The purpose of this study is to analyze how positive behaviors toward influencers (customer interaction) and promoted products (looking for product information) can be achieved, taking into account influencer–product fit, in a fashion marketing campaign. In addition, account following and product involvement are examined as possible moderators in these relationships.
The data were gathered from online participants. The participants were Instagram users who already knew a popular influencer on the platform. The experimental design manipulated the types of picture posted by the influencer to observe customers’ reactions in terms of intention to interact with the influencer’s account and to look for further information about promoted products.
The authors’ findings suggested that influencer–product matches in posts on Instagram encourage users to search for information about promoted products but do not affect their intention to interact with influencers’ accounts. Nevertheless, customers’ reactions toward an influencer’s posts differ based on whether they are followers of the influencer and whether they are highly or lowly involved with the promoted product.
Both brands and influencers should properly manage influencer marketing actions. Brands should control influencers’ audiences and their involvement with featured products so that they are seen to promote them in a natural way. Influencers should endorse branded products that fit their own style; this will increase the interaction on their accounts.
This research contributes to a better understanding of how users can be encouraged to undertake positive online actions as regards influencers (interaction with their accounts) and promoted products (information search) in influencer marketing campaigns.
Esta investigación analiza cómo lograr comportamientos positivos hacia los influencers (mayor interacción del consumidor con la cuenta) y los productos promocionados (búsqueda de información sobre el producto), en función del ajuste influencer-producto en una campaña de marketing de moda. Además, se estudian el seguimiento de la cuenta y la implicación con el producto como posibles moderadores en estas relaciones.
Los datos de la investigación se recogieron a través de una encuesta realizada a usuarios de Instagram que conocían previamente a la influencer estudiada. A través de un diseño experimental se manipularon las publicaciones de la influencer para analizar las reacciones de los consumidores, más concretamente, sus intenciones de interactuar con la cuenta del influencer y de buscar más información sobre los productos promocionados.
Los resultados de la investigación sugieren que un buen ajuste entre los influencers y los productos promocionados incentiva a los usuarios a buscar información sobre éstos productos, pero no afecta a su intención de interactuar con la cuenta de Instagram de la influencer. No obstante, las reacciones hacia las publicaciones de la influencer difieren dependiendo de si los consumidores son o no seguidores de la influencer y del nivel de implicación con el producto promocionado.
El trabajo muestra la necesidad de gestionar adecuadamente las acciones de marketing en las que participan influencers. Las marcas deben conocer el público al que se dirigen los influencers y su implicación con los productos promocionados, para que la publicación promocionada resulte natural. Por su parte, los influencers deben promocionar los productos de marcas que se ajusten a su propio estilo para así incrementar la interacción del público con sus cuentas.
Este trabajo indica algunas de las características que han de tener las campañas de marketing con influencers cuando su objetivo es estimular comportamientos de los consumidores tales como la interacción con la cuenta y la búsqueda de información sobre el producto.
Influencers, seguidores, Instagram, intención de interactuar, búsqueda de información de productos, implicación con el producto
Tipo de trabajo
Trabajo de investigación
Belanche, D., Flavián, M. and Ibáñez-Sánchez, S. (2020), "Followers’ reactions to influencers’ Instagram posts", Spanish Journal of Marketing - ESIC, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/SJME-11-2019-0100Download as .RIS
Emerald Publishing Limited
Copyright © 2020, Daniel Belanche, Marta Flavián and Sergio Ibáñez-Sánchez.
Published in Spanish Journal of Marketing – ESIC. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
Customers increasingly use social networks (SN) to update themselves about brands and products in which they are interested (Casaló et al., 2017; Brandão et al., 2019). Instagram is the fastest growing SN (WebsitePlanet, 2019). This SN is characterized by its visual nature (Belanche et al., 2019; Kim et al., 2017), which allows users (i.e. personal profiles, brands, influencers) to publish visual materials (e.g. photos, videos) that they can edit with tools available on the platform (e.g. filters). In recent times Instagram has focused more on its visual appeal, enhancing the user experience by adding new features (e.g. stories, Instagram TV). This has turned Instagram into one of the most used SNs, recently reaching the figure of one billion active users, half of them using the platform daily (Statista, 2019). In addition, because it has a higher level of engagement than other SNs, it is widely used by brands to promote their products (RivalIQ, 2019). After viewing a product, Instagram users tend to perform positive actions, such as information searches, follow the brand account or make a purchase (Facebook, 2019). This happens particularly in the fashion industry, whose visual nature fits with the essence of Instagram. Thus, Instagram has become a valuable touch point for customers, serving them as a valued and inspirational tool for making their purchase decisions (Facebook, 2019).
Instagram is regarded as the natural platform for developing marketing actions with influencers, so nearly nine out of ten marketers prefer to use it in their influencer marketing campaigns (Relatabe, 2019). Influencers are, in essence, present-day opinion leaders (Casaló et al., 2018) and, like all opinion leaders, they exert unequal influence on the decision-making processes of others (Rogers and Cartano, 1962; Blasco-López et al., 2019). They are considered as role models by other users, who follow their advice because they trust their beliefs and opinions (Casaló et al., 2018). These influencers differ from celebrities in that they were born on SNs, and established their reputations among their followers through the actions they performed on these platforms (Schouten et al., 2019). In contrast, the fame enjoyed by celebrities often stems from their activities outside SNs (e.g. TV, music, sports). Consequently, influencers are able to develop closer ties with their followers, which leads to the establishment of credible and trustworthy relationships (Djafarova and Rushworth, 2017). Their actions are perceived as beneficial by their followers; they provide them with inspiration and help them to discover new brands/products which, in the end, the followers might purchase or recommend (Rakuten Marketing, 2019). Marketers are aware of this process, through which they can bring their products closer to their potential customers, and annually invest more resources in this area to influence marketing campaigns (Influencer Marketing Hub, 2019).
Despite the importance of influencer marketing on Instagram, studies in this research area are scarce (Casaló et al., 2018). Previous studies have focused on the metrics used to measure the impact of influencers’ actions (Arora et al., 2019), the different uses made of the tools provided by the SNs (Erz et al., 2018), brand–influencer relationships in influencer marketing campaigns (Boerman, 2020; Jiménez-Castillo and Sánchez-Fernández, 2019) and the type of content uploaded by influencers (Casaló et al., 2018). However, giving the importance of this phenomenon, more research is needed to understand the efficacy of influencer marketing campaigns in terms of their impact on customers’ online behaviors. Therefore, with the aim of going into greater depth in this research area, the present study analyzes how different online behaviors related to influencers’ accounts (intention to interact) and products (intention to look for information) can be affected by the fit (vs non-fit) of fashion influencer–product posts. The moderating role of the user (follower vs non-follower) and involvement with the product category (fashion) are also included in the model. Our findings shed light on the effect of congruity in the content published by influencers collaborating with brands. Both influencers and brands can apply our findings to plan effective influencer marketing actions that result in positive user reactions.
2. Literature review and hypothesis development
Influencer marketing has become a powerful online instrument in customer persuasion. A survey conducted with more than 800 brand managers estimated that their investment in influencer marketing grew from US$1.7bn in 2016 to US$6.5bn in 2019 (Drummond-Butt, 2019). Moreover, Instagram has recently experienced extraordinary growth, both in terms of its popularity as a SN and as a marketing channel where companies spread their commercial messages (Kim et al., 2017; Serra-Cantallops et al., 2018; Rietveld et al., 2020). In this sense, brands use Instagram not only as a direct channel or advertising medium (Belanche et al., 2019), but also as a platform to better reach their target audiences through Instagram influencers. Consequently, Instagram has become the preferred SN channel for brands to run influencer-based marketing campaigns (Relatabe, 2019; Sanz-Blas et al., 2019).
Influencers use their Instagram accounts to present new products (e.g. fashion outfits) to encourage users to increase their interaction with their accounts (e.g. number of likes, comments, sharing content and attracting new followers) and users’ interest in the promoted products (as a marketing goal). These behavioral intentions have been chosen for this study because they represent essential online behaviors that arise directly as a consequence of viewing the influencer–product posts published on SNs (Jacobsen and Munar, 2012). Influencers try to increase interactions on their accounts as this is key for the successful development of their online communities (Blazevic et al., 2014). The opinion leader-opinion seeker dynamic ensures that the opinion seeker is constantly looking for information and advice from the opinion leader about branded promoted products (Casaló et al., 2018; Flynn et al., 1996). Thus, brands must foster these behaviors as followers’ information seeking is a first step toward their eventual purchase of the promoted products (Haans et al., 2013). Therefore, both influencers and brands can benefit from influencer marketing actions; influencers can increase the interactions in their accounts and brands can bring their products closer to the users, thereby increasing interest in them and positive behavioral intentions.
In this study, we understand intention to interact with an Instagram account as the predisposition to adopt a proactive position after viewing contents published on the account. The interaction possibilities on Instagram have evolved over time, such that users can now express their opinions in many different ways. For instance, they can “like” the publication, add comments, share the content or participate in the activities promoted by the account (e.g. contests, sweepstakes, answer questions, participate in surveys). In addition, customers interested in the promoted products may be prompted to seek information about them (Wei and Lu, 2013). In this way, information about promoted products that have attracted the interest of consumers can be obtained directly on Instagram (e.g. links included in the account itself) or from external sources (e.g. online searches, from family and friends and in physical stores). This is important because, as previously noted, when customers search online for information about a product, their probability of purchasing it increases (Haans et al., 2013).
Thus, after exposure to an influencer’s post on Instagram, users might proactively react to the content, that is, by increasing their intention to interact with the influencer account and by searching for information about the product. On the other hand, the influencer content may be regarded with disdain and decrease users’ intention to interact with the account or look for information about the product. In line with these assumptions, this article analyzes to what extent the intention of users to interact with influencers’ accounts, and their intention to look for information about the promoted products, may be conditioned by the fit of the influencer with the products promoted in his/her posts. Similarly, an analysis is made of the possible moderating effect of being a follower or non-follower of the account in which the post appears, and the level of involvement of the user with the type of product endorsed in the post. Figure 1 summarizes the research model.
2.1 Customer’s reactions to influencers’ fit and non-fit posts
The specialized brand management literature often relates the personality of a brand (Aaker, 1997) with the image that people have of themselves, that is, their self-image (Achouri and Bouslama, 2010). Specifically, individuals seek congruence between the characteristics they associate with their own personality, or self-image, and the characteristics associated with the brand image (Belk, 1988; Sirgy, 1982). In this way, customers express their personalities through the products and brands they use (Phillips, 2003). Just as individuals positively value the fit between their personalities and the personalities associated with the brands and products they use, it seems reasonable to expect that they will also positively value the congruence between the characteristics associated with others’ personalities and the products/brands they promote. In short, generalizing this type of relationship, followers will positively value the fit between the characteristics associated with influencers and those of the brands or products they promote in their posts.
In this study, the fit of promotional posts is analyzed and defined as the degree of similarity that exists between the characteristics associated with influencers and the products and brands promoted on their accounts. Previous research on communication has found that a commercial message placed in a congruent (vs incongruent) context increases its effectiveness (Gunter et al., 2002). In an online context, the customer processing and evaluation of information is improved if it is congruent with the content of the media (Furnham and Budhani, 2002; van Reijmersdal et al., 2010). According to priming theory (Iyengar et al., 1982) and the spreading activation model (Collins and Loftus, 1975), individuals’ minds move through a network of interconnected ideas, which favors the processing of thoughts that fit with their knowledge structure (Janssens et al., 2012). If viewers’ expectations in terms of the content of posts are fulfilled, they are willing to welcome and process the information in messages (Belanche et al., 2017). In addition, customers perceive more positively commercial messages when there is thematic congruence between the message and the context in which it is set (Moorman et al., 2002). On the other hand, when individuals perceive that information does not match their previous perceptions or beliefs, the information is often discarded because of selective perception bias (Klapper, 1960; Das and Teng, 1999). In this situation, it is less costly to discard, and thus not process, inconsistent information than to reinterpret reality to accommodate it to the new information (Klapper, 1960).
Taking these points into account, it is a reasonable assumption that a fit post published by an influencer will increase both the behavioral intentions toward the influencer (interaction with the account) and the promoted products (looking for information). In turn, a lack of fit between a post and an influencer will reduce the probability of a customer performing these actions. In this case, the perceived non-fit between the content and the influencer will probably motivate the user to discard the information. On the basis of this reasoning, the following hypotheses are proposed:
Compared to a non-fit post, a fit post will increase the user’s intention to interact with an influencer’s account.
Compared to a non-fit post, a fit post will increase the user’s intention to look for more information about a product.
2.2 Moderating effects of being a follower or a non-follower
SN platforms, which were initially developed to allow communication between users, are now being used by some intensive users as ideal channels for self-presentation and contacting large groups of followers (Carr and Hayes, 2015). In this new context, influencers are using their skills, knowledge and experience to continually create new content to become opinion leaders and attract a broad follower network (De Veirman et al., 2017). Influencers are generally experts in certain knowledge areas, which gives them great capacity to influence others’ behaviors (Huang et al., 2017), although in some cases their role is limited to that of a brand ambassador or simply a passionate consumer of the brand (Smith et al., 2018). The importance attributed to influencer marketing has grown significantly because of its lower cost in comparison to traditional promotions using celebrities, and its greater effectiveness in attracting followers’ attention (Lou and Yuan, 2019). In addition, message credibility is much higher if celebrities and professional models are not used (Percy and Elliot, 2007) because of greater emitter–receiver similarity (Munnukka et al., 2016). Influencers, thus, are SN users who have developed the capability to influence a large group of followers through the media (Gilani et al., 2018) and may determine their followers’ behavioral decisions (Roelens et al., 2016).
From a hierarchical perspective, the follower concept has been described as synonymous with subordinate, which assigns it an inferior position with respect to leaders (Crossman and Crossman, 2011). Kellerman (2012) adopted this approach and argued that followers had less power, authority and influence than their superiors. In SNs such as Twitter, Facebook and Instagram, the term follower is widely used to describe users who follow an opinion leader or influencer (Casaló et al., 2018). However, in this context, the term need not necessarily be associated with a position of inferiority (Casaló et al., 2017; Gilani et al., 2018). In this case, the leader–follower nexus can be more like a relationship between equals, or between friends who share similar values, problems and needs. For influencers, their number of followers is particularly important, as greater the number, the more widely their publications will be disseminated among the target audience. In fact, influencers with the highest number of followers are usually considered more likeable, simply because they are more popular (De Veirman et al., 2017).
Followers who subscribe to a particular influencer’s account are notified when new content is published. Consequently, it is more likely that their followers will view the content, which allows them to become increasingly familiar with the types of post published by the influencer. Thus, because they can contrast new content with previously posted content, followers can be more sensitive than non-followers to the fit of products promoted by influencers in these new posts. Bearing this in mind and taking into account the potential influence of fit (vs non-fit) posts, it is proposed that degree of fit may have a greater effect on followers (vs non-followers) in their intention to interact with influencers’ accounts and look for information about products promoted in posts. In other words, if an influencer transmits a congruent or incongruent message, its level of fit will be more evident to his/her account followers and affect them more than it would affect non-followers. Therefore, being a follower of an influencer will moderate the influence exerted by the level of fit of a new message on intention to interact with the influencer’s account and to seek additional information about the promoted product. Thus:
Being a follower of an influencer moderates the influence of message fit on intention to interact with the influencer’s account, such that fit is more important for followers than for non-followers.
Being a follower of an influencer moderates the influence of message fit on intention to look for more information about a product, such that fit is more important for followers than for non-followers.
2.3 Moderating effects of involvement with products promoted in posts
Involvement has been defined as the individuals’ degree of interest in a product or the relevance assigned to a purchase situation (Zaichkowsky, 1985; Mittal, 1989). Zaichkowsky (1986) stressed that involvement depends on personal factors (e.g. needs, importance, interest, values) and situational factors (e.g. differentiation of alternatives) and may be associated with different outcomes (e.g. relative importance of the product category, perceived differences in product attributes, preference for a particular brand).
Thus, level of involvement may vary from person to person and, consequently, user behavior may also differ significantly. Consumers with high product involvement usually seek to maximize their satisfaction through a conscious decision-making process (Laurent and Kapferer, 1985). In this way, involved consumers seek out and use more information to make their decisions and are more interested in learning about products. On the other hand, less involved consumers tend to simplify their choices by using other risk-reduction strategies (Lockshin et al., 2006). In the advertising context, it has been noted that greater involvement motivates the adoption of a more attentive state of mind, which allows the consumer to better process information (Gunter et al., 2002; Yoo et al., 2004). Thus, users who are more involved with the promoted product category pay more attention to commercial messages and process them more intensively (Belanche et al., 2017). In addition, when users are involved, they feel less irritated when presented with commercial information (Edwards et al., 2002). On the other hand, individuals with low involvement with a product often pay less attention to commercial messages and are unwilling to make great efforts to process any information received (Bian and Moutinho, 2011). Thus, when there is fit between an influencer and promoted products, greater involvement among his/her followers increases their interest and the attention they pay and, consequently, their intention to interact with the influencer’s account and to look for information about the promoted product. Hence:
Product involvement moderates the influence of fit on intention to interact with the influencer’s account, such that fit is more important for highly involved customers than for lowly involved customers.
Product involvement moderates the influence of fit on intention to look for more information about a product, such that fit is more important for highly involved customers than for lowly involved customers.
An experiment was designed to test the research hypotheses. We focused on popular fashion influencers on Instagram, as the most prototypical example of influencer marketing (Chun et al., 2018). Following Casaló et al. (2018), we selected a specific influencer account with a large and increasing number of followers, focused on the fashion industry, and with growing popularity in the media. The account, which has more than nine million followers, belongs to a young British woman who had not previously enjoyed celebrity status (e.g. in contrast to singers and sportspeople). It shows pictures of her wearing clothes, as a way to influence potential women fashion customers.
The data were obtained by a British market research company. The company addressed our online-based experimental scenario to women who follow the influencer’s Instagram account or are aware of her, her activity and the kinds of pictures she post. Thus, the sample was formed only by women, which is appropriate for our research context and a frequent practice in fashion marketing research (Michon et al., 2008). The study was introduced to the participants as a survey about the Instagram influencer; they were presented with different posts (i.e. a manipulated picture in an Instagram frame) depending on the experimental condition. Some control questions were included at the beginning of the survey to confirm that the participants knew the influencer, her features and the kind of content she posts (e.g. her age and category of products she promotes). Participants who responded incorrectly were automatically excluded from the study. The participants thereafter were introduced to the experimental scenarios. In the fit scenario, the influencer was pictured in an outfit of the style that she normally wears in her account. In the non-fit scenario, the influencer was pictured in an outfit of a style that she does not normally wear in the posts on her Instagram account. The participants then had to complete a questionnaire addressing the variables of the research framework and demographics. The final sample consisted of 304 women, half of whom were randomly assigned to the influencer–product fit condition, and half to the non-fit condition. In terms of age, 35.5 per cent were between 18 and 25, 38.2 per cent were between 26 and 34, 17.8 per cent were between 35 and 44 and 9.6 per cent were aged over 44. In terms of education level, 23.7 per cent had undertaken secondary school studies, 51.6 per cent college studies and 24.3 per cent postgraduate studies. Of the participants, 53.3 per cent were followers of the influencer and 46.7 per cent were not. Most (93.5 per cent) reported they purchased fashion products online once or more each year.
The study variables were measured by 7-point Likert scales (from 1 “strongly disagree” to 7 “strongly agree”) previously validated in the literature. The fit between the influencer and the endorsed product was measured by three items borrowed from Xu and Pratt (2018), “matching,” “compatible” and “aligned.” The scale presented a high level of reliability (Cronbach’s α = 0.98). Intention to interact with the influencer’s account was measured by three items previously used by Casaló et al. (2018) and Algesheimer et al. (2005), for example, “I intend to interact with this Instagram account in the near future” (α = 0.98). Information search intention was measured by three items adapted to a commercial setting by Stewart et al. (2018), from Yang (2012), for example, “I predict I will search for information about this product in the near future” (α = 0.97). Product involvement was measured by five items borrowed from Wang et al. (2012) and Zaichkowsky (1985) assessing users’ level of the product category “interest,” “involvement,” “concern,” “relevance” and “importance” of the product category (α = 0.97). All scale items correlated positively and loaded on a single factor. The item-total correlation was higher than the minimum recommended value of 0.3 (De Vaus, 2001) as presented in Table I.
3.3 Manipulation check
Using the seven-point congruence scale, an independent samples test was performed to check the fit vs non-fit manipulation. The participants agreed that level of fit was higher in the fit post scenario than in the non-fit post scenario (MFit = 5.22, MNon-Fit = 2.65; t = 17.05; p < 0.01), indicating that the manipulation was successful.
To test the research hypotheses that proposed direct effects, that is, H1 and H2, we ran an independent samples test. The results of the analysis indicated that the influence of fit on customer intention to interact with the influencer’s account is not significant (t = 0.65, p > 0.10, MFit = 3.59, MNon-fit = 3.49); thus, the results do not support H1. In turn, the influence of fit on customer intention to look for information about products is positive and significant (t = 7.25, p < 0.01, MFit = 3.31, MNon-fit = 1.93), in support of H2.
To test the interaction effects proposed in H3 and H4 we ran ANOVAs. The analyses confirmed that the direct effect of fit on intention to interact with the account was not significant (H1 was not supported) and its direct influence on intention to look for information about the product was significant (H2 supported). First, we carried out a 2 (fit vs non-fit) × 2 (followers vs non-followers) ANOVA for each of the two dependent variables. The interaction effect of fit and being a follower on intention to interact with the influencer’s account predicted in H3a was not supported (F[1,303] = 0.53, p > 0.10). However, being a follower has a direct influence on intention to interact with the influencer’s account in the future (F[1,303] = 51.83, p < 0.01, MFollowers = 4.30, MNon-Followers = 2.68). Thus, it seems that being a follower (or not) determines the customer’s intention to interact with the influencer’s account. The interaction effect of fit and being a follower on intention to look for information about the product is also significant (F[1,303] = 6.98, p < 0.01), which supports H3b. The direct effect of being a follower does not influence intention to look for information (F[1,303] = 2.05, p >0.10). Thus, the influence of post fit on the customer’s intention to look for information about products is more important for followers than for non-followers. Figure 2 illustrates this moderation effect.
The influence of product involvement on the dependent variables was assessed by another 2 (fit vs non-fit) × 2 (high- vs low-product involvement) ANOVA. Following the standard process, to make a comparison between customers based on their involvement with the product (Jaccard and Wan, 1996; Jin, 2009), the participants were categorized into high- and low-involvement subgroups on the basis of their scores’ median split on the product involvement scale. Around this mean some cases were eliminated (±1/2 standard deviation; García et al., 2008). These analyses revealed a significant interaction effect between fit and product involvement on the customer’s intention to interact with the influencer’s account (F[1,229] = 9.89, p < 0.01), which supports H4a. As depicted in Figure 3, the results indicated a moderation effect, such that fit post increases interaction intentions among customers highly involved with the product, but decreases interaction intentions among customers with low product involvement. Nevertheless, the direct effect of product involvement is also significant (F[1,229] = 71.12, p < 0.01, MHigh Involvement = 4.65, MLow Involvement = 2.57), indicating that customers’ involvement with products increases their intention to interact with the influencer’s account. The results for intention to look for information presented a similar pattern. In support of H4b, the interaction effect between fit and product involvement was significant (F[1,229] = 16.79, p < 0.01). That is, a non-fit post reduces customers’ intention to look for information about a product, but a fit post increases intention to look for information about a product, especially among highly involved customers. Again, the direct effect of product involvement on customers’ intention to look for information is significant (F[1,229] = 236.50, p < 0.01, MHigh Involvement = 4.17, MLow Involvement= 1.36), suggesting that intrinsically motivated customers tend to look for information about products which are important to them. Finally, we confirmed that customers highly and lowly involved with the product category can be followers or non-followers of the influencer. Specifically, we confirmed that both variables (being a follower and product involvement) are lowly correlated (ρ = 0.18), proving that they are independent variables.
During the past years, companies have been collaborating with influencers to bring their products to their target audience in a more natural way. These influencer marketing actions have been widely implemented on Instagram, whose visual nature makes it the platform of choice for marketers (Relatabe, 2019). Despite the growing importance of this phenomenon, little literature has delved into the role played by fit in influencer marketing campaigns (Phua et al., 2018). Therefore, this research aims to shed light on the effects of influencer–product fit on users’ reactions to marketing campaigns involving influencers on Instagram.
The results showed that users’ intention to interact with influencers’ accounts is not highly influenced by the fit or non-fit of the influencer–product posts published. This means that users (especially followers) tend to continue interacting with accounts no matter the degree of fit of new posts. The reason behind this is that users interact with influencers’ accounts because they are already interested in the overall subject that (s)he covers (Casaló et al., 2018). Therefore, as influencers are considered experts in a particular area of knowledge (Rahman et al., 2014), users follow them because they want to keep up to date with developments there. However, our findings revealed that a fit post increases user intention to look for information about the promoted product. In the customer journey, looking for product information usually precedes a final purchase; users want first to be informed before making their consumption decisions (Hajli et al., 2014; Orús et al., 2019). In this sense, message fit is important as it helps users to easily process information about products (Collins and Loftus, 1975; Iyengar et al., 1982). Consequently, it seems that when the message of the influencer–product campaign fits (vs non-fit), users are more prone to begin to look for information about the products shown, an initial step toward an increase in customer conversion rate (Haans et al., 2013).
In addition, we analyzed the effect of being (or not) a follower of the influencer’s account. As noted previously, followers tend to interact with the influencers’ account even if a certain post is incongruent with the influencer’s style. This effect is reflected in the direct relationship found between being (or not) a follower of the account and intention to interact. Followers of accounts (compared to non-followers) tend to interact with them to obtain valuable feedback from the community (Algesheimer et al., 2005; Casaló et al., 2007). Interestingly, being a follower significantly increases the relationship between fit influencer–product posts and intention to look for information about products. Influencers’ followers have greater interest in the products featured in campaigns when posts are congruent, but for non-followers this is of no relevance. Followers (compared to non-followers) know more about the influencer’s areas of expertise (Hwang and Zhang, 2018); thus, they tend to search for more information about products when they match the influencer’s style. Therefore, when the influencer marketing action is congruent with the regular content published by the influencer, followers react more favorably, and look for information about the products advertised. This finding is in line with the results of previous studies that argued that users of specialized SNs tend to follow leaders’ advice and behave accordingly (Casaló et al., 2009).
Finally, the findings showed that product involvement plays an important role in determining the customer’s reaction toward influencers’ Instagram posts. Users who are more involved with products have higher intentions to interact with influencers’ accounts and to look for information about the products advertised. These results are logical, given that users highly involved with certain product categories (e.g. fashion) tend to have more positive attitudes toward commercial campaigns developed for those products (Belanche et al., 2017). In addition, product involvement moderates the relationship between the fit of the influencer marketing campaign and both intention to interact with the influencer’s account and to look for information about the products. In the context of this research, faced with a fit influencer–product post, users more highly involved (vs lowly involved) with the product tend to develop more positive behavioral intentions toward both the influencer account and the promoted products. Consequently, product involvement has a positive impact on their subsequent behaviors toward the influencer and the products advertised, strengthening the effects of fit influencer–product campaigns.
4.1 Theoretical and managerial implications
This research offers interesting theoretical and managerial implications. For researchers the study adds to the scarce literature about influencer marketing and, in particular, about customers’ reactions toward influencers’ posts on SNs (Casaló et al., 2018). Previous research has focused mainly on the role of celebrity endorsements in promotions (Phua et al., 2018). However, influencers are different in nature (Schouten et al., 2019). Thus, given that influencer-based marketing activities on SNs are increasingly commonplace, this research tries to shed light on this phenomenon to reach a deeper understanding of how brands might collaborate with influencers to promote their products. In this sense, this study highlighted that the fit between the influencer and the products promoted is a crucial factor in fostering subsequent positive behavioral intentions (both toward the influencer and the brand) in influencer marketing campaigns. Therefore, this variable should be taken into account in analyses of the impact of influencer marketing actions. In addition, this research highlighted the moderating and direct roles of two personal variables: following the influencer’s account and involvement with the products promoted. In particular, no previous research has explored the role played by users’ involvement with a promoted product in SN influencer marketing campaigns. Our results showed that this variable is important in understanding the efficacy of promotional messages. Thus, it is important for marketers to increase their knowledge of the users interested in particular influencers, as this is a determining factor in achieving a better understanding of the effectiveness of influencer marketing actions.
As for the managerial implications, our findings showed that brands should manage properly the promotion of their products when using influencers. They should undertake prior market research studies to select the most appropriate influencers, individuals whose themes, style and regular content match the products they want to promote in their influencer-based campaigns (Casaló et al., 2018). Ideally, brands should work hand in hand with influencers to generate stories that intertwine their products with the regular content published by the influencer. These measures will create a higher perception of fit between the influencer and the products promoted which, in turn, will foster positive behaviors toward the products. In addition, brands should analyze the level of product involvement of users interested in the influencer with whom they propose to collaborate. This is important because, if these users are involved with the type of products the provider wants to promote in the influencer marketing campaign, the results of this collaboration will be more fruitful. Finally, influencers foster interaction on their accounts to increase user engagement (Hughes et al., 2019). Therefore, when they plan to collaborate with brands, they must be aware of the types of product that the brands offer. These products must match with the content that they regularly publish on their accounts, which will avoid them harming their own images, and will result in greater user interaction. In particular, presenting products which match the influencer’s style has been shown to be a marketing action effective for attracting users highly involved with the products, and for prompting them to interact with the influencer’s account. In this way, both brands and influencers can benefit from influencer marketing actions.
5. Research limitations and future research lines
This research has some limitations that open the door to the development of future research lines. First, the study was conducted with just one influencer who, despite being very popular in the United Kingdom (country of origin of the participants), may have some particularities and special features, in comparison to other similar influencers. Therefore, to generalize the results, future research should replicate the study with other influencers. In addition, although fashion is regarded as the main industry for performing influencer marketing actions (Klear, 2018), examining other industries would enrich the analysis of the effect of congruity on influencer marketing campaigns. In line with previous research into Instagram (Phua et al., 2017) and fashion marketing (Michon et al., 2008), our sample was made up of women. Nevertheless, future research should include a gender-balanced sample to generalize the results, and analyze if gender might play a moderating role in this research context. In addition, this research used normal pictures on Instagram to manipulate the levels of fit between the influencer and the fashion products. However, Instagram now includes several formats (e.g. video) which influencers use to promote products, so future research might compare the effectiveness of these different formats in terms of fit in influencer campaigns.
In the absence of actual behaviors, such as conversion rates on sales, we adopted behavioral intentions, which have been widely used as the antecedents of actual behaviors (Venkatesh and Davis, 2000). However, future research should include behavioral metrics to obtain a more complete view of how influencer–product fit affects users’ responses to influencer-based marketing campaigns. Finally, this research included two main personal features as moderating variables (i.e. following the account and level of product involvement) to help better understand the phenomenon under study. However, future research should consider other personality variables (e.g. narcissism, interpersonal influence), which may serve to clarify the underlying mechanisms of these type of influencer-consumer interactions.
Item-total correlations per variable
|Fit influencer – product||Intention to interact with the influencer||Intention to search for information about the product||Product involvement|
Aaker, J.L. (1997), “Dimensions of brand personality”, Journal of Marketing Research, Vol. 34 No. 3, pp. 347-356.
Achouri, M.A. and Bouslama, N. (2010), “The effect of the congruence between brand personality and self-image on consumers’ satisfaction and loyalty: a conceptual framework”, IBIMA Business Review Journal.
Algesheimer, R., Dholakia, U.M. and Herrmann, A. (2005), “The social influence of brand community: evidence from European car clubs”, Journal of Marketing, Vol. 69 No. 3, pp. 19-34.
Arora, A., Bansal, S., Kandpal, C., Aswani, R. and Dwivedi, Y. (2019), “Measuring social media influencer index-insights from Facebook, twitter and Instagram”, Journal of Retailing and Consumer Services, Vol. 49, pp. 86-101.
Belanche, D., Cenjor, I. and Pérez-Rueda, A. (2019), “Instagram stories versus Facebook wall: an advertising effectiveness analysis”, Spanish Journal of Marketing - Esic, Vol. 23 No. 1, pp. 69-94.
Belanche, D., Flavián, C. and Pérez-Rueda, A. (2017), “Understanding interactive online advertising: congruence and product involvement in highly and lowly arousing, skippable video ads”, Journal of Interactive Marketing, Vol. 37, pp. 75-88.
Belk, R.W. (1988), “Possessions and the extended self”, Journal of Consumer Research, Vol. 15 No. 2, pp. 139-168.
Bian, X. and Moutinho, L. (2011), “The role of brand image, product involvement, and knowledge in explaining consumer purchase behaviour of counterfeits: direct and indirect effects”, European Journal of Marketing, Vol. 45 Nos 1/2, pp. 191-216.
Blasco-López, F., Recuero Virto, N., Aldás Manzano, J. and Cruz Delgado, D. (2019), “Facebook’s power: factors influencing followers’ visit intentions”, Spanish Journal of Marketing - Esic, Vol. 23 No. 1, pp. 95-117.
Blazevic, V., Wiertz, C., Cotte, J., de Ruyter, K. and Keeling, D.I. (2014), “GOSIP in cyberspace: conceptualization and scale development for general online social interaction propensity”, Journal of Interactive Marketing, Vol. 28 No. 2, pp. 87-100.
Boerman, S.C. (2020), “The effects of the standardized Instagram disclosure for micro- and meso-influencers”, Computers in Human Behavior, Vol. 103, pp. 199-207.
Brandão, A., Pinho, E. and Rodrigues, P. (2019), “Antecedents and consequences of luxury brand engagement in social media”, Spanish Journal of Marketing-ESIC,
Carr, C.T. and Hayes, R.A. (2015), “Social media: defining, developing, and divining”, Atlantic Journal of Communication, Vol. 23 No. 1, pp. 46-65.
Casaló, L., Flavián, C. and Guinalíu, M. (2007), “The impact of participation in virtual brand communities on consumer trust and loyalty: the case of free software”, Online Information Review, Vol. 31 No. 6, pp. 775-792.
Casaló, L.V., Flavián, C. and Ibáñez-Sánchez, S. (2017), “Understanding consumer interaction on Instagram: the role of satisfaction, hedonism, and content characteristics”, Cyberpsychology, Behavior, and Social Networking, Vol. 20 No. 6, pp. 369-375.
Casaló, L.V., Flavián, C. and Ibáñez-Sánchez, S. (2018), “Influencers on Instagram: antecedents and consequences of opinion leadership”, Journal of Business Research.
Casaló, L.V., Cisneros, J., Flavián, C. and Guinaliu, M. (2009), “Determinants of success in open source software networks”, Industrial Management and Data Systems, Vol. 109 No. 4, pp. 532-549.
Chun, C.S., Lim, W.M., Tan, R.W. and Teh, E.W. (2018), “Impact of social media influencer on Instagram user purchase intention: the fashion industry”, Doctoral dissertation, UTAR, available at: http://eprints.utar.edu.my/3105/1/fyp_MK_2018_LWM.pdf
Collins, A.M. and Loftus, E.F. (1975), “A spreading-activation theory of semantic processing”, Psychological Review, Vol. 82 No. 6, p. 407.
Crossman, B. and Crossman, J. (2011), “Conceptualising followership – a review of the literature”, Leadership, Vol. 7 No. 4, pp. 481-497.
Das, T.K. and Teng, B.S. (1999), “Cognitive biases and strategic decision processes: an integrative perspective”, Journal of Management Studies, Vol. 36 No. 6, pp. 757-778.
De Vaus, D. (2001), Surveys in Social Research, 5th ed., Social Research Today.
De Veirman, M., Cauberghe, V. and Hudders, L. (2017), “Marketing through Instagram influencers: the impact of number of followers and product divergence on brand attitude”, International Journal of Advertising, Vol. 36 No. 5, pp. 798-828.
Djafarova, E. and Rushworth, C. (2017), “Exploring the credibility of online celebrities’ Instagram profiles in influencing the purchase decisions of young female users”, Computers in Human Behavior, Vol. 68, pp. 1-7.
Drummond-Butt, S. (2019), “Influencer marketing trends in 2019. Learning center”, available at: www.impactbnd.com/blog/influencer-marketing-trends-in-2019-infographic (accessed 17 December 2019).
Edwards, S.M., Li, H. and Lee, J.H. (2002), “Forced exposure and psychological reactance: antecedents and consequences of the perceived intrusiveness of pop-up ads”, Journal of Advertising, Vol. 31 No. 3, pp. 83-95.
Erz, A., Marder, B. and Osadchaya, E. (2018), “Hashtags: motivational drivers, their use, and differences between influencers and followers”, Computers in Human Behavior, Vol. 89, pp. 48-60.
Facebook (2019), “How Instagram boosts brands and drives sales”, available at: bit.ly/2LoWJJd (accessed 11 December 2019).
Flynn, L.R., Goldsmith, R.E. and Eastman, J.K. (1996), “Opinion leaders and opinion seekers: two new measurement scales”, Journal of the Academy of Marketing Science, Vol. 24 No. 2, p. 137.
Furnham, A. and Budhani, S. (2002), “Sex differences in the estimated intelligence of school children”, European Journal of Personality, Vol. 16 No. 3, pp. 201-219.
García, N., Sanzo, M. and Trespalacios, J. (2008), “Can a good organizational climate compensate for a lack of top management commitment to new product development?”, Journal of Business Research, Vol. 61 No. 2, pp. 118-131.
Gilani, P., Bolat, E. and Wilkin, C. (2018), “Leadership chaos: when power is in the hands of followers”, paper presented at the 6th International Studying Leadership Conference, Lancaster, UK.
Gunter, B., Baluch, B., Duffy, L.J. and Furnham, A. (2002), “Children’s memory for television advertising: effects of programme–advertisement congruency”, Applied Cognitive Psychology, Vol. 16 No. 2, pp. 171-190.
Haans, H., Raassens, N. and van Hout, R. (2013), “Search engine advertisements: the impact of advertising statements on click-through and conversion rates”, Marketing Letters, Vol. 24 No. 2, pp. 151-163.
Hajli, N., Lin, X., Featherman, M. and Wang, Y. (2014), “Social word of mouth: how trust develops in the market”, International Journal of Market Research, Vol. 56 No. 5, pp. 673-689.
Huang, C.C., Lien, L.C., Chen, P.A., Tseng, T.L. and Lin, S.H. (2017), “Identification of opinion leaders and followers in social media”, In DATA, pp. 180-185.
Hughes, C., Swaminathan, V. and Brooks, G. (2019), “Driving brand engagement through online social influencers: an empirical investigation of sponsored blogging campaigns”, Journal of Marketing, Forthcoming. Vol. 83 No. 5.
Hwang, K. and Zhang, Q. (2018), “Influence of parasocial relationship between digital celebrities and their followers on followers’ purchase and electronic word-of-mouth intentions, and persuasion knowledge”, Computers in Human Behavior, Vol. 87, pp. 155-173.
InfluencerMarketingHub (2019), “The State of Influencer Marketing 2019: Benchmark Report”, available at: bit.ly/2PGxLWO (accessed 12 December 2019).
Iyengar, S., Peters, M.D. and Kinder, D.R. (1982), “Experimental demonstrations of the “not-so-minimal” consequences of television news programs”, The American Political Science Review, Vol. 76 No. 4, pp. 848-858.
Jaccard, J. and Wan, C. (1996), LISREL Approaches to Interaction Effects in Multiple Regressions, Sage Publications, Thousand Oaks, CA.
Jacobsen, J.K.S. and Munar, A.M. (2012), “Tourist information search and destination choice in a digital age”, Tourism Management Perspectives, Vol. 1, pp. 39-47.
Janssens, W., De Pelsmacker, P. and Geuens, M. (2012), “Online advertising and congruency effects: it depends on how you look at it”, International Journal of Advertising, Vol. 31 No. 3, pp. 579-604.
Jiménez-Castillo, D. and Sánchez-Fernández, R. (2019), “The role of digital influencers in brand recommendation: examining their impact on engagement, expected value and purchase intention”, International Journal of Information Management, Vol. 49, pp. 366-376.
Jin, S.A.A. (2009), “The roles of modality richness and involvement in shopping behavior in 3D virtual stores”, Journal of Interactive Marketing, Vol. 23 No. 3, pp. 234-246.
Kellerman, B. (2012), The End of Leadership, HarperCollins, New York, NY.
Kim, D.H., Seely, N.K. and Jung, J.H. (2017), “Do you prefer, Pinterest or Instagram? The role of image-sharing SNSs and self-monitoring in enhancing ad effectiveness”, Computers in Human Behavior, Vol. 70, pp. 535-543.
Klapper, J.T. (1960), The Effects of Mass Communication, Free Press.
Klear (2018), “The state of influencer marketing. An industry analysis of branded partnerships and the strategies that reach success”, available at: bit.ly/2YJtkyR (accessed 13 December 2019).
Laurent, G. and Kapferer, J.N. (1985), “Measuring consumer involvement profiles”, Journal of Marketing Research, Vol. 22 No. 1, pp. 41-53.
Lockshin, L., Jarvis, W., d’Hauteville, F. and Perrouty, J.P. (2006), “Using simulations from discrete choice experiments to measure consumer sensitivity to brand, region, price, and awards in wine choice”, Food Quality and Preference, Vol. 17 Nos 3/4, pp. 166-178.
Lou, C. and Yuan, S. (2019), “Influencer marketing: how message value and credibility affect consumer trust of branded content on social media”, Journal of Interactive Advertising, Vol. 19 No. 1, pp. 58-73.
Michon, R., Yu, H., Smith, D. and Chebat, J.C. (2008), “The influence of mall environment on female fashion shoppers’ value and behavior”, Journal of Fashion Marketing and Management: An International Journal, Vol. 12 No. 4, pp. 456-468.
Mittal, B. (1989), “Must consumer involvement always imply more information search?”, ACR North American Advances.
Moorman, M., Neijens, P.C. and Smit, E.G. (2002), “The effects of magazine-induced psychological responses and thematic congruence on memory and attitude toward the ad in a real-life setting”, Journal of Advertising, Vol. 31 No. 4, pp. 27-40.
Munnukka, J., Uusitalo, O. and Toivonen, H. (2016), “Credibility of a peer endorser and advertising effectiveness”, Journal of Consumer Marketing, Vol. 33 No. 3, pp. 182-192.
Orús, C., Gurrea, R. and Ibáñez-Sánchez, S. (2019), “The impact of consumers’ positive online recommendations on the omnichannel webrooming experience”, Spanish Journal of Marketing - ESIC, Vol. 23 No. 3, pp. 397-413.
Percy, L. and Elliot, R. (2007), Strategic Brand Management, Oxford University Press.
Phillips, C. (2003), “How do consumers express their identity through the choice of products that they buy”, University of Bath School of Management Working Paper Series, Vol. 17, pp. 2003-2017.
Phua, J., Jin, S.V. and Kim, J.J. (2017), “Uses and gratifications of social networking sites for bridging and bonding social Capital: a comparison of Facebook, twitter, Instagram, and snapchat”, Computers in Human Behavior, Vol. 72, pp. 115-122.
Phua, J., Lin, J.S.E. and Lim, D.J. (2018), “Understanding consumer engagement with celebrity-endorsed E-Cigarette advertising on Instagram”, Computers in Human Behavior, Vol. 84, pp. 93-102.
Rahman, S.U., Saleem, S., Akhtar, S., Ali, T. and Khan, M.A. (2014), “Consumers’ adoption of apparel fashion: the role of innovativeness, involvement, and social values”, International Journal of Marketing Studies, Vol. 6 No. 3, p. 49.
Rakuten Marketing (2019), “Influencer marketing global survey consumers”, available at: www.iab.com/wp-content/uploads/2019/03/Rakuten-2019-Influencer-Marketing-Report-Rakuten-Marketing.pdf (accessed 10 December 2019).
Relatabe (2019), “The 2019 State of Influencer Marketing Report”, available at bit.ly/386eQx8 (accessed 10 December 2019).
Rietveld, R., van Dolen, W., Mazloom, M. and Worring, M. (2020), “What you feel, is what you like influence of message appeals on customer engagement on Instagram”, Journal of Interactive Marketing, Vol. 49, pp. 20-53.
RivalIQ (2019), “2019 Social media industry benchmark report”, available at: bit.ly/2Rq6YAJ (accessed 8 December 2019).
Roelens, I., Baecke, P. and Benoit, D.F. (2016), “Identifying influencers in a social network: the value of real referral data”, Decision Support Systems, Vol. 91, pp. 25-36.
Rogers, E.M. and Cartano, D.G. (1962), “Methods of measuring opinion leadership”, Public Opinion Quarterly, Vol. 26 No. 3, pp. 435-441.
Sanz-Blas, S., Buzova, D. and Miquel-Romero, M.J. (2019), “From Instagram overuse to instastress and emotional fatigue: the mediation of addiction”, Spanish Journal of Marketing – ESIC, Vol. 23 No. 2, pp. 143-161.
Schouten, A.P., Janssen, L. and Verspaget, M. (2019), “Celebrity vs. influencer endorsements in advertising: the role of identification, credibility, and product-endorser fit”, International Journal of Advertising, Forthcoming.
Serra-Cantallops, A., Ramon-Cardona, J. and Salvi, F. (2018), “The impact of positive emotional experiences on eWOM generation and loyalty”, Spanish Journal of Marketing - Esic, Vol. 22 No. 2, pp. 142-162.
Sirgy, M.J. (1982), “Self-concept in consumer behavior: a critical review”, Journal of Consumer Research, Vol. 9 No. 3, pp. 287-300.
Smith, B.G., Kendall, M.C. and Knighton, D. (2018), “Rise of the brand ambassador: social stake, corporate social responsibility and influence among the social media influencers”, Communication Management Review, Vol. 3 No. 1, pp. 6-29.
Statista (2019), “Number of monthly active Instagram users from January 2013 to June 2018 (in millions)”, available at: bit.ly/34WpTHt (accessed 2 December 2019).
Stewart, K., Kammer-Kerwick, M., Koh, H. and Cunningham, I. (2018), “Examining digital advertising using an affect transfer hypothesis”, Journal of Research in Interactive Marketing, Vol. 12 No. 2, pp. 231-254.
van Reijmersdal, E., Smit, E. and Neijens, P. (2010), “How media factors affect audience responses to brand placement”, International Journal of Advertising, Vol. 29 No. 2, pp. 279-301.
Venkatesh, V. and Davis, F.D. (2000), “A theoretical extension of the technology acceptance model: four longitudinal field studies”, Management Science, Vol. 46 No. 2, pp. 186-204.
Wang, X., Yu, C. and Wei, Y. (2012), “Social media peer communication and impacts on purchase intentions: a consumer socialization framework”, Journal of Interactive Marketing, Vol. 26 No. 4, pp. 198-208.
WebsitePlanet (2019), “Which social media platform has the most users?”, available at: bit.ly/2OVieDo (accessed 7 December 2019).
Wei, P.S. and Lu, H.P. (2013), “An examination of the celebrity endorsements and online customer reviews influence female consumers’ shopping behavior”, Computers in Human Behavior, Vol. 29 No. 1, pp. 193-201.
Xu, X. and Pratt, S. (2018), “Social media influencers as endorsers to promote travel destinations: an application of self-congruence theory to the Chinese generation Y”, Journal of Travel and Tourism Marketing, Vol. 35 No. 7, pp. 958-972.
Yang, Z.J. (2012), “Too scared or too capable? Why do college students stay away from the H1N1 vaccine?”, Risk Analysis, Vol. 32 No. 10, pp. 1703-1716.
Yoo, C.Y., Kim, K. and Stout, P.A. (2004), “Assessing the effects of animation in online banner advertising: hierarchy of effects model”, Journal of Interactive Advertising, Vol. 4 No. 2, pp. 49-60.
Zaichkowsky, J.L. (1985), “Measuring the involvement construct”, Journal of Consumer Research, Vol. 12 No. 3, pp. 341-352.
Zaichkowsky, J.L. (1986), “Conceptualizing involvement”, Journal of Advertising, Vol. 15 No. 2, pp. 4-34.
Sokolova, K. and Kefi, H. (2019), “Instagram and YouTube bloggers promote it, why should I buy? How credibility and parasocial interaction influence purchase intentions”, Journal of Retailing and Consumer Services.
Funding: This research was supported by the Spanish Ministry of Economy and Competitiveness (ECO2016-76768-R) the European Social Fund and the Government of Aragon (group “Metodo” S20_17R and LMP65_18).