Keywords

E-Commerce, Social Commerce, User Intention, Social Networking Sites, Social Platform.

Introduction

A lot is happening over social platforms these days. Relationship building or so-called networking has transformed over the last few decades, all thanks to the social media revolution dating back to the emergence of web 2.0 (Zhang et al., 2014; Wang and Zhang, 2012). The total population of active social media users as of 2018 stands at 3.196 billion i.e., 42% of the world population, 13% greater than 2017 (Global Digital Report, 2018). According to the report, the growth in social media reach is even higher in Asia-pacific region with 31% increase over the last year in India. Thus, it can be inferred that compared to developed countries where the annual rate of social media penetration is less than the global average, emerging nations such as India, China, Indonesia etc. are the potential places of intense social networking activities. Taking the case of India which is one of the fastest growing emerging countries, the Social Networking Sites (SNS) such as Facebook, Instagram etc. have become the preferred mode of audience channel (Statista Report, 2017).

Gallup Mobile Retail Panel Study (2012) conducted on social media users revealed that globally almost 94% users visit SNS to socialize i.e. to connect and build relationships with friends and family. Further, 29% users are basically interested in knowing the latest products and services in the market and their reviews and are driven by the need to keep themselves constantly updated and informed. And, 20% users are expressive social media users identified with elaborate product and service reviews, recommendations and shared experiences. With the conversations and interactions revolving around brands and consumption and consumers being highly active in maintaining and following virtual relationships over social media, the social platforms have become highly influential in shaping consumer behavior (Liang et al., 2011).

Witnessing this burgeoning social space of current and potential customers, e-commerce has since long made substantial attempts to be the center of their conversations. Knowing that consumer purchase is largely a social act and is highly dependent on the social relationships of the consumer (Lu et al., 2016), e-marketers have dynamically evolved in their design and approach to build strong relationships with the social media users. As a response to the growing consumer’s need for collaborative spaces in addition to convenient and assured decision making, the marriage of e-commerce and social media has offered a novel platform of “social commerce” abbreviated as SC (Liang et al., 2011; Bhat and Singh, 2017). In order to leverage the social media space and its favorable influence on consumer participation, engagement and value, many e-commerce firms such as Amazon, eBay etc. have evolved themselves by weaving social networking features into their websites, thus, marking the birth of social commerce (Kim and Srivastava, 2007). Meanwhile, Social Networking Sites (SNS) such as Facebook, Twitter etc. which were a huge social space in themselves also started extending their hand towards e-commerce to harness their user’s online social capital.

A number of benefits have been found to accrue to the social commerce firms as a result of enthusiastic user participation, some of which are better understanding of consumer demand through user content analysis leading to increased customer satisfaction and boosted profitability (Curty and Zhang, 2011; Bhat and Singh, 2018). These immense marketing implications have drawn in substantial interest in social commerce from researchers as well as practitioners; however, there is a need to empirically ground the conceptualizations and propositions made in its regard (Zhou et al., 2013). A number of studies have been done to understand SC evolution and its independent standing from the traditional e-commerce (Wang and Zhang, 2012; Huang and Benyoucef, 2013), antecedents of user participation and SC intention for SNS (Gu et al., 2012; Chen and Shen, 2015). However, the other important configuration of social commerce namely the updated e-commerce sites seem to have been missed out in these studies.

Both these social commerce types incorporate different designs to suit their fundamentally different objectives and relationship motives. SNS’s primary objective is to garner user participation so as to amass enough qualitative and useful content on the portal along with a superior system quality. They are usually suited for promotional activities through generation of likes, shares or tweets. For purchase transaction options and even, detailed product descriptions, users end up visiting the e-commerce websites (Huang and Benyoucef, 2013). Also, the user-generated content remains totally outside the control of the marketers and hence, handling negative and unfavorable e-wom is a big issue for firms promoting social commerce on SNS.

