Customer experience in AI-enabled products: Scale development and validation

操作化 比例(比率) 规则网络 计算机科学 知识管理 新产品开发 数据科学 服务(商务) 业务 营销 哲学 物理 认识论 量子力学
作者
Ping Wang,Kun Li,Qinglong Du,Jianqiong Wang
出处
期刊:Journal of Retailing and Consumer Services [Elsevier]
卷期号:76: 103578-103578 被引量:7
标识
DOI:10.1016/j.jretconser.2023.103578
摘要

Artificial Intelligence (AI) is an enabling technology that can be integrated into products to provide emerging capabilities and craft novel customer experience (CX); many companies have widely adopted AI-enabled products to provide customers with service interactions. However, meager researchers have studied CX in AI-enabled products. Utilizing qualitative and quantitative methods, this study developed a scale of CX in AI-enabled products using Churchill's (1979) scale development framework. The scale underwent several stages of development including item generation, scale purification, scale validation, cross-testing, and nomological validity testing. Finally, five dimensions were identified to represent CX in AI-enabled products, such as data capture experience, classification experience, delegation experience, social experience, and anthropomorphic experience. Based on assemblage theory, the study develops the scale from the perspective of integrating human-centric and object-oriented anthropomorphic metaphors. Establishment of this scale extends traditional CX research, expands emerging CX research, and presents the first operationalized definition of the CX in AI-enabled products. The development of this scale provides a framework for marketers to enhance the CX in emerging consumer environments.
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