{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T06:12:04Z","timestamp":1783923124360,"version":"3.55.0"},"publisher-location":"Singapore","reference-count":30,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819220137","type":"print"},{"value":"9789819220144","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T00:00:00Z","timestamp":1783987200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T00:00:00Z","timestamp":1783987200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2027]]},"DOI":"10.1007\/978-981-92-2014-4_3","type":"book-chapter","created":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T05:36:56Z","timestamp":1783921016000},"page":"28-36","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["AI Adoption Phases in\u00a0Business Intelligence: From Outsourcing to\u00a0Human-Centered Systems"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3030-1280","authenticated-orcid":false,"given":"Erik","family":"Cambria","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1082-8755","authenticated-orcid":false,"given":"Rui","family":"Mao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5751-3937","authenticated-orcid":false,"given":"Frank","family":"Xing","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0157-5477","authenticated-orcid":false,"given":"Gianmarco","family":"Mengaldo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,14]]},"reference":[{"issue":"4","key":"3_CR1","doi-asserted-by":"publisher","first-page":"1901","DOI":"10.5267\/j.ijdns.2023.7.003","volume":"7","author":"I Abu-AlSondos","year":"2023","unstructured":"Abu-AlSondos, I.: The impact of business intelligence system (BIS) on quality of strategic decision-making. Int. J. Data Netw. Sci. 7(4), 1901\u20131912 (2023)","journal-title":"Int. J. Data Netw. Sci."},{"issue":"1","key":"3_CR2","doi-asserted-by":"publisher","first-page":"69","DOI":"10.51594\/ijmer.v6i1.710","volume":"6","author":"EM Adaga","year":"2024","unstructured":"Adaga, E.M., Egieya, Z.E., Ewuga, S.K., Abdul, A.A., Abrahams, T.O.: Philosophy in business analytics: a review of sustainable and ethical approaches. Int. J. Manage. Entrepreneurship Res. 6(1), 69\u201386 (2024)","journal-title":"Int. J. Manage. Entrepreneurship Res."},{"issue":"3","key":"3_CR3","doi-asserted-by":"publisher","first-page":"777","DOI":"10.1108\/BPMJ-10-2022-0546","volume":"29","author":"A Al-Okaily","year":"2023","unstructured":"Al-Okaily, A., Teoh, A.P., Al-Okaily, M.: Evaluation of data analytics-oriented business intelligence technology effectiveness: an enterprise-level analysis. Bus. Process. Manag. J. 29(3), 777\u2013800 (2023)","journal-title":"Bus. Process. Manag. J."},{"issue":"4","key":"3_CR4","first-page":"404","volume":"51","author":"A Al-Okaily","year":"2023","unstructured":"Al-Okaily, A., Teoh, A.P., Al-Okaily, M., Iranmanesh, M., Al-Betar, M.A.: The efficiency measurement of business intelligence systems in the big data-driven economy: a multidimensional model. Inf. Discov. Deliv. 51(4), 404\u2013416 (2023)","journal-title":"Inf. Discov. Deliv."},{"issue":"1","key":"3_CR5","first-page":"24","volume":"7","author":"JP Bharadiya","year":"2023","unstructured":"Bharadiya, J.P.: A comparative study of business intelligence and artificial intelligence with big data analytics. Am. J. Artif. Intell. 7(1), 24 (2023)","journal-title":"Am. J. Artif. Intell."},{"issue":"1","key":"3_CR6","first-page":"123","volume":"48","author":"JP Bharadiya","year":"2023","unstructured":"Bharadiya, J.P.: Machine learning and ai in business intelligence: trends and opportunities. Int. J. Comput. (IJC) 48(1), 123\u2013134 (2023)","journal-title":"Int. J. Comput. (IJC)"},{"key":"3_CR7","doi-asserted-by":"crossref","unstructured":"Cambria, E., Howard, N., Hsu, J., Hussain, A.: Sentic blending: scalable multimodal fusion for continuous interpretation of semantics and sentics. In: IEEE SSCI, pp. 108\u2013117. Singapore (2013)","DOI":"10.1109\/CIHLI.2013.6613272"},{"key":"3_CR8","doi-asserted-by":"crossref","unstructured":"Cambria, E., Malandri, L., Mercorio, F., Mezzanzanica, M., Nobani, N.: A survey on XAI and natural language explanations. Inf. Process. Manage. 