Past research offers mixed perspectives on whether domain experience helps or hurts algorithm-augmented worker performance. Reconciling these perspectives, we theorize that intermediate levels of domain experience are optimal for algorithm-augmented performance, due to the interplay between two countervailing forces—ability and aversion. Although domain experience can increase performance via increased ability to complement algorithmic advice (e.g., identifying inaccurate predictions), it can also decrease performance via increased aversion to accurate algorithmic advice. Because ability developed through learning by doing increases at a decreasing rate, and algorithmic aversion is more prevalent among experts, we theorize that algorithm-augmented performance will first rise with increasing domain experience, then fall. We test this by exploiting a within-subjects experiment in which corporate information technology support workers were assigned to resolve problems both manually and using an algorithmic tool. We confirm that the difference between performance with the algorithmic tool versus without the tool was characterized by an inverted U-shape over the range of domain experience. Only workers with moderate domain experience did significantly better using the algorithm than resolving tickets manually. These findings highlight that, even if greater domain experience increases workers’ ability to complement algorithms, domain experience can also trigger other mechanisms that overcome the positive ability effect and inhibit performance. Additional analyses and participant interviews suggest that, even though the highest experience workers had the greatest ability to complement the algorithmic tool, they rejected its advice because they felt greater accountability for possible unintended consequences of accepting algorithmic advice.

The precipitous growth of remote work has given rise to a new phenomenon: the emergence of relocation incentive programs that localities use to compete for the physical presence of remote workers. Remote workers with high general human capital may create value for their new destinations and reverse net talent outflow from smaller cities in middle America and globally. However, localities seeking to attract, retain, and create value from remote workers face significant challenges because such workers may have a low attachment to their new destination. Analogizing these challenges to the problem of creating and capturing value from workers with general human capital, we argue that localities can benefit from using relocation incentive program by leveraging location-specific attributes that create value for the individual and the locality. We examined these ideas in the context of Tulsa Remote, a program that provides relocation incentives and a bundle of services to increase engagement and embeddedness in Tulsa, Oklahoma. We found that Tulsa Remote increased community engagement, real income, and entrepreneurship of remote workers, benefiting both the community and the individual. Tulsa Remote increased the worker’s willingness to stay, and local community engagement is a key driver of this relationship. This work thus suggests that location specificity enables localities to both create and capture value from remote workers. Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2023.17712 .

Firm-induced migration typically entails firms relocating workers to fill value-creating positions at destination locations. But such relocated workers are often exposed to external employment opportunities at their destinations, possibly triggering turnover. We conceptualize the firm-induced migration path, consisting of the relocated workers’ place of origin and destination, as relevant in determining worker performance and turnover postrelocation. Using a unique data set from a large Indian technology firm that hires talent from both large cities and smaller towns, we document robust econometric patterns by exploiting the firm’s randomized assignment of workers to production centers across the country. These production centers are located in the largest technology cluster in India (Bangalore), smaller technology clusters, and noncluster locations. We find that the firm-induced migration path shapes both worker performance and turnover. Compared with workers from large cities, workers from smaller towns achieve higher performance when relocated to Bangalore than to other production centers, but are also more likely to join competing firms. Fine-grained data on employment and human-capital-augmentation opportunities at workers’ destination locations, and on socioeconomic conditions in workers’ places of origin, help us rule in an abductive explanation: across firm-induced migration paths, differences in external labor-market opportunities between workers’ places of origin and their destinations, as well as intrafirm skill-development opportunities at the destination, are related to heterogeneous human-capital outcomes. This paper was accepted by Alfonso Gambardella, business strategy. Supplemental Material: The e-companion and data files are available at https://doi.org/10.1287/mnsc.2022.4361 .

Abstract Research Summary An emerging form of remote work allows employees to work‐from‐anywhere , so that the worker can choose to live in a preferred geographic location. While traditional work‐from‐home (WFH) programs offer the worker temporal flexibility, work‐from‐anywhere (WFA) programs offer both temporal and geographic flexibility. WFA should be viewed as a nonpecuniary benefit likely to be preferred by workers who would derive greater utility by moving from their current geographic location to their preferred location. We study the effects of WFA on productivity at the United States Patent and Trademark Office (USPTO) and exploit a natural experiment in which the implementation of WFA was driven by negotiations between managers and the patent examiners' union, leading to exogeneity in the timing of individual examiners' transition from a work‐from‐home to a work‐from‐anywhere program. This transition resulted in a 4.4% increase in output without affecting the incidence of rework. We also report results related to a plausible mechanism: an increase in observable effort as the worker transitions from a WFH to a WFA program. We employ illustrative field interviews, micro‐data on locations, and machine learning analysis to shed further light on geographic flexibility, and summarize worker, firm, and economy‐wide implications of provisioning WFA. Managerial Summary Work‐from‐anywhere is an emerging form of remote work, in which workers are awarded geographic flexibility, that is, the flexibility to choose where to live. We study the productivity effects of workers moving from a work‐from‐home (WFH) to a work‐from‐anywhere (WFA) regime at the United States Patent and Trademark Office (USPTO). Exploiting a natural experiment, we find that the transition from WFH to WFA resulted in a 4.4% increase in employee output, with no increase in rework. We also report an increase in employee effort after the transition to WFA and document qualitative evidence on how geographic flexibility benefits individual workers and the USPTO (e.g., real estate savings).