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DTSTART;TZID=America/New_York:20261105T090000
DTEND;TZID=America/New_York:20261105T120000
DTSTAMP:20261022T152606Z
CREATED:20261021T172151Z
LAST-MODIFIED:20261022T152606Z
UID:10002135-1793869200-1793880000@www.aom.org
SUMMARY:Advanced Topic Workshop Series: Grounded Theory
DESCRIPTION:Dr. Heather VoughGeorge Mason University \n\n\n\nSession abstract\n\n\n\nIn this session\, we will explore many of the fundamental aspects of using a grounded theory approach for qualitative projects. We will cover topics such as iteration between data collection and analysis\, purposeful and theoretical sampling\, the constant comparative method\, ensuring trustworthiness\, theoretical saturation\, and developing emergent theory. Participants will be given some data to analyze and will have the opportunity to discuss their analysis with other participants\, keeping in mind topics addressed in the session. \n\n\n\nPresenter biography\n\n\n\n\n\n\n\nHeather C. Vough is a Professor of Management in the Donald G. Costello College of Business at George Mason University. Her research is focused on understanding how employees make sense of events and setbacks at work as well as how they construct their identities relative to their careers\, professions\, organizations\, and workgroups. She has used qualitative methods to address these questions across a variety of contexts including architecture firms\, start-ups\, and call centers. She is currently an Associate Editor at Organizational Behavior and Human Decision Processes and was formerly an Associate Editor at the Academy of Management Journal. In both positions\, she was primarily responsible for handling qualitative manuscripts. She also serves on the editorial board of four journals. \n\n\n\n\nRegister Now
URL:https://www.aom.org/event/advanced-topic-workshop-series-grounded-theory/
CATEGORIES:Research Methods Events
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20261117T130000
DTEND;TZID=America/New_York:20261117T160000
DTSTAMP:20261022T152432Z
CREATED:20261021T173235Z
LAST-MODIFIED:20261022T152432Z
UID:10002136-1794920400-1794931200@www.aom.org
SUMMARY:Advanced Topic Workshop Series: Multilevel Analysis
DESCRIPTION:Dr. Gilad ChenUniversity of Maryland \n\n\n\nSession abstract\n\n\n\nThis lecture will discuss basic and advanced multilevel analyses\, using random coefficient modeling (RCM) in R.  The lecture will start by laying out a foundation for analyzing two-level models in RCM\, where the dependent variable is at the lower level (Level 1)\, and predictors are at both the lower level (Level 1) and a higher-level unit (Level 2).  The lecture will then apply RCM to multilevel longitudinal models involving growth\, and consider more complex models\, including 3-level models and models involving contextual influences emanating from multiple units simultaneously.  Empirical examples will be provided\, along with R codes and data to analyze and apply each of the RCM models discussed. \n\n\n\nPresenter biography\n\n\n\n\n\n\n\nDr. Gilad Chen is the Robert H. Smith Chair in Organization Behavior at the University of Maryland’s Robert H. Smith School of Business. He received his bachelor’s degree in Psychology from the Pennsylvania State University in 1996\, and his doctoral degree in Industrial/Organizational Psychology from George Mason University in 2001. Prior to joining the Smith School\, Dr. Chen was on the faculty at the Georgia Institute of Technology and Texas A&M University\, and a visiting scholar at various other universities. \n\n\n\nDr. Chen teaches courses on a variety of organizational behavior\, human resource management\, and methodological topics. His research focuses on work motivation\, adaptation\, teams and leadership\, with particular interest in understanding the complex interface between individuals and the socio-technical organizational context. He has won several research awards\, including the 2007 Distinguished Early Career Contributions Award from the Society for Industrial and Organizational Psychology\, and the 2008 Cummings Scholar Award from the Organizational Behavior Division of the Academy of Management. Dr. Chen is also an elected Fellow of the Academy of Management\, American Psychological Association\, Association for Psychological Science\, and Society of Industrial-Organizational Psychology. His research has appeared in such journals as the Academy of Management Journal\, Journal of Applied Psychology\, Journal of Organizational Behavior\, Personnel Psychology\, Organizational Behavior & Human Decision Processes\, and Research in Organizational Behavior. He served as Editor of the Journal of Applied Psychology\, and as an editorial board member of several leading psychology and management journals. \n\n\n\n\nRegister Now
URL:https://www.aom.org/event/advanced-topic-workshop-series-multilevel-analysis/
CATEGORIES:Research Methods Events
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20261203T090000
DTEND;TZID=America/New_York:20261203T120000
DTSTAMP:20261022T152029Z
CREATED:20261021T174033Z
LAST-MODIFIED:20261022T152029Z
UID:10002137-1796288400-1796299200@www.aom.org
SUMMARY:Advanced Topic Workshop Series: Computational Modeling
DESCRIPTION:Dr. Goran KuljaninDePaul University \n\n\n\nSession abstract\n\n\n\nThis workshop introduces participants to computational modeling. Specifically\, we discuss and demonstrate the use of computational models as a tool to develop more explicit\, transparent\, reproducible\, and insightful theories for organizational science. To do so\, we begin with a discussion to distinguish the difference between narrative construct theorizing (i.e.\, the dominant form of theory development in organizational science) and computational process theorizing (i.e.\, the form of theory development promoted in the workshop). After this initial discussion\, we collectively work through a specific motivated example to demonstrate how to: program a computational model using software (R\, Positron\, and GenAI)\, execute virtual experiments with a developed computational model\, and analyze simulated data from a computational model to derive theoretical insights. During this collective work experience\, participants actively engage in completing various tasks pertaining to process thinking\, programming\, simulation\, and data analysis. By the end of this workshop\, participants will gain an understanding of the importance of computational modeling for effective theory development and how to take the next steps in developing computational models for their own theories of interest. \n\n\n\nPresenter biography\n\n\n\n\n\n\n\nProfessor Goran Kuljanin serves as an Assistant Professor in the Department of Management and Entrepreneurship in the Driehaus College of Business at DePaul University. He primarily teaches courses on business and people analytics. His courses cover topics such as data visualization\, analytical notebooks\, unsupervised and supervised learning\, natural language processing\, web scraping\, time series forecasting\, business process analytics\, and organizational network analysis. \n\n\n\nProfessor Kuljanin’s research focuses on developing computational process models of human functioning and workplace operations to enable process-oriented\, strategic decision-making on human resources management. He has published his research in the Journal of Applied Psychology\, Leadership Quarterly\, Organizational Research Methods\, and Psychological Methods. His research awards include Best Article in Organizational Research Methods in 2013\, the 2015 William A. Owens Scholarly Achievement Award from the Society of Industrial and Organizational Psychology\, and a Monograph Distinction from the Journal of Applied Psychology. As a co-investigator\, he has won grants from the U.S. Army Research Institute for the Behavioral and Social Sciences. \n\n\n\nProfessor Kuljanin’s consulting work focuses on analyzing data on any organization’s most important resource: its people. Specifically\, his consulting work involves applying advanced analytics (e.g.\, machine learning\, dynamical modeling\, organizational network analysis\, natural language processing\, computational process modeling) on employee\, team\, organizational\, and client data to develop effective work strategies to meet organizational objectives. \n\n\n\n\nRegister Now
URL:https://www.aom.org/event/advanced-topic-workshop-series-computational-modeling/
CATEGORIES:Research Methods Events
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20261215T130000
DTEND;TZID=America/New_York:20261215T160000
DTSTAMP:20261026T035405Z
CREATED:20261021T181357Z
LAST-MODIFIED:20261026T035405Z
UID:10002138-1797339600-1797350400@www.aom.org
SUMMARY:Advanced Topic Workshop Series: Large Language Models
DESCRIPTION:Dr. Richard LandersUniversity of Minnesota \n\n\n\nSession abstract\n\n\n\nThe use of large language models (LLMs) in research represents an extraordinarily broad range of options and opportunities\, from consulting ChatGPT like a research assistant to the real-time delivery of LLM-generated content to research participants. Whatever the use case\, the diverse new capabilities brought by LLMs bring an equally diverse new set of challenges and risks. In this lecture\, we’ll first explore what social scientists need to know about the technical foundations of LLMs to make good use of them. Next\, we’ll explore LLM use cases that can be executed with no or limited technical ability. Finally\, we briefly explore more advanced uses of LLMs\, including automated qualitative coding and use of LLM embeddings in natural language models. In the application activity\, participants will work through the engineering steps needed to execute one of the use cases and describe the risks of the approach taken. \n\n\n\nPresenter biography\n\n\n\n\n\n\n\nRichard N. Landers\, Ph.D.\, is the John P. Campbell Distinguished Professor of Industrial-Organizational Psychology at the University of Minnesota. He is Principal Investigator for TNTLAB (Testing New Technologies in Learning\, Assessment and Behavior)\, where his research concerns the use of innovative technologies like games\, gamification\, machine learning\, artificial intelligence\, and virtual reality\, to improve psychometric assessment\, employee selection\, adult learning\, and research methods. He is a Fellow of the Society for Industrial and Organizational Psychology\, American Psychological Association\, and Association for Psychological Science. His work appears primarily in psychology journals\, such as American Psychologist\, Journal of Applied Psychology\, Journal of Business and Psychology\, and Psychological Methods\, and in interdisciplinary outlets\, including Computers in Human Behavior\, International Journal of Human-Computer Studies and Simulation & Gaming. He is the incoming editor-in-chief for the interdisciplinary American Psychological Association journal Technology\, Mind\, and Behavior\, author of a statistics textbook and a research methods textbook\, and editor of two volumes: Social Media in Employee Selection and Cambridge Handbook of Technology and Employee Behavior. Dr. Landers has been featured in the popular press\, including Forbes\, Business Insider\, and Popular Science\, and he regularly consults with industry as president of Landers Workforce Science LLC\, primarily by designing and auditing employee hiring systems incorporating artificial intelligence and machine learning. \n\n\n\n\nRegister Now
URL:https://www.aom.org/event/advanced-topic-workshop-series-large-language-models/
CATEGORIES:Research Methods Events
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20270113T090000
DTEND;TZID=America/New_York:20270113T120000
DTSTAMP:20261022T152232Z
CREATED:20261021T181945Z
LAST-MODIFIED:20261022T152232Z
UID:10002139-1799830800-1799841600@www.aom.org
SUMMARY:Advanced Topic Workshop Series: Interpretive Content Analysis
DESCRIPTION:Dr. Jane LeWHO School of Business \n\n\n\nSession abstract\n\n\n\nThis applied module engages researchers in the practice of doing advanced qualitative analysis. Focused on interpretive analysis\, it focuses on practices of coding and analysis with particular attention paid to the advanced analytic processes that facilitate novel theorizing. Using a combination of learning techniques\, including taught sessions and lab sessions (coding and analysis exercises)\, the module works to demystify the interpretive research process. \n\n\n\nPresenter biography\n\n\n\n\n\n\n\nDr. Jane Lê is Professor of Strategic Management at WHU – Otto Beisheim School of Management. Her observation-based interpretive research explores how people experience and respond to strategic complexity in a multitude of organizational and industrial settings. Jane is passionate about qualitative research and qualitative research methods. She has written on various aspects of the research process\, including data collection and analysis\, the specifics of process research\, studying paradox\, research designs-in-use\, and the practice of innovating research methods. Jane is Associate Editor at Organizational Research Methods and a proud CARMA instructor. Affiliation note for AOM review: The supplied listing says “WHO School of Business”; the presenter biography identifies WHU – Otto Beisheim School of Management. Please confirm the listing before publication. \n\n\n\n\nRegister Now
URL:https://www.aom.org/event/advanced-topic-workshop-series-interpretive-content-analysis/
CATEGORIES:Research Methods Events
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20270125T130000
DTEND;TZID=America/New_York:20270125T160000
DTSTAMP:20261022T193743Z
CREATED:20261021T182739Z
LAST-MODIFIED:20261022T193743Z
UID:10002140-1800882000-1800892800@www.aom.org
SUMMARY:Advanced Topic Workshop Series: Using AI to Enhance Your Research and Data Analysis
DESCRIPTION:Dr. Justin DeSimoneUniversity of Alabama \n\n\n\nSession abstract\n\n\n\nArtificial intelligence (AI) offers myriad opportunities to enhance the research process. To get as much out of AI as possible\, researchers must develop an understanding of the capabilities of AI but should also be aware of critical limitations. This workshop will address five areas researchers should consider when exploring the potential for AI to assist with research and analysis: (1) recognizing potential errors and limitations in AI-generated outputs\, (2) using AI to generate and refine research ideas\, (3) developing research design and measurement using AI\, (4) applying AI’s capabilities to data analysis and interpretation\, and (5) writing alongside AI. Across the workshop\, particular attention will be given to how AI can support (rather than replace) researchers’ critical evaluation skills. Following the lecture\, participants will have the opportunity to apply these concepts through a set of hands-on exercises inspired by each of the five topic areas. Participants should come away from this workshop with an improved sense of how AI can assist the research and data analysis process along with practical strategies for critically evaluating AI-generated output. At the end of this workshop\, participants should be more comfortable integrating AI into their organizational research endeavors. \n\n\n\nPresenter biography\n\n\n\n\n\n\n\nJustin A. DeSimone is an associate professor in the Department of Management at the University of Alabama. His research focuses on research methodology\, including statistics\, measurement\, and innovative assessment techniques. Justin’s work has been published in various outlets including Organizational Research Methods\, the Journal of Management\, and the Journal of Applied Psychology. He has served as an associate editor for Organizational Research Methods and the Journal of Organizational Behavior as well as on the editorial review boards for the Journal of Management and Journal of Business and Psychology. Justin has also served on the executive committee for the Research Methods Division at Academy of Management and is a frequent contributor to CARMA. \n\n\n\n\nRegister Now
URL:https://www.aom.org/event/advanced-topic-workshop-series-using-ai-to-enhance-your-research-and-data-analysis/
