The Communication Quality and Client Tenure as Determinants of Churn in Indonesian Tax Advisory Firms
DOI:
https://doi.org/10.61194/ijcs.v4i3.943Keywords:
communication quality, client tenure, client churn, tax consulting, predictive modelling, indonesiaAbstract
Client churn poses a critical threat to the sustainability of tax consulting firms in Indonesia, particularly following major tax regulatory reforms implemented between 2023 and 2025 that have increased compliance complexity and client uncertainty. In professional service settings where trust and expertise are central, ineffective communication can weaken client relationships and accelerate churn risk. This study examines the influence of consulting service communication quality and client tenure on client churn in Indonesian tax advisory firms, addressing the limited empirical evidence on how tenure moderates communication effectiveness in this context. A quantitative cross-sectional survey was conducted involving 300 active clients of metropolitan tax consulting firms, and the data were analyzed using logistic regression with an interaction model. The results indicate that consulting service communication quality significantly reduces the likelihood of client churn (OR = 0.468, p < 0.001), confirming its role as a key protective factor in client retention. Furthermore, the interaction between communication quality and client tenure is statistically significant (β_interaction = −0.421, p = 0.015), demonstrating that the churn-reducing effect of communication quality is strongest among short-term clients with engagement periods of two years or less. These findings suggest that clear, responsive, and transparent communication is particularly critical during the early stages of the consultant–client relationship. The study contributes to the literature by empirically validating a communication-based predictive model of client churn within the Indonesian tax consulting industry and provides practical implications for firms to prioritize targeted communication strategies as an effective churn mitigation approach during periods of regulatory change.
References
Afrinaldi, F. (2022). A new method for measuring eco-efficiency. Cleaner Environmental Systems, 7. https://doi.org/10.1016/j.cesys.2022.100097
Amin, A., Adnan, A., & Anwar, S. (2023). An adaptive learning approach for customer churn prediction in the telecommunication industry using evolutionary computation and Na"{i}ve Bayes. Appl. Soft Comput., 137(C). https://doi.org/10.1016/j.asoc.2023.110103
Ardhani, D. A., & Ditha Tania, K. (2025). Knowledge Discovery on E-Commerce Customer Churn Using Interpretable Machine Learning: A Comparative Study of SHAP-Based Classifiers. In Journal of Applied Informatics and Computing (JAIC) (Vol. 9, Issue 5). http://jurnal.polibatam.ac.id/index.php/JAIC
Arockia Panimalar, S., Krishnakumar, A., & Senthil Kumar, S. (2025). Intensified Customer Churn Prediction: Connectivity with Weighted Multi-Layer Perceptron and Enhanced Multipath Back Propagation. Expert Syst. Appl., 265(C). https://doi.org/10.1016/j.eswa.2024.125993
Belli, L., Gaspar, W. B., & Singh Jaswant, S. (2024). Data sovereignty and data transfers as fundamental elements of digital transformation: Lessons from the BRICS countries. Computer Law & Security Review, 54, 106017. https://doi.org/https://doi.org/10.1016/j.clsr.2024.106017
Boozary, P., Sheykhan, S., GhorbanTanhaei, H., & Magazzino, C. (2025). Enhancing customer retention with machine learning: A comparative analysis of ensemble models for accurate churn prediction. International Journal of Information Management Data Insights, 5(1), 100331. https://doi.org/https://doi.org/10.1016/j.jjimei.2025.100331
Borraz, F., & Mello, M. (2025). Communication, information and inflation expectations. Central Bank Review, 100224. https://doi.org/https://doi.org/10.1016/j.cbrev.2025.100224
Chang, V., Hall, K., Xu, Q. A., Amao, F. O., Ganatra, M. A., & Benson, V. (2024). Prediction of Customer Churn Behavior in the Telecommunication Industry Using Machine Learning Models. Algorithms, 17(6). https://doi.org/10.3390/a17060231
Chen, H. , & D. P. (2021). Tax enforcement and taxpayer reporting behavior: Evidence from a field experiment. Journal of Accounting Research, 53(2), 467–506.
