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Types of Forecast and Weather-Related Information Used among Tourism
Businesses in Coastal North Carolina
Emily P. Ayscue
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Purpose Methods Analysis/Results
User Profile Discussion/Implications
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Purpose Create a NC Coastal Tourism Business user profile of
climate and weather information
Why? NWS product assessments include:-the effectiveness of terminology in public forecasts and specific geographic areas (Saviers & VanBussum 1997)-economic values of climate and weather forecasts ( Katz & Murphy 1997) -forecast use by demographic and general customer satisfaction (Claes Fornell International 2005) -how the general public receives, perceives, uses and values weather forecasts across a range of contexts (Lazo, Morss & Demuth 2009)
Still a need to investigate into the end-user needs of specific industries including tourism.
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tourism climatology: interactions and relationships of tourism and recreation with climate and weather.
These connections highlight the economic value of climate to tourism destinations
- a resource exploited by tourism, thus justifying the need to explore its opportunities and value to the tourism industry (Matzarakis & Freitas 2001).
What’s the relationship?
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Research Questions
What types of forecasts to do tourism business owners use the most and for what purposes?
What types of forecasts do tourism business owners use the least and why?
What impact does business size, business age and business type have on perceived dependency on climate and weather, forecast usefulness and forecast value?
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Study Area
(North Carolina Department of Cultural Resources 2013)
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Sample
Database of 3391 tourism businesses in 20 NC CAMA counties included:
Business name Person of contact Their position in the company Contact information US Census Bureau North American Industry Classification System
(NAICS) code
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Business Types
Climate and weather dependent coastal tourism businesses identified (Curtis et al. 2009, Gamble & Leonard 2005, Hale & Altalo 2002)
Final Categories adapted (Roehl 1998, p.63) and created: Agriculture (i.e. charter boats, a pier) Outdoor Recreation (i.e. golf clubs, campgrounds) Accommodations (i.e. cottages, inns) Food Services (i.e. chain and local restaurants, grills) Parks and Heritage (i.e. historical gardens) Other (i.e. historical gardens, state parks)
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Sampling Procedure
Database cross-referenced with Chamber of Commerce websites as well as the internet and updated accordingly
1089 businesses were contacted with 177 completed surveys = 16.3% response rate
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Business Types
Agriculture13%
Outdoor Recreation19%
Ac-com-
moda-tions23%
Food Services
30%
Parks and Heritage
8%
Other7%
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Survey Design
Online Qualtrics Survey: September 2013-January 2014
Pilot test list created with NC tourism business owners/managers located outside the study area
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Analysis
DescriptivesFactor Analysis ANOVAMANOVA
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Variables
Independent Variables: Business Type (Curtis et al. 2009, Roehl 1998, p.63) Business Size: Microbusiness, Small Business(SBA 2013) Business Age: Young, Middle-Aged, Old (Robb 2002)
Dependent Variables: Forecast Value (Murphy 1993) Forecast Usefulness (Klopper et al. 2006; Roncolli et al.
2009) Perceived Dependency on Climate and Weather
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General Descriptives of Respondents
Majority either the business owner or manager
Almost all had some level of college educationMost represented a small businessMost represented old businessesAlmost all indicated some level of dependency
on climate and weather
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Sources of Climate and Weather Information
0% 10% 20% 30% 40% 50% 60%
Smartphone
Website
Other
National Weather Service
Local News Station
None4%
One Source51%
Two Sources15%
Three Sources15%
Four Sources14% Five
Sources1%
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Types of Forecasts Used
0% 10% 20% 30% 40% 50% 60% 70% 80%
Don't Use
Hourly
Daily
Weekly
Monthly
Seasonal
Other
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How Hourly Forecasts are Used
0% 5% 10% 15% 20% 25% 30% 35%
Operational Decision Making
Risk Management
Marketing
Investment Decisions
Sustainaiblity Practices
Landscaping
Finance and Budgeting
Other
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Hourly Forecast Use by Business Type
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Non-Use of Hourly Forecasts by Business Type
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How Daily Forecasts are Used
0% 5% 10% 15% 20% 25% 30% 35% 40% 45%
Operational Decision Mak-ing
Risk Management
Marketing
Investment Decisions
Sustainaiblity Practices
Landscaping
Finance and Budgeting
Other
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Daily Forecast Use by Business Type
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Non-Use of Daily Forecasts by Business Type
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How Weekly Forecasts are Used
0 5 10 15 20 25 30 35 40 45 50
Operational Decision Mak-ing
Risk Management
Marketing
Investment Decisions
Sustainaiblity Practices
Landscaping
Finance and Budgeting
Other
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Weekly Forecast Use by Business Type
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Non-Use of Weekly Forecasts by Business Type
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How Monthly Forecasts are Used
0% 1% 2% 3% 4% 5% 6% 7% 8% 9% 10%
Operational Decision Making
Risk Management
Marketing
Investment De-cisions
Sustainaiblity Prac-tices
Landscaping
Finance and Budget-ing
Other
How Monthly Forecasts are Used
