Next Article in Journal
Beyond Urban-Centered Responses: Overcoming Challenges to Build Disaster Resilience and Long-Term Sustainability in Rural Areas
Next Article in Special Issue
Sustainable Consumption and Environmental Change in Taiwan: Understanding Consumer Perceptions and Narratives of Practice
Previous Article in Journal
Village Settlements’ Perspective on Rural Water Accessibility: A Mountainous Water Security Measurement Approach
Previous Article in Special Issue
Consumers’ Risk Perception of Triploid Food: Empirical Research Based on Variance Analysis and Structural Equation Modeling
 
 
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 11
10.3390/su16114369
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

Influencer Marketing Platforms’ Effect on Light Meal Purchase Intention and Behavior

by
Zhaoyue Qin
1,2,†,
Yiming Chen
1,2,†,
Yue Yan
1,2,* and
Yi Huang
1,2
1
School of Business, 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(11), 4369; https://doi.org/10.3390/su16114369
Submission received: 19 March 2024 / Revised: 15 May 2024 / Accepted: 17 May 2024 / Published: 22 May 2024
(This article belongs to the Special Issue Personal and Social Factors Influencing Consumers Attitudes and Behaviors toward Sustainable Foods)
Browse Figures
Versions Notes

Abstract

:
Given the heightened awareness of health and sustainable development in contemporary society, light meals have become a popular dietary choice with considerable momentum. This study focuses on the role of influencer marketing platforms in promoting consumer purchasing intentions towards light meals. By conducting a sample survey of 654 consumers in Suzhou City and employing Structural Equation Modeling (SEM) for empirical analysis, the findings indicate that the intervention of influencer marketing platforms has a significant positive impact on both the purchase intention and purchasing behavior towards light meals, and that purchase intention has a mediating effect between influencer marketing platform intervention and purchase behavior. This research further reveals that while influencers share more lifestyle displays and light meal recipes, marketing platforms should strengthen the integration of sustainable development concepts with the light meals industry to enhance product exposure and promote the spread of ideas. In addition, consumers can reinforce this trend by actively participating in social media, sharing purchasing experiences, and proactively seeking information about light meals and sustainable living, thus achieving a beneficial mutual promotion.

1. Introduction

Social media, as a tool to facilitate communication mechanisms, closely connects individuals through content sharing [1]. In today’s digital society, social media and online platforms play a significant role in influencing consumers [2], with their rise gradually changing consumer shopping decision-making patterns. Consumers increasingly use social media, especially to gather information for decision-making. In this context, influencers have gradually entered the public eye. Ki and Kim (2019) define influencers as “individuals who have established credibility with a large social media audience due to their knowledge and expertise on a specific topic, thereby significantly influencing their followers and peer consumers’ decisions” [3]. Hudders et al. (2021) further explain “Influencers are social media users who, by creating and disseminating online content across various social media platforms, have gained fame online and accumulated a large following. Generally, digital influencers distinguish themselves from ordinary social media users through their extensive reach and influence” [4]. Consumers often follow the advice of influencers and trust their recommendations on products. Influencers are increasingly seen as powerful tools for communication with consumers [5,6]. Compared to celebrities, influencers have established closer connections with their followers and developed more trustworthy and credible relationships [7]. This transformative interaction model has made consumers more reliant on influencers’ opinions, thus having a more direct and profound impact on shopping and lifestyle decisions.
Consequently, influencer marketing has become increasingly popular as a key component of companies’ digital marketing strategies, as evidenced by the global market growth of influencer marketing [8]. Influencer marketing platforms, known for their user-generated content recommendation mechanisms [9,10], have gained considerable attention. When influencers share personal experiences, product evaluations, and usage scenarios, they directly or indirectly shape consumers’ perceptions of various goods, attracting them to make purchases. Essentially, influencers receive compensation for posting content related to products, services, or brands. Owing to their large follower base, influencers can significantly influence potential consumers’ purchasing decisions by disseminating their views on company or brand products and services. Consumers are very likely to follow the purchasing advice of influencers they admire [11].
Health and wellness constitute a significant domain within influencer marketing [12], and following the COVID-19 pandemic there has been an increasing focus on healthy living [13] and sustainable eating practices. This shift has led to an exploration of light meals. Light meals represent a dining form characterized by simplicity, moderation, and a balanced diet, primarily consisting of low-fat, low-sugar, low-salt, and high-fiber foods, which are more natural and sustainable for the environment [14]. The concept of light meals originated in Europe, with the aim of allowing consumers to intake nutrients without burden, promoting healthier diets, and advocating for a positive and proactive attitude and lifestyle. In meeting individual health needs, the concept of light meals is also closely linked with the philosophy of sustainable food.
In recent years, an increasing number of scholars have been studying the influence of influencer marketing platforms on consumer intentions and behaviors. Research shows that factors such as influencer credibility, consistency between the influencer and promoted products, media richness, and parasocial interaction significantly affect consumer behavior [15,16]. Soltani et al. (2021) pointed out that influencer marketing platforms influence food tourists’ behavioral intentions by affecting people’s attitudes towards local foods and food destination images, ultimately shaping food consumption intentions and behaviors [17]. Zheng et al. (2024) indicated that influencer marketing platforms influence food consumption intentions and behaviors through influencers’ intimate self-disclosures, perceived altruistic motives, follower attitudes, information involvement, and product knowledge [18]. However, the existing literature has not yet explored the factors influencing light food consumption intentions and behaviors on influencer marketing platforms. Therefore, this study surveyed consumers in Suzhou to fill this gap.
In contemporary society, the increasing awareness of health and sustainable development has made light meals one of the most sought-after dietary trends. According to the “Light Food Consumption Big Data Report” by Meituan Delivery in China, the size of the domestic light food market exceeded RMB 100 billion in 2022, accounting for about 10% of the total catering revenue within five years. Against the backdrop of the rising popularity of light meals, influencer marketing platforms have emerged as a significant sales channel. Hence, investigating whether influencer marketing platforms can drive consumer purchasing intentions towards light meals is crucial. It not only offers recommendations for current brand and market strategies but also promotes the joint development of healthy eating and sustainable lifestyles in the digital era. At the same time, sustainable development is closely related to regional development, with the key being to balance economic, social, and environmental needs to ensure the quality of life and well-being now and in the future [19]. Knowledge, natural capital, and culture are particularly important in economic development, especially innovation and nature. Therefore, the transition between different stages of economic development is influenced by various factors and is crucial for sustainable economic development [20]. This study takes Suzhou as an example, exploring the connection between influencer marketing platforms and the consumption intentions and behaviors towards light food. Based on this, the main objective of this study is to explore the impact of influencer marketing platforms on consumers’ purchasing intentions and behaviors towards light meals, with the aim of fostering the collective advancement of healthy eating and sustainable lifestyles in the digital age and providing effective recommendations for market strategies.
The remaining parts of this paper are organized as follows: First, we describe the theoretical background of the study and propose the research hypotheses. Second, we present our research methods and model. Third, we conduct an empirical analysis of the data. Finally, we discuss the research findings and the significance of our study.

