Skip to main content

ORIGINAL RESEARCH article

Front. Psychol., 10 August 2022
Sec. Organizational Psychology

Modeling Intention-Based Critical Determinants of E-Commerce Utilization: Emerging Business Models and Transformation in the Digital World

\r\nTianjie Tong&#x;Tianjie Tong1†Yuyu Xiong*&#x;Yuyu Xiong2*†
  • 1Graduate School of Business, SEGI University, Petaling Jaya, Malaysia
  • 2Postgraduate Department, Henan Agricultural University, Zhengzhou, China

Companies in the world today understand that keeping users in touch is essential to enhancing their trust. The primary objective of this study was to determine the intention-based critical determinants of E-commerce utilization in China from the end users’ perspective. We developed a framework that identifies the factors that influence E-commerce utilization in China. Besides, we introduced observational research (data analysis) conducted in a real-world E-commerce sense. Results are based on a sample of 400 respondents by employing a comprehensive questionnaire survey. The structural equation modeling (SEM) and the partial least squares (PLS) regression approach was used to analyze the data. Study results show that perceived usefulness, perceived ease of use, reputation, trust in vendors, and purchase frequency significantly influence consumers’ intention to use E-commerce systems. Research outcomes emphasize transforming social norms, raising consumers’ awareness, redesigning policy frameworks, and highlighting the paybacks that E-commerce offers through integrative and consistent efforts.

Introduction

Global E-commerce purchasing is about to hit US$4.2 trillion, and recent data show almost 27% growth in the E-commerce business (Uzir et al., 2021). According to statistics from the China Internet Network Information Center, the total number of online shopping users in China in June 2019 was 639 million, accounting for 74.8% of the total number of Internet users in the country, an increase of 28.71 million from the end of 2018. This trend of growth in E-commerce businesses is due to excessive use of the Internet and mobiles. E-commerce is altering the mechanisms by which businesses design, manufacture, and give their goods and services, as well as the number of customers who trust businesses. According to Eurostat reports, in 2011, a 65% increase in the number of businesses with an E-commerce thread occurred, and roughly half of such big businesses at the European commission already have it. According to Yang et al. (2021), offerings focused purely on responsible action, including E-commerce, seem to be examples characterized by the high possibility for game-based learning, as customers can be focused forward into financial increasing efficiency. Due to E-commerce, the cash-less economy has come into existence.

E-commerce is a widely accepted business nowadays, but still, it is taking time to be accepted by all human beings. With each coming day, the ease in the business of E-commerce is expanding at a higher rate (Kotha et al., 2004; Vakulenko et al., 2019). Given the wide range of human differences (Bai et al., 2021; Huang et al., 2022), it can be challenging to determine what factors work the best and which will be avoided. It is widely accepted in the field of E-commerce that intention is a reliable predictor of behavioral findings and a proxy for future purchasing behaviors (Chen and Yang, 2021; Xiang et al., 2022), despite being extremely difficult to quantify. A variety of factors influence the likelihood of making an online purchase. There are many different influencing factors that influence both online and offline purchase intention in different ways and to different degrees. Customer demands, power, and utilitarianism are common characteristics of online consumers, and these characteristics are important distinctions between online and offline consumption. Furthermore, consumers’ lack of leisure time and lifestyle can influence their intention to purchase online (Shao et al., 2021; Qiu et al., 2022; Wang et al., 2022).

There have been a lot of studies performed in the field of consumer habits to develop theories based on the concept of intention. According to Oliver’s expectation and disconfirmation theory (EDT), consumer satisfaction is a function of expectations and disconfirmation of expectations. Satisfaction is thought to have an impact on people’s attitudes and their willingness to buy. The theory of reasoned action (TRA) was developed by Oliveira et al. (2017), and it asserts that persons’ quality is measured by their performance expectancy, which is ascertained by the person’s subjective norms. In addition to product uncertainty in E-commerce, we asserted that consumers’ trust in broadcasters can directly impact their perceived intention (PI). Trust can be recognized as a vital antecedent of faith in online transactions in E-commerce, creating a positive attitude toward transaction behavior (Nasiri and Shokouhyar, 2021), which led to purchase intentions. Previous research has shown that trust is linked to purchase intentions (Paul and Rosenbaum, 2020). Li et al. (2020) discovered that consumers’ intentions to engage in a purchase are influenced by their integrity, while one’s ability impacts their intentions to ask questions about the good or service without making a purchase. There is a positive relationship between purchase intention and actual purchasing behavior (Kumar and Ayodeji, 2021). The consumer’s intention to purchase through E-commerce is defined as PI in this study (Salari et al., 2021). PI is a direct predictor of actual purchase behaviors (Truong et al., 2020). Product lack of certainty (i.e., product fit and top-notch uncertainty) is among the essential functional factors that online shoppers take into account during purchase decisions.

Moreover, customer PI depends on transparent information circulation. Any information gap between customers, vendors, and stakeholders misleads the customers and ultimately affects the business of E-commerce (Kolotylo-Kulkarni et al., 2021). Although customers show concerns at the disclosure of secret information, most of the customers are now getting addicted to E-commerce and do not express any type of reservations. During the COVID-19 pandemic, E-commerce has gained a large share of the market (Pantano et al., 2020). At the time of scarcity, customers have fewer options to select (Hamilton et al., 2019); that is why all the customers ignored their reservations and just used E-commerce to fulfill their needs. As culture also affects the intention of the customer, in China, Korean cultural product perception is better, so they purchase across the border through an E-commerce channel (Han and Kim, 2019). E-commerce is directly related to trust, the same as other offline businesses. The trust comes from consumers’ confidence and particularly awareness of the product. Product awareness comes from more promotion on social and multimedia and when a product has more users in the market. Product awareness may directly affect the intention of the consumers of E-commerce in China. According to Chen and Yang (2021), customer engagement in the system creates intentions in the customer’s mind, and customer psychological factor also improves the intention of E-commerce customers in china.

In China, the E-commerce business is spreading quickly. Alibaba and Tencent have been working in E-commerce at the retail and wholesale levels. These companies have set a positive intention in the mind of customers. With each passing day, the perception of online shopping is changing in China, although China’s economy is already mostly cashless. This type of business works in the presence of thirrd party involvement such as money market funds (MMFs), especially in China (Wang and Ben, 2021). These markets have set a system of financial trust and changed the intention of customers. This system of MMF is not only facilitating financial services but also the point of investment for investors around the globe. The maturity of the payment system has changed the PIs of China’s E-commerce. In this study, we can take Pinduoduo as an example.1 This company was established in 2015 and now has more than 824 million users in China. This change has come due to technology acceptance. WeChat has changed the perception, intention, usefulness, and ease of use in China. Besides, this app has shifted the cash economy to a cashless economy (Zhao et al., 2019). WeChat is the first high-ranked payment system globally, and Ali Pay is ranked at the second number (Tran, 2021). In China, the online business system is growing faster, such as Taobao, JD, and Tmall. In the case of grocery retailing, the Chinese market is developing (Olsson et al., 2021), and still, there is the problem of delivery of products while customer perception is good, but the delivery cost and the bottleneck of extra cost are creating problems. According to Geng and Li (2020), two main elements affect the E-commerce intention in China. These are (i) aggressive analysis and (ii) the digitalization of each system. The factorial analysis model depends on these two factors for the E-commerce analysis of China. According to Shan et al. (2020), E-commerce in China is positively associated with the market design and technical performance (Tang et al., 2022). Therefore, the intention-based determinants of E-commerce in different parts of China are perception, availability, usefulness, innovation, technology, urbanization, and technology (Vasic et al., 2019).

The establishment of the E-commerce markets at a bigger rate has damaged offline businesses. All the businesses of whatever criteria, either goods or services, should work on E-commerce. If they ignore the E-commerce concept, they will lose their organizations in the near future. E-commerce in China and around the globe is a matter of serious concern (Xiao et al., 2019). In most of the research, the center of focus is the domestic E-commerce business, but Yoon and Zhang (2018) have worked on the cross-border E-commerce business between China and the world, and it was noticed that E-commerce had changed the intentions of customers not only in China but also in the world.

