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DTSTART;TZID=America/New_York:20261014T130000
DTEND;TZID=America/New_York:20261014T160000
DTSTAMP:20261005T151527Z
CREATED:20260916T201955Z
LAST-MODIFIED:20261005T151527Z
UID:10002121-1791982800-1791993600@www.aom.org
SUMMARY:Concepts & Tools for Research Methods: Machine Learning
DESCRIPTION:Delivery: Live and virtual \n\n\n\nExplore where machine learning fits into contemporary organizational research. Designed primarily for doctoral students building their methods foundation \n\n\n\nWhat to Expect\n\n\n\nThis 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. \n\n\n\nPresenter\n\n\n\nDr. Louis Hickman\, Virginia Tech \n\n\n\nDr. 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. \n\n\n\nRegister for the tutorial series\n\n\n\nOne registration includes all four live tutorials\, their released resources\, and related program activities. \n\n\n\n\nLogin to register
URL:https://www.aom.org/event/machine-learning/
CATEGORIES:Research Methods Events
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20261012T090000
DTEND;TZID=America/New_York:20261012T120000
DTSTAMP:20261005T151801Z
CREATED:20260916T202755Z
LAST-MODIFIED:20261005T151801Z
UID:10002122-1791795600-1791806400@www.aom.org
SUMMARY:Concepts & Tools for Research Methods: Qualitative Data Coding
DESCRIPTION:Delivery: Live and virtual \n\n\n\nBuild a practical foundation for organizing\, coding\, and interpreting qualitative material. Designed primarily for doctoral students building their methods foundation \n\n\n\nWhat to Expect\n\n\n\nIn this session\, I will discuss techniques to help you analyze textual materials and then see patterns in your coding. In the first part of the session\, I will discuss a coding approach that emerged from my own experiences. This approach is based on the metaphor of painting your textual materials. From this\, I will discuss and show generic techniques from Computer Aided Qualitative Data Analysis software to help you see patterns in your data. We also will discuss\, in brief\, non-coding\, advanced analytic techniques that can bridge between coding efforts toward theoretical insights for a qualitative research project. For the hands-on activity\, we will analyze one interview together. We will practice coding this textual document using techniques identified above. Given that this interview is part of a large set of interviews\, we will discuss some analysis ideas to make sense of similar coding efforts across other interviews. Finally\, we will discuss other ways to analyze this interview (and other similar interviews) beyond coding. \n\n\n\nPresenter\n\n\n\nDr. Anne Smith\, University of Tennessee \n\n\n\nAnne Smith (BS\, University of Virginia; PhD\, MBA\, University of North Carolina) is the King & Judy Rogers Professor in Business\, Management & Entrepreneurship Department\, Haslam College of Business\, University of Tennessee. Her research addresses qualitative research practices as well as how organizations change and\, many times\, fail. Anne served as an Associate Editor at Academy of Management Journal (qualitative methods) and has continued to serve on the editorial board of Organization Research Methods (ORM) for over a decade\, with five years as an Associate Editor. She has been a guest co-editor for three ORM special feature topics. Anne also has co-edited Research Methodology in Strategy and Management Emerald book series (2019-2021). Her research has been published in journals such as Organization Science\, Journal of Applied Behavioral Science\, Organization Studies\, ORM\, Qualitative Research in Organizations and Management\, Journal of Management\, Entrepreneurship: Theory & Practice\, Journal of Business Research\, and Journal of Management Inquiry. \n\n\n\nRegister for the tutorial series\n\n\n\nOne registration includes all four live tutorials\, their released resources\, and related program activities. \n\n\n\n\nLogin to register
URL:https://www.aom.org/event/qualitative-data-coding/
CATEGORIES:Research Methods Events
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20261008T130000
DTEND;TZID=America/New_York:20261008T160000
DTSTAMP:20261005T151350Z
CREATED:20260916T201131Z
LAST-MODIFIED:20261005T151350Z
UID:10002120-1791464400-1791475200@www.aom.org
