Mapping eWOM effectiveness for Generation Z consumers: an integrative approach

Panu Alanko

Department of Marketing

Hanken School of Economics

Helsinki

2018

HANKEN SCHOOL OF ECONOMICS

Department of:

Marketing

Type of work: Master’s Thesis

Author:

Panu Alanko

Date:

8.5.2018

Title of thesis:

Mapping eWOM effectiveness for Generation Z consumers: an integrative approach

Abstract: The effectiveness of word-of-mouth (WOM) keeps intriguing both researchers and practitioners as it remains one of the most powerful forces influencing consumer behaviour beyond the direct promotional activities of marketers. Along with the era of the internet, electronic word-of-mouth (eWOM) has further extended this information exchange among consumers due to new communication platforms that enable faster dissemination of information and immediate access to consumer networks online. Being such a prevalent source of information among digital natives, such as Generation Z consumers, eWOM also provides opportunities for businesses to use it to their advantage. However, before exploiting eWOM commercially, practitioners must first understand the determinants of its effectiveness.

Based on the findings of existing research, this thesis provides a holistic view of the impact of eWOM on consumer behaviour. By classifying eWOM effectiveness factors according to the three dimensions of social communication, the study explains the combined effect of both communicator, receiver and stimuli characteristics on the purchase intention of Generation Z consumers. To meet its objectives, the study employs a quantitative research design in which formal hypotheses and a cross-sectional survey technique are used to answer the research questions. As the representatives of Generation Z, a sample of 133 Finnish students is drawn from closed-membership groups of a social networking site to collect individual-level data through a structured online questionnaire. Finally, the relationship between consumer purchase intention and six eWOM effectiveness predictors is statistically analysed by applying hierarchical multiple regression.

In conclusion, a significant relationship between eWOM and purchase intention is found. The findings imply that the effectiveness of eWOM is determined by the interplay among several factors that, however, differ in terms of their individual contribution. In descending order of importance, the receiver’s attitude towards eWOM information, the similarity between the communicator and the receiver, eWOM quality and eWOM quantity all contribute positively to Generation Z consumers’ purchase intention. Thus, the study empirically illustrates relations between the dimensions of the social communication framework and advances a holistic understanding of eWOM effectiveness for both researchers and practitioners.

Keywords: eWOM, word-of-mouth, effectiveness, Generation Z, consumer behaviour, purchase intention

CONTENTS

1 INTRODUCTION....................................................................................... 1

1.1. Context of the study .............................................................................................. 2

1.1.1. Generation Z ............................................................................................. 2

1.1.2. Relevance of product type ........................................................................ 4

1.1.3. Relevance of communication platform ................................................... 5

1.2. Research problem ................................................................................................. 5

1.3. Research aim ......................................................................................................... 7

1.4. Delimitations......................................................................................................... 7

1.5. Thesis structure..................................................................................................... 8

1.6. Key concepts and definitions ................................................................................ 8

2 THEORETICAL BACKGROUND ........................................................... 10

2.1. WOM in marketing literature ............................................................................. 10

2.2. eWOM in marketing literature ........................................................................... 12

2.3. Effectiveness of eWOM communication ............................................................ 14

2.4. Hypotheses development ................................................................................... 16

2.4.1. Effectiveness of eWOM: responses ........................................................ 18

2.4.2. Impact of communicator characteristics ............................................... 19

2.4.3. Impact of stimuli characteristics ........................................................... 20

2.4.4. Moderating effect of receiver characteristics ........................................ 22

2.5. Theoretical framework for the study .................................................................. 23

3 RESEARCH METHODOLOGY .............................................................. 26

3.1. Research design .................................................................................................. 26

3.1.1. Data collection ........................................................................................ 26

3.1.2. Sampling and accessing data ................................................................. 27

3.1.3. Questionnaire design ............................................................................. 29

3.1.4. Construct operationalization ................................................................. 31

3.1.5. Pilot testing and translation of the questionnaire ................................ 34

3.2. Data analysis ....................................................................................................... 35

3.3. Assessing the quality of research ....................................................................... 36

4 EMPIRICAL FINDINGS ......................................................................... 40

4.1. Data screening .................................................................................................... 40

4.2. Descriptive statistics ........................................................................................... 41

4.3. Assessing the reliability of scales ....................................................................... 44

4.4. Assessing statistical assumptions....................................................................... 46

4.5. Correlation analysis ............................................................................................ 47

4.6. Regression analysis ............................................................................................. 49

4.6.1. Testing the assumptions for multiple regression analysis .................... 50

4.6.2. Assessing the regression models ........................................................... 52

4.6.3. Results ................................................................................................. 53

5 DISCUSSION .......................................................................................... 58

5.1. Key findings ........................................................................................................ 58

5.1.1. Relations between eWOM effectiveness factors and purchase intent .. 58

5.1.2. Significance of eWOM effectiveness factors to purchase intent ........... 59

5.1.3. Impact of receiver characteristics on eWOM effectiveness .................. 62

5.1.4. Revised framework ................................................................................. 63

5.2. Theoretical contributions ................................................................................... 64

5.3. Managerial implications ..................................................................................... 66

5.4. Limitations and further research ....................................................................... 70

5.5. Conclusions ......................................................................................................... 72

REFERENCES ............................................................................................ 73

APPENDICES

Appendix 1 Questionnaire in English ....................................................................... 78

Appendix 2 Questionnaire in Finnish ....................................................................... 83

TABLES

Table 1 Key characteristic differences between WOM and eWOM communication.... 13

Table 2 Individual-level research focusing on the influence of eWOM .........................15

Table 3 eWOM effectiveness constructs involved in the study ..................................... 25

Table 4 Summary of the variables related to eWOM communicator and stimuli dimensions ....................................................................................................... 33

Table 5 Summary of the variables related to eWOM receiver and response dimensions ....................................................................................................... 34

Table 6 Results of independent-samples t-test: comparing males and females on the individual items of eWOM quality scale ................................................... 38

Table 7 Sample characteristics ....................................................................................... 41

Table 8 Internet usage in spare time .............................................................................. 42

Table 9 Frequency of online information search according to service category .......... 43

Table 10 The most popular online platforms for searching information about services ............................................................................................................. 44

Table 11 Reliability of scales ........................................................................................... 45

Table 12 Correlations among variables (Pearson’s r) .................................................... 48

Table 13 Results of hierarchical regression analysis for predicting purchase intention ........................................................................................................... 54

Table 14 Findings from the empirical research ............................................................. 57

FIGURES

Figure 1 An integrative framework of the impact of eWOM communication (Cheung & Thadani, 2012) ................................................................................ 17

Figure 2 Theoretical framework to study the impact of eWOM on consumer purchase intention (adapted from Cheung & Thadani, 2012). ...................... 24

Figure 3 Revised empirical framework for studying the impact of eWOM on consumer purchase intention. (Adapted from Cheung & Thadani, 2012) .... 64

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1 INTRODUCTION

In an age when anyone can easily reach thousands of people by sharing and publishing

content online, the digital media space is becoming more saturated with information

than ever before. In fact, as pointed out by Kelly et al. (2010), a significant part of the

information clutter is caused both by consumer-generated media and traditional media

in which advertisements and other commercial messages are customary. As a result,

marketers are struggling to stand out from the crowd while trying to remedy the

decreasing effectiveness of conventional “push” tactics in digital marketing (Kelly et al.,

2010). According to extant studies, the underlying challenge is in the modern

consumers’ negative attitude towards advertising (Truong & Simmons, 2010) and their

desire to decide on sources of information (Kelly et al., 2010) which has led to an

increasing reluctance in receiving commercial messages. Instead of being merely

passive receivers, consumers prefer to take an active role as they source and adopt

information relevant for their needs while disregarding the “push” information of

advertising (Kelly et al., 2010). This shift in power relations is largely facilitated by

technology, such as the internet, ad blocking software, search engines and the variety of

digital platforms that have enabled increasingly non-linear communication between

consumers and companies while allowing the consumers to “pull” relevant information

from the widely accessible online sources (Truong & Simmons, 2010).

Along with this development, new technology has not only allowed consumers to avoid

intrusive advertising from marketers, it has also created an opportunity for digital

influencers to emerge and take marketing space from brands through means of

electronic word-of-mouth (eWOM) (Djafarova et al., 2017; Erkan & Evans, 2016;

Weinswig, 2016; Zangeneha, et al., 2014). On the one hand, digital influencers, such as

bloggers, video bloggers, celebrities and active members of online communities, are

taking advantage of digital media channels to endorse brands, goods and services and

to encourage purchase decisions in their followership (Djafarova et al., 2017). On the

other hand, consumers are also looking up similar advice and expressions of approval

from peer networks, review sites and digital communities of like-minded consumers

when they need information about products and services to support their purchase

decisions (Cheung & Thadani, 2012; Fan et al., 2013; Kozinets et al. 2010). This online

information appears in a variety of forms, such as written texts (e.g. reviews), pictures,

videos and social posts (e.g. tweets, likes, pins) about products and services that appear

on websites, blogs, mobile applications, forums and social media (Rosario et al., 2016).

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More commonly, this type of consumer-to-consumer communication has been

conceptualized as word-of-mouth (WOM) in the marketing literature (Hennig-Thurau

et al., 2004: Kozinets et al., 2010) or as electronic word-of-mouth (eWOM) to separate

between information exchange that occurs online and offline (Cheung & Thadani, 2012;

Rosario et al., 2016).

1.1. Context of the study

Following the aforementioned trends, more and more consumers are becoming “web-

fortified decision makers” (Rosario et al., 2016: 297) with the help of widely accessible

eWOM information, while their exposure to corporate messages and their dependence

on firm-generated information sources is reduced due to advertising avoidance

behaviour and the increasingly non-linear communication between consumers and

firms (Kelly et al., 2010; Truong & Simmons, 2010). Thus, it is worthwhile to further

explore the role of eWOM communication in consumer behaviour and, in particular, its

effectiveness as an information source. However, before presenting the specific

research questions of the study, a few contextual factors need to be considered. First,

the demographic context of this study is discussed and justified with supporting

arguments from media articles and research literature. Second, consumer purchase

behaviour and the role of the product type are deliberated in the context of eWOM

effectiveness. Third, the significance of communication platform type is reflected while

discussing findings from extant eWOM literature.

1.1.1. Generation Z

Given that Generation Z is currently entering the market as the first consumer segment

born during the digital era, firms are eager to learn more about the needs and

behaviours of this particular demographic group. Thus, it offers researchers a fruitful

context to advance such understanding. However, before further exploring the

characteristics of Generation Z consumers, we must first define the concept of

generation. Although definitions vary to some extent, Chaney et al. (2017) remark that

a generation has been historically outlined by the time and space in which a group of

people happen to live in simultaneously. This again, may create a shared sense of

belonging to an entire society that ends up characterising the generational cohort

(Chaney et al., 2017). Some contemporary definitions, however, are more culturally-

inclined. According to Turner (1998, as cited in Chaney et al., 2017), generation is

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defined as “a cohort of persons passing through time who come to share a common

habitus and lifestyle… [and] has a strategic temporal location to a set of resources as a

consequence of historical accident and the exclusionary practices of social closure”.

Therefore, the age of individuals is merely the starting point for observing generational

differences (Chaney et al., 2017).

While ambiguous definitions exist regarding the age range of Generation Z, it is often

described as the demographic group of young adults born in 1995 and later (Chaney et

al., 2017), which makes the age spectrum of Generation Z rather wide. So far, the end of

the said generation has not yet been officially defined thus even children born in the

2010’s may be considered to belong to Generation Z. As such, Generation Z holds a

huge spending power in the future since they are estimated to form 40% of US

consumers already by 2020 (Finch, 2015). As a group of consumers, Generation Z is

typically described as highly educated, mobile, technologically savvy, creative and

highly connected through memberships in various online networks (Chaney et al.,

2017; Ozkan & Solmaz, 2015; Priporas et al., 2017). What is more important,

Generation Z tends to behave differently than earlier generations which is reflected in

their consumer behaviour as well. In fact, Generation Z consumers have been

characterised by higher social consciousness, higher risk aversion, higher expectations

of retailers and lower level of brand loyalty compared to their predecessors (Chaney et

al., 2017; Priporas et al., 2017) which makes them a demanding target segment for

marketers. With such characteristics, marketers may face long-term challenges in

persuading and convincing Gen Z consumers to make large scale purchase decisions

and repeated purchases due to their high perceived risk of making a purchase and low

commitment towards a single provider.

In news media, consumers of Generation Z are reported to avoid online advertising

significantly more than preceding generations while also being identified as avid users

of ad blocker and similar technology (CNBC, 2017). Moreover, as Generation Z

consumers show signs of being more selective by their nature, they are likely to form

tightly bound digital communities and peer networks that are based on shared interests

and values (Fromm, 2016). Thus, they seem to rely more on social reputation and

opinions of the virtual communities than face-to-face recommendations when making

decisions (The Global Consumer Commerce Centre, 2016).

Finally, as pointed out by Chaney et al. (2017), the individuals of Generation Z can

access more information than any other generational cohort before thanks to being

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constantly connected through mobile devices and the Internet of Things. Being

accustomed to such vast amounts of online content, they also prefer to communicate in

written form instead of speaking out loud (Chaney et al., 2017). Therefore, Generation

Z consumers have a high likelihood of becoming engaged and affected by various forms

of electronic word-of-mouth (eWOM), such as written online reviews and

recommendations from peers and other consumers, while intentionally disregarding

the digital marketing messages put forth by firms.

1.1.2. Relevance of product type

In existing literature, various contextual factors have been found to influence the

effectiveness of eWOM. Among the most cited factors is the specific type of product of

which the consumers communicate to each other (Ismagilova et al., 2017; Rosario et al.,

2016). The connection between the product characteristics and eWOM is discussed by

Sweeney et al. (2008), who argue that the importance of WOM to consumers’ decision-

making process is significantly explained by its risk-reducing capabilities. These apply

to different forms of perceived risk which include both product-focused and

performance-related risks, such as functional, time and financial risks. In addition,

WOM can also reduce consumer-focused risks such as psychological and social risks

(Sweeney et al., 2008). Hence, the proportional importance of WOM is likely to be

more significant when the purchase process requires high involvement. Although the

risk of making a purchase decision is prevalent in various situations, the impact of

WOM has been recognized to be greater in the final stages of the buying process and

especially in the context of services due to the intangible and indivisible nature of the

product, which makes the evaluation of product quality difficult without the

experienced opinion of a fellow consumer (Sweeney et al., 2008; Sweeney et al., 2014:

Ismagilova et al., 2017: 12).

Following a similar logic, Zhang et al. (2010) expect WOM to have a bigger impact on

consumers’ perceptions when the quality of a product or a service is dominated more by

an interaction process than a technical outcome. Based on the concept of value co-

creation (See-To et al., 2014) and the interaction between a consumer and a product

during the consumption process, Zhang et al. (2010) deduced that WOM is highly

influential in service contexts in which the perceived quality is more affected by

interaction processes than in goods contexts. Thus, this study is embedded in the

service context in order to collect more meaningful data from the population.

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1.1.3. Relevance of communication platform

Besides the product type, further contextual considerations involve typically the

platform which facilitates eWOM communication because it is one of the key factors

separating online WOM from offline WOM (Cheung & Thadani, 2012; Rosario et al.,

2016). In particular, previous studies have found that the type of platform, e.g. a

discussion forum, a review website or a social networking site, may even moderate the

influence of eWOM (Rosario et al., 2016). This is partially explained by credibility

issues related to potentially unfamiliar eWOM communicator that drives the receivers

to lean on other cues embedded in both the message and the platform to assess the

overall quality of the communication (Cheung & Thadani, 2012). Nonetheless, the

nature of this potential moderating effect is not fully conclusive and extant studies have

ended up with mixed results. On the one hand, the study by Tsao and Hsieh (2015)

found out an interaction effect in which positive eWOM with high-quality content had a

more significant influence on purchase intention when it was published on

independent platforms instead of corporate platforms. However, the result was the

opposite when the quality of eWOM communication was not controlled (Tsao & Hsieh,

2015). On the other hand, some studies have simply identified that the impact of

product reviews is more evident on established platforms than on under-established

platforms (Cheung & Thadani, 2012). Due to these inconclusive results in past studies,

no formal hypothesis regarding the effect of the platform is presented in this study.

Instead, the communication platform is included in the study only as a contextual

factor that is analysed in a descriptive manner.

1.2. Research problem

Although the concept of word-of-mouth, its drivers and motivators, and its influence on

consumer behaviour have been studied to vast extent, the exact mechanism of WOM

effectiveness – i.e. the factors that set a particular reaction in the receiver, keep

intriguing researchers and marketing practitioners (Cheung & Thadani, 2012; Rosario

et al., 2016; Sweeney et al., 2008). For instance, while aiming to quantify the effects of

WOM, Trusov et al. (2009) argue that the true effects of WOM are not yet fully

understood despite its acknowledged significance to the marketing communications

mix. Based on a rigorous analysis of eWOM effectiveness literature, Cheung and

Thadani (2012) argue in similar fashion as they urge researchers to approach the topic

more comprehensively and systematically. They conclude that various theoretical

models and eWOM variables have been studied in a plethora of research contexts but

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the conclusions of eWOM effectiveness nonetheless seem ambiguous. In fact, the focus

of extant research has been on the impact of particular elements of communication,

such as the message, the platform or the information-seeking and information-sharing

consumers, but not necessarily on the combination of all of these elements (Cheung &

Thadani, 2012). Thus, the field of eWOM effectiveness research remains rather

fragmented, despite the remarkable amount of studies, which makes it challenging to

understand the larger picture.

Moreover, previous eWOM studies have mostly represented the current consumers in

the market, such as consumers belonging to generation X and Y (i.e. the millennials),

while our understanding of eWOM influence on Generation Z is still rather limited. As

Generation Z represents the first consumer segment that has fully grown up during the

digital age, they are characterised e.g. by their avid use of the internet (Chaney et al.,

2017). Thus, individuals belonging to Generation Z have a high likelihood of engaging

in eWOM – an online phenomenon that has been prevalent throughout their lives and

to which they are likely to rely as an information source about services. Supporting this

demographical scope, Chaney et al. (2017) discuss the importance of a generational

marketing approach that reflects on the behaviour, the values and the shared culture of

each generational cohort to gain more in-depth understanding of buying decisions in

different consumer segments. In particular, the authors suggest a future research

agenda for generational marketing in which studies should be dedicated to specific

generational cohorts to create typologies that allow comparison of generational

characteristics over time (Chaney et al., 2017).

To conclude, besides shifting the marketing focus from individuals to generational

cohorts (Chaney et al., 2017), it is increasingly important for managers to better

understand the metrics that contribute to eWOM effectiveness if they aim to induce,

monitor and manage eWOM as part of their marketing strategy (Rosario et al., 2016;

Trusov et al., 2009). What is more pressing, however, is that current knowledge of

eWOM effectiveness should be advanced towards a more comprehensive

understanding (Cheung & Thadani, 2012). Thus, with these notions in mind, this study

contributes to an ongoing topic of interest within current marketing research and

creates relevant insights for both marketing researchers and practitioners.

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1.3. Research aim

The aim of the study is to examine the impact of eWOM communication on Generation

Z consumers’ purchase intention in a service context. In other words, the purpose is to

better understand the conditions in which Generation Z consumers base their buying

behaviour on other people’s recommendations and opinions that appear online. Hence,

the study contributes to our understanding of eWOM effectiveness on an individual

level in which consumers communicate with each other. The secondary aim is to learn

more about the nature of the determinants of eWOM effectiveness, namely the message

characteristics and the personal characteristics related to the communicator and the

receiver, while taking a holistic approach to better understand the determinants’

interrelationships. These issues fall under the following research questions.

RQ1: What kind of relationships exist between the determinants of eWOM effectiveness

and the purchase intention of Generation Z consumers?

RQ2: Which determinants of eWOM effectiveness are relatively the most important in

terms of Generation Z consumers’ purchase intention?

RQ3: How do the characteristics of the receiver influence the other determinants of

eWOM effectiveness?

1.4. Delimitations

Although the topic of the study is global, it has some demographical and geographical

delimitations to it. First of all, the research focuses on Generation Z consumers that are

of legal age in Finland because they can give their consent to participate in the survey.

This will avoid any ethical questions concerning the study of minors while removing the

need to obtain a guardian’s permission for research. In addition, adult consumers have

full control over their spending, enabling them to better assess their own consumption

and purchasing behaviour. In practice, the sample is limited to respondents born in

between 1995 and 1999, thus representing the older end of the generation Z age

spectrum. Second, the sample is drawn from a native Finnish-speaking population,

thus the study also represents a rather homogeneous group of consumers in a specific

cultural area. Lastly, the study is embedded in a service context because extant research

(e.g. Racherla & Friske, 2012; Sweeney et al., 2014) has indicated that WOM has a

greater influence on purchase decisions regarding intangible products, such as services.

