1. Measuring the global readiness of airline
websites:
Are they speaking the world’s languages?
Peter O'Connor & Zaheer A. Shaik
ESSEC Business School
Paris, France
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2. Agenda
• Introduction
– Inspiration for GRI, Glocalization, History of GRI, Limitation of
previous studies, Language weights
• Parameters
– Language & Content, Means of payment, Bookings engine, Address
elements, Currency, Email support, Social media, Alliances
• Research method
– Criteria used for evaluation
• Results
• Contribution & Conclusion
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3. INTRODUCTION
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4. Inspiration for GRI
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5. Glocalization
• Dochakuka (meaning indigenization)
• Relevance to tourism
• Airlines
– try to be global to achieve
economies of scale
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6. History of GRI
• Augustine, Surynt, and Jens (2008)
• O'Connor (2011)
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7. Why?
• Measures & Indices aid stakeholders in
understanding the environment
• CPI & GDI
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8. Website with English and
Korean versions is typically
rated globally ready on a par
with another website with
English and Mandarin versions
(other parameters being equal)
Limitation of previous GRI studies
• Language given equal weight
Parameters
Original GRI
GRI of hotel websites
GRI of airline websites
Language
Yes
Yes
Yes (modified)
Content
No
Yes
Yes
Payment option
Yes
Yes
Yes
Bookings option
No
Yes
Yes
Address elements
Yes
Yes
Yes
Logistics/shipping
Yes
Yes
No
Currency
Yes
Yes
Yes
Email support
No
No
Yes
Social media links on the
website
No
No
Yes
Part of alliances
No
No
Yes
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9. Language Weights
Language
Internet users
English
27%
Chinese
24%
Spanish
8%
Japanese
5%
Portuguese
4%
German
4%
Arabic
3%
French
3%
Russian
3%
Korean
2%
Rest
17%
http://www.internetworldstats.com/stats7.htm
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10. PARAMETERS
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11. Language & Content
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12. Means of payment
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13. Bookings Engine
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14. Address Elements
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15. Currency
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16. Email Support
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17. Social Media Links on the website
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18. Part of Alliances
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19. Why logistics/shipping was removed?
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20. RESEARCH METHOD
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21. Case of two airlines
Airline 2
Airline 1
Parameter
English
Mandarin
Parameter
English
Korean
Language
1
1
Language
1
1
Content
1
1
Content
1
1
Payment
0
1
Payment
0
1
Bookings
1
1
Bookings
1
1
Address
1
1
Address
1
1
Currency
1
0
Currency
1
0
Email
0
1
Email
0
1
Social Media
0
0
Social Media
0
0
Alliances
1
1
Alliances
1
1
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22. Effect of weighting language
• Previous studies
– GRI 1 = (6+7)/2 = 65%
– GRI 2 = (6+7)/2 = 65%
• Weighed approach
– GRI 1 = (6*0.27)+(7*0.25)/8 = 42.1%
– GRI 2 = (6*0.27)+(7*0.002)/8 = 20.4%
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23. Criteria used for Evaluation
Parameter
Criteria used to evaluate the website elements
Content
Same language as language version = 1, else = 0
Payment option
Credit card only = 0, else = 1
Bookings engine
Bookings engine in the same language = 1, else = 0
Address elements
US specific address elements only = 0, else = 1
Currency
Currency conversion available = 1, else = 0
Email support
Email reply in same language = 1, else = 0
Social media link on
the website
Link to Facebook (Weibo) in same language = 1
No link or a link to a different language page = 0
Part of any alliance = 1, else = 0
Part of an alliance
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24. Data
98 airlines with 293 language
versions
Language versions
Number of websites
10
1
9
4
8
3
7
4
6
2
5
7
4
11
3
10
2
18
1
38
Total
98
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25. RESULTS
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26. Content – 93%
• Others had parts of their home pages
(images, text, or text in images) in a
different language than intended
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27. Alternate payment methods – 27%
• 73% of websites accepted only Visa or
MasterCard
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28. Bookings engine – 87%
• Most are paying attention to the
importance of language throughout the
entire customer experience
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29. Non-US address – 81% (49% did not
ask for an address)
• Widespread adoption of electronic
ticketing
• Airlines aware of diverse physical location
of users
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30. Currency converter – 26%
• Airlines do not realise the importance of
allowing users to see prices in different
currencies
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31. Emails
• 96 / 293 emails replied
• 77% (74) in the same language (74/293 =
25.3%)
• Average email turnaround time was 37
hours and 17 minutes
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32. Social media – 27%
• Chinese social media links like Weibo also
gaining traction
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33. Airline alliance – 36%
• Star Alliance - 8 airlines
• SkyTeam - 6 airlines
• OneWorld - 5 airlines
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34. GRI
Highest – 59%
Lowest – 1%
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35. CONTRIBUTION & CONCLUSION
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36. Contribution
• Language parameter given weight according
to the online usage of each particular
language
– Website with Mandarin and English versions rated
differently than one with English and Korean
versions
• Without weighting – GRI = 65%
• With language weighting – GRI = 42.1% & 20.4%
• Moving towards weighted GRI where all
parameters are weighted according to
importance
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37. Conclusions
• GRI improved by attributing weights to language –
to be done the same with other parameters
• GRI at 21% lower than previous studies
– Airline may be less globally ready than hotel and
ecommerce websites
– Weighted approach may have significantly reduced the
value
• Improvements in social media (27%) and alliances
(36%)
• Future correlation with other parameters such as
profit, sales, human index etc.
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38. Thank you
Peter O'Connor & Zaheer A. Shaik
zaheer.shaik@essec.edu
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