Influence of social interaction on the purchase behavior of social commerce consumers

Abstract

BACKGROUND:

Compared with traditional e-commerce, a significant feature of social commerce is the more complex network of social relations built by the user through frequent online interaction and communication. This relationship network strengthens the impact of social interaction on its purchase behavior and highlights the necessity to study social interaction in social commerce.

OBJECTIVE:

This research aims to study the mechanism of social interaction on the purchase behavior of social commerce consumers based on the SOR theory.

METHODS:

This research uses SEM to perform hypothesis testing. The survey data of 622 consumers are used to conduct empirical tests on the hypothesis proposed in the research.

RESULTS:

The research results show that user-website interaction, user-seller interaction, user-user interaction, user-online friend interaction can significantly enhance users’ physical presence; user-seller interaction, user-user interaction, and user-online friend interaction can significantly enhance users’ social presence; other variables can significantly enhance users’ flow experience except for the user-seller interaction; the physical presence and social presence significantly affect users’ impulsive purchasing intention, but have no significant effect on repeat purchasing intention; the flow experience significantly affects users’ impulsive purchasing willingness and repeat purchasing willingness.

CONCLUSION:

By connecting social interaction theory, presence theory, flow experience theory with consumer purchase behavior, the research group provides a new lens for how social interaction influence consumers’ purchase behavior in social business platform. The social business platform can learn from the research results of this paper and formulate corresponding measures to promote its own development.

Keywords

Social commerce social interaction presence flow experience impulsive purchasing repeat purchasing

Li XQ. Department of Management Science and Engineering, School of Economics and Management, Fuzhou University. Her main research interests are E-Commerce, Leadership, Innovation Management and Technology Innovation. 769808049@qq.com

Ren Dashuai doctor student, Department of Management Science and Engineering, School of Economics and Management, Fuzhou University. His main research interests are Leadership, Innovation Management and Technology Innovation. 1187303474@qq.com

Jie Zhen is a graduate student of Northeast Petroleum University. Work in the diversified business department of PetroChina North China Oilfield Company. Research direction is equity business management. 1519568506@qq.com

1 Introduction

With the rapid development of e-commerce technology and the rise of social media, social commerce (also known as social e-commerce) has become a new mode of e-commerce development, which is generally regarded as one of the main development directions of e-commerce [1, 2]; the concept of social commerce was first proposed by Yahoo in 2005. Using Web 2.0 and social media (such as micro-blog, SNS, social network sites and online media) to transmit and assist e-commerce companies in achieving commodity purchases and sales through social relationships such as recommendation, sharing, comment, communication and user-generated content, social commerce is a relationship-based online business activity [3].

The “2015 China Online Shopping Market Research Report” released by China Internet Network Information Center (CNNIC) in 2016 shows that “the number of online shoppers in China reached 413 million by December 2015, up 51.83 million over that of December 2014, with a growth rate of 14.3%. Among them, the number of social online shoppers reached 145 million, and the average number of social online shopping per person per year was 7.2 times, increased by 1.2 times compared to 2014, the utilization rate of social online shopping among online shopping users increased from 33.8% to 35.2% ” [4]. The continuous increase in social online shopping usage rate and user scale indicates that social interaction has an increasing impact on online shoppers’ purchase decision [5].

Users can have “many-to-many communications” through social commerce platforms [6], they can also share shopping experiences, consumption experiences and seek shopping advice with friends on social platforms [7]. Compared with communicating with strangers in traditional e-commerce, the social interaction of users in the social e-commerce environment is mostly between users and their online friends. Suggestions come from friends are easier to be accepted and will ultimately influence users’ purchase decision.

In addition, users’ purchase decision in traditional e-commerce is mainly based on the system functions of the platform, such as search engine, personalized and preference-based recommendation, commodity purchase review, etc.; while a significant extension of social commerce is the social interaction factors such as recommendation, sharing, comment, and communication based on social relationships among users, which will have an important effect on the development of e-commerce industry and enterprises. Thus, research on the frontier field of social e-commerce is very remarkable whether in e-commerce research or social network research.

As a research hot spot, social e-commerce has been explored by scholars from different angles. Existing researches use traditional perceived value theory, perceived risk theory, trust theory or information system theory to explore online shopping behaviors. For example, Eder LB and Shen J (2011) explore the effect of perceived ease of use and perceived usefulness on users’ purchase intention based on the technology acceptance model (TAM) [9]. Hajli M (2012) starts from the theory of perceived value and studies the role of perceived risk in user behavior decision-making [10]. Nick Hajli (2015) explores the effect of trust on users’ purchase intention based on trust theory [11, 12].

After reviewing the previous literature, the research group finds some theoretical gaps. First, although scholars have found that presence affects user purchase, research on the driving factors of presence is still very rare. Will the interaction in the business environment enhance the consumer’s sense of presence and thus affect the consumers’ purchase intention? This content needs in-depth study. In addition, the “flow experience” theory provides a new perspective for studying consumer experience and relating consumer online shopping behavior from a psychological perspective. However, at present, combining the theory of “flow experience” with consumers’ online shopping behavior research, there are still few literature on this behavior in social e-commerce. As a key feature of social commerce, will social interaction affect the online experience and flow experience of viewers and then affect their purchasing behavior? These issues also need to be further explored. Based on this, the research draws on the “stimulus-organism-response (S-O-R) model”, takes “social interaction” as the antecedent variable, and uses “presence” and “flow experience” that represent consumer experience factors as intermediaries variables, takes “impulsive purchasing” and “repeat purchasing” as the result variables, and uses social e-commerce website users as the research object to explore the effect and the mechanism of social interaction on consumer purchase behavior.

2 Literature review

2.1 Social interaction

Social interaction was first proposed by scholar Wiener (1948) [13]. Wiener argues that interaction is a two-way communication between information receivers and senders. Human-human or human-machine interaction can ultimately affect the cognition and behavior of others [14].

Social interaction is a process of information exchange and mutual communication between users and the other part. This process is realized by the two parties through continuous information receiving, modifying, and giving feedback. The rise of social media has enriched the methods for information dissemination among users, and made the forms and types of social interaction more diverse.

Hoffman & Nobak (1997) [15] divides interactions into human-machine interaction and human-human interaction; Massey & Levy (1999) [16] proposes the concept of interpersonal interaction and content interaction. Drawing on the research results of Hoffman (1997) [15] and Zhao (1980) [17], and taking into account the role of online friends in the social commerce environment, this research divides social interaction in social e-commerce environment into four categories: user-website interaction, user-seller interaction, user-user interaction, and user-online friend interaction. User-website interaction belongs to human-machine interaction, while the others belong to human-human interaction.

