AMD Call for Submissions: Registered Reports
This is a permanent call for submissions for Academy of Management Discoveries.
14 October, 2026 @ 13:00 – 16:00 EDT
Delivery: Live and virtual
Explore where machine learning fits into contemporary organizational research. Designed primarily for doctoral students building their methods foundation
This tutorial introduces the fundamental concepts and methods of machine learning, with an emphasis on supervised learning. Participants will examine how machine learning models learn from data, how model complexity affects performance, and how regression-based methods support more advanced techniques such as neural networks. Through conceptual explanations and worked examples, the tutorial will build a practical foundation for understanding model selection, regularization, and prediction. Participants will apply these concepts to a dataset by fitting and comparing regularized regression models, evaluating their performance, and identifying signs of overfitting and underfitting.
Dr. Louis Hickman, Virginia Tech
Dr. Louis Hickman is an Assistant Professor of Management at Virginia Tech, a Visiting Scholar at Amazon, and a Senior Fellow at University of Pennsylvania’s Wharton People Analytics. He holds an M.S. in Computer and Information Technology, specializing in natural language processing, and a Ph.D. in Industrial-Organizational Psychology. His research focuses on applications and implications of machine learning, natural language processing, and artificial intelligence in selection, assessment, and training and development. His research won the Personnel Psychology best paper award and twice won the Society for Industrial-Organizational Psychology’s Jeanneret Award for Excellence in the Study of Individual or Group Assessment.
One registration includes all four live tutorials, their released resources, and related program activities.
This is a permanent call for submissions for Academy of Management Discoveries.
Submission Deadline: 31 January 2027 ATTENTION! Please disengage the autopilot. Check the muscle memory. We do not want more of the same. Academy of Management Perspectives (AMP) is different. Are you thinking of submitting a […]
Discoveries-through-Prose empower authors to craft their manuscripts in nontraditional ways that make for tighter, more engaging narratives.
Interested in publishing an Academy of Management Collection? Submit an original essay tied to articles selected from the AOM archive.
Actionable Solutions to Real-World Problems of Practice and Policy Based on Prior Academy of Management Publications
Self-nominations are being sought for the position of editor (or co-editors) of the Academy of Management Perspectives. The person(s) selected will become editor-elect on 1 July 2027 and editor on 1 January 2028. The term of office as editor is three years.
Self-nominations are sought for the following founding leadership roles during the Teaching Community’s three-year pilot: Teaching Community Founding Chair; Teaching Community Committee Leaders
Practical Solutions for Coping with Rising Geopolitical Risk. Submission period 1-30 September 2026
Presented in collaboration with AOM’s Community Accelerator Program, this Spanish-language webinar hosted by the Laboratorio Internacional en Ciencias de la Gestión Pública presents a critical examination of artificial intelligence (AI) […]
Recent decades have seen rising scholarly, policy, and media attention towards the mental health of university populations. While there is strong evidence of mental health concerns among students, there are […]
SEE Connect is an Academy of Management initiative under the Community Accelerator Program (CAP), designed for doctoral students, young researchers, and early-career academics working across all areas of management research – particularly the domains of the AOM Strategic Management (STR), Technology and Innovation Management (TIM), and Organizational Behavior (OB) divisions, spanning strategy, innovation, technology, and organizational studies.
Delivery: Live and virtual Connect the way research data is structured to the statistical models used to analyze it. Designed primarily for doctoral students building their methods foundation What to […]