Advancing Human–AI Synergy in Decision-Making and Management Innovation

Francisco Javier Santos Arteaga is Assistant Professor in the Department of Financial and Actuarial Economics and Statistics at the Complutense University of Madrid. He is also a researcher in the Statistical Methods and Big Data Applied to Social Sciences Group at Complutense, and the Big Data and Artificial Intelligence Group of the Spanish Society of Nephrology. He has published over 130 peer-reviewed articles in the areas of decision making and management and is an academic editor for more than ten international journals. He has participated in multiple national and international competitive research projects ranging from business and management to climate change and medicine. 

Outi Behm is a PhD Student at the Business School at LUT University, Finland. She studies stakeholder collaboration and trust dynamics in green transition investment projects. She is particularly interested in the antecedents and consequences of distrust. She hopes to include aspects of digital collaboration and AI-assisted smart tools in her research to examine how AI could potentially foster trust relations between stakeholders.

Matteo Benuzzi is a postdoctoral researcher at the University of Trento. He received his PhD cum laude at the University of Trento. His research focuses on experimental economics and finance, and sustainable finance, with a particular interest in retail investors’ decisions. His research has been published in journals such as International Review of Financial Analysis, Journal of Behavioral and Experimental Finance, and Annals of Finance. Before starting his PhD, he worked in financial consulting for approximately one year.

Kirsimarja Blomqvist is a Professor for Knowledge management at the Business School at LUT University, Finland. Her research interests relate to trust, knowledge management, innovation, and new digital forms of organizing expertise. She has published over 200 research articles in academic journals, books, and peer-reviewed conferences. She serves in the Editorial boards for Journal of Trust Research, Industrial Marketing Management, and Journal of Organization Design. As engaged scholar she has been developing a large-scale online temporary organizing for solving complex societal problems. She is also a founding member and board member of FINT network of trust researchers around the world.

Sandro Casal is an Associate Professor in the Department of Economics and Management at the University of Trento, Italy, and a member of the Cognitive and Experimental Economics Laboratory (CEEL) at the same institution. His research interests lie in Experimental and Behavioural Economics, Public Finance, Tax Compliance Behaviour, Behavioural Public Policy, and Nudging. He earned his Ph.D. in Economics and Management from the University of Trento in December 2014. Before his current position, he was an Assistant Professor at the University of Trento (2019–2025), and a Postdoctoral Research Fellow at the University of Milan (2015–2019) and at FAU University of Erlangen-Nuremberg (2013–2015). Sandro Casal has authored several academic publications in peer-reviewed journals, including the Journal of Economic Psychology, Journal of Economic Behavior and Organization, Economic Inquiry, and PLOS ONE.

Violeta Cvetkoska is a Professor of Operational Research and Machine Learning for Management, Coordinator of Business Analytics and Data Science, and Head of the EUi Center at the Faculty of Economics-Skopje, Ss. Cyril and Methodius University in Skopje. She is the author of the book Business Analytics. Her research and teaching bridge advanced analytics with managerial decision-making, empowering students and professionals to thrive in data-driven environments. Prof. Cvetkoska is a member of the university’s Science Committee and the National Accreditation Board. Internationally active, she contributes to editorial boards, scientific committees, and professional societies such as AOM, CRORR, SEM, and EURO Working Groups. She will chair the Jury Committee for the EDDA Award at IFORS 2026, the world’s largest Operational Research conference. As a MonsoonSIM-certified trainer, she champions experiential and AI-enhanced learning that promotes synergy between human intuition and algorithmic intelligence.

Debora Di Caprio is Associate Professor at the Department of Economics and Management, University of Trento, Italy. She is also a Researcher in the Big Data and Artificial Intelligence Group of the Spanish Society of Nephrology and the ARES (ARtificial IntElligence for better tranSplant) Research Group of the Hospital Clinic-IDIBAPS of Barcelona, Spain. She has published over 100 peer-reviewed articles in the areas of mathematics, decision theory, and operations research. She is a Department Editor of Healthcare Analytics and an Associate Editor for several academic journals among which Expert Systems with Applications, Decision Analytics, and Supply Chain Analytics.

