The determinants of social commerce system success for SMEs in Thailand

Abstract

Social commerce is growing as a new critical hub of product sourcing for both businesses and individuals. It has become an important strategy that helps companies, especially small and medium enterprises (SMEs), to connect with customers and to gain competitive advantages. Still, there is a scarcity of frameworks for evaluating social commerce system success and identifying what significantly contributes to success. This study aims to investigate the determinants of social commerce system success in the context of Thai SMEs. A questionnaire survey was conducted to evaluate the proposed model. The empirical results provide support for the relationship among six dimensions of the proposed model. System use and user satisfaction relate significantly to the success of a social commerce system, which is measured in terms of organizational benefits. Three factors associate positively to system use, namely system quality, service quality, and trust, all of which lead to user satisfaction. Further research can investigate other social factors or empirically test the model at different levels of success.

Keywords

social commerce system success social media social networks SMEs Thailand

Trust is the most influential factor affecting both use of social commerce and user satisfaction among SMEs in Thailand

Introduction

As the Internet has become substantially important to the conduct of business, markets and businesses are being reformed (DeLone and McLean, 2004). Social media and social networking sites have been utilized worldwide by both consumers and organizations. Many companies attempt to earn competitive advantages by marketing and merchandising through these platforms, which give rise to the concept of social commerce. The term can be defined as one form of commerce that is mediated by social networking sites and other social media that make use of both online and offline environments for business purposes (Wang and Zhang, 2012).

Since its introduction in 2005 by Yahoo, social commerce has been continuously evolving (Wang, 2008). In the context of Thailand, the top three sites of social commerce are Facebook, Instagram, and LINE. The most attractive channel is Facebook, which has over 19 million potential users (Pornwasin, 2013). A survey from the Electronic Transactions Development Agency (ETDA) revealed that approximately 49.7% of the people who use social media had purchased goods via social commerce (Electronic Transactions Development Agency, 2013). This result also suggests that social commerce is likely to grow further.

This study aims to investigate factors that determine social commerce system success, particularly in the context of SMEs in Thailand. Thai SMEs comprise an interesting target group because they are more vulnerable to competition than large organizations, and the use of social media and social networking sites can dramatically affect their markets at less cost. The Thai government also recognizes the importance of social commerce for SMEs and thus supports its use by providing education and funding through the Enhancing SMEs Competitiveness through IT program (Department of Industrial Promotion, 2013; Electronic Transactions Development Agency, 2013). To ensure the success of such programs, identifying and analysing the determinants of social commerce system success is crucial.

SMEs not only contributed 36.6% of the total GDP, they also expand the capacity of labor market. The new workforce includes those who are farmers and are looking for a job when the agricultural season is ended. This can help preventing the large amount of labor emigration into Bangkok area, which may cause more overcrowding problems. The workforce would be spread out through Bangkok, surrounding boundaries, and other regional areas of Thailand. As a result, it would support economic growth in both metropolitan and regional areas (Office of Small and Medium Enterprises Promotion, 2011).

Globalization brings many changes that challenge and create opportunities for business nowadays. This is also a great opportunity for SMEs which have the potential to support the new emerging trends. One of the biggest changes today is undoubtedly the emergence of social commerce. Furthermore, more opportunities are also generated from the innovation of technology, changes in population structure, and consumer behavior. Due to these changing environments, SMEs need to pay attention to what will be the effect on their future business model.

Despite the popularity of online channels, few researches have investigated the social commerce system. Most prior studies have focused on the evolution of social commerce, its integrated viewpoints, the social commerce movement in general, and its possible future trends. Very few studies have examined the determinants of social system success. An extensive literature review turned up insufficient research on many contexts of social commerce systems, which can be summarized as:

A lack of research on success factors of social commerce.

A lack of research on success factors of social commerce in the context of a developing country.

A lack of research on success factors of social commerce in a specific type of business enterprise (for example, SMEs).

A lack of research that would help construct a comprehensive model to measure a social commerce system, its social characteristics, and the impact at the organizational level.

A linkage between social commerce and the information systems (IS) success model.

A lack of research that would integrate social and technical elements–the most vital social element being trust.

Accordingly, this study attempts to fill these gaps and to provide both academic and practical contributions for further implications.

Literature review

Social commerce systems

The popularity of social media and social networking sites has introduced a new interesting area of business: social commerce. With the emergence of Web 2.0, ICTs and e-commerce development, the online communication capabilities increased and new online channels have appeared, for example, blogs, social networking sites, social media, wikis, and communities. These changes allow consumers to become active content creators instead of being only passive consumers as in the old days (Hajli, 2012). The specific characteristic of social commerce is that interactions are communities-based (Stephen and Toubia, 2010) that take place in the context of the demands associated with being part of a group and of with partaking in group collaboration (Vongmahasetha, 2011). This phenomenon is framing a new business model based on communities where the users’ interaction and social influence affect marketing strategies and enable commercial transactions (Hajli, 2012).

