Engagement With Fashion Influencers on Instagram: Impact on Purchase Intentions and Adoption of Fashion Style
Natasha Tombeng (12880981)
Master’s Thesis | Graduate School of Communication Master’s programme Communication Science
Abstract
The current study aimed to investigate the effect of engagement with fashion influencers on Instagram, on one’s purchase intentions and adoption of fashion style. A cross sectional survey was conducted with adults as subjects. 130 participants who followed at least one fashion influencer on Instagram, completed the survey. Findings showed that parasocial relationships and identification did not significantly affect individuals their purchase behaviors and/or adoption of fashion style. However, wishful identification showed a positive significant effect on individuals their purchase behaviors and adoption of fashion style. Further research is needed to clarify the influence of parasocial relationships and identification on one’s purchase intentions and/or adoption of fashion style. This research should consider attitude strength as moderator and the possibility that parasocial relationships and identification might not have a direct media-effect on behaviors.
Engagement With Fashion Influencers on Instagram: Impact on Purchase Intentions and Adoption of Fashion Style
In an era where digital marketing is on top of its game, it may not be surprising that influencer marketing was expected to be worth 2 billion dollars in 2020 (Wong, 2018).
Influencer marketing is the phenomenon where influencers promote products on social media. Fashion influencers in particular, are hired to promote or wear fashion items (Global Yodel, 2017). For a retailer, influencer marketing can be profitable because fashion influencers are perceived as opinion leaders who have influence on their followers (Agarwal, Mahata, & Liu, 2014; Thakur, Angriawan, & Summey, 2016; Djafarova & Rushworth, 2017). As influencer marketing grew in a short time in to a billion dollar industry, it is important to understand which aspects make influencer marketing successful. The current study will focus on
engagement between fashion influencers and their followers. Three forms of engagement will be investigated: parasocial relationships, identification and wishful identification.
Whereas a lot of research has been done on engagement with traditional celebrities and digital influencers in general (e.g. Hwang & Zhang, 2018; Djafarova & Rushworth, 2017; Bergkvist & Zhou, 2016). Little research has been done on engagement with fashion
influencers. Although traditional celebrities and fashion influencers can both be considered as opinion leaders (Thakur et al., 2016; Weisfeld-Spolter & Thakkar, 2011), there are
differences. For instance, traditional celebrities gain fame through acting, sports, music and politics and based on their work and publicity, they obtain fans (McCracken, 1989). Where fashion influencers obtain a fanbase through posting content on social media platforms (Mediakix, 2019). In a comparison study between social media influencers (SMI’s) and traditional celebrities, findings show that ads by SMI’s had greater influence on consumers’ purchase behaviors than traditional celebrities. In extend, Schouten, Janssen & Verspaget (2020) found that influencers are also perceived differently from traditional celebrities, where
influencers are find to be more authentic, similar and trustworthy. An explanation for this can be that influencers use a real-life and authentic setting to promote products (Zhu & Tan, 2007).
Authenticity, similarity and trustworthiness are related to engagement and therefore engagement has the potential to be a key factor in understanding the influence of fashion influencers (Hoffner & Buchanan, 2005; Hwang & Zang, 2018). Fashion influencers might even have a greater influence than traditional celebrities. Because engagement with fashion influencers is found to be stronger because social media platforms make it easier to connect (Turcotte, York, Irving, Scholl, & Pingree, 2015). But also because influencers have been found to interact closely with followers and because influencers focus on creating affinity-based relationships (Liu, Qu, & Zhao, 2017). Because the engagement is found to be stronger, it is interesting to look at how engagement with fashion influencers influences purchase intentions and adoption of fashion style. Also, because research on traditional celebrities might not be applicable for fashion influencers since they are perceived differently and the engagement is found to be less strong (Schouten et al., 2020; Turcotte et al., 2015).
The current study adds to existing research in several ways. There seems to be a lack of research on fashion influencers in specific and on adoption of fashion style. While fashion influencers are well known because of their fashion style, influencer marketing is growing rapidly and it is of importance for the marketer. Therefore, this research can add knowledge so marketers can implement the findings into their marketing strategy (Blair, 2018; Mediakix, 2019). The current study wants to emphasize how fashion influencers in some sense are different from other digital influencers. Where with travel influencers for example,
influencers can influence travelling plans of their followers (Varkaris & Neuhofer, 2017). But when it comes to fashion influencers, it is about influencing one’s identity since clothes are a
tool for expressing one’s identity (Rahman, 2016). Therefore, other constructs might be of importance when investigating the effects of fashion influencers.
The aim of the study is to investigate whether engagement with fashion influencers on Instagram, influences one’s purchase intentions and adoption of fashion style. The current study wants to contribute by giving a better understanding of fashion influencers. We hope that addressing adoption of fashion style as a new construct, opens door for future research. Moreover, the focus is on three forms of engagement which are: parasocial relationships, identification and wishful identification. Research indicates that these three forms may be key concepts to understand why fashion influencers are successful in influencing individuals (Casaló, Flavián, & Ibáñez-Sánchez, 2018; Hwang & Zhan, 2018; Schouten et al., 2020). However, little is known about the specific effects of engaging with fashion influencers.
Theoretical Framework Fashion Influencers
Nowadays, fashion influencers are well known personalities on social media platforms with an expertise in fashion. They obtain a large number of followers (over one thousand followers) on Instagram. Because fashion influencers are aware of their followers and their preferences, influencers can shape their content specifically for their followers (Wei, 2017). They have the ability to influence attitudes, behaviors and decision making of individuals (Leal, Hor-Meyll, & de Paula Pessôa, 2014; Rogers & Cartano, 1962; Godey et al., 2016). Therefore we believe that fashion influencers can influence one’s purchase intentions and adoption of fashion style.
Fashion influencers are considered a trustworthy source, attractive, innovative, authentic and similar (Jin & Phua, 2014; Agrawal, 2016; Thakur et al., 2016; Schouten et al., 2020). They are active on platforms like YouTube, TikTok and twitter (MediaKix, 2019). One of the most well-known fashion influencer is Negin Mirsalehi, based in the Netherlands
with over 5.9 million followers on Instagram. Negin promotes next to fashion products also beauty products, platforms like TikTok, food-products and places like restaurants. Fashion influencers like Negin, became a fashion influencer by posting content on Instagram. The focus of this study will be on fashion influencers on Instagram. It occurs that Instagram is the most used platform for fashion influencers, probably because of its visualization-possibilities (Flynn, 2019). It is considered to be one of the biggest platforms for the fashion industry since fashion brands have significantly more followers than other brands (Buryan, 2016).
Therefore, Instagram is find to be a suitable focus for this study. Parasocial Relationships
Relationships between individuals and fashion influencers are found to be parasocial because of the limited interactions they have; fashion influencers have many followers and therefore limited time to interact with each individual like with a true friend. Despite the limited interactions, individuals form feelings of intimacy with fashion influencers through parasocial interactions (Dibble, Hartmann, & Rosaen, 2016). Because of these feelings, individuals create the illusion of having a true social relationship with a fashion influencer (Rubin & Step, 2000). Once these parasocial relationships are formed, it can influence one’s purchase intentions (Hwang & Zhang, 2018). Parasocial relationships develop when
influencers post content on Instagram which resembles interpersonal interactions. For example, when influencers post videos where they address their audience and share personal information about themselves. Because for their followers, it feels like the influencer is speaking directly to them. Therefore, engaging the audience allows individuals to create a relationship that feels two-sided as a true social relationship (Horton & Wohl, 1956; Perse & Rubin, 1989).
Repeated exposure to content of influencers let individuals perceive influencers as trustworthy. Where trust is found to be an antecedent of parasocial relationships (Stever &
Lawson, 2013; Lee & Watkins, 2016; Djafarova & Rushworth, 2017). Similarity,
accessibility, and familiarity are also antecedents. Where antecedents make it more likely for individuals to develop parasocial relationships (Agarwal, Mahata, & Liu, 2014; Hwang & Zhang, 2018). Once parasocial relationships evolve, fashion influencers become a reliable source and individuals start to perceive them as credible (Rubin, Perse, & Powell, 1985). Individuals tend to refer to influencers they trust and perceive as credible when they intend to purchase a product. Therefore, credibility and trust play a great role in purchase behavior (Bearden, Netemeyer, & Teel, 1989; Wilcox & Stephen, 2013). Because parasocial relationships let influencers perceive as trustworthy and credible, we expect an effect of parasocial relationships on purchase intentions.
H1a: The higher a user’s parasocial relationship with a fashion influencer on Instagram, the higher intentions to purchase the products endorsed by the fashion influencer
Because fashion influencers their expertise lies in fashion, the current study wants to look into the effect of engagement on adoption of fashion style. For many, a fashion style is a way to express our identity, personality and taste (Rahman, 2015). But often, individuals choose to obtain a fashion style which is socially accepted by others (Rahman, 2016). The social cognitive theory (SCT) explains how individuals adopt fashion styles by observing fashion influencers (Bandura, 1986). Fashion influencers frequently receive positive reactions on their fashion style. Positive reactions can be seen as a reward for their fashion style. When an individual observes how influencers are rewarded for their fashion style, it increases the likelihood of adopting the influencer’s fashion style (Bandura, 1977, 1988). Bandura mentions how individuals are more likely to adopt behaviors when individuals emotionally attach to influencers (Bandura, 1977, 1988). Since parasocial relationships let individuals emotionally attach to influencers, we believe that parasocial relationships can be a key
element for adoption of fashion style (Rubin & Step, 2000). However, little is known about the effects of parasocial relationships on adoption of fashion style.
H1b: The higher a user’s parasocial relationship with a fashion influencer on Instagram, the higher intentions to adopt the fashion style of the fashion influencer Identification and Wishful Identification
Next to parasocial relationships, studies showed that identification and wishful identification can also affect one’s purchase intentions and attitudes on fashion (Kelman, 1961; Kapitan & Silvera, 2016; Djafarova & Rushworth, 2017). Identification finds place when an individual can identify themselves with another, as if they are in someone else’s shoes (Cohen, 2001). Multiple antecedents were found for identification. One of the antecedents describes how identification can be provoked when influencers share their thoughts and feelings on Instagram. Because an individual can either identify with these thoughts and feelings or not (Oatley, 1999). Another antecedent is similarity. Because identification requires individuals to imagine themselves as him/her. Similarity can evoke at any level, from demographic similarities (race, age and gender) to similarity of situations (i.e. where both influencer and follower are pregnant) (Cohen, 2001).
Wishful identification finds place when individuals desire to become like someone else (Hoffner & Buchanan, 2005). Similarity is also found to be an antecedent for wishful identification (Bandura, 1986). Besides that, individuals tend to wishfully identify with people who are perceived as attractive, intelligent, admired and successful (Hoffner & Buchanan, 2005). According to the SCT, (wishful) identification increases the chances of individuals observing the influencer with whom they (wishfully) identify with. This effects arises when individuals perceive similarities with a fashion influencer. When they observe influencers their Instagram, Individuals can see how influencers are getting complimented for what they are wearing in the form of likes and comments on Instagram-posts. These compliments
motivate individuals to change attitudes when they feel similar to the one who gets the compliments. Thus (wishful) identification allows individuals to adopt and change behaviors because of the similarities and compliments (Bandura, 1986, 2001; Cialdini, 1993; Kelman, 2006). These changes in behaviors can affect purchase intentions and adoptions in fashion style.
However, we have to keep in mind that for people who wishfully identify with influencers; their motivation to adopt behaviors is higher than for people who identify with influencers. Because wishful identification is the desire to become like someone else. Therefore buying the same products and adopting the influencers’ fashion style makes it easier to become like the influencer. Whereas with identification the motivation of desire to be like someone else, does not apply (Cohen, 2001; Hoffner & Buchanan, 2005). Either way, we believe that identification and wishful identification can be key elements for influencing purchase intentions and adoption of fashion style.
H2a: The higher a user identifies with a fashion influencer on Instagram,
the higher intentions to purchase the products endorsed by the fashion influencer H2b: The higher a user wishfully identifies with a fashion influencer on Instagram, the higher intentions to purchase the products endorsed by the fashion influencer H2c: The higher a user identifies with a fashion influencer on Instagram,
the higher intentions to adopt the fashion style of the fashion influencer
H2d: The higher a user wishfully identifies with a fashion influencer on Instagram, the higher intentions to adopt the fashion style of the fashion influencer
Method Participants
For the current study, a questionnaire was sent out through social media platforms. To participate, individuals had to be users of Instagram and at least 18 years. From the 202
respondents, 156 respondents were suitable to participate because they followed a fashion influencer on Instagram. However, 26 participants reported that they followed a fashion influencer on Instagram but did not fill in the rest of the questionnaire; therefore, they were excluded from the dataset. 130 participants remained who filled in the questionnaire. For each variable, there was found to be one to six cases with missing values. Missing values were coded as 999. The sample consisted an age range from 18 to 58 years. Seventy percent of the sample consisted females (Mage = 26.93, SDage = 6.49) and 30% male (Mage = 27.77, SDage = 7.12). 117 participants were based in the Netherlands and 12 participants were based in either Australia, Belgium, Czech Republic, France, Germany, Hong Kong, Spain, Turkey, UK or Uruguay.
Procedure
A quantitative survey study was conducted. The questionnaire was programmed in Qualtrics. The questionnaire was distributed through convenience sampling, more specifically the snowball sampling method. The distribution found place between the 11th and the 20th of May 2020. Whereas convenience sampling is when the sample is taken from a group who are easy to reach for the researchers (Saunders, Lewis, & Thornhill, 2019). With snowball
sampling, participants who participate in the study, also recruit participants for the same study. Therefore the sample group expands as with a rolling snowball (Goodman, 1961). The participants were contacted through social media platforms which are WhatsApp, Facebook, and Instagram.
Before participating, the participants were made aware of the content of the study. Knowing that the study was carried out under the responsibility of ASCoR, The University of Amsterdam. They were informed that their anonymity was guaranteed; that they were not subjected to any risks or discomforts; and that they were allowed to withdraw at any given time during or after participation. Participants agreed on voluntary base and did not receive
any rewards. They were required to confirm they were 18 years or over. Lastly, they gave informed consent before participation.
The questionnaire started with the question “do you follow a fashion influencer on Instagram?”. This made it possible to filter out participants who don’t follow fashion influencers on Instagram. Because of this question, the questionnaire was terminated for 46 participants who answered no. The whole questionnaire took approximately 5 minutes to complete. After the data was collected, the data was analyzed with the help of SPSS_24. Measurements
The current study measured five constructs: adoption of fashion style, purchase intentions and three forms of engagement: parasocial relationships, identification and wishful identification. In addition, four control variables were measured which were: gender, age, nationality and Instagram usage. To measure the constructs, a seven-point Likert scale was used (1= strongly disagree and 7 = strongly agree) with exception for the control variables. The current study conducted a method in which the participants were asked to name their favorite fashion influencer at the beginning of the questionnaire. This was mandatory before answering the items because the items regarded to their favorite fashion influencer. This method was found to be used in multiple studies regarding influencers (Hoffner & Buchanan, 2015; Chung & Cho, 2017).
Adoption of Fashion Style. There was found to be no measurement for adoption of fashion style. The current study developed a new scale, consisting three items. The first item of this scale was also used in the study from Djafarova and Rushworth (2017); it stated “my favorite fashion influencer inspires my fashion style”. The second item stated “ because of my favorite fashion influencer, my fashion style has changed”. The last item stated “because of my favorite fashion influencer, I want to change my fashion style”.
To analyze the dimensions of the scale, a principal axis factor analysis (PAF) was conducted with a direct oblimin rotation (Field, 2014). To determine the dimensions, several criteria were used. the first criteria, the Kaiser criterion, suggests that all components with eigenvalues lower than 1 should be dropped (Bandalos & Boehm-Kaufman, 2018). For the second criteria, Cattell’s scree tests, components after the left point of inflexion in the scree plot should be dropped (Cattell, 1966). For the current scale, the PAF showed that the items formed an unidimensional scale. The factor showed an eigenvalue above 1 (eigenvalue 2.21). Also, the scree plot showed one clear point after inflexion. This factor explained 74% of the variance with factor loadings from .668 to .892. The scale was measured reliable ( = .821). Lastly, a total score was created by averaging the means of the items. The total score ranged from 1.00 to 6.67 (M = 4.02, SD = 1.36).
Purchase Intentions. To measure purchase intentions, the scale by Kim, Ko, & Kim (2015) was used; which is an adapted scale from the scale by Jiang, Hoegg, Dahl and
Chattopadhyay (2010). It was adapted to fit in the social media context. The scale was found to be reliable and unidimensional. The sample of Kim et al. (2015) only consisted adults. This is of value because the sample of the current study also obtained adults as subjects. Therefore we believed that the scale was suitable for this study.
The scale by Kim et al. (2015) was slightly modified to fit into the Instagram-context of this study; the word “SNS” was replaced with “Instagram”. The scale consisted four items, one item for example stated “I am interested in buying clothes which are shown on his/her Instagram”. For the current scale, the PAF showed that the items formed an unidimensional scale. The factor showed an eigenvalue above 1 (eigenvalue 3.01). Also, the scree plot showed one clear point after inflexion. This factor explained 75% of the variance with factor loadings from .768 to .874. The scale was measured reliable ( = .890). Lastly, a total score
was created by averaging the means of the items. The total score ranged from 1.00 to 7.00 (M = 4.48, SD = 1.22).
Parasocial Relationships. Most of the scales for parasocial relationships were
developed for measuring parasocial relationships with tv-characters. Chung and Cho’s (2017) scale however, was appropriate to measure parasocial relationships with SMI’s. This scale was intended to measure two subconstructs of parasocial relationships, which are friendship and understanding. This scale was measured reliable and this scale was used in a study with adults in a social media environment. Therefore, we believed that this scale was the right measurement for this study.
As expected, the PAF showed that the scale extracted two factors. The first factor showed an eigenvalue of 4.84 and the second factor an eigenvalue of 1.18. Also, the scree plot showed two points after inflexion. Together, the factors explained 66% of the variance. However, the analysis showed different interpretations of the factors from Chung and Cho (2017) their factors. Whereas the subscale friendship of Chung and Cho (2017) consisted three items, the factor of the current scale would consist 6 items. The current study decided to keep the same scale-construction as Chung and Cho (2017) since it was measured reliable and valid. Therefore, the subscale for friendship in this study consisted the same three items as in the subscale of Chung and Cho (2017). One item for example stated “he/she makes me feel comfortable as if I am with a friend”. The scale was measured reliable ( = .760) with factor loadings from .655 to .714. A total score was created by averaging the means of the items. The total score ranged from 1.00 to 7.00 (M = 4.21, SD = 1.37). The subscale for
understanding consisted six items. The scale was measured reliable ( = .855) with factor loadings ranging from -.362 to -.874. Also here, a total score was created by averaging the means of the items. The total score ranged from 1.00 to 6.50 (M = 4.17, SD = 1.18).
Identification. For the construct identification, an adapted version of Cohen’s (2001) scale was used. Initially, Cohen’s (2001) scale was built to measure identification with media characters. Therefore Cohen’s (2001) scale was adapted since the current study wanted to measure identification with fashion influencers. The scale was modified by removing one item. Then for the remaining items, two words were replaced; the word “program” was replaced with “Instagram” and the word “character” was replaced with “him/her” which links to the participants their favorite influencer. One item for example stated “when viewing him/her on Instagram, I can feel the emotions he/she portrays”. Cohen’s (2001) scale was used in previous studies to measure identification with adults and is cited over 600 (Taylor & Francis Online, 2020). In addition with the giving that the scale was modified to fit the Instagram-context, we believed that this measurement was suitable to measure identification with.
For the current scale, the PAF loaded onto three factors. When three factors are extracted, this suggests that three total scores should be created (Bandalos &
Boehm-Kaufman, 2018). However, These findings are not consistent with previous studies where the scale was measured unidimensional (Cohen 2001, 2018). In addition, the screeplot showed one clear point after inflexion. Whereas the thumb rule suggests to retain the factors after inflexion (Cattell, 1966). Lastly, one subscale would be unreliable ( = .651) if the scale would be divided in three subscales. Whereas the scale as a whole would be reliable ( = .858). So based on previous research, the results of the scree plot and the results of
Cronbach’s Alpha, there was decided to create one total score for identification.1 This scale measured an eigenvalue of 4.25. The scree plot showed one clear point after inflexion. The
1 A multiple regression model was run where identification was split up in three sub constructs. A difference in
results was found, where the subconstruct friendship showed to be significant (p = .03). However, since one of the subconstructs of identification was not measured reliable, the current study valued the reliability of the subconstruct above the significance of another subconstruct. Therefore, the current study choose to not split up identification into three sub constructs.
factor explained 47% of the variance with factor loadings from .511 to .745. The scale was measured reliable ( = .858). Lastly, a total score was created by averaging the means of the items. The total score ranged from 1.11 whereas the maximum score was 6.33 (M = 4.02, SD = 1.02).
Wishful identification. To measure wishful identification, the scale by Schouten et al. (2020) was used. Which is an adapted scale from the scale by Hoffner and Buchanan (2005). Schouten et al. (2020) adapted the original scale by excluding one item. We found the adapted scale more suitable because it was applied in a social media context. The scales were
measured reliable, unidimensional and were used to measure wishful identification with adults. Therefore, we believed that the adapted scale was suitable for measuring wishful identification.
The current scale consisted four items. One item for example stated “he/she is the type of person I want to be like myself”. The PAF showed that the items formed an unidimensional scale. The factor showed an eigenvalue above 1 (eigenvalue 2.97). Also, the scree plot
showed one clear point after inflexion. This factor explained 74% of the variance with factor loadings from .645 to .904. The scale was measured reliable ( = .884). Lastly, a total score was created by averaging the means of the items. The total score ranged from 1.00 to 7.00 (M = 4.28, SD = 1.40).
Control variables. The control variables were: gender, age, nationality and Instagram usage. The variable gender measured 91 females and 39 males. The variable age had a range from 18 to 57 years with a mean of 27.18 (SD = 6.67). For nationality, as mentioned before, 117 participants were based in the Netherlands. For the remaining 12, one participant was based in Australia, two in Belgium, one in Czech Republic, one in France, two in Germany, one in Hong Kong, one in Spain, two in Turkey, on in the UK and lastly one in Uruguay.
To measure Instagram usage, the scale of Fardouly, Diedrichs, Vartanian and
Halliwell (2015) was used which consisted two items. The current study wanted to control for the possible effect of Instagram usage. Where greater exposure to Instagram possibly could have greater influence on purchase intentions and adoption of fashion style. This scale was used in multiple studies where the sample consisted adults (Fardouly et al., 2015; Fardouly, Willburger & Vartanian, 2018). Therefore this scale was found suitable for the current study.
The first item of this scale stated “how often do you check Instagram” (1 = not at all, 2 = every few days, 3 = once a day, 4 = every few hrs, 5 = every hr, 6 = every 30 min., 7 = every 10 min., 8 = every 5 min.). The second item stated “overall, how long do you spend on Instagram on a typical day?” (1 = 5 min. or les, 2 = 15 min., 3 = 30 min., 4 = 1 hr, 5 = 2 hrs, 6 = 3 hrs, 7 = 4 hrs, 8 = 5 hrs, 9 = 6 hrs, 10 = 7 hrs, 11 = 8 hrs, 12 = 9 hrs, 13 = 10 hrs or more). The two items correlated (r = .41, p < .00). The PAF showed that the items formed an unidimensional scale. The factor showed an eigenvalue above 1 (eigenvalue 1.41). The scree plot did not showed an inflexion. This can be explained since the analysis consisted only two items. However, it did showed that one factor was above 1. This factor explained 70% of the variance with factor loadings from .636. Lastly, a total score was created by averaging the means of the items. The total score ranged from 1.50 to 7.00 (M = 4.25, SD = 1.03).
Results
This study investigated if parasocial relationships, identification and/or wishful
identification influences individuals their purchase intentions (H1a, H2a and H2b). Therefore, a multiple regression model was conducted. This model was found to be adequate because of the ability to estimate the relationship between multiple independent variables and one continuous dependent variable (Field, 2014). The regression model included purchase intentions as dependent variable; parasocial relationship, identification and wishful
control variables. Beforehand, it was tested if the data met the assumptions for conducting a multiple regression model. The first assumptions suggests that independent and dependent variables must be continuous. Besides that, independent variables must show variance. This implies that independent variables cannot show standard deviations of 0. Both assumptions were met since the variables were continuous and the independent variables showed variance (SDfri = 1.37, SDund = 1.18, SDiden = 1.02, SDwish = 1.40). The next assumption is the
assumption of linearity. To meet this assumption, the relationship between the dependent and independent variable should be linear. A way of assessing is by visual inspections of the scatterplot from the multiple regression model. To obtain the correct scatterplot, a plot was requested when running the multiple regression model of the current model; with ZPRED on the x-axis and ZRESID on the y-axis. The assumption would be violated if the scatterplot shows any sort of curve (Field, 2014). For the current model, the assumption was met since the scatterplot showed no curve.
The next assumption mentions there should be no multicollinearity. A violation of this assumption would affect the precision of the estimate coefficients and can cause to produce biased p-values. To meet this assumption, the criteria states that no variance inflation factor (VIF) higher than 10 or tolerance lower than 0.1, is allowed (Field, 2014). For the current model, multicollinearity was not found (friendship, tolerance = .43, VIF = 2.31;
understanding, tolerance = .27, VIF = 3.77; identification, tolerance = .31, VIF = 3.19; wishful identification, tolerance = .63, VIF = 1.58). The second assumption suggests that residuals of model should be independent. A violation of this assumption can cause a wrong prediction and estimation of the regression vector and can cause invalid distributions of the t and F test. To meet the assumption, the Durbin-Watson statistics should be between 1 and 3 (Field, 2014). For the current model, this assumption was met (Durbin-Watson value = 1.89).
Next, is the assumption of homoscedasticity. To meet this assumption, variance of the dependent variable should be the same at all levels of the dependent variable, this is called equality of variance. When violating this assumption the variance of scores at one level of the dependent variable, is different from the variance of scores at another level. This is called heteroscedasticity. Heteroscedasticity can cause an inconsistency and bias in estimation of the standard error, which is associated with the parameter estimates of the model. A way of assessing the assumption is by visual inspections of the scatterplot from the multiple
regression model. To obtain the correct scatterplot, a plot should be requested when running the multiple regression model; with ZPRED on the x-axis and ZRESID on the y-axis. This assumption would be violated if the scatterplot shows a cone-shaped pattern of
heteroscedasticity. The scatterplot of the current model showed a random array of dots and therefore the assumption was met (Field, 2014). Lastly, data should met the assumption of normality. To meet this assumption, the residuals in the population should be normally distributed. A way of assessing this assumption is by visual inspection. A histogram was requested when running the multiple regression of the current model. The histogram showed a normal distribution of the residuals and therefore the assumption was met (Field, 2014). In conclusion, the assumptions were met for the current model.
The model showed that parasocial relationships, identification and wishful identification explained 29% of the variance of purchase intentions (R2Adjusted = .25). The multiple regression analysis showed that the model as a whole, including the control variables (age, gender, Instagram usage and nationality) explains a significant amount of variance in purchase intentions of the participants (F(8, 115) = 5.99, p < .00). The control variables age, gender, Instagram usage and nationality did not significantly predict purchase intentions (see Table 1). Parasocial relationships did not significantly predict purchase intentions (pfriendship = .01, punderstanding = .03), neither did identification (p = .57). However, a positive significant
effect was found of wishful identification on participants their purchase intentions (p < .00). To summarize, Findings showed that parasocial relationships (H1a) and identification (H2a) did not significantly influence participants their purchase intentions. However, a significant effect of wishful identification (H2b) was found on participants their purchase intentions. Table 1
Multiple Regression Analysis for Predicting Purchase Intentions
Variable B SE B t p 95% CI Gender -0.12 0.21 -.05 -0.59 .56 [-0.54 0.30] Nationality -0.20 0.32 -.05 -0.63 .53 [-0.83 0.43] Age 0.00 0.02 -.01 -0.13 .90 [-0.03 0.03] Instagram usage 0.04 0.10 .03 0.40 .69 [-0.16 0.24] Friendship Understanding -0.30 0.35 0.11 0.16 -.34 .34 -2.85 2.25 .01 .03 [-0.51 -0.91] [0.04 0.66] Identification -0.10 0.17 -.08 -0.58 .57 [-0.43 0.24] Wishful identification 0.45 0.09 .52 5.24 .00 [0.28 0.62] Note. N = 124
The second model tested the effect of parasocial relationships, identification and wishful identification on individuals their adoption of fashion style (H1b, H2c and H2d). Again a multiple regression model was conducted, with adoption of fashion style as dependent variable; parasocial relationships, identification and wishful identification as independent variables; and age, gender, Instagram usage and nationality as control variables. First, it was tested if the data met the assumptions. However, some assumptions only regard to independent variables. Since the independent variables (parasocial relationships, identification and wishful identification) of the current model are the same as the first model, two
suggested that independent variables must show variance and the assumption of no multicollinearity. For both assumptions was found that they were met.
