Subscription intentions for ChatGPT plus: a look at user satisfaction and self-efficacy

Abstract

Purpose

This study examines the key determinants of subscription intentions for ChatGPT Plus (paid version) in business settings, focusing on tasks such as system quality, information support, service quality, perceived intelligence, goal-congruent outcome and self-efficacy.

Design/methodology/approach

The study utilized a survey of office workers, analyzed through structural equation modeling, to explore these determinants.

Findings

The results demonstrate that system quality, service quality and perceived intelligence significantly influence satisfaction, while service quality and perceived intelligence also impact goal-congruent outcomes. Contrary to traditional models, satisfaction does not significantly correlate with usage. Instead, a significant relationship is observed between goal-congruent outcomes and usage. Self-efficacy emerges as a crucial predictor of subscription intentions, further underlined by the significant impact of usage on subscription intention.

Research limitations/implications

The study’s focus on office workers and a single artificial intelligence (AI) chatbot type may limit generalizability. Its findings illuminate several avenues for future research, particularly in diversifying the context and demographics studied.

Practical implications

This research offers actionable insights for businesses and practitioners in the implementation of AI chatbots. It highlights the importance of enhancing system quality, personalization and user confidence to boost subscription intentions, thereby guiding strategies for user engagement and technology adoption.

Originality/value

This study pioneers in investigating subscription intentions towards AI chatbots, particularly ChatGPT, providing a novel framework that expands upon traditional user behavior theories.

Citation

Jo, H. (2024), "Subscription intentions for ChatGPT plus: a look at user satisfaction and self-efficacy", Marketing Intelligence & Planning, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/MIP-08-2023-0411