SUMMARY:Concepts & Tools for Research Methods: Programming Languages
DESCRIPTION:Delivery: Live and virtual \n\n\n\nSee how programming languages support transparent\, repeatable research workflows. Designed primarily for doctoral students building their methods foundation \n\n\n\nWhat to Expect\n\n\n\nThe session provides an introduction to the main languages of data analysis: Python\, R\, and SQL. Attendees will learn the similarities and differences between the languages\, their respective strengths\, and the types of tasks for which each is best suited. After introducing the languages\, attendees will then get hands-on experience programming in each during an online lab session\, in which participants apply the content from the lecture with organizationally relevant exercises. The session will close with a Q&A session and an opportunity to exchange ideas and insights between attendees. \n\n\n\nPresenter\n\n\n\nDr. Ivan Hernandez\, Virginia Tech \n\n\n\nDr. Ivan Hernandez is an Associate Professor and Area Head of Industrial-Organizational Psychology at Virginia Tech. He is currently an Associate Editor for the Journal of Applied Psychology and an editorial board member for Organizational Research Methods and Technology\, Mind\, and Behavior. His work has been published in leading research journals\, including Industrial-Organizational Psychology: Perspectives on Science and Practice\, Organizational Research Methods\, Psychological Methods\, Behavior Research Methods\, Personnel Psychology\, and the Journal of Applied Psychology. He served as team lead of the winning team in the 2023 SIOP Machine Learning Competition and co-organized the 2024\, 2025\, and 2026 SIOP Machine Learning Competitions. \n\n\n\nRegister for the tutorial series\n\n\n\nOne registration includes all four live tutorials\, their released resources\, and related program activities. \n\n\n\n\nLogin to register
URL:https://www.aom.org/event/programming-languages/
CATEGORIES:Research Methods Events
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20261006T090000
DTEND;TZID=America/New_York:20261006T120000
DTSTAMP:20261005T151637Z
CREATED:20260916T175358Z
LAST-MODIFIED:20261005T151637Z
UID:10002119-1791277200-1791288000@www.aom.org
SUMMARY:Concepts & Tools for Research Methods: Data Structures and Statistical Models
DESCRIPTION:Delivery: Live and virtual \n\n\n\nConnect the way research data is structured to the statistical models used to analyze it. Designed primarily for doctoral students building their methods foundation \n\n\n\nWhat to Expect\n\n\n\nData analyzed in organizational research frequently violates the basic regression assumption that errors are independent and unrelated to predictors. This tutorial uses examples and simple illustrations to show why this non-independence can be problematic in both cross-sectional and longitudinal data. The tutorial also gives a high-level overview of common ways to address non-independence\, with a focus on how addressing non-independence can expand a researcher’s research methods repertoire and strengthen the theory-methods-data link. \n\n\n\nPresenter\n\n\n\nDr. Paul Bliese\, University of South Carolina \n\n\n\nPaul D. Bliese is the Department Chair and Jeff B. Bates Chaired Professor in the Management Department at the Darla Moore School of Business at the University of South Carolina. He received a Ph.D. from Texas Tech University. After graduate school\, he worked for 22 years at the Walter Reed Army Institute of Research and retired as a Colonel. Throughout his career\, Dr. Bliese has led efforts to use statistical methods to answer complex organizational problems and advance theory and practice. He developed and maintains the multilevel package for R and has published in a variety of journals\, including the Academy of Management Journal\, the Journal of Applied Psychology\, the Journal of Management\, Organizational Research Methods\, and Organization Science. He served as an Associate Editor for the Journal of Applied Psychology from 2010 to 2017 and as the Editor in Chief for Organizational Research Methods from 2017 to 2021. In 2024\, he began a new role as the first Deputy Editor for Research Methods for the Academy of Management Journal. \n\n\n\nRegister for the tutorial series\n\n\n\nOne registration includes all four live tutorials\, their released resources\, and related program activities. \n\n\n\n\nLogin to register
