{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T01:02:05Z","timestamp":1777424525418,"version":"3.51.4"},"reference-count":94,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2021,7,30]],"date-time":"2021-07-30T00:00:00Z","timestamp":1627603200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,7,30]],"date-time":"2021-07-30T00:00:00Z","timestamp":1627603200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Inf Technol Manag"],"published-print":{"date-parts":[[2021,9]]},"DOI":"10.1007\/s10799-021-00333-9","type":"journal-article","created":{"date-parts":[[2021,7,31]],"date-time":"2021-07-31T02:25:13Z","timestamp":1627698313000},"page":"207-229","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Framework for implementing big data analytics in Indian manufacturing: ISM-MICMAC and Fuzzy-AHP approach"],"prefix":"10.1007","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7921-7306","authenticated-orcid":false,"given":"Amit Kumar","family":"Gupta","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Harshit","family":"Goyal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,7,30]]},"reference":[{"key":"333_CR1","doi-asserted-by":"crossref","unstructured":"Luckow A, Kennedy K, Manhardt F, Djerekarov E, Vorster B, Apon A (2015) Automotive big data: applications, workloads, and infrastructures.\u00a0 Proceedings: IEEE international conference on big data, IEEE Big Data.","DOI":"10.1109\/BigData.2015.7363874"},{"key":"333_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbusres.2016.08.002","author":"Y Wang","year":"2017","unstructured":"Wang Y, Hajli N (2017) Exploring the path to big data analytics success in healthcare. J Bus Res. https:\/\/doi.org\/10.1016\/j.jbusres.2016.08.002","journal-title":"J Bus Res"},{"key":"333_CR3","doi-asserted-by":"publisher","DOI":"10.1111\/jbl.12010","author":"MA Waller","year":"2013","unstructured":"Waller MA, Fawcett SE (2013) Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management. J Bus Logist. https:\/\/doi.org\/10.1111\/jbl.12010","journal-title":"J Bus Logist"},{"key":"333_CR4","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1016\/j.ijpe.2016.03.014","volume":"176","author":"G Wang","year":"2016","unstructured":"Wang G, Gunasekaran A, Ngai EWT, Papadopoulos T (2016) Big data analytics in logistics and supply chain management: certain investigations for research and applications. Int J Prod Econ 176:98\u2013110","journal-title":"Int J Prod Econ"},{"key":"333_CR5","doi-asserted-by":"publisher","DOI":"10.1111\/jbl.12082","author":"T Schoenherr","year":"2015","unstructured":"Schoenherr T, Speier-Pero C (2015) Data science, predictive analytics, and big data in supply chain management: current state and future potential. J Bus Logist. https:\/\/doi.org\/10.1111\/jbl.12082","journal-title":"J Bus Logist"},{"key":"333_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.psep.2018.04.020","author":"MA Moktadir","year":"2018","unstructured":"Moktadir MA, Ali SM, Kusi-Sarpong S, Shaikh MAA (2018) Assessing challenges for implementing Industry 4.0: implications for process safety and environmental protection. Process Saf Environ Prot. https:\/\/doi.org\/10.1016\/j.psep.2018.04.020","journal-title":"Process Saf Environ Prot"},{"key":"333_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijinfomgt.2014.10.007","author":"A Gandomi","year":"2015","unstructured":"Gandomi A, Haider M (2015) Beyond the hype: big data concepts, methods, and analytics. Int J Inf Manage. https:\/\/doi.org\/10.1016\/j.ijinfomgt.2014.10.007","journal-title":"Int J Inf Manage"},{"key":"333_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.bushor.2019.02.001","author":"P Tabesh","year":"2019","unstructured":"Tabesh P, Mousavidin E, Hasani S (2019) Implementing big data strategies: a managerial perspective. Bus Horiz. https:\/\/doi.org\/10.1016\/j.bushor.2019.02.001","journal-title":"Bus Horiz"},{"key":"333_CR9","unstructured":"Axryd S (2019) Why 85% of big data projects fail. DNA. https:\/\/www.digitalnewsasia.com\/insights\/why-85-big-data-projects"},{"key":"333_CR10","unstructured":"Gartner (2015) Gartner says business intelli-gence and analytics leaders must focus on mindsets and culture to kick start advanced analytics. In: Forbes. http:\/\/www.gartner.com\/newsroom\/id\/3130017"},{"key":"333_CR11","unstructured":"Marr B (2015) Where big data projects fail. Forbes. https:\/\/www.forbes.com\/sites\/bernardmarr\/2015\/03\/17\/where-big-data-projects-fail\/#1a6e75eb239f"},{"key":"333_CR12","unstructured":"Patrizio A (2019) 4 reasons big data projects fail\u2014and 4 ways to succeed. In: InfoWorld. https:\/\/www.infoworld.com\/article\/3393467\/4-reasons-big-data-projects-failand-4-ways-to-succeed.html"},{"key":"333_CR13","doi-asserted-by":"publisher","first-page":"1113","DOI":"10.1016\/j.procir.2019.03.262","volume":"81","author":"CG Machado","year":"2019","unstructured":"Machado CG, Winroth M, Carlsson D et al (2019) Industry 4.0 readiness in manufacturing companies: challenges and enablers towards increased digitalization. Procedia CIRP 81:1113\u20131118","journal-title":"Procedia CIRP"},{"key":"333_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2018.04.013","author":"MA Moktadir","year":"2019","unstructured":"Moktadir MA, Ali SM, Paul SK, Shukla N (2019) Barriers to big data analytics in manufacturing supply chains: a case study from Bangladesh. Comput Ind Eng. https:\/\/doi.org\/10.1016\/j.cie.2018.04.013","journal-title":"Comput Ind Eng"},{"key":"333_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.techfore.2019.05.021","author":"D Horv\u00e1th","year":"2019","unstructured":"Horv\u00e1th D, Szab\u00f3 RZ (2019) Driving forces and barriers of Industry 4.0: do multinational and small and medium-sized companies have equal opportunities? Technol Forecast Soc Change. https:\/\/doi.org\/10.1016\/j.techfore.2019.05.021","journal-title":"Technol Forecast Soc Change"},{"key":"333_CR16","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1007\/s11036-013-0489-0","volume":"19","author":"M Chen","year":"2014","unstructured":"Chen M, Mao S, Liu Y (2014) Big data: a survey. Mob Netw Appl 19:171\u2013209","journal-title":"Mob Netw Appl"},{"key":"333_CR17","doi-asserted-by":"publisher","DOI":"10.4018\/978-1-5225-9750-6.ch001","author":"S Dessureault","year":"2016","unstructured":"Dessureault S (2016) Understanding big data. CIM Mag. https:\/\/doi.org\/10.4018\/978-1-5225-9750-6.ch001","journal-title":"CIM Mag"},{"key":"333_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.infsof.2008.09.005","author":"D Laney","year":"2001","unstructured":"Laney D (2001) 3D data management: controlling data volume, velocity, and variety. Appl Deliv Strateg. https:\/\/doi.org\/10.1016\/j.infsof.2008.09.005","journal-title":"Appl Deliv Strateg"},{"key":"333_CR19","volume-title":"The importance of \u2018big data\u2019: a definition","author":"MA Beyer","year":"2012","unstructured":"Beyer MA, Laney D (2012) The importance of \u2018big data\u2019: a definition. CT Gart, Stamford"},{"key":"333_CR20","unstructured":"Zikopoulos PC, DeRoos D, Parasuraman K, et al (2012) Harness the power of big data"},{"key":"333_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.bushor.2017.01.004","author":"I Lee","year":"2017","unstructured":"Lee I (2017) Big data: dimensions, evolution, impacts, and challenges. Bus Horiz. https:\/\/doi.org\/10.1016\/j.bushor.2017.01.004","journal-title":"Bus Horiz"},{"key":"333_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2019.106099","author":"A Belhadi","year":"2019","unstructured":"Belhadi A, Zkik K, Cherrafi A et al (2019) Understanding big data analytics for manufacturing processes: insights from literature review and multiple case studies. Comput Ind Eng. https:\/\/doi.org\/10.1016\/j.cie.2019.106099","journal-title":"Comput Ind Eng"},{"key":"333_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbusres.2016.08.001","author":"U Sivarajah","year":"2017","unstructured":"Sivarajah U, Kamal MM, Irani Z, Weerakkody V (2017) Critical analysis of big data challenges and analytical methods. J Bus Res. https:\/\/doi.org\/10.1016\/j.jbusres.2016.08.001","journal-title":"J Bus Res"},{"key":"333_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2018.08.018","author":"S Tewari","year":"2019","unstructured":"Tewari S, Dwivedi UD (2019) Ensemble-based big data analytics of lithofacies for automatic development of petroleum reservoirs. Comput Ind Eng. https:\/\/doi.org\/10.1016\/j.cie.2018.08.018","journal-title":"Comput Ind