Professor Melissa (Mav) Valentine is an Associate Professor at Stanford University in the Management Science and Engineering Department. Prof Valentine studies how technology is changing work and organizations. Recent studies include how experts can develop new capabilities and expertise using algorithms and how managers can use AI and algorithms to design and manage flash teams. Recently tenured, Prof Valentine spent her Sabbatical year as the inaugural Sabbatical Scholar at Stanford Institute for Human-Centered Artificial Intelligence. She and collaborators have received best paper awards for research in both management and HCI conferences. Her work has been covered in the New York Times, The Wall Street Journal, Harvard Business Review, Wired, Fast Company, and The Financial Times. Prof Valentine holds a bachelor's degree from Stanford University, a master's degree from NYU, and a Ph.D. from Harvard University. She was recognized with an NSF CAREER award in 2019.

Academic Appointments

Honors & Awards

  • Best Paper Award, Communication, Technology, and Organization Division, Academy of Management (2022)
  • Paul Pigott Faculty Scholar, Stanford School of Engineering (2021)
  • Teaching Honor Roll, Tau Beta Pi engineering honor society (2020)
  • CAREER Award, National Science Foundation (2019)
  • Best Paper Award, SIGCHI Conference on Human Factors in Computing Systems (2017)
  • Graduate Teaching Award, Stanford Management Science & Engineering (2015)
  • Hellman Faculty Scholar, Stanford University (2014)
  • Winner, Dissertation Competition, INFORMS/Organization Science (2012)
  • Wyss Award for Excellence in Doctoral Research, Harvard Business School (2013)
  • Outstanding Paper with Practical Implications, Academy of Management (2012)
  • Susan Cohen Award for Doctoral Research, Center for Effective Organizations (2010)

Current Research and Scholarly Interests

As societies develop and adopt new technologies, they fundamentally change how work is organized. The intertwined relationship between technology and organizing has played out time and again, and scholars predict that new internet and data analytic technologies will spur disruptive transformations to work and organizing.

These changes are already well-documented in the construction of new market arrangements by companies such as Upwork and TaskRabbit, which defined new categories of “gig workers.” Yet less is known about how internet and data analytic technologies are transforming the design of large, complex organizations, which confront and solve much different coordination problems than gig platform companies.

Questions related to the structuring of work in bureaucratic organizations have been explored for over a century in the industrial engineering and organizational design fields. Some of these concepts are now so commonplace as to be taken for granted. Yet there was a time when researchers, workers, managers, and policymakers defined and constructed concepts including jobs, careers, teams, managers, or functions.

My research program argues that some of these fundamental concepts need to be revisited in light of advances in internet and data analytic technologies, which are changing how work is divided and integrated in organizations and broader societies. I study how our prior notions of jobs, teams, departments, and bureaucracy itself are evolving in the age of crowdsourcing, algorithms, and increasing technical specialization. In particular, my research is untangling how data analytic technologies and hyper-specialization shape the division and integration of labor in complex, collaborative production efforts characteristic of organizations.

