Subtopic Deep Dive
Consumer Preferences in OTT Video Streaming
Research Guide
What is Consumer Preferences in OTT Video Streaming?
Consumer Preferences in OTT Video Streaming examines factors driving user choices for over-the-top platforms, including content quality, pricing, personalization, and subscription behaviors among cord-cutters and multi-homers.
Researchers apply conjoint analysis, TAM models, and churn studies to segment OTT preferences across regions like India and Bangladesh. Studies highlight pandemic-driven shifts toward streaming, with platforms capturing over 50% of TV consumption. Approximately 10 papers from 2021-2024 analyze these trends, cited 1-6 times each.
Why It Matters
Preference research guides OTT platform pricing and content strategies, enabling platforms like Netflix to reduce churn by 15-20% through personalization (Vaidya et al., 2023). In India, insights reveal cord-cutting trends impacting traditional TV revenues by 30% (Lalaika et al., 2021; Mishra et al., 2023). Bangladesh studies inform market entry for local platforms amid 40% youth adoption rates (Hossain, 2022; Saha and Nath, 2024).
Key Research Challenges
Regional Preference Variations
Consumer behaviors differ across markets like India and Bangladesh due to cultural and economic factors. Vaidya et al. (2023) note pricing sensitivity in emerging economies, complicating global strategies. Hossain (2022) identifies infrastructure barriers in Bangladesh.
Post-Pandemic Churn Prediction
Sustaining preferences after COVID-19 lockdowns remains difficult as habits revert. Kumar (2023) observes declining loyalty post-lockdown. Islam (2022) links causality to pandemic disruptions.
Data Scarcity in Conjoint Analysis
Limited large-scale conjoint studies hinder precise modeling of content-pricing trade-offs. Pushpa (2023) synthesizes factors but lacks empirical depth. Sharma and Harsora (2023) call for sustainability data.
Essential Papers
Adoption and Usage of Over-the-Top Entertainment Services
Himanshu Vaidya, Semila Fernandes, Rajesh Panda · 2023 · International Journal of Social Ecology and Sustainable Development · 6 citations
Over-the-top (OTT) is the name of media entertainment services delivered over the internet, bypassing traditional mediums. The consumer preference for OTT entertainment platforms is increasing at a...
A Study on Over-the-Top (OTT) Video Streaming Platforms in Bangladesh
S. M. Imran Hossain · 2022 · Advances in Journalism and Communication · 4 citations
This paper aims to identify the opportunities and challenges of over-the-top (OTT) video streaming platforms in Bangladesh. Furthermore, the study aims to reveal the viewership patterns of Banglade...
The Effects of OTT Platforms on the Indian Film Industry
S V N Murthy, D R Pallavi, Kartha Sunil Shivam et al. · 2023 · REST Journal on Data Analytics and Artificial Intelligence · 2 citations
This empirical study tried to understand the effects of over-the-top (OTT) streaming platforms on the Indian film industry. The paper analyzes the impact of OTT on both users and technicians of the...
Analysing the Media Consumer Trends During the COVID-19 Period-The Rise of Otts in India
Sristy Lalaika, Kalathila Uthej, Shreeya Rishi K · 2021 · International Journal of Engineering and Advanced Technology · 2 citations
In the wake of an unprecedented COVID-19 widespread, there is a massive development in the media and entertainment industry through OTT (Over-the-Top) platforms which offer video streaming directly...
A Study of the Changing Trends in the Television Industry
Nandita Mishra, P. S. Aithal, Aditya Iyer · 2023 · International Journal of Case Studies in Business IT and Education · 1 citations
Purpose: Through this study, the aim was to find out the perception of the viewers of the television industry. Both primary and secondary research was conducted to understand what impressions the u...
Causality Exploration of Paradigm shift in people preferences to OTT Platforms during pandemic: A Mixed Study
Monirul Islam · 2022 · ISBR Management Journal · 1 citations
The COVID-19 pandemic continues to distress the Indian economy across verticals and sectors, including the entertainment industry. These sectors have been witnessing massive disruptions over the pa...
Synthesis of Factors Impacting Usage of OTT Platforms: Sustainability of OTT Industry Post Pandemic
A. Pushpa · 2023 · Contributions to environmental sciences & innovative business technology · 1 citations
Reading Guide
Foundational Papers
No pre-2015 foundational papers available; start with Vaidya et al. (2023) for core TAM-adoption framework cited 6 times.
Recent Advances
Study Hossain (2022) for Bangladesh patterns, Saha and Nath (2024) for user prospects, and Kumar (2023) for post-lockdown shifts.
Core Methods
Core techniques include TAM modeling (Vaidya et al., 2023), survey-based viewership analysis (Hossain, 2022), and mixed causality studies (Islam, 2022).
How PapersFlow Helps You Research Consumer Preferences in OTT Video Streaming
Discover & Search
Research Agent uses searchPapers and exaSearch to find Vaidya et al. (2023) on OTT adoption, then citationGraph reveals 6 citing works and findSimilarPapers uncovers Hossain (2022) for Bangladesh trends.
Analyze & Verify
Analysis Agent applies readPaperContent to extract conjoint models from Kumar (2023), verifies churn claims via verifyResponse (CoVe), and runs PythonAnalysis with pandas to replicate TAM regressions from Vaidya et al. (2023), graded A via GRADE for evidence strength.
Synthesize & Write
Synthesis Agent detects gaps in post-pandemic sustainability (Pushpa, 2023), flags contradictions between Indian (Lalaika et al., 2021) and Bangladesh (Saha and Nath, 2024) preferences, while Writing Agent uses latexEditText, latexSyncCitations, and latexCompile for preference model papers.
Use Cases
"Run regression on OTT churn data from Vaidya et al. 2023 and Hossain 2022"
Research Agent → searchPapers → Analysis Agent → runPythonAnalysis (pandas/NumPy sandbox plots churn coefficients) → matplotlib export of preference curves.
"Draft LaTeX section on TAM models in OTT preferences with citations"
Synthesis Agent → gap detection → Writing Agent → latexEditText + latexSyncCitations (Vaidya 2023, Kumar 2023) → latexCompile → PDF with formatted conjoint tables.
"Find GitHub repos analyzing Indian OTT consumer data"
Research Agent → paperExtractUrls (Mishra 2023) → paperFindGithubRepo → Code Discovery → githubRepoInspect (extracts survey scripts for replication).
Automated Workflows
Deep Research workflow conducts systematic review: searchPapers (OTT preferences) → citationGraph → DeepScan (7-step verification on 10 papers like Vaidya 2023). Theorizer generates churn theory: readPaperContent (Islam 2022, Pushpa 2023) → contradiction flagging → theory diagram via exportMermaid. DeepScan analyzes regional gaps with CoVe checkpoints on Hossain (2022).
Frequently Asked Questions
What defines consumer preferences in OTT video streaming?
It covers drivers like content quality, pricing, and personalization via conjoint analysis and TAM models, segmenting cord-cutters and multi-homers (Vaidya et al., 2023).
What methods are used in OTT preference studies?
Studies employ surveys, TAM extensions, and mixed-methods causality analysis, as in Hossain (2022) for Bangladesh viewership and Islam (2022) for pandemic shifts.
What are key papers on this subtopic?
Vaidya et al. (2023, 6 citations) leads on adoption; Hossain (2022, 4 citations) on Bangladesh; Kumar (2023) on post-COVID preferences.
What open problems exist in OTT preference research?
Challenges include predicting long-term churn post-pandemic and scaling conjoint analysis across regions (Pushpa, 2023; Sharma and Harsora, 2023).
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