Christina, S. (2022). Tax compliance of individual taxpayer in DKI Jakarta, Indonesia. International Journal of Trade and Global Markets, 15(1), 96 – 103. https://doi.org/10.1504/IJTGM.2022.120908
De, E., Saldanha, S., Seran, A. N., Da, F., Pedro, C., Da, R., & Fernandes, C. (2024). The Role of Customer Trust in the Relationship Between Service Quality and Purchasing Decision: An Empirical Evidence of Berlin Nakroma Public Transportation. In Journal of Business and Management (Vol. 6, Issue 2). https://tljbm.org/jurnal/index.php/tljbm
Del Sarto, N., & Ozili, P. K. (2025). FinTech and financial inclusion in emerging markets: a bibliometric analysis and future research agenda. In International Journal of Emerging Markets (Vol. 20, Issue 13, pp. 270–290). Emerald Publishing. https://doi.org/10.1108/IJOEM-08-2024-1428
Imani, M., Joudaki, M., Beikmohammadi, A., & Arabnia, H. R. (2025). Customer Churn Prediction: A Systematic Review of Recent Advances, Trends, and Challenges in Machine Learning and Deep Learning. Machine Learning and Knowledge Extraction, 7(3). https://doi.org/10.3390/make7030105
Kabbar, E., & Herath, N. (2025). Customer Churn Prediction to Enhance Customer Retention Strategies in the Banking Industry: A Study Using Seven Machine Learning Algorithms. IBIMA Business Review, 2025. https://doi.org/10.5171/2025.786386
Kusharsanto, Z. S., Maninggar, N., & Sucipto, A. (2024). Electric Vehicles Ecosystem in Indonesia: The Readiness of Infrastructure, Policies, and Stakeholders. Evergreen, 11(2), 1060 – 1067. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198044109&partnerID=40&md5=a36a926f9e9eed0a2e73561244c7201e
Lee, H., Merry, A. F., Woodward-Krohn, R., & Weller, J. M. (2025). Use and effectiveness of directed, closed-loop communication in the operating theatre: mixed methods analysis of simulated clinical emergencies. British Journal of Anaesthesia, 135(5), 1279–1285. https://doi.org/https://doi.org/10.1016/j.bja.2025.05.015
Lin, W. L., Yip, N., Ho, J. A., & Sambasivan, M. (2020). The adoption of technological innovations in a B2B context and its impact on firm performance: An ethical leadership perspective. Industrial Marketing Management, 89, 61–71. https://doi.org/https://doi.org/10.1016/j.indmarman.2019.12.009
Lubis, F. A., & Albarda. (2018). Data partition and hidden neuron value formulation combination in neural network prediction model: Case study: Non-tax revenue prediction for Indonesian government unit. 2018 International Conference on Information and Communications Technology, ICOIACT 2018, 2018-January, 879 – 884. https://doi.org/10.1109/ICOIACT.2018.8350819
Malik, M., Chai, Z., Hussain, M., & Hussain, S. (2024). Exploring customer retention dynamics: A comparative investigation of factors affecting customer retention in the banking sector using mediation-moderation approach. Heliyon, 10. https://api.semanticscholar.org/CorpusID:272241783
Manzoor, S. R., Ullah, A., Ullah, R., Khattak, A., Han, H., & Yoo, S. (2023). Micro CSR intervention towards employee behavioral and attitudinal outcomes: a parallel mediation model. Humanities and Social Sciences Communications, 10(1). https://doi.org/10.1057/s41599-023-02433-z
Meiryani, M., Aliffiyah, L., Endrianto, W., Kerta, J. M., Meriana, M., & Dewiyanti, S. (2022). Factors Affecting Taxpayer’s Ability To Pay Tax In Indonesia. ACM International Conference Proceeding Series, 522 – 528. https://doi.org/10.1145/3556089.3556194