When the proposed novel factors are combined with the existing factors, this study provides a comprehensive analysis of the intention-based critical determinants of E-commerce utilization in China, according to the findings. Finally, in terms of the significance of the findings, despite the fact that the research was conducted in a specific region of China, the implications of the findings suggest that consumers’ intention factors to use E-commerce are prevalent. E-commerce has huge research potential in China, and the prospect of E-commerce in China is very good. Social commerce, a new E-commerce model, has a significant impact on consumer behavior. By combining information, experience, and social knowledge, consumers can improve their understanding of online PI. As a result, purchasing decisions on E-commerce platforms will be more precise and intelligent. Even though E-commerce is becoming more popular, it is still in its early stages, and only a few social commerce vendors have made a significant profit from it. Other developing economies face the same E-commerce challenges and proposed factors. As a result, the results of this study are generalizable to other economies and demonstrate the influence of intention-based critical factors on the use of e-commerce. Then, we will apply what we have learned from China in order to forecast where this trend is headed. As a result, the findings will assist the Chinese government in determining end users’ intention to use E-commerce.

The theoretical approach is described in the “Theoretical Background” section, followed by the “Materials and Methods” section, in which we proposed a research model of the intention-based critical determinants of E-commerce utilization. We first described how our evidence-based study was carried out and then revealed the study’s findings (segment 4). The discussion and conclusion are presented in the “Results” and “Discussion” sections, respectively.

Theoretical Background

Technology Adaptation Theory (TAT) presents the intention and behavior of an individual toward the acceptance of technology. E-commerce is derived from technology. This theory consists of two factors: the result of perceived value and a person’s behavioral change due to his/her belief and motivation. According to Bijmolt et al. (2010), E-commerce learning comes from three steps. The first step leads toward the attraction of new customers in second phase, the customer development. In the second stage, the repetitive customer repurchase starts. In the final step, we came to know the loyalty of the customers. According to Gilboa et al. (2019), customers who are regular and loyal to the company are not like ordinary customers in E-commerce. The intentions of a loyal customer are different from others. Nowadays, organizations are working to make their loyal E-commerce customers by delivering batter (Vasic et al., 2019). According to Lowery (2021), the trust of customers in E-commerce changes their intention. It depends on stakeholders’ activities, better image of the web content, customers’ simplicity of using the online platform, and customer firm belief regarding particular retailers. E-commerce trust depends on the elements such as courage, benefits, and skills (Kim and Gupta, 2009). Mental accounting theory expresses the stages of E-commerce transaction, in the first stage, it evaluates the transaction, and in the second stage, it takes the final decision (Kim and Gupta, 2009). After these two-step customers with absolute thinking, the organizational performance of specific E-commerce businesses is assessed (Hamilton et al., 2019).

Transaction in E-commerce has greater importance because many companies or some intermediates do fraud with the end customer (Keshri et al., 2020). Frauds change the intention of customers in E-commerce. According to Kolotylo-Kulkarni et al. (2021), information processing theory gives a choice to the customer in his decision-making process than he takes the decisions at the past E-commerce experience. Past customer experience has an impact on the perception and intention of E-commerce. In previous studies, it has been known that the E-commerce repurchase ratio is very low, and 0.50% of customers do not visit the site again. According to Leopoldo (2019), E-commerce also depends on mobile and the Internet, which develops the infrastructure of E-commerce. E-commerce in the cities is growing much as compared with rural areas, and it is due to facilities of Internet and knowledge gap. According to Cheba et al. (2021), determinants of E-commerce in China are the availability of mobile and Internet, country macroeconomics situation, social condition, environmental performance, and E-commerce business in cities. All these factors affect the E-commerce consumer’s intentional behavior in China and around the globe. In previous research, different aspects have been covered but in this research, the purpose was to judge the impact of intention-based perception, usefulness, reputation, ease of use, belief in vendor, and purchase frequency by E-commerce in China concerning the end customer. The research framework for this study is depicted in Figure 1.

FIGURE 1
www.frontiersin.org

Figure 1. Research framework.

Perceived Usefulness in E-Commerce

Consumers’ general preconceptions of the risks and benefits associated with a customer experience are regarded as perceived values. Beyond the straightforward impact of perceived usefulness (PUSF) on implementation, research has found that PUSF can act as a mediator in a variety of disciplines. Similarly, PUSF has been found to establish a relation between perceptual customization and buying intention in E-commerce (Feng et al., 2021). That is, greater cognitive behavior, or the intensity to which users are satisfied with the information, is given in a specific product and is linked to higher repurchase intention for such a good or service. This connection exists due to the mediating effects of the consumer’s perceived usefulness (Kim et al., 2021). The study explains to the E-commerce consumers how ready and able to use the Internet to purchase goods. Another thing related to customer consent is sharing personal information with invisible representatives. The customer consent to cooperate with a web-based E-commerce system directly linked with the usefulness of the system. In E-commerce, the website’s front page is where potential customers can find out more about the company’s services and expertise (Kim et al., 2021). In general, it is a very simple system, and in the advanced concept of organizations, it is a multidimensional system and very complex (Hopkins, 2022; Kliestik et al., 2022b). Another important aspect examined in this study is the degree of use and repetitive visiting site behavior, which is due to the usefulness perception and their collaboration to share personal and Internet-based data (Nica et al., 2022). Online interactions such as ease of information collection, updated information, and entertainment are not only the choices, which are essential for making a decision but they also create an image with the help of the first website attempt and observations. So, E-commerce is the formative behavior that comes from continuous, repetitive Internet-based activities.

H1: Perceived usefulness has a positive impact on consumer’s intention to use E-commerce systems.

Perceived Ease of Use in E-Commerce

Perceived ease of use in E-commerce is outlined as “the degree to which the person believes that exploitation the system new school are going to be freelance of brainpower” (Kasilingam, 2020). This ease of handling the results with an ideal mindset conveys the idea. Reports tailored to a person’s locus of control, in specific, take place effortlessly to digest. Likewise, Zhang et al. (2020) discovered that a mark promotional material corresponding to a regulative objective looks easier to perform than one that does not. This same easiness of its use had also piqued the interest of researchers interested in areas other than ancient production and design development. In general, a practical amount of physical and mental effort is necessary to know, learn, and operate a specific technological system (Kliestik et al., 2022a). Ease of use may be defined in the way that buyer’s discernment of a by-product, notably in any way commodity is simple to absorb and utilize, shrinks the weight of remembrance, and provides a high level of satisfaction. The ease of use of software, or the transparency of work and easy way finding, is a compulsory situation for E-commerce selling to get through and set off with success (Ashraf et al., 2021).

This quality concerns buyers’ online shopping etiquette structure. Furthermore, customers will shift their attention to other apps whenever the person’s laptop relation to software is not sufficiently affectionate to boost their sales. Thus, assessing the usability of software in E-commerce selling is required. The usability of mobile applications is not enough to determine consumer behavior. It is also an essential element for product developers looking to improve device design. As a result, when consumers come across different mobile applications from E-commerce businesses, they should choose the one with the greatest easiness of its use.

H2: Perceived ease of use has a positive impact on consumer’s intention to use E-commerce systems

Reputation in E-Commerce

Seeing as the rumor mill can negatively impact the reputation of an E-commerce business, the thousand-dollar concern is to save virtual customers’ beliefs in E-commerce and its Internet-based financial activities. Therefore, rumor-based consumers are divided into four categories, namely, online customers, puzzled customers, customer left, and rebuying consumers (Bellini et al., 2020). It is well known that E-commerce consumers face several challenges such as fraudulent selling, quality issue, bad services, and risk factors that affect the reputation of the E-commerce business (Vãtãmãnescu et al., 2020; Andronie et al., 2021). To address these issues, famous E-commerce enterprises such as https://productsearch.alibaba.com/, Walmart, JD, and Taobao frequently and broadly enforce reputation systems (Zhao et al., 2020). A repetitive buyer is someone who buys from the same vendor a second time, and on a daily basis, the repetitive customers spend more and more money (Kolotylo-Kulkarni et al., 2021). Buyback conduct impacts repeat intention activities, and it also shows the trustful relationship between consumers and retailers. It should be focused on when setting seller reputation and purchaser demand (Kasilingam, 2020).

According to Bellini et al. (2020), there are many fake comments and ratings on the popular retailing platform. As a result, consumers may be misled by these fake comments and ratings while online shopping. This type of abnormal rating is known as a malicious rating, and those who give malicious ratings have been known as attackers. These types of malicious can be detected by analyzing the previous rating, and many approaches have been unveiled to detect these attackers (e.g., Ashraf et al., 2021). This reputation may impact the future decision-making of consumers. When there are no nefarious scores, the product reputation can be maintained. To manage these types of fatal attacks, the 3R (repurchase, return, reputation) model has been discovered by researchers.

H3: Reputation of websites used by vendors has a positive impact on consumer’s intention to use E-commerce systems.

