How Continuous Intentions Towards Over the Top Platform are Framed? Stimulus–Organism

Abstract

The current study attempts to measure the determinants of continuous intention of over-the-top (OTT) platforms. In the present context, stimulus–organism–response (S–O–R) model was applied to identify the determinants of continuous intentions. The data were collected from users of the existing OTT platforms. To test the proposed model, we applied the partial least square structural equation modeling (PLS-SEM) technique in accordance with the objective and hypotheses. The proposed model explains 45% of perceived value and 41% of continuance intentions, respectively. The results indicated that there are four significant relationships and one insignificant relationship. To explicitly state; perceived ease of use, perceived customization benefits, mobility has a favorable impact on perceived value, and perceived value significantly influences the continuous intention of OTT platform, and last, entertainment value was found to be insignificant with perceived value. This study provides varied clues to marketers to shape up customers behavior and to ensure the factors affecting decision regarding continuous intention of OTT platform. The current study proposes that continuous intentions can be framed by offering value.

Keywords

Perceived Value Continuous Intentions Over the Top PLS and Mobility Perceived Customization

Introduction

The media sector has seen significant transformation with the advent of internet and wireless technologies. Prior research in the field of online communities revealed that technological advancements have ushered in a new era of consuming content (Chalaby & Plunkett, 2020). Thus, the evolution of visual material consumption habits is centered on over-the-top (OTT) platforms (Federal Communications Commission [FCC], 2004). With around 158 million members, Netflix is presently the largest worldwide OTT services company, operating in approximately 190 nations (Wayne, 2020). Video consumption is particularly growing as a result the popularity of video content in our modern lives (Bentley et al., 2019). The Indian video OTT industry is prophesied to upsurge to US$4 billion by 2025, and then to US$12.5 billion by 2030. India has seen an unprecedented growth in the world market related to OTT streaming apps, on track to overtake the USA as the world’s sixth largest nation by 2024 (Jha, 2020). The reasons for the rise in OTT network memberships were the COVID-19 outbreak and lockdown (Basuki et al., 2022). For fresh releases, filmmakers prefer to use the OTT network. From 2017 to 2022, the streaming business will be responsible for 46% of the total expansion in the Indian information and broadcasting industry (Verma, 2018). Consumption habits of consumers are changing as a result of smartphones which enables them to access content anywhere and anytime, and its consumption is particularly growing (Bentley et al., 2019). As a result, the introduction of new media has impacted not only users’ media consumption habits, but also the scholarly work - particularly in terms of its connection with older media. Several researches has been undertaken to investigate this connection (Dimmick, 1993; Dutta-Bergman, 2004; Kim et al., 2013; Lazarsfeld, 1940; McCombs & Nolan, 1992). Competition in this segment is very intense and competitors, such as Hotstar, Amazon Prime, Netflix, MX Player, Alt Balaji, SonyLiv and Zee 5, are now providing plenty of options to choose from.

However, recent studies have reported high abandonment rate in the context of technology acceptance (Spil et al., 2019). The advent of technology presents an opportunity for the customer to update or switch to different brands that makes the job for OTT industry to hold existing customers. After first purchase, persistent utilization of wellness wearables drops to 70% following a half year and to around 55% later in 1 year (Ledger & McCaffrey, 2014). To ensure the success of OTT industry, the determinants of continuance intentions become imperative to study. Extant studies posited that ‘S–O–R’ system is more reasonable to inspect the effect of both interesting technological environments and customer experience on technology continuance intentions (Kim & Park, 2016; Zhao et al., 2020).

Therefore, the current study concentrates on the ‘S–O–R’ paradigm to OTT platform addressing the require-ment to study the continuous intention in reference of perceived value (PV). The S–O–R framework includes three aspects: stimulus, organism and response. ‘The impact that evokes the person is the stimulus component’ (Eroglu et al., 2001) and the cognitive and affective interim state of consumers is the organism aspect of the S–O–R model, and it depicts the mechanisms that intervene among stimuli and consumer reactions (‘response’) (Loureiro & Ribeiro, 2011; Mehrabian & Russell, 1974). Stimuli are also considered as an external factor that comprises varied environmental inputs. In the present context, we have taken perceived ease of use (PEOU; Islam et al., 2020), perceived enjoyment (Prashar et al., 2017), perceived customization benefits (PCB) and mobility considered as stimuli that predicts the PV of OTT platforms. Customization found to be significant variable to predict value perception among users (Sigala, 2006). Whereas, Yang et al. (2016) concluded that mobility should be appropriate to be considered in the context of technology. Largely, the success and adoption of any technology are depend upon the value that a customer expects or perceives from the product/services (Singh et al., 2021). Hence, PV is treated as critical antecedent that influences the customers’ shopping behavior (Yen, 2022). Contextual to present study, PV (Kim & Park, 2019) is studied as organism in the present study. Value perception is commonly seen in the realm of marketing as a balance among pricing (loss) as well as utility (gain) (Rust & Oliver, 1994). Further, Smith and Colgate (2007) reported that PV had various aspects, notably practical or instrumental, psychological or hedonistic, symbolic or expressive, and expense or sacrifice. In the ‘S–O–R’ framework, response is considered as the final outcome of user’ behavior (Mehrabian & Russell, 1974). Extant studies have taken continuance intentions as a response in the context of MOOCs (Nikhashemi et al., 2021; Zhao et al., 2020). If customers perceive OTT as a valuable platform, this will ensure that they are likely to use OTT platforms in future as well. Therefore, the aim of present study is to identify the determinants of PV so that continuance intentions as response can ensure the success of OTT platforms.

The current examination contributes in varied ways to the existing literature on OTT and the role of PV to predict continuance intentions by addressing the existing research gaps. Researchers have come across have been showing that in the contemporary era, traditional television viewership appears to be declining. The model applied in this study aims to measure what kind of behavior Indian people will exhibit regarding the OTT platform since the mere acceptance of technology does not guarantee its continuous use. Furthermore, the current study has included prominent role of continuance intentions alongside eminent constructs, to be specific perceived ease of use, entertaining value, mobility, PCB to predict PV to use OTT platform in the upcoming times. This study also adds a new dimension of PCB which a subscriber perceives during his OTT streaming experience and subscription. The results of the study ought to give rich bits of knowledge to creating advertising efforts and shows/movies with lucrative offers. The immense potential OTT offers for companies operating in this space is undeniably humongous. Our research tried to study the factors affecting decision regarding continuous intention of OTT platform which is hardly being looked after in the existing literature. This research includes factors that have never been investigated previously using the S–O–R model. As a result, this study manages to come up with some significant findings while contributing to OTT platforms, S–O–R models, and continuous intentions which could give new faces to its elements. This study reveals strong inter-relationships between elements contributing to the continuation of intention within the S–O–R framework for an OTT platform in India.

