Sustainable Supply Chain Management: Evolution & Future - PDF

The current issue and full text archive of this journal is available on Emerald Insight at: www.emeraldinsight.com/0960-0035.htm IJPDLM 50,1 Sustainable supply chain management: continuing evol...

The current issue and full text archive of this journal is available on Emerald Insight at: www.emeraldinsight.com/0960-0035.htm IJPDLM 50,1 Sustainable supply chain management: continuing evolution and future directions 122 Craig R. Carter, Marc R. Hatton, Chao Wu and Xiangjing Chen Department of Supply Chain Management, Received 15 February 2019 Revised 31 May 2019 Arizona State University, Tempe, Arizona, USA 26 September 2019 Accepted 9 October 2019 Abstract Purpose – The purpose of this paper is to update the work of Carter and Easton (2011), by conducting a systematic review of the sustainable supply chain management (SSCM) literature in the primary logistics and supply chain management journals, during the 2010–2018 timeframe. Design/methodology/approach – The authors use a systematic literature review (SLR) methodology which follows the methodology employed by Carter and Easton (2011). An evaluation of this methodology, using the Modified AMSTAR criteria, demonstrates a high level of empirical validity. Findings – The field of SSCM continues to evolve with changes in substantive focus, theoretical lenses, unit of analysis, methodology and type of analysis. However, there are still abundant future research opportunities, including investigating under-researched topics such as diversity and human rights/working conditions, employing the group as the unit of analysis and better addressing empirical validity and social desirability bias. Research limitations/implications – The findings result in prescriptions and a broad agenda to guide future research in the SSCM arena. The final section of the paper provides additional avenues for future research surrounding theory development and decision making. Originality/value – This SLR provides a rigorous, methodologically valid review of the continuing evolution of empirical SSCM research over a 28-year time period. Keywords Environment, Economic performance, Sustainability, Social responsibility, Triple bottom line, Sustainable supply chain management, Systematic literature review Paper type Literature review 1. Introduction Sustainability has increasingly become a part of how firms manage their production and operations, with 82 percent of S&P 500 companies publishing separate sustainability reports in 2016 compared with only 20 percent in 2011 (GreenBiz, 2018). However, over 90 percent of the impact on natural resources, including air, soil and land, is due to supply chain management (SCM) activities, and over 80 percent of greenhouse-gas emissions for consumer-goods products occur in the supply chain (Bové and Swartz, 2016). As noted by Gary Hirshberg, President and CEO of Stonyfield Farms, “Even though we were the first manufacturer in America to offset 100% of the CO2 emissions from our manufacturing plants, these incredible offsets amounted to a rounding error in terms of our total carbon footprint […] despite the great things we did in our plant, unless we tackled our supply chain’s carbon footprint, we were nowhere” (Sustainable Supply Chain Foundation, 2019). For most organizations, focusing on sustainability beyond the four walls – i.e. sustainable supply chain management (SSCM) – has the greatest overall impact (Schmidt et al., 2017). SSCM has also become one of the primary areas of research in the SCM discipline. As evidence of this statement, numerous systematic literature reviews (SLRs) have appeared in International Journal of Physical the literature, to the point where a recent SLR of SLRs was conducted (Carter and Distribution & Logistics Management Washispack, 2018). In their meta-review, Carter and Washispack (2018) note that one of the Vol. 50 No. 1, 2020 pp. 122-146 remaining opportunities for SSCM SLRs is periodic updates of extant, high-visibility SLRs. © Emerald Publishing Limited 0960-0035 This paper was invited by the journal’s outgoing editor, with the objectives of providing DOI 10.1108/IJPDLM-02-2019-0056 an update to Carter and Easton’s (2011) SSCM SLR and offering guidance for future SSCM research. The rationale for this periodic update is that while only nine years have Sustainable passed since Carter and Easton’s work was published, we have seen both a tremendous supply chain growth in the interest in and publication of SSCM research. In addition, a priori, we posited a management substantial increase in both theoretical and methodological rigor. Our findings, based on this update, do indeed suggest that SSCM research continues to evolve in a positive direction in terms of theory and methodological rigor. However, there are opportunities to further improve methodological rigor – including more fully addressing empirical validity 123 and social desirability bias – and to investigate under-researched substantive areas including diversity and human rights. Furthermore, in the final section of the paper we outline two key opportunities – theory development and decision making – that we believe are particularly in need of additional research. Addressing each of these areas of research will result in not only improved theory and methods, but in findings that will benefit SSCM practice by providing rigorous research that will facilitate SSCM measurement and managerial decision making. The remainder of this paper is organized as follows. In the next two sections, we describe the methodology used to collect the study’s data and the analysis of these data. We then present the results of this analysis in a table that updates the findings from Carter and Easton (2011). We conclude by discussing these results, comparing the findings with earlier data and presenting prescriptions for future SSCM research. 