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Diplomarbeit, 2010, 95 Seiten
List of Figures
List of Tables
List of Abbreviations
1.1 Problem Outline
2 Theoretical Background
2.1 Social Networking Sites
2.1.2 Previous Research
2.1.3 Invitation-only Social Networks (ISNs)
2.1.4 Open Social Networks (OSNs)
2.1.5 Theoretical Comparison of ISNs and OSNs
2.2 Previous Research about Product Recommendations
2.2.1 Traditional Word-of-Mouth
2.2.2 Electronic Word-of-Mouth
2.2.3 Comparison of WOM and eWOM
2.2.4 eWOM in Social Networking Sites
2.3 Previous Research about Social Capital and Outcome Expectations
2.3.1 Structural Capital
2.3.2 Relational Capital
2.3.3 Cognitive Capital
2.3.4 Outcome Expectations
3 Conceptual Model and Hypotheses
4 Empirical Research
4.1 Research Methodology
4.2 Description of the Sample
4.3 Common Method Bias Analysis
4.4 Measurement Model Validation
4.5 Structural Model Validation
4.6 T-tests for the Comparison of OSN and ISN
4.6.1 Differences in Relational Capital
4.6.2 Differences in Outcome Expectations
5 Discussion and Implications
5.2 Research Implications
5.3 Managerial Implications
5.3.1 Product recommendation systems for OSNs
5.3.2 Product recommendation systems for ISNs
Figure 1: Timeline of Social Networking Sites Launches (Adapted from Boyd and Ellison 2007, and extended with ISNs and other launches for 2007-2010)
Figure 2: Conceptual Model
Figure 3: Internet Use (in hours)
Figure 4: Social Networking Site Usage
Figure 5: Social Capital Path model with Results for ASW and FB (***p < 0.01, **p < 0.05, p* < 0.1)
Figure 6: Relational Capital Path Model with Results for ASW and FB (*** p < 0.01, ** p < 0.05, * p < 0.1)
Figure 7: Product Recommendation System for OSNs such as FB
Figure 8: Product Recommendations System for ISNs such as ASW
Table 1: Invitation-only Social Networks
Table 2: Open Social Networks
Table 3: Theoretical Comparison of ISNs and OSNs
Table 4: Comparison of WOM and eWOM
Table 5: Layout of Online Survey
Table 6: Reflective multi- and one-item constructs
Table 7: Demographic profile
Table 8: Convergent Validity of the Reflective Multi-item Constructs (First-order Constructs)
Table 9: Discriminant Validity of the Reflective Multi-item Constructs: Construct Correlations and Square Root of AVE (Diagonal Elements) for ASW
Table 10: Discriminant Validity of the Reflective Multi-item Constructs: Construct Correlations and Square Root of AVE (Diagonal Elements) for FB
Table 11: Effect Size f2
Table 12: Descriptive Statistics of ß-Coefficients
Table 13: T-test for Mean Equality
Table 14: Significance of Path-Coefficients (*** p < 0.01, ** p < 0.05, * p < 0.1) and Verification of the Hypotheses (by Mean Comparison) for ASW and FB
Table 15: Member Profile (Appendix)
Table 16: Construct and Related Indicators (Appendix)
Table 17: Cross-loadings for ASW (Appendix)
Table 18: Cross-loadings for FB (Appendix)
Table 19: Common Method Bias for ASW (*** p < 0.01, ** p < 0.05) (Appendix)
Table 20: Common Method Bias for FB (*** p < 0.01, ** p < 0.05) (Appendix)
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The importance of information exchange through the Internet is growing and social networking sites are having a profound impact on the evolution of Internet business and e-commerce (Nielsen 2009). According to Comscore, Facebook, the world’s most popular and well-known social networking site, was the fourth largest site worldwide with 340 million unique users in July 2009 right after Google, Microsoft and Yahoo (Internet World Business 2009) and the number one website in the US as of March 2010 (Hitwise 2010). Facebook’s growth underscores the ongoing shift away from algorithm-based search engines to social search, which are results based on interpersonal interaction (CNN Money 2010; Lampe et al. 2006). Interestingly, according to Nielsen, the growth of social networking for information searches coincides with an increase of global consumer trust in online channels with 70% trusting consumer opinions posted online in 2009 compared to 60% in 2007 (Nielsen 2009). Both of these current trends have been greatly facilitated by social networking sites.
