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Digital technology in financial services is helping consumers gain wider access to investment funds, acquire these funds at lower costs, and customize their own investments. However, direct digital access also creates new challenges because consumers may make suboptimal investment decisions. We address the challenge that consumers often face complex investment decisions involving multiple funds. Normative optimal asset allocation theory prescribes that investors should simultaneously optimize risk–returns over their entire portfolio. We propose two behavioral effects (mental separation and correlation neglect) that prevent consumers from doing so and a new choice architecture of virtually integrating investment funds that can help overcome these effects. Results from three experiments, using general population samples, provide support for the predicted behavioral effects and the beneficial impact of virtual integration. We find that consumers’ behavioral biases are not overcome by financial literacy, which further underlines the marketing relevance of this research.

Financial advisors seek to accurately measure individuals’ risk preferences and provide sound personalized investment advice. Both advice tasks are increasingly offered through automated online technologies. Little is known, however, about what drives individuals’ acceptance of such automated financial advice and, from a consumer point of view, which firms may be best positioned to provide such advice. We generate novel insights on these questions by conducting a real-world empirical study using an interactive automated online tool that employs an innovative computer algorithm to build pension investment profiles, the “Pension Builder,” and a large, representative sample. We focus on the role that two key firm characteristics have on consumer acceptance of pension investment advice generated by computer algorithms running on automated interactive online tools: profit orientation and role in the sales channel. We find that consumers’ perceptions of trust and expertise of the firm providing the automated advice are important drivers of advice acceptance (besides a strong impact of the satisfaction with the consumer–online tool interaction), and that these constructs themselves are clearly influenced by the for-profit vs. not-for-profit orientation and the product provider vs. advisor only role in the sales channel of the firm providing the advice. We discuss the implications of our findings for marketers and policy makers and provide suggestions for future research.

Objective. Quantitatively summarize patient preferences for European licensed relapsing-remitting multiple sclerosis (RRMS) disease-modifying treatment (DMT) options. Methods. To identify and summarize the most important RRMS DMT characteristics, a literature review, exploratory physician interviews, patient focus groups, and confirmatory physician interviews were conducted in Germany, the United Kingdom, and the Netherlands. A discrete choice experiment (DCE) was developed and executed to measure patient preferences for the most important DMT characteristics. The resulting DCE data ( n=799 and n=363 respondents in the United Kingdom and Germany, respectively) were analyzed using Bayesian mixed logit models. The estimated individual-level patient preferences were subsequently summarized using 3 additional analyses: the quality of the choice data was assessed using individual-level R2estimates, individual-level preferences for the available DMTs were aggregated into DMT-specific preference shares, and a principal component analysis was performed to explain the patients’ choice process. Results. DMT usage differed between RRMS patients in Germany and the United Kingdom but aggregate patient preferences were similar. Across countries, 42% of all patients preferred oral medications, 38% infusions, 16% injections, and 4% no DMT. The most often preferred DMT was natalizumab (26%) and oral DMT cladribine tablets (22%). The least often preferred were mitoxantrone and the beta-interferon injections (1%–3%). Patient preferences were strongly correlated with patients’ MS disease duration and DMT experience, and differences in patient preferences could be summarized using 8 principle components that together explain 99% of the variation in patients’ DMT preferences. Conclusion. This study summarizes patient preferences for the included DMTs, facilitates shared decision making along the dimensions that are relevant to RRMS patients, and introduces methods in the medical DCE literature that are ideally suited to summarize the impact of DMT introductions in preexisting treatment landscapes.

© 2019 ISPOR–The Professional Society for Health Economics and Outcomes Research Background: Lack 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. Objectives: 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 be able to predict real-world healthcare choices. Methods: Six 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. Results: Irrespective 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). Conclusions: Our 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.

This multi-method study aims to shed light on digital platforms' decisions regarding their openness. Platform openness results from a series of decisions on how open a platform is regarding: (a) suppliers, (b) customers, (c) complementary service providers, as well as to (d) product categories and (e) channels. By conducting a scoping literature review, we analyze the current body of knowledge about the drivers, dimensions and outcomes of platform openness. Using an expert panel discussion and analysis of real-world digital platforms, we confront this existing knowledge with current business challenges to identify research challenges. We address how future research can advance platform research by tackling these challenges.

