Next Article in Journal
Gradient-Based Automatic Exposure Control for Digital Image Correlation
Previous Article in Journal
The Product–Service System Supply Chain Capabilities and Their Impact on Sustainability Performance: A Dynamic Capabilities Approach
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 15
Issue 2
10.3390/su15021151
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Social Media Marketing as a Segmentation Tool

by
Jorge Serrano-Malebran
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
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(2), 1151; https://doi.org/10.3390/su15021151
Submission received: 22 September 2022 / Revised: 4 November 2022 / Accepted: 5 January 2023 / Published: 7 January 2023
Browse Figures
Review Reports Versions Notes

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.

1. Introduction

Social commerce is becoming “the definitive form of advertising”, where social networks 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, surpassing 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 platforms 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.
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 research 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 evaluation [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 traditionally 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 [23,24,25].

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 [27,28,29,30], social media ads [26,31,32], mobile ads [33,34,35], mobile shopping apps [36,37,38,39] and online shopping on mobiles [40,41,42].
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 [26,33,44,45,46]. 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 technology [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 organization, company or person [50]. From a marketing perspective, social media have become an important channel that is strongly aligned with advertising and marketing communications [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 influence 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 compiling 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 literature 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 potential 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 Mohammadi [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.

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 fashion products are classified as followers, innovators, opinion leaders and communicators, establishing differences at a global and segment level. In another study of fashion consumers, 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 information 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 participants, 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 literature 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. Table 1 and Table 2 shows the measurement scales for each construct.

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 [93,94,95]. 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 [97,98,99]. 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).
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].
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, R 2 specifies the amount of variance explained by the exogenous variables. Table 5 shows the results of R 2 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.
Finally, our objective is to characterize the two user segments by analyzing demographic characteristics such as age, concern for privacy, trust and online shopping. For this purpose, we performed an ANOVA analysis (Table 7).

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 explained 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 adoption 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.

