Research on the Influence Mechanism of Social Media Opinion Leaders on the Purchasing Intentions of Small and Medium-sized Enterprise Consumers

The concept of “opinion leader” originates from Lazarsfeld’s seminal work, “The People’s Choice,” where an opinion leader is defined as an individual who disseminates information and exerts a certain influence on the thoughts of others during political elections [1]. Zhang Xiaofei (2020) posits that with the maturation of the internet commercial ecosystem, the purchasing intentions of small and medium-sized enterprise (SME) consumers on online platforms are also subject to the influence of online opinion leaders [2]. Chen Li (2023) uncovers that the social media opinion leaders affecting the internet consumption intentions of SME consumers encompass not only live streamers on various platforms but also content providers who offer specialized information on social media platforms such as Zhihu and Weibo [3]. SME consumers aspire to acquire more information about products and services through social media opinion leaders, thereby mitigating the risks associated with product selection.

In response to the increasingly prevalent presence of social media opinion leaders, current research predominantly explores the regulation of their conduct to foster the healthy development of the online commercial ecosystem. For instance, Li Ruoyue and Niu Kun (2022) analyze the characteristics and classification of online platform opinion leaders [4], while Zhang Zhaohui et al. (2022) further elucidate the potential influence mechanisms of these characteristics on SME consumers [5]. However, empirical studies that validate the influence mechanism of social media opinion leaders on the purchasing intentions of SME consumers are still quite limited.

To address this research gap, the present study quantitatively verifies the influence mechanism of social media opinion leaders on the purchasing intentions of SME consumers, based on 638 shopping intention surveys.

2

Literature review and proposed hypothesis

2.1

Factors and mechanisms influencing the purchasing intentions of SME consumers on online platforms

Research on the purchasing intentions of small and medium-sized enterprise (SME) consumers is often conducted using consumer psychology, rational choice theory, attitude theory, and the theory of planned behavior. For instance, Fang Lin and Wang Yanjie (2022) employed the SOR model from psychology, which discusses the mechanism of consumer purchasing intention from three aspects: stimulus factors (S), consumer subjects (O), and stimulus response behavior (R) [6]. Building on this foundation, mainstream literature tends to explore further from the perspective of attitude theory. Meng Fei (2012) views attitude as a behavioral tendency composed of three elements: cognition, emotion, and conation [7]. By clarifying the basis of attitude theory, this research measures consumer attitudes and analyzes the factors influencing consumer attitudes from various angles, such as psychology and the external environment, thereby providing support for the study of consumer purchasing intentions. This study also follows the theory of consumer behavior intentions, quantifies the purchasing intentions of SME consumers, and further analyzes the influence mechanism between various factors and social media opinion leaders.

2.2

A research overview of the impact of online social media opinion leaders under the word-of-mouth effect

As a core concept in journalism and communication, human resource management, and marketing, social media opinion leaders are increasingly being incorporated into the analysis of word-of-mouth effects, especially in exploring the impact mechanism of social media opinion leaders on word-of-mouth communication. For example, Tan Xiuli et al. (2017), from the perspective of interpersonal influence, proposed that the professionalism of the communicator has a certain impact on the purchasing intention of small and medium-sized enterprise (SME) consumers [8]; Cong and Zheng (2017) analyzed the influence of recommendations from others in online communities on the purchasing intention of SME consumers and found that compared with recommendations from ordinary SME consumers, recommendations from well-known community opinion leaders will have a greater impact on the purchasing intention of SME consumers [9]. With more and more research related to opinion leaders entering the field of word-of-mouth effects, researchers have reached a consensus on the characteristics of opinion leaders in the marketing field. For instance, Ma Liting and Guo Meiling (2023), based on the analysis of opinion leaders in the live broadcast industry, proposed that the characteristics of opinion leaders in online marketing include product involvement, professionalism, interaction, and popularity [10]. Fang Lin and Wang Yanjie (2022), based on the study of the impact of short videos on online purchasing behavior, pointed out that the characteristics of online opinion leaders include interactive attributes, personal charm attributes, recommendation information characteristics, and marketing information characteristics, which can reduce the psychological defense of the audience and promote the audience’s willingness and behavior to purchase [11]. Chen Jingyi et al. (2023) analyzed the impact mechanism of the online consumer group of internet celebrities from the perspective of value co-creation, proposing that the trust, professionalism, and activity of internet celebrities as opinion leaders all have a significant impact on the purchasing intention of SME consumers [12].

Based on the previous research on the characteristics of online opinion leaders, this paper proposes the first hypothesis and its sub-hypotheses:

H1: Social media opinion leaders have a significant promotional effect on the consumption willingness of small and medium-sized enterprise (SME) consumers.

H1a: The more professional the social media opinion leader’s understanding of the industry, the higher the consumption willingness of SME consumers will be enhanced.

