{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T21:33:52Z","timestamp":1778276032376,"version":"3.51.4"},"reference-count":53,"publisher":"Emerald","license":[{"start":{"date-parts":[[2022,11,23]],"date-time":"2022-11-23T00:00:00Z","timestamp":1669161600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JEIM"],"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>The paper aims to help enterprises gain valuable knowledge about big data implementation in practice and improve their information management ability, as they accumulate experience, to reuse or adapt the proposed method to achieve a sustainable competitive advantage.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>Guided by the theory of technological frames of reference (TFR) and transaction cost theory (TCT), this paper describes a real-world case study in the banking industry to explain how to help enterprises leverage big data analytics for changes. Through close integration with bank's daily operations and strategic planning, the case study shows how the analytics team frame the challenge and analyze the data with two analytic models \u2013 customer segmentation (unsupervised) and product affinity prediction (supervised), to initiate the adoption of big data analytics in precise marketing.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>The study reported relevant findings from a longitudinal data analysis and identified some key success factors. First, non-technical factors, for example intuitive analytics results, appropriate evaluation baseline, multiple-wave implementation and selection of marketing channels critically influence big data implementation progress in organizations. Second, a successful campaign also relies on technical factors. For example, the clustering analytics could promote customers' response rates, and the product affinity prediction model could boost efficient transaction and lower time costs.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>For theoretical contribution, this paper verified that the outstanding characteristics of online mutual fund platforms brought up by Nagle, Seamans and Tadelis (2010) could not guarantee organizations' competitive advantages from the aspect of TCT.<\/jats:p><\/jats:sec>","DOI":"10.1108\/jeim-05-2020-0176","type":"journal-article","created":{"date-parts":[[2022,11,22]],"date-time":"2022-11-22T11:20:07Z","timestamp":1669116007000},"source":"Crossref","is-referenced-by-count":19,"title":["Impact of big data analytics on\u00a0banking: a case study"],"prefix":"10.1108","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4699-7710","authenticated-orcid":false,"given":"Wu","family":"He","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jui-Long","family":"Hung","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lixin","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","published-online":{"date-parts":[[2022,11,23]]},"reference":[{"key":"key2022112216200063200_ref001","article-title":"Interpretable K-Means: clusters feature importances","year":"2020","journal-title":"Towards Data Science"},{"issue":"2","key":"key2022112216200063200_ref002","doi-asserted-by":"crossref","first-page":"439","DOI":"10.2308\/acch-51067","article-title":"Drivers of the use and facilitators and obstacles of the evolution of Big Data by the audit profession","volume":"29","year":"2015","journal-title":"Accounting Horizons"},{"key":"key2022112216200063200_ref003","volume-title":"Transaction Cost Theory, the Resource Based View, and Information Technology Outsourcing Decision. 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(2020), \u201cBig data in the banking sector from a transactional cost theory (TCT) perspective\u2014the case of top Lebanese banks\u201d, ICT for an Inclusive World, Springer, Cham, pp.\u00a0391-405.","DOI":"10.1007\/978-3-030-34269-2_27"},{"issue":"5","key":"key2022112216200063200_ref008","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.elerap.2009.03.002","article-title":"Discovering recency, frequency, and monetary (RFM) sequential patterns from customers' purchasing data","volume":"8","year":"2009","journal-title":"Electronic Commerce Research and Applications"},{"key":"key2022112216200063200_ref009","doi-asserted-by":"crossref","unstructured":"Cho, Y.S., Moon, S.C., Jeong, S.P., Oh, I.B. and Ryu, K.H. 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(2020), \u201cSilhouette index as clustering evaluation tool\u201d, in Jajuga, K., Bat\u00f3g, J. and Walesiak, M. (Eds), Classification and Data Analysis. SKAD 2019. 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