Original Research
Do Millennials’ Motives for Using
Snapchat Influence the Effectiveness
of Snap Ads?
SAGE Open
July-September 2023: 1–28
Ó The Author(s) 2023
DOI: 10.1177/21582440231187875
journals.sagepub.com/home/sgo
Nour El Houda Ben Amor1 and Mohamed Nabil Mzoughi2
Abstract
While the social media app Snapchat has increasingly attracted the attention of millennials around the world, there is limited
empirical research dealing with Snapchat marketing. For businesses, it is imperative to know how effective Snap Ads are at
reaching millennials. Based on the uses and gratifications theory (UGT), this study aims to fill this research gap by identifying
millennials’ motives for using Snapchat and analyzing how they affect their attitudes toward Snap Ads and the subsequent behavioral intentions. Some 265 Saudi, Snapchat-using students aged between 18 and 35 completed an online survey. Our data analysis employed exploratory and confirmatory factor analyses, as well as structural equation modeling. The results indicate that
respondents seek four forms of gratification from using Snapchat, namely information seeking, self-expression, entertainment,
and social interaction. Of these, only information seeking and entertainment were found to have significant positive influences
on attitudes toward Snap Ads, intentions to share then, and purchasing intentions. This research contributes to the theory by
proving that the UGT is suitable for gaining knowledge about consumer behavior on social media in general, as well as by proposing a framework for studying the persuasive effectiveness of Snap Ads in particular. From a practical perspective, this study
offers guidelines on how customized Snap Ads can be conceived for millennials, so they will not irritate them.
Keywords
Snapchat, millennials, uses & gratifications, intention to share Snap Ads, persuasion
Introduction
Having a strong presence on social media presents
opportunities to create value for customers and enhance
the brand–consumer relationship. This is why more than
80% of marketers invest hugely in social media in general and in digital advertising in particular (Faruk et al.,
2021; Ganguly, 2015; Gao & Feng, 2016; Gil-Or, 2010;
Lipschultz, 2017; Smith, 2011; Stelzner, 2016; Tucker,
2016). At the same time, researchers are continually
investigating how digital advertising in general, and
social media advertising in particular, can help persuade
consumers (Faruk et al., 2021).
Snapchat’s marketing potential has gained particular
attention among many practitioners and scholars. Wellknown brands like Sony, Sperry, Victoria’s Secret,
Universal Studios, and GoPro maintain continuous contact with their customers through Snapchat’s Discover
feature (Sashittal et al., 2016). Over the years, Snapchat
has grown impressively in terms of its number of users
and daily views on mobile devices (Frier, 2016; Hatch,
2018; Heine, 2016a; Murphy, 2018; Richter, 2016), and
maintaining this relies upon constantly satisfying users’
needs through this platform (Quinn, 2020). This has led
to Snapchat increasing its net worldwide revenues from
advertising (Snap Ads) to an estimated 2.62 billion US
dollars in 2021 (Statista Research Department, 2021a).
Snapchat engagement is driven by the fear of missing
out (FOMO), which is defined as ‘‘a pervasive apprehension that others might be having rewarding experiences
from which one is absent,’’ so it arouses ‘‘a desire to stay
continually connected with what others are doing’’
(Przybylski et al., 2013). In the Snapchat app, users’
1
Higher Institute of Computer Science and Management of Kairouan,
Kairouan, Tunisia
2
Dar Al Uloom University, Riyadh, Saudi Arabia
Corresponding Author:
Mohamed Nabil Mzoughi, Marketing Department, College of Business, Dar
Al Uloom University, P.O. Box 3535, Riyadh 13314-7222, Saudi Arabia.
Email: m.nabil@dau.edu.sa
Creative Commons CC BY: This article is distributed under the terms of the Creative Commons Attribution 4.0 License
(https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of
the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages
(https://us.sagepub.com/en-us/nam/open-access-at-sage).
2
interactions with brands are ephemeral in nature, but
they are constantly renewed as brands offer time-limited
promotions and photo/video collections that are only
available for 24 hr. Due to this short-lived content,
Snapchatters continually follow their favorite brands to
explore their updates, and they more quickly and frequently share those brands’ content (Utz et al., 2015).
This new Snapchat experience is attractive to businesses,
because it provides opportunities to differentiate and customize offers and customer relationships, whether to trigger a desired behavior or just provide information.
Furthermore, compared to traditional and other forms of
digital advertising, Snap Ads appears to be more efficient
at generating revenue (Hutchinson, 2021), so it will be
interesting to investigate the persuasiveness of Snap Ads.
Due to its relatively young population, which is
mostly aged between 26 and 34 years old, the Kingdom of
Saudi Arabia (KSA) has the highest annual growth rate
for social media users of anywhere in the world
(Radcliffe & Bruni, 2019). About 72.38% of its population is rated as active users of social networking sites
(SNSs), with a daily average of 3 hr of use (Alhubaishy
& Aljuhani, 2021). In 2019, global statistics classified
Snapchat’s monthly active audience in the KSA as the
largest in the world (Fonteneau, 2019), and in 2021, the
country was ranked fifth with a total of 19.7 million
Snapchat users (Statista Research Department, 2021b).
On the global level, Snapchat tends to attract women
more than men, and it is most popular amongst millennials, who are typically, at present, adults aged 18 to 34
(Heine, 2016b; Trending Value, 2017). In the United
States, for example, around 70% of Snapchat users are
women and typically under 34 years old, with 53% of
them being aged 18 to 24 (Aslam, 2021; MacMillan,
2013). Similarly in the KSA, of the 9 million active daily
Snapchat users, 57.9% are aged 18 to 34 with 55% being
women (Ampere Analysis, 2017; Saudi Gazette, 2018).
This trend was observed around the world in 2021, with
61% of active Snapchatters being female (Statista
Research Department, 2021c), and 12.5% of them being
aged 18 to 20 (Statista Research Department, 2021d).
The increase in the number of Snapchat users and
growing investment in Snap Ads in the KSA highlight
the powerful motives for adopting this application, as
well as the effects that these motives have on the persuasiveness of Snap Ads. From a marketing perspective,
Snapchat can help engage customers and drive purchases,
especially when Snap Ads cultivate emotional connections and positive attitudes (Phua et al., 2017b; Pavelle &
Wilkinson, 2020).
Taking into account the uses and gratifications theory, this present research sought to determine millennials’ motives (i.e., the gratifications sought) for using
SAGE Open
Snapchat and examine their impact on attitudes toward
Snap Ads and behavioral intentions.
The following section provides an overview of Snap
Ads’ relevance for marketing to millennials. It also discusses the importance of Snapchat users’ motivations
when investigating Snap Ads’ persuasiveness. It also proposes some hypotheses to link these motives with attitudes toward Snap Ads and behavioral intentions. The
second section then presents the research design, before
the third section details the statistical analyses. The
fourth and final section then presents the findings and
discusses their major implications on both the theoretical
and practical levels.
Literature Review, Theoretical Framework, and
Hypotheses Development
The strong potential of Snap Ads for promoting products
has attracted several brands in recent years, yet there is a
lack of research into the effectiveness of Snap Ads, particularly when it comes to exploring motives that lead to
sharing Snap Ads and how these motives influence consumers’ attitudes and purchasing intentions.
Snap Ads. Snapchat offers different advertising products
with specific marketing objectives, such as Snap Ads,
Filters, Lenses, and Discover, thereby offering an interesting alternative to TV advertising and other major
social media platforms.
A Snap Ad is a full-screen video advertisement with
customized attachments (e.g., web view, app install, longform, and article) based on the advertiser’s objectives
(Felicitas, 2022; L. Johnson, 2016; Snap Inc. Internal
Data, 2017). It offers interesting opportunities for advertisers and a new experience for customers. Snapchat makes
it easier to target particular clientele because it ‘‘takes context into account to serve up the Ad most relevant to the
user.’’ Furthermore, Snapchatters ‘‘can view a product’s
Snap Ad, can swipe up on the Snap Ad to buy the product instantly from the advertiser’s website without leaving
the Snapchat application’’ (McAlone, 2017). Overall,
Snapchat advertising represents a relevant tool for marketers due to its potential to acquaint users with brands
through emotional connections (Sashittal et al., 2016).
Nevertheless, sponsored Snapchat advertising can also
irritate users. A survey among 2,500 millennials and gen
Zers found that 74% of them hate seeing advertising in
their social media feeds, such that 56% of these audiences
decreased their social media usage time. This highlights
an interesting conundrum about how to advertise online
without annoying consumers (Whitman, 2016). There is
no doubt about the challenge of targeting the right audience with attractive and engaging content (W. Wang
Ben Amor and Mzoughi
et al., 2021). The uses and gratifications theory (UGT) of
E. Katz et al. (1973) offers a marketing perspective for
understanding customers’ motivations for engaging with
specific types of social media content (Malthouse et al.,
2013; Smock et al., 2011), so marketers can avoid annoying users and enhance the effectiveness of their Snap Ads.
