The Role of Machine Learning Algorithms in Shaping the Hedonic Treadmill from the Perspective of Mental Accounting

Abstract

The development of subscription-based video streaming services has transformed the way humans consume entertainment by granting users the freedom to watch any film, anytime, and anywhere. The presence of such services has become the biggest disruptor to conventional television stations, DVD producers, and even cinema services. Film consumption behaviors have undergone a shift triggered by the Covid-19 pandemic. Individuals have become accustomed to enjoying films from the comfort of their homes or their familiar surroundings, rather than having to travel far or purchase and store DVD discs. The streaming service business has skyrocketed and amassed a large number of subscribers. This reality indicates a change in the consumer decision-making patterns for video streaming services. Streaming services are perceived to provide more advantages or benefits compared to cost or sacrifices. In accounting studies, this phenomenon is referred to as mental accounting. Thus far, there have been few scientific studies attempting to address the topic of mental accounting in the video streaming service industry, particularly in relation to framing strategies and the creation of hedonic treadmills built from machine learning algorithms. This research aims to explain how successful video streaming service businesses create hedonic treadmill conditions for their customers, binding them to addiction. This study employs a qualitative method with an interpretative approach to video streaming services to design a conceptual model of how video streaming service businesses' machine learning algorithms create hedonic treadmills for their customers. The research findings indicate that machine learning algorithms can play a role in creating hedonic treadmills through the creation of five relevant values: compatibility, variability, originality, personalization, and flexibility through recommendation and prediction systems.