H1b: The deeper the social media opinion leader’s understanding of the industry, the higher the consumption willingness of SME consumers will be enhanced.

H1c: The more interaction the social media opinion leader has with SME consumers, the higher the consumption willingness of SME consumers will be enhanced.

H1d: The higher the popularity of the social media opinion leader, the higher the consumption willingness of SME consumers will be enhanced.

H1e: The more profit-oriented intentions the social media opinion leader demonstrates, the higher the consumption willingness of SME consumers will be enhanced.

In addition to the impact of the characteristics of the social media opinion leader themselves on the consumption willingness of SME consumers, the platform where the opinion leader is located and the information conveyed by word-of-mouth itself also affect the purchasing willingness of SME consumers. Li Kai et al. (2015) proposed that when opinion leaders speak on more objective, high-credibility websites built by third parties, they will gain higher trust from SME consumers, thereby further enhancing the purchasing willingness of SME consumers. On the other hand, the positivity of word-of-mouth itself will also affect the appeal of the opinion leader on the purchasing willingness of SME consumers [13]. Meng Fei (2012), based on trust research, mentioned that the spread of negative information is much greater than that of positive information. Therefore, the higher the positivity of word-of-mouth, the less it may enhance the consumption willingness of SME consumers promoted by the opinion leader. Based on previous research, this paper proposes the second part of the hypothesis as follows:

H2a: As the platform’s influence grows, the impact of social media opinion leaders on the purchasing willingness of SME consumers becomes more apparent.

H2b: As the positive influence of product word-of-mouth grows, the impact of social media opinion leaders on the purchasing willingness of SME consumers becomes less apparent.

Based on the above literature review and the proposed hypotheses, the overall framework of this paper is shown in Figure 1.

3

Competency model for identifying social media opinion leaders

In the social network environment, there are all kinds of users. Opinion leaders are usually individuals living in real life and naturally possess some specific psychological and behavioral characteristics that distinguish them from ordinary individuals. Therefore, the identification characteristics of opinion leaders in social networks can be explored in depth from the new perspective of the competency theory. Based on the competency theory, this paper proposes a competency model for accurately identifying opinion leaders in social networks. The model consists of three basic competency elements, as shown in Table 1. The table presents the names of the competency elements, the definitions of the competency elements (that is, the meanings of the key elements that define the competencies), and the behavior indicators refined layer by layer (which reflect the differences in the behavioral manifestations of the competencies).

Table 1.

Competency Quality Model for Opinion Leaders in Social Networks

Competency elements	Element Definition	Behavioral indicators
Activity (Explicit)	The degree of activity in the process of information dissemination.	Number of followed friends.Number of posts within an average period of time.Number of replies within an average period of time.
Centrality (Implicit)	The quality and effectiveness of the information produced.	Influence: Audience reach, response level, dissemination degree.Professionalism: Degree of deviation from the theme.Innovation: Degree of originality, document similarity.Degree of Focus: Proportions of posts, replies and forwards on specific themes.

3.1

Activity

Activity directly reflects the degree of activity of opinion leaders in the process of information dissemination. The calculation formula for the activity of user u is the weighted sum of three items. (1) $A c t i v i t y (u) = α A T (u) + β A D P (u) + γ A D R (u)$ $$Activity(u)=\alpha A T(u)+\beta A D P(u)+\gamma A D R(u)$$

Among them, α, β, and γ are adjustable parameters. AT(u) represents the number of friends followed by the user. The larger the number of followed friends is, the stronger the user’s willingness to engage in social network activities. ADP(u) represents the average number of posts made by the user per week, and ADR(u) represents the average number of replies made by the user to other documents per week.

3.2

Centrality

Activity focuses on reflecting the degree of active participation of users in the whole process of information dissemination in the social network, while centrality reflects users’ information production capabilities, that is, whether users have a high level of attention and whether they can influence the direction of public opinion. Centrality is calculated according to topics. In this paper, the Latent Dirichlet Allocation (LDA) model for document topic generation is used to classify the topics of users’ posts, and then the user centrality indicator values under each topic are calculated, including four behavioral measurement indicators: influence, professionalism, innovation and degree of focus.

3.2.1

Influence

Influence is mainly based on two types of behavioral information, namely the number of a user’s followers and the number of replies received. Being replied to also includes behaviors such as being forwarded and being “liked” in some social networks. In this paper, influence is quantified as the weighted sum of audience reach, response level and dissemination degree.

Audience reach represents the popularity of a document on a certain theme t published by user u in the community. It is calculated by dividing the number of users who have read documents on such a theme, denoted as “read_u”, by the total number of users in the online community, denoted as “all”. The calculation formula is (2) $C o v_{t} (u) = r e a d_{u} / a l l$ $${Cov}_t(u)={read}_u / all $$

Response level represents the degree of response to a user’s document in the community. It is calculated by dividing the number of users who have replied to the document, denoted as “response_u”, by the number of users who have read the document, denoted as “read_u”. The calculation formula is (3) $r e s p_{t} (u) = r e s p o n s e_{u} / r e a d_{u}$ $${resp}_t(u)={ response }_u / {read}_u$$

Among them, “t” represents a specific theme, and “u” represents a specific user.

