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The Motivation of Online Community Members for Electronic
Word of Mouth
A quantitative research on the factors that are the motivations for member’s activity in online communities.
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The Motivation of Online Community Members for Electronic
Word of Mouth
A quantitative research on the factors that are the motivations for member’s activity in online communities.
Groningen, August 2011
Faculty: Economics and Business
Specialization: Marketing Management Master Thesis
Author: Andrew Richard van der Meer e-mail: avdm_@hotmail.com
S/N: s1675354
Organization: University of Groningen
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Abstract
Internet based communities (eg,Facebook) have allowed consumers to share their opinions and experiences on specific brands with an unlimited amount of other consumers. They engage in electronic word of mouth communication. A model of
Motivations for the reason these consumers participate in the Online Brand Community is developed using findings from research on online communities and normal word of mouth communication. With the use of an online survey, data on the motivations for the posting of community members is collected. The result from the research showed that the Motivations of Anticipated Reciprocity, Increased Recognition and Motivation Not in Self Interest were the reasons community members participated in Online Brand Communities. It was also found that the members of these communities could be
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Index
1. Introduction . . . . 7 1.1 Problem Statement . . . 8 1.2 Managerial Relevance . . . 9 1.3. Academic Relevance . . . 9 1.4 Structure Overview . . . 10 2. Theoretical Framework . . . . 11 2.1 Definitions . . . 11 2.1.1 Definition of a Community . . . . 122.1.2 Definition of an Online Community . . . 12
2.1.3 Definition of an Online Community used in this Paper . 13 2.1.4 Definition of an Online Brand Community . . 14
2.1.4.1 Categories of Online Brand Communities 14 2.2 Word of Mouth . . . 15
2.2.1Word of Mouth Definition . . . . 15
2.2.2 Electronic Word of Mouth (eWOM) difference and definition 16 2.3 Online Participation . . . 17
2.3.1 Anticipated Reciprocity . . . 17
2.3.2 Increased Recognition . . . 18
2.3.3 Sense of Efficacy . . . 19
2.3.4 Community Need Motivation . . . . 19
2.3.5 Sense of Community . . . 20
2.3.6 The Conceptual Model . . . 20
2.4 Conclusion . . . 21
2.5 Membership Classification and Membership Life Cycle . . . 21
2.6 Advantages and Disadvantages for a company when creating communities where consumers are able to contribute information . . . 23
3. Research Design . . . . 25
3.1 Sample and Procedure . . . 25
3.1.1 Sample . . . 26
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3.2 Operationalization of Constructs . . . 27
3.3 Estimation and Assessment . . . 30
3.4 Specify the Measurement of Scaling Procedures . . . . 30
4. Results . . . 31
4.1 Procedure . . . 31
4.2 The Sample and Demographics . . . 31
4.3 Factor Analysis . . . 34
4.4 Reliability Analysis . . . 38
4.4.1 Reliability Analysis for factors found in the factor analysis 38 4.4.2 Reliability Analysis for the factors of the theoretical model 39 4.4.3 The New Model and Hypotheses . . . 40
4.5 Regression . . . 41
4.5.1 The Regression Setup . . . 42
4.5.2 Regression Findings . . . 42
4.6 Membership Lifecycle ANNOVA Test . . . 43
4.6.1 Results from the Tukey Post Hoc . . . 46
4.6.1.1 Motivation Not in Self Interest . . 46
4.6.1.2 Motivation of Increased Recognition . . 47
4.6.1.3 Motivation of Anticipated Reciprocity . 48 4.6.1.4 Posts with a Specific Username . . 49
5. Conclusions . . . 50
5.1 Basic Conclusions . . . 50
5.2 The Hypothesized Relationships . . . 50
5.3 The motivations and the Online Community Membership Lifecycle . 52 5.4 Managerial Implications . . . 53 5.5 Academic Implications . . . 55 5.6 Further Studies . . . 56 6. References . . . . 57 7. Appendix . . . . 60 Appendix 1 Questionnaire . . . 60
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Appendix 3 Frequencies Statistics . . . 67 Appendix 4 Regression Statistics . . . 72
Appendix 5 ANOVA Statistics . . . 73
List of figures and tables
Figures
Figure 1: Conceptual model Motivations for Contributing . . . 20 Figure 2: Membership Lifecycle for Online Communities Kim (2000) . 22 Figure 3: The Different Communities and the Respondents . . . 32 Figure 4: Amount of Members and the Amount of years they have been a Member 33
Figure 5: Histogram showing the number of times each respondent posted in the last
month . . . 34
Figure 6: The total variance explained using the principal components method 35 Figure 7: Model for Motivations for posting in Online Communities . 41
Tables
Table 1: Facebook groups and the current amount of members they have . 26 Table 2: The Different Online communities and Ratios of Male and Female
Respondents . . . 32
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1. Introduction
Currently it is becoming increasingly difficult to find someone who is not a member of some form of online social network platform such as Facebook, Myspace or Hyves. According to Facebook.com statistics (2011), Facebook currently has 750 million active users who spend over 500 billion minutes on the social network per month. Seventy percent of these users reside outside of the United States and Facebook is available in 70 different translations. Each user is connected to, on average 60 group pages and events. Platforms such as these have given consumers access to new areas where they can source and share product information with each other. Where the consumer would previously have laboriously searched for information on products “offline” by means of the print media and verbal contact with a shop assistant or consumer call centre, they are now able to have immediate access to an abundance of information and opinions from several sources online. They are also able to evaluate for themselves what information is credible to them from information that is online, such as product/brand fangroups on the Myspace and Facebook communities. The information that is available on the social networking platforms is mainly provided by individuals, but also to an increasing extent by
companies who wish to connect with the vast number of potential consumers.
You can search for any product/brand information online and you are virtually
guaranteed to find a group of people who are sharing information and experiences about the product/brand among themselves. As a result of this, companies have identified these online communities as sources of quality and credible, consumer provided information about their products or brands.
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reintroduced their Wispa Bar chocolate after there were more than one hundred “Bring back Wispa” communities created on Facebook (Robin Goad nma.co.uk, 13.09.07).
Rozanski et al (1999) found that a small core of focused individuals can have a
significant impact on new product offerings. Wang and Fesenmaier (2003) found that in an online community it is common to find individuals who are incredibly generous with their time and expertise. In online communities it is also common to find individuals who participate and share information without any financial rewards coming to them. Who are these active consumers / focused individuals who join the groups? What are the
motivating factors for them to share information often and with no obvious rewards?
