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Effects of Antecedents of Collectivism on Consumers’ Intention to Use Social Commerce



Sanghyun Kim, Mi-Jin Noh and Kyung-Tag Lee
 
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ABSTRACT

Social commerce (s-commerce), a subset of electronic commerce (e-commerce), involves social interactions and user contributions and facilitates the online buying and selling of a wide range products and services. Given that s-commerce encourages consumers to share product- and service-related information, it reflects collectivism, not individualism. Using the Technology Acceptance Model (TAM), this study employs the Structural Equation Modeling (SEM) method to investigate a research model incorporating consumers’ preferences, reliance, concern and norm acceptance as antecedents of collectivism. The results of a survey of 365 s-commerce users indicate that preferences, reliance and norm acceptance had significant effects on the perceived usefulness of s-commerce. In addition, the goodness-of-fit results indicate that collectivism and perceived ease of use accounted for 65.7% of the variance in the perceived usefulness of s-commerce. This study contributed to the literature by providing useful insights into the factors influencing consumers’ decision to adopt s-commerce.

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  How to cite this article:

Sanghyun Kim, Mi-Jin Noh and Kyung-Tag Lee, 2012. Effects of Antecedents of Collectivism on Consumers’ Intention to Use Social Commerce. Journal of Applied Sciences, 12: 1265-1273.

DOI: 10.3923/jas.2012.1265.1273

URL: https://scialert.net/abstract/?doi=jas.2012.1265.1273
 
Received: March 13, 2012; Accepted: May 17, 2012; Published: June 30, 2012



INTRODUCTION

The explosive growth of social media, particularly Social Networking Sites (SNSs), has provided electronic commerce (e-commerce) with a new paradigm called social commerce (s-commerce). s-commerce, a new business model based on e-commerce, combines social media with e-commerce and enables the buying and selling of a wide range of products and services over the Internet (Marsden, 2011). In other words, s-commerce makes use of social networks formed through e-commerce transactions. s-commerce users collaborate with one another for their online shopping activity by sharing product- and service-related information and exchanging opinions, among others.

S-commerce has attracted increasing attention from vast numbers of online communities. Furthermore, s-commerce has become one of the major forms of online commerce and is an integral part of the social phenomenon involving social media. In particular, s-commerce has enjoyed explosive growth in South Korea (hereinafter “Korea”) because many consumers are interested in acquiring discount coupons and sharing information on products and services by using social media for their online buying and selling activities. Such characteristics of s-commerce reflect collectivism. Pookulangara and Koesler (2011) claimed that collectivism highlights a sense of interdependence; interactions among individuals belonging to a collective group and the prioritization of group goals. Thus, collectivism is often understood as a cultural phenomenon reflecting individuals’ personal tendencies (Pookulangara and Koesler, 2011). In addition, Brandtzaeg (2010) claimed that cultural differences can influence the interaction between new social media technologies (e.g., s-commerce) and users.

S-commerce suggests an interesting paradox for many researchers and practitioners because it is an emerging e-commerce phenomenon. Although a number of studies have examined diverse aspects of e-commerce and m-commerce, including their adoption and diffusion, few have considered the nature of s-commerce to explain consumers’ adoption of it because it is difficult to explain the adoption of a new commerce mechanism (Kim, 2006; Kim and Garrison, 2009). Therefore, there is a need for a better understanding of consumers’ behavior toward s-commerce because s-commerce is a new phenomenon. In addition, given the importance of s-commerce, there is an urgent need for identifying the factors influencing consumers’ intention to adopt s-commerce in the context of collectivism.

However, few studies have examined the effects of collectivism on consumers’ behavior toward s-commerce. In this regard, the main purpose of this study is to understand consumers’ acceptance of s-commerce by using the well-known Technology Acceptance Model (TAM). This study provides an empirical analysis and addresses the following research question: “How does collectivism influence the behavior of s-commerce users?”

For this, this study employs the Technology Acceptance Model (TAM) as a theoretical framework to suggest the research model because previous studies (Massimo et al., 2002; Veiga et al., 2001) have claimed that cultural characteristics, including individualism, collectivism, uncertainty avoidance and power distance, can induce beliefs such as perceived usefulness and perceived ease of use. The s-commerce users are likely to show collectivism because s-commerce represents an online space in which product- and service-related information is widely shared among users, which can encourage them to develop favorable attitudes toward group purchases (or collectivism). This indicates that collectivism, particularly with respect to the consumer’s preferences, reliance, concern and norm acceptance, may influence s-commerce users’ beliefs.

