Copyright 2017©, Mansour T. Alansari

BEYOND 140 CHARACTERS: MARKETING EFFECTIVENESS OF HOTEL

TWITTER ACCOUNTS IN SAUDI ARABIA

by

MANSOUR T. ALANSARI, B.S., MBA

A DISSERTATION

IN

HOSPITALITY ADMINISTRATION

Submitted to Graduate Faculty

of Texas Tech University in

Partial Fulfillment of

the Requirements for

the Degree of

DOCTOR OF PHILOSOPHY

Approved

Dr. Natalia Velikova

Chairperson of the Committee

Dr. Shane Blum

Dr. Tim Dodd

Dr. Tun-Min Jai

Accepted

Mark Sheridan

Dean of the Graduate School

May, 2017

Copyright 2017©, Mansour T. Alansari

Texas Tech University, Mansour Alansari, May 2017

ii

ACKNOWLEGDEMENTS

In the name of Allah, the Most Gracious, the Most Merciful.

First and foremost, I would like to thank my creator, Almighty God, for giving me

a still-functioning body and mind so that I may live life and learn. Without His grace, this

doctoral dissertation could not have become a reality.

I am truly grateful to my dissertation committee chairperson, Dr. Natalia

Velikova, who tolerantly and insightfully guided and supported me these past four years

as I sought the right way to conduct this research. Professor Natalia was the most

important pillar and the cornerstone of this project. She was the one who believed in me

and in my topic. Her suggestions helped me to expand my horizons. Without her

inspiration and encouragement, I would not have reached this level of academic

advancement and would not have completed this study. Thank you very much for all of

the valuable time you spent challenging me to effectively complete this work.

My sincere appreciation and endless thanks are also extended to the other

members of my dissertation committee. I offer genuine thanks to Dr. Shane Blum, whose

editorial expertise and valuable recommendations enhanced the overall quality of this

dissertation. As the chairperson of the department, he is always willing to help doctoral

students; his kindness in helping Saudi students is especially appreciated. I also offer

sincere thanks to Dr. Tim Dodd for providing numerous helpful suggestions, insightful

feedback, great advice, and support. I also want to take this opportunity to sincerely

thank Dr. Catherine Jai for all of the help and support she offered me during the statistical

analysis stage and for all of her generous feedback and advice.


iii

I would like to thank all of my friends and all of the faculty and staff of Texas

Tech University’s Department of Hospitality and Retailing. I offer special thanks to my

first mentor, Dr. Ben Goh; to Drs. Lynn Huffman, Jessica Yuan, Barry McCool, and

Betty Stout; and to all of the other exceptional professors who helped me grow my

knowledge and taught me the importance of a good education. I would like to express my

appreciation to all of my dedicated friends for your support throughout the years I worked

on my doctoral degree.

I have enormous appreciation for the Kingdom of Saudi Arabia, which provided

help and financial support to me during my PhD studies. I offer thanks for the great and

unique opportunities that the most beloved country of Saudi Arabia offers to all Saudi

students. I also send my sincere thanks to the Saudi Arabian Cultural Mission (SACM)

and to the Saudi Students Association of the United States. These organizations helped

me tremendously by distributing the link to my online survey to participants.

I am most appreciative of my lovely parents, Talal Alansari and Amal Badawi,

because of their unconditional love and support, words of wisdom, and absolute

confidence in me. I also offer a heartfelt thanks to my brother and sisters for always

standing by my side, no matter where I was. I thank all of my uncles and aunts for their

friendly advice and inspiration.

Finally, but most importantly, I give my deepest thanks to my precious wife, the

love of my life, my best friend, and the mother of my children – Dania Alansari. Her

support, encouragement, quiet patience, and unwavering love were undeniably the

bedrock upon which the last twelve years of my life were built. I offer a wholehearted


iv

thanks to my children – Taleen, Abdulkarim, Talal, and Diyala – who provided necessary

breaks from my studies and were my motivation to expediently finish my degree. Love!


v

TABLE OF CONTENTS

ACKNOWLEGDEMENTS ........................................................................................................................ ii

ABSTRACT ............................................................................................................................................... vii

LIST OF ABBREVATIONS ...................................................................................................................... ix

LIST OF TABLES ....................................................................................................................................... x

LIST OF FIGURES .................................................................................................................................... xi

CHAPTER I ................................................................................................................................................. 1

INTRODUCTION ....................................................................................................................................... 1 1.1 Background ......................................................................................................................................... 1 1.2 Problem Statement .............................................................................................................................. 4 1.3 Purpose of the Study ............................................................................................................................ 6 1.4 Significance of the Study ..................................................................................................................... 7

CHAPTER II ................................................................................................................................................ 9

LITERATURE REVIEW ........................................................................................................................... 9 2.1 Theoretical Perspective ....................................................................................................................... 9

2.1.1 Attitude-toward-the-ad (Aad) model ........................................................................................ 9 2.1.2 Attitudes-toward-the-website (Aws) model ............................................................................ 12 2.1.3 Social Media Marketing Effectiveness Model ...................................................................... 13

2.2 Twitter Marketing Effectiveness Components Model........................................................................ 19 2.2.1 Electronic Word of Mouth (eWOM) ..................................................................................... 19 2.2.2 Intentions to Book Hotels ...................................................................................................... 20 2.2.3 Photo Presentations on Hotel Social Media .......................................................................... 21 2.2.4 Hyperlink Presentations on Hotel Social Media.................................................................... 23 2.2.5 Product/Service Presentations on Hotel Social Media .......................................................... 24 2.2.6 Consumer Engagement on Hotel Social Media ..................................................................... 25

2.3 Hypothesized Model .......................................................................................................................... 27 2.4 Relevant Consumer Characteristics .................................................................................................. 28

2.4.1 Attitudes toward Social Media .............................................................................................. 29 2.4.2 Consumer Behavior toward Social Media ............................................................................. 33 2.4.3 Consumer Involvement with Social Media ........................................................................... 34

CHAPTER III ............................................................................................................................................ 36

METHODOLOGY .................................................................................................................................... 36 3.1 Research Design ................................................................................................................................ 36 3.2 Pre-test .............................................................................................................................................. 36

3.2.1 Sampling and Data Collection Procedure for the Pre-test ..................................................... 37 3.2.2 Pre-Test Data Analysis .......................................................................................................... 37 3.2.3 Results of the Pre-test ............................................................................................................ 39

3.3 Main Study......................................................................................................................................... 40 3.3.1 Sampling and Data Collection ............................................................................................... 41 3.3.2 Experiment Design ................................................................................................................ 42 3.3.3 Measures ................................................................................................................................ 43

3.4 Pilot Study ......................................................................................................................................... 44 3.5 Data Analysis Procedure .................................................................................................................. 45


vi

CHAPTER IV ............................................................................................................................................ 49

ANALYSIS AND FINDINGS ................................................................................................................... 49 4.1 Data Screening .................................................................................................................................. 49 4.2 Characteristics of Respondents ......................................................................................................... 50 4.3 Respondents’ Behavior toward Using Twitter .................................................................................. 51 4.4 Characteristics of Social Media ........................................................................................................ 52

4.4.1 Consumer Behavior toward Social Media ............................................................................. 52 4.4.2 Consumer Attitudes toward Social Media ............................................................................. 57 4.4.3 Consumer Involvement with Social Media ........................................................................... 59

4.5 Measurement Validity and Reliability ............................................................................................... 60 4.6 Preliminary Analysis ......................................................................................................................... 63 4.7 Measurement Model .......................................................................................................................... 67 4.8 Structural Model ............................................................................................................................... 70

CHAPTER V .............................................................................................................................................. 73

DISCUSSION AND IMPLICATIONS .................................................................................................... 73 5.1 Hypotheses Discussion ...................................................................................................................... 73 5.2 Theoretical Framework Support ....................................................................................................... 81 5.3 Relevant Consumer Characteristics Discussion ............................................................................... 84 5.4 Conclusion, Limitations and Suggestions for Future Studies ........................................................... 86

BIBLIOGRAPHY ...................................................................................................................................... 89

APPENDICES .......................................................................................................................................... 104

Appendix A: Diagram of the Research Design........................................................................... 104

Appendix B: Content Analysis - Pre-test .................................................................................... 105

Appendix C: Human Research Protection Program Approval Letter ......................................... 108

Appendix D: Simulated Twitter Account ................................................................................... 109

Appendix E: Online Survey ........................................................................................................ 118


vii

ABSTRACT

Over the past decade, the rapid advancement of social media (SM) and concurrent

economic conditions created a remarkable proliferation of SM users. SM not only

revolutionized individuals’ lifestyles but also transformed how businesses communicate

and interact with their consumers. Twitter is one of the most popular SM platforms.

Twitter was purposely selected for this study because it is one of the fastest growing SM

platforms (The Statistics Portal, 2015). Moreover, marketers consider Twitter the

second-most-commonly used SM platform, after Facebook (Stelzner, 2014). While the

effects of Facebook on various aspects of business have been studied extensively in the

academic and trade literature, Twitter gets significantly less attention from the academy

and the hospitality industry.

The purpose of this study was to investigate the effect of marketing and advertising

via Twitter on hotels’ marketing effectiveness, which, in turn, may lead to enhanced hotel

performance. The study focused on the market of Saudi Arabia. Specifically, this study

classified by format (e.g. video, photo, and text) and by content (e.g. brand, product, and

engagement) the tweets posted by Saudi hotels. The study used the work of Leung (2012)

as an investigative framework to examine the marketing effectiveness of hotel Twitter

accounts in Saudi Arabia.

The study employed content analysis as a pre-test and a quantitative research design

in the formation of an online survey with an embedded experiment as the main study.

Content analysis was applied in exploring the format and the content of hotels’ tweets to

identify the most popular methods used by Saudi hotels to deliver Twitter messages.


viii

Quantitative data were collected via an online survey that investigated Saudi consumers’

perspectives toward effective tweets using a simulated hotel Twitter account.

The findings suggest that consumers’ attitudes toward hotels’ tweets have positive

effects on their attitudes toward the Twitter accounts, which, in turn, positively affect

their attitudes toward the hotels’ brands and, ultimately, increase positive electronic word

of mouth and consumers’ intent to book. Regarding the tweets’ format and content,

however, the results were contrary to predictions. This study found that when compared

to a plain-text tweet, adding a photo in a tweet did not have a significant effect on

consumers’ attitudes toward that tweet. Additionally, when compared to a tweet that

included brand information, adding information about products/services in a tweet did not

have a significant effect on consumers’ attitudes toward that tweet. Adding a hyperlink

and providing space for customer engagement negatively affected attitudes toward hotels’

tweets.

Overall, it can be concluded that hoteliers need to find ways to use SM to enhance

their guests’ perceptions of their brands. One of the most effective strategies to meet and

exceed guests’ expectations is to provide high-quality customer service and advanced

interactive technology. This study shows that when consumers have positive attitudes

toward hotels’ tweets and Twitter accounts, it ultimately translates into positive attitudes

toward their brands, intentions to book rooms, and the spread of positive electronic word

of mouth.


ix

LIST OF ABBREVATIONS

SM Social Media

CEO Chief Executive Officer

WTTC World Travel and Tourism Council

GCC Gulf Cooperation Council (Kingdom of Saudi Arabia, Kingdom of Bahrain,

Kuwait, Sultanate of Oman, Qatar, and United Arab Emirates.

eWOM Electronic Word-of-Mouth

IRB Texas Tech University Institutional Review Board

ATT Attitude-toward-the-tweet

ATHTA Attitude-toward-hotel-Twitter-account

ATHB Attitude-toward-the-hotel-brand

IHB Intention-of-hotel-booking

IEWOM intention-of-electronic-word-of-mouth

SPSS Statistical Package for the Social Sciences, an IBM software

EFA Exploratory Factor Analysis

SEM Structural Equation Model

CFA Confirmatory Factor Analysis (CFA)

CFI Comparative Fit Index

SRMR Standardized Root Mean Square Residual

NINFI Non-Normed Fit Index

RMSEA Root Mean Square Error of Approximation


x

LIST OF TABLES

Table 1 The Ten Most Popular Websites in 2004 and 2017 ........................................................... 3 Table 2 Categorization of Tweet Content ..................................................................................... 38 Table 3 Tweet Formats Most Frequently Used by Saudi Hotel Tweets ....................................... 39 Table 4 Tweet Content Most Frequently Used by Saudi Hotels .................................................. 40 Table 5 Variables of the Main Study ............................................................................................ 43 Table 6 Frequencies and Percentage of Screening ....................................................................... 50 Table 7 Demographic Characteristics of the Sample .................................................................... 50 Table 8 Respondents' Behavior toward Twitter Usage ................................................................. 51 Table 9 Social Media Usage Behavior.......................................................................................... 54 Table 10 Frequency of Social Media Usage ................................................................................. 56 Table 11 Attitudes toward Social Media ...................................................................................... 58 Table 12 Attitudes toward Social Media Advertisements ............................................................ 59 Table 13 Involvement with Social Media ..................................................................................... 60 Table 14 Scale Items and Factor Analysis Results for Model Constructs .................................... 62 Table 15 Mean, Standard Deviation, Skewness, and Kurtosis of Indicators. ............................... 64 Table 16 Means, Standard Deviations, and Construct Inter-Correlations .................................... 65 Table 17 Means, Standard Deviations and Inter-correlations among Indicators .......................... 66 Table 18 Maximum Likelihood Parameter Estimates for Measurement Model........................... 69 Table 19 Unstandardized Coefficients, Estimated Standard Errors, and Standardized

Coefficients of Direct Effects ............................................................................................... 72


xi

LIST OF FIGURES

Figure 1. The mediating effect of advertising content on cognitive variables ............................. 11 Figure 2. Model of marketing effectiveness of hotel Facebook messages. .................................. 14 Figure 3. Proposed conceptualized model of Twitter marketing effectiveness. ........................... 18 Figure 4. A 3 x 3 design (tweet format and tweet content) of a simulated hotel Twitter

account .................................................................................................................................. 42 Figure 5. Variables coding for data analysis. ............................................................................... 48 Figure 6. Four-factor measurement model of the present study. .................................................. 68 Figure 7. Structural model and hypotheses testing results. .......................................................... 72


1

CHAPTER I

INTRODUCTION

1.1 Background

The dramatic growth of the Internet has been driven by the emergence of two

important phenomena: social media platforms and online search engines (Xiang &

Gretzel, 2010). In the business context, Social Media (SM) is the new communication

channel between service suppliers and customers because it allows them to interact

directly with each other. SM is now one of the most successful advertising and marketing

tools. It is also known as consumer-generated media and as electronic word of mouth

(eWOM). In the business context, this technology allows users to create and exchange

content that informs other users about goods, services, and businesses and provides other

information (Blackshaw & Nazzaro, 2004; Elefant, 2011; Kaplan & Haenlein, 2010).

Facebook, Twitter, YouTube, LinkedIn, Flickr, TripAdvisor, Yelp, Instagram,

Foursquare, and Delicious are all examples of SM, and the list of emerging SM platforms

is still growing fast. Consumer-generated media is unique because it offers customers the

ability to contribute their opinions and feedback, share their experiences, and rate the

products and services provided to them.

The rapid advance of the Internet and concurrent economic conditions have

caused a great proliferation of SM users around the world and in developing countries

especially (Violino, 2011). The CEO of Facebook, which started in 2004, recently made

an announcement that “[they] just passed an important milestone. For the first time ever,

one billion people used Facebook in a single day. On Monday [August 24, 2015], [one in

seven] people on Earth used Facebook to connect with their friends and family”


2

(Zuckerberg, 2015). Approximately 82.4% of Facebook’s daily active users are outside of

North America (Facebook, 2015).

Twitter was officially launched in 2006. Currently, Twitter has more than 250

million monthly active users, and 80% of them log into their Twitter accounts via mobile

devices. Almost 77% of Twitter users are outside of the United States (Twitter, 2015).

Erbar (2014) and Firstpost (2013) reported that the most active Twitter users in the world

(relative to the total number of Internet users) are Saudi Arabians. In the fourth quarter

of 2014, Twitter was one of the top ten most popular SM platforms in Saudi Arabia, with

a 19% penetration rate (The Statistics Portal, 2015). The Economist (2014) stated that

most SM users in Saudi Arabia are between 26 and 34 years old. Given that Saudi Arabia

has the world’s highest penetration rate of Twitter users (GMI, 2016; The Statistics

Portal, 2015), the country represents an enormous market in which to examine hotels’

marketing strategies in their Twitter posts, which was the focus of this study.

SM channels account for the most visited websites (Pratt, 2014) and the top share

(about 72%) of total Internet usage (Bullas, 2014; Nielsen, 2013). Reflecting the

evolution of SM, most websites have transformed from Web 1.0 to Web 2.0 (Kambil,

2008). Table 1 compares the ten most popular websites in 2004 and 2015. This

comparison reveals that the Internet has shifted away from one-way (“unidirectional”)

communication, i.e. Web 1.0 (e.g. About, Ask Jeeves, and AOL), to two-way

(“multidirectional”) communication or interaction, i.e. Web 2.0 or SM (e.g. YouTube,

Facebook, Wikipedia, and Twitter). In addition to SM channels, online search engines

such as Google, Yahoo!, and Bing have integrated some functions that used to be

exclusive to SM platforms, including reviewing services and products, sharing photos


3

and videos, hosting chats, and other interactive functions. This type of media is gaining

attention as one of the most important technological and marketing tools.

Table 1 The Ten Most Popular Websites in 2004 and 2017

Rank 2004 2017

1 Yahoo! Facebook

2 MSN (Microsoft) YouTube

3 AOL Twitter

4 Google LinkedIn

5 eBay Pinterest

6 Ask Jeeves Google Plus +

7 Terra Lycos Tumblr

8 About Instagram

9 Amazon Reddit

10 Monster VK

Note. Adapted from Quinby (2010); ebizMBA (2017)

Several studies have shown that people of different demographics use SM to

communicate with each other (Duggan, Ellison, Lampe, Lenhart, & Madden, 2015;

Guimarães, 2015; Morejon, 2011; PewResearchCenter, 2014). Pew Research Center

(2014) reported that the number of young adult users of SM sites jumped from 9% in

2005 to 90% in 2013. Considering the explosive impact of SM on Internet users and the

way they interact with each other and with businesses, it is important to understand the

opportunity that SM offers the businesses world. Specifically, this study examined the

use of SM by the hotel industry.

The hotel industry is one of the biggest and fastest-growing industries in the

world and plays a significant role in fostering the growth of the global economy. The

number of hotels in Saudi Arabia is dramatically increasing due to the rise of business

and trade and due to religious, heritage, and other types of tourism. Religious tourism is

a major source of revenue for the country. Saudi Arabia has the two holy mosques of

Makkah and Madinah within its borders. Makkah offers the most hotel lodging in the


4

Kingdom: 61,319 hotel rooms. Madinah offers 7,890 hotel rooms, while Al Khobar offers

12,186 rooms, Jeddah offers 11,500 rooms, and Riyadh offers 10,514 rooms (The Hotel

Summit in Saudi Arabia, 2013). According to the World Travel and Tourism Council

(2015), the number of foreign tourists to Saudi Arabia is increasing drastically. It is

expected that the county will attract more than 22 million international tourists in 2025.

Riyadh and Jeddah are two major cities in Saudi Arabia. Riyadh is the capital city and

hosts most of the mega-events, and Jeddah is a commercial city that has a very busy

seaport and an airport. Due to their significant amounts of business tourists, these two

cities are more profitable than the other leading Gulf Cooperation Council (GCC) cities

(The Hotel Summit in Saudi Arabia, 2013). Therefore, the tourism and business

environment in Saudi Arabia demands that hotels be designed and prepared to serve a

large number of visitors every year.

