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Abstract

The study aims to examine user’s perceptions of the service quality of social networking sites (SNSs), contributing to customer satisfaction and usage intention leading to social commerce (s-commerce) intention. An online survey with a structured questionnaire was conducted to obtain contextual data, comprising 549 valid samples. The results indicate that outcome and environment quality have a significant influence on satisfaction, whereas interaction and outcome quality have a positive impact on usage intention of SNSs. Satisfaction is a noble indicator of usage intention; social capital and perceived trusts have a significant effect on s-commerce intention; and usage intention has the greatest influence on s-commerce intention. Perceived trust proves to be an important partial mediator between usage intention and s-commerce intention. Our results highlight the important role of service quality in behavioral perceptions of SNS users, shed much light on the social capital and s-commerce intention, and provide valuable contributions for understanding s-commerce behavior in SNSs context. The results of this study bridge gaps in SNS literature by demonstrating how s-commerce vendors or SNS practitioners can increase service quality, resulting in increased customer satisfaction and usage intention and thereby affecting the acceptance of s-commerce.

Keywords

social commerce social capital perceived trust customer satisfaction usage intention service quality social networking sites

Introduction

In the first decades of the 21st century, there are more than hundreds of social networking sites (SNSs) with plentiful high-tech possibilities, supporting a wide range of interests and practices. Most of these SNSs allow users to present themselves and connect to existing and new users on social networks. Scholars from a variety of fields examine the SNSs to know the practices, implications, culture, sites significance, user engagement, and the intention of social commerce (s-commerce) as well. SNSs have become a perfect online venture for information sharing and social interaction with its comprehensive ease of use and technological capability, helping to build interactive communication and have a greater potential to create value for the organization (Ellison et al., 2007; Raza et al., 2017; Sheikh et al., 2019).

Due to the incredible improvement of the internet and information technologies, SNSs have become the global station of s-commerce (Al-Tit et al., 2020; Hajli, 2014a; Um, 2018); share brand allied information through their ubiquity, flexibility, and interactivity; and have an impact on intention to use, attitude toward services, and making purchase decisions (Ma, 2013). SNSs have an absolutely long-lasting social impact on people’s mind, due to the immediate presence in the lives of its users. Socialnomics (2018) reports that worldwide s-commerce revenue in 2017 was US$41 billion, where Facebook’s advertising revenue was US$10.4 billion and Instagram’s mobile advertising revenue was US$4 billion. In addition, 87% of customers say that SNSs helps them decide what to buy, and 90% of the followers try to reach out to the desired brands via SNSs. In general, referred clients are 18% more anticipated to halt with their current service provider than others, which results in 16% additional profits (Harvard Business Review, 2011). The literature shows that compared with the editorial recommendation, referral communication is more powerful in influencing because of its power of persuasion and credibility (Trusov et al., 2010), and service quality dimensions are the prerequisite of referral communication and have a decisive influence in building sustainable usage intention of SNSs (Hossain & Kim, 2018).

The antecedents of the multidimensional service quality theory to SNSs usage intention were given special attention (Lien et al., 2017; Lu et al., 2009; Zhao et al., 2012). Lien et al. (2017) validate Brady and Cronin’s (2001) multidimensional service quality theory into the SNS context and state that the usage intention of SNSs depends on the primary dimensions (e.g., outcome, environment, and interaction) of service quality. Zhao et al. (2012) state that the transaction-specific satisfaction and cumulative satisfaction of SNS users are obtained through multidimensional service quality dimensions.

Another main research theme in the SNSs study is the relationship between user satisfaction, usage intention, and social capital (Ellison et al., 2007; Phua et al., 2017; Sheikh et al., 2019; Su & Chan, 2017). Social capital refers to the ability of people or groups to access resources integrated into their social network (Bourdieu, 1986) and is often separated into “bridging” and “bonding.” Bridging and bonding social capital are significantly influenced by the amount of SNS use and the SNS-enabled communication practices (Su & Chan, 2017), uses and gratifications (Phua et al., 2017), intention of continuous use (Raza et al., 2017), SNS relationship maintenance behaviors (Ellison, Vitak et al., 2014), and the benefits of SNS friends (Ellison et al., 2007).

A new and timely research theme in the SNSs study is s-commerce, which allows lively interaction between customers and social networking media or s-commerce vendors. SNSs engage in pre-purchase product information sharing through its group interactions, commercial activities, and social media technologies (Liang & Turban, 2011). Excessive use of SNSs will increase s-commerce intention (Pookulangara & Koesler, 2011), where trust has a great impact on purchase intention (Al-Tit et al., 2020; Ventre & Kolbe, 2020). Commercial organizations can add value by integrating the SNSs (Hajli, 2015) and can have a strong impact on income generation (Turban et al., 2011). Rosa et al. (2014) argue that SNSs engage in s-commerce in two ways. First, SNSs provide vendors with virtual space for promotions and transactions such as buying and selling products or services, opening its interfaces to facilitate this process, such as Facebook, YouTube, LinkedIn, and so on. Second, it appears to be traditional electronic commerce sites that use the capabilities of social networks to leverage its power of reach and trust, such as Amazon.com, Americanas, Ponto Frio, Netshoes, and so on.