Mu’ti, D. L., & Prasetyaningrum, P. T. (2025a). Comparative Analysis of Classification Algorithms for Predicting Membership Churn in Fitness Centers: Case Study and Predictive Modeling at EightGym Indonesia. Journal of Information Systems and Informatics, 7(2), 1592–1611. https://doi.org/10.51519/journalisi.v7i2.1120
Mu’ti, D. L., & Prasetyaningrum, P. T. (2025b). Comparative Analysis of Classification Algorithms for Predicting Membership Churn in Fitness Centers: Case Study and Predictive Modeling at EightGym Indonesia. Journal of Information Systems and Informatics, 7(2), 1592–1611. https://doi.org/10.51519/journalisi.v7i2.1120
Nguyen, H. M., Ho, T. K. T., & Ngo, T. T. (2024). The impact of service innovation on customer satisfaction and customer loyalty: a case in Vietnamese retail banks. Future Business Journal, 10(1), 61. https://doi.org/10.1186/s43093-024-00354-0
Nursalim, A. B., Novita, J., & Prawati, L. D. (2023). The Success Factors on Tax Technology Transformation: Assessment of Personality Traits and Digital Maturity among Indonesian Tax Consultants. ACM International Conference Proceeding Series, 16 – 23. https://doi.org/10.1145/3584816.3584819
O. O., O. (2024). Impact of Customer Satisfaction on Organizational Performance in Nigeria’s Brewery Industry. British Journal of Management and Marketing Studies, 7(3), 102–115. https://doi.org/10.52589/bjmms-ql267zrn
Oliveira, V. E. A., da Costa Guimarães, A. C., de Quadros, A. R. S., Mazo, R. N., de Souza Gaspar, R. L., Vieira, A., & Brandão, W. C. (2025). Predicting B2B Customer Churn and Measuring the Impact of Machine Learning-Based Retention Strategies. International Conference on Enterprise Information Systems, ICEIS - Proceedings, 1, 572–581. https://doi.org/10.5220/0013436300003929
Reinhardt, K. (2023). BUSINESS RESEARCH METHODS. SSRN Electronic Journal. https://api.semanticscholar.org/CorpusID:56574235
Ribeiro, H., Barbosa, B., Moreira, A. C., & Rodrigues, R. (2024). Customer Experience, Loyalty, and Churn in Bundled Telecommunications Services. Sage Open, 14(2), 21582440241245190. https://doi.org/10.1177/21582440241245191
Ribeiro, Hugo, Barbosa, Belem, Moreira, Antonio C, & Rodrigues, Ricardo. (2024). Customer Experience, Loyalty, and Churn in Bundled Telecommunications Services. Sage Open, 14(2), 21582440241245190. https://doi.org/10.1177/21582440241245191
Rixen, T., & Unger, B. (2022). Taxation: A Regulatory Multilevel Governance Perspective. Regulation and Governance, 16(3), 621–633. https://doi.org/10.1111/rego.12425
Rokhmawati, A., Sarasi, V., & Berampu, L. T. (2024). Scenario analysis of the Indonesia carbon tax impact on carbon emissions using system dynamics modeling and STIRPAT model. Geography and Sustainability, 5(4), 577 – 587. https://doi.org/10.1016/j.geosus.2024.07.003
Salamah, A. A., Hassan, S., Aljaafreh, A., Zabadi, W. A., AlQudah, M. A., Hayat, N., Al Mamun, A., & Kanesan, T. (2022). Customer retention through service quality and satisfaction: using hybrid SEM-neural network analysis approach. Heliyon, 8(9), e10570. https://doi.org/https://doi.org/10.1016/j.heliyon.2022.e10570
Saleh, S., & Saha, S. (2023). Customer retention and churn prediction in the telecommunication industry: a case study on a Danish university. SN Applied Sciences, 5(7), 173. https://doi.org/10.1007/s42452-023-05389-6
Salome, I. O. (2022). FROM SERVICE QUALITY TO E-SERVICE QUALITY: MEASUREMENT, DIMENSIONS AND MODEL. In 2022 1 Journal of Management Information and Decision Sciences (Vol. 25, Issue 1).
