预测印度消费者的投诉表达或退出:CHAID分析

IF 2.6 Q3 MANAGEMENT
Ami A. Kumar, Anupriya Kaur
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引用次数: 0

摘要

目的本研究旨在根据社会人口统计和心理因素预测消费者投诉状态(投诉者或非投诉者),并进一步辨别消费者群体在决定消费者退出倾向(TE)方面的行为倾向差异行业(酒店和酒店、汽车服务中心和有组织的零售店)。卡方自动交互检测器(CHAID)决策树分析用于对消费者进行分析。调查结果表明:职业;收入教育行业和对投诉的态度是将消费者定性为投诉者或非投诉者的重要因素。进一步的TE的决定因素(令人沮丧的主观规范、成功投诉的可能性、较低的转换成本、糟糕的员工反应、负面的过去经历和投诉过程的简易性)在投诉者和非投诉者群体中差异很大。研究局限性/含义本研究中的研究问题在三个服务部门进行了测试印度的消费者,因此在将这些发现推广到其他部门和国家时应格外小心。建议在其他服务部门和国家进行研究复制,以提高这些发现在更广泛的社会人口样本中的可推广性。实践含义致力于留住消费者并致力于延长其消费者生命周期的公司可以从本研究的结果中受益匪浅,以了解非投诉(退出)行为特有的客户投诉行为。未来的研究人员可能会受益于在不同行业复制和扩展该模型,从而对CCB文献做出进一步贡献。独创性/价值据作者所知,通过CHAID决策树分析,没有证据表明消费者基于投诉行为或社会人口统计和心理因素进行细分。除了说明数据挖掘技术(如CHAID)在CCB领域的使用外,它还通过在印度等非西方环境中进行研究,为现存文献做出了贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predicting complaint voicing or exit amidst Indian consumers: a CHAID analysis
PurposeThe current study aims to predict consumer complaint status (complainers or non-complainers) based on socio-demographic and psychographic factors and further to discern the differences in behavior disposition of consumer groups concerning determinants of consumer's tendency to exit (TE).Design/methodology/approachThe research used survey-based data of 600 Indian consumers of three service sectors (hotel and hospitality, automobile service centers and organized retail stores). Chi-square automatic interaction detector (CHAID) decision tree analysis was used to profile consumers.FindingsThe results indicated that occupation; income; education; industry and attitude toward complaining were significant factors in profiling consumers as complainers or non-complainers. Further, determinants of TE (discouraging subjective norms, perceived likelihood of successful complaint, lower perceived switching cost, poor employee response, negative past experience and ease of complaint process) vary significantly across the groups of complainers and non-complainers.Research limitations/implicationsThe research questions in this study were tested with three service sectors consumers in India, so due care should be exercised in generalizing these findings to other sectors and countries. Study replication across other service sectors and countries is recommended to improve the generalizability of these findings with wider socio-demographic samples.Practical implicationsFirms striving for consumer retention and aim to extend their consumer life cycle can greatly benefit from the results of this study to understand the customer complaint behavior (CCB) specific to non-complaining (exit) behavior. The future researcher may benefit from replicating and extending the model in different industries for further contribution to the CCB literature.Originality/valueTo the best of the author's knowledge, there is no evidence of consumer segmentation based on their complaining behavior or socio-demographic and psychographic factors by employing CHAID decision tree analysis. In addition to illustrating the use of data mining techniques such as CHAID in the field of CCB, it also contributes to the extant literature by researching in a non-Western setting like India.
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CiteScore
6.50
自引率
3.20%
发文量
30
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