RELATED WORKS

Collectivism: Collectivism has been defined as an individual’s or a group’s orientation toward relationships with other individuals or groups. Individualists place greater emphasis on personal interests than on group needs, looking after themselves and ignoring group interests when they conflict with their personal desires. According to this commonly accepted view, collectivism is the opposite of individualism (Hui, 1988; Oyserman et al., 2002) claimed that collectivism has the opposite of individualism. In fact, collectivism prioritizes group goals over personal ones; stresses conformity and in-group harmony; and defines the self in relation to the group (Triandis, 1989). Collectivists place greater emphasis on group interests than on individual needs and desires and tend to focus on the well-being of their group (Wagner and Moch, 1986).

Hofstede (1980) suggested collectivism entails four major cultural variables: power distance, uncertainly avoidance, individualism and masculinity. In addition, Triandis et al. (1988, 1990) introduced several variables for the nature of collectivism, including the concern for the group, interdependence, family integrity, self-reliance and the distance from the group. Wagner and Moch (1986) introduced three dimensions of collectivism: collective beliefs, values and norms. In addition, Jackson et al. (2006) examined the effects of various constructs (e.g., preference, reliance, concern and goal priority) of collectivism on the performance of group members. These characteristics of collectivism can play crucial roles in individuals’ within-group behaviors, particularly in their technology adoption decisions or intentions.

In-group goals take priority over personal goals for collectivists, whereas the opposite tendencies are typically observed for individualists. For collectivists, subjective boundaries of in-groups are clear in distinguishing between in-and out-group members (Chen et al., 1997). Collectivists are concerned about the importance of group harmony and stability (Triandis et al., 1990). Similarly, collectivism reflects collectivistic cultures, which are characterized by homogeneous, interdependent and long-term relationships and common interests. The s-commerce is based mainly on social media, which are online services, platforms or sites that focus on facilitating social networks or relationships among users. This conclusion indicates that the nature of s-commerce reflects the characteristics of collectivism.

s-commerce: s-commerce is a new e-commerce phenomenon and has grown more rapidly than any other form of online commerce. Few could have predicted how pervasive s-commerce would become not only in online environments but also in people’s daily lives. In fact, s-commerce itself is not new. However, what is new is its use of social media (e.g., SNSs) and innovative e-commerce technologies (e.g., social media stores and portable social graphs). This new phenomenon has introduced a wide range of new opportunities for the monetization of social media through e-commerce.

Because the use of social media in online commerce is a new phenomenon, few studies have examined s-commerce adoption. In addition, most of the previous s-commerce studies have provided descriptive analyses of various aspects of s-commerce. For example, Marsden (2011) claimed that s-commerce can deliver important business advantages such as the monetization of social media, the optimization of e-commerce sales and the innovation of business models. In addition, Stephen and Toubia (2010) claimed that firms developing social strategies should take into account the extent to which the socialness of such strategies may influence their business. Some firms are hesitant to use Twitter or Facebook because of the possibility of having to address negative comments or bad publicity. However, many have recognized the benefits associated with forming relationships with their customers through social networks (3dcart, 2010). For consumers, s-commerce can enhance the purchase experience, offering trust, utility and fun in key areas such as the discovery, selection and referral of products (Marsden, 2011). For example, Ticketmaster benefits from Facebook’s focus on friends because people tend to go to concerts with others.

Kim et al. (2011) investigated the effects of various characteristics of s-commerce and s-commerce users that can induce trust in s-commerce and found that variables such as reputation, economic feasibility, information quality, transaction safety and interactions have positive effects on trust and that consumers’ purchase experience and word-of-mouth communication have considerable influence on trust in s-commerce, which in turn has a positive effect on trust performance.

Technology acceptance model (TAM): Several studies have demonstrated that the two beliefs of the TAM (perceived usefulness and perceived ease of use) are significant predictors of an individual’s acceptance in terms of his or her use intentions and actual behaviors toward new technologies such as websites (Fenech, 1998; Lee et al., 2008), e-commerce (Khatibi et al., 2006; Liu and Wei, 2003; Sek et al., 2010; Yoon, 2009), online games (Hsu and Lu, 2004; Wu et al., 2010) and SNSs (Pookulangara and Koesler, 2011). However, these constructs do not fully reflect the specific effects of usage-context factors that may influence an individual’s technology acceptance (Moon and Kim, 2001; Srivastava, 2012). Thus, it is difficult to conclude that these two variables can fully explain consumers’ behavioral intentions and actual behaviors toward new information systems, including social networking and s-commerce.

