Physicians spend more than 5 hours a day working on Electronic Health Record (EHR) systems and more than an hour doing EHR tasks after the end of the workday. In this paper, we investigate how physicians' workflow decisions on when to perform EHR tasks affect: (1) total time on EHR and (2) time spent after work.
Factor analysis is a widely used tool to summarize high dimensional panel data via a small dimensional set of latent factors. Applications, particularly in finance, are often focused on observable factors with an economic interpretation. The objective of this paper is to provide a formal test for the question whether the factor spaces of latent and observable (economic) factors are equal.
This paper investigates how bank supervisors’ enforcement decisions and orders (EDOs) influence the allocation of mortgage lending across demographic groups underlying a banks’ borrower base. Specifically, we investigate how banks’ mortgage lending to minority borrowers relative to white borrowers changes following the resolution of severe EDOs.
Using confidential offer-level data on the US housing market, this paper examines the rounding-off heuristics in the bilateral bargaining process. We demonstrate that home sellers and home buyers follow different rounding-off heuristics. While sellers' list prices cluster more frequently around charm numbers (e.g. 349,999), buyers' offer prices and negotiated final sales prices cluster at salient round numbers.
The paper uses structured machine learning regressions for nowcasting with panel data consisting of series sampled at different frequencies. Motivated by the problem of predicting corporate earnings for a large cross-section of firms with macroeconomic, financial, and news time series sampled at different frequencies, we focus on the sparse-group LASSO regularization which can take advantage of the mixed-frequency time series panel data structures.
It is common wisdom that practice makes perfect. And, in fact, we find evidence that when given a choice between practicing a task and reflecting on their previously accumulated practice, most people opt for the former. We argue in this paper that this preference is misinformed. Using evidence gathered in ten experimental studies (N = 4,340) conducted across different environments, geographies, and populations, we provide a rich understanding of the conditions under which the marginal benefit of reflecting on previously accumulated experience is superior to the marginal benefit of accumulating additional experience.
This paper presents the development, validation, and implementation of a data-driven optimization model designed to dynamically plan the assignment of anesthesiologists across multiple hospital locations within a large multi-specialty healthcare system. We formulate the problem as a multi-stage robust mixed-integer program incorporating on-call flexibility to address demand uncertainty. The optimized dynamic staffing plan has been successfully implemented in the University of Pittsburgh Medical Center healthcare system, leading to estimated annual cost savings of 12\% compared to current practice, or about \$800,000 annually.
This paper surveys the recent advances in machine learning method for economic forecasting. The survey covers the following topics: nowcasting, textual data, panel and tensor data, high-dimensional Granger causality tests, time series cross-validation, classification with economic losses.
This paper uses transaction-level import data at the shipment level to examine how multinational companies importing to the US have restructured their supply chains during the COVID-19 pandemic. We find that companies sourced from fewer locations, reduced the share of imports from China, and increased the share of imports from other Asian countries, such as India and Vietnam, and North American countries, such as Canada and Mexico. For managers, our results imply that a one-size-fits-all mentality regarding supply chain disruption responses is not appropriate, and companies’ disruption-response strategies need to be tailored to individual supply chains’ circumstances.
Quantum computers are not yet up to the task of providing computational advantages for practical stochastic diffusion models commonly used by financial analysts. In this paper we introduce a class of stochastic processes that are both realistic in terms of mimicking financial market risks as well as more amenable to potential quantum computational advantages.
This paper studies a long-term power purchase agreement (PPA) between a firm and a new renewable energy generator.
Ally work, or actions to support those from less advantaged social groups, shows promise in advancing social welfare in workplaces. Although much of the literature has explained factors that predict ally work, in this paper, we shift the conversation to understand the positive spillover of managers’ ally work on observing employees. We focus specifically on self ally work. Drawing from the theory of political ideology-as-motivated cognition, we propose that employees perceive managers who enact self ally work as more liberal (rather than conservative).
Past research in operations management and marketing on inventory levels and product variety has predominantly focused on their effects on brand performance indicators, such as sales and market share, while overlooking the influence on consumers’ perceptions of brands. Brand perceptions, encompassing reputation, quality, credibility, and emotional associations, go beyond typical revenue metrics and offer foresight into a brand’s future performance. Hence, understanding the effects of inventory and product variety on brand perceptions is crucial, and that constitutes the main contribution of this paper.
In the digital age, merchants are presented with an expanding range of resources to enhance operational transparency for customers. Moreover, the digital marketplace increasingly demands improved transparency due to the physical separation between merchants and customers. This paper explores a new method to enhance operational transparency for merchants on digital platforms: displaying real-time videos of their operational processes.
This paper investigates the causal impact of entrepreneurs' prior experience on startup success. Employing within-country changes in Green Card wait lines to instrument for immigrant first-time entrepreneurs' experience, we uncover that startups led by more experienced founders demonstrate superior funding, patenting, and employee growth.
With the increasing prevalence of renewable energy supply contracts, utility suppliers are investing in new green sources and developing allocation policies of those to satisfy renewable targets required by customers. However, the variability of customer demand and the intermittency in supply complicates the supplier's decision process. In this paper, we address these challenges by formulating the utility supplier's problem as a two-stage stochastic program.
The paper studies the nowcasting of Euro area Gross Domestic Product (GDP) growth using mixed data sampling machine learning panel data regressions with both standard macro releases and daily news data.
Feed supplements have recently been touted as an effective means to reducing methane emissions from livestock (e.g., cattle and sheep). In this paper, we examine the environmental implication of this innovation in a supply chain setting.
As deep learning and big data increasingly shape modern artificial intelligence (AI) tools, it is essential to consider the broader impact of integrating AI into workplaces. While AI applications can optimize processes and improve productivity, their long-term effects on workers’ learning curves and overall performance are still underexplored. This paper investigates the intricate relationship between AI-enabled technology and workers’ learning dynamics through a large-scale randomized field experiment conducted on the Instacart platform.
This intro-to-research session is for those students who would like to learn more about research at KFBS, including those who plan to pursue the honors thesis. It is for students who are either curious about research opportunities at Kenan-Flagler, intend to conduct research, or who just want to learn more about the research process.