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Article

Understanding Gen Z and Gen X Responses to Influencer Communications

by
Danijel Bratina
* and
Armand Faganel
Faculty of Management, University of Primorska, 6000 Koper, Slovenia
*
Author to whom correspondence should be addressed.
Adm. Sci. 2024, 14(2), 33; https://doi.org/10.3390/admsci14020033
Submission received: 18 October 2023 / Revised: 26 January 2024 / Accepted: 8 February 2024 / Published: 16 February 2024
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Versions Notes

Abstract

:
The increased popularity of social media has greatly affected the marketing-communications activities of companies. This study seeks to understand how influencers promoting products affect their followers’ purchasing intentions and attitudes towards endorsed products. Our main interest is to get an insight on differences between two generations (X and Z). We construct a structural model, based on the theory of credibility. Findings suggest that influencer endorsements have an impact on both age groups, with a more noticeable effect observed in the younger generation. The loyalty towards influencers emerges as a constructive intermediary factor, amplifying the influence of credibility on purchasing intentions across both generations. When considering attitudes towards the brands endorsed by the influencers, loyalty’s impact is comparatively modest. The results provide a building block in understanding the influencers’ activities in marketing-communications, and how they can be used for communication with different generations.

1. Introduction

Web 2.0 has changed the media landscape in recent years by introducing user-generated content through new media in the form of social networks, allowing for anyone to create marketing-communication messages. Communication using social media influencers has been on a rise since 2011, and is constantly showing above average returns on investments compared to the use of classic media and web advertising such as paid search or social media advertising. Geyser (2023) mentions that the influencer marketing industry is set to grow to USD 21 bn (up USD 5 bn) from 2022, which is still weak compared to search advertising (forecasted to USD 279.30 bn from USD 251.70 bn in 2022), social media advertising (USD 223.11 bn in 2022) and TV (USD 158 bn in 2022) (Majidi 2023). Santora (2022) claims that the influencers’ market size is going to reach as high as USD 85 bn in 2028 mostly on expense of classic media such as TV, radio, and billboards. In recent years, comprehensive research has explored the effect of influencers on consumer buying behavior (Geyser 2023; Leung et al. 2022; Zhang and Wei 2021), showing measurable effects. Such findings are backed up by the financial effects companies are experiencing from collaborations with influencers. The latest data available at the time of writing this paper shows influencers ROAS (return on ad spent) to be 5.20 (Geyser 2023) compared to TV 1.24 (The Nielsen Company 2020), search Ads (McCormick 2022) 2.00 (with high dispersion between 0.5 and 10), Instagram ads 1.46, Facebook ads 1.23, YouTube ads 1.21, TikTok ads 1.06 and Twitter ads 1.04 (Heinecke 2022). Even though the data for influencers are down considerably from the 2021 ROAS of 11, it is still the best investment when ROAS is measured.
Various authors provide several reasons for the popularity of influencers among customers like trustworthiness, personality traits, emotional attachment to followers, and skills (Belanche et al. 2021a; Kapitan and Silvera 2016; Lou and Yuan 2019; Teng et al. 2014). Their effectiveness has also been confirmed in several studies (Jarrar et al. 2020; Kapoor et al. 2021; Leung et al. 2022). A recent study from Goldring and Azab (2020) shows there are significant differences between Gen X and Gen Z consumers when exerting behavior influenced by social interactions (Gen Z being more concerned with ethical and responsible consumption). Pradhan et al. (2023) show Gen Z tends to avoid products promoted by influencers when consumers perceive the communication being controlled by endorsed brands and when the relationship with the influencer is weak. Childers and Boatwright (2021) show that Gen X displays distrust of social media advertising efforts as compared to Gen Z. Gen X perceives influencer posts as traditional advertisements from celebrity endorsers. Several other studies (Pradhan et al. 2023, pp. 30–31) only study the effects on Gen Z. A comprehensive analysis of consumer behavior as a consequence of influencers’ activities between generations is necessary to understand whether different responses arise from generations. Our paper aims to gain insight into the different behavior of generations X and Z through credibility construct (Sobel 1985).
We gather insight on how different generations (X and Z) are affected by influencers as a marketing communication tool. We analyze how influencers affect consumers’ attitudes towards endorsed brands, as well as their intention to buy and compare the magnitudes of effects on both generations. An investigation is performed on relevant credibility dimensions affecting each generation using loyalty towards the influencer as the mediator.