Trust in Vendors in E-Commerce

In an E-commerce environment, trust is both important and vulnerable (McKnight et al., 2002). Consumers’ perception and uncertainty could be reduced by trust. To predict the issues in vendor trust, the customer service life cycle is used in many types of research (Kolotylo-Kulkarni et al., 2021). It consisted of the whole set of the system from collecting the information about any product, purchasing it, disposing, and even replenishing it (Zhou et al., 2018). It also reduces the state of mistrust between vendors and customers and gives a transparent purchasing system. The buyer-perceived system of transparency is the set of three elements that could be transparent: vendor, product, and transaction detail. According to Ribadu and Wan (2019), many customers do not believe in E-commerce; it negatively influences buyers during purchasing through the Internet. Consumer trust, vendor’s reputation in the market, and quality assurance are the main elements that impact E-commerce.

As a result, there is very difficult to establish or maintain trust between vendor and consumer in E-commerce. The E-commerce literature defines consumer trust in e-suppliers as a positive psychological state, based on beliefs in professional knowledge (Kasilingam, 2020), integrity, and generosity to determine the tendency to participate in trust-related behaviors (Cui et al., 2018). Online trust has been classified into four categories: trust based on the first impression, trust based on regular contact, trust on personal characteristics, which finally lead to the success just on a company’s market picture.

H4: Trust in vendors has a positive impact on consumer’s intention to use E-commerce systems

Purchase Frequency in E-Commerce

Purchase frequency is the range of times a client makes a sale in a very given amount of time (Ingham et al., 2015). Further buying rhythm explores the image in the mind of customers that influence consumers to buy through the Internet, even while observing the mediating impact of e-buying expertise. McKnight et al. (2002) differentiate two types of attitudes: acceptance of E-commerce is the first repurchase of goods and buying back and behavioral intention is the repeating behavior of repurchase using the same medium. Yu et al. (2005) claimed that new possible customers of an E-commerce differ from repeat usage in order of acceptance, intentions, and usage. The same occurrence is explained by Oliver (1980)’s Expectation-Confirmation theory. It appears perfectly reasonable to believe that large acceptance ends up with a large frequency of use and, as a result, larger satisfaction. According to Le et al. (2021), the most powerful generator of self-efficacy influences because the skills acquired through Internet use determine this perception. Recurrent Internet users should be more identity and thus more likely to benefit from E-commerce. We can consider the buyers who have more steering professionalism, which connects to the Internet more frequently, are comfortable with it, and have a greater self-efficiency in E-commerce.

H5: Purchase frequency has a positive impact on consumer’s intention to use E-commerce systems

Materials and Methods

Questionnaire Development

The study method was verified quantitatively using a rating scale that had earlier been proven to work to actualize each structure and boost its authenticity. As a result, in the advancement of measurement devices, this was adjusted from validated empirical investigations. We then designed a questionnaire to achieve the study purpose. The very first portion of the questionnaire comprised specimen classification questions. The second section evaluated the structures chosen using a seven-point rating scale (1-strongly disagree, 7-strongly agree). As per the literary works, in IS, common method bias (CMB) is a major worry about data gathering operational definition. We calculated the Harman feature to evaluate CMB, and all predictors were allowed to pass that check (less than 0.5) (Schwarz et al., 2017). The final tools of measurement often used to check the structural model are listed in Appendix A. To make sure a widely accepted set of questions, the survey was performed using established balances. Participants were asked about single-game element key findings (Huang et al., 2020), so they were not anticipated to have previous information on game element design systems.

TABLE 1
www.frontiersin.org

Table 1. Participants’ demography.

Selection of Respondents and Sample Size

To make certain that their results were accurate and meaningful, the researchers conducted a pilot survey before starting the main study (Xue et al., 2014). Random sampling was used to ensure that all members of the subset had an equal chance of being selected as a part of the sampling process in order to avoid an unbalanced representation of the overall population (Tauni et al., 2015). The survey process was divided into two phases. First, questionnaires were handed over to 500 consensual E-commerce website users in both the business-to-customer (B2C) and customer-to-customer (C2C) contexts, with a high prevalence in the propagation of the survey on social networks. We provided an online survey link to respondents via Linkedin, Facebook, and Whatsapp. One month was allotted for the completion of the responses, and they were all completed on time. An in-depth explanation of the questionnaire was given to the participants to ensure that they understood all aspects of the questionnaire. Participants reflected in the following online vendors: AliExpress 13 percent, Amazon 18 percent, Made an impact 3 percent, Continent 1 percent, eBay 20 percent, El Corte Ingles 1 percent, Lot 12 percent, GearBest 8 percent, PCDiga 4 percent, Sporting 2 percent, Worten 4 percent, and on vendors 10 percent. The portrayal of the specimen is outlined in Table 4.

Second, questionnaires were received from the respondents after 1 month. As a result of the questionnaire survey, a total of 400 responses were collected, representing 80% of the original sample. Based on Westland’s sample size calculation formula, the recommended minimum sample size for our model should be 336 (Westland, 2010), while our actual sample size (400 responses) is even more than the minimum recommended value, ensuring that empirical analysis is feasible. Users were asked about the conceptions of their own life experiences with B2C and C2C systems. Besides they were asked about their most utilized E-commerce console, not just one.

Statistical Analysis

AMOS (edition 26) and SPSS (edition 26) softwares are used for statistical tests. Structural equation modeling (SEM) is used to test the hypotheses. SEM is a practical approach for determining the relationship between various variables, providing meaningful and accurate results (Liu M. et al., 2021), with three significant advantages over traditional methods. (i) An accurate assessment of measurement errors. (ii) Using identified variables to approximate underlying features. (iii) Tool for modeling for trend evaluation and implementation based on data compliance. Furthermore, most multinomial strategies tacitly dismiss math errors. The SEM, on either hand, forecasts both variables of the study while accounting for miscalculation (Sardeshmukh and Vandenberg, 2013). The method creates precise and erudite numbers due to its reliability and serviceability.

The SEM method enables the generation of various predictor structures for every component as well as yields audio ramifications. Furthermore, it calculates the mistakes parts of the work carefully. As a consequence, the connection among variables produces accurate results. Furthermore, this can evaluate complicated interactions as well as a wide range of assumptions by incorporating average setups and team market values, what other designs and experiments could do (Agudo-Peregrina et al., 2014). Taking the benefits of SEM in and out of evaluation, we used it with our assessment because it is the most effective method to test the association between the variables under evaluation.

Results

Demography of the Participants

Table 1 presents the demography of the participants. The lower-middle age group (198, 49.50%) has the highest percentage of respondents in our sample, followed by the middle-age group (97, 24.25%), young age group (67, 16.75%), and old age group (38, 9.50%). In our sample, male participants are 236, 59.00% of the whole sample, and outnumbered females 164 (41.00%). With a monthly income of 15,001–20,000 CNY, 185 respondents (26.25%) are from the middle-income class, followed by the upper-middle-income class (198, 39.5%) with a monthly income of 25,001–30,000 CNY. We also divided respondents into groups based on their educational levels; 26.96% have a high school diploma, while 41.18% only have a Bachelor’s degree. The majority of the respondents were married (71.25%), 41.50% are technical persons, and 6.75% worked for the government.

Descriptive Analysis and Correlation Analysis

The hypotheses were tested after the initial screening of responses for usability and reliability (see Table 2). The data in this study were analyzed in two steps. For the model’s validity, we conducted a confirmatory factor analysis (CFA). The proposed research model’s causal structure was tested using structural equation modeling (SEM). Modifying the proposed model using the model-generating (MG) strategy for testing structural equation models was carried out until it met the criteria for theoretical significance and statistical well-fitting (Jöreskog, 1993). The formative and reflective latent variables were measured during the evaluation of the measurement model. Tests of indicator reliability, internal consistency, discriminant validity, and convergent validity were used to evaluate the reflective latent variables’ measurement model (see Table 3). When factor loadings for perceived desirability were examined for indicator reliability, some poor factor loadings were discovered. These items were omitted from consideration for this study. When the test was rerun, the factor loadings for all remaining reflective indicators were found to be greater than or equal to the 0.70 threshold, which was deemed acceptable. To test the construct’s validity, data variance was compared for both discriminant and convergent validity, and both were found to be statistically significant. Table 4 shows that all constructs are significantly higher than the threshold of 0.50. Convergent validity testing necessitates factor loadings and average variance extracted (AVE) of all constructs to be at least 0.50. In addition, correlations between each construct and every other construct were smaller than the square root of the AVE of each one, as shown in Table 4, for discriminant validity using the Fornell and Larcker’s (1981) criterion.

TABLE 2
www.frontiersin.org

Table 2. Results of descriptive statistics.

TABLE 3
www.frontiersin.org

Table 3. Correlation and discriminant validity analysis.

TABLE 4
www.frontiersin.org

Table 4. The results of reliability analysis and factor loadings.

