Understanding Communication Preferences of Information Workers in Engagement with Text-Based Conversational Agents
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910680058167296 |
|---|---|
| author | Bhattacharjee, Ananya Suh, Jina Ershadi, Mahsa Iqbal, Shamsi T. Wilson, Andrew D. Hernandez, Javier |
| author_facet | Bhattacharjee, Ananya Suh, Jina Ershadi, Mahsa Iqbal, Shamsi T. Wilson, Andrew D. Hernandez, Javier |
| contents | Communication traits in text-based human-AI conversations play pivotal roles in shaping user experiences and perceptions of systems. With the advancement of large language models (LLMs), it is now feasible to analyze these traits at a more granular level. In this study, we explore the preferences of information workers regarding chatbot communication traits across seven applications. Participants were invited to participate in an interactive survey, which featured adjustable sliders, allowing them to adjust and express their preferences for five key communication traits: formality, personification, empathy, sociability, and humor. Our findings reveal distinct communication preferences across different applications; for instance, there was a preference for relatively high empathy in wellbeing contexts and relatively low personification in coding. Similarities in preferences were also noted between applications such as chatbots for customer service and scheduling. These insights offer crucial design guidelines for future chatbots, emphasizing the need for nuanced trait adjustments for each application. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_20468 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Understanding Communication Preferences of Information Workers in Engagement with Text-Based Conversational Agents Bhattacharjee, Ananya Suh, Jina Ershadi, Mahsa Iqbal, Shamsi T. Wilson, Andrew D. Hernandez, Javier Human-Computer Interaction Communication traits in text-based human-AI conversations play pivotal roles in shaping user experiences and perceptions of systems. With the advancement of large language models (LLMs), it is now feasible to analyze these traits at a more granular level. In this study, we explore the preferences of information workers regarding chatbot communication traits across seven applications. Participants were invited to participate in an interactive survey, which featured adjustable sliders, allowing them to adjust and express their preferences for five key communication traits: formality, personification, empathy, sociability, and humor. Our findings reveal distinct communication preferences across different applications; for instance, there was a preference for relatively high empathy in wellbeing contexts and relatively low personification in coding. Similarities in preferences were also noted between applications such as chatbots for customer service and scheduling. These insights offer crucial design guidelines for future chatbots, emphasizing the need for nuanced trait adjustments for each application. |
| title | Understanding Communication Preferences of Information Workers in Engagement with Text-Based Conversational Agents |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2410.20468 |