Theoretical Underpinnings

The S–O–R model has been thoroughly evaluated using the measurement theory. The S–O–R model has been investigated in the fields of online learning, mobile auctions and virtual reality (VR) tourism (Islam & Rahman, 2017; Kim et al., 2020). Stimulus is the architecture of an internet business network and the set of its qualities that affect the consumers’ inner status in the virtual environment (Mollen & Wilson, 2010). These metrics illustrate users’ perceptions of the resources they need to access information systems in their day-to-day lives. As a result, the current research used a comprehensive examination of the reflective measurement paradigm (relations between indicators and their corresponding latent constructs).

Perceived Ease of Use

In current scenario, the overall perception of an ease of use can be considered as a measure of how easy a system appears to be used. An intensified use of the system as well as interactions between the users can make it easier to use (Zuniarti et al., 2021). Prior studies looked into several industries, like flight booking apps in Malaysia (Suki & Suki, 2017) and e-shopping through smart device (Agrebi & Jallais, 2015; Alzubi et al., 2018; Vahdat et al., 2021). PEOU alongside social media’s account of use had little effect on PV. Individual judgment was used to determine PV (Holbrook, 1994). As a result, PEOU as well as PV components are identical. Users consider it simple to acquire valuable information online, and they believe it is worthwhile, equivalent to perceived usefulness. Thus, perceived usefulness and customer intention are indirectly related to PEOU (Abdullah et al., 2016). VR technology presents an exciting new way for sports fans to consume content through technology (Kunz & Santomier, 2020). Extant studies found significant influence of perceived ease of use on PV (Chairina, 2021; Kim & Kim, 2020). A propensity to use online platforms is strappingly predisposed by PEOU (Setiawan & Widanta, 2021). Moreover, Chairina (2021) concluded that higher PEOU will result in higher PV.

H1: There is a positive relationship between PEOU and PV of OTT platforms.

Entertainment Value

Consumer satisfaction with services is influenced by a variety of factors, including entertainment (Namkung & Jang, 2007). Previous research has discovered that entertainment has a favorable impact on attitude, that influences the readiness to suggest and the intention to utilize a certain social site (Curras-Perez et al., 2014). Kim et al. (2013) posited that hedonic motivation (joy, happiness, and delight) has an effect on PV, and thus on mobile addiction. According to research on internet services, entertainment has an impact on PV (Kim & Niehm, 2009), TikTok short video (Dwinanda et al., 2022). A recent study has found significant influence of entertainment on PV (Cao et al., 2022). Based on this, we have proposed as follows:

H2: There is a positive relationship between entertainment value (EV) and PV of OTT platforms.

Mobility

Extant studies reported that users value the efficiency and availability of information as an imperative advantage of mobile services due to mobility (Hill & Roldan, 2005). In today’s world, online/e-learning platforms offer an innovative and exciting access option. That is why online/e-learning platforms are valuable due to their mobility. Furthermore, Howcroft et al. (2002) posited that consumers prefer to use the services of various platforms to improve their learning skills. In the present context, mobility can be considered as the access of OTT platforms as it provides freedom of movement Dasgupta and Grover (2019). Moreover, Dasgupta and Grover (2019) concluded that mobility can be one of the reasons to adopt OTT platforms. In addition, (Mallat et al., 2009) postulated that the main benefit of mobile technology is mobility that enables consumers to process information anywhere, anytime. Furthermore, Wang et al. (2020) found that mobility is positively associated with the PV. Based on this, we have inferred as follows:

H3: There is a positive relationship between mobility and PV of OTT platforms.

Perceived Customization Benefits

PCB ‘may be referred to product or service that is tailor-made to a customer’s demands’ (Terblanche & Taljaard, 2018). Extant studies have witnessed that customized product/service can considerably affect the manner in which a customer perceives the value of product/services (Gazley et al., 2015; Zarrad & Debabi 2012). Moreover, organizations are providing customized services to their customers in order to enhance the PV so that customer can be more satisfied (Lusch et al., 2008; Vargo & Lusch, 2016). Therefore, customization in the present context can be considered as an action of modifying something to suit a particular individual or task. The customization benefits that OTT can provide the subscribers are on demand content which is without any ad breaks, zipping and zapping benefits, budgetary control over the subscriber module such as single user or multiple user, single screen or multiple screen, content control and variation. Moreover, customized experiences structure the underpinning of significant value being used (Vargo & Lusch, 2016). Online customization is regarded as an appealing vital instrument that offers advantages and snags for customers and organization too (Canarslan & Bariş, 2022). Thus, customized services or better services can help to develop customer value (Cai et al., 2018). In this view, Zhao and Chen (2021) have also proposed that various perceived benefits significantly influencing PV in the context of green housing. Based on these, it is hypothesized as follows:

H4: There is a positive relationship between PCB and PV of OTT platforms.

Perceived Value

Zeithaml (1988) posited that PV is described as an assessment of the component’s cost versus its utility. The observable valuation is the outcome of this contradiction, and it provides information about the willingness to accept and utilize the tangible or intangible offerings (Zhuang et al., 2010). Previous studies have reported that PV significantly associated with continuous intentions (Li et al., 2021; Tseng et al., 2022; Wang et al., 2020).

Users’ PVs are vital in establishing protracted connections with them, as well as having an emotional influence on their buy intentions (Zhuang et al., 2010). Customers’ confidence, loyalty and contentment are all influenced by PV, which has an impact on their long-term intentions (Alawan et al., 2018). PV has been identified as a constant contributor to continuous intention (Yang & Peterson, 2004). Based on proposed hypotheses, following model is proposed (refer Figure 1). A recent study demonstrated that PV has a considerable influence on the desire to use digital entertainment offerings in the future (Singh et al., 2021).

Figure 1.

Source: The authors.

Based on this, it is hypothesized as follows:

H5: There is a positive relationship between PV and continuous intention of OTT platforms.