Most consumers are members of multiple social networks and use these networks to fulfill different needs and desires (De Valck et al. 2009; Bagozzi and Dholakia 2002). Social networking sites are generally classified into two categories: open social networks (OSNs) and invitation-only social networks (ISNs). Social networks are also organized around either a specific topic or general demographic such as friends or business partners. OSNs have no entry restrictions, whereas ISNs as private social networking sites require invitation, which results in a limitation in its membership base.
Whether social networking sites are OSNs or ISNs, previous research has found that qualitative and rich knowledge contribution is essential to successful online communities. Chiu et al. (2006) suggest that knowledge contribution is influenced by social capital and outcome expectations. Social capital is the network of relationships possessed by an individual or a social network and the set of resources embedded within it. Outcome expectations represent the expectations that an individual has towards the possible outcome of knowledge contribution for him/herself or for the community as a whole. From a marketing perspective, an important possibility to utilize member participation in terms of knowledge contribution is the encouragement of product recommendations between social networking site members, which may strongly influence the purchasing decisions of other members. A key aspect in this case is, whether or not recommenders receive monetary or non-monetary rewards. At first glance, one may think that a monetary reward should increase a member’s willingness to participate. However, this may not be always the case (Trusov et al. 2009) because trust-based relationships within the community and among its members greatly impact consumer behavior and should be cultivated to influence a member’s participation (Andrews et al. 2002; Ridings et al. 2002). The following scenario will show that the decision about implementing a monetary or non-monetary product recommendation system in different social network types is important since different approaches can lead to very different outcomes: ISNs have entry restrictions and are therefore smaller, which increases trust since relationships are based on authentic connections and true information. An ISNs focus is to provide a confidential platform for like-minded individuals who already have strong connections with each other. As such, a monetary incentive for a recommender should not influence the acceptance of recommendations because members trust each other and are intrinsically motivated. In contrast, OSN members usually have weaker connections and therefore, may trust other members less. In this environment with minor trust, it could be expected that some OSN members only recommend products to gain monetary rewards without consideration of whether their input helps the receiver of their recommendation. OSN members will be less trusting in product recommendations than ISN-members because they cannot distinguish between valuable and invaluable products recommendations. This results in a decreasing influence on the purchasing decision, making a monetary rewarded product recommendation system less successful than initially believed.
The scenario outlined above shows that the decision about monetary or non-monetary rewards is not trivial and sometimes not intuitive as different setups may lead to different outcomes depending on whether it is an open or invitation-only social network. Therefore it must first be empirically validated which product recommendation system will be successful prior to implementation. Then, as a next step, social capital and outcome expectations as important drivers of knowledge contribution in terms of product recommendation should be analyzed for both OSNs and ISNs.
The aim of this thesis is to measure and analyze the differences in the impact of social capital and outcome expectation elements on monetary and non-monetary rewarded recommendations between open and invitation-only social networks and to identify which product recommendation system should be implemented for ISNs and OSNs.
This thesis is organized as follows: Chapter 1 gives a brief introduction to the objective of this research. Chapter 2 defines open- and invitation-only social networking sites and compares the social networking sites. Further, the motivations for product recommendations in social networking sites, as well as, the impact of social capital and outcome expectations are discussed. Chapter 3 defines the research model for this study and sets up hypotheses based on previous research in the area of social networking sites. Chapter 4 empirically compares and contrasts factors that influence knowledge contribution in open- and invitation-only social networking sites, using structural equation modeling with data from a survey conducted for this study. Based on the differences between open- and invitation-only social networking sites, this thesis will then identify the appropriate product recommendation system needed for each social network type in chapter 5.
Social networking sites are web-based services where members can create personal profiles, connect with other members, share personal connections and establish or maintain relationships with others (Boyd and Ellison 2007). Social networks are usually organized around a specific subject or general demographic such as friends or business contacts. Social networking sites are one type of online communities (also called virtual communities) (Miller et al. 2009; Rheingold 1993). The term also includes markets and auction sites, electronic bulleting boards, list servers, blog sites, gaming communities and shared interest web sites (Miller et al. 2009). Online communities are defined as social networks where like-minded members share information (Hagel and Armstrong 1997; Chiu et al. 2006) and interact online to pursue personal and common goals (Dholakia and Bagozzi 2004).
