Desirable or Distasteful? Exploring Uncertainty in Human-Chatbot Relationships

Pan, Shuyi; Cui, Jie; Mou, Yi

doi:10.1080/10447318.2023.2256554

Cited by 7 publications

(3 citation statements)

References 45 publications

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“…Therefore, this study assumes that when individuals passively observe the use of ChatGPT in the higher education landscape, it can reduce uncertainty. Thus, it extends the findings of previous studies [24,32] and advances technology adoption by observing and learning to diminish uncertainty. Consequently, the following hypothesis is proposed:…”

Section: H1a the Interactive Urs Implemented By Users Can Influence T...supporting

confidence: 85%

“…A summary of previous studies relevant to the applied theory and context can be found in Table 1. Unfortunately, only one study, conducted by Pan et al (2023) [32] in 2023, systematically addressed users' concerns and uncertainties related to interactions with AI-driven Chatbots. Their research had a specific focus on a Chinese online community and primarily employed sentiment analysis.…”

Section: Previous Studies and Gaps Identificationmentioning

confidence: 99%

“…This conceptualization aligns with a communication concept proposed by Stephens (2012) [50], suggesting that individuals actively seek clarification to enhance their comprehension of a particular issue. The absence of prior research on the role of seeking clarification in reducing uncertainty motivates its inclusion in this research model (see [12,32]). Seeking clarification in this study is distinct from the concept of Interactive URS introduced earlier.…”

Section: Seeking Clarification To Reduce Uncertaintymentioning

confidence: 99%

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From Uncertainty to Tenacity: Investigating User Strategies and Continuance Intentions in AI-Powered ChatGPT with Uncertainty Reduction Theory

Chang,

Lee,

Silalahi

2024

Preprint

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The introduction of ChatGPT in academia has raised significant concerns, particularly regarding issues like plagiarism and the ethical use of generative text for academic purposes. Existing literature has predominantly focused on ChatGPT adoption, leaving a notable gap in addressing the strategies users employ to mitigate these emerging challenges. To bridge this gap, this research utilizes the Uncertainty Reduction Theory (URT) to formulate user strategies for reducing uncertainty. These strategies include both interactive and passive approaches. Concurrently, the study identifies key sources of uncertainty, which include concerns related to transparency, information accuracy, and privacy. Additionally, it introduces the concepts of seeking clarification and consulting peer feedback as mediating roles to facilitate reduced uncertainty. We tested these hypotheses with a sample of Indonesian users (N = 566) using structural equation modeling via Smart-PLS 4.0 software. The results confirm that interactive Uncertainty Reduction Strategies (URS) are more effective in reducing uncertainty when using ChatGPT compared to passive URS. Transparency concerns, information accuracy, and privacy concerns are identified as factors that increase the level of uncertainty. In contrast, consulting peer feedback is the most effective strategy for reducing uncertainty compared to seeking clarification at the individual-system level. Insights from the mediating effects confirm that consulting peer feedback significantly mediates uncertainty sources to reduce uncertainty. The study discusses various strategies for the ethical use of ChatGPT by users in the educational context and contributes significantly to theoretical development.

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Section: H1a the Interactive Urs Implemented By Users Can Influence T...supporting

confidence: 85%

Section: Previous Studies and Gaps Identificationmentioning

confidence: 99%