Reliability Analysis

Cronbach’s alpha was computed to assess the reliability coefficient. The findings demonstrate that the Cronbach’s value for all factors exceeded the lowest required value of 0.70, as recommended by Treiblmaier and Sillaber (2021), verifying the data’s accuracy. It was decided to use composite reliability (CR) assessment to see if all the explanatory variables were consistent. CR values are found to be higher than the recommended threshold of 0.70, as determined by the investigation (Hair et al., 2017). Table 4 presents the conclusion.

Multicollinearity

To test the multicollinearity, regression was used to determine the value systems of the variance inflation factor (VIF) as well as tolerance. As per the f, the value of VIF has to be less than 10, and the tolerance value has to be larger than 0.1. The research results indicate that the model did not have a multicollinearity problem, so the VIF value is as per limit, and the value of tolerance for whole variables ranges within the ideal range and is in line with the observations of Strupeit and Palm (2016). The findings can be seen in Table 5.

TABLE 5
www.frontiersin.org

Table 5. The results of the collinearity diagnostic test.

Factor Analysis

To acquire the attributing design methodology, an exploratory factor analysis (EFA) has been conducted. EFA seeks to explore factorability, i.e., the relationships and clusters of different factors based on cross-comparisons (Mahmood et al., 2019). For even more meaningful results, the factors were derived to use the statistical parameters and then turned with the corresponding Varimax coefficients. The Eigenvectors have been used to assist specify the number of factors. Several tests that were carried out during this stage are crucial to determine whether the EFA might be applied in this study. The Bartlett’s Test of Sphericity (BTS) and Kaiser-Meyer-Olkin (KMO) test were used to evaluate the data fitness. The consequences supplied a significance based on KMO (Kaiser, 1974), implying that principal component analysis can be continued. Table 6 presents the results of the KMO and BTS tests. The substantial significance of 6,874.96 provided by BTS also meets the requirements for EFA. Table 7 results show that almost all factors have a value greater than the standard minimum of 0.4, consequently (Stevens and Stevens, 2001). There are seven important factors that have Eigenvalues larger than one and a total combined variability of 64.930% for the Promax roster using the Kaiser method (Table 8). Since each one of these is true, the data can be trusted to support further assessment (Blunch, 2017).

TABLE 6
www.frontiersin.org

Table 6. Bartlett’s test and Kaiser–Meyer–Olkin (KMO).

TABLE 7
www.frontiersin.org

Table 7. Communality findings.

TABLE 8
www.frontiersin.org

Table 8. Cumulative variance and Eigenvalues.

Later, the models were discovered via confirmatory factor analysis (CFA). CFA can be used to verify the EFA framework of variables. Finding out if a model is normal is the first step in making a model selection. Items with high capacities (greater than 0.7) should always be available (Truong et al., 2020). According to the findings, all levels were found to be greater than 0.7. Since all goods were packed on one’s own constructs, the validity of the quantification model was confirmed (Figure 2). Data can be reused in the measurement model following the completion of the analysis.

FIGURE 2
www.frontiersin.org

Figure 2. Confirmatory factor analysis is a type of statistical analysis that is used to represent a measurement model. Source: Authors’ calculations.

Hypothesis Results and Structural Model

The writers evaluated the proposed prototype and theorized interconnections within a week of acquiring valid and reliable measures. To determine how much variation in the dependent variable could be explained by variation in the independent variable, R2 was a critical step. The value of R2 was 0.54, and the difference between the two levels is greater than the corresponding minimal level of 0.35 (Huang et al., 2020), suggesting an important viewpoint. To investigate the model’s connections, we used structural bend assessment and the SEM method. The assessment created a high f-value, implying that all interconnections were straightforward. Various fit indices were also used to confirm that the data are accurate and completely fit again for the structural equation model. The results indicate that almost all fit indices (i.e., CFI = 0.988, NFI = 0.923, IFI = 0.989, TLI = 0.974, GFI = 0.983, RMSEA = 0.021, X2/df = 1.147, and SRMR = 0.026) meet the standard criteria, indicating that model fit the data adequately (Lucianetti et al., 2018).

Figure 3 depicts a diagrammatic diagram of SEM together with path coefficients. The path coefficients for the variables “perceived usefulness,” “perceived ease of use,” “reputation,” “trust in vendor,” and “purchase frequency” H1 (b = 0.04, p = 0.001), H2 (b = 0.13, p = 0.01), H3 (b = 0.67, p = 0.05), H4 (b = 0.02, p = 0.001), and H5 (b = 0.16, p = 0.05) demonstrate that the factors PUSF, PEUS, REPU, TVEN, and PFRE have a positive and significant effect on customers’ intention to adopt E-commerce. As a result, assumptions 1, 2, 3, 4, and 5 were acknowledged. Table 9 depicts the authenticity of theorized routes and theories.

FIGURE 3
www.frontiersin.org

Figure 3. Structural equation modeling path diagram. Insignificant and significant paths are indicated by dashed and continuous lines, respectively. ***p < 0.00, **p < 0.01, *p < 0.05. Source: Authors calculation.

TABLE 9
www.frontiersin.org

Table 9. Result hypothesis testing.

Testing Endogeneity

This test is mainly used to verify the consistency of study findings. Endogeneity partiality in the information can jeopardize the findings. Furthermore, endogeneity could misrepresent the forecast of posterior probability, presenting a major challenging problem to the authenticity of outcomes. While investigating endogeneity, we used the Heckman test to address these issues. The results produced the very same degree of confidence as the original version, implying that endogeneity partiality is just not prevalent in our conclusion (Table 10).

TABLE 10
www.frontiersin.org

Table 10. Test for endogeneity.

Discussion

This study accomplishes two significant impacts. First, it recommends a conceptual model that combines technology adoption theory with the implication of the E-commerce system of business. As a result, incorporating aspects of adoption studies, combined with E-commerce models, including facets of much other research. Second, we evaluated this study model in terms of actual E-commerce platform utilization. As a consequence of our research, customer E-commerce platform implementation is straightly attributable to the concentration of perceived usefulness, perceived ease of use, reputation, trust in vendor, and purchase frequency, which also produce a customer sense of giving rewards as a result. The study’s findings are consistent with the literature, namely, the Technology Adoption Theory (Venkatesh and Davis, 2000).

Perceived Ease of Use

The results show that perceived ease of use and perceived usefulness have a significant positive impact on IUES. This finding implied that the more an individual felt capable of using an application, the easier it was for them to use an E-commerce system. This result also implied the ease they felt from using the application, as consumers carried over into their perception of ease they felt toward using the application as a customer or seller. Further development of an E-commerce application that is easy to use increases the number of existing customers in general (Huang et al., 2021) and increases the number of E-commerce partners the application has, thus providing more (Liu P. et al., 2021). This finding was consistent with the previous research that showed the significant positive impact of perceived ease of use (Moorthi et al., 2021) and attitude toward E-commerce intention (Lăzăroiu et al., 2020). Although the perceived usefulness result was inconsistent with the aforementioned extant corpus, some of the previous findings still showed similar results (Spruit and Almenar, 2021; Zheng et al., 2021). This result implied that rather than the usefulness of an application, its easiness and attitude toward it were the variables that determined its adoption. In other words, even if an application was provided to be useful, as long as it was hard to use or ignited a negative attitude, no one would want to use it.

Perceived Usefulness

Perceived usefulness has a significant positive result on IUES. Consistent with previous research, perceived usefulness plays an important role in defining the intention of using E-commerce systems (Liu X. et al., 2021; Yang et al., 2022). The usefulness of an application depends on its ease of use (Cremer and Loebbecke, 2021). The previous statement implied that, for individuals, an application would only feel useful when it is easy to use (Yuan et al., 2021). This also means that for application developers to ensure that their application is useful to users, they need to consider the application’s ease of use. Consumer shopping experiences are being improved, and retailers are remaining competitive by implementing E-commerce (Sharma and Aggarwal, 2019). Consumers’ purchasing experiences were examined by Chen and Yang (2021), who studied the impact of mobile retailing. The findings of this study can help other researchers better understand how technology usefulness affects consumer behavior, emphasizing the factors that motivate consumers to adopt the e-commerce shopping experience. Customers’ perceptions of the attributes of e-commerce that influence their cognitive and affective attitudes and online purchase intentions were studied by Ribadu and Wan (2019). Utilitarian attributes are found to be a significant and positive predictor of cognitive and affective attitudes, according to the authors.

