Vidi, twiti, vici? The effects of personalization and informal...

UNIVERSITEIT GENT

FACULTEIT ECONOMIE EN

BEDRIJFSKUNDE

ACADEMIEJAAR 2014 – 2015

Vidi, twiti, vici?

The effects of personalization and informal

language use on conversational human voice

and brand evaluations in webcare

Masterproef voorgedragen tot het bekomen van de graad van

Master of Science in de Bedrijfseconomie

Mieke Booy

onder leiding van

Prof. Dr. Bart Larivière & Drs. Arne De Keyser

UNIVERSITEIT GENT

FACULTEIT ECONOMIE EN

BEDRIJFSKUNDE

ACADEMIEJAAR 2014 – 2015

Vidi, twiti, vici?

The effects of personalization and informal language

use on conversational human voice and brand

evaluations in webcare

Masterproef voorgedragen tot het bekomen van de graad van

Master of Science in de Bedrijfseconomie

Mieke Booy

onder leiding van

Prof. Dr. Bart Larivière & Drs. Arne De Keyser

PERMISSION

Ondergetekende verklaart dat de inhoud van deze masterproef mag geraadpleegd en/of gereproduceerd worden, mits bronvermelding. Mieke Booy

..............................................

I

Nederlandstalige Samenvatting

In deze masterproef is onderzoek gedaan naar het effect van een menselijkere taal

(conversational human voice), personalisering en informeel taalgebruik in webcare-

interventie op Twitter. Er werd eerst onderzocht of een conversational human voice

(CHV) bepaalde factoren zoals brand attitude, vertrouwen, tevredenheid, herkoop

intentie en word-of-mouth gedrag positief beïnvloedt. Ook werd onderzocht of

gepersonaliseerde en/of informele webcare-berichten CHV vergroten en of ze op zijn

beurt ook een positief effect hebben op brand attitude, vertrouwen, tevredenheid, herkoop

intentie en word-of-mouth gedrag. Om dit te onderzoeken, is een experiment uitgevoerd

waarbij 162 respondenten middels een online survey aan stimuli werden blootgesteld en

vervolgens vragen moesten beantwoorden. Uit de resultaten is gebleken dat een grotere

CHV positievere invloed heeft op vertrouwen, tevredenheid, negatieve eWOM-gedrag en

herkoop intentie, maar niet op brand attitude en positieve eWOM-gedrag. Ook is gebleken

uit ons onderzoek dat gepersonaliseerde webcare berichten, informeel taalgebruik of een

combinatie van beide er niet voor zorgt dat CHV hoger gepercipieerd is, noch dat brand

attitude, vertrouwen, tevredenheid, eWOM-gedrag en herkoop intentie er positief door

worden beïnvloed.

Informeel taalgebruik en/of de persoon onthullen achter het webcare-bericht, blijkt dus

in ons onderzoek geen enkel effect te hebben. Dit staat haaks op onze hypotheses en op

de resultaten van vorige studies. Het contrast kan verklaard worden door het feit dat dit

experiment op Twitter uitgevoerd is, een zeer interactieve microblog. De hoge

interactiviteit zorgt al voor een hoge gepercipieerde CHV, wat personalisering of

informeel taalgebruik op de achtergrond kan doen verschijnen. Ook is dit misschien te

wijten aan de limiet van 140 letters per tweet, wat zorgt voor een zeer kleine context. De

respondenten hebben zich hierdoor misschien niet goed genoeg kunnen inleven of details

(zoals een persoonlijke profielfoto) over het oog gezien om daadwerkelijk personalisering

en informaliteit genoeg te kunnen verwerken. Hoe dan ook, door het feit dat informeel

taalgebruik en personalisering geen effect heeft, kunnen bedrijven kiezen welke strategie

ze uiteindelijk nemen. Zo kunnen ze uit praktisch punt opteren om te personaliseren,

zodat ze hierdoor gemakkelijker hun webcare-team kunnen opvolgen. Of misschien

willen ze dat hun antwoord beter aansluit bij het imago dat ze nastreven: een serieuze

bank zal zo eerder een meer informeel antwoord willen geven, terwijl een jonge, hippe bar

eerder formeel zou antwoorden.

III

Acknowledgments

I would not have been able to accomplish this dissertation without the help and support

of certain people whom I would like to thank for.

First of all, I would like to express my gratitude to all the participants for their

enthusiastic participation in the present study. Their feedback and evaluations have been

indispensable for retrieving accurate data and analysis.

Mainly, I would like to thank my family for their support, motivation and tough

love during another master year. They kept believing in my capabilities and reassured me

by using the magical and calming phrase “everything is going to be alright” whenever I

needed to hear it. I would also like to thank my dear friends, for their permanent

comprehension in many breaks that motivated me through lots of ups and downs. They

always provided me energy to keep focusing with the right objective in mind. In particular

I would like to thank Thomas Claesen, my friend and lecturer at Hogeschool Vives, for his

correct and helpful language suggestions, making this dissertation a more pleasant text to

read.

I would also like to thank Dr. Bart Larivière, who introduced me last year to the

basics of service management, research methodology and SPSS with the biggest care and

enthusiasm.

Last and foremost, I would like to express my deepest thanks to my supervisor,

Drs. Arne De Keyser, who guided me through the whole process with great patience and

continuous care for successfully completing this dissertation in a relative short space of

time. His quick, yet thorough suggestions and corrections were highly appreciated. He

will be, without any doubt, an insightful and inspiring professor for the next generation

of students in marketing and service management at the University of Ghent.

IV

Table of Content

Introduction ................................................................................................................................... 1

1. Theoretical Framework ............................................................................................................ 5

1.1 The internet, a user’s world. .............................................................................................. 5

1.2 Webcare ................................................................................................................................ 8

1.3 Conversational human voice .......................................................................................... 13

1.3.1 Message Personalization ........................................................................................ 16

1.3.2 Message Formality ................................................................................................... 19

1.3.3 Combination of message personalization and informality ................................ 22

2. Methodology ............................................................................................................................ 25

2.1 Design and participants ................................................................................................... 25

2.2 Stimulus materials & procedure ..................................................................................... 25

2.3 Pretests ................................................................................................................................ 27

2.4 Measures............................................................................................................................. 29

3. Results ....................................................................................................................................... 33

3.1 Manipulation & Cofound check ..................................................................................... 33

3.2 Findings .............................................................................................................................. 35

4. Conclusions .............................................................................................................................. 48

4.1 Discussion .......................................................................................................................... 48

4.2 Managerial and other implications ................................................................................. 50

4.3 Limitations and directions for future research ............................................................. 51

Bibliography ................................................................................................................................ 53

Appendix ...................................................................................................................................... 61

74

VI

List of Tables

Table A: Demographic information of the participants ........................................................................ 25

Tabel B: Variable description and reliability scales ............................................................................... 30

Table C: Control variables ......................................................................................................................... 31

Table D: Correlations among variables and covariates ........................................................................ 34

Table E1: Explanatory power of linear regression analysis (brand attitude) ..................................... 35

Table E2: Explanatory power of linear regression analysis (satisfaction) ........................................... 36

Table E3: Explanatory power of linear regression analysis (trust) ...................................................... 37

Table E4: Explanatory power of linear regression analysis (PeWOM) ............................................... 38

Table E5: Explanatory power of linear regression analysis (NeWOM) ............................................... 38

Table E6: Explanatory power of linear regression (repurchase intent) ............................................... 39

Table F: Effect message personalization on CHV via linear regression analysis ............................... 40

Table G1: Effect message personalization on variables via linear regression .................................. 41

Table G2: Effect message personalization on variables via T-test ........................................................ 41

Table H: Effect message informality on CHV via linear regression .................................................... 42

Table I1: Effect message informality on outcomes via linear regression ............................................ 43

Table I2: Effect message informality on outcomes via T-test ................................................................ 43

Table I3: Explanatory power of linear regression analysis (brand attitude) ...................................... 44

Table I4: Explanatory power of linear regression analysis (satisfaction) ............................................ 44

Table I5: Explanatory power of linear regression analysis (repurchase intent) ................................. 45

Table J: Combination effect on CHV via linear regression analysis ................................................... 46

Table K: Combination effect on outcomes via linear regression analysis ........................................... 46

Table L: Combination effect on outcomes via T-test .............................................................................. 47

VIII

List of figures

Figure 1: Tweet Belgacom (Eva van Belgacom, 2014a) .......................................................... 18

Figure 2: Tweet McDonald’s (McDonald’s, 2014) .................................................................. 18

Figure 3: Tweet of Starbucks (Starbucks, 2014)....................................................................... 20

Figuur 4: Tweet 2 (Eva van Belgacom, 2014b) ....................................................................... 21

Figure 5: Conceptual model with hypotheses......................................................................... 24

Abbreviations

CHV = Conversational human voice

CMC= Computer-mediated communication

UGC = User-generated content

NeWOM = Negative electronic word-of-mouth

PeWOM= Positive electronic word-of-mouth

1

Introduction

Web 2.0 technologies have dramatically changed the premises of complaint behavior in

recent years (van Noort & Willemsen, 2011). Until recently, complaint behavior connected

the customer and firm within a dyadic framework. Yet today, social media platforms such

as Facebook and Twitter witnessed an explosive growth, and people have instant high-

speed Internet access via their smartphones (Larivière et al., 2013). As a result, the sharing

of negative experiences, venting of frustrations, and retaliation against brands is only one

click away, spreading the negative message across the customer’s entire network (van

Noort & Willemsen, 2011). These negative online opinions about an organization’s

products and services are referred to as negative electronic word-of-mouth (NeWOM).

Research has revealed that this NeWOM can have undesirable effects on consumer

attitudes, customer behavior, buying decisions, brand image and, as a result, on sales and

profitability (Nader, 1980; Broadbridge & Marshall, 1995; TARP, 1981 in Walker, 2006).

Consequently, multiple companies have started to monitor and intervene in online

NeWOM, an activity often referred to as webcare (Fournier and Avery, 2011; Larivière et

al., 2013). Webcare, defined as “the act of engaging in online interactions with (complaining)

consumers, by actively searching the web to address consumer feedback e.g., questions, concerns,

and complaints” (van Noort & Willemsen 2011, p. 133), is believed to restore or even

improve brand evaluations, by attenuating the effects of NeWOM on both the

complaining customers and his/her network of followers (Hong & Lee, 2005; van Noort

& Willemsen, 2011; Davidow, 2003). Gatorade, for example, has created a social media

center that monitors social media 24 hours a day, with the single aim of responding to

online complaints and improving Gatorade’s general online brand sentiment (Fournier &

Avery, 2011). Another example can be found with BestBuy, having over 2000 staffers

responding to Tweet- messages of their customers.

Following the increasing webcare efforts of practitioners, academic research is slowly

starting to explore this new topic in complaint literature. For example, van Noort and

Willemsen (2011) find that consumers evaluate a brand more favorably when the brand

responds to NeWOM than when the brand remains silent. However, their study has also

shown that not all webcare is equally effective and that certain factors need to be taken in

consideration, e.g. the context in which complaints are posted (consumer-generated or

marketer-generated platform) and also the strategy used (proactive or reactive response).

A reactive webcare response, for example, engenders a more positive brand evaluation

2

compared to a proactive response. Other research has also demonstrated that the

effectiveness of webcare depends on the content of the webcare response, i.e. an

accommodative or a notice (Kunz, 2013) and even on the response time (van der Leer,

2013).

All these findings point out that merely engaging in webcare is not good enough. Instead

of trying to respond to all NeWOM, companies should save their efforts and respond only

when webcare is likely to engender positive effects. Therefore, how you react is of even

bigger importance. To illustrate, lots of organizations already have experienced the

backfiring effect. This occurs when the organization’s response to a customer’s complaint

goes down the wrong way and provokes a spiral of even more NeWOM until it reaches a

large amount of potential customers and hence losses. Lots of companies are therefore

afraid to interact online with their customers, because they consider the negative outcomes

that might happen when webcare is applied inappropriately bigger than the positive

effects webcare might provoke when used in an appropriate manner.

Researchers are therefore investigating other factors that can lead to more positive

evaluations of the webcare strategy. An interesting factor in computer-mediated

communication that has been focused on recently is conversational human voice (CHV).

CHV is described as an engaging and natural style of organizational communication perceived

as by the stakeholders (Kelleher, 2009, p. 177). Using a more human voice is shown to play

an important role in the effectiveness of webcare, because it overcomes the shortage of

social cues in computer-mediated-communications (Beldad, de Jong & Steehouder, 2010).

However, conversational human voice does not have the same value in every context, it

can vary depending on the platform on which the webcare is posted and the strategy that

it is used for (van Noort and Willemsen, 2011).

Beside specific context elements, there also might be factors within messages that

enhance CHV. Some literature suggests that personalizing webcare messages, i.e.

disclosing the person behind the message, can enhance a firm’s CHV and eventually result

in more positive evaluations than impersonal webcare responses. Few studies (Kerkhof,

Beukeboom and Utz, 2010; van Noort & Willemsen, 2011; Koot, 2013) support this

hypothesis. Next to message personalization, literature also suggests that message

informality, in contrast to a more distant, corporate tone, enhances CHV (Kaplan &

Haenlein, 2010; Searls & Weinberger, 2000), because companies need to blend in with the

3

medium (in this case: social media) they are using webcare on. One recent study, the

dissertation of Koot (2013), confirmed this hypothesis.

