a study of interpersonal relations needs and referral...

A Study of Interpersonal Relations Needs and Referral Behavior:

The Moderating Effect of eWOM Qualities

BY

Miko, TSE Man Kit

11015845

Sandra, LEE Saminee

11012099

Marketing Concentration

An Honors Degree Project Submitted to the

School of Business in Partial Fulfillment

of the Graduation Requirement for the Degree of

Bachelor of Business Administration (Honors)

Hong Kong Baptist University

Hong Kong

April 2014

BUS 3570 – BBA Honors Project A Study of Interpersonal Relations Needs and Referral Behavior: The Moderating Effect of eWOM Qualities

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Contents

Acknowledgment ....................................................................................................................... 4

Abstract ..................................................................................................................................... 5

1. Introduction ........................................................................................................................... 6

1.1 Background ....................................................................................................................... 6

1.2 Objectives ......................................................................................................................... 7

2. Literature review ................................................................................................................... 9

2.1 Electronic word-of-mouth (eWOM) ................................................................................... 9

2.2 The impact of interpersonal relations needs towards referral behavior ................................ 9

2.2.1 Enhancement of self-image ....................................................................................... 11

2.2.2 Need to belong .......................................................................................................... 12

2.2.3 Concern for others .................................................................................................... 13

2.3 eWOM referral behavior .................................................................................................. 14

2.4 The impact of moderators on the relationship between interpersonal relations needs and

referral behavior .................................................................................................................... 15

2.4.1 Relevance ................................................................................................................. 16

2.4.2 Credibility ................................................................................................................ 17

2.4.3 Timeliness ................................................................................................................ 18

3. Research model ................................................................................................................... 19

4. Research methodology ........................................................................................................ 20

4.1 Research design ............................................................................................................... 20

4.2 Research approach ........................................................................................................... 21

4.2.1 Questionnaire design – format & measurement ......................................................... 21

4.2.2 Questionnaire design – approach ............................................................................... 24

4.2.3 Sampling method selection ....................................................................................... 24

4.2.4 Sample size determination......................................................................................... 25

4.2.5 Data collection procedure .......................................................................................... 25

4.2.6 Data analysis methods ............................................................................................... 26

5. Research analysis and findings ........................................................................................... 27

5.1 Primary data analysis with descriptive statistics ............................................................... 27

5.2 Internet usage behavior and eWOM referral behavior of respondent ................................. 27

5.3 Reliability test ................................................................................................................. 28


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5.4 Multiple Regression ......................................................................................................... 29

5.4.1 Main effect of interpersonal relations needs and referral behavior .............................. 30

5.4.2 Moderating effect of relevance, credibility, and timeliness on relationship between

respective interpersonal relations need and referral behavior .............................................. 32

5.5 Overview of hypotheses ................................................................................................... 36

6. Discussion and implication .................................................................................................. 37

6.1 Interpersonal relations needs on referral behavior............................................................. 38

6.2 Moderators – Relevance, Credibility, Timeliness ............................................................. 40

6.2.1 Moderating effect of relevance between enhancement of self-image and referral

behavior ............................................................................................................................ 40

6.2.2 Moderating effect of credibility between need to belong and referral behavior ........... 40

6.2.3 Moderating effect of timeliness between concern for others and referral behavior...... 42

7. Recommendations ............................................................................................................... 43

8. Limitations and future study .............................................................................................. 47

9. Conclusion ........................................................................................................................... 48

10. Reference ........................................................................................................................... 49

11. Appendix ............................................................................................................................ 59

Appendix A: Questionnaire .................................................................................................. 59

Appendix B: Result of Descriptive Statistics and Frequency Distribution .............................. 64

Appendix C: Cronbach’s Alpha Coefficient .......................................................................... 67

Appendix D: Result of Regression Analysis.......................................................................... 72

Appendix E: Result of Moderating Effect Analysis ............................................................... 75


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Acknowledgment

We would like to take this opportunity to express our greatest gratitude to everyone who

have helped and supported us in the project.

First and foremost, may we hereby show our sincere thanks towards our supervisors, Dr.

Noel Siu, for her valuable time, helpful guidance and advice, as well as the continuous

encouragement throughout the year.

Moreover, we would like to thank Yan Kwan and Morgan Yeung for the enormous

generosity and patience for their voluntary help in our honor’s project.

In addition, we would like to say thank you to our families and friends for their

understanding and unlimited support during the research process, including all the

participants of our survey and interview.

Last but not least, we have to give thanks to our Heavenly Father.

“I can do all things through Christ who strengthened me.”

(Philippians 4:13)


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Abstract

The rising of new online social platforms in recent years provided fertile foundation for

electronic word-of-mouth (eWOM). With the notion of eWOM communication,

marketers explore huge opportunities in promoting their products and services, especially

through online web tools. At the same time, consumers desire to be connected with their

communities. The popularity of social networking sites implies there are psychological

needs behind the users.

In this study, we identified the underlying motives for eWOM information referral

behavior built upon the most prominent study of WOM communication suggested by

Schutz (1966). Meanwhile, information qualities are unprecedented used as the

moderators to facilitate the relationship between them, in which scant research has

examined the impact of those in this intent.

Using convenience sampling and qualitative research method, we distributed 250

questionnaires to three local universities’ students who have adopted and forwarded

information eWOM in online social platforms. The paper found enhancement of self-

image and the need to belong are contributed to the forwarding activities proposed in our

model. Among the three qualities of eWOM as moderators with respective interpersonal

relations needs, relevance is found significant using the multiple regression analysis.

We believe that the research will offer an all-time insight for eWOM literature and

provide valuable meaning to marketers in understanding consumers’ behavior

psychologically and socially so as to design more effective marketing campaigns.


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1. Introduction

1.1 Background

With the advance of internet, WOM communication has extended in the online

environment, especially on the online social platforms. Consumers search for product

information from other customers and give advices on experiences by engaging in

eWOM. They act as both the information receiver and provider. The advent of internet

enables the popularity and effect of WOM entering a new decade. Consumers are not

restricted to people they already know and eWOM generated a pool of useful and

credible source of information. Online comments made by other customers are more

powerful and trustworthy than marketer-generated information (Bickart & Schindler,

2001). However, to generate the WOM from consumers is not an easy task for the

marketers. Consumers tend not to spread the WOM when it is effortful. Thus, adequate

motivations are needed to drive them to do so (Chaiken & Trope, 1999).

Rapid emergence of various forms of online platforms such as social networking sites

and discussion forums mark a considerable change in the practice of companies’

communication strategies with customers (Hagel & Armstrong, 1997; Shea, Enghagen, &

Khullar, 2004). They encouraged consumers to share their comments and views about the

products and services after use (De Bruyn & Lilien, 2008). This topic is chosen because

eWOM is the most effective tool in affecting consumer purchase decision and there will

be more and more consumers seeking and relying on advices online (eMarketer, 2006;

Hennig-Thurau, Gwinner, Walsh & Gremler, 2004). We hope to provide marketers an


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overview of how eWOM communication works and add value to the knowledge of

eWOM literature.

1.2 Objectives

As the change of era in Web 2.0, recent researchers are keen on researching on the

sharing activities of consumer’s product knowledge through various web-based

discussion boards (Lee, Cheung, Lim & Sia, 2006). eWOM or viral marketing has been

proved to have considerably influences on customer economic value perceptions and

customer loyalty intentions (Gruen, Osmonbekov & Czaplewski, 2006). While most

researchers have focused in discovering the result and effect of eWOM, yet, there is little

research in examining the drivers or motivations on consumers for spreading the WOM

of products in the internet environment (Gruen et al., 2006; Olaru, Purchase & Peterson,

2008; Schmitt, Skiera & Van den Bulte, 2011).

In addition, while most studies have only examined the online information qualities for

consumers to adopt eWOM (Brown, Broderick & Lee, 2008; Cheung & Thadani, 2012;

Cheung, Lee & Rabjohn, 2008; Chu & Kim, 2011; Hsu & Tran, 2013), few have

recognized the importance of motivations behind in spreading the WOM in the online

environment. Past researches posited that consumers are more likely to take part in WOM

behavior only when they are highly psychologically motivated (Ajzen & Sexton, 1999;

Fazio & Towles-Schwen, 1999). Psychological factors in the social context plays a more

crucial role in how consumers response. Moreover, the high speed of diffusion of eWOM

and voluminous information available online, information qualities are thus less valued as

people spread information without thorough verification of the comments (Chatterjee,


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2001). In view of this, instead of examining the direct relationship between the factors in

affecting consumers to adopt eWOM information, they are taken as the moderators in our

model, in order to see how they will influence the level of referral behavior.

Thus, we concluded three main objectives for the study.

First, it is to examine the psychological factors, i.e. enhancement of self-image, need to

belong, and concern for others, in affecting consumer behavior behind the referrals

activities. Second, by using the factual quality factors, i.e. relevance, credibility, and

timeliness in the online environment as moderators, we would like to explore how they

could influence the relationships between the interpersonal relations needs and referral

activities. The last objective is to advance the understanding of marketers in seeing a

more comprehensive framework of how eWOM works on consumers.

We hope the study can shed some lights for marketers to take advantage of the eWOM

mechanism in customers’ purchase decision-making. This helps them to better shape

consumer attitudes and behaviors, and thus effectively implement eWOM as a

promotional tool in the integrated marketing communication mix.

In summary, this study proposes a complete model: the interpersonal relation needs

which drive customers to recommend products to others, together with the online

information qualities as the moderators.


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2. Literature review

2.1 Electronic word-of-mouth (eWOM)

Past empirical studies proved traditional WOM a useful tool in influencing customers’

purchase decision (Richins, 1983; Crosby, Evans & Cowles, 1990; Parasuraman, Berry &

Zeithaml, 1991; Morgan & Hunt, 1994; Henning-Thurau, Gwinner & Gremler, 2002).

With the advancement of the internet, consumers are shifting from spreading their

product opinions and reviews from face-to-face to through the internet. Thus, eWOM can

be regarded as the extension of traditional WOM (Cheung et al., 2008). Hennig–Thurau

et al. (2004 p.39) defined eWOM as “any positive or negative statement made by

potential, actual, or former customers about a product or company, which is made

available to a multitude of people and institutions via the internet”. This is regarded as the

most well defined and extensive definition. Since the internet is highly accessible and

consumers communicate on a virtual platform, it is important for marketers to understand

their consumers’ motives in sharing product knowledge and opinions. The emergences of

great amounts of consumer’s reviews create both opportunities and challenges to

marketers (Stauss, 2000).

