Can big data and predictive analytics improve social and environmental sustainability?

Technological Forecasting and Social Change

Volumn 144, Issue , 2019, Pages 534-545

  Dubey, Rameshwar ^a   Gunasekaran, Angappa ^b   Childe, Stephen J ^c   Papadopoulos, Thanos ^d   Luo, Zongwei ^e   Wamba, Samuel Fosso ^f   Roubaud, David ^a  

^a MONTPELLIER BUSINESS SCHOOL   (France)

^b CALIFORNIA STATE UNIVERSITY   (United States)

^c UNIVERSITY OF PLYMOUTH   (United Kingdom)

^d UNIVERSITY OF KENT   (United Kingdom)

^e SOUTHERN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^f TOULOUSE BUSINESS SCHOOL   (France)

Author keywords

Big data; Dynamic capability view; Environmental sustainability; Predictive analytics; Social sustainability; Supply chains

Indexed keywords

BIG DATA; ENTERPRISE RESOURCE MANAGEMENT; ENVIRONMENTAL MANAGEMENT; PREDICTIVE ANALYTICS; SUPPLY CHAINS;

DYNAMIC CAPABILITIES; ENVIRONMENTAL PERFORMANCE; ENVIRONMENTAL SUSTAINABILITY; MANUFACTURING ORGANISATIONS; ORGANISATIONAL CAPABILITIES; SOCIAL AND ENVIRONMENTAL; SOCIAL SUSTAINABILITY; STRUCTURAL EQUATION MODELLING;

SUSTAINABLE DEVELOPMENT;

MANUFACTURING; PREDICTION; SUPPLY CHAIN MANAGEMENT; SUSTAINABILITY;

INDIA;

EID: 85023643537     PISSN: 00401625     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.techfore.2017.06.020     Document Type: Article

References (84)

