Content area
The rapid expansion of over-the-top (OTT) video streaming services has transformed content consumption patterns, particularly among digitally literate populations such as college students. This study explores the factors influencing streaming platform preferences and satisfaction levels among Gen Z college students in India. Employing a combination of descriptive statistics, multinomial logit (MNL) and ordered logit (OLOGIT) models, the analysis identifies key determinants of platform choice, including income, bundling strategies, price sensitivity, and regional disparities. The findings suggest that price discrimination, network effects, and behavioral inertia significantly impact subscription decisions, while content diversity and platform usability shape overall satisfaction. The study also highlights the role of peer recommendations, algorithmic content curation, and bundling incentives in consumer retention. The results offer practical insights for streaming platforms seeking to optimize pricing models and content strategies, and policymakers aiming to regulate the rapidly evolving digital entertainment market.
The rapid expansion of over-the-top (OTT) video streaming services has transformed content consumption patterns, particularly among digitally literate populations such as college students. This study explores the factors influencing streaming platform preferences and satisfaction levels among Gen Z college students in India. Employing a combination of descriptive statistics, multinomial logit (MNL) and ordered logit (OLOGIT) models, the analysis identifies key determinants of platform choice, including income, bundling strategies, price sensitivity, and regional disparities. The findings suggest that price discrimination, network effects, and behavioral inertia significantly impact subscription decisions, while content diversity and platform usability shape overall satisfaction. The study also highlights the role of peer recommendations, algorithmic content curation, and bundling incentives in consumer retention. The results offer practical insights for streaming platforms seeking to optimize pricing models and content strategies, and policymakers aiming to regulate the rapidly evolving digital entertainment market.
Keywords: OTT platforms, Consumer preferences, Price sensitivity, Bundling, Network effects, Digital streaming, Behavioral economics, India
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Introduction
Over two decades ago, movies and television were synonymous with the entertainment industry. However, a new trend has emerged in the recent past. These well-established sources of entertainment have been overtaken by the over-the-top (OTT) platforms, which have transformed entertainment in both the literal and figurative sense. This signals a significant shift in human behavior. Through the lens of economics, the core questions lie in why this transformation has occurred and what it tells about the evolution of economic decisionmaking. This rapid expansion of digital streaming platforms has fundamentally altered the global and regional entertainment landscapes. India is an interesting region to study because of its diverse demographics. One section completely besotted by this phenomenon is college students-a section of the population characterized by high digital literacy and changing preferences that play a paramount role in shaping market dynamics. From a microeconomic perspective, understanding the factors that influence platform choice and satisfaction levels among students offers significant insights into consumer behavior, pricing strategies, and platform competition within the Indian streaming industry.
Consumer choice is broadly guided by the fundamental principles of utility maximization, budget constraints, and substitution effects. Even seemingly trivial decisions such as choosing whether to continue one's subscription with a given service or to switch platforms are dependent on a variety of economic and noneconomic factors such as pricing, content availability, bundling strategies, and the perceived quality of service or lack thereof. Intuitively and corroborated by the stiff competition between platforms, aggressively marketing through promotions and new content to keep the audience engaged, the concept of substitutability between platforms plays a vital role in shaping market competition.
The aim of this study is to explore the content preferences and platform choices of college students in Hyderabad city, and to analyze how microeconomic factors influence their decisions. This paper utilizes survey data collected from 600 respondents and examines variables such as price sensitivity, subscription habits, likelihood of switching, and the role of online ratings in determining content consumption. The empirical analysis employs multinomial logit (MNL) and ordered logit (OLOGIT) models to quantify the effects of income, device usage, regional disparities, and frequency of platform visits on consumer satisfaction and platform preference. The findings suggest that factors such as income levels, regional differences, and a preference for bundling, among others, significantly impact consumer choices. The present study explores several other factors, including but not limited to price elasticity of demand, network effects, and monopolistic competition. The areas covered in this paper contribute to the broader understanding of digital platform economics in emerging markets.
The rest of the paper is structured as follows: the next section provides a brief background on OTT Platforms, followed by literature review. The subsequent section outlines the methodology employed and the econometric models used, followed by results and discussion. The last section offers conclusion, along with the implications, limitations of the present study and the avenues for future research.