2.2 Presence theory

The concept of presence was evolved from the virtual sense of presence, first proposed by the scholar Minsky [18] in 1980. Presence is expressed as an individual’s perception of the surrounding environment, and it reflects the degree of realism that an individual feels in a virtual environment, that is, the feeling of “immersive experience” [19] [20].

Based on the perspective of psychology, Ijsselsteijn [21] divides presence into social presence and physical presence; social presence gives users the feeling of “joining with others” [22] by shaping social scenes that communicate and share with others, while physical presence creates an “immersive” feeling in the process of experiencing services or purchasing products through the interaction between users and services or users and products [23].

At present, presence is widely used in the fields of distance education, communication and human-machine interaction. In recent years, few scholars began to apply it to the study of online purchase behavior, such as page design of shopping websites, network service improvement, and presentation of online advertisements. Fortin (2005) [24] finds that the interaction presented by advertising positively affected the consumers’ presence when studying the impact of presence on consumer purchase behavior. Shin (2011) [25] finds that presence can significantly increase the willingness of consumers to purchase online when study the relationship of presence, consumer trust and purchase intention. Zhao et al (2015) [17] explore the relationship between online interaction and consumer trust in B2C online shopping based on the theory of presence, and found that online interaction can significantly affect consumers’ social presence and physical presence, and further influence consumer trust.

2.3 Flow experience theory

Flow experience theory was proposed by the American psychologist Csikeszentmihalyi [26] in 1975. Flow experience refers to a psychological state in which people are in deep concentration, losing self-consciousness, feeling time distortion and a sense of pleasure when they are immersed in a certain thing. As a self-directed experience, flow experience can stimulate people to generate intrinsic motivation for repeatedly participating in an ctivity [27].

When this theory came up, it has been widely used in distant education, online games, sports, etc. As an important component of consumer experience theory, the application of flow experience in the online consumer behavior, such as the behavioral intentions, cognition and attitude of network users, has received a lot of empirical support [28]. Shiau (2011) [29] and Chang (2012) [30] conduct research on blogs and social networking sites, finding that the heartbeat experience has a positive impact on both the satisfaction of the blog and social networking users and the willingness to continue to use. Lee & Choi (2013) [31], when studying user satisfaction with online courses, finding that the flow experience would influence the continued willingness of online course learning by impact on users’ satisfaction.

Chen, Cong & Kang (2009) [32] explore online consumer shopping behavior based on the flow experience, finding that this experience can significantly affect the number of unplanned purchases and the willingness to repeat purchases. Li (2014) [33] finds that flow experience has a positive impact on the satisfaction of online shopping users.

2.4 Purchase behavior

As an integration of social media and traditional e-commerce, social e-commerce is still the promotion of consumer purchase behavior in essence. In a social e-commerce environment, the distance between users and stores is shortened by the interaction between users and websites, sellers, other users and online friends, and create an “immersive experience” for users, thereby reducing the risk perception of users, enhancing trust and pleasure of online shopping. This kind of immersive feeling is the sense of presence, while the sense of “pleasure” is a flow experience. Prior researches have shown that presence and flow experience can generate impulsive purchasing and repeat purchasing [32, 34]. Drawing on the research results of Chen (2009) [32], this paper divides user purchase behavior into impulsive purchasing behavior and repeat purchasing behavior.

2.4.1 Impulsive purchasing

Impulsive purchasing is a sudden, irresistible and hedonic purchase behavior [35]. It is accompanied with complex emotional pleasures, which can lead to conflicts between control and enjoyment. When users’ emotion overcome rationality and think less of the purchasing consequence, they are more likely to purchase [36]. There are many factors that induce impulsive purchasing, including external environment stimulation, purchase situation factors and individual personality traits; external environment stimulation is a series of marketing stimuli that sellers take to attract users, such as advertising, price promotion, shop furnishings. The purchase scenario factors refer to the atmosphere of marketing, the emotions when purchase and self-regulating resources. Individual personality traits such as impulsive buying tendencies, hedonic needs and economic strength are internal influencing factors. Impulsive purchasing phenomenon also exists in online shopping, and the network environment is much easier to induce impulsive purchasing [37]. Robert (2001) [38] believes that in online shopping, stimulating factors such as attractive pictures, product descriptions and purchase evaluations undermine user self-control, so online shopping is more likely to stimulate users’ impulse purchasing than physical stores. Wells (2011) [39] finds that perceived usefulness, website quality, enjoyment affect users’ impulsive purchasing significantly. In addition, other factors such as word-of-mouth recommendations, product images and advertisements are also proved to promote user’s impulsive purchasing tendency.

2.4.2 Repeat purchasing

Repeat purchasing intention refers to the willingness of online consumers to re-trade with sellers at a certain frequency for a period of time after conducting a transaction on the shopping website [40]. It is a desire or tendency for users to continue to form a trading relationship with sellers [41]. Obtaining user loyalty and willingness to purchase repeatedly is the decisive factor for online sellers to have sustainable competitive advantages; research has shown that elements such as service quality, website design, interactivity, trust, security, satisfaction can largely affect consumers’ repeat purchasing [42]. Gefen (2002) [43] finds that service quality, transfer cost, perceived risk and user trust are important factors influencing online shopping consumers’ willingness to purchase repeatedly. Qureshi (2009) [44] finds that website quality, website reputation and delivery ability can positively affect users’ trust, while trust and website quality can positively affect users’ willingness to purchase repeatedly. Chen, Li, and Song (2015) [45] prove that perceived value, satisfaction and trust significantly affect users’ willingness to purchase repeatedly. In addition, some scholars analyze the formation mechanism of users’ repeat purchasing intention from the perspective of emotion, and find that the positive attitude of users to the website is an important factor affecting users’ repurchase on the website [46].

3 Research framework and research hypotheses

3.1 Research framework

In the social commerce environment, the formation of user purchase decision is a complex process influenced by many factors. Scholars’ research on consumer behavior of social business users is mostly focused on word-of-mouth recommendation [47], user experience [48], website design [49], trust [50], etc.

In recent years, some scholars have found that social interaction is more important than many other explicit factors in influencing the formation of user purchase decision as a procedural factor. But why social interaction can lead to the formation of user purchase decision? Research suggests that social interaction creates an atmosphere, which narrows the psychological and spatial distance between users and businesses at both ends of the network and produces a sense of pleasure for users who exposed to such an atmosphere. This atmosphere is the sense of presence and users’ sense of pleasure is flow experience, both of them serve as the mediating variables for social interaction and user purchase decision.