Shahabikargar, M., Beheshti, A., Zhang, X., Foo, J., & Jolfaei, A. (2025). A comprehensive survey on customer churn analysis studies. Journal of Information and Telecommunication, 0(0), 1–47. https://doi.org/10.1080/24751839.2025.2528440
Sikri, A., Jameel, R., Idrees, S. M., & Kaur, H. (2024). Enhancing customer retention in telecom industry with machine learning driven churn prediction. Scientific Reports, 14(1), 13097. https://doi.org/10.1038/s41598-024-63750-0
Strauß, N., & Šimunović, D. (2025). Communicating net-zero: A conceptual model for effective strategic communications. Public Relations Review, 51(3), 102580. https://doi.org/https://doi.org/10.1016/j.pubrev.2025.102580
Thangeda, R., Kumar, N., & Majhi, R. (2024). A neural network-based predictive decision model for customer retention in the telecommunication sector. Technological Forecasting and Social Change, 202, 123250. https://doi.org/https://doi.org/10.1016/j.techfore.2024.123250
Wagh, S. K., Andhale, A. A., Wagh, K. S., Pansare, J. R., Ambadekar, S. P., & Gawande, S. H. (2024). Customer churn prediction in telecom sector using machine learning techniques. Results in Control and Optimization, 14, 100342. https://doi.org/https://doi.org/10.1016/j.rico.2023.100342
Afrinaldi, F. (2022). A new method for measuring eco-efficiency. Cleaner Environmental Systems, 7. https://doi.org/10.1016/j.cesys.2022.100097 DOI: https://doi.org/10.1016/j.cesys.2022.100097
Amin, A., Adnan, A., & Anwar, S. (2023). An adaptive learning approach for customer churn prediction in the telecommunication industry using evolutionary computation and Na"{i}ve Bayes. Appl. Soft Comput., 137(C). https://doi.org/10.1016/j.asoc.2023.110103 DOI: https://doi.org/10.1016/j.asoc.2023.110103
Ardhani, D. A., & Ditha Tania, K. (2025). Knowledge Discovery on E-Commerce Customer Churn Using Interpretable Machine Learning: A Comparative Study of SHAP-Based Classifiers. In Journal of Applied Informatics and Computing (JAIC) (Vol. 9, Issue 5). http://jurnal.polibatam.ac.id/index.php/JAIC DOI: https://doi.org/10.30871/jaic.v9i5.10811
Arockia Panimalar, S., Krishnakumar, A., & Senthil Kumar, S. (2025). Intensified Customer Churn Prediction: Connectivity with Weighted Multi-Layer Perceptron and Enhanced Multipath Back Propagation. Expert Syst. Appl., 265(C). https://doi.org/10.1016/j.eswa.2024.125993 DOI: https://doi.org/10.1016/j.eswa.2024.125993
Belli, L., Gaspar, W. B., & Singh Jaswant, S. (2024). Data sovereignty and data transfers as fundamental elements of digital transformation: Lessons from the BRICS countries. Computer Law & Security Review, 54, 106017. https://doi.org/https://doi.org/10.1016/j.clsr.2024.106017 DOI: https://doi.org/10.1016/j.clsr.2024.106017
Boozary, P., Sheykhan, S., GhorbanTanhaei, H., & Magazzino, C. (2025). Enhancing customer retention with machine learning: A comparative analysis of ensemble models for accurate churn prediction. International Journal of Information Management Data Insights, 5(1), 100331. https://doi.org/https://doi.org/10.1016/j.jjimei.2025.100331 DOI: https://doi.org/10.1016/j.jjimei.2025.100331
Borraz, F., & Mello, M. (2025). Communication, information and inflation expectations. Central Bank Review, 100224. https://doi.org/https://doi.org/10.1016/j.cbrev.2025.100224 DOI: https://doi.org/10.1016/j.cbrev.2025.100224
