Trade-Off Analysis of Health and Wellness Tourism Destination Attributes: An Outbound U.S. Consumers' Perspective
Frederick J. DeMicco, Ali A. Poorani in Medical Travel Brand Management, 2023
This research applied conjoint methodology to determine the relative importance of the key variables and the incumbent trade-offs that consumers make while choosing a health and wellness tourism destination. Conjoint analysis is an established technique applied in research for evaluating the value proposition or the utilities of products with multiple critical factors (Kohli and Sukumar, 1990). Conjoint analysis has been used for a wide range of applications, including product design, price testing, and service development plans. The underlying benefit is the evaluation of the key attributes and their levels to tease out relationships and variable interplay that would otherwise be opaque. For example, one of the early and seminal studies that evaluated the attributes for designing a new hotel brand for a popular hotel chain applied conjoint methodology (Wind, Green, and Shifflet, 1989). In that study, the researchers evaluated 50 hotel factors with 167 levels in all ranging from building shape to lounge atmosphere. Through an empirical evaluation, the researchers developed the now very successful Courtyard by Marriott hotel brand. Similarly, researchers have identified the most preferred performing arts tourism products as perceived by tourists using conjoint techniques (Kim, Chung, Petrick, and Park, 2018; Ross, Norman, and Dorsch, 2003).
Using economics in health promotion
Robin Bunton, Gordon Macdonald in Health Promotion, 2003
In recent years, the technique of conjoint analysis, initially developed in market research, has been used by health economists to assess the relative importance of different characteristics in the way goods or services are provided. An estimate of the total utility an individual gains from a good or service with particular characteristics can also be calculated, which could be used to forecast the likely uptake if a new health promotion programme were introduced. This technique has been used to a limited extent in health promotion (van der Pol and Ryan 1996; Spoth 1989, 1991, 1992; Spoth and Redmond 1993) and there is scope for further work in this area.
Obtaining the views of the public: using conjoint analysis studies when eliciting preferences in healthcare
David Kernick in Getting Health Economics into Practice, 2018
At a more general level, conjoint analysis is a rigorous survey technique for eliciting patient/community views in healthcare. The technique has been successfully applied in healthcare, and shows great potential as an instrument for establishing patient and community preferences (as well as those of clinicians and policy makers). Important areas of future research relate to experimental design, alternative methods of data collection and analysis and investigation of the underlying axioms of economic theory. Collaborative work with psychologists and qualitative researchers will prove useful when investigating these issues.30
Using conjoint analysis to investigate hospital directors’ preference in adoption of an evidence-based intervention
Published in International Journal of Healthcare Management, 2021
Chunqing Lin, Li Li, Sung-Jae Lee, Liang Chen, Yunjiao Pan, Jihui Guan
Conjoint analysis is a popular marketing research technique that marketers use to determine how consumers make decisions and what they value in products when making a purchase [8]. The statistical technique starts with defining a product with a set of features (attributes), and each attribute can then be broken down into a number of levels. First, the customers would be presented with a series of combination of attributes and levels, and then asked to rate their preference of each combination [9,10]. The statistical analysis of respondents’ preference rating would allow researchers to quantify the value (or the impact score) of each product attributes in terms of its contribution to the customer’s decision. The method has been applied in health research to study individual acceptability of healthcare services, such as HIV testing, vaccine, and microbicides [11–13].
The effect of partner HIV status on motivation to take antiretroviral and isoniazid preventive therapies: a conjoint analysis
Published in AIDS Care, 2018
Hae-Young Kim, Colleen F. Hanrahan, David W. Dowdy, Neil Martinson, Jonathan Golub, John F P Bridges
We conducted a cross-sectional survey using a conjoint analysis to elicit patients’ motivation for IPT and ART. Conjoint analysis refers to the methods that elicit respondents’ preferences by allowing them to make choices over sets of hypothetical alternatives, where each alternative is described by several characteristics (i.e., Attributes) related to health services or goods of interest (Bridges et al., 2011; Louviere, Hensher, & Swait, 2000). It has been applied to measuring preferences for a wide range of health applications including condom use (Bridges, Selck, Gray, McIntyre, & Martinson, 2011), HIV prevention (Newman, Cameron, Roungprakhon, Tepjan, & Scarpa, 2016) and treatment (Kruk et al., 2016), and delivery services (Kruk, Paczkowski, Mbaruku, de Pinho, & Galea, 2009). The advantage of conjoint analysis is the ability to quantify patients’ preferences among different attributes thus helping to design patient-centered interventions and health services (Bridges et al., 2011).
Utilization of an online module bank for a research training curriculum: development, implementation, evolution, evaluation, and lessons learned
Published in Medical Education Online, 2019
Kara E. Sawarynski, Dwayne M. Baxa
Next, an analysis of preferred attributes was conducted by conjoint analysis. Conjoint analysis is a statistical method used in market research to determine which attributes of a product consumers value [18]. It was hypothesized that students would only select short modules to fulfill their course requirement. While shorter modules were preferred, the module duration was not the most significant factor in student module selection (Figure 4). The analysis was limited by the fact that the module library was created for educational purposes, not for the purpose of conducting a research analysis, and thus was not distributed equally among the attributes resulting in the possible 27 permutations (3 factors x 3 levels). Seventeen modules corresponding to the research question generation thread were available to students (Table 1). Of those, nine were actually selected by students resulting in a factoral, non-orthogonal post-hoc analysis. The prediction model calculated by the conjoint analysis correlated well with the data observed (Pearson’s R 0.821, p = 0.003; Kendall’s tau 0.778, p = 0.002). According to the analysis, averaged importance values credited to each factor identified duration as being of least importance (12.149), with media type (37.893) and source (49.958) being substantially more important to students. As dependence of the factors media type and source could not be discounted, it can only be said that the length of the modules was of lesser importance to students’ choice selection (Figure 4). The module combination that students most preferred was short animation style modules curated from an external source. These results were supported by the attribute preferences reported as percentages in Table 2.
Related Knowledge Centers
- Operations Research
- Potentially All Pairwise Rankings of All Possible Alternatives
- Fractional Factorial Design
- Revealed Preference
- Quantitative Marketing Research