Understanding motivations to use
online streaming services:
integrating the technology
acceptance model (TAM) and the
uses and gratiﬁcations theory (UGT)
Comprendiendo las motivaciones
para usar los servicios de streaming
en línea: Integrando el modelo de
on de la tecnología y la
teoría de usos y gratiﬁcaciones
Mark Anthony Camilleri
Department of Corporate Communication, Faculty of Media and Knowledge Sciences,
University of Malta, Msida, Malta and The Business School,
The University of Edinburgh, Edinburgh, UK, and
University of Malta, Msida, Malta
Purpose –The outbreak of the Coronavirus (COVID-19) pandemic and its preventative social distancing
measures have led to a dramatic increase in subscriptions to paid streaming services. Online users are
increasingly accessing live broadcasts, as well as recorded video content and digital music services through
internet and mobiledevices. In this context,this study aims to explorethe individuals’uses and gratiﬁcations
from online streaming technologies during COVID-19.
Design/methodology/approach –This research has adapted key measures from the “technology
acceptance model”(TAM) and from the “uses and gratiﬁcations theory”(UGT) to better understand the
individuals’intentions to use online streaming technologies. A structural equations partial least squares’
conﬁrmatory composite approach was used toanalyze the gathered data.
© Mark Anthony Camilleri and Loredana Falzon. Published in Spanish Journal of Marketing –ESIC.
Published by Emerald Publishing Limited. This article is published under the Creative Commons
Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative
works of this article (for both commercial and non-commercial purposes), subject to full attribution to
the original publication and authors. The full terms of this licence maybe seen at http://
The authors thank the editor and his reviewers for their constructive remarks and suggestions.
Received 25 April2020
Accepted 12 November2020
Spanish Journal of Marketing -
Emerald Publishing Limited
The current issue and full text archive of this journal is available on Emerald Insight at:
Findings –The individuals’perceived usefulness and ease of use of online streaming services were
signiﬁcant antecedents of their intentions to use the mentioned technologies. Moreover, this study suggests
that the research participants sought emotional gratiﬁcations from online streaming technologies, as they
allowed them to distract themselves into a better mood and to relax in their leisure time. Evidently, they were
using them to satisfy their needs for information and entertainment.
Research limitations/implications –This study contributes to the academic literature by generating
new knowledge about the individuals’perceptions, motivations and intentions to use online streaming
technologies to watch recorded movies, series and live broadcasts.
Practical implications –The ﬁndings imply that there is scope for the providers of online streaming
services to improve their customer-centric marketing by reﬁning the quality and content of their recorded
programs and throughregular interactions with subscribers andpersonalized recommender systems.
Originality/value –This study integrates the TAM and UGT frameworks to better understand the effects
of the users’perceptions, ritualized and instrumental motivations on their intentions to continue watching
movies, series and broadcasts through online streaming technologies, during COVID-19.
Keywords Uses and gratiﬁcations theory, TAM, Technology acceptance model, COVID-19, UGT,
Online streaming, Broadcast media, SEM-PLS, Streaming video, COVID-19
Paper type Research paper
osito –El distanciamiento social durante la pandemia del coronavirus (COVID-19) ha llevado a un
atico en las suscripciones a los servicios de transmisi
on de pago. Los usuarios en línea acceden
cada vez m
as a transmisiones en vivo, así como a contenido de video grabado y servicios de música digital. En
este contexto, este estudio explora los usos y las gratiﬁcaciones buscadas por las personas con las tecnologías
on en línea durante la COVID-19.
Diseño/Metodología/Enfoque –En la operacionalizaci
on de las variables se utilizaron las medidas del
“Modelo de Aceptaci
on de Tecnología”(TAM) y la “Teoría de Usos y Gratiﬁcaciones”(UGT). Adem
o SEM-PLS 3 para analizar los datos recopilados de las encuestas.
Hallazgos –La utilidad percibida y la facilidad de uso de los servicios de transmisi
on en línea son
antecedentes signiﬁcativos de la intenci
on de utilizarlos. Adem
as, las personas buscan gratiﬁcaciones
emocionales de tales tecnologías, ya que les permiten distraerse, estar de mejor humor y relajarse en su tiempo
as, las utilizan para obtener informaci
on y entretenimiento.
oricas –Este estudio contribuye a la literatura académica generando nuevos
conocimientos sobre las percepciones, motivaciones e intenciones de los individuos de utilizar tecnologías de
on en línea para ver películas grabadas, series y transmisiones en vivo.
acticas –Los hallazgos implican que hay margen para que los proveedores de servicios
on en línea mejoren su marketing centrado en el cliente reforzando la calidad y el contenido de sus
programas grabados y la publicidad intermitente.
Originalidad/Valor –Este estudio integra las teorías TAM y UGT para comprender mejor el creciente
uso de las tecnologías de transmisi
on para ver películas grabadas, seriesy transmisiones en vivo.
