Place marketing buyer behaviour: a model of Australian convention sites

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Choice experiments in health: The good, the bad, the ugly and toward a brighter future

Compared to many applied areas of economics, health economics has a strong tradition in eliciting and using stated preferences (SP) in policy analysis. Discrete choice experiments (DCEs) are one SP method increasingly used in this area. Literature on DCEs in health and more generally has grown rapidly since the mid-1990s. Applications of DCEs in health have come a long way, but to date few have been 'best practice', in part because 'best practice' has been somewhat of a moving target. The purpose of this paper is to briefly survey the history of DCEs and the state of current knowledge, identify and discuss knowledge gaps, and suggest potentially fruitful areas for future research to fill such gaps with the aim of moving the application of DCEs in health economics closer to best practice. © Cambridge University Press 2009

Estimating preferences for a dermatology consultation using Best-Worst Scaling: Comparison of various methods of analysis

Background: Additional insights into patient preferences can be gained by supplementing discrete choice experiments with best-worst choice tasks. However, there are no empirical studies illustrating the relative advantages of the various methods of analysis within a random utility framework. Methods: Multinomial and weighted least squares regression models were estimated for a discrete choice experiment. The discrete choice experiment incorporated a best-worst study and was conducted in a UK NHS dermatology context. Waiting time, expertise of doctor, convenience of attending and perceived thoroughness of care were varied across 16 hypothetical appointments. Sample level preferences were estimated for all models and differences between patient subgroups were investigated using ovariateadjusted multinomial logistic regression. Results: A high level of agreement was observed between results from the paired model (which is theoretically consistent with the 'maxdiff' choice model) and the marginal model (which is only an approximation to it). Adjusting for covariates showed that patients who felt particularly affected by their skin condition during the previous week displayed extreme preference for short/no waiting time and were less concerned about other aspects of the appointment. Higher levels of educational attainment were associated with larger differences in utility between the levels of all attributes, although the attributes per use had the same impact upon choices as those with lower levels of attainment. The study also demonstrated the high levels of agreement between summary analyses using weighted least squares and estimates from multinomial models. Conclusion: Robust policy-relevant information on preferences can be obtained from discrete choice experiments incorporating best-worst questions with relatively small sample sizes. The separation of the effects due to attribute impact from the position of levels on the latent utility scale is not possible using traditional discrete choice experiments. This separation is important because health policies to change the levels of attributes in health care may be very different from those aiming to change the attribute impact per se. The good approximation of summary analyses to the multinomial model is a useful finding, because weighted least squares choice totals give better insights into the choice model and promote greater familiarity with the preference data

Keep it simple: Easy ways to estimate choice models for single consumers

We show with Monte-Carlo simulations and empirical choice data sets that we can quickly and simply refine choice model estimates for individuals based on methods such as ordinary least squares regression and weighted least squares regression to produce well-behaved insample and out-of-sample predictions of choices. We use well-known regression methods to estimate choice models, which should allow many more researchers to estimate choice models and be confident that they are unlikely to make serious mistakes

Plain Packaging, Pictorial Warnings and Tobacco Products: An Empirical Assessment

Although tobacco marketing is restricted, tobacco product packaging continues to communicate brand imagery, thereby maintaining brand salience and potentially reducing the impact of health warnings. This study used respondent conditioning theory to predict how disruption of brand imagery would affect the attractiveness of known, unknown and generic tobacco packages. A best-worst study found that familiar branding offset the negative connotations created by a pictorial warning label (PWL). These are the first findings to document the combined effect of PWLs and plain packaging; they suggest generic packs will stimulate cessation attempts and deter smoking initiation