A Study of Interpersonal Relations Needs and Referral...
Transcript of 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
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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
Constructs Items Literature
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
Constructs Items Literature
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
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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.
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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.
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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.
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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).
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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
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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
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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.
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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
Independent Variable
Moderator
Moderator
Independent Variable
Credibility
Need to belong Referral
behavior
H2a (+) (0.033) t = 0.579
Dependent Variable
Moderator
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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.
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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.
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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.
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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)
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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
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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
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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
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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
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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
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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.
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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.
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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.
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11. Appendix
Appendix A:
Questionnaire
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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.)
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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
Based on what you have written in Part B,
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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
Based on what you have written in Part B,
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
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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!
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Appendix B:
Result of Descriptive Statistics and Frequency Distribution
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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
Frequency Percent Valid Percent Cumulative
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
Frequency Percent Valid Percent Cumulative
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
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Internet usage frequency
Frequency Percent Valid Percent Cumulative
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
Frequency Percent Valid Percent Cumulative
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
Frequency Percent Valid Percent Cumulative
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
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Appendix C:
Cronbach’s Alpha Coefficient
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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)
Reliability Statistics
Cronbach's Alpha N of Items
.794 3
Item-Total Statistics
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
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Reliability Test: Need to Belong (IV)
Reliability Statistics
Cronbach's Alpha N of Items
.808 4
Item-Total Statistics
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)
Reliability Statistics
Cronbach's Alpha N of Items
.745 2
Item-Total Statistics
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 .
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Reliability Test: Relevance (moderator)
Reliability Statistics
Cronbach's Alpha N of Items
.843 3
Item-Total Statistics
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)
Reliability Statistics
Cronbach's Alpha N of Items
.794 3
Item-Total Statistics
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
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Reliability Test: Timeliness (moderator)
Reliability Statistics
Cronbach's Alpha N of Items
.746 2
Item-Total Statistics
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 .
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Appendix D:
Result of Regression Analysis
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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
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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
a. Dependent Variable: Mean_Recom
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Appendix E:
Result of Moderating Effect Analysis
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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
Model Sum of Squares df Mean Square F Sig.
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
a. Dependent Variable: Mean_Recom
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
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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
internet_usage_freq -.052 .076 -.036 -.690 .491
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
internet_usage_freq -.043 .075 -.030 -.566 .572
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
a. Dependent Variable: Mean_Recom
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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
a. Predictors: (Constant), internet_usage_freq, age
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
Model Sum of Squares df Mean Square F Sig.
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
a. Dependent Variable: Mean_Recom
b. Predictors: (Constant), internet_usage_freq, age
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
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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) 3.121 1.237 2.523 .012
age -.607 .572 -.059 -1.061 .290
internet_usage_freq -.021 .080 -.015 -.261 .795
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
internet_usage_freq -.017 .081 -.012 -.215 .830
Mean_Need .264 .063 .248 4.207 .000
Mean_Credibility .401 .068 .345 5.863 .000
Interaction_Need_Credibility .032 .056 .033 .579 .563
a. Dependent Variable: Mean_Recom
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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
a. Predictors: (Constant), internet_usage_freq, age
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
Model Sum of Squares df Mean Square F Sig.
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
a. Dependent Variable: Mean_Recom
b. Predictors: (Constant), internet_usage_freq, age
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
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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) 3.276 1.285 2.550 .011
age -.550 .590 -.054 -.931 .353
internet_usage_freq -.076 .083 -.053 -.917 .360
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
internet_usage_freq -.071 .083 -.050 -.862 .390
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. Dependent Variable: Mean_Recom