Using Online Communities to increase Customer Loyalty Rosa 306444... · Web viewOther target groups are the companies that want to catch the wave of the online communities but do

From Motivation trough Participation to Loyalty

The Differential Effect of product type in Online Communities

Master’s Thesis

Author: Rosa Boomsma BComStudent id: 306444

Master MarketingErasmus School of EconomicsErasmus University RotterdamSupervisor: Dr. Remco Prins Second supervisor: Dimitrios Tsekouras MScRotterdam, 2009

From Motivation through Participation to loyalty; The Differential Effect Of product type in Online Communities

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Index

Abstract................................................................................................................................3Chapter 1. Introduction...................................................................................................3

1.1. Motivation and contribution..................................................................................31.2. Research questions and sub research questions..................................................31.3. Thesis outline...........................................................................................................3

Chapter 2. Theoretical background and hypotheses......................................................3

2.1. Introduction and contribution of this research...................................................32.2. Online communities................................................................................................32.3. passive vs. active participation..............................................................................32.4. participation as an antecedent of loyalty..............................................................32.5. motives for participation........................................................................................32.6. Product category as a moderator..........................................................................3

Chapter 3. Methodology..................................................................................................3

3.1. Approach.................................................................................................................33.2. Questionnaire construct.........................................................................................33.3. Variables..................................................................................................................33.4. Regression analysis.................................................................................................3

Chapter 4. Results............................................................................................................3

4.1. passive participation:.............................................................................................34.2. active participation.................................................................................................34.3. loyalty.........................................................................Error! Bookmark not defined.

Chapter 5. Conclusion.....................................................................................................3

5.1. Discussion................................................................................................................35.2. Implications of this research..................................................................................35.3. Limitations of this research...................................................................................3

References............................................................................................................................3Appendix 1. Items used..................................................................................................3Appendix 2. Questionnaire.............................................................................................3Appendix 3. Survey invitations......................................................................................3Appendix 4. Research results Thorsten Hennig-Thurau et al......................................3Appendix 5.........................................................................................................FCB Grid 3

Abstract

Members of online communities view (passive participation) or post (active participation) content

for multiple reasons. This present research will examine the moderating effect of ‘product type’

on the relationship between several ‘motives’ to participate and ‘participation’. To underline the

marketing relevance of this research, the relationship between participation and customer loyalty

is examined as well.

In order to generate data to test both the direct and moderating effects, two surveys were taken.

For the first survey, a sample was used of some 305 internet users. This survey provided data on

motives for participation, the type and level of participation and the type of product. For the

second survey, a sample was used of some 130 members from tweakers.net. This survey provided

data on the level of participation and the level of loyalty towards a certain brand.

After defining all variables, the direct effect of several motives on participation was measured,

using the linear regression model. Here, a distinction was made between motives for ‘passive’

participation and ‘active’ participation. Furthermore, there was support for two out of six motives

to have a significant effect on passive participation. However, there was no proof of a moderating

effect caused by ‘product type’. There was support for four out of five motives to have as

significant effect on active participation. For one out of five motives, there was also evidence for a

moderating effect, caused by ‘product type’. Finally, the analysis showed that passive

participation has a significant effect on brand loyalty. However, there was found no relationship

between active participation and brand loyalty.

In general, online communities try to strive for the highest participation. This present research

indicates what motives are important in both passive or active participation. Moreover, lessons

can be drawn from differences between product types. Finally, the relevance and importance of

both active and passive participation within online communities is explained.


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Chapter 1. Introduction

With the growing importance of the internet, a new phenomenon, called web 2.0, is growing in

popularity as well. The term web 2.0 was introduced by Tim O’Reilly (2004), describing an

emerging new user-centered web. On this web, websites are using different interactive web

applications which are created for the people to use. Well-known examples of this approach are

online forums and social networking sites like Facebook or Hyves. These applications are based

on the principle of user generated content, where the content is mainly provided by the user. The

concept can differ and therefore the purposes and benefits differ among websites and applications.

Within the social context for example, the web 2.0. enables people to establish communities

online while it enables offline communities to move toward an online environment. Maintaining

and building relationships is less time consuming, and seeking information with other subject

enthusiasts becomes more efficient. Within a more knowledge based context, (online) companies

can use online user behavior and user generated content to adjust their offerings to meet specific

customers’ needs.

1.1. Motivation and contribution

Implications in the Field of Marketing

Online communities, web 2.0 and virtual worlds have been the subject of attention for many

researchers the last decade. Rightly so, because in 2009 over 67% of the global online population

visited “member communities”, including social networks and blogs (Nielsen Online; 2009).

Developing a platform for customers and potential customers to interact with each other in

relation to a certain brand or product can be an advantage in different ways (Kumar et al.; 2006)

(McWilliam; 2000). For instance, customers are an infinite source of knowledge for innovation.

Moreover, community members can be used as a database with people who share characteristics

(e.g. interests, attitude towards a brand, etc.). Members seem to be willing to interact with

producers during development, driven by interests in innovation (Füler et al.; 2008). Another

advantage is the opportunity to create a buzz by multiplying a brand’s popularity through

consumer articulations on the web. Bringing people together that have a common interest in a

brand, is a type of viral marketing that improves sustainable brand cult (Cova and Pace; 2006).

Another reason in support of using online communities as a marketing tool are the studies on

Word-of-Mouth. These communities provide a platform for sharing experiences, concerning a

brand or product, between the community members. It will add to the company’s brand equity

when more people are interested in, and are talking about, a product or brand. There is even a

good reason to maintain the negative comments. Although bad reviews can have negative

influence, it also helps containing the trustworthiness of the community (Shang et al. 2006).

In general, an online community can only exist if sufficient members participate (Koh et al.;

2007). This can either be in the form of viewing content (passive participation), or actually post

content yourself (active participation). For company initiated communities, interaction among

participants is even more important, the participation has to lead to some extend of brand or

customer loyalty in order to be an attractive investment.

As a consequence, lot has been written about what drives people to participate and contribute to

these online communities (e.g.: Hennig-Thurau and Walsh (2003), Hennig-Thurau et al.; (2003)),

Jang et al; (2008). Or more importantly, how can this be stimulated (e.g.: Koh et al; (2007),

Beenen et al. (2004))? Although these motives and stimuli are relevant, some researchers have

been questioning whether findings on participation would differ between different kinds of

products (Hennig-Thurau and Walsh; 2003), (Shang et al.; 2006). This present research will

examine whether there is a difference between the effect that ‘motives to participate’ have on

participation for different product types. Furthermore, the relationship between participation and

customer loyalty is examined as well.

1.2. Research questions and sub research questions

Main research question

What are the differences between motives between different levels of participation and different

product types, and does this lead to loyalty intentions?


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To make assumptions about what motivates people to read information that is posted on websites,

or to actually contribute content to an online community, influences on participation have to be

examined. Consequently the following research question is formed to examine this.

Research question 1

What motives have a significant influence on passive participation and what motives tend to evoke active participation?

As described above, it is important to see whether some motives have a stronger influence on

either passive or active participation than others. Although conclusions will be drawn from these

measures (question 1), the relationship primarily serves as a benchmark in measuring the effect of

the moderator (question 2):

Research question 2

Do motives for participation differ between product types?

However, even if there appears to be a relationship between one or both forms of participation and

loyalty intentions, the motives behind participating may be the true explanatory variable. The

attitude may already be formed before an internet user reads or posts information in the

community.

Research question 3

To what extent does motivation leading to participation explain the influence on loyalty intention?

Actual value for companies is only created if the customer grows loyalty intentions, directly or

indirectly, caused by passive or active participation. Whether participation has a significant

influence on loyalty intentions, which will be further explained in paragraph 2.5, is to be answered

by research question 3a. Although passive participation cannot exist without the presence of

consumer created content, passive participants may be more inclined to buy the product than

active participants, the other way around, or neither. Moreover, if motives differ between both

forms of participation (research question 1), it is very well possible that the attitude towards a

product, differs as well (question 3b).

Research question 3aIs participation an antecedent of loyalty intention?

MOTIVES TO PARTICIPATE

PASSIVE PARTICIPATION

ACTIVE PARTICIPATION

LOYALTY INTENTIONS

PRODUCT TYPE

Q2

Q1 Q3

Research question 3bWhat form of participation has a stronger influence on loyalty intention?

In the following research model, the three research questions are depicted.

1.3. Thesis outline

The results of the literature review are divided into three chapters. In chapter 2, an introduction is

given on the topic online communities, web 2.0. and how these can be used for business purposes.

Furthermore, the relationship between both forms of participation and loyalty will be discussed.

The set of potential motives for participation will also be discussed in this chapter. To what extent

the relationship between motivation and participation is influenced by the type of product is

discussed at the end of this chapter. The hypotheses will formulated based on and resulting from

the theoretical background. In chapter 3 the design and methodology of the research is explained.

Research results and analyses are given and elaborated on in chapter 4. The results are discussed

in chapter 5, and finally the restrictions and implications of the research are explained.


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Chapter 2. Theoretical background and hypotheses

2.1. Introduction and contribution of this research

In former research, different influences that encourage participation have been investigated. Some

researchers have focused on platform characteristics e.g. information quality , usefulness , size,

levels of interaction (Koh and Kim; 2003), (Jang et al.; 2008). Others have looked at the basic

motivation for participants to exchange information or communicate with each other (Gruen et al.

2005), (Hennig-Thurau et al.2004). These characteristics or motivators to participate are indeed

important to take into consideration, but may vary when other variables such as product type or

community type are taken into account as well. Mot Jang et al. (2008) for example have shown

that the characteristics, influencing community commitment, vary between consumer initiated

communities and company initiated communities. The same differences between motives to

participate could arise between different product types being the topic of a certain community.

Shang et al. (2006) discuss the influence of participation on loyalty in a virtual community of

Apple, but question whether the outcomes would be the same for different types of products.

These authors argue that computers are informational and high involvement products, and

therefore, satisfying informational needs may be the main motivation for participation. Hennig-

Thurau and Walsh (2003) indicated the same limitations to their research. They questioned

whether their conclusions on motives for viewing online content would be the same for high and

low involvement products.

To prove potential differences in member participation among product categories, a direct

influence on participation is used as a benchmark. Instead of examining how Website attributes or

characteristics affect member participation, motivation to participate will be examined. Arguing

that fulfilling a need is the main driver in becoming a member of an online community, this serves

as a basis on which the moderating effects of the type of product are tested.

Two product types are chosen in such a way that differences are most likely to occur. For each of

these product types, examples of empirical communities are gathered to serve as a background for

the research. In chapter 2, this will be described in further detail.