Whereas, the e-commerce websites are primarily customer-oriented and their attempt is to deliver superior quality in terms of information, system performance, web content, web presentation and design and playfulness along with an optimal mix of social media features. They include customer reviews and ratings, referrals and recommendations, shared shopping opportunities, discussion forums and social applications and advertising (Qiu and Benbasat, 2009). E-commerce websites possess a greater control over the social space on their own websites and hence, are able to proactively monitor and respond to any grievance or dissatisfaction surfaced in the website social interactions.

To fulfill their objectives, both the configurations of social commerce are highly dependent on qualitative and meaningful content for others to read which is possible only through consistent and voluntary user participation on the forums. However, the factors influencing the user’s intention to participate on SNS’s social commerce platform may not be the same in case of e-commerce websites too. With their entirely different design and objectives, the e-commerce websites do demand specific enquiry into the user intention to use their social tools and platform (Huang and Benyoucef, 2013).

Yet another reason for exploring the SC commerce intention on e-commerce websites can be ascribed to the unique demographic and cultural characteristics of Asian consumers, here, Indian consumers. Indian consumers are relatively more utilitarian and value conscious as compared to their western counterparts (Bhat & Singh, 2017). Hence, where western consumers may participate in social commerce for collaborative spaces and hedonic enjoyment, Indian consumers may find utilitarian and value propositions to be more interesting. Also, belonging to a collectivist society, Indian consumers tend to express themselves in a high context i.e. implicit manner (McNett, 2015) and hence, more often choose SNS platforms (Facebook) instead of the concerned e-commerce website for expressing their dissatisfactions and grievances in an informal way. This may lead to more uncontrollable content exchange which can damage a firm’s image (Hutter et al., 2013). Thus, it becomes necessary to study the antecedents of social commerce intention on Indian e-commerce websites.

Considering this important call for investigation, the present study aims to uncover the motivations driving the user intention to participate in SC (such as discussion forums, chat rooms, review and recommendation mechanisms, brand communities and social networks) on updated e-commerce websites of India.

Literature Review

Social Media

Highly functional in information exchange, social media is an umbrella term composed of a range of internet-anchored applications offering generation and exchange of user-created content (Kaplan and Haenlein, 2010). Individuals use these applications to share their views, information, feelings and concerns on a range of issues including the products and brands that they use (Liang et al., 2011). Making better use of this platform, consumers consider the shares and opinions of the fellow users instead of relying on the company’s information channels or websites for their purchase decision (Chen and Shen, 2015). The easy and useful exchange of product and brand related information from the trust-worthy source of other already-been consumers is perceived to make the information search and, thus purchase decision simple and easy.

Social Commerce

Social commerce is nothing but a type of e-commerce wherein social platform is interactively used by the customers to create, enquire and share product related information, views and remarks along with making purchases (Ng, 2013). As back as 2005, the maiden attempt was made by Yahoo!, when they gave their users a chance to comment and review the product lists in their project “shoposphere” (Rubel, 2005). This is when “social commerce” is first introduced in the e-commerce scenario. Generally, “social commerce” was understood as the evolved version of e-commerce mediated by social media (Kim, 2013), where this evolution is assumed to be triggered by the birth of “referral economy” and the technological advances of web 2.0 (Harkin, 2007). From the perspective of e-commerce websites, Kim and Srivastava (2007) defined social commerce as the “the utilization of web2.0 features in e-commerce”, mainly the features of user-generated content and information exchange and sharing. However, Social Commerce (SC) has also been referred to “the provision of e-commerce activities and transactions under the social media framework primarily in Social Networking Sites (SNS) using web 2.0” (Liang and Turban, 2011).

Many studies exploring the commercial value of social commerce confirmed its positive influence on users behavioral and usage patterns. For instance, Ohlbrich and Holsing (2012) examined the influence of social features of an e-commerce website on actual transactions made by the user. They found that tagging and high ratings are predictive of user’s actual purchases. Further, in a relatively niche platform of websites offering social TV service, Pagani and Mirabello (2012) found that personally and socially interactive engagement of the viewer enhances both active as well as passive usage.

Investigating the influence of Social Networking Sites (SNS) on user’s intention to enquire and recommend regarding commercial offerings, Liang et al. (2011) found social support and website quality to be the major relational drivers of social commerce. Yet another study by Chu and Kim (2011) examining consumer engagement in SNS’s e-wom found social media to be of profound significance in the promotional mix of e-commerce firms and urged the marketers to identify the social influencers on their SNS portals and encourage their voluntary participation, engagement and favorable endorsements.