Stimulus-Organism-Response theory (SOR), Social Exchange Theory (SET) and Technology Acceptance Model (TAM) are widely used in the study of consumers’ willingness to purchase and behavior in online shopping. This research draws on these theories and constructs a model of the impact mechanism of social interaction on user purchase behavior in a social commerce environment as shown in Fig. 1. In this hypothetical model, social interaction is a pre-variable that affects users’ purchase behavior, and is divided into “user-website interaction (human-machine interaction)”, “user-seller interaction (human-human interaction)”, “user-user interaction” (human-human interaction) and “user-online friends interaction (human-human interaction)”. These four interaction factors correspond to the “stimulus” variables in the “Stimulus-Organism-Response” theory, among which “user-website interaction” can be measured by the “perceived ease of use” in the technology acceptance model, “user-seller interaction” can be measured by the responsiveness in social exchange theory, “user-user interaction” and “user-online friends interaction” can be measured by mutual assistance. As mediators of social interaction affecting users’ purchasing behavior, the sense of presence and flow experience correspond to the “organism” variables in the stimulus-organism-response theory, among which the sense of presence includes spatial presence and social presence. As a result variable, purchase behavior corresponds to the “response” variable of the stimulus-organism-response theory. Drawing on the research results of Chen, Cong & Kang [32], purchase behavior in this research is divided into impulsive purchasing and repeat purchasing.

Fig. 1

Study model.

Fig. 2

Model test results.

3.2 Research hypotheses

3.2.1 Social interaction and presence

The presence theory holds that social interaction can promote presence of users [51]. With the improving level of social interaction, the sensory stimulus obtained by users is closer to the stimulus of the real environment. On a social commerce platform with a high level of social interaction, users can communicate with sellers, other users, online friends, etc. through social media in a two-way manner to get feedback. High social interaction will give users a sense of being in the environment of products and shopping websites [52]. The consumer can search all the information about the products they need more easily by interacting on the website which can recommend products suitable for them according to their individual needs. In addition, consumers who interact on the website are able to perceive a higher interactive response speed and perceived ease of use, so as to facilitate the consumer to obtain detailed information about products and facilitate their shopping. Customers can view 3D merchandise display from different angles and try on merchandise online, all these activities can make them feel real. Therefore, high interactivity will give customers a feeling of being with the product and being in a physical store. In addition, Hassanein et al. [53] find that improving the design of text and pictures in online marketing can enhance the social presence of users significantly. Mollen [54] also shows that the stimulation of the consumer on the website and in the surrounding environment will be reflected in the interactivity, presence and investment. The following assumptions are made based on the above analysis:

Hypothesis H1a: user-website interaction positively affects users’ physical presence;

A good navigation system, web page layout, detailed pictures and text descriptions of products can not only equip the consumer with the products information they need, but also make them feel relaxed when browsing the website. For example, when consumers buy a sofa, the good design of the website and product display can not only equip them with information about the products, but also make them feel at home, which can improve their social presence.

Hypothesis H1b: user-website interaction positively affects users’ social presence;

By interacting with sellers, consumers can understand the product information from sellers, thus having a deeper understanding of the features and functions of the product. For example, sellers can show consumers the picture of the product and recommend a suitable size for the consumer, so that the consumer can imagine the approximate appearance of the product even if they do not see the real one. On the other hand, by interacting with sellers, consumers can get some more comprehensive suggestions about the product from sellers, which can make consumers feel the service of the merchant and increase consumers’ favor for sellers and the product. Based on the above analysis, the following assumptions are made:

Hypothesis H1c: user-seller interaction positively affects users’ physical presence.

Hypothesis H1d: user-seller interaction positively affects users’ social presence.

Liu H et al. [55] believe that three types of interpersonal interactions between users are mainly concerned, which are professionalism, similarity and familiarity. Professionalism reflects the knowledge of community members in a certain field. Professional knowledge sources are vital to the acceptance of information. People are more willing to believe the opinions of experts when they accept social influence. On the online community platform, members with a certain degree of expertise and more professional knowledge will provide useful suggestions. This can increase consumers’ spatial presence. According to the similarity theory, people are more easily to be attracted by those who are similar to them. Al-Natour et al. [56] point out that consumers’ perception of similarity to other members helps them enjoy interaction. When users use the community platform, they will unconsciously resonate with people who are similar to them. In the social business environment, most of the information users want to obtain are from users who have the same preferences as themselves. Common topics appear only when the preferences are similar. Therefore, the similarity will promote the generation of consumers’ social presence. Familiarity refers to the degree of interaction between members of the community platform and their understanding of other members on the platform. Familiarity reflects the interaction frequency and relationship strength of users in social shopping websites. Familiarity can reduce uncertainty, enhance the degree of trust among platform members and promote interaction. When users interact on a community platform, they are not necessarily familiar with each other, and it is difficult to have a sense of trust or feel relaxed to communicate on the platform. Only when the members feel that they are familiar with each other like friends, will they be more enjoyable and relaxed when participating in activities on the community platform, thereby enhancing consumers’ social presence. Based on the above analysis, the following assumptions are made:

Hypothesis H1e: user-user interaction positively affects users’ physical presence.

Hypothesis H1f: user-user interaction positively affects users’ social presence.

Ipsos’ survey shows that Chinese users are more likely to buy something because their friends on social networks like or follow. The purchase behavior in online social groups is different from one-to-one purchase relationship in the traditional business model [57]. The attributes of social networks have gradually turned the purchase relationship to many-to-many trading relationship based on online social groups and social interaction [58], giving birth to the collective buying behavior of group consumers. Therefore, when users’ online friends are closely connected with them, users’ physical presence will be improved.Finally, consumers’ social friends are a group closely connected with consumers who trust online friends a lot. If consumers’ online friends have purchased goods, consumers can get a lot of information from them. Detailed information can thus enhance consumers’ physical presence. Jiang et al. [34] and Zhao et al. [17] find that online interaction can affect consumers’ social presence and physical presence significantly. By interacting with social friends and discussing common topics about products, consumers can make both parties feel each other’s existence psychologically, so that they will feel a sense of authenticity and intimacy. Based on the above analysis, the following assumptions are made:

Hypothesis H1g: user-online friends’ interaction positively affects users’ physical presence.

Hypothesis H1h: user-online friends’ interaction positively affects users’ social presence.

