Improving B2C e-commerce Trust through Social Presence Factors

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1 Improving B2C e-commerce Trust through Social Presence Factors Nur Syadhila Che Lah 1 Sya_ila87@yahoo.com.my Ab Razak Che Hussin 2 abrazak@utm.my Halina Mohamed Dahlan 3 halina@utm.my Author(s) Contact Details: 1,2,3 Faculty of Computer Sciences and Information Systems, University of Technology Malaysia, 81310, Skudai Johor. Abstract In e-commerce field, there are many researchers conducted on different properties of trust but lack of attention has been given to interpersonal based trust. This has lead to limitations for e-commerce growth where social events have not yet fully duplicate into digital form especially in e-commerce environment. Taking the advantage of rapid growth of social network nowadays, the objective of this study is to improve current e-commerce implementation through social presence. In order to achieve this objective, this study has explored e-commerce trust properties with its attributes as suggested by previous researchers. Trust factors based on social presence attributes also have been identified. This research hopes to provide a guideline to online businesses in order to develop a trusted website based on updated trend of e- Commerce which is more customer-oriented. Keywords Trust, risk, e-commerce, interpersonal, social presence, social network, web merchants 1. INTRODUCTION The growth of Internet nowadays is unpredictable and the advantage of this technology can be seen in providing fast and more convenience way in performing daily activities. The true potential of Internet technology is now being exploited at a higher rate as a medium of selling and purchasing goods [1] and creating a new economic ecosystem and electronic market place where it becomes the virtual main street of people around the world. But in addition to that, there also gain in initiative to gain more number of people that will engage and placing more interest in this virtual electronic market place. In e-commerce environment, consumer is often in the inferior position of asymmetric information, looking for e-vendor s trustworthiness and perceived security and privacy, while sellers generally control the price, quality, performance or other information of their goods and services. The requirement for trust is mainly results from the existence of risk in the viral transaction environment itself and it is most agreed that e-commerce can achieve a wide success if general public trusts the virtual environment [2]. Comparing the situation between traditional transaction with real environment where consumer will give their satisfaction on products by experience it physically, e-commerce will require more consumer trust because the absence of human touch and communication. Prior researchers put their concern in understanding consumer trust and factors that will construct consumer trust towards online merchant. Those researchers study on exploring trust factors such as findings on technology related security [3], discussing the issues in behavior and intentions of consumer when performing transaction in e-commerce activities [4], understanding initial stages of consumer trust building in e-commerce, [5] and integrating consumer trust in building e- Commerce website [7]. Recently, scholars focus on understanding the impact of social presence in e-commerce, [6] and the effect of internet trust and social influence towards online purchasing. Yet, there are still no researches conducted focuses on exploring social presence trust factors in detail and integrate this factor into e-commerce website but only study this trust attributes in a separated ways. Thus, this study focus on how consumer trust can be improved in B2C e-commerce website development by integrating with the benefits of trust factors of social presence. This paper is organized as follows: Section 2 described about literature review which consist of the need of social presence in B2C e-commerce including findings of initiative trend of e-commerce nowadays and what can be improved from previous research in order to sustain the growth of e-commerce and explanation about social presence factors. Section 3 explains about deriving factors of social presence in B2C e-commerce and section 4 describes about results obtained which include data collection and analysis. Section 5 discusses about the conclusion of this study. ISSN: P a g e 44

2 2. LITERATURE REVIEW Online shopping including B2C e-commerce could not escape from facing some barriers in terms of service quality, tools, technologies and also trust. Online shopping is more impersonal, more automatic, and responds less effectively to immediate response than traditional shopping. Thus social presence in the context of media supported interactions has the potential to improve the rates of e-commerce adoption and use. Social media applications improve users experience and reduce uncertainty by providing realistic and complete of information, [9, 10, 13]. Social presence is linked to information richness, clearness, and interactivity. Interpersonal interactions (face-to-face or media-supported) help to build trust, and because customers are more influenced by direct relationship with a representative