro- vides a summary of several research directions for scholars to pur- sue, based on the preliminary establishment of the SCI construct within the SSCM literature. First, a formal and rigorous construct development process should be undertaken to more fully develop SCI as a construct that can be employed in future research. SCI has initially been defined along two dimensions, based on conceptual adaptation from the corporate integrity literature (Audi and Mur- phy 2006; Maak 2008). Each of these dimensions needs to be fully
developed and there may be other dimensions that need to be included when considering the broader context of SCI. One approach to formally develop the SCI construct uses Hinkin’s (1995) three-stage method. Other options to further refine SCI include use of a qualitative method such as case studies (Yin 2014) or grounded theory (Glaser and Strauss 1967; Glaser 2001), which have been demonstrated to be fruitful in supply chain management research (e.g., Mello and Flint 2009; Randall and Mello 2011; Saldanha et al. 2015). Adopting a grounded theory approach to further develop and confirm the SCI con- struct would lead to a theoretical framework rooted in practice and grown from interviews and site observations. The insights gleaned from such an endeavor can then be used to assess the validity and generalizability of the SCI construct through quanti- tative analysis.
Second, an abundance of supply chain issues can be explored with the SCI concept. For instance, SCI can be explored as a means of mitigating supply-side risk (Zsidisin et al. 2004), which can affect a firm’s TBL performance (Carter 2000). An organization’s supply-side risk can be impacted by a multitude of factors, including poor financial health, which could trigger unethical sourcing behavior such as seeking shortcuts around ethical practices in the form of kickbacks from suppliers to award contracts (Turner et al. 1994). So, what is the role SCI plays in preventing or assuaging the negative consequences from increasing supply risks in global markets? Firms that have taken measures to prioritize TBL performance have gained com- petitive advantage and in some cases, mitigated certain supply- side risks that may have resulted from having a relationship with suppliers of unknown integrity (Hart 1995; Carter and Rogers 2008; Reuter et al. 2010; Thornton et al. 2013). Build- ing Moral and Structural SCI at the firm level may also be a means for mitigating reputational risk that arises from dealing with suppliers of unknown character or integrity (Min and Galle 1991; Emmelhainz and Adams 1999; Rogers 2011). Such efforts would help fulfill calls for increasing multidisciplinary research on supply chain management to address risks that span functions and organizations (Sanders and Wagner 2011; Sanders et al. 2013; Talluri et al. 2013; Manuj et al. 2014).
Upon refinement of the SCI construct, a third prominent research opportunity lies in exploring its antecedents and out- comes. Regarding antecedents, what are the general characteris- tics or mind-sets of firms that do or do not infuse SCI into their business operations? How are they influenced, either intrinsically or extrinsically, to ensure integrity and sustainability in their supply chain operations? Future research should specifically address both environmental and social antecedents to SCI. Exist- ing research studying the antecedents to socially responsible sup- plier selection (Thornton et al. 2013; Griffis et al. 2014) can be combined with previous business ethics research to develop integrity-based antecedents of SCI. Regarding outcomes, researchers using a more fully developed SCI construct will be able to explore the nature and strength of the relationship between SCI and TBL performance within certain contexts (Elkington 1998, 2004).
Fourth, the multilevel aspects of SCI provide opportunities to integrate organizational and individual-level motives and actions. SCI has been defined in this research as a firm-level concept, but is conceptually related to individual integrity and corporate
Supply Chain Integrity 47
integrity. Researchers have built upon the individual as the unit of analysis to examine firm-level or corporate integrity (Paine 1994; Maak 2008; Maurer 2009). Linking individuals and orga- nizations is a logical progression, as firms are considered corpo- rate citizens of communities and have the ability to manage policies, actions, and ethical concerns of stakeholders (Donaldson 1982; Velasquez and Goodpaster 1983; French 1984, 1995; Maak 2008). Strategic management and supply chain researchers alike are increasingly addressing the microfoundations of organi- zational actions (Oliva and Watson 2011; Felin et al. 2015), rec- ognizing that firm behaviors are essentially dependent on individuals within the firm. Studies can be performed to explore the sociocognitive processes at the micro level to examine how supply chain managers receive, comprehend, and develop mental schema for implementing SCI into their organizations.
Contributions
The SCI concept development, which was adapted from the busi- ness ethics literature, and the subsequent exploratory analysis
have provided insights to the research question of how SCI relates to SSCM. Several contributions to theory and practice have resulted.
