How Does Worker Mobility Affect Business Adoption of a New Technology? The Case of Machine Learning

Author(s) Information

Natarajan Balasubramanian, Syracuse University

Chris Forman, Cornell University

Ruyu Chen, Stanford University



Journal (Year):

Under Review at the Strategic Management Journal

The essence of the paper:

Increases in outward mobility of employees increase costs for adoption of new technologies like ML that involve significant downstream investments in innovation.

Research Question:

Does the risk of employees leaving a firm affect the firm's propensity to adopt new technologies?

What We Know:

The evidence on how employee mobility may affect the propensity to adopt new technologies is limited. On the one hand, employee mobility has been found to benefit firms, for example, by bringing in new ideas from other firms. On the other hand, firms also risk losing valuable knowledge when skilled employees leave.  

Understanding this is particularly important as many new technologies such as ML require firms, especially early adopters, to make significant investments in the human capital of their employees.

Novel Findings:

We find that changes in the enforceability of non-compete agreements that facilitate worker movements are associated with a significant decline in the likelihood of adoption of machine learning by establishments. Moreover, we find that the magnitude of decline depends upon the size of the establishment, the extent of predictive analytics adoption in its industry, and the number of large establishments in the same industry-location.



Full Citation:

How does worker mobility affect business adoption of a new technology? The case of machine learning. Under Review at the Strategic Management Journal.

Abstract: 

We investigate how worker mobility influences the adoption of a new technology. Using data on over 153,000 establishments from 2010 and 2018, and state-level changes to the enforceability of noncompete agreements as an exogenous shock to worker mobility, we find that changes that facilitate worker movements are associated with a significant decline in the likelihood of adoption of machine learning by establishments. Moreover, we find that the magnitude of decline depends upon the size of the establishment, the extent of predictive analytics adoption in its industry, and the number of large establishments in the same industry-location. These results are consistent with the view that increases in outward worker mobility increase costs for adoption of a new technology that involves significant downstream investments in innovation.