Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 304

The DOI matches its Crossref record; 86 of the 86 references it cites were found in a registry or an index.

Understanding the Factors Affecting the Organizational Adoption of Big Data

Sun, Shiwei, Cegielski, Casey G., Jia, Lin, Hall, Dianne J.·2018·Journal of Computer Information Systems
DOI 10.1080/08874417.2016.1222891
Open this paper in CoChat — free account

Abstract

Big data is rapidly becoming a major driver for firms seeking to gain a competitive advantage. Grounded in the Diffusion of Innovation theory (DOI), the institutional theory, and the Tech-nology–Organization–Environment (TOE) framework, this study applies the results of a content analysis to develop a framework to identify the main factors affecting the organizational adoption of big data. The content analysis is based on the retrieval and review of relevant papers in the business intelligence & analytics (BI&A) literature published during the period 2009–2015. The 26 factors identified by this review are then integrated into a TOE framework. The findings of this research enrich the current big data literature and enhance practitioners’ understanding of the decision-making processes involved in a firm’s adoption of big data.

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 · 10.1080/08874417.2016.1222891

Cites

86 of 86 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

Show all 86 references

Research works printed but not linked: 38

  1. Gartner, Gartner predicts business intelligence and analytics will remain top focus for CIOs through 2017, Gartner Newsroom, 2013 [cited 2015 Jan 12]. Available from http://www.gartner.com/newsroom/id/2637615
  2. 10.5437/08956308X5603005 · doi:10.5437/08956308x5603005
  3. Goes, PB. Big data and IS research. MIS Q 2014;38(3):3–8.
  4. 10.1016/j.aei.2012.11.006 · doi:10.1016/j.aei.2012.11.006
  5. Klie L. Getting closer to customers tops big data agenda. CRM Mag. 2013;17(1):15–15.
  6. Rogers EM. Diffusions of Innovations. New York: Free Press; 2003, p. 5.
  7. Tornatzky L, Fleischer M. The process of technology innovation, Lexington, MA: Lexington Books; 1990.
  8. Berinato, S., “With big data comes big responsibility,” Harvard Business Review (92：11), 2014, 100–104.
  9. Blohm, I., Leimeister, JM. and Krcmar, H., “Crowdsourcing: how to benefit from (too) many great ideas,” MIS Quarterly Executive (12:4), 2013, 199–211.
  10. 10.1145/2630787 · doi:10.1145/2630787
  11. 10.2307/41703503 · DOI · Paper page
  12. Davenport, TH., “Analytics 3.0,” Harvard Business Review (91:12), 2013, 64–72.
  13. Davenport, TH., Barth, P. and Bean, R., “How ‘big data’ is different,” MIT Sloan Management Review (54:1), 2013.
  14. Davenport, TH., Patil, DJ., Stafford, M., Bettin, J., Gutiérrez, C., Hren, R. and Singh, H., “Hot jobs: data scientist: interaction,” Harvard Business Review (90:12), 2012, 16–17.
  15. Executive Summaries October 2012, Harvard Business Review (90:10), 2012, 128–131.
  16. Executive Summaries November 2014, Harvard Business Review (92:11), 2014, 141–143.
  17. Goes, PB., “ Big data and IS research,” MIS Quarterly (38:3), 2014, 3–8.
  18. Goyal, M., Hancock, MQ. and Hatami, H., “Selling into micro markets,” Harvard Business Review (90:7-8), 2012, 78–86.
  19. Hayashi, A., “Thriving in a big data world,” Sloan Management Review (55:2), 2012, 34–40.
  20. Horst, P. and Duboff, R., “Don't let big data bury your brand: what capital one learned about over-relying on analytics,” Harvard Business Review (93:11), 2015, 79–86.
  21. 10.1145/2447976.2447984 · doi:10.1145/2447976.2447984
  22. Ignatius, A., “Big data for skeptics,” Harvard Business Review (90:10), 2012, 12–12.
  23. 10.1145/2500873 · DOI · Paper page
  24. Lavalle, S., Lesser, E., Shockley, R., Hopkins, MS. and Kruschwitz, N., “Big data, analytics and the path from insights to value,” MIT Sloan Management Review (52:2), 2011, 21–32.
  25. Lee, Y., Madnick, S., Wang, R., Wang, F. and Zhang, HY., “A Cubic Framework for the Chief Data Officer: Succeeding in a world of big data,” MIS Quarterly Executive (13:1), 2014, 1–13.
  26. Najjar, MS. and Kettinger, WJ., “Data monetization: lessons from a retailer's journey,” MIS Quarterly Executive (12:4), 2013, 213–225.
  27. O'Leary, DE., “Exploiting big data from mobile device sensor-based apps: challenges and benefits,” MIS Quarterly Executive (12: 4), 2013, 179–187.
  28. Panchaksharaiah, U., “How to address big data,” Communications of the ACM, (52:12), 2009, 7–7.
  29. Parmar, R., Mackenzie, I., Cohn, D. and Gann, D., “The new patterns of innovation,” Harvard Business Review (92:1–2), 2014, 86–95.
  30. Piccoli, G. and Pigni, F., “Harvesting external data: the potential of digital data streams,” MIS Quarterly Executive (12:1), 2013, 53–64.
  31. Redman, TC. “Data’s Credibility Problem,” Harvard Business Review (91:12), 2013, 84–88.
  32. Ross, JW., Beath, CM. and Quaadgras, A., “You may not need big data after all,” Harvard Business Review (91:12), 2013, 90–98.
  33. 10.1145/2601074 · doi:10.1145/2601074
  34. Stonebraker, M. and Hong, J., “Researchers’ big data crisis: understanding design and functionality,” Communications of the ACM (55:2), 2012, 10–11. · DOI
  35. 10.1145/2500468.2500471 · doi:10.1145/2500468.2500471
  36. Tallon, PP., Short, JE. and Harkins, MW., “The evolution of information governance at Intel,” MIS Quarterly Executive (12:4), 2013, 189–198.
  37. Thomke, S. and Manzi, J., “The discipline of business experimentation,” Harvard Business Review (92:12), 2014, 70–79.
  38. 10.1145/2617660 · doi:10.1145/2617660

Cited by

See all 304

Metadata

Authors
Sun, S., Cegielski, C. G., Jia, L., & Hall, D. J.
Venue
Journal of Computer Information Systems
Publisher
Informa UK Limited
Published
2016-10-07
Volume
58
Issue
3
Pages
193-203
ISSN
  • 0887-4417 (ISSN-L)
  • 2380-2057
DOI
10.1080/08874417.2016.1222891
OpenAlex
W2527709680
Cited by
304
References
86
FWCI
16.63
Type
journal article
Language
en
Open access
closed

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

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Understanding the Factors Affecting the Organizational Adoption of Big Data. https://app.cochat.ai/papers/10.1080/08874417.2016.1222891
The paper
Sun, S., Cegielski, C. G., Jia, L., & Hall, D. J. (2018). Understanding the Factors Affecting the Organizational Adoption of Big Data. Journal of Computer Information Systems. https://doi.org/10.1080/08874417.2016.1222891

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.