than by indirect relationship with the online merchant, social presence media messages should lead to the better outcomes. Research links social presence with trust, usefulness, and enjoyment, [8, 11, 12, 14]. Customers reviews on the website were found to enhance social presence and can be further improved by providing means for actual interaction with other humans such as via virtual online communities. Trust is build through ongoing social interactions. It is then reasonable to state that the e-vendors social presence replaces the social aspect of trust building in a mediated environment. Social environment can be made by having a communication networks between each entity surrounding its environment. A social presence has come to be viewed as the way individuals represents themselves in their online environment as a computer-mediated communication has emerged nowadays. Social presence has been defined as the sense of being with others and the feeling that one has some level of access or insight into the other s intentional, cognitive, or affective states, [15]. As the technology emerged today, this social presence can be seen in various way of channel in order to create fast and easy approach to be connected within each other. Social presence technologies performance is important as individuals and organizations depend on this advantage to represent face-to-face interaction. This situation also have no exception within e-commerce environment where technology are being adopted to create a sense of social presence by using various type of media tools such as human audio and video and social media integration in order to attract consumer intention towards e-vendor s product. This is important to minimize perceive risk of online transaction and increase consumer trust since this viral marketing have no face-to-face interaction and consumer needs some advice or opinions from others in order to create their initial trust towards e-vendor. Trust in the context of B2C e-commerce is about understanding the online trust. It can be seen incorporated with propensity and experience of consumer, asymmetry of information provided by e-vendor and its reputation, technology specific factor like website quality and stability, interval of space and time of transaction, transaction risk and uncertainty. [16] define trust as the willingness of a party to be vulnerable to the actions of another party based on the expectation that the other will perform a particular action important to the trustee, irrespective of the ability to monitor or control that other party. This definition is quite well accepted among the researchers [17]. The rapid development of e-commerce has also expanded the trust research in the context of B2C e-commerce. Several researchers found that trust is very important, especially in the relationships between consumers and e-vendors [18]. Also, several concepts of consumer s trust in e- Commerce have been introduced during the decade [19]. Based on those formulations and earlier research, three main properties of online or e-commerce trust can be recognized as interpersonal (refers to the belief or confidence in intentions and ability of others), institutional (refers to consumer trust in Internet infrastructure in general), and dispositional (is the extent to which a person displays a tendency to be willing to depend on others) trust.. All properties represent attributes consist of technology based and also consumer characteristic. This study will find out in detail about these properties and attributes. The issue about trust in B2C e-commerce has gained numbers of researches that aims to help business clearly understand the need of characteristic or components that have to focus on because mostly issues raise within this environment is about understanding consumer trust towards e-vendor. This is because doing business online involves some of entities that give influence in engendering trust of customer in order to have intention to participate in online transaction. Trust issue in B2C e-commerce can be seen in various aspects and several or more aspects may result in increasing or lowering the consumer trust towards online business. Example aspects related to trust issue that have been study previously consist of communities, consumer, technology, e-services, e-vendor and also third party. Normally, consumer lives in the society and various communities surrounding them. These communities have potential affect either more or less in the formation of consumer s trust and understanding the use of e-commerce. Relatives, colleagues and friends give advice and hints of links to e-commerce sites to the consumer and this method will have significant effect of the next step of trust formation between consumers. ISSN: P a g e 45

