Weighing of Hotel Website Dimensions and Attributes

Document Sample
Weighing of Hotel Website Dimensions and Attributes Powered By Docstoc
					    Weighing of Hotel Website Dimensions and Attributes


                                        Rob Law
                                    Catherine Cheung

                 School of Hotel & Tourism Management,
            The Hong Kong Polytechnic University, Hong Kong
              {hmroblaw, hmcat}@hmroblaw@polyu.edu.hk


                                          Abstract
The wide establishment of hotel websites has recently drawn the attention of many tourism and
hospitality researchers to investigate the factors that contribute to a successful hotel website,
and the ways of measuring the performance of hotel websites. The published articles in the
hospitality and tourism literature, however, have largely overlooked the decision making
approach which involved the development of weighing and rating scales from website users. To
fill in such a void, this research makes an attempt to develop a weighing model for contents of
hotel websites. The model was built on the basis of input from hotel website users who were
international visitors to Hong Kong. Empirical findings indicated that Reservations Information
and Website Management were the dimensions with the largest and smallest weights; whereas
Transportation and Special Request Forms were the attributes with the largest and smallest
overall weights. The value of a weight thus directly reflects its perceived importance.

Keywords: Hotel, Website, Weight, Performance

1     Introduction

The recent technological advancements, together with the affordable costs of personal
computers, have facilitated the large scale of Internet applications in the world. Most,
if not all, businesses thus have setup their e-channels to enable customers to search
and to purchase on the Internet (Phau and Poon, 2000). Such an Internet application
also directly applies to the hotel industry, a major sector of travel and tourism. Cox
(2002) stated that travel spending is the top growth driver for e-commerce as a whole.
Similarly, O’Connor (2003) emphasized that, by quoting a recent report from Jupiter
Media Metrix, online travel sales will increase more than 3 times from US$18 billion
in 2002 to US$ 64 billion in 2007. In the hotel industry, more than 106 millions hotel
room nights were purchased from hotel GDS e-commerce channels in the world in
2003 (Shellum, 2004). Such a figure represented a 3.3% increase over the
corresponding number in the previous year. Furthermore, Greenspan (2003) predicted
that online hotel room bookings would grow from US$5 billion in 2001 to US$14.8
billion in 2007.
In response to the huge potential market of e-commerce, and the induced revenues
thus generated from the Internet, hotels have largely used various online channels to
distribute their rooms (O’Connor & Frew, 2004). While many hotels choose to sell
their rooms to third-party online travel agents such as expedia and travelocity, a
majority of them have also setup their own websites for direct sales. In spite of the
increasing investment in the development of numerous hotel websites, the existing
hospitality literature only has a limited number of published articles that investigated
the performance of hotel websites. The scarcity of published articles is particularly
true when considering the construction of website users’ perceived weights, or
importance when customers are making online purchase, of different features on hotel
websites. Such a set of weights can unambiguously assist hotel practitioners
determine the perceived importance of their website features. This, in turn, enables
them to more efficiently use their resources on the web. Although hospitality and
tourism researchers have introduced different approaches to evaluate the performance
of websites, these methods are either lack of well-defined objective standard (Cai et
al. 2004; Dube et al., 2003; Doolin et al., 2002) or without direct involvement of
consumers (Morrison et al., 1999; Wöber, 2000; Wöber et al., 2002). For instance, the
traditional techniques of understanding online customers using server logs, hits,
views, and visits are subject to numerous limitations and drawbacks. These
measurement methods are unable to provide enough information about the customers’
view on the importance of hotel website content. Furthermore, these limitations
render hoteliers’ inability to know whether their websites meet the best industrial
practice from a technical and design perspective. Hoteliers, thus, have no way of
making reference of the performance of their websites relative to the industrial
standard. As a result, these traditional techniques do not suffice to serve
benchmarking in the latest competitive hotel business. It has long been agreed that
content delivers the information, raises customers’ awareness, and attracts e-browsers
to become e-buyers (Phau & Poon, 2000; Weber & Roehl, 1998). Research projects
of surveying customers’ preference and then analyzing hotel website contents in terms
of numeric weights and scores, therefore, would benefit hotel practitioners better
understand customers’ need and hotel websites’ performance.

