ELECTRONIC WORD-OF-MOUTH VIA TWITTER

Customer eWOM Motivations and Intentions

Basim Musallam

and Rodrigo Magalhães

2, 3

Researcher, Kuwait Masstricht Business School, Salmiya, Kuwait

Professor of Information Systems and Organization, Kuwait Maastricht Business School, Salmiya, Kuwait

Researcher, CIEO - Research Centre for Spatial and Organizational Dynamics, University of Algarve, Algarve, Portugal

Keywords: Twitter, Electronic Word-of-mouth, Online Consumer Behaviour, Online Consumer Motivation, eWOM

Intentions, Consumer Engagement, Corporate Response Strategy, Interactivity.

Abstract: The study aims to explore customers’ motivational factors to express their opinions in Twitter regarding

company performance, products and services. Using Twitter as the main medium of the study, an electronic

questionnaire has been implemented to gather data. A total of 1,192 complete valid responses have been

collected from 5,011 hits on Twitter. Data were statistically analyzed to extract the strongest factors. The

results show that consumers are driven by multiple motives. Concern for Other Consumers,

Extraversion/Positive Self Enhancement, Venting Negative Feelings and Helping the Company are the

primary factors. Furthermore, we have identified early symptoms of grouped eWOM in Twitter against and

in favour of companies which carry an important message to companies regarding the need to formulate

proactive strategies to deal the with upcoming trend of Twitter powers for companies. The study was carried

out in Kuwait.

This paper is partially financed by the Foundation for Science and Technology, Portugal

1 INTRODUCTION

In an effort to understand what factors companies

should take into consideration when it comes to

dealing with social media, many scholars have tried

to identify the motivations behind user participation

in online media. Nardi et al., (2004) have identified

the motivations behind people participation in online

medium by documenting user lives, opinion

expressions, emotional and thinking outlets.

Keitzmann, et al. (2011) focused on what drives

online conversations and suggested that people seek

self-esteem through being among the opinion leaders

in the medium with trendy and hot ideas. Other

motivations revolve around user desires to meet

similar people to exchange ideas and opinions, with

user messages being heard for humanitarian causes

and positive effects.

The common factors linking users are the social

connections and the information sharing which feed

inner self-motivations to be involved in the social

media sphere (Foster et al., 2010). These

motivational elements have been categorized as both

functional goals for information exchange and

hedonic goals for rich and positive experiences (Hur,

et al., 2009). In order to engage in an infinite loop

between the user, other users, and the company, the

company ultimately should support enriched

conversations in social media (Parent et al., 2011).

This paper follows up the findings and

recommendations of Hennig-Thurau et al., (2004)

and Goldsmith and Horowitz (2006) regarding the

need to refine the mesurements used in these studies.

Additionally, it addresses customer motives within a

new cultural contex with the new meduim of

Twitter. The aim of this exploratory study is to

investigate customer intentions to engage with a

company by commenting on it either positively or

nagatively, through writing tweets in Twitter.

The paper proceeds with the literature review in

Section 2. Section 3 outlines the research

metholdogy and in Section 4 the findings of the

study are presented. Section 5 presents conclusions

and implications for both managerial practice and

suggestions for future research.

558

Musallam B. and Magalhães R..

ELECTRONIC WORD-OF-MOUTH VIA TWITTER - Customer eWOM Motivations and Intentions.

DOI: 10.5220/0003935305580564

In Proceedings of the 8th International Conference on Web Information Systems and Technologies (WEBIST-2012), pages 558-564

ISBN: 978-989-8565-08-2

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

2 BACKGROUND

2.1 eWOM via Twitter

The investigation of Twitter eWOM is timely and

necessary. The most prominent output of the

processes of interaction and communication is the

flow of messages between users, which spread in the

form of eWOM. eWOM has been defined as “a

statement made by potential actual or former

customers about a producer or a company which is

made available to a multitude of people and

institutions via the internet” (Hennig-Thurau et al.,

2004:39)

