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Digital Divide in Tourism

Sifiso Shongwe

School of Hotel and Tourism ManagementHong Kong Polytechnic University, Hong Kong

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Agenda

• Introduction– Problem

– Previous research

• Method

• Evaluation results

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Introduction

• Tourism: International arrivals (UNWTO)– 2012 exceeded the 1 billionth mark– To grow at 3.3% between 2010 and 2030– To grow faster in emerging economies (4.4%)– By 2030:

• To reach 1.8 billion• 57% (over 1 billion) expected in emeging economies

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Introduction

• Digital Divide (ITU). By end of 2013:– 2.7 billion people (39% of world population) on

line– Internet access limited in emerging economies

• 77% of population in developed countries online• 33% of Population in developing countries online

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Problem

– Tourism growth to be fastest in emerging economies (Good)

– internet access limitation in emerging economies (potential bottleneck)

– Lack of quantitative eof how the digital divide relates to tourism destinations.

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Introduction

• Previous research: Digital Divide in Tourism (Mingetti and Buhalis 2010)– High Digital access tourists most likely to book

holidays directly with establishments with high digital access in high digital access destinations

– High digital access destinations mostly found in developed regions.

– Destinations with high digital access more competitive than those with low digital access

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Introduction

• Previous research:– The likelihood of direct online booking reduces

with the reducing digital access level of the tourist and the destination.

– The lower the destination digital access level the higher the total cost of travel due to reliance on intermediearies.

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Research objective

• To test the validity of destination digital access hypothesis, not the digital access tourists.

• To establish an appropriate destination digital access evaluation tool.

• To follow robust quantitative techniques to arrive at conclusions.

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Method

• Evaluation tool:– An evaluation form derived from the modified

balanced score card website evaluation tool.• Subjects for evaluation:

– Websites of National Tourism Organisations (NTO) of UNWTO member states.

• Source of Subjects– UNWTO website, members states section.

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Evaluation tool

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Method

• Evaluation (Primary data)– Member states were rearranged alphabetically

to diffuse the geographical grouping– Each website accessed was scored against the

presence or absence of items listed on the dichotomous website evaluation (1 /0)

– The 1’s were added to record a total score for that particular website.

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Method

• Secondary Data– 2002 ITU Digital Access Index– 2004 UNCTD Digital Access Index– 2004 UNCTD Digital Connectivity Index– 2004 UNCTD Digital Diffusion Index– 2011 UNWTO Annual report– 2012 UNDP Human Development Index

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Method

• Data Coding– Countries were allocated unique numerical

codes for consistency of reference between the different sources of sec0ndary data

– Categorical variables were coded 1-4 according to the country level of development category

• 1 = Low Digital Access / Low Human development• 4 = High digital Access / Very High Human

Develipment

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Method

• Chi-squarer tests to on website accessibility against country’s development category

• Data description conducted on primary data• Normality tests conducted on primary data• Normality tests repeated without outliers.• Hypotheses tested (Oneway ANOVA/T-tests)• Correlation tests and regression analyses.

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Evaluation Results

Table 1. Case Summary

Total High Digital Access

Upper Digital Access

Medium Digital Access

Low Digital Access

N/A

All NTO cases 255 24 52 87 82 10 Evaluated 135 12 35 54 30 4 Not Evaluated 120 12 17 33 52 6

Case not evaluated Total Cases 120 24 17 33 52 6 Not in English 53 8 10 19 14 2 Not Accessible 43 2 1 11 25 4 Other reasons 24 2 6 3 13 0

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Results

Histogram of d$Total

Website Scores

Fre

qu

en

cy

10 20 30 40

01

02

03

04

0

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Box Plot

10

15

20

25

30

35

40

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Normality Test

-2 -1 0 1 2

1015

2025

3035

40

Normal Q-Q Plot

Theoretical Quantiles

Sam

ple

Qua

ntile

s

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Histogram without outliers

Histogram of website scores

Website Scores

Fre

quen

cy

15 20 25 30 35 40 45

010

2030

40

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Box Plot without outliers

15

20

25

30

35

40

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Normality test without outliers

-2 -1 0 1 2

1520

2530

3540

Normal Q-Q Plot

Theoretical Quantiles

Sam

ple

Qua

ntile

s

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Correlation

Correlation Matrix

2012 NTOs Website

Total scores

2012 UNDP Human

Development Index

2002 UN-ITU Digital

Access Index

2004 UNCTD Digital

Access Index

2004 UNCTD Digital

Connectivity Index

2004 UNCTD Digital

Diffusion Index

NTO Website scores 1.00 0.25 0.22 0.21 0.25 0.25 2012 HDI 0.25 1.00 0.69 0.85 0.77 0.83 ITU DAI 0.22 0.69 1.00 0.68 0.64 0.67 UNCTD DAI 0.21 0.85 0.68 1.00 0.87 0.94 UNCTD DCI 0.25 0.77 0.64 0.87 1.00 0.98 UNCTD DDI 0.25 0.83 0.67 0.94 0.98 1.00

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Relative weights

X2012_HDI ITU_DAI_Index UNCTD_DAI UNCTD_DCI UNCTD_DDI

Relative Importance of Predictor Variables

R-Square= 0.094Predictor Variables

% o

f R-S

qu

are

05

10

15

20

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High digital access destinations in well developed regions

Table 2. Digital Access destinations by Human Development Index classification

Total High Digital Access

Upper Digital Access

Medium Digital Access

Low Digital Access

N/A

All Countries 205 25 40 59 55 27 ITU DAI 178 25 40 59 55 0 UNDP HDI 187 24 39 59 55 10

UNDP HDI Categories Very High 47 24 21 0 0 2 High 47 0 18 26 1 2 Medium 47 0 0 33 11 3 Low 46 0 0 0 43 3 None 0 1 1 0 0 0

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Results continued

• High digital access destinations are at the forefront of web applications development.– The Welch test results were:

• F Statistic = 4.962107; p-value = 0.004828367. • The p-value is less than 0.05.

– Each group mean was significantly higher or lower than the next group.

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• High digital access destinations are at the forefront of web application in terms of design, content and production skills. – The Welch test (ANOVA) results for only the

site attractiveness factor were: • F Statistic = 2.056409; p-value = 0.1188848. • The p-value is greater than 0.05.

– The group means are not significantly different.

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• Upper-digital-access destinations have good access to the web. – Out of the total of 43 NTO inaccessible website

cases:• One was from upper digital access category; • 11 were from the medium Digital Access (26%), • 25 were from low digital access category (58%).

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• Upper digital access destinations have less ICT usage than high digital access destinations (UNCTD DCI)– The Welch test (T-Test) results:

• F = 67.898, P-value = 0.0000000039374. • p-value less than 0.05, the difference is significant.

– Upper digital access destinations have significantly less connectivity than high digital access destinations.

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• Low digital access destinations’ websites not used directly to market destinations.– The Welch test (One way ANOVA) results for

only the marketing information factor were: • F Statistic = 3.929009; p-value = 0.01506642. The p-

value is less than 0.05. – Low digital access destinations are at the

trailing end of web marketing

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• Does the digital divide affect the competitiveness of tourism destinations through their website quality?

• Do High digital access destinations have better quality websites than low digital destinations?

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• The Welch test (T-Test) results were: – Total (F = 13.07, p = 0.0012)– ease of navigation (F = 9.04, p = 0.0047)– ease of contact (F = 4.06; p = 0.056)– site attractiveness (F = 2.32, p = 0.137)– marketing information (F = 11.24, p = 0.003)– trip planner assistance (F = 1.62; P = 0.221).

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Conclusion

• Emerging economies have limited access to the internet.– Most websites that could not be accesses were

from the low digital access destinations• Developed countries websites are of better

quality and functionality than websites from developing countries.

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Recommendations

• Websites can be hosted in areas with good internet access.

• International Hotel chains can host the websites in their corporate head office with good internet access

• Strategic alliances can be arranged between DMOs in developing countries to host their websites in developed areas.