Frontiers in Business, Economics and Management ISSN: 2766-824X | Vol. 7, No. 3, 2023 8 Research on the Impact of UGC Quality on Consumers’ Purchase Intention Wenhui Lyu1, a, Mingxia Wei2, b, Brian Sheng-Xian Teo3, * 1Wenhui Lyu, Graduate School of Management and Science University, Shah Alam, Selangor, Malaysia 2Mingxia Wei, School of Management, Henan University of Technology, Zhengzhou, China 3Brian Sheng-Xian Teo, International Academic Affairs Department, Management and Science University, Shah Alam, Selangor, Malaysia a1021683922@qq.com, bweimingxia@haut.edu.cn, * Corresponding author: 1787785388@qq.com Abstract: As the key engine of Internet shopping, online consumer groups in the 5G era has been increasing in recent years, making consumption behavior on Internet a new form of market development. According to the data of China’s Internet Development Status, by 2021, the number of Internet users in China has risen to 1.033 billion, and there are more than 2,000 5G+ industrial network projects under construction, of which the application rate of short video is as high as 90.6%. It can be seen that Internet retail is the current form of market economic development. TikTok, Micro videos, Huoshan short videos, etc. account for half of the short video marketing. The regular operation of the social economy in the face of the COVID-19 outbreak is encouraged by this new form of internet economy, which attracts consumers and encourages purchasing online. This study explains the quality of User-Generated Content (UGC) and examines its impact on consumers’ purchase intentions through a questionnaire poll. Keywords: short video, User- Genrated Content (UGC), Consumers, Purchase intention. 1. Introduction In recent years, e-commerce, short video and live streaming have become growing popular as the vane of marketing dividends, making more businesses and enterprises aware of the value of digital operations. The substantial retail sector has been particularly hard struck by the COVID-19 virus, which has steadily updated the digital economy to the new standard of commerce. Short videos have gained a lot of attention from the public because to the benefits of easy engagement, rich material, and timely browsing, which has satisfied the public’s need for fragmented entertainment[1]. Young people are the core strength of the current consumer market, and short videos with practicality and interest are the reference standards of these main consumers. Because short videos are professional and targeted, vloggers share their experience in using products to enhance their product views and influence. Consumers can learn about the real form and use effect of the desired products through short videos, while merchants can promote their brand culture with the help of vloggers. All three are mutually beneficial and win-win [2]. However, the stormy development of short videos will also lead to a large number of even products. Faced with the complicated e-commerce market, consumers can easily fall into the trap of unscrupulous anchors, and have to spend more energy and time to obtain real product information. Many consumers will choose groups with similar consumption experiences and interests to save costs. Therefore, at this stage, UGC is very important for video e-commerce mode. Through questionnaire survey, this study expounds the quality of short video UGC and analyzes its influence on consumers’ purchase intention. 2. Related Concepts 2.1. User-Generated Content (UGC) As the inevitable outcome of the development of the Internet era, User-Generated Content (UGC) is mainly characterized by the following features: common rather than authoritative, open rather than confidential, original rather than repost. According to this feature, UGC can be defined as: users who participate in Internet interaction voluntarily publish original content with various content forms including picture, text, video or audio. According to the Type Theory, Zhao Yuxiang and other scholars further refined the concept of UGC, arguing that the main users of content generation are social groups, individuals and organizations, and the generation modes include collaborative, independent, competitive and cumulative. Combining with domestic and foreign research at present, it is found that the hotspots of UGC are mainly concentrated in short videos, social media, virtual communities, etc., and the research fields are mainly concentrated in marketing, social science, e-commerce and information knowledge management and other fields. 2.2. Involvement “Involvement” was originally the point of sight of social psychology, that is, the attitude of individuals towards hot events in society. Self-involvement theory lays a theoretical foundation for consumer behavior, which refers to the degree of correlation between self and events that individuals feel after a series of experiences and stimuli. This paper defines consumers’ involvement: according to their own needs, interests, social background and personal values, consumers’ response to external stimuli is characterized by variability and continuity, and the involvement level will have a series of influence on consumer behavior[3]. 