Social Commerce (SC) Intention

Although SC intention has been studied in the case of SNS configuration, the traditional e-commerce websites offering social platforms have been left out. While measuring intentions, studies have emphasised on behavioural intentions as a proxy for consumer behaviour, since, accounting for an individual’s actual behaviour is quite difficult (Zhang et al., 2014). There are many theories endorsing the use of behavioural intentions such as Theory of Reasoned Action (TRA), Theory of Planned Behaviour (TPB), Technology Acceptance Model (TAM) and Stimulus-Organism-Response Theory (SOR). Thus, consumer’s behavioural intentions can be understood to strongly influence their behaviour. From this insight, SC intention in this study has been operationally defined as a participant’s tendency to create, enquire and share valuable information about the products and services on the social platform, which can be made use of by the customers to optimize their purchase decisions (Liang et al., 2011; Bhat and Singh, 2018). From the past literature, attitude and flow has been found to be crucial predictors of user participation on e-commerce platforms (Hausman and Siekpe, 2009). Also, a study conducted by Hajli (2015) throws light on the impact of SC participation on user’s purchase intention towards the website that is the other way around. To further investigate the SC intention, the present study shall use the framework provided by Liang et al. (2011) in the SNS context.

Social Support

One of the antecedents studied by Liang et al. (2011) in their SC intention model is social support, which refers to the supportive content exchanged on the platform making the users feel loved, cared and respected as a consequence of their membership and participation. It is often used as a multidimensional construct composed of two main dimensions reflecting emotional and informational support (Chen and Shen, 2015). As a result of the love and care received and felt giving way to emotional connect as well as the meaningful and valuable information obtained engendering better purchase decisions, the users are drawn to reciprocate towards the platform (Choi et al., 2011). Further, these to-and-fro interactions may even lead to friendly and reliable relationships leading to favourable purchase intentions (Liang et al., 2011). Thus, it is proposed that both emotional as well as informational support dimensions may significantly assist in garnering favourable SC intentions.

H1: Informational Support on the platform significantly influences SC intention towards e-commerce websites.

H2: Emotional Support on the platform significantly influences SC intention towards e-commerce websites.

Flow

It has been found in the past studies that user’s experience of flow on the platform influences their intention to further participate in social commerce. Flow, also known as immersion, is a state of complete absorption as experienced by an individual during an activity (Curty and Zhang, 2011). An experience of flow on a website has been found to gather full absorption and thus escalate user’s engagement in online activities (Zhang et al., 2014) as absorption in such activities induce satisfaction and garner loyalty (Hoffman and Novak, 1996). Studies have established flow as a significant contributor to purchases as well as future online recommendations made by the user (Guo and Poole, 2009). Thus, it is proposed that the flow experience offered by the e-commerce platform will positively influence the user’s SC intention.

H3: The user’s experience of Flow will significantly enhance his intention to use SC platform of e-commerce websites.

Social Presence

A sense of social familiarity and closeness provided by the platform may instil an intention to participate on SC portals. A construct better reflecting such essence is social presence which has been defined as “the degree of salience among communicating individuals” (Sallnas et al., 2000). In other words, Animesh et al. (2011) describe social presence as “the degree of awareness of the other members during interaction and a psychological feeling of closeness”. A sense of having secure social presence during interactions has the propensity to provide a comfortable and emotionally fulfilling environment to the participants (Zhang et al., 2014), which may further translate into increased engagement and intentions. Thus, it is proposed that a better sense of social presence can significantly enhance the user’s intention to participate on SC platform of e-commerce firms.

H4: Social presence on the platform significantly influences SC intention on e-commerce websites.

