https://research.
e-greenation.
org/GIJES Vol.
No.
December - February 2025 E-ISSN : 2986-0326.
P-ISSN : 2986-089X
DOI: https://doi.
org/10.
38035/gijes.
Received: December 05th, 2024.
Revised: December 20th, 2024.
Publish: January 09th, 2025 https://creativecommons.
org/licenses/by/4.
The Role of Artificial Intelligence in Enhancing User Experience on OTT Platforms Keshav Chahwala1.
Chavda Shubham2.
Nihal Zalariya3.
Aryan Dhruv4.
Arya Rakesh Shah5.
Rahul Chauhan6.
Andino Maseleno7.
Unitedworld Institute Management.
Karnavati University.
Gandhinagar, diyanshipatel171003@gmail.
Unitedworld Institute of Management.
Karnavati University.
Gandhinagar.
India.
Unitedworld Institute of Management.
Karnavati University.
Gandhinagar.
India.
Unitedworld Institute of Management.
Karnavati University.
Gandhinagar.
India.
Unitedworld Institute of Management.
Karnavati University.
Gandhinagar.
India.
Unitedworld Institute of Management.
Karnavati University.
Gandhinagar.
India.
International Open University.
Gambia, andino@bahasa.
India.
Corresponding Author: andino@bahasa.
Abstract: This study investigates the impact of Artificial Intelligence (AI) on user experience and engagement with Over-the-Top (OTT) platforms, focusing on Ahmedabad.
India.
The research examines how AI-driven content personalization and predictive analytics influence user satisfaction and retention.
Using a quantitative approach, data was collected from 101 respondents via a structured questionnaire and analyzed using SPSS software.
Key findings reveal that AI-based content recommendations significantly enhance user satisfaction, with noticeable differences in engagement across age groups.
Gender-based analysis shows a variance in time spent on platforms, though content preferences and response to recommendations remain consistent across genders.
The study highlights the growing importance of AI in optimizing user experiences and driving engagement on OTT platforms.
Future research can explore regional variations, ethical challenges, and evolving AI technologies in streaming services to further refine personalization strategies.
Keywords: AI-Driven Personalization.
OTT Platforms.
User Engagement.
Content Recommendations INTRODUCTION In the rapidly evolving landscape of digital entertainment.
Over-the-Top (OTT) platforms have emerged as pivotal players, fundamentally transforming how content is consumed.
The role of Artificial Intelligence (AI) in this transformation is significant, offering innovative solutions to enhance user experience.
As OTT platforms face increasing competition and growing user expectations, leveraging AI to optimize content delivery, personalization, and engagement has 154 | Page https://research.
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This introduction explores how AI is reshaping the OTT sector by synthesizing insights from recent studies on user behavior, content consumption, and market dynamics.
Recent research highlights the complex interplay between user preferences and OTT For instance.
Baber et al.
investigate how factors such as price consciousness, anti-socializing behavior, and content preferences influence online video They reveal that AI-driven personalization can address these diverse needs by recommending content that aligns with individual preferences and viewing habits.
AI algorithms analyze user data to tailor content suggestions, thereby enhancing the relevance of recommendations and potentially increasing user satisfaction and retention.
Moreover.
Friederich et al.
delve into the behavior of digital natives and their interaction with streaming platforms.
Their study emphasizes the importance of understanding the continuance usage of OTT services, suggesting that AI can play a critical role in improving user experience by refining content recommendations and interface design based on user behavior and preferences.
AI technologies, such as machine learning and predictive analytics, enable platforms to anticipate user needs and adapt accordingly, ensuring a more engaging and personalized experience for digital natives.
In a broader context.
Khanna et al.
provide a comprehensive review of OTT platforms, highlighting the need for ongoing research into AIAos role in enhancing user They propose several future research directions, including the application of AI in content creation and market analysis.
AIAos capability to process vast amounts of data and generate actionable insights positions it as a powerful tool for understanding and predicting user preferences, thereby guiding content strategy and development.
Region-specific studies also underscore AIAos potential in optimizing OTT experiences.
For example.
Lo et al.
use the stimulus-organism-response (SOR) paradigm to explore Malaysian viewers' usage intentions of OTT platforms.
Their findings suggest that AI can be instrumental in localizing content and personalizing user interactions based on regional and cultural factors.
By analyzing local user data.
AI can help OTT platforms tailor their offerings to meet specific regional demands, thereby enhancing user satisfaction and engagement.
The role of AI in market expansion is exemplified by Nafees et al.
, who examine NetflixAos successful expansion into the Indian market.
AI-driven market analysis tools were critical in understanding regional content preferences and optimizing strategies for market entry.
This study illustrates how AI can support OTT platforms in navigating new markets by providing insights into consumer behavior and preferences.
Riskos et al.
investigate the impact of hedonic and eudaimonic entertainment on brand engagement using a multiple mediation model for Netflix.
