Browsing by Author "Glaser, Vern L."

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    Algorithmic routines and dynamic inertia: how organizations avoid adapting to changes in the environment
    (Wiley, 2022-04-16) Omidvar, Omid; Safavi, Mehdi; Glaser, Vern L.
    Organizations often fail to adequately respond to substantive changes in the environment, despite widespread implementation of algorithmic routines designed to enable dynamic adaptation. We develop a theory to explain this phenomenon based on an inductive, historical case study of the credit rating routine of Moody’s, an organization that failed to adapt to substantial changes in its environment leading up to the 2008 financial crisis. Our analysis of changes to the firm’s algorithmic credit rating routine reveals mechanisms whereby organizations dynamically produce inertia by taking actions that fail to produce significant change. Dynamic inertia occurs through bounded retheorization of the algorithmic model, sedimentation of assumptions about inputs to the algorithmic model, simulation of the unknown future, and specialized compartmentalization. We enable a better understanding of organizational inertia as a sociomaterial phenomenon by theorizing how—despite using algorithmic routines to improve organizational agility—organizations dynamically produce inertia, with potentially serious adverse consequences.
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    Predictive models can lose the plot. Here's how to keep them on track
    (Sloan Management Review Association, 2023-06-13) Glaser, Vern L.; Omidvar, Omid; Safavi, Mehdi
    Organizations are increasingly turning to sophisticated data analytics algorithms to support real-time decision-making in dynamic environments. However, these organizational efforts often fail—sometimes with spectacular consequences. In 2018, real-estate marketplace Zillow launched Zillow Offers, an “instant buyer” arm of the business that leveraged a proprietary algorithm called Zestimate, which calculated the estimated sales prices of real estate. Based on these calculations, Zillow Offers planned to purchase, renovate, and resell properties for a profit.1 While it had some success for the first few years, the model failed to adjust to the new dynamics of a more volatile market in 2021. Zillow lost an average of $25,000 on every home they sold in the fourth quarter of 2021—resulting in a write-down of $881 million.2 This is an instance of what we call algorithmic inertia: when organizations use algorithmic models to take environmental changes into account, but fail to keep pace with those changes. Here, we explain algorithmic inertia, identify its sources, and suggest practices organizations can implement to overcome it.