Use Of Discrete Choice Experiments Research Articles

Patient and Public Acceptance of Digital Technologies in Health Care: Protocol for a Discrete Choice Experiment.

Strokes pose a particular challenge to the health care system. Although stroke-related mortality has declined in recent decades, the absolute number of new strokes (incidence), stroke deaths, and survivors of stroke has increased. With the increasing need of neurorehabilitation and the decreasing number of professionals, innovations are needed to ensure adequate care. Digital technologies are increasingly used to meet patients' unfilled needs during their patient journey. Patients must adhere to unfamiliar digital technologies to engage in health interventions. Therefore, the acceptance of the benefits and burdens of digital technologies in health interventions is a key factor in implementing these innovations. This study aims to describe the development of a discrete choice experiment (DCE) to weigh criteria that impact patient and public acceptance. Secondary study objectives are a benefit-burden assessment (estimation of the maximum acceptable burden of technical features and therapy-related characteristics for the patient or individual, eg, no human contact), overall comparison (assessment of the relative importance of attributes for comparing digital technologies), and adherence (identification of key attributes that influence patient adherence). The exploratory objectives include heterogeneity assessment and subgroup analysis. The methodological aims are to investigate the use of DCE. To obtain information on the criteria impacting acceptance, a DCE will be conducted including 7 attributes based on formative qualitative research. Patients with stroke (experimental group) and the general population (control group) are surveyed. The final instrument includes 6 best-best choice tasks in partial design. The experimental design is a fractional-factorial efficient Bayesian design (D-error). A conditional logit regression model and mixed logistic regression models will be used for analysis. To consider the heterogeneity of subgroups, a latent class analysis and an analysis of heteroscedasticity will be performed. The literature review, qualitative preliminary study, survey development, and pretesting were completed. Data collection and analysis will be completed in the last quarter of 2023. Our results will inform decision makers about patients' and publics' acceptance of digital technologies used in innovative interventions. The patient preference information will improve decisions regarding the development, adoption, and pricing of innovative interventions. The behavioral changes in the choice of digital intervention alternatives are observable and can therefore be statistically analyzed. They can be translated into preferences, which define the value. This study will investigate the influences on the acceptance of digital interventions and thus support decisions and future research. DERR1-10.2196/46056.

Stakeholder Engagement in Designing Attributes for a Discrete Choice Experiment With Policy Implications: An Example of 2 Swiss Studies on Healthcare Delivery

ObjectivesThe use of discrete choice experiments (DCEs) has become increasingly popular in health policy development by involving and analyzing the preferences of target groups for different aspects of the policy intervention. In this article, we aim to contribute to the standardization of the process of attribute and attribute level development for DCEs with policy relevance. To do so, we propose and empirically illustrate a framework tool for the development and reporting of attributes and attribute levels, with the systematic engagement of relevant stakeholders. MethodsThe framework tool was kept general to allow its use as a standard reporting approach on the development process of a DCE, involving relevant stakeholders at each stage. The proposed 3-stage process consists of (1) collection, (2) selection, and (3) refinement of the DCE attributes and attribute levels. The application of the framework tool is illustrated by 2 projects, Health2040 and COCONUTS—both concerning the preferences of the Swiss population for the future organization of healthcare. ResultsBy engaging stakeholders, we identified and included attributes and attribute levels that would not have been identified solely through the existing literature. In addition, including multiple stakeholders from different professional backgrounds ensured that the selected attributes and attribute levels were policy relevant, were applicable in practice, and reflected the opinions of multiple actors in healthcare. ConclusionThe proposed framework helps to strengthen the standardization of the reporting on attribute and attribute level development with stakeholder engagement and to guide future research teams designing a DCE with policy implications.