2. Methodology In this section of the paper, we describe the methodology, used by Carter and Easton (2011) in their SLR of SSCM research, which we employed for this updated SLR. An SLR allows researchers to perform an objective, transparent and replicable review of the literature (Denyer and Neely, 2004). In particular, we aligned our methodology with Carter and Washispack’s (2018) Modified AMSTAR criteria (see Appendix 1). To allow a direct comparison with Carter and Easton’s (2011) findings, our review incorporated the same seven journals, which are commonly recognized as encompassing the primary outlets for empirical, SCM research (Giunipero et al., 2008; Cantor, 2008): (1) International Journal of Logistics Management. (2) International Journal of Physical Distribution and Logistics Management. (3) Journal of Business Logistics. (4) Journal of Operations Management. (5) Journal of Supply Chain Management. (6) Transportation Journal. (7) Transportation Research Part E. Like Carter and Easton, we adopted Carter and Roger’s (2008, p. 368) definition of SSCM as, “the strategic, transparent integration and achievement of an organization’s social, environmental, 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.” Articles which centered on topics that fell within this definition of SSCM were considered for inclusion in our analysis, based on topic. A broad, secondary inclusion criterion was that the paper employed an empirical methodology, including, “the collection and analysis of primary or secondary data […] as well as conceptual theory building” (Carter and Easton, 2011, p. 50). Following Carter and Easton’s exclusion criteria, we omitted articles that focused on the following subjects: consumer issues (e.g. automobile safety); macro/policy issues (as compared IJPDLM to micro, company and supply chain specific issues); reverse logistics and waste disposal; 50,1 supply chain security; technical issues surrounding life cycle analysis, end-of-life, cost modeling, hazardous materials, etc., including the regulatory issues surrounding these subjects; and papers where sustainability was an ancillary part of the article’s focus. We also excluded papers that employed non-empirical approaches (e.g. mathematical modeling) as well as articles that were opinion based, relied on anecdotal evidence and did not fall under the 124 rubric of conceptual theory building. The time period of Carter and Easton’s (2011) review ended at the end of June 2010. We began data collection by manually reviewing all content published in the seven journals over the eight-year period from July 2010 through the end of June 2018. We reviewed the title, keywords and abstracts of the 2,610 pieces appearing in the above journals over the eight-year time period. The first author reviewed all 2,610 articles for possible inclusion, and the second, third and fourth authors divided the 2,610 articles into three groups with each of these three authors reviewing one of the groups of articles. There were 75 initial disagreements of whether to include or exclude a paper across the 2,610 published papers (an initial inter-coder agreement rate of 97.13 percent). These disagreements were settled through discussion and consensus. This review resulted in the initial inclusion of 194 potential articles. We next performed a keyword search of the same journals and time period, using both the EBSCO and SCOPUS databases, using the same keywords employed by Carter and Easton (2011, p. 51) in the text and abstract fields. No additional papers were identified through this keyword search. The initial shortlist of 194 papers is displayed in Appendix 2. Next, we manually reviewed the full articles for each of the 194 papers on the shortlist. Based on the inclusion and exclusion criteria, we eliminated 30 of these papers (denoted by “excluded” in Appendix 2), resulting in a final set of 164 papers used in our analysis. 3. Data coding The first author coded each of the 164 articles, using Carter and Easton’s (2011) coding scheme (Table I). The second, third and fourth authors coded subsamples of the 164 articles, so that each article was coded by two researchers. Following Carter and Easton (2011), we calculated the reliability of the data coding using the proportion of all pairwise agreements between coders. This resulted in an inter-coder agreement rate of 96.16 percent. Based on the large number of classification categories, this reliability rate is comparable to Cronbach’s (1951) coefficient α (Perreault and Leigh, 1989). This reliability rate is far in excess of the recommended minimum value of 0.70, indicating a high level of replicability of the data coding process. We also evaluated the validity of our SLR methodology using Carter and Washispack’s (2018) Modified AMSTAR criteria. Each of these criteria was met: an a priori selection process with explicit inclusion and exclusion criteria; clearly referenced keywords; the use of multiple databases; the use of two or more reviewers for article selection and article coding and reporting of inter-rater reliability statistics; a shortlist of included and excluded papers; and reporting of aggregate study data in table form. 4. Results Our results are displayed in Table II. Extending Carter and