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Transforming decision-making processes: a research agenda for understanding the impact of business analytics on organisations
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Much attention is currently being paid in both the academic and practitioner literatures to the value that organisations could create through the use of big data and business analytics (Gillon et a...
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“Essentially, those firms that are better in transforming their operations are those that have established firm-wide practices regarding big data analytics and established a data-driven culture [14,17].”
From Exploring the relationship between big data analytics capability and competitive performance: The mediating roles of dynamic and operational capabilities (2020) · cites this paper as [14]
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“Sharma et al. [14] underscore the importance of fostering appropriate decision-making structures, essentially enabling a data-driven culture to diffuse throughout the firm.”
From Exploring the relationship between big data analytics capability and competitive performance: The mediating roles of dynamic and operational capabilities (2020) · cites this paper as [14]
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Citations
Cites
Henderson, R. M., & Clark, K. B. (1990) · Administrative Science Quarterly · doi:10.2307/2393549 · Cited by 8,563
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
Rational choice and the structure of the environment.
Simon, H. A. (1956) · Psychological Review · doi:10.1037/h0042769 · Cited by 5,919
Administrative Behavior: A Study of Decision-Making Processes in Administrative Organization.
Brown, M., & Simon, H. A. (1978) · Contemporary Sociology A Journal of Reviews · doi:10.2307/2065048 · Cited by 4,807
(2007) · Cited by 3,434
Sambamurthy, V., Bharadwaj, A. S., & Grover, V. (2003) · MIS Quarterly · doi:10.2307/30036530 · Cited by 3,418
Contributing Knowledge to Electronic Knowledge Repositories: An Empirical Investigation1
Kankanhalli, A., Tan, B. C. Y., & Wei, K. (2005) · MIS Quarterly · doi:10.2307/25148670 · Cited by 2,831
Self-citationLeadership and Decision-Making
Vroom, V. H., & Yetton, P. (1973) · University of Pittsburgh Press eBooks · doi:10.2307/j.ctt6wrc8r · Cited by 2,533
A primer on decision making : how decisions happen
Heath, C., & March, J. G. (1994) · Medical Entomology and Zoology · Cited by 2,404
(2018) · MIS Quarterly Executive · doi:10.17705/2msqe · Cited by 1,994
Flagged first, then by citations
Research works printed but not linked: 10
- Anderson-Lehman R, 2004, MISQ Executive, 3, 163
- Goodhue DL, 2002, MISQ Executive, 1, 79
- Hogarth RM, 1987, Judgement and Choice, 204
- Kohli R, 2007, MIS Quarterly Executive, 6, 199
- LaValle S, 2011, MIT Sloan Management Review, 52, 21
- Markus LM, 2000, Framing the Domains of IT Management: Projecting the Future through the Past, 173
- Sasisekharan R, 1996, IEEE Expert Intelligent Systems and Their Applications, 11, 37
- Sharma R, 2010, Bridging the Socio-Technical Gap in DSS - Challenges for the Next Decade, 187
- Simoudis E, 1996, IEEE Expert Intelligent Systems and Their Applications, 11, 26
- Watson HJ, 2001, Communications of the AIS, 8, 1
Cited by
Sorted by citations
Wang, Y., Kung, L., & Byrd, T. A. (2018) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2015.12.019 · Cited by 1,571
Mikalef, P., Krogstie, J., Pappas, I. O., & Pavlou, P. (2020) · Information & Management · doi:10.1016/j.im.2019.05.004 · Cited by 1,373
“Essentially, those firms that are better in transforming their operations are those that have established firm-wide practices regarding big data analytics and established a data-driven culture [14,17].”