URL:https://www.aom.org/event/data-structures-and-statistical-models/
CATEGORIES:Research Methods Events
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260601T000000
DTEND;TZID=America/New_York:20260914T235959
DTSTAMP:20260918T180733Z
CREATED:20260918T180602Z
LAST-MODIFIED:20260918T180733Z
UID:10002123-1780272000-1789430399@www.aom.org
SUMMARY:Call for Proposals for Research Methods Training Proposals from DIGs
DESCRIPTION:The AOM Board of Governors Methods Committee seeks strategic project proposals that creatively and meaningfully advance AOM’s Vision 2030: to advance our activities for all management scholars globally by expanding our content\, services\, and community. Funding Year: 2026Funding per Project: $2500 – $10\,000 \n\n\n\nProposal Submission\n\n\n\nThe application process for the strategic project fund is designed to be straightforward and efficient. (Please note some process changes have been adjusted since the initial 2025 launch of the DIG Strategic Project Fund CFP). \n\n\n\nMethods Training Proposal Submissions\n\n\n\nProposals will be submitted through a single-stage\, comprehensive application process*\, due no later than 14 September 2026. Each submission should include: \n\n\n\n\nContacts: Lead DIGs group members and names/affiliations of collaborators.\n\n\n\nSummary: A description of the project idea\, its innovative potential and the project goals.\n\n\n\nTarget audience: Discipline/subject matter\, intended career stage (graduate and doctoral student as primary focus)\, and geographic regions (if applicable).\n\n\n\nOriginality: The project must be original or a substantial extension/enhancement of an existing initiative.\n\n\n\nDIG Sponsorship: A brief statement by a DIG Executive Committee Member (or members if more than one DIG is involved) confirming that the DIG is a formal sponsor of the proposal.\n\n\n\nGovernance and implementation plan: Clear roles and responsibilities for developing\, implementing\, and disseminating the project.\n\n\n\nDeliverables and dissemination: Description of expected outputs and how they will be shared with the broader AOM community.\n\n\n\nTimeline and success metrics: Key milestones and criteria for evaluating project success.\n\n\n\nBudget: Estimated funding request and justification\, consistent with Strategic Project Fund guidelines.\n\n\n\n\nPrinciples for Projects and Project Teams\n\n\n\nThe creativity and passion found in Strategic Project Fund proposals reflect a shared interest in building an Academy that provides ever-better member services—all year long and beyond our current offerings. \n\n\n\nConsistent with the member-service ideal\, Project Teams are guided by the following principles in the execution of funded projects: \n\n\n\n\nProjects are for the benefit of the Academy’s membership\, or service to the broader field\, as approved. Funding supports costs for strategic projects undertaken by volunteers on behalf of the Academy of Management.\n\n\n\nProject work products generated through funded initiatives are considered AOM assets. Proposals should clearly outline plans for dissemination\, reuse\, and long-term accessibility of materials for the broader AOM community.\n\n\n\nProject teams are accountable to the Methods and Teaching Committees of the Board and to the membership\, on whose behalf they are working.\n\n\n\nProject Teams incorporate the feedback of the Board Committee reviewers\, if any\, into their activities.\n\n\n\nProject Teams\, operating under the purview of the Academy and with Academy funding\, promote high quality scholarship and standards in all activities.\n\n\n\nTransparency and fairness are paramount in all activities where members stand to gain from\, or where they may be excluded from opportunities to participate.\n\n\n\nProject Champion/Teams help the Academy and future members to learn from experience\, by documenting the procedures for activities and reporting to Board Committees on progress and against metrics for success\, if requested.\n\n\n\n\nResource Principles (Financial\, Human\, Technical)\n\n\n\nThe Academy responsibly manages its assets (human\, technical and financial). As such\, the following principles apply to the support of funded projects: \n\n\n\n\nOnce the project is approved\, funding will be immediately available for use.