Dissemination degree represents how fast a document spreads in the community. If a text message is forwarded by a follower who also has a large number of his or her own followers, then this text message will be seen by more people in a short period of time. The dissemination degree of a user is the sum of the dissemination degrees of each forwarding user. The calculation formula for the dissemination degree is (4) $d i f f_{t} (u) = \sum_{v} \frac{1}{f o r w a r d (u)} f a n s (v)$ $${diff}_t(u)=\sum\limits_v \frac{1}{{forward}(u)} {fans}(v)$$

Among them, “forward(u)” represents the number of times the text message of user u has been forwarded, and “fans(v)” represents the number of followers of the forwarding user v.

The calculation formula for the influence of user u is the weighted sum of the audience reach, response level and dissemination degree of the documents on theme t by this user. (5) $I n f l u e n c e_{t} (u) = δ {cov}_{t} (u) + ε r e s p_{t} (u) + χ d i f f_{t} (u)$ $${ Influence }_t(u)=\delta {\rm cov}_t(u)+\varepsilon {resp}_t(u)+\chi {diff}_t(u)$$

Among them, δ, ε and χ are adjustable parameters.

3.2.2

Professionalism

Professionalism is mainly analyzed based on users’ dynamic behavior information and behavior content information. The more information a user publishes on a certain theme and the more professional terms it contains, the more interested and professional the user is in this theme. Here, the technique for order preference by similarity to an ideal solution (TOPSIS) is used to measure the overall professionalism of users.

Based on the “document-topic” matrix obtained from Latent Dirichlet Allocation (LDA), the topic with the highest probability d_t in each document is taken as the topic of the document. All the documents published by a user on a certain topic are regarded as a set D_t,u. The maximum value in the set is defined as $d_{t, u}^{+}$ $$d_{t,u}^+$$, and the minimum value is defined as $d_{t, u}^{-}$ $$d_{t,u}^-$$. Here, “t” represents a specific topic, and “u” represents a specific user. The Euclidean distances between d_t of each document and $d_{t, u}^{+}$ $$d_{t,u}^+$$, $d_{t, u}^{-}$ $$d_{t,u}^-$$ are defined as $S_{t, u}^{+}$ $$S_{t,u}^+$$ and $S_{t, u}^{-}$ $$S_{t,u}^-$$ respectively, which indicate whether the document is closer to or further away from the topic.

The calculation formula for user professionalism is (6) $\Pr o f e s s i o n a l i s m_{t} (u) = S_{t, u}^{+} / (S_{t, u}^{+} + S_{t, u}^{-})$ $$\Pr ofessional \ ism_t(u)=S_{t, u}^{+} /\left(S_{t, u}^{+}+S_{t, u}^{-}\right)$$ (7) $S_{t, u}^{+} = \sqrt{\sum_{d_{t} \in D_{t, u}} {(d_{t, u}^{+} - d_{t})}^{2}}$ $$S_{t, u}^{+}=\sqrt{\sum\limits_{d_t \in D_{t, u}}\left(d_{t, u}^{+}-d_t\right)^2} $$ (8) $S_{t, u}^{-} = \sqrt{\sum_{d_{t} \in D_{t, u}} {(d_{t} - d_{t, u}^{-})}^{2}}$ $$S_{t, u}^{-}=\sqrt{\sum\limits_{d_t \in D_{t, u}}\left(d_t-d_{t, u}^{-}\right)^2}$$

3.2.3

Innovation

The user’s innovation is mainly analyzed based on the behavioral content information, considering from two aspects: the degree of originality and document similarity. The degree of originality indicates the proportion of original documents among all the documents published by the user. The higher the degree of originality is, the higher the user’s innovation will be. The calculation formula for the degree of originality is (9) $O r i g i n a l i t y_{t} (u) = 1 - f o r w a r d_{t, u} / S u m_{t, u}$ $${Originality}_t(u)=1-{ forward }_{t, u} / {Sum}_{t, u}$$

Among them, “forward_t,u” represents the number of documents forwarded by user u regarding theme t.

