Social Desire Or Commercial Desire? The Factors Driving Social Sharing And Shopping Intensions On Social Commerce Platforms
Abstract
?This research has been conducted for exploring the role of social desire and commercial desire in driving user’s social sharing and social shopping intentions on SNS. In this study 18 hypothesis were developed to examine the significance of the relationship between seven independent variables and one dependent variable. The two moderators (social desire and commercial desire) were also used in this study. The theory used in this study is Model of goal directed behavior (MGB). A total of 501 questionnaires were distributed to users of SNS according to convenience sampling. The results were obtained by applying factor analysis, regression, and reliability on the data collected from respondents. The findings concluded that people have weaker desire to engage in commercial activities than to engage in social activities on SNS. Moreover, as compared to social desire, commercial desire to be more influential to social sharing and social shopping intentions on SNS. The results also revealed that social attitude, anticipated positive emotion for social activities, and social identity have a positive influence on social desire but negative influence on commercial desire whereas perceived behavioral control for social activities have negative influence on social desire and positive influence on commercial desire. The anticipated positive emotion for commercial activities and perceived behavior control for commercial activities has positive impact on commercial desire. Both social and commercial desire has positive impact on social shopping and social sharing intention.
Keywords: Social commerce, Social desire, Commercial desire, social attitude, anticipated positive emotion, perceived behavioral control, Social identity.
1.1 Background of the study
Social networking site has gained an increasing attention in the modern world. Businesses are focusing more on how to manage commercial activities on SNS. On the other side, the users of social networking site are increasing rapidly as well as the usage time is also achieving continuous growth. The term ‘social commerce’ means the involvement of social media to buy or sell product. Social commerce is the next step in the evolution of e-commerce and is one of the most challenging fields. According to Globalwebindex.net social commerce is listed as one of 16 major trends in 2016 (Mander, 2016). Social commerce was first introduced by Yahoo in 2005 (Curty and Zhang, 2011; Wang and Zhang, 2012). There are two major models of social commerce i.e. commercial and social desire. The businesses that offer more services on SNS got more profit for example Facebook have introduced a “buy now” button to boost sales of the company (Goodwin, 2016). Therefore exploring factors affecting SNS user’s social commerce behavior is very important as social commerce is a new trend nowadays in the development and application of e-commerce and it maximizes shopping efficiency. Social commerce intention is defined as the degree to which the user is willing to share commercial information on SNS. Social sharing and social shopping is also used by (Chen and Shen, 2015 to measure social commerce behavior. Social sharing refers to the degree to which user is willing to share their shopping experience or word of mouth on SNS on the other hand social shopping refers to which users search for comments before making shopping decisions on SNS. Both social shopping and social sharing are two most important dimensions of social commerce.
1.2.??Problem Statement
Many studies have been conducted of using several theories such as relationship quantity theory, trust transfer theory, social support theory to investigate the factors that influence social intention (Chen and Shen, 2015; Hajli, 2014; Hajli 2015; Zhang et.al, 2014 but most of the studies focused only on relational (Hajli, 2014; Lu et.al, 2016) and some on trust related factors (Chen and Shen, 2015; Hajli, 2015). What we have to understand and get awareness about is the user behavior in social commerce context and what factors would arouse the desire that affect the social sharing and shopping intentions. By using MGB as theoretical foundation we can get the awareness about which desire (social or commercial) is more influential to social sharing and social shopping intentions on SNS. There is no research conducted on the topic of social shopping intention and commercial desire. In Pakistan, there is lack of research on this topic.
1.3.??Research Objective
The purpose of this study is to investigate factors that affect user’s social sharing and shopping intentions on social networking sites.
1.4.??Research Question
?What is the impact of social desire and commercial desire in driving user’s social sharing and shopping intentions on social commerce platforms?
Do individuals who possess positive attitude towards social activities are more directed towards commercial activities on SNS or individuals who have positive/negative attitude toward commercial activities are more engaged in commercial activities?
Does strongest engagement in social activities impact social sharing intention on SNS?
?Does strongest involvement in commercial activities have a higher impact on social sharing intention?
Is social identity important for an individual’s desire to engage in social or commercial activity on SNS?
