This project is made possible by the generous efforts of a very large group of contributors and collaborators.
Writing and Editing
Special thanks to Nichole Mumford from CSCMP for facilitating
Research and Translation Teams
Laura Allegue Lara
Communications and Media Team
B. Translation and Reliability
This year, we simultaneously released our online survey instrument in English, Spanish and Simplified Mandarin Chinese translations. Translations of the English language survey to the two other languages were performed by translation teams. The Spanish language translation team consisted of Daniela Muñoz (Mexico), Arianne Mahuad (Peru) and Anderson Bernal (Colombia). The Mandarin translation team consisted of Jay Guo and and Daniel Gao from the Ningbo China Center for Supply Chain Innovation (NISCI) in Ningbo, China, part of the MIT Global Supply Chain and Logistics Excellence (SCALE) Network.
After developing each translation, the MIT CTL research team talked through each translated question with the translation teams, stating and restating the translated words in order to ensure that the correct meaning was captured. Where potential discrepancies were encountered, native speakers were recruited to share their insights and offer suggestions.
Once the survey data was collected, we performed another indirect test of translation accuracy. We used a Cronbach’s alpha test to assess the internal consistency all the responses across translations, as well as within each of the three translations independently. Cronbach’s alpha is a test of a measurement scale’s internal consistency. That is, it tests whether answers show sufficient inter-item correlation to indicate that the tested questions are all measuring respondents’ feelings about the same concept, or domain. Cronbach’s alpha is measured on a scale of 0 to 1, and, generally speaking, scores exceeding 0.60 indicate that the survey instrument is ‘reliable’. The results for all the questions, as well as the three translations are shown below. 
Table 1: Reliability results for survey translations
C. Results of Global Comparisons
Comparisons across regions were statistically tested using a chisquared distribution test with Bonferonni correction. This method tests whether the groups of responses are dissimilar enough to conclude that they must have been drawn from meaningfully different populations. This is a probabilistic test that is commonly employed to compare responses to ordinal survey data like ours. We first tested entire data sets to see where some difference was observed. We then looked for differences in groups, across the Global North–South divide—and to confirm that the effect of this grouping was not just a result of aggregating, we also tested along an East–West divide. The results of those tests are shown in the tables below.
* Indicates statistical significance
All comparisons evaluated at threshold α = 0.05
With Bonferroni correction, global significance threshold < 0.0025, Global North–South threshold < 0.006
† Counts for question scores 1 and 2 were combined to avoid any item showing a count of less than 5, which can compromise the validity of chi-squared tests. For a full discussion of this methodology, see Harvey Russell Bernard, Social Research Methods: Qualitative and Quantitative Approaches (Thousand Oaks, Calif.: Sage, 2000), 563–67.
Table 2: Results of regional comparisons of SCS pressure sources/influences, goals, and investments
1. Alexis H. Bateman, Donna Palumbo-Miele, Suzanne Greene, Ashley Barrington, and Laura Allegue Lara, “State of Supply Chain Sustainability 2020” (Cambridge, Mass. and Lombard, Ill.: MIT Center for Transportation & Logistics and Council of Supply Chain Management Professionals, July 2020), https://ctl.mit.edu/sites/ctl.mit.edu/files/2020-09/State_Supply_Chain_Sustainability_MIT_CTL_CSCMP_0.pdf.
2. Alexis H. Bateman, Kellen Betts, Ken Cottrill, Jason Pang, and Aniruddha Suhas Deshpande, “State of Supply Chain Sustainability 2021” (Cambridge, Mass. and Lombard, Ill.: MIT Center for Transportation & Logistics and Council of Supply Chain Management Professionals, July 2021), 15, https://sscs.mit.edu/wp-content/uploads/2021/07/State-Sustainable-Supply-Chains-MIT-CSCMP.pdf.
3. Bateman et al., “State of Supply Chain Sustainability 2021”, 13.
4. Yossi Sheffi, “Green Takes a Back Seat to Recovery,” in The New (Ab)Normal: Reshaping Business and Supply Chain Strategy Beyond Covid‑19 (Cambridge, Mass.: MIT CTL Media, 2020), 180–90; Bateman et al., “State of Supply Chain Sustainability 2020,” 23–24; Bateman et al., “State of Supply Chain Sustainability 2021,” 13.
5. Bateman et al., “State of Supply Chain Sustainability 2021,” 13, 29.
6. Jum C. Nunnally, Psychometric Theory (New York: McGraw Hill, 1967); David L. Streiner, “Starting at the Beginning: An Introduction to Coefficient Alpha and Internal Consistency,” Journal of Personality Assessment 80, no. 1 (February 2003): 99–103.
Jum C. Nunnally, Psychometric Theory (New York: McGraw Hill, 1967); David L. Streiner, “Starting at the Beginning: An Introduction to Coefficient Alpha and Internal Consistency,” Journal of Personality Assessment 80, no. 1 (February 2003): 99–103.