The effect of trust on user adoption of AI-generated content
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| Vydáno v: | The Electronic Library vol. 43, no. 1 (2025), p. 61-76 |
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Emerald Group Publishing Limited
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| On-line přístup: | Citation/Abstract Full Text Full Text - PDF |
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MARC
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| 001 | 3157265560 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 0264-0473 | ||
| 022 | |a 1758-616X | ||
| 024 | 7 | |a 10.1108/EL-08-2024-0244 |2 doi | |
| 035 | |a 3157265560 | ||
| 045 | 2 | |b d20250101 |b d20250228 | |
| 084 | |a 36136 |2 nlm | ||
| 100 | 1 | |a Zhou, Tao |u School of Management, Hangzhou Dianzi University, Hangzhou, China | |
| 245 | 1 | |a The effect of trust on user adoption of AI-generated content | |
| 260 | |b Emerald Group Publishing Limited |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a PurposeThe purpose of this study is to examine the effect of trust on user adoption of artificial intelligence-generated content (AIGC) based on the stimulus–organism–response.Design/methodology/approachThe authors conducted an online survey in China, which is a highly competitive AI market, and obtained 504 valid responses. Both structural equation modelling and fuzzy-set qualitative comparative analysis (fsQCA) were used to conduct data analysis.FindingsThe results indicated that perceived intelligence, perceived transparency and knowledge hallucination influence cognitive trust in platform, whereas perceived empathy influences affective trust in platform. Both cognitive trust and affective trust in platform lead to trust in AIGC. Algorithm bias negatively moderates the effect of cognitive trust in platform on trust in AIGC. The fsQCA identified three configurations leading to adoption intention.Research limitations/implicationsThe main limitation is that more factors such as culture need 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 knowledge hallucination to engender users’ trust in AIGC and facilitate their adoption.Originality/valueExisting research has mainly used technology adoption theories such as unified theory of acceptance and use of technology to examine AIGC user behaviour and has seldom examined user trust development in the AIGC context. This research tries to fill the gap by disclosing the mechanism underlying AIGC user trust formation. | |
| 651 | 4 | |a China | |
| 653 | |a User behavior | ||
| 653 | |a Anxiety | ||
| 653 | |a Competitive advantage | ||
| 653 | |a Social networks | ||
| 653 | |a Generative artificial intelligence | ||
| 653 | |a Fuzzy sets | ||
| 653 | |a Privacy | ||
| 653 | |a Influence | ||
| 653 | |a Chatbots | ||
| 653 | |a Natural language | ||
| 653 | |a COVID-19 | ||
| 653 | |a Data analysis | ||
| 653 | |a Qualitative analysis | ||
| 653 | |a Algorithms | ||
| 653 | |a False information | ||
| 653 | |a Large language models | ||
| 653 | |a Transparency | ||
| 653 | |a Sociocultural factors | ||
| 653 | |a Research | ||
| 653 | |a Technology | ||
| 653 | |a Adoption of innovations | ||
| 653 | |a Cognition | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Comparative analysis | ||
| 653 | |a Cultural factors | ||
| 653 | |a Responses | ||
| 653 | |a Structural equation modeling | ||
| 653 | |a Empathy | ||
| 653 | |a Cognitive bias | ||
| 653 | |a Trust | ||
| 653 | |a Stimulus | ||
| 653 | |a Linguistic Input | ||
| 653 | |a Literature Reviews | ||
| 653 | |a Student Characteristics | ||
| 653 | |a Science Education | ||
| 653 | |a Influence of Technology | ||
| 653 | |a Environmental Influences | ||
| 653 | |a Intention | ||
| 653 | |a Natural Language Processing | ||
| 653 | |a Grounded Theory | ||
| 653 | |a Science Instruction | ||
| 653 | |a Social Influences | ||
| 653 | |a Language Processing | ||
| 700 | 1 | |a Lu, Hailin |u School of Management, Hangzhou Dianzi University, Hangzhou, China | |
| 773 | 0 | |t The Electronic Library |g vol. 43, no. 1 (2025), p. 61-76 | |
| 786 | 0 | |d ProQuest |t Library Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3157265560/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3157265560/fulltext/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3157265560/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |