Decision-Aware Learning for Global Health Supply Chains

Funded Research Proposal Tsai-Hsuan (Angel) Chung, Phd Candidate at the Wharton School; Hamsa Bastani, Operations, Information, and Decisions, The Wharton School; Osbert Bastani, Penn School of Engineering; Francis Smart, PhD Candidate at the University of Montana; Jatu Abdulai*; Patrick Bayoh*; Musa Komeh*; Vahid Rostami* Abstract: This proposal aims to develop and deploy novelRead More

Differentiation in Microenterprise: A Field Experiment in Zimbabwe

Funded Research Proposal

In explaining variation in productivity in microenterprise, research has focused primarily on the adoption of effective business practices and access to capital, with little focus on strategic positioning. In archival evidence, we find that offering a differentiated product or service is strongly correlated with firm performance. Using a combined sample of nearly 10,000 microenterprises across eight developing countries, we estimated that a standard deviation increase in differentiation is associated with approximately an 11 percent increase in revenues and an eight percent increase in profitRead More

The Impact of Healthcare Price Transparency Rules

Funded Research Proposal

The US government recently required healthcare price transparency from hospitals and insurers in an attempt to control spiraling healthcare costs. On the one hand, price transparency and online search tools have been very effective in increasing competition for other products in the e-commerce space. On the other hand, healthcare is a complex product, often bought through insurance, which makes it unclear how much transparency will affect realized prices. Further, public disclosure of prices might allow healthcare providers to better negotiate with insurance companies, which might even lead to an increase in healthcare prices. Our project uses a large-scale nationwide dataset of healthcare insurance claims to analyze the potential benefits and impact of these regulations, and inform policymakers and the public about the values and risks of these reforms.Read More

Artificial Intelligence, CEO Turnover, and Directional Change in Firm Innovation

Funded Research Proposal

In this project, we aim to investigate the extent to which acquiring AI capabilities can ease firms’ transition of innovation directions in uncertain times of leadership change. To achieve this goal, we plan to identify the challenges that firms face when they attempt to pivot the innovation direction during CEO turnover. Furthermore, we plan to examine how AI can help mitigate these challenges and provide new opportunities for certain types of innovation development. Our study will provide a roadmap for firms to make optimal decisions when allocating AI resources for strategic changes in innovation.Read More

Reality Check: Subjective Well-being and the Value of the Metaverse

Funded Research Proposal

In this project, we adapt a classic philosophical method (Nozick’ 1974 “experience machine”) to understand the ways in which people think about the types of alternate realities presented by the metaverse, and with it, the type of value it may offer in the marketplace.Read More

Resetting the Clock: Dynamic Goal Setting Using Behavior Tracking Technology

Funded Research Proposal

Can companies providing goal-tracking technology innovate their products to better enable consumers to keep going when they inevitably fail (i.e., skip a workout or overeat calories)? Our research aims to fill this gap by testing the effectiveness of one type of goal tracking design: dynamic goal setting. Most traditional goal tracking apps focus on static goals – goals where the consumer aims to complete the same amount of goal behavior each day (i.e., working out 30 minutes a day or walking 10,000 steps a day). However, we suggest behavior tracking apps that use AI technology to vary the amount of goal behavior the consumers engage in from day to day (i.e., work out an average of 30 minutes a day, with some days being 15, some days 45, etc.) will be more effective, a type of goal tracking we term dynamic goal setting.Read More

Exclusivity in the Video Streaming Market

Funded Research Proposal

The main goal of this project is to enhance our understanding of the role that exclusive contracts play in shaping market structure, consumer demand, and innovation. The effect of exclusivity on consumer welfare is ambiguous.Read More

Examining the Role of Local Boards of Health in Local Health Departments in the United States

Funded Research Proposal

Nearly 3,000 local health departments (LHDs) across the United States are tasked with improving, maintaining, and monitoring population health. LHDs are an administrative or service unit of local or state government concerned with health, and carrying out the responsibility for the health of a jurisdiction smaller than a state.Read More

Innovating Shift Work Design to Incorporate Heterogeneous Worker Preferences

Funded Research Proposal

Shifts are the dominant way to work in many contexts requiring 24/7 coverage, including call centers, police departments, and hospitals. While the detriments of shift work are well-documented both at the individual and organizational levels, its deployment is often unavoidable given round-the-clock staffing needs. We explore a potential organizational lever—incorporating heterogeneous preferences over shift design—to mitigate ramifications of shift work in the context of acute care bedside nurses. Using survey, administrative, and shift data, we examine whether and the extent to which individual choice over dimensions of their shifts mitigates the impact of shift work on work (dis)satisfaction and turnover of nurses.Read More

A Swing and a Hit: Optimal Disclosure Policy for Swing Pricing in Mutual Funds

Funded Research Proposal

What is the optimal disclosure policy for swing pricing by open-end mutual funds? Specifically, which disclosure rule, with respect to the swing factor and – thresholds, minimizes run risk in open-end mutual funds? Does the disclosure policy rule that is optimal from a financial stability perspective also preclude the possibility of front running by fund investors? Answering this question will likely also require a model of the optimal swing factor. By addressing this critical gap in the literature, my research aims to contribute to inform policy decisions regarding the proposed SEC rule.Read More

Strategies For Creating Value in Base of the Pyramid Markets: Evidence From a Field Experiment in India

Funded Research Proposal

In contexts with high poverty, impact-driven for-profit firms often take market-based approaches to drive inclusive growth while being financially sustainable. Even with the alignment between “doing well” and “doing good” that these firms supposedly seek to achieve, successful business strategies in these contexts have been associated with breadth of reach (reaching maximum number of beneficiaries) with little or no attention to depth of reach (repeated engagement with each beneficiary). However a focus on breadth alone may lead to tunnel vision when it comes to social impact. This paper draws the distinction between the two approaches and examines their implications for the outcomes for the different actors involved in BoP markets– the organization, its agents and the end beneficiaries. Read More

Private Equity and Productivity in US Healthcare

Funded Research Proposal

Private equity (PE) investment in US healthcare has increased dramatically in recent years. On one hand, widespread PE participation may be one avenue to stem rising costs and increase productivity, which have been elusive objects in US healthcare. Research from other sectors has shown that PE firms apply best-in-class management practices to improve productivity and firm value. On the other hand, high powered financial incentives for PE managers may create conflicts with the interests of consumers and of taxpayers, who finance about 45% of healthcare spending. This project aims to study how PE investment affects quality and productivity of healthcare delivery in the US, issues of first order importance for both policy and business. We have assembled the most comprehensive set of data resources used to study this question – confidential micro data on PE investments in healthcare firms, and national data on services, spending, and outcomes for about 30 million Medicare beneficiaries over 2000–17. We will deploy a differences-in differences research design, exploiting variation in the timing of PE acquisitions across firms over this long panel. We will examine effects on 1) growth 2) operating efficiency, and 3) quality of care delivered, over the short and long-run.Read More

Remanufacturing Consent: How Algorithmic Management Repurpose Workplace Consent

Funded Research Proposal

In recent years, the number of workers in the US who show up to work by turning on an app on their smartphone has dramatically increased. Dubbed on-demand or “gig” workers, these individuals log onto digital platforms and depend on algorithms, rather than managers, to set pay rate, segment work tasks, and supervise and evaluate their actions while on the job. Researchers have sounded alarms about the “encompassing, instantaneous, interactive, and opaque” (Kellogg et al., 2020) nature of such algorithmic management, concluding that it traps workers in an “invisible cage” (Rahman 2021). And yet, many on-demand workers report enjoying management by algorithms, enthusiastically noting the freedom that comes with this type of app-based work. Indeed, data from the Federal Reserve and Bureau of Labor from the past five years indicate a tight labor market alongside an increase in the number of workers in the on-demand economy (Kaplan et al., 2021; Katz & Krueger, 2016; 2019), suggesting that on-demand workers may not experience the work to be as oppressive as some scholars have suggested (Rosenblat, 2018; Ravenelle, 2019; Shapiro, 2018). Moving beyond money as the sole explanatory mechanism of why individuals work hard at their jobs, especially under less-than-ideal conditions, this study takes seriously how on-demand workers describe and value finding freedom and choice in their jobs. In doing so, I identify the limitations of the “carrots and sticks” metaphor that scholars have long used to describe the production of consent in the traditional workplace. Instead, I examine how algorithmic management, in conjunction with the new work arrangements of the gig economy, creates consent through the notion of workers having increased choices—a form of consent that, I argue, is more pervasive, but also more fragile.Read More

Algorithmic Pricing and Transparency in the Gig Economy

Funded Research Proposal

Algorithms control pricing and match customers and workers in the gig economy. However, algorithms face several critiques: they lack transparency, can be biased, and can be inefficient. We empirically analyze these issues and show that algorithms lose efficiency from two sources: competition between platforms and misaligned worker incentives. We model workers’ strategic responses to variation in pricing and estimate counterfactuals on the effects of minimum wage and transparent pricing policies.Read More

Are Startups More Innovative than Incumbent Counterparts? Evidence from AI Companies

Funded Research Proposal

I plan to study whether AI startups are more innovative than their incumbent counterparts. Although the extant literature highlights startups’ initial innovation capacities that help them identify and leverage market and technology opportunities, startups’ innovation competency at the founding stage no longer properly explains differences in competitive advantages in the digital era. Compared to traditional industries, companies in the digital era, such as AI firms leveraging big data, do not require capital-intensive manufacturing facilities to commercialize their ideas because the end products and services are digitized. Hence, the source of entrepreneurial innovation built upon the foundation-based explanation becomes obscure in the digital era, while it remains unclear what types of innovation startups vs. incumbents are better at during the post-foundation stages of startups. I argue that AI startups have competitive advantages in innovation against their incumbent counterparts based on different types of tech-based strategies for algorithm innovation. Read More

Consumer Cryptocurrency Confidence Index (CCCI or C3i)

Funded Research Proposal

The Consumer Cryptoconfidence Index offers the first ongoing, consumer-centered data source specifically intended to capture sentiment related to this market exchange tool. Offering both a novel “snapshot” understanding of the typical consumer’s perception of and interaction with cryptocurrency as well as data that may be fruitfully connected to other marketplace changes, the C3i index will allow us to build our knowledge as the cryptocurrency market evolves. By developing this understanding, we hope to support decision-making among consumers, regulators, investors, and entrepreneurs, while also laying the groundwork for ongoing consumer-centered research in this domain.Read More

AI Decision Aids and Metacognition

Funded Research Proposal

The proposed research focuses on how people use and perceive innovative decision aids, for example, AI tools like ChatGPT. Individuals and organizations are increasingly outsourcing part of their labor or decision-making process to these tools. The proposed research aims to investigate the perception around using AI decision aids, both in terms of its impact on individuals’ metacognitive assessments and observers’ social judgments. Read More

Evolution of Internet Retailing and Buy vs. Make Decisions

Funded Research Proposal

We have obtained data from Digital Commerce 360 which publishes pertinent statistics on top 1000 internet retail sites. One interesting aspect of this data is detailed information regarding outsourcing/insourcing decisions including web hosting, search engine optimization services, product delivery etc. This is a pretty unique data spanning hundreds of companies and a last decade. We wish to use this data to analyze outsourcing decisions of internet retailers and how these choices affect their performance. The data shows variation across time and industries, and it is supplemented with many quantitative metrics for internet retailers including number of visitors, conversion rate and basket size etc. We believe this unique data will allow us to gain unique insights into make vs buy decisions by internet retailers.Read More

Risk in Discovery-Stage Biotech Innovation

Funded Research Proposal

The main goal of this project is to enhance our understanding of what drives firm decisions in discovery-stage drug development, when risk of failure and variance in outcomes are highest. Specifically, this project contributes to existing literature on biopharmaceutical innovation, by testing whether the theory that larger firms pursue novel drugs is valid in the earliest phase of the drug development pipeline. Using data on early-stage VC funding and FOIA’ed biopharmaceutical alliances across thirty years, I use data-driven methods to examine the relationship between the novelty of biotech innovation and investor decisions at the earliest stage of drug development and contextualize its magnitude against other innovation characteristics. In doing so, I resolve a potential discrepancy between academic findings and industry observations.Read More

Innovate or Excavate? A Raw Resource View of Induced Innovation

Funded Research Proposal

This project aims to quantify the effect of innovation on commodity price reversions. Commodity prices tend to be stationary with occasional deviations and then revert to their mean prices. There are two big forces behind this reversion. First, there are firms that invest in research that allows them to substitute away from the expensive commodity. Secondly, the commodity producers will expand extraction capacity in response to increased profitability. The former affects the demand side and the latter the supply side. The goal of this project is to estimate the magnitude of the effect of price reversion due to innovation.Read More