{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,6]],"date-time":"2026-11-06T14:54:40Z","timestamp":1793976880041,"version":"4.8.0"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,3,31]],"date-time":"2022-03-31T00:00:00Z","timestamp":1648684800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,3,31]],"date-time":"2022-03-31T00:00:00Z","timestamp":1648684800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Qual Quant"],"published-print":{"date-parts":[[2023,2]]},"DOI":"10.1007\/s11135-022-01365-1","type":"journal-article","created":{"date-parts":[[2022,3,31]],"date-time":"2022-03-31T18:06:05Z","timestamp":1648749965000},"page":"561-585","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Prediction of production facility priorities using Back Propagation Neural Network for bus body building industries: a post pandemic research article"],"prefix":"10.1007","volume":"57","author":[{"given":"A.","family":"Sivakumar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6413-437X","authenticated-orcid":false,"given":"N. Bagath","family":"Singh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"D.","family":"Arulkirubakaran","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"P. Praveen Vijaya","family":"Raj","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,3,31]]},"reference":[{"issue":"1","key":"1365_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/23311916.2018.1507657","volume":"5","author":"WS Alaloul","year":"2018","unstructured":"Alaloul, W.S., Liew, M.S., Wan Zawawi, N.A., Mohammed, B.S., Adamu, M.: An Artificial neural networks (ANN) model for evaluating construction project performance based on coordination factors. Cogent. Eng. 5(1), 1\u201318 (2018). https:\/\/doi.org\/10.1080\/23311916.2018.1507657","journal-title":"Cogent. Eng."},{"key":"1365_CR2","doi-asserted-by":"publisher","DOI":"10.1080\/15623599.2020.1711494","author":"ZG Al-Kofahi","year":"2020","unstructured":"Al-Kofahi, Z.G., Mahdavian, A., Oloufa, A.: System dynamics modeling approach to quantify change orders impact on labor productivity 1: principles and model development comparative study. Int. J. Constr. Manag. (2020). https:\/\/doi.org\/10.1080\/15623599.2020.1711494","journal-title":"Int. J. Constr. Manag."},{"key":"1365_CR3","doi-asserted-by":"publisher","unstructured":"Annamalai, S., Nagarajan, B., Kumar, H. V., Saravanakumar, S.: Process and analysis with demographic methodological refinement of bus body industry. Mater. Today Proc. (2020). https:\/\/doi.org\/10.1016\/j.matpr.2020.05.482","DOI":"10.1016\/j.matpr.2020.05.482"},{"key":"1365_CR4","doi-asserted-by":"publisher","unstructured":"As\u2019ad, R., Demirli, K., Goyal, S.K.: Coping with uncertainties in production planning through fuzzy mathematical programming: application to steel rolling industry. Int. J. Oper. Res. 22(1), 1\u201330 (2015). https:\/\/doi.org\/10.1504\/IJOR.2015.065937","DOI":"10.1504\/IJOR.2015.065937"},{"issue":"1\u20132","key":"1365_CR5","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1016\/S0925-5273(97)00026-1","volume":"52","author":"I Bernolak","year":"1997","unstructured":"Bernolak, I.: Effective measurement and successful elements of company productivity: the basis of competitiveness and world prosperity. Int. J. Prod. Econ. 52(1\u20132), 203\u2013213 (1997). https:\/\/doi.org\/10.1016\/S0925-5273(97)00026-1","journal-title":"Int. J. Prod. Econ."},{"key":"1365_CR6","doi-asserted-by":"publisher","first-page":"622","DOI":"10.1016\/j.econmod.2013.02.020","volume":"36","author":"G Calcagnini","year":"2014","unstructured":"Calcagnini, G., Travaglini, G.: A time series analysis of labor productivity. Italy versus the European countries and the U.S. Econ. Model. 36, 622\u2013628 (2014). https:\/\/doi.org\/10.1016\/j.econmod.2013.02.020","journal-title":"Econ. Model."},{"issue":"4","key":"1365_CR7","doi-asserted-by":"publisher","first-page":"387","DOI":"10.1080\/0144619042000326738","volume":"23","author":"ARJ Dainty","year":"2005","unstructured":"Dainty, A.R.J., Ison, S.G., Briscoe, G.H.: The construction labour market skills crisis: the perspective of small-medium-sized firms. Constr. Manag. Econ. 23(4), 387\u2013398 (2005). https:\/\/doi.org\/10.1080\/0144619042000326738","journal-title":"Constr. Manag. Econ."},{"issue":"1","key":"1365_CR8","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1007\/s42943-020-00009-1","volume":"15","author":"SG Deshmukh","year":"2020","unstructured":"Deshmukh, S.G., Haleem, A.: Framework for manufacturing in post-COVID-19 world order: an Indian perspective. Int. J. Global Bus. Compet. 15(1), 49\u201360 (2020). https:\/\/doi.org\/10.1007\/s42943-020-00009-1","journal-title":"Int. J. Global Bus. Compet."},{"key":"1365_CR9","doi-asserted-by":"publisher","unstructured":"Dissanayake, M., Fayek, A. R., Russell, A. D., Pedrycz, W.: A hybrid neural network for predicting construction labour productivity. In: Proceedings of the 2005 ASCE International Conference on Computing in Civil Engineering, pp. 819\u2013830 (2005). https:\/\/doi.org\/10.1061\/40794(179)78","DOI":"10.1061\/40794(179)78"},{"issue":"1","key":"1365_CR10","first-page":"1","volume":"55","author":"S Durdyev","year":"2012","unstructured":"Durdyev, S., Ismail, S., Bakar, N.A.: Factors constraining labour productivity\u202f: case study of Turkmenistan. Ipedr 55(1), 1\u20135 (2012)","journal-title":"Ipedr"},{"issue":"8","key":"1365_CR11","doi-asserted-by":"publisher","first-page":"04017045","DOI":"10.1061\/(asce)co.1943-7862.0001340","volume":"143","author":"KM El-Gohary","year":"2017","unstructured":"El-Gohary, K.M., Aziz, R.F., Abdel-Khalek, H.A.: Engineering approach using ANN to improve and predict construction labor productivity under different influences. J. Constr. Eng. Manag. 143(8), 04017045 (2017). https:\/\/doi.org\/10.1061\/(asce)co.1943-7862.0001340","journal-title":"J. Constr. Eng. Manag."},{"issue":"4","key":"1365_CR12","doi-asserted-by":"publisher","first-page":"245","DOI":"10.3846\/13923730.2007.9636444","volume":"13","author":"A Enshassi","year":"2007","unstructured":"Enshassi, A., Mohamed, S., Mayer, P.E.: Factors affecting labour productivity in building projects in the Gaza strip. J. Civ. Eng. Manag. 13(4), 245\u2013254 (2007). https:\/\/doi.org\/10.3846\/13923730.2007.9636444","journal-title":"J. Civ. Eng. Manag."},{"key":"1365_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107091","volume":"102","author":"ZI Erzurum Cicek","year":"2021","unstructured":"Erzurum Cicek, Z.I., Kamisli Ozturk, Z.: Optimizing the artificial neural network parameters using a biased random key genetic algorithm for time series forecasting. Appl. Soft Comput. 102, 107091 (2021). https:\/\/doi.org\/10.1016\/j.asoc.2021.107091","journal-title":"Appl. Soft Comput."},{"key":"1365_CR14","first-page":"609","volume":"86","author":"S Fabricant","year":"1962","unstructured":"Fabricant, S.: Which productivity? Perspective on a current question. Mon. Labourrev 86, 609\u2013613 (1962)","journal-title":"Mon. Labourrev"},{"key":"1365_CR15","doi-asserted-by":"crossref","unstructured":"Gate, B.: Responding to Covid-19\u2014a once-in-a-century pandemic? New England J. Med. pp. 1\u20133 (2020)","DOI":"10.1056\/NEJMp2003762"},{"key":"1365_CR16","doi-asserted-by":"publisher","unstructured":"Gautam, S., Patra, A.K., Brema, J., Raj, P.V., Raimond, K., Abraham, S.S., Chudugudu, K.R.: Prediction of various sizes of particles in deep opencast copper mine using recurrent neural network: a machine learning approach. J. Inst. Eng. (India) Ser. A. (2021). https:\/\/doi.org\/10.1007\/s40030-021-00589-y","DOI":"10.1007\/s40030-021-00589-y"},{"key":"1365_CR17","doi-asserted-by":"publisher","unstructured":"Gerami Seresht, N., Fayek, A.R.: Neuro-fuzzy system dynamics technique for modeling construction systems. Appl. Soft Comput. J.93. (2020). https:\/\/doi.org\/10.1016\/j.asoc.2020.106400","DOI":"10.1016\/j.asoc.2020.106400"},{"issue":"2","key":"1365_CR18","doi-asserted-by":"publisher","first-page":"231","DOI":"10.3846\/13923730.2013.802741","volume":"21","author":"IH Gerek","year":"2015","unstructured":"Gerek, I.H., Erdis, E., Mistikoglu, G., Usmen, M.: Modelling masonry crew productivity using two artificial neural network techniques. J. Civ. Eng. Manag. 21(2), 231\u2013238 (2015). https:\/\/doi.org\/10.3846\/13923730.2013.802741","journal-title":"J. Civ. Eng. Manag."},{"issue":"3","key":"1365_CR19","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1504\/GBER.2017.083964","volume":"19","author":"V Goel","year":"2017","unstructured":"Goel, V., Agrawal, R., Sharma, V.: Factors affecting labour productivity: an integrative synthesis and productivity modelling. Global Bus. Econ. Rev. 19(3), 299\u2013322 (2017). https:\/\/doi.org\/10.1504\/GBER.2017.083964","journal-title":"Global Bus. Econ. Rev."},{"key":"1365_CR20","doi-asserted-by":"publisher","unstructured":"Golnaraghi, S., Zangenehmadar, Z., Moselhi, O., Alkass, S., Vosoughi, A.R.: Application of artificial neural network(s) in predicting formwork labour productivity. Adv. Civil Eng. (2019). https:\/\/doi.org\/10.1155\/2019\/5972620","DOI":"10.1155\/2019\/5972620"},{"key":"1365_CR21","doi-asserted-by":"publisher","unstructured":"Han, J.W., Li, Q.X., Wu, H.R., Zhu, H.J., Song, Y.L.: Prediction of cooling efficiency of forced-air precooling systems based on optimized differential evolution and improved BP neural network. Appl. Soft Comput. J.84 (2019). https:\/\/doi.org\/10.1016\/j.asoc.2019.105733","DOI":"10.1016\/j.asoc.2019.105733"},{"key":"1365_CR22","unstructured":"Healthcare, G.: Fears of a second wave of Covid-19 in Australia after three months of control. Pharm. Technol. (2020)"},{"key":"1365_CR23","doi-asserted-by":"publisher","DOI":"10.1007\/s10479-022-04528-3","author":"SK Jauhar","year":"2022","unstructured":"Jauhar, S.K., Raj, P.V.R.P., Kamble, S., Pratap, S., Gupta, S., Belhadi, A.: A deep learning-based approach for performance assessment and prediction: a case study of pulp and paper industries. Ann. Oper. Res. (2022). https:\/\/doi.org\/10.1007\/s10479-022-04528-3","journal-title":"Ann. Oper. Res."},{"issue":"2","key":"1365_CR24","doi-asserted-by":"publisher","first-page":"95","DOI":"10.3846\/1392-3730.2008.14.4","volume":"14","author":"A Kazaz","year":"2008","unstructured":"Kazaz, A., Manisali, E., Ulubeyli, S.: Effect of basic motivational factors on construction workforce productivity in Turkey. J. Civ. Eng. Manag. 14(2), 95\u2013106 (2008). https:\/\/doi.org\/10.3846\/1392-3730.2008.14.4","journal-title":"J. Civ. Eng. Manag."},{"issue":"5","key":"1365_CR25","doi-asserted-by":"publisher","first-page":"703","DOI":"10.4103\/ijo.IJO_843_20","volume":"68","author":"RC Khanna","year":"2020","unstructured":"Khanna, R.C., Cicinelli, M.V., Gilbert, S.S., Honavar, S.G., Murthy, G.V.S.: COVID-19 pandemic: lessons learned and future directions. Indian J. Ophthalmol. 68(5), 703\u2013710 (2020). https:\/\/doi.org\/10.4103\/ijo.IJO_843_20","journal-title":"Indian J. Ophthalmol."},{"issue":"1","key":"1365_CR26","doi-asserted-by":"publisher","first-page":"04019035","DOI":"10.1061\/(asce)me.1943-5479.0000720","volume":"36","author":"S Kim","year":"2020","unstructured":"Kim, S., Chang, S., Castro-Lacouture, D.: Dynamic modeling for analyzing impacts of skilled labor shortage on construction project management. J. Manag. Eng. 36(1), 04019035 (2020). https:\/\/doi.org\/10.1061\/(asce)me.1943-5479.0000720","journal-title":"J. Manag. Eng."},{"key":"1365_CR27","unstructured":"Lalit Kant, former scientist \u2018G\u2019 and head-Epidemiology and Communicable Diseases Division, I. C. of M. R.: Second wave of corona virus possible, India not at peak of infections yet, say experts. India Today August 6 (2020)"},{"issue":"5","key":"1365_CR28","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1002\/jmv.25701","volume":"92","author":"X Li","year":"2020","unstructured":"Li, X., Wang, W., Zhao, X., Zai, J., Zhao, Q., Li, Y., Chaillon, A.: Transmission dynamics and evolutionary history of 2019-nCoV. J. Med. Virol. 92(5), 501\u2013511 (2020). https:\/\/doi.org\/10.1002\/jmv.25701","journal-title":"J. Med. Virol."},{"issue":"2","key":"1365_CR29","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1016\/j.ijproman.2007.06.003","volume":"26","author":"S Mojahed","year":"2008","unstructured":"Mojahed, S., Aghazadeh, F.: Major factors influencing productivity of water and wastewater treatment plant construction: Evidence from the deep south USA. Int. J. Project Manage. 26(2), 195\u2013202 (2008). https:\/\/doi.org\/10.1016\/j.ijproman.2007.06.003","journal-title":"Int. J. Project Manage."},{"key":"1365_CR30","first-page":"180","volume":"583","author":"J Muliyil","year":"2020","unstructured":"Muliyil, J.: India\u2019s epidemic adviser fears Corona virus crisis will worsen\u201d, Satish Bate\/Hindustan Times Via Getty. News Focus Springer Nat 583, 180 (2020)","journal-title":"News Focus Springer Nat"},{"issue":"6","key":"1365_CR31","doi-asserted-by":"publisher","first-page":"903","DOI":"10.1016\/j.ijproman.2012.11.003","volume":"31","author":"F Nasirzadeh","year":"2013","unstructured":"Nasirzadeh, F., Nojedehi, P.: Dynamic modeling of labor productivity in construction projects. Int. J. Project Manage. 31(6), 903\u2013911 (2013). https:\/\/doi.org\/10.1016\/j.ijproman.2012.11.003","journal-title":"Int. J. Project Manage."},{"key":"1365_CR32","doi-asserted-by":"publisher","unstructured":"Noda, H., Kyo, K.: Bayesian estimation of the CES production function with capital- and labour-augmenting technical change. Asian J. Manage. Sci. Appl, 2(1), 61 (2015). https:\/\/doi.org\/10.1504\/ajmsa.2015.071890","DOI":"10.1504\/ajmsa.2015.071890"},{"key":"1365_CR33","unstructured":"Organisation for European Economic Co-operation (OEEC).: Terminology of productivity. Indus. Eng. Knowl. Centre 1\u201313 (1950)"},{"key":"1365_CR34","doi-asserted-by":"publisher","unstructured":"Patel, G.C., Shettigar, A.K., Krishna, P., Parappagoudar, M.B.: Back propagation genetic and recurrent neural network applications in modelling and analysis of squeeze casting process. Appl. Soft Comput. J. 59, 418\u2013437 (2017). https:\/\/doi.org\/10.1016\/j.asoc.2017.06.018","DOI":"10.1016\/j.asoc.2017.06.018"},{"key":"1365_CR35","doi-asserted-by":"publisher","unstructured":"Ponmalar, V., Aravindraj, V., Nandhini, K.: Study on factors influencing labour productivity in residential buildings in indian scenario. Int. J. Eng. Technol. Manage. Res. 5(2), 239\u2013248 (2020). https:\/\/doi.org\/10.29121\/ijetmr.v5.i2.2018.168","DOI":"10.29121\/ijetmr.v5.i2.2018.168"},{"key":"1365_CR36","unstructured":"Saxena-Editor, S.: Fears of second wave of Covid-19 outbreak loom large in Chinese province Xinjiang. Corona Virus Outbreak, Hindustan Times, July 19 (2020)."},{"key":"1365_CR37","doi-asserted-by":"publisher","DOI":"10.1007\/s11135-021-01207-6","author":"PK Singh","year":"2021","unstructured":"Singh, P.K., Chouhan, A., Bhatt, R.K., Kiran, R., Ahmar, A.S.: Implementation of the SutteARIMA method to predict short-term cases of stock market and COVID-19 pandemic in USA. Qual. Quant. (2021). https:\/\/doi.org\/10.1007\/s11135-021-01207-6","journal-title":"Qual. Quant."},{"key":"1365_CR38","doi-asserted-by":"crossref","unstructured":"Sivakumar, A., Bagath Singh, N., Arulkirubakaran, D.P.V.R.P.: Prediction of equipment effectiveness using hybrid moving average-adaptive neuro fuzzy inference system (MA-ANFIS) for decision support in bus body building industry. Ann. Brazilian Acad. Sci. (article in press) (2022)","DOI":"10.1590\/0001-3765202220210552"},{"issue":"10","key":"1365_CR39","doi-asserted-by":"publisher","first-page":"786","DOI":"10.1061\/(asce)0733-9364(2008)134:10(786)","volume":"134","author":"L Song","year":"2008","unstructured":"Song, L., AbouRizk, S.M.: Measuring and modeling labor productivity using historical data. J. Constr. Eng. Manag. 134(10), 786\u2013794 (2008). https:\/\/doi.org\/10.1061\/(asce)0733-9364(2008)134:10(786)","journal-title":"J. Constr. Eng. Manag."},{"issue":"1","key":"1365_CR40","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1504\/IJESB.2016.078030","volume":"29","author":"J Sp\u00e4th","year":"2016","unstructured":"Sp\u00e4th, J.: Young and flexible: labour contracts in young and incumbent firms. Int. J. Entrep. Small Bus. 29(1), 46\u201365 (2016). https:\/\/doi.org\/10.1504\/IJESB.2016.078030","journal-title":"Int. J. Entrep. Small Bus."},{"key":"1365_CR41","unstructured":"Sumanth, D.J.: Productivity engineering and management: productivity measurement, evaluation, planning, and improvement in manufacturing and service organizations. Mc-Graw Hill (2011)."},{"key":"1365_CR42","unstructured":"The Medical Futurist.: Will there be a second wave of COVID-19?\u2014The Medical Futurist. The Medical Futurist (2020). https:\/\/medicalfuturist.com\/will-there-be-a-second-wave-of-covid-19\/"},{"key":"1365_CR44","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1016\/j.indmarman.2018.09.004","volume":"77","author":"YC Tsao","year":"2019","unstructured":"Tsao, Y.C., Raj, P.V.R.P., Yu, V.: Product substitution in different weights and brands considering customer segmentation and panic buying behavior. Ind. Mark. Manage. 77, 209\u2013220 (2019). https:\/\/doi.org\/10.1016\/j.indmarman.2018.09.004","journal-title":"Ind. Mark. Manage."},{"key":"1365_CR45","doi-asserted-by":"publisher","unstructured":"Woltjer, G., van Galen, M., Logatcheva, K.: Industrial innovation, labour productivity, sales and employment. Int. J. Econ. Bus. (2019). https:\/\/doi.org\/10.1080\/13571516.2019.1695448","DOI":"10.1080\/13571516.2019.1695448"},{"key":"1365_CR46","unstructured":"World Health Organization.: COVID-19 emergency committee highlights need for response efforts over long term. Geneva, WHO, 1st August (2020). https:\/\/www.who.int\/news\/item\/01-08-2020-covid-19-emergency-committee-highlights-need-for-response-efforts-over-long-term"},{"key":"1365_CR47","doi-asserted-by":"publisher","unstructured":"Wu, B., Wang, L., Lv, S.X., Zeng, Y.R.: Effective crude oil price forecasting using new text-based and big-data-driven model. Measur. J. Int. Measur. Conf. 168 (2021a). https:\/\/doi.org\/10.1016\/j.measurement.2020.108468","DOI":"10.1016\/j.measurement.2020.108468"},{"key":"1365_CR48","doi-asserted-by":"publisher","unstructured":"Wu, B., Wang, L., Wang, S., Zeng, Y.R.: Forecasting the U.S. oil markets based on social media information during the COVID-19 pandemic. Energy 226 (2021b). https:\/\/doi.org\/10.1016\/j.energy.2021b.120403","DOI":"10.1016\/j.energy.2021b.120403"},{"key":"1365_CR49","doi-asserted-by":"crossref","unstructured":"Wu, C.X., Liao, M.H., Karatas, M., Chen, S.Y., Zheng, Y.J.: Real-time neural network scheduling of emergency medical mask production during COVID-19. ArXiv (2020).","DOI":"10.1016\/j.asoc.2020.106790"},{"key":"1365_CR50","doi-asserted-by":"publisher","unstructured":"Yang, Z., Mao, L., Yan, B., Wang, J., Gao, W.: Performance analysis and prediction of asymmetric two-level priority polling system based on BP neural network. Appl. Soft Comput.99. (2021). https:\/\/doi.org\/10.1016\/j.asoc.2020.106880","DOI":"10.1016\/j.asoc.2020.106880"},{"key":"1365_CR51","doi-asserted-by":"publisher","unstructured":"Zhang, J., Qu, S.: Optimization of backpropagation neural network under the adaptive genetic algorithm. Complexity (2021). https:\/\/doi.org\/10.1155\/2021\/1718234","DOI":"10.1155\/2021\/1718234"}],"container-title":["Quality &amp; Quantity"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11135-022-01365-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11135-022-01365-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11135-022-01365-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,31]],"date-time":"2023-01-31T17:28:16Z","timestamp":1675186096000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11135-022-01365-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,31]]},"references-count":50,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,2]]}},"alternative-id":["1365"],"URL":"https:\/\/doi.org\/10.1007\/s11135-022-01365-1","relation":{},"ISSN":["0033-5177","1573-7845"],"issn-type":[{"value":"0033-5177","type":"print"},{"value":"1573-7845","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,31]]},"assertion":[{"value":"28 February 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 March 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declared no potential conflicts of interest for the research, authorship and publication of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The authors voluntarily agree to take part in this study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"The authors gives consent for the publication of identifiable details, which can include photograph(s) and\/or videos and\/or case history and\/or details within the text (\u201cMaterial\u201d) to be published in the above Journal and Article. Therefore, anyone can read material published in the Journal.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}