2.2 The impact of interpersonal relations needs towards referral behavior

Existing research adopts various theories in explaining why people take part in WOM

communication. In this study, we try to use the most prominent study of WOM

communication suggested by Schutz (1966) as the base of the model since it is a


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conceptual framework integrating different underlying motives for interpersonal WOM

communication. In Schutz’s study, he proposed a three dimensional theory to predict

interpersonal behavior - FIRO (Fundamental Interpersonal Relations Orientation). It

illustrates that people engage in WOM communication because of their interpersonal

relations needs: inclusion (need to be unique and need to belong), affection (concern for

others) and control (need to be competent).

Little research relates FIRO in explaining eWOM behavior and in fact, psychological

motives contribute greatly to online behaviors (Gruen et al., 2006 & Lee et al., 2006).

Thus, in this study, we will extend the three interpersonal relations needs, i.e. inclusion,

affection and control in FIRO to the online social platforms context that contribute to the

eWOM research area.

Moreover, similar to other research concerning WOM or eWOM, they use interviews and

experiments in a controlled environment in which the generalization of the underlying

motives for eWOM referral activities in the market remains uncertain (Gupta & Harris,

2010; Huang et al., 2007; Schindler & Bickart, 2004; Sundaram, Mitra & Webster, 1998;

Sweeney, Soutar & Mazzarol, 2008). Therefore, we will use surveys to examine eWOM

on different online social platforms so as to enhance the level of generalization and

implication for market and management nowadays.

In this study, we will adopt FIRO as the foundation to explain eWOM information

referral behavior. Three independent variables (IV), i.e. enhancement of self-image, need

to belong, concern for others are selected to be the underlying factors altering the

dependent variable (DV), i.e. referral behavior.


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2.2.1 Enhancement of self-image

According to FIRO, inclusion refers to the need to be part of the group. But at the same

time, it also means to be recognized as unique and valuable (Schutz, 1966). Ho and

Dempsey (2010) elaborated that people with inclusion needs desire to be different among

others because they want to enhance self-image. Thus, under inclusion need, we presume

that the need to enhance self-image is an important driver for interpersonal

communication. In many studies, the need to enhance self-image is proven to be a

stimulator in WOM communication (Chung & Darke, 2006; Dichter, 1966; Sundaram et

al., 1998). Individuals want to receive recognition and approval from others after sharing

opinions so as to enhance self-image and social status (Balasubramanian & Mahajan,

2001; Engel, Blackwell & Miniard, 1993; Sundaram et al., 1998). On the online social

platforms, opinions are shown to everyone that allow individuals to establish themselves

as opinion leaders and signal connoisseurship by posting quality comments (Hennig-

Thurau et al., 2004; Wasko & Faraj, 2005).

Hence, the greater the need to enhance self-image, the higher the chance will be for

customers to refer eWOM information.

H1: Enhancement of self-image is positively related to referral behavior

towards eWOM information


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2.2.2 Need to belong

Suggested by Schutz (1966), inclusion is the interpersonal desire to be viewed as a part of

the organization. “To belong”, “to join” and “togetherness” are some key ideas in

inclusion (Ho & Dempsey, 2010).

It can be further elaborated with social identity theory that individuals may be able to

obtain social existence and recognition from the organization they belong to and they

show strong need to be within the group (Tajfel & Turner, 1986). According to

Baumeister and Leary (1995, p. 522), the need to belong is a “strong desire to form and

maintain enduring interpersonal attachments”. On the online social platforms, people

interact with the members. Prior studies have shown that the higher level of engagement

on the platform, the stronger affiliation and belongings with the online social platform

(McWilliam, 2000; Oliver, 1999). For instance, individuals post their feedback on the

online forum because it represents their contribution and presence among the users in the

platform. This enables them to get social acceptance and attention.

Therefore, the greater the need to belong into a group, the higher will be the chance for

customers to referral eWOM information.

H2: Need to belong is positively related to referral behavior towards eWOM

information.


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2.2.3 Concern for others

Affection refers to the desire to have warm, intimate and personal relationship (Schutz,

1966). It can also be related to altruistic motives or concern for others (Price, Feick &

Guskey, 1995). Altruism refers to the pursuit of devoting the welfare of others other than

oneself (Batson, 1994). Affective consequences affect how consumers make decisions

like buying or not buying (Curtis, Paul & Frank, 2006). A number of scholars have

investigated on the topic of negative affect and affect regulation. It refers to people who

tend to engage in a range of activities to make themselves feel happier in order to change

their state of feelings especially when they experience negative affect. Examples are

listening to uplifting music, eating, exercising, and helping others etc. Specifically,

helping is regarded as a mood-lifting activity since people feel satisfied after serving

others (Bagozzi & Moore, 1994; Cialdini, Darby & Vincent, 1973). In addition,

individuals with altruistic goals share their previous purchasing experience on online

social platforms without expecting returns, but to assist other consumers in buying

decisions and preventing others from negative experience (Ho & Dempsey, 2010).

Hence, the higher the need to concern for others, the higher will be the chance for

customers to referral eWOM information.

H3: Concern for others is positively related to referral behavior towards

eWOM information.


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2.3 eWOM referral behavior

WOM can fulfill interpersonal communication goal (Kraut, Mukhopadhyay, Szczypula,

Kiesler & Scherlis, 1999). People gain power and influence in the virtual community by

contributing into it. After they receive WOM information from the community, they will

evaluate and share useful information with others to show the sense of belonging to the

community, which may result in referral activities (Huang, Chou & Lan, 2007). Referring

electronic content is also regarded as a form of conversation to accomplish interpersonal

communication goals. This is consistent with Dichter’s (1966) suggestion that providing

WOM is often a means of gaining attention, and showing connoisseurship.

According to Chaiken (1980), each receiver has different responses based on the

recipients’ experience, perceptions, and sources. Thus, information system researchers

are inspired to know more about how information adoption process works in human’s

mind. In Lee, Park and Han’s (2008) study, they used the elaboration likelihood model

(ELM) to examine the proportion and quality of negative comments in the context of

information processing. Theoretically, it suggested that information influence people’s

attitude centrally and peripherally. Since eWOM is one kind of social interaction

communication, consumers will evaluate information they received and determine

whether to accept it based on different factors.


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2.4 The impact of moderators on the relationship between interpersonal relations

needs and referral behavior

No identified research proposes information qualities as moderators in altering the

relationship between interpersonal relations and referral behavior. Nevertheless, Chin,

Sheng and Der (2011) suggested that the quantity and quality of eWOM can moderate

consumers’ attitude and behavior. When there are more quality online comments, the

product is perceived to be more popular, and consequently induce more favorable

consumer behavior, i.e. purchase intention. In Leung & Ma (2012), they believe that

eWOM information has a significant role in moderating the relationship between

consumers’ attitude and behavior.

Furthermore, Chung and Darke (2006) examined the relationships among self-image,

product relevance, and WOM communication. People are exposed to various products

every day, but only engage in WOM when they are highly motived by psychological

needs (Ajzen & Sexton, 1999; Fazio & Towles-Schwen, 1999). When consumers have a

need to enhance self-esteem to create social impression, they will engage more in WOM

communication only when the product is perceived to be relevant to them and able to

present the consumer’s extended self. In other words, product relevance is proven to be

influential in moderating the relationship between self-image enhancement and WOM

communication (Chung & Darke, 2006).

In our model, we predict that information qualities are comparatively less valued than

psychological drives when they come to eWOM referral behavior. With the support of

Chin, Sheng & Der’ (2011) study that used eWOM information as moderators, while


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Chung and Darke’ (2006) adopted product relevance, it is sufficient to accept that

information qualities can moderate the relationship. We therefore propose them as the

moderators.

2.4.1 Relevance

Relevance is defined as the extent to which the data meet the needs of users (Lynn, 2001).

The excessive loads of information on the internet have distracted customers from

obtaining useful information. Pham (1998) pointed out that relevancy of information

plays a significant role in consumer decision making. As mentioned, according to Chung

and Darke (2006), consumers are motivated to take part in forwarding activities when the

product or service is viewed as relevant to both senders and receivers and is able to

represent the consumer’s self-image. Thus, if the piece of information is more relevant to

the consumer, provided that the positive relationship between enhancement of self-image

and referral behavior, such relationship is stronger.

H1a: The relevance of information in the online social platforms positively

moderates the relationship between enhancement of self-image and referral

behavior, such that relationship is stronger with higher relevance.


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2.4.2 Credibility

Credibility is defined as how recipients perceive the credibility, trustworthiness, and

competence of a message source, it has nothing to do with the message content (Chaiken,

1980; Petty & Cacioppo, 1986). Credibility is especially critical in the online

environment as people have freedom of speech to express product opinions and there is a

lack of quality control. Rieh (2002) pointed out different factors influencing judgment of

quality and authority in terms of characteristics of information objects, characteristics of

sources, and knowledge etc. The result demonstrates that quality and authority are critical

factors for the internet users in interacting information online. People hope others to

recognize their presences among friends no matter online or offline in order to gain social

acceptance and attention, providing credibility source is vital in building trust with them.

Since the study of Chin, Sheng and Der (2011) supports eWOM qualities to be the

moderators, among the information qualities, we predict information credibility is the

strongest predictor in explaining people’s referral behavior with the need of belong. It is

because credibility is proven to have close linkage with tie strength which is related to the

need to belong (Brown, Broderick & Lee, 2008). Due to the fact that source credibility is

crucial in the context of eWOM, it is served as the moderator in the relationship between

the need to belong and referral behavior.

H2a: The credibility of information in the online social platforms positively

moderates the relationship between the need to belong and referral behavior,

such that relationship is stronger with higher credibility.


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2.4.3 Timeliness

Timeliness or currency represents the user’s perception of the degree to which the

information is up-to-date (Wixom & Todd, 2005). A number of information systems

literatures have included timeliness as one of the aspects in evaluating information

quality (Bailey & Pearson, 1983; Mahmood & Medewitz, 1985; Negash, Ryanb, &

Igbariab, 2003). In this hustle and bustle city, most updated information and news is one

of the indispensable requirements in high quality information. Old news does not appeal

to consumers. Same reason as above, it is supported by Chin, Sheng and Der (2011) that

information qualities can be served as moderator, timeliness is observed to be the most

relevant information quality linked with concern for others in referring activity. The

reason is that people tend to help others with latest news available (Kollock, 1999). For

example, some limited time offers encourage consumers to share the message to their

social circle. The rationale behind is that marketers would like to make use of the linkage

between information timeliness and concern for others in order to successfully promote

their brands. Thus, when the information is recent, current and up-to-date, it moderates

the relationship between concern for others and referral behavior.

H3a: The timeliness of information in the online social platforms positively

moderates the relationship between concern for others and referral behavior,

such that relationship is stronger with higher timeliness.