Website Quality

An antecedent unique to e-commerce success is their website design and content (Liang et al., 2011). Apart from its reliability on the system, website quality is highly valued in terms of service, usefulness, enjoyment and established relationships (Huang and Benyoucef, 2013). Owing to this description, studies on SC have incorporated both the system as well as the service aspects into their conceptualization of website quality (Liang et al., 2011). A better quality experienced on the website assists the users in searching solutions to their issues, provides a sense of lasting satisfaction and establishes the validity of the platform for relationship building (Liang et al., 2011). Thus, it is hypothesised that superior e-commerce website quality will enhance the user’s SC intention.

H5: E-commerce website quality significantly influences the user’s intention to participate on its SC platform.

Methodology

The study is descriptive in nature and cross-sectional in design. Data was collected from 200 people using self-administered questionnaires. The respondents were mainly the individuals who actively indulge on social platforms (discussion forums, review and ratings, comments and recommendations etc.) of e-commerce websites and were approached using purposive sampling. The exclusion criterion was placed as “no purchase made from the website”. To make the questionnaire context relevant, Social Support, Web Quality and Social Intention scales were adapted from Liang et al. (2011) and Social presence scale and Flow scale was adapted from Zhang et al. (2014).

Structure Equation Modelling (SEM) was used for data analysis, since, it is traditionally built on covariances and suits exploratory research (Peter et al., 2007). Since the research field is not yet established, Partial Least Squares was used to account for variations in distribution as well as small sample size. However, it is to be noted that the sample size is sufficiently greater than the threshold of ten times the highest number of paths leading towards a certain construct in the model (Barclay et al., 1995). A total of 200 valid questionnaires were received. The sample group consisted of 65% males and 35% females. In terms of age, the majority of the sample was younger than 25 (60%) and with graduation as education qualification (64%) (Table 1). Data analysis was performed using Structural Equation Modelling (SEM).

Table 1 Demographic Profile of Respondents
Gender	Frequency	Percentage (%)
Male	130	65
Female	70	35
Age
Less than 18
18-25	120	60
26-35	68	34
36-45	6	3
45 above	6	3
Qualification
secondary level	10	5
Graduate	128	64
Post Graduate	60	30
other	2	1
Occupation
Un Employed	7	3.5
Student	110	55
Employed by others	39	19.5
Self- Employed	42	21
others	2	1

Results

Assessment of the Measurement Model

The reliability of each item was assessed by examining simple correlations of indicators with their respective variable. A value greater than 0.7 suggests that the variance between the construct and its indicators is greater than the error variance (Barclay et al., 1995). In our case, all indicators exceed the values recommended in the literature, except a flow indicator (F1) and two social commerce intention indicators (SCI1 and SCI2). These were consequently eliminated from the analysis. To measure the reliability of the scales, we used Cronbach’s alpha (Cronbach, 1951) and composite reliability (CR). All the resulting values were greater than the recommended minimums.

The convergent and discriminant validity were assessed. Convergent validity was assessed using Average Variance Extracted (AVE) (Fornell and Larcker, 1981). AVE estimates the amount of variance that a construct captures from its indicators in relation to the amount of variance due to the measurement error. In our case, the AVE value is greater than the suggested minimum of 0.5. Table 2 shows the values of the indicators in the assessment of the measurement model.

Table 2 Reliability and Validity of Constructs
Construct	Cronbach's Alpha	CR	AVE
Informational Support	0.851	0.918	0.779
Emotional Support	0.911	0.929	0.767
Flow	0.851	0.899	0.654
Social Presence	0.843	0.891	0.627
Web Quality	0.921	0.934	0.764
Social Commerce Intention	0.782	0.852	0.581

Lastly, three SEM methods were used to assess discriminant validity i.e. checking that a construct measures a concept which is different from other constructs: (1) cross-loadings analysis, which looks at whether the average variance between a dimension and its items is greater than the variance between other dimensions in the model (Barclay et al., 1995); (2) the Fornell-Larcker criterion, which analyses whether the correlations between dimensions are lower than the square root of the AVE (Fornell and Larcker, 1981); (3) the HTMT ratio (hetero trait-mono trait), which measures correlations between pairs of constructs and should be around 0.9 (Henseler et al., 1996; Bhat and Singh, 2017). The resulting values are close to the limits recommended in the scientific literature. We therefore consider the discriminant validity in the model to be satisfactory. Tables 2 and 3 show the discriminant validity values for each of the above criteria.