Easton’s (2011) analysis, we compare the 2010–2018 time period (Column 2 of Table II) with the two earlier time periods reported in Carter and Easton (2011) (Columns 3 and 4 of the table). We discuss these results and comparisons in the next section of the paper. Coding family Description of codes Sustainable supply chain Subject Standalone area(s): the article investigates a specific dimension(s) of sustainability management (e.g. the environment) CSR: the article uses framework(s) from the CSR literature Sustainability: the article uses the triple bottom line as a framework for the article’s conceptualization Note: an article could be coded as both “CSR” and “Sustainability,” if both literature 125 bases are used in developing the article’s conceptualization Inferential Coded as “Inferential” if the authors use inferential statistics to test hypotheses or propositions that are stated a priori, or if the authors use an inductive approach (e.g. grounded theory, rigorous analysis of qualitative data) to develop explicitly stated propositions or similar, explicitly stated relationships among variables Coded as “Descriptive” if the authors take a descriptive approach to their analyses (e.g. present summary statistics; compare means; or even use inferential statistics such as ANOVA, but without testing hypotheses or propositions which were developed and presented prior to the statistical analysis of the data) Moderation Coded as “Moderation” if the authors test for moderation/interaction effects Methodology and Methodology: this is the primary methodology used to collect the study’s data. analysis Examples include case study, survey, archival data, literature review, depth interviews and focus group interviews Analysis: the primary approach(es) used to analyze the study’s data. Examples include confirmatory factor analysis, exploratory factor analysis, regression, ANOVA, structural equation modeling and qualitative data analysis Validity: coded as “Yes” if the authors address both reliability and other facets of validity, “Somewhat” if the authors assess reliability but not other facets of validity, and “No” if the authors address neither reliability nor validity Social desirability bias: coded as “Yes” if the authors address this bias, and No if they do not Sample Size 1: the number of organizations/firms from which data were collected Sample Size 2: the total number of respondents/informants (this number is equal to “Sample Size 1” in the case of a single, key informant, or greater than “Sample Size 1” in the case of multiple informants) Unit of analysis: generally one of the following: the “Individual,” the “Function or Group,” the “Firm” (includes a plant or SBU) or the “Supply Chain”. Examples of “Other” include projects and published articles as the unit of analysis Context Key informant: the functional affiliation of the key informant (e.g. procurement, production, distribution and marketing) Industry: the investigated industry; coded as “Multiple” in the case of multiple industries Theoretical lens(es) Assessed whether the authors use any of the following theories as lenses, or even rationale in developing their models: transaction cost economics, the resource-based Table I. view, the knowledge-based view, stakeholder theory, and/or other Coding scheme 5. Discussion and prescriptions for future research 5.1 Subject Environmental facets of SCM continue to lead the focus of research over the 28-year time period (Section A of Table II). While the focus on environmental topics decreased from the 1991–2000 time period to the 2001–2010 time period, this focus increased from 35.42 percent during the 2001–2010 time period to 45.73 percent during the 2010–2018 time period. One explanation for this greater proportion of environmental articles during the most recent time period may be the elevated focus by the media on climate change and by industry due to related supply chain disruptions and emissions reporting consortia such as the GRI. While the environment as a topic increased during the most recent time period, the use of corporate social responsibility as a framework for an article’s conceptualization decreased substantially – from 18.75 to 4.88 percent – between the 2001–2010 and 2010–2018 IJPDLM Percenta Percenta Percenta 50,1 (2010–2018)b (2001–2010) (1991–2000) Section A: subject Environment 45.73 35.42 53.13 Diversity 0.61 4.17 31.25 Human rights/Quality of life 2.44 4.17 9.38 126 Safety 21.34 27.08 12.50 Philanthropy 0.00 0.00 0.00 Corporate social responsibility 4.88 18.75 0.00 Sustainability 48.17 25.00 0.00 Section B: industry Automotive 2.21 4.76 0.00 Consumer products 4.41 11.90 16.67 Food and beverage 7.35 2.38 3.33 Transportation 35.29 23.81 16.67 Multi-industry 38.97 47.62 50.00 Other 11.76 9.52 13.33 Section C: theoretical lens(es) Transaction cost economics 2.44 8.33 3.13 Resource-based view 12.80 16.67 3.13 Knowledge-based view/Organizational learning 1.83 4.17 0.00 Stakeholder theory 15.85 35.42 0.00 Other 57.32 45.83 9.38 Multiple lenses 28.66 33.33 3.13 None 29.88 33.33 87.50 Section D: validity Addressed (reliability and multiple, additional facets of validity) 66.67 64.44 18.75 Partially addressed (reliability, but no additional facets of validity) 9.65 8.89 15.63 Not addressed 23.68 26.67 65.63 Section E: social desirability bias Addressed 23.47 25.00 3.45 Not addressed 76.53 75.00 96.55 Section F: unit of analysis Individual 13.41 17.78 25.00 Function or group 1.22 8.89 9.38 Firm (including plant or SBU) 67.68 60.00 65.63 Supply chain (at least at dyad) 17.68 6.67 0.00 Other 5.49 6.67 