References

  1. Rooney, J. The Ultimate Form of Advertising’ for CMOs—And What’s on the Horizon. 2019. Available online: https://www.forbes.com/sites/jenniferrooney/2019/03/20/why-social-commerce-is-becoming-the-ultimate-form-of-advertising-for-cmosand-whats-on-the-horizon/#2fc6e1596fac (accessed on 3 September 2022).
  2. Williams, R. How Mobile Ads Are Powering a Social Commerce Surge This Holiday Season. 2019. Available online: https://www.marketingdive.com/news/how-mobile-ads-are-powering-a-social-commerce-surge-this-holiday-season/567745/ (accessed on 3 September 2022).
  3. Donthu, N.; Gustafsson, A. Effects of COVID-19 on business and research. J. Bus. Res. 2020, 117, 284–289. [Google Scholar] [CrossRef] [PubMed]
  4. Insider Intelligence-eMarketer. Retail Ecommerce Sales Worlwide, 2019–2024. 2022. Available online: https://www.insiderintelligence.com/chart/242908/retail-ecommerce-sales-worldwide-2019-2024-trillions-change-of-total-retail-sales (accessed on 3 September 2022).
  5. Williamson, D.A. US Social Trends for 2020: eMarketer’s Predictions for the Year Ahead. 2019. Available online: https://www.insiderintelligence.com/content/us-social-trends-for-2020 (accessed on 3 September 2022).
  6. Seo, E.J.; Park, J.W. A study on the effects of social media marketing activities on brand equity and customer response in the airline industry. J. Air Transp. Manag. 2018, 66, 36–41. [Google Scholar] [CrossRef]
  7. Yadav, M.; Rahman, Z. Measuring consumer perception of social media marketing activities in e-commerce industry: Scale development & validation. Telemat. Inform. 2017, 34, 1294–1307. [Google Scholar] [CrossRef]
  8. Sharma, S.; Singh, S.; Kujur, F.; Das, G. Social media activities and its influence on customer-brand relationship: An empirical study of apparel retailers’ activity in India. J. Theor. Appl. Electron. Commer. Res. 2020, 16, 602–617. [Google Scholar] [CrossRef]
  9. Wang, X.; Wang, H.; Zhang, C. A Literature Review of Social Commerce Research from a Systems Thinking Perspective. Systems 2022, 10, 56. [Google Scholar] [CrossRef]
  10. Wibowo, A.; Chen, S.C.; Wiangin, U.; Ma, Y.; Ruangkanjanases, A. Customer behavior as an outcome of social media marketing: The role of social media marketing activity and customer experience. Sustainability 2020, 13, 189. [Google Scholar] [CrossRef]
  11. Eisenbrand, R.; Peterson, S. Megatrend Shoppable Ads & Social Commerce: How Platforms Are Aiming to Become the “Malls” of Tomorrow. 2019. Available online: https://omr.com/en/daily/megatrend-shoppable-ads/ (accessed on 3 September 2022).
  12. Worldwide HQ. Social Commerce 2022: Social Media and Ecommerce Convergence Trends Bring Growth Opportunity for Brands. 2019. Available online: https://www.insiderintelligence.com/insights/social-commerce-brand-trends-marketing-strategies/ (accessed on 3 September 2022).
  13. Bento, M.; Martinez, L.M.; Martinez, L.F. Brand engagement and search for brands on social media: Comparing Generations X and Y in Portugal. J. Retail. Consum. Serv. 2018, 43, 234–241. [Google Scholar] [CrossRef]
  14. Ladhari, R.; Gonthier, J.; Lajante, M. Generation Y and online fashion shopping: Orientations and profiles. J. Retail. Consum. Serv. 2019, 48, 113–121. [Google Scholar] [CrossRef]
  15. Davis, F.D. Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Q. 1989, 13, 319–340. [Google Scholar] [CrossRef] [Green Version]
  16. Lin, X.; Li, Y.; Wang, X. Social commerce research: Definition, research themes and the trends. Int. J. Inf. Manag. 2017, 37, 190–201. [Google Scholar] [CrossRef]
  17. Oleynikova, E.; Zorkin, Y. Social Commerce as a Driver of Sustainable Development of the Information Economy of the City. Procedia Eng. 2016, 165, 1556–1562. [Google Scholar] [CrossRef]
  18. Li, C.Y. How social commerce constructs influence customers’ social shopping intention? An empirical study of a social commerce website. Technol. Forecast. Soc. Chang. 2019, 144, 282–294. [Google Scholar] [CrossRef]
