Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2401.12247 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866929219485827072 |
|---|---|
| author | Cheng, Xusen Bao, Ying Zarifis, Alex Gong, Wankun Mou, Jian |
| author_facet | Cheng, Xusen Bao, Ying Zarifis, Alex Gong, Wankun Mou, Jian |
| contents | Artificial intelligence based chatbots have brought unprecedented business potential. This study aims to explore consumers trust and response to a text-based chatbot in ecommerce, involving the moderating effects of task complexity and chatbot identity disclosure. A survey method with 299 useable responses was conducted in this research. This study adopted the ordinary least squares regression to test the hypotheses. First, the consumers perception of both the empathy and friendliness of the chatbot positively impacts their trust in it. Second, task complexity negatively moderates the relationship between friendliness and consumers trust. Third, disclosure of the text based chatbot negatively moderates the relationship between empathy and consumers trust, while it positively moderates the relationship between friendliness and consumers trust. Fourth, consumers trust in the chatbot increases their reliance on the chatbot and decreases their resistance to the chatbot in future interactions. Adopting the stimulus organism response framework, this study provides important insights on consumers perception and response to the text-based chatbot. The findings of this research also make suggestions that can increase consumers positive responses to text based chatbots. Extant studies have investigated the effects of automated bots attributes on consumers perceptions. However, the boundary conditions of these effects are largely ignored. This research is one of the first attempts to provide a deep understanding of consumers responses to a chatbot. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_12247 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Exploring consumers response to text-based chatbots in e-commerce: The moderating role of task complexity and chatbot disclosure Cheng, Xusen Bao, Ying Zarifis, Alex Gong, Wankun Mou, Jian Artificial Intelligence Artificial intelligence based chatbots have brought unprecedented business potential. This study aims to explore consumers trust and response to a text-based chatbot in ecommerce, involving the moderating effects of task complexity and chatbot identity disclosure. A survey method with 299 useable responses was conducted in this research. This study adopted the ordinary least squares regression to test the hypotheses. First, the consumers perception of both the empathy and friendliness of the chatbot positively impacts their trust in it. Second, task complexity negatively moderates the relationship between friendliness and consumers trust. Third, disclosure of the text based chatbot negatively moderates the relationship between empathy and consumers trust, while it positively moderates the relationship between friendliness and consumers trust. Fourth, consumers trust in the chatbot increases their reliance on the chatbot and decreases their resistance to the chatbot in future interactions. Adopting the stimulus organism response framework, this study provides important insights on consumers perception and response to the text-based chatbot. The findings of this research also make suggestions that can increase consumers positive responses to text based chatbots. Extant studies have investigated the effects of automated bots attributes on consumers perceptions. However, the boundary conditions of these effects are largely ignored. This research is one of the first attempts to provide a deep understanding of consumers responses to a chatbot. |
| title | Exploring consumers response to text-based chatbots in e-commerce: The moderating role of task complexity and chatbot disclosure |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2401.12247 |