Peiman Ghasemi is a Postdoctoral Researcher at the University of Vienna, Department of Business Decisions and Analytics. His research includes soft computing, fuzzy systems, meta-heuristic optimization, data envelopment analysis, and applied operations research. He has authored over 100 peer-reviewed publications and was previously a Research Associate at GUtech in Oman. Dr. Ghasemi holds a Ph.D. and M.Sc. in Industrial Engineering from Azad University. He serves as Associate and Guest Editor for journals with Elsevier and Springer, such as EAAI, DAJOUR and OMR. In 2024, he was ranked among the Top 2% of scientists globally by a Stanford University study.

Vittorio Guida is a postdoctoral researcher at the University of Trento, where he also received his PhD in Economics and Management in 2022. His research interests include behavioral economics, decision-making, and organizational theory, particularly in the context of sustainability and governance. He has collaborated on several interdisciplinary projects involving experimental methods, computational modeling, and environmental policy. He is also experienced in programming and deploying laboratory experiments using oTree.

Vahid Hajipour holds BS, MS, and PhD degrees in industrial engineering. He is an Assistant Professor at Azad University and Head of the FANAP Research Center in Iran, as well as a Visiting Professor at Altinbas University, Turkey. His research spans operations research, decision and business analytics, emerging technologies, and computational intelligence. He has authored four books and over 70 publications in leading journals and conferences. He has received multiple Distinguished Young Researcher Awards and the Distinguished Researcher Award. Dr. Hajipour was inducted into Alpha Iota Delta, the International Honor Society for Decision Sciences and Information Systems, in 2017.

Elina Hyrkäs works as a Postdoctoral researcher and teacher at Oulu Business School, Finland. She has worked as a Research Director at the Oulu Southern Institute, University of Oulu, Finland. She has done scientific research work for over 20 years, both on the national and international levels. Previously, among other things, she has directed a wide cooperation project; the focus of the project was to develop wellness as a field of research in the region of Oulu South. She has also been worked work package leader in the InDemand project (Horizon 2020, three countries in the consortium). InDemand was co-creation project with the eHealth companies and hospitals.  She has also worked other Horizon 2020 projects for example MIDAS, concerning a meaningful data integration in the preventive healthcare related decision making. Her research interests include knowledge management in general and especially AI and KM, data privacy issues, innovation management, open innovation and co-creation.

Paolo Leoni works at the Department of Economics and Management of the University of Trento as the lab manager of the Cognitive and Experimental Economics Laboratory (CEEL). He is responsible for developing experimental software, managing the participant database, coordinating their involvement in studies, and maintaining all hardware. He has collaborated with CEEL researchers on numerous research projects, including studies focused on human–AI interaction in behavioral economics. Before joining the CEEL team, Paolo worked as a laboratory technician at the Department of Psychology and Cognitive Science of UniTrento, specializing in neuroscience research tools.

Päivi Maijanen-Kyläheiko is an Associate Professor at LUT University, Business School, Finalnd, where she is Head of the Viipuri Lab. Her research focuses on strategic management and organizational renewal, particularly in the context of digital transformation, media responsibility, and sustainable business models in media organizations. Her current work explores how generative AI is reshaping journalistic and organizational practices. She served for several years on the executive board of the European Media Management Association. Before academia, she had a long career in the media industry.

Luigi Marengo is professor of Economics at LUISS University in Rome. He is one of the managing editors of Industrial and Corporate Change (Oxford University Press). Before joining LUISS he was professor and Dean of the School of Social Sciences at Scuola Superiore Sant’Anna in Pisa. His main research interests are evolutionary economics, organization theory, complexity, technological change. He has published several articles in Strategic Management Journal, Organization Science, Research Policy, Journal of Industrial Economics, Industrial and Corporate Change, Strategy Science, Journal of Economic Behavior and Organization, Journal of Evolutionary Economics, Journal of Economic Psychology, Organization Studies, and several other journals and collective books. He has also served for nine years as managing editor of Structural Change and Economic Dynamics, published by Elsevier. He graduated at the University of Torino (Italy) and got his PhD with the Science Policy Research Unit (SPRU), Sussex University, UK.

Luigi Mittone is full professor at University of Trento and Visiting Professor of LUT School of Business and Management. He is Director of the Cognitive and Experimental Economics Laboratory (CEEL) at the University of Trento. His main research interests and publications are in the field of experimental and behavioral economics: fiscal evasion theory, consumer behavior; mental modeling of uncertain events; inter-temporal choices, cooperation among agents; organizational behavior. He has authored more than 100 scientific articles and book chapters, many of them in top scientific journals.

