Antecedents for customer satisfaction and willingness to subscribe over-the-top services post the pandemic in India

Over-the-top (OTT) services are online-based video streaming platforms that provide on-demand content, accessible through an internet connection without relying on traditional cable, satellite or IPTV networks (Yoon and Kim, 2024). These services offer personalized viewing experiences with a vast variety of content, including movies, TV shows and original productions (Palomba, 2022). The popularity of OTT video streaming services has grown in recent years (Yousaf et al., 2021). An Increase in Internet usage, improved telecommunication networks and the availability of smart gadgets have fuelled the emergence of OTT services (Gupta and Singharia, 2021). During the pandemic, the distressing effect mirrored people’s behavioral and lifestyle changes, including a shift in media usage (Ghosh, 2021). This transition has led to increased investment in media content production, localization and platform innovation, such as AI-driven recommendations by service providers. Post-pandemic, the OTT services remain the primary source of entertainment.

Despite the growing literature on OTT services, several gaps remain unaddressed. Most studies on OTT platforms have been conducted before or during the pandemic (Gupta and Singharia, 2021; Patnaik et al., 2024). A search was carried out in the Scopus database to identify the number of research papers published post-pandemic on OTT services. The keywords “OTT” or “Over-the-top” and “post-pandemic” or “post-COVID” used for the search resulted in 11 documents. This indicates that there is a huge scope to examine consumer behaviour and subscription pattern post-pandemic. This mandates a study to understand customer satisfaction and willingness to pay for OTT services after the pandemic. The objectives of this study are to understand:

• the antecedents that impact customer satisfaction;

• the impact of customer satisfaction on the willingness to pay for the OTT services; and

• the impact of customer habit as a variable moderating the relationship between customer satisfaction and willingness to pay.

One of the renowned theories used to study consumer behaviour is expectation-confirmation theory (ECT) (Ma et al., 2023). The theory postulates that when the customers’ experience in using a product or service meets or exceeds expectations, then the chance of the customers continuing to use the product or service is high (AlSokkar et al., 2024). This theory is integrated with two other theories, namely, technology acceptance model (TAM) and cognitive dissonance theory (CDT). TAM is used to study the technology adoption of customers based on their perceived usefulness and ease of use (Prastiawan et al., 2021; Kumar et al., 2024). CDT studies the conflicting beliefs arising out of differences between the experience of the customers and their expectations (Wang et al., 2023). ECT, TAM and CDT are integrated because TAM includes the pre-adoption expectations (Jaiswal et al., 2022), CDT demonstrates how customers deal with unmet expectations (Marikyan et al., 2023) and ECT explains the alignment between pre-consumption expectations and experience (Ashraf et al., 2020).

The primary motivations for adopting OTT services are performance expectations, price (Polisetty et al., 2023), brand image (Chen et al., 2023), content availability (Malewar and Bajaj, 2020) and advertising (Agarwal et al., 2023). However, these factors do not predict the long-term usage and customer satisfaction of OTT services (Thu and Hue, 2022). In digital platforms, customer engagement improves perceived usefulness (Tao et al., 2022). The TAM model reiterates that perceived usefulness and ease of use influence customer satisfaction. However, the study does not incorporate these two variables directly. Instead, the study uses the extended version of TAM, which is the unified theory of acceptance and use of technology (UTAUT). The UTAUT model builds on TAM and has four factors: performance expectancy, effort expectancy, social influence and facilitating conditions. The research study emphasizes only on facilitating conditions and social influence as the determinants, because this research studies the post-adoption customer satisfaction and not on initial adoption intent. Moreover, performance and effort expectancies overlap with constructs integrated with TAM and ECT. Hence, these constructs were excluded from the study. The UTAUT model indicates that facilitating conditions is an important antecedent of technology adoption, leading to enhanced user experience (Venkatesh et al., 2003). The CDT suggests that customers are influenced by socially driven expectations and their satisfaction levels increase if their experience meets their expectations (Yang et al., 2024). If the customer expectations are not met then dissatisfaction occurs (Shin and Park, 2021). ECT indicates that when customers find the content on digital platforms are relevant and has variety, then they are more satisfied, leading to an increase in intention to continue usage (Carissa et al., 2023). ECT also explains that customers undergo a conscious evaluation process before they feel satisfied (Mamun et al., 2020). This study introduces a behavioural aspect to ECT. Habit is a behaviour formed among customers because of repeated viewing and usage without a conscious evaluation process (Schnauber-Stockmann and Naab, 2019). Based on the ECT, TAM and CDT theories, customer engagement, facilitating conditions, social influence and content availability are particularly relevant and chosen as the antecedents due to their impact on customer experience and long-term retention.

