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This paper investigates the effect of items' physical position in the best-worst scaling technique. Although the best-worst scaling technique has been widely used in many fields, the literature has largely overlooked the phenomenon of consumers' adoption of processing strategies while making their best-worst choices. We examine this issue in the context of consumers' trust in institutions to provide information about a new food technology, nanotechnology, and its use in food processing. Our results show that approximately half of the consumers used position as a schematic cue when making choices. We find the position bias was particularly strong when consumers chose their most trustworthy institution compared to their least trustworthy institution. In light of our findings, we recommend that researchers in the field be aware of the possibility of position bias when designing best-worst scaling surveys. We also encourage researchers who have already collected best-worst data to investigate whether their data shows such heuristics.

PurposeThe aim is to explore the impact of new menu labels on consumers' actual meal purchases with a field experiment undertaken in a local restaurant.Design/methodology/approachThe author used a field experiment in a natural eating environment at a restaurant to investigate the effect of restaurant menu labelling on consumers' meal choices and opinions on the use of nutritional labels on menus. The experiment included control and treatment conditions in which we offered customers unlabelled and labelled menus, respectively. After individuals' dining experience, the data on meal choices and attitudes to menu labelling was collected via a brief questionnaire. The author then performed inferential statistical analysis to test differences between the control and treatment conditions and logistic regression analysis to explore further what predicts the probability of labels being influential on meal choice.FindingsThe study finds that the information provided to the consumers on restaurant menus matters. The more useful the information is perceived by consumers, the more likely the labels will influence their choices. Calorie content and the walking minutes to burn those calories on labels were considered the most useful aspect of the menu labels.Originality/valueThe study contributes to a better understanding of the impact of menu labelling on actual meal purchases, as well as the best way to communicate calorie and nutrient information to consumers. The author also shares her experience designing a field experiment with a restaurateur for future research.

Providing an opt-out alternative in discrete choice experiments can often be considered to be important for presenting real-life choice situations in different contexts, including health. However, insufficient attention has been given to how best to address choice behaviours relating to this opt-out alternative when modelling discrete choice experiments, particularly in health studies. The objective of this paper is to demonstrate how to account for different opt-out effects in choice models. We aim to contribute to a better understanding of how to model opt-out choices and show the consequences of addressing the effects in an incorrect fashion. We present our code written in the R statistical language so that others can explore these issues in their own data. In this practical guideline, we generate synthetic data on medication choice and use Monte Carlo simulation. We consider three different definitions for the opt-out alternative and four candidate models for each definition. We apply a frequentist-based multimodel inference approach and use performance indicators to assess the relative suitability of each candidate model in a range of settings. We show that misspecifying the opt-out effect has repercussions for marginal willingness to pay estimation and the forecasting of market shares. Our findings also suggest a number of key recommendations for DCE practitioners interested in exploring these issues. There is no unique best way to analyse data collected from discrete choice experiments. Researchers should consider several models so that the relative support for different hypotheses of opt-out effects can be explored.