0.00 Section G: methodologyc Survey 30.49 47.92 78.13 (Multiple) case study 22.56 22.92 9.38 Archival data 22.56 10.42 6.25 Empirical/Systematic literature review 7.32 8.33 0.00 Conceptual theory building 10.98 4.17 3.13 Focus group interviews 0.00 2.08 3.13 Individual interviews 7.93 4.17 0.00 Section H: analysis Descriptive statistics (summary statistics, means testing, rank analysis) 1.83 11.11 54.29 Regression analysis 7.93 19.05 11.43 Table II. Results (continued )  Percenta Percenta Percenta Sustainable (2010–2018)b (2001–2010) (1991–2000) supply chain management Qualitative data analysis (data coding, matrix analysis and content analysis) 34.76 17.46 11.43 CFA 5.49 15.87 5.71 SEM, Path Analysis 21.34 12.70 2.86 EFA 1.83 11.11 2.86 127 Other 1.83 6.35 2.86 ANOVA 4.27 3.17 5.71 Conceptual Theory Building 10.98 3.17 2.86 Section I: moderation Moderation/Interaction effects 28.48 13.04 6.25 Notes: aPercentages in a section may not sum to 100 percent, due to rounding and multiple-category coding; b July 2010 through June 2018; cother methodologies (7.32 percent of total) included experimental designs, event studies and the Delphi method Table II. time periods. There were also decreases in diversity (from 4.17 to 0.61 percent) and human rights/quality of life (from 4.17 to 2.44 percent) as subject foci. Finally, sustainability served as a foundational conceptualization for 48.17 percent of the articles during the 2010–2018 time period (increasing from 25.00 percent during the 2001–2010 time period). The increased focus on the environmental pillar of SSCM is encouraging. However, the decreased emphasis on diversity and human rights/quality of life suggests an opportunity for future research. These topics might be investigated in a standalone fashion using a number of different methodologies. For example, lab experiments could be used to investigate minority supplier selection decisions and decisions surrounding diversity hiring for transportation and warehousing positions. Archival and field studies could be used to identify firms that are outliers in terms of diversity, human rights and working conditions in their supply chains, and investigate potential differences and key success factors for these outlying conditions. Events studies could be employed to study the impact of human rights and working conditions on stock prices – perhaps at both a focal firm vs its supplier. Diversity, human rights and working conditions could also be studied in conjunction with other sustainability topics. For example, researchers might consider the potential tradeoffs between improving one pillar of the triple bottom line (e.g. a decreased carbon footprint resulting from reshoring manufacturing) and possibly harming another (e.g. removing the sole source of viable income from a community in an emerging economy). Such tradeoffs can also occur within a pillar of the triple bottom line. For instance, plastic grocery bags have a lower carbon footprint than paper grocery bags, but plastic bags can cause greater damage to the aquatic ecosystem. Such research might begin by identifying tradeoffs via archival and field research, and then investigating how managers might decide among tradeoffs (e.g. by employing a policy capturing approach). Finally, this research stream could ascertain potential workarounds such that tradeoffs are minimized and synergies created. 5.2 Industry Overall, there were not substantial changes in the industries from which data were collected. The largest change was in the transportation sector, particularly in the case of studies focusing on safety (e.g. Miller and Saldanha, 2016) and emissions (e.g. Rodrigues et al., 2015). Multi-industry studies continue to account for a large proportion of the total. While Carter and Easton (2011) suggested that researchers might narrow their focus to specific industries, with the goals of both developing industry-specific measures and testing the IJPDLM boundaries of theory, this advice does not seem to have been born out. As suggested by 50,1 Carter and Easton (2011), this may be due to the need to generate adequate sample sizes. One possible means of meeting the goals of developing industry-specific measures and testing the boundaries of a theory would be to identify a group of industries with similar characteristics, so that there would be both an adequate sample size and homogeneity in terms of empirical measurement and the characteristic(s) of a particular theory. A similar 128 approach could also be used to identify a second (homogeneous within group) set of industries which may, based on theory, create a boundary condition for the theory being tested. 5.3 Theoretical lenses The use of theory in SSCM research continues to increase. During the 2010–2018 time period, 29.88 percent of studies employed no theoretical lens, compared to 33.33 and 87.50 percent during the 2001–2010 and 1991–2000 time periods, respectively. While recognizing that a theoretical lens is not required across all types of SSCM research and that there are differing missions across the seven journals from which we collected data, this is nonetheless an encouraging trend. The proportion of papers employing the specific theories examined by Carter and Easton (2011) – transaction cost theory, resource-based theory, the knowledge-based view/organizational learning and stakeholder theory – decreased for each theoretical lens during the 2010–2018 time period. Researchers instead employed “Other” theoretical lenses to a greater extent (57.32 percent for the 2010–2018 time period compared to 45.83 