In today’s environment, higher educational institutions edge their policies to increase student enrolment due to marketisation (Faham *et al.*, 2017; Yu *et al.*, 2018). To address concerns related to student recruitment and partnership development, branding has established itself as a key method (Chapleo, 2011; Hemsley-Brown & Goonawardana, 2007). Countries with renowned education systems, such as France and Canada, serve as prime examples. France’s education system, which caters to over 2.7 million students, generates approximately 30 billion Euros annually and stands out due to its substantial state support and focus on international markets (Campus France, 2022). Similarly, the Canadian system, with a population of up to 2.2 million students, is worth more than CAD 40 billion annually (Government of Canada, 2022). As both nations apply marketing to promote their national competitiveness, they make progress in attracting prestigious institutions, influencers, and job opportunities for graduates. They also aim to establish relevant relationships to enhance their position in the international education market. According to Suomi *et al.* (2014), education tends to be an experience-based activity, which makes quality assessment difficult. This led to the scenario where reputation and imagery became measurable elements of quality with prominence in the process of university evaluation and selection (Hemsley-Brown & Oplatka, 2015). Given that students are unable to “sample” their education first and later commit the better part of several years to their studies, the issue of selecting an HEI becomes important and complicated, with no room for errors (Khanna *et al.*, 2014; Hemsley-Brown & Oplatka, 2015; Towers & Towers, 2020). Influential factors such as teachers, friends, digital media, and college-based advisors play a significant role in decision-making. McNicholas & Marcella (2022), Poole *et al.* (2023), and others discuss how these elements contribute to shaping one’s choices.

In this context, the role of SMIs is becoming increasingly crucial in shaping students’ attitudes because their opinion is perceived as authentic and trustworthy (Uzunoğlu & Misci Kip, 2014). Given their ability to rapidly reach a broad and diverse audience, SMIs are more efficient and impactful than traditional advertising campaigns (Jiménez-Castillo & Sánchez-Fernández, 2019). The benefits derived from influencers make them effective spokespersons for brands, including HEIs (Sundermann & Raabe, 2019). Until recently, there have been few systematic analyses of the impact of social media on university brands (Nevzat *et al.*, 2016). Therefore, HEIs attempting to implement social media interaction lack guidance on how it might impact their brand’s value (Minocha *et al.*, 2017). “Edu-influencers” represent a promising and relatively unexplored field of investigation (Shelton *et al.*, 2020; Carpenter & Wilson, 2022).

Despite the impressive growth in the use of social media, academic research lacks the mechanism and implications of an influencer’s role in shaping student attitudes toward HEI brands. To bridge the existing gaps, this research delves into the determinants of influencer marketing success concerning its impact on consumer perceptions of HEIs. The objective is to examine how brand trust is influenced by SMIs’ attributes and communication practices. Drawing from a review of influencer marketing literature and strategies, six factors contributing to brand trust and linked to SMIs’ characteristics were identified and examined in a comparative context of HEIs in France and Canada. As Canada is a member of the Francophonie, students who have mastered French are eligible to study in French and Canadian institutions. This research contributes to the existing literature theoretically and empirically by *1) presenting the critical role of SMIs in influencing the brand image of HEIs; 2) demonstrating the significant role played by perceived trustworthiness, authenticity, and expertise of SMIs in creating positive brand trust in HEIs; 3) highlighting the importance of unique communication patterns in social media and identification between influencers and followers via attitude homophily, interactivity, and social attractiveness; and 4) providing advice to HEIs in terms of collaborating with SMIs for impactful branding.*
# Literature review

## Branding in Higher Education Institutions

In light of this paradigm shift in HEIs, the role of SMIs is becoming interesting to study. Thus, as HEIs have adopted progressive techniques to engage students, SMIs offer powerful means to impact their perceptions and foster a significant evolution in higher education marketing. Extensive research has documented that universities have undergone commercialization processes in recent years. In response to competitive pressures, HEIs increasingly adopt marketing and branding strategies typically linked with the for-profit sector (Hemsley-Brown & Goonawardana, 2007). HEIs have progressively embraced a student-centred marketing orientation. The current landscape of higher education is marked by a noticeable shift towards a market-driven, corporate-style academy (McMillan & Cheney, 1996), with branding now widely acknowledged as an extension of the “student as consumer” concept. This shift in perspective has enabled the adoption of these practices (Hemsley-Brown & Goonawardana, 2007). Recent research has explored branding strategies within, highlighting the need for further investigation into their applicability and impact (Chapleo, 2011). Khanna *et al.* (2014) emphasise the need to “research the factors that help to create and build brands” (p. 124), and several studies have investigated various aspects of branding by testing students (Balaji *et al.*, 2016; Foroudi *et al.*, 2019). HEIs have recently adopted social media (Ngai *et al.*, 2015) for branding, entailing the development of communication and promotional strategies to engage students through various platforms. This type of marketing is particularly important for HEIs to connect with prospective students (Lacka & Wong 2021) because they have a very high social media use (Liu *et al.* 2021). Though HEIs have embraced social media in pedagogy and education delivery, its use for branding institutions is underexplored (Gai *et al.*, 2016). Notable studies by Jan & Ammari (2016) and Royo-Vela & Hünermund (2016) tried to bridge this gap by exploring the impact of HEI marketing endeavours, including their websites, search engine optimisation, and social media optimisation, on students’ decision-making processes. Bolat and O’Sullivan (2017) highlighted the aspect of student-to-student engagement on social media as a precursor of branding. Recent work by Alfonzo (2023) adds to this by explaining how specialists managing HEIs’ social media accounts with high levels of engagement generate and leverage social media content. Social media marketing’s influence on brand equity, studied by Perera *et al.* (2023), established the multidimensional nature of brand equity and how it relates to various marketing efforts. Influencer marketing—a key aspect of social media marketing—remains unexplored in the context of HEIs, yet influencers play a crucial role because of their credibility and connection with their followers (Lou & Yuan, 2019; Kim & Kim, 2021).