Reputation

Furthermore, reputation has a significant positive impact on IUES. Reputation is generally considered to be an important factor in long-term customer relationships and customer trust. Previous research (Akram et al., 2021; Aparicio et al., 2021) has found that perceived reputation significantly impacts online shopping trust and purchase intention. A company’s reputation is built on long-term investments of resources (Karani and Failler, 2020), effort, and attention (Meilatinova, 2021) to customer relationship building. Consumers prefer companies with a good reputation in E-commerce because they perceive less risk and uncertainty and are aware of where to seek assistance. These findings are consistent with theory and prior studies (Qalati et al., 2021).

Trust in Vendor

The hypothesis that IUES is significantly affected by trust in vendor-related hypotheses (F2 > 0.383) and is highly significant (p < 0.000) is supported by the data. The results of the SEM model show that online trust occurs in E-commerce and that trust has a major impact on satisfaction and repeat purchase intention. The value is higher than in prior E-commerce research investigations (Aparicio et al., 2021). In the e-commerce context, trust is a crucial aspect to investigate: many small and medium online sellers do not aim to use their website as a channel since there are numerous obstacles (Qalati et al., 2021), such as trust concerns when doing business at a distance and uncertainties, and increased risks. Moreover, if customers have faith and trust the online vendor that the vendor will be honest and reliable, they will be more likely to feel comfortable making purchases and disclosing sensitive information online (Wang and Emurian, 2005). According to Sharma and Aggarwal (2019), customer happiness can be improved by delivering high-quality products and websites that customers trust. This can be accomplished through delivering a great customer experience and differentiated services, which will lead to positive purchase decisions.

Purchase Frequency

This analysis revealed interesting insights into how consumers changed their online E-commerce purchasing habits regarding purchase frequency. The frequency shifted evidently toward a substantial and increased E-commerce use (Yu and Huang, 2022), with the population ordering more than once per month increased by 44.5%. In addition, the percentage of people never ordering through online channels decreased by more than 50% (Kawasaki et al., 2022). This is a clear sign that purchase frequency has increased the use of online channels in China, as the statistical tests in the previous section also demonstrated. The test results show that the increase in use will also increase the satisfaction with delivery services (Böheim et al., 2021). This could be due to the fact that the most experienced and loyal users frequently use the most reliable and best online retailers and do not experience any unpleasant surprises when doing so (Gian et al., 2019). The navigated and less loyal consumers may incur bad or unreliable websites, and as a result, they will have a worse shopping experience.

Conclusion

This study systematically assessed intention-based critical determinants of E-commerce utilization in China. We identified the potential factors that could discourage or inspire consumers from using E-commerce. The conceptual framework of Technology Adaptation Theory (TAT) presents the intention and behavior of an individual toward the acceptance of technology. The theory contains two factors: the result of perceived value and the other is that people’s behavior changes due to beliefs and motivations. SEM/PLS was used to analyze survey data collected from the four largest cities in Guangdong Province. The empirical analysis discloses that perceived usefulness, ease of use, reputation, trust in vendors, and purchase frequency positively influence E-commerce utilization.

Customers purchase the market image and market reputation of E-commerce channels as well. Additional core functions of E-commerce Internet sites, such as making purchases faster and spending less time on unnecessary activities, have a strong positive effect on behavioral intention. The interface and ease of use are also important factors in the E-commerce customer experience. Companies can improve their trustworthiness in the eyes of their customers by ensuring improved quality; fulfilling the given promise enhances the level of commitment, integrity, and level of trust. This exploration has some limits. Initially, data were collected only from E-commerce customers. In terms of age, the reality that even more than 80% of survey participants are aged 30 years or younger, and the fact that more than 87% of the sample chooses to live in Shenzhen and Guangzhou may impact the outcome acquired. Even though the research results seem to be statically meaningful, so much study with a wider geographical purview will improve the model’s predictive power.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics Statement

The studies involving human participants were approved by the Ethical Committee and responsible authorities of Henan Agricultural University following all guidelines, regulations, legal, and ethical standards as required for humans. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

Both authors listed have made a substantial, direct, and intellectual contribution to the work, and approved it for publication.

Funding

This project was funded by the Postgraduate Education Reform and Quality Improvement Project of Henan Province (YJS2022KC21).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Footnotes

  1. ^ https://en.pinduoduo.com/

References

Agudo-Peregrina, A. F., Hernández-García, A., and Pascual-Miguel, F. J. (2014). Behavioral intention, use behavior and the acceptance of electronic learning systems: differences between higher education and lifelong learning. Comput. Hum. Behav. 34, 301–314. doi: 10.1016/J.CHB.2013.10.035

CrossRef Full Text | Google Scholar

Akram, U., Junaid, M., Zafar, A. U., Li, Z., and Fan, M. (2021). Online purchase intention in Chinese social commerce platforms: being emotional or rational? J. Retail. Consum. Serv. 63:102669. doi: 10.1016/J.JRETCONSER.2021.102669

CrossRef Full Text | Google Scholar

Andronie, M., Lazaroiu, G., Stefanescu, R., Ionescu, L., and Cocosatu, M. (2021). Neuromanagement decision-making and cognitive algorithmic processes in the technological adoption of mobile commerce apps. Oecon. Copernic. 12, 1033–1062. doi: 10.24136/oc.2021.034

CrossRef Full Text | Google Scholar

Aparicio, M., Costa, C. J., and Moises, R. (2021). Gamification and reputation: key determinants of e-commerce usage and repurchase intention. Heliyon 7:e06383. doi: 10.1016/J.HELIYON.2021.E06383

PubMed Abstract | CrossRef Full Text | Google Scholar

Ashraf, A. R., Thongpapanl Tek, N., Anwar, A., Lapa, L., and Venkatesh, V. (2021). Perceived values and motivations influencing m-commerce use: a nine-country comparative study. Int. J. Inf. Manage. 59:102318. doi: 10.1016/j.ijinfomgt.2021.102318

CrossRef Full Text | Google Scholar

Bai, H., McColl, J., and Moore, C. (2021). Motives behind retailers’ post-entry expansion-evidence from the Chinese luxury fashion market. J. Retail. Consum. Serv. 59:102400. doi: 10.1016/J.JRETCONSER.2020.102400

CrossRef Full Text | Google Scholar

Bellini, E., Iraqi, Y., and Damiani, E. (2020). Blockchain-based distributed trust and reputation management systems: a survey. IEEE Access 8, 21127–21151. doi: 10.1109/ACCESS.2020.2969820

CrossRef Full Text | Google Scholar

Bijmolt, T. H. A., Leeflang, P. S. H., Block, F., Eisenbeiss, M., Hardie, B. G. S., Lemmens, A., et al. (2010). Analytics for customer engagement. J. Serv. Res. 13, 341–356. doi: 10.1177/1094670510375603

CrossRef Full Text | Google Scholar

Blunch, N. J. (2017). Introduction to Structural Equation Modeling Using IBM SPSS Statistics and AMOS. 2nd Edn. Thousand Oaks, CA: Sage. doi: 10.4135/9781526402257

CrossRef Full Text | Google Scholar

Böheim, R., Hackl, F., and Hölzl-Leitner, M. (2021). The impact of price adjustment costs on price dispersion in e-commerce. Int. J. Ind. Organ 77:102743. doi: 10.1016/j.ijindorg.2021.102743

CrossRef Full Text | Google Scholar

Cheba, K., Kiba-Janiak, M., Baraniecka, A., and Kołakowski, T. (2021). Impact of external factors on e-commerce market in cities and its implications on environment. Sustain. Cities Soc. 72:103032. doi: 10.1016/j.scs.2021.103032

CrossRef Full Text | Google Scholar

Chen, N., and Yang, Y. (2021). The impact of customer experience on consumer purchase intention in cross-border E-commerce-taking network structural embeddedness as mediator variable. J. Retail. Consum. Serv. 59:102344. doi: 10.1016/J.JRETCONSER.2020.102344

CrossRef Full Text | Google Scholar

Cremer, S., and Loebbecke, C. (2021). Selling goods on e-commerce platforms: the impact of scarcity messages. Electron. Commer. Res. Appl 47:101039. doi: 10.1016/j.elerap.2021.101039

CrossRef Full Text | Google Scholar

Cui, Z., Zhang, H., Chen, X., Zhang, C., Ma, W., Huang, C., et al. (2018). Pursuing sustainable productivity with millions of smallholder farmers. Nature 555, 363–366.