2.3. Perceived Interactivity At present, most of the concepts of perceived interactivity are summarized from the perspective of technical characteristics. Scholar Rice defines perceived interactivity as follows: users exchange roles in the information interaction system, and analyze the interactivity of coded websites through technical features. Some scholars will break the 9 previous academic understanding of perceptual one-to-one interaction, and think that perceived interactivity is the degree of interaction between different communication subjects, information, subjects and media. Expanding the interactivity in the interaction, after generating the interactivity, it has been widely used in the fields of business model, advertising marketing and e-commerce[4]. Perceived interactivity is put forward from the perspective of user perception, corresponding to actual interactivity. 2.4. Purchase Intention The so-called intention is the subjective thinking formed when an individual has a specific behavior or view on things. Purchase intention is the follow-up attitude and inclination of consumers to purchase. Most studies have pointed out that purchase behavior and intention are complementary, and they belong to a collaborative path. In the field of marketing, purchase intention refers to the consumption idea of an individual for a service or product, which is then converted into buying behavior. purchase intention and purchase behavior play an important role in developing marketing theory [5]. 3. Theoretical Model and Research Hypothesis 3.1. Theoretical Model At the present stage, academic theory has confirmed that the central path and the marginal path are the related factors that have a synergistic impact on the attitude of information reception. The situational quality and content quality both map the reliability and scientificity of the obtained information from the objective level, while the source quality reflects the authority of the platform to ensure consumers’ purchasing decisions. The emotional quality is mainly measured in terms of vloggers, and users are usually more inclined to choose entertaining, empathetic and professional vloggers. Therefore, this paper puts situational quality and content quality in the quality of UGC in the central path, while emotional quality and source quality in the marginal path, and studies the influence of four dimensions on consumers’ purchase intention in collaboration, in which the intermediary variable is perceived interactivity, and constructs a model of the influence of UGC quality on consumers’ purchase intention, in which the regulating variable is consumer involvement, and studies the regulating function of consumer involvement when perceived interactivity influences consumers’ purchase intention[6]. Figure 1 is the schematic diagram of theoretical model construction.   Figure 1. Theoretical model 3.2. Research Hypothesis 3.2.1. The Influence of UGC Quality on Consumers’ Purchase Intention This paper analyzes the influence of UGC on consumers’ purchase intention from two aspects: characteristics and manifestions of UGC. High-quality of UGC helps consumers fully grasp of product information, and at the same time reduces the risk of consumers’ purchasing decisions, time- saving, and enhances consumers’ satisfaction and sense of participation. If the quality of UGC is poor, it is difficult for users to obtain the attribute of product, thus they will have a wait-and-see attitude, which will affect their follow-up attention. Therefore, the following hypotheses are put forward: H1: The UGC quality has a positive impact on consumers’ purchase intention. H1a: Situational quality has a positive impact on consumers’ purchase intention. H1b: Content quality has a positive impact on consumers’ purchase intention. H1c: Emotional quality positively affects consumers’ purchase intention. H1d: Source quality positively affects consumers’ purchase intention. 3.2.2. The Influence of UGC Quality on Perceived Interactivity Some scholars[6] have analyzed the content aggregation process of short videoes on TikTok platform in the Web3.0 era, which can be divided into three stages: interactive learning, stimulating participation and practical application. Therefore, high-quality UGC can stimulate the highly perceived interactivity of consumers, which gives consumers a comprehensive understanding of product information, and positively influence their purchase intention. Therefore, the following hypotheses are put forward: H2: The UGC quality has a positive impact on consumers’ perceived interactivity. H2a: Content quality has a positive impact on consumers’ perceived interactivity. H2b: Source quality positively affects consumers’ perceived interactivity. H2c: Situational quality positively affects consumers’ perceived interactivity. H2d: Emotional quality positively affects consumers’ perceived interactivity. 10 3.2.3. The Influence of Perceived Interactivity on Consumers’ Purchase Intention Many scholars[7] focus on the perceived interactivity of self-directed and interaction-oriented types. By comparison and verification, it is found that, compared with self-directed interaction, task-based and interactive-based synergistic effects exert a positive stimulating effect on consumer satisfaction. Self-directed interaction has no obvious such impact on consumer satisfaction. Especially for RED or Xiaohongshu in Chinese Pinyin, a popular e-commerce platform at this moment, information interaction and interpersonal interaction can positively influence consumers’ purchase intention. Effective perceived interactivity can not only enhance consumers’ immersive sense with distance- bridging, but create harmonious and interactive atmosphere, which enhance consumers’ purchase intention. Therefore, the following hypotheses are put forward: H3: Perceived interactivity positively influences consumers’ purchase intention. 