The aim of this study is to improve an empirical understanding of behavioral intentions of SNS users and its antecedents, and thus achieve s-commerce intention. Particularly, the proposed study will validate the primary dimensions of service quality observed by SNSs users, and how s-commerce intention and social capital are generated through customer satisfaction and usage intention in the context. This study also validates whether perceived trust has a mediating effect in the relationship between usage intention and s-commerce intention.

As only limited research is available on the topic, examining the relationship among these constructs will provide a more comprehensive understanding to the literatures, SNSs developers, and s-commerce vendors. First, the results of the current study will highlight the remarkable role of service quality dimensions in behavioral perceptions, shed considerable light on the social capital and s-commerce intention, and provide valuable contributions for understanding s-commerce behavior. Second, the results will subsidize current literatures by providing an investigation of several service marketing concepts, which would provide an improved understanding of users’ perceptions on SNS usage and s-commerce intention. Third, the current study will benefit practitioners (e.g., SNS developers, managers, or service providers, and s-commerce vendors). These findings may extensively assist SNS practitioners and s-commerce vendors in developing and maintaining service marketing tactics leading to higher quality service, enhance sustainable usage intentions and social capital, and thereby engender commerce intention.

Research Model and Hypotheses

Conceptual Research Model

Gronroos (1984) notes that the service quality has two distinct dimensions: technical quality—referring to what the customer receives from service experience—and functional quality—talking about how the service is provided. Parasuraman et al. (1988) propose a five-dimensional SERVQUAL model that comprises reliability, tangibility, assurance, empathy, and responsiveness. However, SERVQUAL has been criticized for its limitations, which leads to the appearance of alternative tools for measuring service quality (Dyke et al., 1997). Rust and Oliver (1994) propose a three-dimensional measurement scale of service quality integrating the service delivery process, service product, and service environment. In addition, a multilevel model is suggested by Dabholkar et al. (1996), where physical aspects, reliability, personal interactions, policy, and problem-solving are its primary dimensions, and promises, appearance, convenience, confidence, doing it right, inspiring, and being courteous and helpful are its secondary dimensions.

Brady and Cronin (2001) notably propose a hierarchical and multidimensional model of service quality, arguing that service quality is the set of states containing three major dimensions: outcome, interaction, and environment quality. Outcome quality is the consumer’s fulfillment of desires that leads to a feeling of experience (Lien et al., 2017; Zhao et al., 2012). Interaction quality is the customer’s perception of the service provider during the service provided (Lien et al., 2017; Lu et al., 2009). Environment quality is the basic requirements of service containing interface design, equipment quality, and service delivery extents (Lu et al., 2009). Despite the high popularity of service quality theory, particularly, SERVQUAL and other theories have been criticized for theoretical, procedural, and interpretative issues, and many new methods for the effective measurement of service quality are under development (Coulthard, 2004; Dyke et al., 1997). In this regard, Brady and Cronin’s model receives enormous attention and is validated in recent literatures (Hossain & Kim, 2018; Lu et al., 2009; Zhao et al., 2012). Therefore, the current study uses Brady and Cronin’s (2001) model in its service quality part.

Bourdieu (1986) notes that social capital is a total of original potential resources associated with having a sustainable network of more or less established relationships of acquaintance and recognition. These resources may include mutual trust, emotional support, and access to social information (Ellison, Gray, et al., 2014), and therefore, social supports have strong positive impact on s-commerce adoption (Hajli, 2014a; Sheikh et al., 2019). Repeated buyer–seller relationship would build when there is a credible trust and social support (Sheikh et al., 2019). A significant number of previous studies explore SNS relationships with social capital theory (Ellison et al., 2007; Raza et al., 2017; Sheikh et al., 2019; Su & Chan, 2017).

S-commerce is a special division of electronic commerce that uses SNSs to build social interaction and collaboration among SNS users with a view to accommodate online commerce. The incorporation of s-commerce creates value for the business by texts shared by SNS users or consumers (Hajli, 2015). It is a special tool for income generation (Turban et al., 2011). Incorporating the SNSs, an organization can build relational quality and social support, which have a positive influence on the adoption of s-commerce (Hajli, 2014a; Sheikh et al., 2019). Ventre and Kolbe (2020) and Al-Tit et al. (2020) argue that trust is an important factor in s-commerce adoption behavior. Trust has been presented as an antecedent of social support and has consequences on s-commerce behavior (Al-Tit et al., 2020; Chen & Shen, 2015). Trust (e.g., competence, integrity, and benevolence) is an inevitable factor in building intention to use s-commerce sites (Qin, 2017).

