Revista Venezolana de Gerencia (RVG)
Año 30 No. 109, 2025, 335-350
Enero-Marzo
ISSN 1315-9984 / e-ISSN 2477-9423
Como citar: Müller-Pérez, J., Acevedo-Duque, Á., Alvarez-Becerra, R., y Verges, I. Y. (2025). A SVoD Platforms: A Comprehensive Analysis through Technology Adoption Models. Revista Venezolana De Gerencia, 30(109), 335-350. https://doi.org/10.52080/rvgluz.30.109.22
A SVoD Platforms: A Comprehensive Analysis through Technology Adoption Models
Müller-Pérez, Jessica*
Acevedo-Duque, Ángel**
Alvarez-Becerra, Rina***
Verges, Irma Yomaira****
Abstract
Nowadays, companies operating in the Over-The-Top (OTT) market, particularly those within the Subscription Video on Demand (SVoD) model, face increasing diversification and competitiveness with numerous alternatives. Unforeseen developments have necessitated adjustments in the pricing strategy of these platforms. Therefore, the objective of this research is to identify the primary factors affecting the intention of continued use of SVoD platforms through the technology acceptance model (TAM), the Value-based Adoption Model (VAM), and the extended Unified Theory of Acceptance and Use of Technology Model (UTAUT), incorporating new variables. An online survey was conducted with 385 OTT users, and PLS-SEM was employed to measure the model. The results revealed that the variable most influencing the intention of continued use was the perceived value of the viewers, followed by personalization and habit. This study represents one of the initial investigations conducted in Mexico on SVoD platforms, providing valuable insights for SVoD platform administrators to understand the key features they should focus on when adjusting or making strategic changes. This knowledge aims to ensure customer satisfaction and prolonged retention. Additionally, it contributes academically by proposing a new model based on the TAM and VAM frameworks.
Keywords: SVoD Platforms; OTT platforms; continuity of use; consumer behavior; digital transformation; sustainability.
Recibido: 22.07.24 Aceptado: 11.11.24
* Faculty of Marketing and Business, Universidad Popular Autónoma del Estado de Puebla, Barrio de Santiago, Puebla 72410, Mexico; Email: jessica.muller@upaep.mx
** Programa de Doctorado en Ciencias Sociales, Universidad Autónoma de Chile, Santiago 7500912, Chile; Email: angel.acevedo@uautonoma.cl. Correspondence: angel.acevedo@uautonoma.cl
*** Faculty of Health Sciences, Universidad Nacional Jorge Basadre Grohmann, Tacna 23001, Peru, Email: ralvarezb@unjbg.edu.pe
**** Grupo de Investigación de Estudios Organizacionales Sostenibles, Universidad Autónoma de Chile, Santiago 7500912, Chile; Email: irma.verges@uautonoma.cl
Plataformas SVoD: Un análisis exhaustivo a través de modelos de adopción de tecnología
Resumen
En la actualidad, las empresas que operan en el mercado Over-The-Top (OTT), en particular las del modelo de vídeo bajo demanda por suscripción (SVoD), se enfrentan a una creciente diversificación y competitividad con numerosas alternativas. La evolución imprevista ha hecho necesario ajustar la estrategia de precios de estas plataformas. Por lo tanto, el objetivo de esta investigación es identificar los principales factores que afectan a la intención de uso continuado de las plataformas SVoD a través del modelo de aceptación de la tecnología (TAM), el modelo de adopción basado en el valor (VAM) y la teoría unificada ampliada del modelo de aceptación y uso de la tecnología (UTAUT), incorporando nuevas variables. Se realizó una encuesta en línea a 385 usuarios de OTT y se empleó PLS-SEM para medir el modelo. Los resultados revelaron que la variable que más influía en la intención de uso continuado era el valor percibido de los espectadores, seguida de la personalización y el hábito. Este estudio representa una de las primeras investigaciones realizadas en México sobre plataformas SVoD, proporcionando información valiosa para que los administradores de plataformas SVoD comprendan las características clave en las que deben centrarse a la hora de realizar ajustes o cambios estratégicos. Este conocimiento tiene como objetivo asegurar la satisfacción del cliente y la retención prolongada. Además, contribuye académicamente al proponer un nuevo modelo basado en los marcos TAM y VAM.
