Abandono en servicios - Una revisión bibliométrica

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Publicado 11-05-2022
Hugo Ribeiro
Belém Barbosa
António C. Moreira
Ricardo Rodrigues

Resumen

El objetivo de este artículo es identificar las investigaciones más impactantes sobre la pérdida de clientes y trazar la estructura conceptual e intelectual de su campo de estudio. Los datos han sido recogidos de la base de datos WoS, que comprenden 338 artículos publicados entre 1995 y 2020. Varias técnicas bibliométricas fueron aplicadas, incluyendo el análisis de co-palabras, cocitaciones, acoplamiento bibliográfico y redes de coautoría. Para realizar los análisis se utilizaron el software R y el Bibliometrix/Biblioshiny. Los resultados identifican los autores, artículos y revistas más influyentes y activos sobre el tema. Más específicamente, a través de las cocitaciones y el acoplamiento bibliográfico, fue posible mapear los artículos más antiguos (análisis retrospectivo) y la investigación más actual (análisis prospectivo). El análisis retrospectivo, basado en las cocitaciones, reveló que los fundamentos de este campo de investigación son constructos como la calidad del servicio, la satisfacción, la lealtad y el cambio de comportamientos. El análisis prospectivo, realizado a través del acoplamiento bibliográfico, reveló que la investigación actual está inmersa en el análisis predictivo, los conglomerados, la minería de datos y los algoritmos. Los resultados proporcionan una sólida orientación para seguir investigando en este campo.

Cómo citar

Ribeiro, H., Barbosa, B., C. Moreira, . A., & Rodrigues, R. (2022). Abandono en servicios - Una revisión bibliométrica. Cuadernos De Gestión, 22(2), 97–121. https://doi.org/10.5295/cdg.211509hr
Abstract 59 | PDF (English) Downloads 24

##plugins.themes.bootstrap3.article.details##

Keywords

Churn de Clientes, Análisis Bibliométrico, Análisis de Cocitación, Acoplamiento Bibliográfico, Mapeo de la Ciencia, Biblioshiny