\n\n\n\nSubmission of the third-party expenses (vendors) should follow established AOM invoicing/expense reimbursement procedures.\n\n\n\nUpon successful project completion\, unused funds\, if any\, will remain available for DIG’s operational use. AOM’s financial policies apply in cases where the award includes additional funds designated for capacity building or other approved activity.\n\n\n\nAll project budget funds are approved on a “not to exceed” basis. Project teams may not re-engage the process to request additional funds in the same funding year. Overdrawing the project budget is not allowed.\n\n\n\nApproved project budget funds not expended in the timeframe to which they were approved do not carry over automatically beyond that time period (future fiscal years).\n\n\n\n\nFunding Preclusions\n\n\n\nThe following activities are precluded from funding: \n\n\n\n\nPayments to members for service.\n\n\n\nContributions to\, or sponsorships of other organizations.\n\n\n\nActivities that can/should be carried out as regular business of the DIGS (events and activities at the annual meeting\, awards\, catering\, regular travel\, program time).\n\n\n\nAOM membership or registration fees for current or former AOM members.\n\n\n\nMembership and registration fees paid to other organizations.\n\n\n\n\nFinancial Procedures\n\n\n\nSpecific financial procedures will accompany the notification of funded projects. \n\n\n\n\n\nOverarching Criteria for Evaluating Proposals\n\n\n\nWhen evaluating proposals\, Board Committees will strive to support projects that creatively and meaningfully advance AOM’s Vision 2030 enhancing our activities for all management scholars globally by expanding our content\, services\, and community. \n\n\n\n\n\n	\n\n\n	\n\n\n	\n		Step 1. Screen for Completeness and Funding Limit	\n	\n					\n\n\n\n\n\n	\n		\n\nBefore evaluating the specifics of a proposal\, Board Committees will FIRST screen the proposal to ensure that it is complete and does not exceed the funding limit per proposal (10k). \n\n	\n\n\n\n\n\n\n\n	\n\n\n	\n		Step 2. Screen for Alignment	\n	\n					\n\n\n\n\n\n	\n		\n\nWhen screening proposals for alignment with the aims of the fund\, the committee will consider the following: \n\n\n\n\nThe project is specific and clear\, and it addresses one or more of the areas of focus outlined in the call for proposals: Research Methods Training 2026 Call for Proposals\n\n\n	\n\n\n\n\n\n\n\n	\n\n\n	\n		Step 3. Evaluate for Potential Support	\n	\n					\n\n\n\n\n\n	\n		\n\nProposals that pass Steps 1 and 2 are evaluated in accordance with the review timeline. Board Committees will provide more favourable evaluations of projects with these attributes: \n\n\n\n\nNew and innovative\, adding value for AOM Members beyond traditional services and the annualmeeting.\n\n\n\nSupported by data indicating a need for the project.\n\n\n\nCan be completed (in phases or fully) within the designated timeframe.\n\n\n\nSubmitted by a Project Team that indicates capacity to complete the project.\n\n\n\nCarried out by a Project Team that reflects appropriate constituencies given the project.\n\n\n\nInvolves members from more than one division or interest group\, or other units of the AOM\, asTeam Members and/or as Collaborators.\n\n\n\nThe action steps\, metrics\, budget/resource requests are realistic.\n\n\n\nThe proposal contains no funding preclusions or other funding concerns.\n\n\n\nThe human\, technical\, and other requests of the HQ are considered and vetted by HQ.\n\n\n\nThe project adheres to the Fund’s principles for project champion/teams and resources.\n\n\n\n\n\nProject Team incorporates Board Committee feedback\, if applicable.\n\n\n\nProject and Project Team promote high quality scholarship/standards\n\n\n\nTransparency and project fairness are achievable\n\n\n\nProgress is measurable and documentable for learning by others\n\n\n\nProject does not obligate the DIG/AOM into the future
URL:https://www.aom.org/event/call-for-proposals-for-research-methods-training-proposals-from-digs/
CATEGORIES:Division & Interest Group (DIG) Events,Research Methods Events
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