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Article

How Key Opinion Leaders’ Expertise and Renown Shape Consumer Behavior in Social Commerce: An Analysis Using a Comprehensive Model

by
Yu-Heng Chen
1,
I-Kai Lin
2,
Ching-I Huang
2 and
Han-Shen Chen
2,3,*
1
Department of Marketing and Logistics Management, Chaoyang University of Technology, Taichung City 413310, Taiwan
2
Department of Health Industry Technology Management, Chung Shan Medical University, Taichung City 40201, Taiwan
3
Department of Medical Management, Chung Shan Medical University Hospital, Taichung City 40201, Taiwan
*
Author to whom correspondence should be addressed.
J. Theor. Appl. Electron. Commer. Res. 2024, 19(4), 3370-3385; https://doi.org/10.3390/jtaer19040163
Submission received: 19 August 2024 / Revised: 18 November 2024 / Accepted: 26 November 2024 / Published: 30 November 2024
(This article belongs to the Topic Digital Marketing Dynamics: From Browsing to Buying)
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Abstract

:
The advent of social commerce platforms fueled by the growing commercialization of social media and networking sites represents a significant evolution in e-commerce dynamics. This study investigates the pivotal role of key opinion leaders (KOLs), particularly YouTubers, in shaping consumer purchasing behavior. Recognizing the powerful influence exerted by KOLs, we examined their ability to promote product diffusion through credibility, specialized knowledge, and strategic word-of-mouth campaigns. This study employs a robust theoretical framework that foregrounds the influence of KOLs while integrating critical constructs, such as perceived value and risk, into a comprehensive model. Our empirical analysis, based on data from 411 valid responses, yields the following insights: the expertise and renown of KOLs exert a profound effect on consumer purchase intentions; consumer perceptions of value positively correlate with trust, whereas perceived risk negatively affects it; and trust mediates the relationship between KOL characteristics (popularity and professionalism) and consumers’ relationship strength with purchase intentions. The findings advocate leveraging KOLs’ renown and expertise while mitigating perceived risks to amplify consumer purchase intentions, thus providing actionable strategies for marketers in the burgeoning social commerce landscape.

1. Introduction

The digital revolution has catalyzed a substantive shift in commercial paradigms epitomized by the integration of social media into the fabric of e-commerce, giving birth to emergent social commerce platforms [1]. These platforms represent a confluence of interactive social media and traditional online marketplaces, signaling a paradigm shift in how consumers discover and purchase products.
Capitalizing on social media as a business conduit, these platforms enable entrepreneurs to venture into commerce with minimal overhead, obviating the need for brick-and-mortar establishments [2]. Thus, economic and operational efficiencies not only introduce lower marketing expenditures for businesses but also foster a community-based platform where consumers can exchange their shopping experiences, thereby amplifying brand resonance [3]. The ascent of social commerce is inextricably tied to advancements in Web 2.0, providing consumers with tools for sharing and consuming commercial information on a social scale [4]. Through the socialization of commerce, customers are empowered to curate and disseminate product knowledge, thereby enhancing product visibility and propagation.
Within this social commerce landscape, key opinion leaders (KOLs) have emerged as influential figures whose endorsements, propelled by their credibility and substantial followings, significantly impact product dissemination [5]. The proliferation of video platforms, such as YouTube, which boasts up to 2.5 billion users worldwide, offers fertile ground for advertisers to leverage YouTubers as KOLs, enhancing their brand penetration [6]. The persuasive power of these KOLs is substantiated by their ability to shape consumer opinions and behaviors, often dictating market trends [7].
In the face of product uncertainty, the information relayed by KOLs serves as a beacon for consumer decisions, with perceived value and risk assessments guiding the purchasing process [8,9]. Various studies suggest that perceived risk plays a pivotal role in constituting perceived value and intention to purchase [10], while endorsement by prominent figures can intensify consumer purchase resolution [11]. Additionally, the sharing of information on social commerce platforms can elevate consumer trust, diminish privacy apprehensions, and enhance decision making and purchasing intentions, especially in emerging markets [12]. The interactive features of social commerce, such as user recommendations and feedback, have been linked to the creation of perceived value, thereby contributing to repeat purchase intentions [13]. Nevertheless, the influence exerted by KOLs in terms of product promotion and information sharing on consumer perceptions of value and risk as well as subsequent buying behaviors within the social e-commerce ecosystem remains a research trajectory ripe for exploration.
This study aims to scrutinize and decipher the nuances of consumer buying behavior and perceptions within the context of social e-commerce and to probe the influence of precursor variables and the mediating role of perceived trust. Focusing on the case study of YouTubers, this study explores the impact of factors, such as KOL popularity, professionalism, and relational strength, alongside perceived value and risk, in an effort to bridge existing academic lacunae and meaningfully contribute to e-commerce discourse.