Tackling brain drain at Chinese CDCs: understanding job preferences of public health doctoral students using a discrete choice experiment survey

BackgroundGiven the demands for public health and infectious disease management skills during COVID-19, a shortage of the public health workforce, particularly with skills and competencies in epidemiology and biostatistics, has emerged at the Centers for Disease Controls (CDCs) in China. This study aims to investigate the employment preferences of doctoral students majoring in epidemiology and biostatistics, to inform policy-makers and future employers to address recruitment and retention requirements at CDCs across China.MethodsA convenience sampling approach for recruitment, and an online discrete choice experiment (DCE) survey instrument to elicit future employee profiles, and self-report of their employment and aspirational preferences during October 20 and November 12, 2020. Attributes included monthly income, employment location, housing benefits, children’s education opportunities, working environment, career promotion speed and bianzhi (formally established post).ResultsA total of 106 doctoral epidemiology and biostatistics students from 28 universities completed the online survey. Monthly income, employment location and bianzhi was of highest concern in the seven attributes measured, though all attributes were statistically significant and presented in the expected direction, demonstrating preference heterogeneity. Work environment was of least concern. For the subgroup analysis, employment located in a first-tier city was more likely to lead to a higher utility value for PhD students who were women, married, from an urban area and had a high annual family income. Unsurprisingly, when compared to single students, married students were willing to forgo more for good educational opportunities for their children. The simulation results suggest that, given our base case, increasing only monthly income from 10,000 ($ 1449.1) to 25,000 CNY ($ 3622.7) the probability of choosing the job in the third-tier city would increase from 18.1 to 53.8% (i.e., the location choice is changed).ConclusionMonthly income and employment location were the preferred attributes across the cohort, with other attributes then clearly ranked and delineated. A wider use of DCEs could inform both recruitment and retention of a public health workforce, especially for CDCs in third-tier cities where resource constraints preclude all the strategies discussed here.

Inequality in Hiring Decisions: Evaluating Recruitment Decisions with Discrete Choice Experiments

Individual characteristics, such as gender, health and nationality, have often shown to influence recruitment decisions. This study employs the use of discrete choice experiments to investigate how different individual characteristics influence hiring decisions for equally qualified job candidates. Two-hundred-and-fifty-five HRM students in The Netherlands participated in fifteen trials where they had to choose between randomly-generated applicant profiles, which differed on: gender, age, nationality, mental health and physical health status. Afterwards, open questions assessed their experiences with the decision-making process. Results show that older age and stigmatized illnesses (e.g. having recurrent depressions; being HIV- positive) constitute the largest disadvantages for hiring decisions. Participants openly confessed to age-discrimination. This study sheds new light on inequality in hiring decisions and the value of discrete choice experiments to assess this.

Age at Diagnosis and Patient Preferences for Treatment Outcomes in AML: A Discrete Choice Experiment to Explore Meaningful Benefits.

The recent expansion of treatment options in acute myeloid leukemia (AML) has necessitated a greater understanding of patient preferences for treatment benefits, about which little is known. We sought to quantify and assess heterogeneity of the preferences of AML patients for treatment outcomes. An AML-specific discrete choice experiment (DCE) was developed involving multiple stakeholders. Attributes included in the DCE were event-free survival (EFS), complete remission (CR), time in the hospital, short-term side effects, and long-term side effects. Continuously coded conditional, stratified, and latent-class logistic regressions were used to model preferences of 294 patients with AML. Most patients were white (89.4%) and in remission (95.0%). A 10% improvement in the chance of CR was the most meaningful offered benefit (P < 0.001). Patients were willing to trade up to 22 months of EFS or endure 8.7 months in the hospital or a two-step increase in long-term side effects to gain a 10% increase in chance of CR. Patients diagnosed at 60 years or older (21.6%) more strongly preferred to avoid short-term side effects (P = 0.03). Latent class analysis showed significant differences of preferences across gender and insurance status. In this national sample of mostly AML survivors, patients preferred treatments that maximized chance at remission; however, significant preference heterogeneity for outcomes was identified. Age and gender may affect patients' preferences. Survivor preferences for outcomes can inform patient-focused drug development and shared decision-making. Further studies are necessary to investigate the use of DCEs to guide treatment for individual patients.

The use of discrete choice experiments in adherence research: A new solution to an old problem.