Theoretical implications This research effort makes three theoretical contributions to the supply chain management literature. First, the SCI concept bridges a previous gap in the SSCM literature relating to interde- pendence of business and ethics decisions (Maak 2008). The SCI concept contributes to emergent SSCM theory by linking the foundational pillars, strategy, transparency, organizational cul- ture, and risk management, through integrity-based approaches to supply chain activities. While the four facets each contribute individually to a firm’s sustainability, SCI provides a founda- tion for understanding how a gestalt is created between two or more of the facets and can help differentiate between firms that are sustainable from those that are less sustainable. Second, using an EN logistic regression and ICOMP model evaluation criterion, this research uncovers and empirically demonstrates at an exploratory level that both Structural and Moral
Table 2: Future research avenues
Topic Possible method Possible contribution
Construct development SCI Construct Refinement and Scale Development
Hinkin’s (1995) three-stage scale development method
Development of SCI construct convergent validity and subdimension discriminant validity
Empirical grounding of SCI Construct Grounded theory (Glaser and Strauss 1967; Mello and Flint 2009)
An empirically grounded theoretical framework for understanding the underlying social process for how organizations develop and maintain SCI
Antecedents and consequences of SCI Antecedents of SCI Case studies (Eisenhardt
1989; Yin 2014) Refined understanding of the underlying dynamics prevalent that either enhance or erode SCI
Relationship between SCI and TBL performance*
Econometrics (Hansen 2015; Wooldridge 2015)
Exploration of the influence on social, environmental, and financial performance by SCI
Multilevel aspects of SCI Individual Employees’ Effects on Firm SCI Multilevel modeling Possible recognition of the need to examine
individual- and firm-level integrity to study SCI SCI and Cross-Organizational Integration Mixed-method approach to
develop a theoretical framework that can be tested empirically
How organizations can influence supply chain partners to increase SCI
Supply chain issues SCI and Reputational Risk Management Laboratory experiments
(Knemeyer and Naylor 2011; Deck and Smith 2013)
Understanding of the effects of SCI on an organization’s reputation
SCI and Supply Chain Risk Management Case studies (Eisenhardt 1989; Yin 2014)
Understanding of how SCI can allow companies to mitigate supply chain risks
Relationship between SCI, Strategy, Risk Management, Transparency, and Corporate Culture (ethics)
Case studies (Eisenhardt 1989; Yin 2014)
Elaboration of the relationships between mechanisms that create enhanced sustainable supply chain performanceMiddle-range theorizing
(Merton 1968; Stank et al. 2017) Role of SCI in Sustainable Supply Chain Management
Case studies (Eisenhardt 1989; Yin 2014)
Theory development. Understanding of the role of SCI as part of the Organizational Culture “pillar” of SSCM; and links to other SSCM pillars
Middle-range theorizing (Merton 1968; Stank et al. 2017)
Note: *Including impact of firm characteristics on the SCI–TBL performance relationship (e.g., supply chain echelon; size; industry; form (public/pri- vate); R&D intensity).
48 V. E. Castillo et al.
dimensions of SCI can help differentiate between firms that are sustainable from those that are less so. Additionally, a firm’s supply chain echelon (manufacturer vs. retailer) and firm size (number of employees vs. total assets) provide at least two contextual explanations addressing why SCI effects might vary across firms in the supply chain. Both findings provide merit to the idea of SCI as underlying SSCM, but further research is needed to generalize the results more broadly. Last, this research effort contributes an agenda to advance theoretical development of SCI and its relation to other constructs. The agenda provides a foundation for further exploration of SCI to more deeply explore the mechanisms, contexts, and boundary conditions of SCI (Busse et al. 2016; Stank et al. 2017).