Palabras clave –Online streaming, Modelo de aceptaci
Teoría de usos y gratiﬁcaciones (UGT), COVID-19
Tipo de papel –Trabajo de investigaci
Relevant academic literature suggests that new media technologies are changing the way
how individuals consume television (Tefertiller, 2018;Aldea and Vidales, 2012;Hirsjärvi
and Tayie, 2011). Today, several media companies are offering video streaming services
that feature high-quality, original content that can be accessed through digital and mobile
technologies (Kostyrka-Allchorne et al., 2017;Groshek and Krongard, 2016). Video
streaming technologies have disrupted the way how individuals consume broadcast media.
Consumers are shifting from linear formats such as real-time television (TV) services that
are accessible through satellite/or cable and subscribing to online streaming services
(Spilker et al., 2020;Sørensen, 2016;Flavi
an and Gurrea, 2007). Online users are accessing
broadcast services through the home internet and/or via mobile devices (Lim et al., 2015;
Simpson and Greenﬁeld, 2012). This is particularly conspicuous among the youngest
demographics, who are increasingly subscribing to online TV channels and video streaming
services (Panda and Pandey, 2017).
One cannot generalize that all young individuals would follow similar consumption
patterns. Therefore, media and entertainment businesses may consider other variables
when they explore their viewers’proﬁles and their consumption behaviors. For instance,
online streaming companies such as Amazon Prime Video, Apple TV, Disneyþ, HBO, Hulu,
Netﬂix and Roku are continuously investing in new programs, as they are operating in an
increasingly competitive environment (WSJ, 2019;Jenner, 2016). Hence, their subscribers can
access a library of movies, series, shows and sports programs, etc. Very often, these media
companies are also using mobile applications (apps) and integrating personalized
recommender systems to enhance their customers’experiences. This way, they improve
their brand equity and service quality to retain existent consumers and attract new ones.
This research explores the consumers’perceptions toward online streaming technologies
and sheds light on their motivations to use them. It presumes that individuals seek
emotional and instrumental gratiﬁcations from watching recorded videos and/or live
broadcasts through digital and mobile devices. Therefore, this contribution builds on the
technology acceptance model (TAM) (Scherer et al., 2019;Munoz-Leiva et al.,2017;Rauniar
et al.,2014;Wallace and Sheetz, 2014; Davis et al., 1989; Davis, 1989) and on the uses and
gratiﬁcations theory (UGT) (Kaur et al.,2020;Dhir et al., 2017a,2017b;Joo and Sang, 2013;
Smock et al.,2011;Stafford et al.,2004;Katz et al.,1973) to investigate the consumers’ease of
use and usefulness of these technologies, as well as their ritualized and instrumental
motivations that would ultimately have a positive and signiﬁcant effect on their behavioral
intentions to use them. Hence, this study relied on TAM’s and UGT’s key measures to
capture the data for this empirical investigation. These two theoretical frameworks were
purposely chosen as they comprise valid and reliable measures that were frequently tried
and tested in academia, in various contexts.
Speciﬁcally, the underlying research questions are: what are the individuals’motivations
for watching online streaming through their digital and mobile devices? Are the streaming
technologies useful andeasy to use? Are they willing to continue using them to watch online
TV channels or recorded video content? To the best of our knowledge, there are no other
studies that have integrated TAM’sand UGT’s key constructs to shed light on the
individuals’motivations for ritualized use and instrumental use of online streaming
technologies, and to reveal their perceived usefulness and ease of use. Therefore, this
research addresses this gap in academic knowledge. In sum, this contribution suggests that
the individuals’motivations to use online streaming technologies to watch live TV channels
and/or recorded videos would have a positive and signiﬁcant effect on their acceptance of
these technologies, and on their intentions to continue using them.
This article is structured as follows: the following section provides a critical review of
key theories that were drawn from relevant marketing and technology literature. It presents
the conceptual framework and formulates the hypotheses for this research. Afterward, the
methodology section describes the method that was used to gather the data from the
respondents. It sheds light on the measures that were used in this quantitative study. Hence,
the results section features an analysis and interpretation of the ﬁndings. In conclusion, this
contribution outlines its theoretical and its practical implications. The authors identify their
research limitations and outline their future research avenues to academia.
