Big data analytics in E-commerce: a systematic review and agenda for future research

Electronic Markets

Volumn 26, Issue 2, 2016, Pages 173-194

  Akter, Shahriar ^a   Wamba, Samuel Fosso ^b  

^a UNIVERSITY OF WOLLONGONG   (Australia)

^b NEOMA BUSINESS SCHOOL   (France)

Author keywords

Big data analytics; Business value; E commerce

Indexed keywords

EID: 84961206877     PISSN: 10196781     EISSN: 14228890     Source Type: Journal    
DOI: 10.1007/s12525-016-0219-0     Document Type: Article

References (160)

1. Agarwal, R., & Dhar, V. (2014). Editorial—big data, data science, and analytics: the opportunity and challenge for IS research. Information Systems Research, 25, 443–448.
2. Agarwal, R., & Weill, P. (2012). The benefits of combining data with empathy. MIT Sloan Management Review, 54, 35.
3. Allen, B., Bresnahan, J., Childers, L., Foster, I., Kandaswamy, G., Kettimuthu, R., Kordas, J., Link, M., Martin, S., Pickett, K., & Tuecke, S. (2012). Software as a service for data scientists. Communications of the ACM, 55, 81–88.
4. Ann Keller, S., Koonin, S. E., & Shipp, S. (2012). Big data and city living - what can it do for us? Significance, 9, 4–7.
5. Bankston, K.S., Soltani, A., (2014). Tiny constables and the cost of surveillance: Making cents out of United States V. Jones. Yale Law Journal Online 123.
6. Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17, 99–120.
7. Barrett, M., Davidson, E., Prabhu, J., & Vargo, S. L. (2015). Service innovation in the digital age. MIS Quarterly, 39, 135–154.
8. Barton, D. (2012). Making advanced analytics work for you. Harvard Business Review, 90(78–83), 128.
9. Barton, D., & Court, D. (2012). Making advanced analytics work for you. Harvard Business Review, 90, 78.
10. Beath, C., Becerra-Fernandez, I., Ross, J., & Short, J. (2012). Finding value in the information explosion. MIT Sloan Management Review, 53, 18–20.
11. Benedettini, O., Neely, A., 2012. Complexity in services: An interpretative framework, POMS 23rd annual conference.
12. Bennett, P., Giles, L., Halevy, A., Han, J., Hearst, M., Leskovec, J., 2013. Channeling the deluge: research challenges for big data and information systems, Proceedings of the 22nd ACM international conference on Conference on information & knowledge management. ACM, pp. 2537–2538.
13. Beskow, L. M., Friedman, J. Y., Hardy, N. C., Lin, L., & Weinfurt, K. P. (2010). Developing a simplified consent form for biobanking. PloS One, 5, e13302.
14. Beulke, D., (2011). Big Data Impacts Data Management: The 5 Vs of big data. URL: http://davebeulke.com/big-data-impacts-data-management-the-five-vs-of-big-data
15. Bhattacherjee, A. (2001). Understanding information systems continuance: an expectation-confirmation model. MIS Quarterly, 25, 351–370.
16. Bialobrzeski, A., Ried, J., & Dabrock, P. (2012). Differentiating and evaluating common good and public good: making implicit assumptions explicit in the contexts of consent and duty to participate. Public Health Genomics, 15, 285–292.
17. Biesdorf, S., Court, D., & Willmott, P. (2013). Big data: What’s your plan? McKinsey Quarterly.
18. Birnik, A., & Bowman, C. (2007). Marketing mix standardization in multinational corporations: a review of the evidence. International Journal of Management Reviews, 9, 303–324.
19. Boja, C., Pocovnicu, A., & Batagan, L. (2012). Distributed Parallel Architecture for "Big Data". Informatica Economica, 16, 116–127.
20. Bose, R. (2009). Advanced analytics: opportunities and challenges. Industrial Management & Data Systems, 109, 155–172.
21. Bouhaddou, O., Bennett, J., Cromwell, T., Nixon, G., Teal, J., Davis, M., Smith, R., Fischetti, L., Parker, D., & Gillen, Z. (2011). The Department of Veterans Affairs, Department of Defense, and Kaiser Permanente Nationwide health information network exchange in San Diego: Patient selection, consent, and identity Matching, AMIA annual symposium proceedings (p. 135). American Medical Informatics Association.
22. Boyd, D., & Crawford, K. (2012). Critical questions for big data: provocations for a cultural, technological, and scholarly phenomenon. Information Communication and Society, 15, 662–679.
23. Bragge, J., Sunikka, A., & Kallio, H. (2012). An exploratory study on customer responses to personalized banner messages in the online banking context. JITTA: Journal of Information Technology Theory and Application, 13, 5–18.
24. Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology qualitative research in psychology, 3, 77–101. Bristol: University of the West of England.
25. Brown, B., Chul, M., Manyika, J., (2011). Are you ready for the era of ‘big data’? McKinsey Quarterly 4, 24–35.
26. Bughin, J., Chui, M., & Manyika, J. (2010). Clouds, big data, and smart assets: ten tech-enabled business trends to watch. The McKinsey Quarterly, 4, 26–43.
27. Bughin, J., Livingston, J., & Marwaha, S. (2011). Seizing the potential of ‘big data’. The McKinsey Quarterly, 4, 103–109.
28. Chandrasekaran, S., Levin, R., Patel, H., Roberts, R., (2013). Winning with IT in consumer packaged goods: Seven trends transforming the role of the CIO. McKinsey & Company, pp. 1–8.
29. Chang, R. M., Kauffman, R. J., & Kwon, Y. (2014). Understanding the paradigm shift to computational social science in the presence of big data. Decision Support Systems, 63, 67–80.
30. Cherif, E., & Grant, D. (2013). Analysis of E-business models in real estate. Electronic Commerce Research, 14, 1–26.
31. Christian, H., (2013). Big data and the creative destruction of today’s business models. AT Kerney, Available: http://www. atkearney. fr/strategic-it/ideas-insights/article/−/asset_publisher/LCcgOeS4t85g/content/big-data-and-the-creative-destruction-of-today-s-business-models/10192.
32. Columbus, L. (2014). Making analytics accountable: 56 % Of executives expect analytics to contribute To 10 % Or more growth in 2014. Forbes. Available at: http://www.forbes.com/sites/louiscolumbus/2014/12/10/making-analytics-accountable-56-of-executives-expect-analytics-to-contribute-to-10-or-more-growth-in-2014/#761c65a95b56 (Accessed on the 2nd of February, 2016).
33. Constantiou, I.D., Kallinikos, J., (2015). New games, new rules: Big data and the changing context of strategy. Journal of Information Technology, 30(1), 44–57.
34. Davenport, T. H. (2006). Competing on Analytics. Harvard Business Review, 84, 98–107.
35. Davenport, T. H. (2010). The New World of “Business Analytics. International Institute for Analytics, 1–6.
36. Davenport, T.H., 2012. The Human Side of Big Data and High-Performance Analytics. International Institute for Analytics, pp. 1–13.
37. Davenport, T. H. (2013a). Analytics 3.0. Harvard Business Review, 91, 64–72.
38. Davenport, T. H. (2013b). Keep up with your quants. Harvard Business Review, 91, 120–123.
39. Davenport, T. H., & Harris, J. G. (2007a). Competing on analytics: The new science of winning. Boston: Harvard Business School Press.
40. Davenport, T.H., Harris, J.G., (2007b). The dark side of customer analytics. Harvard Business Review 85, 37 − +.
41. Davenport, T. H., & Patil, D. (2012). Data scientist: the sexiest job of the 21st century. Harvard Business Review, 90, 70–77.
42. Davenport, T. H., Barth, P., & Bean, R. (2012). How ‘Big Data’is Different. MIT Sloan Management Review, 54, 43–46.
43. De Swert, K., (2012). Calculating inter-coder reliability in media content analysis using Krippendorff’s alpha. Center for Politics and Communication.
44. Delone, W. H. (2003). The DeLone and McLean model of information systems success: a ten-year update. Journal of Management Information Systems, 19, 9–30.
45. DeLone, W. H., & McLean, E. R. (1992). Information systems success: the quest for the dependent variable. Information Systems Research, 3, 60–95.
46. Demirkan, H., Delen, D., (2012). Leveraging the capabilities of service-oriented decision support systems: Putting analytics and big data in cloud. Decision Support Systems.
47. Devaraj, S., Fan, M., & Kohli, R. (2002). Antecedents of B2C channel satisfaction and preference: validating e-commerce metrics. Information Systems Research, 13, 316–333.
48. Dijcks, J. P. (2012). Oracle: Big data for the Enterprise. Oracle: USA.
49. Dimon, R. (2013). Understand: Turning insights into actions. Enterprise Performance Management Done Right: An Operating System for Your Organization, pp.57–77.