Their research highlights how AI can enhance brand engagement by delivering personalized content that aligns with users' entertainment and emotional needs.
AI technologies can segment content into various categories and tailor recommendations to match users' psychological and emotional states, thereby fostering a deeper connection with the brand.
The impact of the COVID-19 pandemic on consumer behavior is another critical area of exploration.
Arya and Chauhan .
address how the pandemic has influenced user contentment with digital platforms.
AI can adapt to these changes by analyzing shifts in user behavior and preferences, allowing OTT platforms to adjust their content strategies and improve user experiences during such disruptive periods.
Kalra et al.
provide a systematic review of customer experiences with OTT platforms, emphasizing the importance of AI in understanding and enhancing these experiences.
AI tools can analyze customer feedback, track user interactions, and identify trends to inform strategies for improving overall satisfaction and engagement.
METHOD
The research methodology for this study is designed to investigate the impact of Artificial Intelligence (AI) on user experience and engagement with Over-the-Top (OTT) platforms, with a focus on the Indian market.
Specifically, the research aims to understand how AI-driven 155 | Page https://research.
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December - February 2025 personalization and content optimization influence user satisfaction and retention.
To achieve these goals, the study employs a quantitative approach using data collected through a structured questionnaire and analyzed using SPSS software.
Objectives A To assess the impact of AI-driven content personalization on user satisfaction with OTT platforms in Ahmedabad.
A To examine the role of AI in enhancing user retention and engagement on OTT platforms.
Hypotheses H1: AI-driven content personalization significantly enhances user satisfaction with OTT H2: The implementation of AI technologies in OTT platforms positively impacts user retention and engagement.
Data for this study was collected from a sample of 97 respondents residing in Ahmedabad.
The data collection process involved distributing a structured questionnaire via Google Forms.
The questionnaire was designed to capture responses on user satisfaction, content personalization, and engagement with OTT platforms, specifically focusing on the influence of AI technologies.
The collected data was analyzed using SPSS software to perform statistical tests and validate the Descriptive statistics were used to summarize the data, while inferential statistics, including correlation and regression analyses, were employed to examine the relationships between AI-driven personalization, user satisfaction, and engagement.
The results of these analyses will provide insights into the effectiveness of AI in enhancing user experiences and driving engagement on OTT platforms.
By aligning the research methodology with the outlined objectives and hypotheses, this study aims to contribute valuable insights into the role of AI in the OTT sector and offer recommendations for optimizing user experiences through advanced technological solutions.
RESULT AND DISCUSSION
The demographic data for this study, as presented in Tables 1 through 4, provides crucial insights into the sample populationAos composition, which aids in interpreting the research A total of 101 respondents participated in the study, contributing to a diverse understanding of how Artificial Intelligence (AI) influences user experience and engagement with Over-the-Top (OTT) platforms in Ahmedabad.
Table 1.
Age Cumulative Frequency Percent Valid Percent Percent Valid 45 and above Total As illustrated in Table 1, the majority of respondents .
1%) fall within the 18-25 age group, which aligns with the significant presence of younger users who are typically more active on OTT platforms.
The age group 26-35 accounts for only 2%, while 3% belong to the 36-45 Respondents aged 45 and above make up a mere 2% of the sample.
This skew towards younger participants suggests that the studyAos findings may be particularly relevant to platforms targeting youth-centric markets, where AI-driven personalization is most effective due to high digital engagement in this demographic.
156 | Page https://research.
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Gender Valid Frequency Percent Valid Percent Male Female Total Cumulative Percent Table 2 shows the gender distribution, with males constituting 84.
2% of the sample and This disparity highlights a male-dominant respondent base, which may reflect broader trends in technology usage or survey participation biases.
Understanding this distribution is essential for interpreting user engagement and satisfaction patterns, as gender-specific preferences may influence how AI algorithms personalize content recommendations.
Table 3.
Occupation Cumulative Frequency Percent Valid Percent Percent Student Valid Working Homemaker Other Total As seen in Table 3, the majority of respondents .
2%) are students, followed by working professionals .
9%).
Homemakers represent 2%, while 5% belong to the AuOtherAy category.
The high proportion of students suggests that this demographic is heavily engaged with OTT platforms, possibly due to greater time availability and a preference for entertainment services.
This also emphasizes the importance of AI-driven personalization in attracting and retaining young users.
Table 4.
Monthly Income (Famil.
Valid Frequency Percent Valid Percent Below 20,000 20,000-50,000 50,000-1,00,000 Above 1,00,000 Total Cumulative Percent Table 4 provides the distribution of respondents based on their family income.
Nearly half of the respondents .
5%) have a family income above C1,00,000, while 22.
8% earn below C20,000.
The remaining fall within C20,000Ae50,000 .
8%) and C50,000Ae1,00,000 .