2. The technology acceptance of online streaming services
Individuals are increasingly consuming the broadcast media through digital and mobile
technologies. Very often, they are watchingTV channels, movies, series, shows, etc. through
online streaming services that are readily available through ubiquitous technologies,
including smartphones or tablets. eMarketer (2019) reported that 70.1% surfed the internet
while watching their favorite movies and shows. Moreover, according to the latest
Global Web Index Trend Report, the individuals who were between 16–24 years, spent
7 ¾ h per day online or on their smartphones or tablets. The individuals from this
demographic segment devoted over 2.5 h a day to social networking and were watching
more than an hour of online TV per day (GWI, 2019). The individuals hailing from the 25–34
age segment have switched from linear TV to online streaming to watch live TV and/or
recorded videos. They subscribed to online services through digital and high-speed mobile
devices, including smartphones and tablets to stream live channels and recorded video
content from anywhere, at any time (eMarketer, 2019;GWI, 2019). Evidently, they were
accessing online streaming through virtual private networks to watch TV programs,
movies, entertainment, sporting events and the like (GWI, 2019). Hence, media and
entertainment businesses are continuously investing on the programming of new content,
including those produced in-house to satisfy their online subscribers. In this light, this study
explores the individuals’perceptions toward online streaming technologies and their
motivations to use them to watch recorded videos and/or live broadcasts. The researchers
relied on TAM’s(Nagy, 2018;Munoz-Leiva et al., 2017;Cha, 2013;Davis, 1989) and UGT’s
key constructs (Kaur et al., 2020; Dhir et al., 2017a, 2017b) to capture the data from their
2.1 The perceived usefulness and ease of use of the technology
TAM has often been used by various researchers to explore the individuals’perceptions
toward the use of different technologies. The model comprises core constructs that measure
the users’motivations to engage with a certain technology, namely, their “perceived ease of
use,”“perceived usefulness”and “attitudes.”The outcome variables are the behavioral
intentions and technology usage (Scherer et al.,2019). Therefore, TAM seeks to explain why
people decide to accept or reject a technology (Davis, 1989;Lee et al.,2010). The individuals’
perceived usefulness and their perceived ease of use are considered as key variables that
directly or indirectly explain the mentioned outcomes (Maranguni
c and Grani
Rauniar et al., 2014). Davis (1989) deﬁned the perceived ease of use as the degree to which a
person believes that using a particular system would be free from effort. The perceived
usefulness is the degree to which a person believes that using a particular system would
enhance his or her job performance (Davis, 1989). In other words, this construct determines
whether individuals would perceive the technology to be useful for what they want to do.
Various researchers reported that there is a positive relationship between the
perceived ease of use and the perceived usefulness (Nagy, 2018;Munoz-Leiva et al., 2017;
Niehaves and Plattfaut, 2014;Wallace and Sheetz, 2014;Joo and Sang, 2013;Liu et al.,
2010;Park, 2010;Davis et al., 1989). Relevant research on the topic of this study reported
that the perceived advantages of online streaming media were also inﬂuenced by the
perceived ease of use of the technology (Tefertiller, 2020;Yang and Lee, 2018;Cha, 2013).
Previously, Rogers (2003) contended that individuals would use certain innovations if
they believe that they provide advantages over extant technologies. These theoretical
underpinning indicated that individuals may be intrigued to use certain technologies
(including online streaming services) if they are easy to use. Conversely, if the
technologies are complex, complicated or difﬁcult to use, they would not perceive their
usefulness. Hence, this research hypothesizes:
H1. The individuals’perceived ease of use of the online streaming technologies will
have a positive and signiﬁcant effect on their perceived usefulness.
The individuals’perceived ease of use and their perceived usefulness of the technologies
precede their intentions to use them (Venkatesh et al.,2003;Venkatesh, 2000). Other studies
indicated that both the individuals’perceived ease of use and their perceived usefulness of
certain technologies were found to have a positive and signiﬁcant effect on their intention to
use them (Joo and Sang, 2013;Jung et al., 2011;Venkatesh, 2000). Yang and Lee’s (2018)
study reported that the individuals’perceived usefulness of streaming media devices was
positively associated with their behavioral intention to use them. This argumentation leads
to the following hypotheses:
H2. The individuals’perceived ease of use of online streaming technologies will have a
positive and signiﬁcant effect on their intentions to use them.
H2a. The individuals perceived usefulness of online streaming technologies is mediating
the relationship between perceived ease of use and intention to use them.
H3. The individuals’perceived usefulness of online streaming technologies will have a
positive and signiﬁcant effect on their intentions to use them.
TAM has been adapted and expanded by various scholars (Venkatesh and Davis, 2000;
Venkatesh, 2000). Many researchers argued that this model has limited predictive power
and its parsimony is one of its key constraint (Venkatesh et al.,2003;Venkatesh, 2000).
Benbasat and Barki (2007) held that TAM ignores the social processes of information
systems. Other researchers, including Legris et al. (2003) recommended that additional
variables from the innovation model ought to be integrated into TAM. Venkatesh and Davis
(2000) extended the original TAM model. They sought to clarify the notions of perceived
usefulness and usage intentions in terms of social inﬂuences and cognitive instrumental
processes. Their revised model was referred to as TAM2. Afterward, Venkatesh et al. (2003)
reﬁned TAM as they included new constructs, including facilitating conditions, social
inﬂuences, as well as demographic variables in their uniﬁed theory of acceptance and use of
technology (or UTAUT). Eventually, Venkatesh and Bala (2008) proposed TAM3. This
model incorporated the effects of trust and perceived risk in the context of e-commerce
technologies. However, these TAM constructs appeared to be more applicable to using
technology for utilitarian motives rather than for hedonic purposes or intrinsic motivations
(Camilleri, 2019;Nikou and Economides, 2017;Vijayasarathy, 2004;Venkatesh, 2000).
2.2 The uses and gratiﬁcations of the technology
The individuals’technology acceptance is inﬂuenced by their extrinsic motivations,
including their perceived usefulness (Joo et al.,2018; Davis et al., 1989; Venkatesh and Davis,
2000). However, TAM did not include a construct that measured the individuals’intrinsic
motivations. Hence, Venkatesh et al. (2012) extended the UTAUT as they included hedonic
motivation (along with price value), in addition to Venkatesh et al.’s (2003) constructs. The
authors contended that many individuals seek intrinsic gratiﬁcations when they use
certain technologies. The users’non-utilitarian gratiﬁcations, including enjoyment and
entertainment, can inﬂuence their behavioral intentions to continue using technologies, such
as mobile devices (Camilleri and Camilleri, 2019;Nikou and Economides, 2017).