Motives for Using Snapchat. In the social media context,
investigations have applied various social psychology
theories and concepts—such as social identity theory
(e.g., D. Lee et al., 2011; H.-L. Yang & Lai, 2011), social
capital theory (e.g., Choi & Scott, 2013), and the need to
belong theory (e.g., Ma & Ma & Chan, 2014; Ma &
Yuen, 2011)—in order to learn about users’ sharing
behaviors. Nevertheless, UGT has been the dominant
social science theory for studying how and why individuals use a specific medium (Smock et al., 2011), and it is
touted as being one of the most influential theories in the
field of communications research (C. A. Lin, 1998). For
many decades now, UGT has served as a cutting-edge
theoretical approach for studying mass communications
media, starting with those currently regarded as traditional (e.g., newspapers, radio, and television) and moving onto the emerging internet-based media (Hossain,
2019; Ruggiero, 2000). UGT seeks to identify the needs
and desires that drive an individual to use a particular
media channel (Smock et al., 2011). Its primary strength
lies in its ability to facilitate investigations of ‘‘mediated
communication situations via a single or multiple sets of
psychological needs, psychological motives, communication channels, communication content, and psychological gratifications within a particular or cross-cultural
context’’ (C. A. Lin, 1998).
Given that UGT is the most effective approach for
identifying the motivations underlying media channel
usage (Hossain, 2019; LaRose & Eastin, 2004; C. A. Lin,
1998; Ruggiero, 2000; Smock et al., 2011), as well as its
extensive application to examining the various motivations in social media (e.g., Kujur & Singh, 2020;
Malthouse et al., 2013), it should serve as a relevant theoretical foundation for this present study.
Motives for using the internet are key to understanding users’ reactions to internet advertising (K. C. Yang,
2004). Gratification emerges from satisfying various
needs by using a specific medium (C. S. Lee & Ma,
2012). According to the UGT, media users have a goaldriven approach in that they search out a medium that
will best meet their needs and reflect their specific
motives (Haridakis & Whitmore, 2006; Leung, 2013;
McLeod & Becker, 1981; West & Turner, 2007). The
UGT explains ‘‘how antecedent conditions lead to felt
needs, motives, attitudes, and behaviors, which produce
outcomes’’ (Cortese & Rubin, 2010), so motives for using
Snapchat depend on the user’s needs, and understanding
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these motives will help to adapt communications and
products to meet customers’ specific needs.
The available literature deals mainly with Facebook
and identifying the specific motives for using it. N. Park
et al. (2009) discovered four primary needs in the
Facebook context, namely information seeking, entertainment, socialization, and self-status seeking. In contrast,
Hunt et al. (2012) found only three major predictive
motives for frequent Facebook use, namely entertainment, interpersonal communication, and self-expression.
Krause et al. (2014) also highlighted three motivations in
the form of entertainment, communication, and habitual
diversion. Sheldon et al. (2021), meanwhile, posited that
the main reasons for using Facebook among baby boomers and traditionalists are diversion, entertainment, relationship maintenance, companionship, and the desire to
meet new people. Based on a review of 49 studies related
to sharing behavior in social media and other online contexts, as well as 53 studies that applied the UGT in the
social media context, Plume and Slade (2018) highlighted
six motivations for sharing tourism-related sponsored
advertising on Facebook: information seeking, entertainment, socializing, self-expression, information sharing,
and altruism. LaRose and Eastin (2004) revealed that
information seeking, entertainment, and social needs are
the most common use and gratification factors in relation
to SNSs. Similarly, C. S. Lee and Ma (2012) found that
the main factors are information seeking, entertainment,
socializing, and self-status seeking.
Regarding the popular social media platforms of
Facebook, Twitter, Instagram, and Snapchat, researchers
have found that the motives for using SNSs are associated with the features and functions offered by each
medium (Phua et al., 2017a, 2017b; Quan-Haase &
Young, 2010). In addition, users remain engaged with
SNSs for as long as their gratifications and needs are satisfied (Y.-C. Ku et al., 2013).
Phua et al., 2017a, 2017b; Quan-Haase & Young, 2010)
compared Facebook, Twitter, Instagram, and Snapchat in
terms of six gratifications: showing affection, following
fashion, demonstrating sociability, passing time, sharing
problems, and improving social knowledge. In terms of
following brands, Snapchat scored higher than the other
platforms according to the last three motives. The authors
therefore posited that Snapchat is the most useful for
entertainment, relaxation, and escape from everyday life.
Furthermore, due to its synchronous and personal nature,
Snapchat offers a means for forgetting problems and getting more involved in the social community. The comparison also revealed that individuals use Instagram more
frequently than Snapchat for showing affection, following
fashion, and demonstrating sociability.
By synthesizing the several studies that have applied
UGT to social and traditional media, Gao and Feng
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(2016) classified gratifications from social media into five
main categories: information seeking, entertainment,
social interaction, self-expression, and impression management. They found that social interaction with friends
through social media and impression management for
maintaining one’s self-image in a social group were perceived as being interconnected by respondents.
The above literature review has found that the four
dominant motivations are information seeking (IS),
entertainment (ET), social interaction (SI), and selfexpression (SE), and these antecede intentions to share
Snapchat Ads and the development of attitudes and purchasing intentions (Gao & Feng, 2016; C. S. Lee & Ma,
2012; N. Park et al., 2009).
Hypotheses Development. The dominant motivations that
drive Snapchat use represent a starting point for examining users’ attitudes toward Snap Ads (ASA), their intentions to share Snap Ads (ISSA), and their purchasing
intentions (PI).
The Impact of Information Seeking. Information seeking
(IS) refers to the search for information and the act of
learning how to make sense of things (Shao, 2009). By
navigating through social media, people can track information about sales, deals, products, events, and businesses (Al-Adwan & Kokash, 2019; J. W. Kim, 2014;
Kwak et al., 2010; Whiting & Williams, 2013). IS is a
motivation that embodies a need for resources and helpful information (Ashley & Tuten, 2015). Thus, information obtained directly from brands can be a major form
of gratification when involving consumers in online
brand communities, such as those on Snapchat (H. W.
Kim et al., 2011; Dholakia et al., 2004; Dunne et al.,
2010; C. S. Lee & Ma, 2012; K.-Y. Lin & Lu, 2011;
Muntinga et al., 2011; N. Park et al., 2009; Pavelle &
Wilkinson, 2020; Raacke & Bonds-Raacke, 2008). This
leads to the following hypothesis:
H1: IS is a significant motive for using Snapchat.
Information about products and events that is presented through advertisements on social media affect
consumers’ attitudes and influence their decisions
(Raacke & Bonds-Raacke, 2008; Rotzoll et al., 1996).
Gratification with social media information is therefore
positively related with attitudes toward product messages
(C. Chung & Austria, 2010, 2012), such that consumers
who use social media to search for information are likely
to feel positively about native advertising (J. Lee et al.,
2016). When IS is the motive for using Snapchat, it is
expected to lead to a favorable ASA. Indeed, Tiany
(2017) confirmed that social IS has a positive impact on
ASA. This leads to the follow hypothesis:
H2: IS as a motive positively affects ASA.
With the exception of the study of Hanson and
Haridakis (2008) that dealt with news sharing on
YouTube, most studies in online and social media contexts have confirmed that IS promotes sharing behavior
(Kairam et al., 2012; J. W. Kim, 2014; J. Lee et al., 2016;
Oh & Syn, 2015; Raacke & Bonds-Raacke, 2008).
H3: IS has a positive effect on consumers’ intentions
to share Snap Ads.
IS increases a customer’s knowledge about a brand
promoted on social media, and this helps the customer
to make better decisions and reinforces the PI (AlAdwan & Kokash, 2019; Hao et al., 2019; E. Katz et al.,
1973; Turcotte et al., 2015), leading to the following
hypothesis:
H4: IS has a positive effect on PI.
The Impact of Entertainment. ET in social media serves
to satisfy various consumer needs (Hung, 2014), such as
a desire to pass time, evade life’s daily routine, vent negative feelings, browse content, share experiences, and
engage in entertaining activities (P. R. Johnson & Yang,
2009; N. Park et al., 2009; Plume & Slade, 2018; QuanHaase & Young, 2010; Raacke & Bonds-Raacke, 2008;
Whiting & Williams, 2013; Zhao & Rosson, 2009). Social
media can be a source of ET, with individuals playing
games, listening to music or jokes, and watching humorous videos (Whiting & Williams, 2013). Furthermore,
interacting with others in an online network can be fun
and enjoyable (Y. C. Ku et al., 2013; Pai & Arnott,
2013). Due to the interactive resources on the platform,
ET via social media can be an immersive experience that
provides significant gratification (C. S. Lee & Ma, 2012;
N. Park et al., 2009; Yu et al., 2020). A comparison
between Facebook, Twitter, Instagram, and Snapchat
found that the last of these was the most useful for ET
(Phua et al., 2017b). This leads to the following
hypothesis:
H5: ET is a significant motive for using Snapchat
ET is positively related to attitudes toward advertising
(Ducoffe, 1995). The greater the enjoyment that is perceived from watching advertising, the more positive the
consumer’s attitude toward advertisements will be
(Blanco et al., 2010; Chowdhury et al., 2006; Tsang et al.,
2004). However, C. Chung and Austria (2010, 2012)
found that ET needs do not significantly influence attitudes toward the advertising content on social media. In
some real-life contexts, using social media has become a
Ben Amor and Mzoughi
common practice in everyday life, and this has weakened
its ability to provide ET (C. Chung & Austria, 2012). The
literature also confirms the existence of a significant relationship between ET motives and attitudes toward sponsored advertising (Celebi, 2015; Ducoffe, 1995, 1996; J.
Lee et al., 2016; Mukherjee & Banerjee, 2017; Oh & Syn,
2015; Plume & Slade, 2018; Turcotte et al., 2015; Zhou &
Bao, 2002), although the findings have been contradictory about the direction of this relationship. On
Snapchat, users can discover innovative entertaining content (Manjoo, 2016), but if they are seeking to satisfy ET
needs when they are suddenly interrupted by Snap Ads,
it can cause an adverse effect (Dehghani et al., 2016). In
this case, ET has a negative impact on ASA (Tiany,
2017). Nevertheless, Plume and Slade (2018) demonstrated that ET has a positive effect on consumers’ intentions to share tourism-related sponsored advertisements
on Facebook. This finding is consistent with previous
results that have shown that consumers who are motivated by ET express positive attitudes toward internet
advertising, and this in turn stimulates their intention to
share it (Celebi, 2015; Zhou & Bao, 2002). Overall, ET
helps explain online advertisement-sharing behaviors
(Kujur & Singh, 2020; Taylor et al., 2012). In SNSs,
brands exploit a platform to diffuse entertaining content
(e.g., sponsored advertising) that enhances interaction,
increases word-of-mouth among users about it (C. S. Lee
& Ma, 2012), and drives the motivation to share it (Hsieh
et al., 2012; Rohm et al., 2013). The present research
adopts the dominant opinion that ET has a positive relation with attitudes toward sponsored advertising on
social media and sharing behaviors, as expressed in the
following hypotheses.
H6: ET positively affects ASA.
H7: ET has a positive effect on consumers’ ISSA.
An entertaining experience on social media is easily
memorized and positively influences attitudes and PI
(Abadi et al., 2011; Cruz & Mendelsohn, 2010;
Topaloğlu, 2012; Zamzuri et al., 2018). In addition,
entertaining online content establishes a positive emotional connection between brands and users (Hudson
et al., 2015; Sheth & Kim, 2017), and this causes users to
share it with their peers and favorably affects their PI
(Dobele et al., 2007). Entertainment-based motives therefore positively affect consumer purchasing behaviors
(Arbabi et al., 2022; Ebrahimi et al., 2022a), as expressed
in the following hypothesis:
H8: ET has a positive effect on PI.
The Impact of Social Interaction. Satisfying the need to
belong to a group and engage in SIs represents the main
5
motivation that drives individuals to use social media
(Alghamdi & Plunkett, 2021; Baek et al., 2011; HennigThurau et al., 2004; Muntinga et al., 2011; N. Park et al.,
2009; Whiting & Williams, 2013). Through interactions,
social media platforms like Snapchat offer the opportunity to build and maintain relationships with people,
groups, and communities. During these interactions,
individuals often share information, knowledge, and
resources, both of a personal nature and in reference to
specific brands. In this sense, social media cultivates trust
and reciprocity among users as well as between users and
brands (Phua et al., 2017a, 2017b; Shipps & Phillips,
2013). This leads to the following hypothesis:
H9: SI is a significant motive for using Snapchat.
Social capital facilitates the SIs of users through social
media (Ghahtarani et al., 2020), leading to strong relationships that feature emotional kinship, trust, and social
support among individuals. This bonding social capital
has been found to be most prevalent in Snapchat, followed by Facebook, Instagram, and Twitter (Phua et al.,
2017a). SI also represents a way to generate online content and share experiences and information (Leung,
2009), and this process cultivates engagement, emotional
attachment, and cognitive involvement among a brand’s
virtual community (Brodie et al., 2013). It also
encourages individuals to develop positive attitudes
toward social network advertising (C. Chung & Austria,
2010; de Gregorio & Sung, 2010). Thus, SI gratification
through social media has a positive relationship with
attitudes toward media content (Chu, 2011; C. Chung &
Austria, 2010), such that the stronger the interaction
with a brand on an online platform, the greater the level
of participation and engagement with the content
(Hamilton et al., 2016; F. Y. Wang et al., 2007).
According to Plume & Slade (2018), SI has a positive
effect on consumers’ intention to share tourism-related
sponsored advertisements on Facebook. All the above
lead to the following hypotheses:
H10: SI positively affects ASA.
H11: SI has a positive effect on consumers’ ISSA.
SI, as an antecedent of emotional and behavioral outcomes, influences consumers’ attitudes and PIs (Coyle &
Thorson, 2001). In social commerce, users observe and
learn from their peers’ behaviors, and such activities
affect an online platform’s use and influence PIs (AlAdwan & Kokash, 2019; L. Chen et al., 2021; Ebersole,
2000; Isa et al., 2016; D. C. Li, 2011; Li et al., 2018;
Stafford & Stafford, 2001; Y. Wang & Yu, 2017). Any
gratification of the social interaction motive favorably
influences consumer purchasing behaviors (Arbabi et al.,
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2022; Ebrahimi et al., 2022a). In reality, the SI motive
can be a tool for both producing value and favorably
impacting buying decisions. In a social network, the
stronger the SI, the stronger the purchasing intention will
be (Ebrahimi et al., 2021), leading to the following
hypothesis.
H12: SI has a positive effect on PI.
The Impact of Self-Expression. Sharing opinions can be a
motive for using SNSs (X. Chen et al., 2015). A. L.
Williams and Merten (2008) found that several students
engaged in SNSs when they perceived them as a means
for SE. In reality, digital platforms enable users to
express their ideas, opinions, and preferences without
constraint, so they facilitate SE (van Dijck, 2013). The
following hypothesis is therefore proposed:
H13: SE is a significant motive for using Snapchat.
To convey their personalities to others and thereby
satisfy their need for SE, consumers associate their selfimage with specific brands’ online images (Cătălin &
Andreea, 2014), leading to positive attitudes (Pelham
et al., 2005). Furthermore, it has been shown that when
online advertising content is perceived to enhance a person’s self-image, it is more likely to be regarded positively
and accepted by that person (Taylor et al., 2012). In addition, if an advertisement’s content allows for self-referencing, it can satisfy the need for self-expression, and
favorable feelings are formed that improve the evaluation
of that advertisement (Sujan et al., 1993). Overall, if a
brand’s image in Snap Ads is perceived to match with the
user’s self-image, that user’s attitude toward these advertisements will be favorable, so:
Facebook. They speculated that this could be explained
by the nature of the sponsored advertising examined.
When looking to satisfy their need for SE, individuals do
not perceive the sharing of marketing generating content
(MGC) to be appropriate, and they prefer to share their
own tourism experiences. In general, consumers are
motivated to share an advertising message when they
perceive it as supporting their own concepts (Taylor
et al., 2012).
H15: SE has a positive effect on consumers’ ISSA
Individuals satisfy their need for SE by buying brands
that they perceive as being appropriate to their selfimage (Cătălin & Andreea, 2014). R. Yang (2018) found
that the perceived self-expressiveness of brand Filters on
Snapchat favorably stimulates PIs, while Flecha et al.
(2019) posited that SE through Snapchat increases
ephemeral user–brand interactions and triggers purchasing actions. Within social media communities, a user’s
SE has a positive relationship with the purchase of digital goods. Hence, the more engagement there is in satisfying SE, the greater the PI will be (Y. Kim et al., 2011;
H. Chen & Chen, 2020; J. Lee et al., 2012).
H16: SE has a positive effect on PI.
Relation Between Attitude and Behavioral Intentions. A
viewer of an online advertisement who experiences a
strong positive attitude toward the content will feel a
greater desire to share it with others. Thus, the attitude
toward an online advertisement is a key determinant of
the intention to share that ad (Huang et al., 2013; J. Lee
et al., 2013; Hsieh et al., 2012; J. Lee et al., 2016).
H17: ASA has a positive effect on ISSA.
H14: SE affects positively ASA.
The SE of users through SNSs acts as a means for
users to disclose themselves to others. This in turn
increases their inclination for online content sharing (N.
Chung et al., 2012; Trammell & Keshelashvili, 2005). R.
Yang (2018) showed that when advertisements on
Snapchat can satisfy SE, the sharing of them increases.
Flecha et al. (2019) demonstrated that gratifying millennials’ need for SE through Snapchat has a positive
impact on the sharing of content (e.g., product images,
promotional videos and events, filters, etc.) and participation. Plume and Slade (2018), meanwhile, suggested
that the sharing of tourism-related sponsored advertisements on SNSs is a way for users to express their personalities, although their empirical findings showed SE to be
the only motivation that did not influence the intention
to share tourism-related sponsored advertisements on
No previous studies have examined the relationship
between attitudes toward advertising and PIs for
Snapchat. However, H. Chen and Lee (2018) found that
young customers tend to have a positive attitude toward
Snapchat that in turn generates favorable feelings toward
the brands advertised on this platform, including for PIs.
More generally, a significant and positive relationship
between attitudes toward online advertisements and PI
has been confirmed in various studies (Chu et al., 2019;
Geetika et al., 2018; C. A. Lin & Kim, 2016; MeghisanToma et al., 2021).
H18: ASA has a positive impact on PI.
By helping users to share positive experiences, a social
media platform can stimulate the purchase of products
(Kaplan & Haenlein, 2010). When an SNS delivers a high
Ben Amor and Mzoughi
7
Figure 1. The conceptual model.
Source. Developed by the authors.
degree of satisfaction, the engagement and participation
of users increase along with their motivation to make an
online purchase (Aluri et al., 2016; Hall et al., 2017;
Hossain & Kim, 2020; Veloutsou et al., 2017). According
to Dones et al. (2018), when users are gratified through
Snapchat, it increases their satisfaction, and this in turn
encourages the quick sharing of Snap Ads and impulsive
purchasing decisions. The sharing of ephemeral Snap
Ads expresses the Snapchatters’ engagement and positive
experiences, potentially leading to an impulsive purchase
(Ho et al., 2019; Marjanovic, ; Dones et al., 2018; Flecha
et al., 2019; Wakefield & Wakefield, 2016).
H19: ISSA has a positive impact on PI.
In light of the hypotheses proposed above, a conceptual
framework was developed (Figure 1). This model involves
four exogenous constructs (i.e., IS, ET, SI, and SE) and
three endogenous ones (i.e., ASA, ISSA, and PI).
Research Methodology
To address the research objectives, a quantitative study
was conducted, with a survey being used to identify people’s motives for using Snapchat and assess their impact
on attitudes toward Snapchat Ads, their intentions to
share them, and their buying intentions.
Sample Selection and Data Collection. The investigation was
conducted among three Saudi universities located in
Riyadh, the kingdom’s capital, between April 15 and
April 30, 2020. A convenience sample of students was
therefore selected from King Saud University (KSU), AlImam University (IMAMU), and King Saud bin
Abdulaziz University for Health Sciences (KSAU-HS).
The population for this study was Saudi millennials
(Generation Y) aged 18 to 35 (Nur’afifah & Prihantoro,
2021; Palacios et al., 2021; Petruch & Walcher, 2021;
Yasri et al., 2020). According to Debevec et al. (2013),
the millennial generation is not homogeneous, because it
includes young and old subgroups. He suggested that the
first group covers those aged 18 to 24 years and the second one includes those aged 25 to 34 years.
Data were collected through a questionnaire, with the
participants being assured about the confidentiality and
anonymity of their data before being asked to give verbal
consent. The questionnaire was written in both English
and Arabic, and each respondent received links to these
two versions through Survey Monkey and Google Forms.
8
Despite respondents having the choice to answer in
Arabic or English, all the respondents completed the
Arabic version. Note that the Arabic versions of the measurement instruments resulted from a meticulous translation and back-translation procedure (McGorry, 2000).
A total of 265 valid responses were returned. In terms
of the ages of respondents, 92.4% were between 18 and
22 years old, and only 7.6% were aged between 23 and
35. For gender, 62.6% of the respondents were female
(N = 166) and 37.4% were male (N = 99). The proportions of students from the KSU, IMAMU, and KSAUHS universities were 66%, 19.3%, and 14.7%, respectively. The questionnaire included three questions that
dealt with the use of social media in terms of familiarity,
frequency of use, and seniority of use. Respondents were
also invited to express their degree of familiarity with five
social media platforms, namely Snapchat, Twitter,
YouTube, Instagram, and Facebook. All the respondents
identified as Snapchat users, and familiarity with
Snapchat was relatively high across all participants compared with the other social media platforms. The percentage of respondents claiming to be ‘‘very’’ to ‘‘extremely’’
familiar with Snapchat was 92%, which was higher than
with Twitter (91%), YouTube (90.6%), Instagram
(86.8%), and Facebook (42.3%). Only 14% of the participants used Snapchat at least once a day, while 65% used
it several times a day. Regarding their seniority with
using Snapchat, 73% of the respondents cited having a
Snapchat account for 3 years or more.
Measures of Reliability and Validity. The measurements for all
the constructs were taken from the literature, and their
wordings were adapted to reflect the use of Snapchat.
The 33 items related to the current research’s variables
were assessed on a seven-point Likert scale that ranged
from ‘‘1 = strongly disagree’’ to ‘‘7 = strongly agree.’’
The list of statements together with their corresponding
constructs is shown in Table A1 (see Appendix).
To ensure accurate results, it was necessary to evaluate
the dimensionality, reliability, and validity of the research
scales (Furr, 2011; Tavakol & Dennick, 2011). As a first
step, the dimensionality and the reliability of the measurements were verified by performing a descriptive analysis with the SPSS software. Next, the validity of the
measures was assessed through a first-order confirmatory
analysis with the Amos software (Roussel et al., 2002),
and this supported the results of the first step.
Principal component analysis (PCA) then confirmed
the unidimensionality of the seven scales. The extractions
of all items were mostly greater than 0.5, with the exception being the SE-related item ‘‘I can keep a record of my
life.’’ This had a communality equal to 0.448, so the item
was removed from the analysis (Evrard et al., 2003). The
scales’ reliability was measured using the Cronbach’s
SAGE Open
alpha coefficient to indicate the level of internal consistency among the items, with a Cronbach’s alpha greater
than .8 being preferred in a confirmatory study (Evrard
et al., 2003). Its values for the IS, ET, SI, SE, ASA,
ISSA, and PI variables were 0.889, 0.860, 0.907, 0.878,
0.944, 0.931, and 0.949, respectively, so the scales used in
this study could be considered reliable (DeVellis, 2012).
The PCA revealed the existence of seven factors, and
the confirmatory factor analysis (CFA) validated this
factorial structure. Through the CFA, the reliability was
re-verified with the Jöreskog’s Rhô, where 0.7 or above is
regarded as an acceptable value for a construct (Fornell
& Larcker, 1981; Didellon & Valette-Florence, 1996).
The standardized regression weights for all the items
were greater than 0.5, and the critical ratio (CR) associated with each factorial contribution was above 1.96, as
recommended by Byrne (2001). The convergent validity
was assessed by calculating the rhô of convergent validity.
According to Fornell and Larcker (1981), this index must
be greater than or equal to 0.5 for each construct. The
conditions for reliability and validity were respected, as
shown in Table A1 (see Appendix), so the dimensions
obtained for all the constructs were deemed to be reliable
and valid.
Hypotheses Verification Methodology. Hypotheses H1, H5,
H9, and H13 state that IS, ET, SI, and SE are relevant
motives for using Snapchat. First, a Varimax rotation
was performed to evaluate the internal structure of
Snapchat’s gratification by integrating items from the
scales purification. This procedure adjusts the coordinates of data produced by a PCA (Allen, 2017). Second,
CFA was performed using the Amos program to analyze
the reliability, convergent validity, and discriminant
validity of the model of Snapchat’s gratification.
Finally, structural equation modeling was used to test
the empirical model with the partial least squares technique (Hair et al., 2014) in the Smart PLS 3.3 software.
We then tested the remaining hypotheses by linking the
motives for using Snapchat to the endogenous variables
(i.e., ASA, ISSA, and PI) (see Figure 1). The partial least
squares structural equation modeling (PLS-SEM)
approach suited this study’s objectives because it is a
causal modeling method that aims to maximize the
explained variance of the dependent latent constructs.
Furthermore, ‘‘it provides more flexibility to explore and
experiment, in contrary to the Covariance-Based
Structural Equation Modeling’ objective that is to reproduce the theoretical covariance matrix, without focusing
on explaining variance’’ (Hair et al., 2017; Hair et al.,
2011). Additionally, the PLS-SEM approach supports
data testing in medium samples (Chin, 2010) and evaluates the results of the measurement model in addition to
Ben Amor and Mzoughi
those of the structural model. This method was therefore
appropriate for testing this research’s framework.
Results
Validation of Snapchat’s Gratification Structure. After purification and validation of the scales, 19 statements that
reflected reasons for using Snapchat were kept. These
items were subjected to PCA using Varimax rotation,
with the results indicating that two statements deviated
from the theoretical structure. Almost all items included
in one factor had loadings greater than 0.60 in that factor and below 0.40 in any other factor, with the exception being the item ‘‘I can get opinions and advice from
my friends.’’ For this item, a cross-loading (0.618–
0.525 = 0.093) with a difference of less than 0.15 from
the item’s highest factor loading was observed, so it was
removed from the analysis (Worthington & Whittaker,
2006). The second item of ‘‘I can express my ideas and
advice to friends’’ was expected to load on the SI factor,
but it had a higher loading on the SE one. This could be
because respondents consider that SE gratification happens not just by expressing their own ideas or opinions
but also when they are perceived as advice by others on
the social platform. In other words, by expressing their
thoughts through Snapchat, individuals hope to convey a
positive self-image. This behavior secures greater gratification when these thoughts are appreciated and considered as valuable advice.
After the structural purification, the KMO measure
of sampling adequacy (KMO = 0.908) and Bartlett’s
Test of Sphericity (x2 = 3193.536, df = 153, p = .000)
indicated that the sample had an appropriate size for the
factor analysis. The analysis yielded four factors that
contribute to explaining motives for using Snapchat, with
these explaining 72.64% of the variance. More specifically, the ‘‘IS’’ factor (a = .089) explained 43.905% of
the variance, while the ‘‘SE’’ factor (a = .901) accounted
for 14.464% of the overall variance. The ‘‘ET’’ factor
(a = .860) represented 7.840% of the variance, while the
‘‘SI’’ factor (a = .912) only explained 6.435% of the
overall variance. We can therefore deduce that the
motives for using Snapchat follow this hierarchy:
IS . SE . ET . SI.
CFA was used to validate the model that integrated
the four identified gratifications. The structural model is
shown in Figure 2. The results (Chi square/degree of
freedom = 1.771 \ 2, p = .000) yielded the standardized
regression weights that linked items to gratifications.
The RMSEA was around 0.05 (0.054), thus reflecting a
good fit (MacCallum et al., 2001; Steiger, 1990). The
GFI (0.913) was above 0.90, which again corresponds to
an acceptable fit (Jöreskog & Sörborn, 1989). The CFI
(0.969) was above the threshold of 0.9 (Bentler, 1990),
9
while the NFI (0.932) was greater than 0.8 (James et al.,
1982). In addition, the standardized root mean residual
value (0.048) was less than 0.08 (Hu & Bentler, 1999).
All these results confirm that the factor structure for
users’ motivations fits with the empirical data.
The reliability and the convergent validity of the two
modified factors were satisfactory. Indeed, the Jöreskog’s
Rhô was 0.917 and 0.896 for SI and SE, respectively,
whereas the Rhô of convergent validity was 0.786 and
0.635 for the same motives. Table A2 (see Appendix)
shows how the discriminant validity was confirmed by
verifying that the square root of every average variance
extracted (AVE) for each latent construct (in the diagonal) was greater than any correlation coefficient (i.e., offdiagonal) for any couple of latent constructs (Fornell &
Larcker, 1981). IS (F1), SE (F2), ET (F3), and SI (F4)
were found to represent significant motives for using
Snapchat, so H1, H5, H9, and H13 were accepted.
Effect of Snapchat’s Gratification on Persuasion
Assessment of the Measurement Model. The level of adequacy between the latent variables and their corresponding measurement items was assessed through individual
item reliability analysis, the convergent validity, and the
discriminant validity. The results in Table A3 (see
Appendix) demonstrate that all items except the ‘‘I use it
to entertain’’ item have loadings greater than 0.70. In
reality, however, when a scale is adapted from another
setting, a loading of 0.5 can be regarded as acceptable
(Chin, 1998), so all the items demonstrated a satisfactory
level of individual reliability (Fornell & Larcker, 1981).
The Cronbach’s alpha (CA), the composite reliability
(CR), and the reliability coefficient (Rho-A) were then
used to assess the measures’ reliability. Table A3 shows
that the CA and CR values were higher than the threshold value of 0.7 (Evrard et al., 2003; Nunnally, 1978) and
that the Rho-A statistics were greater than 0.8 (Henseler
et al., 2009), so all the constructs demonstrated good
reliability.
The average variance extracted (AVE) was then used
to assess the convergent validity of the latent variables.
The AVE values (see Table A3 in the Appendix) are
above 0.5 for all latent variables, thus confirming convergent validity (Fornell & Larcker, 1981).
Three tests were conducted to assess the discriminant
validity: First, an analysis of the cross-loading (Table
A4, Appendix) indicated that all the measurement items
have a higher correlation with the latent variable being
measured than with any other latent variable in the
model (Chin, 1998). Second, AVE analysis (Table A5,
Appendix) demonstrated that each latent variable shares
a greater variance with its own measure than it does with
the measures of other latent variables (Fornell &
Larcker, 1981). Finally, the Heterotrait–Monotrait
10
Figure 2. Confirmed factor structure for Snapchat’s gratification.
SAGE Open
Ben Amor and Mzoughi
11
Table 1. Path Coefficients and t-Statistics.
Hypothesis
Relationship
Standardized Beta (b)
t-Statistic (T)
p-value (p)
Inference
H2
H3
H4
H6
H7
H8
H10
H11
H12
H14
H15
H16
H17
H18
H19
IS ! ASA
IS! ISSA
IS! PI
ET! ASA
ET! ISSA
ET! PI
SI! ASA
SI! ISSA
SI ! PI
SE ! ASA
SE ! ISSA
SE ! PI
ASA ! ISSA
ASA ! PI
ISSA ! PI
.327
2.007
.090
.229
.095
.017
2.268
2.004
.036
.035
2.042
2.116
.772
.870
2.043
4.283
0.108
1.484
2.204
1.154
0.253
2.763
0.050
0.594
0.336
0.455
1.502
16.567
12.325
1.484
.000
.914
.138
.028
.249
.801
.006
.960
.553
.737
.649
.133
.000
.000
.593
Supported (S)
Non supported (N.S)
N.S
S
N.S
N.S
S
N.S
N.S
N.S
N.S
N.S
S
S
N.S
criterion values, which represent the similarity between
latent variables, are below 0.90 (Table A6, Appendix),
thus confirming discriminant validity (Hair et al., 2022;
Henseler et al., 2015).
Assessment of the Structural Model. With the robustness
of the measurement model having been confirmed, the
next step was to evaluate the structural model. This
involved examining the model’s predictive abilities and
the relationships among the latent variables. The path
coefficient (b value) and t-statistic value, the coefficient
of determination (R2), the effect size (ƒ2), and the predictive relevance of the model (Q2) were employed as standards for evaluating the structural model (Hussain et al.,
2018).
The model was validated by using a resampling
method to test the significance of the t-value of the path
coefficients through the nonparametric significance test
known as bootstrapping (Chin, 1998). The results of this
are presented in Table 1, and they reflect the significant
structural relationships among the study’s constructs.
Overall, of the 15 proposed hypotheses, five were confirmed at various significance levels.
Hypotheses 2, 3, and 4 posit that IS has a positive
effect on ASA, ISSA, and PI, respectively. The results
showed IS to have a significant direct effect on ASA
(b = .327; T = 4.283; p = .000) but not on ISSA
(b = 2.007; T = 0.108; p = .914) or PI (b = .090;
T = 1.484; p = .914). Thus, H2 was accepted, while H3
and H4 were rejected.
Hypotheses 6, 7, and 8 suggest that ET influences
ASA, ISSA, and PI, respectively. The results revealed
that ET significantly and positively affects ASA
(b = .229; T = 2.204; p = .028) but not ISSA (b = .095;
T = 1.154; p = .249) or PI (b = .017; T = 0.253;
p = .801). Consequently, H6 was accepted, but H7 and
H8 were rejected.
The findings also revealed that SI has a significant
negative influence on ASA (b = 2.268; T = 2.763;
p = .006) but not on ISSA (b = 2.004; T = 0.050;
p = .960) or PI (b = .036; T = 0.594; p = .553). Thus,
H11 and H12 were rejected, while H10 was also rejected
because SI as a significant motive for using Snapchat
was expected to have a positive impact on ASA.
H14, H15, and H16 link SE to ASA (b = .035;
T = 0.336; p = .737), ISSA (b = 2.042; T = 0.455;
p = .649), and PI (b = 2.116; T = 1.502; p = .133),
respectively, but these were all rejected due to a lack of
significance.
H17 and H18 assert that ASA has positive relationships with ISSA and PI, respectively, and the results did
indeed demonstrate that ASA positively and significantly
influences ISSA (b = .772; T = 16.567; p = .000) and PI
(b = .870; T = 12.325; p = .000), so H17 and H18 were
accepted.
Finally, no significant relationship was found between
ISSA and PI (b = 2.043; T = 1.484; p = .593), so H19
was rejected.
To evaluate the predictive power of the structural
model, the R2 value, which is also called the coefficient of
determination, was employed. This value explains the
variance in the endogenous variable(s) in the model
through its exogenous variable(s). R2 values of .75, .50
and .26 are considered substantial, moderate, and weak,
respectively, when determining the predictive capacity of
a structural model (Hair et al., 2013). Results indicated
R2 values of 15.8% (p = .000) for ASA, 55.4% (p = .000)
for ISSA, and 66.4% (p = .000) for PI. According to
Hair et al. (2013), the proposed research model demonstrated the weakest predictive power for attitudes to
SnapAds and the strongest predictive power for
12
SAGE Open
Table 2. Effect Size F2.
Path
IS !ASA
IS! ISSA
IS! PI
ET! ASA
ET! ISSA
ET! PI
SI! ASA
SI! ISSA
SI ! PI
SE ! ASA
SE ! ISA
SE ! PI
ASA ! ISSA
ASA ! PI
ISSA ! PI
Table 4. Model Fit.
F2
p-value (p)
0.079
0.000
0.017
0.029
0.007
0.000
0.032
0.000
0.000
0.001
0.002
0.010
0.982
0.698
0.004
.025
.969
.411
.165
.629
.933
.235
.968
.974
.946
.872
.551
.000
.000
.728
Table 3. Construct Cross-Validated Redundancy (Q2).
Construct
ASA
ET
IS
ISSA
PI
SE
SI
SSO
SSE
Q2 (=1-SSE/SSO)
1325.000
1060.000
1590.000
795.000
1325.000
1060.000
795.000
1164.082
1060.000
1590.000
405.670
603.121
1060.000
795.000
0.121
0.490
0.545
behavioral intentions. Nevertheless, it has been suggested
that a model where the R2 values are equal to, or greater
than, 10% has adequate predictive power (Falk & Miller,
1992).
The effect size F2 is the change in the R2 when an exogenous variable is removed from the model (Chin, 1998).
Cohen (1992) suggested that the effect size is weak when
the value is greater than 0.002, moderate if the value is
greater than 0.15, and strong if the value is greater than
0.35. Table 2 shows that removing the relationship
between ASA, ISSA, and PI has a substantial effect on
the R2 and improves the model greatly. Eliminating the
relation between IS and ASA also has an impact on the
R2, albeit a weak one because the F2 is 0.079 (\0.15).
The predictive relevance of the proposed research
model was calculated using the non-parametric Stone–
Geisser test (Q2). The Q2 value derives from a blindfolding procedure with an omission distance of 7, and it was
calculated using the cross-validated redundancy
approach (Hair et al., 2012) in order to establish the predictive relevance of the endogenous construct. If the Q2
values are above zero, this indicates that the model is
well reconstructed with good predictive relevance. Table
Estimated model
SRMR
d_ULS
d_G
Chi-Square
NFI
0.051
1.192
0.697
1084.153
0.847
3 indicates that the research model has predictive relevance since all the Q2 values are above zero.
Finally, the model fit was assessed by calculating the
standardized root mean square (SRMS) residual. This
index reflects the average degree of the differences
between the observed correlation and the hypothesized
covariance matrices. An SRMS value of 0.08 or lower
indicates a good fit (Chen, 2007; Hu & Bentler, 1999),
and the result in Table 4 indicates that the model has a
good fit (SRMR = 0.051).
Discussion and Conclusion
Key Findings. No previous study has proposed an empirical model for examining how the motives for using
Snapchat relate to ASA, ISSA, and PI simultaneously.
Thus, by adopting the UGT to identify the main motives
for using Snapchat, this study has helped to gain a better
understanding of how to promote products and increase
sales through Snapchat, specifically by providing insights
into how the motives for using Snapchat can predict
ASA, ISSA, and PI.
The findings reveal that Snapchat users seek various
forms of gratification to fulfill their needs and wants,
and this is consistent with previous research (H. Chen &
Lee, 2018; Dones et al., 2018; Phua et al., 2017a, 2017b;
Tiany, 2017). We found that the main motive for using
Snapchat is IS, followed by SE, ET, and SI.
With ET being placed in the third position, the
respondents clearly did not consider it a priority when
using Snapchat. This is somewhat surprising given that
Phua et al. (2017a) found Snapchat to be more useful for
ET than Facebook, Twitter, and Instagram.
Nevertheless, our finding concurs with that of Tiany
(2017), who found that ‘‘social IS’’ was ranked foremost
by Snapchatters. For Saudi millennials, obtaining useful
information quickly, easily, and at low cost is the leading
form of gratification for this Snapchat community.
SE emerged as the second most important motive for
using Snapchat, confirming that younger people frequently employ a higher level of self-disclosure on
Snapchat (Bayer et al., 2016; Larsen & Kofoed, 2015).
Saudi millennials, especially students, see Snapchat as a
Ben Amor and Mzoughi
digital platform where they can convey their ideas, opinions, and preferences without constraints (van Dijck,
2013; A. L. Williams & Merten, 2008). According to the
current research, expressing one’s personal information
(e.g., interests, feelings, etc.) to others via Snapchat leads
to SE-related gratification. This also occurs when users
perceive that their friends are benefiting from their selfexpressiveness (e.g., they find the advice useful).
Consequently, Snapchat contributes to building a virtual
self-identity through SE-related gratification, which is in
turn crucial for gaining peer acceptance and exchanging
social support (Shao, 2009).
The SI motive reflects a need to maintain contact with
friends, interact with them, show concern, and give support. It remains a form of gratification that is sought out
when using Snapchat, but the number of responses that
cite it as a motive for using Snapchat is low compared to
the three other motives. In reality, relationships on
Snapchat are more restrictive than they are on Twitter,
Instagram, and Facebook, because ‘‘snaps’’ are directed
at a select group of individuals who maintain close, private relationships with the user (Phua et al., 2017b).
Moreover, SIs on Snapchat are ephemeral in nature,
because they are based on transient content (Bayer et al.,
2016), such that users interact ‘‘for fun’’ (e.g., by sharing
funny pictures and selfies) rather than for privacy-related
or sexually motivated reasons (J. E. Katz & Crocker,
2015; Roesner et al., 2014; Utz et al., 2015).
In conclusion, the Saudi millennials conveyed that in
order of importance, IS, SE, ET, and maintaining close
SIs were the main gratifications they sought from
Snapchat use.
In line with earlier studies (e.g., Celebi, 2015; J. Lee
et al., 2016; Oh & Syn, 2015; Plume & Slade, 2018;
Turcotte et al., 2015; Zhou & Bao, 2002), IS and ET
have significant positive influences on ASA. In contrast,
Dehghani et al. (2016) found that ET negatively influences attitudes toward advertisements displayed on
YouTube. They explained this by saying that when users
are motivated to use an SNS for an ET purpose, but they
are interrupted by an irritating and irrelevant advertisement, they are more likely to reject that advertisement.
Thus, the negative relation between ET and attitudes
toward advertisements is due to the lack of congruency
between the expected gratification and the advertising
content. Tiany (2017) also found that ET negatively
influences ASA. Nevertheless, the findings of these two
studies cannot entirely dispute the dominant opinion
that ET is positively linked with attitudes toward advertising for a couple of reasons. First, the ET being sought
on YouTube differs in nature from that being sought on
social media platforms like Snapchat. On YouTube, people look for a good video-viewing experience, and advertising interruptions can degrade this. Being aware of the
13
irritation that advertising can cause, many social media
platforms have introduced a ‘‘skippable’’ form of advertisements. Second, Tiany’s (2017) research found that
the context combined with an advertisement’s content
can unfavorably shape attitudes. In the current study, it
was clear that the Saudi millennials have fun watching
Snap Ads, and they are satisfied with the information
provided by these ads.
In contrast to previous investigations (e.g., Celebi,
2015; J. Lee et al., 2016; Oh & Syn, 2015; Plume &
Slade, 2018; Turcotte et al., 2015; Zhou & Bao, 2002), IS
and ET were found to have no significant direct effect
on ISSA or PI. However, when examining the indirect
effects between these two gratifications and the dependent variables, the relationships were found to be statistically significant (Table A7, see Appendix), thus
highlighting the mediating role of ASA. Furthermore,
this is full mediation, such that the effects of IS and ET
are fully transmitted by ASA.
This study found that SE does not affect ASA, which
contradicts previous studies that have found that advertising content (e.g., brand image, a portrayed situation,
etc.) is evaluated favorably when it stimulates selfreferencing and thereby conveys a positive self-image (Y.
Kim et al., 2011; Sujan et al., 1993; Taylor et al., 2012).
Sujan et al. (1993) posited that when an advertisement’s
content matches the recipient’s self-structure, it gratifies
the SE motive and leads to strong positive feelings about
the ad, which results in a favorable attitude. This process
was not observed in our study, however, which suggests
that Saudi millennials do not perceive Snap Ads as a
means for self-expression.
In accordance with Plume and Slade (2018), no significant relationship was found between SE and the intention to share advertisements. When it comes to SE, Saudi
Millennials do not like sharing MGC, preferring instead
to share their own experiences with friends, brands,
firms, or events on Snapchat.
We found that the SE motive does not influence PI,
unlike in H. Chen and Chen’s (2020) study, where the
authors found that in social network games (SNGs), the
more that users are engaged in expressing their selfimage, the greater their PI for digital goods will be. This
is likely because the main way to express one’s self-image
on SNGs is to create a virtual self-image known as an
‘‘avatar’’ (S.-B. Park & Chung, 2011). Users therefore
invest in virtual goods to decorate their avatars, such as
buying clothes, weapons, accessories, and so on (Schau
& Gilly, 2003). In contrast, Snapchatters can satisfy their
need for SE without intending to buy the promoted
products.
Based on the above discussion, a plausible scenario
could explain why the SE motive does not influence
ASA, ISSA, or PI. Saudi millennials do not consider
14
Snap Ads as a means for SE, so they do not share them
or intend to buy the related product. To express themselves on Snapchat, Saudi Snapchatters prefer to share
their own real-life experiences with brands through the
use of other Snapchat features rather than Snap Ads:
For example, they may use Snap stories to publish personal videos and images, sponsored geo-Filters to add
information to stories by adding a specific geographical
location (e.g., a restaurant, brand event, etc.), sponsored
Lenses that allow linking a brand’s animation to personal images, and Discover, which offers compilations of
personal ‘‘Snaps’’ at events and locations. Overall, SNSs
users have many alternative means for expressing themselves: They can engage in online communications and
conversations (Belk, 2013; Muntinga et al., 2011), voice
opinions about a brand (Kokkoris & K€
uhnen, 2013),
create personal content or contribute to a brand-related
conversation online (Livingstone, 2008; Muntinga et al.,
2011; Schau & Gilly, 2003), or publish selfies (Eagar &
Dann, 2016; Kedzior et al., 2016; Pounders et al., 2016).
In summary, the ephemeral nature of Snapchat’s content
encourages a high degree of freedom and creativity. It
offers a platform for users to experiment with themselves
by trying different styles of presentation (J. E. Katz &
Crocker, 2015; Kwon & Kwon, 2014). Thus, Snapchat
has a significant capacity for gratifying users’ need for
self-expression, but this does not contribute directly to
the effectiveness of Snap Ads.
The present research confirms the divergence that has
been observed in the prior literature with regards to the
relationship between SI and attitudes toward sponsored
content on social media. C. Chung and Austria (2010)
found that gratifying the need for interaction on social
media has a positive impact on attitudes toward social
media marketing messages, while J. Lee et al. (2016)
showed that socialization-based motivation was not a
significant positive predictor of attitudes toward native
advertising.
Our study found that the SI motive actually negatively
influences ASA. In relation to this, Ko et al. (2005) posited that individuals with an SI motivation are more
strongly attached to human–human interactions (i.e.,
communicating with friends and advertisers) than they
are to human–message interactions (i.e., browsing and
sharing messages). They therefore demonstrated that SI
has a positive effect on human–human interaction but a
negative impact on human–message interactions. This
suggests that people using Snapchat for SI purposes are
more focused on exchanging ideas and communicating
with friends, so they are more likely to develop negative
attitudes toward Snap Ads because they interrupt the SI
process. Contrary to H11 and H12, SI was found to not
affect either the intention to share Snap Ads or purchasing intention. These findings are consistent with those of
SAGE Open
J. Lee et al. (2016), who showed that socialization-based
motivations do not affect the intention to share native
advertising. They also concur with those of Harun and
Husin (2019), which affirmed that SI does not influence
millennials’ online purchasing behaviors. In conclusion,
when Snapchat users are seeking to satisfy their need for
information, the appearance of Snap Ads in their feeds
leads to irritation and ultimately a negative attitude
toward that ad. This in turn makes them disinterested in
sharing the advertisement or buying the related product.
Nevertheless, our results indicate that ASA has a positive influence on the intention to share ads and purchasing intention, which concurs with the findings of a
number of authors (e.g., Flecha et al., 2019; Geetika
et al., 2018; Huang et al., 2013).
Moreover, it was expected that the more that individuals favorably evaluate advertisements, the more willing
they will be to share them with their online peers, as well
as intend to purchase the related product (Kenneth &
Kelly, 2020). Nevertheless, our results imply that sharing
Snap Ads does not influence purchasing intention.
Considering that brand identification is defined as ‘‘the
extent to which the consumer sees his or her own selfimage as overlapping with the advertised brand’s image’’
(Tuskej & Podnar, 2018), the persuasion occurs through
a peripheral route for people with a low level of brand
identification. Under such conditions, the advertisement
is evaluated based on its peripheral characteristics, such
as the background music or the celebrity endorsing the
product, rather than the message content in itself
(Kenneth & Kelly, 2020). This assumption is plausible
because the results demonstrated that the SE motive does
not affect the persuasiveness of Snap Ads (H14, H15,
and H16). Additionally, the attitudes formed through the
peripheral route are less strong and less consequential for
persuasion than those created through the central route
(Petty & Hinsenkam, 2017). Consequently, a favorable
ASA directly and positively influences the intention to
share ads and purchasing intention. However, this
impact is not transferred to PI via ISSA, because no significant indirect effect was found to exist between ASA
and PI (T = 0.526; p = .599, see Table A7, Appendix),
so ISSA clearly does not mediate the relationship
between these two variables.
In summary, having been tested with empirical data,
the proposed theoretical framework of our study offers
both theoretical and practical implications, and it also
opens up further opportunities for researching the effectiveness of Snap Ads as a commercial tool.
Implications, Limitations, and Future Directions. This study
makes a theoretical contribution to the literature by
adopting the UGT in order to propose a model for
examining the motives that drive people to use Snapchat
Ben Amor and Mzoughi
and explaining how various forms of gratification impact
the ASA, ISSA, and PI of users. This model can now be
applied by researchers and practitioners to better understand how sponsored advertisements can succeed in the
social media environment. Furthermore, this study validates the UGT for the social media context in addition
to identifying four motives for using Snapchat. The
results here demonstrate that not all these motives influence the effectiveness of Snap Ads, so to effectively promote their products, marketers should focus on Snap
Ads that satisfy the IS and ET needs of users. What is
more, users searching for information through Snapchat
can be irritated by Snap Ads and therefore develop a
negative attitude toward them, so identifying the motives
of the target customers can help brands to customize
their Snap Ads in order to avoid such irritation.
Organizations nurture their relationships with their
audiences through social media platforms in general and
particularly Snapchat. They need to develop a dialog that
is perceived as being vibrant and dynamic, and this can
be achieved through the continual use of new and/or
creative input (Kodish, 2015). Our study has revealed
that the content diffused through Snap Ads should fit
with users’ motives if they are to be successful. In reality,
young adults recognize that Snapchat facilitates congruent communication at the interpersonal level because it
allows for more privacy than other social media platforms (Vaterlaus et al., 2016), and this advantage should
be exploited by practitioners. Thus, to enhance the persuasiveness of Snap Ads, brands should create close, private relationships with users to facilitate congruent
communication, and this could be further reinforced by
customizing Snap Ads. This will be key to influencing
consumer behavior (Salamzadeh et al., 2022).
Marketers should also consider adopting co-creation
behaviors on Snapchat to create effective advertisements.
Moghadamzadeh et al. (2020) demonstrated that cocreation with customers on social media offers an opportunity to understand these customers and develop innovative services and products together with them. This
process can also be used to design innovative and appropriate Snap Ads that will contribute to improving the
brand experience on Snapchat. Rather than creating
advertisements on its own, a brand can ask Snapchatters
to propose ideas that will help its marketers to shape
their advertising. During the co-creation process, users
can express their motives for using Snapchat and enjoy
opportunities to enhance the brand experience and people’s willingness to share Snap Ads, as well as reinforce
buying behaviors. In effect, social media users engaged
in a co-creation process are participating as co-marketers. By contributing to the promotion of the brand, these
15
users make it more credible for their peers, and this in
turn increases purchasing intentions (Ebrahimi et al.,
2022b).
From a practical perspective, satisfying IS- and ETrelated needs seems to be crucial to the success of Snap
Ads among Saudi millennials. This observation agrees
with previous findings that have indicated that Snapchat
addiction derives from its ability to satisfy IS and ET
(Noë et al., 2019; Punyanunt-Carter et al., 2017). Since
millennial Snapchatters believe that Snap Ads gratify
these two needs, we expect that brand addiction can be
achieved by providing useful, informative, and enjoyable
Snap Ads. Brand addiction is defined as a ‘‘self-brand
relationship in daily life and involving positive affectivity
and gratification with a particular brand and constant
urges for possessing the brand’s products/services’’
(Mrad et al., 2020).
Snap Ads should attract customers’ attention, both
cognitively and emotionally, so marketers are urged to
target more effort at ensuring the quality of the delivered
information and the creative aspect of the message. They
should communicate up-to-date information and focus
on offering valuable propositions while applying entertaining techniques. The Snap Ads should then be perceived as a useful source of information and pleasure.
Indeed, Snapchat users need to feel that novelty and creativity are attached to the cognitive message being delivered. Snap Ads can therefore be useful for creating an
experience that provides both utility and joy, which in
turn will nurture positive attitudes and behaviors.
While this study has brought some clarity about
how the main motives for using Snapchat can shape
customers’ attitudes and behavioral intentions, some
limitations have affected this study, and these could be
addressed in future research. First, our study focused
on four important motives for using social media that
were extracted from the literature, but an exploratory
investigation could have determined motives that are
specific to the Snapchat context. By the same token,
the validated model could be applied to other contexts
in order to investigate the effect of cultural differences
among millennials. In addition, this study did not consider the impact of demographic factors (e.g., gender,
ethnicity, and socioeconomic status), so it would be
worthwhile to test whether such factors have a moderating influence.
Finally, this study considered only a limited number
of Saudi millennial students, so caution is needed in
generalizing the results to all the millennials in the
Saudi population. A future study could improve the
external validity by recruiting a larger and more representative sample.
16
SAGE Open
Appendix
Data Tables
Table A1. Constructs’ Reliability and Validity.
Constructs
Information seeking { IS}
(Source : Gao & Feng, 2016)
I can get a large amount of
information quickly and
easily.(IS1)
I can get useful
information.(IS2)
I can get information at a
lower cost.(IS3)
I can get information that I
am interested in. (IS4)
I can use it to collect
information for future use.
(IS5)
I can learn a lot. (IS6)
Entertainment {ET} (Source :
Gao & Feng, 2016)
I use it to entertain. (ET1)
I think it is fun. (ET2)
I feel excited when I use it.
(ET3)
I enjoy using it. (ET4)
Social interaction {SI} (Source :
Gao & Feng, 2016)
I can get information about
my friends. (SI1)
I can communicate and
interact with my friends.
(SI2)
I can show concern and
support to my friends. (SI3)
I can get opinion and advice
from my friends. (SI4)
I can express my ideas and
advice to friends. (SI5)
Self-expression {SE}(Source : Gao
& Feng, 2016)
I can express my personal
interests or preferences.
(SE1)
I can express my feelings.
(SE2)
I can post information about
myself to let others know
about me. (SE3)
I can express my ideas and
opinions. (SE4)
Attitude towards Snap Ads
{ASA} (Sources : J. Lee &
Hong, 2016; Taylor et al.,
2011)
I like banner product and
brand advertising on
Snapchat profiles. (ASA1)
Factors
Mean
SD
0.798
4.505
1.425
0.828
4.528
1.320
0.819
4.894
1.361
0.784
5.245
1.182
0.793
4.290
1.557
0.802
4.483
1.500
0.757
0.883
0.823
5.867
5.630
4.803
1.045
1.086
1.296
0.898
5.226
1.203
0.884
5.849
1.022
0.908
5.950
0.938
0.882
5.758
1.049
0.853
5.019
1.243
0.814
5.441
1.257
0.814
5.449
1.266
0.890
4.984
1.334
0.836
5.132
1.427
0.885
5.022
1.414
2.917
1.660
1
2
3
4
5
0.881
6
7
(continued)
Ben Amor and Mzoughi
17
Table A1. (continued)
Factors
Constructs
Information seeking { IS}
(Source : Gao & Feng, 2016)
1
I like Snap Ads videos created
by the sponsor company of
the product or brand.
(ASA2)
My overall attitude towards
Snap Ads is positive. (ASA3)
I feel positive towards Snap
Ads. (ASA4)
I react favorably to the Snap
Ads. (ASA5)
Intention to share Snap Ads
{ISSA} (Source : Plume & Slade,
2018)
I intend to share Snap Ads.
(ISSA1)
I expect to share Snap Ads.
(ISSA2)
I plan to share Snap Ads.
(ISSA3)
Purchase intention {PI}(Source :
Duffett, 2015)
I will buy products that are
advertised on Snapchat in
the near future. (PI1)
I desire to buy products that
are promoted on
advertisements on Snapchat.
(PI2)
Advertisements on Snapchat
have a positive influence on
my purchase decisions. (PI3)
I am likely to buy some of the
products that are promoted
on Snapchat. (PI4)
I plan to purchase products
that are promoted on
Snapchat. (PI5)
Cronbach alpha (a)
Jöreskog’s Rhô
Rhô of Convergent Validity
.889
0.891
0.577
2
.860
0.852
0.599
3
4
.907
0.910
0.672
.878
0.880
0.649
Mean
SD
0.862
2.954
1.678
0.893
2.966
1.584
0.898
2.867
1.588
0.896
2.758
1.252
0.949
3.294
1.704
0.961
3.320
1.703
0.935
3.143
1.695
0.927
2.871
1.602
0.930
2.811
1.612
0.893
3.056
1.667
0.909
3.347
1.612
0.901
2.947
1.611
5
.944
0.963
0.896
6
7
.931
0.927
0.717
.949
0.943
0.770
Table A2. AVE Analysis.
Factors
F1
F2
F3
F4
F1
F2
F3
F4
0.933
0.466
0.499
0.331
0.950
0.606
0.628
0.995
0.560
0.982
18
SAGE Open
Table A3. Reliability and Convergent Validity.
Constructs
Items
loading
CA
CR
AVE
Rho-A
IS
IS1
IS2
IS3
IS4
IS5
IS6
ET1
ET2
ET3
ET4
SI1
SI2
SI3
SE1
SE2
SE3
SE4
ASA1
ASA2
ASA3
ASA4
ASA5
ISSA1
ISSA2
ISSA3
PI1
PI2
PI3
PI4
PI5
0.731
0.746
0.713
0.721
0.785
0.848
0.671
0.784
0.825
0.848
0.851
0.935
0.866
0.742
0.818
0.838
0.814
0.822
0.747
0.866
0.913
0.918
0.927
0.933
0.906
0.941
0.932
0.868
0.823
0.878
0.891
0.890
0.576
0.893
0.862
0.864
0.616
0.871
0.914
0.915
0.783
0.917
0.879
0.879
0.646
0.881
0.931
0.931
0.732
0.935
0.944
0.945
0.850
0.945
0.950
0.950
0.791
0.951
ET
SI
SE
ASA
ISSA
PI
Table A4. Indicator Item Cross Loading (Chin, 1998’ Criterion).
ASA1
ASA2
ASA3
ASA4
ASA5
ET1
ET2
ET3
ET4
ISSA1
ISSA2
ISSA3
IS1
IS2
IS3
IS4
IS5
IS6
PI1
PI2
PI3
ASA
ET
IS
ISSA
PI
SE
SI
0.822
0.747
0.866
0.913
0.918
0.129
0.164
0.326
0.190
0.731
0.729
0.719
0.309
0.267
0.236
0.195
0.321
0.337
0.808
0.807
0.744
0.222
0.182
0.261
0.283
0.170
0.671
0.784
0.825
0.848
0.255
0.248
0.226
0.360
0.386
0.406
0.411
0.386
0.443
0.245
0.218
0.175
0.297
0.332
0.308
0.345
0.298
0.320
0.410
0.475
0.434
0.247
0.308
0.293
0.731
0.746
0.713
0.721
0.785
0.848
0.339
0.315
0.325
0.735
0.587
0.657
0.719
0.669
0.105
0.189
0.296
0.221
0.927
0.933
0.906
0.248
0.172
0.183
0.183
0.320
0.278
0.614
0.621
0.573
0.642
0.641
0.760
0.784
0.825
0.091
0.168
0.277
0.170
0.625
0.615
0.588
0.278
0.309
0.263
0.226
0.302
0.292
0.941
0.932
0.868
0.161
0.096
0.127
0.151
0.100
0.429
0.500
0.485
0.562
0.114
0.115
0.120
0.310
0.340
0.317
0.356
0.330
0.381
0.077
0.062
0.099
0.015
0.010
0.012
0.051
20.047
0.494
0.510
0.372
0.525
0.027
0.059
20.006
0.244
0.254
0.241
0.347
0.257
0.274
0.009
0.010
0.001
(continued)
Ben Amor and Mzoughi
19
Table A4. (continued)
PI4
PI5
SE1
SE2
SE3
SE4
SI1
SI2
SI3
ASA
ET
IS
ISSA
PI
SE
SI
0.684
0.763
0.065
0.136
0.123
0.149
0.014
0.019
20.010
0.193
0.184
0.421
0.518
0.576
0.508
0.527
0.569
0.500
0.324
0.332
0.307
0.382
0.311
0.435
0.304
0.336
0.299
0.565
0.564
0.042
0.090
0.127
0.140
20.005
0.038
0.044
0.823
0.878
0.012
0.057
0.082
0.110
0.006
0.010
0.005
0.046
0.083
0.742
0.818
0.838
0.814
0.551
0.612
0.598
0.031
20.015
0.578
0.532
0.550
0.480
0.851
0.935
0.866
Table A5. Discriminant Validity (Fornell & Larcker, 1981’ Criterion).
ASA
ET
IS
ISSA
PI
SE
SI
ASA
ET
IS
ISSA
PI
SE
SI
0.856*
0.262
0.368
0.788
0.857
0.148
0.009
0.785*
0.526
0.264
0.229
0.632
0.602
0.759*
0.307
0.367
0.447
0.354
0.922*
0.661
0.126
0.029
0.889*
0.083
0.008
0.804*
0.664
0.885*
SI
*The diagonals represent Square Root of the AVE.
Table A6. Discriminant Validity (HTMT Criterion).
ASA
ET
IS
ISSA
PI
SE
SI
ASA
ET
IS
ISSA
PI
SE
0.259
0.367
0.788
0.853
0.147
0.033
0.525
0.260
0.225
0.632
0.610
0.304
0.367
0.446
0.355
0.661
0.125
0.039
0.085
0.016
0.667
Table A7. Total Indirect Effects.
Relationship
IS !ASA
IS! ISSA
IS! PI
ET! ASA
ET! ISSA
ET! PI
SI! ASA
SI! ISSA
SI ! PI
SE ! ASA
SE ! ISSA
SE ! PI
ASA ! ISSA
ASA ! PI
ISSA ! PI
Standardized Beta (b)
T-Statistics (T)
p-value (p)
.252
.274
4.027
4.179
.000
.000
.176
.187
2.260
2.092
.024
.037
2.207
2.224
2.732
2.677
.006
.008
.027
.031
0.337
0.354
.736
.723
2.033
0.526
.599
20
SAGE Open
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with
respect to the research, authorship, and/or publication of this
article.
Funding
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this
article: The authors extend their appreciation to the Deanship
of Post Graduate and Scientific Research at Dar Al Uloom
University for funding this work.
Ethics Statement
All participants were fully informed why the research is being
conducted, that the anonymity is assured, and that the collected
data will be used exclusively for the research purpose. All participants gave verbal consents to fill out the questionnaire.
ORCID iD
Mohamed Nabil Mzoughi
8054-5526
https://orcid.org/0000-0001-
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