GOOGLES ENTRY INTO ONLINE ACCOMMODATION DISTRIBUTION - Hotel Newsroom

Degree Program "Innovation and Management in Tourism" University of Applied Sciences Salzburg

GOOGLES’ ENTRY INTO ONLINE ACCOMMODATION DISTRIBUTION

UNDERSTANDING TRAVELER’S ACCEPTANCE OF THE GOOGLE HOTEL

FINDER

THESIS SUBMITTED TO THE UOAS SALZBURG IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF

"MASTER OF ARTS IN BUSINESS"

Author: Nadia Pircher, BA

Student number: 1010649032

Date: 1st of December 2012

Supervisor: Prof. (FH) Dr. Roman Egger

Affidavit

I herewith declare on oath that I wrote the present master thesis without the help of third persons and

without using any other sources and means listed herein; I further declare that I observed the

guidelines for scientific work in the quotation of all unprinted sources, printed literature and phrases

and concepts taken either word for word or according to meaning from the Internet and that I

referenced all sources accordingly.

This thesis has not been submitted as an exam paper of identical or similar form, either in Austria or

abroad and corresponds to the paper graded by the assessors.

Salzburg, 1st of December 2012 ___________________________ Nadia Pircher

UoAS Salzburg, Master Program IMT | Nadia Pircher I

I. Table of contents

I. Table of contents ......................................................................................... I II. List of abbreviations .................................................................................. III III. List of illustrations ................................................................................... IV IV. List of tables ............................................................................................. V V. Abstract ............................................................................................ VII 1. Introduction .............................................................................................. 1

1.1 Background of the research problem .................................................... 1 1.2 Significance of the research ................................................................ 2 1.3 Research gap ................................................................................... 3 1.4 Research objectives ........................................................................... 4 1.5 Outline of the thesis .......................................................................... 4

2. Distribution of tourism accommodations ........................................................ 6 2.1 The accommodation .......................................................................... 7 2.2 Distribution channels defined .............................................................. 8 2.3 Accommodation distribution channels ................................................ 10

2.3.1 Electronic distribution channels .......................................................... 11 2.3.2 Computer reservation systems (CRS) and GDS .................................... 12

2.4 Online distribution channels .............................................................. 13 2.4.1 Evolution of online distribution channels .............................................. 15

2.5 Mulitple distribution channels ............................................................ 19 3. Google travel technologies and services ....................................................... 21

3.1 Google information power ................................................................ 21 3.2 Google travel services ...................................................................... 22 3.3 The Google Hotel Finder ................................................................... 23

4. Online travel decision-making for accommodations ....................................... 26 4.1 The travel decision-making process ................................................... 26 4.2 Research on travel decision-making processes .................................... 28

4.2.1 The role of the Internet in travel decision-making ................................. 31 4.2.2 Need for information during the decision-making process ...................... 34

4.3 Purchase decision for accommodations ............................................... 36 4.3.1 Choice attributes for hotels ................................................................ 36 4.3.2 Decision process for hotel selection .................................................... 38 4.3.3 Website attributes affecting online hotel purchase ................................ 39 4.3.4 Hotel purchase with the Hotel Finder ................................................... 41

5. The Technology Acceptance Model .............................................................. 44 5.1 Extensions and modifications of TAM ................................................. 48 5.2 The extended TAM .......................................................................... 49 5.3 Research purpose ........................................................................... 52 5.4 Proposed research model ................................................................. 53 5.5 Research variables and hypotheses ................................................... 54

6. Research methodology .............................................................................. 57 6.1 Research philosophy ........................................................................ 57 6.2 Research approach and strategy ....................................................... 58 6.3 Model building ................................................................................ 58 6.4 Population and sample ..................................................................... 61

UoAS Salzburg, Master Program IMT | Nadia Pircher II

6.5 Questionnaire ................................................................................. 61 6.6 Research method ............................................................................ 62

6.6.1 Model replication .............................................................................. 62 6.6.2 Validity ........................................................................................... 63 6.6.3 Reliability ........................................................................................ 64 6.6.4 Correlation ...................................................................................... 64

6.7 Results .......................................................................................... 65 6.7.1 Demographics and experience ........................................................... 66 6.7.2 Model fit analysis ............................................................................. 67 6.7.3 Assessment of validity and reliability ................................................... 67 6.7.4 Assessment of correlation .................................................................. 73 6.7.5 Discussion ....................................................................................... 75

7. Conclusions ............................................................................................ 77 7.1 Implications ................................................................................... 77 7.2 Limitations and further research ........................................................ 78 7.3 Acknowledgements ......................................................................... 79

VI. List of references ..................................................................................... VI VII. Annex ............................................................................................ XX

UoAS Salzburg, Master Program IMT | Nadia Pircher III

II. List of abbreviations

AGFI ................................................................................................................. Adjusted Goodness of Fit Index

ATT ............................................................................................................................................................ Attitudes

CRO ............................................................................................................................ Central Reservation System

CRS ........................................................................................................................ Computer Reservation System

GDS ........................................................................................................................... Global Distribution System

GFI ..................................................................................................................................... Goodness of Fit Index

GPS ............................................................................................................................ Global Positioning System

IT .................................................................................................................................... Information Technology

ICT .......................................................................................... Information and Communication Technology

INT ................................................................................................................................................ Intentions to use

OTA ................................................................................................................................... Online Travel Agency

PEOU ................................................................................................................................ Perceived Ease of Use

PP ........................................................................................................................................... Perceived Playfulness

PU ......................................................................................................................................... Perceived Usefulness

RMSEA .................................................................................................... Root Mean Square of Approximation

TAM ................................................................................................................... Technology Acceptance Model

TPB ....................................................................................................................... Theory of Planned Behavior

TRA ........................................................................................................................ Theory of Reasoned Action

UGC ................................................................................................................................ User Generated Content

UoAS Salzburg, Master Program IMT | Nadia Pircher IV

III. List of illustrations

Figure 1 The accommodation product ............................................................................................................... 7

Figure 2 The switch company concept ............................................................................................................. 13

Figure 3 Online bookings on the German market .......................................................................................... 16

Figure 4 Online travel distribution pyramid ..................................................................................................... 19

Figure 5 Search engine market share ................................................................................................................. 22

Figure 6 Decision-making process ..................................................................................................................... 26

Figure 7 Travel choice components in the decision-making process .......................................................... 30

Figure 8 Information sources for travel decision-making .............................................................................. 32

Figure 9 Factors influencing the purchase decision for hotels ...................................................................... 34

Figure 10 Information needs in the decision making process ....................................................................... 35

Figure 11 Ranking of important choice attributes .......................................................................................... 37

Figure 12 Hotel decision-making process ........................................................................................................ 39

Figure 13 The Technology Acceptance Model ................................................................................................ 45

Figure 14 The extended TAM ............................................................................................................................ 51

Figure 15 Proposed research model and its relationships .............................................................................. 53

Figure 16 Hypotheses testing ............................................................................................................................. 75

UoAS Salzburg, Master Program IMT | Nadia Pircher V

IV. List of tables

Table 1 Characteristics of tourism services ........................................................................................................ 6

Table 2 Six A's framework for the analysis of tourism destinations .............................................................. 9

Table 3 Examples of online travel agents ......................................................................................................... 18

Table 4 Examples of travel meta sites ............................................................................................................... 19

Table 5 Evaluation of altervative hotel ............................................................................................................. 28

Table 6 Ten-item scale for perceived usefulness ............................................................................................. 46

Table 7 10 item scale for perceived ease of use ............................................................................................... 46

Table 8 Revised six-item scale for perceived usefulness ................................................................................ 47

Table 9 Revised six-item scale for perceived ease of use ............................................................................... 47

Table 10 Perceived usefulness (PU) measurement scale ................................................................................ 59

Table 11 Perceive ease of use (PEOU) measurement scale ........................................................................... 59

Table 12 Perceived Playfulness (PP) measurement scale ............................................................................... 60

Table 13 Attitudes toward using (ATT) measurement scale ......................................................................... 60

Table 14 Intentions to use (INT) measurement scale .................................................................................... 60

Table 15 Model fit analysis .................................................................................................................................. 67

Table 16 Loading estimates of the CFA model ............................................................................................... 68

Table 17 Factor score regression coefficients of the CFA model ................................................................ 69

Table 18 Cronbach's alpha coefficient for PU ................................................................................................. 70

Table 19 Cronbach's alpha coefficient with deleted variable, PU ................................................................. 70

Table 20 Cronbach's alpha coefficient for PEOU .......................................................................................... 70

Table 21 Cronbach's alpha coefficient with deleted variable, PEOU .......................................................... 70

Table 22 Cronbach's alpha coefficient for PP ................................................................................................. 71

Table 23 Cronbach's alpha coefficient with deleted variable, PP ................................................................. 71

Table 24 Cronbach's alpha coefficient for ATT .............................................................................................. 71

Table 25 Cronbach's alpha coefficient with deleted variable, ATT .............................................................. 71

Table 26 Cronbach's alpha coefficient for INT ............................................................................................... 72

Table 27 Cronbach's alpha coefficient with deleted variable, INT .............................................................. 72

Table 28 Inter-item correlation matrix .............................................................................................................. 72

Table 29 Squared multiple correlations of the CFA model ........................................................................... 73

Table 30 Fit summary of the multivariate regression model ......................................................................... 74

UoAS Salzburg, Master Program IMT | Nadia Pircher VI

Table 31 Parameter estimates of the multivariate regression model ............................................................ 74

Table 32 Squared multiple correlations of the multivariate regression model ............................................ 75

UoAS Salzburg, Master Program IMT | Nadia Pircher VII

V. Abstract

Effective hotel distribution is of significant importance for hotel establishments and can be defined by

two main functions: (1) to provide consumers with information and thus facilitate the purchase

decision-making and (2) enable the purchase itself. How to reach the online customer in the most

effective way and which online distribution channels to use. These are fundamental questions in the

present hotel business. With the launch of the Google Hotel Finder, Google opened a new channel for

accommodations and online travel agents to reach the customer. Furthermore, due to the integration

of the Hotel Finder into other Google services such as search, maps and Google+ local, the travel

search experience of the online consumer is enhanced.

Building on the extended technology acceptance model (TAM), the aim of this master thesis is to

provide insight into this field and investigate the acceptance of the Google Hotel Finder tool for online

hotel reservations. Overall, it was found that the adoption of a particular online reservation website can

be predicted by the extended TAM framework. Perceived usefulness, perceived ease of use and

perceived playfulness have an impact on users’ attitudes toward using the Hotel Finder, while

playfulness was found to be a key predictor. Moreover attitudes are key determinants of travelers’

intentions to use the Hotel Finder for online reservations.

UoAS Salzburg, Master Program IMT | Nadia Pircher 1

1. Introduction

1.1 Background of the research problem Efficient tourism distribution is a crucial factor for the success of tourism organizations and thus has

gained increased attention among tourism researchers and various definitions can be found. They are

the bridge between supply and demand (Alcázar Martínez, 2002), in other words they bring producers

of tourism services and their consumers together (Gartner and Bachri, 1994).

Three main waves of technological developments changed distribution systems in tourism enterprises,

namely computer reservation systems (CRS) in the 1970s; Global distribution systems (GDS) in the

1980s and the Internet in the 1990s (Buhalis, 1998). The major technological progress in the

distribution industry was the Internet.

The internet commenced operation in 1969 with four universities connected, mainly for research and

military purposes (Werthner and S. Klein, 1999a). The commercial usage of the Internet began years

later, when companies started to take advantage of the communication protocol of the world wide

web, which in 1993 has been made freely accessible to the public (Kracht and Wang, 2010). After the

public entrance of the Internet, it has grown as a network of networks and currently records 2.3 billion

users worldwide. This represented about 33% of the population worldwide and a 528.1% growth

compared to the year 2000 (World Usage Patterns & Demographics, 2012).

Throughout the world there has been a tremendous growth in the use of the web. Especially online

shopping and purchase for tourism products is one of the fastest growth areas, with online booking of

hotel rooms experiencing the biggest development (Wong and Law, 2005). The Internet provides 24/7

accessibility and allows travelers to undertake reservations online in a short period of time, at much

lower costs and in a more convenient way then with traditional methods. This advantages of IT

changed the way in which customers look for information and how they purchase tourism products

and services today (Buhalis, 2003). The major part of online tourism sales is generated by air travel,

followed by hotels, package tours, rail and rental cars (Marcussen, 2009).

The tourism product can be defined as an ‘amalgam of factors that are combined to provide the tourist

with something they wish to consume’ (Page, 2012, p. 157). Like the tourism product in general, the

accommodation product is complex and diverse. The accommodation is a sub-sector of the hospitality

sector. Hospitality is the very essence of tourism, involving the consumption of food, drink and

accommodation in an environment away from home. In todays’ society, hospitality has become a

commercialized experience, where the guest pays for the service or good they consume at the tourism

destination. Accommodation is only one component of the hospitality sector that comprises amongst

others the following types of establishments; hotels, restaurants, cafes, camping sites, canteens or take-

away food bars (Page, 2012). Online booking of accommodations, the second most important product

in online tourism sales is the focus of this research.

The Internet has significantly changed the way hotels distribute their products and electronic channels

play an increasingly important role in hospitality distribution (Gazzoli et al., 2008). While hospitality

distribution has traditionally been categorized into direct selling channels and intermediaries, over the

last years developments in information and communication technologies (ICTs) offered new


possibilities for hotel distribution, adding new players and shifting the power among suppliers, buyers

and intermediaries (O’Connor and Frew, 2004). Literature suggests that the complexity of distribution

channels is likely to further increase in future (Gursoy, 2010).

Increased market transparency in the e-marketplace and price disparities among the distribution

channels of hotels have changed the behavior of the consumers, who are now shopping around and

search for better deals (Gazzoli et al., 2008; O’Connor and Frew, 2002). However, the complexity of

the travel product hinders the end user in the adoption online booking systems and many studies have

already investigated the attributes of the booking systems influencing online booking behavior (Law

and R. Leung, 2000; Qi et al., 2010).

The Technology Acceptance Model (TAM) (Davis et al., 1989) has extensively been used in research to

measure users adoption of technology systems and has already been extended to predict usage of hotel

reservation websites (Morosan and Jeong, 2006, 2008). Perceived usefulness, perceived ease of use and

perceived playfulness have an impact on attitudes toward using booking systems. In this master thesis

the author is relying on the constructs of the extended TAM to predict users adoption of the Google

Hotel Finder.

1.2 Significance of the research Effective hotel distribution is of significant importance due to the perishability of the accommodation

product. In general, effective hotel distribution has two main functions; (1) to provide the consumers

with information and thus facilitating the purchase decision making and (2) to enable the purchase of

the product itself (O’Connor and Frew, 2004). Multiple channel strategies, where the suppliers applies

more then one distribution channel to reach the customer in the online market, has grown rapidly in

recent years. The Internet has played an important role in this phenomenon as web based technologies

provide numerous possibilities for suppliers to implement multiple channel distribution. However the

use of multiple channels can lead to high distribution costs, segmentation-overlap or cannibalization in

the market (Kang et al., 2007). How to manage these multiple channels effectively will be critical to the

long-term outcomes of the implementation. In this dynamic and volatile distribution landscape

hoteliers have to be up to date about new channels and ensure that each added channel has a

reasonable return on investment.

Presently, the important topics for suppliers to monitor are meta-search, social and mobile. In meta-

search, travel offers and pricing from many sources such as websites are found and compared for the

ease of the consumer (Christodoulidou et al., 2007). Another ‘mega trend’ emerged in recent years

among online travellers is the use of social media and of various user generated content (UGC) during

travel planning process. The term social media can generally be defined “as a group of internet-based

applications that build on the ideological and technological foundation of Web 2.0, and that allow the

creation and exchange of user generated content” (Kaplan and Haenlein 2010, p. 61). So called social

media websites, representing various consumer generated content such as blogs, virtual communities,

social networks, collaborative tagging and sharing of media files like videos and pictures on sites like

Youtube and Flicker have gained importance in online travel search (Xiang and Gretzel, 2010). Finally,

the increasing trend of travel technology also includes the use of mobile services throughout the travel

information process. Due to broad adoption of third generation (3G) mobile phones and services,


mobile has become the third screen beside the desktop and laptop computer (Vinod, 2011). Past

research on preferred channels of interaction with travel services implies increased acceptance toward

mobile applications during the whole consumer process, from information search, to reservation,

payment and especially the activity ‘check in’ (Eriksson, 2012). Along with this developments, new

third-party intermediaries such as Google, Facebook or Smartphone provider could increase their

power as they become the preferred points of entry for consumers in online travel shopping and

online travel purchase (Green and Lomanno, 2012).

In order to reach the customer in the most effective manner, hoteliers need to determine which

channels are currently the most successful in hotel business and which are likely to dominate the

future. Choosing the best mix of channel partners is crucial for the hotel’s success and underestimating

the power of new entrants in the distribution landscape may have consequences. Focusing on one of

the before mentioned topics, this work examines the power of search giant Google, who currently

clearly dominates in general search (Shabat, 2012) and may become equally successful in meta travel

search as well. While Google has been the biggest player in the travel advertising market for years, the

search giant recently entered the vertical distribution chain by offering new search functions and value

added services for their users (Suhayda, 2011). One of these value added services is the Google Hotel

Finder, which holds the ability to change the hospitality distribution market within the next years and is

subject to this study.

1.3 Research gap As already mentioned in chapter 1.2, effective hotel distribution depends on choosing the right

distribution channels. A mix of these different channels is therefore an important part of the hoteliers’

strategic management decision. One of the distribution channels to look at in near future is the Google

Hotel Finder. While Google has started the project with the Google Hotel Finder experiment in 2011,

the term experiment can be leaved out since November 2012. During the ‘experiment’ phase Google

continued to update the users interface and tested additional services such as the mapping tool for

selection by popular areas , because only the satisfied version should be marketed to the masses as the

Google Hotel Finder (“What Is Hotel Finder?,” 2012). And finally in November 2012 Google started

the official Google Hotel Finder service with the new domain http://www.google.com/hotels/.

Interesting is the integration of the tool into other Google services such as ‘search’, ‘maps’ and

‘Google+ local’. With this integration the online user has the possibility to make a reservation directly

on the search engine result page, which makes the use of further booking and rating portals

unnecessary and online reservations for the user easier and faster (Hendele, 2012a). And actual studies

confirm, 80 percent of all online tourists start their search with Google. Furthermore, Google makes

the Hotel Finder service available in many different languages, such as German while prices will be

presented local currencies (Benkert, 2012a). According to Be:con (Benkert, 2012) this is the “start of a

new age in online hotel distribution, while they expect about 70 percent of all hotel rooms to be

booked on the Hotel Finder within the next two years.”

But what thinks the online customer about this new service from Google? This scientific research aims

to examine the potential of the Google Hotel Finder for online hotel distribution and makes an

attempt to identify online travellers adoption of the tool for online hotel reservations.


It has been determined that the extension of technology acceptance model (TAM) framework to the

hotel industry is a good fit for examining online travelers usage and adoption of hotel reservation Web

sites (Morosan and Jeong, 2006, 2008). Building on the extended TAM framework, this study

examines users’ adoption of the Hotel Finder within the online purchase decision-making process for

hotel accommodation products and services.

1.4 Research objectives The main research objectives of this study seek to examine the acceptance of the Hotel finder tool

among online travelers for their online travel decision-making process. Further, it will examine the

effects of perceived usefulness, perceived ease of use, and perceived playfulness on travellers attitude

toward using the Google Hotel Finder portal and aim to provide insight into the question why people

do or do not use the Hotel Finder for online room reservation. The results of this study could assist

hotels to gain a better understanding of Google’s power in travel distribution and seeks to address the

question, if accommodation distribution through the Hotel Finder will be essential in the future.

Moreover, the author aims to find out if the collaboration with Google could increase effectiveness of

hotel distribution in the long term.

Based on the literature presented in the chapters 1.1 to 1.2 and the abovementioned objectives, the

research questions of this study are:

Can consumers’ adoption of the Google Hotel Finder tool be predicted with the extended TAM?

If yes, to which extend perceived usefulness, perceived ease of use and perceived playfulness influence

online travelers acceptance of the Google Hotel Finder for online room reservation?

These research questions are designed to contribute to the growing body of literature on online

distribution in the hospitality sector as well as provide some insights for hospitality organizations and

consumers.

1.5 Outline of the thesis The first chapter provides an introduction into the master thesis. First of all a theoretical framework is

given and the relevance of the research is explained. Then the aims and objectives of the study will be

discussed and the research question is formulated.

In the second chapter distribution of the accommodation product is discussed. First of all the

importance of the accommodation product is argued and various definitions of distribution channels

are provided. Further accommodation distribution is defined and the devolvement from electronic

distribution to online distribution is analyzed in detail. Finally the importance of multiple channel

strategies is discussed.

The third chapter deals with the influence of the most powerful search engine in the world and the

beginnings of Google in vertical travel distribution. Additionally, the launch of the Google Hotel

Finder tool and its functionalities are discussed in detail.

The fourth chapter deals with the travel decision-making process. First, prior research on the decision-

making process is reviewed and how the development of the Internet intensified this process, is


explored. After analyzing the relevance of the Google Hotel Finder during the decision-making

process, the purchase decision of accommodations is discussed and finally the functionalities of the

Hotel finder tool are examined.

In the fifth chapter the Technology Acceptance Model is examined in detail. After exploring the

original TAM proposed by Davis (1986), different extensions and modifications of the original model

are reviewed. Then the research purpose of this study, the proposed research model, research variables

and hypotheses are presented.

In the sixth chapter the research methodology is discussed. First, the research philosophy and the

research approach and strategy of the study are presented. Afterwards the author presents how the

research model is built and how the data for the constructs of perceived usefulness, ease of use and

playfulness will be collected. Further the sample and population size is argued and finally the results of

the study are presented.

In the seventh chapter, the results and outcomes of the research are summarized, recommendations

will be made and conclusively limitations and further research is discussed.


2. Distribution of tourism accommodations Distribution is a part of the marketing functions in an organization, which makes the product and

services accessible and available to the consumer and provides the link between the supply and

demand. Together with the three other variables of the marketing communication mix, namely

product, price and promotion, distribution is a critical aspect of the strategic marketing management

with the overall goal to satisfy consumer needs (Lubbe, 2000). Due to the complexity of the tourism

industry and the intangibility of the tourism product, distribution in tourism is especially problematic

(Pender and Sharpley, 2006). The general characteristics of the tourism product are extensively

discussed in Table 1.

Intangibility Travel product and services are intangible. When purchasing a tourism service the

consumer has no possibility to see, feel or try the product prior to purchase. Sellers

of tourism do not purchase stock; only images and other information related to the

product can be displayed at the point of sale. Due to the intangibility of the tourism

product consumer have to assume a high risk associated with high costs, when

purchasing tourism products. Also the purchased product cannot be returned if the

purchaser is dissatisfied.

Perishability Tourism services are highly time specific and immediately perishable; for example a

hotel bed is available for occupancy at a particular time. If it’s not sold for that time

period, revenue will be lost forever.

Uno-actu

principle

Tourism services require the active participation of the customer (prosumer) and

can’t be stored due to the simultaneous production and consumption.

Information

intensity

Tourism is highly dependent on information in terms to overcome the intangibility

of the product. The delivery of appropriate information to consumers can help in

the selection and decision-making process. Timely and accurate information can

minimize the gap between consumers’ expectations and actual experience.

Intermediaries Due to the distance between the market and the product, tourism distribution

often includes intermediaries, who have a strong influence on consumers purchase

decision.