2. Literature Review

Early research defines social media influencers (SMI) as a form of electronic word of mouth (e-WOM) that uses popularity of individuals to build brand elements (De Veirman et al. 2017). In comparison to classic marketing communication methods, the use of SMI helps in identifying and targeting a narrower audience with higher probability of engagement (in the form of views, comments, likes, and purchases). Schouten et al. (2020) argue that consumers perceive SMI as more effective and reliable as compared to other marketing-communication mix tools. Such evidence is supported by Moshin (2022), stating that 79% of consumers’ purchasing decisions online are highly impacted by user-generated content and SMI.
The main purpose of our study is to research the differences in responses to SMI communications between generations X and Z. One of the main drivers of SMI effectiveness has been identified to be the credibility that creates influence and drives purchase intentions and positive attitudes towards an endorsed brand (Hussain and Ali 2021).
Credibility has earlier been proposed to study celebrities’ communication effects by Ohanian (1990), that, based on previous research from Hovland et al. (1953) and McGuire (1985), proposed that expertise, trustworthiness, and attractiveness are the building blocks of an endorser’s credibility that he defined as: “communicator’s positive characteristics that affect the receiver’s acceptance of a message”. Recently, several authors successfully applied this same concept on influencers, using Ohanian’s model to assess a marketing-communication source credibility for influencers (Breves et al. 2019; Djafarova and Rushworth 2017; Wiedmann and von Mettenheim 2020; Nafees et al. 2021).
Several factors have been investigated for their influence on customers’ purchase intentions. Prior to SMI, key elements such as advertisement features, perceived product value, experience with the product under consideration, and recommendations from acquaintances have been identified as primary factors impacting the decision-making process. Factors such as the credibility of the endorser, the alignment between the product and the endorser, and the reputation of the endorsed brand also play crucial roles (Chetioui et al. 2020). In this paper, we focus on credibility as the main affecting factor, following Hussain and Ali’s (2021) methodology based on Ohanian (1990), depicted in Figure 1, that constructs credibility using three factors, namely: expertise, trustworthiness, and attractiveness. While Nafees et al. (2021) suggest using endorsers’ goodwill instead of attractiveness, most of the studies involving beauty and fashion industry focus on attractiveness. The endorsers’ credibility measure has recently been researched by Schouten et al. (2020), showing advertising effectiveness is greater when using SMI as when using celebrities, and credibility is playing a statistically significant role in this; Weismueller et al.’s (2020) study shows a positive influence of attractiveness, trustworthiness, and expertise of SMI on purchase intentions; Lim et al.’s (2017) research argues that source attractiveness and product match-up have a significant impact on purchase attitudes and intentions. All these findings demonstrate that the endorsers’ credibility framework is applicable to SMI.

2.1. Dimensions of Credibility

Following the above construction of credibility, we therefore operationalize the three building blocks: the influencers’ expertise, trustworthiness, and attractiveness.
Expertise refers to the knowledge of a person about the product category being endorsed, and has been confirmed to positively affect attitudes towards the content being promoted by the communicator (Wiedmann and von Mettenheim 2020). It is thought to be a result of an individual’s knowledge and achievements (Silvera and Austad 2004).
Trustworthiness is defined as the degree to which the receiver of the message trusts the source (Chetioui et al. 2020) and refers to the extent of trust, acceptance, and confidence of the receiver in the source providing the message. Wiedmann and von Mettenheim (2020) believe that trustworthiness is the extent to which the message from a celebrity/SMI expresses their own beliefs or being influenced by the brand they endorse (like paid ads). Schouten et al. (2020) provide evidence that trustworthy endorsers are perceived to induce positive attitudes of consumers. In the context of SMI, trustworthiness is a construct of honesty, dependability, reliability, and sincerity (Chetioui et al. 2020).
Attractiveness deals with communicators’ physical appearance, and is perceived to have a positive effect on advertising campaigns (Silvera and Austad 2004) and increases target audience attention (Lim et al. 2017). Ohanian (1990) defines attractiveness as a latent variable constructed by attractive, classy, beautiful, elegant, and sexy indicators. A similar version of the construct has been proposed by (Wiedmann and von Mettenheim 2020) on influencers with a slightly different phrasing (attractive, charismatic, good-looking, admirable, and beautiful). Much research on the study of attractiveness is limited to product categories that are affected by this feature (mostly fashion and style) and remains to be confirmed in other product categories.