2. Theoretical Background and Research Hypotheses

2.1. Light Meals

The Food and Agriculture Organization of the United Nations states “Sustainable diets are diets with low environmental impacts which contribute to food and nutrition security and to healthy life for present and future generations. Sustainable diets are protective and respectful of biodiversity and ecosystems, culturally acceptable, accessible, economically fair and affordable; nutritionally adequate, safe and healthy; while optimizing natural and human resources” [21].
The concept of light meals originated in 17th century Europe, initially referring to sandwiches and salads served with coffee at coffee-shop tea times. This concept has evolved to today’s definition of light meals as fresh, appropriately portioned, low-calorie, high-fiber, and nutritionally rich foods. This evolution reflects a shift in consumer pursuit from flavor, aroma, and appearance towards a focus on low-calorie, sustainable, healthy eating principles. The core concept of light meals emphasizes low-fat, low-sugar, low-salt foods that are high in fiber, aiming to provide nutrition while easing bodily burdens, making the diet healthier. Light meals, in meeting individual health needs, are closely linked with the concept of sustainable food, emphasizing respect for the environment and sustainable development. By advocating for healthy eating and active lifestyles, light meals not only meet the physical needs of individuals but also promote social awareness and recognition of sustainable food practices.
Light meals emphasize low calories, low fat, and high fiber, focusing on maintaining a healthy diet and moderate control of calorie intake. This concept aligns with the core values of health and environment found in the principles of sustainable food [22]. Compared to other kind of meals, light meals are mostly composed of vegetables, fruits, whole grains, etc., reducing the consumption of high-carbon-footprint meats and processed foods [23,24]. The production of vegetables and fruits often requires less fertilizer and pesticides, reducing pollution to water sources and soil. The production of whole grains also helps maintain soil health and biodiversity, reducing the risks of land degradation and ecosystem damage. Research by Ernstoff A and others focusing on school meals shows that a low-carbon menu can reduce greenhouse gas emissions while maintaining sufficient nutrition, demonstrating the feasibility of choosing sustainable food on a large scale [25]. Moreover, the production of plant-based foods usually requires less land and water resources, reducing pressure on natural resources, helping to preserve the integrity of ecosystems and biodiversity [26]. This resource-efficient production approach is one of the significant contributions of light meals to sustainability and environmental protection. Compared to animal husbandry and meat production, plant-based food production can more efficiently utilize land and water, reducing the need for land reclamation, deforestation, and excessive water resource use. This not only helps slow down ecosystem degradation but also protects and restores biodiversity, maintaining ecological balance. By choosing light meals, we are actually practicing an environmentally friendly lifestyle in our daily diet, making a positive contribution to creating a more sustainable future. Socially, supporting the light meals industry can improve working conditions for farmers and other practitioners, promote the development of community economies, and enhance community welfare. Therefore, choosing light meals not only benefits environmental protection but also helps achieve the goals of social sustainable development.
Moreover, the concept of light meals stresses dietary balance and choices beneficial to the body, encouraging consumers to consider their health and prefer foods that are both healthy and eco-friendly [27]. In terms of ingredient selection, light meals prioritize natural, fresh, and seasonal ingredients to maintain food quality and nutritional value. Compared to general food choices, the ingredients for light meals reduce dependence on chemical agricultural products, making them more natural and helping to decrease negative environmental impacts [14], thereby achieving more sustainable food production. This aligns with the sustainable food movement’s pursuit of using organically grown, pesticide-free, and fertilizer-free ingredients produced using environmentally friendly methods. Additionally, light meals emphasize the balanced distribution of ingredients to maintain a balanced diet, helping to reduce food waste [28] and avoid overconsumption of food resources. This is consistent with the principles of sustainable food, which stress the efficient use of resources during production and consumption. By allocating ingredients wisely, waste can be reduced, lessening the pressure on natural resources from food production, and achieving a more sustainable food supply chain.

2.2. Theory of Planned Behavior (TPB)

In the field of studying consumer purchasing behavior, the Theory of Planned Behavior (TPB) is widely used to explain the determinants of individual behavior. This theory posits that an individual’s behavior is influenced by their beliefs, attitudes, subjective norms, and perceived behavioral control. Specifically, an individual’s beliefs guide their attitudes and subjective norms, while perceived behavioral control influences whether they can perform a specific behavior. With the proliferation of the internet and social media, influencer marketing platforms, as an emerging form of marketing, have an increasingly significant impact on consumer purchasing behavior [29]. Therefore, this study will use the TPB as the theoretical framework to explore the relationship between consumers’ attitudes towards influencer marketing platforms, their purchase intentions, and their purchasing behaviors, as well as any potential mediating effects.

2.3. Hypotheses

2.3.1. Influencer Marketing Platform Intervention and Purchase Intention

Influencer marketing platforms, with their recommendation mechanisms based on user-generated content, receive considerable attention [9,10]. Through influencers sharing their personal experiences, product evaluations, and usage scenarios, they directly or indirectly shape consumers’ views on various goods, attracting them to make purchases. Under the framework of the Theory of Planned Behavior, an individual’s behavior is influenced by their beliefs and attitudes [30]. For consumers, if they believe that influencer marketing platforms can provide useful product information and recommendations, as well as positive evaluations of the products, then they are likely to have a positive attitude towards the platform and exhibit a higher intention to purchase. For example, consumers tend to prioritize products recommended by their favorite YouTubers, with the level of self-disclosure, similarity, and attractiveness of YouTube users positively affecting viewers’ parasocial interactions, brand attitudes, and loyalty to YouTubers [31]. Similarly, perceived social media marketing activities (SMMAs) have a positive impact on brand trust, brand experience, and brand love, which, in turn, positively affects purchase intentions [32]. Influencers’ actions also positively influence people’s attitudes towards influencers, which then positively affects their purchase intentions [33]. Moreover, influenced by media richness and media naturalness, the hyper-social interaction between influencers and consumers strongly promotes purchase intentions [34]. Therefore, this study proposes the following hypothesis:
Hypothesis 1 (H1): 
Influencer marketing platform intervention has a positive effect on purchase intention.

2.3.2. Purchase Intention and Purchase Behavior

The relationship between purchase intention and purchase behavior has been explored in multiple studies. According to the Theory of Planned Behavior, an individual’s purchasing behavior is directly influenced by their purchase intention [35,36]. If consumers exhibit a high purchase intention, meaning they have a positive intention to buy a specific product or service, they are more likely to actually engage in the purchasing behavior. Kamalanon, Vishal, and others have found that green purchase intentions have a positive impact on green purchase behaviors, with attitudes towards green products being the strongest precursor to purchase intentions [36,37]. Imiru’s research in the context of Ethiopia indicates that green purchase intentions play a mediating role between antecedents of green purchase intentions and green purchase behaviors [38]. Additionally, the literature suggests that the relationship between purchase intentions and purchase behaviors is influenced by various factors, such as emotions, cultural factors, value consciousness, and social sustainability. Emotions play a role in strengthening the relationship between the intention to purchase and actual purchase behaviors [39]. Therefore, this study proposes the following hypothesis:
Hypothesis 2 (H2): 
Purchase intention has a positive impact on purchase behavior.

2.3.3. Influencer Marketing Platform Intervention and Purchase Behavior

The relationship between influencer marketing platform intervention and purchase behavior exists to an extent. According to the Theory of Planned Behavior, an individual’s purchasing behavior is influenced by their beliefs and attitudes. In the context of influencer marketing platforms, if consumers have positive beliefs and attitudes towards the platform, meaning they consider the product information and recommendations provided by the platform to be valuable and have a positive evaluation of the products, then they are more likely to engage in purchasing behavior through that platform [30]. Studies show that influencer advertisements on influencer marketing platforms can increase consumers’ impulse to make purchases, with attitudes towards influencer ads playing a key role in driving this impulse [40]. Influencers in influencer marketing platforms influence consumer engagement behaviors, such as content engagement and creation, thereby affecting the likelihood of consumer purchases [41]. Influencers using social media platforms to interact with consumers and build relationships can shape purchase intentions, with hyper-social interaction being a strong driver of purchase intentions [34]. Therefore, this study proposes the following hypothesis:
Hypothesis 3 (H3): 
Influencer marketing platform intervention has a positive impact on purchase behavior.

