Factors affecting performance expectancy and intentions to use ChatGPT: Using SmartPLS to advance an information technology acceptance framework

Mark Anthony Camilleri

Technological Forecasting and Social Change, 2024, vol. 201, issue C

Abstract: Few studies have explored the use of artificial intelligence-enabled (AI-enabled) large language models (LLMs). This research addresses this knowledge gap. It investigates perceptions and intentional behaviors to utilize AI dialogue systems like Chat Generative Pre-Trained Transformer (ChatGPT). A survey questionnaire comprising measures from key information technology adoption models, was used to capture quantitative data from a sample of 654 respondents. A partial least squares (PLS) approach assesses the constructs' reliabilities and validities. It also identifies the relative strength and significance of the causal paths in the proposed research model. The findings from SmartPLS4 report that there are highly significant effects in this empirical investigation particularly between source trustworthiness and performance expectancy from AI chatbots, as well as between perceived interactivity and intentions to use this algorithm, among others. In conclusion, this contribution puts forward a robust information technology acceptance framework that clearly evidences the factors that entice online users to habitually engage with text-generating AI chatbot technologies. It implies that although they may be considered as useful interactive systems for content creators, there is scope to continue improving the quality of their responses (in terms of their accuracy and timeliness) to reduce misinformation, social biases, hallucinations and adversarial prompts.

Keywords: Unified Theory of Acceptance and use of Technology; Information Adoption Model; Chat Generative Pre-Trained Transformer; ChatGPT; AI Chatbot; Natural language generation (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:201:y:2024:i:c:s004016252400043x

DOI: 10.1016/j.techfore.2024.123247

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