Next Article in Journal
Reaction to Idiosyncratic Economic Shocks—Economic Resilience of Small- and Medium-Sized Enterprises
Next Article in Special Issue
Exploring Virtual Fashion Consumption through the Emotional Three-Level Theory: Reflections on Sustainable Consumer Behavior
Previous Article in Journal
Firms’ Capital Structure during Crises: Evidence from the United Kingdom
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 16
Issue 13
10.3390/su16135471
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

The Power of Influencers: How Does Influencer Marketing Shape Consumers’ Purchase Intentions?

by
Yiming Chen
1,2,†,
Zhaoyue Qin
1,2,†,
Yue Yan
1,2,* and
Yi Huang
1,2
1
Business School of Jiangsu University of Science and Technology, Zhenjiang 215600, China
2
Yangtze River Delta Social Development Research Center, Jiangsu University of Science and Technology, Zhangjiagang 215600, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Sustainability 2024, 16(13), 5471; https://doi.org/10.3390/su16135471
Submission received: 27 April 2024 / Revised: 28 May 2024 / Accepted: 24 June 2024 / Published: 27 June 2024
(This article belongs to the Special Issue Insights into Contemporary Consumer Behavior: New Perspectives on Social Media and Technological Innovation)
Browse Figures
Review Reports Versions Notes

Abstract

:
In the current digital wave, social media is not only a hub for information exchange but also a shaper of new business marketing models, as is especially evident in the trend towards light and healthy eating. The influence of the influencer economy on consumer purchasing decisions is increasingly pronounced. This paper systematically investigates the impact of influencer marketing on consumer purchase intentions in social media utilizing the Consumer Attitude Theory. Through a sample survey of 654 consumers and empirical analysis using the fuzzy comprehensive evaluation model, the results show that the influencers’ credibility and professionalism and consumers’ satisfaction with live-streaming sales by influencers have a significant positive impact on enhancing consumers’ purchase intentions. To enhance consumers’ purchase intentions, this study suggests that influencers should transparently disclose their collaborations with brands, showcase the positive experiences of other users, and use relevant research and data to support their product recommendations in order to enhance their credibility. Simultaneously, influencers need to strengthen product knowledge, improve professional image and reputation, and meet consumer needs through personalized recommendations and carefully designed live-streaming content to promote brand-value enhancement.

1. Introduction

With the internet’s robust growth, the emergence of the social media era has heralded a new epoch that integrates business and social interaction. Social media platforms are utilized by billions globally and have swiftly become one of the defining technologies of our time. Given that a substantial audience spends considerable time on various platforms, it is unsurprising that marketers leverage social media as a channel for marketing. Academically, social media is recognized as such and has fostered extensive research on topics related to social media marketing [1]. Social media serves not only as a tool for information dissemination but also as a platform that facilitates interpersonal relationships. Users share their lives, insights, and consumption experiences, creating vast and rich social networks. In this digital wave, social media platforms have become fertile ground for influencers, leading to profound transformations in consumer behavior and purchase intentions [2].
When influencers establish emotional resonance and interaction with consumers, the consumers are more easily influenced and led to make purchasing decisions. Furthermore, the information shared by influencers allows consumers to gain authentic and direct experience of products, which enhances their trust and desire to purchase. Local officials have even experimented with using live streaming to promote local agricultural products [3]. This new marketing approach breaks free from the constraints of traditional advertising. Leveraging social media platforms allows products to spread more quickly and extensively, effectively reaching target audiences and encouraging active participation and purchasing behaviors among consumers.
“Light food” is a form of dining characterized by simplicity, moderation, and balanced nutrition. The concept originated in 17th-century Europe, initially emerging in the context of coffee-shop afternoon teas that featured sandwiches, salads, and other dishes paired with coffee. Evolving through the centuries, today’s definition of light food encompasses fresh, appropriately portioned meals that are low in calories, high in fiber, and rich in nutrients.
Today, as awareness of health and sustainability continues to grow, light food has become one of the most popular dietary trends. The growth rate of light-food orders was rapid from 2015 to 2019. In the first half of 2020, market development was hindered due to the impact of the COVID-19 pandemic. However, with the improvement in the pandemic situation, the light-food market gradually recovered in the second half of 2020, with significant growth in orders, online merchants, and transaction users [4,5,6,7]. Meituan’s “Light Food Consumption Big Data Report” shows that the size of the domestic light-food market reached CNY 100 billion in 2022. It is expected that over the next five years, its market share will gradually increase to about 10% of the total revenue of the catering industry. The rise of this trend not only reflects consumers’ pursuit of a healthy lifestyle but also highlights their concern for environmental protection. Against the backdrop of the increasing popularity of light food, influencers on social media platforms have become significant influencers in guiding consumers’ purchasing decisions. Therefore, a thorough investigation into the role of influencers on social media platforms in promoting consumer purchase intentions towards light food is of great importance. This not only aids in guiding current brand and market strategies but also fosters the joint development of healthy eating and sustainable living in the digital age.
Based on the above context, this paper poses the following questions: “Which influencer marketing factors influence consumer satisfaction with light food?” and “What is the best way for influencers to strategically plan their actions to enhance consumer purchase intentions?”. To answer these questions, this study, leveraging the characteristics of the social media era, applies the Consumer Attitude Theory and employs a fuzzy comprehensive evaluation model to thoroughly examine the impact of influencer marketing on consumer purchase intentions. The results of this research are significant for understanding market mechanisms in the current digital age, advancing commercial development, and promoting the prevalence of healthy eating and sustainable lifestyles.

2. Literature Review and Research Hypotheses

2.1. Influencer Marketing

Social media influencers, as significant representatives of social media, possess large and active fan bases. They have successfully crafted their online identities by posting content in specific domains such as food, travel, fitness, or fashion [8]. These influencers have built deep, intimate relationships with their followers, encouraging active participation, interaction, and support from them [9,10]. The role of social media influencers in brand marketing is undeniable. Compared to traditional celebrities, they have clear advantages in brand endorsements due to their close interaction with fans and unique communication styles [11]. This interactivity provides brands with genuine and appealing endorsement opportunities, enhancing consumer trust [12,13].
From an economic perspective, investment in influencer marketing continues to grow. Investments reached $13 million in 2021 and are predicted to soar to $84.89 million by 2028. This reflects an ongoing increase in budget allocation for influencer marketing in the future, further emphasizing its key role in modern brand strategies [14].
In influencer marketing, factors such as the credibility of the influencer [15], their professionalism, and their ability to sell products through live streaming [16] significantly impact consumer purchase intentions.

2.2. Consumer Attitude Theory

The Consumer Attitude Theory, as one of the core theories in the field of consumer behavior, explores the formation of consumer attitudes towards products, brands, or services, and their mechanisms of influence on purchase intentions [17,18]. According to Douglass et al. (1977), attitude consists of three components: the cognitive, affective, and behavioral components [19]. Reed et al. (2022) proposed that the cognitive component involves individuals’ cognitive evaluations and beliefs about a product or service, the affective component involves emotional responses related to the product or service, and the behavioral component includes actual purchasing and usage behavior [20].
Research indicates that consumers’ cognitive evaluations of brands and products significantly influence their purchase intentions. Batra and Ahtola (1991) validated the hedonic and utilitarian components of attitude, which have varying influences across different product categories [21]. For example, consumers’ trust in influencers (i.e., the cognitive component) and their cognitive evaluations of influencers’ expertise directly affect their attitudes towards the products recommended by influencers and their purchase intentions. Ashraf et al. (2023) demonstrated that influencers’ credibility plays a crucial role in enhancing consumer trust and purchase decisions [22].
The affective component reflects consumers’ emotional responses to and preferences regarding a product or brand. Reed et al. (2002) studied the process of consumers forming attitudes based on external information and personal experiences, emphasizing the role of emotional responses [20]. In influencer marketing, influencers’ professionalism (such as professional insights into product knowledge) can significantly impact consumers’ emotional responses. Consumers are more likely to trust influencers who demonstrate high levels of professionalism, and these influencers enhance consumers’ preferences and trust by sharing professional knowledge and insights, thereby influencing purchase intentions [23].
The behavioral component emphasizes consumers’ actual purchasing and usage behavior, which is particularly important in influencer marketing. In the context of live-streaming sales, influencers directly impact consumers’ purchasing behavior through personalized and precise recommendations. Chen and Zhang (2023) found that live interaction significantly increases consumers’ purchase intentions and reduces uncertainties during the purchasing process [16].

