Professor Rhee’s research lies at the intersection of behavioral strategy and organization theory, with particular emphasis on the role of cognition for innovation, creativity and decision making. He is at the forefront of using advanced technologies in his research, employing machine learning to analyze large-scale textual data from internal corporate documents and various online platforms. His research has been published in leading academic journals, including Academy of Management Journal, Organization Science, and Strategic Management Journal, and has reached a broader audience through features in practitioner-oriented outlets like Harvard Business Review.

Peer-Reviewed Journal Articles

  • Yoon, D., Lee, J. and Rhee, L. 2025. Founder’s Entry Strategy and Funding Performance in the Crowdfunding Industry: The Mediating Role of Founder Attention. Forthcoming at Strategic Entrepreneurship Journal
  • Rhee, L. 2024. CEO Attentional Vigilance: Behavioral Implications for the Pursuit of Exploration. Academy of Management Journal 67(6): 1463-1487
  • Rhee, L. and Leonardi, P. 2024. Borrowing Networks for Innovation: The Role of Attention Allocation in Secondhand Brokerage. Strategic Management Journal 45(7): 1326-1365
  • Joseph, J., Rhee, L. and Wilson, A. 2023. Corporate Hierarchy and Organizational Learning: Member Turnover, Code Change, and Innovation in the Multiunit Firm. Organization Science 34(3): 1332-1352
  • Ocasio, W., Rhee, L. and Boynton, D. 2020. March and the Pursuit of Organizational Intelligence: The Interplay between Procedural Rationality and Sensible Foolishness. Industrial and Corporate Change 29(1): 225-239
  • Rhee, L., Ocasio, W. and Kim, T. 2019. Performance Feedback in Hierarchical Business Groups: The Cross-Level Effects of Cognitive Accessibility on R&D Search Behavior. Organization Science 30(1): 51-69
  • Rhee, L. and Leonardi, P. 2018. Which Pathway to Good Ideas? An Attention-based View of Innovation in Social Networks. Strategic Management Journal 39(4): 1188-1215

Other Publications

  • Leonardi, P. and Rhee, L. 2018. Finding New Ideas When You Don’t Have a Broad Network. Harvard Business Review Online. March 16.
  • Ocasio, W., Rhee, L. and Milner, D. 2017. Attention, Knowledge and Organizational Learning. In Linda Argote and John Levine (Eds.), The Oxford Handbook on Group and Organizational Learning. Oxford University Press
  • Rhee, L. 2015. Cognitive Advantage: Effects of Holistic and Analytic Managerial Attention on Product Innovation. Academy of Management Best Paper Proceeding
  • Rhee, L. and Leonardi, P. 2014. Networks, Attention and Good Ideas: Taking Advantage of Social Structure. Academy of Management Best Paper Proceedings

Current Research Projects

Generative AI and Collaborative Performance: An Attention-Based View

Drawing on the attention-based view (ABV), this study investigates the role of generative AI in enhancing collaborative performance in organizations. Through a field experiment with a longitudinal survey design conducted at an IT service company using ChatGPT Enterprise, we observe that employees using ChatGPT Enterprise attract 23.5% more attention from their colleagues compared to those who do not use this tool. Significantly, the benefits of this generative AI are more pronounced for employees with colleagues from diverse functional areas. Furthermore, we show that increased attention mediated the relationship between generative AI use and improved collaborative performance: employees who garnered greater attention through generative AI achieved higher collaborative outcomes relative to their counterparts. However, we find no evidence that generative AI enhanced employees’ idea generation performance, measured as their ability to produce feasible ideas for problem solving. In our setting, the primary benefit of generative AI manifests through improved collaborative performance rather than enhanced abilities in idea generation. By applying ABV, our study offers a novel perspective that generative AI’s core algorithmic logic serves as a communication aid that enables employees to mobilize support and convert ideas into implementation.

Family CEOs and Problemistic Search in Business Groups

This study examines the role of family CEOs in business groups in their firm’s problemistic search in response to underperformance. We argue that family CEOs, driven by a heightened sense of familial responsibility, intensify their firm’s problemistic search to remedy performance shortfalls. This intensity is amplified by negative media attention on family-dominant governance, reliance on internal sales transactions, and direct kinship to the group chairperson. Analyzing a dataset from South Korean business groups, we provide empirical support for our hypotheses. Using a coarsen exact matching technique, we also show that firms exhibit divergent behavior in problemistic search simply due to the difference between family CEOs and professional CEOs. This study holds theoretical implications regarding performance feedback in the contexts of multilevel hierarchies and multiple goals.

Compensating for Limited Mental Representations: The Role of Distributed Representations in Strategic Decision-Making (with Felipe Csaszar)

Because aggregation structures and mental representations have opposing effects on fallibility—mental representations are a source of errors while aggregation structures aim to overcome these—aggregation structures can be used to compensate for flawed mental representations. Yet given the multiple aggregation structures from which to choose and given that their effects depend on environmental factors, it is not clear which structure is best suited to what circumstances. To provide an answer to that question, we develop a formal model of group decision making among individuals who base their decisions on flawed mental representations. The model predicts the performance of three different aggregation structures (delegation, unanimity, and averaging) under different environments (defined by their munificence, uncertainty, complexity, and attribute dominance). We show that the concept of decision boundary, an idea we borrow from the machine learning literature, explains when and how aggregation structures compensate for flawed representations. This allows us to characterize the conditions under which it is preferable to use different aggregation structures as well as situations where all aggregation structures perform poorly. More generally, our paper provides a theoretical framework to understand how aggregation structure, mental representations, and the task environment jointly determine organizational performance.

The Role of Inventor Turnover in Vicarious Learning from Failures: Evidence from Peers’ Product Recalls (with Linyi Zhang and Tony Tong)

While prior research has long recognized the importance of peers’ failures in shaping a focal firm’s learning, evidence on the specific learning activities firms undertake and the mechanisms through which learning occurs remains limited. We address this gap by introducing a critical yet often overlooked dimension: the role of human resources. In particular, inventor turnover plays a pivotal role in this learning process. Leveraging a unique dataset of product recall events from FDA-regulated industries, we find that peer firms’ product recalls lead to increased exploitation and reduced exploration at focal firms. Our mediation analysis reveals that, following peers’ product recalls, focal firms experience a decline in inventor departures, reinforcing exploitation, while the arrival of new inventors diminishes, constraining exploration. By incorporating inventor turnover into the study of vicarious learning, we provide a more nuanced understanding of how firms internalize and respond to external failure events.