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Discrete choice experiments (DCEs) are regularly used in health economics to elicit preferences for healthcare products and programmes. There is growing recognition that DCEs can provide more than information on preferences and, in particular, they have the potential to contribute more directly to outcome measurement for use in economic evaluation. Almost uniquely, DCEs could potentially contribute to outcome measurement for use in both cost-benefit and cost-utility analysis. Within this expanding remit, our intention is to provide a resource for current practitioners as well as those considering undertaking a DCE, using DCE results in a policy/commercial context, or reviewing a DCE. We present the fundamental principles and theory underlying DCEs. To aid in undertaking and assessing the quality of DCEs, we discuss the process of carrying out a choice study and have developed a checklist covering conceptualizing the choice process, selecting attributes and levels, experimental design, questionnaire design, pilot testing, sampling and sample size, data collection, coding of data, econometric analysis, validity, interpretation and welfare and policy analysis. In this fast-moving area, a number of issues remain on the research frontier. We therefore outline potentially fruitful areas for future research associated both with DCEs in general, and with health applications specifically, paying attention to how the results of DCEs can be used in economic evaluation. We also discuss emerging research trends. We conclude that if appropriately designed, implemented, analysed and interpreted, DCEs offer several advantages in the health sector, the most important of which is that they provide rich data sources for economic evaluation and decision making, allowing investigation of many types of questions, some of which otherwise would be intractable analytically. Thus, they offer viable alternatives and complements to existing methods of valuation and preference elicitation.

The social banking market constitutes a small but rapidly growing submarket of the global banking sector. Due to an explicit commitment to sustainability, social banking is a segment of banking services which is not exclusively focused on economic performance criteria, but pursues ecological and social goal dimensions on an equal footing. Information on the number and reachability of potential social banking customers is essential for social banks to further promote sustainable consumption in finance. In scientific research, social banking is considered a relatively new field, still lacking empirical analyses regarding the market size and specific consumer behaviour. This study addresses the research gap by generating first insights into the German social banking market. Based on an online survey using an adaptive conjoint analysis, a large data set covering 3537 respondents was compiled. Sample 1 comprises 2896 respondents who are customers of three major social banks in Germany. Sample 2 covers the remaining 641 respondents who represent the German adult population and exclusively buy from conventional banks. Logistic regression modelling reveals that social banking customers differ significantly from their conventional counterparts regarding several socio-demographic, behavioural and psychographic factors. In comparison with conventional banking customers, social banking customers tend to be younger, higher educated and located in larger places of residence. Contrary to existing research on socially responsible investors, they are male to a higher proportion than female. Moreover, social banking customers demonstrate stronger sustainable buying patterns and weaker preferences for financial, but stronger preferences for social return than conventional banking customers. The results further indicate a considerable untapped growth potential for social banks by uncovering a market size ranging between 10 and 26% of the German adult population. Finally, suggestions for marketing strategies and future research are given.

The widespread use of mobile applications (apps) offers a new platform for sustaining traditional culture, yet insufficient focus on interface design has hindered user experience. This paper focuses on traditional Chinese medicine (TCM) apps, examining user preferences for interface design elements and their combinations across four dimensions: visual effects, functional attributes, layout, and interaction modes. Utilizing Conjoint Analysis Method (CAM), this study quantitatively explores user preferences for the combination schemes of 18 orthogonal designs. The study collected 464 users’ data through questionnaires and processed the data using CAM and Multivariate Analysis of Variance (MANOVA). The findings indicate that users have a high demand for theme colors (35.09%), text styles (27.32%), and icon styles (14.78%) in medical consultation apps. For health education apps, users exhibit preferences for text styles (24.95%), theme colors (24.58%), and functional architecture (16.30%). In professional learning apps, the preferred elements are theme colors (24.07%), text styles (18.54%), and icon styles (18.03%). Based on these results, the paper proposes corresponding design strategies for each type of TCM app. This study reveals the characteristics of user preferences for different categories of TCM apps, enriching the theories and methods of interaction design for similar mobile applications. Ultimately, this facilitates the integration of traditional Chinese medicine culture with modern digital technology, promoting the digital and sustainable transmission of traditional culture.