2.3. Hypotheses

The credibility of influencers is a key factor affecting consumer purchase intentions [15,22,24,25]. Research indicates that influencers’ credibility significantly influences consumers’ attitudes, thereby affecting their purchase intentions. For instance, Nugroho et al. (2020) pointed out that influencers’ credibility plays a crucial role in enhancing consumer trust and purchase decisions, especially on e-commerce platforms [26]. Martiningsih and Setyawan (2022) found that on the Shopee marketplace, influencers’ attractiveness and expertise have a significantly positive impact on consumer purchase intentions [27]. However, there is limited research on the impact of influencers’ credibility on purchase intentions with regard to light food products. Based on this research gap, this study proposes the following hypothesis, and Figure 1 illustrates the relationship between the hypotheses and Consumer Attitude Theory:
H1: 
Influencers’ credibility positively affects consumer purchase intentions with regard to light food products.
The professionalism of influencers also affects consumer purchase intentions. Research indicates that influencers’ understanding of a brand or product positively affects consumers’ purchase decisions, and their rich professional knowledge has been confirmed as an important characteristic influencing purchase intentions [22,28,29]. In addition, Zaman et al. (2023) studied various characteristics of social media influencers and found that expertise has a significantly positive impact on consumer purchase intentions [30]. These findings collectively reveal the multifaceted impact of influencers’ professionalism on consumer purchasing behavior, from individual characteristics to interactive effects, providing important academic insights for a deeper understanding of this field. However, there is limited research on the specific impact of professionalism on light food products. To fill this research gap, this study proposes the following hypothesis:
H2: 
Influencers’ professionalism positively affects consumer purchase intentions with regard to light food products.
Live-streaming sales significantly impact consumers’ willingness to purchase light food, a phenomenon that has been thoroughly studied in multiple aspects. Chen and Zhang (2023) found that live interactions significantly increase consumers’ purchase intentions and reduce uncertainty during the purchasing process [16]. Weismueller et al. (2020) showed that advertising disclosure and source credibility on social media platforms have a significant impact on consumer purchase intentions [31]. Liu and Zhang (2024) found that consumers can interact in real time with influencers via live streaming platforms to learn about product use and purchase information, which enhances their purchase willingness [32]. In addition, Men and Zheng (2023) pointed out that the alternative learning experience obtained through live shopping reduces consumers’ uncertainty regarding light food products, thereby strengthening their purchasing behavior [33]. However, it is necessary to explore its impact on purchase intentions with regard to light food products. Thus, this study proposes the following hypothesis:
H3: 
Live-streaming sales positively affect consumer purchase intentions with regard to light food products.

3. Research Design

3.1. Sample and Data Collection

Suzhou is an economically developed and diverse city that can provide a broadly representative sample. Additionally, the emerging light-food industry has significant development potential in this city. Therefore, this study distributed questionnaires to residents of Suzhou using the PPS random-sampling method. By employing a three-stage unequal probability sampling approach, we ensured scientific rigor and the representativeness of the sample [34,35].
First, primary sampling units were randomly selected from the urban districts and county-level cities of Suzhou. Then, streets and towns were further sampled from these units. Finally, households within these streets and towns were chosen for the survey. Each stage of sampling was based on the Probability Proportional to Size (PPS) method, ensuring that the probability of selecting each sample unit was proportional to its size. To verify the representativeness of the sample, we conducted mean processing and statistical analysis of the sampling results at each stage, ensuring that the sample was consistent with the population in key demographic characteristics. Additionally, we compared the characteristics of the incomplete survey samples with those of the completed survey samples, confirming no significant differences between the two groups. This further enhanced the representativeness of our sample and the reliability of the survey results.
In the formal survey, 859 questionnaires were distributed, of which 654 were effectively recovered, yielding a response rate of 76.14%.The summary of the study sample is shown in Table 1. It can be seen that the research subjects are mainly concentrated among female users (65.14%) and those aged 18 to 35 (78.9%), reflecting that most users of social media are young female consumers. This suggests that the development potential and innovation direction of influencer marketing need to be closely aligned with the lifestyle concepts and consumption trends of young female consumers. Continuous innovation and optimization of products and services are required to meet their pursuit of a high-quality life. The data show that the residents from the Gusu District-Pingjiang Street area make up the highest proportion, possibly due to a higher concentration of influencers in the area, leading to more active users on social media platforms. This may reflect the influence of influencers in the region and the likelihood of residents participating more on social media platforms, further emphasizing the area’s importance in online marketing and brand promotion. Moreover, most respondents (71.56%) prefer to spend no more than one hour on social media platforms, suggesting an emphasis on efficient information acquisition, which is a reason behind the rise of platforms like TikTok that focus on short videos. This requires influencers to learn how to refine the core information in their content creation and dissemination strategies and to adopt more intuitive and engaging formats, such as short videos, combined images and text, interactive live streaming, etc., to enhance the efficiency of information dissemination and audience engagement [36]. Furthermore, influencers should utilize data-analytics tools to deeply understand the preferences and behavioral characteristics of their target audience to develop more personalized and targeted content strategies. This can attract and maintain consumer attention in a short time and enhance the effectiveness of marketing activities.
The obtained data underwent reliability, validity, and chi-square tests, yielding reliability coefficients of 0.932, 0.858, and 0.902, all of which are greater than 0.8, indicating a high level of reliability in the research data. In the validity analysis, the KMO value was 0.957, and Bartlett’s test of sphericity produced an approximate chi-square of 6338.878, with df = 276 and a p-value of 0.000, indicating high validity of the data. The chi-square test results showed a significant correlation between whether respondents had purchased light food and whether they became interested and continued to search for related information (X2 = 13.707, p = 0.000). The results demonstrate that the data are reliable and valid and that the survey results are both authentic and credible.