Methods

Measurement and Data Collection

Using a quantitative survey design, this study examined continuous intention of using OTT platform. North India region was the source for the OTT user data. The measurement items were taken from previous research, that is, PEOU can be used as an exogenous stimulus (04) items (Davis, 1989; Yang, 2021), (03) items EV (Eighmey & McCord, 1998), PCB (03) (Terblanche & Taljaard, 2018), (03) items for PV (Sirdeshmukh et al., 2002), continuance intentions (3 items) (Bhattacherjee, 2001) and items for mobility (3 items) derived from (Huang et al., 2007) and evaluation of all these factors were ranged from between 1 (strongly disagree) and 5 (strongly agree) (Roberts & Priest, 2006). Research methodology and results can be communicated effectively through the concepts of reliability and validity. The reliability and validity of latent constructs are presented in the Table 2 and Table 3. Further, the indicator loadings of the underlying constructs are also mentioned in the Table 4.

Table 1.

Demographic Details (N = 358)

Variables	Description	Frequency	Percentage
Gender	Male	241	67
Gender	Female	117	33
Income	Less than 500,000	70	20
	510,000–800,000	145	41
	Above 800,000	143	40
Education	Graduate	233	65
Education	More than graduate	125	35
OTT platforms	Disney + Hotstar	138	39
	Amazon Prime	97	27
	Netflix	66	18
	Zee5	30	8
	SonyLiv	27	8

Source: The authors.

Table 2.

Reliability and Validity

Reliability and Convergent Validity	AVE	Cronbach’s Alpha	Items Code	Mean	Standard Deviation
Ease of use (CR = 0.896)		0.689	PEOU1	3.74	0.86
	0.682		PEOU2	3.84	0.83
			PEOU3	3.75	0.88
			PEOU4	3.66	0.86
Entertainment value (CR = 0.868)	0.688	0.773	EV1	4.00	0.72
			EV2	3.96	0.81
			EV3	4.13	0.83
Perceived customization benefits (CR = 0.912)		0.855	PCB1	4.08	0.90
	0.775		PCB2	3.68	1.05
			PCB3	3.77	1.03
Continuance intentions (CR = 0.798)		0.689	CI1	3.10	1.20
	0.57		CI2	3.14	1.17
			CI3	4.18	0.85
Mobility (CR = 0.925)		0.879	M1	2.63	1.26
	0.805		M2	2.18	1.23
			M3	2.41	1.25
Perceived value (CR = 0.911)	0.773	0.853	PV1	3.01	1.11
			PV2	2.98	1.09
			PV3	3.18	1.15

Source: The authors.

Table 3.

Discriminant Validity

Fornell and Larcker
	Continuous Intentions	Perceived Ease of Use	Entertainment Value	Perceived Customization Benefits	Mobility	Perceived Value
Continuous intentions	0.755
Perceived ease of use	0.474	0.826
Entertainment value	0.471	0.547	0.829
Perceived customization benefits	0.533	0.405	0.414	0.88
Mobility	0.375	0.283	0.18	0.265	0.897
Perceived value	0.64	0.405	0.35	0.49	0.553	0.879
HTMT
	Continuous Intentions	Perceived Ease of use	Entertainment Value	Financial Benefits	Mobility	Perceived Value
Continuous intentions
Perceived ease of use	0.629
Entertainment value	0.664	0.681
Perceived customization benefits	0.648	0.476	0.509
Mobility	0.359	0.328	0.217	0.303
Perceived value	0.702	0.474	0.427	0.571	0.634

Source: The authors.

Table 4.

Indicator Reliability

Items	Original Sample (O)	Sample Mean (M)	Standard Deviation (STDEV)	T-statistics (\|O/STDEV\|)	p-Values
CI1 ← continuance intentions	0.85	0.852	0.024	35.797	0.00
CI2 ← continuance intentions	0.708	0.701	0.053	13.265	0.00
CI3 ← continuance intentions	0.697	0.691	0.057	12.296	0.00
EV1 ← entertainment value	0.822	0.821	0.034	24.407	0.00
EV2 ← entertainment value	0.871	0.87	0.022	39.044	0.00
EV3 ← entertainment value	0.792	0.791	0.033	24.006	0.00
PCB1 ← perceived customization benefits	0.873	0.874	0.016	52.936	0.00
PCB2 ← perceived customization benefits	0.875	0.876	0.02	44.605	0.00
PCB3 ← perceived customization benefits	0.892	0.893	0.014	65.982	0.00
M1 ← mobility	0.866	0.865	0.017	50.92	0.00
M2 ← mobility	0.921	0.921	0.009	102.557	0.00
M3 ← mobility	0.903	0.904	0.016	56.212	0.00
PEOU1 ← ease	0.807	0.808	0.025	32.443	0.00
PEOU2 ← ease	0.838	0.836	0.023	36.921	0.00
PEOU3 ← ease	0.845	0.845	0.025	33.756	0.00
PEOU4 ← ease	0.814	0.814	0.023	35.475	0.00
PV1 ← PV	0.903	0.904	0.01	87.626	0.00
PV2 ← PV	0.881	0.88	0.015	57.664	0.00
PV3 ← PV	0.853	0.854	0.019	45.942	0.00

Source: The authors.

The data have been collected from 358 working professionals in Chandigarh and which is considered as a planned and modern city (Chaudhry et al., 2007) (presented in the Table 1). Chandigarh has the highest (Rank 1) per capita income in the country. Moreover, Chandigarh has been adjudged as a leader in e-readiness, according to a report of the Government of India. Electronic readiness takes into account the studied institutions ability to partake in ever changing networked world. It also indicates the penetration and usability of the Information and Communication Technology (ICT) enabled services at the level of individuals, community and governance (Mishra & Fatmi, 2015). Moreover, Disney+ Hotstar is the leading OTT platform and most viewed in Chandigarh as well (BigMediakart, 2021).