What impact did perceived behavioral control have on individuals social activities that engages them to commercial activities on SNS?
What impact did perceived behavioral control have on individuals commercial activities that engages them to social activities on SNS?
1.5.??Significance of the study
?The businesses using social networking platforms will get more benefitted from this study as they get to know that as compare to social desire, commercial desire is more influential to social sharing and social shopping intentions on social networking sites.
1.6.??Limitations and Delimitations
?This study is subject to some limitations. First, the sample consists of mostly students so the perception of other people may not have been obtained. Second, only Facebook is used as a SNS in this study therefore the results are limited to Facebook user’s only and not to other commerce platforms like Twitter etc. Third, in this study MGB is used to investigate that desire plays an important role on users of SNS, therefore other theories could also be used.
1.7.??Organization of the study
?The paper is organized in this manner. Literature review is reported in section 2. Section 3 discusses the methodology. Section 4 explains the results and its discussion and conclusion, policy implications and in Section 5 limitations of the study are described.
2.1. Theoretical background
?The conceptual model of this research is based on Model of Goal Directed Behavior (MGB). The Model of Goal Directed Behavior has been developed on the basis of Theory of Planned Behavior (TPB) where fundamental components of TBP i.e. attitude, subjective norm and perceived behavioral control are viewed with respect to goals rather than behaviors (Hagger and Chatzisarantis, 2009). In MGB, desire plays an important role and is considered to be the strongest predictor that can drive behavioral intentions as compared to attitudes and subjective norms (Sutton, 1998). The “anticipated emotions” and “past behavior” are considered as factors of desire. Bagozzi (1992) suggested that individuals have stronger motivation to conduct goal-directed behavior which means individual are more committed to a specific behavior and has the desire to attain the goal. Therefore according to Bagozzi desire is an important mediator between attitude and behavioral intention in MGB. This study uses MGB as a theoretical foundation to investigate the effects of social desire and commercial desire on social sharing and social shopping intentions on social commerce platforms.
2.2 Empirical studies
?Erdogmus & Tatar (2015) investigated drivers of social commerce through brand engagement. Brand trust and purchase intention has been used as dependent variables while social commerce stimuli and brand engagement are used as independent variables. The study uses S-O-R model to test the proposed model. The results suggest that social commerce stimuli i.e. sales campaign, personalization, interactivity and consumer generated content affects consumers engagement with brand on social media which leads to brand trust and purchase intention.
Bounkhong (2017) identified factors affecting intentions to use social commerce in shopping for fashion products. The dependent variables used in this study were purchase intention whereas perceived ease of use of social commerce, perceived usefulness and perceived enjoyment of social commerce are used as independent variables and attitude has been used as a mediator. The data has been collected from 531 college students who use social media through an online survey. The results show that perceived ease of use, usefulness and enjoyment of social media positively impact attitude and intention of consumers to use social commerce for shopping.
Leone et.al (1999) observed the comparison of three models of attitude-behavior relationships in the studying behavior domain. Three theories have been used in this research i.e. TRA, TPB and TSR. Intention has been used as a dependent variable whereas past behavior, variables of present behavior i.e. attitude, subjective norms, perceived behavioral control and desire are used as independent variables. The data was collected from 240 students of Italian college. The structural equation approach has been used to test the relationship. The results show that past behavior is the strong predictor of both intention and behavior in TRA model while it is a weaker predictor of intention in TPB and TSR models.
Hunter (2006) identified the role of anticipated emotion, desire and intention in the relationship between image and shopping center visits. Shopping center image and frequency of visits has been used as dependent variables whereas desire, intention and positive anticipated emotions are used as independent variables. The data was collected through mail survey. The findings suggest that positive anticipated emotion, desire and intention intervene between shopping center image and frequency of visiting shopping centers. The shopping center managers should allocate resources towards increasing desire, intention and positive anticipated emotions.
