Lindsey D. Cameron is an assistant professor of management at the Wharton School. 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. She recently completed a three-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 the workplace. Her research program is motivated by identifying and understanding how these changes affect how work is being organized and experienced by workers in positive, negative, and neutral ways.
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.
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,.
Description: (Forthcoming) 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.
S. Nurmohamed, C. McCluney, Lindsey Cameron, D. Mayer (Under Review), Show me the money?: The Business vs. Ethical Case for Diversity in Corporations.
Description: Under review at Journal of Applied Psychology.
Lindsey Cameron (Working), Making Out While Driving: Control, Coordination, and its Consequences for Algorithmic Labor.
Description: Target: Administrative Science Quarterly
Lindsey Cameron (Working), The Sound, Smells, and Tastes that Bind Us: Materiality in the Process of Organizational Identification in Diverse Communities.
Description: Target: Organization Science
V. Kamaswaren, Lindsey Cameron, T. Dillahunt (2018), Support for Social and Cultural Capital Development in Real-time Ridesharing Services, Computer-Human Interactions. CHI 2018: ACM Conference on Human Factors in Computing Systems..
G. M. Spreitzer, Lindsey Cameron, L. E. Garrett (2017), Alternative Work Arrangements: Two Images of the New World of Work, Annual Review of Organizational Psychology and Organizational Behavior, 4, pp. 473-499.
Emerging enterprises, the focus in this course, are small, new, fast-growing organizations. Their founders and managers face multifaceted challenges: how to assess the competitive position of their business model and develop a strategy; how to develop the internal organizational structure, culture, and policies for selecting and managing employees; and how to pursue global opportunities. We cover these challenges in separate modules on strategy, human and social capital, and global issues. The human and social capital module covers classic management challenges of aligning interests of the individual and the organization; managing individual psychological needs and social influences; and developing employee capabilities that provide competitive advantage. Also covered are unique challenges that yound organizations face, i.e. building an effective culture; recruiting, selecting, and retaining talent; building systematic approaches to motivating employees; coping with the stresses of rapid growth; and leveraging the benefits (and avoiding the liabilities) of the founder's powerful imprint. The strategy module covers fundamental issues central to the competitiveness of the enterprise. Because the strategy field is broad, MGMT 612 emphasizes topics and frameworks that are most relevant for younger firms, such as innovation, disruption, managing resource constraints, and building capabilities. However, a key insight of the module is the importance of seeing the playing field from the perspective of the competition. Thus, by the end of this section, students will have a robust grounding in strategy that will allow them to succeed, whether their career path leads to a Fortune 100 firm or a garage start up. The global module covers the emerging firm's decision about when (and whether) to internationalize. This decision must address which foreign markets to enter; the mode of entry; the sequence of moves to develop capabilities; what organizational form to choose; where to establish HQ; and how to adapt to the unique economic and institutional features of different markets. In all these issues, the emphasis is on how young, resource-constrained firms can position themselves profitably in globally competitive markets. For the final project, student teams provide integrated analysis across the modules for an emerging enterprise of their choice.
This course is designed to provide students with an understanding of the methodological approaches we commonly think of as qualitive, 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 collections, 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. Ths 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 independly or consecutively.