In a pre-purchase situation, consumer perception of mandarin quality is almost exclusively based on fruit appearance. Determination of consumer requirements in this regard is needed to preserve the current success of this crop in markets worldwide. In this study, the effect on consumer choice of different appearance characteristics that can occur mainly in early-season mandarins was quantified. Two independent Choice-Based Conjoint Analyses were performed to evaluate the effect of different external mandarin factors: (1) two factors linked mainly with harvesting practices: ‘Leaf’ (no leaves but sound calyx/fresh leaf/dehydrated leaf) and ‘Rind Colour’ (orange colour/rind with slightly greenish areas); (2) three factors related to postharvest handling: ‘Calyx Condition’ (sound/blackening/detached),’Waxing’ (absence/presence of wax), and ‘Rind Condition’ (dehydrated/turgid). A total of 280 consumers participated in each study. The evaluation of the factors linked with harvesting revealed four different choice patterns. Leaf presence was appreciated only by a small group of consumers (20%), and the presence of greenish areas on the rind was perceived mostly as a negative characteristic. Among the evaluated postharvest-related factors, ‘Waxing’ and ‘Calyx condition’ had the strongest effect on consumer choice. Consumers showed marked preferences for mandarins that had been waxed and presented shine and gloss. Calyx blackening and detachment had a negative effect mainly on waxed fruit, while rind dehydration more negatively affected the fruit that had not been waxed. Consumer perception of mandarin quality is importantly affected by external mandarin characteristics. The information herein reported can be very useful for the citrus industry for adapting quality control criteria that respond to consumer demands.

This study explores the potential of cross-publisher bundled offers as a strategy for increasing subscription sales in digital journalism. While innovative forms of bundling are an integral part of media distribution in music (e.g., Spotify) and film (e.g., Netflix), their adoption in digital journalism has been limited, despite research showing that bundled access to products can increase consumers’ willingness to pay, especially in younger target groups. Against this background, we conduct a choice-based conjoint analysis using data from a representative survey of the German online population (n = 1,542). Results show that bundling digital journalism has the potential to raise publisher revenues and subscription sales in digital markets. In particular, they highlight that a comprehensive, cross-publisher bundled offer, available at a fixed monthly rate, has the potential to stimulate digital journalism sales among different consumer groups in a relatively balanced way, including those who are typically more reluctant towards journalism. These findings align with the principles of information goods economics, which posit that maximising the size of digital content bundles often tends to be the most profitable distribution strategy. However, it is crucial to examine these findings in the context of the potential negative effects associated with this emerging business model in digital journalism, such as the cannibalisation of print subscriptions, diminished brand identification, and a possible imbalanced distribution of revenues.

The geological structure and stratum lithology have important roles in the seismic stability of complex slopes; however, their roles complicate engineering construction. Four three-dimensional, layered granite slope models with infinite boundaries were modeled via the finite element method. The seismic response characteristics of slopes are systematically analyzed in the time–frequency domain. A frequency-domain analysis method of complex slopes, including modal and spectrum conjoint analysis, is proposed. Modal analysis can directly display the main vibration modes of slopes. The combination of modal and spectral analysis can clarify the inherent characteristics of slopes and reveal the interaction mechanism between the inherent frequency of slopes and their dynamic characteristics. The results illustrate that structural planes have significant effects on the propagation characteristics of waves within rock masses, and complex refraction/reflection phenomena occur near these discontinuities, thus leading to different dynamic response characteristics in the slope. Layered slopes have an apparent magnification effect of slope surface and altitude. The directions of seismic excitation and structural plane types affect the dynamic response of slopes. Horizontal waves mainly affect the middle and upper parts of high-steep slopes, while vertical waves have an obvious influence on the slope crest. Additionally, Fourier spectral analysis shows that structural planes have filtering effects on high-frequency waves. Combined with modal analysis, this finding further explains that the high-frequency section of waves mainly triggers local deformation of slopes, while the low-frequency component controls their overall deformation. The instability regions and evolution process of slopes were predicted based on time–frequency conjoint analysis.

