Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 5, 1434-1444 2024 Publisher: Learning Gate DOI: 10.55214/25768484.v8i5.1846 © 2024 by the authors; licensee Learning Gate © 2024 by the authors; licensee Learning Gate * Correspondence: dsusilo@bundamulia.ac.id Impact of over-the-top video providers on viewing satisfaction and its effect on watching a movie in cinema: Bridging preferences and challenges Daniel Susilo1*, Endik Hidayat2, Hilarius Bambang Winarko3, Jerico John G. Dalangin4 1Universitas Bunda Mulia,Lodan Raya Avenue, Ancol, Northern Jakarta, Indonesia; dsusilo@bundamulia.ac.id (D.S.). 2Badan Riset dan Inovasi Nasional, 40 Gatot Subroto Boulevard, Central Jakarta, Indonesia; endi004@brin.go.id (E.H.). 3Universitas Bunda Mulia,Lodan Raya Avenue, Ancol, Northern Jakarta, Indonesia; hwinarko@bundamulia.ac.id (H.B.W.). 4De La Salle Araneta University, Victoneta Ave, Potrero, Malabon, Philippines; jerico.dalangin@dlsau.edu.ph (J.J.G.D.). Abstract: Netflix, Disney Hotstar, Amazon Prime, and VIU are projected to abolish cinema due to fierce competition. Traditional cinema businessmen blame the COVID-19 pandemic for exacerbating cinema's collapse. Streaming services are more like websites than TV and movie theatres in terms of distribution and accessibility. Streaming platforms provide users more control over content selection and consumption, changing how they watch TV and films. This study compares OTT video provider viewing pleasure to cinema viewing satisfaction. Satisfaction is essential to understanding customer decisions and entertainment. Analysing and comparing both mediums' satisfaction levels will reveal what influences viewers' tastes and entertainment choices. This survey-based investigation was quantitative. OTT users and cinemagoers in Greater Jakarta (Jakarta-Bogor-Depok-Tangerang-Bekasi) were surveyed. June 2024's survey. This study compares OTT and cinema viewing satisfaction. 148 respondents were used in this investigation. This study sheds light on the changing dynamics between OTT video providers and traditional cinema experiences. The research will also help industry practitioners understand viewers' preferences and optimise their offers to increase satisfaction and competitiveness. This study compares OTT video providers' viewing satisfaction to cinema experiences to better understand viewers' leisure preferences. The findings will help industry players decide on content generation, distribution, and user experience improvements. This study contributes to the discussion over entertainment consumption's future and customer pleasure. Keywords: Cinema, OTT video provider, Satisfaction level. 1. Introduction Over the past few years, the entertainment sector has seen a substantial shift in consumer behaviour towards the consumption of digital media, particularly in light of the proliferation of over-the-top (OTT) video providers [1], [2], [3]. Bypassing the conventional means of transmission, these platforms give viewers the ability to watch video content of high quality directly through the internet, which provides both ease and flexibility [4], [5], [6]. As a direct consequence of this development, the level of rivalry between suppliers of OTT services and traditional cinematic experiences has increased, which has led to the formulation of new inquiries concerning the effect that these various choices have on the levels of viewer pleasure [7]). Tight competition and rivalry between OTT Video providers like Netflix, Disney Hotstar, Amazon Prime, and VIU are expected to eliminate the traditional cinema existence [8]. The COVID-19 pandemic is being blamed by some traditional cinema businessmen as an accelerating factor in the decline of cinema [8], [9], [10], [11]. Streaming platforms are more identical to websites in terms of the distribution and accessibility of their material than they are to traditional forms of media 1435 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 5: 1434-1444, 2024 DOI: 10.55214/25768484.v8i5.1846 © 2024 by the authors; licensee Learning Gate consumption like television broadcasts and movie cinema [5], [11], [12]. Streaming platforms have fundamentally altered the way viewers engage with traditional media like television and films by providing a greater degree of flexibility with regard to the selection and consumption of content [13]. The purpose of this study paper is to compare the influence of viewing satisfaction on OTT video providers to the satisfaction levels of seeing a movie in a cinema. Knowledge of consumer choices and involvement in the entertainment scene requires a knowledge of the concept of satisfaction [14], [15], [16]. This study intends to provide insights into the aspects that impact viewers' tastes and form their overall entertainment choices by analysing and comparing the satisfaction levels of both mediums. Previous research has explored the determinants of satisfaction in various entertainment contexts, such as traditional cinema experiences and home viewing [17], [18], [19], [20], [21], [22], [23], [24], [25]. However, as the OTT sector develops further, it becomes important to evaluate how well these digital channels live up to viewers' expectations and challenge conventional cinema [2], [3]. 