Tourism Product Conjoint Analysis
Also known as: Travel Package Conjoint Analysis, Tourism Product Profile Analysis, Holiday Package Part-Worth Estimation, Tourism Attribute Decompositional Preference Analysis
Tourism product conjoint analysis is a decompositional preference-measurement technique that breaks travellers' overall judgments of holiday packages into the separate contributions, or part-worths, of each package attribute. Building on the conjoint framework articulated by Green and Srinivasan (1978), the method presents respondents with whole travel-package profiles, each combining levels of attributes such as price, trip duration, board basis, accommodation class and included activities, and asks them to rate or rank the packages. From these holistic evaluations it statistically recovers how much each attribute level adds to or subtracts from preference, and how important each attribute is overall. Unlike choice-based methods that model selection among alternatives, traditional ratings-based conjoint treats preference as a quantity to be decomposed, making it a natural tool for designing and optimising tourism products and bundles.
Key highlights
- Decomposes holistic, realistic package judgments into part-worths, capturing trade-offs that direct importance ratings miss.
- Yields an additive utility model that predicts preference for any attribute combination, including bundles never tested.
- Quantifies relative attribute importance, showing which features most strongly drive appeal and justify a price premium.
- Supports product design, bundling and market simulation, making it directly actionable for tourism marketing decisions.
Intuition
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How it works
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When to use it
Use tourism product conjoint analysis when you need to design or optimise a multi-attribute travel offering, a tour package, hotel product, cruise itinerary or bundled experience, and you want to know how much each feature contributes to appeal and how features trade off against one another. It is especially valuable for new-product design and bundling, price-versus-feature trade-offs, and identifying which attributes justify a premium. Ratings-based conjoint is appropriate when respondents can meaningfully evaluate whole profiles and when you want full part-worth functions rather than just choice shares; if the research question is fundamentally about choice among a few realistic alternatives, a choice-based experiment may be preferable. The method is less suitable when the number of attributes is large enough to overwhelm respondents, when hypothetical bias is a serious concern, or when the offering cannot be sensibly described as a profile of discrete attributes.
Strengths & limitations
- Decomposes holistic, realistic package judgments into part-worths, capturing trade-offs that direct importance ratings miss.
- Yields an additive utility model that predicts preference for any attribute combination, including bundles never tested.
- Quantifies relative attribute importance, showing which features most strongly drive appeal and justify a price premium.
- Supports product design, bundling and market simulation, making it directly actionable for tourism marketing decisions.
- Preference judgments are hypothetical and based on stated, not actual, behaviour, so they may overstate real demand.
- The additive part-worth model usually ignores interactions among attributes unless explicitly designed in.
- Respondent burden and fatigue rise quickly with the number of attributes and profiles, degrading data quality.
- Ratings-based conjoint measures stated preference rather than choice, so it may not reproduce real market shares as well as choice-based methods.
Common pitfalls
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Applications
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Frequently asked
How does conjoint analysis differ from just asking people how important each feature is?
Direct importance questions ask people to introspect on trade-offs they make poorly, and respondents tend to rate everything as important. Conjoint instead shows complete, realistic packages and asks only for an overall judgment, forcing implicit trade-offs. The analysis then decomposes those holistic judgments into part-worths for each feature level. Because importance is inferred from how much a feature actually swings overall preference, conjoint reveals true relative importance rather than self-reported claims.
What is a part-worth and how is it used?
A part-worth is the utility contribution of a single attribute level, for example the added appeal of full board versus room only, estimated from respondents' overall package evaluations. The model is additive, so the predicted utility of any package is the sum of the part-worths of its levels plus a constant. This lets you score packages you never tested by adding up the relevant part-worths, and to compute attribute importance from the range of an attribute's part-worths.
Should I use ratings-based conjoint or a choice-based experiment for tourism products?
Ratings-based (full-profile) conjoint, the classic Green and Srinivasan approach, gives rich part-worth functions and is excellent for product design and understanding trade-offs. Choice-based conjoint or discrete choice experiments ask respondents to choose among alternatives, which mimics real purchase decisions more closely and is often better for predicting market shares. The choice depends on whether your priority is decomposing preference for design (ratings) or modelling realistic choice and shares (choice-based).
Sources
- 1.Green, P. E., & Srinivasan, V. (1978). Conjoint Analysis in Consumer Research: Issues and Outlook. Journal of Consumer Research, 5(2), 103-123.
- 2.Huybers, T. (2003). Modelling Short-Break Holiday Destination Choices. Tourism Economics, 9(4), 389-405.
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Cite this page
ScholarGate. (2026, June 23). Tourism Product Conjoint Analysis. ScholarGate. https://scholargate.app/tourism/tourism-conjoint-analysis