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In cooperation with a German online retail bank, the aim of this paper is to investigate how the bank should price a new fee-only financial advisory service. Two types of pricing plans differ in terms of their strategies for determining monthly prices: a fixed monthly price that is identical for all clients (i.e. a flat pricing plan) or a monthly price that varies as a function of each client's assets under management (i.e. a volume pricing plan).

With a discrete choice experiment, this article studies client preferences for the two types of plans. To ensure that the respondents understood the financial consequences of their decisions, a price calculator was embedded into the discrete choice experiment to enable the respondents to determine their individual monthly prices based on their assets under management.

Methodologically, the price calculator is useful for simplifying mathematically complex decisions, and it provides additional valuable information for analysis. Substantively, the results show that clients perceive both types of pricing plans as equally attractive; however, the service provider's revenues would increase by up to 12 per cent if it uses the volume pricing plan.

This research extends the stream of literature on the measurement of pricing plan preferences and offers guidance for service industries, such as telecommunications, cloud computing services, insurances, or transportation. It extends the use of discrete choice experiments to study client preferences for different pricing plans and also integrates a decision aid, i.e. a price calculator, in the experiment to assist clients in comparing alternatives more effectively.

Patients and health professionals often make decisions which involve a choice between discrete alternatives. This chapter reviews the econometric methods which have been developed for modelling discrete choices and their application in the health economics literature. We start by reviewing the multinomial and mixed logit models and then consider issues such as scale heterogeneity, estimation in willingness to pay space and attribute non-attendance.

This paper reviews the current literature on theoretical and methodological issues in discrete choice experiments, which have been widely used in non-market value analysis, such as elicitation of residents' attitudes toward recreation or biodiversity conservation of forests.

We review the literature, and attribute the possible biases in choice experiments to theoretical and empirical aspects. Particularly, we introduce regret minimization as an alternative to random utility theory and sheds light on incentive compatibility, status quo, attributes non-attendance, cognitive load, experimental design, survey methods, estimation strategies and other issues.

The practitioners should pay attention to many issues when carrying out choice experiments in order to avoid possible biases. Many alternatives in theoretical foundations, experimental designs, estimation strategies and even explanations should be taken into account in practice in order to obtain robust results.

The paper summarizes the recent developments in methodological and empirical issues of choice experiments and points out the pitfalls and future directions both theoretically and empirically.

The purpose of this paper is to survey climate change impacts on winter sport tourists' activity and destination choice, to estimate shifts in customer demand and to provide recommendations and decision support for destination management.

A total of 540 skiers from Vienna, Austria were surveyed with a standardized online questionnaire. The survey also contained a discrete choice experiment a stated preference method which forces respondents into trade‐off behavior between various possible combinations of destination profiles.

The results show a strong preference for destination attributes promising sufficient (natural) snow conditions. In winters that lack snow, resorts in high destinations gain importance and travel distances lose some relevance. A large proportion of skiers would forgo skiing if it becomes more expensive. Snow independent substitutes are accepted as a short time compensation but not for the whole winter holiday. When asked to trade off additional costs and additional travel distances for a snow secure destination, the majority of winter sport tourists are willing to incur some additional cost but the majority reach thresholds at about 10 percent additional cost and 2h additional driving.

The survey shows, that a discrete choice experiment is a suitable method to cover the complexity of activity and destination choice. Therefore it is an unique individual‐oriented approach to consider customer demand and to evaluate the success of offer setting in tourism management. The sequential presentation of three related choice sets is a novel contribution in the field of choice experiments, and appears to be well suited to simulate climate change‐related effects.

Operatic events are an important sector of the performing arts industry and are currently facing the challenges of decreasing demand and price-based competition from other sectors of the performing arts industry. It is posited that adding value and ensuring satisfaction may enhance consumer loyalty, and therefore, the likelihood of sales and continued subscriptions may be increased. The purpose of this paper is to examine bundling as a marketing management technique for opera companies and hypothesises that offering attractive “package deals” that bundle various benefits with the seat ticket may increase participation and loyalty.

A discrete choice experiment with opera patrons is used to evaluate the application of bundling to improve increased demand and loyalty.

It is concluded that offering bundles creates a greater likelihood of customer satisfaction and favourable behavioural intentions, which may lead to increased loyalty. Participants expressed strong support for value-added elements. Such package deals included a variety of elements: opportunity for a visit backstage to meet the cast, informative introductions to operas and facilitated parking options. Consumers’ level of overall willingness to purchase a bundle was altered based on the attributes that made up that bundle. The findings of this research confirm that the theory of bundling can be applied to arts marketing and provides support for the development of bundling strategies to enhance opera attendance.

It is recognised that the research needs to be tested in different countries in order to know the extent to which the findings of this study can be generalised. Additionally, future research could use other statistical methods such as regression and structural equation modelling to holistically model behaviour. Finally, as well as testing customer-stated intentions, the model also needs to be tested with actual patronage behaviour following the development and application of bundling strategies. Future research could also consider how bundling and other aspects relating to opera attendance could be used as part of the branding strategy associated with opera attendance; in particular how to develop, increase and maintain loyalty and therefore brand resonance in opera attendees.

The findings have useful implications for event organisers and policymakers and suggest bundling strategies that could be utilised. It is has been found that loyalty can be enhanced by adding value and ensuring satisfaction, and therefore, increase the likelihood of sales and repeat purchase.

Opera represents a significant cultural heritage and is a valuable component of the performing arts, both historically and currently. Opera is a form of art whose survival is threatened by an increasingly diminished audience whose average age is steadily increasing. This decrease in audience attendance has led to radical changes in the management and marketing of opera houses, where theaters have moved increasingly towards a business-oriented model where improved branding and bundling strategies can be utilised.