2. Methodology This study employed a quantitative approach using survey methods to gather data from respondents who are users of electronic cinema streaming services (OTT) and cinema visitors in the Greater Jakarta area (Jakarta, Bogor, Depok, Tangerang, Bekasi). The survey was conducted in June 2024, aiming to determine the relationship between viewing satisfaction on OTT platforms and satisfaction with watching movies in cinemas. The study utilized a dataset comprising 148 respondents, who were randomly selected from the population of electronic cinema streaming service users and moviegoers in the Greater Jakarta area. The variable indicators that are of concern in this study can be seen in Table 1. The results of the data obtained from the survey were then analyzed using the regression analysis method. Regression analysis was used to determine whether there was a relationship between viewing satisfaction on OTT and satisfaction watching in cinemas. Through this analysis, information can be found about the extent to which viewing satisfaction on OTT can affect viewing satisfaction in cinemas. 1436 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 5: 1434-1444, 2024 DOI: 10.55214/25768484.v8i5.1846 © 2024 by the authors; licensee Learning Gate 3. Result and Discussion 3.1. Results The data presented in Figure 1 were derived from the responses of the 148 respondents. These respondents span various age ranges, with the majority (45%) being between 25-29 years old, followed by 34% in the 30-34 age range. The remaining respondents are aged 35-39 years (12%) and 40-44 years (9%). This age distribution demonstrates a significant interest among younger individuals in the study Figure 1. Figure 1. Age of respondent. Source: Data Processed by Researcher (2024). Figure 2 illustrates the education levels of the respondents along with their respective percentages. Specifically, 12% of respondents graduated from post-secondary school, 6% completed a diploma, academy, or associate degree, 51% obtained an undergraduate degree, 25% earned a master's degree, and 5% achieved a doctoral degree. Figure 2. Education level. Source: Data processed by researcher (2024). 1437 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 5: 1434-1444, 2024 DOI: 10.55214/25768484.v8i5.1846 © 2024 by the authors; licensee Learning Gate Figure 3 highlights the distribution of respondents' monthly expenses. Approximately 20% of respondents have expenses of less than USD 210. About 23% of respondents spend between USD 210 and USD 420. Around 17% of respondents have monthly expenses ranging from USD 420 to USD 630. About 12% of respondents report expenses between USD 630 and USD 840. The highest percentage, 28%, represents respondents who spend more than USD 840 per month. Figure 3. Household Expenditure per month. Source: Data Processed by Researcher (2024). Figure 4 presents data on the frequency of cinema visits within a month among respondents. Approximately 32% of respondents indicated that they watch a movie in a cinema once a month. About 27% reported visiting the cinema 2-3 times in one month, while only 2% stated they go to the cinema 4- 5 times a month. In contrast, around 34% admitted they rarely visit the cinema or do so less than once a month. Finally, nearly 4% of respondents go to the cinema more than five times in a single month. These statistics shows an overview of the frequency of individuals in attending movies in theaters over the course of one month. Figure 4. Frequency of watching cinema. Source: Data Processed by Researcher (2024). Table 5 indicates the frequency of OTT subscription usage among respondents over period of one month. Around 36% of respondents use their OTT subscriptions fewer than five times per month while 27% access their subscriptions between 5 and 10 times. Nine percent (9%) use them between 11 to 15 times monthly and nearly 27% of respondents access their OTT subscriptions more than 15 times within a single month. 1438 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 5: 1434-1444, 2024 DOI: 10.55214/25768484.v8i5.1846 © 2024 by the authors; licensee Learning Gate Figure 5. OTT Subscriptions Access within month. Source: Data Processed by Researcher (2024). This data describes the use of content viewing devices most used by respondents. Netflix is the most popular platform, with 74% of respondents using it. Disney Hotstar cameShapiro-Wilkith 12% of respondents accessing it. 3% of respondents use VIU, while Amazon Primetime is used by 1% of respondents. None of the respondents used Iflix. In addition, 5% of respondents mentioned using other platforms not listed in the table. This data provides an idea of respondents' preferences in using content- viewing devices. Netflix is the top choice with a high