Big Data and Decision-Making: A Structured Literature Review

Daniela Di Berardino; Simone Vona

Authors

Daniela Di Berardino Associate Professor in Business Administration, University of Chieti-Pescara, Italy
Simone Vona PhD in Accounting, Management and Business Economics University of Chieti-Pescara, Italy

Keywords:

Big Data Analytics, Big Data, strategic management, decisionmaking, structured literature review, bibliometric analysis

Abstract

This study provides a structured literature review on the role of Big Data (BD) and Big Data Analytics (BDA) in supporting the decisionmaking. 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.

References

Alles, M, & Gray, GL (2016). “Incorporating Big Data in Audits: Identifying Inhibitors and a Research Agenda to Address Those Inhibitors”. International Journal of Accounting Information Systems, 22, 44-59. https://doi.org/10.1016/j.accinf.2016.07.004

Ardito, L, Scuotto, V, Del Giudice, M, & Petruzzelli, AM (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

Bholat, D (2015). “Big data and central banks” Big Data Society, 2 (1), 1–6. http://dx.doi.org/10.1177/2053951715579469.

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

Broadbent, J, & Guthrie, J (2008). “Public sector to public services: 20 years of “contextual” accounting research”, Accounting, Auditing & Accountability Journal, 21, 129-169.

Chen, H, Chiang, RH, & Storey, VC (2012). “Business intelligence and analytics: from big data to big impact”, MIS Quarterly 36 (4), 1165–1188. https://doi.org/10.1108/09513570810854383

Crossan, MM, & 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

Davenport, TH (2006). “Competing on analytics”, Harvard Business Review, 84 (1), 98-107.

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

Fredriksson, C (2015), “Knowledge Management with Big Data Creating new possibilities for organization”, XXIV Nordiska kommunforskarkonferensen Gothenburg, November 26–28th 2015.

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

George, G, Osinga, EC, Lavie, D, & Scott, BA (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

Gibson, JJ (1979). The ecological approach to visual perception. Boston: Houghton Mifflin.

Gobble, MAM (2013). “Big data: The next big thing in innovation”, Research Technology Management, 56, 64-67. https://doi.org/10.5437/08956308X5601005

Goes, PB (2014). “Big data and IS research”. MIS Quarterly. 38 (3), iii–viii

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-forbig-business_3.html.

Johnson, BD, (2012). “The Secret Life of Data”, The Futurist, 46, 20–23

Kessler, MM,(1963). “Bibliographic coupling between scientific papers”. Am. Document. 14, 10–25.

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.

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-DataManagement-Controlling-Data-Volume-Velocity-and-Variety.pdf (accessed June 2021)

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

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

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.

Mayer-Schönberger, V., & Cukier, K (2013). Big data: A revolution that will transform how we live, work, and think. Houghton Mifflin Harcourt. Boston, Massachusetts.

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.

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.

Newman, ME, (2004). “Fast algorithm for detecting community structure in networks”. Physical Review E. 69: 066133. https://doi.org/10.1103/PhysRevE.69.066133

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

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/JIC10-2016-0097

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

Van Eck, NJ, & 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

Van Eck, NJ, & 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

White, M (2012). “Digital workplaces: Vision and reality”, Business Information Review, 29 (4), 205–214. https://doi.org/10.1177/0266382112470412

Big Data and Decision-Making: A Structured Literature Review

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