The Making of member-to- community identification and its influence on participation behavior in online community

(Jeyhun Hajiyev)

(Dr. Bing-Jyun Wang)

The Making of member-to- community identification and its influence on

participation behavior in online community

Student : Jeyhun Hajiyev

Advisor: Dr. Bing-Jyun Wang

A ThesisSubmitted to Department of Information Management

College of InformaticsYuan Ze University

in Partial Fulfillment of the Requirementsfor the Degree of

Master of Business Administrationin

Information Management

July 2013Chungli, Taiwan, Republic of China

i

vii

The Making of member-to- community identification and its influence onparticipation behavior in online community

Student: Jeyhun Hajiyev Advisor: Dr. Bing-Jyun Wang

Department of Information Management

College of Informatics

Yuan-Ze University

ABSTRACTOnline community providers are very concerned about the factors that form the member

participation and interaction. Although a number of studies have been conducted to examine the

major determinants of member motivation to participate in online communities, this study

particularly investigates factors creating community identification and its influence on members’

intention to retain the relationship with community and its maintainers, and further continue the

participation.

In theoretical framework, we propose three major dimensions which are community

characteristics, managing characteristics of online community and the personality traits that

define the attractiveness of online community and image. In its turn, this process leads to

members’ actual participation behavior through the community identification. We developed

hypotheses by testing the relationships between the model constructs mentioned above. The

results of the study revealed that Managing characteristics and Community features play

significant roles in establishing Community Identification. Locus of Control and Gender

significantly affect the level of Community Identification. Finally, Community Identification

was found to be positively related to Participation. The theoretical and practical implications

were discussed further.

Keywords: Online community characteristics, Managing characteristics, Personality traits, attractiveness of online community, Community identification, Participation

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Acknowledgements

First of all, I would like to express my great appreciation to my thesis advisor Prof. Bing

Jyun-Wang for his guidance, comments and suggestions during the research process. He helped

me in every single step of the study that motivated me to reach my goal and finish the research

on time. I would also like to express a very special gratitude to my parents, friends outside of

school who inspired me, and encouraged me with valuable attitudes.

In addition, my friends, classmates, labmates at the school also supported me and provided

very useful advice that helped me a lot in order to learn and improve new skills and abilities by

doing the research.

Finally, I would like to thank Yuan Ze University for providing professional educational

services to do master degree with highly qualified teachers and working staff, opportunities to

improve myself from different perspectives, and friendly campus environment that I enjoyed in

the last two years of my life.

Jeyhun Hajiyev

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Table of ContentsTitle Page…………………..………………………………………………………………...i

Letter of Approval.………………………………………………………………………….ii

Letter of Authority……..…………………………………………………………………...iii

Abstract in Chinese…………………………………………………………………...…....vii

Abstract in English………………………………………………………………………...viii

Acknowledgements………………………………………………………………………....ix

Table of Contents...…………………………………………………………………….........x

List of Tables……………………………………………………………………………...xiii

List of Figures……………………………………………………………………………..xiv

Chapter 1 Introduction……………………………………………………………………....1

1.1 Research Background and Motivation…………………………………………...1

1.2 Research Motives…………………………………………………………….......3

1.3 Research Objective……………………………………………………………....4

1.4 Research Structure and Process……………………………………………….....6

Chapter 2 Literature Review………………………………………………………………..8

2.1 Defining online community……………………………………………………...8

2.2 Typology of online communities……………………………………………….14

2.3 Social Identity Theory and Online Community Identification…………………21

2.4 Community Characteristics……………………………………………………..25

2.4.1 Quality of Information……………………………………………………26

2.4.2 Quality of Interaction……………………………………………………..29

2.4.3 Perceived Shared Values……………………………………………….....31

2.5 Managing Characteristics of online community………………………………...34

2.5.1 The Perceived Role of Community Maintainer…………………………....34

2.5.2 Non-Opportunistic Behavior…………………………………………….....37

2.6 Personality Characteristics……………………………………………………....39

2.6.1 Perceived Centrality…………………………………………………….….39

2.6.2 Demographics……………………………………………………………....43

2.6.3 Locus of Control……………………………………………………….…..45

2.6.4 Previous studies on Locus of Control………………………………….…..45

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2.7 Participation in online community..………………………….…………….…….50

Chapter 3 Research Methodology……………………….…………………………….…....55

3.1 Research Model…………………………………………………………….…....55

3.2 Research Hypothesis……………………………………………………….…....56

3.2.1 The relationship between Quality of Information and Community

Identification…………………………………………………………….....56

3.2.2 The relationship between Quality of Interaction and Community

Identification……………………………………………………………….57

3.2.3 The relationship between Perceived Shared Values and Community

Identification………………………………………………………………..60

3.2.4 The relationship between Perceived Role of Maintainer and

Community Identification…………………………………………………..61

3.2.5 The relationship between Perceived Non-Opportunistic Behavior and

Community Identification…………………………………………………..64

3.2.6 The relationship between Perceived Centrality and Community

Identification………………………………………………………………..65

3.2.7 The relationship between Demographics and Community Identification….67

3.2.8 The relationship between Locus of Control and Community

Identification………………………………………………………………..69

3.2.9 The relationship between Community Identification and Participation…….71

3.3 Questionnaire Design and Data Collection……………………………………...72

3.4 Operational Definitions of Study Variables……………………………………..74

3.4.1 Quality of Information……………………………………………………..74

3.4.2 Quality of Interaction………………………………………………………75

3.4.3 Perceived Shared Values…………………………………………………...76

3.4.4 Perceived Role of Maintainer………………………………………….…...77

3.4.5 Perceived Non-Opportunistic Behavior……………………………………78

3.4.6 Perceived Centrality………………………………………………………..79

3.4.7 Locus of Control…………………………………………………………...80

3.4.8 Community Identification……………………………….…………………81

3.4.9 Participation…………………………………………….…………………..82

Chapter 4 Data Analysis and Results…..………………………….………………………..84xi

4.1 Demographic Analysis………………...………………………………….....84

4.2 Descriptive statistics of measurement scales……...……….………………..85

4.3 Correlation Analysis…...…………………………………….……………...91

4.4 Regression Analysis…...…………………………………….……………...93

4.5 ANOVA Analysis………………..…………………………………………95

4.6 Model Testing………...……………………………………………………..98

Chapter 5 Discussion and Conclusion……………………………………………………101

5.1 Discussion…………………………………………………………………..101

5.2 Conclusion………………………………………………………………….104

5.3 Implication………………………………………………………………….105

5.4 Limitation…………………………………………………………………..106

5.5 Suggestion for future Research….……...………………………………….107

References………………………………………………………………………………..109

Appendix 1……………………………………………………………………………….118

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List of Tables

Table 1 Some definitions of online communities……………………………………………8

Table 2 The descriptions of internal and external locus of control………………………...48

Table 3 Common attraction to the online community……………………………………...52

Table 4 Attractions to communities where lurkers and poster differ………………………52

Table 5 Operational definition of Quality of Information………………………………….75

Table 6 Operational definition of Quality of Interaction…………………………………...76

Table 7 Operational definition of Perceived Shared Values………………………………..77

Table 8 Operational definition of Perceived Role of Maintainer…………………………...78

Table 9 Operational definition of Perceived Non-opportunistic Behavior…………………79

Table 10 Operational definition of Perceived Centrality…………………………………...80

Table 11 Operational definition of Locus of Control……………………………………….81

Table 12 Operational definition of Community Identification……………………………..82

Table 13 Operational definition of Participation……………………………………………83

Table 14 Demographic characteristics of Respondents……………………………………..85

Table 15 Descriptive Analysis of Model Constructs………………………………………..87

Table 16 Result of Correlation Analysis (N=214)…………………………………………..92

Table 17 Multiple Regression Analysis of Community, Managing and

Personality Characteristics predicting Community Identification (N=214)...…….94

Table 18 Means, Std. Deviations, and Correlations for Participation behavior

and Predicting Community Identification (N=214)……………………………….95

Table 19 Multiple Regression Analysis of Community Identification

predicting Participation Behavior (N=214)………………………………………..95

Table 20 Demographic factors and Online Community Identification……………………..96

Table 21 Results of PLS analysis: Path Coefficients………………………………………..99

Table 22 Results of Hypotheses……………………………………………………………100

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List of FiguresFigure 1……………………………………………………………………………………….7

Figure 2…………………………………………...…………………………………………55

Figure 3……………………………………………………………………………...………98

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Chapter 1 Introduction

1.1 Research Background and Motivation

Internet has become the source of information, knowledge, building relationships, making

consumption decisions. In fact, it has given an access for people from all around the World to

come together online, share, discuss, support socially and emotionally each other. “Online

community” is one of the popular terms used. Recently, online communities respond to all

people’s consumption and purchase demands, need for social belonging to groups, which can

explain why this phenomenon has become very prominent.

In recent years, the immense growth of companies such as Amazon.com shows that the

easiest and the most sufficient way to make purchase decisions for people is going online

through consumer communities. According to Williams and Cothrel, (2000), Amazon.com has

become one of the most powerful retail platforms, which has established successful relationship

with and among its customers. Although the strength of online communities in social and

business relationship building process has been discovered several years ago, most firms are not

fully aware of the role of online community in enhancing the business opportunities. A vast

number of people around the World communicate with their peers (strong ties) as well as new

people (weak ties) through e-mail or switching on communities, platforms and discussion

forums (Preece, 2000). The author mentions that application of online community in the

customer relationship strategies of companies creates positive reactions for customers and

association between them and firms they want to buy from.

Similar to increasing attention to Computer Mediated Communication phenomenon, online

community study is an emerging research field, which is gaining a lot of attention from different

approaches and disciplines. People use online communities of transaction to do shopping, make

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decisions on their consumption behaviors and learn more about products and services (Hagel and

Armstrong, 1997). In transaction oriented online communities, content is less social due to the

commercial intention of community. However, individuals also can use virtual communities to

discuss shared interests, to develop social relations, to support emotionally and socially and to

explore new identities (Porter, 2004). Nowadays, online communities are studied from different

perspectives. Particularly, technical transformations, marketing perspective as well as

sociological and psychological directions gained more attention and efforts in order to study

online communities. Some authors refer to online communities as the transformation of offline

community characteristics, human socialization and interaction factors into online environment.

However, other stduies indicated that online communities are distinctive in nature from offline

communities, regarding to their specific characteristics, human interaction without borders in

global level, easy way to make friendships who share same interests, emotions.

Recently, Online communities play an important role not only in making friendship,

socializing, discussing contents, sharing emotions, but also in customer decision-making on

purchase intentions, sharing pre- and post-purchase experiences with peers, supporting people

morally and providing essential knowledge related to products, and brands. This is the core of

online communities, especially, which are consumption and brand oriented. By socializing in

these online communities, members get to know each other, comply with the group, generate the

shared values and norms, follow the opinions and preferences of each other’s and conform to the

group.

Although online communities have been investigated in different disciplines, little attention

has been paid to the approach of social relationship building with community members, not only

for short-term goals, but also for long-term perspectives. Chiu et al (2006) emphasized that

online communities bring together people with common interests, goals and attitudes to

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exchange information an engage in social interaction. Social structure is the reason of

sustainable community relationships. Although, many studies addressed the issues related to

personal cognition, usability and internet behaviors previously, the authors strongly offered that

sociability in terms of Social Cognitive Theory and its influence on community user’s behavior

is necessary to study broadly. Therefore, current research aims to concentrate on investigating

the strategy for maintainers of online communities to form platforms of social relationship,

positive social identity and image in the minds of people that can potentially lead to active

participation behavior of online community members. The other suggested elements are

supposed to be studied in building long-term social relationship and identification among

members and between community maintainers and members. Those elements include frequent

responsiveness, mutual commitment, and integrity and so on.

1.2 Research Motives

Antikainen (2007) has highlighted that it is important to start with exploring the phenomenon

(antecedents of member’s trust) in order to understand the leading factors that create community

members’ identification with sponsors and intention to participate. After doing so, it is easier to

start considering how trust and motivation for participation can be built in online community. In

author’s research, attraction refers to conception, which is the major construct of relationship

building in community. The strategy of building successful community relationship might be

useful for all kinds of online communities. Nambisan (2005) had proposed the idea of online

community experience and member perception according to the relationships created among

members. And it has been shown that perceived positive experience with online community

hugely influences customer’s attitude and participation behavior. However, sociability issue has

not been studied in-depth and well-established (Preece and Krichmar, 2003). We believe that the

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social identification and sense of belongingness stay in the central point of sociability issue in

online communities. Based on the current study, we distinguish three major characteristics in

different level likely personality traits, community maintainers’ characteristics and the common

community environment in order to examine their relationship with participation behavior

through the community identification.

1.3 Research Objective

A number of studies have been done related to the online community from different

perspectives. Antikainen (2007) did very useful approach in order to understand the attraction

factors that create the member trust in brand community maintained by company and positively

lead to member’s commitment to the community. The findings of the study also provided some

suggestions such that there are several necessary constructs in online community strategy that

applying them into community and building many-to-many communications will lead to positive

word-of-mouth and high level of member’s loyalty to the community and brand as well. One of

the studies has been done by Dholakia et al. (2004) which provided very significant outcome

about the relationship between social influence factors and member participation in the

community. Value perceptions, namely purposive value, self-categorization, interrelationship,

social enhancement and entertainment values were found to significantly affect the member

participation in the community through group norms and social identity

However, little studies are known on the subject of the membership behavior, and the impact

of community identification on the members’ intention to stay in online community, particularly

in Taiwan. It is important to shed the light on distinctive characteristics which may potentially

affect the actual participation behavior through the role of community identification. Eveleth and

Eveleth (2010) had investigated very interesting study related to team identification and leader-

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member relationship in online groups. The authors proposed that although group identification is

an important factor that creates several outcomes such as satisfaction, turnover and commitment

in one-to-one relationships, it is also necessary a element for online environment. They found

that quality of member-leader relationship as well as team performance significantly affect the

level of identification and team identification leads to behavioral outcomes. However, the

authors investigated the identification by looking at only two factors namely past performance of

group and member’s relationship with team leader. Drawn from the above-mentioned literature,

we would like to extend the study by modeling several other antecedents of identification based

on community experience, management features and personal issues that might affect

identification in online community and play a role in the behavioral outcomes.

The purpose of this research is to develop a theoretical model enabling us to test the

antecedent of online community identification and its influence on the member’s participation

behavior (i.e. sharing information, posting contents, contributing knowledge) in the online

community. Based on the above-mentioned research motivation, the objectives of this study are

as follows:

1. How do different characteristics so-called community, managing and personality may

affect member’s identification with the online community?

2. How does the online community identification influence the participation behavior?

3. Which of the above-mentioned characteristics is more significant in creating positive

community identification, and building sustainable online community relationship?

Main contributions of the current study is the insights on company- and member-sponsored

online community formation and effective management by identifying the major characteristics

and their dimension that lead to successful member-to-community relationships, the influence of

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member’s perceived social identity of community and its organizers, as well as their future

social identification for the long-term relationship.

1.3 Research Structure and Process

This section provides general overview of the entire research process. Chapter 1 is the

Introduction part, which describes the research background, motivation and the objectives as

well as the process of the study. In Chapter 2 we provide the literature review that interprets the

previous studies in detail, definitions of terms and measures we adopt in the current study.

Chapter 3 consists of Research model and Hypothesis development through which the proposed

conceptual model of the study and hypotheses has been depicted. Chapter 4 draws on the

Research Methodology. In other words, this chapter discusses the selected methodology,

questionnaire design, data collection process and data analysis issues have been discussed.

Chapter 5 discusses the results and gives conclusion of the study, further recommendations on

the practical implications, research limitations and prospectives that can be taken into

consideration in the future studies.

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Figure 1 Research Process

Research background and motivation

Literature Review

Research model

And Hypothesis

Design of Questionnaires

Data analysis and testing of Model

Discussion and Conclusion

7

Chapter 2 Literature Review

The major objective of this study is to discuss the antecedents of member-to-community

identification and its influence on member’s participation behavior. This chapter is organized as

following. In section 1, the definitions and previous studies on online community are described.

In section 2, we present the literature of typology of online communities. Section 3 looks into

the background of Social identity theory and member identification in online community. Finally,

in section 4, literatures are presented according to each construct of the proposed model of this

research.

2.1 Defining Online community

The frequently referred definition of an online community was provided by Rheingold (1994)

as: “Social aggregations that appear from the web when enough people carry on those public

discussions long enough, with sufficient human feelings, to form webs of personal relationships

in cyberspace”. According to Bressler and Grantham (2000), online communities are the sets of

individuals who come online with others holding similarities, support each other, and exchange

ideas, and opinions. In previous studies, a vast number of definitions of online communities have

been suggested. Table 1 provides various types of communities and descriptions.

Table 1 Some definitions of online communities

Possible definitions of Online communities Source“An aggregation of people who share a common interest and

communicate through electronic mailing lists, chat rooms, Internet

user groups or any other computer-mediated mechanism” (p. 410)

or related group of activities” (p. 254)

Kim et al.(2008)

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Possible definitions of Online communities Source“A cyber space supported by information technology . . . centered

upon the communications and interactions of participants to

generate specific domain knowledge that enables the participants to

perform common functions and to learn from, contribute to, and

collectively build upon that knowledge” (p. 153)

Hsu et al.(2007)

“Virtual communities comprise people coming together to obtain

information from and give information to other people”

Lin (2007)

In terms of small-group based brand communities “communities

which are comprised of……..typically fewer than ten or so riders

plus a few passengers, that have close friendships with one another

and engage in regular and frequent face-to-face interactions”(p. 46)

Bagozzi and Dholakia

(2006)

“Social aggregations of critical masses of people on the Internet who

engage in public discussions, interactions in chat rooms, and

information exchanges with sufficient human feeling on matters of

common interest to form webs of personal relationships” (p. 416)

Kannan et al.(2000)

“Online communities, which … define as groups of people who

engage in many-to-many interactions online-form wherever people

with common interests are able to interact.”

Williams and Cothrel

(2000)

“Affiliative groups whose online interactions are based upon shared

enthusiasm for, and knowledge of, a specific consumption activity

Kozinets (1999)

“Members feel being part of a larger social group, sense of

interwoven web of relationships with other members, have ongoing

exchanges with other members of commonly valued things, and

have lasting relationships with others”

Figallo (1998)

“Virtual communities which are targeted to enable the interaction

between consumers on their special needs such as fantasy,

interaction, transaction” (p. 131)

Armstrong and Hagel

(1995)

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Preece (2000, p. 10) has defined online community as comprising of:

� “People who interact socially as they strive to satisfy their own needs….”

� “A shared purpose, such as interest, need, information exchange, or service that

provides as reason for the community,”

� “Policies…. That guide people’s interactions,”

� “Computer systems, to support and mediate social interactions

A virtual community is a group of people who may or may not meet each other face-to-face,

and who exchange words and ideas through the online bulletin boards and networks. Community

relationship and interactions occur in the case that people are connected through computer

regardless their geographical location (Williams and Cothrel, 2000). In this process,

communities getg narrower, because only the part of members maintain relationships who have

full trustworthiness, compliance with group, conformity to all actions and behaviors of other

group members. The rest switch off from the community while going into the deep of

socialization, and clarifying the similarities and dissimilarities with the whole group. In other

words, whose similarities in personality, tastes, emotions, and consumption behavior exceed the

dissimilarities with the group, they maintain community relationship, the rest of the users switch

off. Therefore, researches show that there is a high level of involvement by community users

(Hoffman and Novak, 1996). This implies that in interactions involving high interactivity,

stronger relationships between participants may be necessary; in addition, participants should be

responsive and engaged throughout the durations of the interaction. These characteristics of

small-group-based online communities prove actually why they are more successful than

network-based online communities in which lower level of interactivity are more dominant.

Majority of previous studies on online communities were conducted in terms of Information

systems because of the role of technology as the mediator of human interaction in virtual

environment. However, there are social norms and values in social relationship between

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community participants, which are the leading factors of success in online community

development (Wilson and Peterson, 2002).

Based on the study by Baglieri and Consoli (2009),the major elements of online

communities are: (1) People from different places come together in order for satisfying their

needs and get support from peers, (2) shared interests, goals and social exchange between

community members which is particularly necessary in terms of sociability issue, (3) rules and

norms that creates sense of community and togetherness of members, (4) systems and service

quality that facilitate the relationship between community members and provide timely response

to their necessities.

According to Antikainen (2007), it is critical to understand how individual members feel

about the social environment which they belong to, and if they consider this social environment

as a community that they belong to. Ren, Kraut and Kiesler (2007) stated that online community

provides platform for individuals who are interconnected through their shared values, goals,

interests and social needs in particular. The core constructs of online community are emotional

and social support by others within community, their commitment and contributions. Kozinets

(2002) shows that online consumers have more opportunities to take a part in discussions,

involve in contents and inform their peers about consumption behaviors. Because consumer-to-

consumer relationship is more reliable and provides more knowledge and information which

represent other peers’ tastes and desires clearly. While differentiating the types of online

communities, one of the best examples of strong social relationship and member-to-member

commitment are brand communities (Muniz and O’Guinn, 2001). The authors implied that

although brand communities are formed and maintained by the loyal customers of specific

brands, this kind of communities lead to two types of relationships basically: a relationship

between community members who are customers of brands and a relationship between brand

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and the customers. As a result, increasing social ties and exchanges between all parts of brand

communities will lead to higher level of loyalty and long-term relationship in the future.

Most of the previous researches focused on particularly the Technology Acceptance Model

(TAM) phenomenon in the online communities and it is accepted that users switch to the online

communities and websites because of perceived usefulness and perceived ease of use. TAM

(Technology Acceptance Model) is powerful in user’s intentions and behavior on usage (Davis,

1989; Davis et al., 1989). Perceived usefulness is defined as “The prospective user’s subjective

probability that using a specific application system will increase his or her task performance”,

whereas perceived ease of use is defined as “the degree to which the prospective user expects the

target system to be free of effort” (Davis, 1989). Briefly, user’s behavioral intention on usage is

affected by the attitude toward usage. However, it could be assumed that user’s intention on

online community participation is not only defined by the technological factors that facilitate the

usage of website, but it is also defined by the sociological and psychological factors that sense of

community/sense of belongingness, individual-to-individual relationships and interactions,

giving the sense of real membership of community to the members increase the user’s

trustworthiness on online community. In this regard, it is worthwhile to investigate the disparity

of consumer-to-consumer and consumer-to-firm relationship and find out how social exchange

between firms and consumers is built.

“Virtual communities constitutes the characteristics of “real”(offline) communities regarding

to the socialization process, occurrences and consequences which happens between the users in

terms of interaction, communication and sharing” (Rheingold,1993). Thompson (2011) found

that as long as the internet enables a huge platform for people all around the world to keep in

touch with peers, to make new friendships, Online community remains the most significant

medium means of building relationships, seek emotional and social support. In addition to this,

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the author mentions that informal communities provide more relevant learning environment for

employees in work-related issues because they are more able to build informal ties, share their

personal experiences, give feedback which are important for other peers compared to formal

information. The results also indicated that connection and closeness to other peers in

community is necessary for some people. In particular, there are cases in which close, intimate

relationship and dedication with other people are required to make the learning process more

efficient.

As discussed the general overview of online communities above, successful and long-lasting

online communities are beneficial for marketers, firms in order to conduct their businesses

competitively as well as for consumers in order to connect with more expert and knowledgeable

people, opinion leaders, not to fail in their product decision-making and to avoid the ambiguity

when buying from the brands. Therefore, sociability and usability issues are extremely important

to run successful online community for the well being of customers and for the benefit of

business. However, some literatures summarize that there is huge challenge for businesses and

online community providers in terms of conducting online community for building relationship

with customers. The main reason is the difference between online and face-to-face communities

that some users are invisible, just read messages and don’t post, motivation to participate in

online discussions is not high, degree of social order and control is low (Lin, 2007). Several

studies have mentioned the customer-side challenges of online community participation. But it is

important to study the role of online community providers in attempt to build social relationships,

combine online and offline features that might play a critical role in a sustainable member-to-

host relationship in firm-hosted online communities particularly. Lin (2007) also suggests that in

order to sustain the social presence and active participation in online community, it is necessary

to merge online and offline features in community strategy. Preece (2000) emphasized that while

13

conducting online community, it is essential to satisfy all community members’ needs and

contribute to their social necessities and well-being. A number of online communities are

launched. However, not all of them are successful at all. Some people say that online

communities change their lifestyle outstandingly, whereas, others complain about the lack of

online communities in particular issues, such as empty chat rooms, unanswered messages,

disproportionate advertising. According to the evaluation of Preece (2000), sociability in online

communities is more significant than usability. In order to attain the community success,

community providers are required to be sensitive and acknowledge community’s principle and

individual need that change over time. Sociability is linked with planning and developing social

norms and policies which is satisfactory to all community members, and supports the purpose of

community. Sociability is related to social interaction between sides.

2.2 Typology of online communities

Recently, a lot of companies and brands have initiated online consumer communities to gain

their attention. Big brands, such as Nike and Suunto benefited from the advantages of online

communities and recently host their discussion platforms in order to promote products and build

relationships with customers (Wilimzig, 2011). According to Antikainen (2007), although online

communities are hard for companies to maintain relationship, it cannot be ignored as the main

part of relationship marketing. In addition, increasing number of Firm-sponsored online

communities create opportunities for firms to benefit from customer-producer relationships, to

add value to business and product development (Nambisan, 2002). Firms try to reach to their

customers in cost-saving way, to get product ideas and feedbacks, understand their needs and

create the conditions for customers to communicate with their peers (Jeppesen, 2005). However,

majority of communities have failed because of community norms, values and policies (Porter

14

and Donthu, 2008). While discussing the distinctive characteristics of various types of online

communities, consumers pay more attention to social relations and shared interests with other

participants, whereas, other kind of online communities focus on commercial interests (Porter

2004). Literatures showed that these kinds of communities are particularly ran by companies in

order to boost sales. Antikainen (2007) implied that there are two ways that companies can run

online communities; (1) build and maintain online communities and reach to customers and fans;

(2) follow other online communities maintained by various companies and brands as well as

customers. However, the author argues that building company’s own community seems a

confusing term, because they cannot be established by companies, rather they are built by

members, and customers with the help and initiation of companies and some members join, and

become more experts and moderators in the future. Several terms are used to describe the online

communities ran by the companies. For example, business community, business-oriented online

community, commercially based online community. Generally speaking, those communities refer

to ones for commercial purposes that are tools for companies to reach customer data, increase

sales. However, while talking about company sponsored online communities, it is essential to

include some other terms which are mainly building stronger social relationships, new product

development and customer focus (Antikainen, 2007). Because, there are a number of good

examples on company maintained communities, which are successful in customer relations,

long-term loyalty and positive word-of-mouth.

Several studies distinguished online communities as member- and firm-hosted by applying

Porter’s typology of online communities (Jantunen et al., 2008). In Porter’s (2004) typology,

member-sponsored communities are generated and managed by members who are customers.

Firm-sponsored communities are those organized by commercial organizations, firms or

government, NGOs for commercial and non-commercial interests respectively. In other typology,

15

online communities are distinguished as the communities, which foster social relation,

particularly between customers and their peers, and communities, which are platforms for

commercial relations between customers and organizations. The study conducted by

Balasubramanian and Mahajan (2001) suggests that in member-hosted communities, both

providers (organizers) and members contribute to the community, social exchange between

partners actively. Consumers create their own discussion platforms around the brands they buy

from. One of the important characteristics of these communities is that they bring companies and

customers together and help them interact with each other (Wilimzig, 2011). This suggestion

allows us to propose that compared to firm-hosted online communities, member-hosted

communities are for “We-intention”, in which there is shared value system, shared

understandings and goals as well as mutual trust.

Park et al (2011) identified the two types of online consumer communities which are (1) the

communities built for the consumers who are fans of specific brands, and (2) the communities of

consumption-related contents, discussions. Online consumer communities have been defined as

“a group of consumers who share common interest about particular brands or general

consumption-based issues in an online environment”. Previous scholars have distinguished

consumer- and company-generated online communities (Porter and Donthu, 2008). However,

the commercially-oriented online communities have been paid more attention compared to the

communities for social-oriented purpose, social relationship building with consumers (Kannan et

al., 2000). Porter and Donthu (2008) have particularly focused on commercially initiated online

communities which are sponsored and controlled by firms. In their study, sponsoring firm

identifies itself with its name and logo in community’s website. In addition, the firm has

commercial interests and goals in the example of Dell and HP community. The results of this

study revealed that social relationship between community members and community sponsors

16

cannot be fostered in case of commercial interests of companies. In online brand community,

members trust to opinion leaders and find the information valuable provided by them. They

believe that the information provided is objective (Wilimzig, 2011). Antikainen (2007)

mentioned that Suomi24.fi (www.suomi24.fi 1 ) is one of the biggest company-owned

communities in Finland. This community consists of several discussion platforms, which get

more than 10 000 messages and feedbacks every day. In order to deal with this amount of

enquiries, company runs the discussion forums with different topics of discussions and they are

quite successful in maintaining relations.

The success of online communities largely depends on how companies and, brands

understand the importance of consumer community, relationship, support and clear identification

of consumer needs (Baglieri and Consoli, 2009). According to Antikainen (2007), while

companies successfully maintaining communities and involving in discussions actively with an

individual members, they are not only creating value for members and for themselves, but also

for other members of community.

“A company online community is maintained by a company sharing a common interest and

interacting via information and communications technologies” (Antikainen, 2007). Common

interest refers to all kind of interests of all members in the community. However, in some cases,

maintainers’ interests are different from members’ interests at all. They only look for profit

increase, spreading the advertisements to increase sales, and in its turn, this leads to short-term

interaction between companies and customers. This is one of the main reasons why companies

fail in their online community strategy.

Another study shows that the success of firm-sponsored online communities launched for

commercial interests, is based on the personal motivation and willingness of members to

1 http://www.suomi24.fi/ 17

participate in community and involve in discussions with others as well as contribute knowledge

(Wiertz and Ruyter, 2007). Most of the previous studies discusses the consumer-to-consumer

interaction and social exchange in terms of firm-hosted online communities. However, a little

attention paid on consumer-to-community provider social relationship and the major factors of

building real community of values, member-to-firm social identification and mutual integrity

that are for “we-intention”. We-intention referred to the commitment of an individual to

participate in joint action, and involves an implicit and explicit agreement between the

participants to engage in joint action (Cheung and Lee, 2010). In broader meaning, participants

have to commit collectively for the performance of group. Williams and Cothrel (2000) provides

the evidence on the community of Java Center taking place as discussion list. Although Java

Center discussion list providers apply the technological tools in order to increase the usability,

they are mostly concerned about the sociability and motivation factor of community participants.

Senior managers attempt to encourage all users to involve in discussions by sending two types of

messages:

- You should actively participate.

- If I observe that you are not participating, it means I am a participant and notice when

others are not.

On the other hand, the Java Center developers provide informal coaching on how to deal

with the challenges during the community usage, how to participate effectively and how to

benefit from the discussions.

In current research, we particularly focus on this issue, because previous studies dealt with

we-intention in consumer-to-consumer relations. However, it is necessary to investigate this

factor in both member-to-member and member-to-host relations and see if community members

can consider themselves as a part of the group and socially identify with their community. In all

18

kinds of online communities, customer-to-customer relationship and interaction is believed to be

more reliable, providing more feedback and receive quick assistance from their peers. These

customers are tended to be opinion leaders and moderators of discussions and contents generated

in online communities (Dholakia et al., 2009). In this study, it has been argued that reliable

knowledge is constructed through customer-to-customer interaction. The major factors assisting

successful customer community have been shown as following:

- Firms can only moderate discussions in community and establish specific norms and

values in order to manage social relation

- Facilitating the community relations without controlling or obtruding those relationships.

If good management and non-control over contents and discussions in community, it will

lead to social identification with the community (Dholakia et al. 2009)

- By considering the growing number of online communities in recent years, consumers’

social benefits predominate functional benefits (Rosenbaum and Massiah 2007).

We aim to apply the social identification theory particularly to member-to-community and

member-to-maintainer relationship and investigate the antecedents that lead to mutual social

identification among the whole community. On the other hand, we will study the members’

perceptions on social benefits received from community in terms of community relationship, at

the same time clarify what providers, organizers should do in order to give real social benefits

and support to customers and build trustworthy social relations with their community members.

The result of study (Dholakia et al., 2009) shows that in order to generate real trustworthy

community, it is necessary that customers are allowed to generate discussion contents on their

individual motivations, and firms should act as facilitators in these information and social

exchange. In eBay Help Forums, it has been presented that firm’s employees play an important

role in communities. So-called “Pink liners”, are opinion leaders, who often energize

19

interactions, provide social and functional support for customers when they cannot get enough

attention and assistance from peers as well as companies. Community members feel a strong

sense of belonging and identify with whole community, once they are respected and heard by

others. In this regard, we are willing to investigate this factor in our study and see if members

can feel sense of community, communicate with hosts directly in timely manner, and feel shared

values.

The study on service quality in online travel community (Elliot et al., 2012) found that firms

should build a strategy to compensate the lack of face-to-face interaction between them and their

customer, increase the social relationship and engagement to community members. The main

reason is that most of customers seek for social and emotional support, frequent response and

feedback for their enquiries. In this case, they claim that community providers fail to respond

frequently and engage in community. However, the other findings are that trust is not the major

determinant of customer intention to make transaction compared to system and service quality

issues. Because this study focuses on improving service, information and system quality in order

to attract more customers. Fukuyama (1995, p. 27) gives the definitions of trust as following:

“Trust is the expectation that arises within a community of regular, honest, and cooperative

behavior, based on commonly shared norms, and on the part of the members of the community”.

Trust is a positive expectation that a person has for another person, organization, tool, or process

that is based on past performance or experience and truthful future guarantees made by a

responsible person or organization (Shneiderman, 2000). Interpersonal trust and party’s

reliability are interrelated with each other. If interpersonal trust exists between parties, they are

not tended to lie, deceive (Wang, 2009). Trust is believed to be only generated in informal, and

small communities in which there are shared norms and goals (Fu, 2004).

20

In social relationship building process, shared norms determine the level of reliance in

community. Shared norms refer to the moral obligations and mutual reciprocity of individuals

who belong to a community (Fu, 2004). Porter and Donthu (2008) focused on firm-sponsored

online communities and proposed that when community members directly communicate and

interact with the providers of community, they have more trusting beliefs on the firm, and its

community as well as the information and content they provide compared to that provided by

other community members.

2.3 Social Identity theory and Online Community Identification

While looking at the definitions of the phrase of community, it is interrelated with several

aspects such as social grouping, shared understandings and relations, social conventions, sense

of membership (Antikainen, 2007). The success or failure of online communities or social

networks might be related to the level of identification of individuals who come online and join

the groups (Eveleth and Eveleth, 2010).

Tajfel (1982) has emphasized that social identity is the part of the individual’s self-

understanding, which is the level of individual’s knowledge of his/her membership and

belongingness to a social group or community along with the values and the emotional

significance of that membership. Based on this theoretical proposition, sense of connectedness

creates the strong social identity in the community. Lampe et al. (2010) stresses that the Social

Identity Theory is in line with the explanation of psychological consequences through which

individuals perceive themselves belonged to a group, and valued by others. Emotional and social

ties are the main elements of Social Identity phenomenon (Tajfel, 1982). In addition, while an

individual has the perception of similarities with the group or community members, he/she will

be more likely to feel the sense of belongingness and commitment to a group. People who have a 21

sense of community or group are more tended to co-operate with others, perceive peers’

opinions valuable and trustworthy and conform with the group for the benefit of all members

(Antikainen, 2007). Individual’s social identity with the community, is interconnected with the

theory of member commitment. It is particularly applicable in Organizational framework

(Lampe et al. 2010). On the contrary, Ashforth and Mael, (1989) noted that commitment and

identification shouldn’t be linked to each other. In their point, commitment is considered as an

individual’s belief in shared values and goals with the organization and group, or desire to

maintain the relationship with them. If an organization is believed to be reliable and individuals

share similar goals and values, they will more likely to switch to that organization (Ashforth and

Mael, 1989). In this regard, we can propose that individuals are socially identified with the

organizations or other parties in their group, once they have perception on shared similarity,

collective intentions and reliability which lead to the trust in organization, authorities and other

group members.

Sociability issues are more significant than usability or technical issues in online

communities. According to the theory of Social identity, users will contribute to and participate

in online communities when they benefit from them, whether it can be true knowledge, solution,

social support, and it will strengthen the ties between user and community hosts. Members have

different feelings; They might feel themselves similar with other community members and

different from outsiders or they feel similar with outsiders of community to which they belong to,

they cannot find themselves in the community. This is particularly applicable for newcomers in

the community and it depends on the level of attention gained from other community users as

well as community providers (Lampe et al., 2010). User participation and contribution to

community is not associated with how easy the website is to use, rather it is related to social and

22

cognitive factors which lead to consumer identification with community, and commitment

(Lampe et al., 2010).

Hogg and Terry, (2000) mentioned that Social Identity theory has been investigated from

several perspectives such as intergroup behavior, motivational processes, social influence and

norms, solidarity and cohesion, attitudes, behavior and norms, collective behavior as well as the

intergroup relations, which we think that can be beneficial to apply the social identity conception

in interpersonal relationship in to online community. The process of Social identity occurs as

long as subjective uncertainty about other parties is reduced in individual’s mind, attitudes, and

feelings are defined in a positive way (Hogg and Terry, 2000). Another study has investigated

the major determinants of Organizational Identification �������������� �������. The authors

described the member perceived attractiveness of organizational identity in organizational

context. Perceived organizational identity refers to the extent to which organization members

perceive the identity, image of organization and authority to be attractive, trustworthy and

���� ����� ����� ���� ��� ����� ������� ������ ��� ���� (2009) also mentioned the strong

relationship between the identity attractiveness and the strength of social identification. Dutton

et al. (1994) referred to the perceived organizational identity phenomenon. This theory reflects

beliefs of individuals on the similarities and differences of organizational they belong to. Bartel

(2001) proposed that perceived organizational identity means how its members value their

membership, how they feel about the degree of emotional attachment to organization. According

to Antikainen (2007), perceived similarity is one of the major determinants of attractiveness.

Perceived similarity refers to sharing similar values, goals, attitudes and shared ideology

between a member and company. Dutton et al (1994) concluded about the relationship between

organizational identity and strength of identification that high degree of esteem formed from the

membership with organization, relationship with its collective positively affects the degree of

23

organizational identification. Self-esteem gives members motivation to co-operate with

organization, and put more efforts in positive outcomes. The degree of maintaining relationship

with the community is up to the member’s perceived attraction of the community (Antikainen,

2007). The study suggested that intensity and length of communication with the organization

increases the attractiveness of identity of organization hugely and it leads to positive

identification (Dutton et al. 1994).

Lee, (2004) found that trust has a positive impact on organizational improvement as well as

the continuous relationship between employees and authority. The major contribution of this

study is that the organizational identification plays an immense role in trust-to-positive

cooperative behaviors of organization employees. Organizational identification positively

influences the continuous relationship improvement. Perceived usefulness, trust and self-efficacy

have been found to have a huge implication on member behavior in online settings. Online

community participation is greatly influenced by identification and compliance. The effect of

social identity and group norm determine the level of user involvement in online community.

However, members are less likely to be influenced by significant others, because they decide to

stay in online community based on their own desires (Zhou, 2011). The author has examined the

effect of three dimensions of social identity on user participation. It has been found that

cognitive identity refers to the match of personal identity with the group identity, whereas,

affective identity is considered as the sense of membership, emotional attachment to the

community. The latter one, namely evaluative identity reflects the degree of member’s influence

and value in the community, and overall assessment of community relationship. Dutton et al

(1994) concluded that strong identification between employee and the organization encourages

members to contribute more actively to the organization. According to Koh et al (2007), four

major characteristics define the sustainable online community; (1) clear vision and goal, (2)

24

clarity of members’ roles, (3) community management, and (4) strengthening member’s

identification with community and each other. However, the authors particularly focus on the

role of offline interaction as the motivational factor of social identity that can foster online

community activity such as posting.

In the current study, we aim to investigate the factors that lead to online community

identification as well as its influence on the member’s activity and actual participation behavior

in online community. Drawn from the above-mentioned literature on social identity and

attractiveness theories, we might assume that the factors which create a positive and attractive

image of the community, organization can also create the trusty behavior. Therefore, it is

necessary to examine their role in participation behavior through the community identification.

2.4 Community characteristics

Jang et al (2008) addressed 4 types of community characteristics that strongly affect the

community commitment that were namely; quality of information, quality of system, interaction

and rewards for activities of community members. The scholars found that two of the

community characteristics – interaction and community rewards for voluntary activities

significantly influenced the community commitment of individuals.

The types of communities play a different role in the relationship between community

characteristics and community commitment. In comparison with company-created online

community, in consumer- organized online communities there is a huge impact of information

quality and usability factor on member’s relationship with the community. Jang et al (2008) have

implied that consumer- organized communities are based on voluntary participation and

voluntary engagement in community activities as well as deep social relationship with other

consumers. In this regard, the website usefulness and information quality is high. The necessary

25

element of maintaining community relations is adding new contents and posting in order to meet

the requirements of community members (Hagel and Armstrong, 1997). Community organizers

play a distinctive role in attracting new members and maintaining the relationship with existing

community members. The information, company evaluations and product reviews posted by

community managers create more value for its members (Rothaermel and Sugiyama, 2001).

Scholars have investigated the TimeZone which is the prominent and one of the biggest online

communities for mobile phone users. One of the discussion content provided by the community

management is called “Watch school”. Member of this discussion group can learn everything

about designing, repairing of watches. According to Rothaermel and Sugiyama (2001), some

members of the community even emphasized that participation in this discussion group and

mutual involvement by members and community managers turned interests into hobbies.

Therefore, it can be propose that valuable and resourceful contents, postings provided by the

community will create strong member engagement in community relationships.

2.4.1 Quality of Information

Combining content and communication, online community may allow members to be

involved in information exchange, learn from each other, and transfer knowledge. In this regard,

online communities are not only for the exchange of information and resources purposes, but

also for bringing people together to meet their functional and social needs (Rothaermel and

Sugiyama, 2001).

Information quality is considered to be one of the most important determinants that

encourage members to participation in online communities. If a person is willing to get expert

knowledge or information, he/she will be likely to visit a community which is more informative

and believed to be relevant to solve problems and meet the personal requirements (Lin, 1997).

26

Dholakia et al (2004) had examined the antecedents of member participation in firm-hosted

online communities. The authors attempted to highlight the significance of information as the

blood that nourishes the learning process in the community. In firm-hosted online communities,

customers join in order to seek for solutions for their problems and specific needs; in this

process all community users exchange their knowledge and experience. Once they get the

solutions and satisfy with the knowledge and information provided in the community, they

intend to maintain their relations and stay in the community. It has been proposed by Dholakia et

al (2004) that information diversity might influence member’s learning in positive way for two

reasons. In one hand, a huge source of information can provide answers to any specific needs of

customers. On the other hand, information is received from two major directions so-called other

members of the community and maintainers who are the firms or organizations. Thus,

information received from other members includes personal stories, experience, while

information provided by firms is their guidelines (Brown and Duguid, 2002).

Other study related to firm-sponsored online communities refers to the quality content as the

efforts of community organizers to provide an access to variety of information sources, and the

dimensions of information quality include credibility, usefulness, and weight (Porter and Donthu,

2008). Dholakia et al (2004) found that valuable, updated, and accurate information were critical

factors that foster the learning environment, however it also requires the effective management

of site and the learning process. In eBay forums, monitoring the information quality and variety,

encouraging employees to take an active roles to provide support for community users increase

the level of functionality. Further, the functionality of the community site significantly affects

the member’s identification with the community that might be good example for the relation

between Information quality and Community identification (Dholakia et al., 2004). Wang and

Strong (1996) proposed the conceptual framework for the data quality for customers that include

27

accessibility of information, relevance, accuracy and lastly that the information or data must be

easy to interpret. Wang and Fesenmaier (2004) argued that accessibility to information source

and the condition to exchange information or knowledge relevant to specific needs of travel

community members can help them to easily plan their trips. It was found that members of

online travel community were tended to put emphasis on functional, social and hedonic benefits

through seeking and exchanging travel information and experiences as well as their personal

stories. The results of another study indicated that system quality, information quality and

service quality critically influenced member loyalty through satisfaction and behavioral intention

to switch to the online community (Lin and Lee, 2006). Customers join online communities and

search potential sources and unbiased information related to products they want to buy, in the

period of making purchase decision (Dellarocas, 2003; Dellarocas, 2006). Information quality,

system quality and service quality have been considered as the overall quality indicators of

online communities (DeLone and McLean, 2003). Information quality referred to the quality and

weight of information provided for members in online community. Completeness, accuracy,

format of presentation are integral parts of overall quality of content (Nelson et al., 2005).

Brandtzaeg and Heim (2008) attempted to examine the reasons why members of online

community members change their communities. Their findings indicated that law quality content

is one of the potential reasons to stop using the community. The study focused on four popular

Norwegian communities so-called Biip.no, HamarUngdom.no, Nettybe.no and Underskog.no.

Overall, 23% of respondents considered the quality of content as the major motive to continue or

not to stop visiting online communities.

Lin (2007) stated that perceived usefulness and ease of use drastically influenced online

community member’s sense of belonging to the community. In the next step, sense of belonging

positively led to member’s intention to participate. In terms of online features, three quality

28

factors namely information, system and service quality distinctively affected the perceived

usefulness and ease of use. It is to say that Information quality has a strong impact on perceived

usefulness, while system quality and service quality had an effect on both perceived usefulness

and ease of use. Harmon (2004) argued that firms anonymously post the reviews and positive

opinions about their products and services that critically affect the decision-making of customers.

It has been particularly mentioned that companies in music industry are looking for marketers or

specialists who can be engaged in community sites in order to post optimistic views about the

music albums (Mayzlin, 2006). In this regard, we can argue that compared to member-to-

member interaction, firms are attempting to control over contents, and discussions which might

decrease the member commitment to the community relationship and mutual confidence

between member and the community. By enhancing the information accuracy in the online

community, it can be ensured that useful and relevant information will be provided for members.

The findings of Bateman, Gray, and Butler (2006) indicated that community members would be

more active in community discussions and exchange when they have the sense of membership

and emotional connection with community. However, member’s contribution to community

depends on the responsiveness of community.

2.4.2 Quality of Interaction

A number of literatures have emphasized the significance of quality of communication

between online community members and community providers, whether they are brands,

companies or volunteer customers. The findings of study conducted by Adjei et al (2010)

proposed that customer’s interaction with other customers reduces the complexity, ambiguity

and uncertainty more efficiently because of reliance, credibility of information and the most

important issue here is that it increases the profits of firms in particular. Antikainen (2007)

29

provided very useful findings about the social relationship in company online communities. The

Author stressed that the role of relationships is distinctive between different types of online

communities. Particularly in business-related and professional communities, provider’s and

moderator’s role is central from whom community members wish to gain valuable information

and answers for their problems. The community members get disappointed once the moderator

goes passive through the discussions. In this regard, it is important to maintain the exchange of

ideas, dialogue in order to continue the long-term relationship and get benefit from communities.

Yet, it is extremely important to be actively involved in community relationship building.

Although the main target is to facilitate the member-to-member communication for the benefit

of company business, moderator’s presence is required to generate the positive feelings for

customers (i.e listening to their ideas, appreciating their opinions).

Rossi (2011) has provided very interesting and necessary insights about the challenges and

resolutions while collaborating and communicating in b-2-c online communities that are

provided and maintained by organizations. One of the critical problems in maintaining

relationship in community is slow organizational response, risk of deceiving users which lead to

community user’s level of belief on credibility and ability of organization to keep promises. The

findings of the study offer organization and community managers to relieve and encourage open

dialogue and motivate community members to participate. The other suggestion for companies

who conduct online communities is to simplify participation and cut down the distance between

them and their customers, and fans, create the perception in their minds that companies “live”. In

this regard, it is necessary to attend in community activities and be involved in collaborating

with community members directly or indirectly.

In the study done by Antikainen (2007), social relationship and communication

phenomenon have been mentioned from relationship marketing perspective. The relationship

30

process consists of interactions that can be developed in networks of suppliers, distributors and

consumers. The more efforts on maintaining relationship and frequent interactions, the more

favorable word-of-mouth and consumer loyalty will be generated. Instead of pursuing self-

interests, it is necessary to act in mutual respect, and interests, that members will be motivated to

collaborate and with community sponsors and providers (Antikainen, 2007).

2.4.3 Shared Values

Maxham and Netemeyer (2003) had investigated the factors that significantly affect the

employees’ of organization to take extra roles in order to meet the requirements of customers.

The major factor discussed in this study was the congruence between employee’s and the

organization’s values. Scholars indicate that employees and the whole organization become

successful, once there is mutual agreement. People are attached to organization or groups

through the values they share.

“Values form part of individual belief systems and are therefore integral to every

decision and step that a person makes. They are what we represent as human beings. To

be accepted by the community, values have to be shared and created by that community”

(Boon, 1997, p. 85).

According to Money and Graham (1999), organizational values are considered as the values

that management attribute to the group and promote it among its members. Value congruence is

the degree of similarity between the sales person’s values and the values of management. Shared

values are considered as fundamental, enduring and guidance of employee and organizational

behavior as a whole (Chatman, 1991; Kristof, 1996). The fit between organization and

individual affects the individual’s performance, attitudes, citizenship behavior and support to the

organization. Those who fit with the organization are likely to stay and maintain relationship,

31

whereas those who don’t fit with organizational culture, policies leave the organization. This

helps to build an environment where members of organization are similar in attitudes, behavior,

orientations, and reactions and creates homogenous group of people. In terms of small group-

based communities, the theory is applicable, thus who can endure the community rules and

values, can build deep social exchange with other members, and stay in community for a long

time compared to those who cannot endure. While firms create relationship with their customers,

they have to form such a value that customers have to perceive and appreciate this value that is

generated in relationship over time (Gronroos, 2000). Maxham and Netemeyer (2003) proposed

that while representatives share the values with their organization and their traits overlap, they

will make extra efforts in order to solve customer complaints and create positive image of

organization. The results have shown that shared values play critical role in employee’s behavior

and relationship with organization as well as customers. Values play an important role in

recruitment, promotion, motivation and development of employee, discipline and decision

making in the organization (Thomas and Doak, 2000). The authors put the emphasis on value

system operating in South African marketing and communications form and examined the

impact of the values on decision-making, cultural interaction and behavioral outcomes. One of

the companies was Group Africa in which values have been taken as the rules that include

principles and behaviors. The values system is maintained by older group members who are

elected by other employees to ensure that all other individuals advocate the values. The selected

individuals have to guide the group in terms of how they must behave. In addition to this, they

have to make sure that all other employees act consistent with the accepted rules and norms. The

findings of the study have discovered set of norms that grouped into 4 groups so-called (1)

behavioral values, (2) interpersonal values, (3) developmental values, and (4) team building

values that have different implication in personal relationships, working environment and so on.

32

Previous study has shown that correspondence between the personal and firm values positively

leads to the productivity, satisfaction and commitment (O’Reilly et al., 1991). In online

communities, people have opportunities to establish and maintain personal relationships through

shared values and beliefs, or common goals that make the online environment attractive for them.

Preece (2000) indicated that the community policy mainly includes shared norms, rituals, and

rules among the members. Further, policy of the community or group fosters the level of

participation. Arrasvuori and Olsson (2009) suggested that if there is sufficient level of trust,

mutual agreement between the community parties, and members would be less likely to be

active in community activities. Their findings confirmed that by asking on the role of shared

norms, policy and goals in the community, majority of the members believed that “By observing

other people, I can learn what appropriate behavior in a community is”. In social relationship

context, the policy is considered as a set of unwritten norms that have to be learned and

understood by observing others or through a mutual commitment such as giving feedback to

others on their actions.

Winter and Cvetkovich (2010) investigated the role of shared values and trust in the

relationship between Forest Service and the community. They revealed that consistency between

perceived shared values and actions taken by Forest Service is more effective in determinations

of the level of trust compared to the role of shared values alone. Sharratt and Usoro (2003) had

found that the value congruence in online community positively leads to member’s commitment

into the group. In our research setting, we can propose that shared values among all participants

of online community have a significant impact on the level of community identification which

will positively lead to active participation.

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2.5 Managing Characteristics of online community

Eveleth and Eveleth (2010) found that based on social identity theory, in a team development

process leaders play a significant role in influencing the level of identification between an

individual and a group. Those who express well-established leadership behaviors are more likely

to attract members with strong sense of membership and identification. In addition to this, the

authors attempted to apply the opportunities and challenges in face-to-face teams into online

communities in order to provide insights for community managers, promoters, facilitators.

2.5.1 The perceived role of community maintainer

Leaders have an important role in the organizational context. They are considered as the key

persons to build-up positive and productive working environment. They can improve their

followers’ self-esteem by giving support and vision. Over time employees with perceived

support believe that they are significant and valuable to the organization and this message

becomes integrated into beliefs about the self (Gardner, Pierce, Van Dyne and Cummings, 2000).

Managing community relations focuses on connections between people, rather than the assets

that community creates. Thus, informal or social interactions are mostly valued and promoted in

the community (Williams and Cothrel, 2000). The authors suggested that some organizations

who own the online communities like About.com maintain their relationship with users in online

and real life interactions help them to strengthen their social relationship. The term of

Community organizers was presented by Hagel and Armstrong (1997). They are individuals who

maintain the online communities, sometimes for profits, but sometimes for social online service

intentions. Community organizers may support sociability by defining solid community policy,

regulations, or taking actions likely conducting on-topic discussions, encouraging reciprocity

and promoting shared understandings for the common goals (de Souza and Preece, 2004).

34

Another study has introduced the term of online community moderators. Preece (2000)

posited that moderators must be concerned with the balance of control on member behavior as

well as the discussions. Exerting too much control will discourage community members to

participate, whereas, too little control will result in loss of focus, and ambiguity. Collins and

Berge, (1997) found very useful facts after collecting the data from over 150 online community

moderators that can describe the role of online community maintainers as a whole. The authors

show that the major roles of moderators are content creator, facilitator, administrator, editor,

promoter and, expert as well as the participant same with other community members. The

answers of respondents on the role of community maintainers are as following:

- Keeping advertising and commercialized contents out of discussions

- Keeping the group focused on the missions, concern with interpersonal issues between

community members

- Helping with technical problems

- Enhancing the clarity and updating of the information

- Generating useful discussions, interesting posts from different sources

- Evaluating the quality and accuracy of information posted by the community participants

- Assisting community members with their needs

- Behaving as simple as other members of the community

The study provides the potential reasons why people become maintainers/ moderators of the

discussions and the whole community: (1) Voluntary participation- because of the need to

maintain and administer the discussions, (2) Invitation by the former moderators, (3) Initiation

for the community- thus, the increase of people around the discussions, postings created the need

35

to form a community, group and organize the all discussions, membership and facilitate the

access of all community members to each other.

Williams and Cothrel (2000) have contributed very useful insights for running successful

online communities. They have investigated 4 different types of online communities that were

Kaiser Permanente, About.com, Sun Microsystems and Ford that called innovative communities

by the authors. As a result of the study, three critical community activities have been suggested

that can guarantee maintaining the communities for a long period. The first suggestions referred

to Member development, which considers the efforts of community managers to interact with

potential users who are opinion leaders, can influence others and take a leading role in

community activities. Online community organizers need to know them and motivate them to

become potential cooperators. The second suggestion has been referred as Asset Management.

William and Cothrel (2000) have measured Asset as the commitment of members to the

community. In this regard, the managers of community should act in a way to provide services,

contents and quality of relationship that are difficult to find in other platforms. Third proposition

is building effective community relations. In this context, informal and social interactions are

valued and promoted in the community that clear community policies and regulations might

guide whole community to act together. The findings of the study on trust and value found that

providing quality content and enhancing the member embeddedness strongly influenced the

customer’s belief on the sponsor in firm-sponsored online community (Porter and Donthu, 2008).

Embeddedness has been considered as the feeling of members being the insiders of an

organization (Bhattacharaya and Sen, 2003). In their study, authors modeled Effort to providing

quality content, encouraging member embeddedness and supporting the interaction as major

determinants of trusty beliefs of community members on sponsor. To summarize, it might be

proposed that online community organizers/maintainers play very necessary role in encouraging

36

members to become active users of community, engage in community activities, and maintain

their relationship. Therefore, we believed that the role of managing characteristics would be one

of the potential factors that create positive image of community for members and motivate them

commit to community relationships.

2.5.2 Non-opportunistic behavior

Opportunistic behavior has its roots in the transaction cost literature, and it has been defined

as the “Self-interest seeking with guile” (Williamson, 1975). In our research, opportunistic

behavior has been conceptualized as a control and biased behavior on the information.

According to Brown et al (2002), to provide the content on the needs of online community

members which is unbiased and manageable by members is one of the major determinants of

community commitment. The study on banking outlined that when customers use the online

banking service, the risk of opportunistic behavior, poor rules and regulations decrease the level

of trust in online bank service provides (Mukharjee and Nath, (2003). Lack of trust has been

considered as one of the reasons why customers don’t purchase from internet shops (Lee and

Turban, 2001). This paper investigated the antecedents of trust in online shopping. In many

researches trust and confidence are considered an essential determinant of long-term social

relationship. Because reliance, and confidence decrease the level of risk that the trusted party

will not take an advantage or behave opportunistically (Fukuyama, 1995). Ridings and Gefen

(2001) investigated the trust issue in online communities and they suggested that trust is

necessary in online communities in order to foster the confidence and reduce the fear between

parties. They referred to opportunistic behavior as a factor revealing the personal information,

using it for commercial intentions and acting in inappropriate ways that are not acceptable by the

community members. The findings of Ridings and Gefen (2001) identified that online

37

community members will believe and conform to others, once the others are responsible for the

personal inquiries, responsive for the specific needs and trying to act in integrity with the

community members. However, another study found that in a firm-sponsored online community,

firm’s attempts to enhance member embeddedness and encourage the interaction might be seen

as an opportunistic trait of firms in order to create positive image in consumer’s mind, but that

opportunistic behavior does not reduce the community member’s commitment to the community,

because when members are engaged in interaction with the firm, and they are already aware of

commercial purpose, therefore don’t put more emphasis on this issue (Porter and Donthu, 2008).

According to Sultan et al (1999) in online retailing once, customers know more about a

purchase decision and the information or knowledge is consistent with what the consumers look

for, they will trust to retailers. In addition, consumers will be tended to trust retailers, once the

retailers are concerned with their needs, listen to them and take actions in order to deal with their

needs fairly. Mukharjee and Nath (2003) emphasized that the integrity of online banking service

providers is very necessary to build trustworthy relations with customers. Information accuracy

fosters the customer engagement in online transactions. It includes the completeness of

information about the product quality. From this point of view, we may propose that in online

community context, community organizer’s concern with the informational and social needs of

members, efforts to provide unbiased contents and details of knowledge will foster the trusty

behavior of members towards the community organizers. The result of the study (Mukharjee and

Nath, 2003) also provides the evidence that opportunistic behavior so called information

distortion, violation of rules and norms negatively influence the level of online user’s loyalty to

the service providers.

Community Citizenship Behavior Theory suggests that collective behavior is achieved

through the common rules, beliefs and values that all members inside the group are aware of it.

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It restricts members to act in a self-interested way and once the individuals act by their own, it

affects the accomplishment of joint tasks in the group or community. Common rules, goals,

values and norms are guidelines that foster community members to contribute, and avoid

inappropriate traits namely spam, off-topic comments (Bateman, Gray, and Butler, 2006). Thus,

it can be said that out-of-group discussions are not useful for the rest of community that can

cause the avoidance of community membership in the future. From the previous literatures, we

adopted the notion of opportunistic behavior, which has been investigated in several perspectives

such as organizational, economic, community relationships. Those empirical evidences gave us

insight that opportunistic behavior is one of the measures that determine the future relationship

between partners in different environments, and define the level of confidence and

organizational citizenship behavior, as well as commitment. Therefore, in current study, we aim

to examine the relationship between non- opportunistic behavior and participation behavior in

online community through the mediating role of community identification.

2.6 Personality characteristics

Online community studies require understanding the members’ characteristics and examining

how those characteristics influence membership behavior, likely their commitment, engagement

and participation behavior.

2.6.1 Perceived centrality

The idea of perceived centrality comes from the theory of self-esteem which has been

studied in several perspectives, particularly in organization science. Previously, scholars have

distinguished different kinds of self-esteem, such as role-based, task-based self-esteem. After the

extension of the studies related to organizational science, “organization-based self-esteem” has

39

been included to the literatures (Uçar and Ötken, 2010). Organization-based self-esteem refers to

the extent that members of the organization perceive themselves as important, valuable and

influential within the organization (Pierce et al., 1989).

Uçar and Ötken (2010) investigated the mediating role of organization-based self-esteem in

a relationship between Perceived organizational support and organizational commitment.

Perceived organizational support is considered as the employees’ beliefs that they get rewarded

and valued by the organization for their contribution and tasks accomplished by them (Rhoades

& Eisenberger, 2002). According to Uçar and Ötken (2010), self-esteem is a personal

assessment of the individual’s own value. Thus, self-esteem refers to the extent to which

individual evaluates himself as a valued, competent, and necessary (Korman, 1970). The

findings of the study indicated that employees will commit to the organization, once they believe

that the organization care, support and value those employees and it strongly creates sense of

belonging and emotional connection with the organization. In addition to this, organization-

based self-esteem significantly affected the decision to continue the relationship (Uçar and

Ötken, 2010). Bandura (1982) has referred to the self-efficacy theory. His study has particularly

focused on the role of self-efficacy in individual’s performance and accomplishment. It was

proposed that increasing level of self-conception or self-efficacy positively leads to the job

performance. The author concluded that the self-efficacy creates the collective efficacy. It is to

say that the “The strength of groups, organizations, and even nations lies partly in people’s

sense of collective efficacy that they can solve their problems and improve their lives through

concerted effort. Perceived collective efficacy will influence what people choose to do as a

group, how much effort they put into it and their staying power when group efforts fail to

produce results. It should be noted that knowledge of personal efficacy is not unrelated to

perceived group efficacy……, collective efficacy is rooted in self-efficacy”. It is not a chance

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event that we aim to apply the above-mentioned theories in our study and propose the idea of

perceived centrality as one of the constructs for our research model. The findings of the study by

Bandura (1982) have been adopted by Wang and Fesenmaier (2003) in their study related to the

motivational factors that lead to online travel community contribution. The authors have used

self-concept as one of the factors that can potentially influence the level of member’s

contribution to online community. In addition to this, one of the dimensions of self-concept

included self-efficacy. It was suggested that self is derived from adopting the role expectations

of reference groups. The individual behaves in ways which might satisfy the whole group as

well as their personal needs of affiliation and power at last. In online settings, a willingness to

help others, contribution to contents and provision of information can significantly improve

individual’s status and prestige in the online community.

Antikainen (2007) indicated that for members of online community, ending relationship

vanishing from community is easier than any other environments. If a member is not satisfied

with a quality of community, he/she will either switch from the community or become passive

user. Therefore it can be suggested that in all periods of online community visit, members have

feeling that they gain some values, or social benefits which are generated through the attraction

of a community. In this way, we attempt to consider the role/position or activity of community

member also contributes to membership of the online community. There are different kinds of

members in online community. One of them pointed out by Katz (1998), and Schlosser (2005) as

lurkers. They are more likely to participate in online communities to obtain some economic

benefits, regardless of deep social relationship and sense of community. Antikainen (2007)

posits that online community cannot be formed if there are lurkers and if there is no deep social

relationship as well as a sense of community. It can be pointed out that the position and the role

of potential members in online community are critical to form a stable community and

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relationship. Several social theories emphasized that the attractiveness of online communities

depended on individual’s physical attributes, their abilities, roles and personalities (Caspi and

Harbener, 1990). On the other hand, perceived similarity between community members is also

referred as the construct of positive community relationship (Antikainen, 2007). The similarity

in attitudes of members and other parties play an immense role (Antikainen, 2007). In online

community, similarity may occur between members and organizers, or among the members.

Harris et. al (2003) concluded that attractiveness of an online community is influenced by

the factors that are (1) socialization, (2) similarity, (3) compatibility and one of the important

factors which is (4) reputation. Reputation is derived from individual’s abilities, personality,

physical attributes which is referred by Antikainen (2007) in her study. Kollock (1999) have

provided evidence on motivation for contributing to online communities. Three motivation

factors, namely anticipated reciprocity, increased reputation and sense of efficacy are believed

to be effective in community contribution. An individual who contributes, shares knowledge to

the community, expects something that will receive useful feedback. On the other hand, active

participants get more response faster to their questions than passive and unknown users.

Reputation is necessary to active participants who are also contributors, because they need to be

recognized and understood by other community members. Antikainen (2007) implied that

making contribution to the community can help the facilitators/active members to believe that

they have impact and support on the community.

To sum up, the above-mentioned studies have provided evidences on self-categorization,

self-esteem, seeking for prestige and status might influence the members of organizations,

groups or communities to continue their relationships and improve their job performance. The

term of Perceived Centrality has not been widely used in online community studies. Therefore,

in the current research setting, the term of perceived centrality has been adopted from different

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theories and it might be necessary to test the role and position of self in online community and

how this influences the level of involvement and commitment which can contribute to further

studies related to online community, social relationship building and participation behavior.

2.6.2 Demographics

Wang and Fesenmaier (2004) provided evidence on the relationship between the member

demographics (i.e gender, age, and education level) and their influence on participation behavior

in online travel community. It was hypothesized that demographics is one of the major

constructs along-with the perceived needs, membership duration that might significantly affect

the level of member participation behavior. Another study contributed to the internet usage

behavior and its impact on the level of social capital, particularly in social networks and online

communities (Wellman et al., 2001). The authors proposed that those who have a university

degree are more tended to be involved in synchronous online activities, and those who are

without university degree are more engaged in fun activities such as playing games. Latecomers

in the internet were expected to play games or chat in web settings. Thus, individuals who use

internet for a long time, are involved in distinctive types of Internet activities. Synchronous

activities are considered as communication and interaction happen through media such as

Messenger, virtual chat, virtual spaces likely Second life and video conferencing. In online

learning, this is prominent in the form of communication and tutoring between teacher and

student. According to Herring (1993), men are less likely to participate in discussions compared

to women. However, women are tended to ask less information in online discussions. In addition

to this, gender difference might be explained in terms of male and female reactions to the

language they receive and use in online settings. In communication, males and females were

found to be different for the level and type of contributions they made. Other research supported

43

the idea that females are more willing to express personal feelings and build interaction with

others, whereas males are willing to establish control rather than interact as much as females do

(Jaffe et al. 1995). In this regard, Wang and Fesenmaier (2004) also adopted the same theory in

order to examine the relationship between demographics and user behavior and they proposed

that individual with different age and educational background switch to online communities for

various reasons and participation behavior. The findings confirmed that individuals from

distinctive backgrounds have different community membership behavior in online travel

community and the organizers should consider this factor carefully during providing online

community service. In our current study, we can apply the findings of the above-mentioned

study and propose that the different Internet using behaviors and reactions of men and women

can significantly affect their community identification and commitment. Because men in young

ages are tended to express aggressiveness, competitiveness and dominance, while women are

tended to negotiate, connect with others and build-up relationships in order to share their

emotions and feelings (Wallace, 1999).

Kim et al (2007) employed the gender as an independent variable in order to examine its

influence on online information search related to tourism planning, The findings of the study

indicated that woman are more likely to spend sufficient amount of time on the Internet every

week in order to search information and they had quite stronger attitude towards both online and

real information sources. On the other hand, men were more experienced in Internet search and

planning the travel even they spent less time on the Internet. Females found to put more attention

on user-friendly functionalities and wide variety of information and discussion contents.

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2.6.3 Locus of Control

Locus of control is an extension of personality trait. This perception has been adopted in a

number of studies related to Organizational science, human behavior in virtual environment,

online gaming. It embraces every phase of human character, intellect, morality, skill and attitude

that has been built up in the course of one’s life. Hall and Lindzey (1957) proposed the

personality as the core of human being. According to Mayer (2005), personality is an

individual’s pattern of psychological processes which arises from personal feelings, motives, and

opinions. Moreover, previous scholars implied that in work conditions, the personality of

individuals significantly affects their actual behavior and in this regard, Locus of control has

been found the most representative personality trait. In our research setting, we put emphasis on

locus of control in order to investigate member’s community identification and participation

behavior.

2.6.4 Previous studies on Locus of control

Bradley and Sparks (2002) have described Locus of Control as one of the productive and

enduring personality constructs in order to predict individual behavior. According to Kren

(1992), Locus of control is considered as personality trait that describes the extent to which

individuals attribute the cause or control of events to themselves (internal course) or to external

environment (external orientation). Saks (2006) has investigated the antecedents of job and

organization engagement of employees in organizational context. The authors tried to explain

the engagement as organizational commitment and organizational citizenship behavior, while the

commitment has been referred as emotional and intellectual commitment and involvement to the

organization. However, Saks (2006) distinguished engagement and commitment that the latter

refers to an individual’s emotional attachment or attitude on organization. Engagement is the

45

employee’s formal role performance, whereas, organizational commitment refers to voluntary

and informal ties and behaviors of individual in organization who helps co-workers and

organizational leaders. Several studies have adopted the Locus of control or control balance

perceptions in order to examine the human behavior. Ajzen (1991) used the term of perceived

behavioral control in order to explain the relationship between the intention to perform a

behavior and their perception on their control. The author has emphasized that the intention to be

engaged in behavior will strongly influence the performance. In his model, Perceived Behavioral

Control directly influences the intention as well as the behavioral outcomes. The behavioral

outcomes largely depend on the motivation and perceived control which is influenced by

motivation, and opportunities. However, Ajzen (1991) has distinguished locus of control and

perceived behavioral control. Thus, perceived behavioral control refers to the ease or difficulty

of performing the behavior of interest. On the contrary, locus of control is a perception on the

expectancy which remains stable across situations and forms of action, but perceived behavioral

control can be varied regarding to situations and actions. According to Hoffman, Novak and

Schlosser (2003), Locus of control is the personal belief of people that they have influential role

and contribution to the events related to their lives. Therefore this conception is considered as

one of the necessary predictors of personality factors and has been adopted by Koo (2009) in

order to study IT acceptance and online gaming behavior of individuals. Koo (2009) revealed

that playing online games is affected by perceived control, social affiliation and enjoyment.

However, locus of control plays a moderating role in the relationships mentioned above and

external locus of control has a huge implication in playing online games and socially affiliating

with friends virtually. Because perceived power or pleasure gives more incentives to people to

be actively involved in playing games. The more resources and opportunities individuals believe

that they possess, and the fewer obstacles or barriers they have, the greater they will have

46

perceived control over their behavior. Therefore, we can anticipate that individual’s perceived

control over their behaviors as well as their power of influence on relationship, communication

with other people will facilitate their involvement in groups, in relationships with great

motivation. Because it can give the people self-esteem, leadership abilities in order to make

positive changes, and contribute to group they belong to. According to Saks (2006), in

organizational context, job engagement is associated with a sustainable workload, feeling of

control, perceived social capital, reward, supportive work community, fairness and justice. In

other words, the degree of employee’s engagement is linked to the reciprocity of organizational

leaders, mutual commitment, trusting behaviors. Identification with the group or community is

strongly associated with the perceived status in the community, group. People with low level of

perceived control power of change are less likely to be socially identified (Abramas, Hinkle and

Tomlins, 1999).

O’Brien (1986, p. 52) had explained two streams of Locus of Control; (1) external locus of

control, which refers to individual’s perception that the changes, outcomes are determined by the

external factors, such as the powerful decision-makers, opinion leaders or other experienced and

influential people, whereas (2) internal locus of control is considered as person’s belief that

he/she can influence the changes, outcomes through their personal efforts, abilities and attempts

to control the events. We have documented the different descriptions of internal and external

locus of control in Table 2 that have been distinguished by previous authors.

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Table 2 The descriptions of internal and external locus of control

Scholars Results of the studiesSpector (1982) Compared to externals, internals have more self-confidence to

their capability, put more efforts to collect information in

complicated working environment, in this way, their job

performance is more higher. In addition, they are more active in

participating and posting feedbacks, whereas, externals are

tended to be obedient

Wu (1986) Internals are more tended to show high work performance, and

susceptible to encouragement. On the other hand, individuals

with external locus of control are difficult to concentrate on their

task, low job satisfaction, and have feeling of isolation from

their group.

Lu et al. (1997) People with higher locus of control are more predictable to the

job environment and more self-confidence to the job, this they

like to own autonomy and have greater satisfaction

Source: Zheng (2001)

Esterhuysen and Stanz (2004) have investigated the role of Locus of Control in Online

learning. It has been concluded that Locus of Control is one of the critical predictors of work

performance. In online environment, people have more perceived internal locus of control and it

maximizes the outcomes of learning process. Lefcourt (1972) has concluded that individuals

with internal locus of control expectancies are more likely to be cognitively active than people

with external locus of control tendency in-group relations. In small groups of discussions, high

internals should participate more in group discussions, be more task-oriented, play a facilitator

role and be more attentive in group interactions. Internals are those who believe their power on

transformations and influence over other people (Johnson and Hanson, 1979). Their findings

supported the idea that internally oriented individuals, particularly men influence the group

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discussions, communicate with other people regarding to their belief on their responsibilities to

act and contribute, whereas, externally oriented individuals also act in the same way, but they

have belief that he is expected by other group members to do so. Another study suggested that

internals are more committed to the organization, more intrinsically motivated to do well on

their job performances, and remain the relationships with other organization members (Macan et

al., 1996).

The moderating role of perceived control has been examined by (Sullivan and Bhagat, 1992)

in the relationship between job characteristics and satisfaction. The authors investigated the

influence of perceived Locus of Control as one of the individual level characteristics. As a result,

the perceived control of individuals has been found to strongly moderate the relationship

between job stress and satisfaction. Thus, active jobs, active involvement by individuals lead to

higher degree of job satisfaction, while passive job environments create ambiguity and lead to

lower level of job satisfaction. Chartzisarantis et al (2009) concluded that significant others

stand in a distinctive position to influence group norms and behavior when they communicate in

supportive ways and give supportive autonomy rather than controlling ways.

Research has revealed that the difference in personality traits hugely influence behavior in

different social situations. According to Locus of Control theory, individuals with internal locus

of control are more likely to collect information to make decision and more careful in their

personal relationships compared to those with external locus of control. However, the theory of

Locus of Control in Online community setting has not previously been introduced. In the current

study, online community members are playing different roles in group discussions, posting

comments, giving feedback. The members interact not only with other members and peers in

group, but they also need to communicate maintarers/providers of community and contents.

According to social cognitive theory, locus of control represents personality factors that we try

49

to investigate how the locus of control affect the member-community identification and overall

impact members’ actual participation behavior. As mentioned above, internals are more likely to

commit to their organization, group and motivated to show high performance in their tasks. In

summary, we will try to find out how this trend changes in online community environment.

2.7 Participation in Online community

Online participation is considered as the interaction between users and online communities

in Internet settings. In online communities, participation refers to the behavior of members to

provide content to the discussion groups and contribute to their peers as well as other people

seeking support. Online participation is recently a one of the topics addressed for research

purposes. There are several sub-dimensions of online participation behavior that are:

commitment to communities, coordination and interaction (www.wikipedia.org). Encouraging

participation in online communities is one of the hardest tasks for community organizers. If

members don’t actively participate in the community, it will not be successful and flourish

(Bishop, 2007). Several studies have mentioned that it is necessary to motivate the passive

community members to become active opinion leaders and, content contributors, and have

explained the reasons why members are not easily taking a role of active participants (Preece,

2000). There are several reasons why committed members on online communities maintain

relationship with insiders and remain active. As long as members feel the need to contribute,

there is a mutual dependence between the community and the member. There are other reasons

which are motivational factors mainly. Those can be characterized as intrinsic and extrinsic

motivations. Intrinsic motivation is considered as an action that is driven by personal interests

and internal emotions in the task itself, whereas, extrinsic motivation refers to an action that is

influenced by external factors, often for a certain outcome, reward or recognition. The two types

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of motivation contradict each other but often go hand-in-hand in cases where continual

contribution is observed.

Community engagement is one of the theories that explain the member intention to

participate in online community. The theory suggests that members are interested in helping

others, actively participating in joint initiatives, and otherwise acting in such ways that the

community endorses and that enhance its value for themselves and others. Member participation

is considered as “Taking part”, mutual contribution in the case when others help either directly

or indirectly to the community (Vroom and Jago, 1988).

Kollock (1999, p. 227) has identified 3 main motivational factors that lead to the

participation in online community that are anticipated reciprocity, increased recognition and

sense of efficacy. The findings of the study have shown that long-term members of a brand

community are more likely to enjoy higher status inside the community and their membership is

regarded more legitimate. In this regard, long-term membership in a brand community positively

influences the level of social identification with the brand community (Muniz & O’Guinn, 2001).

According to the suggestions of Bishop (2007), in order to become active participant in an

online community the member needs to have a desire and the desire has to be dependable with

his/her goals, values, beliefs and even the social benefits in the future. The results of the study

have indicated that the main reason why member participate or do not participate is that they are

needs- and goal-driven. Thus, a member joins the online community to meet their social and

informational needs. However, their lower needs are met there, whereas, higher needs are met in

other communities. They will switch to those communities easily. Non-active users of online

communities are called lurkers and this term has been studied by several authors previously.

Lurkers are those who join online communities, but do not participate actively or post in boards

(Nonnecke, Andrews and Preece, 2006). The authors examined the difference between lurkers

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and active contributors as well as the reasons that people are lurking while joining the online

communities. The findings show that lurkers are less optimistic compared to the active posters

and contributors. Introverted or passive behavior significantly affect their attitudes on the

community activities. Active posters are more likely to be engaged in ongoing community

activities. Nonnecke, Andrews and Preece, (2006) presented the result on the behavior and

attitudes of lurkers and active users in Table 3 and Table 4 below. Four major reasons for

community participation were identified.

Table 3 Common attraction to the online community

On the other hand, the authors gave the broad definition of the reasons for joining the online

communities in the Table 4.

Table 4 Attractions to community where lurkers and posters differ

Lurkers indicated that they received less benefit than they expected to receive. Additionally,

lurkers are less likely to have sense of community and membership compared to active users.

Therefore they didn’t participate actively in online community.

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Previous literatures have shed a light on the reasons that draw people to online communities.

Luo (2002) investigated the impact of information and entertainment factors on consume

behavior in online settings. The authors found that users were more likely to put emphasis on

informational and entertaining opportunities of internet. And those factors created more value

and positive attitude for users. Wang and Fesenmaier (2004) attempted to model the

participation on online travel community. Thus, the authors tried to understand the needs of

members in online travel community, the relationship between needs and the level of

participation as well as their roles while meeting their needs and contributing to other members

of the community. The findings have shown that online travel community meets their functional,

social, and hedonic needs. On the other hand, in terms of functional needs, members didn’t act in

order to look for more information in the website, rather they put more attention on building

social relationship with other members. The major contribution of this study is that two need

constructs are critical determinant of the level of online community participation; (1) social

needs, and (2) hedonic needs. In this context, travel community members are more likely to

participate in activities by seeking for and exchanging travel information and tips, enjoying

sharing their travel experiences and stories and supports socially their peers inside the

community. The authors suggested that two characteristics must be taken into consideration by

community management in order to increase the level of participation of members, which are (1)

facilitating sociability among the members, and (2) increasing the hedonic experience

(interaction process). Hedonic experience put an emphasis particularly on the activities such as

amusement, fun and excitement. In other study related to the overall participation pattern and its

relationship with contribution to online travel community, the authors found that efficacy is one

of the major factors influencing the active contribution of a member in the community.

Providing support to the well-being of public, norms of reciprocity may also affect the

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individuals to interact with other people. The authors also suggested that online travel

communities should be organized in a way that members or active contributors could get

rewards for making contributions to community activities (Wang and Fesenmaier, 2004).

Wilimzig (2011) referred to participation as posting a comment, or as leading a discussion

and creating content about a brand’s product or service. While respondents of his study asked

the question whether community members should contribute to the community when there is

need, 13% of them strongly agreed, 44% agreed somehow, whereas 19% didn’t agree with the

statement. In addition to this, majority of brand community users agreed that when receive help

from other members of the community, it is necessary to contribute to the community.

Community members believed that identification with the brand community positively leads to

their intention to purchase from the brand and remain the member of the community for a long

time.

In our current study, we examine the role of personality, community features and

management characteristics in creating the community identification and how it affects the

member’s participation behavior in online community. It is necessary to better clarify the factors

from different perspectives that influence the participation behavior of members in online

community.

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Chapter 3 Research MethodologyIn chapter 3, conceptual framework proposed and followed by the hypotheses based on the

structure and the contribution of the current study. Further, Methodology of the study has been

introduced. This chapter is aimed to build an appropriate scale and study methodology which

can better interpret the procedures of data collection as well as the data analysis further. The

current research adopted the survey based on the questionnaire as the methodology and sampling

in order to collect the data. In this chapter, section 1 consists of Research Framework, while in

section 2, Hypotheses were developed. Further we explained Questionnaire design and Data

collection as well as Operational Definitions of study variables in section 3 and 4. The research

model is shown in Figure 2:

3.1 Research Model

Figure 2 Research Model

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In the model of research, we aim to test the relationship between three characteristics so-

called community, managing and personality, and participation behavior through the influence

of community identification. It is estimated that the emergence of community and managing

characteristics as well as personality traits would significantly affect the community

relationships and membership behavior. Next section, we will further elaborate on our research

hypotheses and in detail.

3.2 Research Hypothesis

3.2.1Community characteristics. The relationship between Quality of Information and

Community Identification

In the current research, quality of information refers to the information and knowledge that

is provided by the community members, opinion leaders as well as the content moderators.

Brandtzaeg and Heim (2008) showed that the main factors influencing users to switch off the

online communities are Low Content quality, Low trust, Over-commercialized intentions,

dissatisfaction with moderators and particularly users’ feeling of isolation from the community

insiders as well as the discussions. Hsu et al (2012) had suggested utilitarian experience of

members in online community as one of the determinants of community identification. They

identified utilitarian experience as “the extent to which a community member perceives that their

goals of acquiring product-related information is realized in the virtual community”. The authors

hypothesized this theory to have a positive influence on online community identification. The

results also supported the above-mentioned arguments.

While talking on these issues, one of the necessary characteristics of both types of online

communities is the information quality, which refers to the extent that users face low

updatedness of discussions, contents, not particular focus on what users really demand. It

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includes information accuracy, timeliness, credibility, diversity that covers all dimensions of

consumption behaviors and so on (Gray and Meister, 2004; Hansen, Nohria and Tierney, 1999).

Timeliness is one of the very important aspects of the communication with consumers (Dahui Li,

Glenn Browne, and James Wetherbe, 2006). Other scholars introduced the theory that customers

or members in the online communities exchange information distinctively with one another

compared with employees (who are the employees of firms and organize/moderate online

communities). When help others, users are more likely to recount personal experiences,

outcomes and stories heard from others. In the contrary, while employees interact with

community members, they are more tended to use their formal training and the firm’s service

guidelines (Leigh, Peters and Shelton, 2006; Brown and Duguid, 2002). Based on the

discussions, we come up with the idea of the distinction of interaction between user-to-user and

user-to-firm significantly affects the future motivation and intention of community members to

maintain their relationship with their peers as well as maintainers of communities. By

considering that all the sides are users, consumers in user-hosted online communities compared

to firm-hosted communities, we can propose the following hypothesis:

Hypothesis 1 a: Quality of Information will positively influence the level of Community Identification

3.2.2 The relationship between Quality of Interaction and Community Identification

The frequency, intensity of the direct interaction between community members elevates the

close ties among them, more exchanges of ideas among community members and community

providers enhance opportunities for them to connect socially and create Trustworthy relationship

(Slater et al., 2000). Mukherjee and Nath (2007) implied that communication is considered as

sharing formal and informal information and knowledge and they conceptualize 3 main aspects 57

of communication that are Openness, Quality of Information and Quality of Response. Openness

in communication is positively related to customer trust in online retailing (Mukherjee and Nath,

2007). Based on 3 main aspects of communication, we can suggest that Openness and Quality of

response can be considered as main antecedent of member’s perceived one-to-one, direct

interaction with the community members as well as providers in online community context.

Because the system which has characteristics of social presence, is tended to build online

customer trust in retailer. In terms of our study, it could be proposed that online community

organizers can enhance their social communication by providing openness, authencity of

information, speed of response, involvement in individual communication.

According to the study related to building trust in online relationship banking (Mukherjee

and Nath, 2003), timely interaction enhances confidence by assisting to overcome challenges,

ambiguities and define the perceptions as well as expectations. In this study, openness in

communication between individual customers and bank is considered as one of the leading

constructs of identification between member and community.

One of the studies on relationship marketing (Morgan and Hunt, 1994) shows that an open

communication and frequent interactions among team members are more likely to foster the

trustworthy relationship within the group. There is direct and positive relationship between

direct communication and attachment to a group. Given in the prior studies, repeated interactions

within the community discussions and contents increase the level of user’s trusty behavior in

others. Reciprocity of community members enhance the trust building in their relationship over

time. Because while individual post content or message on a community, expects the

involvement of other peers, their responses and attention. In addition, this involvement, attention

and responsiveness require the skills and expertise in order to provide reliable and accurate

information for the members. Responsiveness leads to integrity and benevolence of community

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members (Ridings, Gefen and Arinze, 2002). The PLS analysis of the study shows that

commitment of and integrity of Virtual community member is positively associated with the

responsiveness, frequent interaction of other people within the community. We can suggest that

there is an impact of direct and open interaction among members and between community

maintainer and members on the community identification. In firm-hosted online communities,

members interacting with peers perceive that they are observed by the firms. It means that host

firm/organizers are observers in the community who just obtain feedback, look the discussions

between members. In this way, if firms are also involved in discussions and provide clear and

accurate social support, knowledge, it will lead to success of community and customers will be

more committed to community as well as the firm.

According to the theory of user- and firm-sponsored online communities’ goals, it results

distinctive user and organizer/host behavior within community, interaction. It has been outlined

that in the communities organized for commercial intentions, more attention paid on collecting

product ideas for new product development, but less attention paid for social interactions to

build real community and maintain it not only pre-purchase period, but also post-purchase

activities, listen to customer after sales, socially support them when they raise question and have

particular, individual needs. By adopting the outcomes of studies related to relationships

between customers and firms as well as relationship between community members, we aim at

using this structure in order to investigate the quality of interaction among the all participants of

online community and its impact on member-to-community identification.

Hypothesis 1 b: Quality of Interaction in the community will positively influence the level of

Community Identification.

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3.2.3 The relationship between Perceived Shared Values and Community Identification

Previous study related to Relationship Marketing has contributed very significant insight

for the correlation between shared values and relationship commitment as well as trust (Morgan

and Hunt, 1994). According to the authors, shared values refer to the mutual beliefs of customers

and marketers about the appropriate, common goals, values as well as unimportant and

inappropriate behaviors that can deteriorate the relationship between the parties. Values have

been considered as essential factors that form the organization’s culture (Schein, 1990). The

author had addressed the question “How is culture created?” in organizational culture context.

Identification with organizational leaders has been considered as one of the major factors. It has

been highlighted that when organizations or groups are created, potential leaders communicate

their values, and views to whole group. Those values and views play role as the guidance and

further, values, beliefs and norms are shared in learning process, become the common rules for

the whole organization or group.

Morgan and Hunt, (1994) have suggested that when exchange partners share the same values,

behaviors, they will more likely to be committed to the relationship. The result of the study also

supported the positive relationship between shared values of relationship partners and

commitment. It was emphasized that providing opportunities and rewards, promoting the

common values and goals will significantly influence the level of commitment and long-term

relationship. In online community context, we believe that shared values and beliefs among the

whole community participants will significantly affect the level of their identification

commitment to the community. Therefore, the further hypothesis is proposed:

Hypothesis 1c: Perceived Shared values will positively influence the community

Identification

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3.2.4 Managing Characteristics. The relationship between the Perceived Role of community Maintainer and Community identification

Zhou (2011) suggested that online community managers can enhance the quality of system

and information, organize offline activities and build member’s confidence to improve social

identity, sense of belongingness and encourage the member to become active participant of the

community. In addition, community maintainers should clearly define their values and vision,

which will enhance mutual commitment to community relationships and acceptance of common

rules, norms by all members. In this regard, we consider managing characteristics as one of the

major determinants of building community identification and try to investigate this factor with

details along with the other characteristics.

According to the McKinsey & Company survey (www.mckinsey.com), the number of

online community users increased compared to previous years. Community management plays a

distinctive role in maintaining relationships with community members. It has been noted that

community management is not only managing and moderating the discussions held in the groups.

It reflects a fundamental role. Community management referred to online engagement such as

listening, monitoring, and getting involved in discussions, supporting to contents and most

importantly, providing insight and feedback. Community management has become a

responsibility to monitor sales, customer service and communication in that it functions as a hub

for many different disciplines integrated with online and offline efforts such as customer/client

service, PR, marketing, business development, building relationships, creating contents,

responding to conversations about the brand and the content, and planning and developing

strategies for increasing engagement and conversion. The results of survey offered 3

propositions to consider while managing online community which are (1) Embed with your

community- Spend time to get to know others in the community and engage in simple and

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personal conversations, (2) Do not just focus of monetizing- The most important strategy to

drive revenue for a business is to build the community, earn members’ trust, and delicately ask

for their feedback and feelings, (3) Do not just listen, get the community involved- Building a

strong loyalty is not just listening but also acting and embedding yourself within the community

and becoming a trusted source in the relationships.

Fisher et al (2007) have contributed to the management and planning of online communities.

The authors have worked closely with the product support teams and online community

maintainers in order to observe their engagement with communities. As a result, 4 major

community management strategies have been defined which are:

� Location and purpose

- Know the space’s purpose

- Build on existing community and brand

� Monitor Social activity

- Know what the space is doing

- Embrace leaders, respect lurkers

� Provide Feedback

- Reward users individually

- Use positive reputation

� Organize and Maintain the Space

- Encourage critical mass

- Exert gentle control

Community without a reason can no longer exist and community maintainers should

understand what their users want, and how much effort they expect to put in. Understanding and

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engaging community users’ goals and interests will help to get a community remain stable and

durable (Fisher et al., 2007). Giving a chance for community members to act on their own desire

and contribute to community is considered as one of the important successful leadership ability

in the community. Some e-commerce sites, such as Amazon.com, have been unusually

innovative about utilizing lurker behavior: there are a number of different levels of involvement

that the site mines, ranging from the very active (lists, reviews) all the way out to the highly

passive (books that users have purchased together, or merely surfed between). Frequent posters

can be valuable members of a community. Not only do they provide the majority of the material

and help set the direction for the community‘s conversations, but they can nurture new members

as well. (Fisher et al., 2007) offered that one way to share the community activities and

acknowledge different members’ status is to give them a separate place to work. The authors

also added the personal opinion of one of the participant on the social benefits of being an expert

member in community discussions and contents:

� “[T]hat group of people that goes out there to newsgroups and sends questions and

answers every day, after a while are more than simply technical guys trying to find

answers to their questions. They‘re some kind of group of friends. You can see from the

text messages that it goes more to the friendship area, it‘s not only trying to solve the

question, it‘s also trying to help people – trying to help friends that are passing through

the same path you passed before. And after that it‘s great to meet those people anywhere

else because you feel like you have friends out there. … Where your only contact is

your keyboard and your screen. It is something that far more than just typical contact.

At least, that‘s what I feel.”

Based on the outcomes of previous literatures, we can suggest the further hypothesis:

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Hypothesis 2 a: Perceived Role of the Community Maintainer will positively influence

the level of Community Identification

3.2.5 The relationship between Perceived Non-Opportunistic Behavior and Community Identification

Previous studies mentioned about the manipulation in discussions, forums and

commercially intended ads which are barrier to the deep social integration of community

members and it is stressed that unbiased contents and also discussions controllable by customers/

members themselves lead to the successful online community (Brown et al., 2002). Over

commercialization intention fosters the dissatisfaction with community participation. The study

conducted by Brandtzaeg and Heim (2008) found that high level of commercial intentions and

behaviors among the community users weaken the social relationship between the sides which

should be taken into consideration by the community organizers. However, little studies are

known on the commercial intentions of community organizers in the case of they are firms/

marketers. In this way, there is distinction between user- and firm-initiated virtual communities.

Firms/marketers are more tended to manipulate and use contents, discussions for commercial

intentions and advertisement. One of the dimensions of firm-sponsored communities mentioned

by Kannan et al (2000) is that in firm-initiated online communities, companies are moderators

and creators of discussions, they also follow the commercially oriented intentions inside the

community.

In some literatures, Firms’ intention to manipulate the information and contents provided

online is referred to Opportunistic behavior. It has been defined as “Self-interest seeking with

guile” (Williamson, 1975).The integrity and avoidance of online retailers from bias and

manipulation, distortion of information is positively associated with customer’s trust. The risk of

retailer’s opportunistic behavior still remains; therefore, it reduces the level of trust in customers’

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online behaviors (Mukherjee and Nath, 2007). The authors also found that as long as customers

believe that online retailers engaged in opportunistic behavior, it would lead to reduce the level

of their belief in retailer.

The study highlighted the implication of opportunistic behavior on the relationship

commitment in relationship marketing (Morgan and Hunt, 1994). The authors concluded that

avoiding from biased behaviors such as taking an advantage of the exchange partners in

relationship marketing will have positive impact on sustainable outcomes, commitment to the

relationship. Mukherjee and Nath (2003) have used the opportunistic behavior as one of the

affective construct of trusty relationship. They postulated that in online environment, customers

cannot directly talk to bank employees as well as see them as in real. In this way, it is difficult

for customer to assess the quality of bank products and services before making transactions.

Controlled, incomplete information could lead their confidence in banks. In this regard, we

propose that information distortion, biased and manipulated contents in online communities

would lead to lower level of members’ belief in community management and negatively

influence the level of community identification and our hypothesis is:

Hypothesis 2 b: Perceived Non-Opportunistic Behavior will positively influence the level of

Community Identification

3.2.6 Personality characteristics. The relationship between Perceived Centrality and Community Identification

According to social identity theory, Tajfel (1978) had concluded that an individual

achieves a social identity through self-awareness of his/her membership in a group. Thus, how

he/she evaluates the personal position in the group that also affects the perception of the identity

of group. Wu and Sukoco (2010) asserted that member’s interest in building relationship with

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other parties, is one the major determinants that shape that member’s willingness to participate

in community, share and contribute.

Little attention has been paid to social motivations likely seeking for a status and prestige that

may potentially affect the member motivation for community relationships (Lampel and Bhalla,

2007).

One of the theories involved in group, community related studies is Status Characteristics

Theory which has been explained by several scholars previously. This theory helps to

understand the perception of power and prestige structures in group settings. In group relations,

individuals value the high status members and they are expected to have prestige and power in

the group. They are acting as opinion leaders and their contributions, suggestions are evaluated

more necessary and they are considered as potential source of information and reference

(Barnum, 2005). Thus, we can suggest that those group members are more central and potential

people for the group. In addition to this, authors emphasized the relationship between the status

characteristics and collective action in order to solve problems, ask the questions and accomplish

the group task.

In our current study, we have adopted Perceived Centrality as one of the major determinants

of community identification. Perceived Centrality can be understood from two perspectives; the

first one is the perception of self, hold by the online community member, thus how the

community members perceive his/her potentials, contribution to the community. Second

perspective refers to the beliefs of other community members about the potential contributor,

high status person. Because motivation for positive self-esteem causes individuals to attempt to

distinguish in-group from the out-group due to their position and acceptance in their group

(Barnum, 2005). Individuals with high-status in the group are more likely to express optimistic

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emotions, gratification in their relationships with other compared to those with lower-status

(Kemper, 1991).

Amazon.com is one of the biggest communities that enhance the close relationship with the

members in order to increase contributions of community users and feedbacks to their

contributions. The initial impact of this mechanism is to provide contributors with direct

feedback about their status seeking. Amazon.com, however, has moved beyond this process by

creating a system that aggregates the views of information receivers. Potential contributors are

aware that other value their contribution and compare to other potential users of the community.

In this regard, we can propose that self-evaluation and evaluations held by other people in group

settings are one of the factors that define the behavior of individuals.

Hypothesis 3a: Perceived Centrality will positively affect the level of Community

Identification

3.2.7 The relationship between Demographics and Community Identification

Several studies have revealed that the socio-demographic factors critically affect the

participation behavior, internet search as well as the communication performance based on the

distinctiveness of males and females (Wang and Fesenmaier, 2004; Herring, 1993; Kim et al,

2007). The previous research demonstrated that social identity theory is essential to understand

the meaning and the outcomes of their identity group membership. In organizational context, by

understanding the value individuals put to their organization membership, different demographic

characteristics cause distinctive identification outcomes (Ely, 1994). The result of the study has

shown the significant disparity between the level of organizational identification and gender of

employees. It is to say that male physicians’ organizational identification level is higher than

female physicians’ (Çelik and Findik, 2012).

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Bhattacharaya and Sen (2003) have investigated the factors that form consumer-company

identification through the strong commitment of consumers to their relationship with the

companies. Social identity and Organizational identification theories had been adopted in this

study, which we think that, might produce very significant outcomes on understanding the

consumer identification with companies. They modeled demographics (i.e age, industry size, and

country of origin, characteristics of company leadership or employees) as one of the components

of the identity. In addition to this, self-categorization in the organization is formed through the

characteristics of individuals such as personality, values and demographics. Another study

examined the relationship between the demographic similarities or difference of members in the

organization and their attitudinal and behavioral outcomes (Tsui and Farh, 1997). The study

distinguished the western conception of relationship with Chinese guanxi in order to get better

understanding the role of demography in the work relationships in Chinese organizations. It was

proposed that difference or similarity in demographic factors significantly affect the

interpersonal relationship and behavior towards the organizational identification. The findings

indicated that relational demography and guanxi are more likely to influence interpersonal trust,

communication. Based on the previous studies, (Tsui and Farh, 1997) used the relational

demography in their model as one of the antecedents of work outcomes in organization through

the social identification. In our conceptual framework, we adopt the process of above-mentioned

organizational identification to the online community identification and propose that

demographics (age, gender and others) will have an influence on the level of community

identification and relationship outcome:

Hypothesis 3b: Demographic factors will positively influence the level of community

identification

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3.2.8 The relationship between Locus of Control and Community Identification

In this relationship locus of control is the representative of individual, and community

identification is the behavior thus we try to investigate the relationship between those two

constructs.

According to Rust et al. (1996) one of the major determinants of customer’s overall

satisfaction is personal interaction component of service. Personality can be believed to play an

important role when it comes to more or less skilled employees. In organizational context,

employee’s self-perception is able to behave in a customer-oriented emergence. In this regard, it

can be suggested that individual’s personality traits can be considered as a self-perception. In

relationship marketing, there are several empirical studies which indicated that customer

orientation is an important and central factor to organization, as well as to help increase

employee’s performance (Brown et al., 2002). In addition to this, they also believe personality

trait as a determinant that leads employees to be more customer oriented. Moreover, scholars

found that personality trait is believed to significantly affect the customer satisfaction or

employee’s job performance (Mowen and Spears, 1999). According to previous studies,

personality trait obviously has a significant impact on performance. Thus we aim to apply the

locus of control to our construct as a sub-dimension of personality characteristics and examine

its influence on the level of member’s identification in online community. In Spector’s (1982)

research, he implied that individuals with internal control are more likely to have self-confidence

to their ability, more attempts to collect information in a complicated work environment, thus

have a higher performance level compared to people with external locus of control. On the other

hand, internals are tended to focus on management issues by active participation and more

feedbacks to their work, whereas, externals accept the leadership of others. Lu et al. (1997)

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proposed that internals are believed to be successful in their job and more self-confident.

Therefore they like to control and influence others when they are given a chance.

However, Lin (2008) has indicated that in IT field, professional are less likely to be

concerned about their users and listen to their opinions, further their behavior will lead to

rigidity and poor coordination, although their job results and performance is high. Externals who

are more obedient and like to act with others, will easily coordinate with their groups and will

concern about others. Other studies mentioned about the difference between male and female

internals and externals. The findings of Johnson and Hanson, (1979) show that internally

oriented individuals, particularly males have influence the group discussions, communicate with

other people regarding to their belief on their responsibilities to act and contribute, whereas,

externally oriented individuals act in the same way, but they have belief that he is expected by

other group members to do so. According to the relationship between Locus of Control and

community identification, we previously mentioned in literature review that, very few studies

are known to investigate this relationship, particularly in online community environment. One of

recent studies in organizational context has proposed that there is positive influence of locus of

control on employee’s organizational identification in Taiwan banking industry (Lee, 2013). The

result of the study revealed that Locus of control significantly affect the level of organizational

identification. Drawn from this point, we can suggest that the role of locus of control changes

according to the environment. In online community environment it can be proposed that locus of

control will significantly affect the level of members’ identification with community:

Hypothesis 3c: Locus of Control will positively influence the level of Community

Identification

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3.2.9 The relationship between Community Identification and Participation

In our study, community identification refers to an individual’s sense of membership,

belonging and, his/her positive attitude for the community which is consistent with the

identification and community citizenship behavior proposed by Chiu et al (2006). Jung et al

(2009) highlighted that the more involved the members with their community or group activities,

the stronger they are willing to retain their participation, take a joint action with brand and

advocate it. Further, members who have strong sense of membership and intend to continue

membership in the community will be more active. The results also confirmed that sense of

community and bond is significant determinant of members’ intention to participate in online

communities.

Chiu et al (2006) stated that community users would not contribute knowledge or actively

participate in community activities, if there is no the sense of togetherness with his/her peers and

mutual contribution. The results show that social interaction ties, reciprocity and the

identification significantly increased the quantity of knowledge, whereas didn’t have effect on

the quality of knowledge in the community. However, trust increased the sharing the high

quality knowledge. The authors explained that community members are willing to share

knowledge due to the fairness, high interactivity, strong feeling of community membership

regardless to trusting other members. Allan, Massiah and Johnson (2013) investigated the

outcome of online community identification and found that consumer’s identification with the

overall community significantly affects the helping behavior for the all members of online

community. The main point of this study is that the authors examined two level of social

identification. The first one is overall community and the second one is sub-group level

identification. And the findings indicated that sub-group identification is more implicative in

sub-group membership behavior compared to overall community identification. However, the

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overall result highlighted that community identification positively leads to the consumer-to-

consumer helping behavior, sense of responsibility and active involvement in community

activities. Another study related to online community participation proposed that identification

maintaining the relationship and being actively engaged in community is dependent on the

identification. Thus identification will encourage members to participate in the community

(Zhou. 2011). The results indicated that online community member participation is formed

through the social identification. In addition to this, members will be actively involved in

community, once its values, norms and visions are clear to whole members. In this regard we

can say that community identification, reciprocity to the community norms can foster high level

of community engagement and active participation Therefore we propose the hypothesis:

Hypothesis 4: The high level of Community Identification will positively influence

member’s participation behavior

3.3 Questionnaire design and Data collection

In order to test the proposed research model and answer the aimed questions of this study, an

online questionnaire is designed. The reason for choosing the online survey is that we believed

that it could reduce the time and help to track the daily changes in survey response rate. On the

other hand, it could be easier to reach a vast number of online community users, and suitable for

respondents to answer the questions and send it back on time. Case (2007, p. 207) had

distinguished advantages and disadvantages of e-mail/web surveys and postal surveys. The

authors emphasized that questionnaires distributed by e-mail or through discussion lists of

particular groups can be easier to answer. Because it doesn’t require too much typing. On the

other hand, online surveys allow us to check the interactivity of responses and automatic

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tabulation of them. About the disadvantage of e-mail survey, it was mentioned that researchers

might send the same surveys several times in order to make sure that the questionnaire had been

delivered to respondents. It could create bias in responses. In the questionnaire, the measures of

the constructs have been adopted from different studies that have been done previously. We

believe that previous authors have contributed very useful insights to the online community

study by testing the reliability and validity of the measures that we aim to use in the current

study. The questionnaire part comprises of overall 70 items based on the related studies done

previously (Dholakia et al, 2009; Porter and Donthu, 2008; Lin, 2007; Mukharjee and Nath,

2007; Morgan and Hunt, 1994; Balasubramanian and Mahajan, 2001; Netemeyer et al., 1997;

Rosenberg, 1965; Graen and Bien, 1995; Wang and Fesenmaier, 2004; Spector, 1988; Koo,

2009; Wilimzig, 2011; Cameron, 2004; Wiertz and Ruyter, 2007 ).

The questionnaire has 5 dimensions namely, Community characteristics, the managing

characteristics, Personality characteristics, Community identification and Participation. To be

more specific, the first three dimensions comprise of sub-dimensions that form the major

constructs of the current research. Community characteristics include Quality of Information and

Quality of Interaction, while The Managing Characteristics have been interpreted with the

Perceived Role of Maintainer and Non-Opportunistic Behavior, and lastly, Personality traits

comprise of the sub-dimensions so-called Perceived Centrality, Demographic factors and Locus

of Control.

In order to get reliable and appropriate research results, we attempted to find the online

communities in Taiwan which can provide use useful and suitable number of responses. In this

regard, we contacted with several online communities such as www.Forumosa.com 2 , and

Pentax-brand community. Forumos.com is believed to be the biggest online community operates

2 http://www.forumosa.com/taiwan/index.php 73

in Taiwan. It started as a discussion group called Oriented.org in 1998. This community consists

of a number of discussion platforms related to topics from the lifestyle, travel to health, sport,

culture and so on. In addition to this, over 1.2 million posts have been recorded in this largest

community. The main feature of this community is that each discussion group is moderated and

managed by specific persons. The moderators are volunteers from different countries, including

Taiwan. We believed that this largest community can provide use very significant responses in

our data collection process and contribute to overall study results. In data collection process, we

got the permission from the platform administrators to post our survey in various discussion

groups that were related to lifestyle, travel, health, sport, cars & motorcycles and other

categories. Pentax community is also one of the brand communities in Taiwan. The members of

Pentax group provided 33 responses back, while 198 Forumosa.com users filled out the

questionnaire. Hence, there were 214 valid responses to analyze the data. The details have been

presented in Chapter 4.

3.4 Operational Definitions of Study Variables

Based on the conceptual framework of our study, operational definitions of variables are

related to Quality of Information, Quality of Interaction, The role of Community Maintainer,

Non-opportunistic behavior, Perceived Centrality, Demographics, Locus of Control, Community

Identification, and Participation. Following section provides the detailed definitions of each

construct.

3.4.1 Quality of Information

Muniz and O’Guin (2001) defined it as the “Quality of information provided through

community”. In another study drawn from Relationship Marketing, content quality is considered

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as information credibility, relevancy, importance and accessibility in order to test the virtual

communication and satisfaction (Mohr and Sohi, 1995).Information quality is measured by the

accuracy, completeness and relatedness to personal needs (Nelson et al 2005; Lin, 2007).

Porter and Donthu (2008) have developed 6-item scale to measure the content quality

provided by the community sponsors in firm-hosted online communities. The items mainly

contain of “…..Makes frequent updates to community content”, “…..Provides content that is

relevant”, “…..Provides important information”. We adopted and edited the measures and items

from Porter and Donthu (2008), (Lin, 2007), Dholakia et al (2009) as well as (Mohr and Sohi,

1995) in order to ask the questions about the role of quality of information in online community

identification. Respondents of the survey were asked to fill in a Likert 6-point scale to express

their opinions, 1 being strongly agree, while 6 being strongly disagree. Table 5 represents the

operational definition of Quality of Information

Table 5 Operational definition of Quality of Information

Variable Operational DefinitionQuality of Information

(Dholakia et al, 2009;

Porter and Donthu,

2008; Lin, 2007)

QINF1 The information provided in the community is quite usefulQINF2 The information provided in the community is valuableQINF3 I go online in the community when I need facts about a

particular subjectsQINF4 The members are provided with information in an

appropriate level of details QINF5 The content and information are frequently updatedQINF6 I can rely on the information and contents provided in the

communityQINF7 I go online in the community when I need to receive clear

answers to my questionsQINF8 The community is a great place to get answer related to my

needs

3.4.2 Quality of Interaction

Jang et al (2007) had considered interactivity as the “Degree of information exchange among

community members and between community members and host of the community”. Antikainen

75

(2007) explained interaction (participation in discussion boards, sending messages and posts) in

firm-sponsored online communities from one-to-one and many-to-many perspectives. Preece

(2001) described usability as the human-computer interaction, whereas sociability refers to

human-to-human communication encouraged by the technology. In our study, we developed 7

items-scale and adopted the majority of items from Mukharjee and Nath (2007) as well as

Morgan and Hunt (1994) to test the quality of interaction. The items mainly included “The

community rules, regulations and policies are clear to members”, “The community creates an

open environment where all member can freely interact with each other” and others”. The list of

items has been depicted in the Table 6.

Table 6 Operational Definition of Quality of Interaction

Variable Operational DefinitionQuality of Interaction

(Mukharjee and Nath,

2007; Morgan and

Hunt, 1994)

QINT1 There are good features that help me to socially interact with whole community

QINT2 The community rules, regulations and policies are clear to members

QINT3 The community creates an open environment where all members can freely interact with each other

QINT4 The responsiveness to content is immediate and timelyQINT5 The community maintainer regularly pay attention to the

feedbacks from community membersQINT 6 The community maintainer is someone who initiate

open dialogues with the community membersQINT7 I feel I am responded on my personal inquiries on timely

fashion

3.4.3 Perceived Shared Values

Shared values has been described as “Explicit or implicit fundamental beliefs, concepts, and

principles that underlie the culture of an organization, and which guide decisions and behavior

of its employees, management, and members” (www.businesdictionary.com 3). Maxham and

Netemeyer (2003) have defined shared values as the overlap of dominant values, and norms of

3 http://www.businessdictionary.com/definition/shared-values.html 76

organization and its employees. The authors emphasized that organizations will work effectively,

once the employees and whole organization share the same values, goals. Because, shared values

play a guiding role in organizational behavior. In order to estimate perceived shared values, we

adopted 4 items from Netemeyer et al (1997), and Balasubramanian and Mahajan (2001). Table

below represents the list of items.

Table 7 Operational definition of Shared values

Variable Operational DefinitionShared Values

(Balasubramanian and

Mahajan, 2001;

Netemeyer et al., 1997)

PSV1 I hold similar values with the communityPSV2 There are well-understood set of norms and values that guide

the behaviors of the communityPSV3 I maintain the values consistent with the goals of the

communityPSV4 Shared values help us to express our opinions and

communicate effectively in the community

3.4.4 Perceived role of Maintainer

Hagel and Armstrong (1997) have used the term “Community organizers” which refers to

the individuals who maintain the community for economic and social purposes. They can form

the community policies, goals and regulations in order to support sociability, encourage mutual

reciprocity and promote shared understanding among the online community members (de Souza

and Preece, (2004). Porter and Donthu (2008) used the measures so-called Perceived effort to

provide quality content, perceived effort to foster member embeddedness, and perceived effort to

encourage interaction to estimate the role of sponsors to foster trust in firm-sponsored online

communities. We attempted to enhance the conception of perceived role of maintainer by

reviewing the previous literatures related to relationship marketing, organization-employee

relationships as well as the different types of online communities. Therefore, items were adopted

from Porter and Donthu (2008) to estimate the perceived role of maintainer in the current study.

77

Table 8 Operational definition of Perceived role of Maintainer

Variable Operational DefinitionPerceived role of

Maintainer

(Porter and Donthu,

2008)

PRM1 The maintainer makes frequent updates in the communitycontent

PRM2 The maintainer provides content that is usefulPRM3 The maintainer provides important informationPRM4 The maintainer provides content that is relevantPRM5 The maintainer provides access to valuable information

equallyPRM6 The maintainer consistently offers constructive ideas and

suggestionsPRM7 The maintainer helps community members by promptly

answering to their questionsPRM8 The maintainer seeks the opinion of members regarding

community policiesPRM9 The maintainer encourages members to take leadership roles

in the communityPRM10 The maintainer allows members to have direct contact with

their representativesPRM11 The maintainer asks members for help in establishing

community policiesPRM12 The maintainer makes an effort to make members feel a part

of the communityPRM13 The maintainer encourages interaction among membersPRM14 The maintainer strongly encourages information sharing

among members

3.4.5 Perceived Non-opportunistic behavior

Opportunism has been defined in relationship marketing as the behavior concerned with

dishonesty and bias by the sales manager (Morgan and Hunt, 1994). Brashear et al (2003)

assumed that when managers are more likely act opportunistically without taking into account

the interests of employees, they will be perceived more dishonest and less reliable for their role

in organization. Porter and Donthu (2008) posited that perceived non- opportunistic behavior of

sponsors in firm-hosted online communities refers to the extent to which online community

members who have confidence that a sponsor attempts to provide access to quality information,

particularly, when that information is not controlled and not harmful for the firm, will believe in

78

less opportunistic behavior of the firm. In the current study, we reviewed opportunistic behavior

from different literatures related to marketing, organizational science as well as the online

community relationships. We further developed 7-time scale in order to measure the construct.

The items have been imported from (Morgan and Hunt, 1994; Mukharjee and Nath, 2007; Porter

and Donthu, 2008).

Table 9 Operational definition of Perceived Non-opportunistic behavior

Variable Operational DefinitionPerceived

Non-opportunistic

Behavior

(Morgan and Hunt,

1994; Mukharjee and

Nath, 2007; Porter and

Donthu, 2008)

PNOB1 The maintainer of our community doesn’t alter the facts and information slightly in order to get what they want

PNOB2 The maintainer doesn’t hold back information that mightbe important for me

PNOB3 The maintainer doesn’t breach formal and informationagreements for their own benefit

PNOB4 The information and content provided in the communityare not divulged

PNOB5 The maintainer is not acting opportunistically in the relationship with community members

PNOB6 Responses I get from community to my posts are fair enough

PNOB7 I believe that community maintainer will assist and support me in my individual inquiries without economic interest

3.4.6 Perceived Centrality

Wasko and Faraj (2005) referred to collective action and social capital theory and used group

level social capital factors in organizations to explain individual-level knowledge contribution in

virtual relationships. Individual’s position in the social networks hugely affects his/her

motivation to actively take part in knowledge contribution. In this regard, Centrality was used as

one of the major determinants of knowledge contribution. (Rosenberg, 1965) emphasized that

those who have high self-esteem will likely to have better relationships and make better

impressions and influence on others compared to people with low self-esteem. The author

developed 10 item self-esteem scale. We adopted the term “Centrality” from Wasko and Faraj

79

(2005) and used it as “Perceived Centrality” in order to estimate the online community

member’s self perception of position and influence on relationships. The items have been

imported from Rosenberg (1965), Graen and Bien (1995) and edited, such as “I am able to do

things as well as most other people”, “On a whole I am satisfied with myself in the group”. The

full items have been shown in Table 10.

Table 10 Operational Definition of Perceived Centrality

Variable Operational DefinitionPerceived Centrality

(Rosenberg,1965;

Graen and Bien, 1995)

PC1 Do you know where you stand with your leader….do youusually know how satisfied your leaders is with what You do?

PC2 How well does your community recognize your potential?PC3 I feel that I am a person of worth, at least on an equal basis

with each otherPC4 I feel that I have a number of good qualities through which

I can influence othersPC5 I am able to do things as well as most other proplePC6 On a whole, I am satisfied with the role of myself in the

group

3.4.7 Locus of Control

In literature review, Locus of Control was referred as personal attributes that has two types;

internal locus of control and external locus of control. O’ Brien (1986, p. 52) considered internal

locus of control as the personal beliefs that changes, transformation and occurrences are

influenced by individual’s control, and attempts, whereas, external locus of control refers to the

perceptions that everything is happened out of control or powerful others take a role in the

transformations. Studies have investigated those two factors in terms of online learning, playing

online games, organizational commitment (Esterhuysen and Stanz, 2004; Koo, 2009; Macan et

al, 1996). We used 9 items in order to test the influence of Locus of Control as the personal train

on the level of community identification and online social relationship building. The items were

mainly adopted from Spector (1988)’s study which had tested the personality trait with 16 items 80

in work environment. In addition to this, we employed some items from Koo (2009), who had

used Locus of Control as the linkage between motivational factors and intention to play online

games. Table 11 provided the operational definition of Locus of Control

Table 11 Operational Definition of Locus of Control

Variable Operational DefinitionLocus of Control

(Spector, 1988;

Koo, 2009)

LOC1 How much I contribute to a conversation depends on howmuch others will allow me to contribute

LOC2 I am usually in control of my behavior, what I express, when I speak

LOC3 If I am aware of a personal communication behavior that is bad, I can control it

LOC4 My life is determined by my own actionsLOC5 On most jobs, people can pretty much accomplish whetever

they set out to accomplishLOC6 Getting job you want is mostly a matter of luckLOC7 Most individuals are capable of doing their jobs well if they

make the effortLOC8 Promotions are given to those who perform well on the jobLOC9 A job is what you make of it

3.4.8 Community identification

Ashforth and Mael (1989) referred organizational identification as the individuals perceive

themselves as members of organization. The more members share same values, norms and

interests with the organization, the more they will identify with the organization. Tajfel (1982)

has identified social identity as individual’s self-perception about membership, attachment to the

social group with emotional significance. According to Bagozzi and Dholakia (2002), online

community identification is the combination of emotional and evaluative elements of social

identification theory which define the level of participation of online community member. Hsu

et al (2012) defined online community identification as a sense of being a member of the online

community and feeling of emotionally connected with other participants in the online

community. In our study, we developed 8 items-scale and adopted the items from several studies

81

done previously (Wilimzig, 2011; Cameron, 2004; Wiertz and Ruyter, 2007). The items

particularly focused on estimating sense of belongingness, emotional attachment such as “I feel

strong sense of belonging to this community”, “I feel emotionally attached to this community”.

Table 12 gives the list of items below.

Table 12 Operational Definition of Community identification

Variable Operational DefinitionCommunity

Identification

(Wilimzig, 2011;

Cameron, 2004;

Wiertz and

Ruyter, 2007)

CI1 I feel myself to be part of this communityCI2 I am proud to be insider of this communityCI3 I am satisfied with my social exchange with communityCI4 I feel strong sense of belonging to this communityCI5 I feel emotionally attached to this communityCI6 My sense of who I am inside the community matches my sense of the

community as a wholeCI7 The relationship I have with this community is one I intend to maintain

indefinitelyCI8 The relationship I have with this community is one to which I am very

committed

3.4.9 Participation

Nonnecke et al (2006) conceptualized two types of participation so-called non-public and

public participation behavior. Non-public participation is also called lurking behavior. It refers

to such an activity that online community members are passive users, don’t post messages or

participate in discussion groups, whereas, active users are actively involved in community

express their opinions, beliefs and contribute to contents. (Zhou, 2011) approached the actual

participation behavior as the frequency of usage and the time spent on using the online

community. Wang and Fesenmaier (2004) measured participation as time spent in online travel

community each week, while active contribution reflected as members’ active or passive

engagement in community activities such as maintaining strong or weak social ties with other

members, contribution to contents. In our study, 7 items-scale was developed to measure the

82

actual participation behavior, and the items were adopted from the study of participation in

online travel community (Wang and Fesenmaier, 2004).

Table 13 Operational Definition of Participation

Variable Operational DefinitionParticipation

(Wang and

Fesenmaier, 2004)

PART1 How long have you been a part of this online communityPART2 On average, how much time do you spend on communicating

with the community members in a weekPART3 On average, how often do you post a message to the discussion

groupsPART4 I provide support for others in their decision-makingPART5 I promote the topic of discussions of the groupPART6 I post the contents which can be helpful for other membersPART7 I enhance my role as a valuable member in the community

83

Chapter 4 Data Analysis and Results

The analytical framework in the current study is set examine the relationships among

Community, Managing, Personality characteristics, Community Identification and Participation

behavior in Online communities. In this chapter, the results of Hypothesis testing, implications

of the findings of correlations between study variables are discussed. Methods of data analysis

adopted in this study include Demographic and Descriptive statistics, Correlation, Multiple

regression analysis and Variance method (One-way ANOVA) in order to test the relationship

between particularly demographic factors and the level of Community Identification.

4.1 Demographic Analysis

We got 231 responses back. 33 of responses were received from Pentax community, while

198 Forumosa.com members answered to our questionnaire. Overall, the rate of valid responses

is 214. Therefore, invalid responses were disregarded first. And then demographic variables

were analyzed. The data consists of 43.9% male and 56.1% female. The largest age group of

respondents is between 25-34 years old with the rate of 43.5% and the second largest group of

online community members is between 18-24 years old (23.8%). It shows, online community

usage is more popular among the young generation particularly. The education level of majority

of respondents is a college degree with 51.4%, whereas, the second prominent degree of

education is master degree in the rate of 26.2%. According to the occupation of the respondents,

14.0% is student, while 7.9% of online community users working in IT/ICT sector and 8.9% of

them are Managers. Table 14 provides the detailed frequency distributions of the demographics

of respondents who are members of two different online communities from which one is the

biggest online platform in Taiwan.

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Table 14 Demographic characteristics of Respondents

Demographic variables Frequency Valid percentage (%)

Gender Male 94 43.9%Female 120 56.1%

Age 18-24 51 23.8%25-34 93 43.5%35-44 41 19.2% 45-64 24 11.2%55-64 5 2.3%65+ 0 0%

Education LevelHigh School 25 11.7%College degree 110 51.4%Master degree 56 26.2%Doctoral Degree 16 7.5%Professional degree 7 3.3%

OccupationStudent 30 14/0%IT/ICT 17 7.9%Manager 19 8.9%Engineer 12 5.6%Medical service 11 5.1%Service sector 10 4.7%Business 10 4.7%Sales Representative/Manager 9 4.2%Teaching/Tutoring 8 3.7%Art and Design 6 2.8%Marketing 3 1.4%

4.2 Descriptive statistics of measurement scales

The scale of Community characteristics consist of 19 items explaining 3 constructs describing

the level of member’s identification with the community: Quality of Information, Quality of

Interaction and Shared values. The Managing characteristics and Personality Features scales

were measured with 21 and 15 items respectively. Lastly, we measure Community Identification

85

with 8 items, while Participation behavior was measured with 7 items. Respondents were asked

to indicate their answers for majority of items that measured by a 6-point Likert scale, ranging

from 1 being “Strongly agree” to 6 being “Strongly disagree”. The findings of Descriptive

statistics indicate that research variables satisfied the criteria for reliability. According to

Nunnally (1978), Cronbach’s Alpha coefficient must be over 0.7 that represents higher

reliability. In our study model, Cronbach’s Alphas for all constructs were between 0.74 and 0.86,

which shows that the variables would have reliability.

The average total score for 3 Community characteristics constructs is 55.836, and the average

item score for Community characteristics is 2.934. Among the items, the one with the highest

mean score was “The members are provided with information in an appropriate level of details”

(M=3.093, SD=1.252). The item with the lowest mean score was “The content and information

are frequently updated” (M=2.686, SD=1.214). In addition to this, the standard deviations of the

items of QINF7, QINF8, QINT6, and PSV3 are obviously larger than others in the context of

Quality of Information, Quality of Interaction and Perceived Shared Values respectively, which

indicates that the sample dispersion of these items are larger, the degree of correspondence

between the items of 3 constructs may be lower, and finally those items are not suitable to

measure the scale of those 3 constructs. The average total score of the Managing Characteristics

is 64.115, and the average item score for the scale of the construct is 3.084. The dimension with

the highest mean score was “The information and content provided in the community are not

divulged” (M=3.285, SD=1.366), whereas, the dimension with the lowest mean score was “The

maintainer makes an effort to make members feel a part of the community” (M=2.832,

SD=1.263). The average total score of the Personality Characteristics was 44.574, and the

average item score for the scale of the construct is 2.971. The item with the highest mean score

was “Getting job you is mostly a matter of luck” (M=3.444), whereas, the item with the lowest

86

mean score was “I am able to do things as well as most other people” (M=2.734). Finally, the

average total score for Community Identification was 32.786 and for Participation was 22.916.

Average of Community Identification items mean was 4.048, while it was 3.273 for the items of

Participation. The item with the highest mean score was found to be “How long have you been a

part of the online community” (M=3.860, SD=1.285) and the item with lowest item score was “I

feel myself to be part of this community” (M=2.813, SD=1.080). Table 15 shows the complete

results of Descriptive Analysis.

Table 15 Descriptive Analysis of Model Constructs

Indicators Items Mean S.D. Cronbach’s

Alpha

Quality of Information

QINF1

QINF2

QINF3

QINF4

QINF5QINF6

QINF7

QINF8

The information provided in the community is quite usefulThe information provided in the community is valuableThe community is a great place to get answer related to my needsThe members are provided with information in an appropriate level of detailsThe content and information are frequently updatedI can rely on the information and contents provided in the communityI go online in the community when I need to receive clear answers to my questionsI go online in the community when I need facts about a particular subject

2.964

2.841

2.897

2.957

3.093

2.6863.000

3.037

3.200

1.191

1.174

1.234

1.252

1.2141.248

1.306

1.307

.762

87

Quality of InteractionQINT1

QINT2

QINT3

QINT4

QINT5

QINT6

QINT7

There are good features that help me to socially interact with whole communityThe community rules, regulation and policies are clear to memberThe community creates an open environment where all member can freely interact with each othersThe responsiveness to content is immediate and timelyThe community maintainer regularly pay attention to the feedbacks from community membersThe community maintainer is someone who initiate an open dialogue with the community membersI feel I am responded on timely fashion

2.9212.841

3.056

2.766

2.817

2.934

3.042

2.993

1.140

1.240

1.175

1.202

1.235

1.311

1.269

.722

Perceived Shared Values

PSV1PSV2

PSV3

PSV4

I hold similar values with the communityThere are well-understood set of norms and values that guide the behaviors of the communityI maintain the values consistent with the goals of the communityShared values help us to express our opinions and communicate effectively in the community

2.918

2.8642.818

3.028

2.962

1.0941.163

1.296

1.206

.743

Perceived RoleOf Maintainer

PRM1

PRM2PRM3PRM4PRM5

PRM6

PRM7

PRM8

PRM9

PRM10

PRM11

PRM12

The maintainer makes frequent updates the community contentThe maintainer provides content that is usefulThe maintainer provides important informationThe maintainer provides content that is relevantThe maintainer provides access to valuable information equallyThe maintainer consistently offers constructive ideas and suggestionsThe maintainer helps community members by promptly answering to their questionsThe maintainer seeks the opinion of members regarding to community policiesThe maintainer encourages members to take leadership roles in the communityThe maintainer allows members to have direct contact with their representativesThe maintainer asks members for help in establishing community policiesThe maintainer makes an effort to make members

2.992

2.930

3.0192.9623.0003.037

3.075

2.944

3.126

2.977

2.963

3.126

2.832

1.190

1.1701.1581.1791.225

1.172

1.262

1.296

1.337

1.202

1.270

1.263

.863

88

PRM13

PRM14

feel a part of the communityThe maintainer encourages interaction among membersThe maintainer strongly encourages information sharing among members

2.897

3.000

1.186

1.289

Perceived Non-OpportunisticBehavior

PNOB1

PNOB2

PNOB3

PNOB4

PNOB5

PNOB6

PNOB7

The maintainer of our community doesn’t alter the facts and information slightly in order to get what they wantThe maintainer doesn’t hold back information that might be important for meThe maintainer doesn’t breach formal and informal agreements for their own benefitThe information and content provided in the community are not divulgedThe maintainer is not acting opportunistically in the relationship with community membersResponses I get from community to my posts are fair enoughI believe that community maintainer will assist and support me in my individual inquiries without economic interests

3.176

3.182

3.230

3.145

3.285

3.215

3.014

3.150

1.293

1.292

1.272

1.366

1.322

1.196

1.340

.782

Perceived CentralityPC1

PC2

PC3

PC4

PC5PC6

Do you know where you stand with your leader…do you know how satisfied your leader is with what you doHow well does your community recognize your potentialI feel that I am a person of worth, at least on an equal basis with each otherI feel that I have a number of good qualities throughwhich I can influence othersI am able to do things as well as most other peopleOn a whole, I am satisfied with the role of myself in the group

2.9733.369

3.126

2.790

2.925

2.7342.897

1.229

1.266

1.205

1.204

1.2481.170

.728

89

Locus of ControlLOC1

LOC2

LOC3

LOC4LOC5

LOC6LOC7

LOC8

LOC9

How much I contribute to a conversation depends on how much others will allow me to contributeI am usually in control of my behavior, what I express, when I speakIf I am aware of a personal communication behavior that is bad, I can control itMy life is determined by my own actionsOn most jobs, people can pretty much accomplish whatever they set out to accomplishGetting job you want is mostly a matter of luckMost individuals are capable of doing their jobs well if they make the effortPromotions are given to those who perform well on the jobA job is what you make of it

2.9701.122

1.161

1.241

1.2651.199

1.3651.207

1.232

1.303

2.748

2.864

3.168

2.9353.093

3.4442.906

2.757

2.818

.722

CommunityIdentification

CI1CI2CI3

CI4CI5CI6

CI7

CI8

I feel myself to be part of this communityI am proud to be insider of this communityI am satisfied with my social exchange with communityI feel strong sense of belonging to this communityI feel emotionally attached to this communityMy sense of who I am inside the community matches my sense of the community as a wholeThe relationship I have with this community is one I intend to maintain indefinitelyThe relationship I have with this community is one to which I am very committed

4.048

2.8131.9212.991

3.0373.1353.117

3.056

2.991

1.0801.1941.206

1.2521.2431.241

1.209

1.271

.820

ParticipationPART1

PART2

PART3

PART4PART5PART6

PART7

How long have you been a part of the online communityOn average, how much time do you spend on communicating with the community members in a weekOn average, how often do you post a message to the discussion groups?I provide support for others in their decision-makingI promote the topic of discussions of the groupI post the contents which can be helpful for other membersI enhance my role as a valuable member in thecommunity

3.2733.860

3.374

3.603

2.9773.0932.822

3.187

1.285

1.408

1.306

1.2651.2261.185

1.361

.736

90

4.3 Correlation Analysis

In correlation analysis, we tested the relationship and the significance of correlations

between the variables of the current study model. Correlation analysis is believed to be simple to

implement and get the correlation significance between the measures. Thirteen variables

including the demographic factors so-called Gender, Age, Education level and Occupation were

measured. In this process, we used Bivariate Correlations which is useful to measure pairwise

associations for a set of variables and display the results in matrix table. The detailed results of

the correlation analysis have been discussed further.

In Table 3, we can see that correlation coefficient is higher in the rate of Pearson

Correlation=.698, p<.01) which is close to one. It shows a good correlation between the selected

variables (Locus of control Community identification), while, p value is .000 in relationship

between most of the variables which is close to 0 and it also shows a validity of variables. From

the correlation analysis, we can conclude that there is stronger positive relationship between

LOC and CI and this correlation is significant at the p<.01 level. Other variables such as QINF

and QINT (.582, p<.01), QINF and CI (.575, p<.01), LOC and PC (.613, p<.01), PRM and

PNOB (.548, p<.01) show significant correlation at the p<.01 level. In addition to this, CI was

significantly correlated with PSV (.532, p<.01), and PRM (.523, p<.01), whereas, PART was

found to be highly correlated with the variables such as PSV (.355, p<.01), PRM (.324, p<.01)

and PC (.319, p<.01).

According to the correlation of Demographic factors with other variables, the analysis

revealed that only Gender is significantly correlated with CI (.158, p<.05) compared to other

factors. In addition to this, there is strong positive correlation between Gender and QINF (.210,

p<.01), Gender and PNOB (.138, p<.05). There is strong relationship between Age and PRM

(.240, p<.01). Education level weakly correlated with Occupation (.137, p<.05). To summarize

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the correlation between demographic factors with other variables, particularly with Community

Identification, it could be concluded that there is no strong correlation between those, excluding

Gender, and in particular, p-value is above the significance level at the rate from .002 to .879

which is very high. Table 16 shows the results of Correlation Analysis.

Table 16 Result of Correlations Analysis (N=214)

*. Correlation is significant at the 0.05 level (2-tailed)**. Correlation is significant at the 0.01 level (2-tailed)

Note: Gen=Gender, Edu=Education, Occ=Occupation, QINF=Quality of Information, QINT=Quality of Interaction, PSV=Perceived Shared Values,PRM=Perceived Role of Maintainer, PNOB=Perceived Non-Opportunistic Behavior, PC=Perceived Centrality, LOC=Locus of Control, CI=Community Identification,PART=Participation

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4.4 Regression Analysis

Multiple regression was conducted to determine the best linear combination of independent

variables and dependent variables. In the first regression analysis, the dependent variable was

Community Identification and the independent variables were QINF, QINT, PSV, PRM, PNOB,

PC, LOC and Demographic variables. The demographic data included Gender, Age, Education

level and Occupation. The results of the regression model were found to be significant. ANOVA

table shows that F (11,202) = 32.743, p<.001, and is significant. This indicates that the

combination of the predictors significantly predicts Community Identification, and the results of

the regression model could hardly occur by chance. The multiple correlation coefficient (R=.80),

and the Adjusted R2 of the model was indicating that 62% of total variance of Community

Identification could be accounted for by Community, Managing and Personality characteristics.

In other words, we can conclude that 62% of the variance in Community Identification can be

predicted from above-mentioned three constructs and their variables. According to Cohen (1988),

this indication is a large effect. From the relationship between Demographics and Community

Identification results, it was revealed that Demographic variables, particularly Education level of

respondents were not a significant predictor of Community Identification. However, Gender had

the influence on the level of Community Identification (Beta value=.165, p=.026). Females were

more likely to identify with the community they are member in. In addition to this, Quality of

Information, Perceived Shared Values, Perceived Role of Maintainer, Locus of Control, and

Perceived Non-Opportunistic Behavior were found to be significant variables in the model. To

be more specific, LOC strongly influences the level of CI (H3c) (Beta value=0.492, t-

value=8.557, and p<.001). Second significant predictor was QINF that affected CI (H1a) (Beta

value=0.277, t-value=4.835, and p<.001 ) followed by PNOB (H2b) (Beta value=0.205, t-

93

value=2.756, and p<.01). However Quality of Interaction (Beta value= -.045, t-value= -.750,

p=.454) and Perceived Centrality (Beta value= -.005, t-value= -.091, p=.928) were not

significant predictors of the level of Community Identification. Thus, H1b and H3a were not

supported. The results of Regression Analysis have been shown in the Table 17.

Table 17 Multiple Regression Analysis of Community, Managing and Personality characteristics predicting Community Identification (N=214)

Note: R2 =. 64; F (11,202) =32.74, p< .001*p< .05; **p< .01

In the next step, we conducted regression analysis in order to estimate the correlation

between Community Identification and predicted Participation behavior in online community.

The results of the ANOVA table showed that F (1, 212) = 30.627, p<0.001, and is significant.

This indicates that Community Identification significantly predicts the Actual Participation

behavior in online communities. The correlation coefficient (R=.35), and the Adjusted R2

indicated that 12% of variance in Participation can be predicted from Community Identification.

In other words, Community Identification was found to be significant variable in the model at

(p<0.001). Coefficient value for Community Identification was 0.267, t-value was above 3.2.

The detailed result of Community identification and Participation correlation and regression

analysis has been shown in Table 18 and Table 19.

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Table 18 Means, Std. Deviations and Correlations for Participation Behavior and predictingCommunity Identification (N=214)

Variable M SD 1 2

Participation 3.27 .61

Predictors Community Identification 3.01 .81 .355 *p< .05; **p< .01

Table 19 Multiple regression analysis of Community Identification predictingParticipation behavior (N=214)

Variable B Std.Error Beta t Sig (p)

Community .267 .048 .355 5.534 .000Identification(Constant) 2.47 .150 16.449 .000

Note: R2 =.13; F(1,212)=30.62, p< .001*p< .05; **p< .01

The result of the multiple regression analysis revealed that Community Identification has a

significant impact on the level of Participation behavior of online community members.