Google Scholar

Feng, Y., Wang, X., and Liang, Z. (2021). How does environmental information disclosure affect economic development and haze pollution in Chinese cities? The mediating role of green technology innovation. Sci. Total Environ. 775:145811. doi: 10.1016/j.scitotenv.2021.145811

PubMed Abstract | CrossRef Full Text | Google Scholar

Fornell, C., and Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: algebra and statistics. J. Mark. Res. 18:382. doi: 10.2307/3150980

CrossRef Full Text | Google Scholar

Geng, J., and Li, C. (2020). Empirical research on the spatial distribution and determinants of regional e-commerce in China: evidence from Chinese Provinces. Emerg. Mark. Financ. Trade 56, 3117–3133. doi: 10.1080/1540496X.2019.1592749

CrossRef Full Text | Google Scholar

Gian, S. H., Kasim, S., Hassan, R., Zakaria, Z., Mahdin, H., Ramli, A. A., et al. (2019). Online activity duration management system for manufacturing company. Acta Electron. Malaysia 3, 1–8. doi: 10.26480/AEM.02.2019.01.08

CrossRef Full Text | Google Scholar

Gilboa, S., Seger-Guttmann, T., and Mimran, O. (2019). The unique role of relationship marketing in small businesses’ customer experience. J. Retail. Consum. Serv. 51, 152–164. doi: 10.1016/j.jretconser.2019.06.004

CrossRef Full Text | Google Scholar

Hair, J. F. Jr., Matthews, L. M., Matthews, R. L., and Sarstedt, M. (2017). PLS-SEM or CB-SEM: updated guidelines on which method to use. Int. J. Multivar. Data Anal. 1:107. doi: 10.1504/ijmda.2017.10008574

PubMed Abstract | CrossRef Full Text | Google Scholar

Hamilton, R., Thompson, D., Bone, S., Chaplin, L. N., Griskevicius, V., Goldsmith, K., et al. (2019). The effects of scarcity on consumer decision journeys. J. Acad. Mark. Sci. 47, 532–550. doi: 10.1007/s11747-018-0604-7

CrossRef Full Text | Google Scholar

Han, J. H., and Kim, H. M. (2019). The role of information technology use for increasing consumer informedness in cross-border electronic commerce: an empirical study. Electron. Commer. Res. Appl. 34:100826. doi: 10.1016/j.elerap.2019.100826

CrossRef Full Text | Google Scholar

Hopkins, E. (2022). Machine learning tools, algorithms, and techniques in retail business operations: consumer perceptions, expectations, and habits. J. Self Governance Manag. Econ. 43–55. Available online at: https://addletonacademicpublishers.com/search-in-jsme/2358-volume-10-1-2022/4197-machine-learning-tools-algorithms-and-techniques-in-retail-business-operations-consumer-perceptions-expectations-and-habits (accessed April 8, 2022).

Google Scholar

Huang, C. C., Jin, H., Zhang, J., Zheng, Q., Chen, Y., Cheung, S., et al. (2020). The effects of an innovative e-commerce poverty alleviation platform on Chinese rural laborer skills development and family well-being. Child. Youth Serv. Rev. 116:105189. doi: 10.1016/j.childyouth.2020.105189

CrossRef Full Text | Google Scholar

Huang, C., Wu, X., Wang, X., He, T., Jiang, F., and Yu, J. (2021). Exploring the relationships between achievement goals, community identification and online collaborative reflection: a deep learning and bayesian approach. Educ. Technol. Soc. 24, 210–223.

Google Scholar

Huang, W., Saydaliev, H. B., Iqbal, W., and Irfan, M. (2022). Measuring the impact of economic policies on Co 2 emissions: ways to achieve green economic recovery in the post-covid-19 era. Clim. Chang. Econ. doi: 10.1142/s2010007822400103

CrossRef Full Text | Google Scholar

Ingham, J., Cadieux, J., and Mekki Berrada, A. (2015). E-Shopping acceptance: a qualitative and meta-analytic review. Inf. Manag. 52, 44–60. doi: 10.1016/j.im.2014.10.002

CrossRef Full Text | Google Scholar

Jöreskog, K. G. (1993). “Testing structural equation models,” in Testing Structural Equation Models, eds K. A. Bollen and J. S. Long (Thousand Oaks, CA: Sage), 294–316.

Google Scholar

Kaiser, H. F. (1974). An index of factorial simplicity. Psychometrika 39, 31–36. doi: 10.1007/BF02291575

CrossRef Full Text | Google Scholar

Karani, P., and Failler, P. (2020). Comparative coastal and marine tourism, climate change, and the blue economy in African large marine ecosystems. Environ. Dev. 36:100572. doi: 10.1016/j.envdev.2020.100572

CrossRef Full Text | Google Scholar

Kasilingam, D. L. (2020). Understanding the attitude and intention to use smartphone chatbots for shopping. Technol. Soc. 62:101280. doi: 10.1016/j.techsoc.2020.101280

CrossRef Full Text | Google Scholar

Kawasaki, T., Wakashima, H., and Shibasaki, R. (2022). The use of e-commerce and the COVID-19 outbreak: a panel data analysis in Japan. Transp. Policy 115, 88–100. doi: 10.1016/j.tranpol.2021.10.023

CrossRef Full Text | Google Scholar

Keshri, A. K., Mishra, B. K., and Rukhaiyar, B. P. (2020). When rumors create chaos in e-commerce. Chaos Solit. Fractals 131:109497. doi: 10.1016/j.chaos.2019.109497

CrossRef Full Text | Google Scholar

Kim, H. W., and Gupta, S. (2009). A comparison of purchase decision calculus between potential and repeat customers of an online store. Decis. Support Syst. 47, 477–487. doi: 10.1016/j.dss.2009.04.014

CrossRef Full Text | Google Scholar

Kim, J., Merrill, K., and Collins, C. (2021). AI as a friend or assistant: the mediating role of perceived usefulness in social AI vs. functional AI. Telemat. Inform. 64:101694. doi: 10.1016/j.tele.2021.101694

CrossRef Full Text | Google Scholar

Kliestik, T., Zvarikova, K., and Lãzãroiu, G. (2022b). Data-driven machine learning and neural network algorithms in the retailing environment: consumer engagement, experience, and purchase behaviors. Econ. Manag. Financ. Mark. 17:57. doi: 10.22381/emfm17120224

CrossRef Full Text | Google Scholar

Kliestik, T., Kovalova, E., and Lãzãroiu, G. (2022a). Cognitive decision-making algorithms in data-driven retail intelligence: consumer sentiments, choices, and shopping behaviors. J. Self Governance Manag. Econ. 30–42. Available online at: https://addletonacademicpublishers.com/search-in-jsme/2358-volume-10-1-2022/4196-cognitive-decision-making-algorithms-in-data-driven-retail-intelligence-consumer-sentiments-choices-and-shopping-behaviors (accessed April 8, 2022).

Google Scholar

Kolotylo-Kulkarni, M., Xia, W., and Dhillon, G. (2021). Information disclosure in e-commerce: a systematic review and agenda for future research. J. Bus. Res. 126, 221–238. doi: 10.1016/j.jbusres.2020.12.006

CrossRef Full Text | Google Scholar

Kotha, S., Rajgopal, S., and Venkatachalam, M. (2004). The role of online buying experience as a competitive advantage: evidence from third-party ratings for e-commerce firms. J. Bus. 77, S109–S133. doi: 10.1086/381639

CrossRef Full Text | Google Scholar

Kumar, V., and Ayodeji, O. G. (2021). E-retail factors for customer activation and retention: an empirical study from Indian e-commerce customers. J. Retail. Consum. Serv. 59:102399. doi: 10.1016/J.JRETCONSER.2020.102399

CrossRef Full Text | Google Scholar

Lăzăroiu, G., Neguriţă, O., Grecu, I., Grecu, G., and Mitran, P. C. (2020). Consumers’ decision-making process on social commerce platforms: online trust, perceived risk, and purchase intentions. Front. Psychol. 11:890. doi: 10.3389/FPSYG.2020.00890

PubMed Abstract | CrossRef Full Text | Google Scholar

Le, H. T. K., Carrel, A. L., and Shah, H. (2021). Impacts of online shopping on travel demand: a systematic review. Transp. Rev. 42, 273–295. doi: 10.1080/01441647.2021.1961917

CrossRef Full Text | Google Scholar

Leopoldo, P. (2019). E-commerce em mercados emergentes: fatores para a internacionalização do calçado brasileiro na américa do sul. Rev. Gest.Tecnol. 20, 165–189.

Google Scholar

Li, X., Zhao, X., Xu, W., and Pu, W. (2020). Measuring ease of use of mobile applications in e-commerce retailing from the perspective of consumer online shopping behaviour patterns. J. Retail. Consum. Serv. 55:102093. doi: 10.1016/J.JRETCONSER.2020.102093

CrossRef Full Text | Google Scholar

Liu, M., Min, S., Ma, W., and Liu, T. (2021). The adoption and impact of E-commerce in rural China: application of an endogenous switching regression model. J. Rural Stud 83, 106–116. doi: 10.1016/j.jrurstud.2021.02.021

CrossRef Full Text | Google Scholar

Liu, P., Li, M., Dai, D., and Guo, L. (2021). The effects of social commerce environmental characteristics on customers’ purchase intentions: the chain mediating effect of customer-to-customer interaction and customer-perceived value. Electron. Commer. Res. Appl. 48:101073. doi: 10.1016/j.elerap.2021.101073

CrossRef Full Text | Google Scholar

Liu, X., Zhou, Y.-W., Shen, Y., Ge, C., and Jiang, J. (2021). Zooming in the impacts of merchants’ participation in transformation from online flash sale to mixed sale e-commerce platform. Inf. Manag. 58:103409. doi: 10.1016/j.im.2020.103409

CrossRef Full Text | Google Scholar

Lowery, P. B. (2021). International Journal of-Special Issue Women’s Heal. London: Psychology Press, 2–13. doi: 10.4324/9781410608536-0

CrossRef Full Text | Google Scholar

Lucianetti, L., Chiappetta Jabbour, C. J., Gunasekaran, A., and Latan, H. (2018). Contingency factors and complementary effects of adopting advanced manufacturing tools and managerial practices: effects on organizational measurement systems and firms’ performance. Int. J. Prod. Econ. 200, 318–328. doi: 10.1016/j.ijpe.2018.04.005

CrossRef Full Text | Google Scholar

Mahmood, A., Akhtar, M. N., Talat, U., Shuai, C., and Hyatt, J. C. (2019). Specific HR practices and employee commitment: the mediating role of job satisfaction. Empl. Relat. 41, 420–435. doi: 10.1108/ER-03-2018-0074

CrossRef Full Text | Google Scholar

McKnight, D. H., Choudhury, V., and Kacmar, C. (2002). Developing and validating trust measures for e-commerce: an integrative typology. Inf. Syst. Res. 13, 334–359. doi: 10.1287/isre.13.3.334.81

PubMed Abstract | CrossRef Full Text | Google Scholar

Meilatinova, N. (2021). Social commerce: factors affecting customer repurchase and word-of-mouth intentions. Int. J. Inf. Manage. 57:102300. doi: 10.1016/J.IJINFOMGT.2020.102300

CrossRef Full Text | Google Scholar

Moorthi, K., Dhiman, G., Arulprakash, P., Suresh, C., and Srihari, K. (2021). A survey on impact of data analytics techniques in E-commerce. Mater. Today Proc. doi: 10.1016/j.matpr.2020.10.867

CrossRef Full Text | Google Scholar

Nasiri, M. S., and Shokouhyar, S. (2021). Actual consumers’ response to purchase refurbished smartphones: exploring perceived value from product reviews in online retailing. J. Retail. Consum. Serv. 62:102652. doi: 10.1016/J.JRETCONSER.2021.102652

CrossRef Full Text | Google Scholar

Nica, E., Sabie, O. M., Mascu, S., and Lu?an, A. G. (2022). Artificial intelligence decision-making in shopping patterns: consumer values, cognition, and attitudes. Econ. Manag. Financ. Mark. 17:31. doi: 10.22381/emfm17120222

CrossRef Full Text | Google Scholar

Oliveira, T., Alhinho, M., Rita, P., and Dhillon, G. (2017). Modelling and testing consumer trust dimensions in e-commerce. Comput. Hum. Behav. 71, 153–164. doi: 10.1016/j.chb.2017.01.050

CrossRef Full Text | Google Scholar

Oliver, R. L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. J. Mark. Res. 17, 460–469.

Google Scholar

Olsson, J., Osman, M. C., Hellström, D., and Vakulenko, Y. (2021). Customer expectations of unattended grocery delivery services: mapping forms and determinants. Int. J. Retail Distrib. Manag. 50, 1–16. doi: 10.1108/IJRDM-07-2020-0273

CrossRef Full Text | Google Scholar

Pantano, E., Pizzi, G., Scarpi, D., and Dennis, C. (2020). Competing during a pandemic? Retailers’ ups and downs during the COVID-19 outbreak. J. Bus. Res. 116, 209–213. doi: 10.1016/j.jbusres.2020.05.036

PubMed Abstract | CrossRef Full Text | Google Scholar

Paul, J., and Rosenbaum, M. (2020). Retailing and consumer services at a tipping point: new conceptual frameworks and theoretical models. J. Retail. Consum. Serv. 54:101977. doi: 10.1016/J.JRETCONSER.2019.101977

CrossRef Full Text | Google Scholar

Qalati, S. A., Vela, E. G., Li, W., Dakhan, S. A., Thuy, T. T. H., and Merani, S. H. (2021). Effects of perceived service quality, website quality, and reputation on purchase intention: the mediating and moderating roles of trust and perceived risk in online shopping. Cogent Bus. Manag. 8:1869363. doi: 10.1080/23311975.2020.1869363

CrossRef Full Text | Google Scholar

Qiu, W., Bian, Y., Zhang, J., and Irfan, M. (2022). The role of environmental regulation, industrial upgrading, and resource allocation on foreign direct investment: evidence from 276 Chinese cities. Environ. Sci. Pollut. Res. 29, 1–17. doi: 10.1007/s11356-022-18607-2

PubMed Abstract | CrossRef Full Text | Google Scholar

Ribadu, M. B., and Wan, W. N. (2019). An integrated approach towards Sharia compliance E-commerce trust. Appl. Comput. Inform. 15, 1–6. doi: 10.1016/j.aci.2017.09.002

CrossRef Full Text | Google Scholar

Salari, M., Javid, R. J., and Noghanibehambari, H. (2021). The nexus between CO2 emissions, energy consumption, and economic growth in the U.S. Econ. Anal. Policy 69, 182–194. doi: 10.1016/j.eap.2020.12.007

CrossRef Full Text | Google Scholar

Sardeshmukh, S., and Vandenberg, R. J. (2013). “Integrating moderation and mediation: a structural equation modeling approach,” in Proceedings of the Academy of Management, Vol. 2013, (Briarcliff Manor, NY: Academy of Management). doi: 10.5465/ambpp.2013.92

CrossRef Full Text | Google Scholar

Schwarz, N., Moretti, M., Bugalho, M. N., Davies, Z. G., Haase, D., Hack, J., et al. (2017). Understanding biodiversity-ecosystem service relationships in urban areas: a comprehensive literature review. Ecosyst. Serv. 27, 161–171.

Google Scholar

Shan, H., Li, Y., Shi, J., and Yao, C. (2020). “Market structure, technical efficiency and performance: an empirical study of e-commerce in China,” in Proceedings of the ACM International Conference Proceeding Series ICMSS 2020 (New York, NY: Association for Computing Machinery), 101–106. doi: 10.1145/3380625.3380626

CrossRef Full Text | Google Scholar

Shao, L., Zhang, H., and Irfan, M. (2021). How public expenditure in recreational and cultural industry and socioeconomic status caused environmental sustainability in OECD countries? Econ. Res. Istraz. 1–18. doi: 10.1080/1331677X.2021.2015614

CrossRef Full Text | Google Scholar

Sharma, H., and Aggarwal, A. G. (2019). Finding determinants of e-commerce success: a PLS-SEM approach. J. Adv. Manag. Res. 16, 453–471. doi: 10.1108/JAMR-08-2018-0074

CrossRef Full Text | Google Scholar

Spruit, D., and Almenar, E. (2021). First market study in e-commerce food packaging: resources, performance, and trends. Food Packag. Shelf Life 29:100698. doi: 10.1016/j.fpsl.2021.100698

CrossRef Full Text | Google Scholar

Stevens, J. P., and Stevens, J. P. (2001). Applied Multivariate Statistics for the Social Sciences. Oxfordshire: Routledge, doi: 10.4324/9781410604491

CrossRef Full Text | Google Scholar

Strupeit, L., and Palm, A. (2016). Overcoming barriers to renewable energy diffusion: business models for customer-sited solar photovoltaics in Japan, Germany and the United States. J. Clean. Prod. 123, 124–136. doi: 10.1016/j.jclepro.2015.06.120

CrossRef Full Text | Google Scholar

Tang, C., Xue, Y., Wu, H., Irfan, M., and Hao, Y. (2022). How does telecommunications infrastructure affect eco-efficiency? Evidence from a quasi-natural experiment in China. Technol. Soc. 69:101963. doi: 10.1016/j.techsoc.2022.101963

