Social Media Marketing as a Segmentation Tool.pdf

Citation: Serrano-Malebrán, J.;
Vidal-Silva, C.; Veas-González, I.
Social Media Marketing as a
Segmentation Tool. Sustainability
2023, 15, 1151. https://doi.org/
10.3390/su15021151
Academic Editor: Jun (Justin) Li
Received: 22 September 2022
Revised: 4 November 2022
Accepted: 5 January 2023
Published: 7 January 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
sustainability
Article
Social Media Marketing as a Segmentation Tool
Jorge Serrano-Malebrá 1 , Cristian Vidal-Silva 2,* and Iván Veas-González 1
1 Department of Administration, Universidad Católica del Norte, Angamos 0610, Antofagasta 1270709, Chile
2 School of Videogame Development and Virtual Reality Engineering, Faculty of Engineering,
University of Talca, Talca 3460000, Chile
* Correspondence: cvidal@utalca.cl; Tel.: +56-9-62002702
Abstract: The aim of this study was to determine consumer segments based on the acceptance of
shoppable ads from fashion brands on online social media platforms. To achieve this objective, we
used the technology acceptance model (TAM) to offer a vision of the perceptions of the shoppable
ads, attitudes and behaviors of social network users, using social media marketing activities as
a background. Second, we searched for fashion social buyer segments using finite mixture partial
least squares (FIMIX-PLS). Third, we sought to characterize these consumer segments. A sample
of 486 users of social networks who accessed through mobile devices was obtained. The inclusion
of social media marketing variables as antecedents of acceptance allowed us, to a large extent, to
understand the intention to buy clothing by these social media users. The a posteriori segmentation
technique helps to identify different types of users who use shoppable ads and their relationship with
age and concerns about privacy, trust and purchases made on the Internet. The results show that,
based on the explained variance and model fit, the proposed variables allow us to explain acceptance,
with two groups of consumers within the sample being found.
Keywords: segmentation; technology acceptance; social media marketing; social commerce;
fashion brands
1. Introduction
Social commerce is becoming “the definitive form of advertising”, where social net-
works offer brands to convert images and videos into moments of purchase through buyable
ads [1]. Mobile devices have closed this gap between promotion and purchase, achieving
smoother and more frictionless experiences [2]. The COVID-19 pandemic has generated
substantial growth for Internet-based businesses as an increasing number of consumers
shop online [3]. By 2020, the global e-commerce market experienced a 27.6 increase, sur-
passing USD 4.28 trillion [4]. Despite these results, eMarketer [5] shows that shoppers are
not using social media as a shopping tool. However, social platforms maintain a relevant
role in stages prior to purchase as a search and influence tool that reaches consumers
through social media marketing. Social media marketing (SMMA) activities have achieved
wide acceptance in business by attracting the attention of companies due to their potential
to generate a greater consumption of their commercial offers [6], as SMMAs allow for
understanding customers and optimizing the company’s commercial strategy [7,8]. Social
media marketing facilitates interaction and an exchange of information, offers personalized
purchase recommendations and allows for recommendations among interested parties
on trend products [9]. In this context, as Wibowo et al. [10] describe, social media plat-
forms have experimented with new mobile-accessible social media ad formats to promote
their products and conduct business transactions such as “shoppable ads”. This format
of personalized ads is presented as promoted ads that the user sees in their social media
feed, characterized by calls to action such as “purchase buttons” from where users can buy
products directly [11]. This is why it is necessary to study the importance of social networks
and their effect on the acceptance of new forms of social commerce and advertising.
Sustainability 2023, 15, 1151. https://doi.org/10.3390/su15021151 https://www.mdpi.com/journal/sustainability

Sustainability 2023, 15, 1151 2 of 16
For consumers who have embraced these types of ads, apparel and fashion products
are the top product that users purchase through shoppable ads [12]. Buyers of fashion
products have different motivations and orientations, which require going deeper into with
the development of mobile devices and social media platforms [13]. Therefore, we believe
it is necessary to know if there are segments of fashion consumers on social networks
in relation to personalized ads. Fashion user segmentation has focused on consumers in
physical stores but not on online consumers [14]. This limitation constitutes a gap in the
knowledge of ads on social networks, especially the identification of different consumer
segments in front of this new format. The questions that guide our work are: what elements
of SMMA affect the acceptance of ads on social networks accessible through mobile devices?