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3. Research model

Figure 1 illustrated the research model used in this study. It depicts the interpersonal

relations needs for consumers in referring the eWOM information, i.e. enhancement of

self-image, need to belong and concern for others, which are proved to be valid

constructs and used by previous scholars in past researches (Gruen et al., 2006; Lee et al.,

2006). Furthermore, we used relevance, credibility, and timeliness to explore the

moderating effect between the respective interpersonal relations needs and referral

behavior, in which scant research has provided insights in this area.

Figure 1: Research model

Independent Variables

Enhancement of self-

image

Need to belong

Concern for others

Referral

behavior

Dependent Variable

Credibility Relevance Timeliness

Moderators

H1

H2

H3

H1a H2a H3a


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4. Research methodology

4.1 Research design

Previous studies usually used qualitative research method to find out the underlying

motives of eWOM communication (Gupta & Harris, 2010; Huang et al., 2007; Schindler

& Bickart, 2004; Sundaram et al., 1998; Sweeney et al., 2008). However, the result found

in this method only based on the data collected from a relatively smaller number of

interviewees. Added to qualitative research, survey will be used as a quantitative research

method to improve the level of the generalization, whilst interview as a qualitative

research method will be used to explain the details of the constructs if necessary.

Moreover, this is a descriptive research which enables us to describe the data collected

through questionnaires. Its aim is to examine the impact of IV (enhancement of self-

image, need to belong, concern for others) on the DV (referral behavior) of eWOM

information and to explore the moderating role of information qualities (relevance,

credibility, timeliness). The descriptive design is an appropriate method in determining

the frequency of a behavior or activity and also in measuring the relationship between

variables (Malhotra, 2010).

Questionnaire sets are expected to be distributed to around 50 respondents for pretest in

order to avoid misunderstanding on the questions (Hair, Wolfinbarger, Ortnau & Bush,

2010). As mentioned, interviews will be conducted in order to give a more thorough

understanding in specific constructs if necessary. It is expected to find 8-10 participants

to avoid data saturation problem (Siegle, 2002).


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4.2 Research approach

4.2.1 Questionnaire design – format & measurement

The questionnaire (see Appendix A) consists of parts A-G with 41 questions in total.

Part A includes screening questions to identify the right target respondent who has the

experiences of adopting online comments written by other platform users in the online

social platform and talking about it with others based on the acceptance of the comment

in the past six months.

Part B requires the respondent to recall a relevant product or service experience to ensure

the consistency of answers in the later part of the survey as they need to refer to the

experience.

Part C includes questions measuring the factors of relevance, credibility, and timeliness

(moderators) of the eWOM information altering the relationship between respective

interpersonal relations needs (IV) and referral behavior (DV).

TABLE 1: Items measuring the factors of relevance, credibility, and timeliness of eWOM information

Constructs Items Literature

Relevance

The comment on that platform is relevant to me.

The comment on that platform is appropriate to me.

The comment on that platform is applicable to me.

Brackstone, (1999)

Credibility

People who left comments in this platform are trustworthy.

People who left comments in this platform are knowledgeable in

evaluating quality of the product/service.

I believe in the comments given by others in the platform.

Wu & Shaffer, (1987)

Timeliness The comment on that platform is most recent.

That comment is most current.

The comment is always up-to-date.

Wixom & Todd, (2005)


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Part D asks about the personal attitude towards the online social platform the respondent

has written above, serving as the control variables in the survey.

Part E measures the referral behavior using seven-point scale. This part refers to the DV

of our model.

TABLE 2: Items measuring the acceptance of eWOM information and referral behavior


Referral behavior

It is likely that I would forward that online information to

others through online.

I want to tell my friends after reading the comment on that

platform.

Schlosser & Lloyd (2006)

Part F includes questions about the interpersonal relations needs (IV) affecting the

referral behavior of eWOM information (DV). This part refers to the main effect of our

model.

TABLE 3: Items measuring the psychological motives towards eWOM referral behavior


Enhancement of

self-image

Recommending a product or service to others can express

my joy about a good buy.

My contributions show others that I am a clever customer. I

want to tell others about a great experience.

I feel good when I can tell others about my buying successes.

Hennig-Thurau et al.,

(2004)

Need to belong I want other people to accept me.

My feelings are easily hurt when I feel that others do not

accept me.

I have a strong need to belong.

I try hard not to do things that will make other people avoid

or reject me.

Leary, Kelly, Cottrell, &

Schreindorfer (2007)


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Concern for others I want to warn others of bad products.

I want to save others from having the same negative

experiences as me.

I want to help others with my own positive experiences.

I want to give others the opportunity to buy the right product.

Hennig-Thurau et al.,

(2004)

Part G includes questions of personal information to collect the demographic statistics of

respondents in this research. There are six questions consisting of gender, age, tertiary

institution, study major, internet usage frequency, and monthly income.

Most of our construct items are developed from previous literature and all the items from

part B-G used the seven-point likert scale of itemized rating scale, ranked from “Strongly

Disagree” to “Strongly Agree”.

Furthermore, some criteria have been considered to develop a more accurate

measurement in the questionnaire. First, as for the question arrangement, we found that

the respondents are confused with the same type of questions and skip reading question

by question after we have done the pretest. Items under the same construct are separately

placed to avoid the confusion of respondents. Furthermore, in regards of the question

content and wordings, they were adjusted to fit into our model. For example, a few lines

of Chinese translations were included for easier understanding. Last but not least, we

amended the questionnaire format and layout. Questionnaires were designed as a booklet

format. We required the respondent to recall a relevant product or service experience, and

we strategically placed the question at the top of the inner pages of the booklet so they

could refer to the experience in the later part of the questionnaire.


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A revised questionnaire was developed after the pre-testing process with 80 samples of

respondents

4.2.2 Questionnaire design – approach

To accurately reflect the results from the proposed model, the screening of respondents is

the one of the most crucial steps in this study.

The first step is to identify the target respondents. Part A is designed to screen the right

respondents, which is the university students who have adopted and talked about

comments of a product or service on any online social platforms in the past six months.

In order to allow participants to respond in a more centralized way, they were asked to

fill in the product or service that they adopted from an online social platform and talked

about with others.

After all these screenings, they could then answer the rest of the questionnaires from Part

C to Part F. They were required to answer the questions based on our hypothesis.

Finally, Part G is to gather their demographic statistics and personal information, which

served as the controlled variables in the model and to examine if the result could be

generalized.

4.2.3 Sampling method selection

The research adopts the non-probability sampling technique – convenience sampling. To

minimize the selection bias from the interviewers, respondents have to answer the


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screening questions in Part A in the questionnaire to be eligible to provide required and

relevant information (Hair et al., 2010).

4.2.4 Sample size determination

According to Malhotra (2010), the sample size is determined based on the expense of

data collection, and the need to have sufficient statistical power. The sample size used in

similar studies was ranging from 200 to 300 (Wangenheim & Bayon, 2004; López &

Sicilia, 2013). In this research, the sample size is 250.

4.2.5 Data collection procedure

80 questionnaires were conducted in Hong Kong Baptist University as the pre-test. A

modified questionnaire was reviewed based on the evaluation of the pre-test. We

abandoned the online data collection method as the data collected were mostly

incomplete and deficient. There are altogether 274 respondents participated in the survey

with 250 usable surveys for the research. The collection period was from 12th

March to

26th March, 2014, lasting for two weeks.

For the target population, a number of scholars investigating similar problems has used

university students as the population due to the fact that they are the leading population in

online social platforms and are composed of the main segment in consuming goods and

services (Hamat, Embi, & Hassan, 2012; Kamtarin, 2012; Quan-Haase & Young, 2010;

Riegner, 2007). Therefore, university student is an ideal population for research related to

eWOM communication. Furthermore, questionnaires were distributed on the campus

with Hong Kong Baptist University from Kowloon, Hong Kong University from Hong


26

Kong Island, and Chinese University from New Territories. These three universities are

chosen because each location of divisions may represent different populations and also

due to the time and money constraints.

4.2.6 Data analysis methods

With the statistics software IBM SPSS Statistics (Version 20), the data was analyzed to

test our research model and hypotheses association with several methods. The first step is

to generate the results from demographic analysis by descriptive statistics and frequency

distribution, together with internet usage behavior and eWOM behavior. In addition to

that, reliability test is conducted to check if the data is consistent internally. Most

importantly, multiple regression analysis and moderating effect analysis are carried out

using the hierarchy multiple regressions.


27

5. Research analysis and findings

5.1 Primary data analysis with descriptive statistics

Among a total number of 274 respondents participated in the survey, 250 of them are

valid who had experience in eWOM adoption and referral in the past six months.

Of 250 valid samples, 46.4% were men and 53.6% were female while the proportion of

three universities are quite evenly distributed with 94 (37.6%) from Hong Kong Baptist

University, 71(28.4%) from The Chinese University of Hong Kong and 85 (34%) from

The University of Hong Kong. Most of them came from Business major (51.6%),

followed by Engineering major (10%), Science (8.4%), and Social Science (8.4%).

Table 5: Result of Demographic Characteristics

Items Category Frequency Percentage

Gender Male 116 46.4

Female 134 53.6

Tertiary Institution Hong Kong Baptist University 94 37.6

The Chinese University of Hong Kong 71 28.4

The University of Hong Kong 85 34.0

Study Major Business 129 51.6

Engineering 25 10.0

Science 21 8.4

Social Science 21 8.4

Others (please refer to Appendix B) 54 21.6

5.2 Internet usage behavior and eWOM referral behavior of respondent

About the internet usage of respondents, 52.8% spent one to three hours a day on the

internet, while 29.2% spent four to six hours and 16% spent more than six hours a day.

Only 2% of respondent used internet for less than an hour a day.


28

Among the 250 respondent, all of them have engaged in eWOM forwarding behavior in

the past six months. Nearly 60% of them have talked about the eWOM they saw online

with others for more than four times.

Table 6: Result of internet usage behavior and eWOM referral behavior

Items Category Frequency Percentage

Internet usage Less than 1 hour a day 5 2.0

1 to 3 hours a day 132 52.8

4 to 6 hours a day 73 29.2

More than 6 hours a day 40 16.0

Frequency of

eWOM referral

1-3 times 105 42.0

4-6 times 84 33.6

7-9 times 29 11.6

10 times or above 32 12.8

5.3 Reliability test

Reliability test was conducted to evaluate the internal consistency and validity of multi-

items scale for each construct (Kerlinger, 1973). The measurement for reliability test is

Cronbach’s Alpha and the general acceptance level is 0.7.

In Table 7 and Appendix C, Cronbach’s Alpha Coefficients of all items ranged from

0.692 to 0.808. Most of the variables were highly reliable and suitable for further data

analysis. As for referral behavior, the Cronbach’s Alpha Coefficients was 0.692 which

was very close to 0.7. It was a marginal case in achieving internal consistency. Yet, as

there are only two items, no item is advised to be deleted and it is still kept for further

analysis.


29

Table 7: Reliability Test Results

Dimensions and Items N of Items Cronbach’s Alpha Coefficients

Referral behavior 2 0.692

Enhancement of self-image 3 0.794

Need to belong 4 0.808

Concern for others 2 0.745

Credibility 3 0.794

Relevance 3 0.843

Timeliness 2 0.746

5.4 Multiple Regression

In this model, we suggest that the eWOM referral behavior (DV) is influenced by

interpersonal relations needs and the relationship (IV) is moderated by the information

qualities (moderator), multiple regression is applied using SPSS. The hypothesis is

justified with Standardized Coefficients (ß Value) and 95% confidential level (p > 0.05).


30

5.4.1 Main effect of interpersonal relations needs and referral behavior

Three interpersonal relations needs are selected to be the IVs while referral behavior as a

DV.

Figure 2: Main effect of interpersonal relations needs and referral behavior

* p < 0.05 ; ** p < 0.01; *** p < 0.001

Referring to Figure 2 and Appendix D, the results show that the relationships between

IVs (enhancement of self-image and need to belong) and DV (referral behavior) are

significant (p < 0.05). It indicates that these two positive relationships are significant,

which support Hypothesis 1 and 2. As for the positive relationship between concern for

others and referral behavior, the standardized coefficient is 0.131 with p = 0.078, which

means the result is insignificant.

Independent Variables

Enhancement of self-image

Need to belong

Concern for others

Referral

behavior

H1 (+) (0.325***), t = 4.069

H2 (+) (0.166**), t = 2.613

H3 (+) (0.131), t = 1.768

Dependent Variables


31

Table 8: Multiple Regression

Dependent Variable: Referral Behavior

Step 1 Model 1 Model 2

Gender 0.075 0.064

Tertiary institution -0.029 0.025

Study major -0.050 -0.022

Internet usage -0.004 -0.060

Step 2

Enhancement of self-image 0.325***

Need to belong 0.166**

Concern for others 0.131

F Value 0.670 13.421

R2 0.011 0.280

Adjusted R2 -0.005 0.259

Change in R2 0.011 0.269

* p < 0.05 ; ** p < 0.01; *** p < 0.001

The above figures are standardized coefficients β.

Referring to Table 8 and Appendix D, two steps are conducted to examine the main effect

of the model. In the first model, control variables including gender, tertiary institution,

study major and internet usage are applied. In the second model, enhancement of self-

image, need to belong and concern for others are also put.

The adjusted R2 is 0.259 which indicates that the association between the interpersonal

relations needs and referral behavior is not very strong. Among the three significant

variables, the standardized coefficients (ß Value) were 0.325, 0.166 and 0.131

respectively. In other words, all the three interpersonal relations needs had positive effect


32

on the referral behavior, whilst enhancement of self-image has the largest ß (ß = 0.325)

which means it has the strongest effect on DV among three.

Moreover, the VIF of the three IVs are 2.139, 1.355 and 1.853 which are all below 3

respectively. It indicates that there is no multicollinearity problem.

5.4.2 Moderating effect of relevance, credibility, and timeliness on relationship between

respective interpersonal relations need and referral behavior

To test the moderating effect of each respective eWOM qualities in the model, i.e.

relevance, credibility, and timeliness, three steps are involved in the test by applying

hierarchical regression analysis suggested by Nually and Bernstein (1994). First, we

inputted the controlled variables, followed by each IV and responsive moderator. Lastly,

the calculated interaction level of the respective constructs is tested. Since we are going

to test each of the relationship, three results of the same procedure will be generated.

Please refer to Appendix E to see the complete results and statistics.


33

Figure 3: H1a - The moderating effect of relevance on the relationship between

enhancement of self-image and referral behavior

Figure 4: H2a - The moderating effect of credibility on the relationship between

need to belong and referral behavior

Figure 5: H3a - The moderating effect of timeliness on the relationship between

concern for others and referral behavior

Relevance

Enhancement of self-image Referral

behavior

H1a (+) (0.115*) t = 2.194

Dependent Variable

Independent Variable

Timeliness

Concern for others Referral

behavior

H3a (+) (-0.092) t = 1.513

Dependent Variable


Moderator

Moderator


Credibility

Need to belong Referral

behavior

H2a (+) (0.033) t = 0.579

Dependent Variable

Moderator


34

From result of Figure 3, relevance shows a significance level (p = 0.029) in moderating

the relationship between enhancement of self-image and referral behavior. Contrary to

our prediction, in Figure 4, credibility fails to prove the effectiveness in serving as a

moderator between need to belong and referral behavior (p = 0.563). In addition to

credibility, timeliness also fails to show a significance (p = 0.132) in moderating the

relationship between concern for others and referral behavior in Figure 5.

Investigating further, we can see from the below Table 9 for the statistical results of the

above proven hypothesis.

The table demonstrates each step in conducting the regression analysis with respective

considerable figures. The increased value in R square in the latter models reveal that the

explanation power of the latter model is larger when more variables are added to the

model. The adjusted R square is 0.325. The standardized coefficient β in the interaction

effect between enhancement of self-image and relevance is 0.115, which confirmed the

positive direction among IV, DV, and moderators.

In summary, H1a is supported with high significance while H2a and H3a are rejected.


35

Table 9: H1a – The moderating effect of relevance on the relationship between

enhancement of self-image and referral behavior

Dependent Variable: Referral Behavior

Model 1 Model 2 Model 3

Step 1

Age -0.076 -0.051 -0.053

Internet usage frequency -0.018 -0.036 -0.030

Step 2

Enhancement of self-image 0.355*** 0.368***

Relevance 0.310*** 0.312***

Step 3

Interaction: Enhance x Relevance 0.115*

* p < 0.05 ; ** p < 0.01; *** p < 0.001

The above figures are standardized coefficients β.

F Value 0.744 37.069 30.668

R2 0.006 0.326 0.339

Adjusted R2

-0.002 0.315 0.325

Δ in R² 0.006 0.320 0.013


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5.5 Overview of hypotheses

Table 10: Overview of hypotheses

Hypothesis Independent Variables Moderators Dependent

Variables

Test Result

Direct Effect

H1 Enhancement of self-image Referral Behavior Supported

H2 Need to belong Referral Behavior Supported

H3 Concern for others Referral Behavior Rejected

Moderating Effect

H1a Enhancement of self-image Relevance Referral Behavior Supported

H2a Need to belong Credibility Referral Behavior Rejected

H3a Concern for others Timeliness Referral Behavior Rejected

Table 10 shows the overview of the hypothesis. To conclude, H1, H2, and H1a are

supported with our statistics output, while H3, H2a, and H3a are rejected.


37

6. Discussion and implication

This research aims at addressing several objectives. First, using FIRO theory suggested

by Schutz (1966) from the interpersonal communication literature as a framework for the

study, we investigate the influence of psychological motivators in the context of eWOM

referral behavior. Meanwhile, we examine information qualities as the moderating effect.

We suggest that the higher the salient motives for interpersonal communication

(enhancement of self-image, need to belong, concern for others), the higher the chance

for referral behavior. Our findings show that out of three motives, enhancement of self-

image and the need to belong are positively related to referral behavior. Other than that,

our research reflects that among all qualities of eWOM (relevance, credibility, and

timeliness), only relevance can moderate the positive relationship of the enhancement of

self-image and the referral behavior.

The study reveals some expected and unexpected findings and it proffers some valuable

insights in explaining the reasons for people in engaging in eWOM forwarding. It also

points out that the information relevance is able to strengthen the relationship. For the

negative result generated from the study, we conducted a focus group interview with 10

students to further investigate on the reasons behind. The 10 students were selected from

Hong Kong Baptist University using convenience sampling. We asked the same

screening questions as we have done in questionnaires before we started the interview.


38

6.1 Interpersonal relations needs on referral behavior

Internet enables people all over the globe communicate with each other and build

relationships. Based on this assumption, we form a model with the psychological motives

being the IVs in altering eWOM referral behavior. The result supports two out of the

three interpersonal relations needs have positive effects on referral behavior.

Of the two motivators under inclusion, enhancement of self-image and need to belong are

both positively related to the referral of online information. The findings are consistent

with the extant literatures that the individuals are likely to publicly talk about online

information with others in order to individualize themselves and show their superb

uniqueness (Chan & Misra, 1990; Taylor, Crocker, Fiske, Sprinzen & Winkler, 1979).

Hence, individuals with those need to be considered as more influential engaged in more

referral behavior as suggested in the model.

Similarly, existing studies suggested that the major purpose of using internet is to keep in

touch with others, form and maintain relationships (Flanagin & Metzger, 2001). The

findings reveal that need to belong also has a significant effect in referral behavior of

online WOM. People interact with the members on the online social platforms by

engaging in exchange of information. Referral of online content represents their

contribution in the group and helps them to obtain more social recognition and attention.

Therefore, individuals with high need to belong disseminate more online information to

others to consolidate their presence in the social circle.

Of the motivator under affection, though concern for others did not significantly

influence the relationship with the referral of online information, the p value (p = 0.078)


39

is close to 0.05. The finding did not support a direct relationship with the previous

research that altruism factors do have an influence in individuals’ behavior. In regards of

the affect aspect, as mentioned in the literature review part, people tend to engage in

different activities to make themselves feel better. In view of this, people may not treat

saving other consumers from a negative experience as an uplifting mood activity. They

may prefer helping others in another way (Curtis, Paul & Frank, 2006).

Considering the strong positive predictor of enhancement of self-image to referral

behavior, this may explain why the predictor concern for others is comparatively

insignificant with other IVs since some of them think that they might lose face in telling

others a negative experience. This reason is supported by the follow-up focus group

interview. Some of the interviewees mentioned the following comments, which provided

some clues to us.

“In fact it somehow depends on the type of product. But all in all, I wouldn’t let

my friends know that I was so dump that I fell into the trap of experiencing such a

negative experience.” (Amy, female)

“I think everyone has different opinions towards different products. I didn’t want

to confuse them with their own experiences.” (Vivian, female)

“How would I uplift my mood? I would chat with my friends; listen to music or be

alone for a while. I would not treat forwarding a product to others as a means to

make myself feeling better. There are other ways to do it.” (Danielle, female)


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6.2 Moderators – Relevance, Credibility, Timeliness

Employing the three information qualities, i.e. relevance, credibility, and timeliness as the

moderators in the model, we discovered that only relevance is supported as a moderator.