3.2.2 Social interaction and flow experience

It is generally believed that the stronger the interactivity of the network platform, the stronger users’ flow experience is [55]. Compared to traditional e-commerce, one of the biggest features of social commerce is the interaction. Online stores provide consumers with corresponding interactive tools, attracting their attention and helping them perceive and experience products in the interaction of online stores; Hoffman [27] divides the social interaction into human-machine interaction and human-human interaction, many scholars adopt this method and further subdivide it. On the basis of human-machine interaction and human-human interaction, Tang Jiageng [59] further divides interaction into consumer-website interaction, consumer-online service staff interaction and consumer-consumer interaction.

From these three aspects, a highly interactive platform can respond to user needs quickly, strengthen the connection between users and sellers, and enable users to obtain more information about products or services based on other users’ evaluation of products or services, and realize human-human interaction. Through such interactive process, the feedback on users’ information as well as needs is strengthened, and users’ sense of control in the purchase process is promoted. After all, timely and clear feedback as well as control are important prerequisite for users to have flow experience [56].

Zhou and Shi [60] study the mechanism of social interaction on the user experience of social business, they divide human-machine interaction into perception control and perception personalization. Their conclusion is that perception control can significantly affect the formation of user flow experience. Zhang, Dai and Peng [61] find that compared with two-dimensional human-machine interaction, three-dimensional human-machine interaction can bring a better user experience and significantly affect user experience. Song and Zinkhan [62] find that unique website information can affect users’ interactive cognition. Skadberg [63] find that the higher the level of interactivity of the website, the higher the level of consumers’ flow experience. Hoffman believes that network interaction can directly affect consumers’ attention, thereby increase consumer perception of flow experience. This paper uses user-website interaction to represent human-machine interaction and proposes the following hypotheses.

Hypothesis H2a: user-website interaction positively affects users’ flow experience.

Zhou and Shi [60] divide human interaction into similarity, professionalism and familiarity, and find that similarity, professionalism and familiarity can significantly affect users’ flow experience. Liu H, Chu H, Huang Q, etc. [55] find that the frequency of user interactions and relationship strength in social websites can increase trust between users and promote interaction between users. Jiang, Zhao and Lei [34] empirical research find that online interaction in B2C online shopping positively affects consumers’ flow experience. Guo [64] also confirms that a clear feedback mechanism in the online shopping environment is a necessary prerequisite variable for flow experience. Al-Natour S and Benbasat I found that users’ perception of similarity to other users can promote the formation of interactions between them. Koufairs [65] finds through empirical research that online consumers’ interaction positively affects their shopping pleasure and concentration. Fan et al [66] find through empirical research that consumer interaction in virtual communities has a significant positive effect on consumers’ flow experience. Based on this, the following hypotheses are proposed.

Hypothesis H2b: user-seller interaction positively affects users’ flow experience.

Hypothesis H2c: user-user interaction positively affects users’ flow experience.

HypothesisH2d: user-online friends interaction positively affects users’ flow experience.

3.2.3 Presence and purchase behavior

Many studies have shown that a significant obstacle to the development of online shopping is that the consumer cannot truly experience the goods or services through their own sense of touch and vision when purchasing goods or services. In order to create a sense of presence to address the defect that users cannot feel the products or services truly, when designing online stores and displaying products, many sellers present them as vividly and realistically as possible. Skadberg et al. [63] regard presence as a feature of the “immersive experience” of web browsing, while “immersive experience can stimulate consumers’ perception of trust, thereby promoting their impulsive purchase behavior”. On the other hand, in a 3D online shopping environment, a high level of physical presence will allow users to have a better evaluation of the product, thereby promoting the purchase intentions of consumers. Shin [25] also reaches the same conclusion. Jiang et al. [34] suggests that physical presence has a significant effect on consumer purchase. Therefore, the physical presence may not only promote consumers’ impulsive purchasing behavior, but also promote their repeat purchasing behavior. Hence, we put forward the following hypotheses:

Hypothesis H3a: physical presence positively affects users’ impulsive purchasing intention.

Hypothesis H3b: physical presence positively affects users’ willingness to purchase repeatedly.

Waller et al. [67] indicate that since trust arises in the social environment, social presence serves as a necessary condition for generating trust, and an environment with rich social presence can stimulate a sense of trust among participants. A study by a well-known e-commerce scholar Gefen [43] points out that social presence can trigger trust in the context of electronic services. Lv HB [68] classifies social presence into conscious social presence, emotional social presence and cognitive social presence. His empirical research finds that conscious social presence and cognitive social presence exert a significant positive impact on trust. Chen et al. [45] find that the stronger the consumers’ sense of presence on clothing sales websites, the higher the level of consumers’ trust, which is more likely to produce repeat purchasing intentions.

Hypothesis H3c: social presence positively affects users’ impulsive purchasing intention.

Hypothesis H3d: social presence positively affects users’ willingness to purchase repeatedly.

3.2.4 Flow experience and purchase behavior

Users lose their sense of time from “flow experience”. This research connects flow experience which means concentration, full engagement and inner pleasure with the “time sense” experienced by individuals during the process of browsing the website. However, concentration and full engagement lead to the loss of “time sense”. In addition, pleasure is also a common variable for measuring flow experience in an event. Just as the important “shopping pleasure” in the offline environment, it is equally important in the online environment because it will affect online shopping in terms of attitude and willingness. Therefore, this research believes that the “shopping pleasure” variable has the same effect on online shopping. This paper describes the state of the consumer in flow experience with the “time sense” and “shopping pleasure” variables. Due to the loss of users’ sense of time and pleasure, online shopping users may purchase products that they are not planned to buy, which is called impulsive purchasing. Based on this, the following hypotheses are made:

Hypothesis H4a: flow experience positively affects users’ impulsive purchasing intention;

In addition, the inner sense of joy awakened by flow experience can enable users to focus on the experience of shopping. However, ignoring the risk of purchase will promote the user’s willingness to buy and repeat purchase behavior. Chen and others [32] find that flow experience affects consumers’ willingness to purchase repeatedly. Jiang et al (2014) [34] also find that flow experience can affect the repeat purchasing intention of B2C online shopping consumers. Based on this, the following hypotheses are made:

Hypothesis H4b: flow experience positively affects users’ repeat purchasing behavior.

4 Methodologies

4.1 Participants and procedure

This paper mainly studies the formation mechanism and influencing factors of consumers’ purchasing willingness in a social commerce environment. The surveyed network users are required to be familiar with various factors and scenarios of the social commerce environment. The data collection is in the form of an online questionnaire which is edited into a web page on the professional questionnaire website “Questionnaire Star”. The data were collected by snowball sampling, that is, the file web address was linked by email, QQ, WeChat and Microblog. Undergraduates, postgraduates and university teachers who have purchasing experience on social e-commerce platforms (such as Mushroom Street, Meilishuo, etc.) in the colleges and universities of the research institutes spread relevant information via their interpersonal network. The online questionnaire was issued for 2 weeks, and a total of 728 electronic questionnaire data were collected. 622 valid questionnaires were collected through the social e-commerce platform, while the other 106 questionnaires were not received.

