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Dr. Majid Majzoubi is an Assistant Professor of Strategic Management at the Schulich School of Business at York University. His research explores the critical interplay between organizational strategy and social evaluation. He investigates how companies strategically position themselves and use communication to navigate the complex judgments of external audiences, such as investors and security analysts, to secure legitimacy and resources. His research has been published in premier journals including Strategic Management Journal, Organization Science, and California Management Review, with additional work under review at top outlets.
A hallmark of his research is the application of advanced computational methods. Majid leverages machine learning, natural language processing (NLP), and generative AI to analyze vast amounts of data, pioneering new ways to address long-standing questions in strategic management.
His contributions have been recognized through multiple awards, including two Seymour Schulich Teaching Excellence Awards and a Foster’s Excellence in Teaching Award. In 2025, he was awarded a prestigious Insight Development Grant from the Social Sciences and Humanities Research Council of Canada as sole Principal Investigator.
Majid teaches Strategic Management at both graduate and undergraduate levels, consistently earning exceptional evaluations. He actively supervises doctoral students and serves the academic community as a reviewer for leading journals and conference organizer.
Recent Publications
Majid Majzoubi, Eric Yanfei Zhao, Tiona Zuzul, Greg Fisher (2025), "The Double-Edged Sword of Exemplar Similarity", Organization Science, 36(1), 121-144.
Abstract
We investigate how a firm’s positioning relative to category exemplars shapes security analysts’ evaluations. Using a two-stage model of evaluation (initial screening and subsequent assessment), we propose that exemplar similarity enhances a firm’s recognizability and legitimacy, increasing the likelihood that it passes the initial screening stage and attracts analyst coverage. However, exemplar similarity may also prompt unfavorable comparisons with exemplar firms, leading to lower analyst recommendations in the assessment stage. We further argue that category coherence, distinctiveness, and exemplar typicality influence the impact of exemplar similarity on firm evaluation. Leveraging natural language processing (NLP) techniques to analyze a sample of 7,603 U.S. public firms from 1997 to 2022, we find robust support for our predictions. By highlighting the intricate role of strategic positioning vis-à-vis category exemplars in shaping audience evaluations, our findings have important implications for research on positioning relative to category exemplars, category viability, optimal distinctiveness, and security analysts.
Majid Majzoubi, Eric Yanfei Zhao (2023), "Going Beyond Optimal Distinctiveness: Strategic Positioning for Gaining an Audience Composition Premium", Strategic Management Journal, 44(3), 737-777.
Abstract
A core question in strategy research is how firms should position themselves to gain favorable audience evaluations. Emphasizing the heterogeneity in audience predispositions, we propose that firms can gain an audience composition premium by strategically positioning themselves to gain more (less) attention from audiences with positive (negative) predispositions toward them. We argue that this approach to strategic positioning is more conducive for firms with high dispersion in their audience predispositions and that firms can increase their ability to gain an audience composition premium by engaging with audiences holding moderately diverse evaluative schemas. We employ recommender systems and topic modeling to analyze 152,312 firm-analyst-year observations from 1997 to 2018 and 297,931 earnings call transcripts of U.S. public firms and find strong support for our predictions.
Managerial Summary
A key question managers encounter is how to increase their firms’ evaluations from external evaluators such as security analysts. In this study, we show that firms can increase their aggregate analyst recommendations by influencing the composition of analysts who opt to cover them and gaining evaluations from analysts who have more favorable predispositions toward them (i.e., by gaining an audience composition premium). Our findings also suggest that gaining an audience composition premium is more important for enhancing a firm’s aggregate analyst recommendations when there is a higher dispersion in analyst predispositions toward the firm. To increase its ability to gain an audience composition premium, the firm should engage with analysts who exhibit a moderate degree of heterogeneity in their evaluative schemas.