3.2. Model Selection

This section describes using descriptive statistical methods to analyze consumer characteristics on trend-setting platforms as a preliminary exploration. In this study, we make use of Consumer Attitude Theory. Given the complexity of the factors influencing consumer purchase intentions, such as influencers’ professional insights, objective recommendations, and the layout style used for live-streaming sales, these factors are often characterized by fuzziness and uncertainty, making them difficult to measure with simple numerical values. Therefore, it is essential to establish a quantitative model suitable for this theory to comprehensively consider these factors.
Consumer Attitude Theory plays an important role in explaining consumer behavior and decision-making processes. For example, Minchev (2021) summarized the formation and impact of consumer attitudes in purchase decisions, focusing on the internal and external factors influencing attitudes in classic behavioral models and demonstrating the importance of attitudes in consumer behavior and purchase decisions [37]. Cerceloiu et al. (2021) used the three components of attitude, cognition, affect, and behavior, to analyze consumer attitudes towards private education services, revealing the critical role of attitudes in consumer decision-making [38]. These studies mainly employed statistical methods such as structural equation modeling (SEM) or regression analysis to analyze the impact of consumer attitudes on purchase intentions.
The fuzzy comprehensive evaluation model can quantify and evaluate ambiguous and difficult-to-quantify factors, translating consumers’ purchase intentions into specific numerical values [39,40]. This allows for a more comprehensive consideration of consumers’ experiences and attitudes on recommendation platforms and more accurately reflects the mechanism of formation of consumer purchase intentions [41]. For instance, Han et al. (2021) used the fuzzy comprehensive evaluation method to assess consumer energy efficiency, demonstrating the effectiveness of this method in handling complex evaluation problems [42]. Li (2021) analyzed consumer decision-making power using a BP neural network and fuzzy comprehensive evaluation model. The study combined fuzzy reasoning and neural networks to predict consumer purchase intentions, showcasing the effectiveness of the fuzzy comprehensive evaluation method in addressing the uncertainty in consumer decision-making [43]. Consumer Attitude Theory involves multiple influencing factors, and the fuzzy comprehensive evaluation model can comprehensively evaluate the relative importance and overall effects of these factors. Additionally, compared to traditional statistical methods, the fuzzy comprehensive evaluation model offers greater flexibility and adaptability in handling nonlinear and complex systems, better reflecting the dynamic changes in consumer behavior.
In summary, although there is currently no literature explicitly using the fuzzy comprehensive evaluation model with Consumer Attitude Theory, studies using these two methods separately have been widely applied and validated in related fields. Therefore, this study chooses to combine Consumer Attitude Theory with the fuzzy comprehensive evaluation model to explore the impact of influencer marketing on consumers’ purchase intentions. The Consumer Attitude Theory provides a theoretical framework for understanding consumer attitudes and behaviors, while the fuzzy comprehensive evaluation model offers a tool for handling multiple indicators and uncertain information. By combining these two methods, we can innovate both theoretically and methodologically, providing a more comprehensive analysis of consumer attitudes. Figure 2 shows the intersection of Consumer Attitude Theory and the fuzzy comprehensive evaluation model, highlighting the theoretical foundation of the research methodology.
Sustainability 16 05471 g002
Figure 2. Illustration of the combination of Consumer Attitude Theory and the fuzzy comprehensive evaluation model [37,38,42,43].
Figure 2. Illustration of the combination of Consumer Attitude Theory and the fuzzy comprehensive evaluation model [37,38,42,43].
Sustainability 16 05471 g002

3.3. Research Method

This study employed the fuzzy comprehensive evaluation method to explore the impact of influencer marketing on consumers’ willingness to purchase light food products. First, the evaluation factor set and evaluation grade set were determined. Then, the entropy weight method was used to calculate the weight values of each dimension. Next, a fuzzy comprehensive evaluation matrix was constructed and a fuzzy comprehensive evaluation was conducted. Finally, through normalization, the normalized fuzzy comprehensive evaluation results were analyzed to determine the weight of the evaluated object in each evaluation grade, ultimately leading to the overall evaluation.

3.4. Model Construction

Trust is a key factor in the consumer decision-making process. When consumers develop a sense of trust in influencers, they are more inclined to believe the information and recommendations provided by the influencers, thereby increasing their willingness to purchase the products recommended by them. This trust can stem from the influencers’ sharing of genuine consumption experiences, objective evaluations, and high-quality content. Sokolova and Kefi (2019) found that there is a significant positive correlation between influencer credibility and consumers’ purchase intentions [44]. Tiwari et al. (2024) also pointed out that influencers can enhance audience identification and purchase intentions through planning, entertaining presentations, and authentic experiences [23].
Xiang et al. (2016) found through their research that professionalism has a significant impact on quasi-social activities [45]. Kenny et al. (1990) pointed out that marketers, through deep insights and professional knowledge about the product, can greatly promote consumers’ purchase intentions; the higher the professional quality, the more likely an effort is to enhance consumers’ final purchasing behavior [46]. In the field of influencer marketing, the professionalism of influencers symbolizes capability and qualification. Consumers are more inclined to purchase products promoted by highly professional influencers. Kim et al. (2022) believe that the professionalism and interactivity displayed by influencers can establish a positive image, thereby significantly enhancing consumers’ purchase intentions [47]. Additionally, Ashraf et al. (2023) found that interactions between influencers and consumers can also strengthen consumers’ purchase intentions [25].
Live-streaming sales, as a new shopping method, offer visibility and interactivity. Platforms also provide personalized recommendations based on individual preferences. The ecological environment of different live-streaming rooms varies due to the host’s attitude, behavior, and personal charm. These differences can affect consumers’ shopping perceptions and purchase intentions [48,49].
Based on the above analysis and Consumer Attitude Theory, this paper selected three categories and nine indicators for evaluation. These are grouped as follows: influencers’ credibility (u1), which includes influencers’ user experience (x1), influencers’ objective recommendations (x2), and influencers’ content presentation quality (x3); influencers’ professionalism (u2), encompassing influencers’ knowledge of product details (y1), influencers’ professional opinions (y2), and influencers’ interactive feedback (y3); and Consumers’ satisfaction with live stream marketing (u3), which consists of personalized promotion in the live stream (z1), layout style of the live stream (z2), and the atmosphere of the live stream (z3). This framework establishes a fuzzy evaluation system, as shown in Table 2.
The evaluation set is constructed as follows:
U = { u 1 , u 2 , u 3 } u 1 = { x 1 , x 2 , x 3 } u 2 = { y 1 , y 2 , y 3 } u 3 = { z 1 , z 2 , z 3 }
To construct the evaluation set, the primary method employed in this research is a questionnaire survey, wherein the scale questions utilized the Likert five-point scale (“Very Satisfied, Somewhat Satisfied, Neutral, Somewhat Dissatisfied, Very Dissatisfied.”). Based on previous research and existing literature [36,50], the evaluation scores of the 11 indicators are categorized into P = {95, 80, 70, 60, 45}. We designate the evaluation scores of the first-level indicators u1, u2, and u3 as {t1, t2, t3}, the values of which are derived from the scores and weights of the secondary indicators.

4. Results

Descriptive statistical data reveal that the study subjects are predominantly female (65.14%) and within the age range of 18 to 35 years old (accounting for 78.9% of the total sample), highlighting the dominant position of young female consumers in the light-food market. This suggests that the potential and innovative directions for the light-food market should closely align with the lifestyle concepts and consumption trends of young female consumers by continuously innovating and optimizing products and services to align with their pursuit of a high-quality life.
The data shows that the residents of the Gusu District-Pingjiang Street area make up the greatest proportion, possibly due to a higher concentration of influencers in the area, leading to more active users on social media platforms. This may reflect the influence of influencers in the region and the likelihood of residents participating more on social media platforms, further emphasizing the area’s importance in online marketing and brand promotion. Moreover, most respondents (71.56%) prefer to spend no more than one hour on social media platforms, suggesting an emphasis on efficient information acquisition, which is a reason behind the rise of platforms like TikTok, which focus on short videos.
This requires influencers to learn how to refine the core information in their content creation and dissemination strategies and to adopt more intuitive and engaging formats, such as short videos, combined images and text, interactive live streaming, etc., to enhance the efficiency of information dissemination and audience engagement [51]. Furthermore, influencers should utilize data-analytics tools to deeply understand the preferences and behavioral characteristics of their target audience to develop more personalized and targeted content strategies. This can attract and maintain consumer attention in a short time and enhance the effectiveness of marketing activities.

4.1. Indicator Weight

Using calculated weights for secondary indicators within three main categories (influencers’ credibility, influencers’ professionalism, and consumers’ satisfaction with live-stream marketing), the following Table 3 displays their weights.

