Although ChatGPT has gained widespread popularity, research on users’ post-adoption behaviors remains limited. This study examined whether information quality (across five distinct attributes) predicts information usefulness and source trust, which, in turn, influences users’ intention to continue using ChatGPT. Data from ChatGPT users were analyzed using structural equation modeling. The results indicate that the accuracy, richness, timeliness, format, and relevance of the information presented by ChatGPT were significantly associated with both information usefulness and source trust. Moreover, the findings showed that both information usefulness and source trust were positively and significantly associated with the continuance usage intention toward ChatGPT. This study sheds light on the factors that drive user perceptions and behaviors and offers a deeper understanding of the continuance usage within the domain of generative AI like ChatGPT. The implications of these findings and directions for future research are discussed.
AbdulkareemAKMohd RamliR (2022) Does trust in e-government influence the performance of e-government? An integration of information system success model and public value theory. Transforming Government: People, Process and Policy16(1): 1–17.
2.
AlAfnanMADishariSJovicM, et al. (2023) ChatGPT as an educational tool: opportunities, challenges, and recommendations for communication, business writing, and composition courses. Journal of Artificial Intelligence and Technology3(2): 60–68.
3.
AshfaqMYunJYuS, et al. (2020) I, chatbot: modeling the determinants of users’ satisfaction and continuance intention of AI-powered service agents. Telematics and Informatics54: 101473.
4.
AslamWHussainAFarhatK, et al. (2020) Underlying factors influencing consumers’ trust and loyalty in e-commerce. Business Perspectives and Research8(2): 186–204.
5.
Baidoo-AnuDAnsahLO (2023) Education in the era of generative artificial intelligence (AI): understanding the potential benefits of ChatGPT in promoting teaching and learning. Journal of AI7(1): 52–62.
6.
BangYCahyawijayaSLeeN, et al. (2023) A multitask, multilingual, multimodal evaluation of ChatGPT on reasoning, hallucination, and interactivity. arXiv preprint arXiv:2302.04023.
7.
BhattacherjeeA (2001) Understanding information systems continuance: an expectation-confirmation model. MIS Quarterly25(3): 351–370.
8.
BhattacherjeeASanfordC (2006) Influence processes for information technology acceptance: an elaboration likelihood model. MIS Quarterly30(4): 805–825.
9.
BilselRUBüyüközkanGRuanD (2006) A fuzzy preference-ranking model for a quality evaluation of hospital web sites. International Journal of Intelligent Systems21(11): 1181–1197.
10.
BuchananJHickmanW (2024) Do people trust humans more than ChatGPT?Journal of Behavioral and Experimental Economics112: 102239.
11.
ChaikenSMaheswaranD (1994) Heuristic processing can bias systematic processing: effects of source credibility, argument ambiguity, and task importance on attitude judgment. Journal of Personality and Social Psychology66(3): 460–473.
12.
ChangHHLuYYLinSC (2020) An elaboration likelihood model of consumer respond action to Facebook second-hand marketplace: impulsiveness as a moderator. Information & Management57(2): 103171.
13.
ChenCCTsengYD (2011) Quality evaluation of product reviews using an information quality framework. Decision Support Systems50(4): 755–768.
14.
ChenYYangLZhangM, et al. (2018) Central or peripheral? Cognition elaboration cues’ effect on users’ continuance intention of mobile health applications in the developing markets. International Journal of Medical Informatics116: 33–45.
15.
ChengYM (2014) Extending the expectation-confirmation model with quality and flow to explore nurses’ continued blended e-learning intention. Information Technology & People27(3): 230–258.
16.
CheungCMLeeMKRabjohnN (2008) The impact of electronic word-of-mouth: the adoption of online opinions in online customer communities. Internet Research18(3): 229–247.
17.
CheungCMThadaniDR (2012) The impact of electronic word-of-mouth communication: a literature analysis and integrative model. Decision Support Systems54(1): 461–470.
18.
CheungR (2014) The influence of electronic word-of-mouth on information adoption in online customer communities. Global Economic Review43(1): 42–57.