50. Dinev, T., & Hart, P. (2006). An extended privacy calculus model for E-commerce transactions. Information Systems Research, 17, 61–80.
51. emarketer, (2013). Ecommerce Sales Topped $1 Trillion for First Time in 2012. Available at: www.emarketer.com/Article/Ecommerce-Sales-Topped-1-Trillion-First-Time-2012/1009649 (Accessed 10 April 2013).
52. Ezzy, D. (2002). Qualitative analysis: Practice and innovation Allen & Unwin. Crows Nest: Allen & Unwins.
53. Ferguson, R. B. (2012). Risky business: how data analytics and behavioral science can help. MIT Sloan Management Review, 54, 1–5.
54. Fisher, D., DeLine, R., Czerwinski, M., & Drucker, S. (2012). Interactions with big data analytics. Interactions, 19, 50.
55. Fosso Wamba, S., Anand, A., & Carter, L. (2013). A literature review of rfid-enabled healthcare applications and issues. International Journal of Information Management, 33, 875–891.
56. Fosso Wamba, S., Akter, S., Coltman, T. W. T., & Ngai, E. (2015a). Guest editorial: information technology-enabled supply chain management. Production Planning and Control, 26, 933–944.
57. Fosso Wamba, S., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2015b). How ‘big data’ can make big impact: findings from a systematic review and a longitudinal case study. International Journal of Production Economics, 165, 234–246.
58. Frost, R., Strauss, J., (2013). E-marketing. Pearson Prentice Hall. Upper Saddle River, NJ.
59. Gantz, J., & Reinsel, D. (2012). The digital universe in 2020: Big data, bigger digital shadows, and biggest growth in the far east (2012). URL: http://www.emc.com/collateral/analyst-reports/idc-the-digital-universein-2020. pdf.
60. Gefen, D. (2000). E-commerce: the role of familiarity and trust. Omega, 28, 725–737.
61. Gefen, D. (2002). Customer loyalty in E-commerce. Journal of the Association for Information Systems, 3, 27–51.
62. Gehrke, J. (2012). Quo vadis, data privacy? Annals of the New York Academy of Sciences, 1260, 45–54.
63. Gentile, B., (2012). Top 5 myths about big data. Available at: http://mashable.com/2012/06/19/big-data-myths/#MwZnjjrOR8qV (Accessed 2nd of March, 2016).
64. George, G., Haas, M. R., & Pentland, A. (2014). Big data and management. Academy of Management Journal, 57, 321–326.
65. Gobble, M. M. (2013). Big data: the next big thing in innovation. Research Technology Management, 56, 64–66.
66. Goes, P.B., (2014). Big Data and IS Research. MIS Quarterly 38, iii-viii.
67. Goff, J., McInerney, P., Soni, G., (2012). Need for speed: Algorithmic marketing and customer data overload, McKinsey Quarterly.
68. Griffin, R. (2012). Using Big Data to Combat Enterprise Fraud. Financial Executive, 28, 44–47.
69. Griffin, J., & Danson, F. (2012). Analytics and the Cloud - the Future is here. Financial Executive, 28, 97–98.
70. Havens, T. C., Bezdek, J. C., Leckie, C., Hall, L. O., & Palaniswami, M. (2012). Fuzzy C-means algorithms for very large data. Fuzzy Systems, IEEE Transactions on, 20, 1130–1146.
71. Hayashi, A. M. (2014). Thriving in a big data world. MIT Sloan Management Review, 55, 35–39.
72. Hayes, A.F., (2011). My macros and code for SPSS and SAS. Retrieved September 27, 2011.
73. Hayes, A. F., & Krippendorff, K. (2007). Answering the call for a standard reliability measure for coding data. Communication Methods and Measures, 1, 77–89.
74. Highfield, (2012). Talking of Many Things: Using Topical Networks to Study Discussions in Social Media. Journal of Technology in Human Services, 30(3–4), 204–218.
75. Holbrook, M. B. (1999). Consumer value: A framework for analysis and research. London: Psychology Press.
76. Hsinchun, C., Chiang, R. H. L., & Storey, V. C. (2012). Business intelligence and analytics: from big data to big impact. MIS Quarterly, 36, 1165–1188.
77. Hull, G. (2015). Successful failure: what foucault can teach us about privacy self-management in a world of facebook and big data. Ethics and Information Technology, 17(2), 89–101.
78. Huwe, T. K. (2012). Big data, big future. Computers in Libraries, 32, 20–22.
79. IBM, (2012). What is big data? Available at: http://www-01.ibm.com/software/data/bigdata/what-is-big-data.html (Accessed 2nd of March, 2016).
