Feeling the love? How consumer's political ideology shapes responses to AI financial service delivery

感情 金融服务 独创性 意识形态 营销 政治 服务(商务) 感觉 适度 价值(数学) 心理学 公共关系 业务 财务 社会心理学 政治学 机器学习 创造力 计算机科学 法学
作者
Aimee Riedel,Rory Mulcahy,Gavin Northey
出处
期刊:International Journal of Bank Marketing [Emerald (MCB UP)]
卷期号:40 (6): 1102-1132 被引量:18
标识
DOI:10.1108/ijbm-09-2021-0438
摘要

Purpose This paper aims, first, to examine artificial intelligence (AI) vs human delivery of financial advice; second, to examine the serial mediating roles of emotion and trust between AI use in the financial service industry and their impact upon marketing outcomes including word of mouth (WOM) and brand attitude; and third, to examine how political ideology moderates' consumers' reactions to AI financial service delivery. Design/methodology/approach A review of the extant literature is conducted, yielding seven hypotheses underpinned by affect-as-information theory. The hypotheses are tested via three online scenario-based experiments ( n = 801) using Process Macro. Findings The results of the three experiments reveal consumers experience lower levels of positive emotions, specifically, affection, when financial advice is provided by AI in comparison to human employees. Secondly, across the three experiments, conservative consumers are shown to perceive somewhat similar levels of affection in financial advice provided by AI and human employees. Whereas liberal consumers perceive significantly lower levels of affection when serviced by AI in comparison to conservatives and human employee financial advice. Thirdly, results reveal affection and trust to be serial mediators which explain consumers' WOM and brand attitudes when financial services are provided by AI. Fourthly, the investment type plays an important role in consumers’ reactions to the use of AI. Originality/value To the best of the authors’ knowledge, this research is one of the first to study political ideology as a potential moderator of consumers’ responses to AI in financial services, providing novel contributions to the literature. It further contributes unique insights by examining emotional responses to AI and human financial advice for different amounts and types of investments using a comprehensive approach of examining both valence and discrete emotions to identify affection as a key explanatory emotion. The study further sheds insights relating to how emotions (affection) and trust mediate the relationship between AI and WOM, and brand attitudes, demonstrating an affect-attitude psychological sequence that explains consumers’ reactions to AI in financial services.
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