{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T09:39:20Z","timestamp":1766050760779,"version":"3.40.3"},"publisher-location":"Cham","reference-count":42,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031763670"},{"type":"electronic","value":"9783031763687"}],"license":[{"start":{"date-parts":[[2024,11,29]],"date-time":"2024-11-29T00:00:00Z","timestamp":1732838400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,29]],"date-time":"2024-11-29T00:00:00Z","timestamp":1732838400000},"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":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-76368-7_5","type":"book-chapter","created":{"date-parts":[[2024,11,28]],"date-time":"2024-11-28T12:15:58Z","timestamp":1732796158000},"page":"59-74","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Predicting Turnover Tendency of Candidates\/Employees Based on Personality Assessment Tests: A Data-Driven Approach"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9616-7467","authenticated-orcid":false,"given":"Berkay","family":"Top\u00e7u","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mukaddes","family":"Altunta\u015f","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dilruba","family":"Top\u00e7uo\u011flu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Talip","family":"Akdemir","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elif","family":"Kurt","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zeynep Deniz","family":"Cankut","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,11,29]]},"reference":[{"issue":"1","key":"5_CR1","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1186\/s40537-019-0191-6","volume":"6","author":"AK Ahmad","year":"2019","unstructured":"Ahmad, A.K., Jafar, A., Aljoumaa, K.: Customer churn prediction in telecom using machine learning in big data platform. J. Big Data 6(1), 28 (2019)","journal-title":"J. Big Data"},{"key":"5_CR2","doi-asserted-by":"crossref","unstructured":"Alduayj, S.S., Rajpoot, K.: Predicting employee attrition using machine learning. In: 2018 International Conference on Innovations in Information Technology (IIT), pp. 93\u201398 (2018)","DOI":"10.1109\/INNOVATIONS.2018.8605976"},{"key":"5_CR3","unstructured":"Amuda, K., Adeyemo, A.: Customers churn prediction in financial institution using artificial neural network (2019)"},{"key":"5_CR4","doi-asserted-by":"crossref","unstructured":"Cappelli, P., Tambe, P., Yakubovich, V.: Artificial intelligence in human resources management: challenges and a path forward. SSRN Electron. J. (2018)","DOI":"10.2139\/ssrn.3263878"},{"key":"5_CR5","doi-asserted-by":"crossref","unstructured":"Chen, X.: Human resource matching support system based on deep learning. Math. Probl. Eng. 2022, 1\u201311 (0022)","DOI":"10.1155\/2022\/1558409"},{"issue":"3","key":"5_CR6","doi-asserted-by":"publisher","first-page":"788","DOI":"10.1108\/IJOA-05-2020-2222","volume":"29","author":"Y Choi","year":"2020","unstructured":"Choi, Y., Choi, J.: A study of job involvement prediction using machine learning technique. Int. J. Organ. Anal. 29(3), 788\u2013800 (2020). https:\/\/doi.org\/10.1108\/IJOA-05-2020-2222","journal-title":"Int. J. Organ. Anal."},{"key":"5_CR7","doi-asserted-by":"publisher","unstructured":"DeNisi, A., Smith, C.: Performance appraisal, performance management, and firm-level performance: a review, a proposed model, and new directions for future research. Acad. Manag. Ann. 8 (2014). https:\/\/doi.org\/10.1080\/19416520.2014.873178","DOI":"10.1080\/19416520.2014.873178"},{"key":"5_CR8","doi-asserted-by":"publisher","unstructured":"Dingli, A., Marmara, V., Sant Fournier, N.: Comparison of deep learning algorithms to predict customer churn within a local retail industry. Int. J. Mach. Learn. Comput. 7, 128\u2013132 (10 2017). https:\/\/doi.org\/10.18178\/ijmlc.2017.7.5.634","DOI":"10.18178\/ijmlc.2017.7.5.634"},{"key":"5_CR9","doi-asserted-by":"crossref","unstructured":"Gao, X., Wen, J., Zhang, C.: An improved random forest algorithm for predicting employee turnover. Math. Probl. Eng. 2019, 1\u201312 (2019)","DOI":"10.1155\/2019\/4140707"},{"key":"5_CR10","doi-asserted-by":"crossref","unstructured":"Guthrie, J.P., Ash, R.A., Stevens, C.D.: Are women \u201cbetter\u201d than men? Personality differences and expatriate selection. J. Manag. Psychol. (2003). https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/02683940310465243\/full\/html","DOI":"10.1108\/02683940310465243"},{"key":"5_CR11","doi-asserted-by":"crossref","unstructured":"Hackston, J.: Using personality type to engage and retain remote, hybrid and office-based workers. Strateg. HR Rev. (2022).  https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/SHR-08-2022-0050\/full\/html","DOI":"10.1108\/SHR-08-2022-0050"},{"key":"5_CR12","unstructured":"Hogan Assessments Systems: Diversity, Equity, and Inclusion. https:\/\/www.hoganassessments.com\/about\/diversity-equity-and-inclusion\/. Accessed 30 Dec 2023"},{"key":"5_CR13","unstructured":"Houston, J., Kester, B.: Talent analytics in practice: go from talking to delivering on big data. In: Global Human Capital Trends 2014, Engaging the 21st-Century Workforce. Deilotte University Press (2014)"},{"key":"5_CR14","unstructured":"Sutton, J.: Understanding the CliftonStrengths\u2122 Assessment: A Guide (2021). https:\/\/positivepsychology.com\/clifton-strengths-assessment\/. Accessed 30 Dec 2023"},{"key":"5_CR15","doi-asserted-by":"publisher","first-page":"1332","DOI":"10.1016\/j.procs.2022.01.169","volume":"199","author":"S Kim","year":"2022","unstructured":"Kim, S., Lee, H.: Customer churn prediction in influencer commerce: an application of decision trees. Procedia Comput. Sci. 199, 1332\u20131339 (2022)","journal-title":"Procedia Comput. Sci."},{"key":"5_CR16","doi-asserted-by":"crossref","unstructured":"Kosti\u0107, S.M., Simi\u0107, M.I., Kosti\u0107, M.V.: Social network analysis and churn prediction in telecommunications using graph theory. Entropy 22(7) (2020)","DOI":"10.3390\/e22070753"},{"key":"5_CR17","unstructured":"Lee, J.W.: Study of enneagram character types and job satisfaction of workers engaged in Korean medical clinics located in changwon city. J. Physiol. Pathol. Korean Med., 753\u2013759 (2012)"},{"key":"5_CR18","unstructured":"Leung, H., Chung, W.: A dynamic classification approach to churn prediction in banking industry. In: 2020 Proceedings of the Americas Conference on Information Systems (AMCIS) (2020). http:\/\/hdl.handle.net\/10722\/289854"},{"key":"5_CR19","doi-asserted-by":"crossref","unstructured":"Li, H., Wang, Q., Liu, J., Zhao, D.: A prediction model of human resources recruitment demand based on convolutional collaborative bp neural network. Comput. Intell. Neurosci. 2022, 1\u201310 (2022)","DOI":"10.1155\/2022\/3620312"},{"key":"5_CR20","doi-asserted-by":"publisher","unstructured":"Li, X., Li, Z.: A hybrid prediction model for e-commerce customer churn based on logistic regression and extreme gradient boosting algorithm. Ing\u00e9nierie des syst\u00e8mes d\u2019information 24, 525\u2013530 (2019). https:\/\/doi.org\/10.18280\/isi.240510","DOI":"10.18280\/isi.240510"},{"key":"5_CR21","unstructured":"Linkedin Talent Solutions: The Rise of HR Analytics (2018). https:\/\/business.linkedin.com\/content\/dam\/me\/business\/en-us\/talent-solutions\/talent-intelligence\/workforce\/pdfs\/Finalv2NAMERRise-of-Analytics-Report.pdf. Accessed 19 Dec 2023"},{"key":"5_CR22","doi-asserted-by":"crossref","unstructured":"Lykourentzou, I., Antoniou, A., Naudet, Y., Dow, S.P.: Personality matters: balancing for personality types leads to better outcomes for crowd teams. ACM Digit. Libr., 260\u2013273 (2016). https:\/\/dl.acm.org\/doi\/abs\/10.1145\/2818048.2819979","DOI":"10.1145\/2818048.2819979"},{"key":"5_CR23","doi-asserted-by":"publisher","first-page":"493","DOI":"10.1108\/JMD-07-2016-0132","volume":"36","author":"PA Maryam Dehghani","year":"2017","unstructured":"Maryam Dehghani, P.A.: An experimental investigation of knowledge acquisition techniques. J. Manag. Dev. 36, 493\u2013513 (2017)","journal-title":"J. Manag. Dev."},{"key":"5_CR24","doi-asserted-by":"crossref","unstructured":"Barrick, M.R., Mount, M.K.: The big five personality dimensions and job performance: a meta-analysis. Wiley Online Libr., 1\u201326 (1991)","DOI":"10.1111\/j.1744-6570.1991.tb00688.x"},{"key":"5_CR25","doi-asserted-by":"crossref","unstructured":"\u00d3skarsdottir, M., G\u00edsladottir, K.E., Stef\u00e1nsson, R., Aleman, D., Sarraute, C.: Social networks for enhanced player churn prediction in mobile free-to-play games. Appl. Netw. Sci. 7(1), 82 (2022)","DOI":"10.1007\/s41109-022-00524-5"},{"key":"5_CR26","doi-asserted-by":"crossref","unstructured":"Pondel, M., et al.: Deep learning for customer churn prediction in e-commerce decision support. Bus. Inf. Syst., 3\u201312 (2021)","DOI":"10.52825\/bis.v1i.42"},{"key":"5_CR27","doi-asserted-by":"publisher","first-page":"789","DOI":"10.1177\/0146167202289008","volume":"28","author":"S Roccas","year":"2002","unstructured":"Roccas, S., Sagiv, L., Schwartz, S.H., Knafo, A.: The big five personality factors and personal values. Personal. Soc. Psychol. Bull. 28, 789\u2013801 (2002)","journal-title":"Personal. Soc. Psychol. Bull."},{"key":"5_CR28","unstructured":"Scikit-Learn Community: Sklearn Linear Model Logistic Regression (2010). https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.linearmodel.LogisticRegression.html. Accessed 11 Jan 2024"},{"key":"5_CR29","unstructured":"Scikit-Learn Community: Sklearn Linear Model Random Forest (2010). https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.ensemble.RandomForestClassifier.html. Accessed 11 Jan 2024"},{"key":"5_CR30","unstructured":"Scikit-Learn Community: Sklearn Neigbours KNearestNeigbours (2010). https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.neighbors.KNeighborsClassifier.html. Accessed 11 Jan 2024"},{"key":"5_CR31","unstructured":"Scikit-Learn Community: Sklearn Neural Network MLPClassifier (2010). https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.neuralnetwork.MLPClassifier.html. Accessed 11 Jan 2024"},{"key":"5_CR32","unstructured":"Scikit-Learn Community: Sklearn SVC SVM (2010). https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.svm.SVC.html. Accessed 11 Jan 2024"},{"issue":"1","key":"5_CR33","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1108\/JTMC-04-2013-0020","volume":"8","author":"L Singer","year":"2013","unstructured":"Singer, L., Millage, P.: The interaction of leadership and personality among Chinese and american nascent entrepreneurs. J. Technol. Manag. China 8(1), 44\u201354 (2013)","journal-title":"J. Technol. Manag. China"},{"key":"5_CR34","unstructured":"The Career Project: Enneagram (2023). https:\/\/www.thecareerproject.org\/personality-types-test\/enneagram\/. Accessed 30 Dec 2023"},{"key":"5_CR35","unstructured":"The Myers-Briggs Company: The history of the MBTI\u00ae assessment (2023). https:\/\/eu.themyersbriggs.com\/en\/tools\/MBTI\/Myers-Briggs-history"},{"key":"5_CR36","doi-asserted-by":"crossref","unstructured":"Tran, H., Le, N., Nguyen, V.H.: Customer churn prediction in the banking sector using machine learning-based classification models. Interdiscip. J. Inf. Knowl. Manag. 18, 087\u2013105 (2023)","DOI":"10.28945\/5086"},{"key":"5_CR37","doi-asserted-by":"publisher","first-page":"60134","DOI":"10.1109\/ACCESS.2019.2914999","volume":"7","author":"I Ullah","year":"2019","unstructured":"Ullah, I., Raza, B., Malik, A.K., Imran, M., Islam, S.U., Kim, S.W.: A churn prediction model using random forest: analysis of machine learning techniques for churn prediction and factor identification in telecom sector. IEEE Access 7, 60134\u201360149 (2019). https:\/\/doi.org\/10.1109\/ACCESS.2019.2914999","journal-title":"IEEE Access"},{"key":"5_CR38","doi-asserted-by":"publisher","unstructured":"Valle, M.A., Ruz, G.A.: Turnover prediction in a call center: behavioral evidence of loss aversion using random forest and na\u00efve bayes algorithms. Appl. Artif. Intell. 29(9), 923\u2013942 (2015). https:\/\/doi.org\/10.1080\/08839514.2015.1082282","DOI":"10.1080\/08839514.2015.1082282"},{"key":"5_CR39","unstructured":"Xgboost Developers: XGBoost Parameters (2022). https:\/\/xgboost.readthedocs.io\/en\/stable\/parameter.html. Accessed 11 Jan 2024"},{"issue":"2","key":"5_CR40","doi-asserted-by":"publisher","first-page":"264","DOI":"10.1080\/17517575.2019.1710862","volume":"16","author":"Q Xie","year":"2022","unstructured":"Xie, Q.: Machine learning in human resource system of intelligent manufacturing industry. Enterp. Inf. Syst. 16(2), 264\u2013284 (2022)","journal-title":"Enterp. Inf. Syst."},{"key":"5_CR41","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Hryniewicki, M.K., Cheng, F., Fu, B., Zhu, X.: Employee turnover prediction with machine learning: a reliable approach. Intell. Syst. Appl. (2018). https:\/\/api.semanticscholar.org\/CorpusID:62981809","DOI":"10.1007\/978-3-030-01057-7_56"},{"key":"5_CR42","doi-asserted-by":"crossref","unstructured":"Zhu, H.: Research on human resource recommendation algorithm based on machine learning. Sci. Program. 2021, 8387277:1\u20138387277:10 (2021)","DOI":"10.1155\/2021\/8387277"}],"container-title":["Lecture Notes in Business Information Processing","Digital Economy. Emerging Technologies and Business Innovation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-76368-7_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,28]],"date-time":"2024-11-28T13:02:38Z","timestamp":1732798958000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-76368-7_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,29]]},"ISBN":["9783031763670","9783031763687"],"references-count":42,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-76368-7_5","relation":{},"ISSN":["1865-1348","1865-1356"],"issn-type":[{"type":"print","value":"1865-1348"},{"type":"electronic","value":"1865-1356"}],"subject":[],"published":{"date-parts":[[2024,11,29]]},"assertion":[{"value":"29 November 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICDEc","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Digital Economy","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Rabat","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Morocco","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 May 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 May 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icdec2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icdec.aten.tn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}