How to reduce termination on freemium platforms—literature review and empirical analysis

Because of growing digitalization, consumers are confronted with more and more new technologies. Digital ecosystems and digital platforms are of great importance in this context. One possibility for monetizing the services offered on digital platforms is the freemium pricing model. In light of increasing competition, it is important for platform operators to know as much as possible about the usage behaviour of their consumers. In particular, they need to know what factors influence the termination behaviour of paying users. The empirical study is based on data from 20,319 paying users of a freemium platform that offers information on equestrian sports. Using a logistic regression, several possible influencing factors on the probability of termination are examined. Among other things, the study shows that the termination probability is greater if the paying user has chosen a shorter subscription. Users who have already terminated their subscription before their current one are also at greater risk of churn. Premium users with high levels of involvement and members who receive notifications about new content on the platform are less likely to quit. The number of video views is also negatively related to the termination probability. Freemium providers can use this information to predict terminations and take appropriate action, such as special offers for users at risk of termination.

What factors influence termination behaviour on freemium platforms? An empirical analysis of the termination probability among paying users

Lehmann‐Zschunke

2024

Acceptance of or resistance to facial recognition payment: A systematic review

Yu,

Wang,

Bian

et al. 2024

With increasing evidence supporting the use of biometric identification methods for authentication, this study aims to enhance our understanding of the factors influencing the acceptance of and resistance to facial recognition payment (FRP) systems. To provide a comprehensive review of these factors, we conducted a systematic literature review (SLR) of empirical studies. We examined 22 key research articles from an initial pool of 1372 publications, identifying 37 factors that influence consumer acceptance or resistance to FRP. These factors were categorized into usage‐related aspects, attitudes and evaluations, user‐related traits, privacy and security concerns, and other factors. Our findings reveal that the most frequently cited factors include performance expectancy, effort expectancy, perceived usefulness, and perceived ease of use. These factors are crucial in contexts where FRP can increase productivity by providing prompt information and effective assistance. This research proposes a collective model for understanding the determinants of FRP acceptance or resistance, integrating theoretical frameworks and empirical findings. The model emphasizes the context‐dependency of user acceptance, highlighting the importance of addressing both technological and psychological factors. It incorporates usage characteristics, user characteristics, and privacy and security concerns, which are mediated by attitudes and evaluations. The proposed model provides a comprehensive framework for understanding and addressing the factors influencing acceptance of and resistance to FRP, guiding FRP service providers in developing effective strategies to increase user adoption, with future research needed to refine and assess the model further.

Exploring AI technology and consumer behavior in retail interactions

Petrescu,

Krishen,

Gironda

et al. 2024

This research systematically reviews artificial intelligence (AI) effects in customer‐interfacing retail applications based on an ecosystem value co‐creation framework. We conduct a bibliometric and conceptual mapping analysis study, focusing on AI‐related implications for consumers' and other stakeholders' well‐being, social interaction, and societal welfare. A co‐citation network visualization of critical AI journal articles is generated, and a network visualization of AI keyword relationships and clustered topic areas is presented and discussed, along with a conceptual map of the relationships between key concepts and substantive AI themes. In an ecosystem context, the multidisciplinary‐based bibliometric and conceptual mapping findings of our analysis reflect the need to focus not only on the positive and negative effects on stakeholder well‐being, social interaction, and societal welfare but also on how effects created in one of these levels impact the value created in the other social layers. Furthermore, the interdisciplinary characteristics necessary in effectively implementing and managing AI technologies emphasize the need for collaboration among multiple organizational departments, technology partners, and other members of the business ecosystem. The findings of this research contribute to assessing both the positive and negative effects of AI and allow for its implementation in a way that is helpful to organizations, employees, consumers, and society. This study should also help managers decide which situations are best suited for using AI and which are not.