Azzurra Morreale is an Associate Professor at LUT Business School (LBS) and Co-Head of the Viipuri Lab at the same institution. She also holds a senior faculty position at Jönköping International Business School (20%) in Sweden and has been recognized as a Docent in Strategic Financing and Decision-Making at LBS. In addition, she is affiliated with the Cognitive and Experimental Economics Laboratory (CEEL) at the University of Trento (Italy) and has served as a visiting scholar at the University of California, Santa Cruz. Her research lies at the intersection of behavioral economics and finance, with a particular focus on real options reasoning and strategic decision-making under uncertainty. More recently, she has begun exploring how artificial intelligence is transforming decision-making processes and financial strategies.

Markus Mäkelä is the Head of AI of the Lappeenranta-Lahti University of Technology, a university executing a comprehensive AI capability development roadmap. Dr. Mäkelä’s background is in both business, where he has worked in or with several industries, and academia. He is a strategy scholar and a former professor of software development with a continuing interest in product development, among other functions.

Elena Nissing is a junior researcher and PhD student at LUT Business School since December 2024. She holds a master's degree in Human-Technology Interaction from RWTH Aachen University, Germany, and has an academic background in cognitive science and experimental methods. Elena also brings professional experience as a user experience researcher in the automotive industry. Her research interests focus on digital collaboration in the age of AI, particularly how to enable effective collaboration on digital platforms. She explores key aspects such as engagement, trust, and asynchronous collaboration, and aims to investigate how AI is perceived in digital collaboration and whether—and how—it can enhance engagement.

Associate Professor Eeva-Liisa Oikarinen earned her PhD in Marketing from Oulu Business School in 2018 and became a Docent in Marketing, specializing in consumer interaction behavior, at the University of Turku in 2023. Her earlier research explored the limits of humor, playfulness and humanness in recruitment advertising, service encounters and innovation management. Passionate about AI-facilitated service interactions and multidisciplinary, she is building a research group in Biomarketing. She leads a project using Biomarketing tools to explain transformative bot-human companionships. She has published in European Journal of Marketing, Journal of Business Research and Journal of Retailing and Consumer Services among others.

Jarno Pasonen is a PhD student and a junior researcher at LUT Business School, where he investigates the transformative role of generative AI in journalistic knowledge work. In his dissertation, he explores how agentic technologies reconfigure boundaries and reshape practices in media organizations, with a focus on agency, leadership, knowledge management, and collaborative innovation. His current research emphasizes qualitative methods, drawing on theoretical insights from information systems and organization studies. Through this work, he aims to contribute to emerging debates on human-AI collaboration, the evolution of professional work, and the changing landscape of journalism and the institutional press.

Khanh Phan is a first-year PhD student at LUT Business School. His research investigates the adoption of artificial intelligence within sustainable entrepreneurship. He focuses on how founders use AI to create and augment ventures with circular economy principles. Also, he studies how qualitative methods can be augmented by the use of local LLMs as a personal side project.

Matteo Ploner is a Full Professor of Economics at the University of Trento, Italy. His research focuses on human decision-making, using rigorous experimental methods to explore how psychological factors influence economic choices. His work spans a wide array of topics, from tax compliance and financial bubbles to energy conservation, consistently bridging the gap between psychology and economics. Professor Ploner’s extensive publication record and his collaborations with prominent international scholars underscore his significant contributions to the field.

Irene Vega Ramon is a junior researcher at LUT university. Her background is in philosophy, business, and data science. In their intersection, she is studying meaning augmentation with language models in organizations. Generally, she is interested in the development, implementation, and use of emerging technologies in practice. She uses longitudinal ethnographic methods. Her supervisors are prof. Paavo Ritala and dr. Letizia Mortara. 

Atta ur Rehman is a marketing professional with a Bachelor's in Marketing from Pakistan and a Master’s from the University of Oulu, Finland. He has around four years of experience in marketing and business development in Pakistan. During his Master’s, he completed an Erasmus internship in the Czech Republic and another in Finland after graduation. He was awarded the Talent Hunt Scholarship during his Bachelor’s and the Finland Scholarship during his Master’s, and also served as a University Ambassador at University of Oulu. He is in the process of being a PhD student and will soon join the Biomarketing research group at Oulu Business School led by Associate Professor Dr. Eeva-Liisa Oika.

