Author Bios (50 Words for each Author)

Danuta de Grosbois, PhD, is an Associate Professor in the Department of Geography and Tourism Studies at Brock University in Canada. Her areas of research interest include performance evaluation in tourism, sustainability reporting in the hospitality and tourism industry and carbon footprint assessment and disclosure.

Abstract (150 Words)

Performance evaluation of tourism destinations is critical to destination competitiveness, success and ability to generate economic benefits for local populations. This paper proposes a two-stage model of tourism destination production process, which during the first stage uses available resources to generate visits to the destination and during the second stage converts the visits into financial results. The model is used to evaluate efficiency of tourism regions in Ontario, Canada, in 2016 and 2017. The efficiency scores are derived using a two-stage network Data Envelopment Analysis (DEA) approach. Findings show that the proposed approach allows to identify variability of efficiency scores across the two stages, analyze spatial distribution of scores and identify trends over time. Four distinct groups of tourism regions are identified with respect to their efficiency patterns. Study findings contribute to the conceptual literature on destination performance and can be used by practitioners to design performance evaluation systems for destinations.

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Evaluating performance of Ontario tourism regions using a two-stage network Data Envelopment Analysis approach

Performance evaluation of tourism destinations is critical to destination competitiveness, success and ability to generate economic benefits for local populations. This paper proposes a two-stage model of tourism destination production process, which during the first stage uses available resources to generate visits to the destination and during the second stage converts the visits into financial results. The model is used to evaluate efficiency of tourism regions in Ontario, Canada, in 2016 and 2017. The efficiency scores are derived using a two-stage network Data Envelopment Analysis (DEA) approach. Findings show that the proposed approach allows to identify variability of efficiency scores across the two stages, analyze spatial distribution of scores and identify trends over time. Four distinct groups of tourism regions are identified with respect to their efficiency patterns. Study findings contribute to the conceptual literature on destination performance and can be used by practitioners to design performance evaluation systems for destinations.