The effect of trust on user adoption of AI-generated content
| Date | 12 December 2024 |
| Pages | 61-76 |
| DOI | https://doi.org/10.1108/EL-08-2024-0244 |
| Published date | 12 December 2024 |
| Author | Tao Zhou,Hailin Lu |
The effect of trust on user adoption
of AI-generated content
Tao Zhou and Hailin Lu
School of Management, Hangzhou Dianzi University, Hangzhou, China
Abstract
Purpose –The purpose of thisstudy is to examine the effect of trust on user adoption of artificial intelligence-
generatedcontent (AIGC) based on the stimulus–organism–response.
Design/methodology/approach –The authors conducted an online survey in China, which is a highly
competitive AI market, and obtained504 valid responses. Both structural equation modelling and fuzzy-set
qualitativecomparative analysis (fsQCA) were used to conductdata analysis.
Findings –The results indicated that perceived intelligence, perceived transparency and knowledge
hallucination influence cognitive trust in platform, whereas perceived empathy influences affective trust in
platform. Both cognitivetrust and affective trust in platform lead to trust in AIGC. Algorithm bias negatively
moderates the effectof cognitive trust in platform on trust in AIGC. The fsQCA identifiedthree configurations
leading to adoptionintention.
Research limitations/implications –The main limitation is that more factors such as cultureneed to be
included to examine their possible effects on trust. The implication is that generative AI platforms need to
improve the intelligence,transparency and empathy, and mitigate knowledgehallucination to engender users’
trust in AIGC and facilitatetheir adoption.
Originality/value –Existing research has mainly usedtechnology adoption theories such as unified theory
of acceptance and use of technology to examine AIGC user behaviour andhas seldom examined user trust
development in the AIGC context. This researchtries to fill the gap by disclosing the mechanism underlying
AIGC user trustformation.
Keywords Technology adoption, Trust,Adoption intention, Stimulus–organism–response,
AI-generated content
Paper type Research paper
1. Introduction
Generative artificial intelligence (AI) products, such as ChatGPT, have received increasing
attention around the world. Powered bylarge language models (LLMs) and massive data
sets, ChatGPT has significantly transformedthe pattern of human–machine interaction.
Motivated by the successof ChatGPT, numerous generative AI productshave emerged in the
market, such as Google Gemini, Anthropic’s Claude, Meta Llama, Baidu’s ERNIE Bot and
Alibaba’s Tongyi Qianwen. Based on LLMs, AI systemsare becoming increasingly human-
like and intelligent. They have shown powerful capabilities in natural language processing,
conversations, questions and answers, text generation and text translation. Research has
shown that generative AI exhibits tremendous potential in various fields, such as enterprise
management (Talaei-Khoei et al., 2024), financialdecision-making (Oehler and Horn, 2024),
healthcare (Howardet al., 2023), science education (Cooper, 2023) and smart libraries(Khan
et al., 2023). The widespread applicationof generative AI in these domains highlights its
This work was supported by National Social Science Foundation of China (24BGL310).
The Electronic
Library
61
Received19 August2024
Revised10O ctober 2024
7 November 2024
Accepted12November2024
TheElectronic Library
Vol.43 No. 1, 2025
pp. 61-76
© Emerald Publishing Limited
0264-0473
DOI 10.1108/EL-08-2024-0244
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/0264-0473.htm
pervasive influence. In the future, generative AI is likely to gradually penetrate into every
aspect of society and people’slives.
However, despite being a significant breakthrough in AI technology, generative AI
also faces numerous challenges, such as knowledge hallucination and biases. Due to the
flaws in algorithms and data sources, generative AI often generates false and biased
information that contradicts facts. This may undermine users’trust in both the platform
and AI-generated content (AIGC) anddecrease their adoption intention of AIGC. The low
adoption rate of AIGC may lead to the failure of generative AI platf orms in the intensely
competitive environments (Lai et al.,2023). Then the research question is howto
engender user trust in AIGC? Previous research has noted the effect of trust on
continuance intention of ChatGPT (Baek and Kim, 2023), user acceptance of AI virtual
assistants (Xiong et al.,2024), students’intention to use ChatGPT (Rahman et al., 2023)
and tourist acceptance of ChatGPT (Xu et al.,2024). However, it has seldom explored the
development mechanism underlying user trust in AIGC. Th is research tries to fill the gap
by using the stimulus–organism–response (SOR) to uncover AIGC user trust formation
and its effect on user adoption.
Drawing on the SOR, this research examined the impact of trust on user adoption of
AIGC. Stimulus reflects the features of generative AI, which include perceived
intelligence, perceived transparency, knowledge hallucination, perceived empathy
and perceived anthropomorphism. Organism includes cognitive trust in a platform,
affective trust in a platform and trust in AIGC. Response refers to the user adoption
intention of AIGC. We conducted an online survey in China, which is a highly
competitive AI market where there are many generative AI products, such as Baidu’s
ERNIE Bot, Ali’s Qianwen and Tencent’s Hunyuan. Baidu, Ali and Tencent are the
largest search engine, e-commerce and social networking companies in China,
respectively. The intense competition is called “the battle of hundreds of LLMs”.
Generative AI platforms are eager to facilitate user adoption and expand the user base.
This fits well with our research purpose. The results will reveal AIGC user trust
development mechanism and its effect on user adoption. They may also help
generative AI platforms adopt measures to engender user trust and promote user
adoption to gain a competitive advantage.
2.Literature review
2.1 Artificial intelligence-generated content user behaviour
As an emerging form of content output, AIGC has attracted the attention of researchers.
Table 1 lists a few popular LLMs in the world. Most of them have over 100 billion
parameters, showing great processing ability. Theyoften own rich functions such as text-to-
text, text-to-imageand text-to-video.
Existing research has examined AIGC user behaviours, such as intermittent
discontinuance, adoption and continuance usage. Zhang et al. (2024) used the grounded
theory to find that factors such as privacy concerns and system qualityinfluence AIGC user
intermittent discontinuance. Mao et al. (2024) reported that information quality, user
perception and technical characteristics influence AIGC user adoption intention.Pham et al.
(2024) noted that perceived warmth and perceivedability influence tourists’use of AIGC to
obtain travel advice. Li (2024) drew on the unified theory of acceptance and use of
technology (UTAUT) to find that perceived anxiety,perceived risk, performance expectancy
and effort expectancy influence designer intention to use AIGC for design assistance.
Similarly, Menon and Shilpa (2023) noted that performance expectancy, effort expectancy
and privacy concerns affect user intention to use generative AI. Ren and Wu (2024) argued
EL
43,1
62
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