Establishing, harnessing, and nurturing industry-academic (I-A) research collaborations is a critical yet complex endeavor that requires mutual trust, alignment, and sustained engagement. This workshop explores structured approaches to creating effective partnerships between academic researchers and industry practitioners. A number of industry-academia collaboration models will be explored that address how industry-academia co-creation processes can be approached in a systematic manner. The workshop also presents a framework, specifically designed to help scholars communicate effectively with a practice-oriented audience. Recognizing that practitioners value clarity, utility, and relevance, the framework guides scholars when translating their theoretical insights into accessible and actionable messages.

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Workshop Leaders:

• • Brian Donnellan, Maynooth University
  • Iris Junglas, College of Charleston

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Workshop Organizers:

• • Aleksi Aaltonen, Stevens Institute of Technology
  • Aron Lindberg, Stevens Institute of Technology
  • Tomislav Karačić, The London School of Economics and Political Science

Workshop Leaders:

• • Kalle Lyytinen, Case Western Reserve University
  • Elizabeth Davidson, Shidler College of Business
  • Youngjin Yoo, The London School of Economics and Political Science
  • Sirkka Jarvenpaa, The University of Texas at Austin
  • Monideepa Tarafdar, University of Massachusetts Amherst
  • Jan Marco Leimeister, University of St.Gallen

After highlighting the strengths and limitations of current AI approaches, current corresponding solutions to support literature reviews will be presented. We will focus on solutions that are destined to an academic audience and we will compare them on dimensions such as system transparency, input data, user control, output presentation, functionality. Hybrid neuro-symbolic systems, which combine transparently logical reasoning for clustering literature with pattern recognition for synthesizing it will be introduced. The focus will then be ARTIREV (https://www.scanlitt.com/), a hybrid AI tool integrating a bibliometric expert system coupled with fine-tuned generative AI (SOCRATES) offering improved transparency, exhaustivity, and reliability. Attendees will experiment with ARTIREV to conduct, on the spot, a literature review on the subject of their choice. They will critically assess the capabilities of the proposed tool, while benchmarking it against any other AI solution of their choice.

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Workshop Leaders:

• • Frantz Rowe, University of Nantes
  • Isabelle Walsh, Scanlitt

As data tools evolve, Information Systems curricula must adapt. While SQL remains foundational, tools like Pandas, Tableau, and PowerBI are used to teach principles of analytics and visualization. This workshop introduces Malloy, a new open-source data language developed by engineers at Google and Meta. Malloy combines the readability of a semantic data model with the power of SQL, enabling reusable joins and calculations while simplifying tasks such as date functions, percent-of-total, and level-of-detail queries. Unlike Tableau or PowerBI, Malloy is code-based, keeping learners closer to the data and reducing reliance on complex interfaces. Compared to Pandas, it provides accessible, database-native analytics without requiring extensive programming expertise. Participants will explore Malloy hands-on using finance, higher education, and sports datasets. Activities include small-group coding, discussion, and Q&A. Attendees will leave with practical teaching materials, datasets, and links to open resources. Basic SQL familiarity is helpful; laptops with internet and GitHub access required.

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Workshop Leaders:

• • Timothy Olsen, Gonzaga University

While much research has focused on how individuals interact with AI, far less attention has been given to how AI may shape human collaboration and collective intelligence. This PDW seeks to spark scholarly dialogue and drive new research on the topic of AI and collective intelligence. Bringing together leading experts from diverse theoretical and methodological backgrounds, the workshop explores a wide range of empirical settings — from robots collaborating with human teams to bots shaping peer production communities. It offers a platform to develop theoretical frameworks, identify promising research directions, and discuss innovative methods and empirical contexts.

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Workshop Leaders:

• • Bei Yan, Stevens Institute of Technology
  • Aron Lindberg, Stevens Institute of Technology
  • Jingyi Sun, Stevens Institute of Technology
  • Lei (Nico) Zheng, University of Massachusetts Boston

Computational Theory Construction is an emerging genre in the Information Systems field. Leveraging computational tools, such as social network analysis, process mining, or machine learning, this kind of research enables theorizing about socio-technical phenomena in innovative ways. However, computationally intensive research lacks established norms and scripts, making it challenging to anticipate the expectations of reviewers and journal editors. This PDW promotes a dialogue between editors and authors. This involves a panel discussion where editors share their experiences and give advice. Furthermore, authors receive feedback from mentors about their ongoing computational theory construction research.

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Workshop Leaders: 

• • Katharina Drechsler, University of Cologne
  • Thomas Grisold, University of St. Gallen
  • Aron Lindberg, Stevens Institute of Technology