Estimating Substitution and Basket Effects in Retail Stores: Implications for Assortment Planning

Many retailers are reducing store footprint and downsizing their assortments accordingly to improve store productivity. Some of the revenue for items removed from the assortment may be recouped by substitution, but also some of the revenue for items kept in the assortment may be lost due to basket abandonment. For a practical setting where baskets may contain any subset of items from thousands of products, estimating both substitution and basket effects is a challenge. To address this, we develop a demand model that combines a multinomial logit (MNL) model to estimate substitution within a subcategory and a purchase-incidence model to estimate basket retention. Using transaction and product availability data from 12 stores of an office supplies retail chain that were dramatically downsized from large- to small-format stores, we show that (i) storewide basket effects are substantial (our model with basket effects predicts out-of-sample transactions with mean absolute percent error (MAPE) of only 7% compared with 22% for a model with only substitution effects), (ii) poor service level can significantly exacerbate lost profit due to abandoned baskets at these stores, and (iii) consideration of the basket effect in assortment selection for the small stores can significantly improve basket retention and increase profits (by up to 16%) at these stores. This paper was accepted by Vishal Gaur, operations management.

APPLICATION OF SCORING AND DECISION MODELS TO EVALUATE PROCUREMENT DETERMINANTS IN COMMUNITY PHARMACY PRACTICE IN NIGERIA

Objectives: There is a need to evaluate the key factors influencing the choice of supply channels used by community pharmacists (CPs). The objectives of the study were to evaluate and score the determinant factors influencing CPs’ procurement decisions from supply channels (pharmaceutical companies-PC, Wholesaler-LW, and Open-Market-OM). Secondly, to evaluate preference decisions based on relative odds ratios using regression models. Methods: A descriptive, cross-sectional study that used structured questionnaires based on World Health Organization’s recommendations for effective procurement decisions. A mixed-sampling method was used to administer the questionnaire to 393 community pharmacists in Southwest, Nigeria. Descriptive and inferential statistics such as Friedman’s test, chi-square, Henry Garrett’s scoring and, multinomial regression (MNL) models were used for data analysis, using SPSS-25. The significance level was set at p<0.05. Results: Results showed that 59.8% (235) of respondents operated as retail practice, 14.8% (62) Wholesale, and 24.4% (96) combined practice. Mean Garrett’s score was highest with ‘quality-assurance (63.36), while ‘Value-added service’ had the least score (38.88) among 10 decision-factors. The median score was 52.82. Individual effects of ‘quality-assurance, competitive-pricing, access-to-credit facilities, flexible payment schedule, range of products, the potential-for-profit, trade-discounts, and value-added service’ were significant determinants of preference decisions (p<0.01; 95% CI) in the MNL model. Interaction effects of competitive pricing and access-to-credit facilities from suppliers had a significant effect on the MNL model (chi-square=493.411; p<0.01; 95% CI). Conclusion: The model predicted preference for supply channels (PC, LW, and OM) at various significance levels of the predictors. The study provided a scoring template for evaluating buying decision parameters. The study provided information that is useful to improve our understanding of buying behavior among CPs in pharmacy practice research Peer Review History: Received: 15 July 2021; Revised: 14 August; Accepted: 7 September, Available online: 15 September 2021 Academic Editor: Dr. Asia Selman Abdullah, Al-Razi university, Department of Pharmacy, Yemen, [email protected] UJPR follows the most transparent and toughest ‘Advanced OPEN peer review’ system. The identity of the authors and, reviewers will be known to each other. This transparent process will help to eradicate any possible malicious/purposeful interference by any person (publishing staff, reviewer, editor, author, etc) during peer review. As a result of this unique system, all reviewers will get their due recognition and respect, once their names are published in the papers. We expect that, by publishing peer review reports with published papers, will be helpful to many authors for drafting their article according to the specifications. Auhors will remove any error of their article and they will improve their article(s) according to the previous reports displayed with published article(s). The main purpose of it is ‘to improve the quality of a candidate manuscript’. Our reviewers check the ‘strength and weakness of a manuscript honestly’. There will increase in the perfection, and transparency. Received file: Reviewer's Comments: Average Peer review marks at initial stage: 5.0/10 Average Peer review marks at publication stage: 7.0/10 Reviewers: Dr. Vanina Doris Edo’o, University of Yaounde I, Yaounde, Cameroun, [email protected] Dr. Muhammad Zahid Iqbal, AIMST University, Malaysia, [email protected] Similar Articles: AWARENESS OF PHARMACISTS TOWARDS ASPARTAME SIDE EFFECTS IN KHARTOUM CITY, SUDAN

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.