Document similarity mainly calculates the similarity among documents within a specific theme. Firstly, each document is represented as a term frequency-inverse document frequency (TF-IDF) weight vector. Then, the cosine method is used to calculate the similarity among documents within specific themes, and the maximum value is selected as the similarity of this user, which is denoted as Similarity_t(u). The calculation formula for user innovation is (10) $I n n o v a t i o n (u) = C r e a t i v i t y_{t} (u) \frac{1}{S i m i l a r i t y_{t} (u)}$ $${Innovation}(u)={ Creativity }_t(u) \frac{1}{{ Similarity }_t(u)}$$

3.2.4

Focus

The degree of focus is represented by the proportion of the number of posts, replies and forwards on a specific theme to the total number of posts, replies and forwards by the user. The higher its value is, the more attention the user pays to the theme area. Use Focuse_t (u) to represent the degree of focus of user u on theme t, where Post_t(u) represents the total number of all posts published by user u on theme Reply_t(u) represents the total number of all replies made by user u regarding theme t, and Forward_t(u) represents the total number of all forwards made by user u regarding theme t. The calculation formula for the degree of focus is (11) $F o c u s_{t} (u) = κ P o s t_{t, u} / \sum_{t = 1}^{n} P o s t_{t, u} + η Re p l y_{t, u} / \sum_{t = 1}^{n} Re p l y_{t, u} + λ F o r w a r d_{t, u} / \sum_{t = 1}^{n} F o r w a r d_{t, u}$ $${ Focus }_t(u)=\kappa { Post }_{t, u} / \sum\limits_{t=1}^n { Post }_{t, u}+\eta {\rm Re} p l y_{t, u} / \sum\limits_{t=1}^n {\rm Re} p l y_{t, u}+\lambda { Forward }_{t, u} / \sum\limits_{t=1}^n { Forward }_{t, u}$$

Among them, κ, η and λ are adjustable parameters.

Finally, influence, professionalism, innovation and degree of focus together form the user’s centrality indicators. Its calculation formula is (12) $C e n t r a l i t y_{t} (u) = ς I n f l u e n c e_{t} (u) + δ Pr o f e s s i o n a l i s m_{t} (u) + τ I n n o v a t i o n_{t} (u) + φ F o c u s_{t} (u)$ $${ Centrality }_t(u)=\varsigma { Influence }_t(u)+\delta {\rm Pr} { ofessional} \ {ism }_t(u)+\tau { Innovation }_t(u)+\varphi { Focus }_t(u)$$

These four parameters, namely ζ, σ, τ and ϕ, are calculated through the Structural Equation Modeling (SEM) and obtained after normalization processing.

4

Sample selection and questionnaire design

In terms of questionnaire design, this paper, based on the preliminary study by Li Kai et al. (2015), has designed a total of 20 items. All items use a five-point Likert scale, where 1 to 5 represents a range from “strongly disagree” to “strongly agree.”

Regarding the distribution of questionnaires and the selection of the sample, in order to enhance coverage, this study primarily targets consumers of small and medium-sized enterprises who have purchased mass consumer goods such as clothing, cosmetics, and food on online platforms. By distributing questionnaires both online and offline, a total of 1000 questionnaires were issued, with 756 collected and 721 valid questionnaires, resulting in a valid recovery rate of 72.1%. The demographic characteristics of the specific respondents are shown in Table 2. The research subjects are mainly concentrated in the age group under 35, and the operating revenue is focused below 20 million yuan, indicating that the consumers of small and medium-sized enterprises influenced by social media opinion leaders have shown corresponding demographic characteristics in terms of age, education, and monthly income distribution. In other words, small and medium-sized enterprise consumers within this category are more likely to identify with the opinions and suggestions of online social media opinion leaders, stimulating their corresponding purchasing intentions.

Table 2.

The demographic characteristics of the specific respondents

Characteristics	Options	Ratio(%)
Gender	Male	46.8
Gender	Female	53.2
Age	25 and below	12.5
	25-35	46.7
	35-45	28.4
	45 and above	12.4
Diploma	below bachelor’s degree	34.2
	bachelor’s degree	53.7
	postgraduate	12.1
Operating Revenue	3 million	54.2
	3 million - 10 million	26.3
	10 million - 20 million	10.5
	20 million and above	8

5

Empirical research

5.1

Reliability and validity analysis

Referencing the study by Ma Liting and Guo Meiling (2015), this research employed the Cronbach’s coefficient to test the reliability of the variables, with results shown in Table 3. The data indicate that the questionnaire possesses good reliability, with an overall Cronbach’s α of 0.953. Specifically, the subscales for product relevance (R), interactivity (I), profitability (Pr), and consumption willingness (W) all achieved values above 0.8, while the subscales for professionalism (P) and familiarity (F) also had Cronbach’s α coefficients above 0.7. Additionally, the questionnaire results passed the KMO and Bartlett tests (see Table 4). Specifically, the KMO value was greater than 0.7 and statistically significant within a 95% confidence interval, indicating that the questionnaire is suitable for factor analysis.