Eng"},{"key":"333_CR25","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-015-0030-3","author":"CW Tsai","year":"2015","unstructured":"Tsai CW, Lai CF, Chao HC, Vasilakos AV (2015) Big data analytics: a survey. J Big Data. https:\/\/doi.org\/10.1186\/s40537-015-0030-3","journal-title":"J Big Data"},{"key":"333_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.techfore.2020.120420","author":"S Bag","year":"2021","unstructured":"Bag S, Pretorius JHC, Gupta S, Dwivedi YK (2021) Role of institutional pressures and resources in the adoption of big data analytics powered artificial intelligence, sustainable manufacturing practices and circular economy capabilities. Technol Forecast Soc Change. https:\/\/doi.org\/10.1016\/j.techfore.2020.120420","journal-title":"Technol Forecast Soc Change"},{"key":"333_CR27","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1080\/00207543.2019.1630770","volume":"58","author":"SS Kamble","year":"2020","unstructured":"Kamble SS, Gunasekaran A (2020) Big data-driven supply chain performance measurement system: a review and framework for implementation. Int J Prod Res 58:65\u201386","journal-title":"Int J Prod Res"},{"key":"333_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2016.02.078","author":"L Li","year":"2017","unstructured":"Li L, Hao T, Chi T (2017) Evaluation on China\u2019s forestry resources efficiency based on big data. J Clean Prod. https:\/\/doi.org\/10.1016\/j.jclepro.2016.02.078","journal-title":"J Clean Prod"},{"key":"333_CR29","first-page":"249","volume":"1","author":"Z Bi","year":"2014","unstructured":"Bi Z, Cochran D (2014) Big data analytics with applications. J Manag Anal 1:249\u2013265","journal-title":"J Manag Anal"},{"key":"333_CR30","doi-asserted-by":"publisher","DOI":"10.1007\/s00170-015-7151-x","author":"J Li","year":"2015","unstructured":"Li J, Tao F, Cheng Y, Zhao L (2015) Big data in product lifecycle management. Int J Adv Manuf Technol. https:\/\/doi.org\/10.1007\/s00170-015-7151-x","journal-title":"Int J Adv Manuf Technol"},{"key":"333_CR31","doi-asserted-by":"publisher","unstructured":"Varela IR, Tjahjono B (2014) Big data analytics in supply chain management: trends and related research. 6th international conference on operations supply chain management. https:\/\/doi.org\/10.13140\/RG.2.1.4935.2563","DOI":"10.13140\/RG.2.1.4935.2563"},{"key":"333_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.bushor.2019.10.001","author":"RH Hamilton","year":"2019","unstructured":"Hamilton RH, Sodeman WA (2019) The questions we ask: opportunities and challenges for using big data analytics to strategically manage human capital resources. Bus Horiz. https:\/\/doi.org\/10.1016\/j.bushor.2019.10.001","journal-title":"Bus Horiz"},{"key":"333_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113450","author":"SJ Barnes","year":"2020","unstructured":"Barnes SJ, Mattsson J, S\u00f8rensen F, Jensen JF (2020) Measuring employee-tourist encounter experience value: a big data analytics approach. Expert Syst Appl. https:\/\/doi.org\/10.1016\/j.eswa.2020.113450","journal-title":"Expert Syst Appl"},{"key":"333_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.comcom.2020.02.052","author":"X Nie","year":"2020","unstructured":"Nie X, Fan T, Wang B et al (2020) Big data analytics and IoT in operation safety management in under water management. Comput Commun. https:\/\/doi.org\/10.1016\/j.comcom.2020.02.052","journal-title":"Comput Commun"},{"key":"333_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2020.106362","author":"J Wang","year":"2020","unstructured":"Wang J, Zheng P, Zhang J (2020) Big data analytics for cycle time related feature selection in the semiconductor wafer fabrication system. Comput Ind Eng. https:\/\/doi.org\/10.1016\/j.cie.2020.106362","journal-title":"Comput Ind Eng"},{"key":"333_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.resconrec.2019.104559","author":"S Bag","year":"2020","unstructured":"Bag S, Wood LC, Xu L et al (2020) Big data analytics as an operational excellence approach to enhance sustainable supply chain performance. Resour Conserv Recycl. https:\/\/doi.org\/10.1016\/j.resconrec.2019.104559","journal-title":"Resour Conserv Recycl"},{"key":"333_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2019.103042","author":"J