3 3. RESEARCH METHODOLOGY This section discussed design methodology used for this research. Methodology used for this study is adapted from [20] which have four major phases: Phase 1: Awareness of the problem, Phase 2: Suggestions, and Phase 3: Evaluation. The first phase is identification of the problems in a real-word that need to be improved and solved. By literature review done, this study found there are some potential limitations which related to social events that need to be duplicated into digital form and by looking ahead of current trend in e-commerce which stated about the importance of social presence in e- Commerce environment, problem statement for this study has been identified. In the second phase, prior to constructing a new integrated B2C e-commerce trust model, the trust attributes and related factors must be identified as the basic construct of the trust model development. The process of identifying trust properties and their attributes begin with collecting and analyzing resources from previous research findings. The first task that needs to be done is investigating all trust properties that influence consumer trust in e-commerce. This activity includes the review process from previous scholars which studies about the online trust dimensions and trust formation in e-commerce. Evaluation of all trust properties in e-commerce can provide the understanding of trust dimensions which consist of all related three trust properties such as e-vendor and consumer characteristics. From the review also, this study needs to identify those related trust attributes for each trust properties which represent social presence place in e-commerce trust dimension. Besides that, this study needs to identify all trust factors which related to social presence that have the impacts on trust perceptions of the consumer. Those suggested activities will be structured in the result tables in section 4. The last phase is about the evaluation of social presence characteristics through survey method. This survey is about validating process of social presence characteristics through respondents answers which represent their trust perceptions towards those characteristics. 4. DERIVING TRUST PROPERTIES AND SOCIAL PRESENCE FACTORS FOR B2C E-COMMERCE A. Findings on B2C E-Commerce Trust Properties and Attributes This study has identified those e-commerce trust properties together with their attributes which consist first with Dispositional, Institutional and lastly is interpersonal trust. All of these three trust properties have their own meaning and attributes that represent the characteristics and example of each online trust properties. Table 1 below is the findings of e- Commerce trust properties with their meaning and followed by their attributes and examples. This result is obtained by literature review from previous findings and has been structured into the table below. Table 1: Trust Properties and Attributes in B2C e-commerce PROPERTIES ATTRIBUTES EXAMPLE Personality [21] DISPOSITIONAL is the extent to which a person displays a tendency to be willing to depend on others across a broad spectrum of situations and persons INSTITUTIONAL refers to consumer trust in Internet infrastructure in general or in some other institutional aspect of e-commerce receptivity to technology innovations, excitement-minded, feeling of control, Internet intelligence, past Internet shopping behavior Experience [21] satisfaction, familiarity with e- Demographics [22] Psychographics [22] structural assurance [3] structural information [3] third-party seals [23] vendor age, gender, marital status INTERPERSONAL word-of-mouth (WOM) [12] social presences activity, interest, attitudes towards e-commerce, education level, perceive enjoyment security and privacy policy laws, security, contact information TRUSTe, WebTrust, BBB Online ISSN: P a g e 46

4 refers to belief or confidence in intentions and ability of others e-vendor s reputation [24] feedback mechanism [25] website quality [26] level of service, functionality of service, branding, e-vendor s size online community, feedback ratings content characteristics, ease of use, navigation, security, privacy, quality of information, site properties B. Findings on Related Social Presence Factors From the research conducted, there are several factors or antecedents of social presence that can engender consumer trust within the context of Interpersonal trust properties in B2C e-commerce. Based on the research also, this study would prefer to place this social presence factors as one of the main attributes that build in the Interpersonal trust property because this trust property as mentioned by previous scholars is related to trust on ability of specific others. All factors are summarizing in Table 2 below based on previous author s findings. Based on the findings, first author [27] stated that webbased consumer-opinion platform is one of the trust antecedents that related to the social presence factors. This type of platform provides consumers the opportunity to read others consumer s opinions and experiences and also writes their experience towards the products. Consumer can share their personal interest and excitement results from product ownership and thus also improved e-vendor reputation by spreading the information. Second authors [28] stated that designation of media interactive provide by e-vendor such as interactive picture and content richness can represent a personal presence in the similar way as personal picture and letters and various choice of language can help in creating an instinct of psychological closeness and warmth. The use of natural and informal language can represent a sense of social presence existing. Customers are more interested to purchase online products when