19.
ChinWW (1998) Commentary: issues and opinion on structural equation modeling. MIS Quarterly22(1): 1–10.
20.
ChongAYL (2013) Understanding mobile commerce continuance intentions: an empirical analysis of Chinese consumers. Journal of Computer Information Systems53(4): 22–30.
21.
ChouWCChengYP (2012) A hybrid fuzzy MCDM approach for evaluating website quality of professional accounting firms. Expert Systems with Applications39(3): 2783–2793.
22.
ChoudhuryAShamszareH (2023) Investigating the impact of user trust on the adoption and use of ChatGPT: Survey analysis. Journal of Medical Internet Research25: e47184.
23.
DedeogluBB (2019) Are information quality and source credibility really important for shared content on social media? The moderating role of gender. International Journal of Contemporary Hospitality Management31(1): 513–534.
24.
De KervenoaelRSchwobAHasanR, et al. (2021) Consumers’ perceived value of healthier eating: a SEM analysis of the internalisation of dietary norms considering perceived usefulness, subjective norms, and intrinsic motivations in Singapore. Journal of Consumer Behaviour20(3): 550–563.
25.
DeLoneWHMcLeanER (1992) Information systems success: the quest for the dependent variable. Information Systems Research3(1): 60–95.
26.
DeLoneWHMcLeanER (2003) The DeLone and McLean model of information systems success: a ten-year update. Journal of Management Information Systems19(4): 9–30.
27.
DwivediYKKshetriNHughesL, et al. (2023) So what if ChatGPT wrote it? Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management71: 102642.
28.
ErkanIEvansC (2016) The influence of eWOM in social media on consumers’ purchase intentions: an extended approach to information adoption. Computers in Human Behavior61: 47–55.
29.
FilieriRAcikgozFNdouV, et al. (2021) Is TripAdvisor still relevant? The influence of review credibility, review usefulness, and ease of use on consumers’ continuance intention. International Journal of Contemporary Hospitality Management33(1): 199–223.
30.
FilieriRAlguezauiSMcLeayF (2015) Why do travelers trust TripAdvisor? Antecedents of trust towards consumer-generated media and its influence on recommendation adoption and word of mouth. Tourism Management51: 174–185.
31.
FilieriRMcLeayF (2014) E-WOM and accommodation: an analysis of the factors that influence travelers’ adoption of information from online reviews. Journal of Travel Research53(1): 44–57.
32.
FitriaTN (2023) Artificial intelligence (AI) technology in OpenAI ChatGPT application: a review of ChatGPT in writing English essay. ELT Forum: Journal of English Language Teaching12(1): 44–58.
33.
FornellCLarckerDF (1981) Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research18: 39–50.
34.
GaoLWaechterKABaiX (2015) Understanding consumers’ continuance intention towards mobile purchase: A theoretical framework and empirical study–a case of China. Computers in Human Behavior53: 249–262.
35.
GorlaNSomersTMWongB (2010) Organizational impact of system quality, information quality, and service quality. The Journal of Strategic Information Systems19(3): 207–228.
36.
HajliN (2018) Ethical environment in the online communities by information credibility: a social media perspective. Journal of Business Ethics149(4): 799–810.
37.
HalilovicSCicicM (2013) Antecedents of information systems user behaviour - extended expectation-confirmation model. Behaviour & Information Technology32(4): 359–370.
38.
HanJTChenQLiuJG, et al. (2018) The persuasion of borrowers’ voluntary information in peer to peer lending: an empirical study based on elaboration likelihood model. Computers in Human Behavior78: 200–214.
39.
HarahapMAKAusatAMARachmanA, et al. (2023) Overview of ChatGPT technology and its potential in improving tourism information services. Jurnal Minfo Polgan12(1): 424–431.
40.
HarrisPRSillenceEBriggsP (2011) Perceived threat and corroboration: key factors that improve a predictive model of trust in internet-based health information and advice. Journal of Medical Internet Research13(3): e51.
41.