1. Ageron, B., Gunasekaran, A., Spalanzani, A., Sustainable supply management: an empirical study. Int. J. Prod. Econ. 140:1 (2012), 168–182.
2. Akter, S., Wamba, S.F., Gunasekaran, A., Dubey, R., Childe, S.J., How to improve firm performance using big data analytics capability and business strategy alignment?. Int. J. Prod. Econ. 182 (2016), 113–131.
3. Amankwah-Amoah, J., Emerging economies, emerging challenges: mobilising and capturing value from big data. Technol. Forecast. Soc. Chang. 110 (2016), 167–174.
4. Armstrong, J.S., Overton, T.S., Estimating nonresponse bias in mail surveys. J. Mark. Res. 14:3 (1977), 396–402.
5. Barney, J., Firm resources and sustained competitive advantage. J. Manag. 17:1 (1991), 99–120.
6. Barratt, M., Oke, A., Antecedents of supply chain visibility in retail supply chains: a resource-based theory perspective. J. Oper. Manag. 25:6 (2007), 1217–1233.
7. Berns, M., Townend, A., Khayat, Z., Balagopal, B., Reeves, M., Hopkins, M., Kruschwitz, N., Sustainability and competitive advantage. MIT Sloan Manag. Rev. 51:1 (2009), 19–26.
8. Boyd, B.K., Takacs Haynes, K., Hitt, M.A., Bergh, D.D., Ketchen, D.J. Jr., Contingency Hypotheses in Strategic Management Research: Use, Disuse, or Misuse?. J. Manag. 38:1 (2012), 278–313.
9. Chen, M., Mao, S., Liu, Y., Big data: a survey. Mob. Netw. Appl. 19:2 (2014), 171–209.
10. Chin, W.W., The partial least squares approach for structural equations modelling. Marcoulides, G.A., (eds.)Modern Methods for Business Research, 1988, Lawrence Erlbaum Associates, Mahwah, NJ, 295–336 (London).
11. Cohen, J., Statistical Power Analysis for the Behavioral Sciences. 2nd ed., 1988, Erlbaum, Hillsdale, NJ.
12. Davidson, R., MacKinnon, J.G., Estimation and Inference in Econometrics. 1993.
13. Deshpandé, R., Farley, J.U., Webster, F.E. Jr., Corporate culture, customer orientation, and innovativeness in Japanese firms: a quadrad analysis. J. Mark. 57:1 (1993), 23–37.
14. Donaldson, L., The Contingency Theory of Organizations. 2001, Sage.
15. Duan, L., Xiong, Y., Big data analytics and business analytics. J. Manag. Anal. 2:1 (2015), 1–21.
16. Dubey, R., Gunasekaran, A., Childe, S.J., Wamba, S.F., Papadopoulos, T., The impact of big data on world-class sustainable manufacturing. Int. J. Adv. Manuf. Technol. 84:1–4 (2016), 631–645.
17. Dubey, R., Luo, Z., Gunasekaran, A., Douglas, M.A., Big data and predictive analytics in humanitarian supply chains: enabling visibility and coordination in the presence of swift trust. Int. J. Logist. Manag., 2018.
18. Eckstein, D., Goellner, M., Blome, C., Henke, M., The performance impact of supply chain agility and supply chain adaptability: the moderating effect of product complexity. Int. J. Prod. Res. 53:10 (2015), 3028–3046.
19. Gold, S., Seuring, S., Beske, P., Sustainable supply chain management and inter-organizational resources: a literature review. Corp. Soc. Responsib. Environ. Manag. 17:4 (2010), 230–245.
20. Grant, R.M., The resource-based theory of competitive advantage: implications for strategy formulation. Calif. Manag. Rev. 33:3 (1991), 114–135.
21. Guide, V.D.R., Ketokivi, M., Notes from the Editors: Redefining some methodological criteria for the journal. J. Oper. Manag. 37 (2015), v–viii.
22. Gunasekaran, A., Jabbour, C.J.C., Jabbour, A.B.L.D.S., Managing organizations for sustainable development in emerging countries: an introduction. Int. J. Sustain. Dev. World Ecol. 21:3 (2014), 195–197.
23. Gunasekaran, A., Papadopoulos, T., Dubey, R., Wamba, S.F., Childe, S.J., Hazen, B., Akter, S., Big data and predictive analytics for supply chain and organizational performance. J. Bus. Res. 70 (2017), 308–317.
24. Gupta, M., George, J.F., Toward the development of a big data analytics capability. Inf. Manag. 53:8 (2016), 1049–1064.
25. Hair, J.F., Tatham, R.L., Anderson, R.E., Black, W., Multivariate data analysis. 6, 2006, Pearson Prentice Hall, Upper Saddle River, NJ.
26. Hartmann, J., Moeller, S., Chain liability in multitier supply chains? Responsibility attributions for unsustainable supplier behavior. J. Oper. Manag. 32:5 (2014), 281–294.
27. Hazen, B.T., Boone, C.A., Ezell, J.D., Jones-Farmer, L.A., Data quality for data science, predictive analytics, and big data in supply chain management: an introduction to the problem and suggestions for research and applications. Int. J. Prod. Econ. 154 (2014), 72–80.
28. Henseler, J., Dijkstra, T.K., Sarstedt, M., Ringle, C.M., Diamantopoulos, A., Straub, D.W., Calantone, R.J., Common beliefs and reality about PLS: comments on Rönkkö and Evermann (2013). Organ. Res. Methods 17:2 (2014), 182–209.
29. Hitt, M.A., Bierman, L., Shimizu, K., Kochhar, R., Direct and moderating effects of human capital on strategy and performance in professional service firms: a resource-based perspective. Acad. Manag. J. 44:1 (2001), 13–28.
