What Do We Know About Customer Churn Behaviour in the Telecommunication Industry? A Bibliometric Analysis of Research Trends, 1985–2019
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DOI: 10.1177/23197145211062687
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- Donthu, Naveen & Kumar, Satish & Mukherjee, Debmalya & Pandey, Nitesh & Lim, Weng Marc, 2021. "How to conduct a bibliometric analysis: An overview and guidelines," Journal of Business Research, Elsevier, vol. 133(C), pages 285-296.
- Pappas, Ilias O. & Woodside, Arch G., 2021. "Fuzzy-set Qualitative Comparative Analysis (fsQCA): Guidelines for research practice in Information Systems and marketing," International Journal of Information Management, Elsevier, vol. 58(C).
- Sourav Bikash Borah & Srinivas Prakhya & Amalesh Sharma, 2020. "Leveraging service recovery strategies to reduce customer churn in an emerging market," Journal of the Academy of Marketing Science, Springer, vol. 48(5), pages 848-868, September.
- Justin Paul & Gabriel R. G. Benito, 2018. "A review of research on outward foreign direct investment from emerging countries, including China: what do we know, how do we know and where should we be heading?," Asia Pacific Business Review, Taylor & Francis Journals, vol. 24(1), pages 90-115, January.
- Sumita Raghuram & Philipp Tuertscher & Raghu Garud, 2010. "Research Note ---Mapping the Field of Virtual Work: A Cocitation Analysis," Information Systems Research, INFORMS, vol. 21(4), pages 983-999, December.
- Denis Bouyssou & Thierry Marchant, 2011.
"Ranking scientists and departments in a consistent manner,"
Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 62(9), pages 1761-1769, September.
- Denis Bouyssou & Thierry Marchant, 2011. "Ranking scientists and departments in a consistent manner," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(9), pages 1761-1769, September.
- Denis Bouyssou & Thierry Marchant, 2011. "Ranking scientists and departments in a consistent manner," Post-Print hal-00606931, HAL.
- Muhammad Khurram Khan, 2021. "Importance of telecommunications in the times of COVID-19," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 76(1), pages 1-2, January.
- Rishikesh Bhaiswar & N. Meenakshi & Deepak Chawla, 2021. "Evolution of Electronic Word of Mouth: A Systematic Literature Review Using Bibliometric Analysis of 20 Years (2000–2020)," FIIB Business Review, , vol. 10(3), pages 215-231, September.
- Manish Bhargava & Awadhesh Bhardwaj & A.P.S. Rathore, 2017. "Prediction model for telecom postpaid customer churn using Six-Sigma methodology," International Journal of Manufacturing Technology and Management, Inderscience Enterprises Ltd, vol. 31(5), pages 387-401.
- Arno de Caigny & Kristof Coussement & Koen W. de Bock, 2018. "A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees," Post-Print hal-01741661, HAL.
- Jeffrey Prince & Shane Greenstein, 2014.
"Does Service Bundling Reduce Churn?,"
Journal of Economics & Management Strategy, Wiley Blackwell, vol. 23(4), pages 839-875, December.
- Jeffrey T. Prince & Shane Greenstein, 2011. "Does Service Bundling Reduce Churn?," Working Papers 2011-05, Indiana University, Kelley School of Business, Department of Business Economics and Public Policy.
- Paul, Justin & Criado, Alex Rialp, 2020. "The art of writing literature review: What do we know and what do we need to know?," International Business Review, Elsevier, vol. 29(4).
- Lemmens, A. & Croux, C., 2006. "Bagging and boosting classification trees to predict churn," Other publications TiSEM d5cb664d-5859-44db-a621-e, Tilburg University, School of Economics and Management.
- Ruth N. Bolton, 1998. "A Dynamic Model of the Duration of the Customer's Relationship with a Continuous Service Provider: The Role of Satisfaction," Marketing Science, INFORMS, vol. 17(1), pages 45-65.
- David Moher & Alessandro Liberati & Jennifer Tetzlaff & Douglas G Altman & The PRISMA Group, 2009. "Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement," PLOS Medicine, Public Library of Science, vol. 6(7), pages 1-6, July.
- Kristof Coussement & Stefan Lessmann & Geert Verstraeten, 2017. "A comparative analysis of data preparation algorithms for customer churn prediction: A case study in the telecommunication industry," Post-Print hal-01745261, HAL.
- Denis Bouyssou & Thierry Marchant, 2011.
"Ranking scientists and departments in a consistent manner,"
Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(9), pages 1761-1769, September.
- Denis Bouyssou & Thierry Marchant, 2011. "Ranking scientists and departments in a consistent manner," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 62(9), pages 1761-1769, September.
- Denis Bouyssou & Thierry Marchant, 2011. "Ranking scientists and departments in a consistent manner," Post-Print hal-00606931, HAL.
- Denis Bouyssou & Thierry Marchant, 2011. "Ranking scientists and departments in a consistent manner," Post-Print hal-02359808, HAL.
- De Caigny, Arno & Coussement, Kristof & De Bock, Koen W., 2018. "A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees," European Journal of Operational Research, Elsevier, vol. 269(2), pages 760-772.
- Raghuram Iyengar & Kamel Jedidi & Skander Essegaier & Peter J. Danaher, 2011. "The Impact of Tariff Structure on Customer Retention, Usage, and Profitability of Access Services," Marketing Science, INFORMS, vol. 30(5), pages 820-836, September.
- Jishnu Bhattacharyya & Soumyadeep Kundu & Manoj Kumar Dash & Shivam Dolhey, 2021. "An Investigation on Consumer Switching Behavior in an Asian Telecommunication Market," International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), IGI Global Scientific Publishing, vol. 12(6), pages 105-125, November.
- Galbi, Douglas A., 2001. "Regulating prices for shifting between service providers," Information Economics and Policy, Elsevier, vol. 13(4), pages 393-410, December.
- Rialp, Alex & Merigó, José M. & Cancino, Christian A. & Urbano, David, 2019. "Twenty-five years (1992–2016) of the International Business Review: A bibliometric overview," International Business Review, Elsevier, vol. 28(6), pages 1-1.
- Kumar, Satish & Pandey, Neeraj & Lim, Weng Marc & Chatterjee, Akash Nil & Pandey, Nitesh, 2021. "What do we know about transfer pricing? Insights from bibliometric analysis," Journal of Business Research, Elsevier, vol. 134(C), pages 275-287.
- Sweeney, Jill & Swait, Joffre, 2008. "The effects of brand credibility on customer loyalty," Journal of Retailing and Consumer Services, Elsevier, vol. 15(3), pages 179-193.
- Galbi, Douglas A., 2001. "Regulating prices for shifting between service providers," Information Economics and Policy, Elsevier, vol. 13(2), pages 181-198, June.
- Verbeke, Wouter & Dejaeger, Karel & Martens, David & Hur, Joon & Baesens, Bart, 2012. "New insights into churn prediction in the telecommunication sector: A profit driven data mining approach," European Journal of Operational Research, Elsevier, vol. 218(1), pages 211-229.
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