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Operational research from Taylorism to Terabytes: A research agenda for the analytics age
Open accessAbstract
The growing attention and prominence afforded to analytics presents a genuine challenge for the operational research community. Many in the community have recognised this growth and sought to align themselves with analytics. For instance, the US operational research society INFORMS now offers analytics related conferences, certification and a magazine. However, as shown in this research, the volume of analytics-orientated studies in journals associated with operational research is comparatively low. This paper seeks to address this paradox by seeking to better understand what analytics is, and how operational research is related to it. To do so literature from a range of academic disciplines is analysed, in what is conceived as concurrent histories in the shared tradition of a management paradigm spread over the last 100 years. The findings of this analysis reveal new insights as to how operational research exists within an ecosystem shared with several other disciplines, and how interactions and ripple effects diffuse knowledge and ideas between each. Whilst this ecosystem is developed and evolved through interdisciplinary collaborations, individual disciplines are cast into competition for the attention of the same business users. These findings are further explored by discussing the implication this has for operational research, as well as considering what directions future research may take to maximise the potential value of these relationships.
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Cites
The Structure of Scientific Revolutions
Kuhn, T. S., & Schlegel, R. (1963) · Physics Today · doi:10.1063/1.3050879 · Cited by 35,538
MapReduce: simplified data processing on large clusters
Dean, J., & Ghemawat, S. (2008) · Communications of the ACM · doi:10.1145/1327452.1327492 · Cited by 18,476
The Analytic Hierarchy Process
Saaty, T. L., & Kearns, K. P. (1985) · Elsevier eBooks · doi:10.1016/b978-0-08-032599-6.50008-8 · Cited by 15,407
The balanced scorecard : measures that drive performance
Caplan, R. S. (1992) · Harvard business review · Cited by 12,085
THE STRUCTURE OF SCIENTIFIC REVOLUTIONS
McGuire, J. E. (1963) · Philosophical Books · doi:10.1111/j.1468-0149.1963.tb00813.x · Cited by 8,547
(1992) · PubMed · Cited by 7,979
Systems Thinking, Systems Practice
Checkland, P. B. (1981) · Cumulative Index of Computer Aided Architectural Design · Cited by 6,256
Business intelligence and analytics: from big data to big impact
Chen, H., Chiang, R. H. L., & Storey, V. C. (2012) · MIS Quarterly · doi:10.2307/41703503 · Cited by 6,250
(2011) · Cited by 5,203
The Psychology of Human-Computer Interaction
Card, S. K., Moran, T. P., & Newell, A. (2018) · CRC Press eBooks · doi:10.1201/9780203736166 · Cited by 4,103
Flagged first, then by citations
Research works printed but not linked: 9
- Agosta, 2004, Data mining is dead – Long live predictive analytics, DM Review, 14, 37
- Basu, A. (2013). Five pillars of prescriptive analytics success. analytics magazine, March/April 2013 (pp. 8–12).
- Churchman, 1967, Wicked problems, Management Science, 14, B141
- Flowers, 1983, The design of colossus, Annals of the History of Computing, 5, 38
- Hurrion, R. D. (1976). The design, use and required facilities of an interactive visual computer simulation language to explore production planning problems, PhD Thesis. University of London.
- Lewis, J. (1996). Reaping the benefits of modern usability evaluation: The simon story. In 1st International conference on advances in applied ergonomics (pp. 752–757).
- Lustig, I., Dietrich, B., Johnson, & Dziekan, C. (2010). The analytics journey. Analytics Magazine, November/December 2010 (pp. 11–13).
- Panian, Z. (2008). A break-through approach to real-time decisioning in business management. In Proceedings of the 2nd WSEAS international conference on management, marketing and finances (pp. 25–30).
- Scott Morton, M. S. (1969). Computer-driven visual display devices – Their impact on the management decision-making process (doctoral thesis). <http://dspace.mit.edu/bitstream/handle/1721.1/48089/impactofinteract00scot.pdf?sequence=1> (Accessed 15.03.13).
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Metadata
- Authors
- Mortenson, M. J., Doherty, N. F., & Robinson, S.
- Affiliations
- Loughborough University (GB)
- Corresponding author
- Robinson, Stewart
- Venue
- European Journal of Operational Research
- Publisher
- Elsevier BV
- Published
- 2015-03
- Volume
- 241
- Issue
- 3
- Pages
- 583-595
- ISSN
- 0377-2217 (ISSN-L)
- OpenAlex
- W2121991049
- Cited by
- 235
- References
- 116
- Citations by year
- 2026: 12 · 2025: 8 · 2024: 16 · 2023: 18 · 2022: 19 · 2021: 35 · 2020: 28 · 2019: 36 · 2018: 26 · 2017: 17 · 2016: 10 · 2015: 9
- FWCI
- 27.33
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- European Journal of Operational Research (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- Loughborough University Institutional Repository (Loughborough University) (submittedVersion, cc-by)
- Loughborough University Research Repository (Loughborough University) (submittedVersion, cc-by)
- Newcastle University ePrints (Newcastle Univesity) (cc-by)
- Funders
- Operational Research Society
- Topics
- Social Sciences › Business, Management and Accounting › Management Information Systems › Big Data and Business Intelligence
- Social Sciences › Business, Management and Accounting › Strategy and Management › Management and Optimization Techniques
- Social Sciences › Decision Sciences › Management Science and Operations Research › Complex Systems and Decision Making
- Keywords
- operational research, data analytics, history of management thought, scientific management, interdisciplinary collaboration, research agenda, INFORMS, management paradigms, knowledge diffusion, business users, analytics adoption
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