2.2. Loyalty as the Mediator between Credibility and Effects of Influencers’ Communications

Loyalty is manifested by a repeated pattern of purchases of the same brand within a product category (Bowen and Chen 2001). In the context of SMIs, the concept of human brand has been proposed by Thomson (2006), where celebrities develop brand-like associations to themselves that result in positive purchasing patterns of endorsed brands, or even their own created brands. Jun and Yi (2020) use Thomson’s human brand in the SMI context by stating the context produced by SMI is their product, followers are consumers, and loyalty is a re-visiting and following of such contents. Our research uses loyalty towards influencers as an intermediate layer between the sources (conceptualized around the construct of credibility) and outcomes that we define as purchase intentions and positive attitudes towards endorsed products. The indirect effect of credibility constructs (through creating loyalty) reflects the attitudinal approach, where loyalty is created through credibility, reflecting an emotional and cognitive intention (McKenna and Bargh 1999; Yeon et al. 2019); while a direct effect of loyalty considers repetitive purchases arising directly from loyalty itself (Moriarty 1990).

Purchase Intention

We operationalize two outcome variables from SMI activities—purchase intention and attitudes towards the products being endorsed. The first is defined as the consumer’s intention to buy a particular product after evaluating available alternatives (Huang et al. 2011). Intentions do not necessarily culminate in an actual purchase, but are regarded as the likelihood of a real purchase occurring. It is beyond the purpose of this paper to study the several steps in the purchase intention creation process; thus, we limit ourselves to recording the level of purchase intention because of SMI activities. SMI are engaged in all the stages of purchase (e.g., awareness, interest, desire, and action—if using the AIDA framework).
The second measure—attitudes towards the endorsed product—has been defined by Mitchell and Olson (1981) as an individual’s assessment of the brand. A positive attitude increases the chances of an actual purchase, while a negative one decreases it. In our study, we assess how consumers’ attitudes are affected by SMI communications, and argue that a higher SMI credibility score would reflect in positive consumer’s attitudes, and, consequently, higher purchase intentions. Such positive correlations have already been explored in the past (Chekima et al. 2020; Wiedmann and von Mettenheim 2020).

2.3. Research Design and Data Collection

We construct a multigroup structural model starting with Hussain and Ali (2021, p. 36) using the same indicators, latent variables, and loyalty as the mediating variable. Initially, we conducted a path analysis to determine the role of the mediating variable, and then used the resulting model in the multigroup structural equation analysis to determine the differences between the two generations of our interest. The conceptual model is shown in Figure 1 below. We constructed the credibility latent variables attractiveness, trustworthiness, and expertise with the same questionnaire as Hussain and Ali (2021). The same procedures have been used to construct loyalty, purchase intention, and attitudes. The validity of the measurement part of the model has been tested with Cronbach’s alpha and confirmatory factor analysis, as follows.
Our study focused exclusively on consumers within the sport apparel goods on the Slovenian market. The method used to collect data was a self-reporting online questionnaire. Sampling was non-random, using snowballing and convenience sampling through contacts from Facebook, Instagram, and Tik-Tok. Questionnaires were collected from 25 May 2022 until 10 June 2022. A total of 96 (48 gen X and 48 Gen Z) full questionnaires have been included in the analysis. Questionnaires were executed in the Slovenian language. For all variables, we used a 7-degree interval scale to determine respondents’ opinions, ranging from “strongly disagree” (1) to “strongly agree (7). Apart from age (to determine generation), we did not collect other demographic data about respondents. To filter out irrelevant respondents, we used two screening questions: “Do you use social media (Instagram, TikTok, Facebook, Twitter, LinkedIn)?” and “Do you follow a sports influencer?” This way, we only included respondents that were to some extent active on social media and had the potential of being affected in their decision making by influencers.