there are emotive descriptions of touch properties and social-rich context such as an example in description of towel: its soft looped design feels smooth and comfortable against your skin, was more appealing to customers than the same towel described as: 100% Egyptian cotton, white, 30cm x 54cm. Based on third author, [29] stated that there is a need of personalization website with social recommendation systems such as rate and review techniques where this system can be used by e-vendor in helping consumer to make the decision about purchasing the products. This happened when the recommendation system could act as an advisor in helping consumer decision making and give the suitable suggestion about any others products that may suite with consumer interest. Based on research findings by [30], he stated that consumers are more paying attention and trust to each other instead of paying attention to marketing promotion or message. He also comes out with statistics that say more than 40% of consumers will check reviews and consumer feedback through online before making purchasing where 60% of them have visited a social network, and half going back every day. 23% of social network users expect e-vendor to listen and gives respond to what is said online by their consumers. This is a sign of there is a need for media integration to e-commerce website such as Facebook and Twitter because consumer now are more addicted with Internet applications and can spend interest in buying online based on social networks platform, because they form trust based on their friends and get advertisement notice for something to purchase via fast spreading of information in this platform. Table 2: Summarization of Social Presence Factors in B2C in e-commerce Authors Sub Attributes Examples T. Henning-Thurau and K. P. Gwinner (2004) web-based consumeropinion platform epinions.com, consumerreview.com, rateitall.com H.Khaled and H. Milena (2007) interactivity of media human audio and video, socially-rich picture content, socially-rich text content, natural and informal language, emotive description human web assistant after-sales support virtual communities chat rooms, message boards Kim Y. and Srivastava J. (2007) personalized greetings social recommendations system David A. Smith (2010) social media integration Facebook, Twitter talking-face displays purchase preference and consumer profile, rate and review techniques ISSN: P a g e 47

5 5. RESULT A. Data Collection In this study, two surveys have been conducted to test the validity of the SP. First survey is a pilot study which consists of 18 questions regarding social presence trust factors. The 5 point Liker scale is used in this survey starting from 1 for strongly disagree to 5 for strongly agree. There are 20 respondents have been involved in this pilot study. They are students from Faculty of Science Computer and Information System, (FSKSM) University of Technology Malaysia Skudai, Johor. The questionnaire from pilot study was analyzed using ANOVAs test in order to validate the reliability of the questions in the survey. The results show that the questionnaire is valid to be distributed in final survey with minor change. 13 out of 18 questions were selected for final survey. The demographic section is remaining the same. The final survey was done among 100 students from the same faculty which consists of 48 male and 52 female students aging from 20 until 40 years old. The explanation of the result from final survey is provided in the following sections. B. Data Analysis There are six main trust factor proposed that can represent social presence in B2C e-commerce environment. Based on data analysis that has been done using data collection from survey questionnaire, the main factors of social presence had been clarified. The attributes of each social presence trust factors is represented by each Likert scale items in the questionnaire in order to validate the proposed factors based on user s perception analysis. Factor analysis method is used to reduce large set of items to a smaller number of components. These techniques are commonly used when designing a questionnaire to see the relationship between the items in the questionnaire and underlying their dimensions. It is also used in general to reduce a larger set of variables to a smaller set of variables that explain the important dimensions of variability. Specifically, factor analysis aims to find underlying proposed factors and grouping each component based on their relationships. In this case, the used of factor analysis is to confirm the trust factors based on its group. C. Trust Factors of Social Presence Survey Analysis Trust Factors of social presence survey have 13 questions relating to public s perceptions about the sense of social interactions in B2C e-commerce website that can increase their trust to shop online. These questions are measured on 1 (strongly disagree) to 5 (strongly agree) point Likert scale. Questions has been structured under all proposed social presence attributes which are web-based consumer-opinion platform, interactivity of media, virtual communities, personalized greetings, social recommendation system and social media integrations. Thus factor analysis has been used to guide the process of grouping items into their components or to check that the proposed grouping structure is consistent with the data. Table 3: KMO and Bartlett s Test Analysis The first output of SPSS factor analysis is shown in Table 3 above which produces Kaiser-Meyer-Olkin measure of sampling adequacy and Bartlett s Test. Value of KMO should be greater than 0.5 to show if the sample is adequate and suitable for conducting factor analysis. From the table analysis above, value of KMO is which greater than 0.5 represent that this sample survey is appropriate for further conducting factor analysis. Table 4: Communalities Analysis ISSN: P a g e 48