Background on OTT Platforms
OTT platforms emerged in the US in the early 2000s. Netflix, one of the most popular OTT platforms, was founded in the US (Lobato, 2019). It marks a shift in the consumption of media from traditional cinema and television to on-demand digital content.
OTTs bypass the need for traditional cable or satellite TV through the dissemination of audiovisual content directly to users over the Internet through a subscription model. The content in question includes movies, television shows, and music. The range of services and extent of access given to users across devices are tied to the subscription cost they are willing to incur.
The demand for streaming continued on an upward trend as broadband access expanded and consumers sought more flexible alternatives to traditional cable television (Tryon, 2013). In India, OTT platforms gained momentum in the mid-2010s. This was propelled by the availability of affordable Internet and mobile proliferation (TRAI, 2022). The Covid-19 pandemic, which led to lockdowns and social distancing measures, catalyzed the adoption of streaming services globally (PwC, 2022). India's emergence as one of the fastest-growing markets for OTTs has motivated platforms to expand their user base rapidly.
The rapid growth of OTTs in India can partially be regarded as a positive externality of the affordability of mobile Internet, facilitated by telecom operators such as Reliance Jio, which drastically reduced data costs (TRAI, 2022). The investment in regional content catered to multiple Indian languages, enabling deeper market penetration (Ernst & Young, 2021). The expansion of vernacular content has allowed streaming platforms to reach wider audiences in semi-urban and rural regions, where traditional television networks were once dominant (FICCI, 2022). Given the pace of growth, the competition among OTT providers has also intensified, leading to aggressive pricing strategies, bundling offers with telecom services, and exclusive content investments. Making OTTs more accessible also brought in product differentiation; subscription-based model (SVOD or subscription video on demand) and ad-supported model (AVOD or advertising-based video on demand) cater to different consumer segments, with SVOD users showing higher engagement levels due to exclusive premium content (PwC, 2022). Other variations include hybrid models or freemium services, which allow users access to limited free content while offering premium upgrades (Boston Consulting Group, 2021). A more recent pattern is the increasing demand for personalized recommendations and AI-driven content curation. When exploited by streaming services, this factor has been found to enhance user retention rates, making algorithmic content suggestions a key differentiator (Deloitte, 2021).
Regulatory overhauls are required in content regulation, data privacy concerns, and net neutrality policies (MeitY, 2022). Presently, content moderation and advertising transparency are largely based on self-regulatory measures, creating variations in how each OTT platform addresses these issues (IAMAI, 2022).
Theoretical Underpinnings
Consumer choice theory serves as the foundational framework for the analysis of streaming platform users' decision-making. As per the standard model of rational choice, individuals seek to maximize their utility subject to budget constraints (Varian, 2014). Contextually, this refers to the allocation of the consumer's finite income among competing services based on perceived value, as influenced by a number of variables. Due to this, users must assess the marginal benefit of subscribing to one service over another.
Budget constraints play a role in decision-making, especially among college students who usually have limited disposable income. Substitution effects are also particularly relevant because consumers may switch between platforms in response to price changes or content exclusivity. The elasticity of demand for streaming services is also critical, as price sensitivity determines the extent to which users are willing to maintain or cancel subscriptions following a price hike.
Network effects, the phenomenon where a product's value increases with the number of users (Katz & Shapiro, 1985), significantly impact streaming services. Direct network effects can manifest in user-generated engagement, such as social media content and online reviews, which can influence subscriber choices. Indirect network effects may arise from platform investments in premium content, algorithmic recommendations, and bundling agreements with telecom providers.
The streaming services market can be likened to the monopolistic competition model, where firms perform product differentiation to maintain a competitive advantage despite facing close substitutes. Product differentiators include exclusive content, differentiated subscription tiers, and AI-driven recommendations. Moreover, the competition among streaming platforms can be characterized as a two-sided market (Rochet & Tirole, 2003), where on the one side, content providers seek to distribute their productions on platforms that guarantee the highest reach and profitability, and on the other, consumers choose platforms that offer the best content variety and affordability.
OTT platforms often implement second-degree price discrimination through tiered pricing models (Shapiro & Varian, 1998). Third-degree price discrimination is prevalent in student discounts and region-specific pricing. Another prevalent pricing strategy is bundling, which is when platforms integrate their services with other service providers to attract costconscious consumers, leveraging complementary goods to enhance perceived value. Bakos and Brynjolfsson (1999) suggest that such strategies increase consumer surplus by allowing users to access multiple services at reduced marginal costs.
