Master thesis_Nadia Pircher_final

Transcription

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
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 1.2 1.3 1.4 1.5 Background of the research problem .................................................... 1 Significance of the research ................................................................ 2 Research gap ................................................................................... 3 Research objectives........................................................................... 4 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 2.3.2 Electronic distribution channels .......................................................... 11 Computer reservation systems (CRS) and GDS .................................... 12 2.4.1 Evolution of online distribution channels .............................................. 15 2.4 Online distribution channels .............................................................. 13 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 4.2.2 The role of the Internet in travel decision-making ................................. 31 Need for information during the decision-making process ...................... 34 4.3.1 4.3.2 4.3.3 4.3.4 Choice attributes for hotels ................................................................ 36 Decision process for hotel selection .................................................... 38 Website attributes affecting online hotel purchase ................................ 39 Hotel purchase with the Hotel Finder................................................... 41 4.3 Purchase decision for accommodations............................................... 36 5. The Technology Acceptance Model .............................................................. 44 5.1 5.2 5.3 5.4 5.5 Extensions and modifications of TAM .................................................
The extended TAM ..........................................................................
Research purpose ...........................................................................
Proposed research model .................................................................
Research variables and hypotheses ...................................................
48 49 52 53 54 6. Research methodology .............................................................................. 57 6.1 6.2 6.3 6.4 Research philosophy ........................................................................
Research approach and strategy .......................................................
Model building ................................................................................
Population and sample .....................................................................
UoAS Salzburg, Master Program IMT | Nadia Pircher
57 58 58 61 I
6.5 Questionnaire ................................................................................. 61 6.6 Research method ............................................................................ 62 6.6.1 6.6.2 6.6.3 6.6.4 Model replication .............................................................................. 62 Validity ........................................................................................... 63 Reliability ........................................................................................ 64 Correlation ...................................................................................... 64 6.7.1 6.7.2 6.7.3 6.7.4 6.7.5 Demographics and experience ........................................................... 66 Model fit analysis ............................................................................. 67 Assessment of validity and reliability ................................................... 67 Assessment of correlation.................................................................. 73 Discussion ....................................................................................... 75 6.7 Results .......................................................................................... 65 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
II
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
III
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
IV
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
V
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
VI
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.
VII
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 takeaway 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
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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 metasearch, 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,
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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.
3
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 decisionmaking process is reviewed and how the development of the Internet intensified this process, is
4
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.
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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.
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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.
Location of the
establishment
(accessibility)
Facilities (bedrooms,
restaurants, meeting
rooms, sports facilities)
Service level
The Accommodation Product
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)
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).
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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
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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 cooperate 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)
9
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)
10
(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
11
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
12
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.
!
Travel
agents
Hotel
CRS
Switch
Company
THISCO
WIZCOM
Amadeus
Galileo
SABRE
Worldspan
Travel
agents
Travel
agents
Travel
agents
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
13
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
14
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 reenter 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
15
Number of people in millions
market. As shown in Figure 3, in 2011 more then 15 million have already booked an overnight stay on
the Internet.
19,3
15,2
13,2
9,6
Flights
Overnight
stays
Train tickets
Rental cars
9,1
Package
holidays
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).
16
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).
17
Online travel agents
Expedia
Web site
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.
18
Travel meta sites
Mobissimo
Web site
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.
?
E-meta travel
sites
E-travel sites
Traditional sales channels, GDS,
group sales, consolidators
Travel products (accomodation, airline seats,
rental cars etc.)
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
19
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.
20
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
21
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).
3,6%
3,3%
1,8%
Google Bing Yahoo others 91,3%
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
22
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
23
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).
24
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
25
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).
!
Need recognition
Information search
Evaluation of alternatives
Purchase decision
Decision making process
!
!
!
!
!
Post-purchase behaviour
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
26
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
27
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
Actual
Marriott
Weighted
Actual
Four Seasons
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
2.66
3.00
3.00
3.00
3.33
3.00
Average
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
28
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.
2.
3.
4.
5.
The generic decision to take a vacation:
Information acquisition to assist decision making:
Joint decision-making by members of the household:
Experiencing of vacation activities: and
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 decisionmaking 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
29
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.
!
!
!
!
!
!
Travel choice components
Destination
Accommodation
Travel Companions
Mode
Departure Date
Duration
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
30
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
31
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.
Internet
Family, Friends, or colleagues
Magazines
Books
Informational brochures
Travel agents
TV
Newspaper
0%
20%
40%
60%
80%
100%
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.
32
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,
33
recommendations of friends, advertisements and local tourism information influence the decision
making for accommodations.
Price
Online reviews
Own experience
Hotel brand / chain
Travel guidebook
Recommendation from friends
Advertisement / brochure
Local tourism information
0% 5% 10% 15% 20% 25% 30% 35% 40%
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.
34
!
!
!
!
!
!
Destination pre-decison,
decision about type of
holiday
Need recognition
Looking for information
about hotels including
location, price, star rating,
user rating on Google Hotel
Finder
Seeking
information
Information search
Making a decision
Booking
Purchase decision
Seeking detailed
information &
expectations
Pre-trip stage
Booking the hotel via Hotel
Finder by being transferred on
hotel website or intermediary
During trip
Post-purchase behaviour
Compare expectation
& actual experience
Share experiences on Hotel
Finder
Sharing,
documentation,
re-experiencing
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 prepurchase 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-
35
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
36
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.
Convienent location
Service quality
Reputation
Friendliness of staff
Price
Room cleanliness
Value for money
Hotel cleanliness
Security
Room standard
Swimming pool
Comfort of bed
Parking facilities
Room size
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
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
37
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
38
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
39
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
40
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.
41
•
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, nonsmoking 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
42
and is transferred to the partners website where the booking can be concluded (“Google Hotel
Finder,” 2012).
43
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
44
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
45
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.
1
2
3
4
5
6
7
8
9
10
Candidate item for psychometric measures for perceived usefulness
Using electronic mail improves the quality of the work I do.
Using electronic mail gives me greater control over my work.
Electronic mail enables me to accomplish tasks more quickly.
Electronic mail supports critical aspects of my job.
Using electronic mail increases my productivity.
Using electronic mail improves my job performance.
Using electronic mail allows me to accomplish more work than would otherwise be
possible.
Using electronic mail enhances my effectiveness of the job.
Using electronic mail makes it easier to do my job.
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.
1
2
3
4
5
6
7
8
9
10
Candidate item for psychometric measures for perceived ease of use
I find it cumbersome to use the electronic mail system.
Learning to operate the electronic mail system is easy for me.
Interacting with the electronic mail system is often frustrating.
I find it easy to get the electronic mail system to do what I want it to do.
The electronic mail system is rigid and inflexible to interact with.
It is easy for me to remember how to perform tasks using the electronic mail system.
Interacting with the electronic mail system requires a lot of my mental effort.
My interaction with the electronic mail system is clear and understandable.
I find it takes a lot of effort to become skillful at using electronic mail.
Overall, I find the electronic mail system easy to use.
Table 7 10 item scale for perceived ease of use
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.
46
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.
1
2
3
4
5
6
Candidate item for psychometric measures for perceived usefulness
Using Chart-Master in my job would enable me to accomplish tasks more quickly.
Using Chart-Master would improve my job performance.
Using Chart-Master in my job would increase my productivity.
Using Chart-Master would enhance my effectiveness on the job.
Using Chart-Master would make it easier to do my job.
I would find Chart-Master useful in my job.
Table 8 Revised six-item scale for perceived usefulness
Item No.
1
2
3
4
5
6
Candidate item for psychometric measures for perceived ease of use
Learning to operate Chart-Master would be easy for me.
I would find it easy to get Chart-Master to do what I want to do.
My interaction with Chart-Master would be clear and understandable.
I would find Chart-Master flexible to interact with.
It would be easy for me to become skillful at using Chart-Master.
I would find Chart-Master easy to use.
Table 9 Revised six-item scale for perceived ease of use
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 selfreported 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.
47
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
48
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
49
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.
50
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.
!
Perceived
usefulness
Prior
experience
Perceived
ease of use
Attitude
Intentions
Perceived
playfulness
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
51
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 TAMrelated 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
52
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.
!
Perceived
usefulness
H1
Perceived
ease of use
H4
H2
H3
Attitudes
H6
Intentions to
use
H5
Perceived
playfulness
Figure 15 Proposed research model and its relationships
Source: own illustration
53
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.
54
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
55
•
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.
56
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.
57
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.
58
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
PU1
PU2
Questionnaire
The Hotel Finder provides useful information about the hotel, location and destination.
PU3
The Hotel Finder provides all my preferred search criteria when looking for an appropriate
hotel.Hotel Finder enables me to compare offers book a hotel at a lower price.
The
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
Construct
PEOU1
PEOU
2PEOU
Questionnaire
It is easy to navigate around the Hotel Finder website.
I can quickly find the information I need.
It is easy for me to become skillful at using the Hotel Finder for room reservation
3
PEOU
I think it is a user-friendly website.
4Table 11 Perceive ease of use (PEOU) measurement scale
59
Construct
PP1
Questionnaire
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
ATT2
Using the Hotel Finder tool is a (good/bad) 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.
Using the Hotel Finder tool is a (wise/foolish) 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 sevenpoint Likert scale, ranging from ‘strongly disagree’ (1) to ‘strongly agree’ (7). Table 14 shows the
questionnaire for measuring behavioral intention to use.
Construct
Questionnaire
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.
60
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
61
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.hotelnewsroom.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).
62
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
63
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
64
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.
65
6.7.1
Demographics and experience
Apprentice ship
7,7%
University degree
48,5%
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
15,4%
reservation over the Hotel Finder
before.
Profile of respondents
Characteristics
Statistics
Gender
Male
47,9%
Female
52,1%
Age
Mean
Standard Deviation
28,98
7,285
Nationality
Austria
39,1%
Germany
28,4%
Italy
17,5%
Switzerland
4,8%
Other
8,9%
Education
Not answered
Advanced school
School leaving examination
Online bookings during last year
None
4,1%
4,7%
34,9%
1
15,4%
2
26%
3
9,5%
4
9,5%
5
5,9%
6
3%
More
Confrontation with the
Google Hotel Finder before the survey
Yes
No
Experience with booking via the Google
Hotel Finder
Yes
No
15,4%
46,2%
53,8%
13,6%
84,4%
66
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.
67
Variable
PU1
Estimate
0,60
Standard Error
0,07
t-value
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.
68
Variable
PU1
PU
0,11
PEOU
0,02
PP
0,00
ATT
0,00
INT
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).
69
Variable
Raw
Alpha
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
Raw
Alpha
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.
Deleted variable
Alpha
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
70
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
Raw
Alpha
0,90
Standardized
0,90
Table 22 Cronbach's alpha coefficient for PP
Deleted variable
Alpha
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
Deleted variable
Alpha
ATT1
ATT2
0,65
0,71
0,86
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
71
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
Raw
Alpha
0,91
Standardized
0,91
Table 26 Cronbach's alpha coefficient for INT
Deleted variable
Alpha
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.
PEOU
PU
PU
1
PP
ATT
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
INT
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.
72
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
73
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
Chi-Square
Value
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
PEOU → PP
0,70
0,38
0,04
0,07
17,27
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.
74
!
!
!
Perceived
usefulness
!
!
!
0.21
0.70
!
Perceived
ease of use
!
0.38
Perceived
playfulness
0.19
!
!
Attitudes
0.42
0.72
!
Intentions to
use
!
!
!
!
!
!
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
75
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.
76
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
77
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
78
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.
79
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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 UoAS Salzburg, Master Program IMT | Nadia Pircher
XX
Annex A)
Questionnaire form
XXI
XXII
XXIII
XXIV
XXV
XXVI
XXVII
XXVIII
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
Konstrukt
Fragebogen
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
Konstrukt
Fragebogen
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
XXIX
Konstrukt
Fragebogen
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
XXX
Annex C)
Questionnaire spreading Google+
XXXI
XXXII
Annex D)
Questionnaire spreading Facebook
XXXIII
Annex E)
Questionnaire spreading Blog
XXXIV

Master thesis_Nadia Pircher_final

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