4.2. Membership Matrix

Utilizing SPSSPRO, the membership matrix for consumer satisfaction across three categories (influencers’ credibility, influencers’ professionalism, and consumers’ satisfaction with live-stream marketing) was computed as shown in Table 4.
For the “Influencers’ credibility” category, a fuzzy comprehensive evaluation was conducted on the three indicators (user experience, objective recommendations, and content presentation quality) against five ratings (very satisfied, somewhat satisfied, neutral, somewhat dissatisfied, very dissatisfied) using the main factor prominence type M(∧,∨) operator. Starting with the evaluation indicator weight vector A (obtained by the entropy weight method), a 3 × 5 weight judgment matrix R was constructed [52], and analysis yielded membership degrees for the five ratings, as follows: 0.226, 0.366, 0.299, 0.083, 0.026. Using the maximum-membership-degree rule, the overall evaluation result for influencers’ credibility was “somewhat satisfied.” This indicates a positive evaluation of the reliability and trustworthiness of influencers as information sources, though the value does not yet reach a “very satisfied” level. This suggests that while consumers generally find the information provided by influencers valuable and trust their recommendations to an extent, there is still room to increase their trust in influencers.
For the “Influencers’ professionalism” category, a fuzzy comprehensive evaluation was performed on the three indicators (knowledge of product details, professional opinions, and interactive feedback) against the five ratings using the same operator. The resulting membership degrees were 0.214, 0.375, 0.302, 0.081, and 0.028, leading to an overall evaluation result of “somewhat satisfied.” This reflects a general approval of the professionalism shown by influencers in product promotion, which positively influences product awareness and trust. However, this also indicates potential for improvement in enhancing influencers’ professional image and providing more in-depth content. Consumers expect more professional, detailed product information and reviews from influencers, reflecting a strong demand for high-quality and in-depth professional content. Therefore, influencers and their partnering brands should focus on enhancing professionalism, deepening their understanding of product characteristics and market trends, and providing more professional advice and solutions. This would not only effectively strengthen consumer purchasing decisions but also build a more stable trust relationship and fan base over the long term.
For the “Consumers’ satisfaction with live-stream marketing” category, a fuzzy comprehensive evaluation was conducted on three indicators (personalized promotion, layout style, atmosphere) against the five ratings using the operator. The resulting membership degrees were 0.218, 0.397, 0.287, 0.057, and 0.041, with an overall evaluation of “somewhat satisfied.” This reflects a high overall acceptance of the live-stream marketing format among consumers, recognizing its effectiveness in providing personalized pushes and enhancing shopping convenience. However, this evaluation also points out opportunities for enhancing the live-stream marketing experience, including optimization of the layout style, strengthening the creation of an ecological atmosphere, and further improving the accuracy of personalized services. Therefore, to meet high consumer expectations and enhance overall satisfaction, practitioners of live-stream marketing need to delve deeper into consumer needs, comprehensively utilize technology and innovative strategies, and continue to optimize live-stream content and formats to foster consumer purchasing behavior and enhance the effectiveness of live-stream marketing.

4.3. Composite Scores of Primary Indicators

The composite scores for the primary indicators u1, u2, and u3 are presented in Table 5.
Based on the calculated results, the composite scores for the three primary indicators are 77.838, 76.876, and 77.817, respectively, showing uniformity. Considering the subjective weighting assigned by each consumer to different indicators, it is challenging to objectively weight with the limited data collected. Noting the closeness of the composite scores for the three primary indicators, an equal weighting approach was adopted to calculate the overall satisfaction score. Thus, the comprehensive consumer-satisfaction score was found to be 77.510. This result, which is relatively high, indicates a positive overall consumer evaluation across the three key dimensions of trust in influencers, their professionalism, and satisfaction with the live-stream market. It demonstrates that influencer marketing strategies effectively enhance consumer purchase intent, establish brand trust, and optimize the shopping experience. These scores reflect consumer acknowledgment of the efforts by influencers to convey brand information, provide professional insights, and enhance the live-stream-interaction experience. However, even with high scores, continual optimization and innovation are necessary to maintain and enhance consumer satisfaction and further strengthen their loyalty and engagement. This requires influencers and brands to pay more attention to consumer feedback while maintaining their current advantages and to continue to adjust and optimize marketing strategies to adapt to market changes and the evolution of consumer needs.

5. Conclusions

Combining descriptive statistical data, this study found that young female consumers are the main users of social media. The occupations of these users are primarily distributed among students, ordinary employees, and corporate managers, with the greatest proportion being students. Residents of the Gusu District-Pingjiang Street area have the highest usage rate, possibly due to the significant influence of local influencers. Most respondents tend to spend no more than one hour on social media platforms, indicating that consumers value efficient information acquisition. Overall, influencer marketing should deeply integrate with the lifestyle concepts and consumption trends of young female consumers and pay attention to the needs of the student demographic. Particularly in content creation and dissemination strategies, it is essential to streamline core information and utilize formats such as short videos, a combination of graphics and text, and live interactions to enhance the efficiency of information dissemination and audience engagement.

5.1. Influencers’ Credibility & Purchase Intentions

The impact of influencer credibility on consumer purchase intentions mainly involves user experience, objective recommendations, and content presentation quality. Consumers are generally satisfied with the objective recommendations and content quality provided by influencers, but their ratings for user experience are somewhat neutral. This indicates that consumers place more emphasis on the objective recommendations regarding products and the quality of services provided by influencers, while the user experience described by the influencers is not a primary focus. This phenomenon might stem from the trust-building process with influencers. In making purchasing decisions, consumers often focus more on the quality of the product itself and the authenticity of the objective recommendations and content quality provided by influencers, whereas personal user-experience evaluations may be less significant. This could be due to the fact that consumers tend to rely on objective data and information to assess the merits of products or services, rather than on individual subjective experiences.
Thus, establishing trust and transparency is key for influencers. Research indicates that consumers are more inclined to trust influencers who transparently disclose their collaborations with brands and provide objective evaluations [53]. Disclosing these collaborations helps to enhance consumer trust, thereby increasing their purchase intentions. In marketing strategies, the theory of social proof can be employed; this theory involves enhancing the credibility of a product or service by presenting positive experiences from other users. In addition, influencers can bolster the persuasiveness of their recommendations by supporting them with relevant research and data.

5.2. Influencers’ Professionalism & Purchase Intentions

The impact of an influencer’s professionalism on consumer purchase intentions primarily involves their knowledge of the product, professional insights, and interactive feedback. Survey results indicate that consumers are quite satisfied with the influencers’ professional insights and product knowledge but rate their interactive feedback low. This suggests that consumers place greater value on influencers’ expertise and knowledge in their respective fields, rather than on their interactive feedback. This may be because, on influencer platforms, consumers primarily seek reliable information, professional advice, and authentic evaluations regarding products or services, rather than just personal interactions with the celebrities. It could also be due to individual differences in personality, profession, income, and age, which might affect the desire for interaction.
Overall, these observations imply that in the influencer-marketing context, consumers value the quality, authenticity, and practicality of content more highly. They expect to gain detailed information on products and user experiences and cost-effectiveness assessments through influencers, and these help them make more informed purchasing decisions. Although interaction between influencers and fans can enhance closeness and a sense of belonging, when it comes to making purchasing decisions, consumers tend to rely more on their judgment of the product’s value rather than on personal interactions with the celebrity.
To meet consumer expectations, influencers should focus on the following areas when promoting products. First, deepen product knowledge. Influencers should enhance their understanding of the products they promote, including their features, functions, advantages, and disadvantages, to ensure that their recommendations are substantive and professional. Second, enhance professional image and credibility. Influencers should elevate their professional image and credibility by sharing relevant industry knowledge, experiences, and practices, establishing trust in their expertise among consumers. Last, invite expert evaluations. Influencers could consider inviting experts or qualified third-party organizations to evaluate and validate products and offer more objective and authoritative professional insights, thereby enhancing the credibility and persuasiveness of their promotions.