UGT assumes that individuals use media technologies to enhance their gratiﬁcations.
This theory is positivistic in its approach and holds heuristic value (Katz et al.,1973). It
seeks to explain why and how individuals are intrigued to use innovative technologies to
satisfy their speciﬁc needs and wants (Dhir et al., 2017a; Chen, 2011;Katz et al., 1973). Thus,
UGT has been widely used to explore the uses of various media, and to better understand
the consumers’motivations for using them. Of course, individuals would have different
motivations for using identical media, and may also exhibit divergent levels of
In the past, UGT was considered as an extension of the needs and motivations theory
(Ray et al.,2019;Nikou, and Economides, 2017;Katz et al.,1973). Its measures were often
used to explore the individuals’intentions to watch speciﬁc programs on television (Stafford
et al.,2004;Harwood, 1999) or to investigate their engagement with digital media, including
internet technologies (Kaur et al.,2020;Shao, 2009;Flavi
an and Gurrea, 2008) and social
media (Dhir et al., 2017a; Mäntymäki and Riemer, 2014;Smock et al.,2011). For example,
Sanz-Blas et al. (2019),aswellasMäntymäki and Islam (2016) have used UGT to shed light
on the adverse effects of social media on teenagers. Other researchers relied on this model to
examine the individuals’gratiﬁcations from mobile instant messaging (Kaur et al.,2020),
food delivery apps (Ray et al.,2019) and digital photo sharing with other social media
subscribers (Malik et al., 2016), among other contemporary topics.
Various studies suggested that individuals are using technologies for different reasons,
including to satisfy their own social and psychological needs (Dhir et al., 2016). Online users
use digital media technologies to access information or to share it with their followers
(Troise and Camilleri, 2020). Others use technologies to buy products (Talwar et al.,2020;
Kaur et al.,2020;Ray et al.,2019) or for entertainment purposes (Kuoppamäki et al., 2017;
Dhir and Torsheim, 2016). Alternately, they use them to communicate, build relationships or
seek affection (Malik et al., 2016;Leung,2015, 2013;Whiting and Williams, 2013).
Some researchers have focused on instant messaging (Ku et al., 2013;Lo and Leung,
2009), on blogging (Hollenbaugh, 2011;Shao, 2009) and on the creation of user generated
content (Herrero and San Martín, 2017;Ye et al., 2011;Van Dijck, 2009). Very often, their
studies shed light on how and why individuals hailing from various demographics and
backgrounds in society (in terms of different genders, age groups and educational levels)
were using these technologies. For instance, individuals may use their mobile devices to
access content (instrumentality) when they are out and about (mobility). Mobile technologies
provide immediate access to a wide array of online information including written content,
images and videos (e.g. via YouTube) (Khan, 2017). Smartphones and tablets allow their
users to entertain themselves by playing games and/or to socialize with other individuals
through social media (Calvo-Porral and Otero-Prada, 2020;Camilleri, 2020;Hajarian et al.,
2020;Calvo-Porral and Nieto-Mengotti, 2019;Dolan et al.,2019;Balakrishnan and Raj, 2012).
Individuals are increasingly subscribing to social media as they offer them different
gratiﬁcations (Dolan et al.,2019;Dhir et al., 2017a;Khan, 2017).
Relevant theoretical underpinnings indicated that the internet provides three types of
gratiﬁcations, including content gratiﬁcation, process gratiﬁcation and social gratiﬁcation (Li
et al., 2017;Stafford et al., 2004). Individuals can use the internet to search for speciﬁc
information. In the meantime, they may enjoy the browsing process during their online
searches (Perks and Turner, 2019;Huang, 2008). Alternately, they may use the internet for
socializing purposes, as it enables them to connect with family, friends and acquaintances.
Several empirical studies have examined the internet’spositive(gratiﬁcations) and its negative
outcomes. For example, LaRose and Eastin (2004) relied on Bandura’s (1991) social-cognitive
approach to investigate the internet users’self-efﬁcacy and their self-disparagement.
Other research investigated the individuals’gratiﬁcations from social networking
services (SNS) including Facebook, Instagram, Twitter and Linkedin, as well as blogs and
review websites (Bevan-Dye, 2020;Capriotti et al.,2020;Belanche et al.,2019;Sanz-Blas et al.,
2019;Leung, 2013;Park et al., 2009). Many authors have used UGT to explore the
gratiﬁcations of social media subscribers as more individuals are becoming devoted,
engaged and highly motivated to upload content in speciﬁc SNS services (Rios Marques
et al.,2020;Malik et al.,2016). They are also listening to music and watching videos (Khan,
2017;Krause et al., 2014), sharing links (Baek et al.,2011), participating in groups (Karnik
et al., 2013;Park et al.,2009), sharing news (Lee and Ma, 2012) and photos (Malik et al.,2016)
through social media.
