The Influence of Key Opinion Consumers on Purchase Intention in Live Streaming Commerce

Abstract

Live streaming commerce has emerged as an innovative e-commerce model. This study, based on the Elaboration Likelihood Model (ELM), aims to explore the impact of Key Opinion Consumers' (KOCs) attributes in live streaming commerce on purchase intentions on short video platforms. A survey was conducted with 411 consumers, and data analysis and hypothesis testing were performed using SPSS 24.0 and AMOS 23.0 software. Research has found that differences in consumers' information processing abilities lead to different pathway selections. Central route factors such as recommendation consistency, product involvement, and professionalism, as well as peripheral route factors such as recommendation timeliness, all have significant positive effects on consumers' purchase intention. However, visual cues in the peripheral route do not have a significant impact. This study aims to provide theoretical support and practical guidance for the development of the live streaming commerce industry, and to help companies adjust their promotion strategies based on differences in consumer information processing, thereby improving purchase conversion rates.

라이브 스트리밍 커머스는 혁신적인 전자상거래 모델이 된다. 본 연구는 정교화 가능성 모델(ELM)에 기반하여 라이브 스트리밍 커머스에서 키 오피니언 소비자(KOCs)의 속성이 짧은 동영상 플랫폼에서 구매 의도에 미치는 영향을 탐구하는 것을 목적으로 한다. 411명의 소비자를 대상으로 설문 조사를 실시하고, SPSS 24.0과 AMOS 23.0 소프트웨어를 사용하여 데이터 분석과 가설 검정을 수행한다. 연구에 따르면 소비자의 정보 처리 능력 차이는 경로 선택에 영향을 미친다. 중심 경로의 추천 동질성, 제품 관여도, 전문성 및 주변 경로의 추천 시효성은 소비자 구매 의도에 모두 긍정적인 영향을 미치는 반면, 주변 경로의 시각적 단서는 유의미한 영향을 미치지 않는다. 본 연구는 라이브 스트리밍 커머스 산업의 발전을 위해 이론적 지원과 실질적인 지침을 제공하고, 소비자 정보 처리의 차이에 따라 기업이 홍보 전략을 조정하여 구매 전환율을 향상시키는 데 도움을 주는 것을 목표로 한다.

I. Introduction

In the digital age, live streaming commerce has emerged as an innovative e-commerce model[1], which involves showcasing products through short video live streaming platforms to attract user attention and drive purchases. This model combines traditional e-commerce with content creation, providing businesses with a new sales channel and offering users a more vivid and interactive shopping experience[2]. The popularity of live streaming commerce is evident. With the rapid proliferation of internet technology and mobile devices, live streaming commerce has entered a period of explosive growth. Globally, TikTok and Facebook are among the most popular social media live shopping platforms[3]. More and more businesses and individual hosts are leveraging their influence and professional skills on short video live streaming platforms to successfully drive product sales and maximize commercial value. According to the latest Statistical Report on China's Internet Development, as of December 2023, the number of internet users in China has reached 1.092 billion, with an internet penetration rate of 77.5%. Specifically, the number of live streaming commerce users is 597 million, an increase of 82.67 million compared to December 2022. The popularity of live streaming commerce is mainly attributed to its ability to break the monotony of traditional e-commerce, making the shopping process more interesting and authentic, thereby significantly increasing user engagement and purchase desire.

Live streaming commerce, is an emerging e-commerce model that utilizes short video platforms to sell products. Through real-time video, hosts introduce and showcase products to the audience (potential consumers), interact with them, and ultimately achieve online sales. Therefore, live streaming commerce involves at least four main entities: platforms, product suppliers, hosts, and consumers.