Unlike traditional economic theory, behavioral economics highlights the role of cognitive biases and heuristics in consumer behavior. One such phenomenon is the status quo bias (Samuelson & Zeckhauser, 1988), which explains why consumers may continue subscribing to a service despite the availability of better alternatives. Streaming platforms exploit this inertia through auto-renewal features and algorithmic personalization.
Loss aversion (Kahneman & Tversky, 1979) suggests that consumers weigh the potential loss of access to familiar content more heavily than the benefits of switching to a new platform. This is particularly relevant for long-term subscribers who have built extensive watch histories and personalized recommendations, creating psychological barriers to switching services.
The theoretical frameworks, such as Eisenmann et al. (2006), ring truer than ever, calling attention to the need for striking a balance between innovation and regulatory innovations to ensure fair competition and consumer protection.
Research Questions
The key research questions are:
1. What are the key factors influencing Gen Z college students' choice of streaming platforms in India?
2. How do economic variables such as income, pricing, and bundling affect students' streaming preferences?
3. What role do noneconomic factors such as user interface, content availability, and regional disparities play in determining platform preference?
4. How does the frequency of usage impact consumer satisfaction with streaming services?
5. To what extent do students perceive streaming platforms as substitutes for one another?
Literature Review
Prominent areas of research include the impact of OTT platforms on content consumption, user preferences, and market competition. A significant proportion of these studies utilize survey-based research, econometric modeling, and content analysis to derive insights. Lotz (2017) found that the proliferation of streaming services in the entertainment market has disrupted traditional broadcasting models by shifting the power from cable networks to digital-first platforms. Wayne (2018) reiterates that the general perceptions of why streaming services have succeeded are advancements in Internet accessibility, changing consumer behaviors, and the appeal of on-demand content. Cha and Chan-Olmsted (2012) used a survey-based approach to investigate the drivers of consumer engagement with OTT platforms. They found that perceived convenience, content diversity, and pricing structures were important. Through qualitative content analysis, Napoli (2019) found that the provision of algorithmic content recommendations based on evolving consumer demands is a key competitive advantage of OTT platforms over traditional media. Jin (2021), through a comparative case-study analysis, concluded that exclusivity plays a crucial role in shaping platform competition.
Sinha and Mandal (2020) analyzed the rapid expansion of Indian OTT platforms and identified regional content adoption as the reason for rapid adoption. This was highly robust in non-metro cities, where consumers overwhelmingly preferred content in their native languages. Mukherjee (2021), through a logistic regression of consumer survey data, revealed that penetration of OTT platforms into low-income demographics was heavily influenced by freemium models and "mobile-based access only" subscriptions.
Bellary et al. (2024) used SEM approach to conclude that user-friendly and "seamless" interfaces and highly personalized recommendations enhanced consumer satisfaction. Singh and Bose (2021) found that a significant proportion of the sample they interviewed valued peer recommendations and online reviews in making subscription decisions.
A time-series analysis showed large spikes in streaming hours during the Covid-19 lockdown (Mehta & Jain, 2021). There was no equitable increase in traditional media formats at the time. This unprecedented surge in viewership incentivized streaming platforms to adopt aggressive pricing strategies and increase investments in original content (Rai, 2022).
In the context of questionnaire creation, the Likert scale derives utility from its reliable and straightforward methodology, capturing attitude measurement efficiently (Likert, 1932). Amiel and Cowell (1997) showcased a three-phased approach consisting of numerical, verbal, and reflective questions to explore complex social phenomena through the elicitation of nuanced responses. A study on poverty and social exclusion (PSE) surveys provided a model for the integration of a diverse set of question formats (Gordon et al., 1999).
Objective
The main objectives of the paper are to:
* Identify and analyze the primary determinants of college students' platform preferences;
* Evaluate the impact of pricing models and income levels on subscription choices;
* Examine the significance of content-related features such as the availability of subtitles and offline downloads; and
* Assess the substitutability of streaming services in the Indian context.
Data and Methodology
Questionnaire
This study employs a mixed methods approach through its utilization of different question types that examine various aspects of OTT preferences. A semi-structured questionnaire (Appendix) is designed tailored to capture the quantitative and qualitative insights into consumer experiences.