Online users are engaging with other individuals through social media to fulﬁll their
socio-cognitive needs or simply to express their feelings. They have different
motivations to use them, including for narcistic, socialization, recognition (status) and/
or for entertainment purposes. It goes without saying that individuals also seek
emotional gratiﬁcations from traditional media, including television and cinemas (Li,
2017;Bartsch, 2012). They engage with different media to distract themselves into a
better mood (Zillmann, 2000). Lonsdale and North (2011) reported that adolescents tend
to regulate their moods by listening to music. Other authors went on to suggest that
media entertainment provide efﬁcient stimuli to individuals to adjust their moods
(Smock et al., 2011;Park et al., 2009;Bumgarner, 2007;Knobloch, 2003)ortoescape
from emotional difﬁculties (Greenwood and Long, 2011;Greenwood, 2008). Hence,
individuals use speciﬁc media to satisfy their needs for information and for
entertainment purposes (Lee et al., 2010;Quan-Haase and Young, 2010;Bumgarner,
2007). They may use media technologies, including mobile devices on a habitual basis
and/or when they have time to spare (Smock et al.,2011).
In this light, this research explores the effect of the individuals’“ritualized use”
and of their “instrumental use”of online streaming technologies (Leung, 2015;Joo and
Sang, 2013;Cooper and Tang, 2009). This study has adapted Joo and Sang’s (2013)
theoretical framework that they used to explore the usage of smartphone devices. In
this case, this empirical investigation is focused on the individuals’consumption
behaviors of online streaming technologies through digital and mobile devices. UGT
was used to explore the individuals’motivations toward online streaming services
that can be accessed through smart TVs, smartphones and tablets. This study
H4. The individuals’motivations to use online streaming technologies for ritual
purposes, will have a positive and signiﬁcant effect on their intentions to use the
H5. The individuals’motivation to use online streaming technologies for instrumental
purposes, will have a positive and signiﬁcant effect on their intention to use of the
Our approach assumes that our respondents:
used smart TVs, smartphones and/or tablets;
were experienced with the use of these technologies (this helped them make
“motivated choices”); and
were using online streaming services to watch live broadcasts and/or recorded
videos. Figure 1 illustrates the hypothesized relationships of this research.
The data was gathered via an online survey questionnaire that was disseminated among higher
education students in a Southern European university. A stratiﬁed sampling approach was used
to select the survey sample. There were more than 10,000 students who were pursuing full time
and part time courses in this institution, who had voluntarily given their consent to receive
requests to participate in academic studies. The targeted research participants received an email
from the university registrar that comprised a hyperlink to this study’s survey questionnaire.
There were 491 respondents who have completed their questionnaire.
This study complied with the research ethic policies of this institution and with the EU’s
general data protection regulation. The research participants indicated the extent of their
agreement with the survey items in a ﬁve-point Likert scale. The responses ranged from 1
“strongly disagree”to 5 = “strongly agree”and 3 signaled an indecision. In the latter part of the
questionnaire, the participants were expected to disclose their age by choosing one of ﬁve age
groups. They indicated their gender that were coded by using the 1 or 0 dummy variable,
where 1 represented the women. The questionnaire was pilot tested among a small group of
postgraduate students (who were not included in the survey results) to reduce the plausibility
ofthecommonmethodbias,asperMacKenzie and Podsakoff’s (2012) recommendations.
3.1 The measures
The survey instrument has adapted measuring items from Davis’(1989) TAM and from
Katz et al. (1973) UGT. The participants were expected to indicate their level of agreement
on the survey items that explored their motivations and perceptions toward the use of online
streaming programs. The constructs included “motivation for ritualized use,”“motivation
for instrumental use,”“perceived usefulness,”“perceived ease of use”and “intention to use
online streaming technologies.”These constructs were tried and tested in several other
studies, and in other contexts (Tefertiller, 2020;Yang and Lee, 2018;Nagy, 2018;Munoz-
Leiva et al.,2017;Kaur, Dhir, Chen, Malibari and Almotairi, 2020;Dhir et al., 2017a,2017b;
Joo and Sang, 2013). The measuring items that were used in this study are presented in
model and the
Intention to Use
3.2 The demographic proﬁle of the respondents
The participants provided their socio-demographic details about their “gender,”“age”and
indicated the “course”that they were studying in the latter part of the survey questionnaire.
Their identity remained anonymous and their responses were kept conﬁdential. Only
aggregate information was used during the analysis of the data. More than two-thirds of the
respondents were women. The sample consisted of 339 women (69%) and 152 men (31%).
There were two individuals who did not indicate their gender. Most of the respondents
(n= 226, 46%) were between 18 and 21years of age. The second largest group (n= 114,
23%) were between 22 and 25 years old. The majority of respondents were pursuing courses
in the faculties of arts (14%), economics, management and accountancy (13%) and applied
sciences (12%). However, the sample included respondents from all areas of studies.
4.1 Descriptive statistics
The respondents agreed with the survey items in the model, as the mean scores (M) were
above the mid-point of 3. The highest mean scores were reported for IU2 (M = 4.273), PU1
(M = 4.184) and PEoU (M = 4.167). Whilst INT2 reported the lowest mean score (M = 3.462).
The standard deviations (SD) ranged indicated that there was a narrow spread around the
mean. The values of the SD ranged from 0.696 (for IU2) to 1.112 (for INT1).
4.2 Conﬁrmatory composite analysis
This study relied on a structural equation modeling (SEM) approach to explore the
measurement quality of this research model (Ringle et al.,2014). SEM-partial least squares
(PLS) 3 conﬁrmatory composite analysis’algorithm revealed the results of the reﬂective
measurement model (Hair et al.,2020).
The measuring items
Motivation for ritualized use
RU1 I watch online streaming services to break the routine
RU2 I watch online streaming services in my free time
RU3 Watching online streaming services is a form of entertainment
Motivation for instrumental use
IU1 I watch informative programs, including news and talk shows through online streaming services
IU2 I watch entertainment programs, including movies and series through online streaming services
IU3 I watch online streaming services as they offer advertising options, e.g. no advertising, limited
advertising or all advertising will be presented in free viewing mode
Perceived ease of use
PEOU1 It is an easy task for me to access the online streaming services of live or recorded programs
PEOU2 I ﬁnd it easy to access online streaming services through digital and mobile devices, including
smart TVs, smartphones and tablets
PU1 The online streaming services allow me to view what I want in a faster way than traditional TV
PU2 The online streaming services enhance my experience of watching informative or entertainment
PU3 I can watch online streaming services in any place I like if there is a good Wi-Fi or network connection
Intention to use
INT1 I will continue using digital and mobile devices, including smart TVs, smartphones and tablets
to watch online streaming
INT2 I shall spend more money on digital and mobile devices to access informative and entertainment
programs through online streaming services
The values of the standardized loadings were higher than the recommended threshold of
0.7 (Hair et al., 2020) and had an associated t-statistic above 61.96. The composite reliability
values were between 0.821 and 0.929. The values of average variance extracted (AVE)
conﬁrmed the constructs’convergent validities as it explained more than 50 % of the
variance of their items. In other words, the values for AVE were higher than 0.5 (Hair et al.,
2011). There was evidence of discriminant validity as the square root value of AVE was
greater than the correlation values among the latent variables (Fornell and Larcker, 1981).
This study also examined heterotrait-monotrait (HTMT) ratio of the correlations, thus it re-
conﬁrmed the presence of discriminant validity across the constructs. The HTMT values
were lower than 0.9 (Henseler et al., 2015) as shown in Table 2.
4.3 Structural model assessment
The assessment criteria involved an examination of the collinearity among the constructs.
The results indicated that there were no collinearity issues as the variance inﬂation factors
have exceeded the recommended threshold of 3.3 (Hair et al., 2020). The PLS algorithm
revealed the model’s predictive power, in terms of the coefﬁcient of determination (R
) of the
endogenous latent variables. It also shed light on the effect (¾
) of each exogenous construct
on the endogenous constructs.
Afterward, a bootstrapping procedure was used to explore the statistical signiﬁcance
and relevance of the path coefﬁcients. The signiﬁcance of the hypothesized path coefﬁcients
in the inner model were evaluated by using a two-tailed t-test at the 5% level (Hair, Ringle
and Sarstedt, 2011). Table 3 presents the results of the hypotheses of this study. It tabulates
the ﬁndings of the standardized beta coefﬁcients (original sample and sample mean), the
conﬁdence intervals, ¾
,t-values and the signiﬁcance values (p). Table 4 features the results
of the mediating relationship.
H1: This study reported that there was a positive and signiﬁcant effect between the
individuals’perceived ease of use and the perceived usefulness of the streaming
= 0.424, t= 10.086 and p<0.001. This result validates the TAM. The
analysis and an
assessment of the
Construct Items Loadings CR AVE 1 2 3 4 5
1 Instrumental use IU1 0.831 0.821 0.607
IU2 0.822 0.779 0.442 0.737 0.402 0.609
2 Intention Int1 0.938 0.929 0.868 0.338 0.932 0.485 0.808 0.703
3 Perceived ease of use PEoU1 0.92 0.925 0.861 0.572 0.411 0.928 0.507 0.555
4 Perceived usefulness PU1 0.83 0.894 0.737 0.303 0.676 0.424 0.859 0.686
5 Ritualized use RU1 0.863 0.852 0.659 0.44 0.555 0.438 0.533 0.812
Notes: The discriminant validity was calculated by using the Fornell-Larcker criterion. The values of square
root of the AVE were presented in bold font. The AVEs for each construct were greater than the correlations
among the constructs. The shaded area features the results from the HTMT criterion (Henseler et al.,2015)
ﬁndings suggest that the individuals who perceived the ease of use of these online
technologies will probably perceive their usefulness as well. H2 revealed that there was no
direct relationship between the individuals’perceived ease of use of the streaming
technologies and their intention to use them. However, there was an indirect effect of
perceived usefulness on perceived ease of use –intentions link. The mediating analysis
reported that there was full mediation from the perceived usefulness construct on this
= 0.285, t= 7.396 and p<0.001. H3 indicated there was a positive and
direct relationship between the respondents’perceived usefulness of the streaming
technologies and their intentions to use them, where
= 0.509, t= 13.48 and p<0.001. H4:
The ﬁndings suggest that the participants’motivations for the ritualized use of the streaming
technologies (to watch entertaining programs such as movies and/or recorded TV series) was a
signiﬁcant antecedent of their intentions to use the mentioned technologies, where
t= 5.678 and p<0.001. In conclusion, the ﬁndings from H5 show that the students’
instrumental motivations to use live streaming technologies (e.g. to watch the news and/or
informative programs) was not a signiﬁcant precursor of their intentions to use them.