Managerial implications The SCI concept as presented in this research also has impor- tant managerial implications. This research suggests the impor- tance of incorporating business strategy decisions with value- laden decision making in the supply chain realm. The research provides new insights on the structural and moral areas upon which managers should focus. Due to the scale of global con- nectivity in business, increased visibility and transparency into supply chain practices around the world can impact a firm’s reputation. Therefore, firms seeking competitive advantage by accessing suppliers and resources abroad are often faced with the dilemma of ensuring responsible sourcing or prioritizing environmental stewardship at the cost of financial performance. This is exemplified in the case of conflict minerals and natural resource scarcity that causes buyers of 3TG minerals to map their supply chains to ensure they are not financing guerilla warfare or slave labor (Bell et al. 2012, 2013). SCI provides a means for managing and mitigating the supply chain risk (Manuj and Mentzer 2008; Rao and Goldsby 2009) that arises from having to make decisions with incomplete information such as in the case of working with suppliers of unknown char- acter and integrity. For example, actively building Structural SCI attributes, such as unity of character or consistency, at the firm level can help to demonstrate a dedication to a gestalt of important virtues and increase reputational capital gained through supply chain transparency. Similarly, managers can seek to strengthen their firm’s Moral SCI by enhancing their recognition of the needs of a multitude of stakeholders, trying to objectively understand how those stakeholders are impacted, and committing to reducing negative impacts. Such activities also help to mitigate the effect of an unexpected disruption resulting from suppliers’ potential use of questionable resources or practices.
This research also demonstrates a way for practitioners to increase organizational legitimacy in downstream markets and with supply chain partners. SCI may also create the ability to demonstrate that materials have been sourced ethically and responsibly. This is an important implication because building trust with supply chain partners and external stakeholders influ- ences a firm’s performance (Chun et al. 2013). Additionally, the increasingly recognized importance of SSCM to firm perfor- mance and reputation means that supply chain managers will not only be looked at to deliver economic-based efficiencies within the constraints of customers’ desires, but will also be expected to provide environmental and social leadership as a way of creating competitive differentiation and building loyal customer bases. This is especially true in light of the growing sustainability focus of customers from the millennial generation who have shown their desire to buy from sustainable companies (Solomon 2014). Therefore, managers should recognize the incentives for building SCI to achieve superior sustainability performance.
Finally, this research suggests that managers should recog- nize the risks and benefits accruing through ethical behaviors. Being held responsible for a violation of Moral SCI, for example, should spur managers to address the decision-mak- ing frameworks within their firms so as to mitigate the occur- rence of such events. Likewise, recognizing the potential benefits of Structural SCI should spur managers to find supply chain partners with SCI, and in doing so developing a specific set of values from which to gain a gestalt of character, and increasing transparency across the supply chain. The impor- tance of firms having strong SCI continues to rise as cus- tomers continue to demand that firms take responsibility for the actions of their suppliers as well (Klassen and Vereecke 2012).
CONCLUSION
As firms grapple with ongoing issues of SSCM in an ever-more complex world, the concept of SCI provides a promising ave- nue of future research that explores the interdependence of the SSCM facets in the supply chain discipline. The efforts of this research demonstrate the promise of the SCI construct, and the potential to help firms develop more sustainable supply chains. As sustainability continues to evolve to more fully embrace a TBL approach in both research and practice, SCI provides new insights to how SSCM can be achieved and offers opportunities for managers to frame their decision making.
APPENDIX A1: KLD ESG Proxies for Supply Chain Integrity
SCI dimension
KLD ESG item name KLD ESG item definition
Structural SCI Supply chain management
This indicator measures the severity of controversies related to the environmental impact of a company’s supply chain and the sourcing of natural resources. Factors affecting this evaluation include, but are not limited to, a history of widespread or egregious environmental impacts in a
Continued.