80. Ioannidis, J. P. (2013). Informed consent, big data, and the oxymoron of research that is not research. The American Journal of Bioethics, 13, 40–42.
81. Jacobs, A., (2009). The Pathologies of Big Data. Association For Computing Machinery. Communications of the ACM 52, 36.
82. Jao, J., (2013). Why big data Is A must In ecommerce. Available at: http://www.bigdatalandscape.com/news/why-big-data-is-a-must-in-ecommerce (Accessed 2nd of March, 2016).
83. Johnson, B. D. (2012a). The secret life of data. The Futurist, 46, 20–23.
84. Johnson, J. E. (2012b). Big data + big analytics = big opportunity. Financial Executive, 28, 50–53.
85. Kalakota, R., & Whinston, A. B. (1997). Electronic commerce: A manager’s guide. Reading: Addison-Wesley Professional.
86. Kang, K.-D., Son, S. H., & Stankovic, J. A. (2003). Differentiated real-time data services for E-commerce applications. Electronic Commerce Research, 3, 113–142.
87. Kaplan, B. (2014). Selling health data: De-Identification, privacy, and speech. Forthcoming: Cambridge Quarterly of Healthcare Ethics, 24(3):256–71. doi: 10.1017/S0963180114000589.
88. Kauffman, R. J., Srivastava, J., & Vayghan, J. (2012). Business and data analytics: new innovations for the management of E-commerce. Electronic Commerce Research and Applications, 11, 85–88.
89. Kim, G., Shin, B., & Kwon, O. (2012). Investigating the value of sociomaterialism in conceptualizing it capability of a firm. Journal of Management Information Systems, 29, 327–362.
90. Kiron, D., Prentice, P. K., & Ferguson, R. B. (2012a). Innovating with analytics. MIT Sloan Management Review, 54, 47–52.
91. Kiron, D., Shockley, R., Kruschwitz, N., Finch, G., & Haydock, M. (2012b). Analytics: the widening divide. MIT Sloan Management Review, 53, 1–22.
92. Kiron, D., Prentice, P. K., & Ferguson, R. B. (2014a). The analytics mandate. MIT Sloan Management Review, 55, 1–25.
93. Kiron, D., Prentice, P. K., & Ferguson, R. B. (2014b). Raising the bar with analytics. MIT Sloan Management Review, 55, 29–33.
94. Koch, C. (2013). Compilation and synthesis in big data analytics, big data (pp. 6–6). Berlin: Springer.
95. Kohli, A. K., & Jaworski, B. J. (1990). Market orientation: the construct, research propositions, and managerial implications. The Journal of Marketing, 54, 1–18.
96. Koirala, P., (2012). What is Big Data Analytics and its Application in E-Commerce? www.venturecity.com.
97. Kopp, M., (2013). Seizing the big data opportunity, Ecommerce Times. Available at: http://www.ecommercetimes.com/story/78390.html (Accessed 2nd of March, 2016).
98. Koutsabasis, P., Stavrakis, M., Viorres, N., Darzentas, J. S., Spyrou, T., & Darzentas, J. (2008). A descriptive reference framework for the personalisation of e-business applications. Electronic Commerce Research, 8, 173–192.
99. Krippendorff, K. (2004). Reliability in content analysis. Human Communication Research, 30, 411–433.
100. Krippendorff, K., (2007). Computing Krippendorff’s alpha reliability. Departmental Papers (ASC), 43. Available at: http://repository.upenn.edu/cgi/viewcontent.cgi?article=1043&context=asc_papers (Accessed 2nd of March)
101. Kung, D. S., Gordon, L. C., Lin, F., Shayo, C., & Dyck, H. (2013). 2013. IT-based System with Dynamic Pricing Algorithm. Business Journal for Entrepreneurs: Business Analytics.
102. Lane, J., (2012). O Privacy, Where Art Thou?: Protecting Privacy and Confidentiality in an Era of Big Data Access. Chance, 25(4), 39–41.
103. LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., & Kruschwitz, N. (2011). Big Data, Analytics and the Path from Insights to Value. MIT Sloan Management Review, 52, 21–32.
104. Leavitt, N. (2013). Bringing Big Analytics to the Masses. Computer, 46, 20–23.
105. Leloup, B., & Deveaux, L. (2001). Dynamic pricing on the internet: theory and simulations. Electronic Commerce Research, 1, 265–276.
106. Liebowitz, J. (2013). Big data and business analytics. Boca Raton: CRC Press.
107. Lim, E. P., Chen, H., & Chen, G. (2013a). Business Intelligence and Analytics: Research Directions. ACM Transactions on Management Information Systems, 3, 17.
108. Lim, M.K., Bahr, W., Leung, S.C., (2013b). RFID in the warehouse: A literature analysis (1995–2010) of its applications, benefits, challenges and future trends. International Journal of Production Economics, 145(1).