In internet culture the 1% rule is that the majority of visitors to a community will be lurkers (people who just read what is available and do not participate). The theory is that 1% or less of the people who view content on the internet actually create it. (e.g., for every one person who posts on a forum, there are at least ninety-nine other people viewing that forum but not posting). So what is motivating the 1% of people to post on online communities and impacting the community?
In their research Lin and Lee (2006) found that members of an online community are motivated to share relevant information and that this information may have a negative or positive impact on the companies activity. Online communities can therefore have an effect on a company financially and can also have an effect on its brand image.
Understanding the posting motivations will assist in the interpretation of the posting and will be helpful to managers if they are considering starting their own online community for their brand. It will also help the manager’s community to be more trustworthy and relevant in the eyes of potential members of the online brand community.
1.1 Problem Statement
A better understanding of the members in online communities is required. The aim of this research is to investigate the motivation of Online Brand Community members for
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What motivates a member to comment on the online community?
What types of people join online communities and do they actively return to the community after they have joined?
Who are the different types of community members and do they have similar posting motivations to other similar types of community members?
1.2 Managerial Relevance
This research project will attempt to provide detailed findings on the motivations for the commenting of individuals in online communities. These findings will allow for brand managers to have a clearer picture on the motivations of the consumer depending on where they are on the online community lifecycle. It will also assist brand managers in browsing through the product groups, to interpret product information from the
consumer’s point of view.
Additionally, the research will be valuable to companies that are considering using social networking communities as a marketing tool for their brand. Online communities can affect brand image and therefore it is important to have a better understanding about the reasons behind Word of Mouth in online communities. The research will also be useful to managers because there will be a better understanding of the reasons for commenting and managers will be able to use this understanding to make better decisions when
considering information from their online community.
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.Academic Relevance
There is much research on Electronic Word of Mouth (eWOM) and why people engage in eWom. As online brand communities are fairly new in the marketing world no
extensive research exists on the reason why members post on them. As yet to my
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This study will extend our knowledge of online communities. At present the majority of research has been done on what an online community is and what the factors are that make up an online community. This thesis research will shed light on the activity behavior of members in online communities. The understanding of online communities will also be improved by the research into the motivations of consumers that are joining the online communities and, if they are actively involved or are they more likely just to join in a once off action.
This study will extend the knowledge on Word of Mouth. It will add to the Electronic Word of Mouth theory which will be combined with findings from other studies to make the understanding of Word of Mouth more complete. The research that has been done on Word of Mouth motivation online, has been done on online shopping sites and not on the aspect of online brand communities. This research will fill that gap in.
The motives of the people who read the posting and the impact from that information on their behavior has been studied, but the focus has not been on the motives of the people who post the information in the first place. This research will deal with this problem in the Word of Mouth literature.
1.4 Structure Overview
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2. Theoretical Framework
A survey done by comScore, an internet marketing research company in 2007 found that 24% of internet users access online reviews before paying for a service delivered offline. Zhu and Zhang (2010) find online customer reviews have become an important resource for consumers wishing to find information on products or services. Chevalier and
Mayzlin (2006) agree by saying that online user reviews have become an important source of information to consumers, substituting and complimenting other forms of business to consumer and offline Word of Mouth communication about products. This product information in consumer reviews from experienced consumers, that consumers are now searching for, can be found in online communities.
2.1 Definitions
2.1.1 Definition of a Community
A community is an organization of individuals or small groups that have an intention to get together and have a sense of mutual responsibility (Jang et al, 2008).
Walden (2000) views the term “community” as the chance to interact with real people. He views any kind of situation in which an individual can view the opinions of others or make known their opinions, as a community.
There are two types of communities according to Gusfield (1975):
1. a relational community concerned with human relationships (eg, hobby clubs and fan clubs)
2. traditional geographic community (eg, neighborhood, or a town)
Within these two types of communities there are three core components of a community according to Muniz and O’Guinn (2001) and they are:
1. a connection so that members feel different from those that are not in the community. 2. the presence of shared rituals and traditions that perpetuate the community’s history, culture and consciousness
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At this stage in the definition of a community it is also important to note that a
community is in a physical place where members interact with each other in a “real” or physical environment.
As seen from the above, current research describes a wide array of definitions on what a community is. Today the concept of a community has broadened with the advent of the internet and social networking platforms.
2.1.2 Definition of an Online Community
For the purposes of this paper a definition of an online community needs to be identified. There has been some recent research done in the various areas involving online
communities and the different authors have similar views on what an online community is. The main differences of an online community, to a community, are that an online community is in cyberspace and does not have any physical qualities. Lin and Lee (2006) define the online community as a social relationship aggregation, facilitated by internet based technology in which users communicate and build personal relationships. Wellman and Gulia (1999) define the online community as a relational community, concerned with social interaction among its members. Bagozzi and Dholakia (2002) view online
communities as computer mediated social spaces with intentional actions, in which content is created by members through ongoing communication processes. An online community is a virtual community that exists online who’s members enable its existence through taking part in membership rituals (Kim, 2000).
Online communities are not exactly relational communities. They do not fit into Gusfield’s (1975) definition of a type of community (either relational or traditional geographic community), because the members of the online community interact with each other on a specific site in cyberspace. The members will have an emotional
attachment to the online community which is the same as the attachment that members of traditional communities have to their physical geographic location communities.
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Examples of online communities are chat rooms and other posting pages, eg, Facebook, Myspace pages, Hyves, Blogspot, E-bay, Wikipedia Youtube, Linkedin, usenet.com and Alibaba. Subjects in the communities can vary between anything from hobbies,
spirituality, fan clubs to corporate community practices.
2.1.3 Definition of an Online Community used in this Paper
To develop a complete definition for an online community the following factors and components of online communities have been considered.
Kim (2000) provides five factors that a community requires to be called an online community:
1. clear purpose and vision- The group has meaning and it is clear what the group is about.
2. flexible and small scale places- The platform or technology that supports the groups online space is able to change.
3. members role (who designs the activities for the groups)- The community members design discussions and activities for the community.
4. leadership of community moderators (eg the leaders)- In Facebook you get administrators, officers and members. Members have different classes, responsibilities and levels of access to the community.
5. online and offline events- Members interact with each other and organize events offline in some cases.
Preece et al (2000) suggested four components of online communities:
1. people- Communities have members.
