Due to the intense competition among generative AI platforms, users may switch from a platform to an alternative one. This may lead to user attrition and undermine the competitive advantage. The purpose of this research is to examine user switching intention between generative AI platforms based on the push-pull-mooring (PPM). Push factors include information hallucination, privacy risk, and dissatisfaction, while pull factors include perceived interactivity, perceived anthropomorphism, perceived personalization, and user experience. Mooring factor is switching costs. Both structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) were used to conduct data analysis. The results validate the hypotheses. The fsQCA found that dissatisfaction is a common core condition triggering switching intention.
BaekTHBakpayevMYoonS, et al. (2022) Smiling AI agents: How anthropomorphism and broad smiles increase charitable giving. International Journal of Advertising41(5): 850–867.
2.
BaekTHKimM (2023) Is ChatGPT scary good? How user motivations affect creepiness and trust in generative artificial intelligence. Telematics and Informatics83: 102030.
3.
BalakrishnanJAbedSSJonesP (2022) The role of meta-UTAUT factors, perceived anthropomorphism, perceived intelligence, and social self-efficacy in chatbot-based services?Technological Forecasting and Social Change180: 121692.
4.
BalakrishnanJDwivediYK (2024) Conversational commerce: Entering the next stage of AI-powered digital assistants. Annals of Operations Research333: 653–687.
5.
BangYCahyawijayaSLeeN, et al. (2023) A multitask, multilingual, multimodal evaluation of ChatGPT on reasoning, hallucination, and interactivity. In: Proceedings of the 13th International Joint Conference on Natural Language Processing, pp.675–718.
6.
BhattacherjeeA (2001) Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly25(3): 351–370.
7.
BlutMWangCWünderlichNV, et al. (2021) Understanding anthropomorphism in service provision: A meta-analysis of physical robots, chatbots, and other AI. Journal of the Academy of Marketing Science49: 632–658.
8.
Calahorra-CandaoGMartín-de HoyosMJ (2024) The effect of anthropomorphism of virtual voice assistants on perceived safety as an antecedent to voice shopping. Computers in Human Behavior153: 108124.
9.
ChangHHLiSYWongKH (2017) Applying push-pull-mooring to investigate channel switching behaviors: M-shopping self-efficacy and switching costs as moderators. Electronic Commerce Research and Applications24: 50–67.
10.
ChangI-CLiuC-CChenK (2014) The push, pull and mooring effects in virtual migration for social networking sites. Information Systems Journal24(4): 323–346.
11.
ChenY-HKengC-J (2019) Utilizing the push-pull-mooring-habit framework to explore users’ intention to switch from offline to online real-person English learning platform. Internet Research29(1): 167–193.
12.
ChengS (2024) Understanding users’ voluntary switching behavior for user interfaces of intelligent personal assistant devices. Information Systems Frontiers: 1–24.
13.
ChengXZhangXCohenJ, et al. (2022) Human vs. AI: Understanding the impact of anthropomorphism on consumer response to chatbots from the perspective of trust and relationship norms. Information Processing & Management59(3): 102940.
14.
ChengYJiangH (2020) How do AI-driven chatbots impact user experience? Examining gratifications, perceived privacy risk, satisfaction, loyalty, and continued use. Journal of Broadcasting & Electronic Media64(4): 592–614.
15.
DaiBDengA (2018) Analysis on the influencing factors of social media discontinuance and switching behaviors. Information Science36(5): 64–70.
16.
FanLSuhY-H (2014) Why do users switch to a disruptive technology? An empirical study based on expectation-disconfirmation theory. Information & Management51(2): 240–248.
17.
FarukLIDRohanRNinrutsirikunU, et al. (2023) University students’ acceptance and usage of generative AI (ChatGPT) from a psycho-technical perspective. In: Proceedings of the 13th International Conference on Advances in Information Technology, pp.1–8.
18.
FeathermanMSMiyazakiADSprottDE (2010) Reducing online privacy risk to facilitate e-service adoption: The influence of perceived ease of use and corporate credibility. Journal of Services Marketing24(3): 219–229.