Ngo","year":"2020","unstructured":"Ngo J, Hwang BG, Zhang C (2020) Factor-based big data and predictive analytics capability assessment tool for the construction industry. Autom Constr. https:\/\/doi.org\/10.1016\/j.autcon.2019.103042","journal-title":"Autom Constr"},{"key":"333_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.ssci.2020.104656","author":"A Ajayi","year":"2020","unstructured":"Ajayi A, Oyedele L, Akinade O et al (2020) Optimised big data analytics for health and safety hazards prediction in power infrastructure operations. Saf Sci. https:\/\/doi.org\/10.1016\/j.ssci.2020.104656","journal-title":"Saf Sci"},{"key":"333_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.jairtraman.2019.101721","author":"I Hausladen","year":"2020","unstructured":"Hausladen I, Schosser M (2020) Towards a maturity model for big data analytics in airline network planning. J Air Transp Manag. https:\/\/doi.org\/10.1016\/j.jairtraman.2019.101721","journal-title":"J Air Transp Manag"},{"key":"333_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2016.09.011","author":"K Fang","year":"2016","unstructured":"Fang K, Jiang Y, Song M (2016) Customer profitability forecasting using big data analytics: a case study of the insurance industry. Comput Ind Eng. https:\/\/doi.org\/10.1016\/j.cie.2016.09.011","journal-title":"Comput Ind Eng"},{"key":"333_CR41","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2019.106100","author":"X Li","year":"2019","unstructured":"Li X, Zhao X, Pu W et al (2019) Optimal decisions for operations management of BDAR: a military industrial logistics data analytics perspective. Comput Ind Eng. https:\/\/doi.org\/10.1016\/j.cie.2019.106100","journal-title":"Comput Ind Eng"},{"key":"333_CR42","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2019.03.181","author":"RD Raut","year":"2019","unstructured":"Raut RD, Mangla SK, Narwane VS et al (2019) Linking big data analytics and operational sustainability practices for sustainable business management. J Clean Prod. https:\/\/doi.org\/10.1016\/j.jclepro.2019.03.181","journal-title":"J Clean Prod"},{"key":"333_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/j.techfore.2017.06.020","author":"R Dubey","year":"2019","unstructured":"Dubey R, Gunasekaran A, Childe SJ et al (2019) Can big data and predictive analytics improve social and environmental sustainability? Technol Forecast Soc Change. https:\/\/doi.org\/10.1016\/j.techfore.2017.06.020","journal-title":"Technol Forecast Soc Change"},{"key":"333_CR44","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2018.06.097","author":"R Dubey","year":"2018","unstructured":"Dubey R, Gunasekaran A, Childe SJ et al (2018) Examining the role of big data and predictive analytics on collaborative performance in context to sustainable consumption and production behaviour. J Clean Prod. https:\/\/doi.org\/10.1016\/j.jclepro.2018.06.097","journal-title":"J Clean Prod"},{"key":"333_CR45","unstructured":"Ernst, Young (2020) Indian manufacturing firms rank big data, predictive analytics as top investment priorities. In: Hindu Business line. https:\/\/www.thehindubusinessline.com\/economy\/indian-manufacturing-firms-rank-big-data-predictive-analytics-as-top-investment-priority-ey\/article30989322.ece"},{"key":"333_CR46","unstructured":"Economic times (2019) Big data plays a crucial role in the manufacturing sector. Economic times.: https:\/\/economictimes.indiatimes.com\/small-biz\/sme-sector\/big-data-plays-a-crucial-role-in-manufacturing-sector-ashutosh-sharma-secy-department-of-sc-tech\/articleshow\/69692549.cms?from=mdr"},{"key":"333_CR47","doi-asserted-by":"publisher","DOI":"10.1016\/j.compind.2020.103368","author":"RD Raut","year":"2021","unstructured":"Raut RD, Yadav VS, Cheikhrouhou N et al (2021) Big data analytics: implementation challenges in Indian manufacturing supply chains. Comput Ind. https:\/\/doi.org\/10.1016\/j.compind.2020.103368","journal-title":"Comput Ind"},{"key":"333_CR48","doi-asserted-by":"publisher","DOI":"10.1016\/j.tre.2017.04.001","author":"D Arunachalam","year":"2018","unstructured":"Arunachalam D, Kumar N, Kawalek JP (2018) Understanding big data analytics capabilities in supply chain management: unravelling the issues, challenges and implications for practice. Transp Res