Palabras clave: Plataformas SVoD; plataformas OTT; continuidad de uso; comportamiento del consumidor; transformación digital; sostenibilidad.
1. Introduction
Many companies are undergoing digital transformation, developing new skills and adopting a business mindset to adapt to changes impacting all organizational operations. The pandemic, coupled with the rapid growth of the Internet and smartphone usage, has significantly accelerated the adoption of Over The Top (OTT) services, reshaping the media industry (Yoon & Kim, 2022).
Over The Top (OTT) services are digital platforms offering communication and content distribution through Internet networks, utilizing Information and Communication Technologies (ICT). These services include OTT telephony (calls, video calls, and conferences), OTT messaging (text, images, voice, audio, and video), OTT television, and cloud services like data storage (Chakraborty et al, 2023). The emergence of OTT video platforms like Netflix and Amazon Prime Video has transformed the film and television landscape, particularly during the COVID-19 pandemic, raising concerns about the future of traditional television (Camargo, 2020; Mulla, 2022; Puthiyakath & Goswami, 2021)the usage of over the top (OTT.
These advancements present challenges for business development strategies (Yang, 2022), with Price Waterhouse Coopers noting that Mexico has the largest and most competitive OTT sector, projected to grow at a compound annual growth rate (CAGR) of 11.26% over the next five years.
Internet users are drawn to OTT platforms for their accessibility across devices and locations (Periaiya & Nandukrishna, 2024). However, acquiring and retaining customers poses a significant challenge, as OTT platforms rely on attracting new users while satisfying existing ones. In the context of Subscription Video on Demand (SVoD), this is a service type that allows users to access an entire library of videos with the convenience and flexibility to watch them from any device. It enables unlimited viewing for users who pay a monthly, annual, or other subscription fee (Mulla, 2022).
In this model, there is a significant increase in investment efforts to create original and exclusive content to capture a share of the market. Examples include Netflix, which initiated the streaming war by producing original content in 2012. Some Mexican companies in this space are Blim TV and Claro Video. On the other hand, global players like Disney+, Amazon Prime Video, HBO Max, Paramount+, among others, have also entered the competition (Camargo, 2020).
This research aims to identify key factors influencing the intention to continue using SVoD platforms among individuals aged 18 to 35 in Mexico. It will analyze factors such as perceived value, utility, enjoyment, ease of use, price, personalization, habit, and social network exposure. By integrating the TAM, VAM, and extended UTAUT2 models, the study introduces new variables to enhance predictive power and contributes to the literature on SVoD platforms in Mexico, offering a framework for stakeholders in their development.
2. Predicting SVoD use: Models and influencing factors
The Technology Acceptance Model (TAM), developed by Davis in 1985, explains factors influencing the adoption of technology through perceived usefulness and ease of use. In streaming platforms, TAM assesses user attitudes, enhancing performance (De la Hera, 2022). Previous research has focused on usage intention and willingness to pay, utilizing theories such as TAM, TRA, and TPB (Kim et al, 2007; Putra et al, 2022). In contrast, the Value-Based Adoption Model (VAM), established in 2007, examines technology adoption from a value perspective, particularly regarding mobile commerce (Kim et al, 2007). While TAM has limitations in predicting behavioral intentions, VAM emphasizes that consumers derive benefits that are both useful and enjoyable while incurring costs (Liao et al, 2022).
Once models of technology use were exposed, there are variables surrounding SVOD usage that we need to determine in order to sustain the research hypothesis of this study. They are: Perceived ease of use, Perceived Usefulness Enjoyment, Perceived Value, social media, habits and personalization.
