Spatial Yield (2007-2012)
Project Overview
Full Title: Enhancing the Financial Economic and Sustainable Yield from Tourism
Project Leader: Professor David Simmons
New Report: Summary on Yield-Relevant Tourism Decision Making Report - view here. For the full report scroll to bottom of page.
Who are the ‘high yielding’ visitors to New Zealand? An answer to this question is central to the New Zealand Tourism Strategy. To date a research contribution from the NZ Tourism Strategy implementation fund has enabled considerable advancement of sector benchmarks which will result in the development of tools for enhancing tourism yield (see past project: Yield Reports).
During the course of this research, which has focussed largely on supply (business) dimensions, crucial new research questions have emerged. The hypothesis that now requires testing is that an integrated demand side perspective, as manifest in tourist expenditure and activity patterns (itineraries), allows additional, effective growth of yield per visitor.
The proposed research is able to draw on previous and current research investments. The aim of the proposed programme is to integrate earlier research into yield and spatial behaviour, along with refined and disaggregated measures of tourist activity, time and expenditure budgets, and then simulate tourist decision making via agent based modelling.
Lincoln University’s tourism research and education centre has won a contract from the Foundation for Research Science Technology (FRST), to carry forward research into tourism yield by examining the travel expenditure and activity patterns of tourists.
The “spatial yield” programme aims to improve the financial yield per visitor by developing a model for identifying the spending patterns of various types of tourists and itineraries, so that new tourism products and interventions can be developed.
Programme Summary Statement (from FRST application)
Yield growth is a key goal for the New Zealand Tourism Strategy and is FRST’s priority goal for its investment in tourism research. Improving public and private sector engagement in tourism and increasing Māori relevant research and research capacity are secondary goals. This programme addresses all of these.
The research will examine growing tourism yield from a demand side perspective. We hypothesise that it is possible to identify which tourists (and their itineraries) generate different yield outcomes. This knowledge will enable product, policy and marketing interventions with the potential to grow yield per visitor. We will achieve this by developing refined and disaggregated measures of tourist activity, time and expenditure budgets; integrating these outputs from a supply side analysis of tourism yields, and then simulating tourist decision making via agent based modelling for scenario analyses.
This work will inform a new sector model of tourist decision making which will allow interactive exploration of high yield tourism outcomes. Model outputs can include tourist types, itinerary types and policy, planning and product development interventions. While applicable at the national (itinerary) level, the model will also be tested in two regional contexts, where its outcomes can contribute to regional and Māori socio-economic development.
The outcome of this research will be growth in yield per visitor through policy and planning shifts among lead tourism public and private sector agencies. The Ministry of Tourism and Local Government New Zealand will use the model to develop policies for national and local tourism planning and management. The Tourism Industry Association NZ will use the model to identify high yielding segments and products for the tourism industry and communicate ways to develop these to its members. The output model will be used by Tourism NZ to inform the future marketing and promotion of high-yield tourist itineraries, including country of origin metrics, and high yield product developments.
The research team has extensive expertise in researching: tourism systems and planning (Simmons), yield, modelling and information systems (Smallman, Doscher, Forer) and tourist behaviour (Moore, Becken). The programme builds New Zealand’s tourism research skills base through involvement of a new researcher (Smallman) and the new computational and modelling skills he brings with him. Two PhDs will be completed in the programme and significant opportunities have been created for Māori research capacity development.
The research leaders already work closely with national and regional stakeholders and their current supply focused yield research is informing a review of the national tourism strategy. The project will be overseen by an advisory group comprising representatives from the Ministry of Tourism, Tourism Association NZ, the Department of Conservation, Local Government NZ, Destination Fiordland, the New Zealand Māori Tourism Council and Ngai Tahu Tourism. Tourism NZ has signalled its willingness to engage with researchers once bid outcomes are known.
Advisory Board members have agreed to annual workshops and detailed inputs into shaping the analytical pathways at key stages (tourist types, tourist decision ‘rules’, and scenario analyses) in the research processes. These relationships and structures underscore the pathways to optimise research uptake and implementation.
A special characteristic of our programme comprises a contribution to Māori tourism development underpinned by a carefully prepared programme of Māori research capacity building via the University’s relationship with Ngai Tahu.
Programme Structure
The research programme is arranged in three Objectives: (refer diagram 1: app 1)
1. Determine current patterns of tourist itineraries, and analyse these along spatial and temporal dimensions to identify opportunities for yield enhancement (e.g. Information provision, destination sequencing, product and itinerary gap analyses).
Report Date: August 2008.
A report on itinerary and tourist yield prototypes, including gap analysis for identification of improvements in yield metrics (opportunities for product development, information distribution, and cost recoveries) presented to the sector policy, management and marketing agencies.
This component of the programme is assessed as low risk because data already collected and computational pathways already piloted. Supply side yield metrics have recently been determined (Moriarty et al, 2007).
2. Develop in-depth understanding of tourist decision making processes, with emphases on understanding itinerary, activity and expenditure choices.
Report Date: June 2009.
A report on tourist decision making sequence, process and identification of major decision making constraints in itinerary formation (for efficiencies in marketing and information service provision) made available to regional marketing, management and policy agencies, and through the TIA to business proprietors.
For this component there is risk associated with primary data collection, specifically concerning the willingness of visitors to engage and the accuracy of their recall of their daily activity and expenditures. It would possible to sample visitors once visitor types are known, but this brings with it the additional risk of creating a methodological tautology –i.e. sampling within visitor types to ultimately determine (similar) visitor types. Consequently, we do not favour this approach. However, risk is mitigated through the research team’s considerable experience in work of this nature. We are confident of formulating approaches based on interviews along itinerary interventions which will ensure the required level of accuracy.
3. Develop an agent-based simulation of tourist decision making Model
Date: November, 2010. Verified simulation of tourist decision-making model.
This is new science with relatively high risk. The modeling of intelligent agents (tourists) during highly discretionary activities (travel patterns and activities) is the most challenging component of the programme in that it seeks to take behavioural models and apply them to highly discretionary tourist behaviour. Risks introduced by this approach will be managed through ongoing involvement of the advisory board and development and release of pragmatic outcomes as model development progresses.
Project Management
The programme will be led by Prof Simmons, who will also lead Obj 3. and sector communication. Obj.1 will be led by Assoc Prof Becken and Obj. 2 will be led by Dr. Moore. The development of the agent based model (Obj 3) will be a collective exercise between researchers and stakeholders and will led by Prof Simmons with the assistance of Prof Smallman who has recently brought to New Zealand considerable expertise in project management and systems modelling.