About

Natalia Levina is a Full Chair Professor at New York University Stern School of Business, where she joined in 2001. She received her Ph.D. in Information Technology from MIT’s Sloan School of Management and holds a B.A. in Computer Science and Mathematics from Boston University, as well as an M.A. in Mathematics from Boston University. Her research focuses on how people span organizational, professional, cultural, and other boundaries while producing and using technological innovations. Currently, her studies include the evaluation and adoption of Artificial Intelligence in medicine and HR, open innovation, theories of smart contracts, and firm-community relationships in crowdsourcing. Prof. Levina is recognized as an innovative teacher, having developed a course on “Digital Innovation & Crowdsourcing,” which has received excellent evaluations across undergraduate, MBA, and Executive MBA programs. She has been nominated for the Best Professor Award by NYU Stern’s EMBA students and is a frequent speaker at academic and industry conferences. Her research has been published in top journals such as Information Systems Research, MIS Quarterly, Organization Science, and Harvard Business Review. She has received numerous awards for her published work, served in editorial roles for leading journals, and held significant positions in academic conferences and research groups. Her contributions to the field have been recognized through various research grants and her appointment as a Fellow of the Association for Information Systems in 2022.

Research topics

• Computer Science
• Artificial Intelligence
• Political Science
• Knowledge management
• Engineering
• Epistemology
• Psychology
• Law
• Sociology
• Social psychology
• Mathematics
• Economics
• Philosophy
• Data science
• Engineering ethics

Selected publications

• PREDICTING THE DEGREE OF SUSCEPTIBILITY OF TEETH TO EXTERNAL STIMULI AFTER THE BLEACHING PROCEDURE

  The actual problems in dentistry · 2025-02-10

  articleOpen access

  The subject of the study is that currently many people strive to have their teeth white. However, after the bleaching procedure, some patients may experience hyperesthesia, which is an increased sensitivity of the teeth. Despite the fact that there is information in the scientific literature about hyperesthesia after bleaching, there is not enough information about its features and manifestations depending on various factors. Purpose: to study the features of hypersensitivity of teeth after the bleaching procedure. Methodology. In the course of the study, the characteristics of hypersensitivity of teeth in 50 people after the bleaching procedure were examined. All participants in the procedure underwent chemical bleaching using the Opalescence Boost PF system. The IBM SPSS Statistics 23 program was used to compare and analyze the results of the study. The Wilcoxon T-test was used to determine the statistical significance of the differences between the samples. The differences were considered statistically significant at p < 0.05. Results. After the teeth whitening procedure, there was a tendency to decrease the intensity of sensitivity after two weeks, however, with the use of additional whitening products, sensitivity could increase. Conclusions. In this regard, it seems necessary to implement a set of measures aimed at preventing tooth hypersensitivity after the bleaching procedure in both the short and long term.

  PublisherOA PDFDOI

• Benefits, risks, and opportunities for knowledge pursuit in organizations

  Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) · 2025-12-17

  articleOpen access1st authorCorresponding

  Modern organizations face growing institutional and competitive pressures to adopt AI for predictive data science and to generate knowledge from vast digital datasets. While AI adoption promises new insights, it also engenders hidden capability traps, risking the conflation of reality with algorithmic representations and the neglect of non-digital or analogue dimensions of organizational life. This paper introduces the concept of epistemic stance—the underlying approach and orientation to generating knowledge in organizations—to critically examine the organizational implications of predictive data science. It unpacks the components and promises of a data science epistemic stance, highlights its epistemic risks, and explains its appeal to modern organizations. The paper argues that organizations can strengthen their knowledge capabilities by combining multiple epistemic stances through carefully designed sociotechnical systems.

  PublisherOA PDF

• Disinformation and Misinformation in the Age of AI: Putting IS field at the Center

  CBS Research Portal (Copenhagen Business School) · 2025-01-01

  articleSenior author

  Disinformation and misinformation threaten democratic institutions, public discourse, and social cohesion. With predictive algorithms and GenAI enabling easy generation of deep fakes, these threats are amplified at unprecedented scale and present an urgent need to understand and counter them. This panel brings together IS scholars from different disciplinary backgrounds who investigate the causes, mechanisms, and consequences of disinformation and propose effective interventions. Despite valuable contributions from IS scholars, disciplines like Political Science and Media Studies often dominate the conversation. This panel argues that IS should take center stage in the public and academic discourse on this topic by leveraging its unique, multidisciplinary lenses. Drawing on theoretical, empirical, and design-based approaches, panelists will debate specific dilemmas associated with this phenomenon and engage the audience in provocative discussions. The goal is to mobilize the IS community to lead this critical area of research and amplify its insights across academia, industry, and policymaking.

