Telehealth Utilization among Low Income Population during COVID-19: An Analysis of COVID-19 Research Database

The COVID-19 Research Database is a public data platform. This platform is a result of private and public partnerships across industries to facilitate data sharing and promote public health research. We analyzed its linked database and examined claims of 2,850,831 unique persons to investigate demographic, socio-economic, and behavioral causes for telehealth utilization in the low-income population. Our results suggest that patients who had higher education, income, and full-time employment were more likely to use telehealth. Patients who had unhealthy behaviors such as smoking were less likely to use telehealth. Our findings suggest that interventions to bolster education, employment, and healthy behaviors should be considered to promote the use of telehealth services

Experimental Evaluation of Sponsored Search Auction Mechanisms

The theory of sponsored search has been developing rapidly although with disagreement in scientific circles on answers to some basic questions about sponsored search. This study focuses on two of these questions, namely, if a search engine seeks to maximize profits, 1) what should its pricing policy be and 2) what should its ranking policy be. This paper uses experiments with economically motivated human subjects to address these questions. We evaluate six different sponsored search auction formats with two different pricing policies (Pay-per-transaction & Pay-per-click) and three different ranking policies (Rank by relevance, Rank by click-through rate, & Rank by both relevance and click-through rate). Our results suggest that Pay-per-click is superior and the reason behind its superiority is behavioral in nature whereas the ranking policy has significant effect on search engine revenue and advertiser profit

Buyers’ Dynamic Click Behavior on Digital Sales Platforms with Complementarities

With numerous options available on digital platforms, buying products is becoming an increasingly complex decision-making task. Many well-known digital sales platforms like Amazon, Uber, Etsy, or Airbnb try to offer products or services that best match the buyers’ search criteria but Amazon for example often also lists products that can possibly complement the best match (Sloane, 2018). Buyers have access to product and price information, and they have to consider multiple factors in making their decisions on checking available options (Karimi et al., 2015). The buyer needs to know how the platform chooses which products or services to display. The buyers’ decision might also be impacted by the way sellers of products are charged to display their products or services on the platform and by the order in which the products are displayed on the screen. Buyers have to keep track of the prices and deals offered related to the various products they have checked on the platform and also consider their opportunity cost of search. As the complexity and cost of the search process increases, there are searches that often end without success. Understanding better how buyers make click decisions dynamically can help platforms increase the success of product searches, buyer satisfaction and ultimately, profitability. In this study we focus on platforms which offer both primary products (products who best match the buyers’ search criteria) and secondary products (products who complement the primary products) and they rank these products on a buyer’s screen either by relevance or by click-through rate ((Hao et al., 2020). We aim to find answer to the following question: To what extent do product values and product prices determine the order in which the buyer clicks through the primary and secondary products. To answer this question, we create a dynamic model that predicts each step in a buyer’s click strategy. The model incorporates rational decision-making as well as known behavioral biases. Under naturally occurring circumstances information, such as the value of a product to a buyer, is strictly private and unavailable. Therefore, we use lab experiments with human subjects to test our model. The model is able to predict a higher percentage of buyer click behavior than existing static search models. Unlike static search models our model predicts a non-zero percentage of clicks on more than two products and provides some guidance on the factors that can lead buyers to make that decision. This study contributes to the theory of shopping on digital platforms because it is a model of sequential search that incorporates rational decision making as well as known human behavioral biases to explain how buyers shop in sequence given the information they discover. As far as we know this is also the first dynamic model that incorporates product complementarities as part of the decision-making environment

A Market-Based Approach to Facilitate the Organizational Adoption of Software Component Reuse Strategies

Despite the theoretical benefits of software component reuse (and the abundance of component-based software development on the vendor side), the adoption of component reuse strategies at the organizational level (on the client side) remains low in practice. According to research, the main barrier to advancing component-based reuse strategies into a robust industrial process is coordination failures between software producers and their customers, which result in high acquisition costs for customers. We introduce a component reuse licensing model and combine it with a dynamic price discovery mechanism to better coordinate producers’ capabilities and customer needs. Using an economic experiment with 28 IT professionals, we investigate the extent to which organizations may be able to leverage component reuse for performance improvements. Our findings suggest that implementing component reuse can assist organizations in addressing the issue of coordination failure with software producers while also lowering acquisition costs. We argue that similar designs can be deployed in practice and deliver benefits to software development in organizations and the software industry

Exact Solutions for the Wick-Type Stochastic Schamel-Korteweg-de Vries Equation

We consider the Wick-type stochastic Schamel-Korteweg-de Vries equation with variable coefficients in this paper. With the aid of symbolic computation and Hermite transformation, by employing the (G′/G,1/G)-expansion method, we derive the new exact travelling wave solutions, which include hyperbolic and trigonometric solutions for the considered equations

Strategy Dynamics in Markets of Software Components

In this paper we propose a dynamic model of a software market for component reuse. We investigate the market dynamics using experiments with economically motivated human subjects. Our results suggest that the introduction of the software component market reduces production costs and increases vendor profits. The dynamic interactions in the component market helped vendors coordinate better their production decisions and resulted in production cost savings. The component market can thrive on a balance between competition and cooperation of software vendors. These experimental results could be applied with some modifications to the development of software products in general