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Modelling tourism and hospitality employment clusters: a spatial econometric approach
Authors:Anjali Chhetri  Prem Chhetri  Colin Arrowsmith  Jonathan Corcoran
Affiliation:1. School of Mathematical and Geospatial Sciences, RMIT University, Melbourne, AustraliaAnjali.chhetri@rmit.edu.au;3. School of Business IT and Logistics, RMIT University, Melbourne, Australia;4. School of Geography, Planning and Environmental Management, The University of Queensland, Brisbane, Australia;5. University of Queensland Ringgold Standard Institution – School of Geography, Planning and Environmental Management, The University of Queensland, Brisbane, Australia
Abstract:ABSTRACT

Geographic clustering of industries for mutual economic benefit has long been recognised. The concept of ‘externalities’ introduced by Alfred Marshall early last century rely on agglomeration of specialised industry within a geographic area. However, only recently has cluster modelling been applied to the tourism and hospitality (T&H) industry. The aim of this paper, therefore, is twofold: first is to develop a cluster-based theoretical framework for delineating geographic boundaries of T&H clusters, and second is to identify the underlying factors that drive their form and shape.

Drawing on employment data as the basis for co-location of T&H firms, spatial econometrics techniques are applied to model the spatial clustering of T&H employment in Victoria, Australia. Results show that rural tourism regions have higher levels of employment in tourism operational services whereas employment in city-based regions is more concentrated in hospitality services. Our findings, when normalised as a percentage of total employment, show that rural and regional Victoria ranks most highly as employers in the T&H industry. Adopting a range of spatial metrics, we show that T&H clustering throughout Victoria is largely driven by six location-specific factors: (1) the availability of tourism attractions; (2) proximity to the coast; (3) the road density network; (4) accessibility to employment within the Melbourne CBD; (5) the scale of the regional economy; and, (6) the advantages and disadvantages associated with economic resources. We conclude that the cluster-led strategy pose a number of challenges for tourism planners to promote regional tourism. Nevertheless, results from this study indicate that T&H employment clustering creates a more cohesive spatial structure that could support economic development and better connectedness of tourism destinations. These clusters could act as service hubs to their wider catchment areas where visitors are encouraged to stay overnight and travel to specific sights during the day.
Keywords:Spatial cluster  tourism and hospitality sector  employment  GIS  spatial autocorrelation
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