Healthy Food Environments in Food Pantries: Lessons Learned from a Sodium Reduction Intervention

In the United States, food pantries increasingly serve as regular food sources for low income households experiencing high rates of chronic disease, including hypertension. Sodium consumption is a modifiable risk factor for hypertension, so pantry customers would benefit from access to low-sodium foods. Pantry customers often experience difficulty acquiring healthy foods, however; little is known about pantry foods’ sodium content specifically. This study assesses the sodium content of pantry foods and lessons learned from an adaptable intervention to support pantries in adopting policies and environmental changes to make healthy, lower-sodium foods appealing and accessible. We conducted sodium assessments of food at 13 food pantries, tracked implementation of intervention strategies, and interviewed 10 pantry directors. More than half of food items in 11 categories met sodium standards for foods to be chosen “often”. Pantry directors reported valuing the intervention approach and implemented six of nine behavioral economics strategies, especially those targeting the visibility and convenience of foods, along with layout changes and expanded customer choice. One pantry adopted an agency-specific nutrition policy and 12 adopted a coalition-level policy. Results can inform intervention efforts to make available healthy options appealing and easy to select while also improving the customer experience in food pantries.

Constrained Assortment Optimization Under the Paired Combinatorial Logit Model

Assortment optimization involves selecting a subset of products to offer to customers in order to maximize revenue. Often, the selected subset must also satisfy some constraints, such as capacity or space usage. Two key aspects in assortment optimization are (1) modeling customer behavior and (2) computing optimal or near-optimal assortments efficiently. The paired combinatorial logit (PCL) model is a generic customer choice model that allows for arbitrary correlations in the utilities of different products. The PCL model has greater modeling power than other choice models, such as multinomial-logit and nested-logit. In “Constrained Assortment Optimization Under the Paired Combinatorial Logit Model,” Ghuge, Kwon, Nagarajan, and and Sharma provide efficient algorithms that find provably near-optimal solutions for PCL assortment optimization under several types of constraints. These include the basic unconstrained problem (which is already intractable to solve exactly), multidimensional space constraints, and partition constraints. The authors also demonstrate via extensive experiments that their algorithms typically achieve over 95% of the optimal revenues.

The Effects of Organizational Design and Decision Making Process on Organizational Performance

This research was aimed to analyze organizational performance through organizational design and decision making process. Garuda Indonesia was chosen as the case study object of this research, because the condition of organizational design and decision making process were taken through decentralization method. This research result referred that the organizational design and decision making process could affect positively on organizational performance, which in this recent research, the organizational design was proven to deliver positive effects on organizational performance, but only on organic org. form. Garuda Indonesia as a full service airline needed innovation to keep improving and being customer choice, which the strategic decision making was taken through decentralization method according to the dynamic needs in the middle of competitive environment with full of uncertainties.

Investigate the Influence of Online Ratings and Reviews in Purchase Behavior Using Customer Choice Sets

The exponentially growing online reviews have become a great wealth of information into which many researchers have started tapping. Using online reviews as a source of customer feedback, product designers are able to better understand customers’ preferences and improve product design accordingly. However, while predicting future product demand as a function of product attributes and customer heterogeneity has proved to be effective, not many literatures have studied the impact of non-product-related features, such as number of reviews and average ratings, on product demand using a large-scale dataset. As such, this paper proposes a data-driven methodology to investigate the influence of online ratings and reviews in purchase behavior by using discrete choice analysis. In the absence of information about the true customer choice set, we generate an estimated customer choice set based on a probability sampling using customer clustering and product clustering. In order to examine the effect of number of reviews and average rating, we have computed, for all the laptops in the choice set of each customer, the number of reviews and thus average rating at the date of this particular customer’s review. Using laptops for our case study, our experiment has shown that the number of reviews and average ratings are statistically significant, and the inclusion of these features will greatly improve the predictive ability of the model.

A Study on Factor Influencing Customer Choice of Store Selection and Behaviour in Organized Retail Outlets in Madurai

Retailing is a major business in India, organized retailers are entering in to Indian markets to reach mass sales and maximize profit , in this stage retailers should aware of the factor that influencing customer choice of store and customer behaviour, loyalty of the customer is a tool to reach profitability and with hold in market, and objective of this study is to know the factor which influences customer choice of store selection and their behaviour in organized retail out lets in Madurai, 175 samples were selected using simple random sampling method, data collected with the structured interview schedule, SPSS package has been used for statistical analysis.

Breaking the Symmetry in Queues with Delayed Information

Giving customers queue length information about a service system has the potential to influence the decision of a customer to join a queue. Thus, it is imperative for managers of queueing systems to understand how the information that they provide will affect the performance of the system. To this end, we construct and analyze a two-dimensional deterministic fluid model that incorporates customer choice behavior based on delayed queue length information. Reports in the existing literature always assume that all queues have identical parameters and the underlying dynamical system is symmetric. However, in this paper, we relax this symmetry assumption by allowing the arrival rates, service rates, and the choice model parameters to be different for each queue. Our methodology exploits the method of multiple scales and asymptotic analysis to understand how to break the symmetry. We find that the asymmetry can have a large impact on the underlying dynamics of the queueing system.