Paavo Ritala is a Professor of Strategy and Innovation at the Business School at LUT University, Finland. His main research themes include ecosystems and platforms, the role of data, algorithms, and digital technologies in organizations, and circular and regenerative economy. His research has been published in outlets such as Journal of Management, Journal of Management Studies, Research Policy, and Academy of Management Perspectives. Prof. Ritala is the Co-Editor-in-Chief of R&D Management and serves on the editorial review boards of Journal of Product Innovation Management and Organization & Environment.

Wojciech Sałabun (Senior Member, IEEE) received his M.S. (2010) and Ph.D. (2018) degrees in information technology and computer science from the West Pomeranian University of Technology in Szczecin, Poland. Since 2019, he has been an Assistant Professor and Head of the Research Team on Intelligent Decision Support Systems at the same university. In 2022, he was appointed Institute Professor at the National Institute of Telecommunications in Warsaw. He is the author of over 200 scientific articles and has completed more than 2000 peer reviews. He serves as Associate Editor for several high-impact journals and Editor-in-Chief of one impact factor journal. He is also actively involved as chair in numerous international scientific conferences. His research interests include soft computing, multi-criteria decision-making (MCDM), uncertainty modeling, and artificial intelligence methods.

Andrii Shekhovtsov earned his M.S. degree in Information Technology and Computer Science from the West Pomeranian University of Technology in Szczecin, Poland, in 2022. He is currently pursuing his Ph.D. at the same institution, focusing his research on personalised decision models in multi-criteria decision-making. He is an active member of the Research Team on Intelligent Decision Support Systems at the West Pomeranian University of Technology in Szczecin and also serves as a research specialist at the National Institute of Telecommunications in Warsaw, Poland. With over 70 publications in the decision-making domain, he has been recognised with multiple scholarships for his outstanding scientific contributions.

Tuuli Toivikko (M.Sc. in Knowledge Management and Leadership, M.A. in Media Studies) is a junior researcher at the Business School at LUT University, Finland. She is working on her doctoral dissertation focused on Technology-mediated collaboration in large groups focuses on the social and digital affordances for knowledge work. Toivikko is also a researcher involved in a project related to artificial intelligence and trust, funded by the Strategic Research Council (SRC) within the Research Council of Finland. Prior to joining LUT and engaging in research, Toivikko's previous experience was in communications and project management.

Shayala Yesmin is a PhD researcher in Business Studies at LUT Business School, based in Lahti, Finland. Currently working as a junior researcher in the Sales Team at LUT Business School. Her research focuses on the intersection of digital technologies and AI-powered value creation within B2B digital platform settings. Before joining LUT University, Shayala completed a master’s degree in Digital Marketing and Corporate Communication from the University of Jyväskylä. With over seven years of experience in university teaching across business and marketing disciplines, she gained a strong foundation in academic instruction, curriculum development, and student mentoring. Her scholarly contributions include peer-reviewed journal articles and international book chapters on emerging topics such as blockchain, the metaverse, and digital payment systems.

DAY 1: Tuesday, 26.08.2025
LUT Business School, Lappeenranta, Finland (Room: 1247)

1) For clarity and brevity, only the presenting authors are listed in this program. Full lists of co-authors are included in the presentations themselves.

DAY 2: Wednesday, 27.08.2025
LUT Business School, Lappeenranta, Finland, (Room: 1247)

2) For clarity and brevity, only the presenting authors are listed in this program. Full lists of co-authors are included in the presentations themselves.

Organizers
This workshop is organized by Viipuri Lab, LUT Business School (Päivi Maijanen & Azzurra Morreale), in collaboration with University of Trento (Luigi Mittone) and Oulu Business School (Eeva-Liisa Oikarinen).

(in alphabetical order by presenter's surname)

Francisco Javier Santos Arteaga and Debora Di Caprio. Multiple Criteria Decision-Making meets Machine Learning (and they hit it off).
Multiple Criteria Decision-Making (MCDM) models and Machine Learning (ML) techniques implement mathematical categorization structures designed to evaluate and rank a series of alternatives and classify them in terms of their similarities. The outcomes provided by each of these structures are highly compatible and can be used to reciprocally improve their respective functionalities. ML techniques can define continuous evaluation frameworks to enhance the strategic qualities inherent to the rankings defined by MCDM models. At the same time, MCDM models deliver ordered rankings of alternatives that can be used by ML techniques to enhance their categorization capacities. These complementarities will be illustrated through a series of numerical simulations designed to both intuitively describe the functioning of these categorization structures and their interactions and foster the applicability of the resulting hybrid models to real life evaluation scenarios. 

