Exploring visitor movement patterns in natural recreational areas

Orellana, Daniel; Bregt, A.K.; Ligtenberg, A.; Wachowicz, Mónica

doi:10.1016/j.tourman.2011.07.010

Cited by 150 publications

(97 citation statements)

References 18 publications

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“…These technologies facilitate the collection of different types of, and more accurate, CB data. For instance, empirical material can be collected and analysed through data mining techniques to examine tourists' spatial and temporal activities (Chang & Caneday, 2011;Orellana et al, 2012;Shoval & Isaacson, 2007).…”

Section: Technologymentioning

confidence: 99%

Consumer behaviour in tourism: Concepts, influences and opportunities

Cohen

Prayag

Moital

2013

Current Issues in Tourism

554

333

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Although consumer behaviour (CB) is one of the most researched areas in the field of tourism, few extensive reviews of the body of knowledge in this area exist. This review article examines what we argue are the key concepts, external influences and opportune research contexts in contemporary tourism CB research. Using a narrative review, we examine the CB literature published in three major tourism journals from 2000 to 2012. Of 519 articles identified and reviewed, 191 are included in this article. We examine the development of and scope for future research on nine key concepts, including decision-making, values, motivations, self-concept and personality, expectations, attitudes, perceptions, satisfaction, trust and loyalty. We then examine three important external influences on tourism behaviour, technology, Generation Y and the rise in concern over ethical consumption. Finally, we identify and discuss five research contexts that represent major areas for future scholarship: group and joint decision-making, under-researched segments, cross-cultural issues in emerging markets, emotions and consumer misbehaviour. Our examination of key research gaps is concluded by arguing that the hedonic and affective aspects of CB research in tourism must be brought to bear on the wider CB and marketing literature.Keywords: consumption; travel; behaviour; marketing; research agenda Introduction Consumer behaviour (CB) involves certain decisions, activities, ideas or experiences that satisfy consumer needs and wants (Solomon, 1996). It is 'concerned with all activities directly involved in obtaining, consuming and disposing of products and services, including the decision processes that precede and follow these actions' (Engel, Blackwell, & Miniard, 1995, p. 4). CB remains one of the most researched areas in the marketing and tourism fields, with the terms 'travel behaviour' or 'tourist behaviour' typically used to describe this area of inquiry. Few comprehensive reviews of the literature on CB concepts and models exist in the field of tourism. Exceptions include Moutinho (1993) who reviews the social and psychological influences on individual travel behaviour with the aim of developing a model of tourist behaviour and Dimanche and Havitz (1995) Tourism, 2014 Vol. 17, No. 10, 872 -909, http://dx.doi.org/10.1080/13683500.2013 four concepts (ego-involvement, loyalty and commitment, family decision-making and novelty-seeking) in an attempt to methodologically advance CB in tourism studies.A lack of comprehensive reviews is not only due to the extensive breadth of the topic area itself, but also because travel behaviour is generally considered as a continuous process that includes varied yet inter-correlated stages and concepts that cannot always be analysed separately (Mill & Morrison, 2002). Tourism researchers have reviewed individual concepts (e.g. Riley, Niininen, Szivas, & Willis, 2001, on loyalty), specific influences (e.g. Moutinho, 1993, on social influences on CB), and particular research contexts (e.g. Hong, Lee, L...

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Section: Technologymentioning

confidence: 99%

Consumer behaviour in tourism: Concepts, influences and opportunities

Cohen

Prayag

Moital

2013

Current Issues in Tourism

554

333

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“…Combining research with individual respondents, such as that done by Orellana et al (2012) and Dias et al (2008), with group analysis methods such as those introduced by Laube and Purves (2006), could improve representativeness, and will also provide insight into group dynamics.…”

Section: Hiker Movementsmentioning

confidence: 99%

“…Hadwen et al, 2007;Lyon et al, 2011;Wimpey & Marion, 2011). An area's recreational quality could be negatively influenced by conflicts in recreational behaviour (Ligtenberg et al, 2008;Orellana, 2012). Therefore, park managers not only need information on ecological and environmental values, but also require spatio-temporal data on visitor flows.…”

Section: Introductionmentioning

confidence: 99%

“…Analysing tourist behaviour -such as places visited, time spent and facility use -can help managers adapt infrastructure and facilities to offer more diverse options to different visitor groups (Dye & Shaw, 2007;Holyoak & Carson, 2009;Wolf et al, 2012) or to route visitors to a range of park locations to avoid overcrowding and to achieve greater matching of visitor and interest (O'Connor et al, 2005;Lyon et al, 2011). Moreover, spatial visitor flow information could be used to define ecological zones and facilitate recreation routing to avoid ecological carrying capacity overload in natural areas (Freuler & Hunziker, 2007;Lyon et al, 2011;Orellana et al, 2012). …”

Section: Introductionmentioning

confidence: 99%

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Analysing hiker movement patterns using GPS data: Implications for park management

Meijles

Bakker

Groote

et al. 2014

Computers, Environment and Urban Systems

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Analysing hiker movement patterns using GPS data: Implications for park management Meijles, E.; de Bakker, M.; Groote, P.; Barske, R. Take-down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons the number of authors shown on this cover page is limited to 10 maximum. AbstractIn natural areas, park management organisations need to cater for economic, environmental, recreation and social demands and values. However, multifunctional land use also creates conflicts. Increased numbers of people using an area could exceed its ecological carrying capacity. The recreational quality of areas could be negatively impacted by conflicts in recreational behaviour. Therefore, park managers require spatio-temporal data on visitor flows, but there appears to be a shortage of suitable visitor data. If there are data available, these often do not deliver the information required by managers and there is little guidance on appropriate monitoring variables. This paper therefore combines user movement analysis with environmental and ecological factors for natural resource management. Through a case study we describe the entire working process from field data acquisition to usable park management information. GPS and itinerary data from 138 visitors to the Drents-Friese Wold National Park (the Netherlands) were collected to estimate visitor densities and distribution patterns within the park. Data acquisition is efficient in the working process, but careful error handling is a time consuming but necessary part of it. We introduce the definition of 'hard' and 'soft' errors to make spatial analysis more flexible. We show that walking speed, trip time and spatial distributions varied between park visitor groups. Classification and Regression Tree (CART) analysis showed that factors such as the presence of marked trails, distance to facilities but not land use (such as forest or arable land) explained visitor distributions. Patterns differ between respondent groups based on group size and composition, which is also true for itinerary variables such as walking speed and trip time. The combination of high resolution location data with itinerary information from respondents provides a good impression of the different walking preferences of different respondent groups. We conclude therefore that combining GPS data with itinerary information is a useful tool in profiling different natural park visitors. This is useful information for park managers in steering tourists and in catering for different visitor demands in natural parks.

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“…The collection of such methods that can be used to discover (non-trivial) patterns and knowledge from large data sets is called data mining (Fayyad, Piatetsky-Shapiro, & Smyth, 1996). Several data mining techniques have already been frequently applied to tourism data, including regression techniques (Song & Li, 2008;Witt & Witt, 1995), clustering (Bloom, 2005;Cini, Leone, & Passafaro, 2010;Dolničar & Leisch, 2003;Dolničar, 2004;Tchetchik et al, 2009), sequential pattern mining (Orellana, Bregt, Ligtenberg, & Wachowicz, 2012;Shoval & Isaacson, 2007a), and classification (Law & Au, 2000;Law, Bauer, Weber, & Tse, 2006). Association rule learning is concerned with discovering associations between variables without fixing the output variable, as is the case in classification.…”

Section: Introductionmentioning

confidence: 99%