Social networking sites can generally be classified into two categories: open social networks and invitation-only social networks. OSNs have no entry restrictions; whereas ISNs are private social clubs that limit its membership base by invitation only. Figure 1 shows a timeline of social networking site launches.
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Figure 1 : Timeline of Social Networking Sites Launches (Adapted from Boyd and Ellison 2007, and extended with ISNs and other launches for 2007-2010)
Previous research has analyzed social networking sites from many different perspectives. Boyd and Ellison (2007) focused on the history and development of social networking sites and gave a detailed overview of the existing sites. Hargittai (2007) analyzed the usage of social networking sites based on demographic characteristics and social surroundings and found that gender, race, ethnicity and parental education have an influence on the usage of social networking sites. Joinson (2008) examined a variety of uses of the social networking site Facebook that illuminated different patterns of usage. Possibilities to observe other members profiles led to an increased frequency of site visit or content on the site such as applications increased the time spent on site. Lampe et al. (2008) reported that the use and perception of Facebook sometimes changed over time, likely due to changes in an individual’s social context or an introduction of major features to the site.
Lampe et al. (2007) stated that the amount of profile information disclosure is positively correlated with more developed friendships. This is especially true for members who filled out profile fields related to common referents such as same high school, hometown, major or classes rather than individual likes or dislikes. Gilbert et al. (2008) investigated differences in social networking site use of rural and urban MySpace users. The key findings are that rural users have more trust issues than urban users and tend to have fewer connections. However, rural users have the same proportion of strong and weak ties as urban user.
Various researches such as Mazer et al. (2007) and Hewitt and Forte (2006) analyzed social networking sites from an academic setting and focused on the relationship between faculty and students. The first study found that a faculty member’s presence and information submitted about himself on Facebook leads to higher motivation of the students, more affective learning and positive climate in the classroom, but there were also concerns about the credibility of the professor. Contrary, Hewitt and Forte (2006) found no influence on the perception of the professor being on Facebook but one third of the respondents, mostly women, argued that faculties should not be present on Facebook at all due to privacy and identity management issues. Pasek et al. (2009) worked in this space as well by studying the relationship between Facebook use and academic performance and found no relationship. Lampe et al. (2006) focused more on the students themselves and found that students used Facebook to investigate people they had previously met offline rather than as opposed to initiating new connections with strangers. They also found that students did not anticipate that their profiles were being searched or viewed by people they are not friends with such as faculty members and/or other people outside their offline network.
Valkenburg et al. (2006) researched the consequences of adolescents’ use of social networking sites for their self-esteem and well-being, and found that the tone of feedback received on their profile affected self-esteem and by extension the individual’s well-being. DiMicco et al. (2008) analyzed a social networking site employed in a corporate setting and found different use and user motivations, such as broadening their network, when compared to usage of open social networking sites.
Other researchers found user concerns about privacy. Gross and Acquisti (2005) quantified social networking site users’, especially Facebook member’s willingness to share personal information and concluded that users were unconcerned about privacy implications at that time. However, Stutzmann (2006) studies showed that traditional identity information such as name shifted to more detailed information such as photo, political views and sexual orientation. Hodge (2006) also studied information disclosure and privacy setting in social networking sites and how users are less protecting their privacy online. He concluded that social networking site users give up their protection rights by subscribing to these sites and making information publicly available to everyone. Especially with police and other law enforcement officials using the information lately for legal actions and he underscored the importance of laws to protect user privacy.
Despite all this research, there is scarce research on social capital and social networking sites. Ellison et al. (2006; 2007) reported on the creation and maintenance of social capital in the context of Facebook use and found that certain types of use lead to higher social capital. In other words, individuals can benefit from their network if they use Facebook in the right way. Steinfield et al. (2008) followed Ellison et al.’s (2007) approach to explore the longitudinal effects of the relationship between Facebook use and social capital. They found support for it but concluded that the investigation should be continued over time to further explore the relationship. Steinfield et al. (2009) investigated the relationship of social capital and the use of internal social networking sites and found contextual evidence that intensity of use yields greater social capital.
Research on social networks has shown that social networking sites can be extremely useful for marketers depending on the context. First, social networking sites are organized around topics such as leisure, entertainment, and pop culture trends, which creates an important source of information for marketing research (Kozinets 2002; Bickart and Shindler 2001). This gives marketers more detailed information about individual and group behavior at little or no cost (Kozinets 1999). Second, social networking sites can be treated as a unique market segment based upon member interests and demographics, which gives marketers the chance to target marketing communications in a more efficient and appropriate way and build a stronger relationship with the customer by interacting with them directly (Kozinets 1999). Finally, social networking sites may be used as a tool to help create awareness for and interest in products, generate positive Word-of-Mouth-communication, enhance brand loyalty and most importantly, increase sales (Armstrong and Hagel 1996; Hagel and Armstrong 1997).
ISNs are private social networking sites or a type of so-called niche communities that require an invitation and are limited in its membership base. They target a more selected audience by restricting access and therefore appear more exclusive (Boyd and Ellison 2007).
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Table 1: Invitation-only Social Networks
The number of ISNs increased in the recent years. Beautifulpeople.com is a social network to create relationships for social and business contacts with other beautiful people, which was launched in 2001. People have to apply for a membership with a photo and short profile and existing members give their votes for the applicants within the first 48 hours (BeautifulPeople 2010). Decayenne is a worldwide invitation-only social network also founded in 2001 with the aim of promoting international social and business contacts. Currently there are about 31,000 members (Decayenne 2010). Diamond-lounge is an exclusive and private online dating and socializing site for “attractive and successful people”, founded in 2007 (Diamond Lounge 2010). An invitation or online application is necessary to be a member (Holahan 2007). Internations is an invitation-only social network to connect people who live and work abroad, which was founded in 2007 (Internations 2010). Affluence.org is an exclusive network for rich people who connect, share information and have important discussions on the page. It was initially launched in 2008. The requirements for a membership are very high. People have to prove that they have a yearly income of $300,000, a minimum net-worth of $3,000,000 or they have to apply with accurate information and invite other people that independently meet the financial requirements to show that they are an influencer (Affluence.org 2010).
ASmallWorld (ASW), the ISN, which is analyzed in this study, is an invitation-only online social network, founded in 2004, which aims to help confidentially connecting an existing international community of people with similar backgrounds, interests and perspectives online. Members are already directly or indirectly connected by three degrees of separation and want to stay in touch and share information. The aggregated source of valuable information, advice and help from trusted members enables individuals to manage their private, social and business lives (ASW 2010a).
Trust and confidence is the basic premise of ASW. In order to build a trusted community and a reliable source of information, the membership is by invitation-only and only 10 to 20% of the community is granted invitation rights. These trusted and loyal members have to fulfill certain criteria in order to achieve invitation rights to the community (ASW 2010e; Frank 2007). The organization polices itself by punishing untruthfulness or misrepresentation of one self’s identity (use of a pseudonym or alias, or inaccurate profile information) with suspension or termination of the member-account (ASW 2010c).
Members participate in different discussions and offer information, help and advice. Most popular topics are business opportunities (e.g. “I have a client with 4 billion dollars looking to invest” with over 49,060 views and 500 posts), “Free offers from to ASW members” (5 threads with over 78,460 views and 2,500 offers and posts), “top” or “best” lists (e.g. “Best club in your city” with over 22,600 views and 500 posts) or groups that find together (e.g. Hoteliers on ASW with over 21,900 views and 500 posts). Threads are limited to 500 posts and are usually continued in another thread if necessary. Individuals also ask for travel advice, what product to buy next or general discussions. Usually threads do not stay unanswered and members reciprocate in different ways (ASW 2010d).
Another aspect of ASW is that it can open doors to new opportunities. For example, members get together offline or identify themselves as a member of ASW to get access or gain other exclusive benefits in clubs, restaurants or special events (Stechlin 2007).
Another characteristic of ASW is that it only allows English as a language so that all discussions and content on the website is accessible and understood among its members. The social networking site refers to English as being the members base most common primary or secondary language (ASW 2010c).
Members of ASW refer to themselves as likeminded members because they share a vision, have similar backgrounds, interests and perspectives (ASW 2010a).
The ASW community is important to marketers because it provides a trusted environment for luxury-brand advertisement. Manufacturers can increase the awareness of their brands by reaching an influential and sophisticated membership base with more than 520,000 members, whom can be described as opinion leaders. However, to protect the user experience, ASW tries to prevent explicit promotion or over-commercialism by members (ASW 2010c; ASW 2010b).