## Social Media Influencers’ role in branding

Influencers on various social media platforms such as Instagram, Facebook, Twitter (X), and TikTok are distinguished by their recognised expertise and perceived knowledge in specific subject matters, which is evident in their consistent creation and dissemination of content. Social media users actively subscribe to these influencers (Geyser, 2022), esteemed for their perceived reliability in providing information or guidance (Freberg *et al.*, 2011). SMIs’ communication differs from traditional methods as their content appears more organic, engaging, authentic, and personal (Liu, 2021). The engaging nature of influencers has become a key marketing strategy (Lou and Yuan, 2019), and companies collaborate with SMIs as independent third-party endorsers of products and services (Freberg *et al.*, 2011; Lou, 2022). Previous research has shown that HEI branding generates awareness and recognition from multiple stakeholders (Chapleo, 2011). SMIs could be educators, student advocates, and other influencers who regularly post content related to universities and higher education. Though practical relevance is increasing, scholarly research has yet to be developed on the phenomena of influencers in the context of HEIs. “Cool” influencers, often considered “close friends” or even “family members” by their followers (Reinikainen *et al.*, 2020), find themselves at the centre of marketing, with brands seeking their endorsement (Ember, 2015) to gain the trust and attention of their followers. Therefore, influencers with specific characteristics are well-received by followers, and their content generates better communication outcomes (Lou & Yuan, 2019). Studies have highlighted the underlying dynamics of such traits and how they lead to favourable outcomes such as brand trust (Kim & Kim, 2021; Masuda, 2022), which can be extended to the context of HEIs.

## The cultural dimension

Researchers widely agree that a thorough understanding of culture and cultural differences is an important prerequisite for successful international advertising (Zhou *et al.*, 2015). Recent studies indicate that in both France and Canada, social networking platforms are used by most of the population; Instagram is popular in both nations among 18 to 44-year-olds, with 71.6% of users in Canada and 74.9% in France (Statista, 2022a,b). Cultural contexts can influence customers’ social media usage behaviours (Okazaki & Taylor, 2013), so it remains crucial to understand cultural differences to make marketing messages relevant to local markets (Berthon *et al.*, 2012).
# Conceptual development and hypotheses