  19. Hsiao, M.H. Influence of interpersonal competence on behavioral intention in social commerce through customer-perceived value. J. Mark. Anal. 2021, 9, 44–55. [Google Scholar] [CrossRef]
  20. Hansen, J.M.; Saridakis, G.; Benson, V. Risk, trust, and the interaction of perceived ease of use and behavioral control in predicting consumers’ use of social media for transactions. Comput. Hum. Behav. 2018, 80, 197–206. [Google Scholar] [CrossRef] [Green Version]
  21. Ko, H.C. Social desire or commercial desire? The factors driving social sharing and shopping intentions on social commerce platforms. Electron. Commer. Res. Appl. 2018, 28, 1–15. [Google Scholar] [CrossRef]
  22. Lal, P. Analyzing determinants influencing an individual’s intention to use social commerce website. Future Bus. J. 2017, 3, 70–85. [Google Scholar] [CrossRef]
  23. Alalwan, A.A.; Rana, N.P.; Dwivedi, Y.K.; Algharabat, R. Social media in marketing: A review and analysis of the existing literature. Telemat. Inform. 2017, 34, 1177–1190. [Google Scholar] [CrossRef] [Green Version]
  24. Jung, A.R. The influence of perceived ad relevance on social media advertising: An empirical examination of a mediating role of privacy concern. Comput. Hum. Behav. 2017, 70, 303–309. [Google Scholar] [CrossRef]
  25. Lee, J.; Hong, I.B. Predicting positive user responses to social media advertising: The roles of emotional appeal, informativeness, and creativity. Int. J. Inf. Manag. 2016, 36, 360–373. [Google Scholar] [CrossRef]
  26. Lin, C.A.; Kim, T. Predicting user response to sponsored advertising on social media via the technology acceptance model. Comput. Hum. Behav. 2016, 64, 710–718. [Google Scholar] [CrossRef]
  27. Hajli, N.; Wang, Y.; Tajvidi, M.; Hajli, M.S. People, Technologies, and Organizations Interactions in a Social Commerce Era. IEEE Trans. Eng. Manag. 2017, 64, 594–604. [Google Scholar] [CrossRef] [Green Version]
  28. Makmor, N.; Aziz, N.; Alam, S. Social Commerce an Extended Technology Acceptance Model: The Mediating Effect of Perceived Ease of Use and Perceived Usefulness. Malays. J. Consum. Fam. Econ. 2019, 22, 119–136. [Google Scholar]
  29. Habib, S.; Hamadneh, N.N. Impact of Perceived Risk on Consumers Technology Acceptance in Online Grocery Adoption amid COVID-19 Pandemic. Sustainability 2021, 13, 221. [Google Scholar] [CrossRef]
  30. Ying, Z.; Jianqiu, Z.; Akram, U.; Rasool, H. TAM model evidence for online social commerce purchase intention. Inf. Resour. Manag. J. 2021, 34, 86–108. [Google Scholar] [CrossRef]
  31. Abed, S. An Empirical Examination of Factors Affecting Continuance Intention Towards Social Networking Sites. In Proceedings of the I3E: Conference on e-Business, e-Services and e-Society, Swansea, UK, 13–15 September 2016; Volume 9844, pp. 228–239. [Google Scholar] [CrossRef] [Green Version]
  32. Zhao, J.; Wang, J. Health advertising on short-video social media: A study on user attitudes based on the extended technology acceptance model. Int. J. Environ. Res. Public Health 2020, 17, 1501. [Google Scholar] [CrossRef] [Green Version]
  33. Bakar, M.S.A.; Bidin, R. Technology Acceptance and Purchase Intention towards Movie Mobile Advertising among Youth in Malaysia. Procedia Soc. Behav. Sci. 2014, 130, 558–567. [Google Scholar] [CrossRef] [Green Version]
  34. Bakr, Y.; Tolba, A.; Meshreki, H. Drivers of SMS advertising acceptance: A mixed-methods approach. J. Res. Interact. Mark. 2019, 13, 96–118. [Google Scholar] [CrossRef]
  35. Salem, M.; Baidoun, S.; Wady, R.; Alhajsaleh, N.; Hamouda, M. Factors affecting consumer attitudes towards SMS advertising in the Palestinian banking sector. The moderating role of national culture. J. Mark. Commun. 2022, 1–22. [Google Scholar] [CrossRef]