The primary objective of this research is to make an attempt to build a multi-criteria
decision making model that incorporates the opinions of hotel website users into the
weights of included hotel website dimensions and attributes. It has been known that
the system acceptability, or the overall acceptance, of a website comprises many
major components. For example, Huizingh (2000) stated that the relevant aspects of
websites are primarily the design and content components. Lu and Yeung (1998),
however, argued that some of the major website components are system feasibility,
functionality, and social acceptability. While agreeing with the usefulness of website
design features like frame layout and background contrast (van Schaik & Ling, 2001),
this research focuses on functionality component because of its direct relevancy to
contents. That is, this research concentrates on investigating the website dimensions
and attributes that are related to the extent which web pages provide sufficient
information about the products and services being promoted on hotel websites.
Research outcomes are, therefore, expected to contribute to the eventual development
of a repeatable and measurable way that has the potential to form a long-term metric
that would assist refinement of hotels’ e-business programs.

Having introduced the research background in the previous paragraphs, the remaining
sections of this paper go as follows. First, a methodology section presents the survey
procedure and the modeling process. After that, there is a section to discuss the
findings, and to analyze the empirical results. The last section concludes the paper,
and offers suggestions for future research possibilities.

2    Methodology
2.1 The Survey

This research is part of a large-scale Omnibus Survey for International visitors in
Hong Kong, which was conducted by the School of Hotel & Tourism Management at
the Hong Kong Polytechnic University in 2003. The survey used a non-random
sampling method, by following the instructions from the Airport Authority, to
interview visitors from 7 major market regions, including Chinese Mainland, Taiwan,
Singapore, Malaysia, the United States, Australia, and Western Europe. In addition to
the qualifying question of having visited hotel website(s) in the past 12 months, the
questionnaire comprised 2 major components. The first component required
respondents to assign a numeric percentage weight (i.e. out of 100) to 5 dimensions
and 40 attributes, which will be shown later in this paper, that were associated with
these dimensions. A higher percentage represented that the dimension or attribute is
more important when the respondent was considering making online reservations. The
second component was to seek for demographic characteristics of the respondent. The
questionnaire was modified from the hotel website evaluation list developed by
Chung and Law (2003), which in turn, largely followed prior studies of Morrison et
al. (1999), van Hoof et al. (1999), and Weeks and Crouch (1999).

The initial questionnaire was pilot tested by 20 international visitors at the Hong
Kong International Airport in late-September 2003. Other than a few minor
suggestions for rewording, the respondents did not find any major problems with the
questionnaire. The suggested changes were made to the questionnaire, and a Chinese
version of the questionnaire was also developed for visitors from Chinese Mainland
and Taiwan. The large-scale survey was performed in late-October 2003 in the
departure hall of the Hong Kong International Airport. At the end of the survey, 2,400
travelers were approached, and 284 completed and usable questionnaires were
received. The next section presents the demographic characteristics of these
respondents, and their perceived weights for the included hotel website dimensions
and attributes.
2.2 The Modeling Process

The weight modeling approach for hotel website attributes was modified from the
multi-criteria weighing model presented by Law (2003), and the process goes as
follows. Let D be the dimension vector that consists of the essential dimensions of a
hotel website. That is, D = [d1, d2, …, dm] for a hotel website with m dimensions.
Furthermore, let A be the attribute vector of the first dimension with i attributes. In
other words, A = [a1 ,a2, …, ai]. Similarly, let B be the attribute vector of the second
dimension with j attributes, namely B = [b1, b2, …, bj]. This attribute vector
assignment process continues until the last attribute vector M = [m1, m2, …, mp],
where M has p attributes.

Next, the importance for D, A, B, …, M in terms of numeric weights are determined
by a group of hotel website users who are asked to provide a numeric percentage
weight for each element in D, A, B, …, M. The relative importance (i.e. the relative
weight) for a specific element can then be obtained by computing the ratio of the
weight of this specific element to the sum of all elements in the corresponding vector.
In other words, the summation of all relative importance (weights) of a specific vector
equals to 100%. Having collected all data from respondents, the overall weight (w) of
each dimension and attribute can be derived by averaging the individual weights.

Finally, the overall relative weights for all attributes, in contrast to the relative
weights within a dimension, will be computed. For an attribute ei in dimension e, its
overall weight Wei, can be computed by: Wei = we × wei, where we and wei represent
the dimension weight of e and the attribute weight of ei.