3.2.4. Mediating Function of Perceived Interactivity Some scholars[8] have studied the marketing value made by vloggers. If the vlogger has a distinctive style, and the food expression is very interesting, it is easier to enhance consumers’ purchase intention. In this process, user’s quasi- interaction plays an intermediary role. From the perspective of communication and users’ social needs, positive emotional energy can promote users’ interaction, and also contribute to the improvement of video content quality, which has great propaganda effect. Therefore, high-quality UGC can enhance consumer interaction, improve user recognition, and influence consumer willingness and attitude. Therefore, the following hypotheses are put forward: H4: Perceived interactivity plays a partial intermediary role in consumers’ purchase intention and the quality of UGC. H4a: Perceived interactivity plays a part of intermediary role in consumers’ purchase intention and content quality. H4: Perceived interactivity plays a partial intermediary role in consumers’ purchase intention and source quality. H4c: Perceived interactivity plays a partial intermediary role in consumers’ purchase intention and situation quality. H4d: Perceived interactivity plays a part of mediating role in consumers’ purchase intention and emotional quality. 3.2.5. The Regulating Effect of Consumer Involvement Some scholars[9] put forward from the perspective of perceived interactivity that among self-oriented, interactive and task-oriented perceived interactivitys, task-oriented and interactive orientation have a positive impact on consumer satisfaction, and consumer involvement plays a positive role in regulating consumer satisfaction, while self-oriented interaction has negative influence on regulating consumer satisfaction. Therefore, the following hypotheses are put forward: H5: Consumer involvement positively regulates consumers’ purchase intention and perceived interactivity. 4. Research Design and Questionnaire Collection 4.1. Variable Measurement The items selected in this study are all based on the gauges developed, verified and improved by researchers at home and abroad. According to the current situation of short video development and relevant theoretical basis of users, the specific expressions are appropriately adjusted, and the final draft of the questionnaire is finally formed. 4.1.1. The Measurement of UGC The UGC quality is divided into four dimensions: source quality, content quality, situational quality and emotional quality. As shown in Table 1. Table 1. Quality Measurement Indicators of User-genearated Content variable dimension number Items UGC quality Situational quality QJ1 QJ2 QJ3 QJ4 High correlation between the answers and users’ needs when browsing short videos. Short video is immediate. Short video content is smooth and graceful. Most of the information in short videos has high application value. Content quality NR1 NR2 NR3 NR4 NR5 Compared with pictures or text comments, the obtained information browsed in short video is more accurate. The answer from short video are highly targeted. The answer from short video is more reasonable. Product information in short video is extremely reliable. The content browsed from short video is more comprehensive. Source quality LY1 LY2 LY3 The short video contains external links to help users purchase. Short video bloggers are professional and authoritative. Short video bloggers actually use the recommended products. Emotional quality QG1 QG2 QG3 QG4 QG5 QG6 Short video bloggers inspire and encourage consumers. Short video bloggers behave appropriately and have civilized speech. Short video bloggers take all works seriously. Short video bloggers fully understand the recommended products. Short video bloggers have a strong sense of empathy and identity. Short video bloggers are humorous and funny. 11 4.1.2. Perceived Interactivity Measurement Generally, perceived interactivity mainly reflects the perceived interaction between vloggers, platforms and themselves during the period of purchasing products and browsing information through short videos. The following are the measurement indicators of perceived interactivity: Table 2. Measurement indicators of perceived interactivity variable number Items Perceived interactivity HDX1 HDX2 HDX3 HDX4 Short videos are easier to learn and apply. Free to watch short video content according to personal preference. Able to communicate video content with other users. Vloggers can give timely help during browsing short video. 4.1.3. Consumer Involvement Measurement Regarding consumer involvement, there are mainly the following measurement items: Table 3. Measurement Indicators of Consumer Involvement variable number Items Consumer involvement SRD1 SRD2 SRD3 SRD4 It is very important to choose the right products. Consumption through short video platform is a huge effort. Short video purchase is very interesting. Short video purchase is very meaningful. 4.1.4. Purchase Intention Measurement Referring to Dodds (1991) and other scholars’ measurement scale of consumer purchase intention, the following measurement items are obtained: Table 4. Measurement Indicators of Consumers’ Purchase Intention variable number Items Consumers’ puchase intention GMYY1 GMYY2 GMYY3 Willing to buy products promoted by short videos Recommend products pushed by short videos to friends and relatives. Continue to buy in the short video platform 4.2. Questionnaire Design Based on the theoretical model and research hypothesis, the questionnaire is designed by comprehensively considering the intelligibility and scientificity of the selected topic. It includes four variables: consumer involvement, UGC quality, purchase intention and perceived interactivity. For the core variables involved in the questionnaire, the scoring method of Likert 5 scale is applied, with 5 points indicating great agreement, 4 points more agreement, 3 points general agreement, 2 points less agreement, and 1 point extreme disagreement. 4.3. Questionnaire Distribution and Data Collection In this study, data in this survey was collected through two channels: real distribution and Internet. The paper questionnaires were distributed to relatives and friends who had experience of consumption in short video platform, while electronic questionnaires were randomly collected by means of Wen JuanXing, an online questionnaire collection site. During the normalization stage of COVID-19 epidemic, Wen JuanXing not only reduced contact between people, but also improved the efficiency of answering questions. A total of 241 questionnaires were collected, of which 223 were valid. 5. Statistics and Analysis 5.1. Basic Information of the Questionnaire The statistical software SPSS20.0 was used to sort out the data of 223 questionnaires, and the following basic information of the respondents was obtained: the sex of the respondents: male and female accounted for 48.9% and 51.1% respectively, and female were slightly more than male. The age range of the respondents was 19-35 years old, and young people had a high internet attention. Junior college and above accounted for 78.5%, which indicated that consumers had a high level of education. In purchase preference of the sample, daily necessities explained the highest proportion (71.9%), followed by fashion clothes (42.3%). 5.2. Descriptive Statistics of Variables According to the descriptive statistics of variables, the average range of each item is 3.8-4.22, the standard deviation of variables is concentrated in the range of 0.79-1.07, and the absolute value and peak value of skewness of related variables are all below 3, which is consistent with the normal distribution referred to the criteria. In the content quality aspect, the average value of “The answer from short video are highly targeted” is the highest. It can be seen that the targeted information obtained by users in the process of browsing short videos is very important. In the source quality part, the mean value of “Short video bloggers actually use the recommended products” is the highest, which shows that users pay more attention to the real use experience of products. In the situational side, the average value of “Short video content is smooth and graceful” is the highest, which shows that the content performance in the process of short video browsing is very important. In the emotional part, the average value of “Short video bloggers take all works seriously” is the highest. It can be seen that users understand the importance of vloggers’ intentions. In terms of perceived interactivity survey, the average value of “Free to watch short video content according to 12 personal preference” is the highest. It can be seen that users pay more attention to the platform to capture personal interests. In terms of consumer involvement, the score of “Consumption through short video platform is a huge effort” is the highest, indicating that it is very important for consumers to buy through short video. In terms of consumers’ purchase intention, the average value of “Willing to buy products promoted by short videos” is the highest. It can be seen that the higher the quality of user content generation, the more conductive it is to improve consumers’ purchase intention. 5.3. Hypothesis Test 5.3.1. Correlation Regression Analysis 5.3.1.1 The Relationship between Consumers’ Purchase Intention and the UGC Quality In Model 1, the independent variable is the basic information of the investigated subject, and the dependent variable is the consumer’s purchase intention, which is analyzed by regression, as shown in Table 5. In addition, the independent variables include UGC quality, situational quality, content quality, source quality and emotional quality, and the Model 2-Model 6 are obtained to test the extent to which different variables affect consumers’ purchase intention. The quality of UGC is increased in Model 2, and the revised R2 is 0.656, which means that the explanatory power of variables to the model rises to 65.6% after adding UGC. It can be seen that the quality of UGC positively affects consumers’ purchase intention. H1 is supported. Content quality is added in Model 3, and the revised R2 is 0.479, which shows that the explanatory degree of variables to the model rises to 47.9% after adding content quality. It can be seen that content quality positively affects consumers’ purchase intention. H1a is supported. Situational quality is plused to Model 4, and the revised R2 is 0.565, indicating that the explanatory degree of variables to the model rises to 56.5% after adding situational quality. It can be seen that situational quality positively affects consumers’ purchase intention. H1b supported. Source quality is added to Model 5, and the revised R2 is 0.473, indicating that after adding source quality, the explanatory degree of variables to the model rises to 47.3%. It can be seen that source quality positively affects consumers’ purchase intention. H1c is supported. Add emotional quality in Model 6, and the revised R2 is 0.618. It explains that the explanatory degree of variables to the model rises to 61.8% after adding emotional quality. It can be seen that emotional quality positively affects consumers’ purchase intention. H1d is supported. Table 5. Correlation regression analysis of UCG quality and consumers’ purchase intention Purchase intention Independe-nt variable Control variable UGC quality Situational quality Content quality Emotional quality Source quality Academic degree Age Monthly income Consumption frequency M1 -0.303 0.076 -0.073 0.301 M2 0.756*** -0.125 0.025 -0.024 0.062 M3 0.606*** -0.172 0.003 -0.033 0.127 M4 0.664*** -0.197 -0.003 -0.054 0.152 M5 0.591*** -0.172 0.073 -0.044 0.125 M6 0.714*** -0.141 0.081 -0.015 0.088 Fitting index R Revised r F 0.171 0.161 17.525 0.661 0.656 494.044 0.486 0.479 209.580 0.572 0.565 319.447 0.481 0.473 204.163 0.624 0.618 410.517 5.3.1.2 The Relationship between Perceived Interactivity and the Quality of UGC In Model 1, the independent variable is the basic information of the investigated subject, and the dependent variable is the perceived interactivity. Regression analysis is carried out, as shown in Table 6. In addition, the independent variables include UGC quality, situational quality, content quality, source quality and emotional quality, and the M2-M6 model are obtained to test the extent to which different variables affect consumers’ purchase intention. The quality of UGC is increased in Model 2, and the revised R2 is 0.607, which shows that the explanatory power of variables to the model rises to 60.7% after adding UGC. It can be seen that the quality of UGC positively affects perceived interactivity. H2 is supported. Content quality is added in Model 3, and the revised R2 is 0.462, which shows that the explanatory degree of variables to the model rises to 46.2% after adding content quality. It can be seen that content quality positively affects perceived interactivity. H2a is supported. Situational quality is plused to Model 4, and the revised R2 is 0.523, which shows that the explanatory degree of variables to the model rises to 52.3% after adding situational quality. It can be found that situational quality positively affects perceived interactivity. H2b is supported. Source quality is added in Model 5, and the revised R2 is 0.484, indicating that after adding source quality, the explanatory degree of variables to the model rises to 48.4%. It can be seen that source quality positively affects perceived interactivity. H2c is supported. Add emotional quality in Model 6, and the revised R2 is 0.596, which shows that the explanatory degree of variables 13 to the model rises to 59.6% after adding emotional quality. It can be seen that emotional quality positively affects perceived interactivity. H2d is supported. Table 6. Correlation regression analysis of UGC quality and perceived interactivity Purchase intention Independent variable Control variable UGC quality Situational quality Content quality Emotional quality Source quality Academic degree Age Monthly income Consumption frequency M1 -0.381 0.071 -0.064 0.363 M2 0.654*** -0.227 0.027 -0.022 0.156 M3 0.511*** -0.271 0.009 -0.029 0.216 M4 0.559*** -0.292 -0.007 -0.048 0.238 M5 0.526*** -0.264 0.069 -0.038 0.206 M6 0.633*** -0.238 0.076 -0.013 0.174 Fitting index R Adjusted r F 0.246 0.237 27.862 0.613 0.607 323.552 0.468 0.462 144.161 0.529 0.523 205.886 0.492 0.484 165.066 0.602 0.596 304.537 5.3.1.3 The Relationship between Consumers’ Purchase Intention and Perceived Interactivity In Model 1, the independent variable is the basic information of the subject under investigation, and the dependent variable is the consumer’s purchase intention. In addition, the independent variable has been added with perceived interactivity, and the regression analysis is carried out, the revised R2 is 0.615. It can be seen that the explanatory power of the model rises to 61.5% after the increase of perceived interactivity, indicating that perceived interactivity positively affects the consumer’s purchase intention, so H3 is supported. Table 7. Regression analysis of consumers’ purchase intention and perceived interactivity Purchase intention Independent variable Control variable Perceived interactivity Academic degree Age Monthly income Purchase frequency M1 -0.303 0.076 -0.073 0.301 M2 0.773*** -0.009 0.021 -0.025 0.022 Fitting index R Adjusted r F 0.171 0.161 17.525 0.621 0.615 405.353 5.4. Mediating Effect Analysis The mediating effect of perceived interactivity is measured by hierarchical regression method. The independent variable is the basic information of the respondent, and the dependent variable is the consumer’s purchase intention. The quality of UGC is increased in Model 2, and as independent variables, the perceived interactivity and the quality of UGC are increased in Model 3, so as to verify the influence of intermediary effect on consumers’ purchase intention. In Model 2, the quality of UGC has a direct effect on consumers’ purchase intention, and at the same time, it increases the perceived interactivity. The revised R2 value is 0.721, which indicates that the perceived interactivity plays a partial intermediary role between consumers’ purchase intention and the quality of UGC. Therefore, H4 is supported. After perceived interactivity is added to Model 3, R2 value increases to 0.669, which the fitting degree of dependent variables