Choice-based conjoint (CBC) analysis features prominently in market research to predict consumer purchases. This study focuses on two principles that seek to enhance CBC: incentive alignment and adaptive choice-based conjoint (ACBC) analysis. While these principles have individually demonstrated their ability to improve the forecasting accuracy of CBC, no research has yet evaluated both simultaneously. The present study fills this gap by drawing on two lab and two online experiments. On the one hand, results reveal that incentive-aligned CBC and hypothetical ACBC predict comparatively well. On the other hand, ACBC offers a more efficient cost-per-information ratio in studies with a high sample size. Moreover, the newly introduced incentive-aligned ACBC achieves the best predictions but has the longest interview time. Based on our studies, we help market researchers decide whether to apply incentive alignment, ACBC, or both. Finally, we provide a tutorial to analyze ACBC datasets using open-source software (R/Stan).

A major challenge with the increasing use of Artificial Intelligence (AI) applications is to manage the long-term societal impacts of this technology. Two central concerns that have emerged in this respect are that the optimized goals behind the data processing of AI applications usually remain opaque and the energy footprint of their data processing is growing quickly. This study thus explores how much people value the transparency and environmental sustainability of AI using the example of personal AI assistants. The results from a choice-based conjoint analysis with a sample of more than 1.000 respondents from Germany indicate that people hardly care about the energy efficiency of AI; and while they do value transparency through explainable AI, this added value of an application is offset by minor costs. The findings shed light on what kinds of AI people are likely to demand and have important implications for policy and regulation.

PurposeTo minimize customer churn, many service providers offer consumers the option of automatic contract renewal at the end of a contract period. Such agreements are known as rollover service contracts (RSCs). This research quantifies the effect of RSCs and other related factors, such as incentives, on consumers' service choice decisions.Design/methodology/approachThe study adopts choice-based conjoint analysis to assess the effect of RSCs on consumers' choices and to determine whether effect size varies when selecting a cell phone network or gym/leisure club provider, which represent lower-priced utilitarian and higher-priced hedonic services.FindingsIt was found that RSCs produce negative perceptions and intended behaviors for the majority of consumers across different product types. Nevertheless, as explained by social exchange theory, many individuals may be persuaded to enter into a RSC on the basis of reciprocity if they are offered an incentive such as a price discount or free product add-on.Originality/valueIn the marketing domain, this is the first comprehensive study to quantify the role of contract type among a range of other factors in consumers' decision-making when selecting a service. The authors' results offer context-specific implications for service marketers. First, RSCs are perceived more negatively in high-priced hedonistic categories, especially among those with lower incomes. Second, price discounts are more effective than product add-ons for motivating hedonic purchases, while product add-ons work better with utilitarian services.

PurposeDecisions based on life cycle sustainability assessment (LCSA) pose a multi-criteria decision issue, as impacts on the three different sustainability dimensions have to be considered which themselves are often measured through several indicators. To support decision-making at companies, a method to interpret multi-criteria assessment and emerging trade-offs would be beneficial. This research aims at enabling decision-making within LCSA by introducing weights to the sustainability dimensions.MethodsTo derive weights, 54 decision-makers of different functions at a German automotive company were asked via limit conjoint analysis how they ranked the economic, environmental, and social performance of a vehicle component. Results were evaluated for the entire sample and by functional clusters. Additionally, sustainability respondents, i.e., respondents that dealt with sustainability in their daily business, were contrasted with non-sustainability respondents. As a last step, the impact of outliers was determined. From this analysis, practical implications for ensuring company-optimal decision-making in regard to product sustainability were derived.Results and discussionThe results showed a large spread in weighting without clear clustering. On average, all sustainability dimensions were considered almost equally important: the economic dimension tallied at 33.5%, the environmental at 35.2%, and the social at 31.2%. Results were robust as adjusting for outliers changed weights on average by less than 10%. Results by function showed low consistency within clusters hinting that weighting was more of a personal than a functional issue. Sustainability respondents weighted the social before the environmental and economic dimension while non-sustainability respondents put the economic before the other two dimensions. Provided that the results of this research could be generalized, the retrieved weighting set was seen as a good way to introduce weights into an operationalized LCSA framework as it represented the quantification of the already existing decision process. Therefore, the acceptance of this weighting set within the respective company was expected to be increased.ConclusionsIt could be shown that conjoint analysis enabled decision-making within LCSA by introducing weights to solve a multi-criteria decision issue. Furthermore, implications for practitioners could be derived to ensure company-optimal decision-making related to product sustainability. Future research should look at expanding the sample size and geographical scope as well as investigating the weighting of indicators within sustainability dimensions and the drivers that influence personal decision-making in regard to weighting sustainability dimensions.