Both reasons for community members to read customer articulations on the internet, as to post

articulations themselves, are researched (Hennig-Thurau and Walsh; 2003) (Hennig-Thurau et al.;

2004). The former form of participation can be described as passive participation or Lurking,

while the latter is referred to as active participation or Posting. Although these authors elaborated

on the motives for both types of member behavior, these were never combined in one study. This

research aims to measure both forms of participation combined in one research.

2.2. Online communities

The definition of the term “Community” has been formulated many times, over many disciplines,

including psychology, sociology and biology. Although these definitions vary across disciplines

and authors, there is some significant overlap. There are three major criteria in order to explain

communities, which we can find in most of the definitions. 1) Members of a community tend to

come or live together, in physical or virtual spaces. 2) There has to be some interaction in order to

build relationships. 3) Community members bond through common characteristics, experiences or

values (Jang et al.; 2008), (Koh and Kim; 2003). Gusfield (1975) describes two different types of

communities. The traditional territorial or geographic community and the relational community

revolving around member relationships. Considering the absence of a physical place to meet,

virtual communities could be regarded as relational communities. However, members can become

attached to these virtual spaces which eventually can become a substitute for a geographical place.

The term “Online Communities”, as the term “Communities”, comes with multiple definitions.

What these definitions have in common, is that online communities are regarded as a computer-

mediated space. The definition by Bagozzi and Dholakia (2002), captures the main idea: “We

view virtual communities to be mediated social spaces in the digital environment that allow

groups to form and be sustained primarily through ongoing communication processes”. This can

either serve as supplement or substitution to physical communities (Bagozzi and Dholakia; 2002).

Although it is often described as a social space, communities not always serve solely as means to

build social relationships, as will be explained.

According to Bagozzi and Dholakia (2002) communities share several characteristics. 1)

Communities often exist around a certain common interest. 2) Members from the community feel

a certain belonging to this group, which separates them from other groups. 3) Most communities

have written or unwritten rules and habits in expressing themselves. These are so called


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netiquettes. 4) Unlike traditional media, content is created and published by active participants.

This concept is in line with the core concept behind Web 2.0 mentioned earlier. Web 2.0

applications have shifted online activities from browsing to interacting and contributing. 5)

Nonverbal expressions and physical appearance is often filtered out. However, with the continues

technological developments, image supported communication becomes more easy.

Rheingold (2000) argues that although there are resemblances between online (virtual)

communities and physical communities, a large difference lies within the interaction. Outside the

virtual world, people first meet face to face, get to know one another, and possible form

relationships. Within online communities, people get to know one another, build relationships, and

possible get face to face. Another difference is whether or not participation is voluntary or not.

participation in online communities is often a matter of choice, while in the physical world

membership may be imposed by, for example, geographic location (Bagozzi and Dholakia; 2002).

For that reason the entry and exit barriers are often lower for virtual communities, which makes

motivating participation more important (Valtersson; 1996).

As mentioned earlier communities vary in purpose. Hagel and Armstrong have among others

classified online communities. According to these authors, there are four types of communities

(Armstrong and Hagel; 2000): 1) Communities of transaction facilitate the buying and selling of

products or services, often supported with information and customer reviews. Examples are

Amazon.com and ebay.com. 2) Communities of interest serve as a platform for participants to

interact about specific topics or content of interest. Examples are tweakers.net and flickr.com. 3)

Communities of relationship is a platform for building relationships, based on experiences,

interests, skills, etc. Examples are facebook.com or myspace.com. 4) Communities of fantasy

allow members to create a personality, and take on this persona, which can be different from who

they are or look like in real life. Communication often takes place between these characters in a

virtual fantasy world. Examples are secondlife.com or theworldofwarcraft.com.

Besides classifications based on the purpose that the online community serves, it can also be either

company initiated or not company initiated (Plant; 2004). It can even be classified based on

functionalities. L.Casaló et al. (2007) have defined ‘Brand community’ as: ‘a set of individuals

who voluntarily relate to each other for their interest in some brand or product’. Therefore, these

brand communities are, from a marketing perspective, an interesting kind.

In seeing whether there are differences in motives between product types, the focus of the research

will be on communities concerning a specific product group. Although opinions are spread in all

kinds of forms and ditto communities, communities of interest and communities of transaction are

the most common places for consumer articulations (Hennig-Thurau et al.; 2004). For this reason,

this study uses communities of interest and transaction as units of observation. Another reason is

the popularity of these websites. Bol.com, for example, is a Dutch community of transaction with

reviews and ratings on multimedia products and has had 80 million unique visitors last year (NRC

Handelsblad; 2009). The more popular a community is, the more likely people are familiar with

the website which will be beneficial for the research.

2.3. passive vs. active participation

As stated before, one of the characteristics of online communities is that the content is delivered

mainly by members of that community. In order to make online communities viable, attracting

people to become member and motivating these members to actively participate, are two crucial

factors.

In this research this division between active participation (posting) and passive participation

(lurking) was made (Jang et al.; 2008). is maintained for the two reasons. One, motives for

obtaining product related information can be different compared to motives for making this

information available. Two, when the antecedents are different, the level of loyalty intention is

likely to be different as well. For example; if customers are less motivated to post information

about a clothing brand, but rather read the information, the community could pick up a more

active, contributing role. Consequently, if passive participation appears to lead to loyalty

intentions, companies could consider making the information freely available instead of making

subscription obligatory.

With the evolution of the internet technology, the terms posting and viewing become rather

narrow. It often depends on the type of community, what kinds of applications are adopted.

Designing your own products in communities of transaction1 and living a second life in

communities of fantasy2 can hardly been described as posting nor viewing. Nevertheless,

contributions still come in the form of posting and viewing opinions, questions, information and

knowledge within the community (Koh and Kim; 2003). From a Marketing perspective, this

research will focus on consumers that will either articulate themselves or view articulations

1 http:// http://nikeid.nike.com; last visited June 20092 http://secondlife.com; last visited June 2009


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http://nikeid.nike.com/




LOYALTY INTENTIONS

PRODUCT TYPE

concerning certain products or brands. These kind of consumer articulations are referred to as

electronic word-of-mouth (eWOM); “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” (Hennig-Thurau et al.; 2004).

Many researchers have tried to discover what web-site attributes, web-site content, types of

remuneration, or other factors may influence member participation. These studies resulted in

multiple challenges for having a sustainable, attractive and effective community (Koh et al.;

2007). People not only have to be motivated to participate, but have to have the opportunity as

well. For instance, creating awareness around the social presence of other members has appeared

to be important in effective communication (Fulk et al.; 1990). Moreover, members have to be

motivated by being treated in a common need (Andrews; 2002). There has to be some leadership

to a certain extent in creating the right social climate. Equally important is the technological

infrastructure and architecture, to give the motivated members the opportunity to participate

(Godwin; 2004). Other stimulants suggested are: clear definition of members’ roles, online/offline

events, opinion leaders, basic guidelines and useful content (William and Cothrel; 2000), (Kim;

2000).

2.4. participation as an antecedent of loyalty

loyalty can be defined as “a deeply held commitment to repurchase or repatronize a preferred

product or service consistently in the future” (Oliver; 1997). loyalty is seen as one of the key

objectives of marketing managers, since it increases the level of future buying behavior which is

key in achieving success and sustainability over time (Flaviàn et al.; 2006). In order to achieve

commitment towards a certain brand (brand loyalty) or product, a relationship has to be built

between the customer and the brand or product. Communities can facilitate this relationship by

bringing customers with a mutual interest in the product together to interact (Hagel III and

Armstrong; 1997).

loyalty can be seen from two different perspectives. One perspective is behavioral loyalty, which

measures the likeliness that a customer will purchase a product once again. Attitudinal loyalty, on

the other hand, is more long term oriented and represents the commitment or preferences a

customer associates with a brand (Jacoby and Keyner; 1973). Several authors argue that attitudinal

loyalty is a better indicator of brand loyalty than actual behavioral loyalty (Füllerton; 2003)

(Shang et al.; 2006). Füllerton (2003) argues it is affective commitment that leads to purchase

behavior. Reasoning is that behavioral loyalty, possibly caused by high switching costs or

dependency, is not the kind of loyalty one would like to measure, nor achieve to increase.

Therefore, the aspects used by Füllerton (2003) to measure affective commitment will serve as a

basis in measuring attitudinal loyalty in this present paper. These aspects are the following:

forsaking the alternative, the sacrifice a consumer will give the pledge of continuity and the

resistance to change.

Several scientific articles have examined this relationship between participation within virtual

communities on customer loyalty. Although nor loyalty nor participation is always measured in

the same way, there is some overlap in the aspects that these different forms of loyalty attempt to

measure.

Shang et al. (2006) examined the effects of consumers’ lurking and posting behaviors in online

communities on brand loyalty. In line with Füllerton (2003), these authors consider attitudinal

loyalty as a better indicator of brand loyalty compared to behavioral loyalty. In this research the

authors only found support for the relationship between lurking and brand loyalty. They argue that

these results imply that the contribution by active participants have a promotional effect, on

visitors of these communities. T.W. Gruen et al. equally have made a distinction between two

forms of loyalty which is quite similar to the division mentioned by Shang et al.(2006), Jacoby

and Keyner (1973) and Füllerton (2003). On one side they describe loyalty in the form of

intention to repurchase the product; this is comparable to the behavior loyalty. On the other side

they describe the form of Word-of-Mouth, which resembles attitudinal loyalty. These authors


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found evidence for the influence of “know-how exchange” on loyalty in the form of providing

Word-of-Mouth. However, instead of measuring the frequency or the type of participation, the

quality of the content was measured. The quality was quantified by importance, usability and

trustworthiness of the information posted, valued by the respondent.

Another research that measured a more subjective aspect of participation was by L.Casaló et al.

(2007). They explored the effects of member participation on trust and loyalty towards free

software products available on the same community website. Here, participation was based on the

value of the contribution and the intrinsic motivation and enthusiasm. In contrast to findings

mentioned earlier, this study has shown that starting a virtual brand community and encouraging

member participation can lead to loyalty from a behavioral point of view. loyalty was based on

variables like repeated usage of the product but also the amount of usage.

Jang et al. (2008) found evidence for attitudinal loyalty towards the community (community

commitment) to have an influence on brand loyalty. loyalty was here measured by both brand

attachment aspects (attitudinal loyalty) and the level of repurchases (behavioral loyalty). In

addition to this, Kim et al. (2004) found a positive influence of behavioral guest loyalty on the

number of products purchased. “Guest loyalty” is another way of looking at the level or type of

participation within an online community (Kim et al.; 2004). Guest loyalty is comparable to

member participation, both attempt to measure the quantity or quality of the relationship between

user and community. For example, guest loyalty can be measured by the number of repeated visits

to the website. These findings imply that there could be a relationship between the nature of the

participation and the developed loyalty. Due to the hypothetical nature of the questions within this

present research however, it is not possible to measure the repeated visits to the site, nor the

attitude towards a specific community.