Giantari and Solimun (2013) observed the role of perceived behavioral control and trust as mediator of experience on online purchasing intentions relationship. Purchase Intention has been used as dependent variable and perceived behavioral control and trust are used as independent variables. The data was collected from 150 respondents by using convenience sampling at three state universities in Denpasar City. The techniques used to analyze the data are SEM and PLS. The results show that previous purchasing experiences directly effect on online purchasing intention whereas perceived behavioral control and trust as mediators also have positive impact on purchase intention via online. The online shopping websites should modify and convince the consumers that transactions are secured on online websites which generated trust among customers.
Li (2017) examined how social commerce constructs influence customer’s social shopping intention. The dependent variable is purchase intention and the independent variables are social commerce constructs, social presence, social support, trust, closeness and familiarity. The data was collected by applying stimulus-organism-response model. The result shows that social commerce constructs significantly impact social interactions and cognitive states (social presence, social support) and affective states (closeness and familiarity) but did not significantly influence on social shopping intention. Furthermore social presence and support has positive impact on intention to trust.
Heijden, Verhagen & Creemers (2017) observed online purchase intentions: contributions from technology and trust perspectives. Purchase intention has been used as dependent variable whereas two different perspectives i.e. technology-oriented perspective and trust-oriented perspective are used as independent variables. The data was collected from 228 potential online shoppers. The results show that trust-antecedent (perceived risk) and technology-antecedent (perceived ease of use) directly affect attitude towards online purchasing.
Monsuwe (2004) investigated what drives consumers to shop online. Intention to purchase online has been used as dependent variable whereas attitude, ease of use, usefulness and enjoyment and trust are used as independent variables. The data was collected through TAM model by consumers of US and Europe who shops online. The result shows that attitude towards shopping online is positively affected by independent variables.
Hwang et.al (2016) explored factors that influence consumer’s attitudes and purchase intentions for smart clothing. Attitude and purchase intention has been used as independent variables whereas perceived usefulness, compatibility, comfort, and perceived risk has been used as independent variables. The data has been collected from 720 random samples of college students and faculty. Structural equation modeling has been used to analyze the results. The results reveal that perceived usefulness is the strong predictor of attitude and purchase intention, compatibility along with comfort is the predictor for usefulness and perceived performance risk, aesthetic attributes and concern for the environment are also the significant predictors of attitude.
Park, Lee & Han (2007) investigated the effect of on-line consumer’s reviews on consumer purchasing intention. Purchase intention has been used as dependent variable whereas on-line reviews and quality or quantity of the product are used as independent variables. The findings suggest that as more and more on-line reviews has been received; it has positive impact on consumer’s purchase intention and those customers that have high-involvement with the product quality the review also affect it.
Hutter et.al (2013) observed the impact of user interactions in social media on brand awareness and purchase intention. Purchase intention, word of mouth and brand engagement has been used as dependent variables while awareness of brand, and user interactions are used as independent variables. The AMOS 18 and SEM technique has been used to analyze the data collected from Facebook page. The findings suggested that engagement with Facebook page i.e. social media activities has positive impact on brand awareness, word of mouth and purchase decision of consumers.
Wu et.al (2013) investigated how can online store layout design and atmosphere influence consumer shopping intention on a website. The dependent variables used in this study were consumers shopping intention whereas the independent variables used in this study were store layout design and atmosphere. The data has been collected from 626 respondents of internet users. The SEM technique has been used to analyze the results. The results showed that store layout design has positive impact on emotional arousal and attitude towards the website which has positive influence on purchase intention. In contrast atmosphere has more influence on emotional arousal than design.
Wann-Yih Wu, Chia-Ling Lee, Chen-Su Fu, Hong-Chun Wang, (2013). Investigate that how can an online store layout design and atmosphere influence consumer shopping intention on a websites? Purchase intention is dependent variable whereas attitude towards the websites. Attitude towards the websites have a significant and positive effect on online consumer purchase intention. The result of this study proves that the store layout has significant impact on emotional attitude and attitude towards the websites, and thus has a positive impact on purchase intention.
Kim Hongyoun Hahn, Jihyun Kim, (2009). Investigate the study of the effect of offline brand trust and perceived internet confidence on online shopping intention in the integrated multi-channel context. Consumer trust is a independent variable whereas behavioral intention towards online store is a dependent variable. There is a positive relationship between consumer trust and behavioral intention.
