Speaking vs. listening? Balance conversation attributes of voice assistants for better voice marketing

被引:19
|
作者
Hu, Peng [1 ]
Gong, Yeming [3 ]
Lu, Yaobin [2 ]
Ding, Amy Wenxuan [4 ]
机构
[1] Anhui Agr Univ, Sch Humanities & Social Sci, Management & Psychol, Changjiang West Rd 130, Hefei, Peoples R China
[2] Huazhong Univ Sci & Technol, Sch Management, Informat Syst & Mkt, Luoyu Rd 1037, Wuhan, Peoples R China
[3] EMLYON Business Sch, AIM Artificial Intelligence Management Inst, Business Intelligence Ctr, Management Sci, Ecully Campus, Ecully, France
[4] EMLYON Business Sch, AIM Artificial Intelligence Management Inst, Business Intelligence Ctr, Artificial Intelligence & Business Analyt, Shanghai Campus, Shanghai, Peoples R China
基金
中国国家社会科学基金;
关键词
Artificial intelligence; Voice marketing; Trust in voice assistants; Social presence; Speaking -listening congruency; SOCIAL PRESENCE; SIMILARITY-ATTRACTION; MODERATING ROLE; HUMAN REALISM; METHOD BIAS; TRUST; INFORMATION; CUSTOMER; CONSISTENCY; TECHNOLOGY;
D O I
10.1016/j.ijresmar.2022.04.006
中图分类号
F [经济];
学科分类号
02 ;
摘要
Artificial Intelligence (AI) is shaping marketing in an unprecedented way. Empowered by AI, voice assistants are increasingly capable of speaking and listening like humans, offering a great opportunity for a new marketing approach - voice marketing. This research examines how conversation attributes of voice assistants determine consumer trust and intention to engage in voice shopping. Using a sequential mixed-method design, three studies consistently show that consumers perceive the speaking attribute of voice assistants as more human-like than the listening attribute. We find that such incongruency between the two conversation attributes can undermine consumers' trust in voice assistants, leading to reduced willingness to accept product recommendations from voice assistants and shop via voice assistants, which would hamper the development of voice marketing. Accordingly, this research suggests that AI giants with strong technological strength and capital support should distribute more resources to advance the underlying technologies enabling human-like listening (e.g., natural language understanding and voice recognition). But for AI startups with limited financing ability and technical talents, they may consider appropriately reducing investments in the underlying technologies enabling humanlike speaking (e.g., natural language generation and voice synthesis) to enhance the congruency level between the conversation attributes of voice assistants.(c) 2022 Elsevier B.V. All rights reserved.
引用
收藏
页码:109 / 127
页数:19
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