Shang et al. (2006) only found support for lurking to have an influence on loyalty; other authors

however did not make the distinction between these forms of participation (Jang et al. 2008),

(T.W. Gruen), (L.Casaló et al. 2007), (Kim et al. 2004). The quantification of participation

appeared to vary across researches as well. Kim et al. (2004) measured participation as both the

number of visits as the repetition of visits. Others however, looked at the quality aspect of

participation (Gruen et al.; 2007) (Casaló et al.; 2007). Even though the variables used may vary,

and found relationships are therefore not completely consistent, all findings support the

assumption there is a relationship between participation and (brand) loyalty. Therefore we expect

that:




LOYALTY INTENTIONS

PRODUCT TYPE

H1a: “active participation” to have a significant positive influence on loyalty intentions.

H1b: “passive participation” to have a significant positive influence on loyalty intentions.

2.5. motives for participation

In 2003, Hennig-Thurau and Walsch researched motives for community members to read

consumer articulations. Later, in 2004, partly the same authors researched motives for community

members to actually post articulations on web sites (Hennig-Thurau et al.; 2004). Possible

motivations , formerly used in traditional Word-of-Mouth literature, were gathered and these

were tested as eWOM in an online community environment. Both studies examined the

dimensionality of the entire set of items. The underlying structure that was derived through a

principal component analysis will not be maintained to serve as a basis for the hypotheses.

However, these factors are used to give some structure (appendix 1) in explaining the theoretical

background of the different motives. It is very well possible that this present study will result in

different underlying components, however, the components found by Hennig-Thurau et al.(2003)

(2004) give a good indication of what one may expect.

Both motives for passive as for active participation were measured by Hennig-Thurau et al. (2003)

(2004), although not in one study, the significant difference was acknowledged. In addition, Koh

and Kim (2003) also found that viewing and posting require different stimuli in promoting

participation. They recommend treating both activities as separate member choices in further


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LOYALTY INTENTIONS

PRODUCT TYPE

research. Comparing motives between posting and viewing is difficult in that it are two different

activities. Whether one activity has a bigger influence on loyalty, however, will be looked into in

this present research.

In order to answer research question 1, a hypothesis will be formulated for each motive, based on

former literature. Consequently, these hypotheses will be tested based on their influence on either

passive or active participation.

Some variables measure, to a large extent, the same underlying motivation for both active and

passive participation. However, some variables are only applicable for just one form of activity. In

order to learn “what products are new” or “dissonance reduction” active participation is not

required. Therefore these variables were derived within the context of passive participation. For

other motives action is needed and therefore eleven motives are formulated within the context of

active participation.

2.5.1. passive participation

2.5.1.1. Obtaining buying- related information

The reason for members to read articulations about a product, in order to make the right buying

decision, can be divided in two motives. On the one hand consumers want to reduce the risk of

buying the wrong product. On the other hand, the internet can be used as a medium in comparing

different products in a timely manner. This relates to consumer’s self-perceived lack of time.

These two motives are referred to as self-involvement motives (Schiffman and Kanuk; 1987).

Hennig-Thurau et al. have found these motives to be the most important in reading customer

articulations (Hennig-Thurau et al.; 2004). Therefore we expect that:

H2a: “Risk reduction” as a motive to read customer articulations to have a significant positive influence on passive participation.

H2b: “Reduction of search time” as a motive to read customer articulations to have a significant positive influence on passive participation.

2.5.1.2. Social orientation through information

“Determination of social position” and “dissonance reduction” concern the benefits derived

through comparing evaluations, opinion and experiences with other members. A product can be

evaluated on multiple aspects, such as prestige and quality. Consumers can determine their social

position by comparing their evaluation with those of others (schiffman and kanuk). Cognitive

dissonance can be reduced by having the confirmation one made the right buying decision in case

of doubt due to contradicting messages. Opinions by others within virtual communities are

regarded as neutral and unbiased (Hennig-Thurau and Walsch; 2003). Moreover, content from

forums is thought of as more relevant, credible and is able to create more empathy amongst

readers than content from corporate websites (Schindler and Bickart; 2003). The reason for this is

that one does not benefit, unlike a company, from enhanced messages. Therefore opinions by

others are a appropriate source to recover from cognitive incongruence (Hennig-Thurau and

Walsch; 2003). Another possibility is by the comfort of knowing that others experience the same

problem as you are (Sweeney & hausknecht). Therefore we expect that:

H3a: “Determination of social position” as a motive to read customer articulations to have a significant positive influence on passive participation.

H3b: “Resonance reduction” as a motive to read customer articulations to have a significant positive influence on passive participation.

2.5.1.3. Community membership

Reading content on a community website may be due to a certain feeling of “belonging to the

community”. A possible antecedent is one may feel part of the community through participating in


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the experiences from other members. The feeling of belonging to a community can be achieved

when one reads and therefore experiences the consumer articulations. Therefore we expect that:

H4: “Belonging to the community” as a motive to read customer articulations to have a positive significant influence on passive participation.

Another motive which is not necessarily related to buying a product is “to learn what products are

new”. Opinion leaders, who are experimental and open to new experiences, are likely to seek

information about new products (Sun et al.; 2006). Curiosity and Novelty are two factors that

explain the need to learn what is new in the marketplace (Henning-Thurau et al.; 2004). In order

to learn ‘What new products or topics are in’ active participation is not required. Therefore this

variable will be considered in the context of passive participation and we expect that:

H5: “To learn what products are new” as a motive to read customer articulations to have a positive significant influence on passive participation.

2.5.1.4. Top learn to consume a product

“To learn how a product is to be consumed” measures whether members participate in order to

get help in using the purchased product. actively posting a question about using a product is not

always needed. With the growing number of posts, the improving search engines and the time it

saves on waiting before the question is answered, finding written solutions is a more

accommodating alternative. In their article, Henning-Thurau et al. (2004) found that seeking

information on how to use a product had a stronger relation with visiting communities, than

posting on communities. Therefore we expect that:

H6: “To learn how a product is to be consumed” as a motive to read customer articulations to have a positive significant influence on passive participation.

2.5.2. active participation

2.5.2.1. Platform assistance

When consumers have questions or complaints addressed at the company, the community can be

used as a medium to contact the company, in seeking redress. This can be done through the

operating staff, formulated as “Problem solving through the platform operator”. The consumer

who wants to contact the company in order to file a complaint, may experience a lower barrier in

posting the complaint on the internet due to convenience or low costs. Another reason could be

the collective power of multiple members filing the same complaint, in the search for redress.

Since this research will not use the context of company-initiated communities, it is not likely

respondents declare they would post for this reason. Operators from not-company-initiated

communities do not have the responsibility to act as a mediator. Even if the community is

company-initiated, it will be managed by the company, and therefore negative eWOM may be

deleted (Jang et al.; 2008). However, since no empirical evidence was found for this reasoning,

we expect that:

H7a: “Problem solving through the platform operator” as a motive to post articulations to have a positive significant influence on active participation.

H7b: “Convenience of articulation” as a motive to post articulations to have a positive significant influence on active participation.

H7c: “Collective power” as a motive to post articulations to have a positive significant influence on active participation.

2.5.2.2. Concern for other consumers

“Help others in their buying decisions” and “Save others form negative experiences” describes

the motive of helping others in making the right buying decision. Giving advice on a product, is

expected to have different motives than seeking advice. The first motive is more driven by

positive experiences, while the second motive is predominantly driven by negative experiences. In

prior research positive WOM appeared to be driven by altruistic motives and self enhancement

(Sundram et al.; 1998). Consumers distribute negative WOM however out of frustration reduction

or vengeance (Duan, Gu and Whinston; 2008). Nevertheless, one motive does not exclude the

other, and a motive may well be a combination of factors. Here, only altruistic intentions are

measured or pro-social behavior. Therefore we expect that:

H8a: “Help others in their buying decisions” as a motive to post articulations to have a positive significant influence on active participation.

H8b: “Save others form negative experiences” as a motive to post articulations to have a positive significant influence on active participation.


P a g e | 19

2.5.2.3. Extraversion, Positive self-enhancement & Venting negative feelings

Another type of articulating eWOM is ‘Extraversion’ or ‘Venting negative feelings’ without the

purpose of helping others. Extraversion stands for “Being predominantly concerned with and

obtaining gratification from what is outside the self” (Merriam Webster Dictionary). Venting

negative or positive feelings, makes dissonance reduction possible and therefore readers may

unintended benefit from the articulations. Nevertheless, this motive attempts to only address the

intention driven by self-interest. On one hand the participant benefits from extraversion, showing

other members their buying success and experience. In addition, Hennig-Thurau et al. (2004) have

found evidence for extraversion to have an influence on posting behavior. On the other hand, the

participant benefits from venting negative feelings about their buy or experiences. Part of venting

negative feelings is creating catharsis as a form of revenge (Hennig-Thurau et al.; 2004).

Therefore we expect that:

H9a: “extraversion” as a motive to post articulations to have a positive significant influence on active participation.

H9b: “venting negative feelings” as a motive to post articulations to have a positive significant influence on active participation.

The last motive is based on the theory of self-enhancement. Showing other members that one

understands the product. This behavior can only be gratified trough social interaction and is driven

by one’s desire for positive recognition from others (Hennig-Thurau et al.; 2004). For two

reasons, we expect the motive “self-enhancement” to have a positive influence on active

participation. First of all, the motive “social benefits” was found to be the most important

antecedent in posting. “Self-enhancement” is based on positive recognition from others (engel et

al.; 1993) (Sundram et al.; 1998) from others and is therefore related to the theory of affiliation.

Secondly, self-enhancement” in combination with “extraversion” was found to have a positive

influence on posting behavior (Hennig-Thurau et al.; 2004). Therefore we expect that:

H10: “Self-enhancement” as a motive to post articulations to have a significant influence on active participation.

2.5.2.4. Social benefits

Within the context of active participation, the motivation “social benefits” is partly in line with the

motive “belonging to a virtual community”. The common need amongst these motives is the need

for affiliation; which is measured with this motivation (Hennig-Thurau et al.; 2004). Members

may want to post content in order to notify others of their presence. This way one is able to

receive social benefits from other members. Hennig-Thurau et al. (2004) have found this motive

to be the most important in posting customer articulations. Therefore we expect that:

H11: “Social benefits” as a motive to post articulations to have a significant influence on active participation.