2. Literature Review and Hypothesis Development

2.1. Perceived Trust

Perceived trust is a cornerstone in the foundation of commercial transactions, and it has been extensively examined in the context of consumer behavior research [8,14]. At its core, perceived trust encapsulates a consumer’s confidence in an entity under conditions of perceived risk [15]. This construct extends within the realm of e-commerce to encompass trust in the online vendor, characterized by the security of Internet operations, the safeguarding of privacy, and the integrity of informational content [16].
Within the context of e-commerce, trust has been identified as a pivotal determinant of consumer engagement and transactional completion, incorporating trust in the vendor, the underlying institutional framework, and the online market as a whole [16]. The factors contributing to the formation of this trust include the seller’s dependability, branding, market stature, reputation, consistency of behavior, and the buyer’s personal experiences [17]. Conversely, the onus on vendors to ensure secure transactional processes and instill buyers’ confidence is recognized as integral to successful commercial relationships [18,19].
Empirical studies have consistently validated the positive correlation between perceived trust and consumers’ purchase decisions and intention to repurchase [20,21], with brand trust emerging as a noteworthy predictor of consumer purchasing decisions [21]. In the social commerce arena, perceived trust is an inevitable and critical construct.

2.2. Key Opinion Leaders and Social Media

The ascension of social media has precipitated the rise of key opinion leaders (KOLs) as formidable influencers in consumer purchasing decisions [22,23,24]. The sway of KOLs is anchored in their expertise and domain [25]. Their endorsements have been shown to expedite decision making and engender brand trust [26]. As pivots within social networks, KOLs possess the capacity to impart both objective product details and subjective experiential feedback, which has been shown to fortify consumer perceptions and purchase intentions [7,27].
In this context, the image of KOLs and the forms of content they create have a profound impact on consumers’ perceived trust. Zhang et al. [28] found that virtual and real KOLs have significant differences in their influence on consumers’ purchase intentions for different product types (search goods and experience goods). Ma et al. [29] highlighted that authentic and ethical livestreamers promote a prevention focus among viewers, thereby enhancing their online purchase intentions. Meng and Wei [30] revealed that variables, such as a KOL’s visibility, product involvement, and interactivity, are associated with consumer trust, which in turn affects their purchase intentions. These findings demonstrate that the influence of KOLs and the establishment of trust are not unidimensional but rather shaped by multiple factors.

2.2.1. Popularity and Perceived Trust

The popularity of KOLs within social media landscapes augments consumers’ recognition of informational credibility [31]. The “celebrity effect” heightens the appeal of KOL through increased exposure and societal influence [32]. Research illustrates that KOLs’ popularity and perceived trust critically influence purchasing intentions, mediated by virtual interaction and emotional engagement [33], whereas high-audience-rated bloggers and vloggers exert formidable positive impacts on consumer purchase behaviors [34,35]. Thus, we hypothesize the following:
H1: 
The popularity of KOLs exerts a positive effect on consumers’ perceived trust.

2.2.2. Expertise and Perceived Trust

KOLs, distinguished by their professional acumen, contribute information anchored in expertise and draw consumer allegiance [36]. The expertise of KOLs has been shown to correlate significantly with consumer trust and consequent purchasing behaviors [37]. Chen et al. [38] revealed that KOL popularity, expertise, and product involvement directly favor purchasing intentions, whereas online community dynamism largely depends on expert knowledge sharing [39]. Hence, we propose the following:
H2: 
The expertise of KOLs exerts a positive effect on consumers’ perceived trust.

2.2.3. Relationship Strength and Perceived Trust

The relationship strength between individuals signifies the depth of interpersonal connection, which is pertinent to recommendation efficacy [40]. Augmented relationship strength predicts higher trust and likelihood of purchasing [41]. Studies have demonstrated that the dyadic relationship strength between the informant and the consumer significantly affects purchasing behaviors [42]. Empirical findings by Hayes et al. [43] further indicate that the robustness of consumer–brand relationships positively influences brand attitudes and consequent purchase actions. We thus posit the following:
H3: 
The strength of the relationship between KOLs and consumers has a positive effect on consumers’ perceived trust.