Table 3

Reliability and validity analysis

Unobserved Variables	Observed Variables	Cronbach’s α coefficient	The overall Cronbach’s α coefficient
Professionalism(P)	P1,P2,P3	0.735	0.953
Product Relevance(R)	R1,R2,R3	0.898
Interactivity(I)	I1,I2,I3	0.867
Familiarity(F)	F1,F2,F3	0.758
Profitability(Pr)	Pr1,Pr2,Pr3	0.807
Consumption Willingness(W)	W1,W2,W3	0.892

Table 4

Reliability and validity analysis

Methods of Validation		Results
KMO Test		0.867
Bartlett’s Test of Sphericity	Approximate chi-square value	1235.2
	DF	146
	P-value	0.0023

5.2

Correlation analysis

In this study, following previous research, a causal effect analysis between variables was conducted. Based on the Pearson correlation coefficient analysis, the results as shown in Table 5 indicate that there is a positive correlation between the characteristic variables of social media opinion leaders and the purchasing intention of small and medium-sized enterprise (SME) consumers. The correlation coefficients are all less than 0.8, suggesting that there is no significant multicollinearity between the variables. This implies that the variables in this study can be used for subsequent regression analysis.

Table 5.

Variable correlation analysis

	Professionalism (P)	Product Relevance (R)	Interactivity (I)	Familiarity (F)	Profitability (Pr)	Platform Influence (PI)	Word-of-mouth Positivity (WP)	Consumption Willingness (W)
Professionalism(P)	1.000
Product Relevance(R)	0.152***	1.000
Interactivity(I)	0.376***	0.683***	1.000
Familiarity(F)	0.324***	0.452***	0.237***	1.000
Profitability(Pr)	0.387***	0.685***	0.764***	0.214***	1.000
Platform Influence(PI)	0.483***	0.021***	0.178***	0.082***	0.375***	1.000
Word-of-mouth Positivity(WP)	0.495***	0.715***	0.359***	0.264***	0.394***	0.176***	1.000
Consumption Willingness(W)	0.441***	0.236***	0.592***	0.479***	0.187***	0.473***	0.597***	1.000

5.3

5.3 Regress analysis

1)

Influence mechanism of social media opinion leader characteristics. As shown in Table 5, the characteristics of online social media opinion leaders, specifically professionalism (P), product relevance (R), and interactivity (I), have a significant positive effect on the purchasing intention of small and medium-sized enterprise (SME) consumers, with marginal effect coefficients of 1.132, 0.693, and 0.871, respectively. Therefore, hypotheses H1a, H1b, and H1c are supported. However, the familiarity (F) and profitability (Pr) of online social media opinion leaders do not significantly affect the purchasing intention of SME consumers. In other words, hypotheses H1d and H1e are not supported.

The conclusions drawn here differ from those of previous studies. On one hand, Tong Wanju and Xu Heiping (2022) suggested that in e-commerce live streaming, the professionalism of social media opinion leaders has no impact on consumer behavior. In contrast, this study finds that in the context of e-commerce live streaming, the professionalism of social media opinion leaders significantly promotes consumer behavior. This may be because SME consumers watch live streaming content with a shopping mindset, thus having certain expectations and understanding of the content. Compared to accidentally browsing related product information on Weibo, Zhihu, or other platforms, the professionalism of social media opinion leaders plays an important role in the product recognition of SME consumers, and their professionalism can better reflect the word-of-mouth effect inherent in the product. On the other hand, the study by Ma Liting and Guo Meiling (2022) posits that the popularity of social media opinion leaders significantly enhances the purchasing intention of SME consumers. However, this study’s survey of SME consumers did not find a similar mechanism of influence, indicating that the notion that “the popularity of social media opinion leaders affects the purchasing intention of SME consumers” is not absolute. For the same product, as SME consumers become increasingly aware of marketing tactics, the opinions of well-known social media opinion leaders may merely reflect the sales strategies of businesses, thereby diminishing their influence on the purchasing intention of SME consumers. Interestingly, this study’s empirical findings reveal that the profitability of social media opinion leaders does not have a negative effect on the purchasing intention of SME consumers, but rather show that “it does not significantly affect the purchasing intention of SME consumers.” This empirical result indicates that in the current business environment, a certain level of profitability demonstrated by social media opinion leaders does not weaken the purchasing intention of SME consumers, as long as their analysis of the product is objective and they provide convincing value judgments. SME consumers can accept the profits obtained by social media opinion leaders based on their influence and objective evaluation of products or services.