According to Heidi Cohen (2011), “social commerce is a shopping-oriented social media marketing that touches buyers before, during and after their purchase.” It integrates many online collective business practices, such as, group buying, social shopping, mobile apps, and social features retailing into social media (Cohen, 2011). In other words, social commerce systems can facilitate the purchasing process for both buyers and sellers, from basic transactions to customer service. Three phrases of marketing (pre-sale, on-line sale, and after sale) are also carried out through the system.

Some scholars claim that social commerce improves customer relationship management (CRM) through social interaction and the delivery of massive information (Hajli, 2012). Similar to e-commerce, information can be produced, captured, processed, and presented on the social commerce platforms to help customer and business decision-making (Molla and Licker, 2001). Organizations can use such systems to market their products, offer service online, and provide interactive communication with customers. Conversely, customers can also reach the organization conveniently and share information with the business provider and other customers.

Since a social commerce system can be considered as a kind of information system (IS), it is reasonable to provide justification for the application of IS theories. Moreover, since this study emphasizes the success of social commerce systems, it is appropriate to focus on IS success theories.

Information system success model

Though an abundance of empirical studies of IS success have been published, the true meaning of IS success has always been ambiguous and remains a controversial issues among IS researchers. This is because success is a multidimensional concept that can be evaluated at different levels and has several assessment criteria (Molla and Licker, 2001).

One of the most widely accepted models of IS success is DeLone and McLean’s IS Success Model (DeLone and McLean, 2003). This model has been extended and used for research in many fields of IS including e-commerce (DeLone and McLean, 2004) (Molla and Licker, 2001). Its framework is based on communication research (Shannon and Weaver, 1949) and information theory (McGill et al., 2003) and has been refined based on criticisms from many IS researchers. Eventually, DeLone and McLean (2003) proposed a comprehensive model for the measurement of IS success as shown in Figure 1.

Figure 1.

The DeLone and McLean IS success model. (DeLone and McLean, 2003).

The DeLone and McLean IS success model consists of six interrelated dimensions, namely, system quality, information quality, service quality, intention to use/use, user satisfaction, and net benefits. The dependent variables indicate the causality flowing in the same direction as the information process (DeLone and McLean, 2003). For instance, higher system quality leads to higher use and user satisfaction, which eventually results in higher net benefits.

According to the authors, the intention to use or use dimensions are normally voluntary. The difference between these is that intention to use implies an attitude or intention towards using a system, whereas use suggests that the system exists and has been adopted (Nantapanuwat et al., 2010). Typically, most IS researchers select one of the two dimensions when investigating the model.

The measures for each dimension varied greatly in many studies that attempted to test, validate, and develop the model (Urbach and Muller, 2012). In order to apply the model, it is important to understand each stipulated definition and its common measures. The system quality success dimension measures the desirable characteristics of an IS in terms of technical success. It can be measured in terms of ease of use, reliability, functionality, flexibility, portability, integration, and so on (DeLone and McLean, 2003; Urbach and Muller, 2012). The information quality success dimension concerns the desirable characteristics of an IS output in terms of semantic success. This dimension can be measured in terms of accuracy, timeliness, availability, completeness, conciseness, consistency, and relevance (DeLone and McLean, 2003; Urbach and Muller, 2012). Service quality success concerns the quality of support provided to users by a particular IS department and IT support personnel (Urbach and Muller, 2012). Chang and King (2005) proposed measurement items for the service quality that included flexibility, interpersonal quality, intrinsic quality, IS training, and responsiveness. Furthermore, the Intention to Use/Use success dimension can be measured by frequency of use, time of use, number of accesses, usage, and dependency (DeLone and McLean, 2003). The user satisfaction construct focuses on users’ level of satisfaction with systems, content, support services, and usage of an IS (Petter et al., 2008) as measured in terms of adequacy, effectiveness, efficiency, information satisfaction, overall satisfaction, and system satisfaction (Urbach and Muller, 2012).

Net Benefits seems to be a broad construct, but it can be narrowed down by focusing on the party concerned and the objective of the analysis. DeLone and McLean (2003) suggest that researchers clearly declare two issues when investigating the model. The first issue is to determine just who are the stakeholders receiving benefits from the system? The stakeholder can be the designer, sponsor, user, or someone else. In an e-commerce context, primary users of the system are customers and suppliers. The second issue that must be addressed is that of just what is being analyzed. The implementation of a system can affect individual users, organizations, industries, economies, and even society (DeLone and McLean, 2004). Therefore, IS researchers should take these two issues into consideration before investigating the IS system success.

Importance of trust

There is a social factor that should also be considered when designing and implementing a social commerce system, namely, trust. Trust is a significant social factor that is critical in human interaction, especially in the online shopping environment (Gefen et al., 2003a). It is defined as an expectation or confidence that something will happen as predicted, or in this case, that technology will perform consistently in a predictable manner.