  36. Natarajan, T.; Balasubramanian, S.A.; Kasilingam, D.L. Understanding the intention to use mobile shopping applications and its influence on price sensitivity. J. Retail. Consum. Serv. 2017, 37, 8–22. [Google Scholar] [CrossRef]
  37. Natarajan, T.; Balasubramanian, S.A.; Kasilingam, D.L. The moderating role of device type and age of users on the intention to use mobile shopping applications. Technol. Soc. 2018, 53, 79–90. [Google Scholar] [CrossRef]
  38. Kasilingam, D.L. Understanding the attitude and intention to use smartphone chatbots for shopping. Technol. Soc. 2020, 62, 101280. [Google Scholar] [CrossRef]
  39. Ngubelanga, A.; Duffett, R. Modeling mobile commerce applications’ antecedents of customer satisfaction among millennials: An extended tam perspective. Sustainability 2021, 13, 5973. [Google Scholar] [CrossRef]
  40. Sohn, S. A contextual perspective on consumers’ perceived usefulness: The case of mobile online shopping. J. Retail. Consum. Serv. 2017, 38, 22–33. [Google Scholar] [CrossRef]
  41. Jain, N.K.; Kaul, D.; Sanyal, P. What drives customers towards mobile shopping? An integrative technology continuance theory perspective. Asia Pac. J. Mark. Logist. 2021. [Google Scholar] [CrossRef]
  42. Groß, M.; Sohn, S. Understanding the consumer acceptance of mobile shopping: The role of consumer shopping orientations and mobile shopping touchpoints. Int. Rev. Retail. Distrib. Consum. Res. 2021, 31, 36–58. [Google Scholar] [CrossRef]
  43. Chin, W.W.; Todd, P.A. On the Use, Usefulness, and Ease of Use of Structural Equation Modeling in MIS Research: A Note of Caution. MIS Q. 1995, 19, 237–246. [Google Scholar] [CrossRef]
  44. Gupta, M.; Sinha, N.; Singh, P.; Liébana-Cabanillas, F. Instagram Advertising among Young Consumers in Wearable Fitness Trackers: The Moderating Role of Technology Acceptance Factors. J. Glob. Mark. 2021, 34, 411–432. [Google Scholar] [CrossRef]
  45. Wang, G.; Tan, G.W.H.; Yuan, Y.; Ooi, K.B.; Dwivedi, Y.K. Revisiting TAM2 in behavioral targeting advertising: A deep learning-based dual-stage SEM-ANN analysis. Technol. Forecast. Soc. Chang. 2022, 175, 121345. [Google Scholar] [CrossRef]
  46. Guerreiro, J.; Loureiro, S.M.C.; Ribeiro, C. Advertising acceptance via smart speakers. Span. J. Mark. ESIC 2022, 26, 286–308. [Google Scholar] [CrossRef]
  47. Shaouf, A.; Lü, K.; Li, X. The Effect of Web Advertising Visual Design on Online Purchase Intention. Comput. Hum. Behav. 2016, 60, 622–634. [Google Scholar] [CrossRef]
  48. Gao, S.; Zang, Z. An empirical examination of users’ adoption of mobile advertising in China. Inf. Dev. 2016, 32, 203–215. [Google Scholar] [CrossRef]
  49. Tran, T.P. Personalized ads on Facebook: An effective marketing tool for online marketers. J. Retail. Consum. Serv. 2017, 39, 230–242. [Google Scholar] [CrossRef]
  50. Susanto, H.; Fang Yie, L.; Mohiddin, F.; Rahman Setiawan, A.A.; Haghi, P.K.; Setiana, D. Revealing social media phenomenon in time of COVID-19 pandemic for boosting start-up businesses through digital ecosystem. Appl. Syst. Innov. 2021, 4, 6. [Google Scholar] [CrossRef]
  51. Miranda González, F.J.; Rubio Lacoba, S.; Chamorro Mera, A.; Correia Loureiro, S.M. Determinantes de la intención de uso de Facebook en el proceso de decisión de compra. Investig. Eur. Dir. Econ. Empresa 2015, 21, 26–34. [Google Scholar] [CrossRef] [Green Version]
  52. Liang, T.P.; Turban, E. Introduction to the Special Issue Social Commerce: A Research Framework for Social Commerce. Int. J. Electron. Commerce 2011, 16, 5–14. [Google Scholar] [CrossRef] [Green Version]
  53. Huang, L.; Lu, X.; Ba, S. An empirical study of the cross-channel effects between web and mobile shopping channels. Inf. Manag. 2016, 53, 265–278. [Google Scholar] [CrossRef]
  54. Khan, I. Do brands’ social media marketing activities matter? A moderation analysis. J. Retail. Consum. Serv. 2022, 64, 102794. [Google Scholar] [CrossRef]