There is a growing number of publications examining the relationship between s-commerce intention, customer satisfaction, SNS usage intentions, and social capital (Hajli, 2014a, 2015; Kim & Park, 2013; Ng, 2013; Pookulangara & Koesler, 2011; Um, 2018), and few of them are presented in Table 1. According to Hajli (2014a, 2015), relational quality (e.g., satisfaction and trust) and social support (e.g., informational and emotional support) have a noteworthy positive impact on s-commerce intention. The author suggests that with social media gaining popularity and familiarity among users, social interactions among consumers encourage them to get involved in online activities and s-commerce. Usage intention in the form of electronic word-of-mouth (eWOM) is of great importance in s-commerce (Kim & Park, 2013); trust with familiarity and closeness stimulates purchase intention (Ng, 2013). Pookulangara and Koesler (2011) argue that the SNS user’s usage intention has a considerable positive impact on online buying intention. In addition, Liang et al. (2011) demonstrate that social constructs (e.g., informational and emotional support), relationship quality (e.g., trust, customer satisfaction, and commitment), and website quality (service and system quality) have a significantly positive association with s-commerce intention and continuance intention.

Table 1.

S-Commerce Literatures.

Source	Purpose	Variables	Results
Pookulangara & Koesler (2011)	This study examines the effect of Hofstede’s cultural dimensions on the social network and its influence on purchase intention	Usefulness of social network, ease of social network, intention of using social network, subjective norm, social search, and self-efficacy	The study validates the influence of intention to use social networks on purchasing intention and culture as a moderator, as well as other relationships
Liang et al. (2011)	It examines the user’s social sharing and social shopping intention in social networking sites	Social support (emotional and informational), relationship quality (trust, satisfaction, commitment), and website quality (system, service)	S-commerce intention and continuance use intention of social network are significantly predicted. Relationship quality has been shown to mediate between user and social networking website
Kim & Park (2013)	This study explores some key factors of s-commerce, which have impact on trust in s-commerce. It also examines the influence of trust in purchase intention and WOM	Information quality, reputation, transaction safety, size, communication, economic feasibility, and WOM	All factors, except economic feasibility, have a positive impact on trust, and trust is an important predictor of purchase intention and WOM intention
Hajli (2015)	This study aims to develop s-commerce constructs and to explore the role of s-commerce constructs on trust and buying intention	S-commerce constructs (recommendation and referrals, ratings and reviews, forums and communities) and trust	Intention to buy and trust are highly dependent on s-commerce constructs. Trust is also proven to be a good predictor of intention to buy
Um (2018)	This study examines the antecedents and consequences of consumers’ attitude toward s-commerce sites	Usefulness, ease, trust, shopping enjoyment, shopping risk, and social presence	The usefulness, shopping enjoyment, ease, and shopping risk have an impact on consumer’s attitude toward s-commerce sites. Attitudes have positive impact on the intention to use of s-commerce, eWOM intention and e-purchase intention
Al-Tit et al. (2020)	This study examines the drivers of s-commerce intention	Social support (informational, emotional), trust, and s-commerce constructs	Social support has positive impact on trust and s-commerce intention, and trust is a good mediator in this relationship

Source. Literature analysis.

Note. s-commerce = social commerce; WOM = word-of-mouth; eWOM = electronic WOM.

Based on the forgoing arguments, the current study continues to explore the relationships among SNS service quality, social capital, and s-commerce intention. The study proposes the following integrated research model, as shown in Figure 1. In this integrated model, Model 1 represents the service quality dimensions and their effects, while Model 2 represents s-commerce constructs.

Figure 1.

Conceptual research model.

Hypotheses Development

It has been figured that customer satisfaction is mainly forecasted by outcome quality of services (Hossain & Kim, 2018; Lien et al., 2017). Customers pay higher attention to outcome quality when judging cumulative satisfaction (Zhao et al., 2012). Interaction quality reflects the quality of a customer’s interaction with the seller in the service delivery process (Lu et al., 2009) and has a significant impact on consumer satisfaction (Hossain & Kim, 2018; Zhao et al., 2012). Surprisingly, Lien et al. (2017) found a negligible impact of interaction quality on customer satisfaction in WeChat. Hossain and Kim (2018) note that the environment quality is the important dimensions of user satisfaction in SNS. Kuo et al. (2009) argue that system reliability, visual design, navigation, and connection qualities are treated as value-added service quality features, which have influence on customer satisfaction and value. Transaction-specific satisfaction and cumulative satisfaction are the most influential attributes to mobile value-added services (Zhao et al., 2012). Therefore, the following hypotheses are proposed:

Hypothesis 1 (H1): Service quality dimensions (H1a—outcome quality, H1b—interaction quality, and H1c—environment quality) have a significant positive influence on SNS users’ satisfaction.