The results indicated that there were signiﬁcant f
values between perceived usefulness
and intention f
= 0.360 and between perceived ease of use and perceived usefulness
= 0.219). Figure 2 sheds light on the explanatory power of this research model. It
Testing of the
H1 Perceived ease of use !
0.424 0.422 [0.345, 0.497] 0.219 10.086 0.000 Supported
H2 Perceived ease of use !
intention to use streaming
0.069 0.068 [0.009, 0.158] 0.006 1.695 0.091 Not
H3 Perceived usefulness !
intention to use streaming
0.509 0.508 [0.434, 0.577] 0.360 13.480 0.000 Supported
H4 Ritualized use !intention to
use streaming technologies
0.236 0.235 [0.152, 0.322] 0.072 5.678 0.000 Supported
H5 Instrumental use !intention
to use streaming technologies
0.041 0.044 [0.037, 0.136] 0.002 0.940 0.348 Not
H2 Perceived ease of use !
0.285 [0.162, 0.277] 7.396 0.000H2a Perceived ease of use !
perceived usefulness !
0.216 Supported (full
Notes: *The direct effect was not signiﬁcant, p= 0.091. The total effect (including the effect from the
mediating construct) was very signiﬁcant, where p<0.001
illustrates the total effects, outer loadings and the coefﬁcient of determination (R
) values of
the constructs. The students’indicated that they were committed to continue using the
online streaming technologies (R
= 0.517) as they perceived its usefulness (R
5.1 Theoretical implications
This contribution explored the individuals’motivations to use streaming technologies to
watch live broadcast programs and/or recorded content (Tefertiller,2020, 2018;Steiner and
Xu, 2018;Panda and Pandey, 2017;Sørensen, 2016;Groshek and Krongard, 2016). It
differentiated itself from other research, as it integrated valid measures that were drawn
from TAM (Nagy, 2018;Munoz-Leiva et al., 2017;Niehaves and Plattfaut, 2014;Cha, 2013;
Davis, 1989) and UGT (Steiner and Xu, 2018;Riddle et al.,2018;Joo and Sang, 2013;Bondad-
Brown et al., 2012;Katz et al.,1973).
The critical review of the relevant literature reported that both theories were widely used
(and cited) in academia to investigate the individuals’behavioral intentions to adopt new
technologies, in different contexts (Manis and Choi, 2019;Liu et al., 2010;Benbasat and
Barki, 2007). In essence, TAM suggests that the individuals’perceptions about the ease of
use and the usefulness of certain technologies would predict their intentions to use them
again in the future (Scherer et al.,2019;Munoz-Leiva et al.,2017;Rauniar et al.,2014;Wallace
and Sheetz, 2014; Davis et al., 1989; Davis, 1989). Moreover, UGT assumes that individuals
seek to gratify their intrinsic and extrinsic needs through habitual consumptions of media
technologies (Kaur et al., 2020;Perks and Turner, 2019;Ray et al., 2019;Li et al.,2017;Joo
and Sang, 2013;Bartsch, 2012;Chen, 2011;Smock et al.,2011;Stafford et al.,2004;Katz et al.,
The ﬁndings from this research indicated that the research participants perceived the
ease of use and the usefulness of the streaming technologies. The results conﬁrmed that they
found it easy and straightforward to use their smart TVs, smartphones or tablets to access
online streaming services. The respondents believed that the streaming technologies
allowed them to view TV programs and/or recorded videos in a faster way than traditional
TV subscriber services or satellite TV. They perceived the usefulness of online TV and/or
illustration of the
video streaming services, as they enhanced their experience of watching informative and/or
entertainment programs, particularly when they used their mobile devices (Nikou and
Economides, 2017;Balakrishnan and Raj, 2012). Hence, the research participants were
committed to continue using their smart devices to access their favorite online programs
through streaming technologies. The regression analysis revealed that there were highly
signiﬁcant correlations between TAM’s core constructs including the perceived ease of use
and the perceived usefulness of online streamingservices. Both of these constructs were also
signiﬁcant antecedents of the individuals’intentions to continue using the mentioned
The individuals’ritualized motivations to use the streaming technologies were found to
have a very signiﬁcant effect on their intention to use them. The respondents were using
online streaming technologies on a habitual basis, to break the routine. These ﬁndings are
consistent with the relevant literature concerning UGT, where the researchers concluded
that many often, individuals consider the media technologies as a form of entertainment
(Dhir et al., 2017b;Li, 2017;Bartsch, 2012;Smock et al.,2011) as individuals. In this case, the
research participants sought emotional gratiﬁcations from the streaming technologies.