  55. Kim, Y.J.; Han, J. Why smartphone advertising attracts customers: A model of Web advertising, flow, and personalization. Comput. Hum. Behav. 2014, 33, 256–269. [Google Scholar] [CrossRef]
  56. Gao, L.; Waechter, K.A.; Bai, X. Understanding consumers’ continuance intention towards mobile purchase: A theoretical framework and empirical study – A case of China. Comput. Hum. Behav. 2015, 53, 249–262. [Google Scholar] [CrossRef]
  57. Schlosser, A.E.; Shavitt, S.; Kanfer, A. Survey of Internet users’ attitudes toward Internet advertising. J. Interact. Mark. 1999, 13, 34–54. [Google Scholar] [CrossRef]
  58. Celebi, S.I. How do motives affect attitudes and behaviors toward internet advertising and Facebook advertising? Comput. Hum. Behav. 2015, 51, 312–324. [Google Scholar] [CrossRef]
  59. Ramadhani, S.; Suroso, A.; Ratono, J. Consumer attitude, behavioral intention, and watching behavior of online video advertising on youtube. J. Apl. Manaj. 2020, 18, 493–503. [Google Scholar] [CrossRef]
  60. Hur, H.J.; Lee, H.K.; Choo, H.J. Understanding usage intention in innovative mobile app service: Comparison between millennial and mature consumers. Comput. Hum. Behav. 2017, 73, 353–361. [Google Scholar] [CrossRef]
  61. Ariely, D. Controlling the information flow: Effects on consumers’ decision making and preferences. J. Consum. Res. 2000, 27, 233–248. [Google Scholar] [CrossRef]
  62. Yim, M.Y.C.; Chu, S.C.; Sauer, P.L. Is Augmented Reality Technology an Effective Tool for E-commerce? An Interactivity and Vividness Perspective. J. Interact. Mark. 2017, 39, 89–103. [Google Scholar] [CrossRef]
  63. Coyle, J.R.; Thorson, E. The Effects of Progressive Levels of Interactivity and Vividness in Web Marketing Sites. J. Advert. 2001, 30, 65–77. [Google Scholar] [CrossRef]
  64. Fiore, A.M.; Kim, J.; Lee, H.H. Effect of image interactivity technology on consumer responses toward the online retailer. J. Interact. Mark. 2005, 19, 38–53. [Google Scholar] [CrossRef]
  65. Wu, G. The Mediating Role of Perceived Interactivity in the Effect of Actual Interactivity on Attitude Toward the Website. J. Interact. Advert. 2005, 5, 29–39. [Google Scholar] [CrossRef]
  66. Choi, Y.K.; Hwang, J.S.; McMillan, S.J. Gearing up for mobile advertising: A cross-cultural examination of key factors that drive mobile messages home to consumers. Psychol. Mark. 2008, 25, 756–768. [Google Scholar] [CrossRef]
  67. Robins, F. The marketing of 3G. Mark. Intell. Plan. 2003, 21, 370–378. [Google Scholar] [CrossRef]
  68. Bleier, A.; Eisenbeiss, M. The Importance of Trust for Personalized Online Advertising. J. Retail. 2015, 91, 390–409. [Google Scholar] [CrossRef]
  69. Enwereuzor, I.K. Capturing consumers’ experiences of unsolicited mobile advertising. Telemat. Inform. 2017, 34, 948–960. [Google Scholar] [CrossRef]
  70. Flanagin, A.; Metzger, M.; Pure, R.; Markov, A.; Hartsell, E. Mitigating risk in ecommerce transactions: Perceptions of information credibility and the role of user-generated ratings in product quality and purchase intention. Electron. Commer. Res. 2014, 14, 1–23. [Google Scholar] [CrossRef] [Green Version]
  71. Figueroa-Fadic, C.; Tello-Álvarez, C.; Veas-González, I.; Vidal-Silva, C.L. Razones para co-destruir en plataformas hoteleras usando boca a boca electrónico (e-WOM) negativo. Inform. Tecnol. 2021, 32, 37–46. [Google Scholar] [CrossRef]
  72. Laughlin, J.; MacDonald, J. Identifying market mavens online by their social behaviors in community-generated media. Acad. Mark. Stud. J. 2010, 14, 55–70. [Google Scholar]
  73. Kawakami, T.; Parry, M. The Impact of Word of Mouth Sources on the Perceived Usefulness of an Innovation. J. Prod. Innov. Manag. 2013, 30. [Google Scholar] [CrossRef]
  74. Sen, S.; Lerman, D. Why are you telling me this? An examination into negative consumer reviews on the Web. J. Interact. Mark. 2007, 21, 76–94. [Google Scholar] [CrossRef]
