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The Role of Big Data and Predictive Analytics in Retailing
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“Nevertheless, the benefits of big data analytics appear not to be derived from the enormous size of the data, but only emerge when working with fine-grained data (Bradlow et al., 2017;Martens et al., 2016).”
From COVID‐19 Pandemic in the New Era of Big Data Analytics: Methodological Innovations and Future Research Directions (2021) · cites this paper as Bradlow et al., 2017
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Cites
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482
(2003) · Journal of Machine Learning Research · doi:10.5555/944919.944937 · Cited by 32,337
Statistical Analysis With Missing Data
Lahiff, M., Little, R. J. A., & Rubin, D. B. (1989) · Journal of the American Statistical Association · doi:10.2307/2289883 · Cited by 17,385
Data clustering: 50 years beyond K-means
Jain, A. K. (2009) · Pattern Recognition Letters · doi:10.1016/j.patrec.2009.09.011 · Cited by 10,021
Information rules - a strategic guide to the network economy
Shapiro, C., & Varian, H. (1999) · The Journal of Economic Education · doi:10.2307/1183273 · Cited by 5,225
Information Rules: A Strategic Guide to the Network Economy
Shaw, M. J., Shapiro, C., & Varian, H. R. (2000) · Academy of Management Review · doi:10.2307/259025 · Cited by 4,416
Detecting influenza epidemics using search engine query data
Ginsberg, J., Mohebbi, M. H., Patel, R. S., Brammer, L., Smolinski, M. S., & Brilliant, L. (2009) · Nature · doi:10.1038/nature07634 · Cited by 4,295
The Calculation of Posterior Distributions by Data Augmentation
Tanner, M. A., & Wong, W. H. (1987) · Journal of the American Statistical Association · doi:10.1080/01621459.1987.10478458 · Cited by 3,929
Big Data : The Management Revolution
マカフィー, ア., ブリニョルフソン, エ., & 有賀, 裕. (2013) · Harvard business review · Cited by 3,545
The Parable of Google Flu: Traps in Big Data Analysis
Lazer, D., Kennedy, R., King, G., & Vespignani, A. (2014) · Science · doi:10.1126/science.1248506 · Cited by 2,523 · cited 1×
“Later a deeper analysis showed that these 'theory-free' projections were overpredicting actual cases by 100%, among other problems that arose (see Lazer et al. 2014 for a review).”
Background
Research works printed but not linked: 2
- Cooper, 1988, Vol. 1
- Van Bommel, 2014, Digitizing the Consumer Decision Journey, McKinsey Quarterly
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Wamba, S. F., Dubey, R., Gunasekaran, A., & Akter, S. (2019) · International Journal of Production Economics · doi:10.1016/j.ijpe.2019.09.019 · Cited by 840
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Fildes, R. A., Ma, S., & Kolassa, S. (2019) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2019.06.004 · Cited by 357
Sheng, J., Amankwah‐Amoah, J., Khan, Z., & Wang, X. (2021) · British Journal of Management · doi:10.1111/1467-8551.12441 · Cited by 369
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Self-citationAnnosi, M. C., Brunetta, F., Bimbo, F., & Kostoula, M. (2021) · Industrial Marketing Management · doi:10.1016/j.indmarman.2021.01.005 · Cited by 304
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Grover, P., & Kar, A. K. (2017) · Global Journal of Flexible Systems Management · doi:10.1007/s40171-017-0159-3 · Cited by 219
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Theodorakopoulos, L., & Theodoropoulou, A. (2024) · Human Behavior and Emerging Technologies · doi:10.1155/2024/3641502 · Cited by 153
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Battisti, S., Agarwal, N., & Brem, A. (2021) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2021.121392 · Cited by 163
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Impact of Big Data and Machine Learning on Digital Transformation in Marketing: A Literature Review
Miklošík, A., & Evans, N. (2020) · IEEE Access · doi:10.1109/access.2020.2998754 · Cited by 164
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Choudhury, A., Behl, A., Sheorey, P. A., & Pal, A. (2021) · Benchmarking An International Journal · doi:10.1108/bij-08-2020-0461 · Cited by 163
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Aljumah, A. I., Nuseir, M. T., & Alam, M. M. (2021) · Business Process Management Journal · doi:10.1108/bpmj-11-2020-0527 · Cited by 130
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Ardito, L., Cerchione, R., Mazzola, E., & Raguseo, E. (2021) · Journal of Knowledge Management · doi:10.1108/jkm-04-2021-0325 · Cited by 108
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Agrawal, S. R., & Mittal, D. (2022) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2022.102966 · Cited by 77
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Sheehan, D., Hardesty, D. M., Ziegler, A. H., & Chen, H. A. (2019) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2019.06.001 · Cited by 88
