Discovering the Relationship Between Big Data, Big Data Analytics, and Decision Making: A Structured Literature Review
Keywords:
Big Data Analytics, Big Data, strategic management, decision making, structured literature review, bibliometric analysis
Abstract
This paper focuses on providing a structured literature review on the role of Big Data (BD) and Big Data Analytics (BDA) in supporting decision making. The study aims to systematize the knowledge, the primary results, and research gaps related to BD and BDA in strategic management and in decision making by providing a future research agenda. Adopting the methodology of Massaro et al. (2015), the structured literature review investigates this phenomenon analyzing a sample of 97 articles published in high-level scientific journals ranked in ABS list in the Marketing, Strategic Management, Ethics, Gender, and Social Responsibility area. Bibliometric analysis, content analysis, and the PRISMA protocol have been used for the review. The study unveils the subject of decisions, factors influencing good decisions, and the main effects of using BD and BDA in decision making. New organizational factors, data chain dynamics, and inhibitors should be explored to remove the obstacles in decision making. The relationship between BD/BDA and decision making remains underexplored in public organizations, non-profit organizations, and small and medium-sized firms.
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References
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26. Newell, S. & Marabelli, M. (2015). “Strategic opportunities (and challenges) of algorithmic decision-making: a call for action on the long-term societal effects of ’datafication’”. Journal of Strategic Information Systems 24 (1), 3–14. http://dx.doi.org/10.1016/j.jsis.2015.02.001.
27. Newman, M.E. (2004). “Fast algorithm for detecting community structure in networks”. Physical Review E. 69: 066133. https://doi.org/10.1103/PhysRevE.69.066133
28. Page, M.J., McKenzie, J.E., Bossuyt, P.M. et al. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Systematic Review, 10, 63-89. https://doi.org/10.1016/j.ijsu.2021.105906
29. Secundo, G., Del Vecchio, P., Dumay, J., & Passiante, G. (2017). “Intellectual capital in the age of Big Data: establishing a research agenda”. Journal of Intellectual Capital, 18(2), 242-261. 10.1108/JIC-10-2016-0097
30. Tranfield, D., Denyer, D., & Smart, P. (2003). “Towards a Methodology for Developing Evidence-Informed Management Knowledge by Means of Systematic Review”. British Journal of Management. 14, 207-222. https://doi.org/10.1111/1467-8551.00375
31. Van Eck, N.J. & Waltman, L. (2009). “How to normalize cooccurrence data? An analysis of some well-known similarity measures”, Journal of the American Society for Information Science and Technology, 60(8), 1635–165. https://doi.org/10.1002/asi.21075
32. Van Eck, N.J. & Waltman, L. (2014). “Visualizing Bibliometric Networks”, in Ding Y, Rousseau R, Wolfram D (Eds.), Measuring scholarly impact: Methods. Springer, 285-320. 10.1007/978-3-319-10377-8_13
33. White, M. (2012). “Digital workplaces: Vision and reality”, Business Information Review, 29 (4), 205–214. https://doi.org/10.1177/0266382112470412
34. Wolfswinkel, J.F., Furtmueller, E., & Wilderom, C.P.M. (2011). “Using grounded theory as a method for rigorously reviewing literature”, European Journal of Information Systems, 22 (2011), pp. 45-55.
2. Ardito, L., Scuotto, V., Del Giudice, M., & Petruzzelli, A.M. (2019). “A bibliometric analysis of research on Big Data analytics for business and management”, Management Decision, 57, 1993–2009. https://doi.org/10.1108/MD-07-2018-0754
3. Bholat, D. (2015). “Big data and central banks” Big Data Society, 2 (1), 1–6. http://dx.doi.org/10.1177/2053951715579469.
4. 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. https://doi.org/10.1080/1369118X.2012.678878
5. Broadbent, J. & Guthrie, J. (2008). “Public sector to public services: 20 years of “contextual” accounting research”, Accounting, Auditing & Accountability Journal, 21, 129-169.
6. Chen, H., Chiang, R.H., & Storey, V.C. (2012). “Business intelligence and analytics: from big data to big impact”, MIS Quarterly 36 (4), 1165–1188. https://doi.org/10.1108/09513570810854383=
7. Crossan, M.M. & Apaydin, M.. (2010). “A multi-dimensional framework of organizational innovation: A systematic review of the literature”, Journal of Management Studies, 47, 1154-1191. https://doi.org/10.1111/j.1467-6486.2009.00880.x
8. Davenport, T.H. (2006). “Competing on analytics”, Harvard Business Review, 84 (1), 98-107.
9. Fosso Wamba, S., 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. https://doi.org/10.1016/j.ijpe.2014.12.031
10. Fredriksson, C. (2015). “Knowledge Management with Big Data Creating new possibilities for organization”, XXIV Nordiska kommunforskarkonferensen Gothenburg, November 26–28th 2015.