percentage, followed by Disney Hotstar, VIU, and Amazon Primetime. Iflix was not popular among respondents. There were also some respondents who used other platforms not specifically mentioned in this data. Figure 6 illustrates the primary content viewing devices used by respondents. Netflix emerges as the most popular platform, utilized by 74% of respondents followed by Netflix, Disney Hotstar with around 12% of users from the respondents. VIU accounts has 3% of users, while Amazon Primetime is used by 1%. None of the respondents reported using Iflix. Additionally, 5% of respondents mentioned using other platforms not listed in the table which might include free streaming platforms that are not commonly out in the market. The data highlights valuable insights regarding the preferences of respondents in terms of content viewing platform. Notably, Netflix stands out as the most preferred choice by a significant majority, followed by Disney Hotstar, VIU, and Amazon Primetime. Iflix did not gained significant usage among the pool of respondents for this study, and there were also mentions of other platforms not specified in the provided data but it is assumed by the researchers that these are platforms that provide free streaming services such as Kissasian and Movie7.to. 1439 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 5: 1434-1444, 2024 DOI: 10.55214/25768484.v8i5.1846 © 2024 by the authors; licensee Learning Gate Figure 6. Most popular platform. Source: Data Processed by Researcher (2024). The test for normality was carried out using the Kolmogorov-Smirnov method to test the hypothesis proposed in this study that refers to the data used as shown in Table 2. This normality test aims to check whether the data collected follows a normal distribution. Both table data were confirmed to be normally distributed using the Kolmogorov-Smirnov and Shapiro-Wilk methods with p-values greater than commonly used significance levels. Source: Data Processed by Researcher (2024). Regression analysis was condcuted to see the causal relationship between variables. In the summary model results, several metrics provide information about the quality of the regression model. It can be understood in the Table 3. Source: Data Processed by Researcher (2024). The results of the summary model show that there is a positive relationship between satisfaction with OTT and satisfaction with watching in cinemas. The correlation coefficient (R) has a value of 0.409a, which indicates the strength and direction of the relationship between the two variables. The R-value ranges from -1 to 1, where getting closer to 1 indicates a stronger positive relationship. In this case, the R-value obtained showed a fairly strong positive relationship between satisfaction with OTT and 1440 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 5: 1434-1444, 2024 DOI: 10.55214/25768484.v8i5.1846 © 2024 by the authors; licensee Learning Gate satisfaction with watching in cinemas. Moreover, the coefficient of determination (R Square) is 0.168, indicating the extent to which the variability in viewing satisfaction can be accounted for by satisfaction on OTT. According to this model, approximately 16.8% of the variance in viewing satisfaction may be accounted for by contentment with over-the-top (OTT) services. A higher R Square value indicates a stronger ability of the model to account for the variances observed in the dependent variable. The Adjusted R Square value, denoted as 0.162, represents a modified version of the R Square metric that takes into account the number of predictors included in the model. This metric offers a more precise approximation of the model's performance within the broader population. Furthermore, it is noteworthy that the Standard Error of the Estimate, with a value of 2.344, serves as an indicator of the model's precision in forecasting real values. A model's predictive accuracy improves as the value of the Standard Error of the Estimate decreases. Overall, the summary model's findings suggest a direct relationship between satisfaction with Over- The-Top (OTT) platforms and enjoyment of the cinema-going experience. However, the degree to which contentment on OTT platforms may explain the differences in viewing pleasure is rather little, totaling only 16.8%. Additionally, it is crucial to acknowledge that the R Square value is adjusted according to the number of predictors incorporated in the model, hence impacting the Adjusted R Square value. Source: Data Processed by Researcher (2024). The ANOVA analysis yielded findings regarding the statistical significance of the regression model. The inclusion of the components in the ANOVA table enhances the comprehensiveness of the findings, allowing for a more nuanced comprehension of the results Table 4. The utilisation of the sum of squares serves as a metric for quantifying the extent of variability attributed to individual components. In this particular instance, the Sum of Squares for the regression model is