INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue II, February 2026

Page 26

www.rsisinternational.org

Adoption of OTT platforms: Analyzing User Behavior through the

UTAUT2 Model

Mr. Smit Gamit

Dr. Pratha Jhala

Research Scholar, Department of Business and Industrial Management, Veer Narmad South Gujarat

University Surat.

Assistant Professor, Department of Business and Industrial Management, Veer Narmad South Gujarat

University Surat.

DOI:

https://doi.org/10.51583/IJLTEMAS.2026.1502000004

Received: 14 February; Accepted: 17 February; Published: 23 February

ABSTRACT

Purpose: This study applied the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) to examine

factors influencing the adoption of OTT platforms among youth in Navsari city, India. With the growing

popularity of OTT platforms, understanding their key adoption determinants can help service providers enhance

user engagement and satisfaction.

Design/methodology/approach: A quantitative research approach was employed, using a structured

questionnaire to collect data from 211 youth respondents. Partial Least Squares Structural Equation Modelling

(PLS-SEM) was used to analyze relationships among constructs and assess the model’s predictive power.

Findings: Results indicate that Facilitating Conditions (FC), Habit (H), and Price Value (PV) significantly

influence behavioral intention and actual OTT usage. Facilitating Conditions (FC) emerged as the strongest

predictor, emphasizing the role of accessibility and resources. However, Effort Expectancy (EE), Performance

Expectancy (PE), Social Influence (SI), and Hedonic Motivation (HM) did not significantly affect behavioral

intention. The Q²predict values for Behavior (0.479) and Behavioral Intention (0.449) confirm good predictive

relevance.

Practical implications: OTT providers should enhance accessibility, affordability, and user engagement.

Personalized recommendations, seamless user experience, and cost-effective subscription models can further

boost adoption among youth.

Originality/value: This study applies UTAUT2 in OTT adoption research, contributing to technology

acceptance literature. The finding that EE, PE, SI, and HM do not significantly influence behavioral intention

contrasts with previous studies, opening avenues for further research.

Keywords: OTT platforms, UTAUT2 model, Youth, Behavioral Intention, Price Value, Facilitating Conditions,

Habit.

INTRODUCTION

The advent of Internet and smartphones in 21st century has made information technologies an indispensable part

of human life, that were mostly available only to organisational users during the late 20th century. Technology

adoption and diffusion research is a mature stream of exploration within the contemporary information systems

(IS) literature and IS researchers are in continuous quest to understand various factors influencing individual

acceptance and use of emerging information technology (IT) (Hughes et al., 2016, 2017, 2020). This widespread

research stream has witnessed assortment of research methodologies examining multitude of technologies in

range of countries with the extant literature revealing numerous theories, contexts, units of analysis and research

INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue II, February 2026

Page 27

www.rsisinternational.org

methods (Dwivedi & Williams, 2008; Choudrie & Dwivedi, 2005; Williams et al., 2009). The varying research

contexts based on technology, user type, location, adoption time and task performed gave rise to many competing

theories and models. For instance, Technology Acceptance Model (TAM), Diffusion of Innovation (DoI),

Theory of Planned Behaviour (TPB), and Task Technology Fit (TTF) Theory that were mostly deployed to

examine assortment of adoption and diffusion-related issues (Dwivedi et al., 2006, 2007; Dwivedi and

Weerakkody, 2007; Kapoor et al., 2014). Based on exhaustive review of eight dominant technology adoption

models, Venkatesh et al. (2003) developed unified theory of acceptance and use of technology (UTAUT) in the

organisational context emphasising on the utilitarian value (extrinsic motivation) of organisational users after

elimination of similar/redundant constructs (see Venkatesh et al., 2003 for review). The rise of consumer

technologies necessitated the extension of UTAUT model to consumer context emphasising on hedonic value

(intrinsic motivation) of technology users. This led to incorporation of three new constructs such as hedonic

motivation, price value, and habit to original UTAUT, the new extended version is popularly refereed as

UTAUT2. However, in UTAUT2, voluntariness of use was dropped as moderator since consumers have no

organisational mandate and in many situations, consumer behaviour is voluntary (Venkatesh et al., 2012). The

predictive ability of UTAUT2 theory is much higher in comparison to UTAUT; explaining about 74 percent of

the variance on consumers’ behavioural intention to and 52 percent of the variance in consumers’ technology

usage of focal technology (Venkatesh et al., 2016).

OTT platforms provides wide range of content, including movies, TV shows, documentaries, and original

programming, is a significant factor. Exclusive or popular content can attract viewers to a specific platform.

(Kumari, T. 2020). OTT Platforms provides opportunity to viewers to compare subscription fees, free trials, and

bundling options to determine the best value for their money. (Kumari, T. 2020). OTT platforms that offer

superior video and audio quality tend to attract more viewers. High-quality video streaming, including 4K and

HDR content, can enhance the viewing experience. (Kumari, T. 2020). OTT platforms has user friendly

interfaces. Viewers prefer platforms that are easy to use on various devices, such as smartphones, tablets, smart

TVs, and computers. (Kumari, T. (2020). Viewers want to watch content on their preferred devices without

compatibility issues. (Vahoniya, D. R., Darji, D. R.,Baruri, S., & Halpati, J. R. (2022).Offline Viewing) OTT

platforms provides opportunity to download content for offline viewing. This feature adds value for viewers who

want to watch content without an internet connection. (Dasgupta, D. S., & Grover, D. P. 2019). Development of

technology that enables machine learning provides better viewing experience. As System sends you notifications

and recommendations which suggest you shows with genres you’ve seen before. (Vahoniya, D. R., Darji, D. R.,

Baruri, S., & Halpati, J. R. (2022). Word-of-mouth recommendations, reviews, and ratings from friends, family,

or online communities can influence a viewer's decision to try a particular OTT platform. (Dasgupta, D. S., &

Grover, D. P. 2019).Multi-User Profiles: OTT platforms provides multiple user profiles within a single

subscription can be appealing for families or households with different viewing preferences. (Vahoniya, D. R.,

Darji, D. R., Baruri, S., & Halpati, J. R. 2022). OTT platforms that offer accessibility features like subtitles,

closed captions, and multiple language options can attract a more diverse audience. (Vahoniya, D. R., Darji, D.