The sample is consisted of 316 males (50.80%) and 306 females (49.20%). Most of them are between 19-30 years old (93.89%), meaning that the sample is young; 569 people (91.74%) have college degrees or above; 511 people (82.16%) have more than 1000 yuan monthly income; 401 people (64.47%) have more than 1000 yuan annual online shopping expenses; 523 people (84.08%) have one year or more accumulated online shopping experiences; 328 people (52.73%) purchase 3 or more times online a month. Detailed sample demographic characteristics are shown in Table 1:

Table 1
Study sample characteristics table (n = 622)

Research indicator Problem option Sample size Percentage (%)

Sex female 306 49.20%

male 316 50.80%

Age 18 years old and below 21 3.38%

19-24 years old 393 63.18%

25-30 years old 191 30.71%

31-45 years old 15 2.41%

45 years old (excluding) or more 2 0.32%

Education Junior high school and below 15 2.41%

High school/secondary school/technical school 38 6.11%

University Specialist / Bachelor 460 73.95%

Master and above 109 17.52%

Personal monthly income (yuan) Below 1000 yuan 111 17.84%

1000–3000 yuan 290 46.62%

3001–5000 yuan 99 15.92%

5001–8000 yuan 73 11.74%

Above 8000 yuan 49 7.88%

Annual online shopping fee Below 500 yuan 106 17.04%

501–1000 yuan 115 18.49%

1001–2000 yuan 127 20.42%

2001–3000 yuan 82 13.18%

Above 3001 yuan 192 30.87%

Cumulative online shopping period Less than half a year 51 8.20%

Half - 1 year 48 7.72%

Between 1-2 year 78 12.54%

Between 2-3 year 88 14.15%

Above 3 year 357 57.39%

Monthly online shopping frequency 1-2 times 294 47.27%

3-4times 186 29.90%

5-6times 68 10.93%

7 times and above 74 11.90%

Social e-commerce platform Taobao micro (324) 52.09%; Tmall (182) 29.26%; Jingdong Enjoy (73) 11.74%; Mushroom Street (7)

used in online shopping 1.13%; WeChat store (5) 0.80%; Meilishuo (4)0.64%; Red (3) 0.48%; wechat business (2) 0.32%;

recently Vancl Star (1) 0.16%; Another (21)3.38%

Research indicator	Problem option	Sample size	Percentage (%)
Sex	female	306	49.20%
	male	316	50.80%
Age	18 years old and below	21	3.38%
	19-24 years old	393	63.18%
	25-30 years old	191	30.71%
	31-45 years old	15	2.41%
	45 years old (excluding) or more	2	0.32%
Education	Junior high school and below	15	2.41%
	High school/secondary school/technical school	38	6.11%
	University Specialist / Bachelor	460	73.95%
	Master and above	109	17.52%
Personal monthly income (yuan)	Below 1000 yuan	111	17.84%
	1000–3000 yuan	290	46.62%
	3001–5000 yuan	99	15.92%
	5001–8000 yuan	73	11.74%
	Above 8000 yuan	49	7.88%
Annual online shopping fee	Below 500 yuan	106	17.04%
	501–1000 yuan	115	18.49%
	1001–2000 yuan	127	20.42%
	2001–3000 yuan	82	13.18%
	Above 3001 yuan	192	30.87%
Cumulative online shopping period	Less than half a year	51	8.20%
	Half - 1 year	48	7.72%
	Between 1-2 year	78	12.54%
	Between 2-3 year	88	14.15%
	Above 3 year	357	57.39%
Monthly online shopping frequency	1-2 times	294	47.27%
	3-4times	186	29.90%
	5-6times	68	10.93%
	7 times and above	74	11.90%
Social e-commerce platform	Taobao micro (324) 52.09%; Tmall (182) 29.26%; Jingdong Enjoy (73) 11.74%; Mushroom Street (7)
used in online shopping	1.13%; WeChat store (5) 0.80%; Meilishuo (4)0.64%; Red (3) 0.48%; wechat business (2) 0.32%;
recently	Vancl Star (1) 0.16%; Another (21)3.38%

4.2 Measures

Most of the variables involved in the research are from the present research. The variable measure items are obtained from the existing English literature through two-way translation and modified according to the basic research situation and the expert opinions in the field.

The social interaction is mainly composed of four dimensions: user-website interaction, user-seller interaction, user-user interaction and user-online friend interaction [69 –72]. In addition to user-seller interaction, which consists of four items, the rest are composed of three items. The measurement of presence is mainly derived from Barfield [73]’s maturity scale, which consists of two dimensions: spatial presence and social presence, and each consists of four items. Representative item for spatial presence is “when shopping on this website, I feel unconsciously that I am on the site.” Representative item for social presence is: “when shopping on this website, I feel a sense of closeness between me, the online store and other parties.” The measurement of flow experience is mainly derived from the Chang [74] mature questionnaire, which consists of four items. One example is: “when shopping on this website, I feel that time passes quickly”. The measurement items for impulsive buying and repeat buying come from the maturity scales of Vijayasarathy [75] and Kim [76], both of which are composed of 3 items. Representative item for impulsive purchasing is: “when shopping on this site, I often buy some products that I didn’t intend to buy”, representative item for repeat purchasing is: “I will recommend my relatives and friends to buy products through this website”.

4.3 Confirmatory factor analysis

A principal component analysis is conducted with the Varimax method. The data analysis results show that the KMO value of the sample is 0.938, greater than the recommended value of 0.5, indicating that the data collected are very suitable for principal component analysis, with the results of principal component analysis shown in Table 2. There are 9 factors in the rotated matrix, explaining the variance of 81.68%. All variables have relatively high load and low load characteristics in the correlation factor, indicating that the scale has relatively good discriminant validity and convergence validity. With Harman’a single factor detection, the variance of each factor detected was no more than 20%, indicating that the common method bias in the study is not a serious problem.

