Building Stronger Brand Evangelism for Sustainable Marketing through Micro-Influencer-Generated Content on Instagram in the Fashion Industry

Abstract

Micro-influencers have become powerful sources of information for consumers in the digital age. Marketers have strategically collaborated with micro-influencers as brand endorsers to generate valuable content, which attract the consumers and encourage them to engage with micro-influencers, leading to brand evangelism. This reflects the sustainable consumer-brand relationships. In Southeast Asia, Instagram stands out as the preferred choice for fashion influencers for sharing product information and boosting consumer engagement. The current study is the first one incorporating literature-based frameworks, consisting of consumer-based digital content marketing, information relevance theory, observational learning theory, consumer-influencer engagement behavior, and brand evangelism, into a unified framework to deal with the research gaps. The quantitative method was applied through using partial least square structural equation modeling (PLS-SEM). The SmartPLS v. 3.3.9 software application was applied to explore the proposed model. The data were acquired from 499 Thai consumers who have followed and engaged the content with fashion micro-influencers on Instagram. The results revealed that the positive effect of topicality of content is the strongest antecedent that motivates consumer-influencer engagement, while novelty, understandability, reliability, interestingness, and influencers’ authenticity of content also have a positive influence on consumer-influencer engagement. Finally, the findings showed that consumer-influencer engagement have vital effects on brand evangelism.

1. Introduction

Influencer marketing can ensure sustainable marketing and influencers have recently strengthened collaborations with brands [1]. The influencer marketing investment amount is globally estimated at USD 15 billion by 2022. The influencer marketing investments are increasingly made by marketers, aiming to supplement existing marketing communication and publicize social media influencer-generated content to followers on social networks and target customers [2]. Instagram has become the ideal platform for brands to use influencer-based marketing campaigns and paid collaboration between influencers and brands is often found in sponsored content [3]. Instagram was launched in 2010 and has achieved substantial growth due to extensive use and high popularity, especially among young users [4]. The platform provides rich photo and video content, with more than 1 billion monthly active users worldwide in 2021, while half of the users use the platform daily [5]. Being recognized as the fastest-growing social networks in Southeast Asia, Instagram stands out as the primary choice among social media influencers for the purpose of providing product information and promoting consumer engagement [6,7,8]. In addition, Instagram is the most popular platform among influencers in the fashion industry. Since fashion significantly contributes to the global economy, it is considered one of the most critical industries [9]. People display their social status through wearing classy attires [10]. As for the clothing inspirations of consumers, the content created by fashion influencers is used to find inspiration [11]. Consumers are becoming more fashion-conscious and fashion trends are very influential for buying behaviors [12]. Such trends are mostly stimulated by fashion influencers. Fashion influencers are those who create fashion content and have the potential to persuade consumers [13].

Consumers are allowed to access more sources of information that are not restricted to only brand-generated content and they rely more on product recommendations, content (such as videos), and information shared by other consumers on social media than product promotion by brands. The consumers trust other users on social media because they believe that these contributors share both negative and positive experiences straightforwardly, without having any commercial interest, thus being neutral to the judgment of products and services. [14]. In contrast, in the viewpoint of the consumers, brand-generated content is suspicious [15], and such content would emphasize only positive aspects of their products to maintain their business benefits [14]. As a result, the brand’s content creation is not as efficient as it should be, compared to the cost of brand investment [15]. Social media users can achieve a large number of followers by providing a unique and attractive content and building a close relationship with their followers. Then, these users become social media influencers, as well as serving as idols for their followers [16]. Social media influencers have become sources of information with an influence on digital users [17]. These influencers provide updates and information about brands and deliver delightful content that is useful for brands because social media influencers are recognized as reliable experts in their areas of interest [8]. The consumers usually rely on social media influencers for information and knowledge regarding their experiences. The content created by social media influencers is found to have more attractiveness than advertising scripts written by a marketing professional [18]. Compared to other celebrities, consumers perceived that social media influencers are more accessible and reliable [19] and that the consumers normally refer to the information provided by influencers prior to making a purchase [20].

Marketers strategically select social media influencers to endorse their brand and generate shareable content (including papers, pictures, and video clips) that utilizes popular social media influencers to build their brand [17]. Recent evidence indicates that brand-embedded content created by social media influencers efficiently develops positive attitudes toward both the influencer and endorsed brand. As a result, consumer engagement with the influencer-created content became increased [21]. Consumer-influencer engagement behavior is conceptually a kind of engagement with social media influencers, which describes the consumer’s intention to consume and contributes to content created by social media influencers [22]. Consumer-influencer engagement behavior reflects how social media influencers are popular and powerful, serving as a key factor driving the close relationships between social media influencers and endorsed brands [21]. Consumer-influencer engagement has been widely interested by both academicians and practitioners. Meanwhile, it significantly helps facilitate the enhancement of loyalty and brand evangelism [23,24].

According to the literature review, most of the previous studies examined consumer-influencer engagement, placing emphasis on macro-influencers or celebrities [25,26,27,28,29,30,31,32,33,34,35,36,37,38]. Few studies have investigated the role of micro-influencers [24,39,40,41,42], while recent industry reports revealed that micro-influencers with small network sizes gained the attention and achieved higher-quality engagement rates through intimate communication with a smaller group of followers to a greater extent than macro-influencers [7]. Although micro-influencers are followed by fewer followers than macro-influencers, the demographic aspects of the followers of macro-influencers and celebrities are far more diverse. On the contrary, the followers of micro-influencers are normally more specific in terms of age groups and gender. A large number of the followers of celebrities are just viewers and might not pay much attention to the created content. Therefore, micro-influencers tend to generate more sales opportunities and better conversions, compared to macro-influencers. In the viewpoint of consumers, they can be perceived as friends, which contribute to the relationship between the brand and consumers. They are basically specialized experts who are more energetic [39] and they can communicate product information to their fan club easily [43], especially among the younger generations. Young social media users turn their attention to Instagram to seek inspiration from their followed micro-influencers [40]. Micro-influencers can affect consumer engagement and relationships between consumers and brands [44]. Nevertheless, there have been few studies on micro-influencers building consumer engagement and sustainable consumer-brand relationships. Therefore, the main research objective is to identify the role of fashion micro-influencers who generated content on Instagram in determining consumer-influencer engagement and lead to brand evangelism.

Considering all the aforementioned significant matters, the objective of this research study is to examine the role of fashion micro-influencers who generate content on Instagram in determining consumer-influencer engagement and leading to brand evangelism.

This study proposes an empirical research model based on the consumer-based digital content marketing concept, information relevance theory, observational learning theory, consumer-influencer engagement behavior, and brand evangelism to empirically test a conceptual framework, which have disseminated the body of knowledge and fill the research gaps as follows. A previous study examined from a utilitarian or functional perspective [45,46], including topicality, novelty, understandability, and reliability, but overlooked the hedonic perspective. According to the research of Chen et al. [46] and Zhang and Choi [47], they extended the body of knowledge and include the interestingness of content as a hedonic aspect of judging the relevance of information theory by studying the significant impact of influencer-generated content on promoting the relationships between influencers and their followers. However, it was found that all of the previous three research studies were conducted from only two perspectives: a functional and a hedonic perspective. In fact, the aspect of an authenticity perspective may also be studied in the context of influencer-generated content because this factor is the internal motivation and inner need of influencers that create social media content in order to express their images [24] and represents the influencers’ trustworthiness perceived by consumers as a result of their content created to review products that they have tried and liked [48]. The significance of the authenticity perspective is supported by Hollebeek [49], who identified the importance of three motives of brand-generated content to the consumers, comprising the functional motive, hedonic motive, and authenticity motive, according to the consumer-based digital content marketing concept. The said concept focuses only on the context of brand-generated content, while there has been no previous study applying this concept to study in the context of influencer-generated content. Thus, to fill the research gap, this study is the first one incorporating two theoretical frameworks, consisting of the relevance of the information theory and consumer-based digital content marketing concept, in order to combine three motive factors (functional, hedonic, and authenticity motive) into a unified framework.