Perceived ease of use is a critical variable influencing system usage, defined as the belief that using a system requires minimal effort (Bhattacherjee, 2001). User-friendly applications are more likely to be accepted (Davis, 1989). This perception encompasses individuals' confidence when interacting with technology (Palumian et al, 2021). In Subscription Video on Demand (SVoD) platforms, perceived ease of use reflects users' beliefs that accessing these services is straightforward, encouraging ongoing engagement (Amalia et al, 2021). Thus, the hypothesis is: H1: Ease of use positively affects perceived value.
Perceived usefulness refers to the extent to which users believe a system enhances job performance (Davis, 1989). In OTT services, utility is defined by how effectively platforms help users achieve their goals. Key factors influencing the intention to continue using OTT services include perceived usefulness, ease of use, enjoyment, and customization (Pan & Cho, 2022). Therefore, the following hypothesis is proposed: H2: Perceived usefulness positively affects perceived value.
Motivations for using technology can be extrinsic or intrinsic, with perceived enjoyment positively influencing technology acceptance (Davis et al, 1992; Venkatesh, Viaswanath & Davis, 2000). Enjoyment enhances personal comfort (Yousaf et al, 2021), and streaming platforms often exhibit hedonic value (Chen & Lin, 2018). Thus, the hypothesis is proposed: H3: Enjoyment positively affects perceived value.
Perceived value is a widely studied variable in marketing, defined as the consumer's assessment of a product's utility (Zeithaml, 1988). In the context of OTTs, perceived value measures whether the service is worth paying for, leading to the hypothesis: H4: Perceived value positively affects the intention for continued use of SVoD platforms (Dhiman et al, 2022; Yoon & Kim, 2022).
Social media, rooted in Web 2.0, allows user-generated content creation and exchange (Kaplan & Haenlein, 2010). It significantly influences business success, with customer comments impacting brand reputation (Adhitya et al, 2021). Regarding streaming platforms, exposure to movie-related content can shape behavioral intentions (Adhitya et al, 2021; Nata et al, 2022). Therefore, the hypothesis is: H5: social media positively affects the intention for continued use of SVoD platforms.
Habit refers to the degree to which consumers automatically perform actions with technology (Alghatrifi & Khalid, 2019). It is a driving factor for loyalty and repeat purchases (Mishra, 2022). Thus, the hypothesis is: H6: Habit positively affects the intention for the continued use of SVoD platforms.
Video service personalization enhances users' Quality of Experience (QoE) (Gao, 2023). Companies, like Netflix, create personalized consumption experiences to drive user engagement (Rodríguez, 2023). Hence, the hypothesis is: H7: Personalization positively affects the intention for the continued use of SVoD platforms.
3. Methodological perspective of the study
A non-experimental, cross-sectional quantitative approach was used (Díaz & Cavazos, 2014). The study targeted Mexican men and women aged 18 and older subscribed to an SVoD platform, requiring specific user knowledge (Amalia et al, 2021; Apasrawirote & Yawised, 2022). An online questionnaire was distributed via WhatsApp, email, and social media, aligning with the digital nature of SVoD services (Azzahro et al, 2020; Pereira & Tam, 2021; Yousaf et al, 2021).
The study examined four independent variables (Perceived Value, Social Networks, Habit, and Customization) and one dependent variable (Continued Use Intention). Perceived Value was measured using three formative variables: Perceived of Use, Perceived Usefulness, and Enjoyment, with items adapted from prior studies (Hsu et al, 2017; Lestari et al, 2020; Periaiya & Nandukrishna, 2024). Responses were rated on a 5-point Likert scale from "Completely Disagree" to "Completely Agree" (Fanani et al, 2022; Guo, 2022; Tseng et al, 2022).