R., Baruri, S., & Halpati, J. R. 2022).

LITERATURE REVIEW

Technology Acceptance and Behavioral Intentions

The Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) is widely used to explain consumer

and organizational adoption of digital technologies (Yuliani et al., 2024). The model extends the original

UTAUT framework by incorporating additional constructs such as hedonic motivation, price value, and habits,

making it more comprehensive for understanding user behavior in the digital era (Venkatesh et al., 2012).

Studies confirm that performance expectancy and effort expectancy significantly influence behavioral intentions,

with users prioritizing perceived benefits and ease of use when adopting new technology (Enriquez et al., 2024).

Social influence also plays a pivotal role, especially in environments where peer recommendations and societal

trends impact decision-making (Hakimi et al., 2024).

INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue II, February 2026

Page 28

www.rsisinternational.org

Factors Influcencing Technology Adopiton

Several studies emphasize the role of psychological and behavioral factors in technology adoption. Matt et al.

(2015) employed the Push-Pull-Mooring (PPM) framework to analyze switching behaviors in technology use.

Stock and Schulz (2015) investigated the role of consumer predispositions in adoption speed, indicating that

early adopters are influenced by cognitive biases. Choi (2016) explored the impact of social presence and privacy

concerns on smartphone-based social networking site (SNS) usage, finding that enjoyment significantly

moderates adoption intention. Furthermore, Limayem et al. (2007) demonstrated that habit plays a crucial role

in continued information system (IS) usage.

Use of Technology in different sector

he adoption of mobile banking and FinTech services has been extensively studied through TAM and UTAUT

models. Shaikh and Karjaluoto (2016) identified key factors influencing mobile banking adoption in Finland,

highlighting trust and perceived risk as major determinants. Alalwan et al. (2016) extended TAM to analyze

mobile banking adoption in Jordan, finding that social influence and performance expectancy significantly

impact user intention. The IoT adoption framework by Gao and Bai (2014) integrates TAM and social trust

factors, emphasizing the need for security assurances in IoT applications. Similarly, Cimperman et al. (2016)

applied UTAUT to study older adults' acceptance of home telehealth services, concluding that effort expectancy

and social influence are critical factors. Alazzam et al. (2016) further extended UTAUT2 to analyze electronic

health record (EHR) system adoption in Jordanian hospitals, reinforcing the importance of facilitating conditions

and performance expectancy.

UTAUT2 Application in various sector

The application of UTAUT2 in higher education highlights key factors affecting technology adoption among

students and faculty. Research indicates that performance expectancy is the strongest predictor of technology

acceptance, as students perceive digital tools as enablers of academic success (Hakimi et al., 2024). Social

influence, particularly from peers and instructors, further shapes students' willingness to adopt learning

management systems and digital resources (Enriquez et al., 2024). Perceived ease of use, an extension of effort

expectancy, also affects adoption rates, demonstrating the necessity for user-friendly digital interfaces in

educational settings (Sembiring et al., 2024).

In the business sector, UTAUT2 is frequently employed to assess the adoption of digital tools, including e-

accounting and e-commerce platforms. Research on MSMEs (Micro, Small, and Medium Enterprises) suggests

that facilitating conditions, such as digital infrastructure and government support, significantly impact adoption

rates (Sembiring et al., 2024). Furthermore, hedonic motivation and price value contribute to adoption decisions,

particularly among businesses evaluating cost-effectiveness and perceived enjoyment in technology use (Yuliani

et al., 2024). The integration of user experience studies within the UTAUT2 framework highlights the

importance of intuitive and accessible technology solutions in driving successful digital transformation in small

businesses (Hakimi et al., 2024).

Challenges in UTAUT2 Application and Contextual Adaptation

Despite its effectiveness, UTAUT2 faces challenges related to contextual adaptation across different industries

and user demographics (Yuliani et al., 2024). Studies argue that while the model comprehensively explains

behavioral intentions, its applicability varies based on cultural, technological, and economic factors (Sembiring

et al., 2024).

Some researchers emphasize the need for empirical refinements, including the incorporation of external factors

such as privacy concerns, regulatory policies, and user trust in AI-driven technologies (Hakimi et al., 2024).

Additionally, studies suggest extending the model to incorporate dynamic factors like evolving consumer

preferences and emerging technologies (Enriquez et al., 2024).

INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue II, February 2026

Page 29

www.rsisinternational.org

Evolution and Global Expansion of OTT platforms

The development of Over-the-Top (OTT) platforms has transformed the global media landscape, enabling

content distribution that bypasses traditional cable and satellite providers (Khanna et al., 2024). Initially

restricted to specific markets, these services have expanded worldwide, driven by increased internet penetration

and the growing demand for on-demand content (Vaidya et al., 2023). The COVID-19 pandemic further

accelerated this shift, with significant growth in subscription-based and ad-supported streaming services, as

consumers sought alternative entertainment options during lockdowns (Patni & Ansari, 2024).

Technology Advancements in OTT Services

Innovation in digital technology has played a crucial role in the rise of OTT platforms, improving content

accessibility and streaming quality. High-speed internet, 5G deployment, and AI-powered recommendation

algorithms have enhanced user experience and engagement (Khanna et al., 2024). Additionally, cloud computing

and content delivery networks (CDNs) have facilitated seamless streaming across multiple devices, reducing

buffering and enhancing video quality (Vaidya et al., 2023). These technological improvements have positioned

OTT services as dominant players in the entertainment industry, competing directly with traditional media.