  Publisher

• Human-AI Medical Decision-Making Under Uncertainty

  Academy of Management Proceedings · 2025-07-01

  article

  AI-based decision support systems are becoming increasingly common in healthcare, promising improved accuracy and efficiency in diagnosis, treatment, and prediction. At the same time, due to the high-stakes and highly regulated context of medical decisions, uncertainty is neither an exception, nor a mere individual decision characteristic. Rather, uncertainty is a continuously constructed and enacted phenomenon by both focal medical professionals and the array of actors and technologies involved in healthcare tasks. The purpose of this panel symposium is to engage a group of panelists in a formal, moderated, interactive discussion on how uncertainty is dealt with by (a) medical doctors and healthcare professionals, (b) AI systems, and (c) human-AI ensembles. We would like to stimulate discussions around the changing nature of medical decision-making revolving around the new types of uncertainty emerging in the interactions between human and AI technologies. We would also like to examine the consequences of such interactions for the way medical decision making is enacted and organized.

  PublisherDOI

• Exercising Judgment in Organizations

  Academy of Management Proceedings · 2025-07-01

  article

  Judgment is a fundamental concept in management research and relates to several subfields, ranging from human resources (Grandey, Houston & Avery, 2019) and entrepreneurship (Foss & Klein, 2012; Foss, Klein, & Bjørnskov, 2019) to strategic decision-making (Priem, 1994) and business ethics (Mudrack & Mason, 2013). The concept's popularity has resulted in a diversity of understandings and applications – some emphasizing the technical aspects of judgment like precision and accuracy, while others more concerned about judgment as a skillful practice (Tsoukas, Hadjimichael, Nair, Pyrko, & Woolley, 2024). At the same time, judgment is critical for navigating contemporary issues, such as developing leadership traits and character (Crossan, Crossan, Newstead, & Sturm, 2024), evaluating the role of AI in everyday work (Lebovitz, Lifshitz-Assaf, & Levina, 2022), entrepreneurial decision making under conditions of uncertainty and unknowingness (Shepherd, Williams & Patzelt, 2015) and understanding how managers form views and interpret ambiguous evidence in a way that will lead to a good decision (Likierman, 2020) – especially in light of the need to address wicked problems and grand societal challenges (Ackermann, Pyrko, & Hill, 2024). To this end, this symposium aims to focus scholarly attention on the role of judgment in business and management, reflect on its characteristics in a fast-changing world, and discuss the implications and future research directions for judgment as an area of study in business and management research.

  PublisherDOI

• Striving for Responsible AI: Governing AI Implementation through a Technological Platform

  Academy of Management Proceedings · 2025-07-01

  articleOpen accessSenior author

  The societal, technical, and organizational risks posed by artificial intelligence (AI) increasingly motivate organizations to invest in governance efforts to promote responsible AI development and use. Over three years, we studied a European telecommunications company to investigate the micro-processes of AI governance. Beyond creating new roles (e.g., governance officer) and governance bodies (e.g., ethical council), the organization saw an AI development platform as a key part of governing AI. Our findings revealed a frequent misalignment between the platform‘s intended use and its actual adoption in practice. Instead of relying on the platform, organizational actors often relied on simpler shadow tools to perform AI governance activities. Establishing, encoding, and enforcing rules around responsible AI unfolded through a series of discursive practices including frequent model review meetings, community workshops, and self-regulating standards. These practices socialized values and norms around AI and helped bridge the gap between aspiration-driven policy and day-to-day practice. Our findings highlight the role of discourse in governing through technological tools as means for overcoming limitations of bureaucratic controls.

  PublisherOA PDFDOI

• Predictive data science as an epistemic stance: Benefits, risks, and opportunities for knowledge pursuit in organizations

  Strategic Organization · 2025-12-17 · 1 citations

  article1st authorCorresponding

  Modern organizations face growing institutional and competitive pressures to adopt artificial intelligence for predictive data science and to generate knowledge from vast digital datasets. While artificial intelligence adoption promises new insights, it also engenders hidden capability traps, risking the conflation of reality with algorithmic representations and the neglect of non-digital or analogue dimensions of organizational life. This article introduces the concept of epistemic stance—the underlying approach and orientation to generating knowledge in organizations—to critically examine the organizational implications of predictive data science. It unpacks the components and promises of a data science epistemic stance, highlights its epistemic risks, and explains its appeal to modern organizations. The article argues that organizations can strengthen their knowledge capabilities by combining multiple epistemic stances through carefully designed sociotechnical systems.