Background Prioritising scarce resources for investment in innovation by publically funded health systems is unavoidable. Many healthcare systems wish to foster transparency and accountability in the decisions they make by incorporating the public in decision-making processes. This paper presents a unique conceptual approach exploring the public’s preferences for health service innovations by viewing healthcare innovations as ‘bundles’ of characteristics. This decompositional approach allows policy-makers to compare numerous competing health service innovations without repeatedly administering surveys for specific innovation choices. Methods A Discrete Choice Experiment (DCE) was used to elicit preferences. Individuals chose from presented innovation options that they believe the UK National Health Service (NHS) should invest the most in. Innovations differed according to: (i) target population; (ii) target age; (iii) implementation time; (iv) uncertainty associated with their likely effects; (v) potential health benefits; and, (vi) cost to a taxpayer. This approach fosters multidimensional decision-making, rather than imposing a single decision criterion (e.g., cost, target age) in prioritisation. Choice data was then analysed using scale-adjusted Latent Class models to investigate variability in preferences and scale and valuations amongst respondents. Results Three latent classes with considerable heterogeneity in the preferences were present. Each latent class is composed of two consumer subgroups varying in the level of certainty in their choices. All groups preferred scientifically proven innovations, those with potential health benefits that cost less. There were, however, some important differences in their preferences for innovation investment choices: Class-1 (54%) prefers innovations benefitting adults and young people and does not prefer innovations targeting people with ‘drug addiction’ and ‘obesity’. Class- 2 (34%) prefers innovations targeting ‘cancer’ patients only and has negative preferences for innovations targeting elderly, and Class-3 (12%) prefers spending on elderly and cancer patients the most. Conclusions DCE can help policy-makers incorporate public preferences for health service innovation investment choices into decision making. The findings provide useful information on the public’s valuation and acceptability of potential health service innovations. Such information can be used to guide innovation prioritisation decisions by comparing competing innovation options. The approach in this paper makes, these often implicit and opaque decisions, more transparent and explicit.

Stated preference elicitation techniques, such as discrete choice experiments and best-worst scaling, are now widely used in health research to explore the public’s choices and preferences. In this paper, we propose an alternative stated preference elicitation technique, which we refer to as ‘trio-wise’. We explain this new technique, its relative advantages, modeling framework, and how it compares to the best-worst scaling method. To better illustrate the differences and similarities, we utilize best-worst scaling Case 2, where individuals make best and worst (most and least) choices for the attribute levels that describe a single profile. We demonstrate this new preference elicitation technique using an empirical case study that explores preferences among the general public for ways to involve them in decisions concerning the health care system. Our findings show that the best-worst scaling and trio-wise preference elicitation techniques both retrieve similar preferences. However, the capability of our trio-wise method to provide additional information on the strength of rank preferences and its ability to accommodate indifferent preferences lead us to prefer it over the standard best-worst scaling technique.

Stated preference elicitation techniques, such as discrete choice experiments and best-worst scaling, are now widely used in health research to explore the public's choices and preferences. In this paper, we propose an alternative stated preference elicitation technique, which we refer to as 'trio-wise.' We explain this new technique, its relative advantages, modeling framework, and how it compares to the best-worst scaling method. To better illustrate the differences and similarities, we utilize bestworst scaling Case 2, where individuals make best and worst (most and least) choices for the attribute levels that describe a single profile. We demonstrate this new preference elicitation technique using an empirical case study that explores preferences among the general public for ways to involve them in decisions concerning the health care system. Our findings show that the best-worst scaling and trio-wise preference elicitation techniques both retrieve similar preferences. However, the capability of our triowise method to provide additional information on the strength of rank preferences and its ability to accommodate indifferent preferences lead us to prefer it over the standard best-worst scaling technique.

This paper explores consumers’ repayment decisions and their time preferences. We do this through a hypothetical study using a stated-preference approach. In our experiment, participants are asked to make repayment decisions over time, under different loan sizes. We report five choice trajectories: minimum delay, monotonically decreasing, trajectory with one contradicting choice, trajectory with more than one contradicting choice, and maximum delay. These choice trajectories are taken into account in our modelling approach. Our analysis uses choice models that jointly estimate the discount rate and the probability of choice trajectories. Observed heterogeneity in repayment behaviour is further analysed using sociodemographic factors, and tested for the two loan sizes. We report heterogeneity in consumer repayment decisions, and what happens in the decision-making process for participants in different sociodemographic groups, for different loan sizes. These findings suggest that decision-makers can tailor their strategy for mitigating consumer debts by targeting different groups in the population demonstrating different choice behaviour and decision-making.