Probably, they allowed them to relax in their free time. Other theoretical underpinnings
reported that individuals use certain technologies to distract themselves into a better mood
(Lonsdale and North, 2011; Park et al.,2009;Knobloch, 2003;Zillmann, 2000). Most of the
respondents indicated that they were using these technologies to satisfy their needs for
information and entertainment. These ﬁndings are consistent with previous studies (Lee
et al.,2010;Quan-Haase and Young, 2010;Bumgarner, 2007).
The survey respondents revealed that they used online streaming technologies for
instrumental purposes to watch informative programs, including news and talk shows, as
well as entertainment programs, including movies and series through online streaming
services. Other researchers also reported that there were many instances where individuals
beneﬁted of their smartphones and tablets’instrumentality and mobility, as they enabled
them to access online content, including recorded videos, live streams and/or intermittent
marketing content,when they were out and about.
The participants indicated their agreement with the survey item about the advertising
options of online streaming services. This research suggests that they were aware that
subscribed users of online streaming technologies can limit or block intrusive and/or
repetitive advertisements they receive whilst using online streaming technologies (Belanche
et al.,2019). Previous studies also reported that online users were increasingly applying ad
blockers (Redondo and Aznar, 2018;Lim et al.,2015). The practitioners who are using digital
marketing platforms, including online streaming websites to promote their products and/or
services, ought to reﬁne the quality and content of their customer centric marketing. Their
underlying objective is to engage their audiences with relevant, helpful information that
complements, rather than detracts from their overall online experience.
5.2 Practical implications
This research postulates that the respondents are consuming free-tier and/or paid streaming
services through different digital media including mobile devices such as smartphones and
tablets. It conﬁrmed that online streaming technologies can improve the consumers’
experiences of watching live broadcasts and/or recorded programs. The research
participants perceived their ease of use and their usefulness as they can be accessed in any
place, at any time, through decent Wi-Fi and/or network connections. The ﬁndings are
consistent with the U&G theory as the participants indicated that the media technologies
were entertaining. Hence, they were committed to continue using them. They indicated that
they would continue using them in the foreseeable future. On the other hand, this study
revealed that the respondents’instrumental motivations to use online streaming services did
not predict their intentions to use them (even though these technologies allowed their
subscribers to limit or block online advertisements).
Most probably, the respondents were accessing on-demand streaming services in the
comfort of their home, rather than from mobile technologies, when they were out and about.
The reason for this behavior could be that they prefer watching online programs through
big screens as opposed to watching them through their mobile devices’smaller screens. The
latest TVs may offer quality, high resolution images and better sound than smartphones
and tablets. Thus, smart TVs (that are using Apple and/or Android systems, etc.) may be
considered more appropriate to watch recorded movies and/or TV series. It is very likely
that the participants would also perceive the ease of use and the usefulness of these
technologies for other purposes, including digital gaming, video conferencing, et cetera.
Recently, the unprecedented outbreak of the Coronavirus pandemic and its preventative
social distancing measures has led to a considerable increase in the use of digital media
(Camilleri, 2020). There was also a surge in the subscriptions to paid streaming services
(Marketwatch, 2020). As a result, more digital advertisements (ads) were featured in online
streaming services. They are usually presented to free tier consumers as skippable or non-
skippable streaming or static ads that appear before, during or after they access online
broadcasts and/or recorded programs. Alternately, online users may decide to subscribe to
the streaming services, if they want to block the marketing messages they receive
(Tefertiller, 2020;Kim et al., 2017). This way, they could have more control over their online
There are several media companies in the market that are offering competitive streaming
packages. Very often, they are producing new programs, including movies, series, et cetera.
Consumers may be intrigued to upgrade their services to beneﬁt of secure, reliable, low
latency streaminginfrastructures and to gain access to more exclusive content in an ad-free,
interactive environment. They may also appreciate if the service providers would increase
their engagement with them by using customer-centric recommender systems. Consumers
may be informed about their favorite programs through regular notiﬁcations to their mobile
apps (if they subscribe to them). These alerts ought to be related to their personal
preferences. As a result, the consumers would continue entertaining themselves with online
streaming technologies as they perceive their instrumentality, ease of use and the usefulness
of their services.
6. Limitations and future research avenues
This research investigated the individuals’attitudes and perceptions toward online streaming
of recorded movies, series or lives television programs, including news, entertainment shows,
quizzes, et cetera. This contribution did not specify whether they were accessing free or paid
online streaming. Therefore, further research can distinguish among different service providers
of online streaming, and those that are operating in different settings. This research was carried
out among university students, who were mostly young women. The respondents attended a
higher education institution from a Southern European context. The researchers decided not to
tweak the data to correct for age or gender imbalance.
Future studies may consider different constructs from other theoretical models to explore
the individuals’acceptance and motivations to use online streaming technology. Although
there are many researchers who have appraised and used TAM’s and UGT’s measures,
others have indicated that their measures have inherent limitations, as reported within the
literature review section of this paper. Perhaps, further research may involve interpretative
studies to investigate the individuals’in-depth opinions and beliefs on the latest
developments in broadcast media. Inductive studies can reveal other important factors
about the individuals’consumption behaviors, and may probably shed more light on why,
where, when and how they are using online streaming technologies. This way, service
providers of recorded video content and/or live broadcasts will be in a better position to
understand their audiences’expectations.
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