2.4. Credibility Variables and Measurement Model

We used three of five questions used by Wiedmann and von Mettenheim (2020)—attractive, charismatic, and admirable—to construct attractiveness using statements: “[Social media influencer] is attractive/charismatic/admirable”, where [Social media influencer] was the best-known SMI in sports apparel to the respondent. A high Cronbach’s alpha (0.921) suggests strong reliability of the construct.
Expertise was been measured by statements: “[SMI] is an expert in his/her field”, “[SMI] is knowledgeable in his/her field” (adapted from Wiedmann and von Mettenheim (2020)). Cronbach’s alpha is 0.905, with all factor loadings statistically significant. For the trustworthiness latent variable construct, we selected all five from the same paper above, being: [SMI] is dependable/honest/reliable/sincere/trustworthy. Cronbach’ alpha for the validity test is 0.907. All credibility factors’ indicators are highly correlated, suggesting a construct using latent variables could have been omitted with little difference to the final model.
The mediating variable loyalty has been operationalized using three questions: “I will continue following [SMI]”, “I will follow [SMI] as long as relevant content is provided”, “I will recommend [SMI] to others”. Loyalty, as conceptualized, reflects behavioral patterns towards the SMI, and not a particular brand being promoted. Although we did not measure respondents’ loyalty to the brands, we feel that such factors should be included in future research (i.e., random effects models accounting for brands as categorical variables). Consistency analysis shows Cronbach’s alpha to be 0.965.
Dependent latent variables in our model represent purchase intentions and attitudes towards the brand. Chetioui et al. (2020) suggest two questions to determine purchase intentions: “I frequently buy products promoted by [SMI] I follow” and “I recommend products promoted by [SMI] I follow to others” (Cronbach’s alpha 0.926). Attitudes towards brands do not involve a particular brand being bought, but generally encompass attitudes towards brands advertised by [SMI] respondents follow, measured by the following questions (Belanche et al. 2021b): “I trust brands promoted by [SMI]” and “I have positive opinions about brands promoted by [SMI]”.
Initially, we conducted the CFA using SPSS AMOS 23 software to assess measurement model validity. The model is depicted in Figure 2. All indicators show normal distribution, and standardized estimates are above 0.5 and statistically significant, meaning no indicator shall be omitted (Table 1).
Using suggested reporting for model fit according to Meyers et al. (2005), the criteria values and critical thresholds in the chi-squared test is nonsignificant (p = 0.166, should be > 0.05; RFI = 0.926, should be > 0.9, IFI = 0.99, should be > 0.9, RMSEA = 0.036, should be < 0.05). Modification indices analysis suggested no meaningful covariates to improve the model fit. We also tested for discriminant variability using Fornell Larcker criterion, and found all squares of average variances extracted were higher than relative correlations among constructs.

Path Analysis for Both Generations (X and Z)

After assessing the measurement model, we constructed the structural model formulating our hypotheses first, building the model using the mediating latent variable of loyalty to assess direct and indirect effects of credibility constructs on the whole sample, and at last discriminating for the moderating variable generation.