percent during the 2001–2010 time period and 9.38 percent during the 1991–2000 time period). The most common theoretical lenses within the “Other” category for the 2010–2018 time period were institutional theory (6.10 percent), resource dependence theory (3.05 percent), behavioral decision theory (2.44 percent), high reliability theory (2.44 percent) and normal accident theory (2.44 percent). Finally, several studies (5.49 percent) employed a theory building approach as their sole lens. There seem to be at least two likely reasons for these trends. Fist, the dominant theories from the earlier time periods have been extensively tested and used in the SSCM and broader SCM literature. Thus, we may have reached a point of saturation (real or perceived). Second, the SCM discipline as a whole seems to be reaching a point of maturation, where scholars are facile with a broader palette of theories and can draw upon the most appropriate theory(ies) to help answer a study’s specific research questions. Our discipline is also increasingly developing SCM-specific theory (Carter, 2011). The use of a wider range of established theories is encouraging, suggesting that SSCM researchers are employing a wider and likely more appropriate palette of theories to advance research. While the use of multiple theoretical lenses within a study decreased somewhat (from 33.33 percent during the 2001–2010 time period to 28.66 percent during the current time period), this may be due to a deeper and more thoughtful reliance on a single theory to develop hypotheses or inductively develop propositions or taxonomies. We advocate that researchers should continue to employ the most appropriate theoretical lens(es), by developing a wider understanding and appreciation of different, potentially relevant theories that might apply to SSCM phenomena. In addition, we encourage researchers to consider potential tensions between theories, and the application of extant theories to SSCM phenomena. For example, agency theory suggests that a buying firm will be held accountable for the sustainability of its suppliers (Eisenhardt, 1989), while the lens of psychological distance suggests that supplier sustainability would be discounted since it occurs at a distance from the buying firm (Trope and Liberman, 2010). Both of these approaches – using a single theory or complementary theories and using competing theories – provide opportunities to test the boundaries of extant theory within the SSCM context.  Our findings suggest that one promising, under-researched lens is behavioral decision Sustainable theory. Managers are not rational agents (Kahneman, 2011), perhaps particularly in the case supply chain of SSCM. For example, Gattiker and Carter (2010) employ intra-organizational influence management theory to investigate the most effective influence tactics to gain the commitment of supply managers to sustainability initiatives; they find that inspirational appeals are significantly more effective than rational persuasion (i.e. making a business case) in gaining commitment. Behavioral decision theory would be a valuable lens to investigate related dynamics, 129 cognition and decision making surrounding SSCM. 5.4 Methodology and analysis 5.4.1 Validity. The proportion of studies that have not addressed validity has not changed substantially between the 2010–2018 (23.68 percent) and the 2001–2010 (26.67 percent) time periods. This is a disturbing finding. Validity is often referred to as the sine qua none of empirical research (Kerlinger, 1986; Campbell and Stanley, 1963), meaning that without demonstrating validity, researchers cannot be certain about their results. We strongly advocate that authors, along with editors and reviewers, ensure that empirical validity is clearly demonstrated where appropriate. Obviously, the way in which validity is assessed varies across methodologies, as does the nomenclature (e.g. “reliability” and “validity” vs “credibility” and “trustworthiness”). 5.4.2 Social desirability bias. Social desirability bias exists when study participants tend to answer questions, share perspectives and/or behave in a manner that they believe will be viewed favorably by the researcher and group or societal norms (Crowne and Marlowe, 1960). For some studies, such as those that do not involve human subjects, social desirability bias is largely not applicable. For studies where social desirability bias was applicable, we coded whether the study addressed social desirability bias. The proportion of applicable studies that addressed social desirability bias decreased slightly from 25.00 percent during the 2001–2010 time period to 23.47 percent during the 2010–2018 time period. Social desirability can be potentially mitigated, for example, via ensuring anonymity, using appropriate question wording and designing experiments so that the research objectives are not understood or communicated to subjects until the subjects are debriefed. Researchers can measure the extent of social desirability bias through the use of a social desirability scale. Crowne and Marlowe (1960) develop one of the earliest scales for assessing social desirability bias. More recent scales, such as the BIDR-16, offer a smaller number of scale items that also leverage the multi-dimensional nature of social desirability bias (Hart et al., 2015). Of course, researchers should do both – attempt to mitigate social desirability bias and test for the presence of the bias – when appropriate. And again, editors and reviewers should help to ensure that the effects of social desirability bias are minimized and measured. 