60(103111) (2023)","DOI":"10.1016\/j.ipm.2022.103111"},{"key":"3_CR9","unstructured":"Cambria, E., Malandri, L., Mercorio, F., Nobani, N., Seveso, A.: XAI meets LLMs: a survey of the relation between explainable AI and large language models. arXiv preprint arXiv:2407.15248 (2024)"},{"issue":"6","key":"3_CR10","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1109\/MIS.2023.3329745","volume":"38","author":"E Cambria","year":"2023","unstructured":"Cambria, E., Mao, R., Chen, M., Wang, Z., Ho, S.B.: Seven pillars for the future of artificial intelligence. IEEE Intell. Syst. 38(6), 62\u201369 (2023)","journal-title":"IEEE Intell. Syst."},{"issue":"20","key":"3_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12559-026-10569-8","volume":"18","author":"E Cambria","year":"2026","unstructured":"Cambria, E., Mao, R., Hussain, A., Oatley, K., Hinton, G.: Artificial intelligence as the fourth decentering revolution: from cosmic, biological, and psychological displacement to cognitive decentering. Cogn. Comput. 18(20), 1\u201313 (2026). https:\/\/doi.org\/10.1007\/s12559-026-10569-8","journal-title":"Cogn. Comput."},{"key":"3_CR12","doi-asserted-by":"crossref","unstructured":"Cambria, E., Mao, R., Zhang, X., Xiao, L., Shen, T., Anand, A.: SenticNet 9: generative commonsense for emotion AI via conceptual primitive discovery and time shift mechanism. IEEE Trans. Comput. Soc. Syst. 13 (2026)","DOI":"10.1109\/TCSS.2026.3677113"},{"key":"3_CR13","doi-asserted-by":"crossref","unstructured":"Cambria, E., Rajagopal, D., Olsher, D., Das, D.: Big social data analysis. In: Akerkar, R. (ed.) Big Data Computing, chap.\u00a013, pp. 401\u2013414. Chapman and Hall\/CRC (2013)","DOI":"10.1201\/b16014-19"},{"issue":"8","key":"3_CR14","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1145\/1978542.1978562","volume":"54","author":"S Chaudhuri","year":"2011","unstructured":"Chaudhuri, S., Dayal, U., Narasayya, V.: An overview of business intelligence technology. Commun. ACM 54(8), 88\u201398 (2011)","journal-title":"Commun. ACM"},{"key":"3_CR15","doi-asserted-by":"crossref","unstructured":"Doshi, R., Hiran, K.K., Mijwil, M.M., Anand, D.: To that of artificial intelligence, passing through business intelligence. In: Handbook of Research on AI and Knowledge Engineering for Real-Time Business Intelligence, pp. 1\u201316. IGI Global (2023)","DOI":"10.4018\/978-1-6684-6519-6.ch001"},{"key":"3_CR16","doi-asserted-by":"crossref","unstructured":"Du, K., Mao, R., Xing, F., Cambria, E.: Explainable stock price movement prediction using contrastive learning. In: CIKM, pp. 529\u2013537 (2024)","DOI":"10.1145\/3627673.3679544"},{"issue":"5","key":"3_CR17","doi-asserted-by":"publisher","first-page":"285","DOI":"10.51594\/estj.v4i5.616","volume":"4","author":"EO Eboigbe","year":"2023","unstructured":"Eboigbe, E.O., Farayola, O.A., Olatoye, F.O., Nnabugwu, O.C., Daraojimba, C.: Business intelligence transformation through ai and data analytics. Eng. Sci. Technol. J. 4(5), 285\u2013307 (2023)","journal-title":"Eng. Sci. Technol. J."},{"issue":"1","key":"3_CR18","doi-asserted-by":"publisher","first-page":"215","DOI":"10.32996\/jbms.2024.6.1.14","volume":"6","author":"MR Hasan","year":"2024","unstructured":"Hasan, M.R., Ray, R.K., Chowdhury, F.R.: Employee performance prediction: an integrated approach of business analytics and machine learning. J. Bus. Manage. Stud. 6(1), 215\u2013219 (2024)","journal-title":"J. Bus. Manage. Stud."},{"issue":"3","key":"3_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.joitmc.2023.100111","volume":"9","author":"H Hmoud","year":"2023","unstructured":"Hmoud, H., Al-Adwan, A.S., Horani, O., Yaseen, H., Al Zoubi, J.Z.: Factors influencing business intelligence adoption by higher education institutions. J. Open Innov. Technol. Market, Complex. 9(3), 100111 (2023)","journal-title":"J. Open Innov. Technol. Market, Complex."