2. shared purpose-The members are in the online community because they all share a specific interest.
3. policies- The online community has rules.
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The definition for an online community used by this paper is developed by taking the above mentioned definitions and factors of online communities into consideration. The developed definition is: an online community is a group of peoplewho have a common interest in a subject and interact with each other through the use of internet based information technology.
2.1.4 Definition of an Online Brand Community
The topics in online communities are infinite, and not all subjects are important to consumers wanting to buy a product. Potential consumers are only interested in
information on online communities that focus on the brand that they are interested in. An Online Brand Community is a specialized, non geographically bound community, based upon social relationships among admirers of a brand in cyberspace (Jang et al, 2008). It is these communities that are discussing products in any way that is relevant for this paper. These communities that focus on a product are called Online Brand Communities. The definition of an Online Brand Community used by this paper is: An Online Brand
Community is an Online Community that is focused on a brand or a product.
2.1.4.1 Categories of Online Brand Communities
Online Brand Communities are created by profit and non profit organizations. Non profit communities are mainly created by individuals and the profit organizations communities are created by the company involved with selling the brand.
Porter (2004) also agrees that there are two categories of online communities. 1. member initiated communities
2. organization sponsored communities
The difference between the two revolves around trust. The advantage of the consumer initiated community is that the information from other users is more trustworthy /credible because negative consumer responses can be screened out of the company initiated communities. Member initiated communities tend to manage negative opinions and information quite well as the information is not as controlled as in organization
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community has is that detailed product information can be given. Despite the discussions about what constitutes an online community, researchers agree that online communities can be created with the presence of people that have specific purposes, who interact with each other, under the governance of certain policies and with the aid of computer
technologies.
Online Communities are important to this thesis because they are the platform that the users of the Online Brand Communities use for Word of Mouth marketing.
2.2 Word of Mouth
Anderson (1998) finds that extremely satisfied and extremely dissatisfied customers are more likely to initiate Word of Mouth transfers. The members who are posting in the communities are not compensated for their sharing. Why do these people share their information, what are the motivations behind their information sharing?
2.2.1 Word of Mouth Definition
The meaning of Word of Mouth or WOM is the passing of information from person to person (Shorter Oxford English Dictionary (Sixth edition). Anderson (1998) defines WOM as informal communications between private parties concerning the evaluation of goods and services. Word of Mouth can be any form of human communication like face to face conversation, e– mail, telephone and more relevant for this research, online community forums. Grewal et al (2003) show that WOM is valued by marketers because of the personal nature that is involved with Word of Mouth communication and this gives the information more credibility than traditional marketing methods.
Anderson (1998) states that it is widely held that when a customer is loyal and satisfied they will engage in WOM favorable to the brand. So when a customer is loyal and satisfied they are motivated to post. Anderson (1998) finds that in contrast to the preceding, there is evidence that there is a negative or inverse relationship between
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A study of Coca Cola’s customers shows that dissatisfied customers partake in twice as much WOM activities as satisfied customers although other tests have resulted in dissatisfied customers partaking in as much as 10 times more WOM than satisfied customers (Henning-Thurau et al, 2004).
With the emergence of internet applications such as Facebook, Youtube and Myspace a new means of communicating WOM has been developed. With the increasing use of the internet as a means to research product information, WOM has become an even more important resource for marketers and consumers. Certain elements of WOM have
changed in this new area of WOM because of the internet element that has been added to WOM. Word of Mouth over the internet and in online communities is referred to as Electronic Word of Mouth (eWOM).
2.2.2 Electronic Word of Mouth (eWOM) difference and definition
As explained in the online community section earlier, online communities have unlimited amounts of members that do not necessarily know or have never even met each other. According to Cheung et al (2009) the attraction to consumer forums (online
communities) is due to a new form of WOM communication comprising information on opinions and recommendations on products from consumers. Researchers refer to this online information sharing as eWOM. Cheung et al (2009) state that eWOM joins consumers and extends and opens up the WOM network to the entire internet world. Henning-Thurau et al (2004) define eWOM as any positive or negative statement made by potential, actual, or former customers about a product or company, which is made available to a multitude of people and institutions via the internet.
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buyers looking for product information (Cheung et al, 2009). The importance of eWOM in communities is not as simple as a meeting place for contributing members to have a conversation about a product. The importance is the people who are reading the
information when they are trying to make a buying decision about the discussed product.
According to Henning-Thurau et al (2004) given the conceptual closeness of eWOM and traditional WOM communication, consumer motives that have been identified in the literature as being relevant for traditional WOM also can be expected to be of relevance for eWOM. Thus the WOM motives identified by Kollock (1999) and Smith (1992) which are focused on in the following section (2.3 Online Participation) can be seen as relevant to the eWOM research in this paper.
2.3 Online Participation
Why people participate in online communities has been a question that researchers have studied. They theorised that several motivations lead people to contribute to online communities. In this section the possible motivations for the sharing of information in online communities is addressed. Kollock (1999) outlines three possible motivations that are in the self interest of the contributor: Anticipated Reciprocity; Increased Recognition; and Sense of Efficacy. Then two other motivations that are in the interest of others are identified: Community Need Motivation Kollock (1999) and Sense of Community Smith (1992). This study will test these theorized motivations empirically which has not been done before.
2.3.1 Anticipated Reciprocity
Anticipated Reciprocity is when members are motivated to contribute information in an online community and expect information in return. If a member contributes regularly then they become known to other members in the group. This is a motivation to
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communities get more responses faster to questions than unknown participants (Rheingold, 1993).
According to Kollock (1999) the factors that affect Anticipated Reciprocity are ongoing interaction, identity persistence, knowledge of previous interactions and strong group borders.
This motivation will work well in information sharing forums like Facebook where all group discussions are available for all members to see.
H1: Anticipated Reciprocity positively affects online participation
2.3.2 Increased Recognition
Increased Recognition is a motivation because of the effect of contributions on a member’s reputation. When the contributions of the member are recognized by other members in the group then the member gets a reputation. This reputation is especially important in communities like E-Bay and Marktplaats because of the financial aspect with these communities and with a good reputation others are more likely to buy from the member. The member’s reputation will be used as a measure of trustworthiness /
credibility of the member, when another member wishes to do business with them. E-Bay members have the option to rate their experience with someone and they, likewise, can rate you. This has an effect on the reputation score. Rheingold (1993) identifies that the desire for prestige is one of the key motivations that individuals contribute to a group.