2)

Platform influence and the positive valence of word-of-mouth: The moderating roles. This study also quantified the moderating effects of platform influence and the positive valence of word-of-mouth in the relationship between social media opinion leaders and the purchasing intentions of small and medium-sized enterprise (SME) consumers. As shown in Table 6, the interaction terms professionalism (P) & platform influence (PI) and product relevance (R) & platform influence (PI) are positively significant, with marginal effect coefficients of 0.671 and 0.565, respectively. This suggests that as platform influence increases, the enhancement of the social media opinion leader’s professionalism will more markedly increase the purchasing intentions of SME consumers. Similarly, the deeper understanding of product content by social media opinion leaders, with the increase in platform influence, will also more significantly enhance the purchasing intentions of SME consumers due to product relevance. This is because platform influence itself brings corresponding traffic; at this time, more SME consumers will pay more attention to the professionalism of social media opinion leaders and their understanding of the product, thereby making corresponding decision intentions based on professional product information. However, the interactivity of social media opinion leaders does not play a more significant role in promoting the purchasing intentions of SME consumers with the expansion of platform influence. This result is also because this study is not aimed at the fast-paced scenario of live broadcasting but is mainly based on articles and product reviews on platforms like Weibo and Zhihu. At this time, the interaction between social media opinion leaders and SME consumers is not immediate, so the frequency of interaction between the two will not produce a significant difference with the expansion of platform influence.

Furthermore, the questionnaire results show that the interaction terms familiarity(F) & word-of-mouth positivity(WP) and product relevance (R) & word-of-mouth positivity(WP) have a corresponding promoting effect on the purchasing intentions of SME consumers. Word-of-mouth positive valence refers to the positive evaluation of the product in advance, that is, the higher the positive evaluation of the product or service in advance, the more significant the role of the familiarity(F) of social media opinion leaders and product relevance (R) on the purchasing intentions of SME consumers. The main reason for the above conclusion is that after having a positive word-of-mouth, SME consumers have specific targets when purchasing, and their mechanism of action is different from that of purchasing without specific targets. SME consumers are more eager to obtain the opinions of well-known social media opinion leaders, using their reputation as the basis for purchase. At the same time, SME consumers also hope to further understand and judge whether the positive word-of-mouth of products or services is well deserved through the analysis and discussion of social media opinion leaders on the product.

Therefore, from the above moderating effects, it can be seen that platform influence and the positive word-of-mouth effect are the results of the interaction of specific characteristics of social media opinion leaders under certain conditions, reflecting the potential pattern switching in the purchasing of SME consumers. This is an issue that has not been fully discussed in previous marketing and communication research.

Table 6.

Regress results

Variable	Estimate	Std. Error	Wald	P-value
Professionalism(P)	1.132	0.371	9.72	0.003***
Product Relevance(R)	0.693	0.241	9.23	0.002***
Interactivity(I)	0.871	0.255	10.86	0.004***
Familiarity(F)	0.545	0.346	2.27	0.121
Profitability(Pr)	0.182	0.378	0.31	0.621
Professionalism(P)#Platform Influence(PI)	0.671	0.364	3.25	0.071*
Product Relevance(R)#Platform Influence(PI)	0.565	0.226	5.81	0.014**
Interactivity(I)#Platform Influence(PI)	0.293	0.373	0.65	0.431
Familiarity(F)#Platform Influence(PI)	0.489	0.677	0.45	0.473
Profitability(Pr)#Platform Influence(PI)	1.045	0.765	2.13	0.117
Professionalism(P)#Word-of-mouth Positivity(WP)	0.635	0.761	0.46	0.446
Product Relevance(R)#Word-of-mouth Positivity(WP)	1.542	0.526	11.12	0.002***
Interactivity(I)#Word-of-mouth Positivity(WP)	0.963	0.635	2.31	0.153
Familiarity(F)#Word-of-mouth Positivity(WP)	1.623	0.631	6.62	0.011***
Profitability(Pr)#Word-of-mouth Positivity(WP)	1.437	0.738	2.91	0.087*
Observations	721

5.4

Further analysis

Utilizing Python web scraping technology, this study captures live broadcast barrage from social media opinion leaders and conducts fuzzy classification in pairs to further verify that in addition to paying attention to the product’s inherent characteristics, SME consumers also understand the promoted products by perceiving the characteristics of opinion leaders (professionalism, product relevance, interactivity, familiarity, and profitability).

5.4.1

Barrage scraping analysis process

Python web scraping was employed to extract barrage comments from live broadcast websites of social media opinion leaders on platforms such as Weibo and Zhihu, totaling 257,102 comments over a 30-day period. The following processes were then undertaken: (1) Duplicate text data were removed, and barrage without practical significance was filtered out, as well as barrage that was too short to be analyzed; (2) Word segmentation was performed on the captured barrage to identify high-frequency words and noun phrases, which were then quantified and assigned scores; (3) The identified nouns and phrases were included in the evaluation system, and an iterative method was used to calculate the weights at each level, resulting in scores, frequency of occurrence, and weights for each feature dimension, and finally calculating the scores and total occurrences for the primary dimensions, and deducing the overall evaluation result from the secondary constructs.

