For the last decade we have been using double-blind survey techniques and randomized sampling to construct management data on over 10,000 organizations across 20 countries. On average, we find that in manufacturing American, ...

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 ...

Past research has produced contradictory insights into how prior collaboration between organizations—their relational embeddedness—impacts collective collaborative performance. We theorize that the effect of relational ...

This article explores the consequences of psychological ownership going beyond the specific relationship with the possession to guide behavior in unrelated situations. Across seven studies, we find that psychological ...

The use of machine learning (ML) for productivity in the knowledge economy requires considerations of important biases that may arise from ML predictions. We define a new source of bias related to incompleteness in real ...

We study how learning by experience across projects affects an entrepreneur's strategic foresight. In a quantitative study of 314 entrepreneurs across 722 crowdfunded projects supplemented with a program of qualitative ...

Given the increasingly specific ways marketers can target ads, many consumers and regulators are demanding ad transparency: disclosure of how consumers’ personal information was used to generate ads. We investigate how and ...

Software platforms create value by cultivating an ecosystem of complementary products and services. Existing explanations for how a prospective complementor chooses platforms to join assume the complementor has rich ...

Organizations often respond to institutional pressures by symbolically adopting policies and procedures but decoupling them from actual practice. Literature has examined why organizations decouple from regulatory pressures. ...

Supervised machine learning (ML) methods are a powerful toolkit for discovering robust patterns in quantitative data. The patterns identified by ML could be used for exploratory inductive or abductive research, or for ...