2.3.4. Influencer Marketing Platform Intervention, Purchase Intention, and Purchase Behavior

According to the mediating model of the Theory of Planned Behavior, an individual’s purchasing behavior may be mediated by their purchase intentions [42]. In the context of influencer marketing platforms, if consumers hold positive beliefs and attitudes towards the platform, this will enhance their purchase intentions. The enhancement of purchase intentions will, in turn, further encourage them to engage in actual purchasing behavior. In the shopping process, influencer marketing platforms act as a medium, directly affecting the consumer’s purchase decision chain [43]. First, through influencers’ promotional activities on influencer marketing platforms, product information is widely disseminated, thereby triggering consumers’ purchase intentions. Second, this purchase intention is converted into actual purchase behavior. Consumers are often more inclined to complete shopping transactions on relevant platforms after being recommended by influencers. Therefore, purchase intention plays a key mediating role throughout the purchase process, linking influencer marketing activities and purchase behavior, directly shaping the consumer’s shopping experience. This mediating effect highlights the importance of purchase intention in the influencer marketing process, providing important clues for a deeper understanding of consumer behavior and the impact of social media marketing. Therefore, this study proposes the following hypothesis:
Hypothesis 4 (H4): 
Purchase intention has a mediating effect between influencer marketing platform intervention and purchase behavior.
Based on the above hypotheses, the following theoretical framework was constructed (see Figure 1):

3. Methods

3.1. Sample and Data Collection

Suzhou City, located at the core of the Yangtze River Delta region [44], is the largest economic center of Jiangsu Province, as a national scenic tourist city with prominent industries. As a national high-tech industrial base, it is expected to showcase the unique development trends of the light meals industry in terms of technology and innovation. Suzhou City has always focused on sustainable development in various aspects. Its enterprises actively pursue green and low-carbon transformation in production and business activities, consistent with the concept of green and low-carbon development [45]. At the same time, Suzhou City actively advocates for sustainable living concepts, and citizens have gradually focused on health and sustainable development, promoting attention and demand for sustainable food. Overall, the emerging light meals industry has significant development potential in Suzhou City, making the survey of its permanent residents representative for analysis.
The survey targeted residents of Suzhou City, employing a three-stage sampling method to ensure the randomness of the sampled population. Administrative districts were selected using the PPS sampling method from all districts in Suzhou City, from which communities and streets were further selected, and permanent residents were finally sampled using systematic sampling. The final questionnaire was measured on a 5-point Likert scale, ranging from “very unwilling” (1) to “very willing” (5). In the formal survey, 859 questionnaires were distributed; 654 valid questionnaires were collected, with a total recovery rate of 76.14%. The data from the formal survey passed reliability, validity, and chi-squared tests, confirming the survey results as credible.
The profile of the study sample is summarized in Table 1. The age of those who have used influencer marketing platforms mainly ranged from 18 to 35 years, and they were predominantly female. The user group mainly consisted of students, ordinary employees, and business managers, with an average monthly income of RMB 4000–6000. The duration spent browsing influencer marketing platforms was generally within 1 h. The distribution of permanent residences was relatively even, with the most people living in Gusu District, Pingjiang Road.

3.2. Model Construction and Measurement

This study uses Structural Equation Modeling (SEM) to analyze data, aiming to delve into the impact of influencer marketing platforms on light meal consumption intentions and behaviors. SEM is widely used in social commerce research as it can effectively analyze the path relationships among complex variables, identify the intrinsic structural relationships between variables, and verify the validity of model hypotheses [46,47,48]. By considering the complex relationships among multiple latent influencing factors, SEM can simultaneously take into account various factors. By measuring latent variables, it captures concepts that are not observable, providing a more comprehensive analysis.
Latent variables are those that cannot be directly observed. In this study, it is posited that before engaging in purchasing behavior, consumers are first attracted by influencer marketing platforms, which include characteristics of the platform itself, marketing content features, and personal attributes of the influencers. This paper categorizes these aspects under “influencer marketing platform intervention”, which further affects residents’ purchase intentions for the “planted” light meal ideas, leading ultimately to a decision on whether to engage in purchasing behavior based on the residents’ self-judgment and cognition.
Drawing from theory and combined with the issues involved in the questionnaire, this study selects influencer marketing platform intervention, purchase intention, and purchase behavior as latent variables. The corresponding measurable variables and related questions are presented in Table 2. Among them, the influencer marketing platform intervention scale adapts questions on influencer personal characteristics and platform characteristics from the questionnaires of Shafiq R (2011) [49] and Hussain S (2021) [50], the purchase intention scale is partially adapted from Chun C (2018) [51], and the purchase behavior scale adapts the 6 items of Humaira A (2016) [52].

3.3. Model Settings

Based on the hypotheses formulated and the variables defined in this study, the AMOS 24 software (IBM, Armonk, NY, USA) was utilized. Following the conventions of Structural Equation Modeling (SEM) path diagrams, a causal relationship path diagram of the model was constructed as follows (see Figure 2):

4. Results

The statistical results indicate that, among the sample, 65.14% of users who use influencer marketing platforms are women, showing that women use these platforms more than men. This may be because women are more active in socializing and consuming and are more interested in topics related to influencers, such as fashion, beauty, and fitness. In terms of age distribution, the main users of influencer marketing platforms are young people, likely because they are more familiar with and open to new social media and digital platforms. They have stronger social needs and a desire to explore, and they tend to interact with and share content and communities of interest on influencer marketing platforms. Regarding usage time, there are many users who browse for less than one hour per day, which may reflect users’ fragmented usage habits of influencer marketing platforms. In subsequent discussions, we will consider how to improve the efficiency of influencer marketing platforms so that influencers can attract consumers’ attention more quickly. In terms of the distribution of permanent residents, the sample is evenly distributed, with the majority being from Pingjiang Road in Gusu District, accounting for 13.15%. This may be due to Pingjiang Road being located in the center of Suzhou, serving as a historical and cultural district and a gathering place for influencers, attracting more young people or internet users.

4.1. Model Validation

4.1.1. Correlation Analysis

To enhance the robustness of the model, a correlation analysis was conducted among the variables. The Pearson correlation coefficient was chosen for this analysis, considering its effective capacity to measure linear relationships between interval variables.
As shown in Table 3, the computation revealed a correlation coefficient of 0.557 between purchase behavior and influencer marketing platform intervention, with a significance level of 0.01. This indicates a significant positive correlation between purchase behavior and influencer marketing platform intervention. The correlation coefficient between purchase behavior and purchase intention was 0.618, also showing a significance level of 0.01, which suggests a significant positive correlation between purchase behavior and purchase intention. Furthermore, the correlation coefficient between influencer marketing platform intervention and purchase intention was 0.510, with a significance level of 0.01, indicating a significant positive correlation between influencer marketing platform intervention and purchase intention.
We found that although consumers express lower purchase intentions, they exhibit higher actual purchasing behaviors, which differs from the conclusions of previous studies. However, this result is consistent with the studies of Tufail et al. (2022) and Chen et al. (2022). Tufail et al. (2022) indicated that consumers’ actual purchasing behavior of suboptimal food was higher despite expressing lower intentions due to factors like quality concerns and unappealing appearance, as per the Behavioral Reasoning Theory [53]. Chen et al. (2022) found that consumers exhibited higher actual purchasing behavior when influenced by search advertising, despite expressing lower intentions to buy due to factors like SMS advertising fatigue [54]. We believe that actual purchasing behavior may be driven by the attractiveness, nutritional value, taste, or quality of light meal products, which prompt their decision to buy. The lower purchase intentions may be influenced by other factors, such as price, brand perception, shopping experience, or overall awareness and attitudes towards light meal products. Additionally, external factors such as shopping environment, promotional activities, or social influence might also affect consumers’ purchase intentions. This discrepancy between purchasing behavior and purchase intentions provides valuable insights for marketing strategies in the light meals market, emphasizing the importance of understanding consumers’ true needs and motivations.

4.1.2. Improper Estimates

From the data presented in the Table 4, it is evident that the standardized factor loadings for each question exceed 0.5, and the residuals are all positive and significant. This demonstrates compliance with the estimation criteria. The Composite Reliability (CR) values are all above 0.7, and the Average Variance Extracted (AVE) values exceed 0.5, meeting the criteria for convergent validity. The moderate fit is within an acceptable range, demonstrating combined reliability and convergent validity.
During the model validation process, the examination of improper estimates indicated that the model did not exhibit any violations, thereby affirming the structural equation model’s rationality. This demonstrates the consistency of the proposed model in theoretical construction, providing a reliable foundation for subsequent exploration of causal relationships.
This study tested the reliability and validity of the questionnaire items during the data verification stage. The results indicate good reliability and validity for the items in the questionnaire (KMO value = 0.957), making them suitable for factor analysis. Consequently, we conducted Confirmatory Factor Analysis (CFA). It is evident that the CMIN/DF is 1.945, and the GFI, AGFI, NFI, TLI, IFI, and CFI all exceed the standard of 0.9. The RMSEA is 0.046, which is less than 0.08. The majority of the fit indices meet the general standards for SEM research. Therefore, it can be concluded that this structural model has a good fit and possesses satisfactory adaptability. Please see the details of the test results in Table 5.

4.2. Hypothesis Testing

In this section, we will test our hypotheses, examining both the direct and indirect effects of influencer marketing platform interventions on consumer purchase intentions and behavior.

4.2.1. Assessment of the Structural Model

Our analysis produced the model shown in Figure 3, which indicates that all key independent variables have measurable effects on their respective dependent variables. This suggests that the model has a certain degree of credibility, which can be seen from the more detailed analysis in the following sections.

4.2.2. The Relationship between Influencer Marketing Platform Interventions and Purchase Intentions

In Table 6, we can see that influencer marketing platform intervention has a significant positive effect on purchase intention (β = 0.581, p < 0.001); thus, H1 is supported. This means that, through the promotion and intervention of influencer marketing platforms, consumers are more willing to purchase light meal products. This trend may stem from the influence and fan base of influencers on social media, as well as their personal recommendations and insights about the products, enhancing consumers’ trust and interest in light meal products. This finding provides empirical support for the role of social media in promoting the purchase intention of sustainable foods, and it also offers businesses effective marketing strategies through the use of influencer marketing platforms.

4.2.3. The Relationship between Purchase Intention and Purchasing Behavior

Purchase intention has a significant positive effect on purchase behavior (β = 0.510, p < 0.001), confirming H2. This demonstrates that consumers are more likely to convert their positive purchase intentions into actual purchasing behaviors when they express a strong intention to purchase. This provides empirical support for the significance of purchase intention in the purchasing decision process, while also emphasizing the effectiveness of influencer marketing platforms in driving purchasing behavior. In the modern consumer environment, consumers often realize their consumption goals through actual purchasing behavior after expressing their purchase intentions. Therefore, understanding and guiding consumers’ purchase intentions is a crucial part of successful marketing for businesses. Furthermore, the validation of H2 also emphasizes the effectiveness of influencer marketing platforms in driving purchasing behavior. Through the recommendations and introductions by influencers, consumers not only express their purchase intentions but are also more inclined to convert these intentions into actual purchasing behaviors. As opinion leaders on social media, influencers’ promotional influence and trustworthiness can facilitate consumers’ purchasing decisions. Therefore, utilizing influencer marketing platforms can more effectively guide consumers to realize their purchase intentions, thereby promoting the occurrence of purchasing behaviors.

4.2.4. The Relationship between Influencer Marketing Platform Interventions and Purchasing Behavior

Influencer marketing platform intervention has a significant positive effect on purchase behavior (β = 0.306, p < 0.001), supporting H3. As a new type of marketing channel, influencer marketing platforms can effectively guide consumer purchasing behavior through the influence and fan base of influencers. After consumers encounter product information and recommendations from influencers on social media, they not only express a positive purchase intention but are also more likely to complete the purchase. This indicates that under the influence of influencer marketing platforms, consumers are more likely to translate their positive purchase intentions into actual buying behaviors, offering empirical evidence of influencer marketing’s effectiveness in promoting sales of light meal products, and highlighting the importance of purchase intention in the decision-making process.

4.2.5. The Relationship among Influencer Marketing Platform Interventions, Purchase Intention, and Purchasing Behavior

From the bootstrap mediation effect test shown in the Table 7, the direct effect of influencer marketing platform intervention on purchase behavior via purchase intention is 0.306, with a 95% confidence interval not including 0, indicating that the direct effect is significant. The indirect effect is 0.302, with a 95% confidence interval not including 0, indicating that the indirect effect is significant. The mediation proportion is 49.7%, supporting H4. This highlights the important role of purchase intention in explaining the mechanism by which influencer marketing affects purchasing behavior, further emphasizing the criticality of purchase intention in this relationship. This validation not only provides a deeper understanding of the impact of purchase intention on purchasing behavior but also underscores the significant role of influencer marketing platforms in guiding consumers to form purchase intentions and, ultimately, realize purchasing behavior.

5. Conclusions

In contemporary society, as awareness of health and sustainable development continues to rise, light meals have emerged as a highly sought-after dietary trend. Against this backdrop, influencer marketing platforms have increasingly become important drivers in shaping consumer perceptions. This study aims to delve into the effect of influencer marketing platforms on consumers’ purchase intentions towards light meals.
The results here confirm that the intervention of influencer marketing platforms has a significant positive impact on both the purchase intention and purchasing behavior towards light meals. This breakthrough reveals the role of influencer marketing platforms as actual guides leading consumers towards more sustainable food choices. Consumers show a preference for light meal products that align with the concept of sustainable development in their shopping decisions, highlighting the significant role of influencer marketing platforms in stimulating purchase intentions for light meals. This provides novel insights into the influence mechanism of influencer marketing platforms in promoting consumers’ choices of sustainable foods.
Furthermore, we found that the formation of purchasing intentions is influenced not only by influencer marketing platforms but also by the authenticity, professionalism, and personalized recommendations of the platforms’ content. Through survey research, it was found that most consumers generally believe in the significant impact of influencers, emphasizing their notable role in stimulating purchase intentions, which aligns with the results of earlier studies [55,56,57]. The professionalism, authenticity, and popularity of influencers have a crucial impact on consumer shopping decisions, making consumers more trusting and inclined to purchase light meal products promoted by influencers. In addition, the platform offers consumers genuine shopping experiences and firsthand product feedback through features such as user reviews and product ratings. This enhances consumers’ confidence in light meal products and solidifies their purchasing decisions. Moreover, the direct interaction and close relationship between influencers and consumers emotionally engage consumers with influencer recommendations, thereby boosting their interest and intention to purchase light meal products.
Finally, the focus on influencer marketing platforms plays a pivotal role in consumers’ shopping behaviors. Factors such as the platform’s personalization and precise recommendations, layout style and user interface, and the ecosystem and atmosphere make consumers feel comfortable and pleased, thereby boosting their confidence in shopping. The comprehensive, authentic, and clear presentation of light meal marketing content, along with the provision of professional and targeted services, further stimulates consumers’ interest and intention to purchase.
Overall, this study delves into the key role of influencer marketing platforms in promoting consumers’ light meal shopping behaviors, highlighting the positive impact of sustainable development on light meal shopping intentions and the importance of influencers in guiding and influencing consumer shopping behaviors.

