Lindsey D. Cameron is an assistant professor of management at the Wharton School, University of Pennsylvania. Her research focuses on how changes in the modern workplace (e.g., algorithms/machine learning, short-term employment contracts, variable pay) affect work and workers. Professor Cameron has an on-going, five-year ethnography of the largest employer in the gig economy, the ride-hailing industry, exploring how algorithms are fundamentally reshaping the nature of managerial control and how workers navigate this new workplace. Professor Cameron also studies gig workers and technology on a variety of other platforms (e.g., TaskRabbit, Instacart, Amazon Flex). She is currently studying how the COVID-19 pandemic is affecting gig workers as well as examining how ride-hailing workers on three continents navigate disputes. Professor Cameron’s research is published or forthcoming in a number of leading journals including, Organization Science, Journal of Applied Psychology, Organizational Behavior and Human Decision Process, Annual Review of Organizational Psychology and Organizational Behavior, and proceedings of the Association of Computing Machinery, and the Academy of Management. Insights from her research have also appeared in top popular press outlets, including NPR’s Marketplace, Forbes, Kiplinger’s, Fast Company and People+Strategy.
In her prior career, Lindsey spent over a decade in the U.S. intelligence and diplomatic communities as a technical and political analyst and completed several overseas assignments in the Middle East, Africa, and Europe. She holds a PhD in Management from the University of Michigan, MS in Engineering Management from the George Washington University, and an SB from Harvard University in Electrical Engineering and Computer Science. She also studied Arabic intensively at the American University of Cairo. She has trained in large group facilitation, and is an experienced practitioner and teacher in mindfulness and non-dual awareness practices, holding lineage in a tradition and having trained at several centers in the US.
Lindsey Cameron, “(Relative) Freedom in Algorithms: How Digital Platforms Repurpose Workplace Consent”. In Proceedings of the Eighty-first Annual Meeting of the Academy of Management. Online ISSN: 2151-6561, edited by Sonia Taneja, (2021)
Lindsey Cameron and H. Rahman, “(Not) Seeing Like an Algorithm: Managerial Control and Worker Resistance in The Platform Economy”. In Proceedings of the Eighty-first Annual Meeting of the Academy of Management. Online ISSN: 2151-6561, edited by Sonia Taneja, (2021)
Lindsey Cameron (2021), “Making Out” While Driving: The Relational and Efficiency Game in the Gig Economy, Organization Science, conditionally accepted.
Lindsey Cameron and Hatim Rahman (2021), Expanding the Locus of Resistance: The Constitution of Control and Resistance in the Gig Economy, Organization Science, conditionally accepted.
Lindsey Cameron, B. Thomason, V. Conzon (2021), Risky Business: Gig Workers and the Navigation of Ideal Worker Expectations During the COVID-19 Pandemic, Journal of Applied Psychology, conditionally accepted.
Lindsey Cameron (2020), “Making out While Driving?”: The Relational and Efficiency Game in the Gig Economy, In Guclu Atinc (Ed.), Proceedings of the Eightieth Annual Meeting of the Academy of Management. Online ISSN: 2151-6561.
Description: Under Review at Organization Science.
Lindsey Cameron, C. Chan, M. Anteby (Under Review), Heroes from Above and Below: Workers’ Responses to the Moralization of their Work.
Description: Under Review at Organizational Behavior and Human Decision Processes.
Lindsey Cameron (Under Revision), The Good Bad Job: Autonomy and Control in the Algorithmic Work Environment.
Description: Revise and Resubmit Requested at Administrative Science Quarterly.
Lindsey Cameron, A. Hafenbrack, G. M. Spreitzer, L. Noval, C. Zhang, S. Shaffakat (2019), Helping Others by Being in the Present Moment: Mindfulness and Prosocial Behavior at Work, Organizational Behavior and Human Decision Processes.
Lindsey Cameron, L. E. Garrett, G. M. Spreitzer (2019), Contingent, Contract, and Alternative Work Arrangements, Oxford Bibliographies in Management.
This course is about managing during the early stages of an enterprise, when the firm faces the strategic challenge of being a new entrant in the market and the organizational challenge of needing to scale rapidly. The enterprises of interest in this course have moved past the purely entrepreneurial phase and need to systematically formalize strategies and organizational processes to reach maturity and stability, but they still lack the resources of a mature firm. The class is organized around three distinct but related topics that managers of emerging firms must consider: strategy, human and social capital, and global strategy.
This course is designed to provide students with an understanding of the methodological approaches we commonly think of as qualitative, with special emphasis on ethnography, semi- structured interviews, case studies, content analysis, and mixed-methods research. The course will cover the basic techniques for collecting, interpreting, and analyzing qualitative (i.e. non-numerical) data. In the spring quarter, the course will operate on two interrelated dimensions, one focused on the theoretical approaches to various types of qualitative research, the other focused on the practical techniques of data collection, such as identifying key informants, selecting respondents, collecting field notes and conducting interviews. In the fall semester, the course will operate on two interrelated dimensions, one focused on the theoretical approaches on building arguments and theory from qualitative data, the other focused on the practical techniques of data collection, such as analyzing data, writing, and presenting findings. Note: This class is part of a two-part sequence which focuses on qualitative data collection and analysis. The first of this course, offered in the Spring, focuses on data collection and the second half of the course, offered the following Fall, will focus on qualitative data analysis. Each course is seven weeks long. Students may take either class independently or consecutively.
Wharton’s Lindsey Cameron explains why algorithms that monitor worker productivity, like the ones used by Amazon, are a bad idea.Knowledge @ Wharton - 9/27/2021