Hsu Meng-Hsiang, Chuang Li-Wen, Hsu Cheng-Se, (2014). The study is provide a better picture of features effecting behavioral decisions in online shopping intention by identifying different targets of trust. Perceived risk and attitude towards online shopping are independent variables and intention to purchase is dependent variable. Trust in the web site has a positive effect on attitude toward online shopping. Attitude toward online shopping has a positive effect on intention to purchase.
Wei Wu, Vivian Huang, Xiayu Chen, Robert M. Davison, Zhongsheng Hua, (2018). The study is to explore how the shopper’s social value perception affects their purchase intention in online shopping context through its distinct role and relationship with other value dimension. The social value is independent variables whereas purchase intention is a dependent variables. Consumer perceived utilitarian value is positively associated with a consumer purchase intention and Consumer perceived social value is positively associated with their purchase intention.
Torben Hansen (2008), taking a hierarchical value-attitude-behavior approach, this study empirically tests relations of consumer personal values, attitude, social norm, perceived behavioral control (PBC) and willingness to buy groceries online. Attitude and behavioral perceived control are independent variables whereas Willingness to buy (WiB) is dependent variables. The data were collected from an online (web-based) survey of Swedish consumers using self-administrated questionnaires. One hundred and 10 respondents had carried out an online grocery buying. The perceived behavioral control is positively related to WiB grocery online. The more favorable a person’s attitude is towards some considered behavior. Attitude towards online grocery shopping is positively related to WiB groceries online.
Shwu-Ing Wu (2003), the purpose of the report here was to examine internet user concerns and perception of online shopping. The independent variables are consumer purchase preference and the attitude toward online shopping whereas dependent variable is online shopping rate. The attitude of internet users toward online shopping was measured using the Fishbein model. The result show that Fishbein model can effectively measure consumer attitudes and the examined consumer’s characteristics were important influence factors on consumer attitudes and online shopping decisions.
W.C. May So, T.N. Danny Wong, Domenic Sculli, (2005), study to investigate web-shopping behavior in Hong Kong: identification of the general attitude towards web-shopping: relationships between past web-shopping experience, attitude towards web-shopping, adoption decisions, search behavior and web-shopping intentions; and influences of promotional offers and product categories on web-shopping intentions. Attitude and web-shopping experience are independent variables whereas Web-shopping intention is dependent variables. The results suggest that the web-shopping intentions of the survey group are relatively low. Some possible reasons suggested below may partly account for the unfavorable attitude towards web-shopping and the reluctance to adopt web-shopping.
Kuo‐Lun Hsiao, Judy Chuan‐Chuan Lin, Xiang‐Ying Wang, Hsi‐Peng Lu, Hueiju Yu, (2010), To improve understanding of the reasons why people trust the information about product recommendations on social shopping networks of websites an online survey. Trust in product and trust in websites are independent variables whereas dependent variable is intention to purchase product. The results suggested that consumers’ trust in a website and their purchase intentions were two important factors enhancing the willingness to purchase the socially-recommended products from the website. The results imply that the members with an intention to purchase products are more likely to shop online.
Zeithaml et al. (1996) suggest that positive behavioral intentions are reflected in the service provider’s ability to get its customers to communicate concerns to other customers and Clearly, it is likely that there is a relationship between customer experience and behavioral intentions, customer experience is independent variable whereas behavioral intensions are dependent variable. Customers expresses their behavioral intensions by repeating their purchases again and again. However, a repeat customer is not necessarily completely satisfied there are levels of customer loyalty and the relationship is not necessarily linear. Behavioral intentions will differ based on customer experience level.
Dasthi et al. (2016), Performed a study on the elements influencing customer intention to purchase products and services in social commerce. Study revealed that social commerce has a direct influence on social support and relationship quality. Social commerce is dependent variable whereas, Relationship quality is independent variable. Social media comprises of socialization components like social supports of online users which boosts trust, satisfaction, and commitment. Whereas, relationship quality is comprised of trust, satisfaction and commitment that has increased the Consumer’s willingness to purchase in social commerce.