Table 1 Characteristics of tourism services Source: own illustration (Lubbe, 2000; Pender and Sharpley, 2006)

For the purpose of this thesis the author concentrates on distribution of one service product in the

destination, namely the accommodation product. Accommodation provides the base from which

tourism can engage in the process of staying at a destination. Like tourism in general, accommodation

assumes many forms and not all of them fit the conventional image of the hotel (Page, 2012). This

diversity of the accommodation sector will be discussed in the following chapter.


2.1 The accommodation Accommodation is the largest subsector within the tourism market (Cooper et al., 1998). The majority

of the tourists require overnight accommodation during their travel or stay at a destination, and

second, the accommodation usually represents the biggest part of the tourist expenditure (Pender and

Sharpley, 2006). Such as the tourism product in general, the accommodation product is complex and

diverse, and the following appearances can be observed. As mentioned before the accommodation

product is highly fragmented and goes beyond the classical image of the hotel. Although the classical

hotel is the most significant and widely recognized form of accommodation (Holloway, 1998), a wide

range of other types of accommodations are available. While they are diverse in terms of style, size,

location, ownership or the level of service they provide, the different types of accommodation include

bed-and breakfasts, apartments, farm stays, backpacker hostels, cruise ships and even camp-sites and

caravan parks (Pender and Sharpley, 2006). The diversity of accommodation types shows the scope of

the sector to adapt their products to the changing customers needs (Page, 2012).

The accommodation product constitutes a fundamental component of the tourism experience. Thus,

the accommodation presents more than the tangible elements of a room, a bed or a meal; one of the

core functions of the accommodation is to meet the customers’ needs and expectations in order to

enrich the entire holiday experience (Pender and Sharpley, 2006). Some researchers have

conceptualized accommodation as a product, which represents different factors and facilities as shown

in Figure 1. While luxury hotels emphasize high service standards and image, economy

accommodations focus on price.

Figure 1 The accommodation product Source: (Page, 2012, p. 157)

Such as the tourism sector, the accommodation sector is characterized by constant changes, innovation

and product diversification. Whilst the serviced accommodation was dominant before 1945, a rapid

growth of the non-serviced types of accommodation happened after 1945. Today the distinction

between serviced and non-serviced accommodation is blurring with the growth of apartment hotels

(Page, 2012).

The Accommodation Product

Location of the establishment (accessibility)

Facilities (bedrooms, restaurants, meeting

rooms, sports facilities) Service level

Image (how customers view the establishment) Price

Ability to differentiate the product to different

customers, and incentives to encourage key clients (ex. rewards for frequent

use)


In addition to the general characteristics of tourism products shown in the chapter before, all types of

accommodation are confronted with some common characteristics:

• Seasonality - periods of demand are concentrated on the peak season, while off-peak times are

characterized by lower demand.

• Occupancy levels – related to the issue of seasonality, demand for room is spread across

seasons and according to weeks and days. With occupancy level accommodation tries to sell its

rooms in order to spread occupancy across the year and avoid to many peak times.

• Location – the location is of paramount importance for the establishment of accommodation

units and often follows the distance-decay principle; the prestigious properties are located in

the central locations with greater access to attractions and facilities, while in rural

environments the absence of the hectic town and landscape attractions are dominant. At the

same time, gateways such as airports or railway stations remain high value locations.

• Grading systems – in grading systems, hotels and accommodations are assigned to a category

in relation to its facilities and services. Star ratings are very common for hotels.

• High fixed costs – hotels typically suffer from high fixed costs as a proportion of total

operation costs. Thus, the level of business needs to optimize occupancy levels to cover such

fixed costs (Page and Connell, 2006; Page, 2012).

2.2 Distribution channels defined Kotler et al. (1996) defined distribution as a pattern of interdependent organizations involved in the

process of making a product or service known to possible consumers. Distribution is the bridge

between supply and demand (Gartner and Bachri, 1994). In tourism, distribution is the link between

tourism suppliers and destinations and the consumers in the market (Knowles and Grabowski, 1999).

Alcázar Martínez (2002, p. 17) defined tourism distribution as “making the product available to the

consumer in the quantity needed at the right time, place, state and possession utility to the consumer,

thereby facilitating sales.” Tourism distribution can be understood on two levels; while basic

distribution is understood as merely intermediation activity, consisting of bringing buyers and sellers

together, augmented distribution refers to additional value creation by intermediaries. Offering value in

terms of service, price, availability, information or security is critical for customer acquisition and

retention (Bigné, 2011). Hence the primary distribution functions for tourism are information and

travel arrangement services. Many tourism distribution channels provide information for potential

tourists, bundle tourism products and also enable the costumer to make and pay for reservations

(Buhalis and Laws, 2001).

Prior research showed that various attempts were made to define the tourism distribution channel

concept. Middleton (1994, p. 202) defined distribution channels as “any organized and serviced system,

created or utilized to provide convenient points of sale and /or access to consumers, away from the

location of production and consumption.” This definition failed to provide information on the channel

members involved and mainly focused on distribution from the supply side. Local distribution

channels, such as tourism offices and incoming travel agents at destinations were ignored, while this

definition assumed that access points for consumers were located away from the location of

production. The study ignored the promotion and marketing activities undertaken by tourism


distribution channels and underestimated their information provision function (Buhalis and Laws,

2001). Wanhill (1993, p. 189) included the role of intermediaries into the definition of distribution

channels and suggested that “the principal role of intermediaries is to bring buyers and sellers together,

either to create markets where they previously did not exist or to make existing markets work more

efficiently and thereby to expand market size.” While Wanhill highlighted the role of intermediaries

and assumed their presence in all distribution systems, direct distribution systems did not involve any

intermediaries. Furthermore Mill and Morrison (1992, p. 471) quoted in their study McIntosh

definition of a distribution channel as “an operating structure, system or linkages of various

combinations of travel organizations, through which a producer of travel products describes and

confirms travel arrangements to the buyer.” Wynne et al. (2001, p. 425) described the purpose of a

distribution channel as followed: “Quite simply... to make the right quantities of the right product or

service available at the right place, at the right time.” Following Stern and El-Ansary (1988) and Wynne

et al. (2001) identified three essential functions of distribubion channels:

1. Adjusting the discrepancy of assortments and thereby supporting economies of scope

2. Routinizing transactions to minimize the cost of distribution

3. Facilitating the search process of both producers and consumers.

To conclude, a very general definition of tourism distribution channels was provided by the World

Tourism Organization (1975, quoted in Buhalis and Laws, 2001, p. 8):

A distribution channel can be described as a given combination of intermediaries who co-

operate in the sale of a product. It follows that a distribution system can be and in most

instances is composed of more than one distribution channel, each of which operates parallel

to and in competition with other channels.

It has to be reminded that tourism is not a single homogeneous activity or market. The tourism market

presents a complex structure of interrelated sectors, each sector showing own characteristics and

different consumer behaviors (Swarbrooke and Horner, 2007). Beside the transportation to the

destination, the destination itself is an important component of the tourism market. Cooper et al.

(1998) define destinations as the focus of facilities and services designed to meet the needs of the

tourists. According to Buhalis (2000) most destinations comprise the components shown in Table 2,

which are known as the 6 As framework for the analysis of a tourism destination.

Attractions natural, man-made, artificial, purpose built, heritage, special events

Accessibility entire transportation system comprising of routes, terminals and vehicles

Amenities accommodation and catering facilities, retailing, other tourist services

Available packages pre-arranged packages by intermediaries and principals

Activities all activities available at the destination for consumers during their visit

Ancillary services services used by tourists such as banks, telecommunications, post, hospitals, etc.

Table 2 Six A's framework for the analysis of tourism destinations Source: (Buhalis, 2000)


Tourism distribution can be applied to a variety of tourism services. The following services are the

most common ones offered:

• transportation – international and domestic flights, car rentals, rail tickets and cruise lines

• accommodation – hotel and other accommodation

• travel insurance – travel insurance policies only, insuring against loss, theft and other damage

during vacations

• currency exchange services and visas

• guided tours, sightseeing tours, excursions and entertainment tickets

• specialized services – restaurants, concert or sports tickets, conference registrations, ski passes,

specific languages or sports training

• package holiday – vacation or incentive trips including transportation and accommodation:

package holidays may include other services as well, such as entertainment, leisure activities

and travel insurance (Bigné, 2011).

2.3 Accommodation distribution channels As explained in the chapter above, distribution channels are a crucial factor for the success of tourism

organizations. Thus, they gained increased attention among tourism researchers and, as shown in

chapter 2.2, various definitions were outlined over the last decade. Buhalis (2001, p. 8) sees the primary

functions of a distribution channel as follow:

The primary distribution functions for tourism are information, combination and travel

arrangement services. Most distribution channels therefore provide information for

prospective tourists; bundle tourism products together: and also establish mechanisms that

enable consumers to make, confirm and pay for reservations.

The core product of hotels, the accommodation, is perishable, which makes accommodation

distribution especially important in today’s hotel sector. The sale of each room, each night at the

optimum price is critical to the overall profitability of a hotel, as an unsold room is a lost business

forever (O’Connor and Frew, 2004). Although demand or accommodation is increasing, the hotel

market is characterized by high capital costs, increasing competition and shrinking margins (Vialle,

1995). According to Go and Pine (1995) hotel distribution channels provide:

Sufficient information to the right people at the right time and in the right place to allow a

purchase decision to be made, and provide a mechanism where the consumer can make a

reservation and pay for the required product.

Effective information distribution is important since consumers are dependent on accurate, timely and

high quality information to help them to plan and choose between various options. Convenience in

terms of finding appropriate information and facilitating reservations and payment process is also

critical for successful distribution (Poon, 1994). One of the key enablers in distribution information

and facilitating a convenient reservation process is information and communication technology (ICT)


(O’Connor and Frew, 2004). The following chapter examines the ways in which ICT is being applied

to distribution in the hotel industry.

2.3.1 Electronic distribution channels Research provides various definitions about ICT. Peppard (1993 cited in Buhalis, 2003) for instance

defined IT as “the enabling mechanism that facilitates the processing and flow of information in an

organization and between organization, including the information the business creates, uses and stores,

as well as the technologies used to produce a product or provide a service.” According to Buhalis

(2003) ICTs include “the entire range of electronic tools, which facilitate the operational and strategic

management of organizations by enabling them to manage their information, functions and processes

as well as to communicate interactively with their stakeholders for achieving their mission and

objectives.“ In the tourism industry ICT transformed distribution to an electronic marketplace, where

access to information was improved and interactivity between suppliers and consumers was

empowered. ICT reduced the cost of each transaction, reduced print and distribution costs, allowed

for short notice changes, supported one-to-one interaction with the consumer and enabled

organizations to reach a broad audience (Buhalis, 2003; Buhalis, 1998).

However, prior to the evolution of ICT and the Internet the tourism market was not as complex as the

current environment of distribution. The tourism industry was traditionally characterized by its use of

intermediaries (Pender and Sharpley, 2006). Intermediation, which means to act as a middleman, refers

to the selling of products and services to customers or other intermediaries (Egger and Buhalis, 2008;

Kracht and Wang, 2010).

Traditionally, hospitality products were distributed via intermediaries such as outbound travel agencies,

tour operators and incoming travel agencies or handling agencies. (Buhalis and Licata, 2002).

Outbound travel agencies used to be one of the most important elements of the tourism distribution

channel, providing a convenient location for the purchase of travel. They acted as booking agents, as

well as source of information and travel service advice. The main role of tour operators in the

distribution channel was the package tour, when different services are bundled into tourism packages

and offered for sale. Incoming travel agencies primarily served as intermediaries between tour

operators and suppliers, they were responsible for the planning and execution of tour packages in the

destination including hotel transfer, sightseeing and other special arrangements (Buhalis & Laws,

2001). A traditional booking required customers to use more then one distribution channel, on the one

hand distribution of information was needed to make the client aware of the product and provide

them with information and on the other hand a distribution channel that allowed the customer to

purchase the product. Thus, both an advertising medium, such as a travel guidebook or a brochure,

and an interactive medium such as a travel agent were needed to make the reservation. Travel agents

used to call the hotel and a telesales agent was necessary to complete the booking transaction.

With the development of ICT in tourism this inefficient way of making a hotel reservation was

enhanced by the improvement of transactions and the enabling of making bookings at a fraction of the

time and cost (Gursoy, 2010; O’Connor and Frew, 2002). Among large hotel organizations and hotel

chains electronic distribution concepts quickly gained acceptance and several authors, most particularly

Buhalis (1998) identified electronic distribution as a mean of enabling hotels to improve their


competitiveness and performance. One can separate three main areas of technological developments

that were established by ICT in tourism enterprises. (1) The computer reservation systems (CRS)

which were developed in the 1970’s; (2) The global distribution systems (GDS) that established in the

1980’s and (3) the Internet which developed rapidly in the 1990’s (Buhalis, 1998). While CRS and GDS

facilitated the intermediation process by increasing efficiency in processing internal information and

managing distribution, today the Internet and ICT facilitate the communication between suppliers,

intermediaries and consumers around the world (Buhalis & Jun, 2011).

2.3.2 Computer reservation systems (CRS) and GDS A CRS is a travel supplier’s own computerized reservation system (Inkpen, 1998) and airlines became

pioneers in using this technology for their distribution mix and strategy. Very soon hotel chains and

tour operator followed their example. The term CRS is mainly used to describe a database which

manages and distributes the inventory of tourism enterprises to remote sales offices such as travel

agents and other external partners. CRS enabled suppliers to facilitate yield management by controlling

and promoting their products globally, improved the communication and operational strategy of the

industry (Buhalis, 1998).

A GDS is a network of large-scale computer reservation systems, which link suppliers to intermediaries

anywhere in the world and provide them with rapid search, booking and confirmation facilities. In

hospitality, GDS are dependent upon modern hotel CRSs, which provide full details of properties,

locations, room types, availability, prices and booking conditions (Bowie and Buttle, 2004).

GDSs were formed from alliances of several CRSs by expanding the capacity of the network. In the

late 1970s Sabre established the first GDS, followed by Amadeus, Galileo and later Worldspan and

very soon this four major GDS dominated the travel market (Bowie and Buttle, 2004; Buhalis, 1998).

The potential of GDS war first demonstrated by the airline sector, giving travel agents direct access to

the information needed to book an airline ticket. When GDS needed to increase their revenues to meet

their high operating costs, they began to offer spare capacity to non-air travel products. Hotel rooms

were the first complementary products added to the system and hotels distributed their product over

the GDS by loading their room types, descriptions and price categories directly onto the airline

reservation system database. However, the database structure originally designed to distribute airline

seats turned out to be unsuitable for the use with the very diverse and complex hotel product

(O’Connor, 2004).

As a result, hotel companies began to develop their own separate computerized systems (CRS) with a

database structure more appropriate to the hotel products. Initially these systems helped to manage

inventory for an entire hotel group at central reservation offices (CRO). The CRO kept track of the

rates, availability, special packages, negotiated rates and descriptions of each property and enabled

customers to book any room in the chain by contacting a single central office. Subsequently the CRO

were integrated into travel agencies through electronic connections with the GDS. Because each GDS

serviced different geographical markets, hotels needed to be represented on each of them in order to

gain maximum market share (O’Connor and Frew, 2000; O’Connor, 2004).

To resolve the problem of connecting several different hotels’ CRS to the major four GDS, the leading

hotel brands developed a ‘universal switch’ mechanism. The switch enables each hotel CRS to connect


with each of the GDS and allows all the linked intermediaries to make reservations at minimized

reservation costs. There are two main switch companies, called Thisco and WizCom (Bowie and

Buttle, 2004). WizCom, the first switching service of the hotel industry was founded in 1987 to provide

GDS connectivity, CRS and information services. WizCom is today owned subsidiary of Cendant

Corporation and the world leader in reservations transactions processing of the hotel and car rental

industries. Pegasus Solutions was founded in 1989 for the hotel industry as The Hotel Industry Switch

Company (THISCO) with the aim to connect hotel reservation systems to the major GDS (Buhalis,

2003).

In Figure 2 the concept of the switch company is shown. Where a switch company is used, only a

single interface is needed to link the hotel CRS with the entire GDS marketplace.

Figure 2 The switch company concept Source: Own illustration (Bowie and Buttle, 2004; O’Connor, 2004)

As the various accommodation types differ in size, ownership and services, their ICT utilization in

distribution vary enormously. While larger accommodation establishments and hotel chains are in a

greater need of ICT and connection to the major GDS to manage their inventory, the majority of

smaller and medium-sized enterprises use less technology and rely often on manual processes (Buhalis,

2003).

2.4 Online distribution channels Since the GDS is a closed network, information is available only to the connected users, the suppliers

and intermediaries, while end-users don’t have access to the system. The emerge of the Internet

improved hotel representation and reservation processes dramatically, by allowing end-users direct

access to the suppliers’ booking engines (Bowie and Buttle, 2004). As a result, each of the participants

in the electronic distribution chain has begun to sell its products directly over the Web. Hotel chain

CRS, the GDS, third party reservation system providers, destination management organizations and

even the Switch companies have introduced consumer-orientated web sites with the aim of making

business directly with the customer. Companies from outside the travel sector and ‘new’ intermediaries

took advantage of the Internet boom and have also entered online distribution, which led to a

rearrangement of the traditional distribution channel partners’ relationship. Instead of cooperating with

each other as they did in the past, most have started to compete with each other by creating their own

!

Hotel CRS

Switch Company THISCO

WIZCOM

Amadeus Galileo SABRE

Worldspan

Travel agents

Travel agents

Travel agents

Travel agents


website that provide information and booking facilities for the online customer (O’Connor and Frew,

2002, O'Connor, 2004).

Intermediation

In the past, intermediaries were the norm rather then the exception (Buhalis and Laws, 2001). Even

today intermediaries play an important economic role, both on physical markets and on information

markets; in other words, they provide economies of distribution by increasing the efficiency of the

distribution process and deliver the right product at the right place at the right time. The focus of

intermediaries is on quality reliance, variety and the offer of specific product information (Egger and

Buhalis, 2008). Research defined three basic functions of intermediaries:

1. Intermediaries create economies of scope by adapting large product quantities for the

convenience of the customer by offering a large assortment of products and services at the

right time and place.

2. Intermediaries have the possibility to standardize transactions and automate activities due to

large quantities and delivery frequency, which makes the exchange between buyers and sellers

more efficient and effective. In consequence of automation and routinized transactions

intermediaries can minimize the cost of distribution.

3. Intermediaries facilitate the searching process of both, suppliers and consumers by providing a

place for them to find each other. While producers are not sure about customer needs,

customers are not sure if their needs can be satisfied. Intermediaries reduce the uncertainty in

regard to customer satisfaction (Wynne et al., 2001).

However, the direct contact between costumer and supplier facilitated through the web can replace the

role of traditional intermediaries and lead to disintermediation and reintermediation due to the creation

of new channels and new intermediaries. In the tourism context disintermediation mainly affects tour

operators, travel agencies and the GDS (Egger and Buhalis, 2008).

Disintermediation

The trend towards disintermediation is driven by the suppliers and by the consumers and realized by

cybermediaries, which entered the online marketplace and made the exchange between purchasers and

producers easier (Egger and Buhalis, 2008). According to Kracht and Wang (2010) the term

disintermediation “is commonly used to refer to the partial or complete replacement of an

intermediary of the functions it performs.”

ICT has not affected all sectors equally. Certain sectors, such as the airlines have been early adopters of

technology and set up websites, call centers or retail outlets through which they pursue direct sales

strategies to gain strategic advantage (Buhalis and Laws, 2001; Werthner and Klein, 1999b). The

Internet has also given hotels the opportunity to disintermediate travel agents by selling directly to

customers via the web (Kracht and Wang, 2010).

While the fist online ventures focused on disintermediation and direct links between suppliers and

customers the industry recognizes very soon that consumers do not want to deal with multiple

suppliers to compare offers and prices. Indeed, customers face a lot of problems when trying to


purchase direct from the supplier on the Internet. On one hand the Internet provides a lot of

information, but on the other hand it requires a lot of knowledge and experience of where to look for

accurate information about suppliers and destinations. It is time consuming to visit each supplier and

destination site to make comparisons and usually the booking of different parts of a trip through the

same channel is not possible (Wynne et al., 2001).

Reintermediation

Due to afore mentioned challenges customers face in direct bookings, in the majority of cases the

Internet did not remove the need for intermediaries, who exist to simplify the buyers decision process.

Intermediaries have the possibility to offer precise information in a uniform layout, which facilitate

comparison and match the customer needs. Moreover they offer the possibility to book parts of the

entire trip through one channel. For this reason, rather then mass disintermediation, new virtual

intermediaries entered the market (Bennett and Lai, 2005; Wynne et al., 2001).

Reintermediaton refers to the process through which a once disintermediate player, by adopting new

ways for conducting transactions and usually with the utilization of ICT and Internet tools, tries to re-

enter the tourism distribution channel by reassessing their intermediary role. Though, other definitions

from different researchers exist to describe the reintermediation process. While some researchers use

reintermediation to describe the re-entrace of disintermediated intermediaries, others also include the

entrance of new intermediaries into the market (Bennett and Lai, 2005; Egger and Buhalis, 2008;

Kracht and Wang, 2010). In referring to intermediaries that perform their middleman activity in the

electronic environment a variety of terms are used. The terminologies have been summed up by

Kracht and Wang (2010) as followed; cybermediaries denote those electronic intermediaries which are

new to the industry. Synonyms, such as e-intermediaries or e-mediaries have been used by other

authors (Anckar, 2003; Buhalis and Licata, 2002; Dale, 2003), while the term e-mediaries in addition to

name new electronic players is also used to indicate traditional ones, such as CRSs, GDSs or suppliers

such as airlines and hotels who use the internet to communicate directly with the consumer.

2.4.1 Evolution of online distribution channels Since 1996 most hospitality companies have began experimenting with web distributing and in recent

years, distributing hotel products through the Internet has become one of the fastest growing methods

of distribution (Buhalis, 2003). To hospitality practitioners, the Internet offers a means to sell their

products to global customers without any geographical or time constraints. Similarly, consumers can

search for their needed information and directly communicate with suppliers at any time and in any

place (Waller, 2003 cited in Law and Hsu, 2005). As recent statistics on Internet sales confirm, hotel

bookings, represents almost 50% of all Internet transactions worldwide (“The Simple Facts for

Booking online,” 2012). The European online travel market have reached 25% of the total market for

travel and tourism services, while the direct sellers are becoming increasingly important, accounting for

nearly two-thirds of online sales. In 2008, the breakdown of the European market by type of service

was as follows: air travel 54%; hotels (and other types of accommodation) 19,5%; package tours 15%;

rail 7,5%; rental cars 4% (Marcussen, 2009). When looking at online booking preferences on the

German market, hotel rooms represent after flights the most popular travel product in the online travel


market. As shown in Figure 3, in 2011 more then 15 million have already booked an overnight stay on

the Internet.

Figure 3 Online bookings on the German market Source: (Statistica, 2011)

Web-based distribution provides hotels with the opportunity to eliminate the middleman from the

distribution process and proposes the disintermediation of the traditional travel agent by selling their

products directly to the customer. This enables them to lower their distribution costs by bypassing

switch companies, GDS and travel agents. However, the introduction of online booking and payment

also created an opportunity for third-party travel websites and online travel agents to distribute hotel

and other travel products from different suppliers on the Web by enabling customers to search,

compare and purchase an entire trip online (Gursoy, 2010). In general online hospitality distribution

channels can be categorized into hotel websites and online intermediary sites. While the hotel website

is owned and managed directly by the hotel owner, online agencies are third-party intermediaries

between the hotels and the online customer (Cantoni et al., 2011).