The study was undertaken with in the scope of working professionals in the Chandigarh region. Researcher personally visited to the mentioned destination and contacted with the professionals using non-probability sampling approach in the absence of the sampling framework. These respondents were then contacted after getting their contact numbers, and data collection was done. Out of 400,388 working professionals agreed to spare some time for giving their responses and eventually 358 valid responses were collected. Out of the total sample size, 241 were male which accounted for 67.3% of the sample and 117 were female which accounted for 32.7%. On the income front, 70 respondents were below the income level of 5 lakhs, 149 respondents were between the income range of 500,000 and 800,000 and 139 respondents were 800,000 lakh and above. Moreover, we also tried to collect the education orientation of our respondents. We found that out of the total sample, 233 were graduates which accounted for 65% of the total sample and 125 respondents were post graduates and above which accounted for 35%. The researchers infused an open-ended question which aimed at identifying the most preferred OTT platform among these respondents. Disney + Hotstar, Amazon Prime, Netflix, Zee 5 and SonyLiv were the most preferred and accepted platforms by the respondents.

The completed questionnaires were presented to experts from prestigious universities prior to data collection to ensure its face and content validity. The questionnaire was screened depending on the participants’ understanding and usability of the OTT platform. The convenience sampling technique was utilized because there was no representative structure or list of such consumers. The possible users of streaming services, digital/social media platforms were considered appropriate (Saha, 2021). Therefore, the questionnaire was distributed via online social media sites since India is in incessant stage for the development of OTT platforms which is paving way for the upsurge and evolution of the media and entertainment industry.

Data Analysis and Results

The present study has used PLS-SEM approach as it is variance-based methods (Lohmoller, 1989). While analysing the techniques, PLS-SEM that is based on a composite model, takes into account common, particular, and error differences, and so employs all fluctuations from the independent variable to predict changes in the dependent variable (Hair et al., 2017).

As a result, utilizing the PLS-SEM, which was done in smart PLS, the recommended look into the model was validated. PLS-SEM has established as a well-known and well-established process that has been strongly suggested by experts in a variety of sectors, including marketing and strategic management (Hair et al., 2012).

A structured equation model was assessed by using 5,000 bootstraps using Smart PLS software. For a sample size greater than 100, standardized root mean squares (SRMR) values should be less than 0.08 according to Cho et al., 2020; Henseler et al., 2016. Furthermore, Bentler (1995) used covariance residuals to calculate SRMR, with smaller values denoting a better fit. It is basically a report of how much discrepancy there is between observed data and model predictions. If the count of observed variables (p) is large, the SRMR produces more appropriate realistic rejection levels for close fit tests and superior saturation for its population value (Maydeu-Olivares et al., 2017) As a way to overcome the difficulties linked with inferring the magnitude of unstandardized misfit effect sizes like SRMR, he advocated standardized effect sizes of model misfit. However, the value for SRMR was reported to be 0.07 that undermines the model fit and R square presented in Figure 2.

Figure 2.

Source: The authors.

Discussion and Conclusions

In India, there has been an impeccable increase in the use of OTT services. In the contemporary era, traditional television viewership appears to be dwindling, because mere acceptance of a technology does not guarantee its continued use; therefore, the model aims to determine the behavior of continuous intention of the Indian people about OTT platforms. Based on our results, we present practical advice for OTT service providers to make their services simple to use in order to boost their subscribers’ PV. According to the S–O–R paradigm, certain aspects of surroundings instigate an individual’s perceptual and sentimental condition, which then compels numerous behavioral responses. The contemporary investigation empirically creates a framework to evaluate the appropriateness of the S–O–R model to the OTT platform context. In the domain of an OTT platform in India, our research reveals strong inter-relationships between elements substantial contribution of continuous intention inside the S–O–R framework. The S–O–R posits an intermediary component of six elements that are evaluated under S (stimulus), O (organism) and R (continuous intention) (response). This model highlights the crucial finding that there are a total of five key relationships that may be seen here and also presented in the Table 5. To put it another way, PEOU has an affirmative impact on OTT platforms’ PV followed by customization benefits, mobility which showed a positive impact on PV and PV significantly influences the continuous intention of the OTT platform. Off-late users have much more control in their hands regarding to the choice of what they want to watch, where they want to watch without being time bounded along with their language preferences. All these led to the significant relationship among the above-mentioned factors. Our study supports the previous literature who advocated for positive relation between ease of use (Setiawan & Widanta, 2021), mobility (Hill & Roldan, 2005), customization benefits (Terblanche & Taljaard, 2018) and PV (Yang & Peterson, 2004). The association between factors like EV and PV, on the other hand, was shown to be inconsequential. Reason could be human beings are said to be a social being, and during the time of pandemic, isolation has become a new normal which is impacting the relationship of entertainment and PV. However, the feeling of loneliness has started to develop more among individuals. Thus, our study does not support the prior literature of significant relationship between entertainment and PV. The study’s findings show that the S–O–R framework is realistic and effective for achieving the study’s goals.

Table 5.

Structural Relationships

Proposed Relationships	Beta Value	T-statistics (\|O/STDEV\|)	p-Values	Remarks
Perceived ease of use → perceived value	0.118	2.132	0.034	Accepted
Entertainment value → perceived value	0.09	1.631	0.103	Not accepted
Perceived customization benefits → perceived value	0.296	5.866	0.00	Accepted
Mobility → perceived value	0.425	12.333	0.00	Accepted
Perceived value → continuance intentions	0.643	22.909	0.00	Accepted

Source: The authors.

PV has been considered as a center construct in the present study because it is the ultimate determining factor of the continuous intention, and however, it has been influenced with above-mentioned factors such as perceived ease of use, customization benefits and mobility which was the strongest predictor to the PV. As now individuals have much more control in their hands regarding to the choice of what they want to watch, where they want to watch without being time bounded along with their language preferences. All these led to the significant relationship among the above-mentioned factors. Our study supports the previous literature which advocated for positive relation between ease of use and PV (Chairina, 2021; Setiawan & Widanta, 2021), mobility and PV (Hill & Roldan, 2005; Wang, 2014; Wang et al., 2020), customization benefits and PV (Kim et al., 2021; Terblanche & Taljaard, 2018), PV and continuance intentions (Singh et al., 2021; Tseng et al., 2022; Wang et al., 2020). The association between factors like entertainment value and perceived value, on the other hand, was shown to be inconsequential. Reason could be human beings are said to be a social being and during the time of pandemic, isolation have become a new normal which is impacting the relationship of entertainment and PV. However, the feeling of loneliness has started to develop more in individuals. So, our study does not support the prior literature of significant relationship between entertainment and PV. Another reason could be different versions of entertainment with those of different individual OTT subscribers. For example, a user might subscribe one platform for sports, and the same user can subscribe a different OTT platform for movies or short videos.