Customer engagement is a crucial determinant of customer satisfaction that has gained significant attention in recent years. Customer engagement is defined as the cognitive, physical and emotional attachments in the relationship with an OTT video service provider (Suryavanshi et al., 2022). Customer engagement is how a customer interacts with a brand via its website and/or any other computer-mediated entity (Kumar and Jhawar, 2024). The literature has extensively demonstrated a relationship between customer engagement and customer satisfaction (Agyei et al., 2021). Moreover, businesses must redefine their marketing activities to engage customers, as engaged customers are more loyal and develop stronger brand relationships, leading to increased satisfaction and retention (Bertini et al., 2024). Based on the literature review cited above, the following hypothesis is proposed:

Facilitating conditions refer to the availability of technological infrastructure and support services for using OTT video streaming facilities (Shah and Mehta, 2023). The infrastructure includes devices that customers use to connect to the Internet, access to payments, the subscription model and the physical environment in which they prefer to use the services. Existing literature has established that conducive technological environments have a significant impact on actual usage and behavioral intentions (Lee et al., 2021). The UTAUT model proposed by Venkatesh et al. (2003) suggests that a user’s perception of facilitating conditions has a direct impact on technology acceptance since the technological environment either supports or hinders the technology adoption. Venkatesh et al. (2012) identified that a well-established operational infrastructure enhances the satisfaction level of the customers, thereby influencing the willingness to pay. Based on the arguments presented, the following hypothesis is proposed:

Social influence refers to the extent to which individuals are affected by the opinions and behaviors of key individuals in their social circles, such as friends, family and colleagues (Bhattacharyya et al., 2022). This is the degree to which customers believe in the people who matter to them when making decisions about adopting technology, including OTT platforms. Gerli et al. (2022) define social influence as the extent to which an individual feel that it is important to adopt a technology due to the influence of people in the closer circle. Research suggests that customers are frequently influenced by their social networks when choosing and engaging with OTT services, as peer recommendations play a crucial role in shaping perceptions and usage behaviors (Sharma et al., 2023). Furthermore, Gunawan et al. (2022) discovered that customers’ intentions to use a specific technology are affected by the opinions of people important to them. Similarly, Shah and Mehta (2023) found comparable social effects on customer satisfaction in the adoption of technology. Based on the literature review, the following hypothesis is proposed:

Content availability refers to the plethora of content offered by OTT providers in terms of genre, quality, quantity, language and recommendations that are accessible to a large customer base (Hallur et al., 2023). OTT platforms strategically persuade users to subscribe and engage with their services. Exclusive content enhances the perceived value of these platforms, making it a key determinant of customer preference and retention (Sundaravel and Elangovan, 2020). The use of an advertisement-based monetization approach by OTT video streaming providers enables them to offer free services, which expands user engagement and enhances overall platform usage (Arakkal and Menon, 2024). Based on these insights, the following hypothesis is proposed:

Customer satisfaction in OTT video streaming services is determined by comparing customers’ direct experiences with their expectations (Situmorang and Harmawan, 2022). Extensive literature has researched customer satisfaction as a mediating the link between quality, engagement, trust, value, loyalty and purchase intentions (Chakraborty et al., 2023; Pappachan, 2023; Ul Haq and Awan, 2020). However, while numerous studies found a positive relationship between customer satisfaction and engagement, there is an ongoing debate about the direction of relationship. Some studies have found satisfaction to be an antecedent of customer engagement where satisfied users are more likely to engage more with OTT platforms (Yoon and Kim, 2024). Conversely, others argue that customer satisfaction to be an effect of customer engagement (Habib et al., 2022). Willingness to pay is primarily influenced by the quality and variety of the content provided by OTT services (Nagaraj et al., 2021). The “willingness” indicates the customers’ decision to subscribe repeatedly. When the purchase becomes repetitive, OTT service providers must improve technology and quality and produce more exclusive content to satisfy customers. The aforementioned discussion lends support to the following hypotheses:

The repetitiveness of past behaviour is one of the key antecedents of present actions (Tsai, 2023) and habit indicates the consequences of previous behaviours or experiences (Lee and Cho, 2021). Karunakaran et al. (2023) define habitual behaviour as the ability with which the customers engage in certain actions suggesting that habits can strengthen or weaken the effect of satisfaction on payment decisions. Santos and Schlesinger (2021) studied how habits related to media consumption can influence customer loyalty and the intention to pay. Polisetty et al. (2023) suggest that customer habits are due to prolonged use of OTT services, which in turn enhances perceived value and willingness to pay. Moreover, Verplanken and Orbell (2022) suggested that habits can regulate the impact of satisfaction on user behaviour, implying that customers who have developed strong habits of using OTT services are more likely to transition to paid subscriptions. Gupta and Singharia (2021) further emphasize habit as a significant mediator in customer retention, reinforcing that habitual engagement can increase the impact of customer satisfaction on WTP. Based on the arguments presented, the hypothesis is formulated:

Based on the extensive literature review and the hypotheses developed, the theoretical framework for the study is proposed. The framework is demonstrated in Figure 1.

In this study, a quantitative survey method is used to test the theoretical framework. Items were selected based on a thorough literature review. The questionnaire was sent to three experts: the marketing head of a consulting firm, the expert from the retailers’ association of India and the Coimbatore management association to check the suitability of the questionnaire. Nine questions were reworded or rephrased, based on their suggestions. The final questionnaire consisted of 65 questions and two sections. Section 1 asked questions to capture the respondents’ demographic profiles. Section 2 contains questions related to these constructs. To measure the response of each variable, a five-point Likert scale was used (strongly agree = 5, agree = 4, neutral = 3, disagree = 2, strongly disagree = 1). The questionnaire is given in Appendix Table A1. A pilot study was conducted with 35 respondents and the questionnaire was tested for incorrect wording and clarity. Suitable modifications were made to the questionnaire and the final version was used to elicit responses.

The target population considered for the study comprised active users of all major OTT service providers in the state of Tamil Nadu in India. A two-stage sampling method is used to effectively reach out to the active OTT users. In the first stage, the dealers were sampled and in the second stage the customers were sampled. The research objectives were explained to the dealers and they were requested to connect their customers for data collection. This process narrowed the sampling focus in a systematic way rather than an individual-level broad survey. By selecting dealers first, the study ensured that the sample includes customers from all major OTT platforms and helps avoid over-representation of any single service provider. This is a cross-sectional study. An online survey was created using Google Forms. A total of 200 responses were collected during October 20, 2023, to November, 2023. Table 1 presents the demographic profile of the respondents.

Table 1 shows that the respondents in the (18–24) age group have the highest percentage (62.0%), which shows that majority of young population are using OTT platforms, followed by the above-45 age group being the next highest percentage (14.5%). The majority of the respondents are female (61%) when compared to male respondents. The respondents comprised of varied categories such as students, employed/self-employed people, homemakers. The majority of the respondents were students, with the highest percentage (50.5%) followed by homemakers with 33.5%. This analysis helped to understand the OTT consumption pattern among the various categories of the population.