Theory can play an important role in providing the framework, and underpinning the design and implementation of interventions to improve medication adherence. Interventions that are grounded in a theory are relatively more effective in improving medication adherence than interventions which do not have a theoretical support. However, a large body of adherence research does not appear to be linked to any theory or model, which therefore may have resulted in interventions that are either ineffective or not sustainable. Interventions that are based on theory have mainly employed socio-behavioural models to explain, and design interventions to address, the complex phenomenon of adherence. Yet, the effectiveness of these interventions is inconclusive, supporting the argument that socio-behavioural models alone have limited applicability in explaining behaviour associated with medication-taking. An important reason for this limitation may be the complex and dynamic nature of adherence. There is a need to include a wide variety of factors in a model and examine adherence in the context of its three phases (initiation, implementation, and discontinuation). One possible way forward is to also examine medication-taking behaviour from an economic perspective, for example, by using a discrete choice experiment (DCE), which provides a different approach to understanding human behaviour about medication-taking and the complexities of decision-making in adhering to medication. DCEs can help in understanding how patients decide to initiate, continue or discontinue taking medication, factors that influence their decision, and the relative importance of those factors, which can assist researchers to prioritise interventions to improve medication adherence. Integration of multiple theories is needed to examine adherence from multiple perspectives and design interventions that are effective and sustainable. This commentary focusses on the pros and cons of some of the commonly used socio-behavioural models in adherence research and suggests a way forward by incorporating DCEs in adherence research.

Can healthcare choice be predicted using stated preference data?

Lack of evidence about the external validity of Discrete Choice Experiments (DCEs)-sourced preferences inhibits greater use of DCEs in healthcare decision-making. This study examines the external validity of such preferences, unravels its determinants, and provides evidence of whether healthcare choice is predictable. We focused on influenza vaccination and used a six-step approach: i) literature study, ii) expert interviews, iii) focus groups, iv) survey including a DCE, v) field data, and vi) in-depth interviews with respondents who showed discordance between stated choices and actual healthcare utilization. Respondents without missing values in the survey and the actual healthcare utilization (377/499 = 76%) were included in the analyses. Random-utility-maximization and random-regret-minimization models were used to analyze the DCE data, whereas the in-depth interviews combined five scientific theories to explain discordance. When models took into account both scale and preference heterogeneity, real-world choices to opt for influenza vaccination were correctly predicted by DCE at an aggregate level, and 91% of choices were correctly predicted at an individual level. There was 13% (49/377) discordance between stated choices and actual healthcare utilization. In-depth interviews showed that several dimensions played a role in clarifying this discordance: attitude, social support, action of planning, barriers, and intention. Evidence was found that our DCE yields accurate actual healthcare choice predictions if at least scale and preference heterogeneity are taken into account. Analysis of discordant subjects showed that we can even do better. The DCE measures an important part of preferences by focusing on attribute tradeoffs that people make in their decision to participate in a healthcare intervention. Inhibitors may be among these attributes, but it is more likely that inhibitors have to do with exogenous factors like goals, religion, and social norms. Con-ducting upfront work on constraints/inhibitors of the focal behavior, not just what promotes the behavior, might further improve predictive ability.

Are Healthcare Choices Predictable? The Impact of Discrete Choice Experiment Designs and Models

BackgroundLack of evidence about the external validity of discrete choice experiments (DCEs) is one of the barriers that inhibit greater use of DCEs in healthcare decision making. ObjectivesTo determine whether the number of alternatives in a DCE choice task should reflect the actual decision context, and how complex the choice model needs to be to be able to predict real-world healthcare choices. MethodsSix DCEs were used, which varied in (1) medical condition (involving choices for influenza vaccination or colorectal cancer screening) and (2) the number of alternatives per choice task. For each medical condition, 1200 respondents were randomized to one of the DCE formats. The data were analyzed in a systematic way using random-utility-maximization choice processes. ResultsIrrespective of the number of alternatives per choice task, the choice for influenza vaccination and colorectal cancer screening was correctly predicted by DCE at an aggregate level, if scale and preference heterogeneity were taken into account. At an individual level, 3 alternatives per choice task and the use of a heteroskedastic error component model plus observed preference heterogeneity seemed to be most promising (correctly predicting >93% of choices). ConclusionsOur study shows that DCEs are able to predict choices—mimicking real-world decisions—if at least scale and preference heterogeneity are taken into account. Patient characteristics (eg, numeracy, decision-making style, and general attitude for and experience with the health intervention) seem to play a crucial role. Further research is needed to determine whether this result remains in other contexts.