Implications

Theoretical Implications

Extant research studies have overlooked the need to study the continuous intention in reference to PV. In three aspects, this study adds to the existing literature. First, the current study adds to our understanding of users’ continuous intention in OTT services. However, there is no scholarly study on how users’ perceptions of OTT platform values affect their continuous intention using the S–O–R model. Furthermore, the research presents actual and up-to-date findings for the S–O–R model in the S–O–R paradigm. Previous research studies have looked into the OTT platform using other theories like TAM (Cebeci et al., 2019) and gratification (Menon, 2022; Sahu et al., 2021), also studied both together (Camilleri & Falzon, 2020), but only a few attempts have been made to look into the OTT platform employing the S–O–R model (Duatibumi & Setyowardhani, 2021). Third, the factors which have been included (perceived ease of use, EVs, mobility and PCB) and analysed using the S–O–R model have not been studied earlier under the same construct (Duatibumi & Setyowardhani, 2021). This research is a small effort toward the direction of enriching and adding new relevant findings in the wings of OTT platform, S–O–R model and continuous intention.

Managerial/Practical Implications

OTT platforms in last few years have come a long way and has created a distinct and indelible impression in the minds of consumers. Suddenly, the entertainment industry has changed its pattern and the industry has realized the true potential of OTT platforms. Pandemic and lockdown gave the much-needed impetus which probably was missing earlier. It can be seen that almost every big media house has created an OTT platform and majority of content, cinema, daily soaps, reality shows have been aired simultaneously on TV, movie halls and OTT platforms. This new normal has created a lot of buzz across the industry, and now, almost every organization or media house has a sleuth on content lined up to be aired on OTT platforms. The TV industry has seen a sudden change in the viewership pattern, and it is evident that in this speed, the OTT viewership can take over TV viewership especially in metros.

The study tried to understand the subscription pattern where PEOU showed an affirmative impact on PV. The PCB also found to be significantly related with PV. This result clearly highlights the impact customization benefits can have on subscription as per the pocket size of OTT users. Mobility in OTT platforms also showed having positive impact on the PV and reiterates the fact that today is the time for grabbing the attention and user on the run. Gone are the days when users were given limited options. Offers like 1-month free trial, opt out when you desire, pro rata refund in case of pre-mature closure of subscription provides the much-needed freedom to the subscribers for getting hooked to the OTT platforms on the go. Keeping all these results under consideration, we further found that PV significantly influences the continuous intention of OTT platform, and this important finding can be explored by the organization or companies who want to identify and record the reasons for irregularity of user OTT subscription when it comes to OTT subscription and its intention.

Therefore, the current study will facilitate brands and companies to understand the equivalence and reason behind the sudden spike in OTT subscription and the declining trend of traditional television viewership. It will also help the cable or direct to home providers to understand the motto and advantage behind viewers’ preference to OTT platforms. The current examination also suggesting the determinants of PV and continuance intentions to use OTT platforms. This will help them to come up with more focused and engaging content for viewers across all demography’s. The model will set a benchmark for all related stakeholders to analyse and take into account the motto of users behind choosing of a particular OTT platforms and factors driving their decision. The model also tried to inculcate the variables suggested the ‘S–O–R’ model and the researchers tried to study and give a direction towards PV of the OTT platforms. PCB were also being thoroughly studied which will further give new ideas and avenues to marketers for content design and implementation. The model highlights the role of PV toward OTT platforms and how variables like mobility and ease of use PCB related to OTT subscription effects the behavioral aspect of a subscriber. This study also shows how powerful customized benefits, offers and vouchers can be, as well as their importance in maintaining people’s interest in the OTT platform. This research even disclosed that entertainment no longer have any significant relationship with the PV. The service provider could come up with certain features like watch-together, which help in entertainment the OTT users where they can communicate with other individuals and share viewpoint. As such features like listen-together have already been introduced in certain music applications like Spotify.

Limitations and Future Research Scope

Unlike all studies our study also possesses few challenges that should be considered in future studies. First and foremost, we exclusively gathered information from respondents in India. As a result, care can be taken while extending the findings of our study to OTT service consumers in other nations. Some elements influencing continuous intention decisions may be more relevant in shaping PV experience in particular nations due to cultural variations. As a result, our research does not shed light on such cultural disparities. Second, it is important to emphasize that the findings of our research cannot be applied to all potential users of OTT services. Our research focuses on existing users’ intentions to persist with them. Because the impact of referral systems on current subscribers is larger than that on new subscribers, this could limit the generalizability of our findings. Additionally, when a potential consumer contemplates using an OTT platform for the first time, they may be drawn in by intriguing original dramas/series. As a result, the result of our research should be applied with caution to potential users. Third, our research was largely silent on income level, gender and generation, all of which could have a substantial impact on continuous intentions. Future research can try to analyse and include other factors such as word of mouth, influence of celebrity marketing and availability of language options. Future research can try to analyse and include other factors such as word of mouth, influence of celebrity marketing and availability of language options, as during our research, we analysed that our participants were indirectly using such terminologies which indicated that these factors could play a significant role in decision-making when it comes to continuous intention.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

References

Abdullah

, Ward

, & Ahmed

(2016). Investigating the influence of the most commonly used external variables of TAM on students’ perceived ease of use (PEOU) and perceived, usefulness (PU) of e-portfolios. Computers in Human Behavior, 63, 75–90. 10.1016/j.chb.2016.05.014

Agrebi

, & Jallais

(2015). Explain the intention to use smartphones for mobile shopping. Journal of Retailing and Consumer Services, 22, 16–23. 10.1016/j.jretconser.2014.09.003

Alawan

A. A.

, Dwivedi

Y. K.

, Rana

N. P.

, & Algharabat

(2018). Examining factors influencing Jordanian customers’ intentions and adoption of internet banking: Extending UTAUT2 with risk. Journal of Retailing and Consumer Services, 40, 125–138. 10.1016/j.jretconser.2017.08.026

Alzubi

M. M.

, Al-Dubai

M. M.

, & Farea

M. M.

(2018). Using the technology acceptance model in understanding citizens’ behavioural intention to use m-marketing among Jordanian citizen. Journal of Business and Retail Management Research, 12(2), 224–231.