Because of the way that people communicate in online communities ie: everybody can see everyone’s comments, helpful and quality information that is posted by specific members will be recognized easily within the community.
According to Kollock (1999) the factors that affect Anticipated Reciprocity also affect recognition.
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Sense of Efficacy is when members contribute information because they feel that their contribution affects the environment or group. There is well-developed research literature that has shown how important a Sense of Efficacy is (Bandura, 1995). Bandura (1995) shows that making high quality contributions to the group helps members believe that they have an impact on the group and supports their own self-image as an efficacious person. Facebook sports group discussion topics are good examples, as Facebook allows the users to create new topics and instantly reply to other topics created by other
members. Kollock (1999) recognizes that if a person is being motivated by a Sense of Efficacy then contributions will be increased until it is noticeable that there are changes in the group due to the user’s actions. Kollock (1999) also recognizes that members who are contributing due to a Sense of Efficacy are likely to contribute more when large numbers of members are involved, so that the contributor will feel like they are having a greater impact because of their actions.
H3: Sense of Efficacy positively affects online participation
2.3.4 Community Need Motivation
The difference with this motivation to the previous three is that the previous motivations act in self interest. The motivation of Community Need refers to contribution for the need of the community. The group is in need of a piece of information, the contributor has it and freely provides the information for the good of the group. Eg, Linux operating system has many developers that contribute free code because there is a need for it. So the
contributions for a needs motivation will be to the extent of the specific need of the group. In groups where contributors are likely to fulfill the Community Need Motivation there are central meeting areas where the needs of the group are discussed and displayed.
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much is being improved. A member who contributes to a group purely for the good of the group without any selfish needs is rare Kollock (1999).
H4: Community Need Motivation positively affects online participation
2.3.5 Sense of Community
Smith (1992) suggests that there is a fifth motivating factor, and that is a Sense of Community. He suggests that people are social beings and it is motivating to many
people to receive direct responses to their contributions. Many online communities have a function for members to comment directly on other members contributions and this motivation will be prominent in Online Brand Communities that offer this reply ability to their site.
H5: Sense of community positively affects online participation
2.3.6 The Conceptual Model
Figure 1 is the model that will be used for measuring the motivations for contributing in online communities. Figure 1. Conceptual model Motivations for contributing.
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2.4 Conclusion
With the knowledge of what Online Brand Communities are and with the knowledge of what Electronic Word of Mouth is, further research can be done on the motivations of contributors in Online Brand Communities. In the conceptual model it is hypothesized that the identified motivations, as discussed before, affect the participation in Online Brand Communities. The research will reveal whether there is a relationship between the different motivations and participation in the communities.
Once the motivations for posting have been identified, the actual members who are posting are still anonymous. These members need to be identified, this can be done with the use of the Membership Lifecycle for Online Communities [section 2.5]. The
Membership Lifecycle will identify different categories of online community members and the different motivations that have been identified can be applied to these different groups in the online community.
2.5 Membership Classification and Membership Life Cycle.
Previously mentioned are some reasons why community members contribute, but there are also members that do not contribute. This research paper considers “active” members to be the members that post in the community, inactive members are the members that do not contribute to the community. Many people join communities and do not contribute, this is called lurking (Preece, 2009).The reasons that Preece et al (2004) found for members non – contribution are; getting what they needed without having to participate actively, not being able to use the software because of poor usability, wanting to learn more about the community before diving in, thinking that they were being helpful by not posting and not liking the dynamics that they observed within the group.
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Figure 2: Membership Lifecycle for Online Communities (Kim, 2000)
1. Peripheral (i.e. Lurker), This member is an outsider and has no structured participation.
Eg, occasionally goes onto Youtube to view a video.
2. Inbound (i.e. Novice) – Newcomer is invested in the community and heading towards full participation.
Eg, starts to comment on other users content on Youtube.
3. Insider (i.e. Regular) – Full committed community participant.
Eg, regularly interacts with other users and regularly posts videos on Youtube.
4. Boundary (i.e. Leader) – Recognized for their contributions by other leaders and integrates with other leaders to improve the community. Their opinions are considered important in the community. They will possibly have the power to
discipline other members that do not adhere to the rules. These people are the leaders of a community.
Eg. This user will post videos about Youtube and other members will trust their given information because they are viewed as experts within the community.
5. Outbound (i.e. Elder) – Process of leaving the community due to new relationships, new positions, new outlooks.
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Eg. A leader from Youtube leaving because they get a new job that takes up too much time and they are unable to contribute to the group.
This online community life cycle is important to this research because the different phases have different meanings to an online community. For example a boundary user has more credibility than an inbound user. As mentioned earlier it is very important to have credibility in an online community because there is more suspicion viewed on Online Word of Mouth because the members do not know if the other members information is trustworthy. Therefore it is more likely that members who are further along the membership life cycle have better credibility than an unknown member that has just joined because of their reputations.
As McKnight and Kacmar (2006) found, information credibility is a vital predictor of an on-line consumer’s further action. A person who believes that the information is credible has no reason not to adopt it (Cheung et al, 2009). This shows that it is important to be viewed as a credible source.
For each phase of the lifecycle the respondents motivations for posting are there.
Although it is not clear if there is a difference in the motivations for posting between the different phases. This will be measured in the results chapter of this paper.
2.6 Advantages and Disadvantages for a company when creating
communities where consumers are able to contribute information
The internet has given consumers far more powerful tools to utilize and made their search results far more visible. What sites like Facebook do for brands has many benefits.
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However, on the other side of the coin, there are reasons that show the creation of a brand community to be a bad strategy. Chevalier and Mayzlin (2006) identify some of these reasons; the first is that it is not clear why members will provide information that they are not in any way compensated for. Chevalier and Mayzlin (2006) also suggest that
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3. Research Design
This study will try to explain which motivations are driving consumers to post
information on Online Brand Communities. The next part of this thesis is a questionnaire (Appendix 1) circulated among the members of Online Brand Communities. To test the proposed hypotheses the questionnaire will be distributed between members of online communities for the brands: Playstation, XBox and Wii. The questionnaire will be distributed through Facebook and the link will be placed in discussion topics on the communities for the aforementioned brands.
3.1 Sample and Procedure
An online questionnaire will be sent to the current members of online communities for the Playstation, XBox and Wii brands. These brands are selected to be test brands because they are large, global, universal brands with multiple online communities on various social networks with many members.