Changing Consumer Behaviors and Engagement with OTT

Consumer preferences have shifted significantly in response to the accessibility and convenience of OTT

platforms. Personalized recommendations, interactive content, and subscription-based models have increased

user retention and engagement (Patni & Ansari, 2024). Studies highlight that younger demographics, particularly

Gen Z and millennials, prefer streaming services over traditional TV due to the flexibility of on-demand content

and multi-device compatibility (Vaidya et al., 2023). Furthermore, social and cultural influences, such as peer

recommendations and regional content preferences, continue to shape adoption patterns (Khanna et al., 2024).

Regulatory and Policy Challenges in OTT Streaming

As OTT platforms disrupt conventional broadcasting norms, regulatory bodies worldwide are addressing

concerns related to content moderation, data privacy, and digital rights management (Khanna et al., 2024).

Countries have introduced policies to ensure compliance with local content regulations and fair competition

among streaming services (Vaidya et al., 2023). Additionally, issues related to subscription pricing, advertising

transparency, and consumer data protection remain critical areas for future research and policy intervention

(Patni & Ansari, 2024).

Historical Narratives and Their Influence on Media Trends

Although the historical context of the Ottoman Empire is not directly related to OTT platforms, historical

narratives play an essential role in shaping contemporary media discourses (Başkan, 2023). Many streaming

services incorporate historical dramas and documentaries, leveraging historical storytelling to engage global

audiences. This highlights the interconnectedness between past and present in shaping content preferences and

media consumption trends.

Theoretical Framework and Hypothesis development

Performance Expectancy

Venkatesh et al. (2003) defined performance expectancy as “the degree to which an individual believes that

using the system will help a person to attain gains in job performance”. Previous research reports that

performance expectancy was a significant forecaster of behavioral intention (Venkatesh et al., 2003).

H1: Performance Expectancy has a significant effect on behavioral intention to use OTT platform.

INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue II, February 2026

Page 30

www.rsisinternational.org

Effort Expectancy

Effort expectancy is defined as “the degree of ease associated with the use of the system”. Previous research

supports that latent variables related to effort expectancy that was significant in determining a person’s intention

to adopt new technology (Zhou et al., 2010; Venkatesh et al., 2012)

H2: Effort Expectancy has a significant effect on behavioral intention to use OTT platform.

Social Influence

Social influence means the extent to which a person perceives how vital others believe he or she should use the

technology. Previous research supports that social influence was significant in determining an individual’s

intention to use new technology (Moore and Benbasat, 1991; Venkatesh et al., 1996; Thompson et al., 1991).

H3: Social Influence has a significant effect on behaviroal intention to use OTT platform.

Facilitating Conditions

Facilitating conditions means the extent of availability of technical support for using the new technology

(Venkatesh et al., 2003).

H4: Facilitating conditions has a significant effect on behaviroal intention to use OTT platform.

H5: Facilitating conditions has a significant effect on use behavior to OTT platforms.

Hedonic Motivation

Brown and Venkatesh (2005) defined hedonic motivation as an enjoyment or happiness resultant from using a

technology and play significant part in determining new technology adoption.

H6: Hedonic Motivation has a significant effect on behavioral intention to use OTT platforms.

Habit

Habit is differentiated in two distinct ways. The first habit viewed as prior behaviour (Kim and Malhotra, 2005)

and second, habit is where an individual believes the behaviour to be automatic (Lamayem et al., 2007).

Venkatesh et al. (2012) modeled habit as having direct and indirect effect through behavioural intention.

H7: Habit has a significant effect on behaviroal intention to use OTT platform.

H8: Habit has a significant effect on use behavior of OTT platform.

Price Value

Price value is defined as the level of an individual’s understanding of the monetary costs and benefits of using a

system, PV is one of the factors affecting behavioural intentions of individuals to accept something (Moorthy et

al., 2019; Venkatesh et al., 2012).

H9: Price value has a significant effect on behavioral intention to use OTT platform.

Behavioural Intention

Based on primary theory for all of the intention models discussed above we expect that behavioral intention

would be best forecaster of actual behavior.

H10: Behavioral Intention has significant effect on use behavior of OTT platform.

INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue II, February 2026

Page 31

www.rsisinternational.org

H11: Behaviroal Intention mediates the relationship between facilitating condition and use behavior of OTT

platform.

H12: Behaviroal Intention mediates the relationship between Habit and use behavior.

Use Behavior

The number of times an individual uses information technology is referred to as use behavior (Ramírez-Correa

et al., 2019). There is evidence that the cultural dimension, collectivism, and uncertainty avoidance have

significant moderating effects on the use behavior of customers engaged in online banking (I. U. Khan, Hameed

and Khan, 2017).

Fig. 1 The UTAUT2 model (Venkatesh et al. 2012)

RESEARCH METHODOLOGY

Research question

The pertinent research question that led to this study forward was: Which factors influence adoption of OTT

platform among youths? To answer this question the Unified Theory of Acceptance and Use of Technology 2

(UTAUT2) was used (Venkateh et al, 2012).

Figure 2 proposes the final hypothesized structural model for the study. It consist of 7 exogenous variable

(Performance Expectancy, Effort Expectancy, Social Influence, Facilitating conditions, Hedonic motivation,

Price value, Habit) and 2 endogenous variables (Behavioral intention and Use Beavior). Intention is hypothesized

to act as a mediator between all relationships of exogenous and behavior.

Researach design and Sample

A quantitative research design was employed, using a structured questionnaire to collect data from 211 youth

respondents of Navsari city. In testing the model, structural equation modelling approach was used (Byrne, 2016;

Hair et al; 2019) where assessment of the measurement model and assessment of the structural model was done.

INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue II, February 2026

Page 32

www.rsisinternational.org

Items used for the study

The 32 items used in the scale are as below:

Performance Expectancy

1. OTT platforms provide content relevant to my interests.

2. OTT platforms offer features (e.g., recommendations) that enhance my viewing experience.

Effort Expectancy

1. It requires minimal effort to search for content on OTT platforms.

2. The interface of OTT platforms is user-friendly.

3. I find it simple to customize my viewing preferences on OTT platforms.

Social Influence

1. I often discuss OTT shows and movies with people in my social group.

2. I feel motivated to use OTT platforms because others in my group use them.

3. My decision to subscribe to OTT platforms is influenced by popular trends.

4. I often see positive reviews about OTT platforms on social media.

Facilitating Conditions

1. I can easily afford to subscribe to OTT platforms providing content.

2. I have reliable internet access to watch content on OTT platform.

3. Using OTT platforms fits well within my daily routine.

Hedonic Motivation

1. Using OTT platforms is enjoyable.

2. I have fun exploring content on OTT platforms.

3. OTT platforms provide a stress-relieving experience.

4. Watching content on OTT platforms is entertaining.

5. OTT platforms add excitement to my daily routine.

Price Value

1. OTT platforms offer good value for the money I spend.

2. The cost of subscribing to OTT platforms is reasonable.

3. The benefits I get from OTT platforms justify the expense.

4. I feel the subscription fees are worth the content available.

Habit

1. Using OTT platform would become a habit for me.

2. I use OTT platforms as a way to relax without actively deciding to do so.

3. I automatically open an OTT app when I have free time.

Behavioral Intention

1. I intend to use OTT platforms regularly to watch my favorite shows/movie.

2. I plan to increase my time spent on OTT platforms in the near future to watch content.

3. I am willing to pay for premium OTT services to access better content.

4. I am likely to subscribe to additional OTT platforms.

INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue II, February 2026

Page 33

www.rsisinternational.org

Behavior

1. I watch content on OTT platforms at least once a week.

2. I have a paid subscription to at least one OTT platform.

3. I frequently switch between different OTT platforms to explore content.

4. I actively participate in online discussions or reviews about content available on OTT platform.

Structural Equation Modeling

Structural Equation Modeling (SEM) is a powerful multivariate statistical technique used to analyze complex

relationships between observed and latent variables.

It extends traditional regression and factor analysis by allowing researchers to test theoretical models involving

multiple dependent and independent variables simultaneously.

SEM is widely applied in social sciences, psychology, business, education, and healthcare to assess relationships

among constructs, validate measurement models, and examine causal pathways.

Figure 2 Hypothesized Model

RESULTS OF STRUCTURAL EQUATION MODELLING

Assessing reflective measurement model:

The first step in reflective measurement model assessment involves examining the indicator loadings. Loadings

above 0.708 are recommended, as they indicate that the construct explains more than 50 per cent of the

indicator’s variance, thus providing acceptable item reliability.

The second step is assessing internal consistency reliability, most often using Jöreskog’s (1971) composite

reliability. Higher values generally indicate higher levels of reliability.

INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue II, February 2026

Page 34

www.rsisinternational.org

Table 1: Assessing Reflective Measurement Model:

Construct

Items

Loadings

Rho_a

Rho_c

AVE

Performance Expectancy (PE)

PE5

0.887

0.71

0.712

0.873

0.775

PE6

0.874

Effort Expectancy (EE)

EE3

0.719

0.766

0.826

0.861

0.676

EE4

0.852

EE6

0.885

Social Influence (SI)

SI3

0.739

0.779

0.787

0.857

0.6

SI4

0.819

SI5

0.745

SI6

0.793

0.727

0.735

0.845

0.646

Facilitating Conditions (FC)

FC1

0.812

FC2

0.765

FC3

0.833

0.906

0.944

0.928

0.722

Hedonic Motivation (HM)

HM1

0.795

HM2

0.906

HM3

0.855

HM4

0.853

HM5

0.835

Habit (H)

0.869

0.767

0.769

0.866

0.683

0.812

0.796

Price Value (PV)

PV1

0.830

0.828

0.829

0.886

0.659

PV2

0.792

PV3

0.802

PV4

0.823

Behavioral Intention (BI)

BI1

0.735

0.812

0.814

0.877

0.64

BI2

0.795

BI3

0.807

BI4

0.859

Behavior

(B)

0.727

0.773

0.776

0.855

0.596

0.748

0.820

0.791

INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue II, February 2026

Page 35

www.rsisinternational.org

The table provides key measures of construct reliability and validity using Cronbach’s alpha, Composite

Reliability (rho_a & rho_c), and Average Variance Extracted (AVE).

Assessing Internal Consistency Reliability

Cronbach’s Alpha (α): Measures internal consistency (should be ≥ 0.7). Composite Reliability (rho_a & rho_c):

Should be ≥ 0.7 for good reliability. (Hair et al. 2019). All constructs have Cronbach’s alpha & Composite

Reliability > 0.7, indicating strong internal consistency.

Assessing Convergent Validity

Average Variance Extracted (AVE): Measures how much variance is captured by a construct vs. measurement

error. Should be ≥ 0.5 for acceptable validity. (Fornell & Larcker 1981). All constructs have AVE > 0.5,

confirming convergent validity (each construct explains at least 50% of its variance).

Discriminant Validity Assessment (Fornell-Larcker Criterion)

The Fornell-Larcker criterion (1981), which checks discriminant validity by ensuring that each construct shares

more variance with its indicators than with other constructs. The diagonal values (highlited below) represent the

square root of AVE for each construct. Discriminant validity is confirmed if the diagonal values are higher than

all off-diagonal values in the same row/column. Each highlite diagonal value (√AVE) is greater than the

correlations in its row/column. This confirms that each construct has more shared variance with its own

indicators than with other constructs.