  75. Yan, Q.; Wu, S.; Wang, L.; Wu, P.; Chen, H.; Wei, G. E-WOM from e-commerce websites and social media: Which will consumers adopt? Electron. Commer. Res. Appl. 2016, 17, 62–73. [Google Scholar] [CrossRef]
  76. Mehrad, D.; Mohammadi, S. Word of Mouth impact on the adoption of mobile banking in Iran. Telemat. Inform. 2017, 34, 1351–1363. [Google Scholar] [CrossRef]
  77. Fuentes, C.; Svingstedt, A. Mobile phones and the practice of shopping: A study of how young adults use smartphones to shop. J. Retail. Consum. Serv. 2017, 38, 137–146. [Google Scholar] [CrossRef]
  78. Kang, J.Y.M.; Johnson, K.K. F-Commerce platform for apparel online social shopping: Testing a Mowen’s 3M model. Int. J. Inf. Manag. 2015, 35, 691–701. [Google Scholar] [CrossRef]
  79. Menon, R.V.; Sigurdsson, V.; Larsen, N.M.; Fagerstrøm, A.; Foxall, G.R. Consumer attention to price in social commerce: Eye tracking patterns in retail clothing. J. Bus. Res. 2016, 69, 5008–5013. [Google Scholar] [CrossRef]
  80. Scuotto, V.; Del Giudice, M.; della Peruta, M.R.; Tarba, S. The performance implications of leveraging internal innovation through social media networks: An empirical verification of the smart fashion industry. Technol. Forecast. Soc. Chang. 2017, 120, 184–194. [Google Scholar] [CrossRef]
  81. Workman, J.E.; Studak, C.M. Fashion consumers and fashion problem recognition style. Int. J. Consum. Stud. 2006, 30, 75–84. [Google Scholar] [CrossRef]
  82. Cardoso, P.R.; Costa, H.S.; Novais, L.A. Fashion consumer profiles in the Portuguese market: Involvement, innovativeness, self-expression and impulsiveness as segmentation criteria. Int. J. Consum. Stud. 2010, 34, 638–647. [Google Scholar] [CrossRef]
  83. Shim, S.; Bickle, M.C. Benefit Segments of the Female Apparel Market: Psychographics, Shopping Orientations, and Demographics. Cloth. Text. Res. J. 1994, 12, 1–12. [Google Scholar] [CrossRef]
  84. Bauer, H.; Neumann, M.; Barnes, S.; Huber, F. Segmenting cyberspace: A customer typology for the Internet. Eur. J. Mark. 2007, 41, 71–93. [Google Scholar] [CrossRef]
  85. Koç, T.; Akbıyık, A. Social Network Sites (SNS) and Their Irrepressible Popularity: Can They Really Cause an Addiction? In Managing Social Media Practices in the Digital Economy; IGI Global: Hershey, PA, USA, 2020; pp. 234–263. [Google Scholar]
  86. Tabassum, S.; Pereira, F.S.; Fernandes, S.; Gama, J. Social network analysis: An overview. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 2018, 8, e1256. [Google Scholar] [CrossRef]
  87. Serrano-Malebrán, J.; Arenas-Gaitán, J. When does personalization work on social media? a posteriori segmentation of consumers. Multimed. Tools Appl. 2021, 80, 36509–36528. [Google Scholar] [CrossRef]
  88. Subtel Chile. Séptima Encuesta de Acceso, Usos y Usuarios de Internet en Chile. 2016. Available online: https://www.subtel.gob.cl/wp-content/uploads/2015/04/Informe-VII-Encuesta-de-Acceso-Usos-y-Usuarios-de-Internet_VF.pdf (accessed on 3 September 2022).
  89. Swinyard, W. The hard core and Zen riders of Harley Davidson: A market-driven segmentation analysis. J. Target. Meas. Anal. Mark. 1996, 4, 337–362. [Google Scholar]
  90. Allred, C.; Smith, S.; Swinyard, W. E-shopping lovers and fearful conservatives: A market segmentation analysis. Int. J. Retail. Distrib. Manag. 2006, 34, 308–333. [Google Scholar] [CrossRef]
  91. Workman, J.E.; Cho, S. Gender, Fashion Consumer Groups, and Shopping Orientation. Fam. Consum. Sci. Res. J. 2012, 40, 267–283. [Google Scholar] [CrossRef]
  92. Gudergan, S.P.; Ringle, C.M.; Wende, S.; Will, A. Confirmatory tetrad analysis in PLS path modeling. J. Bus. Res. 2008, 61, 1238–1249. [Google Scholar] [CrossRef]
  93. Fornell, C.; Larcker, D.F. Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 1981, 18, 39–50. [Google Scholar] [CrossRef]