This makes a theoretical contribution by advancing performing arts research and furthering the notion that bundling can increase the likelihood of opera attendance, satisfaction, ongoing loyalty, and also addresses a managerial need of an arts marketing organisation.

The purpose of this paper is to introduce the concept of robust design in retail service literature.

Using a Taguchi design comprising of inner L₈ (2⁷) and outer 2² arrays, a discrete choice task was designed for 233 students respond to. Signal‐to‐noise (S/N) ratio was used to test design robustness.

Negative effects of uncontrollable design factors on service choice were minimized through the use of inner and outer Taguchi arrays. The single composite measure of consumer choice called S/N ratio accounted both for mean and variance of choice probabilities.

Use of student sample was a major limitation. Also, the interaction between design factors was not tested as it required the use of more complicated designs.

This method can be used to improve design robustness by minimizing the impact of uncontrollable noise factors. Use of S/N ratio can help to select the design that simultaneously maximized the choice probabilities and minimized performance variation.

The paper makes important methodological and operational contributions to the retail service design literature. First, the concept of controllable and uncontrollable factors in choice‐based designs is introduced. Second, the use of S/N ratio as a single, composite measure of design robustness was incorporated. Operationally, this study highlights the impact of less‐studied concepts of wayfinding and customer incompatibility on satisfaction of retail customers.

This article presents the results of a study using discrete choice analysis (DCA) in the dine‐in pizza industry. DCA offers an effective approach for incorporating customer preferences into operating decisions in service businesses. Our results show how customers tradeoff among several determinant attributes (e.g. price, waiting time, quality) when choosing a dine‐in pizza restaurant. The article also offers evidence that managers’ perceptions of customer choice patterns are not the same as customers’ actual choice patterns for the businesses we examined. Finally, we show how our results can be easily incorporated into a decision support system for structuring service operations according to customer preferences.

The purpose of this research is to test how varying the numbers of attributes and alternatives affects the use of heuristics and selective information processing in discrete choice experiments (DCEs). The effects of visual attribute and alternative non-attendance (NA) on respondent choices are analyzed.

Two laboratory experiments that combined eye tracking and DCEs were conducted with 109 and 117 participants in the USA. The DCEs varied in task complexity by the number of product attributes and alternatives.

Results suggest that participants ignore both single attributes and entire alternatives. Increasing the number of alternatives significantly increased attribute NA. Including NA in choice modeling influenced results more in more complex DCEs.

The current experiments did not test for choice overload. Future studies could investigate more complex designs. The choice environment affects decision-making. Future research could compare laboratory and field experiments.

Private and public sectors often use DCEs to determine consumer preference. Results suggest that DCEs with two alternatives are superior to DCEs with four alternatives because NA was lower in the two-alternative design.

This empirical research examined effects of attribute and alternative NA on choice modeling using eye tracking and DCEs with varying degrees of task complexity. Results suggest that accounting for NA reduces the risk of over- or understating the impact of attributes on choice, in that one avoids claiming significance for attributes that might not truly be preferred, and vice versa.

Questionnaire measures of consumers’ willingness to pay (WTP) and price sensitivity are biased, yet these declarative methods can aid managerial decision-making. Additional choices involve which question formats to use (open-ended or discrete choice) and how many questions (unique versus multiple). This paper aims to inform such choices for online data collection with an empirical evaluation of the size of the bias induced by four methods (price acceptability, price judgements, multiple discrete choices and single discrete choices) in a realistic choice context.

An experimental framework collects online data about a staple product whose price should be well known. Price sensitivity, WTP and their confidence intervals are derived from a logistic binary model of acceptability, then ranked to evaluate the size of the bias of each method, relative to an indirect benchmark.

Online data collections with self-administrated questionnaires lower respondents’ involvement and create substantial bias; hypothetical methods overestimate WTP and underestimate price sensitivity, especially with methods using unique questions (both discrete choice and price acceptability). Multiple questions (price judgements and repeated random discrete choices) increase attention to price information and reduce the bias. The round price effect also is notable in data collected by open-ended methods.

To measure declarative WTP and price sensitivity with online data collections, researchers should use a random discrete choices method. Price acceptability questions and split tests are not recommended. Price judgements provide reliable information about consumer reactions to prices, but the strong round price bias is problematic.

This study adds to marketing and economic literature by comparing actual measurement methods used by firms, rather than hypothetical versions, and offers strong external validity.

Using a discrete choice experiment (DCE), this study aims to better understand the job preference of postgraduate students studying at the Kwame Nkrumah University of Science and Technology-Institute of Distance Learning, Ghana and also rank the attributes of a job they deem important.

The research adopted a positivist epistemological design contextualised within a deductive approach and case study strategy. Primary survey data was collected from a stratified random sample of 128 postgraduate students with multi-sectorial career prospects. Sample students were subjected to a DCE in which their stated preferences were collected using closed-ended questionnaires with 28 pairs of hypothetical job profiles. Respondents’ preferences from the DCE data were then modelled using the conditional logit.

The research reveals that: salary in the range GHC 2,800.00 to GHC 3,400.00 ($1 = GHS 5.3); supportive management; very challenging jobs; and jobs located in the city were the top attributes that were significant and had the most impact in increasing the utility of selecting a particular job. Interestingly, jobs with no extra hours workload were not significant hence, had a negative impact upon student preferences.

This novel research is the first to use a DCE to better elicit preference and trade-offs of postgraduate students in a developing country towards varying job characteristics that have an impact on their future employment decisions. Knowledge advancements made provide invaluable insight to employers and policymakers on the key criteria that should be implemented to retain the best candidate.