{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,10]],"date-time":"2025-04-10T14:12:15Z","timestamp":1744294335735,"version":"3.37.3"},"reference-count":65,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2019,3,1]],"date-time":"2019-03-01T00:00:00Z","timestamp":1551398400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"}],"funder":[{"DOI":"10.13039\/501100001824","name":"Czech Sciences Foundation","doi-asserted-by":"crossref","award":["16-19590S"],"id":[{"id":"10.13039\/501100001824","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Decision Support Systems"],"published-print":{"date-parts":[[2019,3]]},"DOI":"10.1016\/j.dss.2019.01.002","type":"journal-article","created":{"date-parts":[[2019,1,9]],"date-time":"2019-01-09T12:26:04Z","timestamp":1547036764000},"page":"33-45","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":149,"special_numbering":"C","title":["Two-stage consumer credit risk modelling using heterogeneous ensemble learning"],"prefix":"10.1016","volume":"118","author":[{"given":"Monika","family":"Papouskova","sequence":"first","affiliation":[]},{"given":"Petr","family":"Hajek","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.dss.2019.01.002_bb0005","first-page":"117","article-title":"Classification methods applied to credit scoring: systematic review and overall comparison","volume":"21","author":"Louzada","year":"2016","journal-title":"Surv. Oper. Res. Manag. Sci."},{"issue":"10","key":"10.1016\/j.dss.2019.01.002_bb0010","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0139427","article-title":"Determinants of default in P2P lending","volume":"10","author":"Serrano-Cinca","year":"2015","journal-title":"PloS one"},{"key":"10.1016\/j.dss.2019.01.002_bb0015","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.elerap.2017.06.004","article-title":"Cost-sensitive boosted tree for loan evaluation in peer-to-peer lending","volume":"24","author":"Xia","year":"2017","journal-title":"Electron. Commer. Res. Appl."},{"key":"10.1016\/j.dss.2019.01.002_bb0020","first-page":"1","article-title":"Dynamic weighted ensemble classification for credit scoring using Markov Chain","author":"Feng","year":"2018","journal-title":"Appl. Intell."},{"key":"10.1016\/j.dss.2019.01.002_bb0025","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1016\/j.ejor.2014.04.001","article-title":"Development and application of consumer credit scoring models using profit-based classification measures","volume":"238","author":"Verbraken","year":"2014","journal-title":"Eur. J. Oper. Res."},{"key":"10.1016\/j.dss.2019.01.002_bb0030","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.dss.2016.06.014","article-title":"The use of profit scoring as an alternative to credit scoring systems in peer-to-peer (P2P) lending","volume":"89","author":"Serrano-Cinca","year":"2016","journal-title":"Decis. Support. Syst."},{"key":"10.1016\/j.dss.2019.01.002_bb0035","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.ejor.2015.05.030","article-title":"Benchmarking state-of-the-art classification algorithms for credit scoring: an update of research","volume":"247","author":"Lessmann","year":"2015","journal-title":"Eur. J. Oper. Res."},{"key":"10.1016\/j.dss.2019.01.002_bb0040","doi-asserted-by":"crossref","first-page":"910","DOI":"10.1016\/j.ejor.2016.01.054","article-title":"Exposure at default models with and without the credit conversion factor","volume":"252","author":"Tong","year":"2016","journal-title":"Eur. J. Oper. Res."},{"key":"10.1016\/j.dss.2019.01.002_bb0045","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1016\/j.ejor.2017.05.017","article-title":"Enhancing two-stage modelling methodology for loss given default with support vector machines","volume":"263","author":"Yao","year":"2017","journal-title":"Eur. J. Oper. Res."},{"key":"10.1016\/j.dss.2019.01.002_bb0050","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1016\/j.ejor.2015.10.001","article-title":"A new mixture model for the estimation of credit card exposure at default","volume":"249","author":"Leow","year":"2016","journal-title":"Eur. J. Oper. Res."},{"key":"10.1016\/j.dss.2019.01.002_bb0055","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.ijforecast.2011.01.006","article-title":"Benchmarking regression algorithms for loss given default modeling","volume":"28","author":"Loterman","year":"2012","journal-title":"Int. J. Forecast."},{"key":"10.1016\/j.dss.2019.01.002_bb0060","doi-asserted-by":"crossref","first-page":"780","DOI":"10.1016\/j.ejor.2017.04.008","article-title":"Fuzzy decision fusion approach for loss-given-default modeling","volume":"262","author":"Nazemi","year":"2017","journal-title":"Eur. J. Oper. Res."},{"key":"10.1016\/j.dss.2019.01.002_bb0065","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.eswa.2016.12.020","article-title":"A comparative study on base classifiers in ensemble methods for credit scoring","volume":"73","author":"Abellan","year":"2017","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.dss.2019.01.002_bb0070","first-page":"946","article-title":"Bank lending policy, credit scoring and value-at-risk","volume":"86","author":"Roszbach","year":"2003","journal-title":"J. Bank. Financ."},{"key":"10.1016\/j.dss.2019.01.002_bb0075","doi-asserted-by":"crossref","first-page":"1060","DOI":"10.1057\/jors.2012.120","article-title":"On the suitability of resampling techniques for the class imbalance problem in credit scoring","volume":"64","author":"Marqu\u00e9s","year":"2013","journal-title":"J. Oper. Res. Soc."},{"key":"10.1016\/j.dss.2019.01.002_bb0080","doi-asserted-by":"crossref","first-page":"3446","DOI":"10.1016\/j.eswa.2011.09.033","article-title":"An experimental comparison of classification algorithms for imbalanced credit scoring data sets","volume":"39","author":"Brown","year":"2012","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.dss.2019.01.002_bb0085","doi-asserted-by":"crossref","first-page":"671","DOI":"10.2307\/2526573","article-title":"Collateral and rationing: sorting equilibria in monopolistic and competitive credit markets","volume":"28","author":"Besanko","year":"1987","journal-title":"Int. Econ. Rev."},{"key":"10.1016\/j.dss.2019.01.002_bb0090","doi-asserted-by":"crossref","first-page":"1920","DOI":"10.1016\/j.jbankfin.2009.04.012","article-title":"The determinants of net interest income in the Mexican banking system: an integrated model","volume":"33","author":"Maudos","year":"2009","journal-title":"J. Bank. Financ."},{"key":"10.1016\/j.dss.2019.01.002_bb0095","doi-asserted-by":"crossref","first-page":"7838","DOI":"10.1016\/j.eswa.2010.04.054","article-title":"Vertical bagging decision trees model for credit scoring","volume":"37","author":"Zhang","year":"2010","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.dss.2019.01.002_bb0100","doi-asserted-by":"crossref","first-page":"534","DOI":"10.1016\/j.eswa.2009.05.059","article-title":"A data driven ensemble classifier for credit scoring analysis","volume":"37","author":"Hsieh","year":"2010","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.dss.2019.01.002_bb0105","doi-asserted-by":"crossref","first-page":"1351","DOI":"10.1016\/j.eswa.2009.06.083","article-title":"Support vector machine based multiagent ensemble learning for credit risk evaluation","volume":"37","author":"Yu","year":"2010","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.dss.2019.01.002_bb0110","doi-asserted-by":"crossref","first-page":"1114","DOI":"10.1108\/K-01-2014-0016","article-title":"Modeling credit scoring using neural network ensembles","volume":"43","author":"Tsai","year":"2014","journal-title":"Kybernetes"},{"key":"10.1016\/j.dss.2019.01.002_bb0115","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.knosys.2016.04.013","article-title":"Classifiers consensus system approach for credit scoring","volume":"104","author":"Alaraj","year":"2016","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.dss.2019.01.002_bb0120","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.eswa.2016.07.017","article-title":"A new hybrid ensemble credit scoring model based on classifiers consensus system approach","volume":"64","author":"Alaraj","year":"2016","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.dss.2019.01.002_bb0125","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1023\/B:MACH.0000015881.36452.6e","article-title":"Is combining classifiers with stacking better than selecting the best one?","volume":"54","author":"Dzeroski","year":"2004","journal-title":"Mach. Learn."},{"key":"10.1016\/j.dss.2019.01.002_bb0130","doi-asserted-by":"crossref","first-page":"576","DOI":"10.1007\/s10696-015-9226-2","article-title":"A novel multistage deep belief network based extreme learning machine ensemble learning paradigm for credit risk assessment","volume":"28","author":"Yu","year":"2016","journal-title":"Flex. Serv. Manuf. J."},{"key":"10.1016\/j.dss.2019.01.002_bb0135","doi-asserted-by":"crossref","first-page":"5737","DOI":"10.1016\/j.eswa.2015.02.042","article-title":"Enhancing accuracy and interpretability of ensemble strategies in credit risk assessment. A correlated-adjusted decision forest proposal","volume":"42","author":"Florez-Lopez","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.dss.2019.01.002_bb0140","doi-asserted-by":"crossref","first-page":"1434","DOI":"10.1016\/j.eswa.2007.01.009","article-title":"Credit risk assessment with a multistage neural network ensemble learning approach","volume":"34","author":"Yu","year":"2008","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.dss.2019.01.002_bb0145","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.eswa.2009.05.024","article-title":"Least squares support vector machines ensemble models for credit scoring","volume":"37","author":"Zhou","year":"2010","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.dss.2019.01.002_bb0150","doi-asserted-by":"crossref","first-page":"1789","DOI":"10.1016\/j.eswa.2014.10.016","article-title":"Classification restricted Boltzmann machine for comprehensible credit scoring model","volume":"42","author":"Tomczak","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.dss.2019.01.002_bb0155","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1016\/j.engappai.2016.12.002","article-title":"A deep learning approach for credit scoring using credit default swaps","volume":"65","author":"Luo","year":"2017","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.dss.2019.01.002_bb0160","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1016\/j.ijforecast.2011.07.006","article-title":"Instance sampling in credit scoring: an empirical study of sample size and balancing","volume":"28","author":"Crone","year":"2012","journal-title":"Int. J. Forecast."},{"key":"10.1016\/j.dss.2019.01.002_bb0165","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.eswa.2018.01.012","article-title":"A novel ensemble method for credit scoring: adaption of different imbalance ratios","volume":"98","author":"He","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.dss.2019.01.002_bb0170","doi-asserted-by":"crossref","first-page":"6609","DOI":"10.1016\/j.eswa.2015.04.042","article-title":"Example-dependent cost-sensitive decision trees","volume":"42","author":"Bahnsen","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.dss.2019.01.002_bb0175","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10693-008-0033-8","article-title":"The sensitivity of the loss given default rate to systematic risk: new empirical evidence on bank loans","volume":"34","author":"Caselli","year":"2008","journal-title":"J. Financ. Serv. Res."},{"key":"10.1016\/j.dss.2019.01.002_bb0180","doi-asserted-by":"crossref","first-page":"2510","DOI":"10.1016\/j.jbankfin.2010.04.011","article-title":"Forecasting bank loans loss-given-default","volume":"34","author":"Bastos","year":"2010","journal-title":"J. Bank. Financ."},{"key":"10.1016\/j.dss.2019.01.002_bb0185","doi-asserted-by":"crossref","first-page":"528","DOI":"10.1016\/j.ejor.2014.06.043","article-title":"Support vector regression for loss given default modelling","volume":"240","author":"Yao","year":"2015","journal-title":"Eur. J. Oper. Res."},{"key":"10.1016\/j.dss.2019.01.002_bb0190","first-page":"22","article-title":"Modeling exposure at default and loss given default: empirical approaches and technical implementation","volume":"8","author":"Yang","year":"2012","journal-title":"J. Credit Risk"},{"key":"10.1016\/j.dss.2019.01.002_bb0195","first-page":"1","article-title":"Exposure at default modeling - a theoretical and empirical assessment of estimation approaches and parameter choice","author":"Gurtler","year":"2018","journal-title":"J. Bank. Financ."},{"key":"10.1016\/j.dss.2019.01.002_bb0200","doi-asserted-by":"crossref","first-page":"3460","DOI":"10.1016\/j.patcog.2013.05.006","article-title":"EUSBoost: enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling","volume":"46","author":"Galar","year":"2013","journal-title":"Pattern Recogn."},{"key":"10.1016\/j.dss.2019.01.002_bb0205","series-title":"2009 IEEE Symp. Comput. Intell. Data Min","first-page":"324","article-title":"Diversity analysis on imbalanced data sets by using ensemble models","author":"Wang","year":"2009"},{"key":"10.1016\/j.dss.2019.01.002_bb0210","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.knosys.2013.07.008","article-title":"Feature selection in corporate credit rating prediction","volume":"51","author":"Hajek","year":"2013","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.dss.2019.01.002_bb0215","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.asoc.2017.10.037","article-title":"Predicting corporate investment\/non-investment grade by using interval-valued fuzzy rule-based systems - a cross-region analysis","volume":"62","author":"Hajek","year":"2018","journal-title":"Appl. Soft Comput. J."},{"key":"10.1016\/j.dss.2019.01.002_bb0220","doi-asserted-by":"crossref","first-page":"656","DOI":"10.1016\/j.ejor.2017.02.037","article-title":"Cost-based feature selection for support vector machines: an application in credit scoring","volume":"261","author":"Maldonado","year":"2017","journal-title":"Eur. J. Oper. Res."},{"key":"10.1016\/j.dss.2019.01.002_bb0225","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.dss.2017.10.007","article-title":"Integrated framework for profit-based feature selection and SVM classification in credit scoring","volume":"104","author":"Maldonado","year":"2017","journal-title":"Decis. Support. Syst."},{"key":"10.1016\/j.dss.2019.01.002_bb0230","doi-asserted-by":"crossref","first-page":"2052","DOI":"10.1016\/j.eswa.2013.09.004","article-title":"Genetic algorithm-based heuristic for feature selection in credit risk assessment","volume":"41","author":"Oreski","year":"2014","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.dss.2019.01.002_bb0235","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.neucom.2016.12.045","article-title":"Multi-objective evolutionary feature selection for online sales forecasting","volume":"234","author":"Jim\u00e9nez","year":"2017","journal-title":"Neurocomputing"},{"key":"10.1016\/j.dss.2019.01.002_bb0240","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.eswa.2017.02.017","article-title":"A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring","volume":"78","author":"Xia","year":"2017","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.dss.2019.01.002_bb0245","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1023\/A:1021709817809","article-title":"Combining classifiers with meta decision trees","volume":"50","author":"Todorovski","year":"2003","journal-title":"Mach. Learn."},{"key":"10.1016\/j.dss.2019.01.002_bb0250","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/j.eswa.2010.06.048","article-title":"A comparative assessment of ensemble learning for credit scoring","volume":"38","author":"Wang","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.dss.2019.01.002_bb0255","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.eswa.2017.10.022","article-title":"A novel heterogeneous ensemble credit scoring model based on bstacking approach","volume":"93","author":"Xia","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.dss.2019.01.002_bb0260","doi-asserted-by":"crossref","first-page":"1469","DOI":"10.1007\/s10994-017-5642-8","article-title":"Adaptive random forests for evolving data stream classification","volume":"106","author":"Gomes","year":"2017","journal-title":"Mach. Learn."},{"key":"10.1016\/j.dss.2019.01.002_bb0265","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1007\/s00521-016-2304-x","article-title":"Comparison of individual, ensemble and integrated ensemble machine learning methods to predict China's SME credit risk in supply chain finance","volume":"28","author":"Zhu","year":"2017","journal-title":"Neural Comput. & Applic."},{"key":"10.1016\/j.dss.2019.01.002_bb0270","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.jretconser.2015.07.003","article-title":"A hybrid data mining model of feature selection algorithms and ensemble learning classifiers for credit scoring","volume":"27","author":"Koutanaei","year":"2015","journal-title":"J. Retail. Consum. Serv."},{"key":"10.1016\/j.dss.2019.01.002_bb0275","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1016\/j.ejor.2010.09.029","article-title":"Multiple classifier architectures and their application to credit risk assessment","volume":"210","author":"Finlay","year":"2011","journal-title":"Eur. J. Oper. Res."},{"key":"10.1016\/j.dss.2019.01.002_bb0280","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.knosys.2011.06.020","article-title":"Two credit scoring models based on dual strategy ensemble trees","volume":"26","author":"Wang","year":"2012","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.dss.2019.01.002_bb0285","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1016\/j.eswa.2017.08.002","article-title":"Forest PA: constructing a decision forest by penalizing attributes used in previous trees","volume":"89","author":"Adnan","year":"2017","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.dss.2019.01.002_bb0290","doi-asserted-by":"crossref","first-page":"1576","DOI":"10.1108\/K-09-2015-0228","article-title":"Customer credit scoring using a hybrid data mining approach","volume":"45","author":"Abedini","year":"2016","journal-title":"Kybernetes"},{"key":"10.1016\/j.dss.2019.01.002_bb0295","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1023\/A:1007421302149","article-title":"Using model trees for classification","volume":"32","author":"Frank","year":"1998","journal-title":"Mach. Learn."},{"year":"2017","series-title":"Data Mining: Practical Machine Learning Tools and Techniques","author":"Witten","key":"10.1016\/j.dss.2019.01.002_bb0300"},{"key":"10.1016\/j.dss.2019.01.002_bb0305","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1002\/isaf.325","article-title":"Credit scoring, statistical techniques and evaluation criteria: a review of the literature","volume":"18","author":"Abdou","year":"2011","journal-title":"Intell. Syst. Accounting, Financ. Manag."},{"key":"10.1016\/j.dss.2019.01.002_bb0310","doi-asserted-by":"crossref","first-page":"961","DOI":"10.1109\/TKDE.2012.50","article-title":"A novel profit maximizing metric for measuring classification performance of customer churn prediction models","volume":"25","author":"Verbraken","year":"2013","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.dss.2019.01.002_bb0315","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","article-title":"An introduction to ROC analysis","volume":"27","author":"Fawcett","year":"2006","journal-title":"Pattern Recogn. Lett."},{"key":"10.1016\/j.dss.2019.01.002_bb0320","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: synthetic minority over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"10.1016\/j.dss.2019.01.002_bb0325","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/S0020-0255(01)00146-3","article-title":"Combining GP operators with SA search to evolve fuzzy rule based classifiers","volume":"136","author":"S\u00e0nchez","year":"2001","journal-title":"Inf. Sci."}],"container-title":["Decision Support Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0167923619300028?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0167923619300028?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2019,2,25]],"date-time":"2019-02-25T17:30:39Z","timestamp":1551115839000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0167923619300028"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,3]]},"references-count":65,"alternative-id":["S0167923619300028"],"URL":"https:\/\/doi.org\/10.1016\/j.dss.2019.01.002","relation":{},"ISSN":["0167-9236"],"issn-type":[{"type":"print","value":"0167-9236"}],"subject":[],"published":{"date-parts":[[2019,3]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Two-stage consumer credit risk modelling using heterogeneous ensemble learning","name":"articletitle","label":"Article Title"},{"value":"Decision Support Systems","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.dss.2019.01.002","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2019 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}]}}