Search results

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.

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

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.

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.

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.

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

This research aims to examine the integral elements of omnichannel retailing, an evolving approach that blends online and offline shopping experiences. It focuses on how various factors – merchandise variety, monetary saving, personal interaction, complaint handling, social influence, perceived crowd and skepticism – affect relative advantage, consumer satisfaction and word-of-mouth (WOM) advocacy in an omnichannel context.

A comprehensive survey was conducted with 258 participants, and the data were analyzed using partial least squares structural equation modeling (PLS-SEM). This methodology provided insights into the complex relationships between different omnichannel retailing factors and their impact on customer satisfaction and WOM.

The study found that monetary saving influences both relative advantage and satisfaction. Personal interaction was observed to affect complaint handling and relative advantage. Importantly, relative advantage was found to impact both satisfaction and WOM. Additionally, the study highlighted the roles of social influence and satisfaction in enhancing WOM.

This research adds to the existing literature by providing a nuanced understanding of the dynamics of consumer engagement in omnichannel retailing. It bridges a gap in existing research by concurrently examining the impact of online and offline retail factors on consumer satisfaction and WOM in an omnichannel setting.

This study aims to investigate the differences in consumer reviews across multiple e-commerce platforms to better assist consumers in making informed decisions. By examining the specific content of these differentiated reviews, the study seeks to provide insights that can enhance e-commerce services and improve consumer satisfaction.

The research utilizes the latent Dirichlet allocation (LDA) method for text analysis to identify the varying concerns of consumers across different e-commerce platforms for the same product. Additionally, the study expands the sentiment dictionary to address polysemy issues, allowing for a more precise capture of sentiment differences among consumers. A non-parametric test is employed to compare reviews across multiple platforms, providing a comprehensive analysis of review disparities.

The findings reveal that consumer concerns and sentiments vary significantly across different e-commerce platforms, even for the same product. The combination of text analysis and non-parametric testing highlights the objectivity of the research, offering valuable evidence and recommendations for improving e-commerce services and enhancing the shopping experience.

This study is original in its approach to combining text analysis with non-parametric testing to examine multi-platform review differences. The research not only contributes to the understanding of consumer behavior in the context of e-commerce but also provides practical suggestions for platforms and consumers, aiming to optimize service quality and consumer satisfaction.