Contemporary scholarly investigations have scrutinised the characteristics and narrative methodologies employed by SMIs (Zhou *et al.*, 2021). This study draws from the persuasion theory (McGuire *et al.*, 2001), the source attractiveness model (McGuire *et al.*, 1985), and the source credibility model (Ohanian, 1990) to explain how consumer perception of SMIs’ endorsements can enhance their trust in brands. Persuasion is a process to change someone’s behaviour or attitude (Dotson & Hyatt, 2000). Influencing people to buy a product can be seen as persuasion. According to McGuire *et al.* (2001), persuasion theory consists of two parts: input variables (communication variables) and output mediational steps (persuasion). Studies on consumer evaluations of information quality and credibility have used this concept widely (Huang *et al.*, 2018) and have shown that message source characteristics and attributes play a more prominent role in persuasion than arguments themselves (Farace *et al.*, 2017). Based on this theory, followers’ perceptions of influencers (such as trustworthiness) can affect their behaviour (i.e., trust toward the brand). According to the source attractiveness model (McGuire, 1985), an attractive source tends to increase persuasion. Influencers’ attractiveness induces followers to mimic their popularity and lifestyle (Okazaki *et al.*, 2014), leading to such behavioural changes across dimensions as a positive brand image (Hermanda *et al.*, 2019) and improved purchase intention (Torres *et al.*, 2019). Past research has examined the endorser’s attractiveness as an important determinant of persuasion (Buunk & Dijkstra, 2011; Till & Busler, 2000). Additional characteristics have been recently associated with this concept, including attitude homophily (Kim & Kim, 2021) and interactivity (Jun & Yi, 2020). Attitude homophily is related to similarity, and SMIs perceived by their followers as being like them may also be an effective marketing tool (Sokolova & Kefi, 2020). Interactivity, characterised as the bidirectional communication between influencers and their followers, manifests through comments and feedback on the influencer’s social network account; this serves as a motivational factor for individuals to consistently partake in online communities, consequently contributing to heightened levels of engagement (Islam and Rahman, 2017). Credibility is one of the most critical determinants of the persuasiveness of a source (Hovland *et al.*, 1953; Ohanian, 1990). It can be argued that SMIs who are perceived as credible have an enhanced persuasive association with their endorsed brands. The source credibility model explains the extent to which the source is perceived as imparting information on a product (Ohanian, 1990); thus, the credibility of a source depends on their “trustworthiness” and “expertness” (Hovland *et al.*, 1953). Brand trust can increase when consumers feel that the brand is sincere (Hernandez-Fernandez & Lewis, 2019); if followers feel that SMIs enjoy creating content without expecting external compensation, they are more confident that the content reflects the influencer’s thinking rather than being manipulated or edited by a third-party intervention. The literature review identified six attributes as key influencers of brand awareness and purchase intention. We include all six constructs in our model as predictors of brand trust. The conceptual framework is summarised in Figure 1 and is explained further below.

## Social Attractiveness

Social attractiveness refers to the likability of a speaker (Sokolova & Kefi, 2020) and a person’s ability to influence the mental state of others and be accepted and approved by society. Communication formed in the presence of social attraction leads to a likability that changes the audience’s attitude. High social attractiveness leads to a positive evaluation of the content by followers regarding credibility and expertise (Lou & Yuan, 2019). A large number of followers associates high social capital with social attractiveness, which leads to a positive evaluation of a person’s level of expertise (Jin & Phua, 2014). Also, social attractiveness has been found to positively influence users’ perceived trustworthiness (Toma, 2014; Masuda *et al.*, 2022). Following the source credibility theory, relational trust is influenced by two dimensions of source credibility: expertise and authenticity. The cultivation of authenticity enables influencers to establish trust with their audiences, thereby exerting a discernible impact on the behaviours of the latter. The following hypotheses were proposed:

```
*H1: Social Attractiveness is positively
                            associated with SMIs’ a) perceived expertise; b) perceived authenticity;
                            c) perceived trustworthiness.*
```

### Attitude Homophily

A high degree of congruence between a follower’s ideal self-image and the SMI’s image leads to more effective endorsement outcomes through the mechanism of attitude homophily. As discussed, attitude homophily relates to perceived similarity, which enhances the effectiveness of endorsements when congruence exists between the SMI’s image and the follower’s ideal self. In the health sector, Wang *et al.* (2008) found that homophily drove the persuasion process, with perceptions of credibility—including expertise—based on similarity. Simply put, the more homophilous the online information stimuli, the more likely people are to adopt the proposed advice. This congruence fosters an increased perception of expertise among followers. Additionally, gaps in the literature regarding the relationship between attitude homophily and perceived authenticity emphasize the importance of investigating this association. Sokolova and Kefi (2020) suggest that homophily also affects an SMI’s perceived authenticity and trustworthiness, which is particularly evident in industries like beauty (Ladhari *et al.*, 2020) and healthcare (Wang *et al.*, 2008). For example, homophily based on factors like attitude, value, context, and appearance has been shown to enhance the perceived expertise of vloggers in the beauty industry, leading to stronger emotional attachment (Ladhari *et al.*, 2020). Perceived similarity further creates positive feelings between groups, reduces uncertainty (Simons *et al.*, 1970), and increases engagement as well as perceived information quality (Wang *et al.*, 2008; Zhang *et al.*, 2018). These elements together enhance the authenticity and credibility of SMIs. Therefore, we hypothesize:

```
*H2: Attitude Homophily is positively
                            associated with SMIs’ a) perceived expertise; b) perceived authenticity;
                            and c) perceived trustworthiness.*
```

<figure><img src="image_1.png" /><figcaption>Figure 1 — Proposed Research Model — Note: ***p*-value = 0.000 **p*-value > 0.000</figcaption></figure>

<figure><img src="image_2.png" /><figcaption>Figure 1.1 — Proposed Research Model — Note: ***p*-value = 0.000 **p*-value > 0.000</figcaption></figure>

