Big Data and Decision-Making: A Structured Literature Review
Keywords:
Big Data Analytics, Big Data, strategic management, decisionmaking, structured literature review, bibliometric analysisAbstract
This study provides a structured literature review on the role of Big Data (BD) and Big Data Analytics (BDA) in supporting the 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, providing a future research agenda. Adopting the methodology of Massaro et al. (2015), the review investigates this phenomenon through a longitudinal approach, 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. The study unveils the subject of decisions, factors influencing good decisions and the main effects of using BD and BDA in decision-making. Public sector, non-profit organizations and SMEs deserve more attention. Similarly, new organizational factors, data chain dynamics and inhibitors must be explored to remove the obstacles in decision-making.
Downloads
Download data is not yet available.
References
1. Alles, M, & Gray, GL (2016). “Incorporating Big Data in Audits: Identifying Inhibitors and a Research Agenda to Address Those Inhibitorsâ€. International Journal of Accounting Information Systems, 22, 44-59. https://doi.org/10.1016/j.accinf.2016.07.004
2. Ardito, L, Scuotto, V, Del Giudice, M, & Petruzzelli, AM (2019). “A bibliometric analysis of research on Big Data analytics for business and managementâ€, 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 phenomenonâ€, 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 researchâ€, Accounting, Auditing & Accountability Journal, 21, 129-169.
6. Chen, H, Chiang, RH, & Storey, VC (2012). “Business intelligence and analytics: from big data to big impactâ€, MIS Quarterly 36 (4), 1165–1188. https://doi.org/10.1108/09513570810854383
7. Crossan, MM, & Apaydin, M (2010). “A multi-dimensional framework of organizational innovation: A systematic review of the literatureâ€, Journal of Management Studies, 47, 1154-1191. https://doi.org/10.1111/j.1467-6486.2009.00880.x
8. Davenport, TH (2006). “Competing on analyticsâ€, 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 studyâ€. 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 organizationâ€, XXIV Nordiska kommunforskarkonferensen Gothenburg, November 26–28th 2015.
11. Gandomi, A, & Haider, M (2015). “Beyond the hype: Big data concepts, methods, and analyticsâ€, International Journal of Information Management, 35, 137–144. https://doi.org/10.1016/j.ijinfomgt.2014.10.007
12. George, G, Osinga, EC, Lavie, D, & Scott, BA (2016). “From the editors: Big data and data science methods for management researchâ€, Academy of Management Journal, 59(5), 1493–1507. https://doi.org/10.5465/amj.2016.4005
13. Gibson, JJ (1979). The ecological approach to visual perception. Boston: Houghton Mifflin.
14. Gobble, MAM (2013). “Big data: The next big thing in innovationâ€, Research Technology Management, 56, 64-67. https://doi.org/10.5437/08956308X5601005
15. Goes, PB (2014). “Big data and IS researchâ€. MIS Quarterly. 38 (3), iii–viii
16. Hartmann, PM, Zaki, M, Feldmann, N, & Neely, AD (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, BD, (2012). “The Secret Life of Dataâ€, The Futurist, 46, 20–23
18. Kessler, MM,(1963). “Bibliographic coupling between scientific papersâ€. Am. Document. 14, 10–25.
19. Kitchin, R, & McArdle, G, (2016). “What makes big data, big data? Exploring the ontological characteristics of 26 datasetsâ€. 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 organizationsâ€, 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 reviewâ€, Journal of Knowledge Management, 19(3), 530–558. https://doi.org/10.1108/JKM-11-2014-0466
23. Markus, ML (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’â€. Journal of Strategic Information Systems 24 (1), 3–14. http://dx.doi.org/10.1016/j.jsis.2015.02.001.