Basuki

, Tarigan

, Siagian

, Limanta

, Setiawan

, & Mochtar

(2022). The effects of perceived ease of use, usefulness, enjoyment and intention to use online platforms on behavioral intention in online movie watching during the pandemic era. International Journal of Data and Network Science, 6(1), 253–262. 10.5267/j.ijdns.2021.9.003

Bentler

P. M.

(1995). EQS structural equations program manual. Multivariate Software.

Bentley

, Silverman

, & Bica

(2019). Exploring online video watching behaviours. In Proceedings of the 2019 ACM International Conference on Interactive Experiences for TV and Online Video, pp. 108–117. 10.1145/3317697.3323355

Bhattacherjee

(2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351–370.

Cai

, Wohn

D. Y.

, Mittal

, & Sureshbabu

(2018, June 26–28). Utilitarian and hedonic motivations for live streaming shopping. In Proceedings of the 2018 ACM International Conference on Interactive Experiences for TV and Online, Video (pp. 81–88). Association for Computing Machinery. 10.1145/3210825.3210837

10.

Camilleri

M. A.

, & Falzon

(2020). Understanding motivations to use online streaming services: Integrating the technology acceptance model (TAM) and the uses and gratifications, theory (UGT). Spanish Journal of Marketing—ESIC, 25(2), 217–238.

11.

Canarslan

N. Ö.

, & Bariş

(2022). Flow experience and consumer willingness to pay in online mass customization processes. International Journal of Online Marketing, 12(1), 1–21.

12.

Cao

, Li

, Wang

, & Ai

(2022). The impact of self-efficacy and perceived value on customer engagement under live streaming commerce environment. Security and Communication Networks, 2022. 10.1155/2022/2904447

13.

Cebeci

, Ince

, & Turkcan

(2019). Understanding the intention to use Netflix: An extended technology acceptance model approach. International Review of Management and Marketing, 9(6), 152.

14.

Chairina

R. R. L.

(2021). The effect of perceived usefulness and perceived ease of use on perceived value and actual usage of technology on the online service of Pt. Garuda Indonesia Tbk. Journal of Research in Business and Management, 9(4), 59–65.

15.

Chalaby

J. K.

, & Plunkett

(2020). Standing on the shoulders of tech giants: Media delivery, streaming television and the rise of global suppliers. New Media & Society, 1461444820946681. 10.1177/1461444820946681

16.

Chaudhry

, Singh

, & Tewari

V. P.

(2007). Non-market economic valuation in developing countries: Role of participant observation method in CVM analysis. Journal of Forest Economics, 13(4), 259–275.

17.

Cho

, Hwang

, Sarstedt

, & Ringle

C. M.

(2020). Cutoff criteria for overall model fit indexes in generalized structured component analysis. Journal of Marketing Analytics, 8(4), 189–202. 10.1057/s41270-020-00089-1

18.

Curras-Perez

, Ruiz-Mafe

, & Sanz-Blas

(2014). Determinants of user behaviour and recommendation in social networks: An integrative approach from the uses and gratifications perspective. Industrial Management & Data Systems. 10.1108/IMDS-07-2014-0219

19.

Dasgupta

, & Grover

(2019). Understanding adoption factors of over-the-top video services among millennial consumers. International Journal of Computer Engineering and Technology, 10(1). http://hdl.handle.net/10739/2286

20.

Davis

F. D.

(1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 318–340. 10.2307/249008

21.

Dimmick

(1993). Ecology, economics, and gratification utilities. In Alexander

, Owers

, & Carveth

(Eds.), Media economics (pp. 135–136). Lawrence Erlbaum.

22.

Duatibumi

, & Setyowardhani

(2021). SBM ITB proceedings. https://journal.sbm.itb.ac.id/index.php/ProceedingSBMITB/article/view/4749/1636

23.

Dutta-Bergman

M. J.

(2004). Complementarity in consumption of news types across traditional and new media. Journal of Broadcasting & Electronic Media, 48(1), 41–60. 10.1207/s15506878jobem4801_3

24.

Dwinanda

, Syaripuddin

F. A.

, & Hendriana

(2022). Examining the extended advertising value model: A case of TikTok short video ads. Mediterranean Journal of Social & Behavioral Research, 6(2), 35–44.

25.

Eighmey

, & McCord

(1998). Adding value in the information age: Uses and gratifications of sites on the World Wide Web. Journal of Business Research, 41(3), 187–194. 10.1016/S0148-2963(97)00061-1

26.

Eroglu

S. A.

, Machleit

K. A.

, & Davis

L. M.

(2001). Atmospheric qualities of online retailing: A conceptual model and implications. Journal of Business Research, 54(2), 177–184. 10.1016/S0148-2963(99)00087-9

27.

Federal Communications Commission. (2004). Annual assesment of the status of competition in the market for the delivery of video programming: Tenth annual report. MB Docket. FCC. https://www.fcc.gov/document/annual-assessment-status-competition-market-8

28.

Gazley

, Hunt

, & McLaren

(2015). The effects of location-based-services on consumer purchase intention at point of purchase. European Journal of Marketing, 49(9/10), 1686–1708. 10.1108/EJM-01-2014-0012

29.

Hair

J. F.

, Ringle

C. M.

, & Sarstedt

(2012). Partial least squares: the better approach to structural equation modeling? Long Range Planning, 45(5–6), 312–319.

30.

Hair Jr

J. F.

, Matthews

L. M.

, Matthews

R. L.

, & Sarstedt

(2017). PLS-SEM or CB-SEM: updated guidelines on which method to use. International Journal of Multivariate Data Analysis, 1(2), 107–123.

31.

Henseler

, Hubona

, & Ray

P. A.

(2016). Using PLS path modeling in new technology research: Updated guidelines. Industrial Management & Data Systems, 116(1), 2–20. 10.1108/IMDS-09-2015-0382

32.

Hill

T. R.

, & Roldan

(2005). Toward third generation threaded discussions for mobile learning: Opportunities and challenges for ubiquitous collaborative environments. Information Systems Frontiers, 7(1), 55–70. 10.1007/s10796-005-5338-7

33.

Holbrook

M. B.

(1994). The nature of customer value: An axiology of services in the consumption experience. Service Quality: New Directions in Theory and Practice, 21(1), 21–71.

34.

Howcroft

, Hamilton

, & Hewer

(2002). Consumer attitude and the usage and adoption of home-based banking in the United Kingdom. International Journal of Bank Marketing, 20(3), 111–121. 10.1016/j.ijinfomgt.2021.102452

35.