Hypothesis 2 (H2): Service quality dimensions (H2a—outcome quality, H2b—interaction quality, and H2c—environment quality) have a significant positive influence on SNS users’ usage intentions.

eWOM, a special form of usage intention, is an added value in s-commerce than in electronic commerce as consumers tend to share their opinion about the product or services online (Kim & Park, 2013). Relationship quality (e.g., commitment, trust, and satisfaction) and social support (e.g., informational and emotional) are the dominant predictors of s-commerce intention (Sheikh et al., 2019). Usefulness has significant positive influence on purchase intention and perceived trust (Ventre & Kolbe, 2020). Relational quality satisfaction and trust are important to stimulate s-commerce intention (Hajli, 2014a; Sheikh et al., 2019). Trust has a good influence in removing uncertainty and becoming a good predictor of s-commerce intention (Ng, 2013). Intention to use SNS has also significant positive impact on online shopping behavior (Pookulangara & Koesler, 2011). According to Raza et al. (2017), the source of bridging and bonding social capital is the user’s continuance intention to use Facebook. Phua and Jin (2011) have addressed that SNSs use positively affects social bridging and bonding. Ellison et al. (2014) note that maintaining relationships through Facebook, including showing sympathy and congratulating others, leads to social bridging and bonding.

Researchers suggest that excessive use of social media increases s-commerce intention enormously (Pookulangara & Koesler, 2011) and social capital (Phua & Jin, 2011; Sheikh et al., 2019). In return, if SNS users accumulate higher levels of social capitals that form social support, then SNS users are likely to engage in s-commerce transactions (Hajli, 2014a; Ng, 2013). Familiarity and closeness with online friends influence buying intention and trust in SNS community as well (Ng, 2013). Integration of s-commerce adds value to business by sharing consumers’ opinions through texts (Hajli, 2015), which has a strong impact on income generation (Turban et al., 2011). Furthermore, Um (2018) has been considered perceived social pressure and trust to examine the attitude toward s-commerce sites and report that there is a substantial positive association among intention to use s-commerce, eWOM, and e-purchase intention. Therefore, the following hypotheses are proposed:

Hypothesis 3 (H3): Satisfaction has a significant positive influence on (H3a) usage intentions, (H3b) social capital, and (H3c) s-commerce intention.

Hypothesis 4 (H4): Usage intentions have a significant positive influence on (H4a) perceived trust, (H4b) social capital, and (H4c) s-commerce intention.

Hypothesis 5 (H5): Social capital has a significant positive influence on s-commerce intention.

Several pioneering research on s-commerce indicate that trust is positively associated with s-commerce (Al-Tit et al., 2020; Hajli, 2014a; Ventre & Kolbe, 2020). Trust can facilitate the interaction of consumers and encourage them to stick to their current network and has influence on perceived usefulness and intention to buy (Hajli, 2014b). Ng (2013) validates trust as an important mediator of intention to buy through s-commerce and it can be transferable from one source to another source. As trust provides power of control to the consumers over the transactions, it minimizes the behavioral hesitation to buying intention in s-commerce, which in turn produces repeated buyer–seller relationships (Ba & Pavlou, 2002; Sheikh et al., 2019). Although there is some research with the mediation effect of trust on s-commerce (Ng, 2013), there is no research conducted focusing on the mediating effect of trust between usage intention of SNSs and s-commerce intention. Therefore, the following hypotheses are formulated:

Hypothesis 6 (H6): Perceived trust has a direct positive effect on s-commerce intention (H6a) and a mediation effect on the relationship between usage intention of SNSs and s-commerce intention (H6b).

Method

Research Design

The study used structural equation modeling (SEM) in AMOS-24 software for data analysis and hypothesis testing. SEM provides a real-time evaluation of the measurement and structural model. In particular, it clearly outperforms partial least square in terms of parameter consistency, reliability, validity, and model fit indices, and explains the relationships among constructs, which confirm its additional strength (Anderson & Gerbing, 1988). The bootstrap function in the SEM is operated to observe the mediation impact of perceived trust between the usage intention of SNSs and s-commerce intention.

The survey questionnaire was initially developed in English for U.S. participants. It is then converted into Korean by native speakers for Korean participants. Inconsistencies with the original version were discussed among translators and resolved to minimize misunderstandings. A 7-point Likert-type scale was used (strongly disagree = 1 and strongly agree = 7) to correctly measure the responses, facilitate interpretation of findings, and strengthen the results of survey questions. To achieve the richness of constructs, increased reliability, and reduced measurement errors, this study measures all constructs using multiple items. To further purify and validate the measurement tools, a pilot study was conducted with 10 samples and revealed that the instrument design is well synchronized and understandable. It took 10 to 15 min to complete the survey. Data were collected through an online survey and was launched from October 3–12, 2018.

Participants

The participants are restricted to SNS users because the prime focus of the study is to observe the relationship among the use of SNSs, social capital, and s-commerce intention. Using random sampling, we collect 634 samples, and after cleansing the data and removing the unacceptable responses, we finally considered a total of 549 valid samples (e.g., 249 from the United States and 300 from Korea). The sample size is adequate for statistical analysis used in this study, mainly a confirmatory factor analysis for measurement model validation and partial least squares for structural model estimation (Hair et al., 2010). Table 2 shows that gender distribution is relatively balanced between the male and female samples. Respondents in the 31 to 40 years age group are higher (36.42%) and followed by 20 to 30 years (33.51%). Overall, 59% of participants use Facebook and 28% use Instagram. While 38.43% of respondents have been using SNSs for more than 5 years, 39.7% use it between 30 min to 1 hr per day.