3    Results and Discussions

The demographic profile of the respondents is presented in Table 1. Among these
respondents, most of them were males, in the age groups of 26-35 and 36-45, received
college/university diploma/degree or above education, with personal income of US$
10,000-49,999, and were mainly from Taiwan, the United States, and Western
Europe.
                        Table 1 Demographic Characteristics

    Variable                                              N                %
    Gender (N=284)
     Male                                                195             68.66
     Female                                              89              31.34
    Age (N=284)
     Under 18                                             2               0.70
     18-25                                               29              10.21
     26-35                                               114             40.14
     36-45                                               80              28.17
     46-55                                               38              13.38
     56-65                                               17               5.99
      66 or above                                           4             1.41
    Education (N=284)
      Less than secondary/high school                     2                0.70
      Completed secondary/high school                    14                4.93
      Some college or university                         50               17.61
      Completed college/university diploma/degree        135              47.54
      Completed postgraduate degree                      83               29.23
    Income (N=284) (US$)
      Less than 10,000                                    15               5.28
      10,000-29,999                                       79              27.82
      30,000-49,999                                       61              21.48
      50,000-69,999                                       51              17.96
      70,000-99,999                                       32              11.27
      100,000 or more                                     46              16.20
    Country of Residence
      Chinese Mainland                                    27               9.51
      Taiwan                                              59              20.77
      Singapore                                            8               2.82
      Malaysia                                             9               3.17
      United States of America                            58              20.42
      Australia                                           34              11.97
      Western Europe                                      57              20.07
      Others                                              32              11.27

Table 2 shows the Cronbach’s alpha measures of the 5 dimensions. The values, which
measured the proportion of variance that was attributable to the true score of variance
that the study intended to evaluate, revealed the consistency of the evaluation and the
homogeneity of the items in the scale. In this research, results showed good reliability
for all dimensions. The first 4 dimensions had strong alpha values of over 0.8, and the
alpha for Contact Information was 0.7519.
                             Table 2 Reliability Analysis

                  Dimensions                           Cronbach’s Alpha
           Reservations Information                         0.8801
             Facilities Information                         0.8711
         Surrounding Area Information                       0.8666
             Website Management                             0.8238
              Contact Information                           0.7519

Following the modeling process as outlined in the previous section, the weights of
different dimensions and their associated attributes were computed. Table 3 lists the
weights (relative importance) of the 5 dimensions for hotel websites. Similarly, Table
4 to Table 8 presents the dimension and overall weights of different attributes. As
previously stated, the total sum of weights for all dimensions (i.e. Table 3) and
attribute weights in each dimensions (i.e. Tables 4 to 8) amounted to 100 percent.
Likewise, the sum of all overall weights should add up to 100 percent.
                               Table 3 Dimension Weights

                                                              Weight
                         Dimensions                        (importance      s.t.d.
                                                              mean)
Reservations Information                                      22.24         5.09
Facilities Information                                        21.70         4.79
Contact Information                                           20.81         5.75
Surrounding Area Information                                  18.76         5.44
Website Management                                            16.49         5.75


Table 4 Weights of the Attributes in the Dimension of Reservations Information

                               Dimension Weight
        Attributes                                  s.t.d.      Overall Weight
                               (importance mean)
Room Rates                           12.28           4.56            2.73
Check Rates and
                                      11.80          3.84            2.62
Availability
Online/Real Time
                                      10.86          2.59            2.41
Reservations
Security Payment Systems              10.33          2.90            2.30
View or Cancel
                                      10.09          2.40            2.24
Reservations
Reservation Policies                  9.67           2.58            2.15
Check In and Check Out
                                      9.64           2.58            2.14
Time
Worldwide Reservations
                                      9.43           2.86            2.10
  Phone Number
Payment Options                       8.89           4.63            1.98
Special Request Forms                 7.02           3.04            1.56


Table 5 Weights of the Attributes in the Dimension of Facilities Information

                                Dimension Weight                    Overall
         Attributes                                  s.t.d.
                                (importance mean)                   Weight
Hotel Location Maps                   12.14          4.58            2.63
Hotel Features                        11.91          4.63            2.59
Guest Room Facilities                 11.58          4.56            2.51
Photos of Hotel Features              10.90          3.15            2.37
Hotel Descriptions                    10.77          2.86            2.34
Hotel Promotions                       9.20          3.47            2.00
Restaurants                            8.48          2.90            1.84
Frequent Guest Program                 7.68          3.54            1.67
Virtual Tours                          7.58          3.19            1.64
Meeting Facilities                   6.34            3.54          1.38
Employment Opportunities             3.43            3.53          0.74

Table 6 Weights of the Attributes in the Dimension of Contact Information

                               Dimension Weight                   Overall
          Attributes                                  s.t.d.
                               (importance mean)                  Weight
Telephone Number                     16.80             5.09        3.50
Address                               16.65            5.32         3.46
E-mail Address                        15.32            4.94         3.19
Fax Number                            12.22            4.90         2.54
Contact Person                        11.22            4.72         2.34
Frequently Asked Questions            9.98             5.63         2.08
Feedback Form                         9.27             5.55         1.93
Online Forum                          8.54             4.61         1.78


Table 7 Weights of the Attributes in the Dimension of Surrounding Area Information

                               Dimension Weight                    Overall
          Attributes                                  s.t.d.
                               (importance mean)                   Weight
Transportation                       22.62             8.96         4.42
Airport Information                  21.32             6.32         4.00
Main Attractions of the City         20.09             5.42         3.77
General Information of the
                                     19.55             6.89         3.67
City
Public Holidays                      15.15             6.67         2.84



Table 8 Weights of the Attributes in the Dimension of Website Management
                                Dimension
         Attributes               Weight           s.t.d.  Overall Weight
                               (importance
                                   mean)
Up-to-date Information on          24.90          11.29         4.11
the Site
Multilingual Site                  21.29           8.11         3.51
Site Map                           19.44           6.55         3.21
Search Function                    19.26           6.53         3.18
Links to other related             15.11           7.19         2.49
businesses

In general, the computed weights of different dimensions did not differ greatly among
each other. While Reservations Information received the largest weight of 22.24,
Website Management had the smallest weight of 16.49. The attribute weights in
individual dimensions, however, differ largely. For instance, in the dimension of
Contact Information, the weight of Telephone Number was 16.80 whereas the
corresponding figure for Online Forum was 8.54, indicating a weight difference of
almost 50%.

4    Conclusions

This study has built a model for weighing different dimensions and attributes of hotel
websites. Although the study is limited in scope of time frame and the number of
respondents, research findings should provide insight for hospitality researchers and
practitioners to determine the weights, or relative importance, of content features on
hotel websites. In particular, hotel practitioners should be able to draw on the findings
of this study to utilize their limited resources on website development. Examples of
such resources utilization include concentrating on the attributes with large weights
like Up-to-date Information on the Site and consider dropping the attributes with low
weights like Employment Opportunities. In other words, it is worthwhile to
continuously refine the model to reflect customers’ ongoing needs.

In the context of online reservations of hotel rooms, the leading online travel agents
like expedia and travelocity get the largest share from online customers. Nevertheless,
most travelers who presently use the Internet to search and purchase travel
services/products are discouraged with the inflexibility of many online travel agents,
and half of the consumers who use online travel agents have had problems with
website contents (Anonymous, 2004). Hence, an examination of website contents
would be beneficial to hotel managers and customers.

A recently published article indicated that hoteliers have realized the fact that they are
losing control of their rooms and revenues, and that hotels can generate higher rates
from their own websites than through third-party websites (Shellum, 2004). O’Connor
and Frew (2002) made a similar positive claim, saying that hotel websites would be
the channel with the highest growth and the largest volume. In order to have a better
online market share of their rooms, many international hotel chains have introduced
various lowest rate guarantee programs to improve the reputation of their websites
and to attract consumers in making reservations (Shellum, 2004). Toms and Taves
(2004) have found that the reputation of a website could influence the success of a
website. The findings of this research, thus, make a contribution for hoteliers to
determine whether the contents of their websites meet the requirements of the
customers.

Although the survey was conducted in Hong Kong, the empirical findings do have a
wide application scope. This wide applicability is largely due to the weights that were
computed on the basis of international hotel website users’ views. In other words, the
research findings have a good potential application to hotel websites in general. A
natural extension of this research is to determine the rating criteria, again on the basis
of hotel website users, for specific attributes. Such a set of rating criteria, coupled
with the established weights, can then be used to perform the actual evaluation of
hotel websites. Another future research possibility is to qualitatively examine the
attributes and their derived overall weights in detail. In conclusion, it is important for
hotel practitioners to know the performance of their websites against the industrial
standard, and to judge their websites’ performance versus their competitors. This
research, therefore, offers insights for the development of a website measurement
standard, at least for specific target markets.

Acknowledgement

This research was supported, in part, by a research grant funded by the Hong Kong
Polytechnic University (under Contract Number: G-T868).

References
Anonymous (2004). Online Frustrations. Hotel Asia Pacific, 5(2), 31.
Cai, L., Card, J.A. & Cole, S.T. (2004). Content Delivery Performance of World Wide Web
       Sites of US Tour Operators Focusing on Destinations in China. Tourism Management,
       25, 219-227.
Chung, T. & Law, R. (2003). Developing a Performance Indicator for Hotel Websites.
       International Journal of Hospitality Management, 22, 119-125.
Cox, B. (2002). Online Travel – Still an E-commerce Star? [Accessed on May 22, 2004].
       www.internetnews.com/ec-news/article.php/1437521.
Doolin, B., Burgess, L. & Cooper, J. (2002). Evaluating the Use of the Web for Tourism
       Marketing: a Case Study from New Zealand. Tourism Management, 23, 557-561.
Dube, L. Le Bel, J. & Sears, D. (2003). From Customer Value to Engineering Pleasurable
       Experiences. Cornell Hotel & Restaurant Administration Quarterly, October-December,
       124-130.
Greenspan, R. (2003). Hotel Industry Makes Room for Online Bookings. [Accessed on May 26,
       2004] www.clickz.com/stats/markets/travel/article.php/1567141.
Huizingh, E.K.R.E. (2000). The Content and Design of Web Sites: an Empirical Study.
       Information & Management, 37, 123-134.
Law, R. (2003). Towards a Multi-criteria Weighting Model for Travel Websites. Review of
       Business Research. 1(2), 114-118.
Lu, M.T. & Yeung, W.L. (1998). A Framework for Effective Commercial Web Application
       Development. Internet Research: Electronic Networking Applications and Policy, 8(2),
       166-173.
Morrison, A.M., Taylor, S., Morrison, A.J. & Morrison, A.D. (1999). Marketing Small Hotels
       on the World Wide Web. Information Technology & Tourism, 2(2), 97-113.
O’Connor, P. (2003). On-line Pricing: An Analysis of Hotel-company Practices. Cornell Hotel
       and Restaurant Administration Quarterly, February, 88-96.
O’Connor, P. & Frew, A.J. (2002). The Future of Hotel Electronic Distribution: Expert and
       Industry Perspectives. Cornell Hotel and Restaurant Administration Quarterly, June,
       33-45.
O’Connor, P. & Frew, A.J. (2004). An Evaluation Methodology for Hotel Electronic Channels
       of Distribution. International Journal of Hospitality Management, 23, 179-199.
Phau, I. & Poon, S.M. (2000). Factors Influencing the Types of Products and Services
       Purchased Over the Internet. Internet Research: Electronic Networking Applications
       and Policy, 10(2), 102-113.
Shellum, S. (2004). Hitting Back. Hotel Asia Pacific, 5(2), 19-24.
Toms, E.G. & Taves, A.R. (2004). Measuring User Perceptions of Web Site Reputation.
       Information Processing and Management, 40, 291-317.
van Hoof, H., Ruys, H. & Combrink, T. (1999). Global Hoteliers and the Internet: Use and
      Perceptions. International Journal of Hospitality Information Technology, 1(1), 45-61.
van Schaik, P. & Ling, J. (2001). The Effects of Frame Layout and Differential Background
      Contrast on Visual Search Performance in Web Pages. Interacting with Computers, 13,
      513-525.
Weeks, P. & Crouch, I. (1999) Sites for Sore Eyes: An Analysis of Australian Tourism and
      Hospitality Web Sites. Information Technology & Tourism, 2(3/4), 153-172.
Weber, K. & Roehl, W. (1999). Profiling People Searching for and Purchasing Travel Products
      on the World Wide Web. Journal of Travel Research, 37, 291-298.
Wöber, K. (2000). Benchmarking Hotel Operations on the Internet: A Data Envelopment
      Analysis Approach. Information Technology & Tourism, 3, 195-211.
Wöber, K.W., Scharl, A., Natter, M., & Taudes, A. (2002). Success Factors of European Hotel
      Websites. Information and Communication Technologies in Tourism 2002 [Wober,
      K.W., Frew, A.J., & Hitz, M. eds]. New York: Spring-Verlag Wien, pp. 397-406.

				
DOCUMENT INFO