The next assumption mentioned that independent and dependent variables must be continuous. For this model, the variables were continuous and therefore the assumption was met. Also the assumption of linearity was met; through visual inspections it was noticed that the scatterplot showed no curve. The fifth assumption mentioned that residuals in the model should be independent. This assumption was met (Durbin-Watson value = 2.07). The next assumption, regarded the assumption of homoscedasticity. This assumption was assessed by visual inspections. The scatterplot showed a random array of dots and thus the assumption was met. Lastly, there was the assumption of normality. Again assessed by visual inspections. The histogram showed a normal distribution, therefore the assumption was met. In
conclusion, the assumptions were met for the current multiple regression model. The second multiple regression model showed that parasocial relationships, identification and wishful identification explained 21% of the variance of fashion style adoption (R2Adjusted = .16). The multiple regression analysis showed that the model as a whole, including the control variables (age, gender, Instagram usage and nationality) explains a significant amount of variance in adoption of fashion style of the participants (F(8, 116) = 3.95, p < .00). The control variables age, gender, Instagram usage and nationality, showed no significant effect (see Table 2). Parasocial relationships (pfriendship = .22, punderstanding = .78) and identification (p = .28) also showed no significant effect. However, a positive significant effect was found of wishful identification on participants their adoption of fashion style (p < .00). Findings showed that parasocial relationships (H1b) and identification (H2b) did not significantly influence participants their adoption of fashion style. However, wishful identification (H2d) did significantly influence participants their adoption of fashion style.
Table 2
Multiple Regression Analysis for Predicting Adoption of Fashion Style
Variable B SE B t p 95% CI Gender -0.27 0.25 -.09 -1.07 .29 [-0.76 0.23] Nationality -0.17 0.38 -.04 -0.45 .65 [-0.92 0.58] Age -0.01 0.02 -.06 -0.66 .51 [-0.05 0.02] Instagram usage 0.13 0.12 .10 1.08 .28 [-0.11 0.36] Friendship -0.15 0.12 -.15 -1.23 .22 [-0.39 0.09] Understanding -0.05 0.18 -.04 -0.28 .78 [-0.41 0.31] Identification 0.21 0.20 .16 1.08 .28 [-0.18 0.60] Wishful identification 0.40 0.10 .41 4.00 .00 [0.20 0.60] Note. N = 125 Discussion
This study investigated the effect of engagement with fashion influencers on one’s purchase intentions and adoption of fashion style. A cross sectional survey was conducted with adults as subjects. The findings showed that parasocial relationships and identification did not significantly affect individuals their purchase intentions and adoptions of fashion style. However, wishful identification showed to have a positive significant effect on individuals their purchase intentions and adoptions of fashion style.
As expected, findings showed that wishful identification with fashion influencers affects individuals their purchase intentions and adoptions of fashion style. These findings are consistent with previous literature which found that individuals are likely to imitate behaviors of people they desire to become like (Djafarova & Rushworth, 2017). In regard to this study, this would imply that individuals are likely to purchase products and/or adopt fashion styles promoted by their favorite fashion influencer when wishful identification finds place.
It seems that wishful identification is an important mediator. But we could learn more about how wishful identification influences outcomes by investigating the mechanisms behind it. In that light, Hoffner and Buchanan (2005) explains how an increase of attention can influence purchase intentions and adoption of fashion style. They suggest that individuals experience an increase of attention when they are exposed to content of influencers they wishfully identify with. This increase of attention leads to an attitude change of the individual. Because when an individual views an advertisement with a great amount of attention, it leads to extensive processing of this ad. Which can lead to greater elaboration of attitude change, for example an attitude change in purchase intentions or fashion style (Chattopadhyay & Nedungadi, 1992; Petty & Cacioppo, 1986). Therefore, individuals can be influenced by content of their favorite fashion influencers.
This study has been unable to demonstrate the influence of parasocial relationships and identification on individuals their purchase intentions and adoptions of fashion style. This might occurred because not all media effects are direct but can also happen indirect (Petty & Cacioppo, 1986). The elaboration likelihood model (ELM) explains this in the way that there are two pathways to process advertisements, the central and the peripheral route. Both can lead to attitude changes. The type of pathway can depend on the degree of motivation an individual has to process the information on the advertisement. People who are highly motivated, take the central route. These individuals are more likely to elaborate all the information of the advertisement because they are very interested. Where greater elaboration of the content can leads to greater change in attitude. The advantages of the central route is firstly, attitude changes due to central routes can predict behavior more than the attitude changes due to peripheral routes. Second, attitude changes due to central routes are more likely to last longer than attitude changes due to peripheral routes (McNeill, 1989).
The peripheral route is where people have low motivation. When viewing
advertisements with low motivation, elaboration will be lower and individuals will seek for easy cues instead of elaborating all the information of the advertisement (Petty &
Cacioppo,1986). When Individuals seek for cues, they can get cued by the attractiveness of influencers (Kelman, 1961). This cue signals how use of the advertised product, can make the viewer look more attractive; because the influencer who promotes the products, is find to be attractive. As a result of this cue, the viewer perceives the brand’s name and therefore develop positive feelings towards that brand (Kotler, 2015). The next time the individual encounters the brand again, he/she will notice this brand. Because of this brand awareness and the positive feelings that he/she developed when watching the advertisement, the individual’s purchase intentions will increase (Kotler, 2015; Kelman, 1961). This is an example of how media-effects can be indirect.