5.3. Live-Streaming Sales & Purchase Intentions

The impact of live-stream marketing on consumer purchase intentions mainly involves personalized and precise recommendations, layout style, and the ecological atmosphere of the live stream. Survey results show that consumers are relatively satisfied with the personalized and precise aspects of live-stream marketing, yet they rate the layout style and ecological atmosphere as average. This might be due to personalized and precise recommendations that more directly meet consumers’ individual needs and shopping preferences, enhancing their experience of a more personalized and customized shopping journey. This aspect can be optimized through big-data analysis and personalized-recommendation algorithms, which provide precise product recommendations based on consumers’ past purchases, preferences, and behavior patterns, thus enhancing the personalized nature of their shopping experience.
However, the average ratings for layout style and atmosphere may be due to these factors having a less direct impact on the shopping experience compared to personalized recommendations. Consumers are more concerned with the quality and price of products and the accuracy of personalized recommendations. Although layout style and atmosphere do enhance the shopping environment, their overall impact on the consumer shopping experience is relatively minor; hence, the average ratings.
This indicates that consumers prefer a personalized shopping experience and expect live-stream marketing to be more aligned with their needs and interests. Key to this is for influencers to enhance their sensitivity to market trends and consumer preferences and carefully design their live-stream content. First, they should consider market sensitivity and content design. Influencers should capture potential shopping hotspots and trending items through market research and industry analysis. By leveraging big data and AI technologies to deeply analyze fans’ behaviors and preferences, influencers can choose products that align with their audience’s interests, thereby personalizing the shopping experience. Second, they should consider content planning and production. During live-stream events, influencers should focus on content planning and production, carefully designing the live-stream program to enhance its entertainment value and appeal. By integrating their personal style and brand image, they can make the live-stream content more attractive and relatable, thus better engaging consumers and boosting sales. By implementing these measures, influencers can effectively meet consumer expectations for personalized shopping experiences and live-stream marketing, enhancing sales outcomes and brand value.

6. Discussion

This paper systematically explores the impact of influencer marketing on consumer purchase intentions in the era of social media, with empirical analysis focusing on the light-food market. Utilizing data from 654 consumers in Suzhou, a comprehensive evaluation system was established using Consumer Attitude Theory and a fuzzy comprehensive evaluation model, creating an integrated evaluation system covering influencer credibility and professionalism and consumers’ satisfaction with live-stream marketing. This study quantifies how these factors influence the consumer decision-making process, offering valuable insights into consumer attitudes and preferences with regard to influencer marketing.
The study examines the effects of influencer credibility and professionalism and consumers’ satisfaction with live-stream marketing on consumer purchase intentions, revealing that consumers tend to rely more on influencers’ objective recommendations and professional insights rather than just direct interaction experiences when making purchase decisions. These findings align with the research of Hu et al. (2020), who noted that consumer purchase intentions increasingly depend on the credibility and expertise of influencers [54]. This contrasts with the views of Miglė et al. (2023), who suggest that consumer purchase intentions are primarily influenced by the personal charm of influencers and the interactive experiences they provide [55].
Research has found that the credibility of influencers has a significant positive impact on consumer purchase intentions for light food products. This result is consistent with the study by Weismueller et al. (2020), which found that advertising disclosure and source credibility significantly affect consumer purchase intentions, especially on social media platforms [31]. Chetioui et al. (2020) studied the influence of fashion influencers on consumer brand attitudes and purchase intentions, finding that influencer trustworthiness and content quality significantly impact consumer purchase intentions [56]. These studies indicate that the influence of influencers’ credibility is consistent across different product categories and markets.
We found that the professionalism of influencers positively affects consumers’ willingness to purchase light food products. This is consistent with the findings of Hughes et al. (2019), who, through empirical research on social media influencers, discovered that bloggers’ expertise and blog content significantly affect online brand engagement and purchase intentions [57]. Additionally, Belanche et al. (2021) studied the impact of influencer–consumer–product fit on purchase intentions, finding that high fit significantly enhances consumers’ product attitudes and purchase intentions [58]. These findings collectively reveal the multifaceted impact of influencers’ professionalism on consumer purchasing behavior.
Furthermore, this paper reveals that live-streaming sales play a significant role in enhancing consumer purchase intentions, consistent with the views of Qin et al. [59]. Han et al. (2020) support this finding in their study of the impact of advertising disclosure on consumer purchase intentions, discovering that clear advertising disclosure reduces consumer purchase intentions, while subtle advertising disclosure is more effective [60]. Furthermore, Gomes et al. (2022) studied the impact of digital influencer marketing on consumer purchase intentions for fashion products, finding that content quality and pseudo-social interaction significantly influence purchase intentions [28]. These studies indicate that the effectiveness of live-streaming sales is consistent across different product categories and markets. However, despite consumer satisfaction with personalized pushes and precise marketing in live streams, the ratings for layout style and atmosphere remain generally low. This supplements the findings of Jerry et al. (2023), who discovered that the dynamic features of live streams, atmospheric cues, and perceived interactivity significantly affect consumers’ immersive experiences and social interactions, subsequently influencing purchase intentions [61].

7. Implications and Limitations

7.1. Theoretical Implications

This study makes significant theoretical contributions in several areas. Firstly, by combining Consumer Attitude Theory and the fuzzy comprehensive evaluation model, we systematically explored the impact of influencer marketing on consumer purchase intentions, filling a gap in previous research. Secondly, this study expanded the application of the fuzzy comprehensive evaluation model in consumer behavior analysis, demonstrating its effectiveness in addressing complex evaluation problems. Through empirical analysis of 654 consumers, the results showed that influencer trustworthiness, professionalism, and satisfaction with live-streaming sales have a significant positive effect on enhancing consumer purchase intentions. Lastly, the research revealed the comprehensive impact of multiple factors on consumer purchase intentions, providing practical guidance for brands in formulating digital-marketing strategies. This study not only enriches the theories of consumer behavior and marketing but also offers new insights and methods for understanding the role of influencers in digital marketing.

7.2. Practical Implications

This study provides significant insights, offering specific strategic guidance for influencers and brands to enhance their competitiveness and influence in the market. For influencers, transparency and objectivity are key to enhancing trustworthiness. Influencers should maintain transparency during the marketing process and provide genuine usage experiences and objective evaluations. Additionally, influencers need to continuously improve their professional knowledge, thoroughly understand the products they recommend, and offer detailed product information and professional insights. This not only enhances the credibility and persuasiveness of their recommendations but also effectively meets consumers’ demand for high-quality content, further increasing their market influence.
For brands, personalization and precise targeting play a crucial role in live-streaming sales. Utilizing big-data analysis and personalized-recommendation algorithms to provide precise product recommendations based on consumers’ purchase history and behavior patterns can significantly enhance the personalization of the shopping experience [32,62]. Brands can fully leverage these technical means to deliver the right products to the right consumers at the right time, thereby increasing purchase conversion rates. Additionally, brands should focus on collaborating with influencers, selecting those who align with the brand image and product characteristics to promote their products, further enhancing brand awareness and reputation. These strategies not only help increase consumers’ purchase intentions but also provide strong support for brands to win more loyal customers in a highly competitive market. By effectively combining the influence of influencers with the technological advantages of brands, both parties can achieve mutual benefits and occupy a more advantageous position in the market.