OSNs are online social networking sites that have no entry restrictions.
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Table 2: Open Social Networks
The first social networking site founded in 1997 was SixDegrees.com, where members were allowed to create profiles and connect to their friends. The site closed in 2000 because of limited business functionality and the little usage of the site for members after accepting their friends, especially because the majority of their extended network was not online (Boyd and Ellison 2007).
Friendster launched in 2002 with the promise of more engaging sites due to detailed profiles, photo sharing, messages and testimonials. But, the page faced some issues to provide a well-functioning page with servers that could handle member growth and new features to bond members to the site. In 2003, professional social networking sites such as LinkedIn and Xing launched to target business people. MySpace was also founded in 2003 and became very popular because it addressed Friendster’s shortcomings (e.g. by quickly adding features on members demand). Music bands especially, promoted themselves and connected to their fans online.
This thesis explains OSNs with the example of Facebook, which will be analyzed more detailed based on the data of the empirical model.
Facebook (FB) is an open social networking site, launched in 2004, which helps to maintain and develop social relationships among friends, family and co-workers (Facebook 2010e). In 2004, it was a relatively closed private and intimate community restricted to Harvard students and later on to any student with an “.edu” email address. FB relaxed this rule in September 2005 and transformed it into an open social network (Boyd and Ellison 2007). FB became a fast-growing global social networking site, which became almost the size of MySpace by the end of October 2008. It exceeded the size by mid 2009 and even became the world’s fourth largest website in the world (Nielsen 2009).
Members of the social networking site are connected by six or less degrees of separation, which mean that (almost) every person is connected to every other person through six contacts (or less). But, the network usually consists of many networks within the community (Facebook 2010c).
Similar to ASW, FB tries to provide a trusted environment for interaction. Personal information must be truthful and members can build trust and reputation through their identity and network (Facebook 2010f). FB intends for the website to be available for everyone in the world across geographic and national boundaries, irrespective of a member’s level of participation or contribution. Millions of members share content such as web links, news stories, blog posts, notes, photo albums, etc. on a daily basis, further establishing and broadening these social relationships (Facebook 2010d; Facebook 2010f).
FB members can share with other members which products or services they like. Almost 10 million members share on their profile that they like FB and therefore get updated about new developments on the site (Facebook 2010h).
Currently, FB is available in more than 70 languages and over 300,000 users helped to translate the pages. While about 70% of the membership base lives outside the US, there are no requirements for a consistent shared language (Facebook 2010d).
FB is not just one network, but rather a collection of many networks that are based around a workplace or school (Facebook 2010c). Thus, the shared vision of members is limited to a network since members usually only interact in their own network and restrict non-member access.
FB provides advertisers with a platform to reach exact target audiences with relevant messages and enable businesses to spread information virally among more than 400,000,000 members (Facebook 2010a). Finally, FB restricts members from using their profiles for their own commercial advantage (Facebook 2010g).
ISNs and OSNs are different types of social networking sites. They especially differentiate themselves with the number of members. OSNs usually have at least a few million members, whereas in ISNs the membership is kept small with less than a million members worldwide. The aim of OSNs is to help maintain or develop social relationships with friends, family and co-workers and share all kind of happenings with them. ISNs want to connect an existing community of like-minded people who share similar backgrounds, interests and perspectives and to manage their private, social and business lives. In OSNs, members will be able to find a wide range of their offline networks, which are communities by themselves. This occurs due to the openness of OSN to everybody, so that (almost) every person is connected to every other person through six contacts (or less). In ISNs you have be invited from a trusted member who fulfills different requirements to join the social networking site. Therefore, members are connected with any other person through two or three other members.
In OSNs, the site is usually translated into many languages and makes it possible to communicate in almost every language. This may not be a problem since members of OSN are usually limited to communicating with their own network and do not have full access to others profiles. ISNs want to make it easier to understand the conversations for everyone and limit the site to one or a few languages. Members also feel more connected to other members outside their networks because they share similar backgrounds, interest and perspectives, so that it is easier to trust a friend’s friend and be connected with those members. Table 3 gives an overview of OSNs and ISNs.