HoodaAGuptaPJeyarajA, et al. (2022) The effects of trust on behavioral intention and use behavior within e-government contexts. International Journal of Information Management67: 102553.
42.
HsuCLChangKCChenMC (2012) The impact of website quality on customer satisfaction and purchase intention: perceived playfulness and perceived flow as mediators. Information Systems and e-Business Management10: 549–570.
43.
HuhJShinW (2014) Trust in prescription drug brand websites: website trust cues, attitude toward the website, and behavioral intentions. Journal of Health Communication19(2): 170–191.
44.
HuschensMBrieschMSobaniaD, et al. (2023) Do you trust ChatGPT?–Perceived credibility of human and AI-generated content. arXiv preprint arXiv:2309.02524.
45.
JinXLLeeMKCheungCM (2010) Predicting continuance in online communities: model development and empirical test. Behaviour & Information Technology29(4): 383–394.
46.
KimHNiehmLS (2009) The impact of website quality on information quality, value, and loyalty intentions in apparel retailing. Journal of Interactive Marketing23(3): 221–233.
47.
KimJHKimJParkJ, et al. (2023) When ChatGPT gives incorrect answers: the impact of inaccurate information by generative AI on tourism decision-making. Journal of Travel Research64(1): 51–73.
48.
KimY (2016) Trust in health information websites: a systematic literature review on the antecedents of trust. Health Informatics Journal22(2): 355–369.
49.
KrasnovaHVeltriNFElingN, et al. (2017) Why men and women continue to use social networking sites: the role of gender differences. The Journal of Strategic Information Systems26(4): 261–284.
50.
LaiCYCheungKYChanCS (2023) Exploring the role of intrinsic motivation in ChatGPT adoption to support active learning: an extension of the technology acceptance model. Computers and Education: Artificial Intelligence5: 100178.
51.
LarsenTJSørebøAMSørebøØ (2009) The role of task-technology fit as users’ motivation to continue information system use. Computers in Human Behavior25(3): 778–784.
52.
LeeDYLehtoMR (2013) User acceptance of YouTube for procedural learning: an extension of the technology acceptance model. Computers & Education61: 193–208.
53.
LeeJCTangYJiangS (2023) Understanding continuance intention of artificial intelligence (AI)-enabled mobile banking applications: an extension of AI characteristics to an expectation confirmation model. Humanities and Social Sciences Communications10(1): 1–12.
54.
LeeYWStrongDMKahnBK, et al. (2002) AIMQ: a methodology for information quality assessment. Information & Management40(2): 133–146.
55.
LiHLiuY (2014) Understanding post-adoption behaviors of e-service users in the context of online travel services. Information & Management51(8): 1043–1052.
56.
LienCHCaoY (2014) Examining WeChat users’ motivations, trust, attitudes, and positive word-of-mouth: evidence from China. Computers in Human Behavior41: 104–111.
57.
LimSHKimD (2012) The role of trust in the use of health infomediaries among university students. Informatics for Health and Social Care37(2): 92–105.
58.
LimayemMHirtSGCheungCM (2007) How habit limits the predictive power of intention: the case of information systems continuance. MIS Quarterly31(4): 705–737.
59.
LuJBaiH (2021) Information usefulness and attitude formation: a double-dependent variable model (DDV) to examine the impacts of online reviews on consumers. Journal of Organizational and End User Computing33(6): 1–22.
60.
LucyLBammanD (2021) Gender and representation bias in GPT-3 generated stories. In: Proceedings of the third workshop on narrative understanding, pp. 48–55. Association for Computational Linguistics.
61.
LuoCLuoXRBoseR (2018) Information usefulness in online third party forums. Computers in Human Behavior85: 61–73.
62.
MarikyanDPapagiannidisSRanaOF, et al. (2024) Working in a smart home environment: examining the impact on productivity, well-being and future use intention. Internet Research34(2): 447–473.
63.
MatuteJPolo-RedondoYUtrillasA (2016) The influence of eWOM characteristics on online repurchase intention: mediating roles of trust and perceived usefulness. Online Information Review40(7): 1090–1110.
