The rapid adoption of remote work led to a sharply reduced presence of office workers in urban centers, weakening cities' traditional role as a center for production. Despite the adverse effect of remote work on cities, we highlight that cities' role as a center for consumption remains strong and may have risen with increased time flexibility from workers.

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.

We reassess whether and to what degree the hiring, development, and promotion decisions of S&P 500® companies have led to misrepresentation of and bias against their minority executives. Instead of the US population benchmark that has conventionally been used to measure misrepresentation, and from such misrepresentation attribute the presence and magnitude of racial bias and discrimination, we measure misrepresentation in US executives using the benchmark of the racial/ethnic densities (RAEDs) of their college cohort peers.

We use construal level theory to investigate how the way employees construe where work occurs—defined as work context construal—influences perceptions of harm and the ethical framing of risk-mitigating behaviors. We hypothesize that high-level (abstract) work context construals (vs. low-level, concrete ones) reduce perceptions of potential harm which, in turn, leads to framing risk-mitigating behaviors as less of an ethical obligation.

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.

Scholars are increasingly recognizing that allyship affects allies themselves. Although existing scholarship covers a multitude of constructs, most of the literature focuses on social evaluations and their effects on allyship persistence. We posit that the dual focus on social evaluations and allyship persistence has limited the theoretical insights and applied relevance of scholarship on the consequences of allyship for allies.

We present a classical enhancement to improve the accuracy of the Hybrid variant (Hybrid HHL) of the quantum algorithm for solving linear systems of equations proposed by Harrow, Hassidim, and Lloyd (HHL). We achieve this by using higher precision quantum estimates of the eigenvalues relevant to the linear system, and a new classical step to guide the eigenvalue inversion part of Hybrid HHL.