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Table 3: Theoretical Comparison of ISNs and OSNs
Product recommendations have a variety of sources. Andreasen’s (1968) four product recommendation sources are: 1) Personal source providing personalized information or 2) Non-personalized information, 3) Impersonal source providing personalized information or 4) Non-personalized information. Senecal and Nantel (2004) translated the typology of information sources stated by Andreasen (1968) into computer-mediated environments. They grouped online product recommendation sources into the following categories: 1) other consumers such as relatives, friends and acquaintances, 2) human experts such as salespersons and independent experts and 3) expert systems such as recommender systems. Electronic Word-of-Mouth can be described as an information source that belongs to personal sources providing personalized and non-personalized information. However, the Internet is an impersonal source providing personalized information (Senecal and Nantel 2004).
In the following, the previous research about traditional Word-of-Mouth (WOM) and electronic Word-of-Mouth (eWOM) helps to understand and compare these sources of product recommendations. Furthermore, eWOM can be observed in the social networking site setting.
Word-of-Mouth (WOM) is the communication and mutual exchange of positive, neutral and negative information about products and services between individuals. Recommendations are a positive form of WOM (Anderson 1998). Several studies proved that WOM significantly influences the following aspects of the consumer behavior.
Previous research has recognized the importance of WOM and found it to be more effective than e.g. printed advertisement, radio advertisement and personal selling (Herr et al. 1991; Katz and Larsfeld 1955). Katz and Lazarfeld (1955) conducted the earliest study on the influence of WOM, and found that it is especially effective on the purchase of household goods and food products. Subsequent researcher expanded the studies in other industries and areas such as for automotive diagnostic centers (Engel et al. 1969) and services (Mangold et al. 1999). Heer et al. (1991) studied the effect of WOM on product judgments by analyzing vividly presented information and found that WOM had a stronger influence on individuals due to its vividness when compared to printed information.
Arndt’s (1967) approach to WOM was to identify the specific factors that influenced a consumer’s decision and found that positive WOM increased the likelihood of purchase, whereas negative WOM decreased it. Contrary to Katz and Larsfeld (1955), who argued that opinion-leaders may control purchasing decisions; Arndt (1967) suggested that the WOM process is an aggregation of opinions, where consumers seek social support from a group.
Many researchers have focused on the characteristics of the communicator, who is the one who expresses a product recommendation (recommender), to identify interpersonal influence (Bearden and Etzel 1982; Bearden et al. 1989; Price et al. 1989). Bearden and Etzel (1982) examined the influence of reference groups on consumer perceptions towards products and brands, and found differences between publicly and privately consumed products and luxuries and necessities. Bearden et al. (1989) analyzed consumer susceptibility to interpersonal influence. Price et al. (1989) examined the impact of similarity between the communicator and the consumer on the extent of informational influence and found that source similarity (e.g. tastes and preferences) is more important than expertise.
Other researchers have examined the extensiveness of interpersonal influence on WOM (Brown and Reinigen 1987; Goldenberg et al. 2001). Brown and Reinigen (1987) investigated the strength of ties between the communicator and the decision-maker in the context of choosing a piano teacher. They found that weak ties are important in bridging referral flows, but that strong ties are more influential in decision making and much more effective than referrals. Consumers tend to choose more similar personal sources of information for a referral flow. Contrary, Goldenberg et al. (2001) showed that the impact of weak ties on information dissemination is at least as strong as the influence of strong ties. The relative impact of each may depend on factors such as personal network size, number of weak ties and advertising.
The expansion of the Internet in the last decade has made electronic Word-of-Mouth (eWOM), also called Word-of-Mouse an important source of consumers’ product evaluations. Consumers gather product information from other consumers by reading comments or by posting their own experiences with a product (Henning-Thurau et al. 2004). Online communication generally takes places through different channels such as emails, online discussion forums, instant messaging programs, electronic bulletin board systems, newsgroups, blogs, review sites and social networking sites (Goldsmith 2006).