This effect is mainly explained by the intangible and indivisible nature of experience

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goods that does not allow the assessment of quality in advance. Therefore, the service

experiences that are expressed through WOM help the individual to assess the quality

of services before the purchase decision (Racherla & Friske, 2012; Sweeney et al.,

2014).

1.5. Thesis structure

The study is structured as follows. First, an introduction to the topic is given to provide

contextual information and motivation for the study. In addition, the research aim and

the purpose of study are discussed. Second, the literature review and theoretical

framework for the study are presented together with the hypotheses. Third, research

methods are discussed. More specifically, the reasons for choosing a quantitative

research design are explained in addition to the choices made regarding data collection

process and statistical data analysis techniques. In the fourth chapter, the empirical

findings are reported together with the different phases of statistical data analysis.

Lastly, the contribution and implications of the study are presented in a conclusive

manner from both the researcher’s and practitioner’s perspectives. Moreover, the

limitations of the study are discussed alongside with the opportunities for further

research in the field of eWOM effectiveness.

1.6. Key concepts and definitions

Some of the key concepts that relate to the research topic are briefly described below.

The purpose of the list is to facilitate understanding and help the reader follow the

logic, core literature and argumentation right from the beginning. The definitions are

based on the literature available hence they can also be found within the following

chapters of the thesis.

Word-of-mouth (WOM) refers to the information exchange about products, brands

and services that typically occurs between consumers in a dialogical manner (Kozinets

et al., 2010). As WOM has been found to influence consumer attitudes and behaviour, it

may e.g. increase or decrease the likelihood of purchase depending on the

communication context (Sweeney et al., 2008). According to marketing literature,

WOM is defined as “oral, person-to-person communication, between a receiver and a

communicator, whom the receiver perceives as non-commercial, concerning a brand,

product, service or organization” (Ismagilova et al., 2017: 7).

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Electronic Word-of-mouth (eWOM) extends the concept of conventional WOM

beyond the physical boundaries of the participants. Simply put, eWOM involves

consumer-to-consumer communication that occurs online (Cheung & Thadani, 2012).

It has been described as any statement about a product or company, which is made

available on the internet (Hennig-Thurau et al., 2004). These include e.g. written

reviews, social posts, pictures and videos that consumers publish on websites, blogs,

mobile applications, forums and social media (Rosario et al., 2016).

Word-of-mouth marketing (WOMM) refers to a specific tactic that is used to

create competitive advantage through generating word-of-mouth and affecting the

course of the communication (Kozinets et al., 2010). Simply put, any business activity

that encourages customers to give positive word-of-mouth about products, brands and

services could be considered as WOMM. More recently, electronic WOMM may also

refer to viral marketing in which people share and distribute commercial content online

without being paid to do so (Ho & Dempsey, 2010).

In a context where eWOM represents another form of social communication, receiver

and communicator refer to the different opinion-seeking and opinion-sharing actors

of the online dialogue, whereas stimuli refers to the vehicle of communication

(message) (Cheung & Thadani, 2012). Response, on the other hand, is defined as the

outcome of communication, i.e. the change in the behaviour or mindset of the receiver

(Cheung & Thadani, 2012: Sweeney et al., 2008). Therefore, the effectiveness of

eWOM is determined by the interplay of the receiver, communicator and stimuli

characteristics and their proportional contribution to producing a specific outcome

(Cheung & Thadani 2012), such as purchase intention.

Platform or digital platform refers to the specific online channel that facilitates the

communication between the receiver and the communicator (Cheung & Thadani,

2012). Although the number and diversity of online platforms is constantly evolving,

most eWOM studies focus only on a single type of platform (Ismagilova et al., 2017:

26), that include e.g. review websites (Zhang et al., 2010), social networking sites (Chu

& Kim, 2011) and social media (Erkan & Evans, 2016; Djafarova & Rushworth, 2017).

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2 THEORETICAL BACKGROUND

The following chapter discusses the theoretical background of the study. First, the

broader concepts of word-of-mouth (WOM) and electronic word-of-mouth (eWOM) are

presented together with findings from extant literature. Then, the scope is narrowed

down to discuss the impact of eWOM and its various constituents while developing the

hypotheses of the study. Finally, the theoretical framework is summarized for the

specific context of this study. The literature for this study was retrieved by searching

electronic databases, including EBSCOhost, ScienceDirect and Google Scholar, to find

scholarly journals that discuss the effectiveness of WOM and eWOM. Search terms,

such as “word-of-mouth”, “electronic word-of-mouth”, “online recommendations” and

“reviews” were used together with sub-search terms such as “effectiveness”, “influence”

and “impact”. To narrow down the number of sources, peer-reviewed journal articles

focusing on either WOM or eWOM were primarily selected for the review.

2.1. WOM in marketing literature

Being one of the oldest information sources between people, word-of-mouth (WOM)

has intrigued scientists already for decades (Ismagilova et al., 2017: 5–7) while its

significance to consumer decision-making has been widely recognized by both

marketing researchers and practitioners (Chu et al., 2011; Kozinets et al., 2010;

Sweeney et al., 2008). Traditionally, WOM has been conceptualized as naturally

occurring information exchange among consumers that heavily influences their buying

decisions (Kozinets et al., 2010). In essence, WOM involves different forms of

consumer-to-consumer communications in which the interpersonal influence of the

sender may change the receiver’s behaviour and attitudes towards products and

services, thus leading to an increased likelihood of purchase (Sweeney et al., 2008).

Although the exact definitions of WOM vary among researchers, Ismagilova et al.

(2017) proposed a comprehensive definition based on their review of existing WOM

literature. Hence, word-of-mouth is outlined as “oral, person-to-person

communication between a receiver and a communicator, whom the receiver perceives

as non-commercial, concerning a brand, product, service or organization”

(Ismagilova et al., 2017: 7). As described above, consumers tend to lean on the opinions

of peer consumers due to higher perceived trustworthiness of WOM information,

instead of purely relying on commercial or other company-generated communications

such as advertising (Chu et al., 2011; Rosario et al., 2016; Tsao & Hsieh, 2015).

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However, companies and marketers may also become involved in WOM generation by

intentionally targeting consumers with commercial messages. More specifically, this is

referred as word-of-mouth-marketing (WOMM) that entails various marketing

techniques that aim to affect the mutual communication between consumers (Kozinets

et al., 2010). Therefore, WOM should not be used interchangeably with WOMM as a

term to describe both consumer-generated WOM and company-generated WOM, such

as viral marketing messages (Ho & Dempsey, 2010). Thus, in the context of this study,

WOM is referred to as communication that flows directly from one consumer to

another. In the following section, the marketer’s role in the circulation of WOM

communication is further discussed.

On the one hand, WOM can be seen as naturally occurring dialogue involving only

consumers. Among the theoretical frameworks presented by Kozinets et al. (2010), the

organic inter-consumer influence model reflects this view, while it is perceived to be the

earliest and the simplest understanding of WOM where product and brand-related

messages are exchanged “between one consumer and another without direct

prompting, influence, or measurement by marketers” (Kozinets et al., 2010: 72). On the

other hand, WOM can be seen as communication involving both consumers and firms

that enter the dialogue either indirectly or directly. Hence, Kozinets et al. (2010)

discuss the differences between the linear marketer influence model and the network

co-production model. In the first model, the marketer aims to affect supposedly

influential consumers, i.e. opinion leaders (Sweeney et al. 2008), through indirect

means of advertising and promotions. This again, multiplies the reach of WOM as the

assumed opinion leaders spread product and brand-related messages to more than one

consumer at a time. In the latter model, consumers are assumed to be co-producers of

WOM thus forming consumer networks where market messages and meanings flow to

multiple directions. In this context, marketers aim to directly influence consumers or

opinion leaders through personal, one-to-one messages that eventually get circulated

within the network (Kozinets et al., 2010). According to Kozinets et al. (2010), all of the

abovementioned WOM influence models currently coexist, although the network co-

production model reflects the most recent understanding of WOM in the current era of

digitalization. This is mainly for two reasons. Firstly, the internet has increased the

speed of information diffusion while the same technology has also enabled firms to

actively manage and measure their WOMM activities at an unprecedented level.

Secondly, due to the introduction of the internet and especially social media,

12

consumers are increasingly connected to each other through online communities that

allow multidirectional communication (Kozinets et al., 2010).

2.2. eWOM in marketing literature

In recent years, electronic word-of-mouth (eWOM) has attracted more and more

attention and it has been considered an impactful marketing force (Cheung & Thadani,

2012; Erkan & Evans, 2016). According to Rosario et al. (2016), this is largely due to the

emergence of new digital media platforms that have provided consumers with

numerous ways of exchanging information about products and services – for example,

by consuming and generating online reviews, tweets, blog posts, likes, pins, images and

video testimonials. The digital platforms themselves include, but are not limited to,

communication channels such as online discussion forums, consumer review sites,

blogs, microblogs, chat rooms and social networking sites (Rosario et al., 2016). Hence,

the consumption of conventional word-of-mouth has become more diverse thus

increasing its significance as a source of product information and a phenomenon of

contemporary consumer behaviour (Cheung & Thadani, 2012; Chu et al., 2011).

As said, electronic word-of-mouth can occur in many forms and in various settings

within the online environment, which has led some researchers to study and define

specific type of eWOM in depth. For instance, Tsao and Hsieh (2015) argue that one of

the most influential presentations of eWOM communication is an online consumer

review that aims to inform about a product. By definition, an informative review

includes more detailed information about the qualities and performance of a product,

whereas a recommendation review provides either positive or negative assessment of

the product. These again, may occur in a diverse set of media that vary according to

their level of interactivity (synchronous vs. asynchronous) and scope of communication

(one-to-one vs. many-to-many) (Tsao & Hsieh, 2015). However, to approach eWOM

holistically, a more general definition is required. Thus, for the rest of the study, I refer

to the widely accepted definition to describe eWOM communication as “any positive or

negative statement made by potential, actual or former customers about a product or

company, which is made available by a multitude of people and institutions via the

internet” (Hennig-Thurau et al., 2004: 39).

Although a majority of WOM-related studies have been conducted before the age of the

internet, similar core concepts and theoretical principles have also been adapted in

later studies that focus on consumer-to-consumer communication in an online context

13

(Cheung & Thadani, 2012). Thus, at its core, eWOM is another form of word-of-mouth

in which consumers exchange information about products and companies online. In

other words, eWOM can be seen as an extension to conventional WOM where various

digital platforms serve as the mediator of consumer-to-consumer communication

(Cheung & Thadani, 2012; Chu et al., 2011). However, despite the many similarities

that eWOM and WOM share with each other, some significant differences do exist

between the two types of communication. In fact, both Cheung and Thadani (2012) and

Tsao and Hsieh (2015) identified four differentiating dimensions that are characteristic

of eWOM communication. The main differences between WOM and eWOM are

summarized in table 1.

Table 1 Key characteristic differences between WOM and eWOM communication

Key characteristic

WOM eWOM

Form of communication

Linear Nonlinear

Dissemination One-to-one Many-to-many

Scalability Low High

Accessibility Temporal Continuous

Measurability Low High

Anonymity Low High

Adapted from Cheung and Thadani (2012) and Tsao and Hsieh (2015).

First, as pointed out in literature (Cheung & Thadani, 2012; Tsao & Hsieh, 2015),

eWOM enables multi-way communication, higher scalability and faster distribution of

information than traditional WOM. For instance, the sender and the receiver of eWOM

do not need to share the same space at the same time while they are also able to

forward the information to multiple directions within their own networks. Thus,

eWOM is more likely to reach a larger number of people than conventional WOM

(Cheung & Thadani, 2012; Tsao & Hsieh, 2015). Second, eWOM communication can be

accessed on a continuous basis, since a piece of online information is archived after it

has been created (Cheung & Thadani, 2012). Thus, eWOM is more persistent than

offline WOM (Hennig-Thurau et al., 2004).

Third, the authors (Cheung & Thadani, 2012; Tsao & Hsieh, 2015) continue that eWOM

communication is more observable than traditional WOM which enables a more

14

detailed measurement and analysis of information. For example, online WOM

information can be retrieved and studied based on its volume and message

characteristics. Lastly, the identity of eWOM sender is not always known by the receiver

thus making communication less contingent upon social cues. Compared to traditional

WOM communication, in which the participants of offline dialogue are known and

present in the same space, both sides of eWOM communication may remain

anonymous to each other throughout the information exchange. Due to this

characteristic, however, the importance of other contextual cues is emphasized in

evaluating overall eWOM credibility. As mentioned above, an examination of message

characteristics such as the quality of information (e.g. valence, choice of words,

justification of arguments) may provide the receiver with relevant indications of source

credibility (Cheung & Thadani, 2012; Tsao & Hsieh, 2015).

2.3. Effectiveness of eWOM communication

While some past studies have concluded that WOM affects the majority of all purchase

decisions (Kozinets et al., 2010), others state that it still remains one of the least

understood forms of communication despite its acknowledged effectiveness (Trusov et

al., 2009). Due to the decreased consumer trust in organizations (Sweeney et al.,

2008), the increasingly negative attitudes towards advertisements and the subsequent

reduction in the effectiveness of traditional marketing communication efforts (Trusov

et al., 2009), marketers have become especially interested in studying WOM in their

search for a cost-efficient marketing tool. Moreover, the rise of the internet has brought

up new opportunities, venues and means of communication for both consumers and

firms to engage in WOM (Sweeney et al., 2008; Trusov et al., 2009). Thus, the ongoing

digitalization is emphasizing the need to understand the conditions in which WOM

influences consumer behaviour the most.

Along with this line of thought, a plethora of studies focusing on the influence of eWOM

communication has emerged during the last few years. Although not being an entirely

separate phenomenon from offline WOM, the electronically facilitated information

exchange has provided a more recent context for researchers to study the effects of

consumer-to-consumer communication. In particular, from their review of existing

studies concerning eWOM impact, Cheung and Thadani (2012) deduced that current

research has been conducted mainly on two levels. First, market-level research has

studied eWOM impact by measuring market-level parameters, such as the relationship

15

of online review valence, i.e. the ratio of positively and negatively laden words

(Ismagilova et al., 2017: 52), and product sales. Second, individual-level research has

focused on the impact of eWOM as a process of personal influence thus examining the

impact of communication between a sender and a receiver (Cheung & Thadani, 2012).

Hence, this study adds to the findings of the latter school of research by focusing on the

effects of information exchange within an individual. Such an approach is also

represented in the example studies described in table 2.

Table 2 Individual-level research focusing on the influence of eWOM

Author Topic Context Explanatory factors

Chu & Kim, 2011 Social factors that

influence consumer’s engagement in eWOM

Social networking sites as a mediator

for eWOM communication

Tie strength, Homophily, Trust, Interpersonal

influence

Fan et al., 2013

Factors affecting perceived eWOM

credibility and information adoption

Customer reviews in an online shopping

context

Source credibility, eWOM quantity, eWOM quality,

Consumer expertise, Consumer involvement

See-To et al., 2014

Interaction effect of trust, value co-creation and

eWOM to purchase intention

Social networking sites as a mediator

for eWOM communication

Source of eWOM, Value co-creation, Trust

Tsao & Hsieh, 2015 Factors of eWOM quality

affecting consumer’s purchase intention

Different types of products and online

platforms as a mediator for eWOM

communication

eWOM quality, eWOM credibility, eWOM

platform, product type

Erkan & Evans, 2016

Key factors of eWOM source that affect

consumer’s purchase intention

Social media networks as a

mediator for eWOM communication

Information quality, Information credibility, Needs of information,

Attitude towards information

To describe a few of these examples, Chu and Kim (2011) studied empirically how social

networking sites serve as a vehicle for consumers to engage in eWOM behaviour. More

specifically, they examined how the personal characteristics of both communicator and

receiver affected opinion-seeking, opinion-giving and opinion-passing behaviour in the

given online platform, while applying a theory of interpersonal influence. As a result,

they found that the communicator’s trustworthiness and the tie strength between the

two parties increased the receiver’s engagement in eWOM behaviour. In addition, the

findings indicated that the receiver’s susceptibility to both normative influence and

informational influence were positively associated with his or her engagement in

16

eWOM (Chu & Kim, 2011). Similarly, Fan et al. (2013) studied the impact of

communicator, message and receiver characteristics on perceived eWOM credibility

but with different variables and a theoretical framework reflecting the Elaboration

Likelihood Model. As a result, they found that both communicator and message

characteristics, i.e. source credibility, eWOM quality and eWOM quantity, significantly

contributed to eWOM credibility, while the receiver characteristics, i.e. expertise and

involvement, did not (Fan et al., 2013). On the other hand, Tsao and Hsieh (2015)

focused on the relationship between eWOM quality, eWOM credibility and purchase

intention, while also examining the moderating effect of the platform and the product

type. Their findings provide further evidence of the positive relationship between

eWOM quality and eWOM persuasiveness as they deduce that online reviews of greater

detail increase the receiver’s trust in the information. However, the hypothesized

influence of the eWOM platform type on eWOM persuasiveness did not occur in the

findings, although an interaction effect between eWOM quality and eWOM platform

was significant. Lastly, they concluded that the product type influences the

persuasiveness of eWOM after finding that online reviews of credence goods – whose

quality is difficult to estimate – are more persuasive than reviews of search goods (Tsao

& Hsieh, 2015).

Based on the review of literature, it seems that many existing studies have chosen to

work with a few specific eWOM effectiveness variables that relate to either message

characteristics and receiver characteristics (e.g. Erkan & Evans, 2016), communicator

characteristics (e.g. Chu & Kim, 2011; Fan et al. 2013) or the platform and product

characteristics (e.g. Tsao & Hsieh, 2015), but not necessarily all of them

simultaneously. While some studies are more holistic than others, existing research

seems to provide rather specialised insights about eWOM effectiveness instead of a

general view over the phenomenon. Therefore, a need for more comprehensive studies,

that illustrate the big picture behind eWOM effectiveness, is reasonably justified.

2.4. Hypotheses development

As argued by Kozinets et al. (2010), it is important to consider the various components

of WOM holistically in order to better understand its effectiveness. Therefore,

researchers should not focus only on individual factors, but rather on a set of factors

and the internal relationships within it. Reflecting this thought, the literature analysis

and the subsequently proposed framework (figure 1) by Cheung and Thadani (2012)

17

calls for a comprehensive approach as it integrates existing research by identifying and

grouping together the different dimensions of eWOM effectiveness. More specifically,

their approach is organized according to the dimensions of social communication – a

theoretical concept originally presented by Carl Hovland (1948, as cited by Cheung &

Thadani, 2012). Thus, it is proposed that the influence of eWOM is determined by the

interplay of the communicator, the receiver, the stimuli (message), the

response, and the related factors within them. Similar to the approach by Cheung and

Thadani (2012), and the classification presented by Sweeney et al. (2008), these four

dimensions of social communication are adapted in this study to form an integrative

and diverse view of the determinants of eWOM effectiveness. Contrary to the rather

dispersed field of existing research that has traditionally focused on the factors of only

one or two of the abovementioned dimensions, this study aims to incorporate relevant

factors from each of them for a broader empirical view. However, acknowledging the

limited resources of this research and the complexity of eWOM phenomenon, creating

a fully comprehensive study model is not deemed viable.

Figure 1 An integrative framework of the impact of eWOM communication (Cheung & Thadani, 2012)

18

In figure 1, a conceptual framework originally proposed by Cheung and Thadani (2012)

is presented. The framework draws from the findings of extant eWOM research in an

attempt to model the mechanics of communication effectiveness. The main elements of

the model are the four dimensions of social communication, including the

communicator, the receiver, the stimuli (message) and the response, that each may

involve numerous subsets of factors respectively (Cheung & Thadani, 2012). As follows,

each dimension of eWOM communication is discussed together with the related factors

and hypotheses before presenting the theoretical framework for this study.

2.4.1. Effectiveness of eWOM: responses

In this framework, response is determined by the receiver’s reaction to the online

information exchange as outlined by Cheung & Thadani (2012). Thus, the response is

the outcome of an interplay among the other elements of eWOM communication.

Namely, the receiver characteristics, the sender characteristics and the message

characteristics that all affect the response either directly or indirectly (Cheung &

Thadani, 2012). In the existing literature, a positive relationship between eWOM and

consumers’ purchase intentions has been well established (Tsao & Hsieh, 2015; Zhang

et al., 2010), while other widely studied eWOM responses include information

adoption, information usefulness and purchase decision (Cheung & Thadani, 2012).