Table 2.

Demographic Characteristics.

Characteristics	N = 549n (%)
Gender
Female Male	270 (49.18)279 (50.81)
Age group (years)
20–30	184 (33.51)
31–40	200 (36.42)
41–50	124 (22.58)
Above 50	41 (7.46)
Mostly used SNS
Facebook	326 (59.38)
Twitter	50 (9.10)
Instagram	154 (28.05)
Snapchat	6 (1)
Others	13 (2.36)
Time spent on SNSs per day
<30 min	156 (28.44)
30 min–1 hr	218 (39.70)
1–2 hr	129 (23.49)
More	46 (8.37)
Experience using SNSs
<6 months	30 (5.28)
6 months–1 year	82 (14.93)
1–3 years	108 (19.67)
3–5 years	119 (21.67)
>5 years	211 (38.43)

Source. Survey results.

Note. SNS = social networking site.

Measurement

The measurement items of outcome, environment, and interaction quality are borrowed from Lu et al. (2009), Lien et al. (2017), and Hossain and Kim (2018). The measurements of satisfaction and usage intention are adopted from Hossain and Kim (2018) and that of social capital comes from Su and Chan (2017) and Hajli (2015). Perceived trust and s-commerce intention are adopted from Chen and Shen (2015) and Hajli (2014a, 2014b, 2015).

Method Bias Test

Because most of the measurement elements are psychometric measures and the data come from only one source at a time, we test the common method variance (CMV). Podsakoff et al. (2003) notes that a CMV problem may arise when all the items fall into a single construct or first constructs explain the majority of the variance of the data. Harman’s single-factor test was performed to examine the CMV problem in this study. The results show that the first factor explains 34.7% of the variance, and several factors carry eigenvalues greater than one, revealing that there is no CMV problem in the data.

Empirical Results and Discussions

Reliability and Validity Measurements

A confirmatory factor analysis is performed to assess the validity and reliability of the measurement model. First, we examine the convergent validity by assessing each factor loading; the results show that all items are loaded well above 0.70 for the construct to which they belong, thus exceeding the suggested critical value (Fornell & Larcker, 1981). For a few items, the standardized factor loadings below the critical value of 0.70 are removed. Second, we inspect the construct reliability for each construct by examining composite reliability (CR), average variance extracted (AVE), and Cronbach’s alpha. In this analysis, all are greater than the standard value, such as values more than .70 for CR, more than 0.50 for AVE, and more than .70 for Cronbach’s alpha, thus signifying good internal consistency in the measurement model (Hair et al., 2010). Table 3 presents the measurement objects in detail with the corresponding standardized factor loadings, CR, AVE, and Cronbach’s alpha values of the measurement model.

Table 3.

Measurement Model Analysis.

Constructs and items	Standardized factor loadings	Composite reliability	Average variance extracted	Cronbach’s α
OQT		.84	0.64	.88
OQT2	0.73
OQT4	0.80
OQT5	0.84
EQT		.81	0.58	.80
EQT1	0.76
EQT2	0.77
EQT5	0.74
IQT		.94	0.75	.93
IQT1	0.86
IQT2	0.85
IQT3	0.87
IQT4	0.85
IQT5	0.87
PT		.90	0.67	.91
PT1	0.84
PT2	0.79
PT3	0.81
PT4	0.82
PT5	0.78
SCP		.91	0.76	.90
SCP4	0.88
SCP5	0.88
SCP6	0.85
SAT		.93	0.71	.92
SAT1	0.86
SAT2	0.86
SAT3	0.87
SAT4	0.81
SAT5	0.80
USE		.78	0.54	.77
USE2	0.79
USE4	0.68
USE5	0.70
S-COM		.92	0.70	.92
S-COM1	0.88
S-COM2	0.72
S-COM3	0.85
S-COM4	0.83
S-COM5	0.87

Note. OQT = outcome quality; EQT = environment quality; IQT = interaction quality; PT = perceived trust; SCP = social capital; SAT = satisfaction; USE = usage intention; S-COM = social commerce intention.

This study assesses discriminant validity according to the suggestion of Fornell and Larcker (1981). In this respect, the value of the correlations between the items of any two constructs must be lower than the square root of the AVE shared by items within a construct. All the square root of AVE values surpass the respective correlation, representing an adequate discriminant validity in the measurement model. Discriminant validity is also assessed based on AVE, maximum shared variance (MSV), and average shared variance (ASV). The decision criteria to confirm the discriminant validity are AVE > MSV and ASV (Hair et al., 2010). Table 4 shows detailed statistics of discriminant validity for the measurement model.

Table 4.

Discriminant Validity.