Part E Logist Transp Rev. https:\/\/doi.org\/10.1016\/j.tre.2017.04.001","journal-title":"Transp Res Part E Logist Transp Rev"},{"key":"333_CR49","doi-asserted-by":"publisher","DOI":"10.4018\/irmj.2018100101","author":"H Gangwar","year":"2018","unstructured":"Gangwar H (2018) Understanding the determinants of big data adoption in India. Inf Resour Manag J. https:\/\/doi.org\/10.4018\/irmj.2018100101","journal-title":"Inf Resour Manag J"},{"key":"333_CR50","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijpe.2014.12.032","author":"D Dutta","year":"2015","unstructured":"Dutta D, Bose I (2015) Managing a big data project: the case of Ramco cements limited. Int J Prod Econ. https:\/\/doi.org\/10.1016\/j.ijpe.2014.12.032","journal-title":"Int J Prod Econ"},{"key":"333_CR51","unstructured":"Dremel C (2017) Barriers to the adoption of big data analytics in the automotive sector. AMCIS 2017\u2014America\u2019s conference on information systems: a tradition of innovation. ISBN: 9780996683142"},{"key":"333_CR52","doi-asserted-by":"publisher","DOI":"10.1016\/j.bushor.2017.01.002","author":"A Alharthi","year":"2017","unstructured":"Alharthi A, Krotov V, Bowman M (2017) Addressing barriers to big data. Bus Horiz. https:\/\/doi.org\/10.1016\/j.bushor.2017.01.002","journal-title":"Bus Horiz"},{"key":"333_CR53","doi-asserted-by":"crossref","unstructured":"Malaka I, Brown I (2015) Challenges to the organisational adoption of big data analytics: a case study in the South African telecommunications industry. In: ACM international conference proceeding series","DOI":"10.1145\/2815782.2815793"},{"key":"333_CR54","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2018.04.055","author":"M Shukla","year":"2019","unstructured":"Shukla M, Mattar L (2019) Next generation smart sustainable auditing systems using big data analytics: understanding the interaction of critical barriers. Comput Ind Eng. https:\/\/doi.org\/10.1016\/j.cie.2018.04.055","journal-title":"Comput Ind Eng"},{"key":"333_CR55","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbusres.2020.03.028","author":"M Yasmin","year":"2020","unstructured":"Yasmin M, Tatoglu E, Kilic HS et al (2020) Big data analytics capabilities and firm performance: an integrated MCDM approach. J Bus Res. https:\/\/doi.org\/10.1016\/j.jbusres.2020.03.028","journal-title":"J Bus Res"},{"key":"333_CR56","doi-asserted-by":"publisher","DOI":"10.1016\/j.promfg.2017.02.094","author":"MW Waibel","year":"2017","unstructured":"Waibel MW, Steenkamp LP, Moloko N, Oosthuizen GA (2017) Investigating the effects of smart production systems on sustainability elements. Procedia Manuf. https:\/\/doi.org\/10.1016\/j.promfg.2017.02.094","journal-title":"Procedia Manuf"},{"key":"333_CR57","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2016.07.013","author":"RY Zhong","year":"2016","unstructured":"Zhong RY, Newman ST, Huang GQ, Lan S (2016) Big Data for supply chain management in the service and manufacturing sectors: challenges, opportunities, and future perspectives. Comput Ind Eng. https:\/\/doi.org\/10.1016\/j.cie.2016.07.013","journal-title":"Comput Ind Eng"},{"key":"333_CR58","doi-asserted-by":"publisher","DOI":"10.1002\/csr.1316","author":"O Chkanikova","year":"2015","unstructured":"Chkanikova O, Mont O (2015) Corporate supply chain responsibility: drivers and barriers for sustainable food retailing. Corp Soc Responsib Environ Manag. https:\/\/doi.org\/10.1002\/csr.1316","journal-title":"Corp Soc Responsib Environ Manag"},{"key":"333_CR59","doi-asserted-by":"publisher","DOI":"10.1108\/jm2-08-2012-0026","author":"SJ Gorane","year":"2015","unstructured":"Gorane SJ, Kant R (2015) Modelling the SCM implementation barriers. J Model Manag. https:\/\/doi.org\/10.1108\/jm2-08-2012-0026","journal-title":"J Model Manag"},{"key":"333_CR60","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2013.12.071","author":"MR Shaharudin","year":"2015","unstructured":"Shaharudin MR, Zailani S, Tan KC (2015) Barriers to product returns and recovery management in a developing country: investigation using multiple methods. J Clean Prod. https:\/\/doi.org\/10.1016\/j.jclepro.2013.12.071","journal-title":"J Clean Prod"},{"key":"333_CR61","doi-asserted-by":"publisher","DOI":"10.5860\/choice.51-6848","author":"RM