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Nilashi, M., Baabdullah, A. M., Abumalloh, R. A., Ooi, K., Tan, G. W., Giannakis, M., & Dwivedi, Y. K. (2023) · Annals of Operations Research · doi:10.1007/s10479-023-05272-y · Cited by 73
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Kakatkar, C., & Spann, M. (2019) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2018.10.001 · Cited by 59
Micu, A., Căpățînă, A., Cristea, D. S., Munteanu, D., Micu, A., & Şarpe, D. A. (2021) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2021.121289 · Cited by 59
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Johnson, D. S., Muzellec, L., Sihi, D., & Zahay, D. L. (2019) · Journal of Research in Interactive Marketing · doi:10.1108/jrim-12-2018-0157 · Cited by 75
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Wong, E. Y. C., & Yan, W. (2018) · International Journal of Retail & Distribution Management · doi:10.1108/ijrdm-06-2017-0130 · Cited by 55
Chen, Y., & Chi, T. (2021) · Sustainability · doi:10.3390/su13168983 · Cited by 54
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Perakis, G., Sim, M., Tang, Q., & Xiong, P. (2018) · Management Science · doi:10.1287/mnsc.2022.4446 · Cited by 55
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Raji, M. A., Olodo, H. B., Oke, T. T., Addy, W. A., Ofodile, O. C., & Oyewole, A. T. (2024) · GSC Advanced Research and Reviews · doi:10.30574/gscarr.2024.18.3.0089 · Cited by 52
van Osselaer, S. M. J., Fuchs, C., Schreier, M., & Puntoni, S. (2020) · Journal of Retailing · doi:10.1016/j.jretai.2019.12.006 · Cited by 52
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Mäntymäki, M., Hyrynsalmi, S., & Koskenvoima, A. (2020) · Information Systems Frontiers · doi:10.1007/s10796-019-09913-1 · Cited by 45
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Nordin, F., & Ravald, A. (2022) · Journal of Business Research · doi:10.1016/j.jbusres.2023.113872 · Cited by 70
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Rose, N., & Dolega, L. (2021) · Applied Spatial Analysis and Policy · doi:10.1007/s12061-021-09397-0 · Cited by 44
Cui, L., Chen, Y., Deng, J., & Han, Z. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124409 · Cited by 48
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Cirqueira, D., Hofer, M., Nedbal, D., Helfert, M., & Bezbradica, M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-48861-1_8 · Cited by 44
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Singh, J., Arnold, T. J., Brady, M. K., & Brown, T. J. (2019) · Journal of Retailing · doi:10.1016/j.jretai.2019.06.003 · Cited by 43
Antipov, E. A., & Pokryshevskaya, E. B. (2020) · Journal of Revenue and Pricing Management · doi:10.1057/s41272-020-00236-4 · Cited by 43
Digitalization and Strategic Transformation of Retail Chain Stores: Trends, Impacts, Prospects
Malenkov, Y. A., Kapustina, I., Kudryavtseva, G. V., Шишкин, В. И., & Шишкин, В. И. (2021) · Journal of Open Innovation Technology Market and Complexity · doi:10.3390/joitmc7020108 · Cited by 43
Saha, L., Tripathy, H. K., Nayak, S. R., Bhoi, A. K., & Barsocchi, P. (2021) · Sustainability · doi:10.3390/su13095279 · Cited by 41
Enhancing consumer perceptions of privacy and trust: a GDPR label perspective
Fox, G., Lynn, T., & Rosati, P. (2022) · Information Technology and People · doi:10.1108/itp-09-2021-0706 · Cited by 71
Digital transformation of supply chain management in retail and e-commerce
Gong, S. (2023) · International Journal of Retail & Distribution Management · doi:10.1108/ijrdm-02-2023-0076 · Cited by 40
RetractedReimagining personalization in the physical store
Scholdra, T. P., Wichmann, J. R. K., & Reinartz, W. (2023) · Journal of Retailing · doi:10.1016/j.jretai.2023.11.001 · Cited by 52
Metadata
- Authors
- Bradlow, E. T., Gangwar, M., Kopalle, P., & Voleti, S.
- Affiliations
- University of Pennsylvania (US)
- Indian School of Business (IN)
- Dartmouth College (US)
- Corresponding author
- Kopalle, Praveen
- Venue
- Journal of Retailing
- Publisher
- Elsevier BV
- Published
- 2017-03
- Volume
- 93
- Issue
- 1
- Pages
- 79-95
- ISSN
- 0022-4359 (ISSN-L)
- OpenAlex
- W2599766447
- Cited by
- 516
- References
- 86
- Citations by year
- 2026: 39 · 2025: 59 · 2024: 62 · 2023: 57 · 2022: 71 · 2021: 81 · 2020: 63 · 2019: 47 · 2018: 35 · 2017: 2
- FWCI
- 68.71
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Retailing (publishedVersion)
- Digital Eprints Services at ISB (DESI) (Indian School of Business) (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Management Information Systems › Big Data and Business Intelligence
- Social Sciences › Business, Management and Accounting › Marketing › Consumer Market Behavior and Pricing
- Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
- Keywords
- big data, predictive analytics, retail trade, customer data, product data, geospatial data, retail channels, Bayesian analysis, hierarchical models, data augmentation, field experiments, data quality, privacy, data ethics
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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