4.5 ANOVA analysis

It was anticipated that the demographic characteristics of online community members would

significantly influence the level of community identification and as mentioned in literature

review, gender, age, education factors have an impact on individual’s behavior. We used one-

way ANOVA analysis in order to measure the importance of differences by categories of the

demographic factors. Because one-way analysis of variance is useful to determine whether there

is any significant difference between the means of two or more independent groups. The results

of analysis provided very useful insight related to the level of community identification between

males and females, age groups, education level and occupation. 95

To begin with, we can underline that there is significant relationship between Gender and

Community identification (Sig. value-p is .011). ANOVA analysis revealed that there is

considerable difference between females and males on their community identification. Females

(56.1%) are more likely to identify with their community. Age was not a predictor of community

identification compared. Detailed results of variance analysis has been shown in the Table 20.

Table 20 Demographic factors and Online Community Identification (ANOVA)

DependentVariable

Demographicfactors

Categories N Mean SD F value p-value

Communityidentification

Gender MaleFemale

94 2.984 .8103120 3.026 .8073 2.840

1.132

2.125

.011

.342

.079

Age 18-2425-3435-4445-5455-6465+

51 2.936 .683493 3.032 .881741 2.866 .903424 3.271 .68375 3.175 .9253- - -

Education Level

High schoolCollege.dMaster.dDoctoral.dProfessional.d

25 3.350 1.0110110 2.942 .771456 2.942 .761216 2.937 .68317 3.500 .9157

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DependentVariable

Demographicfactors

Categories N Mean SD F value p-value

Community Identification

Occupation StudentIT/ICTManagerEngineerMedical serv.Service BusinessSalesTeaching/TutArt/DesignMarketing

30 2.866 .709317 2.852 .604719 2.302 .473812 2.979 .867311 3.136 1.328110 2.812 1.1430 10 2.925 .65939 3.444 .77848 2.969 .84456 2.979 .69103 4.250 .3307

1.336 .085

Education was weakly related to Online Community Identification. Members with high

school and professional degree were more likely to identify with their community respectively.

However, online community members in 35-44, and 55-64 age groups were more active in

community activities compared to other age groups. According to the Occupation of online

community members, the analysis revealed that there is no statistically significant difference

between the mean Community Identification for all age groups (Sig. value is .085, p>.05). Post-

hoc tests showed that most of the values for occupations are greater than .05. Overall,

Occupation was not found to be related to the level of Community Identification. The results of

ANOVA analysis showed that Demographic variables are not strongly predicting the level of

Community Identification in Online Communities. Only Gender can have influence on members’

behavior in communities. This could happen maybe because there is relationship between gender,

education level, occupation and purpose of using online communities. Thus, the members,

particularly male members use online community to collect information, share their experiences

with other members, but not strongly identify with the community.

In summary, H3b which postulated that there is significant relationship between

Demographics and Community Identification was not strongly supported. Rather, this

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hypothesis was partially supported and it can be stated that Gender has a positive influence on

the level of Community Identification.

4.6 Model Testing

In this study we used “SmartPLS” to conduct the path analysis according to our study model

and test the hypotheses. Bootstrapping (resample=500) method was used to determine the

confidence interval for each path.

Figure 3 Model Testing

According to the path analysis, we can see that Community identification (R2=64%) was

significantly affected by Locus of Control (0.492, p<.001), followed by Perceived Non-

Opportunistic Behavior (0.205, p<.01). Quality of Information was also found to be positively

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related to the level of Community Identification (Beta value=0.277, t-value=4.83 and p<.001)

which had been found in Regression analysis as well. Perceived Shared Values and Perceived

Role of Maintainer were also positively correlated with the level of Community Identification.

However, the path coefficients with standardized values are between 0.1 and 0.2. To be more

specific, Path coefficient for Perceived Shared values was (Beta value=0.155, t-value=2.82,

p<.01) and for Perceived Role of Maintainer, it was (Beta value=0.126, t-value=1.98, p<.05).

According to Demographic factors, we found again that Gender significantly predicts the level

of Community Identification in Online communities (Beta value=0.165, t-value=1.97 p<.05).

We conducted the path analysis to measure the relationship between Community Identification

and Participation behavior. The result of analysis proved that Community Identification notably

affects the participation behavior of online community members (R2=13%) and path coefficient

was (Beta value=0.355, t-value=5.53, p<.001). Quality of Interaction and Perceived Centrality

were not found to be connected with the level of Community Identification (-0.045) and (-0.005).

Hypothesis 1b and Hypothesis 3a were not supported.

The results of Regression and ANOVA tests also showed that from Demographic factors, only

Gender was significantly related to the Community Identification compared to other factors. So,

the Path analysis supported the results of Regression and ANOVA testing. By testing of different

types of analysis, we came up with the results of our study hypotheses. The outcomes of path

coefficients in the structural model of hypothesis testing are presented in Table 21 and Table 22

Table 21 Results of PLS analysis: Path Coefficients

Structural Paths Path Coefficients T-ValueQINF CI 0.277*** 4.83

QINT CI -0.045 -0.75

PSV CI 0.155** 2.81

PRM CI 0.126* 1.98

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Structural Paths Path Coefficients T-ValuePNOB CI 0.205** 2.76

PC CI -0.005 -0.09

LOC CI 0.492*** 8.56

Gender CI 0.165* 1.97

Age CI -0.049 -0.96

Education CI 0.015 0.33

Occupation CI 0.029 0.65

CI PART 0.355*** 5.53

Note: *p<.05; **p<.01; ***p<.001

Table 22 Results of Hypotheses

Hypothesis Content Result

H1a Quality of Information positively affects the level of SupportedCommunity Identification

H1b Quality of Interaction positively affects the level of Not supportedCommunity Identification

H1c Perceived Shared Values positively affects the level of SupportedCommunity Identification

H2a Perceived Role of Maintainer positively affects the level of SupportedCommunity Identification

H2b Perceived Non-Opportunistic Behavior positively affects SupportedThe level of Community Identification

H3a Perceived Centrality positively affects the level of Not supportedCommunity Identification

H3b Demographic factors positively affect the level of Partially Community Identification supported

H3c Locus of Control positively affects the level of Supported Community Identification

H4 Community Identification positively affects the actual SupportedParticipation behavior

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Chapter 5 Discussion and Conclusion

In this chapter we will provide the discussion based on the result of analysis and model

testing. In addition to this, conclusion, limitation and implications will be discussed in Section 2.

5.1 Discussion

The aim of this study was to investigate the antecedents that create the Community

Identification and how this leads to actual participation behavior in online communities. The

results of hypotheses indicated that Community features, Managing and Personality

characteristics significantly predict the level of Community Identification that create sense of

belongingness and sense of community in the online community members. However, all three

characteristics are not affecting Community Identification at the same level. The findings

showed that Quality of Information has immense effect on Community Identification.

Conversely, Quality of Interaction, particularly among the community members is not effective

in bringing people into online communities and remaining them for a long time. However,

previous study had concluded that open and frequent communication among the community

members positively influences the trusty behavior and long-term relationship (Morgan and Hunt,

1994). Wang (2009) found that members who are willing to obtain useful information would not

have feeling of belonging to the online community. Members come to online communities

because of useful and important information and after they get what they need, they might go

away. The author further added that particularly website providers have to design a mechanism

to motivate users to maintain interaction in online communities. As a result, H1b was not

supported in our study. We found positive relationship between Perceived Shared Values and the

level of Community Identification (H1c). The theoretical propositions had been adopted from

organizational as well as online community studies. Chatman (1991) and Kristof (1996) had 101

stated that shared values are fundamental guides for employees in their organizational behavior.

The fit of values between employees and organization positively leaded to individual

performance, citizenship behavior and sense of membership. In online community context,

community policies had been considered as shared norms, values and rules among members

(Preece, 2000). Further, Arrasvuori and Olsson (2009) revealed that mutual goals, shared beliefs

and values significantly affect the community membership. Another study had found that shared

values and goals strongly predict the level of commitment in relationship (Morgan and Hunt,

1994). In our study, the findings discovered that perceived shared values among the online

community members is one of the determinants of community identification and long-term

relationship.

According to H2a which represents the impact of Perceived Role of Maintainer on

Community Identification, the results supported the idea that online community members are

more interested in interacting with community maintainers. Therefore, the more interaction,

openness between providers and community member, the higher there will be Community

Identification. Consistent with the findings of Porter and Donthu (2008), attempts to foster

interaction, embeddedness by online community providers strongly affect the level of

membership. In addition to this, Williams and Cothrel (2000) had provided very useful insight

on online community management. Thus, in order to maintain the community stable, and create

sense of community in members, efforts should be made to interact with potential users, opinion

leaders and give them chance to take leadership role in the community activities. Mukharjee and

Nath, (2007) had hypothesized that in customer-retailer relationship, the risk of opportunism and

self-interest by the retailer reduces customer’s online buying behavior. The study related to firm-

sponsored online community, it was found that opportunistic behavior of firms in online

communities has negative relationship with trusty behavior (Porter and Donthu, 2008). However,

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Brown et al (2002) found that providing unbiased contents that are related to needs of members

create commitment in online community. Our finding based on the relationship between

Perceived Non-Opportunistic behavior and Community Identification is consistent with the

majority of studies related to online community, customer-retailer relationship mentioned in

Literature Review. As a result, the H2b was strongly supported in our study.

The result of H3c revealed that Locus of Control strongly affects the level of CI, whereas

Perceived Centrality was found to be negatively related to CI (H3a). It was mentioned in

literature that individuals with internal LOC are more self-confident and tended to have more

autonomy on their work. Koo (2009) showed that externals have more social affiliation with

peers. Therefore we can conclude that in our study majority of members of online communities

were externals. More external LOC, the more there will be identification and commitment to the

group and there will be less implication of Perceived Centrality on CI. Thus, people with

external LOC are less likely to put emphasis on their role and centrality in community

relationships, social exchange and they are not likely to have autonomy over others. Further, we

found that External LOC is negatively related to PC and Internal LOC is positively related to PC.

We estimated Demographic factors as one of the major personal characteristics that might

influence the level of Community Identification significantly. However, only Gender factor was

positively correlated with the CI in online communities. In broadly speaking, females were

found to be strongly identified with the group they belong to. Literatures had mentioned about

the difference between genders, education level and their impact on internet usage behavior.

Wang and Fesenmaier (2004) had revealed those factors as well as Age influence participation

behavior in online travel community. Our finding is consistent with the study of Herring (1993)

that females are more tended to participate in discussions compared to men. We had mentioned

in literature that females were more willing to express personal feelings and build strong

103

relationship with others (Jaffe et al. 1995). In organizational context, male physicians were

strongly identified with the organization than their female counterparts (Çelik and Findik, 2012).

Overall, H3b was partially supported that gender was significantly related to the level of

Community identification. Although other factors didn’t predict the Community Identification, it

could be stressed that females are more likely to take part in information exchange, share and

support others, and build a stable relationship with other members for a long time regardless of

finding answers to their inquiries. Education level affected the participation behavior of

members, but it cannot play a role in making strong community identification.

According to relationship between CI and Participation behavior, our finding supported that

there is positive association. Thus, H4 was highly supported. Our finding is consistent with

previous studies that community members would contribute knowledge or involve in discussions,

once there is sense of togetherness with other members (Chiu et al. 2006), and consumer

identification with whole community significantly influence the helping behavior in online

community (Allan, Massiah, and Johnson, 2013).

5.2 Conclusion

The overall results of the study showed that Community feature, Managing and Personality

characteristics are necessary predictors in order for creating community identification and

commitment of members. However, the most important factor is the role of community

providers/managers that shape the social exchange and the level of attachment of members to the

community. In this regard, we would propose that if online community maintainers want to

increase the quality and sustainability of the community, they have to focus on the factors which

can give social benefits, sense of togetherness and access to huge source of information related

to personal needs of members. On the other hand, the quality of interaction, shared values and

104

goals between community providers and members, unbiased behavior by providers and the level

of their engagement in community activities considerably affect the attitudes of members

towards the online community.

According to other factors, it is worthy to say that difference in personality of individuals

influence the level of community identification distinctively. Therefore, it is important for

community maintainers to understand personal needs, why people invest their time and attention

in the activities of sharing information, contributing and interacting with others.

Community Identification plays a distinctive role in participation behavior of members. To

conclude, Community Identification stands in the center of building sustainable online

community and maintaining it for a long-term period.

5.3 Implication

This study has several contributions that can be applied in future studies while developing a model

for online community participation. We think that this study can give the guidance for companies as well

as the customers when they initiate an online platforms to attract people, motivate them to be involved in

discussions and strength the ties with community providers.

1. Recently, it is needed to move from understanding member’s needs and motivation to understand

their participation from social and collective process. Because they study also supported the idea that

collective action, social exchange and social presence regardless the personal needs are more

effective in driving people in to communities.

2. Implication for companies, and brands is that they have to give the voice to all members either they

are opinion leaders or ordinary members in discussion platforms. This will increase member’s

belongingness to the group. Further companies and brands might answer the question “Are online

relationships narrowly supported or broadly supported”

3. This study provided useful findings on the relationship among the study constructs. It gave a path to 105

future studies that can conduct longitudinal research in order to understand how social exchange and

participation evolves over time. It is particularly applicable in company-created communities that

they can track how their members act in community activities in different time.

5.4 Limitation

First of all, the advantages of conducting the research in online environment compared to

offline environment are adaptability, easiness as well as cost and time efficiency. Because it is

easy to notice the ongoing phase of the data collection and the need for additional data further.

We had the chance to reach both of the online communities and ask members’ opinions through

the online questionnaire. However, we didn’t have the access to majority of discussion platforms,

particularly in Pentax community in order to observe the conversations among the community

members and analyze two online communities in depth. Observation approach is one of

successful methods used by online community researchers in order to get better understanding of

a community. It is good way to observe the community activities (Preece, 2000). Although it has

some difficulties, this method would create more opportunities to test the validity and

importance of each construct of the model in online community relationships, and ask open

questions or do interview with particularly facilitators or opinion leaders in discussion forums.

As Antikainen (2007) concluded, respondents would be encouraged to respond spontaneously if

they were not given complete choices in questionnaires. It would help us to reveal more facts

about the community relationships and particularly the role of Community Identification.

Another shortcoming of the study is that we were provided with very little amount of responses

from Pentax, whereas, the other community provided huge amount of responses in return. Two

online communities differ from each other. Thus, Pentax was related to the brand, while

Forumosa.com was one of the biggest platforms containing of discussions related to various

106

topics. We would like to get at least equal amount of responses in order to compare the opinions

and behaviors of members of two different community environments and provide useful insight

to the result of study.

5.5 Suggestion for Future Research

Consumer- created, company and brand communities differ from each other. In company online

communities, community providers or discussion moderators can be companies or their representatives

and customers have an access to companies. In customer-created online communities, customers share

their opinions, product reviews with others who can also be moderators of discussions. In Brand

communities, there can be exchange among customers, or between customers and brand. In this regard,

while choosing the subjects of the research, different type of online communities might give different

results, contributions to study model. One of our research subjects was customer-created discussion

platform containing of different topics, groups, while another was brand community. We found that

Quality of Interaction and Role of self were not significant components of community relations and

participation intention. However, as we mentioned above, interaction differs based on the type of

community. It would be worth in future to focus on one type of online community and test the

antecedents of online community attractiveness, identification. On the other hand, the future study can be

extended in this way:

1. Choose different types of online communities as the subjects of research and compare them in a study

to see difference in terms of reason-result action (Apply same research model and test if the model

works for customer- and company-created online communities at the same level or they differ from

each others)

2. Extend the period of Data collection (observation) to get sufficient and equal amount of responses in

order to compare them and get reliable and valid results.

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Trust is necessary in building relationship among members and between community provider

and member. It is particularly important for e-commerce communities. Thus, community

managers must support trust (Kollock, 1999). In this regard, we believe that while studying

company online communities by applying the current research model, it would be worthy to test

Trust as one of the major antecedents of building online community relationship. Previous

studies also supported the idea that relationship among community members and between

member and community providers particularly in company-created online communities

considerably differ from each other. Therefore, companies must be more concerned in

maintaining engagement, cooperating in community activities. By taking into account the above-

mentioned issues, the future research can contribute to the online community strategies of

companies.

108

References

1. Abrams, D., S. Hinkle and M. Tomlins. (1999). “Leaving Hong Kong? : The roles of attitudes, subjective norm, perceived control, social identity and relative deprivation”. International Journal or Intercultural Relations, 23(2): 319-338

2. Adjei, M. T., Stephanie M. N., and Charles H. N. (2010). “The influence of C2C communications in online brand communities on customer purchase behavior”. Journal of the Academy of Marketing Science. 38(5): 634-653

3. Ajzen, I. (1991). “The theory of Planned Behavior”. Organizational behavior and human decision processes, 50(): 179-211

4. Antikainen, M. (2007). “The attraction of company online communities. A multiple case study”. Academic dissertation. Department of Management Studies, University of Tampere, Finland

5. Arrasvuori, J., and Olsson, T. (2009). “A model for analyzing Online Communities”. International Journal of Business and Information, 4(2): 115-135

6. Armstrong, A., and Hagel III, J. (1995). “Real profits from virtual communities”. The McKinsey Quarterly, 3(): 126-14

7. Asforth, B. E., and Mael, F. (1989). “Social identity theory and the organizations”. Academy of Management review, 14(): 20-39

8. Baglieri, D., and Consoli, R. (2009). “Collaborative innovation in tourism: managing virtual communities”. The TQM journal, 21(4): 353-364

9. Bagozzi, R. P., and Dholakia, U. M. (2006). “Antecedents and purchase consequences of customer participation in small group brand communities”. International Journal of Research in Marketing, 23(1): 45-61

10. Balasubramanian, S., and Mahajan, V. (2001). “The economic leverage of the virtual community”. International Journal of Electronic Commerce, 5(3): 103-138

11. Bandura, A. (1982). “Self-efficacy mechanism in human agency”. Stanford University, American Psychologist, 37(2): 122-147

12. Barnum, C. (2005). “The effects of status and group membership modeled in a Graph-Theoretic Setting”, (2): 125-153

13. Bartel, C. A., Saavedra, R. and Van Dyne, L. (2001). “Design conditions for learning in community service contexts”. Journal of Organization Behavior, 22(4): 367–385

14. Bateman, J. P., Gray, H. P., and Butler, S. B. (2006). “Community commitment: how affect, obligation and necessity drive online behaviors”. Twenty-Seventh International Conference on Information Systems, Milwaukee

15. Bhattacharya, C. B., and Sen, S. (2003). “Consumer-company identification: A framework for understanding consumers’ relationships with companies”. Journal of Marketing, 67(2): 76-88

16. Bishop, J. (2007). “Increasing participation in online communities: A framework for human-computer interaction”. Computers in Human Behavior 23(4): 1881-1893

17. Boon, M. (1997), “The African way the power of interactive leadership”. Second Edition. Johannesburg: Zebra Press

18. Bradley, G. L., and Sparks, B. A. (2002). “Service Locus of Control: Its conceptualization and Measurement”. Journal of Service Research, 4(4): 312-324

19. Brandtzaeg, P.B., and Heim, J. (2008). “User loyalty and online communities: Why members of online communities are not faithful, In Proceedings of the Second International Conference on Intelligent Technologies for Interactive Entertainment (Article 11).Brussels,

109

Belgium: ICST20. Brashear. T. G., Boles, J., Bellenger, D., and Brooks, C. M. (2003). “An empirical test of

trust-building processes and outcomes in sales manager-salesperson relationships”. Journal of Academy of Marketing Science, 31(2): 189-200

21. Bressler, S., and Grantham, C. (2000). “Community of commerce”. New York: McGraw-Hill

22. Brown, J. S., and Duguid, P. (2002). “The Social Life of Information”. Harward Business Press

23. Brown, T. J., Mowen, J. C., Donavan, T. D., and Licata, J. W. (2002). “The customer orientation of Service workers: personality trait effects on self and supervisor performance ratings”. Journal of Marketing Research 39(1): 350-370

24. Cameron, J. E. (2004). “A three factor model of social identity”. Self and Identity, 3(3):239-262 http://www.tandfonline.com/doi/abs/10.1080/13576500444000047

25. Case, Donald O. (2007). “Looking for information: a survey of research on information seeking, needs, and behavior”. Second edition

26. Caspi, A., and Harbener, E. (1990). “Continuity and change: Assortative marriage and the consistency of personality in adulthood”. Journal of Personality and Social Psychology, 58 (2): 250-258

27. Chatman, J. A. (1991). “Matching people and organizations: Selection and socialization in public accounting firms”. Administrative Science Quarterly, 36(3): 459-484

28. Chatzisarantis, N. L. D., Hagger, M. S., Wang, C. K. J., and Thogersen-Ntoumani, C. (2009). “The effects of social identity and perceived autonomy support on health behavior within the Theory of Planned Behavior”. Journal of Current Psychology, 28(1): 55-68

29. Cheung, C. M. K., and Lee, M. K. O. (2010). “A theoretical model of intentional social action in online social networks”. Decision Support Systems, 49(1): 24–30

30. Chiu, C. M., Hsu, M. H., and Wang, E. T. G. (2006). “Understanding knowledge sharing in virtual communities: An integration of social capital and social cognitive theories”. Decision Support Systems, 42 (3): 1872-1888

31. Cohen, J. (1988). “Statistical power and analysis for the behavioral sciences (2nd Edition). Hillsdale, NJ: Lawrence Erlbaum Associates

32. Collins, M. P., and Berge, Z. L. (1997). “Moderating online electronic discussion groups”. Paper presented at the 1997 American Educational Research Association (AREA) Meeting, March 24-28, Chikago, IL

33. Çelik, A., and Findik, M. (2012). “The effect of Perceived Organizational Support on Organizational Identification”. World Academy of Science, Engineering and Technology, 68

34. Dahui, L., Browne, G., and Wetherbe, J. (2006). “Why Do Internet Users stick with a specific web Site?A relationship Perspective”. International Journal of Electronic Commerce, 10(4): 105-141

35. Davis, F. (1989). “Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology”. MIS Quarterly, 13(3): 319-340

36. Davis, F. D., Bagozzi, R. P., and Warshaw, P. R. (1989). “User acceptance of Computer technology: A comparison of two theoretical models”. Management Science, 35(8): 982-1002

37. Dellarocas, C. (2006). “Strategic Manipulation of Internet opinion Forums: Implications for consumers and firms”. Management Science, 52(10): 1577-1593

38. Dellarocas, C. (2003). “The Digitization of Word-of-Mouth: Promises and Challenges of Online Feedback Mechanisms”. Management Science, 49(10): 1407-1424

110

39. DeLone, W. H., and McLean, E. R. (2003). “The DeLone and McLean Model of Information Systems Success: A ten-year update”. Journal of Management Information Systems, 19(4): 9-30

40. De Souza, C. S., and Preece, L. (2004). “A framework for analyzing and understandingonline communities. Interacting with Computers”. The Interdisciplinary Journal of Human-Computer Interaction, 16(3): 579-610