6. Discussion

6.1. Research Contributions

Although numerous studies have widely discussed the theoretical models and hypotheses using purchase intention as a mediator between attitudes and behaviors, this study demonstrates significant originality in several aspects. Firstly, it focuses on the light meals sector, a relatively new and rapidly evolving market that has received little attention in prior research. Secondly, by conducting a large-scale field survey in Suzhou, this study provides empirical data that reflect the actual behaviors and attitudes of consumers in a specific region of China, adding geographical and cultural dimensions to the research. Lastly, this research explores the role of influencers in promoting sustainable development concepts and the integration process within the light meals industry, offering new strategic recommendations for marketing practices. This not only enriches the application scope of purchase intention theories but also provides practical guidance for marketing strategies in the light meals market, which has rarely been addressed in previous studies. Through these original contributions, this paper not only fills gaps in the existing literature but also provides new theoretical and empirical foundations for future research.

6.2. Practical and Social Implications

This study plays a crucial role in profoundly revealing the connections between influencers, platforms, and consumers. Influencers should focus on building trust and credibility and providing informational value in their content to facilitate consumer knowledge acquisition, positive attitudes, and behavioral change [58,59]. For influencers, there is a need to more deeply integrate the concepts of a healthy lifestyle and sustainable food. By sharing more lifestyle displays and light meal recipes, influencers can further solidify their image and closely associate themselves with the concepts of health and sustainable food. Influencers focusing on sustainability can emphasize the adoption of sustainable foods in daily life to guide their followers to more actively engage with this idea. Additionally, actively participating in social media activities, such as giveaways, challenges, and user-generated content, is also an effective way to inspire fans to actively participate and share their real-life experiences of health and sustainable living.
For platforms, first, leveraging the influence of social media and influencers to combine the concept of sustainable development with the light meals industry can not only increase the exposure of light meal products but also promote the spread of sustainable food concepts. By enhancing health awareness, information support may trigger online health information seeking, thereby encouraging people to adopt healthier lifestyles [60]. Second, platforms can encourage more diverse content about light meals and sustainable living to support influencer creativity, meeting the needs of different consumers. Providing professional information on health and sustainable diets can help consumers better understand the advantages of products and the concepts behind them. Through creating engaging interactive social media activities, platforms can attract user participation and strengthen the market dissemination of light meals and sustainable food. This view is consistent with previous research in the field of social media by William et al. [61] and Qiao [62].
For consumers, engaging more actively with social media to share their purchase experiences and healthy lifestyles, interacting with others, and forming communities are beneficial. Consumer engagement in social media brand communities can enhance participation and promote the sharing of purchasing experiences, healthy lifestyles, and interaction, ultimately fostering a solid relationship between consumers and brands [63]. Actively seeking information about light meals and sustainable living can lead to a more comprehensive understanding of products and concepts. When shopping, there is a tendency to choose light meal products that align with eco-friendly principles to support the market development of sustainable food. This finding is consistent with previous research showing that consumers actively participate in social media discussions about natural foods, reflecting people’s attitudes and beliefs. They share experiences and healthy lifestyles, form communities, and influence food marketing and public policy [64]. Such consumer decisions can encourage brands and businesses to pay more attention to the production of sustainable and healthy foods.
Furthermore, this study provides support for local policy development and sustainable management. By deeply exploring the impacts of influencer marketing platforms on the intentions and behavior towards the consumption of light meals, we can not only understand the driving factors behind consumer shopping behavior but also offer targeted policy recommendations and management plans to local governments and administrative departments. Given the positive influence on the intention to consume light meals, the government can increase efforts to promote healthy eating, enhance consumer awareness and acceptance of light meal products, and promote sustainable development and healthy lifestyles. At the same time, recognizing the importance of influencer marketing platforms in guiding and influencing consumer shopping behavior, the government can establish corresponding platform regulatory measures to protect consumer rights and ensure the proper functioning of the market [65,66].

6.3. Limitations and Future Research

Despite its contributions, this study still has some limitations, opening pathways for future research. First, data collection was limited to 654 consumers in Suzhou, China, and consumers from different countries and regions may have different preferences and behavior patterns. Therefore, future research should conduct sample surveys in a broader geographical scope to more comprehensively understand the impacts of influencer marketing platforms on consumers with different cultural and regional backgrounds. Second, although the research on influencer marketing platforms is innovative, the self-developed scale may have some shortcomings. We hope that future studies will apply it in different scenarios to verify its effectiveness. Lastly, this study primarily conducted quantitative empirical tests. In future research, qualitative empirical tests could be introduced to more comprehensively understand the impact of influencer marketing platforms on consumer decision-making through in-depth interviews, observations, and content analysis.