The questionnaire was divided into four sections: an introduction with a subscription filter, user behavior items regarding platform usage (Apasrawirote & Yawised, 2022; Gupta et al, 2021), items to explore relationships among the dimensions, and demographic questions. The population consisted of Mexican individuals aged 18 and older subscribed to an SVoD platform, and a sample of 385 surveys was collected using non-probabilistic convenience and snowball sampling (Bae, 2018; Khatib, 2019).
SPSS version 25 was used for descriptive analysis, followed by Partial Least Squares Structural Equation Modeling (PLS-SEM) for model evaluation, using SmartPls4 software. The measurement model was validated through reliability and discriminant validity, with internal consistency assessed via Cronbach's alpha.
4. Results and Findings of SVoD use in Mexico
The demographic profile of respondents was as follows: 55.2% female, 43.8% male, and 0.8% non-binary. All participants were aged 18 to 35, with 32.2% between 18-20, 43% between 21-23, 16.8% between 24-26, 3.4% between 27-29, 2.8% between 30-32, and 1.8% between 33-35. In terms of occupation, 84.8% were university students, 9.3% employed, 4.6% master’s students, and small percentages were freelancers, doctoral students, unemployed, and homemakers. Regarding state of origin, 56.9% were from Puebla, 34.7% from Veracruz, and smaller percentages from Tlaxcala, Mexico City, Coahuila, and Oaxaca (Table 1).
Table 1
Respondent demographic profile
|
Ìtem |
Frequency |
Percentage |
|
Age |
||
|
18 to 20 21 to 23 24 to 26 27 to 29 30 to 32 More than 32 |
125 167 65 13 13 12 |
32.2% 43% 16.8% 3.4% 2.8% 1.8% |
|
Gender |
||
|
Female Male Others |
214 170 3 |
55.2% 43.8% 0.8% |
|
Occupation |
||
|
Undergraduate student Master student PhD student Employee Freelancer Unemployed Housewife |
329 18 1 36 2 1 1 |
84.8% 4.6% 0.3% 9.3% 0.5% 0.3% 0.3% |
|
Place of origen |
||
|
Puebla Veracruz Ciudad de México Tlaxcala Coahuila Oaxaca |
221 134 11 12 6 4 |
56.9% 34.7% 2.8% 3.1% 1.5% 1% |
The most used SVOD platforms were Netflix (89.9%), Disney+ (70.4%), and HBO Max (65.2%). Nearly half (47.9%) of respondents had subscriptions for over two years. Daily usage showed that 37.1% spent at least two hours on the platforms, while 21.6% used them three times a week (Graphic 1).
Graphic 1
Use of SVOD Platforms. Source: Own elaboration based on data analysis
For model evaluation, reliability, convergent validity, and discriminant validity were assessed. Reliability was confirmed with factor loadings above 0.70, and items below this threshold were removed and recalculated. Construct reliability was also tested using Cronbach’s Alpha, composite reliability, and rho_A, all exceeding 0.70 (table 2).
Table 2
Model measurement adjustment
|
Ítems |
Loading >0,70 |
AVE > 0,50 |
Cronbach’s Alpha >0,70 |
CR >0,70 |
HTMT |
|
Perceived Ease of Use |
|||||
|
PEU1 PEU2 PEU3 PEU4 |
0.880 0.879 0.889 0.891 |
0.783 |
0.908 |
0.911 |
Yes |
|
Perceived Usefulness |
|||||
|
PU1 PU4 |
0.924 0.913 |
0.844 |
0.815 |
0.818 |
Yes |
|
Enjoyment |
|||||
|
E1 E3 E4 |
0.922 0.928 0.928 |
0.858 |
0.917 |
0.918 |
Yes |
|
Perceived Value |
|||||
|
PV1 PV2 PV3 PV4 PV5 |
0.802 0.880 0.878 0.897 0.896 |
0.759 |
0.920 |
0.927 |
Yes |
|
Personalization |
|||||
|
PER1 PER2 PER3 PER4 PER5 |
0.887 0.886 0.816 0.869 0.878 |
0.753 |
0.918 |
0.921 |
Yes |
|
Social Media |
|||||
|
SM1 SM2 SM3 SM4 SM5 |
0.818 0.866 0.827 0.863 0.763 |
0.686 |
0.885 |
0.891 |
Yes |
|
Hábits |
|||||
|
HA1 HA2 HA3 HA4 HA5 |
0.771 0.814 0.872 0.918 0.883 |
0.728 |
0.906 |
0.912 |
Yes |
|
Intention of continuity of use |
|||||
|
ICU1 ICU2 ICU3 ICU4 |
0.895 0.909 0.854 0.878 |
0.782 |
0.907 |
0.907 |
Yes |
Convergent validity was assessed using Average Variance Extracted (AVE), which should exceed 0.5 to confirm that each variable correlates more with its construct than with others. All values surpassed 0.5, indicating proper convergence (Table 2).