Kim et al. (2007), investigated the social factors influencing virtual community members’ satisfaction. After the investigation the Study found that the online community members have a strong identification and strong desires for social presence and social comparison. Social Comparison is dependent variable whereas, strong desires are independent variable. Desires for social presence and social comparison impact members’ satisfaction with the use of e-WOM in a virtual community.
Hajli (2015) conducted a study on the role of social media in facilitating online communication between customers through social commerce constructs, leading to online social support. Online communication between customers is dependent variable whereas social commerce construct is independent variable. This research found that the social media and social networking sites proves to be a supportive environment for consumers as well as developing online communication. Social commerce constructs, which have a significant effect on consumer behavior, can be powerful tools for social media strategists
Chen &Shen (2015), examined the impact of social, psychological factors such as social influence, social identity, and social presence on the relationship quality of online users. These users are dependent variable and social influence and social identity are independent variables. This study concludes that social influence is linked from the different social interactions with people online. Advanced social influence gives birth to higher social identity and social presences which results in user’s satisfaction and trust with the online communities. All these components influence the user relationship quality with the groups of social media.
2.3. Conceptual model
?
2.4 Model Hypothesis:
?H1: Social attitude have positive and significant effect on social desire in social activities on SNS.
?H2: Social attitude have positive and significant effect on commercial desire in commercial activities on SNS.
H3: Commercial attitude have positive and significant effect on social desire in social activities on SNS.
H4: Commercial attitude have positive and significant effect on commercial desire in commercial activities on SNS.
H5: Anticipated positive emotions for social activities have positive and significant effect on social desire in social activities on SNS.
H6: Anticipated positive emotions for social activities have positive and significant effect on commercial desire in commercial activities on SNS.
H7: Anticipated positive emotions for commercial activities have positive and significant effect on social desire in social activities on SNS.
H8: Anticipated positive emotion for commercial activities have positive and significant effect on commercial desire in commercial activities on SNS.
H9: Perceived behavioral control for social activities have positive and significant effect on social desire in social activities on SNS.
H10: Perceived behavioral control for social activities have positive and significant effect on commercial desire in commercial activities on SNS.
H11: Perceived behavioral control for commercial activities have positive and significant effect on social desire in social activities on SNS.
H12: Perceived behavioral control for commercial activities have positive and significant effect on commercial desire in commercial activities on SNS.
H13: Social identity is insignificant on social desire in social activities on SNS.
?H14: Social identity is insignificant on commercial desire in commercial activities on SNS.
?H15: Social desire in social activities have positive and significant effect on social sharing intention on SNS.
H16: Social desire in social activities have positive and significant effect on social shopping intention on SNS.
H17: Commercial desire in commercial activities have positive and significant effect on social sharing intention on SNS.
H18: Commercial desire in commercial activities have positive and significant effect on social shopping intention on SNS.
3.1.??Research purpose
?In this study explanatory research purpose is used. The term explanatory research refers to the research whose primary purpose is to explain the effect and cause relationship between the dependent and independent variables and to make amendments to previous studies. The purpose of this study is to investigate the role of social desire and commercial desire in driving user’s social sharing and social shopping intentions on social commerce platform.
3.2.??Research approach
?In this study quantitative research purpose is used. Quantitative or deductive approach refers to the numerical analysis of data that has been collected through questionnaire, survey or by using statistical techniques. The theory is also being tested in context to the paper. In this study data has been collected through questionnaire from users of social networking sites. The MGB Theory (Model of Goal directed Behavior) is used as a theoretical foundation in this study to observe the role of social and commercial desire in driving social sharing and social shopping intentions of users on SNS.
3.3.??Research design
In this study correlational research design is used. The correlation research examines the relationship between two or more variables which entails that either the variables have positive or a negative relationship. This research explores the relationship between social and commercial oriented factors that drive customers to have desire to engage in social and commercial activities on SNS.
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3.4.??Sampling technique
This study uses convenience sampling technique to collect data. Convenience sampling include those members who are conveniently available to participate in research. In this study the data has been collected by users of social media who are aware of social commerce websites and do shopping through SNS.
3.5.??Target audience/Population
The target audiences of this study are users of social networking sites who are engaged in social or commercial activities or those people who have awareness of social commerce including male, females and mainly students of Iqra University.