Most of the questions are structured, i.e., definite, concrete, predetermined, and presented with the same wording and in the same order to all respondents. However, this questionnaire is structured and not highly structured because it incorporates a few open-ended questions to capture consumers' opinions fully (Kothari & Garg, 2024). The language of the questionnaire is made easy to understand, and the sequence and length of the questionnaire are logical.
Most questions are closed-ended for uniformity in the format of answers to facilitate analysis; Likert scales are employed due to their strength in respondent and stimulus-centered studies, multiple-choice options, and dichotomous (yes/no) formats to ensure clarity, homogeneity, and ease of response. Specific open-ended questions were included to gather qualitative insights.
The questionnaire is demarcated into eight constructs:
1. Demographics: Captures key characteristics such as age, gender, and income levels, allowing for the identification of trends and patterns in responses.
2. Streaming Habits: Examines the frequency with which respondents visit streaming platforms, along with typical viewing duration, most used device, whether they usually use data or Wi-Fi, etc.
3. Platform Preferences: Explores the respondent's choice of platform, the extent of their subscriptions, and their perceptions of their most-used service.
4. Content Preferences: Exploration into preferences in terms of preferred genres,language, and subtitle preferences.
5. Social Influence and Peer Recommendations: Evaluates the impact of the same on respondents' content choices and platform subscriptions.
6. Price Sensitivity: Deals with the impact of price changes on subscription decisions.
7. Willingness to Pay: Revolves around assigning a numerical estimation to the willingness of the respondents to pay for premium features.
8. Substitution and Cross-Platform Use: Pertains to the frequency with which respondents alternate between different platforms, whether they cancel subscriptions temporarily when doing so, and their perspective on the extent to which different streaming services are perfect substitutes.
Target Population and Sampling
The study aims for a sample size of approximately 600-650 respondents to ensure robustness in statistical inference and generalizability of findings. Given the budget and time constraints, a multipronged approach of distributing the survey both online and offline to colleges in Hyderabad and a college in Bengaluru was undertaken. The data were collected from November 10 to December 15, 2024.
A stratified sampling approach was pursued to capture diversity in age groups, fields of study, and regional representation. Given logistical constraints, convenience sampling was also employed. Prior to the full deployment of the survey, a pretest was conducted with six respondents to identify potential improvements in the questionnaire. Post-acquisition of the questionnaire responses, improper or incomplete responses were removed through datacleaning.
Approval was obtained from the appropriate sources prior to the dissemination of the survey within college premises to manage axiological concerns. The participation of respondents in the survey was voluntary and was done with informed consent. The respondents' identities were protected by anonymizing the data and excluding any identifiable information.
Data Analysis Approach
Descriptive statistics, regression analysis, and multinomial logit models are used to examine the relationships between constructs. Descriptive statistics provide an overarching understanding of the data and a comprehensive overview of the diverse nature of the question types utilized.
OLOGIT regression was used with user satisfaction ratings as the dependent variable to account for the occurrence of scores being inherently ranked but not necessarily equidistant, estimating the probability of satisfaction falling into different ranked categories rather than assuming a continuous relationship.
...(1)
where j represents the threshold for category j, Xik are independent variables affecting satisfaction rankings.
An MNL model was used for its appropriateness in handling the categorical dependent variable of the most used platform, i.e., where respondents select one platform from multiple alternatives. It estimates how factors such as income, bundling, device used, and regional distribution influence the probability of selecting a particular streaming platform.
...(2)
where P Yi m represents the probability of individual i choosing platform m, and X i includes factors like income and region.
Results and Discussion
Descriptive Statistics
Demographic Analysis
The demographics of the respondents are presented in Table 1. The percentage distribution of the age of the respondents is representative of the typical distribution of age groups found in a university setting.
The gender distribution of the respondents shows the number of male respondents being only slightly higher than that of the female respondents, i.e., 51.4% and 48.6%, respectively.
The majority of students who responded hailed from law and science & technology backgrounds, with management and humanities coming third and fourth. Around 67% of the respondents were undergraduates, while the remaining were postgraduates.
