A DESCRIPTIVE STATISTICAL ANALYSIS OF REFERRAL CONSUMER BEHAVIOR INFLUENCING THE USE OF CHATGPT FOR FITNESS

Abstract

This paper examines the impact of referral behaviour on the use of ChatGPT to find fitness through a descriptive statistical analysis. This
analysis is conducted using 415 valid responses that were obtained among the users of health and fitness applications. The primary aim is to
learn how social referrals influence the adoption decision of users, and how they stay active using apps related to fitness. In this study, one of
the statements will be the dependent variable, i.e., I use this app because my friend/relative uses this app, which depicts referral-based adoption
behaviour. The independent variables are intention to persist in using health and fitness applications, being a user tracking fitness activities via
the application and user satisfaction with health and fitness platforms. Describing statistics was used to compare the trends of responses and
relationships between the variables. The results indicate that referral behaviour plays an important role in determining the intention of users to
adopt and be continued to use ChatGPT in terms of fitness. Those who had firm plans of continuing use and had already developed past
practices of using fitness-trackers were more prone to adopting the use of the app by social suggestions. Further, there was a correlation between
increased satisfaction and increased acceptability toward usage through referrals, and this implies that satisfied users are more likely to believe
in recommendations by their social networks. In general, the paper provides a significant focus on the role of social influence and positive user
experiences in marketing AI-based fitness apps and provides the insights to enhance the user engagement and retention measures.