7.3. Limitations and Further Study

Although this study provides some meaningful conclusions, there are still some limitations. First, the sample consists of residents from Suzhou city, which may present regional and sample biases; future research could use a larger sample size to verify the universality and robustness of the results. Second, this study lacks deeper theoretical support and explanations for consumer purchase intentions. Hence, future studies will combine more empirical data and case analyses to further explore the underlying drivers and mechanisms of consumer behavior. Moreover, integrating theoretical and methodological contributions from other fields such as psychology, sociology, and communication studies could provide a deeper analysis of the cognitive and perceptual processes and the underlying psychological and social mechanisms that shape consumer behavior towards influencer marketing, thereby enhancing the depth and breadth of the research and offering theoretical support for more effective marketing strategies.

Author Contributions

Overall research design, Y.C.; writing—original draft preparation, Y.C., Z.Q., Y.Y. and Y.H.; writing—review and editing, Y.Y. and Y.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Training Program of Innovation and Entrepreneurship for Undergraduates: A Study on the Influence of Social Platforms on Light Food Shopping Decisions Based on a Survey of Suzhou Residents (202310289015Z), and the General Project of Philosophy and Social Science Research in Colleges and Universities in Jiangsu Province: Digital Risk Visualization and Analysis of Listed Companies in Jiangsu Province Based on Text Mining (2023SJYB1514).

Institutional Review Board Statement

Ethical approval is not required for this study.

Informed Consent Statement

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

Data Availability Statement

Data supporting the study are available upon reasonable request from the corresponding author.