1.2 Problem Statement

SM allows hotels to communicate with their guests in real time. Several studies

have suggested that SM has not only replaced traditional forms of advertising and one-

way marketing but has also improved consumer engagement, brand awareness, and

customer service (Aluri, Slevitch, & Larzelere, 2015; Barreda, Bilgihan, Nusair &

Okumus, 2015; Ngai, Tao, & Moon, 2015).

One of the most innovative marketing and advertising instruments embraced by

hotels in Saudi Arabia is the involvement of consumers via SM. Increasing numbers of

hotel managers in Saudi Arabia use various marketing and promotion tools to entice

customers to their lodges. For instance, hotels such as Hilton and Marriott and many local


5

hotels are heeding advice to increase and improve their SM presences and practices in

Saudi Arabia, which would help grow their businesses and attract more hotel guests.

Despite the broad integration of SM by hotels in Saudi Arabia, however,

examination into the effectiveness of SM practices is still lacking. Measuring the

effectiveness of SM marketing by businesses is a significant challenge (Palmer &

Koenig-Lewis, 2009). In fact, Mickey (2011) reports that 61% of marketing executives

claim that one of the top obstacles to be measured by businesses is the effectiveness of

SM.

The academic approach is similar. While it is assumed that SM is a useful tool

for improving marketing and advertising efforts in the hospitality industry, only a handful

of academic studies have empirically supported this claim, and only a few have discussed

this notion from a quantitative point of view (Aluri et al., 2015; Kim, Lim, & Brymer

2015; Leung, 2012; Leung, Bai, & Stahura, 2015). Several studies have shown that the

theories and approaches of traditional marketing cannot be used with SM marketing (Gil-

Or, 2010; Tariq & Wahid, 2011). Theories, research, and studies into the effectiveness of

SM marketing by the hospitality industry are particularly scarce. This lack of research

and knowledge about the effectiveness of SM marketing creates a need for contributions

to theoretical and practical knowledge about SM marketing, which, in turn, will promote

understanding of consumers’ perceptions and attitudes toward SM marketing. SM is a

revolutionary tool that needs additional research into theoretical approaches and

managerial applications. Thus, the current study sheds light on theoretical models of SM

marketing and contributes to existing knowledge about marketing and social media.


6

Furthermore, despite the growing adoption of SM in Saudi Arabia, to the

researcher’s knowledge, the marketing effectiveness of hotel SM sites in Saudi Arabia

has not been previously studied. Therefore, this exploratory study examined Twitter

usage in the hotel industry in Saudi Arabia from both guest and SM content perspectives.

This study can help hotels in Saudi Arabia improve their SM marketing practices.

Additionally, this research and its implications may increase the importance of SM as a

marketing tool for businesses, particularly the hotel industry.

1.3 Purpose of the Study

The purpose of this study was to investigate the influence that Twitter marketing

and advertising efforts have in improving hotels’ marketing effectiveness, which, in turn,

may enhance hotel performance. This study focused on the market of Saudi Arabia.

Twitter was purposely selected because it is one of the most used and one of the

fastest growing SM platforms in Saudi Arabia (Erbar, 2014; Firstpost, 2013; The

Statistics Portal, 2015). Moreover, Stelzner (2014) stated that marketers generally

identify Twitter as the second-most-commonly used SM platform, after Facebook. While

the effects that Facebook has on various aspects of business have been studied

extensively in the academic and professional trade literature, Twitter gets significantly

less attention from the academy and from the hospitality industry. Measuring the

marketing effectiveness of Twitter will help answer the following important questions:

how marketing through Twitter can entice guests to join a hotel’s Twitter account, and

whether positive attitudes toward a hotel’s Twitter account result in guests’ intentions to

book that hotel and strengthen guests’ intentions to spread electronic word of mouth

(eWOM).


7

On the theoretical level, this study used the attitudes-toward-the-advertisement

model for traditional media and its extensions, which incorporate websites and social

media as new advertising and marketing platforms. This study empirically tested the

relationships suggested by the previous models in the Twitter context and aimed to offer

a new step forward in theoretical knowledge about social media’s marketing

effectiveness.

1.4 Significance of the Study

This research has both theoretical and practical significance. First, this research

was one of the first studies to contribute to the existing literature on SM for hotel

marketing. The marketing effectiveness of the hotel-Twitter-account model proposed in

this study provides essential insight into marketing through social media in the hospitality

industry context. From the theoretical perspective, this study offers an important

conceptual model for understanding consumers’ hotel-booking behavior and intentions to

spread positive eWOM. In particular, this study tested the application of the traditional

attitudes-toward-the-advertisement theory to social media. Furthermore, this study

extended this theoretical approach to examining the advertisement itself by incorporating

various components of tweets (a new form of advertisement) into the model.

To the researcher’s knowledge, only one previous study developed a classification

strategy for SM messages: Leung (2012). Leung’s study was limited to investigating the

marketing effectiveness of one SM platform: Facebook. Different SM platforms have

different characteristics, however, and different marketing strategies are needed for each.

Twitter, for instance, provides businesses with diverse marketing tools and focuses more

on mobile marketing. Additionally, the rapid advancement of technologies and SM


8

platforms creates considerable room for further investigation and the replication of

previous studies. Leung did not study the influence of consumer characteristics, which

we believe can provide significant insights into attitudes toward SM hotel marketing.

Additionally, the sample that Leung considered was Facebook users in the United States,

who may have different characteristics than Twitter users in Saudi Arabia, who were

targeted in this study.

This study investigated the effectiveness of hotel Twitter marketing by examining

the content and the format of posted hotel tweets. Specifically, this study classified tweets

that Saudi hotels posted by tweet format (e.g. video, photo, and text) and by tweet content

(e.g. brand, product, and consumer engagement). This study examined the perceived

marketing effectiveness of Twitter posts based on these different tweet formats and

different tweet contents in terms of a variety of consumer measures. These consumer

measures included attitude-toward-hotel-twitter-account, attitude-toward-the-tweet,

attitude-toward-the-hotel-brand, the intention to book a hotel room, and the intention to

engage in electronic word of mouth. The practical significance of this study is that it

considered the use of Twitter by the hotel industry. As mentioned previously, the study

focused on Saudi Arabia, which gets little attention in the academic and business

literature. This study will help hotel managers in Saudi Arabia to understand how to use

Twitter to maximize their marketing effectiveness and to understand their consumers

better, which will help them build tailored marketing strategies that target hotel

customers more effectively. This study also offers a new conceptual model that is based

on a synthesis of previous research, adds to the understanding of social media

advertising, and can guide future research.


9

CHAPTER II

LITERATURE REVIEW

This chapter presents a review of the literature and an acknowledgment of the

prior marketing, advertising, hospitality, tourism, and technology research that

contributes to the subsequent analysis of hotel social media (SM) marketing

effectiveness. Consideration of past studies was undertaken to evaluate the empirical

support for the theoretical background of this research. Furthermore, relevant studies

regarding consumer characteristics such as attitudes toward SM, behavior toward SM,

and involvement with SM are investigated.

2.1 Theoretical Perspective

This section introduces the theoretical framework that helps to identify

characteristics and attributes that may impact consumers’ attitudes and behaviors, which

in turn, may influence their hotel-booking intentions and their eWOM. This study uses

the following fundamental theoretical models: Attitude-toward-the-ad (Aad) (Mitchell &

Olson, 1981), Attitude-toward-the-website (Aws) (Stevenson, Bruner II, and Kumar,

2000), and the Facebook Marketing Effectiveness Model (Leung, 2012). A combination

of these models guided the development of the conceptual model of the marketing

effectiveness of hotel Twitter accounts, which was the focus of this study.

2.1.1 Attitude-toward-the-ad (Aad) model

Mitchell and Olson (1981) were among the first scholars to introduce the Aad

concept. Their approach focused on the affective reactions of individuals toward specific

advertisements after ad exposure. This approach ignores strictly cognitive reactions – for


10

example, ad cognitions and brand cognitions (MacKenzie, Lutz, & Belch, 1986; Mitchell

& Olson, 1981; Shimp, 1981).

The Aad model suggests that when consumers are exposed to a possibly

persuasive advertisement, they develop attitudes toward the ad, such as brand attitudes

and purchase intentions, that then influence the advertisement’s effectiveness (Lutz,

Mackenzie, & Belch, 1983). The Aad model has been tested in various research contexts.

Over the time, several major hypotheses were generated to test the relationships between

Aad , attitudes toward brands, and purchase intent. This study focused on Mitchell’s and

Olson’s (1981) original approach to the mediating effects of advertising content on

cognitive variables.

Specifically, Mitchell and Olson (1981) suggested a direct, one-way causal flow

from Aad to attitude toward the brand (Ab). Mitchell and Olson (1981) used Fishbein’s

attitude theory to investigate whether advertising’s effects on brand attitudes have other

mediators besides product attribute beliefs. The researchers found that by itself, Aad can

accurately reflect consumers’ overall evaluation of an advertising stimulus. Thus, Aad

should be treated as a separate measure from Ab. Furthermore, the mediating effect of

Aad can be interpreted as capturing the conditioning effect of pairing an unknown brand

name (the unconditioned stimulus) with a highly valenced visual stimulus (the

conditioned stimulus). On this interpretation, the evaluation of the advertisement in

general (Aad) or of a prominent part of the advertisement – for example, a picture –

becomes associated with the brand name. The authors suggested that this direct influence

is independent of a message's effect on the formation of beliefs about a product’s

attributes. In other words, consumers’ brand attitudes are largely determined by the


11

effects of the advertisements themselves, rather than solely dependent upon consumers’

beliefs about product attributes.

In sum, Mitchell and Olson’s (1981) results indicate that individuals can develop

different perceptions of brands based only on visual information that provides no explicit

brand information. That is, consumers seem to be able to convert visual information into

beliefs about the attributes of an advertised brand (see Figure 1).

Figure 1. The mediating effect of advertising content on cognitive variables

Source: Adapted from Mitchell and Olson (1981)

In the same vein, Mitchell and Olson (1979) and Shimp and Yokum (1980) tested

the path of the relationship between consumers’ attitudes toward an ad and their purchase

intentions and purchase behavior. Mitchell and Olson found that advertisements greatly

influenced consumers’ brand attitudes and purchase intentions. Likewise, Shimp and

Yokum claimed that the frequency with which consumers purchased an advertised

brand’s products increased with their favorable evaluations of the advertisement.

With reference to the current study, to better understand the marketing

effectiveness of Twitter, it is important to base studies on the theoretical approach of


12

consumer attitudes toward ads. In fact, Mitchell and Olson themselves (1981)

recommended using this approach to research advertising effects. They suggested that

considering Aad a separate construct can provide diagnostic information about an

advertisement's attitudinal impact on consumers. In the framework of the current study,

Aad is substituted for attitudes toward the tweet. In the social media context, a company’s

Twitter account is an advertising medium and a tweet is a modern-day advertisement.

2.1.2 Attitudes-toward-the-website (Aws) model

Following the same logic, the development of the World Wide Web introduced a

new advertising medium, thereby creating the need to study the hierarchy-of-effects of

websites. The evaluation of website advertising, in turn, created the most useful parallel

for examining the effectiveness of SM advertising.

To test the effectiveness of website advertising, many academic studies have used

the attitude toward the website (Aws) model. Chen and Wells (1999) defined Aws as “web

surfers' predispositions to respond favorably or unfavorably to web content in natural

exposure situations” (p. 29). They found that interaction with a website and the features

of the site was positively correlated with attitude toward the website (Cho & Leckenby,

1999). Choi, Miracle, and Biocca (2001) claimed that attitude toward a website is also

influenced by the animated features of the site.

The Aws model was developed first by Stevenson et al. (2000) to show how

consumers react to ads embedded within websites. Later, the model was further examined

and modified by Bruner II and Kumar (2000) to measure how complexity of web

advertising affects the advertising hierarchy-of-effects. They suggested that Aws has an

essential influence on Aad, brand attitude, and purchase intention (Bruner II & Kumar,


13

2000; Stevenson, Bruner II, & Kumar, 2000). Importantly, they also found that the more

positive a consumer’s Aws is, the more positive that consumer’s attitudes will be toward

the ad, the brand, and their purchase intentions.

These studies were followed by others that investigated the marketing

effectiveness of website media. The introduction of social media in the mid 2000’s

presented new opportunities for marketers, however, as well as new areas of research for

scholars. Leung (2012) was among the first researchers to study the marketing

effectiveness of Facebook in the hotel industry context.

2.1.3 Social Media Marketing Effectiveness Model

This study used the work of Leung (2012) as an investigative framework to

examine the marketing effectiveness of hotel Twitter accounts in Saudi Arabia. The

rationale for basing this dissertation on Leung’s research was as follows: first, Leung

developed a model to investigate the effectiveness of Facebook marketing in the hotel

industry, and this study also uses the context of social media effectiveness. The model

has proven to be an effective and useful measure for examining a variety of attitudes

toward Facebook that relate to perceptions of hotel brands and to individuals’ intentions,

including their intentions to engage in electronic word of mouth (eWOM) and their hotel-

booking intentions.

Leung (2012) proposed an original model to investigate consumers’ attitudes

toward hotels’ Facebook pages, their messages, and their brands, and to investigate how

different Facebook messages’ contents and formats influence consumers’ hotel-booking

intentions and intentions to spread positive eWOM (see Figure 2).


14

Figure 2. Model of marketing effectiveness of hotel Facebook messages.

Source: Leung (2012).

Leung’s model holds that consumers’ hotel-booking intent and eWOM are guided

by three considerations: 1) message content (e.g. brand, product, and involvement); 2)

message format (e.g. word, picture, and web link); and 3) consumers’ attitudes toward the

Facebook page, messages, and brand. With regard to message content, Leung found that

Facebook messages featuring products and allowing consumer involvement generated

more positive attitudes toward hotels’ Facebook pages than did messages that focused on

a hotel’s brand. Leung also claimed that brand and involvement messages increase both

hotel-booking intentions and eWOM to a greater degree than do product messages. With

regard to message format, Leung found that picture messages positively influence

consumers’ attitudes toward sites more than do words and web link messages. Leung also

argued that words and web link messages have greater impacts on hotel guests’ booking


15

intentions than do picture messages. Leung also claimed that attitudes toward a hotel’s

Facebook page have a significant impact on attitudes toward that hotel’s message,

resulting in more positive attitudes toward that hotel’s brand. Hotel booking rates and

positive eWOM are boosted by positive consumer attitudes toward the Facebook page

and the message itself.

This study used Leung’s (2012) model of the marketing effectiveness of

Facebook to empirically validate its fit in a different SM context: Twitter. For the

purposes of this study, however, the original model was modified as follows. Leung

initially argued that Facebook’s message content and format have a direct effect on

consumers’ attitudes toward Facebook pages. Attitudes toward Facebook pages, in turn,

have a direct impact on consumers’ attitudes toward Facebook messages. When they

were tested empirically, however, the results of Leung’s study showed that there was no

effect of message format and content on consumers’ attitudes toward Facebook pages.

This is hardly surprising because the relationship path proposed in the original model

seems counterintuitive. On the contrary, it seems logical to expect that one’s reaction to

the format and content of a SM message will likely influence how one reacts to that

message before one reacts to the entire SM site. Numerous studies have shown that

advertising messages’ format/content (or message characteristics) first shape and directly

influence consumers’ attitudes toward an ad itself (e.g., Baker & Lutz 1988; Edell &

Staelin 1983; Jamalzadeh, Behravan, & Masoudi, 2012; Greene, 1992; Mitchell, 1986;

Raluca & Ioan, 2010; Shimp, 1981; Solomon, 2003; Zabadi, Shura, & Elsayed, 2012).

For instance, Solomon (2003) offered a list of factors that can directly affect attitudes

toward an ad, such as the evaluation of an ad’s executional characteristics (its message


16

format), attitudes toward an ad’s promoter, consumers’ moods and feelings induced by

the ad, and reactions generated by the ad. Other studies have claimed that attitudes

toward the brand are influenced directly by attitudes toward the ad (Biehal, Stephens, &

Curio, 1992; Greene, 1992; Homer, 1990; Lutz, 1985; MacKenzie & Lutz, 1989;

Mitchell, 1986; Mitchell & Olson, 1977; Raluca & Ioan, 2010; Shimp, 1981; Solomon,

2003).

Thus, this study assumed that the content and the format of messages, rather than

the entire SM page or account, have a direct impact on consumers’ attitudes toward

messages. Therefore, the model proposed by Leung (2012) was modified to suit this

assumption.

By logical extension, then, it is assumed that consumers’ attitudes toward SM

messages positively or negatively influence their attitudes toward SM sites. In other

words, we propose that favorable/unfavorable attitudes toward a SM messages are

reflected in consumers’ favorable/unfavorable attitudes toward the SM platform on which

that message is posted. This assumption is based on previous findings in the literature.

For example, a study by Raney, Arpan, Pashupati, & Brill (2003) found that positive

attitudes toward entertaining advertisements on a website improve consumers’ attitudes

toward revisiting that site considerably more than do sites without entertaining

advertisements. Likewise, Paquette (2013) claims that “users who have more positive

attitudes toward advertising are more likely to join a brand or a retailer’s Facebook group

to receive promotional messages” (p. 10). Moreover, the impact of attitudes toward sites

or SM pages on attitudes toward brands has been investigated by several studies. For

instance, Jee and Lee (2002) argued that positive attitudes toward a site lead to positive


17

attitudes toward a brand, which, in turn, increase consumers’ purchasing intentions and/or

their revisiting of the site. Additionally, Leung et al. (2015) claims that attitudes toward

hotel brands are positively affected by attitudes toward their SM sites.

Furthermore, Leung’s (2012) conceptual model provides a useful foundation for

the investigation of the marketing effectiveness of a different type of SM platform –

Twitter – that has not yet been fully explored. Twitter is one of the most popular SM

platforms in the world and uses marketing, advertising approaches, and strategies that

differ from those of Facebook. Smith, Fischer, and Yongjian (2012) claimed that Twitter

provides more brand-central posts than does Facebook. This implies that the role of

brands on Twitter may be larger than the role of brands on Facebook. Moreover, previous

research has found that it is easier for businesses to get “followers” on Twitter than to get

“likes” on their Facebook pages (Kirtiş & Karahan, 2011; Jackson, 2015; Wolfe, 2016).

Thus, because of the very nature of its format, Twitter offers instant access to a larger

number of consumers. In addition, Twitter is more popular with and viewed more

favorably by young audiences than Facebook is because young consumers prefer to

receive information quickly and in small portions (Jackson, 2015). Additionally, Jackson

argued that Twitter is directed more toward audiences who live in cities than toward

audiences who live in rural areas. It is also suggested that Twitter is an ideal marketing

tool for SM marketers because people prefer to use Twitter as a primary source of new

content (Jackson, 2015; Williamson, 2016). Williamson also emphasized Twitter’s

advantages over Facebook: Twitter reaches people outside of individuals’ own circles of

connections; it has a better mobile user interface (UI); it offers better and real-time

engagement tools and capabilities; and it advertises accurately to a wide array of target


18

audiences. Thus, previous research suggests that there are essential differences between

Facebook and Twitter that ultimately translate into different marketing and advertising

strategies: they reach different audiences, they deliver different messages, and they

deliver messages at different speeds. Specifically, the conceptual model proposed by this

study can be a source of information on using Twitter as a SM platform for marketing

and advertising for scholars interested in hospitality marketing and the hotel industry.

Moreover, this study tested a new and unique market – Saudi Arabia – while most

previous studies investigated the market of the United States. Additionally, the

conceptual model of the current study focuses more on hotel tweets for marketing and

advertising and their impact on booking intent and eWOM than on hotel SM sites in

general. Figure 3 presents the conceptual framework for the marketing effectiveness of

hotel Twitter account in Saudi Arabia.