Author Contributions

Overall research design, Z.Q.; writing—original draft preparation, Z.Q., Y.C., Y.Y., and Y.H.; writing—review and editing, Y.Y. and Z.Q. 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 by 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 four 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. Ghazwani, S.S.; Alzahrani, S. The Use of Social Media Platforms for Competitive Information and Knowledge Sharing and Its Effect on SMEs’ Profitability and Growth through Innovation. Sustainability 2024, 16, 106. [Google Scholar] [CrossRef]
  2. Dash, G.; Sharma, C.; Sharma, S. Sustainable Marketing and the Role of Social Media: An Experimental Study Using Natural Language Processing (NLP). Sustainability 2023, 15, 5443. [Google Scholar] [CrossRef]
  3. Ki, C.W.C.; Kim, Y.K. The mechanism by which social media influencers persuade consumers: The role of consumers’ desire to mimic. Psychol. Mark. 2019, 36, 905–922. [Google Scholar] [CrossRef]
  4. Hudders, L.; De Jans, S.; De Veirman, M. The commercialization of social media stars: A literature review and conceptual framework on the strategic use of social media influencers. Int. J. Advert. 2021, 40, 24–67. [Google Scholar] [CrossRef]
  5. Luo, B.; Nie, M.; Ji, H. The Influence of Internet Celebrities’ Expertise and Attraction on Residents’ Intention to Purchase Household Energy-Saving Products in the Context of an Online Community. Energies 2023, 16, 3332. [Google Scholar] [CrossRef]
  6. Khare, A.; Sadachar, A.; Chakraborty, S. Influence of celebrities and online communities on Indian consumers’ green clothing involvement and purchase behavior. J. Fash. Mark. Manag. 2022, 26, 676–699. [Google Scholar] [CrossRef]
  7. 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]
  8. Mero, J.; Vanninen, H.; Keränen, J. B2B influencer marketing: Conceptualization and four managerial strategies. Ind. Mark. Manag. 2023, 108, 79–93. [Google Scholar] [CrossRef]
  9. Pradhan, B.; Kishore, K.; Gokhale, N. Social media influencers and consumer engagement: A review and future research agenda. Int. J. Consum. Stud. 2023, 47, 2106–2130. [Google Scholar] [CrossRef]
  10. Vrontis, D.; Makrides, A.; Christofi, M.; Thrassou, A. Social media influencer marketing: A systematic review, integrative framework and future research agenda. Int. J. Consum. Stud. 2021, 45, 617–644. [Google Scholar] [CrossRef]
  11. Koay, K.Y.; Cheah, C.W.; Yap, J.Y. Self-influencer congruence, parasocial relationships, credibility, and purchase intentions: A sequential mediation model. J. Relatsh. Mark. 2024, 23, 1–20. [Google Scholar] [CrossRef]
  12. Añaña, E.; Barbosa, B. Digital Influencers Promoting Healthy Food: The Role of Source Credibility and Consumer Attitudes and Involvement on Purchase Intention. Sustainability 2023, 15, 15002. [Google Scholar] [CrossRef]
  13. Jevtic, M.; Matkovic, V.; Paut Kusturica, M.; Bouland, C. Build Healthier: Post-COVID-19 Urban Requirements for Healthy and Sustainable Living. Sustainability 2022, 14, 9274. [Google Scholar] [CrossRef]
  14. Limbu, Y.B.; McKinley, C.; Ganesan, P.; Wang, T.; Zhang, J. Examining How and When Knowledge and Motivation Contribute to Organic Food Purchase Intention among Individuals with Chronic Diseases: Testing a Moderated Mediation Model. Sustainability 2023, 15, 14584. [Google Scholar] [CrossRef]
  15. De Keyzer, F. #ThisIsSustainable: The Effect of Disclosures in Influencer Marketing for Sustainable Food. Sustainability 2023, 15, 9501. [Google Scholar] [CrossRef]
  16. Calvo-Porral, C.; Rivaroli, S.; Orosa-González, J. The Influence of Celebrity Endorsement on Food Consumption Behavior. Foods 2021, 10, 2224. [Google Scholar] [CrossRef] [PubMed]
  17. Soltani, M.; Soltani Nejad, N.; Taheri Azad, F.; Taheri, B.; Gannon, M.J. Food consumption experiences: A framework for understanding food tourists behavioral intentions. Int. J. Contemp. Hosp. Manag. 2021, 33, 75–100. [Google Scholar] [CrossRef]
  18. Zheng, L.; Huang, B.; Qiu, H.; Bai, H. The role of social media followers’ agency in influencer marketing: A study based on the heuristic–systematic model of information processing. Int. J. Advert. 2024, 43, 554–579. [Google Scholar] [CrossRef]
  19. Manioudis, M.; Meramveliotakis, G. Broad strokes towards a grand theory in the analysis of sustainable development: A return to the classical political economy. New Political Econ. 2022, 27, 866–878. [Google Scholar] [CrossRef]
  20. Meramveliotakis, G.; Manioudis, M. History, Knowledge, and Sustainable Economic Development: The Contribution of John Stuart Mill’s Grand Stage Theory. Sustainability 2021, 13, 1468. [Google Scholar] [CrossRef]
  21. Köksal, E.; Bilici, S.; Çıtar Dazıroğlu, M.E.; Erdoğan Gövez, N. Validity and reliability of the Turkish version of the Sustainable and Healthy Eating Behaviors Scale. Br. J. Nutr. 2023, 129, 1398–1404. [Google Scholar] [CrossRef]
  22. Gialeli, M.; Troumbis, A.Y.; Giaginis, C.; Papadopoulou, S.K.; Antoniadis, I.; Vasios, G.K. The Global Growth of ‘Sustainable Diet’ during Recent Decades, a Bibliometric Analysis. Sustainability 2023, 15, 11957. [Google Scholar] [CrossRef]
  23. Volanti, M.; Arfelli, F.; Neri, E.; Saliani, A.; Passarini, F.; Vassura, I.; Cristallo, G. Environmental Impact of Meals: How Big Is the Carbon Footprint in the School Canteens? Foods 2022, 11, 193. [Google Scholar] [CrossRef]
  24. Ernstoff, A.; Tu, Q.; Faist, M.; Del Duce, A.; Mandlebaum, S.; Dettling, J. Comparing the Environmental Impacts of Meatless and Meat-Containing Meals in the United States. Sustainability 2019, 11, 6235. [Google Scholar] [CrossRef]
  25. Kluczkovski, A.; Menezes, C.A.; da Silva, J.T.; Bastos, L.; Lait, R.; Cook, J.; Cruz, B.; Cerqueira, B.; Lago, R.M.; Gomes, A.N.; et al. An Environmental and Nutritional Evaluation of School Food Menus in Bahia, Brazil That Contribute to Local Public Policy to Promote Sustainability. Nutrients 2022, 14, 1519. [Google Scholar] [CrossRef]
  26. Carey, C.N.; Paquette, M.; Sahye-Pudaruth, S.; Dadvar, A.; Dinh, D.; Khodabandehlou, K.; Liang, F.; Mishra, E.; Sidhu, M.; Brown, R.; et al. The Environmental Sustainability of Plant-Based Dietary Patterns: A Scoping Review. J. Nutr. 2023, 153, 857–869. [Google Scholar] [CrossRef] [PubMed]
  27. Sonnenberg, L.; Gelsomin, E.; Levy, D.E.; Riis, J.; Barraclough, S.; Thorndike, A.N. A traffic light food labeling intervention increases consumer awareness of health and healthy choices at the point-of-purchase. Prev. Med. 2013, 57, 253–257. [Google Scholar] [CrossRef]
  28. Bock, M.; Meyerding, S.G.H. Consumer Perception of Food Product Packaging Materials Sustainability versus Life Cycle Assessment Results: The Case of Processed Tomatoes—A Quantitative Study in Germany. Sustainability 2023, 15, 16370. [Google Scholar] [CrossRef]
  29. Icek, A. From intentions to actions: A theory of planned behavior. In Action Control: From Cognition to Behavior; Springer: Berlin/Heidelberg, Germany, 1985; pp. 11–39. [Google Scholar]
  30. Pilatin, A.; Dilek, Ö. Investor intention, investor behavior and crypto assets in the framework of decomposed theory of planned behavior. Curr. Psychol. 2024, 43, 1309–1324. [Google Scholar] [CrossRef]
  31. Koay, K.Y.; Cheah, C.W.; Goon, S.W.U. How Do Perceived Social Media Marketing Activities Foster Purchase Intentions? A Multiple Sequential Mediation Model. J. Glob. Mark. 2023, 36, 210–224. [Google Scholar] [CrossRef]
  32. Su, B.-C.; Wu, L.-W.; Wu, J.-P. Exploring the Characteristics of YouTubers and Their Influence on Viewers’ Purchase Intention: A Viewers’ Pseudo-Social Interaction Perspective. Sustainability 2023, 15, 550. [Google Scholar] [CrossRef]