Acknowledgments

We thank the academic editor and two anonymous reviewers for their contributions to improving the manuscript. Our gratitude is extended to each of the parties listed above.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Appel, G.; Grewal, L.; Hadi, R.; Stephen, A.T. The future of social media in marketing. J. Acad. Mark. Sci 2020, 48, 79–95. [Google Scholar] [CrossRef]
  2. Romero-Rodriguez, L.M.; Castillo-Abdul, B. Toward state-of-the-art on social marketing research in user-generated content (UGC) and influencers. J. Manag. Dev. 2023, 42, 425–435. [Google Scholar] [CrossRef]
  3. Zhu, D.; Chang, Y. Not doing your job is still admirable: The impact of the county magistrate’s live broadcast of goods on purchase intention. Nankai Manag. Rev. 2023, 26, 177–187. [Google Scholar]
  4. Eftimov, T.; Popovski, G.; Petković, M.; Seljak, B.K.; Kocev, D. COVID-19 pandemic changes the food consumption patterns. Trends Food Sci. Technol. 2020, 104, 268–272. [Google Scholar] [CrossRef] [PubMed]
  5. Qi, X.; Ploeger, A. Explaining Chinese Consumers’ Green Food Purchase Intentions during the COVID-19 Pandemic: An Extended Theory of Planned Behaviour. Foods 2021, 10, 1200. [Google Scholar] [CrossRef] [PubMed]
  6. Mason, A.N.; Narcum, J.; Mason, K. Social media marketing gains importance after COVID-19. Cogent Bus. Manag. 2021, 8, 1870797. [Google Scholar] [CrossRef]
  7. Bilińska-Reformat, K.; Dewalska-Opitek, A. E-commerce as the predominant business model of fast fashion retailers in the era of global COVID-19 pandemics. Procedia Comput. Sci. 2021, 192, 2479–2490. [Google Scholar] [CrossRef]
  8. Haenlein, M.; Anadol, E.; Farnsworth, T.; Hugo, H.; Hunichen, J.; Welte, D. Navigating the New Era of Influencer Marketing: How to Be Successful on Instagram, TikTok, & Co. Calif. Manage. Rev 2020, 63, 5–25. [Google Scholar]
  9. Johnstone, L.; Lindh, C. Sustainably sustaining (online) fashion consumption: Using influencers to promote sustainable (un)planned behaviour in Europe’s millennials. J. Retail. Consum. Serv. 2022, 64, 102775. [Google Scholar] [CrossRef]
  10. Bu, Y.; Parkinson, J.; Thaichon, P. Influencer marketing: Homophily, customer value co-creation behaviour and purchase intention. J. Retail. Consum. Serv. 2022, 66, 102904. [Google Scholar] [CrossRef]
  11. Djafarova, E.; Rushworth, C. Exploring the Credibility of Online Celebrities’ Instagram Profiles in Influencing the Purchase Decisions of Young Female Users. Comput. Hum. Behav 2017, 68, 1–7. [Google Scholar] [CrossRef]
  12. Breves, P.; Liebers, N.; Motschenbacher, B.; Reus, L. Reducing Resistance: The Impact of Nonfollowers and Followers Parasocial Relationships with Social Media Influencers on Persuasive Resistance and Advertising Effectiveness. Hum. Commun. Res 2021, 47, 418–443. [Google Scholar] [CrossRef]
  13. Coco, S.L.; Eckert, S. #sponsored: Consumer Insights on Social Media Influencer Marketing. Public Relat. Inq 2020, 9, 177–194. [Google Scholar]
  14. Barta, S.; Belanche, D.; Fernández, A.; Flavián, M. Influencer marketing on TikTok: The effectiveness of humor and followers’ hedonic experience. J. Retail. Consum. Serv. 2023, 70, 103149. [Google Scholar] [CrossRef]
  15. Ao, L.; Bansal, R.; Pruthi, N.; Khaskheli, M.B. Impact of Social Media Influencers on Customer Engagement and Purchase Intention: A Meta-Analysis. Sustainability 2023, 15, 2744. [Google Scholar] [CrossRef]
  16. Chen, C.; Zhang, D. Understanding consumers’ live-streaming shopping from a benefit–risk perspective. J. Serv. Mark. 2023, 37, 973–988. [Google Scholar] [CrossRef]
  17. Shanbhogue, A.V.; Ranjith, V.K. Effects of Consumer Dispositional Attitude on Purchase Intention in an Emerging Market. F1000Research 2023, 12, 384. [Google Scholar] [CrossRef] [PubMed]
  18. Argyriou, E.; Melewar, T.C. Consumer attitudes revisited: A review of attitude theory in marketing research. Int. J. Manag. Rev. 2011, 13, 431–451. [Google Scholar] [CrossRef]
  19. Fishbein, M.; Ajzen, I. Belief, attitude, intention, and behavior: An introduction to theory and research. Philos. Rhetor. 1977, 10, 130–132. [Google Scholar]
  20. Reed, A.; Wooten, D.B.; Bolton, L.E. The temporary construction of consumer attitudes. J. Consum. Psychol. 2002, 12, 375–388. [Google Scholar] [CrossRef]
  21. Batra, R.; Ahtola, O.T. Measuring the hedonic and utilitarian sources of consumer attitudes. Mark. Lett. 1991, 2, 159–170. [Google Scholar] [CrossRef]
  22. Ashraf, A.; Hameed, I.; Saeed, S.A. How do social media influencers inspire consumers’ purchase decisions? The mediating role of parasocial relationships. Int. J. Consum. Stud. 2023, 47, 1416–1433. [Google Scholar] [CrossRef]
  23. Goranova, P. Consumer attitudes in omni-channel marketing. Izvestia Journal of the Union of Scientists-Varna. Econ. Sci. Ser. 2018, 7, 196–202. [Google Scholar]
  24. Tiwari, A.; Kumar, A.; Kant, R.; Jaiswal, D. Impact of fashion influencers on consumers’ purchase intentions: Theory of planned behaviour and mediation of attitude. J. Fash. Mark. Manag. Int. J. 2024, 28, 209–225. [Google Scholar] [CrossRef]
  25. Cabeza-Ramírez, L.J.; Fuentes-García, F.J.; Cano-Vicente, M.C.; González-Mohino, M. How generation X and millennials perceive influencers’ recommendations: Perceived trustworthiness, product involvement, and perceived risk. J. Theor. Appl. Electron. Commer. Res. 2022, 17, 1431–1449. [Google Scholar] [CrossRef]
  26. Nugroho, D.R.; Linardi, J.K.; Setyawati, A.; Firadus, M.I. The Effect of Influencer Credibility Towards Consumer Trust in E-Commerce and Purchase Intention of Low-Cost Carrier Ticket. Proc. Int. Conf. Econ. Bus. Stud. ICOEBS 2020 2020, 3, 582–589. [Google Scholar]
  27. Martiningsih, D.A.; Setyawan, A.A. The Impact of Influencers’ Credibility towards Purchase Intention. In International Conference on Economics and Business Studies (ICOEBS 2022); Atlantis Press: Amsterdam, The Netherlands, 2022; pp. 196–204. [Google Scholar]
  28. Gomes, M.A.; Marques, S.; Dias, Á. The impact of digital influencers’ characteristics on purchase intention of fashion products. J. Glob. Fash. Mark. 2022, 13, 187–204. [Google Scholar] [CrossRef]
  29. Melo, I.; Reis, J.L. The Social and Financial Impact of Influencers on Brands and Consumers. In Marketing and Smart Technologies: Proceedings of ICMarkTech 2022; Springer Nature: Singapore, 2023; Volume 2, pp. 563–574. [Google Scholar]
  30. Zaman, S.A.A.; Anwar, A.; Haque, I.U. Examining the Mediating Effect of Online Engagement and Online Reviews: The Influence of Influencer Credibility on Consumer Purchase Intentions. Pak. Bus. Rev. 2023, 24, 389–410. [Google Scholar] [CrossRef]
  31. Weismueller, J.; Harrigan, P.; Wang, S.; Soutar, G. Influencer Endorsements: How Advertising Disclosure and Source Credibility Affect Consumer Purchase Intention on Social Media. Australas. Mark. J. 2020, 28, 160–170. [Google Scholar] [CrossRef]
  32. Liu, J.; Zhang, M. Formation mechanism of consumers’ purchase intention in multimedia live platform: A case study of taobao live. Multimed. Tools Appl. 2024, 83, 3657–3680. [Google Scholar] [CrossRef]
  33. Men, J.; Zheng, X.; Davison, R.M. The role of vicarious learning strategies in shaping consumers’ uncertainty: The case of live-streaming shopping. Internet Res. 2023, 34, 1066–2243. [Google Scholar] [CrossRef]
  34. Ozturk, O. Post-stratified probability-proportional-to-size sampling from stratified populations. J. Agric. Biol. Environ. Stat. 2019, 24, 693–718. [Google Scholar] [CrossRef]
  35. Sohil, F.; Sohail, M.U.; Shabbir, J. Optimum second call imputation in PPS sampling. PLoS ONE 2022, 17, e0261834. [Google Scholar] [CrossRef] [PubMed]
  36. Cacioppo, J.T.; Petty, R.E. The need for cognition. J. Personal. Soc. Psychol. 1982, 42, 116. [Google Scholar] [CrossRef]
  37. Minchev, A. An examination of attitudes in some widespread models of consumer behaviour. Econ. Stud. 2021, 73, 61–73. [Google Scholar] [CrossRef]
  38. Lucian, C.M.; Diaconu, V.I. Consumer Attitude in the Context of Private Education Services. In Proceedings of the 5th International Scientific Conference on Recent Advances in Information Technology, Tourism, Economics, Management and Agriculture—ITEMA, Online-Virtual, 21 October 2021; Volume 2021, pp. 2683–5991. [Google Scholar]
  39. Vivek, K.; Subbarao, K.V.; Routray, W.; Kamini, N.R.; Dash, K.K. Application of Fuzzy Logic in Sensory Evaluation of Food Products: A Comprehensive Study. Food Bioprocess Technol. 2020, 13, 1–29. [Google Scholar] [CrossRef]
  40. Juhua, F. Evaluation of Online Shopping Consumer Satisfaction Based on Fuzzy Comprehensive Evaluation Method. Mark. Res. 2017, 6, 31–32. [Google Scholar]
  41. Zadeh, L.A. Fuzzy sets. Inf. Control 1965, 8, 338–353. [Google Scholar] [CrossRef]
  42. Han, S.; Xu, P.; Li, Y.; Ji, S.; Han, D.; Yang, L. Energy efficiency evaluation of consumer based on fuzzy comprehensive evaluation method. IOP Conf. Ser. Earth Environ. Sci. 2021, 784, 012006. [Google Scholar] [CrossRef]
  43. Li, W. Consumer Decision-Making Power Based on BP Neural Network and Fuzzy Mathematical Model. Wirel. Commun. Mob. Comput. 2021, 2021, 1–9. [Google Scholar]