  PublisherDOI

• Walls Without Walls: Performing Self-Regulated Learning With Virtual Spaces

  Academy of Management Proceedings · 2024-07-09

  articleSenior author

  Learning activities through virtual spaces in a hybrid environment has grown in significance since the beginning of the COVID-19 pandemic, giving an impetus for developing new digital spaces that can emulate or reconstruct traditional physical working environments. In this paper, we present a qualitative comparative field study investigating how people engage in self-regulated learning activities at a university library and with a digital study platform, ST.com. We adopt an agential realist perspective to analyze how self-regulated learners structure the learning environment with the digital learning space. We analyze how the digital elements in the virtual spaces are not standalone entities but become meaningful when learners actively include as well exclude elements while performing the learning activities – a process we call agential cutting following an agential realism ontology. We discuss how our theorizing about self-regulated learning in hybrid environments informs a broader understanding of how knowledge work practices move into virtual spaces.

  PublisherDOI

• Technology-Centric Contestation over Symbolic and Social Boundaries: The Social Justice Implications of Covid-19 Contact Tracing Technologies

  MIS Quarterly · 2024-08-07 · 17 citations

  article

  Information systems (IS) can amplify or reduce social justice by limiting or expanding access to material and nonmaterial resources. Using the lens of symbolic and social boundaries, we focus on the role of cultural discourse in demarcating and contesting boundaries associated with IS design and adoption. We examine news media-based journalistic discourse surrounding contact tracing systems implemented in South Korea and Singapore during the COVID-19 pandemic to develop a process model of technologycentric contestation. We found that public contestation over competing societal values, including public health, individual rights, and social justice, influenced how contact tracing systems were adopted and redesigned. Through the (re)design and adoption actions, symbolic and social boundaries were demarcated and contested. Our study illustrates the critical role of news media in counteracting the consequences of IS design and adoption decisions that limit access to resources for marginalized social groups.

  PublisherDOI

• Digitization of Transaction Terms within TCE: Strong Smart Contract as a New Mode of Transaction Governance

  MIS Quarterly · 2024-06-01 · 28 citations

  article

  We use transaction cost economics (TCE) to define the “digitization of transaction terms” shift parameter that describes the institutional changes associated with increased digitization in society. We then draw on legal scholarship to analyze how strong smart contracts, which refer to agreements with automatic execution and enforcement that are not reversible by courts, rely on a new level of digitization of transaction terms. Specifically, these contracts may rely on standard digital infrastructures such as blockchain systems that guarantee automatic execution and non-reversibility. Strong smart contracts represent a distinct mode of transaction governance compared to markets, hierarchies, or hybrids. This is because each classic governance mode is distinguished by how ex post adaptation is handled—through public courts, managerial fiat, or both. In contrast, strong smart contracts prevent ex post adaptation altogether. We propose that when strong smart contracts can be fully specified, they may dominate other governance modes based on certain trade-offs. These trade-offs include weighing the benefits of avoiding the holdup problem and lowering contract enforcement costs against the downsides of high ex ante specification costs and the elimination of flexibility to make ex post adjustments in a changing environment. Our discussion elaborates on which institutional conditions can further facilitate this institutional shift.

  PublisherDOI

Recent grants

• VOSS Collaborative Research: Open Innovation Intermediaries: Brokering Technology and the Wisdom of the Crowds

  NSF · $270k · 2011–2015

Frequent coauthors

• Brian S. Butler

  University of Maryland Eastern Shore

  37 shared

• Kalle Lyytinen

  Case Western Reserve University

  37 shared

• Jr. Richard J. Boland

  University of Pittsburgh

  36 shared

• Matt Fineout

  Case Western Reserve University

  36 shared

• Wendy Jansen

  Breda University of Applied Sciences

  36 shared

• Will Rifkin

  36 shared

• Deborah Dougherty

  36 shared

• John Venable

  Curtin University

  36 shared

Education

• Ph.D., Sloan School of Management

  Massachusetts Institute of Technology

  2001

Awards & honors

• Best Published Paper of 2021 Award in Information Systems
• Best Published Paper of 2015 Award in Information Systems
• Best Published Paper of 2015 award from the Academy of Manag…
• Best Published Paper of 2014 award from the Academy of Manag…
• Alfred P. Sloan Foundation Industry Studies Fellowship