Table 2: Fornell-Larker Criterion

0.772

0.607

0.800

0.486

0.357

0.822

0.618

0.557

0.538

0.804

0.611

0.582

0.468

0.556

0.826

0.611

0.428

0.707

0.644

0.674

0.849

0.410

0.240

0.654

0.462

0.379

0.619

0.881

0.637

0.612

0.537

0.654

0.535

0.586

0.393

0.812

0.519

0.476

0.472

0.478

0.546

0.564

0.385

0.539

0.775

Discriminant Validity Assement – Heterotrait-Monotrait (HTMT) ratio

As a replacement of Fornell-Larcker criterion (1981), Henseler et al. (2015) proposed the heterotrait-monotrait

(HTMT) ratio of the correlations (Voorhees et al., 2016). The HTMT is defined as the mean value of the item

correlations across constructs relative to the (geometric) mean of the average correlations for the items measuring

the same construct. Discriminant validity problems are present when HTMT values are high. Henseler et al.

(2015) propose a threshold value of 0.90 for structural models with constructs that are conceptually very similar,

for instance cognitive satisfaction, affective satisfaction and loyalty. In such a setting, an HTMT value above

0.90 would suggest that discriminant validity is not present. But when constructs are conceptually more distinct,

a lower, more conservative, threshold value is suggested, such as 0.85 (Henseler et al., 2015).

INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue II, February 2026

Page 36

www.rsisinternational.org

Table 3: Heterotrait-Monotrait Ratio

0.763

0.609

0.425

0.820

0.707

0.715

0.789

0.734

0.578

0.750

0.720

0.462

0.847

0.793

0.783

0.548

0.306

0.882

0.664

0.514

0.792

0.799

0.740

0.659

0.838

0.669

0.513

0.667

0.586

0.604

0.638

0.707

0.667

0.524

0.676

Constructs (BI ↔ EE (0.425) , H ↔ EE (0.578), PE ↔ PV (0.513), PE ↔ SI (0.524), SI ↔ PV (0.676) meet

the HTMT discriminant validity criteria. As their values do not exceed the 0.85 threshold. For construct (FC ↔

PV (0.838)) these values are below 0.90 but near 0.85.

Assessing Structural model

Collinearity statistics (VIF) – Outer Model List

Before assessing the structural relationships, collinearity must be examined to make sure it does not basis the

regression results. Ideally, the VIF values should be close to 3 and lower. If collinearity is a problem, a frequently

used option is to create higher-order models that can be supported by theory (Hair et al., 2017a).

Table 3: Collinearity Statistic (VIF) – Outer Model List

VIF

1.330

1.486

1.741

1.619

BI1

1.369

BI2

1.773

BI3

1.875

BI4

2.258

EE3

1.429

EE4

1.684

EE6

1.653

FC1

1.379

FC2

1.417

FC3

1.556

INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue II, February 2026

Page 37

www.rsisinternational.org

1.815

1.571

1.466

HM1

2.285

HM2

3.833

HM3

2.531

HM4

3.134

HM5

1.872

PE5

1.436

PE6

1.436

PV1

1.917

PV2

1.649

PV3

1.908

PV4

1.901

SI3

1.542

SI4

1.666

SI5

1.356

SI6

1.611

The outer model assesses the relationship between indicators and their latent constructs. Most outer model VIF

values are below 3, indicating low multicollinearity among constructs. HM2(3.833), HM4 (3.134) and

HM3(2.531) indicates moderate multicollinearity in Hedonic motivation (HM) indicators. Overall the outer

model does not exhibit serious multicollinearity issues.

Collinearity statistics (VIF) – Inner Model List

Table 4: Collinearity statistics (BIF) – Inner Model List

VIF

BI -> B

1.716

EE -> BI

2.486

FC -> B

1.643

FC -> BI

2.200

H -> B

1.713

H -> BI

2.092

HM -> BI

3.402

PE -> BI

1.937

PV -> BI

2.122

SI -> BI

1.717

INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue II, February 2026

Page 38

www.rsisinternational.org

The inner model examines relationships between latent variables. Most inner model VIF values are below 3,

suggesting that multicollinearity among latent constructs in not severe.

Path Co-efficient and P value results

If collinearity is not an issue, the next step is examining the R square value of endogenous constructs. The R

square measures the variance, which is explained in each of the endogenous constructs and is therefore a measure

of the model’s explanatory power (Shmueli and Koppius, 2011). The R square ranges from 0 to 1, with higher

values indicating a greater explanatory power. As a guideline, R square values of 0.75, 0.50, and 0.25 can be

considered as substantial, moderate and weak (Henselar et al., 2009; Hair et al., 2012).

Table 5: Path Co-effieicent (R

Statistics)

Original

sample (O)

Sample mean

(M)

Standard

deviation

(STDEV)

T statistics

(|O/STDEV|)

P values

0.528

0.544

0.064

8.212

0.505

0.53

0.062

8.092

Both Behavior (B) and Behavior (BI) have significant positive effects, as indicated by path coefficients, low

standard deviations, and p-value of 0.000. The model demonstrates stable and precise estimates, with no signs

of variability issue.

Path Co-efficient – Confidence interval bias corrected.

Table 6: Path Co-efficient – Confidence interval bias corrected values

Original sample (O)

Sample mean (M)

Bias

2.5%

97.5%

BI -> B

0.270

0.274

0.004

0.082

0.470

EE -> BI

0.004

-0.004

-0.009

-0.134

0.168

FC -> B

0.311

0.303

-0.009

0.136

0.496

FC -> BI

0.227

0.228

0.001

0.062

0.398

H -> B

0.281

0.286

0.006

0.089

0.455

H -> BI

0.359

0.360

0.002

0.151

0.553

HM -> BI

-0.188

-0.184

0.005

-0.422

0.013

PE -> BI

-0.067

-0.064

0.004

-0.220

0.072

PV -> BI

0.343

0.340

-0.003

0.179

0.511

SI -> BI

0.117

0.121

0.004

-0.039

0.275

Significant Relationships (Support for Hypothesis)

 BI → B (0.270): Behavioral intention significantly influences behavior.

 FC → B (0.311): Facilitating conditions positively affect use behavior.

 FC → BI (0.227): Facilitating conditions impact behavioral intention.

 H → B (0.281): Habit influences use behavior.

 H → BI (0.359): Habit affects behavioral intention.

