Lindsey Cameron

Lindsey Cameron
  • Assistant Professor of Management
  • Dorinda and Mark Winkelman Distinguished Faculty Scholar

Contact Information

  • office Address:

    2027 SH-DH
    3620 Locust Walk
    Philadelphia, PA 19104

Research Interests: algorithmic management, artificial intelligence, gig economy/future of work, contemporary careers, worker well-being, labor issues, field research

Links: Personal Website, CV

Overview

Lindsey D. Cameron is an assistant professor of management and the Dorinda and Mark Winkelman Distinguished Faculty Scholar at the Wharton School, University of Pennsylvania and holds an appointment in the sociology department. She is a Faculty Affiliate at the Berkman Klein Center for Internet and Society at Harvard University, the Data and Society Research Institute in New York City, and a former fellow (member) at the Institute for Advanced Study in Princeton. A scholar of the future of work, her research focuses on how algorithmic management and artificial intelligence is changing the modern workplace, with an emphasis on the gig economy. Professor Cameron has an on-going, eight-year ethnography of the largest sector of the gig economy, the ride-hailing industry, examining how algorithms management changes managerial control. She recently completed a study on how the COVID-19 pandemic affected workers on various gig platforms (TaskRabbit, Instacart, AmazonFlex, Uber, Lyft, DoorDash). She is currently completing a project on how the gig economy’s business model adapts in the Global South, with a focus on the implications for management and workers. Other ongoing research projects focus on workforce development, particularly as it relates to technology.

Professor Cameron’s work has been published in leading academic journals, including Administrative Science Quarterly, Organization Science, Journal of Applied Psychology, Organizational Behavior and Human Decision Process, Academy of Management Annals, Journal of Management Inquiry, Annual Review of Organizational Psychology and Organizational Behavior, Research in Organizational Behavior, Platforms and Society, and proceedings of the Association of Computing Machinery and the Academy of Management. She has won eighteen best paper awards and several teaching awards, including the Wharton Teaching Excellence Award. She has published opinion pieces in Harvard Business Review, Fast Company, Kiplinger’s, Labor and Employment Relations Association Perspectives, Newsweek, and the Society of Human Resource Management’s flagship magazine People & Strategy . Her research has been mentioned in numerous media outlets, including the Washington Post, the Boston Globe, Bloomberg, NPR’s Marketplace, Fast Company, the World Economic Forum, CNBC, Forbes, The Skim, the Philadelphia Inquirer, Newsweek, and Inc.

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Research

Pennsylvania Joint Taskforce on Worker Misclassification
PA Senate Democratic Policy Committee Hearing: Worker Misclassification and the Future of Work
Pennsylvania State Senate Democratic Policy Committee Hearing: Testimony on Ride-hailing Companies

  • Lindsey Cameron and Meuris, J. (2026), The Perils of Pay Variability: Determinants of Worker Aversion to Variable Compensation in Low- and Middle-Wage Jobs, PLOS ONE. Abstract

    A substantial proportion of the labor force in low- and middle-wage jobs is prone to pay variability, or variance in earnings from paycheck to paycheck. Emerging research suggests that pay variability can be detrimental to workers’ well-being and enhance their likelihood of exiting their job. Using several iterations of qualitative and quantitative data, we identify and evidence critical determinants of workers’ experience of pay variability and their likelihood of voluntary turnover. Combining insights from exploratory interviews and surveys with ride-hailing drivers (N = 64) and cognitive appraisal theory, we predicted that pay variability is most likely to result in voluntary turnover when (a) workers were more frequently earning lower-than-average paychecks, (b) felt limited control over their earnings, and (c) their household was dependent upon their paycheck. Using matched survey and archival data from a sample of truck drivers (N = 711) from a national transportation company, we subsequently found that pay variability is positively associated with the likelihood of turnover, but this relationship significantly varied with the predicted contextual moderators. Additional quantitative analyses demonstrated that this relationship was observed even among the highest performing drivers and, surprisingly, likely resulted in less annual pay for those who left. Supplemental qualitative data provided further support for the predicted moderators. Taken together, our findings explain why and when variable compensation represents a source of precarity for low-and middle-wage workers that can motivate turnover. Theoretical and practical implications are discussed.

  • Weigel, M. and Lindsey Cameron (2026), From Amway to Uber: A genealogy of sponsored entrepreneurship, Platforms and Society. Abstract

    This article develops a new history of gig work by turning to the margins of standard histories of capitalism. In contrast to prior research, which has used historical analogies to describe the contemporary gig economy, we develop a genealogy of three organizational forms that have sponsored entrepreneurship in the United States since the early twentieth century: multi-level marking organizations, franchises, and platforms. We analyze how all three organizational forms have restructured work by appealing to individuals, communities, and states, promising solutions to forms of marginalization and exclusion that existing employment laws and norms created. However, we find that sponsored entrepreneurship tends to perpetuate and even exacerbate the inequalities that it purports to remedy.

  • Lindsey Cameron, Mayberry, K. Maffie, M. (2026), Voice Activism: How Digital Counterpublics Cultivate Change in the Gig Economy, Industrial Labor Relations Review (forthcoming).
  • Lindsey Cameron, Hill, J. Dubal, V. Schor, J. Baretto, I. Vianna, F. Alcadippani da Salveira, R. Amrute, S. Bhallamudi, I. Weigel, M. Ticona, J. Caza, B. Reid, E. Wohl, H. Van Doorn, N. Ravenelle, A. Aninch, E. Ghaedipour, F. Conzon, V. (2026), Thinking with and Beyond Labor and Technology: An Investigation of How the Ghost Variables of Race, Caste, Gender, Immigration Status, and Class Shape Gig Work, Journal of Management Inquiry (conditionally accepted).
  • Lindsey Cameron, Conzon, V. Lam, L. (2025), Selling the self: Neo-normative control and the platform paradox, Research in Organizational Behavior. Abstract

    The platform economy is an increasingly large segment of the contemporary economy that continues to attract workers, drawing on narratives of self-empowerment and entrepreneurship that are idolized within American culture. Yet, the realized lived experiences of workers are often anything but that of a flourishing entrepreneur, with many describing economic precarity, invasive and unpredictable algorithmic control, and demanding hours. What keeps people invested in returning to platform work? We label this observed contradiction the platform paradox, and we theorize how this paradox is continuously regenerated through neo-normative control, a modern form of workplace control that encourages workers to express their ‘authentic’ selves, individuality, and emotions in ways that align with organizational goals. Drawing on extant scholarship, we identify three neo-normative control mechanisms—framing self-as-product, whole self-integration, and hyper-gamification—that support platform workers in experiencing themselves as independent entrepreneurs while also increasing their control by the platform and its algorithmic management system. We illustrate how these controls collectively contribute to adverse consequences on two core work experiences: worker skill and worker time. We propose future research directions to further unpack this paradox and for the field of platform scholarship at-large, discussing implications for inequality.

  • Lindsey Cameron (2024), The Making of the “Good Bad” Job: How Algorithmic Management Manufactures Consent through Constant and Confined Choices, Administrative Science Quarterly , 69 (2), pp. 458-514. Abstract

    This research explores how a new relation of production—specifically, the shift from human managers to algorithmic managers on digital platforms—manufactures workplace consent. While most research argues the task standardization and surveillance that accompanies algorithmic management will give rise to the quintessential “bad job” (Kalleberg, 2000), I find that, surprisingly, many workers report liking and finding choice while working under algorithmic management. Drawing on a seven-year qualitative study of the largest sector in the gig economy, the ridehailing industry, I describe how workers navigate being managed by an algorithm. I begin by showing how algorithms segment the work at multiple sites of human-algorithm interactions and that this configuration of the work process allows for more frequent and narrow choice. I find individuals use two sets of tactics. In engagement tactics, individuals generally follow the algorithmic nudges and do not try to get around the system, while in deviance tactics individuals manipulate their input into the algorithm. While the behaviors associated with these tactics are practical opposites, they both elicit consent, or active, enthusiastic participation to align one’s efforts with managerial interests, and workers seeing themselves as skillful agents. However, this choice-based consent can mask the more structurally problematic elements of the work, contributing to the growing popularity of what I call the “good bad” job.

  • Kulkarni, M. Lindsey Cameron, Gehman, J. Glaser, V. Greenwood, M. Islam, G. Lindebaum, D. Mantere, S. Pachidi, S. Rahman, H. Vaara, E. Van den Broek, E. (2024), The Future of Research in an Artificial Intelligence Driven World, Journal of Management Inquiry. Abstract

    Current and future developments in artificial intelligence (AI) systems have the capacity to revolutionize the research process for better or worse. On the one hand, AI systems can serve as collaborators as they help streamline and conduct our research. On the other hand, such systems can also become our adversaries when they impoverish our ability to learn as theorists, or when they lead us astray through inaccurate, biased, or fake information. No matter which angle is considered, and whether we like it or not, AI systems are here to stay. In this curated discussion, we raise questions about human centrality and agency in the research process, and about the multiple philosophical and practical challenges we are facing now and ones we will face in the future.