5.4.2

Weight calculation model

The fuzzy analytic hierarchy process (FAHP) can effectively address the issue of imprecise indicator values, converting qualitative evaluations into quantitative assessments. 1)

Calculation model for the average score of individual feature values

Suppose the scores given by n users for feature i in the barrage comments of a social media opinion leader are $x_{i 1}, x_{i 2}, \dots, x_{i n}$ $$x_{i1}, x_{i2}, \ldots, x_{in}$$, then the average score for feature i is (13) ${\bar{x}}_{i} = \frac{\sum_{j = 1}^{n} x_{i j}}{n}$ $$\bar {x}_i=\frac{\sum\nolimits_{j=1}^n x_{ij}}{n}$$ 2)

Calculation of the average score for individual secondary constructs

Suppose under secondary construct k, all features (m features) are $x_{k 1}, x_{k 2}, \dots, x_{k m}$ $$x_{k1}, x_{k2}, \ldots, x_{km}$$, and the number of mentions for m features are $p_{k 1}, p_{k 2}, \dots, p_{k n}$ $$p_{k1}, p_{k2}, \ldots, p_{kn}$$, respectively, then the average score for secondary construct k is (14) ${\bar{Y}}_{k} = \frac{p_{k 1} x_{k 1} + p_{k 2} x_{k 2} + \dots + p_{k m} x_{k m}}{p_{k 1} + p_{k 2} + \dots + p_{k m}} = \frac{\sum_{j = 1}^{m} p_{k j} x_{k j}}{\sum_{i = 1}^{m} p_{k i}}$ $$\bar{Y}_k=\frac{p_{k 1} x_{k 1}+p_{k 2} x_{k 2}+\cdots+p_{k m} x_{k m}}{p_{k 1}+p_{k 2}+\cdots+p_{k m}}=\frac{\sum_{j=1}^m p_{k j} x_{k j}}{\sum_{i=1}^m p_{k i}}$$ 3)

Calculation of the average score for individual primary constructs

Suppose under primary construct r, all features (g secondary constructs) are $y_{r 1}, y_{r 2}, \dots, y_{r m}$ $$y_{r1}, y_{r2}, \ldots, y_{rm}$$, and the number of mentions for m features are $q_{r 1}, q_{r 2}, q_{r m}$ $$q_{r1}, q_{r2}, q_{rm}$$, respectively, then the average score for primary construct r is (15) ${\bar{Z}}_{r} = \frac{q_{r 1} y_{r 1} + q_{r 2} y_{r 2} + \dots + q_{r m} y_{r m}}{q_{r 1} + q_{r 2} + \dots + q_{r m}} = \frac{\sum_{j = 1}^{m} q_{r j} y_{r j}}{\sum_{j = 1}^{m} q_{r j}}$ $$\bar{Z}_r=\frac{q_{r 1} y_{r 1}+q_{r 2} y_{r 2}+\cdots+q_{r m} y_{r m}}{q_{r 1}+q_{r 2}+\cdots+q_{r m}}=\frac{\sum_{j=1}^m q_{r j} y_{r j}}{\sum_{j=1}^m q_{r j}}$$

The steps for the fuzzy comprehensive evaluation in this study are: (1) Determine the evaluation project set $F = (f_{1}, f_{2}, \dots f_{n})$ $$F=(f_1, f_2, \ldots f_n)$$ that can construct F=(Opinion Leader Characteristics,Word-of-Mouth Characteristics). The indicators for opinion leader characteristics and word-of-mouth characteristics in the fuzzy comprehensive evaluation refer to the research by Liu Fengjun et al. (2020), and then f_1OLC = (Professionalism, Product Relevance, Interactivity, Familiarity, and Profitability), f_2WMC = (Platform Influence, Word-of-Mouth Positivity) [14]. (2) After word segmentation processing of the barrage, nouns or phrases with a subject-verb-object relationship are obtained, then according to the similarity matching principle, the corpus is summarized into feature word categories, and through the marketing dictionary and related literature, each primary dimension is interpreted, and feature words are included under the primary dimension to construct a feature word library. This determines the scoring scale for each item and refines the scoring, E=(Very Like,Like,Neutral,Dislike,Very Dislike), where “Very Like” is given 5 points, and “Very Dislike” is given 1 point. In the captured barrage comments, the degree adjectives of feature words can be specifically divided into the following categories: 5 = (Very Good, Very Like, Very Satisfied, Super Strong, ...), 4 = (Good, Like, Satisfied, ...), 3 = (Not Bad, OK, Can, ...), 2 = (Just Pass, Make Do, Very Average, ...), 1 = (Bad, Really Poor, Terrible, Ugly, ...). (3) After counting the frequency of occurrence of each feature word, calculate the corresponding weight and score for each corpus.

5.4.3

Data Analysis

Using the above processing methods, the corresponding weights and scores for each corpus are shown in Figure 2. From Figure 2, it can be seen that the final comprehensive score of the product is 4.61. Consumers’ comments mostly involve opinion leader characteristics (72.9%), while comments on word-of-mouth characteristics are relatively less (27.7%). In terms of opinion leader characteristics, SME consumers have a high evaluation of the professionalism of opinion leaders (4.78), accounting for 42.5% of the comments. In terms of word-of-mouth characteristics, SME consumers have a high evaluation of platform influence (4.93), accounting for 63.5% of the comments.