Based on these limitations of the TAM, previous studies have considered some exogenous variables for national and individual cultures to extend the TAM (Srite and Karahanna, 2006; McCoy et al., 2007). Such exogenous variables (e.g., the national culture) can have considerable influence on individuals’ acceptance of new technologies because their adoption decisions depend mainly on these variables. In fact, many studies have examined the effects of such usage-context variables within the TAM in various technology adoption contexts. For example, Turel and Connelly (2011) provided an empirical analysis of the relationship between perceived usefulness and collectivism to examine individuals’ acceptance of e-collaboration tools, focusing on collectivistic cultures. In addition, some studies have claimed that a collectivistic culture is a typical type in culture in which individuals follow group norms (Srite and Karahanna, 2006). In this regard, the present study employs the TAM as a research framework to propose a research model for investigating the effects of collectivism on perceived usefulness and the relationships between the TAM variables in the context of s-commerce.

RESEARCH MODEL AND HYPOTHESES

Figure 1 shows the research model, which introduces the strategic rationale for including collectivism in the analysis of consumers’ intention to use s-commerce. Based on the TAM, the research model introduces four variables (the consumer’s preferences, reliance, concern and norm acceptance) to explain collectivism. These variables are expected to directly influence perceived usefulness. In addition, the research model includes the TAM’s two beliefs to verify the relationships between the TAM variables in the context of s-commerce.

Hypothesis development: Previous studies (Srite and Karahanna, 2006; McCoy et al., 2007) have defined collectivism as a type of national or individual culture and claimed that collectivism is a key determinant of IT adoption (Folorunso et al., 2006; Veiga et al., 2001; Zeng et al., 2009). Jackson et al. (2006) considered many cultural variables and emphasized that the variables such as consumers’ preferences, reliance, concern, norm acceptance and goal priority are the key dimensions of collectivism in the context of group performance.

Fig. 1: The proposed research model and hypotheses

In addition, Turel and Connelly (2011) focused on psychological collectivism (e.g., consumers’ preferences, reliance, concern and norm acceptance) to explain the use of e-collaboration tools. Following previous research, the present study proposes a research model and develops some theoretical hypotheses about the four dimensions of collectivism and the perceived usefulness of s-commerce.

The first antecedent factor of collectivism is the preference, which this study defines as an individual’s belief that collective efforts are superior to individual efforts (Turel and Connelly, 2011). Because s-commerce can provide consumers with social or collective shopping opportunities, consumers who prefer to be part of a group tend to believe that such preferences can strengthen the perceived usefulness of s-commerce. However, no study has provided an empirical analysis of consumers’ preferences in the context of their intention to adopt s-commerce. In this regard, the following hypothesis is proposed:

Hypothesis 1; H1: The consumer’s preference has a positive effect on the perceived usefulness of s-commerce

The second variable in the proposed research model is reliance, which this study defines as the extent to which a group member relies on other members. Collectivists believe that they have some responsibility for the entire group and this responsibility is shared among all group members. Thus, each group member is comfortable relying on other members (Jackson et al., 2006; Turel and Connelly, 2011). In s-commerce, social groups have extensive relationships and individuals in collectivistic cultures are more likely to show a high level of interdependence than those in individualistic cultures, who tend to show a high level of self-reliance. In this regard, Triandis (1989) strenuously insisted that in collectivistic cultures, social relationships have the tendency to be more persistent and spontaneous and tend to occur within larger groups.

One of the major concerns in individualistic cultures is self-reliance, whereas it is conformity with others in public settings (e.g., s-commerce) (Triandis, 1989). Because consumers who rely on others in s-commerce tend to purchase the same products at discounted prices, they try to acquire a price advantage by relying on others. Such tendencies in s-commerce derive from the nature of s-commerce, that is, collectivism, which can have positive effects on the perceived usefulness of s-commerce. In this regard, hypothesis 2 is proposed:

Hypothesis 2; H2: The consumer’s reliance on others has a positive effect on the perceived usefulness of s-commerce

The third variable in the research model is concern defined as something that interests individuals because of the size of the group they belong to. A group member’s concern for the well-being of other members often motivates collectivists. Self-interest is not the main motive in collectivistic cultures (Jackson et al., 2006; Turel and Connelly, 2011). In this regard, Triandis (1989) claimed that collectivists tend to be concerned more about what happens to other group members than about what happens to themselves. In addition to subordinating personal goals to collective goals, collectivists tend to be concerned about the impact of their actions on their in-group members and have interdependent relationships with their in-group members (Hui and Triandis, 1986).

In general, interpersonal behavior naturally occurs within in-groups in s-commerce. In addition, s-commerce users are interested in the behavior of other users. This natural collectivistic behavior, particularly toward s-commerce users, may have positive effects on the perceived usefulness of s-commerce. However, no study has examined consumers’ concern as a dimension of collectivism in the context of their intention to adopt s-commerce. In this regard, the following hypothesis is proposed:

Hypothesis 3; H3: The consumer’s concern has a positive effect on the perceived usefulness of s-commerce

The fourth variable for collectivism is norm acceptance, which is defined as the extent to which individuals are willing to accept a normal social behavior as informal guidelines in a particular social group. Such normal social behaviors reflect what is appropriate or inappropriate. Turel and Connelly (2011) claimed that social norms form the basis for group members’ collective expectation and play an important role in social control and order by exerting pressure on others to conform.

In addition, collectivists obey social norms and rules of the in-group to build solid harmony among group members (Jackson et al., 2006; Turel and Connelly, 2011). If there is a conflict between individual and group goals, then it is considered socially desirable to prioritize collective goals over individual ones (Triandis et al. 1990). In addition, social norms are related to transactions in collectivistic cultures. For example, s-commerce is based on group purchase deals, which require users to accept certain social norms. By accepting such social norms, s-commerce users can derive many benefits in terms of prices and service. For collectivists (s-commerce users), their perception of the usefulness of s-commerce may be enhanced by accepting social norms. In this regard, the following hypothesis is proposed:

Hypothesis 4; H4: The consumer’s norm acceptance has a positive effect on the perceived usefulness of s-commerce

Previous studies have employed the TAM to examine individuals’ behavioral intentions and actual behaviors toward the use of new technologies (e.g., e-commerce, m-commerce and SNSs). The TAM considers two major beliefs (perceived usefulness and perceived ease of use) as the key determinants of technology adoption. Perceived usefulness is defined as the extent to which individuals believe that a new technology would improve job performance, whereas perceived ease of use is defined as the extent to which individual believe that using a new technology would require little effort (Davis, 1989).

Many studies based on the TAM have demonstrated that perceived usefulness is a strong determinant of consumers’ acceptance, adoption and use behaviors (e.g., Agrawal et al., 2000; Rigopoulos et al., 2008). In addition, the relationships between the TAM variables have been verified in various technology adoption contexts. Similarly, the present study assumes that the ultimate reason why consumers exploit s-commerce is that this technology can facilitate their online shopping and provide them with many benefits, including efficient and effective online shopping. Because s-commerce is a new e-commerce phenomenon, the TAM should be used for predicting consumers’ intention to use s-commerce. The present study examines the effects of perceived ease of use on perceived usefulness and on the intention to use s-commerce. In this regard, the following hypothesis is proposed:

Hypothesis 5; H5: Perceived usefulness has a positive effect on the intention to use s-commerce
Hypothesis 6a; H6a: Perceived ease of use has a positive effect on perceived usefulness
Hypothesis 6b; H6b: Perceived ease of use has a positive effect on the intention to use s-commerce

RESEARCH METHODOLOGY

Sample and the measurement: A survey was conducted to test the proposed model and hypotheses. A total of 375 responses was obtained by using various survey methods, including online, offline, on-site, telephone, email and fax solicitations but excluded 10 responses because of missing items. As shown in Table 1, the respondents reflected a demographically diverse group and their average age was 34.5. All the respondents were s-commerce users for various reasons (e.g., information sharing and purchasing discount coupons).

Items to measure each variable in the research model were mainly adopted from previous studies (Kim, 2008; Oyserman et al., 2002; Teoh and Cyril, 2008). However, each item was modified to measure the respondent’s psychological feeling with respect to his or her intention to use s-commerce. All items were measured using a seven-point Likert-type scale ranging from “strongly disagree” (1) to “strongly agree” (7).

RESULTS AND DISCUSSION

Analysis of the measurement model: Before testing the research model, a measurement model was created and tested through a Confirmatory Factor Analysis (CFA) with AMOS 7.0. To measure the fit of the measurement model, this study employed several fit indices, including the Normed Fit Index (NFI), the Goodness-of-fit Index (GFI), the Adjusted Goodness-of-fit Index (AGFI), the Comparative Fit Index (CFI), relative χ22/df) and the Root Mean Square Error of Approximation (RMSEA).

A good fit is demonstrated if NFI, GFI and CFI values exceed 0.90 (Bentler, 1990), the AGFI exceeds 0.8 and the RMSEA is less than 0.05 (Browne and Cudeck, 1993). In addition, the χ2/df value range from less than 3 to as high as 5 (Goodhue, 1995). The results for the measurement model with 23 items measuring the seven variables indicate that the model provided a good fit to the data. Table 2 shows the results.

Table 1: Demographic characteristics

Table 2: Fit indices for the measurement model

Table 3: Results for reliability and construct validity

Table 4: Results for discriminant validity
Square root of the AVE is indicated along the bold diagonal

Reliability and construct validity: After purifying the overall fit of the measurement model, construct validity was examined by testing item reliability, internal consistency and discriminant validity. Individual item loadings for item reliability were assessed. Satisfactory item reliability is generally demonstrated if the loading for an item exceeds 0.7 for the proposed factor and is less than 0.4 for other factors (Chin, 1998). The results indicate that all items exceeded the threshold, implying that the items were sufficient for assessing each construct individually.

To test internal consistency, Cronbach’s alpha, which is the most widely used tool in social sciences, was considered. Teo et al. (1999) suggested 0.7 as the minimum threshold for Cronbach’s alpha in an exploratory study. As shown in Table 3, Cronbach’s alpha ranged from 0.796 to 0.965, exceeding the threshold.

Discriminant validity was tested by considering the Average Variance Extracted (AVE) and Pearson’s correlation. For sufficient discriminant validity, the square root of each latent variable’s AVE should exceed the vertical and horizontal correlations between the variables (Fornell and Larcker, 1981). The results indicate that the square root of each latent variable’s AVE exceeded the correlation for each latent variable, demonstrating sufficient discriminant validity. Table 4 presents the result of discriminant validity.

Structural model assessment: The casual relationships between the variables were examined by using Structural Equation Modeling (SEM) approach with AMOS 7.0. The SEM approach provides two important pieces of information-the standardized path coefficient (β) and the squared multiple correlation (R2)-which are used as indicators of how well the structural model predicts hypothesized relationships. In particular, the standardized path coefficient shows the strength of the causal relationship between two variables. The results for all fit indices indicate that the structural model provided a good fit to the data (n = 375). The NFI (0.98), the GFI (0.95) and the CFI (0.97) all exceeded the required threshold (0.90) and the AGFT (0.92) exceeded the minimum threshold (0.80). The RMSEA (0.024) was less than 0.05 and the χ2/df value (1.57) was less than 3. Thus, each hypothesis was tested by using the standardized path coefficient. The results provide support for all hypotheses (Fig. 2).

Among the four antecedents of collectivism, consumers’ preferences were hypothesized to have a positive effect on perceived usefulness. Consumers’ preferences had a positive effect on perceived usefulness (β = 0.385, t = 5.204), providing support for H1; their reliance had a positive effect on perceived usefulness (β = 0.299, t = 4.340), providing support for H2; their concern had a positive effect on perceived usefulness (β = 0.382, t = 4.887), providing support for H3; and their norm acceptance had a positive effect on perceived usefulness (β = 0.537, t = 6.479), providing support for H4.

Fig. 2: The results of the structural model analysis. Regular numbers are standard egress weight or factor loadings, Numbers with ( ) are t-value, **p<0.01, ***p<0.001, R2 = Squared multiple correlations

Then, the hypotheses about the TAM variables were tested. Perceived usefulness had a significant positive effect on the intention to use s-commerce (β = 0.429, t = 6.151), providing support for H5 and perceived ease of use had a significant positive effect on both perceived usefulness and the intention to use s-commerce (β = 0.541, t = 7.518; β = 0.537, t = 8.859, respectively), providing support for H6a and H6b, respectively.

The second crucial SEM information is the squared multiple correlation (R2) between exogenous and endogenous variables in the research model. The R2 value measures the percentage of the variance explained by each construct in the model (Wixom and Watson, 2001). That is, the variance of an exogenous variable can be explained by changes in each endogenous variable. The results indicate that the four antecedents of collectivism and perceived ease of use explained 65.7% of the variance in perceived usefulness. In addition, perceived usefulness and perceived ease of use explained 72.1% of the variance in the intention to use s-commerce. Figure 2 illustrates the SEM test of the proposed model, including the standardized path coefficients as well as their significance levels and variance explained.

CONCLUSIONS

This study proposes a research model theorizing consumers’ intention toward s-commerce adoption. The proposed research model was tested by using the SEM method. The results indicate the validity of the research model and provide support for all hypotheses. The results provide new insights into consumers’ intention to adopt s-commerce. The four antecedents (consumers’ preferences, reliance, concern and norm acceptance) of collectivism had significant effects on the perceived usefulness of s-commerce. In addition, the TAM variables were valid in the context of s-commerce. These variables, together with perceived ease of use, explained 65.7% of the variance in perceived usefulness. In addition, perceived usefulness and perceived ease of use explained 72.1% of the variance in the intention to use s-commerce.

The results have important implications for IS researchers and practitioners. In terms of IS research, this study proposes a unique characteristic (collectivism) of s-commerce to explain consumers’ intention to use the technology, providing new insights into technology acceptance and use. In particular, no study has conceptualized consumers’ psychological behaviors in terms of their preferences, reliance, concern and norm acceptance to explain their intention to adopt s-commerce. The results suggest that collectivism has considerable influence on online buyers who prefer to be in-group members and prioritize in-group goals over personal ones. Thus, s-commerce firms should formulate marketing strategies that focus on such consumers.

This study develops and validates an instrument for measuring these new variables. The study contributes to the literature on general technology acceptance and provides additional insights into the effects of new technologies on consumers’ attitudes and behaviors. Finally, the study contributes to the literature by extending the well-established technology acceptance model and thus suggesting interesting avenues for future research on s-commerce.

For IS practitioners, the results provide some clues for successful s-commerce implementation, including new factors that may have considerable influence on consumers’ intention toward s-commerce. In addition, the results have important implications for researchers exploring other online topics, including ubiquity commerce (u-commerce). That is, this study’s results for s-commerce are expected to facilitate future research on u-commerce and other types of online business models.

However, this study has some limitations. As in many studies employing survey methods, self-report bias is a source of concern. In addition, to make the research model parsimonious, this study focused only on the effects of collectivism, demonstrating that perceived usefulness is an important antecedent of collectivism. In this regard, future research should consider other factors that may explain consumers’ behaviors in the context of s-commerce.

ACKNOWLEDGMENT

This research was supported by Kyungpook National University Research Fund, 2010.

REFERENCES
1:  Agrawal, R., V. Sambamurthy and R.M. Stair, 2000. Research report: The evolving relationship between general and specific computer self-efficacy: An empirical assessment. Inform. Syst. Res., 11: 418-430.
Direct Link  |  

2:  Bentler, P.M., 1990. Comparative fit indexes in structural models. Psychol. Bull., 107: 238-246.
CrossRef  |  PubMed  |  Direct Link  |  

3:  Brandtzaeg, P.B., 2010. Towards a unified Media-User Typology (MUT): A meta-analysis and review of the research literature on media-user typologies. Comput. Hum. Behav., 26: 940-956.
CrossRef  |  Direct Link  |  

4:  Browne, M.W. and R. Cudeck, 1993. Alternative Ways of Assessing Model Fit. In: Testing Structural Equation Models, Bollen, K.A. and J.S. Long (Eds.). SAGE Publication, Newbury Park, USA., ISBN-13: 9780803945074, pp: 136-162.

5:  Chin, W., 1998. The Partial Least Squares Approach for Structural Equation Modeling. In: Modern Methods for Business Research, Marcoulides, G.A. (Ed.). Lawrence Erlbaum Associates, New Jersey, pp: 295-336.

6:  Chen, C.C., J.R. Meindl and R.G. Hunt, 1997. Testing the effects of vertical and horizontal collectivism: A Study of reward allocation preference in China. J. Cross-Cult. Psychol., 28: 44-70.
CrossRef  |  Direct Link  |  

7:  Davis, F.D., 1989. Perceived usefulness, perceived ease of use and user acceptance of information technology. MIS Quart., 13: 319-340.
CrossRef  |  Direct Link  |  

8:  Fenech, T., 1998. Using perceived ease of use and perceived usefulness to predict acceptance of the World Wide Web. Comp. Networks ISDN Syst., 30: 629-630.
CrossRef  |  

9:  Folorunso, O., A.O. Gabriel, S.K. Sharma and J. Zhang, 2006. Factors affecting the adoption of E-commerce: A study in Nigeria. J. Applied Sci., 6: 2224-2230.
CrossRef  |  Direct Link  |  

10:  Fornell, C. and D.F. Larcker, 1981. Evaluating structural equation models with unobservable variables and measurement error. J. Market. Res., 18: 39-50.
CrossRef  |  Direct Link  |  

11:  Goodhue, D.L., 1995. Understanding user evaluations of information systems. Manage. Sci., 41: 1827-1844.
Direct Link  |  

12:  Hsu, C.L. and H.P. Lu, 2004. Why do people play on-line games? An extended TAM with social influences and flow experience. Inform. Manage., 41: 853-868.
CrossRef  |  Direct Link  |  

13:  Hofstede, G., 1980. Culture's Consequences: International Differences in Work-Related Values. Sage Publication, Thousand Oaks, CA., USA., ISBN-13: 9780803914445, Pages: 475.

14:  Hui, C.H., 1988. Measurement of individualism-collectivism. J. Res. Personality, 22: 17-36.
CrossRef  |  Direct Link  |  

15:  Hui, C.H. and H.C. Triandis, 1986. Individualism-collectivism: A study of cross-cultural researchers. J. Cross-Cultural Psychol., 17: 225-248.
Direct Link  |  

16:  Jackson, C.L., J.A. Colquitt, M.J. Wesson and C.P. Zapata-Phelan, 2006. Psychological collectivism: A measurement validation and linkage to group member performance. J. Applied Psychol., 91: 884-899.
CrossRef  |  Direct Link  |  

17:  Khatibi, A., A. Haque and K. Karim, 2006. E-commerce: A study on internet shopping in Malaysia. J. Applied Sci., 6: 696-705.
CrossRef  |  Direct Link  |  

18:  Kim, S.H., 2006. Impact of mobile-commerce: Benefits, technological and strategic issues and implementation. J. Applied Sci., 6: 2523-2531.
CrossRef  |  Direct Link  |  

19:  Kim, S., 2008. Appropriation of wireless technology: Direct impacting factors on the youth's adoption intention and usage of the wireless application protocol phone. Inform. Technol. J., 7: 1116-1124.
CrossRef  |  Direct Link  |  

20:  Kim, S.H. and G. Garrison, 2009. Investigating mobile wireless technology adoption: An extension of the technology acceptance model. Inform. Syst. Front., 11: 323-333.
CrossRef  |  Direct Link  |  

21:  Kim, S.H., H.S. Park and G.A. Kim, 2011. An empirical study on the factors influencing the trust of social commerce (s-Commerce). J. Bus. Res., 26: 93-119.

22:  Lee, K.C., N. Chung and I. Kang, 2008. Understanding individual investor's behavior with financial information disclosed on the web sites. Behav. Inform. Technol., 27: 219-227.
CrossRef  |  Direct Link  |  

23:  Liu, X. and K.K. Wei, 2003. An empirical study of product differences in consumers E-commerce adoption behavior. Electron. Commerce Res. Appl., 2: 229-239.
CrossRef  |  Direct Link  |  

24:  Marsden, P., 2011. Social commerce: Monetizing social media. Syzygy Group. http://socialcommercetoday.com/social-commerce-monetizing-social-media-syzygy-group-whitepaper/.

25:  Massimo, S.K., P.T. Mburu and K. Mutua, 2002. Utilization of internet facilities in botswana an empirical investigation. J. Applied Sci., 2: 84-91.
CrossRef  |  Direct Link  |  

26:  McCoy, S., D.F. Galletta and W.R. King, 2007. Applying TAM across cultures: The need for caution. Eur. J. Inform. Syst., 16: 81-90.
CrossRef  |  Direct Link  |  

27:  Moon, J.W. and Y.G. Kim, 2001. Extending the TAM for a world-wide-web context. Inform. Manage., 38: 217-230.
CrossRef  |  

28:  Oyserman, D., H.M. Coon and M. Kemmelmeier, 2002. Rethinking individualism and collectivism: Evaluation of theoretical assumptions and meta-analyses. Psychol. Bull., 128: 3-72.
CrossRef  |  

29:  Pookulangara, S. and K. Koesler, 2011. Cultural influence on consumers usage of social networks and its impact on online purchase intentions. J. Retailing Consum. Serv., 18: 348-354.
CrossRef  |  Direct Link  |  

30:  Rigopoulos, G., J. Psarras and D.T. Askounis, 2008. A TAM model to evaluate user`s attitude towards adoption of decision support systems. J. Applied Sci., 8: 899-902.
CrossRef  |  Direct Link  |  

31:  Sek, Y.W., S.H. Lau, K.K. Teoh, C.Y. Law and S.B. Parumo, 2010. Prediction of user acceptance and adoption of smart phone for learning with technology acceptance model. J. Applied Sci., 10: 2395-2402.
CrossRef  |  Direct Link  |  

32:  Stephen, A.T. and O. Toubia, 2010. Deriving value from social commerce networks. J. Marketing Res., 47: 215-228.
CrossRef  |  Direct Link  |  

33:  Srite, M. and E. Karahanna, 2006. The role of espoused national cultural values in technology acceptance. MIS Q., 30: 679-704.
Direct Link  |  

34:  Srivastava, R., 2012. Will mobile marketing in communicating medical products to doctors be appreciated as a service in pharmaceutical industry? An exploratory study. Asian J. Market., 6: 1-9.
CrossRef  |  Direct Link  |  

35:  Teo, T.S.H., V.K.G. Lim and R.Y.C. Lai, 1999. Intrinsic and extrinsic motivation in internet usage. Omega, 27: 25-37.
CrossRef  |  Direct Link  |  

36:  Teoh, K.K. and E.U. Cyril, 2008. The role of presence and para social presence on trust in online virtual electronic commerce. J. Applied Sci., 8: 2834-2842.
CrossRef  |  Direct Link  |  

37:  Triandis, H.C., 1989. The self and social behavior in differing cultural contexts. Psychol. Rev., 96: 506-520.
CrossRef  |  Direct Link  |  

38:  Triandis, H.C., R. Bontempo, M.J. Villareal, M. Asai and N. Lucca, 1988. Individualism and collectivism: Cross-cultural perspectives on self-group relationships. J. Personality Soc. Psychol., 54: 323-338.
Direct Link  |  

39:  Triandis, H.C., C. McCusker and C.H. Hui, 1990. Multimethod probes of individualism and collectivism. J. Personality Soc. Psychol., 59: 1006-1020.
CrossRef  |  Direct Link  |  

40:  Turel, O. and C.E. Connelly, 2011. Team spirit: The influence of psychological collectivism on the usage of e-collaboration tools. Group Decis. Negotiation, 10.1007/s10726-011-9245-7

41:  Veiga, J.F., S. Floyd and K. Dechant, 2001. Towards modelling the effects of national culture on IT implementation and acceptance. J. Inform. Technol., 16: 145-158.
CrossRef  |  Direct Link  |  

42:  Wagner, J.A. and M.K. Moch, 1986. Individualism-collectivism: Concept and measure. Group Organiz. Manage., 11: 280-304.
CrossRef  |  Direct Link  |  

43:  Wixom, B.H. and H.J. Watson, 2001. An empirical investigation of the factors affecting data warehousing success. MIS Quart., 25: 17-41.
CrossRef  |  Direct Link  |  

44:  Wu, J.H., S.C. Wang and H.H. Tsai, 2010. Falling in love with online games: The uses and gratifications perspective. Comput. Hum. Behav., 26: 1862-1871.
CrossRef  |  Direct Link  |  

45:  Yoon, C., 2009. The effects of national culture values on consumer acceptance of e-commerce: Online shoppers in China. Inform. Manag., 46: 294-301.
CrossRef  |  Direct Link  |  

46:  Zeng, F., L. Huang and W. Dou, 2009. Social factors in user perceptions and responses to advertising in online social networking communities. J. Interact. Advertising, 10: 1-13.
Direct Link  |  

47:  3dcart, 2010. Social commerce: What it means to your business. http://www.3dcart.com/whitepapers/SocialCommerce.pdf.

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