The rise of live streaming commerce is the result of the comprehensive impact of various factors. Firstly, changes in consumer demand play a key role. As lifestyles and shopping habits evolve, consumers have increasingly high expectations for shopping experiences. Live streaming commerce meets the demand for participation, real-time interaction, and entertainment by providing instant product displays, interactive shopping experiences, and real-time communication with hosts and other viewers, thereby attracting the attention and participation of a large number of users. Secondly, the widespread use of mobile internet provides strong technical support for live streaming commerce. With the popularity of smartphones and mobile devices, users can conveniently access the internet anytime and anywhere, enabling them to participate in live streaming commerce easily. Additionally, the social shopping experience of live streaming commerce also attracts user interest. Through interaction with hosts and other viewers, consumers can build trust, share shopping experiences, and enhance the fun and social aspects of shopping. Finally, the enhancement of content creation and interactivity has also driven the development of live streaming commerce. It has attracted a large number of creative content creators and professional hosts who, through various content creation and interactive forms, have captured user attention and participation, thereby driving the thriving development of the live streaming commerce industry. In conclusion, the rise of live streaming commerce is the result of the combined effect of various factors, including changes in consumer demand, the prevalence of mobile internet, the social shopping experience, and the impact of content creation and interactivity[4][5][6].

In recent years, with the emergence of live streaming commerce as a new media form, it has garnered extensive academic attention. At the foundational level, Chinese scholars such as Lange, C., & Costley, J. (2020), Tully, C., & Alfaraz, C. (2017), and Su, Z. (2021) have conducted studies on the dissemination of across various platforms. These studies summarized the communication characteristics of as a new media form, analyzed the existing problems, and predicted future development trends[7][8][9]. More specifically, different researchers have explored aspects such as platforms, suppliers, and the characteristics of hosts. In the live streaming commerce model, platforms play a critical "channel" role, providing technical support and traffic resources to facilitate transactions. Dumrongsiri et al. (2008) analyzed direct sales and retail channels, finding that adding direct sales channels increases overall profit[10]. Yang et al. (2018) further studied the relationship between supplier characteristics and channel choice, discovering that medium-sized suppliers benefit most from direct sales[11]. Additionally, Khouja et al. (2010), Li Mingfang (2016), and Cao Yu (2021) examined the selection of online channels[12][13][14]. Interestingly, Wang et al. (2019) and Xing Peng et al. (2022) investigated the impact of platform commission rates and host commission ratios on the overall revenue of live streaming commerce[15][16]. Their studies found that the quality of the host's service is positively correlated with the host's commission ratio and negatively correlated with the platform's commission rate.

Research on the characteristics of hosts is quite extensive. As hosts act as persuaders and guides in live streaming commerce, their traits directly influence consumer purchasing behavior. Studies by Wang Wei et al. (2016) and Rausser et al. (2015) found that the degree of exaggeration in the persuader's information delivery affects the persuasion effect, thus influencing audience attitudes and behaviors[17][18]. Meng Lu et al. (2020; 2021) conducted a series of studies on the impact of hosts' influencer identities on consumer behavior[19][20]. Wu and Fang (2021) discovered that the "internet celebrity" status of hosts increases consumer trust, thereby enhancing purchase intentions[21]. Wongkitrungrueng and Apiradee (2020), using Facebook as an example, found that customer trust in hosts affects their engagement[22]. Huang Minxue et al. (2021) further distinguished the impact of product types and host types on consumer purchase intentions and behaviors[23]. A review of existing research reveals a relative scarcity of systematic studies on consumer information processing methods. In particular, the influence of key opinion consumers on purchase intentions has received limited attention.

Consumers' purchasing decisions are not purely individual choices but are also influenced by social factors. One significant pathway of influence is through consumers who propagate word-of-mouth information about products, known as Key Opinion Consumers (KOCs)[24][25]. This study is based on the Elaboration Likelihood Model (ELM) and aims to explore the impact of KOCs live streaming attributes on purchase intentions on short video platforms. Research shows that KOCs can influence other online consumers by showcasing products they have used and providing feedback or endorsements[26]. In general, KOCs promote consumers' attitudes and behaviors towards purchasing, a process known as internalization. When consumers accept information from a certain source, it can influence their personal opinions[27].

In summary, this study starts from consumers' information processing and explores whether different traits of KOCs affect consumer behavior. It is hoped that through this study, live streaming platforms can adjust their promotion strategies and content creation based on differences in consumer information processing, thereby increasing purchase conversion rates. This study also aims to provide theoretical support and practical guidance for the industry's development. In the e-commerce industry, adjusting product promotion strategies and content creation methods based on consumer information processing can improve purchase conversion rates. Additionally, on live streaming platforms, providing content with higher quality and trustworthiness can meet the needs of different consumer information processing styles, attract more users, and increase platform activity.