BackgroundDebating big data: a literature review on realizing value from big data
Günther, W. A., Rezazade Mehrizi, M. H., Huysman, M., & Feldberg, F. (2017) · The Journal of Strategic Information Systems · doi:10.1016/j.jsis.2017.07.003 · Cited by 880
Mikalef, P., Boura, M., Lekakos, G., & Krogstie, J. (2019) · British Journal of Management · doi:10.1111/1467-8551.12343 · Cited by 835
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Mikalef, P., Boura, M., Lekakos, G. D., & Krogstie, J. (2019) · Journal of Business Research · doi:10.1016/j.jbusres.2019.01.044 · Cited by 819
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University of Virginia, Abbasi, A., Sarker, S., University of Virginia, Aalto University, Chiang, R., & University of Cincinnati (2016) · Journal of the Association for Information Systems · doi:10.17705/1jais.00423 · Cited by 688
Big data analytics in E-commerce: a systematic review and agenda for future research
Akter, S., & Wamba, S. F. (2016) · Electronic Markets · doi:10.1007/s12525-016-0219-0 · Cited by 684
Müller, O., Fay, M., & Brocke, J. V. (2018) · Journal of Management Information Systems · doi:10.1080/07421222.2018.1451955 · Cited by 578
Assessing business value of big data analytics in European firms
Côrte-Real, N., Oliveira, T., & Ruivo, P. (2017) · Journal of Business Research · doi:10.1016/j.jbusres.2016.08.011 · Cited by 538
Rana, N. P., Chatterjee, S., Dwivedi, Y. K., & Akter, S. (2021) · European Journal of Information Systems · doi:10.1080/0960085x.2021.1955628 · Cited by 500
‘Datafication’: making sense of (big) data in a complex world
Lycett, M. (2013) · European Journal of Information Systems · doi:10.1057/ejis.2013.10 · Cited by 441
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Ríkharðsson, P., & Yigitbasioglu, O. M. (2018) · International Journal of Accounting Information Systems · doi:10.1016/j.accinf.2018.03.001 · Cited by 503
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Popovič, A., Hackney, R., Tassabehji, R., & Castelli, M. (2016) · Information Systems Frontiers · doi:10.1007/s10796-016-9720-4 · Cited by 392
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Ashrafi, A., Ravasan, A. Z., Trkman, P., & Afshari, S. (2019) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2018.12.005 · Cited by 412
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“[95] 2014 x x Sviokla [96] 1986 x x Thow-Yick and Huu-Phuong [97] 1990 x”
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Côrte-Real, N., Ruivo, P., & Oliveira, T. (2020) · Information & Management · doi:10.1016/j.im.2019.01.003 · Cited by 306
Torres, R., Sidorova, A., & Jones, M. C. (2018) · Information & Management · doi:10.1016/j.im.2018.03.010 · Cited by 319
Utilizing big data analytics for information systems research: challenges, promises and guidelines
Müller, O., Junglas, I. A., Brocke, J. V., & Debortoli, S. (2016) · European Journal of Information Systems · doi:10.1057/ejis.2016.2 · Cited by 282
Saldanha, T. J. V., Mithas, S., & Krishnan, M. S. (2017) · MIS Quarterly · doi:10.25300/misq/2017/41.1.14 · Cited by 282
Self-citationBig data and business analytics: A research agenda for realizing business value
Mikalef, P., Pappas, I. O., Krogstie, J., & Pavlou, P. A. (2019) · Information & Management · doi:10.1016/j.im.2019.103237 · Cited by 253
Big data analytics and enterprises: a bibliometric synthesis of the literature
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Vaio, A. D., Hassan, R., & Alavoine, C. (2021) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2021.121201 · Cited by 255
Bader, V., & Kaiser, S. (2019) · Organization · doi:10.1177/1350508419855714 · Cited by 232
Božič, K., & Dimovski, V. (2019) · The Journal of Strategic Information Systems · doi:10.1016/j.jsis.2019.101578 · Cited by 254
Ghasemaghaei, M. (2019) · Decision Support Systems · doi:10.1016/j.dss.2019.03.004 · Cited by 255
Artificial intelligence as digital agency
Ågerfalk, P. J. (2020) · European Journal of Information Systems · doi:10.1080/0960085x.2020.1721947 · Cited by 217
Organizational intention to adopt big data in the B2B context: An integrated view
Sun, S., Hall, D. J., & Cegielski, C. G. (2020) · Industrial Marketing Management · doi:10.1016/j.indmarman.2019.09.003 · Cited by 212
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Wang, Y., & Byrd, T. A. (2017) · Journal of Knowledge Management · doi:10.1108/jkm-08-2015-0301 · Cited by 205
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Vudugula, S., Chebrolu, S. K., Bhuiyan, M., & Rozony, F. Z. (2023) · doi:10.63125/s5skge53 · Cited by 202
Chatterjee, S., Rana, N. P., & Dwivedi, Y. K. (2021) · Information Technology and People · doi:10.1108/itp-08-2020-0603 · Cited by 194
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Hazen, B. T., Skipper, J. B., Boone, C. A., & Hill, R. R. (2016) · Annals of Operations Research · doi:10.1007/s10479-016-2226-0 · Cited by 175
Industrial and Commercial Training
(1992) · Industrial and Commercial Training · doi:10.1108/eb004126 · Cited by 169
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Wang, Y., Kung, L., Gupta, S., & Ozdemir, S. (2019) · British Journal of Management · doi:10.1111/1467-8551.12332 · Cited by 173
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Murawski, M., & Bick, M. (2017) · Business Process Management Journal · doi:10.1108/bpmj-06-2016-0126 · Cited by 152
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Barbosa, M. W., de la Calle, A., Ladeira, M. B., & de Oliveira, M. P. V. (2017) · International Journal of Logistics Research and Applications · doi:10.1080/13675567.2017.1369501 · Cited by 148
Digital economy in a global perspective: is there a digital divide?