The income distribution across the sample is somewhat even, with the greatest majority of respondents (16.3%) having an annual household income of 10,00,001- 15,00,000. The proportion decreases at the 15,00,000 mark (10.5%) and continues to do so, with an annual income of 30,00,000 being the smallest in proportion (7.34%).
A large proportion of the sample comprises respondents from Telangana (Table 2). Several other states are represented. Due to low representation, the states were grouped into regions to allow for statistically sound analysis.
Streaming Behavior Patterns Analysis
A Spearman rank correlation between viewing frequency and viewing duration found a weak positive association between the two, indicating that people who stream daily tend to watch longer, while some may do quick, frequent sessions instead. The p-value of 0.0032 indicates statistical significance at 1% level, meaning the relationship is unlikely due to random chance.
A Pearson chi-square test (2 (6) = 67.48, p = 0.000) indicates a significant relationship between the primary streaming device and the method of Internet access. A majority of laptop (53.68%) and smart TV (9.20%) users rely on Wi-Fi, while smartphone (57.38%) and tablet (11.48%) users primarily use mobile data or both (Table 3).
Table 4 presents platform preferences. In Likert scale rankings, downloadability for offline viewing shows a fairly equal distribution among the ratings. Even though the majority (27.62%) rated it a 4, it appears not to be a dominant priority. It was found that an overwhelming majority of respondents use Wi-Fi more frequently than Data, which is in line with the lack of importance placed on downloadability. The importance of the user interface was confirmed as the highest proportion (35.54%) rated it a 5, followed by 4 (30.31%). In the case of overall satisfaction, most respondents (45.71%) gave a 3, indicating neutral satisfaction. Notably, higher ratings (4 and 5) are nearly equal (24.05% each), while dissatisfaction levels are significantly lower (1.19% for 1). This indicates that most platforms generally meet expectations but do not exceed them dramatically. In the case of the importance of availability of specific genres, a gradual increase from 1 to 4 (7.12% to 30.96%) with a drop at 5 (9.25%) was observed, suggesting that while genres are important, they are not the decisive factor for most. As for the importance of subtitles, the highest proportion of votes was for 3 (43.23%), indicating a midpoint response on the scale. 24.19% of respondents ranked it a 5, showing that approximately a quarter of users see it as very important, but it is not a universal priority. The questions pertaining to recommendation algorithm importance and similarity between OTT platform content offerings reflect fairly balanced responses while pointing towards mid-level importance for most respondents.
When it comes to checking social media for recommendations (Table 5), the responses are fairly spread out, but most concentrated at ranks 2 and 3, suggesting that social media is not a primary influence for most. The proportions for asking friends for streaming recommendations suggest that it is not highly preferred, with the highest majority of respondents choosing 3 (32%), and 2 following closely with 28.75%. The social aspects of OTT platforms are not valued very highly. Around 46.25% of people chose 3, while only 11.25% of respondents selected 4 or 5. The avoidance of both content and the OTT platforms that receive poor reviews on social media mirrors each other's distribution, with a relatively high proportion at 3 and 4, indicating that negative social media reception does influence some decisions.
A clear trend emerges wherein as the price increase becomes steeper, the average likelihood of cancelation rises. Given this, the lowest impact is seen at the lowest and minimal price increase of 30, which was found to be a price point where it was unlikely for users to consider canceling the service. Although a price hike of 50 exhibited a slightly higher willingness to cancel, the effect remained moderate. The likelihood of cancelation showed a significant increase with a rise of 100, indicating that this is likely the threshold at which a sizeable proportion of consumers start to reassess their subscriptions. The highest likelihood of cancelation is seen at 200, suggesting that a steep price rise would lead to a major drop in subscriber retention. These findings align with price sensitivity theories. Figure 1 illustrates the average Likert scale responses for the likelihood of canceling a streaming subscription under different price increase scenarios ( 30, 50, 100, and 200), with 1 being the least likely and 5 being the most likely to cancel a subscription.
Econometric Analysis
The MNL logistic regression (Table 6) was chosen to explore those factors that influence the most-used streaming platform of the given respondent. Amazon Prime was taken as the base category. The model was found to be statistically significant (LR 2 = 307.19, p < 0.001), with a pseudo R2 of 0.1394, indicating a moderate fit. Out of the variables chosen to be represented in this regression, several key variables emerged as significant predictors of platform choice. Those respondents who had higher satisfaction levels with streaming platforms overall were more likely to use Apple TV, Rakuten Viki, SonyLIV, and ZEE5, suggesting that these platforms are characterized by higher user satisfaction.