3.6.??Sample size
The data was collected from 500 users of social networking sites.
3.7.??Statistical techniques
The Statistical Package for the Social Sciences (SPSS) software and PLS-Structural Equation Modeling (SEM) have been used in this study for analysis of data. The test applies on the data includes, reliability analysis, factor analysis and the regression analysis.
The Structural Equation Modeling is a hybrid statistical technique used to analyze the structural relationships between measured variables and latent constructs. This technique is the combination of factor analysis and multiple regression analysis. Through this technique researchers can specify confirmatory factor analysis models, regression models and complex path models.?
The Statistical Package for the Social Sciences (SPSS) is software used by researchers for performing statistical analysis of data. It is one of the popular statistical packages that can perform highly complex data manipulation and analysis easily.
3.8.??Questionnaire and Measurement Instrument
The data has been collected through questionnaire which was based on a 5-point Likert scale from strongly disagree (1) to strongly agree (5). The questionnaire was adapted from past studies. The validation of questionnaire is done by random people who share their knowledge on social media.
3.9.??Ethical consideration
The information that has been collected from the respondents through questionnaire is for the fulfillment of the research objective and it should be kept confidential and will only be used for this research purpose and will cause no harm to the dignity of research participants. Furthermore any type of personal information will not be circulated anywhere.
4.1??Data analysis
?The exploration of the data is morally based on the opinion from the participants that answered to the research instruments. The questionnaire consisted of 40 items, each and every item or questions was examined so that the reader can get the well idea of the research and can repeat this research in any manner useful to them. The data collected through the survey is calculated as a whole taking every response in to account, there were very simple questions mentioned in the questionnaire for the ease of the respondents. For analyzing the research model PLS-SEM partial least squares and SPSS method to structural equation modeling was chosen. Data was examined by using the smart PLS 3.1.6 (Ringle, Wende, & Becker, 2014).
4.1.1??Descriptive statistics
According to responses, with respect to gender most of the respondents among our sample are male i.e. 72.5% whereas females are 27.5%. The educational profile of the respondents shows that 37.3% respondents are graduates, whereas 41.5% are under graduates and 16.4% are postgraduates and 4.8% are in other. In terms of age it shows that 54.9% respondents lie at the category of less than 25 years old, whereas 26.1% were in the category of 26-30 years, 9.6% were in the category of 31-35 years old, 6.2% were in the category of 36-40 years old, and only 3.2% respondents are from above 40 years old. The occupation of the respondents as 38.7% lie in Private sector, 20.4% lie in Public sector, 1% is in category of semi-private, 8.8 % are self-employed, and 31.1% are in other field. With the reference of daily time spends on the Social Networking Sites shows that 25.1% are less than 1 hour, whereas 51.1% are 1-2 hours, 23.8% are Above 2 hours. Lastly the category which shows the number of friends on social networking sites (SNSs) that 54.9% respondents lie in 1-200 category, whereas 33.3% are in 201-400, and 11.8% are in above 400.
4.2. Reliability Analysis
?Reliability in statistics is the overall consistency of a measure/ items or instruments. Cronbach’s Alpha should be more than 0.55 means 55% (Tabachnick and Fiddell, 2007), in this case all of our 7 constructs have a positive result and are higher than 0.55. This table shows that the variables have a cronbach’s alpha greater than 0.5 which meets the given criteria showing all variables are reliable.
Anticipated positive emotions for commercial activities has an alpha of 0.913, anticipated positive emotions for social activities has an alpha of 0.886, commercial attitude has an alpha of 0.925, commercial desire has an alpha of 0.937, perceived behavioral control for commercial activities has an alpha of 0.716, perceived behavioral control for social activities has an alpha of 0.755, social attitude has an alpha of 0.900, social desire has an alpha of 0.945, social identity has an alpha of 0.936, social Sharing intention has an alpha of 0.926, and social shopping intention has an alpha of 0.925.