2.5. Hypotheses Development

Our initial challenge was to explore the connection between credibility factors and the outcome variables of purchase intention and attitude. Chopra et al. (2021), Casaló et al. (2020), Djafarova and Rushworth (2017), and others have shown the positive impact of credibility on purchase intentions and attitudes. Attractiveness positively affects trust in the promoted brand in the fashion industry, affecting purchase intentions (Lou and Yuan 2019; Woodburn 2004) and attitudes (Wiedmann and von Mettenheim 2020). We thus hypothesize that attractiveness positively influences purchase intentions and attitudes towards the endorsed brand (product).
H1a. 
Attractiveness has a positive effect on purchase intentions.
H1b. 
Attractiveness has a positive effect on attitudes.
We similarly construct the other four hypotheses for the remaining two constructs of the credibility dimensions, basing our reasoning on McGinnies and Ward (1980), who found a positive correlation between trustworthiness and persuasion power; Schouten et al. (2020) and Sakib et al. (2020) provide evidence of a positive correlation between trustworthiness and purchase intention/attitudes; and Chetioui et al. (2020) and De Veirman et al. (2017) find a positive correlation between expertise and purchase intention/attitudes. For the latter, Wiedmann and von Mettenheim (2020), as well as AlFarraj et al. (2021), found no significant relationships between expertise and outcome variables on purchase intention and attitudes.
H2a. 
Trustworthiness has a positive effect on purchase intentions.
H2b. 
Trustworthiness has a positive effect on attitudes.
H2c. 
Expertise has a positive effect on purchase intentions.
H2d. 
Expertise has a positive effect on attitudes.
Loyalty is a widely used measure that reflects repetitive purchasing patterns of a preferred brand from consumers. In the web 2.0 context, loyalty represents a follower intention to read and get engaged from SMI’s content, as well as recommend the SMI they follow to relatives (Belanche et al. 2021b). Perceived credibility is directly linked to the willingness of a consumer to follow a SMI’s account and recommend their media to others. Credibility constructs have also been found to positively affect loyalty: attractiveness (Chekima et al. 2020; Wiedmann and von Mettenheim 2020), trustworthiness (Wiedmann and von Mettenheim 2020), and expertise (Urrutikoetxea Arrieta et al. 2019). Beside this indirect (mediating) effect through credibility, loyalty itself affects consumer purchase intentions and attitudes. Such a concept can also be applied to loyalty towards SMIs, and has been researched by Ki et al. (2020), providing evidence that loyal followers develop positive purchase intentions and attitudes towards products (brands) endorsed by SMIs. Coherent with studies that examined the mediating role of loyalty on brand purchases and attitudes, we propose that loyalty to an SMI to have the same role in our model transferring attachment to SMI towards purchasing intentions and attitudes of the endorsed brand/product. We thus formulate the following hypotheses:
H3a. 
Loyalty mediates the positive effect of attractiveness on purchase intentions.
H3b. 
Loyalty mediates the positive effect of attractiveness on attitudes.
H3c. 
Loyalty mediates the positive effect of trustworthiness purchase intentions.
H3d. 
Loyalty mediates the positive effect of trustworthiness on attitudes.
H3e. 
Loyalty mediates the positive effect of expertise on purchase intentions.
H3f. 
Loyalty mediates the positive effect of expertise on attitudes.
H3g. 
Loyalty has a positive effect on purchase intentions.
H3h. 
Loyalty has a positive effect on attitudes.

Generations

Our research’s main purpose is to investigate whether the model depicted in Figure 1 works differently for generation X (born between 1965 and 1980) than generation Z (born between 1995 and 2010). We have omitted the millennial generation, with the assumption it would be somehow a mixture of the two, expecting a significant difference in behavior for the two analyzed generations. To omit generating too many hypotheses, we will report on statistically significant differences between the two models, and elaborate the causes using multigroup SEM analysis.
There is a considerable gap between the two generations when consuming influencers’ content. A total of 77% of Gen Z follows influencers, opposed to 24% of Gen X, a 3 times difference (Aubertine 2023). A similar report from Hub-Spot and Brandwatch (2022) on U.S. consumers shows 55% of Gen Z consider influencer recommendations, while only 24% of Gen X and 28% of Gen Z have bought products on social media directly (18% of Gen X).
Another study (Moesser 2022) claims that Gen Z online behavior prefers people over marketers, with 11% of them relying on influencers for product recommendations, while Gen X are more value-driven preferring, cross-checking sources of product promotions using blogs, websites, reviews, and videos, and being more susceptible to long term strategies. Subsequently, the author suggests a “be-nice and create good mood” approach to Gen Z communications (using videos, games, and live streams) and “provide factual benefits of solutions offered” approach to Gen X (using in-depth videos, email marketing, blogs, review sites, and infographics). Similar findings (Sway Group 2023) suggest a personalized “feel good” approach to Gen Z and a “facts rich” approach to Gen X, when communicating.