To examine this trend in detail, some researches such as Godes and Mayzlin (2004) and Chevalier and Mayzlin (2006) tracked online conversations to find out the influence of eWOM. For instance, Godes and Mayzlin (2004) tracked how the amount and dispersion of online conversation and different Usenet groups’ communications influenced the success of new TV shows. They found the dispersion of conversations to be more valuable than the volume, but they did not identify whether the important factors that lead to success were online conversations or other exogenous factors. In another study, Chevalier and Mayzlin (2006) observed the effect of eWOM on product sales, specifically book reviews. In analyzing book reviews, the researchers found that reviews are on average, more positive than negative. At one site, new and favorable reviews resulted in increased book sales. However, this trend works both ways because a negative review adversely impacts books sales. These results underscore the power of eWOM in product sales.
Senecal and Nantel (2004) investigated the influence of eWOM on consumers’ product choices, taking into consideration the different effects of online recommendation sources, product and website types. They found that recommender systems are the most influential source, despite the fact that human experts possess more expertise and other consumers are trustworthier. Also, the influence of experience products (qualities cannot be determined before purchasing e.g. wine) is greater than that of search products (qualities are accessible prior purchase e.g. calculator) (Senecal and Nantel 2004). Furthermore, the type of website has little to no influence on product choices and the authors suggested that consumers focus more on the source of the recommendation itself rather than on the type of website where it is found. Cheung et al. (2008) analyzed the influence of source credibility and information quality on eWOM information use and consumer adoption. The attributes that significantly influenced the use and adoption of information were the relevance and the comprehensiveness of the information quality. This means that the eWOM message has to be relevant and complete in order to be useful to the consumer and to help in the decision-making process. Park and Lee (2009) measured the influence of a website’s reputation as a dimension of source credibility and found that established reputations have a greater impact on eWOM messages than less established ones. They also analyzed the impact of positive and negative eWOM on products and found that negative eWOMs had a greater impact than positive eWOM. Zhang et al. (2010) came to the same conclusion as they examined the usefulness and persuasiveness of product reviews on eWOM users. In their study, they analyzed the persuasiveness of eWOM through the regulatory focus theory, which, according to Higgins (1997), states that individuals use promotion and prevention as self-regulatory systems to achieve their personal goals. In Zhang et al.’s (2010) product review model, consumers identify useful information to help achieving desirable outcomes while avoiding undesirable outcomes. Positive product reviews describe satisfaction with a product and provide the opportunity to gain positive outcomes. As a result, positive product reviews are more persuasive than negative reviews. Accordingly, negative product reviews describe dissatisfaction and may lead to undesired outcomes. Thus, they are associated with negative consumption goals and negative reviews are more persuasive than positive ones.
Another author also considered the recommender source as a relevant decision aid (Smith et al. 2005). Smith et al. (2005) outlined the importance of peer recommender to the consumer. They found that product recommendations influence consumers in the actual product choices, the amount of search effort in the decision-making process and the level of user interest in sponsored advertisement. Peer recommenders are generally preferred over editorial recommendations and a lack in expertise and rapport of the peer is counter-balanced by independent search. In general, consumers want to reduce the effort for online searches by using product recommendations. The provision of peer recommenders regardless of their profile may lead to information overload. Park and Lee (2008) concluded that consumers might become more dissatisfied and confused due to too much information. The authors studied information overload qualitatively and categorized messages into two categories: simple recommendations and attribute-value information. While simple recommendations provide emotional, abstract and subjective opinions and are easy to understand, attribute-value information are rational, objective and concrete and rely on specific product attributes and takes longer to process. This may also explain Bickart and Schindler’s (2001) results that showed online forums to have a greater impact on product evaluation than marketer-generated sources or corporate websites. Subjective product recommendations in online forums have a greater impact than objective and rational marketer generated sources.
Depending on the common interest of an online community, members are willing to participate and provide product and service related information. Individuals who are committed to an online community are more likely to show a positive attitude and commitment to the products and brands favored by online community members. Community members can act as objective sources of information that also create new uses and benefits from the brand (Kim et al. 2008). Kim et al. (2008) studied the role of online communities and its relationship to brand commitment. They stated that brand commitment leads to more positive WOM. This can help firms to build a strong relationship with their customers and stimulate favored outcomes such as repurchase, WOM, cross-over buying, co-production. Thus, the development of strong customer commitment itself does not assure desired outcomes, its impact is realized through brand commitment.