Huang

J. H.

, Lin

Y. R.

, & Chuang

S. T.

(2007). Elucidating user behavior of mobile learning: A perspective of the extended technology acceptance model. The Electronic Library, 25(5), 585–598.

36.

Islam

J. U.

, & Rahman

(2017). The impact of online brand community characteristics on customer engagement: An application of stimulus-organism-response paradigm. Telematics and Informatics, 34(4), 96–109.

37.

Islam

, Abid

, Salman

, & Ahmer

(2020). How perceptions about ease of use and risk explain intention to use mobile payment service in Pakistan? The mediating role of perceived trust. Pakistan Journal of Commerce and Social Sciences, 14(1), 34–48.

38.

Jha

(2020). India is the world’s fastest growing OTT market: PwC report. Mint. https://www.livemint.com/news/india/india-is-the-world-s-fastest-growing-ott-market-pwc-report-11603355739242.html

39.

Kim

, & Kim

(2020). Exploring the key antecedents influencing consumer’s continuance intention toward bike-sharing services: Focus on China. International Journal of Environmental Research and Public Health, 17(12), 4556.

40.

Kim

, & Niehm

L. S.

(2009). The impact of website quality on information quality, value, and loyalty intentions in apparel retailing. Journal of Interactive Marketing, 23(3), 221–233. 10.1016/j.intmar.2009.04.009

41.

Kim

J.-H.

, & Park

J.-W.

(2019). The effect of airport self-service characteristics on passengers’ perceived value, satisfaction, and behavioral intention: Based on the SOR model. Sustainability, 11(19), 5352. 10.3390/su11195352

42.

Kim

J. J.

, Ariza-Montes

, & Han

(2021). The role of expected benefits towards smart hotels in shaping customer behavior: Comparison by age and gender. Sustainability, 13(4), 1698.

43.

Kim

, Kim

, & Jeong

S. H.

(2013). Effect of smartphone use on traditional media uses: Application of media substitution hypothesis. Journal of Media Economics and Culture, 11(1), 88–119.

44.

Kim

M. J.

, Lee

C. K.

, & Jung

(2020). Exploring consumer behavior in virtual reality tourism using an extended stimulus-organism-response model. Journal of Travel Research, 59(1), 69–89. 10.1177%2F0047287518818915

45.

Kunz

R. E.

, & Santomier

J. P.

(2020). Sport content and virtual reality technology acceptance. Sport, Business and Management, 10(1), 83–103. 10.1108/SBM-11-2018-0095

46.

Lazarsfeld

P. F.

(1940). Radio and the printed page; An introduction to the study of radio and its role in the communication of ideas. Duell, Sloan, & Pearce.

47.

Ledger

, & McCaffrey

(2014). Inside wearables: How the science of human behavior change offers the secret to long-term engagement. Endeavour Partners, 200(93), 1.

48.

, Nishimura

, Yagami

, & Park

H. S.

(2021). An empirical study on online learners’ continuance intentions in China. Sustainability, 13(2), 889.

49.

Lohmoller

J. B.

(1989). Predictive vs. structural modeling: Pls vs. ml. In Lohmöller

J.-B.

(Ed.), Latent variable path modeling with partial least squares (pp. 199–226). Physica-Verlag.

50.

Loureiro

, & Ribeiro

(2011). The effect of atmosphere on emotions and online shopping intention: Age differentiation. In MacCarthy

(Ed.), Marketing in the age of consumerism: Jekyll or Hyde? Proceedings of the Australian and New Zealand Marketing Academy Conference (pp. 1–8). ANZMAC-Pandora.

51.

Lusch

R. F.

, Vargo

S. L.

, & Wessels

(2008). Toward a conceptual foundation for service science: Contributions from service-dominant logic. IBM Systems Journal, 47(1), 5–14. 10.1147/sj.471.0005

52.

Mallat

, Rossi

, Tuunainen

V. K.

, & Oorni

(2009). The impact of use context on mobile services acceptance: The case of mobile ticketing. Information & Management, 46(3), 190–195. 10.1016/j.im.2008.11.008

53.

Maydeu-Olivares

, Fairchild

A. J.

, & Hall

A. G.

(2017). Goodness of fit in item factor analysis: Effect of the number of response alternatives. Structural Equation Modeling, 24, 495–505. 10.1080/10705511.2017.1289816

54.

McCombs

, & Nolan

(1992). The relative constancy approach to consumer spending for media. Journal of Media Economics, 5(2), 43–52. 10.1080/08997769209358222

55.

Mehrabian

, & Russell

J. A.

(1974). An approach to environmental psychology. The MIT Press.

56.

Menon

(2022). Purchase and continuation intentions of over-the-top (OTT) video streaming platform subscriptions: A uses and gratification theory perspective. Telematics and Informatics Reports, 5, 100006.

57.

Mishra

, & Fatmi

S. N.

(2015). E-readiness of India with reference to national e-governance plan. International Journal of Computer Applications, 123(8), 21–26.

58.

Mollen

, & Wilson

(2010). Engagement, telepresence and interactivity in online consumer experience: Reconciling scholastic and managerial perspectives. Journal of Business Research, 63(9–10), 919–925. 10.1016/j.jbusres.2009.05.014

59.

Namkung

, & Jang

(2007). Does food quality really matter in restaurants? Its impact on customer satisfaction and behavioral intentions. Journal of Hospitality & Tourism Research, 31(3), 387–409. 10.1177/1096348007299924

60.

Nikhashemi

S. R.

, Knight

H. H.

, Nusair

, & Liat

C. B.

(2021). Augmented reality in smart retailing: A Symmetric Approach to continuous intention to use retail brands’ mobile AR apps. Journal of Retailing and Consumer Services, 60, 102464. 10.1016/j.jretconser.2021.102464

61.

Prashar

, Sai Vijay

, & Parsad

(2017). Effects of online shopping values and website cues on purchase behaviour: A study using S–O–R framework. Vikalpa, 42(1), 1–18. 10.1177%2F0256090916686681

62.

Roberts

, & Priest

(2006). Reliability and validity in research. Nursing Standard, 20(44), 41–46.

63.

Rust

, & Oliver

(1994). Service quality: Insights and managerial implications from the frontier. In Rust

& Oliver

(Eds.), Service quality: New directions in theory and practice (pp. 1–19). SAGE Publications.

64.