2.3. Perceived Value and Perceived Trust

Perceived value is a consumer-determined assessment of net benefits that considers both tangible costs and inherent benefits [44]. It equates to a subjective calculus of product value, balancing quality (as a benefit) with both monetary and non-monetary costs [45,46]. This assessment encompasses consumers’ evaluation of a product’s worth relative to its pricing and communicated value [47,48,49]. Research indicates that perceived value influences consumers’ repurchase intentions and is directly linked to consumer trust and purchasing intentions, with emotional value shown as especially pivotal [13,50,51,52] identify the significant impact of perceived value on impulse buying in e-commerce, while Sullivan and Kim [20] integrate it as a principal component in e-commerce trust development. This study supports the following hypothesis:
H4: 
Perceived value exerts a positive effect on perceived trust.

2.4. Perceived Risk

Perceived risk epitomizes the potential negative outcomes a consumer anticipates when considering a purchase, including the time, physical, self-esteem, and financial loss dimensions [53]. When faced with latent purchasing risks, consumers often use online word of mouth to glean information to abate these risks [54,55,56]. Global consumer behavior attests to the reliance on online reviews to diminish perceived risks [57,58,59], and perceived risk is inversely related to perceived trust [60,61]. Furthermore, risk perception has been shown to significantly influence purchasing decisions on social commerce platforms [8]. Based on these findings, we hypothesize the following:
H5: 
Perceived risk exerts a negative effect on perceived trust.

2.5. Purchase Intention

Purchase intention reflects a consumer’s predisposition to buy a product or service, which is indicative of the final purchasing act [62,63]. Strong purchase intention increases the likelihood of purchasing behavior [64]. In other words, when consumers have a more positive perception of goods or services, they are more likely to purchase and subsequently make a purchase [65,66]. This propensity is linked not only to personal evaluations but is also influenced by external factors, such as societal norms, brand allegiances, and service quality. Cumulative research suggests that emotional connections [7], pleasurable online shopping experiences [67], and immersive environments [68] enhance purchase intentions. Acknowledging purchase intention as a harbinger of actual purchasing activity [69], we propose the following:
H6: 
Perceived trust exerts a positive effect on purchase intention.
H7: 
Perceived trust mediates the relationship between KOLs’ popularity and purchase intention.
H8: 
Perceived trust mediates the relationship between KOLs’ expertise and purchase intention.
H9: 
Perceived trust mediates the relationship between the strength of the relationship between KOLs and consumers and their purchase intentions.

3. Research Methodology

3.1. Research Framework

Capitalizing on extant scholarship, this study extrapolates a conceptual framework postulating that antecedents, such as KOLs’ popularity, professionalism, and relationship strength with consumers, are predictors of perceived trust. In this model, perceived value and risk are posited to mediate the effects of KOLs on consumers’ purchase intentions. Figure 1 illustrates the framework.

3.2. Questionnaire Design

The questionnaire used in this study is divided into two sections. The first section focuses on the main research variables of the questionnaire items, encompassing the popularity of key opinion leaders, professionalism, and the strength of the relationship with consumers. It further investigates seven core variables: perceived value, perceived risk, perceived trust, and purchase intention. The scale for the popularity of key opinion leaders was adapted from Ladhari et al. [34], and it consists of four items: the professionalism scale for key opinion leaders was based on Zhou and Huang [37], with four items; the relationship strength scale for key opinion leaders was drawn from Touni et al. [42], containing four items; the perceived value scale was adapted from Sullivan and Kim [20] and Wu et al. [70], comprising three items; the perceived risk scale was from Faqih [61], with three items; the perceived trust scale was based on Sullivan and Kim [20], consisting of four items; and the purchase intention scale was derived from Farivar and Wang [35], with three items. These scales used a 7-point Likert scale, asking respondents about their level of agreement with each item, ranging from strongly disagree (1) to strongly agree (7). The second section collected basic information from the respondents, including gender, age, education level, and personal monthly income.
After the compilation of the survey items, to avoid misunderstanding by the respondents due to unclear item semantics or incomplete descriptions, which could lead to responses that do not reflect their true situations, thus collecting answers that do not correspond with the facts, an expert validity review was conducted. The study invited nine experts, including educational scholars and e-commerce platform operators, each with over ten years of working experience. After obtaining the experts’ consent, the expert-reviewed questionnaires were sent for filling out, where they reviewed and corrected the accuracy, appropriateness, and wording of each questionnaire item. After collating all expert opinions, suggestions for semantic and item modifications and deletions were integrated to compile a pilot survey. A total of 85 pilot questionnaires were issued, with 76 valid questionnaires retrieved, followed by item and reliability analyses to confirm the item and reliability of each construct. An official questionnaire was designed based on this procedure. After official questionnaire retrieval, responses that were incomplete, failed the reverse-item test, or showed uniform answers were considered invalid.