Table 2
Maximum variance after rotation factor load matrix

Factor

1 2 3 4 5 6 7 8 9

CS1 0.828

CS2 0.868

CS3 0.839

CS4 0.797

PH1 0.748

PH2 0.827

PH3 0.816

PH4 0.823

SH1 0.787

SH2 0.802

SH3 0.748

SH4 0.708

FE 1 0.635

FE 2 0.638

FE 3 0.847

FE 4 0.830

SF1 0.748

SF2 0.856

SF3 0.820

RP1 0.806

RP2 0.677

RP3 0.795

IP1 0.850

IP2 0.742

IP3 0.768

CW1 0.730

CW2 0.770

CW3 0.704

CC1 0.591

CC2 0.827

CC3 0.816

In this paper, the measurement model and the structural model are tested with the two-step method respectively, with the reliability and validity of the measurement model based on the confirmatory factor analysis (CFA) shown in Table 3. There are 31 items measuring 9 variables: user-website interaction (CW), user-seller interaction (CS), user-user interaction (CC), user-online friend interaction (SF), physical presence (PH), social presence (SH), flow experience (FE), impulsive purchasing (IP) and repeat purchasing (RI). As demonstrated in Table 4, except that the standard load of flow experience measurement item 4 is slightly less than 0.7, the standard load of all other items is greater than 0.7, and both are significant at the 0.001 level; the Cronbach’s Alpha value of the variable is greater than 0.7, indicating the measure has a better reliability, and the average variance of the variables, such as AVE, is greater than 0.5, indicating the good convergence validity of the data.

Table 3

Subscale validity test

Variables	Items	Standard load	CR	AVE	Cronbach’s Alpha
User-website interaction	CW1	0.860	0.900	0.751	0.899
	CW2	0.877
	CW3	0.863
User-seller interaction	CS1	0.883	0.926	0.759	0.927
	CS2	0.899
	CS3	0.853
	CS4	0.851
User-user interaction	CC1	0.798	0.862	0.677	0.843
	CC2	0.829
	CC3	0.841
User- social friends interaction	SF1	0.741	0.894	0.739	0.888
	SF2	0.901
	SF3	0.926
Physical presence	PH1	0.869	0.941	0.799	0.936
	PH2	0.888
	PH3	0.931
	PH4	0.888
Social Presence	SH1	0.773	0.906	0.708	0.912
	SH2	0.866
	SH3	0.866
	SH4	0.857
Flow experience	FE1	0.827	0.841	0.572	0.896
	FE2	0.824
	FE3	0.705
	FE4	0.657
Impulsive purchase	IP1	0.832	0.844	0.644	0.846
	IP2	0.787
	IP3	0.789
Repetitive purchase	RI1	0.870	0.899	0.748	0.896
	RI2	0.824
	RI3	0.900

Table 4

Correlations and AVE of Study Variables

	Mean	Std.	CW	CS	CC	SF	PH	SH	FE	IP	RI
CW	5.0156	1.12673	0.867
CS	4.8806	1.04381	0.616^**	0.872
CC	4.9317	1.23428	0.508^**	0.428^**	0.823
SF	4.7650	1.29210	0.505^**	0.369^**	0.515^**	0.860
PH	4.2036	1.25703	0.448^**	0.454^**	0.360^**	0.351^**	0.894
SH	4.1693	1.16133	0.420^**	0.438^**	0.406^**	0.375^**	0.712^**	0.841
FE	4.6589	1.25028	0.588^**	0.380^**	0.477^**	0.562^**	0.491^**	0.478^**	0.757
IP	4.3212	1.31423	0.379^**	0.316^**	0.321^**	0.357^**	0.512^**	0.521^**	0.516^**	0.803
RI	5.1719	1.15469	0.636^**	0.503^**	0.635^**	0.602^**	0.307^**	0.275^**	0.545^**	0.353^**	0.865

Note: The diagonal bold number is AVE square root, the others are correlation coefficients, *, **, *** indicate significant at 0.05, 0.01, 0.001 levels, respectively.

In order to further test the discriminant validity of the measure, the variable correlation coefficient is compared with the size of the AVE square root, as shown in Table 4. It can be seen from the table that all the average variances obtained, such as the square root of AVE (the diagonal number in the table), are greater than the factor. The correlation coefficient between the two indicates that the variable has good discriminant validity.

5 Tests of hypotheses

Based on the confirmation of the reliability and validity of the measurement questionnaire and the measurement model, this research analyzes the structural relationship of the theoretical model with AMOS 21 and calculates the fitting index of the hypothesis model and the various path coefficients based on the maximum likelihood estimation method. The value is estimated, the hypothesis is tested and the path influence relationship between the variables is examined. Table 5 lists the recommended reference range of the research model fitting index and the actual test result fitting index of the model. All the fitting indices are better than the recommended values, so in general, the theoretical model and the data fitting hypothesis of this study are better, making the theoretical model acceptable.

Table 5
Model fitness standard values and actual measured values

Fitness index X²/df RMSEA GFI AGFI CFI NFI IFI

Recommended value <3 <0.08 >0.90 >0.80 >0.90 >0.90 >0.90

Structural equation model 957.592/391 = 2.449 0.050 0.903 0.877 0.962 0.937 0.962

Fitness index	X²/df	RMSEA	GFI	AGFI	CFI	NFI	IFI
Recommended value	<3	<0.08	>0.90	>0.80	>0.90	>0.90	>0.90
Structural equation model	957.592/391 = 2.449	0.050	0.903	0.877	0.962	0.937	0.962

The standardized path coefficients, T values, P values, and the results of the research hypothesis test for the structural relationship between the structural model variables are summarized in Table 7. It shows that except for the assumption that the four hypotheses H1b, H2b, H3b, and H3d are not supported, the other hypotheses are supported, among which H1e, H1g, H1h, and H3a are significant at the 0.05 level, while H1a, H1c, H1d, H1f, H2a, H2c, H2d, H3a, H3c, H4a and H4b are significant at the 0.001 level, with the resulting model path coefficients shown in Table 6.

Table 6

Hypothesis test results

Hypothesis	relationship	Standardized path	T-value	P-value	Conclusion
Hypothesis H1a	CW → PH	0.230	3.453	^***	support
Hypothesis H1b	CW → SH	0.097	1.565	0.118	not support
Hypothesis H1c	CS → PH	0.269	4.316	^***	support
Hypothesis H1d	CS → SH	0.275	4.683	^***	support
Hypothesis H1e	CC → PH	0.136	2.550	0.011^*	support
Hypothesis H1f	CC → SH	0.213	4.156	^***	support
Hypothesis H1g	SF PH	0.090	2.206	0.043^*	support
Hypothesis H1h	SF → SH	0.099	2.388	0.017^*	support
Hypothesis H2a	CW → FE	0.443	8.671	^***	support
Hypothesis H2b	CS → FE	–0.045	–1.087	0.277	not support
Hypothesis H2c	CC → FE	0.206	5.180	^***	support
Hypothesis H2d	SF → FE	0.284	8.457	^***	support
Hypothesis H3a	PH → IP	0.166	2.290	0.022^*	support
Hypothesis H3b	PH → RI	–0.005	–0.102	0.919	not support
Hypothesis H3c	SH → IP	0.349	4.170	^***	support
Hypothesis H3d	SH → RI	–0.046	–0.799	0.425	not support
Hypothesis H4a	FE → IP	0.444	7.521	^***	support
Hypothesis H4b	FE → IP	0.851	13.661	^***	support

^*, ^**, ^*** indicate significant at the 0.05, 0.01, and 0.001 levels, respectively.

5.1 Results

The empirical results show that physical presence (0.166, P<0.05), social presence (0.349, P<0.001) and flow experience (0.444, P<0.001) are decisive factors affecting users’ impulsive purchasing intention. Flow experience (0.851, P<0.001) plays an important role in the users’ repeat purchasing intentions. In terms of social interaction, users interact with websites (0.230, P<0.001), users interact with sellers (0.269, P<0.001), users interact with users (0.136, P<0.05), and users interact with online friends (0.090, P<0.05), thus affecting users’ physical presence significantly and positively. In addition, user-seller interaction (0.275, P<0.001), user-user interaction (0.213, P<0.001), user-online friend interaction (0.099, P<0.05) affect users’ social presence significantly and positively. However, the impact of user interaction with the website on social presence is not significant. Finally, the impact of the user-seller interaction on flow experience is not significant, while the user-website interaction (0.443, P<0.001), user-user interaction (0.206, P<0.001), user-online friends interaction (0.284, P<0.001) affect users’ flow experience significantly and positively.

5.1.1 Detailed explanation of the results

Social interaction enhances the spatial presence of social e-commerce users significantly.

The research results above show that user interaction with the website can largely enhance the social presence of social e-commerce users. Shopping websites with good interactivity often have very specific and detailed page layouts, product descriptions, etc., which can facilitate users’ understanding of the functional characteristics of the products. At the same time, the convenient navigation system can prompt users to find the products they are interested more quickly on the website, and offline physical shopping scenarios are simulated even on some shopping websites with virtual reality technology. Users are more easily to be immersed in the virtual online shopping world through these measures and methods. The immersive experience can enhance users’ physical presence. In addition, in social commerce online shopping, the interaction between users and sellers mainly focuses on the communication between users and customers service. The effect and quality of the communication are mainly reflected in the timeliness, accuracy and assistance of the response, while the interaction between users and online friends is reflected in mutual assistance and sharing. Although these three types of interactions are human-to-human communication, this type of communication can inspire users to remember previous physical shopping, and recall the previous shopping scene, thus promoting the production of users’ physical presence.

Apart from the unimportant interaction between users and websites, other types of social interactions can affect users’ social presence significantly and positively.

User-seller interaction, user-user interaction and user-online friend interaction with significant social characteristics focus more on the communication between people. Good interaction can promote two-way and friendly communication between the two parties, thus creating a warm atmosphere of social presence. Besides, the high-quality interaction between users and sellers, such as product information consultation, bargaining, etc., can make users feel that they are communicating with sellers in a physical store, even giving them a feeling that the store and the product are really in front of them. The interaction between users and their online friends is often reflected in the product service evaluation and shopping experience sharing. The interaction between the two parties is based on mutual assistance and non-utility, thus making it more likely to have resonance and emotional communication between users. Compared with the interaction between users and sellers, this kind of interaction is easier, thus enhancing users’ social presence. The reason why the interaction between users and websites does not have a significant effect on the social presence may be that this interaction focuses more on the user and the website product or service. However, the interaction lacks sociality, that is, failing to deal directly with people, thereby making it difficult for users to have a feeling of “being with others”, so the social presence is not obvious.

Except that the interaction between users and sellers is unnoticeable, other types of social interactions can significantly affect users’ flow experience.

The interaction between users and websites serves as an important factor influencing the improvement of the user’s flow experience. Since the easiness of searching for product information about shopping websites, the detailed product introductions and targeted recommendations based on user needs can strengthen users’ feedback, sense of control and satisfaction during the purchase process and improve the online shopping experience in a social business environment to a certain extent, thereby promoting the generation of flow experience. User-user interaction and user-online friend interaction are mainly manifested as interpersonal communication during the virtual online shopping process. Since these two relationships are based on mutual assistance and non-utility, users can relax their vigilance and shop online easily in this condition, thus generating a sense of joy and entering a state of flow experience easily. In this research, the reason why the interaction between users and sellers does not have a significant effect on flow experience may be that they are often stakeholders, and sellers’ description of the product information is often exaggerated or even false. Therefore, users are often alert when communicating with sellers, thus making them difficult to concentrate or to be happy, so it is impossible for them to achieve a state of flow experience.

Presence is an important factor influencing the impulsive purchasing of social business users, but it has no significant impact on the willingness to repeat purchasing.

Presence is a kind of “immersive” feeling generated by users in a virtual environment. Both physical presence and social presence are important factors which can help users generate impulsive purchasing intentions. In a social business environment, the vivid and anthropomorphic design, attractive pictures and purchase evaluations of shopping websites stimulate the sense of physical presence of users, thus giving them a feeling that online stores and products are in front of them. At the same time, external stimulus factors such as advertisements and price promotions release a signal to users that the product is of good quality and low price, thereby destroying their self-control ability, making it easier to stimulate their impulsive purchasing intention. In addition, the positive social presence generated by positive interpersonal communication allows users to relax alertness, shorten the distance and generate trust between users and sellers, which leads to users’ impulsive purchasing intentions ultimately. The reason that physical presence and social presence do not affect the willingness to repeat purchasing significantly may be that users’ repeat purchasing of a certain product is based on the evaluation of shopping satisfaction. If users are not satisfied with the purchased product, they may not buy again. The sense of presence is just a sense of reality generated by users in the virtual environment, but it cannot be guaranteed that users will be satisfied with the purchased product, so they don’t want to buy again.

Flow experience is a key factor affecting the willingness of social business users to buy impulsively and repeatedly

Impulsive purchasing is a sudden and hedonic buying behavior with complex emotions. The impulsive purchasing behavior is often caused by the irresistible and strong purchasing tendency of users because of various stimuli in the scene. Impulsive purchasing is often manifested as users’ pursuit of the buying experience, so enhancing their virtual experience can enable them to have a timely and clear sense of feedback and control, thus generating a flow experience to stimulate users’ impulsive purchasing intention. The willingness to repeat purchasing is a desire or tendency for users to establish a continuous trading relationship with merchants. As a self-purposed experience, users will feel the loss of self-awareness and time and have a sense of pleasure in flow experience. These reactions and emotions can stimulate people’s inherent motivation to repeatedly participate in an activity, known as the repetitive purchasing willingness.