is improved. Therefore, the correlation coefficient decreases to 0.303. It can be seen that perceived interactivity plays a part of intermediary function between consumers’ purchase intention and content quality, so it is assumed that H4a is valid. The situation quality in the model Model 2 directly affects the purchase intention. After the increase perceived interactivity in model Model 3, the R2 value increases to 0.695. After the intermediary variable is added, the fitting degree of the dependent variable is improved, and the correlation coefficient drops to 0.373. It can be seen that the perceived interactivity plays a part of the intermediary function between the consumer’s purchase intention and the situation quality, so it is assumed that H4b is true. The source quality in the Model 2 directly affects the purchase intention. After the model Model 3 increases the perceived interactivity, the R2 value increases to 0.660. After the intermediary variable is added, the fitting degree of the dependent variable is enhanced, and the correlation coefficient drops to 0.275. It can be seen that the perceived interactivity plays a part of the intermediary function between the consumer’s purchase intention and the source quality, so it is assumed H4c supported. The emotional quality in the Model 2 directly affects the purchase intention. After the Model 3 increases the perceived interactivity, the R2 value increases to 0.701. After 14 the intermediary variable is added, the fitting degree of the dependent variable is improved, and the correlation coefficient drops to 0.427. It can be seen that the perceived interactivity plays a part of the mediating function between the consumer’s purchase intention and emotional quality, so it is assumed H4d valid. 6. Research Conclusion and Suggestions 6.1. Research Conclusion Taking consumers on short video as the research object, this paper discusses the influence of the quality of UGC on consumers’ purchase intention, and draws the following conclusions. First of all, the quality of UGC exert a positive effect on consumers’ purchase intention and perceived interactivity. Second, perceived interactivity positively affects consumers’ purchase intention. Thirdly, the quality of UGC has a direct effect on consumers’ purchase intention, and plays an intermediary role in the path of consumers’ purchase intention by means of perceived interactivity. Finaly, consumers’ involvement and perceived interactivity positively influnce their purchase intention. 6.2. Suggestion on Management 6.2.1. Focus on the role of UGC Quality in Consumers’ Purchase Intention. The quality of UGC has a direct effect upon consumers’ purchase intention, and indirectly promotes consumers’ purchase behavior intention. Therefore, in the process of controlling the content quality, the concept of “gatekeeper” should be introduced, so as to eliminate the release of violent or vulgar information from the source, maintain the market operation order of short video platform, and improve the quality of short video content[10]. Great products must be persuasive in guiding consumers to buy. Therefore, it is necessary to ensure that the price and quality of products in different channels are restrained, thus the user’s immersive experience can be improved to some extent[11]. Vloggers mainly emphasizes his image and style, facing consumers with friendly and full emotions, ameliorating its authority in product categories, and finally enhancing the product value [12]. 6.2.2. Give Full Play to the Interactive Effect of Consumers’ Perception. The effective interaction is extremely beneficial to the establishment of harmonious relationship between vloggers and consumers. Therefore, short video platform should improve the mechanism of the operation and audit in food quality, pay attention to consumer experience, and continuously increase the product browsing of short videos. Vloggers should constantly polish the content quality, strengthen the adhesion between their published information and the audience, make full use of the community economy, build a fan team with similar values and shopping needs, and advance user stickiness in communication with commentators [13]. Merchants who publish their products on the short video platform should concentrate on online marketing channels. Besides, they should build product brands by improving product quality, enhance consumer loyalty, carry out discount promotion activities for consumers. In addtion, they also should enhance the attention of products in short video platform, and communicate with users more, so as to enhance the relationship with users[14]. 6.2.3. 5.2.3 Pay attention to the Role of Consumer Involvement in Short Video Marketing Abandon the traditional one-way marketing model, pay attention to consumers’ right of choice and voice, tap the need of potential users’ s with the help of big data, and fully implement the concept of customer first. Involvement is the starting point of users purchase behavior, so it should be correctly judged that consumers buy in order to reduce risks or personal preferences. Only by understanding users’ consumption habits can users be guided to buy. If users’ consumption preferences are relatively fixed, they can recommend alternative services or products [15]. For example, if young people like fashionable products, fashionable clothes, digital products, etc. can be recommended to themso as to improve their satisfaction. 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