电商交易背景下S银行信用卡APP智能客服满意度研究

A Study on Satisfaction of Intelligent Customer Service in S Bank’s Credit Card APP under the E-Commerce Transaction Context

摘要: 近年来，电商交易与移动支付高频发生，信用卡线上结算后持续引发账单核对、分期办理、退款争议、风险拦截与跨境支持等交易后服务需求。信用卡APP内嵌智能客服凭借即时承接与自助闭环能力，成为连接电商交易事件与金融服务响应的关键平台触点，其服务体验直接影响用户对该渠道的忠诚意向。本文以S银行信用卡APP内嵌智能客服为研究对象，基于S-O-R逻辑与SERVPERF框架，构建电子服务质量、总体满意度与渠道忠诚意向(LOY)之间的作用机制模型，其中LOY由持续使用意向、推荐意愿与渠道优先选择意向三类行为意向表征。基于300份有效问卷数据，本文采用结构方程模型(SEM)与组态分析(fsQCA)进行检验。研究发现，在净效应层面，可靠性、响应性、保证性与移情性显著提升总体满意度，有形性影响不显著；总体满意度显著促进渠道忠诚意向，并发挥关键传导作用。在组态层面，高满意存在多条等效路径，核心组合主要集中于可靠性与响应性、可靠性与保证性，以及响应性与保证性的协同；低满意则主要表现为可靠性与响应性同时不足的缺口。结果表明，在电商交易后的线上服务情境中，准确可靠与响应效率是驱动智能客服满意度并形成渠道忠诚的基础条件。

Abstract: In recent years, e-commerce transactions and mobile payments have become increasingly frequent, and credit-card settlements completed online continue to trigger high-frequency post-transaction service needs, such as bill verification, installment processing, refund/dispute handling, risk interception, and cross-border support. Embedded AI customer service in credit-card mobile apps, with its real-time handling and self-service closure capability, has become a critical platform touchpoint linking e-commerce transaction events to financial service responses; the service experience at this touchpoint directly shapes users’ loyalty intention toward the channel. Focusing on the embedded AI customer service in S Bank’s credit-card app, this study develops a mechanism model relating e-service quality, overall satisfaction, and channel loyalty intention (LOY) based on the S-O-R logic and the SERVPERF framework. LOY is conceptualized as a behavioral intention manifested through continuance intention, recommendation intention, and channel preference (first-choice) intention. Using 300 valid questionnaires, the study conducts empirical tests via structural equation modeling (SEM) and configurational analysis (fsQCA). The net-effect results show that reliability, responsiveness, assurance, and empathy significantly enhance overall satisfaction, while tangibles are not significant; overall satisfaction significantly promotes channel loyalty intention and plays a key transmitting role. At the configurational level, multiple equivalent paths lead to high satisfaction, with core combinations concentrated on the synergy between reliability and responsiveness, reliability and assurance, and responsiveness and assurance; low satisfaction is mainly characterized by concurrent deficiencies in reliability and responsiveness. Overall, in post-transaction online service contexts following e-commerce transactions, accuracy/reliability and response efficiency constitute the foundational conditions for improving AI customer service satisfaction and forming channel loyalty.