26 3.1.1 Sample
The sample for the survey is drawn from the following facebook groups: Table 1: Facebook groups and the current amount of members they have.
More respondents will also be drawn from: http://www.community.eu.playstation.com/, http://www.xbox.com/en-US/community and http://www.wiiaddiction.com/ .
In the different Facebook groups it is possible to contact members individually and send them the link to the questionnaire via Facebook messaging. The link will also be placed in threads on the community forums as well as in current threads that are highly active. All these members are considered relevant respondents because they are members of an online community. It must however be noted that the respondents are more likely to be the active members of the community rather than the inactive members (lurkers). The active members are targeted because they are the members implementing eWOM.
The questionnaire for the online community members is used to test the proposed
hypotheses. This study will try to identify the motivating factors for the members to post on the walls or in forums of their communities. It is important for the sample to reveal their motivations through the questionnaire by providing information about their activities and feelings while posting.
3.1.2 Procedure
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possible to calculate how many people will be exposed to the link because of the anonymous nature of the link placement.
The link will be continually placed on forums and community walls and individual Facebook members will be continually approached until the final count of 150 responses is reached. It is expected that there will be some negativity towards the placement of links that do not involve the brand that the communities focus on. It is against the community rules in many of the communities to post “advertising” or spam the members. Facebook in particular monitors for spamming and will block or cancel members that repeatedly spam using the Facebook service.
3.2 Operationalization of Constructs
This section provides a justification for the use of the different measurement items that make up the possible motivations for posting. The measurement items are turned into questions in the questionnaire which can be found in appendix 1.
The motivations are adopted from Kollock (1999) and Smith (1992) however, they did not statistically test their theories, so there are no existing measures or previous studies to base questions on in the questionnaire. The scales designed for the questionnaire are not adapted from any other studies, they are original questions. The questions are based on the theoretical explanations of each motivation from Kollock (1999) and Smith (1992) which are highlighted in the theoretical framework of this paper.
The first part of the questionnaire is aimed at discovering the demographics of the respondent. (Question 1) finds the age of the respondent, (Question 2) finds the sex of the respondent, (Question 3) finds the education level of the respondent and (Question 4) discovers which product the respondents community focuses on.
(Question 5) measures the approximate length of time that the member has been
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the frequency of the respondents visits to their community. This is measured in (Question 7) and will show the approximate amount of times that the member logged onto their community in the last month. (Question 8) is linked with (Question 7) but measures how many times the member posted in the last month. (Questions 5 – 8) determine if the member is active in the groups and how active they are.
Anticipated Reciprocity
(Question 9) consists of five questions that are answered through a likert scale. The aim of the questions is to measure the motivation of Anticipated Reciprocity. The questions are based upon the factors that Kollock (1999) identified that affect Anticipated
Reciprocity. Questions 9.1 and 9.2 measure the members ongoing interaction. Question 9.3 measures identity persistence. Questions 9.4 and 9.5 measure the knowledge of previous interactions.
Increased Recognition
(Question 10) consists of five questions that are answered through a likert scale. The aim of the questions is to measure Increased Recognition. The questions are based upon the factors that Kollock (1999) identified that had an effect on Increased Recognition.
Questions 10.1 and10.2 measure the information quality of the member’s posts. This will be shown in impressive technical details, elegant writing and high quality information. Question 10.3 measures the willingness to help others. Question 10.4 measures if the respondents realize that their input is being recognized by others. Question 10.5 measures if the respondents wish to participate in a highly recognizable position in the community.
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discovers if the respondent posts pictures or videos in the community. Question 11.4 discovers to what extent the member reads the posts and information in the community.
(Question 12) consists of 9 statements, Sense of Efficacy, Community need Motivation and Sense of Community are all measured in this question.
Sense of Efficacy
Sense of Efficacy is measured in question 12.1 and 12.3 where the questions aim to find out if the respondents are aware they are trying to affect or impact the group. Question 12.2 finds the quality of the respondents posts in the community which indicates a Sense of Efficacy.
Community Need Motivation
(Questions 12.4 – 12.6) are the measures for Community Need Motivation.
Question 12.4 measures the respondents affection for the community. Question 12.5 measures the respondents willingness to provide information without being rewarded for it. Question 12.6 shows to what extent the respondent is providing information for the good of the group.
Sense of Community
(Questions 12.7 – 12.9) measures the respondent’s motivation for Sense of Community. Questions 12.7 and 12.8 measures the social motive that the respondent has, to talk about the brand with other members. Question 12.9 finds the respondents view on the other members of the community in terms of friendship.
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3.3 Estimation and Assessment.
After all the data is collected from the survey, analysis will be done to test the hypothesis. SPSS version 16 will be used to execute the tests on the data so that reliable conclusions can be deduced.
This paper will use Factor Analysis, Regression and One Way ANNOVA testing. Firstly a Factor Analysis will be performed, this will identify and confirm the constructs as individual motivations. Once these motivations have been identified a Regression test will be applied showing to what degree each motivation affects participation in Online Communities compared to the other Motivations. Finally a One Way ANNOVA will be implemented to identify what motivations are used by the members of the Online Communities depending where they are on the Membership Lifecycle.
3.4 Specify the Measurement of Scaling Procedures
The data is measured in interval scale, this is because regression or factor analysis will be used for the data analysis. Every question in the questionnaire will be answered with a 7 point likert scale - 1 being strongly agree and 7 being strongly disagree. The likert scale is used because of its ease of use for the respondent as well as the relative ease to construct and measure the likert scale.
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4. Results
This chapter deals with the results of this study. First the sample will be discussed. Secondly, the procedure of the analysis will be discussed. Thirdly, the factor analysis to ascertain if the key constructs appear will be discussed. Thereafter the reliability analysis for each construct will be discussed. After the reliability analysis a regression of the constructs will be done. Finally an ANNOVA test will also be performed to link the individual motivations to each step of the membership lifecycle seen in figure 2.
4.1 Procedure
The analysis of the data was done with the use of the statistics software SPSS version 16. The first stage of the analysis was the sample size. A factor analysis was then performed followed by a reliability analysis on the constructs found in the factor analysis, followed by a regression of each individual construct. In the final step the hypotheses were tested and conclusions deduced.
4.2 The Sample and Demographics
The total sample consisted of 152 respondents. No record has been kept of how many people were approached to answer the questionnaire therefore a response percentage cannot be shown. All the members of the sample were members of an online community. The total number of respondents was reached within a total of 3 months of direct
approaches and posting the questionnaire link on the online community walls.