19.
GkinkoLElbannaA (2022) Hope, tolerance and empathy: Employees’ emotions when using an AI-enabled chatbot in a digitalised workplace. Information Technology & People35(6): 1714–1743.
20.
Guertin-LahoudSCoursarisCKSénécalS, et al. (2023) User experience evaluation in shared interactive virtual reality. Cyberpsychology, Behavior, and Social Networking26(4): 263–272.
21.
GurungARajaMK (2016) Online privacy and security concerns of consumers. Information & Computer Security24(4): 348–371.
22.
HaridasanACFernandoAGBalakrishnanS (2021) Investigation of consumers’ cross-channel switching intentions: A push-pull-mooring approach. Journal of Consumer Behaviour20(5): 1092–1112.
23.
HongHXuD (2017) An empirical study of mobile social app continuance intention: Integrating flow experience and switching costs. International Journal of Networking and Virtual Organisations17(4): 410–424.
24.
HongWThongJYL (2013) Internet privacy concerns: An integrated conceptualization and four empirical studies. MIS Quarterly37(1): 275–298.
25.
HowardDJKerinRA (2004) The effects of personalized product recommendations on advertisement response rates: The “try this. It works!” technique. Journal of Consumer Psychology14(3): 271–279.
26.
HsiaoK-LChenC-C (2022) What drives continuance intention to use a food-ordering chatbot? An examination of trust and satisfaction. Library Hi Tech40(4): 929–946.
27.
HsiehJ-KHsiehY-CChiuH-C, et al. (2012) Post-adoption switching behavior for online service substitutes: A perspective of the push-pull-mooring framework. Computers in Human Behavior28(5): 1912–1920.
JangYJChoS-BKimWG (2013) Effect of restaurant patrons’ regret and disappointment on dissatisfaction and behavioral intention. Journal of Travel & Tourism Marketing30(5): 431–444.
30.
KangMShinD-HGongT (2016) The role of personalization, engagement, and trust in online communities. Information Technology & People29(3): 580–596.
31.
KimJKangSBaeJ (2022) Human likeness and attachment effect on the perceived interactivity of AI speakers. Journal of Business Research144: 797–804.
32.
KimJSKimMBaekTH (2024) Enhancing user experience with a generative AI chatbot. International Journal of Human–Computer Interaction: 1–13.
33.
LeeES (1966) A theory of migration. Demography3(1): 47–57.
34.
LeeJ-CWangJ (2023) From offline to online: Understanding users’ switching intentions from traditional wealth management services to mobile wealth management applications. International Journal of Bank Marketing41(2): 369–394.
35.
LiXLeeGJXYuenKF (2024) Consumer acceptance of urban drone delivery: The role of perceived anthropomorphic characteristics. Cities148: 104867.
36.
LiXSungY (2021) Anthropomorphism brings us closer: The mediating role of psychological distance in user-AI assistant interactions. Computers in Human Behavior118: 106680.
37.
LiuZZhaoYChenS, et al. (2021) Exploring askers’ switching from free to paid social Q&A services: A perspective on the push-pull-mooring framework. Information Processing & Management58(1): 102396.
38.
MaXHuoY (2023) Are users willing to embrace ChatGPT? Exploring the factors on the acceptance of chatbots from the perspective of AIDUA framework. Technology in Society75: 102362.
39.
MendeMScottMLvan DoornJ, et al. (2019) Service robots rising: How humanoid robots influence service experiences and elicit compensatory consumer responses. Journal of Marketing Research56(4): 535–556.
40.
MengMZhuQ (2021) A review of research on user experience at home and abroad. Research on Library Science9: 9–19.
41.
Mohd-AnyAASarkerMHuiFLZ (2024) Understanding users’ switching intention of cloud storage services: A push-pull-mooring framework. Journal of Consumer Behaviour23(2): 748–768.
42.
MonoarfaTASumarwanUSurosoAI, et al. (2023) Switch or stay? Applying a push-pull-mooring framework to evaluate behavior in e-grocery shopping. Sustainability15(7): 6018.