3. Results

We first conducted statistical tests for differences in all used variables between the two generations. Averages, variances, and statistical significance of differences are shown in Table 2.
Expectedly, there is significantly more connection between Gen Z and their influencers of choice compared to generation X in all factors (credibility measures and outcomes).
Next, we conducted a mediation test using AMOS software to assess direct, indirect (through loyalty), and total effects of the three credibility constructs (attractiveness, trustworthiness, and expertise), on outcome variables (purchase intention and attitudes), on the whole sample (both generations), and for each generation separately. Table 3 represents the findings for the whole sample, showing all effects to be statistically significant. The results are, however, different for the two generations, represented below in the group analysis discussion.
As per previous research, loyalty does have a mediating effect on purchase intentions and attitudes for expertise and attractiveness. Our research shows no mediating effect of loyalty on attractiveness and trustworthiness towards attitude. Analysis with standardized regression coefficients is shown in Figure 3.
Direct effect of loyalty on purchase intention has a value of 0.47 and is statistically significant (p < 0.000, H3g), while the direct effect of Loyalty on Attitude has a value of −0.02 and is not significant (H3h). All loadings (apart for loyalty towards attitude) are coherent with previous research (Hussain and Ali 2021; Jun and Yi 2020; Ki et al. 2020).

Multigroup SEM Analysis

We compared the unconstrained 2 group model with the fixed structural weights model to check for any statistically significant differences between the base (non-generation dependent) and multigroup model and found difference (Table 4), assuming the unconstrained model to be correct:
Table 5 shows loadings for the two generations. For each, we tested significance for difference by comparing the unconstrained model with a model where we fixed the tested loadings for both generations.
We found a statistical difference in expertise over purchase intention (both factors being small), attractiveness to loyalty, purchase intentions, and attitude all stronger in Gen Z, truthfulness towards attitude stronger for Gen Z, loyalty to purchase intention strong, and loyalty to attitude insignificant for Gen Z, but significant and small for Gen X.

4. Discussion

Our framework follows Hussain and Ali (2021) and adds the demographic dimensions of generations X and Z as moderating factors to the effects of SMI credibility power to affect consumer purchase intentions and attitudes. We confirmed that all three constructs of credibility (expertise, trustworthiness, and attractiveness) have a positive effect on purchase intentions and attitudes. This validates the findings of Wiedmann and von Mettenheim (2020), Schouten et al. (2020), and Weismueller et al. (2020), indicating that individuals who perceive social media influencers as experts, trustworthy, and appealing are more inclined to purchase products endorsed by them and foster positive attitudes toward these products. Hussain and Ali (2021) found expertise to be the most affecting credibility factor on purchase intention, and trustworthiness the most influential factor over attitude. Our study does not confirm such findings. The highest weight for purchase intention is from attractiveness (0.28; 0.19 for expertise and 0.16 for truthfulness) and the highest weight for attitude is from attractiveness (0.43; 0.27 for expertise and 0.39 for truthfulness), if we only consider the direct effect. Weights are similar to Schouten et al. (2020, p. 16), and when compared to Weismueller et al.’s (2020) study, our findings show higher coefficients (0.14 from attractiveness, while trustworthiness and expertise are not significant in their research). Including the total effect, truthfulness emerges as the most influential factor towards purchase intention (total effect weight of 0.39; 0.27 for attractiveness and 0.32 for expertise), while attractiveness has the highest weight of total effect on attitude (0.46; compared to 0.38 for trustworthiness and 0.35 for expertise).
Loyalty is affected by all the three credibility constructs, with the largest weight being truthfulness, confirming that the three dimensions increase consumer’s loyalty towards influencers, which is consistent with previous research by Belanche et al. (2021b) and Urrutikoetxea Arrieta et al. (2019).
Hypotheses H3g and H3h claimed that loyalty has a positive effect on purchase intentions (H3g) and attitude (H3h). The prior literature suggests positive effects for both relationships (Hussain and Ali 2021; Kanwar and Huang 2022; Ki et al. 2020). Our study confirmed a positive and strong relationship between loyalty and purchase intention. We find, however, the relationship between loyalty and attitude negative and statistically insignificant. Multigroup analysis shows the problem exists only for generation Z, while the relationship is positive and statistically significant for generation X, although small (0.11).
We find loyalty also to be effectively acting as a mediating variable in the relationship between credibility factors and purchase intention. Mediation works as leverage, increasing the effect on purchase intention by roughly 33–50%. Such effect is, however, not present for attitudes (except for attraction), where the effects are direct only.