## Interactivity

Research indicates that interaction and collaboration between influencers and consumers enhance the effects of eWOM while simultaneously improving consumer engagement. Thus, Kretz and de Valck (2010) examined how fashion bloggers build expertise by demonstrating and arguing about the performance of products. Accordingly, we propose that interactivity positively affects an influencer’s perceived expertise. Studies indicate that interactivity with social influencers impact information signals, affecting the level of trustworthiness regarding the disseminated information (Luo *et al.*, 2013). Jun and Yi (2020) showed that influencers’ interactivity significantly and positively affected their perceived authenticity. People’s experience of trust in brands with respect to social media marketing increases with their online interactivity (Tatar & Eren-Erdo Gmus, 2016). Online interpersonal interactions influence social media users’ perceptions of the credibility and trustworthiness of the information provided (Sundar, 2008; Kim *et al.*, 2012). Thus, we hypothesise:

```
*H3: Interactivity is positively
                            associated with SMIs’ a) perceived expertise; b) perceived authenticity;
                            c) perceived trustworthiness.*
```

## SMIs’ perceived expertise and brand trust

Expertise is conceptualized as the perception that “a communicator is a source of valid claims” (Hovland *et al.*, 1953), and it exerts a significant positive influence on both brand attitude and purchase intention (Till & Busler, 2000). Persuasion theory suggests that followers are more inclined to trust brands when they perceive influencers as credible experts (Sternthal *et al.*, 1978). The persuasive influence of a source exhibiting expertise is significantly greater than that of a non-expert source, with individuals being more inclined to align with the opinions of experts over those of non-experts (Horai *et al.*, 1974). Furthermore, Delgado *et al.* (2005) define brand trust as “the confident expectations of the brand’s reliability and intentions,” suggesting that expertise can directly and positively enhance brand trust. In the context of social media influencers (SMIs), perceived expertise plays a crucial role in shaping followers’ trust in the brand. When influencers lack demonstrable expertise, their perceived credibility diminishes, weakening brand trust (Sokolova & Kefi, 2020). Given the strong influence of perceived expertise on brand trust, we propose the following hypothesis*:*

```
*H4: SMIs’ perceived expertise is
                            positively associated with brand trust.*
```

## SMIs’ perceived SMI’s perceived trustworthiness and brand trust

Trust is a fundamental driver of social relationships, fostering consumer confidence and reducing uncertainty (Gopichandran & Chetlapalli, 2013; Chaudhuri & Holbrook, 2001). In the context of social media influencers (SMIs), followers evaluate an influencer’s attributes—such as trustworthiness—based on their individual assessments, which can directly influence their behavior and trust towards the associated brand. According to Rogers and Bhowmik (1970), credibility is a key factor in determining one’s ability to trust and rely on a source, with Sternthal *et al.* (1978) suggesting that credibility is built upon two key components: trustworthiness and expertise. Trustworthiness reflects how the audience perceives the speaker’s claims and is primarily based on the speaker’s honesty and the extent to which the audience feels they care about them, or goodwill (Sokolova & Kefi, 2020). SMIs who are perceived as trustworthy can significantly enhance brand trust (Leite & Baptista, 2022), as trustworthiness plays a crucial role in establishing a strong connection between the influencer and their audience. Wiedmann and von Mettenheim (2021) further demonstrated that trustworthiness has the most substantial impact on brand trust among all influencer attributes.

Therefore, given the essential role of perceived trustworthiness in shaping brand trust, we hypothesize:

```
*H5: Perceived trustworthiness is
                            positively associated with brand trust.*
```

## SMI’s perceived authenticity and brand trust

Authenticity in sociology is conceptualized as the ability to appear true to oneself and others, encompassing attributes such as sincerity, genuineness, truthfulness, and originality (Vannini & Franzese, 2008; Molleda, 2010). In the context of marketing communication, Baker and Martinson (2002) argue that authenticity depends on the communicator’s openness and personal identification as the persuader. Social media influencers (SMIs) who endorse products or services are often perceived as embodying authenticity (Boerman *et al.*, 2017), a quality increasingly valued in marketing research.This perception is driven by the growing consumer demand for authentic brands and products (Chronis & Hampton, 2008). When followers believe that influencers genuinely enjoy creating content and are not solely motivated by external compensation, they are more likely to view the content as a reflection of the influencer’s true preferences, rather than being manipulated by third parties. Authenticity thus becomes a key factor in fostering trust, as consumers are more likely to trust influencers whose content aligns with their intrinsic motivations. Given the significance of authenticity in shaping consumer trust, we hypothesize:

```
*H6: SMIs’ perceived authenticity is
                            positively associated with brand trust.*
```

