Weismayer, C.; Lalicic, L. at ENTER2016 eTourism Conference

1. ENTER 2016 Research Track Slide Number 1
An Exploration of User-driven
Assessments of Travel Enhancing
Apps
Christian Weismayer and Lidija Lalicic
MODUL University Vienna, Austria
christian.weimayer@modul.ac.at
lidija.lalicic@modul.ac.at
http://www.modul.ac.at

2. ENTER 2016 Research Track Slide Number 2
Table of Content
• Introduction
• Literature review
• Method
• Results
• Conclusion

3. ENTER 2016 Research Track Slide Number 3
Introduction
• Smartphones are a dominant fore shaping visitors’ behavior (Wang et al., 2014; Neuhofer
et al., 2015)
• Apps to be innovative, creative and support role while travelling (Wang et al., 2014)
• Various application distribution platform allow users to search, buy and
deploy apps (Harman et al., 2012; Maalej & Nabil, 2013)
• App reviews serve as a communication channel between users and
developers (Panichella et al., 2013)
• The importance of reviews for an app success
• App reviews are like snapshots of tourists 'experience (Wang et al., 2012)
• Effective design of travel apps can enhance the travel experience

4. ENTER 2016 Research Track Slide Number 4
Aims of the Study
1. To explore users praise or citizen of apps
2. Indicate the various flaws
3. To offer insights into the different paid and non-paid
apps and app categories
4. Recommendations how to steer the experience by
effective app design

5. ENTER 2016 Research Track Slide Number 5
Literature Review
• App reviews:
– Short and precise
– Continuously comments, experiences and ideas to optimize the app (Khalid
et al., 2015; Seyff et al., 2011)
– User requirements, bug report feature request, documentation of user
experience with features (Guzman & Maalej, 2012)
– Six categories: feature request, opinion asking, problem discovery,
solution proposal, information seeking and information giving (Pachinalla et al.,
2015)

6. ENTER 2016 Research Track Slide Number 6
Literature Review
• Mobile applications and travel (Wang et al., 2012;2014)
– Communication
– Entertainment
– Online social networking
– Information search
– Facilitation
• Mediates the behavioral and psychological dimensions of the
tourist experience (Tussyadiah, 2015; Larsen & Larsen, 2007)

7. ENTER 2016 Research Track Slide Number 7
Method
• 2 distribution platforms; Google Play and Apple Store
• 5 categories (communication, entertainment, facilitation, information and social
marketing) (Tussyadiah & Zach, 2012)
• 5 highest rank apps + balanced star rating
Sample:
• 240 reviews in 12 reviews per category
• Content analysis with 20 items: praise, helpfulness, shortcoming, crash, feature
removal/request, missing feature, apps, recommendation, noise, dissuasion – advise not to use/buy the
app, content request, improvement request, dispraise, other feedback – refer to feedback from other
reviewers, how to use the app, compatibility, hidden costs, network problem, privacy issues,
unresponsive (Pagano et al., 2013; Khalid, et al., 2015)
• Binary coding

8. ENTER 2016 Research Track Slide Number 8
Results
• Price vs. No Price
• iPhone apps: 0, .99, 2.99
• Android apps: 0, .75, .99, 2.99
• Price levels collapsed into two categories:
free apps (209 reviews) vs. paid apps (31 reviews)
• Method: Canonical variates analysis (CVA)
Software: R (R Development Core Team, 2005)
Package: BiplotGUI (la Grange et al., 2009)

9. ENTER 2016 Research Track Slide Number 9
Price vs. no-price percentage mean values
Items located above the line are
mentioned more often in paid
app reviews (e.g. helpfulness,
improvement requests).
Items located below the line are
mentioned more often in free
app reviews (e.g.
unresponsiveness).
Items located on the line show
balanced occurrence
frequencies (e.g. shortcomings).
Free app reviews
Paidappreviews

10. ENTER 2016 Research Track Slide Number 10
Price vs. no-price classification regions
Paid app reviews are located
closer to:
feature removal/request,
content request,
missing features,
network problems,
compatibility,
improvement requests,
helpfulness

11. ENTER 2016 Research Track Slide Number 12
Axes predictives of 5 app categories
(=> multiple discriminating functions)
• Items on the right side are well
represented by the 1st
dimension
(content request, privacy issues,
compatibility, missing features,
improvement requests, praise, other
feedback)
=> app development needs and
functional feedback
• Items with additional high marginal
contributions on the second
dimension: dissuasion, network
problems, crash reports, how to and
dispraise.
=> non-functional feedback

12. ENTER 2016 Research Track Slide Number 13
Review content representation
Review content representation:
1–communication (well by 1st
&2nd
)
2–entertainment (moderately by 1st
)
3–facilitation (well by 1st
)
4–information (well by 1st
and 2nd
)
5–social marketing (moderately by 2nd
)

13. ENTER 2016 Research Track Slide Number 14
Review content dimensions
requests complaints compliments divers problems

14. ENTER 2016 Research Track Slide Number 16
Conclusion and Recommendations
• Reviewers of paid apps give feedback how to improve the app
• Reviewers of free app complain without constructive feedback
=> paid app developers might benefit more from user-driven assessments
• Different topics between different app categories
=> different treatment of app reviews
⇒ first insight on how to handle reviews
Limitations
• Small sample especially for paid apps
• Exploratory setting (measurement constructs)
• Measurement scales (0-1)