 PV → BI (0.343): Price value strongly influences behavioral intention.

INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue II, February 2026

Page 39

www.rsisinternational.org

 H→BI→B (0.034): Partial mediation effect of behavioral intention on Habit and use behavior.

 PV→BI→B (0.04): Partial mediation effect of behavioral intention on Price value and use behavior.

Non-Significant Relationships (Hypothesis Not Supported)

 EE → BI (0.004): Effort expectancy has no impact on behavioral intention.

 HM → BI (-0.188): Hedonic motivation does not significantly affect behavioral intention.

 PE → BI (-0.067): Performance expectancy has no significant impact on behavioral intention.

 SI → BI (0.117): Social influence does not significantly impact behavioral intention.

F square (Effect size) value

Researchers can also asses how the removal of a certain predictor construct affects an endogenous construct’s R

square value. This metric is referred to as the f² effect size. The guidelines for assessing f² values of 0.02, 0.15,

and 0.35 can be considered as small, medium and large effects (Cohen,1988).

Table 7: f

Effect size

f-square (f

)

BI -> B

0.090

EE -> BI

0.000

FC -> B

0.125

FC -> BI

0.048

H -> B

0.097

H -> BI

0.124

HM -> BI

0.021

PE -> BI

0.005

PV -> BI

0.112

SI -> BI

0.016

The analysis of f² effect sizes reveals that Facilitating Conditions (FC), Habit (H), and Price Value (PV) are the

key drivers in the model. Among them, FC → B (0.125), H → BI (0.124), and PV → BI (0.112) demonstrate a

moderate impact, indicating that facilitating conditions play a notable role in influencing behavior, habit

significantly affects behavioral intention, and price value is an important determinant of behavioral intention.

Several factors exhibit a small but meaningful effect on the model. Specifically, Behavioral Intention (BI)

impacts Behavior (B) (0.090) with a small effect, while Habit (H) also influences Behavior (B) (0.097) in a

similar manner. Additionally, Facilitating Conditions (FC) contribute to Behavioral Intention (BI) (0.048), albeit

with a minor effect, and Hedonic Motivation (HM) has a very small effect on Behavioral Intention (0.021).

On the other hand, some factors show negligible or no effect on behavioral intention. Effort Expectancy (EE)

(0.000) has no influence, Performance Expectancy (PE) (0.005) has an almost insignificant impact, and Social

Influence (SI) (0.016) contributes only marginally.

These results suggest that while facilitating conditions, habit, and perceived value are significant predictors,

effort expectancy, performance expectancy, and social influence do not play a major role in shaping behavioral

intention.

INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue II, February 2026

Page 40

www.rsisinternational.org

Q square value

To assess the PLS path model’s predictive accuracy is by calculating the Q square value (Geisser, 1974; Stone,

1974). As a guideline, Q square values should be larger than zero for a specific endogenous construct to indicate

predictive accuracy of the structural model for that construct. As a rule of thumb, Q square values higher than 0,

0.25 and 0.50 depict small, medium and large predictive relevance. of the PLS-path model.

Table 8: Predictive accuracy by Q

value

Q²predict

RMSE

MAE

0.479

0.731

0.554

0.449

0.751

0.536

Q square predict value for Behavior (0.479) and Behavioral Intention (0.449) are above zero indicating good

predictive relevance of the model.

CONCLUSION

This study investigated the adoption and usage behavior of OTT platforms among the youth of Navsari city using

the UTAUT2 model. A total of 211 respondents participated in the study, providing insights into the key

determinants of behavioral intention and actual behavior. The reliability and validity of the measurement model

were confirmed through Cronbach’s alpha, composite reliability, AVE, and discriminant validity tests (Fornell-

Larcker and HTMT criteria). Additionally, the structural model exhibited stable estimates with no severe

multicollinearity issues, as reflected by the VIF values.

Key findings indicate that Facilitating Conditions (FC), Habit (H), and Price Value (PV) significantly influence

behavioral intention and actual behavior. Behavioral Intention (BI) was found to be a significant predictor of

Behavior (B), confirming the core premise of the intention-behavior relationship in technology adoption. Among

these factors, Facilitating Conditions (FC) emerged as a crucial driver, significantly influencing both Behavioral

Intention and Behavior, highlighting the importance of external support in driving OTT platform usage.

Conversely, Effort Expectancy (EE), Performance Expectancy (PE), Social Influence (SI), and Hedonic

Motivation (HM) did not significantly impact Behavioral Intention. This suggests that the youth of Navsari may

prioritize practical factors such as Facilitating Conditions and Price Value over ease of use, social influence, or

enjoyment when forming their intention to use OTT platforms.

The model exhibited strong predictive relevance, as indicated by the Q²predict values for Behavior (0.479) and

Behavioral Intention (0.449), both above zero. Effect size analysis further revealed that Facilitating Conditions,

Habit, and Price Value had a moderate impact on behavioral intention and behavior, while other factors

demonstrated small or negligible effects.

These findings provide valuable insights for OTT platform providers and policymakers aiming to enhance

adoption and engagement among youth. Strategies should focus on improving facilitating conditions, reinforcing

habitual usage, and emphasizing price value to drive continued usage. Future research could explore additional

moderating variables or incorporate alternative theoretical frameworks to further refine predictive power.

Limitation and Future Scope of Research

This study has certain limitations that should be considered while interpreting the findings. First, the research

was conducted among youth in Navsari city, which may limit the generalizability of the results to other regions

or age groups. A broader geographic scope could provide a more comprehensive understanding of OTT platform

INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue II, February 2026

Page 41

www.rsisinternational.org

adoption. Second, while the sample size of 211 respondents offers valuable insights, a larger sample covering

different cities or states could enhance the robustness and reliability of the conclusions. Third, the study

employed a cross-sectional design, capturing consumer behavior at a single point in time. Since digital

consumption habits evolve rapidly, a longitudinal approach could help track changes in user preferences and

engagement over time.