109. Loebbecke, C., & Picot, A. (2015). Reflections on societal and business model transformation arising from digitization and big data analytics: a research agenda. The Journal of Strategic Information Systems, 24, 149–157.
110. Loveman, G. (2003). Diamonds in the data mine. Harvard Business Review, 81, 109–113.
111. Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., Byers, A.H., (2011). Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute.
112. Martin, K.E., (2015). Ethical Issues in the Big Data Industry. MIS Quarterly Executive 14, 67–85.
113. McAfee, A., Brynjolfsson, E., Davenport, T.H., Patil, D.J. and Barton, D., (2012). Big data. The management revolution. Harvard Business Review, 90(10), 61–67.
114. Mehra, G., (2013). 6 uses of big data for online retailers, Practical Ecommerce. Available at: http://www.practicalecommerce.com/articles/3960-6-Uses-of-Big-Data-for-Online-Retailers (Accessed 2nd of March, 2016).
115. Melville, N., Kraemer, K., & Gurbaxani, V. (2004). Review: information technology and organizational performance: an integrative model of it business value. MIS Quarterly, 28, 283–322.
116. Miller, G., (2013). 6 ways To use “big data” To increase operating margins By 60 %. Available at: http://upstreamcommerce.com/blog/2012/04/11/6-ways-big-data-increase-operating-margins-60-part-2 (Accessed 2nd of March, 16).
117. Minelli, M., Chambers, M., & Dhiraj, A. (2013). Business analytics (pp. 99–125). Big Data, Big Analytics: Emerging Business Intelligence and Analytic Trends for Today’s Businesses.
118. Mithas, S., Lee, M. R., Earley, S., Murugesan, S., & Djavanshir, R. (2013). Leveraging big data and business analytics. IT Professional, 15, 18–20.
119. Narver, J. C., & Slater, S. F. (1990). The effect of a market orientation on business profitability. The Journal of Marketing, 54, 20–35.
120. Nelson, R. R., Todd, P. A., & Wixom, B. H. (2005). Antecedents of information and system quality: an empirical examination within the context of data warehousing. Journal of Management Information Systems, 21, 199–235.
121. Ngai, E. W. T., & Wat, F. K. T. (2002). A literature review and classification of electronic commerce research. Information Management, 39, 415–429.
122. Ngai, E. W. T., Moon, K. K. L., Riggins, F. J., & Yi, C. Y. (2008). RFID research: an academic literature review (1995-2005) and future research directions. International Journal of Production Economics, 112, 510–520.
123. Ngai, E. W. T., Xiu, L., & Chau, D. C. K. (2009). Application of data mining techniques in customer relationship management: a literature review and classification. Expert Systems with Applications, 36, 2592–2602.
124. Nunan, D., & Di Domenico, M. (2013). Market research and the ethics of big data. International Journal of Market Research, 55, 505–520.
125. Ohata, M., & Kumar, A. (2012). Big data: a boon to business intelligence. Financial Executive, 28, 63–64.
126. Orlikowski, W. J. (2007). Sociomaterial practices: exploring technology at work. Organization Studies, 28, 1435–1448.
127. Orlikowski, W. J., & Scott, S. V. (2008). 10 sociomateriality: challenging the separation of technology, work and organization. The Academy of Management Annals, 2, 433–474.
128. Pantelis, K. and Aija, L., (2013, October). Understanding the value of (big) data. In Big Data, 2013 IEEE International Conference on (pp. 38–42). IEEE.
129. Piatetsky, G., (2014). Big data market to reach $50 billion by 2018. Wikibon. Available at: http://www.kdnuggets.com/2014/02/wikibon-big-data-market-to-reach-50-billion-by-2018.html (Accessed 2nd of March, 2016).
130. Porter, M. E., & Millar, V. E. (1985). How information gives you competitive advantage. In Harvard Business Review, 63, 149–160. Reprint: Service.
131. Rajpurohit, A., (2013). Big data for business managers—Bridging the gap between potential and value, Big Data, 2013 I.E. International Conference on. IEEE, pp. 29–31.
132. Ramaswamy, S., (2013). What the Companies Winning at Big Data Do Differently. Bloomberg, June: http://www. bloomberg. com/news/2013–06-25/what-the-companies-winning-at-big-data-do-differently. html.