Figure 3. Proposed conceptualized model of Twitter marketing effectiveness.


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2.2 Twitter Marketing Effectiveness Components Model

2.2.1 Electronic Word of Mouth (eWOM)

The importance of understanding the intentions of electronic word of mouth

(eWOM) is an essential aspect of predicting the marketing effectiveness of SM. eWOM

is comprised of online reviews and feedback concerning consumers’ experiences with the

services and products they purchase (Hennig-Thurau, Gwinner, Walsh, & Gremler, 2004;

Leung, 2012; Leung et al., 2015; Stauss, 2000). Cheung, Lee, and Thadani's study (2009)

revealed that “[eWOM] communication has become a dominating channel that influences

buying decisions of consumers on the Web” (p. 501).

Additionally, several studies have found that eWOM significantly impacts

consumers’ behavior in online shopping and product selection via Internet channels

(Bickart & Schindler, 2001; Senecal & Nantel, 2004; Xia & Bechwati, 2008). SM has

also been found to help in developing positive eWOM, which can improve customers’

participation and interaction with firms (Kim & Hardin, 2010). Similarly, other

consumers’ recommendations of products and services are more important and interesting

to future customers than product and service information itself (Ridings and Gefen,

2004). Twitter followers’ purchasing intentions and product involvement can be affected

by eWOM spread by celebrities on Twitter, especially those with high numbers of

followers (Jin & Phua, 2014).

In the hospitality field, the use of eWOM supports the examination of hotel

guests’ reviews and comments as examples of customers’ perceptions of their

experiences (Lee, Law, & Murphy, 2011; O’Connor, 2010; Stringam & Gerdes, 2010).

Previous researchers have agreed that online comments and reviews of hotel services are

important to hotel ratings (Lee et al., 2011; O’Connor, 2010; Stringam & Gerdes, 2010).


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It can be further argued that eWOM generally illicits more credibility and trust than does

traditional media (Blackshaw & Nazarro, 2006). To this end, studies have found that

consumers believe that eWOM is a more reliable source of information than advertising

and marketing messages delivered by companies themelves because eWOM is perceived

to be more organic and, therefore, more authentic (Bickart & Schindler, 2002; Kempf &

Smith, 1998; Walsh, Mitchell, Jackson, & Beatty, 2009). Thus, it is assumed that eWOM

can help researchers to understand the marketing effectiveness of Twitter from the guest

perspective.

2.2.2 Intentions to Book Hotels

To understand consumers’ intentions to book hotels, it is important to understand

whether consumers’ attitudes toward brands can influence their purchasing decisions or

intentions. Purchase intentions are consumers’ actual willingness to act toward an object

or brand (i.e. to buy) and are developed as a result of their decision-making processes

(Dodds, Monroe, & Grewal, 1991; Wells, Valacich, & Hess, 2011). Purchase intentions

are one of the most important characteristics of the behaviors or attitudes related to

purchasing decisions (Zeithaml, Berry, and Parasuraman, 1996). Similarly, other studies

have found that attitudes toward brands are one of the key dimensions that facilitate

purchasing intentions by linking current and future purchasing behaviors to consumers’

experiences, satisfaction, and knowledge (Kapferer, 2008; Keller, 2008). Early studies

suggested that when consumers become more aware of and knowledgeable about a brand

through the information they receive via advertising or word of mouth, they decide to

purchase that brand based on their favorable feelings toward that brand (Lavidge &

Steiner, 1961). Similar results were obtained by a more recent study related to SM


21

activities, in which it was found that consumers’ purchasing intentions can be influenced

by their perceptions of a brand, which are developed via SM interactions with that

specific brand (Hutter, Hautz, Dennhardt, & Füller, 2013).

With regard to the hospitality and tourism industries, several studies have reported

a strong relationship between purchase intentions and consumers’ behaviors or attitudes

(Ajzen & Driver, 1992; Buttle & Bok, 1996; Jeong, Oh, & Gregoire, 2003; Law & Hsu;

2005; Leung, 2012; Leung et al., 2015). For instance, the quality of a hotel website

influences guests’ purchasing intentions (Jeong et al., 2003; Law & Hsu, 2005). SM sites

are not the only thing that impact hotel guests’ purchasing intentions; booking intentions

are also influenced by attitudes toward hotel brands (Leung et al., 2015). Thus, it is

essential to apply consumers’ intentions to book to the study of hotel Twitter accounts’

marketing effectiveness.

This study focused on the effectiveness of hotel tweets’ marketing and advertising

in terms of their impact on eWOM and booking intentions. Thus, the followings sections

provide an overview of the literature on various aspects of tweets. They consider their

format and content, including photos, hyperlinks, product/services presentations, and

consumer engagement.

2.2.3 Photo Presentations on Hotel Social Media

Twitter, the microblogging social network, provides users various types of

formatting styles. Tweet formats include plain-text, emoji, photos, videos, GIFs, polls,

and quote tweets. Recently, Twitter rolled out new features that allow users to add

photos, videos, GIFs, polls, and quote tweets without having them count toward the 140-

character limit (Wong, 2016). Several studies have claimed that text alone is insufficient


22

to communicate information. These studies have emphasized the meaning and

importance of photos (Cho, Phillips, Hageman, & Patten, 2009; Davison, 2007; Graves,

Flesher, & Jordan, 1996; MacKenzie, 1986). Photos and other visual elements are

claimed to be more influential means of interaction than text becuase they more

effectively draw peoples’ attention and influence their perceptions of a given message’s

quality and effectiveness (Cho et al., 2009; Davison, 2007; Graves et al., 1996;

MacKenzie, 1986). Tweets with photos can convey emotions and beauty more accurately

than can text alone becuase their visual elements can be used to enrich and contribute to

the visual experience of the text’s content (Xi, 2012). The impact of a picture is said to be

greater than the impact of plain-text because a picture’s visual quality grabs attention and

engages potential customers (Zhang, Wang, & Tangshan, 2013). A study investigating

the use of Twitter in the Spanish hotel industry claims that “photos, as a particular media

type, generate more retweets … and [favorites] … than other media types do” (Bonsón,

Bednárová, & Wei, 2016, p. 77). Additionally, many previous studies have found that

including photos in tweets results in boosted numbers of retweets (Alboqami et al., 2015;

Boyd, Golder, & Lotan, 2010; Suh, Hong, Pirolli, & Chi, 2010; Zarrella, 2009). Photo

presentations were found to be a key predictor of consumers’ attitudes toward websites,

and behavioral intentions were found to be strongly influenced by these attitudes (Jeong

& Choi, 2005). Using photos on a SM site might motivate Twitter users and increase

their intentions to book hotels because aesthetic ambiance has been found to be one of

travelers’ top priorities in planning trips (Vogt & Fesenmaier, 1998). Furthermore,

beautiful and pleasant information is very important in online interactions (Wang &


23

Fesenmaier, 2004). Thus, this study addressed photos as the visual tweet format in order

to understand their effect on attitudes toward hotels’ Twitter accounts.

2.2.4 Hyperlink Presentations on Hotel Social Media

Hyperlinks, also known as “web links” or “links,” are highlighted words or

images that connect directly to a specific page or object in another location or file (Zhang

et al., 2013). Because Twitter limits the length of a tweet to 140 characters, hyperlinks

can be used to deliver more details and more complex information than can fit within the

140 characters of a tweet. The use of hyperlinks on Twitter provides individuals an

advanced way to share and enrich their ideas, opinions, and stories, and it offers users an

opportunity to become more involved with the topic at hand when sharing or retweeting

(De Maeyer, 2013; Hsu & Park, 2011). Additionally, Twitter allows an individual to

share information on a particular topic in a single tweet via hyperlink (Holton, Baek,

Coddington, & Yaschur, 2014). Websites such as Bitly (https://bitly.com/), Tinyurl

(tinyurl.com), Google URL Shortener (goo.gl), and Ow via Hootsuite (ow.ly) are used to

simplify and compress long hyperlinks so that they can be embedded in a tweet of limited

size.

Several studies have found that adding hyperlinks to tweets can benefit customers.

Hyperlinks were included in almost 26.2% of millions of tweets collected for research

(Gao, Zhang, Li, & Hou, 2012), and the number of tweets with hyperlinks is rising

(Techcrunch, 2010). Hotels in Spain use hyperlinks, or website links, more than any other

type of media on their Twitter pages. This is believed to be caused by Twitter's limitation

of tweets to 140 characters (Bonsón et al., 2016). Tweets that include hyperlinks attract

more consumers than do those that do not have hyperlinks in them (Alboqami et al.,


24

2015). A few studies have also claimed that including links in tweets is important for

retweeting purposes (Boyd et al., 2010; Suh et al., 2010; Zarrella, 2009). In fact, “tweets

that include links are 86% more likely to be retweeted” (Cooper, 2013, para. 24). A

Microsoft study found that hyperlinks can increase tweets’ credibility, which, in turn, can

make them more likely to be retweeted (Morris et al., 2012). Another study revealed that

Twitter users found beneficial or interesting information in 84% of tweets with

hyperlinks and that 67% of these hyperlinks referred users to news sources (Gao et al.,

2012, p. 2535). Therefore, it seemed hyperlinks were an important tweet format to

include in this study.

2.2.5 Product/Service Presentations on Hotel Social Media

Information about products and services offered by hotels is valued by consumers

(Kaplanidou & Vogt, 2006). Today, most corporations introduce their products/services

through SM to a vast community of customers by posting short messages, photos, and

hyperlinks about their new products/services and their existing products/services, by

posting tips on how to use their products/services, and via other activities, all of which

are impossible via traditional marketing and advertising means (Roberts & Kraynak,

2008; Weinberg, 2009). This is because marketing products and services via SM is

considered one of the most inexpensive and cost-effective ways of marketing and

advertising in today’s marketplace (Green, 2007; Paridon & Carraher, 2009; Park,

Rodgers, & Stemmle, 2011; Parsons, 2009). Additionally, SM employs a pull marketing

strategy that allows large numbers of consumers to easily access information about

products/services they are interested in (Akar, 2010; Sigala, Christou, & Gretzel, 2012).

Also, many customers prefer to go to SM sites to learn about products/services and to


25

gain information they are seeking because they realize that these SM platforms are more

powerful, reliable, and trustworthy than other sources of information provided by

marketers (Bernoff & Li, 2008; Canhoto & Clark, 2013; Chu & Kim, 2011; Park & Cho,

2012). In a survey of high-level managers of various large, global organizations, there

was a general agreement that customers are more influenced by products and services

advertised, designed, and promoted through SM sites (Sinclare & Vogus, 2011). The

presentation of attractive products and services through SM can benefit firms by

enhancing their selling environments, drawing their consumers’ attention, and increasing

their consumers’ engagement (Anderson, Swaminathan, & Mehta, 2013). Visual

presentations of otherwise intangible products and services are very influential in the

tourism and hospitality industry (Morgan, Pritchard, & Abbott, 2001). Messages about

products and services promote intentions to book and engender positive attitudes toward

hotels’ SM sites, especially when product messages are posted in text and hyperlink

formats (Leung, 2012). Valuable and useful product information shared by

knowledgeable people and consumers may help encourage other customers to purchase

and may spread eWOM behavior (Chu & Kim, 2011). For example, it is easier today than

ever before for guests to forward information presented in a hotel's Twitter account about

their favorite products and services; they need only click the “retweet” or “favorite”

button. This action may better illustrate their attitudes toward the hotel’s tweets.

2.2.6 Consumer Engagement on Hotel Social Media

Nowadays, due to the huge influence of various Internet sites and SM in

particular, businesses cannot fully control how their brands and their products/services

are communicated to customers. In this multidimensional communication network – and


26

especially in the hospitality industry – the engagement of both customers and businesses

becomes increasingly important. Therefore, engagement in SM communities has received

increased attention from both marketers and researchers in recent years. The concept of

consumer engagement has been acknowledged in a variety of fields, including hospitality

(Cabiddu, Lui, & Piccoli, 2013), community engagement in tourism (Hamilton &

Alexander, 2013), online reviews and engagement in hotels (Park & Allen, 2013),

customer engagement with tourism brands (So, King, & Sparks, 2014), customer

engagement behaviors and hotels’ responses (Wei, Miao, & Huang, 2013), and travelers’

engagement in consumer-generated media creation (Yoo & Gretzel, 2011). So et al.

(2014) discussed in great detail customer engagement with tourism brands and its

measurement. Some researchers believe that customer engagement is an interaction

among a variety of motivational aspects (Bijmolt et al., 2010; Marketing Science

Institute, 2010; van Doorn et al., 2010; Verhoef, Reinartz, & Krafft, 2010). Others claim

that customer engagement is a multidimensional concept encompassing both behavioral

and psychological characteristics (Brodie, Hollebeek, Juric, & Ilic, 2011; Hollebeek,

2009; Hollebeek, 2011; Patterson, Yu, & De Ruyter, 2006; Vivek, 2009). Customer

engagement can also be defined as a form of connection that clients make with other

clients, businesses, and particular brands (Smith & Wallace, 2010).

Several studies have found that engagement with consumers can boost

consumers’ attitudes and decisions to purchase. One of the best uses of SM in the travel

market is to involve consumers and interact with them throughout the entire buying

process to fully understand their needs and enhance the consumer-purchaser relationship

(Green, 2007). Guest and hotelier engagement on SM platforms can influence guests’


27

purchasing decisions (Levy, Duan, & Boo, 2013). Engagement can generate new ideas

that enrich consumer experiences and raise the competitive advantage of companies

(Prahalad & Ramaswamy, 2004; Ramaswamy & Gouillart, 2010). Customer engagement

adds value for firms in the tourism and hospitality industries by increasing customer

loyalty (So et al., 2014). Customer engagement is also a valuable and essential key to

improving advertising’s effectiveness and to managing retention and the interactions and

loyalty between customers and firms (Calder, Malthouse, & Schaedel, 2009; Hollebeek,

2011). Businesses in the hospitality industry should make it a practice to track and

promote customer engagement (Wei et al., 2013). The proliferation of SM platforms

allows consumers to play a substantial role by engaging with other consumers through

engagement behaviors that go beyond transactions (Verhoef et al., 2010).

Twitter provides consumers a space in which to engage and interact instantly with

businesses. In fact, some hospitality businesses have already effectively increased via

Twitter their two-way interaction and engagement with their guests. For instance,

Marriott Hotels, which has over 200,000 followers on its Twitter page, developed a social

media center called “M Live” to “listen” instantaneously to their guests (Golden &

Caruso-Cabrera, 2016). Previous research shows that one tweet from a hotel's twitter

account increased engagement by 3,000%, so the reach of SM platforms can be

significant (Schools, 2014).

2.3 Hypothesized Model

The model proposed by this study suggests that the four features described above

(photos, hyperlinks, products, and engagement) have direct effects on hotel guests’

attitudes toward tweets, which, in turn, have direct impacts on their attitudes toward


28

hotels’ Twitter accounts. Hotel consumers’ attitudes toward hotel Twitter accounts are

assumed to further influence attitudes toward hotel brands, which then influence both

their intentions to engage in eWOM and their intentions to book (see Figure 3).

Therefore, the study offered the following eight directional hypotheses:

H1. Adding a photo will positively affect attitudes toward hotel tweets.

H2. Adding a hyperlink will positively affect attitudes toward hotel tweets.

H3. Adding information about products/services will positively affect attitudes

toward hotel tweets.

H4. Providing space for consumer engagement will positively affect attitudes

toward hotel tweets.

H5. Positive attitudes toward hotel tweets will positively affect attitudes toward

hotel Twitter accounts.

H6. Positive attitudes toward hotel Twitter accounts will positively affect

attitudes toward hotel brands.

H7. Positive attitudes toward hotel brands will positively affect intentions to

engage in eWOM.

H8. Positive attitudes toward hotel brands will positively affect hotel booking

intentions.

2.4 Relevant Consumer Characteristics

The proposed model focuses on hotel Twitter accounts and how tweets influence

the behaviors and intentions of hotel guests. The model does not take into consideration

any consumer characteristics. Several hospitality and tourism studies, however, have

found that consumer characteristics such as SM involvement, attitude toward SM, and


29

behavior toward SM can increase the effectiveness of SM platforms in marketing and

advertising hotels (e.g. Bai et al., 2008; Berthon, Pitt, & Campbell, 2008; Boateng &

Okoe, 2015; Christodoulides, Jevons, & Bonhomme, 2012; Hutter et al., 2013; Leung,

2012; Leung et al., 2015; Nassar, 2012; Paris, Lee, & Seery, 2010). Thus, it stands to

reason that consumer characteristics related to SM use can provide significant insights

into the impact of Twitter on hotel marketing.

To understand the influence of SM marketing, it is important to consider the

perceived value it creates for consumers. The benefit or added value might differ based

on hotel guests’ knowledge and experience and on other factors related to SM. For

instance, consumers with greater SM experience and SM knowledge perceive the

marketing or advertising activities of hotel SM sites to be more valuable and beneficial

than do consumers with little experience (Leung et al., 2015). Therefore, the level of

effectiveness of SM sites might vary across consumers. Moreover, consumer SM

attitudes, behaviors, and involvement can be diverse and result in different intentions. In

this case, hotel guests’ experience and knowledge about a hotel's SM site becomes a

determinant of SM attitudes, behaviors, and involvement, which also provide a more

thorough understanding of SM marketing effectiveness. Thus, there is a need to discuss

some of these additional consumer characteristics because they could potentially

influence intentions to book and eWOM.

2.4.1 Attitudes toward Social Media

Attitude is defined as “a person’s enduring favorable or unfavorable evaluation,

emotional feeling, and action tendencies toward some object or idea” (Kotler & Keller,

2006, p. 194). Additionally, attitude is “a lasting, general evaluation of individuals,


30

objects, advertisements or issues” (Solomon, Bamossy, Askegaard, & Hogg, 2013, p.

292). Attitudes determine whether individuals like or dislike objects, advertisements,

ideas, or issues. Therefore, attitudes can influence consumers’ behavior toward products

or services (Kotler & Keller, 2006). Thus, attitudes of individuals toward advertising

influence their responses to advertising efforts and, by extension, their purchasing

intentions (Mitchell & Olson, 1981).

Brand awareness can affect consumers’ attitudes toward SM advertising, which

can consequently impact their behavioral responses (Chu & Kim, 2011; Chu, Kamal &

Kim, 2013). For example, a study in China about gender differences in attitudes and

behaviors toward SM explained how essential SM platforms can be for modern Chinese

consumers, particularly the millennial generation (Ly & Hu, 2015). Moreover, the study

discussed SM usage and how it varied between male and female Chinese consumers;

females tend to be more interested in utilizing SM sites than males do (Ly & Hu, 2015).

Additionally, most Chinese customers utilize SM to socialize and share information with

one another, find different events, follow celebrities, and purchase products (Ly & Hu,

2015).

Typically, consumers have positive attitudes toward SM advertising (Boateng &

Okoe, 2015). Companies’ reputations are considered an important factor in consumers’

responses to advertisements (Boateng & Okoe, 2015). Positive reputations positively

influence decision-making behaviors, including purchasing advertised products and

services and taking favorable actions toward products or services promoted through SM.

Additional factors that influence consumers’ attitudes toward SM marketing

include SM use, SM knowledge, being affected by the Internet and SM,


31

following/monitoring SM, foresight about SM, and fear of marketing via SM (Akar &

Topçu, 2011). Even further, family income was the only aspect that had a significant

impact on attitudes toward SM marketing (Akar & Topçu, 2011).

Studies also have shown that consumer attitudes toward SM advertising are an

important contributor to its effectiveness (Edwards, Li, & Lee, 2002; Chu et al., 2013).

Consumers’ negative attitudes toward advertising can be influenced by the putatively

intrusive and disturbing nature of online advertising (Edwards et al., 2002).