Van","year":"2014","unstructured":"Van RM (2014) Think bigger: developing a successful big data strategy for your business. Choice Rev Online. https:\/\/doi.org\/10.5860\/choice.51-6848","journal-title":"Choice Rev Online"},{"key":"333_CR62","unstructured":"Douglas M (2013) Big data raises big questions. Gov Technol"},{"key":"333_CR63","doi-asserted-by":"publisher","DOI":"10.1377\/hlthaff.2014.0522","author":"D Fallik","year":"2014","unstructured":"Fallik D (2014) For big data, big questions remain. Health Aff. https:\/\/doi.org\/10.1377\/hlthaff.2014.0522","journal-title":"Health Aff"},{"key":"333_CR64","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.1974.5408524","author":"JN Warfield","year":"1974","unstructured":"Warfield JN (1974) Developing interconnection matrices in structural modeling. IEEE Trans Syst Man Cybern. https:\/\/doi.org\/10.1109\/TSMC.1974.5408524","journal-title":"IEEE Trans Syst Man Cybern"},{"key":"333_CR65","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1016\/j.ijpe.2015.10.024","volume":"171","author":"V Jain","year":"2016","unstructured":"Jain V, Raj T (2016) Modeling and analysis of FMS performance variables by ISM, SEM and GTMA approach. Int J Prod Econ 171:84\u201396. https:\/\/doi.org\/10.1016\/j.ijpe.2015.10.024","journal-title":"Int J Prod Econ"},{"key":"333_CR66","doi-asserted-by":"publisher","DOI":"10.1016\/j.ergon.2013.08.005","author":"E Cagno","year":"2014","unstructured":"Cagno E, Micheli GJL, Jacinto C, Masi D (2014) An interpretive model of occupational safety performance for small- and medium-sized enterprises. Int J Ind Ergon. https:\/\/doi.org\/10.1016\/j.ergon.2013.08.005","journal-title":"Int J Ind Ergon"},{"key":"333_CR67","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2012.10.042","author":"K Mathiyazhagan","year":"2013","unstructured":"Mathiyazhagan K, Govindan K, NoorulHaq A, Geng Y (2013) An ISM approach for the barrier analysis in implementing green supply chain management. J Clean Prod. https:\/\/doi.org\/10.1016\/j.jclepro.2012.10.042","journal-title":"J Clean Prod"},{"key":"333_CR68","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1016\/j.procir.2014.07.125","volume":"26","author":"AK Digalwar","year":"2015","unstructured":"Digalwar AK, Giridhar G (2015) Interpretive structural modeling approach for development of electric vehicle market in India. Procedia CIRP 26:40\u201345","journal-title":"Procedia CIRP"},{"key":"333_CR69","doi-asserted-by":"publisher","DOI":"10.1016\/j.sbspro.2015.03.200","author":"DK Dewangan","year":"2015","unstructured":"Dewangan DK, Agrawal R, Sharma V (2015) Enablers for competitiveness of Indian manufacturing sector: an ISM-Fuzzy MICMAC analysis. Procedia Soc Behav Sci. https:\/\/doi.org\/10.1016\/j.sbspro.2015.03.200","journal-title":"Procedia Soc Behav Sci"},{"key":"333_CR70","doi-asserted-by":"publisher","DOI":"10.1504\/IJBIR.2017.085103","author":"DK Dewangan","year":"2017","unstructured":"Dewangan DK, Agrawal R, Sharma V (2017) Enablers of eco-innovation to enhance the competitiveness of the Indian manufacturing sector: an integrated ISM-fuzzy MICMAC approach. Int J Bus Innov Res. https:\/\/doi.org\/10.1504\/IJBIR.2017.085103","journal-title":"Int J Bus Innov Res"},{"key":"333_CR71","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.rser.2016.04.033","volume":"62","author":"S Sindhu","year":"2016","unstructured":"Sindhu S, Nehra V, Luthra S (2016) Identification and analysis of barriers in implementation of solar energy in Indian rural sector using integrated ISM and fuzzy MICMAC approach. Renew Sustain Energy Rev 62:70\u201388","journal-title":"Renew Sustain Energy Rev"},{"key":"333_CR72","doi-asserted-by":"publisher","DOI":"10.1504\/IJPMB.2017.080941","author":"R Attri","year":"2017","unstructured":"Attri R, Grover S (2017) Developing the weighted ISM-MICMAC framework for process design stage of production system life cycle. Int J Process Manag Benchmark. https:\/\/doi.org\/10.1504\/IJPMB.2017.080941","journal-title":"Int J Process Manag Benchmark"},{"key":"333_CR73","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijpe.2018.07.012","author":"SK Mangla","year":"2018","unstructured":"Mangla SK, Luthra S, Rich N et al (2018) Enablers to implement sustainable initiatives in agri-food