5. Conclusions

Our study evaluates the validity of the model proposed by Hussain and Ali (2021) for two different generations in terms of interaction with social media influencers (namely, generation X born between 1965 and 1980, and generation Z born between 1996 and 2010). Our findings confirm the validity of the model on the whole sample, but with significant differences between the two groups. Generation Z has an expectedly higher affinity towards SMI in terms of credibility dimensions and loyalty, as well as the impact of these dimensions on their purchase intentions and attitudes towards the products endorsed by SMI they follow. Though with lower weights, generation X follows similar relationships between the dimensions. We thus confirm the validity of the model. Our evidence shows that the highest impact on purchase intention can be attributed to truthfulness for purchase intentions and attractiveness for attitude, which is different from Hussain and Ali (2021), who claimed expertise and attraction to be the most influential ones. Such differences could arise from different industries being studied (fashion for Hussain and Ali (2021), sport apparel for our study). A wider study should investigate this phenomenon further.
The lower weights for the impact of credibility factors on loyalty, purchase intentions, and attitude have been expected due to significantly lower interaction intensity between Gen X and the SMI they follow. The impact of loyalty on purchase intention is statistically significant for Gen Z, but both weights are high, confirming that SMI are one of the most effective communication tools. The impact of loyalty on attitudes is insignificant for Gen Z and small (but significant) for Gen X. The argument we propose, to be researched in the future, is that attitudes towards a product (brand) are a result of many past activities from the brand, and an endorsement produces only a little differential change on a long-term measure such as attitudes, while purchase intention is a much shorter-term planned activity that could be triggered by SMI activity, especially for respondents in the market for such a product.
Our research should be confirmed on a larger sample (of respondents and influencers analyzed) to test for validity. Another limitation is the non-inclusion of external variables that could affect consumer decision making and attitudinal changes (such as wishful identification theory) and could be included in an extended model. Further research should address our model’s fallacies by including more influencers’ properties in the model and/or enhancing the list of variables reflecting effects on consumers.
Our findings can find many applications in practice, such as assessing the performance of influencers’ activities, while accounting for generational differences, as well as during the influencer selection process using their credibility dimension scores to forecast their performance.