CATEGORIES:Research Methods Events
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20270208T130000
DTEND;TZID=America/New_York:20270208T160000
DTSTAMP:20261022T151716Z
CREATED:20261021T183207Z
LAST-MODIFIED:20261022T151716Z
UID:10002141-1802091600-1802102400@www.aom.org
SUMMARY:Advanced Topic Workshop Series: Panel analysis
DESCRIPTION:Dr. DJ SchepkerUniversity of South Carolina \n\n\n\nSession abstract\n\n\n\nThis workshop covers the primary methods used in analyzing panel data\, a data structure in which the same units are observed repeatedly over time. We will explore why that structures creates both opportunities and challenges for statistical analysis. Participants will be introduced to pooled OLS\, econometric fixed effects and random effects\, and mixed effect models as means to analyze panel data\, with particular attention paid to how each model treats stable differences across units and separates within-unit from between-unit variation. Participants will learn how to think about model choice using the underlying research question\, assumptions about unobserved heterogeneity\, and tools such as the Hausman test. The workshop will also emphasize interpretation of model coefficients in common panel models and common mistakes that arise when researchers analyze panel data. At the end\, participants will be provided a real world data and sample code to analyze the data using the models discussed in the workshop. The goal will be to conduct analysis and interpret the results in line with the various tests performed and a hypothetical research question that could be explored with the data. \n\n\n\nPresenter biography\n\n\n\n\n\n\n\nDonald J. “DJ” Schepker is a Professor of Strategic Management and Moore Distinguished Fellow in the Darla Moore School of Business at the University of South Carolina\, where he also serves as the Research Director in the Center for Executive Succession. His research focuses on relational dynamics in top management teams and boards of directors\, including work on executive succession planning\, the causes and consequences of executive succession\, corporate governance\, and board decision making. His research has appeared in outlets such as the Academy of Management Journal\, Strategic Management Journal\, Journal of Applied Psychology\, Journal of Management\, and Journal of Management Studies. Dr. Schepker currently serves as a Senior Associate Editor for The Leadership Quarterly. He received his Ph.D. in Strategic Management from the University of Kansas and B.S. from Babson College.journals. \n\n\n\n\nRegister Now
URL:https://www.aom.org/event/advanced-topic-workshop-series-panel-analysis/
CATEGORIES:Research Methods Events
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20270223T090000
DTEND;TZID=America/New_York:20270223T120000
DTSTAMP:20261022T151416Z
CREATED:20261021T183525Z
LAST-MODIFIED:20261022T151416Z
UID:10002142-1803373200-1803384000@www.aom.org
SUMMARY:Advanced Topic Workshop Series: Introduction to Causal Inference
DESCRIPTION:Dr. Michael WithersUniversity of Notre Dame \n\n\n\nSession abstract\n\n\n\nOrganizational researchers often seek to draw causal inferences from observational data. However\, this is a challenging task\, given that the relevant counterfactual outcomes are by definition unobservable. Observational settings can also involve confounding that further complicates causal inference. To address these issues\, a number of methodological approaches have emerged\, each with its own set of identifying assumptions. Some approaches adjust for observed differences between units\, while others leverage temporal variation\, discontinuities\, instrumental variables\, or other features of the research setting to identify causal effects. Despite the benefits they provide\, these approaches support a causal interpretation only when their underlying assumptions hold. Although researchers can often evaluate the plausibility of these assumptions and test some of their observable implications\, the identifying assumptions themselves generally cannot be established from the observed data alone. As such\, applying these approaches requires careful consideration and evaluation. In this session\, we focus on the assumptions that make causal inference possible and on the ways those same assumptions constrain it. \n\n\n\nPresenter biography\n\n\n\n\n\n\n\nMichael C. Withers is a professor in the Department of Management & Organization at the University of Notre Dame’s Mendoza College of Business. He received his Ph.D. in strategic management from the W. P. Carey School of Business at Arizona State University. His research focuses on how organizations manage external dependencies and adapt to changing competitive environments through strategic decision-making\, executive leadership transitions\, and corporate governance mechanisms. His work has appeared in leading journals\, including Academy of Management Journal\, Academy of Management Review\, Strategic Management Journal\, Organization Science\, Journal of Management\, and Journal of Applied Psychology. He is a Senior Editor of the Journal of Management and an Associate Editor of Organizational Research Methods. \n\n\n\n\nRegister Now
URL:https://www.aom.org/event/advanced-topic-workshop-series-introduction-to-causal-inference/
CATEGORIES:Research Methods Events
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