Matteo Benuzzi and Vittorio Guida: Can AI Curb the Disposition Effect? An Experimental Study with ChatGPT.
This study investigates how Large Language Models (LLMs), specifically ChatGPT, can help investors overcome the disposition effect—the tendency to sell winning positions prematurely and hold onto losing ones. Through simulation and human-based experiments, ChatGPT consistently demonstrates an anti-disposition bias, advising to sell losing positions more frequently. Human participants using ChatGPT also exhibit this beneficial anti-disposition effect compared to those not using it. However, highly skilled traders benefit less, potentially experiencing decreased performance. Additionally, LLM-generated strategies, while generally effective, may sometimes appear erratic or overly sensitive to short-term market fluctuations.

Violeta Cvetkoska: Brains, Bytes, and Benchmarks: Redefining Managerial Ability through DEA and AI Synergy.
As data multiplies and intelligence evolves, the future will be shaped not by competition between humans and machines, but by the synergy they create together. In a world where data drives decisions and AI reshapes workflows, the role of the human manager is under transformation, not extinction. This talk explores how managerial ability can be quantified and redefined in environments where algorithms and humans must collaborate rather than compete. Using Data Envelopment Analysis (DEA), the leading non-parametric method for performance benchmarking, the presentation demonstrates how managerial effectiveness can be measured in complex, data-rich systems. It also addresses the subtle balance between human intuition and AI logic, proposing a model where synergistic intelligence, a fusion of human insight and machine precision, becomes the cornerstone of superior decision-making. Drawing on practical cases and theoretical insights, the session reframes managerial evaluation and training to embrace this hybrid approach. The goal is to understand not just what managers do better than AI, but how they can do better with AI.

Peiman Ghasemi: AI-Assisted Decision-Making for Equitable Disease Testing: A Multi-Period Optimization Approach.
This study presents an AI-assisted, bi-objective optimization framework to support decision-making in managing contagious disease testing. Focusing on the strategic planning of mobile testing units and equitable resource distribution, we develop a multi-period team orienteering model that incorporates real-world constraints such as testing capacity, geographic clustering, and patient risk profiles. By categorizing populations into "standard" and "risky" groups based on disease background, and clustering areas by demographic factors like age and contact rate, the model enables health managers to make more informed, equitable decisions. We further explore the implications of quarantine versus non-quarantine scenarios, highlighting how algorithmic planning can enhance fairness and efficiency in public health interventions. This approach exemplifies the synergy between human expertise and AI-based decision support in managing complex, high-stakes resource allocation problems.

Vahid Hajipour: AI’s Flywheel Effect: Where Research Fuels Markets.
AI's flywheel effect creates a powerful cycle where research accelerates market transformation. This talk reveals how breakthrough AI technologies move from labs to industry, fueling disruption and revenue growth. Through real-world examples, we'll show how research-application feedback loops generate compounding value. Learn to overcome adoption barriers and align R&D with market needs - mastering this cycle is essential for competitive advantage in the AI age.

Luigi Marengo: Aspirations, Search, and Performance with Human and Artificial Agents.
When search is guided by aspiration levels, should managers and policy makers encourage high aspirations? Past work has shown that optimal aspiration (the aspiration level that leads to the highest level of performance in the long run) is lower in more turbulent environments. This past work assumes that aspirations are specified as absolute performance, but search is often triggered by performance shortfalls relative to others. Using a simple and analytically tractable model, we show that in such cases, the optimal aspiration may instead increase with turbulence (with rank-based aspirations) or stay constant (with average-based aspirations). Our analyses have interesting implications for target setting and for understanding how aspiration specification impacts exploration in organizations and societies. I will also discuss how our results could apply to organizations formed by both human and artificial (intelligent) agents.

Markus Mäkelä: The Strategy of AI-Powered Product Development: A Rising Capability Opportunity.
Like many activities, product development can be strongly supported by artificial intelligence. This presentation explores how generative AI can combine with product development activities for a company aiming to create a strategic capability out of product development, so as to attain a period of sustained competitive advantage.

Elena Nissing: Digital Collaboration – Future Research Ideas.
In times of remote work and asynchronous collaboration, it is important to ensure that individuals can work together effectively as a team regardless of their location. It is essential to understand how trust in team members and tools can be established and maintained, and how the team members' engagement can be ensured. Digital collaboration platforms that increasingly integrate AI can play a decisive role in this. In this presentation, I will briefly explain my research focus on digital collaboration in the age of AI, with a focus on trust and engagement, and then outline my future research ideas.

Eeva-Liisa Oikarinen: Biomarketing tools reveal the secrets of Transformative Bot-Human Companionship.
AI companionships, including bot assistants (e.g., ChatGPT) and AI friends (e.g., Replika), often evoke human-like emotions and can develop into significant relationships. This project examines transformative bot companionships (OuluBot, ChatGPT, Replika) and their impact on young adults and the elderly experiencing loneliness. Data collection includes interviews, surveys, Critical Incident Technique, longitudinal studies, and experimental multimodal physiological data at the LeaF research facility. Project goals are: 1) Advance marketing science through the proposed paradigm of Biomarketing, 2) Identify factors enhancing well-being in human-bot interactions, 3) Develop methods to measure and promote well-being. Results will benefit public services and AI-driven health solutions.

Jarno Pasonen: Shifting Boundaries of Agency: Understanding Generative AI Integration in Journalism.
When generative AI enters professional settings like journalism, it disrupts established boundaries of agency. Drawing on a growing set of over 50 interviews in four Finnish media organizations, we develop a model explaining how professionals confront, interpret, and respond to this disruption. We show that agentic ambiguity arises at the human-AI boundary, prompting boundary work practices aimed at either guarding or aligning agency. These responses reflect and reinforce the perceived empowerment in the evolving dynamics of human-AI assemblages. Our study advances understanding of human-AI interaction by illuminating how professionals recalibrate agency amid technological change in the context of journalism – an industry where human creativity and expertise have always been central to its professional ethos.

Matteo Ploner: Chatbot Influence in Strategic Decision Making: Investigating AI Bias in Prisoner’s Dilemma Interactions.
This study examines the influence of AI-driven chatbots on decision-making within a one-shot Prisoner’s Dilemma context. By manipulating chatbot bias toward either cooperation or defection and varying user awareness of these biases, the study observes significant effects on participants’ choices and beliefs. However, contrary to expectations, awareness of biases amplifies rather than diminishes the influence on cooperative behavior. These findings highlight the role AI systems may play in shaping strategic human decisions and highlight the need for transparency in their deployment.

Irene Vega Ramon: Language models as technologies of meaning.
The AI program is grounded in the social question of what distinguishes human beings from everything else. By adopting the aim of emulating humans, the AI program perceives humans through certain salient features. In organizations, we identify a gradual category shift in how we think and theorize about humans and machines. Two grounding features to theorize the “humanness” of AI are rationality—at the core of the faculty of reason—and more recently, language—at the core of the faculty of meaning. These enact different organizational realities, opening new opportunities or threats.

Atta ur Rehman: Understanding the transformative potential of generative AI through Agentic service interactions: A multimethod study.
GenAI holds transformative potential across numerous industries, and academia is no exception. This study will examine this potential in academic knowledge work by employing a multimethod design. It begins with a systematic literature review, proceeds to an experiment comparing survey-based measures across service interaction styles, and culminates in a multimodal investigation capturing physiological and reflective data. The study aims to advance theoretical understanding of human-AI co-creative dynamics within academic context, identify how service interaction style influences researcher agency and identity, and develop guidelines for designing and integrating GenAI systems that foster positive cognitive engagement and emotional well-being.

Paavo Ritala: Rational and anthropomorphic features of AI: Are we expecting too much or too little?
Artificial intelligence (AI) has long held the promise of imitating, replacing, or even surpassing human intelligence. Now that the abilities of AI systems have started to approach this initial aspiration, organization and management scholars face a challenge in how to theorize this new phenomenon, which potentially changes the way we view technology: not as a tool, but as something that enters previously human-only domains. Two core assumptions about AI are often put forward in the leading organization and management journals: rationality and anthropomorphism. I will discuss the organizational implications and boundary conditions of these assumptions in the context of theorizing AI. More broadly, I invite scholars to build a distinctive organization and management theory scaffolding within the broader social science of AI. This presentation is based on a recent paper published in Journal of Management Studies: https://onlinelibrary.wiley.com/doi/full/10.1111/joms.13246