There is a significant relationship between consumers’ attitudes toward SM

advertising and their brand consciousness, with the perceived quality of this relationship

influencing consumers’ behavioral responses (Chu et al., 2013). This theory explains how

consumers’ attitudes toward advertising can influence their responses, which, in turn,

impact their buying intentions (Mitchell & Olson, 1981).

In sum, previous research has shown that consumers’ attitudes toward SM can

have a significant effect on consumers’ perceptions of SM in general. With regard to

Twitter specifically, this study addressed consumer attitudes and how they influence

consumers’ perceptions in the following three ways: consumers’ attitudes toward specific

tweets, consumers’ attitudes toward hotel Twitter accounts, and consumers’ attitudes

toward hotels’ brands as they are conveyed through SM accounts.

Attitudes toward hotel tweets: The types of tweets a hotel posts through its Twitter

account may influence consumers’ experiences. Tweets often vary: some hotels opt for

informative strategies by conveying specific deals and packages currently offered, while

others prefer more indirect approaches, such as including photographs of the resort and

messages intended to convey the benefits of taking a vacation. Others value tweets for


32

consumer engagement. With this in mind, customer attitudes toward hotels’ tweets likely

indicate customer engagement and intention to purchase.

Attitudes toward hotel Twitter accounts: Types of hotel tweets may shape consumer

experiences and, therefore, may influence how hotel brands are ultimately perceived. For

example, a hotel that constantly tweets specific deals might eventually be perceived by

consumers as a budget brand. Conversely, a hotel that tweets images intended to

showcase a its luxuriousness might influence perceptions so that it comes to be seen as a

premium brand. Additionally, the level of hotel engagement on a Twitter account might

result in expectations for service; a Twitter account that communicates and engages with

potential customers could be perceived as one that puts the needs of its customers first,

while one that does not engage on SM might be seen as providing substandard customer

service. Customer attitudes toward hotel Twitter accounts should thus translate into

customer engagement and into intention to purchase.

Attitudes toward hotel brand: Attitude toward a brand is “an individual’s internal

evaluation of the brand” (Mitchell & Olson, 1981, p. 318). Additionally, attitude is “a

relatively enduring, unidimensional summary evaluation of the brand that presumably

energizes behavior” (Spears & Singh, 2004, p. 55). Also, attitude toward a brand is

“implicit in beliefs, feelings, behaviors and other components and expressions of

attitudes” (Giner-Sorolla, 1999, p. 443). As these definitions imply, attitude toward a

brand is the basis of one approach to measuring how consumers evaluate a brand. In other

words, attitude toward a brand can be viewed as the extent to which consumers favor the

brand. Structured advertising influences consumers’ beliefs and evaluations and,

therefore, influences consumers’ attitudes toward a brand (Shimp, 1981). Measuring


33

attitude toward a brand is one method by which to examine what kind of advertising,

marketing, promotion, or other activity related to the brand would have the most positive

influence on consumer attitudes toward the brand. As a result, a company could know

how to improve its advertising, marketing promotion, or other activities related to its

brand in the most effective way.

The overall perception of a hotel's Twitter account is often generated on SM via

an initial introduction to the hotel's brand. Thus, whether consumers are motivated or

influenced by a specific account depends on whether the brand advertised aligns with

their travel goals and needs. A business traveler, for example, might be drawn toward

accounts that advertise the convenient amenities necessary for business travel, such as

access to high-speed Internet and conference room availability. Family-oriented travelers

might look for accounts that convey a brand of fun and hospitality, while newlyweds

might seek opulence. Thus, the types of tweets conveyed through a Twitter account

influence perceptions toward hotels’ brands, with customers identifying most with the

brand that aligns best with their needs.

2.4.2 Consumer Behavior toward Social Media

Consumer behavior has been described as a process that starts with a pre-purchase

stage and then continues on to purchase and post-purchasing. Consumer decision-making

processes have been examined in the virtual world (De Valck, Van Bruggen, &

Wierenga, 2009). Communications and interactions with other individuals via SM have a

major impact on decision-making processes, including behavior, the evaluation of post-

purchase, and need recognition (De Valck et al., 2009). SM marketing played an

important role in the formation of relationships among consumers by posting information


34

about experiences during both the early and the mature periods of Internet utilization

(Chen, Fay, & Wang, 2011). Consumer behavior has played a significant role in changing

media and marketing processes because consumers have increased their intervention in

business marketing strategies (Berthon et al., 2008). The entire business landscape is

shifting because customers are gradually performing activities that used to be controlled

by firms. Additionally, due to SM, consumers are more actively contributing to the

marketing content of businesses (Ly & Hu, 2015). Thus, in order to create a mutually

beneficial customer-business relationship using SM, businesses need to have an

understanding of what motivates consumer behavior.

2.4.3 Consumer Involvement with Social Media

To better understand consumer involvement with SM, researchers need to

examine the origin and the meaning of the concept of “involvement” and to identify types

of SM users by their levels involvement with SM. Involvement is defined as “a person’s

perceived relevance of the object based on their inherent needs, values, and interests”

(Zaichkowsky, 1985, p. 342). Numerous research studies claim that the use and

purchasing of products or services increases when consumers’ levels of involvement with

those products or services are high (Clarke & Belk, 1979; Greenwald & Leavitt, 1984;

Krugman, 1962; Krugman, 1965; Petty, Cacioppo, & Schumann, 1983; Wright, 1973).

Users have been clustered into two major groups based on their involvement with SM:

active and passive (Alarcón-Del-Amo, Lorenzo-Romero, & Gómez-Borja, 2011). Active

SM users engage more in SM activities, such as updating, communicating, reviewing,

and searching (Constantinides, Carmen Alarcón del Amo del, & Romero, 2010). In

contrast, passive SM users are less involved with SM activities (Constantinides et al.,


35

2010). Hutton and Fosdick (2011) claimed that active SM consumers who follow a

brand's SM site are more positive toward that brand and more willing to purchase the

products/services offered by that brand. Consumers’ involvement with SM can also

increase their trust in a brand and their intention to purchase (Hajli, 2014). Thus, by

studying consumers’ involvement with SM, this study focused on describing the

importance of active SM users and on understanding their contributions to SM marketing

activities.

Christodoulides et al. (2012) found that user-generated content involvement can

have a significant effect on consumers’ perceptions of a brand, which can positively

influence consumer-based brand equity. In the hotel industry, Kim et al. (2015) examined

the effect of managing SM on hotel performance. Guest involvement in online reviews or

ratings can positively impact perceptions of hotel performance. Therefore, previous

research shows that there is a need to understand the impact of consumer involvement

with SM because it is one of the most important consumer characteristics related to the

study of SM marketing effectiveness. With these constructs in mind, this study sought a

more comprehensive understanding of how customer attitudes toward SM, their behavior

on SM, and their involvement with SM can influence their perceptions of hotel Twitter

marketing.


36

CHAPTER III

METHODOLOGY

3.1 Research Design

This study employed content analysis as a pre-test and quantitative research

design in the form of an online survey with an embedded experiment as the main study.

Content analysis was applied in exploring the format and content of hotels’ tweets in

order to identify the most popular methods Saudi hotels use to deliver Twitter messages.

The data collected during the pre-test assisted the researcher in designing an experiment

using the most prevalent tweet formats and contents. Quantitative data were collected

through an experiment of a simulated hotel Twitter account that was used in an online

survey to investigate Saudi consumers’ perspectives toward effective tweets (Creswell &

Plano-Clark, 2011). Appendix A presents a full diagram of this dissertation’s research

design.

3.2 Pre-test

In the pre-test, the researcher explored the content of multiple existing Saudi

Arabian hotel Twitter accounts to identify the differences within and between the

contents of these accounts. The researcher used content analysis to develop a

classification of the tweets posted on these accounts. Yin (2003) explained how multiple

or collective case studies can be used to predict either “(a) similar results (a literal

replication) or (b) contrasting results but for predictable reasons (a theoretical

replication)” (p. 47).


37

3.2.1 Sampling and Data Collection Procedure for the Pre-test

In the pre-test, six sample Saudi Arabian hotel Twitter accounts were thoroughly

explored. Tweets posted between September 5, 2015 and October 5, 2015 were reviewed

by the researcher (see Appendix B). Only tweets that were posted by hotels and that were

re-tweeted and mentioned by consumers were included in the study.

The researcher used a purposive sampling strategy (intensity sampling) to select

the hotels (Creswell, 2009; Creswell & Plano Clark, 2011). The researcher identified six

of the most active Saudi hotels on Twitter by considering their numbers of followers and

their numbers of tweets, which were ranked by the well-known SM statistical website

Socialbakers (2015). The main purpose of the pre-test was to develop a categorization for

the formats and the contents of tweets posted on hotel Twitter accounts. The dates,

contents, and formats of the tweets, as well as their numbers of re-tweets and their

numbers of mentions, were recorded and classified. Additionally, an examination of

Twitter followers, re-tweets, and mentions further explored the marketing effectiveness

of the contents posted.

3.2.2 Pre-Test Data Analysis

Content analysis was used to determine the most-frequently used formats and

contents of existing hotel tweets. The procedures were based on Leung’s (2012)

categories for Facebook messages’ formats and contents. Table 2 shows detailed

descriptions of the six categories of tweet contents.

The researcher identified patterns of occurrence in the data generated from the

tweets. The data were then combined into categories that matched Leung’s (2012)

categories, shown in Table 2. After all the data were patterned into the categories, the

researcher interpreted the results using the frequencies method.


38

Table 2 Categorization of Tweet Content

Tweet Content Description

Brand Focuses on tweets about hotel news, advertisements, hotel honor

and awards, staffing, and social activities.

Product Pays attention to new and existing hotel properties, food and

beverage, restaurants, amenities, room services, events activities,

holiday products, and website, SM and mobile app.

Engagement Focuses on Twitter fans’ replies and actions, such as questions,

experience sharing, mentions, retweets, likes, and picture

captions.

Promotion Spotlights on the tweets that discuss deals, promotions, special

offers, sales, and packages.

Information Consists of the information that is not directly related to the hotel,

such as travel tips, destination information, trip diary, travel

sayings, food recipes, food trends, holiday greetings, safety, guest

trends, and other not information not directly related to hotel.

Reward Includes the prize that Twitter fans win from the hotel without

any purchase, such as contests, guesses, spins, games, giveaways,

free stays, and free points.

Source: Leung (2012)

The validity and reliability of the collected data were assessed and controlled

using different methods. In content analysis research, “validity” refers to the accuracy,

trustworthiness, and credibility of findings (Lincoln & Guba, 1985; Creswell & Plano-

Clark, 2011). Internal validity in the content analysis was used to ensure that the data

collected were compatible with reality. The most common internal validity approaches

used in content analysis are triangulation, member checks, and peer review (Creswell &

Plano-Clark, 2011). In this study, a peer review approach was employed by an additional

researcher (who was not associated with this study) to ensure the internal validity of the

data. Additionally, external validity was checked. Tweets from six sample Saudi hotel

Twitter accounts were collected over a period of four weeks to ensure the external

validity of the data.


39

Stenbacka (2001) stated that reliability in qualitative research is related to the

quality of content analysis. To ensure the reliability of the data collected in the pre-test,

the audit trail approach was utilized. An audit trail is an overall description of how a

study was conducted, from the start to the final reporting of the results (Lincoln & Guba,

1985). Thus, memos were kept throughout the research process and other researchers

were asked to review the trail of the analysis in order to ensure the reliability of the data.

3.2.3 Results of the Pre-test

In terms of tweet format, four types of formats were identified as those most

frequently used in the tweets: video, photo, text, and hyperlink. The results of the tweet

format categorization showed that “Text” was the most popularly used tweet format,

followed by “Photos” and “Hyperlinks.” “Videos” was the least used tweet format; only

two tweets posted videos. Table 3 presents the results of the pre-test in terms of the

number of Saudi hotel tweets by tweet format.

Table 3 Tweet Formats Most Frequently Used by Saudi Hotel Tweets

Tweet format No. of tweets Percent

1 Text 334 50.68

2 Photos 236 35.81

3 Hyperlinks 123 18.66

4 Videos 2 0.30

Thus, the findings suggest that Saudi hotels are more acquainted with posting text,

photos, and hyperlinks in tweets via their Twitter accounts and that the utilization of

videos on Twitter is still narrow. Therefore, the current study considered only text,

photos, and hyperlinks in the main study.

In terms of tweet content, a categorization was identified that included six types:

brand, product, engagement, promotion, information, and reward. The results of the tweet


40

content categorization showed that “Brand” was the most commonly used tweet content,

followed by “Product” and “Engagement.” “Information,” “Promotion,” and “Reward”

were less popularly used tweet contents. Thus, the results of the pre-test suggest that

Saudi hotels are more familiar with posting about brands, products, and engagement on

Twitter accounts and that the deployment of the other tweet contents is still limited.

Therefore, this study considered only brand, product, and engagement because of their

frequent utilization. Table 4 presents the results of the pre-test with regard to the number

of Saudi hotel tweets by tweet content.

Table 4 Tweet Content Most Frequently Used by Saudi Hotels

Tweet format No. of tweets Percent

1 Brand 445 33.18

2 Product 356 26.55

3 Engagement 256 19.09

4 Information 148 11.04

5 Promotion 133 9.92

6 Reward 3 0.22

For logistical reasons, though, it was decided that the 4 x 6 design would be

difficult to implement and to report. Thus, the research design was reduced to a 3 x 3 and

only the most frequently used tweet formats and content features were considered: text,

photo, and hyperlink (format), and brand, product, and consumer engagement (content).

3.3 Main Study

The purpose of the main, quantitative study was to explore the marketing

effectiveness of diverse types of hotel Twitter accounts. To achieve this goal, the study

implemented an online survey with an embedded experiment based on the tweet

categorization of the pre-test.


41

3.3.1 Sampling and Data Collection

A convenience sampling strategy was used in this study (Creswell, 2009;

Creswell & Plano Clark, 2011). The sample consisted of two groups of consumers: (a)

hotel visitors in Saudi Arabia (reached via SM) and (b) Saudi citizens currently residing

and/or studying in the United States but with previous experiences staying at Saudi

hotels. The two groups were chosen based on their importance to the hospitality industry

and because they represent significant and heavy SM users (Mohammed Bin Rashid

School of Government, 2014).

Permission to conduct this study was obtained from the Saudi Association Clubs,

the Saudi Cultural Mission, and hotels in Saudi Arabia. The researcher programmed the

survey and sent it to these organizations. They then distributed a link to the online survey

to potential participants. Because of the nature of the study, the link was also distributed

through various SM platforms, including Twitter and Facebook.

The questionnaire started with two screening questions: “Have you ever stayed in

a hotel in Saudi Arabia?” and “Do you have a Twitter account?” If respondents passed

the screening by answering “yes” to both questions, they proceeded to the experiment,

which involved reviewing and evaluating a simulated Twitter account. Respondents were

asked to indicate their attitudes toward tweets, their attitudes toward the hotel Twitter

account, their attitudes toward the hotel brand, their intentions to book, and their

intentions to engage in eWOM. The respondents were then asked to provide their SM

characteristics, including their behavior, their attitudes, and their involvement with SM.

Finally, participants were asked to provide their demographic information.


42

The study was submitted for approval by the Texas Tech University Institutional

Review Board (IRB). The Human Research Protection Program approved this research

on March 21, 2016. Please see Letter of Approval in Appendix C.

3.3.2 Experiment Design

A simulated Twitter account was created for a fictitious “M Hotel” brand. Nine

conditions were created that included different tweet formats and contents. To control

ensure the ecological validity of the experiment, tweets were designed to imitate as

closely as possible the real tweets collected in the pre-test. A 3 x 3 design (tweet format

and tweet content) provided nine unique tweets, which were posted on the “M Hotel”

Twitter account (see Appendix D). The sample was randomly split into nine groups

(groups were relatively equal in their numbers of respondents). Each participant saw and

evaluated only one condition. Respondents then proceeded to a set of questions that

assessed their attitudes toward the tweets they had just viewed and additional attitudes

and behavioral measures. The survey measures contained only closed-ended questions.

Figure 4 represents the experimental design.

Tweet Format

Text Photos Hyperlinks

Tw

eet

Con

ten

t Brand Text & Brand

(Condition #1)

Photos & Brand

(Condition #4)

Hyperlinks &

Brand

(Condition #7)

Product Text & Product

(Condition#2)

Photos & Product

(Condition #5)

Hyperlinks &

Product

(Condition #8)

Engagement Text & Engagement

(Condition #3)

Photos & Engagement

(Condition #6)

Hyperlinks &

Engagement

(Condition #9)

Figure 4. A 3 x 3 design (tweet format and tweet content) of a simulated hotel Twitter

account


43

3.3.3 Measures

The online survey included a variety of measures designed to obtain information

essential to the purpose of the study. To measure marketing effectiveness, two exogenous

variables were used: tweet format (text, photo, and hyperlink) and tweet content (brand,

product, and engagement). Intention-of-hotel-booking (IHB) and intention-of-electronic-

word-of-mouth (IEWOM) were used as endogenous variables. Other endogenous

variables included the following consumer attitudes: attitude-toward-the-tweet (ATT),

attitude-toward-hotel-Twitter-account (ATHTA), and attitude-toward-the-hotel-brand

(ATHB). Additional variables were utilized to measure consumer characteristics related

to SM: SM usage behavior, attitudes toward SM, and SM involvement (Table 5). For a

complete set of measures and their sources, see Appendix E.

Table 5 Variables of the Main Study

Exogenous Variable

Endogenous Variable

1. Tweet Format (Photo and Hyperlink)

2. Tweet Content (Product and

Engagement)

• Attitudes (ATHTA, ATT, and

ATHB)

• Intentions (IHB and IEWOM)

Measures for ATHTA were adapted from various studies and included attitude-

toward-the-website (Chen & Wells, 1999), website advertising (Bruner II & Kumar,

2000), attitude-toward-the-hotel-Facebook-page (Leung, 2012), and perceived usefulness

and ease of use (Davis, 1989). Measures for ATT and ATHB were adapted from studies

on attitude-toward-the-ad (e.g., Batra & Ray, 1986; Leung, 2012; Leung et al., 2015;

MacKenzie & Lutz, 1989; MacKenzie et al., 1986; Mitchell & Olson, 1981) and attitude-

toward-the-brand (Chaudhuri & Holbrook, 2001; Cronin, Brady, & Hult, 2000; Leclerc,

Schmitt, & Dubé, 1994; Leung, 2012; Leung et al., 2015). Additionally, the

measurements for IHB and IEWOM were developed based on the scales for hotel


44

booking intention and the scale for intention to spread positive WOM (e.g., Chiang &

Jang, 2006; Gruen, Osmonbekov, & Czaplewski, 2006; Leung, 2012; Leung et al., 2015).

Some of the measures were modified to fit the context of this study.

3.4 Pilot Study

A pilot study was conducted to gain information about the data collection process

as well as to identify potential problems with the questionnaire. The main purposes for

the pilot study were to determine whether the instrument could be clearly understood by

participants and to make sure the instrument was reliable.

The original survey was developed in English. Before conducting the pilot study,

however, the lead researcher, who was bilingual, translated the survey from English into

Arabic. To reduce the potential for biases resulting from the translation, another

individual, who was also bilingual, translated the Arabic version back into English

without having seen the original English version. The two versions of the survey were

compared, and minor changes were made to ensure accurate translation. Participants were

permitted to complete the questionnaire in the language of their preference. For the pilot,

the survey was active for a week, between April 10, 2016 and April 17, 2016. This initial

effort resulted in 188 responses. Of these, 41 were from the English version and 147 were

from the Arabic version. The subjects were informed that they were participating in a

pilot study and that they had only one week to complete the survey. A time check found

that most of the participants had been able to complete the survey in seventeen minutes or

under.