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Of the 152 respondents, 133 respondents were male and 19 were female. It was seen that many females answered in the Wii group, with 6 out of the 16 respondents being female. Whereas the ratio of female respondents was much lower for Playstation and Xbox communities.
Table 2: The Different Online Communities and the Ratios of Male and Female Respondents
Sex Playstation Xbox Wii Total
Male 62 61 10 133
Female 10 3 6 19
Total 72 64 16 152
Of the 152 respondents, 79 respondents had high school education (51.97%), 58 respondents had university education (38.16%) and 15 respondents had a higher university education (9.87%).
The minimum age of respondents was 14 years and the maximum was 44 years. The average age of respondents was 23 years.
The respondents have been members of their communities between 0 and 5 years. 14(12.8%) members were members for 0 years, 42(38.5%) respondents were members for 1 year, 37(33.9%) respondents were members for 2 years, 9(8.3%) respondents were
72 64 16
The Different Communities and the Respondents
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members for 3 years, 6 (5.5%) respondents were members for 4 years, and only 1(0.7%) respondent was a member for 5 years.
Figure 4
3 respondents claim to be members of 0 communities but this is impossible as they would have to be a member of a community to have access to the questionnaire link. 75
respondents were members of 1 community. 52 respondents were members of 2 communities. 17 respondents were members of 3 communities. 4 respondents are members of 4 communities. 1 respondent was a member of 5 communities.
Respondents visited their communities between 1 and 100 times a month. The most common number of times visited per month were 5(16 respondents), 10(24 respondents), 15(12 respondents), 20(14 respondents) and 30(14 respondents) times a month.
Respondents commented on their communities between 0 and 400 times a month. 77.3% of respondents commented on their community 0 – 5 times a month. Only 7 respondents
0 5 10 15 20 25 30 35 40 45 0 1 2 3 4 5
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commented 50 – 400 times per month. This suggests that there were some opinion leaders in the sample.
Figure 5: Histogram showing the number of times each respondent posted in the last month
4.3 Factor Analysis
(Appendix 2)
A major part of this research was to find if the theoretical motivations of Anticipated Reciprocity, Increased Recognition, Sense of Efficacy, Community Need Motivation and Sense of Community actually exist. This can be seen with the use of a factor analysis.
To create the factor analysis, the variables that make up the above factors were all used. For the Motivation of Anticipated Reciprocity variables 14 – 18 were used, for the
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Efficacy, the variables 32 – 34 for Community Need Motivation and the variables 35 – 37
for Sense of Community were used. These motivations make up the model and it was expected that these constructs show up in the results of the factor analysis.
A correlation matrix was created. For the factor analysis to be valid the variables must be correlated. A Bartlett test of sphereicity was performed, and the null hypothesis is that the correlation matrix is an identity matrix and is rejected by the Bartlett test of sphereicity. The appropriate chi – square statistic was 1.904 with 171 degrees of freedom which is significant at the 0.05 level. The KMO test was also performed on the correlation matrix and the value of the KMO was 0.915 which is above 0.5 which shows that the
correlations between the variables were appropriate and factor analysis could be used. According to these tests factor analysis is an appropriate technique for analyzing the correlation matrix.
Principal components analysis was used. The result using the determination based on eigenvalues, factors with an eigenvalue greater than 1 were retained. The other factors were not included in the model. From the factor analysis data it can be seen that there were four clear factors that could be extracted. These four factors account for 68% of the cumulative variance. The first 3 factors account for 63% of the cumulative percentage of variance which would indicate that there should be 3 factors resulting from the factor analysis because they account for the minimum of 60% of the variance. But it is decided based on the eigenvalues and percentage of variance that 4 factors will be used for the final model of this research.
Figure 6: The total variance explained using the principal components method Initial Eigenvalues
Component Total % of Variance Cumulative %
1 9.061 47.692 47.692
2 1.630 8.577 56.269
3 1.190 6.266 62.535
4 1.172 5.644 68.179
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Next the rotated component matrix was interpreted, in this matrix the four factors can be linked with the variables.
Table 3: The Rotated Component matrix from the Factor Analysis
Variables Component
1 2 3 4
14:Intention to quit .132 .157 .686 .410
15:Keeps up to date with
community .292 .374 .710 -.049
16:Posts with a specific
Username .015 .271 .086 .730
17:Remember names of
other members .228 .345 .593 -.274
18:Knowledge increased .235 .145 .602 .020
19:Posts high quality
information .211 .780 .169 .271
20:Posts in depth details .328 .714 .165 .260
21:Helps others with
problems .439 .640 .246 .062
22:Feel important when
helping others .299 .786 .224 .057
23:Would like to be an
administrator .224 .699 .270 -.198
29:Posts to improve the
group .729 .398 .117 -.110
30:Posts quality information
to improve the group .751 .400 .190 .004
31:Contributions are useful
to the group .610 .377 .278 .270
32:Enjoys browsing in the
group .587 -.068 .396 .288
33:Posts so that others can
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34:Posts information because it is good for the community
.803 .368 .117 -.115
35:Post to socialise with
others .700 .331 .293 -.234
36:Enjoys talking about the
product .666 .070 .312 .117
37:Considers other members
as friends .428 .339 .377 -.502
The rotated factor matrix was created using the varimax procedure. The results were, 8 variables (29, 30, 31, 32, 33, 34, 35, 36) correlate to component 1. These variables are all variables that indicate motivations that are not in self interest and Sense of Efficacy. The variables used to measure Sense of Efficacy concentrate on improving the community and can therefore be paired with the motivations that are not in self interest. Factor 1 can be named Motivation Not In Self Interest. Five variables (19, 20, 21, 22, 23) correlate to component 2 and are indicating a sense of increased recognition. Component 2 can be named Motivation of Increased Recognition. Four variables (14, 15, 17, 18) correlate to component 3 and indicate a motivation of Anticipated Reciprocity. Component 3 can be named Motivation of Anticipated Reciprocity. One variable (16) correlates to component 4. It was the variable that users post with a specific username. Component 4 can be named Motivation to Post with a Specific Username. No variable correlates highly with more than 1 factor.