6 Discussion

6.1 Theoretical contribution

This research has two contributions. First, the research explores the function mechanism and influence of social interaction on social e-commerce users’ purchase behaviors under the guidance of the theory of presence, flow experience and SOR. Second, previous researches on the purchase decision of social commerce are mainly based on the traditional e-commerce research framework and influencing factors, while ignoring the basic characteristics of social commerce. The remarkable difference between social commerce and traditional e-commerce lies in social interaction whose role cannot be ignored. Thus, this research starts from the characteristics of social interaction and explores its impact on user purchase decision, and the research model will better explain the purchase decision of social commerce users.

6.2 Practical implication

This research also has important implications for social commerce and related platforms to promote social commerce development as follows:

Social commerce merchants must take into account the vividness, convenience and humanization of the website when designing the website, which plays an important role in improving users’ purchase decision. This research conclusion is consistent with previous research conclusions. That is, vivid website, beautiful web page and convenient website navigation can promote users’ online browsing time. Besides, the detailed product information, the refined online service and the diversified interaction mode can stimulate users’ presence. The sense of feeling and flow experience make up for the shortcomings of online shopping compared with the traditional offline physical shopping that cannot really touch the goods, thus promoting the impulse to purchase and the willingness to purchase repeatedly.

In addition, social e-commerce merchants should pay more attention to human-human interaction in social e-commerce while highlighting user-website interaction, such as human-machine interaction; the difference between human-human interaction lies in the significant difference between social e-commerce and traditional e-commerce. Based on the construction of the social interaction network between users and sellers, users and users as well as users and online friends, users can experience the presence and flow experience in the communication with members of social relations, which will greatly promote users’ impulsive and repetitive purchasing intentions.

China’s e-commerce is at the forefront of the world due to its unique advantages, and social e-commerce is a new stage in the development of e-commerce. Therefore, conclusions of this research have important reference significance for other developing countries. When developing social e-commerce, other developing countries should also pay attention to the satisfaction of users’ flow experience and sense of presence, because this can enhance users’ desire to repeat purchasing.

6.3 Limitations and future research

Although this research has made some meaningful conclusions, there are still some shortcomings. The limitations of the research are mainly reflected in the following aspects:

In the process of issuance and collection of questionnaires, due to time and energy constraints, the number of samples collected is limited. The sample is also those who have experience in purchasing in a social commerce environment, there are still room to research on the purchase intention of those who don’t have such experiences. In the future, the role of social interaction in user experience, satisfaction and trust can be further explored.

7 Conclusion

In conclusion, in view of the fact that few scholars at home and abroad have explained the consumer behavior of social e-commerce platforms from the perspective of presence theory and flow experience theory, there is a big theoretical gap that needs to be studied in this respect. Under the guidance of social interaction theory, presence theory, and flow experience theory, this research combines consumer purchase behavior and discovers important factors influencing the purchase behavior of consumers on social e-commerce platforms through an empirical analysis of 622 data collected from consumers on Chinese social e-commerce platforms. This research not only provides a new perspective on how social interaction affects consumers’ buying behavior on social e-commerce platforms and fills in the theoretical gaps in the past, but also guides social e-commerce platforms on how to stimulate consumers to repeat purchasing and provide countermeasures. In addition, this research can also provide reference for the development of social e-commerce in other developing countries and regions.

Footnotes

Acknowledgments

I would like to express my gratitude to all those who helped us during the writing of this article. There is no conflict of interest in this paper.

Author contributions

CONCEPTION: Li Xq

METHODOLOGY: Li Xq

DATA COLLECTION: Ren Dashuai and Li Xq

INTERPRETATION OR ANALYSIS OF DATA: Ren Dashuai, Li Xq and Jie Zhen

PREPARATION OF THE MANUSCRIPT: Ren Dashuai and Jie Zhen

REVISION FOR IMPORTANT INTELLECTUAL CONTENT: Ren Dashuai and Jie Zhen

SUPERVISION: Ren Dashuai

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Factor
	1	2	3	4	5	6	7	8	9
CS1	0.828
CS2	0.868
CS3	0.839
CS4	0.797
PH1		0.748
PH2		0.827
PH3		0.816
PH4		0.823
SH1			0.787
SH2			0.802
SH3			0.748
SH4			0.708
FE 1				0.635
FE 2				0.638
FE 3				0.847
FE 4				0.830
SF1					0.748
SF2					0.856
SF3					0.820
RP1						0.806
RP2						0.677
RP3						0.795
IP1							0.850
IP2							0.742
IP3							0.768
CW1								0.730
CW2								0.770
CW3								0.704
CC1									0.591
CC2									0.827
CC3									0.816

Influence of social interaction on the purchase behavior of social commerce consumers

Abstract

BACKGROUND:

OBJECTIVE:

METHODS:

RESULTS:

CONCLUSION:

Keywords

1 Introduction

2 Literature review

2.1 Social interaction

2.2 Presence theory

2.3 Flow experience theory

2.4 Purchase behavior

2.4.1 Impulsive purchasing

2.4.2 Repeat purchasing

3 Research framework and research hypotheses

3.1 Research framework

3.2.1 Social interaction and presence

3.2.2 Social interaction and flow experience

3.2.3 Presence and purchase behavior

3.2.4 Flow experience and purchase behavior

4 Methodologies

4.1 Participants and procedure

4.3 Confirmatory factor analysis

Table 5 Model fitness standard values and actual measured values Fitness index X2/df RMSEA GFI AGFI CFI NFI IFI Recommended value <3 <0.08 >0.90 >0.80 >0.90 >0.90 >0.90 Structural equation model 957.592/391 = 2.449 0.050 0.903 0.877 0.962 0.937 0.962

5.1.1 Detailed explanation of the results

6 Discussion

6.1 Theoretical contribution

6.2 Practical implication

6.3 Limitations and future research

7 Conclusion

Footnotes

Acknowledgments

Author contributions

References

Table 5
Model fitness standard values and actual measured values

Fitness index X²/df RMSEA GFI AGFI CFI NFI IFI

Recommended value <3 <0.08 >0.90 >0.80 >0.90 >0.90 >0.90

Structural equation model 957.592/391 = 2.449 0.050 0.903 0.877 0.962 0.937 0.962