Economists, psychologists, sociologists, and management theorists have seen trust as an important social factor, especially in online shopping adoption (Gefen et al., 2003a). The intention to use and usage of online purchasing activities are built on the combination of trust and the Technology Acceptance Model, also known as TAM (Gefen et al., 2003a; Gefen et al., 2003b). Six studies find that the relationship between trust and online shopping intentionality and usage is significantly positive (Bhattacherjee, 2002; Gefen, 2000; Gefen, 2002; Gefen et al., 2003a; McKnight, et al., 2002; Yoon, 2002). Another of Gefen’s studies also revealed that trust in e-vendors significantly influences customers’ intentions to purchase online (Gefen et al., 2003b). Importantly, trust is one of the key factors in building and maintaining successful customer relationships (Casielles et al., 2005) in which customer loyalty is found to be a driver for the success of online business (Reichheld et al., 2000). From the review of many empirical studies, trust can be conceived as a significant social factor in the IS success framework.

Many IS researchers are aware of the importance of trust and include trust as a variable in their proposed model. Hajli (2012) proposed a social commerce adoption model that includes trust as a key construct. According to DeLone and McLean’s model (2003), trust positively leads to intention to buy. Another good example is Molla and Licker’s e-commerce system success model. They also believe that trust is an important construct leading to higher usage and higher customer e-commerce satisfaction, which means greater e-commerce success (Molla and Licker, 2001).

Most research measures and analyzes trust at an individual level. But some studies have found trust to be an important construct in the field of information system success at the organizational level. Nattapol et al. (2010) proposed and empirically validated the success of a knowledge management system in the banking industry. The authors incorporated trust, one of the key founding constructs of the model. The authors also found that trust has a significant positive relationship with use and user satisfaction constructs (Nattapol et al., 2010). Lippert and Swiercz (2005) also conducted a study of trust in technology in human resource information systems. They proposed that trust in technology plays an important role in influencing the successful implementation of human resource information systems (Lippert and Swiercz, 2005). As stated earlier, this study also aims to measure social commerce system success at the organizational level with trust as one of the antecedent variables.

Conceptual framework

Rationale for using the Delone and Mclean IS success model

With a comprehensive set of constructs and linked relationships, the model is considered suitable to be used as a base model for the social commerce systems success. All of the proposed measures that are significant in the social commerce systems success can be divided into the updated Delone and Mclean IS success model’s six dimensions. The widespread use of social commerce is also considered to have had an important influence on the social commerce systems success. For these reasons, the updated Delone and Mclean IS success model (2003) is considered to be an appropriate model to be used in conducting research on the social commerce system.

Although this study shares some similarities with Molla and Licker’s study of 2001, it differs in various important aspects. In 2001, Molla and Licker’s study focused on extending Delone and Mclean’s original framework of 1992, whereas this study seeks to extend the Delone and Mclean IS success model (2003), the updated and more comprehensive model, to the area of social commerce systems. Even though the proposed social commerce system success model has similar constructs to Molla and Licker’s (system quality, information quality, service quality, trust, use, user satisfaction, and social commerce system success), there is a direct linked relationship from use toward success construct, which is not present in Molla and Licker’s model. Furthermore, the proposed model provides empirical hypotheses testing and validation on all proposed relationships, as compared to previous research. However, Molla and Licker did not provide an empirical study or a validation of their model. Furthermore, the model proposes two interesting and important additional constructs, namely, trust and customer satisfaction, which were accepted in many later researches. Lastly, this model focuses on a specific group, comprised of small and medium enterprises in Thailand, a developing country in Southeast Asia.

Proposed conceptual framework

A conceptual framework for studying social commerce systems (see Figure 2) was developed in accord with the updated DeLone and McLean IS success model (DeLone and McLean, 2003) along with several of the studies reviewed above. The main constructs leading to social commerce system success in terms of organizational benefits involve six components: information quality, system quality, service quality, trust, use, and user satisfaction. In order to measure net benefits accurately, the model should clearly and carefully define the stakeholder who receives the benefits and at what level it is tested (DeLone and McLean, 2003). DeLone and McLean (2004) also reviewed the determinants of the “net benefits” of an e-commerce system at different levels: individual, group, organizational, industry, and national levels. In the current study, the key stakeholders are SMEs in Thailand, so the main focus of net benefits analysis is at the organizational level. Figure 2 states the net benefit as social commerce systems success (organizational benefit).

Figure 2.

Social commerce systems success model.

Prior studies have found positive relationships between information quality, system quality, and service quality and use and user satisfaction in the updated DeLone and McLean IS Success Model (DeLone and McLean, 2003; Wang, 2008). Empirical research supporting the relationships between the constructs is summarized in Table 1. Trust is also an important variable affecting use and user satisfaction (Molla and Licker, 2001), as it is one of the vital factors for successful e-vendor and information technology adoption (Gefen et al., 2003a). In the proposed model, some relationships were excluded from DeLone and McLean’s IS Success Model (2003) to avoid complexity and in accord with the cross-sectional nature of this study (Wang and Liao, 2008). For instance, the feedback relationship of User Satisfaction to Use and the feedback relationships of net benefits to use and to user satisfaction were excluded.