Are there different profiles of users who use shoppable ads to buy fashion products through
social media?
The aim of this study is to determine consumer segments based on the acceptance of
buyable ads from fashion brands on social networks accessed through mobiles. To meet
this objective, the TAM technology acceptance model was used [15] to offer a vision of
the users’ perceptions of the shoppable ads, attitudes and behaviors, using the activities
of social media marketing as a background. Second, we searched for fashion consumer
segments using FIMIX-PLS. Third, we sought to characterize these consumer segments. The
main contribution of this work is that most TAM-based investigations do not address the
heterogeneity of consumer behavior. This work contributes to the research by addressing a
gap in the literature on new ad formats in the fashion industry affected by technological
and social transformations. In addition, its results contribute to the literature on social
media and social commerce, where ads play an important role [16,17].
To meet the proposed objective, this work is structured as follows. The next section
discusses the literature of the topics of this work: social commerce and ads, SMMA, TAM
and consumer segmentation. In the third section, the hypotheses are developed and the re-
search model is set out. The article continues with the methodology and results, concluding
with a discussion of this study’s theoretical contributions, conclusions and implications.
2. Literature review
2.1. The Role of Ads in Social Commerce
Social commerce represents a new form of e-commerce mediated by social networks
involving consumers and advertisers [16,18]. It involves various business activities that
help the consumer in the pre-purchase stages, purchase decisions and post-purchase evalu-
ation [16]. Social commerce is characterized by the use of social networking technologies,
online communities and e-commerce [19]. From this technological change, social media
give advertisers the possibility of offering commercial transactions, which have tradition-
ally been attributed to e-commerce [20]. The new commercial functionalities of social media
platforms are beginning to be accepted by the platforms, advertisers and users. Shoppable
ads use calls-to-action such as “buy buttons” in ads that appear in users’ feeds, allowing
for direct purchases without leaving the app [21,22].
Consumer behaviors towards these new functionalities must be studied because
people consider social networks to be part of their daily life and because the ads on these
platforms are highly personalized [23–25].
2.2. Technology Acceptance Model (TAM)
When analyzing the intention to use technology, Davis [15] proposed the technology
acceptance model (TAM) based on the theory of reasoned action (TRA) to explain the user’s
intention to use information technology. Lin and Kim [26] proposed that the TAM is a valid
typology for explaining the decision-making process of adopting an advertising innovation
in social networks. Hence, different experiences applying TAM exist in order to analyze
the acceptance of social commerce [27–30], social media ads [26,31,32], mobile ads [33–35],
mobile shopping apps [36–39] and online shopping on mobiles [40–42].

In the TAM model, the perceived utility and perceived ease of use are two fundamental
determinants of technology adoption [15]. Chin and Todd [43] demonstrated that users who
perceive a new technology to be useful or easy to use are more likely to adopt it. A positive
effect of perceived ease of use was also found in the perceived utility of a new technology.
The literature has supported the positive and significant relationship between the perception
of user-friendliness and attitude in the context of advertisements [26,33,44–46]. Davis [15]
describes the perception of ease of use as “the degree to which a person believes that using
a particular system requires little effort”. To the extent that a greater ease of use leads to
a better performance, ease of use would have a direct effect on the perceived usefulness
and attitude toward a technology.
According to TAM, the main antecedent and key mediator of the influence of other
variables on the intention of use is the attitude of a person toward the use of a technol-
ogy [15]. The relationship between attitude and intention has been validated in the field
of advertisements; for example, in web ads [47], in mobile ads [48] and in social media
ads [25,49]. As a result, attitude is expected to be a strong predictor of the use of trendy,
shoppable ads on mobile social media. The following hypotheses are proposed:
Hypothesis 1. Ease of use is positively related to the perceived usefulness of shoppable ads.
Hypothesis 2. Perceived utility is positively related to the attitude toward shoppable ads.
Hypothesis 3. Ease of use is positively related to the attitude toward shoppable ads.
Hypothesis 4. Perceived utility is positively related to the intention to use buyable ads.
Hypothesis 5. Attitude is positively related to the intention to use buyable ads.