  44. Sokolova, K.; Kefi, H. Instagram and YouTube bloggers promote it, why should I buy? How credibility and parasocial interaction influence purchase intentions. J. Retail. Consum. Serv. 2020, 53, 101742. [Google Scholar] [CrossRef]
  45. Xiang, L.; Zheng, X.; Lee, M.K.; Zhao, D. Exploring consumers’ impulse buying behavior on social commerce platform: The role of parasocial interaction. Int. J. Inf. Manag. 2016, 36, 333–347. [Google Scholar] [CrossRef]
  46. Chan, K.K.; Misra, S. Characteristics of the opinion leader:a new dimension. J. Advert. 1990, 19, 53–60. [Google Scholar] [CrossRef]
  47. Kim, H.J.; Chan-Olmsted, S. Influencer Marketing and Social Commerce: Exploring the role of influencer communities in predicting usage intent. J. Interact. Advert. 2022, 22, 249–268. [Google Scholar] [CrossRef]
  48. Zhu, P.; Liu, Z.; Li, X.; Jiang, X.; Zhu, M.X. The influences of livestreaming on online purchase intention: Examining platform characteristics and consumer psychology. Ind. Manag. Data Syst. 2023, 123, 862–885. [Google Scholar] [CrossRef]
  49. Shang, Q.; Ma, H.; Wang, C.; Gao, L. Effects of background fitting of e-commerce live streaming on consumers’purchase intentions: A cognitive-affective perspective. Psychol. Res. Behav. Manag. 2023, 16, 149–168. [Google Scholar] [CrossRef] [PubMed]
  50. Likert, R. A technique for the measurement of attitudes. Arch. Psychol. 1932, 22, 55. [Google Scholar]
  51. Geng, R.; Wang, S.; Chen, X.; Song, D.; Yu, J. Content marketing in e-commerce platforms in the internet celebrity economy. Ind. Manag. Data Syst. 2020, 120, 464–485. [Google Scholar] [CrossRef]
  52. Sun, Y.; Yang, Y.; Liu, S.; Li, Q. Research on Transportation Carbon Emission Peak Prediction and Judgment System in China. Sustainability 2023, 15, 14880. [Google Scholar] [CrossRef]
  53. Hwang, Y.; Jeong, S.H. This is a sponsored blog post, but all opinions are my own: The effects of sponsorship disclosure on responses to sponsored blog posts. Comput. Hum. Behav. 2016, 62, 528–535. [Google Scholar] [CrossRef]
  54. Hu, T.; Shi, B. More proximal, more willing to purchase: The mechanism for variability in consumers’ purchase intention toward sincere vs. exciting brands. Front. Psychol. 2020, 11, 541190. [Google Scholar] [CrossRef] [PubMed]
  55. Černikovaitė, M.E.; Karazijienė, Ž. The role of influencers and opinion formers marketing on creative brand communication. Creat. Stud. 2023, 16, 371–383. [Google Scholar] [CrossRef]
  56. Chetioui, Y.; Benlafqih, H.; Lebdaoui, H. How fashion influencers contribute to consumers’ purchase intention. J. Fash. Mark. Manag. Int. J. 2020, 24, 361–380. [Google Scholar] [CrossRef]
  57. Hughes, C.; Swaminathan, V.; Brooks, G. Driving brand engagement through online social influencers: An empirical investigation of sponsored blogging campaigns. J. Mark. 2019, 83, 78–96. [Google Scholar] [CrossRef]
  58. Belanche, D.; Casaló, L.V.; Flavián, M.; Ibáñez-Sánchez, S. Understanding influencer marketing: The role of congruence between influencers, products and consumers. J. Bus. Res. 2021, 132, 186–195. [Google Scholar] [CrossRef]
  59. Qin, C.; Zeng, X.; Liang, S.; Zhang, K. Do Live Streaming and Online Consumer Reviews Jointly Affect Purchase Intention? Sustainability 2023, 15, 6992. [Google Scholar] [CrossRef]
  60. Han, H.; Yi, J.; Jun, S.; Ahn, S. How do followers infer the motives behind an influencer’s advertising disclosures? Asia Pac. J. Mark. Logist. 2021, 33, 1159–1174. [Google Scholar] [CrossRef]
  61. Shiu, J.Y.; Liao, S.T.; Tzeng, S.Y. How does online streaming reform e-commerce? An empirical assessment of immersive experience and social interaction in China. Humanit. Soc. Sci. Commun. 2023, 10, 224. [Google Scholar] [CrossRef]
  62. Xu, M.; Chen, P. Big data analysis based on the correlation between live-streaming with goods, perceived value and consumer repurchase. Appl. Math. Nonlinear Sci. 2023, 9. [Google Scholar] [CrossRef]
Sustainability 16 05471 g001
Figure 1. Theoretical model of consumer attitudes.
Figure 1. Theoretical model of consumer attitudes.
Sustainability 16 05471 g001
Table 1. Descriptive statistics.
Table 1. Descriptive statistics.
ConstructFeaturesAmountPercentage
GenderFemale42665.14%
Male22834.86%
AgeUnder the age of 18304.59%
18–25 years old34853.21%
26–35 years old16825.69%
36–45 years old9314.22%
46–55 years old131.99%
Above the age of 5620.31%
Current careerStudents24437.31%
General staff12118.50%
Business managers8312.69%
Government/Agency cadres/Civil servants599.02%
Professionals406.12%
Ordinary workers304.59%
Freelancers253.82%
Self-employed/Contractors192.91%
Other182.75%
Commercial service workers152.29%
The length of time consumers browse on the influencer’s marketing platformLess than one 30 min20431.19%
30 min–1 h26440.37%
1 h–2 h11918.20%
2 h–4 h416.27%
More than 4 h263.98%
Place of residenceGusu District-Pingjiang Street8613.15%
Industrial Park-Louwei Street7711.77%
Kunshan City-Bacheng Town7110.86%
Kunshan City-Zhangpu Town6910.55%
Wuzhong District-Luzhi Town6810.40%
Wuzhong District-Jinting Town659.94%
Zhangjiagang City-Yangshe Town629.48%
Wuzhong District-Guoxiang Street599.02%
Kunshan City-Yushan Town517.80%
Zhangjiagang City-Hucheng Street467.03%
Table 2. Fuzzy comprehensive evaluation system.
Table 2. Fuzzy comprehensive evaluation system.
Primary IndicatorsSecondary IndicatorsDescriptions
▪
influencers’ credibility
User experienceInfluencers share their real-life experiences of recommended products.
Objective recommendationsInfluencers share their objective recommendations to the consumer.
Content presentation qualityThe attractiveness of influencers’ explanation of the product.
▪
influencers’ professionalism
Knowledge of product detailsInfluencers have a comprehensive understanding of product details.
Professional opinionsInfluencers have sufficient professional understanding of the product.
Interactive feedbackCommunication between influencers and consumers.
▪
Satisfaction with live-stream marketing
Personalized promotionAccurate and personalized promotion of products, such as salads, fruit and vegetable juices, oatmeal, etc.
Layout styleThe layout style of the live-stream platform, which may be simple, clean, fancy, eye-catching, etc.
AtmosphereThe overall environment and atmosphere related to the live stream, such as anchors, products, live-streaming platforms, etc.
Table 3. Secondary-indicator weights.
Table 3. Secondary-indicator weights.
Primary IndicatorsItemInformation Entropy Value (e)Information Utility Value (d)Weight (%)
Influencers’ credibilityUser experience0.7650.23530.988
Objective recommendations0.7220.27836.615
Content-presentation quality0.7540.24632.397
Influencers’ professionalismKnowledge of product details0.7190.28137.459
Professional opinions0.7640.23631.484
Interactive feedback0.7670.23331.057
Consumers’ satisfaction with live-stream marketingPersonalized promotion0.6310.36940.211
Layout style0.730.2729.408
Atmosphere0.7210.27930.381
Table 4. Membership matrix for the three categories.
Table 4. Membership matrix for the three categories.
Primary IndicatorVery SatisfiedSomewhat SatisfiedNeutralSomewhat DissatisfiedVery Dissatisfied
MembershipInfluencers’ credibility0.2260.3660.3000.0830.026
Normalized Membership [Weight]0.2260.3660.2990.0830.026
MembershipInfluencers’ professionalism0.2140.3750.3010.0810.028
Normalized Membership [Weight]0.2140.3750.3020.0810.028
MembershipConsumers’ satisfaction with live-stream marketing0.2200.4020.2910.0580.041
Normalized Membership [Weight]0.2180.3970.2870.0570.041
Table 5. The composite scores for the primary indicators u1, u2, and u3.
Table 5. The composite scores for the primary indicators u1, u2, and u3.
Primary IndicatorVariableVery WillingSomewhat WillingNeutralSomewhat UnwillingVery UnwillingPredicted Result Y
u1Coefficient0.2260.3660.2990.0830.02677.838
test value9580706045
u2Coefficient0.2140.3750.3020.0810.02876.876
test value9580706045
u3Coefficient0.2180.3970.2870.0570.04177.817
test value9580706045
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Chen, Y.; Qin, Z.; Yan, Y.; Huang, Y. The Power of Influencers: How Does Influencer Marketing Shape Consumers’ Purchase Intentions? Sustainability 2024, 16, 5471. https://doi.org/10.3390/su16135471

AMA Style

Chen Y, Qin Z, Yan Y, Huang Y. The Power of Influencers: How Does Influencer Marketing Shape Consumers’ Purchase Intentions? Sustainability. 2024; 16(13):5471. https://doi.org/10.3390/su16135471

Chicago/Turabian Style

Chen, Yiming, Zhaoyue Qin, Yue Yan, and Yi Huang. 2024. "The Power of Influencers: How Does Influencer Marketing Shape Consumers’ Purchase Intentions?" Sustainability 16, no. 13: 5471. https://doi.org/10.3390/su16135471

APA Style

Chen, Y., Qin, Z., Yan, Y., & Huang, Y. (2024). The Power of Influencers: How Does Influencer Marketing Shape Consumers’ Purchase Intentions? Sustainability, 16(13), 5471. https://doi.org/10.3390/su16135471

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop