# A Study on the Influence of Interactivity on Behavior Intention in Platform B2C Model

Authors: Hao Sun
Journal: Advances in Multimedia
 (2022)

---

## Abstract

The progress of information technology and the popularity of the Internet have led to the rapid development of the network economy, and as a new shopping method, online shopping is gradually accepted and respected by consumers. With the development and maturity of online shopping market, the concept of consumers has also been changing, gradually changing from “panning for cheap” and “panning for convenience” to “panning for quality,” paying more attention to the quality of goods and services. B2C is more trusted by consumers than C2C in terms of reputation and quality assurance, so it has gradually become a hot spot in online shopping. With the popularization of online shopping, the development of e-commerce is increasingly characterized by the platform, and it is more and more difficult for shopping websites to attract online consumers with rich product information and functions alone. Using college students with online shopping experience in T-mall as the research subjects, descriptive analysis, exploratory factor analysis, correlation analysis, and regression analysis were used to study the influence of interactivity on customers’ behavioral intention under the platform-based model from a perception perspective.


---

## Body

## 1. Introduction

The progress of information technology and the popularity of the Internet have led to the rapid development of the network economy, and as a new shopping method, online shopping is gradually accepted and respected by consumers. With the development of China's online shopping market, the concept of consumers is also changing, gradually upgrading from "cheap" or "convenient" to "focusing on product quality. Because B2C is more trusted by consumers than C2C [1] in terms of reputation and quality, it has gradually become a hot spot for the development of online shopping. With the popularization of online shopping, the development of B2C e-commerce is increasingly characterized by the platform, and it is more and more difficult for shopping websites to attract online consumers with rich product information and functions alone [2].Internet shopping [3, 4] is the process of transferring goods and services from the seller to the consumer through the Internet. In China, there are many ways for Internet users to shop, including T-mall, Taobao, Jindo, and Jingdong. People’s lives are becoming more convenient and modern because of the rise of these shopping platforms. The mode of network shopping contains two kinds. The transaction subjects of network shopping are individual sellers, traditional channel merchants, and pure network retailers. The transaction objects are tangible goods and intangible services [5]. According to the different degrees of electronic network, shopping transaction process can be divided into fully electronic and not fully electronic network shopping. Among them, B2C is the process of transferring goods or services from merchants to consumers through the Internet, which is a value-based business activity on interactive relationship, and its business model includes independent sales and platform [6, 7]. Autonomous sales [8] refers to the merchant shopping site to establish an independent network platform to sell their own products or services; products or services sales of the various aspects of the operator to complete the independent sales of B2C shopping site revenue are mainly the difference between the purchase and sale of goods; platform type refers to the third-party merchants to provide a trading platform, allowing the seller and the seller in the transaction. Platform-type refers to the third-party merchants to provide a trading platform, allowing sellers and sellers to independently carry out online transactions on the platform, but is not responsible for sales-related links, such as T-mall. For example, T-mall, platform-type shopping sites mainly earn revenue from commission services [9].Due to the infinite space, long tail effect, and diminishing marginal cost of the network economy [10], the development of e-commerce [11] has shown the characteristics of departmentalization and platformization, and independent sales shopping websites have also started to develop gradually in the direction of platformization, and third-party merchants have been introduced to Jingdong Mall and Amazon [12]. The market share of shopping through the platform-based approach is expanding rapidly, and the scope of business and product categories offered by shopping websites is also expanding rapidly [13, 14].As the economy and technology develop, the Internet is taking on an increasingly important role in people’s lives, and the Internet is becoming more and more popular in people’s life and study. As of January 2021, the number of cell phone users worldwide is 5.22 billion, the number of Internet users is 4.66 billion, and the number of social media users is 4.2 billion. Currently, 5.22 billion people worldwide use cell phones, equivalent to 66.6% of the world’s total population. Since January 2020, the number of cell phone users has grown by 1.8% (93 million), while the total number of mobile connections (one person with multiple devices) has increased by 0.9% (72 million) to 8.02 billion (in January this year.) In January 2021, the number of people using the Internet worldwide reached 4.66 billion, an increase of 316 million or 7.3% over the same period last year. Currently, the global Internet penetration rate is 59.5%. However, the new crown virus outbreak has had a significant impact on the reported number of Internet users, so the actual number may be higher. Currently, there are 4.2 billion social media users worldwide. This number has increased by 490 million in the past 12 months, a year-over-year increase of more than 13 percent. The number of social media users now represents more than 53 percent of the global population. According to the latest information from China Internet Network Information Center (CNNIC) [15], by the end of the year, the number of people using the Internet in China was 100 million, an increase year-on-year, with students, self-employed people, and freelancers taking up the top two positions, respectively. At the same time, the number of people accessing the Internet through cell phones has also increased very rapidly, accounting for a proportion of the total, further promoting the popularity of the Internet.With the development and popularity of Internet technology [16, 17], China’s online shopping market continues to develop rapidly, and shopping websites are built in more diverse forms and styles, with richer functions [18]. Most shopping websites are equipped with rich product categories, beautiful pictures, clear site navigation, intelligent site search, and other elements [19]. It is difficult for shopping websites to attract online consumers at this time just by virtue of the rich product information and functions mentioned above. The progress of information technology and the spread of the Internet have led to the rapid development of the online economy, and online shopping is gradually accepted and respected by consumers. By the end of 2013, the number of people shopping through the Internet in China was 302 million, and the scale of online shopping was 185 million, while the improvement of online payment and related measures furthered the development of online shopping [20]. Compared to the traditional environment, the purchase behavior in the Internet environment is very different from it, and consumers lack tangible contact with products and salespeople [21]. In the Internet environment, shopping websites not only assume the function of displaying goods and information, but also the function of answering and solving customers’ questions, and enhancing customers’ perception of shopping websites and their goods through interaction and communication between customers and websites. Especially in the current context where customers pay more attention to consumer experience and perception, interactivity plays a more important role [22].As an emerging shopping method, online shopping brings great convenience. Internet shopping has the following advantages: save shopping time, reduce shopping fatigue, save shopping cost, complete goods, and convenient payment. We can pay directly to each other through the one-stop service provided by the e-commerce website. But due to the virtual nature of the Internet environment, compared to the real world, the purchase behavior in the internet environment has more uncertainty and risk, and the online purchase behavior is very different from the purchase behavior in reality. Unlike the physical consumer environment, the virtual shopping environment in the online shopping environment makes it more difficult to form trust as consumers lack tangible contact with goods, services, and people, which brings a nonnegligible problem to online shopping, namely, trust [23, 24].As the Internet technology develops and becomes more popular, China’s online shopping market continues to develop rapidly, and shopping websites are built in more diverse forms and styles, with richer functions. Compared with the traditional environment, the purchasing behavior in the Internet environment is very different from it, and consumers lack tangible contact with products and salespeople. In the Internet environment, shopping websites not only take the function of displaying products and information, but also take the function of answering and solving customers’ questions, enhancing customers’ perception of shopping websites and their products through interaction and communication between customers and websites. Especially in the current context that customers pay more attention to consumer experience and perception, interactivity plays a more important role.Most shopping websites are equipped with rich product categories, beautiful pictures, clear site navigation, intelligent site search, and other elements. It is difficult for shopping websites to attract online consumers at this time just by virtue of the rich product information and functions mentioned above. In the traditional environment, customers experience and perceive the store inside and enhance their understanding of products and information through communication and interaction with sales staff.This study aims to investigate the influence of interactivity on customers’ behavioral intention in the platform model, which helps to break the black box between interactivity and customers’ behavioral intention in the platform model and provides theoretical support for better understanding the relationship between the two. Secondly, we introduce variables such as customer perceived value and trust to explore the influence of interactivity on customers’ behavioral intention in the platform model from the perspective of perception, which expands the research field of interactivity. Third, we introduce online shopping experience as a moderating variable to explain the mechanism of interactivity on purchase intention in platform-based shopping sites, which completes the previous theories. The flow of this study is shown in Figure1.Figure 1
Flow chart of this study.


## 2. Theoretical Model and Research Hypothesis

The stimulus-organism-response (S-O-R) model was developed by Mehrabian based on the development of environmental psychology. This model assumes that external stimuli influence the behavioral decisions made by individuals by affecting their internal states. In the stimulus-organism-response model, the stimulus is the factor that arouses or stimulates the individual; the organism represents the emotional and cognitive state, which is the internal processing and structure between the external stimulus and the final response or behavior; the response is the individual’s tendency or avoidance behavior, which is the behavioral decision made by the individual based on the stimulus and the organism and represents the final outcome. The stimulus-organism-response model has been studied in depth and has been applied to the field of marketing, where the external stimulus consists of a situation and a product, assuming that the situation exists objectively and that the customer’s perception of the situation will have an effect on his or her psychological response and thus influence the consumer’s behavioral decision. The theoretical model is shown in Figure2.Figure 2
Theoretical model.


### 2.1. Reliability

Reliability, or dependability, reflects the stability and consistency of the measurement results. In this study, based on pretesting and mass distribution of questionnaires, the formal questionnaire study was statistically and analytically analyzed using SPSS, mainly using single overall correlation coefficients and Cronbach’s coefficients for positive research data.The following equation represents the Cronbach’s coefficient.(1)alpha=kk−11−∑i=1kSi2Sx2,where k is the number of items used for measurement, Si2 is the variance of the ith item score (measured value), and Sx2 is the variance of the total scale score.The variance is calculated as(2)varx1,x2,⋯,xn=x1−x¯2+x2−x¯2+⋯+x3−x¯2n−1,where x¯ is average value.


### 2.2. KOM Test

The KMO test [25] is a measure of sampling adequacy (MSA) test proposed by Kaiser, Meyer, and Olkin. This test is a test of the relative magnitude of the simple and partial correlation coefficients between the original variables. The calculation formula is
(3)KMO=∑∑i≠jrij2∑∑i≠jrij2+∑∑i≠jrijn1,2⋯k2.The principle of the test: If a single co-factor does exist in the raw data, then the bias correlation coefficient among the covariates should be minimal, when the KMO value is near one, and thus, the raw data are appropriate for factor analysis.


### 2.3. Bartlett’s Spherical Test

Suppose there arer subgroups and the standard deviation of each group is s12,s22,⋯,sr2, from these standard deviations, it is also possible to calculate the MSe. The statistic K2 approximately follows a chi-square distribution with r−1:
(4)K2=1cn−rlnMSe−∑ri=1ni−1lnsi2,(5)c=1+13r−1∑ri=11ni−1−1n−r.It is not difficult to show that the MSe is taken logarithmically and multiplied by own degrees of liberty, the criterion difference was taken numerically and summed by certain weights (degrees of liberty), and at last, the 2 parts were subtracted to obtain the major part of theK2 equation.If the same amount of data is available for each group, then the equation simplifies to(6)K2=1cn−rlnMSe−lnsi2,c=1+13r−1r2n−r−1n−r.


### 2.4. Pearson Simple Correlation Coefficient

In statistical terms, the Pearson product-moment correlation coefficient (PMCC), which is abbreviated sometimes asr or ρ, is a measurement of the correlation (linear correlation) between 2 covariates X and Y, taking on values ranging from [-1,+1]. The PMCC is extensively used in scholarly studies to gauge the intensity of structural linear relationship between 2 covariates. It was originally formulated by Carl Pearson in the 1880s from ideas proposed by Francis Galton, but following the development of a comparable yet marginally different original idea, the coefficient of correlation is often termed “ Pearson’s r”.Pearson’s linear correlation coefficient [26] is only another of the numerous possibilities. In order to employ Pearson’s linear correlation coefficient, it has to be assumed that the data are obtained in pairings from a regular circulation and the data have to be isometric, especially at least in the field of logic. If both conditions are not met, one possibility is for Spearman’s rank correlation coefficient to be used instead of Pearson’s linear correlation coefficient.The Pearson product moment coefficient of correlation between both covariates is to be defined as the entropy of the product of the covariance of these covariates and the standard deviation of both.(7)ρXY=covX,YσXσY=EX−μXY−μYσXσY.The above equation defines the overall correlation coefficient, which is generally denoted by the Greek letterρ. If the overall covariance and normal deviation are replaced by the calculated covariance and formal deviation for the sample, the sample’s correlation coefficient is typically expressed as r.
(8)r=∑ni=1Xi−X¯Yi−Y¯∑ni=1Xi−X¯2∑ni=1Yi−Y¯2.Another formula for defining the correlation coefficient equivalent to the above equation is defined by the product of the mean values of the variables after normalization. Assuming that the sample can be written as (X1, Y1), the sample Pearson correlation coefficient is
(9)r=1n−1∑ni=1Xi−X¯sXYi−Y¯sY.


## 2.1. Reliability

Reliability, or dependability, reflects the stability and consistency of the measurement results. In this study, based on pretesting and mass distribution of questionnaires, the formal questionnaire study was statistically and analytically analyzed using SPSS, mainly using single overall correlation coefficients and Cronbach’s coefficients for positive research data.The following equation represents the Cronbach’s coefficient.(1)alpha=kk−11−∑i=1kSi2Sx2,where k is the number of items used for measurement, Si2 is the variance of the ith item score (measured value), and Sx2 is the variance of the total scale score.The variance is calculated as(2)varx1,x2,⋯,xn=x1−x¯2+x2−x¯2+⋯+x3−x¯2n−1,where x¯ is average value.


## 2.2. KOM Test

The KMO test [25] is a measure of sampling adequacy (MSA) test proposed by Kaiser, Meyer, and Olkin. This test is a test of the relative magnitude of the simple and partial correlation coefficients between the original variables. The calculation formula is
(3)KMO=∑∑i≠jrij2∑∑i≠jrij2+∑∑i≠jrijn1,2⋯k2.The principle of the test: If a single co-factor does exist in the raw data, then the bias correlation coefficient among the covariates should be minimal, when the KMO value is near one, and thus, the raw data are appropriate for factor analysis.


## 2.3. Bartlett’s Spherical Test

Suppose there arer subgroups and the standard deviation of each group is s12,s22,⋯,sr2, from these standard deviations, it is also possible to calculate the MSe. The statistic K2 approximately follows a chi-square distribution with r−1:
(4)K2=1cn−rlnMSe−∑ri=1ni−1lnsi2,(5)c=1+13r−1∑ri=11ni−1−1n−r.It is not difficult to show that the MSe is taken logarithmically and multiplied by own degrees of liberty, the criterion difference was taken numerically and summed by certain weights (degrees of liberty), and at last, the 2 parts were subtracted to obtain the major part of theK2 equation.If the same amount of data is available for each group, then the equation simplifies to(6)K2=1cn−rlnMSe−lnsi2,c=1+13r−1r2n−r−1n−r.


## 2.4. Pearson Simple Correlation Coefficient

In statistical terms, the Pearson product-moment correlation coefficient (PMCC), which is abbreviated sometimes asr or ρ, is a measurement of the correlation (linear correlation) between 2 covariates X and Y, taking on values ranging from [-1,+1]. The PMCC is extensively used in scholarly studies to gauge the intensity of structural linear relationship between 2 covariates. It was originally formulated by Carl Pearson in the 1880s from ideas proposed by Francis Galton, but following the development of a comparable yet marginally different original idea, the coefficient of correlation is often termed “ Pearson’s r”.Pearson’s linear correlation coefficient [26] is only another of the numerous possibilities. In order to employ Pearson’s linear correlation coefficient, it has to be assumed that the data are obtained in pairings from a regular circulation and the data have to be isometric, especially at least in the field of logic. If both conditions are not met, one possibility is for Spearman’s rank correlation coefficient to be used instead of Pearson’s linear correlation coefficient.The Pearson product moment coefficient of correlation between both covariates is to be defined as the entropy of the product of the covariance of these covariates and the standard deviation of both.(7)ρXY=covX,YσXσY=EX−μXY−μYσXσY.The above equation defines the overall correlation coefficient, which is generally denoted by the Greek letterρ. If the overall covariance and normal deviation are replaced by the calculated covariance and formal deviation for the sample, the sample’s correlation coefficient is typically expressed as r.
(8)r=∑ni=1Xi−X¯Yi−Y¯∑ni=1Xi−X¯2∑ni=1Yi−Y¯2.Another formula for defining the correlation coefficient equivalent to the above equation is defined by the product of the mean values of the variables after normalization. Assuming that the sample can be written as (X1, Y1), the sample Pearson correlation coefficient is
(9)r=1n−1∑ni=1Xi−X¯sXYi−Y¯sY.


## 3. Data Analysis and Discussion

In order to ensure the consistency with the preresearch sample, this study still selects university students and friends in Jinan, Qingdao, Weihai, and Shanghai as the main survey subjects, using a combination of online and offline questionnaires: online, mainly using the Internet questionnaire star and attitude 8 website release question, and offline, mainly using snowball and random column intercept. This study issued a total of questionnaires, of which 200 were distributed online and 100 offline; a total of 267 were returned, excluding 14 questionnaires with incomplete answers and 39 questionnaires that did not pass the screening questions; 214 valid questionnaires were returned; and the questionnaire efficiency rate was 84.6%. The formal study sample statistics are shown by Table1.Table 1
Formal research sample statistics.
Demographic characteristicsGenderMale34.6%Female65.4%Ages20 years old and below0.5%21-25 years old59.8%26-30 years old24.3%30-40 years old7.5%Over 40 years old7.9%Education levelHigh school and below1.9%College8.9%Undergraduate41.6%Master45.3PhD and above2.3%Average monthly consumer spending500 RMB and below11.7%501-1000 RMB31.3%1001-2000 RMB34.6%2001-4000 RMB13.6%4000 RMB and above7%Status of online shoppingCumulative shopping trips1-2 times4.2%3-5 times11.7%6-10 times12.6%11-20 times23.8%More than 20 times47.7%Average amount per purchaseUnder 50 RMB4.2%51-100 RMB41.6%101-200 RMB40.2%201-500 RMB9.8%500 RMB and above4.2%The time since the first online purchaseWithin 1 year10.7%1-2 years18.2%2-5 years53.7%More than 5 years17.3%


### 3.1. Reliability Analysis of Prestudy Data

In this study, the analysis of the data was used to purify the questionnaire using CITC. In addition, Cronbach’s coefficient and standard deviation will be used to measure the validity of the questionnaire in this paper.After and reliability analysis, it can be seen from Figures3–9 that the CITC value of interactivity CON is 0.152, and its absolute value is less than 0.3, and the Cronbach’s coefficient increases from 0.779 to 0.789 after deleting the question item, which satisfies the deletion criteria, where the CITC value of question item MUT4 is -0.105, and its absolute value is less than 0.3, and after deleting the question item Cronbach’s coefficient increased from 0.799 to 0.822, satisfying the deletion condition. Therefore, to ensure the reliability of the study scalar, the question items CON1 and MUT4 were removed, and after deleting these two items, the Cronbach’s coefficient rose to 0.833, which has high credibility. As can be seen in Figures 3–6, all question items of the other quantities do not satisfy the removal condition, the single item-total correlation coefficient (CITC) is greater than 0.3, and the Cronbach’s coefficient increases after the removal of any of the question items. Therefore, the CPV, PT, OSE, and BI measures did not meet the deletion criteria, and all of them were retained. After processing, the remaining measurement items had good internal consistency.Figure 3
Results of INT analysis.Figure 4
Results of MUT analysis.Figure 5
Results of RES analysis.Figure 6
Results of CPV analysis.Figure 7
Results of PT analysis.Figure 8
Results of OSE analysis.Figure 9
Results of BI analysis.


### 3.2. Descriptive Statistical Analysis of Study Variables

In addition to the descriptive statistical analysis of the demographic characteristics of the sample, this study also conducted descriptive statistical analysis of the study variables and calculated the means and standard deviations of the study variables to provide a better and more comprehensive presentation of the sample data, as shown in Table2. The statistical results showed that the mean values of each study variable ranged from 4.37 to 5.99, and the standard deviations of each measurement question item were in the range of 0.885-1.501, and the data distribution was generally reasonable. The coefficients of skewness all have absolute values between 0.330 and 1.577, all of which are less than 3, and the coefficients of kurtosis have absolute values between 0.046 and 3.906, all of which are less than 10, so they are basically consistent with the assumption of normal distribution.Table 2
Means and standard deviations of study variables.
Variable nameMeasurement question itemsAverage valueStandard deviationSkewnessKurtosisControllabilityCON15.461.137-0.8540.619CON25.661.087-1.5773.906CON34.711.501-0.471-0.701CON44.901.389-0.534-0.527MultidirectionalityMUT15.900.976-1.4833.314MUT25.850.912-0.9351.202MUT35.641.019-0.9910.902ResponsivenessRES14.881.239-0.575-0.046RES25.191.086-0.7780.565RES34.811.227-0.5770.097RES44.371.342-0.100-0.666Customer perceived valueCPV14.861.238-0.7550.473CPV25.081.052-0.502-0.189CPV34.961.011-0.448-0.058Platform trustPT15.071.070-0.512-0.175PT25.400.972-0.6940.390PT35.310.968-0.6840.463PT45.220.962-0.7190.230Behavioral intentionsBI15.481.010-0.8580.743BI24.561.372-0.140-0.614BI35.221.099-0.6050.116BI45.031.182-0.330-0.381Online shopping experienceOSE15.791.136-1.3722.060OSE25.571.261-0.9510.598OSE35.831.130-1.2011.465OSE45.990.885-0.8740.853The results of the overall reliability analysis of this study are shown in Table3. The overall Cronbach’s coefficient value of 0.912 is much greater than 0.7, indicating that this questionnaire has good reliability in general.Table 3
Results of overall reliability analysis.
Number of measurement itemsCronbach’s270.912


### 3.3. Factor Analysis of Customer Perceived Value

As shown in Table4, the KMO=0.666 and Bartlett’s sphere check pass the test of significance as well, and the sample results show that the measure is appropriate for conducting factor analysis. As shown in Table 4, an explanatory factor with an eigenvalue greater than 1 is obtained by factor analysis, which can explain 66.231% of the variance.Table 4
Customer perceived value KMO and Bartlett’s text test results.
KMO0.666Bartlett’s sphere testApprox., chi-square149.730Degree of freedom3Significance level0Total explained variance66.231%As can be seen from the Table5, the factor loadings of customer perceived value after transfer ranged from 0.767 to 0.857, and there was no cross-factor loading, indicating good discriminant validity among the variable scales.Table 5
Customer perceived value factor loadings.
Item no.Factors1Guaranteed quality of goods0.767Reasonable price of goods0.815Good value for money0.857


### 3.4. Related Analysis

We first test whether there is a correlation between the three dimensions of interactivity (control, multidirectionality, and responsiveness) and customer perceived value. The results of the correlation analysis are shown in Table6.Table 6
Correlation analysis of interactivity and customer perceived value.
CPVRESCorrelation coefficient0.474Significance0CONCorrelation coefficient0.388Significance0.002MUTCorrelation coefficient0.536Significance0The components of interactivity all had a significant positive correlation with perceived customer valence. The correlation coefficient of responsive RES with perceived customer value was 0.474, the correlation coefficient of control with perceived customer value was 0.388, and the correlation coefficient of bidirectional with perceived customer value was 0.536. The correlation coefficients between each variable were less than 0.8, so there was no covariance problem, and it was suitable for the next step of analysis.Figure10 examines the relationship between the variables in the hypothesis. The data show that the variables of interactivity, customer perceived value, platform trust, and customer behavioral intention are significantly correlated with each other, and each hypothesis is initially verified, where 1 represents gender; 2 represents age; 3 represents education level; 4 represents average monthly consumption expenditure; 5 represents average daily online time; 6 represents responsiveness; 7 represents control; 8 represents bidirectionality; 9 represents perceived value; 10 represents platform trust wide; 11 represents online shopping experience; and 12 represents behavioral intention.Figure 10
Correlation analysis of formal research study variables.


## 3.1. Reliability Analysis of Prestudy Data

In this study, the analysis of the data was used to purify the questionnaire using CITC. In addition, Cronbach’s coefficient and standard deviation will be used to measure the validity of the questionnaire in this paper.After and reliability analysis, it can be seen from Figures3–9 that the CITC value of interactivity CON is 0.152, and its absolute value is less than 0.3, and the Cronbach’s coefficient increases from 0.779 to 0.789 after deleting the question item, which satisfies the deletion criteria, where the CITC value of question item MUT4 is -0.105, and its absolute value is less than 0.3, and after deleting the question item Cronbach’s coefficient increased from 0.799 to 0.822, satisfying the deletion condition. Therefore, to ensure the reliability of the study scalar, the question items CON1 and MUT4 were removed, and after deleting these two items, the Cronbach’s coefficient rose to 0.833, which has high credibility. As can be seen in Figures 3–6, all question items of the other quantities do not satisfy the removal condition, the single item-total correlation coefficient (CITC) is greater than 0.3, and the Cronbach’s coefficient increases after the removal of any of the question items. Therefore, the CPV, PT, OSE, and BI measures did not meet the deletion criteria, and all of them were retained. After processing, the remaining measurement items had good internal consistency.Figure 3
Results of INT analysis.Figure 4
Results of MUT analysis.Figure 5
Results of RES analysis.Figure 6
Results of CPV analysis.Figure 7
Results of PT analysis.Figure 8
Results of OSE analysis.Figure 9
Results of BI analysis.


## 3.2. Descriptive Statistical Analysis of Study Variables

In addition to the descriptive statistical analysis of the demographic characteristics of the sample, this study also conducted descriptive statistical analysis of the study variables and calculated the means and standard deviations of the study variables to provide a better and more comprehensive presentation of the sample data, as shown in Table2. The statistical results showed that the mean values of each study variable ranged from 4.37 to 5.99, and the standard deviations of each measurement question item were in the range of 0.885-1.501, and the data distribution was generally reasonable. The coefficients of skewness all have absolute values between 0.330 and 1.577, all of which are less than 3, and the coefficients of kurtosis have absolute values between 0.046 and 3.906, all of which are less than 10, so they are basically consistent with the assumption of normal distribution.Table 2
Means and standard deviations of study variables.
Variable nameMeasurement question itemsAverage valueStandard deviationSkewnessKurtosisControllabilityCON15.461.137-0.8540.619CON25.661.087-1.5773.906CON34.711.501-0.471-0.701CON44.901.389-0.534-0.527MultidirectionalityMUT15.900.976-1.4833.314MUT25.850.912-0.9351.202MUT35.641.019-0.9910.902ResponsivenessRES14.881.239-0.575-0.046RES25.191.086-0.7780.565RES34.811.227-0.5770.097RES44.371.342-0.100-0.666Customer perceived valueCPV14.861.238-0.7550.473CPV25.081.052-0.502-0.189CPV34.961.011-0.448-0.058Platform trustPT15.071.070-0.512-0.175PT25.400.972-0.6940.390PT35.310.968-0.6840.463PT45.220.962-0.7190.230Behavioral intentionsBI15.481.010-0.8580.743BI24.561.372-0.140-0.614BI35.221.099-0.6050.116BI45.031.182-0.330-0.381Online shopping experienceOSE15.791.136-1.3722.060OSE25.571.261-0.9510.598OSE35.831.130-1.2011.465OSE45.990.885-0.8740.853The results of the overall reliability analysis of this study are shown in Table3. The overall Cronbach’s coefficient value of 0.912 is much greater than 0.7, indicating that this questionnaire has good reliability in general.Table 3
Results of overall reliability analysis.
Number of measurement itemsCronbach’s270.912


## 3.3. Factor Analysis of Customer Perceived Value

As shown in Table4, the KMO=0.666 and Bartlett’s sphere check pass the test of significance as well, and the sample results show that the measure is appropriate for conducting factor analysis. As shown in Table 4, an explanatory factor with an eigenvalue greater than 1 is obtained by factor analysis, which can explain 66.231% of the variance.Table 4
Customer perceived value KMO and Bartlett’s text test results.
KMO0.666Bartlett’s sphere testApprox., chi-square149.730Degree of freedom3Significance level0Total explained variance66.231%As can be seen from the Table5, the factor loadings of customer perceived value after transfer ranged from 0.767 to 0.857, and there was no cross-factor loading, indicating good discriminant validity among the variable scales.Table 5
Customer perceived value factor loadings.
Item no.Factors1Guaranteed quality of goods0.767Reasonable price of goods0.815Good value for money0.857


## 3.4. Related Analysis

We first test whether there is a correlation between the three dimensions of interactivity (control, multidirectionality, and responsiveness) and customer perceived value. The results of the correlation analysis are shown in Table6.Table 6
Correlation analysis of interactivity and customer perceived value.
CPVRESCorrelation coefficient0.474Significance0CONCorrelation coefficient0.388Significance0.002MUTCorrelation coefficient0.536Significance0The components of interactivity all had a significant positive correlation with perceived customer valence. The correlation coefficient of responsive RES with perceived customer value was 0.474, the correlation coefficient of control with perceived customer value was 0.388, and the correlation coefficient of bidirectional with perceived customer value was 0.536. The correlation coefficients between each variable were less than 0.8, so there was no covariance problem, and it was suitable for the next step of analysis.Figure10 examines the relationship between the variables in the hypothesis. The data show that the variables of interactivity, customer perceived value, platform trust, and customer behavioral intention are significantly correlated with each other, and each hypothesis is initially verified, where 1 represents gender; 2 represents age; 3 represents education level; 4 represents average monthly consumption expenditure; 5 represents average daily online time; 6 represents responsiveness; 7 represents control; 8 represents bidirectionality; 9 represents perceived value; 10 represents platform trust wide; 11 represents online shopping experience; and 12 represents behavioral intention.Figure 10
Correlation analysis of formal research study variables.


## 4. Conclusion

This paper composes and analyzes the existing related literature and results, proposes a theoretical model of interactivity-customer behavioral intention and related hypotheses under the platform-based model, and conducts empirical investigation and analysis, and draws the following conclusions.(1)
Interactivity (including control, bidirectionality, and responsiveness) is significantly related to customer perceived value and has a positive and significant impact on customer perceived value. In the Internet environment, consumers are constantly interacting with shopping sites, online suppliers, and other consumers, and this interaction further affects consumers’ experience and perception(2)
Interactivity (including control, two-way, responsiveness) is significantly related to platform trust and has a positive and significant impact on platform trust. Positive interactive experiences and perceptions of each other help increase consumers’ trust propensity, which in turn influences their behavioral intentions and decisions(3)
Between interactivity (including control, two-way, responsiveness) and customers’ behavioral intention, customer perceived value plays a mediating role: among them, between responsiveness, control, and customers’ behavioral intention, customer perceived value plays a partial mediating role; between two-way and customers’ behavioral intention, customer perceived value plays a full mediating role(4)
The moderating effect of online shopping experience on the relationship between interactivity and customer perceived value and platform trust is not significant. The study confirms that consumers’ personal experience in online shopping does not have a moderating effect on the relationship between interactivity, customer perceived value, and platform trust. This may be related to consumers’ risk preferences, consumption tendencies, and habitsThis study introduces customer perceived value and platform trust to explore the mechanism of interaction on customer behavioral intention in platform-based model, which not only enriches the interaction-related research in online shopping model, but also provides guidance and inspiration for online marketing management and customer relationship management in reality.Research findings show that interactivity is significantly related to customers’ perceived value and platform trust; thus, the interactivity experience of shopping websites is crucial. In reality, the website design should be improved to enhance the perceived usefulness and perceived ease of use of the shopping website so that customers can easily and quickly find and buy the products they want. Especially nowadays, the pace of people’s life is accelerating, and they pay more attention to the convenience of shopping. Whether we can provide customers with a simple and easy-to-use system and reliable and useful product information is increasingly becoming the key to attract and retain customers. As more and more customers regard online shopping as a kind of enjoyment experience, we should continuously develop the entertainment and enjoyment type of the website to improve customer experience and perceived value; shopping websites should pay attention to website design and construction, design a clear navigation and positioning system to help customers understand where they are, get the product information they need accurately and timely, and make the shopping process faster and clearer. The website should focus on customer experience, strengthen customized page technology, give customers more space to operate, and enhance website browsing experience and perception control.We should establish an active online virtual communication platform for consumers to exchange knowledge and experience, and the interaction mechanism within the platform should be improved as much as possible to strengthen the communication between consumers. These measures will help increase customer dependence and trust on the website.This study investigates the effects of three dimensions of platform-based B2C shopping site interactivity on customer behavioral intention, which provides a basis for companies to develop and manage shopping sites. This study introduces customer perceived value and trust to investigate the impact of platform-based B2C shopping site interactivity on customer behavioral intention, which provides a realistic guidance and basis for companies to use B2C shopping sites more specifically to develop strategies, cultivate customers, and achieve marketing goals.This study aims to investigate the influence of interactivity on customers’ behavioral intention in the platform-based model, which helps to break the black box between interactivity and customers’ behavioral intention in the B2C platform-based model and provides theoretical support to better understand the relationship between the two. Second, we introduce variables such as customer perceived value and trust to explore the influence of interactivity on customer behavioral intention in the platform-based B2C model from the perspective of perception, which expands the research field of interactivity. Third, we introduce online shopping experience as a moderating variable to explain the mechanism of interactivity on purchase intention in platform-based shopping sites, which completes the previous theories.



---

*Source: 8914315-2022-11-17.xml*