6 Table 4 above is the result of communalities analysis before and after extraction that shows how much of the variance for the variables have been accounted for by the extracted factors. Principal component analysis used by factor analysis works on initial assumptions that all variance is common, therefore the extraction of communalities are all 1 as shown is column Initial. The communalities in column labeled Extraction reflect common variance in data structure. For instance, over 70% of the variance in variable (a) is accounted while 67.8% of variance in variable (k) is accounted. If the communality is very low for an item, it suggests that it does not share much in common with the extracted components. This generally implies that it is unrelated to the other items in the sample set and from the analysis above, item (c) shown very low extraction value (0.394) compare to others thus this item will be excluded for further analysis. The next output from factor analysis is total variance explained as the data shown in Table 5 below. Total variance represents the percentage of total variance in the items explained by a component which in this case it consist of 13 components. The variables are standardized because of correlation matrix used, which means that each variable has variance of 1 and total variance should equal with number of variables used in this sample set. Table 5: Total Variance Explained (13 Items) ISSN: P a g e 49

7 The eigenvalues associated with each factor also displays the result in terms of percentage of variance explained which in this case, factor 1 contribute to 28.68% of total variance and commonly first few factors explain relatively large amount of variance whereas subsequent factors explain only small number of variance. This analysis then further extracts all factors with eigenvalues greater than 1 which in this case leaves result with five factors. The eigenvalues associated with five factors again displayed including percentage of variance in the column labeled Extraction Sums of Squared Loadings. Values in this part are the same with the values before extracts except values for discarded factors ignored which be a reason of table blank after five factors extracted. In the last column of table above that labeled with Rotation Sum of Squared Loadings displayed the eigenvalues of the selected factors after rotation. This rotation has the effect of optimizing the factor structure and one consequence for the data is the equalization of relative important factors obtained. From the analysis table above, factor 1 accounted more variance than the remaining four factors before rotation, however after extraction it accounts for only % of variance with the rest of %, %, % and % of variance. After determination of five factors from total variance explained above, this analysis is repeated using principal component analysis that includes the extraction step from 12 items with the given value of factors obtained. In this case, author stated values of five that represent number of main factors obtained before and repeating the same steps of factor analysis. From this, a new total variance has been obtained and the result is shown in Table 6 below. Result shows that five factors obtained including component number 5 with low total variance (0.992). The first component accounted % of total variance with the subsequent components %, 9.453%, 8.878% and 8.266%. The purpose of rotated component matrix is to reduce the variables which have no significant relationship among each factor. This rotation does not change anything but automatically load those variables into their factors and makes the interpretation of the analysis easier. From Table 7, the result obtained shows that item (l) does not belong to any component factor and leave component number 5 with blank values. This is because factor loadings which less than 0.5 will not been displayed because author asked for these loading to be suppressed. From the analysis also, this result can be computed that item (f), (i), (m) is highly loaded into factor number 1, item (d), (g) loaded into factor number 2, item (b), (j), (a) loaded into factor number 3 and the last item (h), (e), (k) were highly loaded into factor number 4. The next step is to categorize those factors with their items into appropriate factor proposed that represent their relationships and relation with those items identified. Table 8 below is the grouping of items identified with their actual definitions that categorized based on four factors extracted before. Table 6: Total Variance Explained (12 Items) ISSN: P a g e 50