Data analysis was done using WarpPLS software. This software is based on partial least squares method. The analysis is done using structural equation modelling (SEM) using factor-based PLS. This method generates estimates for both composites and factors that fully account for the measurement error (Kock, 2019).

Cronbach’s alpha for all the constructs was > 0.7 (Hair et al., 2017), indicating that the model has internal consistency. Constructs that have average variance extracted (AVE) values greater than 0.5 are said to have convergent validity or be unidimensional. All the constructs had AVE values greater than 0.5. Furthermore, the composite reliability values of all the constructs are > 0.7, indicating reliability and validity. A confirmatory factor analysis was done to understand the factor loadings. All the items in the questionnaire loaded onto their respective constructs. The results are given in Table 2.

The variance inflation factor (VIF) values were checked to see if multicollinearity exists among constructs. Table 3 shows the VIF values of the constructs. The values are less than the threshold value of 5 (Kim, 2019), indicating that there is no multicollinearity.

The correlation values of the constructs were squared and compared with AVE values. It was found that the squares of correlations were less than AVE values. This indicates the discriminant validity of the questionnaire (Fornell and Larcker, 1981). The correlation values of the independent variables and the square root values of AVE are shown in Table 4. The values in brackets represent the square root of AVEs. The other values represent the correlation coefficient between the constructs.

The descriptive analysis results are presented in Table 5, which shows that the majority of the respondents (40.5%) use three OTT platforms. About 59% of the respondents preferred OTT platforms as their mode of entertainment. More of the respondents get information about OTT platforms through social media (44.5%), followed by advertisements (22.5%). OTT providers can use social media to promote their platforms and reach a wider audience of customers. About 72.5% of respondents reported increased OTT usage during lockdown, as users sought entertainment, escapism and social engagement during isolation. However, post-pandemic retention is a key challenge. OTT providers must now shift from acquisition to retention strategies, leveraging content diversification, flexible pricing models and ad-supported services to sustain long-term growth. In addition, the pandemic-driven rise of OTT consumption accelerated the decline of traditional media, reinforcing OTT’s role as the dominant mode of digital entertainment.

Using the SEM process, the hypothesised linkages among the constructs were tested. The beta value for the direct linkage between CE → WTP (β = 0.29, p = < 0.01), FC → WTP (β = 0.29, p = < 0.01), SI → WTP (β = 0.09, p = 0.01), CA → WTP (β = 0.31, p = < 0.01), CS → WTP (β = 0.80, p = < 0.01) shows positive and significant impact, lending support to the acceptance of H1, H2, H3, H4 and H5. But the moderating link between the customer satisfaction and willingness to pay (H6) proved to be insignificant (β = 0.07, p = < 0.23). The results fail to establish the moderation effect of habit and hence hypothesis H6 is rejected. The summary of hypothesis test results in given in Table 6. The validated model is given in Figure 2.

It is evident from the results that the antecedents in the validated model have a significant impact on the customer’s willingness to pay. The H1 hypothesis is supported by the study, which indicates that customer engagement has a significant effect on customer satisfaction. Customer engagement, such as repeatedly viewing the contents, interaction with the media content and easy navigation, creates a customer experience that impacts customer satisfaction. This study is consistent with the findings by Nagaraj et al. (2021) and Bhale and Bedi (2022), because users feel that the media service is aligned towards their preferences, which leads to their satisfaction. Customers are more likely to follow and stay informed about OTT services since they are more prevalent among them. This confirms the fact that customers respond to the cognitive, visual and engaging forms of content or features in OTT services. This also means that the OTT service providers must work on their marketing efforts to keep customers engaged with their brand.

The H2 hypothesis is supported by the study, which indicates that facilitating conditions have a significant effect on customer satisfaction. Facilitating conditions are the external factors that support or enable customers to use a service without any difficulty. This is in accordance with the study by Venkatesh et al. (2003); Shin and Park (2021) and Kim et al. (2024) which suggests that reliable infrastructure reduces problems and ensures that customers can efficiently use the service. This study augments the findings by Bhattacharyya et al. (2022) who found that facilitating conditions enhance the perceived quality, which in turn increases customer satisfaction. Facilitating conditions will always be a key element for customer satisfaction and OTT service providers will have to develop ways to ensure that customers are aware of the technical as well as non-technical requirements they must meet to stream OTT services efficiently. This comprises Internet connectivity, infrastructure, a proper subscription model and payment convenience.