Wojciech Sałabun: Modeling Uncertainty for Human–AI Decision Support: A Novel Approach with Asymmetric Interval Number.
Human–AI decision-making requires robust uncertainty modeling to ensure reliability and interpretability. Traditional interval numbers are simple but limited, while fuzzy sets, although expressive, can be computationally heavy. Asymmetric Interval Numbers (AINs) introduce a balanced alternative—retaining interval simplicity while embedding expected values to better capture asymmetric uncertainty. This framework provides well-defined arithmetic operations, theoretical results on symmetry and asymmetry, and practical integration with computational tools. In this presentation, we will introduce AINs, demonstrate their use in modeling uncertain data, and discuss their potential in AI-assisted decision-making scenarios. Through illustrative examples, we will show how AINs enhance interpretability, improve communication between human decision-makers and AI systems, and open new avenues for research and application in management and engineering contexts.

Andrii Shekhovtsov: Enhancing Human–AI Collaboration in Multi-Criteria Decision-Making: The pymcdm Python Library.
Effective decision-making in complex environments increasingly relies on synergistic interaction between human expertise and AI-driven computational tools. Multi-Criteria Decision-Making (MCDM) provides structured methods to evaluate alternatives, yet practical application often suffers from a lack of accessible, extensible tools. The open-source Python library pymcdm bridges this gap, offering a versatile platform for researchers and practitioners. It supports a wide range of MCDM methods, weighting techniques, and visualization tools, enabling informed and transparent decisions. Its modular design fosters both experimentation with novel models and integration into real-world decision-support systems. The presentation will demonstrate key functionalities, illustrate usage through practical examples, and highlight research outcomes based on pymcdm, showcasing its role in advancing human–AI synergy in decision-making.

Tuuli Toivikko: Social and digital enablers for AI-supported knowledge co-creation.
AI-based technology holds new potential for digital knowledge co-creation because it enhances the effectiveness of knowledge processes. To understand the perceived potential of technology for knowledge processes within large groups of experts, it is necessary to explore the potential of AI-based tools used in digital communities of experts sharing and creating knowledge, as well as to comprehend both digital and social affordances. This presentation discusses how advanced technologies, particularly AI, are shaping new ways of working. It also highlights how solving complex challenges often requires a collaboration of expertise from diverse fields and actors, emphasising the crucial role of AI and digital collaboration platforms alongside this expertise.

Organizing committee

Päivi Maijanen and Azzurra Morreale, Viipuri Lab (Research laboratory for experimental research), LUT Business School

Luigi Mittone, Cognitive and Experimental Economics Laboratory (CEEL), University of Trento, Italy

Eeva-Liisa Oikarinen, University of Oulu, Finland
 

Scientific committee

Mohsen Afsharian, Leibniz FH University of Applied Sciences, Hannover, Germany

Debora Di Caprio, University of Trento, Italy

Peiman Ghasemi, University of Vienna, Wien, Austria

Angappa Gunasekaran, Penn State Harrisburg School of Business Administration, Harrisburg, Pennsylvania, United States

Adolfo Hernández Estrada, Universidad Complutense de Madrid, Spain 

Paavo Ritala, LUT Business School, Finland

Wojciech Sałabun, National Institute of Telecommunications - the State Research Institute, Warszawa, Poland

Francisco Javier Santos-Arteaga, Universidad Complutense de Madrid, Spain

Madjid Tavana, La Salle University, Philadelphia, Pennsylvania, United States

We have agreed discount prices for the workshop participants with Original Sokos Hotel Lappee. The hotel locates in the city center with good connections to LUT University. More details can be found below. Please note that there might be a limited amount of discount rooms available in the hotel.
 

Original Sokos Hotel Lappee

Discount prices are 115 €/night for a single person room and 135 €/night for a two persons room and you can get the discount by using code BERW25. Please make the discount reservation here: www.sokoshotels.fi, or by sending an email to: lappee.lappeenranta@sokoshotels.fi (discount room reservations should be done before 05.08.2025)

Original Sokos Hotel Lappee
Brahenkatu 1
53100 Lappeenranta
+358 10 762 1000
lappee.lappeenranta@sokoshotels.fi