Hypothesis 6. Intent to use is positively related to the current usage of shoppable ads.
2.3. Social Media Marketing Activities (SMMA)
Social media enable user interactions or connections with content created by an organi-
zation, company or person [50]. From a marketing perspective, social media have become
an important channel that is strongly aligned with advertising and marketing communi-
cations [51], where these platforms deliver features that allow ads and transactions [52].
Marketing in social networks attracts the attention of companies due to its potential to
generate a greater consumption of their commercial offers [25], and it is considered the
essence and one of the most important activities of social commerce [52,53]. Yadav and
Rahman [7] define SMMA as “a process by which companies create, communicate and
deliver online products/services offered through social media platforms”, and identify
the constructs that make up the perception of activities of social media marketing (SMM)
in the context of social commerce (social-commerce): information quality, interactivity,
personalization, trend and word of mouth recommendation. SMMA has the ability to influ-
ence brand experience, attitude and purchase intention towards the brand [54]. Below is
a review of the literature on the importance of each of these elements of SMM in the context
of shoppable ads.
2.3.1. Informativeness
The primary use of advertising for customers is to acquire product information [55].
The quality of the information reflects the beliefs of individuals about the information
capacity of mobile online stores, including the relevance, sufficiency and timeliness of the
content presented [56]. The literature indicates that the quality of information is a strong
predictor of the perceived usefulness of online stores through mobile phones [40]. Various
authors have evaluated the quality of the information as a precedent of ad acceptance due
to its effect on consumer perceptions, such as the perception of utility and the perception of

ease of use, in web ads [57], social media ads [25,58,59] and mobile ads [60]. The following
hypotheses are proposed:
Hypothesis 7. The informativeness is positively related to the perceived usefulness of the buyable ads.
Hypothesis 8. The informativeness is positively related to the perceived ease of use of the buyable ads.
2.3.2. Interactivity
Sponsored advertising on social media represents a place for interactive ads [26].
A greater interactivity provides consumers with the possibility of more effectively compil-
ing information about products by permitting the visual examining of virtual products [61].
In the context of new technologies in electronic commerce, Yim, Chu and Sauer [62] suggest
that there is a positive relationship between interactivity and the perception of utility.
Lin and Kim [26] indicate which elements of interactivity positively affect the perception
of usefulness and the perception of ease of use in ads on social networks. The litera-
ture suggests that interactivity has a positive effect on consumer perceptions of online
stores [63,64], websites [65] and in mobile advertising [66]. Therefore, we establish the
following hypotheses:
Hypothesis 9. Interactivity is positively related to the perceived usefulness of shoppable ads.
Hypothesis 10. Interactivity is positively related to the perceived ease of use of shoppable ads.
2.3.3. Personalization
Robins [67] found that mobile device users prefer personalized ads based on their
interests and relevance to them. Bleier and Eisenbeiss [68] point out that a greater depth of
personalization corresponds to a greater relevance of the content to the individual; therefore,
it should be seen as more useful. When customers receive personalized content, they are
more likely to focus on the content and not overlook the information and investigate the
details of the advertisement [55]. In the context of mobile ads, the personalization of
mobile advertising is positively related to consumer perceptions of ads [48,69] . Therefore,
the following hypotheses are presented:
Hypothesis 11. Personalization is positively related to the perceived usefulness of shoppable ads.
Hypothesis 12. Personalization is positively related to the perceived ease of use of shoppable ads.
2.3.4. Word of Mouth-WOM
In the pre-purchase stage, individuals seek information from others as a risk reduction
strategy [70,71]. In the post-purchase stage, consumers communicate verbally with a wide
variety of goals and motivations, primarily to help other consumers and prevent poten-
tial mistakes [72]. In this sense, Kawakami and Parry [73] point out that word-of-mouth
communications impact the perception of an innovation’s utility . In the context of product
reviews on the web, the literature suggests that the value given through WOM determines
the review’s perceived usefulness [74]. Yan et al. [75] showed that elements of e-WOM,
such as the volume of information and scores, have a positive effect on the perception of
the usefulness of e-commerce communications in social networks. Mehrad and Moham-
madi [76] show that word of mouth has a positive effect on the perception of utility, on the
perception of ease of use and on attitudes in the adoption of mobile banking. Therefore,
the following hypotheses are presented:
Hypothesis 13. WOM is positively related to the perceived usefulness of shoppable ads.