Construct	MSV	ASV	S-COM	OQT	EQT	IQT	PT	SCP	SAT	USE	Tolerance	VIF
S-COM	0.44	0.36	0.84
OQT	0.56	0.49	0.58	0.80							0.33	3.01
EQT	0.54	0.47	0.52	0.74	0.76						0.31	3.20
IQT	0.40	0.35	0.61	0.63	0.63	0.86					0.36	2.71
PT	0.51	0.46	0.63	0.71	0.69	0.56	0.82				0.27	3.63
SCP	0.46	0.39	0.63	0.58	0.59	0.64	0.65	0.87			0.44	2.27
SAT	0.56	0.53	0.62	0.75	0.74	0.63	0.72	0.68	0.84		0.24	4.02
USE	0.50	0.46	0.67	0.66	0.62	0.64	0.68	0.64	0.71	0.74	0.36	2.74

Note. Model fit indices: χ²/df = 2.328, RMSEA = 0.049, CFI = 0.961, GFI = 0.891, AGFI = .868, IFI = 0.961, NFI = 0.934, TLI = 0.956. Bold diagonal values are the square root of AVE. MSV = maximum shared variance; ASV = average shared variance; S-COM = s-commerce intention; OQT = outcome quality; EQT = environment quality; IQT = interaction quality; PT = perceived trust; SCP = social capital; SAT = satisfaction; USE = usage intention; VIF = variance inflation factor; RMSEA = root mean square error of approximation; CFI = comparative fit index; GFI = goodness of fit index; AGFI = adjusted goodness of fit index; IFI = incremental fit index; TLI = Tucker–Lewis index; AVE = average variance extracted; NFI = normalized fit index.

The lower panel of Table 4 shows the overall model fit statistics from the measurement model. The results show the following values: ratio of chi-square to degrees of freedom (χ²/df) = 2.328, root mean square error of approximation (RMSEA) = 0.049, comparative fit index (CFI) = 0.961, adjusted goodness of fit index (AGFI) = 0.868, incremental fit index (IFI) = 0.961, and the Tucker–Lewis index (TLI) = 0.956, representing a good model fit (Hair et al., 2010; Hu & Bentler, 1999). In addition, variance inflation factor (VIF) is considered to overcome the problem of multicollinearity among independent variables. The VIF value varies from 2.27 to 4.02, within the acceptable limit of 10.0, proving that there is no multicollinearity issue in the model (Hair et al., 2010). The tolerance value varies from 0.24 to 0.44, which represents the additional strength of the model.

Hypotheses Testing

With the satisfactory reliability and validity of the measurement model, this study proceeded to the evaluation of the hypothesized paths by structural model analysis. The structural model explains 79% of the variance in satisfaction, 92% of the variance in usage intention, 64% of the variance in social capital, 84% of the variance in perceived trust, and 63% of the variance in s-commerce intentions, representing a higher explanatory power of the model. It also shows a good fit of the model (χ²/df = 2.544; RMSEA = 0.053; CFI = 0.954; AGFI = 0.855; TLI = 0.948). Figure 2 shows the path analysis results.

Figure 2.

Path analysis results.

Using structural model with maximum likelihood method, 10 out of 14 hypotheses are found to be significant. Figure 2 indicates that outcome quality (β = .350, p < .001) and environment quality (β = .529, p < .001) significantly affect satisfaction, thus H1a and H1c are supported. In case of predicting usage intention, outcome (β = .209, p < .01) and interaction quality (β = .401, p < .001) show significant effect, supporting H2a and H2b. However, interaction quality does not affect satisfaction and environment quality does not affect usage intention significantly, thus H1a and H2c are not supported. Although, satisfaction exhibits a significant effect on usage intention (β = .471, p < .001) but has no significant effect on social capital and s-commerce intention, which supports H3a and rejects H3b and H3c. Moreover, usage intention shows a significant influence on perceived trust (β = .917, p < .001), social capital (β = .710, p < .001), and s-commerce intention (β = .958, p < .001) that supports H4a, H4b, and H4c. Accordingly, social capital (β = .180, p < .001) and perceived trust (β = .251, p < .01) also show a significant effect on s-commerce intention, thus supporting H5 and H6a.

The Mediating Effect of Perceived Trust

As a post hoc analysis, this study performed a bootstrap to examine the mediation effect in the model according to the guidelines of Hayes (2009). AMOS-24 software was used to bootstrapping with 549 samples and the process was repeated 5,000 times. Table 5 shows the bootstrapped results, revealing that usage intention has a significant total (β = .945, p < .001), direct (β = .745, p <.001), and indirect (β = .200, p <.01) effect on s-commerce intention. The results also evidenced that bias corrected and percentile method with a 95% confidence interval (CI) linked to the indirect effect do not include zero, which represents a partial mediation of perceived trust (Baron & Kenny, 1986), thus supporting H6b.

Table 5.

Bootstrapping Results.

Total effect of usage intention on s-commerce intention	Direct effect of usage intention on s-commerce intention	Indirect effect of usage intention on s-commerce intention (through perceived trust)
			Bias corrected		Percentile
			95% CI		95% CI
			Lower	Upper	Lower	Upper
.945***	.745***	.200**	.012	.307	.009	.308

Note. s-commerce = social commerce; CI = confidence interval.

p < .01. ***p < .001.