30. Hitt, M.A., Xu, K., Carnes, C.M., Resource based theory in operations management research. J. Oper. Manag. 41 (2016), 77–94.
31. IGEL, https://igel.wharton.upenn.edu/research/knowledgewharton-and-igel-reports/, 2014 (Accessed 23/06/2017).
32. ILO, http://www.ilo.org/wcmsp5/groups/public/—dgreports/—nylo/documents/statement/wcms_547544.pdf, 2017 (date of access: 11th April, 2017).
33. Jabbour, C.J.C., Mauricio, A.L., Jabbour, A.B.L.D.S., Critical success factors and green supply chain management proactivity: shedding light on the human aspects of this relationship based on cases from the Brazilian industry. Prod. Plan. Control 28:6–8 (2017), 671–683.
34. Kache, F., Seuring, S., Challenges and opportunities of digital information at the intersection of big data analytics and supply chain management. Int. J. Oper. Prod. Manag. 37:1 (2017), 10–36.
35. Kearns, G.S., Lederer, A.L., A resource-based view of strategic IT alignment: how knowledge sharing creates competitive advantage. Decis. Sci. 34:1 (2003), 1–29.
36. Keeso, A., Big data and environmental sustainability: a conversation starter. Working Paper 14-04, Smith School Working Paper Series, 2014 ((http://www.smithschool.ox.ac.uk/library/working-papers/workingpaper%2014-04.pdf)(Date of access: 15th April, 2015)).
37. Ketokivi, M.A., Schroeder, R.G., Perceptual measures of performance: fact or fiction?. J. Oper. Manag. 22:3 (2004), 247–264.
38. Khazanchi, S., Lewis, M.W., Boyer, K.K., Innovation-supportive culture: the impact of organizational values on process innovation. J. Oper. Manag. 25:4 (2007), 871–884.
39. Koseleva, N., Ropaite, G., Big data in building energy efficiency: understanding of big data and main challenges. Procedia Eng. 172 (2017), 544–549.
40. LaValle, S., Lesser, E., Shockley, R., Hopkins, M.S., Kruschwitz, N., Big data, analytics and the path from insights to value. MIT Sloan Manag. Rev., 52(2), 2011, 21.
41. Leidner, D.E., Kayworth, T., Review: a review of culture in information systems research: toward a theory of information technology culture conflict. MIS Q. 30:2 (2006), 357–399.
42. Liu, X., Zhang, C., Corporate governance, social responsibility information disclosure, and enterprise value in China. J. Clean. Prod. 142 (2017), 1075–1084.
43. Liu, H., Ke, W., Wei, K.K., Gu, J., Chen, H., The role of institutional pressures and organizational culture in the firm's intention to adopt internet-enabled supply chain management systems. J. Oper. Manag. 28:5 (2010), 372–384.
44. Longoni, A., Cagliano, R., Environmental and social sustainability priorities: their integration in operations strategies. Int. J. Oper. Prod. Manag. 35:2 (2015), 216–245.
45. Marques, F., Sérgio Miranda Mendonça, P., José Chiappetta Jabbour, C., Social dimension of sustainability in retail: case studies of small and medium Brazilian supermarkets. Soc. Respon. J. 6:2 (2010), 237–251.
46. Mata, F.J., Fuerst, W.L., Barney, J.B., Information technology and sustained competitive advantage: a resource-based analysis. MIS Q. 19:4 (1995), 487–505.
47. Matthias, O., Fouweather, I., Gregory, I., Vernon, A., Making sense of big data – can it transform operations management?. Int. J. Oper. Prod. Manag. 37:1 (2017), 37–55.
48. McAfee, A., The impact of enterprise information technology adoption on operational performance: an empirical investigation. Prod. Oper. Manag. 11:1 (2002), 33–53.
49. McAfee, A., Brynjolfsson, E., Davenport, T.H., Patil, D.J., Barton, D., Big data. The management revolution. Harv. Bus. Rev. 90:10 (2012), 61–67.
50. Moshtari, M., Inter-organizational fit, relationship management capability, and collaborative performance within a humanitarian setting. Prod. Oper. Manag. 25:9 (2016), 1542–1557.
51. Mueller, M., Dos Santos, V.G., Seuring, S., The contribution of environmental and social standards towards ensuring legitimacy in supply chain governance. J. Bus. Ethics 89:4 (2009), 509–523.
52. Nonaka, I., Toyama, R., Konno, N., SECI, Ba and leadership: a unified model of dynamic knowledge creation. Long Range Plan. 33:1 (2000), 5–34.
53. Oliver, C., Sustainable competitive advantage: combining institutional and resource-based views. Strateg. Manag. J. 18:9 (1997), 697–713.
54. Oztekin, A., Big data analytics for creating a marketing strategy in healthcare industry. Annals of Operations Research, 2017, 1–XX (accepted).
55. Pagell, M., Wu, Z., Building a more complete theory of sustainable supply chain management using case studies of 10 exemplars. J. Supply Chain Manag. 45:2 (2009), 37–56.
56. Park-Poaps, H., Rees, K., Stakeholder forces of socially responsible supply chain management orientation. J. Bus. Ethics 92:2 (2010), 305–322.
57. Peng, D.X., Lai, F., Using partial least squares in operations management research: a practical guideline and summary of past research. J. Oper. Manag. 30:6 (2012), 467–480.