Saha

(2021). Consumption pattern of OTT platforms in India. International Journal of Modern Agriculture, 10(2), 641–655.

65.

Sahu

, Gaur

, & Singh

(2021). Applying niche and gratification theory approach to examine the users’ indulgence towards over-the-top platforms and conventional TV. Telematics and Informatics, 65, 101713.

66.

Setiawan

, & Widanta

(2021). The effect of trust on travel agent online use: Application of the technology acceptance model. International Journal of Data and Network Science, 5(3), 173–182. https://dx-doi-org.web.bisu.edu.cn/10.5267/j.ijdns.2021.6.015

67.

Sigala

(2006). Mass customisation implementation models and customer value in mobile phones services: Preliminary findings from Greece. Managing Service Quality: An International Journal, 16(4), 395–420. 10.1108/09604520610675720

68.

Singh

, Singh

, Kalinić, Z., & Liébana-Cabanillas

F. J.

(2021). Assessing determinants influencing continued use of live streaming services: An extended perceived value theory of streaming addiction. Expert Systems with Applications, 168, 114241.

69.

Sirdeshmukh

, Singh

, & Sabol

(2002). Consumer trust, value, and loyalty in relational exchanges. Journal of Marketing, 66(1), 15–37.

70.

Smith

J. B.

, & Colgate

(2007). Customer value creation: A practical framework. Journal of Marketing Theory and Practice, 15(1), 7–23. 10.2753/MTP1069-6679150101

71.

Spil

T. A.

, Kijl

, & Romijnders

(2019). The adoption and diffusion of wearables. In International Working Conference on Transfer and Diffusion of IT, pp. 31–47. Springer, Cham.

72.

Suki

N. M.

, & Suki

N. M.

(2017). Light ticket booking app on mobile devices: Examining the determinants of individual intention to use. Journal of Air Transport Management, 62, 146–154. 10.1016/j.jairtraman.2017.04.003

73.

Terblanche

N. S.

, & Taljaard

(2018). The perceived value and perceived benefits experienced by customers using travel agents. South African Journal of Business Management, 49(1), 1–13.

74.

Tseng

, Lee

, Chen

, & He

(2022). Understanding Agri-food traceability system user intention in respond to COVID-19 Pandemic: The comparisons of three models. International Journal of Environmental Research and Public Health, 19(3), 1371.

75.

Vahdat

, Alizadeh

, Quach

, & Hamelin

(2021). Would you like to shop via mobile app technology? The technology acceptance model, social factors and purchase intention. Australasian Marketing Journal, 29(2), 187–197. 10.1016%2Fj.ausmj.2020.01.002

76.

Vargo

S. L.

, & Lusch

R. F.

(2016). Institutions and axioms: An extension and update of service-dominant logic. Journal of the Academy of Marketing Science, 44(1), 5–23.

77.

Verma

U. K.

(2018). Video on demand: Entertainment reimagined. https://www.pwc.in/assets/pdfs/publications/2018/video-on-demand.pdf

78.

Wang

(2014). Antecedents and consequences of perceived value in mobile government continuance use: An empirical research in China. Computers in Human Behavior, 34, 140–147.

79.

Wang

, Teo

T. S.

, & Liu

(2020). Perceived value and continuance intention in mobile government service in China. Telematics and Informatics, 48, 101348.

80.

Wayne

M. L.

(2020). Netflix nations: The geography of digital distribution. Journal of Communication, 70(4), E14–E16.

81.

Yang

(2021). Augmented reality in experiential marketing: The effects on consumer utilitarian and hedonic perceptions and behavioural responses. In Lee

Z. W. Y.

, Chan

T. K. H.

, & Cheung

C. M. K.

(Eds.), Information technology in organisations and societies: Multidisciplinary perspectives from AI to Technostress (pp. 147–174). Emerald Publishing. 10.1108/978-1-83909-812-320211006

82.

Yang

, & Peterson

R. T.

(2004). Customer perceived value, satisfaction, and loyalty: The role of switching costs. Psychology & Marketing, 21(10), 799–822. 10.1002/mar.20030

83.

Yang

, Yu

, Zo

, & Choi

(2016). User acceptance of wearable devices: An extended perspective of perceived value. Telematics and Informatics, 33(2), 256–269.

84.

Yen

Y.-S.

(2022). Channel integration affects usage intention in food delivery platform services: The mediating effect of perceived value. Asia Pacific Journal of Marketing and Logistics, Vol. ahead-of-print No. ahead-of-print. 10.1108/APJML-05-2021-0372

85.

Zarrad

, & Debabi

(2012). Online purchasing intention: Factors and effects. International Business and Management, 4(1), 37–47.

86.

Zeithaml

V. A.

(1988). Consumer perceptions of price, quality, and value: A means-end model and synthesis of evidence. Journal of Marketing, 52(3), 2–22. 10.1177%2F002224298805200302

87.

Zhao

, & Chen

(2021). Exploring residents’ purchase intention of green housings in China: An extended perspective of perceived value. International Journal of Environmental Research and Public Health, 18(8), 4074.

88.

Zhao

, Wang

, & Sun

(2020). Technological environment, virtual experience, and MOOC continuance: A stimulus–organism–response perspective. Computers & Education, 144, 103721. 10.1016/j.compedu.2019.103721

89.

Zhuang

, Cumiskey

K. J.

, Xiao

, & Alford

B. L.

(2010). The impact of perceived value on behavior intention: An empirical study. Journal of Global Business Management, 6(2), 1.

90.

Zuniarti

, Yuniasih

, Martana

I. K.

, Setyaningsih

E. D.

, Susilowati

I. H.

, Pramularso

E. Y.

, & Astuti

(2021). The effect of the presence of e-commerce on consumer purchasing decisions. International Journal of Data and Network Science, 5(3), 479–484. 10.5267/j.ijdns.2021.3.005

How Continuous Intentions Towards Over the Top Platform are Framed? Stimulus–Organism– Response Model Perspective

Abstract

Keywords

Introduction

Theoretical Underpinnings

Perceived Ease of Use

Entertainment Value

Mobility

Perceived Customization Benefits

Perceived Value

Methods

Measurement and Data Collection

Data Analysis and Results

Discussion and Conclusions

Implications

Theoretical Implications

Managerial/Practical Implications

Limitations and Future Research Scope

Footnotes

Declaration of Conflicting Interests

Funding

References