8 Table 7: Rotated Component Matrix Factor Factor 1 Factor 2 Factor 3 Factor 4 Table 8: Factor Analysis Result Components Personalized Greetings Icon, Address Link of Social Media, Updated Promotions on Social Media Online Product Suggestions and Product s Rating Online Discussion Forums, Community Review Platforms and Social Media Comment Platform Online Chat Application, Human Audio and Video and Complete Product Descriptions According to result obtained in Table 8 above, the next step is to assign those four factors based on social presence attributes that represent trust factors in B2C e-commerce. This social presence attributes is proposed earlier as can be referred in Table 1, summarization of social presence factors in B2C in e-commerce. The first factor which includes ISSN: P a g e 51

9 personalized greetings icon, seller s Facebook fan pages and Twitter address link and updated promotions on seller s Facebook and Twitter are related to social media integration because those items accounted represent social network attributes such as integration of Facebook fan pages. Second factor which includes online product suggestions and product s rating is clearly related to social recommendation system because of highly similarity of these social presence techniques. The third factor which includes online discussion forums, community review platforms and consumer comment platform through Seller s Social Media is highly related to web-based consumer opinion platform and the last factor which includes online chat application, discount gifts and complete product descriptions is related to media interactivity. Summarization of these actual social presence trust factor is shown in Table 9 below. 6. CONCLUSION Table 9: Extracted Factor Analysis with Social Presence Trust Factors Factor Factor 1 Factor 2 Factor 3 Factor 4 Social Presence Trust Factors Social Media Integration Social Recommendation Systems Web-Based Consumer Opinion Platforms Media Interactivity This study has begun with proposing all related social presence trust factors in order to improve B2C e-commerce trust model. All those factors were identified by literature review analysis from previous scholars finding and then incorporated all of them in one category in order to highlight in detail about social presence sub-attributes. After that, data collection was conducted using questionnaire distribution for 100 respondents and was then analyzed by using method of factor analysis in SPSS. Results from this analysis are to validate the reliability of those social presence trust factors proposed earlier. The final trust factors obtained by this study are social media integration, social recommendation system, web-based consumer opinion platform and media interactivity together with their sub-attributes as highlighted earlier in this paper. The aim of this study is to contribute to the further discussion on proposed social presence trust factors. This step will open a new way to investigate more such as the effectiveness of each sub-attribute of social presence obtained and it also can be tested in some other place in order to verify more about the results. Besides that, this result also can be tested in other e-commerce domain such as consumer-to-consumer (C2C) and business-to-business (B2B). Hopefully, this study will give a clear guideline for other researchers in order to improve trust intention in e-commerce. REFERENCES [1] Lee, MKO & Turban, E (2001), A Trust Model for Consumer Internet Shopping, International Journal of Electronic Commerce, September 2001, vol. 6, no. 1, pp [2] Tan, Y.-H., and Thoen, W., "Toward a Generic Model of Trust for Electronic Commerce," International Journal of Electronic Commerce, Volume 5, Number 2, , pp [3] Yousafzai, S. Y., Pallister, J. G., & Foxall, G. R. (2005). Strategies for Building and Communicating Trust in Electronic Banking: A Field Experiment. Psychology & Marketing, 22(2), [4] Chen YH, Barnes S (2007). Initial trust and online buyer behavior. Ind. Manage. Data Syst., 107(1): [5] Pennanen, K., (2009). The Initial Stage of Consumer Trust Building in e-commerce. [6] Kim, Y.A., Srivastava, J., (2007). Impact of Social Influence in E-Commerce Decision Making [7] Othman, N.Z., Che Hussin, A.R., and Daud, A.R. (2008). Trust Mechanisms: An Integrated Approach for E- Commerce Website Development Process. 3 rd International Symposium on Information Technology, 26 29th August Vol 1, pp. [8] Turban, E.; King, D.; Lee, J.; Warkentin, M.; and Chung, H. Electronic Commerce 2002, 2d ed. Mahwah, NJ: Prentice Hall, [9] Short, J., Williams, E., & Christie, B. (1976). The social psychology of telecommunications. London: John Wiley & Sons. [10] M. Kaplan, A., Haenlein, M., (2009). The Challenges and Opportunities of Social Media. [11] Flavian C, Guinaliu M (2006). Consumer trust, perceived security and privacy policy: Three basic elements of loyalty to a web site. Ind. Manage. Data Syst. 106: [12] Gefen, D., W. Straub, D., (2004). Consumer trust in B2C e-commerce and the importance of social presence: experiments in e-products and e-services. [13] Zhang, H., Chen, D., Sun, R. (2010). The study of consumer trust in B2C E-commerce based on reputation score, information disclosure, and Online Consumer Review quality. ISSN: P a g e 52

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