64.
MillerH (2005) Information quality and market share in electronic commerce. Journal of Services Marketing19(2): 93–102.
65.
MolinilloSAguilar-IllescasRAnaya-SánchezR, et al. (2021) Social commerce website design, perceived value and loyalty behavior intentions: the moderating roles of gender, age and frequency of use. Journal of Retailing and Consumer Services63: 102404.
66.
MouJShinDHCohenJ (2017) Understanding trust and perceived usefulness in the consumer acceptance of an e-service: a longitudinal investigation. Behaviour & Information Technology36(2): 125–139.
67.
MunYYYoonJJDavisJM, et al. (2013) Untangling the antecedents of initial trust in web-based health information: the roles of argument quality, source expertise, and user perceptions of information quality and risk. Decision Support Systems55(1): 284–295.
68.
NahFFHDavisS (2002) HCI research issues in e-commerce. Journal of Electronic Commerce Research3(3): 98–113.
69.
NicolaouAIIbrahimMVan HeckE (2013) Information quality, trust, and risk perceptions in electronic data exchanges. Decision Support Systems54(2): 986–996.
70.
NiuBMvondoGFN (2024) I am ChatGPT, the ultimate AI chatbot! Investigating the determinants of users’ loyalty and ethical usage concerns of ChatGPT. Journal of Retailing and Consumer Services76: 103562.
71.
ParkNRomanRLeeS, et al. (2009) User acceptance of a digital library system in developing countries: an application of the technology acceptance model. International Journal of Information Management29(3): 196–209.
72.
PengLLiaoQWangX, et al. (2016) Factors affecting female user information adoption: an empirical investigation on fashion shopping guide websites. Electronic Commerce Research16: 145–169.
73.
PetterSDeLoneWMcLeanE (2008) Measuring information systems success: models, dimensions, measures, and interrelationships. European Journal of Information Systems17: 236–263.
74.
PetterSDeLoneWMcLeanER (2013) Information systems success: the quest for the independent variables. Journal of Management Information Systems29(4): 7–62.
75.
PettyRECacioppoJT (1986) The Elaboration Likelihood Model of Persuasion. New York, NY: Springer.
76.
PhamHCDuongCDNguyenGKH (2024) What drives tourists’ continuance intention to use ChatGPT for travel services? A stimulus-organism-response perspective. Journal of Retailing and Consumer Services78: 103758.
77.
PodsakoffPMMacKenzieSBLeeJY, et al. (2003) Common method biases in behavioral research: a critical review of the literature and recommended remedies. Journal of Applied Psychology88(5): 879–903.
78.
RemondiCCompareATascaGA, et al. (2020) Insecure attachment and technology addiction among young adults: the mediating role of impulsivity, alexithymia, and general psychological distress. Cyberpsychology, Behavior, and Social Networking23(11): 761–767.
79.
Salehi-EsfahaniSRavichandranSIsraeliA, et al. III (2016) Investigating information adoption tendencies based on restaurants’ user-generated content utilizing a modified information adoption model. Journal of Hospitality Marketing & Management25(8): 925–953.
80.
SangWJiSPuB, et al. (2023) The effect of tourism live streaming information quality and source credibility on users’ continuous viewing intention: an application of elaboration likelihood model. Asia Pacific Journal of Tourism Research28(3): 177–190.
81.
SetiaPSetiaPVenkateshV, et al. (2013) Leveraging digital technologies: how information quality leads to localized capabilities and customer service performance. MIS Quarterly37(2): 565–590.
82.
ShenXLCheungCMLeeMK (2013) What leads students to adopt information from Wikipedia? An empirical investigation into the role of trust and information usefulness. British Journal of Educational Technology44(3): 502–517.
83.
ShroutPEBolgerN (2002) Mediation in experimental and nonexperimental studies: new procedures and recommendations. Psychological Methods7(4): 422–445.
84.
SillenceEBriggsPHarrisPR, et al. (2007) How do patients evaluate and make use of online health information?Social Science & Medicine64(9): 1853–1862.
