{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T17:12:48Z","timestamp":1777050768911,"version":"3.51.4"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,8,26]],"date-time":"2020-08-26T00:00:00Z","timestamp":1598400000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2020,8,26]],"date-time":"2020-08-26T00:00:00Z","timestamp":1598400000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100000038","name":"NSERC","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Big Data"],"published-print":{"date-parts":[[2020,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Prediction using machine learning algorithms is not well adapted in many parts of the business decision processes due to the lack of clarity and flexibility. The erroneous data as inputs in the prediction process may produce inaccurate predictions. We aim to use machine learning models in the area of the business decision process by predicting products\u2019 backorder while providing flexibility to the decision authority, better clarity of the process, and maintaining higher accuracy. A ranged method is used for specifying different levels of predicting features to cope with the diverse characteristics of real-time data which may happen by machine or human errors. The range is tunable that gives flexibility to the decision managers. The tree-based machine learning is chosen for better explainability of the model. The backorders of products are predicted in this study using Distributed Random Forest (DRF) and Gradient Boosting Machine (GBM). We have observed that the performances of the machine learning models have been improved by 20% using this ranged approach when the dataset is highly biased with random error. We have utilized a five-level metric to indicate the inventory level, sales level, forecasted sales level, and a four-level metric for the lead time. A decision tree from one of the constructed models is analyzed to understand the effects of the ranged approach. As a part of this analysis, we list major probable backorder scenarios to facilitate business decisions. We show how this model can be used to predict the probable backorder products before actual sales take place. The mentioned methods in this research can be utilized in other supply chain cases to forecast backorders.<\/jats:p>","DOI":"10.1186\/s40537-020-00345-2","type":"journal-article","created":{"date-parts":[[2020,8,26]],"date-time":"2020-08-26T00:12:03Z","timestamp":1598400723000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":139,"title":["Prediction of probable backorder scenarios in the supply chain using Distributed Random Forest and Gradient Boosting Machine learning techniques"],"prefix":"10.1186","volume":"7","author":[{"given":"Samiul","family":"Islam","sequence":"first","affiliation":[]},{"given":"Saman Hassanzadeh","family":"Amin","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,8,26]]},"reference":[{"key":"345_CR1","unstructured":"Clark KB, Fujimoto T. Product development performance: strategy, organization, and management in the world auto industry. 1991."},{"key":"345_CR2","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.cie.2018.11.042","volume":"127","author":"L Guo","year":"2019","unstructured":"Guo L, Wang Y, Kong D, Zhang Z, Yang Y. Decisions on spare parts allocation for repairable isolated system with dependent backorders. Comput Ind Eng. 2019;127:8\u201320.","journal-title":"Comput Ind Eng"},{"issue":"5","key":"345_CR3","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1108\/09600030810882816","volume":"38","author":"CR Carter","year":"2008","unstructured":"Carter CR, Rogers DS. A framework of sustainable supply chain management: moving toward new theory. Int J Phys Distrib Logistics Manag. 2008;38(5):360\u201387.","journal-title":"Int J Phys Distrib Logistics Manag"},{"key":"345_CR4","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1016\/j.ijpe.2019.07.007","volume":"219","author":"F Mohebalizadehgashti","year":"2020","unstructured":"Mohebalizadehgashti F, Zolfagharinia H, Amin SH. Designing a green meat supply chain network: a multi-objective approach. Int J Prod Econ. 2020;219:312\u201327.","journal-title":"Int J Prod Econ"},{"key":"345_CR5","volume-title":"Designing and managing the supply chain: concepts, strategies and case studies","author":"D Simchi-Levi","year":"2008","unstructured":"Simchi-Levi D, Kaminsky P, Simchi-Levi E, Shankar R. Designing and managing the supply chain: concepts, strategies and case studies. New York: Tata McGraw-Hill Education; 2008."},{"key":"345_CR6","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1016\/j.ijpe.2019.06.002","volume":"219","author":"L Yu","year":"2020","unstructured":"Yu L, Duan Y, Fan T. Innovation performance of new products in China's high-technology industry. Int J Prod Econ. 2020;219:204\u201315.","journal-title":"Int J Prod Econ"},{"key":"345_CR7","volume-title":"Fundamentals of quality control and improvement","author":"A Mitra","year":"2016","unstructured":"Mitra A. Fundamentals of quality control and improvement. New York: Wiley; 2016."},{"issue":"4","key":"345_CR8","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1016\/j.orl.2017.04.007","volume":"45","author":"Y Xu","year":"2017","unstructured":"Xu Y, Bisi A, Dada M. A finite-horizon inventory system with partial backorders and inventory holdback. Oper Res Lett. 2017;45(4):315\u201322.","journal-title":"Oper Res Lett"},{"issue":"3","key":"345_CR9","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/S0925-5273(96)00107-7","volume":"48","author":"BR Sarker","year":"1997","unstructured":"Sarker BR, Mukherjee S, Balan CV. An order-level lot size inventory model with inventory-level dependent demand and deterioration. Int J Prod Econ. 1997;48(3):227\u201336.","journal-title":"Int J Prod Econ"},{"key":"345_CR10","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.ijpe.2017.02.002","volume":"186","author":"X Wan","year":"2017","unstructured":"Wan X, Sanders NR. The negative impact of product variety: forecast bias, inventory levels, and the role of vertical integration. Int J Prod Econ. 2017;186:123\u201331.","journal-title":"Int J Prod Econ"},{"key":"345_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijpe.2019.09.024","author":"X Wan","year":"2019","unstructured":"Wan X, Britto R, Zhou Z. In search of the negative relationship between product variety and inventory turnover. Int J Prod Econ. 2019. https:\/\/doi.org\/10.1016\/j.ijpe.2019.09.024.","journal-title":"Int J Prod Econ"},{"issue":"16","key":"345_CR12","doi-asserted-by":"crossref","first-page":"7005","DOI":"10.1016\/j.eswa.2014.05.012","volume":"41","author":"JA Rodger","year":"2014","unstructured":"Rodger JA. Application of a fuzzy feasibility Bayesian probabilistic estimation of supply chain backorder aging, unfilled backorders, and customer wait time using stochastic simulation with Markov blankets. Expert Syst Appl. 2014;41(16):7005\u2013222.","journal-title":"Expert Syst Appl"},{"issue":"2","key":"345_CR13","doi-asserted-by":"crossref","first-page":"534","DOI":"10.1016\/j.ijpe.2007.06.012","volume":"114","author":"MP De Brito","year":"2008","unstructured":"De Brito MP, Carbone V, Blanquart CM. Towards a sustainable fashion retail supply chain in Europe: organisation and performance. Int J Prod Econ. 2008;114(2):534\u201353.","journal-title":"Int J Prod Econ"},{"key":"345_CR14","doi-asserted-by":"crossref","first-page":"540","DOI":"10.1016\/j.compchemeng.2018.11.014","volume":"121","author":"BM Tosarkani","year":"2019","unstructured":"Tosarkani BM, Amin SH. An environmental optimization model to configure a hybrid forward and reverse supply chain network under uncertainty. Comput Chem Eng. 2019;121:540\u201355.","journal-title":"Comput Chem Eng"},{"key":"345_CR15","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.eswa.2015.12.032","volume":"51","author":"A Srivastav","year":"2016","unstructured":"Srivastav A, Agrawal S. Multi-objective optimization of hybrid backorder inventory model. Expert Syst Appl. 2016;51:76\u201384.","journal-title":"Expert Syst Appl"},{"issue":"3","key":"345_CR16","first-page":"55","volume":"1","author":"G Ridgeway","year":"2006","unstructured":"Ridgeway G. gbm: Generalized boosted regression models. R package version. 2006;1(3):55.","journal-title":"R package version"},{"key":"345_CR17","doi-asserted-by":"crossref","DOI":"10.1201\/9780429292859","volume-title":"Data mining with R: learning with case studies","author":"L Torgo","year":"2011","unstructured":"Torgo L. Data mining with R: learning with case studies. New York: Chapman and Hall\/CRC; 2011."},{"key":"345_CR18","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. SMOTE: synthetic minority over-sampling technique. J Artif Intell Res. 2002;16:321\u201357.","journal-title":"J Artif Intell Res"},{"issue":"4","key":"345_CR19","doi-asserted-by":"crossref","first-page":"40","DOI":"10.4018\/jiit.2007100103","volume":"3","author":"R Carbonneau","year":"2007","unstructured":"Carbonneau R, Vahidov R, Laframboise K. Machine learning-Based Demand forecasting in supply chains. Int J Intell Inf Technol (IJIIT). 2007;3(4):40\u201357.","journal-title":"Int J Intell Inf Technol (IJIIT)"},{"key":"345_CR20","doi-asserted-by":"crossref","unstructured":"Hearst MA, Susan TD, Edgar O, John P, Bernhard S. Support vector machines. In: IEEE intelligent systems and their applications. 1998. p. 18\u201328.","DOI":"10.1109\/5254.708428"},{"issue":"3","key":"345_CR21","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/0893-6080(89)90003-8","volume":"2","author":"KI Funahashi","year":"1989","unstructured":"Funahashi KI. On the approximate realization of continuous mappings by neural networks. Neural Netw. 1989;2(3):183\u201392.","journal-title":"Neural Netw"},{"issue":"3","key":"345_CR22","doi-asserted-by":"crossref","first-page":"1140","DOI":"10.1016\/j.ejor.2006.12.004","volume":"184","author":"R Carbonneau","year":"2008","unstructured":"Carbonneau R, Laframboise K, Vahidov R. Application of machine learning techniques for supply chain demand forecasting. Eur J Oper Res. 2008;184(3):1140\u201354.","journal-title":"Eur J Oper Res"},{"key":"345_CR23","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1016\/j.proeng.2011.12.707","volume":"29","author":"WANG Guanghui","year":"2012","unstructured":"Guanghui WANG. Demand forecasting of supply chain based on support vector regression method. Procedia Eng. 2012;29:280\u20134.","journal-title":"Procedia Eng"},{"issue":"2","key":"345_CR24","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1109\/72.80341","volume":"2","author":"S Chen","year":"1991","unstructured":"Chen S, Cowan CF, Grant PM. Orthogonal least squares learning algorithm for radial basis function networks. IEEE Trans Neural Netw. 1991;2(2):302\u20139.","journal-title":"IEEE Trans Neural Netw"},{"issue":"3","key":"345_CR25","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1016\/j.cie.2011.11.015","volume":"62","author":"K Shin","year":"2012","unstructured":"Shin K, Shin Y, Kwon JH, Kang SH. Development of risk based dynamic backorder replenishment planning framework using Bayesian Belief Network. Comput Ind Eng. 2012;62(3):716\u201325.","journal-title":"Comput Ind Eng"},{"issue":"4","key":"345_CR26","doi-asserted-by":"crossref","first-page":"842","DOI":"10.1016\/j.ijforecast.2011.11.003","volume":"28","author":"Y Acar","year":"2012","unstructured":"Acar Y, Gardner ES Jr. Forecasting method selection in a global supply chain. Int J Forecast. 2012;28(4):842\u20138.","journal-title":"Int J Forecast"},{"key":"345_CR27","doi-asserted-by":"crossref","unstructured":"de Santis RB, de Aguiar EP, Goliatt L. Predicting material backorders in inventory management using machine learning. In 2017 IEEE Latin American Conference on Computational Intelligence (LA-CCI). 2017. p. 1\u20136.","DOI":"10.1109\/LA-CCI.2017.8285684"},{"issue":"1","key":"345_CR28","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1016\/j.ijforecast.2017.11.004","volume":"35","author":"D Prak","year":"2019","unstructured":"Prak D, Teunter R. A general method for addressing forecasting uncertainty in inventory models. Int J Forecast. 2019;35(1):224\u201338.","journal-title":"Int J Forecast"},{"key":"345_CR29","unstructured":"Dancho M. Use Machine Learning to Predict and Optimize Product Backorders. Business Science Article. Business Science Article. 2017. https:\/\/www.business-science.io\/business\/2017\/10\/16\/sales_backorder_prediction.html. Accessed 15 Feb 2020."},{"issue":"1","key":"345_CR30","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.ijforecast.2018.01.004","volume":"35","author":"F Petropoulos","year":"2019","unstructured":"Petropoulos F, Wang X, Disney SM. The inventory performance of forecasting methods: evidence from the M3 competition data. Int J Forecast. 2019;35(1):251\u201365.","journal-title":"Int J Forecast"},{"key":"345_CR31","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/S0925-2312(01)00702-0","volume":"50","author":"GP Zhang","year":"2003","unstructured":"Zhang GP. Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing. 2003;50:159\u201375.","journal-title":"Neurocomputing"},{"issue":"1","key":"345_CR32","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.ijforecast.2017.11.005","volume":"35","author":"L Yu","year":"2019","unstructured":"Yu L, Zhao Y, Tang L, Yang Z. Online big data-driven oil consumption forecasting with Google trends. Int J Forecast. 2019;35(1):213\u201323.","journal-title":"Int J Forecast"},{"issue":"4","key":"345_CR33","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1016\/j.ijforecast.2006.03.001","volume":"22","author":"RJ Hyndman","year":"2006","unstructured":"Hyndman RJ, Koehler AB. Another look at measures of forecast accuracy. Int J Forecast. 2006;22(4):679\u201388.","journal-title":"Int J Forecast"},{"issue":"3","key":"345_CR34","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1016\/j.ijforecast.2015.12.003","volume":"32","author":"S Kim","year":"2016","unstructured":"Kim S, Kim H. A new metric of absolute percentage error for intermittent demand forecasts. Int J Forecast. 2016;32(3):669\u201379.","journal-title":"Int J Forecast"},{"issue":"3","key":"345_CR35","doi-asserted-by":"crossref","first-page":"588","DOI":"10.1016\/j.ejor.2018.04.034","volume":"281","author":"A Mart\u00ednez","year":"2020","unstructured":"Mart\u00ednez A, Schmuck C, Pereverzyev S Jr, Pirker C, Haltmeier M. A machine learning framework for customer purchase prediction in the non-contractual setting. Eur J Oper Res. 2020;281(3):588\u201396.","journal-title":"Eur J Oper Res"},{"issue":"2","key":"345_CR36","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.ijforecast.2017.09.007","volume":"34","author":"S De Baets","year":"2018","unstructured":"De Baets S, Harvey N. Forecasting from time series subject to sporadic perturbations: effectiveness of different types of forecasting support. Int J Forecast. 2018;34(2):163\u201380.","journal-title":"Int J Forecast"},{"issue":"4","key":"345_CR37","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1007\/s10462-011-9272-4","volume":"39","author":"SB Kotsiantis","year":"2013","unstructured":"Kotsiantis SB. Decision trees: a recent overview. Artif Intell Rev. 2013;39(4):261\u201383.","journal-title":"Artif Intell Rev"},{"issue":"1","key":"345_CR38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-014-0007-7","volume":"2","author":"MM Najafabadi","year":"2015","unstructured":"Najafabadi MM, Villanustre F, Khoshgoftaar TM, Seliya N, Wald R, Muharemagic E. Deep learning applications and challenges in big data analytics. J Big Data. 2015;2(1):1.","journal-title":"J Big Data"},{"key":"345_CR39","doi-asserted-by":"crossref","first-page":"744","DOI":"10.1016\/j.scitotenv.2018.01.266","volume":"627","author":"K Khosravi","year":"2018","unstructured":"Khosravi K, Pham BT, Chapi K, Shirzadi A, Shahabi H, Revhaug I, Bui DT. A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran. Sci Total Environ. 2018;627:744\u201355.","journal-title":"Sci Total Environ"},{"key":"345_CR40","unstructured":"Chiabaut J. U.S. Patent No. 8,761,022. Washington: U.S. Patent and Trademark Office. 2014."},{"key":"345_CR41","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ins.2013.12.060","volume":"266","author":"L Rutkowski","year":"2014","unstructured":"Rutkowski L, Jaworski M, Pietruczuk L, Duda P. The CART decision tree for mining data streams. Inf Sci. 2014;266:1\u201315.","journal-title":"Inf Sci"},{"issue":"3","key":"345_CR42","doi-asserted-by":"crossref","first-page":"769","DOI":"10.1016\/j.patcog.2012.09.005","volume":"46","author":"Y Ye","year":"2013","unstructured":"Ye Y, Wu Q, Huang JZ, Ng MK, Li X. Stratified sampling for feature subspace selection in random forests for high dimensional data. Pattern Recogn. 2013;46(3):769\u201387.","journal-title":"Pattern Recogn"},{"key":"345_CR43","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1007\/978-3-030-12854-8_10","volume-title":"Decision and Inhibitory Trees and Rules for Decision Tables with Many-valued Decisions","author":"F Alsolami","year":"2020","unstructured":"Alsolami F, Azad M, Chikalov I, Moshkov M. Multi-pruning and Restricted Multi-pruning of Decision Trees. Decision and Inhibitory Trees and Rules for Decision Tables with Many-valued Decisions. Cham: Springer; 2020. p. 153\u2013174."},{"key":"345_CR44","unstructured":"Lee S, Gonzalez J, Wright M. Interpretable few-shot image classification with neural-backed decision trees. 2020."},{"key":"345_CR45","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.ijpe.2018.11.024","volume":"208","author":"OM Araz","year":"2019","unstructured":"Araz OM, Olson D, Ramirez-Nafarrate A. Predictive analytics for hospital admissions from the emergency department using triage information. Int J Prod Econ. 2019;208:199\u2013207.","journal-title":"Int J Prod Econ"},{"issue":"6","key":"345_CR46","doi-asserted-by":"crossref","first-page":"971","DOI":"10.1007\/s10994-019-05787-1","volume":"108","author":"G Biau","year":"2019","unstructured":"Biau G, Cadre B, Rouvi\u00e8re L. Accelerated gradient boosting. Machine Learning. 2019;108(6):971\u201392.","journal-title":"Machine Learning"},{"key":"345_CR47","first-page":"503","volume":"6","author":"D Ernst","year":"2005","unstructured":"Ernst D, Geurts P, Wehenkel L. Tree-based batch mode reinforcement learning. J Mach Learn Res. 2005;6:503\u201356.","journal-title":"J Mach Learn Res"},{"issue":"3","key":"345_CR48","doi-asserted-by":"crossref","first-page":"456","DOI":"10.1080\/07350015.2016.1200981","volume":"36","author":"Y Yang","year":"2018","unstructured":"Yang Y, Qian W, Zou H. Insurance premium prediction via gradient tree-boosted tweedie compound poisson models. J Bus Econ Stat. 2018;36(3):456\u201370.","journal-title":"J Bus Econ Stat"},{"issue":"3\/4","key":"345_CR49","doi-asserted-by":"crossref","first-page":"441","DOI":"10.2307\/1422689","volume":"100","author":"C Spearman","year":"1987","unstructured":"Spearman C. The proof and measurement of association between two things. Am J Psychol. 1987;100(3\/4):441\u201371.","journal-title":"Am J Psychol"},{"issue":"3","key":"345_CR50","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1016\/S0377-2217(97)00012-X","volume":"104","author":"R Ernst","year":"1998","unstructured":"Ernst R, Powell SG. Manufacturer incentives to improve retail service levels. Eur J Oper Res. 1998;104(3):437\u201350.","journal-title":"Eur J Oper Res"},{"issue":"10","key":"345_CR51","doi-asserted-by":"crossref","first-page":"734","DOI":"10.1108\/09590550510622281","volume":"33","author":"P Appelqvist","year":"2005","unstructured":"Appelqvist P, Gubi E. Postponed variety creation: case study in consumer electronics retail. Int J Retail Distrib Manag. 2005;33(10):734\u201348.","journal-title":"Int J Retail Distrib Manag"}],"container-title":["Journal of Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-020-00345-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s40537-020-00345-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-020-00345-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,8,25]],"date-time":"2021-08-25T20:11:34Z","timestamp":1629922294000},"score":1,"resource":{"primary":{"URL":"https:\/\/journalofbigdata.springeropen.com\/articles\/10.1186\/s40537-020-00345-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,26]]},"references-count":51,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,12]]}},"alternative-id":["345"],"URL":"https:\/\/doi.org\/10.1186\/s40537-020-00345-2","relation":{"has-preprint":[{"id-type":"doi","id":"10.32920\/23823900.v1","asserted-by":"object"},{"id-type":"doi","id":"10.32920\/23823900","asserted-by":"object"}],"is-supplemented-by":[{"id-type":"doi","id":"10.32920\/23823900","asserted-by":"object"},{"id-type":"doi","id":"10.32920\/23823900.v1","asserted-by":"object"}]},"ISSN":["2196-1115"],"issn-type":[{"value":"2196-1115","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,8,26]]},"assertion":[{"value":"7 May 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 August 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 August 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"There are no financial and non-financial competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"65"}}