Table 1.

Summary of empirical studies related to the D and M IS systems success. (Petter, DeLone, and McLean, 2008).

Antecedents	→	Explained Constructs	Individual	Overall results (Ind.)	Organization	Overall result (Org.)
System quality	→	Use	–	9 of 21 found a positive association	–	2 of 6 found a positive association
System quality	→	User satisfaction	++	21 of 21 positive	O	2 of 3 positive
System quality	→	Net benefits	+	15 of 22 positive	+	4 of 5 positive
Information quality	→	Use	–	3 of 6 positive	O	1 of 1 positive
Information quality	→	User satisfaction	++	15 of 16 positive	O	3 of 3 positive
Information quality	→	Net benefits	+	9 of 11 positive	O	3 of 4 positive
Service quality	→	Use	O	0 of 3 positive	O	3 of 3 positive
Service quality	→	User satisfaction	–	6 of 12 positive	O	3 of 4 positive
Service quality	→	Net benefits	+	4 of 7 positive	O	3 of 3 positive
Use	→	User satisfaction	+	4 of 5 positive	O	0 of 1 positive
Use	→	Net benefits	+	16 of 22 positive	+	5 of 6 positive
User satisfaction	→	Net benefits	++	14 of 14 positive	O	2 of 2 positive

Notes: ++ strong support.

+ moderate support.

− mixed support.

O insufficient data.

The relationship between the taxonomic constructs in the IS success model can be applied to social commerce systems success as well because social commerce combines related issues of people, management, technology, and information, all of which lie within the IS disciplinary boundary (Wang and Zhang, 2012). Thus, the hypotheses of social commerce system success are based on prior empirical evidence that allows for possibilities for related associations among the proposed model’s variables.

In a social commerce environment, information is transmitted through the use of social media and social networking sites. Information starts from the concept of user-generated content and evolves to localized or globalized crowd-sourced content and to communities of users; it is co-created content from both consumers and marketers (Wang and Zhang, 2012). As emphasized by many studies of information systems, information quality is one of the crucial determinants of system use and user satisfaction (Molla and Licker, 2001). To examine the effects of information quality in the context of social commerce, the following hypotheses are proposed:

H1: Information quality is positively related to the use of social commerce.

H2: Information quality is positively related to user satisfaction with social commerce.

Behyar, Heidari, and Bayat (Behyar et al., 2011) studied and proposed a framework that shows positive associations between innovation and ease of use, considered to be a system’s quality measure (DeLone and McLean, 2004), and user satisfaction with social commerce. According to Table 1, many empirical studies show that system quality has a strong positive relation to user satisfaction, and some reveal that system quality has a positive relation to the use of the system (Petter et al., 2008; Urbach and Muller, 2012). Compatible with social commerce, the following hypotheses focus on system quality:

H3: System quality is positively related to the use of social commerce.

H4: System quality is positively related to user satisfaction with social commerce.

Service quality has been both criticized and supported by many researchers as an additional needed dimension in DeLone and McLean’s IS success model (2003; 2004). It is believed to have a positive association with use and user satisfaction dimensions. The need for service quality obviously comes up in the context of e-commerce as there has been a higher demand from customers for good support from Web providers (DeLone and McLean, 2004). In terms of social commerce, e-retailers also take advantage of social media and services for their customer-oriented businesses (Zhou et al., 2013). One study of social commerce included customer support, or service quality, as an independent variable affecting user satisfaction (Behyar et al., 2011). Therefore, the following hypotheses are proposed:

H5: Service quality is positively related to the use of social commerce.

H6: Service quality is positively related to user satisfaction with social commerce.

Trust is defined as, “a motivating factor to gain more confidence among internet users by providing prompt and information rich services” (Behyar et al., 2011). Prior empirical studies found a positive relationship between trust and intention to use an e-communication channel (Currall and Inkpen, 2000). Such studies have found that trust is a significant factor for any kind of interaction via the Internet. Hajli (2012) found that at least one social commerce characteristic affected trust and that the trust variable strongly influenced intention to buy or usage among consumers in a social commerce environment. Moreover, trust shows a significant and positive relation to user satisfaction (Behyar et al., 2011). To affirm trust as an important variable in social commerce system success, the hypotheses are:

H7: Trust is positively related to the use of social commerce.

H8: Trust is positively related to user satisfaction with social commerce.

