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Media and Communication (ISSN: 2183–2439)
2022, Volume 10, Issue 2, Pages X–X
https://doi.org/10.17645/mac.v10i2.4737
Article
Why Do People Return to Video Platforms? Millennials and Centennials
on TikTok
Pedro Cuesta‐Valiño 1,*, Pablo Gutiérrez‐Rodríguez 2, and Patricia Durán‐Álamo 1
1Department of Economics and Business Management, Universidad de Alcalá, Spain
2Department of Business Administration, Universidad de León, Spain
* Corresponding author (pedro.cuesta@uah.es)
Submitted: 26 July 2021 | Accepted: 6 September 2021 | Published: in press
Abstract
While some social networks like Facebook are losing interest among digital influencers, TikTok continues to grow, capturing
and impacting centennials and millennials alike. This situation highlights the new generations’ increasing interest in short
video formats, which are also becoming a new window of communication between companies and consumers. TikTok
allows users to create, share, and discover short, user‐generated videos in hopes of attracting viewers. But it is necessary
to understand the variables that attract and engage users of these particular social networks. This article analyses the vari‐
ables of continuance motivation, video sharing behaviour, and video creation capabilities, which allow users to enjoy such
networks, and service providers and companies to obtain results from them. The aim is to understand how these variables
motivate social media users to return to and spend more time on this video‐sharing platform. This is measured through
the stickiness variable. In this context—and due to the particular relevance of the topic—the authors also aim to reveal any
potential differences in the behaviour of centennials and millennials when using TikTok. Therefore, a cross‐sectional study
was conducted through a questionnaire answered by 2,301 millennials and centennials who use TikTok. The data were
analysed through a structural equation model to measure the relevance of each of the variables to stickiness. The results
provide guidelines for improving research on video social media platforms, as well as an opportunity to explore the impor‐
tance of the selected variables to the stickiness variable across different user segments.
Keywords
centennials; continuance motivation; millennials; social networks; stickiness; TikTok; video creation; video sharing
behaviour
Issue
This article is part of the issue “New Narratives for New Consumers: Influencers and the Millennial and Centennial
Generations” edited by Luis M. Romero‐Rodríguez (Rey Juan Carlos University), Santiago Tejedor (Autonomous University
of Barcelona) and Inmaculada Berlanga (International University of La Rioja).
© 2022 by the author(s); licensee Cogitatio (Lisbon, Portugal). This article is licensed under a Creative Commons Attribu‐
tion 4.0 International License (CC BY).
1. Introduction
The digital era, in which users are increasingly online,
presents challenges and opportunities for both online
service providers and companies that want to com‐
municate and sell their products. Social media has
transformed the landscape of interaction between peo‐
ple, and between brands and consumers. In this situa‐
tion, the best‐known social networks such as Facebook,
YouTube, WhatsApp, or Instagram have added millions
of users worldwide. Faced with these successful formats,
TikTok has differentiated itself as a mobile application
that serves mainly to create and share short, entertain‐
ing videos. The essence of this social network is what
is known as user‐generated content; in other words,
the content on the network is generated by the users
themselves. TikTok’s success has been immediate, and it
became the most downloaded non‐game mobile app of
Media and Communication, 2022, Volume 10, Issue 2, Pages X–X 1
2020, also ranking second in consumer spending (Sydow,
2020). This confirms the rapid growth of the industry of
short video platforms (Liu et al., 2019).
It is therefore necessary to better understand the
variables and strategies that determine a development
that ensures billions of people are so in love with social
networks that they spend several hours a day posting and
commenting on them. This variable is stickiness, and it
is of great importance in the study of social networks.
Nevertheless, there is another range of different vari‐
ables that have a direct and indirect influence on social
networks’ stickiness. This study examines stickiness to
TikTok, the international version of China’s mobile short
video platform (Chen et al., 2019). Different studies have
shown the evident impact of variables such as continu‐
ance motivation (Hsu et al., 2015; Klobas et al., 2018; Wu
et al., 2010), sharing behaviour (Khan, 2017; Törhönen
et al., 2020), and perceived video creation ability (Chiang
& Hsiao, 2015) on TikTok’s stickiness. This article aims to
go beyond existing research to find out whether there is
a difference between the behaviour of centennials and
millennials on TikTok based on the variables analysed.