  • Rahman, H. Karunakaran, A. Lindsey Cameron (2023), Taming Platform Power: Taking Accountability Into Account In the Management of Platforms, Academy of Management Annals. Abstract

    Research on multi-sided platforms emphasizes how platform owners have accumulated significant power over other platform actors, such as producers and customers, arguing for the need to balance such power with accountability. We review two perspectives on platform accountability: a) a bottom-up, emergent perspective that focuses on the collective action taken by lower-powered platform actors such as producers (e.g., gig workers, app developers) to enhance rule adequacy and push back against platform owners’ power, and b) a top-down, institutional perspective that emphasizes the importance of preventing extractive opportunism and maintaining a level playing field among different platform actors by enabling legal, regulatory, and governance changes. The bottom-up perspective’s overarching focus is on procedural (rule-focused) fairness, while the top-down perspective’s focus is largely on distributive (outcome-focused) fairness. While both perspectives are important, they have limitations regarding platform accountability, especially given the power and informational asymmetries inherent among platform actors. Therefore, synthesizing across literatures, we provide a framework for platform accountability that accounts for both procedural and distributive fairness, and is based on a fundamental premise: multi-sided platforms require multi-sided accountability systems. Thus, our review proposes an approach for enforcing platform accountability that has the potential to rebalance the power between high-powered and low-powered platform actors.

  • Lindsey Cameron, Lamers, L. Leicht-Deobald, U., Lutz, C., Meijerink, J., Mohlmann, M. (2023), Algorithmic Management: Its Implications for Information Systems Research, Communications of the Association for Information Systems. Abstract

    In recent years, the topic of algorithmic management has received increasing attention in information systems (IS) research and beyond. As both emerging platform businesses and established companies rely on artificial intelligence and sophisticated software to automate tasks previously done by managers, important organizational, social, and ethical questions emerge. However, a cross -disciplinary approach to algorithmic management that brings together IS perspectives with other (sub-)disciplines such as macro – and micro -organizational behavior, business ethics, and digital sociology is missing, despite its usefulness for IS research. This article engages in cross-disciplinary agenda setting through an in-depth report of a professional development workshop (PDW) entitled “Algorithmic Management: Toward a Cross-Disciplinary Research Agenda” delivered at the 2021 Academy of Management Annual Meeting. Three leading experts (Mareike Möhlmann, Lindsey Cameron, and Laura Lamers) on the topic provide their insights on the current status of algorithmic management research, how their work contributes to this area, where the field is heading in the future, and what important questions should be answered going forward. These accounts are followed up by insights from the breakout group discussions at the PDW that provided further input. Overall, the experts and workshop participants highlighted that future research should examine both the desirable and undesirable outcomes of algorithmic management and should not shy away from posing ethical and normative questions.

  • Lindsey Cameron, Chan, C., Anteby, M (2022), Heroes from Above But Not (Always) From Within: Gig Workers Responses to the Public Moralization of their Work, Organizational Behavior and Human Decision Processes. Abstract

    How do individuals react to the sudden public moralization of their work and with what consequences? Extant research has documented how public narratives can gradually moralize societal perceptions of select occupations. Yet, the implications of how workers individually respond and form self-narratives in light of—or in spite of—a sudden moralizing event remain less understood. Such an understanding is even more critical when workers are weakly socialized by their organization: a situation increasingly common today. During the COVID-19 pandemic, radically shifting public narratives suddenly transformed grocery delivery work, previously uncelebrated, into highly moralized “heroic” pursuits. Drawing on interviews (n=75), participant artifacts (n=85), and archival data (e.g., newspaper articles), we find that these workers (here, shoppers on the platform organization Instacart), left mainly to themselves, exhibited varying responses to this moralizing and that their perceived relations to the organization, customers, and tasks shaped these responses. Surprisingly, those who facilely adopted the hero label felt morally credentialled and were thus likely to minimize their extra-role helping of customers and show low commitment to the organization; in contrast, those who wrestled with the hero narrative, sought to earn those moral credentials, were more likely to embrace extra-role helping and remain committed to moralized aspects of the work. Our study contributes to literatures on the moralization of work and narratives by explaining why some workers accept a moralized narrative and others reject or wrestle with it, documenting consequences of workers’ reactions to such narratives, and suggesting how a moralized public narrative can backfire.

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Teaching

Past Courses

  • MGMT6120 - Managing Emerg Entrprse

    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.