85.
SongJ (2007) Trust in health infomediaries. Decision Support Systems43(2): 390–407.
86.
SongJBakerJLeeS, et al. (2012) Examining online consumers’ behavior: a service-oriented view. International Journal of Information Management32(3): 221–231.
87.
SussmanSWSiegalWS (2003) Informational influence in organizations: an integrated approach to knowledge adoption. Information Systems Research14(1): 47–65.
88.
TangJTETangTIChiangCH (2014) Blog learning: effects of users’ usefulness and efficiency towards continuance intention. Behaviour & Information Technology33(1): 36–50.
89.
TeoTSSrivastavaSCJiangLI (2008) Trust and electronic government success: an empirical study. Journal of Management Information Systems25(3): 99–132.
90.
ThompsonB (2004) Exploratory and Confirmatory Factor Analysis: Understanding Concepts and Applications. Washington, DC: American Psychological Association.
91.
ThongsriNTripakOBaoY (2024) Do learners exhibit a willingness to use ChatGPT? An advanced two-stage SEM-neural network approach for forecasting factors influencing ChatGPT adoption. Interactive Technology and Smart Education: 1–18.
92.
TiwariCKBhatMAKhanST, et al. (2023) What drives students toward ChatGPT? An investigation of the factors influencing adoption and usage of ChatGPT. Interactive Technology and Smart Education21(3): 333–355.
93.
Van BulckLMoonsP (2024) What if your patient switches from Dr. Google to Dr. ChatGPT? A vignette-based survey of the trustworthiness, value, and danger of ChatGPT-generated responses to health questions. European Journal of Cardiovascular Nursing23(1): 95–98.
94.
WangZWaltherJBPingreeS, et al. (2008) Health information, credibility, homophily, and influence via the internet: web sites versus discussion groups. Health Communication23(4): 358–368.
95.
WhiteJFuQHaysS, et al. (2023) A prompt pattern catalog to enhance prompt engineering with ChatGPT. arXiv preprint arXiv:2302.11382.
96.
WuJJChenYHChungYS (2010) Trust factors influencing virtual community members: a study of transaction communities. Journal of Business Research63(9-10): 1025–1032.
97.
XuMDuHNiyatoD, et al. (2024) Unleashing the power of edge-cloud generative AI in mobile networks: a survey of AIGC services. IEEE Communications Surveys & Tutorials26(2): 1127–1170.
98.
XuRFengYChenH (2023) ChatGPT vs. Google: a comparative study of search performance and user experience. arXiv preprint arXiv:2307.01135.
99.
ZahediFMSongJ (2008) Dynamics of trust revision: using health infomediaries. Journal of Management Information Systems24(4): 225–248.
100.
ZainalNTAHarunALilyJ (2017) Examining the mediating effect of attitude towards electronic words-of-mouth (eWOM) on the relation between the trust in eWOM source and intention to follow eWOM among Malaysian travellers. Asia Pacific Management Review22(1): 35–44.
101.
ZhengYZhaoKStylianouA (2013) The impacts of information quality and system quality on users’ continuance intention in information-exchange virtual communities: an empirical investigation. Decision Support Systems56: 513–524.
102.
ZhouLZhangZZhaoL, et al. (2022) How user personality and information characteristics influence the creative information quality on open innovation platforms: an elaboration likelihood model. Kybernetes51(7): 2305–2325.
103.
ZhouT (2011a) An empirical examination of users’ post-adoption behaviour of mobile services. Behaviour & Information Technology30(2): 241–250.
104.
ZhouT (2011b) Examining the critical success factors of mobile website adoption. Online Information Review35(4): 636–652.
105.
ZhouT (2022) Understanding online health community users’ information adoption intention: an elaboration likelihood model perspective. Online Information Review46(1): 134–146.
106.
ZwijnenbergNCHendriksMDammanOC, et al. (2012) Understanding and using comparative healthcare information; the effect of the amount of information and consumer characteristics and skills. BMC Medical Informatics and Decision Making12: 1–11.