2. Conceptual Framework and Hypothesis
Many researchers have studied the concept of sticki‐
ness in recent years. Zott et al. (2000) define stickiness
as the power of a website to retain and attract new
customers. This power motivates them to stay on the
platform (Marchand, 2000). According to Hsu and Liao
(2014, p. 836) “a website is considered sticky when its
users spend an above‐average amount of time brows‐
ing it, when they visit the site frequently.” The main
means of generating stickiness on a website or social
platform are the content (Hu et al., 2020; Lu & Lee,
2010; Xu et al., 2018) and communication style (Fitriani
et al., 2020). Lu and Lee (2010) indicate that if you know
your audiences, companies from all sectors can create
specific contents which give them the opportunity to
increase stickiness. Different researchers have studied
the influence of stickiness in many sectors, including Lien
et al. (2017) in instant messaging applications such as
WeChat, Wang et al. (2016) in e‐commerce, and Chiang
and Hsiao (2015) in video platforms like YouTube. Others
such as Zhang et al. (2017) have studied the term to
give companies practical guidance on how to encour‐
age customer engagement and increase the stickiness
of company social networks through content. Recently,
other researchers have focused on the role of digital influ‐
encers in generating follower stickiness (Hu et al., 2020),
focusing on parasocial relationships and wishful identifi‐
cation as a key to social network stickiness.
Stickiness on social media platforms and smart‐
phones has also received some attention. Wu et al.
(2016) argue that, in the case of smartphones, stickiness
is achieved by making whatever is appropriate to influ‐
ence users’ affective state, such as emotions, part of their
life. Another way of generating stickiness on social media
platforms takes the form of communities and external
influences. According to Yen (2016), stickiness to the
social media site occurs when an individual’s intention to
contribute knowledge is highly influenced by their peers.
In this sense, Chiang and Hsiao (2015) employ the uses
and gratifications theory to research factors that influ‐
ence the stickiness of YouTube. They also use social cog‐
nitive theory to test how YouTube users’ continuance
motivation and sharing behaviour are influenced by envi‐
ronmental and personal factors. However, while many
studies have examined social media platforms and smart‐
phone stickiness from the individual and collective point
of view, little research has been done on TikTok stickiness
and video behaviour.
Motivation seems to have a more relevant role
than emotions, insomuch as motivations can explain a
user’s satisfaction with social network services regard‐
less of whether the users feel positive or negative emo‐
tions (Pappas et al., 2020). From another perspective,
Camilleri and Falzon (2020) indicate the importance of
ritualized motivations in the use of streaming technolo‐
gies, and Fitriani et al. (2020) show that the utilitarian
motivation (credibility) influences channel engagement.
As a consequence, it can be said that motivation is an
extension of demand, which will lead to the emergence
of behaviour (Bi & Tang, 2017).
Continuance motivation has a strong relationship
with uses and gratifications theory because satisfaction
is treated as an important antecedent of continued use
of social media (Hsu et al., 2015; Wu et al., 2010). Linking
in with this point, Wu et al. (2010) show that gratifica‐
tions have a significant role in continuance motivation,
whereas Park et al. (2021) identify social reward as an
important motivational factor. Nevertheless, it is known
that there are other constructs such as “presence” that
do not have a relationship with continuance motivation
(Wu et al., 2010).
On video creation platforms, motivations—such
as enjoyment and social interaction—have a positive
impact on continued engagement and, furthermore,
they are necessary to drive the continuity of the activ‐
ity (Lee & Quillian, 2019; Törhönen et al., 2020). Other
factors have been shown to have significant influence on
continuance motivation too (Chiang & Hsiao, 2015) but
some of these motivations—such as social interaction—
represent a way for customers to show their brand incli‐
nations to other people while they use social channels
(Lee & Quillian, 2019). In the same study, continuance
motivation refers to the ongoing internal drive to watch
and share videos through TikTok.