Shared opinions and reactions are passed

electronically from person to person, thus creating a

mechanism that impacts consumers’ decision

making. This mechanism is effective, especially due

to the memory features of Twitter (Jones et al.,

2009) which allows eWOM to be accessed at any

moment, as eWOM is visible not only online but

also offline with high accessibility and forwarding

capabilities through mobile devices (Hennig-Thurau,

et al., 2010).

eWOM has a multitude of features which are

appropriate for business purposes. One distinctive

feature is that companies have no control over

eWOM once unleashed online coupled with hybrid

interpersonal mass communication channels (Jansen,

et al., 2009). It is driven by online interactivity of

consumers seeking, giving, and passing opinions and

information, which reinforces credibility and

reliability (Chu and Kim, 2011). Twitter offers

eWOM anonymity and limitless geographical reach.

Another feature is endogenous to Twitter, where

eWOM plays a dual role as the informant precursor

and the recommender outcome. It thrives on the

push-push-pull communication with positive

feedback mechanism creating a ripple effect (Huang,

et al., 2009) that is not matched by any other

communication channel in terms of speed. A high

number of positive eWOM messages will influence

more purchases, which in turn will restart the whole

cycle on a larger scale (Duan et al., 2008).

These features offer companies unprecedented

chances to monitor eWOM´s spreading scale, and,

for purposes of researching the behaviour of re-

senders, it gives companies the opportunity to have a

physical look at the written comments of consumers

electronically documented (Huang et al., 2009).

2.2 Customer Motivations and

Intentions

Customers are the key players in Twitter. They are

part of the majority of the Twitter environment, the

receivers who act as the real engines to stimulate

eWOM. Those are the ones that the company strives

to please while aiming toward getting closer to them.

Thus, to build relationships with them is crucial and

this requires understanding their behaviours and

motivations in engaging with Twitter (Hanna et al.,

2011).

Many scholars have investigated consumer

behaviours in eWOM, from seeking eWOM,

providing eWOM and passing on eWOM The

research model used in this study (see Figure 1) built

upon the work of Hennig-Thurau et al., (2004),

which in turn is the outcome of previous WOM

models. Also, the work of Goldsmith and Horowitz

(2006) is adapted to the model.

Our model is built on four “utility types” or

reasons for users to provide eWOM messages. The

Focus-Related Utility encompasses the individual

motivation to add value to online community with

user input; Consumption Utility revolves around the

consumption of other user inputs and gaining

external opinions; Approval Utility deals with formal

and informal praising for user contributions and thus

feeds own satisfaction of peer approvals;

Homeostase Utility is built on balance theory which

states that individuals try to restore the balance once

its original state has been changed, i.e. equilibrium

changes with satisfaction or dissatisfaction which, in

turn, drive people to express their emotions and

revert to their equilibrium state.

Hennig-Thurau et al., (2004) have identified

eight factors that influence consumer motivation in

composing eWOM messages. The economic

incentives factor does not apply to the Twitter

environment and thus was eliminated from the

study. However, the remaining factors are all tightly

related to the reasons of why customers use Twitter

as a preferred eWOM format.

Concern for Other Consumers, Helping the

Company, and Social Benefits fall under Focus-

Related Utility. Advice Seeking belongs to

Consumption Utility; Extraversion/Positive Self-

Enhancement falls under Approval Utility and

Venting Negative Feelings is related to Homeostase

Utility.

One extra factor, the exertion of (Collective)

Power over Companies), which falls under Focus-

Related Utility has been extracted from the platform

assistance factor which focuses on operator

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assistance and convenience (Hennig-Thurau et al.,

2010). Lastly, Influence of Others, a factor that

touches on the way that customers are influenced by

other as well as being prone to imitate others

(Goldsmith and Horowitz, 2006).

2.2.1 Venting Negative Feelings

This factor is the most obvious one when it comes to

negative eWOM, capturing the way that negative

experiences influence intentions (Jones et al., 2009).

Venting negative feelings is the natural process for

users to restore the balance and get back to their

equilibrium state once they are exposed to an

unsatisfactory experience (Hennig-Thurau et al.,

2004). It serves the goal of lessening frustration and

diminishing discontent with negative experience and

resulting anxiety. Thus the following hypothesis is

formed:

H1: Venting negative feelings positively impacts

user intentions to engage in Twitter messages about

companies.

2.2.2 Concern for Others

Customers are driven by their motivation to engage

in eWOM communications in order to create and

maintain social relationships, to share their

satisfaction, dissatisfaction, and to keep the

information circulating online (Chu and Kim, 2011).

The factor has altruism in its heart, where the act of

doing something good for others has no reciprocity

element in it. People enjoy giving and aiding others

with their inputs without expectations of anything in

return. Such motive is derived from traditional

WOM but Hennig-Thurau et al., (2004) argued its

relation to eWOM with the genuine desire to help

others is emphasized in eWOM intentions. This

feeds the following hypothesis:

H2: Concern for others positively impacts user

intentions to engage in Twitter messages about

companies.

2.2.3 Social Benefits

Twitter users follow each other and form affiliations

to create virtual bonds which act as forms of social

integration and identification, which are perceived as

social benefits. The genuine desire to communicate

with similar minded people and to have their values

and inputs assist their virtual social interaction

influence the psychological need to flock around

those who share their values and interests. This

factor is used to pose the following hypothesis:

H3: Social benefits positively impacts user

intentions to engage in Twitter messages about

companies.

2.2.4 Extraversion/Positive Self

Enhancement

Customers are motivated by their desire to receive

gratification from others. They share their

experience and spread eWOM while enhancing the

perception of source expertise (Jalilvand et al.,

2010). User articulation in eWOM serves the

purpose of feeding self-related needs that vary from

self-image and prestigious intellectual perception, to

social status. In the Twitter environment, customer

eWOM can be used to reflect a level of knowledge

about given products or services. It creates the

impression of one being an insider regarding the

company’s internal affairs, while projecting oneself

as mass influencer and opinion expert. The factor

helps to form the following hypothesis:

H4: Extraversion/positive self enhancement

positively impacts user intentions to engage in

Twitter messages about companies.

2.2.5 Helping the Company

The customer desire to give the company something

in return for gained satisfaction and appreciation

drives this factor. This motive is the result of the

same psychological background as altruism, as the

customer feels that companies with good

performance deserve to be rewarded with positive

eWOM. This is also a form of commitment to the

company and to the novelty of its product (Jalilvand,

et al. 2010). The factor is hypothesized to impact

user intentions in Twitter as follows:

H5: Helping the company positively impacts user

intentions to engage in Twitter messages about

companies.

2.2.6 Collective Power Over Companies

Unsatisfied customers engage in collective

complaints against companies using the power of

their virtual social influence. The accumulated

number of customer messages resulting from the

consumption of a company’s products and services

acts as a powerful tool against the company’s image

and reputation (Hennig-Thurau et al., 2004). Twitter

users are in their millions; this is combined with the

long-term availability of memory arising from stored

messages, to form a very real collective power of

customers which threatens the company with public

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criticism. The factor is reflected in the following

hypothesis:

H6: Collective power over companies positively

impacts user intentions to engage in Twitter

messages about companies.

2.2.7 Influence of Others

In the Twitter sphere, customers are prone to be

influenced by the behaviours and practices of other

users. They imitate what has worked for others and

copy them, especially if the approaches used by

others have been proven fruitful (Goldsmith and

Horowitz, 2006). It can be argued that peer influence

plays a significant role since users follow those who

are like-minded. Hence, the adoption level of similar

practices is justified by a higher number of positive

eWOM (Doh and Huang, 2010). The factor is used

to propose the following hypothesis:

H7: Influence of others positively impacts user

intentions to engage in Twitter messages about

companies.

2.2.8 Advice Seeking to Get Information

This factor serves both sides of seeking opinion;

prior- and post-purchase or consumption of products

or services. Customers provide their feedback based

on their consumption and elaborate on their

experience to provide insights to others (Goldsmith

and Horowitz, 2006). It is a way to: (a) offer or

solicit objective opinions regarding how to solve a

problem, (b) enlighten others with required skills for

consumption and (c) obtain more specific and useful

feedback. The next hypothesis addresses this factor:

H8: Advice seeking positively impacts user

intentions to engage in Twitter messages about

companies.

3 METHODOLOGY

Adhering to the aims of this study, we have adopted

Twitter as a research tool for both sampling and

collecting data. Using it as both the environment

containing the eWOM phenomena under

investigation and as the tool deployed to apply the

theory has proved to be quite beneficial.

The questionnaire is the primary data collection

instrument in this study. It has been designed to

assess the motivations behind customer intentions

into composing eWOM messages in Twitter about

company products, services or performance. The

aim of the questionnaire is to determine the main

factors influencing consumers in mentioning

companies on Twitter. All factors have been adapted

from the reviewed literature as shown in Figure 1.

The questionnaire was divided into two parts

consisting of 39 statements. It made use of a Likert

five-point scale, ranging from 1=Strongly Disagree

to 5=Strongly Agree. The first part includes five

statements covering general information i.e. age,

gender, education and nationality (GNR1-4) and one

elimination statement for validity purpose (GNR5).

The second part was divided into nine sections and

designed to investigate consumer behaviours in

engaging in eWOM. A first draft of the

questionnaire written in English and Arabic and

presented in electronic format was piloted before

implementation.

The first section of second part was dedicated for

the dependent variable of the study. It aimed at

assessing users intentions when they express

themselves in Twitter, either positively or

negatively, reporting on their experiences with a

particular company. Statements were built on the

eWOM definition by Hennig-Thurau et al., (2004)

(see Table 1).

The sampling was deliberately restricted to

Twitter users. The implications of using Twitter as a

research tool were also put into practice in adopting

it as the vehicle for creating the viral effect of

eWOM in the form of an electronic survey link. The

electronic format questionnaire link was distributed

strictly online via Twitter users who are the market

mavens as characterized by Kaplan and Haenlein

(2011a). These are the opinion leaders in the Kuwait

Twitter-sphere who have acted as our distribution

nodes for our survey in the Twitter network.

The sample of opinion leaders was selected from

the pool of 1,000 top ranked Twitter users in

Kuwait, in terms of number of followers with a

minimum of 1,000 followers covering both genders

and different age groups. Among the top tanked,

users who are actively involved in the Twitter-

sphere with emphasis on bloggers with Twitter

accounts, were identified and selected as market

mavens. Political figures, corporate accounts and

official personnel accounts have been omitted from

the sample.

The data collection process started by contacting

thirty influential users in the Kuwait Twitter sphere,

from which twenty three agreed to spread the

questionnaire link by re-tweeting it to their follower

networks. The retweets were intended to cause a

ripple effect aimed at reaching the maximum

possible number of users.

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The data analysis was conducted with tools for

statistical analysis such as factor analysis, regression

analysis, and variance extract. The strength between

independent variables and the dependent variable

was examined in order to identify the most effective

and significant factors contributing to eWOM

intentions.

Table 1: Statement of eWOM inentions.

Indicator: It is likely that I

... write a tweet regarding my positive experience with

my bank/telecom company

... write a tweet regarding my negative experience with

my bank/telecom company

... forward a tweet (Re-tweet) about other people

experiences with my bank/telecom company

... participate in Twitter conversation regarding my

bank/telecom performance

4 RESULTS

We have used free Twitter applications to monitor

the questionnaire link distribution and responses

network. The electronic link was re-tweeted 351

times within 72 hours, at which point it reached the

cut-off date. The sample caused the desired ripple

effect for the questionnaire link by being the source

of retweets.

The link was visited 5,011 times from which

2,755 were abandoned visits with respondents

leaving the first page without filling in the

questionnaire. Out of the remaining 2,256 responses,

917 were only partially filled which can be

explained by the fact that most participants accessed

the link through smart phones and any mobile

network malfunction would have reset the survey.

We ended up with 1,339 usable responses.

In order to exclude random and non-pertinent

responses, these were filtered by asking respondents

whether their responses reflect their opinions about

telecommunication companies or banks inside

Kuwait or outside it. The filter eliminated 147

responses of users who based their opinions on firms

outside Kuwait which left us with 1,192 full

responses to conduct the remaining analysis.

In order to investigate the significance of the

research model, we have applied standard linear

regression to the model’s variables as shown in

Figure 1. The model’s significance level (p-value) of

.000

shows that there is high level of significance

between all factors in the model. The Pearson’s

Correlation Coefficient gives the indication that

variance among all eight factors leading to the

dependent variable is 27.8%.

The regression analysis results were used to

investigate the validity of the hypotheses and to test

the level of significance with which each factor

influences and predicts the dependent variable. As

shown in Figure 1, two variables have fallen short in

terms of statistic significance with the coefficient p-

value > 0.05; namely Influence of Others (0.569)

and Advice Seeking (0.468).

Based on the regression analysis, we conclude

that hypotheses H7 and H8 cannot be accepted,

while all remaining hypotheses (H1, H2, H4, H5 and

H6) are accepted. We recall the fact that hypothesis

H3 related to Social Benefits has been omitted from

proposed design, with the corresponding statements

being grouped with other factors.

5 CONCLUSIONS

Our results concur with the findings by Hennig-

Thurau et al., (2004) and show that the factors

adapted from the model by Goldsmith & Horowitz

(2006), which fall under Consumption Utility do not

impact customer intentions and are not applicable in

the Twitter sphere, at least not in Kuwait. Twitter is

a nurturing medium for eWOM when it comes to

composing and passing messages with absence of

advice seeking motive. Our findings also support

Kwon and Sung’s (2011) when these authors state

that users are prone to tweet less frequently when it

comes to company products and services, as

opposed to mentioning company names in tweets.

We attribute this to customers being driven to tweet

with their opinions and sentiments about events and

incidents that happen in their environment. Thus has

more of broadcasting nature than requesting

assistance and information.

The study aims to explore and identify what

triggers consumers into contributing to Twitter’s

eWOM by specifically mentioning companies in

their tweets. Thus the managerial impactions of our

study are quite pertinent.

Individual and personalized approaches with the

help of friendly, informal language and tone will

absorb frustration and anger from customers and let

them feel they are special, and that the company is

there to listen and to give them attention. It will

satisfy the positive self-enhancement motivation of

customers who will receive replies that solve their

problems, while feeding their ego and image. This

illustrates precisely Keitzmann et al., (2011)

contention that conversations are driven by opinion

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Figure 1: The research model showing standard linear regression results.

leaders’ self esteem. Such an approach will

humanize company brand and boost customer

perception about the human characteristics of

Twitter’s electronic format (Kwon and Sung, 2011).

Companies need to pay attention to

onlinegrouping against them and try to absorb it

with proper responsive strategies. Online consumer

activists and groups can cause catastrophic uproars

which should be handled with care at the early

stages. At the end of the day, what has shaken

governments can also shake the ground beneath

companies, especially where those grounds have

proven to be slippery.

Embracing negative eWOM in Twitter and

offering consumers sincere ears are the online shock

absorbers. In the age of Twitter, social media tools

should be integrated into the company’s strategy in

order to monitor the effects of the company’s

decisions and to encourage users to express

themselves freely and not in a controlling way.

Once the company understands customer

motivations and acts accordingly this will pave the

way for engaging in conversations where customer

sentiments thrive. The conversation feedback is what

makes the difference, be it recommendations,

suggestions or even questions, as suggested by

Jalilvand, et al. (2010). This interactive

communication pays dividends when it comes to

market research (Kaplan and Haenlein, 2011b).

Customers acting as co-producers with their unique

inputs create substantial amounts of data, which can

be pulled up at any given and used as market

intelligence.

Future research should revolve around fine

tuning factors of extraversion and positive self-

enhancement motivations in order to explore how to

deal with them and their noise should bring valuable

insights to academic level and business level

likewise. The replication of the study in other

regions with high rates of Internet penetrations

should provide insights into key cultural differences.

Finally, an attempt to investigate the motives to

retweet tweets with company names should capture

the majority of lurking Twitter users who would

rather keep to low presence and contribute with

retweets rather than forming their own tweets with

their names.

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