6.2.1 Moderating effect of relevance between enhancement of self-image and referral

behavior

Among the three qualities, relevance is the only effective moderator in moderating the

relationship between enhancement of self-image and referral behavior. It implies that the

relationship between enhancement of self-image and referral behavior will be stronger for

the higher relevancy of a eWOM message. With the support from Pham (1998), the

relevancy of information is a vital part in consumer decision making. In this information

glut era, consumers tend to screen the information which matches with their needs and

image (Shenk, 1997). It is reasonable that they are more likely to forward the product to

their friends when the message is relevant to both the sender and receiver given that the

sender have the need to be considered as more influential among their friends.

6.2.2 Moderating effect of credibility between need to belong and referral behavior

On the other hand, contradict with our hypothesis, credibility is proven to be insignificant

in affecting the strength between the relationship of need to belong and referral behavior.

Previously we had an assumption that people with strong need to belong to the social

circle have more concern in the information credibility as it is a tool to reflect their

presence and contribution in the group. Disregarding the fact that quality of comments is


41

fluctuant since everyone can leave comments on the online social platforms, the

credibility of the messages is losing its prominence in affecting the information receiver.

The analysis from our focus group interview showed that 8 out of 10 of the interviewees

agreed that credibility is important when they adopted the comments in making a high-

involvement purchase decision. However, 6 out of the 8 interviewees think that

credibility is not that important when they forward the information to others. The

followings interviewees summarized their attitude towards forwarding information to

others as follows:

“When I got a message which was beneficial to others or it was fun to share, I

would immediately forward it to others. My first reaction didn’t response to its

credibility but the worthiness to forward to others…somehow I would like to show

that I was the first one to forward the message.” (Amy, female)

“There were too many messages flooding in various online social platforms like

Facebook and Whatsapp. So, once I found the information was relevant to me and

my friends, I would immediately forward it to them. I might not consider the

credibility of the message.” (Gladys, female)

“Anyone could post on the online social platforms. To be honest, who cares

about the credibility? It depends on you who believe it or not!” (Jason, male)

“I seldom refer a product or service to others unless I really tried that product,

not to mention evaluating the credibility of the information. Beside, why do I need


42

to promote the product or service to others? I am not the spokesperson for the

company.” (Roy, male)

This provides clues for us to understand how the young adults’ responses to the

information they received in this data smug age. Most of them disregard the credibility of

the eWOM when it comes to the forwarding activities. It may be inferred as the

interpersonal relations needs, which explain why credibility cannot serve as an effective

moderator between need to belong and referral behavior.

6.2.3 Moderating effect of timeliness between concern for others and referral behavior

From our findings, timeliness did not serve as a significant predictor in moderating the

relationship between concern for others and referral behavior. It contradicted to our

assumption that when the information is more timely and recent, the relationship between

concern for others and referral behavior will be strengthened. However, from the analysis

of the focus group interview, we found that such relationship is still valid when it

specifically applies to limited time offer promotion message. People are more likely to

refer the information to their friends because they care about them, and therefore want to

share with them.

“It depended on the type of message. If there was a limited time offer promotional

message, I would definitely forward it to my friends just because I wanted them to

get the discount.” (Rachel, female)

“Some of the platforms like OpenRice, the old comments are still useful for me to

judge if this was a good restaurant or not.” (Jason, male)


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7. Recommendations

With the increasing usage and popularity of social media, many studies emphasize on the

value for marketers to recognize the market mavens (Feick & Price, 1987; Laroche, Pons,

Zgolli, Cervellon & Kim, 2003) because more consumers access to online information

and use their comments as an indicator in decision-making.

Compared with information qualities, our findings suggest that interpersonal

communication needs can induce more referral conversations. Hence, we recommend

marketers to develop viral marketing campaigns that resonate with the target audience

and provide the audience a reason to share the information with others.

Among the three interpersonal communication needs, the findings in our research suggest

that individual is motivated to forward eWOM in their social network as a way to stand

out from the crowd and as a symbol of contribution to the group. On the other hand, the

results of our research also find that the higher the relevancy of the message, the stronger

the motivation for consumers to forward the product to their friends given their need to

enhance self-image.

Thus, we suggest that marketers, when designing marketing strategies, should consider

incorporating the above motivations.

In regard to the marketing strategies planning, the company can adopt the following

suggestions:

First and foremost, based on general finding of our model, marketers should develop a

platform for consumers to exchange eWOM of your product and service. Owing to the


44

growing lack of trust in market-generated information, more consumers seek information

from online WOM from other customers. It is necessary to provide a platform for them to

exchange information online. Some may claim that eWOM is difficult to control, but it

does more good than harm to the company image if they are properly monitored. Even if

a customer has posted a negative comment, company should react promptly to deal with

the concern and in this way the brand image can be promoted. Company can also learn

from the feedbacks from the consumers in order to improve the quality of service.

Second, also based on the general findings of our model, marketers should create

meaningful and relevant content, as well as making “sharing” easy and user-friendly.

Content generated in social media platform should be relevant, appealing and meaningful.

Meanwhile, consumers should be encouraged to share the content through various easy

and user-friendly tools. Companies should design campaigns that have a “Share to

Friends” function. In some campaigns that ask a consumer to create their own content,

they may be interesting in showing to their friends what they have created. Make the

sharing option as simple as possible and to create something interesting that audience

find worth sharing.

Third, based on the supported hypothesis H1, marketers should encourage customers to

share their buying success on the designated platform. Findings show that recommending

a product or service to others can express one’s joy about a good buy and be viewed as a

smart consumer. Marketers can take advantage of this psychological drive and launch a

membership class on the platform to award active eWOM contributors. Members may be


45

classified from ordinary users to platinum users, and the latter may receive samples in the

new product launch as rewards.

Fourth, based on the supported hypothesis H2, marketers should design interesting

campaigns that start a conversation in the social group. Successful viral marketing

campaigns usually have a sense of humor or some surprise elements. When people are

pleasantly amazed, they engage in more forwarding behavior because it can be a good

topic to start and talk about in the social circle.

Fifth, based on the supported moderating effect of information relevance H1a, marketers

should incorporate relevant key words in promotional content. Online social platform

users normally find the comments of the products by sorting keywords, it is then crucial

for marketers to decide what keywords to be included in their online promotion contents

so that their products or services could be easily searched.

Sixth, again based on the supported hypothesis of H1a, marketers should make good use

of the Search Engine Optimization (SEO). In regards of the shopping websites, by

developing an excellent recommendation engines, users are offered with relevant

products or services based on what they have searched. This kind of engine enlightens

users in discovering other offerings which they might not aware they need to buy, based

on their habits and patterns. Some multi-national firms like Amazon and eBay have been

using this and they are proved to be successful.

Last but not least, based on the supported hypothesis of H1a, when people consider a

comment to be relevant to their self-image, they express themselves by increasing the


46

amount and valence of eWOM communication. Therefore, marketers should strengthen

image association between their brand and information receivers. Companies like Acuvue

Hong Kong and Red Bull are successful examples in creating strong linkage between the

products or services and the customers’ sense of self. They target at outgoing university

students and launch adventurous interactive marketing campaign. It generates favorable

eWOM every year when the game season starts.


47

8. Limitations and future study

In spite of the findings and insights related to eWOM are found in the study, some

limitations are inevitably existed.

First, due to time and budget constraints, we used convenience sampling as the method of

research design which induced bias problem. Besides, only three local universities are

selected for the distributions of questionnaires, which may not represent the whole

sample of university students in Hong Kong.

Second, the marginal significance of the information quality “relevance” and “timeliness”

may due to the small sample size. It may lack of generalization because our target

respondents are university students only. It is suggested that future study may expand the

target respondents to more online users aged from 17-25.

Third, eWOM is an extensive topic which includes a wide array of theories supporting

different hypothesis. In this research, our model is restricted to the FIRO theories for the

IVs while only three qualities of information qualities are included. Future study may

consider including more argument qualities such as accuracy, helpfulness, and

comprehensiveness.

Last but not least, the field of study is too board which may affect the accuracy of the

results. Since our research is based on all kinds of online social platforms, responses

mainly depend on the platforms in which the respondents have recent experiences.


48

9. Conclusion

Various forms of social media platforms are emerging, and with more consumers being

gravitated towards the internet, marketers have to catch the attention of the audience and

stand out from the battle field. With less perceived credibility in market-generated

information, there has been an increasing trend for consumers to use online WOM in

purchase decision-making process.

The contribution of the study is to provide insights into the important drives associated

with the referral behavior of eWOM and how the qualities of eWOM information serve

as the moderating effect. The findings mostly support the proposed hypothesis. Three

interpersonal relations needs based on FIRO model are positively related to referral

behavior of online comments. As for relevance and timeliness of comment, they proved

to have marginal significant influence in altering the relationship.

Our research contributes a better understanding in eWOM area. We have proved that the

FIRO model of interpersonal relations communication can be also applied in the online

context. Moreover, most existing studies have only examined the online information

qualities as IVs for consumers to adopt eWOM, we shed a new insight to use quality of

information as moderators and it is consistent with our assumption that factual

information qualities are less influential than psychological motives when they come to

eWOM spreading nowadays.

Despite of the limitations, we hope the model can add value to the knowledge of eWOM

area and assist in future research.


49

10. Reference

Ajzen, I., & Sexton, J. (1999). Depth of processing, belief congruence, and attitude-

behavior correspondence. In S. Chaiken & Y. Trope (Eds.). Dual process theories

in social psychology, NewYork, NY: Guilford Press, 97, 117-138.

Bagozzi, R. P., & Moore, D. J. (1994). Public-Service Advertisements - Emotions and

Empathy Guide Prosocial Behavior. Journal of Marketing, 58(1), 56-70.

Bailey, J.E. & Pearson, S.W. (1983). Development of a tool for measuring and analyzing

computer user satisfaction. Management Science, 29(5), 530–545.

Balasubramanian, S., & Mahajan, V. (2001). The Economic Leverage of the Virtual

Community. International Journal of Electronic Commerce, 5(Spring), 103-138.

Batson. C.D. (1994). Why act for the public goods? Four answers. Personality and

Social Psychology, 20(5), 603-610.

Baumeister, R.F., & Leary, M.R. (1995). The need to belong: Desire for

interpersonal attachments as a fundamental human motivation. Psychological

Bulletin, 117, 497-529.

Bickart, B., & Schindler, R.M. (2001). Internet forums as influential sources of

consumer information. Journal of Interactive Marketing, 15(Summer), 31-52.

Brown, J., Broderick, A. J., & Lee, N. (2008). Word of mouth communication within

online communities: Conceptualizing the online social network. J. Interactive

Mark, 21, 2-20.


50

Chaiken, S. (1980). Heuristic versus systematic information processing and the use of

source versus message cues in persuasion. Journal of Personality and Social

Psychology, 39(5), 752-766.

Chaiken, S., & Trope, Y. (1999). Dual-Process Theories in Social Psychology. New York,

NY: Guilford Press, xiii.

Chan, K.K., & Misra, S. (1990). Characteristics of the opinion leader: a new dimension. J

Advert, 19(3), 53-60.

Chatterjee. P. (2001), Online reviews: do consumers use them?. Advances in Consumer

Research, 28, 129-133.

Cheung, C. M. K., Lee, M. K. O., & Rabjohn, N. (2008). The impact of electronic

word of-mouth. Internet Research, 18(3), 229-247.

Cheung, C.M.K., & Thadani, D.R. (2012). The impact of electronic word-of-mouth

communication: A literature analysis and integrative model. Decision Support

Systems, 54(1), 461-470.

Chin-Lung, L., Sheng-Hsien, L., & Der-Juinn, H. (2011). The Effects of Online Reviews

on Purchasing Intention: The Moderating Role of Need For Cognition. Social

Behavior & Personality: An International Journal, 39(1), 71-81.

Chu, S.C., & Kim, Y.J. (2011). Determinants of consumer engagement in electronic

word-of-mouth (eWOM) in social networking sites. International Journal of

Advertising, 30(1), 47-75.

Chung, C., & Darke, P. (2006). The consumer as advocate: self-relevance, culture,

and word-of mouth. Marketing Letters, 17(4), 269-279.


51

Cialdini, R. B., Darby, B. L., & Vincent, J. E. (1973). Transgression and Altruism - Case

for Hedonism. Journal of Experimental Psychology, 9(6), 502-516.

Crosby, Lawrence A., Evans, Kenneth R. & Cowles, Deborah. (1990). Relationship

Quality in Services Selling: An Interpersonal Influence Perspective. Journal of

Marketing, 54, 68-81.

Curtis P. Haugtvedt, Paul Herr & Frank Kardes. (2006). The Nature and Role of Affect

in Consumer Behavior. Handbook of Consumer Psychology.

De Bruyn, A. & Lilien, G. (2008). A Multi‐Stage Model of Word‐Of

Mouth Influence. International Journal of Research in Marketing, 25, 151‐163.

Dichter, E.,1966. How Word of Mouth Advertising works. Harvard Business Review.

44(6), 147-161.

eMarketer. (2006). Online Travel Worldwide. Accessed February 15.

Engel, J.F., Blackwell, R.D., & Miniard, P.W. (1993). Consumer Behavior (8th ed.).

Fort Worth: Dryden Press.

Fazio, R. H., & Towles-Schwen, T. (1999). The MODE model of attitude-behavior

processes, In S. Chaiken and Y. Trope (Eds.). Dual-process theories in social

psychology, NewYork,NY: Guilford Press, 97-116.

Feick, L.F., & Price, L.L. (1987). The market maven: a diffuser of marketplace

information. J Mark, 51(1), 83-97.

Flanagin, A.J., & Metzger, M.J. (2001). Internet use in the contemporary media

environment. Hum Comm Res, 27(1), 153-181.


52

Gruen, Thomas W., Osmonbekov, Talai & Czaplewski, Andrew J. (2006). eWOM:

The Impact of Customer-to-Customer Online Know-How Exchange on

Customer Value and Loyalty. Journal of Business Research, 59(4), 449-456.

Gupta, P. & Harris, J. (2010). How e-WOM recommendations influence product

consideration and quality of choice: A motivation to process information

perspective. Journal of Business Research, 63(1), 1041-1049.

Hagel, J.III, & Armstrong, A.G. (1997). Net Gain: Expanding Markets through Virtual

Communities. The McKinsey Quarterly, 1, 140-153.

Hair, Jr. J.F., Wolfinbarger, M.F., Ortnau, D.J., & Bush, R.P. (2010). Essentials of

Marketing Research, New York: McGraw-Hill Irwin.

Hamat, A., Embi, M. A., & Hassan, H. A. (2012). The use of social networking sites

among Malaysian university Students. International Education Studies, 5(3),

56-67.

Hennig-Thurau, T., Gwinner, K.P. & Gremler, D.D. (2002). Understanding

relationship marketing outcomes: an integration of relational benefits and

relationship quality. Journal of Service Research, 4(3), 230-247.

Hennig-Thurau, T., Gwinner, K.P., Walsh, G., & Gremler, D.D. (2004). Electronic

word-of-mouth via consumer-opinion platforms: what motivates consumers to

articulate themselves on the Internet?. Journal of Interactive Marketing, 18(1),

38-52.

Ho, J.Y.C., & Dempsey, M. (2010). Viral Marketing: Motivations to Forward Online

Content. Journal of Business Research, 63(9-10), 1000-1006.


53

Hsu, Yi, & Tran, Thi Hong Chau. (2013). Social Relationship Factors Influence on

eWOM Behaviors in Social Networking Sites: Empirical Study: Taiwan and

Vietnam. International Journal of Business, Humanities and Technology, 3(3),

22-31.

Huang, L.S., Chou, Y.J., & Lan, I.T. (2007). Effects of Perceived Risk, Message Types,

and Reading Motives on the Acceptance and Transmitssion of Electronic Word-

of-Mouth Communication. Contemporary Management Research, 3(4), 299-312.

Kamtarin, M. (2012). The effect of Electronic Word of Mouth, Trust and Perceived

Value on behavioral Intention from the Perspective of Consumers. International

Journal of Academic Research in Economics and Management Sciences, 1,

56-66.

Kerlinger, Fred N. (1973). Foundations of Behavioral Research. 2nd edition.

Kollock. P. (1999). The economies of online cooperation: gifts, and public goods in

cyberspace, in: M.A. Smith, P. Kollock (Eds.), Communities in Cyberspace, 220-

239.

Kraut, R., Mukhopadhyay, T., Szczypula, J., Kiesler, S., & Scherlis, B. (1999).

Information and Communication: Alternative Uses of the Internet in Households.

Information Systems Research, 10(4), 287-303.

Laroche, M., Pons, F., Zgolli, N., Cervellon, M.C., & Kim, C. (2003). A model of

consumer response to two retail sales promotion techniques. J Bus Res, 56, 513-

522.


54

Lee, J., Park, D.-H. & Han, I. (2008). The effect of negative online consumer reviews

on product attitude: An information processing view. Electronic Commerce

Research and Applications, 7(3), 341.

Lee, M.K.O., Cheung, C.M.K., Lim, K.H., & Sia, C.L. (2006). Understanding

customer knowledge sharing in web-based discussion boards: an exploratory

study. Internet Research, 16(3), 289-303.

Leung, N.Y., & Ma, K.C. (2012). Factors Affecting Hong Kong Customers’ E-loyalty

and Repeated Purchase Intention in Online Chinese Retail Stores and the

Moderating Effect of Electronic Word-of-Mouth. Hong Kong Baptist University,

6-12.

López, M., & Sicilia, M. (2013). How WOM marketing contributes to new product

adoption: testing competitive communication strategies. European Journal of

Marketing, 47(7), 1089-1114.

Lynn, Peter. (2001). A Quality Framework for Longitudinal Studies. ULSC, 1-14.

Mahmood, M.A. & Medewitz, J.N. (1985), Impact of design methods on decision support

system success: an empirical assessment. Information and Management, 9(3),

137-151.

Malhotra, N.K. (2010). Marketing Research: An Applied Orientation (6thed.). Boston:

Pearson Prentice Hall.

McWilliam, G. (2000). Building Stronger Brands Through Online Communities. Sloan

Management Review, 41(Spring), 43-54.


55

Morgan, RM., & Hunt, S.D. (1994). The commitment-trust theory of relationship

marketing. Journal of Marketing, 58, 20-38.

Negash, S., Ryanb, T. & Igbariab, M. (2003). Quality and effectiveness in web-based

customer support systems. Information & Management, 40, 757–768.

Nunally, J.C., Bernstein, I.H. (1994). Psychometric Theory. New York: McGraw-

Hill

Olaru, D., Purchase, S., & Peterson, N. (2008). From customer value to repurchase

intentions and recommendations. Journal of Business & Industrial Marketing,

23(8), 554-565.

Oliver, R.L. (1999). Whence Customer Loyalty?. Journal of Marketing, 63(4), 33-44.

Parasuraman, Berry & Zeithaml. (1991). Understanding Customer Expectations of

Service. Sloan Management Review, Spring, 39-48.

Petty, R. E., & Cacioppo, J. T. (1986). Communication and persuasion: Central and

peripheral routes to attitude change. New York: Springer-Verlag.

Pham, M.T. (1998). Representativeness, Relevance, and the Use of Feelings in Decision

Making. Journal of Consumer Research, 25(2), 144-159.

Price, L.L., Feick, L.F., & Guskey, A. (1995). Everyday market helping behavior. J

Public Policy Mark, 14(2), 255-266.

Quan-Haase, A., & Young, A.L. (2010). Uses and Gratifications of Social Media: A

Comparison of Facebook and Instant Messaging. Bulletin of Science, Technology

& Society, 30, 350-361.


56

Richins, Marsha L. (1983). Negative Word-of-Mouth by Dissatisfied Consumers: A

Pilot Study. Journal of Marketing, 47(Winter), 68-78.

Riegner, C. (2007). Word of mouth on the web: The impact of web 2.0 on consumer

purchase decisions. Journal of advertising research, 47(4), 436-447.

Rieh, S.Y. (2002). Judgment of information quality and cognitive authority in the web.

Journal of the American Society for Information Science and Technology, 53(2),

145-61.

Rosenberg, M. J., & Hovland, C. I. (1960). Cognitive, affective, and behavioral

components of attitudes, In C. I. Hovland, & M. J. Rosenberg (Ed.), Attitude

organization and change: An analysis of consistency among attitude components,

New Haven, CT: Yale University Press, 1-14.

Schindler, Robert M. & Bickart, Barbara. (2005). Published Word of Mouth:

Referable. Consumer-Generated Information on the Internet, 35-62.

Schlosser, A. E., & Lloyd, S. M. (2006). Converting Web Site Visitors into Buyers: How

Web Site Investment Increases Consumer Trusting Beliefs and Online Purchase

Intentions. Journal of Marketing, 70(2), 1-148.

Schmitt, P., Skiera, B., & Van den Bulte, C. (2011). Referral Programs and Customer

Value. Journal of Marketing, 75(January), 46 -59.

Schutz WC. (1966). FIRO: A Three Dimensional Theory of Interpersonal Behavior.