3.3. Sample and Data Collection

Acknowledging the evolution of research methodologies in alignment with technological advancements, this study pivoted from traditional to online questionnaire distribution via social media platforms, in line with recent research suggestions [71,72]. The survey was distributed through word of mouth, Facebook, Instagram, and Line groups, as well as other community channels. Taking research ethics into account, the first page of the questionnaire informed the participants about the purpose of the study and anonymous data collection, allowing all participants to fill out the questionnaire with the assurance of privacy.
Considering the study aims and hypothesis testing requirements, Structural Equation Modelling (SEM) was selected for statistical evaluation. The appropriateness of the SEM sample size was determined by the item number, with optimal ratios between 10:1 and 15:1 [73]. Consequently, for the 25 survey items, a sample size of 250 to 375 was targeted. Generation Z was identified as the demographic focus given its inherent digital fluency and pivotal role as a market influencer [74,75]. Data were gathered from July to August 2023, with 489 responses, 411 of which were deemed valid post-validation. Demographic characteristics are presented in Table 1.

3.4. Methods for Data Analysis

A quantitative paradigm underlies this study, anchored in a survey data analysis using IBM SPSS Statistics 26 and AMOS 24. Analytical techniques encompass descriptive statistics, reliability coefficients, validity assessments, and confirmatory analyses via maximum likelihood estimates in SEM to explore the veracity of the proposed hypotheses and to evaluate the model’s overall goodness of fit.

4. Analysis and Results

4.1. Measurement Model: Reliability and Validity

A two-stage analytical method was used in this study. The first stage was a confirmatory factor analysis (CFA), and the second stage involved the analysis of the overall model fit. CFA is a part of SEM analysis that is used to test the relationships between observed variables and latent (factor) variables to determine whether the observed variables actually represent the latent variables. Generally, CFA is used to assess psychometrics, construct validity, test method effects, and to check for model invariance among groups. Because this study used questionnaires developed by other researchers, it was necessary to use CFA to test the appropriateness of the measurement tools for the study population.
This study consisted of six sub-constructs: “Key Opinion Leaders’ Visibility”, “Key Opinion Leaders’ Expertise”, “Relationship Strength between Key Opinion Leaders and Consumers”, “Perceived Value”, “Perceived Risk”, “Perceived Trust”, and “Purchase Intention”. The CFA was conducted separately for each construct. Item deletion was based on the principle that items with a factor loading of less than 0.5 were removed. CFA was performed repeatedly to determine the RMSEA of the sub-constructs. If the RMSEA was greater than 0.08, it indicated that the fit did not meet the acceptable standards; thus, the model was revised repeatedly based on the modification indices (MIs) until the RMSEA of the sub-constructs was less than 0.08 or the sub-construct became a saturated model. This item deletion process was conducted for each construct individually, resulting in a reduction from five items to four for “Key Opinion Leaders’ Visibility” and from four items to three for “Perceived Risk”.
After confirming the sub-construct items of the questionnaire, the Composite Reliability (CR) and the convergent validity of each construct were tested. The CR value is a combination of the reliabilities of all of the measurement variables in a ratio ranging from 0 to 1, with higher values indicating higher proportions of true variance to total variance and, hence, higher internal consistency. Fornell and Larcker [76] suggested that the CR value for latent variables should be 0.60 or above. The convergent validity of latent variables is best represented by the Average Variance Extracted (AVE), with Fornell and Larcker [76] and Bagozzi and Yi [77] suggesting that the AVE for latent variables should exceed 0.50.
The CR values for the constructs in this study ranged from 0.844 to 0.926, indicating good internal consistency of the questionnaire. The AVE values ranged from 0.597 to 0.807, all exceeding the suggested value of 0.50, indicating good convergent validity. The standardized regression weights of all items ranged from 0.561 to 0.926, and the t-values were higher than 1.96, indicating statistical significance. The factor loadings, CR values, and AVE values for each construct are summarized in Table 2. The table shows that all constructs meet the requirements for convergent validity, indicating the good internal quality of the measurement model.
Discriminant validity analysis in SEM involves measuring two different concepts and analyzing their correlations. If the correlations are very low, it indicates that the concepts have discriminant validity. According to Hair et al. [78], the correlation coefficient between two different concepts should be less than the square root of the average explained variance (AVE) for each concept. Table 3 compares the correlation coefficients of all constructs in this study with the square root of the AVE. The square root values of the AVE for each construct were greater than the correlation coefficients between the constructs, meeting the suggested standards of Hair et al. [78], indicating that the constructs have discriminant validity. Based on the test results of the measurement model evaluation, the measurement model in this study has good internal and external qualities.