Moreover, the studies of Chen et al. [46] and Zhang and Choi [47] apply the theory of information relevance that focuses on the factors including: novelty, understandability, reliability, and interestingness of content, but the topicality of content is not incorporated into the theoretical framework. Both studies did not carry out a hypothesis test and there was no empirical evidence showing the reason why topicality of content should be excluded from the theory of information relevance in the context of influencer-generated content. Based on the aforementioned studies, influencers are active in several fields and they create content mostly based on their own expertise. Therefore, topicality is not included in their study. From the above statement, it is insufficient to conclude that topicality deserves to be excluded from the theory of information relevance. This is contrary to the theory stating that topicality is the most basic condition to which consumers perceived information to be pertinent to the topic of their current interest and current needs and is the most important criterion at all the stages of the search process of the content. [45,50]. To address this gap, this study incorporates the topicality of content into a framework based on the original theory (the relevance of information).

Finally, given the increasing importance of social media influencer marketing, previous studies have applied the influencers’ credibility model (i.e., attractiveness, trustworthiness, expertise) to followers’ purchase intentions [8,26,27], investigated how the social media micro-influencer characteristics affect brand love and brand engagement [24], numerous studies have determined match-up congruence between influencers and the product fostering consumer purchase intentions [28,29,30], and the majority of studies have examined the parasocial interaction of digital influencers with buying intent [31,32]. Notwithstanding, the relationship between micro-influencer-generated content with consumer-influencer engagement behavior and the latter role in driving sustainable brand evangelism remains an issue in the literature and this study aimed to address this issue. Brand evangelism can be regarded as a superior stage of word-of-mouth communication among consumers [51], persuade others to engage with and buy the same brand, discourage others from buying other brands [52], and even degrade other brands [53]. It is challenging to gain an in-depth understanding of brand evangelism, which has become increasingly challenging in today’s business environment [54].

These research findings have offered interesting results that highlight for marketers seeking to collaborate with fashion micro-influencers and jointly produce attractive content to boost consumption, support, and create their own content relevant to fashion micro-influencers’ endorsement on the Instagram platform, which will enhance brand evangelism. The current study was conducted based on the related studies, research hypotheses, and conceptual framework. The theoretical model testing was empirically conducted with the data acquired from a web-based survey of Thai consumers who have experience in following fashion micro-influencers on Instagram. In this study, structural equation modeling (SEM) is used to conduct hypothesis testing. The results, discussion, and conclusion are presented in the following section, respectively.

2. Theoretical Background and Hypothesis Development

2.1. The Concept of Influencer Marketing

Influencer marketing is a communication strategy in which brand managers and popular social media influencers in relation to their brand positioning cooperate to promote brands to target customers [33,55], while key content creators are assigned to initiate an authentic conversation and engagement in the brand’s products and messages [16]. With a large fan base, influencers can serve as competent marketing agents who regularly create valuable content on social media for brand endorsement and increase marketing value through cultivating several followers [20]. Unlike conventional word-of-mouth marketing, influencer marketing enables marketers to more efficiently handle and understand marketing outcomes. Marketers are allowed to gain access to a certain number of views, likes, comments, posts of influencers, and feedback toward their products and services [3]. Thus, influencer marketing has been recognized as an important marketing tool for global brands. With the current trends and high recognition for platforms, including Instagram, Facebook, and YouTube, ordinary people are able to serve as social media influencers [56]. Many businesses have devised influencer marketing strategies to strengthen their relationship with customers through enhancing customer interaction, while having social media influencers to encourage the engagement of target buyers with the brand [57].

2.2. Social Media Influencers

Social media influencers are referred to as individuals who can interact with social media users and promote products to the targeted customers, with many followers on either one or several social media platforms (e.g., Instagram, Facebook, YouTube, or personal blogs) [44]. They are considered to be accessible, natural, and genuine to the public eye, as well as having an informal and casual communicative style; thus, followers feel more familiar with them than other celebrities and also consider them as more genuine; more people can be reached by engaging followers and attracting digital natives [18]. Followers are those who wish to view content from the influencer; they voluntarily interact with the influencer and other followers who follow the same influencer [58]. They can promote brands and products to attract potential customers and encourage engagement with the brand [6]. Influencers are influential and have large audiences, so various brands seek alliances with influencers [59]. In addition, influencers can be classified by the number of followers as follows: (1) Mega-influencers are a type of influencers who are followed by from 1 million to an unlimited numbers of followers; (2) Macro-influencers normally have from 100,000 to 1 million followers; (3) Micro-influencers are followed by from 1000 to 100,000 followers; (4) Nano-influencers, a new type of influencer with less followers than micro-influencers, are usually followed by below 1000 followers [60].

2.3. The Role of Micro-Influencer-Generated Content

Micro-influencers receive attention and high-quality engagement rates by communicating intimately with a narrowed group of followers than macro-influencers [7]. They are basically specific experts who are more strenuous. Therefore, brands can access target groups if they decide to collaborate with micro-influencers who have the potential to get in touch with their specific audience [39] and they have been able to create a buzz that is proved to be more effective and cost-efficient [61], as well as achieving high conversion rates. They look more natural and seem to treat their followers similar to friends [6], which contributes to the relationships between the brand and customers [39]. In addition, consumers consider the content to be preferable to the advertising, especially the content created by micro-influencers. The information sharing performed by micro-influencers has a choice of communication tools that marketers use to communicate with consumers [41]. Micro-influencers are appealing because they are able to create premium content [62] and they certify the brand through uniquely creating personal content. The content is normally enjoyable, informative, and consistent with their characteristics [58]. Thus, working with micro-influencers helps brands create genuine content and optimize budgets for customer engagement. Several fashion, luxury, and beauty professionals choose micro-influencers because it is believed that these influencers are able to communicate with their audience more effectively [63].

2.4. Theory of Information Relevance and Observational Learning Theory

From the 1970s, information relevance has been utilized as a criterion for evaluating whether the information retrieved is relevant to information needs. Because the information search for decision-making can serve and fulfill utilitarian purposes of users, it can be neutrally measured from utilitarian benefits. In this regard, the meanings of multidimensional cognitive concepts largely depend upon the benefit, value, or utility of a piece of information as perceived by users [46]. Past studies have revealed that the information relevance theory can be evaluated from its topicality, novelty, understandability, and reliability [45]. After that, the research of Zhang and Choi [47] combines the interestingness of content into the research model to explore the influencer-generated content and influencer popularity based on the concept of information relevance. They revealed that interestingness should be classified as a part of information relevance in the influencer marketing context. Interesting content is likely to be another significant informational characteristic that enables audiences to evaluate information relevance [64]. It can be referred to as the huge impact of an activity on an individual emerged from the interaction between a person and an activity, resulting in a positive psychological condition and satisfaction [46]. However, emotional attachment can also be acquired from information search, leading to hedonic impacts, such as amusement from viewing content [45]. Reading social media content for pleasure can be employed as one of the aspects for evaluating information relevance [65].

Consumers are those who actively participate in social media content in order to satisfy specific requirements, especially, information search [66]. They are unable to view all content and they are unwilling to view irrelevant content. Therefore, they may evaluate the benefit of information based on some specific indications so that they are encouraged to continue reading [67], and the usefulness of information is proven to have an impact on consumer engagement [68].

Recent studies have found that social media influencer-generated content can fulfill consumer satisfaction, for example, the information search can strengthen the connection between social media influencers and customers [69]. As such, social media influencer-generated content, including blogs, vlogs, and reviews, provide significant messages that fulfill individual satisfaction and consequently encourage the engagement of consumers with social media influencer-generated content [2]. Regarding this study rationale, the theory of information relevance is adopted to identify how consumer satisfaction is probably influential in encouraging engagement with social media influencer channels. These intentions are likely to demonstrate in the intention to consume, contribute to social media influencer-generated content, or create content on social media platforms [22]. Additionally, the relationships between customer engagement, social media influencer-generated content, and endorsed brands can be elaborated by the observational learning theory that posits the consumer attitudes and behaviors derived from social interactions and learning. As social media influencers are considered as reliable outside parties, the social learning of consumers happens when social media influencers express their influence through engaging with consumers. The endorsed brand learning of consumers is performed through the consumption, contribution, and creation of content on social network platforms and probably builds close relations with the endorsed brands [2,22]. Thus, the association between information relevance theory and observational learning theory provides support to an inspection, the links to consumer-influencer engagement behavior, and may develop brand evangelism that represents sustainable consumer-brand relationships.

2.5. Consumer-Based Digital Content Marketing Concept

Digital content marketing has been utilized across various sectors, comprising durable goods, consumer packaged goods, and services [49]. This approach is designed to build, strengthen, or sustain customer relationships, raise brand awareness, encourage engagement, or help develop customer loyalty [70]. Under the context of content marketing, digital content marketing reveals the execution of activities on digital platforms, for example, the corporate web page, online communities, blogs, vlogs, social network, and mobile apps [71]. The Uses and Gratification Theory, which explains why individuals select or interact with specific content or platforms to respond to their needs, is applied in this study, as well as identifying three consumer-based digital content marketing antecedents, including the functional motive, hedonic motive, and authenticity motive [49].

The functional motive was defined as the basic requirements of consumers for information or learning about brands acquired from digital content marketing. Consumers are likely to access social media content for practical reasons, aiming to gain insight into brands so as to properly make a purchase decision [49]. The businesses build brand communities on social media and use utilitarian informational content to promote consumer-brand and consumer-consumer interactions, resulting in better engagement [72]. Considering the context of influencer marketing, social media users wish to acquire useful information from influencer-generated content, and the cognitions of users have a significant impact on an influencer’s reputation [47].

The hedonic motive was defined as the underlying emotional requirements of consumers for brand-relatedness, interestingness, amusement, attractiveness, pleasantness, enjoyment, and relaxation acquired from digital content marketing [73]. The hedonic motive involves the role of driving consumers’ emotions and experiences in selecting and interacting with digital content marketing, including a requirement for amusement [49]. Considering the context of influencer marketing, social media users wish to view interesting content generated by influencers since it affects the emotional attachment and encourages people to follow influencers and introduce influencers to other people [47].

The authenticity motive was defined as the underlying requirements of consumers for brand-related righteousness, integrity, and meaning acquired from digital content marketing [74]. Meanwhile, the functional and hedonic motive of consumers represent generic antecedents based on U&G Theory. Hollebeek [49] represents the authenticity motive to adapt the third social driver that reflects requirements of actors for connection with other people. In the context of influencers, Rapp [48] describes that the authenticity is defined as the beauty of influencers whose content do not oversell product reviews and only who only review products that they have tried and liked, as well as providing content based on their love [75]. Moreover, Pornsrimate and Khamwon [24] refer to the influencers who use their internal motivation and inner need to create content on social media in order to express their image or personality.

Based on the association between information relevance theory, and consumer-based digital content marketing. This study provides the topicality, novelty, understandability, and reliability of content that are considered as a functional motive for consumers’ underlying utilitarian information; the interestingness of the content is considered a hedonic motive for consumers’ underlying emotional information and the influencers’ authenticity of content is considered an authenticity motive, according to the concept of the consumer-based digital content marketing literature [49].

2.6. Consumer-Influencer Engagement Behaviors

Digital consumer engagement behavior is defined as a behavioral manifestation of consumers toward social media content. It is widely acceptable that engagement with the digital behavior of consumers consists of three characteristics as follows: consuming, contributing, and creating social media content [76]. These three characteristics of digital engagement behavior indicate low, moderate, and high levels of engagement with digital behavior [77]. To consume available social media content is considered as the low level of engagement with digital behavior. In this state, consumers read or view content, but have no active contribution or creation of content [76]. The consumers have active contributions when commenting and sharing similar opinions, representing a moderate level of engagement with digital behavior [78]. Creation occurs after consumers upload, publicize, or create content on the brand platforms. Creation indicates the high level of engagement with digital behavior [22].

As social media influencers significantly affect the decision-making of consumers, brands are more likely to share brand-embedded content via channels of social media influencers [2]. Considering the context of social media influencers, the digital engagement behaviors of consumers are regarded as consumer-influencer engagement behaviors. As for the concept of consumer-influencer engagement, it involves the intention of consumers to engage with content created by social media influencers, and indicates their intention to consume, contribute, and create content relevant to the brand endorsement of social media influencers [79]. Brand-related content may be consumed by viewing and looking for brand-related posts on social media channels of influencers. They have active contributions when they like, comment, and share brand-related posts with similar opinions. Creation takes place after consumers upload stories and video clips related to influencers and brands on social media channels [22]. These three consumer-influencer engagement behavior dimensions indicate the effectiveness of social media influencer-generated content in consumer engagement [2].

2.7. Brand Evangelism

The term “brand evangelism” was initially mentioned by Kawasaki (1991) to express strongly sustainable consumer-brand relationships involving a high level of word-of-mouth communication [51]. Although word-of-mouth and brand evangelism have certain similarities, brand evangelism is more powerful and extends beyond the sharing of brand-related content. Brand evangelism can be thought of as an advanced stage of word-of-mouth, as it helps convince other people to engage with the same brand, encourage purchasing intentions, and degrade competitors in the market [80]. Thus, brand evangelism is more emotionally oriented than word-of-mouth [81] and can serve as unofficial brand ambassadors, as it is considered a marketing tool that is far more effective than positive word-of-mouth communication [52]. Brand evangelists are willing to convince other people to pay attention to their affection for a brand’s style and disseminate positive comments about a brand [82], have deep emotional relationships with a brand, and are sincerely connected to a brand [83].

The consumers who have become brand evangelists feel compelled to share their passion for their brand with other people [80], express their dislike of other brands selling the same product [84], and actively inform others about their good brand experience. They also show strong support to the brand by not only sharing good recommendations but also purchasing products, spreading positive feedback, and complimenting the brand and they have a strong desire to endorse the brand and persuade others to buy it, as well as criticizing competing products and attempting to defend the brand [85]. The marketers always prefer to develop such a sustainable bond between brand and consumers and, as a result, consumers become more loyal to an organization. The consumers assist marketers in brand promotions and it depends on their willingness to become brand evangelists [86] and unpaid brand spokespersons [87]. According to the behavioral model of Becerra and Badrinarayanan [88] adopted in this study, brand evangelism is characterized by three brand-related behaviors: the desire to buy the brand’s products (purchasing intentions), the inclination to praise the brand (positive brand referrals), and the proclivity to make negative comments against competing brands (oppositional brand referrals).

2.8. Research Framework and Formulation of Hypotheses

When searching for information, consumers are encouraged to engage with media content. The studies propose that consumer motivations for the information search have a positive relationship with the intent to consume, contribute, and create content on social media platforms [89]. The consumers are more likely to rely on social media influencers’ advice when making a decision, so they are encouraged to browse and view content, such as current trends, stories, and updates about brands on social network channels of influencers [79]. Moreover, information-seeking motives of consumers stimulate the intent to engage in social media channels of influencers in the form of liking, commenting, and sharing content generated by the social media influencers [90]. The consumers also agree to create posts about current trends shared by social media influencers on social media channels, in order to show that they are prominent and distinctive [21]. Thus, the combination of information relevance theory, consumer-based digital content marketing, and consumer-influencer engagement behavior lends support to an examination.

2.8.1. Relationship between Topicality of Content and Consumer-Influencer Engagement

Topicality has been classified as the first and most fundamental condition for relevance [46]. Topicality evaluates the extent to which consumers perceived information related to their interested topic and current needs [45]. The research of Albassam and Ruthven [50] found that topicality is the most significant criterion at all stages of searching for video content. Topicality is the matching of information contained in the video with the search topic or interest of consumers. In the present time when information is abundant on the online platform, it is difficult for consumers to read every content, and they refuse to read irrelevant information [67]. Therefore, consumers choose only what they are interested in. If consumers are confident that the information they have received or the information they search for is about a topic that they are interested in, their desire to read or access that information will become increased. If the connection between the information’s relevancy and the information needs of consumers is high, it represents topical relevance [45], probably affecting customer engagement. Moreover, according to the research of Weng and Menczer [91] on topicality and the impact on Twitter, they found that high topicality diversity is not the factor contributing to the growth of individual social impact. To concentrate on one or more topics of interest among followers is likely to be a sign of expertise. The active followers tend to gain several followers by ensuring focused topical interest. Focused topical preferences enhance the content appeal of common users. Based on this, the following hypothesis was proposed in order to ensure the influence of topicality of content on consumer-influencer engagement.

Hypothesis 1. 

The topicality of content has a significant positive impact on consumer-influencer engagement on Instagram.

2.8.2. Relationship between Novelty of Content and Consumer-Influencer Engagement

The novelty of content refers to at which level the information is perceived to be up to date [46], uniqueness of the information [92], difference from existing knowledge [45], or fresh experience, so a novelty of information is identified by new and unusual experiences that are different from past experiences [93]. If the influencer-generated content have never been seen or have been rarely seen, it should be easier to draw the attention of consumers because they are normally attracted to unique and distinctive information [94]. The consumers consider new messages as internal rewards as they are equipped with unusual and unique information. Such unique and distinctive information draws the attention of the consumers [92]. In contrast, if consumers have been familiar with the content, it probably does not cause any intellectual change and will decrease their motivation and interest in such content [45]. The research of Hamzah et al. [95] found that the novelty of content significantly affects the engagement of consumers with brand-related posts on social networks. Moreover, Tafesse [96] investigated audience response on Facebook business pages, and the results report that brand-generated novel content significantly has a positive impact on audience’s likes and shares. Similarly, Mendelson [92] revealed that novelty of the communication can contribute to higher consumer engagement. Consequently, the following hypothesis was formulated.

Hypothesis 2. 

The novelty of content has a significant positive impact on consumer-influencer engagement on Instagram.

2.8.3. Relationship between Understandability of Content and Consumer-Influencer Engagement

The understandability of content refers to at which level users are certain that information is readable and understandable [45]. Due to the overwhelming amount of online information, straightforward information can enable users to quickly understand and process the information as required [97], while users are likely to discard complicated content publicized on social media [98]. Straightforward information can effectively encourage users to be willing to read and have positive emotions that can help strengthen the emotional relationship between users and influencers [46]. According to past studies, the understandability of content contributes to the information quality and positively affects influencer-generated content adoption among users [99]. Additionally, the understandability of content positively connected to the amusement of viewing blogs and the intent to visit a tourist destination [46] and can also significantly have a positive impact on the emotional attachment between influencers and their followers [47]. Therefore, the following hypothesis was created to investigate the association between the understandability of content and consumer-influencer engagement.

Hypothesis 3. 

The understandability of content has a significant positive impact on consumer-influencer engagement on Instagram.

2.8.4. Relationship between Reliability of Content and Consumer-Influencer Engagement

In this study, the reliability of content refers to at which level the information is considered real, correct, or reliable [45]. If consumers are certain that the content created by an influencer is reliable, they will admit it and pay greater attention to the content or influencer. Otherwise, they will not continue to view the content [98]. Influencer-generated content might affect influencers who are regarded by their followers as opinion leaders [100]. Credible information is more interesting and trustworthy than incredible information because it can reduce the perceived confusion of consumers about the information and ensure more effectiveness [101]. The information for consumers provided by a reliable endorser needs to be trustworthy. It is likely for the influencers who were trusted by their followers to be able to raise positive attitudes toward the endorsed brands and stimulate the buying decisions of customers [27]. According to previous studies, the importance of the social influencer-generated reliability of content contributes to the information quality and emotional attachment between influencers and their followers. This eventually causes social media users to follow or introduce influencers to other followers [47]. Based on this, the hypothesis was established as follows:

Hypothesis 4. 

The reliability of content has a significant positive impact on consumer-influencer engagement on Instagram.

2.8.5. Relationship between Interestingness of Content and Consumer-Influencer Engagement

The interestingness of content represents the level of attraction perceived from viewing the content published on social media, including perceived enjoyment, satisfaction, and delight acquired from the content [46]. The interestingness of content enables brands to develop emotional connections with consumers. The reason is that interesting information is more likely to be accepted by consumers, meet the entertainment requirements of consumers, and stimulate positive emotional conditions [102], and if people perceived interestingness from the content, they are likely to continue reading it [46]. The consumers are encouraged to experience a sense of enjoyment by viewing interesting posts created by social media influencers, such as funny content, or sharing entertaining videos clips and stories with friends who are of similar opinions [89]. The consumers willingly feel pleasure and share their experiences with similar-minded friends in the form of liking, commenting, and sharing the social network content [22]. Previous studies have also revealed that hedonic motivations affect the intention of consumers to create posts on social networks and proposed that consumers voluntarily upload content on social networks [78]. Muntinga et al. [76] stated that the seeking of entertaining or interesting content boosts online engagement (consumption, creation, and contribution). Furthermore, consumers are more willing to engage with social media influencer-generated content when they perceived that the content is interesting or entertaining [79]. Thus, the following hypothesis was formulated.

Hypothesis 5. 

The interestingness of content has a significant positive impact on consumer-influencer engagement on Instagram.

2.8.6. Relationship between Influencers’ Authenticity of Content and Consumer-Influencer Engagement

The influencers’ authenticity of content refers to the fact the influencers use their internal motivation and inner need to create content in order to express their images, provide content based on their passion or inspiration, and generate content that they have tried and liked. Numerous research illustrated that influencers’ authenticity of content influences consumers’ behavioral intentions, such as the intention to suggest, take advice, and buy the products [24], as well as affecting the attachment bonds between the influencers and the followers [103], and engaging them to the brand [104]. Therefore, the following hypotheses were formulated to explore the association between influencers’ authenticity of content and consumer-influencer engagement.

Hypothesis 6. 

The influencers’ authenticity of content has a significant positive impact on consumer-influencer engagement on Instagram.

2.8.7. Relationship between Consumer-Influencer Engagement and Brand Evangelism

Influencer-generated content can affect the opinions and behaviors of the followers [3]. Influencers provide their followers with knowledge and advice by regularly creating meaningful content on social media channels. Normally, they are specialized in specific areas, including fashion, beauty, cuisine, healthy life, lifestyle, or excursion, while trying to build close, sustainable relationships with them [44]. The marketers strategically select social media influencers to endorse brands, aiming to generate shareable content that take advantage of the popular social media influencer for brand development [17]. According to recent studies, the content sharing by social media influencers efficiently moves positive attitudes toward the influencers as well as the brands [21]. In addition, the previous results can also apply to consumer-influencer relationships. If consumers greatly interact with social media influencers and build a strong emotional connection, they will attempt to continuously maintain the relationship [7]. Furthermore, as for the concept of consumer-influencer engagement, it involves the intention of consumers to engage with content created by social media influencers, and indicates their intent to consume, contribute, and create content relevant to the endorsement of social media influencers [79]. Consumer-influencer engagement behavior represents how the popular and powerful social media influencers significantly develop close relationships between social media influencers and endorsed brands [21]. Meanwhile, it significantly helps facilitate the enhancement of loyalty [23] and brand evangelism [24]. Thus, the following hypothesis was formulated.

Hypothesis 7. 

Consumer-influencer engagement has a significant positive impact on brand evangelism.

In this study, the conceptual framework was developed according to related research and theories, as shown in Figure 1.

3. Method

3.1. Sample Characteristics

The target population was Instagram users, aged between 18–30 years old, which is the largest group of Instagram followers [105], as well as the target group that is following and engaged with the content of fashion micro-influencers on Instagram. A micro-influencer is referred to as an opinion leader who has between 1000 and 100,000 followers [60] and they habitually post several photos or videos on fashion each week. This study was conducted with the audience who follow micro-influencers on Instagram because they can communicate product information to their followers easily [43], especially among the younger generations [40].

Moreover, the Instagram platform was chosen to be the subject of this study because Instagram is the fastest growing social networks platform [30] and the number of Instagram users in Thailand is on rising trend [106]. As of June 2022, official statistics from NapoleonCat [107] show that the estimated number of Instagram users in Thailand were 20,242,500 accounts, representing 28.90% of the Thai population. In addition, this study focuses on the fashion industry sector because it produces a significant contribution to the global economy [9]. The consumers are likely to become more sensitive to fashion and their buying behaviors are largely affected by fashion trends [12]. Instagram is the platform mostly used by fashion influencers and this popularity tends to exist in the years ahead. In addition, the engagement rate on Instagram is higher than other social network platforms [9]. The sample size for structural equation modeling should be at a minimum of 10 times the number of items [108]. As for the proposed model, it comprised seven items of one dependent variable and thirty-one items of seven independent variables. Therefore, a sample size at a minimum of 310 was suitable.

3.2. Data Collection Procedure

This method is based on the data collected by HypeAuditor [109], which reports the top fashion micro-influencers on Instagram in Thailand with followers ranging between 1000 and 100,000. There are a total of 42 accounts of fashion micro-influencers. The researcher coordinated with 42 fashion micro-influencers to ask for their cooperation to share the questionnaire online with their Instagram followers. In this regard, there are a total of 16 fashion micro-influencers who cooperated in sharing the questionnaire link. To collect the cross-sectional data, the respondents were invited to fill in an online survey from fashion micro-influencers from June to July 2022. The first part of the online survey consisted of three screening questions aiming to ensure that each respondent was eligible to participate in this study: (1) Are you currently following fashion micro-influencers? (2) Have you ever engaged with content of fashion micro-influencers whom you are following? (3) Is your age between 18 and 30? The respondents were allowed to proceed with the survey after answering the screening questions. In order to avoid repetitive responses, the same Internet Protocol address was only permitted to submit data once. At the end of the data collection period, 545 questionnaires were returned from 16 fashion micro-influencers. A total of 46 returned questionnaires were discarded because they were not consistent with the inclusion criteria. Therefore, the data analysis was conducted using the data of 499 respondents.

3.3. Instrumental

The quantitative method and the data acquired from a closed-ended questionnaire were used to evaluate the constructed model. The first section of the questionnaire contained screening questions. The second section presented personal information. The final section included the measurement scales adapted from related studies. Similar to many of the previous studies, a five-point Likert scale of agreement was used in this study, ranging from strongly disagree (1) to strongly agree (5), to measure thirty-one items corresponding to eight constructs. The adaptation of the topicality of content measurements was performed based on Xu and Chen [45] and Albassam and Ruthven [50]. The scale to measure the novelty, understandability, reliability, and interestingness of content were adapted from Chen et al. [46] and Hollebeek [49]. The scale to measure the authenticity of influencers was adapted from Rapp [48] and Pornsrimate and Khamwon [24]. The scale to measure the consumer-influencer engagement was based on the studies of Vale and Fernandes [110] and Piehler et al. [78]. Finally, the scale to measure brand page evangelism was based on the studies of Riorini and Widayati [111], Swimberghe et al. [82], Munasinghe and Dissanayake [112], and Pornsrimate and Khamwon [24].

4. Data Analysis and Results

4.1. Descriptive Analysis

The data in this study were obtained from 499 respondents. Most of the respondents were 354 females (70.94%), followed by 88 males (17.64%), and 57 LGBTQ+ (11.42%). Of all the respondents, 245 were between 26 and 30 years old (49.10%). For the educational background, 414 respondents (82.97%) graduated with a bachelor’s degree. All the 261 respondents were students (52.30%) and 201 respondents earned 10,000–30,000 baht each month (40.28%). An overview of the demographic characteristics of the respondents is presented in

Table 1. Demographic characteristics of respondents (N = 499).

Variable	Category	Frequency	Percentage
Gender	Female	354	70.94
	Male	88	17.64
	LGBTQ	57	11.42
Age	18–21 years old	183	36.67
	22–25 years old	71	14.23
	26–30 years old	245	49.10
Education level	Below bachelor’s degree	46	9.22
	Bachelor’s degree	414	82.97
	Master’s degree	33	6.61
	Doctoral degree	6	1.20
Occupation	Government officers/employees	38	7.62
	Students	261	52.30
	Private company employees	84	16.83
	Business owners	116	23.25
Income	<10,000 baht	185	37.07
	10,000–30,000 baht	201	40.28
	30,001–50,000 baht	64	12.83
	>50,000 baht	49	9.82

.

4.2. Data Analysis

The optimal approach for responding to the research objectives is SEM because it is able to analyze indices of fit and relationships between various dependent and independent variables [113]. In this study, the partial least squares structural equation modeling (PLS-SEM),using the SmartPLS v. 3.3.9 software application [114], method was adopted for evaluation of the measurement and structural model, aiming to estimate casual-predictive relations [115]. PLS-SEM can help clarify and forecast, as well as assuring the practical relevance of causal explanations and showing that it is more effective than the regression analysis [116], and predict constructs measured by a large number of indicators and second order constructs [117]. Moreover, PLS-SEM does not only have high power for small sample size research, but it is also commonly regarded as the preferred method for studies with the aim of developing and exploring theory [116].

4.3. Common Method Bias Test

Because the data collection was created from a single survey, the evaluation of a possible common method bias should be performed [100]. Common method variance can inflate noticeable correlations, resulting in false support for the tested theories [118]. Therefore, it is necessary to remove the impact of common method variance in a cross-sectional research design [119]. Harman’s single-factor test [120] was used in this study to examine CMV. The principal component analysis (PCA) is used to conduct the test as proposed by Tehseen et al. [121]. Based on the unrotated principal axis factoring analysis, a single factor indicates 38.466% of the variance (

Table 2. Common method variance via single factor.

Factor	Initial Eigenvalues			Extraction Sums of Squared Loading
	Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %
1	12.375	39.918	39.918	11.924	38.466	38.466
2	3.526	11.376	51.293
3	1.363	4.395	55.689
4	1.266	4.085	59.774

Extraction method principal axis factoring.

), which is below 50%. The findings revealed that every indicator met the pass criteria of the test [122]. In addition, the R² value is 0.592 (Figure 2), which is below 0.7, and the VIF value is 2.45 (VIF = 1/(1 − R Square). If all the VIFs derived from a comprehensive collinearity test are equal to or below 3.3, the model should be without a common method bias, as proposed by Kock [123]. Therefore, a common method bias was not found in this study, representing that there is no major problem affecting variable correlation.

4.4. Multicollinearity Test

When two or more correlated predictors in a model provide redundant information about the response, multicollinearity occurs. This study tested the multicollinearity between the antecedents of endogenous constructs [124] and verified that the inner VIF is less than 5 (

Table 3. Collinearity statistics (VIF).

	BEV	CIE	CSM	CTB	CRT	AUT	INT	NOV	OBR	PBR	PIT	REL	TOP	UND
BEV	1.000		1.000	1.000	1.000				1.000	1.000	1.000
CIE
CSM
CTB
CRT
AUT		1.897
INT		2.275
NOV		2.099
OBR
PBR
INT
REL		2.242
TOP		1.760
UND		2.054

Inner VIF values.

), as recommended by Fernández-Portillo et al. [125]. Thus, there is no multicollinearity in this study.

4.5. Measurement Model Analysis

After 499 respondents completely filled in the questionnaires, the convergent validity was tested based on the confirmatory factor analysis. The results revealed that the standardized factor loading of every item was greater than 0.6 [108], as shown in Figure 2 and

Table 4. Standardized item loadings, construct reliability, and convergent validity.

Constructs	Items	Loading	CA	AVE	CR
Topicality of content (TOP)			0.823	0.739	0.895
	TOP1: Micro-influencer provides the fashion content that is relevant to
	the interested topic of the followers.	0.836
	TOP2: Micro-influencer provides the fashion content that is highly	0.878
	related to followers’ needs.
	TOP3: Micro-influencer provides the fashion content that matches the followers’ interests.	0.865
Novelty of content (NOV)			0.799	0.617	0.866
	NOV1: Micro-influencer-generated fashion content is new and up to date.	0.781
	NOV2: Micro-influencer-generated fashion content is trendy.	0.746
	NOV3: Micro-influencer-generated fashion content has a lot of information that I had not been aware of before.	0.778
	NOV4: Micro-influencer-generated fashion content is uniquely different from others.	0.834
Understandability (UND)			0.793	0.827	0.906
	UND1: Micro-influencer-generated fashion content is understandable.	0.928
	UND2: Micro-influencer-generated fashion content is easy to read or watch.	0.891
Reliability of content (REL)			0.809	0.723	0.887
	REL1: Micro-influencer-generated fashion content is reliable.	0.866
	REL2: Micro-influencer-generated fashion content is accurate.	0.839
	REL3: Micro-influencer-generated fashion content is consistent with facts.	0.846
Interestingness of content (INT)			0.764	0.679	0.864
	INT1: Micro-influencer-generated fashion content is interesting.	0.834
	INT2: Micro-influencer-generated fashion content is entertaining.	0.796
	INT3: Micro-influencer-generated fashion content is attractive.	0.841
Influencers’ authenticity of content (AUT)			0.718	0.623	0.830
	AUT1: Micro-influencer uses inner desire, when he/she generated fashion	0.674
	content to represent his/her images.
	AUT2: Micro-influencer uses inner desire, when he/she generated fashion	0.885
	content to represent his/her personality.
	AUT3: Micro-influencer-generated content only reviews fashion products	0.793
	that he/she has tried and liked.
Consumer-influencer engagement (CIE)—Consuming (CSM)			0.931	0.936	0.967
	CSM1: I often view pictures or photos posted by fashion micro-influencers	0.967
	on Instagram.
	CSM2: I often watch videos posted by fashion micro-influencers on	0.968
	Instagram.
Consumer-influencer engagement (CIE)—Contributing (CTB)			0.712	0.773	0.872
	CTB1: I often like content posted by fashion micro-influencers on	0.839
	Instagram.
	CTB2: I often comment on images or videos posted by fashion	0.917
	micro-influencers on Instagram.
Consumer-influencer engagement (CIE)—Creating (CRT)			0.858	0.875	0.933
	CRT1: I often post pictures or photos related to content posted by	0.934
	fashion micro-influencers on Instagram.
	CRT2: I often upload videos related to content posted by fashion	0.937
	micro-influencers on Instagram.
Consumer-influencer engagement (second-order construct)			0.922	0.727	0.940
	Consuming	0.935
	Contributing	0.857
	Creating	0.957
Brand evangelism (BEV)—Purchase intentions (PIT)			0.882	0.894	0.944
	PIT1: If my favorite micro-influencer, whom I follow, creates content	0.945
	about any brand’s fashion products, I intend to buy that brand’s products.
	PIT2: If my favorite micro-influencer, whom I follow, creates content	0.946
	about any brand’s fashion products, I often buy that brand’s products.
Brand evangelism (BEV)—Positive brand referral (PBR)			0.889	0.818	0.931
	PBR1: If my favorite micro-influencer, whom I follow, creates content	0.906
	about any brand’s fashion products, I will recommend my friends to buy
	that brand’s products.
	PBR2: If my favorite micro-influencer, whom I follow, creates content	0.909
	about any brand’s fashion products, I will help share the positive news
	about that brand.
	PBR3: If my friends would like to buy fashion products, I recommend	0.897
	my friends to buy products of the brands that my favorite
	micro-influencers, whom I follow, create content for.
Brand evangelism (BEV)—Oppositional brand referral (OBR)			0.940	0.944	0.971
	OBR1: If my favorite micro-influencer, whom I follow, creates content	0.972
	about any brand’s fashion products, I will help edit the information
	for that brand in case someone makes a negative comment.
	OBR2: If my favorite micro-influencer, whom I follow, creates content	0.971
	about any brand’s fashion products, I will help protect that brand
	in case of negative mentions.
Brand evangelism (second-order construct)			0.940	0.736	0.951
	Purchase intentions	0.897
	Positive brand referral	0.961
	Oppositional brand referral	0.876

CA = Cronbach’s Alpha, AVE = Average Variance Extracted, CR = Composite Reliability.

. The Cronbach’s Alpha (CA) values were above 0.7, compared with the model ranging between 0.712 and 0.940. All the values exceeded the recommended threshold of 0.7 [126], demonstrating the highly reliable scales. The result of the Average Variance Extracted (AVE) showed that each component in the model was greater than 0.5; a threshold value suggested by Fornell and Larcher [127], with values ranging from 0.617 to 0.944. The Construct Reliability (CR) of each construct was greater than 0.8 and the model ranged from 0.830 to 0.971, which fulfilled the threshold criterion of 0.8 according to Nunnally [128]. Thus, the scale’s convergent validity is at a high level. In summary, the measurements were authentic and accurate. The results are presented in Table 4.

To confirm discriminant validity, the cross-loading and the square root of the AVE, ASV, and MSV were tested. The comparison of the bolded diagonal of each construct was performed with its correlation coefficients corresponding with other constructs. The associated correlation coefficient of each construct was less than the AVE square root. It is advisable that the AVE square root must be higher than the value in each row and column. The findings revealed that the correlation of each construct is greater than its correlation with other constructs. Likewise, the maximum shared variance (MSV) should be less than the AVE, but higher than the average shared variance (ASV). The acquired results revealed that the scales’ discriminant validity is satisfactory [127]. Based on

Table 5. Discriminant validity.

	BEV	CIE	CSM	CTB	CRT	AUT	INT	NOV	OBR	PBR	PIT	REL	TOP	UND
BEV	0.858
CIE	0.767	0.853
CSM	0.708	0.704	0.967
CTB	0.682	0.655	0.666	0.879
CRT	0.723	0.644	0.878	0.742	0.936
AUT	0.453	0.349	0.272	0.415	0.293	0.789
INT	0.412	0.341	0.262	0.416	0.280	0.563	0.824
NOV	0.427	0.382	0.311	0.429	0.327	0.520	0.630	0.785
OBR	0.846	0.691	0.642	0.610	0.651	0.391	0.318	0.352	0.971
PBR	0.822	0.722	0.669	0.648	0.673	0.447	0.408	0.417	0.771	0.904
INT	0.556	0.690	0.630	0.611	0.661	0.396	0.398	0.397	0.648	0.826	0.946
REL	0.456	0.364	0.293	0.428	0.299	0.640	0.629	0.550	0.403	0.439	0.405	0.851
TOP	0.401	0.398	0.318	0.468	0.330	0.464	0.559	0.567	0.289	0.400	0.403	0.501	0.860
UND	0.324	0.259	0.196	0.327	0.207	0.504	0.603	0.609	0.211	0.332	0.336	0.590	0.553	0.910

Bold values indicate square root of AVE in the diagonal.