58. Podsakoff, P.M., MacKenzie, S.B., Lee, J.Y., Podsakoff, N.P., Common method biases in behavioral research: a critical review of the literature and recommended remedies. J. Appl. Psychol. 88:5 (2003), 879–903.
59. Quinn, R.E., Rohrbaugh, J., A spatial model of effectiveness criteria: towards a competing values approach to organizational analysis. Manag. Sci. 29:3 (1983), 363–377.
60. Ross, J.W., Beath, C.M., Quaadgras, A., You may not need big data after all. Harv. Bus. Rev. 91:12 (2013), 90–98.
61. Russom, P., Big data analytics. TDWI Best Practices Report, Fourth Quarter, 2011, 1–35 ((https://vivomente.com/wp-content/uploads/2016/04/big-data-analytics-white-paper.pdf)(Date of access: 10th April, 2017)).
62. Schoenherr, T., Cheri, S.-P., Data science, predictive analytics, and big data in supply chain management: current state and future potential. J. Bus. Logist. 36:1 (2015), 120–132.
63. Sengers, F., Wieczorek, A.J., Raven, B., Experimenting for sustainability transitions: a systematic literature review. Technol. Forecast. Soc. Chang., 2016, 10.1016/j.techfore.2016.08.031.
64. Seuring, S., Müeller, M., Core issues in sustainable supply chain management–a Delphi study. Bus. Strateg. Environ. 17:8 (2008), 455–466.
65. Shrivastava, P., Guimaraes-Costa, N., Achieving environmental sustainability: the case for multi-layered collaboration across disciplines and players. Technol. Forecast. Soc. Chang. 116 (2017), 340–346.
66. Song, M.L., Fisher, R., Wang, J.L., Cui, L.B., Environmental performance evaluation with big data: theories and methods. Ann. Oper. Res., 2016, 1–14.
67. Song, M., Cen, L., Zheng, Z., Fisher, R., Liang, X., Wang, Y., Huisingh, D., How would big data support societal development and environmental sustainability? Insights and practices. J. Clean. Prod. 142 (2017), 489–500.
68. Sousa, R., Voss, C.A., Contingency research in operations management practices. J. Oper. Manag. 26:6 (2008), 697–713.
69. Teece, D.J., Pisano, G., Shuen, A., Dynamic capabilities and strategic management. Strateg. Manag. J. 18:7 (1997), 509–533.
70. Vachon, S., Klassen, R.D., Environmental management and manufacturing performance: the role of collaboration in the supply chain. Int. J. Prod. Econ. 111:2 (2008), 299–315.
71. Waller, M.A., Fawcett, S.E., Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management. J. Bus. Logist. 34:2 (2013), 77–84.
72. Wamba, S.F., Akter, S., Edwards, A., Chopin, G., Gnanzou, D., How ‘big data’ can make big impact: findings from a systematic review and a longitudinal case study. Int. J. Prod. Econ. 165 (2015), 234–246.
73. Wamba, S.F., Gunasekaran, A., Akter, S., Ji-Fan Ren, S., Dubey, R., Childe, S.J., Big data analytics and firm performance: Effects of dynamic capabilities. J. Bus. Res. 70 (2017), 356–365.
74. Wang, G., Gunasekaran, A., Ngai, E.W.T., Papadopoulos, T., Big data analytics in logistics and supply chain management: certain investigations for research and applications. Int. J. Prod. Econ. 176 (2016), 98–110.
75. Wang, Y., Kung, L., Byrd, T.A., Big data analytics: understanding its capabilities and potential benefits for healthcare organizations. Technol. Forecast. Soc. Chang., 2016, 10.1016/j.techfore.2015.12.019.
76. WEF, https://www.weforum.org/agenda/2016/10/inequality-in-india-oxfam-explainer/, 2016 (date of access 5th June, 2017).
77. White, M., Hill, S., McGovern, P., Mills, C., Smeaton, D., ‘High-performance’ management practices, working hours and work–life balance. Br. J. Ind. Relat. 41:2 (2003), 175–195.
78. Wolf, J., Sustainable supply chain management integration: a qualitative analysis of the German manufacturing industry. J. Bus. Ethics 102:2 (2011), 221–235.
79. Wu, Z., Pagell, M., Balancing priorities: decision-making in sustainable supply chain management. J. Oper. Manag. 29:6 (2011), 577–590.
80. Wu, J., Guo, S., Li, J., Zeng, D., Big data meet green challenges: big data toward green applications. IEEE Syst. J. 10:3 (2016), 888–900.
81. Yakovleva, N., Sarkis, J., Sloan, T., Sustainable benchmarking of supply chains: the case of the food industry. Int. J. Prod. Res. 50:5 (2012), 1297–1317.
82. Zhang, Y., Ren, S., Liu, Y., Si, S., A big data analytics architecture for cleaner manufacturing and maintenance processes of complex products. J. Clean. Prod. 142 (2017), 626–641.
83. Zhou, Z.H., Chawla, N.V., Jin, Y., Williams, G.J., Big data opportunities and challenges: discussions from data analytics perspectives [discussion forum]. IEEE Comput. Intell. Mag. 9:4 (2014), 62–74.
84. 3p Contributors, How big data can make your company more sustainable. Triple Pundit: People, Planet and Profit (Feb 10th 2016), 2016 (http://www.triplepundit.com/2016/02/big-data-can-make-company-sustainable/(date of access: 16th April, 2017)).