5.4.3 Unit of analysis. The most common unit of analysis (Section F of Table II) is the firm. The focus on the firm as the unit of analysis increased somewhat from the 2001–2010 time period (60.00 percent) to the 2010–2018 time period (67.68 percent). This may be due to the increased use of archival data, described in the next section. An even more dramatic increase appears at the supply chain level, which encompasses the interorganizational dyad through broader interorganizational networks. Only 6.67 percent of the 2001–2010 time period articles employed this supply chain-level unit of analysis, compared to 17.68 percent during the 2010–2018 time period. While not displayed in the table, we also coded articles that employed a multi-level approach (6.10 percent for the 2010–2018 time period). Wichmann et al. (2016), for example, investigate both individual and social network characteristics surrounding a large-scale SSCM initiative. Our findings suggest that there continue to be opportunities to explore group-level phenomena (only 1.22 percent of articles used the function or group as the unit of analysis IJPDLM during the 2010–2018 time period). For example, how does group decision making differ 50,1 from individual decision making for SSCM phenomena? And, are there differences in group decision making between SSCM and non-SSCM phenomena? Most SSCM initiatives involve multiple functions (Carter et al., 2007; Wichmann et al., 2016). Our findings suggest that there is an almost “blank canvas” for studies that investigate the cross-functional interactions surrounding SSCM. In addition, given the inherent nesting of individuals within 130 groups within firms within networks of firms, there are vast opportunities to take a multi-level perspective – conceptually and methodologically – in future SSCM research. 5.4.4 Methodology. Surveys continued to be the most commonly employed primary methodology during the 2010–2018 time period (30.49 percent) (Section G of Table II). However, unlike the prior two time periods, where surveys were the dominant methodology, there is more balance across methodologies during the 2010–2018 time period. Over 22 percent of the authors employed a case study methodology, and a similar percentage of authors used archival data. The use of archival data increased substantially, from 10.42 to 22.56 percent between the 2001–2010 and 2010–2018 time periods. This may be due to an increased awareness of the availability of archival data, along with an increased acceptance of this approach (Carter et al., 2008). The use of conceptual theory building also increased substantially, from 4.17 to 10.98 percent, across the two most recent time periods. We believe that this is a positive sign. Journals in the SCM discipline have begun to both recognize and encourage high-quality, rigorous conceptual theory building as the discipline continues to mature. The use of conceptual theory building helps the discipline to develop our own, SCM-specific theories rather than relying solely on theory developed in other disciplines. Whether conceptual theory building is used as a standalone methodology or incorporated into another methodology, researchers could consider how SSCM might differ from other SCM phenomena as they develop theory and/or integrate extant theory. In addition to the categories of methodologies displayed in Table II, 3.66 percent of the articles utilized an experimental design. Given the underrepresentation of this methodology compared to the other methodologies displayed in Table II, we believe that there is an opportunity to use experiments to better understand SSCM phenomena. As one example, researchers might employ a laboratory experimental design to examine group-level decision making concerning whether to implement different types of sustainable supplier development programs. 5.4.5 Analysis. As shown in Section H of Table II, the use of descriptive statistics as a primary analysis has almost disappeared from the SSCM literature (1.83 percent during the 2010–2018 time period). Conversely, the use of qualitative data analysis almost doubled between the two most recent time periods, from 17.46 to 34.76 percent. Qualitative data analysis was used with case study and individual interview data, as well as some archival data (e.g. content analysis). There are likely numerous, additional data sources which could be subjected to qualitative analysis. Researchers could present a small group of subjects with an SSCM problem or scenario, video record the subsequent discussion and then qualitatively analyze the discussion to generate understanding about the decision-making process. As another example, researchers could take a hybrid, ethnography-case study approach to follow the development and implementation of SSCM initiatives within organizations. The increased use of structural equation modeling (21.34 percent during the 2010–2018 time period vs 12.70 percent during the 2001–2010 time period) may explain the decreased use of regression analysis (7.93 percent in the current time period vs 19.05 percent during the 2001–2010 time period) and exploratory factor analysis (1.83 vs 11.11 percent in the 2010–2018 and 2001–2010 time periods, respectively). We also included a code for econometric modeling. Over 12 percent of the articles used econometric modeling during the Sustainable 2010–2018 time period. We believe that this is a positive trend for SSCM research. Such supply chain approaches allow researchers to rigorously investigate inferential relationships by management accounting for endogeneity effects (Semadeni et al., 2014) and investigating temporal effects. Finally, there was a substantial increase in investigating moderation/interaction effects across the two most recent time periods (28.48 vs 13.04 percent) (Section I of Table II). This increase suggests that researchers are moving beyond examining linear effects, to also 131 considering the contexts in which theoretical assertions and inferential relationships exist. Moving forward, SSCM scholars might consider moderators such as SSCM vs non-SSCM phenomena, social vs environmental initiatives, perceived project risk and adversarial vs collaborative supply chain relationships. 