Purchase intention refers to the potential willingness to purchase a product or a service

during an undefined period of time, while purchase decision is based on the actualized

choice of buying a product or a service (Coyle & Thorson, 2001). On the other hand,

information adoption refers to the process of accepting given eWOM communication

and applying it to make decisions (Cheung & Thadani, 2012), which has been found to

have a positive relationship with purchase intention (Erkan & Evans, 2016).

As noted by Cheung and Thadani (2012), the majority of eWOM studies have only

considered one or two response variables while the interrelationships between them

have been left with little attention. Among the most studied response variables is

consumer purchase intention (e.g. Coyle & Thorson, 2001; Prendergast et al., 2010)

which is also adopted for the purpose of this study. This is done mainly for two reasons.

First, the relationship between consumer purchase intention and the other variables

applied in this study has not yet been studied extensively. Thus, the impact of both

communicator, receiver and message characteristics on purchase intention can be

better understood. Second, examining how eWOM communication affects consumer

19

purchase intention will provide managerial insights that can help identify opportunities

for the firms’ marketing strategy (e.g. leveraging WOMM tactics to drive business

outcomes).

2.4.2. Impact of communicator characteristics

In the context of eWOM, communicator refers to the source of information, thus

making it a key element in determining the overall communication effectiveness. As

discussed by Cheung and Thadani (2012), a communicator is identified as the person

sharing his or her opinion about a product to an online audience. Whereas in

traditional WOM both the receiver and the communicator share the same physical

space in an offline dialogue, the participants of eWOM can share information in a

virtual environment beyond their personal social networks. Thus, the identity and

credibility of the communicator are not always verifiable which can raise several

concerns for the receiver. (Cheung & Thadani, 2012).

First, the potentially unfamiliar communicator can raise concerns regarding source

credibility, which has been found to be among the key factors affecting eWOM

effectiveness (Cheung & Thadani, 2012). According to definition, source credibility

describes whether the communicator, not the message itself, is believable and

competent enough to transfer product or service knowledge to the receiver of

communication (Cheung & Thadani, 2012). Among the frequently studied attributes of

source credibility are expertise (Sweeney et al., 2008) and trustworthiness (Cheung et

al., 2008) that are subjectively evaluated by the receiver after a piece of information has

been shared online by the communicator. On the one hand, credibility can be

understood as something that a source already has due to e.g. a public status of an

industry expert. On the other hand, as a result of the accumulated eWOM information

that is archived online, the communicator can also develop his or her credibility by

sharing truthful and knowledgeable information over time. Either way, sources with

higher credibility have been empirically shown to have a more significant impact on

information adoption than sources with low-credibility (Cheung et al., 2008).

Therefore, the first hypothesis of the study is formulated as:

H1) Credibility of the eWOM communicator is positively associated with purchase

intention.

20

Second, concerns regarding other interpersonal characteristics, such as source

similarity, can also have an impact on WOM effectiveness (Sweeney et al., 2008). As

interpersonal characteristics have been found to be important in traditional WOM

(Sweeney et al., 2008), they continue to play a role in eWOM even if the participants of

the dialogue no longer meet face-to-face. In fact, interpersonal characteristics are

mediated by the online platform (Chu & Kim, 2011; Shan & King, 2015). Among the

attributes of source similarity are homophily and social ties that were classified as being

characteristics of the communicator by Cheung and Thadani (2012). As proposed in the

studies by Chu and Kim (2011) and Shan and King (2015), social ties, i.e. tie strength

can be categorized either as strong or weak depending on the relative closeness of the

bond between groups of individuals. In brief, strong social ties are established through

personal relationships, such as family and friends, whereas weak social ties constitute

less personal relationships that involve a variety of acquaintances (Chu & Kim, 2011;

Shan & King, 2015). Hence, stronger social ties are likely to positively impact eWOM

adoption due to higher perceived reliability and rapport.

Moreover, the concept of homophily is defined as an individual tendency to interact

with other people that are “congruent or similar in certain attributes” (Chu & Kim,

2011: 54) thus contributing to our understanding of overall source similarity. In other

words, high levels of homophily among a group of people would indicate they share

similar attributes related to e.g. gender, age, values, lifestyle and attitudes. Previous

studies have found that individuals with high levels of homophily tend to socialize with

each other more often, thus they are more likely to transfer knowledge and accept

information from each other (Chu & Kim, 2011; Sweeney et al., 2014). Based on these

theoretical notions, the second hypothesis of the study is:

H2) Source similarity is positively associated with purchase intention.

2.4.3. Impact of stimuli characteristics

According to the definition by Cheung and Thadani (2012), stimuli refers to the

information or the message that is forwarded from the communicator to the receiver, as

also illustrated in the framework. Due to the distinct characteristics of eWOM, the

message itself plays an essential role in determining the final impact of communication.

Compared to traditional WOM dialogue, the non-linear and anonymous nature of

eWOM communication may prevent the receiver from sufficiently assessing the

communicator’s credibility. Thus, the online message may reveal a variety of cues that

21

affect the decision to either adopt or ignore the information provided (Cheung &

Thadani, 2012).

Although previous research has established various factors related to message

characteristics, among the most frequently studied factors are eWOM quality and

eWOM quantity (Cheung & Thadani, 2012). In essence, the quality of information is

determined by the nature of message (Sweeney et al., 2008) and the validity of

argumentation (Cheung et al., 2009) that have been measured by e.g. information

content, accuracy, timeliness and comprehensiveness in past studies (Cheung &

Thadani, 2012). As argued by Park et al. (2007), online reviews have no predefined

format hence their content can reveal important cues to the receiver about the author of

the message. If the identity of the communicator is unknown, the level of logical and

credible reasoning used in the review becomes even more important in deciding

whether to accept or reject the information (Park et al., 2007). In conclusion, if the

quality of argumentation is perceived to be robust enough, the receiver is likely to react

either positively or negatively towards the information depending on the valence of

argument (Cheung et al., 2009). Related to argumentation, previous studies have also

found that eWOM messages providing two-sided comments (i.e. for and against the

product or service) add to the integrity of the information (Cheung & Thadani, 2012;

Cheung et al., 2009). Thus, a balanced ratio of eWOM sidedness, i.e. both positive and

negative comments included in the message, is expected to contribute to the overall

credibility and quality of communication. Considering these findings, the following

hypothesis is proposed.

H3) eWOM quality is positively associated with purchase intention.

Similarly, the bare volume of information – i.e. “the total number of eWOM units sent

about a particular object” (Rosario et al., 2016: 301) may lead to increased eWOM

effectiveness as consumers tend to perceive high quantities of reviews as a sign of

popularity (Park et al., 2007). Some studies have found that high quantities of eWOM

create a positive awareness effect even if the valence of reviews is perceived to be

negative (Cheung & Thadani, 2012), which highlights the so called “bandwagon” effect

(Rosario et al., 2016). Thus, high eWOM quantity is expected to have a positive

relationship with purchase intention.

H4) eWOM quantity is positively associated with purchase intention.

22

2.4.4. Moderating effect of receiver characteristics

According to the framework, receiver refers to the individual that is affected by the

information (Cheung & Thadani, 2012). In other words, eWOM receiver is the person

responding to the product- or brand-related communication from other consumers

online. However, the influence of eWOM varies according to the personal

characteristics of the receiver and his or her previous experiences with the discussed

product or service (Cheung & Thadani, 2012). Thus, the factors related to individual

characteristics should be considered together with the other determinants of eWOM

effectiveness to see how they interact.

Among other personal factors, such as consumer expertise, consumer involvement (Fan

et al., 2013) and information needs (Erkan & Evans, 2016), previous research has also

studied the receiver’s overall attitude towards eWOM information (Park & Kim,

2008; Park et al., 2007). In fact, the consumer’s attitude towards eWOM information

has been empirically demonstrated to have a positive relationship with consumer

purchase intention (Chang et al., 2005; Erkan & Evans, 2016) which makes it an

interesting factor for this study. In addition, extant studies (e.g. Chu & Kim, 2011; Tsao

& Hsieh, 2015) have referred to social influence when explaining eWOM effectiveness

on consumer decision-making as it “tends to amplify the influence that eWOM has on

consumers” (Tsao & Hsieh, 2015: 516). Hence, the more consumers are susceptible to

interpersonal influence, the more they are expected to engage in their eWOM

intentions (Chu & Kim, 2011). In essence, such influence accounts for “the changes in

an individual’s reaction to given matters in response to or in deference to other people’s

opinions” (Tsao & Hsieh, 2015: 512). More specifically, interpersonal influence consists

of two dimensions: normative and informational influence. On the one hand, normative

influence affects attitudes, norms and values, and it is defined by the level of conformity

to others’ expectations (Chu & Kim, 2011; Tsao & Hsieh, 2015). On the other hand,

informative influence affects adoption of knowledge, thus it is defined by the level of

information acceptance from others (Chu & Kim, 2011; Tsao & Hsieh, 2015). However,

the two dimensions of interpersonal influence are not mutually exclusive thus they can

overlap and affect consumer decision-making in parallel. For example, a consumer

more susceptible to normative influence would be more dependent on the acceptance of

peers and other social circles than on the information of knowledgeable others,

although both knowledge and social acceptance may have an effect on the final

decision.

23

Given these findings from previous research, it can be deduced that the receiver’s

characteristics are likely to moderate the relationship between the determinants of

eWOM effectiveness and eWOM response. In other words, the better the attitude

towards eWOM and the higher the sensitivity to interpersonal influence, the greater the

impact of other eWOM attributes on the outcome. While both the receiver’s attitude

towards eWOM information and susceptibility to interpersonal influence are expected

to regulate the proportional impact of the other determinants on purchase intention, it

remains to be explored whether either of the moderators is stronger than the other.

Therefore, the following hypotheses are presented:

H5) As the receiver’s attitude towards eWOM information increases, the relationship

between eWOM communicator variables, eWOM stimuli variables and purchase

intention also increases.

H6) As the receiver’s susceptibility to interpersonal influence increases, the

relationship between eWOM communicator variables, eWOM stimuli variables and

purchase intention also increases.

2.5. Theoretical framework for the study

As follows, a theoretical framework for examining the impact of eWOM communication

on consumer’s purchase intention is presented in figure 2. The framework adopts an

integrative approach similar to the theoretical model proposed by Cheung & Thadani

(2012), to involve variables related to the different determinants of eWOM

effectiveness. Although the proposed framework is less comprehensive than the

original model and employs alternative variables, it follows a similar line of thought by

combining ideas from past research to form an overview of eWOM effectiveness. In

particular, the original framework was streamlined by focusing on only one response

variable while using various composite variables as predictors, e.g. susceptibility to

interpersonal influence, source similarity and eWOM quality, that better capture the

multidimensionality of the complex constructs. Nonetheless, this framework draws

from the same notion of social communication (Cheung & Thadani, 2012) in which

overall eWOM persuasiveness is a function of “who says what to whom with what

effect” (Racherla & Friske, 2012: 551).

24

Figure 2 Theoretical framework to study the impact of eWOM on consumer purchase intention (adapted from Cheung & Thadani, 2012).

In the framework, specific eWOM variables related to the receiver, the sender and

the stimuli are involved and their relationship to the response element is examined.

Based on the findings of previous research, both communicator and stimuli

characteristics are tested for a direct effect to eWOM response whereas the receiver

characteristics are tested for both direct effect and moderating effect which was also

proposed by Cheung and Thadani (2012). Logically, a moderating effect can be

expected as the receiver is in the middle of the online information exchange – i.e. being

exposed to the information that he or she evaluates according to its content and the

characteristics of the sender before deciding on the subsequent action. Thus, depending

on the receiver’s attitude and susceptibility to interpersonal influence, the magnitude

and direction of the relationship between communicator characteristics and eWOM

response and/or message characteristics and eWOM response may change. According

to the objectives of this study, the eWOM response element is defined as purchase

intention, which has also been the focus of most studies in the past (e.g. Erkan & Evans,

2016; Park et al., 2007). However, since the relationship between consumer purchase

intention and the other variables used in this study has not been comprehensively

studied, the combined impact of these predictors on purchase intention can be better

understood with the help of this framework. In addition, the role of the goods type is

eWOM stimuli

eWOM communicator

H1.

H2.

H3.

.

H4.

.

H5.

H6.

eWOM response

eWOM receiver

Source

credibility

Source similarity

Purchase intention:

Gen Z consumers in a service

context

eWOM quality

eWOM quantity

Direct effect

Moderating effect

Susceptibility to

interpersonal

influence

Attitude towards

eWOM

information

25

considered by anchoring the framework to a service context in which eWOM has been

found to be most useful for consumer’s decision-making (e.g. Sweeney et al., 2014).

Table 3 eWOM effectiveness constructs involved in the study

Source credibility

Source similarity

eWOM quality

eWOM quantity

Receiver susceptibility

to interpersonal

influence

Receiver attitude towards eWOM

information

The expertise and

trustworthiness of the

communicator

The homophily

and tie strength

between the communicator

and receiver

The credible reasoning and

objective argumentation embedded in the message

The volume of eWOM messages

available in online

archives

The extent to which the receiver is

affected by social influence

(normative and informative)

The personal stance the

receiver has adopted towards eWOM

information

In accordance with the findings from existing literature, this study employs the above

mentioned conceptual constructs that contribute to eWOM effectiveness: source

credibility, source similarity, eWOM quality, eWOM quantity, receiver susceptibility to

interpersonal influence and receiver attitude towards eWOM information (table 3).

These effectiveness constructs are based on one or more representative sub-scales that

each relate to one of the main dimensions of eWOM communication (communicator,

receiver, stimuli). Based on this approach, the study provides an integrative view over

some of the key factors that contribute to eWOM effectiveness in terms of consumer’s

purchase intention while clarifying the relative importance of these factors for a specific

consumer segment, i.e. the Generation Z. Hence, each of the effectiveness constructs is

used as an independent variable while the purchase intention is used as the dependent

variable in the upcoming analysis. As a result, our understanding of the importance of

eWOM in consumer’s decision-making process is improved by applying a previously

identified theoretical approach in an empirical context and testing the six hypotheses

about the interrelationships of various eWOM effectiveness constructs.

26

3 RESEARCH METHODOLOGY

In the following chapter, the methodological choices for conducting the empirical

research are explained. Both research design and data collection are discussed together

with the proposed measures and data analysis techniques.

3.1. Research design

Although the impact of eWOM can be examined both quantitatively (e.g. Park et al.,

2007; Trusov et al., 2009; Zhang et al., 2010) and qualitatively (e.g. Sweeney et al.,

2008), statistical methods are better suited for answering the specific research

questions of this thesis because they enable the description, measurement and

comparison of relationships between variables. Hence, this thesis is based on a

quantitative research design that draws upon theoretical concepts and measures

available in previous research. According to this deductive approach, insights generated

by the literature review are used to form relevant topics, conceptual constructs and

specific scales for the investigation. In this case, investigation is based on an online

survey which will provide the required dataset for conducting further analysis. That is,

identifying relationships between the selected constructs to make statistical inferences

regarding eWOM effectiveness. In conclusion, this study aims to examine the

relationships between previously identified conceptual constructs while generalizing

the results to a specific population.

3.1.1. Data collection

According to the objectives of the study, the data were collected among young

consumers of Generation Z that forms approximately one fourth of the total population

in Finland alone (Statistics Finland, 2017). As advised by Saunders et al. (2009: 217–

220) and Hair et al. (2010: 175–176), a sufficiently representative sample is required to

make confident generalisations about the population. Thus, a critical mass of at least

100 respondents was targeted by using a cross-sectional survey instrument online

which allowed convenient data collection from such a big population. The target sample

size was determined according to the guidelines by Pallant (2010: 150) and Hair et al.

(2010: 175–176), who propose an approximate sample of 90–120 respondents for a

study with six independent variables, thus yielding roughly 15–20 observations per

predictor.

27

Although some eWOM studies have conducted designed experiments with simulated

eWOM scenarios (e.g. Smith et al., 2005; Park & Kim, 2008) or analysed WOM

effectiveness based on actual online information and consumer activity (e.g. Trusov et

al., 2009), other studies have also used traditional surveys as the instrument for data

collection (e.g. Sweeney et al., 2014). While the first-mentioned artificial experiments

provide high precision, designing them for a larger sample can become costly and they

are often conducted in a laboratory environment that is unlikely to be related with the

real world (Saunders et al., 2009: 142–144). On the contrary, recall-based survey

methods provide a more cost-effective way of collecting quantitative data in a natural

context while enabling the researcher to statistically analyse particular relationships

between variables (Saunders et al., 2009: 144–145). Thus, a survey instrument was

deemed appropriate for the purpose of the study. However, its limitations in describing

real-world behaviour should also be acknowledged. For example, the respondent may

experience a lack of memory or knowledge regarding a particular event while an

interpretive bias could also occur when answering the questionnaire (Saunders et al.,

2009: 363–365). In addition, the ability of a survey to understand a specific context is

always limited by the number of variables involved in the study (Saunders et al., 2009:

144–146). Therefore, compared to live observation of the subjects, a questionnaire-

based survey may only provide an estimation of the reported behaviour, which in this

case refers to receiving and reacting to eWOM communication.

3.1.2. Sampling and accessing data

Generation Z consumers were specifically chosen as the target group of the study due to

their extensive experience with the online environment, in which they choose to spend

an increasing amount of time. As the youngest generation currently in the market –

born in 1995 and after (Chaney et al., 2017), they have grown up during the age of the

internet while becoming highly familiar with various platforms in which eWOM may

occur. As a result, this group of consumers has a high likelihood of engaging in eWOM

thus increasing the probability for extracting meaningful data.

As it stands, the oldest Generation Z consumers turned 22 at the time of the data

collection (December 2017), thus it could be assumed that students were widely

represented in the total population. For this reason, the researcher considered a sample

of students to be appropriate. However, to avoid any ethical controversy, a minimum

age limit of 18 years was set for the study. Only adults were involved in the sample

28

because they are able to give their consent for participation. As a result, the youngest

individuals of Generation Z (born 2000 and after) were disregarded thus limiting the

scope of the study. However, a sufficient amount of the total population is eligible for

the research according to this criterion hence enabling fluent data collection. This

sampling approach is also supported by the fact that consumers of legal age have full

control over their spending and financial resources. Thus, they are more capable to

evaluate their own consumption and buying behaviour than minors who are still

dependent on their guardians and do not possess as much experience in the market as

an adult consumer.

To access data, the researcher decided to draw the sample from closed community

groups within a social networking site. For this purpose, Facebook was chosen as the

most suitable platform since it involves the majority of Finnish users. Here, the term

‘closed group’ refers to a private group that has limited access to members only and in

which all members and group publications need to be approved by dedicated

administrators. As followed, the administrators of 30 individual Facebook groups were

contacted in order to negotiate access and a permission to publish the survey within the

closed group. The closed groups were filtered according to specific keywords that

allowed to find groups with newly enrolled freshmen members from both universities

and universities of applied sciences. In addition, the administrators of 5 other groups

were approached. These closed groups included final year students in upper secondary

schools which allowed the younger end of the sample to be reached. The

aforementioned search engine criteria were specifically used to find relevant Facebook

groups that have a high likelihood of having members within the desired age cohort.

As a result of the Facebook inquiry, the study was granted access by the administrators

of 11 closed groups consisting of first-year students in universities and universities of

applied sciences. In addition, the study was granted access by the administrators of 2

closed Facebook groups that included senior upper secondary students. According to

plan, a direct link to the questionnaire and a covering note was posted to a total of 13

groups during weeks 49-51 in December 2017. In four of the groups, the researcher was

allowed to join the group as a new member and publish the post himself while the

remaining posts were directly published by the administrators. The questionnaire was

accessible online for four weeks until the minimum sample size (N=100) was

surpassed. During this time, reminders were sent to the participating groups to

motivate the potential respondents. Since the respondents themselves eventually

29

decided whether to answer to the questionnaire or not, a self-selective sample was

formed (Saunders et al., 2009: 362–363). Finally, the questionnaire was closed on 31st

December 2017.