For a detailed weight analysis of the 257,102 barrage comments, see Table 7. From the perspective of the number of times SME consumers pay attention, the total proportion of opinion leader characteristics (162.3%) is significantly higher than the total proportion of word-of-mouth characteristics (68%), indicating that SME consumers’ attention to products recommended by social media opinion leaders depends more on the inherent characteristics of the opinion leaders themselves. Further analysis of the characteristics of internet celebrities reveals that the professionalism of opinion leaders is more focused on by SME consumers; while in terms of word-of-mouth characteristics, SME consumers pay more attention to platform influence.

Table 6.

Regress results

Characteristics Indicator	Opinion Leader Characteristics				Word-of-Mouth Characteristics
Characteristics Indicator	Professionalism	Product Relevance	Interactivity	Familiarity	Profitability	Platform Influence	Word-of-Mouth Positivity
Frequency of Mentions	80546	25728	69742	54637	27572	57834	63745
Proportion of Mentions	63.2%	10.7%	53.7%	22.1%	12.6%	22.7%	45.3%

6

Conclusions and implications

6.1

Conclusions

Social media opinion leaders provide quality information about products and services to small and medium-sized enterprise (SME) consumers through platforms such as Weibo and Zhihu. Influenced by the word-of-mouth effect, these opinion leaders can affect the purchasing intentions of SME consumers, but the mechanisms by which they influence these intentions are varied.

Surveys indicate that the professionalism of social media opinion leaders, their understanding of products, and their interaction with SME consumers significantly enhance the purchasing intentions of these consumers. However, this study also found that the popularity and profitability of social media opinion leaders do not markedly affect the purchasing intentions of SME consumers. Therefore, it can be observed that with the current economic growth slowdown, SME consumers are more focused on the quality of products and services themselves, rather than just the influence of social media opinion leaders. How to better recommend products or services to SME consumers through social media opinion leaders is a new issue that the online word-of-mouth effect needs to study.

Platform influence and the positive valence of word-of-mouth have a moderating effect on the role of social media opinion leaders, where platform influence mainly affects the professionalism and product relevance of opinion leaders, while the positive valence of word-of-mouth more significantly affects the popularity and product relevance characteristics of opinion leaders. The impact of platform influence and the positive valence of word-of-mouth on social media opinion leaders precisely illustrates that different characteristics of opinion leaders have different patterns of influence on the purchasing intentions of SME consumers under different circumstances. For example, when the positive valence of word-of-mouth is introduced, it means that SME consumers have a preliminary understanding of the product, and at this time, they pay more attention to the popularity of social media opinion leaders and their introduction to the product. However, if SME consumers do not have prior knowledge of the product, they pay more attention to the professional characteristics of the opinion leaders and their understanding of the product.

6.2

Implications

Firstly, for social media opinion leaders, due to changes in the economic environment, the consumption behavior of SME consumers has become more rational. They can accept the profit orientation of social media opinion leaders in product promotion, but they also demand that these leaders deeply understand the products and provide comprehensive and profound opinions from a professional perspective to reduce potential blind spots in the consumption process. At the same time, as consumer goods become more segmented, SME consumers do not place as much emphasis on the personal influence of social media opinion leaders as before, but rather on what they have done. Under such circumstances, social media opinion leaders need to pay more attention to their professional literacy to provide more value to SME consumers in consumption guidance.

Secondly, for product and service providers, it is necessary to value the role of social media opinion leaders, as the third-party evaluation function plays a decisive role in the purchasing intentions of SME consumers. Therefore, while refining their products, providers also need to pay attention to the channels of social media opinion leaders, rely on their paths, understand the needs of SME consumers, and make product iterations.

Lastly, for online platforms, they should be aware of the guiding role of social media opinion leaders and can drive consumption by SME consumers on the platform by providing more opportunities for objective evaluation for these leaders. With a value co-creation attitude, platforms, social media opinion leaders, and product providers should work together to provide higher quality consumption content for SME consumers.

Language:: English

Publication timeframe:: 1 times per year
Journal Subjects:: Life Sciences, Life Sciences, other, Mathematics, Applied Mathematics, General Mathematics, Physics, Physics, other

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Research on the Influence Mechanism of Social Media Opinion Leaders on the Purchasing Intentions of Small and Medium-sized Enterprise Consumers

Zhenlin Ning

Published Online: Feb 27, 2025

Received: Oct 03, 2024

Accepted: Jan 29, 2025

DOI: https://doi.org/10.2478/amns-2025-0090

KeywordsWord-of-mouth effect, Opinion leaders, SME consumers

© 2025 Zhenlin Ning, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Keywords
Word-of-mouth effect, Opinion leaders, SME consumers