Analysing International City Tourism

3211094156......Page 1
Title page\r......Page 3
Copyright  Page\r......Page 4
Table of Contents\r......Page 5
Introduction......Page 7
City Tourism Management\rand Trends......Page 9
1.2 \rIntroduction......Page 10
1.3  Services and functions of city tourism organizations......Page 11
1.4  A longitudinal study of European city tourism offices’ functions and services......Page 14
1.5  Comparing city tourism offices’ responsibilities between 1995 and 1999......Page 15
1.5.1  Structure of functions provided by European city tourism offices......Page 18
1.5.2 \rStructure of changed functions......Page 23
1.5.3  Nature of the changes in functions provided by European city tourism offices......Page 24
1.6 \rSummary and conclusions......Page 27
2.2 \rIntroduction......Page 30
2.3 \rSources and users of tourism statistics......Page 31
2.4 \rCity tourism statistics......Page 33
2.5  Methods for measuring city tourism statistics......Page 36
2.6.1  Availability of European city tourism statistics......Page 40
2.6.2  Comparability of European city tourism statistics......Page 42
2.7 \rConclusion......Page 44
3.2 \rIntroduction......Page 47
3.3 \rQuantifying European city tourism......Page 48
3.3.1 \rEuropean city tourism compared to overalltourism – the status quo......Page 49
3.4 \rForecasting European City Tourism......Page 51
3.4.1 \rAssessing Forecast Accuracy......Page 53
3.4.2 \rNaïve Forecasting......Page 54
3.4.3 \rSingle Moving Average (SMA)......Page 55
3.4.4 \rExponential smoothing......Page 56
3.4.5 \rAutoRegressive Integrated Moving Average(ARIMA)......Page 57
3.4.6 \rForecasts of European urban tourismcompared to tourism overall......Page 59
3.4.7 \rEffect of economic crisis on tourism forecasts......Page 60
3.5 \rConclusions......Page 61
4.2 \rIntroduction......Page 63
4.3  A conceptual framework of tourism seasonality......Page 64
4.4 \rMeasuring and analysing seasonality......Page 67
4.5.1  Measuring and benchmarking the amplitude of seasonality......Page 69
4.5.2 \rBenchmarking seasonal demand patterns......Page 73
4.6 \rConclusions......Page 78
Monitoring Demand\rand Competition......Page 82
5.2 \rHeterogeneity in terms of what?......Page 83
5.3 \rAnalytical tools and techniques......Page 85
5.4.1 \rStudy objectives and data......Page 89
5.4.2 \rResults......Page 90
5.5 \rComparison and method bias......Page 94
5.6.1 \rTypology based on the DTRN solution......Page 96
5.6.2 \rTypology based on the TRN solution......Page 98
5.7.1 \rComparison based on demographic and travelcharacteristics......Page 100
5.7.2 \rRecommendation......Page 106
6.2 \rConsumer information search behaviour......Page 109
6.2.1 \rOnline search theory......Page 110
6.3  Examining the relationship between online information search and city tourism......Page 112
6.4.1  The demand for Stockholm on the ECM web portal......Page 115
6.4.2  Market-specific enquiries on Salzburg’s tourist office website......Page 117
6.4.3 \rBarcelona on ‘Google Trends’......Page 119
6.5 \rConclusions......Page 122
7.1 \rIntroduction......Page 127
7.2 \rDestination image measurement......Page 129
7.3  PBMS-based destination image measurement......Page 132
7.4.1 \rData......Page 135
7.4.2 \rResults......Page 138
7.5 \rConclusions, limitations and future work......Page 143
8.2 \rThe city database......Page 147
8.3 \rThe missing value problem......Page 153
8.4 \rClustering guest mix profiles......Page 155
8.5 \rLongitudinal analysis of guest mixprofiles......Page 163
8.6 \rConclusions......Page 168
9.2 \rIntroduction......Page 170
9.3.1 \rThe destination competitiveness analysis:A review of the literature......Page 172
9.4  Towards a competitiveness model for cities to play: The methodology\r......Page 174
9.4.1 \rThe survey instrument......Page 175
9.4.2  The factors measured and the main features of the model......Page 176
9.4.3 \rThe set of summary indicators......Page 180
9.5.1 \rThe ‘ultimate‘ cities......Page 182
9.5.3 \rThe ‘new old’ city......Page 184
9.5.4 \rThe ‘young trendy’ cities......Page 185
9.6 \rDiscussion and conclusions......Page 186
10.2  Tourist cities on the Internet and the fuzzy concept of destination image......Page 190
10.3  Analytical tools for data capturing and similarity processing......Page 192
10.4.1  How European cities position themselves on the Internet......Page 194
10.4.2 \rRetrieving Google search engine data......Page 196
10.4.3 \rConnotative space maps......Page 202
10.5 \rPrecautions and conclusions......Page 206
Tourists’ Assessment\rof Typical City Offerings......Page 210
11.2 \rIntroduction......Page 211
11.3  Tourism studies about the city of Heidelberg......Page 212
11.4 \rHeidelberg as a tourism destination......Page 213
11.5  General trends for incoming tourism in Heidelberg......Page 215
11.6 \rVisitor activities......Page 218
11.7 \rTourist mobility in Heidelberg......Page 221
11.8 \rConclusions......Page 222
12.2 \rLiterature review......Page 225
12.3 \rConceptual model and empirical test......Page 228
12.4.1 Generating hypotheses with Inferred Causation tools......Page 231
12.4.2  Testing the revised Authenticity Model with the hold-out sample......Page 233
12.4.3  One step toward generalisation: Another attraction in another city......Page 234
12.5 \rConclusions......Page 236
Biographies......Page 240
Subject Index......Page 244