5.5 Conclusions based on SLR Overall, our findings suggest that SSCM research continues to mature, by employing a broader range of theoretical lenses and thus greater richness, a more balanced breadth of methodologies and increased rigor in terms of data analysis. At the same time, there are some continuing weaknesses and imbalances in the research published during the 2010–2018 time period. As described in Sections 5.1 through 5.4, these deficits and omissions can be viewed as opportunities for future research. Specifically, there are opportunities to investigate diversity (e.g. diversity spend and diversity in a largely Caucasian male-dominated SCM workforce) and human rights (e.g. working conditions at supplier facilities) as subjects, incorporate the group as the unit of analysis and employ a multi-level perspective. SSCM researchers must also better assess empirical validity and social desirability bias, and editors and reviewers should incorporate both as criteria in evaluating manuscripts for publication. 6. Final thoughts about future directions We see two major and impactful opportunities for future SSCM research: first, continuing to develop and refine middle-range theory that will ultimately result in a grand theory of SSCM, and second, creating a better understanding of how managers can make effective decisions surrounding SSCM. We discuss these opportunities in the two subsections that follow. 6.1 Theory development Our discipline saw initial efforts to develop middle-range theory using a SLR (Seuring and Müller, 2008), conceptual theory building (Carter and Rogers, 2008) and grounded theory (Pagell and Wu, 2009) approaches. Since that time, much of the research has focused on using both deductive (e.g. Kim et al., 2019) and inductive (e.g. Fayezi et al., 2018) approaches. We have also seen more recent conceptual theory building efforts at the middle range (e.g. Busse, 2016), including calls to expand our paradigm of SSCM and questioning our assumptions of current theorizations (e.g. Matthews et al., 2016; Pagell and Shevchenko, 2014). As a next step forward, additional theory development of SSCM phenomena seems warranted. One clear approach is for researchers to continue to develop middle-range theory. The above examples include multiple methodological approaches for doing so. Beyond the obvious benefits of incorporating multiple methods in building SSCM theory, it is important for researchers to consider multiple forms of theorization. For example, the introduction of propositions is a popular outcome or “product” of theory development (Brief, 2003). In the case of the above, initial efforts to develop middle-range theory, all three sets of IJPDLM authors introduce propositions based on their theory building (although each of the three 50,1 papers introduces a framework or model in addition to developing propositions). Yet there are many other approaches to theory building, including the use of taxonomies and typologies. These approaches are frequently found in the general management literature. Makadok and Coff (2009), for example, develop a taxonomy of interorganizational governance forms and their relationships with efficiency. Mitchell et al. (1997, p. 853) develop a typology of 132 stakeholders, based on whether stakeholders possess power, legitimacy and urgency, and by doing so move stakeholder theory into the realm of “full theoretical status.” SSCM researchers should keep each of these options in mind when engaging in theory building research. For instance, is there a typology of the supply chain – or as a more tractable unit of analysis, a portion of the supply chain such as a triad – related to sustainability? To ask this question a bit differently, are some triadic forms more likely to have higher sustainability performance than others? Finally, there is an opportunity to develop a grand theory of SSCM. Such an effort might integrate existing theory. As one example, complex adaptive systems theory (Kauffman, 1993) could be one particularly germane theoretical lens, given the inter-relationships of not only multiple organizations in the supply chain, but also the potential tradeoffs across outcomes (discussed in the next section). The development of a grand theory of SSCM might also include a comparison and contrasting of initial theory development efforts – for instance, Seuring and Müller (2008), Carter and Rogers (2008), Pagell and Wu (2009) and others that follow. 