Coinciding users' goals: the mediating role of goal-congruent outcomes in predicting ChatGPT plus users' continuance intention
Abstract
Subscription retention is crucial for a firm's sustained success, with the expectation-confirmation model (ECM) commonly used to explain users' intentions to maintain subscriptions. However, ECM shows limitations when applied to ChatGPT Plus, as users may continue their subscriptions even amidst fluctuating satisfaction. ChatGPT, a well-known artificial intelligence (AI) tool, frequently generates incorrect or misleading responses, yet users often retain their subscriptions. This study seeks to address gaps in ECM by empirically investigating the mediating role of goal-congruent outcomes to better identify the factors influencing users' subscription continuance intentions.
Study specs
The study employs an empirical approach, likely involving a modified Expectation-Confirmation Model (ECM) framework to analyze user behavior and outcomes associated with subscription continuance.
- Institution
- Yuan Ze University
- Study Type
- Experimental Study
- Year
- 2025
- Human Data Platform
- Prolific
- Source
- View Source Google Scholar
Measured Outcomes
The relationship between goal-congruent outcomes and users' intention to continue subscribing to ChatGPT Plus, particularly in scenarios where satisfaction varies.
Peer Review & Critical Discussion
Potential Selection Bias in 2023 Cohort
The participant pool shows a concerning overrepresentation of users from high-income demographics. Looking at Table 3, we can see that 78% of respondents had annual incomes above $75k, which significantly limits the generalizability of these findings to broader populations.
Non-naive Participants Issue
I've noticed a methodological concern regarding participant naivety. Given that Prolific users often complete multiple studies, there's a real risk that participants had prior exposure to similar experimental paradigms, which could confound the results.
RLHF Applicability to This Study Design
The implications for RLHF training pipelines are understated. If we accept the authors' conclusions about preference stability, this has direct consequences for how we should structure reward model training. The temporal decay effect described in Section 4.2 is particularly relevant.
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