  • MGMT6130 - Manag Estab Enterprise

  • MGMT9180 - Personnel Economics A

    This is a half-semester PhD course in the Management Department that is also open to any current PhD students at Wharton. The canonical model in economics views an agent as a fully rational, atomistic individual making optimal choices under scarcity. This approach has been very powerful theoretically and empirically to explain and to predict behavior in the workplace. This model has also been enriched to accommodate other phenomena arguably affecting behavior in the workplace like the social context (e.g. peer effects, altruism, or social comparison), non-standard time preferences, loss aversion, and cognitive costs. Incorporating these ideas into the standard model can be accomplished in various ways but the real stress test for these theories is whether they predict behavior more generally (i.e. we don't just use theory to explain one choice but choices more generally) and to generate empirical predictions that can be tested using experiments. In this mini-course we start-off with a tour de force of the fundamental principal-agent model and the various behavioral extensions. The core of the course is, however, not theoretical but a practical course on how to design field experiments to test these ideas.

  • MGMT9320 - Prosem in Mgmt

    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.

  • MGMT9700 - Research Methods in Mgmt

    Students taking the course will be introduced to the seminal readings on a given method, have a hands-on discussion regarding their application often using a paper and dataset of the faculty member leading the discussion. The goal of the course is to make participants more informed users and reviewers of a wide variety of methodological approaches to Management research including Ordinary Least Squares, Discrete Choice, Count Models, Panel Data, Dealing with Endogeneity, Survival/failure/event history and event studies, experiments, factor analysis and structural equation modeling, hierarchical linear modeling, networks, comparative qualitative methods, coding of non-quantitative data, unstructured text and big data simulations.

  • MGMT9703 - Qualitative Methods

    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 (in-person and digital), 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. Students will expected to collect and analyze data about a topic of their choosing.

Awards And Honors

  • Responsible Research in Management Award, 2025
  • Penn Postdoctoral Association Mentorship Award, 2025
  • Best Published Paper Award – AOM CTO Division, 2025
  • Honorable Mention, Distinguished Scholarly Article Award – American Sociological Association, Labor and Labor Movements Division, 2025
  • Winner, Responsible Business Education – Academic Research with Impact Award, 2025
  • ONYX Magazine Women on the Move, Most Impactful and Influential Women Honoree, 2025
  • Best 40-Under-40 MBA Professor, Poets & Quants, 2023
  • Faculty Associate, Berkman Klein Center for Internet and Society, Harvard University, 2023
  • Wharton Teaching Excellence Award, 2023
  • CTO Best Published Paper Award, 2023
  • MOC Best Symposium Award, 2023
  • Member of the Institute of Advanced Studies, Princeton, 2023
  • Aspen Ideas Fellow, 2022
  • AOM HR Division Best Overall Paper Award, 2022
  • AOM Showcase Symposium, 2022
  • MOC Best Symposium Award, 2021
  • OMT Best Paper Award (top 10% of submissions), 2021
  • OCIS Best Paper Award (top 10% of submissions), 2021
  • Winner, Industry Studies Dissertation Award, 2021
  • Runner-Up, Louis Pondy Best Dissertation Paper, 2021
  • Finalist, Grigor McClelland Doctoral Dissertation Award, 2021
  • Industry Studies Association’s Giarratani Rising Star Award, 2020
  • MOC Division Best Paper Award Nominee, 2020
  • Wharton Dean's Research Grant, 2020
  • Psychology of Technology Dissertation Award, 2020
  • Likert Dissertation Prize, 2020
  • Mack Institute Research Fellowship for Innovation Management, 2019
  • Center for Advanced Studies of the Behavioral Sciences Summer Institute, Stanford University, 2019
  • OMT Above and Beyond the Call of Duty Reviewer Award, 2019
  • Ruth and Gilbert Whitaker Doctoral Fellowship, 2018
  • OB Doctoral Student Consortium, 2018
  • Pre-Doctoral Fellow, Wharton School, University of Pennsylvania, 2017
  • Bouchet Honor Society, 2017
  • AOM Showcase Symposium, 2014
  • Rotary Ambassadorial Scholar, 2005
  • Coca Cola National Scholar, 2001
  • Siemens-Westinghouse (formerly Intel) Competition, Semi-finalist, 2001
  • USA Today All-Academic Team, 2001
  • Bill Gates Millenium Scholar, 2000

In the News

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Activity

Latest Research

Lindsey Cameron and Meuris, J. (2026), The Perils of Pay Variability: Determinants of Worker Aversion to Variable Compensation in Low- and Middle-Wage Jobs, PLOS ONE.
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In the News

Automation Doesn’t Just Cut Jobs. It Slows Career Progression

Automation is often seen as destroying jobs, but new Wharton research shows it also can quietly block workers from moving into better-paid roles.…Read More

Knowledge @ Wharton - 2026/06/9
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Awards and Honors

Responsible Research in Management Award 2025
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