For future research, several directions can be explored to build upon these findings. Expanding the study to a

larger and more diverse sample across different regions and demographics would enhance generalizability.

Conducting longitudinal studies could provide deeper insights into how user behavior changes over time,

particularly in response to technological advancements and market trends. A comparative analysis between

different age groups, regions, or cultural backgrounds could reveal variations in adoption patterns. Additionally,

integrating new variables such as content quality, personalized recommendations, and user engagement factors

could offer a more comprehensive understanding of OTT platform usage. Researchers could also explore

alternative theoretical models, such as TAM, TPB, or hybrid frameworks, to assess OTT platform adoption from

different perspectives. Lastly, with the increasing role of AI-driven recommendations, future studies could

investigate how personalization impacts consumer behavior and brand loyalty in the OTT industry. These

directions could help deepen the understanding of OTT platform adoption and contribute to more effective

strategies for service providers and policymakers.

REFERENCE

1. Chuah, S. H. W., Rauschnabel, P. A., Krey, N., Nguyen, B., Ramayah, T., & Lade, S. (2016). Wearable

technologies: The role of usefulness and visibility in smartwatch adoption. Computers in Human

Behavior, 65, 276-284.

2. Gu, R., Jiang, Z., Oh, L. B., & Wang, K. (2014). Exploring the influence of optimum stimulation level

on individual perceptions of IT innovations.

3. Gao, L., & Bai, X. (2014). A unified perspective on the factors influencing consumer acceptance of

internet of things technology. Asia Pacific Journal of Marketing and Logistics, 26(2), 211-231.

4. Matt, C., Hess, T., & Heinz, S. (2015). Should we take a closer look? Extending switching theories

from singular products to complex ecosystem structures.

5. Shaikh, A. A., & Karjaluoto, H. (2016, January). Mobile banking services continuous usage--case study

of Finland. In 2016 49th Hawaii International Conference on System Sciences (HICSS) (pp. 1497-

1506). IEEE.

6. Wang, Y. S., Li, H. T., Li, C. R., & Zhang, D. Z. (2016). Factors affecting hotels' adoption of mobile

reservation systems: A technology-organization-environment framework. Tourism management, 53,

163-172.

7. Alalwan, A. A., Dwivedi, Y. K., Rana, N. P., & Williams, M. D. (2016). Consumer adoption of mobile

banking in Jordan: Examining the role of usefulness, ease of use, perceived risk and self-

efficacy. Journal of Enterprise Information Management, 29(1), 118-139.

8. Stock, R. M., & Schulz, C. (2015). UNDERSTANDING CONSUMERS'PREDISPOSITIONS

TOWARD NEW TECHNOLOGICAL PRODUCTS: TAXONOMY AND IMPLICATIONS FOR

ADOPTION BEHAVIOUR. International Journal of Innovation Management, 19(05), 1550056.

9. Cimperman, M., Brenčič, M. M., & Trkman, P. (2016). Analyzing older users’ home telehealth services

acceptance behavior—applying an Extended UTAUT model. International journal of medical

informatics, 90, 22-31.

10. Choi, S. (2016). The flipside of ubiquitous connectivity enabled by smartphone-based social

networking service: Social presence and privacy concern. Computers in Human Behavior, 65, 325-333.

11. Chang, H. H., Fu, C. S., & Jain, H. T. (2016). Modifying UTAUT and innovation diffusion theory to

reveal online shopping behavior: Familiarity and perceived risk as mediators. Information

Development, 32(5), 1757-1773.

12. Limayem, M., Hirt, S. G., & Cheung, C. M. (2007). How habit limits the predictive power of intention:

The case of information systems continuance. MIS quarterly, 705-737.

INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue II, February 2026

Page 42

www.rsisinternational.org

13. Alazzam, M. B., BASARI, A., SIBGHATULLAH, A. S., RAMLI, M. R., JABER, M. M., & NAIM,

M. H. (2016). PILOT STUDY OF EHRS ACCEPTANCE IN JORDANHOSPITALS BY

UTAUT2. Journal of Theoretical & Applied Information Technology, 85(3).

14. Cebeci, U., Ince, O., & Turkcan, H. (2019). Understanding the intention to use Netflix: An extended

technology acceptance model approach. International Review of Management and Marketing, 9(6),

152-157.

15. Momani, A. M. (2020). The unified theory of acceptance and use of technology: A new approach in

technology acceptance. International Journal of Sociotechnology and Knowledge Development

(IJSKD), 12(3), 79-98.

16. Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information

technology: extending the unified theory of acceptance and use of technology. MIS quarterly, 157-178.

17. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model:

Four longitudinal field studies. Management science, 46(2), 186-204.

18. Xue, L., Rashid, A. M., & Ouyang, S. (2024). The unified theory of acceptance and use of technology

(UTAUT) in higher education: a systematic review. Sage Open, 14(1), 21582440241229570.

19. Polisetty, A., Sowmya, G., & Pahari, S. (2023). Streaming towards innovation: Understanding

consumer adoption of OTT services through IRT and TAM. Cogent Business & Management, 10(3),

2283917.

20. Lubis, F. G., Miralaksmi, A., Christian, N., & Hendijani, R. B. (2023). Intention To Use Netflix In

Indonesia: A Modified Technology Acceptance Model. International Journal of Innovative Research

and Advanced Studies (IJIRAS), 10(10), 19-28.

21. Yoon, J. H., & Kim, H. K. (2023). An Empirical Analysis of Factors Affecting OTT Service Users’

Switching Intention: Focusing on Netflix and the Perspective of the Push-Pull-Mooring

Framework. International Journal of Human–Computer Interaction, 1-10.

22. Saha, S., & Srivastava, A. (2023, February). Digitized Entertainment–Factors influencing Consumer

Adoption of OTT Platforms. In 2022 OPJU International Technology Conference on Emerging

Technologies for Sustainable Development (OTCON) (pp. 1-6). IEEE.