## Moderating role of message type

A significant impact may also be exerted by the way SMIs communicate, particularly in the type of message they select to accompany their social media posts. The text they choose can influence their followers’ perceptions. Warner and Forward (2016) explain that rational and emotional messages are designed differently. Rational messages convey factual information, require logical reasoning, and engage reflective cognitive processes. In contrast, emotional messages evoke a range of emotional responses, both positive and negative, and appeal to more immediate, instinctive reactions. These differences in message type can influence various interactions and affect how followers perceive influencers. In this context, we expect the message type to play a key moderating role in the relationships between the three SMI power variables (social attractiveness, attitude homophily, and interactivity) and the three SMI perceived characteristics variables (perceived expertise, authenticity, and trustworthiness). Specifically, the type of message (emotional or rational) may alter how these power dynamics and perceived traits affect followers’ trust and engagement. Accordingly, the following hypotheses were proposed:

```
*H7a-b-c-d-e-f-g-h-i: The message type
                            (emotional and rational) moderates the relationships between SMI power
                            variables (attitude homophily, social attractiveness, and interactivity)
                            and SMI perceived characteristics variables (perceived expertise,
                            authenticity, and trustworthiness) in the research
                            model.*
```

## Methodological approach

Influencers are harnessing the power of social media to distribute materials that cater to the needs of both students and educators. These materials range from instructional concepts to motivational resources, all crafted with the educational context in mind. Instagram has quickly ascended to become a leading platform for sharing imagery and video content. It is a preferred venue for influencers and brands to conduct influencer marketing campaigns. Given its prominence, this study focuses on Instagram as the principal channel for influencers specialising in higher education to connect with their target audience. To illustrate this, we created vignettes that depict education advocates as SMIs designed to mimic an authentic Instagram post. We included a diverse array of male and female influencers, each with an identical follower count. Accompanying their image was an emotional or rational message promoting a fictional university to prevent preconceived notions about the institution from swaying the participants. Profiles of “fictitious influencers” were used, ensuring that the panel of Instagram users—our study’s respondents—remained unbiased toward any profile they have been exposed to. Participants were randomly shown a single Instagram post before being asked to complete a subsequent questionnaire. Sample vignettes are provided in Appendix 1. The survey’s constructs were based on a multi-item structure, with items derived from previous studies. Table 1 lists these items and their sources. Harman’s one-factor analysis was performed to address potential biases, indicating no common method bias. This was further validated by including a common method factor in a second measurement model. Additionally, the variance inflation factor (VIF) indicators were used to confirm the validity of the collected data, effectively reducing the risk of multicollinearity bias.

<table><caption>Table 1 — Questionnaire Measures</caption><tr><td>ConstructItemMeasureSocial Attractiveness (Duran & Kelly, 1988)SA1I
                        think this influencer could be my friendSA2I wish I could converse with this
                        influencerSA3I think this influencer and I would be able to establish a
                        friendly bondSA4I think this influencer is similar to me in his/her way of
                        beingAttitude Homophily (Lou & Kim, 2019)AH1This influencer and I have a
                        lot in commonAH1This influencer and I are very similarAH3This influencer
                        seems to share my valuesAH4This influencer thinks like
                        meInteractivity(Thorson & Rodgers, 2006)I1Interacting with this
                        influencer would be like having a real conversationI2I perceive this
                        influencer as being responsive to my information needsI3This influencer
                        would respond to me quickly and efficientlyI4This influencer allows me to
                        communicate directly with him/herPerceived Expertise (Lou & Kim,
                        2019)PE1I find this influencer to be knowledgeable on the subjectPE2I
                        consider this influencer to be an expert in this fieldPE3I consider this
                        influencer to be experienced enough to share statements about this topicPE4I
                        consider this influencer to be sufficiently qualified in his/her field to
                        make these types of claimsPerceived Authenticity (Moulard et al.,
                        2016)PA1This influencer has a true passion for his/her fieldPA2This
                        influencer wants to do their best to share their contentPA3This influencer
                        is dedicated to what they do on this social network (Instagram)PA4This
                        influencer is sincere in his/her content creationPerceived Trustworthiness
                        (Lou & Kim, 2019)PT1I find this influencer honestPT2I find this
                        influencer trustworthyPT3I believe this influencer is telling the truthPT4I
                        believe what this influencer says and that they would not try to take
                        advantage of their followersBrand Trust(Chaudhuri & Holbrook, 2001)BT1I
                        trust this influencer and the brand they recommendBT2I can trust this
                        influencer and what they recommendBT3This influencer is an honest person
                        with quality recommendationsBT4I would not hesitate to invest in what this
                        influencer recommends</td></tr></table>

All measurement scales have been adopted from the literature. The social attractiveness scale was based on Duran and Kelly (1988) (4 items). The measures of attitude homophily, perceived expertise, and trustworthiness consist of 4 items each and are derived from the work of Lou and Kim (2019). The measure of interactivity, also based on 4 items, is derived from the work by Thorson and Rodgers (2006). The concept of perceived authenticity was based on the scale established by Moulard *et al.* in 2016 and has 4 items. Finally, brand trust was assessed according to the work of Chaudhuri and Holbrook, 2001 (4 items). Table 1 depicts the measures of each variable in the questionnaire and the related literature. The data collection lasted over two months. To collect high-quality data, screening questions were placed at the beginning of the survey to verify whether the respondents were regular users of social media platforms, allowing us to recruit only those who were. All participants were social media users, most indicating their preference for Instagram and excellent knowledge of the platform and SMIs, principally due to their age and lifestyle. Data collection was carried out anonymously. As, this research was conducted in a French-speaking context, so the questions were adapted for a French panel. To evaluate the efficacy and clarity of both rational and emotional messages, a sequence of pre-tests and pilot studies was undertaken to ensure the survey’s reliability and the validity of its content. A group of 20 individuals, representing our target demographic, were enlisted to evaluate and classify the message types during this preliminary phase. This step was crucial to ensure that the study’s participants correctly identified the messages. The study recruited 345 individuals, with 276 questionnaires completed thoroughly and discarding any that were incomplete or unclear. The survey was administered via Qualtrics™ and targeted a student demographic in France and Canada—known for their high social media engagement and online information-seeking behaviour. The survey was distributed either through a panel via email, which included a URL to the survey, or through an anonymous link. To minimise context effects on measurement, the questionnaire items were randomised. The survey featured closed-ended questions on a five-point Likert scale, asking respondents to indicate their level of agreement with each statement, ranging from “strongly disagree” to “strongly agree.” This questionnaire was tailor-made for this study and was translated into French using a back-translation method (Brislin, 1970). Academic experts also reviewed it for readability. Following best practices for survey development and to refine the questionnaire, a pilot test was conducted with 20 individuals matching the target demographic. Feedback from this group was solicited for the improvement and finalisation of the survey, and these individuals were excluded from the final sample (Martins *et al.*, 2019). The final sample comprised 67.03% female respondents and 32.97% male respondents. The participants’ levels of education were structured as follows: 34.55% of respondents were high school graduates; 17.45% had a bachelor’s degree; 14.18% were at the graduate level; and 26.91% were at the postgraduate level, with approximately 4% of respondents having a PhD (see Table 2). Furthermore, 51.45% of those surveyed studied in France, compared to 48.55% in Canada. Structural equation modeling (SEM) was used to test the model and the research hypotheses on SmartPLS4. The results indicated that the psychometric quality of the constructs was satisfactory.

<table><caption>Table 2 — Sociodemographic Information and Education Level of the Respondents</caption><tr><td>CharacteristicsFrequency%GenderMale9132.97Female18567.03Country of
                        StudyFrance14251.45Canada13448.55Marital
                        StatusSingle20976.84Married155.51Divorced20.74Common Law4616.91Age
                        Group18-25 years old22682.1826-35 years old3312.0036-49 years old145.0950
                        and above20.73Education LevelNo Education82.91High School9534.55Bachelor’s
                        Degree4817.45Graduate3914.18Post-Graduate7426.91PhD114.00</td></tr></table>
# Results

The first step in validating the measurement model was to evaluate the item loadings on the relevant constructs to assess the reliability of the indicators. In the second step, composite reliability and Cronbach’s alpha were used to assess each construct’s internal consistency. Third, convergent validity was examined by comparing the average variance extracted (AVE) values of all the indicators for each construct. To assess discriminant validity, we used the heterotrait–monotrait correlation criterion (HTMT). Table 3 presents the results of the discriminant validity analysis according to the HTMT criterion. All the values were below 0.90, which confirms discriminant validity. As part of the structural model assessment, the first step is to ensure that there are no significant levels of collinearity between predictor constructs, which could create redundancy problems. A VIF can be used to determine this. All VIF values (see Table 4) in the research model were less than 5, demonstrating no critical multicollinearity issues. The significance of the hypothesised relationship between constructs was tested using bootstrapping in SmartPLS4. Except for H1a, H2b, and H5, the model was largely supported. The results are presented in Tables 5a and 5b. Both perceived expertise (β = 0.271, *p*= 0.000) and perceived trustworthiness (β = 0.577, *p*= 0.000) scored the highest compared with perceived authenticity (β = 0.029, *p*= 0.603). Regarding associations between personal attributes and characterisation, interactivity had relatively strong associations (β = 0.386, 0.364, and 0.359) with perceived expertise, authenticity, and trustworthiness, respectively. Attitude homophily also maintained a significant but weaker association (β = 0.239, 0.085, and 0.158, respectively). Social attractiveness had the lowest association overall (β = 0.069 only for the path to perceived expertise).

<table><caption>Table 3 — Assessment of discriminant validity using HTMT — Note: HTMT = heterotrait-monotrait criterion</caption><tr><td>AHBTIPAPEPTSAAHBT0.580I0.6290.602PA0.4430.6460.598PE0.5650.7600.6540.691PT0.5460.8660.6470.7770.766SA0.8270.6320.6600.4750.5310.578</td></tr></table>

The convergent validity of the measurement model was assessed through an analysis of the AVE and composite reliability (CR). Table 4 indicates that convergent validity was supported here since the AVE was greater than 0.50 for each construct, suggesting that its measures better explained the variance of each construct than by error. Table 4 also demonstrates that the CR was systematically superior to the AVE for each construct, supporting convergent validity. Regarding discriminant validity, the data indicated that each construct correlated more with its measures (manifest variables) than with other constructs (latent variables). VIF indicators are satisfying and comply with norms, statistical requirements, and Cronbach’s alpha indicators. As listed in Table 4, Cronbach’s alpha ranged from 0.811 to 0.904 for each construct. Moreover, the smallest CR was 0.822, higher than the recommended threshold of 0.7. These assessments confirmed each construct’s internal consistency. Furthermore, all AVE values exceeded the minimum level of 0.50, indicating high convergent validity.

<table><caption>Table 4 — Results of measurement model analysis</caption><tr><td>ConstructItemCronbach’sAlpha (α)CRAVEVIFSocial
                        AttractivenessSA1SA2SA3SA40.8690.8700.7182.4032.2322.1011.877Attitude
                        HomophilyAH1AH2AH3AH40.9040.9120.7763.4213.2453.3543.372InteractivityI1I2I3I40.8110.8160.6371.6831.8412.0891.983Perceived
                        ExpertisePE1PE2PE3PE40.8680.8710.7182.0461.6492.5312.941Perceived
                        AuthenticityPA1PA2PA3PA40.8020.8220.6201.5032.0971.9441.419Perceived
                        TrustworthinessPT1PT2PT3PT40.8910.8920.7553.0972.4572.7761.871Brand
                        TrustBT1BT2BT3BT40.8890.9030.7512.9873.4602.5971.684</td></tr></table>

The structural model was constructed based on the hypothesised variable relationships (see Figure 2). The results of the tested model are shown in Table 5a. Twelve hypotheses were tested in the general research model, resulting in nine hypotheses being supported and validated. The model controlled for age, marital status, and gender. Based on the main effect results, no significant relationship was found between attitude homophily and perceived authenticity or between social attractiveness and perceived expertise. Similarly, the effect of perceived authenticity on brand trust was not significant.

A moderated relationship between SMI power variables (social attractiveness, attitude homophily, and interactivity) and SMI perceived characteristics variables (perceived expertise, authenticity, and trustworthiness) was also tested (see Figure 2.1). A moderation analysis was conducted for these paths, and the results show a significant impact only of interactivity on perceived expertise (β**= 0.297, *p*= 0.023) and interactivity on perceived trustworthiness (β**= 0.268, *p*= 0.043), therefore only validating hypotheses H7g and H7i. Table 5b presents the results of the tested model.

<table><caption>Table 5a — Results of structural model assessment</caption><tr><td>Structural Pathβp-valueHypothesesH1aSocial Attractiveness → SMI’s
                        PerceivedExpertise0.0690.350RejectedH1bSocial Attractiveness → SMI’s
                        PerceivedAuthenticity0.1570.027AcceptedH1cSocial Attractiveness → SMI’s
                        PerceivedTrustworthiness0.1910.010AcceptedH2aAttitude Homophily→ SMI’s
                        PerceivedExpertise0.2390.001AcceptedH2bAttitude Homophily → SMI’s
                        PerceivedAuthenticity0.0850.293RejectedH2cAttitude Homophily → SMI’s
                        PerceivedTrustworthiness0.1580.040AcceptedH3aInteractivity → SMI’s
                        PerceivedExpertise0.3860.000AcceptedH3bInteractivity → SMI’s
                        PerceivedAuthenticity0.3640.000AcceptedH3cInteractivity → SMI’s
                        PerceivedTrustworthiness0.3590.000AcceptedH4SMI’s Perceived Expertise →
                        Brand Trust0.2710.000AcceptedH5SMI’s Perceived Authenticity → Brand
                        Trust0.0290.603RejectedH6SMI’s Perceived Trustworthiness →
                        Brand Trust0.5770.000AcceptedControl variablesAge Group → Brand
                        Trust0.0570.160Gender → Brand Trust0.1190.114Marital Status → Brand
                        Trust0.0370.291</td></tr></table>

<figure><img src="image_3.png" /><figcaption>Figure 2 — Standardized Results — Note: ***p*-value = 0.000 **p*-value > 0.000</figcaption></figure>