New York: Holt, Rinehart, & Winston


57

Shea, L., Enghagen, L., & Khullar, A. (2004). Internet Diffusion of an E-Complaint:

A Content Analysis of Unsolicited Responses. Journal of Travel & Tourism

Marketing, 17(2/3), 145-165.

Shenk, D. (1997). Data Smog: Surviving the Info Glut. Technology Review, 1(4), 18-26.

Siegle, D. (2002), Qualitative Research, Principles and Methods in Educational Research.

http://www.gifted.uconn.edu/siegle/research/Qualitative/qualitativeInstructorNote

s.html

Stauss, B. (2000). Using new media for customer interaction: a challenge for

relationship marketing. in Hennig-Thurau, T. and Hansen, U. (Eds). Relationship

Marketing, Springer, 233-253.

Sundaram, D.S., Mitra, K., & Webster, C. (1998). Word of-Mouth Communications:

A Motivational Analysis. Advances in Consumer Research, 25, 527-531.

Sussman, S.W. and Siegal, W.S. (2003). Informational influence in organizations: an

integrated approach to knowledge adoption. Informational Systems Research,

14(1), 47-65.

Sweeney, J.C., Soutar, G.N. & Mazzarol, T. (2008), Factors influencing word of mouth

effectiveness: receiver perspectives, European Journal of Marketing, 42(3/4),

344-364.

Tajfel, H. & Turner, J.C. (1986). The social identity theory of intergroup behavior. In S.

Worchel & W.G. Austin (Eds.). Psychology of intergroup relations, 7-24.

Taylor, S.E., Crocker, J., Fiske, S.T., Sprinzen, M., & Winkler, J.D. (1979). The

generalizability of salience effects. J Pers Soc Psychol, 37(3), 357–68.


58

Wangenheim, F. V., & Bayon, T. (2004). The effect of word of mouth on services

switching: measurement and moderating variables. European Journal of

Marketing, 38(9/10), 1173-1185.

Wasko, M. M. & Faraj, S. (2005). Why should I share? Examining social capital and

knowledge contribution in electronic networks of practice. MIS Quarterly, 29,

35–57.

Wixom, B.H. & Todd, P.A. (2005). A theoretical integration of user satisfaction and

technology acceptance. Information Systems Research, 16(1), 85-102.

Wu, C. & Shaffer, D. (1987). Susceptibility to persuasive appeals as a function of source

credibility and prior experience with the attitude object. Journal of Applied

Psychology, 52, 677-688.


59

11. Appendix

Appendix A:

Questionnaire


60

Survey on electronic word of mouth (網上口碑)

We are final-year students studying in BBA (Hons) Marketing at Hong Kong Baptist University.

We are conducting a study about electronic word of mouth (eWOM) on online social platforms.

Please spare 5 minutes to complete ALL the questions. Your responses will be kept strictly

confidential and your participation is very important to us. Thank you.

Part A

1. Are you a university student?

□ Yes □ No (End of the questionnaire )

2. Have you ever used the following online social platforms? (You may choose more than one

answer.)

□ Blogs □ Forums

□ Mobile apps

□ Online guide, i.e. OpenRice

□ Online shopping websites □ Social Media

□ Others, please specify: __________________

□ No (End of the questionnaire, thank you.)

3. Have you ever adopted comments (採納意見) of a product or service written by other

platform users in the online social platforms?

□ Yes □ No (End of the questionnaire, thank you.)

4. During the past six months, how many times have you adopted comments of a product or service written by other platform users in the online social platforms?

□ 1 – 3 times □ 4 – 6 times □ 7 – 9 times □10 times or above

5. Have you ever talked about (與別人提及您所採納的意見) that product or service with

others based on the acceptance of the comment?

□ Yes

□ No (End of the questionnaire, thank you.)


61

6. During the past six months, how many times have you talked about that product or service

with others?

□ 1 – 3 times □ 4 – 6 times □ 7 – 9 times □ 10 times or above

Part B

7. Please think of ONE product or service that you adopted the comments from an online

social platform and talked about it with others (採納意見並與別人提及它).

Product or service:____________________________

(e.g. Restaurant / Cosmetics / Clothes)

Online social platform:_________________________

(e.g. OpenRice / Instagram / Taobao)

* Please remember your answer as you need to refer to it in the later parts of the survey.

* 請記住您的答案，以便填寫問卷的後半部分。

Part C

Based on what you have written in Part B,

Strongly

Disagree <

Strongly

> Agree

8. The comment on that platform is relevant to

me.

1 2 3 4 5 6 7

9. People who left comments in this platform

are trustworthy.

1 2 3 4 5 6 7

10. The comment on that platform is most

recent.

1 2 3 4 5 6 7

11. The comment on that platform is appropriate

to me.

1 2 3 4 5 6 7

12. People who left comments in this platform

are knowledgeable in evaluating quality of

the product or service.

1 2 3 4 5 6 7

13. That comment is most current. 1 2 3 4 5 6 7

14. The comment on that platform is applicable

to me.

1 2 3 4 5 6 7

15. I believe in the comments given by others in

the platform.

1 2 3 4 5 6 7

16. The comment is always up-to-date. 1 2 3 4 5 6 7

Part D



62

Bad < > Good

17. What is your personal attitude towards

the online social platform?

1 2 3 4 5 6 7

Dislike < > Like

1 2 3 4 5 6 7

Unpleasant < > Pleasant

1 2 3 4 5 6 7

Unfavorable

<

>

Favorable

1 2 3 4 5 6 7

Part E


Strongly

Disagree <

Strongly

> Agree

18. I am likely to accept the comment on that

platform.

1 2 3 4 5 6 7

19. It is possible for me to forward that

information to others.

1 2 3 4 5 6 7

20. I agree with the opinion suggested on that

platform.

1 2 3 4 5 6 7

21. It is likely that I would forward that online

information to others through online.

1 2 3 4 5 6 7

22. I closely followed the suggestions of the

comment on that platform.

1 2 3 4 5 6 7

23. I want to tell my friends after reading the

comment on that platform.

1 2 3 4 5 6 7

Part F

Based on your personal attitude,

Strongly

Disagree <

Strongly

> Agree

24. Recommending a product or service to

others can express my joy about a good buy.

1 2 3 4 5 6 7

25. I want other people to accept me. 1 2 3 4 5 6 7

26. I want to warn others of bad products. 1 2 3 4 5 6 7

27. My contributions show others that I am a

clever customer.

1 2 3 4 5 6 7

28. My feelings are easily hurt when I feel that

others do not accept me.

1 2 3 4 5 6 7

29. I want to save others from having the same

negative experiences as me.

1 2 3 4 5 6 7


63

30. I want to tell others about a great

experience.

1 2 3 4 5 6 7

31. I have a strong need to belong. 1 2 3 4 5 6 7

32. I want to help others with my own positive

experiences.

1 2 3 4 5 6 7

33. I feel good when I can tell others about my

buying successes.

1 2 3 4 5 6 7

34. I try hard not to do things that will make

other people avoid or reject me.

1 2 3 4 5 6 7

35. I want to give others the opportunity to buy

the right product.

1 2 3 4 5 6 7

Part G

Gender □ Male □ Female

Age □ Below 16 □ 16 to 25 □ Above 25

Tertiary

Institution

□ Hong Kong Baptist University

□ The Chinese University of Hong Kong

□ The University of Hong Kong

Study Major □ Arts

□ Architecture

□ Business

□ Communication

□ Education

□ Engineering

□ Law

□ Medicine

□ Science

□ Social Sciences

□ Others

Internet Usage

Frequency

□ Less than 1 hour a

day

□ 4 to 6 hour a day

□ 1 to 3 hour a day

□ More than 6 hours a day

Monthly Income □ Below HK$1,000

□ HK$3,000 - 5,000

□ HK$1,000 - 3,000

□ Above HK$5,000

- End of the Questionnaire -

Thank you for your time!


64

Appendix B:

Result of Descriptive Statistics and Frequency Distribution


65

Questionnaire - Part G

Gender

Frequency Percent Valid Percent Cumulative

Percent

Valid

Male 116 46.4 46.4 46.4

Female 134 53.6 53.6 100.0

Total 250 100.0 100.0

Tertiary institution


Percent

Valid

Hong Kong Baptist University 94 37.6 37.6 37.6

The Chinese University of

Hong Kong 71 28.4 28.4 66.0

The University of Hong Kong 85 34.0 34.0 100.0

Total 250 100.0 100.0

Study Major


Percent

Valid

Arts 20 8.0 8.0 8.0

Architecture 6 2.4 2.4 10.4

Business 129 51.6 51.6 62.0

Communication 5 2.0 2.0 64.0

Education 4 1.6 1.6 65.6

Engineering 25 10.0 10.0 75.6

Law 5 2.0 2.0 77.6

Medicine 12 4.8 4.8 82.4

Science 21 8.4 8.4 90.8

Social Sciences 21 8.4 8.4 99.2

Others 2 .8 .8 100.0

Total 250 100.0 100.0


66

Internet usage frequency


Percent

Valid

Less than 1 hour a day 5 2.0 2.0 2.0

1 to 3 hour a day 132 52.8 52.8 54.8

4 to 6 hour a day 73 29.2 29.2 84.0

More than 6 hours a day 40 16.0 16.0 100.0

Total 250 100.0 100.0

Questionnaire - Part A – Q 5

Talked about the product or service with others


Percent

Valid Yes 250 100.0 100.0 100.0

Questionnaire - Part A – Q 6

Frequency of talking about the product or service with others


Percent

Valid

1-3 times 105 42.0 42.0 42.0

4-6 times 84 33.6 33.6 75.6

7-9 times 29 11.6 11.6 87.2

10 times or above 32 12.8 12.8 100.0

Total 250 100.0 100.0


67

Appendix C:

Cronbach’s Alpha Coefficient


68

Reliability Test: Referral behavior (DV)

Reliability Statistics

Cronbach's Alpha N of Items

.692 2

Item-Total Statistics

Scale Mean if

Item Deleted

Scale Variance if

Item Deleted

Corrected Item-

Total Correlation

Cronbach's Alpha

if Item Deleted

Recom1 4.68 1.743 .531 .

Recom3 4.84 1.524 .531 .

Reliability Test: Enhancement of self-image (IV)



.794 3


Scale Mean if

Item Deleted

Scale Variance if

Item Deleted

Corrected Item-

Total Correlation

Cronbach's Alpha

if Item Deleted

Enhance1 10.10 4.304 .590 .769

Enhance3 9.84 4.017 .700 .654

Enhance4 10.04 4.050 .625 .734


69

Reliability Test: Need to Belong (IV)



.808 4


Scale Mean if

Item Deleted

Scale Variance if

Item Deleted

Corrected Item-

Total Correlation

Cronbach's Alpha

if Item Deleted

Need1 12.45 11.068 .586 .777

Need2 13.24 9.502 .679 .733

Need3 12.94 10.041 .687 .728

Need4 12.68 11.569 .554 .791

Reliability Test: Concern for others (IV)



.745 2


Scale Mean if

Item Deleted

Scale Variance if

Item Deleted

Corrected Item-

Total Correlation

Cronbach's Alpha

if Item Deleted

Concern3 5.07 1.301 .594 .

Concern4 5.01 1.225 .594 .


70

Reliability Test: Relevance (moderator)



.843 3


Scale Mean if

Item Deleted

Scale Variance if

Item Deleted

Corrected Item-

Total Correlation

Cronbach's Alpha

if Item Deleted

Relevance1 10.03 3.413 .662 .830

Relevance2 10.33 3.612 .741 .754

Relevance3 10.19 3.382 .728 .762

Reliability Test: Credibility (moderator)



.794 3


Scale Mean if

Item Deleted

Scale Variance if

Item Deleted

Corrected Item-

Total Correlation

Cronbach's Alpha

if Item Deleted

Credibility1 8.94 4.081 .656 .700

Credibility2 8.90 3.989 .602 .759

Credibility3 8.59 4.155 .655 .702


71

Reliability Test: Timeliness (moderator)



.746 2


Scale Mean if

Item Deleted

Scale Variance if

Item Deleted

Corrected Item-

Total Correlation

Cronbach's Alpha

if Item Deleted

Timeliness1 4.70 1.401 .595 .

Timeliness3 4.87 1.368 .595 .


72

Appendix D:

Result of Regression Analysis


73

Simple Regression: Referral behavior and interpersonal relations needs

Descriptive Statistics

Mean Std. Deviation N

Mean_Recom 4.7620 1.11774 250

gender 1.54 .500 250

tert_institution 1.96 .847 250

stud_major 4.64 2.798 250

internet_usage_freq 2.59 .777 250

Mean_Enhance 4.9960 .96687 250

Mean_Need 4.2760 1.04939 250

Mean_Concern 5.0400 1.00321 250

Model Summary

Model R R

Square

Adjusted R

Square

Std. Error of the

Estimate

Change Statistics

R Square

Change

F

Change

df1 df2 Sig. F

Change

1 .104a .011 -.005 1.12072 .011 .670 4 245 .613

2 .529b .280 .259 .96229 .269 30.104 3 242 .000

a. Predictors: (Constant), internet_usage_freq, gender, stud_major, tert_institution

b. Predictors: (Constant), internet_usage_freq, gender, stud_major, tert_institution, Mean_Need, Mean_Concern,

Mean_Enhance

ANOVAa

Model Sum of Squares df Mean Square F Sig.

1

Regression 3.367 4 .842 .670 .613b

Residual 307.722 245 1.256

Total 311.089 249

2

Regression 86.996 7 12.428 13.421 .000c

Residual 224.093 242 .926

Total 311.089 249

a. Dependent Variable: Mean_Recom

b. Predictors: (Constant), internet_usage_freq, gender, stud_major, tert_institution

c. Predictors: (Constant), internet_usage_freq, gender, stud_major, tert_institution, Mean_Need,

Mean_Concern, Mean_Enhance


74

Coefficientsa

Model Unstandardized

Coefficients

Standardized

Coefficients

t Sig. Collinearity

Statistics

B Std. Error Beta Tolerance VIF

1

(Constant) 4.688 .366 12.825 .000

gender .168 .143 .075 1.172 .242 .986 1.014

tert_institution -.038 .088 -.029 -.434 .664 .916 1.092

stud_major -.020 .026 -.050 -.752 .453 .918 1.090

internet_usage_freq -.006 .093 -.004 -.067 .946 .962 1.039

2

(Constant) 1.374 .473 2.903 .004

gender .143 .125 .064 1.144 .254 .957 1.045

tert_institution .033 .076 .025 .431 .666 .906 1.103

stud_major -.009 .023 -.022 -.389 .698 .897 1.115

internet_usage_freq -.086 .080 -.060 -1.065 .288 .951 1.051

Mean_Enhance .375 .092 .325 4.069 .000 .467 2.139

Mean_Need .177 .068 .166 2.613 .010 .738 1.355

Mean_Concern .146 .083 .131 1.768 .078 .540 1.853



75

Appendix E:

Result of Moderating Effect Analysis


76

Moderating Effect:

Information relevance on enhancement of self-image and referral behavior

Model Summary

Model R R

Square

Adjusted R

Square

Std. Error of the

Estimate

Change Statistics

R Square

Change

F

Change

df1 df2 Sig. F

Change

1 .077a .006 -.002 1.11890 .006 .744 2 247 .476

2 .571b .326 .315 .92511 .320 58.161 2 245 .000

3 .582c .339 .325 .91799 .013 4.812 1 244 .029

a. Predictors: (Constant), internet_usage_freq, age

b. Predictors: (Constant), internet_usage_freq, age, Mean_Relevance, Mean_Enhance

c. Predictors: (Constant), internet_usage_freq, age, Mean_Relevance, Mean_Enhance,

Interaction_Enhance_Relevance

ANOVAa


1

Regression 1.863 2 .932 .744 .476b

Residual 309.226 247 1.252

Total 311.089 249

2

Regression 101.413 4 25.353 29.625 .000c

Residual 209.676 245 .856

Total 311.089 249

3

Regression 105.468 5 21.094 25.031 .000d

Residual 205.621 244 .843

Total 311.089 249


b. Predictors: (Constant), internet_usage_freq, age

c. Predictors: (Constant), internet_usage_freq, age, Mean_Relevance, Mean_Enhance

d. Predictors: (Constant), internet_usage_freq, age, Mean_Relevance, Mean_Enhance,

Interaction_Enhance_Relevance


77

Coefficientsa

Model Unstandardized Coefficients Standardized Coefficients t Sig.

B Std. Error Beta

1

(Constant) 6.394 1.340 4.771 .000

age -.778 .650 -.076 -1.196 .233

internet_usage_freq -.026 .091 -.018 -.283 .778

2

(Constant) 1.935 1.185 1.634 .104

age -.525 .538 -.051 -.976 .330


Mean_Enhance .411 .068 .355 6.025 .000

Mean_Relevance .386 .073 .310 5.272 .000

3

(Constant) 1.814 1.177 1.541 .125

age -.542 .534 -.053 -1.015 .311


Mean_Enhance .425 .068 .368 6.258 .000

Mean_Relevance .388 .073 .312 5.338 .000

Interaction_Enhance_Relevance .124 .057 .115 2.194 .029



78

Moderating Effect:

Information credibility on need to belong and referral behavior

Model Summary

Model R R

Square

Adjusted R

Square

Std. Error of the

Estimate

Change Statistics

R Square

Change

F

Change

df1 df2 Sig. F

Change

1 .077a .006 -.002 1.11890 .006 .744 2 247 .476

2 .490b .241 .228 .98197 .235 37.843 2 245 .000

3 .492c .242 .226 .98330 .001 .335 1 244 .563


b. Predictors: (Constant), internet_usage_freq, age, Mean_Credibility, Mean_Need

c. Predictors: (Constant), internet_usage_freq, age, Mean_Credibility, Mean_Need, Interaction_Need_Credibility

ANOVAa


1

Regression 1.863 2 .932 .744 .476b

Residual 309.226 247 1.252

Total 311.089 249

2

Regression 74.845 4 18.711 19.405 .000c

Residual 236.244 245 .964

Total 311.089 249

3

Regression 75.169 5 15.034 15.549 .000d

Residual 235.920 244 .967

Total 311.089 249



c. Predictors: (Constant), internet_usage_freq, age, Mean_Credibility, Mean_Need

d. Predictors: (Constant), internet_usage_freq, age, Mean_Credibility, Mean_Need, Interaction_Need_Credibility


79

Coefficientsa


Coefficients

Standardized Coefficients t Sig.

B Std. Error Beta

1

(Constant) 6.394 1.340 4.771 .000

age -.778 .650 -.076 -1.196 .233


2

(Constant) 3.121 1.237 2.523 .012

age -.607 .572 -.059 -1.061 .290


Mean_Need .266 .062 .249 4.251 .000

Mean_Credibility .404 .068 .348 5.945 .000

3

(Constant) 3.095 1.240 2.497 .013

age -.592 .574 -.058 -1.032 .303


Mean_Need .264 .063 .248 4.207 .000

Mean_Credibility .401 .068 .345 5.863 .000

Interaction_Need_Credibility .032 .056 .033 .579 .563



80

Moderating Effect:

Information timeliness on concern for others and referral behavior

Model Summary

Model R R

Square

Adjusted R

Square

Std. Error of the

Estimate

Change Statistics

R Square

Change

F

Change

df1 df2 Sig. F

Change

1 .077a .006 -.002 1.11890 .006 .744 2 247 .476

2 .437b .191 .178 1.01365 .185 27.976 2 245 .000

3 .445c .198 .182 1.01100 .008 2.289 1 244 .132


b. Predictors: (Constant), internet_usage_freq, age, Mean_Timeliness, Mean_Concern

c. Predictors: (Constant), internet_usage_freq, age, Mean_Timeliness, Mean_Concern,

Interaction_Concern_Timeliness

ANOVAa


1

Regression 1.863 2 .932 .744 .476b

Residual 309.226 247 1.252

Total 311.089 249

2

Regression 59.354 4 14.839 14.442 .000c

Residual 251.735 245 1.027

Total 311.089 249

3

Regression 61.694 5 12.339 12.072 .000d

Residual 249.395 244 1.022

Total 311.089 249



c. Predictors: (Constant), internet_usage_freq, age, Mean_Timeliness, Mean_Concern

d. Predictors: (Constant), internet_usage_freq, age, Mean_Timeliness, Mean_Concern,

Interaction_Concern_Timeliness


81

Coefficientsa


Coefficients

Standardized Coefficients t Sig.

B Std. Error Beta

1

(Constant) 6.394 1.340 4.771 .000

age -.778 .650 -.076 -1.196 .233


2

(Constant) 3.276 1.285 2.550 .011

age -.550 .590 -.054 -.931 .353


Mean_Concern .410 .067 .368 6.094 .000

Mean_Timeliness .151 .064 .142 2.360 .019

3

(Constant) 3.397 1.284 2.645 .009

age -.584 .589 -.057 -.991 .323


Mean_Concern .379 .070 .341 5.421 .000

Mean_Timeliness .175 .066 .165 2.661 .008

Interaction_Concern_Timeliness -.085 .056 -.092 -1.513 .132


a study of interpersonal relations needs and referral...

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