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Discrete choice models can forecast market shares and individual choice probabilities with different price and alternative set scenarios. This work introduces a method to personalize choice models involving causal variables, such as price, using rich observational data. The model provides interpretable customer- and context-specific preferences, and price sensitivity, with an estimation procedure that uses orthogonalization. We caution against the nalive use of regularization to deal with the high-dimensional observational data challenge. We experiment with the attended home delivery (AHD) slot choice problem using data from a European online retailer. Our results indicate that while the popular non-personalized multinomial logit (MNL) model does very well at the aggregate (day-slot) level, personalization provides significantly and substantially more accurate predictions at the individual-context level. But the nalive personalization approach using regularization without orthogonalization wrongly predicts that the choice probability will increase if the slot price increases, rendering it unfit for forecasting demand with pricing scenarios. The proposed method avoids this problem. Further, we introduce features based on potential consideration sets in the AHD slot choice context that increase accuracy and allow for more realistic substitution patterns than the proportional substitution implied by MNL.

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IntroductionWilson's disease (WD) is associated with a variety of movement disorders and progressive neurological dysfunction. The aim of this study was to correlate baseline brain magnetic resonance imaging (MRI) features with clinical phenotype and long-term outcomes in chronically treated WD patients.MethodsPatients were retrospectively selected from an institutional database. Two experienced neuroradiologists reviewed baseline brain MRI. Functional assessment was performed using the World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0) scale, and disease severity was classified using the Global Assessment Scale for Wilson's Disease (GASWD).ResultsOf 27 patients selected, 14 were female (51.9%), with a mean (standard deviation [SD]) age at onset of 19.5 (7.1) years. Neurological symptoms developed in 22 patients (81.5%), with hyperkinetic symptoms being the most common (70.4%). Baseline brain MRI showed abnormal findings in 18 cases (66.7%), including T2 hyperintensities in 59.3% and atrophy in 29.6%. After a mean (SD) follow-up of 20.9 (11.0) years, WD patients had a mean score of 19.2 (10.2) on WHODAS 2.0 and 6.4 (5.7) on GASWD. The presence of hyperkinetic symptoms correlated with putaminal T2 hyperintensities (p = 0.003), putaminal T2 hypointensities (p = 0.009), and mesencephalic T2 hyperintensities (p = 0.009). Increased functional disability was associated with brain atrophy (p = 0.007), diffusion abnormalities (p = 0.013), and burden of T2 hyperintensities (p = 0.002). A stepwise regression model identified atrophy as a predictor of increased WHODAS 2.0 (p = 0.023) and GASWD (p = 0.007) scores.ConclusionsAtrophy and, to a lesser extent, deep T2 hyperintensity are associated with functional disability and disease severity in long-term follow-up of WD patients.

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Retailers face increasing competitive pressure to determine how best to deliver products purchased online to the end customer. Grocery retailers often require attended home delivery where the customer must be present to receive the delivery. For attended home delivery to function, the retailer and customer must agree on a delivery time slot that works for both parties. Using online data from a grocery retailer, we observe customer preferences for three delivery service attributes associated with each time slot: speed, precision, and timing. We define speed as the expected time between the placement of an order and its delivery, precision as the duration of the offered time slot, and timing as the availability of choices across times of the day and days of the week. We show that customers not only value speed as an attribute of delivery service but that precision and timing are also key drivers of the customer's time slot selection process. We also observe substantial customer heterogeneity in the willingness of customers to pay for time slots. Customers that differ in their loyalty to the retailer, basket value, basket size, and basket composition exhibit distinct differences in their willingness to pay. We show that retailers with the capability to tailor their time slot offerings to specific customer segments have the potential to generate approximately 9% more shipping revenue than those who cannot. Our findings inform practitioners seeking to design competitive fulfillment strategies and academics customer behavior in the attended home context.

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This chapter aims to contribute to the understanding of how artificial intelligence (AI) technologies can promote increased business revenues, cost reductions, and enhanced customer experience, as well as society's well-being in a sustainable way. However, these AI benefits also come with risks and challenges concerning organizations, the environment, customers, and society, which need further investigation. This chapter also examines and discusses how AI can either enable or inhibit the delivery of the goals recognized in the UN 2030 Agenda for Sustainable Business Models Development. In this chapter, the authors conduct a bibliometric review of the emerging literature on artificial intelligence (AI) technolo¬gies implications on sustainable business models (SBM), in the perspective of Sustainable Development Goals (SDGs) and investigate research spanning the areas of AI, and SDGs within the economic group. The authors examine an effective sample of 69 publications from 49 different journals, 225 different institutions, and 47 different countries. On the basis of the bibliometric analysis, this study selected the most significant published sources and examined the changes that have occurred in the conceptual framework of AI and SBM in light of SDGs research. This chapter makes some significant contributions to the literature by presenting a detailed bibliometric analysis of the research on the impacts of AI on SBM, enhancing the understanding of the knowledge structure of this research topic and helping to identify key knowledge gaps and future challenges. © 2024, IGI Global. All rights reserved.

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