4.1.3??Factor Analysis
Factor analysis is a technique of data reduction which is designed to represent a wide range of attributes on a smaller number of dimensions on the basis of their similarities. Correlation matrix (CM) shows that how every of the item is linked with every of the new. If value lies in the range of 0.01 to 0.3 it means relationship between variables is weak. Moreover, if it lies in the range of 0.31 to 0.7 then it is moderate relationship between variables and if it is greater than 0.7 so it represents high correlation. The extraordinary correlations designate that dual matters are related and will perhaps be gathered composed by the factor analysis (FA). Correlation Matrix indicated how each questions are related to each other. Low correlation means the items were not in same factors. Moderate correlation you can see in perceived behavioral control for commercial activities and perceived behavioral control for social activities lies in range of 0.3-0.7 so its moderate relationship between variables, and other nine variables have a high correlation with their respective variables as the value is greater than or equals to 0.7.
4.1.3??Regression Analysis
?The degree to which one variable is dependent on other is regression. Regression analysis is performed to determine the relationships between the variables.
APMSA -> SD
?
The hypothesis is about the belongings of APMSA on SD and it shows the strong and positive relationship as (P< 0.1, β = 0.238).
Wahnbaeck and Roloff (2017), This suggests that both anticipated positive emotion for social activities and positive emotion for commercial activities have potential influence on desires to engage in social and commercial activities on SNS.
Hunter (2006), confirmed that anticipated positive emotions lead to a higher desire to visit shopping malls
CA -> CD
?The hypothesis is about the belongings of CA on CD and it shows the strong and positive relationship as (P< 0.1, β = 0.082).
(MIC, 2017; Nielsen, 2016; SUMO, 2016), this suggests that an individual’s attitude toward social commerce may also include attitude toward social activities and attitude toward commercial activities.
CA -> SD
?The hypothesis is about the effects of CA on SD. Moreover, results show positive but insignificant impact as (P>0.1, β = 0.004).
CD -> SSHAREI
?The hypothesis is about the belongings of CD on SSHAREI and it shows the strong and positive relationship as (P< 0.1, β = 0.505).?
CD -> SSHOPI
?The hypothesis is about the belongings of CD on SSHOPI and it shows the strong and positive relationship as (P< 0.1, β = 0.571).
PBCCA -> CD
?The hypothesis is about the belongings of PBCCA on CD and it shows the strong and positive relationship as (P< 0.1, β = 0.288).
Huang and Benyoucef (2015), social and commercial attitude, perceived behavior control may have positive influence on commercial desire
PBCCA -> SD
?The hypothesis is about the effects of PBCCA on SD. Moreover, results show positive but insignificant impact as (P> 0.1, β = 0.054).
Perugini and Bagozzi (2001), pointed that perceived behavioral control strong related to desires.
?Previous research prove that perceive behavioral control strong related to desires to conduct knowledge contribution behavior in virtual communities. (Tsai and Bagozzi, 2014).
PBCSA -> CD
?The hypothesis is about the belongings of PBCSA on CD and it shows the strong and positive relationship as (P< 0.1, β = 0.072).
Perugini and Bagozzi (2001), pointed that perceived behavioral control strong related to desires.
?Previous research prove that perceive behavioral control strong related to desires to conduct knowledge contribution behavior in virtual communities. (Tsai and Bagozzi, 2014).
PBCSA -> SD
?The hypothesis is about the effects of PBCSA on SD. Moreover, results show positive but insignificant impact as (P> 0.1, β = 0.029).
SA -> CD
?The hypothesis is about the effects of SA on CD. Moreover, results show negative and insignificant impact as (P>0.1, β = -0.068).
Turban et al., (2016), social factors may have weaker influence on social desire than on commercial desires.
Huang and Benyoucef, (2017), social attitude may have lower related on social desire.
SA -> SD
?The hypothesis is about the belongings of SA on SD and it shows the strong and positive relationship as (P< 0.1, β = 0.450).
SD -> SSHAREI
?The hypothesis is about the belongings of SD on SSHAREI and it shows the strong and positive relationship as (P< 0.1, β = 0.165).
Kim and Preis (2016), It can be inferred that an individual’s desire to engage in social and commercial activities may drive his or her social sharing intention and social shopping intention on SNS.
Most people use Facebook mainly for sharing and receiving information (Zhang et al., 2017). ?