Discriminant validity was assessed using three criteria, starting with the Fornell-Larcker criterion, which requires the square root of each construct to be greater than the highest correlation with other constructs. The results confirmed that this condition was met, validating the model’s discriminant validity (Table 3).
Table 3
Model's discriminant validity
|
E |
H |
ICU |
PEU |
PU |
PV |
PER |
SM |
|
|
Enjoyment |
0.926 |
|||||||
|
Habits |
0.603 |
0.854 |
||||||
|
ICU |
0.741 |
0.769 |
0.851 |
|||||
|
P_Ease of Use |
0.750 |
0.466 |
0.639 |
0.885 |
||||
|
P_usefulness |
0.768 |
0.641 |
0.713 |
0.707 |
0.918 |
|||
|
Perceived_Value |
0.748 |
0.736 |
0.799 |
0.636 |
0.741 |
0.871 |
||
|
Personalization |
0.755 |
0.744 |
0.790 |
0.622 |
0.678 |
0.796 |
0.868 |
|
|
Social_Media |
0.614 |
0.751 |
0.750 |
0.546 |
0.640 |
0.774 |
0.756 |
0.828 |
Note: E: Enjoyment, PEU: Perceived ease
The second criterion involved analyzing cross-loadings, where each indicator’s loading should be higher than all its cross-loadings (Fazal‐E‐Hasan et al, 2020; Hair Jr. et al, 2019)this research advances knowledge of how consumers’ confidence in sharing personal information develops hope, consequently enabling them to attain their goals and repeat their purchases. For practitioners, it offers a better understanding of how investments are successful in aiding consumers to attain their goals and generate repeat purchase intentions in an online shopping environment.”,”author”:[{“dropping-particle”:””,”family”:”Fazal-E-Hasan”,”given”:”Syed Muhammad”,”non-dropping-particle”:””,”parse-names”:false,”suffix”:””},{“dropping-particle”:””,”family”:”Ahmadi”,”given”:”Hormoz”,”non-dropping-particle”:””,”parse-names”:false,”suffix”:””},{“dropping-particle”:””,”family”:”Mortimer”,”given”:”Gary”,”non-dropping-particle”:””,”parse-names”:false,”suffix”:””},{“dropping-particle”:””,”family”:”Lings”,”given”:”Ian”,”non-dropping-particle”:””,”parse-names”:false,”suffix”:””},{“dropping-particle”:””,”family”:”Kelly”,”given”:”Louise”,”non-dropping-particle”:””,”parse-names”:false,”suffix”:””},{“dropping-particle”:””,”family”:”Kim”,”given”:”Hyoje (Jay. Table 4 confirms that each construct has values higher than their cross-loadings, meeting this requirement.