In conclusion, the online questionnaire was posted to a total of 13 Facebook groups that

had student members from both secondary and tertiary educational institutes. All of

the groups involved Finnish-speaking students. The first 11 groups included first-year

students and their tutors from universities and universities of applied sciences. Among

them were KIK-fuksit 2017 (162 members), LUT fuksit 2017 – kauppatieteet

(244 members), Vaasan yliopiston fuksit 2017-2018 (449 members), Preemion

fuksit 2017 (119 members), Lapin oikeustieteellisen fuksit 2017 (204 members),

Laurea Lohjan fuksit syksy 2017 (110 members), Laurea Tikkurila fuksit

kevät ’17 (146 members), Tekun fuksit 2017 (163 members), PT-fuksit 2017 (207

members), Gulis 2017 (227 members), TiTe-fuksit 2017 (99 members). The last 2

groups included senior upper secondary students. Among them were Torkkelin abit

2018 (94 members) and Linnankosken abit 2017-2018 (161 members). As a result,

the sampling pool involved a total of 2 385 students. Out of these 191 students took the

survey and filled in the questionnaire. After filtering out the eligible respondents (see

3.1.3. for filtering questions), the final sample size was 135 thus yielding a response rate

of 5.6%.

3.1.3. Questionnaire design

The quantitative scales employed in the study were adapted from existing studies to

ensure sufficient validity. Hence, a cross-sectional survey instrument was compiled by

using relevant measures that relate to the conceptual constructs previously identified in

the literature. The questionnaire included statements regarding the determinants of

eWOM effectiveness and the respondent’s intention to purchase. In addition,

demographic information was collected together with some general data related to

consumption of services and internet usage.

Furthermore, the questionnaire was anchored in a service context in order to collect

more meaningful data. As pointed out in the literature, eWOM is likely to have a

significant influence on purchase decisions regarding experience goods, such as

services, rather than tangible goods (Racherla & Friske, 2012; Sweeney et al., 2014).

Hence, the respondents were asked to consider situations in which they typically

30

searched for online information about services and to identify the types of services they

sought information about.

In addition, the respondents were asked to categorize the online channels in which

eWOM communication had previously occurred to them in order to allow an

examination of the communication platform. As there is a wide range of different

online platforms, the study was not tied to any specific online channel. Instead, this

contextual factor was addressed by outlining three platform categories that represented

some of the typical venues for online information exchange – namely social networking

sites, review websites and discussion forums. In addition, a fourth category was left

open for any additional platforms not included under the three main categories. As a

result, the respondents were able to indicate the most relevant eWOM platforms based

on their own experience – either by selecting a pre-determined category or describing

any platform in their own words. The decision to include these platform options was

done mainly for three reasons. First, the potential effect of the platform can be

examined to some extent by comparing scores in between the groups of different

platform users if desired. Second, there are numerous alternative platforms available

online, but not all of them can be outlined individually. Hence, categorization is

deemed appropriate. Third, the presented platform categories are wide enough to

facilitate online information exchange with both existing personal connections and

other connections. Thus, a wide range of communicators, such as friends, opinion

leaders, influencers and unknown consumers, is included under the scope of the study.

Both of the abovementioned contextual factors were outlined in the first two sections of

the questionnaire (Q1-Q7, see Appendix 1 & 2) before proceeding to the questions that

relate to the theoretical constructs. In these sections, there were also general questions

about the respondents’ age, gender, education level and time spent online. The two

screening questions employed in the questionnaire were respondent’s age (Q1) and

his/her previous experience with receiving eWOM about services (Q5). The following

six sections (Q8–Q15, see Appendix 1 & 2) of the questionnaire were arranged

according to the main constructs and the related 35 items respectively. In other words,

each individual section had an amount of 7-point Likert-scale items related to each of

the main eWOM dimensions – the communicator, the stimuli, the receiver or the

response, but none of them were mixed in between sections. This was done to ensure a

conceptually cohesive structure, to improve overall clarity and keep the respondent as

31

engaged as possible. After designing the first draft of the questionnaire, an online

version of the questionnaire was built and published using Webropol 3.0 software.

By framing the questionnaire into lived events, the research adopts a recall approach

that has been used in similar survey-based WOM studies (e.g. Sweeney et al., 2014).

However, this specific approach can also be criticised because it is highly contingent on

the respondent’s memory and provides only a general view of the studied phenomenon.

Hence, a weak memory of a specific event could distort the results and increase the

probability of response bias. Another approach widely employed in eWOM studies is a

simulated decision-making task (e.g. Smith et al., 2005) in which a factorial experiment

is usually created to control the effect of different variables in a laboratory

environment. Nonetheless, such an approach is rather resource-intensive and more

demanding since the researcher him- or herself is required to create hypothetical

scenarios for the experiment which increases the likelihood of experimenter bias.

Therefore, a self-administered and recall-based questionnaire, which allows cost-

efficient data collection in a natural context, was perceived to be the most feasible data

collection tool for this study.

3.1.4. Construct operationalization

Based on the proposed research model, the eWOM effectiveness variables listed below

and their interrelationships were studied together with the purchase intention variable.

All of the variables were measured on Likert-type scales that were retrieved from past

studies and adapted to fit the specific context of this study. Only minor changes were

made to the translations of individual items while the overall meaning of each item was

kept as close to the original as possible. More specifically, in the source credibility scale

(Cheung et al., 2008), the online platform that was pre-specified in the original items

was simply replaced by the word ‘online’ since this study was not delimited to a specific

platform. In addition, the words ‘product’ and ‘brand’ that appeared in the items of

several scales (Cheung et al., 2008; Chu & Kim, 2011; Coyle & Thorson, 2001; Park &

Kim, 2008) were replaced by the word ‘service’ because this study was solely focused on

purchase intention regarding services. Finally, the attitude scale by Park and Kim

(2008) was adjusted by omitting the word ‘online’ from each item since this study was

not limited only to purchases made on the internet but purchases in general (both

offline and online). Thus, all of the abovementioned changes were justified by the

contextual scope of the study.

32

In the context of this study, source credibility is based upon the communicator’s

expertise and trustworthiness that both include two items adapted from Cheung et al.

(2008). On the other hand, source similarity involves both a 4-item homophily scale

adapted from Sweeney et al. (2014) and a 5-item tie strength scale adapted from Shan

& King (2015). Both eWOM (message) quantity and quality are measured on 2-item

and 4-item scales adapted from Park, Lee and Han (2007), while a 2-item scale is

adapted for eWOM sidedness (Cheung et al., 2009) as part of the quality construct. The

receiver’s susceptibility to interpersonal influence is examined through the

measures of normative influence (4 items) and informational influence (2 items)

adapted from Chu & Kim (2011). Moreover, the receiver’s overall attitude towards

eWOM information employs a 4-item scale adapted from Park and Kim (2008).

Finally, purchase intention is studied with three items adapted from Coyle and

Thorson (2001).

All of the 35 items were measured on a 7-point Likert-type scale (Strongly disagree–

Strongly Agree) which allowed for more variance among the responses than e.g. 5- or 6-

point scales. Two of the adapted items employed a reverse-coded scale. All of the

adapted scales are summarized in tables 4 and 5 while the final version of the

questionnaire is attached in appendices (see Appendix 1 & 2 for language versions).

33

Table 4 Summary of the variables related to eWOM communicator and stimuli dimensions

Construct Variable Adapted items Author

Source credibility

Expertise (source_EXP)

The person who left comments online is knowledgeable in evaluating the quality of

service.

Cheung et al., 2008

The person who left comments online is an expert in evaluating the quality of service.

Trustworthiness (source_TRUST)

The person who left comments online is trustworthy.

Cheung et al., 2008

The person who left comments online is

reliable.

Source similarity

Homophily (source_HPHLY)

You and the person who gave WOM have a close relationship.

You and the person who gave WOM have a similar outlook on life.

You and the person who gave WOM share common interests.

You and the person who gave WOM have similar likes and dislikes.

Sweeney et al., 2014

Interpersonal Tie

Strength (source_TIE) I am committed to maintain my relationship

with this person. Shan & King,

2015

I want our relationship to last for a long time.

I feel very strongly linked to this person.

I would not feel very upset if our relationship were to end in the near future.

I am oriented towards continuing this relationship in the long term.

The relationship with this person is important

to me.

eWOM quality

Overall quality (message_QUAL)

Each review has sufficient reasons supporting the opinion.

Each review is objective.

Each review is understandable.

Each review is credible.

Park, Lee & Han, 2007

Recommendation

sidedness (message_SDNES)

The review includes both pros and cons of the discussed service.

Cheung et al., 2009

The review includes only one-sided comments.

eWOM quantity

Volume (message_VOL)

The number of recommendations is large.

The quantity of review information is large.

Park, Lee & Han, 2007

34

Table 5 Summary of the variables related to eWOM receiver and response dimensions

Construct Variable Adapted items Author

Susceptibility to interpersonal

influence

Normative influence

(receiver_NORM)

When buying services, I generally purchase those brands that I think others will approve of.

If other people can see me using a service, I often purchase the brand they expect me to buy.

I achieve a sense of belonging by purchasing the same services and brands that others purchase.

Chu & Kim, 2011

Informational influence

(receiver_INFO)

If I have little experience with a service, I often ask my friends about it.

I often consult other people to help choose the best alternative available among services.

I frequently gather information from friends or family about a service before I buy.

Attitude towards eWOM

information

Attitude

(receiver_ATT)

When I buy services, I always read reviews presented online.

Park & Kim, 2008

When I buy services, the reviews presented online are helpful for my decision making

When I buy services, the reviews presented online make me confident in purchasing the product.

If I don’t read the reviews online when I buy services, I worry about my decision.

eWOM Response Purchase intention

(response_PUR)

It is very likely that I will buy the service. Coyle & Thorson,

2001

I will purchase the service the next time I need it.

I will definitely try the service.

3.1.5. Pilot testing and translation of the questionnaire

Along with the process of finalising the questionnaire, pilot tests were conducted

among a small convenience sample of 10 university students to see whether the

contents of the questionnaire were sufficiently comprehensible. Several test rounds

were conducted in an iterative manner and written feedback was collected each time to

improve the flow and clarity of the questionnaire. As followed, the feedback was mainly

concerned with the number of different questionnaire sections and the order in which

the questions were presented. Thus, the questionnaire was divided into dedicated

sections according to the individual scales, allowing the researcher to direct the

respondent’s attention to a single subject at a time.

35

As per plan, the questionnaire was first designed in English and then translated into

Finnish to enable data collection from a Finnish population. The translation process

was conducted in an iterative manner in which the source content (items in English)

and the target content (items in Finnish) were compared side by side, translated, tested

and then improved to reach sufficient internal equivalence. The contents of the original

items and the adapted items were evaluated and refined based on the feedback from

both pilot test participants and thesis supervisor. Most of the feedback was concerned

in clearly separating similar items from each other and using consistent terminology

throughout the questionnaire.

3.2. Data analysis

The survey data were statistically analysed using SPSS software. Before exploring data

in depth, the dataset was screened to verify that the basic assumptions for statistical

analysis were met. For example, the distribution of variables, missing values and

outliers were checked (Pallant, 2010: 43–50). Furthermore, as advised by Hair et al.

(2010: 125–127) the scales involved in the questionnaire were examined by using

Cronbach’s alpha which provides an approximation of each scale’s internal consistency.

In other words, each scale was entered separately into reliability analysis by calculating

the Cronbach’s alpha, which is based on the scale-specific items’ ability to measure the

desired phenomena. This procedure ensures that the interpretation of the combined

items within each scale is feasible. In case the Cronbach’s alpha of a specific scale falls

below the desired level, less consistent items can be excluded from the scale (Hair et al.,

2010: 125–127: Pallant, 2010: 97–101).

To study the relationships between several eWOM constructs and their ability to

predict a single eWOM response (purchase intention), a multiple regression analysis

was applied. According to Hair et al. (2010: 161–167), multiple regression analysis

provides a statistical tool to illustrate and measure the strength of linear relationships

between many independent variables and one dependent variable. In addition, it allows

an assessment of the independent variables’ relative importance and predictive power

both independently and collectively (Hair et al., 2010: 169–173) thus the applied

technique is in line with the objectives of the study.

However, as pointed out in methodology literature, multiple regression analysis is

dependent on using metric variables (Hair et al., 2010: 161–162, 181–182). Hence, the

mean of each multi-item scale was calculated by summating items that connected

36

conceptually (Hair et al., 2010: 124–127). This computation procedure allowed the

scales to be treated as continuous although the single items were measured on an

ordinal, Likert-type scale. As a result, the eWOM scales related to the communicator,

the receiver and the stimuli were used as independent variables and the scale related to

eWOM response (purchase intention) was used as the dependent variable in the

regression model. To test interaction effects between the variables, different models

with and without the moderator variable were constructed and compared to see which

of them provided the most accurate explanation in variance regarding the outcome

(purchase intention).

Lastly, the specific assumptions related to statistical applications of multiple regression

analysis were examined to ensure the model provides grounds for viable

interpretations. Thus, statistical tests to detect any issues with multicollinearity were

used together with observations of normality and homoscedasticity to validate the

model (Hair et al., 2010: 181–186, 200–205).

3.3. Assessing the quality of research

As the sampling, measurement operationalization and data collection processes have

been made as transparent as possible, the repeatability of the study is improved.

According to Saunders et al. (2009: 156–157), repeatability and equivalence increase

the overall reliability of the research if another experiment could be conducted with a

similar sampling method in the future. However, there were few other inherent factors

in the data collection process that affect the overall quality of the research. First, it is

recognized that a homogeneous sample involving only students from a small

geographical area, such as Finland, may lead to biased results. Thus, the results may

not be generalizable to total population within the scope of this study. Second, the final

sample size of 135 may be sufficient for meaningful statistical analysis, but it might not

provide enough grounds to make confident generalizations either. With the total

population of Finnish Generation Z consumers being approximately 1,375 million

(Statistics Finland, 2017), a more confident sample size would have been in the range of

500–1000 (confidence level 95%, margin of error < 5%).

In terms of data quality, it is also a concern that the link of the online questionnaire was

public to the members of the selected Facebook groups. Thus, it could have been

accessed multiple times by the same respondent, which creates an opportunity for

duplicate answers among the dataset. This issue could have been avoided by

37

distributing the questionnaire directly through a traceable email link, but due to

restrictions with data access it was not a feasible option.

Since the online survey was in fact based on self-reporting of the respondents, the

potentially underlying response bias should also be addressed to decrease sample error

while increasing the external validity i.e. the accuracy of the research in generalizing

results. Existing literature suggests various ways to assess this issue, such as analysing

non-response bias or non-response error (Lindner et al., 2001). According to

Armstrong and Overton (1977), non-response bias may occur when a specific group of

potential survey respondents are unwilling to provide answers. With low response rates

within the sample, this phenomenon creates a suspicion of whether the persons who

responded are significantly different from those who did not. If indeed there was a

substantial gap between the two groups, the results would not directly reflect the way

the entire sample would have answered the survey (Armstrong & Overton, 1977).

Although the best remedy against non-response bias is to increase the overall response

rate of the study, other approaches have been suggested as well. To assess the effect of

potential non-response bias, Armstrong and Overton (1977: 2) discuss extrapolation as

a method in which the “subjects who respond less readily” are assumed to be “more like

nonrespondents”. In similar manner, Lindner et al. (2001) discuss a method in which

the early respondents are compared to the late respondents. More specifically, it is

noted that studies that have previously addressed non-response bias have primarily

used a technique that splits the sample in two distinctive sub-samples while testing

whether any significant differences exist between the early and late respondents on any

given variable (Lindner et al., 2001: 51). To illustrate the method, Lindner et al. (2001:

45) cite Connors and Elliot (1994: 16): “Respondents were grouped as early or late

respondents. The two groups were compared on their responses to the Likert scale

questions using t-tests”. Hence, a similar approach is deemed appropriate and adopted

for the purpose of this study.

As follows, the entire dataset (N=133) was split in two groups: early (N=66) and late

respondents (N=67). In this case, the latter group represents the respondents that did

not take the survey but belong to the population nonetheless. By running an

independent-samples t-test with the split dataset, the potential differences among the

two groups were analysed on all of the seven scales that were previously combined into

the following composite variables: source credibility, source similarity, eWOM quality,

eWOM quantity, susceptibility to interpersonal influence, attitude towards eWOM

38

information and purchase intention. As a result, the Levene’s test returned a

significance level p>.05 for all of the variables except for Purchase intention which

yielded a significance level p=.027, thus implying that the variance for early and late

respondents is not equal regarding this specific variable. To verify whether the groups

differed significantly from each other, the results of the t-test were then examined on

each variable. However, no evidence was found regarding significant differences (p>.05

for all variables) (Pallant 2010: 241–242).

Alongside with non-response bias, the effect of the gender was investigated. As noted in

section 4.2, the division between male (N=39) and female respondents (N=94) was

rather unequal in the sample. Hence, an independent-samples t-test was conducted to

see whether any significant differences existed in between men and women (Pallant

2010: 241–242). The results implied a highly significant difference between the groups

regarding the scores of eWOM quality variable (p=.004), while the there was no

evidence of group differences regarding the other variables (p>.05 for all variables).

Thus, a further inspection of the eWOM quality variable was conducted by running an

independent-samples t-test with the six individual items belonging to that specific scale

(table 6).

Table 6 Results of independent-samples t-test: comparing males and females on the individual items of eWOM quality scale

Male

(N=39)

Female

(N=94)

Scale item

(eWOM quality)

M SD M SD t-test

Each review has sufficient reasons supporting the opinion.

6.00 1.30 5.46 1.47 2.02*

Each review is objective. 4.97 1.95 4.47 1.66 1.52

Each review is understandable. 5.82 1.12 5.15 1.59 2.40*

Each review is credible. 5.33 1.83 4.93 1.67 1.25

The review includes both pros and cons of the discussed service.

5.97 1.11 5.32 1.42 2.57*

The review includes only one-sided comments.

5.77 1.27 5.61 1.25 .683

*Significant when p < .05 (2-tailed)

39

Overall, the descriptive analysis reveals that men (M=5.65, SD=.87) perceive the

quality of eWOM messaging slightly more important than women (M=5.15, SD=.88; t

(131) =2.95, p=.004). When looking at the individual items, the group means differ on

three occasions that imply a stronger emphasis on the strength and clarity of

argumentation among male respondents. Thus, the effect of the gender should be

considered when interpreting the results of the research.

40

4 EMPIRICAL FINDINGS

In the following chapter, the results of the empirical research are examined. The

descriptive statistics and statistical assumptions are first discussed, which is followed

by a reliability evaluation of the scales and a correlation analysis. Lastly, multiple

regression analysis is applied to examine the linear relationships between the variables.

4.1. Data screening

Before proceeding to statistical analysis, the dataset was screened for potential errors,

cleaned and prepared for further inspection. As advised in the methodology literature,

data screening should be conducted to confirm that data are reliable and valid for

testing theory (Hair et al., 2010: 33–37).

First of all, the respondents were screened for eligibility to ensure that all of the cases in

the dataset provide meaningful insights. Out of the 192 individuals who took the

survey, 135 respondents were eligible according to the pre-determined sample criteria

that were measured with two filtering items in the questionnaire. The first filtering

question (Q1) targeted consumers in the desired age group of 18–22, while the second

filtering question dismissed respondents who had not received eWOM in the service

context (Q5). Hence, only those respondents who had experienced eWOM were

included in the dataset. As a result of the preliminary screening, 57 invalid cases were

removed from the dataset thus leading to a sample size of 135.

Second, all of the variables in the dataset were checked for any errors, such as missing

values or out-of-range values. As a result of the descriptive analysis, all values in the

dataset were within the expected range except for two cases. For an unknown reason,

there were two responses in which the value of a single Likert-type item was recorded

zero (on a scale from 1–7). Given the extremely small amount of the cases with out-of-

range units (1,5% of total sample), the two responses were omitted from the dataset

instead of unit imputation, thus leading to a reduced sample size of 133. This approach

is supported by Hair et al. (2010: 44–49) who suggest deletion as an option when low

levels of missing data (<15% of the data) occur. No other errors or missing values were

identified.

Third, duplicate cases were also inspected because the online questionnaire could have

been entered and answered multiple times by a single respondent. Such potentially

41

problematic cases were examined by using the Identify Duplicate Cases -function in

SPSS. All 35 Likert-type items were entered into the analysis to check whether 100%

duplicates exist in the dataset. However, fully matching cases were not discovered.

Lastly, the negatively framed questionnaire items were examined and re-coded. The

first item belonged to the Social tie scale (I would not feel very upset if our relationship

were to end in the near future) while the other item measured the Quality of the eWOM

message (The review includes only one-sided comments). Both items were reverse

coded before proceeding with the analysis.

4.2. Descriptive statistics

After cleaning the dataset, the total sample size was 133. In addition to the Likert-type

items, some categorical data were also collected to outline the sample demographics.

The key characteristics of the sample are presented in table 7.

Table 7 Sample characteristics

Gender

Frequency Percentage

Male 39 29.3%

Female 94 70.7%

Age

Frequency Percentage

18 12 9.0%

19 26 19.5%

20 44 33.1%

21 33 24.8%

22 18 13.5%

Employment

Frequency Percentage

Full-time student 130 97.7%

Employed part-time 3 2.3%

Total (N) 133 100%

42

As presented in table 7, the division between genders is rather unequal. Compared to

male respondents, there are more than twice the number of female respondents in the

sample. Hence, the effect of gender should be considered when interpreting the results

as mentioned in the previous chapter. The reason for the uneven distribution may lie in

the proportional number of female students in tertiary educational institutions.

According to recent statistics, there are more female than male students in Finnish

universities and universities of applied sciences (Statistics Finland, 2014; Statistics

Finland, 2017b), thus there is a high likelihood of developing a female majority in a

sample that involves 97.7% students. On the contrary, the sample is rather normally

distributed according to the respondents’ age. Although being slightly negatively

skewed (zskewness -0.104) towards the higher end of the age spectrum, the target group of

18–22 year old consumers is rather well represented.

Furthermore, three other background questions were employed to record information

about the respondent’s internet usage, the type of services the respondents typically

search information about, and the most popular online platforms in which the

information search occurs.

Table 8 Internet usage in spare time

Daily time spent online

(h)

Frequency Percentage

1–3 51 38.3%

4–6 60 45.1%

7–9 12 9.0%

10–12 7 5.3%

Missing 3 2.3%

Total (N) 133 100%

As presented in table 8, the majority of respondents spend between 4 and 6 hours

online on a daily basis (M= 4.4 hrs, Mdn= 4.0 hrs). More specifically, the respondents

were asked how much time they spent online on average in their daily spare time.

Hence, the results reflect the amount of time the respondents themselves choose to

spend online instead of being forced to do so due to e.g. studies or work.

43

To highlight the range of services that are relevant in the young consumers life, the

respondents were asked to identify the type of service(s) they typically searched online

while looking for information. The most frequently mentioned services included leisure

items such as entertainment, food and restaurants, travel and accommodation, while

the likes of banking, public transport and housing were mentioned less than 10 times

each. Such miscellaneous services that received less than 10 mentions each were

labelled under ‘Other’. All of the mentioned services are presented by category in table

9.

Table 9 Frequency of online information search according to service category

Type of service

Frequency Percentage

Entertainment 65 30.8%

Food and restaurants 51 24.2%

Travel and accommodation 45 21.3%

Electronics and technology 13 6.2%

Health and beauty care 11 5.2%

Other (misc.) 26 12.3%

Total 211 100%

As follows, the respondents were also asked about the type of platforms in which their

information search usually occurs. In the questionnaire, the respondents were able to

choose one or more categories that represent the type of online platforms that

effectively enable consumer-to-consumer communication. Since there are various

online platforms that could potentially be relevant for the respondents, only the

broader categories were presented in the questionnaire to entail as many options as

possible. Moreover, the respondents were able to choose another option in which they

could fill in any platform they saw relevant.

44

Table 10 The most popular online platforms for searching information about services

Online platforms

Frequency Percentage

Review websites 86 64.7%

Social networking sites 85 63.9%

Discussion forums 52 39.1%

Other 18 13.5%

Based on the results in table 10, the most popular type of platforms for searching

information about services were review websites (e.g. TripAdvisor, Yelp) and social

networking sites (e.g. Facebook, Instagram, YouTube) that both gathered a nearly equal

proportion of users. This could be explained by the added credibility in review websites

and social networking sites in which users more typically post and discuss using their

own name and profile picture, while discussion forums may favour more anonymous

communication that entail only nicknames and avatars. The last category ‘Other’ was

chosen by 18 respondents who also provided a description of the type of platform they

used. The most frequently mentioned platforms were Google and search engines (N=7)

that allowed to read other consumer’s comments about a service by typing in the

service provider’s name and relevant keywords such as “kokemuksia” (experiences). In

addition, some mentioned the service provider’s own website or online store (N=5) in

which other customer’s comments and reviews may be displayed. Further mentions

were given to the online community Reddit (N=2), the anonymous mobile

communication platform Jodel (N=2) and blog portals (N=1). One respondent had also

argued that the choice of platform is dependent on the type of product or service.

4.3. Assessing the reliability of scales

Based on the theoretical framework presented in Chapter 2, the study employed six

independent variables and a single dependent variable that were measured by 35

Likert-type items in the questionnaire. To construct the variables for further analysis,

all of the 35 items were combined into individual composite measures i.e. averaged

total scores that serve as substitute variables (Hair et al., 2010: 124–125). Hence, seven

scales were created respectively: source credibility, source similarity, eWOM quality,

eWOM quantity, susceptibility to interpersonal influence, attitude towards eWOM

45

information and purchase intention. As outlined in the theoretical framework, the first

six scales relate to the various determinants of eWOM effectiveness while the last scale

reflects the receiver’s response to eWOM communication. Since the scales are based on

items adopted from previous research, they have been tested and validated at least

once, thus ensuring sufficient content validity. However, the reliability of the scales still

needs to be established through testing the scales’ internal consistency (Hair et al.,

2010: 125). For this purpose, the Cronbach’s alpha coefficient was calculated.

Table 11 Reliability of scales

Summated scale(1

Number of items

Mean Std. deviation Cronbach’s

alpha

Source credibility

4 5.12 .91 .69

Source similarity 10 2.50 .86 .86

eWOM quality 6 5.30 .90 .63

eWOM quantity 2 5.04 1.08 .51

Susceptibility to interpersonal

influence 6 4.22 1.02 .77

Attitude towards eWOM

information 4 4.98 1.02 .71

Purchase intention (eWOM

response)

3 4.93 1.01 .79

1) Measured on 7-point Likert-scale

As presented in table 11, the reliability of each scale varied significantly according to the

analysis. While source similarity, susceptibility to interpersonal influence, attitude

towards eWOM information and purchase intention scales exceeded the acceptable

critical value of .70 (Hair et al., 2010:125), the three other scales did not. According to

the widely accepted understanding, the result implies that the reliability of source

credibility, eWOM quality and eWOM quantity scales may be contested. Thus, the

aforementioned scales should be treated with caution for their internal consistency may

be of lower quality. However, it has been argued that even a lower limit of .60 is

acceptable for the Cronbach’s alpha coefficient (Hair et al., 2010: 125) thus implying

moderate reliability for source credibility and eWOM quality scales. Nonetheless, the

46

scale measuring eWOM quantity is considered to have extremely low internal

consistency (Cronbach’s alpha=.51) which may be partially explained by the small

number of items in the scale. In fact, the uneven reliability values among variables may

be explained by the varying number of items in each sub-scale, since Cronbach’s alpha

coefficient is very sensitive to both the number of items and the sample size (Hair et al.,

2010:124-126). Thus, increasing either the number of items or the sample size could

have remedied the problem and yielded a more balanced result.

When examining the Item-Total statistics in SPSS, the option of reducing items of a

single scale was also considered. Depending on the case, this procedure can improve

the Cronbach’s alpha coefficient by eliminating poorly correlated items from a total

scale with low reliability scores (Pallant, 2010: 97–100). However, further investigation

revealed that deleting items from the scales that scored low would not have improved

the scales’ reliability enough to exceed the critical value of .70, thus the original scales

were retained for further analysis.

4.4. Assessing statistical assumptions

Before proceeding to regression analysis, the composite variables (summated scales)

were entered into univariate analysis to examine the distribution, kurtosis, skewness

and potential outliers in the data. As a result of the statistical tests for normality, all of

the composite variables returned a p-value < .05 in Kolmogorov-Smirnov test thus

rejecting the null hypotheses of normality. On the other hand, only three composite

variables (Source similarity, eWOM quality, eWOM quantity) returned a p-value < .05

in Shapiro-Wilk test, thus providing mixed results regarding the normality of the

variables. In literature, it is noted that even though both statistical tests are commonly

used to evaluate normal distribution (Hair et al., 2010: 73–74: Pallant, 2010: 63), they

might yield different results within the same sample. Hence, other indicators of

normality should also be assessed.

When examining the skewness and kurtosis values of the composite variables, it was

noted that all values were within an acceptable range of -.56 and .19 except for the

source similarity variable that was more positively skewed (S=1.02) and had a

significantly higher peak (K=2.00) than the other variables. According to Hair et al.

(2010: 72–73) the suggested limit for both values is ±1.96 (.05 significance level), thus

a slight deviation from normality may exist based on these numbers.

47

However, according to Hair et al. (2010: 72–74) and Field (2009: 345), both statistical

tests and graphical plots should be used to estimate whether severe deviations from

normality occur since it is the shape of the distribution that may reveal underlying

problems. Furthermore, Pallant (2010:59–65) argues that none of the aforementioned

criteria should be employed alone but they should be evaluated together to form an

overview and better understand whether the distribution of data is problematic. Hence,

the visual data of the histograms, normal quantile-quantile plots and boxplots were

assessed. All histograms showed a distribution that followed a bell-shaped curve

reasonably well, while the data on the normal plots followed a fairly straight line. From

the boxplots it could be seen that all variables except the receiver attitude towards

eWOM information had one outlier within the data while the source similarity variable

had also a single data point with an extreme value. However, the effect of the outliers

was found negligible when the values between the variable mean and 5% trimmed

mean (Pallant, 2010:62–63) were contrasted. The largest difference between the two

values was .04 hence the outliers are not likely to have a significant influence on the

mean.

To conclude, lack of normality is a more pressing issue in smaller samples (N<50)

while its effects are disputable in reasonably large samples (Hair et al., 2010: 72). This

view is also supported by Field (2010: 45), who argues that in larger samples (N>30)

the sampling distribution can be taken as normal based on the central limit theorem.

Considering the sample size (N=133) and the abovementioned evidence, the data are

deemed approximately normally distributed.

4.5. Correlation analysis

To examine the relationships between the composite variables, a correlation analysis

was used. More specifically, Pearson correlation coefficient was applied to quantify and

describe the linear relationships since it is the most suitable measure for studying the

interval data at hand (Pallant, 2010:128). Here, the correlation coefficient (r) implies

the strength and the direction of association among metric variables on a range from 0

to ±1 (Hair et al., 2010: 156-157). Since all of the independent variables were expected

to have a positive relationship with the dependent variable according to hypotheses, a

one-tailed test was seen appropriate.

According to Pallant (2010: 134, 157–158), at least a moderate association (r>.30)

between the predictors and the response variable is deemed ideal to conduct multiple

48

regression analysis. However, an overly strong association (r>.70) between the

independent variables could result in undesirable multicollinearity issues in the final

model (Pallant, 2010: 134, 157–158). The results of the correlation analysis are

presented in table 12.

Table 12 Correlations among variables (Pearson’s r)

Source

credibility Source

similarity eWOM quality

eWOM quantity

Susceptibility to

interpersonal influence

Attitude towards eWOM

information

Purchase intention

Source credibility

1

Source similarity

.228*** 1

eWOM quality

.421*** .098 1

eWOM quantity

.208*** .211*** .125* 1

Susceptibility to

interpersonal influence

.167** .308*** .303*** .216*** 1

Attitude towards eWOM

information

.159** .182** .245*** .319*** .162** 1

Purchase intention

.108 .270*** .277*** .327*** .284*** .452*** 1

Note: N=133, ***p<.01, **p<.05, *p<.10 (one-tailed) From the index we can see that the strength of the correlations vary from small to

medium (Pallant, 2010: 134) while all of the relationships are also positive. While the

correlations were statistically significant (p<.05) among almost every variable, the

source credibility variable (predictor) was not significantly correlated with the purchase

intention variable (response). With its overall low score (r=.108, p=.109), source

credibility suggests only 1.2% shared variance with purchase intention thus making it

the weakest predictor of the group. On the other hand, source credibility is moderately

correlated with another predictor, eWOM quality (r=.421, shared variance 17.7%),

which suggests that the two constructs are connected – the higher the quality of the

message, the higher the communicator’s credibility. However, no preliminary signs of

multicollinearity were identified between the two variables according to the correlation

49

coefficient criteria (r<.70). Nonetheless, since the following regression model is not

restricted to choosing only one independent variable, the lowest scoring variable

(source credibility) is not dropped from analysis but instead it is included to examine

the combined effect of the assumed predictors.

4.6. Regression analysis

To examine the relationships between the determinants of eWOM effectiveness and

purchase intention, hierarchical multiple regression analysis was applied to study the

predictors’ main effects and interaction effects. This dependence technique was

specifically chosen since it describes and quantifies the strength of linear relationships

between many independent (predictor) variables and one dependent (response)

variable, while it also allows an assessment of the independent variables’ relative

importance and predictive power (Hair et al., 2010: 161–162).

As presented in the literature review, the division between independent and dependent

variables is based on theoretical considerations and empirical findings that emerge

from previous research. While the response variable is defined as purchase intention,

the predictors include the following scales: (1) source credibility, (2) source similarity,

(3) eWOM quality, (4) eWOM quantity, (5) attitude towards eWOM information and

(6) susceptibility to interpersonal influence. Out of these, the last two variables were

also hypothesized to moderate the relationships between the other four predictors and

the response variable. For this reason, the main predictors, the moderators and the

interaction terms were added to the model in a hierarchical manner. This approach

allowed for a comparison between the models and their relative contribution to

predicting the outcome.

Drawing from the original research questions, the purpose of the regression analysis

was to describe the relationships between the determinants of eWOM effectiveness

(RQ1) and eWOM response, and study the extent to which these determinants can

predict the eWOM response (RQ2). Furthermore, adding the moderator effect into the

analysis allows an examination of the effects of the receiver’s characteristics (RQ3). To

summarize, the following questions are answered by the analysis:

1) What kind of associations exist between the predictors and the eWOM response?

2) How much variance in eWOM response is accounted by the predictors?

50

3) Among the given set of variables, which is the best predictor of eWOM response?

4) Are the relationships between the predictors and the response significantly affected

by the assumed moderators?

Regarding the generalizability of the results, the ratio between the number of cases and

the number of independent variables should also be considered when multiple

regression analysis is conducted. As argued by Pallant (2010: 150), different authors

provide researchers with varying guidelines about sample size requirements. While

some have suggested a minimum of 15 cases per predictor, others have proposed a

different formula for calculating the optimal ratio of cases vs. predictors. For instance,

the minimum sample size N could be estimated by N > 50 + 8m, in which m is the

number of independent variables (Pallant, 2010: 150). With all of the six predictors

included, the first approach would imply a minimum sample size of 90 respondents

while the second formula would suggest a sample larger than 98 respondents. In

addition, Hair et al. (2010: 175–176) propose a minimum sample size of 100 for most

research contexts while a ratio of 20 observations per variable is argued to be desirable.

Based on these principles, the minimum sample size would be approximately 120 cases.

Therefore, the total sample size (N=133) is deemed sufficient for conducting multiple

regression analysis with the intended six independent variables. However, it is noted

that the sample may be undersized for a regression model with all the main predictors

(6 variables) and interaction terms (8 variables) included. That said, an ideal sample

size would be closer to 200 cases according to guidelines.

Before entering into regression analysis, all of the variables were centred without fully

standardizing them. As advised by Field (2009: 740–741), the mean was subtracted

from the raw data to set the mean of each scale to zero. This procedure was conducted

to reduce potential collinearity issues and improve the interpretation of the regression

coefficients when both the interaction and main effects are present (Jaccard et al.,

1990).

4.6.1. Testing the assumptions for multiple regression analysis

Since the regression analysis was conducted in a hierarchical manner, four models were

created to estimate each model’s relative contribution to predicting the eWOM

response (purchase intention). More specifically, the analysis was conducted in the

following sequence. Model 1 included the four main independent variables, namely

51

source credibility (IV1), source similarity (IV2), eWOM quality (IV3) and eWOM

quantity (IV4). In Model 2, attitude towards eWOM information (IV5) was added to

examine its direct effect before analysing its hypothesized moderation effect. In model

3 the second hypothesized moderator, susceptibility to interpersonal influence (IV6),

was included. Lastly, all interaction terms were added to model 4 that involved 14

independent variables in total. This model entailed the four main predictors (IV1–IV4),

the two moderators (IV5 and IV6) and eight interaction terms. As advised by Hair et al.

(2010: 180–181), the interaction terms were aggregated as a product of the predictor X1

and the assumed moderator X2. Hence, each of the main predictors (IV1–IV4) was

multiplied by attitude towards eWOM information (IV5) and susceptibility to

interpersonal influence (IV6).

Before examining the goodness of fit and model accuracy, the assumptions for multiple

regression analysis were assessed. First, the multicollinearity diagnostics in SPSS were

observed but they did not show any problematic correlations among independent

variables in any of the four models. Both Tolerance and VIF values were clean and

within the critical limits (TOL > .10, VIF < 10) (Pallant, 2010: 157–160). Hence, the

original interpretation of the correlation analysis was supported and all predictors were

deemed sufficiently independent of each other. Second, the normal probability plot of

regression standardized residual showed data points that were approximately following

the diagonal line, while the points in the scatterplot were arranged around the zero line

in the shape of a random cloud. Similarly, the distribution on histogram followed a bell

shaped curve as expected. Thus, no severe deviations from normality, linearity or

homoscedasticity were observed based on the visual analysis of the charts (Hair et al.,

2010: 220–221; Pallant, 2010: 157–160). Third, the regression residuals, i.e. the

observed values subtracted from the predicted values, were further analysed by plotting

each independent variable against the standardized residuals. Although some partial

regression plots were better defined than the others, none of them showed systematic

pattern or curvature that would imply a deviation from linearity (Hair et al., 2010: 221-

222).

Based on the results from data screening, the potentially outlying cases were not

considered an issue (see Chapter 4.4). To ensure that the regression model was not

significantly affected by outliers, the standardized regression residuals on the

scatterplot were yet again examined. The chart revealed only few possible outliers that,

however, were not removed from the dataset because all cases were found to be within

52

the intended range (1–7 on a Likert-scale). Hence, the outliers represented real values

on the higher end of the scale as deduced previously. Furthermore, none of the

potentially outlying data points exceeded a standardised residual of ±3.3 that is used as

the critical limit for defining influential outliers (Pallant 2010: 159).

To conclude, all the main assumptions for regression analysis were met according to

the aforementioned evidence. In other words, no sign of violating normality, linearity

or homoscedasticity was identified.

4.6.2. Assessing the regression models

In model 1, the coefficient of determination R square returned a value of .209 (p<.01)

while the value of adjusted R square was only slightly lower at .184. Out of the four

predictors, all but one made a statistically significant unique contribution to predicting

purchase intention. Both eWOM quality (Beta=.269) and quantity (Beta=.271) were

highly significant to the model (p<.01) while source similarity (Beta=.212) was also

significant on a lower level (p<.05) However, it turned out that source credibility

(Beta=-.111) was not significant even at the lowest significance threshold of p < .10.

Compared to the first block of predictors, model 2 had an R square of .331 (p<.01) thus

making the increase (ΔR2=.122, p<.01) highly significant. Although R square increases

every time a new predictor is added, the results reveal that including attitude towards

eWOM information (Beta=.313, p<.01) actually made a significant unique contribution

although the interaction variables did not. However, it was noted that two of the

interaction variables (IV1: source credibility x IV5: attitude towards eWOM

information and IV3: eWOM quality x IV5: attitude towards eWOM information)

made a unique contribution to predicting the response on the lowest significance

threshold of p < .10, but the effects were deemed negligible since the desired

significance level for the study had been set to p < .05. Thus, only the direct

relationships were found to be significant instead of the hypothesized moderator

relationships between the predictors and the response. The findings were supported by

the comparative measure of adjusted R square, in which the increment (Adjusted

ΔR2=.098) was also evident. Therefore, the overall model accuracy was improved from

model 1 despite the alternative combinations of variables.

In model 3, the second hypothesized moderator, susceptibility to interpersonal

influence, and the related interaction terms were included respectively thus yielding an

53

R square of .246 (p<.01). However, neither the new predictor nor the interaction terms

made a significant unique contribution to the model which made the increment

(ΔR2=.038) clearly smaller compared to the first two models. This conclusion was also

supported by the relatively small increase in adjusted R square (Adjusted ΔR2=.007)

that provides a comparative measure for models 1 and 3 that each have a different

amount of variables. Therefore, the model accuracy was not significantly improved by

adding the sixth variable and its interaction terms.

Lastly, all predictors (IV1: source credibility, IV2: source similarity, IV3: eWOM

quality, IV4: eWOM quantity, IV5: attitude towards eWOM information, IV6:

susceptibility to interpersonal influence) and all of the eight interaction terms were

included in model 4 (R2=.358, p<.01) – i.e. the full model. Yet again, the increase in R

square was rather small in magnitude (ΔR2=.027) when compared to model 2 and the

total amount of variables added. Moreover, the difference between R square (.358) and

adjusted R square (.281) was greater in model 4 thus increasing the gap from .49 to .77

when compared with model 2. In other words, the proportional change in the

explanatory power of the model was not significant compared to model 2, while none of

the interaction terms had made a significant unique contribution either. The full model

was deemed redundant because it did not add any value to the analysis above the first

three models.

4.6.3. Results

The overall results from the regression analysis and the first three models are

summarized in table 13. However, as explained above, the fourth model was not

reported in the table because the interaction terms were not found statistically

significant. Thus, adding the last model to the results table would not have increased

the informativeness of the report.

54

Table 13 Results of hierarchical regression analysis for predicting purchase intention

Variables

Model 1: Std. Beta

coefficients (t-values)

TOL

Model 2: Std. Beta

coefficients (t-values)

TOL

Model 3: Std. Beta

coefficients (t-values)

TOL

Direct effects

IV1: Source credibility

-.111 (-1.237) .773 -.101 (-1.191) .761 -.140 (-1.515) .718

IV2: Source similarity

.212 (2.583)** .920 .201 (2.557)** .880 .164 (1.829)* .759

IV3: eWOM quality

.269 (3.098)*** .821 .194 (2.303)** .765 .238 (2.620)** .740

IV4: eWOM quantity

.271 (3.323)*** .927 .191 (3.375)** .844 .274 (3.305)*** .889

IV5: Attitude

towards eWOM

information

.313 (3.776)*** .794

IV1 x IV5 -.173 (-1.870)* .638 IV2 x IV5 -.057 (-.687) .797 IV3 x IV5 .152 (1.670)* .660 IV4 x IV5 .065 (.818) .869

IV6:

Susceptibility to

interpersonal influence

.135 (1.489) .740

IV1 x IV6 -.100 (-1.083) .720 IV2 x IV6 .074 (.849) .815 IV3 x IV6 -.042 (-.455) .734 IV4 x IV6 -.081 (-.916) .788

F 8.433*** 6.759*** 4.463*** R2 .209 .331 .246

ΔR2 .209 .122*** .038 Adjusted R2 .184 .282 .191

Note: N=133, ***p<.01, **p<.05, *p<.10 TOL = Tolerance

Although the explained variance in purchase intention was highest in model 4, the

result has to be reflected against the alternative combinations of variables to facilitate

meaningful comparison. As discussed previously, the only significant leap from the

explanatory power of model 1 was achieved in model 2 (ΔR2=.122, Adjusted ΔR2=.098)

in which the fifth predictor (IV5: attitude towards eWOM information) was added

along with the interaction terms. On the contrary, the overall comparison implied a

lower fit for models 3 and 4 in which the gap between R square and adjusted R square

increased more than the total increment in the models’ explanatory power. In fact, the

adjusted R square was roughly the same in models 2 and 4, because neither the second

hypothesized moderator (IV6: susceptibility to interpersonal influence) nor the

55

interaction terms were statistically significant in the last full model. Hence, the

inclusion of the fifth predictor alone made a relatively stronger contribution in model 2

than the other additional predictors together in models 3 and 4. As a result, it can be

inferred that the goodness-of-fit in model 2 (R2=.331, p<.01) is relatively the highest of

all models which implies that approximately 33% of the variance in the predicted

behaviour is explained by this particular model.

As always, the results of any study should also be reflected against the specific research

context. Predicting human behaviour differs significantly from mathematical and

natural sciences in which high precision in prediction is typically more attainable. On

the contrary, when human behaviour and social interactions are involved, even models

with lower prediction accuracy are more than acceptable. Therefore, similarly

conducted studies may provide an appropriate point of comparison for the output of

this research. Given the results of the regression analysis, model 2 yielded a fairly

moderate R square of .331 which is, in fact, quite well in line with some other studies of

the same field. Although the results cannot be directly compared, they provide some

direction of the outcome that other researchers have achieved in eWOM effectiveness

studies by using the same kind of statistical technique. For instance, Racherla and

Friske (2012) analysed the usefulness of consumer reviews by applying multiple

regression analysis (R2=.29) while Fan and Miao studied the adoption of eWOM

information (2013) and its effect on purchase intention (2012) by constructing several

regression models where the R squared landed within a range of .35 < R2 < .58. Hence,

the model created for this study is placed along the “mid-range” in terms of its

predicting accuracy when compared with the aforementioned studies.

Beyond the statistical significance and the goodness-of-fit, the substantive significance

of model 2 can also be estimated by the complementary measures of effect size and

statistical power (Cohen, 1988; Selya et al., 2012). Here, the first measure indicates the

magnitude of the effect while the latter presents the likelihood of making significant

findings in the data if they exist. According to Cohen’s guidelines (1988), the effect size

can be calculated for regression as follows: 𝑓2 =𝑅2

1−𝑅2. Hence, model 2 reaches a

moderate effect size (f2=.49) with an R squared of .331. Furthermore, the model’s

statistical power reaches a high level of .99, thus there is a high probability of

recognizing the effect if it exists in the data.

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To examine the individual contribution of the predictors, the Beta coefficients that

made a significant unique contribution to predicting purchase intention were compared

in model 2. As presented in table 13, all five main predictors were positively associated

with purchase intention except for source credibility which had a negative association

with the response variable. As presumed from the results of the correlation analysis,

source credibility did not make a strong unique contribution either (Beta=-.111,

p=.203). Therefore, H1. was not supported. Among all predictors, the most significant

unique contribution was made by the receiver’s attitude towards eWOM information

(Beta=.313, p=.000). The second best unique contribution was made by source

similarity (Beta=.201, p=.012), while eWOM quality (Beta=.194, p=.023) and eWOM

quantity (Beta=.191, p=.019) were ranked third and fourth according to their unique

contribution in the model. Thus, H2., H3. and H4. were all supported. The results

imply that the receiver’s personal stance towards other consumers’ reviews, statements

and recommendations is in a key role in determining the strength of purchase

intention. In fact, attitude towards eWOM information predicts the outcome over 1.5

times more than eWOM quantity. From the relationship between source similarity and

purchase intention, it can be interpreted that the likelihood of purchase is significantly

influenced by the closeness of the relationship between the communicator and the

receiver, and their similarity in taste, lifestyle and interests. Finally, the results indicate

that the likelihood of purchase is positively affected by higher quality of argumentation

and message content online, while the higher volume of eWOM information also has a

similar effect.

Overall, since the analysis provided statistically significant results for examining the

relationships between the determinants of eWOM effectiveness and the eWOM

response, the main goals of the study were met. However, not all of the hypothesized

relationships were found to be significant. Based on the results of statistical analysis,

both H1. and H6. were rejected while the following hypotheses H2.–H4. were

supported. Due to slightly weaker statistical evidence, H5. was only partially

supported. All original hypotheses and the subsequent results of the statistical analysis

are presented in table 14.

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Table 14 Findings from the empirical research

Hypothesis Result

H1. Credibility of the eWOM communicator is positively associated with

purchase intention. Not supported

H2. Source similarity is positively associated with purchase intention Supported

H3. eWOM quality is positively associated with purchase intention. Supported

H4. eWOM quantity is positively associated with purchase intention. Supported

H5. As the receiver’s attitude towards eWOM information increases, the

relationship between eWOM communicator variables, eWOM stimuli

variables and purchase intention increases.

Partially supported

H6. As the receiver’s susceptibility to interpersonal influence increases,

the relationship between eWOM communicator variables, eWOM

stimuli variables and purchase intention increases.

Not supported

As outlined in table 14, the hypothesized moderator relationships were not supported

by the empirical findings. In models 2 and 3, the interaction terms were included in the

analysis but none of them made a truly significant unique contribution. Even though

two of the interaction terms (IV1: source credibility x IV5: attitude towards eWOM

information and IV3: eWOM quality x IV5: attitude towards eWOM information) in

model 2 made a small unique contribution (Beta=-.173, p=.064 and Beta=.152, p=.098)

on the lowest significance threshold (p<.10), the empirical evidence provides only

partial if any support to hypothesis H5. Due to these small interaction effects, statistical

evidence for the moderator relationships was neither provided by the final model 4

which combined a total of 14 variables including the interaction terms. The reasons for

such a result are manifold. Testing moderation with such many variables can be

challenging, hence the result may indicate a problem of overfitting the model in which

the differences between variables are no longer detected. Nonetheless, the model

involved only those relationships that were justified by underlying theory which leaves

room for further discussion and post hoc -analysis regarding the potentially latent

relationships in between variables. Given the scope of the research, however, such

analyses are not included in this report.

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5 DISCUSSION

In this chapter, the results and implications of the research are discussed. First, the key

findings of the empirical study are presented. Second, the results are reviewed from

both theoretical and managerial perspectives. Lastly, the final conclusions of the study

are summarized after discussing the limitations of the study and the opportunities for

future research.

5.1. Key findings

In order to close the loop and conclude the study in an iterative manner, the final result

should always be connected with the starting point of the research. Therefore, the key

findings of this study are reflected against the findings from extant literature and the

research questions that were presented alongside with the research problem. Moreover,

the findings are explained through a revised theoretical framework that bridges theory

and empirical evidence by illustrating the discovered relationships between the

conceptual constructs.

5.1.1. Relations between eWOM effectiveness factors and purchase intent

Although past research has provided a vast base of knowledge and empirical evidence

about the impact of eWOM on consumer behaviour (e.g. Park et al., 2007; Sweeney et

al., 2008; Cheung & Thadani, 2012), the influence mechanism of the said consumer-to-

consumer communication keeps intriguing researchers even today (e.g. Erkan & Evans,

2016; Djafarova & Rushworth, 2017). Continuing this tradition and building on the

findings of extant eWOM literature, a theoretical framework originally proposed by

Cheung & Thadani (2012) was adapted and tested empirically in a new demographical

context to answer the research questions of this study. Hence, the study further

strengthens our current understanding of the relationship between eWOM and

consumer purchase intention while focusing on the determinants of eWOM

effectiveness.

The first research question was specifically aimed to describe the relationships between

the determinants of eWOM effectiveness and the eWOM response. In this case, the

determinants of eWOM effectiveness were classified into four main predictors, namely

source credibility, source similarity, eWOM quality, eWOM quantity, and two

moderators defined as receiver’s attitude and susceptibility to interpersonal influence.

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Furthermore, the eWOM response was defined as purchase intention of Generation Z

consumers. According to findings, the relationships between the determinants and the

response were mainly direct and positive except for source credibility which was

negatively associated with purchase intention. This implies that source credibility

contributes significantly less to predicting purchase intention than the other

determinants involved in this study. However, despite this result, only four

relationships between the aforementioned determinants and purchase intention were

statistically significant. In fact, only receiver’s attitude towards eWOM information,

source similarity, eWOM quality and eWOM quantity made a significant unique

contribution to predicting the respondents’ eWOM intention. Regarding the indirect

relationships that were hypothesized to exist between the two moderators and the four

main predictors, only weak statistical evidence was found.

5.1.2. Significance of eWOM effectiveness factors to purchase intent

The second research question aimed to identify the determinants of eWOM

effectiveness that are relatively the most important in determining the strength of

purchase intention. In other words, the goal was to rank the determinants according to

their individual contribution in the regression model. Even though the model itself was

found significant, some of the regression coefficients did not make a significant

contribution to predicting the response variable. Thus, the hypotheses were only

partially supported. Given these empirical results, some of the findings are in line with

previous research while some of them are not.

Firstly, the results of the regression analysis indicated that the most important

determinant out of the six alternatives was in fact receiver’s attitude towards eWOM

information. Thus, the receiver’s thoughts and feelings about the relevance and

usefulness of review information are emphasized in the overall results due to the large

proportional contribution of the attitude variable. The second most important factor

proved to be source similarity which shifts attention from the receiver towards the

communicator. More specifically, attention is drawn to the characteristics of the

relationship between the two actors – their interpersonal closeness and similarity

which increases consumer purchase intention. On the one hand, the significance of

source similarity has been acknowledged rather well in studies related to conventional

WOM (Brown & Reingen, 1987; Sweeney et al., 2008), but on the other hand, its role

has not been as clear in the context of eWOM communication. The reason for this lies

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mainly in the differences between offline and online communication: it is usually easier

to assess the similarity of the communicator in a live situation whereas it might be

impossible to find cues about source similarity on the internet (Cheung & Thadani,

2012). Therefore, its relevance for digital conversations could be questioned. However,

as presented in the findings of this study, source similarity does play a role in eWOM as

well.

By looking at extant studies on the impact of eWOM, a similar finding was made by

Rosario et al. (2016) who argued that source homophily influences the effectiveness of

eWOM stronger than source trustworthiness. Similarly, Chu and Kim (2011) found that

tie strength – another dimension of source similarity, was positively associated with

consumers’ overall eWOM behaviour. Along with the same school of thought, Sweeney

et al. (2014) discovered that homophily between the receiver and the sender enhanced

the influence of eWOM message although positive eWOM was less dependent on other

conditional factors than negative eWOM. In addition, the findings of this study also

complement the work of Djafarova and Rushworth (2017), who concluded that lower

scale influencers, such as bloggers and other social media personalities, are more

powerful than traditional celebrities in influencing the buying decision of young

females (18–30 years old). In fact, in their study about the credibility of online

influencers, lower scale influencers were perceived to be more credible and relevant

because the respondents could better relate to them (Djafarova & Rushworth, 2017).

Thus, it can be concluded that the ability to identify with the information source has a

significant role in determining the effect of eWOM among Generation Z consumers.

The third and the fourth most important factors, eWOM quality and eWOM quantity,

were both related to the stimuli i.e. message characteristics. Although both factors were

almost equally important according to statistics, profound, clear and objective

argumentation was ranked slightly more significant than the amount of such

information. Therefore, the results emphasize quality over quantity in terms of eWOM

content and its influence on consumer purchase intention. This, however, is not

completely in line with findings from previous research. The importance of eWOM

quantity was pointed out by Rosario et al. (2016), who found the positive volume of

information to be the most significant determinant of eWOM effectiveness for service

products. Here, the authors stated that the bandwagon effect best explains the

dynamics of such communication effectiveness, which, nonetheless, was not evident in

the results of this study. As stated previously, eWOM quantity made a smaller

61

contribution in predicting purchase intention than eWOM quality and, in fact, it was

the least significant contributor of the four key determinants of eWOM effectiveness

found in this study. This indicates that Generation Z consumers are less affected by the

volume of online information than the other factors involved, which could be explained

by the fact that they have grown in a data-rich environment where online information

has always been abundant. Thus, the weight of other factors than volume is greater

when separating relevant and influential information from the rest.

Nonetheless, the contribution of the remaining factors was still far behind the effect of

eWOM quantity. In fact, the least important determinants of eWOM effectiveness were

source credibility and receiver’s susceptibility to interpersonal influence that did not

make a statistically significant unique contribution to purchase intention. As credibility

has been found to be among the key factors that predict consumer activity online

(Cheung et al., 2009; Fan et al., 2013), the conclusions of this study are somewhat

controversial. For example, the finding about the low significance of source credibility

is in conflict with the study by Racherla and Friske (2012), who discovered that the

usefulness of online reviews is enhanced by the reviewers’ high expertise and high

reputation – both of which represent the dimensions of source credibility. However, it

has been argued that source credibility is not only build upon the communicator

characteristics, but also on the quality of the argument and strength of persuasion

(Djafarova & Rushworth, 2017). Thus, the interplay among the message and

communicator characteristics is emphasized in providing cues about source credibility

online. This could also explain the result of this study since quality of eWOM was

operationalized as a separate variable from source credibility. If source credibility and

eWOM quality were not distinctive enough conceptually but rather different

dimensions of the same effectiveness factor, it would make more sense why the former

variable did not make a significant unique contribution to predicting purchase

intention, but the latter did.

Considering this surprising outcome and the possibility that source credibility was in

fact outweighed by other predictors, the first suspect would typically be collinearity

between variables. However, the multicollinearity diagnostics in SPSS did not show

alarming signals which suggests that the two factors were in fact independent, not

adjacent. Moreover, the correlations between all independent variables were below the

critical threshold of r < .70 (Pallant, 2010: 157–158). Nonetheless, we should consider

this scenario since correlations among independent variables can affect the explanation

62

of regression results even at lower thresholds when their mutual correlation is greater

than their individual correlation with the dependent variable (Hair et al. 2010: 203–

204). Thus, the unexpectedly reversed and nonsignificant relationship between source

credibility and purchase intention is perhaps influenced by the relatively high

correlation (r=.421, 17.7% shared variance) between source credibility and eWOM

quality which is higher than either predictor’s correlation with the response variable

(r=.108 and r=.277). Therefore, it is concluded that the importance of source credibility

is relative of the other predictors included in the model and the result can be

interpreted only in the context of this study (Hair et al., 2010: 199–200). In fact, if

source credibility had been studied in isolation of the other variables, the result might

have been different or even close to extant studies that have employed a different set of

variables. The findings of this study do not imply that neither source credibility nor

receiver’s susceptibility to interpersonal influence matter at all. Instead, the results can

be interpreted in such a way that the influence of eWOM is better explained by the

other factors involved in this specific model.

5.1.3. Impact of receiver characteristics on eWOM effectiveness

The third research question aimed to find out whether the characteristics of the

receiver influence the remaining determinants of eWOM effectiveness. Thus, the

hypotheses (H5. and H6.) about moderating relationships between the variables was

tested. The results of the regression analysis, however, indicate that the chosen

measures of receiver characteristics, attitude towards eWOM information and

susceptibility to interpersonal influence, do not significantly moderate the other

determinants except for source credibility and eWOM quality that were both influenced

by the receiver’s attitude to some extent. More specifically, the negative relationship

between source credibility and purchase intention increased while the positive

relationship between eWOM quality and purchase intention decreased. Since the

statistical evidence was not unanimous about the moderation effect, the influence of

receiver characteristics on source and message characteristics is rather negligible and

should be studied further.

Nonetheless, the findings do indicate that the receiver’s attitude towards eWOM

information is single-handedly the most significant determinant out of the six

effectiveness determinants involved in the study. Therefore, the receiver’s

characteristics play an important role in defining the influence of eWOM exposure and

63

the following behavioural reaction, but not in the way that was originally hypothesized.

In fact, the relationship between the receiver’s attitude and purchase intention proved

to be direct not a moderating one. Given this result, the findings are in line with the

study by Erkan and Evans (2016) who also discovered a significant, positive

relationship between attitude and purchase intention in their study about the influence

of eWOM. On the contrary, the receiver’s susceptibility to interpersonal influence was

not found to be among the determinants of eWOM effectiveness although extant

research (Chu & Kim, 2011) has found a statistically significant linkage between

consumers’ susceptibility to interpersonal influence and their engagement in eWOM

behaviour, such as opinion seeking. Therefore, the findings of this study indicate that

Generation Z consumers’ confidence in buying is driven more by the receiver’s personal

stance and own assessment of the helpfulness of information than his or her need of

social acceptance, belongingness and conformity to external information. Thus it can be

interpreted that the influence of eWOM is not as much based on their dependence on

other consumers’ opinions but more on their positive perception of such opinions as a

useful source of information.

5.1.4. Revised framework

The theoretical framework of this study involved six hypothesized determinants of

eWOM effectiveness that were regressed on a purchase intention variable. As expected,

the empirical analysis revealed that the influence of eWOM is based on both the

characteristics of the communicator, the stimuli and the receiver that each have

different subsets of variables. Therefore, the results illustrate the underlying logic of

social communication in which the communication process is explained by the

interplay among these three dimensions and the response. This principle is similarly

represented in the early works of Carl Hovland (1948, as cited by Cheung & Thadani,

2012) that summarized social communication as the function of “who says what to

whom and with what effect” (Cheung & Thadani, 2012: 463; Racherla & Friske, 2012:

551).

64

Figure 3 Revised empirical framework for studying the impact of eWOM on consumer purchase intention. (Adapted from Cheung & Thadani, 2012)

However, as a result of the empirical research, the framework of the study was slightly

revised (Figure 3). The main differences with the original framework were related to

the hypothetical relationship between source credibility and purchase intention (H1.)

and the moderating effect of receiver’s susceptibility to interpersonal influence (H6.)

that both proved to be less significant than expected. Therefore, susceptibility to

interpersonal influence was removed from the illustration as it did not increase the

informativeness of the model. On the contrary, source credibility remained in the

model because of its moderator relationship with receiver’s attitude towards eWOM

information. Another change to the original framework was done when the receiver’s

attitude towards eWOM information turned out to have a significant direct relationship

with purchase intention. Thus, the hypothesized moderator (H5.) became the most

significant main predictor of eWOM response.

5.2. Theoretical contributions

Based on the aforementioned key findings, this study contributes to the existing

literature of eWOM effectiveness in three different ways. First, the study advances our

understanding of eWOM effectiveness factors in a holistic manner. Instead of focusing

solely on a single dimension of eWOM communication, the complementary dimensions

eWOM stimuli

eWOM communicator

-.101

.201**

.194**

. .191**

.

.330***

.

eWOM receiver

R2=.331***

Note: N=133, ***p<.01, **p<.05, *p<.10

.152*

-.173*

eWOM response

Source credibility

Source similarity

Purchase intention: Gen Z consumers in a service

context

eWOM quality

eWOM quantity

Attitude towards eWOM

information

Direct effect

Moderating effect

65

of both communicator, receiver and stimuli characteristics are integrated to understand

their impact on consumer behaviour as a whole. By exploring empirically all of the

dimensions, the study is among the first to develop current knowledge towards a more

comprehensive understanding as requested by Cheung & Thadani (2012). The strength

of this approach is its ability to evaluate the joint contribution of various determinants

of eWOM effectiveness as well as the determinants’ interrelationships and their relative

contribution to eWOM outcomes. Based on the findings, it is concluded that the

statistical differences between the studied determinants of eWOM effectiveness were

rather small, except for receiver attitude towards eWOM information which stood out

as the most significant predictor in the regression model. Hence, the importance of

receiver characteristics should not be neglected when examining the influence of

eWOM. However, the final outcome is hardly explained by any single factor, thus it is

essential to consider eWOM effectiveness as a complex interplay of many factors that

may vary in their relative weight depending on the context.

Second, the study incorporates the perspective of Generation Z consumers – an

emerging consumer group that has not been studied extensively in the context of

eWOM effectiveness. Contrary to their predecessors, Generation Z represents the first

generational cohort that has been born and raised during the digital age which makes

them a relevant subject for studies. After all, they have had a constant access to online

information sources unlike the older generations who have experienced eWOM only at

a later stage. The internet, social media and apps being an essential part of their life,

eWOM is likely to remain an important information source among Generation Z in the

future as well. Thus, this study offers researchers a way to better understand how this

online information exchange affects their purchase behaviour.

Third, the results of the empirical study show source credibility in a different light than

before. In fact, the impact of source credibility on consumer purchase intention was

significantly lower than the other factors employed in this study although it has been

conventionally identified as a strong antecedent of eWOM influence. Given the

surprisingly low significance of source credibility and its inverse relationship to

consumer purchase intention, this study encourages researchers to further explore

source credibility dimensions to better understand its role in eWOM context and the

reason behind its unexpectedly negative association. As noted previously, the

importance of source credibility is relative of the other predictors included in the model

(Hair et al., 2010: 199–200) and the result does not imply that source credibility per se

66

would be insignificant. Instead, a more meaningful interpretation would be that the

dimensions of source credibility (i.e. expertise and trustworthiness) are more

frequently assessed by the quality of the content (i.e. argumentation and sidedness) as

pointed out by Djafarova and Rushworth (2017). On the one hand, this is explained by

the distinct characteristics of eWOM communication in which anonymity between

participants is rather common (Cheung & Thadani, 2012), thus the receiver has to look

for other cues of credibility when the source is unknown to the receiver. On the other

hand, in a situation where identity cues about the source are available or the source is

personally known to the receiver, relatability plays an important role in determining

the credibility of eWOM (Djafarova & Rushworth, 2017). Thus, in this research context,

it seems that the impact of credibility might not be explainable by two dimensions only

but rather a combination of dimensions that overlap with both stimuli and

communicator characteristics.

Finally, as reported in the analysis chapter, the respondents had also indicated the

types of services of which they used to search online information about. Therefore, as a

theoretical sidenote, the results may provide some relevant insights even if the data

were analysed only descriptively. In fact, the most frequently mentioned services

included various leisure items, e.g. entertainment, food and restaurants, travel and

accommodation, while the likes of banking, public transport and housing were

mentioned less frequently. Following a similar line of thought as Zhang et al. (2010),

the findings may imply that eWOM information is more useful and relevant for certain

type of services. More specifically, services in which interaction processes and

experiential outcomes are more dominant than technical outcomes (Zhang et al., 2010).

Therefore, the potential impact of the service category on eWOM effectiveness should

be further studied to confirm whether such an association actually exists.

5.3. Managerial implications

In the modern business landscape, companies aiming to reach Generation Z consumers

simply cannot refrain themselves from online marketing activity because the digital

space is widely populated by their target audience. In fact, somewhere in the digital

marketplace, the brands, the products and the services are being assessed in an ongoing

dialogue in which information flows from one consumer to another. Hence, companies

should become aware of the effects of such information exchange and proactively

engage in the discussion.

67

As has been shown in previous research, such consumer-to-consumer communication

i.e. eWOM affects the perception and behaviour of the receiver in a way that has direct

managerial implications (e.g. Park et al., 2007; Prendergast et al. 2010) especially to

firms in service business that co-create value together with their customers through

intangible assets (e.g. Sweeney et al., 2014). This understanding has been further

supported and improved by the results of this study which elaborated on the

interrelationships of the underlying eWOM effectiveness constructs among a group of

young consumers in a service context. Given the ever increasing importance of service

economy, the undiscovered potential of Generation Z consumers as the emerging

consumer group, and the continuously growing amount of public WOM information

recorded and archived online, marketers should be motivated to track, monitor and

engage in eWOM whenever possible. In fact, marketers can use the existing product

reviews, forum discussions and other relevant consumer generated content in their

advantage by retrieving such publicly available data and analysing the success factors

behind it. Similarly, managers can use the results of the research in planning their

digital marketing strategies. A deeper understanding of the effectiveness and

mechanisms of eWOM communication provides practitioners with opportunities to

improve their digital outreach within the Generation Z segment.

First, we can find an interesting perspective on the subject by juxtaposing source

credibility with source similarity. Here, credibility is defined as the perceived expertise

and trustworthiness of the communicator (Cheung et al., 2008; Sweeney et al., 2008)

while similarity refers to closeness of the tie (Sweeney et al., 2014) and homophily

(Shan & King 2015) between the communicator and the receiver. In light of the

findings, formal expertise is less important than the ability to identify oneself with the

communicator. Hence, the shorter the “mental” distance between the two, the more

effective the message. Communicators who share the same taste, interests, lifestyle and

values as the receiver are likely to contribute more to purchase intention of Generation

Z consumers than communicators who are less similar than their audience. In other

words, the communication from peer consumers (e.g. a specific demographic group),

friends and family members is likely to be more influential than communication from

people that perhaps do not belong to the inner social circle, but represent another

demographic group or lead a different lifestyle.

Given these results, marketers aiming to leverage word-of-mouth marketing (WOMM)

through influencer partnerships should consider an alternative source of information in

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the field of online advocates that is typically crowded by celebrities, journalists,

commercial brand ambassadors, bloggers and industry-specific thought leaders –

individuals that have typically become influential through their formal expertise and

public status. Instead, more focus should be given to regular consumers and small-scale

(micro) influencers who are more relatable – people who pass on opinions and

experiential knowledge about the services, products and brands they encounter in

everyday life. Therefore, marketers who wish to promote and encourage eWOM among

their customers need not only consider celebrity influencers but also the vast crowd of

micro influencers that may emerge within the target group. For example, an active

member of a digital community or a discussion forum, in which like-minded consumers

may exchange information with each other, could rise above the power of a public

influencer as long as the communicators profile resonates enough with the receiver.

Considering the reasons behind this phenomenon, the following interpretation can be

made: Generation Z consumers seek the opinion of “a peer” because they share the

same taste and interests. On average, an expert or a heavy-user might have different

needs and requirements than the regular consumer which creates an expectation gap.

For example, the expert might be looking for a specific quality in a service that the

regular consumer does not value as much. Hence, there is likely to be a better match

when the information is exchanged between two persons with similar needs. Another

interpretation is more or less related to the characteristics of online communication:

source similarity may also mitigate the problem of inauthentic communicators and fake

information that occurs all around the internet. Hence, Generation Z consumers would

be more receptive to an advice from an individual they know personally or from an

individual they can relate to and identify with. Given this source similarity aspect, a

simple rule of thumb can be proposed to practitioners who wish to apply WOMM

tactics in creating awareness among Generation Z. If the marketing activities target e.g.

young and sporty male consumers, practitioners should encourage and facilitate

communication that flows from such male communicators to male receivers. The same

principal applies whether the target group is female students or teenagers – eWOM

communication from a similar reference group is likely to contribute to the purchase

intention of the target group.

Secondly, further managerial insights can be gained by comparing the quantity and

quality of eWOM information. As defined in the literature section, quantity is simply

defined as the volume of eWOM information available online (Rosario et al., 2016),

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while quality refers to the depth and breadth of argumentation and objectivity of the

information (Cheung & Thadani, 2012; Cheung et al., 2009). According to the findings

of the study, both quantity and quality matter to Generation Z consumers. Therefore,

marketers should encourage and facilitate eWOM whenever and wherever possible by

providing the consumers with tools to express and exchange information with each

other.

The quantity of eWOM communication can be increased e.g. through well-managed

brand communities and social media channels, but also through enabling online

comments and reviews on corporate websites and online stores. Overall, managers

should consider how to embed opportunities for increasing eWOM in their service

process. That is, encouraging their customers to publish and share reviews,

recommendations and experiences about the service. This could be done, for example,

by posting a review after the service encounter and inviting friends to try the service to

earn a free service voucher. However, as pointed out by Trusov et al. (2009),

stimulating the creation of eWOM among customers through financial incentives may

decrease the effectiveness of such communication. Therefore, managers should not rely

only on paid referral programs but plan alternative ways to generate “organic” eWOM

around their services.

As a rule of thumb, the more there are reviews available online, the higher the quantity

of eWOM information and the stronger the impact on purchase intention. The

reasoning behind the effect has been previously presented as a sense of added trust in

the receiver that is created by high amounts of eWOM information (Cheung & Thadani,

2012). For Generation Z, it can be similarly interpreted that the high volume of

information creates “safety in numbers” i.e. the bandwagon effect (Rosario et al., 2016)

which primarily reduces the risk of making a wrong purchase decision. However, bare

volume is not enough in determining the impact of eWOM communication on purchase

intention. Actually, as a third implication for practitioners, high quality of eWOM

information is even more influential for Generation Z. Thus, practitioners should also

consider ways of enhancing the content of communication – for example, by

encouraging and facilitating the use of pictures, videos and illustrative arguments. In

fact, marketers should not censor the contents of critical reviews, since objectivity is

also one of the quality criteria that adds to the overall effectiveness of the

communication.

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Finally, in a time when a wide variety of fake information and scams prevail over the

internet, it might seem strange that trustworthiness, which is one of the dimensions of

source credibility, was not represented stronger in the results. Instead, similarity

between the receiver and the communicator was highlighted as a particularly

influential factor. Thus, it seems that identifying oneself with the communicator is a

better way in reducing the risk of purchase decision. As pointed out by Rosario et al.

(2016), the receivers of eWOM can evaluate similarity between themselves and the

communicator by observing various cues available online: e.g. real usernames,

geographic location, profile pictures and other information disclosed by the

communicators. Therefore, managers should facilitate these sort of identification

functionalities on their own platforms, such as websites and online stores, in which

customer reviews can be posted.

5.4. Limitations and further research

Regarding the limitations of the research, one of the key issues arises from the cultural

and demographical context of the study. Since the sample was drawn from a Finnish

population, it may not reflect the effectiveness of eWOM communication in other

cultural contexts although eWOM is considered a global phenomenon. It is also noted

that not all members of Generation Z consumers were represented in the study.

Instead, only the adult members (over 18 years old) of this specific demographic group

were represented for the reasons previously described in the sampling strategy. In fact,

the boundaries of Generation Z still remain rather undefined while the end of the said

generation has not been officially stated. Hence, the demographical representativeness

of the study is limited and appropriate consideration should be used when interpreting

the results. Another demographical limitation is related to the unequal division

between male and female respondents. Since women were represented stronger than

men in the sample, the effect of the gender was analysed in chapter 3. The results

indicated that the quality of eWOM message (i.e. rational argumentation and the clarity

of messaging) was slightly more important to male respondents than female

respondents. Hence, the relative significance of the eWOM quality variable might have

been further increased if more men would have participated.

Overall, the measurement scales constructed for this study should be further improved

to ensure a sufficient validity and decrease measurement error. Future research could

even use alternative scales that employ a different set of existing items or develop and

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test new scales in order to measure the variables better. The most significant challenge,

however, lies in the process of selecting the most suitable variables to begin with. The

decision to apply only seven variables in this study can also be considered a limitation

since failing to choose the most relevant measures increases the chance of false

conclusions. After all, there is a wide variety of factors affecting eWOM and a decision

to include some variables while excluding others should not be made lightly. Since a

fully comprehensive model has not yet been attained, a thorough literature review is

recommended to avoid potential researcher bias when selecting the variables.

Looking forward, the study provides a theoretical framework to be further developed

and tested by other researchers in the field. As we are only beginning to see the end of

Generation Z somewhere in the near future, a similar study could be replicated with

another sample that is more representative of the whole generational segment. In

addition, the various determinants of eWOM effectiveness may be studied further to

estimate their relative importance in affecting the consumer’s behaviour since only a

fraction of potential determinants were included in this study. For example, it would be

interesting to look deeper into the similarity factor and its potential role in creating

social media bubbles that strengthen the confirmation bias of like-minded people.

Moreover, the interplay among multiple response variables could be studied further as

suggested by Cheung and Thadani (2012). Such research could improve the integrative

model of eWOM effectiveness by connecting eWOM communication with behavioural

patterns that precede the final outcome: e.g. mapping the steps of information adoption

before a purchase decision.

Another perspective to studying eWOM effectiveness is provided by the variety of

technology and communication platforms that continue to develop as we speak. In fact,

the technology that facilitates consumer-to-consumer communication is inevitably

going to affect the way we perceive and exchange information online, thus widening the

gap between conventional WOM and electronic WOM. Since the effect of the platform

or the format of the messaging (e.g. text, image, video, endorsement by like and share)

were not thoroughly discussed in this study, there is still room to dig deeper into these

aspects of eWOM communication. Along with the new ways of sharing our experiences

and expressing our opinions, new challenges could emerge. For example, future

researchers may revisit the current studies about the determinants of eWOM

effectiveness while considering other situational factors such as virtual reality

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platforms, the ever increasing information clutter online and fake online reviews

manufactured by artificial intelligence.

5.5. Conclusions

In conclusion, this study provides further evidence about the significant relationship

between electronic word-of-mouth and consumer purchase intention. The findings

support our current understanding of the phenomenon through considering the eWOM

effectiveness mechanism from three different perspectives that were brought to a new

demographic context. More specifically, the characteristics of the receiver, the

communicator and the message were reflected against the outcome to evaluate the

effectiveness factors for Generation Z consumers. The results were clear: in addition to

the receiver’s positive stance towards eWOM information, the closeness and

identifiability of the communicator anticipate the consumers’ desire to buy. Regarding

the content of communication, however, quality is more important than quantity

although both factors have a significant impact on the young consumers’ buying

behaviour.

As the study provides arguments and empirical evidence to highlight the effectiveness

mechanism of eWOM, its main contribution is in explaining the factors that drive

purchase intention. Therefore, both researchers and practitioners are able to improve

their understanding about the phenomenon in the context of Generation Z consumers.

However, the study provides less details about the marketer’s role in facilitating such

communication. In a nutshell, managers should adopt an integrative view of marketing

to create an outstanding customer experience which ultimately feeds positive eWOM

about their business. As noted by Meyer and Schwager (2007), such a result is best

achieved by establishing transparency and facilitating seamless cooperation between

the organizational capabilities, thus transforming marketing into a customer-focused

way of doing business in which the customer experience becomes a shared

responsibility. After all, the primary concern of marketing is not only to put out alluring

promises to create demand in the audience, but to ensure such promises are redeemed

every day in every customer interaction (Grönroos, 2006). Through harnessing the

power of these internal marketing resources, marketers can best leverage the power of

eWOM and facilitate the creation of “external marketing forces” – their satisfied

customers that turn into influencers as they spread the word about their positive

experiences online.

73

REFERENCES

Armstrong, J.S., & Overton, T.S. (1977). Estimating nonresponse bias in mail surveys. Journal of Marketing Research, 14(3), 396–402.

Brown, J.J., & Reingen, P.H. (1987). Social ties and word-of-mouth referral behavior. Journal of Consumer Research, 14(December 1987), 350–362.

Chaney, D., Touzani, M., & Slimane, K.B. (2017). Marketing to the (new) generations: Summary and perspectives. Journal of Strategic Marketing, 25(3), 179–189.

Chang, M.K., Cheung, W., Lai, V.S. (2005). Literature derived reference models for the adoption of online shopping. Information management, 42(4), 543–559.

Cheung, C.M.K., Lee, M.K.O., & Rabjohn, N. (2008). The impact of electronic word-of-mouth: The adoption of online opinions in online communities. Internet Research, 18(3), 229–247.

Cheung, C.M.K., & Thadani, R.T. (2012). The impact of electronic word-of-mouth communication: A literature analysis and integrative model. Decision Support Systems, 54(1), 461–470.

Cheung M.Y., Luo C., Sia, C.L., & Chen, H. (2009). Credibility of electronic word-of-mouth: Informational and normative determinants of on-line consumer recommendations. International Journal of Electronic Commerce, 13(4), 9–38.

Chu, S., & Kim, Y. (2011). Determinants of consumer engagement in electronic word-of-mouth (eWOM) in social networking sites. International Journal of Advertising, 30(1), 47–75.

CNBC (2017, January 11), Generation Z likely to avoid advertising, use ad block and skip content: Study. CNBC, Retrieved from: http://www.cnbc.com/2017/01/11/generation-z-avoids-advertising-uses-ad-blockers-and-skips-content.html. Accessed 13.2.2017.

Cohen, J. (1988). The statistical power analysis for the behavioral sciences (2nd edition). New Jersey: Erlbaum.

Coyle, J.R., & Thorson, E. (2001). The effects of progressive levels of interactivity and vividness in web marketing sites. Journal of Advertising, 30(3), 65–77.

Djafarova, E., & Rushworth, C. (2017). Exploring the credibility of online celebrities' Instagram profiles in influencing the purchase decisions of young female users. Computers in Human Behaviour, 68(March 2017), 1–7.

74

Erkan, I., & Evans, C. (2016). The Influence of eWOM in social media on consumers’ purchase intentions: An extended approach to information adoption. Computers in Human Behavior, 61(August 2016), 47–55.

Fan, Y.W., Miao, Y.F., Fang, Y.H., & Lin, R.Y. (2013). Establishing the adoption of electronic word-of-mouth through consumer’s perceived credibility. International Business Research, 6(3), 58–65.

Fan, Y.W., & Miao, Y.F. (2012). Effect of electronic word-of-mouth on consumer purchase intention: The perspective of gender differences. International Journal of Electronic Business Management, 10(3), 175–181.

Finch, J. (2015, May 4). What is Generation Z and what does it want? Fast Company, Retrieved from: https://www.fastcompany.com/3045317/what-is-generation-z-and-what-does-it-want.

Field, A. (2009). Discovering statistics using SPSS (3rd Edition). London: Sage Publications.

Fromm, J. (2016, December 19). What marketers need to know about social media and gen Z. Forbes, Retrieved from: http://www.forbes.com/sites/jefffromm/2016/12/19/what-marketers-need-to-know-about-social-media-and-gen-z/#1ad8513256a7.

Gasca, P. (2014, July 7). Start preparing your business for the generation after Millennials. Entrepreneur, Retrieved from: https://www.entrepreneur.com.

Global Consumer Commerce Centre. (2016). Gen Z are focused on social reputation and rely more heavily on virtual communities than face-to-face recommendations. Retrieved from: http://www.iorma.com/news/gen-z-are-focused-on-social-reputation-and-rely-more-heavily-on-virtual-communities-over-face-to-face-for-recommendations.

Grönroos, C. (2006). Adopting a service logic for marketing. Marketing Theory, 6(3), 317–333.

Hair, J.F., Black, W.C., Babin, B.J., & Anderson, R.E. (2010). Multivariate data analysis: A global perspective (7th edition). New Jersey: Pearson Education.

Hennig-Thurau, T., Gwinner, K.P., Walsh, G., & Gremler, D. (2004). Electronic word-of-mouth via consumer opinion platforms. Journal of Interactive Marketing, 18(1), 18–52.

Ho, J.Y.C., & Dempsey, M (2010).Viral marketing: Motivations to forward online content. Journal of Business Research, 63(9), 1000–1006.

75

Ismagilova, E., Dwivedi, Y.K., Slade, E., Williams, M.D. (2017). Electronic word of mouth (eWOM) in the marketing context. http://dx.doi.org/10.1007/978-3-319-52459-7_1

Jaccard, J., Wan, C.K., & Turrisi, R. (1990). The detection and interpretation of interaction effects between continuous variables in multiple regression. Multivariate Behavioral Research, 25(4), 467–478.

Lindner, J.R., Murphy, T.H., & Briers, G.E. (2001). Handling nonresponse bias in social science research. Journal of Agricultural Education, 42(4), 42–53.

Kelly, L., Kerr, G., & Drennan, J. (2010). Avoidance of advertising in social networking sites: The teenage perspective. Journal of Interactive Advertising, 10(2), 16–27.

Kozinets, R.V., de Valck, K., Wojnicki, A.C., & Wilner, S.J.S. (2010). Networked narratives: Understanding word-of-mouth marketing in online communities. Journal of Marketing, 74(March 2010), 71–89.

Meyer, C., & Schwager, A. (2007). Understanding customer experience. Harvard Business Review, 85(2), 116–126.

Mishra, A., Maheswrappa, S.S., Maity, M., & Samu, S. (2017). Adolescent’s eWOM intentions: An investigation into the roles of peers, the Internet and gender. Journal of Business Research. https://doi.org/10.1016/j.jbusres.2017.04.005

Nowak, K.L., & McGloin, R. (2014). The influence of peer reviews on source credibility and purchase intention. Societies, 4(December 2014), 689–705.

Ozkan, M., & Solmaz, B. (2015). Mobile addiction of Generation Z and its effects on their social lives. Procedia – Social and Behavioral Sciences, 205(May 2015), 92–98.

Park, D., & Kim, S. (2008). The effects of consumer knowledge on message processing of electronic word-of-mouth via online consumer reviews. Electronic Commerce Research and Applications, 7(4), 399–410.

Park, D., Lee, J., & Han, I. (2007). The Effect of On-line Consumer reviews on Consumer Purchasing Intention: The Moderating Role of Involvement. International Journal of Electronic Commerce, 11(4), 125–148.

Pallant, J. (2010). SPSS Survival Manual (4th edition). Berkshire: McGraw-Hill Education.

Prendergast, G., Ko, D., & Yuen, S.Y.V. (2010). Online word of mouth and consumer purchase intentions. International Journal of Advertising, 29(5), 687–708.

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Priporas, C.V., Stylos, N., & Fotiadis, A.K. (2017). Generation Z consumer’s expectations of interactions in smart retailing: A future agenda. Computers in Human Behavior, 77(December 2017), 374–381.

Racherla, P., & Friske, W. (2012). Perceived usefulness of online consumer reviews: An exploratory investigation across three service categories. Electronic Commerce Research and Applications, 11(6), 548–559.

Rosario, A.B., Sotgiu, F., De Valck, K., & Bijmolt, T.H.A. (2016). The effect of electronic word of mouth on sales: A meta-analytic review of platform, product, and metric factors. Journal of Marketing Research, 53(June 2016), 297–318.

Saunders, M., Lewis, P., & Thornhill, A. (2009). Research methods for business students (5th edition). Harlow: Pearson Education Limited.

See-To, E.W.K., & Ho, K.K.W. (2014). Value co-creation and purchase intention in social network sites: The role of electronic Word-of-Mouth and trust – A theoretical analysis. Computers in Human Behavior, 31(February 2014), 182–189.

Selya, A.S., Rose, J.S., Dierker, L.C., Hedeker, D., & Mermelstein, R.J. (2012). A practical guide to calculating Cohen’s f2, a measure of local effect size from proc mixed. Frontiers in Psychology, 3(April 2012), 1–6.

Shan, Y., & King, K.W. (2015). The effects of interpersonal tie strength and subjective norms on consumer’s brand-related eWOM referral. Journal of Interactive Advertising, 15(1), 16–27.

Smith, D., Menon, S., & Sivakumar, K. (2005). Online peer and editorial recommendations, trust, and choice in virtual markets. Journal of Interactive Marketing, 19(3), 15–37.

Statistics Finland. (2017)a. Population. Retrieved from: http://www.tilastokeskus.fi/tup/suoluk/suoluk_vaesto_en.html.

Statistics Finland. (2017)b. Number of university degrees fell by 3 per cent from the previous year. Retrieved from: https://www.stat.fi/til/yop/2016/yop_2016_2017-05-10_tie_001_fi.html.

Statistics Finland. (2014). Korkeakouluissa opiskelevat naiset valmistuvat miehiä todennäköisemmin ja nopeammin. Retrieved from: https://www.stat.fi/artikkelit/2014/art_2014-09-29_002.html.

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.

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Sweeney, J., Soutar, G., & Mazzarol, T. (2014). Factors enhancing word-of-mouth influence: positive and negative service-related messages. European Journal of Marketing, 48(1/2), 336–359.

Trusov, M., Bucklin, R.E., & Pauwels, K. (2009). Effects of word-of-mouth versus traditional marketing: findings from an internet social networking Site. Journal of Marketing, 73(September 2009), 90–102.

Truong, Y., & Simmons, G. (2010). Perceived intrusiveness in digital advertising: strategic marketing implications. Journal of Strategic Marketing, 18(3), 239–256.

Tsao, W.C., & Hsieh, M.T. (2015). eWOM persuasiveness: Do eWOM platforms and product type matter? Electronic Commerce Research, 15(4), 509–541.

Weinswig, D. (2016, October 5). Influencers are the new brands. Forbes, Retrieved from: http://www.forbes.com.

Wong, K. (2016, December 13). How millennials and generation Z transformed tourism marketing through sharing. Forbes, Retrieved from: http://www.forbes.com.

Zangeneha, S.K., Mohammadkazemib, R., & Rezvanib, M. (2014). Investigating the effect of electronic word of mouth on customer’s purchase intention of digital products. Management Science Letters, 4(11), 2433–2440.

Zhang, J.Q., Craciun, G., & Shin, D. (2010). When does electronic word-of-mouth matter? A study of consumer product reviews. Journal of Business Research, 63(12), 1336–1341.

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APPENDIX 1 QUESTIONNAIRE IN ENGLISH

Welcome to the survey! This study is conducted for a Master’s thesis in the Business & Management program at Hanken.

The study aims to examine the effectiveness of online consumer-to-consumer communication among 18–22 year old individuals. Hence, the term “word-of-mouth” has been established to describe the information exchange regarding products and services that occurs in between consumers. The focus of this research is in the various determinants of electronic word-of-mouth effectiveness that relate to the communicator, the message and the receiver.

In the questionnaire, you will find seven sections that are all answered anonymously. The estimated length of the questionnaire is 5–7 minutes. After you have filled in all of the sections, you will be guided from a thank you -page to another online form in which you can choose to participate in a lottery for gift cards. NB! Your contact information will be remain confidential and they cannot be connected with any of the answers you have given in the questionnaire.

If you have any questions regarding the survey, feel free to contact me.

Panu Alanko MSc Student, HANKEN panu.alanko@student.hanken.fi +358 50 414 2001

1. Age

18

19

20

21

22

23 or older

2. Gender

Male Female

3. Choose the option that best describes your current situation.

Full-time employment

Part-time employment

79

Student

Unemployed

4. How many hours do you spend online in your spare time?

The average amount of time I spend online (hours per day): __________

5. Regarding services, have you read reviews, experiences and opinions of other consumers online?

Yes No

6. About which services do you most often search information online?

Please identify the type of service (e.g. travel, accommodation, restaurant, telecommunications, banking, entertainment):__________________

7. Please identify one or more online platforms in which you mostly seek information about services.

Social networking site (e.g. Facebook, YouTube, Instagram)

Discussion forum (e.g. Suomi24, MuroBBS)

Review website (e.g. Yelp, TripAdvisor, Vertaa.fi)

Other, please identify____________

8. When you receive information about services from other consumers online, it is important that…

The person who left comments online is knowledgeable in evaluating the quality of service.

Strongly Disagree 1

2 3 4 5 6 7 Strongly Agree

The person who left comments online is an expert in evaluating the quality of service.

1

2 3 4 5 6 7

The person who left comments online is trustworthy.

1

2 3 4 5 6 7

The person who left comments online is reliable.

1

2 3 4 5 6 7

80

9. When you consider the relationship between you and the person who commented the service online, to what extent to do you agree with the following statements?

I am committed to maintain my relationship with this person.

Strongly Disagree 1

2 3 4 5 6 7 Strongly Agree

I want our relationship to last for a long time.

1

2 3 4 5 6 7

I feel very strongly linked to this person.

1

2 3 4 5 6 7

I would not feel very upset if our relationship were to end in the near future.

1

2 3 4 5 6 7

I am oriented towards continuing this relationship long term.

1

2 3 4 5 6 7

The relationship with this person is important to me.

1

2 3 4 5 6 7

10. When you receive information about services from other consumers online, it is important that…

You and the person who gave WOM have a close relationship.

1

2 3 4 5 6 7

You and the person who gave WOM have a similar outlook on life.

1

2 3 4 5 6 7

You and the person who gave WOM share common interests.

1

2 3 4 5 6 7

You and the person who gave WOM have similar likes and dislikes.

1

2 3 4 5 6 7

11. When you consider the online reviews and comments about services from other consumers, it is important that…

Each review has sufficient reasons supporting the opinion.

Strongly Disagree 1

2 3 4 5 6 7 Strongly Agree

Each review is objective. 1

2 3 4 5 6 7

Each review is understandable. 1

2 3 4 5 6 7

Each review is credible. 1

2 3 4 5 6 7

The review includes both pros and cons of the discussed service.

1

2 3 4 5 6 7

81

The review includes only one-sided comments.

1

2 3 4 5 6 7

12. When you consider the online reviews and comments about services from other consumers, it is important that…

The number of reviews is large. 1

2 3 4 5 6 7

The quantity of review information is large.

1

2 3 4 5 6 7

13. If the comments, reviews and opinions I read about the service support the purchase decision…

It is very likely that I will buy the service.

Strongly Disagree 1

2 3 4 5 6 7 Strongly Agree

I will purchase the service the next time I need a service.

1

2 3 4 5 6 7

I will definitely try the service. 1

2 3 4 5 6 7

14. When you consider your own purchase behaviour in general, to what extent do you agree with the following statements?

When buying services, I generally purchase those services that I think others will approve of.

Strongly Disagree 1

2 3 4 5 6 7 Strongly Agree

If other people can see me using a service, I often purchase the brand they expect me to buy.

1

2 3 4 5 6 7

I achieve a sense of belonging by purchasing the same services that others purchase.

1

2 3 4 5 6 7

If I have little experience with a service, I often ask my friends about it.

1

2 3 4 5 6 7

I often consult other people to help choose the best alternative available among services.

1

2 3 4 5 6 7

I frequently gather information from friends or family about a service before I buy.

1

2 3 4 5 6 7

82

15. When you consider your own purchase behaviour in general, to what extent do you agree with the following statements?

When I buy services, I always read reviews available online.

1

2 3 4 5 6 7

When I buy services, the reviews available online are helpful for my decision making.

1

2 3 4 5 6 7

When I buy services, the reviews available online make me confident in purchasing the product.

1

2 3 4 5 6 7

If I don’t read the reviews available online when I buy services, I worry about my decision.

1

2 3 4 5 6 7

Thank you for taking the survey!

Contact information for the lottery

If you want to participate in a lottery for S-group gift cards (à 10 EUR), please fill in your name and email in the below questionnaire. Your contact information are retained confidential and they will only be used to pick the winners of the lottery. There are five gift cards raffled among all respondents. The winners will be contacted individually.

The lottery will be conducted by 31st December 2017.

83

APPENDIX 2 QUESTIONNAIRE IN FINNISH

Tervetuloa vastaamaan kyselyyn! Tutkimus toteutetaan osana pro gradu -tutkielmaa Hanken Business & Management -koulutusohjelmassa.

Tutkimuksen tavoitteena on selvittää verkossa käytävän vertaisviestinnän vaikuttavuutta 18–22-vuotiaiden kuluttajien keskuudessa. Ilmiötä on vakiintunut kuvaamaan englanninkielinen termi sähköinen ”word-of-mouth”, joka viittaa kuluttajien väliseen tiedonvaihtoon Internetissä koskien tuotteita ja palveluita. Tutkimus keskittyy vertaisviestinnän eri vaikuttavuustekijöiden välisiin suhteisiin sekä viestin lähettäjän, viestin sisällön että vastaanottajan osalta.

Kyselyssä on seitsemän osiota, joihin vastataan anonyymisti. Kyselyn arvioitu kesto on 5-7 minuuttia. Täytettyäsi kaikki osiot, sinut ohjataan kiitos-sivulta lomakkeelle, jossa voit halutessasi osallistua S-ryhmän lahjakorttien arvontaan. Huom! Yhteystietosi säilytetään luottamuksellisesti, eikä niitä ole mahdollista yhdistää kyselyssä antamiisi vastauksiin.

Mikäli sinulla on kysyttävää tutkimuksesta, otathan ystävällisesti yhteyttä.

Panu Alanko MSc Student, HANKEN panu.alanko@student.hanken.fi +358 50 414 2001

1. Ikä*

18

19

20

21

22

23 tai yli

2. Sukupuoli

Mies Nainen

3. Valitse vaihtoehto, joka kuvaa parhaiten nykyistä tilannettasi.

Kokoaikatyö

Osa-aikatyö

84

Opiskelija

Työtön

4. Kuinka paljon aikaa vietät verkossa vapaa-ajallasi?

Keskimäärin verkossa viettämäni aika (tuntia per päivä) (____)

5. Oletko lukenut verkossa muiden kuluttajien antamia arvioita, kokemuksia tai mielipiteitä palveluista?*

Kyllä Ei

6. Mistä palveluista etsit useimmiten tietoa verkossa?

Kirjoita kenttään palvelutyyppi (esim. matkailu, majoitus, ravintola, televiestintä, pankki- ja vakuutuspalvelut, viihde):______________

7. Valitse alla olevista vaihtoehdoista yksi tai useampi verkkoalusta, jonka kautta etsit useimmiten tietoa palveluista.

Yhteisöpalvelu (esim. Facebook, YouTube, Instagram)

Keskustelupalsta (esim. Suomi24, MuroBBS)

Arviointisivusto (esim. Yelp, TripAdvisor, Vertaa.fi)

Muu, mikä? _________________________

8. Kun mietit palveluista lukemiasi kommentteja verkossa, on tärkeää että…

Henkilö, joka kommentoi palvelua verkossa on pätevä arvioimaan palvelun laatua.

Vahvasti eri mieltä 1

2 3 4 5 6 7 Vahvasti samaa mieltä

Henkilö, joka kommentoi palvelua verkossa on asiantuntija palvelun laadun arvioinnissa.

1

2 3 4 5 6 7

Henkilö, joka kommentoi palvelun laatua verkossa on totuudenmukainen.

1

2 3 4 5 6 7

Henkilö, joka kommentoi palvelun laatua verkossa on luotettava.

1

2 3 4 5 6 7

85

9. Koskien sinun ja palvelua kommentoineen henkilön välistä suhdetta, mitä mieltä olet seuraavista väittämistä?

Olen sitoutunut säilyttämään suhteeni tähän henkilöön.

Vahvasti eri mieltä 1

2 3 4 5 6 7 Vahvasti samaa mieltä

Haluan suhteemme kestävän pitkään.

1

2 3 4 5 6 7

Tunnen olevani vahvasti yhteydessä tähän henkilöön.

1

2 3 4 5 6 7

En olisi juurikaan harmissani, jos suhteemme päättyisi lähitulevaisuudessa.

1

2 3 4 5 6 7

Olen asennoitunut jatkamaan suhdettamme pitkällä tähtäimellä.

1

2 3 4 5 6 7

Suhteeni tähän henkilöön on minulle tärkeä.

1

2 3 4 5 6 7

10. Kun mietit palveluista lukemiasi kommentteja verkossa, on tärkeää että…

Palvelua kommentoineella henkilöllä on läheinen suhde sinuun.

1

2 3 4 5 6 7

Palvelua kommentoineella henkilöllä ja sinulla on samanlainen elämänkatsomus.

1

2 3 4 5 6 7

Palvelua kommentoineella henkilöllä ja sinulla on samanlaiset kiinnostuksen kohteet.

1

2 3 4 5 6 7

Palvelua kommentoineella henkilöllä ja sinulla on samanlainen maku.

1

2 3 4 5 6 7

11. Kun mietit palveluista lukemiasi kommentteja ja arvioita verkossa, on tärkeää että…

Jokaiselle arviolle on annettu riittävät perustelut, jotka tukevat esitettyä mielipidettä.

Vahvasti eri mieltä 1

2 3 4 5 6 7 Vahvasti samaa mieltä

Jokainen arvio on puolueeton. 1

2 3 4 5 6 7

Jokainen arvio on ymmärrettävä. 1

2 3 4 5 6 7

Jokainen arvio on uskottava. 1

2 3 4 5 6 7

Arvio sisältää sekä hyviä että huonoja puolia palvelusta.

1 2 3 4 5 6 7

86

Arvio sisältää vain yksipuolisia kommentteja.

1

2 3 4 5 6 7

12. Kun mietit palveluista lukemiasi kommentteja ja arvioita verkossa, on tärkeää että…

Palvelua koskevien arviointien määrä on suuri.

Vahvasti eri mieltä 1

2 3 4 5 6 7 Vahvasti samaa mieltä

Arviointien sisältämän tiedon määrä on suuri.

1

2 3 4 5 6 7

13. Mikäli palveluista lukemani kommentit, arviot ja mielipiteet tukevat ostopäätöstä…

On erittäin todennäköistä, että ostan palvelun.

Vahvasti eri mieltä 1

2 3 4 5 6 7 Vahvasti samaa mieltä

Ostan palvelun seuraavalla kerralla kun tarvitsen sitä.

1

2 3 4 5 6 7

Tulen ehdottomasti kokeilemaan palvelua.

1

2 3 4 5 6 7

14. Kun mietit yleisesti omaa ostokäyttäytymistäsi, miten hyvin seuraavat väittämät kuvaavat sinua?

Kun ostan palveluita, ostan yleensä niitä merkkejä jotka uskon muiden ihmisten hyväksyvän.

Vahvasti eri mieltä 1

2 3 4 5 6 7 Vahvasti samaa mieltä

Jos muut ihmiset voivat nähdä minun käyttävän palvelua, ostan usein merkkiä, jonka he odottavat minun ostavan.

1

2 3 4 5 6 7

Koen yhteenkuuluvuuden tunnetta ostamalla samoja palveluita ja merkkejä, joita muut ostavat.

1

2 3 4 5 6 7

Jos minulla on vain vähän kokemusta tietystä palvelusta, kysyn siitä usein ystäviltäni.

1

2 3 4 5 6 7

Kysyn usein neuvoa muilta ihmisiltä valitakseni parhaan vaihtoehdon tietyn tyyppisten palveluiden joukosta.

1

2 3 4 5 6 7

Hankin palveluista usein tietoa ystäviltä tai perheenjäseniltä ennen ostopäätöstä.

1

2 3 4 5 6 7

87

15. Kun mietit yleisesti omaa ostokäyttäytymistäsi, miten hyvin seuraavat väittämät kuvaavat sinua?

Kun ostan palveluita, luen niistä aina arvioita verkossa.

Vahvasti eri mieltä 1

2 3 4 5 6 7 Vahvasti samaa mieltä

Kun ostan palveluita, verkossa esitetyt arviot ovat hyödyllisiä oman päätökseni kannalta.

1

2 3 4 5 6 7

Kun ostan palveluita, saan verkossa olevista arvioista varmuutta ostoksen tekemiseen.

1

2 3 4 5 6 7

Jos en lue verkossa olevia arvioita kun ostan palveluita, huolehdin tekemästäni päätöksestä.

1

2 3 4 5 6 7

Kiitos vastauksesta!

Yhteystiedot arvontaa varten

Jos haluat osallistua arvontaan S-ryhmän lahjakorteista (à 10 EUR), täytäthän alla olevalle lomakkeelle nimesi ja sähköpostiosoitteesi. Yhteystietosi säilyvät luottamuksellisena ja niitä käytetään vain arvonnan voittajien valitsemiseen kaikkien osallistujien joukosta. Yhteensä lahjakortteja arvotaan viisi kappaletta. Voittajiin otetaan henkilökohtaisesti yhteyttä palkinnon toimittamiseksi.

Arvonta suoritetaan 31.12.2017 mennessä.