4.2. Model Fit Testing

Confirmatory factor analysis was performed using AMOS 24.0. The study found χ2/df (1.978) values below the recommended thresholds of 3 [77]; AGFI (0.901), NFI (0.907), CFI (0.913), and IFI (0.905) values surpassed 0.9 [79]; and RMSEA (0.073), RMR (0.076), and SRMR (0.075) values were below the recommended threshold of 0.08 [77]. This indicates that the overall model had a good fit.

4.3. Overall Model Path Analysis

Structural Equation Modelling (SEM) was conducted to examine the relationships between variables. The structural model analysis diagram is shown in Figure 2. H1: The visibility of key opinion leaders has a significantly positive impact on perceived trust (β = 0.163, p < 0.001). H2: Key opinion leaders’ expertise has a significantly positive effect on perceived trust (β = 0.199, p < 0.05). H3: The strength of the relationship between key opinion leaders and consumers has a significantly positive impact on perceived trust (β = 0.177, p < 0.001). H4: Perceived value has a significantly positive effect on perceived trust (β = 0.471, p < 0.001). H5: Perceived risk has a significant negative impact on perceived trust (β = −0.276, p < 0.05). H6: Perceived trust has a significantly positive effect on purchase intention (β = 0.749, p < 0.001).
Therefore, it can be known that H1, H2, H3, H4, H5, and H6 are all established and significant. Table 4 shows the path analysis and hypothesis testing results of this study.

4.4. Mediation Effect Analysis

This study examined the existence of a mediation effect according to the conditions set by Baron and Kenny [80]. The results in Table 5 show that in the path from visibility to perceived trust to purchase intention, visibility had a significant impact on perceived trust (β = 0.331, p < 0.001), visibility had a significant impact on purchase intention (β = 0.275, p < 0.001), and perceived trust had a significant impact on purchase intention (β = 0.651, p < 0.001). When considering the influence of visibility and perceived trust on purchase intention simultaneously, the predictive power of β for visibility was reduced from 0.331 to 0.067, and the explanatory power R2 increased from 0.075 to 0.428, thereby confirming the mediation effect.
On the path from expertise to perceived trust to purchase intention, expertise had a significant impact on perceived trust (β = 0.413, p < 0.05), expertise had a significant impact on purchase intention (β = 0.374, p < 0.001), and perceived trust had a significant impact on purchase intention (β = 0.651, p < 0.001). When considering the influence of expertise and perceived trust on purchase intention simultaneously, the predictive power of β for expertise reduced from the original 0.413 to 0.126, and the explanatory power R2 increased from the original 0.140 to 0.437, thereby confirming the mediation effect.
The results in Table 5 show that in the path from relationship strength to perceived trust to purchase intention, relationship strength has a significant impact on perceived trust (β = 0.271, p < 0.001), relationship strength has a significant impact on purchase intention (β = 0.178, p < 0.05), and perceived trust has a significant impact on purchase intention (β = 0.651, p < 0.001). When considering the impact of relationship strength and perceived trust on purchase intention simultaneously, the predictive power of β for relationship strength decreased from the original 0.271 to 0.002, and the explanatory power R2 increased from the original 0.032 to 0.424, thereby confirming the mediation effect.

