Lindsey Cameron

Lindsey Cameron
  • Assistant Professor of Management

Contact Information

  • office Address:

    2027 SH-DH
    3620 Locust Walk
    Philadelphia, PA 19104

Research Interests: algorithms, gig economy/future of work, contemporary careers, financial well-being, labor issues, field research

Links: Personal Website, CV

Overview

Lindsey D. Cameron is an assistant professor of management at the Wharton School, University of Pennsylvania. Her research focuses on how algorithmic management is changing the modern workplace, especially individual’s behaviors at work. 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. She is currently studying how the COVID-19 pandemic is affecting gig workers on different platforms (e.g., TaskRabbit, Instacart, AmazonFlex) as well as examining how ride-hailing drivers on three continents navigate disputes. Professor Cameron’s work has been published in leading academic 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. She has also published opinion pieces in Fast Company, Kiplinger’s, and the Society of Human Resource Management’s flagship magazine People & Strategy and her research has been mentioned in numerous media outlets including Bloomberg, NPR’s Marketplace, Fast Company, the World Economic Forum, CNBC, Forbes, The Skim, and Inc.

In her prior career, Professor Cameron 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.

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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, Laura Lamers, Ulrich Leicht-Deobald, Christoph Lutz, Jeroen Meijerink, Mareike Mohlmann (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, Curtis Chan, Michel Anteby (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.

  • Lindsey Cameron and Jirs Meuris, “The Perils of Pay Variability: Determinants of Worker Aversion to Variable Compensation in Low and Middle Wage Jobs”. In Proceedings of the Eighty-second Annual Meeting of the Academy of Management. Online ISSN: 2151-6561, edited by Sonia Taneja, (2022)

  • Lindsey Cameron (2022), “Making Out” While Driving: The Relational and Efficiency Game in the Gig Economy, Organization Science.

  • Lindsey Cameron and Hatim Rahman (2022), Expanding the Locus of Resistance: The Constitution of Control and Resistance in the Gig Economy, Organization Science.

  • 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, 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.

  • 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 (Under Revision), The Good Bad Job: Autonomy and Control in the Algorithmic Work Environment.

    Description: Revise and Resubmit Requested at Administrative Science Quarterly.

Teaching

Current Courses (Spring 2023)

  • 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.

    MGMT6120001 ( Syllabus )

    MGMT6120002 ( Syllabus )

    MGMT6120003 ( Syllabus )

  • 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.

    MGMT9320401 ( Syllabus )

All 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.

  • 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.

Awards and Honors

  • Aspen Ideas Fellow, 2022
  • AOM HR Division Best Overall Paper Award, 2022
  • AOM Showcase Symposium, 2021-2022
  • 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, 2018-2019
  • OB Doctoral Student Consortium, 2018
  • Pre-Doctoral Fellow, Wharton School, University of Pennsylvania, 2017-2018
  • Bouchet Honor Society, 2017
  • AOM Showcase Symposium, 2014
  • Rotary Ambassadorial Scholar, 2005
  • Coca Cola National Scholar, 2001
  • USA Today All-Academic Team, 2001
  • Siemens-Westinghouse (formerly Intel) Competition, Semi-finalist, 2001
  • Bill Gates Millenium Scholar, 2000

In the News

Knowledge at Wharton

Wharton Stories

Activity

Latest Research

Lindsey Cameron, Laura Lamers, Ulrich Leicht-Deobald, Christoph Lutz, Jeroen Meijerink, Mareike Mohlmann (2023), Algorithmic Management: Its Implications for Information Systems Research, Communications of the Association for Information Systems.
All Research

In the News

Hero Worship: What Happens When Jobs are Suddenly Moralized

Grocery delivery workers were hailed as heroes during the pandemic, but not every gig worker considered themselves worthy. New research from Wharton’s Lindsey Cameron explores the business consequences of becoming an overnight hero.…Read More

Knowledge at Wharton - 11/29/2022
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Wharton Magazine

Data: Superstitious Investors, Mindfulness at Work, and More
Wharton Magazine - 04/17/2020

Wharton Stories

Research Spotlight: Prof. Lindsey Cameron on Drivers in the Gig Economy

In Wharton Social Impact’s “Research Spotlight” series, we highlight recent research by Wharton professors and doctoral students whose research focuses on the intersection of business and impact. This month, we spoke with Lindsey Cameron, assistant professor of management at Wharton, about her research on workers’ experiences in the gig economy…

Wharton Stories - 11/29/2021
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