Product Ranking on Online Platforms

On online platforms, consumers face an abundance of options that are displayed in the form of a position ranking. Only products placed in the first few positions are readily accessible to the consumer, and she needs to exert effort to access more options. For such platforms, we develop a two-stage sequential search model where, in the first stage, the consumer sequentially screens positions to observe the preference weight of the products placed in them and forms a consideration set. In the second stage, she observes the additional idiosyncratic utility that she can derive from each product and chooses the highest-utility product within her consideration set. For this model, we first characterize the optimal sequential search policy of a welfare-maximizing consumer. We then study how platforms with different objectives should rank products. We focus on two objectives: (i) maximizing the platform’s market share and (ii) maximizing the consumer’s welfare. Somewhat surprisingly, we show that ranking products in decreasing order of their preference weights does not necessarily maximize market share or consumer welfare. Such a ranking may shorten the consumer’s consideration set due to the externality effect of high-positioned products on low-positioned ones, leading to insufficient screening. We then show that both problems—maximizing market share and maximizing consumer welfare—are NP-complete. We develop novel near-optimal polynomial-time ranking algorithms for each objective. Further, we show that, even though ranking products in decreasing order of their preference weights is suboptimal, such a ranking enjoys strong performance guarantees for both objectives. We complement our theoretical developments with numerical studies using synthetic data, in which we show (1) that heuristic versions of our algorithms that do not rely on model primitives perform well and (2) that our model can be effectively estimated using a maximum likelihood estimator. This paper was accepted by Gabriel Weintraub, revenue management and market analytics.

Cost-effectiveness of a Transdiagnostic Psychotherapy Program for Youth With Common Mental Health Problems

Objectives: Our objective was to evaluate the cost-effectiveness of the transdiagnostic psychotherapy program Mind My Mind (MMM) for youth with common mental health problems using a cost-utility analysis (CUA) framework and data from a randomized controlled trial. Furthermore, we analyzed the impact of choice of informant with respect to both quality-of-life reporting and preference weights on the Incremental Cost Effectiveness Ratio (ICER). Methods: A total of 396 school-aged youth took part in the 6-month trial. CUAs were carried out for the trial-period and for four one-year extrapolation scenarios. Costs were based on a combination of budget and self-reported costs. Youths and parents were asked to report on the youth’s quality-of-life three times during the trial using the Child Health Utility 9D (CHU9D). Parental-reported CHU9D was used in the base case together with preference weights of a youth population. Analyses using self-reported CHU9D and preference weights of an adult population were also carried out. Results: The analysis of the trial period resulted in an ICER of €170,465. The analyses of the one-year scenarios resulted in ICERs between €23,653 and €50,480. The ICER increased by 24% and 71% compared to the base case when using self-reported CHU9D and adult preference weights, respectively. Conclusion: The MMM intervention has the potential to be cost-effective, but the ICER is dependent on the duration of the treatment effects. Results varied significantly with the choice of respondent and the choice of preference weights indicating that both factors should be considered when assessing CUA involving youth.

Determining Suitable Locations for Regional Waste Final Processing Site (WFP) Using the Simple Additive Weighting (SAW) (Case Study: Malang City and Malang Regency)

Garbage or waste is basically a residual material resulting from human activities and natural processes that have no economic value anymore. The volume of waste in Malang City and Malang Regency every year always increases, so the existing waste final processing site will no longer be able to accommodate the pile of garbage. Therefore the Malang City Government plans to collaborate with the Malang Regency Government in making an integrated regional waste landfill and processing site. In this study, an analysis of the determination of the appropriate location for regional waste final processing for Malang City and Malang Regency was carried out using the Simple Additive Weighting (SAW) method. The SAW method is a weighted summation method, which can make a more precise assessment, based on the predetermined criteria and preference weights. Preference weights were determined by pairwise comparison method. The criteria used are geological hazardous areas, distance from drinking water sources, land slope level, distance from settlements, protected areas and distance from airports. The final result in this study is a map of the appropriate location for a regional waste final processing site and an analysis of the location for the best regional final processing site. The location map is classified into three, namely: not feasible with a total area of 54,774.33 ha, less feasible with a total area of 170,846.49 ha and feasible with a total area of 130,096.63 ha.

ANALISIS SURVEI KEPUASAN PELANGGAN TERHADAP E-COMMERCE DENGAN METODE SIMPLE ADDITIVE WEIGHTING

Online shopping is the process of purchasing goods / services by consumers to realtime sellers, without services, and through the internet. The development of online business in Indonesia is now very rapid, one of them by shopping online. Online stores or we often call e-commerce is a form of change that is presented by the internet in terms of innovation in shopping by providing various facilities in the transaction process. The aim of the writer is to determine the best e-commerce, therefore the author uses the SAW (Simple Additive Weighting) method because the method is able to make a more precise assessment because it is based on the value of criteria and preference weights that have been determined, besides that the SAW method also can select the best alternative from a number of alternatives because there is a ranking process after determining the weights for each attribute.

AN AHP BASED OPTIMAL DISTRIBUTION MODEL AND ITS APPLICATION IN COVID-19 VACCINATION

COVID-19 is causing a large number of causalities and producing tedious healthcare management problems at a global level. During a pandemic, resource availability and optimal distribution of the resources may save lives. Due to this issue, the authors have proposed an Analytical Hierarchy Process (AHP) based optimal distribution model. The proposed distribution model advances the AHP and enhances real-time model applicability by eliminating judgmental scale errors. The model development is systematically discussed. Also, the proposed model is utilized as a state-level optimal COVID-19 vaccine distribution model with limited vaccine availability. The COVID-19 vaccine distribution model used 28 Indian states and 7 union territories as the decision elements for the vaccination problem. The state-wise preference weights were calculated using the geometric mean AHP analysis method. The optimal state-level distribution of the COVID-19 vaccine was obtained using preference weights, vaccine availability and the fact that a patient requires exactly vaccine doses to complete a vaccination schedule. The optimal COVID-19 vaccine distribution along with state and union territory rank, and preference weights were compiled. The obtained results found Kerala, Maharashtra, Uttarakhand, Karnataka, and West Bengal to be the most COVID-19 affected states. In the future, the authors suggest using the proposed model to design an optimal vaccine distribution strategy at the district or country level, and to design a vaccine storage/inventory model to ensure optimal use of a vaccine storage center covering nearby territories.

Valuing informal carers’ quality of life using best-worst scaling—Finnish preference weights for the Adult Social Care Outcomes Toolkit for carers (ASCOT-Carer)

This study developed Finnish preference weights for the seven-attribute Adult Social Care Outcomes Toolkit for carers (ASCOT-Carer) and investigated survey fatigue and learning in best-worst scaling (BWS) experiments. An online survey that included a BWS experiment using the ASCOT-Carer was completed by a sample from the general population in Finland. A block of eight BWS profiles describing different states from the ASCOT-Carer were randomly assigned to each respondent, who consecutively made four choices (best, worst, second best and second worst) per profile. The analysis panel data had 32,160 choices made by 1005 respondents. A scale multinomial logit (S-MNL) model was used to estimate preference weights for 28 ASCOT-Carer attribute levels. Fatigue and learning effects were examined as scale heterogeneity. Several specifications of the generalised MNL model were employed to ensure the stability of the preference estimates. The most and least-valued states were the top and bottom levels of the control over daily life attribute. The preference weights were not on a cardinal scale. We observed the position effect of the attributes on preferences associated with the best or second-best choices. A learning effect was found. The established preference weights can be used in evaluations of the effects of long-term care services and interventions on the quality of life of service users and caregivers. The learning effect implies a need to develop study designs that ensure equal consideration to all profiles (choice tasks) in a sequential choice experiment.

Do You Prefer Safety to Social Participation? Finnish Population-Based Preference Weights for the Adult Social Care Outcomes Toolkit (ASCOT) for Service Users

Introduction. The Adult Social Care Outcomes Toolkit (ASCOT) was developed in England to measure people’s social care–related quality of life (SCRQoL). Objectives. The aim of this article is to estimate preference weights for the Finnish ASCOT for service users (ASCOT). In addition, we tested for learning and fatigue effects in the choice experiment used to elicit the preference weights. Methods. The analysis data ( n = 1000 individuals) were obtained from an online survey sample of the Finnish adult general population using gender, age, and region as quotas. The questionnaire included a best-worst scaling (BWS) experiment using ASCOT. Each respondent sequentially selected four alternatives (best, worst; second-best, second-worst) for eight BWS tasks ( n = 32,000 choice observations). A scale multinomial logit model was used to estimate the preference parameters and to test for fatigue and learning. Results. The most and least preferred attribute-levels were “I have as much control over my daily life as I want” and “I have no control over my daily life.” The preference weights were not on a cardinal scale. The ordering effect was related to the second-best choices. Learning effect was in the last four tasks. Conclusions. This study has developed a set of preference weights for the ASCOT instrument in Finland, which can be used for investigating outcomes of social care interventions on adult populations. The learning effect calls for the development of study designs that reduce possible bias relating to preference uncertainty at the beginning of sequential BWS tasks. It also supports the adaptation of a modelling strategy in which the sequence of tasks is explicitly modelled as a scale factor.

What’s important when caring for a loved one? Population-based preference weights for the Adult Social Care Outcomes Toolkit for informal carers (ASCOT-Carer) for Austria

Purpose The Adult Social Care Outcomes Toolkit for informal carers (ASCOT-Carer) can be used to assess long-term care-related quality of life (LTC-QoL) of adult informal carers of persons using LTC services. The ASCOT-Carer instrument has been translated into several languages, but preference weights reflecting the relative importance of different outcome states are only available for England so far. In this paper, we estimated preference weights for the German version of the ASCOT-Carer for Austria and investigated the value people place on different QoL-outcome states. Methods We used data from a best–worst scaling (BWS) experiment and estimated a scale-adjusted multinomial logit (S-MNL) model to elicit preference weights for the ASCOT-Carer domain-levels. Data were collected using an online survey of the Austrian general population (n = 1001). Results Top levels in the domains of ‘Space and time to be yourself’, ‘Occupation’ and ‘Control over daily life’ were perceived as providing the highest utility, and states with high needs in the same domains seen as particularly undesirable. ‘Personal safety’ was the only domain where levels were roughly equidistant. In all other domains, the difference between the top two levels (‘ideal state’ and ‘no needs’) was very small. Conclusion The paper provides preference weights for the German version of ASCOT-Carer to be used in Austrian populations. Furthermore, the results give insight into which LTC-QoL-outcomes are seen as particularly (un)desirable, and may therefore help to better tailor services directed at informal carers and the persons they care for.

Understanding Treatment Strategies and Preferences in Nonmetastatic Castration-Resistant Prostate Cancer From the Japanese Physician Perspective

PURPOSE Sixteen percent (16%) of patients with castration-resistant prostate cancer (CRPC) show no bone metastasis at diagnosis. However, 33% will become metastatic within 2 years. The goal of treatment in patients with nonmetastatic CRPC (nmCRPC), therefore, is to delay symptomatic metastases without undue toxicity. With novel antiandrogen treatments of different strengths and limitations available, physician preferences for nmCRPC treatment in Japan should be understood. METHODS A discrete choice experiment was conducted. Physicians chose between two hypothetical treatments in nmCRPC defined by six attributes: risk of fatigue, falls or fracture, cognitive impairment, hypertension, rashes as side effects of treatment, and extension of time until cancer-related pain occurs. Relative preference weights and relative importance were estimated by hierarchical Bayesian logistic regression. Physicians were also asked to make treatment decisions based on four hypothetical patient profiles to understand the most important factors driving decision making. RESULTS A total of 151 physicians completed the survey. Extension of time until cancer-related pain occurs was the most important attribute (relative importance, 32.3%; CI, 31.3% to 33.3%). Based on summed preference weights across all attributes, preferences for hypothetical treatment profiles I, II, and III were compared. A hypothetical treatment profile with better safety though shorter extension time was preferred (I: mean [standard deviation] = 1.7 [1.6 to 2.1]) over treatment profiles with lower safety but longer extension time (II: −2.7 [−2.8 to −2.6] and III: −0.2 [−0.3 to −0.1]). Treatment characteristics were more important factors for physicians' decision making than patient characteristics in prescribing treatment. CONCLUSION Physicians preferred a treatment with better safety profile, and treatment characteristics were the most important factors for decision making. This might have implications in physicians' decision making for nmCRPC treatment in the future in Japan.