Discussion

The findings of the current study empirically validate that the service quality dimensions, outcome and environment quality, have a positive and significant influence on the satisfaction of SNS users. In case of predicting continuous usage intention, outcome and interaction quality show significant effect. These associations are reliable with preceding studies by Lien et al. (2017) and Hossain and Kim (2018), among others. Surprisingly, the results do not support the paths of interaction quality to satisfaction and environment quality to usage intention, which does not corroborate the results by Zhao et al. (2012) and Hossain and Kim (2018).

Of the three dimensions of service quality, outcome quality was found to have a positive and significant impact on both satisfaction and usage intention. In traditional services, customers focused on what they gained from using the services. As the consumer experience increases, they become more familiar with the services process, making interaction and environmental context less concern. Thus, SNS users would definitely place importance on the outcome quality. On the contrary, as the satisfaction of a particular transaction emphasizes the experience of service encounter and the consumption situation, customers focus more on the implementation of the service process and the environmental parameters. Thus, the interaction and environment quality play an inevitable role in understanding comprehensive service quality issues.

However, the two service quality dimensions, interaction and environment show a negligible impact on satisfaction and usage intention, respectively, in this study. One possible reason could be that users do not interact directly with the service system all the time but can judge what they are actually getting from the service. Users become familiar and confident about the service process and physical attributes, thus proving that SNS users place a higher value on the outcome quality, rather than interaction and environment quality.

In addition, corroborating existing research on SNSs and social capital (Ellison et al., 2007; Raza et al., 2017), this study proves that usage intention significantly affects social capital, suggesting it is the best predictors of social capital. Relationship maintenance, such as congratulating, sympathizing, accommodating, and helping each other, leads to bridging and bonding social capital. Individuals have perceived that s-commerce is a commerce channel that further facilitates as a social platform for interacting with other favorable and compassionate people. SNS members can contribute to diverse group activities and provide hands to others through their social interactions and communication on a particular SNS platform. These advances integrate consumers into the value-making process for business enterprises through the social support they provide on the internet.

Following the relational qualities such as trust, satisfaction, and user loyalty, and social support (e.g., in the form of social capital), the current study demonstrates that s-commerce intention is highly projected, which are consistent with previous studies by Hajli (2014a), Um (2018), and Al-Tit et al. (2020). The proposed model implies that social constructs (e.g., emotional and informational support) and the ability to improve the quality of relationships in business world have a countless impact on individuals to engage in s-commerce.

Conversely, the results show that satisfaction has a negligible impact on s-commerce intention and social capital. One possible explanation is that with the development of the internet and social media, consumers are increasingly playing a dual role of information providers and information seekers. Before placing an order, everyone collects as much information as needed, which forces them to continue using SNSs, and creates valuable social relationships among customers, thus minimizing the focus on satisfaction. Second, commercial firms are happy to see consumers become sources of information through content generation; so firms took interactive relationships building strategy as a core concern, allowing a better customer relationship management. This has been achieved through social interactions and supports that build customer loyalty. On the contrary, social interactions and support can increase mutual trust and favorable attitudes toward the SNS and are more likely to accept the adoption of s-commerce.

Furthermore, perceived trust is decidedly anticipated through usage intention and is proven to have direct influence on s-commerce intention, supporting the earlier studies of Um (2018) and Hajli (2014b). A trustworthy partner is more likely to be popular for other partners in a group, which structures the belief systems of an individual. The trusted relationships with referral mechanism provide s-commerce vendors with a powerful vehicle to surpass service quality, deepen seller–customer relationships, and endorse products successfully. The result also shows that perceived trust partially mediate the influence of usage intention on s-commerce intention, which is in line with Ng (2013). The total effect of usage intention on s-commerce intention is .945, including both the direct (.745) and indirect (.200) effect. Total effect reveals the presiding role of usage intention on s-commerce intention. In particular, s-commerce intention is boosted by the mediated effect of perceived trust, indicating that s-commerce customers need authorization through social processes before making the final decision toward s-commerce. In other words, trust provides consumers with awaited confidence in online settings, which will help to diminish hesitations in s-commerce.

Conclusion

SNS is not just a communication tool but also an artificial structural equivalent of social interaction that provides users with social support and s-commerce opportunity. Particularly, the results reveal that (a) environment quality and interaction quality are the best predictors of satisfaction and continuous usage intention of SNSs, respectively; (b) satisfaction has a positive impact on usage intention, which in turn influences s-commerce intention, perceived trust, and social capital; and (c) perceived trust is proven to have a direct and partial mediation effect on s-commerce intention, and social capital also has a positive influence on s-commerce intention.

Theoretical Contributions

As a theoretical contribution, this study is a first initiative that explains the adoption behavior of s-commerce through the service quality of SNS as a hierarchical reflective model. The comprehensive initiative presents the multidimensional service quality model for achieving and managing total service quality of SNS. This study explains the additional details of the variables, showing their respective influence on customer satisfaction and usage intention of SNSs. The study asserts that to achieve total SNSs service quality, SNSs or s-commerce sites should consider three main dimensions, namely, outcome quality, interaction quality, and environment quality. As the outcome quality is related to what the customers really received from the service rendered or is related to valence, punctuality, and tangibles, the manager should focus on these. Consumers are concerned about informing and dealing with problems and complaints during the interaction process and suggest that information and problem-solving have important implications for interaction quality. Excellency in attitude, problem-solving, expertise, and information should be the focal dimension of interaction quality in service delivery. Moreover, service environment is related to tangible characteristics of the service and plays a decisive role in consumers’ perception of overall service quality, which in turn affects user satisfaction and usage intention leading to s-commerce intention.

Second, this study empirically proves the different functional mechanisms of the impact of satisfaction and usage intention on social capital and s-commerce intention. Individuals do not generate social capital or feel interactively connected with others solely because of SNSs, but an effective service quality shapes their insights of their social network on SNSs that allow them to have social support. Social capital plays a great role in s-commerce adoption behavior. Social capital could be understood as a form of capital (e.g., financial or human capital) which is anchored in the relationships between individuals and can be measured at the individual or collective level. It encompasses personal relationships, trust and cooperative norms, social network support, and civic engagement with those who have a significant connection with s-commerce intention. This is increasingly considered as a key determinant of a firm’s long-run financial performance in a competitive market. It is an organization’s primary concern regarding its product’s performance in the market place. In addition, usage intentions that involve referrals, interactive value creation, and stable usage are more likely to be communicative spirits on SNSs and then influence social capital and s-commerce intention. The integration of SNSs into s-commerce facilitates the value creation for businesses through texts generated by consumers. S-commerce intention is highly dependent on continuous usage intention of social networks as well. As s-commerce users tend to share their opinions and feelings about products or services, usage intention is more important in s-commerce. Closeness, familiarity, and trust in social network community have a significant impact on intention to buy in s-commerce.

Third, the study’s findings provide a better understanding of SNS users’ perceived trust and the influences they exert on their s-commerce intention. Customers gain trust on SNSs when they continually use the SNS and have a direct impact on their purchase decision, which also mediates the relationship between usage intention and s-commerce intention. As trusted partner’s recommendations have a notable contribution in s-commerce shopping, this issue has become a vital phenomenon in the context of s-commerce. SNS users are encouraged to participate in sharing particularized information, recommending, comparing, and criticizing on the products of services in both online or offline marketplace and groups thereby leading to s-commerce.

Practical Contributions

First, to maintain good relations with customers, SNS managers and those who intend to conduct s-commerce through SNSs need to consider how to provide adequate service quality and creative functions of the sites. They also need to think about how to promote a supportive environment in the related communities. Thus, this research finding should be simulated in the development of management strategies and themes corresponding to user preferences of SNS quality, behavioral attitude, social capital, and s-commerce adoption. The current study highlights outcome, interaction, and environment qualities, and managers or practitioners should focus on the information shared and key features of SNSs and s-commerce sites. These findings will lead to the development of strategies that will enable effective marketing communication through SNSs and attract users who embrace the sites.

Second, given the importance of customer satisfaction and usage intention, managers or practitioners must regularly integrate and develop useful functions or various value-added features so that customers can meet, share information or experience, generate social capital, and build trust in the virtual world. Customers join SNSs and online communities to get information, and share experiences and interaction with peers, which can build trust and stimulate the intention of s-commerce. This suggests that SNSs or s-commerce sites would gather and meet customers by making online communities. Firms should make an appropriate plan to properly review SNS discussions that have countless impact on purchase decision. This improves the channel of communication through customers and unlocks opportunities for marketing strategies that can benefit both sellers and customers.

Limitation and Future Research Guidelines

Although this study offers a number of noteworthy contributions to the practitioners and managers, it still has few drawbacks. The potential drawback is that the current study accomplished its analysis based on survey data, which have the inherent constraint of survey research. Despite the fact that this study did everything possible to ensure the survey was conducted rigorously, what respondents said might not be the same as what they would do. Therefore, the result may differ from the actual behavior. In future, there may be space for a more unified model that incorporates other longitudinal methods for attaining better understandings. Finally, it is also recommended to conduct a similar study in other service industries with a larger sample so that the results can be generalized to a larger population in the world.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research,authorship,and/or publication of this article.

Funding

The author(s) received no financial support for the research,authorship,and/or publication of this article.

ORCID iD

Minho Kim

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A Comprehensive Study on Social Commerce in Social Networking Sites

Abstract

Keywords

Introduction

Research Model and Hypotheses

Conceptual Research Model

Hypotheses Development

Method

Research Design

Participants

Measurement

Method Bias Test

Empirical Results and Discussions

Reliability and Validity Measurements

Hypotheses Testing

The Mediating Effect of Perceived Trust

Discussion

Conclusion

Theoretical Contributions

Practical Contributions

Limitation and Future Research Guidelines

Footnotes

Declaration of Conflicting Interests

Funding

ORCID iD

References