133. Riggins, F. J. (1999). A framework for identifying web-based electronic commerce opportunities. Journal of Organizational Computing and Electronic Commerce, 9, 297–310.
134. Rouse, M., (2011). big data (Big Data). Available at: http://searchcloudcomputing.techtarget.com/definition/big-data-Big-Data (retrieved 11.03.13).
135. Russom, P., (2011, September). The three Vs of big data analytics. TDWI Best Practices Report, Fourth Quarter. 18:1–35.
136. Schneier, B., (2013). The US Government has betrayed the internet. we need to take it back. The Guardian. Sept 5, 2013.
137. Schroeck, M., Shockley, R., Smart, J., Romero-Morales, D., & Tufano, P. P. (2012). Analytics: The real-world use of big data. NY, USA: IBM Institute for Business Value.
138. Shah, S., Horne, A., Capellá, J., (2012). Good data won’t guarantee good decisions. Harvard Business Review 90, 23–25.
139. Shanks, G., Sharma, R., Seddon, P., Reynolds, P., (2010). The impact of strategy and maturity on business analytics and firm performance: A review and research agenda. ACIS 2010 Proceedings.
140. Sharma, R., Mithas, S., & Kankanhalli, A. (2014). Transforming decision-making processes: a research agenda for understanding the impact of business analytics on organisations. European Journal of Information Systems, 23, 433–441.
141. Smith, R., & Shao, J. (2007). Privacy and E-commerce: a consumer-centric perspective. Electronic Commerce Research, 7, 89–116.
142. Szongott, C., Henne, B. and von Voigt, G., (2012, June). Big data privacy issues in public social media. In Digital Ecosystems Technologies (DEST), 2012 6th IEEE International Conference on (pp. 1–6). IEEE.
143. Spiggle, S. (1994). Analysis and interpretation of qualitative data in consumer research. Journal of Consumer Research, 21, 491–503.
144. Strawn, G. O. (2012). Scientific research: how many paradigms? Educause Review, 47, 26.
145. Tankard, C. (2012). Big Data Security. Network Security, 2012, 5–8.
146. The Economist, (2011). Building with big data: The data revolution is changing the landscape of business. Available at: http://www.economist.com/node/18741392 (Accessed 2nd of March, 2016).
147. Vaidhyanathan, S., & Bulock, C. (2014). Knowledge and dignity in the era of “big data”. The Serials Librarian, 66, 49–64.
148. Vaithianathan, S. (2010). A review of E-commerce literature on india and research agenda for the future. Electronic Commerce Research, 10, 83–97.
149. Viaene, S., & Van den Bunder, A. (2011). The secrets to managing business analytics projects. MIT Sloan Management Review, 53, 65–69.
150. Wagner, E. (2012). Realities learning professionals need to know about analytics. T+D, 66, 54–58.
151. Waller, M. A., & Fawcett, S. E. (2013). Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management. Journal of Business Logistics, 34, 77–84.
152. Wamba, S. F., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2015). How ‘big data’can make big impact: findings from a systematic review and a longitudinal case study. International Journal of Production Economics, 165, 234–246.
153. White, M. (2012). Digital workplaces vision and reality. Business Information Review, 29, 205–214.
154. Williamson, O. E. (1979). Transaction-cost economics: the governance of contractual relations. Journal of Law and Economics, 22, 233–261.
155. Williamson, O. E. (1981). The economics of organization: the transaction cost approach. American Journal of Sociology, 87, 548–577.
156. Williamson, O. E. (2000). the new institutional economics: taking stock, looking ahead. Journal of Economic Literature, 38, 595–613.
157. Wixom, B. H., & Todd, P. A. (2005). A theoretical integration of user satisfaction and technology acceptance. Information Systems Research, 16, 85–102.
158. Wixom, B. H., Yen, B., & Relich, M. (2013). Maximizing value from business analytics. MIS Quarterly Executive, 12, 111–123.
159. Zeng, D., & Lusch, R. (2013). Big data analytics: perspective shifting from transactions to ecosystems. IEEE Intelligent Systems, 28, 2–5.
160. Zhao, D. (2013). Frontiers of big data business analytics: patterns and cases in online marketing. Big Data and Business Analytics, p. 43.