{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T22:15:47Z","timestamp":1780524947383,"version":"3.54.1"},"reference-count":106,"publisher":"Emerald","issue":"6","license":[{"start":{"date-parts":[[2023,9,19]],"date-time":"2023-09-19T00:00:00Z","timestamp":1695081600000},"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"],"published-print":{"date-parts":[[2023,11,8]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>The purpose of this research study was to understand the simultaneous competitive and social gains of machine learning (ML) and artificial intelligence (AI) usage in organizations. There was a knowledge hiatus regarding the contribution of the deployment of ML and AI technologies and their effects on organizations and society.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>This study was grounded on the dynamic capabilities (DC) and ML and AI automation-augmentation paradox literature. This research study examined these theoretical perspectives using the response of 239 Indian organizational chief technology officers (CTOs). Partial least square-structural equation modeling (PLS-SEM) path modeling was applied for data analysis.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>The results indicated that ML and AI technologies organizational usage positively influenced DC initiatives. The findings depicted that DC fully mediated ML and AI-based technologies' effects on firm performance and social performance.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Research limitations\/implications<\/jats:title><jats:p>This study contributed to theoretical discourse regarding the tension between organizational and social outcomes of ML and AI technologies. The study extended the role of DC as a vital strategy in achieving social benefits from ML and AI use. Furthermore, the theoretical tension of the automation-augmentation paradox was explored.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Practical implications<\/jats:title><jats:p>Organizations deploying ML and AI technologies could apply this study's insights to comprehend the organizational routines to pursue simultaneous competitive benefits and social gains. Furthermore, chief technology executives of organizations could devise how ML and AI technologies usage from a DC perspective could help settle the tension of the automation-augmentation paradox.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Social implications<\/jats:title><jats:p>Increased ML and AI technologies usage in organizations enhanced DC. They could lead to positive social benefits such as new job creation, increased compensation to skilled employees and greater gender participation in employment. These insights could be derived based on this research study.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>This study was among the first few empirical investigations to provide theoretical and practical insights regarding the organizational and societal benefits of ML and AI usage in organizations because of their DC. This study was also one of the first empirical investigations that addressed the automation-augmentation paradox at the enterprise level.<\/jats:p><\/jats:sec>","DOI":"10.1108\/jeim-09-2022-0307","type":"journal-article","created":{"date-parts":[[2023,9,19]],"date-time":"2023-09-19T07:01:07Z","timestamp":1695106867000},"page":"1556-1582","source":"Crossref","is-referenced-by-count":26,"title":["Automation-augmentation paradox in organizational artificial intelligence technology deployment capabilities; an\u00a0empirical investigation for achieving simultaneous economic and social benefits"],"prefix":"10.1108","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1652-2308","authenticated-orcid":false,"given":"Amit","family":"Kumar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Som Sekhar","family":"Bhattacharyya","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bala","family":"Krishnamoorthy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"140","published-online":{"date-parts":[[2023,9,19]]},"reference":[{"issue":"1","key":"key2025061015092672500_ref105","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/0960085X.2020.1721947","article-title":"Artificial intelligence as digital agency","volume":"29","year":"2020","journal-title":"European Journal of Information Systems"},{"key":"key2025061015092672500_ref001","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.jbusres.2021.05.026","article-title":"Unpacking the role of innovation capability: exploring the impact of leadership style on green procurement via a natural resource-based perspective","volume":"134","year":"2021","journal-title":"Journal of Business Research"},{"issue":"3","key":"key2025061015092672500_ref002","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1108\/17410391311325225","article-title":"Cloud computing adoption by SMEs in the north east of England: a multi\u2010perspective framework","volume":"26","year":"2013","journal-title":"Journal of Enterprise Information Management"},{"issue":"3","key":"key2025061015092672500_ref003","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1177\/002224377701400320","article-title":"Estimating nonresponse bias in mail surveys","volume":"14","year":"1977","journal-title":"Journal of Marketing Research"},{"key":"key2025061015092672500_ref004","article-title":"Ethical framework for artificial intelligence and digital technologies","volume":"62","year":"2022","journal-title":"International Journal of Information Management"},{"key":"key2025061015092672500_ref005","article-title":"Role of institutional pressures and resources in the adoption of big data analytics powered artificial intelligence, sustainable manufacturing practices and circular economy capabilities","volume":"163","year":"2021","journal-title":"Technological Forecasting and Social Change"},{"issue":"2","key":"key2025061015092672500_ref006","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1108\/JEIM-07-2020-0284","article-title":"A framework for understanding artificial intelligence research: insights from practice","volume":"34","year":"2021","journal-title":"Journal of Enterprise Information Management"},{"issue":"5-6","key":"key2025061015092672500_ref007","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1016\/j.lrp.2012.10.001","article-title":"Hierarchical latent variable models in PLS-SEM: guidelines for using reflective-formative type models","volume":"45","year":"2012","journal-title":"Long Range Planning"},{"issue":"4","key":"key2025061015092672500_ref008","first-page":"ix","article-title":"Artificial intelligence in organizations: current state and future opportunities","volume":"19","year":"2020","journal-title":"MIS Quarterly Executive"},{"issue":"3","key":"key2025061015092672500_ref009","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1016\/j.jom.2008.03.002","article-title":"Empirical elephants\u2014why multiple methods are essential to quality research in operations and supply chain management","volume":"26","year":"2008","journal-title":"Journal of Operations Management"},{"key":"key2025061015092672500_ref010","doi-asserted-by":"crossref","unstructured":"Brynjolfsson, E., Rock, D. and Syverson, C. 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