Chang, V., Hall, K., Xu, Q. A., Amao, F. O., Ganatra, M. A., & Benson, V. (2024). Prediction of Customer Churn Behavior in the Telecommunication Industry Using Machine Learning Models. Algorithms, 17(6). https://doi.org/10.3390/a17060231 DOI: https://doi.org/10.3390/a17060231
Chen, H. , & D. P. (2021). Tax enforcement and taxpayer reporting behavior: Evidence from a field experiment. Journal of Accounting Research, 53(2), 467–506. DOI: https://doi.org/10.1111/1475-679X.12429
Christina, S. (2022). Tax compliance of individual taxpayer in DKI Jakarta, Indonesia. International Journal of Trade and Global Markets, 15(1), 96 – 103. https://doi.org/10.1504/IJTGM.2022.120908 DOI: https://doi.org/10.1504/IJTGM.2022.120908
De, E., Saldanha, S., Seran, A. N., Da, F., Pedro, C., Da, R., & Fernandes, C. (2024). The Role of Customer Trust in the Relationship Between Service Quality and Purchasing Decision: An Empirical Evidence of Berlin Nakroma Public Transportation. In Journal of Business and Management (Vol. 6, Issue 2). https://tljbm.org/jurnal/index.php/tljbm
Del Sarto, N., & Ozili, P. K. (2025). FinTech and financial inclusion in emerging markets: a bibliometric analysis and future research agenda. In International Journal of Emerging Markets (Vol. 20, Issue 13, pp. 270–290). Emerald Publishing. https://doi.org/10.1108/IJOEM-08-2024-1428 DOI: https://doi.org/10.1108/IJOEM-08-2024-1428
Imani, M., Joudaki, M., Beikmohammadi, A., & Arabnia, H. R. (2025). Customer Churn Prediction: A Systematic Review of Recent Advances, Trends, and Challenges in Machine Learning and Deep Learning. Machine Learning and Knowledge Extraction, 7(3). https://doi.org/10.3390/make7030105 DOI: https://doi.org/10.3390/make7030105
Kabbar, E., & Herath, N. (2025). Customer Churn Prediction to Enhance Customer Retention Strategies in the Banking Industry: A Study Using Seven Machine Learning Algorithms. IBIMA Business Review, 2025. https://doi.org/10.5171/2025.786386 DOI: https://doi.org/10.5171/2025.786386
Kusharsanto, Z. S., Maninggar, N., & Sucipto, A. (2024). Electric Vehicles Ecosystem in Indonesia: The Readiness of Infrastructure, Policies, and Stakeholders. Evergreen, 11(2), 1060 – 1067. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198044109&partnerID=40&md5=a36a926f9e9eed0a2e73561244c7201e
Lee, H., Merry, A. F., Woodward-Krohn, R., & Weller, J. M. (2025). Use and effectiveness of directed, closed-loop communication in the operating theatre: mixed methods analysis of simulated clinical emergencies. British Journal of Anaesthesia, 135(5), 1279–1285. https://doi.org/https://doi.org/10.1016/j.bja.2025.05.015 DOI: https://doi.org/10.1016/j.bja.2025.05.015
Lin, W. L., Yip, N., Ho, J. A., & Sambasivan, M. (2020). The adoption of technological innovations in a B2B context and its impact on firm performance: An ethical leadership perspective. Industrial Marketing Management, 89, 61–71. https://doi.org/https://doi.org/10.1016/j.indmarman.2019.12.009 DOI: https://doi.org/10.1016/j.indmarman.2019.12.009
Lubis, F. A., & Albarda. (2018). Data partition and hidden neuron value formulation combination in neural network prediction model: Case study: Non-tax revenue prediction for Indonesian government unit. 2018 International Conference on Information and Communications Technology, ICOIACT 2018, 2018-January, 879 – 884. https://doi.org/10.1109/ICOIACT.2018.8350819 DOI: https://doi.org/10.1109/ICOIACT.2018.8350819