, each measured variable is distinct and discriminant from one another.

4.6. Path Coefficient and Structural Model Analysis

The evaluation of the structural model was performed using bootstrapping with 5000 subsamples from the original set of data [129]. The coefficient of determination, denoted R-Squared (R²), is a statistical measure in a model that determines how well the independent variables explain the variance in the dependent variable. This means R² shows the predictive ability of the model. The R² value for brand evangelism is 0.592, which indicates 59.20% of the variance in brand evangelism while the other variables have an influence on the remaining 40.80%. Moreover, the R² value for consumer-influencer engagement is 0.225. All the R² values met the threshold value of 0.20 according to Cohen [130]. The correlation between the independent factors and their influence on the dependent variable were characterized by the structural model (path coefficient). The SEM method, particularly the maximum likelihood estimation, is able to efficiently assess complicated models [131]. The detailed path coefficient effect sizes of this model are shown in Figure 2 and

Table 6. Path coefficients and hypotheses testing.

Hypotheses	Path	Path Coefficients (β)	t Statistic	p-Value	Results
H1	TOP → CIE	0.234 ***	4.135	0.000	Supported
H2	NOV → CIE	0.179 **	3.248	0.002	Supported
H3	UND → CIE	0.134 *	2.122	0.026	Supported
H4	REL → CIE	0.138 *	2.227	0.041	Supported
H5	INT → CIE	0.159 **	2.483	0.005	Supported
H6	AUT → CIE	0.110 *	2.042	0.047	Supported
H7	CIE → BEV	0.767 ***	43.428	0.000	Supported

*** = p-value ≤ 0.001, ** = p-value ≤ 0.01, * = p-value ≤ 0.05.

and the structural model’s results are demonstrated in Figure 3.

The goodness of fit of a variance-based model is identified by SRMR (standardized root mean square residual). The SRMR is a measure of fit, which is described as the standardized differentiation between the observed and predicted correlations. The SRMR has a positive bias, with an increased bias for small N and low df studies. As the SRMR is an absolute fit measure, a perfect fit is indicated by a value of zero. The SRMR has no penalty for model complexity. Normally, a good fit is indicated by a value of equal to or less than 0.080 [132]. In this study, the results showed an SRMR of 0.066.

4.7. Hypothesis Testing

The entire path coefficients and hypotheses are shown in Figure 2 and Table 6. The results revealed that topicality, novelty, understandability, reliability, interestingness, and influencers’ authenticity of content have positive and significant influences on consumer-influencer engagement (β = 0.234, t = 4.135), (β = 0.179, t = 3.248), (β = 0.134, t = 2.122), (β = 0.138, t = 2.227), (β = 0.159, t = 2.483), and (β = 0.110, t = 2.042). Therefore, the hypotheses (H1-H6) were proven and accepted as true. For the hypothesis (H7), the consumer-influencer engagement positively and significantly affected brand evangelism (β = 0.767, t = 43.428). Therefore, this hypothesis was accepted.

5. Discussion and Implications

5.1. Discussions

This research explored the impact of fashion micro-influencer-generated content, which has become a major marketing tool for brands all over the world. The results of this research reveal that fashion micro-influencer-generated content has a substantial impact on consumer-influencer engagement since it contributes to brand evangelism. This reflects the sustainable consumer-brand relationships. The first significant finding is that the topicality of content has a greater impact on consumer-influencer engagement (H1). This finding expands the existing body of knowledge in the context of influencer-generated content, according to the studies of Chen et al. [46] and Zhang and Choi [47], which were conducted in the same context and ignored the topicality of content into the theoretical framework. If the consumer-perceived information related to their interested fashion topic, they will pay more attention to consume the influencers’ content (e.g., watch fashion videos), contribute to the influencers’ content (e.g., comment on fashion images/videos), or publicize the related influencers’ content to other followers (e.g., post photos, upload fashion videos).

Secondly, the findings reveal that the novelty of content has an impact on consumer-influencer engagement (H2). This research finding expands the existing body of fact of Zhang and Choi [47] that the novelty of content contributes to consumers’ emotional attachments to influencers, while this study found that the novelty of content can build the consumer-influencer engagement. Accordingly, micro-influencers should attentively consider whether the fashion content is up to date, trendy, and different from other influencers because if the influencer-generated content is neither inventive nor notable among users, it will be denied. Moreover, if the micro-influencer-generated fashion content has a large amount of information that has never been unveiled to consumers, they will be excited about the new content and have a stimulated interest in following the content and promoting the consumer-influencer engagement.

Regarding the third finding, it is indicated that the understandability of content has an impact on consumer-influencer engagement (H3). This research finding expands the existing body of work by Zhang and Choi [47] that indicates that the understandability of content contributes to consumers’ emotional attachments to influencers, while this study found that the understandability of content is influential to consumer-influencer engagement. The reason could be because consumers are likely to search for useful information instantly and that comprehensible information can enable users to save their resources and time in a dynamic, changing, and information-abundant environment [99]. This finding can assist influencers in generating content. Furthermore, the reliability of the content is found to have a positive impact on consumer-influencer engagement (H4). If consumers are certain that the content created by an influencer is reliable, correct, and conforms to facts, they will admit it and focus more on the content of the influencer. Subsequently, they will have more engagement with the fashion content of micro-influencers. Otherwise, they will not read the content further [98].

In addition, the fifth crucial finding showed that the interestingness of content has a positive influence on consumer-influencer engagement (H5). This result expands the existing body of knowledge of Xu and Chen [45], which examined the characteristics of information relevance based on a functional perspective but overlooked a hedonic perspective. The interestingness of content is considered a hedonic motive for consumers’ underlying emotional requirements. Considering the social media context, consumers are stimulated to experience a sense of enjoyment by viewing interesting posts created by social media influencers, such as funny content or sharing entertaining video clips and stories with similar-minded friends [89], and have more engagement with the micro-influencers’ content about the brands or products that they are interested in or entertained by. This justifies the importance of the interestingness of content in driving consumer-influencer engagement behavior.

Moreover, this study provides primary evidence that influencers’ authenticity of content has significantly affected consumer-influencer engagement (H6). This result expands the existing body of knowledge of Xu and Chen [45], which only focused on a functional motive, as well as the studies of Chen et al. [46], which studied a functional motive and a hedonic motive in the context of influencer-generated content, but overlooked the authenticity motive. Authenticity is important for social media influencers [133]. Consumers prefer to engage with micro-influencers and use inner desire when they generate fashion content to represent their images or personality, because the consumers can feel the true identity of micro-influencers rather than macro-influencers or celebrities. Internet celebrities are regularly reconciling between their own reliability and the sponsored content they create [75]. Due to this issue, celebrities look for authenticity by means of “staging” [134,135]. Moreover, the consumers pay more attention to engage with micro-influencers if they review the fashion product that has actually been consumed and admired. Consumers need attractive social media influencers serving as a credible source of information and they also need an endorser who is able to provide authentic information through communicating with them in a friendly manner [27].

Finally, the significant findings suggest that enhancing followers’ engagement with influencers can increase brand evangelism (H7). This finding expands the existing body of knowledge of influencer marketing. Micro-influencer-generated content can contribute to fashion brand evangelism more effectively if there is a stronger followers’ engagement between micro-influencer-generated content on Instagram and followers. Consumer-influencer engagement behavior reflects that the popular and powerful social media influencers serve as a key factor driving close relationships between social media influencer-endorsed brands [21]. Brand evangelism strongly reflects sustainable consumer-brand relationships [88]. The marketers should develop such a strong sustainable bond between the brand and consumers and maintain long-term consumer-brand relationships, which has become increasingly challenging in today’s fashion business on social media networking.