II. Theoretical Background

1. Elaboration Likelihood Model(ELM)

The Elaboration Likelihood Model (ELM) was proposed by Petty and Cacioppo in 1986 as a psychological theory. This model is used to explain how individuals process information and make decisions, particularly in the context of persuasion[28]. ELM suggests that individuals process information through two routes: the central route and the peripheral route. The central route involves users forming attitudes and making decisions by deeply thinking, analyzing, and evaluating the relevance and value of information[29]. For example, consumers may deeply analyze the product features, performance, and price presented by the host, and make rational decisions based on this information. The peripheral route, on the other hand, involves users processing information superficially, mainly relying on simple heuristics and emotional factors to form attitudes and decisions[30]. For instance, consumers often process information based on the host's personal charm, brand image, or other emotional factors, rather than the characteristics of the product itself. These two paths are not mutually exclusive. For the same information, the audience often chooses to process it through both paths, with the difference lying in which path serves as the dominant force. ELM has been widely applied in research fields such as information technology[31], advertising[32], social media[33], and e-commerce[34]. For example, Shi et al. studied the information posted by 1250 Twitter users and found that both the central and peripheral routes of ELM have a positive impact on individual communication behaviors[35].

2. Model Construction and Research Hypotheses

This study is based on the Elaboration Likelihood Model (ELM) and constructs a model diagram for the purchase intention of live streaming users. The central route factors include recommendation consistency, product involvement, and expertise, while the peripheral route factors include recommendation timeliness and visual cues, as shown in Figure 1.

Fig. 1. Research Model

RC(Recommendation Consistency)

PI(Product Involvement)

PR(Professionalism)

RT(Recommendation Timeliness)

VC(Visual Cues, BI(Buying Intention)

Purchase Intention is commonly used to represent the intent to buy. In the model, to distinguish it from Product Involvement (PI), we use the term Buying Intention. Throughout the main text, we primarily use purchase intention.

2.1 The Influence of the Central Route on Buying Intention

Recommendation Consistency(RC) refers to the degree of similarity between oneself and others in terms of interests, values, and attitudes toward certain things[36]. This similarity can manifest in aspects such as brand, features, functions, and more. Recommendation consistency positively influences consumers' purchase intentions by enhancing cognitive consistency, reducing purchase risk, and increasing brand loyalty. Consumers prefer products that are similar to their existing ones because it aligns with their cognition and preferences, reduces uncertainty during purchase, and strengthens loyalty to specific brands. Shi's research indicates that consumers whose characteristics and attitudes align more with the atmosphere of online communities are more likely to develop purchase intentions, especially when the recommendation consistency within the online community is strong[35].

Product involvement refers to consumers' judgment of the importance of a product based on their interests, hobbies, and values, which then affects their cognitive perception of the product[37]. Goldsmith suggests that when consumers perceive a product to be related to their interests and hobbies, they will invest significant effort to understand the product more deeply[38]. Awad and Ragowsky also mention in their research that long-term product involvement by Key Opinion Leaders (KOLs) can increase consumer trust and endorse the information they recommend, leading to purchase behavior[39].

Professionalism refers to the relevant professional knowledge, experience, or skills possessed by communicators and disseminated to their fans or a wider audience[40]. Bansal et al. found in their study of consumer behavior that information provided by individuals with higher levels of expertise in an industry is often more trusted[41]. Consumers are more likely to be influenced by recommendations from professionals, leading to purchase behavior.

Thus, the following hypotheses are developed:

H1: Key Opinion Consumers' (KOCs) recommendation consistency positively influences consumer buying intention.

H2: Key Opinion Consumers' (KOCs) product involvement positively influences consumer buying intention.

H3: Professionalism positively influences consumer buying intention.

2.2 The Impact of Peripheral Route on Buying Intention

Each product has its own peak period, during which information dissemination can reach a larger audience. Therefore, the timeliness of recommendations is crucial for influencing purchase intentions. Zhang and Watts (2008) showed in their study that the speed of information updates reflects the timeliness of the information source, which affects consumers' purchase intentions[42].