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Monthly Forecast Use by Business Type
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Non-Use of Monthly Forecasts by Business Type
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How Seasonal Forecasts are Used
0% 1% 2% 3% 4% 5% 6% 7% 8% 9%
Operational Decision Making
Risk Management
Marketing
Investment De-cisions
Sustainaiblity Prac-tices
Landscaping
Finance and Budget-ing
Other
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Seasonal Forecast Use by Business Type
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Non-Use of Seasonal Forecasts by Business Type
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How Other Forecasts are Used
0% 1% 2% 3% 4% 5% 6% 7% 8% 9%
Operational Decision Making
Risk Management
Marketing
Investment Decisions
Sustainaiblity Prac-tices
Landscaping
Finance and Budgeting
Other
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Other Forecast Use by Business Type
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Zero
One
Two
Three
Four
Five
Six
0% 10% 20% 30% 40% 50% 60%
Zero
One
Two
Three
Four
Five
Six
0% 5% 10% 15% 20% 25% 30% 35% 40%
Zero
One
Two
Three
Four
Five
Six
Seven
0% 5% 10% 15% 20% 25% 30% 35% 40%
Zero
One
Two
Three
Four
Five
Six
0%10%
20%30%
40%50%
60%70%
80%90%
100%
Zero
One
Two
Three
Four
Five
Six
0%10%
20%30%
40%50%
60%70%
80%90%
100%
Hourly Daily Weekly
Monthly Seasonal
Forecast Usefulness
Other
Zero
One
Two
Three
Four
Five
0%10%
20%30%
40%50%
60%70%
80%90%
100%
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Forecast Value
One
Two
Three
Four
Five
0% 10% 20% 30% 40% 50% 60%
Hourly
One
Two
Three
Four
Five
0% 5% 10% 15% 20% 25% 30% 35% 40% 45%
Daily
One
Two
Three
Four
Five
0% 5% 10% 15% 20% 25% 30% 35% 40%
Weekly
One
Two
Three
Four
Five
0%10%
20%30%
40%50%
60%70%
80%90%
100%
Monthly
One
Two
Three
Four
Five
0%10%
20%30%
40%50%
60%70%
80%90%
100%
Seasonal
One
Two
Three
Four
Five
0%10%
20%30%
40%50%
60%70%
80%90%
100%
Other
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Not Dependent6%
Somewhat Dependent34%
Not Sure1%
Dependent21%
Very Dependent38%
Perceived Dependency on Climate and Weather
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Factor Analysis
Short-Range Value ( = .738) Long-Range Value ( = .738)
Forecast Value
Forecast Usefulness
Short-Range Usefulness ( = .876) Long-Range Usefulness ( = .735)
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ANOVA
Accommodations (M = 3.15 + 1.5, p = .001)
Food Services (M = 2.91 + 1.3, p = .000)
Other (M = 3.08 + 1.5, p = .016)
Statistically significant difference in levels of Perceived Climate and Weather Dependency based on Business type F (5, 171) = 10.785; p < .05.
Agriculture (M = 4.52 + 1.0)
Outdoor Recreation (M = 4.55 + .80)
Accommodations (M = 3.15 + 1.5, p = .00)
Food Services (M = 2.91 + 1.3, p = .00)
Parks and Heritage (M = 3.29 + 1.5, p = .026)
Other (M = 3.08 + 1.5, p = .007)
Perceived Dependency on Climate and Weather
<
<
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MANOVA
Statistically significant difference in forecast value based on business type F (15, 466.436) = 1.996; p = .014; Wilk’s λ = .842; partial η2 = .056; power = .940.
Significant univariate main effects for business types was obtained for short-range value F (5, 171) = 4.385; p = .001; partial η2 = .114; power = .964.
Agriculture (M = 3.17 + 1.0) )Accommodations (M = 1.98 + 1.0, p = .001)
Outdoor Recreation (M = 2.79 + 1.3) Accommodations (M = 1.98 + 1.0, p = .034)
Short-Range Forecast Value
<
<
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User Profile
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Forecast usefulness partly predicted by value and relevance/importance (Ziervogel & Downing 2004) to the business.
Other factors that seem to impact forecast use include: perceived dependency on climate and weather forecast accuracy (Hartmann et al. 2002) forecast reliability lack of detail in forecasts
cloud cover predictions could help develop a likelihood scale for sunshine or cloud cover.
Marketing strategies can be long or short-term
Discussion
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Forecasts can be a barrier to business Indicated forecast unimportance may relate to:
uncertainty in climate and weather dependency Habitual use of climate and weather forecast distorting actual
extent of use Magnitude of decision being made with a forecast should be
considered Thought that alternate sources of climate and weather data
would entail wind, flora and fauna as forecast indicators (Roncolli 2009).
Technical language is a suggested barrier to forecast use (Ziervogel and Downing 2004).
Potential users may not even know a forecast is available (Ziervogel & Downing 2004)
Discussion Contd.
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Low Diversity in Business Sizes
Respondents who selected the source of National Weather Service (NWS) might have been referring to the NWS website
Did not ask respondents about the times of year in which they use different forecasts. Administering the survey at different times of the year or replicating this survey with a seasonality measure might affect respondents’ answers.
Limitations
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Partnerships between indoor and outdoor businesses in the event of adverse weather
Outreach to sectors uncertain about their climate and weather dependency through targeted resources
Forecasts outside of precipitation and temperature are also useful.
Implications
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Investigate the types of Marketing Strategies being developed
“Sustainability” is vague, what sustainable initiatives are taken related to climate and weather?
Further investigation of the relationship between Impact of climate and weather on state and federal parks
Perceived monetary value of the weather forecasts produced by private and public meteorology groups. Only done for the general public. (Lazo et al. 2009)
Qualitative research to uncover alternative environmental indicators used for forecasting
Future Directions
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THANK YOU
Dr. Scott Curtis: ChairDr. Huili Hao: Committee MemberDr. Burrell Montz: Committee Member
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Questions?