2.5.2.5. Advice seeking

“Advice seeking” measures whether members participate in order to give help in using an already

purchased product. actively posting a question concerning the product enables the members to get

more specific information on their problem (Hennig-Thurau et al.; 2004). Hennig-Thurau et al.

have found evidence for “advice seeking” to have an influence on posting behavior. Therefore we

expect that:

H12: “Advice seeking” as a motive to post articulations to have a significant influence on active participation.

2.5.2.6. Helping the company

Another motive is the desire to “help the company”, by posting positive eWOM online (Hennig-

Thurau et al.; 2004). This motive is based on the same background as the first motive and is also a

form of altruism, although this time towards the company. An underlying intention is supported

by the “Equity Theory”. It argues that when customers feel their benefits to be higher than their

costs, they may feel the need to equalize this input/output ratio (Oliver and Swan; 1989). One way

of repaying the company, is spreading positive word of mouth. The results for this motive within

the research of Henning-Thurau et al. (2004) showed no significant influence. However, both ‘The

desire to help the company’ as well as ‘Positive expressions’ are a result from a positive product


P a g e | 21

experience. Therefore we do expect for this motive to have a positive influence on active

participation.:

H13: “Helping the company” as a motive to post articulations to have a significant influence on active participation.

2.6. Product category as a moderator

Shang et al. (2006) mention that information searching is proposed as the main purpose for

consumers to participate in virtual communities. A consumer’s need to find information about a

certain product is driven by that consumer’s involvement in a certain product. In their research,

the hypothesis was tested whether affective or cognitive involvement has different influences on

participation. Cognitive involvement in a product is often the case when consumers are driven by

utilitarian motives. With some products consumers are more interested in comparing the benefits

with the costs, and the functional performance of a product than for example the emotional

aspects. Affective involvement on the other hand, can be reached if interests are driven by self-

enhancement or by expressing one’s self-image. Cognitive involvement often corresponds with

cognitive products, also referred to as utilitarian products or informative products. Affective

involvement, in contrast, often corresponds with affective products, also referred to as symbolic

products, holistic products or hedonic products. The two product types are depicted in the “FCB-

grid”, a conceptual model to reflect how consumers have a different approach to buying each of

the four product types (affective-, cognitive-, high-involvement- and low involvement products).

Buying informative products or cognitive products often requires more information than affective

products since its buyer is more concerned with its functionality and its cost. The situation can

therefore be described as learn-feel-do. Whereas the buying process for affective products follows

the sequence feel-learn-do. These products are related to one’s self-esteem and specific

information is less important (Vaughn; 1986). It goes without saying that these product categories

are not exclusive. Many products contain intangible attributes, however some products contain

these products to a greater extent, and therefore are called symbolic products (Solomon; 1983).

Examples of cognitive products are electronics and houses, while fashion apparel or music are

more affective products.

Shang et al. found that cognitive involvement had an influence on lurking, while affective

involvement did not. Although the purpose of this present research is not to measure the

difference between lurking and posting, it attempts to find support for the type of product having

effect on motives for participation. In previous research suggestions have been made to measure

whether the type of products (cognitive vs. affective) would influence the motives for

participation (Shang et al.; 2006) (Hennig-Thurau et al.; 2004).

In order to research possible differences between the two types of products, the motives for

participation discussed earlier, act as a basis. This approach serves two purposes. One, it gives us

the opportunity to see whether there is a difference between the role of virtual communities in the

buying process when it comes to different product types. Two, it enables us to sketch what the

drivers are in buying a cognitive versus an affective product.

2.6.1. Social oriented motives

People have a certain idea of who they are, which is called the self-concept. This concept is based

on the perceptions of others, which can be communicated through social contact (Solomon; 1983).

The products we use play a important role in these perceptions because symbolic attributes of

products we use tells others who we are, what we do or what we believe in. It also works the other

way around. The social meaning inherited in symbolic attributes, determine how we behave, and

influences the social role we pursue. Likeminded people will interpret the symbolism behind a

product in the same way, and therefore will confirm one’s self-concept (Solomon; 1983). Because

of the relation between symbolic attributes and one’s social position, we expect affective products

to play more predominant role in the social behavior within online communities.

Within the context of passive participation two motives were discussed earlier in this research, in

which the emphasis is more on the social aspect in comparison to the product aspect. These are

‘Determination of social position’ and ‘Belonging to a virtual community’. The former concerns

comparing one’s opinion with those of others, the latter concerns the pleasure of being a part of a

group and its experiences. Our taste in music can represent a part of who we are, music related

opinions or experiences are therefore a mean to determine one’s social position. Equally it enables

us to enjoy belonging to a community with like-minded people. On the contrast, it is harder to

identify oneself with a more functional product like a digital camera. Within the context of active

participation the social oriented motives that were discussed earlier in this research were ‘Social

benefits’ and ‘Positive self-enhancement’. The former is comparable to ‘Belonging to a virtual

community’, with the main difference being the interactive character. The latter concerns a

behavior which can only be gratified trough social interaction and is driven by one’s desire for


P a g e | 23

positive recognition from others (Hennig-Thurau et al.; 2004). Due to the social awareness this

motivation requires from both receptor as the messenger we expect that affective products are

more suitable in seeking positive recognition than informative products. Therefore we expect that:

H14a: “Determination of social position” as a motive to read articulations is more important for affective products than for informative products.

H14b: “Belonging to a virtual community” as a motive to read articulations is more important for affective products than for informative products.

H14c: “Social benefits” as a motive to post articulations is more important for affective products than for informative products.

H14d: “Positive self-enhancement” as a motive to post articulations is more important for affective products than for informative products.

2.6.2. Functional oriented motives

Consumers are more concerned with the practical and functional performance when buying or

evaluating informative products. Whether or not an informative product has met its (functional)

expectation is rather objective, whilst the performance of a more affective product has more

subjective nature (Vaughn; 1986). Advice on buying a certain television based on its image

quality is often considered more valuable in making a buying decision than an opinion on what is

the best music CD available. Therefore we expect that all motives concerning using the product

after purchase or advice giving or seeking have a stronger influence within the context of

informative products.

H15a: “Advice seeking” as a motive to post articulations is more important for informative products than for affective products.

H15b: “Help others in their buying decisions” as a motive to post articulations is more important for informative products than for affective products.

Post purchase advice seeking, concerns seeking help in using the product. Learning how to watch

a movie or how to listen to music (affective products) is for the same reasons different from

seeking help in using a kitchen tool (informative/functional product). Using an affective product is

often less complex and its performance depends on one personal opinion or taste. Therefore we

expect that:

H15c: “To learn how a product is to be consumed” as a motive to read articulations is more important for informative products than for affective products.

2.6.3. Word of Mouth due to positive or negative experiences

Affective or symbolic products, as compared with utilitarian products, are purchased and

evaluated largely based on emotional aspects, according to the feel-learn-do sequence (Vaugn;

1983). Pre-purchase advice giving is based on experience, and therefore resembles engaging in

word of mouth. Word of mouth can be either positive or negative and is a result of consumer

involvement in the product. Consequently, consumers will engage more easily in WoM when the

experience involves more affective elements, good or bad (Westbrook; 1987). Therefore we

expect that symbolic products are better capable of evoking emotions, positive or negative, and

therefore lead to eWoM in more cases. Moreover, Carrol and Ahuvia (2006) found that affective

products tend to evoke stronger emotional responses, and therefore consumers can develop a more

passionate and emotional attachment for a brand. Moreover, these authors found that self-

expressive brands have a greater effect on positive word-of-mouth than utilitarian products. For

these reasons, we expect that positive electronic word-of-mouth will have a stronger influence on

active participation in relation to affective products than for cognitive products.

H16a: “Extraversion” as a motive to post articulations is more important for affective products than for informative products.

H16b: “Helping the company” as a motive to post articulations is more important for affective products than for informative products.


P a g e | 25




LOYALTY INTENTIONS

PRODUCT TYPE




LOYALTY INTENTIONS

InformativeAffective

PRODUCT TYPE

Table 2

Table 1

H1a

H1b

2.6.4. Research model overview & hypotheses

The model below gives us an idea of the framework that will provide us information to draw the

conclusions. In addition, the hypotheses that were formulated earlier, are depicted in Table 1 and

Table 2.

Hypothesis motive

H2A Risk reduction

Reading

H2B Reduction of search timeH3A Determination of social positionH3B Resonance reductionH4 Belonging to the communityH5 To learn what products are newH6 To learn how a product is to be consumedH7A Problem solving through the platform operator

Posting

H7B Convenience of articulationH7C Collective powerH8A Help others in their buying decisionsH8B Save others form negative experiencesH9A extraversionH9B venting negative feelingsH10 Self-enhancementH11 Social benefitsH12 Advice seekingH13 Helping the company

Table 1

Hypothesis motive ActivityModerating

effectH14A Determination of social position Posting

AffectiveH14B Belonging to a virtual community Reading

H14C Social benefits Posting

H14D Positive self-enhancement PostingH15A To learn how a product is to be consumed Reading

InformativeH15B Advice seeking PostingH15C Help others in their buying decisions PostingH16A Helping the company Posting

AffectiveH16B Extraversion Posting

Table 2


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Chapter 3. Methodology

3.1. Approach

In the first two chapters an overview is given of previous literature, which serves as a

foundation on which this present thesis will build its contribution. This foundation contains

the following aspects. Firstly the relevance of this research, as an extension on former

research, is explained. Secondly, a background is given on the phenomenon online

communities and their commercial value, viewed from a marketing perspective. Finally, the

components used in our research model are supported which lead to the formulation of the

hypotheses. For the operationalization of the research model (paragraph 2.6.4), data had to be

gathered and variables had to be defined. This was done according to the following procedure.

The items for the components from the research model had to be created in order to measure

the different variables. Then, a principal component analysis was used to reveal possible

underlying components. The underlying components were then tested on reliability, using the

Cronbach’s alpha statistic, and served as new variables. After the new variables were created

and formulated, the direct relationships between motives and participation were tested using a

linear regression model. This relationship enables us to test the moderating effect of the

product type.

3.2. Questionnaire construct

Because passive and active participation are two different activities, different items were used

to measure the motives. The items measuring ‘motives to participate’ and ‘loyalty intentions’

were already constructed in earlier research and therefore derived from three articles. The

items that were used for passive participation were derived from Hennig-Thurau et al. (2003)

and are listed in Error: Reference source not found. These authors tested all the items on

expert validity, criterion validity and convergent validity. The items used for active

participation were derived from a follow up article to the first article (Hennig-Thurau et al.;

2004). Again, the items measuring the motives for active participation were critically assessed

by both experts and users and are listed in Error: Reference source not found. loyalty was

measured by four items, based on items suggested by Quester and Lim (2003) and

F.Reichheld (2003). Four of the seven items measuring loyalty were maintained due to length

restrictions. An ordinal scale for visiting frequency (Lurking) and posting frequency (Posting)

were constructed. The measurement scales that were used for the variables active and passive

participation and the items that were used for loyalty are depicted in Tabel 19.