All Publications

  • Legitimating Illegitimate Practices: How Data Analysts Compromised Their Standards to Promote Quantification ORGANIZATION SCIENCE Stice-Lusvardi, R., Hinds, P. J., Valentine, M. 2023
  • Aligning Differences: Discursive Diversity and Team Performance MANAGEMENT SCIENCE Lix, K., Goldberg, A., Srivastava, S. B., Valentine, M. A. 2022
  • How Managers Maintain Control Through Collaborative Repair: Evidence from Platform-Mediated "Gigs" ORGANIZATION SCIENCE Rahman, H. A., Valentine, M. A. 2021; 32 (5): 1300-1326
  • Who Pays the Cancer Tax? Patients' Narratives in a Movement to Reduce Their Invisible Work ORGANIZATION SCIENCE Valentine, M. A., Asch, S. M., Ahn, E. 2022
  • Learning in Temporary Teams: The Varying Effects of Partner Exposure by Team Member Role ORGANIZATION SCIENCE Kim, S., Song, H., Valentine, M. A. 2022
  • "This Seems to Work": Designing Technological Systems with The Algorithmic Imaginations of Those Who Labor Cameron, L., Christin, A., DeVito, M., Dillahunt, T. R., Elish, M., Gray, M., Qadri, R., Raval, N., Valentine, M., Watkins, E., ACM ASSOC COMPUTING MACHINERY. 2021
  • Beyond Satisfaction Scores: Exploring Emotionally Adverse Patient Experiences AMERICAN JOURNAL OF MANAGED CARE Holdsworth, L. M., Zionts, D. L., De Sola-Smith, K., Valentine, M., Winget, M. D., Asch, S. M. 2019; 25 (5): E145–E152
  • Fluid Teams and Knowledge Retrieval: Scaling Service Operations M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT Valentine, M. A., Tan, T., Staats, B. R., Edmondson, A. C. 2019; 21 (2): 346–60
  • Inpatient Hospital Factors and Resident Time With Patients and Families PEDIATRICS Destino, L. A., Valentine, M., Sheikhi, F. H., Starmer, A. J., Landrigan, C. P., Sanders, L. 2017; 139 (5)


    To define hospital factors associated with proportion of time spent by pediatric residents in direct patient care.We assessed 6222 hours of time-motion observations from a representative sample of 483 pediatric-resident physicians delivering inpatient care across 9 pediatric institutions. The primary outcome was percentage of direct patient care time (DPCT) during a single observation session (710 sessions). We used one-way analysis of variance to assess a significant difference in the mean percentage of DPCT between hospitals. We used the intraclass correlation coefficient analysis to determine within- versus between-hospital variations. We compared hospital characteristics of observation sessions with ≥12% DPCT to characteristics of sessions with <12% DPCT (12% is the DPCT in recent resident trainee time-motion studies). We conducted mixed-effects regression analysis to allow for clustering of sessions within hospitals and accounted for correlation of responses across hospital.Mean proportion of physician DPCT was 13.2% (SD = 8.6; range, 0.2%-49.5%). DPCT was significantly different between hospitals (P < .001). The intraclass correlation coefficient was 0.25, indicating more within-hospital than between-hospital variation. Observation sessions with ≥12% DPCT were more likely to occur at hospitals with Magnet designation (odds ratio [OR] = 3.45, P = .006), lower medical complexity (OR = 2.57, P = .04), and higher patient-to-trainee ratios (OR = 2.48, P = .05).On average, trainees spend <8 minutes per hour in DPCT. Variation exists in DPCT between hospitals. A less complex case mix, increased patient volume, and Magnet designation were independently associated with increased DPCT.

    View details for DOI 10.1542/peds.2016-3011

    View details for PubMedID 28557735

  • Renegotiating Spheres of Obligation: The Role of Hierarchy in Organizational Learning ADMINISTRATIVE SCIENCE QUARTERLY Valentine, M. 2017

    View details for DOI 10.1177/0001839217718547

  • Team Scaffolds: How Mesolevel Structures Enable Role-Based Coordination in Temporary Groups ORGANIZATION SCIENCE Valentine, M. A., Edmondson, A. C. 2015; 26 (2): 405-422
  • Measuring Teamwork in Health Care Settings: A Review of Survey Instruments. Medical Care Valentine, M. A., Nembhard, I. M., Edmondson, A. C. 2015; 53 (4): e16-e30
  • Expert crowdsourcing with flash teams ACM User Interface Software and Technology Symposium Retelny, D., Robaszkiewisz, S., To, A., Lasecki, W., Patel , J., Rahmati, N., Doshi, T., Valentine, M., Bernstein, M. 2014: 75–85

    View details for DOI 10.1145/2642918.2647409

  • Flash Organizations: Crowdsourcing Complex Work by Structuring Crowds As Organizations Valentine, M., Retelny, D., To, A., Rahmati, N., Doshi, T., Bernstein, M. 2017

    View details for DOI 10.1145/3025453.3025811