The Influences of Short Video Content Marketing on the Purchase Intention: The Case of “Haohuanluo” in China ()
1. Introduction
1.1. Background
With the continuous development of the Internet, the commercial use of 5G networks and the introduction of national policies, the short video industry has matured rapidly. Internet restrictions have been further lifted, and the viewing and application experience of short videos has also been improved. In 2021, the promulgation of the Detailed Rules for the Review of Online Short Video Content marked an important regulatory milestone in this field, which further regulated the steady development of the short video industry. At the same time, with the development of big data and cloud computing technology, the published short video content can further accurately obtain the needs of users, thereby influencing consumers’ access to product information and purchase decisions. Due to the support of national policies and the rapid development of the Internet industry and online social media, short videos are becoming the most popular and preferred entertainment method for all people.
The core of short video content marketing is to convey brand value, build resonance with consumers, and stimulate their purchase intentions. Therefore, when conducting short video content marketing, brands need to pay attention to consumer needs, ensure the quality of content, arouse consumers’ thinking and resonance, create an immersive experience, and achieve satisfactory results.
1.2. Literature Review
1.2.1. Domestic Research
Firstly, in terms of research on short video content marketing and purchase intention, Qin and Wang (2022) found that the dimensions of content marketing of short videos for evaluating products have a direct impact on purchase intention, and trust and perceived value play a partial mediating role between content marketing and purchase intention. Zhang (2023) concluded that interactive content can best arouse consumers’ attention, dramatic content can best mobilize consumers’ interest, and content that leads to place orders can increase transactions and sharing.
Regarding content marketing, Zhou and Chen (2013) believe that content marketing refers to the dissemination of various forms of media content, such as e-magazines, microblogs, videos, etc., to convey product or brand information and stimulate consumer behavior. Additionally, Sun (2017) divides content marketing into three dimensions: informational content, entertaining content, and emotional content. Meanwhile, brand identity plays a partly mediating role in the process of content marketing influencing purchase intention. At the same time, consumers’ product involvement plays a moderating role in the process of content marketing’s impact on purchase intention and brand identity. In another research, Pei (2022) found that in the process of short video content marketing, functional content, entertainment content, and social interaction content all positively affect purchase intention. Moreover, functional content, entertainment content, and social interaction content all have a positive impact on brand identity. In addition, brand identity plays a mediating role in the influence mechanism of the three dimensions of short video content marketing on purchase intention.
Referring to the dimension division of perceived value, Li et al. (2017) respectively looked at the driving role of perceived value in consumers’ purchase behavior from the perspectives of products, services, and society, and found that high perceived value can help increase customers’ willingness to spend on the Internet.
What’s more, in terms of the research on purchase intention, Liang et al. (2020) conducted a research based on the Stimulus-Organism-Response (SOR) theoretical model, and revealed that products recommended by friends can promote perceived pleasure and perceived diagnosis significantly and positively affect consumers’ purchase intention. Kui et al. (2020) found that the usefulness, ease of use, and entertainment in short videos positively affect users’ book purchase intentions through emotional arousal and perceived matching.
1.2.2. Overseas Research
There are also many scholars who have conducted in-depth research of content marketing, perceived value and purchase intention around the world.
Regarding content marketing, perceived value and purchase intention, recently, Hilmiyah, Suhud, and Fawaiq (2024) conducted research on male skin care brands in Jakarta and revealed that content marketing has been shown to have a positive impact on brand image, brand trust, and perceived value. They also found that the brand image and perceived value had a positive impact on purchase intention.
Regarding content marketing and purchase intention, Prasetya and Susilo (2022) found that content marketing strategy and marketing content of Instagram account such as special deals, flash promos, giveaways, and campaigns can influence consumer purchase intention. Meanwhile, Jafarova and Tolon (2022) revealed that content marketing activities in social media have a positive effect on purchase intention and brand loyalty. Later, Wu, Wang and Wang (2024) concluded that consumer purchase intention and brand resonance were positively affected by content marketing; while brand resonance has a positive effect on consumer purchase intention; brand resonance is a mediating variable in the relationship of content marketing on consumer purchase intention.
Moreover, researchers also pointed out, Digital Content Marketing (DCM) of e-commerce is critical to stimulating both immediate and long-term online purchase intention (OPI). The immediate OPI is positively affected by increasing perceived value through Mixed Reality (MR) in DCM. Regarding the long-term OPI, increased customer engagement with DCM under MR environment can build brand trust and significantly affect the long-term OPI (Li, Chan, Chow, Zhang et al., 2022).
Furthermore, some research had tried to figure out the relationship between perceived value, perceived risk and purchase intention. For instance, Gülsoy and Koçer (2023) found that digital content marketing is an important strategy in consumers’ preferences for online purchasing. Gao (2018) pointed out the informational content and the entertaining content have significant positive effect on brand identity, while perceived risk is negatively correlated with purchase intention. While Subasinghe and Weerasisri (2019) concluded that perceived trust influences the impact of digital content marketing on the purchase intention of consumers towards natural personal care items.
2. Theoretical Framework
Stimulus-Organism-Response (SOR) theory is based on the theoretical model framework of behaviorism, which was proposed by Mehrabian and Russell (1974), and refers to the response of people when they are affected by a certain stimulus or environment. This theory is closely related to the influence of short video content and consumers’ purchase intention, and the short video content marketing information of Haohuanluo is the stimulus factor in the theory, the consumer’s perceived value is the internal psychological activity factor of the organism, and the purchase intention is the behavioral expression factor. The theory is shown in Figure 1.
Figure 1. SOR theory.
3. Methodology
3.1. Research Design
The main content of this paper used SPSS 27.0 system software to carry out in-depth processing and analysis of the collected information and data, so as to draw valuable results and conclusions. Firstly, through the analysis of demographic characteristics, we can better understand the composition of the research sample in this paper, so as to better carry out follow-up research work. Secondly, the analysis of the sample information can better understand the background of the research object, as well as the relevant measurement data, to ensure the accuracy and reliability of the research results. Finally, through the analysis of the reliability and validity of the measurement scale, the reliability and validity of the measurement scale in this paper can be better reflected, so as to provide more solid support and guarantee for the research results.
3.2. Research Hypothesis
Content marketing is a method for branding that generates and spreads useful brand content to attract the target audience (Brieger, 2013). Based on the above analysis, the following hypotheses are proposed:
H1: Content marketing has a positive impact on consumers’ perceived value
H1a: Entertaining content in content marketing has a positive impact on consumers’ perceived value
H1b: Functional content in content marketing has a positive impact on consumers’ perceived value
H1c: Social-interactive content in content marketing has a positive impact on consumers’ perceived value
In terms of the influence of perceived value on purchase intention, based on the SOR theory, consumers will perceive products or services when they are affected by the environment, thereby affecting their purchase intention, which has also been confirmed by many scholars. Liu et al. (2021) confirmed that consumers’ perceived willingness and perceived value in e-commerce live streaming have a positive impact on purchase intention. Zhang and Gong (2018) found that perceived usefulness and perceived ease of use have a direct impact on consumers’ use of facial recognition payments. In summary, the following hypotheses are proposed:
H2: Perceived value has a positive impact on consumers’ purchase intention
In terms of the relationship between content marketing and purchase intention, consumers have purchase intention due to the influence of content marketing information. In related research, Zhu (2020) confirmed that the three dimensions of content marketing will have a positive impact on consumers’ willingness to purchase mobile phones. Ji et al. (2023) empirically studied the positive impact of different dimensions of short video content marketing of clothing brands on consumers’ purchase intention and communication intention. Therefore, based on the above analysis and literature review, the following hypotheses are proposed:
H3: Content marketing has a positive impact on consumers’ purchase intentions
H3a: Entertaining content in content marketing has a positive impact on consumers’ purchase intentions
H3b: Functional content in content marketing has a positive impact on consumers’ purchase intentions
H3c: Social-interactive content in content marketing has a positive impact on consumers’ purchase intentions
In terms of the mediating role of perceived value, through the search of literature, perceived value plays a mediating role in many studies. In a related study, Liu et al. (2019) explored the positive effect of the description of product details on consumers’ purchase intention through the effect of perceived value. Yan et al. (2021) found that perceived value mediates the relationship between live streaming characteristics and purchase behavior. To sum up, consumers will be affected by many aspects before purchasing, and after understanding the product, they will be psychologically stimulated, and then they will perceive it psychologically, and after judging after integrating many factors, they will produce purchase behavior. Therefore, it is necessary to use perceived value as a mediating variable in research. Therefore, the following assumptions are drawn:
H4: Perceived value mediates the relationship between content marketing and consumers’ purchase intentions
3.3. Construction of Research Model
By reviewing the literature, it is found that the content quality of short videos will greatly affect the purchase decision of consumers. Short videos can help customers obtain more product-related information, so as to generate curiosity, make customers perceive the value of the product, reduce customers’ distrust of the product, and even influence consumers’ purchase decisions. By watching short videos, Internet users can perceive their own perceived value of the brand or product, learn about the brand or product by browsing short videos, and finally generate purchase intentions.
Short video content marketing may further influence consumers’ purchase intentions by influencing perceived value. Based on the references, the independent, intermediate, and dependent variables were designed as content marketing, perceived value, and purchase intention, respectively. The theoretical analysis framework is shown in the figure below (as shown in Figure 2).
Figure 2. Construction of a research model based on SOR theory.
3.4. Measurements of Variables
3.4.1. Content Marketing
This paper studies the short video content of the brand “Haohuanluo”, and selects three dimensions of content marketing which are entertaining content, functional content and social-interactive content from the existing studies of short video content marketing mentioned in literature review. Among them, entertaining content refers to the content that short videos can bring pleasure and emotional resonance to consumers. Functional content refers to content that consumers can obtain useful information from short videos and can influence consumption decisions. Social-interactive content refers to the informational content that consumers obtain information from short videos and can interact with it and make remarks, referring to relevant research on short video content marketing by Gao (2022), Pei (2022) and Wu, Wang and Wang (2024).
This study draws on the mature content marketing scale with appropriate modifications, and the specific items are shown in Table 1 below.
3.4.2. Perceived Value
Perceived value was defined as customer’s perceived preference for the evaluation of product attributes, product performances etc. (Flint, Woodruff, and Gardial, 1997).
Table 1. Short video content marketing scale.
Variables |
Items |
Entertaining content |
1) I think the short video content can make me feel relaxed and interesting 2) I think the short video content makes me feel excited and excited 3) I think the content of this short video makes me feel curious 4) I think the short video content can immerse me in it |
Functional content |
1) I was able to get some information about the brand and the product from this short video content 2) I was able to get valuable information from the short video content 3) By watching this short video, I can answer some questions related to it 4) I was able to get some useful information from the short video content |
Social-interactive content |
1) By watching this short video content, I found people who have the same needs as me 2) By watching this short video content, I can interact with people who are similar to me 3) By watching the short video content, I may click like and favorite button, etc. |
Based on the knowledge and research of relevant short video content marketing research (Gao, 2022), this paper sorts out the questions in this paper, with a total of 5 items, as shown in Table 2.
Table 2. Perceived value scale.
Variables |
Items |
Perceived usefulness |
1) Watching this short video content helps me understand the product 2) By watching this short video, I can get the information I need 3) By watching this short video content, it is helpful for my consumption decision |
Perceived risk |
4) By watching the short video content, I can effectively reduce the risk that the products I bought do not match my own needs 5) By watching this short video content, I can reduce my asset risk |
3.4.3. Purchase Intention
Purchase intentions can help managers classify locations and consumer groups through the channel (Morwitz et al., 2007). This paper has been appropriately modified according to the existing scale used by Pei (2022) in a short video content marketing research and by Wu, Wang and Wang (2024), with a total of 4 items, as shown in Table 3.
Table 3. Purchase intention scale.
Variables |
Items |
Purchase intention |
1) I would consider buying products from this brand if there is a need 2) I may recommend and share the brand’s products with
others 3) I might think that there would be no problem with the products that appear in the brand’s short videos 4) I think the products recommended in the brand’s short
videos may be worth buying |
3.5. Questionnaire Design
To ensure the reliability and validity of the scale, the items of the scale are all referred to the mature scales that have been studied and concluded by the academic community. Over a period of more than half a month, a total of 311 questionnaires were distributed. To improve the validity of the questionnaire, the questionnaire with less than 1 minute filling time was regarded as invalid, so 288 valid questionnaires were received, and the questionnaire validity rate reached 92%.
4. Data Analysis
4.1. Descriptive Statistical Analysis
Descriptive statistical analysis is the statistics of population-related variables based on the basic information of the questionnaire, including factors such as gender, age, occupation, education and income level, as shown in Table 4.
As can be seen from the table, women (57.6%) fill in more questionnaires than men (42.4%). While participants of 21 to 25-year-old consumers are the most, accounting for 47.9%, and people of 15 to 20 years old accounting for 29.2%. In the proportion of academic qualifications, undergraduate students accounted for the highest proportion of 60.4%, followed by high diploma, accounting for 26.4%, it can be seen that the main respondents of this questionnaire are college students. In terms of consumption level, the consumption level of 1001 - 2000 yuan in the survey data is the main level, accounting for 50.0%, followed by 1000 yuan and below, accounting for 26.7%. In terms of occupation percentage, the top three respondents are students (64.9%), staff at enterprises (16.3%), and house husband/wife/unemployed (8.7%). Questionnaires are mostly distributed during the lunch break, and the frequency of watching short videos during breaks will also increase probably. In the proportion of monthly income, the survey subjects are mainly college students, and the proportion of monthly income also shows a certain pattern: the largest proportion is 1000 - 4000 yuan (45.1%), followed by less than 1000 yuan (35.1%). As a result, the data presents a relatively good income profile for consumers.
Table 4. Demographics of participants.
|
Frequency |
Percentage |
Effective percentage |
Cumulative percentage |
Gender |
Female |
166 |
57.6 |
57.6 |
57.6 |
Male |
122 |
42.4 |
42.4 |
100.0 |
Total |
288 |
100.0 |
100.0 |
|
Age |
15 - 20 years old |
84 |
29.2 |
29.2 |
29.2 |
21 - 25 years old |
138 |
47.9 |
47.9 |
77.1 |
26 - 30 years old |
34 |
11.8 |
11.8 |
88.9 |
31 - 40 years old |
32 |
11.1 |
11.1 |
100.0 |
Total |
288 |
100.0 |
100.0 |
|
Education level |
High school |
18 |
6.3 |
6.3 |
100.0 |
High diploma |
76 |
26.4 |
26.4 |
26.4 |
Undergraduate |
174 |
60.4 |
60.4 |
86.8 |
Master and above |
20 |
6.9 |
6.9 |
93.8 |
Total |
288 |
100.0 |
100.0 |
|
Consumption level |
<1000 Yuan |
77 |
26.7 |
26.7 |
26.7 |
1001 - 2000 Yuan |
144 |
50.0 |
50.0 |
76.7 |
2001 - 3000 Yuan |
35 |
12.2 |
12.2 |
88.9 |
3001 - 4000 Yuan |
22 |
7.6 |
7.6 |
96.5 |
>4000 Yuan |
10 |
3.5 |
3.5 |
100.0 |
Total |
288 |
100.0 |
100.0 |
|
Occupation |
Student |
187 |
64.9 |
64.9 |
93.1 |
Staff at enterprises |
47 |
16.3 |
16.3 |
16.3 |
House husband/wife/
unemployed |
25 |
8.7 |
8.7 |
25.0 |
Civil servant |
5 |
1.7 |
1.7 |
26.7 |
Freelancer and
entrepreneurs |
20 |
6.9 |
6.9 |
100.0 |
Retired people |
1 |
0.3 |
0.3 |
28.1 |
Others |
3 |
1.0 |
1.0 |
27.8 |
Total |
288 |
100.0 |
100.0 |
|
Monthly income |
<1000 Yuan |
101 |
35.1 |
35.1 |
82.6 |
1000 - 4000 Yuan |
130 |
45.1 |
45.1 |
47.6 |
4001 - 7000 Yuan |
28 |
9.7 |
9.7 |
92.4 |
7001 - 10,000 Yuan |
22 |
7.6 |
7.6 |
100.0 |
>10,000 Yuan |
7 |
2.4 |
2.4 |
2.4 |
Total |
288 |
100.0 |
100.0 |
|
4.2. Reliability Analysis
As can be seen from Table 5, the Cronbach’s alpha values of the three dimensions of content marketing are 0.878, 0.871, and 0.824, respectively, and the overall Cronbach’s alpha value is 0.891, indicating that the credibility is greater than 0.7, indicating that the content marketing scale studied in this paper has a high degree of credibility and reliability, and the Cronbach’s alpha values of the two dimensions of perceived value are 0.857, 0.770, the overall Cronbach’s alpha value is 0.821, and the credibility is greater than 0.7, indicating that the perceived value scale studied in this paper has great reliability and credibility The value is 0.887, which indicates that the credibility is greater than 0.7, indicating that the content marketing scale studied in this paper has high credibility and reliability.
Table 5. Reliability test.
Concept |
Variables |
Dimensions of Cronbach’s Alpha |
Overall Cronbach’s Alpha |
Result |
Content marketing |
Entertaining content |
0.878 |
0.891 |
Passed |
Functional content |
0.871 |
|
Passed |
Social-interactive content |
0.824 |
|
Passed |
Perceived value |
Perceived usefulness |
0.857 |
0.821 |
Passed |
Perceived risk |
0.770 |
|
|
Purchase intention |
|
0.877 |
0.877 |
Passed |
4.3. Validity Analysis
Validity analysis refers to the validity test of the questionnaire content to test whether the questionnaire scale has validity. The test method in this paper is mainly factor analysis.
In summary, the KMO value was greater than 0.7, the KMO value and the Bartlett sphere test results met the requirements of factor analysis, the commonality of the common factor variance value was greater than 0.4, and the total variance that could be explained by each common factor exceeded 50%, indicating that the questionnaire in this study had good reliability and validity, and had the conditions for correlation analysis (Table 6, Table 7).
4.4. Correlation Analysis
Correlation analysis is used to measure how closely correlates between two variable factors. In addition, there is a significant correlation between the variables, which provides the basis for the next part of the regression analysis (Table 8).
Table 6. KMO and Bartlett test.
|
|
Content marketing |
Perceived value |
Purchase intention |
The number of KMO sampling appropriateness |
|
0.889 |
0.766 |
0.833 |
Bartlett sphericity test |
Approximate chi-square |
1645.977 |
593.125 |
577.464 |
|
Degree of freedom |
55 |
10 |
6 |
|
Significance |
0.000 |
0.000 |
0.000 |
Table 7. Total variance explained.
|
|
Item |
Initial eigenvalues |
Extract the sum of squares of the load |
|
|
Total |
Variance percentage |
Cumulative % |
Total |
Variance percentage |
Cumulative % |
Content Marketing |
Entertaining Content (EC) |
EC1 |
5.269 |
47.896 |
47.896 |
5.269 |
47.896 |
47.896 |
EC2 |
1.564 |
14.218 |
62.114 |
1.564 |
14.218 |
62.114 |
EC3 |
1.227 |
11.151 |
73.264 |
1.227 |
11.151 |
73.264 |
EC4 |
0.470 |
4.274 |
77.538 |
|
|
|
Functional Content (FC) |
FC1 |
0.442 |
4.021 |
81.559 |
|
|
|
FC2 |
0.412 |
3.747 |
85.306 |
|
|
|
FC3 |
0.381 |
3.461 |
88.767 |
|
|
|
FC4 |
0.342 |
3.111 |
91.878 |
|
|
|
Social Interactive Content (SIC) |
SIC1 |
0.334 |
3.032 |
94.910 |
|
|
|
SIC2 |
0.286 |
2.597 |
97.507 |
|
|
|
SIC3 |
0.274 |
2.493 |
100.000 |
|
|
|
Perceived Value |
Perceived Usefulness (PU) |
PU1 |
2.931 |
58.623 |
58.623 |
2.931 |
58.623 |
58.623 |
PU2 |
1.030 |
20.591 |
79.214 |
1.030 |
20.591 |
79.214 |
PU3 |
0.384 |
7.687 |
86.901 |
|
|
|
Perceived Risk (PR) |
PR1 |
0.334 |
6.687 |
93.587 |
|
|
|
PR2 |
0.321 |
6.413 |
100.000 |
|
|
|
Purchase Intention |
Purchase Intention (PI) |
PI1 |
2.925 |
73.130 |
73.130 |
2.925 |
73.130 |
73.130 |
PI2 |
0.408 |
10.192 |
83.322 |
|
|
|
PI3 |
0.363 |
9.065 |
92.387 |
|
|
|
PI4 |
0.305 |
7.613 |
100.000 |
|
|
|
Table 8. Correlation analysis of variables.
|
EC |
FC |
SIC |
PV |
PI |
EC |
1 |
|
|
|
|
FC |
0.467** |
1 |
|
|
|
SIC |
0.501** |
0.470** |
1 |
|
|
PV |
0.529** |
0.520** |
0.508** |
1 |
|
PI |
0.524** |
0.538** |
0.522** |
0.548** |
1 |
Note: **At the 0.01 scale (two-tailed), the correlation is significant.
4.5. Regression Analysis
Regression analysis is a statistical method to study the quantitative relationship between quantitative data. In terms of the relationship between content marketing and perceived value, the three dimensions of content marketing were taken as independent variables, and perceived value was analyzed as dependent variables. As shown in Table 9, it shows that content marketing significantly affects perceived value, which proves that the significance study of this regression model is meaningful. The results show that there is an obvious positive influence relationship between the significant influence variables of content marketing, so the assumptions H1, H1a, H1b, and H1c are valid. At the same time, VIF < 5 proves that there is no multicollinearity between the three independent variables, and the results of this operation are accurate and reliable. The perceived value proves that the significance of the regression model is meaningful.
Table 9. Regression analysis of content marketing and perceived value.
Model |
Denormalized factor |
Normalization factor |
t |
Sig. |
Collinearity statistics |
B |
Standard Error |
Beta |
Tolerance |
VIF |
1 |
(constant) |
1.073 |
0.178 |
|
6.036 |
0.000 |
|
|
EC |
0.241 |
0.047 |
0.281 |
5.097 |
0.000 |
0.681 |
1.469 |
FC |
0.255 |
0.050 |
0.278 |
5.138 |
0.000 |
0.708 |
1.413 |
SIC |
0.206 |
0.048 |
0.236 |
4.276 |
0.000 |
0.677 |
1.476 |
R2 |
0.413 |
Adjusted R2 |
0.407 |
F |
66.570 |
Note: Dependent variable is perceived value.
Regarding the relationship between perceived value and purchase intention, the independent variable adopts two dimensions of perceived value, and the dependent variable adopts purchase intention. As shown in Table 10, it is proved that perceived value significantly affects purchase intention. There is a clear positive influence relationship between the variables, so it is assumed that H2 is true. There is no multicollinearity between the three independent variables.
Table 10. Regression analysis of perceived value and purchase intention.
Model |
Denormalized factor |
Normalization factor |
t |
Sig. |
Collinearity statistics |
B |
Standard Error |
Beta |
Tolerance |
VIF |
1 |
(constant) |
1.142 |
0.215 |
|
5.312 |
0.000 |
|
|
PU |
0.348 |
0.058 |
0.333 |
5.993 |
0.000 |
0.794 |
1.259 |
PR |
0.311 |
0.055 |
0.312 |
5.624 |
0.000 |
0.794 |
1.259 |
R2 |
0.302 |
Adjusted R2 |
0.297 |
F |
61.744 |
Note: The dependent variable is purchase intention.
In terms of the relationship between content marketing and purchase intention, as shown in Table 11, the independent variable adopts the three dimensions of content marketing, and the purchase intention is used as the dependent variable. The significance of the regression model is significant, and content marketing has a significant impact on purchase intention. There is a significant positive relationship between the variables, and it is assumed that H3, H3a, H3b, and H3c are true. There is no multicollinearity between the three independent variables.
Table 11. Regression analysis of content marketing and purchase intention.
Model |
Denormalized factor |
Normalization factor |
t |
Sig. |
Collinearity statistics |
B |
Standard Error |
Beta |
Tolerance |
VIF |
1 |
(constant) |
0.480 |
0.210 |
|
2.287 |
0.023 |
|
|
EC |
0.265 |
0.056 |
0.258 |
4.749 |
0.000 |
0.681 |
1.469 |
FC |
0.328 |
0.059 |
0.299 |
5.604 |
0.000 |
0.708 |
1.413 |
SIC |
0.263 |
0.057 |
0.252 |
4.622 |
0.000 |
0.677 |
1.476 |
R2 |
0.428 |
Adjusted R2 |
0.422 |
F |
70.818 |
Note: The dependent variable is purchase intention.
In terms of the mediating test of perceived value, it can be seen from the above analysis that content marketing has a positive effect on perceived value and purchase intention, and perceived value has a positive effect on consumers’ purchase intention, which has met the conditions of intermediary effect test, so the mediating role of perceived value can be tested. The results of the mediation model show that content marketing can positively predict perceived value (β = 0.70, t = 14.18, p < 0.001) as shown in Table 12. When both content marketing and perceived value enter the regression equation, content marketing (β = 0.66, t = 8.95, p < 0.001) and perceived value (β = 0.26, t = 3.87, p < 0.01) can significantly and positively predict purchase intention.
Table 12. Regression analysis of the relationship between variables in the mediation model (N = 288).
Variable |
Model 1 |
Model 2 |
Model 3 |
β |
t |
β |
t |
β |
t |
CM |
0.85 |
14.58** |
0.70 |
14.18** |
0.66 |
8.95** |
PV |
|
|
0.26 |
3.87** |
R2 |
0.42 |
0.41 |
0.45 |
F |
212.81*** |
201.31*** |
119.11*** |
Note: Model 1—Content Marketing (CM) Influences Purchase Intention (PI); Model 2—Content Marketing (CM) Affects Perceived Value (PV); Model 3—Content marketing (CM) and perceived value (PV) influence purchase intention (PI). *** means p < 0.001, ** means p < 0.01.
The Bootstrap 95% CI of the total effect, direct effect, and indirect effect did not include 0, indicating that the mediating effect was significant. Therefore, perceived value plays a partial mediating role in the impact of content marketing on purchase intention, and the mediating effect accounts for 21.1% of the total effect, assuming that H4 is true, as shown in Table 13.
Table 13. Analysis of the mediating effect of perceived value.
Effect |
Effect value |
Standard
error |
Bootstrap 95% CI |
Ratio to
total effect |
Min |
Max |
Total effect |
0.85 |
0.05 |
0.73 |
0.96 |
100% |
Direct effects |
0.66 |
0.07 |
0.52 |
0.81 |
77.6% |
Indirect effects |
0.18 |
0.05 |
0.08 |
0.29 |
21.1% |
4.6. Results and Discussions
After regression analysis, the hypotheses proposed in this study have been verified, as shown in Table 14 below. As shown in Table 14, hypotheses are all passed and show us some implications.
Firstly, content marketing has a positive effect on purchase intention. The more interesting and practical the content is, the higher the purchase intention. Also, most of the buyers also seek interaction online, while interactive content providing by marketers can meet their social needs, such as Q&A sections and communicate with other people by posting reviews, which can also result in higher purchase intention.
Secondly, perceived value plays a mediating role between content marketing and purchase intention. While perceived value can be valued by perceived usefulness and perceived risks, marketers should state more practical information in the video within a short period of time. Meanwhile marketers and platforms should also provide more valid and safe online purchasing and paying environment.
Thirdly, marketers on social media can increase the consumers’ perceived value by increasing entertaining content, functional content and social-interactive content in the short video, thus results in the increase of consumers’ purchase intention, instead of losing potential buyers.
Table 14. Results.
Hypothesis |
Content |
Results |
H1 |
Content marketing has a positive impact on consumers’ perceived value |
Supported |
H1a |
Entertaining content in content marketing has a positive impact on consumers’ perceived value |
Supported |
H1b |
Functional content in content marketing has a positive impact on consumers’ perceived value |
Supported |
H1c |
Social-interactive content in content marketing has a positive impact on consumers’ perceived value |
Supported |
H2 |
Perceived value has a positive impact on consumers’ purchase intentions |
Supported |
H3 |
Content marketing has a positive impact on consumers’ purchase intentions |
Supported |
H3a |
Entertaining content in content marketing has a positive impact on consumers’ purchase intentions |
Supported |
H3b |
Functional content in content marketing has a positive impact on consumers’ purchase intentions |
Supported |
H3c |
Social-interactive content in content marketing has a positive impact on consumers’ purchase intentions |
Supported |
H4 |
Perceived value plays a mediating role between content marketing and consumers’ purchase intentions |
Supported |
5. Research Summary and Prospects
5.1. Conclusions
Through short video content marketing, Haohuanluo can effectively disseminate the information of products or services, present valuable and innovative content, and stimulate consumers’ curiosity and resonance. Entertaining content can arise the interests of consumers, while functional content can win consumers’ trust, and reducing consumers’ sense of uncertainty. And social-interactive content can meet the desire of communicating with people. The content mentioned above makes consumers think that the product or service meets their expectations or meets a high standard, which in turn motivates them to potentially take the next step, which is to enhance the purchase intention.
It is also found that perceived value can be used as a mediating variable between content marketing and purchase intention. Increasing entertainment, functional and interactive content can result in higher perceived value. While higher perceived usefulness and lower perceived risk can lead to higher purchase intention. Sellers should clearly clarify the information of products in the video, and guide potential consumers to watch more related videos, and reduce the asset risks such as using professional payment and logistics method, in order to increase users’ purchase intentions and thus increase the order rate.
5.2. Limitations and Prospects
5.2.1. Limitations
First, in terms of the research dimension, this paper only takes three dimensions of content marketing, namely entertaining content, functional content and social interactive content. However, some scholars have conducted more dimensional research, but this paper only takes three of them, so it has certain limitations.
Second, in terms of questionnaire design, the research in this paper was carried out in the form of a questionnaire survey. When the questionnaire was distributed, the short video of the “Haohuanluo” brand was sent to the respondents, but there is no guarantee that participants will not be affected by other objective conditions, and there will be some deviations.
Thirdly, due to the limitations of time, place and other factors, the questionnaire samples in this paper are mainly young people, and there is a lack of respondents aged 41 and above. The education level and consumption level of the respondents are relatively concentrated.
Lastly, the influences of the three types of content of stimulus stage on the two aspects of organism stage are not specifically examined in this study.
5.2.2. Prospects
First, more dimensions can be added to the existing three dimensions of content marketing. Second, this study only investigated by quantitative questionnaires, and future research can use in-depth interview method and experimental method to conduct the research. Third, the type of population and the number of surveys can be expanded to obtain more representative empirical results. Finally, more in-depth research can be carried out to explore the psychological processes involved in the organism stage, specifically how different content types trigger different cognitive and affective responses.