  94. Henseler, J.; Hubona, G.; Ray, P.A. Using PLS path modeling in new technology research: Updated guidelines. Ind. Manag. Data Syst. 2016, 116, 2–20. [Google Scholar] [CrossRef]
  95. Wright, R.T.; Campbell, D.E.; Thatcher, J.B.; Roberts, N. Operationalizing multidimensional constructs in structural equation modeling: Recommendations for IS research. Commun. Assoc. Inf. Syst. 2012, 30, 23. [Google Scholar] [CrossRef] [Green Version]
  96. De Souzabido, D.; Da Silva, D. Smartpls 3: Specification, estimation, evaluation and reporting. Adm. Ensino Pesqui. RAEP 2019, 20, 465–514. [Google Scholar]
  97. Gbongli, K.; Xu, Y.; Amedjonekou, K.M. Extended technology acceptance model to predict mobile-based money acceptance and sustainability: A multi-analytical structural equation modeling and neural network approach. Sustainability 2019, 11, 3639. [Google Scholar] [CrossRef] [Green Version]
  98. Li, W. The Role of Trust and Risk in Citizens’ E-Government Services Adoption: A perspective of the extended UTAUT model. Sustainability 2021, 13, 7671. [Google Scholar] [CrossRef]
  99. Sharmin, F.; Sultan, M.T.; Badulescu, D.; Badulescu, A.; Borma, A.; Li, B. Sustainable destination marketing ecosystem through smartphone-based social media: The consumers’ acceptance perspective. Sustainability 2021, 13, 2308. [Google Scholar] [CrossRef]
  100. Kumar, V.V.; Chatterjee, B.; Tankha, G. The Development and Validation of a Measure to Assess COVID-19 Stress among College Students for Sustainable Education. Sustainability 2021, 13, 9828. [Google Scholar] [CrossRef]
  101. Mohd Dzin, N.H.; Lay, Y.F. Validity and reliability of adapted self-efficacy scales in Malaysian context using PLS-SEM approach. Educ. Sci. 2021, 11, 676. [Google Scholar] [CrossRef]
  102. Permarupan, P.Y.; Al Mamun, A.; Samy, N.K.; Saufi, R.A.; Hayat, N. Predicting nurses burnout through quality of work life and psychological empowerment: A study towards sustainable healthcare services in Malaysia. Sustainability 2020, 12, 388. [Google Scholar] [CrossRef]
  103. Herlambang, H.; Fitri, A.D.; Mukminin, A.; Muhaimin, M.; Hidayat, M.; Elfiani, E.; Shafira, N.N.A.; Puspasari, A.; Tarawifa, S.; Salam, A.A.; et al. Quality assurance for sustainable higher education: Structural equation modeling. Sustainability 2021, 13, 4954. [Google Scholar] [CrossRef]
  104. Ejarque, A.T.; Campos, V. Assessing the economy for the common good measurement theory ability to integrate the SDGs into MSMEs. Sustainability 2020, 12, 10305. [Google Scholar] [CrossRef]
  105. Sethar, W.A.; Pitafi, A.; Bhutto, A.; Nassani, A.A.; Haffar, M.; Kamran, S.M. Application of Item Response Theory (IRT)-Graded Response Model (GRM) to Entrepreneurial Ecosystem Scale. Sustainability 2022, 14, 5532. [Google Scholar] [CrossRef]
  106. Becker, J.M.; Rai, A.; Ringle, C.; Völckner, F. Discovering Unobserved Heterogeneity in Structural Equation Models to Avert Validity Threats. MIS Q. 2013, 37, 665–694. [Google Scholar] [CrossRef] [Green Version]
  107. Ong, C.S.; Lai, J.Y. Gender differences in perceptions and relationships among dominants of e-learning acceptance. Comput. Hum. Behav. 2006, 22, 816–829. [Google Scholar] [CrossRef]
  108. Vermunt, J.; Magidson, J. Latent class cluster analysis. Appl. Latent Cl. Anal. 2002, 11, 89–106. [Google Scholar] [CrossRef] [Green Version]
  109. Steuer, J. Defining Virtual Reality: Dimensions Determining Telepresence. J. Commun. 1992, 42, 73–93. [Google Scholar] [CrossRef]
  110. Prasarnphanich, P.; Wagner, C. The Role of Wiki Technology and Altruism in Collaborative Knowledge Creation. J. Comput. Inf. Syst. 2009, 49, 33–41. [Google Scholar] [CrossRef]
Sustainability 15 01151 g001 550
Figure 1. Proposed model.
Figure 1. Proposed model.
Sustainability 15 01151 g001
Sustainability 15 01151 g002 550
Figure 2. Results of the proposed model. H = Hypothesis, * = p < 0.5, ** = p < 0.01, *** = p < 0.001, N.S. = not significant.