These figures indicate that OTT users in this sample come from diverse economic backgrounds, suggesting that AIAos role in engagement and satisfaction transcends income levels, although higher-income families might have greater access to multiple platforms and premium This demographic analysis offers a well-rounded context for interpreting how AI-driven personalization influences user experience and engagement.
It also sheds light on the sampleAos representativeness and possible biases, ensuring that the findings are correctly positioned within the Indian OTT landscape.
157 | Page https://research.
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ANOVA between Age and OTT Factors Use OTT platforms Between Groups Within Groups Total Between Groups Which OTT Within Groups Total Time spending on Between Groups OTT platforms Within Groups Total Between Groups Types of content Within Groups Total Between Groups Content recommendations Within Groups Total Sum of Squares Mean Square Sig.
The ANOVA results in Table 5 analyze the relationship between age and key factors associated with OTT platform usage.
The significance values (Sig.
) indicate whether age groups differ meaningfully in their interaction with these factors.
For the factor "Use of OTT platforms," the Sig.
value is 0.
777, indicating no significant difference between age groups in terms of their platform usage frequency.
Similarly, for "Which OTT platform," the Sig.
value is 0.
suggesting that preferences for specific platforms do not vary significantly with age.
The factor "Time spending on OTT platforms" also shows a non-significant result (Sig.
= 0.
, implying that time spent on these platforms is consistent across different age groups.
However, "Types of content" approached significance (Sig.
= 0.
, suggesting a possible trend where content preferences may vary across age groups, although not strongly enough to confirm a statistically significant difference.
The most notable finding is in "Content recommendations," where the Sig.
value is 0.
010, indicating a significant difference between age groups in how they perceive or respond to personalized recommendations.
This suggests that AIdriven personalization may be more or less effective depending on the userAos age, with younger or older groups potentially responding differently to curated content.
Overall, these results provide valuable insights into how user behavior and AI effectiveness on OTT platforms may vary with age, particularly in the realm of content recommendations.
Table 6.
ANOVA between Gender and OTT Factors Between Groups Within Groups Total Between Groups Which OTT Within Groups Total Time spending on Between Groups OTT platforms Within Groups Use OTT Sum of Squares Mean Square Sig.
158 | Page https://research.
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No.
December - February 2025 The ANOVA results in Table 6 explore the relationship between gender and key OTT platform factors to determine if male and female users exhibit significant behavioral differences.
For the factor "Use of OTT platforms," the Sig.
value is 0.
548, indicating no significant difference in the frequency of OTT usage between genders.
Similarly, the factor "Which OTT platform" shows a Sig.
value of 0.
146, suggesting that platform preferences do not vary significantly based on gender.
This implies that both male and female users engage with similar platforms without major variation.
A significant difference emerges for "Time spending on OTT platforms" (Sig.
= 0.
, indicating that the amount of time spent on OTT services varies meaningfully between male and female users.
This could reflect differing media consumption habits or lifestyle patterns between genders.
The factor "Types of content" shows no significant difference (Sig.
= 0.
, suggesting that both genders have relatively similar content preferences.
Lastly, the factor "Content recommendations" presents a Sig.
value of 0.
888, indicating no notable difference between how males and females respond to AI-driven content recommendations.
In summary, while most OTT factors show no significant gender-based differences, the amount of time spent on platforms stands out.
This finding suggests that gender may play a role in engagement duration but not in content preferences or response to AI personalization.
CONCLUSION
This study provides valuable insights into the role of AI-driven personalization in shaping user experience and engagement on OTT platforms, with a focus on the Indian market, specifically Ahmedabad.
The results suggest that AI-based recommendations significantly enhance user satisfaction, highlighting the importance of personalized content in improving viewer experiences.
However, factors such as the time spent on OTT platforms vary between genders, indicating that demographic characteristics like gender and age can influence engagement patterns.
While AI-driven content recommendations show significant variation across age groups, the study did not find notable differences in platform preference or content type based on either gender or age.
The findings open several avenues for further research.
Expanding the sample size beyond Ahmedabad to other regions in India could provide more generalizable insights into AIAos impact across diverse demographics.
Additionally, future studies can focus on the effectiveness of specific AI technologies, such as predictive analytics or sentiment analysis, to identify which algorithms yield the highest user satisfaction.
Another area of interest would be exploring the ethical implications of AI in personalization, including privacy concerns and algorithmic biases.
Longitudinal studies can also track changes in user behavior over time as AI technologies continue to evolve.
The findings have significant global implications for the OTT industry, particularly in regions where streaming services are rapidly expanding.
As AI technologies become more integral to digital media platforms worldwide, understanding the nuances of user behavior across different cultural and demographic segments will be crucial.
Countries with high youth populations or growing internet penetration can leverage AI personalization to boost user engagement and retention.
Furthermore, global OTT platforms like Netflix.
Disney , and Amazon Prime can utilize these insights to refine their content recommendation engines and optimize user experiences across various markets.
This study contributes to the broader 159 | Page https://research.
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REFERENCES