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4.4 Reliability Analysis
(
Appendix 3) The results of the factor analysis did not exactly show the expected constructs of the theoretical model. Therefore, a reliability analysis was done on both the factors for the factor analysis components and the theoretical analysis factors.4.4.1 Reliability Analysis for factors found in the factor analysis
A reliability analysis was needed to see if the scale produces consistent results. Internal Consistency Reliability was used on the factors that were identified in the factor analysis. The Cronbachs Alpha was used to measure the reliability of the factors, it varies from 0 – 1 and a value of 0.6 or less means that there is unsatisfactory internal consistency
reliability.
For the Motivation Not in Self Interest of the factor analysis, variables
29,30,31,32,33,34,35,36,37 were tested. The reliability analysis results confirm that the scale produces consistent results with a Cronbachs Alpha of .922.
Reliability Statistics
Cronbach's Alpha N of Items
0.922 9
For the Motivation of Increased Recognition of the factor analysis, variables 19,20,21,22,23 were tested. The reliability analysis results confirm that the scale produces consistent results with a Cronbachs Alpha of .899.
Reliability Statistics
Cronbach's Alpha N of Items
0.889 5
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Reliability Statistics
Cronbach's Alpha N of Items
0.729 4
The final Motivation of the Need to Post With a Specific Username consisted of only 1 variable (16) and a reliability analysis of this factor was not possible because there was only 1 variable.
In conclusion, all factors have scales that are consistent if multiple measurements were made on them. Regression analysis is possible on the four factors.
4.4.2 Reliability Analysis for the factors of the theoretical model.
The reliability analysis was also done for the factors of the theoretical model to see if a regression analysis could also be done. This was necessary because the factors found from the factor analysis were not the same factors as expected from the theoretical research.
These are the results of the reliability analysis for the theoretical model motivations.
For the motivation of Anticipated Reciprocity variables 14 – 18 were tested. The
reliability analysis results confirm for this factor that the scale produces consistent results with a Cronbachs Alpha of .679.
Reliability Statistics
Cronbach's Alpha N of Items
0.679 5
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Reliability Statistics
Cronbach's Alpha N of Items
0.889 5
The motivation of Sense of Efficacy was measured with variables 29 – 31. The reliability analysis results confirm for this factor that the scale produces consistent results with a Cronbachs Alpha of .878.
Reliability Statistics
Cronbach's Alpha N of Items
0.878 3
For the motivation of Community Need variable 32 – 34 were tested. The reliability analysis results confirm for this factor that the scale produces consistent results with a Cronbachs Alpha of .799.
Reliability Statistics
Cronbach's Alpha N of Items
0.799 3
For the motivation of Sense of Community variables 35 – 37 were tested. The reliability analysis results confirm for this factor that the scale produces consistent results with a Cronbachs Alpha of .757.
Reliability Statistics
Cronbach's Alpha N of Items
0.757 3
In conclusion all factors have scales that are consistent if multiple measurements were made on them. Regression analysis is possible on the five factors.
4.4.3 The New Model and Hypotheses
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amended. The new model is used because in the factor analysis data reduction 2 of the 3 methods (eigenvalues and percentage of variance is above 5) suggests using these four factors [as seen in figure 6]. The reliability analysis is also positive for these factors. Figure 7 is the new model for the motivations that contribute to Participation in Online Brand Communities.
Figure 7: Model for Motivations for posting in Online Communities
The main difference in this model is that all the motivations that were not self centered (Community Need motivation and Sense of Community) and Sense of Efficacy were combined to make up 1 motivation of: The Motivation Not in Self Interest. The new Hypotheses are also represented in the new model.
H1: Anticipated Reciprocity positively affects online participation H2: Increased Recognition positively affects online participation
H3: Motivation Not in Self Interest positively affects online participation
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4.5 Regression
(Appendix 4)An important part of this study is to determine which factors (the motivations) directly affect the Participation in the Online Brand Community. These relationships will be measured with the use of a regression. The hypotheses involved are H1, H2, H3, H4 and they all suggest that each motivation has a positive effect on the Participation in the Online Brand Community. The results for the hypotheses will be discussed in the conclusions section of this paper.
4.5.1 The regression Setup
The regression has 1 dependent variable which is the Participation in Online Community (POC) and 4 independent variables of Anticipated Reciprocity, Increased Recognition, Motivation Not in Self Interest and Posts with a Specific Username. The dependent variable (POC) is made up of 3 components:
1. How many times did you visit your community in the last month 2. How many communities for your brand are you a member of 3. How many times did you post in your community in the last month
The 4 Independent Variables are kept the same as the constructs found from the factor analysis. Eight variables making up Motivation Not in Self Interest, 5 variables making up Increased Recognition, 4 variables making up Anticipated Reciprocity and 1 variable for Posts with a Specific Username. All of the variables that make up each Motivation were transformed using SPSS into 1 numerical value for each Motivation, then a regression was run and the results are described below.
4.5.2 Regression Findings
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Table 4: Coefficients table from the Regression Analysis
Factor Participation in Online Brand Community
Beta Value Standardized Beta T-Value Sig. (Constant) 33.717 8.887 .000 3 Anticipated Reciprocity 18.071 0.323 4.552 .000 4 Posts with a Specific Username -6.105 -0.110 -1.542 .125 2 Increased Recognition 17.358 0.309 4.343 .000 1 Motivation Not in Self Interest 13.614 0.252 3.569 .000
All the factors were significant except for Posts With a Specific Username. This shows that there were 3 motivations that influence participation in an online community. The motivation of Anticipated Reciprocity was the motivation that had the most influence on Participation in the Community with a Beta and Standardized Beta value of 18.071 and 0.323 respectively. While the motivation of Increased Recognition had a slightly smaller influence on Participation in the Community with a Beta and Standardized Beta values of 17.358 and 0.309 respectively. Motivation Not in Self Interest also positively affected Participation in the Community with Beta and Standardized Beta values of 13.614 and 0.252 respectively. The interpretation of these findings is discussed in the conclusions section.
It was expected from the research that there were 5 motivations affecting Participation, but this turned out not to be entirely true. The surprising finding from the results was that 4 motivations were identified from the factor analysis.
4.6 Membership Lifecycle ANOVA Test
(Appendix 5)Now that the motivations have been identified and tested they can be linked to the
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Specific Username are tested to see how active each motivation is in each phase of the lifecycle.