Murthy, K. V. B., Kalsie, A., & Shankar, R. (2021) · Transnational Corporation Review · doi:10.1080/19186444.2020.1871257 · Cited by 144
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Cao, G., Duan, Y., & Banna, A. E. (2018) · Industrial Marketing Management · doi:10.1016/j.indmarman.2018.08.002 · Cited by 155
Agag, G., Shehawy, Y. M., Almoraish, A., Eid, R., Lababdi, H. C., Labben, T. G., & Abdo, S. S. (2023) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2023.103663 · Cited by 153
Business analytics and business value: A comparative case study
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Su, X., Zeng, W., Zheng, M., Xiao-li, J., Lin, W., & Xu, A. (2021) · European Journal of Innovation Management · doi:10.1108/ejim-10-2020-0431 · Cited by 127
Troilo, G., Luca, L. M. D., & Guenzi, P. (2017) · Journal of Product Innovation Management · doi:10.1111/jpim.12395 · Cited by 131
Dwivedi, R., Nerur, S., & Balijepally, V. (2023) · International Journal of Information Management Data Insights · doi:10.1016/j.jjimei.2023.100185 · Cited by 126
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Ghasemaghaei, M. (2018) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2018.12.011 · Cited by 104
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Suuronen, S., Ukko, J., Eskola, R., Semken, R. S., & Rantanen, H. (2022) · CIRP - Journal of Manufacturing Science and Technology · doi:10.1016/j.cirpj.2022.02.016 · Cited by 76
Herath, S., Shrestha, Y. R., & von Krogh, G. (2024) · European Journal of Information Systems · doi:10.1080/0960085x.2024.2330402 · Cited by 118
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Naseer, A., Naseer, H., Ahmad, A., Maynard, S. B., & Siddiqui, A. M. (2021) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2021.102334 · Cited by 95
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Dubey, R., & Gunasekaran, A. (2015) · Industrial and Commercial Training · doi:10.1108/ict-08-2014-0059 · Cited by 94
Defining business analytics: an empirical approach
Power, D., Heavin, C., McDermott, J., & Daly, M. (2018) · Journal of Business Analytics · doi:10.1080/2573234x.2018.1507605 · Cited by 94
Ghasemaghaei, M., & Turel, O. (2020) · Information Systems Journal · doi:10.1111/isj.12310 · Cited by 94
Chen, L., Liu, H., Zhou, Z., Chen, M., & Chen, Y. (2022) · Decision Support Systems · doi:10.1016/j.dss.2022.113745 · Cited by 92
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On neural networks and learning systems for business computing
Li, Y., Jiang, W., Liu, Y., & Wu, T. B. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.09.054 · Cited by 94
Business Performance Analytics: exploring the potential for Performance Management Systems
Raffoni, A., Visani, F., Bartolini, M., & Silvi, R. (2017) · Production Planning & Control · doi:10.1080/09537287.2017.1381887 · Cited by 89
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Baabdullah, A. M. (2023) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2023.102745 · Cited by 98
Malomo, F., & Sena, V. (2016) · Policy & Internet · doi:10.1002/poi3.141 · Cited by 83
van Rijmenam, M., Erekhinskaya, T., Schweitzer, J., & Williams, M. (2018) · Long Range Planning · doi:10.1016/j.lrp.2018.05.007 · Cited by 81
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Xiao, X., Tian, Q., & Mao, H. (2020) · IEEE Access · doi:10.1109/access.2020.2968734 · Cited by 81
Benzidia, S., Bentahar, O., Husson, J., & Makaoui, N. (2023) · Annals of Operations Research · doi:10.1007/s10479-022-05157-6 · Cited by 84
