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BY 4.0 license Open Access Published by De Gruyter July 26, 2021

Taking It a Step Further: When do Followers Adopt Influencers’ Own Brands?

Solon Magrizos EMAIL logo , Grigorios Lamprinakos , Yanling Fang and Dimitrios Drossos

Abstract

In this study, we investigate the factors affecting consumers’ purchase intention toward influencers’ personal owned brands. By using the theoretical lens of the Theory of Planned Behaviour (TPB) we explore consumers’ purchase intentions towards influencers own brands and discuss the importance of previously held attitudes, subjective norms and perceived behavioural control. We further develop TPB by adding two further constructs, that of price and self-identity. The reported moderator role of self-identity in the relationship between price and purchase intention under the context of influencers’ personal owned brands suggests that the ‘fan’ status of followers makes them more tolerant to price increases. We discuss theoretical implications and offer suggestions for marketers and consumers alike.

1 Introduction

As brands have started abandoning traditional advertising platforms, they have increasingly focused on social media influencers to endorse their products and services across their followers. This collaboration worked well for brands which appear more credible, likeable and authentic, and for influencers who are seen as opinion leaders and enjoy celebrity status and increased compensation (Ryu and Park 2020). The win-win relationship between influencers and brands has received plenty of research focus, mainly focusing on its boundary conditions. For example, whether an influencer’s brand endorsement will be successful might depend on their perceived fit (Breves et al. 2019; Djafarova and Rushworth 2017), the influencer’s perceived credibility (Lou and Kim 2019) and authenticity (Audrezet, de Kerviler, and Moulard 2020) or on if and how the sponsorship is disclosed to followers (Stubb and Colliander 2019).

A relatively new but rather limited stream of literature addresses the benefits associated with influencers’ personal brand building. Motivated by the potentially higher compensation and successful examples of influencers such as Kylie Jenner and Huda Kattan, many more move away from brand partnerships and attempt to start their own lines of products which ranged from clothes and cosmetics to gym equipment and snack food. Although influencers’ personal owned brands promotion is achieved in most cases through the same techniques as in sponsored posts, and the success of both marketing techniques rely on their followers, there are significant differences as well.

First, influencers’ personal owned brands are more deeply intertwined with influencers and therefore, are more likely to appeal to followers. Further that, these brands often do not have offline physical stores, and sell products exclusively through e-commerce platforms. Since, the brand is directly and exclusively associated with the influencer, personal attributes that the persona holds, are directly attributed to the brand, affecting the marketing strategy to be followed. That is, influencers’ personal owned brands are by definition newly created brands with no pre-existing attitudes and associations, that may affect consumers evaluations. Finally, they have important and contradictory implications for perceived authenticity. While influencers’ own brands are likely to fit their personality and lifestyle, increasing ‘passionate authenticity’ (Audrezet, de Kerviler, and Moulard 2020), at the same time, they are likely to be seen as actively selling their products, interested in extrinsic rewards and thus face reduced authenticity and source credibility (Lou and Yuan 2019).

Motivated by the lack of research on influencer’s private labelled brands, we aim to explore under which conditions followers’ attitudes and purchase intentions will be mostly influenced by recommendations towards these private owned brands. We use the theoretical lens of the Theory of Planned Behaviour which we also attempt to explore further by including new constructs (self-identity and price) to achieve a deeper understanding of consumers’ purchasing intention. The sufficiency of the TPB has been a subject of a long-standing debate (Conner and Armitage 1998). while even the person responsible for this theory suggested that it is “open for inclusion of additional predictors” that explain part of the individual’s behaviour (Ajzen 1991 p. 199).

In doing so, this paper aims to make several theoretical contributions. From a theoretical perspective it advances the discussion of celebrities creating own brands rather than endorsing a product/service than belongs to corporation (Santos, Barros, and Azevedo 2019). By employing the Theory of Planned Behaviour as a theoretical lens to study the concept of Influencer’s own brands we also advance the theoretical discussion of celebrity branding in its most novel form such as social media influencers. Next, we uncover the importance of perceived self-identity as the influencers’ fan. Previous literature on advertising (i.e. source attractiveness model, developed by Baker and Churchill 1977) has examined the importance of the endorser’s attractiveness but the degree to which social media influencers have celebrity-level fans is less established. Finally, not only we examine how perceived price affects behaviour, we explore if this relationship is affected by the individual’s self-identity concluding that influencers develop a parasocial relationship (Sokolova and Kefi 2020) with their followers so that these fans are -to a degree- immune to their price perceptions.

2 Literature Review

According to Ajzen (1991, behavioral intention is the most accurate indicator of an individual’s prospect to undertake a certain behavior and comprises an immediate antecedent of behavior. While the relationship between intention and actual behavior varies significantly depending on situational (Jarvenpaa, Tractinsky, and Saarinen 1999) and dispositional factors (McKnight, Cummings, and Chervany 1998) intention can be used as the best predictor of behavior (Ajzen 1991). According to Theory of Planned Behaviour (TPB), behavioural intentions of various kinds can be accurately predicted from attitudes toward the behavior, subjective norms, and perceived behavioural control; and these intentions, together with actual behavioural control, account for considerable variance in actual behavior (Ajzen 2002).

Previous literature, has used TPB to explain consumers’ intention to visit green hotels (Han, Hsu, and Sheu 2010), students’ intention to attend peer-assisted study sessions (White et al. 2008), and consumers’ purchase intentions toward organic food (Dutta and Singh 2014; Irianto 2015), green products (Ko and Jin 2017; Paul, Modi, and Patel 2016; Sparks and Shepherd 1992; Yadav and Pathak 2017), fashion and skincare goods (Goldsmith et al. 2005; Hsu, Chang, and Yansritakul 2017; Kim and Karpova 2010), sports products (Kim and James 2016; Lings and Owen 2007; Madrigal 2000), Halal products (Ali et al. 2018), etc.

Since TBP is a general model, designed to explain most human behaviors (Ajzen 1991), it is reasonable to assume that a TPB-based model could effectively explain online consumer behavior as well. Many studies have applied the Theory of Planned Behaviour to study people’s intention in purchasing products online with mixed results (Cheng and Huang 2013; Hsu et al. 2006; Moon, Chadee, and Tikoo 2008; Nguyen et al. 2019; Pavlou 2002). For example, Lee and Ngoc (2010) examined Vietnamese students’ online shopping intention and found that perceived behaviour control variable had the greatest effects on the intention. Hsu et al. (2006) and White et al. (2008) concluded that attitude and perceived behavioural control significantly predicted consumers’ purchase intention, but subjective norms did not. In other relevant lines of research, has been found that attitude and subjective norms significantly affected consumer’s purchase intention, while the effect of perceived behavioural control was not significant (Fielding, McDonald, and Louis 2008; Pavlou 2002).

TPB has proved to be a useful theoretical framework and one of the most influential theories in explaining and predicting a wide range of consumers’ purchase intention and especially online purchase intention (Pavlou and Fygenson 2006). However, the variation in results demonstrated previously, had been predicted by Ajzen (1991) who concluded that the degree to which attitude, subjective norm, and perceived behavioural predict intention will vary significantly across behaviours and situations. It is thus, important to continue to advance the model and apply it to different contexts and populations as a consensus on what drives consumer behaviour is unlikely to be reached (Abbasi et al. 2021; David and Rundle-Thiele 2018).

3 Underlying Dimensions of TPB

Attitudes: As mentioned previously according to the TPB theory, intention can be predicted based on attitudes toward the behavior, subjective norms and perceived behavioral control. Attitude is defined as people’s internal evaluation of an object or action (Hoyer and MacInnis 1997; Sallam and Wahid 2012; Yadav and Pathak 2017). According to Theory, attitude toward the behaviour is an essential predictor of behavioural intentions. Attitudes regarding specific behaviour are developed from people’s evaluation about the possible consequences of engaging in that particular behaviour. When people are satisfied with the perceived outcomes related to that behaviour, they are more likely to form a favourable or positive attitude toward the behaviour and thus a stronger intention to perform that particular behaviour (Ajzen 1991).

In the context of consumers’ purchase intention, an extensive line of research studied the positive relationship of brands and products attitudes on purchase intention (Goldsmith, Lafferty, and Newell 2002; Hoyer and MacInnis 1997; Sallam and Wahid 2012; Shimp and Gresham 1985). Moreover, and similar to what was mentioned before, parallel lines of research claimed a positive relationship between attitude toward purchasing behaviour and purchase intention (Crespo and Bosque 2008; Hsu, Chang, and Yansritakul 2017; Lee and Ngoc 2010; Ming-Shen et al. 2007; Pavlou 2002; Rise, Sheeran, and Hukkelberg 2010; Wu 2006).

In the social media context, followers in most cases, hold positive attitudes towards people they follow and consumers report higher trust on the influencers they follow and their online recommendations. Influencers who have developed loyal audiences are more likely, to be both attractive/likeable and also be perceived as authentic and experts (Kapitan and Silvera 2016). Based on that notion, we hypothesize that in most cases people hold favourable attitudes toward influencers they follow and due to that, their purchase intention toward their personal owned brands would also be higher.

H1:

Attitude toward purchasing from influencers’ personal owned brands, will positively influence the consumers’ purchase intention.

3.1 Inclusion of Additional Constructs in the TPB: Self-Identity and Price

Researchers have long recognized that the predictability of the theory of planned behaviour in different contents can be further enhanced when including additional predictors that capture a significant proportion of behavioural intentions’ variance (Abbasi et al. 2021; Fielding, McDonald, and Louis 2008; Hsu, Chang, and Yansritakul 2017; Kim and James 2016). Thus, Theory of Planned Behaviour is assumed to have greater explanatory power of purchasing intention in different contexts when certain validated additional constructs are included into the model, as have been shown in previous lines of research. Some scholars employed environmental knowledge (Yadav and Pathak 2017) or environmental concerns (Paul, Modi, and Patel 2016) as new constructs in their studies to understand consumers’ purchase intention toward green products. Further, self-identity (Fielding, McDonald, and Louis 2008; Yazdanpanah and Forouzani 2015) and price (Hsu, Chang, and Yansritakul 2017; Lodorfos, Mulvana, and Temperley 2006)  could be considered as effective predictors of consumers’ online purchase intention, in addition to the three original constructs. Inspired by the previous mentioned findings included in the consumer behavior literature, we explore the inclusion of two additional constructs, self-identity and price, into the original model. Each variable and their relationships with purchase intention toward influencers’ personal owned brands is discussed in detail below followed by the proposed hypotheses.

Self-identity: Self-identity refers to “salient and enduring aspects of one’s self-perception” (Dean, Raats, and Shepherd 2012; Rise, Sheeran, and Hukkelberg 2010). People could maintain multiple vias of self-identification based on different situational contexts, but in most cases they are highly motivated to exhibit coherent traits across identifications in order to avoid cognitive dissonance and behavioural ambivalence (Abbasi et al. 2021; Biddle et al. 1985; Stets and Burke 2000). Therefore, people are more likely to engage in role-congruent behaviours in order to reinforce their self-identities (Callero 1985). Many empirical studies have explored the importance of self-identity as an additional construct to the prediction of behavioural intention across a wide range of domains, including environmental behaviour intention (Fielding, McDonald, and Louis 2008; Nigbur, Lyons, and Uzzell 2010; Sparks and Shepherd 1992), student attendance intention (White et al. 2008), consumer purchase intention (Ali et al. 2018; Dean, Raats, and Shepherd 2012; Kim and James 2016; Puntoni 2001; Yazdanpanah and Forouzani 2015), etc. People who perceive themselves as sports fans are more likely to purchase sports team sponsors’ products (Kim and James 2016; Lings and Owen 2007; Madrigal 2000). Similarly, other lines of research demonstrated that people that perceived themselves as environmental conscious tend to buy more environmentally sustainable products. Based on that notion, people who consider themselves as influencers fans, are more likely to have higher purchase intention toward the brands endorsed by those influencers as they tend to self-identify themselves based on similarities with the specific group of people (Beck 2019; Srivastava 2015). This is especially true taking into considerations that previous studies (e.g. Koklic et al. 2019) have uncovered that including dimensions of personal norm to the TPB model can increase its explanatory power.

In the case of influencers’ personal owned brands, consumers’ self-identification we assume to have a significant effect on purchase intention due to the desired self-association with influencers. Influencers’ personal owned brands are more deeply intertwined with influencers. People who perceive themselves as loyal fans of influencers should have higher purchase intention toward influencers personal owned brands. In other words, the stronger the consumers’ sense of self-identity as influencers’ fans, the higher the purchase intention toward the influencers’ personal owned brands:

H4:

Self-identification as influencers’ fan will positively influence the consumers’ purchase toward influencers’ personal owned brands.

3.1.1 Price

The relation between price and purchase intention is a topic that had been extensively studied over the years. Many pieces of research recognized the significant influence of price on the consumers’ purchase intention (Lodorfos, Mulvana, and Temperley 2006). The effects of price on purchase intention are more even more consequential in the context of online buying (Chu and Lu 2007; Maia et al. 2019) as people can compare price easily with reduced search cost. While that’s true, parallel lines of research has identified certain conditions where price is not consider a significant determinant of consumers’ purchase intention (Irianto 2015; Mirabi, Akbariyeh, and Tahmasebifard 2015) as the role of price on purchase intention is related to the perceived value, rather than the actual value. People’s self-identity, loyalty or trust toward the brand all contribute to the perceived value. When the perceived value is high, consumers often willing to pay more and are more tolerant to the product price.

In the case of influencers’ personal owned brands, the influence of the price on the consumers’ purchase intention remains unclear. On one hand, this kind of brands often sell their products through online vendors, indicating that consumers should have been more price sensitive. On the other hand, since the leading consumers of these brands are fans of the influencers, they exhibit a certain degree of loyalty and trust toward the influencers and influencers’ personal owned brands. Thus, the price may have less influence on their purchase intention. In light of the above, this paper decided to explore the effect of price on the purchase intention toward influencers’ personal owned brands.

Aside the perceptual processes underlying the relationship between price and purchase intention a vast number of studies indicates that price in most cases and for the majority of consumer products directly and indirectly affects purchase intentions also thought psychological associative processes. Given that, we hypothesized that:

H5:

Price will have a significant influence on the consumers’ purchase toward influencers’ personal owned brands.

3.2 Moderating Role of Self-Identity

We further propose that self-identity as influencers’ fan would moderate the relations between price and purchase intention. According to Zheng (2019), fan’s purchase intentions and behaviours were different from typical consumers. Compared to regular consumers, fans pay more attention to the psychological satisfaction rather than practical factors, such as product price or quality. For influencers’ fans, the perceived value of the products from influencers’ personal owned brands is higher than the actual value of the products. Therefore, they are willing to pay more for these brands. That is, their self-identities as influencers’ fans would make them more tolerant to the product price. In light of past literature, we could expect the self-identity to be a moderator of the relationship between price and purchase intention. Thus, we hypothesized that:

H6:

Self Identity as the influencer’s fan will moderate the relationship between pricey and consumers’ purchase intention toward that influencer’s personal owned brand.

Following the discussion above, the theoretical frameworks in this study are proposed, see Figure 1.

Figure 1: 
Proposed theoretical framework.
Figure 1:

Proposed theoretical framework.

4 Methodology

4.1 Sample and Data Collection

This study aims to test the predictive power of the extended Theory of Planned Behaviour model on the consumers’ purchase intention toward influencers’ personal owned brands. Due to the positivist nature of this research, a deductive approach and quantitative empirical methods are employed in this study.

The study context has been Chinese influencers and instagrammers. We chose to focus solely in China because due to the rapid development of e-commerce it is extremely easy and profitable for influencers to start their own brands there. Multiple e-commerce platforms, such as Taobao and Wechat-shop, provide opportunities for the public to open online stores with a lower start-up risk and costs making China the country with the most influencers’ personal owned brands. Further, in China, beside the beauty and fashion related fields, influencers have built their personal brands in the food field, stationery field, fitness supplies field, baking supplies field, etc. (Liu 2019; Shan, Chen, and Lin 2019). The influencer economy is booming in China, partly due to the facts that the Chinese often have more trust on social ties and connections. This feature brings more power to the influencers and makes it easier for them to promote and develop their personal brands in China (China Briefs 2019; Liu 2019).

The target sample for this study therefore is Chinese consumers with some knowledge about influencers and influencers’ personal owned brands. The influencers and influencers’ personal owned brands are relatively new social phenomena, some people may not be familiar with these. For this reason, an online survey has been considered as an appropriate data collection technique in this study. People who are active on the internet are more likely to know about influencers and influencers’ personal owned brands.

4.2 Survey and Measurement

The survey was created in English and then translated in Mandarin. To increase the efficiency and increase the internal validity of our questionnaire, we used back and forth translation method as proposed by previous equivalent lines of research (Behling and Law 2000).

Attitude was measured following Nguyen et al. (2019) (sample item “Purchasing products from influencers personal owned brands is a good idea”), subjective norm was measured with three items following Paul, Modi, and Patel (2016) (sample item “people I value or around me would purchase products from influencers’ personal owned brands”) and perceived behavioural control was measured with three items adapted from Yadav and Pathak (2017) (“I have resources and time to buy products from influencers personal owned brands if I want to”). Further, measurement of self-identity and purchase intention employed items adapted from Kim and James 2016 (sample items “I think it is appropriate for me as a fan of influencers to purchase products from influencers’ personal owned brands” and “I intend to buy products from influencers’ personal owned brands” respectively). Finally, perceived price was measured with two items previously used by Hsu, Chang, and Yansritakul (2017) such as “it is acceptable to pay more for products from influencers’ personal owned brands”.

5 Data Analysis and Results

5.1 Reliability Analysis and Descriptive Statistics

The reliability of the scale was examined by calculating Cronbach’s alpha value (α) for each independent variable. The results are displayed in the last column of Table 1. The Cronbach’s alpha values for all variables in this study were found larger than the threshold value (0.7), ranging from 0.768 to 0.892. Table 1 also demonstrates the descriptive statistics for the dependent and independent variables of this study.

Table 1:

Descriptive statistics for dependent and independent variables.

Mean Std. dev. Cronbach's alpha (α)
Dependent variable
Purchase intention 2.5 1 /
Independent variables
Attitude 2.67 0.71 0.892
Subjective norm 2.38 0.83 0.768 (0.647)
Perceived behaviour control 3.59 0.82 0.846
Self-identity 2.69 0.93 0.780
Product price 2.09 0.85 0.856

5.2 Hypothesis Testing and Moderator Analysis

Table 2 depicts the bivariate correlations for each pair of variables in this study. The statistical significance associated with the correlation coefficient was also tested and shown in the table. Hypothesis 1, 2, 4 and 5 were supported. Attitude toward purchase from influencers’ personal owned brands had a positive and significant relationship with consumers’ purchase intention, also suggesting that attitude was the most influential determinants of consumers’ purchase intention toward influencers’ personal owned brands in China. Subjective norm toward purchase from influencers’ personal owned brands was also positively related to the consumers’ purchase intention. Finally, a positive relationship was found between self-identity and price, and consumers’ purchase intention toward influencers’ personal owned brands.

Table 2:

Correlation matrix of key independent variables and dependent variable.

Purchase intention Attitude Subjective norms Perceived behaviour control Self-identity Product price
Purchase intention 1.00
Attitude 0.73 1.00
Subjective norms 0.53 0.56 1.00
Perceived behaviour control 0.09 0.17 0.1 1.00
Self-identity 0.29 0.26 0.18 0.26 1.00
Product price 0.52 0.44 0.47 0.14 0.46 1.00
  1. Bold coefficients indicate statistical significance at p < 0.05.

However, H3 was not supported. The correlation between perceived behaviour control and consumers’ purchase intention is small and also not statistically significant, indicating that perceived behaviour control was not an important factor that predicts consumers purchase intention toward influencers’ personal owned brands in China.

Hypothesis Six (H6) was tested through a moderation analysis. The outcome of moderator analysis was presented in Table 3. This result confirmed the moderator role of price in the relationship between price and consumers’ purchase intention toward influencers’ personal owned brands. The coefficient for the interaction of price and self-identity is −0.1801. This value quantifies how the effect of the variable on the outcome changes as the moderator changes by one unit (Hayes 2017). This result showed that the effect of price on purchase intention changes as the self-identity changes. On the other hand, the predictive power of the model is increased by almost 2.3% by considering the moderator role of self-identity. This model could explain 61.5% of the variance in the purchasing intention. Therefore, the H6 was supported. Price would change the relationship between price and consumers’ purchase intention toward influencers’ personal owned brands in China.

Table 3:

Results of moderator analysis.

Predictor Purchase intention
B SEB R 2 ΔR 2
Model summary 0.615 0.0229
Constant −1.2931** 0.4973
Price 0.7583*** 0.2267
Self-identity 0.3906* 0.1529
Price × self-identity −0.1801* 0.0707
Attitude 0.7860*** 0.1062
Subjective norms 0.1551 0.0907
Perceived behaviour control −0.0656 0.0754
  1. n = 116. *p < 0.05; **p < 0.01; ***p < 0.001.

5.3 Theory of Planned Behaviour and the Extended Model

Table 4 displays the results for the multiple regression models. Each model including multiple independent constructs to predict consumers’ purchase intention. First, Model 1 was regressed with the three original constructs from the Theory of Planned Behavior, more specifically attitude, subjective norm, perceived behavioral control, to predict the purchase intention. The R-squared value indicated the proportion of the total variation in the dependent variable that is explained by the independent variables including in that particular model (Agresti and Finlay 2013). The variables in Model 1 could explain 55.7% of the variance in the purchasing intention. Attitude was the strongest predictor and followed by the subjective norm, while perceived behaviour control did not appear to be a significant predictor on purchase intention. This result also confirmed the hypothesis testing results for the H1–H3.

Table 4:

Regression analysis results predicting the purchase intention.

Model 1 Model 2 Model 3 Model 4 Model 5
Attitude 0.898*** (0.108) 0.866*** (0.109) 0.820*** (0.108) 0.815*** (0.108)
Subjective norms 0.214** (0.091) 0.207** (0.091) 0.133 (0.092) 0.138 (0.093)
Perceived behaviour control −0.048 (0.078) −0.079 (0.079) −0.063 (0.075) −0.074 (0.077)
Self-identity 0.070 (0.097) 0.126* (0.071) 0.050 (0.076)
Product price 0.573*** (0.107) 0.252** (0.084) 0.228* (0.092)
Constant (intercept) −0.236 (0.345) 1.112*** (0.266) −0.362 (0.349) −0.310 (0.334) −0.353 (0.341)
R-squared 0.557 0.271 0.569 0.591 0.592
  1. +p < 0.10; *p < 0.05; **p < 0.01; ***p < 0.001. Standard errors in parentheses.

Model 2 only included the two new constructs, which were self-identity and product price, to predict purchase intention. This model could only explain 27.1% of the variance in the purchase intention. Model 3 regressed with three original constructs from the Theory of Planned Behavior and self-identity, while Model 4 included three original constructs and product prices. The r-squared value indicated that these two models could explain 56.9 and 59.1% variance in the purchase intention, respectively. In terms of the influence of each independent variable, attitude still had a strong and significant impact on purchase intention in both models. Subjective norm appeared to be an important predictor to purchase intention in Model 3, however its impact was reduced in the Model 4 when the price was adding into the model. Self-identity’s influence on purchase intention was enhanced in Model 3 compared to Model 2. The significant impact of self-identity and product price on purchase intention showed in these models also confirmed the hypothesis testing results for the H4 and H5.

All five variables were included in Model 5. This model could explain 59.2% of the variance in the purchasing intention. Compared to Model 1, we could find that the predictive power of the Theory of Planned Behavior was improved by adding the new constructs into the model. Regarding the predictive power of each independent variable, attitude was found to be the strongest predictor of consumers’ purchase intention toward influencers’ personal owned brands in China. Comparing Model 4 and Model 5, we see that the relationship between product price and purchase intention is reduced when self-identity was added into the model. This result supported the H6, which also confirmed the moderator role of self-identity in the relationship between price and purchase intention. On the other hand, when new constructs were adding into the model, the predictive power of subjective norm also reduced.

6 Discussion

Overall, in line with previous literature, the findings of this study confirmed the predictive power of the Theory of Planned Behaviour on people’s behavioural intention. A vast number of previous lines of research have also successfully used the TPB as a theoretical framework in order to explain intention toward purchasing or other commerce activity (Pavlou 2002) or even actual purchasing behavior (Khalifa and Limayem 2003). In terms of independent variables in the Theory of Planned Behaviour however, not all three constructs significantly influenced behavioural intention across studies. While most studies advocate the importance of attitudes toward the behavior in question, some of these studies have found subjective norms to be equally predictive of purchasing behavior (e.g. Khalifa and Limayem 2003), while others have found perceived behavioral control to be highly influential (e.g. Tan and Teo 2000).

In the context of influencers owned brands, attitude and subjective norm were found to be significant determinants of purchase intention, while perceived behaviour control was not. An unexpected finding in this study was the significant contribution of subjective norm on behavioural intention. Subjective norm was the weakest predictors of behaviour intention in most earlier studies (Ajzen 1991; Hsu et al. 2006; Ming-Shen et al. 2007; Paul, Modi, and Patel 2016; Picazo-Vela et al. 2010; Puntoni 2001). However, the current research, found that subjective norm was an important predictor of purchase intention, after the attitude. A potential explanation is that the majority of respondents were below 30 years old- subjective norms may be more influential on people among this age group. Another point of consideration is the context of the study, Chinese culture is collectivist in nature, which means that Chinese people are more likely to be influenced by their significant others. That is, the subjective norm will have a larger influence on their behavioural intention. Thus, it is not surprising that subjective norm appears to be an influential factor influence consumers’ purchase intention toward influencers’ personal owned brands in China.

Several lines of research have demonstrated the strong positive relationship between attitudes and the purchase intention (Goldsmith, Lafferty, and Newell 2002; Hoyer and MacInnis 1997; Pavlou 2002). In line with previous work, our findings suggest that the most important predictor of purchase intention towards influencers own brands is attitudes towards purchase. While attitudes reflect internal evaluations of constructs, external influences may play an important role on behavioral intention formation. Subjective norms as a construct is related to the opinion of an individual’s significant others. Based on that, subjective norms can be considered as the perceived appraisal of social force to either engage or avoid a behaviour (Dutta and Singh 2014). Previous lines or research have extensively demonstrated the increased correspondence between favourable subjective norms and increased behavioural intention (Ajzen 1991; Paul, Modi, and Patel 2016). In line with previous findings and as hypothesized our results show that subjective norms are indeed a significant contributor in the efficiency of the TBP when applied in the context of predicting behavioural intentions towards influencers’ owned brands.

Regarding the effects of perceived behaviour control, similarly, to relevant lines of research investigating the predictive validity of the factors included in the TPB, perceived behaviour control was not a strong determinant of purchase intention in the context of influencers’ personal owned brands (e.g. Crespo and Bosque 2008; Fielding, McDonald, and Louis 2008; Picazo-Vela et al. 2010; Sparks and Guthrie 1998; Wu 2006). Perceived behaviour control had the highest mean (3.59 of 5), implying that people participating in this research believed that they perceived to possess an increased level of control toward the behaviour of purchasing influencers’ personal owned brands.

Moreover, and crucial to the contribution of the present research on the overall literature investigation the predictive efficiency of the TPB in different contexts, we also extended the Theory of Planned Behaviour by adding new constructs into the model. Our findings supported the models’ predictive value in determining the consumers’ purchase intention. Among the added constructs (self-identity and price) and in line with previous research (Chu and Lu 2007) price was reported to have a more substantial influence on the consumers’ purchase intention. This may be because of the feature of influencers’ personal owned brands. Online shopping provides a more direct way to compare price during the shopping process. Thus, consumers engaged in online shopping would care more about the price, which making price become an essential determinant of purchase intention in this case. Finally, the findings indicated that including new constructs would increase the predictive power of the model. The explanatory power of the typical TPB model was increased by adding self-identity and price, and increased again by considering self-identity as a moderator in the relationship between price and purchase intention. Our proposed model could be considered as a potential model to study the purchase intention toward influencers’ personal owned brands in different cultures.

From a practical perspective, as a cumulative body of work on Internet purchasing emerges, a better understanding of the key elements that drive purchasing intentions is needed. This study is one of the first studies focuses on influencers’ personal owned brands, rather than influencer marketing, which has already received a lot of attention. On this basis, this study further attempted to explore new important factors that influence consumers’ purchase intention. Two new constructs, self-identity and price, were confirmed influence consumers’ purchase intentions. Furthermore, this study is an initial attempt to examine the moderator role of self-identity in the relationship between price and purchase intention.

Our findings suggest that attitude was the most influential determinants of consumers’ purchase intention toward influencers’ personal owned brands. This result highlights the importance of developing favourable attitudes toward purchasing influencers’ personal owned brands among potential consumers. According to Schiffman and Kanuk (2010), creating favorable association with particular behaviours is the key of creating favourable attitudes toward that particular behavior. Therefore, influencers should take advantage of their position as opinion leaders on social media, and associate this favourable attitude towards themselves with their brands encouraging people on purchasing these privately labeled products. Furthermore, our results advocate the significance of the subjective norms on consumers’ purchase intention towards influencers owned brands. Next, influencers and marketers should also employ omni channel promotional communication about influencers’ personal owned brands in order to evoke positive attitudes among a larger population. While targeting directly the consumer has be proven an efficient direct technique, targeting significant others and indirectly driving attitudes and purchasing intention, according to our data seems an important determinant of purchase intention as well. Therefore, establishing a wider acceptance toward purchasing from influencers’ personal owned brands among the public is crucial.

Moreover, the results of this study also indicated that price was an important predictor of consumers purchase intention toward influencers’ personal owned brands. Thus, some important implications regarding the pricing strategies about these brands could be suggested. First of all, the products from these brands should be reasonably priced. Marketers should offer price promotion activities to enhance consumers’ purchase intention. Although consumers are often price-sensitive in nature, this does not mean that brands with the lowest price would have ultimate successes. This study demonstrated the fact that self-identity would moderate the relationship between price and purchase intention. People with stronger self-identity as influencers’ fans would be less price-sensitive when they purchase from influencers’ personal owned brands. Thus, influencers and brand managers should be motivated to focus on deepening consumers’ self-identity as influencers’ fans and connecting this self-identity with the behaviour of purchasing products from influencers’ personal owned brands. In other words, they should evoke fan engagement with the influencers and influencers’ personal owned brands. In this way, the perceived value of products from influencers’ personal owned brands for these fans would increase as well as the price tolerance for these brands among these fans.

A final set of practical contributions comes with a warning for responsible use of social media. The continuous receival of messages via social media and the ability to ‘comment’, ‘like’ or interact with these influencers, often gives followers an illusion of intimacy (parasocial relationship; Dibble, Hartmann, and Rosaen 2016). This can lead to unethical marketing practices from the influencer, who in the case of their owned brand has stronger motives to promote its products. For example, while an advertiser’s message is easily identified by followers and most often mentioned as such, when influencers promote their own brand, they will do so more discreetly but more often by e.g. keeping the product in the background while doing something else. These cues are caught by only the most loyal fans and further increase their self-identity and lead to an often-unreasonable willingness to buy and pay a premium for these products. It is not up to these influencers to promote their products more ethically, however. Policymakers need to intervene and mandate explicit mentions of even indirect promotion of products on social media while it is up to followers as well to examine when their own attitudes and self-identity leads to unreasonable purchases.

Although the study successfully identified factors that affect consumers’ purchase intentions toward influencers’ personal owned brands, some limitations of the current study and suggestions for future studies should be addressed. The first limitation is related to the convenience sampling employed in this study. In this study, the majority of the participants are in the same age group and have similar backgrounds, and the number of participants is not very large. These issues raise concerns for the generalisability of the study results. Generalisability refers to the extent that the research findings are applicable to other settings (Saunders, Lewis, and Thornhill 2019). Due to the unique feature of influencers’ personal owned brands and composition of the sample, the findings may not be able to generalize to a larger population or more diverse context. Considering this, future studies may consider using different sampling approaches with a more diverse sample in order to get results. Finally, we did not measure consumers’ actual purchase behaviour. Although the behavioural intention is an essential predictor of actual behaviour, there is still an intention-behaviour gap existing (Allom and Mullan 2012; Godin and Conner 2008; Godin, Conner, and Sheeran 2005). In addition to this, other variables, such as past experience and shopping habits, would also be potential factors that influence consumers’ purchase intention. Thus, future researchers could explore the relationship between purchase intention and other new constructs.


Corresponding author: Solon Magrizos, University of Birmingham, Birmingham, UK, E-mail:

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Received: 2021-01-11
Accepted: 2021-07-13
Published Online: 2021-07-26

© 2021 the author(s), published by De Gruyter, Berlin/Boston

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