Hotel websites

Hotel websites can be distinguished into hotel company websites and independent hotel websites.

Most of the major hotel chains maintain a website with promotion and booking purposes for their

entire hotel product, including a search engine, which makes it easy for potential customers to find the

product that meets their needs in terms of location and any other desired criteria. Independent

websites tend to be more varied and often harder to find (O’Connor, 2004). In general, hotels can use

the Internet to promote themselves in three different ways; first they can have a simple website with

process, location and pictures for promotion purposes, second they can enable interaction with a

booking engine and third integrate business processes such as eCustomer Relationship Marketing

(Cantoni et al., 2011).

19,3

15,2 13,2

9,6 9,1

Flights Overnight stays

Train tickets Rental cars Package holidays

Num

ber o

f pe

ople

in m

illio

ns


Intermediary sites

Another possibility for hotels to take advantage of the Web is the distribution through online travel

intermediary sites. As the reintermediation process explains, the huge success of the Internet led to the

evolution of new forms intermediaries.

“Online travel intermediaries are online travel agencies that conduct hotel bookings in their attempt to

earn a share of the travel markets” (Christodoulidou et al. 2007, p. 227). They try to sell hotel rooms

from a number of different hotels because they want to offer a full range of hotel types to potential

customers (Christodoulidou et al., 2010). While on a hotel company website, customers are limited to

viewing and purchasing just the products of a single organization, on intermediary websites they have

the possibility to see a more comprehensive offer that might satisfy their needs. In addition to

commercial information and booking facilities, most intermediary sites also provide other useful

information to the customer, including travel advice, destination guides, on-site attractions or local

weather conditions (O’Connor, 2004).

In 1995 online travel agencies (OTA) were the first intermediaries in the online distribution market

who attempt to disintermediate traditional travel agents. After the launch of Travelocity by GDS

owner Sabre, the non-tourism organization Microsoft followed with Expedia. These OTA’s allowed

consumers online access to the information of GDSs at minimal cost. The business model of OTAs is

to provide travelers with the information of GDS in a user-friendly view on the browser. This

circumstance favored on one hand the position of the GDS in the online distribution industry and on

the other hand allowed the GDS-based OTA market to develop early in the Internet era. However, the

link with GDS forced them to accept high switching costs (Granados et al., 2008).

In 1998, Priceline began selling airline tickets by using the demand collection system by allowing

customers to search for offers that match the price they are willing to pay (Buhalis and Licata, 2002),

while presently beside this system they also offer the traditional retail method. In the same year

Lastminute.com was founded with the purpose of selling distressed travel products efficiently at short

notice and cheap prices that otherwise would go unsold (Kracht and Wang, 2010). Later, some of

major airlines reintermediated the online travel distribution with the lauch of Orbitz, by using a new

technology developed by ITA Software1. With the help of this technology Orbitz was able to increase

product and price transparency by displaying a higher number of search results in a more user friendly

way. While Orbitz was gaining market share, GDS-baded OTA were not able to compete with this

marekt transparency (Granados et al., 2008). Following the example of Orbitz, Opodo was launched

by nine major airlines in Europe (Egger, 2005). Table 3 shows examples of OTAs.

1 ITA Software utilizes the same databases that are used by GDS to construct travel products (i.e. combining complex pricing structures with flight schedules to generate offers), but it uses a distributed IT architecture with powerful servers and Linux-based applications to provide a more comprehensive set of travel search results (Granados et al., 2008).


Online travel agents Web site

Expedia www.expedia.com

Travelocity www.travelocity.com

Orbitz www.orbitz.com

Opodo www.opodo.com

Priceline

www.priceline.com

Lastminute.com

www.lastminute.com

Table 3 Examples of online travel agents

Not only GDS and airlines, also tour operators had to rethink their distribution strategy by selling their

products to customers directly via their own website (i.e. www.thomascook.com, www.tui.com)

(Kracht and Wang, 2010). In addition, several destinations developed destination management systems

in order to present the destination as a holistic entity and distribute their accomodations and services

online. Internet portals (i.e. Yahoo) as well as vertical portals (i.e. www.ski.com) developed online

travel services, usually by sourcing their travel offers from externatl OTA and suppliers (Buhalis and

Licata, 2002). Numerous infomediaries such as Tripadvisor and HolidayCheck entered the market by

offering to travelers the possibility to report on their travel experiences, describe offers and evaluate

tourism products and services (Egger and Buhalis, 2008). An infomediary is an electronic intermediary

that provides and/or controls information flow in cyberspace, often aggregating information and

selling it to others (Buhalis and Jun, 2011).

In 2000, an additional new form of intermediation emerged on the market, when SideStep first

launched its meta-search web-browser toolbar plug-in product and followed with the first meta-search

website five years later (Kracht and Wang, 2010). In Christodoulidou et al. (2006 cited in

Christodoulidou et al., 2007) travel meta search is defined as “a vertical search engine focused on

finding and comparing travel accommodations and pricing from many sources (i.e. web sites) with a

single query from one site, the home of the meta search engine.”

While online travel agents provide full range of services, destination content and completed the

booking transactions process, travel meta sites only facility the travel purchase process. On a meta

search engine potential travelers can search for tourism products and services which meet their budget

and needs. Travel meta sites make comparison easy, as results from many travel web sites are listed

simultaneously in terms of convenience and price. For the actual booking the customer is redirected to

the source, i.e. travel supplier or OTA, were the booking can be generated. Meta sites are then

compensated for their role in the booking process, usually in form of commission payment

(Christodoulidou et al., 2007). In table 4 , examples of travel meta search engines are shown.


Travel meta sites Web site

Mobissimo www.mobissimo.com

SideStep www.sidestep.com

Cheap flights www.cheapflights.com

Kayak www.kayak.com

Kelkoo www.kelkoo.com

Table 4 Examples of travel meta sites

Travel meta search leader Kayak was launched in 2005 and became just within two years the travel

meta site with the highest number of vistis. Like Orbitz, also Kayak uses web application technologies

by ITA Software which reduce the number of clicks necessary to filter and alter search results

(Granados et al., 2008). As a result, this travel metas sites are able to present best fares to its users in a

fraction of time and are beginning to establish themselves as influential players in the distribution

landscape (Christodoulidou et al., 2010). The balance of power between various third-party players is

shown in Figure 4.

Figure 4 Online travel distribution pyramid Source: (Christodoulidou et al., 2010)

2.5 Mulitple distribution channels The suitability of travel products and especially hotel products as well as few barriers to entry have

resulted in a very large number of companies facilitating the sale of hotel rooms online (O’Connor,

2004). In general, distribution channel structures are of two main types: direct and indirect. A direct

distribution channel is made up of the supplier and the consumer only, where suppliers sell their

product directly to the consumer. If the distribution channel involves one or more intermediaries, it is

considered to be indirect (Pearce and Taniguchi, 2008). Although hospitality companies see the

Internet as a means of reducing distribution costs and wish to reduce or eliminate intermediation by

encouraging direct communication with customers through the own company website, the role of the

intermediary is well established (Bowie and Buttle, 2004). Intermediaries have been positioned

themselves in a very competitive situation and encourage online travelers to make room reservations

?

E-meta travel sites

E-travel sites

Traditional sales channels, GDS, group sales, consolidators

Travel products (accomodation, airline seats, rental cars etc.)


through their travel portals by offering deeply discount prices, opportunities to compare rates of

different hotels, and providing additional information about destinations and attractions (Morosan and

Jeong, 2006). It is currently estimated that there are 35 000 websites from which consumers can book a

hotel room. As a result, multiple-channel strategies are required to interconnect with the wide range of

distribution in the online marketplace and to evaluate which channels, or combination of channels

should a hotel be using (Buhalis, 2003). The reason for hoteliers for employing multiple channels

involving a mix of direct and indirect channels is to increase market share, respond to the preferences

of different market segments, reduce costs and take advantage of technological changes (Kang et al.,

2007; Bowie and Buttle, 2004). The diversity of the used distribution channels, with the usage of

multiple channels being very common, was highlighted in the research of Pearce and Schott (2005).

Their study complemented multiple channels strategies by examining the use of distribution channels

by the visitors’ perspective and showed that travelers use a range of different distribution channels to

make travel and accommodation arrangements at New Zealand destinations. Next to this, they also

analyzed the factors that influenced the usage of these channels.

However, the constantly increasing complexity of the distribution network and the rapid changing

environment make this a difficult task to fulfill. While the influence of intermediary sites is increasing,

no single channel seems to be emerging as being dominant and thus most hotel companies will have to

make use of more simultaneous routes to the customer (O’Connor and Frew, 2002). Research claimed

that channel management is essential for competitive distribution and hotel companies need to

understand the profitability of each channel (O’Connor and Frew, 2004) and be aware of the travelers

adoption and usage of online reservation sites (Morosan and Jeong, 2006).

One distribution channel to consider for present multi-channel strategies may be the Google Hotel

Finder, being on the market since July 2011 (Fox, 2011). How Google entered the vertical travel

distribution and which role the search giant will play in the future hotel distribution will be discussed in

the following chapter.


3. Google travel technologies and services As discussed in the previous chapters, the World Wide Web has changed travel distribution and thus,

hospitality distribution dramatically. While there are new categories of intermediaries, the travelers have

numerous possibilities for online reservation of hotel products (Kracht and Wang, 2010). For many

travelers the complex distribution and information structure leads to frustration in the travel planning

process. Though it is easy to find plenty of information, that data often exist in a very rough format

and makes it difficult for users to compare travel products and hotel organizations. In order to get the

best deal most travelers browse around to feel confident about their choices. By now, meta travel sites

try to counter this development, but the issue often seems more complex. Nowadays travelers would

like to know if the hotels in Rome are less expensive then in Paris, which hotel is the cheapest in the

historic center of Rome or what is the three-months average price of a special hotel to simplify

comparison (Rheem, 2012).

Considering the trend of search engine usage in online travel search, Google is among all search

engines regularly the first stop for many travelers to find anything they need online. And it is the same

search engine that recently aimed to make the web more useful for online travelers by improving the

quality of travel information available to the consumer. With the launch of several travel-related search

technologies or the integration of new search functionalities into the Google maps service, the search

giant has huge potential to simplify travel information search by addressing exactly the consumer

information needs. If users are able to find all the information they need through Google, in a quick

and easy way and in a familiar format they trust, in future they might be less likely to search through

other intermediaries (Hotel Price Listings, 2011). How the future of travel information search in the

hospitality sector could look like, will be shown later in this work.

3.1 Google information power Without a doubt, no other invention empowered individuals and transformed access to information

the same way as Google. Google’s ability to produce speedy and relevant response to hundreds of

millions information queries every day, made Google to the most powerful search engine in the world

(Vise and Malseed, 2005). Over time the word ‘googling’ integrated itself in everyday language and can

found it in the dictionary as ‘searching the web’ (Reischl, 2008).

The success of Google is based on the Page-Rank algorithm, which delivers the most relevant search

results at the top of the list. Everything began, when Larry Page and Sergey Brin (Google cofounders)

were Ph.D. students at Stanford University in computer science and Larry Page got the crazy idea to

download the entire web onto his computer. After about a year he had some portion of it, but Page

was optimistic (Halici and Mayer, 2007). Today the Google network consists of thousands of

computers, which find and fetch data on the web. Data is then sorted and stored as an index of words

in a huge database (Bachmann and Peek, 2007). The company Google was founded in 1996, shortly

after the founders have developed the PageRank algorithm, the beta-version of their search engine.

When the google.com domain was registered in 1997, it quickly gained in popularity, as the search engine

was better than everything known so far (Vise and Malseed, 2005). Since then Google is one of the

fastest growing companies worldwide. Besides being the best-known search engine, Google is also a


successful advertising company making most of its revenue with the ads shown next to the search

results. As Figure 5 shows, today, it holds a worldwide market share of over 90%. All the other search

engines are far behind, with Microsoft’s Bing holding 3,5% Yahoo 3.3%, while the remaining 1,8% are

held by other search engines (state: January 2012).

Figure 5 Search engine market share Source: adapted from (Shabat, 2012)

Beside the vision to “organize the world’s information and make it universally accessible and useful”,

Larry Page and Sergey Brin also follow the “Don’t be evil” motto (Vise and Malseed, 2005). Rather

then monopolize the hospitality industry, the company aims to make the web more useful, by

organizing and presenting information needed for online hotel booking in a more efficient way (R.

Cole, 2009). The popularity of Google among its users might be explained with the fact that the

Google web search service is totally for free to users and the web giant continuously offers new

services, likewise for free. Thus, Google is no longer a purely search engine for the consumer, but

offers plenty of technologies and search-related services for other purposes (Schreder et al., 2008). One

of these search-services Google is providing is called the Google Hotel Finder. This service assists

travelers in finding a hotel that fits their search criteria and aims to make online travel planning for

hotel products fast and easy. How hotel distribution and online booking through the Google Hotel

Finder works will be discussed in detail in the following chapters.

3.2 Google travel services As discussed at the beginning of chapter 3, there is a certain frustration among many online travelers,

when searching for travel related information on the Internet. By facilitating information search with

the launch of several travel-related services, Google tried to improve the online travel information

experience in order to solve or minimize the online search frustration of travelers. While Google was

the biggest player in the travel advertising space for years, the search giant recently entered the vertical

distribution chain by offering new search functionalities and value added services for their users. In

order to increase its reach, Google offers all this search functionalities for free. Nevertheless, for the

91,3%

3,6% 3,3% 1,8%

Google

Bing

Yahoo

others


online traveler Google delivers not just the cheapest, but also the most comprehensive, as well as the

fastest travel search (Suhayda, 2011).

However, before discussing these services into more detail, the author aims to define Google’s online

travel services. Just to mention some, with services such as maps, places, youtube or panoramio,

Google provides a broad range of services, largely consisting of information exchange and information

processing activities. As the travel industry is largely information driven and the aforementioned is

essential to support customers in their travel decision-making (Van Riel et al., 2004), these services aim

at supporting the information search activity. However, according to Grönroos et al. (2000) online

travel services are a composite offer, consisting of a core service and an auxiliary service. While the

core service for online intermediaries is selling travel products, auxiliary services are supporting

services that facilitate the use of the core service. Supporting services could for example be destination

information, weather forecasts, information about attractions and activities (Van Riel et al., 2004).

Consistent with this definition of online travel services and the definition of accommodation

distribution given in chapter 2.3, in this study travel-related services, refer to those services that

provide the customer with necessary information about availability, prices and facilities. This

information is needed in order to support their purchase-decision, enable the customer to make a

reservation and pay for the required product (O’Connor and Frew, 2002). So far Google offers two

travel-related services meeting the requirements of this definitions; the Google Flight Search tool and

the Google Hotel Finder tool, which is subject of this thesis.

The Google Hotel Finder was initiated at the beginning as an experiment in April 2011, when Google

acquired ITA Software for approx. $700 million (D. King, 2010). ITA’s primary product, QPX, is a

search and pricing system built in to airline and travel company websites typically used by travelers to

search for flights, fares and related information. The company’s clients include major airlines and

online travel companies such as Orbitz or Kayak. ITA’s fare search and pricing platform helps Google

to conduct online airline-fare searches and gives the search engine control over collecting information

on airfares, flight availability and flight times. With the aim to create a flight comparison tools that

makes it easier for users to compare prices and find the best deal, the Flight Search tool Google could

soon play a major role in online air travel search (Thomson, 2011). Next to facilitate air travel search,

Google offered a similar tool for hotel products. With real-time pricing and inventory provided by

ITA Software plus the local hotel information, Google already has in their database from maps, places

or panoramio, Google was able to create a website which enables customers to search for hotels in a

more convenient way (“What Is Hotel Finder?,” 2012).

3.3 The Google Hotel Finder In July 2011 Google started the Hotel finder as an experiment in the U.S., but already in October the

Hotel Finder was extended to European cities (Fox, 2011). The tool enables users to search for a hotel

within a named destination by popular areas or by editing the shape within an area of a city. The hotels

within the shape are also displayed on a list with an image to the left, including sorting possibilities

according to the lowest price or user ratings. Another feature in the hotel listing is the ‘compared to

typical’ selection, which shows the hotel price on the desired travel dates, compared to the typical price

over the last year. There are a lot of other tools, including a ‘shortlist’ feature where preferred hotels


can be listed. When selecting one of the listed hotels, a clear overview, photos, reviews and the exact

location on the map is shown. By clicking on the ‘book’ button, the user is forwarded to an online

travel agency or the hotel website for the actual booking (Schaal, 2011).

Until November 2012, the Google Hotel Finder was called an experiment. The term ‘experiment’ was

used because Google contiuend to update the users interface and tested additional services such as the

mapping tool for selection by popular areas. Only the satisfied version should be marketed to the

masses as the Google Hotel Finder (“What Is Hotel Finder?,” 2012). This was the case in November

2012 and according to Be:con, a consultance company, who provides hotels with the interface needed

for direct connection to the Google Hotel Finder, the distribution platform very soon could dominate

the online travel distribution and rating of hotel accommodations (Benkert, 2012a). The Hotel finder

opens a new channel of distribution for accommodations. Also if for single hotels it is currently

difficult to use the full service of the new channel, the optimization of the Google+ local profile is a

good start to guarantee an attractive presentation of the own organization on the Google Hotel Finder.

While Google uses different data sources for displaying hotels on the Hotel Finder platform, the hotel

description comes largely from information provided through Google places and Google+ local. Thus,

to guarantee an inclusion, hotel owner need to open a Google places account or Google+ profile and

provide the necessary information. Furthermore, the Hotel Finder includes regularly updated photos

from the owner and from VFM Leonardo2. To update or add pictures, those have to be updated in the

places account or directly with VFM Leonardo (“How to use Hotel Finder,” 2012).

In the beginning phase of the experiment, the ‘book button’ listed, most of the time only rates of

OTAs such as booking.com and some major hotel chains, which are already supplying availability to

Google such as Best Western. The hotels’ direct site usually appeared at the bottom of the list with no

rate attached (Freed, 2011).

In the meantime, Google has allowed selected technology providers to deliver hotels with the

appropriate software necessary to connect with the Hotel Finder portal in order to provide real-time

rates and inventory. Only if the hotel’s own website and the actual rate appears on the ‘book button’,

the potential customer will conduct the booking directly on the owners website. So far the companies

seekda, Be:con, Bocco Group and MICROS-Fidelio offer interfaces to the Hotel Finder and thus, for

their clients the possibility to provide real-time rates and availability to the platform. Next to this

companies, merely a few Hotels dispose over a direct interface; as already mentioned the hotel chain

Best Western is directly connected to Google since autumn 2011 and Accor will follow very soon,

while only a handful of private owned hotels are directly connected (Hendele, 2012b). Precondition for

a direct connection with the Google Hotel Finder is an application programming interface (API),

which enables the communication between Google and the hotels’ own CRS. Up till now Google only

accepts a few partners, but according to experts within a short period of time Google will offer an

official API interface for hotels an enable them to update data and transfer clients to their own

webpage. Of course Google doesn’t offer this connection for free, Google earns with every client they

2 VFM Leonardo is a technology and online media company for the global hospitality industry, which provide hotel companies with technology, sales tools and a global travel media network that enables them to better distribute their hotels in the market (VFM Leonardo, 2012).


transfer to one of the partners following the principle ‘cost per click’. For every click to the partners

offer a provision of 0,2%3 goes to Google, independent from a later booking or not (“Google

Hotelfinder,” 2012).

3 example: price per day x length of stay x 0,2


4. Online travel decision-making for accommodations

4.1 The travel decision-making process Tourism research mainly sees travel planning as a complex and multi-faceted decision-making process

(Jeng and Fesenmaier, 2000) and, over the last 30 years, a significant amount of research has been

carried out in relation to this subject. Understanding the traveler’s decision-making behavior and the

various aspects of a tourist’s decision are of great importance for tourism organizations (Hyde and

Decrop, 2011; Jones and M.-M. Chen, 2011).

Studies on tourist consumer behavior from the perspective of decision-making processes began to

appear in the 1970s. Most models explain the tourist-decision making process focused on the classical

buyer behavior theory, stating that travel decision is complex and follows a hierarchical structure of

subsequent sub decisions, varying in number between three and five (van Raaij and Francken, 1984;

Um and Cropmton, 1990). Usually, these sub decisions include the following five-stages: needs

motivation, problem recognition, information search, evaluation of alternatives and decision, purchase

and post-purchase evaluation (Engel et al., 1995). Adapting this to the travel and tourism context, the

decision-making process starts when a traveler recognizes the need to travel. A motivated traveller may

then search and process travel information to obtain several alternatives. The next step is the actual

travel phase and finally concludes with the post-trip evaluation (Ayeh et al., 2012).

Figure 6 Decision-making process Source: adapted from (Ayeh et al., 2012)

Research has shown that various variables influence this decision making process. On the one hand,

external influences including culture, socioeconomic status, reference groups and household can affect

the decision about hospitality products. On the other hand, internal influences affect consumers’

choices as well. Those influences consist of personal needs and motives, experience, personality and

self-image (Reid and Bojanic, 2010). The model illustrated in Figure 6 show the major steps in the

decision making process and will be described in the following points.

Need recognition. The decision-making process begins with the recognition of a need, problem or

unfulfilled desire, which occurs when a consumer realizes a difference between the actual state and the

desired state (Reid and Bojanic, 2010). For example, the consumer might get influenced by an online

! !!!!!

Need recognition

Information search

Evaluation of alternatives

Purchase decision

Post-purchase behaviour

Decision m

aking process


advertisement or the discounts offered by travel agents for international travels that could arouse the

need book an international vacation (Srinivasan, 2004). In the next step the individual will explore the

possibilities of getting the best deal (Reid and Bojanic, 2010).

Information search. Once the need is raised, consumers seek for relevant information that helps

them to plan the vacation and choose between various options. Moutinho et al. (2011) defined

information search as “an expressed need to consult various sources prior to making a purchase

decision.” According to other authors information search can be defined as “the motivated activation

of knowledge stored in memory or acquisition of information from the environment” (Engel et al.,

1995). The individual’s primary force for information search in the course of travel planning is to

enhance the quality of the trip by decreasing the level of associated uncertainty (Fodness and Murray,

1997). Tourism information search includes internal search as well as multiple external information

sources. Usually people first try to search for information internally. Personal experiences and past

information searches are most of the time used as the basis for tourism planning. The information is

processed and stored in the tourists’ long-term memory, which then forms their prior knowledge and

is used to make the travel decision. If the prior gained knowledge is not sufficient for decision-making,

the tourist then starts searching for relevant information in external sources. In the case of searching

for information relating to travel planning information this is predominantly done externally.

Nowadays, the tourist has a wide choice of external sources. Research showed, that friends,

guidebooks, regional and destination information brochures, and tourist boards were very important

channels for the tourist. However, the most trustful external source still is family and friends, followed

by people with shared interests (Bieger and Laesser, 2004). The trustworthiness of online information

sources varies depending on the search goal (Dickinger, 2011).

Evaluation of alternatives. Once relevant travel information is found, the consumer needs to

evaluate the set of alternatives that are available. Rather the alternatives available on the market

influence the consumer’s purchase decision, than the awareness of all the products and brands offered

in the marketplace. The awareness set are all the alternatives the consumer is aware of and from this

decision stage the customer comes to the evoked set from which, then, the final purchase decision will

be made. Before the final decision is made, different types of sets have to be taken into account in the

various stages of the decision. The total available set consists of all the possible tourist alternatives in a

particular product category that is available on the market. The unawareness set is composed of all the

tourist product alternatives that the tourist is not aware of, while those the consumer is aware of are

within the awareness set. Among all the product alternatives that the tourist is aware of, only some of

them will be considered as important for the purchase and make up the consideration set or evoked set

usually consisting of two or three alternatives from which the final decision is made. The inept set is

composed of those alternatives the consumer dislikes and therefore unworthy for further

consideration. The inert set is consisting of alternatives the tourist is indifferent towards because they

are not perceived as having particular advantages (Bretbacher and Egger, 2010; Hyde, 2008; Moutinho

et al., 2011).