Visual cues refer to the form in which information is presented to consumers, such as text, images, and videos[43]. Lurie and Mason found in their study on incorporating visual cues into word-of-mouth that different visual cues can have suggestive effects on consumer behavior. Filieri et al. studied the impact of verbal and visual cues on consumer intentions and behaviors. Their findings indicate that nonverbal, visual cues possess a stronger predictive capability in determining consumers' intention and behavior[44].

Based on this, the study proposes the following hypotheses:

H4: The timeliness of Key Opinion Consumers' (KOCs) recommendations positively influences consumers' buying intentions.

H5: The visual cues of Key Opinion Consumers (KOCs) positively influence consumers' buying intentions.

III. Methodology

1. Sampling and Data Collection

To ensure the reliability and validity of the scale, this study borrowed and modified scales used by scholars both domestically and internationally, forming an initial scale. Semantic adjustments were made to fit Chinese language habits and cultural background. Subsequently, the scale was further optimized through interviews with multiple respondents. Finally, a small-sample survey was conducted to improve the effectiveness and feasibility of the questionnaire. After these steps and data analysis, the final test questionnaire was obtained, consisting of two parts: the first part covers the social demographic characteristics of the survey subjects (such as gender, age, education, and occupation), and the second part includes questions related to the assumed variables.

This study targets consumers with purchasing experience on the TikTok platform to more accurately analyze and understand their consumption behavior and its influencing factors. The questionnaires were distributed through research institutions, survey collection platforms, and email. After excluding questionnaires with excessive missing responses and other invalid submissions, a total of 411 valid questionnaires were collected, resulting in a valid response rate of 84.2%. The demographic characteristics are shown in Table 1.

Table 1. Basic Information of Samples

2. Measurement of Variables

In this study, each item is measured using a 5-point Likert scale, where 1 indicates "strongly disagree" and 5 indicates "strongly agree" (see Table 2).

Table 2. Measurement of variables

IV. Data Analysis and Results

1. Reliability and Validity Analysis

Using SPSS 24.0 and AMOS 23.0, the variables were tested for reliability and validity, and the results are shown in Table 3. The Cronbach's α reliability coefficient for each variable was greater than 0.8, indicating high internal consistency and good reliability. Principal component analysis with varimax rotation was used to conduct factor analysis on the scale. The Bartlett's sphericity test result was 0.000, and the KMO value was 0.893, meeting the factor analysis assumptions. In the exploratory factor analysis, the factor loading values were all greater than 0.6. The variables were divided into six factors, which together accounted for 72.149% of the total variance.

Table 3. Factor Load and Model Fitting

The stability of the scale was confirmed through confirmatory factor analysis (CFA) using structural equation modeling (SEM). The SEM effectively demonstrated the relationships and strengths between observed variables and latent variables. The model fit indices were as follows: χ²/df = 1.874, RMSEA = 0.046, SRMR = 0.040, CFI = 0.962, IFI = 0.962, TLI = 0.956, indicating a satisfactory fit. The composite reliability (CR) values were all above 0.8, indicating good convergent and discriminant validity. The average variance extracted (AVE) for each variable was above 0.5, and the correlations between factors were all less than the square root of the AVE, indicating good discriminant validity. Overall, the scale demonstrated good validity and reliability.

2. Correlation Analysis

According to the Fornell-Larcker criterion, the square root of the AVE diagonal value should be greater than the corresponding correlation value[46]. This study meets this criterion, as shown in Table 4.

Table 4. Differential Validity

3. Hypothesis Test

The results of the structural equation model (SEM) fitting are shown in Figure 2, indicating a good fit for the SEM (χ²/df = 1.874, RMSEA = 0.046, SRMR = 0.040, CFI = 0.962, TLI = 0.956, IFI = 0.962). Among the five hypotheses, four were supported by significant results, while the control variables (gender, age, education, and occupation) did not show significant effects, as shown in Table 5. Specifically, hypotheses H1, H2, H3, and H4 were supported, while hypothesis H5 was not. The lack of significant impact from control variables suggests that in the current outdoor product market, factors such as age, education, and occupation are not the main determinants of consumer purchase intention compared to ordinary consumer goods.