23. Mulla, T. (2022). Assessing the factors influencing the adoption of over-the-top streaming platforms:

A literature review from 2007 to 2021. Telematics and Informatics, 69, 101797.

24. Govind, A. (2022). A study on factors influencing customer’s adoption of Over-The-Top (OTT)

platform over other conventional platforms.

25. Vahoniya, D. R., Darji, D. R., Baruri, S., & Halpati, J. R. (2022). Awareness, Preferences, Perception,

and Satisfaction about the Over-The-Top (OTT) Platforms/Players in Anand City, Gujarat, India. Asian

Journal of Agricultural Extension, Economics & Sociology, 40(12), 254-264.

26. Priya, R., Mondal, D. P., & Paldon, T. (2021). Understanding the intentions of students to use OTT

platforms. International Journal of Innovative Research in Technology, 8(1), 671-677.

27. TS, S., & Sumathy, M. (2021). User Perception Towards OTT Video Streaming Platforms in Kerala

(With Special Reference to Thrissur). Analytical Commerce and Economics, 2, 27-32.

28. Jhala, B. D., & Patadiya, V. B. (2021). A STUDY ON CONSUMER BEHAVIOUR TOWARDS OTT

PLATFORMS IN INDIA DURING COVID ERA. Advance and Innovative Research, 120.

29. Kumari, T. (2020). A study on growth of over the top (OTT) video services in India. International

Journal of Latest Research in Humanities and Social Science (IJLRHSS), 3(9), 68-73.

30. Sundaravel, E., & Elangovan, N. (2020). Emergence and future of Over-the-top (OTT) video services

in India: an analytical research. International Journal of Business, Management and Social

Research, 8(2), 489-499.

31. Gangwar, V. P., Sudhagoni, V. S., Adepu, N., & Bellamkonda, S. T. (2020). Profiles and Preferences

of OTT users in Indian Perspective. European Journal of Molecular & Clinical Medicine, 7(8), 5106-

5142.

32. Nijhawan, G. S., & Dahiya, S. (2020). Role of COVID as a Catalyst in increasing adoption of OTTs in

India: A Study of evolving consumer consumption patterns and future business scope. Journal of

Content, Community and Communication, 12, 298-311

33. Dasgupta, D. S., & Grover, D. P. (2019). Understanding adoption factors of over-the-top video services

among millennial consumers. International Journal of Computer Engineering and Technology, 10(1).

INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue II, February 2026

Page 43

www.rsisinternational.org

34. Sant, S. (2019). A study on Factors Leading to Adoption of OTT Services among Millennial Consumers

in India.

35. Yuliani, P. N., Suprapti, N. W. S., Widagda K., I. Gst. Ngr. J. A., & Piartrini, P. S. (2024). The

Literature Review on UTAUT 2: Understanding Behavioral Intention and Use Behavior of Technology

in the Digital Era. International Journal of Social Science and Business, 8(2), 208–222.

https://doi.org/10.23887/ijssb.v8i2.77311

36. Acosta Enriquez, B. G., Ramos Farroñán, E. V., Villena Zapata, L., Mogollón García, F. S., Rabanal-

León, H. C., Morales Angaspilco, J. E., & Saldaña Bocanegra, J. C. (2024). Acceptance of Artificial

Intelligence in University Contexts: A Conceptual Analysis Based on UTAUT2 Theory. Heliyon,

10(19), e38315.

https://doi.org/10.1016/j.heliyon.2024.e38315

37. Hakimi, T. I., Jaafar, J. A., Mohamad, M. S., & Omar, M. (2024). Unified theory of acceptance and

use of technology (UTAUT) applied in higher education research: A systematic literature review and

bibliometric analysis. Multidisciplinary Reviews, 7(12), 2024303.

https://doi.org/10.31893/multirev.2024303

38. Sembiring, F., Sarumpaet, S., & Metalia, M. (2024). UTAUT model in explaining intentions and actual

behavior of e-accounting users: A literature review.

https://doi.org/10.53402/ajebm.v3i1.421

39. Patni, Y., & Ansari, S. (2024). A Comprehensive Review of Literature on OTT Platforms.

https://doi.org/10.62737/p4xdrx40

40. Vaidya, H., Fernandes, S., & Panda, R. (2023). Adoption and Usage of Over-the-Top Entertainment

Services. International Journal of Social Ecology and Sustainable Development, 14(1), 1–18.

https://doi.org/10.4018/ijsesd.319718

41. Khanna, P., Sehgal, R., Gupta, A., Dubey, A. M., & Srivastava, R. (2024). Over-the-top (OTT)

platforms: a review, synthesis and research directions. Marketing Intelligence & Planning.

https://doi.org/10.1108/mip-03-2023-0122

42. Başkan, A. (2023). History, international relations and the ottoman empire: a review article. Tarih

Incelemeleri Dergisi.

https://doi.org/10.18513/egetid.1336760

43. C, K. (2024). Exploring adoption of Connected TV for OTT Streaming – A Synthesis of Technology

Acceptance Model (TAM) and Uses and Gratification Theory (UGT). Journal of Informatics

Education and Research, 4(3).

https://doi.org/10.52783/jier.v4i3.1808

44. Vaghela, N., & Pandya, K. (2024). The next click: factors driving ott platform adoption. ShodhKosh

Journal of Visual and Performing Arts, 5(1).

https://doi.org/10.29121/shodhkosh.v5.i1.2024.3195

45. Paul, S., Soni, S., Kushwah, P. S., & Bisen, N. (2024). Factors Influencing Choice of OTT Platforms

Among Students. International Journal For Multidisciplinary Research, 6(6).

https://doi.org/10.36948/ijfmr.2024.v06i06.34180

46. Khanna, P., Sehgal, R., Gupta, A., Dubey, A. M., & Srivastava, R. (2024). Over-the-top (OTT)

platforms: a review, synthesis and research directions. Marketing Intelligence & Planning.

https://doi.org/10.1108/mip-03-2023-0122