Journey from light viewer to heavy viewer when you get engaged in OTT
A Hidden Markov Model approach
Bibliographic Data
| ID | 21582675 |
|---|---|
| Authors | Dongnyok Shim (0000-0003-1406-9984, Konkuk University, corresponding author) |
| Year | 2025 |
| Volume | 37 |
| Issue | 4 |
| Pages | 185-205 |
| Publication date | 2025-10-02 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Journal of Media Economics (JOURNAL) |
| Journal identifiers | ISSN: 0899-7764 • E-ISSN: 1532-7736 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/08997764.2025.2535329 |
| OpenAlex | W4412592885 |
| Language | EN |
| References cited | 42 |
This study examines the dynamic journey of Over-The-Top (OTT) users as they transfer from light to heavy viewers, employing a Hidden Markov Model (HMM) approach to capture the evolving nature of viewer engagement. Unlike previous static analyses, we model the consumer learning process through which viewers discover their utility of OTT services and adjust their time allocation decisions. Analysis of three-year panel data from the Korean Media Panel survey reveals three distinct engagement states – light, moderate, and heavy viewers – with substantial transition probabilities between states. Service preferences (Netflix, YouTube, local OTT) and infrastructure factors (unlimited data plans, tablet ownership) significantly influence state transitions, while socio-demographic characteristics and motivational innovativeness dimensions affect viewing time differently across engagement states. Netflix preference shows stronger association with transitions to higher engagement states, while YouTube demonstrates better retention of heavy viewers. Age consistently predicts viewing time, while income and innovativeness dimensions exhibit complex patterns that vary between weekday and weekend viewing, reflecting different opportunity costs. These findings contribute to understanding the state-dependent nature of OTT consumption and provide implications for content strategy, platform competition, and customer retention in the evolving OTT market
Advertising · Art · Business · Machine learning · Markov chain · Computer Science · Consumer Market Behavior and Pricing · Consumer Retail Behavior Studies · Technology Adoption and User Behaviour · Artificial Intelligence
The impact of social media on risk perceptions during the Mers outbreak in South Korea
Does the internet kill newspapers? The case of South Korea
Measuring Trends in Leisure
Factors affecting consumers’ willingness to subscribe to over-the-top (OTT) video streaming services in India
Leisure Luxuries and the Labor Supply of Young Men
Allocation of Time to the Mass Media
| Highly cited | No |
|---|