References
Adebiyi, S. O., Oyatoye, E. O., & Amole, B. B. (2016). Relevant drivers for customers` churn and retention decision in the Nigerian mobile telecommunication industry. Journal of Competitiveness, 6(3), 52-67. https://doi.org/10.7441/joc.2016.03.04
Ahn, J. H., Han, S. P., & Lee, Y. S. (2006). Customer churn analysis: Churn determinants and mediation effects of partial defection in the Korean mobile telecommunications service industry. Telecommunications Policy, 30(10-11), 552-568. https://doi.org/10.1016/j.telpol.2006.09.006
Ahn, J., Hwang, J., Kim, D., Choi, H., & Kang, S. (2020). A Survey on churn analysis in various business domains. IEEE Access, 8, 220816-220839. https://doi.org/10.1109/access.2020.3042657
Al-Mashraie, M., Chung, S. H., & Jeon, H. W. (2020). Customer switching behavior analysis in the telecommunication industry via push-pull-mooring framework: A machine learning approach. Computers & Industrial Engineering, 144. https://doi.org/10.1016/j.cie.2020.106476
Amin, A., Shah, B., Khattak, A. M., Moreira, F. J. L., Ali, G., Rocha, A., & Anwar, S. (2019). Cross-company customer churn predic-tion in telecommunication: A comparison of data transformation methods. International Journal of Information Management, 46, 304-319. https://doi.org/10.1016/j.ijinfomgt.2018.08.015
Amiri, H., & Daume III, H. (2016). Short text representation for detecting churn in microblogs. Paper presented at the Thirtieth AAAI Conference on Artificial Intelligence.
Anderson, E. W., Fornell, C., & Lehmann, D. R. (1994). Customer satisfaction, market share, and profitability - Findings from Sweden. Journal of Marketing, 58(3), 53-66. https://doi.org/10.1177/002224299405800304
Aria, M., & Cuccurullo, C. (2017). Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959-975. https://doi.org/10.1016/j.joi.2017.08.007
Athanassopoulos, A. D. (2000). Customer satisfaction cues to support market segmentation and explain switching behavior. Journal of Business Research, 47(3), 191-207. https://doi.org/10.1016/s0148-2963(98)00060-5
Aydin, S., & Ozer, G. (2005). The analysis of antecedents of customer loyalty in the Turkish mobile telecommunication market. European Journal of Marketing, 39(7-6), 910-925. https://doi.org/10.1108/03090560510601833
Bansal, H. S., Irving, P. G., & Taylor, S. F. (2004). A three-component model of customer commitment to service providers. Journal of the Academy of Marketing Science, 32(3), 234-250. https://doi.org/10.1177/0092070304263332
Becker, J. U., Spann, M., & Schulze, T. (2015). Implications of minimum contract durations on customer retention. Marketing Letters, 26(4), 579-592. https://doi.org/10.1007/s11002-014-9293-2
Benedek, G., Lubloy, A., & Vastag, G. (2014). The Importance of Social Embeddedness: Churn Models at Mobile Providers. Decision Sciences, 45(1), 175-201. https://doi.org/10.1111/deci.12057
Bolton, R. N. (1998). A dynamic model of the duration of the customer's relationship with a continuous service provider: The role of satisfaction. Marketing Science, 17(1), 45-65. https://doi.org/10.1287/mksc.17.1.45
Buckinx, W., & Van den Poel, D. (2005). Customer base analysis: Partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting. European Journal of Operational Research, 164(1), 252-268. https://doi.org/10.1016/j.ejor.2003.12.010
Burez, J., & Van den Poel, D. (2007). CRM at a pay-TV company: Using analytical models to reduce customer attrition by targeted marketing for subscription services. Expert Systems with Applications, 32(2), 277-288. https://doi.org/10.1016/j.eswa.2005.11.037
Burez, J., & Van den Poel, D. (2009). Handling class imbalance in customer churn prediction. Expert Systems with Applications, 36(3), 4626-4636. https://doi.org/10.1016/j.eswa.2008.05.027
Burnham, T. A., Frels, J. K., & Mahajan, V. (2003). Consumer switching costs: A typology, antecedents, and consequences. Journal of the Academy of Marketing Science, 31(2), 109-126. https://doi.org/10.1177/0092070302250897
Callon, M., Courtial, J. P., & Laville, F. (1991). Co-word analysis as a tool for describing the network of interactions between basic and technological research - The case of polymer chemistry. Scientometrics, 22(1), 155-205. https://doi.org/10.1007/bf02019280