Using discrete choice experiments to inform the design of complex interventions

BackgroundComplex health interventions must incorporate user preferences to maximize their potential effectiveness. Discrete choice experiments (DCEs) quantify the strength of user preferences and identify preference heterogeneity across users. We present the process of using a DCE to supplement conventional qualitative formative research in the design of a demand creation intervention for voluntary medical male circumcision (VMMC) to prevent HIV in Tanzania.MethodsThe VMMC intervention was designed within a 3-month formative phase. In-depth interviews (n = 30) and participatory group discussions (n = 20) sought to identify broad setting-specific barriers to and facilitators of VMMC among adult men. Qualitative results informed the DCE development, identifying the role of female partners, service providers’ attitudes and social stigma. A DCE among 325 men in Njombe and Tabora, Tanzania, subsequently measured preferences for modifiable VMMC service characteristics. The final VMMC demand creation intervention design drew jointly on the qualitative and DCE findings.ResultsWhile the qualitative research informed the community mobilization intervention, the DCE guided the specific VMMC service configuration. The significant positive utilities (u) for availability of partner counselling (u = 0.43, p < 0.01) and age-separated waiting areas (u = 0.21, p < 0.05) led to the provision of community information booths for partners and provision of age-separated waiting areas. The strong disutility of female healthcare providers (u = − 0.24, p < 0.01) led to re-training all providers on client-friendliness.ConclusionThis is, to our knowledge, the first study documenting how user preferences from DCEs can directly inform the design of a complex intervention. The use of DCEs as formative research may help increase user uptake and adherence to complex interventions.

RM3 - IS PATIENT CHOICE PREDICTABLE? THE IMPACT OF DISCRETE CHOICE EXPERIMENT DESIGNS AND MODELS

Increased use of discrete choice experiments (DCEs) in healthcare requires establishing whether stated preferences are predictive of observed healthcare utilization. This study aimed to determine whether the number of alternatives in a DCE choice task should reflect the actual decision context, and how complex the choice model needs to be to predict real-world choices correctly at an aggregate and individual level in healthcare. Two randomized controlled trials (RCTs) involving choices for influenza vaccination and colorectal cancer screening were used. Each RCT had three study conditions: DCE choice tasks with (i) two alternatives, (ii) three alternatives, or (iii) both. Two samples of 1,200 respondents each were randomly assigned to one of the conditions. Each respondent answered 16 DCE choice tasks (for the derivation of the decision model) plus a choice task mimicking the real-world choice (to keep the decision context the same). The data was analysed in a systematic way using random-utility-maximization (RUM) and random-regret-minimization (RRM) choice processes with scale and/or preference heterogeneity. Irrespective of the number of alternatives per choice task, the choice to opt for influenza vaccination and colorectal cancer screening was correctly predicted by DCE at an aggregate level, if scale and preference heterogeneity were taken into account. At an individual level, three alternatives per choice task and using heteroscedastic model plus preference heterogeneity seemed to be most promising, correctly predicting the real-world choice in 81.7% and 87.9% of the cases for influenza vaccination and colorectal cancer screening respectively. No evidence was found that RRM outperformed RUM. Our study shows that DCEs hold the potential of being externally valid at an aggregate level if at least scale and preference heterogeneity are taken into account. Further research is needed to determine if this result remains in other contexts, and to optimise choice prediction at an individual level.

Choosing between Alternative Motor Insurance Policies: A Discrete Choice Experiment

The research expounds the use of Discrete Choice Experiment (DCE) approach and Random Utility Theory (RUT) in analyzing alternative motor insurance policy choice behaviour for drivers in Sunyani municipality in Brong-Ahafo, Ghana. We design a DCE to identify the factors (premium cost, claims settlement, customer satisfaction, and proximity) that influences an individual choice for insuring with a particular insurance company given the existence of an alternative. Probit model was used in estimating the parameters of the consideration sets of the DCE. The research sampled 100 respondents comprising mainly of private and commercial drivers. The magnitude of estimates from both probit model and estimated marginal effects indicates respondents highly value insurers with the attribute of paying claims promptly, charging moderate premium, and closer in terms of proximity. However, customer satisfaction will result in a disutility of choice of insurer. Also, the levels of customer satisfaction can be traded off for other attributes.

Use of discrete choice experiments to facilitate design of effective environmentally friendly agricultural policies

Appropriate environmentally friendly agricultural technologies and practices must be implemented in China (and many other countries) to reduce farmland non-point source pollution and meet sustainability objectives. A major hindrance to their implementation is that Chinese farmers have very low willingness to accept such practices. Discrete choice experiments (DCEs) have known value for identifying goods or policies with attractive attributes, and thus exploring target groups’ choice behavior and addressing such problems. Sound questionnaire design is of paramount importance for robust results. This article describes the nature and theoretical foundations of DCEs and considers their use in diverse applications abroad, and their limited but growing applications in China. It then presents a case study, focused on the detailed design of DCEs intended to gauge paddy farmers’ willingness to accept several agriculturally friendly techniques and related compensation methods, problems encountered, and predictions for future studies.

