ENTER2018 eTourism Conference presentation

1. ENTER 2018 Research Track Slide Number 1
Time-varying browsing behavior of
hotel website users
Rob Lawa
, Elise Wonga
Dimitrios Buhalisb
, and Richard Hattera
a
School of Hotel and Tourism Management
The Hong Kong Polytechnic University, Hong Kong
rob.law@polyu.edu.hk, elise.wong@outlook.com,
richard.hatter@hotel-icon.com
b
Department Tourism and Hospitality
Bournemouth University, England
dbuhalis@bournemouth.ac.uk

2. ENTER 2018 Research Track Slide Number 2
Agenda
 Introduction
 Literature Review
 Research Methodology
 Findings & Discussion
 Conclusion & Future Research

3. ENTER 2018 Research Track Slide Number 3
Introduction – Hotel ICON
Source: https://www.hotel-icon.com/

4. ENTER 2018 Research Track Slide Number 4
Introduction
 Accurate and timely information is always
emphasized in hospitality and tourism.
 Many marketers ignored the relationship
between time and consumer behaviors when
developing marketing research (Jacoby et al.
1976).
 There are limited studies on user’s time-varying
behaviors on websites in tourism.

5. ENTER 2018 Research Track Slide Number 5
Introduction
 Capturing users’ actual behavior through traditional
methods is difficult (Kellar et al., 2008).
 Prior studies have utilized web data in analyzing
consumers’ online behaviors (Bucklin & Sismeiro, 2003;
Moe, 2003; Kellar et al., 2008; Su & Chen, 2015; Suneetha
& Krishnamoorthi, 2009).
 Murphy et al.(2001) advocated the use of weblog data to
understand web users’ behaviors that can serve as a
useful source to improve website design and
effectiveness.

6. ENTER 2018 Research Track Slide Number 6
Introduction
 In hospitality and tourism, web data have been occasionally used, and
most studies focused on identifying:
– Users’ access path on websites (Leung & Law, 2008;
Schegg, Steiner, Gherissi-Labben, & Murphy, 2005), and
– Users’ characteristics (Park & Chung, 2009).
 Leung et al. (2016) examined users’ behavior considering time-varying
differences, but their study only focused on weekday-weekend
differences.
 Differences on monthly, daily, and hourly basis remain unknown.
 This study aims to extend current knowledge on consumers’ time-
varying behavior in the online platform by using weblog data from a
hotel website.

7. ENTER 2018 Research Track Slide Number 7
Literature Review
 Previous studies have identified “how” travelers navigate through
hotel websites and provided useful insights to improve website
performance:
– Schegg et al. (2005) used content analysis to examine the
most common navigation path from weblog data
collected from 15 Swiss hotel web servers.
– Leung and Law (2008) used weblog data from a 5-star
hotel in Hong Kong and identified users’ information and
access path preference, to determine the weakness of
the hotel’s website.

8. ENTER 2018 Research Track Slide Number 8
Literature Review
 Park and Chung (2009) identified users’ motivation and involvement
based on their access paths and length of stay respectively, which
were then used to predict their actual purchase behaviors.
 Courture et al. (2015) integrated the use of weblog data and
questionnaire to study the relationship between tourism
innovativeness and information searching, purchasing, and
communication behavior on tourism website.
 Leung et al. (2016) showed that users have different browsing
patterns during weekends and weekdays based on web log data.

9. ENTER 2018 Research Track Slide Number 9
Literature Review
 Weblog data contain rich temporal and behavioral data, which
provide more accurate information than when using traditional
methods (Schegg et al., 2005).
 Peterson (2004) stated that a good website analysis tool must provide
all the “who,” “where,” “what,” “how,” and “when” information of
the website user; otherwise, managers cannot acquire all necessary
information to develop complete e-marketing plans.
 Most existing studies have focused on identifying users’ profiles and
examining users’ navigation behaviors on websites.
 Time-varying behaviors have not been fully examined to date.

10. ENTER 2018 Research Track Slide Number 10
Literature Review
 Chetthamrongchai and Davies (2000) found that time attribute
predicts consumer behavior better than other common attributes
used in retail marketing research.
 The “time” element affects users’ online behavior and thus deserves
much research attention.
 To fill this research gap, the present study aims to identify users’
time-varying behaviors on hotel websites, particularly focusing on
number of visitors, number of pages viewed, and average length of
visit on a hotel website on a monthly, daily, and hourly basis.

11. ENTER 2018 Research Track Slide Number 11
Research Methodology
 Data source: Hotel A, a major tourist destination in Hong
Kong.
 A total of 365 weblog files from September 1, 2015 to
August 31, 2016 were collected from Hotel A’s website
server.
 The analyzed information include: total number of visits,
number of pages viewed, and average length of visit per
day; total visit number and page viewed per hour; the
most active countries/regions in each month; desktop and
mobile device behavior in each hour.

12. ENTER 2018 Research Track Slide Number 12
Research Methodology
 Unusual activities, such as zero and large visitor numbers
were eliminated.
 All data were then imported into SPSS to detect outliers.
 Monthly and daily browsing behaviors are examined using
one-way ANOVA test.
 The hourly browsing behavior data are not normally
distributed and comparison was conducted using non-
parametric tests.

13. ENTER 2018 Research Track Slide Number 13
Findings & Discussion
General statistics of the hotel website user activities
 A total of 278 daily and 1668 hourly data were investigated.
 Hotel A’s website had 1,309,109 visitors (4,709 visitors on average
per day), with 4,043,290 pages viewed (3.09 pages viewed per visitor
on average).
 Most visitors were from Hong Kong (41.89%), followed by the United
States (12.51%), Mainland China (7.54%), the United Kingdom
(3.39%), and Japan (2.91%).
 Over 58% of the visits to the website were from desktop device and
41% were from portable devices (mobile: 36.02%; tablet: 5.16%).

14. ENTER 2018 Research Track Slide Number 14
Findings & Discussion
Month-varying browsing behaviors - Visitors number
 There is a significant difference in monthly visitor numbers (F (11,
266) = 33.6, P<0.001), average number of pages viewed per visitor
(F(11,266)=17.38, P<0.001), and average length of visit
(F(11,266)=241.02, P<0.001).
 The post-hoc tests show that August 2016 had the highest volume of
visitors (M=5620), followed by July 2016 (M=5568) and April 2016
(M=5342).
 On the contrary, October 2015 (M=4015), November 2015 (M=4109),
and December 2015 (M=4088) recorded the lowest number of
visitors.
 Seasonality is observed because the number of visitors from
September 2015 to January 2016 is lower than that from February
2016 to August 2016.

15. ENTER 2018 Research Track Slide Number 15
Findings & Discussion
Month-varying browsing behaviors - Avg. no. of pages / visitor
 Visitors viewed a larger number of pages in September
2015 (M=3.22), October 2015 (M=3.41), and November
2015 (M=3.48).
 Interestingly, in the months that recorded highest total
number of visitors, such as February 2016 (M=2.57),
March 2016 (M=2.93), and August 2016 (M=2.83), users
viewed a smaller number of pages on average.

16. ENTER 2018 Research Track Slide Number 16
Findings & Discussion
Month-varying browsing behaviors - Avg. length of stay
 The average length of stay on the website is around 5 min.
 Visitors usually stayed longer from September 2015 to
January 2016 (mean range= 6:48 to 8 minutes) than from
February 2016 to August 2016 (mean range= 3:19 min to
3:38 min).

17. ENTER 2018 Research Track Slide Number 17
Findings & Discussion
Month-varying browsing behaviors
 In general, Hotel A’s website had fewer visitors from September 2015
to January 2016, and the visitors usually stayed long and viewed a
large number of pages.
 According to Park and Chung (2009), e-buyers stayed long but
browsed a few number of pages.
• Therefore, from September 2015 to January 2016, most
visitors of Hotel A’s website could be browsers.
 Hotel A’s practitioners may consider providing persuasive promotions
to convert e-browsers into e-buyers in those months.

18. ENTER 2018 Research Track Slide Number 18
Findings & Discussion
Day-varying browsing behaviors
 There is a significant difference in daily visitor numbers (F (6,
271)=13.61, P<0.001). However, no significant differences are found
for the average number of pages viewed per visitor (F(6, 271)=2.38;
P>0.05) and average length of visit (F(6, 271)=0.84, P>0.05).
 Higher visitor number is found during weekdays than weekends.
Tuesday recorded the highest visitor number (M=5,107), followed by
Wednesday (M=4,984), and Thursday (M=4,980).
 Saturday (M=4,130) and Sunday (M=4,233) recorded the lowest
visitor volume.

19. ENTER 2018 Research Track Slide Number 19
Findings & Discussion
Day-varying browsing behaviors
 Post hoc comparison (i.e., Games–Howell test) reveals that the visitor
numbers on Saturdays and Sundays are smaller than that on each
weekday.
 Workers usually participated in out-of-home activities on Saturdays
and rest at home on Sundays (Baht & Srinivasan, 2005).
 Users may not be willing to use their holidays to search for hotel-
related information.
 Thus, a lower number of visitors used Hotel A’s website on Saturdays
(M=4,130), compared with that on Sundays (M=4,233), is reasonable.

20. ENTER 2018 Research Track Slide Number 20
Findings & Discussion
Hour-varying browsing behaviors - Visitors number

21. ENTER 2018 Research Track Slide Number 21
Findings & Discussion
Hour-varying browsing behaviors - Avg. no. of pages viewed

22. ENTER 2018 Research Track Slide Number 22
Findings & Discussion
Hour-varying browsing behaviors
 The difference in number of pages viewed found in each
hourly section is related to the type of devices used to
access the website.
 The results show that users viewing the website via
portable devices viewed 50% fewer pages than desktop
device users.
 Over 50% of portable device users were accessing Hotel
A’s website at hourly sections 2 (20.92%) and 4 (34.7%).
– This explains why the overall pages viewed on sections 2
and 4 are less than those in the other sections.

23. ENTER 2018 Research Track Slide Number 23
Conclusions
 The findings show that numbers of visitors on Hotel A’s website differ
among months, with significantly less visitors from September 2015
to January 2016.
 Users accessed the website on weekdays during working hours via
desktop devices. After work, users tended to use portable devices to
access the hotel website and viewed 50% fewer pages than when
using desktop device.
– Hotel managers should ensure that the mobile version of
their website is fully functional.
 The number of visitors during weekdays is more than that during
weekends.
– More promotion can be implemented during weekdays
while maintenance services may be conducted during
weekends to reduce disturbances to users.

24. ENTER 2018 Research Track Slide Number 24
Future Research
 The weblog data used for this study were collected from a
single hotel, thereby limit the generalizability of the
results to users of other hotel websites.
 Future studies can extend the scope of this study by
including data from other hotels.
 Future research can also examine visitors’ time-varying
behavior on hotel websites focusing on their access paths
to identify the effect of geographical location on users’
online behaviors.

25. ENTER 2018 Research Track Slide Number 25
THANK YOU!