Evernden, Nichola

Parker, Pete, Dr.

Sliskovic, Laurel

Vaugeois, Nicole L., Dr.

English

ScholarWorks@UMass Amherst

University of Massachusetts AmherstScholarWorks@UMass AmherstTravel and Tourism Research Association:Advancing Tourism Research Globally 2015 ttra International ConferenceDesigning systems for community based visitordata: A collaborative case study on VancouverIslandNicole L. Vaugeois Dr.Vancouver Island UniversityPete Parker Dr.Vancouver Island UniversityNichola EverndenThe Sociable ScientistsLaurel SliskovicThe Sociable ScientistsFollow this and additional works at: https://scholarworks.umass.edu/ttraThis Event is brought to you for free and open access by ScholarWorks@UMass Amherst. It has been accepted for inclusion in Travel and TourismResearch Association: Advancing Tourism Research Globally by an authorized administrator of ScholarWorks@UMass Amherst. For moreinformation, please contact scholarworks@library.umass.edu.Vaugeois, Nicole L. Dr.; Parker, Pete Dr.; Evernden, Nichola; and Sliskovic, Laurel, "Designing systems for community based visitordata: A collaborative case study on Vancouver Island" (2015). Travel and Tourism Research Association: Advancing Tourism ResearchGlobally. 12.https://scholarworks.umass.edu/ttra/ttra2015/Academic_Papers_Visual/12Extended Abstract: Title: Designing systems for community based visitor data: A collaborative case study on Vancouver Island The context Vancouver Island, British Columbia relies heavily on tourism as a core contributor to the economy.  The region provides visitors with adventure, culinary and agri-tourism experiences in both terrestrial and marine settings.  Despite the fact that the last provincial visitor research was undertaken in British Columbia in 1995, the Vancouver Island region has been proactive in researching and profiling its visitors. In 2003 a region wide visitor research model provided tourism stakeholders with comprehensive data on visitors in all four seasons, and again in 2008, a region wide study provided updated information on visitors.  While useful, these efforts have relied on significant funding from external sources and are difficult to maintain on an ongoing basis.   Many of the communities in the region are small and at early stages of tourism development. Budgets to invest in tourism marketing are often limited and unstable, and capacity to undertake market research among any staff members is weak.  These communities have limited access to models that enable them to understand their own visitors and therefore they rely on outdated information from prior regional visitor studies or macro level data sources from urban areas or provincial and national sources.  This data is unlikely to represent the profile of visitors and if used to guide decisions, could result in risky and ineffective investments for the communities. In an effort to address these systemic issues and enhance the availability of marketing intelligence, researchers piloted a visitor experience study with the City of Nanaimo and the community of Tofino in 2013.  After a successful pilot, the model was refined and expanded in 2014 to include the community of Ucluelet and the Cowichan Valley.   Literature review The literature on visitor experience has expanded rapidly in recent years (Sharpley and Stone, 2010; Lugosi and Walls, 2013). Tourism researchers have made tremendous contributions to understand the nature of the visitor experience including tourist satisfaction (Alegre and Garau, 2010; Rucks and Geissler, 2011; Zabkar et al, 2010), segmentation (Andriotis and Agiomirgianakis, 2008; Arimond et al, 2003) and spatial analysis (Edwards and Griffin, 2013).  Additionally, the focus on methodological advancements in this research has also expanded the ability of researcher to analyze and interpret visitor data (Stradling et al, 2007; Priporas and Vassiliadis, 2013).   Many of the studies used to produce this data are conducted as single investigations on visitors with the intent of enhancing academic knowledge about visitor experience.  While useful, there remains a need to ensure that those involved in destination marketing can access visitor research to improve their investment decisions (Liburd, 2011).  In particular, enhanced knowledge about visitor origin, satisfaction, behavior patterns, spending and planning behaviors enable informed investments in tourism marketing. There is a need for tourism researchers to design models to gather visitor data at the community level on an ongoing basis that result in enhanced marketing intelligence for stakeholders.  The purpose of this study was to design and pilot such a model with communities on Vancouver Island, British Columbia. The methodological contribution This paper contributes to current methodology in tourism research by describing a model whereby academic researchers and destination marketing bodies collaborate on studying visitor research (Fyall et al, 2012). The innovative design of the model incorporates the participation of businesses, the use of pre-consent ballots, and a post-experience on-line visitor survey.  The emphasis on co-creating the study with community based partners and the format used in knowledge mobilization also addresses criticisms in the literature (Hudson, 2013). It also aligns with and capitalizes on many of the notions expressed in Liburd’s (2011) Tourism research 2.0, particularly challenging notions of ownership, quality control, and dissemination of tourism knowledge.  And finally, it validates the idea of modernizing methods to gather data in innovative ways (Carson, 2008). The methodology The model was developed in a collaborative manner utilizing the resources and capacities of researchers at a local University, a private research firm, destination marketing organizations and tourism businesses.  The survey instrument was designed by the research team with input on community specific response options. When finalized, it was hosted on Survey Monkey and used to solicit responses from the sample. Data was gathered on visitor motivations, travel planning behavior and sources, overall experience, satisfaction, group composition, spending and suggested enhancements.  The sample was obtained through ballot locations placed at 10-12 locations within the community. Destination marketing organizations selected a range of businesses and attractions within their community where visitors were likely to frequent while on vacation.  These included coffee shops, special events, retail outlets, attractions, visitor information centers and accommodators.  Each business hosted a ballot box explaining the study and inviting participation at a later date via email. In exchange, visitors were able to enter their name in to win a series of prizes determined by the destination which included both experiences and tangible products.   These ballots were gathered bi-weekly by the destination and sent to the researchers who then sent a request to participate in the study to the emails provided.  Data were later analyzed in SPSS by the University partner and results were shared in an 8-10 page user friendly visitor profile for the community. Results were also shared publicly in a stakeholder event. Key findings The model was successfully piloted over a two year period due to: a) active engagement of the partners, b) a sense of ownership of the results, c) affordability and value for the community marketing organizations, d) immediate reporting of results in a user friendly and accessible format, e) opportunities to compare results with other communities, and f) the perception of credibility due to involvement of the University partner.  The model was successful in gathering data from visitors in each of the locations with sample sizes ranging from 350 to 700 completed surveys and response rates of 49-53%.   Destination marketing organizations were provided the first community specific data sets on visitors to enable them to understand their current visitors. Feedback from the destination marketing organizations expressed strong support for the model based on comparing the value of the information with the financial investment required and the ease of application for their staff.  The initial communities continued the use of the model to gather data on visitors in a different season, and they actively recruited additional communities to adopt the model in the second and subsequent seasons. While the survey instrument used between communities remained fairly consistent, the profiles of the visitors that emerged were often quite different. This observation validated the importance of having micro level or community specific data for tourism development. The model continues to grow in application to additional communities with the intention of moving to other regions of the province. Additionally, the model is planned to be used in a pilot with the BC Ocean Boating Tourism Association to provide sector specific data on visitors in collaboration with marinas along the BC coast. While successful, the partners have actively sought ways to refine the model.  At post season sharing sessions, enhancements to the model have been shared and include: 1) training of the businesses where ballot boxes are placed, 2) the importance of professionally designed and small ballot boxes and posters, 3) selecting both tangible and intangible prizes (i.e. return visits) to elicit participation, and 4) fine tuning the locations of ballot boxes to the highest yield locations such as retail and coffee shop locations where diverse visitor groups are likely to frequent.  Contributions to applied tourism This model has tremendous potential to contribute to applied tourism in contexts outside of BC and is scalable to understand sector groups, events, regions, and tourism routes.  If shared, this model also presents opportunities to modernize research methodology in visitor research to provide communities and regions with accessible, affordable and localized data sets and to enhance research capacity and knowledge sharing between tourism researchers and destination marketing organizations.   References Alegre, J., & Garau, J. (2010). Tourist satisfaction and dissatisfaction. Annals of Tourism Research, 37(1), 52-73.  Andriotis, K., & Agiomirgianakis, G. (2008). Measuring tourist satisfaction: A factor-cluster segmentation approach. Journal of Vacation Marketing, 14(3), 221-235.  Arimond, G., Achenreiner, G., & Elfessi, A. (2003). An innovative approach to tourism market segmentation research: An applied study. Journal of Hospitality & Leisure Marketing, 10(3-4), 25-56.  Carson, D. (2008). The "blogoshere" as a market research tool for tourism destinations: A case study of Australia's northern territory. Journal of Vacation Marketing, 14(2), 111-119.  Crouch, G. I. (2000). Services research in destination marketing: A retrospective and prospective appraisal. International Journal of Hospitality & Tourism Administration, 1(2), 65-86.  Deborah Edwards, & Tony Griffin. (2013). Understanding tourists' spatial behaviour: GPS tracking as an aid to sustainable destination management. Journal of Sustainable Tourism, 21(4), 580.  Fyall, A., Garrod, B., & Wang, Y. (2012). Destination collaboration: A critical review of theoretical approaches to a multi-dimensional phenomenon. Journal of Destination Marketing & Management, 1(1–2), 10-26.  Hudson, S. (2013). Knowledge exchange: A destination perspective. Journal of Destination Marketing & Management, 2(3), 129-131.  Liburd, J. J. (2011). Tourism research 2.0. Annals of Tourism Research, 39(2), 883.  Lugosi, P., & Walls, A. R. (2013). Researching destination experiences: Themes, perspectives and challenges. Journal of Destination Marketing & Management, 2(2), 51-58.  Priporas, C., & Vassiliadis, C. A. (2013). An analysis of visitor behaviour using time blocks: A study of ski destinations in Greece. Tourism Management, 34, 61-70.  Rucks, C. T., & Geissler, G. L. (2011). The overall theme park experience: A visitor satisfaction tracking study. Journal of Vacation Marketing, 17(2), 127-138.  Sharpley, R., & Stone, P. R. (2010). Tourist experience: Contemporary perspectives. Hoboken: Taylor & Francis Ltd. Stradling, S. G., Anable, J., & Carreno, M. (2007). Performance, importance and user disgruntlement: A six-step method for measuring satisfaction with travel modes. Transportation Research Part A, 41(1), 98-106.  

Designing systems for community based visitor data: A collaborative case study on Vancouver Island

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Designing systems for community based visitor data: A collaborative case study on Vancouver Island

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