There are few studies on the relationship between
continuance motivation and stickiness. These aim to
identify a positive relationship between motivation and
people’s preference for a choice of media or content
(Konstantinos et al., 2002). In the case of online games,
Wu et al. (2010) find that the continuance motivation
in this type of amusement enhances the stickiness of
entertainment media. Following the same relationship,
Media and Communication, 2022, Volume 10, Issue 2, Pages X–X 2
stickiness can be further increased if there is motiva‐
tion for continuity on platforms that use video such as
YouTube (Chiang & Hsiao, 2015).
Taking into account all of the above, the following
hypothesis is proposed:
H1: Continuance motivation has a positive relation‐
ship on stickiness.
Another construct that is perceived as a trigger in the
use of social platforms is “sharing behaviour.” A user who
arrives in a social media environment or a virtual commu‐
nity is not just looking for information or knowledge; this
user also harnesses the platform to meet other people,
develop a sense of belonging and build friendships (Chiu
et al., 2006). This triggers a consumers’ referral tendency,
which reflects their propensity to share information with
friends, relatives, and other social groups (Koster et al.,
2020). As Lee and Ma (2011) note, social media sharing
experience and socializing are the two most salient fac‐
tors that influence intention to share. The rise in popular‐
ity of social video content could be ascribed to “increas‐
ing prosumerism, the development of live streaming
technologies, and popular social video sharing sites such
as YouTube, Twitch, Snapchat and Instagram” (Törhönen
et al., 2020, p. 166). And in this process, Vermeulen et al.
(2018) show that users enjoy receiving likes and positive
comments on their positive emotions.
There is a wealth of research analysing sharing
behaviour through social media platforms such Facebook
or Instagram and video content platforms such as
YouTube, Twitch, or TikTok. Vermeulen et al. (2018) focus
on how adolescents use different social media plat‐
forms, while Lim et al. (2015) study how users of var‐
ious online social networks create and share informa‐
tion. Ma and Chan (2014) explore the factors contribut‐
ing to knowledge‐sharing behaviour on Facebook and
Twitter, and Lee et al. (2015) examine the motivations
that drive users to share photos on Instagram. In the
video content industry, Khan (2017) analyses what moti‐
vates user participation and consumption on YouTube,
while Törhönen et al. (2020) look at why people create
content on video platforms.
The relationship between sharing behaviour and
stickiness is well known. In recent years, some research
has shown a strong connection between both constructs.
As Yen (2016, p. 127) says, “members’ knowledge‐
sharing intention significantly affects collaborative stick‐
iness intention”; thus, social interactions could help to
create stickiness (Xu et al., 2018). Taking account of this
correlation, it is considered that enhancing the collabo‐
ration between others “will make users stick to the ser‐
vice and further motivate them to use” (Lien et al., 2017,
p. 409) the services that the platforms offer.
In this sense, users that maintain a closer relation‐
ship and share moments and content with others in an
online community will develop the power to absorb large
amounts of information, generating more stickiness
between them (Hsu & Liao, 2014). Furthermore, Chiang
and Hsiao (2015) reflect in relation to video platforms
that sharing behaviours are important antecedents of
YouTube’s stickiness.
Therefore, the following research hypothesis is
proposed:
H2: Sharing behaviour has a positive influence on
stickiness.
Different users may be driven by different motivations
for sharing content on social platforms. In online net‐
working environments, content creation is referred to
as user‐generated content, which gives to the users the
opportunity to report their opinions, thoughts, and orig‐
inal and artistic content with others online (Boyd &
Ellison, 2008). But, as Weeks et al. (2017) state, not
every social media user writes a post on Facebook, repub‐
lishes stories on Instagram, or generates and shares
news videos. In this case—TikTok—video is the princi‐
pal content created by users, so it is necessary to ana‐
lyse this construct and its influence on the use of social
media platforms.