Comment on: Patients' preferences for anti-osteoporosis drug treatment: a cross-European discrete choice experiment: reply.

_SIR,_ We are grateful to Dr Armstrong for sharing his views and concerns about our recent paper that elicited patients’ preferences for osteoporosis drug treatment using a cross-European discrete-choice experiment (DCE). First, the correspondent questioned the use of DCE in the area of osteoporosis therapy. Although the application of DCEs within healthcare is relatively new, DCEs are nowadays increasingly used to elicit preferences for various healthcare conditions and purposes. Previous DCEs were already conducted in osteoporosis, suggesting that respondents were able to discriminate between medication attributes and that a DCE could be suitable in the area of osteoporosis. Our study confirms this finding. [...]

Using Discrete Choice Experiments to Inform the Benefit-Risk Assessment of Medicines: Are We Ready Yet?

There is emerging interest in the use of discrete choice experiments as a means of quantifying the perceived balance between benefits and risks (quantitative benefit-risk assessment) of new healthcare interventions, such as medicines, under assessment by regulatory agencies. For stated preference data on benefit-risk assessment to be used in regulatory decision making, the methods to generate these data must be valid, reliable and capable of producing meaningful estimates understood by decision makers. Some reporting guidelines exist for discrete choice experiments, and for related methods such as conjoint analysis. However, existing guidelines focus on reporting standards, are general in focus and do not consider the requirements for using discrete choice experiments specifically for quantifying benefit-risk assessments in the context of regulatory decision making. This opinion piece outlines the current state of play in using discrete choice experiments for benefit-risk assessment and proposes key areas needing to be addressed to demonstrate that discrete choice experiments are an appropriate and valid stated preference elicitation method in this context. Methodological research is required to establish: how robust the results of discrete choice experiments are to formats and methods of risk communication; how information in the discrete choice experiment can be presented effectually to respondents; whose preferences should be elicited; the correct underlying utility function and analytical model; the impact of heterogeneity in preferences; and the generalisability of the results. We believe these methodological issues should be addressed, alongside developing a ‘reference case’, before agencies can safely and confidently use discrete choice experiments for quantitative benefit-risk assessment in the context of regulatory decision making for new medicines and healthcare products.

Testing on-site sampling correction in discrete choice experiments

This study uses truncated Poisson regression to correct endogeneity of discrete choice experiments (DCEs) with data from on-site sampling, which has been commonly used to collect samples in the field of tourism research. We find that goodness of fit of model is significantly improved with on-site sampling correction. Estimated parameters with onsite sampling correction also match the predictions of economic theory better. Estimated willingness to pay for attributes are substantially underestimated due to sample selection bias. These results question the use of DCEs without on-site sampling correction for tourism policy making.

Efficiency of the coordinate-exchange algorithm in constructing exact optimal discrete choice experiments

ABSTRACTThe use of discrete choice experiments (DCEs) for modeling real marketplace choices, in both fundamental and applied research, has gained much attention recently. To improve the quality of designing DCEs, most researchers have drawn on optimal design theory. Because of the nonlinearity of the probabilistic choice models, to construct a proper choice design, one needs the help of efficient search algorithms, among which the coordinate-exchange algorithm (CEA) has shown itself to work very well under the widely used multinomial logit discrete choice model. However, due to the discrete nature of the choice design, there are no computationally feasible ways to verify that the resulting design is indeed optimal or efficient. In this article, an approach of evaluating the performance of the CEA for Bayesian optimal designs is proposed. This approach gives a lower bound of the efficiency of the resulting design under the continuous/approximate optimal design framework where well-established mathematical ...

The use of discrete choice experiments to inform health workforce policy: a systematic review.