Callon, M., Courtial, J. P., Turner, W. A., & Bauin, S. (1983). From translations to problematic networks - An introduction to co-word analysis. Social Science Information Sur Les Sciences Sociales, 22(2), 191-235. https://doi.org/10.1177/053901883022002003
Carrizo-Moreira, A., Freitas-da Silva, P. M., & Ferreira-Moutinho, V. M. (2017). The effects of brand experiences on quality, satisfac-tion and loyalty: An empirical study in the telecommunications multiple-play service market. Innovar, 27(64), 23-38. https://doi.org/ 10.15446/innovar.v27n64.62366
Chen, P. Y., & Hitt, L. M. (2002). Measuring switching costs and the determinants of customer retention in Internet-enabled busi-nesses: A study of the Online brokerage industry. Information Systems Research, 13(3), 255-274. https://doi.org/10.1287/isre.13.3.255.78
Cobo, M. J., Lopez-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). Science mapping software tools: review, analysis, and cooperative study among tools. Journal of the American Society for Information Science and Technology, 62(7), 1382-1402. https://doi.org/10.1002/asi.21525
Coussement, K. (2014). Improving customer retention management through cost-sensitive learning. European Journal of Marketing, 48(3-4), 477-495. https://doi.org/10.1108/ejm-03-2012-0180
Coussement, K., & Van den Poel, D. (2009). Improving customer attrition prediction by integrating emotions from client/company interaction emails and evaluating multiple classifiers. Expert Systems with Applications, 36(3), 6127-6134. https://doi.org/10.1016/j.eswa.2008.07.021
De Caigny, A., Coussement, K., & De Bock, K. W. (2018). A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees. European Journal of Operational Research, 269(2), 760-772. https://doi.org/10.1016/j.ejor.2018.02.009
de Haan, E., Verhoef, P. C., & Wiesel, T. (2015). The predictive ability of different customer feedback metrics for retention. Interna-tional Journal of Research in Marketing, 32(2), 195-206. https://doi.org/10.1016/j.ijresmar.2015.02.004
Eck, N. J. v., & Waltman, L. (2009). How to normalize co-occurrence data? An analysis of some well‐known similarity measures. Journal of the American society for information science and technology, 60(8), 1635-1651. https://doi.org/10.1002/asi.21075
Ellegaard, O., & Wallin, J. A. (2015). The bibliometric analysis of scholarly production: How great is the impact? Scientometrics, 105(3), 1809-1831. https://doi.org/10.1007/s11192-015-1645-z
Eshghi, A., Haughton, D., & Topi, H. (2007). Determinants of customer loyalty in the wireless telecommunications industry. Tele-communications Policy, 31(2), 93-106. https://doi.org/10.1016/j.telpol.2006.12.005
Ferreira, F. A. F. (2018). Mapping the field of arts-based management: Bibliographic coupling and co-citation analyses. Journal of Business Research, 85, 348-357. https://doi.org/10.1016/j.jbusres.2017.03.026
Fischbach, K., Putzke, J., & Schoder, D. (2011). Co-authorship networks in electronic markets research. Electronic Markets, 21(1), 19-40. https://doi.org/10.1007/s12525-011-0051-5
Ganesh, J., Arnold, M. J., & Reynolds, K. E. (2000). Understanding the customer base of service providers: An examination of the differences between switchers and stayers. Journal of Marketing, 64(3), 65-87. https://doi.org/10.1509/jmkg.64.3.65.18028
Garfield, E., & Sher, I. H. (1993). KEYWORDS-PLUS(TM) - Algorithmic derivative indexing. Journal of the American Society for Information Science, 44(5), 298-299. https://doi.org/10.1002/(sici)1097-4571(199306)44:5<298::aid-asi5>3.0.co;2-a
Gentile, C., Spiller, N., & Noci, G. (2007). How to sustain the customer experience:: An overview of experience components that co-create value with the customer. European management journal, 25(5), 395-410. https://doi.org/ /10.1016/j.emj.2007.08.005
Gerpott, T. J., & Ahmadi, N. (2015). Regaining drifting mobile communication customers: Predicting the odds of success of winback efforts with competing risks regression. Expert Systems with Applications, 42(21), 7917-7928. https://doi.org/10.1016/j.eswa.2015.05.011
Haddaway, N. R. A. U. P. C. C., & McGuinness, L. A. (2021). PRISMA2020: R package and ShinyApp for producing PRISMA 2020 compliant flow diagrams (Version 0.0.2): Zenodo. Retrieved from http://doi.org/10.5281/zenodo.5082518
Hadden, J., Tiwari, A., Roy, R., & Ruta, D. (2007). Computer assisted customer churn management: State-of-the-art and future trends. Computers & Operations Research, 34(10), 2902-2917. https://doi.org/10.1016/j.cor.2005.11.007