Table 4
Cross-loads analysis
|
E |
H |
ICU |
PEU |
PU |
PV |
PER |
SM |
|
|
E1 |
0.922 |
0.537 |
0.696 |
0.730 |
0.710 |
0.697 |
0.695 |
0.599 |
|
E3 |
0.928 |
0.599 |
0.684 |
0.660 |
0.719 |
0.711 |
0.695 |
0.581 |
|
E4 |
0.928 |
0.539 |
0.680 |
0.694 |
0.703 |
0.670 |
0.707 |
0.525 |
|
HA1 |
0.712 |
0.763 |
0.693 |
0.533 |
0.647 |
0.644 |
0.683 |
0.625 |
|
HA2 |
0.390 |
0.820 |
0.564 |
0.301 |
0.456 |
0.606 |
0.548 |
0.639 |
|
HA3 |
0.400 |
0.876 |
0.619 |
0.324 |
0.501 |
0.608 |
0.595 |
0.678 |
|
HA4 |
0.475 |
0.920 |
0.680 |
0.358 |
0.547 |
0.630 |
0.611 |
0.643 |
|
HA5 |
0.557 |
0.881 |
0.700 |
0.445 |
0.563 |
0.641 |
0.710 |
0.618 |
|
ICU1 |
0.708 |
0.662 |
0.872 |
0.603 |
0.640 |
0.704 |
0.698 |
0.609 |
|
ICU2 |
0.693 |
0.634 |
0.887 |
0.601 |
0.651 |
0.693 |
0.699 |
0.607 |
|
ICU3 |
0.638 |
0.622 |
0.848 |
0.560 |
0.625 |
0.655 |
0.669 |
0.633 |
|
ICU4 |
0.624 |
0.686 |
0.876 |
0.538 |
0.585 |
0.710 |
0.684 |
0.670 |
|
ICU5 |
0.486 |
0.668 |
0.769 |
0.413 |
0.533 |
0.637 |
0.610 |
0.674 |
|
PE1 |
0.691 |
0.674 |
0.726 |
0.550 |
0.596 |
0.696 |
0.886 |
0.661 |
|
PE2 |
0.678 |
0.641 |
0.673 |
0.583 |
0.616 |
0.717 |
0.885 |
0.661 |
|
PE3 |
0.547 |
0.659 |
0.614 |
0.436 |
0.539 |
0.673 |
0.818 |
0.693 |
|
PE4 |
0.680 |
0.589 |
0.677 |
0.556 |
0.582 |
0.656 |
0.866 |
0.590 |
|
PE5 |
0.667 |
0.663 |
0.729 |
0.563 |
0.604 |
0.711 |
0.880 |
0.680 |
|
PEU2 |
0.658 |
0.443 |
0.619 |
0.879 |
0.676 |
0.616 |
0.569 |
0.535 |
|
PEU3 |
0.608 |
0.431 |
0.548 |
0.889 |
0.620 |
0.575 |
0.537 |
0.503 |
|
PEU4 |
0.699 |
0.382 |
0.551 |
0.891 |
0.601 |
0.514 |
0.548 |
0.435 |
|
PU1 |
0.673 |
0.551 |
0.638 |
0.659 |
0.924 |
0.703 |
0.623 |
0.591 |
|
PU4 |
0.740 |
0.630 |
0.674 |
0.639 |
0.913 |
0.657 |
0.622 |
0.585 |
|
PV1 |
0.533 |
0.535 |
0.563 |
0.443 |
0.552 |
0.802 |
0.609 |
0.573 |
|
PV2 |
0.695 |
0.623 |
0.738 |
0.613 |
0.683 |
0.879 |
0.739 |
0.669 |
|
PV3 |
0.684 |
0.690 |
0.738 |
0.561 |
0.685 |
0.878 |
0.714 |
0.701 |
|
PV4 |
0.619 |
0.671 |
0.706 |
0.553 |
0.630 |
0.897 |
0.680 |
0.716 |
|
PV5 |
0.708 |
0.671 |
0.716 |
0.584 |
0.663 |
0.896 |
0.713 |
0.700 |
|
SM1 |
0.656 |
0.666 |
0.704 |
0.579 |
0.616 |
0.773 |
0.731 |
0.815 |
|
SM2 |
0.479 |
0.615 |
0.615 |
0.467 |
0.502 |
0.628 |
0.607 |
0.865 |
|
SM3 |
0.387 |
0.586 |
0.524 |
0.361 |
0.453 |
0.574 |
0.557 |
0.827 |
|
SM4 |
0.478 |
0.640 |
0.654 |
0.423 |
0.542 |
0.662 |
0.621 |
0.866 |
|
SM5 |
0.507 |
0.587 |
0.579 |
0.398 |
0.513 |
0.532 |
0.588 |
0.764 |
|
PEU1 |
0.695 |
0.387 |
0.535 |
0.880 |
0.597 |
0.536 |
0.544 |
0.448 |
The third criterion for discriminant validity, the Heterotrait-Monotrait (HTMT) ratio, is more reliable than the Fornell and Larcker (1981) criterion. An HTMT value below 0.90 indicates valid discriminant validity between reflective constructs. Table 5 shows that all values are below 0.90, confirming this validity.