The combined effect, however, of personalization and informality has been

considered in literature to have no or even negative effects on conversational human voice,

because companies are perceived to be intrusive and overdoing it. Until now, only one

study we know of (Koot, 2013) confirmed this hypothesis.

Because of the scarcity of research confirming above described hypotheses, more research

is needed to confirm these findings across other settings. Therefore, we will investigate in

this dissertation if the use of message personalization and message informality in webcare

strategy increases conversational human voice and if they also have positive effects on

important variable outcomes, such as brand attitude, trust, satisfaction, word-of-mouth

behavior and repurchase intent. Also, the combined effect of message informality and

message personalization on will be questioned more thoroughly.

By doing so, this study adds to the literature in multiple ways. First, we will

investigate for the first time the effects of message personalization and informality of

webcare on Twitter, a platform that is massively used by organizations to monitor

NeWOM and interact with customers. The high level of interactivity and limitations of

characters makes this microblog a whole new setting, which can lead to different outcomes

than above mentioned research. Second, we want to reaffirm relevant research by

completing their hypotheses. Even though the personalization or informality on webcare

is highly recommended, few organizations are applying this (or any specific) webcare

strategy in practice. Companies still don’t know when and how webcare works best in

order to fully exploit its benefits and opportunities, which often leads to lack of webcare

strategy.

This paper is organized as follows: in the first part, we will start with a theoretical

review on two related concepts that are frequently associated in conjunction with social

media: Web 2.0 and user-generated content (1.1). Second, the concept of webcare (1.2) and

conversational human voice (1.3) will be explained in more detail. This will include a

thorough description of both factors we want to investigate in this dissertation: message

personalization (1.3.1) and message informality (1.3.2), leading to our hypotheses. In the

second part, the methodology of the research will be presented by describing the design

and participants (2.1), procedure (2.2), pretests (2.3) and measures used (2.4). In the third

part, we will analyze the data we received and interpret them in function of our seven

4

hypotheses. In the fourth and last part, we will reflect on the results of the thesis, by

providing the general discussion (4.1) and managerial implications (4.2). We conclude the

paper with its limitations and implications for further studies (4.3).

5

1. Theoretical Framework

1.1 The internet, a user’s world.

Since 2005, people have coined the term “Web 2.0” to refer to the change of the Internet

into an interactive medium providing the possibility for the average user (and not only a

small group of institutions) to upload and share their information (Constantinides &

Fountain, 2008; Hennig-Thurau et al., 2010; van Noort & Willemsen, 2011). This user-

generated content (UGC) disperses through various forms of media content such as blogs,

discussion forms, pins, video, audio files, wikis, chats, tweets, and any other content

created by users of an online system or service, often social media Web sites. UGC is

published and shared effortlessly, which makes it possible for users to reach almost

everyone anywhere and anytime (Chua, Juanzi & Moens, 2014; Larivière et al., 2013).

Deriving from this features, the emergence of Web 2.0 enables consumers to take a more

active role as market players by sharing effortlessly their reviews, opinions and

experiences with and opinions on goods and services (Hennig-Thurau et al., 2004;

Larivière et al., 2013). The real time exchange of this user-generated content through high-

tech mobile phones and portable computers has become an integral element of consumer

behavior and consequently a mass phenomenon (Larivière et al., 2013). What before the

emergence of Web 2.0 was mainly a one-to-one communication, is now a one-to-many or

even many-to-many communication and leads to an abundance of electronic word-of-

mouth (eWOM) which is defined as “any positive or negative statement made by potential,

actual or former customers about a product or company, which is made available to a multitude of

people and institutions via the internet” (Hennig-Thurau et al., 2004, p. 39). Social media are

ideal tools for eWOM, because they make it easy to spread and create brand-related

information in your network compounded of friends, family and acquaintances

(Oosterveer, 2011; Walker, 2006). Because of the fact that eWOM-messages permanently

exist and are easily searchable via Web search engines and other services, it is easy to reach

large populations and thereby influencing public opinion, consumer decisions, consumer

expectations, pre-usage attitudes as well as post-purchase product perceptions

(Oosterveer, 2011; Walker, 2006).

6

Opportunities for businesses

For businesses and organizations, above described effects of the emergence of Web 2.0

cuts in two ways. First of all, it provides a whole spectrum of innovative opportunities.

Social media tools allow firms to access without any trouble millions of people, which

allows them through f.eg. a Facebook-like to inform about the brand, making them

familiar with the firm and by doing so creating brand awareness or in best-case scenario

even boost sales (Dimitriadis, 2014). Furthermore, the availability of and convenient

access to information on social network sites such as Twitter and Facebook can be useful

to identify service mistakes and product failures, which allows the business to take

remedial action and provides an opportunity to take action more quickly than it used to.

Some even argue that, because of this reason, dissatisfied customers are more beneficial to

a company than satisfied customers (Walker, 2006). To illustrate its relative importance, a

study of 1981 has shown that only seventy percent of consumers who experiences service

or good failures, do not lodge complaints (Day et al., 1981 in Walker, 2006). However,

current technologies has made it easier for customers to complaint and for businesses to

find this complaints not aimed directly at them. They can actually reach out to (potential)

customers that otherwise could not be reached (and vice versa). This makes it easier to

develop and enhance customer relationships by increasing the ability of firms to interact

in firm-customer dialogue and by strengthening their communications (Constantinides &

Fountain, 2007; Dimitriadis, 2014).

Another interesting opportunity given by Web 2.0 is that, by monitoring the complaint

data over time, companies can identify special needs and thus new market opportunities,

or even complete new product or service ideas (Walker, 2006). Also, the web provides in-

depth information about consumer preferences and lifestyles, which enables customized,

addressable messaging and even micro-targeting (Larivière et al., 2013).

Challenges for businesses

Web 2.0 also brings along some dangers lurking around the corner for most business. Long

established corporate communication strategies and business models are threatened and

have to be revised according to the era in which Web 2.0 plays a significant role. While

communicating, assessing and distributing information related to consumer decision-

making, one has to take into account the broad reach of eWOM that affects brand image

and perceptions (Constantinides & Fountain, 2008; van Noort & Willemsen, 2011).

Especially when this communication is negative, one cannot overestimate the impact.

7

Online consumers appear as fierce brand arbiters and commentators, providing judgment

and critique of companies and brands without any geographic or temporal limitations

(Hennig-Thurau et al., 2004; van Noort & Willemsen, 2011). Moreover, the online

environment provides little social context such as physical context, verbal nuances and

social characteristics, which, in combination with a high level of anonymity, can make the

messages easily exaggerated or even misunderstood by other customers, with all its

consequences for the mentioned brand in question (Oosterveer, 2011; Peña & Hancock,

2006). These negative online complaints expressed between consumers are referred to as

negative electronic word-of-mouth (NeWOM) (van Noort & Willemsen, 2011). NeWOM

messages are permanently accessible because they easily are found via Web search

engines and other services, making the negative messages last in time and creating viral

effects (Malthouse, 2007).

It has been shown that NeWOM is perceived as more reliable, credible and

trustworthy than business-to-consumer communication and consequently a very

persuasive source of consumer information (van Noort & Willemsen, 2011). Indeed,

research has repeatedly been demonstrating that NeWOM causes negative effects on all

stages of the consumer decision-making process, i.e. brand evaluation, brand choice,

purchase behavior and brand loyalty (Bailey, 2004; Maheswaran & Meyers-Levy, 1990;

Park & Lee, 2008). Moreover, NeWOM seems to have stronger effects than positive eWOM

(PeWOM), a phenomenon referred to as the negativity effect (Ahluwalia, 2002). This

asymmetric effect drives consumers to pay more attention to NeWOM than to PeWOM,

which consequently influences the decisions and attitudes of future potential customers

and finally causes damage to the company that is accused of in NeWOM (Sen and Lerman,

2007; Peña & Hancock, 2006).

Nevertheless, besides the threat of losing possible future customers, losing current

customers is even worse, since it costs five times more to attract a new customer as it does

to retain a current one (Walker, 2006). Customers who have learned of negative

experiences of their friends, family or acquaintances are more likely to be wary about

certain goods or services the next time they evaluate purchase alternatives (Walker, 2006).

Therefore, organizations should turn dissatisfied customers into satisfied customers

before they switch brands by means of handling effectively NeWOM and its potential

damage (van Noort & Willemsen, 2011; Walker, 2006).

8

1.2 Webcare

Given the above described potential of NeWOM to damage the image of companies,

businesses are increasingly worrying about detecting, controlling and preventing

NeWOM and its potential trigger effects (Malthouse, 2007). Nevertheless, NeWOM is

difficult to monitor because of its high speed and enormous reach (Dekay, 2012; van Noort

& Willemsen, 2011). Metaphorically speaking, brands are uninvited crashers in the Web

2.0 party: the web was not made to sell products, but for people and their conversations

which leads to a shift in market power from marketers to consumers (Fournier & Avery,

2011).

Be that as it may, businesses should see this threats as challenges and look for a way to

turn them into opportunities by exploiting efficiently the little power they have on the

Internet. Therefore, marketers should not worry about dominating NeWOM, however,

they should recognize that in this Web 2.0 era, NeWOM is insurmountable and that they

need to find a way to fit in and participate in conversations about their brands (Deighton

& Komfeld, 2009). These kind of interventions are also referred to as webcare, which is

defined by van Noort and Willemsen (2011) as “the act of engaging in online interactions with

(complaining) consumers, by actively searching the web to address consumer feedback (e.g.,

questions, concerns, and complaints)” (van Noort & Willemsen, 2011, p. 1).

What could webcare offer?

Literature suggests that webcare is a supportive tool for several important marketing

aspects. The main purpose of webcare is the improvement and/or restoration of brand

evaluations of the complaining customers and those who have been exposed to their

NeWOM (van Noort & Willemsen, 2011). By doing so, webcare can mitigate the

detrimental effects of NeWOM or even turn negative eWOM into positive eWOM (Hong

& Lee, 2005). Needless to say that webcare also is seen as an encouraging means for

customer relationship management (Hong & Lee, 2005). When NeWOM is resolved

adequately by webcare, they openly demonstrate that they take complaints seriously, take

responsibility for their actions and care about their customers’ problems (Kerkhof,

Beukeboom & Utz, 2010; van Noort & Willemsen, 2011). Hereby, customers feel that their

voices are being heard which can restore or even enhance their satisfaction and trust

9

(Dekay, 2012). Literature suggests that restoration also has a positive effect on eWOM

behavior and even more, on repurchase intentions (Anderson, 1998). Moreover, when the

company succeeds in enhancing trust and satisfactions, they can prevent customers from

switching to another company (Hong & Lee, 2005). Since recruiting new customers entails

greater marketing expenditures than investing in existing customers, engaging in webcare

is of crucial importance for the company’s economic situation (Hong & Lee, 2005;

Davidow, 2003; van Noort & Willemsen, 2011).

Besides brand management and customer relationship management, webcare also

contributes to reputation management. A well-timed response to online complaints can

solve the issue with the complainant and thus stop unnecessary follow-up attacks from

other consumers exposed to the publicly communicated complaint and so prevent further

damage of the company’s reputation (Davidow, 2003; Hong & Lee, 2005; van Noort &

Willemsen, 2011).

The T-Mobile case illustrates adequately how an indifferent sense of webcare can

influence eWOM behavior, brand evaluations, customer relationship management and

reputation management. Youp van ‘t Hek, a famous Dutch comedian, started complaining

on Twitter about a service failure of T-Mobile. He explained that he tried to solve a

problem with his son’s iPhone by calling the helpdesks, but was kept waiting for hours to

subsequently be transferred to another desk. Frustrated by the waiting time and lack of

attentiveness, he kept on tweeting about the situation… and his 45503 followers kept re-

tweeting. On top of that, he wrote a column in NRC (a well-known and respected journal

in Holland) concerning his situation, calling on those with bad helpdesk experiences to

send him their stories. Research agency Buzzcapture pointed out that the number of

negative statements related to T-Mobile increased in one week by 20 percent to 64 percent

and calculated the damage to its reputation at €200.000 to €300.000 (van ‘t Hek, 2010;

Buzzcapture, 2010).

Even though literature strongly states that using webcare in complaint handling is an

efficient tool, little research has confirmed this position. Exceptions are the research of

van Noort & Willemsen (2011) and Kerkhof, Beukeboom & Utz (2010). Confirming the

expectations, the study of Kerkhof, Beukeboom & Utz (2010) demonstrated that webcare

interventions positively influences consumer’s brand evaluations. Van Noort &

Willemsen (2011) and Kunz (2013) also reached the same conclusion.

10

Webcare in practice

Increasingly, organizations such as Mobile Vikings, KLM and ING are, due to above

mentioned benefits, convinced that implementing webcare in their social media marketing

strategy is of great importance. Recent studies also have revealed that, all too often,

companies want to interact online, but they don’t know how and they consequently tend

to deal with online critic by censuring NeWOM or even by making it impossible for

consumers to put any kind of negative feedback online (i.e. blocking) (Willemsen, 2013).

