KMIS 2025 Abstracts

Full Papers Short Papers
Full Papers
Paper Nr: 12
Title:

Exercise Evaluation Creating Knowledge to Improve Information Resilience
Authors:

Harriet Lonka and Harri Ruoslahti
Abstract: Carrying out and evaluating exercises is typically seen as a very pragmatic means to improve performance of various security related organizations. In this research, we question this traditional perception of exercising and broaden the perspective to look at the evaluation of exercises as an integral part of the preparedness related knowledge management. This study utilizes as its theoretical framework a systemic model for information resilience created in the context of national preparedness by the Academy of Finland project IRWIN (Information Resilience in a Wicked Environment). The findings of the EU project INEGMA-E2 are used as study material to unveil the specific features of evaluation systematics in reflection to information resilience. Exercises are understood as systems and evaluation as a process that captures the influencing factors of this system. In connection to information resilience, exercise evaluation concepts need to consider e.g. individual agency and group decision making features, while reflecting them with the development goals of the exercise system. Improvement of exercise evaluation as a process enhances the creation of validated knowledge for systemic development of preparedness at all levels. Exercise evaluation can also contribute to improving information resilience in connection to preparedness related governance and decision making.
Download
Paper Nr: 40
Title:

From Knowledge to Action: Understanding Coordination Practices in Community-Led Urban Sustainability Projects
Authors:

Leidy J. Palma-Huertas and Néstor A. Nova
Abstract: This study explores how community-based initiatives coordinate knowledge and collective action in urban agriculture and organic waste management in Bogotá, Colombia. Grounded in coordination theory and following a design science research approach, the study examines how interdependencies between tasks and knowledge sources are addressed in grassroots sustainability projects. The discussion is supported by a case study in a community-driven urban agriculture and organic waste recovery setting. We identify four core community needs through qualitative methods: resource management, knowledge management, collaboration, and organization. The findings show that coordination mechanisms are shaped by sociotechnical variables such as the nature and origin of knowledge, its degree of codification, organizational learning trajectories, and the availability of technological infrastructures. These factors configure dynamic conditions that affect both the technical feasibility and social legitimacy of coordination practices. The study highlights coordination as a situated and adaptive process, offering an analytical framework to understand knowledge flows in community-led innovation.
Download
Paper Nr: 65
Title:

Digital Transformation and Automation: Application and Effectiveness of Chatbots in Bulgarian Entrepreneurship
Authors:

Ana Todorova and Irina Kostadinova
Abstract: In view of the growing importance of digitalisation, this study examines the adoption and use of chatbot solutions based on artificial intelligence among Bulgarian entrepreneurs. The aim is to identify the main reasons for their implementation, the perceived benefits and challenges encountered, and the main areas of application. A survey was conducted among 401 business owners in Bulgaria. The results show that 38% (154) of the surveyed entrepreneurs use chatbots and the analysis focuses on their data. The main factors driving implementation are increased efficiency, improved customer service and reduced costs. Improved operational efficiency and customer satisfaction were identified as the leading benefits. However, entrepreneurs face significant challenges, mainly related to the ability of chatbots to handle complex queries, the need for staff training and customer acceptance. Chatbots are most often used in marketing and sales, but they also have applications in customer service, internal processes and data analysis. In conclusion, despite the still relatively low adoption rate among all respondents, Bulgarian entrepreneurs with experience using chatbots see clear benefits related to efficiency and customer service. Successful expansion of this technology requires addressing current challenges, especially in terms of functionality and the human factor.
Download
Paper Nr: 66
Title:

Systematic Literature Review (SLR): Knowledge Management (KM) Processes and Artificial Intelligence (AI)
Authors:

Nada S. AlMuzaini, Boyka Simeonova and Mat Hughes
Abstract: The purpose of this systematic literature review is to identify the gaps and limitations within Knowledge Management (KM) processes through the lens of Artificial Intelligence (AI). Using a systematic literature review methodology, 42 academic articles were identified and analysed through content analysis to examine how KM processes are addressed within AI-related research. The studies were thematically coded and categorised to uncover prevailing patterns and insights. The review finds that the integration of AI into KM is still in its nascent stages, with fragmented and evolving research. Five core themes emerged from the analysis: (1) AI and human collaboration, (2) Trust and ethics, (3) Ingenuity, (4) Organisational performance and (5) Information security. Each theme highlights both opportunities and challenges of AI within KM processes. In addition, the review identifies limitations within each theme and offers suggestions for future research. This paper provides a comprehensive overview of how AI intersects with KM processes and demonstrates the value of applying a systematic literature review to organise and explore this emerging area of research.
Download
Paper Nr: 78
Title:

Semi-Supervised Object Labeling on Video Data with Collaborative Classification and Active Learning
Authors:

Bruno Padilha and João Eduardo Ferreira
Abstract: Streaming applications in video monitoring networks generate datasets that are continuously expanding in terms of data amount and sources. Thus, given the sheer amount of data in these scenarios, one big and fundamental challenge is how to reliably automate data annotation for downstream tasks such as object detection, image classification, object tracking among other functionalities. In this work, we propose a novel active learning strategy based on multi-model collaboration able to self-annotate training data, providing only a small initial subset of human verified labels, towards incremental model improvement and distribution shifts adaptation. To validate our approach, we collected approximately 50,000 hours of video data sourced from 193 security cameras from University of São Paulo Monitoring System (USP-EMS) during the years 2021-2023, totaling 7.3TB of raw data. For experimental purposes, this work is focused on identification of pedestrians, cyclists and motorcyclists resulting in 3.5M unique objects labeled with accuracy between 92% to 96% for all cameras. Time-stamped data along with our incremental learning method also facilitate management of naturally occurring distribution shifts (e.g., weather conditions, time of the year, dirty lenses, out-of-focus cameras). We are currently working to release this dataset in compliance with local data privacy legislation.
Download
Paper Nr: 112
Title:

Agentic RAG-Based Legal Advisory Chatbot: A Knowledge-Driven Approach for Vietnamese Legal System
Authors:

Pham Thi Xuan Hien, Duong Ngoc Thao Nhi and Pham Thi Ngoc Huyen
Abstract: This paper presents an innovative Agentic Retrieval-Augmented Generation (RAG) approach for developing a legal advisory chatbot specifically designed for the Vietnamese legal system. While traditional RAG systems face limitations in handling complex legal inquiries requiring multi-source integration and domain expertise, our framework addresses these challenges through a centralized Router Agent that coordinates multiple specialized agents via a unified Tools/MCP Server infrastructure. The system incorporates automatic data collection from official legal sources, processing 54,000 legal documents, and provides multi-modal search capabilities including vector search, graph database queries, web search, and external API integration. Experimental evaluation on 1,247 real-world legal queries demonstrates significant improvements with 82.3% accuracy, outperforming baseline systems by 17.2% in context precision and 29.7% in context recall. Human expert evaluation confirms practical applicability with 4.18/5.0 overall satisfaction while maintaining compliance with Vietnamese legal standards.
Download
Paper Nr: 127
Title:

Assessing Value Co-Creation in Blockchain Enabled Learning Certificates: A Knowledge Management Perspective
Authors:

Nathalya Guruge and Jyri Vilko
Abstract: Blockchain enabled learning certificates promise immutable, transparent proof of skills and achievements, yet their potential for sustained value co - creation remains underexplored. Grounded in Nonaka and Takeuchi’s SECI model, Service-Dominant Logic, and the Co-Creation Triad, this position paper advances an integrative analytical model to evaluate how blockchain credentials instantiate knowledge‐conversion processes, operant resource integration, and stakeholder engagement structures. A dual‐stream methodology first maps construct from 50 prior studies to these lenses - revealing a research landscape heavily focused on technical architectures but largely neglectful of on-chain Socialization, Internalization, and ongoing co-creation incentives. We then apply our model to four illustrative platforms (LearnCoin, Blockcerts, Badgr, and the Learning Economy Foundation), systematically coding each system’s support for explicit knowledge externalization/combination, smart-contract-driven workflows, and dialogic customization. Our cross-case analysis confirms universal strengths in artifact codification and protocol automation but identifies persistent gaps in reflective learning cycles and sustained co-creation mechanisms. We conclude by calling for next‐ generation credential designs that embed on-chain communities of practice, adaptive operant resources, and multi-phase token economies, thereby charting a research and design roadmap for transforming blockchain certificates into living ecosystems of shared learning value.
Download
Paper Nr: 130
Title:

A Contingency View of CISO–Board Interactions in Information Security Governance
Authors:

Sara Nodehi, Tim Huygh, Laury Bollen and Remko Helms
Abstract: This study investigates how Chief Information Security Officers (CISOs) work together with board members to attain Information Security Governance (ISG). Based on a qualitative exploratory workshop involving CISOs, this study examines CISO–board relationships and governance decision-making. Five governance classes—board involvement, communication strategy, influence mechanisms, reporting structures, and information security budgeting—were established through thematic analysis and were discovered to vary considerably across organizational contexts. CISOs, rather than applying a uniform approach, adopt context-specific and even contradictory governance strategies contingent upon organization culture, leadership, and structural attributes. These strategic trade-offs are viewed as deliberate adaptive responses to diffuse authority, asymmetrical information, and incongruent expectations. By analyzing ISG as a relational and contingent practice, the research contributes theoretical understanding by illustrating how the application of contingency thinking can explain differences in ISG arrangements between contexts, highlighting the value of adaptive, context-sensitive governance approaches. Additionally, this paper provides practitioner-useful guidance to improve board engagement, strategic communication, and organizational alignment in security governance.
Download
Paper Nr: 138
Title:

Integrating Information Retrieval and Large Language Models for Vietnamese Legal Document Query Systems
Authors:

Pham Thi Xuan Hien, Duong Ngoc Thao Nhi and Pham Thi Ngoc Huyen
Abstract: The complexity of Vietnamese legal documents poses significant challenges in accessing legal information for both professionals and the general public. Traditional legal information retrieval methods are time-consuming and require specialized expertise to navigate the intricate hierarchy of laws, decrees, and regulations. This paper introduces a Vietnamese legal document query system that integrates Information Retrieval (IR) techniques with Large Language Models (LLMs) to automate legal document access and provide accurate, context-aware responses to legal queries. The proposed system employs a Retrieval-Augmented Generation (RAG) architecture, combining vector-based document retrieval with LLMs to generate precise, context-informed answers. Key components include a document indexing module processing 45,000 Vietnamese legal documents, a vector database for semantic search, and an LLM-powered response generation interface. The system leverages 350,000 legal Q&A pairs from authoritative sources to understand complex legal terminology and provide contextually relevant responses. In a comprehensive evaluation, the system was assessed using both performance metrics (via RAGAs framework, including context recall and ROUGE scores) and user studies involving legal professionals and law students. The results indicate that integrating IR with LLMs substantially improves the relevance and accuracy of legal responses, reducing response time by 58%. Users reported high satisfaction levels (average 4.23/5) with the system's ability to answer complex legal queries, achieving 89% accuracy across 12 legal categories. Overall, our findings demonstrate that IR-augmented LLM systems can effectively automate legal information access, alleviating professional workloads and democratizing legal knowledge access in Vietnamese legal contexts.
Download
Paper Nr: 167
Title:

PROM: Personal Knowledge Graph Construction with Large Language Models
Authors:

Aishan Maoliniyazi, Chaohong Ma, Xiaofeng Meng and Bingbing Xu
Abstract: The growing volume of digital information requires effective Personal Knowledge Management. Personal Knowledge Graphs (PKGs), which model knowledge as connected entities and relationships, show potential. Chats or natural voice conversations contain abundant context information about users’ thoughts and preferences, which is beneficial for constructing PKGs. However, constructing PKGs from unstructured natural conversations is still challenging. The main obstacle comes from two aspects: inherently complex and context-dependent conversations. In this paper, we present PROM, a novel framework of personal knowledge graph construction with LLMs. PROM effectively constructs PKGs from natural conversations. Particularly, PROM constructs PKGs with rich knowledge information, preserves context information for knowledge provenance, and fuses different kinds of contexts for structural and semantic coherence. Specifically, PROM constructs knowledge triples (subject, predicate, object) from conversational text and integrates them into a coherent PKG with the help of LLMs. We propose a multi-strategy knowledge fusion technique to resolve conflicts and unify information from different sources for structural and semantic consistency. Moreover, we design an API proxy engine to facilitate consistent knowledge extraction from different LLM backends. The proxy system is flexible and cost-effective. It can adapt different triple extraction strategies from LLMs and unify the results with a knowledge fusion strategy. We evaluate PROM in different scenarios. The experiments show that PROM is able to construct comprehensive and context-aware PKGs from unstructured conversations and can support personal knowledge discovery.
Download
Short Papers
Paper Nr: 11
Title:

System, Structures and Processes in Evaluation of Civil Protection Exercises
Authors:

Harri Ruoslahti and Harriet Lonka
Abstract: Civil protection exercises are activities that simulate real-life emergencies, where participants can practice, review, and test the exercise system, and its structures and processes. The Union Civil Protection Mechanism (UCPM) exercises all require systematic evaluation. This article applies the framework of systems theory to the practical evaluation of civil protection exercises with the research question (RQ): how does systems theory apply to the evaluation practices of civil protection exercises? The method of this study combines a descriptive literature review, Delphi workshops, and analysis of expert interview transcripts on the approaches behind the concepts which are then examined in the context of the evaluation of civil protection exercises. Organizational structures of exercise systems outline 'what' direct the activities that achieve the goals of the exercise. Structures are the combinations of relations between the organizational elements that form organizational activities, and may include rules, roles, methods, technologies, applications, and responsibilities of the exercise participants. Organizational processes of an exercise include the activities that establish the goals of the exercise. In an exercise evaluation context, processes focus on how operations and human interactions are carried out by the people who realize and manage the scenarios of the exercise. This three-dimensional approach can help address the complex interplay of factors within civil protection exercises. The contribution of this study is that it clarifies the theoretical background of literature on systems theory, organizational structures, and processes as they relate to the evaluation of civil protection exercises, which can have a practical contribution to the training of exercise evaluators.
Download
Paper Nr: 34
Title:

Embedding Knowledge Management in R&D Capability Transformation in Software Startups
Authors:

Nabil Georges Badr
Abstract: Organizational development drives growth, especially for startups. This study presents a longitudinal action research engagement exploring the strategic integration of Knowledge Management (KM) practices within Organizational Development (OD) initiatives to catalyse scalable transformation in a software startup. Grounded in dynamic capability theory and implemented through the continual improvement framework, the intervention addressed operational inefficiencies, role ambiguity, and delivery challenges across R&D functions. By layering KM methodologies, such as centralized repositories, stakeholder-driven assessments, and iterative feedback loops into OD processes, the engagement reconstructed team structures, codified decision-making routines, and fostered a culture of collaborative innovation. Through cross-functional restructuring, strategic role definition, and embedded governance practices, KM was operationalized as both an infrastructural asset and a dynamic capability enabler. The findings underscore KM’s pivotal role in enhancing adaptability, aligning leadership vision with execution, and sustaining high-performance trajectories under volatile growth conditions. This research contributes to startup literature by framing KM not merely as a support function, but as a strategic lever for organizational resilience, learning, and value creation.
Download
Paper Nr: 35
Title:

Towards a Systemic Approach to Knowledge Integration in Learning Health Ecosystems: AI and DLT Perspectives
Authors:

Nabil Georges Badr
Abstract: A Learning Health System (LHS) is an essential paradigm for addressing the evolving complexities of healthcare systems, fostering continuous improvement, adaptability, and stakeholder collaboration. By integrating knowledge management with technological advancements, LHS enhances data-driven decision-making and the responsiveness of healthcare interventions. Artificial Intelligence (AI) has emerged as a powerful tool within Learning Health Systems, yet its evolving nature presents challenges related to ethical, traceable, and trustworthy data management. Distributed Ledger Technology (DLT) offers immutable and transparent data governance, yet its full potential remains unrealized due to the absence of integrated frameworks that could reinforce accountability and reliability in AI-driven processes. Addressing this gap is critical for developing robust, ethical, and efficient healthcare solutions. This paper examines the synergistic potential of AI and DLT within LHS, proposing a framework that leverages systematic knowledge integration, predictive analytics, and proactive interventions. By harnessing AI-driven automation, IoT-enabled data collection, and the secure, decentralized architecture of DLT, LHS can advance evidence-based healthcare, mitigate disparities, and promote equitable access to high-quality care.
Download
Paper Nr: 39
Title:

Enhancing Aviation Safety Analysis in MROs: A Complex Emergent Model with a Predictive Approach
Authors:

Victoria Grech and Joseph Paul Zammit
Abstract: This study explores the aviation industry's shift from reactive and proactive safety strategies towards predictive safety management, focusing on Maintenance, Repair and Overhaul (MRO) operations. It introduces a novel complex emergent safety model designed to integrate predictive analytics into existing Safety Management Systems (SM) via occurrence reporting data. Moving beyond traditional linear causation models, the proposed framework leverages machine learning and data mining techniques to identify hazards and assess risks, thereby reducing the frequency and severity of incidents and minimising maintenance disruptions. Using the DMADOV methodology, the study aims to extract actionable insights from unexploited safety data, despite challenges such as data quality variations and the stochastic nature of safety. Ultimately, this research advocates for a unified, AI-driven approach to enhance safety capabilities across the aviation industry.
Download
Paper Nr: 43
Title:

Data-Driven Culture Requires Overcoming Data Governance and Data Literacy Challenges
Authors:

Carlos Alberto Bassi, Jacqueline David-Planas, Solange Nice Alves-Souza and Luiz Sergio de Souza
Abstract: Organizations are progressively working towards becoming data driven. To achieve this, they need to cultivate a comprehensive culture grounded in behaviors, practices, and training, while also ensuring governance in data sharing, collection, policies, tools, and processes. Data Governance (DG) and Data Literacy (DL) offer essential resources to support this cultural shift. However, the implementation of DG and DL faces a variety of organizational challenges. This study aims to identify and analyze these challenges, with a focus on their intersections and implications for building a sustainable Data-Driven Culture (DDC). The challenges for this research were derived from a scoping review of the literature of case studies on the implementation of DG and DL, complemented by data collected through a completed web form and interviews with professionals involved in the DG initiatives. The analysis revealed a significant overlap in the challenges of DG and DL, highlighting the importance of integrated strategies. Organizations that prioritize and address these shared challenges will significantly accelerate the development of a robust DDC and enhance the value derived from data.
Download
Paper Nr: 45
Title:

Contribution of Knowledge Management to Innovation Capabilities in the Manufacturing Industry Through Machine Learning
Authors:

Juan Ibujés-Villacís and Michael S. Simba-Herrera
Abstract: Knowledge management has been fundamental for organizations to improve their ability to innovate. The objective of this research is to design and develop machine learning models that impact predictive analytics, identifying the determinants of knowledge management (KM) that influence innovation capabilities (IC) in the manufacturing industry. Given the quantitative nature of the research, in a first stage, information on factors related to KM and IC was collected and processed. In a second phase, six models were developed to predict which manufacturing companies innovate in their production processes based on a set of KM factors. Information from 142 manufacturing companies in the province of Pichincha, Ecuador, was used for the study. The results show that all the factors of KM contribute to innovation capabilities, with organizational structure, technology, people and incentives standing out in particular. This study is pioneering in Ecuador and reinforces the strategic value of corporate governance as a driver of industrial innovation and provides a useful empirical framework to guide decision making and business policy formulation. In addition, this study contributes to the field of knowledge management by providing empirical evidence on the key factors on which manufacturing companies should focus their efforts to develop innovation capabilities in processes, products and services.
Download
Paper Nr: 87
Title:

Multivariate Automatic Tuning of Isolation Forest for Anomaly Detection in Critical Infrastructures: A Solution for Intelligent Information Systems
Authors:

David Saavedra Pastor, José Vicente Berná Martínez, Lucia Arnau Muñoz and Carlos Calatayud Asensi
Abstract: The Isolation Forest (IF) algorithm is effective in detecting anomalies in critical infrastructure, but its performance depends on the proper setting of five hyperparameters: sample size, number of trees, maximum tree depth, maximum number of features and detection threshold. Static tuning of these parameters is inefficient and poorly adaptable to dynamic environments. This paper proposes a multivariate autotuning method that automatically optimises these hyperparameters by: (1) adaptive adjustment of the sample size based on the standard deviation of the anomaly scores, (2) selection of the number of trees according to F1-score stabilisation, (3) control of the maximum depth based on the average isolation rate, (4) adjustment of the maximum number of features according to the variance of the data, and (5) optimisation of the detection threshold by minimisation of a cost function. The auto-tuning procedure has been validated in the detection of anomalies in drinking water networks, showing an F1-score improvement of 7.5% and a reduction of the execution time by 22.55% compared to static configurations, demonstrating its feasibility for real-time systems.
Download
Paper Nr: 89
Title:

The Role of Context to Detect Conflict Expression in Text
Authors:

Philippe Herr and Nada Matta
Abstract: The notion of context, present since Antiquity, has gained increasing importance across various fields such as linguistic semantics, cognitive psychology, artificial intelligence (AI), and natural language processing (NLP) since the 1980s. In text analysis, a distinction is made between “internal context” (textual elements surrounding a linguistic item) and “external context” (circumstances surrounding the production of a fact or process). Context is thus crucial both for determining the meaning of linguistic signs and for interpreting texts Although NLP and generative AI systems simulate linguistic exchanges, they often lack explicit internal representations of contextualization processes This paper aims to shed light on what is meant by “context,” with a particular focus on “cultural context.” It specifically investigates the expression of conflictual elements that can be identified in texts through the activation of context.
Download
Paper Nr: 91
Title:

LLM-Assisted Augmented Intelligence for Context-Aware Decision Support: Current Trends and Integrated Approach
Authors:

Alexander Smirnov, Andrew Ponomarev, Nikolay Shilov and Tatiana Levashova
Abstract: The growing complexity of technical, social, and business systems created and managed by humans determine the need for effective decision support. Recent advancements in AI push the boundary of what can be accomplished using AI tools and what are possible modes of human-AI interaction, bringing a concept of augmented intelligence, extending intellectual capabilities of human by variety of AI-based tools, while leaving final decision-making (as well as some other operations, e.g., goal-setting, coordination, control) to a human. This paper explores possibilities of using augmented intelligence for decision support. Starting with a general structure of decision-making process, it highlights and reviews current trends in several branches of AI, that are most important for decision support. Then, it proposes an integrated approach combining conversational, generative, and evaluative AI. Distinguishing features of the proposed approach are integration and mutual enrichment of data- and model-based techniques, as well as using modern LLMs as a basis for human-AI interaction during decision-making.
Download
Paper Nr: 116
Title:

Developing Context-aware Applications in Automotive Environments
Authors:

Julian Dörndorfer, Markus Schmidtner and Holger Timinger
Abstract: IT companies like Google and Apple are entering the automotive market with disruptive technologies such as autonomous driving, enabled by numerous sensors evaluated by software. These sensors allow vehicles to recognize their context and passengers and react accordingly by adjusting features like interior temperature based on solar radiation and temperature. The domain-specific modeling language (DSML) SenSoMod incorporates these sensors early in software architecture design. Established automakers face challenges integrating more software components in the product development process (PEP). Optimizing the supply chain has led to a lack of common understanding of requirements and development. This multilayered process must now meet additional requirements for complex software development. SenSoMod can help by uniformly considering sensors across the supply chain, aiding in the implementation of context-aware software in vehicles. In this paper, experts from the automotive industry have assessed SenSoMod by its potential benefits and challenges in the automotive development process.
Download
Paper Nr: 117
Title:

Guiding Improvement in Data Science: An Analysis of Maturity Models
Authors:

Christian Haertel, Tom Engelmann, Abdulrahman Nahhas, Christian Daase and Klaus Turowski
Abstract: Maturity models (MMs) can help organizations to evaluate and improve the value of emerging capabilities and technologies by assessing strengths and weaknesses. Since the field of Data Science (DS), with its rising importance, struggles with successful project completion because of diverse technical and managerial challenges, it could benefit from the application of MMs. Accordingly, this paper reports on a structured literature review to identify and analyze MMs in DS and related fields. In particular, 18 MMs were retrieved, and their contribution toward the individual stages of the DS lifecycle and common DS challenges was assessed. Based on the outlined gaps, the development of a meta-maturity model for DS can be pursued in the future.
Download
Paper Nr: 119
Title:

Innovation as Mindset: Management’s Moderating Role in the Relationship Between Organizational Culture and Human Resources
Authors:

Luca Furlani, Rita Giordano, Filippo Frangi, Alessandra Luksch and Antonio Ghezzi
Abstract: Innovation has been traditionally studied through two main lenses: innovation as product and innovation as process. Recently, a third perspective – innovation as a mindset – has emerged, focusing on the relationship between innovation, organizational culture and individual involvement. A review of the literature of innovation as mindset reveals fragmented field, lacking of a comprehensive definition and showing inconsistencies with established innovation management studies. This study investigates these inconsistencies, assesses the relevance of the mindset perspective to innovation studies, and examines the effects on innovation of three key actors: organizations, individuals, and management. Based on an analysis of three Italian SMEs, the research highlights the joint influence of organization and individuals on the innovation capability of the organization itself. In addition, it has emerged the moderating role of management in the relationship between organization and individuals. Management fosters a climate for innovation by enabling open communication, supporting individual expression, and translating organizational values into actionable practices. The study proposes a framework where the organization and the individuals have a direct impact on innovation, while management acts as a facilitator and moderator.
Download
Paper Nr: 128
Title:

Assessing Value Creation in Professional Communities: Perspectives on Collaboration in Demographic Change
Authors:

Roope Laakkonen, Jyri Vilko and Timo Pohjosenperä
Abstract: This research explores how value is constructed in professional networks and how participation, collaboration and inclusion shape members' experiences at different career stages. As demographics and technology change, professional networks are increasingly expected not only to share knowledge but also to facilitate renewal and engagement. Using qualitative interview data, the study identifies the key mechanisms through which communities generate knowledge, social, emotional and symbolic value. The findings highlight that participation requires accessible structures, low-threshold opportunities and an open culture. For participation to be meaningful, especially for young members, it requires not only content but also visibility, recognition and the opportunity to influence. The study deepens our understanding of value as a relational and community-constructed phenomenon and offers practical recommendations for strengthening community-based collaboration in a changing expert environment.
Download
Paper Nr: 145
Title:

The Role of Formal Knowledge Management Practices in Reducing Burnout Among Healthcare Professionals
Authors:

Rita Marques, Andreia Luís, Liliana Martins, Gisela Cotrim and Marta Correia Sampaio
Abstract: This study examines relationship between formal knowledge management practices and burnout among Portuguese healthcare professionals. Drawing upon the Maslach Burnout Inventory Scale and an adapted formal knowledge management practices scale, we examine the extent to which the adoption of structured knowledge management strategies is associated with lower levels of emotional exhaustion, depersonalisation, and reduced personal accomplishment. Our sample (N=218) reveals elevated burnout rates, with 39% presenting high emotional exhaustion and 45.4% reporting low personal accomplishment. Statistical analysis indicates that greater adoption of formal knowledge management practices correlates negatively with burnout, particularly in preserving personal accomplishment (ρ=-0.320, p<0.001). The private sector demonstrates higher formal knowledge management practices adoption rates than the public sector. Age and professional experience influence burnout patterns, with younger and less experienced professionals displaying higher rates. This study contributes to the literature by clarifying formal knowledge management practice’s protective role in occupational well-being and suggests that organisational policy reforms promoting knowledge sharing could mitigate burnout in healthcare contexts.
Download
Paper Nr: 148
Title:

Adaptive Fog Computing Architecture Based on IoT Device Mobility and Location Awareness
Authors:

Narek Naltakyan
Abstract: In the modern world, we are increasingly seeing the involvement of IoT devices in our daily lives, whether in the form of sensors, smart homes, smart cars, or several other devices and equipment. Fog computing is a concept that is intended to stimulate the development of such systems. However, a problem arises when the operation of a given device or equipment depends on the location. This paper considers a new adaptive fog computing architecture that dynamically manages IoT device mobility through intelligent location awareness and geographic area orchestration. In the proposed system, devices are divided into two parts according to location: static and dynamic, which allows optimizing resource allocation and service approach. The architecture features include dynamic geographic area division, adaptive scaling of fog nodes, and the use of a smart location service that can work both in the cloud and in the fog, depending on the number and density of user requests. The system automatically scales infrastructure based on real-time demand and the geographic distribution of devices, while maintaining quality of service.
Download
Paper Nr: 159
Title:

Sentiment Analysis of Social Media Use in Public Transportation in Sweden
Authors:

Azadeh Sarkheyli and Elnaz Sarkheyli
Abstract: The increasing impact of social media on public transportation is transforming communication strategies and user engagement. These platforms offer real-time service updates while allowing users to voice their concerns and suggestions, fostering trust and enhancing customer satisfaction. This research investigates public perceptions of the communication methods used by public transportation services in Sweden, highlighting user preferences for different social media platforms and content types. The study employs a four-step methodology. First, a literature review examines the advantages and challenges of integrating social media into public transportation systems. Next, a survey assesses Swedish users’ interactions with social media in this context. The third phase involves sentiment analysis and text mining of the survey responses to evaluate public opinion. Finally, the research proposes potential steps for collecting and analyzing social media data. The findings contribute to a better understanding of effective communication strategies, ultimately improving the responsiveness of public transportation systems.
Download
Paper Nr: 161
Title:

Open Knowledge Fuelling Open Innovations in Public-Private Collaboration
Authors:

Nina Helander, Krishna Venkitachalam and Hannele Väyrynen
Abstract: Despite recognizing the importance of knowledge in innovation, the link between publicly available knowledge and innovation remains unclear. A unified view of open innovation (OI) and the public sector's knowledge ecosystem, along with public-private collaboration, is essential. Successful knowledge-based innovation requires functional systems to overcome barriers in open knowledge and innovation. This paper emphasizes the need for consideration of open innovations converted by private sector from public sector knowledge sources. Empirical study consisted of two exemplary cases is carried out to understand how the private sector can benefit from public knowledge.
Download
Paper Nr: 168
Title:

Support Tools for Hybrid Research, EDIGA Project
Authors:

Libertad Tansini and Regina Motz
Abstract: In the digital age, where social interactions extend into complex virtual environments, digital ethnographies have become essential methodologies for understanding identity and community formation. However, the integration of diverse data types (textual, visual, and relational) poses significant technological challenges. This paper presents an integrated platform designed to address the fragmentation of tools in qualitative research workflows. The system comprises: (1) a mobile application for multimodal data collection; (2) a digital field diary with collaborative annotation features; (3) an image anonymization tool with automatic face/blurred text detection; and (4) a web portal for unified data visualization and analysis. Developed through an iterative design process with researchers from the EDIGA project, a transnational study on teenage gender identities in digital spaces, the platform solves critical points identified in traditional approaches: data silos in cloud storage, inconsistent file naming, and disconnection between collection and analysis tools. The proposed architecture enables end-to-end management of ethnographic data while maintaining GDPR compliance through built-in anonymization features. The paper contributes both a technical framework for integrated ethnographic tools and practical insights on overcoming interoperability challenges in qualitative research software.
Download
Paper Nr: 57
Title:

Knowledge Management in Finnish Comprehensive Security Ecosystem
Authors:

Jussi Kosonen
Abstract: Society is exposed to a wide range of threats that can jeopardise the continuity of organisations and the security of citizens. In previous years, deliberate hybrid influence from authoritarian countries has increased significantly. Finland's comprehensive security is a cooperative concept for implementing preparedness and crisis management. Organisations involved in comprehensive security require knowledge to prepare and respond appropriately to crises. This study aimes to determine how knowledge is managed within the Finnish comprehensive security knowledge network. A theory-guided mixed methods study investigated the security-related knowledge management practices of 54 diverse Finnish organisations involved in comprehensive security. The study identifies knowledge management in a four-layer architecture: institutional, organisational, interaction, and knowledge layers, all of which need to be aligned to facilitate effective knowledge management. According to the findings, networked knowledge management works partly well, but there is potential for improvement in the breadth and depth of knowledge-sharing. This study suggests proposals for the development of knowledge networks and management for comprehensive security.
Download
Paper Nr: 74
Title:

Hybrid Influence on Rescue Services
Authors:

Harri Ruoslahti and Ilkka Tikanmäki
Abstract: Society may face significant threats from hybrid influence, which blends physical, psychological, and technological methods to disrupt, manipulate, or confuse members and actors of society. Rescue services, which are an integral part of society, may become affected by or even a direct target for hybrid operations. This study examines the impacts of hybrid influence on rescue services. The need for enhanced situational awareness, coordinated responses, and resilience-building measures becomes emphasised. Hybrid influence and hybrid war, situational awareness, and responses to hybrid threats were investigated using a structured literature review. The findings highlight the significance of comprehensive security models and international cooperation in effectively tackling hybrid threats. It is recommended to conduct further research to deepen our understanding and develop robust strategies and practical secure knowledge management and information sharing systems to protect rescue services from hybrid influence.
Download
Paper Nr: 79
Title:

A Method to Construct Dynamic, Adaptable Maturity Models for Digital Transformation
Authors:

Jan M. Pawlowski
Abstract: Maturity models are a well-established method to assess and improve Digital Transformation processes in organizations. However, many models lack theoretical foundations and specificity for a branch or sector. The Dynamic Adaptable Maturity Model development method (DA3M) provides the steps to develop scientifically sound, specific and relevant maturity models to be used to improve organizational performance. The method was successfully valeted in different branches – in this paper, the method is used to develop a maturity model with a focus on human aspects and to create an adaptation for Higher Education Institutions.
Download
Paper Nr: 109
Title:

Extending Federated Data Platforms in the Cloud Continuum for Manufacturing and Intralogistics
Authors:

Krista Mätäsniemi, Teemu Toroi and David Hästbacka
Abstract: Federated data platforms (FDPs), or data spaces, are frameworks that facilitate data sharing between diverse data sources and stakeholders. FDPs are critical enablers in the 2020 European Data Strategy where industrial data is a strategic investment area. Industrial use cases can involve high data volumes and velocity despite operating in resource-constrained environments. FDPs have been proven in inter-company data exchanges and are typically implemented on scalable cloud platforms. This study explores the possibility of extending FDPs in the cloud continuum (CC) by examining how the technical requirements of FDP building blocks align with the drivers, characteristics, and enablers of CC layers. The study introduces an analysis framework for finding the optimal placement of building blocks along CC. It also shows that transferring FDP components connected to high data volumes closer to the edge can make FDPs more effective and better suited for manufacturing and intralogistics use cases. The study concludes that there is an architectural fit between FDP functionalities and the characteristics of the CC, and suggests that FDP usability and performance should be studied empirically.
Download
Paper Nr: 113
Title:

Beyond Black Boxes: Adaptive XAI for Dynamic Data Pipelines
Authors:

Otmane Azeroual
Abstract: The increasing use of real-time data streams in application areas such as the Internet of Things (IoT), financial analytics, and social media demands highly flexible and self-adaptive data pipelines. Modern AI techniques enable the automatic adjustment of these pipelines to dynamically changing data landscapes; however, their decision-making processes often remain opaque and difficult to interpret. This paper presents and evaluates novel approaches for integrating Explainable Artificial Intelligence (XAI) into self-adaptive real-time data pipelines. The goal is to ensure transparent and interpretable data processing while meeting the requirements of real-time capability and scalability. The proposed methods aim to strengthen trust in automated systems and simultaneously address regulatory demands. Initial experimental results demonstrate promising improvements in both explainability and adaptivity without significant performance degradation.
Download
Paper Nr: 126
Title:

Distributors’ Attitudes Towards AI Tools in Business-to-Business Sales Channels
Authors:

Tommi Mahlamäki and Johannes Kuoppala
Abstract: This study explores B2B distributors’ attitudes toward artificial intelligence (AI) tools, particularly chatbots, as part of digital self-service in sales channels. AI-powered chatbots enable distributors to independently access information and are becoming increasingly common in the B2B context. A survey of 83 global distributors revealed generally positive attitudes toward AI tools. Among the respondents, 60% were open to interacting with chatbots, 27% were neutral, and only 10% were opposed. A majority of respondents (69%) agreed that chatbots are useful for information search. Chatbots were valued for their speed, ease of access, and ability to reduce search time, though they were not seen as suitable for complex support situations. Most respondents preferred a combination of information channels in their search process, and over 80% agreed that digital tools cannot fully replace human support. The findings highlight the importance of offering flexible, hybrid service models when serving B2B distributor partners.
Download
Paper Nr: 133
Title:

Measuring the Maturity of Knowledge-Based Management of Human Resources
Authors:

Sini Tenhovuori and Nina Helander
Abstract: In today’s volatile and demanding environments, organizations face increasing pressure to balance employee well-being (Kim & Cho, 2024). Given that personnel expenses often constitute the largest share of organizational budgets (Kuntaliitto, 2024), knowledge-based human resource management (KHRM) becomes essential for organizations to survive and remain competitive. The purpose of this research was to investigate the factors influencing the development of knowledge-based management of human resources and to develop an assessment model for evaluating the maturity level of knowledge-based management of human resources. The developed assessment model was tested in Finnish public sector context. The model complements existing maturity frameworks by operationalizing maturity dimensions into measurable statements, enabling organizations to assess not only structural readiness but also perceived satisfaction and cultural alignments. Measuring employee satisfaction as part of determining maturity levels is important, as previous studies have shown that employee satisfaction supports the adoption of new ways of working in organizations. In addition, this model can be used to measure the realization of the benefits achieved with KHRM in the organization. This model has dual focus on objective capability and subjective experience, and it offers a novel contribution to the maturity model literature and supports more holistic HR development strategies.
Download
Paper Nr: 149
Title:

Toolchain for Dataset Description with Contextual Recommendation from Machine-Actionable Data Management Plans
Authors:

Mohamed-Anis Koubaa, Nan Liu, Fabia Martens, Andreas Schmidt, Karl-Uwe Stucky and Wolfgang Süß
Abstract: By harnessing the power of interconnected research project information, this position paper introduces a novel system designed to automate and enhance the metadata description process for research data. The system effectively leverages existing structured data from RDMO (Research Data Management Organiser), drawing insights from research projects, measurement equipment, sensors, and simulations to provide context-aware suggestions for metadata fields. We argue that this system significantly reduces the manual burden on researchers, improves the quality and consistency of metadata, and ultimately champions the FAIR principles (Findable, Accessible, Interoperable, Reusable) for all research data.
Download
Paper Nr: 157
Title:

Intelligent Knowledge Management for Enhancing Sustainable Food Systems: The Case of Sweden
Authors:

Azadeh Sarkheyli
Abstract: Intelligent Knowledge Management (IKM) aims to establish intelligent integration of the food system to capture, organize, analyze, and utilize information and knowledge that promotes sustainable food production. With the growing importance of sustainable food systems, understanding consumer behavior, customer needs, food preferences, producer demands, and local regulations is necessary. However, integration challenges within the Swedish food system create significant obstacles. Inappropriate Knowledge Management systems, system complexity, dynamic environments, inability to learn from and reuse data, information overload, and insufficient data collection and analysis contribute to these challenges. This study uses a case study approach and literature review to collect and analyze data. The proposed solution is an IKM conceptual model based on the knowledge-based theory of the firm, leveraging AI-powered techniques to manage and analyze large datasets from various stakeholders in the food supply chain. This model enhances forecasting and planning capabilities, improving decision-making processes. Future research should further develop the IKM system to achieve the potential results outlined in this paper.
Download
Paper Nr: 169
Title:

Use of a Collaborative Tool for Knowledge Acquisition and Refreshment
Authors:

Emine Bilek
Abstract: The paper deals with knowledge management, organizational learning and collaborative tools in general and its possible application at the Institute for the Digital Transformation of Application and Living Domains (IDiAL) which focus is on the main topics digital transformation of application and living domains at Fachhochschule Dortmund University of Applied Sciences and Arts in particular. Firstly, the different forms of knowledge management and organizational learning are discussed, followed by a description and comparison of different collaborative tools. Then IDiAL, its development and its focus will be descripted. In addition to the main areas of research and the transdisciplinary collaboration between the institute's scientists, this article describes how the use of a collaborative tool is used at IDiAL for organizational learning with sample contents and how this has improved communication in general and administrative processes at the institute's head office.
Download
Paper Nr: 179
Title:

Elevating Data Science Maturity: Toward a Process Model that Harnesses MLOps
Authors:

Christian Haertel, Daniel Staegemann, Matthias Pohl and Klaus Turowski
Abstract: Data Science (DS) uses advanced analytical methods, such as Machine Learning, to extract value from data to improve organizational performance. However, numerous DS projects fail due to the complexity and difficulty of handling various managerial and technical challenges. Because of shortcomings in existing DS methodologies, new standardized approaches for DS project management are needed that respect both the business and data perspectives. In this paper, the concept for a DS process model to address common problems in DS, including a low level of process maturity and a lack of reproducibility, is outlined. This artifact is developed using the Design Science Research methodology and relies on MLOps principles to support the development and operationalization of the analytical artifacts in DS projects.
Download