3.2.1. Survey one: motives

The empirical data was gathered with the use of an online survey. The website

www.studentenenquete.nl provides free use of the web based software Limesurvey version

1.81+. The original items were translated in Dutch, and subjected to several users of online

communities for their judgment, until agreement was reached on the translation.

With the use of the described software, potential participants were invited by means of an e-

mail message. Participants were found amongst family, friends and acquaintances. In order to

motivate people to complete the survey, two completed surveys were rewarded with a

voucher of € 25,- each. The address list contained 1125 e-mail addresses, resulting in 147

completed surveys after the first invitation and an additional 168 completed surveys after

sending a reminder. The survey was divided in two identical parts, each part containing

questions concerning passive and active participation. In the first part, however, the questions

concerned electronic goods (informative products) and the second part concerned music,

books and films (affective products). Although a distinction is made between posting (active

participation) and viewing (passive participation), members can still be “active” in viewing

posts. Therefore the likelihood of passive and active participation for that person was

measured. If the participant indicated that the probability of (active or passive) participating in

the community was very unlikely (“1” on a 7-point scale), the motive questions (for active or

passive participation) were then skipped.

3.2.2. Survey two: loyalty

The second part of the data was gathered to examine the influence of active and passive

participation on loyalty intentions. In the first part of the survey the target group consisted of

anyone who is familiar with communities of interests or communities of transaction. In this

part, however, loyalty intentions can only be measured on the basis of actual behavior and

attitude. Therefore the survey was spread through ‘tweakers.net’, the largest online consumer


P a g e | 29

electronics community in the Netherlands. A link to the survey was posted on the community

forum, were 130 participants completed the survey. The community members were first asked

to indicate their lurking and posting frequency. Lurking was formulated as number of visits

per month, and posting was described as the number of visits during which the member added

or (or changed) content to the website. Then the member was asked if they occasionally reads

or posts about a particular brand. If the answer was yes, the question was asked to state about

which brand he or she posts or reads more than other brands. To have the least possible bias in

the dataset, the cases without a certain brand name (68 cases) were deleted. Reasoning is that

without a certain brand in mind, one’s brand loyalty is impossible to measure. Finally, loyalty

was measured (Tabel 19), using the four items constructed by Quester and Lim (2003) and

F.Reichheld (2003).

In both surveys, some demographic control variables were also measured. Table 3 provides

the basic demographic distribution on both surveys.

Variable CategoriesPercent

1st Survey(395)

Percent2nd Survey

(62)

Gender Female 48,1% 8%Male 51,9% 92%Total 100,0% 100%

Education Primary school 0,3% 0%High school 4,1% 11%Intermediate Vocational Training 14,2% 18%Higher Vocational Education 50,1% 35%University 31,1% 34%Other 0,3% 2%Total 100,0% 100%

Age Younger that 20 years 6,1% 15%20 - 25 years 47,8% 42%25 - 30 years 27,8% 16%30 - 35 years 9,1% 15%35 - 40 years 4,1% 10%40 years or older 5,1% 3%Total 100,0% 100%

Hours per day online

1 hour per day 20.3%Between 1 and 3 hours per day 43.3%Between 3 and 5 hours per day 24.6%More than 5 hours per day 11.4%I don't know 0.5%Total 100.0%

Hours per day online

1 hour per day 0%Between 1 and 3 hours per day 23%

Between 3 and 5 hours per day 34%Between 5 and 7 hours per day 19%Between 7 and 9 hours per day 6%More than 9 hours per day 18%Total 100%

Table 3 Demographic profile on the sample

3.3. Variables

After the data was gathered through the surveys, a principal component analysis was

conducted, using the Varimax rotation method. This analysis was performed to examine the

independence of the items that were identified for passive and active participation. Before this

analysis was performed, few measures have been taken to optimize the dataset. Firstly, all

cases that had the same values for all items, and of which the credibility was questioned, were

not selected. Secondly, all broken cases were deleted. Finally, when a participant indicated it

was “very unlikely” that one would participate (actively or passively), the survey skipped the

motivational questions (for active or passive participation). Reasoning was that participants

who indicate it is very unlikely for them to participate in the future, probably cannot relate to

any motive at all. To have the least “random” answers as possible, these cases were therefore

left out of the analysis..

To ensure the internal consistency, a reliability test was performed. This test measured the

reliability of the items within each component, with the use of the Cronbach’s alpha statistic

(α).

For passive participation, five out of seven motives showed strong correlation among the

items measured. The two items for the motive ‘Resonance reduction’ loaded high on both

‘Comparing evaluations’ and ‘Problem in common’, as Table 4 shows us. The motive ‘To

learn how a product is to be consumed’ was measured by two items, one of those appeared to

load equally high on two factors and was therefore deleted. The reliability statistic (α) scored

for four out of six motives below .7, which is a common used benchmark. However, since

these four motives only exist out of 2 items, a score above .53 is satisfying.

When looking at the underlying components (Table 4), the principal component analysis

showed us, the following theoretical reasoning was defined. The first revealed component is


P a g e | 31

a combination of ‘Determination of social position’ with the first item for ‘Dissonance

reduction’ (for a full overview of the items see Error: Reference source not found). This last

item describes the comfort one gets through reading positive evaluations about their bought

product. Although ‘Determination of social position’ initially was defined as a social oriented

motive, the motive also concern comparing one’s evaluations with those of others. Therefore

this new component is called ‘Comparing evaluations’. Factor 6 is a combination of

‘Dissonance reduction’ and ‘Learn how to consume a product’. Both items address a certain

problem in using the product, and therefore this new variable is renamed into ‘Problem in

common’. The items of the remaining four variables are allocated to the remaining factors

according to their initial structure.

New VariableComponent

Item 1 2 3 4 5 6 α

Comparing evaluationsDetermination social position 2 ,847 ,765Determination social position 1 ,734Resonance reduction 1 ,651

Risk ReductionRisk reduction 1 ,839

,777Risk reduction 2 ,803

Reduction of search time

Reduction of search time 2 ,828,613

Reduction of search time 1 ,376 ,700Learn what products are new

Learn about new products 1 ,854,586

Learn about new products 2 ,731Belonging to a virtual community

Belonging to a virtual community 1 ,832,532

Belonging to a virtual community 2 ,422 ,687

Problem in commonLearn how to consume a product 1 ,300 ,779

,630Resonance reduction 2 ,442 ,683

Table 4 Principal component analysis within the passive participation construct

For active participation five out of six factors reveal a combination of items from different

motives, as is depicted in Table 5. Therefore these items were aggregated into one separate

motive. Items that did not load highly on one distinctive factor were deleted, including the

complete motive ‘Save others from negative experiences’. The reliability statistic was

satisfying for all motives, since all motives showed a Cronbach’s alpha of above .7.

When looking at the underlying components the principal component analysis showed us, we

can define the following new variables. The first new variable combines ‘Desire to help the

company’ with ‘Extraversion’. This combination can be explained due to the fact that both

motives are a form of positive word-of-mouth, and can be linked to altruism. This new

variable is called positive expressions. The second new variable exists of ‘Social benefits’ and

‘Positive self-enhancement’. Since self-enhancement can only be gratified trough social

interaction and is driven by one’s desire for positive recognition from others (Hennig-Thurau

et al.; 2004), it is arguable that this item could correlate with the ‘Social benefits’. Hennig-

Thurau et al. (2004) found that the motives ‘Problem solving through platform operator’,

‘Collective power’ and ‘Convenience of articulation’ to be one factor, and called it ‘Power

through articulation’. This name is maintained for the third new variable. The fourth new

variable is a combination of the motives ‘Advice seeking’ and ‘Help others in their buying

decisions’, and is renamed into ‘Product advicing’. Although ‘Advice seeking’ is based on

self-benefit and ‘Help others in their buying decisions’ on altruism, both motives concern

giving or seeking advice about a certain product. The fifth last new variable consists of the

items measuring the motive ‘Venting negative feelings’, and therefore is not renamed.

To measure the relationship between participation and loyalty we used the data from the

second survey. No principal component analysis was performed for the variable ‘loyalty’,

since there was no interest in finding or testing an underlying structure. Again the reliability

was tested and showed a Cronbach’s alpha of .817, which is above .7 and therefore satisfying.

The eventual variable was created by taking the average value over all four items.

New Variable Component 1 2 3 4 5

Positive expressions

Desire to help the company 1 ,795,864Desire to help the company 2 ,597

Extraversion 1 ,726Extraversion 3 ,784

Social benefits

Positive self-enhancement ,707,829Social benefits 1 ,679

Social benefits 2 ,696Social benefits 3 ,708

Power through articulation

Problem solving through operator 1 ,801

,836Problem solving through operator 2 ,782

Convenience of articulation 1 ,677Collective power ,562

Product advising

Advice seeking 1 ,711,848Advice seeking 2 ,812

Help other with buying decision 1 ,598Help other with buying decision 2 ,650

Venting negative feelings

Venting negative feelings 1 ,769 ,868Venting negative feelings 2 ,811Venting negative feelings 3 ,866

Table 5 Principal component analysis within the active participation construct


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3.4. Regression analysis

After defining the variables to work with, different models are used to test the hypotheses

from chapter two. These models will be analyzed and discussed in chapter four. This chapter

is divided in three parts. Firstly, all hypotheses regarding passive participation, except for the

influence on loyalty, were tested. Secondly, these tests were repeated for all hypotheses

regarding active participation. These tests regarding both forms of participation were

performed in two steps. The first step was to look at the direct effect between motivation and

participation. Here a linear regression model was used, where all variables and control

variables were taken into account. The second step was to test for a moderating effect, caused

by the type of product. To find support for this effect, we used three types of analyses. 1)

Interaction terms were created by multiplying the motive variables with a dummy variable.

This Dummy variable contained the values one and zero. The value ‘one’ equaled Electronics

or ‘Informative Products’, ‘zero’ equaled ‘Books/Films/Music’ or ‘Affective Products’. The

moderating effect was then tested by adding the created interaction terms in a regression

model, together with the motive variables. 2) The data for informative products was separated

from the data for affective products. Then the a regression analysis was performed for each

‘Product type’. This way, both situations could be analyzed separately and independent. 3) A

one-way ANOVA analysis was performed for the relationship between motives and product

type. This analysis shows whether the mean values for each motivation significantly differs

among product types. Finally, the hypotheses regarding loyalty were tested. Again a linear

regression model was used, with loyalty as the explained variable and passive and active

participation as explaining variables.

