{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T05:21:01Z","timestamp":1771046461013,"version":"3.50.1"},"reference-count":37,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2021,5,20]],"date-time":"2021-05-20T00:00:00Z","timestamp":1621468800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["IIS-2006387,IIS-2040799"],"award-info":[{"award-number":["IIS-2006387,IIS-2040799"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Manage. Inf. Syst."],"published-print":{"date-parts":[[2021,6,30]]},"abstract":"<jats:p>As an emerging measure of proactive talent management, talent turnover prediction is critically important for companies to attract, engage, and retain talents in order to prevent the loss of intellectual capital. While tremendous efforts have been made in this direction, it is not clear how to model the influence of employees\u2019 turnover within multiple organizational social networks. In this article, we study how to exploit turnover contagion by developing a Turnover Influence-based Neural Network (TINN) for enhancing organizational turnover prediction. Specifically, TINN can construct the turnover similarity network which is then fused with multiple organizational social networks. The fusion is achieved either through learning a hidden turnover influence network or through integrating the turnover influence on multiple networks. Taking advantage of the Graph Convolutional Network and the Long Short-Term Memory network, TINN can dynamically model the impact of social influence on talent turnover. Meanwhile, the utilization of the attention mechanism improves the interpretability, providing insights into the impact of different networks along time on the future turnovers. Finally, we conduct extensive experiments in real-world settings to evaluate TINN. The results validate the effectiveness of our approach to enhancing organizational turnover prediction. Also, our case studies reveal some interpretable findings, such as the importance of each network or hidden state which potentially impacts future organizational turnovers.<\/jats:p>","DOI":"10.1145\/3439770","type":"journal-article","created":{"date-parts":[[2021,5,21]],"date-time":"2021-05-21T02:08:36Z","timestamp":1621562916000},"page":"1-18","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":19,"title":["Exploiting Network Fusion for Organizational Turnover\u00a0Prediction"],"prefix":"10.1145","volume":"12","author":[{"given":"Mingfei","family":"Teng","sequence":"first","affiliation":[{"name":"Rutgers University, New Jersey, Newark, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hengshu","family":"Zhu","sequence":"additional","affiliation":[{"name":"Talent Intelligence Center, Baidu, Inc., Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chuanren","family":"Liu","sequence":"additional","affiliation":[{"name":"The University of Tennessee, Knoxville, Tennessee, Knoxville, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hui","family":"Xiong","sequence":"additional","affiliation":[{"name":"Rutgers University, New Jersey, Newark, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,5,20]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Theresa Agovino. 2019. To Have and to Hold. Amid One of the Tightest Labor Markets in the Past 50 Years employee retention is more critical than ever. (2019)."},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.5120\/ijca2016910497"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.5555\/2188385.2188395"},{"key":"e_1_2_1_4_1","unstructured":"Heather Boushey and Sarah Jane Glynn. 2012. There Are Significant Business Costs to Replacing Employees."},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3021380"},{"key":"e_1_2_1_6_1","volume-title":"Regression Models and Life-Tables","author":"Cox David R.","unstructured":"David R. Cox. 1992. Regression Models and Life-Tables. Springer, New York. 187--220 pages."},{"key":"e_1_2_1_7_1","unstructured":"Zhiyong Cui Kristian Henrickson Ruimin Ke and Yinhai Wang. 2018. High-Order Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting. (2018)."},{"key":"e_1_2_1_8_1","unstructured":"Mich\u00ebl Defferrard Xavier Bresson and Pierre Vandergheynst. 2016. Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering. (2016)."},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.5465\/amj.2009.41331075"},{"key":"e_1_2_1_10_1","unstructured":"Kate Heinz. 2020. Dangers of Turnover: Battling Hidden Costs. Revenue Loss Shouldn\u2019t be Your Only Concern\u2014Understand the True Costs of Turnover. (2020)."},{"key":"e_1_2_1_11_1","volume-title":"Breckenridge","author":"Jackofsky Ellen F.","year":"1986","unstructured":"Ellen F. Jackofsky, Kenneth R. Ferris, and Betty G. Breckenridge. 1986. Evidence for a curvilinear relationship between job performance and turnover.Journal of Management 12, 1 (1986), 105--111."},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.2307\/257166"},{"key":"e_1_2_1_13_1","volume-title":"Kipf and Max Welling","author":"Thomas","year":"2016","unstructured":"Thomas N. Kipf and Max Welling. 2016. Semi-Supervised Classification with Graph Convolutional Networks. (2016)."},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.2307\/258835"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.2307\/257015"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.2307\/256629"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098107"},{"key":"e_1_2_1_18_1","volume-title":"ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1715--1724","author":"Li Yan","unstructured":"Yan Li, Jie Wang, Jieping Ye, and Chandan K. Reddy. 2016. A multi-task learning formulation for survival analysis. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1715--1724."},{"key":"e_1_2_1_19_1","unstructured":"Yaguang Li Rose Yu Cyrus Shahabi and Liu Yan. 2017b. Graph Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting. (2017)."},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3392734"},{"key":"e_1_2_1_21_1","volume-title":"Simon","author":"March James G.","year":"1958","unstructured":"James G. March and Herbert A. Simon. 1958. Organizations."},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.2307\/256158"},{"key":"e_1_2_1_23_1","doi-asserted-by":"crossref","unstructured":"W. H. Mobley S. O. Horner and A. T. Hollingsworth. 1978. An evaluation of precursors of hospital employee turnover.Journal of Applied Psychology 63 4 (1978) 408--414.","DOI":"10.1037\/\/0021-9010.63.4.408"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3376927"},{"key":"e_1_2_1_25_1","unstructured":"Jiezhong Qiu Tang Jian Ma Hao Yuxiao Dong and Tang Jie. 2018. DeepInf: Social Influence Prediction with Deep Learning. (2018)."},{"key":"e_1_2_1_26_1","volume-title":"SIGKDD Workshop on Fintech (SIGKDD Fintech\u201918)","author":"Rebane Jonathan","year":"2018","unstructured":"Jonathan Rebane and Isak Karlsson. 2018. Seq2Seq RNNs and ARIMA models for cryptocurrency prediction: A comparative study. In SIGKDD Workshop on Fintech (SIGKDD Fintech\u201918) , 2--6."},{"key":"e_1_2_1_27_1","volume-title":"Signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular data domains. CoRR abs\/1211.0053","author":"Shuman David I.","year":"2012","unstructured":"David I. Shuman, Sunil K. Narang, Pascal Frossard, Antonio Ortega, and Pierre Vandergheynst. 2012. Signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular data domains. CoRR abs\/1211.0053 (2012). arxiv:1211.0053 http:\/\/arxiv.org\/abs\/1211.0053"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11635"},{"key":"e_1_2_1_29_1","volume-title":"Le","author":"Sutskever Ilya","year":"2014","unstructured":"Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. 2014. Sequence to sequence learning with neural networks. Advances in Neural Information Processing Systems 4, (Jan. 2014), 3104--3112. arxiv:1409.3215"},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","unstructured":"Mingfei Teng Hengshu Zhu Chuanren Liu Chen Zhu and Hui Xiong. 2019. Exploiting the contagious effect for employee turnover prediction. In The 33rd AAAI Conference on Artificial Intelligence (AAAI\u201919) The 31st Innovative Applications of Artificial Intelligence Conference (IAAI\u201919) The 9th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI\u201919). AAAI Press 1166--1173. DOI:https:\/\/doi.org\/10.1609\/aaai.v33i01.33011166","DOI":"10.1609\/aaai.v33i01.33011166"},{"key":"e_1_2_1_31_1","volume-title":"Boudreau","author":"Trevor Charlie O.","year":"1997","unstructured":"Charlie O. Trevor, Barry Gerhart, and John W. Boudreau. 1997. Voluntary turnover and job performance: Curvilinearity and the moderating influences of salary growth and promotions.Journal of Applied Psychology 82, 1 (1997), 44--61."},{"key":"e_1_2_1_32_1","volume-title":"Attention is all you need. Advances in Neural Information Processing Systems (Dec","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in Neural Information Processing Systems (Dec. 2017), 5999--6009. arxiv:arXiv:1706.03762v5"},{"key":"e_1_2_1_33_1","unstructured":"Neo Wu Bradley Green Xue Ben and Shawn O\u2019Banion. 2020. Deep transformer models for time series forecasting: The influenza prevalence case. arxiv:2001.08317 http:\/\/arxiv.org\/abs\/2001.08317"},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2018.2873341"},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380299"},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P16-2034"},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3234465"}],"container-title":["ACM Transactions on Management Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3439770","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3439770","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3439770","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:01:52Z","timestamp":1750197712000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3439770"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,20]]},"references-count":37,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2021,6,30]]}},"alternative-id":["10.1145\/3439770"],"URL":"https:\/\/doi.org\/10.1145\/3439770","relation":{},"ISSN":["2158-656X","2158-6578"],"issn-type":[{"value":"2158-656X","type":"print"},{"value":"2158-6578","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,20]]},"assertion":[{"value":"2020-01-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-11-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-05-20","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}