  33. 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. 2024, 28, 209–225. [Google Scholar] [CrossRef]
  34. Chidiac, D.; Bowden, J. When media matters: The role of media richness and naturalness on purchase intentions within influencer marketing. J. Strateg. Mark. 2023, 31, 1178–1198. [Google Scholar] [CrossRef]
  35. Rodrigues, M.; Proença, J.F.; Macedo, R. Determinants of the Purchase of Secondhand Products: An Approach by the Theory of Planned Behaviour. Sustainability 2023, 15, 10912. [Google Scholar] [CrossRef]
  36. Kamalanon, P.; Chen, J.-S.; Le, T.-T.-Y. Why Do We Buy Green Products? An Extended Theory of the Planned Behavior Model for Green Product Purchase Behavior. Sustainability 2022, 14, 689. [Google Scholar] [CrossRef]
  37. Gupta, V. Validating the theory of planned behavior in green purchasing behavior. SN Bus. Econ. 2021, 1, 146. [Google Scholar] [CrossRef]
  38. Imiru, G.A. The Mediating Role of Green Purchase Intention between Antecedents of Green Purchase Intention and Consumer’s Green Buying Behaviour: Empirical Evidence in Ethiopia. Int. J. Mark. Stud. 2023, 15, 31. [Google Scholar] [CrossRef]
  39. Atmoko, W.B.; Noor, F.M.; Yulandari, A. Bridging Intention-Behavior Gap on Green Products Consumption. Return: Study of Management. Econ. Bussines 2023, 2, 251–259. [Google Scholar]
  40. Yan, M.; Kwok, A.P.K.; Chan, A.H.S.; Zhuang, Y.S.; Wen, K.; Zhang, K.C. An empirical investigation of the impact of influencer live-streaming ads in e-commerce platforms on consumers’ buying impulse. Internet Res. 2022, 33, 1633–1663. [Google Scholar] [CrossRef]
  41. Wang, P.; Huang, Q. Digital influencers, social power and consumer engagement in social commerce. Internet Res. 2023, 33, 178–207. [Google Scholar] [CrossRef]
  42. Shanbhag, P.R.; Pai, Y.P.; Kidiyoor, G.; Prabhu, N. Development and initial validation of a theory of planned behavior questionnaire: Assessment of purchase intentions towards products associated with CRM campaigns. Cogent Bus. Manag. 2023, 10, 2229528. [Google Scholar] [CrossRef]
  43. Pangarkar, A.; Rathee, S. The role of conspicuity: Impact of social influencers on purchase decisions of luxury consumers. Int. J. Advert. 2023, 42, 1150–1177. [Google Scholar] [CrossRef]
  44. Yuan, Y.; Li, H.; Zhang, X.; Hu, X.; Wang, Y. Emerging Location-Based Service Data on Perceiving and Measuring Multifunctionality of Rural Space: A Study of Suzhou, China. Sustainability 2019, 11, 5862. [Google Scholar] [CrossRef]
  45. Zhang, M. The Problems and Countermeasures of Suzhou Enterprises’ Development under the Background of “Double Carbon”. Front. Sustain. Dev. 2022, 2, 1–6. [Google Scholar] [CrossRef]
  46. Chen, J.; Liao, J. Antecedents of Viewers’ Live Streaming Watching: A Perspective of Social Presence Theory. Front. Psychol. 2022, 13, 839629. [Google Scholar] [CrossRef]
  47. Prentice, C.; Han, X.Y.; Hua, L.-L.; Hu, L. The Influence of Identity-Driven Customer Engagement on Purchase Intention. J. Retail. Consum. Serv 2019, 47, 339–347. [Google Scholar] [CrossRef]
  48. Sun, Y.; Shao, X.; Li, X.; Guo, Y.; Nie, K. How Live Streaming Influences Purchase Intentions in Social Commerce: An IT Affordance Perspective. Electron. Commer. Res. Appl 2019, 37, 100886. [Google Scholar] [CrossRef]
  49. Shafiq, R.; Raza, I.; Zia-ur-Rehman, M. Analysis of the factors affecting customers’ purchase intention: The mediating role of perceived value. Afr. J. Bus. Manag. 2011, 5, 10577. [Google Scholar]
  50. Hussain, S.; Li, Y.; Li, W. Influence of platform characteristics on purchase intention in social commerce: Mechanism of psychological contracts. J. Theor. Appl. Electron. Commer. Res. 2021, 16, 1–17. [Google Scholar] [CrossRef]
  51. Chun, C.S.; Lim, W.M.; Tan, R.W.; Teh, E.W. Impact of Social Media Influencer on Instagram User Purchase Intention: The Fashion Industry; UTAR: Kampar, Malaysia, 2018. [Google Scholar]
  52. Humaira, A.; Hudrasyah, H. Factors influencing the intention to purchase and actual purchase behavior of organic food. J. Bus. Manag. 2016, 5, 581–596. [Google Scholar]
  53. Tufail, H.S.; Yaqub, R.M.S.; Alsuhaibani, A.M.; Ramzan, S.; Shahid, A.U.S.; Refat, M. Consumers’ Purchase Intention of Suboptimal Food Using Behavioral Reasoning Theory: A Food Waste Reduction Strategy. Sustainability 2022, 14, 8905. [Google Scholar] [CrossRef]
  54. Chen, B.; Li, L.; Wang, Q.; Li, S. Promote or inhibit? Research on the transition of consumer potential purchase intention. Ann. Oper. Res. 2022, 1–20. [Google Scholar] [CrossRef] [PubMed]
  55. Schimmelpfennig, C.; Hunt, J.B. Fifty years of celebrity endorser research: Support for a comprehensive celebrity endorsement strategy framework. Psychol. Mark 2020, 37, 488–505. [Google Scholar] [CrossRef]
  56. 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]
  57. Chetioui, Y.; Benlafqih, H.; Lebdaoui, H. How fashion influencers contribute to consumers’ purchase intention. J. Fash. Mark. Manag. 2020, 24, 361–380. [Google Scholar] [CrossRef]
  58. Rosenthal, A.; Guedes, A.M.M.; dos Santos, K.M.O.; Deliza, R. Healthy food innovation in sustainable food system 4.0: Integration of entrepreneurship, research, and education. Curr. Opin. Food Sci. 2021, 42, 215–223. [Google Scholar] [CrossRef]
  59. María, S.-V.; Rosa-Díaz, I.M. Promoting Sustainable Lifestyle Habits: “Real Food” and Social Media in Spain. Foods 2022, 11, 224. [Google Scholar] [CrossRef] [PubMed]
  60. McKinley, C.J.; Paul, J.W. Informational social support and online health information seeking: Examining the association between factors contributing to healthy eating behavior. Comput. Hum. Behav. 2014, 37, 107–116. [Google Scholar] [CrossRef]
  61. Housley, W.; Webb, H.; Williams, M.; Procter, R.; Edwards, A.; Jirotka, M.; Burnap, P.; Stahl, B.C.; Rana, O. Interaction and transformation on social media: The case of Twitter campaigns. Soc. Media+ Soc. 2018, 4, 2056305117750721. [Google Scholar] [CrossRef]
  62. Qiao, F. Conceptualizing interactivity on social media and exploring the effects of interactivity on consumers’ engagement with online social-interactions. Online J. Commun. Media Technol. 2019, 9, 1–13. [Google Scholar] [CrossRef]
  63. Santos, Z.R.; Coelho, P.S.; Rita, P. Fostering Consumer–Brand Relationships through social media brand communities. J. Mark. Commun. 2022, 28, 768–798. [Google Scholar] [CrossRef]
  64. Fatemi, H.; Kao, E.; Schillo, R.S.; Li, W.; Du, P.; Jian-Yun, N.; Dube, L. Using social media to analyze consumers’ attitude toward natural food products. Br. Food J. 2023, 125, 3145–3159. [Google Scholar] [CrossRef]
  65. Li, C.; Li, H.; Tao, C. Evolutionary game of platform enterprises, government and consumers in the context of digital economy. J. Bus. Res. 2023, 167, 113858. [Google Scholar] [CrossRef]
  66. McKnight, S.; Kenney, M.; Breznitz, D. Regulating the platform giants: Building and governing China’s online economy. Policy Internet 2023, 15, 243–265. [Google Scholar] [CrossRef]
Sustainability 16 04369 g001
Figure 1. Theoretical framework.
Figure 1. Theoretical framework.
Sustainability 16 04369 g001
Sustainability 16 04369 g002
Figure 2. Causal relationship path diagram.
Figure 2. Causal relationship path diagram.
Sustainability 16 04369 g002
Sustainability 16 04369 g003
Figure 3. Path diagram with coefficients.
Figure 3. Path diagram with coefficients.
Sustainability 16 04369 g003
Table 1. Descriptive statistics.
Table 1. Descriptive statistics.