Author Contributions

Conceptualization, D.B.; Software, D.B.; Validation, D.B. and A.F.; Formal analysis, D.B.; Investigation, D.B.; Resources, A.F.; Data curation, D.B.; Writing—original draft, D.B. and A.F. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, no personal data was collected.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Data used in this research have not been approved for public sharing.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Conceptual model.
Figure 1. Conceptual model.
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Figure 2. CFA used in factor analysis.
Figure 2. CFA used in factor analysis.
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Figure 3. Path analysis of total, direct, and indirect effects.
Figure 3. Path analysis of total, direct, and indirect effects.
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Table 1. Regression weights.
Table 1. Regression weights.
VariableCredibility
Dimension		EstimateComposite
Reliability (CR)		Average Variance
Extracted (AVE)
EXP1Expertise0.963
EXP2Expertise0.963
EXP3Expertise0.9270.860.90
PI2Purchaseintentions0.912
PI1Purchaseintentions0.9450.920.86
ATT3Attitude0.900
ATT1Attitude0.9210.910.82
ATTR1Attractiveness0.813
ATTR2Attractiveness0.956
ATTR3Attractiveness0.9270.930.81
TRU1Truthfulness0.859
TRU2Truthfulness0.808
TRU3Truthfulness0.824
TRU4Truthfulness0.793
TRU5Truthfulness0.7820.910.67
LOY1Loyalty0.947
LOY2Loyalty0.941
LOY3Loyalty0.9630.970.90
Table 2. Average values, variances, and t-test for differences.
Table 2. Average values, variances, and t-test for differences.
VariableAverage
(Gen Z)		Variance
(Gen Z)		Average
(Gen X)		Variance
Gen (X)		t-Test for Difference
(p Values)
Experience4.602.383.572.420.001
Attractiveness4.662.663.682.600.003
Trustworthiness4.372.463.702.000.020
Loyalty4.632.643.872.070.017
Purchase intention4.762.503.721.930.000
Attitudes4.712.383.842.600.008
Table 3. Total, direct, and indirect effects of credibility dimensions on outcome variables.
Table 3. Total, direct, and indirect effects of credibility dimensions on outcome variables.
Total EffectDirect EffectIndirect Effect
ValueHypothesisValueHypothesisValueResult
Expertise →: Purchase intention0.318 ***H2c0.153 ***H3e0.170 **Partially mediated
Expertise → Attitude0.347 ***H2d0.250 ***H3f0.097 *Partially mediated
Attractiveness → Purchase intention0.268 **H1a0.121 ***H3a0.132 **Partially mediated
Attractiveness → Attitude0.457 ***H1b0.503 **H3bNot significantDirect
Trustworthiness → Purchase intention0.395 ***H2a0.116 **H3c0.279 ***Partially mediated
Trustworthiness → Attitude0.380 ***H2b0.468 **H3dNot significantDirect
*** = p < 0.001, ** = p < 0.01, * = p < 0.05.
Table 4. Chi-squared tests for difference between unconstrained and constrained model.
Table 4. Chi-squared tests for difference between unconstrained and constrained model.
ModeldfChi sqp
Structural weights2366.1050.000
Table 5. Multigroup weights analysis.
Table 5. Multigroup weights analysis.
Weight of CoefficientComplete ModelGen ZGen XChi sq. p Value between the Tested Models
Expertise → Loyalty0.280.210.290.697
Expertise → Purchase intention0.190.140.220.050 *
Expertise → Attitude0.270.270.330.108
Attractiveness → Loyalty0.240.330.130.023 *
Attractiveness → Purchase intention0.290.290.210.024 *
Attractiveness→ Attitude0.430.540.480.024 *
Truthfulness → Loyalty0.460.450.440.856
Truthfulness → Purchase intention0.160.180.100.291
Truthfulness → Attitude0.390.460.200.066 *
Loyalty → Purchase intention(H3g) 0.470.510.440.008 **
Loyalty → Attitude(H3h) −0.11−0.110.140.008 **
** = p < 0.01, * = p < 0.05.
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Bratina, D.; Faganel, A. Understanding Gen Z and Gen X Responses to Influencer Communications. Adm. Sci. 2024, 14, 33. https://doi.org/10.3390/admsci14020033

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Bratina D, Faganel A. Understanding Gen Z and Gen X Responses to Influencer Communications. Administrative Sciences. 2024; 14(2):33. https://doi.org/10.3390/admsci14020033

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Bratina, Danijel, and Armand Faganel. 2024. "Understanding Gen Z and Gen X Responses to Influencer Communications" Administrative Sciences 14, no. 2: 33. https://doi.org/10.3390/admsci14020033

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Bratina, D., & Faganel, A. (2024). Understanding Gen Z and Gen X Responses to Influencer Communications. Administrative Sciences, 14(2), 33. https://doi.org/10.3390/admsci14020033

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