Hypothesis 14. WOM is positively related to the perceived ease of use of shoppable ads.

2.3.5. Trendiness
The idea of shopping involves gathering information on trends and fashion [77].
Consumers have accepted Facebook as a form of fashion advertising, and since most of its
members are young adults, they tend to be interested in finding unique styles by searching
for information on a brand’s pages and through recommendations. Kang and Johnson [78]
found that behaviors such as social searching (e.g., exploring new fashion / product trends)
have a positive effect on consumer perceptions. Various authors have evaluated trend
elements and their influence on the degree of acceptance of new fashion-related technology;
for example, fashion image search applications [60], social commerce [79] and in social
networks [78,80].
Hypothesis 15. Trendiness is positively related to the perceived usefulness of shoppable ads.
Hypothesis 16. Trendiness is positively related to the perceived ease of use of shoppable ads.
Figure 1 summarizes our research framework connecting the SMMA and TAM hypothesis.
Figure 1. Proposed model.
2.4. Consumer Segmentation
The literature review shows different ways to segment buyers of fashion products.
Based on fashion innovation, Workman and Studak [81] proposed that consumers of fash-
ion products are classified as followers, innovators, opinion leaders and communicators,
establishing differences at a global and segment level. In another study of fashion con-
sumers, Cardoso et al. [82] found three types of fashion consumers: enthusiastic, moderate
and apathetic consumers. Shim and Bickle [83] identified three fashion lifestyle segments:
practical, conservative and apathetic users. In the cases described, we can find similarities
in consumer segments. In addition, previous studies related to online purchases have
delved into consumer segmentation. In a study of online consumers, Barnes et al. [84]
described three segments: risk doubters, open-minded online shoppers and secretive infor-
mation seekers. In a study on targeting personalized ads on Facebook, Tran [49] identified
three market segments: ad lovers, ad ushers and ad haters.
3. Methodology
3.1. Sample
The data collection process was carried out through a survey aimed at users of mobile
social networks, specifically the Facebook mobile application. The collection was between
June and August in Chile. The choice of the Facebook mobile application was due to the
following reasons:
• Facebook has remained a social media platform with many users worldwide [84,85].
• Chile is an important country in terms of the percentage of reach of Facebook users [86].
• The Facebook mobile platform has pioneered commerce tools [87].

A sampling of quotas based on age and sex ranges was used to select the partici-
pants, the quotas being selected according to the profile of smartphone users in Chile [88].
To eliminate possible ambiguities in the questionnaire, a pilot was applied with 40 users
of social networks on mobile devices. After applying minor changes to the instrument,
face-to-face surveys were applied. The exclusion of invalid surveys provided a final sample
of 486 social media users on mobile devices. A total of 58.8% were women. The average
age was 30 years. The majority had high school studies, 18.9%, or university studies, 65.6%,
and 84.3% had made at least one purchase through the Internet during the last year.
3.2. Measurement Scales
The measures for the constructs in this study were adopted from the existing litera-
ture to fit the context of shoppable ads and social commerce. The variables—quality of
information, interactivity, personalization, WOM and trendiness—were adapted from the
work of Yadav and Rahman [7] on the perceptions of social media marketing activities
in the context of e-commerce. The measures of the TAM model were adapted from the
work of Davis [15] on the acceptance of information technologies. All of the scales were
measured with items on a 7-point Likert scale, ranging from “totally disagree” to “totally
agree”, except for socio-demographic variables and others related to purchases over the
Internet. Tables 1 and 2 shows the measurement scales for each construct.
Table 1. Measurement scale (I).
Construct Items Source
Interactivity
(INT)
The shoppable ads in the mobile application of Facebook
enables me to share and update about the existing ads
[7]
The brands in the mobile application of Facebook interact
regularly with the followers and fans
The brands in the mobile application of Facebook facilitate
communication with my family and friends
Informativeness
(INF)
offers accurate information about the products
offers useful information about the products
The information that is provided in the shoppable ads in
the mobile application of Facebook is comprehensible
Personalization
(PERS)
The mobile application of Facebook offers shoppable
fashion ads in accordance with my interests
I feel that my needs are satisfied using the shoppable
fashion ads in the mobile application of Facebook
The mobile application of Facebook facilitates the
personalized search for information
Trendiness
(TREND)
The shoppable ads in the mobile application of
Facebook are trendy
Using shoppable ads in the mobile application of
Facebook is trendy
Fashion products are available as shoppable ads in
the mobile application of Facebook
Word of mouth
(WOM)
I would recommend my friends to use shoppable
fashion ads in the mobile application of Facebook
I would encourage my friends and acquaintances to
use shoppable fashion ads in the mobile application
of Facebook
I would like to share my shopping experiences with
my friends and acquaintances in the mobile application of Facebook

Table 2. Measurement scale (II).
Construct Items Source
Perceived usefulness
(PU)
The shoppable ads in the mobile application of
Facebook can be useful in my life
[15]
The use of the shoppable ads in the mobile
application of Facebook enables me to carry
out transactions more quickly
application of Facebook will increase my
productivity
application of Facebook will increase my efficiency
application of Facebook will enable me to carry out
shopping tasks more quickly
Perceived ease of use
(PEOU)
Using shoppable ads in the mobile application of
Facebook does not require a great mental effort
I believe that I am capable of using shoppable
ads in the mobile application of Facebook without
the help of an expert
Learning to operate the shoppable fashion ads in
the mobile application of Facebook is easy for me
In general, I believe that the shoppable fashion ads
in the mobile application of Facebook is easy to use
Working with the shoppable fashion ads in the
mobile application of Facebook is not complicated;
it is easy to understand what is happening
Attitude toward use
(A)
In general, I like the shoppable ads in the mobile
application of Facebook
In general, I am in favor of the shoppable ads in the
mobile application of Facebook
In general, it seems to me that the shoppable ads in
the mobile application of Facebook is something good
Most of the shoppable ads in the mobile application
of Facebook are pleasant
Behavioral intention to use
(BI)
After seeing shoppable ads in the mobile application
of Facebook, I am interested in shopping
After seeing shoppable ads in the mobile application
of Facebook, I am willing to buy the product that is
advertised
After seeing shoppable ads in social networks, I will
probably buy the product that is advertised
Actual use
(AU)
I tend to use shoppable fashion ads in the mobile
application of Facebook often
I spend a lot of time using shoppable fashion ads
in the mobile application of Facebook
I make an effort to use shoppable fashion ads in
the mobile application of Facebook
3.3. Statistical Tools
Consumer-revealed segmentation can show potential segments for a product and
provide an understanding of the segment’s motives, lifestyles or needs [89]. Identifying
segments disclosed by the consumer from a set of variables can show different levels of
response from each segment to a product [90]. Individual differences in psychographic
characteristics can be found in groups with the same demographic characteristics, leading
to different purchasing processes [91]. To segment shoppable ads consumers, a posteriori

segmentation is proposed based on the latent constructs: the perception of trend, privacy
concern and purchase intention. For this segmentation, we first tested a model using
a structural equation modeling approach, specifically PLS-SEM, to evaluate the validity,
reliability and proposed hypotheses. This technique evaluates causal relationships between
latent variables [92]. The evaluation of the measurement and structural models was
according to the recommendations of the previous literature [93–95]. Then, in the second
step, we identified the consumer segments of shoppable ads using the finite segmentation
technique (FIMIX-PLS). This technique allows us to calculate the parameters and segments
of the observations simultaneously. In step 3, we compared the differences in the model
between the segments using PLS-multi-group analysis (MGA). We analyze the different
behaviors of each segment in the proposed model with this technique. Finally, in step
4, the resulting segments were characterized by analysis of variance (ANOVA) for the
variables: age, concern for privacy, trust and online purchases. Data were analyzed using
SmartPLS 4 statistical software [96].
4. Results
First, we assessed the reliability and validity of the scales to evaluate the measurement
model based on the recommendations for studies based on new technologies [97–99].
For each of the proposed model’s latent constructs, we reviewed each item’s latent loads
that presented loads more significant than 0.7 [100,101]. Then, we evaluated the criteria of
composite reliability using Cronbach’s alpha and average variance, whose values indicated
adequate convergent validity. Finally, this work evaluated the discriminant validity of
the model using the Fornell–Larcker and heterotrait–monotrait criterion [102,103], which
showed sufficient values for assessing the structural model. In the case of the structural
model, the results of the model adjustment using the standardized root mean square (SRMR)
indicator gave a value of 0.05, a value lower than the recommended value of 0.08, indicating
am adequate goodness of fit [104,105]. To evaluate segmentation through FIMIX-PLS, we
used the guidelines and criteria from [106,107]. This research conventionally applied the
Bayesian information criterion (BIC), the Akaike information criterion (AIC) and the Akaike
consistent information criterion (CAIC) [108] to determine the number of segments that
best fit the data. This allowed us to relate the various solutions of the models depending on
their precision and parsimony. Given that the AIC and AIC3 criteria decrease indefinitely,
we adopted the BIC and CAIC that reach their minimum value for two segments. Following
the previous recommendations [109] and according to the resulting size of the segments,
we chose the two-segment solution (Table 3).
Table 3. FIMIX criteria.
1 Segment 2 Segments 3 Segments 4 Segments 5 Segments
AIC 5530.034 5385.715 5330.515 5288.739 5269.292
AIC3 5551.034 5428.715 5395.515 5375.739 5371.292
AIC4 5571.034 5471.715 5460.515 5462.739 5480.29
BIC 5617.945 5565.722 5602.618 5652.939 5718.589
CAIC 5638.945 5608.722 5667.618 5739.939 5827.589
EN 0.508 0.511 0.607 0.614
Segment 1 is larger than segment 2 (Table 4), with 313 and 173 users, respectively.
The estimated structural model was evaluated through the standardized root mean squared
residual (SRMR), the path coefficients and the R squared values. The SRMR value is a rough
measure of the overall fit of the model. In our case, the model has an SRMR value of 0.051
at the global level, 0.048 for segment 1 and 0.061 for segment 2. These values indicates
a good general fit of the model [110].

Table 4. Segments size.
Segment 1 Segment 2 Total
313 173 486
64.4% 35.6% 100%
The path coefficients specify the intensity of the relationships between the independent
and dependent variables, while the R squared values determine the predictive power of
the structural models. Understood as multiple regression results, R2 specifies the amount
of variance explained by the exogenous variables. Table 5 shows the results of R2 globally
and for each group. Using the bootstrapping technique, we calculated the path coefficients
and t statistics for the relationships expressed in the hypotheses. After this, a multi-group
analysis was performed between the two segments using multi-group analysis (MGA-PLS)
to compare the differences between them. We can see in Table 6 that a large part of the
proposed hypotheses was confirmed except for hypotheses 10, 11, 12, 14, 15 and for the
global model. Figure 2 shows the results of the proposed model.
Table 5. Explained variance of endogenous variables (R2).
Constructs Global Seg. 1 Seg. 2
PU 0.560 0.774 0.284
PEOU 0.282 0.395 0.181
ATSA 0.425 0.692 0.123
BI 0.574 0.804 0.311
AU 0.287 0.411 0.117
Figure 2. Results of the proposed model. H = Hypothesis, * = p < 0.5, ** = p < 0.01, *** = p < 0.001,
N.S. = not significant.

Table 6. Path model—multi-group analysis.