They act so, because they fear a wrong response might make it even worse (Willemsen,

2013; Dekay, 2014). Indeed, several cases have reported in which a company has suffered

massively in terms of reputation and customer management due to an inappropriate

response (Cortjens & Umblijs, 2012). When an organization does engage in webcare, but

does not employ it effectively, its response to a customer’s complaint may go down the

wrong way and provoke a spiral of negative effects, wherein a response to NeWOM

engenders an even more NeWOM until further NeWOM has reached a large amount of

potential customers. This is referred to as the backfiring effect. There have been reported

several cases in which webcare failed and evoked this backfiring effect. The situation of

Sanoma Media, for example, demonstrates that losing patience against a customer in

public is everything but an effective response strategy. Their epic tweet “What do you

want? Act like a teacher or be helped?” in response to a customer’s complaint about their

service, has been put under the magnifying social media glass with all its consequences.

Another way for webcare to misfire, is using automatized tweets. American Airlines

applied webcare by using extremely friendly automatized messages to respond their

customers. However, this was discovered quickly by customers when they called all sort

of names to American Airlines, who kept responding with the same friendly messages

that made no sense at all (Gijzemijter, 2013). From the moment customers realize messages

are automatized, they might feel the company doesn’t take their complaints seriously.

Not apologizing or recognizing their mistakes, is also a classic fail. An employee from

HEMA, a famous Dutch discount retail chain, once took out a diabetic customer from the

fitting room while she was injecting insulin, because they thought she was a junkie. On

Facebook, HEMA replied the incident without apologizing, which caused an enormous

chain of negative comments.

Probably the biggest blunder of all, is the one of T-Mobile. They posted publicly on

Twitter the login credentials of a customer, thinking they sent a direct message. The tweet

with this personal information was massively posted and retweeted. As a consequence,

11

the customer’s subscription was changed and the received a torrent of text messages of

people who retrieved his number. If you want to use webcare, you should at least

familiarize yourself with the social media platform in order to avoid mistakes like these

(Gijzemijter, 2013).

Even though above mentioned cases seem easy to prevent, they demonstrate that merely

engaging in webcare may not be sufficient because it can also backfire on a company and

undermine its effects. Instead, how you apply webcare may be of even greater importance.

Therefore, an adequate response is vital for customer relationship with complainants as

well as other potential customers and a guideline for an organization’s webcare policy is

needed (van Noort & Willemsen, 2011). Fortunately, more and more research are

investigating which webcare-strategies are more effective to counter negative critic and

the effects of it to the people who are reading it. Two factors have already been considered

important for efficiency: the platform on which NeWOM occurs and the communication

strategy, which will be further discussed in following paragraphs.

Response Strategy: platform and communication strategy

A precondition for engaging in online interaction with the customers, is the need for

companies to know on which platforms these online conversations are more likely to

happen. NeWOM takes place on both brand-generated platforms, such as organizational

blogs, the official website, as on consumer-generated platforms such as social network sites,

consumer blogs, review sites, etc. According to a research by TNS NIPO (2011), 70% of the

consumers post their complaints in consumer-generated platforms and the remaining 30%

occur in brand-generated platforms (in van Noort & Willemsen, 2011). The study of van

Noort & Willemsen (2011) has shown that this condition, in combination with the

following, needs to be considered when engaging in webcare.

Companies can also decide whether to engage in a reactive or proactive webcare

strategy. When applying proactive webcare, the company responds unsolicitedly to

NeWOM. In this case, the company’s reaction is the initiative response to NeWOM. With

a reactive webcare strategy, on the contrary, the company responds only when the

customer explicitly or implicitly asked to do so. According to van Noort and Willemsen

(2011), consumers are increasingly aware of the possibility that their online complaints are

12

monitored and responded accordingly by the company. Therefore, consumers also post

complaints on the web as a way to attract the attention of companies, even if they are not

addressed directly to them (van Noort & Willemsen, 2011). By doing so, they in fact

express their expectations. The study of van Noort & Willemsen (2011) has shown that,

when companies engage in reactive webcare, they meet in the consumers’ expectations by

which they can gain trust, stop circulation of NeWOM and therefore restore customer

satisfaction. In contrast, when companies engage in proactive webcare, they can damage

trust and negatively affect satisfaction and eWOM. The same study also revealed that they

only appreciate proactive webcare when the complainant expresses their dissatisfaction

on a brand-generated platform and not on a consumer-generated platform (van Noort &

Willemsen, 2011). The study of Kunz (2013) on the contrary, suggests that webcare

communication strategy (i.e. proactive or reactive) is not an important factor and does not

positively influence the effect of webcare on the attitudes and evaluations of the

complainants, but that the content is of much more importance and value (Kunz, 2013).

They found, however, that an accommodative webcare response (i.e. a financial

compensation or an apology) has a more positive effect on consumers’ evaluations than a

simple notice (Kunz, 2013).

These investigations are especially concerned about the context of webcare. Usually, the

software tool (f.eg Hootsuite, Obi4Wan, Coosto) or software company (f.eg. The Webcare

Company) deals with finding the NeWOM, being it brand-generated or consumer-

generated, asking for a response or not from the company. Hereafter, the webcare team

has to elaborate the most crucial part of the webcare: formulating the appropriate

response. Mostly, the webcare representative knows which kind of compensation the

company is willing to give, however, how exactly to put it in words is in his or her power.

This is a crucial and delicate part, in which it is very common and easy to make mistakes

(cfr. the Sanoma-case p. 6). Setting up the right tone and voice is an example which is in

hands of the webcare representative and contributes to how the customer perceives the

messages. In following paragraphs, we will discuss the challenges of doing so.

13

1.3 Conversational human voice

As mentioned earlier, Web 2.0 presents a wide range of opportunities and also challenges

for businesses. One mayor challenge is the communication style. The shared use of

Internet-supported applications for social interaction is often referred to as “computer-

mediated communication” (CMC) (Peña & Hancock, 2006).

CMC has received a lot of attention from communication researchers, especially in

work and organizational contexts which leads to several theoretical approaches to the

question (Peña & Hancock, 2006). Relevant to this dissertation, is the cues-filtered out

approach, which focuses on the absence of cues in computer-mediated social interaction

that normally regulate interaction and impression formation between communicators.

Examples are verbal nuances (the voice or facial expression of whom you are talking with),

physical context (the setting), and observable information about social characteristics

(gender, age, race) (Culnan & Markus, 1987 in Peña & Hancock, 2006 and in Anderson,

Park & Walther, 1994; Beldad, de Jong & Steehouder, 2010). The social presence theory, for

example, suggests that because of the diminished bandwidth of cues, CMC predicts a

decrease in social presence1 which renders communicators less salient to each other than

in face-to-face communication (Beldad, de Jong, Steehouder, 2010). Another example is

the reduced context cues perspective, which postulates that the lack of social cues and

anonymity of CMC-interactions encourages states of depersonalization and self-

absorption among the communicators, resulting in impulsive negative communication

(Kiesler et al., 1984; Siegel, Dubrovsky, Kiesler, & McGuire, 1986; Sproull & Kiesler, 1986

in Peña & Hanckock, 2006; Anderson, Park, Walther , 1994).

In order to overcome this shortage of cues, businesses have been recommended to make

the human voice of the people from the organization heard through online communication

(Searls & Weinberger, 2000; Beldad, de Jong & Steehouder, 2010; González-Herrero &

Smith, 2008). This is often referred to as conversational human voice (CHV) and is defined

by Kelleher as “an engaging and natural style of organizational communication as perceived by

an organization’s publics based on interactions between individuals in the organization an

individuals in publics” (Kelleher, 2009, p. 177). A company applies a high degree of

conversational human voice when their messages welcome conversational

communication (i.e. invites people to a conversation), openly invite to candid dialogue

1 Social presence can be considered as the degree of being connected by CMC to another intellectual entity through a text-based platform (Beldad, De Jong, Steehouder, 2010).

14

and promptly provide feedback that addresses criticism with a direct, but constructive

manner. A conversational human voice is also more likely to be perceived by the customer

when the message includes features of communication that otherwise would not be

associated with traditional corporate communication. For example, talking with a sense of

humor, admitting mistakes, treating others more as human and referring to competitors

(Searls & Weinberger, 2000).

There exist several reasons why CHV is believed to be an effective mechanism in

online corporate communication. First, incorporating a more humanized voice makes

consumers sense they are having a one-to-one conversation instead of a one-to-many

conversation, which in turn causes a better relationship with customers (Locke et al., 2004

in van Noort & Willemsen, 2011; Mayer Sobko & Utz, 2003). Second, by using a CHV, the

company seems to focus on creating a dialogue rather than solely on commercial and

profit-driven motives which makes the company appear more authentic in its intentions

(Searls & Weinberger, 2000; Kwon & Sung, 2011). Thirdly, companies can be perceived as

more trustworthy by using a more human tone of voice. Many users state that they find it

hard to build trust with someone they cannot see face-to-face. This negative effect on

trustworthiness when interacting in CMC technology could lead to people’s reluctance to

engage in any form of online “faceless and intangible transaction” (e.g. online purchasing)

and consequently weaken the performance, reputations and appearance (Riegelsberger,

Sasse & McCarthy, 2002; Bordia, 1997; Beldad, de Jong & Steehouder, 2010). However,

companies may promote online trust by imitating social cues (Beldad, de Jong &

Steehouder, 2010).

Last few years, empirical research within computer science, communication sciences

and public relations literature has confirmed that a more human or conversational voice

positively affects online communication. A study by Kelleher & Miller (2006)

demonstrates that conveying a conversational human voice in blogs correlates

significantly with relational outcomes pertinent to public relations such as trust,

satisfaction, control mutuality and commitment. Likewise, an investigation by Sweetser &

Metzgar (2007) confirms that employing a CHV in organizational blogs wins over publics

and thus is an appropriate relational maintenance strategy in online contexts, especially

in crisis situations. Also, in the research by Yank, Kang & Johnson (2010), CHV appears to

be a key variable in enhancing positive attitudes towards a company that uses blogs to

communicate with its stakeholders. Other empirical studies show that perceived social

15

presence positively correlates with the user’s trust and also the enjoyment they receive

from using the site (Cyr et al., 2007 in Beldad, de Jong & Steehouder, 2010).

Nevertheless, all these studies focus on the effects of CHV in messages initiated by

the organization and relatively little attention has been devoted within the academic

literature to the merits and effects of conversation human voice for webcare management.

A study of Kelleher & Miller (2006) however, reveals interesting results which are relevant

to webcare. This research points out that blogs carry an advantage over more traditional

corporate web pages in communicating with a conversational human voice and that

conversational human voice correlates positively with relational outcomes. These

outcomes are explained by the interactivity of blog platforms which mimics one-to-one

communication and thereby making them a suitable location to communicate in a

conversational style (Kelleher & Miller, 2006). From this study, it is relevant for us that

applying webcare anyhow increases conversational human voice, because you are

engaging in a conversation, which implies interactivity. Also, using webcare on

microblogs, such as Twitter, might increase conversational human voice even more,

because interactivity is extremely high. However, the question here is whether the same

kind of effects occur when a brand is confronted with NeWOM and whether choosing a

social medium to communicate is sufficient to be perceived as more humanized and

committed.

We argue that using a more conversational human voice in webcare on an interactive

platform, such as the microblog Twitter, has more positive effects on brand evaluations.

By using a higher degree of CHV, the customer might feel like they are having a one-to-

one conversation instead of a one-to-many conversation, which can make the company

perceived to be more attentive. A more human voice makes them also appear more

authentic in their intentions and not just driven by commercial motives, because by using

a more appropriate voice for Twitter they seem less intrusive. This can lead to a higher

degree of trust, a more attractive appearance and/or maybe intentions for future

repurchases. Therefore, we will investigate in this thesis following hypothesis:

H1: Conversational human voice leads to positive effects on brand attitude (a), trust (b),

satisfaction (c), positive word-of-mouth (d), negative word-of-mouth (e) and repurchase

intent (f).

16

The research of van Noort & Willemsen (2011) is one of the few studies that already

examined the direct effects of conversational human voice on webcare. They found that

an organization’s CHV can vary depending on the platform on which the webcare is used

and its webcare strategy (proactive vs. reactive). Reactive webcare is perceived as more

human on both consumer-generated and brand-generated platforms. A proactive

strategy, however, is only perceived as more humanized when it is generated on brand-

generated platform. When reactive is provided on consumer-generated platform, they are

more likely to have negative results on conversational human voice, which in turn can

result in negative brand evaluations (van Noort & Willemsen, 2011). This study

demonstrates that engaging in webcare can strengthen an organization’s conversational

human voice in certain contexts, but it can also decrease an organization’s conversational

human voice in other contexts. Nevertheless, they propose that some message elements,

for example the use of language, may compensate the relatively low CHV in certain

webcare contexts (Kelleher & Miller, 2006; Kelleher, 2009; Kerkhof, Beukeboom & Utz,

2010). In order to analyze by which means the use of language can affect the perception of

an organization and its conversational human voice, we will discuss in further detail two

potential antecedents of conversational human voice: message personalization and

message informality which will bring us to our hypotheses.

1.3.1 Message Personalization

Companies can enhance conversational human voice in webcare by disclosing the

individual behind their communications and hereby showing who is actually responding

on behalf of the organization (Kerkhof, Beukeboom & Utz, 2010; Pollach, 2005; Willemsen,

2014). In contrast to these personalized messages, impersonalized messages do not reveal the

person behind the message and in consequence respond on behalf of the organization

(Willemsen, 2014). Management literature recommends the use of personalization in order

to give large and faceless corporations a more human aspect (Kerkhof, Beukeboom & Utz,

2010; Pollach, 2005; Willemsen, 2014). By presenting the individual behind the company,

the organization demonstrates a commitment to engage in interpersonal communication

between the human-being representing the company and the company’s stakeholders

(Rybalko & Seltzer, 2010). Also, when companies reveal the people behind the

organization, they appeal to the customers’ emotion, because people relate more easily to

other human beings than to a faceless department (Pollach, 2005). Especially in social

17

media context, and even more Twitter, there has been argued that providing more detailed

information about the person tweeting on behalf of the company is very important

(Rybalko & Seltzer, 2010; Huibers, 2012). Twitter is a microblogging platform that creates

a high level of interactivity and is thus well-suited for interpersonal communication

(Huibers, 2010; Pollach, 2005; Kwon & Sung, 2011)

A higher degree of personalization can be obtained by using a personal picture of the

marketer in question, by revealing his/her name and contact information on the

concerned profile and by using personal signatures while responding (Kwon & Sung,

2011; Beldad, de Jong & Steehouder, 2010).

Besides sharing information about the human being behind the organization, there

also exist certain specific grammatical elements that can enhance the CHV (Willemsen,

2014). The use of first-person pronouns (such as “I”, “We”, “Us”, “Our”) suggests that the

author is communicating personal beliefs instead of facts and this in turn helps to build

relationship with stakeholders (Pollach, 2005; Kwon & Sung, 2011). The use of “I” instead

of “We” suggests an even larger personalization, because this means the message is

coming from the author and only the author (Willemsen, 2014). Also, the use of second-

person (“You”, “You’re”) draws the audience into the discourse, making the conversation

more dialogical. Pollach (2005) additionally suggests that using verbs in the imperative

form too can involve readers in the conversation, because imperative forms imply a very

direct and persistent way of communicating (f.eg. “Join us at the event”) (Pollach, 2005).

Despite of the expected advantages of message personalization, few organizations

actually seem to be using personalized messages in their online communications. Rybalko

& Seltzer’s research (2010) has shown that only 26.9% of the Fortune 500 companies1

clearly identifies who is tweeting on their behalf.

An example of an organization that responds with a personalized tweet is given in Figure

1:

1 Fortune 500 is an annual list that ranks top 500 U.S. companies by their gross

revenue.

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Figure 1: Tweet Belgacom (Eva van Belgacom, 2014a)

By using the image of a greeting woman for profile picture instead of the official logo,

Belgacom succeeds in making their tweet more personal. Also, the name of the Twitter

account, “Eva from Belgacom”, indicates that customers will communicate with a person,

symbolized by “Eva”, and not merely with the company as a whole. Another

personalization effect is that the webcare representative closes with her name “Wendy”.

The tweet of McDonald’s (figure 2), on the contrary, does not give such direct evidence of

personalization:

Figure 2: Tweet McDonald’s (McDonald’s, 2014)

Their profile picture is the famous logo of McDonald’s, the person who wrote the tweet

does not close with his or her name and talks in first personal plural, suggesting that a

plural concept, i.e. the company, is communicating with them. However, using “We can

assure…” instead of “McDonald’s can assure…” already indicates a little degree of

personalization.

The fact that the majority of businesses does not engage in personalized webcare,

might be because few studies actually tested the effects of message personalization, and

moreover, they are very recent. The first study of Kerkhof, Beukeboom & Utz (2010),

affirmed that personalized messages enhance an organization’s CHV, which in turn

generates more favorable attitudes toward the product, increases purchase intentions and

19

corporate credibility. Likewise, Koot (2013) confirmed in his master’s thesis that

personalization in webcare strategy directly enhances the CHV.

However, because little research tested the effect of personalization on conversational

human voice and still the majority of organizations responds with impersonal messages

in webcare without knowing its effectiveness, we feel the need to explore this element in

further detail. Therefore, in line with the suggestions of above mentioned management

literature (Kwon & Sung, 2011; Pollach, 2005; Rybalko & Seltzer, 2010; Willemsen, 2014)

and previous studies (Kerkhof, Beukeboom & Utz, 2010; Koot, 2013), we believe that

companies can create a more “humanized” voice by no longer communicating in the name

of a faceless and abstract organization, but instead revealing through their messages the

people working “behind the scenes”. To that end, this thesis will investigate following

hypothesis:

H2. Personalized messages create a higher degree of perceived conversational human voice than impersonalized messages.

We also argue that revealing the people behind the organization may have positive effects

on several other brand evaluations. Trust in the organization might increase, because not

knowing who you are talking to in an online environment can be a huge uncertainty for

trustworthiness. Brand attitude and satisfaction also might be affected positively, because

customers feel that someone is personally taking care of their complaints. Finally, it might

evoke repurchase intent, because customers could conclude that in future repurchases, the

same care will be taken. To that end, we will also investigate succeeding hypothesis:

H3. Personalized messages create more positive effects on brand attitude (a), trust (b),

satisfaction (c), negative eWOM-behavior (d), positive eWOM-behavior (e) and repurchase

intent (f) than impersonalized messages.

1.3.2 Message Informality

Besides using message personalization, literature suggests the use of informal language

within messages to increase an organization’s perceived CHV (van Noort & Willemsen,

2011; Willemsen, 2014; Sparks et al. 1997 in Huibers, 2012). Businesses have to use a tone

and language that is appropriate for dialogue in a dynamic online environment, which is

quite different from the more distant, formal corporate language used in traditional

corporate communication (González-Herrero & Smith, 2008).

20

Formal messages are messages that are restricted to formal regulations and

prescriptions that are typically associated with corporate communication (Kramer, 2001).

The register is characterized by a larger emotional distance between the sender and the

audience, but also between the sender and the topic (Kramer, 2001). It is grammatically

correct and logically organized (Kramer, 2001). Informal messages, in contrast, make use

of a more casual language which is applied in everyday human-to-human speech

(Kramer, 2001). It suggests that the relationship between the communicator, audience and

topic are relatively close (Kramer, 2001). Informal messages may include CMC-

conventions that are considered as surrogates for non-verbal communication and can be

employed to help nuancing certain emotions to overcome the impersonal nature of

corporate communications (Kwon & Sung, 2011; Peña & Hancock, 2006). Examples are

applying abbreviations (such as ‘LOL’, which is short for laughing out loud, or ‘OMG’,

which is short for ‘oh my god’), emotes, which are preprogrammed scripts used to express

personal actions and states (e.g. *facepalm*), emoticons (keyboard characters resembling

facial expressions f.eg. ‘:-)’ or ‘:-D’), capitalization (e.g. ‘NICE’), repeated punctuation

(‘Yes!!!’), or intentional misspellings for emphasis (‘Whaaaaat?’) (Willemsen, 2014; Kwon &

Sung, 2011; Peña & Hancock, 2006). Other illustrations of informal elements that can be

used in messages are the use of irony, humor, sarcasm, colloquialisms or addressing to the

customer like they know him for years (Kwon & Sung, 2011; Kramer, 2001). There exist

lots of examples of informal tweets put by organizations on the internet, as the tweet of

Starbucks illustrates in Figure 3:

Figure 3: Tweet of Starbucks (Starbucks, 2014)

The long “yaaaaaaay” mimics colloquial language and by using an emoticon (the hearts)

they are trying to convey feelings, which also implies there is a short distance between the

sender and receiver of the message. We can find an example of an informal tweet in figure

1, already mentioned previously. Wendy addresses the customer with the formal Dutch

pronoun “uw” and uses the formal closure “Met vriendelijke groeten”. Moreover, the

21

tweet doesn’t hold any misspellings or abbreviations. However, there are inconsistencies

in the webcare strategy of Belgacom. Figure 4 demonstrates that another webcare

representative, Wim, replies in a more formal language by using abbreviations “MVG”

and talking in the informal Dutch pronoun “je”:

Figuur 4: Tweet 2 (Eva van Belgacom, 2014b)

Management literature always strongly recommended the use of informal language,

especially in social media because this is a more hip and modern medium and corporates

should try to blend in with other users by engaging in informal communication (Kaplan

& Haenlein, 2009; Searls & Weinberger, 2000; Kwong & Sung, 2011; Willemsen, 2014).

Accordingly, one could also conclude that using informal language in webcare could be

more effective than formal language because it enhances CHV: it creates the illusion of

face-to-face communication and it can arouse empathy, familiarity, trust and equality

(Willemsen, 2014).

However, as with message personalization, research concerning the effects of informality

in webcare on CHV is still in its infancy. The only study we know that actually did, is the

dissertation of Koot (2013). This study confirmed that the use of informal messages

enhances the conversational human voice (Koot, 2013). Therefore, because of the scarcity

of research investigating the matter and since lots of organizations follow literature

suggestions by using informal webcare on social media Web sites without really knowing

its effectiveness, we would like to gain deeper insights in the question in this study. We

believe that using an informal tone of voice is more appropriate for a modern, interactive

medium like social media and is thus perceived to be more human and more inviting for

dialogue than using an artificial, formal language. This leads to the formulation of

following hypothesis:

H4. An informal webcare response enhances perceived conversational human voice more than a formal webcare response.

22

As with message personalization, we also argue that message informality positively

affects important consumer evaluations towards the organization. By using a more

informal language where people also expect an informal tone, the intentions of the

company might be perceived as more reliable, which can increase trust. Responding in a

more colloquial way might be seen by the customer as more “hip” and “innovative”

resulting in positive brand attitudes and satisfaction, even leading to positive word-of-

mouth. Customers might want to be perceived as related to this kind of organizations,

leading to repurchases. To that end, we will also investigate following hypothesis:

H5. Informal messages have a more positive effect on brand attitude (a), trust (b), satisfaction (c), positive eWOM-behavior (d), negative eWOM-behavior (e) and repurchase intent (f) than formal messages.

1.3.3 Combination of message personalization and informality

After reading above described literature and research suggesting that both message

personalization as message informality enhances conversational human voice and in turn

positively affects brand evaluations, one would assume that the combined effects of both

message elements (informality and personalization) would have the most positive effect

on conversational human voice, which will in turn have the most positive effect on brand

evaluations. In our knowledge, the master’s thesis of Koot (2013) is the only study that

recently has examined this effects (Koot, 2013). The findings in this study of the combined

effect of impersonality and personalization were inconsistent with the assumed

hypothesis that the combined effect results in a higher degree of conversational human

voice. However, it was found that personalization can augment the CHV of formal

messages, and informality can enhance the CHV of impersonalized messages. Therefore,

they recommend that organizations use either personalized formal messages or

impersonalized informal messages in their webcare strategy. They even deduced that the

use of both message personalization as message informality could bring negative brand

evaluations by the customers. Based upon this results, Willemsen (2014) also suggests that

one has to be cautious with combining informality and personalization in webcare and

that the choice of the strategy might depend on the image or target group the organization

is striving for.

23

Nevertheless, these are the results of just one study, which means that more investigation

is needed in order to pronounce upon the question on solid ground. Therefore, we would

like to investigate this in further detail. We state that organizations need to know its

limitations in blending in with the customers through online communication. An

exaggeration by the combination of personalization and informality might come across as

too artificial or even as an intrusion, leading to a decreased level of human voice.

Therefore, we formulate the effects of the combination of message personality and

informality by following hypothesis:

H6: The combination of an informal and personal webcare strategy does not enhance conversational human voice more than a formal, personal or informal, impersonal webcare strategy.

Likewise, we assume that a combination of message personalization and informality

creates the same negative affect on other brand evaluations. To this end, we will

investigate also following hypothesis:

H7: The combination of an informal and personal webcare strategy creates more positive effects on brand attitude (a), trust (b), satisfaction (c), eWOM-behavior (d), repurchase intent (e).

An overview of the hypotheses we will investigate in this dissertation is displayed in a

conceptual model, figure 5:

24

Figure 5: conceptual model with hypotheses

Covariates

Besides message personalization and informality, several consumer characteristics may

have an impact on conversational human voice, brand evaluations, trust, eWOM-

behavior, etc. In order to assess their impact, we will therefore measure for exploratory

reasons demographic factors such as age, gender, educational level and income. We will

also measure behavior on social network sites by asking their Twitter activity, i.e. in what

extent they post and read tweets and visit their profile (Subrahmanyam, 2008); profile

settings (public or private); Twitter experience; previous directed and undirected

complaints on Twitter, attitude towards complaint handling, attributions of blame and

finally in what degree they expect interactivity (Labrecque, 2014).

25

2. Methodology

2.1 Design and participants

The design of this study was a scenario-based experiment with a 2 (formalization: informal

vs. formal) x 2 (personalization: impersonal vs. personal) between subject design. The

scenario described a bad network experience with a fictitious telecom provider BelCom,

followed by a public response of BelCom to this complaint on Twitter.

Subjects for this research are 162 respondents. The respondents were contacted through

Facebook, e-mail, Twitter, etc., through which they were asked to visit a Qualtrics website

that contained the questionnaire. They were randomly assigned to one of the four

experimental conditions. As demonstrated in table A, the sample consisted of 43.8% males

and 56.2% females. The biggest part of the participants (84.2%) were aged from 21-30

years. Most participants (67.3%) obtained a university degree and the medium income was

between €1500 and €3000.

Table A: Demographic information of the participants

Sex Average age Education Income

Man: 43.8%

Woman: 56.2% 21-30

Not-University:

32.7%

University: 67.3%

€ 0-1500: 17.3%

€1500-3000:

> € 3000:

Would rather

not say:

17.3%

39.5%

29.0%

14.2%

2.2 Stimulus materials &procedure

Participants were presented with a fictional scenario in which they were asked to imagine

themselves in the role of a customer, Sam Janssens, who experienced service failure with

an also fictitious telecom provider BelCom. There was opted for a fictitious company,

because this ensures that all participants evaluate equally the brand and thus results are

more “clean”. Also, there was chosen for the service industry (telecom provider), because

according to Walker (2006) complaints may be of greatest value to organizations that

primarily provide services. This is because services are more heterogeneous or variable

26

than products are. This variation is more likely to give rise to more perceived problems

and thus more customer complaints.

The scenario was the following: Sam Janssens had been experiencing since 2 days

network problems. To check whether his cellphone didn’t break down, he turned on and

off his cell phone, but this seemed to work fine. Assuming his telecom provider BelCom

was the one to blame, he shared this experience by sharing a Tweet (see appendix A.1, in

Dutch), which is translated as follows:

“Not available via cellphone, since there’s no connection. Can you please fix

this @BelCom?? #bigfail #unreliable #sigh

Next, the participants were presented to one of the four responses of BelCom. They were

not only exposed to the actual tweet of BelCom, but also had access to the profile

description which is also possible in real life (See appendix A.2).

For the manipulation of message personalization, specific message elements were

altered along the conceptualization of Kerkhof, Beukeboom, Utz (2010), Koot (2013) and

Willemsen (2014). The personalized messages were written in the first person singular

(f.eg. “I will”, “Excuse me”) and signed by an individual customer service representative

of BelCom, named Elisa. To make this more clear, and because of the limited characters in

Twitter (140), her job description, i.e. webcare representative, was mentioned in her

Twitter profile. Also, to make it even more personal, the profile picture was a picture of a

woman, representing Elisa. The impersonalized messages, in the contrary, were written

in the first person plural (“We will”,” Excuse us”) and thus in the name of the company.

In order to make this more apparent, the description of this Twitter profile explicitly

mentioned it was the official Twitter-account of BelCom and its profile picture was their

“official” logo.

For the manipulation of message informality, there were also specific message

elements modified in order to transmit either a formal or informal tone of voice. Because

no other study, with exception of Koot (2013), has studied formalization before, we based

our alterations on the definition of formal and informal language by Kramer (2001):

Formal language is characterized by a greater emotional distance between the

communicator and the audience, between the communicator and the topic, than the informal

or familiar register. Formal register is grammatically accurate and logically organized, it does

not include contractions, colloquial language or slang. […] Informal language is the

language of everyday speaking and writing, casual conversation between friends and

27

associates, personal letters and writing close to general speeches. It may include shortened

forms of words (Kramer, 2001, p. 231).

Following this definition, the formal tweets applied strict rules by e.g. opening with a

formal greeting “Dear @SamJanssens” and ending with “Sincerely”, whilst the informal

message used more daily and colloquial speech and greeted with “Hi @SamJanssens!” and

ended with the abbreviated “Grtz!”. The formal tweet also used the Dutch personal

pronoun “U” which implies a greater distance between the sender and the receiver,

whereas the informal response used “je”, a more informal pronoun that implies little

distance. Also, the informal tweet used abbreviations such as “PM” (Private Message),

“Grtz” (Greetings) and “Thx” (Thanks), excessive exclamations (!!!) and smileys “:-)”,

which are message elements people use when chatting with people they know better.

2.3 Pretests

Pretest 1

To make sure whether the participants correctly recognized the different response

strategies, we carried out a manipulation check by conducting a 2x2 pretest among 40

participants. After each exposure to one of the four possible answers (personalized and

formal, personalized and informal, impersonalized and formal, impersonalized and

informal), we first checked message formality by asking the respondents to evaluate on a

7-point Likert scale to which degree the message was perceived as formal (with 1= very

informal and 7= very formal), as also was applied in the dissertation of Koot (2013).

Second, we asked participants to evaluate the message personalization by asking them to

evaluate on a 7-point Likert scale how personal the message was (with 1= very impersonal,

7=very personal). In addition, we did a reality check to find out how convincing and

credible the scenario was by asking them on a 7-point Likert scale to which degree they

found that the scenario was credible and the possibility of it to happen in real life.

Unexpectedly, the results of this pretest indicated that we needed to make some

serious alterations. Means were mainly around the average: Mformal = 3.38, Minformal = 2.42,

which means that formal messages were evaluated more informal than the informal

messages. The results of Koot showed a similar effect, which made us conclude that, apart

from making the tweets more formal or informal, we also needed to alter the question.

28

Since our participants had to evaluate short tweet-messages instead of long emails, we

had to be more specific to the concept of “informality”.

The data of personalization-check did not provide the appropriate results either.

Since it was a webcare message on Twitter, most people already found it very “personal”,

which demonstrated they didn’t really interpreted the term correctly. Means were slightly

better, yet not convincing: Mmpersonal =3.14 and Mpersonal = 4.1. This made us realize messages

had to be made more personal and we had to formulate clearly the concept of “personal”.

The most unexpected results, though, were the realism check. They was surprisingly

low and led us to the conclusion that messages had to be made in some way more realistic.

Nevertheless, feedback from the participants made us clear that the situation was

perceived unrealistic because of the webcare itself. Using Twitter to complain about an

experience and the company responding to it, just did not make any sense according to

the participants, even though in reality Belgian companies such as Mobile Vikings,

Telenet, Proximus, Brussels Airlines, etc. are increasingly applying webcare. This

perceived unrealism could be attributed to Belgium’s inactivity on and ignorance of

Twitter. A study of 2010 has shown that Twitter still is not really integrated in Belgium,

and the active users are estimated to 300.000.1 Compared to the 5.2 million accounts in

Holland and 107.7 million in USA, one can understand the lack of knowledge of Twitter

by the participants (Peeters, 2010). Nevertheless, in order to draw accurate conclusions,

this needs to be investigated in more detail.

Pretest 2

Owing to the unexpected and unfavorable results the first pretest provided us, we set up

a new pretest, taking into account the feedback the participants in the first test had given

us. First of all, we changed the representation of the tweets by using banners which made

it possible for the participants to take a look at the Twitter account. When there was a

personal webcare representative, for example, we made sure this was explicitly noticeable

through the account by mentioning her function in the company. We also made some

adjustments to the tweets itself: by changing some specific words, we made them look

more formal or informal.

Most change, however, was made to the questions we asked. As already mentioned,

the effect of message informality on webcare hasn’t been studied before, so instead of

1 It has to be noted that estimation of Twitter usage by country is difficult, since users are not obliged to declare their country of origin/residence.

29

merely asking in which degree the tweet was formal, we turned to the definition of Kramer

(2010) of formality (cfr. supra p. 22-23) and reformulated the questions as following: “The

language BelCom used, I could also use with my friends”, “BelComs language suggests

we have a close relationship” and “BelCom uses casual, colloquial language”. To check

personality, we reformulated the question in order to prevent misapprehension into “it is

clear who is talking out of BelCom”. Realism check was also altered, by asking more

specific questions, for example, if today’s technology permits the use of webcare, which is

based on the scale of Larivère, Van Vaerenbergh & Vermeir (2012). All items of the

conditions and realism check can be consulted in appendix B.

As a result to these modifications, the outcome of the second pretest were more

favorable. An analysis of variance (ANOVA) showed that the respondents in the personal

condition perceived the personal webcare post significantly (p=0) more personal (M =

5.63, SD=1.83) than respondents in the impersonal condition (M=1.53, SD=1.172). Also,

informal messages were significantly (p=0) more informal (M=5.7, SD=1.21) than formal

messages (M=2.23, SD=1.39). In addition, realism was measured across the four conditions

via ANOVA and results demonstrated that all participants equally (p=.589) perceived the

tweets highly realistic (M= 5.72, SD = 0.91).

2.4 Measures

All constructs are acquired directly from previous literature in order to verify the

legitimacy of the metrics under investigation. Table B provides an overview of the

dependent variables and the cronbach alphas of the constructs. Likewise, Table C provides

a summary of a description of the covariates and their cronbach alphas. For all scales,

cronbach alpha is amply above the acceptable range (i.e. > .60) which proves the items

constitute a reliable scale (De Pelsmaecker & Van Kenhove, 2006). Consequently, we

averaged the items to form an index measure.

30

Tabel B: Variable description and reliability scales

Construct Description Cronbach

alpha Source

Conversational

Human Voice

M= 4,78

SD=.83

Ten items on a 7-point Likert scale that

assesses the perceived human voice as Searls

& Weinberger (2000) describe it (see p. 10)

α = 0.84

Kelleher &

Miller (2006)

Brand Attitude

M= 3,71

SD = 1.41

A three item, 7-point semantic differential

scale that evaluates general attitude of the

brand.

α =0.76

Reinders et al

(2008)

Trust

M= 5.03

SD = 0.99

A three item, 7-point, Likert scale that

assesses the perceived trustworthiness of

BelCom by the participants.

α = 0.76

DeWitt et al.

(2007)

Satisfaction

M=4.51

SD = 1.11

A tree item, 7-point Likert scale that assesses

the general satisfaction perceived by the

participants.

α =0.79

Maxham III

& Netemeyer

(2002)

PeWOM -

intentions

M=3.52

SD=1.01

A five, 7-point Likert scale that assesses the

likelihood that customers would spread

PeWOM.

α = 0.89

Maxham III

& Netemeyer

(2002)

NeWOM-

intentions

M=3,45

SD = 1,12

A five, 7-point Likert scale that assesses the

likelihood that the respondents would spread

NeWOM.

α = 0.91

Maxham III

& Netemeyer

(2002)

Repurchase

intent

M= 4,78

SD = .94

A tree item, 7-point Likert scale that evaluates

the degree in which respondents intent to

keep using the services BelCom provides. α = 0.87

Maxham III

& Netemeyer

(2002)

Conversational Human Voice

Participants were asked to indicate the perceived human voice conveyed in the webcare

strategy. The level of conversational human voice was measured by ten items on a 7-point

Likert scale (1= “Not at all agree” and 7= “Completely agree”), which were adopted from

Kelleher & Miller (2006) who based themselves on Searls & Weinberger (2000) (Cfr. supra

p. 10). The scale included items such as “BelCom is open to dialogue”, “BelCom treats the

customer as a human”, “BelCom communicates on a human tone”, “BelCom welcomes

31

conversational communication”, etc. The items proved to constitute a reliable scale and

therefore formed an index measure.

In appendix C, all items from mentioned measures can be consulted in more detail.

2.5 Control variables

Table C provides a description of the covariates and their cronbach alphas where scales

consisted of multiple items. Likewise, Cronbach alpha is amply above the acceptable

range (i.e. > .60) which proves the items constitute a reliable scale. We averaged this items

to form an index measure. In appendix D, all covariates can be consulted in further detail.

Table C: Control variables

Variables Description Cronbach

alpha Source

De

mo

gra

ph

ic f

act

ors

Age Age of the participants: younger than 21, 21-

30, 31-40, 41-50, 51-60, older than 60 /

Own scale

Education Educational level of the participant. / Own scale

Gender Gender of the participant: male or female. / Own scale

Income

The monthly household income of the

participant, represented by 3 ordered

categories.

/

Own scale

So

cia

l N

etw

ork

s B

eh

av

ior

Twitter activity Does the participant have twitter profile: yes or

no. /

Own scale

Previous directed

complaints on

social media

M=1.68

SD=1.07

Questions about the extent that the participant

has complaint to a company via social media

and directly interacted with companies on

social media.

α=.79

Own scale

Previous

undirected

complaints on

social media

M=1.86

SD=1.19

Questions about how many times the

participant has shared through social media a

bad experience with a company, not directly

towards the company in question.

α=.88

Own scale

Expected

interactivity

M=4.98

SD=1.44

Questions about what the respondent expects

from the company, after sharing experiences

with the company on social media.

α=.93

Labrecque

(2014)

32

Oth

ers

Failure severity

M=5.19

SD=1.29

A three item, 7-point Likert scale that evaluates

the severity perceptions of failures. α=.89

Van

Vaerenbergh et

al. (2012)

Attributions of

blame

M=2.9

SD=.98

A three item, 7-point Likert scale that assesses

the extent to which customers hold the seller

responsible for a failure. α=.81

Maxham III &

Netemeyer

(2002)

Attitude towards

complaint

handling

M=3.35

SD = 1.33

How participants feel about the way BelCom

handled the complaint, measured through a

three item, 7-point semantic differential scale.

α = .8

Reinders et al;

(2008)

33

3. Results

3.1 Manipulation & Cofound check

Similar to the pretest, checks were carried out to make sure respondents processed the

stimulus material properly. As intended, informal messages (M= 5.0, SD=1.2) were

perceived to be significantly (p=0.00) more informal that formal messages (M=2.3, SD=1.1,

p=0.00) Personal messages (M=5.6, SD= 1.37) were also perceived to be significantly

(p=0.00) more personal than impersonal messages (M=2.05, SD=1.29).

We also checked realism by conducting an ANOVA-test. Means do not differ between

conditions, which means that every condition is perceived equally as high realistic

(M=5.51, SD=0.98).

In order to determine the need of control for other variables, confound checks were

performed by calculating Pearson correlations for covariates such as demographic

variables and social network behavior. The results are presented in table D.

Table D illustrates that age is a covariate that we should take in account in further

analysis: it correlates significantly (p<0.05) with CHV (r=-0.256, p=0.001), trust (r=-0.186,

p=0.018), satisfaction(r=0.253, p=0.001), PeWOM (r=-0.257, p=0.001) and repurchase intent

(r=-0.226, p=0.004). Even though correlations are weak, this age will be included in further

analysis. In order to do so, 2 dummy variables were made: young age (0-30) and middle

(31-50).

Attributions of blame seem to correlate positively with satisfaction (0.17, p=0.0031),

PeWOM (r=0.22, p=0.023) and repurchase intent (r=0.175, p=0.002). Perception of failure

severity also correlates significantly with PeWOM (r=-0.156, p=0.047) and NeWOM

(r=0.169, p=0.0032). Therefore, these two covariates will also be included in further

analysis.

Income also seems to be an important control variable. It correlates significant and

negatively with satisfaction (r=-0.168, p=0.033) and positively with PeWOM (r=-0.176,

p=0.025). Therefore, we will include the control variable income in further analysis. Before

analyzing, two dummy variables were created: low income (€0-€1500) and middle income

(€1500-€3000).

Finally, we can deduce from table D that attitude towards complaint handling needs

to be included in further analysis because it correlates significantly (p<0.05) with all

34

variables: CHV (r=-0.404), trust (r=-0.3), satisfaction (r=-0.0567), brand attitude (r=0.74),

PeWOM (r=-0.48), NeWOM (r=0.49), repurchase intent (r=-0.337)

It also confirms that the sex, education, having Twitter, directed and undirected

complaints, expected interactivity and higher education are not related to the measures

(p>0.05) and thus omitted from further analyses.

Table D: Correlations among variables and covariates

CHV Trust Satisfaction Brand

attitude PeWOM NeWOM

Repurchase

intent

Co

va

ria

tes

Age -0.256

p=0.001

-0.186

p=0.018

-0.253

p=0.001

0.052

p=0.526

-0.257

p=0.001

0.129

p=0.103

-0.226

0.004

Sex 0.068

p=0.39

0.084

p=0.291

0.09

p=0.256

-0.112

p=0.157

0.068

p=0.39

-0.046

p=0.563

-0.048

p=0.546

Blame 0.031

p=696

0.038

p=0.631

0.17

p=0.031

-0.046

p=0.563

0.222

p=0.005

-0.122

p=0.121

0.175

p=0.026

Failure

severity

0.029

p=0.715

0.035

p=0.657

-0.110

p=0.164

0.079

p=0.32

-0.156

p=0.047

0.169

p=0.032

-0.023

p=0.771

Income -0.154

p=0.051

-0.101

p=0.626

-0.168

0.033

0.144

p=0.148

-0.176

p=0.025

0.082

p=0.031

-0.109

p=0.166

Education 0.132

p=0.094

0.101

p=0.199

0.132

p=0.095

0.026

p=0.746

0.008

p=0.919

0.024

p=0.761

0.115

p=0.146

Twitter 0.051

p=0.515

0.08

p=0.31

0.018

p=0.824

-0.041

p=0.605

0.085

p=0.284

-0.034

p=0.67

0.055

p=0.487

Expected

interactivity

-0.001

p=0.988

0.092

p=0.245

0.144

p=0.067

0.147

p=0.063

-0.064

p=0.419

0.065

p=414

-0.142

p=0.071

Directed

complaints

-0.031

p=0.692

0.071

p=0.372

-0.036

p=0.65

0.042

p=0.595

0.075

p=0.342

0.052

p=0.509

-0.116

p=0.142

Undirected

complaints

-0.033

p=0.672

0.032

p=0.685

-0.078

p=0.326

0.022

p=0.78

0.132

p=0.094

0.048

p=0.546

-0.019

p=0.807

Attitude

towards

complaint

handling

-0.404

p=0

-0.3

p=0

-0.567

p=0

0.74

p=0

-0.48

p=0

0.49

p=0

-0.37

p=0

35

3.2 Findings

To test our hypotheses, seven linear regression models were developed for each

hypotheses in order to account for the variance of the dependent variable with and

without covariates. In order to add credibility to the conclusions, we also conducted T-

tests.

1. Effects of conversational human voice

It was hypothesized that conversational human voice affects outcomes such as brand

attitude (a), trust (b), satisfaction (c), positive word-of-mouth (d), negative word-of-

mouth (e) and repurchase intent (f) in a positive way (H1). To test H1, we performed

multiple linear regressions with conversational human voice as the independent variable

and brand attitude, trust, satisfaction, word-of-mouth behavior and repurchase intent as

dependent variable by turns. Afterwards, we included covariates that were proved to

have a significant correlation with the variable in question (cfr. table D).

In table E1, the results of the regression analysis of brand attitude are given:

Table E1: Explanatory power of linear regression analysis (brand attitude)

In the first model, we only included conversational human voice, in the second model, we

also included attitude towards complaint handling, since there was found a significant

correlation between attitude towards complaint handling and brand attitude (cfr. table D).

In the first model, we can observe that the variance in brand attitude can be explained

for 5.4 % (R²a = 0.054) by conversational human voice. It demonstrates that, when using

conversational human voice, brand attitude decreases (β=-0.245, p=0.002). This is not in

line with our hypothesis, since we stated that conversational human voice would

Model 1 Model 2

Stand. β T-value Sign. p Stand. β T-value Sign. p

CHV -0.245 -3.19 0.002 0.064 1.108 0.269

attitude_complaint 0.766 13.178 0.000

R²_adj 0.054 0.545

36

positively affect brand attitude. In the second model, however, we see that

54.5%(R²a=0.54) of the variance in brand attitude can be explained. Nevertheless, whilst

the coefficient of attitude towards complaints handling is significant (β =0.766, p=0), the

coefficient of CHV is not (β=0.064, p=0.269). This means that the variance in brand attitude

is explained by the covariate attitude towards complaint handling, and not by CHV which

in conclusion makes CHV a factor to be neglected. We can thus reject hypothesis 1a which

stated that conversational human voice affects positively brand attitude.

Similarly, we checked the regression of satisfaction in order to know how

conversational human voice is affecting satisfaction. Results are presented in table E2:

Table E2: Explanatory power of linear regression analysis (satisfaction)

The first model includes CHV and shows that 30% of the variance in satisfaction is

explained by conversational human voice (R²a=0.3, p=0). It means that, when we use a

conversational human voice, satisfaction increases (β=0.735, p=0). This is in line with

hypothesis 1c.

In the second model, we also included attitude towards complaint handling, age and

attributions of blame, because correlations were proved to be significant (cfr. table D). In

this model, the variance explained is higher (R²a=0.472, p=0) and we see that, when

applying CHV, satisfaction increases (β=0.355, p=0), although in lower degree than

previous model, because here, also covariates like attitude towards complaint handling

(β=-0.402, p=0), young age (β=0.296, p=0,018), and attributions of blame (β=0.164,

p=0.005), play a significant role in the regression model. Still we can assume that

conversational human voice affects satisfaction positively and thus we accept hypothesis

1c.

Model 1 Model 2


CHV 0.735 8.364 0 0.355 5.555 0

attitude_complaint 0.402 -6.388 0.000

age_young 0.296 2.387 0.018

age_middle 0.237 1.943 0.054

blame 0.164 2.848 0.005

R²_adj 0.3 0.472

37

Next, we similarly analyzed the regression of trust, and in which way conversational

human voice and some covariates are significant. Results are shown in table E3:

Table E3: Explanatory power of linear regression analysis (trust)

Model 1 Model 2


CHV 0.562 8.584 0 0.507 6.96 0

attitude_complaint -0.078 -1.083 0.28

age_young 0.145 1.028 0.306

age_middle 0.064 0.461 0.645

R²_adj 0.311 0.313

The first model only includes CHV, and we can see that 31.1% of the variance in trust is

explained by CHV (R²a=0.311, p=0). The table shows that, when using CHV, trust

increases (β=0.562, p=0). And this supports hypothesis 1c. In the second model, there were

also included covariates that proved to be correlated significantly (cfr. table D). However,

the coefficients are not significant (p>0.05), and thus we can omit these covariates in the

regression. This means that CHV explains 31.3% of the variance in trust and that when

using CHV, trust increases (β=0.507, p=0) and therefore affirms hypothesis 1c.

To check in which degree conversational human voice affects word-of-mouth behavior,

we look at both positive and negative word-of-mouth. Analysis of regression was first

made with PeWOM and the results are presented in table E4:

38

Table E4: Explanatory power of linear regression analysis (PeWOM)

The first model only includes CHV and in this model, variance in PeWOM is explained

for 14.1% by conversational human voice (R²a=0.141, p=0). The results also demonstrate

that, when using CHV, PeWOM increases, which is in line with our hypothesis (β=0.383,

p=0). The second model, however, also includes age, failure severity, income, attributions

of blame and attitude towards complaint handling, because significant correlations were

confirmed (cfr. table D). Variance that can be explained is higher in this model (R²a=0.334,

p=0). Nevertheless, the role of CHV (β=0.147, p=0.066) is not significant, the only variable

that significantly explains the variance in PeWOM is attributions of blame (β =0.205,

p=0.005). Therefore, we reject hypothesis 1d.

When analyzing the regression with NeWOM, results presented in Table E5 were given:

Table E5: Explanatory power of linear regression analysis (NeWOM)

Model 1 Model 2

Stand. β T-value Sign. p Stand. Β T-value Sign. p

CHV 0.383 5.246 0 0.147 1.854 0.066

attitude_complaint -0.395 -5.115 0

age_young 0.249 1.66 0.098

age_middle 0.122 0.84 0.403

income_low 0.039 -0.508 0.6122

income_middle 0.084 -1.105 0.271

Failure_severity -0.084 -1.146 0.254

blame 0.205 2.848 0.005

R²_adj 0.141 0.334

Model 1 Model 2

Stand. β T-value Sign. p Stand. Β T-value Sign. p

CHV 0.377 -5.15 0 0.278 -3.465 0.001

attitude_complaint 0.342 4.267 0

income_low 0.019 -0.222 0.824

income_middle -0.05 -0.605 0.546

Failure_severity 0.14 1.899 0.06

39

As with other analyses, the first model only includes CHV, and as a result, variance of

13.7% (R²=0.137, p=0) in NeWOM is explained by CHV. We can see that, when using a

more human voice, NeWOM decreases (β=-0.377, p=0), which is in line with our

hypothesis.

The second model includes attitude towards complaint handling, income and failure

severity, because these covariates were proven to be correlated significant with NeWOM

(cfr. table D). In this model, more variance in NeWOM is explained (R²a=0.281, p=0). It

shows however, that only the coefficients of CHV (β=-0.278, p=0.001) and attitude towards

complaint handling (β=0.342, p=0) are significant. When using a more human voice,

people tend to engage less in NeWOM. This is in line with hypothesis 1e, stating that a

more human voice affects positively word-of-mouth behavior.

A last variable we stated to be affected by CHV, was repurchase intent. As we did in

previous analysis, regression was analyzed and the results are given in Table E6:

Table E6: Explanatory power of linear regression (repurchase intent)

The first model only includes CHV and demonstrates that 11.6% of the variance in

repurchase intent is explained by CHV (R²a=0.116, p=0). When using CHV, repurchase

intent increases (β=0.349, p=0), as we predicted in our hypothesis.

The second model also includes attitude towards complaint handling, age and

attributions of blame, since they were considered to correlate with repurchase intent (cfr.

table D). In this model, more variance is explained (R²a=0.21), however, only the

coefficients of CHV (β=0.212, p=0.007) and attitude towards complaint handling (β=-0.258,

R²_adj 0.137 0.281

Model 1 Model 2


CHV 0.349 4.714 0 0.212 2.17 0.007


age_young 0.007 0.046 0.964

age_middle -0.153 -1.025 0.307

Blame 0.168 2.396 0.018

R²_adj 0.116 0.21

40

p=0.001) are considered to be significant. The effect of CHV is smaller, because variance is

also explained by attitude towards complaint handling. However, it is supports

hypothesis 1f, because when using a more CHV, repurchase intent increases, even though

it is a rather small increase.

After conducting these analysis in order to check hypothesis 1, we can generally conclude

that they support a big part of our first hypothesis (Hb,c,e,f): when an organization applies

a more human voice in their webcare strategy, they affect positively satisfaction, trust,

negative word-of-mouth behavior and repurchase intent, even though little. However,

what is not in line with our hypothesis, is that there is no significant relationship between

the use of CHV and the outcomes brand attitude and PeWOM.

2. Effects from message personalization on CHV

In the second hypothesis, we argued that when personalizing messages in webcare, the

perceived conversational human voice would increase. As we did with previous

hypothesis, we analyzed this through linear regression analysis. Results are provided in

table F:

Table F: Effect message personalization on CHV via linear regression analysis

In contrary to our expectations, no significant associations are found between

personalization and conversational human voice. A T-test confirms this outcome: there is

no significant difference (p=0.238>0.05) between the means of conversational human voice

of impersonal messages (M=4.7) and personal messages (M=4.85). Therefore, we can reject

our second hypothesis. This is also in contrast with the study of Koot (2013) and Kerkhof,

Beukeboom & Utz (2010) where message personality is found to directly enhance

conversational human voice.

Dependent

variable

R²a F Sign. p Stand. β T

Conversational

human voice

0.002 2.88 0.258 0.089 1.135

41

3. The effects of message personalization on outcomes

As we stated in hypothesis 3, we argue that personalization of message elements might

increase brand evaluations such as brand attitude (a), trust (b), satisfaction (c), PeWOM

(d) NeWOM (e) and repurchase intent (f). In order to investigate this hypothesis, we

conducted a linear regression analysis. The results are presented in table G1:

Table G1: Effect message personalization on variables via linear regression

As the results demonstrate, in contrast to our expectations, there are no significant (p>

0.05) associations between message personality and above mentioned brand evaluations.

An independent T-test confirms this, results are demonstrated in table G2:

Table G2: Effect message personalization on variables via T-test

Dependent

Variable

R² a R² dF F Sign.

(p)

Standardized

β

T

Brand

attitude

-0.006 0.000 1 0.021 0.886 0.032 0.144

Satisfaction -0.005 0.002 1 0.021 0.615 -0.04 -0.504

Trust -0.001 0.005 1 0.813 0.369 -0.071 -0.901

PeWOM -0.005 0.001 1 0.14 0.709 0.709 -0.374

NeWOM -0.002 0.006 1 0.773 0.381 0.075 0.879

Repurchase

Intent

-0.006 0.00 1 0.072 0.789 -0.021 -2.68

Personal Mean Sign.

Brand attitude Impersonal

Personal

3.70

3.73

0.078

Satisfaction Impersonal

Personal

4.56

4.47

0.563

Trust Impersonal

Personal

4.76

4.63

0.349

PeWOM Impersonal

Personal

3.56

3.50

0.509

NeWOM Impersonal

Personal

3.4

3.50

0.378

Repurchase intent Impersonal

Personal

4.79

4.75

0.129

42

It shows that means of brand attitude do not differ (p>0.05) between impersonal or

personal messages. Concluding from the results presented in table G1 and G2, we can

reject hypothesis 3 stating that the use of personalization enhances variable outcomes. This

is also in contrast with the study of Koot (2013) and Kerkhof, Beukeboom & Utz (2010),

where personalization was found to have a positive effect on brand attitude.

4. The effects of message informality on conversational human voice

In hypothesis 4, we state that using more informal elements enhance the perceived

conversational human voice. By using a linear regression model, we conveyed following

results, which are represented in table H:

Table H: Effect message informality on CHV via linear regression

From the table we can deduce that there is no significant (p>0.05) relation between using

message formalization and conversational human voice. Thus, we reject the hypothesis

that message informality enhances conversational human voice. We reaffirm this position

after conducting an independent T-test, of which the results demonstrated that the means

do not differ significantly (p=0.258) in conversational human voice when personal

(M=4.85) or impersonal (M=4.71).

5. The effects of message informality on other variables

In our fifth hypothesis, we formulated that using informal messages would increase brand

attitude (a), trust (b), satisfaction (c), PeWOM (d), NeWOM (e) and repurchase intentions

(f). As with previous hypotheses, we checked any effects by modelling a linear regression.

Results are shown in table I1:

Dependent

Variable

R² a Significance (p) F Standardized (β) T

CHV -0.003 0.497 0.464 -0.054 -0.681

43

Table I1: Effect message informality on outcomes via linear regression

From this table we can derive that there is a significant (p≤0.05) relationship between

message informality and brand attitude (a), satisfaction (c) and repurchase intent (e).

A T-test gives us the same results:

Table I2: Effect message informality on outcomes via T-test

To investigate more thoroughly, we further included in a second model covariates that

were proved to be significant in table D.

Dependent

variable

R²a Sign. (p) F Stand. β T

Brand

attitude

0.032 0.013 4.975 -0.194 -2.502

Satisfaction 0.024 0.027 4.975 0.174 2.231

Trust -0.006 0.785 2.316 0.022 0.273

PeWOM 0.007 0.147 2.12 0.115 1.1459

NeWOM 0.008 0.13 1.084 0.119 -1.522

Repurchase

intent

0.023 0.031 4.741 0.17 2.17

Formalization Mean Sign.

Brand attitude Informal

Formal

3.99

3.44

0.013

Satisfaction Informal

Formal

4.3

4.7

0.027

Trust Informal

Formal

4.67

4.71

0.785

PeWOM Informal

Formal

3.41

3.63

0.147

NeWOM Informal

Formal

3.58

3.531

0.130

Repurchase intent Informal

Formal

4.62

4.93

0.031

44

The results of the linear regression analysis from brand attitude are demonstrated in table

I3:

Table I3: Explanatory power of linear regression analysis (brand attitude)

Model 1 Model 2


message formality -0.194 -2.502 0.013 -0.082 -1.535 0.127

attitude_complaint 0.727 13.656 0.000

R²_adj 0.032 0.548

In the first model, only CHV is included. In this model, variance in brand attitude

predicted by conversational human voice is very low (R²a=0.032%). It also demonstrates

that, when using more informal elements, brand attitude slightly decreases (β=-0.194),

which is in contrast to our hypothesis and also with former studies. In the second model,

however, covariates attitude towards complaint handling is included. Variance explained

is higher (R²a=0.584), but only due to the inclusion of the covariate (β=0.727, p=0), since

the coefficient of CHV seems not to contribute to the variance (β=0.082, p=0.127).

As presented in table I1, there is also a significant relationship (p≤0.05) between

satisfaction (c) and message informality. In table I4, results are presented:

Table I4: Explanatory power of linear regression analysis (satisfaction)

Likewise, the variance predicted by CHV in satisfaction is very low (R²=0.024). It also

shows that when using more informal elements, satisfaction increases slightly (β= 0.174).

We also took into account the covariates that were proven to be correlated with satisfaction

Model 1 Model 2


message formality 0.174 2.231 0.027 0.108 1.715 0.088


age_young 0.423 3.166 0.002

age_middle 0.309 2.328 0.021

blame 0.179 2.87 0.005

R²_adj 0.024 0.379

45

(cfr. table D) in a second model. We can see that variance is higher (R²a=0.379) and not

predicted by message informality (β=0.108, p>0.05), but rather by covariates. Therefore,

we can exclude that message informality affects satisfaction.

A last significant relationship has been shown between message informality and

repurchase intent (p≤0.05). In order to investigate the relation more thoroughly, we also

included in a second model the control variables. Table I5 contains the results received:

Table I5: Explanatory power of linear regression analysis (repurchase intent)

The variance in the first model predicted is extremely low (R²=0.029) and it demonstrates

that when using more informal elements, repurchase intentions are affected slightly (β=

0.17). In the second model, we included covariates such as age, attributions of blame and

attitude towards complaint handling because these appeared to be significant in the

cofound check (cfr. table D). In this second model, we can see that the variance explained

is higher (R²a=0.188), but this is not explained by CHV (β=0.118, p=0.105), however,

covariates attitude towards complaint handling (β=-0.317, p=0.001), attributions of blame

(β=0.178, p=0.013), and middle age (β=-0.18, p=0.021), play a significant role. Thus, we

can conclude that message formality does not affect positively repurchase intent.

Concluding, from results presented in tables E1-5, we reject hypothesis 5 that using

informal message elements affects positively certain variable outcomes.

6. The combination effect of personalization and informality on CHV

In hypothesis 6, we argued that by using message informality and message

personalization at the same time, conversational human voice would decrease. As with

previous hypothesis, we conduct a linear regression analysis to investigate the effects.

Results are shown in table J:

Model 1 Model 2


message formality 0.170 8.364 0.031 0.118 1.632 0.105


age_young 0.094 0.613 0.541

age_middle -0.1 -0.658 0.021

blame 0.178 2.5 0.013

R²_adj 0.023 0.188

46

Table J: Combination effect on CHV via linear regression analysis

It shows that there is no significant relationship between the combined use of message

informality and personalization on conversational human voice (p>0.05), which rejects

our hypothesis. An independent T-test also gives the same result: means do not differ

(p=0.715) between conversational human voice between combination of personalization

and informality (M=4.82) and no combination (M=4.76). This means that using both

informal and personal elements, has no affect on conversational human voice.

7. The combination effect of personalization and informality on other variable

outcomes

In our last hypothesis (H7), we similarly assumed that using both message informality

and message personalization would affect brand attitude (a), trust (b), satisfaction (c),

word-of-mouth behavior (d) and repurchase intent (e) negatively. Results are given in

Table K:

Table K: Combination effect on outcomes via linear regression analysis

As the table shows, there has not been established any significant (p>0.05) relationships

between the combination effect of message informality and message personalization and

Variable R²a Sign. (p) F Standardized

(β)

T

CHV -0.007 0.594 0.634 0.035 0.248

Variable R² a F Sign.

(p)

Standardized

β

T

Brand

attitude

0.02 2.123 0.099 -0.298 -0.298

Satisfaction -0.015 1.798 0.15 -0.031 -0.219

Trust -0.008 0.574 0.633 0.128 0.898

PeWOM -0.001 0.92 0.433 0.093 0.658

NeWOM 0.002 1.084 0.358 -1.05 -0.745

Repurchase

intent

0.13 1.704 0.168 0.071 0.504

47

any of the investigated dependent variables. To confirm this, we also conducted a T-test,

which lead to the same conclusions:

Table L: Combination effect on outcomes via T-test

Difference in means does not seem to differ significantly (p>0.05).

From above established observations, we conclude that using both message formalization

and personalization does not have any effect on variable outcomes and thus reject our last

hypothesis (H7).

Combination Mean Sign.

Brand attitude combination

no combination

3.44

3.82

0.127

Satisfaction combination

no combination

4.63

4.46

0.375

Trust combination

no combination

4.71

4.68

0.872

PeWOM combination

no combination

3.65

3.47

0.314

NeWOM combination

no combination

3.30

3.50

0.316

Repurchase intent combination

no combination

4.94

4.71

0.165

48

4. Conclusions

4.1 Conclusion and discussion

The advent of Web 2.0 has presented major challenges for traditional business strategies.

Consumers can share effortlessly their negative experience with products and services

anywhere, anytime and to anyone, which causes serious reputation and image damage to

the business complaint about. In order to counter with this NeWOM, an increasing

number of organizations are engaging in webcare. However, real-life examples have

demonstrated that merely engaging in webcare is not enough: inappropriate responses

can provoke a backfiring effect, which turns NeWOM into more NeWOM instead of

attenuating this effects and thus causes more damage to the organization in question.

Therefore, it is important for businesses to know how to apply webcare and fully exploit

its benefits effectively, so that businesses can engage determinedly in an elaborate webcare

strategy. Previous research and literature has suggested that the use of a conversational

human voice in computer-mediated communication contributes to the effectiveness of the

webcare strategy. However, up till now, little research has been investigating which

specific elements can enhance this conversational human voice and if these elements also

affect positively brand evaluations, such as trust, satisfaction, attitude, etc. Therefore, this

study wanted to examine such effects of two elements: message personalization and

message informality.

First, it was investigated whether there was a positive relationship between conversational

human voice and important variable outcomes (hypothesis 1). Second, it was analyzed if

message personalization (hypothesis 2) and message informality (hypothesis 4) indeed are

factors that increase conversational human voice. Third, it was studied if message

personalization (hypothesis 3) and message informality (hypothesis 5) positively affect

important evaluations for the company, such as brand attitude (a), trust (b), satisfaction

(c), PeWOM (d), NeWOM (e) and repurchase intent (f). Last, the combination effect of

message personalization and message informality was measured on both conversational

human voice (hypothesis 6) and brand evaluations (hypothesis 7). In order to investigate

these hypotheses, we conducted a scenario-based experiment, where a customer

49

complained on Twitter about his telecom provider to which the telecom provider

answered in four possible ways: informal and personal, informal and impersonal,

impersonal and formal, formal and personal.

The results demonstrate that using a conversational human voice positively affects trust,

satisfaction, repurchase intent and negative word-of-mouth towards the organization,

which is in respect with our first hypothesis. However, brand attitude and positive word-

of-mouth does not seem to be influenced by conversational human voice. Thus, our first

hypothesis is partially confirmed and therefore also partially in line with the study of

Kerkhof, Beukeboom & Utz (2010), van Noort & Willemsen (2011) and Koot (2013).

Personalizing messages, by revealing the person responding through the name and

profile picture, did not seem to have a positive effect on CHV or on the brand evaluations

we investigated. This is in contrast with our second and third hypothesis and also with

the findings from Koot (2013) and Kerkof, Beukeboom & Utz (2010). This difference in

results might be due to the platform: in our study webcare was applied on Twitter, while

in the other studies, webcare was applied on a consumer forum or on Facebook.

Message informality, i.e. using a more colloquial and day-to-day language, did not

enhance conversational human voice or any brand evaluations in our research. Therefore,

we also reject hypothesis 4 and 5, which is also in contrast with the study of Koot (2013).

Our last hypotheses followed these findings: the combination effect did not have any

effect on CHV or on the brand evaluations, which brings us to the rejection of hypothesis

6 and 7. This is in contrast with the findings of Koot’s research (2013), where the

combination of both elements even conducted negative effects.

We conclude that, in contrast to our expectations, most of the hypotheses presumed were

not supported by the research we made. The only hypothesis that was confirmed by our

study, is that conveying a more conversational human voice has positive effects on most

brand evaluations (i.e. trust, satisfaction, NeWOM-behavior and repurchase intent).

However, message informality or personalization do not have any effects on

conversational human voice or brand evaluations. Even though these results might be

disappointing, they also require some interpretations. First of all, Twitter is a microblog,

which means a high degree of interactivity. The research of Kelleher & Miller (2006)

argued that interactivity is a factor that increases conversational human voice, because it

mimics one-to-one communication and thus a conversational style. This might explain

50

why the use of informal and/or personal messages might not have an effect anymore,

because conversational human voice already is perceived as high. We observed something

similar when our first pretest failed and we retrieved feedback from the respondents. They

considered the fact that organizations respond to your Tweet and specific complaint

already very “personal” and unbelievable. However, this might also be due to the fact that

our respondents were not familiar with webcare on Twitter.

Second, tweets are limited to 140 characters, which makes the message context very

small comparing to the blogs and messages in previous studies. This might make it

difficult for respondents to imagine themselves in the context the same way as it were in

real life. They also might have overlooked at some items, such as the name of the

respondent in the end of the tweet, which we tried to compensate by also revealing a part

of the profile. Even though the manipulation check assured that formal/informal and

personal/impersonal were distinguished significantly by the participants, maybe this

elements were still not present enough due to the context. This could be corrected by

presenting a thread of tweets, instead of only one, to create a more explaining context.

4.2 Managerial and other implications

The results of our study also implies some practical implications, even though they in

general do not support all of our hypotheses.

First of all, our study suggests that customer service should try to use a more human voice

when engaging in webcare, because this seems to effect positively trust, satisfaction,

PeWOM and repurchase intent. Enhancing trust online, is of great importance, since

online credibility is an issue that is difficult to overcome. Also, if PeWOM-behavior is

affected positively, this means that when using a more human voice, a backfiring effect is

less likely to happen.

Also, because of the high interactivity, businesses might consider Twitter as a very

efficient platform on which to engage in webcare.

Another suggestion is that, since message personalization and message informality

don’t seem to have any effects on Twitter, it does not matter for businesses in which

strategy to engage. So maybe the choice of whether engaging in a personal or impersonal

strategy should be seen from a more practical point of view: revealing the person behind

the webcare response can be practical for a follow-up of their customer service personnel.

51

Also, businesses can decide to respond more formal or informal, depending on the image

they want to reflect of their businesses.

4.3 Limitations and directions for future research

Like any research, this study had certain limitations that should need to be taken into

account. To start with, a convenience sample of respondents participated in the study and

therefore the outcomes should not be generalized to a wider public. The outcomes may,

for example, also depend on the target group of the organization. Older people might

appreciate a more formal approach, while a young, hip target group would rather expect

organizations to blend in with this modern environment. Future research would therefore

do well to investigate how certain target groups react to different strategies.

Also, this study only included one specific fictitious complaint towards a telecom

provider. Being informal or more personal could be different for other services, brands or

product types or even the brand image the organization wants to reflect to their customers.

Customers would want to have a persistent and continuous “fit” between the

representation on the internet of the organization and their corporate image in order to be

perceived as credible and reliable. In this case, people would like a serious bank to

respond more formal than for example a trendier brand like Abercrombie. Future

research could investigate if a strategy that fits to the corporate image creates more

positive brand evaluations.

Another limitation is that in this study, the stimulus materials were exposed on

Twitter. Even though Twitter is a commonly used platform for webcare, the insights

should not automatically be assumed to be generalized to other consumer-generated

platform, like for example Facebook, Instagram or to brand-generated platforms. The fact

that our outcomes are different from the results that the research of Koot (2013) and

Kerkhof, Beukeboom & Utz (2010) attained, illustrate this even more. Furthermore, we

only examined the effects for reactive webcare. The study by van Noort & Willemsen

(2011) has demonstrated that reactive webcare is more positive for conversational human

voice than proactive webcare. Therefore, results can be found different for proactive

webcare. Further research is therefore needed to see if message informality and

personalization have the same effects in other webcare strategies.

Finally we would like to conclude by emphasizing that, since personalization and

informality according to our study does not seem to have any effects when engaging in

52

webcare on Twitter, the challenge for future research is to find factors that do enhance

conversational human voice or that have positive outcomes for brand evaluations.

53

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Appendix

Appendix A: Questionnaire

1. Introduction and scenario

Beste deelnemer,

In het kader van mijn masterproef aan de Universiteit van Gent doe ik, in samenwerking met prof. dr. Bart Larivière en drs. Arne De Keyser, onderzoek naar het gebruik van webcare door bedrijven op Twitter.

Straks krijgt u een beschrijving te lezen van een situatie waarin u zich zo goed als mogelijk moet inleven om de vragen te kunnen beantwoorden. Hierover worden dan later vragen gesteld. Om betrouwbare resultaten te verkrijgen, is het belangrijk dat u deze vragen aandachtig leest en met

zorg beantwoordt. Het invullen van de vragenlijst zal ongeveer 10 minuten in beslag nemen en u helpt mij er enorm mee!

Uiteraard is de vragenlijst anoniem en zal alle informatie voor academische doeleinden worden gebruikt.

Alvast hartelijk bedankt voor uw medewerking!

Mieke Booy

_____________________________________________________________________________

Op de volgende pagina wordt u geconfronteerd met een korte beschrijving over de interactie tussen een klant, Sam, en telecomprovider, BelCom. Het is de bedoeling om u in te leven in de situatie van Sam en de case te lezen alsof uzelf betrokken partij bent (u moet zichzelf dus in de plaats

van Sam stellen). Nadien volgt een reeks vragen over deze case en is het terug de bedoeling dat u antwoordt op deze vragen alsof u zelf deze situatie meemaakt.

Al de hele dag kan Sam geen verbinding maken met het netwerk van zijn telecombedrijf BelCom, waardoor Sam geen telefoons kan ontvangen, noch andere mensen kan bellen. Sam heeft al meermaals de telefoon opnieuw opgestart en deze blijkt correct te werken. Geïrriteerd na het mogelijks missen van de oproepen en berichten van zijn contacten, besluit Sam BelCom te contacteren om dit probleem op te lossen.

Sams ongenoegen wordt via volgende bericht op Twitter aan BelCom gecommuniceerd:

62

2. Experimental material

BelCom reageert als volgt op Sams tweet-bericht:

Response 1: Impersonal & Informal

Response 2: Impersonal & Formal

63

Response 3: Personal & Informal

Response 4: Personal & Informal

64

3. Questions

72

Appendix B: Manipulation Check

Construct Item(s) Scale Source

Realism

What happens in this scenario could also

happen in real life.

Current technology allows for the above

scenario to happen in real life.

Today’s online tracking technologies allow

for the above scenario to happen in real life.

This scenario could happen to me or

someone else in the future.

The scenario is not hard to imagine.

The scenario is clear to me.

7-point

Likert

own scale +

Van

Vaerenbergh,

Larivière &

Vermeir

(2012).

Message

personalization

It is clear which person behind BelCom is

talking to me.

7-point

Likert Own scale

Message

informality

The language BelCom used, I could also

use with my friends.

BelComs language suggests we have a

close relationship.

BelCom uses casual, colloquial language.

7-point

Likert

Own scale,

based on

Kramer

(2001)

Appendix C: Measures Outcome Variables

Construct Item(s) Scale Source

Conversational

human voice

BelCom is open to dialogue.

BelCom treats the customer as a human.

BelCom communicates on a human tone

BelCom invites people to conversation.

BelCom use sense of humor

BelCom would admit mistakes

BelCom uses a conversation-style

communication

BelCom provides prompt feedback on a

direct, but uncritical manner.

BelCom tries to make communication

with their customers attractive.

BelCom tries to engage an interesting

conversation.

7-point

Likert

Kelleher &

Miller (2006)

73

Brand attitude

Given the scenario, how would you describe

your feeling against BelCom?

Good – Bad

Positive- Negative

Favorable-Unfavorable

Semantic

differantial

scale

Reinders et

al. (2008)

Trust

The firm puts the customer’s interests first.

I can count on the firm to respond to my

requests.

The firm can be relied upon to keep its

promises.

7-point

Likert

DeWitt et al.

(2007)

Satisfaction

I am satisfied with my overall experience

with BelCom.

As a whole, I am not satisfied with BelCom

How satisfied are you overall with the

quality of BelCom’s service?

7-point

Likert

Maxham III

& Netemeyer

(2002)

Satisfaction

Complaint

service

handling

I’m satisfied with my overall experience

with BelCom’s complaint service handling

As a whole, I am not satisfied with

BelCom’s complaint service handling.

How satisfied are you overall with the

quality of BelCom’s complaint service

handling?

7-point

Likert

Maxham III

& Netemeyer

(2002)

Positive eWOM

When I see my friends and family, I am

likely to say positive things about my

experience with BelCom

I am likely to spread PeWOM about

BelCom to friends and family via Twitter.

I am likely to spread PeWOM about

BelCom to friends and family via other

social media.

I would recommend BelCom to others.

If my friends were looking for telecom

provider, I would tell them to choose

BelCom.

7-point

Likert

Maxham III

& Netemeyer

(2002)

Negative

eWOM

When I see my friends and family, I am

likely to say negative things about my

experience with BelCom

I am likely to spread NeWOM about

BelCom to friends and family via Twitter.

I am likely to spread NeWOM about

BelCom to friends and family via other

social media.

I wouldn’t recommend BelCom to others.

If my friends were looking for telecom

provider, I would not tell them to choose

BelCom

7-point

Likert

Keiningham

et al. (2014)

74

Repurchase

intent

In the future, I will contintue using

BelCom for these services.

As long as BelCom delivers its current

services, I will not switch to another

service provider.

In the near future, I will not use BelCom

any longer.

7-point

Likert

Maxham III

& Netemeyer

(2002)

Appendix D: Measures Covariates

Variables Description Scale Source

De

mo

gra

ph

ic f

act

ors

Age

younger than 21

21- 30

31-40

41-5

51-60

older than 60

/ Own scale

Education

What is your educational level?

Grammar school

High school or equivalent

Vocational/Technical school

Some college

Bachelor’s degree

Master’s degree

Doctoral degree (Ph.D)

Professional degree (MD, JD, etc.)

/ Own scale

Gender Gender of the participant: male or female. / Own scale

Income

What is your current household income?

<1500

1500-3000

>3000

Would rather not say

/ Own scale

So

cia

l N

etw

ork

s

Be

ha

vio

r

Twitter activity Does the participant have twitter profile:

yes / no. / Own scale

Previous directed

complaints on

social media

How many times in the past year have you…

complained to a company via social

media (Never- All the time)

7-point

Likert

Own scale

75

directly interacted with companies on

social media (Never – All the time)

Previous

undirected

complaints on

social media

How many times in the past year have you…

shared your experience about a company

via social media, not directly towards the

company? (Never – All the time)

shared your emotions to your followers

after a company’s service failure via

social media? (Never – All the time)

7-point

Likert

Own scale

Expected

interactivity

If I post tweets concerning my experiences

with a company, I expect…

that the company will talk back to me if I

post a message

the company would respond to me

quickly and efficiently.

that the company allows me to

communicate directly with it.

the company listens to what I have to say.

7-point

Likert

Labrecque

(2014)

Oth

ers

Failure severity

In my opinion, the problem that I

experienced was a:

minor problem – major problem

big inconvenience-small inconvenience

major aggravation-minor aggravation

7-point

Likert

Van

Vaerenbergh

et al. (2012)

Attributions of

blame

To what extent was BelCom responsible

for the problem that you experienced?

(Not at all responsible – Totally

responsible)

The problem that I encountered was all

BelCom’s fault (Totally disagree-Totally

agree)

To what extent do you blame BelCom

for this problem? (Not at all –

Completely)

7-point

Likert

Maxham III &

Netemeyer

(2002)

Attitude towards

complaint

handling

Given the scenario, how would you describe

your feelings towards the service and

solution to the problem?

Good-Bad

Positive-Negative

Semantic

differenti

al scale

Reinders et al;

(2008)

76

Favorable-Unfavorable

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