Iglesias, O., Singh, J. J., & Batista-Foguet, J. M. (2011). The role of brand experience and affective commitment in determining brand loyalty. Journal of Brand Management, 18(8), 570-582. https://doi.org/10.1057/bm.2010.58
Jahromi, A. T., Stakhovych, S., & Ewing, M. (2014). Managing B2B customer churn, retention and profitability. Industrial Marketing Management, 43(7), 1258-1268. https://doi.org/10.1016/j.indmarman.2014.06.016
Jones, M. A., Mothersbaugh, D. L., & Beatty, S. E. (2000). Switching barriers and repurchase intentions in services. Journal of Retail-ing, 76(2), 259-274. https://doi.org/10.1016/s0022-4359(00)00024-5
Keaveney, S. M. (1995). Customer switching behavior in-service industries - An exploratory-study. Journal of Marketing, 59(2), 71-82. https://doi.org/10.2307/1252074
Keaveney, S. M., & Parthasarathy, M. (2001). Customer switching behavior in online services: An exploratory study of the role of selected attitudinal, behavioral, and demographic factors. Journal of the Academy of Marketing Science, 29(4), 374-390. https://doi.org/10.1177/03079450094225
Kessler, M. M. (1963). Bibliographic coupling between scientific papers. American Documentation, 14(1), 10-25. https://doi.org/10.1002/asi.5090140103
Kumar, V., Leszkiewicz, A., & Herbst, A. (2018). Are you back for good or still shopping around? Investigating customers’ repeat churn behavior. Journal of Marketing Research, 55(2), 208-225. https://doi.org/10.1509/jmr.16.0623
Kyei, D. A., & Bayoh, A. T. M. (2017). Innovation and customer retention in the Ghanaian telecommunication industry. International Journal of Innovation, 5(2), 171-183. https://doi.org/10.5585/iji.v5i2.154
Lemmens, A., & Croux, C. (2006). Bagging and boosting classification trees to predict churn. Journal of Marketing Research, 43(2), 276-286. https://doi.org/10.1509/jmkr.43.2.276
Lemon, K. N., & Verhoef, P. C. (2016). Understanding customer experience throughout the customer journey. Journal of Marketing, 80(6), 69-96. https://doi.org/10.1509/jm.15.0420
Mahajan, V., Misra, R., & Mahajan, R. (2015). Review of data mining techniques for churn prediction in telecom. Journal of Informa-tion and Organizational Sciences, 39(2), 183-197.
Meyer, C., & Schwager, A. (2007). Understanding customer experience. Harvard Business Review, 85(2), 116-26,157.
Moreira, A. C., Silva, P., & Moutinho, V. (2016). Differences between stayers, switchers, and heavy switchers: A study in the tele-communications service market. Marketing Intelligence & Planning, 34(6), 843-862. https://doi.org/10.1108/MIP-07-2015-0128
Mozer, M. C., Wolniewicz, R., Grimes, D. B., Johnson, E., & Kaushansky, H. (2000). Predicting subscriber dissatisfaction and improving retention in the wireless telecommunications industry. Ieee Transactions on Neural Networks, 11(3), 690-696. https://doi.org/10.1109/72.846740
Neslin, S. A., Gupta, S., Kamakura, W., Lu, J. X., & Mason, C. H. (2006). Defection detection: Measuring and understanding the predictive accuracy of customer churn models. Journal of Marketing Research, 43(2), 204-211. https://doi.org/10.1509/jmkr.43.2.204
Newman, M. E., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical review E, 69(2), 026113. https://doi.org/10.1103/physreve.69.026113
Orman, G. K., & Labatut, V. (2009). A comparison of community detection algorithms on artificial networks. Paper presented at the International conference on discovery science. https://doi.org/10.1007/978-3-642-04747-3_20
Peters, H. P. F., & Vanraan, A. F. J. (1991). Structuring scientific activities by coauthor analysis - An exercise on a university-faculty level. Scientometrics, 20(1), 235-255. https://doi.org/10.1007/bf02018157
Polo, Y., & Sese, F. J. (2009). How to Make Switching Costly The Role of Marketing and Relationship Characteristics. Journal of Service Research, 12(2), 119-137. https://doi.org/10.1177/1094670509335771
Pons, P., & Latapy, M. (2005). Computing communities in large networks using random walks. Paper presented at the International symposium on computer and information sciences. https://doi.org/10.1007/11569596_31
Prince, J., & Greenstein, S. (2014). Does service bundling reduce churn? Journal of Economics & Management Strategy, 23(4), 839-875. https://doi.org/10.1111/jems.12073
Pritchard, A. (1969). Statistical bibliography or bibliometrics. Journal of documentation, 25(4), 348-349.
Rajan Sachdeva, R. R. S. (2017). Evaluating prediction of customer churn behavior based on artificial bee colony algorithm. Interna-tional Journal of Engineering and Computer Science, 6(1). https://doi.org/10.18535/ijecs/v6i1.32
Ramos-Rodriguez, A. R., & Ruiz-Navarro, J. (2004). Changes in the intellectual structure of strategic management research: A bibliometric study of the Strategic Management Journal, 1980-2000. Strategic Management Journal, 25(10), 981-1004. https://doi.org/10.1002/smj.397
Reichheld, F. F., & Sasser, W. E. (1990). Zero defections - Quality comes to services. Harvard Business Review, 68(5), 105-111.
Rust, R. T., & Zahorik, A. J. (1993). Customer satisfaction, customer retention, and market share. Journal of Retailing, 69(2), 193-215. https://doi.org/10.1016/0022-4359(93)90003-2
Sirapracha, J., & Tocquer, G. (2012). Customer experience, brand image and customer loyalty in telecommunication services. Paper presented at the Int Conf Econ Bus Mark Manag.
Small, H. (1973). Co‐citation in the scientific literature: A new measure of the relationship between two documents. Journal of the American Society for information Science, 24(4), 265-269. https://doi.org/10.1002/asi.4630240406
Small, H. (1999). Visualizing science by citation mapping. Journal of the American Society for Information Science, 50(9), 799-813. https://doi.org/10.1002/(sici)1097-4571(1999)50:9<799::aid-asi9>3.0.co;2-g
Team, R. C. (2021). R: A language and environment for statistical computing.
Tsai, C. F., & Lu, Y. H. (2009). Customer churn prediction by hybrid neural networks. Expert Systems with Applications, 36(10), 12547-12553. https://doi.org/10.1016/j.eswa.2009.05.032
Ullah, I., Raza, B., Malik, A. K., Imran, M., Ul Islam, S., & Kim, S. W. (2019). A churn prediction model using random forest: Analysis of machine learning techniques for churn prediction and factor identification in telecom sector. Ieee Access, 7, 60134-60149. https://doi.org/10.1109/access.2019.2914999
Van den Poel, D., & Lariviere, B. (2004). Customer attrition analysis for financial services using proportional hazard models. Euro-pean Journal of Operational Research, 157(1), 196-217. https://doi.org/10.1016/s0377-2217(03)00069-9
van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523-538. https://doi.org/10.1007/s11192-009-0146-3
Verbeke, W., Dejaeger, K., Martens, D., Hur, J., & Baesens, B. (2012). New insights into churn prediction in the telecommunication sector: A profit driven data mining approach. European Journal of Operational Research, 218(1), 211-229. https://doi.org/10.1016/j.ejor.2011.09.031
Verbeke, W., Martens, D., Mues, C., & Baesens, B. (2011). Building comprehensible customer churn prediction models with advanced rule induction techniques. Expert Systems with Applications, 38(3), 2354-2364. https://doi.org/10.1016/j.eswa.2010.08.023
Wei, C. P., & Chiu, I. T. (2002). Turning telecommunications call details to churn prediction: a data mining approach. Expert Systems with Applications, 23(2), 103-112. https://doi.org/10.1016/s0957-4174(02)00030-1
Yan, E. J., & Ding, Y. (2011). Discovering author impact: A PageRank perspective. Information Processing & Management, 47(1), 125-134. https://doi.org/10.1016/j.ipm.2010.05.002
Zeithaml, V. A., Berry, L. L., & Parasuraman, A. (1996). The behavioral consequences of service quality. Journal of Marketing, 60(2), 31-46. https://doi.org/10.2307/1251929
Zhang, J., Yu, Q., Zheng, F. S., Long, C., Lu, Z. X., & Duan, Z. G. (2016). Comparing keywords plus of WOS and author keywords: A case study of patient adherence research. Journal of the Association for Information Science and Technology, 67(4), 967-972. https://doi.org/10.1002/asi.23437
Zhao, D. Z., & Strotmann, A. (2008). Evolution of research activities and intellectual influences in information science 1996-2005: Introducing author bibliographic-coupling analysis. Journal of the American Society for Information Science and Technology, 59(13), 2070-2086. https://doi.org/10.1002/asi.20910
Zupic, I., & Cater, T. (2015). Bibliometric methods in management and organization. Organizational Research Methods, 18(3), 429-472. https://doi.org/10.1177/1094428114562629
Sección
Artículos