6.2 Decision making surrounding SSCM At the micro (individual), meso (group) and macro (firm and supply chain) levels, there is an opportunity to better understand how managers make decisions surrounding SSCM. As described in the prior section, SSCM decisions, in particular, involve uncertainty and tradeoffs. Tradeoffs can exist between two or more of the dimensions of the triple bottom line (for instance, driving smaller and lighter vehicles may decrease carbon output but result in decreased driver safety) and even within a dimension of the triple bottom line (e.g. a closed-loop system in which plastic is recycled would likely reduce the amount of plastics in landfills and water systems, but could increase the system’s carbon footprint). In many cases, managers may be unaware of these tradeoffs (Rogers et al., 2019). In addition, uncertainty exists due to a lack of metrics both within and across organizations (Carter and Washispack, 2018). Construal-level theory is one potential lens that might be employed to investigate how managers value different TBL outcomes (Trope and Liberman, 2010). The theory proposes that as the distance of an event – spatial and/or temporal – increases, decision makers will increasingly discount the consequences of the event. This theory could be used to posit how supply chain managers evaluate the tradeoffs discussed above. One useful methodology for examining managerial judgments surrounding SSCM tradeoffs is policy capturing (Bottenberg and Christal, 1961, 1968). This approach essentially entails identifying tradeoffs across different criteria (e.g. economic, environmental and social performance) at multiple levels (for example at low, medium and high levels) and presenting managers will all possible combinations – in the above example 27 (3 × 3) scenarios. This methodology allows researchers to understand the true evaluation and tradeoff preferences of participants, since participants are presented with “tough choices” (e.g. medium economic performance, low social performance and high environmental performance). Related to tradeoff preferences, the metrics that exist to measure SSCM, particularly across two or more organizations, are at an early stage of development (Goldsby and Zinn, 2018, p. 238). One encouraging initial effort is the aggregated, longitudinal GhG data collected through CDP. Researchers should consider similar opportunities to accumulate Sustainable longitudinal data relating to the plant, firm and/or supply chain levels. For example, supply chain the high performance manufacturing project (www.linkedin.com/in/hpm-high-performance- management manufacturing-6b51575b/) – a multi-university initiative that has collected operations data globally, at the plant level, since 1989 – could be modified to collect social and environmental performance data. Finally, researchers might move beyond what appears to be a paradigm that SSCM is 133 “good,” by asking questions such as: What is “good”? Is SSCM always “good”? What are the potential tradeoffs, and unintended consequences of SSCM? And, once identified, how can tradeoffs be mitigated or even turned into complementarities? 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(2011), “The influence of green practices on supply chain performance: a case study approach”, Transportation Research Part E: Logistics and Transportation Review, Vol. 47 No. 6, pp. 850-871. Bajec, P., Tuljak-Suban, D. and Krmac, E. (2015), “Do ISO standards favour logistics provider efficiency, competitiveness and sustainability? A Slovenian perspective”, International Journal of Logistics Management, Vol. 26 No. 2, pp. 275-295. Bals, L. and Tate, W.L. (2018), “Sustainable supply chain design in social businesses: advancing the theory of supply chain”, Journal of Business Logistics, Vol. 39 No. 1, pp. 57-79. Bask, A. and Rajahonka, M. (2017), “The role of environmental sustainability in the freight transport mode choice”, International Journal of Physical Distribution and Logistics Management, Vol. 47 No. 7, pp. 560-602. Bell, J.E., Autry, C.W., Mollenkopf, D.A. and Thornton, L.M. (2012), “A natural resource scarcity typology: theoretical foundations and strategic implications for supply chain management”, Journal of Business Logistics, Vol. 33 No. 2, pp. 158-166. Bendoly, E., Craig, N. and DeHoratius, N. (2018), “Consistency and recovery in retail supply chains”, Journal of Business Logistics, Vol. 39 No. 1, pp. 26-37 (excluded). Beske, P. (2012), “Dynamic capabilities and sustainable supply chain management”, International Journal of Physical Distribution and Logistics Management, Vol. 42 No. 4, pp. 372-387. Britto, R.A., Corsi, T.M. and Grimm, C.M. (2010), “The relationship between motor carrier financial performance and safety performance”, Transportation Journal, Vol. 49 No. 4, pp. 42-51. Brockhaus, S., Kersten, W. and Knemeyer, A.M. (2013), “Where do we go from here? progressing sustainability implementation efforts across supply chains”, Journal of Business Logistics, Vol. 34 No. 2, pp. 167-182. IJPDLM Browne, M. and Gomez, M. 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(2013), “Do owner–operators pose a safety risk on the nation's highways?”, Transportation Research Part E: Logistics and Transportation Review, Vol. 59 No. 1, pp. 34-47. Cantor, D.E., Corsi, T.M. and Grimm, C.M. (2017), “The impact of new entrants and the new entrant program on motor carrier safety performance”, Transportation Research Part E: Logistics and Transportation Review, Vol. 97, pp. 217-227. Cantor, D.E., Corsi, T.M., Grimm, C.M. and Özpolat, K. (2010), “A driver focused truck crash prediction model”, Transportation Research Part E: Logistics and Transportation Review, Vol. 46 No. 5, pp. 683-692. Cantor, D.E., Corsi, T.M., Grimm, C.M. and Singh, P. (2016), “Technology, firm size and safety: theory and empirical evidence from the us motor-carrier industry”, Transportation Journal, Vol. 55 No. 2, pp. 149-167. Cantor, D.E., Macdonald, J.R. and Crum, M.R. 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