Video content creation is a dynamic activity that
encompasses personal satisfaction and social approval
(Balakrishnan & Griffiths, 2017). So, the influence of per‐
ceived video creation ability—which is defined by Chiang
and Hsiao (2015, p. 90) as “a person’s judgment of his
or her ability to create valuable or interesting video,” an
opinion that is shared by Page et al. (2014)—must be
taken into account. In this field, Kwane et al. (2020) indi‐
cate that perceived competence influences whether to
engage or not to engage, and to disclose or not disclose
personal information. Meanwhile, Kim et al. (2017) indi‐
cate that if the focus is on social approval, enjoyment
and social recognition are important motivators for con‐
tent creation. Furthermore, negative comments have a
huge impact on users’ desire to share content. As Lortie
and Guitton (2013) point out, for some users the fear of
being insulted, cursed at, or receiving any other offen‐
sive comment is a reason to abstain from uploading per‐
sonal videos.
Cao et al. (2021) indicate that content creation is the
highest level of engagement. This theory has been stud‐
ied in relation to YouTube by Balakrishnan and Griffiths
(2017) who found that content creation had a consid‐
erable effect on addiction. Following this perception,
Lortie and Guitton (2013) point out that social involve‐
ment may have a possible influence on internet addic‐
tion behaviours, while Zhang et al. (2017) show that cus‐
tomer engagement has a direct and positive influence on
customer stickiness and indirect influence through cus‐
tomer value creation.
If we focus on the values that have a relationship with
perceived value, Yang and Lin (2014) showed that hedo‐
nic and social value had a significant impact on sticki‐
ness. Yen (2016) identifies that social capital and social
identity have an effect on knowledge‐sharing intention,
Media and Communication, 2022, Volume 10, Issue 2, Pages X–X 3
which subsequently has a positive relation on collective
stickiness intention towards social networks. Hsu and
Chuan‐Chuan Lin (2016) reveal that attitude and satis‐
faction appear to have significant and positive effects on
stickiness. Consequently, we consider that if a user feels
a perceived video creation ability, the user is going to
develop stickiness to the platform.
Taking into account all of the above, the following
hypothesis is proposed:
H3: Perceived video creation has a positive relation‐
ship on stickiness.
Figure 1 summarizes the relationships proposed in the
model between the different variables.
3. Method
A descriptive transversal method was carried out to
develop the research through a questionnaire that was
answered by people between 16 and 40 years old
(centennials and millennials) who used TikTok in Spain.
The questionnaire was answered between February and
May 2021. A total of 2,301 validated questionnaires
were collected.
A focus group was held before starting the survey
in order to determine that the items in the question‐
naire were relevant, and it was made up of eight people:
four regular TikTok users (two millennials and two cen‐
tennials), two executives of social networks companies,
and two university professors who teach management
on social media. The final questionnaire was obtained as
a result of this qualitative research. This questionnaire,
in its first part, includes questions about demographic
characteristics and items related to the use of TikTok by
respondents. And the second section of the question‐
naire examines the four dimensions analysed of the pro‐
posed model through a 5‐point Likert‐type scale from
1 (completely disagree) to 5 (completely agree). These
measurement scales, that were adapted from the liter‐
ature review, help to guarantee the validity of the mea‐
surement scales and comprise the following: three items
for continuance motivation, three for sharing, three for
video creation, and three for stickiness (Chiang & Hsiao,
2015; see Table 1).
A pretest was developed in January 2021 and was
answered by 60 people between 16 to 40 years old
(30 centennials and 30 millennials) who used TikTok.
The objective of this pretest of the questionnaire con‐
sisted in assessing whether people understood the
items of the questionnaire, as well as validate that the
scales were perfectly constructed. Considering all these
aspects, the final questionnaire was launched on the
main social networks in February 2021. The discretionary
non‐probabilistic sampling by quotas method was used
in order to obtain a representative sample of the Spanish
population of TikTok.
The total number of validated questionnaires
received was 2,301. The configuration of the sample was
43% male and 57% female. By ages, 62% were 16–25
years old and 38% were 26–40 years old. By educa‐
tional level, 9% had a basic education, 30% intermediate
studies, and 61% higher education. By TikTok usage fre‐
quency, 47% used it every day, 14% every 2–3 days, 16%
every 1–4 weeks, and 24% less than once a month. And
finally, from the point of view of TikTok usage time on
a typical day, 35% used it between 0–15 minutes, 22%
between 16–30 minutes, 25% between 31–60 minutes,
and 18% for more than one hour (see Table 2).