The hypothesis H3 is supported by the study, which indicates that social influence has a significant effect on customer satisfaction. In digital environments, user behaviour is influenced by peer group recommendations, reviews and feedback. This is supported by the studies by Latiff et al. (2022); Chang and Chiu (2023) and Bhattacharyya et al. (2022). The customers feel more confident in their decisions when supported by the experience of others (Ameen et al., 2021). Customers who follow recommendations from their social group experienced a more personalized and satisfactory service (Gupta and Singharia, 2021). This implies that customers’ friends, family and co-workers, guide or influence them in the usage of OTT services. The reviews and opinions of a customer’s closer circle are well taken, and they play a major role in determining whether or not to purchase the services.

The hypothesis H4 is supported by the study, which indicates that content availability has a significant effect on customer satisfaction. The increasing demand for personalized, diverse and relevant content has made content availability a central factor in influencing customer satisfaction. This finding is augmented by Malewar and Bajaj (2020); Yousaf et al. (2021); Sharma and Mishra (2023). Content library and personalized recommendations is a major factor that enhances customer satisfaction. This indicates that customer satisfaction is related to the content that is delivered to customers in terms of genre, quality, languages and the exclusivity of the shows, movies and series that are offered. The availability of original content serves as a key driver of satisfaction because it offers a unique value proposition to the users. As a result, OTT video streaming platforms in India are gaining popularity among customers.

The hypothesis H5 is supported by the research that shows customer satisfaction has a substantial influence on willingness to pay. Customer satisfaction is considered a key determinant of the customer’s willingness to pay for OTT services, as the experience leads to higher perceived value, making them to subscribe more (Kim et al., 2017; Guo, 2022). Satisfied customers are willing to pay for additional services such as ad-free streaming. Satisfaction driven by content availability, facilitating conditions, social influence and customer engagement significantly influences the willingness to pay.

The hypothesis H6 is not supported by the study, indicating that habit does not moderate the relationship between customer satisfaction and willingness to pay. This is consistent with the results by Gupta and Singharia (2021). Habits indicate routine behaviour, but those habits need not involve the conscious decision-making process. This finding indicates that habitual use of an OTT platform does not moderate perceived value or satisfaction. The customer’s repeated behaviour does not appear to have a major influence on the customer’s willingness to pay. Customers’ willingness to pay is unaffected by the frequency with which they stream, the number of hours they enjoy streaming, their preferences for watching OTT services alone or with friends and family and other habits. But this finding differs from the previous studies by Soren and Chakraborty (2023) and Sadana and Sharma (2021), who found that habitual users of streaming platforms are more likely to subscribe to premium plans. This posits that habitual usage of OTT services post-pandemic did not convert to their willingness to pay. There is a lack of conscious evaluation in the habitual behaviour (Yoon and Kim, 2024), and willingness to pay is more driven by satisfaction. Paying for OTT services requires evaluating content quality, price and benefits, making it a conscious decision rather than an automatic habitual action. The study suggests that habitual usage does not directly translate into payment decisions, making satisfaction a stronger predictor of WTP. This is a major contribution to the research on OTT platforms.