Table 3 Structural Analysis Model Results
Relationship	Path	Effect	R2	RMR
Informational Support →social Commerce Intention	0.089	0.009	0.524	0.06
Emotional Support→social Commerce Intention	0.149	0.017
Flow→social Commerce Intention	0.346	0.142
Social Presence→social Commerce Intention	-0.091	0.009
Web Quality→social Commerce Intention	0.428	0.239

Assessment of the Structural Model

The recommended method of assessing the structural model is to start by evaluating the square of the coefficient of multiple correlations (R²), which indicates the amount of variance in the construct due to the model. In our case, R² for the social commerce intention variable is 0.524 and thus far greater than the recommended minimum (0.1) (Cronbach, 1951). We then studied the standardised regression coefficients to find the relative importance of the factors in the endogenous variables. Values greater than 0.3 are recommended (Cronbach, 1951; Bhat and Singh, 2017). However, values greater than 0.2 are permitted in exploratory studies or when they are applied to different sectors. The emotional support-social commerce intention relationship has a lower coefficient than the recommended minimum and the informational support-social commerce intention and social support- social commerce intention relationships are not significant.

The effect size (f²) also verified the suitability of the model. This coefficient measures whether independent latent variable impacts substantially on a dependent latent variable. f² values of between 0.02 and 0.15, between 0.15 and 0.35 and 0.35 or greater indicate whether an exogenous latent variable has small, medium or big effect, respectively (Chin, 1998). Four of the relationships in table 3 show a small or negligible effect, whilst one has a medium effect. Lastly, the SRMR (Standardized Root Mean Square Residual) (Cronbach, 1951) enables us to compare the difference between the observed and predicted correlations and thus adjust the model. A value less than 0.08 are considered acceptable. The value in our model is on the limit, meaning the proposed model adjustment is partially correct (Figure 1).

The results of the analyses confirm and verify the relevance of hypotheses 3 and 5. Conversely, the analyses provide no empirical support for hypotheses 1 and 4. Based on Chin (1998), hypothesis 2 is not supported by sufficient empirical evidence either, since the path coefficient is less than 0.2 despite its significance. However, given the exploratory nature of SC research, recent studies have accepted relationships between variables with a path coefficient less than the minimum value cited in the literature as being significant (Cronbach, 1951).

Discussion

The findings of the study reflect the fundamental differences expected between the factors influencing SC intention towards SNS and e-commerce websites. The Social Intention model employed in the study identified four such factors shaping SC intention on e-commerce sites, mainly website quality, flow experience, social presence and informational support. This finding is in line with Liang et al. (2011) examining the antecedents of SC intention on SNS, however, differs in terms of their varying significance. As compared with the antecedents found in SNS studies, website quality and flow or immersive experience offered by the e-commerce website were more crucial in forming SC intentions.

The study also identified that emotional support is not as important in shaping SC intentions on e-commerce as on SNS. This is in contrast with the findings of studies examining SNS SC intention wherein emotional support is a crucial motivator for the users to participate and stay (Chen and Shen, 2015; Choi et al., 2011). From this finding, it may be inferred that although emotional backup and bonding in user interactions may enhance their engagement over SNS where their primary motive is socialization, it is not sufficient to hold the users on e-commerce SC where users mainly participate to make purchase decisions.

On the other hand, social presence and informational support perform better in and are more crucial in forming SC intentions on e-commerce sites, which are insignificant when it comes to SNS (Liang et al., 2011). The failure of social presence in shaping SC intentions on SNS can be attributed to the lack of common relational anchors on such sites. Informational support may also not be a viable predictor of SNS SC intention because the focus of SNS is more on generating and building a wide range of user content and relationships, rather than offering a space for product specific information exchange as is the focus of e-commerce websites. Also, the product related information exchange taking place on SNS is not perceived to be trustworthy and reliable by the participants.

Conclusion

Thus, the present study contributed to the literature of SC intention by identifying and distinguishing antecedents of user’s intention to participate on SC platform of e-commerce websites from SNS. The study contributes to the previous SC intention models based on TAM and SOR by validating in the unique context of e-commerce websites in India. The study also contributes to theory by modifying TPB by including Social Support as one of the factors.