BackgroundDiscrete choice experiments have become a popular study design to study the labour market preferences of health workers. Discrete choice experiments in health, however, have been criticised for lagging behind best practice and there are specific methodological considerations for those focused on job choices. We performed a systematic review of the application of discrete choice experiments to inform health workforce policy.MethodsWe searched for discrete choice experiments that examined the labour market preferences of health workers, including doctors, nurses, allied health professionals, mid-level and community health workers. We searched Medline, Embase, Global Health, other databases and grey literature repositories with no limits on date or language and contacted 44 experts. Features of choice task and experimental design, conduct and analysis of included studies were assessed against best practice. An assessment of validity was undertaken for all studies, with a comparison of results from those with low risk of bias and a similar objective and context.ResultsTwenty-seven studies were included, with over half set in low- and middle-income countries. There were more studies published in the last four years than the previous ten years. Doctors or medical students were the most studied cadre. Studies frequently pooled results from heterogeneous subgroups or extrapolated these results to the general population. Only one third of studies included an opt-out option, despite all health workers having the option to exit the labour market. Just five studies combined results with cost data to assess the cost effectiveness of various policy options. Comparison of results from similar studies broadly showed the importance of bonus payments and postgraduate training opportunities and the unpopularity of time commitments for the uptake of rural posts.ConclusionsThis is the first systematic review of discrete choice experiments in human resources for health. We identified specific issues relating to this application of which practitioners should be aware to ensure robust results. In particular, there is a need for more defined target populations and increased synthesis with cost data. Research on a wider range of health workers and the generalisability of results would be welcome to better inform policy.Electronic supplementary materialThe online version of this article (doi:10.1186/1472-6963-14-367) contains supplementary material, which is available to authorized users.

Discrete Choice Experiments in Health Economics: A Review of the Literature

Discrete choice experiments (DCEs) are increasingly used in health economics to address a wide range of health policy-related concerns. Broadly adopting the methodology of an earlier systematic review of health-related DCEs, which covered the period 2001-2008, we report whether earlier trends continued during 2009-2012. This paper systematically reviews health-related DCEs published between 2009 and 2012, using the same database as the earlier published review (PubMed) to obtain citations, and the same range of search terms. A total of 179 health-related DCEs for 2009-2012 met the inclusion criteria for the review. We found a continuing trend towards conducting DCEs across a broader range of countries. However, the trend towards including fewer attributes was reversed, whilst the trend towards interview-based DCEs reversed because of increased computer administration. The trend towards using more flexible econometric models, including mixed logit and latent class, has also continued. Reporting of monetary values has fallen compared with earlier periods, but the proportion of studies estimating trade-offs between health outcomes and experience factors, or valuing outcomes in terms of utility scores, has increased, although use of odds ratios and probabilities has declined. The reassuring trend towards the use of more flexible and appropriate DCE designs and econometric methods has been reinforced by the increased use of qualitative methods to inform DCE processes and results. However, qualitative research methods are being used less often to inform attribute selection, which may make DCEs more susceptible to omitted variable bias if the decision framework is not known prior to the research project. The use of DCEs in healthcare continues to grow dramatically, as does the scope of applications across an expanding range of countries. There is increasing evidence that more sophisticated approaches to DCE design and analytical techniques are improving the quality of final outputs. That said, recent evidence that the use of qualitative methods to inform attribute selection has declined is of concern.

Testing a discrete choice experiment including duration to value health states for large descriptive systems: Addressing design and sampling issues

There is interest in the use of discrete choice experiments that include a duration attribute (DCETTO) to generate health utility values, but questions remain on its feasibility in large health state descriptive systems. This study examines the stability of DCETTO to estimate health utility values from the five-level EQ-5D, an instrument with depicts 3125 different health states. Between January and March 2011, we administered 120 DCETTO tasks based on the five-level EQ-5D to a total of 1799 respondents in the UK (each completed 15 DCETTO tasks on-line). We compared models across different sample sizes and different total numbers of observations. We found the DCETTO coefficients were generally consistent, with high agreement between individual ordinal preferences and aggregate cardinal values. Keeping the DCE design and the total number of observations fixed, subsamples consisting of 10 tasks per respondent with an intermediate sized sample, and 15 tasks with a smaller sample provide similar results in comparison to the whole sample model. In conclusion, we find that the DCETTO is a feasible method for developing values for larger descriptive systems such as EQ-5D-5L, and find evidence supporting important design features for future valuation studies that use the DCETTO.

Pricing plans for a financial advisory service

Purpose – 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). Design/methodology/approach – 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. Findings – 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. Originality/value – 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.