CrossRef Full Text | Google Scholar

Tauni, M. Z., Fang, H. X., Rao, Z. U. R., and Yousaf, S. (2015). The influence of investor personality traits on information acquisition and trading behavior: evidence from Chinese futures exchange. Pers. Individ. Dif 87, 248–255. doi: 10.1016/j.paid.2015.08.026

CrossRef Full Text | Google Scholar

Tran, L. T. T. (2021). Managing the effectiveness of e-commerce platforms in a pandemic. J. Retail. Consum. Serv. 58:102287. doi: 10.1016/J.JRETCONSER.2020.102287

CrossRef Full Text | Google Scholar

Treiblmaier, H., and Sillaber, C. (2021). The impact of blockchain on e-commerce: a framework for salient research topics. Electron. Commer. Res. Appl. 48:101054. doi: 10.1016/j.elerap.2021.101054

CrossRef Full Text | Google Scholar

Truong, D., Pan, J. Y., and Buaphiban, T. (2020). Low cost carriers in Southeast Asia: how does ticket price change the way passengers make their airline selection? J. Air Transp. Manag. 86:101836. doi: 10.1016/j.jairtraman.2020.101836

CrossRef Full Text | Google Scholar

Uzir, M. U. H., Al Halbusi, H., Thurasamy, R., Thiam Hock, R. L., Aljaberi, M. A., Hasan, N., et al. (2021). The effects of service quality, perceived value and trust in home delivery service personnel on customer satisfaction: evidence from a developing country. J. Retail. Consum. Serv. 63:102721. doi: 10.1016/J.JRETCONSER.2021.102721

CrossRef Full Text | Google Scholar

Vakulenko, Y., Shams, P., Hellström, D., and Hjort, K. (2019). Online retail experience and customer satisfaction: the mediating role of last mile delivery. Int. Rev. Retail. Distrib. Consum. Res. 29, 306–320. doi: 10.1080/09593969.2019.1598466

CrossRef Full Text | Google Scholar

Vasic, N., Kilibarda, M., and Kaurin, T. (2019). The influence of online shopping determinants on customer satisfaction in the Serbian market. J. Theor. Appl. Electron. Commer. Res. 14, 70–89. doi: 10.4067/s0718-18762019000200107

PubMed Abstract | CrossRef Full Text | Google Scholar

Vãtãmãnescu, E. M., Alexandru, V. A., Mitan, A., and Dabija, D. C. (2020). From the deliberate managerial strategy towards international business performance: a psychic distance vs. global mindset approach. Syst. Res. Behav. Sci. 37, 374–387. doi: 10.1002/sres.2658

CrossRef Full Text | Google Scholar

Venkatesh, V., and Davis, F. D. (2000). A theoretical extension of the technology acceptance model: four longitudinal field studies. Manage. Sci. 46, 186–204.

Google Scholar

Wang, K., Wang, H., and Li, S. (2022). Renewable quantile regression for streaming datasets. Knowl. Based Syst. 235:107675. doi: 10.1016/j.knosys.2021.107675

CrossRef Full Text | Google Scholar

Wang, Z., and Ben, S. (2021). Effect of consumers’ online shopping on their investment in money market funds on ecommerce platforms. Inf. Syst. E Bus. Manag. 20, 325–346. doi: 10.1007/s10257-021-00516-5

CrossRef Full Text | Google Scholar

Wang, Y. D., and Emurian, H. H. (2005). An overview of online trust: concepts, elements, and implications. Comput. Hum. Behav. 21, 105–125.

Google Scholar

Westland, J. C. (2010). Lower bounds on sample size in structural equation modeling. Electron. Commerce Res. Appl. 9, 476–487.

Google Scholar

Xiang, H., Chau, K. Y., Iqbal, W., Irfan, M., and Dagar, V. (2022). Determinants of social commerce usage and online impulse purchase: implications for business and digital revolution. Front. Psychol. 13:837042. doi: 10.3389/fpsyg.2022.837042

PubMed Abstract | CrossRef Full Text | Google Scholar

Xiao, J., Wu, Y., Xie, K., and Hu, Q. (2019). Managing the e-commerce disruption with IT-based innovations: insights from strategic renewal perspectives. Inf. Manag. 56, 122–139. doi: 10.1016/j.im.2018.07.006

CrossRef Full Text | Google Scholar

Xue, P., Mak, C. M., and Cheung, H. D. (2014). The effects of daylighting and human behavior on luminous comfort in residential buildings: a questionnaire survey. Build. Environ. 81, 51–59. doi: 10.1016/j.buildenv.2014.06.011

CrossRef Full Text | Google Scholar

Yang, F., Tang, J., Men, J., and Zheng, X. (2021). Consumer perceived value and impulse buying behavior on mobile commerce: the moderating effect of social influence. J. Retail. Consum. Serv. 63:102683. doi: 10.1016/J.JRETCONSER.2021.102683

CrossRef Full Text | Google Scholar

Yang, L., Xu, M., and Xing, L. (2022). Exploring the core factors of online purchase decisions by building an E-Commerce network evolution model. J. Retail. Consum. Serv. 64:102784. doi: 10.1016/j.jretconser.2021.102784

CrossRef Full Text | Google Scholar

Yoon, S., and Zhang, D. (2018). Social media, information presentation, consumer involvement, and cross-border adoption of pop culture products. Electron. Commer. Res. Appl. 27, 129–138. doi: 10.1016/j.elerap.2017.12.005

CrossRef Full Text | Google Scholar

Yu, J., Ha, I., Choi, M., and Rho, J. (2005). Extending the TAM for a t-commerce. Inf. Manag. 42, 965–976. doi: 10.1016/j.im.2004.11.001

CrossRef Full Text | Google Scholar

Yu, N., and Huang, Y.-T. (2022). Why do people play games on mobile commerce platforms? An empirical study on the influence of gamification on purchase intention. Comput. Hum. Behav. 126:106991. doi: 10.1016/j.chb.2021.106991

CrossRef Full Text | Google Scholar

Yuan, C., Moon, H., Wang, S., Yu, X., and Kim, K. H. (2021). Study on the influencing of B2B parasocial relationship on repeat purchase intention in the online purchasing environment: an empirical study of B2B E-commerce platform. Ind. Mark. Manag 92, 101–110. doi: 10.1016/j.indmarman.2020.11.008

CrossRef Full Text | Google Scholar

Zhang, Z., Liu, S., and Niu, B. (2020). Coordination mechanism of dual-channel closed-loop supply chains considering product quality and return. J. Clean. Prod. 248:119273. doi: 10.1016/j.jclepro.2019.119273

CrossRef Full Text | Google Scholar

Zhao, W., Wang, A. Q., and Chen, Y. (2019). How to maintain the sustainable development of a business platform: a case study of pinduoduo social commerce platform in China. Sustain 11:6337. doi: 10.3390/su11226337

CrossRef Full Text | Google Scholar

Zhao, Y., Wang, L., Tang, H., and Zhang, Y. (2020). Electronic word-of-mouth and consumer purchase intentions in social e-commerce. Electron. Commer. Res. Appl. 41:100980. doi: 10.1016/j.elerap.2020.100980

CrossRef Full Text | Google Scholar

Zheng, Y., Han, W., and Yang, R. (2021). Does government behaviour or enterprise investment improve regional innovation performance?–Evidence from China. Int. J. Technol. Manag. 85, 274–296. doi: 10.1504/IJTM.2021.115266

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhou, L., Wang, W., Xu, J., Liu, T., and Gu, J. (2018). Perceived information transparency in B2C e-commerce: an empirical investigation. Inf. Manag. 55, 912–927. doi: 10.1016/j.im.2018.04.005

CrossRef Full Text | Google Scholar

Appendix

Appendix A
www.frontiersin.org

Appendix A. Survey questionnaire.

Keywords: intention, critical factors, consumers, E-commerce, China

Citation: Tong T and Xiong Y (2022) Modeling Intention-Based Critical Determinants of E-Commerce Utilization: Emerging Business Models and Transformation in the Digital World. Front. Psychol. 13:889147. doi: 10.3389/fpsyg.2022.889147

Received: 03 March 2022; Accepted: 07 June 2022;
Published: 10 August 2022.

Edited by:

Naeem Akhtar, University of Engineering and Technology, Lahore, Pakistan

Reviewed by:

Surya Prakash Pati, Indian Institute of Management Kozhikode, India
Gheorghe Popescu, Dimitrie Cantemir Christian University, Romania
Muhammad Irfan, Beijing Institute of Technology, China
Freddy Paz, Pontifical Catholic University of Peru, Peru

Copyright © 2022 Tong and Xiong. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Yuyu Xiong, xyy45856003@126.com

These authors have contributed equally to this work and share first authorship

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.