ConstructFeaturesAmountPercentage
GenderMen22834.86%
Women42665.14%
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%
OccupationStudents24437.31%
Business managers8312.69%
General staff12118.50%
Professionals406.12%
Government/agency cadres/civil servants599.02%
Freelancers253.82%
Ordinary workers304.59%
Self-employed/contractors192.91%
Commercial service workers152.29%
Other182.75%
Average monthly incomeUnder RMB 200015924.31%
RMB 2001–400017326.45%
RMB 4001–600019730.12%
RMB 6001–80008713.30%
Above RMB 8000385.81%
The length of time consumers browse on the influencer marketing platformLess than 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 residenceIndustrial Park, Louwei Street7711.77%
Gusu District, Pingjiang Road8613.15%
Kunshan City, Bacheng Town7110.86%
Kunshan City, Yushan Town517.80%
Kunshan City, Zhangpu Town6910.55%
Wuzhong District, Guoxiang Street599.02%
Wuzhong District, Jinting Town659.94%
Wuzhong District, Luzhi Town6810.40%
Zhangjiagang City, Hucheng Street467.03%
Zhangjiagang City, Yangshe Town629.48%
Table 2. Latent variables’ and observable variables’ settings.
Table 2. Latent variables’ and observable variables’ settings.
Latent VariablesSecondary VariablesObservable VariablesObservable Variable Description
Influencer marketing platform interventionPlatform
characteristics		P1_1The platform’s personalization and precise recommendations attract me to use it.
P1_2The layout style and user interface of the platform make me feel comfortable.
P1_3I like the ecosystem and atmosphere of the platform.
P1_4The light meal marketing content is comprehensive, true and clear, which gains my trust.
P1_5The light meal marketing content can provide me with professional and targeted services.
Marketing content featuresP1_6The diversity of presentation in light meal marketing content stimulates my interest.
P1_7Influencers have a good understanding of light meals.
P1_8Influencers have purchased and consumed many light meal categories.
Influencers’ personal attributes P1_9Influencers’ comments on light meal products are trustworthy.
P1_10The popularity of the influencer increases my trust.
P1_11Influencers actively respond to consumer topics and answer various product questions.
Purchase intentions I1Actively responding and participating in topics or live broadcasts initiated by influencers.
I2Interacting with other influencers on the platform.
I3I believe in the recommendations of the influencer marketing platform and try to purchase the “planted” light meals.
I4Repurchasing light meals that I have “harvested” from the platform.
I5Sharing experiences of “planted”/“harvested” on the platform with people around me.
Purchase behavior B1After being “planted” by the platform, I search for related information about light meal products on relevant websites.
B2After being “planted” by the platform, I inquire about the product information from people who have purchased the light meal product.
B3I am considering ordering light meal products recommended by influencers on the platform.
B4I am very willing to browse the light meal products recommended by the influencer marketing platform to help me make purchasing decisions.
B5The recommendations of the influencer marketing platform play an important role in my purchase of light meal products.
B6I plan to frequently purchase light meal products on the influencer marketing platform in the future.
B7I share the usage experience of the light meal product on the “planted” platform.
B8I share the usage experience of the light meal product with people around me.
Table 3. Pearson correlation coefficient.
Table 3. Pearson correlation coefficient.
AverageStandard DeviationInfluencer Marketing Platform InterventionPurchase IntentionPurchase Behavior
Influencer marketing platform intervention3.7710.7151
Purchase intention3.6510.7920.510 ***1
Purchase behavior3.7260.7250.557 ***0.618 ***1
Note: *** p < 0.001.
Table 4. Parameter estimates between latent variables and observable variables.
Table 4. Parameter estimates between latent variables and observable variables.
EstimateS.E.C.R.PFactor LoadingCRAVE
P1_1<---Influencer marketing platform intervention1.000 0.6950.9320.556
P1_2<---Influencer marketing platform intervention1.2140.07216.823***0.844
P1_3<---Influencer marketing platform intervention0.9880.07113.910***0.691
P1_4<---Influencer marketing platform intervention1.0850.07414.641***0.727
P1_5<---Influencer marketing platform intervention1.0570.07114.836***0.737
P1_6<---Influencer marketing platform intervention1.0470.07014.854***0.739
P1_7<---Influencer marketing platform intervention1.0850.07414.565***0.725
P1_8<---Influencer marketing platform intervention1.1230.07315.434***0.766
P1_9<---Influencer marketing platform intervention1.1790.07615.557***0.776
P1_10<---Influencer marketing platform intervention1.0860.07115.377***0.766
P1_11<---Influencer marketing platform intervention1.0270.07014.622***0.727
I1<---Purchase intention1.000 0.7860.8610.554
I2<---Purchase intention0.8670.06014.525***0.682
I3<---Purchase intention0.7950.04916.369***0.756
I4<---Purchase intention1.0750.06317.117***0.789
I5<---Purchase intention0.7820.05215.092***0.702
B1<---Purchase behavior1.000 0.6890.9040.540
B2<---Purchase behavior1.1880.07914.995***0.769
B3<---Purchase behavior1.2760.08914.416***0.747
B4<---Purchase behavior1.1370.07814.580***0.743
B5<---Purchase behavior1.0430.07214.438***0.736
B6<---Purchase behavior1.2220.08514.335***0.732
B7<---Purchase behavior1.3510.09314.461***0.743
B8<---Purchase behavior1.0650.07613.937***0.718
Note: *** p < 0.001.
Table 5. Structural equation model fit.
Table 5. Structural equation model fit.
Fit IndicesAcceptable RangeMeasurementsMatch
CMIN 484.316
DF 249
CMIN/DF<51.945Yes
GFI>0.90.928Yes
AGFI>0.90.913Yes
RMSEA<0.080.046No
IFI>0.90.962Yes
NFI>0.90.925Yes
TLI(NNFI)>0.90.958Yes
CFI>0.90.962Yes
SRMR<0.050.047Yes
Table 6. Path coefficients.
Table 6. Path coefficients.
EstimateS.E.C.R.PStandardized Coefficient
Purchase intention<---Influencer marketing platform intervention0.7400.07310.087***0.581
Purchase behavior<---Purchase intention0.3800.0448.617***0.520
Purchase behavior<---Influencer marketing platform intervention0.2850.0505.662***0.306
Note: *** p < 0.001.
Table 7. Bootstrap mediation effect analysis for the impact of influencer marketing platform intervention on purchase behavior through purchase intention.
Table 7. Bootstrap mediation effect analysis for the impact of influencer marketing platform intervention on purchase behavior through purchase intention.
PathEffectSEBias Corrected (95%)Percentile Method (95%)Effect
LLCIULCIPLLCIULCIP
Direct effect0.3060.0530.1980.4120.0010.1980.4120.00150.3%
Indirect effect0.3020.0380.2350.3850.0010.2300.3790.00149.7%
Total effect0.6080.0400.5310.6870.0010.5300.6850.001
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

Qin, Z.; Chen, Y.; Yan, Y.; Huang, Y. Influencer Marketing Platforms’ Effect on Light Meal Purchase Intention and Behavior. Sustainability 2024, 16, 4369. https://doi.org/10.3390/su16114369

AMA Style

Qin Z, Chen Y, Yan Y, Huang Y. Influencer Marketing Platforms’ Effect on Light Meal Purchase Intention and Behavior. Sustainability. 2024; 16(11):4369. https://doi.org/10.3390/su16114369

Chicago/Turabian Style

Qin, Zhaoyue, Yiming Chen, Yue Yan, and Yi Huang. 2024. "Influencer Marketing Platforms’ Effect on Light Meal Purchase Intention and Behavior" Sustainability 16, no. 11: 4369. https://doi.org/10.3390/su16114369

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