Malik, M., Chai, Z., Hussain, M., & Hussain, S. (2024). Exploring customer retention dynamics: A comparative investigation of factors affecting customer retention in the banking sector using mediation-moderation approach. Heliyon, 10. https://api.semanticscholar.org/CorpusID:272241783 DOI: https://doi.org/10.1016/j.heliyon.2024.e36919
Manzoor, S. R., Ullah, A., Ullah, R., Khattak, A., Han, H., & Yoo, S. (2023). Micro CSR intervention towards employee behavioral and attitudinal outcomes: a parallel mediation model. Humanities and Social Sciences Communications, 10(1). https://doi.org/10.1057/s41599-023-02433-z DOI: https://doi.org/10.1057/s41599-023-02433-z
Meiryani, M., Aliffiyah, L., Endrianto, W., Kerta, J. M., Meriana, M., & Dewiyanti, S. (2022). Factors Affecting Taxpayer’s Ability To Pay Tax In Indonesia. ACM International Conference Proceeding Series, 522 – 528. https://doi.org/10.1145/3556089.3556194 DOI: https://doi.org/10.1145/3556089.3556194
Mu’ti, D. L., & Prasetyaningrum, P. T. (2025a). Comparative Analysis of Classification Algorithms for Predicting Membership Churn in Fitness Centers: Case Study and Predictive Modeling at EightGym Indonesia. Journal of Information Systems and Informatics, 7(2), 1592–1611. https://doi.org/10.51519/journalisi.v7i2.1120
Mu’ti, D. L., & Prasetyaningrum, P. T. (2025b). Comparative Analysis of Classification Algorithms for Predicting Membership Churn in Fitness Centers: Case Study and Predictive Modeling at EightGym Indonesia. Journal of Information Systems and Informatics, 7(2), 1592–1611. https://doi.org/10.51519/journalisi.v7i2.1120 DOI: https://doi.org/10.51519/journalisi.v7i2.1120
Nguyen, H. M., Ho, T. K. T., & Ngo, T. T. (2024). The impact of service innovation on customer satisfaction and customer loyalty: a case in Vietnamese retail banks. Future Business Journal, 10(1), 61. https://doi.org/10.1186/s43093-024-00354-0 DOI: https://doi.org/10.1186/s43093-024-00354-0
Nursalim, A. B., Novita, J., & Prawati, L. D. (2023). The Success Factors on Tax Technology Transformation: Assessment of Personality Traits and Digital Maturity among Indonesian Tax Consultants. ACM International Conference Proceeding Series, 16 – 23. https://doi.org/10.1145/3584816.3584819 DOI: https://doi.org/10.1145/3584816.3584819
O. O., O. (2024). Impact of Customer Satisfaction on Organizational Performance in Nigeria’s Brewery Industry. British Journal of Management and Marketing Studies, 7(3), 102–115. https://doi.org/10.52589/bjmms-ql267zrn DOI: https://doi.org/10.52589/BJMMS-QL267ZRN
Oliveira, V. E. A., da Costa Guimarães, A. C., de Quadros, A. R. S., Mazo, R. N., de Souza Gaspar, R. L., Vieira, A., & Brandão, W. C. (2025). Predicting B2B Customer Churn and Measuring the Impact of Machine Learning-Based Retention Strategies. International Conference on Enterprise Information Systems, ICEIS - Proceedings, 1, 572–581. https://doi.org/10.5220/0013436300003929 DOI: https://doi.org/10.5220/0013436300003929
Reinhardt, K. (2023). BUSINESS RESEARCH METHODS. SSRN Electronic Journal. https://api.semanticscholar.org/CorpusID:56574235
Ribeiro, H., Barbosa, B., Moreira, A. C., & Rodrigues, R. (2024). Customer Experience, Loyalty, and Churn in Bundled Telecommunications Services. Sage Open, 14(2), 21582440241245190. https://doi.org/10.1177/21582440241245191
Ribeiro, Hugo, Barbosa, Belem, Moreira, Antonio C, & Rodrigues, Ricardo. (2024). Customer Experience, Loyalty, and Churn in Bundled Telecommunications Services. Sage Open, 14(2), 21582440241245190. https://doi.org/10.1177/21582440241245191 DOI: https://doi.org/10.1177/21582440241245191
Rixen, T., & Unger, B. (2022). Taxation: A Regulatory Multilevel Governance Perspective. Regulation and Governance, 16(3), 621–633. https://doi.org/10.1111/rego.12425 DOI: https://doi.org/10.1111/rego.12425
Rokhmawati, A., Sarasi, V., & Berampu, L. T. (2024). Scenario analysis of the Indonesia carbon tax impact on carbon emissions using system dynamics modeling and STIRPAT model. Geography and Sustainability, 5(4), 577 – 587. https://doi.org/10.1016/j.geosus.2024.07.003 DOI: https://doi.org/10.1016/j.geosus.2024.07.003
Salamah, A. A., Hassan, S., Aljaafreh, A., Zabadi, W. A., AlQudah, M. A., Hayat, N., Al Mamun, A., & Kanesan, T. (2022). Customer retention through service quality and satisfaction: using hybrid SEM-neural network analysis approach. Heliyon, 8(9), e10570. https://doi.org/https://doi.org/10.1016/j.heliyon.2022.e10570 DOI: https://doi.org/10.1016/j.heliyon.2022.e10570
Saleh, S., & Saha, S. (2023). Customer retention and churn prediction in the telecommunication industry: a case study on a Danish university. SN Applied Sciences, 5(7), 173. https://doi.org/10.1007/s42452-023-05389-6 DOI: https://doi.org/10.1007/s42452-023-05389-6
Salome, I. O. (2022). FROM SERVICE QUALITY TO E-SERVICE QUALITY: MEASUREMENT, DIMENSIONS AND MODEL. In 2022 1 Journal of Management Information and Decision Sciences (Vol. 25, Issue 1).
Shahabikargar, M., Beheshti, A., Zhang, X., Foo, J., & Jolfaei, A. (2025). A comprehensive survey on customer churn analysis studies. Journal of Information and Telecommunication, 0(0), 1–47. https://doi.org/10.1080/24751839.2025.2528440 DOI: https://doi.org/10.1080/24751839.2025.2528440
Sikri, A., Jameel, R., Idrees, S. M., & Kaur, H. (2024). Enhancing customer retention in telecom industry with machine learning driven churn prediction. Scientific Reports, 14(1), 13097. https://doi.org/10.1038/s41598-024-63750-0 DOI: https://doi.org/10.1038/s41598-024-63750-0
Strauß, N., & Šimunović, D. (2025). Communicating net-zero: A conceptual model for effective strategic communications. Public Relations Review, 51(3), 102580. https://doi.org/https://doi.org/10.1016/j.pubrev.2025.102580 DOI: https://doi.org/10.1016/j.pubrev.2025.102580
Thangeda, R., Kumar, N., & Majhi, R. (2024). A neural network-based predictive decision model for customer retention in the telecommunication sector. Technological Forecasting and Social Change, 202, 123250. https://doi.org/https://doi.org/10.1016/j.techfore.2024.123250 DOI: https://doi.org/10.1016/j.techfore.2024.123250
Wagh, S. K., Andhale, A. A., Wagh, K. S., Pansare, J. R., Ambadekar, S. P., & Gawande, S. H. (2024). Customer churn prediction in telecom sector using machine learning techniques. Results in Control and Optimization, 14, 100342. https://doi.org/https://doi.org/10.1016/j.rico.2023.100342 DOI: https://doi.org/10.1016/j.rico.2023.100342
Yum, K., & Yoo, B. (2023). The Impact of Service Quality on Customer Loyalty through Customer Satisfaction in Mobile Social Media. Sustainability, 15(14). https://doi.org/10.3390/su151411214 DOI: https://doi.org/10.3390/su151411214
Zhang, B. (2023). Customer Churn in Subscription Business Model-Predictive Analytics on Customer Churn. In BCP Business & Management FIBA (Vol. 2023). DOI: https://doi.org/10.54691/bcpbm.v44i.4971
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