Data from the pilot study were analyzed using SPSS 22.0. Frequencies were

checked for all variables. Factor analysis was used to evaluate the reliability of the


45

following variables: attitude-toward-the-tweet, attitude-toward-hotel-Twitter-account,

attitude-toward-the-hotel-brand, intention of hotel booking, and intention of electronic

word-of-mouth. Reliability was examined using Cronbach’s alpha coefficients. All scales

yielded reliability scores above .89.

Slight modifications were made as a result of the pilot study. Some questions

were reworded in minor ways in an effort to elucidate them for participants. Furthermore,

new options were added in response to subjects’ “Other (please, specify)” responses. For

example, when participants were asked about the types of SM platforms they used, some

of the “other” answers suggested “WhatsApp”, which initially was not among the options

in the survey. “WhatsApp” is a popular SM platform in Saudi Arabia, however, so it was

added to the list in the final version. Finally, factor analysis of the pilot study data helped

the researcher to be aware of joint variations in response to unobserved latent variables.

After conducting the factor analysis, the researcher created new items for “intentions of

hotel booking” and deleted some old items that had not loaded properly. Furthermore,

changes were made to the formats of these new items. These efforts helped to increase

the overall content validity of the constructs.

3.5 Data Analysis Procedure

The data collected in the online survey were analyzed using SPSS 24.0 and

Mplus 7. First, the data were pre-screened to eliminate incomplete responses. Next,

descriptive statistics were conducted to check for errors in data entry and missing data. A

structural model was used to test the relationship between the latent variables, which

included tweet format (photo and hyperlink), tweet content (product and engagement),

attitudes toward hotel tweets, attitudes toward hotel Twitter account, attitudes toward


46

hotel brand, intentions of eWOM, and intentions of hotel booking (see Table 5).

Moreover, an exploratory factor analysis (EFA) was used to select the appropriate

indicators to perform the structural equation model (SEM) for the hypotheses. A

confirmatory factor analysis (CFA) was then conducted and the variance-covariance

matrices from Mplus software version 7 were utilized (Muthen & Muthen, 1998-2016)

with Maximum Likelihood estimation.

Treatments of the different conditions – tweet format (text, photo, and hyperlink)

and tweet content (brand, product, and engagement) – were differentiated by creating

four dummy variables. These dummy variables were coded as follows: a 0,1 dummy

variable was used in which the value of 0 was given to the control conditions and the

value of 1 was given to the treatment, or testing, condition. The control conditions were

the text condition for tweet format and the brand condition for tweet content. Thus, four

dummy variables were created for data analysis: photos, hyperlinks, product, and

engagement. In all treatments, the controlling conditions (text and brand) were given the

value of 0. Thus, condition #1 (text and brand) was coded as (0,0,0,0) – all of the testing

conditions (photos, hyperlinks, product, and engagement) were given the value of 0.

Condition #2 (text and product) was coded as (0,0,1,0) – the testing condition (product)

was given the value of 1, and all other treatment conditions (engagement, photos, and

hyperlinks) were given the value of 0. Condition #3 (text and engagement) was coded as

(0,0,0,1) – the testing condition (engagement) was given the value of 1, and all other

testing conditions (photos, hyperlinks, and product) were given the value of 0. Condition

#4 (photos and brand) was coded as (1,0,0,0) – the testing condition (photos) was given

the value of 1, and all other testing conditions (hyperlinks, product, engagement) were


47

given the value of 0. Condition #5 (photos and product) was coded as (1,0,1,0) – the

testing conditions (photos and product) were given the value of 1, and the testing

conditions (hyperlinks and engagement) were given the value of 0. Condition #6 (photos

and engagement) was coded as (1,0,0,1) – the testing conditions (photos and engagement)

were given the value of 1, and the testing conditions (hyperlinks and product) were given

the value of 0. Condition #7 (hyperlinks and brand) was coded as (0,1,0,0) – the testing

condition (hyperlinks) was given the value of 1, and the testing conditions (photos,

product, and engagement) were given the value of 0. Condition #8 (photos and

engagement) was coded as (0,1,1,0) – the testing conditions (hyperlinks and product)

were given the value of 1, and the testing conditions (photos and product) were given the

value of 0. Condition #9 (hyperlinks and engagement) was coded as (0,1,0,1) – the testing

conditions (hyperlinks and engagement) were given the value of 1, and the testing

conditions (photos and product) were given the value of 0 (see Figure 5 for more details).

Then, comparative fit index (CFI), standardized root mean square residual (SRMR), non-

normed fit index (NINFI or TLI), and root mean square error of approximation (RMSEA)

were used to evaluate the quality of the model.


48

Tweet Format


Tw

eet

Con

ten

t Brand

Text & Brand

(Condition #1)

(0,0,0,0)

Photos & Brand

(Condition #4)

(1,0,0,0)

Hyperlinks & Brand

(Condition #7)

(0,1,0,0)

Product

Text & Product

(Condition#2)

(0,0,1,0)

Photos & Product

(Condition #5)

(1,0,1,0)

Hyperlinks & Product

(Condition #8)

(0,1,1,0)

Engagement

Text & Engagement

(Condition #3)

(0,0,0,1)

Photos &

Engagement

(Condition #6)

(1,0,0,1)

Hyperlinks &

Engagement

(Condition #9)

(0,1,0,1)

Figure 5. Variables coding for data analysis.

Note: Four dummy variables were created for data analysis, namely, picture, hyperlink,

product and engagement. In all treatments, the controlling conditions (text and brand)

were given the value of 0. For example, condition #2 (text and product) was coded as

(0,0,1,0) where the testing condition (product) was given the value of 1 and all other

treatment conditions (engagement, photos, hyperlinks) were given the value of 0.

In terms of the inferences process, validity and reliability of measurement were

evaluated using a conducting factor analysis and a Cronbach’s alpha, respectively. A

principal axis factor analysis was utilized by using Varimax with Kaiser Normalization

rotation on all the scale items included in the hypothesized model (Figure 3). The number

of constructs proposed in this study matched the total number of factors that resulted

from the factor analysis (Hair, Black, Babin, & Anderson, 2010). Additionally, external

validity was evaluated to preclude convenience sampling, which limits generalizability.

An Estimated Cronbach’s alpha was used to evaluate the reliability of the instrument.

Alpha values at 0.5 were considered acceptable (Nunnally, 1978).


49

CHAPTER IV

ANALYSIS AND FINDINGS

This chapter presents the analysis of the data and the study results. Validity,

reliability analysis, and demographics results are provided. Lastly, the statistical results of

the main experiment using SEM are discussed.

4.1 Data Screening

Due to the nature of the study, participants were recruited via SM platforms,

including Facebook, Twitter, and WhatsApp. Two screening questions were asked to

determine respondents’ eligibility to participate in the research project: (a) Have you ever

stayed in a hotel in Saudi Arabia? and (b) Do you have a Twitter account? Respondents

who answered “no” to either of the screening questions were redirected to a thank-you

message at the end of the survey and excluded from participating in the study.

A total of 1,138 respondents initiated the survey. The results of the screening

questions revealed that 92% of respondents had stayed in a hotel in Saudi Arabia

(n=1,045, 91.83%) and that more than half of them had a Twitter account (n=659,

57.91%). After additional data screening, all incomplete responses were deleted, resulting

in a final sample of 615 responses. Details regarding the selection of the participants are

presented in Table 6.


50

Table 6 Frequencies and Percentage of Screening (N=1138)

Frequency Percentage Cumulative P.

1 Have you ever

stayed in a hotel in

Saudi Arabia?

Yes 1045 91.83 91.83

No 93 8.17 100.00

2 Do you have a

Twitter account?

Yes 659 57.91 63.06

No 386 33.92 100.00

Dropped after

answering “No”

in the 1st

questions

93 8.17

4.2 Characteristics of Respondents

The ages of the participants varied from 17 to 61, though the vast majority

(almost 60%) were under 34 years old. The biggest age group was those between 25 and

34 years old (45.37%). The majority of the participants were male (64.07%). In terms of

highest level of education, the majority of the respondents had already achieved either a

bachelor’s degree (39.02%) or a graduate degree (27.15%). Only 11.38% reported that

their highest level of education was high school. Most of the respondents were Saudi

citizens (83.41%). A detailed demographic profile of the sample is shown in Table 7.

Table 7 Demographic Characteristics of the Sample (N=615)


What is your gender?

Male 394 64.07 74.34

Female 136 22.11 100

No answer, missing 85 13.82

Age

17 – 24 86 13.98 13.98

25 – 34 279 45.37 59.35

35 – 44 118 19.19 78.54

45 – 54 35 5.69 84.23

Over 54 97 15.77 100

What is the highest education level you achieved?

Less than high school 6 0.98 1.13

High school graduate 70 11.38 14.34


51


Some college work 47 7.64 23.21

Bachelor’s degree 240 39.02 68.49

Graduate degree 167 27.15 100


What is your nationality?

Saudi Arabia 513 83.41 98.49

Other 17 2.76 100


4.3 Respondents’ Behavior toward Using Twitter

“Both send and receive” was the highest reported use of Twitter (65.69%). The

number of tweets most participants sent was “less than 4 tweets in a week” (36.91%).

With regard to the effectiveness of Twitter in daily-life communication, the vast majority

of respondents (about 73%) indicated that they found Twitter at least moderately

effective. Almost 24% reported that it was “Extremely effective.” In terms of Twitter

usage, most respondents were heavy users of Twitter. Almost 71% of the sample had

being using Twitter for at least 4 years, and about 29% had used Twitter for more than 4

years. More information about behavior toward Twitter is presented in Table 8.

Table 8 Respondents' Behavior toward Twitter Usage


I use Twitter to

Send only 11 1.79 1.79

Receive only 198 32.20 34.09

Both send and receive 404 65.69 100


How many tweets do you send in a week?

Less than 4 tweets 227 36.91 55.23

4 – 7 tweets 81 13.17 74.94

8 – 14 tweets 58 9.43 89.05

Over 14 tweets 45 7.32 100


How effective is Twitter in daily life communication?

Not effective at all 42 6.83 6.90

Not effective 50 8.13 15.11


52


Slightly effective 68 11.06 26.27

Moderately effective 103 16.75 43.19

Effective 112 18.21 61.58

Very effective 87 14.15 75.86

Extremely effective 147 23.90 100


How long have you been using Twitter?

Less than 1 year 58 9.43 9.52

1 – 2 years 137 22.28 32.02

3 – 4 years 240 39.02 71.43

5 - 6 years 116 18.86 90.48

Over 6 years 58 9.43 100


4.4 Characteristics of Social Media

4.4.1 Consumer Behavior toward Social Media

Because the study examined the effectiveness of Twitter as a marketing tool, it

was important to understand the participants’ overall behavior toward SM usage,

especially their experiences with various SM platforms. Table 9 displays the overall

behavior of the respondents toward SM usage. A total of 23 SM platforms were used.

WhatsApp was the most popular (17.51%), followed by Twitter (15.39%), Snapchat

(13.61%), YouTube (13.48%), Instagram (13.08%), and Facebook (11.63%). In terms of

the frequencies of usage of these six SM platforms, respondents reported that they used

WhatsApp “All the time” (66.08%) and that “At least once a day” they used Twitter

(51.54%), Snapchat (50.88%), YouTube (53.54%), Instagram (48.95%), or Facebook

(47.54%) (Table 10).

There were 20 reasons reported for using SM. The top four most frequent reasons

were “To connect with friends” (15.87%), “For the latest news” (13.06%), “To connect

with family” (12.66%), and “For entertainment” (11.98%). With regard to brands or


53

companies that respondents followed on SM, the majority followed at least one brand or

a company on SM (72%). Almost 40% followed 1 to 5 companies on SM, 18.29%

followed 6 to 15, and 14.10% followed more than 16 brands or companies. To understand

why respondents followed brands’ SM pages, it was important to consider the reasons or

activities that they engaged with these pages. Twenty-three percent of respondents

indicated that they follow brands’ SM pages to “Search for discounts, offers, and

promotions,” 22.90% indicated that they follow brands’ SM pages to “Find new

products/services,” and 22.33% indicated that they follow brands’ SM pages to “Search

for information about a certain products/services.”

Regarding products or services that respondents purchased because of

advertisements on SM in the last year, 45.07% bought 1 to 5 products or services, and

almost 29% purchased more than 6 products or services. “A friend or colleague with

prior knowledge of the products/services” was the most preferred source of information

(19.87%), followed by “A brands' own website” (19.68%), “Social media” (17.67%), and

“Online search” (17.18%).


54

Table 9 Social Media Usage Behavior (N = 615) Freq. Percentage %

Which of these social media platforms do

you use? a

WhatsApp 521 17.51

Twitter 458 15.39

Snapchat 405 13.61

YouTube 401 13.48

Instagram 389 13.08

Facebook 346 11.63

Tango 145 4.87

LinkedIn 121 4.07

Google Plus 90 3.03

Foursquare 31 1.04

Vine 20 0.67

Pinterest 18 0.61

Flickr 13 0.44

Telegram 5 0.17

Line 4 0.13

Imo 1 0.03

iMessages 1 0.03

Yelp 1 0.03

Email 1 0.03

Path 1 0.03

Skype 1 0.03

Talk 1 0.03

Hangouts 1 0.03

What is the primary reason that you use

social media? a

To connect with friends 514 15.87

For the latest news 423 13.06

To connect with family 410 12.66

For entertainment 388 11.98

For information on products/services 256 7.90

For information on brands 247 7.63

For political updates 234 7.22

For update in my professional field 227 7.01

To find old friends 175 5.40

For employment opportunities 134 4.14

For spiritual inspiration 113 3.49

To find new friends 104 3.21

For sport news 4 0.12


55

Freq. Percentage %

For educational purpose 3 0.09

For reading and discussions 2 0.06

For marketing an enterprise 1 0.03

For news about technology and alternative energy 1 0.03

To connect with other colleagues at work 1 0.03

For new announcements 1 0.03

For new ideas and inspiration 1 0.03

How many brands or companies do you

follow on social media?

0 167 28.02

1-5 236 39.60

6-15 109 18.29

16-30 53 8.89

31-60 20 3.36

Over 60 11 1.85

Which of the following do you do on the

brands’ social media pages? a

Search for discounts, offers, and promotions 244 23.28

Find new products/services 240 22.90

Search for information about a certain product/service 234 22.33

Discuss products or services with other followers 125 11.93

Connect with like-minded people 103 9.83

Give feedback on products/services 102 9.73

How many products/services have you

purchased as a result of advertisements on

social media within the last year?

0 155 26.36

1-5 265 45.07

6-10 95 16.16

11-15 31 5.27

16-20 15 2.55

Over 20 27 4.59

Which source of information on a

product/service do you prefer? a

A friend or colleague with prior knowledge of the

products/services. 326

19.87

A brands' own website 323 19.68

Social media 290 17.67

Online search 282 17.18

Physical shops or dealerships 163 9.93

Television adverts 92 5.61

Television programs 58 3.53

Forums 57 3.47

Magazines articles 50 3.05

a. Dichotomy group tabulated at value 1.


56

Table 10 Frequency of Social Media Usage (N = 615)

All the time

At least once a

day

At least once a

week

At least once a

month

Once in a few

months

How often do you use

the following SM

platforms?

WhatsApp 66.08 33.14 0.58 0.00 0.19

Twitter 15.86 51.54 22.03 7.05 3.52

Snapchat 35.59 50.88 9.52 3.26 0.75

YouTube 21.97 53.54 21.21 2.78 0.51

Instagram 20.42 48.95 23.56 5.50 1.57

Facebook 14.20 47.54 21.45 10.43 6.38

Tango 5.59 16.78 34.97 32.17 10.49

LinkedIn 4.20 15.13 41.18 27.73 11.76

Google Plus 7.78 24.44 26.67 15.56 25.56

Foursquare 6.67 16.67 36.67 33.33 6.67

Vine 5.00 20.00 45.00 20.00 10.00

Pinterest 5.56 5.56 55.56 27.78 5.56

Flickr 15.38 7.69 7.69 53.85 15.38

Telegram 0.00 20.00 40.00 20.00 20.00

Line 0.00 50.00 50.00 0.00 0.00

Imo 100.00 0.00 0.00 0.00 0.00

iMessages 100.00 0.00 0.00 0.00 0.00

Yelp 0.00 100.00 0.00 0.00 0.00

Email 100.00 0.00 0.00 0.00 0.00

Path 0.00 100.00 0.00 0.00 0.00

Skype 0.00 100.00 0.00 0.00 0.00

Talk 0.00 100.00 0.00 0.00 0.00

Hangouts 0.00 0.00 0.00 100.00 0.00


57

4.4.2 Consumer Attitudes toward Social Media

To examine attitudes toward SM, the researcher asked respondents to indicate

their level of agreement with five items, each of which employed a 7-point Likert scale

anchored with 1 “Strongly disagree” and 7 “Strongly agree.” The majority of respondents

(86.48%) at least somewhat agreed with the statement that “Social media is more

reachable than mass media (e.g. TV and Radio).” With “Social media is important in

today’s marketplace,” about 89% at least somewhat agreed. Eighty-six percent of

respondents also indicated that they at least somewhat agree with the statement that

“Social media provides effective platforms to new products/services.” Additionally,

88.44% of respondents at least somewhat agreed that “Advertisements via social media

are an effective way for consumers to try new products/services.” It was found that

87.02% of respondents at least somewhat agreed with the last statement: “Overall, I feel

that companies should use social media in today’s business.” More detailed information

on attitudes toward SM can be found in Table 11.

For respondents’ attitudes toward SM advertisements, “I pay little or no attention

to advertisements on social media” was the highest reported item (44.21%), followed by

“I pay lots of attention to advertisements on social media” (32.46%). Almost 22% of

respondents would like SM to ban advertisements, and only 1.75% did not know if there

were advertisements on SM (Table 12).


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Table 11 Attitudes toward Social Media (N = 615)

Strongly

disagree

Disagree

Somewhat

disagree

Neither

agree nor

disagree

Somewhat

agree Agree

Strongly

agree

% % % % % % %

Social media is more reachable than

mass media (e.g. TV and Radio).

2.28 2.11 2.28 6.84 11.75 31.05 43.68

Social media is important in today’s

marketplace.

1.93 1.23 2.98 5.09 10.70 31.58 46.49

Social media provides effective

platforms to new products/services.

2.11 0.70 2.81 7.89 15.61 33.51 37.37

Advertisements via social media are

an effective way for consumers to

try new products/services.

2.46 1.05 2.46 8.60 17.89 32.81 34.74

Overall, I feel that companies should

use social media in today’s business.

2.28 1.58 2.28 6.84 14.21 29.30 43.51


59

Table 12 Attitudes toward Social Media Advertisements (N = 615)

Freq. Percentage Cumulative

Percentage

I pay little or no attention to advertisements on

social media.

252 44.21 76.67

I pay lots of attention to advertisements on social

media.

185 32.46 32.46

I would like social media to ban advertisements. 123 21.58 98.25

I did not know there were advertisements on

social media.

10 1.75 100.00

4.4.3 Consumer Involvement with Social Media

Table 13 displays respondents’ involvement with SM. Almost 60% of

respondents reported that the time they spend online (generally, not necessarily on SM)

ranged between 1 and 29 hours per week (59.37%), and 40.62% reported spending more

than 30 hours online per week. With regard to SM specifically, 61.60% reported spending

1 to 29 hours per week, and 38.41% reported spending at least 30 hours per week on SM.

About 53% of respondents reported that they followed between one and 149 people on

Twitter, and almost 47% followed at least 150 people on Twitter. With regard to number

of followers, almost 60% of respondents reported having 1 to 149 followers, and the

other 40% had at least 150 followers. To understand their involvement with SM, the

researcher asked respondents to identify the age at which they first started using SM.

Almost 50% of the participants reported that they were 20 to 29 years old when they first

started using SM, 28.94% said they were 10 to 19 years old, and almost 21% were older

than 30 years. For more details about consumer involvement with SM, see Table 13.


60

Table 13 Involvement with Social Media (N = 615)

Freq. Percentage

Cumulative

Percentage

How many hours do you

spend online per week? 1 - 29 hours 320 59.37 59.37

30 - 59 hours 125 23.19 82.56

60 - 89 hours 55 10.20 92.76

90 - 119 hours 28 5.19 97.96

Over 119 hours 11 2.04 100.00

How many hours do you

spend on social media per

week?

1 - 29 hours 332 61.60 61.60

30 - 59 hours 111 20.59 82.19

60 - 89 hours 57 10.58 92.76

90 - 119 hours 25 4.64 97.40

Over 119 hours 14 2.60 100.00

How many people do you

follow on Twitter?

1 - 149 285 52.88 52.88

150 - 299 94 17.44 70.32

300 - 449 60 11.13 81.45

450 - 599 36 6.68 88.13

Over 599 64 11.87 100.00

How many followers do you

have on your Twitter

account?

1 - 149 322 59.74 59.74

150 - 299 77 14.29 74.03

300 - 449 43 7.98 82.00

450 - 599 21 3.90 85.90

Over 599 76 14.10 100.00

Approximately, how old

were you when you first

started using social media?

10 - 19 156 28.94 28.94

20 - 29 268 49.72 78.66

30 - 39 76 14.10 92.76

40 - 49 30 5.57 98.33

Over 49 9 1.67 100.00

4.5 Measurement Validity and Reliability

Construct validity was evaluated using a factor analysis (Kline, 2015) with a

principle components extraction method and a Varimax with Kaiser Normalization

rotation on all model scale items. Cronbach’s alphas were calculated to examine

reliability of measurement.

Table 14 shows the factor analysis results for the model, which consisted of five

major constructs. Each of the five constructs was measured using a seven-point Likert


61

scale. Constructs that measured attitudes-toward-hotel-Twitter-account and attitudes-

toward-the hotel-brand had scales anchored with “Strongly disagree” (1) and “Strongly

agree” (7). Attitudes-toward-the-tweet was measured using semantical differential scales

– for example, to indicate whether the respondents felt the tweet was “Bad” (1) or

“Good” (7). Intentions to book and eWOM were measured using “Extremely unlikely”

(1) and “Extremely likely” (7). More information about scale items is presented in Table

14.

First, the attitudes-toward-the tweets scales were tested. After the fifth and sixth

items were dropped because of cross loadings, the scale had an excellent internal

reliability (α = 0.93) and a good convergent validity (factor loadings ranged from 0.78 to

0.86). The attitudes-toward-hotel-Twitter-account scale was composed of three items.

The internal reliability of this scale was excellent (α = 0.86) and its convergent validity

was good (factor loadings ranged from 0.66 to 0.82). The attitudes-toward-the-hotel-

brand (ATHB) scale consisted of four items. Factor loadings for this scale ranged from

0.67 to 0.83, which indicates a good convergent validity. This scale also had an excellent

internal reliability (α =0.87). The intentions-of-hotel-booking scale could not be tested for

internal reliability because it consisted of only one item. The convergent validity was

good (the factor loading was 0.56), but it loaded under another factor, intentions of

eWOM. The intentions-of-electronic-word-of-mouth scale had an excellent internal

reliability (α = 0.93) and an excellent convergent validity (factor loadings ranged from

0.82 to 0.84). Based on the results of this exploratory factor analysis (EFA), all of the

model’s indicators were selected for the structural equation model (SEM) for hypotheses

testing.


62

Table 14 Scale Items and Factor Analysis Results for Model Constructs

(1=Strongly Disagree / 7=Strongly Agree)

Variables/Source Indicators Items Factor

Loading

Cronbach's

Alpha (α)

Attitudes-toward-the-tweet

ATT1 Bad: Good 0.78 0.93

ATT2 Unlikable: Likable 0.82

ATT3 Unfavorable: Favorable 0.86

ATT4 Negative: Positive 0.83

ATT5* Uninteresting: Interesting *drop

ATT6* Boring: Exciting *drop

Attitudes-toward-hotel-Twitter-

account

ATHTA1 It provides useful information. 0.82 0.86

ATHTA2 I am satisfied with this hotel Twitter account. 0.78

ATHTA3 I would like to follow this hotel Twitter account. 0.66

Attitudes-toward-the-hotel-

brand

ATHB1 I like this hotel brand. 0.70 0.87

ATHB2 The products and services of this brand are valuable. 0.71

ATHB3 This brand is different from other hotel brands. 0.83

ATHB4 I would be loyal to this hotel brand. 0.67

Intentions-of-hotel-booking IHB I would consider this hotel for booking. 0.56 -

Intentions-of-electronic-word-

of-mouth

IEWOM1 I would re-tweet these tweets. 0.82 0.93

IEWOM2 I would mention the tweets to other people on Twitter. 0.84

IEWOM3 I would post a tweet of my experience on this hotel

Twitter account.

0.82

IEWOM4 I would recommend the hotel to other people on Twitter. 0.84

Note: *Items were dropped because of cross loadings.


63

4.6 Preliminary Analysis

An analysis of the normality and homoscedasticity of the data was conducted

before the analysis of the measurement construct and structural equation model (SEM)

was started. Kurtosis values should not be greater than ten with no problem of

multivariate normality (Kline, 2015). In this study, Kurtosis values ranged from -1.20 to -

0.21, indicating that the data did not have a serious normality problem. Additionally, the

normality of the data was confirmed using the Skewness values, which ranged from +1 to

-1. More details about the normality and homoscedasticity are displayed in Table 15.

Sample size is very important to having stable results. “Ten participants or

observations per estimation parameter” seemed to be a good rule of thumb and was the

general consensus (Brown, 2006). In this study, there were 45 estimated parameters,

suggesting a targeted sample size of 450 subjects. Hence, the actual sample size (615)

was acceptable.

Mplus software version 7 was used to examine the variance-covariance matrices

(Muthen & Muthen, 1998– 2016). A two-step model-building rule was utilized.

Measurement and path models were included in this model-building analysis (Kline,

2015). To benefit from all of the responses provided in the dataset, including the missing

data, a Maximum Likelihood estimation was used. Moreover, a confirmatory factor

analysis (CFA) and a structural equation model fit (SEM) were obtained by utilizing

numerous model-fit indexes. Hu and Bentler (1999) argued for the comparative fit index

(CFI) ≥ .95, non-normed fit index (NINFI or TLI) ≥ .95, root mean square error of

approximation (RMSEA) ≤ .06, and standardized root mean square residual (SRMR) ≤

.08. Thus, this study used these values as cut-off lines. Furthermore, the study examined


64

Chi-square (χ2) differences to compare the model fit among models.

Table 15 Mean, Standard Deviation, Skewness, and Kurtosis of Indicators.

Construct Name Indicators Mean S.D. Skewness Kurtosis

Attitudes-toward-the-tweet ATT1 4.62 1.77 -0.42 -0.57

ATT2 4.55 1.74 -0.38 -0.59

ATT3 4.61 1.79 -0.42 -0.67

ATT4 4.77 1.67 -0.48 -0.40

ATT5* 4.27 1.93 -0.23 -1.02

ATT6* 4.22 1.84 -0.22 -0.86

Attitudes-toward-hotel-Twitter-

account

ATHTA1 4.43 1.61 -0.60 -0.57

ATHTA2 4.39 1.62 -0.47 -0.63

ATHTA3 3.92 1.81 -0.13 -1.20

Attitudes-toward-the-hotel-brand

ATHB1 4.40 1.64 -0.45 -0.62

ATHB2 4.34 1.44 -0.31 -0.21

ATHB3 4.37 1.53 -0.36 -0.44

ATHB4 3.71 1.65 -0.06 -0.82

Intentions of hotel booking IHB 4.33 1.60 -0.49 -0.50

Intentions of electronic word-of-

mouth

IEWOM1 3.69 1.79 -0.07 -1.14

IEWOM2 3.68 1.75 -0.02 -1.08

IEWOM3 4.06 1.84 -0.20 -1.04

IEWOM4 3.98 1.78 -0.16 -0.96

Note: * Items dropped because of cross-loadings.

ATT1-4 are the item codes of attitudes-toward-the-tweet scale, ATHTA1-3 are the item

codes of attitudes-toward-hotel-Twitter-account scale, ATHB1-4 are the item codes of

attitudes-toward-the-hotel-brand scale, IHB is the item code of the intentions of hotel

booking scale, and IEWOM1-4 are the item codes of the intentions of electronic word-

of-mouth scale. Scale items are shown in Table 14.

A construct inter-correlation test was employed to examine the relationship

between the variables in the proposed model. The results showed that all variables and

their indicators were highly correlated with each other (Table 16 and Table 17). For

example, attitudes toward hotel Twitter account was found to be highly correlated with

attitudes toward the tweet, attitudes toward hotel brand, intentions of hotel booking, and

intentions of eWOM.


65

Table 16 Means, Standard Deviations, and Construct Inter-Correlations

Mean S.D. ATHTA ATT ATHB IHB IEWOM

ATHTA 4.25 1.68 1.00

ATT 4.51 1.79 0.57*** 1.00

ATHB 4.21 1.56 0.67*** 0.57*** 1.00

IHB 4.33 1.60 0.63*** 0.55*** 0.62*** 1.00

IEWOM 3.85 1.79 0.62*** 0.53*** 0.64*** 0.68*** 1.00

Note: ATT1-4 are the item codes of attitudes-toward-the-tweet scale, ATHTA1-3 are the

item codes of attitudes-toward-hotel-Twitter-account scale, ATHB1-4 are the item codes

of attitudes-toward-the-hotel-brand scale, IHB is the item code of the intentions of hotel

booking scale, and IEWOM1-4 are the item codes of the intentions of electronic word-of-

mouth scale. Scale items are shown in Table 14. *p<.05, **p<.01, ***p<.001 (two-tailed)


66

Table 17 Means, Standard Deviations and Inter-correlations among Indicators 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

1 ATHTA1 1.00

2 ATHTA2 0.75*** 1.00

3 ATHTA3 0.61*** 0.68*** 1.00

4 ATT1 0.45*** 0.54*** 0.43*** 1.00

5 ATT2 0.46*** 0.53*** 0.44*** 0.76*** 1.00

6 ATT3 0.44*** 0.51*** 0.42*** 0.76*** 0.80*** 1.00

7 ATT4 0.33*** 0.42*** 0.36*** 0.64*** 0.72*** 0.73*** 1.00

8 ATT5 0.41*** 0.46*** 0.44*** 0.65*** 0.69*** 0.73*** 0.65*** 1.00

9 ATT6 0.38*** 0.48*** 0.46*** 0.63*** 0.66*** 0.69*** 0.65*** 0.81*** 1.00

10 ATHB1 0.54*** 0.61*** 0.52*** 0.49*** 0.46*** 0.44*** 0.42*** 0.43*** 0.43*** 1.00

11 ATHB2 0.53*** 0.61*** 0.56*** 0.50*** 0.50*** 0.47*** 0.42*** 0.49*** 0.51*** 0.76*** 1.00

12 ATHB3 0.39*** 0.45*** 0.44*** 0.36*** 0.36*** 0.35*** 0.34*** 0.37*** 0.35*** 0.62*** 0.63*** 1.00

13 ATHB4 0.45*** 0.46*** 0.50*** 0.43*** 0.46*** 0.39*** 0.36*** 0.45*** 0.44*** 0.58*** 0.66*** 0.59*** 1.00

14 IHB 0.53*** 0.58*** 0.56*** 0.50*** 0.53*** 0.47*** 0.43*** 0.48*** 0.46*** 0.60*** 0.59*** 0.42*** 0.49*** 1.00

15 IEWOM1 0.46*** 0.50*** 0.58*** 0.43*** 0.46*** 0.41*** 0.34*** 0.46*** 0.46*** 0.53*** 0.56*** 0.42*** 0.57*** 0.64*** 1.00

16 IEWOM2 0.48*** 0.49*** 0.58*** 0.42*** 0.47*** 0.41*** 0.35*** 0.44*** 0.42*** 0.52*** 0.55*** 0.44*** 0.57*** 0.63*** 0.88*** 1.00

17 IEWOM3 0.40*** 0.46*** 0.49*** 0.40*** 0.43*** 0.39*** 0.37*** 0.41*** 0.38*** 0.47*** 0.51*** 0.42*** 0.44*** 0.59*** 0.70*** 0.72*** 1.00

18 IEWOM4 0.48*** 0.51*** 0.56*** 0.46*** 0.47*** 0.40*** 0.37*** 0.45*** 0.42*** 0.53*** 0.55*** 0.43*** 0.54*** 0.63*** 0.79*** 0.81*** 0.80*** 1.00

Mean 4.43 4.39 3.92 4.62 4.55 4.61 4.77 4.27 4.22 4.40 4.34 4.37 3.71 4.33 3.69 3.68 4.06 3.98

S.D. 1.61 1.62 1.81 1.77 1.74 1.79 1.67 1.93 1.84 1.64 1.44 1.53 1.65 1.60 1.79 1.75 1.84 1.78

Note: ATT1-4 are the item codes of attitudes-toward-the-tweet scale, ATHTA1-3 are the item codes of attitudes-toward-hotel-

Twitter-account scale, ATHB1-4 are the item codes of attitudes-toward-the-hotel-brand scale, IHB is the item code of the

intentions of hotel booking scale, and IEWOM1-4 are the item codes of the intentions of electronic word-of-mouth scale. Scale

items are shown in Table 14. *p<.05, **p<.01, ***p<.001 (two-tailed)


67

4.7 Measurement Model

Figure 6 shows the measurement model, which consisted of four latent constructs.

The first latent construct, attitudes-toward-hotel-Twitter-account (ATHTA), was

estimated using three indicators. Each of the other latent constructs, including attitudes-

toward-the-tweet (ATT), attitudes-toward-hotel-brand (ATHB), and intentions-of-

electronic-word-of-mouth (IEWOM), was estimated using four indicators. Moreover, the

maximum-likelihood method in the Mplus software was used to estimate and examine the

measurement model (Table 18). A confirmatory factor analysis (CFA) was conducted –

χ2 (113) = 588.92, p<.001, CFI = .95, TLI = .94, RMSEA = .08, SRMR = .04 – and

indicated a good fit between the model and the data (MacCallum, Browne, & Sugawara,

1996). Standardized parameter estimates suggested that the latent variables had been

effectively measured using their respective indicators (factor loadings > .72), as is shown

in Figure 6. The un-standardized parameter estimates are provided in Table 18. The cut-

off criterion for good discriminant validity is determined by calculating the standardized

estimated error-correlations between latent factors, and Brown (2006) suggests that it

should be below 85.


68

Figure 6. Four-factor measurement model of the present study.

Note: χ2 (113) = 588.92, p<.001, CFI = .95, TLI = .94, RMSEA = .08, SRMR = .04.

Standardized coefficients are shown in Table 18. *p<.05, **p<.01, ***p<.001 (two-

tailed).

ATT is the code of attitudes-toward-the-tweet and ATT1-4 represents its scale, ATHTA

is the code of attitudes-toward-hotel-Twitter-account and ATHTA1-3 represents its scale,

ATHB is the code of attitudes-toward-the-hotel-brand and ATHB1-4 represents its scale,

and IEWOM is the code of the-intentions-of-electronic-word-of-mouth and IEWOM1-4

represents its scale.


69

Table 18 Maximum Likelihood Parameter Estimates for Measurement Model

Unstandardized "B" Standardized "β"

Parameter Estimate SE Estimate SE

Pattern Coefficients

ATT factor

ATT1 1 na -

0.85*** 0.01

ATT2 1.04*** 0.04

0.90*** 0.01

ATT3 1.07*** 0.04

0.90*** 0.01

ATT4 0.88*** 0.04

0.79*** 0.02

ATHTA factor

ATHTA1 1 na -

0.81*** 0.02

ATHTA2 1.11*** 0.04

0.89*** 0.01

ATHTA3 1.07*** 0.05

0.77*** 0.02

ATHB factor

ATHB1 1 na -

0.84*** 0.01

ATHB2 0.91*** 0.03

0.88*** 0.01

ATHB3 0.78*** 0.04

0.71*** 0.02

ATHB4 0.88*** 0.04

0.74*** 0.02

IEWOM factor

IEWOM1 1 na -

0.92*** 0.01

IEWOM2 0.99*** 0.02

0.93*** 0.01

IEWOM3 0.89*** 0.03

0.80*** 0.02

IEWOM4 0.95*** 0.03

0.88*** 0.01

Note: ATT is the code of attitudes-toward-the-tweet and ATT1-4 represents its scale,

ATHTA is the code of attitudes-toward-hotel-Twitter-account and ATHTA1-3 represents

its scale, ATHB is the code of attitudes-toward-the-hotel-brand and ATHB1-4 represents

its scale, and IEWOM is the code of the-intentions-of-electronic-word-of-mouth and

IEWOM1-4 represents its scale. Scale items are shown in Table 14. *p<.05, **p<.01,

***p<.001 (two-tailed).


70

4.8 Structural Model

The hypothesized conceptual model (Figure 3) specified the relationship between

the latent variables – tweet format (photo and hyperlink), tweet content (product and

engagement), attitudes toward hotel tweets, attitudes toward hotel Twitter account,

attitudes toward hotel brand, intentions of eWOM, and intentions of hotel booking. After

the CFA was conducted, the variance-covariance matrices were utilized using Mplus

software version 7 (Muthen & Muthen, 1998-2016) with Maximum Likelihood

estimation.

To distinguish the treatments of the different conditions – tweet format (text,

photo, and hyperlink) and tweet content (brand, product, and engagement) – four dummy

variables were created. The dummy variables were coded as follows: a 0,1 dummy

variable was used in which the value of 0 was given to the control conditions and the

value of 1 was given to the treatment, or testing, condition. The control conditions in the

present study were the text condition, in the case of tweet format, and the brand

condition, in the case of tweet content. Thus, four dummy variables were created for data

analysis. For more details, see Figure 5.

The results of the SEM model recommended a good fit – χ2 (160) = 550.13,

p<.001, CFI = .95, TLI = .94, RMSEA = .06, SRMR = .06 – revealing good model fit for

the hypotheses (Hu & Bentler, 1999). Therefore, no model modification was needed. The

standardized parameter estimates (β) are presented in Figure 7, and the unstandardized

parameter estimates (B) are shown in Table 19.

Of the eight hypotheses proposed, six were supported (Table 19). Hypotheses

1&2 tested the effect of tweet format (photo and hyperlink) on attitudes-toward-hotel-

tweets. Contrary to what was predicted, photo did not have a significant effect on


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attitudes-toward-hotel-tweets when compared to text (Hypothesis 1 was not supported).

On the other hand, hyperlink showed a significant effect on attitudes-toward-hotel-tweets

when compared to text (hypothesis 2 was supported). Contrary to the prediction,

however, hyperlink decreased attitudes-toward-hotel-tweets (β= -.15, p< .01).

Hypothesis 3 tested the effect of tweet content (information about hotel products

compared to information about hotel brand) on attitudes-toward-hotel-tweets. This

hypothesis was not supported. The effect of tweet content (space for consumer

engagement compared to brand) on attitudes-toward-hotel-tweets (Hypothesis 4) was

supported (β= -.13, p< .05), however. This result differed from the result predicted:

engagement was found to decrease attitudes-toward-hotel-tweets.

Hypothesis 5 tested whether attitudes-toward-hotel-tweets positively affect

attitudes-toward-hotel-Twitter-account. Hypothesis 5 was supported (β= .66, p< .001).

Hypothesis 6 examined the effect of attitudes-toward-hotel-Twitter-account on attitudes-

toward-hotel-brand. Hypothesis 6 was also supported (β= .81, p< .001). Hypothesis 7

investigated whether attitudes-toward-hotel-brand significantly influence intentions-of-

eWOM. Hypothesis 7 was supported (β= .72, p< .001). Hypothesis 8 tested whether

attitudes-toward-hotel-brand significantly impact intentions-of-hotel-booking. Hypothesis

8 was supported (β= .70, p< .001). The overall structural model is presented in Table 19.


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Figure 7. Structural model and hypotheses testing results.

Note: All are standardized estimates. χ2 (160) = 550.13, p<.001, CFI = .95, TLI = .94,

RMSEA = .06, SRMR = .06. Standardized coefficients are shown. *p<.05, **p<.01,

***p<.001 (two-tailed); ns: non-significant.

Table 19 Unstandardized Coefficients, Estimated Standard Errors, and Standardized

Coefficients of Direct Effects Hypothesis Direct Effect Path B SE β Result

H1 Photo ATT -0.14 0.15 -0.05 Not supported

H2 Hyperlink ATT -0.48** 0.15 -0.15 Supported, but opposite direction

H3 Product ATT -0.03 0.15 -0.01 Not supported

H4 Engagement ATT -0.40* 0.15 -0.13 Supported, but opposite direction

H5 ATT ATHTA 0.58*** 0.04 0.66 Supported

H6 ATHTA ATHB 0.86*** 0.05 0.81 Supported

H7 ATHB IEWOM 0.87*** 0.05 0.72 Supported

H8 ATHB IHB 0.81*** 0.04 0.70 Supported

Note: ATT is the code of attitudes-toward-the-tweet, ATHTA is the code of attitudes-

toward-hotel-Twitter-account, ATHB is the code of attitudes-toward-hotel-brand,

IEWOM is the code of the intentions-of-electronic-word-of-mouth, and IHB is the code

of the intentions-of-hotel-booking. *p<.05, **p<.01, ***p<.001 (two-tailed).


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CHAPTER V

DISCUSSION AND IMPLICATIONS

5.1 Hypotheses Discussion

The purpose of the study was to investigate the influence of various factors of

hotels tweets on customers’ decisions to spread positive eWOM and their intentions to

book a hotel room. Specifically, the purpose of this study was to measure the marketing

effectiveness of Twitter use in the hotel industry in Saudi Arabia. A model was

developed based on the framework of social media marketing effectiveness developed by

Leung (2012). The hypothesized model was tested using an online survey with an

embedded experiment.

The first hypothesis discussed the impact of adding a photo to a hotel tweet on

consumers’ attitudes toward that tweet. We predicted that the relationship would be

significant and the impact would be positive. The hypothesis was not supported,

however. This study found that photos do not affect or positively change customers’

attitudes toward hotel tweets. Contrary to the prediction, the results indicated that hotel

guests like and prefer to retweet plain-text tweets more than they prefer to retweet tweets

that include a photo. This result is inconsistent with the original proposal of Mitchell’s

and Olson’s (1981) attitude model, which suggests that the evaluation associated with a

prominent part of an advertisement, such as a picture, becomes associated with the brand

name. Studies on social media suggest that tweets with photos generate a greater number

of engagements, have excessive effect, and express the emotions and beauty of

information better than does plain-text (Bonsón et al., 2016; Geerlings, 2014; Xi, 2012;

Zhang et al., 2013). For example, a study investigating the use of Twitter in the Spanish


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hotel industry (Bonsón et al., 2016) found that “photos, as a particular media type,

generate more retweets … and [favorites] … than other media types do” (p. 77). The

results of this study, however, were contrary to these previous findings.

We suggest several reasons why tweets with text were more appealing to Saudi

consumers than tweets with photos in this study. Twitter was initially established as a

plain-text only platform, so many people still prefer sending and receiving text tweets.

This may be perceived as similar to sending and receiving text messages. The appeal of

Twitter over text messaging, however, is that it reaches a broader audience. It could be

that most people are accustomed to the simple format of plain-text messaging. It is more

straight-forward and may be perceived as a more direct way to deliver messages via

Twitter. During the pre-test in the present study, the researcher found text to be one of the

most popular types of format used in Twitter by the top six hotels in Saudi Arabia.

Therefore, text is also the most common way for well-known Saudi hotels to deliver

information. Saudi hotel guests may simply be used to this format. Furthermore, this

study used an experimental design by creating a fictitious hotel. It could be that many

respondents did not trust the photo presented in tweets from the simulated hotel’s

account. Nevertheless, the results of this study suggest that Saudi hotels should focus

more on marketing using informative texts about their brands, products/services, policies,

locations, and other useful information for guests. Using a photo may be an option, but

according to the results of this study, it does not have a greater effect that text tweets do.

The second hypothesis tested the relationship between adding a hyperlink to a

hotel tweet and hotel guests’ attitudes toward that tweet. We hypothesized that the

relationship would be significant and positive. This hypothesis was supported, but the


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direction of the relationship was opposite to the one we predicted. We found, in fact, that

adding a hyperlink to a hotel tweet decreases hotel guests’ attitudes toward that tweet.

Contrary to the predictions, the results indicated that plain-text tweets are preferred to a

greater degree by hotel guests than are tweets including a hyperlink. These results are

inconsistent with previous research that found that adding hyperlinks to SM messages is

positively correlated with consumers’ attitudes toward the messages (Alboqami et al.,

2015; Bonsón et al., 2016; Boyd et al., 2010; Cooper, 2013; Suh et al., 2010; Zarrella,

2009). At the same time, the results of our research were in line with a recent study by

Microsoft and Columbia University researchers who found that about 60% of tweets with

links never get clicked or read (Jain, 2016; Morris, 2016). The negative attitudes we

found toward the tweet with a hyperlink may indicate consumers’ concerns about their

privacy and security. This may be especially the case for unknown businesses (like the

simulated hotel created for the purposes of this study). When a company is unknown or

new, there is a lack of trust between consumers and the business. Consumers, therefore,

may think that a hyperlink in a tweet can navigate them to a vulnerable web page. Hence,

new hotels need to be aware of such concerns and try to be more informative in their

tweets to first build trust with their guests. In their initial marketing through Twitter and

other SM platforms, new hotel businesses need to ensure their guests that they are

looking after their well-being, showing integrity, and protecting their information. If they

do, guests will feel safe and secure when they click on all links provided by the hotels

they follow.

The third hypothesis revolved around the relationship between information about

products/services added to a hotel tweet and its influence on hotel guests' attitudes toward


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that tweet. We predicted that the relationship would be significant and that the influence

would be positive. The hypothesis was not supported, however. We found that the hotel

guests’ attitudes did not change if the hotels added information about products/services to

their tweets. Again, these results seem contrary to the findings of prior studies, which

argued that customers’ attitudes are positively influenced by products/services advertised

via SM sites (Sinclaire & Vogus, 2011). From our study, it appears that guests prefer

marketing and advertising information that is not related to products/services. Therefore,

we suggest that if a new hotel wants to achieve satisfactory attitudes from their guests,

they need to focus on marketing their brands first. Only after consumers develop trust in

the brand can they trust information posted about products/services from new or

unknown hotels.

The fourth hypothesis investigated the effect of consumer engagement on hotel

guests’ attitudes toward tweets. Engagement was tested by providing a space in the

simulated tweets that allowed hotel guests’ to reply and act, including by asking

questions, sharing experiences, mentioning, retweeting, liking, and adding picture

captions. We hypothesized that the impact of this type of tweet on hotel guests’ attitudes

toward such tweets would be positive and significant. This hypothesis was supported, but

the direction of the relationship was opposite to the direction we predicted. We found that

tweets with a space for engagement do not affect or positively change customers’

attitudes toward hotel tweets. The results indicated that hotel guests like and prefer to

retweet and interact with tweets that include brand information more than they do tweets

that include a space for engagement. This result was also contrary to the findings of other

studies. For instance, engagement has been found to be positively correlated with


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customers’ experiences, attitudes, and purchasing decisions (Green, 2007; Golden &

Caruso-Cabrera, 2016; Levy et al., 2013; Prahalad & Ramaswamy, 2004; Ramaswamy &

Gouillart, 2010; Schools, 2014). Our findings, however, indicate that providing a space

for consumer engagement in a hotel tweet negatively affects attitudes toward that tweet.

One possible explanation for this result might be that the posted tweets were from

a fictitious hotel and, therefore, did not reflect consumers’ preferences. Respondents in

this study were unfamiliar with the simulated hotel brand and simply did not have any

previous experience to reflect upon. Secondly, the simulated tweets were presented in a

static format. So while participants could see a space for engagement, they could not

interact directly in that space.

Similar to our previous conclusions, we suggest that new hotels need to build their

brand images first by posting tweets that are appealing, favorable, useful, and interesting

to their guests’ preferences and experiences. Once consumers have sufficient experience

with a hotel brand, they may be more willing to share their experiences and preferences

by engaging with hotel Twitter accounts. As has been shown by numerous consumer

relationship management (CRM) studies, building relationships with a company

facilitates brand loyalty, retention, repeat purchases, WOM/eWOM, and customer

satisfaction (Heller Baird & Parasnis, 2011; Noor, 2012; Patil, 2014).

We also speculate that respondents may be more engaged with their issues and

problems instantly and directly by, for example, reaching out to management via phone

or email, rather than through a space on Twitter provided for guests’ feedback. This may

be especially relevant when the issue at hand could reveal private information that the

guest does not want to be made public. Hotels need to listen to their guests and interact


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with them instantaneously. Some of these suggestions might help to influence hotels

guests’ engagement and positively affect their attitudes toward brands.

Hypothesis five examined the relationship between the attitude toward the hotel

tweet and the attitude toward the hotel Twitter account. The prediction was that the

relationship would be significant and positive. The hypothesis was supported. We found

that hotel guests’ favorable attitudes toward hotel tweets boosted their attitudes toward

hotel Twitter accounts. Generally, the more hotel guests liked the tweets posted by a

hotel, the more time they spent following and engaging with that hotel’s Twitter account.

This finding accords with our predictions and the notion, forwarded by Raney et al.

(2003), that positive attitudes toward entertaining advertising on a website increase

consumers’ intentions to revisit that site considerably more than do sites without

entertaining advertising. Likewise, Paquette (2013) found that “users who have more

positive attitudes toward advertising are more likely to join a brand or a retailer’s

Facebook group” (p. 10). Also, Leung et al. (2015) suggested that customers’ enjoyment

and experiences of SM pages have a positive influence on their attitudes toward hotels’

SM pages.

Therefore, a hotel’s tweets are important predictors of whether guests will like the

hotel’s Twitter account. Hotels need to evaluate the types of tweets that they post in order

to discover the types that best influence their guests’ attitudes. Tweets posted by a hotel

should be more attractive and interesting to the hotel’s guests to enrich their attitudes

toward the entire Twitter account. The use of Twitter proves to be the venue for

marketing. The results of this study emphasize the value of tweets as a means to improve

a chain of consumer attitudes toward the hotel.


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The sixth hypothesis tested the next link in this chain of relationships: the link

between attitudes toward the hotel Twitter account and attitudes toward the hotel brand.

The prediction was that the relationship would be significant and positive. The hypothesis

was supported. We found that hotel guests’ attitudes toward hotel brands are positively

influenced by their attitudes toward hotels’ Twitter accounts. This result also supports the

findings of Leung (2012) and Leung et al. (2015), which claimed that attitudes toward a

hotel’s SM page have a positive impact on attitudes toward the hotel’s brand. The results

suggest that hotels need to take advantage of the marketing opportunities that Twitter

provides to feature their brand names and images. Doing so motivates and influences

their guests’ perceptions of their brands. Brand awareness can also be increased by letting

guests know that the hotel is available on Twitter to listen to and respond to their

inquiries. Strong marketing via Twitter has a direct effect on positive consumer attitudes

toward brands.

The next two hypotheses tested consumers’ attitudes toward a brand on their

intentions to spread positive electronic word-of-month (hypothesis seven) and to book a

room (hypothesis eight). Both hypotheses were supported. We found that the more

positive and favorable consumers felt toward a hotel brand, the more likely they were to

spread positive eWOM about the hotel. This result is in line with previous research that

found that spreading of eWOM is higher when consumers feel more interested in the

brands they follow (Leung, 2012; Leung et al., 2015; Yeh & Choi, 2011). Several

traditional marketing studies have also claimed that consumers’ satisfaction with a brand

greatly influences their WOM. Individuals are more likely to engage in WOM when their

excitement toward a brand is very high (Aaker, 1997; Roberts, 2004) or, on the contrary,


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very low (Richins, 1983). An important predictor of the spread positive eWOM is

whether a brand is relevant to its consumers’ lives. This effect was found to be more

prominent in the offline context than in the online context, however (Lovett, Peres, &

Shachar, 2013). Nevertheless, our results suggest that consumers are more likely to

spread positive eWOM about new brands online if those brands use SM platforms,

especially if consumers use Twitter as a platform to engage with the hotels’ brands. Also,

the simplicity of Twitter might be the reason for the fast spread of eWOM, which

increases awareness of hotels’ brands quickly and easily. To generate positive eWOM

about their brands on Twitter, hotels need to professionally interact with their guests.

Larger hotels may consider using celebrities to endorse their brands and spread eWOM.

Moreover, hotels should always maintain control of their brands’ reputations by listening

to and engaging with their guests, employees, and other groups on Twitter. This strategy

can help them to generate more brand awareness and allow them to benefit from

individuals’ engagement.

The eighth and final hypothesis tested whether the relationship between attitudes

toward hotel brand and intentions of hotel booking was significant and positive. This

hypothesis was also supported. The findings clearly demonstrate the impact of hotel

guests’ favorable attitudes toward hotel brands on their booking intentions. This finding

is compatible with those of previous studies that have suggested that purchase intentions

are affected positively and directly by attitudes toward a brand (Bruner II & Kumar,

2000; Leung et al., 2015; Mitchell & Olson, 1981). In other words, the more customers

like a hotel brand, the more likely they are to book a room in that hotel. This is hardly

surprising, as this relationship has been effectively demonstrated by traditional


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marketing. Furthermore, this research seems to support the notion that the same

principles are applicable to marketing via Twitter. Therefore, hotels should use their

Twitter accounts to sustain and promote the positioning of their brand names, which will

enhance their guests’ hotel booking intentions.

Additionally, Smith (2016) claimed that more than three-quarters of active

Twitter users access Twitter on their mobile phones. Thus, hotels also need to increase

their brand awareness and presence on Twitter by targeting mobile users, being mobile

friendly, and utilizing different Twitter tools, e.g. mentioning and adding hashtags.

5.2 Theoretical Framework Support This study used Leung’s (2012) model of the marketing effectiveness of

Facebook in the hotel industry. Leung’s original model suggested that the format and the

content of Facebook messages have a direct, positive effect on consumers’ attitudes

toward Facebook pages, which then influence their attitudes toward the messages

themselves. These positive effects result in positive attitudes toward hotel brands and

ultimately lead to positive eWOM and intent to book.

For the purposes of this study, the model was modified and applied in a different

context (Twitter) and in a different cultural setting (Saudi Arabia). With regard to the

modification of the model, we suggested that the format and the content would influence

attitudes toward messages before they had any impact on other attitudes, such as attitudes

toward SM pages and hotel brands.

Leung initially argued that Facebook message content and message format have

direct effects on consumer attitudes. When tested empirically, however, Leung’s findings

showed that message format and content have no effect on consumers’ attitudes toward

Facebook pages. Leung found that the test of the interaction effect (format x content) was


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not significant. Although, Leung found that among the two main effects, the impact of

Facebook message format was significant, while the impact of Facebook message content

was non-significant. Therefore, Leung argued that both message format and message

content have no effect on consumers’ attitudes and/or their intentions to engage in

eWOM and hotel booking. Thus, Leung dropped these constructs from the originally

hypothesized model and simplified the model to examine only the relationships between

consumers’ attitudes, their eWOM, and their hotel room booking intentions.

In our study, we did not find that Twitter messages’ format or content had any

positive influence on consumers’ attitudes toward hotel Twitter accounts or brands.

Adding a photo did not have any effect on these attitudes, and adding a hyperlink had a

negative effect. Featuring a product in a message did not result in positive consumer

attitudes, while adding a space for consumer engagement had a negative impact on these

attitudes.

Although these results were contrary to our predictions, they were consistent with

Leung’s (2012) original model. Neither study found that SM message format or content

can be used to predict consumers’ positive attitudes toward a company. This is not to say

that SM message format or content should not be taken into consideration when

developing marketing strategies via SM. Here is why.

There are several reasons why the effect of SM messages was not found. First and

foremost, simulated SM accounts were used in both studies. Second, because the

accounts were simulated, respondents did not have any prior knowledge about the

companies or experiences with the hotels. This implies a lack of any previous relationship

with the hotels and, most importantly, a lack of trust in the brands. In real life situations,


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respondents may be willing to click on a link provided by a trusted brand or pay more

attention to a photo of a hotel they are familiar with. In the simulations, however, these

attitudes were not evident. Likewise, consumers may be willing to leave feedback on

otherwise engage with a familiar hotel Twitter account, but in our simulated situation,

this was not the case. In addition, guests may be willing to receive information about

products/services provided by a well-known hotel that they are aware of, but in our

simulated situation, this willingness was not shown. Overall, both Leung’s research and

our study demonstrate that forcing respondents to evaluate only simulated hotels’ SM

accounts may have a negative or zero impact on their attitudes and behaviors toward

those hotels’ SM accounts.

It is important to note, here, that although the simulated Twitter account did not

work as intended in examining the relationships between tweets and consumer attitudes,

it actually provided many useful insights for hospitality marketers. In a way, a simulated

Twitter account can be seen as a projection of a new hotel: customers are unfamiliar with

the brand name, and they do not have any experience or trust in the company. What this

study shows is that new hotels face many challenges at first. A new company needs to

start developing their presence on Twitter with simple, informative, plain-text tweets.

Once the company develops a number of followers, they can start making their tweets

more interactive. This study also suggests that once consumers have positive attitudes

toward a hotel’s tweets, they start having positive attitudes toward the hotel’s entire

Twitter account, which, in turn, improve their attitudes toward the hotel’s brand and

result in intent to book and the spread of electronic word of mouth. The findings for these

relationships were consistent with the theoretical model of the study and, once again,


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empirically supported the hypothesis that attitudes toward SM accounts have a direct

impact on consumers’ attitudes toward brands and lead to boosted booking intentions and

positive eWOM.

In sum, we found that SM messages are important predictors of whether guests

like or follow hotels’ SM pages. This conclusion is opposite to Leung’s suggestion that

only after consumers develop positive attitudes toward SM pages can they develop

positive attitudes toward messages posted by those pages. We believe that companies

need first to attract their consumers to join their SM sites by taking care of the messages

posted in their SM account. Thus, once consumers have positive attitudes toward a SM

site’s messages, they will be more likely to join or follow the site.

Lastly, the more positive attitudes consumers develop toward a hotel brand, the

more likely they are to book hotel rooms in that brand’s hotels and the more willing they

are to spread positive eWOM about that brand. All hotels, and especially newcomers,

need to take this strategy into consideration to attract new customers and retain existing

customers. Hotel managers should understand that SM marketing is different from

traditional marketing. SM requires them to build their brand names and images by first

listening to their consumers and responding to them instantly. Thus, we believe that

companies need to consider these approaches to earn their consumers’ trust and

confidence in their hotel brands.

5.3 Relevant Consumer Characteristics Discussion

This study also examined consumer characteristics regarding behavior toward

SM, attitude toward SM, and involvement with SM. We found that these characteristics

predict hotel guests’ perceptions toward SM marketing. Generally, it appears that a great


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number of respondents used various types of SM almost every day. The reasons they

reported for using SM included connecting with friends and family, entertainment, news,

and other reasons. Thus, it can be assumed that hotel guests’ positive or negative

experiences with hotels can be spread easily and swiftly to many of their followers on

SM.

With regard to the brands or firms they follow on SM, it seems that the vast

majority of consumers (72% in this sample) follow at least one brand or company. Most

of the respondents followed the brands or companies that they did to search for discounts,

offers, and promotions; to find new products/services; or to search for information about

a certain products/service. Thus, it seems that respondents prioritized information about

products and services above information about brands or companies on SM. This

behavior was only evident for the brands that the respondents knew and trusted (and

therefore followed on SM). Brands need to use SM to provide whatever satisfies their

customers’ needs, experiences, and preferences. Generally, it appears that Saudi

customers prefer recommendations from their friends and colleagues with prior

knowledge about products/services, and then the brands’ or businesses’ own websites.

This shows the value of word of mouth and the value of information provided on

companies’ websites more than it does the value of information on SM. This might be

due to trust concerns about some of the information provided via SM. When asked about

their attitudes toward SM, however, the majority of Saudi respondents believed that SM

plays an important role in today’s marketplace and should be embraced by many

businesses. Therefore, businesses should use SM to build trust, build their reputations,


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enhance awareness of their brands, and gain competitive advantage by reaching the

masses and saving costs.

When it comes to involvement with SM, respondents who started using SM when

they were 10 years old and older said they spent an average of 30 hours per week (more

than 4 hours per day) online and more than 4 hours per day on SM. Regarding the

number of followers on Twitter, more than half of the respondents followed and were

followed by less than 150 people. Almost half of the respondents who used SM were

young consumers (between 20 and 29 years old). Thus, it seems that involvement with

SM is gaining popularity, which means that SM offers great opportunities for hoteliers to

effectively engage with their guests in two-way conversations.

Overall, we suggest that hoteliers find ways to enhance their guests’ experiences

with their brands through SM. One of the most effective ways to improve their

interactions with their guests is to provide a trusted customer review system on their SM

sites. Different strategies for marketing hotels’ products/services via SM seem necessary

for success in today’s highly competitive business world because guests have an open

space of choices from which to select what they follow and recommend and where they

book. Thus, hoteliers need to provide high-quality customer service and advanced

interactive marketing technology to meet and exceed their guests’ expectations.

5.4 Conclusion, Limitations and Suggestions for Future Studies

The investigation of SM consumers’ attitudes warrants continuous effort from

both academic scholars and industry practitioners. This study extended the model of

social media marketing effectiveness to investigate the impact of Twitter consumers’

intentions to book hotels in Saudi Arabia and to engage in positive eWOM. The size of


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the Saudi population that uses new technologies and SM and the economic prosperity of

Saudis increase the demand for hospitality researchers to study this phenomenon.

Businesses in general and hospitality and tourism firms in particular should take

advantage of the opportunity to attract Saudi consumers to SM sites.

While this study added knowledge to the theoretical background on the

effectiveness of SM and offered many valuable insights for the hotel industry

practitioners, it is important to acknowledge its limitations. The sampling method used is

one limitation. The study utilized a convenience sampling strategy, so the results cannot

be generalized because the sample was limited to the context of Saudi Arabia. To

generalize these findings, replication of this study in a range of locations is

recommended. Moreover, future studies could use the theory and method applied in this

study to investigate how the hospitality and tourism industries apply different SM

platforms and to investigate other businesses contexts. For instance, similar studies could

be conducted in other Gulf Cooperation Council (GCC) countries (e.g. Kuwait, United

Arab Emirates, Qatar, Oman, and Bahrain) or in other regions. Consumers in other

regions might act and feel differently about the use of different SM sites as marketing

tools.

Furthermore, the simulated hotel Twitter account developed and designed by the

researcher might be the reason that some of the results differed from what was expected

and predicted. This study clearly points out that hotel guests need to trust hotel brands

before they can have favorable attitudes toward them. Trust, in turn, could be one of the

mediating factors that affect hotel guests’ attitudes toward hotels. Perhaps using a real

hotel brand to test the model proposed in this research would provide more accurate and


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reliable results that would be more useful to the hospitality industry. Loyalty toward a

specific hotel brand can also be a mediating factor that influences hotels guests’ attitudes

and intentions. So, future research testing real-brand SM accounts should examine trust

and brand loyalty as mediating factors.

Additionally, depending on shifts in the ever-developing, sophisticated

technologies used by different SM platforms, some factors used in this study might need

to be adjusted in future research. For example, videos and snapchatting are

transformative means to market and advertise businesses. Thus, future research could

investigate the impact of these technologies on the consumer attitudes tested in this study.

A general limitation of research in the social sciences is the risk of respondents’

answering untruthfully or inaccurately; therefore, some of the results may not precisely

represent consumers’ experiences and attitudes. Such concerns are always out of the

researcher’s control. Additionally, inaccurate results are usually attributed to the number

of variables used (fatigue effect) or to the way the survey was presented. Although the

timing records show that on average, participants took between 10 and 20 minutes to

complete the survey, future studies might consider being more concise in their

questionnaires.

This research has shed light on the study of SM marketing effectiveness in the

hospitality industry. It offered empirical support to the theoretical model and extended

knowledge of the marketing effectiveness of SM sites in the hotel industry. Future

research is warranted to further examine the relationships between new SM platforms in

the hospitality and tourism industries.


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104

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APPENDICES

Appendix A

Diagram of the Research Design


105

Appendix B

Content Analysis - Pre-test (September 5, 2015 – October 5, 2015)

Boudl Hotels & Resorts @BoudlHotels:

Number of Followers 89,177, Followings 4,165, Tweets 6,529.

Type: Regional Hotel & Resort Company.

Bio: Welcome [into] the world of comfort, luxury and refinement, where you can enjoy

fine, distinct and superior services, comfort and peace and all means of [entertainment].

.Saudi Arabia and Kuwait Location:

Phone: 920000666

Website: boudl.com.sa

Joined: June 2011.

Tweet Format Posts/Month Tweet Content Times

Text

205

Brand 110

Product 34

Engagement 190

Promotion 3

Information 92

Photos

132

Brand 107

Product 101

Engagement 6

Promotion 57

Information 32

Videos 2

Brand 2

Product 2

Hyperlinks

62

Brand 6

Product 5

Promotion 56

Carawan Al Fahad Hotel @Carawanalfahad:

Number of Followers 11,195, Followings 26, Tweets 1,091.

Type: National Hotel Company.

Bio: 4-Star hotel includes recreation facilities, spa, convention center, and luxury

wedding hall. It is in the Heart of Riyadh City on Ourabah Street crossing King Fahd

Road close to Kingdom Tower.

Location: Riyadh, Saudi Arabia.

Phone: +966 (11) 217-2345

Website: carawan-alfahad.com

Joined: March 2013


Text

5

Brand 5

Product 3

Engagement 5

Photos 5

Brand 5

Product 5


106

Four Seasons Riyadh @FSRiyadh:

Number of Followers 5,667, Followings 266, Tweets 5,227.

Type: Multinational Hotel Company.

Bio: We are Four Seasons Hotel Riyadh at Kingdom Centre. Please follow us on Twitter

for up-to-the-tweet Hotel news and updates.

Location: Riyadh, Saudi Arabia.

Phone: +966 (11) 211-5000

Website: fourseasons.com/riyadh/

Joined: March 2009


Text

31

Brand 27

Product 21

Engagement 4

Promotion 4

Information 6

Photos 2

Brand 2

Promotion 1

Hyperlinks

28

Brand 27

Product 22

Engagement 1

Promotion 3

Information 4

Makkah Hilton & Towers @Hilton_Makkah:

Number of Followers 5,584, Followings 5,707, Tweets 1,781. Type: Multinational Hotel Company.

Bio: Overlooking the Holy Haram Mosque and the Kaaba, the Makkah Hilton & Towers

is set in the heart of Makkah.

Makkah, Saudi Arabia Location: .

Phone: +966 (12) 534-0000

Website: http://www3.hilton.com/en/hotels/saudi-arabia/makkah-hilton-hotel-

MAKHITW/index.html

Joined: December 2011


Text

21

Brand 1

Product 21

Engagement 3

Photos

10

Brand 7

Product 9

Information 2

Hyperlinks 7

Brand 3

Product 3


107

Burj Rafal Hotel Kempinski @BurjRafalHotel:

Number of Followers 2,794, Followings 88, Tweets 928.

Type: Multinational Hotel Company.

Bio: Burj Rafal Hotel Kempinski is a luxurious 5 Star Hotel in Riyadh, Kingdom of

Saudi Arabia.

Saudi Arabia. Riyadh, Location:

Phone: +966 (11) 511-7777

Website: kempinski.com/burjrafal

Joined: January 2014


Text

31

Brand 29

Product 28

Engagement 15

Promotion 6

Reward 1

Photos

30

Brand 29

Product 28

Engagement 2

Reward 1

Hyperlinks

2

Brand 1

Product 1

Engagement 1

Reward 1

Sofitel Al Khobar @SofitelAlKhobar:

Number of Followers 2,544, Followings 1,092, Tweets 1,706. Type: Multinational Hotel Company.

Bio: Luxury hotel located at Khobar Corniche, offering a breathtaking view of the

Arabian Gulf and the city.

Location: Al Khobar, Saudi Arabia.

Phone: +966 (13) 881-7000

Website: 5988sofitel.com/

Joined: February 2010


Text

41

Brand 34

Product 33

Engagement 26

Promotion 1

Information 7

Photos

39

Brand 34

Product 34

Engagement 1

Information 4

Hyperlinks

6

Brand 6

Product 6

Engagement 1

Promotion 2

Information 1


108

Appendix C

Human Research Protection Program Approval Letter


109

Appendix D

Simulated Twitter Account

• Text and Brand (Condition #1)


110

• Text and Product (Condition #2)


111

• Text and Engagement (Condition #3)


112

• Photos and Brand (Condition #4)


113

• Photos and Product (Condition #5)


114

• Photos and Engagement (Condition #6)


115

• Hyperlinks and Brand (Condition #7)


116

• Hyperlinks and Product (Condition #8)


117

• Hyperlinks and Engagement (Condition #9)


118

Appendix E

Online Survey

Introductory Statement of Confidentiality and Rights

We are inviting you to participate in our research project that involves the completion of

an online survey. The purpose of the survey is to assess your perceptions and opinions as

Saudi hotel guests. The results of this study will be shared with Saudi Hotels, so you will

have the chance to provide valuable information they may use. The approximate time to

complete the survey is about 10-15 minutes.

The survey is not designed to sell you anything or solicit money from you in any way.

You will not be contacted on a later date for any sales or solicitation. Participation is

voluntary. You may withdraw at any time or skip any questions you do not wish to

answer.

All responses are anonymous. No personal data will be asked and the information

obtained will be recorded in such a manner that you cannot be identified. The data will be

used solely for statistical analysis and no other purpose, and will be available only to the

research group working on this project.

If you have any questions or if you would like to know the results of the study, please

contact Mr. Mansour Alansari at 806.252-2428 or email at [email protected]

This study has been approved by the Human Research Protection Program (HRPP) at

Texas Tech University (tel. 806. 742-2064). If you have any questions about the study or

your rights as a participant, please mail your inquiries to Human Research Protection

Program, Office of the Vice President for Research, Texas Tech University, Lubbock,

Texas 79409

Please click to the next page to access the survey

mailto:[email protected]


119

Screening Questions

If you are using your cell phone, please use the Landscape Orientation mode.

1. Have you ever stayed in a hotel in Saudi Arabia?

• Yes …. [Continue survey]

• No ….. screen out [Thank you for your cooperation, but you don’t meet the

requirement for this study]

2. Do you have a Twitter account?

• Yes …. [Continue survey]

• No ….. screen out [Thank you for your cooperation, but you don’t meet the

requirement for this study]

Experiment Section

In this section the sample is split into the equal number of respondents for the nine

blocks. Each set of participants sees only one condition of the experiment and

respondents then proceed to the next set of attitudes questions.

Tweet Format


Tw

eet

Con

ten

t

Brand Brand & Text

(Condition #1)

Brand & Photos

(Condition #4)

Brand & Hyperlinks

(Condition #7)

Product Product & Text

(Condition #2)

Product & Photos

(Condition #5)

Product & Hyperlinks

(Condition #8)

Engagement Engagement &

Text

(Condition #3)

Engagement &

Photos

(Condition #6)

Engagement &

Hyperlinks

(Condition #9)


120

Attitudes Measures (ATHTA, ATT, and ATTHB)

Variables Measure Source*

• Attitude-

toward-

the-tweet

(ATT)

3. How do you feel about these tweets?

(Likert scales 1 to 7)

3-1. Bad / Good

3-2. Unlikable / Likable

3-3. Unfavorable / Favorable

3-4. Negative / Positive

3-5. Uninteresting / Interesting

3-6. Boring / Exciting

Adapted from Batra

& Ray, 1986; Leung,

2012; Leung et al.,

2015; MacKenzie &

Lutz, 1989;

MacKenzie et al.,

1986; Mitchell &

Olson, 1981

• Attitude-

toward-

hotel-

Twitter-

account

(ATHTA)

4. Please, indicate your level of agreement with

each of these statements about this hotel

Twitter account. (Likert scales 1=Strongly

Disagree/ 7=Strongly Agree)

4-1. It provides useful information.

4-2. I’m satisfied with this hotel Twitter

account.

4-3. I would like to follow this hotel Twitter

account.

Adapted from

Bruner II & Kumar,

2000; Chen & Wells,

1999; Davis, 1989;

Leung, 2012

• Attitude-

toward-

the-hotel-

brand

(ATHB)


each of these statements about this hotel brand

after reviewing its Twitter account.

(Likert scales 1=Strongly Disagree/ 7=Strongly

Agree)

5-1. I like this hotel brand.

5-2. The products and services of this hotel

brand are valuable.

5-3. This brand is different from other hotel

brands.

5-4. I would be loyal to this hotel brand.

Adapted from

Chaudhuri &

Holbrook, 2001;

Cronin, Brady, &

Hult, 2000; Leclerc,

Schmitt, & Dubé,

1994; Leung, 2012

Intentions Measures (IHB and IEWOM)


o The-

intention-

of-hotel-

booking

(IHB)

6. Please, indicate how likely you are to book this

hotel after reviewing this Twitter account.

(Likert scale 1 =Extremely Unlikely/ 7= Extremely

Likely)

6.1. I would consider this hotel for booking.

Adapted from

Chiang & Jang,

2006; Leung, 2012

7. Using the information provided in this hotel

Twitter account, how would you book this hotel?

(You can choose more than one answer)

• Hyperlinks provided

• Phone number provided


121

• Email account provided

• Twitter secure messaging system

• Other (please specify) _____________

o The-

intention-

of-

electronic

-word-of-

mouth

(IEWOM)

8. Please, indicate how likely you are to recommend

this hotel Twitter account to other people on

Twitter.

(Likert scales 1 =Extremely Unlikely/ 7= Extremely

Likely)

8-1. I would re-tweet these tweets.

8-2. I would mention the tweets to other people

on Twitter.

8-3. I would post a tweet of my experience on this

hotel Twitter account.

8-4. I would recommend the hotel to other people

on Twitter.

Adapted from

Gruen,

Osmonbekov, &

Czaplewski, 2006

Twitter Behavior Measures


o Twitter

behavior Please, tell us how you use Twitter in general.

9. I use Twitter to …

o Send only

o Receive only

o Both send and receive

10. How many tweets do you send in a week?

o Less than 4 tweets

o 4 – 7 tweets

o 8 – 14 tweets

o Over 14 tweets

11. How effective is Twitter in a daily life

communication? (Likert scale 1=Not Effective at all/

7=Extremely Effective)

12. How long have you been using Twitter?

o Less than 1 year

o 1 – 2 years

o 3 – 4 years

o 5 - 6 years

o Over 6 years

Adapted from

Shultz, 2010


122

Behavior and Attitudes toward Social Media Measures


• Behavior

toward

social

media

13. Which of these social media platforms do you

use? (Please select 'none' if you do not use any)

Skipping Logic to question 19

• Facebook

• Twitter

• Google+

• LinkedIn

• YouTube

• Instagram

• Snapchat

• Vine

• Flickr

• Pinterest

• Foursquare

• WhatsApp

• Tango

• None

• Other (Please specify) ______________

14. How often do you use [name of SM platform

that is carried over from question 13]?

o Once in a few months

o About once a month

o Several times a month

o About once a week

o Several times a week

o Daily

o Several times a day

o All the time

15. What is the primary reason that you use

social media?

• To connect with friends

• To find new friends

• To find old friends

• To connect with family

• For information on brands

• For information on products/services

• For the latest news

• For entertainment

• For spiritual inspiration

• For political updates

Adapted from

Anonymous, n.d.;

Heinonen, 2011;

Ly & Hu, 2015;

Valenzuela, Park,

& Kee, 2008


123

• For update in my professional field

• For employment opportunities

• Other (Please specify) ______________

16. How many brands or companies do you follow

on social media? (Please select '0' if you do not

follow any)

Skipping Logic to question 18

o 0

o 1-5

o 6-15

o 16-30

o 31-60

o Over 60

17. Which of the following activities do you

perform on the brands’ social media pages?

• Find new products/services.

• Search for information about a certain

product/service.

• Search for discounts, offers, and

promotions about products/services.

• Discuss products or services with other

followers.

• Give feedback to the brand on

products/services.

• Connect with like-minded people.

18. How many products/services have you

purchased as a result of advertisements on social

media within the last year?

o 0

o 1-5

o 6-10

o 11-15

o 16-20

o Over 20

19. Which source of information on a

product/service do you prefer?

• A brands' own website

• A friend or colleague with prior

knowledge of the products/services

• Social media


124

• Television programs

• Television adverts

• Magazines articles

• Online search

• Physical shops or dealerships

• Forums

• Attitudes

toward

social

media


each of these statements about social media.

(Likert Scales 1=Strongly Disagree/7=Strongly

Agree)

20-1. Social media is more reachable than mass

media (e.g. Television and Radio).

20-2. Social media is important in today’s

marketplace.

20-3. Social media provides effective platforms

to new products/services.

20-4. Advertisements via social media are an

effective way for consumers to try new

products/services

20-5. Overall, I feel that companies should use

social media in today’s business

21. Please choose which statement suits your

attitude toward advertisements on social media.

(Likert Scales 1=Strongly Disagree/7=Strongly

Agree)

21-1. I pay lots of attention to advertisements on

social media.

21-2. I pay little or no attention to

advertisements on social media.

21-3. I would like social media to ban

advertisements.

21-4. I didn't know there were advertisements on

social media.

21-5. Other (please specify)

_________________

Adapted from

Akar & Topçu,

2011; Boateng &

Okoe, 2015; Cha,

2009; Chiou,

Chen, Huang,

Huang, & Hu,

2008; Chu, Kamal,

& Kim, 2013; Ly

& Hu, 2015


125

Involvement with Social Media Measures

Variables Measures Source*

• Social

media

involvement

22. How many hours do you spend online per week?

________

23. How many hours do you spend on social media per

week?________

24. How many people do you follow on

Twitter?________

25. How many followers do you have on your Twitter

account? ________

26. Approximately, how old were you when you first

started using social media? _________ (years old)

Adapted

from Ha &

Hu, 2013

Demographic Measures

Variables Measures

o Demographic 27. What is your nationality?

_________________________________

28. What is your gender?

o Male

o Female

29. What is the highest education level you

achieved?

o Less than high school

o High school graduate

o Some college work

o Bachelor’s degree

o Graduate degree

30. In which year were you born? _________

* Some measures were adjusted to fit the context of the current study

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