Profiting from big data analytics: The moderating roles of industry concentration and firm size
Raguseo, E., Vitari, C., & Pigni, F. (2020) · International Journal of Production Economics · doi:10.1016/j.ijpe.2020.107758 · Cited by 79
Gao, J., & Sarwar, Z. (2022) · Information Technology and Management · doi:10.1007/s10799-022-00380-w · Cited by 88
Behl, A., Dutta, P., Lessmann, S., Dwivedi, Y. K., & Kar, S. (2019) · Information Systems and e-Business Management · doi:10.1007/s10257-019-00452-5 · Cited by 78
The role of big data and predictive analytics in the employee retention: a resource-based view
Singh, R., Sharma, P., Foropon, C. R. H., & Belal, H. M. (2022) · International Journal of Manpower · doi:10.1108/ijm-03-2021-0197 · Cited by 91
Kowalczyk, M., & Buxmann, P. (2015) · Decision Support Systems · doi:10.1016/j.dss.2015.08.010 · Cited by 86
The Shift to Socio-Organizational Drivers of Business Intelligence and Analytics Acceptance
Grublješič, T., Coelho, P. S., & Jaklič, J. (2019) · Journal of Organizational and End User Computing · doi:10.4018/joeuc.2019040103 · Cited by 76
Rahman, M. S., Hossain, M. A., & Abdel Fattah, F. A. M. (2022) · Journal of Enterprise Information Management · doi:10.1108/jeim-05-2020-0185 · Cited by 74
Towards a data science toolbox for industrial analytics applications
Flath, C. M., & Stein, N. (2017) · Computers in Industry · doi:10.1016/j.compind.2017.09.003 · Cited by 74
Exploring perceptions of data analytics in the internal audit function
Tang, F., Norman, C. S., & Vendrzyk, V. P. (2017) · Behaviour and Information Technology · doi:10.1080/0144929x.2017.1355014 · Cited by 74
Torres, R., & Sidorova, A. (2019) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.05.028 · Cited by 79
Galliers, R. D., Newell, S., Shanks, G., & Topi, H. (2017) · The Journal of Strategic Information Systems · doi:10.1016/j.jsis.2017.08.002 · Cited by 70
Gestaltungsbereiche der Digitalen Transformation von Unternehmen: Entwicklung eines Reifegradmodells
Berghaus, & Back (2016) · Die Unternehmung · doi:10.5771/0042-059x-2016-2-98 · Cited by 70
Metadata
- Authors
- Sharma, R., Mithas, S., & Kankanhalli, A.
- Affiliations
- University of Wollongong (AU)
- University of Maryland, College Park (US)
- National University of Singapore (SG)
- Venue
- European Journal of Information Systems
- Publisher
- Informa UK Limited
- Published
- 2014-07
- Volume
- 23
- Issue
- 4
- Pages
- 433-441
- ISSN
- 0960-085X (ISSN-L)
- 1476-9344
- OpenAlex
- W2003813760
- Cited by
- 516
- References
- 69
- Citations by year
- 2026: 33 · 2025: 45 · 2024: 51 · 2023: 67 · 2022: 58 · 2021: 61 · 2020: 44 · 2019: 51 · 2018: 43 · 2017: 26 · 2016: 21 · 2015: 14
- FWCI
- 44.91
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- European Journal of Information Systems (publishedVersion)
- Research Online (University of Wollongong) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Figshare (submittedVersion, cc-by)
- Funders
- University of Oxford
- Australian Research Council
- Topics
- Social Sciences › Business, Management and Accounting › Management Information Systems › Big Data and Business Intelligence
- Social Sciences › Business, Management and Accounting › Management Information Systems › Information Technology Governance and Strategy
- Social Sciences › Business, Management and Accounting › Strategy and Management › Competitive and Knowledge Intelligence
- Keywords
- business analytics, big data, decision-making, organizational impact, value creation, research agenda
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 ›
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