Chapter 4. Results

4.1. passive participation:

4.1.1. Direct effect of Motivation on participation

In order to test whether there is a direct relationship between motives and participation, two

regression models were constructed. Both models test the relationship between motives as

independent variables and passive participation as dependent variable. However, for the

second model some control variables were added to the model. These control variables are

gender, education, age and the number of hours per day one spends on the internet. After first

creating a model with only motive variables, a second model was created where the control

variables were added to the model (Table 6). These control variables were added to see

whether it had any substantial effect on the initial results. Although the level of education and

the ‘Hours online per day” both have an influence on passive participation, they did not have

a large influence on one of the coefficients from the original model. Therefore, only the

second model is depicted in Table 6.

Model 1 indicates that a relationship exists for three out of six motives. The coefficients for

‘Risk reduction’, ‘Reduction of search time’ and ‘Belonging to a virtual community’ all three

significantly differ from zero. The variables ‘Risk reduction’ and ‘Reduction of search time’

both have a positive influence on viewing, while ‘Belonging to a virtual community’ has a

negative effect on viewing. Therefore, we found support for hypothesis H1a and H1b. This

indicates that the first two motives are better reasons for community visitors to participate.

Because both variables have an equally large standardized Beta, they both seem to have an

equally strong influence on passive participation. Since the motivation ‘Belonging to a virtual

community’ has a negative effect on passive participation, we did not find support for

hypothesis H3a. The three remaining variables do not show any significant relationship and

therefore there is no support for the hypotheses H3a, H3b, H5 and H6.

Model 1 Unstandardized Coefficients

Standardized Coefficients Sig.

B BetaComparing evaluations 0,048 0,030 0,465


P a g e | 35

Risk Reduction 0,252 0,155 0,000*Reduction of search time 0,268 0,165 0,000*Learn what products are new 0,033 0,021 0,612Belonging to a virtual community -0,208 -0,128 0,002*Problem in common -0,058 -0,036 0,379

Gender -0,046 -0,014 0,736

Education 0,198 0,100 0,015**Age 0,003 0,015 0,720

Hours online per day 0,051 0,092 0,026**a. Dependent Variable: passive participation

Table 6 Effect of motives and control variables (*P < 0.01,**P < 0.05, ***P < 0.1)

4.1.2. Direct & interaction effect of product type

After testing the direct effect between ‘motives’ and ‘passive participation’, the moderating

effect of ‘product type’ was tested. Again, the six motive variables were used as a basis. In

Model 2 the variable ‘product type’ was added to test the direct effect of informative products

on ‘passive participation’ In Table 9 we observe that this dummy variable, which was added

to model 1, shows support for a significant negative effect of ‘Informative products’

(Electronics) on the direct relationship. This indicates that the influence within an informative

product context differs significantly (at level p < 0.1) from the influence within an affective

product context. Implications are that in case of affective products, people tend to view

content on communities more often. The additional dummy variable did not have a large

influence on the remaining variables.

Model 2Unstandardized

Coefficients Standardized Coefficients

Sig.B BetaComparing evaluations 0,052 0,032 0,429Risk Reduction 0,307 0,189 0,000*Reduction of search time 0,272 0,168 0,000*Learn what products are new 0,007 0,004 0,916Belonging to a virtual community -0,219 -0,135 0,001*Problem in common -0,036 -0,023 0,586Electronics -0,252 -0,077 0,079***

Table 7 Effect of motives and product type (*P < 0.01,**P < 0.05, ***P < 0.1)

To see whether there is a moderating effect, interaction variables were created and added to

the model. The interaction term measures the influence of the combined effect between

‘Motivation’ and ‘Informative Products’ on ‘passive participation’. Each interaction variable

was added separately to the six motives to have as little bias on the individual effect. The six

models that were created are depicted in Tabel 8. The models show whether the coefficients

significantly differed from zero. As we can see, although we added the interaction variables,

the coefficients for the motives remain more or less the same. Moreover, none of the Beta’s

from the interaction variables differ significantly from zero. Therefore, there is no support for

hypotheses H14b and H15a.

Models Sign 3 4 5 6 7 8

Comparing evaluations + ,470 ,650 ,566 ,530 ,571 ,650Risk Reduction + ,080 ,000 ,000 ,000 ,000 ,000Reduction of search time + ,000 ,000 ,001 ,000 ,000 ,000Learn what products are new + ,675 ,816 ,785 ,931 ,789 ,816Belonging to a virtual community - ,002 ,002 ,002 ,002 ,120 ,002Problem in common - ,384 ,335 ,301 ,334 ,318 ,335Electronics*CE + ,172Electronics*RR - ,932Electronics*RST - ,477Electronics*LNP + ,916Electronics*BVC - ,521Electronics*PIC - ,932

Tabel 8 (*P < 0.01,**P < 0.05, ***P < 0.1)

The next step is to test the same motive variables, but this time within the context of each

product category. Within each context, the direct relationship between the different motives

and passive participation was measured. Within the context of informative products, Table 9

shows that there is support for a significant direct relationship between the motives ‘Risk

reduction’, ‘Reduction of search time’, ‘Belonging to a virtual community’ and ‘passive

participation’. Again, ‘Risk reduction’ and ‘Reduction of search time’ both have a positive

influence on viewing, while ‘Belonging to a virtual community’ has a negative effect on

viewing. In contrast with the aggregated model (Table 6), “Risk reduction” is the strongest

relation with a standardized Beta of .219, which would indicate this motivation is even more

important within communities concerning informative products.



B BetaComparing evaluations ,050 ,031 ,565Risk Reduction ,388 ,219 ,000*Reduction of search time ,241 ,137 ,012**


P a g e | 37

Learn what products are new ,028 ,016 ,770Belonging to a virtual community -,252 -,152 ,005*Problem in common -,017 -,010 ,856a. Dependent Variable: passive participationb. Selecting only cases for which product type = Electronics

Table 9 (*P < 0.01,**P < 0.05, ***P < 0.1)

Within the context of affective products, the model (Table 10) again shows there is support

for a direct relationship between motives ‘Risk reduction’, ‘Reduction of search time’,

‘Belonging to a virtual community’ and ‘passive participation’. In contrast to model 11

however, ‘Reduction of search time’ is the strongest relation with a standardized Beta of .217.

Moreover, ‘Belonging to a virtual community’ still has a negative influence on ‘passive

participation’, but is not as significant (P < 0.1).



B BetaComparing evaluations ,057 ,034 ,586Risk Reduction ,206 ,131 ,040**Reduction of search time ,321 ,217 ,001*Learn what products are new ,014 ,009 ,882Belonging to a virtual community -,177 -,111 ,079***Problem in common -,067 -,043 ,498a. Dependent Variable: passive participationb. Selecting only cases for which product type = books/films/music

Table 10 (*P < 0.01,**P < 0.05, ***P < 0.1)

Comparing the results for informative products with those for affective products, we find

indications of some differences. Firstly, ‘Reduction of search time’ seems be more important ,

and ‘Risk reduction’ less important, within the context of affective products. Secondly,

‘Belonging to a virtual community’ is less negative. This indicates people are less reluctant

towards this motive when it comes to affective products in comparison to informative

product.

4.1.3. ANOVA analysis

Independent of the direct effect between motives and passive participation, a One-way

ANOVA analysis is used to see whether the mean values of the different motives significantly

differ between informative and affective products (see Table 11). Looking at the variables,

there seems to be a clear difference in means between informative and affective products.

Except for the motives ‘Learn what products are new’ and ‘Comparing evaluations’ the means

from the four other variables are significantly different between product types. Although these

results do allow for any conclusions about the ‘Level of participation’, the motives were

measured with the context of ‘passive participation’. To see which mean is higher, for

affective or informative products, the values are depicted in Table 11. For four out of six

variables the mean is higher for informative products. For the motives ‘Learn about new

products’ and ‘Belonging to a virtual community’ the means are higher for affective products.

Therefore these motives appear to be a more important factor in visiting virtual communities

for affective products than for informative products. For ‘Belonging to a virtual community’

this is in line with a lower standardized beta for this variable within the context of affective

products, compared to informative products (Table 9 & Table 10).

One-way ANOVA Means books/films/music Means Electronics Significance

Comparing evaluations -0.102 < 0.075 ,35

Risk Reduction -0.320 < 0.236 ,000*

Reduction of search time -0.126 < 0.093 ,009*

Learn what products are new 0.078 > -0.058 ,105

Belonging to a virtual community 0.121 > -0.089 ,013**

Problem in common -0.247 < 0.182 ,000*

Table 11 (*P < 0.01,**P < 0.05, ***P < 0.1)

4.2. active participation

4.2.1. Direct effect of Motivation on participation

In order to test the direct relationship between motives and active participation, the same

models were used as for passive participation. Again the control variables ‘Gender’,


P a g e | 39

‘Education’, ‘Age’ and the number of hours per day one spends on the internet were

measured as well. After first creating a model with only motive variables, a second model was

created where the control variables were added to the model. The control variables however,

did not have a noteworthy influence on one of the coefficients from the original model.

Therefore only the second model is depicted in Table 12. Moreover, the control variables do

not have a significant influence on active participation of their own.

When we look at the direct effect of the motives for active participation , we see that all

variables have positive coefficients. However, only the coefficients for ‘Positive expressions’,

‘Social benefits’, ‘Power through articulation’ and ‘Venting negative feelings’ all four

significantly differ from zero. Therefore we can state that these four motives have an

influence on posting on online communities, and therefore hypotheses H7, H8, H9a, H10,

H11, H12 and H13 are supported. The coefficient of the variable ‘Venting negative feelings’

does not significantly differs from zero, and therefore H9b is not supported.



B BetaPositive expressions 0,322 0,238 0,000*

Social benefits 0,236 0,175 0,001*

Power through articulation 0,162 0,120 0,018**

Product advising 0,174 0,129 0,012**

Venting negative feelings 0,102 0,076 0,135

Gender -0,237 -0,088 0,102

Education -0,127 -0,077 0,140

Age 0,011 0,054 0,300

Hours online per day -0,016 -0,032 0,543a. Dependent Variable: active participation

Table 12 Effect of motives and control variables (*P < 0.01,**P < 0.05, ***P < 0.1)

4.2.2. Direct & interaction effect of product type

After testing the direct effect between ‘motives’ and ‘active participation’, the moderating

effect of ‘product type’ was tested. Again, the motive variable were used as a basis. In model

12 the variable ‘product type’ was added to the model, to test the direct effect on ‘active

participation’ In Table 13 we see that the dummy variable, which was added to the model,

does not show support for a significant negative effect of ‘Informative products’ (Electronics)

on the direct relationship. This indicates that the influence within an informative product

context does not differ significantly from the influence within an affective product context.