11. Gandomi, A. & Haider, M. (2015). “Beyond the hype: Big data concepts, methods, and analytics”, International Journal of Information Management, 35, 137–144. https://doi.org/10.1016/j.ijinfomgt.2014.10.007
12. George, G., Osinga, E.C., Lavie, D., & Scott, B.A. (2016). “From the editors: Big data and data science methods for management research”, Academy of Management Journal, 59(5), 1493–1507. https://doi.org/10.5465/amj.2016.4005
13. Gibson, J.J. (1979). The ecological approach to visual perception. Boston: Houghton Mifflin.
14. Gobble, MAM. (2013). “Big data: The next big thing in innovation”, Research Technology Management, 56, 64-67. https://doi.org/10.5437/08956308X5601005
15. Goes, P.B. (2014). “Big data and IS research”. MIS Quarterly. 38 (3), iii–viii
16. Hartmann, P.M., Zaki, M., Feldmann, N., & Neely, A.D. (2014). Big Data for Big Business? A Taxonomy of Data-Driven Business Models Used by Start-Up Firms. Cambridge Service, pp:1-29. Available at: http://cambridgeservicealliance.blogspot.co.uk/2014/04/big-data-for-big-business_3.html.
17. Johnson, B.D. (2012). “The Secret Life of Data”, The Futurist, 46, 20–23
18. Kessler, M.M. (1963). “Bibliographic coupling between scientific papers”. Am. Document. 14, 10–25.
19. Kitchin, R. & McArdle, G. (2016). “What makes big data, big data? Exploring the ontological characteristics of 26 datasets”. Big Data Society 3 (1), 1–10. http://dx. doi.org/10.1177/2053951716631130.
20. Laney, D. (2001). 3D Data Management: Controlling Data Volume, Velocity and Variety. META Group Research Note, 6.http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf (accessed June 2021)
21. Li, L., Lin, J., Ouyang, Y., & Luo, X. (2021). “Evaluating the impact of big data analytics usage on the decision-making quality of organizations”, Technological Forecasting and Social Change, 175 (February) https://doi.org/10.1016/j.techfore.2021.121355
22. Massaro, M., Dumay, J., & Garlatti, A. (2015). “Public sector knowledge management: A structured literature review”, Journal of Knowledge Management, 19(3), 530–558. https://doi.org/10.1108/JKM-11-2014-0466
23. Markus, M.L. (2015). New games, new rules, new scoreboards: the potential consequences of big data. Journal of Information Technologies 30 (1), 58–59. http://dx.doi.org/10.1057/jit.2014.28.
24. Mayer-Schönberger, V. & Cukier, K. (2013). Big data: A revolution that will transform how we live, work, and think. Houghton Mifflin Harcourt. Boston, Massachusetts.
25. Namvar, M. & Cybulski, J. (2014). BI-based organizations: a sensemaking perspective. In: Proceedings of the Thirty-Fifth International Conference on Information Systems, Auckland, New Zealand, December 14–17.
26. Newell, S. & Marabelli, M. (2015). “Strategic opportunities (and challenges) of algorithmic decision-making: a call for action on the long-term societal effects of ’datafication’”. Journal of Strategic Information Systems 24 (1), 3–14. http://dx.doi.org/10.1016/j.jsis.2015.02.001.
27. Newman, M.E. (2004). “Fast algorithm for detecting community structure in networks”. Physical Review E. 69: 066133. https://doi.org/10.1103/PhysRevE.69.066133
28. Page, M.J., McKenzie, J.E., Bossuyt, P.M. et al. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Systematic Review, 10, 63-89. https://doi.org/10.1016/j.ijsu.2021.105906
29. Secundo, G., Del Vecchio, P., Dumay, J., & Passiante, G. (2017). “Intellectual capital in the age of Big Data: establishing a research agenda”. Journal of Intellectual Capital, 18(2), 242-261. 10.1108/JIC-10-2016-0097
30. Tranfield, D., Denyer, D., & Smart, P. (2003). “Towards a Methodology for Developing Evidence-Informed Management Knowledge by Means of Systematic Review”. British Journal of Management. 14, 207-222. https://doi.org/10.1111/1467-8551.00375
31. Van Eck, N.J. & Waltman, L. (2009). “How to normalize cooccurrence data? An analysis of some well-known similarity measures”, Journal of the American Society for Information Science and Technology, 60(8), 1635–165. https://doi.org/10.1002/asi.21075
32. Van Eck, N.J. & Waltman, L. (2014). “Visualizing Bibliometric Networks”, in Ding Y, Rousseau R, Wolfram D (Eds.), Measuring scholarly impact: Methods. Springer, 285-320. 10.1007/978-3-319-10377-8_13
33. White, M. (2012). “Digital workplaces: Vision and reality”, Business Information Review, 29 (4), 205–214. https://doi.org/10.1177/0266382112470412
34. Wolfswinkel, J.F., Furtmueller, E., & Wilderom, C.P.M. (2011). “Using grounded theory as a method for rigorously reviewing literature”, European Journal of Information Systems, 22 (2011), pp. 45-55.
Published
2023-07-31
How to Cite
Di Berardino, D., & Vona, S. (2023). Discovering the Relationship Between Big Data, Big Data Analytics, and Decision Making: A Structured Literature Review. European Scientific Journal, ESJ, 19(19), 1. https://doi.org/10.19044/esj.2023.v19n19p1
Section
ESJ Social Sciences
Copyright (c) 2023 Daniela Di Berardino, Simone Vona
This work is licensed under a Creative Commons Attribution 4.0 International License.
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