Besides the evoked set evaluation, the evaluation of alternatives can, especially, for hotels be based on

a systematic evaluation, where different choice attributes will be evaluated within the multi attribute

approach model (Srinivasan, 2004). This model assumes that consumers evaluate each of a product’s


attributes and then arrive at an overall assessment, or score, for the product that can be compared to

alternative products. After the comparison, the consumer will choose the product with the highest

rating. An example of the multi attribute model for hotels is shown in Table 5. The weighted rating is

computed by multiplying the importance rating by the actual rating. Then the final average for the

weighted rating is simply the sum of the scores for each attribute. The ratings are based on a four-point

scale: 1 = poor, 2 = fair, 3 = good, 4 = excellent. As Table 5 reveals, for the consumer in this example

the price is the most important factor in choosing a hotel, followed by location and service quality.

According to the overall assessment, the Holiday Inn received the highest weighted average total

across the three choice attributes and is the preferred hotel of the customer (Reid and Bojanic, 2010).

Attribute Importance Holiday Inn Marriott Four Seasons

Actual Weighted Actual Weighted Actual Weighted

Price 0.50 4 2 3 1.5 2 1

Location 0.30 2 0.6 3 0.9 4 1.2

Service Quality 0.20 2 0.4 3 0.6 4 0.8

Average 2.66 3.00 3.00 3.00 3.33 3.00 Table 5 Evaluation of altervative hotel Source: (Reid and Bojanic, 2010, p. 108)

Purchase decision. The fourth stage in the consumer decision-making process is the purchase

decision. The decision is made based on the perceived risk associated with the purchase and the

willingness of the individual to take the risk. Before taking a decision, the consumer will have to

evaluate the consequences and possible outcomes of the purchase. While this risk factor may be a

competitive advantage for hotel chain organizations, whose standardized products and services are well

known to customers, independent hotel organizations must work very hard to establish themselves and

convince the consumer. Timely and accurate information about the product and services, as well as

own experience with the product or recommendations of other people reduce the risk associated with

the purchase and can minimize the gap between consumers’ expectations and the actual experience

(Reid and Bojanic, 2010).

Post-purchase evaluation. In the last stage of the decision-making process, the consumer compares

the actual experience with the expectations prior to purchase. Post consumption feelings are based on

two factors; the consumer’s expectations and the actual performance of the hotel organization. The

last makes it very important for the hotel to deliver the promised products and services in advertising

or distribution channels. Negative post consumption feelings lead to dissatisfaction with the customer

and prevent them to repurchase the product or make recommendations. Thus, a key implication for

hospitality managers to keep customers satisfied is to deliver the promised product (Reid and Bojanic,

2010).

4.2 Research on travel decision-making processes A travel decision is an outcome of a mental process whereby one action is chosen from a set of

available alternatives. Decision process models describe how information is acquired and related in


order to make a decision (Moutinho et al., 2011). Many of the models deal with the five different steps

in the decision process, namely need recognition, information search, evaluation of alternatives,

purchase decision and post-purchase behavior, which are illustrated and discussed in chapter 4.1. The

on-going sequences in vacation decision-making have been extensively discussed in research literature

(van Raaij and Francken, 1984). However, recent literature shows that other perspectives, such as

hedonic, implicit and adaptive decision-making processes are also relevant (Decrop and Snelders, 2005;

Hyde and Decrop, 2011). Different approaches are discussed as follow.

At the beginning of their study, Van Raaij and Francken (1984), pointed out that the vacation

represents an optional expenditure for most households and they presented a five-step, sequential

model of the vacation. The authors assumed that vacation-decision making always commences with

the generic decision of whether to go on a vacation or not and involves a period of joint decision

making by the members of the household. The five steps in vacation decision-making, as presented by

these authors, are the following:

1. The generic decision to take a vacation:

2. Information acquisition to assist decision making:

3. Joint decision-making by members of the household:

4. Experiencing of vacation activities: and

5. Subsequent satisfaction and complaints about the vacation.

Typical for traditional models of decision-making, also the model of Van Raaij and Francken (1984)

assumed that consumers followed an on-going sequences of steps and apply theses steps to decision-

making for all types of vacations. Similar are the models developed by Moutinho (1987), Woodside and

Lysonski (1989) and Um and Cropmton (1990) which heavily relied on the hierarichal evolution of

vacation decision-making and were based on the assuption that a traveler is a rational decision maker.

In contrast, recent studies have recognized, that travel decision-making is more complex and diverse.

While adopting the five-step model of decision-making developed by researchers in 1970 (van Raaij

and Francken, 1984) is a useful framework, contemporary researchers do not assume that consumers

always apply each of these steps or adopt the invariant sequence (Decrop and Snelders, 2004; Hyde,

2004; Woodside and MacDonald, 1994).

A further limitation of the traditional models is that they focused on just one facet of vacation

decision-making, the choice of destination. However, the conceptual framework developed by Dellaert

et al. (1998) indicated that multi-faceted tourist travel decisions involve subsequent, yet interrelated

choices for different parts of a single trip, such as the destination, accommodation or travel duration.

Additionally, they defined an average period for the timing of the choices between decision-making

and the travel moment, as well as constraints that may limit the number of possible travel alternatives.

Similarly, Woodside and MacDonald (1994) introduced the ‘trip frame’ concept, which described the

decision-making process on hand of the major elements of the vacation. These included destination

choice, route choice, accommodation choice, choice of activities, choice of areas to visit, choice of

attractions and choice of visiting shops. First, the decision whether to take a holiday had to be made.

Second, if it was decided to take a holiday, a vacation sub-decision had to be made about the different

components of the travel package shown in Figure 7 including the destination, type of


accommodation, travel companions, travel mode and duration of the holiday. Generally these

decisions were taken prior to the trip, while other decisions about attractions to visit, food locations or

activities were more often made during the vacation (Dellaert et al., 1998). For each of these elements

of choice different motives and information search procedures existed and also the choices for each

element could be made at different points in time. Thus, the decision process was viewed as a complex

multi-faceted process consisting of a number of separate, yet interrelated choices (Woodside and

MacDonald, 1994).

One of these travel sub-decisions is the accommodation choice, which is subject of this thesis.

Accommodation includes not only lodging, but also the general infrastructure (i.e. pool) of the staying

(Decrop and Snelders, 2004). While the choice of the accommodation will depend on the available

accommodation at the destination, the destination choice will also depend on the various requirements

that travelers have about the accommodation (Dellaert et al., 1998). Similar results were obtained by

Pearce and Schott (2005) in their study about travellers use of distribution channels for travel

decisions. They argued that travelers tend to book accommodations in advance in order to ensure

room availability, as the availability-related aspect was the most important factor influencing how

travelers book the accommodation.

Figure 7 Travel choice components in the decision-making process Source: adapted from (Dellaert et al., 1998)

Hyde (2004) indicated that the overall travel planning process might exist of a plurality of vacation

decision-making processes, including decisions made before departure and the decisions made during

the vacation. While decisions made before the departure are deliberated, well reasoned and accurate

and follow the classic decision process, on vacation decisions are usually less deliberated and simple.

Decrop and Snelders (2004) investigated the decision-making process of Belgian households when

planning a summer vacation. They found that planning for the summer vacation is a moderately

involving process, which is ongoing throughout the year and does not end once the vacation is

! !!!!!

Destination

Accommodation

Travel Companions

Mode

Departure Date

Duration

Travel choice components


booked. The latter is especially true for information search, which turns out to be an ongoing activity

during the travel planning process. They noted that there is no particular order in which plans evolve

or decisions are made. There are often several vacation plans at a time and usual steps in the decision

making process such as need recognition, information search or evaluation of alternatives do not

follow a particular order.

Later, Decrop and Snelders (2005) illustrated the decision-making process on hand of six types of

travelers, namely: habitual, rational, hedonic, opportunistic, constrained and adaptable. The study

suggested that not all tourists follow a sequential evolution of plans, but adapt their choices to

characteristics of the trip, while others tend to follow a strict decision pattern. The habitual

vacationers, for instance follow decision rules in an unconscious and routine way while the rational

visitor uses well-defined decision criteria and strategies. The hedonic vacationer is inspired by tourist

information and tends to collect tourist data at any occasion; hence their decision-making is more

influenced by emotional factors than by reasonable and subsequent patterns. The opportunistic

traveler could be seen as a unplanned vacationers whereas the constraint traveler is constraint to make

decisions based on stable factors such as travel companions. Therefore this study showed, that

decision-making is not individual but involve groups. Decision-making of the adaptive traveler is very

flexible, as they adapt their plans according to the situation, which means that they often revise their

decisions.

Jeng and Fesenmaier (2000) decomposed the decision-making process into three stages, in which all

sub-decisions have a different importance and might condition subsequent sub-decisions. Most

importance was assigned to the core decisions, which are planned in detail and considerable time

before the actual trip. Also secondary decisions are planned prior to the travel behavior, but they are

still flexible and might be adapted during the vacation. En route decisions are planned while on

vacation and involve the elevated evaluation of alternatives. The tourists’ core decisions include choice

of primary destination, travel dates, members of the travel party, accommodation, travel route and

budget. Secondary decisions include choices of secondary destinations, activities and attractions and

en-route decisions include choice of dining or shopping options. This study revealed that travel

planning follows a hierarchical process in which en route decisions contingent on prior decisions.

Jeng and Fesenmaier (2002) propose that vacation decision-making is based on three key

characteristics including multidimensionality, sequentially and contingency. Multidimensionality is a

term used to portrait vacation decision-making as a complex process involving multiple decisions.

Sequentially is the notion that vacation decision-making proceeds as an evolving sequence of choices.

Contingency implies that decisions taken by the consumer early in the travel decision-making process,

limit consumer choices in following decisions. To sum up, travel decisions are complex and include a

plurality of decisions, which may be sequent and often dependent on previous decisions.

4.2.1 The role of the Internet in travel decision-making The Internet intensified the complexity of the travel decision-making process and affected the

information search strategies and purchase decision of today’s travelers (Hyde, 2009). Though the

Internet made it easier for travelers to collect information and purchase travel products, the world of

vacationing and vacation decision-making has changed considerably with the growth of the Internet


and development of online intermediaries (Hyde and Decrop, 2011; Jun et al., 2007). The Internet has

revolutionized the way in which consumers search for travel information and purchase vacation

products online. Nowadays the use of online travel services is the most popular way consumers

purchase their travel tickets and other tourism related products (D.J. Kim et al., 2007). Even though

online searching became the primary and dominant source for tourism information, a clear

differentiation between off- and online information search is not always possible as individuals use

various sources online, offline or a mixture between both. Thus, tourism information search involves

interactive characteristics and has developed itself to a very time-consuming activity (Ho et al., 2012).

As Figure 8 shows, the Internet is the leading source for travel planning information. 87% of the US

population uses the Internet to find specific travel information. Another study of PhoCus Whrigt Inc.

(Rheem, 2012) shows that also the majority of travelers in the German and U.K market are conducting

their travel planning online, using their computers at first hand.

Figure 8 Information sources for travel decision-making Source: (Google/OTX, 2011)

There is a significant amount of online information available and up-to-date information on

inventories and pricing simplified the comparison and booking of travel products for customers. The

Internet empowered the new tourist who became knowledgeable, more independent and sophisticated

using a range of tools, which made it possible for travelers to search, compare and book hotel products

and services all at once. The consumers’ online travel decision process usually involve multiple

selections of suppliers, comparisons of facilities, prices and availability, which enables customers to

reach optimal decisions through more adequate information than with traditional sources (Hyde, 2009;

Jang, 2004).

In their study on decision-making for city travel Dunne et al. (2011) noted that the Internet enhances

last minute decisions. Unlike the traditional and extensive decision-making process, the authors

observed a dramatic truncation in the decision-making process where information search on the

vacation and the actual booking took place within a couple of days or even hours. In many cases no

clear differentiation between the stages of information search, evaluation of alternatives and purchase

decision was evident, as many of the observed people carried out these steps almost simultaneously.

Ready access to information, low cost airfares, accommodations and immediate booking possibilities

via the Internet enabled these spontaneous actions.

0% 20% 40% 60% 80% 100%

Newspaper

TV

Travel agents

Informational brochures

Books

Magazines

Family, Friends, or colleagues

Internet


Jun et al. (2007) examined online and offline information search and purchase behavior in the pre-trip

contexts. They found out that the principles of the theory of case-based vacation planning developed

by Stewart and Vogt (1999) were consistent in the online or web environment. Though travellers

develop plans before their trip, these plans are often subject to change, especially with regard to on-site

activities, suggesting that good information about activities should be available at the destination.

Indeed, individuals use the Internet differently for travel information search and product purchase in

the pre-trip stage. During the pre-trip phase more information searching occurs, while the purchase

happens at the destination. This study also found that individuals use various tools online, offline and

on/offline for information search. In the work of Jun et al. (2007) blurring boundaries between online

and offline sources become evident.

Dickinger and Stangl (2012) examined the influence of the actual search goal on search behavior. They

stated that there are differences regarding the search depending on search motivation, which can either

be goal directed or experiential. Goal-oriented search behavior is driven by functional benefits, which

involve external motives to use the Internet with the aim to find specific information for problem

solving. Experiential or non-directed search is driven by hedonic benefits and involves internal

motives. While in goal-oriented search behavior people use the Internet for information search, the

latter use the Internet for entertainment, fun and emotional satisfaction. The results show that there

are significant differences between the experiential search task and the goal-directed search task. While

ease of use, usefulness and an adequate level of quality are important factors for goal-directed users,

the entertainment factor is more important for experiential users. Nevertheless usefulness and content

quality were significant also in experiential search and might therefore be seen as basic requirements.

Ho et al. (2012) surveyed the search behavior for tourism information using online and offline sources.

Their research focused on how individuals search for information switching from online to offline

sources and how they use these multiple information sources. The study showed that the search

process implicated four stages: a start to online searching, online searching, an end of online searching

and offline searching. Furthermore web users employed five strategies throughout an online search

session, including using a search engine, using keywords, using a landmark website, comparing search

results, and browsing webpages. The information collected by the searcher was then summarized and

compared, which usually corresponded to the end of online searching. Although nearly all web users

found relevant tourism information on the Internet they tend to search for more information using

offline sources. In the end web users often exchange and share the summarized information with

others.

In their research on information search for vacation decision-making by couples Bronner and Hoog

(2011) noted a preference for the social context in information search. Discussions and personal

information sources on vacation options are of great importance for the decision-making process.

While in offline information search word-of mouth is a very common source of information, user

generated content (UGC), such as consumer reviews of hotels are popular to influence travel decisions.

As a study on factors influencing the hotel selection among German travelers confirms, online reviews

on hotels are the second most important reason for booking an accommodation or not. As Figure 9

shows, is the price with 35% the biggest influence on the purchase decision, followed by 27% for

online reviews and 10% own experience. Also the brand of the hotel, travel guidebooks,


recommendations of friends, advertisements and local tourism information influence the decision

making for accommodations.

Figure 9 Factors influencing the purchase decision for hotels Source: (Henning, 2009)

4.2.2 Need for information during the decision-making process According to Clawson and Knetsch (1966 cited in Xiang et al., 2008) travel information search

activities could generally be grouped into three stages with different communication and information

needs. (1) The pre-consumption stage, (2) the consumption stage and (3) the post-consumption stage.

Information search is therefore more than just a pre-purchase alternative evaluation.

Internet technologies provide the consumer in the pre-consumption stage with information that could

arouse the need for travelling even before the web is used to obtain travel information necessary for

planning trips and to evaluate, compare and search for alternatives. During the actual trip, in the

consumption stage, the Internet is used in relation to tourism experiences, to stay connected and to

obtain valuable information to a specific place and moment in time. In the post-consumption stage the

consumer concentrates on sharing and documenting the travel information and communication in

order to be able to relive the holiday experience (Gretzel et al., 2006). The different information needs

that can be seen in the decision-making process and where exactly the Google Hotel Finder may

become relevant for the online traveller is illustrated in Figure 10.

0% 5% 10% 15% 20% 25% 30% 35% 40%

Local tourism information

Advertisement / brochure

Recommendation from friends

Travel guidebook

Hotel brand / chain

Own experience

Online reviews

Price


Figure 10 Information needs in the decision making process Source: own illustration

Pre-purchase information attracted major attention in tourism research areas and is considered to be

the key component in the decision-making process (Bieger and Laesser, 2004; Vogt and Fesenmaier,

1998). As the pre-purchase phase starts with the need for recognition, the information search is

primarily goal-directed with the main aim to resolve an immediate purchase problem (Pan and Turner,

2006). It is in the pre-purchase phase in the decision-making process where the Google Hotel Finder

becomes relevant for the online traveler. The Google Hotel Finder could provide the consumer with

relevant and accurate information and could thus facilitate the purchase decision and at the same time,

enable the online booking.

Pre-purchase information has been defined as “information seeking and processing activities which

one engages into facilitate decision making regarding some goal object in the marketplace” (Kelly, 1968

cited in Vogt and Fesenmaier, 1998). According to Bieger and Laesser (2004) the traveler is, in the pre-

purchase information search phase, the traveller is still completely free in his decisions, while decisions

in this phase will contingent those in the subsequent phases. Unlike ongoing information search, the

pre-purchase search is characterized by the buyers’ short-term involvement with the consumption

problem and related risk reduction and include beside internal sources mainly external information

such as family and friends, the Internet, travel magazines or travel agents. The importance of pre-

! !!!!!

Need recognition

Information search

Pre-trip stage

Purchase decision

Post-purchase behaviour

During trip

Destination pre-decison, decision about type of

holiday

Seeking information

Making a decision

Booking

Seeking detailed information & expectations

Compare expectation & actual experience

Sharing, documentation, re-experiencing

Looking for information about hotels including

location, price, star rating, user rating on Google Hotel

Finder

Booking the hotel via Hotel Finder by being transferred on hotel website or intermediary

Share experiences on Hotel Finder


purchase information search is reflected by the fact that it leads to purchase decision (Pan and Turner

2006).

In the need for recognition phase different media, both online and offline, as well as personal

recommendations from friends and family may arouse the users interest for a certain holiday

destination or vacation. The decision for a specific hotel is usually not a part of this information phase

(Hinterholzer and Jooss, 2010).

Once the decision to book a trip is made; the potential traveler comes into the information search

phase and will start searching for necessary information that could be used for an eventual booking

decision in the next step. During this phase various aspects of the trip, such as travel companions,

transportation and accommodation are planned. Thereby the consumer has plenty of on- and offline

information sources at his disposal. In the context of the World Wide Web these information sources

are, for instance general search engines, destination websites, hotel websites, intermediary websites or

rating platforms. For accommodation decisions, the Google Hotel Finder may be a relevant

information source for the future. When using the Google Hotel Finder the choice of the hotel will be

based on variables including location, date of the vacation, price, star rating and user rating. Nowadays

information based on user rating plays a major role in the accommodation decision (Cox et al., 2009).

In the purchase decision stage the consumer makes the decision to book or not book the holiday,

which is based on prior research about travel related information. Again the user has plenty of

possibilities where to book accommodation, transportation or a packaged holiday. Nowadays, different

distribution channels, including hotel websites, intermediary sites or airlines enable booking facilities.

Also with the Google Hotel Finder the potential traveler has the possibility to book the chosen hotels

either directly on the hotel website, hotel chain website or through one of the offered intermediaries.

After the trip, in the post purchase behavior phase, experiences can be shared on the Google Hotel

Finder.

4.3 Purchase decision for accommodations In the following chapter the factors influencing the purchase decision for accommodations will be

discussed. Considering the explosive increase in the number of online hotel reservations, hotel

marketers need to understand the determinants of customers’ online hotel reservation intention and

their purchase behavior.

A lot of research on the topic of how guests select a hotel to stay in has concentrated on which hotel

attributes guests care about. However, there is little known about the actual decision-making process

itself. This is because the focus of attention has been on determining ‘choice attributes’, without any

research into the actual selection process (Jones and M.-M. Chen, 2011). Knowing the attributes that

determine accommodation choice and how potential customers use these attributes in the purchase

decision process enables hotels to make optimal hotel distribution decision.

4.3.1 Choice attributes for hotels Hotel selection and attributes that are important to travelers have been researched extensively. The

attributes, which directly influence the selection of a certain hotel, are called determinant indicators


that may arouse the customers’ intention to purchase and differentiate the product from the

competitor. The services and facilities offered by a hotel may be decisive for customers to choose one

accommodation over the other (Sohrabi et al., 2012). Atkinson (1988) found that cleanliness of the

accommodation, safety and security, value for money and polite services staff are reasons for travelers

to choice a certain hotel. In the same year Wilensky and Buttle (1988) attempted to predict customer

choice of hotel organizations and concluded that personal service, an attractive infrastructure, standard

of services and good value for money are significant for the hotel selection of customers. Numerous

authors carried out similar studies on hotel choice attributes in the last decades, thus a look at the

review of 21 studies published on hotel attributes conducted by Dolnicar and Otter (2003) will provide

an overview of past research. The review study allowed an insight of the studies and provided a

ranking list with the various attributes rated as most important criteria by the majority of reviewed

literature. The most important criterion, which was ranked first or second by seven studies, was the

“convenience of the location”. The next most important factor was “service quality”, followed by

“reputation” and “friendliness of staff”. In Figure 11 the complete top ranking hotel attributes is listed.

Figure 11 Ranking of important choice attributes Source: (Dolnicar and Otter, 2003)

When looking at these results it has to be noted that in their analysis Dolnicar and Otter (2003)

included studies with different definitions of importance and different target groups.

To get a general idea, the author now aims to look at the differences in choice attributes for the two

major guest groups served by hotels: business and leisure travelers. A study amongst the business and

leisure travelers' perceived importance and performance of hotel selection factors in the Hong Kong

hotel industry identified service quality, business facilities, value, room and front desk, food and

recreation, and security to be decisive aspects. While no difference between business and leisure

travelers was observed in the selection criteria, business facilities where more important for business

travelers (Chu and T. Choi, 2000). In contrast to the work of Chu and Choi (2000), researchers

0% 10% 20% 30% 40% 50% 60% 70% 80% 90%

Room size

Parking facilities

Comfort of bed

Swimming pool

Room standard

Security

Hotel cleanliness

Value for money

Room cleanliness

Price

Friendliness of staff

Reputation

Service quality

Convienent location


Kashyap and Bojanic (2000) found differences in the value perceptions of business and leisure

travelers. While, on the one hand for business travelers the quality of the room was not of significant

importance, the quality of public areas was of high value. On the other hand, the quality of room and

price were rated as important for leisure travelers. Yavas and Babakus (2005) investigated the

congruence between hotel choice criteria for the business and leisure segment. By employing the factor

congruency technique they examined if business and leisure guests utilize similar choice factors when

choosing a hotel. After clustering the different attributes into five dimensions, namely general

amenities, convenience, core service, room amenities and ambiance, results showed that hotel choice

attributes for both of the guest segments did not correspond neatly. Of similar importance was the

general amenities dimension, which incorporated items such as access to computer/modem,

entertainment lounges, fitness center and meeting facilities. The order of importance of the other four

factors was different. For leisure travelers the second most important dimension was core service,

including service related items such as location, room rates, room comfort and promptness of service.

Third was the convenience dimension, including check in and check out or ease of making the

reservation. Factor four was ambiance, comprising the attractiveness of exterior and interior design. Of

least importance for the leisure travelers segment were the good working condition of room amenities,

such as TV, light, heating or air conditioning. For business travelers, the second most important factor

was the convenience dimension; the core service dimension was on third place, followed by room

amenities and ambiance.

Unfortunately, the approaches on hotel attributes differ very strongly in terms of attributes included,

segments studied and data analysis instruments, which makes it difficult to generalize results or even to

end up with a list of the 50 most important hotel attributes (Dolnicar and Otter, 2003).

4.3.2 Decision process for hotel selection After the choice attributes have been discussed, the author aims to review consumer decision-making

for hotel selection. Indeed, few hospitality studies make a distinction between consumer choices and

the decision-making process, yet evaluative criteria and choice criteria are two distinctive things. While

choice is an outcome of decision-making, very few have explored how choice attributes are actually

used in the decision-making process (Jones and M.-M. Chen, 2011).

Jones and Chen (2011) identified the decision-making process of consumers in choosing when hotel

and developed a basic model, which is illustrated in Figure 12. As the Figure reveals, the typical hotel

selection process is a two-stage process, which is made up of forming a consideration set and a smaller

choice set, from which the final selection is made. These findings are consistent with the proposed

model of consumer decision-making for high involvement goods (Engel et al., 1995), which is based

on the construction of a consideration set, followed the formation of a smaller choice set for the final

selection. A consideration set is a set of brands evaluated when making a choice. The consideration set

contains brands consumers are choosing among, whereas decisions tend to be easier when the

consideration set contains brands that can be easily compared. Thus knowing which attributes

influence the consideration set, and which might be used to make the final choice are central to

understanding hotel selection. The amount, quality and format of the information can affect the

decision-making process for hotels, as useful and relevant information facilitates the construction of

the consideration set and potential consumers can focus on and compare those attributes that are most


important to their decision. If the available information is ambiguous, consumers are less likely to

assume the risk of purchasing an unknown hotel product (Hoyer and Macinnis, 2008).

Figure 12 Hotel decision-making process Source: (Jones and Chen, 2011, p. 87)

The size of the consideration and choice set may be influenced by the size of the hotel market from

which the selection is being made. Furthermore the criteria used for formulating the two sets may

include many be a selection of the attributes that previous research on choice attributes of hotels have

identified. In previous research authors tend to name between 38 and 166 attributes for hotel selection,

while in the study of Jones and Chen (2011) consumers used a much smaller number of attributes in

forming the consideration and choice set. 24 different attributes were used in forming consideration

sets, the most popular being non-smoking, swimming pool, high-speed Internet, hot tub, fitness

center, room service and price range. Only 19 attributes were used to select from the choice set, the

most popular including comparison, picture, reviews, star-ratings and sort by price. These findings

show that the attributes used in forming the consideration set are different from those used in the

choice set. Interestingly, the major part of the 19 attributes used for selection in the choice set are

features of the website, rather than specific hotel attributes, and only the combination of both leads to

the final decision for a certain hotel. Attributes influencing the consumers’ decision of making

reservations and bookings of hotel products and services online are reviewed in the next step.

4.3.3 Website attributes affecting online hotel purchase Although plenty of choices are available on the Internet to choose from, perceived risk and

uncertainty, security issues and lack of personal service often prevent consumers from completing

transactions online, resulting in “lookers”. Less time spent on waiting, greater usability and more time

on enjoyment and simple pricing are some of the features increasing likelihoods of making reservations

and bookings of travel services online (Buhalis and Law, 2008). The aim of this chapter is to

investigate website attributes that affect hotel reservation intentions of online customers.

Research suggests that travel website quality factors are positively correlated to customer satisfaction,

which in turn, is significantly correlated to purchase intention. The main components of website

quality are content richness, functionality and ease of use or usability. Usability generally refers to the

website design, layout, graphics and format of the information, navigation or the degree of ease to use


the website. Functionality is associated with the supply of sufficient and efficient information about the

products and services, such as customer information, reservation information or surrounding area

information (Law and Bai, 2008). Suárez Álvarez et al. (2007) suggest that the quality of the website on

the Internet is associated with personalized and individualized service and with the ease of using and

accessing the distribution channel. Travelers expect websites to be interactive and secure. They are

looking for reduced waiting time during browsing, which implies good page loading speed and

navigation efficiency. The traveler appreciates finding value added services on the website such as

direct and contextualized access to other websites (i.e Google maps) that are enabled through the

mash-up technology (Petr, 2009).

Law and Hsu (2005) attempted with their study to investigate the importance of specific attributes of

hotel websites from the perspective of consumers. Their goal was to find out which dimensions should

be included in a hotel website. Findings of the study suggest that first of all the reservation transaction

should be easy accessible and clearly displayed. Information regarding room rates, availability, and

policy should be presented on the website. Especially information about room rates was very

important and accurate information should be provided. For instance, what type of room is available

including view, size and number of beds and what does the rate include (e.g. breakfast). Potential

customers should be enabled to make online reservations with ease and be informed about cancellation

policies. Moreover potential guests would like to have pictures of the hotel facilities, location, rooms

and other features. Basic contact and access information, such as telephone number, address, e-mail,

local transportation or the closest airport/train station should also be available on the website. If

international visitors should be addressed a multilingual site would be necessary. Also the information

should be presented clearly and long download time must to be avoided.

Jeong and Lambert (2001) investigated customer perceived quality of information of lodging websites

and identified four constructs to be significant indicators to predict the customers’ purchase decision.

These indicators included perceived usefulness, perceived ease of use, perceived accessibility and

attitude. They concluded that information quality could be measured in information content,

information format and physical environment, whereas information should be accurate, current,

relevant, secure, valid and complete. Focusing on the user-friendliness, information format included

design, format and links, which measured a customer’s physical movement through the Internet. The

physical environment referred to a customer’s ease of accessibility to the system and information.

Jeong et al. (2001) examined customer perception of hotel websites and concluded that color

combinations, ease of use, navigation, quality, information completeness, accuracy, and currency were

crucial factors influencing the customers purchase decision.

Yoon (2002) investigated the relationship between trust and the purchase intention of online

consumers and found out that website trust and satisfaction were mediating variables in influencing

customer’s purchase intention. Another study by Greenfield Online (1998, cited in Yoon, 2002)

confirmed the importance of awareness in creating online trust. The study suggested that as reasons

for non-purchasing online to be payment security, payment-clearing structure, company credibility and

absence of privacy policy. And when asked what constitutes trust, online purchasers answered

company awareness, brand familiarity, and recommendation by friends or family. Thus, the awareness

of the name of the company’s websites should be considered as an essential ingredient for garnering


trust toward online websites. Beatty and Ferrell (1998) also found that the familiarity could increase

trust, consequently, the possibility of purchasing online may be affected by consumers’ familiarity with

a particular brand. Additionally, payment security and privacy concerns were a major concerns to many

consumers while making purchases online (Ahuja et al., 2003).

In their study about Hong Kong residents’ perception of travel websites, Qi et al., (2010) findings

showed that experienced as well as inexperienced online travelers considered online payment security

as the most important factor when making a online hotel purchase. Price and website reputation were

indicated as the second most important factors, whereas to experienced travelers, payment security and

price were equally important, payment security was considered as more important than price by

inexperienced travelers. As well, website usability was less important to experienced travelers, yet it was

more important to inexperienced travelers.

4.3.4 Hotel purchase with the Hotel Finder With the aim to make it easier for online travelers to find, compare and book hotels across the web,

Google launched the Hotel Finder as a new meta search product in July of 2011 (Fox, 2011). With a

special focus on usability, the tool assists travelers in finding a hotel that fits their search criteria and

therefore makes the booking process fast and easy. Nevertheless before investigating the Hotel Finder

functionalities into detail, the author aims to remind that the state of the tool was experiential until

recently. With current updates and changes Google aimed to further improve the service, which finally

in November 2012 became officially all over the world. As of lately the Hotel Finder is also available in

different languages, including German and hotel prices are displayed in Euros (Benkert, 2012a).

The Hotel Finder enables potential travelers to:

• Find hotels according to their preferred search criteria such as travel dates, price, location and

user ratings

• Find accurate and current information about chosen hotels

• Find relevant information about the destination or location

• Keep track on preferred hotels in a shortlist

• Book a room or connect directly with the hotel or seller to ask for additional information

To start with their searching process on the Hotel Finder home page, online travelers can type the

name of a city, landmark, hotel name or address into the search bar on top of the page. It is usual for

customers to start their search with a location. After entering a location, the Hotel finder shows a large

map and a list of hotels that match the users search specifications. Now the user has plenty of

possibilities to narrow the results down and customize the search according to their preferences.

One way to find an appropriate hotel is to specify an area on the map and filter the hotels to the most

popular areas of town, hotels within a specific area, or hotels within a certain distance of a landmark or

location. Only hotels within the highlighted area of the map are shown in the list of results and each

hotel is shown with a red dot on the map. By clicking on a dot, detailed information about the hotel is

shown, as well as a booking link and the possibility to add the hotel to the shortlist. When clicking the

dropdown menu in the top right corner of the map the user has different available options to choose

from.


• Hotels in selected area: When searching for a location, the map will show a rectangular border

around the city or a specific location. By dragging the corners of the rectangle the user can

limit the results to a specific region on the map.

• Hotels by travel time: This option enables the user to limit the results to hotels in the vicinity

of a special landmark, address, hotel or city center. The user has the possibility to specify the

desired proximity in terms of walking time, public transit travel time or travel time by car. Also

the preferred maximum travel time can be selected.

• Hotels in popular areas: The map can also help the user to identify popular areas and regions

in a city still unknown to the traveler. By clicking on smaller area or larger area the user can

adjust the size of the highlighted area.

Another possibility to find a suitable hotel is to filter by preferences and limit the search to hotels that

match the users preferred price, hotel class or user rating. The following are the specifications the user

can apply:

• Date: on a three-month calendar and up to 90 days in the future the user can choose the dates

of the hotel stay.

• Price: the user can specify an ‘up to’ price per night or specify a price range by using the

dropdown menu.

• Class & User rating: to see only hotels within or above a certain class, the user can select the

minimum number of stars in the hotel class dropdown menu. To see only hotels that have

user rating above a certain average, the user can specify the minimum rating.

• Amenities: when clicking the amenities dropdown menu, the user can downsize the hotel

results to hotels, which offer specific amenities such as pool, restaurant, air-conditioned, non-

smoking room and many more.

Furthermore, the user can sort the results by different factors including the price, magic, hotel class, user

rating, price compared to usual and travel time.

To see more detailed information about a certain hotel, the user can simply click on the hotel in the

results list and then choose by various tabs. In the overview one can find the hotels most relevant

information at one sight including a short description of the hotel, contact information and address,

photos and user reviews. Under photos the user can see all available photos of the hotel and enlarge

them into the gallery view. All reviews written by other Google users and links to review around the

web can be found. Under location the user can get an interactive view of the nearby location of the

hotel and a 360-degree street-level imagery with Street View, if available.

To keep track of the viewed hotels, the user can add them to a shortlist by clicking the ‘Add to

shortlist’ button. To later view the saved results a click on the shortlist bar is enough.

Finally, if the user wishes to book a chosen hotel he or she can click the ‘Book’ button below the hotel

name. While primarily the cheapest booking option is shown, by clicking on ‘More’ all available

booking options for the hotel are shown, which may include different OTA’s as well as the hotel

owner or hotel chain website. The user can choose to book the hotel through any of these channels


and is transferred to the partners website where the booking can be concluded (“Google Hotel

Finder,” 2012).


5. The Technology Acceptance Model The study of human decision-making resulted in models that posit the mental process that humans use

to make decisions. Most of these models have been used within organizational contexts to predict

which employees are likely to accept new technology and why (Willis, 2008). In particular the Theory

of Planned Behavior (TPB) (Ajzen, 1985) and the Theory of Reasoned Action (TRA) (Ajzen and

Fishbein, 1980) have been used to predict many types of behavior, nonetheless have been less

successful in predicting technology acceptance. This led to the development of the Technology

Acceptance Model (TAM) (Davis, 1986, 1989; Davis et al., 1989).

The TAM was proposed by Davis (1986) and aimed to explain and predict user acceptance of IT in the

workplace. It is based upon the TRA, which states that consumers’ beliefs influence attitudes, while

attitudes shape behavioral intention (Ajzen and Fishbein, 1980). In the TAM, the two main constructs

to predict users’ attitudes and intentions to use new technologies are perceived usefulness and

perceived ease of use (Figure 13).

Prior to the work of Davis (1986), several studies emphasized the importance of perceived usefulness

and perceived ease of use in predicting a persons behavior. Robey (1979), for instance, found a high

correlation in his work that existed between perceived usefulness and system usage. The importance of

perceived ease of use on innovation adoption could also be found in the study of Tornatzky and K.

Klein (1982). They found that the complexity of an innovation was one of the main factors influencing

its adoption. Later, Bandura (1982) showed the importance of considering both perceived ease of use

and perceived usefulness in predicting human behavior. He suggested that behavior would be best

predicted by self-efficacy and outcome judgments. Self-efficacy was similar to perceived ease of use

and measured on how well one can deal with a given situation. The outcome judgment was similar to

perceived usefulness and was defined as the extent to which successful behavior was believed to be

linked to effective results. Similarly, Swanson (1982) showed evidence that perceived ease of use and

perceived usefulness were both important determinants for predicting behavior. In Swanson’s work

perceived ease of use could be compared with the associated cost of access, while perceived usefulness

was found to be similar to information quality. In the end, Davis (1986) provided evidence that people

tend to use or reject a system within an organization to the extend that they believed it would help

them to perform their job better (perceived usefulness) and also that the use of system is free of effort

(perceived ease of use).

More generally, perceived usefulness refers to the assumption that using a new system would increase

job performance within an organizational context. Ease of use refers to the degree to which the

potential user expects the system to be free of effort (Chung and Tan, 2004). TAM also incorporates a

causal relationship between ease of use and perceived usefulness, suggesting that an individual’s

perception of how easy or difficult it is to use a system will influence his or her perceptions about the

usefulness of the system, since it is perceived as being more useful if it is easier to use. Hence, a system

or technology that is perceived as easier to use than another system would be more likely to be

accepted by users (Davis, 1989).

Two other constructs in TAM are attitude towards use and behavioral intention to use. Attitude was

defined as an individual’s inclination to exhibit a certain response toward a concept or object. Attitude


toward an object is generally viewed as a function of an individual’s beliefs about the object and the

evaluative responses associated with these beliefs (Fishbein, 1963). It is generally agreed that favorable

attitudes toward a new system or technology result in a strong intention to use this technology (Shih,

2004). According to Wen (2009) the attitude concept can be used to explain customers’ actions since

attitude is a behavioral disposition. The theory of planned behavior (Ajzen, 1985) proposes three

conceptually independent determinants of intention. Among those three determinants, attitude has

being tested and confirmed as most significant determinant which exert significant influence on

consumers’ intention and behavior in various studies.

Behavioral intention to use is a measurement of the likelihood a person will employ a certain

application (Ajzen and Fishbein, 1980). According to Zeithaml et al. (2002) purchase intention is one

dimension of behavioral intention and has been used to predict actual behavior in hospitality and

tourism businesses (Ajzen and Driver, 1992). Although an objective measurement for the actual

behavior would be ideal, it is difficult to obtain. However, there is enough evidence to suggest that

there is a positive relationship between the intentional behavior and actual behavior and in many

studies the intentional behavior is defined as the consumers’ intention to use a new technology

(Morosan and Jeong, 2008). It is important to understand that customers purchase intention can

usually be predicted by their intention. Dodds et al. (1991) suggested that purchase intention was the

possibility of consumers purchasing certain products or brands; Burton et al. (1998) indicated the

purchase intention as the probability of purchasing products.

Reasons to study TAM include improving user acceptance by changing the nature of a system

involved, predicting how users will respond to changes, understanding why people resist using

computers and understanding determinants of technology adoption (Adams et al., 1992; Davis, 1989).

Figure 13 The Technology Acceptance Model Source: (Davis et al., 1989)

To develop measurement scales for perceived ease of use and perceived usefulness, Davis (1989)

referred to psychometric scales used in psychology. Typically individuals were asked to respond to

various questions that pertained to a given context. Afterwards the responses were analyzed and used

as an indication of a person’s belief for the context considered. In case of TAM, Davis developed

psychometric scales for both perceived ease of use and perceived usefulness in three stages: a

pretesting phase, an empirical field study, and a laboratory experiment. During the pretesting phase a

ten-item scale was developed and then the reliability and validity of the ten item scales was tested. To

do so, Davis (1989) conducted a field study with 112 employees working for IBM in Toronto, Canada,

where the participants were asked to rate the usefulness and ease of use of two systems that the


employees were already using in the organization. Further analyses were used and all the tests showed a

high reliability and validity for the ten-item scales shown in Table 6 and 7.

Item No. Candidate item for psychometric measures for perceived usefulness 1 Using electronic mail improves the quality of the work I do. 2 Using electronic mail gives me greater control over my work. 3 Electronic mail enables me to accomplish tasks more quickly. 4 Electronic mail supports critical aspects of my job. 5 Using electronic mail increases my productivity. 6 Using electronic mail improves my job performance. 7 Using electronic mail allows me to accomplish more work than would otherwise be

possible. 8 Using electronic mail enhances my effectiveness of the job. 9 Using electronic mail makes it easier to do my job. 10 Overall, I find the electronic mail system useful in my job. Table 6 Ten-item scale for perceived usefulness Source: (Davis, 1989, p. 326)

Item No. Candidate item for psychometric measures for perceived ease of use 1 I find it cumbersome to use the electronic mail system. 2 Learning to operate the electronic mail system is easy for me. 3 Interacting with the electronic mail system is often frustrating. 4 I find it easy to get the electronic mail system to do what I want it to do. 5 The electronic mail system is rigid and inflexible to interact with. 6 It is easy for me to remember how to perform tasks using the electronic mail system. 7 Interacting with the electronic mail system requires a lot of my mental effort. 8 My interaction with the electronic mail system is clear and understandable. 9 I find it takes a lot of effort to become skillful at using electronic mail. 10 Overall, I find the electronic mail system easy to use. Table 7 10 item scale for perceived ease of use Source: (Davis, 1989, p. 326)

Davis (1989) also asked the participants from IBM to report their attitude towards the two systems

they were rating, using a scale developed by Ajzen and Fishbein (1980) for operationalizing attitude

toward behavior. The scale measured five different types of attitude that a person may have toward a

system on a seven-point scale with a mid-point standing for ‘neutral’ as shown below.

All things considered, my using electronic mail in my job is: Neutral

Good :_:_:_:_:_:_:_: Bad Wise :_:_:_:_:_:_:_: Foolish Favorable :_:_:_:_:_:_:_: Unfavorable Beneficial :_:_:_:_:_:_:_: Harmful Positive :_:_:_:_:_:_:_: Negative

The participants had to report their actual usage of the systems on a six position categorical scale with

the following labels: Don’t use at all, Use less than once each week, Use about once each week, Use several times a

wee, Use about once each day and Use several times each day.


The obtained results showed that self-reported usage was significantly correlated with both perceived

ease of use and perceived usefulness for the two systems in use at IBM, thus confirming Davis (1986)

original TAM model.

However, later Davis (1989) refined both ten-item scales to develop two shorter six-item scales (Table

8 and 9), because a shorter scale might be more practical in real world situations. Davis (1989) used the

six-item scales to conduct a laboratory study with 40 participants to validate the original TAM model.

The systems evaluated were two IBM PC-based graphics systems, Chart-Master and Pen-draw, which

the participants had never used before. After a one-hour experiment with each system he asked them

to rate their perceived usefulness and perceived ease of use for both systems.

Item No. Candidate item for psychometric measures for perceived usefulness 1 Using Chart-Master in my job would enable me to accomplish tasks more quickly. 2 Using Chart-Master would improve my job performance. 3 Using Chart-Master in my job would increase my productivity. 4 Using Chart-Master would enhance my effectiveness on the job. 5 Using Chart-Master would make it easier to do my job. 6 I would find Chart-Master useful in my job. Table 8 Revised six-item scale for perceived usefulness Source: (Davis, 1989, p. 340)

Item No. Candidate item for psychometric measures for perceived ease of use 1 Learning to operate Chart-Master would be easy for me. 2 I would find it easy to get Chart-Master to do what I want to do. 3 My interaction with Chart-Master would be clear and understandable. 4 I would find Chart-Master flexible to interact with. 5 It would be easy for me to become skillful at using Chart-Master. 6 I would find Chart-Master easy to use. Table 9 Revised six-item scale for perceived ease of use Source: (Davis, 1989, p. 340)

Once again, Davis (1989) used the measurement scales by Fishbein and Ajzen (1980) to measure the

attitude of the participants. In order to conduct the measurement the participants had to report their

self-predicted future by using both systems. Such self-predictions were amongst the most accurate

predictors available for an individual’s future behavior at that time (Warshaw and Davis, 1985). In

Davis’ (1989) study, both perceived usefulness and ease of use were significantly correlated with self-

reported indicants of system use.

In a later development of TAM Davis et al. (1989) included behavioral intentions as a new variable that

was directly influenced by the perceived usefulness of a system. They suggested that if an individual

perceives a given system as useful he or she might form a strong intention to use this system without

forming any attitude towards it. This first modified version of TAM was tested within a 107 people

sample and the main finding indicated a direct influence of perceived usefulness and perceived ease of

use on behavioral intention, thus eliminating the need for an attitude construct from the model.


5.1 Extensions and modifications of TAM TAM became one of the most widely applied models for explaining and predicting usage intentions

and acceptance behaviors for information technologies (Venkatesh, 2000). With more than 700

citations to the original proposal for TAM, Davis’ research (Davis, 1989) has been adapted and

extended in many ways (Chuttur, 2009). Though the results showed slight modifications in

explanations of TAM, in general most of the researchers agreed that perceived usefulness was the main

determination of the actual use of the system, while perceived ease of use had a strong influence on

perceived usefulness and a slight effect on the use of the system (Venkatesh, 2000).

One of the important extensions was made by Venkatesh and Davis (2000) who proposed a theoretical

extension of TAM, which is also referred to as TAM2. TAM2 revised the original TAM and proposed

the extension with two determinants: social influence processes and cognitive instrumental processes.

Social influences represented the social forces (subjective norm, voluntariness and image) that

influence an individual’s decision to accept a new system, while job relevance, output quality and result

demonstrability represented the factors of cognitive instrumental processes. In addition, the factor of

experience decreased the influence from subjective norm on the individual’s behavior.

Another important extension of TAM was proposed by Venkatesh (2000) who identified the

antecedents to the perceived ease of use variable in the TAM model. The two antecedents proposed by

Venkatesh (2000) were anchors and adjustments. Anchors were considered as general beliefs about

computers and computer usage, and adjustments represented beliefs that were shaped based on direct

experience with the system. All variables showed strong evidence in explaining perceived ease of use

for a given system.

A variety of researchers proposed the extension of self-efficacy in TAM (Igbaria and Iivari, 1995; C.

Pan, 2003; Yi and Hwang, 2003). Bandura (1982) found that the individual’s beliefs and behaviors were

influenced by self-efficacy, which referred to the individual’s judgments of their capabilities and skills

and how one could perform with those skills. Further, Compeau and Higgins (1995) defined computer

self-efficacy as the individual’s “judgment of one’s capability to use a computer”. Igbaria and Iivari

(1995) introduced self-efficacy into TAM, while self-efficacy was jointly influenced by computer

experience and organization support and also computer anxiety was both affected by self-efficacy,

computer experience, and organization support. The results suggested that perceived ease of use was

significantly affected by self-efficacy, computer experience and organization support. Perceived

usefulness was significantly affected by computer anxiety, computer experience, organization support,

and perceive ease of use, whereas only computer experience and perceived usefulness directly

influenced system usage.

Other studies extended TAM to the context of the Web with the aim to understand the individual’s

beliefs or motives to use the Internet and to show how factors affect individual’s acceptance of the

Web. Teo et al., (1999) investigated TAM using the Web as the application and found that usefulness

and ease of use predicted usage, however that usefulness had a stronger effect. Lederer et al. (2000)

studied TAM and Web usage and identified antecedents of both perceived usefulness and perceived

ease of use. They demonstrated that ease of understanding and ease of finding predicted ease of use,

and that information quality predicted usefulness of websites. Moon and Y. Kim (2001) introduced in


their study on Web acceptance introduced playfulness as a new factor in TAM. Results showed that

perceived ease of use and perceived usefulness were important to the user’s perception of the Web

systems. The perception of playfulness appeared to influence user’s attitude toward using the World

Wide Web. Thus, perceived playfulness may be considered as important in the design of Web systems

or in other words they must provide more concentration, curiosity, and enjoyment.

Playfulness has also been considered as important, when addressing the individual’s motivation toward

the acceptance of websites (Morosan and Jeong, 2006, 2008) and would have been one of the

determinants in the extended TAM model tested in this thesis and thus be discussed in detail in the

next chapter. Also Chuan-Chuan Lin and Lu (2000) addressed in their paper the acceptance of a

Website and extended TAM by the features information quality of a Website, response time and

system accessibility of a website. The findings showed that acceptance behavior in the voluntary usage

environment, such as the Internet, could be predicted by TAM. In addition, perceived usefulness of a

Web user was significantly affected by the quality of information provided and the amount of time that

users spend on waiting for the responses of the Web. This implies that webpage providers not only

need to make the content informative and accurate, nevertheless also need to design a speedy website

and keep loading time low.

In order to investigate the individual’s motivation toward the acceptance of websites, Van der Heijden

(2003) expanded the constructs of TAM with the constructs of perceived enjoyment and perceived

attractiveness. Davis et al. (1992) first introduced the concept of perceived enjoyment, which refers to

the extent to which the activity of using the computer is perceived to be enjoyable in its own right,

apart from any performance consequences that may be anticipated. Perceived attractiveness was the

new construct, defined in this paper as “the degree to which a person believes that the website is

aesthetically pleasing to the eye (Van der Heijden, 2003, p. 544). Based on this extended TAM, 825

users of a portal website were surveyed. The results clearly supported the constructs of the perceived

enjoyment extended TAM. Further, perceived attractiveness contributed to feelings of usefulness,

enjoyment and ease of use and revealed that visual attractiveness is a much more powerful concept

than is previously assumed. However, an inclusion of the visual attractiveness construct makes sense,

only when intrinsic motivation is explicitly included. Intrinsic motivation refers to behaviors performed

out of interest and enjoyment (Deci, 1971). It seems plausible to suggest that the

attractiveness/enjoyment couple is the intrinsic motivation counterpart of the ease-of-use/usefulness

couple and the relationship between this constructs are worth to be further explored (Van der Heijden,

2003).

A number of other researchers proposed extension to the TAM; for instance constructs such as

compatibility (L. Chen et al., 2002), cost (J.-H. Wu and S.-C. Wang, 2005), and trust (Yu et al., 2005),

have been added to the model. An extension of TAM for the context of hotel room reservation

websites will be examined in this master thesis.

5.2 The extended TAM The original TAM proved to be lacking in capturing the specific contexts of technology adoption. Ease

of use and usefulness were believed to be fundamental in determining the acceptance and use of

various, corporate ITs. These beliefs, however, may not explain the user's behavior toward newly


emerged ITs, such as the voluntary usage environment of the World-Wide-Web (WWW) (Moon and

Y. Kim, 2001). In their study Moon and Y. Kim (2001) introduced playfulness as a new intrinsic

motivation factor and extended and empirically validated the TAM for the WWW context. The

playfulness dimension reflected the user’s intrinsic beliefs in WWW acceptance. Likewise, to explicitly

model the role of intrinsic motivation in TAM, Davis et al. (1992) introduced the concept of perceived

enjoyment. Perceived enjoyment was defined as “the extent to which the activity of using the

computer is perceived to be enjoyable in its own right, apart from any performance consequences that

may be anticipated” (Davis et al., 1992: 1113).

With some exceptions (Davis et al., 1992; Van der Heijden, 2003; Moon and Y. Kim, 2001) in

technology acceptance research, most of the work has been conducted from an extrinsic motivation

perspective. Motivation theorists have often distinguished the effects of extrinsic and intrinsic

motivation on individuals’ behaviors (Deci and Ryan, 1985; Deci, 1975). Extrinsic motivation refers to

the performance of an activity and was perceived to help to achieve valued outcomes that are distinct

from the activity itself, such as improving job performance. In other words, extrinsic motivation refers

to behaviors carried out to obtain contingent outcomes. Intrinsic motivation refers to the performance

of an activity for no apparent reason other than the process of performing it out of interest and

enjoyment (Deci, 1971, 1975).

Morosan and Jeong (2006, 2008) extended the original TAM to predict the usage of hotel room

reservation websites and argued that in a travel context relying only on the traditional TAM constructs

(usefulness and ease of use) could be misleading as today’s websites are characterized not only by

functionality, but also by playfulness or enjoyment. On hotel reservation websites the role of

playfulness applications is increasing and it is necessary to examine adoption of such websites from the

functional as well as from the playfulness perspectives. Thus perceived playfulness should be added to

the model. Liu and Arnett (2000) found that playfulness was one of the most important factors

associated with website success. Playfulness is believe to be a key factor that affects users’ adoption of

a new system (Moon and Y. Kim, 2001) and refers to an individual’s tendency to interact

spontaneously with a system or technology (Hackbarth et al., 2003). Playfulness encompassed a

multifaceted construct including cognitive, social, and physical spontaneity, joy, and sense of humor

(Webster and Martocchio, 1992). In their paper C.S. Lin et al. (2005) investigated the value of

perceived playfulness in expectation-confirmation theory when studying continued use of a web site

and found out that perceived playfulness was a key predictor for users’ online behavior and contributes

significantly to the users’ intent to reuse a website. Users with pleasant and enjoyable experiences

tended to establish favorable attitudes toward Web portals.

In an online environment, prior experience with online reservation plays an important role, as it links

previous behavior with the probability of that behavior being repeated. Indeed, one key factor that can

reduce uncertainty with online purchasing is past behavioral experiences. Thus, consumers who have

purchased from the Internet are more likely to make online hotel room reservations (Cho, 2004).

These findings are supported by the study of Jensen (2011) who found out that experience with online

travel shopping appears to be the main predictor for both online travel search and online purchase.

Moreover Lohse et al. (2000) found out, that length of Web browsing time as well as frequency and

amount of time using the Internet per visit are positively related to intention of online purchasing.


Prior practice with online hotel reservations enhances the users experience with hotel reservation

websites, which is important for the adoption of online travel portals. As travellers will accumulate

knowledge about making hotel reservations on the Internet, they will become more skilled at this and

will enjoy the process of making an online booking (Morosan and Jeong, 2006).

According to Morosan and Jeong (2006) in travel, playfulness and prior experience, along with

usefulness and ease of use, are believed to influence the adoption of hotel reservation websites. Their

proposed extended TAM model is shown in Figure 14.

Figure 14 The extended TAM Source: adapted from (Morosan and Jeong, 2006)

Out of the 914 respondents, surveyed for their research, more than half had visited the selected

website before and 18 percent commented that they would revisit the website if it offered cheap deals

and discounts. Nine percent of the respondents indicated that they would come back because of the

ease of use of the website. Only one percent commented that they were concerned about security

issues when making en online reservations. Therefore, in spite of the fact that companies claimed that

their websites were fully secure and trustful, online travelers still worried about online security and may

preferred talking to a human being when making a room reservation (Morosan and Jeong, 2006).

The model was tested and resulted in a good fit for examining adoption of hotel reservation websites.

As predicted by the traditional TAM, travelers’ attitude towards the use of reservation websites was

positively correlated to their intention to use the website for reservations. Further, perceived

playfulness was found to have the greatest influence on intentions even if not hypothesized by the

theory. In this model, perceived ease of use had the greatest impact on attitude, followed by perceived

usefulness, perceived playfulness and prior experience. The impact of prior experience in the tested

model was marginal and indicated that prior experience had only a small effect in shaping travelers’

attitude and intention to use reservation websites (Morosan and Jeong, 2006).

A couple of years later, Morosan and Jeong (2008) tested again a modified version of the TAM

focusing on users’ perception of hotel-owned and third party reservation websites. In this experimental

study, the authors tested whether the extended variant of TAM could be used to assess users

perception of two different channels for hotel online reservations. One of the objectives of this study

!

Perceived usefulness

Perceived ease of use

Perceived playfulness

Prior experience Attitude Intentions


was to find out whether there existed differences in intentions to use between the two most common

online reservation channels. To assess the adoption of hotel reservation websites with the extended

TAM model this study used five constructs including perceived usefulness, perceived ease of use,

perceived playfulness, attitude and intentions to use. 914 respondents participated in this study and

more than half of them had visited the given websites before. Again the model showed good reliability

and fit for both types of websites and the results were rather similar in both subsamples. The TAM-

related and the extended model hypotheses were validated. In the hotel website subsample, both

attitude and perceived playfulness were found to have a significant relationship with the intentions to

use the website with perceived usefulness being the strongest predictor for attitude towards using.

However, in the third party subsample perceived ease of use appeared to be the strongest predictor for

attitude toward using the website. Further, perceived playfulness and attitude had significant impacts

on intentions to use, at which attitude had a stronger relationship than perceived playfulness.

Interestingly the survey showed only one significant difference between the hotel website and the third

party website subsample. Attitude and intention to use were significantly higher in the third party

subsample, indicating that online travelers rather used third party than hotel owned websites for their

room reservations.

5.3 Research purpose With the growing online market, the Internet has been adopted by many travel organizations as well as

non-travel organizations as a competitive tool to provide travelers with online reservation

opportunities. There is also a continuous increase in the number of travelers that use the Internet to

make their hotel arrangements (“Online Travel Market,” 2012), with intermediaries being very

competitive in persuading online travelers to make room reservations through their website (Morosan

and Jeong, 2008). The Hotel Finder is a novel reservation tool with high functionality and the potential

to revolute the online market for hotels.

A significant amount of research has been concentrating on the consumer’s perspective towards online

distribution channels. In particular understanding user’s adoption of new electronic distribution

channels in the hotel industry and specifically the online purchase intention and channel choice have

been a topic of research (Card et al., 2003; Jeong and Lambert, 2001; W.G. Kim and D.J. Kim, 2004;

W.G. Kim et al., 2006). Jeong and Lambert (2003) used lodging websites for their research and

identified that perceived usefulness and attitudes were significant indicators to predict the customers’

purchase behavior. However, at the moment these determinants have not yet been investigated on the

specific example of Google’s reservation tool.

Thus, first of all the overall aim of this thesis is to find out whether the Google reservation website will

be adopted as a tool by potential travelers to make their travel arrangements. It will aim at providing

more insight into the question why people do or do not use the Hotel Finder for hotel room

reservations. Secondly this thesis aims to develop suggestions for future distribution channel strategies

of industry practitioners to take advantage of traveler’s adoption of the Hotel Finder as a reservation

website.

Due to the successful application of the extended TAM framework for the Internet environment in

general as well as for specific websites, the extended version of the TAM model for hotel reservations


websites has been chosen as proposed research model for this master thesis. Reservation websites and

respectively the Hotel Finder represent efficient technological tools for online travellers to make

reservation transactions. Therefore the TAM is used as a theoretical foundation in this study to

examine users acceptance of this novel technology. The application of the TAM framework offers the

possibility to examine the effects of perceived usefulness, ease of use and playfulness on traveller’s

attitude toward using the Hotel Finder for their future room reservations. This thesis will as well

examine traveler’s prior knowledge and online reservation experiences the Hotel Finder tool.

Considering the outcome of the literature review, the author will focus on the application of the

extended TAM framework for a specific hotel reservation website and not for hotel reservation

websites in general as most of the previous work has focused on (Morosan and Jeong 2006, 2008).

Due to this, the experimental study of this master thesis aims to test whether the extended variant of

the TAM can be used to evaluate the users perception of the Hotel Finder website and will aim at

representing explicit results with direct implications for hotel practitioners.

5.4 Proposed research model Morosan and Jeong (2006, 2008) applied Davis’ original TAM and highlighted the need for adapting

the theoretical framework to the context of hotel room reservation websites, for the purpose of which

the extended TAM was created. As today’s websites are characterized not only by functionality, but

must be entertaining, concentrating and fun, the construct of playfulness has been included in the

model. Also the prior experience is believed to influence adoption of hotel reservation websites, which

explains the extension of the prior experience dimension.

The use of the TAM construct to predict users adoption of technology in various contexts has

extensively been discussed extensively earlier in this work. Based on the literature review, for the scope

of this study the extended TAM model for hotel reservation website adoption has been chosen to

predict the adoption of the Hotel Finder. The model has already been successfully applied by Morosan

and Jeong (2006, 2008) in the surveyed context of hotel reservation websites and was found to have

good reliability and validity. It provides all dimension and attributes, which support the users adoption

of Google’s new technology offered for the reservation of hotel rooms.

Figure 15 Proposed research model and its relationships Source: own illustration

!




Attitudes Intentions to use

H1

H4

H2

H3

H5

H6


Figure 15 illustrates the proposed research model. It indicates that in addition to the functional factors

(usefulness and ease of use) perceived playfulness should be relevant to intentional use. To assess how

the TAM can be used to predict the adoption of the Hotel Finder for hotel reservations, this study

developed a set of hypotheses to test. The relationships of the extended TAM have been tested in the

context of the online reservation environment (Morosan and Jeong 2006, 2008). To revalidate these

relationships and to predict adoption of the Hotel Finder within the proposed TAM construct, a total

of six hypotheses have been proposed.

5.5 Research variables and hypotheses Previous research was reviewed to ensure that a comprehensive list of research variables were included

in the proposed research model.

In the context of online hotel reservations, perceived usefulness can be defined as ‘the extent to which a

consumer believes that online hotel reservation websites will provide access to useful information,

facilitate comparison shopping, and enable quicker shopping’ (Vijayasarathy, 2004). The perceived

usefulness construction is measured by dimensions including completeness of information; is the

provided information relevant, informative, meaningful, important, helpful, or significant for

customers’ decision-making process. And also the information must be unambiguous, clear, or

readable (Jeong and Lambert, 2001). Other measurements include whether or not the website provides

links to complementary service providers and booking possibilities (Morosan and Jeong, 2006), if the

website is clear about payment security and privacy concerns and if it enables customization and

product assortment (Qi et al., 2010).

Perceived ease of use refers to the degree to which the potential user expects the use of the reservation

website to be free of effort (Chung and Tan, 2004). The perceived ease of use dimension consists of

such attributes as user-friendly and convenience to use the site, controllable, clear and understandable,

well structured, flexible, easy to become skillful, loading speed of the page, ease of comparing hotel

products, easy to navigate and to make a reservation (Jeong and Lambert, 2001). An additional

measurement for this construct is the websites find-ability and accessibility. As most online travelers

are searching for hotel products by means of search engines an appropriate search engine strategy is

very important for reservation websites so that potential customers can easily find the site. Websites

must also be furthermore accessible by users making use of different types of web browsers

(Constantinides, 2004).

The first hypothesis examines the link between the user’s beliefs about perceived ease of use and

perceived usefulness. The traditional TAM suggests that an individual’s perception of how easy or

difficult it is to use a system will influence his or her perceptions about the usefulness of the system,

since it is perceived as being more useful if it is easier to use (Davis, 1989). Previous TAM research

demonstrates strong empirical support for a positive relationship between perceived ease of use and

perceived usefulness. In the website environment this relationship is expected to hold, as the easier a

website is to learn, use and navigate, the more useful it would be perceived by it’s user (Van der

Heijden, 2003). Therefore an individual’s perceived ease of use about finding relevant information and

making a reservation on the Hotel Finder portal is expected to have a positive influence on the user’s

perception of usefulness in their interaction with the Hotel Finder.


Hypothesis 1. There is a positive relationship between perceived ease of use and perceived usefulness

of using the Hotel Finder for room reservation.

Empirical support for the relationship between usefulness, ease of use and attitude has been provided

by a number of studies. Users may adopt a positive attitude toward the website when they perceive that

seeking related and accurate information over this website is both useful and easy (Shih, 2004). The

positive relationship has also been demonstrated in the context of the WWW. Individuals who

believed that using IT would have positive outcomes also tended to have a positive attitude toward

using it (Moon and Y. Kim, 2001). Therefore, individuals who perceive the interaction with the Hotel

Finder as useful will have a positive attitude toward using it.

Hypothesis 2. There is a positive relationship between perceived usefulness and attitude toward using

the Hotel Finder for room reservation.

In technology acceptance research in the context of hotel reservation websites, ease of use was found

to have the greatest impact on attitude. It seems that today’s travel websites should be easy to use in

order for users to adopt them as a reservation tool for hotel rooms (Morosan and Jeong 2006, 2008).

Since this thesis aims to examine users adoption of the Hotel Finder, ease of use may be relevant for

users having little or no experience with this specific website. Therefore the following hypothesis is

proposed:

Hypothesis 3. There is a positive relationship between perceived ease of use and attitude toward using

the Hotel Finder for room reservation.

Literature suggested two possible approaches on the trait of playfulness. One the one hand playfulness

was defined as a motivational characteristic of individuals and, on the other hand, playfulness was

defined as a situational characteristic of the interaction between an individual and the situation

(Barnett, 1990; Liebermann, 1977). Webster and Martocchio (1992) argued that individuals with a

higher perception of playfulness demonstrated better performance and higher affective responses to

computer training tasks than individuals with less perceived playfulness. The majority of research on

playfulness as the individual’s interaction state were based on the Csikszentmihalyi's (1975) flow

theory. He defined flow as “the holistic sensation that people feel when they act with total

involvement.” When in the flow state, a person may have more voluntary interaction with his or her

environment.

The first to extend the TAM model by the concept of enjoyment were Davis et al. (1992). Perceived

enjoyment equalized perceived playfulness and referred to the extent to which the activity of using the

website was perceived to be enjoyable in its own right, apart from any performance outcomes (Davis et

al., 1992). To explain the effect of playfulness on the individual’s technology acceptance, Moon and Y.

Kim (2001) suggested to consider playfulness as an intrinsic belief or motive, which was shaped from

the individual’s experiences with the environment. In this thesis, playfulness is examined as an intrinsic

motivation that is formed from the individual’s subjective experience with the Hotel Finder. Intrinsic

motivation referred to the performance of an activity for no apparent reason other than the process of

performing it (Deci, 1975). On the basis of Csikszentmihalyi's and Deci’s works, three dimensions of

perceived playfulness could be defined. The extent to which the individual:

• Perceives that his or her attention is focused on the interaction with the Hotel Finder


• Is curios during the interaction

• Finds the interaction intrinsically enjoyable or interesting (Moon and Y. Kim, 2001).

Csikszentmihalyi (1975) argued that the feasibility of the activity encourages the state of playfulness.

Technologies that are easier to use will be less threatening to the individual and increase enjoyment of

interacting with ITs. Research has also found support for this positive relationship: the easier the

system is to use, the more enjoyable it is (Van der Heijden, 2003). Therefore, perceived ease of use is

expected to have a positive influence on user’s perception of playfulness in their interaction with the

Hotel Finder and therefore the following hypotheses is proposed:

Hypothesis 4. There is a positive relationship between perceived ease of use and perceived

playfulness of using the Hotel Finder for room reservation.

A few studies have been carried out in which the perceived enjoyment concept has been investigated in

relationship with system usage. Igbaria et al. (1996) studied the effect of perceived enjoyment in

microcomputer usage by professionals and managers in North America. They found strong

relationships between perceived enjoyment and system usage. Especially in the context of the WWW

perceived enjoyment was found as a key driver of usage. Also M.A. Atkinson and Kydd (1997) tested

the relationship of usefulness and enjoyment with Internet usage and found perceived enjoyment to be

a significant predictor for using the Internet for entertainment purposes. Similarly, Moon and Kim

(2001) examined the influence of perceived usefulness and playfulness on WWW use and found

considerable impact on attitude and intention to use. Further, the construct of playfulness has been

validated by previous studies to play an important role in predicting travelers’ attitude toward room

reservation websites. Findings suggested that reservation websites should be fun, entertaining and

capable of retaining the attention of potential customers. In addition, perceived playfulness was found

to have a positive impact on users’ behavior to use these websites for reservations (Morosan and Jeong

2006, 2008). Therefore, individuals who perceive the interaction with the Hotel Finder as playful will

have a positive attitude toward using it for hotel reservation.

Hypothesis 5. There is a positive relationship between perceived playfulness and attitude toward

using the Hotel Finder for room reservation.

Attitude has been characterized as a person’s inclination to exhibit a certain response towards a concept

or object (Vijayasarathy, 2004) and favorable attitudes toward a new system or technology result in a

strong intention to use this technology (Shih, 2004). For this study attitude refers to the extent to

which a consumers likes room reservation portals and considers online reservations through one of

those websites to be a good idea. The dimension of intentions to use is used in this thesis as a surrogate

for actual use, and is defined as a consumer’s intent to use the Hotel Finder for room reservation.

There is ample evidence for the positive relationship between attitude and intention (Moon and Y.

Kim, 2001; Swanson, 1982). Thus, the following hypothesis is proposed:

Hypothesis 6: There is a positive relationship between consumers’ intention to use the Hotel Finder

for room reservation and their attitude towards it.

To sum up, this research focuses on the relationship between perceived usefulness, perceived ease of

use, perceive playfulness, attitude toward using and behavioral intention to use the Hotel Finder tool

for online room reservations.


6. Research methodology After reviewing the literature on the particular topics for this thesis, the author can move on the

research. Research involves the collection of data in a systematic way and in particular with a clear

purpose. Further research implicates interpretation of the data collected. Research can therefore be

defined as “something that people undertake in order to find out things in a systematic way, thereby

increasing their knowledge” (Saunders et al., 2007, p. 5). Systematic means that research is based on

logical relationships and will involve an explanation of the methods used to collect the data, will argue

why the results obtained are meaningful, and will explain any limitation that are associated with them

(Ghauri and Gronhaug, 2005).

Research methodology is associated with different kinds of research design. A research design provides

a framework for the collection and analysis of data of a study (Bryman and Bell, 2011) and can be seen

as a strategy that enables researchers to find answers to the questions and research objectives studied

for any research project. Saunders et al. (2007, p. 602) defined research methodology as “the theory of

how research should be undertaken, including the theoretical and philosophical assumptions upon

which research is based and the implications of these for the method or methods adopted.” It is driven

by assumptions and consists of research questions or hypotheses, a conceptual approach to a topic, the

method to be used in the study- and their justification- and consequently, the data and sources (Grix,

2004).

In this chapter the accurate method for this study will be discussed and the author will demonstrate

which processes are most appropriate for the specific research in the field of user’s adoption of the

Hotel Finder tool for online room reservation.

6.1 Research philosophy A research philosophy is a belief about the way data about a phenomenon should be collected and

analyzed (Levin, 1988). It contains important assumptions about the way, in which one view and

interprets the world. Indeed, the social world can be understood and interpreted in many different

ways.

One major way of thinking about research philosophy is epistemology. Epistemology regards the

question of what is regarded as acceptable knowledge in an area of study and is concerned with the

ways in which the reality can be known (Saunders et al., 2007).

In behavioral sciences, the positivist suggests that human behaviors can be explained and predicted in

terms of cause and effect (May, 1997). Positivism emphasizes the importance of an objective scientific

method, principally consisting of observations, experiments and survey techniques. To generate a

research strategy, existing theory will be used to develop hypotheses. These hypotheses will be

empirically tested by analyzing quantitative collected data with statistically valid techniques, leading to

the further development of theory (Saunders et al., 2007). The main aim of positivism is to generalize

the results to the larger population within a deductive approach. The deductive approach implies, that

the, with literature review developed theory, is tested by empirical observations.


Based on the research question and objectives of this study, the positivistic perspective will be used to

evaluate users perception of the Hotel Finder for room reservations.

6.2 Research approach and strategy Derived from the research philosophy is the determination of the research approach and strategy. It is

normally argued that research approaches are attached to different research philosophies. By adopting

a positivistic approach in this master thesis focusing on the development of a theory and hypotheses,

the use of the deductive approach is implied (Saunders et al., 2007). Deductive theory represents the

most common method for analyzing the relationship between theory and research. Based on literature

of a particular domain and on theoretical considerations in relation to that domain, hypotheses are

deduced and explain causal relationships between variables. Then the hypotheses need to be translated

into operational terms, that is, indicating exactly how the concepts or variables are to be measured. To

test the hypotheses, quantitative data is collected in relation to the concepts that make up the

hypotheses (Bryman and Bell, 2011, p. 11).

The quantitative research strategy refers to the collection of numerical data and involves a deductive

approach to the relationship between theory and research, whereat the testing of theories is

emphasized (Bryman and Bell, 2011). The survey strategy is usually associated with the deductive

approach. Surveys are popular as they allow the collection of a large amount of data from a sizeable

population in a very economic way. Often obtained by using a questionnaire, these data are

standardized and allow easy comparison. Quantitative data obtained by questionnaire surveys can be

analyzed quantitatively by using descriptive statistics. Further, data collected by using a survey can be

used to test proposed relationships within a research model. With the use of a sample in a survey

strategy, findings that are representative of the whole population can be generated at economic cost.

However, the research needs to ensure a representative sample by designing data collection instrument

and trying to ensure a good response rate (Saunders et al., 2007, pp. 136-137).

In this study the deductive approach has been used to develop hypotheses out of the examined theory.

Thereby the TAM model is investigated in relation to Hotel Finder adoption and thus hypotheses have

been deduced, which in turn will be empirically tested by collecting quantitative data with the use of

survey questionnaires.

6.3 Model building The measurement model was adopted from previous research studies (Davis et al., 1989; Moon and Y.

Kim, 2001; Morosan and Jeong, 2006, 2008; C. Pan, 2003) that have showed reliability and validity

evidence and a total of 24 questions items were used in the questionnaire. Scales were developed for

measuring each of the constructs in the proposed model.

Respondent’s perceptions were measured with three constructs: perceived usefulness (PU), perceived

ease of use (PEOU), and perceived playfulness (PP).

Perceived usefulness was operationalized with six items. All those items were a brief statement,

measuring the extent to which the Hotel Finder tool was viewed as a useful tool for users to make an

online reservation. Table 10 shows the questionnaire for measuring the perceived usefulness construct.


Likewise, the perceived ease of use construct was measured with four items to assess how easy, quickly

and how user-friendly it was for users to navigate the Hotel Finder website and to make a reservation.

Table 11 shows the questionnaire for measuring the perceived ease of use construct. Finally, perceived

playfulness was operationalized with six items to evaluate the extent to which the Hotel Finder tool

was entertaining, enjoyable and fun.

All measurement items in each construct were followed by a 7-point Likert scale, ranging from

‘strongly disagree’ (1) to ‘strongly agree’ (7). The Likert scale is a widely used format devolved by

Rensis Likert (Likert, 1932) for asking attitude questions. Respondents are typically asked their degree

of agreement with a series of statements or questions that together form a multiple-indicator measure.

The scale is supposed to measure the intensity with which respondents feel about an issue (Bryman

and Bell, 2011, p. 715). In TAM research typically 5-point and 7-point Likert type scales are used and

most studies are not conclusive on the difference between 5 and 7 points on a scale, while both 5 and 7

point provide accurate and reliable responses. In their study Alwin and Krosnick (1991) concluded that

as more points are added, a scale becomes more reliable, but only up to a certain point. Consequently

they argue that 7 is slightly more reliable than 5 and hence the 7-point Likert scale is used in this

research study.

Construct Questionnaire

PU1 The Hotel Finder provides useful information about the hotel, location and destination.

PU2 The Hotel Finder provides all my preferred search criteria when looking for an appropriate

hotel. PU3 The Hotel Finder enables me to compare offers book a hotel at a lower price.

PU4 The map helps me with the selection of a hotel.

PU5 The Hotel Finder enables me to make a room reservation more quickly.

PU6 The Hotel Finder makes it easier for me to make a room reservation.

Table 10 Perceived usefulness (PU) measurement scale


PEOU1 It is easy to navigate around the Hotel Finder website.

PEOU

2

I can quickly find the information I need.

PEOU

3

It is easy for me to become skillful at using the Hotel Finder for room reservation

PEOU

4

I think it is a user-friendly website.

Table 11 Perceive ease of use (PEOU) measurement scale



PP1 When interacting with the Hotel Finder, I do not realize the time elapsed.

PP2 Using the Hotel Finder for seeking an appropriate hotel gives enjoyment to me.

PP3 Using the Hotel Finder for seeking an appropriate hotel makes me happy.

PP4 Using the Hotel Finder for seeking an appropriate hotel is fun and entertaining.

PP5 I browse the Hotel Finder website for pleasure.

PP6 Browsing the Hotel Finder website arouses my imagination.

Table 12 Perceived Playfulness (PP) measurement scale

Travelers attitude toward using the Hotel Finder for room reservations were operationalized with five

items on a semantic-differential scale, following the recommendation of Ajzen and Fishbein (1980).

They suggested that attitude could be predicted from a person’s salient belief. The procedure they

proposed was used and proven successfully in many studies (Moon and Y. Kim, 2001; Morosan and

Jeong, 2006, 2008). Hence, present research implemented the same approach to measure the traveler’s

attitude toward using the Hotel Finder website for online room reservation.

Construct All things considered, using the Hotel Finder tool for room reservation is a ______ idea: ATT1 Using the Hotel Finder tool is a (good/bad) idea.

ATT2 Using the Hotel Finder tool is a (wise/foolish) idea.

ATT3 Using the Hotel Finder tool is a (worthless/valuable) idea.

ATT4 Using the Hotel Finder tool is a (undesirable/desirable) idea.

ATT5 Using the Hotel Finder tool is a (positive/negative) idea.

Table 13 Attitudes toward using (ATT) measurement scale

Intentions were measured with four items. All those items were a brief statement followed by a seven-

point Likert scale, ranging from ‘strongly disagree’ (1) to ‘strongly agree’ (7). Table 14 shows the

questionnaire for measuring behavioral intention to use.


INT1 I will visit the Hotel Finder website again.

INT2 I will frequently use the Hotel Finder website in future.

INT3 I will recommend the Hotel Finder website to others.

INT4 When I need to make a room reservation, the Hotel Finder website is the first site I will visit.

Table 14 Intentions to use (INT) measurement scale

Additionally, overall respondents’ online experience and Internet use were measured, as well as their

socio-demographic profile such as age, gender and academic major. Online reservation experience was

measured with items such as number of online reservations made during the past year, degree of use of

online reservation websites and prior experience with the Hotel Finder website. The complete

questionnaire is shown in Appendix A.


6.4 Population and sample Sampling techniques are closely associated with quantitative methods such as survey and experiments

(Finn et al., 2000). As the main empirical part of this research deals with a survey, it is essential to

define a sample. Basically sampling is the process of selecting people for a certain study. Thus a sample

is a sub-set of the population selected for inclusion in the research. Therefore the sample is smaller

than the population from which it is drawn. The main objective of sampling is to obtain a

representative selection of the sample units within the population (Finn et al., 2000, p.108). Bryman

and Bell (2011, p.176) define a sample as the “segment of the population that is selected for

investigation. It is a subset of the population. The method of selection may be based on probability or

non-probability approach.” With probability samples the chance, or probability, of each case being

selected from the population is known and is usually equal for each case and is often associated with

survey and experimental research strategies (Saunders et al., 2007). Based on the research question and

objectives, probability sampling has been chosen as a suitable sampling frame for this research. Due to

time and cost constraints the researcher aims to achieve a sample size of at least 100 or more

respondents. Further the simple random sampling technique has been chosen. The simple random

sample is the most basic form of probability sample. With simple random sampling each unit of the

population has an equal probability of inclusion in the sample (Bryman and Bell, 2011, p.179).

For this research, the author aims to investigate the acceptance of the Google Hotel Finder for online

room reservation. Tourism providers and tourists are the main entities in the tourism industry and the

main subjects of this study. Especially tourism providers and tourists with a special interest and affinity

for online booking portals and individuals who prefer to go online to find product information and

book their vacations through online distribution channels. As these people are more likely to be found

in online social networks and tourism blogs, the survey was randomly spread via these networks and

blogs over the Internet.

6.5 Questionnaire The method used for this kind of research is of quantitative nature and a structured questionnaire

consisting of closed-ended questions for the main part was used. As the Google Hotel Finder is a

relatively novel tool for conducting online hotel reservations, all participants which have not been

confronted with the website yet, were given the task to explore the Hotel Finder for at least 15 minutes

before answering the questions. In the questionnaire the respondents were asked to answer 28

questions regarding their opinion and feeling about the Google Hotel Finder tool. The closed-ended

questions were mainly ranking questions, where the respondent has to choose his agreement from a

range of seven possibilities or options. A seven-point-Likert scale was applied to the greater part of the

questions. The questionnaire was composed of questions concerning the respondent’s attitude towards

the Google Hotel Finder tool for online room reservations, following the pattern of previous TAM

surveys. The last part of the survey dealt with general socio-demographic facts. Information about

gender, age, nationality, highest level of education and prior experience with online room reservations

were important to the researcher.

The online survey tool soSci Survey (soscisurvey.com) was used to create the questionnaire. With this

service, the questionnaire was create using custom templates and spread via the link


https://www.soscisurvey.de/GoogleHotelFinder sent by email to participants or posted on websites

and networking platforms. Companies, such as seekda or Be:con, which have specialized in online

distribution solutions for hotels and provide the software for hotels to connect with the Google Hotel

Finder, were asked to support the spreading of the questionnaire through their networks. Further the

company some communications, offering online marketing services and social media assistance for hotels

was willing to assist in the distribution of the questionnaires over the marketing blog www.hotel-

newsroom.de. Examples of the questionnaire spreading can be found in Annex C. The result was some

kind of snowballing effect, promising a simple random sample including respondents who belong to a

variety of demographic groups.

Before the actual survey was carried out, a pre-testing of the questionnaire was conducted to clarify

whether the questionnaire is clear in terms of wording, layout and comprehensiveness. The testing

group consisted of friends and relatives and the size of this group did not exceed ten persons.

As already said, the researcher aims to achieve a sample size of at least 100 respondents. The study

population consists of adult (over 18) Internet users, since the questionnaire can only be worked on via

the Internet. The questionnaire was started on the 5th of November 2012; a reminder was sent on 12th

of November and lasted in total for 14 days. Within this period, 169 completed questionnaires were

collected.

6.6 Research method

6.6.1 Model replication The advancement of knowledge requires the critical evaluation of prior studies and many studies that

follow the heels of prior studies are similar enough to be considered as replications. In research the

term replication often refers to a study that duplicates some or all of the processes of prior studies.

Extension on the other hand, resembles a prior study and usually replicates part of it, but goes further

and adds at least one new variable (Goodwin, 2010, p. 108). In order to replicate a previous study it

must be capable of replication, in other words it must be replicable. Therefore, if a researcher does not

disclose his procedures in great detail, replication is impossible (Bryman and Bell, 2011).

A model replication or re-examination has been generally conducted in a variety of research fields to

assess the consistency, reliability, and validity of the measurement scales of the previous research work

(Sundaravej, 2006). It has been relatively straightforward and therefore quite common for researchers

to replicate and extend the Technology Acceptance Model, in order to enhance confidence in the

theory and its findings. Several of these attempted to improve the generalizability of the model through

its replication in different settings and for different applications (Davis et al., 1989; Lederer et al., 2000;

Teo et al., 1999; Venkatesh, 2000).

Even though, replication adopts instruments used in prior studies, researchers must be aware that a

methodological approach may be altered in a new study and the adapted model needs to be retested.

Therefore a model validation is fundamental for the replication of previous research (Sundaravej,

2006).


In research two main objectives of the model replication have been identified. On the one hand the

aim is to explore new findings (Kacmar, 1999; Segars and Grover, 2012), and on the other hand to

confirm the previous study (Adams et al., 1992; Szajna, 1996). Related with these objectives are the two

steps of the standardized instrument for a research developed by Doll et al. (1994). First the previous

study is explored by developing a hypothesized measurement model from the analysis of empirical data

of prior research, and second the study is confirmed by testing the hypothesized measurement model

with new gathered data.

The current master thesis first analyzed the extended TAM model developed by Morosan and Jeong

(2008) to examine users adoption of hotel reservation websites and will apply the constructs of the

model to the setting of a specific website, namely the Hotel Finder website for room reservation.

Further the results of the study will be analyzed in order to confirm or reject the extended TAM as a

model for the measurement of hotel reservation website acceptance and usage.

6.6.2 Validity Two of the most important criteria for the evaluation of research are validity and reliability.

Measurement validity is concerned with the integrity of the conclusions that are generated from

research and applies primarily to quantitative research (Bryman and Bell, 2011).

Internal validity in relation to questionnaires therefore refers to the ability of the questionnaire to

measure what the author intends it to measure and actually represents the reality of what is being

measured. Often when discussing the validity of questionnaires, researchers distinguish between a

number of types of validiy which reflect different ways of testing the validity of a concept. Content

validiy for instance, measure if the questions in the questionnaire provide adequate coverage of the

research question (Saunders et al., 2007) and deals with how representative and comprehensive the

items are in creating the measurement scale. It is assessed by examining the process by which scale

items are generated (Straub, 1989). In this research, definitions of perceveid ease of use, perceived

usefulness, and perceived playfulness are proposed based on the review of theory and research in

reservation website acceptance. Predictive validity is concerned with the ability of the questions to

make accurate predictions. If the measurement questions in the questionnaire are used to predict

consumers’ future buying behavior then a test of predictive validity will measure the extent to which

they actually predict these consumers’ buying behavior. To assess predictive validity often a statistical

analysis such as correlation is used. Construct validity is an issue of measurement between constructs

and refers to the extent to which the used measurement questions actually measure the presence of

those constructs and thus is a reasonable operationalization of the construct. Construct validity is

normally used when referring to constructs such as attitude scales (Saunders et al., 2007).

For the current study, confirmatory factor analysis is used to assess the convergent and discriminant

construct validity. Confirmatory factor analysis can be used to assess the overall fit of the entire

measurement model and to obtain the final estimates of the measurement model parameters.

Convergent and discriminant validity are both considered subcategories or subtypes of construct

validity and are related to each other. Only if evidence for both convergent and discriminant validity is

demonstrated, then evidence for construct validity is confirmed. To establish convergent validity the

author needs to show that measured, which should be related are in reality related. To establish


discriminant validity, the author needs to show that measures that should not be related are in reality

not related. To estimate the degree to which measures are related to each other, the correlation

coefficient is used (Trochim, 2006a).

6.6.3 Reliability Although the terms reliability and validity seem to be almost like synonyms, they have quite different

meanings in relation to the evaluation of measures of concepts. Reliability is fundamentally concerned

with issues of consistency of measures (Bryman and Bell, 2011) and therefore with the robustness of

the questionnaire. It refers to whether of not the questions will produce consistent findings at different

times and under different condidions, such as with different samples (Saunders et al., 2007).

Three prominent factors involved when considering whether a measure is reliable are stability, internal

reliability and inter-observer consistency. Stability determines whether or not a measure is stable over

time and there will be little variation over time in the results obtained. The most obvious way of testing

for the stability of a measure is the test-retest method. This involves collecting data from the same

questionnaire and sample on one occasion and then again on another occasion. Internal reliability

refers to the degree to which the indicators that make up a scale are consistent. When a multiple-item

measure is used to form an overall score out of the respondent’s answers, the possibility is raised that

the indicators do not relate to the same thing and lack coherence. Therefore internal reliability involves

correlating the responses to each question in the questionnaire with those to other questions in the

questionnaire, in other words internal reliability measures if the respondents’ scores on any one

indicator tend to be related to their scores in the other indicators. There are a variety of methods for

calculating internal reliability, whereat Cronbach’s alpha nowadays is one of the most frequently used

methods. Its use has grown as a result of its incorporation into computer software for quantitative data

analysis. Finally, the inter-observer consistency helps to translate data into categories, such as

categorize open-ended questions or classify subjects’ behavior in structured observations (Bryman and

Bell, 2011; Saunders et al., 2007).

In this master thesis Cronbach’s alpha method and inter-item correlation matrix is used to assess the

internal reliability. The Cronbach’s alpha test essentially calculates the average of all possible split-half

reliability coefficients. A computed alpha coefficient will vary between 1 (denoting perfect internal

reliability) and 0 (denoting no internal reliability). The figure 0.80 is typically employed as a rule of

thumb to denote an acceptable level of internal reliability, though many researchers accept a slightly

lower figure (Bryman and Bell, 2011, p. 159).

6.6.4 Correlation Correlation is the extent to which two variables are related to each other (Saunders et al., 2007, p. 589),

whereat a single number describes the degree of relationship between two variables (Trochim, 2006b).

This number is called the correlation coefficient and enables the researcher to quantify the strength of

the linear relationship between two variables. This coefficient, usually represented by the letter r, can

take on any value between -1 and +1. While a value of +1 represents a perfect positive correlation, a

value of -1 represents a perfect negative correlation. In a positive correlation the increasing values of

one variable leads to an increasing of values in the other variable. On the other hand, in a negative


correlation as the values of one variable increase those of the other variable decrease (Saunders et al.,

2007, p. 459). To establish discriminant validity, the correlation among constructs is calculated.

6.7 Results Results of the research can be discussed in three different areas: construct validity, reliability, and

correlation. For the current study, coefficient factor analysis was used to determine the convergent and

discriminant construct validity. Cronbach’s Alpha was employed to assess the internal consistency

reliability. The inter-item correlation was utilized to explain the construct reliability. And finally, the

regression analysis method explored the relationship between variables.


6.7.1 Demographics and experience The mean age of all respondents

was 28,98 years with ages ranging

from 18 to 61. About half of the

respondents were male (47,9%) and

half female (52,1%). Majority of the

participants were from Austria and

Germany, 17,5% from Italy, about

5% from Switzerland and the rest

from other nationalities. The

majority of all participants, namely

48,5% had high educational level

equal to a university degree. As

expected, most of the respondents

have experience with online room

reservations and made more than

one online booking during the last

year. About half of the respondents

have already been in contact with

the Google Hotel Finder and even

13,6% has already done a

reservation over the Hotel Finder

before.

Profile of respondents

Characteristics Statistics

Gender

Male 47,9%

Female 52,1%

Age

Mean 28,98

Standard Deviation 7,285

Nationality

Austria 39,1%

Germany 28,4%

Italy 17,5%

Switzerland 4,8%

Other 8,9%

Education

Not answered 4,1%

Advanced school 4,7%

School leaving examination 34,9%

Apprentice ship 7,7%

University degree 48,5%

Online bookings during last year

None 15,4%

1 15,4%

2 26%

3 9,5%

4 9,5%

5 5,9%

6 3%

More 15,4% Confrontation with the Google Hotel Finder before the survey

Yes 46,2%

No 53,8% Experience with booking via the Google Hotel Finder

Yes 13,6% No 84,4%


6.7.2 Model fit analysis For the present study, 25 variables were selected and classified into five constructs in the extended

TAM model. The variables PU1 to PU6 are related to the factor or construct perceived usefulness

(PU), the variables PEOU1 to PEOU4 are related to the factor perceived ease of use (PE), the

variables PP1 to PP6 are related to the factor perceived playfulness (PP), the variables ATT1 to ATT5

to the factor attitude toward use (ATT) and the variables INT1 to INT4 are related to the factor

intentions to use (INT).

First of all an analysis of model fitness is performed. Fit indices of the postulated confirmatory factor

analysis are shown in Table 15.

Index Value

Chi-Square 508,61

Chi-Square DF 265

Goodness of Fit Index (GFI) 0,80

Adjusted GFI (AGFI) 0,75

Root Mean Square Error of Approximation (RMSEA) Estimate 0,07

Table 15 Model fit analysis

The model fit chi-square is 508.6 (df=265). This shows that statistically the confirmatory factor model

for the test scores has to be rejected. However, due to its tendency to be sensitive to sample size (Lee,

2009), the following indices were also applied. The Root Mean Square Error of Approximation

(RMSEA) estimate is 0.07, which is near the recommended value of 0.05 for a good model fit. The

author further focused on the fit indices GFI (Goodness of Fit Index) developed by Jöreskog and

Sörbom (1981). The GFI and the Adjusted Goodness of Fit Index (AGFI) are positive and have

values of .80 and .75. They are close and respectively exactly meet the recommended limits of .80.

(Ahn et al., 2007), which also indicate a good model fit. All things considered, the model resulted in

good fit for the data.

6.7.3 Assessment of validity and reliability As discussed in chapter 6.2.2 construct validity is an issue of measurement between constructs with the

concern to test if the items selected for a given construct are a reasonable operationalization of the

construct (Saunders et. al, 2007).

With a Confirmatory Factor Analysis (CFA) construct validity is assessed. In Table 16 the loading

estimates of the CFA model together with the standard error estimates and the t-values are shown. To

test the significance of the parameter estimates, the t-values are compared with the critical value of a

standardized normal distribution. Estimates with t-values greater than 1.96 are significant at α=.05. In

table 2 all the t-values for the loading estimates are greater than 2. This shows that the suggested

relationships between all variables and constructs are significant and validity of the extended TAM

model is confirmed.


Variable Estimate Standard Error t-value

PU1 0,60 0,07 8,02

PU2 0,69 0,07 9,66

PU3 0,65 0,07 8,86

PU4 0,24 0,08 2,87

PU5 0,69 0,07 9,68

PU6 0,80 0,07 11,82

PEOU1 0,83 0,07 12,63

PEOU2 0,76 0,07 11,17

PEOU3 0,78 0,07 11,65

PEOU4 0,90 0,06 14,52

PP1 0,70 0,07 10,07

PP2 0,80 0,07 12,16

PP3 0,78 0,07 11,63

PP4 0,85 0,06 13,31

PP5 0,81 0,07 12,25

PP6 0,75 0,07 11,05

ATT1 0,74 0,07 10,63

ATT2 0,75 0,07 10,82

ATT3 0,74 0,07 10,67

ATT4 0,70 0,07 10,02

ATT5 0,89 0,06 14,13

INT1 0,84 0,06 13,19

INT2 0,87 0,06 13,93

INT3 0,89 0,06 14,50

INT4 0,80 0,07 12,19

Table 16 Loading estimates of the CFA model

Next the factor score regression coefficients are calculated. The factor score regression coefficients are

used for the calculation of the values for a certain construct. A high coefficient shows a strong

relationship between variable and construct and thus, has a strong influence on the calculation of the

factor scores. As Table 17 shows, all coefficients with a value higher than .10 are marked. It is obvious

that most of the variables load on the ‘correct’ constructs. Only variable PU4 can’t be assigned to any

of the constructs and the variable PEOU4, in addition to a strong influence on the construct PEOU,

shows also a certain loading on PU.


Variable PU PEOU PP ATT INT

PU1 0,11 0,02 0,00 0,00 0,01

PU2 0,16 0,03 0,01 0,00 0,01

PU3 0,13 0,02 0,01 0,00 0,01

PU4 0,03 0,01 0,00 0,00 0,00

PU5 0,16 0,03 0,01 0,00 0,01

PU6 0,27 0,05 0,01 0,01 0,02

PE1 0,05 0,21 -0,01 0,00 0,03

PE2 0,04 0,15 -0,01 0,00 0,02

PE3 0,04 0,16 -0,01 0,00 0,02

PU4 0,10 0,39 -0,01 0,00 0,05

PP1 0,01 0,00 0,11 0,01 0,01

PP2 0,01 -0,01 0,19 0,02 0,02

PP3 0,01 -0,01 0,16 0,02 0,02

PP4 0,01 -0,01 0,26 0,03 0,02

PP5 0,01 0,00 0,19 0,01 0,01

PP6 0,01 0,00 0,14 0,01 0,01

ATT1 0,00 0,00 0,01 0,15 0,03

ATT2 0,01 0,00 0,01 0,15 0,03

ATT3 0,00 0,00 0,01 0,15 0,03

ATT4 0,00 0,00 0,01 0,13 0,02

ATT5 0,00 0,00 0,01 0,39 0,01

INT1 0,03 0,03 0,02 0,04 0,18

INT2 0,04 0,04 0,03 0,05 0,22

INT3 0,05 0,04 0,03 0,06 0,27

INT4 0,02 0,02 0,02 0,03 0,13

Table 17 Factor score regression coefficients of the CFA model

While the construct validity is a measurement between constructs, the reliability is a measurement

within a construct. The concern on reliability is how well a set of instrument variables selected for a

given construct measures the same construct. In the following section the reliabilities of the different

constructs are examined.

First, analysis on the reliability of the scales was conducted by calculating Cronbach’s alpha for each

construct. Positive correlation is needed for the alpha coefficient because variables measure a common

entity. As Table 18 shows, the overall standardized Cronbach’s alpha coefficient for the scale PU is

0.79 and can be interpreted as an acceptable lower bound for the reliability coefficient. In literature a

value of 0.80 and slightly lower figures are accepted as a tolerable limit (Bryman and Bell, 2011, p. 159).


Variable Alpha

Raw 0,78

Standardized 0,79

Table 18 Cronbach's alpha coefficient for PU

Table 19 shows the change of Cronbach’s Alpha, if a variable is deleted from the scale. If the

standardized alpha decreases after removing a variable from the construct, then this variable is strongly

correlated with other variables in the scale. On the other hand, if the standardized alpha increases after

removing a variable from the construct, the construct is more reliable without this variable. One

variable of the PU construct shows significant increase in the standardized alpha coefficient, if it is

deleted. This is the variable PU4, which seems to be not adequate for the underlying construct PU.

Deleted variable Correlation with total Alpha

PU1 0,53 0,76

PU2 0,63 0,74

PU3 0,65 0,73

PU4 0,25 0,82

PU5 0,57 0,75

PU6 0,64 0,73

Table 19 Cronbach's alpha coefficient with deleted variable, PU

The reliability coefficient for the scale PEOU is 0.90 and therefore significantly higher than for the

scale PU.

Variable Alpha

Raw 0,90

Standardized 0,90

Table 20 Cronbach's alpha coefficient for PEOU

By elimination of individual variables, the values of Cronbach’s alpha only change marginally with

slightly lower values of the reliabilities, as shown in Table 21. This indicates a high internal consistence

of the PEOU scale.


PEOU1 0,79 0,86

PEOU2 0,72 0,88

PEOU3 0,74 0,88

PEOU4 0,82 0,85

Table 21 Cronbach's alpha coefficient with deleted variable, PEOU


The results for the PP construct are very similar to those of the PEOU construct. The high reliability

of 0.90 and again little variation of the reliabilities with deleted variables show high quality of the PP

scale.

Variable Alpha

Raw 0,90

Standardized 0,90

Table 22 Cronbach's alpha coefficient for PP


PP1 0,67 0,90

PP2 0,74 0,89

PP3 0,73 0,89

PP4 0,80 0,88

PP5 0,77 0,88

PP6 0,72 0,89

Table 23 Cronbach's alpha coefficient with deleted variable, PP

A little bit lower, but still satisfactory is Cronbach’s Alpha for the scale ATT. When looking at the

individual variables, there is no evidence that the reliability of the scale can be improved by elimination

of some variable.

Variable Alpha

Raw 0,87

Standardized 0,87

Table 24 Cronbach's alpha coefficient for ATT


ATT1 0,65 0,86

ATT2 0,71 0,84

ATT3 0,68 0,85

ATT4 0,64 0,86

ATT5 0,82 0,82

Table 25 Cronbach's alpha coefficient with deleted variable, ATT


With a value of 0.91 the scale INT shows the highest value for Cronbach Alpha of all analyzed factors.

Further Table 27 indicates, that all single items of the scale correlate significantly with the total score

and form a highly homogenous subset.

Variable Alpha

Raw 0,91

Standardized 0,91

Table 26 Cronbach's alpha coefficient for INT


INT1 0,79 0,89

INT2 0,82 0,88

INT3 0,86 0,87

INT4 0,74 0,91

Table 27 Cronbach's alpha coefficient with deleted variable, INT

Additionally, the correlations among variables are presented in Table 28. The inter-item correlation

matrix (Pearson correlations) reflects the self-determining relationship between variables. All the

variables are moderately correlated and are statistically significant with a mean correlation of 0.67.

Especially there is a strong relationship between the constructs PU and PEOU with a correlation

coefficient of 0.82. The construct INT correlates with all other factors at least by 0.70. The results of

the inter-item correlation matrix provide more evidence to prove the reliability of the extended TAM

model.

PU PEOU PP ATT INT

PU 1

PEOU 0,82 1

PP 0,56 0,43 1

ATT 0,60 0,56 0,62 1

INT 0,79 0,76 0,70 0,81 1

Table 28 Inter-item correlation matrix Note: all correlations are highly significant (p < .001)

Finally the explained variance estimates for the variables and for the constructs are reported as squared

multiple correlations. In the Table 29 the R-squares show the percentages of variance of the variables.

These values can be interpreted as the reliability of the variable as an indicator of its associated latent

construct. Most of these percentages are quite high for the variables, but there are some where this is

not true. So only six percent of the variation of PU4 can be explained by the factors. For the variables

PU1 and PU3 the explained variation is rather low as well. All in all perceived usefulness is the

construct where the explained variance turns out to be the lowest of all scales.


Variable Error Variance R-Square

PU1 0,64 0,36

PU2 0,52 0,48

PU3 0,58 0,42

PU4 0,94 0,06

PU5 0,52 0,48

PU6 0,36 0,64

PEOU1 0,32 0,68

PEOU2 0,42 0,58

PEOU3 0,39 0,61

PEOU4 0,19 0,81

PP1 0,50 0,50

PP2 0,35 0,65

PP3 0,39 0,61

PP4 0,27 0,73

PP5 0,35 0,65

PP6 0,43 0,57

ATT1 0,46 0,54

ATT2 0,44 0,56

ATT3 0,46 0,54

ATT4 0,50 0,50

ATT5 0,21 0,79

INT1 0,29 0,71

INT2 0,24 0,76

INT3 0,20 0,80

INT4 0,36 0,64

Table 29 Squared multiple correlations of the CFA model

6.7.4 Assessment of correlation Like other researchers (Shih, 2004), the author also applied a multiple regression path model in order

to test the proposed hypotheses in the applied model.

In the multiple regression path model, PU, PEOU and PP are predictors, that have direct effects on

Attitude toward using. Also there is a direct effect from Attitudes on Intentions to use. Further, there

are indirect effects in the multiple regression model. On the one hand the model shows indirect effects

of PU, PEOU and PP on intention to use. On the other hand there is assumed that there are direct

effects from PU on PEOU and on PP. As a result PEOU remains the only exogenous variable in the

model. In addition to the direct effect on Attitude toward using, the PEOU construct has also indirect

effects on this construct. This indirect effects on Attitude are indicated by the following causal chains;

(1) PEOU → PU → ATT and (2) PEOU → PP → ATT. Similarly, PEOU has indirect effects on


Intentions to use, for example PEOU → PU → ATT → INT. Indirect effects on INT also exists for

PU and PP, i.e. PP → ATT → INT.

Table 30 shows some model fit indices of the direct and indirect effects in the model. The model fit

chi-square is 114.1 with four degrees of freedom. This would be a quite clear signification for rejection

of the model on statistical grounds. Moreover the RMSEA estimate is rather high, which indicates bad

fit as well. On the other hand the values of the fit indices GFI and AGFI show a very good model fit,

which leads to the conclusion that the results are not unique. Thus, the hypothesized model needs to

be further tested.

Index Value

Chi-Square 114,22

Chi-Square DF 4

Pr > Chi-Square < .001

Goodness of Fit Index (GFI) 0,82

Adjusted GFI (AGFI) 0,32

RMSEA Estimate 0,41

Table 30 Fit summary of the multivariate regression model

Table 31 shows the parameter estimates of the various effects in our model. All the path effects are

statistically significant (p < .05) so that the proposed research model seems to be reasonable. However,

the strength of the various effects is quite different. There is a strong influence of the PEOU construct

on PU. Likewise Attitude toward using has great impact on Intention to use. Also the values of the

coefficients of the effects from PEOU on PP and PP on ATT are rather high with .40. All other

coefficients show relatively low significance.

Path Estimate Standard Error t-value

PEOU → PU 0,70 0,04 17,27

PEOU → PP 0,38 0,07 5,71

PEOU → ATT 0,19 0,09 2,21

PU → ATT 0,21 0,08 2,57

PP → ATT 0,41 0,06 6,71

ATT → INT 0,72 0,04 19,10

Table 31 Parameter estimates of the multivariate regression model

Based on these estimates for the individual coefficients the various hypotheses can be discussed. As

showed in Table 31 all estimates are significant (p < 0.05), which leads to the acceptance of all six

hypotheses. However, the significance of the impact has been found to be different for the six

hypotheses. In Figure 16 the results of the hypotheses testing are shown.


Figure 16 Hypotheses testing

Finally the R-Square values for the endogenous variables are shown in Table 32. While the total

variance is relatively high for the constructs of ATT, INT and PU and determines the strength of linear

relationship between the variables, the value for PP construct has to be indicated as rather low.

Error Variance R-Square

Attitudes 0,57 0,41

Intentions to use 0,47 0,52

Perceived playfulness 0,85 0,15

Perceived usefulness 0,52 0,48

Table 32 Squared multiple correlations of the multivariate regression model

6.7.5 Discussion The following section reflects on the results of the conducted research and discusses the outcomes of

the research in relation to the research questions of the master thesis. Based on the aims and objectives

of the study, the research questions were the following.

Can consumers’ adoption of the Google Hotel Finder tool be predicted with the extended TAM?

If yes, to which extend perceived usefulness, perceived ease of use and perceived playfulness influence

online travelers acceptance of the Google Hotel Finder for online room reservation?

In previous research, the extended TAM model with five constructs including perceived usefulness,

perceived ease of use, perceived playfulness, attitudes and intention to use was successfully applied in

previous research to predict online travelers adoption of hotel room reservation websites. In the

context of the hotel industry, perceived usefulness, ease of use and playfulness have an impact on

attitudes toward using reservation websites in general. This study argued that the usage of a specific

reservation website, the Google Hotel Finder can be predicted by the extended TAM framework.

Overall, results of the study reveal that the consumers perceived usefulness, perceived ease of use and

perceived playfulness play a critical role in influencing travelers’ intentions to use the Hotel Finder for

!!!

!!!

!!

!

!!

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!!!!!!!




Attitudes Intentions to use

0.70

0.38

0.21

0.19

0.42

0.72


room reservation. Thus, results of this study confirm that the extended TAM model can be used to

predict consumers’ usage behavior of the Google Hotel Finder and all proposed hypotheses could be

supported.

In this model, perceived playfulness is the key influential factor to predict users’ attitude to use the

Hotel Finder tool, followed by perceived usefulness and perceived ease of use. Prior studies identified

perceived usefulness to be the most influential predictor for online travelers attitude towards using of

room reservation websites. This inconsistency calls for further research in this area and suggest that

today’s online travelers are looking for fun and entertainment when looking for an appropriate

accommodation on the web. Additional features are necessary to keep the users attention and allow

travelers to conduct a more pleasant and less stressful online reservation process. During the

experiment phase Google has continued to ad functionality and improve the user interface. Consumers

have the possibility to limit search results by a selected area on the map, nearby a landmark of address

by defining proximity in terms of desired travel time.

The constructs of perceive usefulness and perceived ease of use are revealed to have a similar impact

on user’s attitude to use the Hotel Finder portal. The possibility for customization including sorting

possibility and using the filters gives the consumers the possibilities to see only hotels which meet their

preferences, enhances perceived usefulness of the Hotel Finder tool. Further, a multitude of

information and specific content is provided. Detailed information includes photos, user ratings and

reviews, amenities and information about the location and surrounding areas. Moreover the ease of

comparing different offers and the possibility to conduct the booking either on third-party providers

or direct from the hotel has direct impacts on traveler’s attitude toward using the Hotel Finder tool.

Additionally the Hotel Finder has to be user-friendly, easy to navigate and fast in order to be adopted

as a reservation tool.

Moreover, great impact of perceived ease of use on perceived usefulness was found. This would

complement the outcomes of previous studies on this topic and confirm that the adoption of a new

system is highly dependent upon its user-friendliness.

As predicted by the traditional TAM literature, travelers’ attitude toward using the Google Hotel

Finder for online bookings had a significant positive relationship with intentions to use the tool.


7. Conclusions

7.1 Implications This study attempted to examine the impact of usefulness, ease of use and playfulness on traveler’s

intentions to use the Google Hotel Finder for online hotel reservation. The extended TAM model was

proved to be feasible for the context of the hotel industry in general and also for a specific hotel

reservation portal.

The outcomes of this study reveal that online travelers perceive the Hotel Finder as a useful tool for

online hotel reservation. Moreover ease of use and perceived playfulness enhance consumers to use the

portal for seeking online information about hotels and make hotel reservations.

First of all this study provides industry practitioners with more inside into traveler’s needs and

preferences in online distribution. To take advantage of the traveler’s adoption of the Google Hotel

Finder for online booking, and to further increase acceptance and improve popularity of the service

amongst online travelers, Google should further focus on:

• The Google Hotel Finder to be efficient, fast and provide the user with rich content

information. Above all detailed information about the accommodation, but also efficiency and

speed enhances the attitude toward the service.

• Increasing user-friendliness. Consumers’ acceptance of a new service is highly dependent upon

its user-friendliness. Moreover ease of use was found to be an important antecedent of

attitude toward using. The Hotel Finder should be easy to use and allow travelers to learn how

to use the service easily.

• With perceived playfulness being the most important determinant of consumer’s attitudes, the

Hotel Finder should be fun, entertaining and capture users attention during interacting with

the website. Interactive features like the selection on the map is already a very good approach.

Further, Google should focus on adding virtual tours of accommodations or even online

games incorporating the accommodation or destination. Playfulness should refer to the

consumers’ tendency to interact spontaneously with the Hotel Finder.

Next, this study provides hoteliers and practitioners in this industry with suggestions on multiple

channel strategies usage. Which channels are currently the most successful in hotel business and which

are likely to dominate the future are important issues for choosing the best mix of channel partners.

With the Google Hotel Finder, Google opened a new channel for hotel organizations and online travel

agents to reach customers. Furthermore due to the integration of Google search, maps and Google+

local, a better travel search experience is offered to the consumer. While online travel agents such as

Expedia see advantages in Google Hotel Finder participation (Schaal, 2012a), the Hotel Finder also

provides hotels with a worthwhile alternative to bypass the online travel agency channels.

The fact that Google ended the experimental phase of the Hotel Finder only recently and the service

by now is available in different languages makes it more attractive. Moreover prices are available in

local currencies and Google confirmed to put additional effort into commercialization of the Hotel

Finder. Following the example in the US, according to experts Google very soon will reposition the


Hotel Finder on top of organic search results in line with the sponsored links (Benkert, 2012b; Schaal,

2012b). This matter will enhance the public excitement and increase usage and traffic dramatically

within the next months. Increasing popularity of the Hotel Finder will lead to increasing price

transparency among third party travel websites and the number of competitors for a possible booking

will grow. This is a chance for small suppliers and single hotels to compete with the big OTA. On the

Google Hotel Finder every competitor has the same representation with only one difference, the price.

This price transparency will encounter great popularity amongst users and if price parity can be

guaranteed the Google Hotel Finder will be successful in future.

Considering the abovementioned potential of Google in online distribution and the outcomes of this

study that online travelers accept the Hotel Finder as a useful tool for online reservation, hoteliers and

online distribution companies should consider the following:

• Hotels can benefit enormously by having their prices and availability direct on Google, as it

offers direct connections to a huge and permanent increasing number of potential travelers.

• Due to the integration of the Google Hotel Finder into other services such as search, maps or

Google+ local, the influence of the Hotel Finder will further increase and expand its reach.

• No commission payment, Google’s price model is based on the cost-per-click (CPC) system.

• The direct connection and implementation of the own booking engine into the Google Hotel

Finder enables hotels to bypass the middleman and can benefit from bookings through the

own website.

7.2 Limitations and further research Due to a few limitations, interpretation of the results should be done with caution. The first limitation

of this work mainly concerns the surveyed sample and sample size. Although the range in terms of age

distribution was relatively large (43 years) the majority of all respondents were comparable young.

Furthermore, the surveyed sample had on average a very high level of education. Half of all

respondents indicated a level of education equal to university degree. Most of the respondents did

already perform bookings via the Internet and the number of online bookings processed during the

last year was relatively high, which indicates high familiarity with online reservation websites. About

half of al participants have already been confronted with the Hotel Finder before this survey.

Considering that the Hotel Finder is a rather novel tool for online booking and was an experiment till

recent, this is a very large proportion. Moreover the sample size (n=169) might not be representative,

although comparable research in this area was also conducted with smaller samples between 100 – 400

respondents.

The second limitation concerns the online survey. Due to time constraints, the questionnaire was

conducted on the Internet and spread via online networks and blogs. Though users with no personal

experience were given the task to simulate making a reservation using the Hotel Finder, there is limited

control about this. In addition, the TAM methodology is weak outside the task environment (Morosan

and Jeong, 2008).

The third limitation is related to the nature of the TAM model. In this study intentions to use the

Hotel Finder are proposed as the last construct in the model. Assuming that intention leads to


behavior, intentions to use were used as a surrogate for actual behavior. This practice is very common

in TAM literature, as intentions can be used as indicator to predict actual behavior (Moon and Y. Kim,

2001; Shih, 2004).

This study provides insight into users’ adoption of the Google Hotel Finder tool. Since this research

area is very limited and specific, a few directions for further research are outlined. First to increase the

generalizability, this study can be replicated using a larger sample of actual travelers and within a task

setting environment. Second, further research should examine travelers’ adoption of other specific

OTA websites. Replicating and extending this study to other providers in online distribution industry

might lead to different results provide the opportunity for comparison.

7.3 Acknowledgements The author would like to thank her family members and friends for their support and assistance during

the whole studies. Furthermore many thanks go to Prof. Dr. Roman Egger who was very supportive

during the whole process of planning and writing the master thesis and helped to steering this work

into the right direction.

UoAS Salzburg, Master Program IMT | Nadia Pircher VI

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VII. Annex

Annex A) Questionnaire form ................................................................. XXI Annex B) Questionnaire in German ....................................................... XXIX Annex C) Questionnaire spreading Google+ ........................................... XXXI Annex D) Questionnaire spreading Facebook ........................................ XXXIII Annex E) Questionnaire spreading Blog ............................................... XXXIV

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Annex A) Questionnaire form

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Annex B) Questionnaire in German

Konstrukt Fragebogen

PU1 Im Google Hotel Finder finde ich nützliche Informationen zum Hotel, zur Lage und Destination.

PU2 Der GHF verfügt über alle Suchkriterien welche ich zur Suche eines geeigneten Hotels benötige.

PU3 Der GHF ermöglicht mir Angebote zu vergleichen und ein Hotel billiger zu buchen.

PU4 Die Landkarte hilft mir ein passendes Hotel auszuwählen.

PU5 Der GHF ermöglicht eine schneller Hotelbuchung.

PU6 Mit dem GHF fällt es mir leichter ein Hotel zu buchen.

Wahrgenommener Nutzen


PEOU1 Die Navigation auf der GHF Website fällt mir sehr leicht.

PEOU 2 Ich kann alle nötigen Informationen schnell finden.

PEOU 3 Die Benützung und Hotelbuchung über den GHF fällt mir sehr leicht.

PEOU 4 Der GHF ist meiner Meinung nach sehr benutzerfreundlich.

Wahrgenommene Bedienbarkeit


PP1 Während ich auf der GHF Website surfe, vergesse ich die Zeit.

PP2 Ein passendes Hotel über den GHF zu suchen macht mir Spass.

PP3 Ein passendes Hotel über den GHF zu suchen macht mich glücklich.

PP4 Ein passendes Hotel über den GHF zu suchen ist lustig und unterhaltsam.

PP5 Ich surfe auf der GHF website zum Vergnügen.

PP6 Surfen auf der GHF website regt meine Fantasie an.

Wahrgenommene Verspieltheit

Konstrukt Alle Aspekte berücksichtigt, ist die Benützung des GHF zur Hotelbuchung eine ____ Idee: ATT1 Die Benützung der GHF ist eine (gute/schlechte) Idee.

ATT2 Die Benützung der GHF ist eine (kluge/unkluge) Idee.

ATT3 Die Benützung der GHF ist eine (wertlose/nützliche) Idee.

ATT4 Die Benützung der GHF ist eine (unerwünschte/erwünschte) Idee.

ATT5 Die Benützung der GHF ist eine (positive/negative) Idee.

Einstellung zur Nutzung

UoAS Salzburg, Master Program IMT | Nadia Pircher XXX


INT1 Ich werde die GHF Website in Zukunft wieder besuchen.

INT2 Ich werde die GHF Website in Zukunft oft benützen.

INT3 Ich werde die GHF Website weiterempfehlen.

INT4 Wenn ich in Zukunft ein Hotel buchen will, ist die GHF Website meine erste Wahl.

Absicht zur Nutzung

UoAS Salzburg, Master Program IMT | Nadia Pircher XXXI

Annex C) Questionnaire spreading Google+

UoAS Salzburg, Master Program IMT | Nadia Pircher XXXII

UoAS Salzburg, Master Program IMT | Nadia Pircher XXXIII

Annex D) Questionnaire spreading Facebook

UoAS Salzburg, Master Program IMT | Nadia Pircher XXXIV

Annex E) Questionnaire spreading Blog

GOOGLES ENTRY INTO ONLINE ACCOMMODATION DISTRIBUTION - Hotel Newsroom

Documents

Transcript of GOOGLES ENTRY INTO ONLINE ACCOMMODATION DISTRIBUTION - Hotel Newsroom