4. Results
Partial least squares (PLS) combines the benefits of multi‐
ple regression and principal component analysis in a sin‐
gle technique. Its use has been shown to be particularly
valid when, from a large number of independent vari‐
ables, a set of dependent variables is predicted. A num‐
ber of observable variables represents a variable that
is not directly observable and is called a latent variable.
To configure these variables, researchers have previously
Connuance
movaon
Sharing
behavior Sckiness
H1
H3
H2
Video
creaon
Figure 1. Proposed model.
Media and Communication, 2022, Volume 10, Issue 2, Pages X–X 4
Table 1. Items, factor loading, reliability, and validity.
Factor Loadings Sources
Continuance motivation
Reliability and validity measures: Cronbach’s alpha =0.94, Composite reliability =0.94, AVE =0.89
I have the motivation to continue sharing videos on this social media 0.94
If I could, I would like to continue sharing videos on this social media 0.95 Chiang and Hsiao (2015)
The past experience motivates me to continue sharing videos on this social media 0.94
Sharing behaviour
Reliability and validity measures: Cronbach’s alpha =0.86, Composite reliability =0.91, AVE =0.78
I usually actively share my experiences with others on this social media 0.91
I have contributed knowledge to other members on this social media 0.89 Chiang and Hsiao (2015)
I have tried to share my videos with other members on this social media 0.85
Video creation
Reliability and validity measures: Cronbach’s alpha =0.87, Composite reliability =0.92, AVE =0.79
I can create some interesting video 0.83
I am good at creating some interesting video 0.93 Chiang and Hsiao (2015)
I often create some interesting video 0.91
Stickiness
Reliability and validity measures: Cronbach’s alpha =0.84, Composite reliability =0.90, AVE =0.76
I would stay longer on this social media than on others 0.78
I would stay on this social media as often as I can 0.93 Chiang and Hsiao (2015)
I am willing to continuously visit this social media 0.89
turned to theory. This technique is capable of express‐
ing theoretical concepts through complex variables (con‐
structs) to study their relationships, using a structural
model. The configuration of the observable indicators
and their relationships is caused by the theoretical con‐
cepts, i.e., the theoretical concept previously analyzed
must be the cause of the union of the observable indi‐
cators (Benítez et al., 2020). In this case, the reflective
measurement model is used because the outer weights
are the correlations between the construct and the indi‐
cators. The analyses of this study have been carried out
using SmartPLS 3.3.2 (Ringle et al., 2015).
Table 2. Sample information.
Gender % Total 2,301
Male 43.1 922
Female 56.9 1,309
Age % Total 2,301
16–25 (centennials) 61.7 1419
26–40 (millennials) 38.3 882
Educational level % Total 2,301
Basic education 8.9 204
Intermediate studies 30.2 694
Higher education 61.0 1,403
TikTok usage frequency % Total 2,301
Every day 46.9 1,080
Every 2–3 days 13.6 314
Every 1–4 weeks 15.9 365
Less than once a month 23.6 542
TikTok usage time on a typical day % Total 2,301
0–15 minutes 35.0 806
16–30 minutes 22.1 508
31–60 minutes 25.3 583
More than 1 hour 17.6 404
Media and Communication, 2022, Volume 10, Issue 2, Pages X–X 5
Assessing the reliability and validity of PLS modeling
(PLS‐SEM) should be the first step to be taken. The first
result to analyze is the relationship that each item shows
with its own latent construct (see Table 1). In this case,
all loadings have a higher value on their construct than
on any other and exceed the value of 0.78. Items with
loadings of 0.707 or more are accepted as they reach the
level of acceptable reliability (Hair et al., 2011). The next
step is to assess internal consistency. For this purpose,
Cronbach’s alpha has traditionally been used and, more
recently, composite reliability. For this study, all con‐
structs (reflective measures) obtain a coefficient for both
indices above 0.84, as shown in Table 1. Therefore, even
in the case of demanding strict reliability, the values
exceed 0.80 (Nunnally & Bernstein, 1994). Internal con‐
sistency is also measured through the average variance
extracted (AVE), in this case, in all the coefficients of each
construct the AVE exceeds 0.76. A value at least equal to
0.5 is recommended. Finally, the discriminant validity in
the model is recently measured through the heterotrait‐
monotrait ratio of correlations. If the value is less than
0.90, discriminant validity between two reflective con‐
structs has been established (all values of the model coef‐
ficients are below 0.72).