5. Discussion

This study adopted variables, such as popularity, professionalism, relationship strength, and perceived trust, to explore the impact of KOLs on consumer purchasing behavior. The results show that the popularity of KOLs has a significant positive effect on perceived trust (H1), which is consistent with the findings of Ladhari et al. [34]. This indicates that the celebrity effect makes consumers trust the products and services that they endorse. Therefore, the popularity and influence of KOLs on social media are crucial for brand-building and product promotion. In addition, this study also shows that the professionalism of KOLs has a significant positive effect on perceived trust (H2), which is consistent with He and Jin [81]. The strength of the relationship between KOLs and consumers also has a significant positive impact on perceived trust (H3), consistent with the findings of Touni et al. [42]. Therefore, popularity, professionalism, and relationship strength with the audience of KOLs will make consumers trust KOLs. Among these, the influence of professionalism is the greatest (β = 0.199), which may explain why consumers tend to rely on professional and objective opinions when choosing products, consistent with Liu et al. [82].
However, it is important to note that the image of a KOL may not always be stable. When a KOL encounters negative events, scandals, or negative comments from the Internet, these factors can significantly impact consumers’ perceived trust and purchase intentions. Algi and Irwansyah [31] found that kindness and integrity are key factors in building trust in sellers and products. Temessek et al. [83] indicated that high trust in advance may amplify the negative effects of perceived betrayal, leading to more negative customer responses. Therefore, how businesses choose truly trustworthy KOLs is a crucial task.
Furthermore, this study incorporated perceived value and risk into the discussion. The results show that perceived value has a significant positive effect on perceived trust (H4), which is consistent with the results of Sullivan and Kim [20] and Sharma and Klein [84]. This means that the better the balance between the costs and benefits perceived by consumers, the higher the perceived value and the more they trust KOLs. Additionally, perceived risk has a significant negative effect on perceived trust (H5), which is consistent with the results of Sullivan and Kim [20] and Faqih [61]. This indicates that when consumers develop purchase intentions or behaviors through KOL recommendations or channels, the greater the danger and the loss of time, self-esteem, and money they incur, the less they trust KOLs, thus reducing trust. Perceived trust has a significant positive effect on purchase intention (H6), consistent with the findings of Ponte et al. [85], Dam [86], and Konuk [87]. An increase in trust in KOLs enhances consumers’ intent to purchase products they recommend, endorse, or group-buy, meaning that the level of perceived trust can predict their purchasing behavior.
However, this study also explored the mediating effect of perceived trust on popularity, professionalism, relationship strength, and purchasing intention. The results showed that perceived trust had a mediating effect on purchase intention among the three factors. Dabbous et al. [88] suggested that trust plays a fully mediating role between brand popularity and purchase intention, which is in line with the results of this study.

6. Conclusions

6.1. Research Conclusions

The results show that the popularity of KOLs has a positive impact on perceived trust. This indicates that consumers are more inclined to trust the products or services recommended by well-known KOLs. In addition, the professionalism of KOLs and the strength of their relationship with consumers also increases consumer trust in KOLs, thereby enhancing their willingness to purchase the recommended products. The higher the consumers’ perceived value, the higher their trust in KOLs, and thus the greater their purchase intention. Conversely, the higher the consumers’ perceived risk, the lower their trust in KOLs, and the lower their purchasing intention. Finally, perceived trust plays a mediating role between the popularity of KOLs, their professionalism, the strength of their relationship with consumers, and purchase intention, meaning that consumers’ perceived trust can partially explain the influence of KOLs on their purchasing intentions.
This study makes several important contributions. (1) It comprehensively explores multiple variables, including popularity, expertise, relationship strength, perceived trust, perceived value, and perceived risk, and it reveals how they interact, particularly in terms of their influence on consumer purchase behavior. (2) It provides an in-depth examination of the mediating role of perceived trust between popularity, expertise, relationship strength, and purchase intention, offering a deeper understanding of how KOLs influence consumer behavior. (3) The findings of this study are consistent with those of several previous studies, further confirming the impact of factors, such as popularity, expertise, and relationship strength, on consumer behavior and expanding the understanding of these factors in the context of social media and KOL influence.

6.2. Management Recommendations

The popularity, expertise, and relationship strength of KOLs all have a positive effect on consumers’ perceived trust and thus increase their purchasing intentions. Therefore, in managerial practice, KOLs can enhance their own influence by increasing their popularity, strengthening their professional image, and establishing good interactive relationships with the audience. On the corporate side, companies can effectively utilize the influence of KOLs’ social media through collaboration to promote their brands and market products, attract more target consumers, and increase their trust in the brand. In addition, companies should focus on the selection and management of KOLs, ensuring that their values and images match those of the corporate brand to maximize the benefits of KOL marketing.
Consumers’ perceived risk and trust had a significant negative effect. In marketing practice, understanding the importance of consumers’ perceived risk in building trust is crucial. Enterprises can reduce consumers’ concerns about the risks of products or services by transmitting real and objective information in collaboration with KOLs. Simultaneously, consumers’ perceived value has a positive and significant effect on their perceived trust. Therefore, as opinion leaders, KOLs can enhance consumers’ positive impressions of products or services through social media interactions, further increasing their perceived value and trust.

6.3. Research Limitations

The subjects of this study were mostly young people aged 19–25. As such, we lack data from other age groups. Future studies should focus on different age groups. In particular, research on middle-aged and elderly groups is insufficient, and these groups play an important role in consumer decision making.
In addition, this study focused only on KOLs on the YouTube platform. Future research could consider expanding to other social platforms, such as Instagram and TikTok, to further compare the differences in KOL influence and consumer behavior across different platforms. Furthermore, research could explore this topic from the perspective of ethical marketing practices, particularly regarding whether KOLs disclose if they receive payment or material benefits when recommending products, as this could have different effects on consumer trust and behavior.
Moreover, this study was conducted only with participants from Taiwan, which limits a thorough understanding of the purchasing behavior of consumers from different countries. Future research could more deeply compare the influence and trustworthiness of KOLs on consumers in different countries, helping to reveal the effectiveness and applicability of KOL marketing strategies in cross-cultural environments. Furthermore, through cross-national comparative studies, the commonalities and differences in consumer acceptance and behavioral patterns of KOLs in a globalized environment can be explored, further expanding the understanding and application of KOL marketing.

Author Contributions

All four authors contributed to the completion of this article. Y.-H.C. was the first author, who analyzed the data and drafted the manuscript; I.-K.L. contributed to reviewing the manuscript and revising the results and conclusion; C.-I.H. contributed to reviewing and revising the literature, results, and conclusion; H.-S.C. acted as the corresponding author on their behalf throughout the revision and submission process. All authors have read and agreed to the published version of the manuscript.

Funding

This study received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data supporting the findings of this study are available from the corresponding author, H.-S.C., upon reasonable request.

Acknowledgments

We would like to express our sincere appreciation to all of the experts who took the time to review this article and provide valuable comments.

Conflicts of Interest

The authors declare no conflicts of interest.

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Jtaer 19 00163 g001
Figure 1. Research framework.
Figure 1. Research framework.
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Figure 2. Structural Equation Modelling diagram. Note: * p < 0.05; *** p < 0.001.
Figure 2. Structural Equation Modelling diagram. Note: * p < 0.05; *** p < 0.001.
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Table 1. Demographic analysis.
Table 1. Demographic analysis.
N = 411ItemPopulationPercentage (%)
GenderMale8721.2
Female32478.8
Age19–25 years32478.8
26–35 years8721.2
Level of
Education		High school/vocational or below276.6
College/university34283.2
Master’s or above4210.2
Monthly
Personal
Income		Less than TWD 20,00025261.3
TWD 20,001~TWD 40,00012029.2
TWD 40,001~TWD 60,000215.1
TWD 60,001~TWD 80,00030.7
TWD 80,001~TWD 100,00061.5
Above TWD 100,00192.2
Table 2. Results related to factor loading, reliability, and validity.
Table 2. Results related to factor loading, reliability, and validity.
VariablesItemsStandardized
Factor Loading		CRAVECronbach’s α
Popularity1. I think my favorite YouTuber is very famous0.923 ***0.9010.6960.854
2. I think my favorite YouTuber has a lot of fans0.896 ***
3. I think the popularity of my favorite YouTuber is increasing0.726 ***
4. I think there are a lot of comments under each video my favorite YouTuber posts0.776 ***
Expertise5. I think the YouTuber I follow can clearly understand the products they recommend and how to use them0.831 ***0.9120.7220.871
6. I think the YouTuber I follow can convey the information about recommended products effectively and accurately0.873 ***
7. I think the YouTuber I follow can give professional answers to related questions about the recommended products0.889 ***
8. I think the YouTuber I follow can give a professional assessment of the products0.804 ***
Relationship Strength9. I think the YouTuber I follow and I have a lot in common0.865 ***0.8530.5970.752
10. I think the image of the YouTuber I follow matches how I see myself0.794 ***
11. I think I fully understand the YouTuber I follow0.834 ***
12. Whether the reviews are good or bad, I will support the YouTuber I follow0.561 ***
Perceived Value13. I think buying products recommended by YouTubers can save me time0.922 ***0.9190.7920.868
14. I think buying products recommended by YouTubers does not require too much energy0.919 ***
15. I think buying products recommended by YouTubers will make me feel like I am getting my money’s worth0.825 ***
Perceived Risk16. I think there is a risk in buying products recommended by YouTubers0.762 ***0.8440.6430.721
17. I think there is a greater risk in group buying from YouTubers than from other purchasing channels0.811 ***
18. I think buying products recommended by YouTubers might result in receiving goods that do not meet my expectations0.831 ***
Perceived Trust19. I think buying products recommended by YouTubers meets expectations0.805 ***0.9260.7580.891
20. I think the information about products recommended by YouTubers is truthful0.910 ***
21. I think there is a guarantee when buying products recommended by YouTubers0.877 ***
22. I think the products recommended by YouTubers are trustworthy0.888 ***
Purchase Intention23. I would buy products recommended by YouTubers0.892 ***0.9260.8070.880
24. I am interested in buying products recommended by YouTubers0.926 ***
25. I might buy products recommended by YouTubers in the future0.876 ***
Note 1: CR: Composite Reliability; AVE: Average Variance Extracted. Note 2: *** p < 0.001.
Table 3. Discriminant validity test.
Table 3. Discriminant validity test.
MeanStandard Deviation1234567
1. KOL Popularity5.381.150.834
2. KOL Expertise5.151.140.361 **0.850
3. KOL Relationship Strength4.031.300.168 *0.254 **0.773
4. Perceived Value4.041.460.274 **0.450 **0.386 **0.890
5. Perceived Risk4.431.20−0.046−0.225 **−0.279 **−0.307 **0.802
6. Perceived Trust4.551.060.331 **0.413 **0.271 **0.621 **−0.275 **0.871
7. Purchase Intention4.551.300.275 **0.374 **0.178 *0.621 **−0.196 *0.651 **0.898
Note 1: The values in bold font are the square roots of the AVE; the non-diagonal numbers represent the correlation coefficients of each dimension. Note 2: * p < 0.05; ** p < 0.01.
Table 4. Results of the path analysis and confirmation of hypotheses.
Table 4. Results of the path analysis and confirmation of hypotheses.
Hypothesized PathsPath CoefficientVerification Results
H1Popularity → Perceived Trust0.163 ***Supported
H2Expertise → Perceived Trust0.199 *Supported
H3Relationship Strength → Perceived Trust0.177 ***Supported
H4Perceived Value → Perceived Trust0.471 ***Supported
H5Perceived Risk → Perceived Trust−0.276 *Supported
H6Perceived Trust → Purchase Intention0.749 ***Supported
Note: * p < 0.05; *** p < 0.001.
Table 5. Mediating effect regression analysis.
Table 5. Mediating effect regression analysis.
Perceived TrustPurchase Intention
Model 1Model 2Model 3Model 4
Popularity0.331 ***0.275 *** 0.067
Perceived Trust 0.651 ***0.629 ***
R 2 0.1090.0750.4240.428
Agj   R 2 0.1030.0690.4200.420
F16.556 ***11.021 ***99.456 ***50.167 ***
Degrees of Freedom(136)(136)(136)(136)
Perceived TrustPurchase Intention
Model 1Model 2Model 3Model 4
Expertise0.413 *0.374 *** 0.126
Perceived Trust 0.651 ***0.599 ***
R 2 0.1700.1400.4240.437
Agj   R 2 0.1640.1330.4200.429
F27.728 ***21.921 ***99.456 ***52.104 ***
Degrees of Freedom(136)(136)(136)(136)
Perceived TrustPurchase Intention
Model 1Model 2Model 3Model 4
Relationship Strength0.271 ***0.178 * 0.002
Perceived Trust 0.651 ***0.651 ***
R 2 0.0730.0320.4240.424
Agj   R 2 0.0660.0250.4200.416
F10.688 ***4.41799.456 ***49.360 ***
Degrees of Freedom(136)(136)(136)(136)
Note 1: The values in the table are standardized regression coefficients (β). Note 2: * p < 0.05; *** p < 0.001.
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MDPI and ACS Style

Chen, Y.-H.; Lin, I.-K.; Huang, C.-I.; Chen, H.-S. How Key Opinion Leaders’ Expertise and Renown Shape Consumer Behavior in Social Commerce: An Analysis Using a Comprehensive Model. J. Theor. Appl. Electron. Commer. Res. 2024, 19, 3370-3385. https://doi.org/10.3390/jtaer19040163

AMA Style

Chen Y-H, Lin I-K, Huang C-I, Chen H-S. How Key Opinion Leaders’ Expertise and Renown Shape Consumer Behavior in Social Commerce: An Analysis Using a Comprehensive Model. Journal of Theoretical and Applied Electronic Commerce Research. 2024; 19(4):3370-3385. https://doi.org/10.3390/jtaer19040163

Chicago/Turabian Style

Chen, Yu-Heng, I-Kai Lin, Ching-I Huang, and Han-Shen Chen. 2024. "How Key Opinion Leaders’ Expertise and Renown Shape Consumer Behavior in Social Commerce: An Analysis Using a Comprehensive Model" Journal of Theoretical and Applied Electronic Commerce Research 19, no. 4: 3370-3385. https://doi.org/10.3390/jtaer19040163

APA Style

Chen, Y.-H., Lin, I.-K., Huang, C.-I., & Chen, H.-S. (2024). How Key Opinion Leaders’ Expertise and Renown Shape Consumer Behavior in Social Commerce: An Analysis Using a Comprehensive Model. Journal of Theoretical and Applied Electronic Commerce Research, 19(4), 3370-3385. https://doi.org/10.3390/jtaer19040163

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