研究生: |
蔡佳穎 Jia-Ying Tsai |
---|---|
論文名稱: |
生成式 AI 客服品質如何影響消費者滿意度與忠誠度之研究 Research into the Effects of Generative AI Customer Service Quality on Consumer Satisfaction and Loyalty |
指導教授: |
欒斌
Pin Luarn |
口試委員: |
陳正綱
葉穎蓉 |
學位類別: |
碩士 Master |
系所名稱: |
管理學院 - 企業管理系 Department of Business Administration |
論文出版年: | 2023 |
畢業學年度: | 112 |
語文別: | 中文 |
論文頁數: | 68 |
中文關鍵詞: | 生成式AI 、核心服務品質 、服務補救品質 、對話品質 、AICSQ |
外文關鍵詞: | generative AI, e-core service quality, e-service recovery quality, conversational quality, AICSQ |
相關次數: | 點閱:391 下載:20 |
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近年來,許多企業採用聊天機器人作為客服的管道,可以提高服務效率。然而過去所使用的基於規則的聊天機器人,對於消費者來說不夠人性化。而生成式AI的出現正好能彌補基於規則的聊天機器人的不足。本研究也針對生成式AI的技術和發展沿革進行了描述,生成式AI的特點使得它適合使用在客服領域。
為了衡量生成式AI應用在客服領域的成效和未來性,本研究使用E-S-QUAL、E-RecS-QUAL、IPO架構,然而這三個架構有不足之處,本研究引入衡量AICSQ的構面補充研究架構,來衡量生成式AI客服的核心服務品質、服務補救品質和對話品質。並且衡量這三項服務品質對消費者滿意度與忠誠度的影響,了解消費者對於生成式AI的態度與想法。
本研究使用問卷進行量化分析,分析方式包含敘述性統計、信度分析以及迴歸分析。分析結果顯示:生成式AI核心服務品質、生成式AI服務補救品質、生成式AI對話品質分別對滿意度及忠誠度有正面顯著影響,滿意度對忠誠度有正面顯著影響,以及滿意度對三項服務品質對忠誠度有部分中介效果。
總而言之,想要使消費者提高滿意度與忠誠度,生成式AI核心服務品質的表現最佳、其次生成式AI對話品質、最後是生成式AI服務補救品質。可以期待生成式AI客服在未來的發展,成為解決消費者疑難雜症的工具。
In recent years, many businesses have adopted chatbots as a channel for customer service, improving service efficiency. However, the rule-based chatbots used in the past were not sufficiently humanized for consumers. The advent of generative AI is perfectly poised to address the shortcomings of rule-based chatbots. This study describes the technology and evolution of generative AI, highlighting its characteristics that make it well-suited for use in customer service domains.
To assess the effectiveness and future potential of generative AI in customer service, this study employs the E-S-QUAL, E-RecS-QUAL, and IPO frameworks. However, recognizing the limitations of these frameworks, the study introduces the AICSQ dimensions to supplement the research framework, measuring the e-core service quality, e-service recovery quality, and conversational quality of generative AI customer service. It also evaluates the impact of these three service qualities on consumer satisfaction and loyalty, to understand consumer attitudes and perceptions towards generative AI.
This research uses a questionnaire for quantitative analysis, including descriptive statistics, reliability analysis, and regression analysis. The results reveal that the e-core service quality of generative AI, its e-service recovery quality, and conversational quality each have a significant positive impact on satisfaction and loyalty. Satisfaction significantly influences loyalty, and partially mediates the effect of the three service qualities on loyalty.
In conclusion, to enhance consumer satisfaction and loyalty, the core service quality of generative AI performs best, followed by conversational quality, and finally service recovery quality. The future development of generative AI customer service is promising, potentially becoming a tool for resolving consumer issues and queries.
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