1.1 Background
Social networks are the grouping of individuals, and online social network platforms are now one of the most popular online communities. Most online social network services are used for sharing what you’ve done or what you’re doing, but this may not be the only thing they can do. Companies have been devoting their efforts to explore the opportunities of social network over the past years. The increased popularity of social network has opened opportunities for electronic commerce, often referred to as social commerce. Social network not only provides a new platform for pioneers to innovate, but also raise a variety of new research problems for electronic commerce researchers.
There’s more evidence that online social network can be a conduit to social commerce [24]. Social network provide an open platform for social commerce consumers and vendors to search, share and advertise product information. From a survey conducted by Gartner [7], 40% of consumers regularly search products information on social media, 34% are more likely to share product information on social media with their friends than in e-commerce sites, and 77% of online shoppers use reviews. The survey also showed that 75% trust personal recommendations, and 75% are more likely to purchase if a friend endorses. This open up the gate to provide product information meeting consumer’s personal preference based on social relation. At the same time, 81% of consumers receive advice from friends. The result implies that a social support mechanism for product selection would be helpful to consumers. In this study, the phenomena were addressed based on consumer purchase behavior. According to OTX’s purchase intention survey [65], 70% of consumers visit social media websites to collect information on a product. According to the Nielsen Global Online Consumer Survey of over 25,000 Internet consumers from 50 countries in 2009, 90%
of consumers trust the opinions of personal acquaintances [18]. IBM’s survey in 2011 found that 50% of 16-64 year olds who use online social networking sites such as Twitter and Facebook admit to using these online social networks to assist with
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shopping decisions while 35% stated they use online social networks to rank products and services. These respondents believe it is important to be able to use online social networks to assist with buying decisions [75].
1.2 Research Problem
Behind the visible action of making a purchase in social commerce lies a decision process that must be investigated. Consumer behavior involves study of how and why they buy. It blends the elements from psychology, sociology and economics. It also tries to assess the influence on the consumer from groups such as family, friends, reference groups and society in general. Consumer purchase decision is the processes undertaken by consumer in regard to a transaction during the purchase. A typical consumer purchase decision making process is depicted in Figure 1.1.
I need a digital camera, which model should I choose?
After survey, several products are found
Product candidates are identified by comparing various criteria and listening to others’ opinions
Select final decision from product candidates
Share user experience with others
Figure 1.1 A typical consumer purchase behavior
Social commerce is a subset of electronic commerce (EC) that uses social network to supports social interaction, to assist in the online buying and selling of products and services. It includes tools that enable consumers to get advice from trusted individuals, find goods and services and then purchase them. Social commerce helps consumers make smart and savvy purchase, and consumers now are looking for ways to leverage each other’s expertise, understand what they are purchasing, and make more informed and accurate purchase decisions.That is, they are increasingly influenced by online social networks when it comes to purchase decision making. Despite its growing interests, however, there are relatively few studies on social commerce support mechanism. For the purpose of helping consumer with making purchasing decision, it is desired to have proper social commerce support mechanisms based on online social networks. Moreover, as research suggests that customers value and respect personal sources more than other sources [29, 57], it would be ideal to construct decision support groups from their online social networks.
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In real life, we are constantly influenced by other factors than just information, such as friends, social classes and psychological needs when making purchase decision. A consumer can obtain information from several sources:
Personal experience: past purchase history, experience of similar products etc.
Personal sources: family, friends, colleagues etc.
Commercial sources: advertising, company websites, and salespeople.
In social commerce context, consumers also collect information from these sources.
As social relation is the core of social commerce, in this study it was used to be the infrastructure of social commerce support mechanisms.
Consider three common scenarios of product purchasing (see Figure 1.2). Suppose that customer A wants to buy a digital camera,
Scenario 1: He/she has searched for various products, and at this point he is interested in several models. However, he/she is unable to make up his/her mind, so his/her friends or family are consulted to rank the products for him/her.
Scenario 2: He/she has identified price, megapixels and LCD size as selection criteria and needs someone to recommend products based on them.
Scenario 3: He/she has no idea about digital camera and just asks his/her friends or family to tell him/her what factors should be considered and what to buy.
Naturally, the following research problems arise when designing social commerce support mechanisms:
For scenario 1, how to find adequate group with similar taste or preference so that consumer can get advice from the group and the group can rank the products for consumer.
For scenario 2, how to design the recommendation mechanism so as to utilize friend network to recommend items based on consumer’s selection criteria, that is, how to design a personalized while socialized recommendation results.
For scenario 3, how to build up functionalities so that consumers can discover product information based on personal and/or commercial sources.
To address these scenarios and meet the requirements of social commerce, in this study the corresponding mechanisms were designed. For more vivid picture of the study, Figure 1.2 serves as the research paradigm.
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I need a digital camera, which model should I choose?
Problem Recognition Information Search Evaluation of Alternatives Purchase Decision Post-purchase Behavior
I need a digital camera, which model should I choose?
Share user experience with others
Scenario 2 Scenario 2
Social Recommendation Mechanism
Scenario 3 Scenario 3
Social Intelligence Mechanism
Scenario 1 Scenario 1
Social Support Mechanism
Selection Criteria Product Candidate List Product Ranking List
Selection Criteria Product Candidate List Product Ranking List
Selection Criteria Product Candidate List Product Ranking List
Figure 1.2 Scenarios of this study and corresponding support mechanisms
1.3 Research Contributions
While there are on-going researches on social network and its effects on business, there is relatively little solid research on social commerce support. The contributions of this study are listed as follow.
Social Support Mechanism for scenario 1:
This work designed a mechanism to find the most fit group for consumer, and an adaptive majority voting method was used to rank items.
1. Regarding support group selection, other than selecting group members based on some predefined measures, in this study the agglomerative hierarchical clustering method was used to find the proper home group for consumer.
2. Concerning improved voting method, social power was used to weight each voting and suggest the ranking of items.
Social Recommendation Mechanism for scenario 2:
This study proposed a system framework for personalized (personal preference) recommendation results based on socialized information sources (friend networks)
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1. For personalized recommendation, an assistant tool based on QOC schema was designed to recommend proper items based on consumer’s preference. And a recommendation conflict resolution was also proposed to solve the recommendation inconsistency on certain product.
2. As for socialized recommendation, by introducing social impact theory of social psychology into social network analysis process, a new decision group recruiting method was designed to select friends with higher impact power.
Social Intelligence Mechanism for scenario 3:
This research suggested a set of functions to collect information from personal or commercial channel based on trustworthy sources.
1. With regard to sources selecting function, source credibility including friendship, social similarity, network centrality and expertise was used to recruit proper members. A PageRank-like index based on post-reply was proposed to measure expertise on products.
2. As to information collecting function, artificial intelligence techniques were used to reduce human’s intervention.
1.4 Outline of the Study
The remaining part of this paper is organized as follows. In chapter 2, existing literatures related to this study were reviewed. The corresponding support mechanisms were demonstrated in chapter 3, 4 and 5 respectively. The system framework, experiment and discussions are also included in each chapter. Finally, chapter 6 concludes research contributions and presents future research directions.
1.5 Chapter Summary
In this chapter, the applications of social network in electronic commerce environment were introduced and pointed out the imperious demands of support mechanisms. In addition, the research questions this study tried to address were also highlighted, and the important contributions were also spotlighted in this chapter.
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