H
Global Segment 1 Segment 2 Dif. (Seg1 vs. Seg2) p-Value
Path p-Value Path p-Value Path p-Value MGA-PLS Parametric
H1 PEOU-PU 0.125 ** 0.001 0.130 * 0.024 0.144 ** 0.003 0.600 0.800
H2 PU-ATSA 0.505 *** 0.000 0.508 *** 0.000 0.605*** 0.000 0.929 0.158
H3 PEOU-ATSA −0.100 * 0.015 N.S 0.105 0.367 *** 0.000 1.000 *** 0.000 ***
H4 PU-BI 0.348 *** 0.000 0.436 *** 0.000 0.640 *** 0.000 0.999 *** 0.004 ***
H5 ATSA-BI 0.266 *** 0.000 0.268 *** 0.000 0.325 *** 0.000 0.805 0.406
H6 BI-AU 0.033 *** 0.000 0.306 *** 0.000 0.782 *** 0.000 1.000 *** 0.000 ***
H7 INF-PU 0.213 ** 0.007 0.215 * 0.011 N.S 0.512 0.052 0.112
H8 INF-PEOU 0.352 *** 0.000 0.349 *** 0.000 0.491 *** 0.000 0.850 0.299
H9 INT-PU 0.113 ** 0.001 N.S 0.113 0.254 *** 0.000 0.947 0.119
H10 INT-PEOU N.S 0.254 N.S 0.115 0.421 *** 0.000 1.000 *** 0.000 ***
H11 PERS-PU N.S 0.054 N.S 0.115 N.S 0.146 0.316 0.651
H12 PERS-PEOU N.S 0.936 N.S 0.709 N.S 0.439 0.205 0.408
H13 WOM-PU 0.162 *** 0.000 N.S 0.052 0.392 *** 0.000 0.986 ** 0.028 **
H14 WOM-PEOU N.S 0.144 N.S 0.574 −0.244 * 0.024 0.104 0.203
H15 TREND-PU N.S 0.358 N.S 0.746 N.S 0.188 0.710 0.583
H16 TREND-PEOU 0.148 ** 0.002 N.S 0.122 0.266 ** 0.002 0.821 0.363
H = Hypothesis, * = p < 0.5, ** = p < 0.01, *** = p < 0.001, N.S = not significant.
Finally, our objective is to characterize the two user segments by analyzing demo-
graphic characteristics such as age, concern for privacy, trust and online shopping. For this
purpose, we performed an ANOVA analysis (Table 7).
Table 7. Descriptive analysis and p-values ANOVA.
Variable Segment N Media p-Value
Age
1 313 30.5
0.049
2 173 28.5
Total 486
Perceived usefulness
1 313 0.1051
0.000
2 173 −2181
Total 486
Trust
1 313 −0.4203
0.004
2 173 0.2290
Total 486
Internet purchases in the last year
1 313 2.242
0.043
2 173 1.823
Total 479 0.00000
5. Discussion
Using social media for business transactions is an emerging area of social commerce
that represents a challenge for both professionals and academics. To address this limitation,
the main objective of this research was successfully achieved by determining consumer
segments of shoppable ads based on the acceptance of fashion brand ads on social media
accessed via mobile devices. Next, the discussion will go deeper into the matter following
the operational objectives set out in the research.
To fulfill the first objective of this study, we evaluated an extended TAM model with
SMMA factors using a PLS-SEM structural equation modeling approach. The results of this
evaluation indicate that the proposed model explains the acceptance of shoppable ads in
mobile social media. This statement is based on the reliability and validity of the scales
and the values of explained variance. The results show that the set of selected variables can
explain 28.2% of the perceived ease of use, 42.5% of the attitude towards shoppable ads,
56% of the perceived usefulness, 57.4% of the intention of use and 28.7% of current use.
Regarding the SMMA variables and their relationship with perceived usefulness and
perceived ease of use, we found four cases. The first refers to informativeness, which has
a positive and significant relationship with both perceptions. Sohn [40] indicates that the
aspects related with information are important when the perceptions of mobile online
shops are evaluated. The second is the case of personalization, which is not related to
any perception. This could be explained because when people are exposed to highly
personalized ads on social media, they think that marketers track their information and

use it for marketing purposes, which raises concerns about consumer privacy [25]. Third,
interactivity and WOM have significant effects on perceived usefulness but not on the
perceived ease of use. In this context, ref. [66] showed that the interactive characteristics of
ads play an important role in the perceptions of mobile advertising. Finally, the trendiness
has a significant effect on the perceived ease of use but not on perceived usefulness.
Hur et al. [60] point out that the users who are interested in new fashion articles adopt
fashion services simply because they are fun to use instead of being beneficial or useful.
The global results mask what happens within the groups and therefore the second
objective of this study is more important. This sought to identify the profiles of fashion
consumers based on their perception of personalization through segmentation via an FIMIX-
PLS analysis. We found two segments. When analyzing them independently, interesting
differences emerge. Segment 2, with respect to TAM variables, is more influenced by
antecedent variables, and also its attitude is the result of the perceived utility and perceived
ease of use. Despite this, the explained variance is much higher for segment 1. Therefore, it
is more important to try to explain the behaviors of the segments.