},{"key":"3_CR20","doi-asserted-by":"crossref","unstructured":"Kazemeini, A., Roy, S., Mercer, R., Cambria, E.: Interpretable representation learning for personality detection. In: Proceedings of ICDM Workshops, pp. 158\u2013165 (2021)","DOI":"10.1109\/ICDMW53433.2021.00026"},{"key":"3_CR21","doi-asserted-by":"crossref","unstructured":"Kazienko, P., et al.: AI overload: A multi-level taxonomy and the path forward. IEEE Intell. Syst. 41(2) (2026)","DOI":"10.1109\/MIS.2026.3666685"},{"issue":"1","key":"3_CR22","volume":"4","author":"AB Mohammed","year":"2024","unstructured":"Mohammed, A.B., Al-Okaily, M., Qasim, D., Al-Majali, M.K.: Towards an understanding of business intelligence and analytics usage: evidence from the banking industry. Int. J. Inf. Manage. Data Insights 4(1), 100215 (2024)","journal-title":"Int. J. Inf. Manage. Data Insights"},{"key":"3_CR23","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1007\/978-3-540-48716-6_9","volume":"2","author":"S Negash","year":"2008","unstructured":"Negash, S., Gray, P.: Business intelligence. Handb. Decis. Support Syst. 2, 175\u2013193 (2008)","journal-title":"Handb. Decis. Support Syst."},{"issue":"9","key":"3_CR24","doi-asserted-by":"publisher","first-page":"64","DOI":"10.9734\/jsrr\/2023\/v29i91785","volume":"29","author":"O Olaniyi","year":"2023","unstructured":"Olaniyi, O., Abalaka, A., Olabanji, S.O.: Utilizing big data analytics and business intelligence for improved decision-making at leading fortune company. J. Sci. Res. Rep. 29(9), 64\u201372 (2023)","journal-title":"J. Sci. Res. Rep."},{"key":"3_CR25","doi-asserted-by":"crossref","unstructured":"Ong, K., Mao, R., Satapathy, R., Cambria, E., Sulaeman, J., Mengaldo, G.: Explainable natural language processing for corporate sustainability analysis. Inf. Fusion 115(102726) (2025)","DOI":"10.1016\/j.inffus.2024.102726"},{"key":"3_CR26","doi-asserted-by":"crossref","unstructured":"Qazi, A., Raj, R.G., Tahir, M., Cambria, E., Syed, K.B.S.: Enhancing business intelligence by means of suggestive reviews. Sci. World J. 879323 (2014)","DOI":"10.1155\/2014\/879323"},{"key":"3_CR27","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1016\/j.inffus.2022.10.009","volume":"91","author":"J Wen","year":"2023","unstructured":"Wen, J., Jiang, D., Tu, G., Liu, C., Cambria, E.: Dynamic interactive multiview memory network for emotion recognition in conversation. Inf. Fusion 91, 123\u2013133 (2023)","journal-title":"Inf. Fusion"},{"key":"3_CR28","doi-asserted-by":"crossref","unstructured":"Yeo, W., Satapathy, R., Cambria, E.: Plausible extractive rationalization through semi-supervised entailment signal. In: ACL, pp 5182\u20135192 (2024)","DOI":"10.18653\/v1\/2024.findings-acl.307"},{"key":"3_CR29","doi-asserted-by":"crossref","unstructured":"Yeo, W., Satapathy, R., Goh, S.M., Cambria, E.: How interpretable are reasoning explanations from prompting large language models? In: Proceedings of NAACL, pp. 2148\u20132164 (2024)","DOI":"10.18653\/v1\/2024.findings-naacl.138"},{"key":"3_CR30","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1007\/s10462-024-11077-7","volume":"58","author":"W Yeo","year":"2025","unstructured":"Yeo, W., Van Der Heever, W., Mao, R., Cambria, E., Satapathy, R., Mengaldo, G.: A comprehensive review on financial explainable AI. Artif. Intell. Rev. 58, 189 (2025)","journal-title":"Artif. Intell. Rev."}],"container-title":["Lecture Notes in Computer Science","Trends and Applications in Knowledge Discovery and Data Mining"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-92-2014-4_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T05:36:58Z","timestamp":1783921018000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-92-2014-4_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,14]]},"ISBN":["9789819220137","9789819220144"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-981-92-2014-4_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,14]]},"assertion":[{"value":"14 July 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PAKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific-Asia Conference on Knowledge Discovery and Data Mining","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hong Kong","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 June 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 June 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pakdd2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.pakdd2026.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}