Moreover, the additional dummy variable did not have a large influence on the remaining

variables.



B BetaPositive expressions 0,310 0,230 0,000*Social benefits 0,232 0,172 0,001*Power through articulation 0,175 0,130 0,011**Product advising 0,140 0,104 0,044**Venting negative feelings 0,098 0,073 0,152

Electronics 0,111 0,041 0,434a. Dependent Variable: active participation

Table 13 (*P < 0.01,**P < 0.05, ***P < 0.1)

To measure any moderating effect caused by the variable ‘product type’, each interaction

variable was separately added to the model. Noteworthy is the transition of the effect of

‘Positive Expressions’ on ‘active participation’. This variable showed a significant effect (P <

0.01) in model 12, while in Model 13 the effect is largely explained by the interaction term.

This implicates that ‘Positive expressions’ is a more important motivation within the context

of informative products compared to the context of affective products. Therefore hypotheses

H16a and H16b are not supported. All other interaction variables do not have a significant

effect on active participation. Therefore there is no support for H14a, H14c, H14d and H16.

Models Sign 13 14 15 16 17

Positive expressions + ,136 ,000 ,000 ,000 ,000Social benefits + ,001 ,004 ,001 ,001 ,001Power through articulation + ,015 ,011 ,203 ,013 ,012Product advising + ,032 ,029 ,028 ,030 ,323Venting negative feelings + ,177 ,171 ,122 ,445 ,128Electronics * PE + ,027**Electronics * SB - ,332Electronics * PTA + ,533Electronics * VNF + ,882Electronics * PA + ,566

Table 14 (*P < 0.01,**P < 0.05, ***P < 0.1)


P a g e | 41

The next step is to test the same motive variables, but this time within the context of each

product category. Within each context, the direct relationship between the different motives

and passive participation was measured.

Within the context of informative products model 18 shows there is support for a direct and

significant relationship between the dependent variable ‘active participation’ and the

independent variables ‘Positive expressions’ (P < 0.01) , ‘Social benefits’ (P < 0.05), ‘Power

through articulations’(P < 0.05) and ‘Product advising’ (P < 0.1). ‘Positive expressions’ has

the strongest relationship with a standardized beta of .314. The weakest relationship is from

‘Product Advising’ with a standardized beta of .131.


Standardized Coefficients

B Beta Sig.Positive expressions ,447 ,314 ,000*Social benefits ,179 ,131 ,047**Power through articulation ,213 ,149 ,024**Product advising ,175 ,126 ,054***Venting negative feelings ,097 ,075 ,253a. Dependent Variable: active participationb. Selecting only cases for which product type = Electronics

Table 15 (*P < 0.01,**P < 0.05, ***P < 0.1)

When comparing the results from model 19 (Table 16) with model 18 (Table 15) we notice a

substantial difference among the coefficients. Within the context of affective products the

model shows there is only support for a direct relationship between ‘Social benefits’ and

‘active participation’. These results show that ‘Social benefits’ has a stronger influence within

the context of affective products compared to the situation depicted in Table 15. Although

model 19 does not depict the moderating effects, the result for Social benefits’ is in support of

H14c. Moreover the results show that ‘Positive expressions’, ‘Power through articulations’

and ‘Product advising’ only show a significant influence on active participation when it

comes to informative products. Regarding ‘Product advising’, these results are in support of

H15b and H15c.



B Beta Sig.Positive expressions ,141 ,112 ,165

Social benefits ,329 ,245 ,003*Power through articulation ,138 ,110 ,174Product advising ,091 ,068 ,397Venting negative feelings ,048 ,032 ,694a. Dependent Variable: active participationb. Selecting only cases for which product type = books/films/music

Table 16 (*P < 0.01,**P < 0.05, ***P < 0.1)

Comparing the results for informative products with those for affective products, we find

indications for some differences. Firstly, ‘Positive expressions’ seems be more important, in

contrast to what was expected, within the context of informative products. Secondly, ‘Social

benefits’ are clearly more important when it comes to affective products, in comparison to

informative products. This is in support of what was expected (H14c).

4.2.3. ANOVA – Analysis

Independent of the direct effect between motives and active participation, an ANOVA-

analysis is used to see whether the mean values of the different motives significantly differ

between informative and affective products. Looking at the variables, there seems to be a

difference in means between informative and affective products. The differences between

product types are significant for ‘Social benefits’ ( P < 0.1), ‘Product advice’( P < 0.01) and

‘Venting negative feelings’( P < 0.01). To see which mean is higher, for affective or

informative products, the values for each variable are depicted in Table 17. “Social benefits”

is the only variable, where the mean is significantly higher for affective products. This is in

line with the results found using model 19. The mean value for ‘Product advice’ was

significantly higher for informative products, which is in line with the results found though

model 18. In using previous models, no sign was found for the difference in mean value for

‘Venting negative feelings’ though.

Variable Means books/films/music Means Electronics Significance

Positive expressions 0.067005 > -0.04894 ,124

Social benefits 0.111844 > -0.08169 ,071***


P a g e | 43

Power through articulation 0.084343 > -0.0616 ,143

Product advising -0.19173 < 0.140034 ,006*

Venting negative feelings -0.10706 < 0.078198 ,006*

Table 17 (*P < 0.01,**P < 0.05, ***P < 0.1)

4.3. loyalty

The variable loyalty was measured with the use of four items. The average of these items was

used as a measurement for loyalty. To see whether the relationship between the form of

participation and loyalty was significant, a regression analysis was used. Table 18 shows a

significant relationship between the number of visits per month to the website, passive

participation, and the level of loyalty. Therefore hypothesis H1a is supported. The coefficient

for active participation however, did not significantly differ from zero. Therefore active

participation, the number of times the member added content, does not show any relation to

loyalty. Consequently there is no support for H1b.



B Beta Sig.

How many times did you visit tweakers.net during the last month? 0,520 0,287 0,030**

How many times did you contributed to tweakers.net, by adding adjusting content or during the last month? 0,120 0,142 0,274

Gender 0,328 0,121 0,362

Education 0,015 0,023 0,865

Age -0,011 -0,114 0,399

Hours online per day 0,027 0,101 0,437

a. Dependent Variable: loyalty

Table 18 (*P < 0.01,**P < 0.05, ***P < 0.1)

Chapter 5. Conclusion

5.1. Discussion

This research explored the impact of different motives on participation, and attempted to find

evidence for a moderating effect caused by the type of product. Moreover, in favor of the

usefulness for marketers, the relation between participation and attitudinal loyalty was

explored. Results show a direct effect for some of the motives on participation. Moreover,

when it comes to motives to participate, there are signs these differ between product types.

However, little proof has been found for a moderating effect caused by product type.

Furthermore, passive participation was proven to have a significant influence on loyalty,

while this was not the case with active participation. The purpose of this chapter is to discuss

our findings and elaborate on possible implications, based on these findings. The chapter

concludes with some limitations and suggestions for further research.

5.1.1. passive participation

In the previous chapter, the results showed that ‘Risk reduction’ and ‘Reduction of search

time’ are motives for people to view content on communities about a certain product (Table

6). Both motives imply that the main reasons for viewing content are product related. People

want to save time and risk in preparation of buying a product. These motives were in line

with our expectations. However, the motives ‘Comparing evaluations’, ‘Belonging to a virtual

community’, ‘Learn what products are new’, and ‘Problem in common’ were not found to be

reasons to view content. The first two are both socially oriented motives, which may imply

that people are less interested in social benefits. However, social benefits may be more

difficult to achieve without interaction. This would explain why the motive ‘Social benefits’

does have an influence on active participation. For the same reason, the motivation ‘Problem

in common’ could be less accommodating if one cannot (actively) ask specific questions

about their problem.


P a g e | 45

The motivation ‘Learn what products are new’ is a motivation that was constructed with

people like opinion leaders in mind (Sun et al.; 2006). This group could very well be

underrepresented, and therefore show little influence.

The moderating effect of the product type on the relationship between motives and viewing

was not found. This implies that however there is evidence that the motives for viewing

content about informative products differ from affective products, this could not be explained

by the level of passive participation. Reason for this inability, could be found in the construct

of the survey that was used. This will be explained when the limitations of the research are

discussed.

Looking at the motives, independently from the level of passive participation, there is

evidence that some reasons apply more to informative products than to affective products.

First of all, we find ‘Belonging to a virtual community’ to have a more positive (or less

negative) influence on viewing. This confirms that affective products are more

accommodating in assessing what kind of members are joining the community and whether

the people in the community are ‘like-minded’. Second, ‘Risk reduction’ and ‘Reduction of

search time’ appear to be more valued reasons within the context of informative Products.

Reasoning could be that informative products are easier to evaluate on the basis of consumers

articulations.

5.1.2. active participation

In case of active participation, four out of five motives seem to evoke Posting. This is

according to what was expected in advance. However, ‘Venting negative feelings’ appeared

no reason for people to post their opinions on the internet. Possible explanation is the aspect

of catharsis as a form of revenge. People who have these kinds of emotions could merely act

out of impulsive reaction, instead of rational. The survey construction was not based on

previous experience, but on expected behavior. This could be the reason it was harder for

people to predict such impulsive behavior.

In contrast to the motives for passive participation, a moderating effect of the product type on

one of the five relationships between motives and viewing was found. However, this

moderating effect shows the exact opposite of the expected findings. ‘Positive expressions’

about affective products was expected to have a stronger influence on active participation.

The affective attachment to affective products, due to the symbolic attributes these products

often entail, was the reason for this discrepancy. The opposite result was found in Table 14. In

contrast to the moderating effect, and in favor of previous reasoning, the mean score for this

motivation was higher for affective products.

In addition to the moderating effect of ‘Positive Expressions’ there were other reasons to

believe there is a noteworthy difference between the product types. As just mentioned, in

comparing the likelihood of the different motives to occur, there was a difference between

product types. Three motives were expected to differ amongst product types. The first

motivation was ‘Positive expressions’. There was no support in favor of this expectation, as is

previously discussed. The second motive was ‘Social benefits’, which was expected to be of

greater importance within communities about affective products instead. The third motive was

‘Product advising’ which was expected to be of importance within communities about

informative products. For both of these motives, results were found in favor of the

expectations (Table 17).