determined to be 161.492, whereas the Sum of Squares for the residual or error is calculated to be 802.318. Degrees of Freedom (df) indicates the number of degrees of freedom associated with each component. The regression model has a df of 1, while the remainder or error has a df of 146. The Mean Square is calculated by dividing the Sum of Squares by the corresponding Degrees of Freedom. The mean square for the regression model is 161.492, and the mean square for the remainder or error is 1441 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 5: 1434-1444, 2024 DOI: 10.55214/25768484.v8i5.1846 © 2024 by the authors; licensee Learning Gate 5.495. The F value, calculated by dividing the regression model's Mean Square by the residual or error Mean Square, is 29.387. A very small p-value (Sig.) (.000b) indicates that the relationship between satisfaction on OTT and satisfaction in cinema is statistically significant. Based on the results of this ANOVA analysis, it can be concluded that the regression model has significance in explaining the relationship between satisfaction with OTT and satisfaction with cinema. The presence of very small p-values suggests that this relationship could not have come about by chance. This is also confirmed based on the results of the Coefficients analysis which is known that viewing satisfaction on OTT (Over-The-Top) video contributes significantly to viewing satisfaction in cinema (Table 5). Based on the results of the Coefficients analysis presented in table 5, there is information about the contribution of the independent variable (viewing satisfaction on OTT) to the dependent variable (viewing satisfaction in the cinemas). The Unstandardized Coefficient indicates the magnitude of the independent variable's influence on the dependent variable in the original unit, and in this case, the Unstandardized Coefficient for satisfaction on OTT is 0.605. This shows that each one-unit increase in satisfaction on OTT correlates with a 0.605-unit increase in satisfaction with cinemas. Standard Error (Std. Error) measures how accurate the estimated regression coefficient is, and in the Coefficients table, the Std. Error for satisfaction on OTT is 0.112. Standardized Coefficients (Beta) are coefficients that have been standardized and measure the strength and direction of influence relative to the independent variable on the dependent variable. In this model, the Standardized Coefficient (Beta) for satisfaction on OTT is 0.409, which shows that satisfaction on OTT has a positive effect with moderate strength on satisfaction in cinemas. Furthermore, the t-value is employed to denote the statistical significance of the regression coefficient. The Coefficients table reveals that the t-value for the relationship between contentment and OTT is 5.421, suggesting a substantial degree of statistical significance. A p -value of 0.000, denoting statistical significance, suggests that the regression coefficient is highly significant. 3.2. Theoretical Discussion and Implications Based on the findings obtained from the comprehensive analysis of coefficients, it can be inferred that there exists a substantial relationship between contentment with over-the-top (OTT) platforms and pleasure with theatres. Any increase in satisfaction with OTT is associated with an increase in satisfaction with cinemas. This is indicated by a positive coefficient, a significant t-value, and a very small p-value. Thus, the results of the analysis show that the higher the level of satisfaction in OTT, the higher the level of satisfaction in cinemas. The utilisation of the sum of squares serves as a metric for quantifying the extent of variability attributed to individual components. In this particular instance, the Sum of Squares for the regression model is determined to be 161.492, whereas the Sum of Squares for the residual or error is calculated to be 802.318. Degrees of Freedom (df) indicates the number of degrees of freedom associated with each component. The regression model has a df of 1, while the remainder or error has a df of 146. The Mean Square is calculated by dividing the Sum of Squares by the corresponding Degrees of Freedom. The mean square for the regression model is 161.492, and the mean square for the remainder or error is 5.495. The F value, calculated by dividing the regression model's Mean Square by the residual or error Mean Square, is 29.387. A very small p-value (Sig.) (.000b) indicates that the relationship between satisfaction on OTT and satisfaction in cinema is statistically significant. Based on the results of this ANOVA analysis, it can be concluded that the regression model has significance in explaining the relationship between satisfaction with OTT and satisfaction with cinema. The presence of very small p-values suggests that this relationship could not have come about by chance. This is also confirmed based on the results of the Coefficients analysis which is known that viewing satisfaction on OTT (Over-The-Top) video contributes