Supply Chain Integrity 49
APPENDIX A1: (Continued)
SCI dimension
KLD ESG item name KLD ESG item definition
firm’s supply chain, legal cases, resistance to improved practices, and criticism by nongovernmental organizations (NGOs) and/or other third-party observers
Supply chain labor standards
This indicator evaluates how well companies manage risks of production disruptions and brand value damage due to substandard treatment of workers in the company’s supply chain. Companies that establish labor management policies meeting stringent international norms, implement programs to verify compliance with the policies, and introduce incentives for compliance among suppliers score higher
Supply chain This indicator measures the severity of controversies related to a firm’s supply chain. Factors affecting this evaluation include, but are not limited to, a history of involvement in supply chain- related legal cases, widespread or egregious instances of abuses of supply chain employee labor rights, supply chain employee safety, resistance to improved practices, and criticism by NGOs and/ or other third-party observers
Ownership strength
The company owns between 20% and 50% of another company KLD has cited as having an area of social strength, or is more than 20% owned by a firm that KLD has rated as having social strengths. When a company owns more than 50% of another firm, it has a controlling interest, and KLD treats the second firm as if it is a division of the first
Ownership concern
The company owns between 20% and 50% of a company KLD has cited as having an area of social concern, or is more than 20% owned by a firm KLD has rated as having areas of concern. When a company owns more than 50% of another firm, it has a controlling interest, and KLD treats the second firm as if it is a division of the first
Transparency strength
The company is particularly effective in reporting on a wide range of social and environmental performance measures or is exceptional in reporting on one particular measure. In 2005, KLD added the Transparency Strength, which incorporates information from the former Environment: Communications Strength (ENV-str-E) as part of its content
Transparency concern
The company is distinctly weak in reporting on a wide range of social and environmental performance measures. In 2005, KLD added the Transparency Concern
Moral SCI Innovative giving
This indicator evaluates company charitable giving programs. Companies whose programs support affordable housing, access to health care, K-12 public education, initiatives to relieve hunger, or in-kind giving and other programs targeted at disadvantaged communities, score higher
Community engagement
The company has a notable community engagement program concerning involvement of local communities in areas where the firm has major operations
Community impact
This indicator measures the severity of controversies related to a firm’s interactions with communities in which it does business. Factors affecting this evaluation include, but are not limited to, a history of involvement in land use and/or development-related legal cases, widespread or egregious community impacts due to company operations, and criticism by NGOs and/or other third-party observers
Human rights violations
This indicator measures the severity of controversies related to the impact of a firm’s operations on human rights. Factors affecting this evaluation include, but are not limited to, a history of involvement in human rights-related legal cases, widespread or egregious complicity in killings, physical abuse, or violation of other rights, resistance to improved practices, and criticism by NGOs and/or other third-party observers
Social opportunities
This indicator evaluates company efforts that benefit the disadvantaged. Detailed definition omitted due to space limitations, see KLD ESG (2015)
Labor rights strength
The company has outstanding transparency on overseas sourcing disclosure and monitoring, or has particularly good union relations outside the United States, or has undertaken labor rights-related initiatives that KLD considers outstanding or innovative. In 2002, the Labor Rights Strength was added
Moral SCI (continued)
Labor rights concern
The company’s operations have had major recent controversies primarily related to labor standards in its supply chain. KLD started assigning concerns for this issue in 1998 and subsequently renamed it from International Labor Concern. KLD subsequently created the Labor Rights Concern using data from the International Labor Concern. KLD started assigning concerns for this issue in 1998
Continued.
50 V. E. Castillo et al.
APPENDIX A2: EN logistic regression and ICOMP
This is a brief presentation of the underlying mathematics of the EN variable selection algorithm used in our logistic regression and the ICOMP criterion used to evaluate model fit, based on Bozdo- gan and Pamukcu (2016), Zou and Hastie (2005), and Bozdogan (2000, 2004). Briefly, for an a strictly between 0 and 1, and a non- negative regularization parameter k, EN logistic regression solves the problem
min b0; b
1 n Devianceðb0; bÞ þ kPaðbÞ
� � ;
where
Devianceðb0; bÞ ¼ �2log LSaturated � 2log LFittedModel
measures the “goodness of fit” of the EN logistic regression model. The saturated model is the “benchmark”—it is the model which fits best in the sense of having smallest �2log L, minus twice the log likelihood of the model.