Those respondents who expressed a higher preference for bundling their subscription purchases with other services were significantly more likely to select Disney+ Hotstar as their most used platform, reinforcing the platform's strategy of integrating with telecom and other services, for example, through the provision of the lowest tier Disney+ Hotstar as an added bonus when purchasing an annual phone-plan subscription from Airtel. However, this is not universally held true because bundling negatively impacts the preference for SonyLIV and Rakuten Viki, implying that users of these platforms may not prioritize package deals. This means consumer preferences can differ, and a one-subscription-model-fits-all approach is an unlikely solution. Income level emerged as a significant factor for Apple TV, suggesting that higher-income users are more inclined towards this service. Even though the monthly subscription cost of Apple TV is only 100, making it one of the least expensive platforms to subscribe to, its compatibility and association with Apple likely deters non-Apple users from subscribing to the platform. A similar, though marginally significant, effect is observed for SonyLIV and YouTube Premium.
Regional differences also increase the likelihood of choosing Apple TV, Rakuten Viki, SonyLIV, YouTube Premium, and ZEE5 over Amazon Prime, highlighting the geographical variation in preferences. The number of streaming subscriptions also emerges as a significant predictor, with users subscribed to multiple services being more likely to use Apple TV, Rakuten Viki, ZEE5, and YouTube Premium. These services are more uncommon and regarded as a luxury by some: Apple TV, due to its association with Apple products; Rakuten Viki, by reason of its steep pricing and international (Korean) content; and YouTube Premium, because most prefer to utilize the ad-supported free model of YouTube.
Gender significantly impacts YouTube Premium, where women are less likely to select it over other streaming services. At the same time, age negatively influences the preference for Apple TV, Rakuten Viki, SonyLIV, and ZEE5, suggesting that younger users are more likely to opt for these platforms.
Overall, the findings suggest that premium and global platforms (Apple TV, Rakuten Viki, and YouTube Premium) are preferred by higher-income, younger users with multiple subscriptions, whereas Disney+ Hotstar benefits significantly from bundling strategies.
The OLOGIT model (Table 7) analyzes the factors affecting satisfaction levels with streaming platforms. It found that those users who expressed a high preference for frequently pausing or canceling their subscriptions exhibit significantly lower levels of satisfaction, a trend that suggests interlinkages between dissatisfaction with content, pricing, or platform policies and pausing or canceling of services (-0.674, p < 0.01). A strong positive correlation between users who are not deterred by cancelation fees in the selection of their subscriptions implies that users who commit to subscriptions despite cancelation fees are generally content with the service. A marginally significant negative relationship suggests that as income increases, satisfaction slightly decreases. This could indicate that higher-income users have more alternatives or greater expectations from streaming services, a possibility that is reinforced by the fact that a greater majority of lower-income brackets are likely to have multiple subscriptions, whereas those from higher income brackets appear to prefer quality over quantity or have other sources of entertainment and do not therefore subscribe to as many platforms. Regional differences marginally influence satisfaction, with certain regions showing lower levels. This could be due to scarce content availability in certain languages, pricing, or platform accessibility. The likelihood ratio chi-square test is significant (p < 0.01), indicating a good model fit.
Variance inflation factor (VIF) tests were conducted for both regression models to assess multicollinearity. The results indicated that multicollinearity was not a concern, as all VIF values were within acceptable limits.
Conclusion
This study analyzed a variety of factors that influence streaming platform preferences and satisfaction among college students in India. Through the use of descriptive statistics, regression models, and theoretical frameworks, the findings indicate that platform choice is a complex concept that is driven by a combination of economic and behavioral factors. The results from the multinomial logistic regression indicate that income, bundling preferences, and regional differences are the key drivers that affect platform selection. This is further demonstrated by the OLOGIT model, which demonstrates that the satisfaction levels of consumers are shaped by content availability, platform usability, and consumer perceptions of substitutability. Overall, the combined insights from this study are cognizant of the evolving nature of consumer preferences in the ever-growing digital streaming markets and have practical implications for those seeking to optimize pricing models, improve user engagement, and tailor content strategies to diverse audience segments.
Beyond serving as conduits for content distribution, OTTs influence consumer and student behavior through a combination of technology, economics, and social dynamics. These platforms offer tailored experiences to students that promote increased engagement, convenience, and loyalty to the platform, bolstered by network effects and peer suggestions. The design and strategic decisions of platforms, which range from exclusive regional content to partnerships with telecom providers, facilitate content discovery and affect perceived value, utility maximization, and the propensity to switch or maintain subscriptions among college students in India. Consequently, the platform itself emerges as a pivotal entity in shaping the preferences, satisfaction, and economic decisions of its users.
Implications: The findings of this study have nuanced implications for both policymakers and market participants in the Indian streaming industry. The consumption patterns of college students in the context of the rapid adoption of OTT platforms necessitate a significant reevaluation of regulatory frameworks, pricing strategies, and content accessibility measures.
Given the rise of streaming platforms as the primary source of entertainment for a large proportion of the population, replacing more traditional forms of entertainment such as network television, regulatory bodies must ensure adequate consumer protection. As examined in this paper, currently, self-regulatory mechanisms and advertising transparency are the primary means of content moderation for streaming service providers. However, these methods may not be sufficient to address concerns such as misleading promotions, pricing practices, or data privacy issues. Policies akin to those implemented for traditional broadcasters may be required to maintain content integrity while balancing creative freedoms.
Another area that requires attention is the question of digital inclusion and accessibility. The results unanimously indicate that regional disparities are present in the usage of streaming platforms. Policymakers should focus on bridging the digital divide by incentivizing infrastructure expansion, particularly in semi-urban and rural areas. Measures such as subsidies for Internet access and the promotion of regional content could enhance equitable access to digital entertainment.
An overwhelming majority of respondents who use their smartphones for streaming rely on mobile data for their streaming needs, pointing to the need for robust net neutrality policies to prevent discriminatory pricing practices by Internet service providers.
As for the market implications of the findings of this paper, the analysis suggests that price sensitivity is a critical factor in subscription choices. Platforms have already shown success by introducing flexible pricing models; this can be explored further in terms of tiered pricing, freemium models, and discounted student subscriptions. The sensitivity of consumers to price hikes is indicative of a threshold at which cancelation becomes significantly more likely. Varied streaming services that cater to different demographics can conduct market studies to determine the threshold level for their demographics to make more informed and sustainable pricing adjustments.
The value placed on bundling as a means of influencing platform choice, as seen most prominently in the case of Disney+ Hotstar, suggests that strategic partnerships with telecom providers and digital payment services can enhance subscription uptake. However, the negative impact of bundling on some platforms (e.g., SonyLIV and Rakuten Viki) indicates that onesize-fits-all bundling strategies may not be universally effective. Platforms must tailor bundling approaches based on their target demographics.
The issue with regional disparities in subscribing to streaming services is not only a policy issue but also a market issue, highlighting the importance of greater investment in vernacular programming and prioritization of localized content offering, be it unique, new programming, or subtitled and dubbed versions of international content. Those platforms that leverage regional content can gain a competitive edge in non-metro markets.
The results show that personalized recommendations and user interface quality have a big impact on customer happiness. OTT platforms ought to concentrate on boosting search capabilities, optimizing interface design, and refining recommendation algorithms via AI-powered personalization.As a significant percentage of users depend on free-tier services, platforms that cater to budget-conscious customers may find that ad-supported models (AVOD) provide an additional source of income. A balance between monetization and user experience is necessary since too much advertising may discourage user participation. According to the study's findings about platform substitutability, differentiation tactics are essential in the OTT market's growing level of competition. To preserve brand loyalty and lower churn rates, platforms need to concentrate on unique content, cutting-edge features for user interaction, and focused marketing.
Limitations: The sample size is limited. The primary focus on college students limits the generalizability of the findings to other demographic groups, such as working professionals and older consumers, who would enable the capturing of a broader spectrum of streaming behavior. The data in this study were self-reported, introducing social desirability and recall biases, which may threaten the accuracy of the data. The cross-sectional study also lacks in collecting changes in preference and behavior over time. Certain factors, such as changes in Internet infrastructure, government regulations, and global streaming trends, were not explicitly modeled in the analysis. Future studies can take these limitations into account to realize more comprehensive results.
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