CoChat
Sign Up
Cited by 240Big Data and Business IntelligenceManagement and Optimization TechniquesComplex Systems and Decision Making

The DOI matches its Crossref record; 116 of the 116 references it cites were found in a registry or an index.

Operational research from Taylorism to Terabytes: A research agenda for the analytics age

Open access
Mortenson, Michael J., Doherty, Neil F., Robinson, Stewart·2015·European Journal of Operational Research
DOI 10.1016/j.ejor.2014.08.029
Open this paper in CoChat — free accountOpen PDF

Abstract

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.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.ejor.2014.08.029

Cites

116 of 116 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Flagged first, then by citations

Show all 116 references

Research works printed but not linked: 9

  1. Agosta, 2004, Data mining is dead – Long live predictive analytics, DM Review, 14, 37
  2. Basu, A. (2013). Five pillars of prescriptive analytics success. analytics magazine, March/April 2013 (pp. 8–12).
  3. Churchman, 1967, Wicked problems, Management Science, 14, B141
  4. Flowers, 1983, The design of colossus, Annals of the History of Computing, 5, 38
  5. 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.
  6. 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).
  7. Lustig, I., Dietrich, B., Johnson, & Dziekan, C. (2010). The analytics journey. Analytics Magazine, November/December 2010 (pp. 11–13).
  8. 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).
  9. 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).

Cited by

Sorted by citations

Items 101–200 of 240

Related

See all

Metadata

Authors
Mortenson, M. J., Doherty, N. F., & Robinson, S.
Affiliations
Loughborough University (GB)
Corresponding author
Robinson, Stewart
ORCID
Venue
European Journal of Operational Research
Publisher
Elsevier BV
Published
2015-03
Volume
241
Issue
3
Pages
583-595
ISSN
0377-2217 (ISSN-L)
DOI
10.1016/j.ejor.2014.08.029
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
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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review