Park, S.; Tussyadiah, I.; Zhang, Y. at ENTER2016 eTourism Conference

1. ENTER 2016 Research Track Slide Number 1
Assessment of perceived risk in
mobile travel booking
Sangwon Parka
, Iis Tussyadiahb
, & Yuting Zhanga
a
University of Surrey, UK
b
Washington State University, USA
sangwon.park@surrey.ac.uk
iis.tussyadiah@wsu.edu
yz00167@surrey.ac.uk

2. ENTER 2016 Research Track Slide Number 2
Introduction
• Mobile: Wireless interface and location-based services 
obtaining information on own schedule (spontaneous
needs) and personalized information (mobility-related
desires) (Wang, Park & Fesenmaier, 2012)
• Of smartphone users in the UK, 16% - hotel bookings; 14%
- air ticket bookings; 8% - attractions and activities (Expedia
Media Solution, 2014)
• The fastest growing mobile booking market, China (China Internet
Watch, 2015) - About 38% of online travellers make bookings
through mobile. (eRevMax, 2014)

3. ENTER 2016 Research Track Slide Number 3
Introduction
• Perceived risk: One of the main barriers that make
consumers reluctant to perform purchasing decisions (Kim,
Ferrin & Rao, 2008).
• People are uncertain not only about the services they look
for, but also about the soundness of the underlying
technology platform.
• Tourism is intangible and perishable products.

4. ENTER 2016 Research Track Slide Number 4
Purpose of the research
1. To propose the multi-facets of risk perceived by
travellers when they use mobile devices to purchase
travel products
2. To identify the antecedent to and effects of the risk on
online travellers’ behaviours

5. ENTER 2016 Research Track Slide Number 5
Multi-dimensions of
perceived risk
• Perceived facets of risk (Featherman & Pavlou 2003; Forsythe & Shi, 2003; Kim et al., 2005;
Mitchell, 1992)
 Financial risk
 Time risk
 Physical risk
 Psychological risk
 Privacy risk
 Security risk
 Performance risk
 Social risk
 Device (or technology) risk(Kim et. al., 2013; Yang & Zhang, 2009)

6. ENTER 2016 Research Track Slide Number 6
Antecedents of Perceived Risk
• Consumer innovativeness - manifest in novelty seeking
tendency (Hirunyawipada & Paswan, 2006)
• Trust (Mayer, Davis, & Schoorman, 1995; Cheung & Lee, 2000)
• Visibility (Vishwanath & Goldhaber, 2003), or observability (Roger, 1995).
• Collection of personal information (Anuar & Gretzel, 2013; Junglas & Spitzmüller,
2005)

7. ENTER 2016 Research Track Slide Number 7
Consequences of Perceived Risk
• Perceived risk is negatively associated with consumer
behaviours.
• Perceived usefulness (Featherman & Pavlou, 2003)
• Attitude toward online consumptions (van der Heijden, Verhagen, &
Creemers, 2003) and intention to purchase the products using the
technology (Kim, Ferrin, & Rao, 2008)

8. ENTER 2016 Research Track Slide Number 8
Proposed model

9. ENTER 2016 Research Track Slide Number 9
Data collection
• Online marketing research company - www.sojump.com
in July, 2014
• A couple of filtering questions were asked:
– (1) “Have you ever used smartphone in everyday life?”
– (2) “Did you use smartphone to search for information
about accommodation in the most recent trip?”
• Of 1,300 invitations, 411 respondents (18 years and
older): 31.6% response rate

10. ENTER 2016 Research Track Slide Number 10
Measurements
• Measurement items were drawn from related literature
and revised to accommodate mobile travel booking
context.
• To minimize potentials of the measurement error,
– Face validity test by inviting academic experts (Belanche, et al.,
2012).
– A back translation method (Brislin, 1986; Park & Reisinger, 2012)
– Preliminary study (Saunders et al., 2009)

11. ENTER 2016 Research Track Slide Number 11
Data Analysis
• SEM using M-Plus: to perform a second-order CFA of
perceived risk facets with goodness-of-fit indexes as well
as AIC (Kline, 2011).
• PLS using SmartPLS - to estimate the proposed
relationships (Chin, 2010)
• Common method bias – the marker variable method (or
latent variable approach) (Podsakoff et al. 2003)

12. ENTER 2016 Research Track Slide Number 12
Variables Frequency %
Gender (N = 411)
Female 236 57.4
Male 175 42.6
Age (N = 411)
18-20 84 20.4
21-24 103 25.1
25-30 136 33.1
31-35 63 15.3
36-45 22 5.4
46 or above 3 .7
Annual household income (N = 411)
Less than ¥20,000 88 21.4
¥20,000 to ¥ 59,999 86 20.9
¥60,000 to ¥99,999 90 21.9
¥100,000 and above 77 18.7
Do not wish to report 70 17.0
Education level (Age 18 and over) (N = 411)
Less than high school 7 1.7
High school 41 10.0
Bachelor at College/University 311 75.7
Master 49 11.9
PhD or higher 3 .7
Employment (N = 411)
Students 113 34.8
Employee in companies 249 60.6
Civil servant 11 2.7
Unemployed 8 1.9

13. ENTER 2016 Research Track Slide Number 13
Estimating measurement model
• Collinearity between security and privacy risk (Bhatnagar et al. 2000 &
Kim et al. (2008)
Those two types of risk were combined.
• The variance explained of 10.6% for social risk implies that
it is not important and salient.
• The correlation values of social risk not only show
inconsistent relationships with other constructs, they are
also low in magnitude (i.e., r < 0.16) (Featherman & Pavlou 2003; Luo et al.
2010).
 Elimination of social risk

14. ENTER 2016 Research Track Slide Number 14
Second-order CFA of perceived risk
facets model

15. ENTER 2016 Research Track Slide Number 15

16. ENTER 2016 Research Track Slide Number 16
Discussion
• Confirming a multidimensional perceived risk
– Excluding social risk, and combining security and privacy risk
– Validating the inclusion of device risk
• The antecedents of perceived risks
– Negative influences: consumer innovativeness, trust, and visibility
– Positive influence: collection
• The consequences of perceived risk
– Negative effects on perceived usefulness, attitude, and
behavioural intention

17. ENTER 2016 Research Track Slide Number 17
Implications
• Implications for service providers as well as designers of
mobile application to reduce perceived risk.
– Promoting the inhibitor factors (innovativeness, trust,
visibility) and repressing the promoter of perceived
risk (collection): for instance, an easy-to-follow
instruction
– Offering a showcase for demonstrating the positive
outcomes: for instance, highlighting positive reviews
and/or testimonials at point of sale

18. ENTER 2016 Research Track Slide Number 18
Future Research
• Estimating the causality of the relationship between trust
and risk to provide further support for the theorizing of
perceived risk (e.g., Trust  Risk vs. Risk  Trust)
• Considering other factors that may increase or reduce
perceived risk in different consumption situations across
different tourism destinations

19. ENTER 2016 Research Track Slide Number 19
Thank you for your attentions!
Any questions and suggestions?