significantly to viewing satisfaction in cinema (Table 5). Based on the results of the Coefficients analysis presented in table 5, there is information about the contribution of the independent variable (viewing satisfaction on OTT) to the dependent variable 1442 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 5: 1434-1444, 2024 DOI: 10.55214/25768484.v8i5.1846 © 2024 by the authors; licensee Learning Gate (viewing satisfaction in the cinemas). The Unstandardized Coefficient indicates the magnitude of the independent variable's influence on the dependent variable in the original unit, and in this case, the Unstandardized Coefficient for satisfaction on OTT is 0.605. This shows that each one-unit increase in satisfaction on OTT correlates with a 0.605-unit increase in satisfaction with cinemas. Standard Error (Std. Error) measures how accurate the estimated regression coefficient is, and in the Coefficients table, the Std. Error for satisfaction on OTT is 0.112. Standardized Coefficients (Beta) are coefficients that have been standardized and measure the strength and direction of influence relative to the independent variable on the dependent variable. In this model, the Standardized Coefficient (Beta) for satisfaction on OTT is 0.409, which shows that satisfaction on OTT has a positive effect with moderate strength on satisfaction in cinemas. Furthermore, the t-value is employed to denote the statistical significance of the regression coefficient. The Coefficients table reveals that the t-value for the relationship between contentment and OTT is 5.421, suggesting a substantial degree of statistical significance. A p -value of 0.000, denoting statistical significance, suggests that the regression coefficient is highly significant. Based on the findings obtained from the comprehensive analysis of coefficients, it can be inferred that there exists a substantial relationship between contentment with over-the-top (OTT) platforms and pleasure with theatres. Any increase in satisfaction with OTT is associated with an increase in satisfaction with cinemas. This is indicated by a positive coefficient, a significant t-value, and a very small p-value. Thus, the results of the analysis show that the higher the level of satisfaction in OTT, the higher the level of satisfaction in cinemas. 4. Conclusion The study examined OTT video provider viewing satisfaction and cinema movie viewing pleasure. The study investigated if the rise of OTT platforms affects traditional cinema experiences. The research and analysis show that OTT video provider viewing satisfaction affects cinema movie enjoyment. As OTT usage and contentment rise, cinema satisfaction falls. Several causes may explain this observation. First, OTT services allow audiences to watch films at home, which may raise their expectations of the theatrical experience. This can increase cinema criticism and lower satisfaction. Second, OTT platforms offer additional movie options and personalised suggestions to suit individual tastes. Cinema theatres may have fewer movie choices and showtimes, which may lower pleasure. OTT watching satisfaction may affect cinema experiences, but the traditional cinema sector is resilient. The immersive big-screen experience, surround sound, and community during shared viewing are not replicated by OTT providers. OTT viewing satisfaction and movie experience satisfaction are linked, but further research is needed to determine the causes attributed for the preference of OTT as some might also consider the cost implication of going to Cinemas as compared to staying at home to watch movies through OTT. It is pertinent that both businesses must understand viewer preferences and find ways to develop to fulfil and address modern audiences' needs. In order to stay relevant, the film industry must adapt to OTT video providers. Cinemas may offer a unique and memorable movie- watching experience, but OTT services offer convenience, content variety and possibly cost-effective that is preferentially beneficial to one who chooses it. Both industries must strike a balance between these two spheres to succeed. Acknowledgment: This research is part of the Collaboration research by Universitas Bunda Mulia, Jakarta under funding provided by Competitive Fundamental Research Grant (PFR-KEMDIKBUDRISTEK-BIMA 2024) with Contract Grant: 789/LL3/AL.04/2024.The author expresses gratitude to the support from the Indonesian Ministry of Education, Culture, Higher Education, Research and Technology; Universitas Bunda Mulia; BRIN, and De La Salle Araneta University, Philippines. 1443 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 5: 1434-1444, 2024 DOI: 10.55214/25768484.v8i5.1846 © 2024 by the authors; licensee Learning Gate Copyright: © 2024 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). References [1] Y. N. Katherine Chen, “Competitions between OTT TV platforms and traditional television in Taiwan: A Niche analysis,” Telecomm. 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