Background and ObjectiveNon-participation in colorectal cancer (CRC) screening needs to be decreased to achieve its full potential as a public health strategy. To facilitate successful implementation of CRC screening towards unscreened individuals, this study aimed to quantify the impact of screening and individual characteristics on non-participation in CRC screening.MethodsAn online discrete choice experiment partly based on qualitative research was used among 406 representatives of the Dutch general population aged 55–75 years. In the discrete choice experiment, respondents were offered a series of choices between CRC screening scenarios that differed on five characteristics: effectiveness of the faecal immunochemical screening test, risk of a false-negative outcome, test frequency, waiting time for faecal immunochemical screening test results and waiting time for a colonoscopy follow-up test. The discrete choice experiment data were analysed in a systematic manner using random-utility-maximisation choice processes with scale and/or preference heterogeneity (based on 15 individual characteristics) and/or random intercepts.ResultsScreening characteristics proved to influence non-participation in CRC screening (21.7–28.0% non-participation rate), but an individual’s characteristics had an even higher impact on CRC screening non-participation (8.4–75.5% non-participation rate); particularly the individual’s attitude towards CRC screening followed by whether the individual had participated in a cancer screening programme before, the decision style of the individual and the educational level of the individual. Our findings provided a high degree of confidence in the internal–external validity.ConclusionsThis study showed that although screening characteristics proved to influence non-participation in CRC screening, a respondent’s characteristics had a much higher impact on CRC screening non-participation. Policy makers and physicians can use our study insights to improve and tailor their communication plans regarding (CRC) screening for unscreened individuals.

Consumers are widely adopting Artificially Intelligent Voice Assistants (AIVAs). AIVAs now handle many different everyday tasks and are also increasingly assisting consumers with purchasing decisions, making AIVAs a rich topic for marketing researchers. We develop a series of propositions regarding how consumer decision-making processes may change when moved from traditional online purchase environments to AI-powered voice-based dialogs, in the hopes of encouraging further academic thinking and research in this rapidly developing, high impact area of consumer-firm interaction. We also provide suggestions for marketing managers and policymakers on points to pay attention to when they respond to the proposed effects of AIVAs on consumer decisions.

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.

Charitable organizations send out large volumes of direct mailings, soliciting for money in support of many good causes. Without any request, donations are rarely made, and it is well known that each request for money by a charity likely generates at least some revenues. Whether a single request from a charity increases the total amount donated by an individual is however unknown. Indeed, a response to one request can hurt responses to others. The net effect is therefore not easily observable, certainly not when multiple charities address the same individuals. In this paper we alleviate these observational difficulties by carrying out a field experiment in which five large charities cooperate. With the unique data that we collect, we study the impact of sending more requests on total donations. The results indicate that there is a negative competitive effect on requests from other charities, but this effect dies out rapidly. Soon after the mailing has been sent, it is only a strong cannibalization of the charity's own revenues that prevails. This empirical finding suggests the important conclusion that not much coordination across charities is needed to increase revenues. We also demonstrate that charities need sophisticated evaluation tools that do not ignore the effects of cannibalization.

Preference studies are becoming increasingly important within the medical product decision-making context. Currently, there is limited understanding of the range of methods to gain insights into patient preferences. We developed a compendium and taxonomy of preference exploration (qualitative) and elicitation (quantitative) methods by conducting a systematic literature review to identify these methods. This review was followed by analyzing prior preference method reviews, to cross-validate our results, and consulting intercontinental experts, to confirm our outcomes. This resulted in the identification of 32 unique preference methods. The developed compendium and taxonomy can serve as an important resource for assessing these methods and helping to determine which are most appropriate for different research questions at varying points in the medical product lifecycle. ispartof: DRUG DISCOVERY TODAY vol:24 issue:7 pages:1324-1331 ispartof: location:England status: published

Contains fulltext : 188598.pdf (Publisher’s version ) (Open Access) Objectives: To improve information for patients and to facilitate a vaccination coverage that is in line with the EU and World Health Organization goals, we aimed to quantify how vaccination and patient characteristics impact on influenza vaccination uptake of elderly people. Methods: An online discrete choice experiment (DCE) was conducted among 1261 representatives of the Dutch general population aged 60 years or older. In the DCE, we used influenza vaccination scenarios based on five vaccination characteristics: effectiveness, risk of severe side effects, risk of mild side effects, protection duration, and absorption time. A heteroscedastic multinomial logit model was used, taking scale and preference heterogeneity (based on 19 patient characteristics) into account. Results: Vaccination and patient characteristics both contributed to explain influenza vaccination uptake. Assuming a base case respondent and a realistic vaccination scenario, the predicted uptake was 58%. One-way changes in vaccination characteristics and patient characteristics changed this uptake from 46% up to 61% and from 37% up to 95%, respectively. The strongest impact on vaccination uptake was whether the patient had been vaccinated last year, whether s/he had experienced vaccination side effects, and the patient’s general attitude towards vaccination. Conclusions: Although vaccination characteristics proved to influence influenza vaccination uptake, certain patient characteristics had an even higher impact on influenza vaccination uptake. Policy makers and general practitioners can use these insights to improve their communication plans and information regarding influenza vaccination for individuals aged 60 years or older. For instance, physicians should focus more on patients who had experienced side effects due to vaccination in the past, and policy makers should tailor the standard information folder to patients who had been vaccinated last year and to patient who had not. 10 p.