This study reaffirms the ECT that customer experience affects their satisfaction and future behaviour intentions. ECT has traditionally been applied to offline consumer satisfaction, but this study extends ECT to the OTT industry by incorporating digital engagement, content availability and technological factors as key determinants of satisfaction. The role of facilitating conditions as a determinant of customer satisfaction is explained in the UTAUT model, where it directly influences behavioural intention through technological infrastructure and service support. In addition, the study integrates Cognitive Dissonance Theory (CDT) to explain dissatisfaction and subscription churn when user expectations are not met. This study develops a holistic theoretical framework by integrating ECT, TAM and CDT to explain different stages of the OTT user journey. Pre-adoption expectations (derived from TAM) shape initial perceptions of OTT services. Post-adoption satisfaction (derived from ECT) determines continued usage and retention. Dissatisfaction and churn behavior (derived from CDT) occur when expectations are disconfirmed. Unlike previous studies that focus solely on performance vs expectation alignment, this study demonstrates that OTT satisfaction is also driven by engagement experiences and social influence. The study builds an integrated framework by selectively combining constructs of the UTAUT model (an extended version of TAM), CDT and ECT. While TAM provides the basis for understanding the pre-adoption expectations, ECT focuses on satisfaction through expectation-experience alignment. UTAUT complements by introducing contextual factors like social influence and facilitating conditions, both of which are highly relevant to OTT platforms. Furthermore, while prior research suggests that habitual media consumption leads to sustained OTT usage and payments (Verplanken and Orbell, 2022), this study finds no significant moderating effect of habit on the relationship between satisfaction and WTP. This study redefines the role of habit in digital media consumption and provides a comprehensive, multi-theory approach to understanding OTT adoption, satisfaction and retention.

From a practical perspective, this theory suggests that service providers should carefully manage and shape the expectations of the customers. The study supports the theory that when customer expectations are built based on individual preferences, personalized marketing efforts can improve the chance of meeting customer expectations. OTT service providers should ensure that they offer original and a variety of content. This will help in getting the attention of customers towards the OTT platforms. The subscription model plays a significant role in developing the intention to use the services, as reported by the results of this study. Customer’s inclination to use OTT services is impacted by social influence and facilitating conditions. OTT service providers should provide services with bare minimum infrastructure requirements, such as low-speed internet access, smartphone device specifications, registration procedures and so on. The availability of content has a significant influence on customer usage. The study’s findings will assist decision-makers and OTT service providers in grasping and developing various tactics for customers of different age groups and experiences. The marketing plan for prospective OTT subscribers may be developed using the findings of this study, and this will help in targeting the different customer segments, such as female and male, young and middle-aged, repeat and new customers.

The competition among OTT service providers is surging. Customers will profit from the competition because they will have access to better and more original content. The research contributes to understanding OTT usage, mainly by getting to know the factors influencing the willingness to pay for the subscriptions. Moreover, the majority of respondents were aware of the OTT platforms through social media. Service providers should use social networking sites and applications to engage customers and build user communities where they can discuss the content, thereby maintaining the “fan base” of the respective providers. Hence, this study offers useful guidance to service providers in comprehending customers’ willingness to pay and recommends realistic ideas to improve streaming services. Consumer behaviour can also vary according to the product or service-focused OTT providers, but the disparity is hard to analyse due to the sample size. The research should be widened to include users from different states, countries and cultures who may have different perspectives on the use of OTT video streaming services. This will enable researchers to perform cross-cultural research to better understand the problem.

This study was approved by PSG Institute of Management Ethics Review Board, under the approval number 2023/2. All procedures followed the board’s ethical guidelines and standards.

1. Age

   • 18–24

   • 25–34

   • 35–44

   • Above 45

2. Gender

   • Male

   • Female

   • Others

3. Occupation

   • Student

   • Employee

   • Self employed

   • Family/Household

4. What is your monthly income?

   • Below 10,000

   • 10,001–20,000

   • 20,001–40,000

   • 40,000–80,000

   • Above 80,000

   • No Income

5. Do you watch OTT content?

   • Yes

   • No

6. How many OTT platforms do you use?

   • 1

   • 2

   • 3

   • 4

   • More than 4

7. Out of these, what do you use the most for entertainment in lockdown?

   • Television

   • OTT platforms

   • YouTube

   • Social Media

8. How did you come to know about your current OTT services?

   • Social Media

   • Peer Influence

   • Advertisements

   • Discounts/Referrals

Source: Created by authors