Table 5. Structural Equation-AMOS Model Path Analysis Results

Fig. 2. Structural Model Analysis Results

V. Conclusions

1. Conclusion and Implications

The study results indicate that KOCs attributes in the central route have a significant positive impact on consumer purchase intentions. The path coefficient for recommendation consistency is 0.127, for product involvement is 0.177, and for expertise is 0.230. Among these, expertise has the strongest positive impact on consumer purchase intentions, followed by product involvement, with recommendation consistency having the smallest positive impact. The influence of KOCs attributes in the peripheral route on consumer purchase intentions is uncertain. Recommendation timeliness has a significant positive impact on consumer purchase intentions, with a path coefficient of 0.326, the highest among all dimensions. However, the impact of KOCs' visual cues on consumer purchase intentions is not significant.

The results demonstrate that consumers are more likely to be influenced by KOCs recommendations that align with their hobbies and values, especially those from KOCs who deeply research products, express personal opinions, and possess relevant experience and abilities. Recommendation timeliness also affects purchase intentions; timely recommendations can stimulate consumers' purchasing impulses. However, compared to text information, visual information in the form of pictures and videos is more intuitive, but its slower production speed leads to uncertain effects on consumers.

In summary, KOCs recommendations have varying degrees of impact on consumer purchase intentions in terms of recommendation consistency, product involvement, expertise, and recommendation timeliness, while the impact of visual cues on consumer purchase intentions is not significant.

Based on the conclusions of this study, businesses can achieve precise brand communication and targeted traffic by effectively utilizing both central and peripheral routes to accurately identify Key Opinion Consumers (KOCs). In terms of the central route, companies can identify KOCs from three perspectives: recommendation consistency, product involvement, and expertise. Specifically, businesses should analyze consumer group characteristics and preferences of target consumers within their industry through big data analysis and accumulated experience. This involves detailed analysis of member systems, activity participation, and evaluation of KOCs' professional abilities, such as the quantity of product sharing and dissemination of product knowledge. By doing so, companies can select high-quality KOCs, establish a KOCs database, and achieve precise guidance and management, thereby influencing a broader consumer base. In the peripheral route, businesses can enhance the timeliness of KOCs recommendations by updating product information frequently, integrating multi-channel promotions, and organizing offline events. They can also assess the effectiveness of KOCs recommendations based on the content of the product information and the degree of alignment with product promotion strategies.

This study focuses on the emerging phenomenon of live streaming commerce on short video platforms, which has significant practical implications. Given the large base of short video users in China, this study is highly representative. The innovation of this research lies in introducing the unique perspective of Key Opinion Consumers (KOCs) and deeply analyzing the influencing factors of KOCs in live streaming commerce from both central and peripheral routes. By exploring the multiple mechanisms through which KOCs affect consumers' purchase intentions, this study finds that KOCs' roles in live streaming commerce extend beyond direct product recommendations and significantly impact consumer purchase decisions through various pathways. This finding provides businesses with new perspectives and a foundation for developing live streaming commerce strategies while offering theoretical support for further academic exploration of consumer behavior on short video platforms.

2. Research Limitation and Future Directions

While this study examines the impact of KOCs characteristics on consumer purchase intentions, several limitations exist. Firstly, the study primarily focuses on the characteristics of KOCs, overlooking the influence of individual consumer characteristics and environmental factors. Future research could broaden its scope by considering more potential influencing factors, such as consumers' socio-economic backgrounds and the competitive status of the product market, to establish a more comprehensive model. This would allow for a deeper exploration of how KOCs on short video platforms affect consumer purchase intentions.

Secondly, the use of a single methodology limits the comprehensive understanding of the research topic. Future studies could adopt a mixed-method research design, combining quantitative and qualitative data to better understand the research problem. Mixed methods can provide more comprehensive and in-depth insights, enhance the credibility and scientific rigor of the research results, and offer more persuasive conclusions for both academic and practical fields.

Lastly, this study primarily focuses on Chinese consumers. To enhance the generalizability of the research, future studies could investigate consumers in other countries and regions. For instance, conducting a comparative analysis between China and South Korea could explore whether differences exist in consumer psychology between countries with similar cultural backgrounds but different levels of economic development.