Discrete-choice experiments (DCEs) have become a commonly used instrument in health economics and patient-preference analysis, addressing a wide range of policy questions. An important question when setting up a DCE is the size of the sample needed to answer the research question of interest. Although theory exists as to the calculation of sample size requirements for stated choice data, it does not address the issue of minimum sample size requirements in terms of the statistical power of hypothesis tests on the estimated coefficients. The purpose of this paper is threefold: (1) to provide insight into whether and how researchers have dealt with sample size calculations for healthcare-related DCE studies; (2) to introduce and explain the required sample size for parameter estimates in DCEs; and (3) to provide a step-by-step guide for the calculation of the minimum sample size requirements for DCEs in health care. Electronic supplementary material The online version of this article (doi:10.1007/s40271-015-0118-z) contains supplementary material, which is available to authorized users.

ObjectiveThe Assessment of Burden of COPD (ABC) tool supports shared decision making between patient and caregiver. It includes a coloured balloon diagram to visualise patients’ scores on burden indicators. We aim to determine the importance of each indicator from a patient perspective, in order to calculate a weighted index score and investigate whether that score is predictive of costs.DesignDiscrete choice experiment.Setting and participantsPrimary care and secondary care in the Netherlands. 282 patients with chronic obstructive pulmonary disease (COPD) and 252 members of the general public participated.MethodsRespondents received 14 choice questions and indicated which of two health states was more severe. Health states were described in terms of specific symptoms, limitations in physical, daily and social activities, mental problems, fatigue and exacerbations, most of which had three levels of severity. Weights for each item-level combination were derived from a Bayesian mixed logit model. Weights were rescaled to construct an index score from 0 (best) to 100 (worst). Regression models were used to find a classification of this index score in mild, moderate and severe that was discriminative in terms of healthcare costs.ResultsFatigue, limitations in moderate physical activities, number of exacerbations, dyspnoea at rest and fear of breathing getting worse contributed most to the burden of disease. Patients assigned less weight to dyspnoea during exercise, listlessness and limitations with regard to strenuous activities. Respondents from the general public mostly agreed. Mild, moderate and severe burden of disease were defined as scores ConclusionsThe ABCIndex is a new index score for the burden of COPD, which is based on patients’ preferences. The classification of the index score into mild, moderate and severe is predictive of future healthcare costs.Trial registration numberNTR3788; Post-results.

Background: The Assessment of Burden of COPD (ABC) tool is an instrument that supports shared decision making between patients and physicians. It includes a coloured balloon diagram to visualize a patient’s scores on a questionnaire about the experienced burden of COPD and several objective severity indicators. An algorithm translates these scores into a treatment advice to be discussed with the patient. The resulting individual care plan includes personalized treatment goals framed in the patient’s own words. In an 18-months cluster randomized controlled trial this tool was found to be effective in improving disease-specific quality of life.Aim: The aim of this study was twofold. Firstly, to determine the importance of each item of the experienced burden of disease, from a patient-perspective, in order to calculate a weighted index score on a 0-100 scale. Secondly, to investigate whether the ABC Index score can be used to develop a grouping of COPD patients into mild, moderate, and severe burden of disease that is predictive of future healthcare utilization and costs.Methods: The importance weights were determined in a discrete choice experiment (DCE) among 282 COPD patients and 252 members of the general public. Respondents received 14 choice questions; in each question they were asked which of two health states was more severe. Health states were described in terms of specific symptoms, limitations, mental problems, fatigue and exacerbations. Weights for each item-level combination were derived statistically from the likelihood of each health state to be considered worse than the other. Weights were re-scaled to a range from 0 (best) to 100 (worst). The capability of the ABC Index to predict healthcare utilization and costs was explored by dividing the ABC Index into three classes (mild, moderate, severe burden of disease) and comparing the average resource utilization and costs per class. Multi-level regression models were used to find the most discriminative cut-off points to define the classes.Results: For patients, fatigue, limitations in moderate physical activities, number of exacerbations, dyspnea at rest, and concern about breathing getting worse were the most important drivers of the burden of COPD. Little weight was attached to dyspnoea during exercise, listlessness and limitations with regard to strenuous activities. Respondents from the general public mostly agreed with this. Mild, moderate and severe burden of disease were defined as scores 40, respectively. This categorisation was most predictive of healthcare utilisation and annual costs: €1200, €2500 and €9500 per year, respectively.Conclusion: The ABC tool is an instrument for shared decision making, which scores can be aggregated into an overall score of the experienced burden of disease that is based on the importance that patients themselves assign to the various items. Patients can be grouped into mild, moderate and severe burden of disease and this grouping is predictive of their future healthcare utilization and costs. Hence, at a group level, the ABC Index can be used for monitoring and it may guide contracting between health insurers and healthcare providers.

Background Several epidemiological studies have investigated the effect of the quantity of green space on health outcomes such as self-rated health, morbidity and mortality ratios. These studies have consistently found positive associations between the quantity of green and health. However, the impact of other aspects, such as the perceived quality and average distance to public green, and the effect of urban green on population health are still largely unknown. Methods Linear regression models were used to investigate the impact of three different measures of urban green on small-area life expectancy (LE) and healthy life expectancy (HLE) in The Netherlands. All regressions corrected for average neighbourhood household income, accommodated spatial autocorrelation, and took measurement uncertainty of LE, HLE as well as the quality of urban green into account. Results Both the quantity and the perceived quality of urban green are modestly related to small-area LE and HLE: an increase of 1 SD in the percentage of urban green space is associated with a 0.1-year higher LE, and, in the case of quality of green, with an approximately 0.3-year higher LE and HLE. The average distance to the nearest public green is unrelated to population health. Conclusions The quantity and particularly quality of urban green are positively associated with small-area LE and HLE. This concurs with a growing body of evidence that urban green reduces stress, stimulates physical activity, improves the microclimate and reduces ambient air pollution. Accordingly, urban green development deserves a more prominent place in urban regeneration and neighbourhood renewal programmes.

Objectives: This study aimed 1) to quantify the strength of patient preferences for different aspects of early assisted discharge in The Netherlands for patients who were admitted with a chronic obstructive pulmonary disease exacerbation and 2) to illustrate the benefits of latent class modeling of discrete choice data. This technique is rarely used in health economics. Methods: Respondents made multiple choices between hospital treatment as usual (7 days) and two combinations of hospital admission (3 days) followed by treatment at home. The latter was described by a set of attributes. Hospital treatment was constant across choice sets. Respondents were patients with chronic obstructive pulmonary disease in a randomized controlled trial investigating the cost-effectiveness of early assisted discharge and their informal caregivers. The data were analyzed using mixed logit, generalized multinomial logit, and latent-class conditional logit regression. These methods allow for heterogeneous preferences across groups, but in different ways. Results: Twenty-five percent of the respondents opted for hospital treatment regardless of the description of the early assisted discharge program, and 46% never opted for the hospital. The best model contained four latent classes of respondents, defined by different preferences for the hospital and caregiver burden. Preferences for other attributes were constant across classes. Attributes with the strongest effect on choices were the burden on informal caregivers and co-payments. Except for the number of visits, all attributes had a significant effect on choices in the expected direction. Conclusions: Considerable segments of respondents had fixed preferences for either treatment option. Applying latent class analysis was essential in quantifying preferences for attributes of early assisted discharge.

Background: Patients' preferences are important for shared decision making. Therefore, we investigated patients' and urologists' preferences for treatment alternatives for early prostate cancer (PC). Methods: A discrete choice experiment was conducted among 150 patients who were waiting for their biopsy results, and 150 urologists. Regression analysis was used to determine patients' and urologists' stated preferences using scenarios based on PC treatment modality (radiotherapy, surgery, and active surveillance (AS)), and risks of urinary incontinence and erectile dysfunction. Results: The response rate was 110 out of 150 (73%) for patients and 50 out of 150 (33%) for urologists. Risk of urinary incontinence was an important determinant of both patients' and urologists' stated preferences for PC treatment (P

The ABC tool is an instrument to support shared decision making between patient and caregiver. It includes a colored balloon diagram to visualize patients9 scores on the subjective burden of COPD questionnaire and objective severity indicators. We determined the importance of each item of the burden of disease from a patient-perspective, in order to calculate a weighted index score that can be related to costs. We conducted a Discrete Choice Experiment (DCE) among COPD patients in a cluster-RCT of the ABC tool. Each COPD patient received 14 choice questions, in which he was asked which of two health states was more severe. States were described in terms of the 15 items of the ABC questionnaire: dyspnoea (at rest; during physical activity), coughing, sputum production, limitations (in strenuous physical; moderate physical; daily and social activities), feeling depressed, concern about breathing getting worse, worrying, listlessness, tension, fatigue and exacerbations. Each item had 3 levels. Weights for each item-level combination were derived statistically from the likelihood of each health state to be considered worse than the other. Weights were re-scaled to generate the ABC index score, ranging from 0 (best) to 100 (worst). 282 patients completed the DCE. The highest weights were assigned to dyspnoea at rest, limitations in moderate physical activities, daily and social activities, concern about breathing getting worse, fatigue, and exacerbations. Mild, moderate and severe burden of disease were defined as ABC index scores 40, respectively. This categorisation was most predictive of mean annual healthcare costs: €1200, €2500 and €9500, respectively.

In managed care markets, health insurers bargain over the price and quality of health care services. Selective contracting is an important tool for health insurers in negotiations with health care providers. Empirical research in the United States showed that health insurers using exclusionary provider networks obtain higher discounts than insurers without restrictions on provider choice (Staten, Dunkelberg, and Umbeck 1987; Melnick et al. 1992; Sorensen 2003;). Research on health plan choice in the United States, however, showed that consumers are reluctant to choose a health plan with stringent restrictions on provider choice. Consumers dislike or even distrust some of these very restrictive forms of managed care (Feldman et al. 1989; Gawande et al. 1998; Chu-Weininger and Balkrishnan 2006; Miller 2006;). As an alternative to exclusionary networks, health insurers can also use preferred provider networks in which enrollees still have the option to seek out-of-network care. In that case, the bargaining position of health insurers largely depends on their ability to channel enrollees toward preferred providers (Pauly 1987). In turn, this ability depends on the attractiveness of the network and consumers' propensity to switch to another provider. The latter is limited by the status quo bias. Status quo bias arises once consumers are reluctant to leave their current provider even if better alternatives are readily available (Neipp and Zeckhauser 1985; Samuelson and Zeckhauser 1988; Strombom, Buchmueller, and Feldstein 2002;). Switching costs, uncertainty about other alternatives, and the relationship consumers have with their current provider determine the importance of the status quo bias. Despite the widespread use of preferred provider networks in the United States, surprisingly little is known about the insurer's ability to channel enrollees toward preferred providers. Most research focused on the impact of price on health care utilization or on health plan choice (Glied 2000; Zweifel and Manning 2000;). In a recent paper, Wu (2008) finds that health plans that successfully channel patients can extract greater discounts but that it still remains unclear which strategies are effective in directing patients to preferred providers. Furthermore, it is unclear whether the effectiveness of channeling incentives differs for different provider types. In many countries with social health insurance systems (e.g., Germany, the Netherlands, Switzerland), health insurers are nowadays provided with incentives and tools, such as selective contracting, to act as prudent buyers of care on behalf of their enrollees. We examine the potential effectiveness of patient channeling by third parties, as this is required before one can reap the benefits from exclusionary and preferred provider networks. We particularly focus on the impact of status quo bias on provider choice. We confine our investigation to GPs and pharmacies because these are most frequently consulted and because consumer sensitivity to channeling incentives may differ most strongly between these providers. These questions are investigated in the context of the Dutch health care system, in which GPs perform an important role as gatekeeper. In 2006, the Dutch health care system was profoundly reformed to provide competing health insurers with incentives to selectively contract providers (Van de Ven and Schut 2008). The results are relevant for all countries in which health insurers or other third parties aim to influence provider choice.

Customers can interact with and create value for firms in a variety of ways. This article proposes that assessing the value of customers based solely upon their transactions with a firm may not be sufficient, and valuing this engagement correctly is crucial in avoiding undervaluation and overvaluation of customers. We propose four components of a customer’s engagement value (CEV) with a firm. The first component is customer lifetime value (the customer’s purchase behavior), the second is customer referral value (as it relates to incentivized referral of new customers), the third is customer influencer value (which includes the customer’s behavior to influence other customers, that is increasing acquisition, retention, and share of wallet through word of mouth of existing customers as well as prospects), and the fourth is customer knowledge value (the value added to the firm by feedback from the customer). CEV provides a comprehensive framework that can ultimately lead to more efficient marketing strategies that enable higher long-term contribution from the customer. Metrics to measure CEV, future research propositions regarding relationships between the four components of CEV are proposed and marketing strategies that can leverage these relationships suggested.

Charities rely mainly on direct mailings to attract the attention of potential donors. Individuals may feel irritated by these mailings, in particular when they receive many mailings. This might harm the revenues charities receive from their mailing activities. Moreover, target selection by charities likely results in many mailings being sent to the best donors, and hence they might become most irritated. As such, irritation with direct mailings could well be endogenously determined. To ensure exogenous variation in irritation, we performed a unique controlled field experiment in cooperation with five of the largest charities in the Netherlands. Our analysis reveals that direct mailings do result in irritation, but surprisingly, this irritation affects neither stated nor actual donating behavior.

Efficient contracting of health care requires effective consumer channeling. Little is known about the effectiveness of channeling strategies. We study channeling incentives on pharmacy choice using a large scale discrete choice experiment. Financial incentives prove to be effective. Positive financial incentives are less effective than negative financial incentives. Channeling through qualitative incentives also leads to a significant impact on provider choice. While incentives help to channel, a strong status quo bias needs to be overcome before consumers change pharmacies. Focusing on consumers who are forced to choose a new pharmacy seems to be the most effective strategy.

The authors propose a dynamic direct mailing response model with competitive effects. Purchase and promotion history are incorporated to map the dynamic competitive interactions among the firms sending the mailings. The authors investigate the impact of direct mailings on the revenues of each firm and its competitors over time. The model accounts for endogeneity of the mailing decision and for unobserved heterogeneity across households. The model is considered in a charitable giving setting, in which households often receive many direct mailings of different charities within a short period and competition is strong. The authors construct a unique database by merging the databases of three large charity organizations in the Netherlands. This results in household-level data on the direct mailings households received from and their donations to each of the three charities. The results show that a charity's own mailings are short-term substitutes; that is, an extra mailing cannibalizes the revenues of subsequent mailings. Furthermore, competitive charitable direct mailings tend to be short-term complements; that is, the direct mailings increase the total pie that is divided among the charities. In the long run, these effects die out. The results are also interpreted from a behavioral perspective.

