{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T11:52:59Z","timestamp":1780401179501,"version":"3.54.1"},"reference-count":163,"publisher":"Emerald","issue":"1","license":[{"start":{"date-parts":[[2021,9,30]],"date-time":"2021-09-30T00:00:00Z","timestamp":1632960000000},"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":[[2022,2,18]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>The study presents various barriers to adopt big data analytics (BDA) for sustainable manufacturing operations (SMOs) post-coronavirus disease (COVID-19) pandemics. In this study, 17 barriers are identified through extensive literature review and experts\u2019 opinions for investing in BDA implementation. A questionnaire-based survey is conducted to collect responses from experts. The identified barriers are grouped into three categories with the help of factor analysis. These are organizational barriers, data management barriers and human barriers. For the quantification of barriers, the graph theory matrix approach (GTMA) is applied.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>The study presents various barriers to adopt BDA for the SMOs post-COVID-19 pandemic. In this study, 17 barriers are identified through extensive literature review and experts\u2019 opinions for investing in BDA implementation. A questionnaire-based survey is conducted to collect responses from experts. The identified barriers are grouped into three categories with the help of factor analysis. These are organizational barriers, data management barriers and human barriers. For the quantification of barriers, the GTMA is applied.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>The study identifies barriers to investment in BDA implementation. It categorizes the barriers based on factor analysis and computes the intensity for each category of a barrier for BDA investment for SMOs. It is observed that the organizational barriers have the highest intensity whereas the human barriers have the smallest intensity.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Practical implications<\/jats:title><jats:p>This study may help organizations to take strategic decisions for investing in BDA applications for achieving one of the sustainable development goals. Organizations should prioritize their efforts first to counter the barriers under the category of organizational barriers followed by barriers in data management and human barriers.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>The novelty of this paper is that barriers to BDA investment for SMOs in the context of Indian manufacturing organizations have been analyzed. The findings of the study will assist the professionals and practitioners in formulating policies based on the actual nature and intensity of the barriers.<\/jats:p><\/jats:sec>","DOI":"10.1108\/jeim-03-2021-0154","type":"journal-article","created":{"date-parts":[[2021,9,29]],"date-time":"2021-09-29T12:55:32Z","timestamp":1632920132000},"page":"179-213","source":"Crossref","is-referenced-by-count":34,"title":["Analysis of barriers intensity for investment in big data analytics for sustainable manufacturing operations in post-COVID-19 pandemic era"],"prefix":"10.1108","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7978-4391","authenticated-orcid":false,"given":"Narender","family":"Kumar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0448-8918","authenticated-orcid":false,"given":"Girish","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9057-2189","authenticated-orcid":false,"given":"Rajesh Kr","family":"Singh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"140","published-online":{"date-parts":[[2021,9,30]]},"reference":[{"key":"key2022092113265921100_ref001","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/j.jclepro.2017.12.053","article-title":"The drivers of sustainable manufacturing practices in Egyptian SMEs and their impact on competitive capabilities: a PLS-SEM model","volume":"175","year":"2018","journal-title":"Journal of Cleaner Production"},{"issue":"5","key":"key2022092113265921100_ref002","doi-asserted-by":"publisher","DOI":"10.1108\/IJPPM-03-2020-0137","article-title":"Applying Industry 4.0 technologies in the COVID\u201319 sustainable chains","volume":"70","year":"2021","journal-title":"International Journal of Productivity and Performance Management"},{"key":"key2022092113265921100_ref003","first-page":"1","article-title":"Factors influencing to the implementation success of big data analytics: a systematic literature review","year":"2017"},{"issue":"1","key":"key2022092113265921100_ref004","doi-asserted-by":"crossref","first-page":"11290","DOI":"10.5465\/ambpp.2015.11290abstract","article-title":"Investigating the determinants of Big Data Analytics (BDA) adoption in emerging economies","volume":"2015","year":"2015","journal-title":"Academy of Management Proceedings"},{"key":"key2022092113265921100_ref005","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.resconrec.2016.01.004","article-title":"Outsourcing decisions in reverse logistics: sustainable balanced scorecard and graph theoretic approach","volume":"108","year":"2016","journal-title":"Resources, Conservation and Recycling"},{"issue":"11","key":"key2022092113265921100_ref006","doi-asserted-by":"crossref","first-page":"1436","DOI":"10.1108\/ECAM-01-2018-0035","article-title":"Challenges and drivers for data mining in the AEC sector","volume":"25","year":"2018","journal-title":"Engineering, Construction and Architectural Management"},{"key":"key2022092113265921100_ref007","first-page":"282","article-title":"Product data analytics service model for manufacturing company","year":"2015"},{"issue":"2","key":"key2022092113265921100_ref008","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1111\/1467-8551.12333","article-title":"Big data\u2010savvy teams' skills, big data\u2010driven actions, and business performance","volume":"30","year":"2019","journal-title":"British Journal of Management"},{"issue":"1","key":"key2022092113265921100_ref009","first-page":"e12151","article-title":"The barriers to big data adoption in developing economies","volume":"87","year":"2020","journal-title":"The Electronic Journal of Information Systems in Developing Countries"},{"key":"key2022092113265921100_ref010","first-page":"7","article-title":"Deployment model of Big Data for port logistics","volume-title":"International Information Institute (Tokyo)","year":"2015"},{"issue":"3","key":"key2022092113265921100_ref011","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1016\/j.bushor.2017.01.002","article-title":"Addressing barriers to big data","volume":"60","year":"2017","journal-title":"Business Horizons"},{"issue":"8","key":"key2022092113265921100_ref012","doi-asserted-by":"crossref","first-page":"793","DOI":"10.1108\/IJPDLM-02-2019-0038","article-title":"Where is supply chain resilience research heading? 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