ObjectivesWearable digital health technologies (DHTs) have the potential to improve chronic kidney disease (CKD) management through patient engagement. This study aimed to investigate and elicit preferences of individuals with CKD toward wearable DHTs designed to support self-management of their condition.MethodsUsing the results of our review of the published literature and after conducting qualitative patient interviews, five-choice attributes were identified and included in a discrete-choice experiment. The design consisted of 10-choice tasks, each comprising two hypothetical technologies and one opt-out scenario. We collected data from 113 adult patients with CKD stages 3–5 not on dialysis and analyzed their responses via a latent class model to explore preference heterogeneity.ResultsTwo patient segments were identified. In all preference segments, the most important attributes were the device appearance, format, and type of information provided. Patients within the largest preference class (70 percent) favored information provided in any format except the audio, while individuals in the other class preferred information in text format. In terms of the style of engagement with the device, both classes wanted a device that provides options rather than telling them what to do.ConclusionsOur analysis indicates that user preferences differ between patient subgroups, supporting the case for offering a different design of the device for different patients’ strata, thus moving away from a one-size-fits-all service provision. Furthermore, we showed how to leverage the information from user preferences early in the R&D process to inform and support the provision of nuanced person-centered wearable DHTs.

IntroductionWearable Digital Health Technologies (WDHTs) can support and enhance self-management by giving individuals with chronic conditions more control over their health, safety and wellbeing. Involving patients early on in the design of these technologies facilitates the development of person-centered products. It may increase the potential uptake of (and adherence to) any intervention they are designed to deliver. This research aims to elicit chronic kidney disease (CKD) patients’ preferences for WDHTs that may help patients manage their conditions.MethodsWe used discrete choice experiments (DCE) to elicit preferences for WDHTs characterized by their generalizable characteristics. The study design was informed by a multi-stage mixed-method approach (MSMMA). This included a review of the published literature, focus group interviews and one-to-one interactions with CKD patients to identify relevant characteristics (that is, attributes and levels) associated with wearable DHTs. We collected the data from 113 patients (age ≥18 years) with stage 3 or above CKD. The analysis started with a conventional multinomial logit model and was extended by investigating heterogeneity in preferences via latent class models.ResultsOur MSMMA yielded ten potential attributes for consideration in a choice task. The final list included five attributes, cross-checked and validated by the research team, and patient representatives. The most preferred attributes of WDHTs were device appearance, format and type of information provided, and mode of engagement with patients. Respondents preferred a discreet device, which offered options that individuals could choose from and provided medical information.ConclusionsWe show how to use MSMMA to elicit user preferences in (and to inform the) early stages of the development of WDHTs. Individuals with CKD preferred specific characteristics that would make them more likely to engage with the self-management support WDHT. Our results provide valuable insights that can be used to inform the development of different WDHTs for different segments of the CKD patients population, moving away from a one-size-fits-all provision and resulting in population health gains.

Background and Objectives Pregnant women living in rural areas considering their preferred place of birth may have to ‘trade-off’ travel time/distance and other attributes of care (e.g. the full choice of birthplace options is rarely available locally). This study assesses the preferences and trade-offs of recent mothers who live in remote and rural areas of Great Britain. Methods An online survey, informed by qualitative research, was administered to women living in rural areas who had given birth in the preceding 3 years. The survey included a discrete choice experiment (DCE) to elicit women’s preferences and trade-offs for place of birth. The DCE presented women with a series of eight choice tasks in which place of birth was defined by four attributes: (1) type of facility, (2) familiarity with staff, (3) understanding options and feel relaxed and reassured and (4) the travel time to the place of intrapartum care. DCE data were analysed using an error components logit model to identify preferences. Results Across 251 survey responses, holding everything else equal, respondents preferred: intrapartum care in locations with more specialist staff and equipment, locations where they understood their options and felt reassured and where travel time was minimal. Women were willing to travel (92–183 min) to a well-staffed and equipped facility if they understood their options and felt relaxed and reassured. Willingness to travel was reduced if the care received at the specialist facility was such that they did not understand their options and felt tense and powerless (41–132 min). Conclusion These insights into the preferences of recent mums from remote and rural areas could inform future planning of rural intrapartum care.