The influence of brand credibility and brand loyalty on customer satisfaction and continued use intention in new voice assistance services based on AI

IF 4 Q2 BUSINESS
Luis Matosas-López
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引用次数: 0

Abstract

The use of Voice Assistance Systems based on Artificial Intelligence (VASAI) -such as Siri, Alexa, and many others- is becoming more and more popular; however, studies on this topic are still scarce. One of the topics that has only been tangentially addressed is the impact of brand-related issues (such as brand credibility and brand loyalty) on customer satisfaction and continued use intention of VASAI. The present study addresses this topic by postulating a structural model for its evaluation. The author's structural model also examines the influence of system quality constructs (system stability, system agility, and anthropomorphism), and information quality constructs (information exhaustiveness or information up-to-datedness) as independent variables. The researcher uses a questionnaire, based on previous literature, which is administered to a sample of 651 participants. The proposed structural model is evaluated by applying PLS-SEM analysis. The results show that brand credibility influences the constructs of customer satisfaction (β = 0.289/p-value < 0.001) and continued use intention (β = 0.304/p-value < 0.001). Similarly, the findings indicate that brand loyalty has a moderating effect on the relationships between brand credibility and consumer satisfaction, on the one hand, and brand credibility and continued use intention, on the other. In view of the results, the author concludes that some brand-related constructs have an impact on customer satisfaction and intention to continue using VASAI, indicating the critical importance of brand management for the success and future development of these technologies.

Abstract Image

品牌可信度和品牌忠诚度对基于人工智能的新型语音辅助服务的客户满意度和持续使用意愿的影响
基于人工智能的语音辅助系统(VASAI)--如 Siri、Alexa 等--的使用正变得越来越流行;然而,有关这一主题的研究仍然很少。与品牌相关的问题(如品牌可信度和品牌忠诚度)对客户满意度和继续使用 VASAI 的意愿的影响是其中一个仅被略微涉及的主题。本研究针对这一主题,提出了一个评估结构模型。作者的结构模型还研究了作为自变量的系统质量建构(系统稳定性、系统敏捷性和拟人化)和信息质量建构(信息详尽度或信息更新度)的影响。研究人员根据以往的文献,对 651 名参与者进行了问卷调查。通过 PLS-SEM 分析,对提出的结构模型进行了评估。结果表明,品牌可信度影响顾客满意度(β = 0.289/p-value<0.001)和持续使用意向(β = 0.304/p-value<0.001)。同样,研究结果表明,品牌忠诚度一方面对品牌可信度与消费者满意度之间的关系有调节作用,另一方面对品牌可信度与持续使用意向之间的关系也有调节作用。鉴于上述结果,作者得出结论,一些与品牌相关的建构对客户满意度和继续使用 VASAI 的意向有影响,这表明品牌管理对这些技术的成功和未来发展至关重要。
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来源期刊
CiteScore
5.40
自引率
16.70%
发文量
46
期刊介绍: Data has become the new ore in today’s knowledge economy. However, merely storing and reporting are not enough to thrive in today’s increasingly competitive markets. What is called for is the ability to make sense of all these oceans of data, and to apply those insights to the way companies approach their markets, adjust to changing market conditions, and respond to new competitors. Marketing analytics lies at the heart of this contemporary wave of data driven decision-making. Companies can no longer survive when they rely on gut instinct to make decisions. Strategic leverage of data is one of the few remaining sources of sustainable competitive advantage. New products can be copied faster than ever before. Staff are becoming less loyal as well as more mobile, and business centers themselves are moving across the globe in a world that is getting flatter and flatter. The Journal of Marketing Analytics brings together applied research and practice papers in this blossoming field. A unique blend of applied academic research, combined with insights from commercial best practices makes the Journal of Marketing Analytics a perfect companion for academics and practitioners alike. Academics can stay in touch with the latest developments in this field. Marketing analytics professionals can read about the latest trends, and cutting edge academic research in this discipline. The Journal of Marketing Analytics will feature applied research papers on topics like targeting, segmentation, big data, customer loyalty and lifecycle management, cross-selling, CRM, data quality management, multi-channel marketing, and marketing strategy. The Journal of Marketing Analytics aims to combine the rigor of carefully controlled scientific research methods with applicability of real world case studies. Our double blind review process ensures that papers are selected on their content and merits alone, selecting the best possible papers in this field.
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