5.1.3. loyalty

Although no evidence was found for the relationship between active participation and loyalty,

evidence for passive participation to have an influence on loyalty was found. These findings

are in line with the results Shang et al. (2006) found. They reasoned that posting is more

driven by social factors, and not by personal considerations. Our findings do support this

reasoning, since ‘Risk reduction’ and ‘Reduction of search time’ were found to have the

strongest influence on viewing, while posting was strongly linked to socially oriented motives

like ‘Social benefits’ and ‘Positive expressions’. Moreover viewing comments posted by

others can have a promotional effect, and therefore evoke loyalty (Shang et al.; 2006).

5.2. Implications of this research

Possible target groups for this research are mainly researchers in the areas of community

participation, web site evaluation, web site development, commercial value of online

communities, or functionalities of online communities. Other target groups are the companies


P a g e | 47

that want to catch the wave of the online communities but do not know what their customers

seek in a online community, or what kind of community fits their business.

Most companies whom have invested in web 2.0. technologies say that they are pleased with

their results (McKinsey Quarterly; 2007), however companies feel unconfident in applying

web 2.0 applications in the right way to their business (Dösinger et al. 2007). This research

aims to provide directions for companies already owning or planning to initiate an online

community. By reading this present research, lessons can be drawn from differences in

motives between products and types of participation. Whether communities concern a certain

brand or concern a certain theme or topic, in both cases the goal is to strive for the highest

participation. Therefore, knowing what drives people to view or to post content is crucial. To

give an idea of how the results could ad value, some practical implications are given. For

example, one of the implications could be that product reviews and ratings are accessible for

anyone, since they are the main reasons for lurkers to visit. active participants however, are

merely driven by social factors, and therefore emphasis could be on the social interaction.

Making members aware of the presence of other members, could stimulate the number of

postings.

Implications also concern company initiated brand communities, since the relevance and

importance of passive participation within online communities is explained. Although this

research did not show for active participation to directly have an influence on loyalty, active

participation is at least equally important. In order to have participants visiting the

community, others have to provide the content that is viewed. Therefore active participation

has to be encouraged just as much, or even more. Especially when it concerns product

experiences, reviews and ratings. These consumer articulations are more credible in

comparison to content created by the company behind the product or brand (Schindler and

Bickart; 2003). However, if passive participation has a direct influence on loyalty, this should

be encouraged as well. A possible implication could be to make it as easy as possible to enjoy

the community without signing up or paying contribution.

5.3. Limitations of this research

The main limitation of this research is the construction of the first field research. Initially

several online communities were approached to ask for incorporating actual members into this

research. This way the research could be based on actual experience instead of predictions

made by the participants from the sample. Unfortunately, no community was willing to

cooperate, and therefore the survey was taken by acquaintances, family and friends. The

questions were expressed in a hypothetical way, so even with little knowledge of communities

in general everybody could take part. The problem of this construct lies in the fact that

participation was difficult to measure. This has implications for the direct relationship, which

was used as the basis for the moderating effect.

Another difficulty was the construction of the second survey. This survey was taken by actual

members. However, the amount of participants to the survey was not large enough to measure

much variance. The largest part of the participant that volunteered, checked the highest level

of passive participation. Considering the short time frame in which the survey was taken, the

likelihood of overrepresentation of participants that visit the community quit frequently is

therefore high. Suggestion for future research would be to gather data from actual members.

This enables measuring the actual post and viewing behavior. Moreover it is advisable take

the survey over an extended period of time.

The direct relationship between motives and participation is suggested to be revised as well.

The motives that were used, which were drawn from previous research, are based on

traditional Word-of-mouth theories. Instead of deriving the motives from these theories, they

could be gathered through qualitative research instead. This way the motives are more

compatible with today’s communities.


P a g e | 49

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http://www.informatik.umu.se/nlrg/valter.html

Appendix 1. Items used

Items used Categories passive participation

How often did you visit the community last month?

Couple times per weekAbout once a weekAbout a couple of times but not every weekMaybe once this month I did not visit the website this month

active participation

How often did you post, contribute or adjust properties on the website last month?

Almost every time I visited the websiteAbout half the time I visited the websiteMaybe once this monthI did not contribute at all this month

loyalty

Do you read or comment about branded products ? If yes, about which brand do you mostly read or comment?

Yes, namelyI don't knowNo

It is important for me to buy <brand> over another brandAlthough another brand is on sale, I still buy <brand>I always think of <brand> over other brands when I consider buying this type of product I would recommend <brand> to people I know

Strongly disagree – Strongly agree

Tabel 19 Construct for the variables participation and loyalty

ViewingHennigHennig-Thurau et al. (2003)

Previous literature Items used Factor found

Risk reduction because contributions by other customers help me to make the right buying decisions to benefit from others’ experiences before I buy a good or use a service Obtaining

buying related informationReduction of search

time

because here I get information on the quality of products faster than elsewhere because one saves a great deal of time during shopping when informing oneself on such

sites before shoppingDetermination of social position

because I can see if I am the only one who thinks of a product in a certain way because I like to compare my own evaluation with that of others Social

orientation through

informationDissonance reduction

because through reading one can get the confirmation that one made the right buying decision

because I feel much better when I read that I am not the only one who has a certain problem

Belonging to a virtual community

because I really like being part of such a community because I enjoy in participating in the experiences of other community members Community

membershipTo learn what products are new

because I am interested in what is new because I get to know which topics are ”in”

To learn how a product is to be consumed

because I find the right answers when I have difficulties with a product to find advice and solutions for my problems

To learn to consume a

product

ViewingHennigHennig-Thurau et al. (2003)

Previous literature Items used Factor foundProblem solving through platform operator

because I believe the platform operator knows the person in charge within the company and will convey my message.

because the platform operator will stand up for me when speaking to the company.Platform assistance

Convenience of articulation

because it is more convenient than writing to or calling the company. because it is not that costly.

Collective Power because I believe companies are more accommodating when I publicize the matter. because one has more power together with others than writing a single letter of

complaint.Help others in their buying decisions (positive WoM)

because I want to warn others of bad products because I want to save others from having the same negative experiences as me

Concern for other consumers

Save others form negative experiences(negative WoM)

because I want to help others with my own positive experiences because I want to give others the opportunity to buy the right product

Social benefits because I believe a chat among like-minded people is a nice thing. because it is fun to communicate this way with other people in the community. because I meet nice people this way

Social benefits

Advice seeking because I expect to receive tips or support from other users. because I hope to receive advice from others that helps me solve my problems.

Advice seeking

Helping the company because I am so satisfied with a product that I want to help the company to be

successful. because in my own opinion, good companies should be supported..

Helping the company

Extraversion because this way I can express my joy about a good buy. because I feel good when I can tell others about my buying successes because I can tell others about a great experience.

Extraversion /Positive self-enhancementPositive self-

enhancement because my contributions show others that I am a clever customer

Venting of negative feelings

because the company harmed me, and now I will harm the company! because I want to take vengeance upon the company. because my contributions help me to shake off frustration about bad buys. because I like to get anger off my chest

Venting of negative feelings


P a g e | 53

Table 20: Structure of motives and items used for passive participation

Table 21 Structure of motives and items used for active participation

Appendix 2. Questionnaire

Only hard copy

Appendix 3. Survey invitations

Survey 1: e-mail invitation and reminder

Hoi!Voor mijn studie aan de Erasmus Universiteit Rotterdam houd ik een onderzoek in het kader van mijn scriptie.

Je zou mij een groot plezier doen door deel te nemen aan dit onderzoek. Onder de deelnemers wordt bovendien 2 x een Irischeque t.w.v. € 25 uitgereikt!

Let wel op! Je maakt alleen kans op een Irischeque wanneer je de vragenlijst volledig hebt ingevuld! Het invullen van de vragenlijst kan tot en met morgen, winnaars worden bekend gemaakt vóór 15 juni.Het invullen van de vragenlijst duurt ongeveer 10 minuten.

Om aan het onderzoek deel te nemen kun je op onderstaande link klikken.

Groetjes,

{ADMINNAME} ({ADMINEMAIL})

Klik hier om aan het onderzoek deel te nemen:{SURVEYURL}

Hoi {FIRSTNAME},

Onlangs heb ik (en {ATTRIBUTE_1}) je uitgenodigd om deel te nemen aan mijn online enquête.

Nu zag ik dat je hier nog niet aan toegekomen was. Je zou me echt een groot plezier doen door me alsnog te

helpen. Bovendien maak je dan nog steeds kans op één van de twee Irischeques t.w.v. € 25 !

Let wel op! Je maakt alleen kans op een Irischeque wanneer je de vragenlijst volledig hebt ingevuld! Het invullen van de vragenlijst kan t/m 7 juni, winnaars worden bekend gemaakt vóór 15 juni.Het invullen van de vragenlijst duurt ongeveer 10 minuten.

Om aan het onderzoek deel te nemen kun je op onderstaande link klikken.

Groetjes,

{ADMINNAME} ({ADMINEMAIL})

Klik hier om aan het onderzoek deel te nemen:{SURVEYURL}


P a g e | 55

http://www.vvvirischeque.nl/pagina/home/default.aspx

http://www.vvvirischeque.nl/pagina/home/default.aspx

Survey 2: Survey 2: Thread posted on tweakers.net

Appendix 4. Research results Thorsten Hennig-Thurau et al

Visit frequency

Comment writing

problem solving through platform operatorPlatform assistance -.18** -.4

Convenience of articulationVentilating negative feelings Ventilating negative feelings -.10** .01Concern for other consumers Concern for other consumers .13** .16**Extraversion/positive self-enhancement Extraversion/positive self-enhancement .15** .12**To express positive feelings

Social benefits .37** .34**Self-enhancementEconomic incentives Economic incentives .10** .18**Helping the company Helping the company -.01 -.03advice seeking advice seeking .10** .06*

Table 1 Standardized regression coefficient (Thorsten Hennig-Thurau et al 2003)

Effect on viewing behaviour

Risk reduction 2.027 Obtaining buying related informationReduction of search of time 2.155

determination of social position 2.529 Social orientation through informationDissonance reduction 2.912belonging to a virtual community 2.854

Community membershipTo learn what products are new in the marketplace

2.954

Remuneration 3.253 RemunerationTo learn how a product is consumed 2.579 To learn to consume a productTable 2 Importance of motives: scale ranges from 1 =”fully agree” to 5 = “fully disagree” (Thorsten Hennig-Thurau et al 2004)


P a g e | 57

Appendix 5. FCB Grid

Figure 1 FCB Grid (Vaughn, 1980, 1986)

Documents

Using Online Communities to increase Customer Loyalty Rosa 306444... · Web viewOther target groups are the companies that want to catch the wave of the online communities but do