The Standardized Root Mean Square Residual
(SRMR) was established as a goodness‐of‐fit measure
for PLS‐SEM, indicating how well a model fits the sam‐
ple data. For this model, SRMR is 0.051, suggesting a
good fitting model. The results of the model also suggest
that the variables explain a good amount of variance in
stickiness, with R2value of 0.36.
The results of the model are presented by divid‐
ing the sample between millennials and centennials.
The results (see Figures 2 and 3) show how stickiness
is related to each of its causal variables in each of the
segments analysed. With coefficients of 0.23 and 0.33,
for centennials and millennials respectively, the results
suggest that sharing behaviour is the most important
positive influence on stickiness, followed by video cre‐
ation. The coefficient of the relationship between these
variables is 0.21 for centennials and 0.28 for millennials.
In both cases, it is a positive relationship. Finally, contin‐
uance motivation is also influencing positively on stick‐
iness in a relevant way (with coefficient values of 0.18
and 0.13 for centennials and millennials, respectively).
Consequently, all hypotheses proposed (H1, H2, and H3)
are not rejected in both established segments.
5. Discussion and Conclusions
The relationships proposed in the model have been gen‐
erally accepted in the literature (Chiang & Hsiao, 2015;
Törhönen et al., 2020). However, with the model vali‐
dated in this work, the authors of this study have gone
beyond the existing literature in the sense that they
include all the variables in the same model for the case of
a social network based on videos. In addition, the differ‐
ences have been analysed for the case of centennials and
millennials. This study shows that the sharing behaviour
variable is the variable that has the strongest relationship
with stickiness. The study also examines the important
impact of continuance motivation in the constant use of
this platform. One of the variables included in this study
was video creation. In this type of video‐based social
network, the perceived ability to create videos plays
an important role in social network stickiness. If users
Connuance
movaon
Sharing
behavior
0.9330.889
0.946
0.936
0.940
0.928
Sckiness
0.184
0.215
0.230
Video
creaon
S-1
S-2
S-3
SB-1
SB-2
SB-3
0.822
0.910
0.889
0.762
0.910
0.810
CM-1
CM-2
CM-3
VC-1
VC-2
VC-3
Figure 2. Model result for centennials.
Media and Communication, 2022, Volume 10, Issue 2, Pages X–X 6
Connuance
movaon
Sharing
behavior
0.9360.885
0.960
0.945
0.953
0.940
Sckiness
0.131
0.277
0.330
Video
creaon
S-1
S-2
S-3
SB-1
SB-2
SB-3
0.891
0.916
0.902
0.821
0.916
0.858
CM-1
CM-2
CM-3
VC-1
VC-2
VC-3
Figure 3. Model result for millennials.
feel that they have the ability to create good and valu‐
able content on TikTok, their stickiness towards the plat‐
form is likely to be higher. Therefore, the results of
this study again demonstrate the evident effect of shar‐
ing behaviour, continuance motivation, and video cre‐
ation on stickiness. The findings are consistent with pre‐
vious studies in various contexts (e.g., Chiang & Hsiao,
2015; Cuesta‐Valiño et al., 2020; Törhönen et al., 2020).
In these studies, sharing behaviour shows a strong influ‐
ence on stickiness, while continuance motivation has a
weak influence. In the case of video creation perception,
this variable did not obtain either a direct or indirect rel‐
evant relationship with stickiness in different social net‐
works or digital services. Moreover, these results occur
in both segments analysed: centennials and millennials.
Indeed, the main differences emerge in the greater rele‐
vance of sharing behaviour for millennials than for cen‐
tennials. Thus, millennials show a strong relationship
between sharing behaviour and stickiness that is not
comparable with the relationship in the case of centenni‐
als. The video creation variable also has a greater impor‐
tance for millennials. The opposite is true for the variable
that has the least influence on stickiness: Continuance
motivation shows greater relevance for centennials than
for millennials.
Given the importance of continuous motivation, in
terms of the practical implications, these results allow
us to establish that companies have the opportunity to
motivate their audiences to increase adherence to the
social network and, in turn, increase engagement. To do
this, they could comment on and/or share fan publica‐
tions to give them greater visibility. This type of action
is especially useful in the case of centennials. This study
provides the keys for social network developers to con‐
tinue to expand into the creation of simpler interfaces,
ensuring that audiences feel comfortable creating visual
content. If they perceive that they are able to create
content, so stickiness to the platform will grow. These
types of actions are especially noteworthy for millenni‐
als. Finally, companies on TikTok should try to provide a
unique experience, creating a connection to attract users
to share that content with their like‐minded peers. They
can incentivize their followers and customers through
TikTok by rewarding the most creative content of the
week or month and they can reward the profiles with
the most activity and engagement. Given that the indus‐
try of short and fast consumption videos is growing (Liu
et al., 2019) and has a special impact on centennials and
millennials, it is important that companies begin to famil‐
iarize themselves with the production of digital content
based on this format, thus building a bridge with these
potential audiences. These types of actions are relevant
to both centennials and millennials but above all to mil‐
lennials, who choose to promote particular experiences
that reflect their individuality. Thus, it is important that
companies understand how to identify what users are
trying to express.
In short, the results of this study once again demon‐
strate the evident effect of sharing behaviour, con‐
tinuance motivation and video creation on stickiness.
The article expands our understanding of social media
network stickiness—specifically on TikTok. While stud‐
ies on stickiness have previously focused on platforms
such as YouTube, Facebook, Twitter, and general apps
or websites, this study centres on TikTok, currently one
of the most important platforms and one that makes
Media and Communication, 2022, Volume 10, Issue 2, Pages X–X 7
use of video formats. It also provides some guidelines to
enhance investigation into video platforms, which are dif‐
ferentiated by the type of content that users upload and
consume, providing an opportunity to explore the rela‐
tionship between the range of variables and segments.
Acknowledgments
The authors want to thank the collaboration to the
Research Group of University of Alcalá: Consumer
Behaviour, Organisational, and Market Analytics.
Conflict of Interests
The authors declare no conflict of interests.
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About the Authors
Pedro Cuesta‐Valiño has a PhD in business and is a professor of marketing at Universidad de Alcalá
(Spain). His priority line of research is consumer and organizational behaviour. He is the author of
more than 80 research papers that have been published in several academic journals. He is also the
executive editor of International Journal of Internet Marketing and Advertising, and he is part of the
editorial board of International Journal of Consumer Studies,Spanish Journal of Marketing, and Revista
Portuguesa de Estudos Regionais.
Pablo Gutiérrez‐Rodríguez has a PhD in marketing and he is a professor and researcher at Universidad
de León. His research has been published in several academic journals, specialized in management
(Corporate Social Responsibility and Environmental Management,Journal Retailing and Consumer
Services, or Economic Research) and in numerous chapters published in the most relevant interna‐
tional editorials in economics. He is a regular lecturer at numerous national and international market‐
ing conferences.
Patricia Durán‐Álamo has a double degree in journalism and audiovisual communication from the Rey
Juan Carlos University of Madrid and specialized in communication and corporate identity from the
International University of La Rioja. She is a lecturer in the marketing and market research area of
the Universidad de Alcalá. As a professional, she manages the communication of companies through
offline communication strategies (press releases, PR, among others) and online (social networks, chat‐
bots, SEO, SEM, etc.) of technology‐based companies.
Media and Communication, 2022, Volume 10, Issue 2, Pages X–X 10