A regret lower bound for assortment optimization under the capacitated MNL model with arbitrary revenue parameters

In this note, we consider dynamic assortment optimization with incomplete information under the capacitated multinomial logit choice model. Recently, it has been shown that the regret (the cumulative expected revenue loss caused by offering suboptimal assortments) that any decision policy endures is bounded from below by a constant times $\sqrt {NT}$ , where $N$ denotes the number of products and $T$ denotes the time horizon. This result is shown under the assumption that the product revenues are constant, and thus leaves the question open whether a lower regret rate can be achieved for nonconstant revenue parameters. In this note, we show that this is not the case: we show that, for any vector of product revenues there is a positive constant such that the regret of any policy is bounded from below by this constant times $\sqrt {N T}$ . Our result implies that policies that achieve ${{\mathcal {O}}}(\sqrt {NT})$ regret are asymptotically optimal for all product revenue parameters.

Optimal Policy for Dynamic Assortment Planning Under Multinomial Logit Models

We study the dynamic assortment planning problem, where for each arriving customer, the seller offers an assortment of substitutable products and the customer makes the purchase among offered products according to an uncapacitated multinomial logit (MNL) model. Because all the utility parameters of the MNL model are unknown, the seller needs to simultaneously learn customers’ choice behavior and make dynamic decisions on assortments based on the current knowledge. The goal of the seller is to maximize the expected revenue, or, equivalently, to minimize the expected regret. Although dynamic assortment planning problem has received an increasing attention in revenue management, most existing policies require the estimation of mean utility for each product and the final regret usually involves the number of products [Formula: see text]. The optimal regret of the dynamic assortment planning problem under the most basic and popular choice model—the MNL model—is still open. By carefully analyzing a revenue potential function, we develop a trisection-based policy combined with adaptive confidence bound construction, which achieves an item-independent regret bound of [Formula: see text], where [Formula: see text] is the length of selling horizon. We further establish the matching lower bound result to show the optimality of our policy. There are two major advantages of the proposed policy. First, the regret of all our policies has no dependence on [Formula: see text]. Second, our policies are almost assumption-free: there is no assumption on mean utility nor any “separability” condition on the expected revenues for different assortments. We also extend our trisection search algorithm to capacitated MNL models and obtain the optimal regret [Formula: see text] (up to logrithmic factors) without any assumption on the mean utility parameters of items.

Impact of COVID-19 Pandemic on Willingness to Consume Insect-Based Food Products in Catalonia

Edible insects are being considered as a sustainable source of protein and are continuously appearing in markets in the West. The impact of COVID-19 on the willingness to consume (WTC) two products enriched with insect ingredients, jam and yogurt, was analyzed. A semistructured questionnaire was applied using the Qualtrics© consumer panel. Data was collected from 799 and 481 consumers before and during the COVID-19 lockdown in Catalonia (Spain), respectively. The multinomial logit (MNL) model was used to analyze the determinant factors affecting consumers’ WTC insect-based products and the impact of COVID-19 on such heterogeneity. Results showed that the outbreak of COVID-19 caused a significant decrease in the WTC. Findings also revealed that consumers who contracted the COVID-19, strictly followed the regulations during the confinement, and are well informed about symptoms were more likely to reject the consumption of the insect-based products. Both before and during the lockdown, results showed that young and employed consumers, with low-income level, who give importance to the environmental attribute in food are prone to consume insect-based food products. The COVID-19 outbreak had a homogenizing impact on consumers’ WTC with respect to the gender variable. Consumers’ affirmation towards strict food safety standards of the insect-based products should be remarked.

Exploring Smallholder Farmers’ Preferences for Climate-Smart Seed Innovations: Empirical Evidence from Southern Ethiopia

Rapid plant breeding is essential to overcome low productivity problems in the face of climatic challenges. Despite considerable efforts to improve breeding practices in Ethiopia, increasing varietal release does not necessarily imply that farmers have access to innovative varietal choices. Prior research did not adequately address whether varietal attributes are compatible with farmers’ preferences in harsh environmental conditions. With an agricultural policy mainly aiming to achieve productivity maximization, existing breeding programs prioritize varietal development based on yield superiority. Against this background, we estimated a multinomial logit (MNL) model based on choice-experiment data from 167 bean growers in southern Ethiopia to explore whether farmers’ attribute preferences significantly diverge from those of breeders’ priorities. Four important bean attributes identified through participatory research methods were used. The results demonstrate that farmers have a higher propensity toward drought-tolerant capability than any of the attributes considered. The model estimates further show the existence of significant preference heterogeneity across farmers. These findings provide important insight to design breeding profiles compatible with specific producer segments. We suggest demand-driven breeding innovations and dissemination strategies in order to accelerate the adoption of climate-smart and higher-yielding bean innovations that contribute to achieve the national and global sustainability goals in Ethiopia.

Fail to Yield? An Analysis of Ambulance Crashes in Taiwan

Introduction—Medical emergency vehicles help patients get to the hospital quickly. However, there were more and more ambulance crashes on the road in Taiwan during the last decade. This study investigated the characteristics of medical emergency vehicle crashes in Taiwan from January 2003 to December 2016. Methods—The ordered logit (OL) model, multinominal logit (MNL) model, and partial proportional odds (PPO) model were applied to investigate the relationship between the severity of ambulance crash injuries and its risk factors. Results—We found the various factors have different effects on the overall severity of ambulance crashes, such as ambulance drivers’ characteristics and road and weather conditions. When another car was involved in ambulance crashes, there was a disproportionate effect on the different overall severity, as found by the PPO model. Conclusions—The results showed that male ambulance drivers and car drivers who failed to yield to an ambulance had a higher risk of severe injury from ambulance crashes. Ambulance crashes are an emerging issue and need further policies and public education regarding Taiwan’s ambulance transportation safety.

Hierarchical Bayes Conjoint Choice Models - Model Framework, Bayesian Inference, Model Selection, and Interpretation of Estimation Results

Choice-based conjoint (CBC) is nowadays the most widely used variant of conjoint analysis, a class of methods for measuring consumer preferences. The primary reason for the increasing dominance of the CBC approach over the last 35 years is that it closely mimics real choice behavior of consumers by asking respondents repeatedly to choose their preferred alternative from a set of several offered alternatives (choice sets). Within the framework of CBC analysis, the multinomial logit (MNL) model is the most frequently used discrete choice model due to the existence of closed form solutions for conditional choice probabilities. The popularity of CBC and the MNL model has grown even more since the introduction of hierarchical Bayesian (HB) estimation techniques that accommodate individual consumer heterogeneity in choice data, and which have now become state-of-the-art in marketing theory and practice. Still, researchers and practitioners have to make further decisions under this framework (CBC, MNL, HB estimation), such as how to represent preference heterogeneity. Here, using a normal distribution (and therefore a unimodal distribution) has become the standard approach in the marketing literature. However, the thin tails of the normal distribution suggest that the standard HB-MNL model should not be the “go-to” approach to approximate multimodal preference distributions, because individual preference patterns lying at the tails of the normal distribution (i.e., that do not fit well with the assumption of a unimodal distribution) tend to be shrunk to the population mean. This shrinkage, especially in multimodal data settings, could mask important information (e.g., new or different structures in the data). A mixture of normal distributions avoids this limited flexibility of the most simple continuous approach of assuming a unimodal prior heterogeneity distribution. There are currently two prominent HB-CBC modeling approaches embedding the mixture-of-normals (MoN) approach: the more widespread MoN-HB-MNL model, and the Dirichlet process mixture (DPM)-HB-MNL model. In this article, we review the prominent HB-MNL model (with its normal prior), the MoN-HB-MNL model, and the DPM-HB-MNL model and apply them to an empirical multi-country CBC data set. We compare the statistical performance of the three models in terms of goodness-of-fit and predictive accuracy, show how to include consumer background characteristics in the upper level of these models, and illustrate how to interpret the estimation results (with a special focus on cross-county heterogeneity). In sum, our article serves as a kind of user guide to the estimation and interpretation of Hierarchical Bayes Conjoint Choice Models. For our data, we observed that all three choice models (both with and without consumer background characteristics) resulted in a one-component solution. The DPM-HB-MNL model nevertheless yielded a higher cross-validated hit rate compared to the MoN-HB-MNL and the HB-MNL models due to its even more flexible prior assumptions. The two latter models tended to slightly overfit our empirical data, which was reflected by higher goodness-of-fit statistics but a lower predictive accuracy compared to the DPM-HB-MNL model. We showed that this result could be attributed to the weaker extent of Bayesian shrinkage of these two models. The DPM-HB-MNL model showed a stronger shrinkage effect and seems therefore somewhat more robust against overfitting. Including consumer background characteristics in terms of country of origin information for the respondents did not improve the statistical model performance (especially not the predictive performance). Still, using the country of origin information for respondents in a post-hoc segmentation analysis helped us to explain some differences in brand preferences between the five countries.