Before beginning the ANOVA a new data variable had to be created. It was called the Lifecycle variable. In this variable the Lifecycle has 5 values, 1 = peripheral, 2 = inbound, 3 = insider , 4 = boundary, 5 = outbound. This variable was the independent variable in the test. This variable was created first by transforming the data from the amount of times a respondent posted and visited their online community in the last month( amount of times posted on community + amount of times visited) . The results computed for this variable were between 1 and 500. Then based on the theoretical information on the Membership Lifecycle provided by (Kim, 2000) it was determined where each respondent lay on the membership lifecycle based on the newly formed variable. The boundaries determined were:
Table 5. The Lifecycle Phase Variables and Values
Lifecycle Phase Posts_Visits Variable Lifecycle Variable Value
Peripheral 1-5 1
Inbound 6-13 2
Insider 14-70 3
Boundary 71-500 4
Outbound 0 5
Therefore a respondent with a value of lifecycle posts_visits between 6-13 had a value of 2 and is a member of the Inbound Phase, and a respondent with the value of between 71 and 500 had a value of 4 and is a member of the Boundary Phase.
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Table 6. Number of respondents for each Lifecycle phase Lifecycle
Phase Number of Respondents
Peripheral 20
Inbound 42
insider 77
Boundary 11
Total 150
Once the independent variable has been completed an ANOVA test could then be
performed on the dependent variables representing the motivations that resulted from the factor analysis.
Firstly the homogeneity of variances was tested. The results were significant above 0.05 for the motivations of Anticipated Reciprocity, Increased Recognition and Motivation Not in Self Interest showing that the variances of these groups were similar. Posts with a Specific Username was significant below 0.05 and therefore there was no similarity in the groups being compared to this motivation.
The results of the ANOVA test were all significant for Anticipated Reciprocity, Increased Recognition and Motivation Not in Self Interest.There was a significant effect of
Anticipated Reciprocity [F(3, 146) = 23.721, p = 0.000]. There was a significant effect of Increased Recognition [F(3, 146) = 7.565, p = 0.000]. There was a significant effect of Motivation Not in Self Interest [F(3, 146) =3.307, p = 0.022]. This result shows that there is a significant difference between the motivations and the phases of the lifecycle. The Motivation of Posts with a Specific Username was insignificant [F(3, 146) =1.691, p = 0.171] and will have to be tested with a robust test of equality of means.
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there is homogeneity of variance between these motivations. As a result of the Welch test it is determined that Posts with a Specific Username(0.159) was insignificant therefore there was no significant homogeneity of variance for this motivation .
After the robust test of equality of means was analyzed the multiple comparisons table was viewed to see which groups differed from each other with the use of the Tukey post-hoc test tool. There was a statistically significant difference between the groups as determined by the one way ANOVA.
4.6.1 Results from the Tukey post-hoc Key for Tukey Tables
N Not Significant Y Significant
4.6.1.1 Motivation Not in Self Interest
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Table 7. Tukey results for Motivation Not in Self Interest
Peripheral Inbound Insider Boundary
Peripheral N N Y
Inbound N N Y
Insider N N N
Boundary Y Y N
The following graph shows the scores for the tested variable of Motivation Not in Self Interest. The graph spikes drastically for the boundary phase.
4.6.1.2 Motivation of Increased Recognition
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of Increased Recognition where as a respondent in the boundary phase does so to a lesser extent, possibly because they are already at the highest level of recognition.
Table 8. Tukey Results for Increased Recognition
Peripheral Inbound Insider Boundary
Peripheral N N Y
Inbound N N Y
Insider N N Y
Boundary Y Y Y
The following graph shows the scores for the tested variable of Increased Recognition.
4.6.1.3 Motivation of Anticipated Reciprocity
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Table 9. Tukey results for Anticipated Reciprocity
Peripheral Inbound Insider Boundary
Peripheral N Y Y
Inbound N Y Y
Insider Y Y N
Boundary Y Y N
The following graph shows the scores for the tested variable of Anticipated Reciprocity.
4.6.1.4 Posts With a Specific Username
There was no difference between any of the phases showing that all members always participate with the same username.
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5. Conclusions
The aim of this research was to investigate the motivation of online community members for posting in the community. The results revealed that there are 3 posting motivations for online communities members. The motivations are: Anticipated Reciprocity, Increased Recognition and Motivation to Post Not in Self Interest.
5.1 Basic Conclusions
The results showed that members that are involved in their communities do post online and that the members are more likely to be lurkers instead of actively participating all the time, ie members are reading what others are posting rather than contributing.
The results confirm that many active members stay members and remain contributing for long periods of time. It is also worthy to note that the members that are placing the majority of the information on the online communities are the members that are higher up in the Membership Lifecycle. Ie the Insiders and Boundary members.
The vast majority of respondents are in their teens and early twenties, this leads to the conclusion that the members of online communities are still in their youth. This factor needs to be taken into account by managers because depending on their target market, online communities might not be successful if they are targeting a market of an older age group.
5.2 The Hypothesized Relationships.
H1: Anticipated Reciprocity positively affects online participation
Of all the motivations, Anticipated Reciprocity is the greatest motivation for community members to post on their communities. Hypothesis 1 is supported.
Members are motivated to post, expecting information in return. This shows in the social nature of all the online communities, with vast discussions occurring with multiple members sharing information for specific topics.
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Members of communities are also motivated to post because of Increased Recognition. Hypothesis 2 is supported.
Members are shown to be motivated by Increased Recognition when they post with high quality information and in depth details so that the member can feel a “prestige” for helping others that are less knowledgeable.
H3: Motivation Not in Self Interest positively affects online participation
Members of online communities Motivated Not in Self Interest do also post in the online community, but do so to a lesser extent than Anticipated Reciprocity and Increased Recognition. Hypothesis 3 is supported.
Wang and Fesenmaier (2003) found that in an online community it is common to find individuals who are incredibly generous with their time and expertise. In online
communities it is also common to find individuals who participate and share information without any financial rewards coming to them. Wang and Fesenmaier’s statement is supported. It is found that there are people in the communities willing to contribute without receiving any monetary or recognition rewards. The results show that there are members who post on the communities to fill information gaps within the communities, to help out other members who are looking for information and members who post because they feel that they are making a positive difference to the community. Although this motivation is far more complicated than a member simply giving information for free. This motivation for posting includes the Sense of Efficacy, a Community Need motivation and Sense of Community of the member.
H4: Posts with a Specific Username positively affects online participation
Posts with a Specific Username is not a significant motivation for community members to post in an online community. Hypothesis 4 is not supported.