<table><caption>Table 5b — Results of structural model assessment – Moderation Analysis</caption><tr><td>Structural Pathβp-valueHypothesesH1aSocial Attractiveness → SMI’s
                        PerceivedExpertise0.1390.184RejectedH1bSocial Attractiveness → SMI’s
                        PerceivedAuthenticity0.2920.002AcceptedH1cSocial Attractiveness → SMI’s
                        PerceivedTrustworthiness0.3170.006AcceptedH2aAttitude Homophily→ SMI’s
                        PerceivedExpertise0.2780.001AcceptedH2bAttitude Homophily → SMI’s
                        PerceivedAuthenticity-0.0410.678RejectedH2cAttitude Homophily → SMI’s
                        PerceivedTrustworthiness0.1330.231RejectedH3aInteractivity → SMI’s
                        PerceivedExpertise0.2240.011AcceptedH3bInteractivity → SMI’s
                        PerceivedAuthenticity0.2310.004AcceptedH3cInteractivity → SMI’s
                        PerceivedTrustworthiness0.2090.015AcceptedH4SMI’s Perceived Expertise →
                        Brand Trust0.2710.000AcceptedH5SMI’s Perceived Authenticity → Brand
                        Trust0.0290.606RejectedH6SMI’s Perceived Trustworthiness →
                        Brand Trust0.5770.000AcceptedH7aMessage x Social attractiveness → SMI
                        Expertise-0.0950.533RejectedH7bMessage x Social attractiveness →
                        SMIAuthenticity-0.2320.092RejectedH7cMessage x Social attractiveness →
                        SMITrustworthiness-0.2060.173RejectedH7dMessage x Attitude Homophily →
                        SMIExpertise-0.0820.591RejectedH7eMessage x Attitude Homophily →
                        SMIAuthenticity0.2380.131RejectedH7fMessage x Attitude Homophily →
                        SMITrustworthiness0.0490.754RejectedH7gMessage x Interactivity → SMI
                        Expertise0.2970.023AcceptedH7hMessage x Interactivity → SMI
                        Authenticity0.2580.050RejectedH7iMessage x Interactivity → SMI
                        Trustworthiness0.2680.043AcceptedControl variablesAge Group → Brand
                        Trust0.0570.160Gender → Brand Trust0.1190.114Marital Status → Brand
                        Trust0.0370.291</td></tr></table>

<figure><img src="image_4.png" /><figcaption>**Figure 2.1** — Standardized Results — Note: ***p*-value = 0.000 **p*-value > 0.000</figcaption></figure>

Lastly, we conducted a multi-group analysis to confirm that our proposed model was moderated by the country of study (France and Canada). However, as demonstrated in Table 5c, there were no significant differences between the subgroups of French and Canadian respondents.

<table><caption>Table 5c — Multi-Group Analysis</caption><tr><td>Welch-Satterthwaite testDifference (Canada - France)t value (Canada
                        vs France)p value(Canada vs France)H1aSocial Attractiveness → SMI’s
                        PerceivedExpertise0.2781.8880.061H1bSocial Attractiveness → SMI’s
                        PerceivedAuthenticity0.2031.3740.172H1cSocial Attractiveness → SMI’s
                        PerceivedTrustworthiness0.2071.2960.197H2aAttitude Homophily→ SMI’s
                        PerceivedExpertise-0.1571.0730.285H2bAttitude Homophily → SMI’s
                        PerceivedAuthenticity-0.0790.4740.636H2cAttitude Homophily → SMI’s
                        PerceivedTrustworthiness-0.1741.0780.283H3aInteractivity → SMI’s
                        PerceivedExpertise-0.1771.4650.145H3bInteractivity → SMI’s
                        PerceivedAuthenticity-0.1120.9400.349H3cInteractivity → SMI’s
                        PerceivedTrustworthiness-0.0040.0310.975H4SMI’s Perceived Expertise → Brand
                        Trust0.0540.5030.616H5SMI’s Perceived Authenticity → Brand
                        Trust0.0220.1880.851H6SMI’s Perceived Trustworthiness →
                        Brand Trust-0.0150.1470.883Control variablesAge Group → Brand
                        Trust0.0650.8020.424Gender → Brand Trust0.0080.0490.961Marital Status →
                        Brand Trust-0.0420.5880.558</td></tr></table>

Using SmartPLS4, we tested the squared multiple correlation (R<sup>2</sup>) for explanatory power and predictive relevance. The R<sup>2</sup> and adjusted R<sup>2</sup> values for the endogenous constructs are shown in Table 6. R<sup>2</sup> measures the percentage of variance explained by the independent constructs in the model. The R<sup>2</sup> model for the endogenous constructs ranged from 0.282 to 0.651, indicating a good amount of variance in the constructs being explained by the explanator constructs and the hypothesised models’ good ability to explain the variance in the outcome construct.

<table><caption>Table 6 — R<sup>2</sup>, R<sup>2</sup> Adjusted</caption><tr><td>Model QualityR2R2
                        AdjustedBT0.6510.643PA0.2820.274PE0.3670.360PT0.3750.368Fit SummarySaturated
                        modelEstimated
                        modelSRMR0.0700.106d_ULS2.4245.613d_G0.8671.054Chi-square1359.7691518.114NFI0.7740.748</td></tr></table>