Henning-Thurau et al. (2004) identified four key motives for sharing and articulating eWOM. The key motives are social benefits, economic incentives, concern for others and self-enhancement. A social benefit can be achieved through participation in an online community when an individual is identified and socially integrated as a member and receives social benefits from this membership. Economic incentives serve as an approval and a sign of appreciation for the members’ behavior, whereas self-enhancement is the personal desire of being recognized by others and to achieve a social status. Another motive is the concern for others where the member behaves in a way that benefits others. Goldsmith and Horowitz (2006) extended the research by further analyzing the motives for seeking eWOM. They found that consumers seek information online to reduce the perceived risk of a purchase, find lower pricing and attain other accessible pre-purchase information.
WOM is usually verbal person-to-person communication (Arndt 1967). eWOM is a written person-to-person communication directed to many individuals (Henning-Thurau et al. 2004). Though these two may seem similar at a first glance, many differences between WOM and eWOM exist, which can be structured in the aspects of communication, source and contribution, as shown in Table 4. The first key difference is the type of communication. In traditional WOM the communication takes place face-to-face. eWOM takes place through a variety of channels such as email, online discussion forums, instant messaging programs, electronic bulletin board systems, newsgroups, blogs, review sites and social networking sites (Goldsmith 2006), and has the ability to reach a broader audience. Second, traditional WOM between consumers and recommenders center around a personal relationship in some capacity (Brown and Reinigen 1987), whereas eWOM may also come from individual’s with little to no prior ties. This makes it difficult to rely on source similarity to identify the information credibility (Park and Lee 2009) and explain the persuasiveness of WOM (Knapp and Daly 2002). But eWOM has the advantage that the identity is kept anonymous and confidential (Goldsmith and Horawitz 2006). The final and third key difference is that eWOM facilitates contribution of opinions about products. eWOM has an added advantage of being more detailed, faster, accessible and not one-time spoken communication (Bickart and Schindler 2001). In addition, it is bringing individuals together online, even though they are geographically dispersed (Henning-Thurau et al. 2004).
Both, WOM and eWOM are an interpersonal interaction that influences decision-making (Arndt 1967; Senecal and Nantel 2004).
Abbildung in dieser Leseprobe nicht enthalten
Table 4: Comparison of WOM and eWOM
Social networking sites are becoming an increasingly more important channel for eWOM because they enhance the ability of the consumer to share and provide information and advice about products and services. The main objectives of social networking sites are to share experiences and establish or maintain relationships with others (Boyd and Ellison 2007). Active and constant communication with friends and acquaintances through different channels such as forums, blogs, groups and instant messaging (Goldsmith 2006) may strengthen the relationship within personal social networks. The variety of online communication channels in social networking sites, give consumers many options to engage in eWOM behavior and share their product-related experiences or seek advice. Despite the huge impact of eWOM on purchasing decisions and the accessibility of consumer generated product recommendations, there is only little research on eWOM behavior and the influence on decision-making in social networking sites. Brown et al. (2007) analyzed how eWOM impacts decision-making and attitude formation in the context of social networking sites and explained the role of tie strength, homophily and source credibility in the evaluation of marketing information. Relationships in online social networks relate more to the interaction of members and the website as a whole rather than to individual-to-individual connections. The strength of relationships is less important in the online context than offline. As a result, website reciprocity and emotional closeness are more important than the strength of the relationship between individuals. Homophily refers to the similarity between an individual’s psychological attributes and the content of the web site. Source credibility is based on attributes such as site trustworthiness and actors’ expertise.
Social capital is composed by the network of relationships possessed by an individual or a social network and the set of resources embedded within it. It is examined along with the three dimensions suggested by Nahapiet and Ghoshal’s (1998) study: 1) structural 2) relational and 3) cognitive capital. They stated that social capital shows a mutual reaction: it is created and maintained through exchange but also facilitates exchange. Time, interaction, interdependence and closure are mentioned as the relevant factors to develop social capital in an organizational setting. Social relations are built and strengthened over time and social capital again depends on a stabile and persistent social structure. Interaction can strengthen social relationships and has to be maintained to continue this relationship. Social capital can be increased by interaction and a high level of mutual interdependence. Finally, closure is an important feature of social relationships and helps to build social capital. Members distinguish themselves from non-members and closure facilitates the development of trust, norms, identity, shared codes and languages (Nahapiet and Ghoshal 1998).
Hung and Li (2007) investigated the influence of social capital on eWOM in online communities and stated that social capital triggers consumer learning and behavioral outcomes.
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