supply chains. Int J Prod Econ. https:\/\/doi.org\/10.1016\/j.ijpe.2018.07.012","journal-title":"Int J Prod Econ"},{"key":"333_CR74","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijpe.2017.02.020","author":"D Kannan","year":"2018","unstructured":"Kannan D (2018) Role of multiple stakeholders and the critical success factor theory for the sustainable supplier selection process. Int J Prod Econ. https:\/\/doi.org\/10.1016\/j.ijpe.2017.02.020","journal-title":"Int J Prod Econ"},{"key":"333_CR75","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2019.04.182","author":"F Zhou","year":"2019","unstructured":"Zhou F, Lim MK, He Y et al (2019) End-of-life vehicle (ELV) recycling management: improving performance using an ISM approach. J Clean Prod. https:\/\/doi.org\/10.1016\/j.jclepro.2019.04.182","journal-title":"J Clean Prod"},{"key":"333_CR76","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2019.05.253","author":"MS Kaswan","year":"2019","unstructured":"Kaswan MS, Rathi R (2019) Analysis and modeling the enablers of green lean six sigma implementation using interpretive structural modeling. J Clean Prod. https:\/\/doi.org\/10.1016\/j.jclepro.2019.05.253","journal-title":"J Clean Prod"},{"key":"333_CR77","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijinfomgt.2018.10.009","author":"NP Rana","year":"2019","unstructured":"Rana NP, Barnard DJ, Baabdullah AMA et al (2019) Exploring barriers of m-commerce adoption in SMEs in the UK: developing a framework using ISM. Int J Inf Manage. https:\/\/doi.org\/10.1016\/j.ijinfomgt.2018.10.009","journal-title":"Int J Inf Manage"},{"key":"333_CR78","doi-asserted-by":"publisher","DOI":"10.3390\/app9020233","author":"M Ahmad","year":"2019","unstructured":"Ahmad M, Tang XW, Qiu JN, Ahmad F (2019) Interpretive structural modeling and MICMAC analysis for identifying and benchmarking significant factors of seismic soil liquefaction. Appl Sci. https:\/\/doi.org\/10.3390\/app9020233","journal-title":"Appl Sci"},{"key":"333_CR79","doi-asserted-by":"publisher","DOI":"10.1108\/IJM-07-2019-0354","author":"MA Moktadir","year":"2019","unstructured":"Moktadir MA, Dwivedi A, Ali SM et al (2019) Antecedents for greening the workforce: implications for green human resource management. Int J Manpow. https:\/\/doi.org\/10.1108\/IJM-07-2019-0354","journal-title":"Int J Manpow"},{"key":"333_CR80","doi-asserted-by":"publisher","DOI":"10.1016\/j.resconrec.2009.06.004","author":"G Kannan","year":"2009","unstructured":"Kannan G, Pokharel S, Kumar PS (2009) A hybrid approach using ISM and fuzzy TOPSIS for the selection of reverse logistics provider. Resour Conserv Recycl. https:\/\/doi.org\/10.1016\/j.resconrec.2009.06.004","journal-title":"Resour Conserv Recycl"},{"key":"333_CR81","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijpe.2012.01.043","author":"K Govindan","year":"2012","unstructured":"Govindan K, Palaniappan M, Zhu Q, Kannan D (2012) Analysis of third party reverse logistics provider using interpretive structural modeling. Int J Prod Econ. https:\/\/doi.org\/10.1016\/j.ijpe.2012.01.043","journal-title":"Int J Prod Econ"},{"key":"333_CR82","doi-asserted-by":"publisher","DOI":"10.1504\/ijaom.2009.030671","author":"RK Mudgal","year":"2009","unstructured":"Mudgal RK, Shankar R, Talib P, Raj T (2009) Greening the supply chain practices: an Indian perspective of enablers\u2019 relationships. Int J Adv Oper Manag. https:\/\/doi.org\/10.1504\/ijaom.2009.030671","journal-title":"Int J Adv Oper Manag"},{"key":"333_CR83","doi-asserted-by":"publisher","DOI":"10.1007\/s13198-012-0088-7","author":"R Attri","year":"2013","unstructured":"Attri R, Grover S, Dev N, Kumar D (2013) An ISM approach for modelling the enablers in the implementation of total productive maintenance (TPM). Int J Syst Assur Eng Manag. https:\/\/doi.org\/10.1007\/s13198-012-0088-7","journal-title":"Int J Syst Assur Eng Manag"},{"key":"333_CR84","first-page":"1171","volume":"2319","author":"R Attri","year":"2013","unstructured":"Attri R, Dev N, Sharma V (2013) Interpretive structural modelling (ISM) approach: an overview. Res J Manag Sci 2319:1171","journal-title":"Res J Manag Sci"},{"key":"333_CR85","doi-asserted-by":"publisher","DOI":"10.1016\/j.asej.2012.08.002","author":"N Dev","year":"2013","unstructured":"Dev N, Samsher KSS, Attri R (2013) GTA-based framework for evaluating the role of design parameters in cogeneration cycle power plant efficiency. Ain Shams Eng J. https:\/\/doi.org\/10.1016\/j.asej.2012.08.002","journal-title":"Ain Shams Eng J"},{"key":"333_CR86","doi-asserted-by":"publisher","DOI":"10.1016\/j.resconrec.2010.12.002","author":"A Diabat","year":"2011","unstructured":"Diabat A, Govindan K (2011) An analysis of the drivers affecting the implementation of green supply chain management. Resour Conserv Recycl. https:\/\/doi.org\/10.1016\/j.resconrec.2010.12.002","journal-title":"Resour Conserv Recycl"},{"key":"333_CR87","doi-asserted-by":"publisher","DOI":"10.1108\/01443579410062086","author":"A Mandal","year":"1994","unstructured":"Mandal A, Deshmukh SG (1994) Vendor selection using interpretive structural modelling (ISM). Int J Oper Prod Manag. https:\/\/doi.org\/10.1108\/01443579410062086","journal-title":"Int J Oper Prod Manag"},{"key":"333_CR88","doi-asserted-by":"publisher","DOI":"10.1108\/17410400410569116","author":"S Jharkharia","year":"2004","unstructured":"Jharkharia S, Shankar R (2004) IT enablement of supply chains: modeling the enablers. Int J Product Perform Manag. https:\/\/doi.org\/10.1108\/17410400410569116","journal-title":"Int J Product Perform Manag"},{"key":"333_CR89","doi-asserted-by":"publisher","DOI":"10.1080\/07421222.2018.1451951","author":"V Grover","year":"2018","unstructured":"Grover V, Chiang RHL, Liang TP, Zhang D (2018) Creating strategic business value from big data analytics: a research framework. J Manag Inf Syst. https:\/\/doi.org\/10.1080\/07421222.2018.1451951","journal-title":"J Manag Inf Syst"},{"key":"333_CR90","doi-asserted-by":"publisher","DOI":"10.1287\/mnsc.17.4.b141","author":"RE Bellman","year":"1970","unstructured":"Bellman RE, Zadeh LA (1970) Decision-making in a fuzzy environment. Manag Sci. https:\/\/doi.org\/10.1287\/mnsc.17.4.b141","journal-title":"Manag Sci"},{"key":"333_CR91","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2012.04.014","author":"K Govindan","year":"2013","unstructured":"Govindan K, Khodaverdi R, Jafarian A (2013) A fuzzy multi criteria approach for measuring sustainability performance of a supplier based on triple bottom line approach. J Clean Prod. https:\/\/doi.org\/10.1016\/j.jclepro.2012.04.014","journal-title":"J Clean Prod"},{"key":"333_CR92","doi-asserted-by":"publisher","DOI":"10.1016\/0165-0114(85)90090-9","author":"JJ Buckley","year":"1985","unstructured":"Buckley JJ (1985) Fuzzy hierarchical analysis. Fuzzy Sets Syst. https:\/\/doi.org\/10.1016\/0165-0114(85)90090-9","journal-title":"Fuzzy Sets Syst"},{"key":"333_CR93","doi-asserted-by":"publisher","DOI":"10.1016\/0022-2496(77)90033-5","author":"TL Saaty","year":"1977","unstructured":"Saaty TL (1977) A scaling method for priorities in hierarchical structures. J Math Psychol. https:\/\/doi.org\/10.1016\/0022-2496(77)90033-5","journal-title":"J Math Psychol"},{"key":"333_CR94","doi-asserted-by":"publisher","DOI":"10.1016\/0377-2217(90)90057-I","author":"TL Saaty","year":"1990","unstructured":"Saaty TL (1990) How to make a decision: the analytic hierarchy process. Eur J Oper Res. https:\/\/doi.org\/10.1016\/0377-2217(90)90057-I","journal-title":"Eur J Oper Res"}],"container-title":["Information Technology and Management"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10799-021-00333-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10799-021-00333-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10799-021-00333-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,6]],"date-time":"2023-01-06T00:16:34Z","timestamp":1672964194000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10799-021-00333-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,30]]},"references-count":94,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2021,9]]}},"alternative-id":["333"],"URL":"https:\/\/doi.org\/10.1007\/s10799-021-00333-9","relation":{},"ISSN":["1385-951X","1573-7667"],"issn-type":[{"value":"1385-951X","type":"print"},{"value":"1573-7667","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,30]]},"assertion":[{"value":"22 July 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 July 2021","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}