Table 5
Criteria for Heterotrait–Monotrait ratio (HTMT)
|
E |
H |
ICU |
PEU |
PU |
PV |
PER |
SM |
|
|
Enjoyment |
|
|
|
|
|
|
|
|
|
Habits |
0.652 |
|
|
|
|
|
|
|
|
ICU |
0.813 |
0.845 |
|
|
|
|
|
|
|
P_Ease of Use |
0.824 |
0.505 |
0.702 |
|
|
|
|
|
|
P_usefulness |
0.889 |
0.743 |
0.832 |
0.819 |
|
|
|
|
|
Perceived_Value |
0.809 |
0.801 |
0.871 |
0.688 |
0.851 |
|
|
|
|
Personalization |
0.821 |
0.811 |
0.865 |
0.678 |
0.783 |
0.864 |
|
|
|
Social_Media |
0.671 |
0.836 |
0.831 |
0.596 |
0.747 |
0.845 |
0.833 |
|
The structural model results, including path coefficients, are shown in Graphic 2. The analysis used the Bootstrap process without sign change, complete Bootstrap with 5000 subsamples, BCa Bootstrap, a one-tailed test, and a 5% significance level.
Graphic 2
Results of the structural modelSource: own elaboration. Path coefficients and p-values are show
When analyzing construct indicators, the variance inflation factor (VIF) should be considered (Akram et al, 2021). Values above 5 are critical, but Table 6 shows they are below this threshold. It also displays significant path coefficient values (p < 0.05) for hypothesis testing. The enjoyment variable (β = 0.409, p < 0.000) significantly influences perceived value, confirming hypothesis 3. Perceived utility (β = 0.388, p < 0.000) positively impacts perceived value, confirming hypothesis 1. Ease of use (β = 0.056, p < 0.190) does not affect perceived value, leading to the rejection of hypothesis 2.
For continued use of SVOD platforms, perceived value significantly affects it (β = 0.308, p < 0.000), confirming hypothesis 4. Habit (β = 0.256, p < 0.001) and personalization (β = 0.303, p < 0.000) positively impact continued use, confirming hypotheses 6 and 7. However, social networks (β = 0.060, p < 0.166) do not influence continuity, rejecting hypothesis 5 (Table 6).
Table 6
Hypotheses test
|
Hyphoteses |
VIF |
Coefic. Path |
P-value |
5% |
95% |
f2 |
Supported |
|
H1 PU → PV |
2.694 |
0.388 |
0.000*** |
0.261 |
0.549 |
0.151 |
Supported |
|
H2 PEU → PV |
2.527 |
0.056 |
0.190 ns |
-0.051 |
0.158 |
0.003 |
Rejected |
|
H3 E → PV |
3.079 |
0.409 |
0.000*** |
0.261 |
0.549 |
0.146 |
Supported |
|
H4 PV → ICU |
3.515 |
0.308 |
0.000*** |
0.203 |
0.442 |
0.101 |
Supported |
|
H5 SM → ICU |
3.225 |
0.120 |
0.022* |
-0.047 |
0.159 |
0.004 |
Supported |
|
H6 HA → ICU |
2.868 |
0.256 |
0.000*** |
0.111 |
0.339 |
0.061 |
Supported |
|
H7 PER → ICU |
3.403 |
0.264 |
0.000*** |
0.185 |
0.415 |
0.091 |
Supported |
Note: Bootstrapping was performed on 5000 subsamples, full, one-tailed t-value; 1.645 (p < 0.05 *), 2.33 (p < 0.01 **), and 3.092 (p < 0.001 ***), ns: not significant.