27. Newman, ME, (2004). “Fast algorithm for detecting community structure in networksâ€. Physical Review E. 69: 066133. https://doi.org/10.1103/PhysRevE.69.066133
28. Page, MJ, McKenzie, JE, Bossuyt, PM. 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 agendaâ€. 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 Reviewâ€. British Journal of Management. 14, 207-222. https://doi.org/10.1111/1467-8551.00375
31. Van Eck, NJ, & Waltman, L (2009). “How to normalize cooccurrence data? An analysis of some well-known similarity measuresâ€, Journal of the American Society for Information Science and Technology, 60(8), 1635–165. https://doi.org/10.1002/asi.21075
32. Van Eck, NJ, & Waltman, L (2014). “Visualizing Bibliometric Networksâ€, 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 realityâ€, Business Information Review, 29 (4), 205–214. https://doi.org/10.1177/0266382112470412
2. Ardito, L, Scuotto, V, Del Giudice, M, & Petruzzelli, AM (2019). “A bibliometric analysis of research on Big Data analytics for business and managementâ€, 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 phenomenonâ€, 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 researchâ€, Accounting, Auditing & Accountability Journal, 21, 129-169.
6. Chen, H, Chiang, RH, & Storey, VC (2012). “Business intelligence and analytics: from big data to big impactâ€, MIS Quarterly 36 (4), 1165–1188. https://doi.org/10.1108/09513570810854383
7. Crossan, MM, & Apaydin, M (2010). “A multi-dimensional framework of organizational innovation: A systematic review of the literatureâ€, Journal of Management Studies, 47, 1154-1191. https://doi.org/10.1111/j.1467-6486.2009.00880.x
8. Davenport, TH (2006). “Competing on analyticsâ€, 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 studyâ€. 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 organizationâ€, XXIV Nordiska kommunforskarkonferensen Gothenburg, November 26–28th 2015.
11. Gandomi, A, & Haider, M (2015). “Beyond the hype: Big data concepts, methods, and analyticsâ€, International Journal of Information Management, 35, 137–144. https://doi.org/10.1016/j.ijinfomgt.2014.10.007
12. George, G, Osinga, EC, Lavie, D, & Scott, BA (2016). “From the editors: Big data and data science methods for management researchâ€, Academy of Management Journal, 59(5), 1493–1507. https://doi.org/10.5465/amj.2016.4005
13. Gibson, JJ (1979). The ecological approach to visual perception. Boston: Houghton Mifflin.
14. Gobble, MAM (2013). “Big data: The next big thing in innovationâ€, Research Technology Management, 56, 64-67. https://doi.org/10.5437/08956308X5601005
15. Goes, PB (2014). “Big data and IS researchâ€. MIS Quarterly. 38 (3), iii–viii
16. Hartmann, PM, Zaki, M, Feldmann, N, & Neely, AD (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, BD, (2012). “The Secret Life of Dataâ€, The Futurist, 46, 20–23
18. Kessler, MM,(1963). “Bibliographic coupling between scientific papersâ€. Am. Document. 14, 10–25.
19. Kitchin, R, & McArdle, G, (2016). “What makes big data, big data? Exploring the ontological characteristics of 26 datasetsâ€. 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 organizationsâ€, 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 reviewâ€, Journal of Knowledge Management, 19(3), 530–558. https://doi.org/10.1108/JKM-11-2014-0466
23. Markus, ML (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’â€. Journal of Strategic Information Systems 24 (1), 3–14. http://dx.doi.org/10.1016/j.jsis.2015.02.001.
27. Newman, ME, (2004). “Fast algorithm for detecting community structure in networksâ€. Physical Review E. 69: 066133. https://doi.org/10.1103/PhysRevE.69.066133
28. Page, MJ, McKenzie, JE, Bossuyt, PM. 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 agendaâ€. 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 Reviewâ€. British Journal of Management. 14, 207-222. https://doi.org/10.1111/1467-8551.00375
31. Van Eck, NJ, & Waltman, L (2009). “How to normalize cooccurrence data? An analysis of some well-known similarity measuresâ€, Journal of the American Society for Information Science and Technology, 60(8), 1635–165. https://doi.org/10.1002/asi.21075
32. Van Eck, NJ, & Waltman, L (2014). “Visualizing Bibliometric Networksâ€, 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 realityâ€, Business Information Review, 29 (4), 205–214. https://doi.org/10.1177/0266382112470412
Downloads
Published
2023-05-25
How to Cite
Berardino, D. D., & Vona, S. (2023). Big Data and Decision-Making: A Structured Literature Review. European Scientific Journal, ESJ, 17, 374. Retrieved from https://test.eujournal.org/index.php/esj/article/view/16776
Issue
Section
ESI Preprints
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