The study also holds practical implications for the Indian e-commerce industry which is expanding tremendously supported by rising internet penetration, government relaxation of FDI norms and Start-up India program (IBEF Report, 2018). The budding e-commerce players can design their platform keeping in mind the significance of social support, social presence, website quality and flow in enhancing user participation as determined by the study. The study suggests that they should address the social commerce needs of their consumers at par with the SNS platforms, however, should align their social features with their main objective of commercial transactions. They should focus more on providing and encouraging informational support which is more instrumental in driving user participation on e-commerce than emotional support. The study also provides an understanding of the how the determinant of user SC intention on e-commerce websites is different than those on SNS. And thus, it may prove helpful to the e-commerce firms in dealing with the issue of keeping the user-generated content within their reach.

As with any empirical study, the present study was also bound by a few limitations and is subject to cautious interpretation. The relatively small sample size of 201 may be a limitation in generalizing the findings considering that the study was exploratory in nature. Future research can address this limitation by extending the study to similar websites offering different products and services, with a varying range of user base. Research can also be undertaken to asses within-factor influences, which may possess an indirect bearing on SC intention. It is suggested that other mediating (such as trust, satisfaction or user engagement) (Chen and Shen, 2015; Bhat, 2016) and moderating (such as age, gender or user experience in time) variables may be included in the study to better explain SC intention.

References

1. Ajzen, I. (1991). The theory of planned behaviour. Organizational Behaviour and Human Decision Process, 50(2), 179-211.

2. Animesh, A., Pinsonneault, A., Yang, S.B., & Oh, W. (2011). An odyssey into virtual worlds: Exploring the impacts of technological and spatial environments on intention to purchase virtual products. Management Information Systems Quarterly, 35(3), 789-810.

3. Barclay, D., Higgins, C., & Thompson, R. (1995). The partial least squares (PLS) approach to causal modelling: Personal computer adoption and use as an illustration. Technology Studies, 2(2), 285-309.

4. Bhat, I.H. (2016). Validating a retail service quality instrument in grocery specialty stores. International Journal of Information, Business and Management, 8(2), 205-218.

5. Bhat, I.H., & Singh, S. (2017). Examining mediating effect of trust between store image and loyalty in grocery speciality stores in Hyderabad. The Journal of Indian Management & Strategy, 22(3), 59-63.

6. Bhat, I.H., & Singh, S. (2017). Exploring the relationship between store image and store loyalty of an organized grocery retail. Clear International Journal of Research in Commerce & Management, 8(10), 393-401.

7. Bhat, I.H., & Singh, S. (2018). Examining the moderating effect of shopping value on private-label and loyalty in Indian grocery stores. Management & Marketing, 13(1), 748-760.

8. Chen J., &. Shen, X.L., (2015). Consumer’s decisions in social commerce context: An empirical investigation. Decision Support Systems, 79(1), 55-64.

9. Chu, S.C., & Kim, Y. (2011) Determinants of consumer engagement in electronic word-of-mouth (eWOM) in social networking sites. International Journal of Advertising: The Review of Marketing Communications, 30(1), 47-75.

10. Crocker J., & Canevello, A. (2008). Creating and undermining social support in communal relationships: The role of compassionate and self-image goals. Journal of Personality and Social Psychology, 95(3), 555-575.

11. Cronbach, L.J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297-334.

12. Curty, R., & Zhang, P. (2011). Social commerce: Looking back and forward in Proceedings ASIST. New Orleans.

13. Fornell, C., & Larcker, D.F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.

14. Gu, B., Park, J., & Konana, P. (2012). The impact of external word-of-mouth sources on retailer sales of high involvement products. Information Systems Research, 23(1), 182-196.

15. Guo, Y.M., &. Poole, M.S. (2009). Antecedents of flow in online shopping: A test of alternative models. Information Systems Journal, 9(4), 369-390.

16. Hajli, N. (2015). Social commerce constructs and consumer’s intention to buy. International Journal of Information Management, 35(2), 183-191.

17. Harkin, F. (2007) The wisdom of crowds. Financial Times.

18. Hausman, A.V., & Siekpe, J.S. (2009). The effect of web interface features on consumer online purchase intentions. Journal of Business Research, 2(1), 5-13.

19. Hoffman, D.L., & Novak, T.K. (1996). Marketing in hypermedia computer-mediated environments: Conceptual foundations. Journal of Marketing, 60(3), 50-68.

20. Huang, Z., & Benyoucef, M. (2013). From e-commerce to social commerce: A close look at design features. Electronic Commerce Research and Applications, 12(4), 246-259.

21. Hutter, K., Hautz, J., Dennhardt, S., & Füller, J. (2013). The impact of user interactions in social media on brand awareness and purchase intention: the case of MINI on Facebook. Journal of Product & Brand Management, 22(6), 342-351.

22. Kaplan, A.M., & Haenlein, M. (2010). Users of the world, unite! The challenges and opportunities of social media. Business Horizons, 53(1), 59-68.

23. Liang, T.P., & Turban, E. (2011). Introduction to the special issue social commerce: A research framework for social commerce. International Journal of Electronic Commerce, 16(2), 5-14.

24. Liang, T.P., Ho, Y.T., Li, Y.W., & Turban, E. (2011). What drives social commerce: The role of social support and relationship quality. International Journal of Electronic Commerce, 16(2), 69-90.

25. Lu, B., Fan, W., & Zhou, M. (2016) Social presence, trust and social commerce purchase intention: An empirical research. Computers in Human Behaviour, 56(1), 225-237.

26. McNett, J.M. (2015). Hall's high and low context. Wiley Encyclopaedia of Management.

27. Mehrabian, A., & Russell, J.A. (1974). An approach to environmental psychology. The MIT Press.

28. Ng, C.S.P. (2013). Intention to purchase on social commerce websites across cultures: A cross-regional study. Information & Management, 50(8), 609-620.

29. Olbrich, R., & Holsing, C. (2011). Modeling consumer purchasing behavior in social shopping communities with clickstream data. International Journal of Electronic Commerce, 16(2), 15-40.

30. Pagani, M., & Mirabello, A. (2011). The influences of personal and social-interactive engagements in social TV Web sites. International Journal of Electronic Commerce, 16(2), 41-67

31. Porter, C.E., & Donthu, N. (2008). Cultivating trust and harvesting value in virtual communities. Management Science, 54(1), 113-128.

32. Qiu, L., &Benbasat, I. (2009). Evaluating anthropomorphic product recommendation agent. Journal of Management Information System, 25(4), 145-181.

33. Rubel, S. (2005). Trends to Watch Part II: Social commerce. Micro Persuasion. Retrieved fromhttp://socialcommercetoday.com/steve-rubels-original-2005-social-commerce-post/

34. Sallnas, E.L., Rassmus, G.K., & Sjostrom, C. (2000). Supporting presence in collaborative environments by haptic force feedback. ACM Transactions on Computer-Human Interaction, 7(4), 461-476.

35. Stephen, A.T., & Toubia, O. (2010). Deriving value from social commerce networks. Journal of Marketing Research, 47(2), 215-228.

36. Wang, C. & Zhang, P. (2012). The evolution of social commerce: The people, management, technology and information dimension. Communications of the Association for Information Systems, 31(5), 105-127.

37. Wang, C., & Zhang, P. (2012) the evolution of social commerce: The people, management, technology and information dimensions. Communications of the Association for Information Systems, 31(1), 105-127.

38. Zhang, H., Lu, Y., Gupta, S., & Zhao, L (2014). What motivates customers to participate in social commerce? The impact of technological environments and virtual customer experiences. Information & Management, 51(8), 1017-1030.

39. Zhang, K.Z.K., & Benyoucef, M. (2016). Consumer behaviour in social commerce: A literature review. Decision Support Systems, 86(1), 95-108.

40. Zhou, L., Zhang, P., & Zimmermann, H.D. (2013). Social commerce research: An integrated view. Electronic Commerce Research and Applications, 12(2), 61-68.