43.
MoonB (1995) Paradigms in migration research: Exploring “moorings” as a schema. Progress in Human Geography19(4): 504–524.
PappasIOWoodsideAG (2021) Fuzzy-set qualitative comparative analysis (fsQCA): Guidelines for research practice in information systems and marketing. International Journal of Information Management58: 102310.
46.
PillaiRGhanghorkarYSivathanuB, et al. (2023) Adoption of artificial intelligence (AI) based employee experience (EEX) chatbots. Information Technology & People37(1): 449–478.
47.
PolyportisAPahosN (2024) Understanding students’ adoption of the ChatGPT chatbot in higher education: The role of anthropomorphism, trust, design novelty and institutional policy. Behaviour & Information Technology: 1–22.
48.
RaginC (2008) Redesigning Social Inquiry: Fuzzy Sets and Beyond. Chicago, IL: University of Chicago Press.
49.
RavensteinEG (1885) The laws of migration. Journal of the Statistical Society48(2): 167–227.
SekarputriJAFitrianiWRHidayantoAN, et al. (2024) The roles of privacy, security, and dissatisfaction in affecting switching intention on messenger applications. Multimedia Tools and Applications83: 45625–45651.
52.
SunYLiuDChenS, et al. (2017) Understanding users’ switching behavior of mobile instant messaging applications: An empirical study from the perspective of push-pull-mooring framework. Computers in Human Behavior75: 727–738.
53.
TanYMinDLiY, et al. (2023) Can ChatGPT replace traditional KBQA models? An in-depth analysis of the question answering performance of the GPT LLM family. Lecture Notes in Computer Science14265: 348–367.
54.
Van DisEABollenJZuidemaW, et al. (2023) ChatGPT: Five priorities for research. Nature614(7947): 224–226.
55.
WuKVassilevaJZhaoY (2017) Understanding users’ intention to switch personal cloud storage services: Evidence from the Chinese market. Computers in Human Behavior68: 300–314.
56.
XiangGChenQLiQ (2022) How attachment affects users’ continued use intention of tourism mobile platform: A user experience perspective. Frontiers in Psychology13: 995384.
57.
XuHWangJTaiZ, et al. (2021) Empirical study on the factors affecting user switching behavior of online learning platform based on push-pull-mooring theory. Sustainability13(13): 7087.
58.
XuYYangYChengZ, et al. (2014) Retaining and attracting users in social networking services: An empirical investigation of cyber migration. The Journal of Strategic Information Systems23(3): 239–253.
59.
YinCZhouYHeP, et al. (2023) Research on the influencing factors of the switching behavior of Chinese social media users: QQ transfer to WeChat. Library Hi Tech41(3): 771–787.
60.
YoonCLimD (2021) Customers’ intentions to switch to internet-only banks: Perspective of the push-pull-mooring model. Sustainability13(14): 8062.
61.
ZhaXYangHYanY, et al. (2018) Exploring the effect of social media information quality, source credibility and reputation on informational fit-to-task: Moderating role of focused immersion. Computers in Human Behavior79: 227–237.
62.
ZhangHLiuCWangD, et al. (2023) Research on the influencing factors of ChatGPT users’ intention. Information Studies: Theory & Application46(4): 15–22.
63.
ZhangZHeHCaoG (2019) The influence of perception characteristics and personalization on the willingness to use mobile phone Reading continuously. Library and Information Service63(7): 80–85.
64.
ZhouPZhaoSMaY, et al. (2023) What influences user participation in an online health community?The Stimulus-Organism-Response Model Perspective. Aslib Journal of Information Management75(2): 364–389.
65.
ZhouTLuY (2011) The effect of interactivity on the flow experience of mobile commerce user. International Journal of Mobile Communications9(3): 225–242.
66.
ZhouTMiQ (2024) Examining user switching between social Q&A platforms: A push–pull-mooring perspective. Universal Access in the Information Society23: 1333–1342.
