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Assortative Matching of Tourists and Destinations: Agents or Algorithms?

Ralf Buckley and Mary-Ann Cooper
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Ralf Buckley: International Centre for Ecotourism Research, Griffith University, Gold Coast 4222, Australia
Mary-Ann Cooper: Instituto Profesional de la Fundacion Duoc UC de la Pontificia Universidad Católica, 2336 Viña del Mar, Chile

Sustainability, 2021, vol. 13, issue 4, 1-10

Abstract: We propose that assortative matching, a well-established paradigm in other industry sectors and academic disciplines, can underpin the concept of destination matching. This provides a new foundation to integrate research concepts and terminology in destination marketing and destination choice. We argue that the commercial tourism industry already applies destination matching approaches, with three historical phases. Initially, matching of tourists and destinations relied on the tacit expertise of specialist agents. This still applies in specialist subsectors. For generalist travel and accommodation, human agents were partially replaced by online travel agents, OTAs, which are customised algorithms operating only in the travel sector. These still exist, but their share price trends suggest decreasing significance. Currently, automated assortative algorithms use multiple sources of digital data to push appealing offers to potential purchasers, across all retail sectors. Digital marketing strategies for tourism products, enterprises, and destinations are now just one category of generalised product–purchaser matching, using entirely automated algorithms. Researchers do not have access to proprietary algorithms, but we can identify which components they incorporate by analysing their underlying patents. We propose that theories of destination marketing and choice need to reflect these recent and rapid real-world changes via deliberate analysis of destination matching.

Keywords: marketing; choice; brand; image; congruence; assortative; fashion (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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