SD -> SSHOPI
?The hypothesis is about the belongings of SD on SSHOPI and it shows the strong and positive relationship as (P< 0.1, β = 0.160).
Meng and Choi, (2016), confirmed that an individual’s intentions in a social commerce context may include social sharing intention, such as the intention to share word of mouth and shopping experience, and social shopping intention, such as the intention to search for online friends’ shopping experiences and the intention to make a shopping decision.
(Bapna et al., 2017), social identity to have social or commercial desires after viewing the shopping information shared by their Facebook friends.
SI -> CD
?The hypothesis is about the belongings of SI on CD and it shows the strong and positive relationship as (P< 0.1, β = 0.308).
Bagozzi and Dholakia, (2002), the relationship between social identity and desire has been supported in previous research.
Tsai and Bagozzi, (2014), defined social identity as member’s identification with his or her small friendship group in the virtual community.
SI -> SD
?The hypothesis is about the belongings of SI on SD and it shows the strong and positive relationship as (P< 0.1, β = 0.156). Bagozzi and Dholakia, (2002), the social identity is highly related to desire, social identity is based on a individual’s preference for a social group.
5.1?Conclusion
?In recent years, a continuous growth in users of social networking sites and usage time has been observed by companies due to advancement of technology which attracts the businesses to conduct commercial activities on SNS. This research has used social sharing and social shopping intentions to represent social commerce intention and explored the determinants of both types of intentions. The MGB theory has been used as a theoretical foundation to investigate the role of social and commercial desire as drivers of user’s social sharing and shopping intention on SNS. The dependent variables used in this study were social sharing and social shopping intention while the independent variables were social attitude, commercial attitude, anticipated positive emotion for social activities, anticipated positive emotion for commercial activities, perceived behavioral control for social activities, perceived behavioral control for commercial activities, and social identity. The moderators were social desire and commercial desire. The data has been collected from 501 Facebook users through questionnaire by convenience sampling. The techniques used to analyze the data were SPSS and SEM. The results revealed that social attitude, anticipated positive emotion for social activities, and social identity have a positive influence on social desire and a negative influence on commercial desire whereas perceived behavioral control for social activities have negative influence on social desire and positive influence on commercial desire. The anticipated positive emotion for commercial activities and perceived behavior control for commercial activities has positive impact on commercial desire. Both social desire and commercial desire have positive impact on social shopping and social sharing intention.
5.1?Managerial implications / Recommendations
?The findings of this study help to deepen our knowledge about social commerce behavior of users of SNS. Businesses use SNS to increase the company’s profitability, which is associated commercial activity. The results revealed that both social desire and commercial desire lead towards intention to engage in commercial activities on SNS. The ultimate goal of businesses is to drive consumers to use commercial functions on SNS. Thus, it is to be suggested that SNS service providers and companies should focus on strengthening consumers’ attitude towards commercial activities, providing sufficient resources, and increasing their perceived positive emotion for commercial activities on SNS by creating safe shopping environment on SNS that is as convenient as e-commerce websites. They should try to satisfy users’ need by providing relevant brand awareness to their followers which results in making SNS users more excited to view the commercial messages on their Timeline. Improvements in these aspects can help create a higher commercial desire in consumers, which can further drive consumers to have higher social sharing and shopping intentions and raise the profitability of the company and SNS. SNS service providers are advised to focus on effective integration of social and commercial functions to enable consumers to make purchases with ease and at their convenience. This can also attract businesses to continue the use of the site for higher competitiveness and profitability, resulting in a win-win situation for both businesses and SNS service providers.?
5.2???Future Recommendations
There are several limitations related to this study. First, the sample size consists of mainly male users of social networking sites therefore future studies should consider female users as females have more intention towards shopping through social networking sites or other groups. Second, the survey was administered to users of Facebook only therefore future research can consider other social commerce websites. Third, this study has used MGB theory to investigate the effect of social desire and commercial desire on social sharing and social shopping intentions in a social commerce context and also examine factors that affect these two types of desires therefore future research can also use Theory of Planned Behavior (TPB), social support theory, relationship quality theory and trust transfer theory to examine the factors that influence social commerce intention.