Customer Lifetime Value (CLV) is one of the key metrics in marketing and is considered an important segmentation base. This paper studies the capabilities of a range of models to predict CLV in the insurance industry. The simplest models can be constructed at the customer relationship level, i.e. aggregated across all services. The more complex models focus on the individual services, paying explicit attention to cross buying, but also retention. The models build on a plethora of approaches used in the existing literature and include a status quo model, a Tobit II model, univariate and multivariate choice models, and duration models. For all models, CLV for each customer is computed for a four-year time horizon. We find that the simple models perform well. The more complex models are expected to better capture the richness of relationship development. Surprisingly, this does not lead to substantially better CLV predictions.

Dealers may contribute to brand retention through their sales and service efforts. In this study we investigate the degree to which dealers contribute to brand retention and how this contribution is moderated by brand tier. To this end we distinguish between economy, volume and prestige brands. We also investigate how the effectiveness of dealer instruments to increase dealer retention differs across these brand tiers. We collected data on brand retention and dealer retention among consumers who recently purchased a new car. Our findings show that dealers selling volume brands are able to improve brand retention rates. In contrast, dealers of prestige and economy brands are unable to affect brand retention. In line with the notion of brand-dealer fit we also find that the effects of dealer extrinsic service quality and dealer payment equity on dealer retention differ between prestige, volume, and economy brands. Extrinsic dealer service quality has the smallest effect for dealers selling economy brands, while dealer payment equity is the most important determinant of dealer retention for these dealers. (C) 2006 New York University. Published by Elsevier Inc. All rights reserved.

One of the aims of direct marketing in practice is to target the most profitable customers in the database at hand. This selection is often done based on observed behavior in the past. As a consequence, databases arising from the responses to direct mailings are not a random sample from all potential respondents. When not all heterogeneity is observed, part of the target selection rule will be based on the unobserved heterogeneity, so selection is endogenous. Treating an endogenously selected sample as a random sample results in inconsistent parameter estimates, which in general also harms the predictive performance of the model. We develop an adjustment to the likelihood of the model that corrects for the endogenous sample selection. We apply this technique to the selection of mail targets for a charitable organization. In the application we also show that, based on a model for the response rate and the amount donated simultaneously, we can create a target selection rule that maximizes expected revenues. Such a selection rule outperforms selection rules based on response rates or donated amount only. The traditional approach of maximizing response is therefore not the optimal approach to target selection.

customs are generally perceived as a time-consuming impediment to international trade. However, few studies have empirically examined the determinants and the impact of this type of government-imposed transaction costs. This paper analyses the role of firm size as a determinant of customs-related transaction costs, as well as the effect of firm size on the relationship between these costs and the international trade intensity of firms. The results of this study indicate that customs-related transaction costs repress international trade activities of firms, even at low levels of these costs. The paper identifies transaction-related economies of scale, simplified customs procedures and advanced information and communication technology as main determinants of customs-related transaction costs. It is shown that when these factors are taken into account, firm size has no effect on customs-related transaction costs. Policy implications are considered for firm strategy and public policy.

This paper presents a transaction costs analysis of the firm size and export intensity relationship. We submit that relation-specific investments and the costs of safeguarding these investments play a significant role in export relationships. Firm size related differences with respect to these factors are used to explain the different relationships between firm size and export intensity that have been found in previous studies. The theoretical framework is tested empirically, and support is found for different industries. © 2002, Academy of International Business. All rights reserved. ispartof: Journal of International Business Studies vol:33 issue:3 pages:603-613 status: published

We formulate a theoretical model in which we postulate that if customers' behavior is perceived as not optimal, customers will adjust this behavior based on their current satisfaction and payment equity. Furthermore, customers will also include new experiences. In our empirical study we particularly investigate customer referrals and the amount of services purchased. Our results show positive effects of current satisfaction and payment equity on referrals, while also changes in satisfaction and payment equity affect customer referrals. With respect to the amount of services purchased, our estimation results reveal a positive significant effect of only changes in satisfaction.

For effective Customer Relationship Management (CRM), it is essential to have information on the potential value of customers. Based on the interplay between potential value and realized value, managers can devise customer specific strategies. In this article, we introduce a model for predicting the potential value of a current customer. Furthermore, we discuss and apply different modeling strategies for predicting this potential value.

Being able to accurately predict what a customer will purchase next is of paramount importance to successful online retailing. In practice, customer purchase history data is readily available to make such predictions, sometimes complemented with customer characteristics. Given the large assortments maintained by online retailers, scalability of the prediction method is just as important as its accuracy. We study two classes of models that use such data to predict what a customer will buy next: A novel approach that uses latent Dirichlet allocation (LDA), and mixtures of Dirichlet-Multinomials (MDM). A key benefit of a model-based approach is the potential to accommodate observed customer heterogeneity through the inclusion of predictor variables. We show that LDA can be extended in this direction while retaining its scalability. We apply the models to purchase data from an online retailer and contrast their predictive performance with that of a collaborative filter and a discrete choice model. Both LDA and MDM outperform the other methods. Moreover, LDA attains performance similar to that of MDM while being far more scalable, rendering it a promising approach to purchase prediction in large assortments.

Background: Screening for prostate cancer (PC) may save lives, but overdiagnosis and overtreatment are serious drawbacks. We aimed to determine men's preferences for PC screening, and to elicit the trade-offs they make. Methods: A discrete choice experiment (DCE) was conducted among a population-based random sample of 1000 elderly men (55-75-years-old). Trade-offs were quantified with a panel latent class model between five PC screening aspects: risk reduction of PC-related death, screening interval, risk of unnecessary biopsies, risk of unnecessary treatments, and out-of-pocket costs. Results: The response rate was 46% (459/1000). Men were willing to trade-off 2.0% (CI: 1.6%-2.4%) or 1.8% (CI: 1.3%-2.3%) risk reduction of PC-related death to decrease their risk of unnecessary treatment or biopsy with 10%, respectively. They were willing to pay \[euro]188 per year (CI: \[euro]141-\[euro]258) to reduce their relative risk of PC-related death with 10%. Preference heterogeneity was substantial, with men with higher educational levels having a lower probability to opt for PC screening than men with lower educational levels. Conclusion: Men were willing to trade-off some risk reduction of PC-related death to be relieved of the burden of biopsies or unnecessary treatments. Increasing knowledge on overdiagnosis and overtreatment, especially for men with lower educational levels, is warranted to prevent unrealistic expectations from PC screening. © 2013 Cancer Research UK. All rights reserved.

Health-adjusted life expectancy (HALE) is one of the most attractive summary measures of population health. It provides balanced attention to fatal as well as non-fatal health outcomes, is sensitive to the severity of morbidity within the population, and can be readily compared between areas with very different population age structures. HALE, however, cannot be calculated at the small-area level using traditional life table methodology. Hence we propose a Bayesian random-effects modeling approach that recognizes correlations and pools strength between sexes, age-groups, geographical areas, and health outcomes. This approach allows for the calculation of HALE for areas as small as 2000 person years at risk and with relatively modest health state survey sample sizes. The feasibility of the Bayesian approach is illustrated in a real-life example, which also shows how differences in areas' health performances can be adequately quantified. Such information can be invaluable for the appropriate targetting and subsequent evaluation of urban regeneration, neighborhood renewal, and community-based initiatives aimed at improving health and reducing health inequalities.

The geographical distribution of nursing homes can significantly distort small-area life expectancy estimations. Consequently, uncorrected life expectancies should not be used for small-area life expectancy comparisons. Instead, several nursing home corrections have been proposed. The practical use of these corrections, however, is severely limited by data availability. This paper introduces a new, model-based nursing home correction that requires considerably less detailed nursing home data. A formal comparison shows that the proposed model-based approach is the recommended correction for all small-area life expectancy estimations where detailed previous residential address information of the nursing home population is not available. This makes the approach highly relevant for a wide range of empirical applications.

BACKGROUND: Guidelines underline the role of individual preferences in the selection of a screening test, as insufficient evidence is available to recommend one screening test over another. We conducted a study to determine the preferences of individuals and to predict uptake for colorectal cancer (CRC) screening programmes using various screening tests. METHODS: A discrete choice experiment (DCE) questionnaire was distributed among naive subjects, yet to be screened, and previously screened subjects, aged 50-75 years. Subjects were asked to choose between scenarios on the basis of faecal occult blood test (FOBT), flexible sigmoidoscopy (FS), total colonoscopy (TC) with various test-specific screening intervals and mortality reductions, and no screening (opt-out). RESULTS: In total, 489 out of 1498 (33%) screening-naive subjects (52% male; mean age +/- s.d. 61 +/- 7 years) and 545 out of 769 (71%) previously screened subjects (52% male; mean age +/- s.d. 61 +/- 6 years) returned the questionnaire. The type of screening test, screening interval, and risk reduction of CRC-related mortality influenced subjects' preferences (all P

Objectives: Discrete choice experiments (DCEs) in health economics commonly present choice sets in an unlabeled form. Labeled choice sets are less abstract and may increase the validity of the results. We empirically compared the feasibility, respondents' trading behavior, and convergent validity between a labeled and an unlabeled DCE for colorectal cancer (CRC) screening programs in The Netherlands. Methods: A labeled DCE version presented CRC screening test alternatives as "fecal occult blood test," "sigmoidoscopy," and "colonoscopy," whereas the unlabeled DCE version presented them as "screening test A" and "screening test B." Questionnaires were sent to participants and nonparticipants in CRC screening. Results: Total response rate was 276 (39%) out of 712 and 1033 (46%) out of 2267 for unlabeled and labeled DCEs, respectively (P < 0.001). The labels played a significant role in individual choices; approximately 22% of subjects had dominant preferences for screening test labels. The convergent validity was modest to low (participants in CRC screening: r = 0.54; P = 0.01; nonparticipants: r = 0.17; P = 0.45) largely because of different preferences for screening frequency. Conclusion: This study provides important insights in the feasibility and difference in results from labeled and unlabeled DCEs. The inclusion of labels appeared to play a significant role in individual choices but reduced the attention respondents give to the attributes. As a result, unlabeled DCEs may be more suitable to investigate trade-offs between attributes and for respondents who do not have familiarity with the alternative labels, whereas labeled DCEs may be more suitable to explain real-life choices such as uptake of cancer screening.

Background Discrete choice experiments (DCEs) allow systematic assessment of preferences by asking respondents to choose between scenarios. We conducted a labelled discrete choice experiment with realistic choices to investigate patients' trade-offs between the expected health gains and the burden of testing in surveillance of Barrett esophagus (BE). Methods Fifteen choice scenarios were selected based on 2 attributes: 1) type of test (endoscopy and two less burdensome fictitious tests), 2) frequency of surveillance. Each test-frequency combination was associated with its own realistic decrease in risk of dying from esophageal adenocarcinoma. A conditional logit model was fitted. Results Of 297 eligible patients (155 BE and 142 with non-specific upper GI symptoms), 247 completed the questionnaire (84%). Patients preferred surveillance to no surveillance. Current surveillance schemes of once every 1–2 years were amongst the most preferred alternatives. Higher health gains were preferred over those with lower health gains, except when test frequencies exceeded once a year. For similar health gains, patients preferred video-capsule over saliva swab and least preferred endoscopy. Conclusion This first example of a labelled DCE using realistic scenarios in a healthcare context shows that such experiments are feasible. A comparison of labelled and unlabelled designs taking into account setting and research question is recommended.

In contrast to books and compact discs, the number of complex services offered on the Internet is still small. The decision-making process for complex services is different because it has an additional intermediate step of “indication of interest.” The Web site is (a) visited and searched for information; subsequently, (b) a request for the service is made, which may lead to (c) a purchase. The authors acquired a unique data set from an online Dutch financial service provider, which offers services such as mortgage loans and insurance on the Internet on behalf of financial institutions. They also obtained information on whether the request for the service resulted in a purchase. The authors used the available information to predict the purchase using a latent class probit model. A direct managerial application of this model is the ability to identify and select profitable applicants, resulting in significant profit improvements for the company.

Acquisition channels are important predictors of customer loyalty in the first stages of a business–consumer relationship. Although some researchers have provided examples of the differences in the value of the customers businesses acquire via different channels, they have not considered the impact of acquisition channels on loyalty and cross-buying. Using probit-models we explored how retention rates and cross-selling opportunities differ among the various acquisition channels a financial-services provider uses. Our results indicate that the direct-mail acquisition channel performs poorly on retention and cross-selling, while radio and TV perform poorly for retention only. The firm's Web site seems to perform well for retention. The theoretical and practical implications of our results are discussed.

Administratieve verplichtingen en vertragingen, die zijn verbonden aan douaneprocedures, kunnen aanzienlijke kosten met zich meebrengen bij internationale transacties. Weinig studies hebben deze kosten echter empirisch onderzocht. In dit artikel onderzoeken wij de omvang en de determinanten van de kosten van douaneprocedures en het effect van deze kosten op de intensiteit van internationale handelsactiviteiten van ondernemingen in Nederland. De resultaten geven aan dat schaalvoordelen van transacties, vereenvoudigde douaneprocedures en het gebruik van geavanceerde informatie- en communicatietechnologie de hoogte van de kosten van douaneprocedures grotendeels bepalen. Het blijkt dat deze kosten daadwerkelijk de intensiteit van de internationale handelsactiviteiten verminderen. Mogelijkheden voor ondernemingen en de overheid om de kosten van douaneprocedures te kunnen verminderen, worden besproken.

Marketing problems sometimes pertain to the analysis of dichotomous dependent variables, such as “buy” and “not buy” or “respond” and “not respond.” One outcome can strongly outnumber the other, such as when many households do not respond (e.g., to a direct mailing). In such situations, an efficient data-collection strategy is to sample disproportionately more from the smaller group. However, subsequent statistical analysis must account for this sampling strategy. In this article, the authors put forward the econometric method that can correct for the sample selection bias, when this method does not lead to a loss in precision. The authors illustrate the method for synthetic and real-life data and document that reductions of more than 50% in sample sizes can be obtained.

In intertemporal models of household consumption or portfolio choice, household behaviour depends on, for example, the household's rate of time preference, the rate of risk aversion, and the household's information set. In this paper we use a survey of Dutch households which contains direct subjective information on risk aversion and time preference and on interest in financial matters. We first describe these data and analyze how they relate to household characteristics and household income. We then investigate whether these variables are related to households' financial decisions on home ownership, mortgages and ownership of risky assets. Our results are broadly in accordance with economic theory.