5.2. Theoretical Contributions

Firstly, given the increasing importance of social media influencer marketing, previous studies have applied an influencers’ credibility model on followers’ purchase intentions, the social media micro-influencer characteristics affecting brand love and brand engagement, match-up congruence between influencers and the products fostering consumer purchase intentions, parasocial interaction of social media influencers with purchase intentions, influencer-generated content contributing to emotional attachment and information quality, and influencer travel blog content affecting perceived enjoyment. Nevertheless, this research expands the scope of studies to include social media micro-influencer marketing in relation to the relationship between micro-influencer-generated content and consumer-influencer engagement behavior by proposing an empirical research model based on a consumer-based digital content marketing concept, information relevance theory, observational learning theory, consumer-influencer engagement behavior, and brand evangelism to empirically test a conceptual framework. This has extended the body of knowledge.

Second, the previous research of Hollebeek [49] developed the concept of consumer-based digital content marketing (including functional, hedonic, and authenticity motives) in the form of the generation and publicizing of relevant and meaningful brand-related content to existing or future customers on digital platforms to boost proper brand engagement. Meanwhile, this article develops and extends the said research by applying the consumer-based digital content marketing concept to the context of influencer-generated content, aiming to foster consumer-influencer engagement.

Third, prior studies of Xu and Chen [45] explored the attribute of information relevance in only one aspect considered as a functional motive, including topicality, novelty, understandability, and reliability. Meanwhile, Chen et al. [46] and Zhang and Choi [47] conceptualized two aspects of a functional and a hedonic motive, including novelty, understandability, reliability, and interestingness, but they did not include topicality into the theory of information relevance. However, the results of this study revealed that topicality of content is a variable that has the most influence over consumer-influencer engagement. Thus, this study extends the body of knowledge that includes the topicality of content into the information relevance theory. If consumers are confident that the information they have received or the information they search for is about a topic that they are interested in, their desire to read or access that information will increase. If the connection between the information’s relevancy and the information needs of consumers is high, it represents topical relevance [45]. It is difficult for consumers to read all the content and they refuse to read irrelevant information [67], thus, the content that draws interest from consumers is supposed to have an impact on consumer-influencer engagement.

Furthermore, this article reinforces the current body of knowledge of Xu and Chen [45] in the context of influencer-generated content by highlighting the importance of the interestingness of content that it should be included as a hedonic motive for judging the relevance of information theory. The interestingness of content is likely to be another significant informational characteristic that enables audiences to evaluate information relevance [64]. In addition, based on the consumer-based digital content marketing concept, this research aims to expand the existing body of fact and the research results found that an authenticity motive should be considered as a component of the relevance of information theory. In summary, this study intends to extend the body of fact to identify the importance of the information relevance theory into three perspectives, comprising a functional, a hedonic, and an authenticity motive by exploring the significant impact of micro-influencer-generated content on the enhancement of consumer-influencer engagement.

Finally, most research focuses on studying consumers’ engagement in social media that influences customer satisfaction, commitment, brand trust, brand affect, word-of-mouth, or brand loyalty. However, this research places emphasis on brand evangelism, which is vital in social media networking sites because brand evangelism reflects that consumers strongly embrace the brand in a sustainable manner and develop a profound emotional bond with it. Brand evangelists can be seen as an advanced level of positive word-of-mouth because they intend to convince others to buy the brand, deter others from buying competing brands, and may even degrade the competitors of their cherished brands.

5.3. Managerial Implications

The results of this study encourage marketers and social media influencers to jointly create valuable content to stimulate consumption and contributions to content created by social media influencers. In addition, the content also stimulates consumers to generate their own content relevant to the endorsement of social media influencers on Instagram or other social network platforms, causing the brand evangelism to become strengthened. This reflects the sustainable consumer-brand relationships. The results provide practical implications for both influencers and brands intending to cooperate with micro-influencers. Furthermore, marketers are able to inspect whether the micro-influencer-generated content can draw consumers’ attention during the process of selecting influencers, especially from the perspective of users’ information requirements. Since micro-influencers obtain information from marketers and share such information with other users, they are able to play a critical role in providing propaganda for the brand. The marketers need to consider whether their strategies of using micro-influencers in digital marketing campaigns can effectively build sustainable brand evangelism.

In addition, to build brand evangelism, marketers must first understand the motivational factors that influence distinct consumer-influencer engagement behaviors. Once customers turn into brand evangelists, they are eager to act as a brand’s unpaid spokesperson, actively disseminate favorable brand experiences to their friends, urge others to buy the same brand, and discourage others from buying competing brands. As a result, it is a difficult task for competing brands to attract their attention. The motivational factors aim to foster consumer-influencer engagement as follows:

First of all, the topicality of content has the greatest impact on consumer-influencer engagement in fashion products. Thus, the marketers should focus on studying the customer insight in relation to consumers’ current interests in the fashion design, style, and trends in order to cooperate with micro-influencers in providing the fashion content that is highly related to consumers’ needs and matches the consumers’ interests.

Second, the novelty of content also has a greater impact on consumer-influencer engagement in the fashion industry. Accordingly, micro-influencers should focus more on considering whether the influencers’ content is innovative, up-to-date, fresh, and creative because if the influencer-generated content is neither inventive nor notable among users, it will be denied. Meanwhile, brands should opt to collaborate with micro-influencers that are uniquely different from others (such as macro-influencers or mega-influencers) so as to stimulate consumers’ interests in the content and differentiate the brand, encouraging consumers to consume the influencers’ content (e.g., watch fashion videos), contribute to the influencers’ content (e.g., comment on fashion images/videos), or share the related influencers’ content to other followers (e.g., post photos, upload fashion videos).

Third, the understandability of content also plays an important role in consumer-influencer engagement, thus influencer-generated content should be straightforward, particularly in the fashion field with specific expertise that is not familiar to consumers. Meanwhile, micro-influencers can provide explanations when they create content in order to ensure the accuracy and reliability of the fashion content, as well as the trust of consumers. Most importantly, the marketers and influencers should consider and ensure the content to be presented to consumers be consistent with the facts, causing the information to be shared to other consumers properly.

Moreover, the results reveal that the interestingness of content is important to driving the consumers’ intent to consume, contribute, and generate content that is initially created by social media influencers. Therefore, it would be beneficial if marketers and micro-influencers create entertaining and attractive content for the purpose of communicating brand-related stories, including pictures, animation, and video clips, that may be used to enhance the communication of brand-related information. Finally, marketers should collaborate with micro-influencers in presenting content that uses inner desire to represent the influencers’ images and personality through the influencers’ own words, pictures, or videos in a unique manner. Most importantly, they should express sincerity by providing honest content through friendly communication.

5.4. Limitations and Future Research

This research has studied only the scope of micro-influencer-generated content on Instagram, so the acquired data may be limited to Instagram users only. At present, consumers access various types of content through a variety of social media platforms, such as Facebook, Twitter, YouTube, TikTok, and others. Therefore, future research on consumer-influencer engagement may be conducted on a variety of social media platforms. Moreover, this study explores the association between influencers and social media users from the perspective of users’ information requirements. Next, research should integrate other moderating variables, such as gender or generation cohort, into the connection between influencer-generated content and consumer-influencer engagement. Secondly, the impact of influencer-generated content on consumer-influencer engagement is evaluated in this study. Next, research should examine whether influencer-generated content affects the attitudes of consumers toward influencers. Thirdly, the samples in this study were users with a Thai nationality, so the results are likely to be different in other countries due to differences in cultural values.