A fundamental issue faced by operations management researcher relates to striking the right balance between rigor and relevance in their work. Another important aspect of operations management research relates to influencing and positively impacting businesses and society at large. We constantly struggle to achieve these objectives.

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.

The case study "Electronic Financial-Advisor for Tech Savvy" (EFforTS, or Efforts) examines a Robo-Advisor start-up based in Raleigh, North Carolina, founded by tech-industry entrepreneurs. Efforts developed an algorithm-based online investment platform tailored for technology professionals, gaining attention through successful social media marketing.

We study the problem of dynamically assigning jobs to workers with two key aspects: (i) workers gain or lose familiarity with jobs over time based on whether they are assigned or unassigned to the jobs, and (ii) the availability of workers and jobs is stochastic. This problem is motivated by applications in operating room management, where a fundamental challenge is maintaining familiarity across the workforce over time by accounting for heterogeneous worker learning rates and stochastic availability.

While failure and success have important implications for individual and organizational performance, not all failure and success are equally significant in influencing performance. A key, but unexamined, element is one's expectation of the outcome.

Rumors are ubiquitous in the workplace, particularly regarding organizational changes. These rumors significantly influence worker behavior by introducing uncertainty, and thus, affect productivity and team performance. However, no studies have provided empirical evidence for these impacts due to data limitations on rumors and workers' behaviors in completing tasks.

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.

Ride-hailing services are an essential mode of transportation for millions worldwide. The rapid growth of this service has raised concerns about its environmental impact. To address these concerns, ride-hailing companies are adding or introducing environmentally friendly vehicles (e.g., electric vehicles) to their platforms. However, it is not clear how adding such "green" vehicles will affect the environment and customers. To our knowledge, this paper is the first to analyze this question theoretically.

Healthcare services provided to patients with similar health conditions are known to vary. Standardization of healthcare delivery is a relatively new, yet hotly debated approach to address clinical variations. Previous research on process standardization in health services has focused on measuring adherence to established protocols that are available only for a limited set of disease states. We create an alternate construct that quantifies process standardization measured in terms of consistency of services rendered, and apply it to the healthcare setting using detailed nonpublic inpatient discharge data from about 35 million inpatient stays at 296 acute care hospitals in California between 2008-2016.

Editorial to the JOM special issue on Pre-approved Research Designs for Field Experiments.

We study a multi-armed bandit problem with dependent arms. The decision maker dynamically chooses an arm out of finitely many arms to maximize her total expected discounted net benefit over an infinite time horizon. Pulling each arm incurs a particular cost and provides a certain reward. Arms' rewards are dependent on each other through a common parameter unknown to the decision maker. Thus, by pulling one arm, the decision maker also collects information about the other arms.