People use social commerce systems on information technology (IT) platforms developed from blogs and e-commerce sites, which in turn generate social media and social networking sites (Wang and Zhang, 2012). Research shows a positive association between the use of social media and customer relationships (Wang, 2011). Customer relationship data are collected as a measure of an organization’s benefits (Parthasarathy and Bhattacherjee, 1998). Table 1 (above) shows data from various studies indicating that the use of information systems positively affects user satisfaction and net benefits. Thus, the current study proposes the following hypotheses:

H9: Use of social commerce is positively related to user satisfaction.

H10: Use of social commerce is positively related to social commerce systems success.

According to Table 1, there are many studies that have empirically validated and found a strong result between user satisfaction and net benefits. Such research indicates that success, measured by user satisfaction, positively relates to organizational performance (Petter et al., 2008). In the context of social commerce, the following hypothesis is proposed:

H11: User satisfaction with social commerce is positively related to social commerce system success.

Methodology

Quantitative method and research tool development

The survey instrument uses a 7-point Likert scale to let the respondents indicate their most appropriate answers. The measurement scales were developed and modified from Cao, Zhang, and Seydel (2005), DeLone and McLean (2003), Liu and Arnett (2000), Seddon and Klew (1996), and Teo and Too (2000).

The Likert scale was developed by Dr. Rensis Likert, a sociologist at the University of Michigan and was originally published in “A Technique for the Measurement of Attitudes” (Likert, 1932). The primary purpose of the scale was to create a means of measuring psychological attitudes in a statistically scientific way. Likert’s 5-point and 7-point scales are commonly used in quantitative research. However, this study adopted a 7-point scale instead of 5-point scale for two reasons. First, some IS researchers, such as Seddon and Klew (1996), employed the 7-point scale in their study of IS success. Therefore, it is appropriate to follow this basis in order to be comparable with other studies of IS success. The second reason is that more scale points offer a slightly better result, but this can diminish if there are more than 11 scale points. Thus, a 7-point scale was employed.

A questionnaire was used as a tool for conducting the survey. To ensure the reliability and validity, the questionnaire was translated into Thai to make it easy and understandable for Thai respondents. Then it was translated back into English. The drafted version of the questionnaire was reviewed by two IS researchers in Thailand as a peer review and then revised based on their feedback. Furthermore, the pre-test was conducted with 10 SME respondents who had experience in using social commerce systems. Following the pre-test, the wording was modified in line with their comments and suggestions.

Data collection and sampling

The survey was conducted in the Bangkok Metropolitan Region, where SMEs are overwhelmingly located. The research questionnaires were distributed and collected from hundreds of selected respondents by one of the authors to minimize the possibility of misinterpretation. The participants were owners and employees of SMEs in Thailand who had experience using social commerce systems for their businesses.

According to Hair et al, (1998), a rule of thumb for sample size calculation is that the sample size should be at least five times the total number of variables. Furthermore, the appropriate sample size should be more than 200 (Comrey and Lee, 1992). In this study, the number of participants exceeded this requirement; 350 questionnaires were distributed, and 298 useful questionnaires were returned, a response rate of 85.14%. The demographic profile of respondents is summarized in Table 2.

Table 2.

Descriptive statistics of respondents.

Respondent characteristics	Frequency	Percentage
Gender
Male	158	53.02
Female	140	46.98
Age (in years)
<25	38	12.75
26 – 35	119	39.93
36 – 45	70	23.49
46 – 55	54	18.12
>55	17	5.70
Education
<Bachelor’s degree	26	8.72
Bachelor’s degree	154	51.68
Master’s degree	112	37.58
Doctoral degree	6	2.01
Experience using social commerce system (SCS)
<1 year	57	19.13
1 – 3 years	111	37.25
4 – 6 years	84	28.19
>6 years	46	15.44
Type of SCS being experienced
Microblogging	82	11.07
Blogs	72	9.72
Social networking sites	293	39.54
Social media sharing	136	18.35
Group buying	61	8.23
Virtual worlds	10	1.35
Online gaming	20	2.70
Wikis	67	9.04
Objective to use SCS
Promote marketing strategies and sales	266	35.14
Improve customer relationship management	186	24.57
Reduce marketing and transaction costs	173	22.85
Implement customer survey	121	15.98
Others	11	1.45
Organization size
Small enterprise	246	82.55
Medium enterprise	52	17.45
Type of organization
Manufacturing	68	22.82
Trading	163	54.70
Service	67	22.48
Location
Bangkok	232	77.85
Nonthaburi	12	4.03
Nakhon Pathom	12	4.03
Pathum Thani	15	5.03
Samut Prakan	16	5.37
Samut Sakhon	5	1.68
Others	6	2.01

Remark: Type of SCS being experienced and objective to use SCS can have multiple answers.

Data analysis and results

Analysis of measurement reliability and validity

Measurement of validity in terms of reliability and design were assessed. Cronbach’s alpha was used to evaluate the reliability of the instrument. The values of Cronbach’s alpha were well above 0.8 (see Table 3), exceeding the recommended level of 0.7 (Gravetter and Forzano, 2012; Hair et al., 1998). This indicates sufficient reliability of the scale.