41. Dholakia, U. M., Bagozzi, R. P., and Pearo, L. K. (2004). “A social influence model of consumer participation in network- and small-group base virtual communities”. International Journal of Research in Marketing, 21(3): 241-263

42. Dholakia, U. M., Blazevic, V., Wiertz, C., and Algesheimer, R. (2009). ‘‘Communal Service Delivery: How Consumers Benefit from Participation in Firm-Hosted Virtual P3 Communities”. Journal of Service Research, 12 (November), 208-226

43. Dukerich, J. M., Golden, B., and Shorteli, S. M. (2002). “Beauty is in the eye of the beholder: The impact of organizational identification, identity and image on physician cooperative behavior”. Adminstrative Science Quarterly, 47(3): 507-533

44. Dutton, J. E., Dukerich, J. M., and Harquail, C. V. (1994). “Organizational images and member identification”. Administrative Science Quarterly, 39(2): 239-263

45. Elliot, S., et al., (2012). “Understanding service quality in a virtual travel community environment”. Journal of Business Research (2012), doi: 10.1016/j.jbusres.2012.03.011

46. Esterhuysen, S., and Stanz, K. (2004). “Locus of Control and online learning”. South African Journal of Industrial Psychology, 30(1), pp. 63-71

47. Eveleth, D. M., and Eveleth, A. B. (2010). “Team identification, Team performance and leader-member exchange relationships in virtual groups: Findings from massive multiplayer online role play games”. International Journal of Virtual Communities and Social networking, 2(1): 52-66

48. Figallo, C. (1998). “Hosting web communities: Building relationships, increasing customer loyalty and maintaining a competitive edge”. New York: John Wiley

49. Fisher, D., Turner, T. C., and Smith, M. A. (2007). “Space planning for online community”. Microsoft Research, Microsoft Live Labs, and Microsoft Internet Research Center, Redmond, Washington

50. Fu, Q. (2004). “Trust, social capital, and organizational effectiveness”. Blacksburg, VA: Virginia Polytechnic Institute and State University Retrieved May 20, 2007, from http://scholar.lib.vt.edu/theses/available/etd05122004155926/unrestricted/qhfumajorpaper.pdf

51. Fukuyama, F. (1995). “Trust: Social Virtues and the Creation of Prosperity”. NY: Free Press, Simon and Schuster

52. Gardner, D. G., Pierce, J. L., Van Dyne, L., and Cummings, L. L. (2000). “Relationship between pay level, employee stock ownership, self-esteem and performance”. Australia and New Zealand Academy of Management Proceedings, Sydney, Australia

53. Graen, G. B., and Uhl-Bien, M. (1995). “Relationship-based approach to leadership: Development of Leader-Member Exchange (LMX) theory of Leadership Over 25 Years: Applying a Multi-Level Multi-Domain Perspective”. The Leadership Quarterly, 6(2): 219-247

54. Gray, P. H., and Meister, D. B. (2004). “Knowledge sourcing effectiveness”. Management Science, 50(6): 821-834

55. Gronroos, C. (2000). “Creating a relationship dialogue: Communication, interaction, value”. Marketing Review, 1(1): 5-14

111

56. http://www.businessdictionary.com/definition/shared-values.html57. http://en.wikipedia.org/wiki/Online_participation58. Hagel, J. I., and Armstrong, A. G. (1997). “Net Gain: Expanding Markets through Virtual

Communities”. Boston, MA: Harvard Business School Press59. Hall, C. S., and Lindzey, G. (1957). “Theories of personality”. New York: John Wiley and

Sons60. Hansen, M. T., Nohria, N., and Tierney, T. (1999). “What’s your strategy for managing

knowledge”. Harward Business Review, 77(2): 106-11661. Harmon, A. (2004). “Amazon glitch unmasks war of reviewers”. New York Times (February

14)62. Harris, H., Brewster, C., and Sparrow, P. (2003). “International human resource

management”. CIPD, London63. Herring, S. C. (1993). “Gender and democracy in computer-mediated communication”.

Electronic Journal of Communication, 3 (2): 1-1764. Hoffman, D.L., and Novak, T.P. (1996). "Marketing in Hypermedia Computer-Mediated

Environments: Conceptual Foundations". Journal of Marketing, 60(3): 50-6865. Hoffman, D. L., Novak, T. P., and Schlosser, A. (2003). “Consumer attitudes towards

software filters and online content ratings: A Policy Analysis”. Journal of Public Policy and Marketing, 22(1): 41-57

66. Hogg, M. A., and Terry, D. J. (2000). “Social Identity and Self-Categorization Processes in Organizational Contexts,” Academy of Management Review, 25(1): 121-140

67. Hsu, M-H., Ju, T. L., Yen, C-H., and Chang, C-M. (2007). “Knowledge sharing behavior in virtual communities: The relationship between trust, self-efficacy, and outcome expectations”. International Journal of Human-Computer Studies, 65(2): 153-169

68. Hsu, C. P., Chiang, Y. F., and Huang, H. C. (2012). “How experience driven community identification generates trust and engagement”. Online Information Review, 36(1): 72-88

69. Jaffe, J. M., Lee, Y. E., Huang, L., and Oshagan, H. (1995). ”Gender, Pseudonyms and CMC: Masking Identities and Baring Souls”. Paper presented at the 45th Annual Conference of the International Communication Association

70. Jang, H. Y., Olfman, L., Ko, I., Koh, J., and Kim, K. (2008). “The influence of online brand community characteristics on community commitment and brand loyalty”. International Journal of Electronic Commerce, 12(3): 57-80

71. Jang, H. Y., Ko, I.S., & Koh, J. (2007). The influence of online brand community characteristics on community commitment and brand loyalty. Proceeding of the 40th Hawaii International Conference on System Sciences, Hawaii, USA. 1-10

72. Jantunen, S., Smolander, K., Malinen, S., Virtanen, T., and Kujala, S. (2008). "Utilizing Firm-Hosted Online Communities: Research challenges and needs," Automated Software Engineering - Workshops, 2008. ASE Workshops 2008. 23rd IEEE/ACM International Conference on

73. Jeppesen, L. B. (2005). “User Toolkits for innovation: Consumers Support each other”. Journal of Product Innovation Management, 22(4): 347-362

74. Johnson, D. L., and Hanson, P. G. (1979). “Locus of Control and Behavior in Treatment groups”. Journal of Personality Assessment, 43(2): 177-183

75. Johnson, Z., Massiah, C., and Allan, J. (2013). “Community identification increase consumer-to-consumer helping, but not always”. Journal of Consumer Marketing, 30(2): 121-129

76. Jung, H-S., Kim, Y and Kook, Y. R. (2009). “A study on the effects of online brand

112

community identity on the characteristics of community activity and behavioral responses”, Agent and Multi-Agent Systems: Technologies and Applications. 5559: 523-533

77. Kannan, P. K., Chang, A., and Whinston, B. (2000). “Electronic communities in e-business: Their role and issues”. Information Systems Frontiers, 1(4): 415-426.

78. Katz, J. (1998). “Luring the lurkers”. Retrieved 1 March 1999,From http://slashdot.org/story/98/12/28/1745252/luring-the-lurkers

79. Kim, D-Y., Lehto, X. Y., and Morrison, A. M. (2007). “Gender difference in online travel information search: Implications for marketing communications on the internet”. Journal of Torusim Management, 28(2): 423-433

80. Kim, J. W., Choi, J., Qualls, W., and Han, K. (2008). “It takes a marketplace community to raise brand commitment: The role of online communities”. Journal of Marketing Management, 24(3-4): 409-431

81. Koh, J., Kim, Y. G., Brian, B., and Bock, G. (2007). “Encouraging participation in virtual communities”. Communications of the ACM, 50(2): 69-73

82. Kollock, P. (1999). “The Economies of Online Cooperation: Gifts and Public Goods in Cyberspace”. In Smith, Marc; Kollock, P. Communities in Cyberspace. London: Routledge, pp: 220–239

83. Koo, Dong-Mo. (2009). “The moderating role of Locus of Control on the links between experiential motives and intention to play online games”. Computers in Human behavior,25(2): 466-474

84. Korman, A. K. (1970). “Toward a Hypothesis of Work Behavior”. Journal of Applied Psychology, 54(1): 31-41

85. Kozinets, R. V. (1999). “E-tribalized marketing? : The strategic implications of virtual communities of consumption”. European Management Journal, 17(3): 252-264

86. Kozinets, R. V. (2002). “The Field behind the Screen: Using Netnography for Marketing Research in Online Communities”. Journal of Marketing Research, 39(1): 61-72

87. Kren, L. (1992). “The moderating effect of Locus of Control on performance incentives and participation”. Human Relations, 45(9): 991-1012

88. Kristof, A. L. (1996). “Person-organization fit: An integrative review of its conceptualizations, measurement, and implications”. Personnel Psychology, 49(1): 1-49

89. Lampe, C., Wash, R., Velasquez, A., and Ozkaya, E. (2010). “Motivations to Participate in online communities”. In Proceedings of the 28th international conference on Human factors in computing systems, pp. 1927-1936, Atlanta, Georgia, USA

90. Lampel, J., and Bhalla, A. (2007). “The role of status seeking in online communities: giving the gift of experience”. Journal of Computer-Mediated Communication, 12(2), Article 5

91. Lee, M., and Turban, E. (2001). “A trust model for consumer internet shopping”. International Journal of Electronic Commerce, 6(1): 75-91

92. Lee, Hyun-Jung. (2004). “The role of competence-based trust and organizational identification in continuous improvement”, Journal of Managerial Psychology, 19(6): 623-639

93. Lee, Hung-Wen. (2013). “Locus of Control, Socialization and organizational identification”. Management Decision, 51(5)

94. Lefcourt, H. M. (1972). “Recent Developments in the study of Locus of Control”. Progress in experimental Personality Research, (6): 1-39

100.Leigh, T. W., Peters, C., and Shelton, J. (2006). “The consumer quest for authencity: The multiplicity of meanings within MG subculture of Consumption”. Journal of the Academy of Marketing Science, 34(4): 481-493

113

101.Lin, C. A. (1997).“Exploring Potential Predictors of Personal Computer Adoption”. Paper presented at the meeting of the Association of Education in Journalism and Mass Communication, Chicago

95. Lin, Hsiu-Fen, and Lee, Gwo-Guang. (2006). “Determinant of success for online communities: an empirical study”. Behavior Information Technology, 25(6): 479-488

96. Lin, Hsiu-Fen. (2007). “The role of online and offline features in sustaining virtual communities: and empirical study”. Internet Research, 17(2): 119-138

97. Lin, Z. Y. (2008). “The core self-evaluation of IT professionals influence on job satisfaction and life satisfaction”. National Central University of Information Management, Master Thesis

98. Lu, S. Z., Yu, K. C., and Cheng, X. C. (1997). “Organizational Behavior-Theory and Practice”, Taipei: Wu Nan

99. Luo, X. (2002). “Uses and gratifications theory and e-consumer behaviors: A structural equation modeling study”. Journal of Interactive Advertising, 2(2): 34-41

100.Macan, T. H., Trusty, M. L., and Trimble, S. K. (1996). “Spector’s Work Locus of Control Scale: Dimensionality and Validity Evidence”. Educational and Psychological Measurement, 56(2): 349-357

101.Maxham, III, J. G., and Netemeyer, R. G. (2003). “Firms reap what they sow: The effects of shared values and perceived organizational justice on customers’ evaluations of compliant handling”. Journal of Marketing, 67(1): 46-62

102.Mayer, J. D. (2005). “A tale of two visions: Can a new view of personality help integrate psychology? ”. American Psychologist, 60(4): 294-307

103.Mayzlin, D. (2006). “Promotional chat on the internet”. Marketing Science, 25(2): 157-165104.Mohr, J.J. and R.S. Sohi. "Communication Flows in Distribution Channels: Impact on

Assessments of Communication Quality and Satisfaction," Journal of Retailing, (71 :4), 1995, pp. 393-416

105.Money, R. B., and Graham, J. L. (1999). “Salesperson performance, pay, and job satisfaction: Test of a model using data collected in the United States and Japan”. Journal of International Business Studies, 30(1): 149-151

106.Morgan, R. M., and Hunt, S. D. (1994). “The commitment-trust theory of relationship Marketing”. Journal of Marketing, 58(3): 20-38

107.Mowen, J. C., and Spears, N. (1999). “A hierarchical model approach to understanding compulsive buying among college students”. Journal of Consumer Psychology, 8(4): 407-430

108.Mukherjee, A., and Nath, P. (2003). "A model of trust in online relationship banking". International Journal of Bank Marketing, 21(1): 5 – 15

109.Mukherjee, A., and Nath, P. (2007). “Role of electronic trsut in online reatiling:A re-examination of the commitment-trust theory”. European Journal of Marketing, 41(9): 1173-1202

110.Muniz, A. M., and O’Guinn, T. C. (2001). “Brand Community”. Journal of Consumer Research, 27 (4): 412-432

111.Nambisan, S. (2002). “Designing virtual customer environments for new product development: Toward a theory”. The Academy of Management review, 27(3): 392-413

112.Nambisan, P. (2005). “Online community experience: Impact on customer attitudes”. Ph.D Dissertation, Rensselaer Polytechnic Institute, 112 pages

114

113.Nelson, R. R., Todd, P. A., and Wixom, B. H. (2005). “Antecedents of Information and system quality: An empirical examination with the context of data warehousing”. Journal of Management Information Systems, 21(4): 199-235

114.Nonnecke, B., Andrews, D., and Preece, J. (2006). “Non-public and public online community participation: Needs, attitudes and behavior”. Electronic Commerce Research6(1): 7-20

115.Nunnally. J. (1978). “Psychometric Theory”. 2nd Edition, New York: McGraw-Hill 116.O’Brien, G. E. (1986). “Psychology of work and unemployment”. New York: John Wiley

and Sons117.O’Reilly, C. A., Jennifer, C., Caldwell, D. F, (1991). People and organizational culture: A

profile comparison approach to assessing person-organization fit. Academy of Management Journal, 34: 487-516

118.Park D-H., Lee, J., and Han, I. (2011). “The different effects of online consumer reviews on consumers’ purchase intentions depending on trust in online shopping malls. An advertising perspective”. Journal of Internet Research, 21(2): 187-206

119.Pierce, J. L., Gardner, D. G., Cummings, L. L., and Dunham, R. B. (1989). “Organization-based self-esteem: Construct definition, measurement, and validation”. The Academy of Management Journal, 32(3): 622-648

120.Porter, C. A. (2004). “A Typology of Virtual Communities: A Multi-Disciplinary Foundation for Future Research”. Journal of Computer Mediated Communication, 10(1), Article 3, Available at: http://jcmc.indiana.edu/vol10 /issue1/porter.html

121.Porter, C. E., and Donthu, N. (2008). “Cultivating Trust and Harvesting Value in Virtual Communities”. Management Science, 54(1): 113-128

122. Preece, J. (2000). “Online communities: Designing usability, Supporting sociability”. Chichester, UK: John Wiley and Sons

123.Preece, J., and Krichmar, M. D. (2003). “Online communities”. In Jacko. J., and Sears. A. eds. Handbook of Human-Computer Interaction, Lawrence Erlbaum Associates Inc., Mahwah. NJ, 596-620

124.Ren, Y., Kraut, R., and Kiesler, S. (2007). "Applying Common Identity and Bond Theory to Design of Online Communities." Organization Studies, 28(3): 377–408

125.Rheingold, H. (1993). “The Virtual Community: Homesteading on the Electronic Frontier”. Reading, MA: Addison-Wesley, New York

126.Rheingold, H. (1994). “A slice of life in my virtual community”. In L. M. Harasim (Ed.), Global Networks: Computers and International Communication (pp. 57-80). Cambridge, MA: MIT Press

127.Rhoades, L., and Eisenberger, R. (2002). “Perceived Organizational Support: A Review of the Literature”. Journal of Applied Psychology, 87(4): 698-714

128.Ridings, C. M, and Gefen, D. (2001). “The development of trust in online communities”. In Proceedings of International Resource Management Association Intrenational Conference (IRMA), Toronto, Ontario, Canada, pp. 374-7

129.Ridings, C. M., Gefen, D., and Arinze, B. (2002). “Some Antecedents and effects of Trust in Virtual Communities”. Journal of Strategic Information Systems 11(3-4): 271-295

130.Ely, R. J. (1994). “The effects of organizational demographics and social identity relationships among professional women”. Administrative Science Quarterly, 39(1): 203-238

115

131.Rosenbaum, M. A., and Massiah, C. A. (2007). “When customers receive support from other customers: Exploring the influence of inter-customer social support on customer voluntary performance”. Journal of Service Research, 9 (3): 257-270

132.Rossi, C. (2011). “Online consumer communities, collaborative learning and innovation”. Measuring Business Excellence, 15(3): 46-62

133.Rothaermel, F. T., and Sugiyama, S. (2001). “Virtual internet communities and commercial success: Individual and community-level theory grounded in the atypical case of TimeZone.com”. Journal of Management, 27(3): 297-312

134.Saks, A. M. (2006). “Antecedents and consequences of employee engagement”. Journal of Managerial Psychology, 21(7): 600-619

135.Schein, E. (1990). “Organizational Culture”. American Psychologist, 45(2): 109-119136.Schlosser, A. E. (2005). “Posting versus Lurking: Communicating in a multiple audience

context”. Journal of Consumer Research, 32(2): 260-265137.Sharrat, M., and Usoro, A. (2003). “Understanding Knowledge –Sharing in online

communities of practice”. Electronic Journal on Knowledge Management, 1(2): 187-196138.Shneiderman, B. (2000). “Universal usability”. Communications of the ACM, 43(5): 84-91,

http://doi.acm.org/10.1145/332833.332843139.Shneiderman, B. (2000). “Designing trust into online experiences”. Communications of the

ACM,43(12): 57-59140.Slater, M., Sadagic, A., and Schroeder, R. (2000). “Small-group behavior in a virtual and

real environment: A comparative study”. Presence, Teleoperators and Vurtual Environments, 9(1): 37-51

141.Spector, P. E. (1988). “Development of the work locus of control scale”. Journal of Occupational Psychology, 61(4): 335-340

142.Sullivan, S. E., and Bhagat, R. S. (1992). "Organizational stress, job satisfaction and job performance: where do we go from here?" , Journal of Management, 18(2): 353–74

143.Sultan, F., Qualls, W., and Urban, G. L. (1999), “Design and evaluations of a true based advisor on the internet”. Working paper, Northeastern University, Boston, MA

144.Szmigin, I., Canning, L., and Reppel, A. E. (2005). ”Online community: enhancing the relationship marketing concept through customer bonding.” International Journal of Service Industry Management, 16(5): 480 – 496

145.Tajfel, H. (1978). “Social categorization, social identity, and social comparison”. In H. Tajfel (Ed.), Differentiation between social groups: Studies in the social psychology of intergroup relations (pp. 61-67). London: Academic Press

146.Tajfel, H. (1982). “Social Identity and Intergroup Relations”. Cambridge, England: Cambridge University Press

147.Thomas, A., and Doak, R. (2000). “The development of share values: impact on employee behavior and on customer perception of service”. South African Journal of Business Management, 31(1):17-30

148.Thompson, T. L. (2011). “Work-learning in informal online communities: evolving spaces”.Information Technology & People, 24(2): 184-196

149.������� !�� #��� ��� ���� �� !�� �������� $!���'��@����@� ���� \��������'� ��� ^ @������������Identification”. Dogus University Journal, 10(2): Start Page: 284

150.Tsui, A. S., and Farh, J-L. L. (1997). “Where Guanxi Matters: Relational Demography and Guanxi in the Chinese context”. Work and Occupations, 24(1): 56-79

116

151.Uçar, D., and Ötken, A. B. (2010). “Perceived organizational support and organizational commitment: The mediating role of organization based self-esteem”. Dokuz Eylul Univeristy, Journal of Economic and Management Science Faculty, 25(2): 85-105

152.Vroom, V., and Jago, A. G. (1988). “The new leadership: Managing participation”. Prentice-Hall, Englewood Cliffs, NJ

153.Wallace, P. (1999). “The Psychology of the internet”. Cambridge, UK, Cambridge University Press

154.Wang, R., and Strong, D. (1996). “Beyond Accuracy: What Data Quality Means to Data Consumers”. Journal of Management Information Systems, 12(4): 5-34

155.Wang, Y., and Fesenmaier, D. R. (2003). “Assessing motivation of contribution in online communities: An empirical investigation of an online travel community”. Journal of Electronic Markets, 13(1): 33-45

156.Wang, Y., and Fesenmaier, D. R. (2004). “Towards understanding members’ general participation in and active contribution to an online travel community”. Tourism Management. 25(6): 709-722

157.Wang, Jau-Shyong. (2009). “Trust and relationship commitment between direct selling distributors and customers”. African Journal of Business Management, 3 (12): 862-870

158.Wasko, M. M. and Faraj, S. (2005). “Why should I share? Examining social capital and knowledge contribution in electronic networks of practice”. MIS Quarterly, 29 (1): 35-57

159.Wellman, B., Haase, A. Q., Witte, J., and Hampton, K. (2001). “Does the internet increase,decrease or supplement social capital?: Social networks, Participation, and Community Commitment”. American Behavioral Scientist, Vol. 45(3): 436-455

160.Wiertz, C., and Ruyter, K. (2007). “Beyond the call of duty: Why customers contribute to firm-hosted commercial online communities”. Organization Studies 28(03): 347-376

161.Wilimzig, B. J. (2011). “Online communities: Influence on Members Brand Loyalty and purchase intent”. Research papers. Paper 153 http://opensiuc.lib.siu.edu/gs_rp/153

162.Williams, R.L., and Cothrel, J. (2000). “Four smart ways to run online communities”, Sloan Management Review, pp. 81–91

163.Williamson, O. (1975). “Markets and Hierarchies”. New York: Free Press164.Wilson, S. M., and Peterson, L. C. (2002). “The anthropology of online communities”.

Annual Review of Anthropology, 31 (1): 449-467165.Winter, P.L., and Cvetkovich, G.T. (2010). “Shared values and trust: the experience of

community residents in a fire-prone ecosystem. In: Pye, J.M., Raushcer, H.M., Sands, Y., Lee, D.C., and Beatty, J.S., eds. Advances in threat assessment and their application to forest and rangeland management. Gen. Tech. Rep. PNW-802. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station: 409-418

166.Wu, W-Y., and Sukoco, B. M. (2010). “Why should I share? Examining consumers`motives and trust on knowledge sharing”. Journal of Computer Information Systems, 50(4): 11-19

167.Zheng, Z. M. (2001). “Locus of Control, Job characteristics and Job Performance Analysis -High-Tech Industry workers as an Example”. National Central University of Human Resource Management, Master thesis

168.Zhou, T. (2011). “Understanding online community user participation: a social influence perspective”. Internet research, 21(1): 67-81

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Appendix 1

Identification and Participation in online community

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:

: Jeyhun Hajiyev

E-mail: ceyhunhajiyev@gmail.com

Mobile Phone: 0989641782

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