The predictive power of perceived value, social networks, habit, and personalization on the intention to continue using SVOD platforms is moderate (adjusted R² = 0.699). Effect sizes for perceived value, habit, and personalization are moderate (f² = 0.101, 0.061, and 0.091), while social networks have a small effect (f² = 0.004). Perceived utility, ease of use, and enjoyment also demonstrate moderate predictive power on perceived value (adjusted R² = 0.626), with perceived utility and enjoyment showing moderate effect sizes (f² = 0.151 and 0.146) and ease of use a small effect (f² = 0.003). The mediating effect of perceived value on the proposed variables was also evaluated (Table 7).
Table 7
Mediating effect
|
Items |
Path |
p-value |
T-value |
Decision |
|
PU PV ICU |
.126 |
.001** |
3.285 |
Supported |
|
PEU PV ICU |
.018 |
.203 ns |
.831 |
No Supported |
|
E PV ICU |
.133 |
.001** |
3.262 |
Supported |
Note: *P < 0.1; ** P < 0.01; *** P < 0.001.
In the table 7, the results show that there are no significant differences between the relationships. In other words, the findings show no statistically significant differences when examining the impact of the perceived value as a mediator.
The Mexican streaming market is expanding, presenting challenges such as intense competition from established and emerging platforms like Mercado Play and the rivalry between SVODs and hybrid/free services (Aja, 2023). To address potential issues like those faced by Cinépolis Klic (Noguez, 2023), a thorough analysis is essential.
This research highlights that Mexicans aged 18 to 35 primarily choose SVODs based on perceived value, which includes functional, epistemic, emotional, and conditional aspects. Enjoyment significantly contributes to perceived value, while perceived utility also plays a crucial role. Although ease of use typically affects loyalty, it did not impact perceived value in this context.
Additionally, personalization is vital for user intent, and habit influences continued use. Social networks also positively affect platform usage. These insights can help SVOD executives enhance user experience (UX), which is critical for customer perception and loyalty (Huang et al, 2021).
5. Conclusion
In relation to the streaming market in Mexico, everything indicates that, although it is on the rise, company directors have various concerns. For instance, there is the constant increasing competition from both established and newly created platforms, such as Mercado Play this year, the competition of international giants against local options; and, the competition between SVODs and hybrid and/or free options. The research contributes to the limited literature on SVOD platforms in Latin America, particularly Mexico, by introducing a new predictive model that integrates variables from the TAM, VAM, and UTAUT2 frameworks.
Besides, perceived value is related to the overall evaluation of service benefits and can be considered crucial for explaining the effects of customer experiences with the platform. It is interesting to note that in previous works, there was no doubt that perceived value would affect the continued use of streaming platforms, making it evident that the variable remains important and vital, especially in the Mexican context. Additionally, it has been explained that perceived value is composed of different variables, such as functional, epistemic, emotional, and conditional values, all of which also affect the intention of continued use of the platforms. In this case, the variables of perceived utility, ease of use, and enjoyment were chosen to measure perceived value.
The insights gained are valuable for SVOD decision-makers, helping them address competition and enhance customer retention through improved satisfaction and user experience strategies. However, the study acknowledges limitations, including its focus on a specific age range and demographic. Future research could investigate individual SVOD platforms and explore additional variables affecting perceived value and continuity of use.
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