Melissa Valentine
Associate Professor of Management Science and Engineering
Bio
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
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Associate Professor, Management Science and Engineering
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Faculty Affiliate, Institute for Human-Centered Artificial Intelligence (HAI)
Honors & Awards
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Best Paper Award, Communication, Technology, and Organization Division, Academy of Management (2022)
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Paul Pigott Faculty Scholar, Stanford School of Engineering (2021)
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Teaching Honor Roll, Tau Beta Pi engineering honor society (2020)
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CAREER Award, National Science Foundation (2019)
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Best Paper Award, SIGCHI Conference on Human Factors in Computing Systems (2017)
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Graduate Teaching Award, Stanford Management Science & Engineering (2015)
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Hellman Faculty Scholar, Stanford University (2014)
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Winner, Dissertation Competition, INFORMS/Organization Science (2012)
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Wyss Award for Excellence in Doctoral Research, Harvard Business School (2013)
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Outstanding Paper with Practical Implications, Academy of Management (2012)
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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.
2024-25 Courses
- Contemporary Themes in Work and Organization Studies
MS&E 388 (Aut) - Managing Data Science Organizations for Innovation and Impact
MS&E 284 (Win) - Navigating an Academic Career: Topics for PhD Students
MS&E 380 (Aut) - The Future of Work: What Will it Mean to Build AI-Augmented Organizations?
MS&E 184 (Aut) -
Independent Studies (1)
- Directed Reading and Research
MS&E 408 (Aut, Win, Spr, Sum)
- Directed Reading and Research
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Prior Year Courses
2023-24 Courses
- Contemporary Themes in Work and Organization Studies
MS&E 388 (Aut) - Flash Teams: Theory and Practice
MS&E 184 (Aut) - Managing Data Science Organizations for Innovation and Impact
MS&E 284 (Win)
2021-22 Courses
- Contemporary Themes in Work and Organization Studies
MS&E 388 (Spr) - Data Science of Organizations
MS&E 284 (Spr) - Future of Work: Issues in Organizational Learning and Design
MS&E 184 (Spr)
- Contemporary Themes in Work and Organization Studies
Stanford Advisees
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Doctoral Dissertation Advisor (AC)
Adrienne Baer -
Master's Program Advisor
Divya Agarwal, Hannah Lee, Joyce Lin, Ian MacKinnon, Finn Mallery, Francesco Marchioni, Adhara Martellini, Ayo Odeyinde, Jackson Painter, Montanna Riggs, Hiya Shah, Emma Thygesen, Zoe von Gerlach -
Doctoral Dissertation Co-Advisor (AC)
Ryan Stice-Lusvardi -
Doctoral (Program)
Chris Dylewski, Ramesh Manian, Nicholas Okafor, Amanda Pratt
All Publications
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Legitimating Illegitimate Practices: How Data Analysts Compromised Their Standards to Promote Quantification
ORGANIZATION SCIENCE
2023
View details for DOI 10.1287/orsc.2022.1655
View details for Web of Science ID 000926670500001
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Aligning Differences: Discursive Diversity and Team Performance
MANAGEMENT SCIENCE
2022
View details for DOI 10.1287/mnsc.2021.4274
View details for Web of Science ID 000828394000001
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How Managers Maintain Control Through Collaborative Repair: Evidence from Platform-Mediated "Gigs"
ORGANIZATION SCIENCE
2021; 32 (5): 1300-1326
View details for DOI 10.1287/orsc.2021.1428
View details for Web of Science ID 000718956600008
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ALGORITHMS AT WORK: THE NEW CONTESTED TERRAIN OF CONTROL
ACADEMY OF MANAGEMENT ANNALS
2020; 14 (1): 366–410
View details for DOI 10.5465/annals.2018.0174
View details for Web of Science ID 000510825600012
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Who Pays the Cancer Tax? Patients' Narratives in a Movement to Reduce Their Invisible Work
ORGANIZATION SCIENCE
2022
View details for DOI 10.1287/orsc.2022.1627
View details for Web of Science ID 000866656300001
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Learning in Temporary Teams: The Varying Effects of Partner Exposure by Team Member Role
ORGANIZATION SCIENCE
2022
View details for DOI 10.1287/orsc.2022.1585
View details for Web of Science ID 000804394200001
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"This Seems to Work": Designing Technological Systems with The Algorithmic Imaginations of Those Who Labor
ASSOC COMPUTING MACHINERY. 2021
View details for DOI 10.1145/3411763.3441331
View details for Web of Science ID 000759178500022
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Beyond Satisfaction Scores: Exploring Emotionally Adverse Patient Experiences
AMERICAN JOURNAL OF MANAGED CARE
2019; 25 (5): E145–E152
View details for Web of Science ID 000472058400003
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Fluid Teams and Knowledge Retrieval: Scaling Service Operations
M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT
2019; 21 (2): 346–60
View details for DOI 10.1287/msom.2017.0704
View details for Web of Science ID 000469773500007
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WHEN EQUITY SEEMS UNFAIR: THE ROLE OF JUSTICE ENFORCEABILITY IN TEMPORARY TEAM COORDINATION
ACADEMY OF MANAGEMENT JOURNAL
2018; 61 (6): 2081–2105
View details for DOI 10.5465/amj.2016.1101
View details for Web of Science ID 000457063100004
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Inpatient Hospital Factors and Resident Time With Patients and Families
PEDIATRICS
2017; 139 (5)
Abstract
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
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Renegotiating Spheres of Obligation: The Role of Hierarchy in Organizational Learning
ADMINISTRATIVE SCIENCE QUARTERLY
2017
View details for DOI 10.1177/0001839217718547
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Team Scaffolds: How Mesolevel Structures Enable Role-Based Coordination in Temporary Groups
ORGANIZATION SCIENCE
2015; 26 (2): 405-422
View details for DOI 10.1287/orsc.2014.0947
View details for Web of Science ID 000355095100006
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Measuring Teamwork in Health Care Settings: A Review of Survey Instruments.
Medical Care
2015; 53 (4): e16-e30
View details for DOI 10.1097/MLR.0b013e31827feef6
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Expert crowdsourcing with flash teams
ACM User Interface Software and Technology Symposium
2014: 75–85
View details for DOI 10.1145/2642918.2647409
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Flash Organizations: Crowdsourcing Complex Work by Structuring Crowds As Organizations
2017
View details for DOI 10.1145/3025453.3025811