Table 3.

Summary of confirmatory factor analysis.

Factor	Item	β	SE	t value	R²	Cronbach’s alpha
Information Quality	X1	0.79	(0.05)	15.70**	0.67	0.87
	X2	0.63	(0.04)	14.87**	0.57
	X3	0.69	(0.04)	17.56**	0.78
	X4	0.73	(0.05)	13.96**	0.55
System Quality	X5	0.67	(0.05)	12.99**	0.47	0.92
	X6	0.87	(0.05)	17.25**	0.90
	X7	0.74	(0.05)	14.24**	0.54
	X8	0.79	(0.05)	16.21**	0.67
Service Quality	X9	0.59	(0.05)	11.99**	0.44	0.92
	X10	0.72	(0.05)	14.23**	0.53
	X11	0.70	(0.05)	14.44**	0.54
	X12	0.77	(0.04)	19.65**	0.84
Trust	X13	0.79	(0.05)	17.30**	0.69	0.90
	X14	0.79	(0.04)	18.51**	0.77
	X15	0.76	(0.04)	19.37**	0.79
Use	Y1	0.62	–	–	0.39	0.88
	Y2	0.74	(0.07)	11.33**	0.62
	Y3	0.68	(0.06)	10.98**	0.56
	Y4	0.60	(0.04)	14.38**	0.55
User Satisfaction	Y5	0.69	–	–	0.62	0.93
	Y6	0.75	(0.05)	14.53**	0.68
	Y7	0.77	(0.05)	14.63**	0.69
	Y8	0.73	(0.05)	16.05**	0.79
SCSS	Y9	0.64	–	–	0.74	0.96
	Y10	0.70	(0.04)	17.98**	0.76
	Y11	0.74	(0.05)	14.69**	0.64

Notes. SCSS is abbreviated from Social Commerce System Success.

The assessment of convergent and discriminant validity typically has focused on the Pearson product-moment correlation coefficient. Results show that all observed variables have high loading on their related factors and low cross loadings. In other words, they relate highly to each other and less highly to measures of other constructs. Also, all loadings were significant at the 0.01 level. This indicates good convergent and discriminant validities (Hair et al., 1998). Therefore, the constructs are appropriate for use in data analysis. A summary of the correlation coefficient analysis of the observed variables is presented in Appendix 1.

The measurement model was tested by conducting a confirmatory factor analysis (CFA). A rule of thumb for factor loading states that at least 0.50 of the value is acceptable (Hair et al., 1998). According to Table 3, all factor loadings were well above 0.50 and were significant at the 0.01 level. This ensures adequate convergent and discriminant validity. In addition, the values of standard error (SE), t-value, and coefficient of determination (r-squared) were satisfied (Kline, 2011) and are summarized in Table 3.

Correlation coefficient matrix revealed that the correlation coefficients were in the range of 0.50–1.00, and were significant at the 0.01 level. The highest correlation coefficient occurs between system quality and user satisfaction at the value of 1.00. The second highest correlation coefficient is between system quality and social commerce system success, plus user satisfaction and social commerce system success. The full analysis of correlation coefficient matrix is presented in Table 4.

Table 4.

Analysis of correlation coefficient matrix.

	IQ	SQ	SVQ	Trust	Use	US	SCSS
IQ	1.00
SQ	0.50**	1.00
SVQ	0.68**	0.55**	1.00
Trust	0.62**	0.66**	0.61**	1.00
Use	0.70**	0.65**	0.68**	0.73**	1.00
US	0.50**	0.56**	0.55**	0.66**	0.65**	1.00
SCSS	0.61**	0.76**	0.62**	0.62**	0.74**	0.76**	1.00

Notes: IQ: information quality; SQ: system quality; SVQ: service quality; US: user satisfaction; SCSS: social commerce system success

Model testing results

Model hypothesized relationships were tested by using Linear Structure Relations (LISREL), which is designed for structural equation modeling (SEM) and path analysis. SEM provides more precise analysis and is more capable than prior generation regression models (Bowen and Guo, 2011).

Table 5 illustrates the seven common model-fit indices used to assess the model’s overall goodness of fit. The X²/degree of freedom is 0.90, GFI is 0.97, AGFI is 0.92, CFI is 1.00, RMR is 0.02, SRMR is 0.02, and RMSEA is 0.00, and all are significant at the 0.01 level. The fit indices suggest that the model fits the data well.

Table 5.

Goodness-of-fit measures of the research model.

Fit index	Value	Recommended value
X²/degree of freedom	0.98	≤3.00
Goodness-of-fit index (GFI)	0.96	≥0.90
Adjusted goodness-of-fit index (AGFI)	0.92	≥0.80
Comparative fit index (CFI)	0.99	≥0.95
Standardized root mean square residual (SRMR)	0.03	≤0.08
Root mean square error of approximation (RMSEA)	0.02	≤0.05

The path analysis diagram and the significance levels of relationship are presented in Figure 3. Eleven proposed hypotheses are investigated, and the results are as follows.

Figure 3.

Path analysis and hypotheses testing results.

H1: Information quality is found to have a positive β of 0.32 at p = 0.01. It can be seen that information quality significantly relates to use of social commerce. Therefore, H1 is accepted.

H2: Information quality is found to have a negative relationship with user satisfaction (β of −0.27 at p = 0.01). Therefore, H2 is rejected.

H3: System quality is found to have a negative relationship with use of social commerce (β of −0.18 at p = 0.01). Therefore, H3 is rejected.

H4: System quality is found to have a positive relationship with use satisfaction (β of 0.12 at p = 0.01). Therefore, H4 is accepted.

H5: Service quality is found to have a significant positive relationship with use of social commerce (β of 0.38 at p = 0.01). Therefore, H5 is accepted.

H6: Service quality is found to have a positive relationship with user satisfaction (β of 0.03 at p = 0.01). Therefore, H6 is accepted.

H7: Trust is found to have a significant positive relationship with use of social commerce (β of 0.51 at p = 0.01). Therefore, H7 is accepted.

H8: Trust is found to have a positive relationship with user satisfaction (β of 0.21 at p = 0.01). Therefore, H8 is accepted.

H9: Use is found to have a significant positive relationship with user satisfaction (β of 0.72 at p = 0.01). Therefore, H9 is accepted.

H10: Use is found to have a significant positive relationship with social commerce systems success (β of 0.54 at p = 0.01). Therefore, H10 is accepted.

H11: User satisfaction is found to have a significant positive relationship with social commerce systems success (β of 0.41 at p = 0.01). Therefore, H11 is accepted.

Discussion and implications

Summary of findings

The study proposes and investigates a model of social commerce systems success based on the DeLone and McLean IS success model (DeLone, and McLean, 2004) in the context of a developing country, Thailand. As predicted, most hypothesized relationships between constructs were significantly or marginally supported, whereas only H2 and H3 were not.

The highest positive relationship appeared in the path coefficient of use to user satisfaction. Also, both use of social commerce and user satisfaction were found to be highly influential factors of social commerce system success, with use producing a slightly greater impact. To explain, the study found that Thai organizational users considered social commerce as an easy and user-friendly platform. They received good feedback from using the platform and therefore were confident in using it in their business. In addition, many Thai SME users found that social commerce systems can support their business in terms of communicating with customers, reaching new market segments, support marketing activities and sales, and increasing their competitive latency.

The findings obviously indicate that trust, as an antecedent factor, is the most influential factor affecting both use of social commerce and user satisfaction. This is because Thai users are also concerned with the reliability, ability, and ethics of the system. System credibility influences Thai SME users’ behavior. If users find that a system is reliable, they tend to feel confident in using it and would prefer to continue doing so.

Considering other key foundation factors, service quality also has a high impact on use and a positive effect on user satisfaction. This indicates that service is very important when using the system, but it may not necessarily generate user satisfaction. This implies that Thai SME users feel more confident in using the system when there is a supportive service regardless of the results of the support they get from the system provider.

Information quality also has a significant impact on use of social commerce but has a negative relationship with user satisfaction. However, use has the highest impact on social commerce system success. Thus, improving information quality can lead to higher system usage and greater success of the system. Moreover, it was found that a good alignment and accuracy of data can support the usage of the system.

While other technical constructs are consecutively less influential on use, system quality results in a negative relationship toward use. Studies show mixed support on this particular relationship, both positive and negative. It may be the case that heavily used systems are often fixed quickly, without sufficient testing and integration of the technical quality of a system (Weill and Vitale, 1999) and thus resulting in lower quality systems. Also, low quality systems which meet basic requirements are acceptable for some people, but may not be adequate for others (McGill et al., 2003). Despite the result that system quality appears to have a negative relationship with Use, it has a positive effect on user satisfaction. Although system quality may not encourage Thai SME users to use the system, after using the system, users found social commerce systems to be comfortable and convenient platforms with variable multimedia functions, which in turn boosts satisfaction among users.

Academic implications

Even though acceptance of social commerce systems is widespread, this study is one of the first to investigate these systems. For scholars, the study proposes a comprehensive theoretical model that extends DeLone and McLean’s IS Success Model (2003) so as to be able to apply to the emergent system, social commerce. It also integrates the Trust variable as one of the key factors of the model. There has been inadequate empirical research on the IS success model from the perspective of organization (Petter et al., 2008), and this study fills that gap by conducting research from the organizational perspective to validate the social commerce system success model. The results confirm many positive relationships between the model’s constructs (see Figure 3). Nevertheless, it does find negative relationships in system quality to use and information quality to user satisfaction.

The study resulted in consistencies and contradictions relative to the previous researches. The relationship between use and user satisfaction was found to be positive, which is consistent with studies conducted by Chiu et al. (2007), Halawi et al. (2007), and Guimaraes et al. (1996). However, those empirical studies were conducted at an individual level of analysis. With regard to an organizational level of analysis, the studies’ applicability is very limited, therefore, it is important to note that this study found the positive relationships at the organizational level. The positive relationship between use and social commerce system success, also known as net benefits, is consistent with Leclercq (2007), Zhu and Kraemer (2005), Deveraj and Kohli (2003), Teng and Calhoun (1996), and Belcher and Watson (1993), while the positive relationship between user satisfaction and social commerce system success is consistent with Gelderman (1998) and Law and Ngai (2007).

As for foundation factors, the positive relationship between system quality and user satisfaction is consistent with Scheepers et al. (2006) and Benard and Satir (1993). Similarly, the positive relationship between service quality and user satisfaction is consistent with Coombs et al. (2001), Thong et al. (1996), and Thong et al. (1994). With the exception of Fitzgerald and Russo (2005), there are very few studies that analyze the organizational level and that have found a positive relationship between information quality and use, but now this study helps fill this gap. In addition, the positive relationship between service quality and use is consistent with Fitzgerald and Russo (2005), Caldeira and Ward (2002), and Gill (1995).

In the context of social commerce systems among Thai SME users, the relationship between system quality and use was found to be negative, which is consistent with Premkumar et al. (1994), whereas Fitzgerald and Russo (2005) and Caldeira and Ward (2002) found this relationship to be positive. Another contradiction is the negative relationship between information quality and user satisfaction found in the study. However, the relationship was found to be positive in the findings of Scheepers et al. (2006), Coombs et al. (2001), and Teo and Wong (1998).

Not only does our study validate previous findings on IS success, but it also integrates another vital dimension, namely, trust. Trust has the greatest effect on both use and user satisfaction. Similar to Tsai and Wu (2011), this indicates that trust is also a significant antecedent for social commerce system success.

Practical implications

The current study does not only provide academic implications, but it also makes practical contributions. This is one of the first studies in the field of information technology and IS conducted in a developing country, let alone in Thailand. It is also one of the first studies to investigate the determinants of social commerce system success from the organizational viewpoint. This provides the Thai government and software developers with an empirical analysis that is crucial for understanding user behavior and to encourage adoption amongst SMEs.

In light of its consistent support for SMEs in terms of social commerce systems, the Thai government has shown that it sees social commerce systems as vital strategies that will not only support SMEs but also generate a higher gross domestic product (GDP). To make SMEs more competitive and better prepared to take advantage of business opportunities, the Thai government needs to come up with consistent policies and practical plans that support the acceptance and potential usage of social commerce. Although we found that many Thai SMEs already use the system in their businesses and are willing to continue doing so, users seem to lack the knowledge necessary for utilizing social commerce effectively. Therefore, the government should support SME entrepreneurs and employees by organizing free seminars and related workshops in order to foster better use of social commerce. The government should not only provide such education, but it should also serve as a good example, for instance, by using social commerce as one of its communication channels. The government can also help promote SMEs via its online channel. Another strategy would be to organize a trade fair or exhibition that allows software developers to meet face-to-face with people in the SME sector as well as potential business partners and users. But the government also needs to commit to long-term sustainability of such strategies to ensure the successful use of social commerce systems by SMEs.

Software developers can attract more users by understanding user behavior when they start using social commerce systems. To attract more SME users, they need to understand that the most important quality that the users are looking for is a reliable and sophisticated platform. Also, they should develop an easy and user-friendly platform that enables SMEs to gain competitiveness and optimal results. It is also a plus if the outputs can be tracked and measured at any time.

Limitations

Despite scrupulous attention on methodology and statistical analysis, this study is not without limitations. First of all, the small sample size creates a limitation in terms of generalizability. The study was conducted in one developing country, Thailand, and was further narrowed down to the Bangkok Metropolitan Region. The adoption and usage of social commerce may be different in other developing countries. To achieve a better understanding of social commerce system success, a similar study should be conducted across countries or regions. Secondly, in order to avoid unnecessary complexity, some path relationships between constructs in this model were different from the original DeLone and McLean IS Success Model (2003). The study excluded the reversed relationship of User Satisfaction to Use and the reversed relationship of Social Commerce Systems Success (Net Benefits) to Use and User Satisfaction. Further studies should take these relationships into account in order to provide a more comparable model. Moreover, data collection was done at a single point of time, not longitudinal. The adoption of social commerce in a developing country such as Thailand is still at the beginning stage and has yet to grow. Therefore, the results could be different if the data were collected at different times. Consequently, future research can test the model in other contexts and can involve other social and organizational factors in order to strengthen it. To extend the application of the model and overcome the limitations, future research will be necessary.

Footnotes

Appendix 1

Correlation coefficient analysis of observed variables.

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