Finally, it sought to explain the segments through variables not considered in the
proposed model: age, concern for privacy, trust and purchases in the last year. The ex-
plained variance of behavioral intention improves when evaluating the results of each
segment compared to the global evaluation. Next, we will discuss the main findings of
the segmentation.
• Segment 1: Rational Shoppers.
This is the largest segment: 64.4% of the sample belongs here. With respect to the
antecedent variables of SMMA, it only presents statistically significant relationships
for the relationship between information quality with perceived ease of use and
perceived usefulness. According to the results of the ANOVA analysis, this segment
is characterized by an average age greater than that of segment 2 (30.5 years), a high
concern for privacy and low confidence in shoppable ads, although its average online
shopping is higher. In general, this segment values the information presented at the
time of making purchases because it allows the subjects to reduce the risk caused
by using shoppable ads on social networks via mobiles. In addition, this group of
consumers has similar elements to “apathetic” [82] and to “ad haters” [49] due to the
fact that, in the shopping activity, they are very prudent and do not present impulsive
behaviors when faced with fashion advertisements.
• Segment 2: Occasional Shoppers.
This segment is the smallest, representing 35.6% of the sample. The results show
that the antecedent variables have a greater impact on acceptance than segment 1.
Interactivity and WOM have an effect on the perceived utility. With regard to the
perceived ease of use, the information quality, interactivity, trendiness and WOM have
an effect. The results of the ANOVA analysis show that this segment is younger, has
less of a concern for privacy, a greater trust in shoppable ads and generates fewer
online purchases than segment 1. This segment is more influenced by the variables
proposed as the background of acceptance. This group has a similarity with the
segment “moderate” [82] and with “ad lovers” [49], as they have a greater behavioral
intent after seeing a personalized ad for fashion products.
6. Implicationes for Academia, Managers and Society
For the academia, the work adds knowledge for future studies that explore the adop-
tion behavior of consumers toward the online clothing trade through the use of social
networks. As Yadav and Raman [7] proposed, the SMMA scale should be tested in various
industry contexts. We tested the scale in the context of shoppable mobile social ads and
fashion products. Including SMMA variables as antecedents of acceptance can greatly
assist in understanding the purchase intent for apparel. The application of a posteriori
segmentation helps to understand, in a better way, the different types of users exposed to
buyable ads on social networks accessed through mobile phones and their relationship with

age, concern for privacy, trust and purchases made on the Internet. While the measures
and scales were tested in a fashion brand mobile commerce context, the methodology can
be applied to other types of products or services.
For the industry, the first implication for fashion clothing companies is that it is
observed that, at a global level and for each segment, the variables of the SMMA produce
different effects on the acceptance of mobile social commerce of clothing and on the
intention of purchase. This suggests that consumers value that apparel ads contain valuable
information, are interactive, contain trending elements and produce WOM when browsing
their social media via mobiles. Although the literature indicates that highly personalized
ads can cause privacy concerns, the results show that, for this sample, personalization has
no effect on the acceptance of shoppable ads.
7. Limitations and Future Lines of Research
The results of this study must be viewed in the context of its limitations. First, this
research used data from a country undergoing technological development; the results could
vary in a country already having a high technological development. Second, the study was
applied to only one social network, Facebook, but it works on multiple platforms. Third,
this study only focused on advertisements for fashion brands.
In the future, the study of user behaviors regarding the use of social commerce on
mobile devices in technologically developed countries should continue to be studied in
depth. One must evaluate and compare other purely mobile social media sites such as
Instagram or Tik-Tok. Finally, it is necessary to evaluate other types of brands or products
(e.g., sports brands, health, consumer technology, etc.).
Author Contributions: Conceptualization, J.S-M. and I.V.-G.; methodology, J.S.-M.; software, C.V.-S.;
validation, J.S.-M., I.V.-G. and C.V.-S.; formal analysis, C.V.-S.; investigation, J.S.-M.; writing—original
draft preparation, J.S.-M.; writing—review and editing, C.V.-S. All authors have read and agreed to
the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Data available on request from the authors.
Conflicts of Interest: The authors declare no conflict of interest.
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