Further,
PaðbÞ ¼ ð1 � aÞ
2 k b k22 þa k b k1¼
X j¼1
ð1 � aÞ 2
b2j þ ajbjj � �
APPENDIX A1: (Continued)
SCI dimension
KLD ESG item name KLD ESG item definition
Access to finance
This indicator evaluates the extent to which a company is taking advantage of opportunities for growth and strengthening reputation through providing lending, financing, or products to underrepresented or underbanked communities. Top performing companies will offer products and services to communities with limited or no access to financial products
Indigenous peoples relations strength
The company has established relations with Indigenous peoples near its proposed or current operations (either in or outside the United States) that respect the sovereignty, land, culture, human rights, and intellectual property of Indigenous peoples. In 2000, KLD added the Indigenous Peoples Relations Strength. In 2004, KLD moved the Indigenous Peoples Relations Strength from Community to Human Rights
Indigenous peoples relations concern
The company has been involved in serious controversies with Indigenous peoples (either in or outside the United States) that indicate the company has not respected the sovereignty, land, culture, human rights, and intellectual property of Indigenous peoples. KLD started assigning concerns for this issue in 2000
Raw material sourcing
This indicator evaluates how companies manage the risks of damaging their brand value by sourcing or utilizing raw materials with high environmental impact. Companies that have policies and procedures to source materials with lower environmental impact and participate in initiatives to reduce environmental impact of raw materials production score higher
Child labor This indicator measures the severity of child labor controversies in a firm’s supply chain. Factors affecting this evaluation include, but are not limited to, a history of involvement in child labor- related legal cases, widespread or egregious instances of child labor in the firm’s supply chain, resistance to improved practices, and criticism by NGOs and/or other third-party observers
Political accountability strength
The company has shown markedly responsible leadership on public policy issues and/or has an exceptional record of transparency and accountability concerning its political involvement in state- or federal-level U.S. politics, or in non-U.S. politics. In 2005, KLD added the Political Accountability Strength
Political accountability concern
The company has been involved in noteworthy controversies on public policy issues and/or has a very poor record of transparency and accountability concerning its political involvement in state- or federal-level U.S. politics, or in non-U.S. politics. In 2005, KLD added the Political Accountability Concern
Human rights policies and initiatives
The company has undertaken exceptional human rights initiatives, including outstanding transparency or disclosure on human rights issues, or has otherwise shown industry leadership on human rights issues not covered by other MSCI human rights ratings
Supply Chain Integrity 51
is the penalty function in EN logistic regression model. We rely on the model selection using ICOMP criterion to choose the best subset of “predictors” for each data set. The analytical form of ICOMP we score is given by
ICOMPðENet Logistic RegÞ ¼ n logð2pÞ þ n logðr̂2Þ þ n þ2C1ð dCovðb̂ÞMisspecÞ
where
r̂2 ¼ 1 n
Xn i¼1
ðyi � ŷiÞ2
is the estimated variance of the residuals of the EN logistic regression model, and
dCovðb̂ÞMisspec ¼ F̂�1R̂F̂�1 ¼
r̂2 n 0
0 2r̂ 4
n
" #" n r̂2
nSk 2r̂3
nSk 2r̂3
nðKt�1Þ 4r̂4
# r̂2 n 0
0 2r̂ 4
0
" #
is the estimated robust covariance matrix of the coefficients with
Sk ¼ Coefficient of skewness ¼ 1 n
Pn i¼1
ê3i
� � r̂3
and
Kt ¼ Coefficient of kurtosis ¼ 1 n
Pn i¼1
ê4i
� � r̂4
This form of ICOMP takes into account skewness and kurtosis in our data sets, and it penalizes the presence of skewness and kurtosis as we fit the EN logistic regression models. We mini- mize ICOMP to choose the best fitting model as our figures of merit, or the performance measure.
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SHORT BIOGRAPHIES
Vincent E. Castillo (MBA University of San Diego) is a PhD Candidate in Supply Chain Management at The University of Tennessee. His research interests include omnichannel distribu- tion, urban delivery, sharing economy, and sustainability topics within the logistics and supply chain management domains. He is a member of the Council of Supply Chain Management Pro- fessionals, Decision Sciences Institute, Academy of Management, and The PhD Project.
Diane A. Mollenkopf (PhD Drexel University) is the McCormick Associate Professor of Logistics in the Department of Marketing and Supply Chain Management, Haslam College of Business at The University of Tennessee, Knoxville. She teaches at the undergraduate, masters, and PhD levels. Her research inter- ests relate to logistics/supply chain integration and supply chain sustainability. Her research has been published in leading logis- tics and supply chain management journals.
John E. Bell (PhD Auburn University) is the Myers Supply Chain Fellow & Associate Professor of Supply Chain Manage- ment at The University of Tennessee. He also holds an MS from the Air Force Institute of Technology and a BS from the U.S. Air Force Academy. He is an Associate Editor at three leading journals in the field of Supply Chain Management, and he has published over 30 peer-reviewed articles at top academic jour- nals.
Hamparsum Bozdogan (PhD University of Illinois at Chicago) is the Toby McKenzie Professor in Information Complexity and Model Selection in the Department of Business Analytics and Statistics at The University of Tennessee in Knox- ville. Dr. Bozdogan is the leading expert in information-theoretic model selection and information complexity in multivariate mod- els in high dimensions. He has authored over 70 peer-reviewed research articles; edited six books, and writing two of his own books.
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