In regard to this study, it would imply that individuals who desire to become like an influencer are highly motivated to elaborate all the information on advertisement. Their high motivation is driven by the desire they have (Bandura, 2001). Therefore, there is greater elaboration of the information on the advertisement. This suggests that individuals processes all the information of the advertisement. Not only the attractiveness of the influencer but, for example, also what the influencer is wearing. The strength of the influence of the
advertisement is dependent of the judgement the individual creates towards the advertisement (Petty & Cacioppo, 1986). In example if the individual judges the outfit to be good looking, it therefore might be more tempting to purchase that outfit. This is likely when individuals have the desire to become like an influencer. Because wearing the same clothes and having the same fashion style as the influencer, is a step closer to becoming like the influencer (Bandura, 2001).
Whereas with identification and parasocial relationships, the motivation of desiring to become like someone else does not apply (Cohen, 2001; Hoffner & Buchanan, 2005).
Therefore, these individuals are less motivated to elaborate the information on
advertisements. Because of the lower elaboration, they will judge advertisements based on cues (peripheral route) like the attractiveness of the influencer (Kelman, 1961). But not on all the information presented in the advertisement. For instance, the individual does not elaborate the outfit the influencer is wearing but only the attractiveness of the influencer. But when there is no awareness of the outfit, they cannot be influenced to purchase the products or adopt the fashion style of the influencer (Petty & Cacioppo, 1986; Kelman, 1961; Kotler & Keller, 2011). Therefore, we might have not been able to demonstrate the influence of parasocial relationships and identification.
As for the limitations of this study a small sample size was found, but more important, there were limitations regarding the characteristics of the participants. The questionnaire was distributed among the researcher’s network and the researcher got acquainted with the majority of the sample through the fashion industry. Therefore, the sample mainly consisted people who work in fashion or people who are highly involved in fashion. The limitation here is that if an individual is highly involved in an area, in this case fashion, individuals might have created strong attitudes in regard to this area. Where strong attitudes are less likely to change by the influence of influencers. Because these individuals are more likely to resist attempts of change regarding fashion because of their strong attitudes (Hovland, 1957). Since most of the participants in this study were highly involved in the fashion industry, it was less likely that fashion influencers had an influence on participants their purchase intentions and adoptions of fashion style. However, their strong attitudes is not a prevention of identifying and/or engaging parasocial relationships with influencers. This could explain why participants did experience identification and parasocial relationships with influencers. But did not
experience a significant influence on their purchase intentions and adoptions of fashion style, due to their strong attitudes. Therefore, the current sample is not representative of the
population. To prevent this limitation, future research should consider attitude strength as moderator when measuring the influence of engagement with fashion influencers.
Besides that, we found the concept of letting questionnaire-items only regard to their favorite fashion influencer, might not be the best approach. Labelling someone as their favorite fashion influencer does not imply that this specific influencer has the most influence on one’s purchase intentions and/or adoption of fashion style. For example, studies found that individuals use Instagram to seek out parasocial interactions in order to feel less lonely or more socially accepted (Ballantina & Martin, 2005; Hwang & Park , 2007). If this is the case, influencers who parasocially interact with these individuals; might become their favorite influencers because these influencers gratify the needs of those individuals. This would mean that influencers can be labelled as someone’s favorite fashion influencer because of the parasocial interactions. But not per se because of their high competence in being a fashion-expert. In addition, Judd, James-Hawkins, Yzerbyt and Kashima (2005) shows how being perceived as warm can be negatively related to high competence. A fashion influencer who is perceived as warm because of the parasocial interactions, might not be very competent as fashion-expert. Additionally, fashion influencers who are highly competent might not have a warm personality. This would explain the current findings of why parasocial relationships did not had an influence. Because fashion influencers who have lower competence in fashion but are perceived as warm and therefore chosen as their favorite influencer, might not have influence on individuals their fashion style and purchase intentions. A suggestion for future research would be to not specify the questionnaire to only their favorite fashion influencer. This would avoid the chance of people choosing fashion influencers who they like the most because of their warm personality; but who may not be competent enough to influence
attitudes. But for instance, let individuals report the fashion influencer who inspires their fashion style the most or all fashion influencers in general.
For fashion brands, 2020 is not a year to look forward to because the fashion industry is being challenged by multiple areas. Since Brexit is official and Trump elected as president, the fashion industry is afraid of the impact of trade disputes and tariffs on their global supply chains. Besides that, sustainability and digitization makes the fashion industry more complex and challenging as well since consumers demand sustainable products and expect innovative digitalized content. Now more than ever, it is a good strategy for fashion players to focus on how to use social media channels since this is one of the few factors they can control
(McKinsey & Company, 2019). Therefore, it is valuable for the fashion industry to know that wishful identification with influencers can lead to higher purchase intentions and adoption of fashion style. The fashion industry is considered one of the most important industries in the world so it is important to keep doing research in order to maximize their profits (McKinsey & Company, 2016).
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Appendix A
The Questionnaire From the Current Study Dear participant,
First of all I would like to thank you for taking your time to visit the link that was sent to you. I am Natasha Tombeng and I am a Master student at the University of Amsterdam. As part of my thesis, I am conducting a survey and I hereby would like to invite you to participate in my study about people's relationships with fashion-influencers on Instagram.
The survey regards questions about your purchase behavior, fashion style and relationships with fashion-influencers.
We consider your anonymity as very important and that is why I want to address that this research is being carried out under the responsibility of the ASCoR, The University of Amsterdam and therefore the following is guaranteed:
1. Your identity and answers remain anonymous and is filed as confidential. The purpose of your data is solely for this study and no information will be passed on to third parties unless you first give your expressed permission.
2. You are aware your participation is on voluntary base and you are allowed to withdraw at any given point without needing to give a reason. Please be aware that you are able to withdraw after participating, contact natasha.tombeng@student.uva.nl when needed.
3. Participating in the research will not entail you being subjected to any appreciable risk or discomfort, the researchers will not deliberately mislead you, and you will not be exposed to any explicitly offensive material.
If you would like to receive further information about this study, feel free to contact
natasha.tombeng@student.uva.nl
If you have any comments or experience complaints about this research, you can contact the designated member of the Ethics Committee representing the ASCoR, at the following address: ASCoR secretariat, Ethics Committee, University of Amsterdam, Postbus 15793, 1001 NG Amsterdam; 020‐ 525 3680; ascor‐secr‐fmg@uva.nl.
By clicking the 'I agree'-button, you declare the following statement:
"I am 18 years or older and I participate this study fully anonymous on voluntary base. I am aware of the nature of this study and have been well informed before starting the survey. I
know my data will not be given to third parties without my consent and I know I can withdraw at any given point without needing to give a reason."
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I agree (1)o
I do not agree (2)Skip To: End of Survey If By clicking the 'I agree'-button, you declare the following statement: "I am 18 years or older... = I do not agree
Page Break
End of Block: Introduction Start of Block: Suitability
Do you follow at least one fashion influencer on Instagram?
A fashion influencer promotes fashion brands through his/her Instagram
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Yes (1)o
No (2)Skip To: End of Survey If Do you follow at least one fashion influencer on Instagram?A fashion influencer promotes fashion... = No
How often do you check Instagram?
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not at all (1)o
every few days (2)o
once a day (3)o
every few hours (4)o
every hour (5)o
every 30 minutes (6)o
every 10 minutes (7)Overall, how long do you spend on Instagram on a typical day?
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5 min or less (1)o
15 min (2)o
30 min (3)o
1 hr (4)o
2 hrs (5)o
3 hrs (6)o
4 hrs (7)o
5 hrs (8)o
6 hrs (9)o
7 hrs (10)o
8 hrs (11)o
9 hrs (12)o
10 hrs or more (13)End of Block: Suitability Start of Block: Demographics
What gender do you identify as?
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Male (1)o
Female (2)What is your age?
________________________________________________________________
In which country do you currently live? ▼ Afghanistan (1) ... Zimbabwe (1357)
End of Block: Demographics
Start of Block: Who is your favorite fashion influencer?
I would like to ask you to choose one of your favorite fashion influencers whom you follow on Instagram. Please keep this particular fashion influencer in mind when answering the following questions.
(name does not have to be accurate)
________________________________________________________________
End of Block: Who is your favorite fashion influencer? Start of Block: Parasocial relationship part 1
Please answer following statements regarding your relationship with your favorite fashion influencer Strongly disagree (22) Disagree (23) Somewhat disagree (24) Neither agree nor disagree (25) Somewhat agree (26) Agree (27) Strongly agree (28) He/she makes me feel comfortable as if I am with a friend (8)
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I would like to have a friendly chat with him/her (9)o
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If he/she were not a well-known fashion influencer, we would have been good friends (10)o
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I think I understand him/her quite well (11)o
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When he/she behaves in a certain way, I know the reasons for his/her behavior (12)o
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End of Block: Parasocial relationship part 1 Start of Block: Parasocial Interactions part 2
Please answer following statements regarding your favorite fashion influencer Strongly disagree (22) Disagree (23) Somewhat disagree (24) Neither agree nor disagree (25) Somewhat agree (26) Agree (27) Strongly agree (28) I can feel his/her emotions in certain situations (8)
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He/she seems to understand the kind of things I want to know (9)o
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He/she reminds me of myself (10)o
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I can identify with him/her (11)o
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End of Block: Parasocial Interactions part 2 Start of Block: Identification part 1
The following statements are about how you see your favorite fashion influencer Strongly disagree (22) Disagree (23) Somewhat disagree (24) Neither agree nor disagree (25) Somewhat agree (26) Agree (27) Strongly agree (28) When viewing his/her Instagram, I feel as if I am part of his/her actions (8)
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When I am viewing his/her Instagram, I forget myselfand get fully absorbed (9)
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I think I have a good understanding of his/her character (10)o
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I tend to understand the reasons why he/she does what he/she does (11)o
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When viewing him/her on Instagram, I can feel the emotionshe/she portrays (12)
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End of Block: Identification part 1 Start of Block: Identification part 2
The following statements are about how you see your favorite fashion influencer Strongly disagree (22) Disagree (23) Somewhat disagree (24) Neither agree nor disagree (25) Somewhat agree (26) Agree (27) Strongly agree (28) When viewing his/her Instagram, I feel I could really get inside his/her head (8)
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When I view key-moments of his/her life on Instagram, I feel I know exactly what he/she is going through (10)o
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I want him/her to succeed in achieving his/her goals (11)o
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When he/she succeeded, I felt joy, but when he/she failed, I was sad (12)o
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End of Block: Identification part 2 Start of Block: Wishful Identification
Please answer following statements regarding your favorite fashion influencer Strongly disagree (22) Disagree (23) Somewhat disagree (24) Neither agree nor disagree (25) Somewhat agree (26) Agree (27) Strongly agree (28) He/she is the type of person I want to be like myself (8)
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Sometimes I wish I could be more like him/her (9)o
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o
o
He/she is someone I would to pursue to be like (10)o
o
o
o
o
o
o
I would like to do the kind of things he/she does (11)o
o
o
o
o
o
o
End of Block: Wishful Identification Start of Block: Fashion style