Figure 2. Results of the proposed model. H = Hypothesis, * = p < 0.5, ** = p < 0.01, *** = p < 0.001, N.S. = not significant.
Sustainability 15 01151 g002
Table
Table 1. Measurement scale (I).
Table 1. Measurement scale (I).
ConstructItemsSource
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)		The shoppable ads in the mobile application of Facebook
offers accurate information about the products
The shoppable ads in the mobile application of Facebook
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
Table 2. Measurement scale (II).
Table 2. Measurement scale (II).
ConstructItemsSource
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
The use of the shoppable ads in the mobile
application of Facebook will increase my
productivity
The use of the shoppable ads in the mobile
application of Facebook will increase my efficiency
The use of the shoppable ads in the mobile
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
Table
Table 3. FIMIX criteria.
Table 3. FIMIX criteria.
 1 Segment 2 Segments 3 Segments 4 Segments 5 Segments
AIC5530.0345385.7155330.5155288.7395269.292
AIC35551.0345428.7155395.5155375.7395371.292
AIC45571.0345471.7155460.5155462.7395480.29
BIC5617.9455565.7225602.6185652.9395718.589
CAIC5638.9455608.7225667.6185739.9395827.589
EN 0.5080.5110.6070.614
Table
Table 4. Segments size.
Table 4. Segments size.
 Segment 1 Segment 2 Total 
313173486
64.4%35.6%100%
Table
Table 5. Explained variance of endogenous variables ( R 2 ).
Table 5. Explained variance of endogenous variables ( R 2 ).
Constructs  Global    Seg. 1    Seg. 2  
PU0.5600.7740.284
PEOU0.2820.3950.181
ATSA0.4250.6920.123
BI0.5740.8040.311
AU0.2870.4110.117
Table
Table 6. Path model—multi-group analysis.
Table 6. Path model—multi-group analysis.
H GlobalSegment 1Segment 2Dif. (Seg1 vs. Seg2) p-Value
Pathp-ValuePathp-ValuePathp-ValueMGA-PLSParametric
H1PEOU-PU0.125 **0.0010.130 *0.0240.144 **0.0030.6000.800
H2PU-ATSA0.505 ***0.0000.508 ***0.0000.605***0.0000.9290.158
H3PEOU-ATSA−0.100 *0.015N.S0.1050.367 ***0.0001.000 ***0.000 ***
H4PU-BI0.348 ***0.0000.436 ***0.0000.640 ***0.0000.999 ***0.004 ***
H5ATSA-BI0.266 ***0.0000.268 ***0.0000.325 ***0.0000.8050.406
H6BI-AU0.033 ***0.0000.306 ***0.0000.782 ***0.0001.000 ***0.000 ***
H7INF-PU0.213 **0.0070.215 *0.011N.S0.5120.0520.112
H8INF-PEOU0.352 ***0.0000.349 ***0.0000.491 ***0.0000.8500.299
H9INT-PU0.113 **0.001N.S0.1130.254 ***0.0000.9470.119
H10INT-PEOUN.S0.254N.S0.1150.421 ***0.0001.000 ***0.000 ***
H11PERS-PUN.S0.054N.S0.115N.S0.1460.3160.651
H12PERS-PEOUN.S0.936N.S0.709N.S0.4390.2050.408
H13WOM-PU0.162 ***0.000N.S0.0520.392 ***0.0000.986 **0.028 **
H14WOM-PEOUN.S0.144N.S0.574−0.244 *0.0240.1040.203
H15TREND-PUN.S0.358N.S0.746N.S0.1880.7100.583
H16TREND-PEOU0.148 **0.002N.S0.1220.266 **0.0020.8210.363
H = Hypothesis, * = p < 0.5, ** = p < 0.01, *** = p < 0.001, N.S = not significant.
Table
Table 7. Descriptive analysis and p-values ANOVA.
Table 7. Descriptive analysis and p-values ANOVA.
VariableSegmentNMediap-Value
Age131330.50.049
217328.5
Total486 
Perceived usefulness13130.10510.000
2173−2181
Total486 
Trust1313−0.42030.004
21730.2290
Total486 
Internet purchases in the last year13132.2420.043
21731.823
Total4790.00000
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Serrano-Malebran, J.; Vidal-Silva, C.; Veas-González, I. Social Media Marketing as a Segmentation Tool. Sustainability 2023, 15, 1151. https://doi.org/10.3390/su15021151

AMA Style

Serrano-Malebran J, Vidal-Silva C, Veas-González I. Social Media Marketing as a Segmentation Tool. Sustainability. 2023; 15(2):1151. https://doi.org/10.3390/su15021151

Chicago/Turabian Style

Serrano-Malebran, Jorge, Cristian Vidal-Silva, and Iván Veas-González. 2023. "Social Media Marketing as a Segmentation Tool" Sustainability 15, no. 2: 1151. https://doi.org/10.3390/su15021151

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop