Högskolan i Skövde

Artificial Intelligence (AI) can take Business Intelligence (BI) to the next level by empowering users in their daily decision-making tasks. Just like Self-Service Business Intelligence (SSBI), AI integrated business analytics comes with many benefits, but also with numerous implementation challenges. In fact, typical SSBI implementation challenges like data quality, data governance, and employee training are equally relevant when integrating AI. Hence, lessons learned from development of SSBI education could increase data literacy and AI literacy. Two case studies of SSBI education in large BI consultancy firms have identified five SSBI education steps: (1) increase the interest of using data; (2) introduce data to all users; (3) clean and define data to create standard reports; (4) develop SSBI data governance and (5) become self-reliant on accessing and using data. SSBI education can create a foundation that leads to being better prepared for the implementation and use of more advanced AI analytics.

Participation in a Megagame and a consecutive facilitated debriefing can enable large groups to co-construct awareness and knowledge about the complex relations involved in developing sustainable futures for current societies. “Switching the current” is a megagame where 20 to 100 participants collaboratively experience the challenges involved in shaping future energy consumption patterns and energy production solutions for their region, just and sustainable from a social, economic and environmental perspective. This paper explores how collective discussions can be visualized to better understand how in-game experiences are connected to real-world phenomena. Doing so, a time-snake method has been developed which visually depicts how reflective group discussions move between “game content” and “real world phenomena”, as well as between “attention to details” and “a broad systems perspective”. The comprehensive analysis and comparison of two debriefing sessions shows how participating in a Megagame can develop awareness and understanding of complex relations in shaping sustainable futures of societal energy systems. It also demonstrates how applying time-snake visualizations can help to evaluate the richness of the reflective co-construction that occurs in facilitated debriefings. © 2025 The Authors.

Challenges and opportunities for becoming a data-driven organization have been investigated in the literature mostly from the perspective of large organizations. In contrast to previous research, this paper targets a public school in a small municipality that sets up a pilot project for monitoring IoT sensors for indoor and outdoor climate. The public school intended to become more data-driven in its decision-making. Data was collected through interviews, documents, and ethnographic studies. The main findings are that the pilot experienced: i) technical challenges due to faulty sensors and IoT interoperability problems, ii) development challenges due to an unbalanced cross-functional network and poor requirement documentation, iii) raised awareness on indoor and outdoor climate among children and teachers, iv) mixed results of using the dashboards' visualizations, and v) lack of supporting guidelines on how to integrate the system into the organization's daily work.

This chapter explains how the transfer of knowledge and understanding from a simulation game to the real world not only depends on the set-up and execution of the debriefing phase but also on the design and implementation of preceding phases. Learning transfer is characterized as participants’ enactments—in future contexts—of the outcomes of a game. A number of interconnected building blocks that influence and enhance the learning transfer are thoroughly explored. These are participants’ motivations, skills, and opportunities, the constitution of the simulation game, facilitation style, and debriefing set-up.

Two extreme case studies are highlighted in this follow-up chapter (this is a practical case study following up on Chap. 4 with a theoretical base) on how facilitators can enhance learning via interventions. Case study 1 consists of a positive open simulation game (learning results and goals achieved), containing optimized conditions for learning and several facilitated interventions per game phase. The open simulation game case study delivered learning early on in the gameplay and resulted in mainly third-order learning (learning to learn, a well-developed role perspective on how one can add value from their role). Case study 2 consists of a negative rule-based simulation game (goals were not achieved, few learnings) with diminished learning conditions and the lack of impact of facilitated interventions per game phase. The learning results of the negative case study 2 were limited because participants did not perceive the game as useful and experienced the mainly content-oriented interventions by the facilitator not as helpful. The extraneous cognitive load resulting from the gameplay disturbed the effect of the process interventions by the facilitator, so participants did not learn from the guidance the facilitator tried to provide. Discussing these two opposite extreme case studies provides an overview of what conditions favour learning and what interventions contribute to learning. In the previous chapter, it was highlighted that facilitators can enhance learning complexity reduction (or attenuation) and skill enlargement (amplification). In the case studies, the types of interventions performed are highlighted and explained so they provide two contextualized examples of how interventions added or left out have effect on learning combined with the internal conditions of the rule-based and the open simulation game.

Studies on Critical Success Factors (CSFs) in Healthcare Information Systems (HIS) development projects have traditionally often been limited to retrospectively identifying CSFs in a finished project. In this paper, we focus on how to prospectively apply and manage CSFs in HIS projects. Based on a holistic perspective and systems thinking, an inductive research strategy was applied and a single in-depth case study was conducted. The findings include detailed descriptions that contribute to further understanding of how to prospectively apply and manage CSFs in HIS projects. The analysis reveals that CSFs must be applied differently and managed on various system levels. Furthermore, it shows how interactions exist between different system levels, both in the case of a specific CSF and between different CSFs on various system levels. Our analysis framework and findings indicate new directions for future research: how to prospectively apply and manage CSFs in HIS development projects can now be investigated both in a more holistic way and more in detail. Finally, healthcare practitioners can use the descriptions as practical checklists for guiding them in how to realize situational adaptation of CSFs in HIS projects across different system levels.

Self-service business intelligence (SSBI) enables all users, including those with limited technical skills, to perform business intelligence (BI) tasks without the support of BI experts. SSBI reduces pressure on BI experts, gives more freedom to self-reliant users and speeds up decision-making. Recent research has illustrated how organisations experience numerous challenges when trying to obtain SSBI benefits. The AQUIRE framework organises 37 identified SSBI challenges in five categories: Access and use of data, Data Quality, User Independence, creating Reports and Education. SSBI literature does poorly address how these challenges can be tackled. This research study aimed to identify strategies on how to manage those 37 SSBI challenges. The performed case study includes 24 semi-structured interviews with respondents from two organisations which have been heavily involved in SSBI implementation. The results reveal how nine identified SSBI success factors are related to the 37 AQUIRE challenges and how they can be addressed over time.

This paper presents a quantitative agent-based simulation model of the everyday payment system used to simulate the business and consumer consequences of loss of functionality, or disruptions of the payment system for the food and fuel retailing markets as well as the bank sector in order to address resilience. The simulation model is used in a gaming simulation approach that couples a role-playing game with the simulation model in order to provide crisis management team-training to decision-makers in a multi-organisational context. Drawing primarily on resilience engineering and crisis response, the concepts of core values, coping strategies, and resilience value networks were used to guide the design of the simulation model. The ultimate aim of this study is to explore the collaborative responses from the key actors during the disruption scenario in order to evoke and facilitate collective resilience.

Situation pictures are helpful to make sense of what is happening and to prevent further escalation. These situation pictures are typically text- or map-based and focus on the current effects of the crisis. For long-lasting transboundary crises that impact many critical infrastructures and different parts of society directly and indirectly, such situation pictures have limitations. Crisis management teams might benefit from continuous monitoring of societal performance indicators, so the current situation can easily be compared with historical and future data to reveal trends and escalations. This research project explored how a successful approach for systematic monitoring of indicators in crime prevention could be transferred to crisis management. Several pilot studies revealed nine challenging pitfalls and six promising possibilities. The findings of this study can inform future research on how continuous systematic situation monitoring can strengthen societal resilience.

A definition of information fusion (IF) as a field of research can benefit researchers within the field, who may use such a definition when motivating their own work and evaluating the contributions of others. Moreover, it can enable researchers and practitioners outside the field to more easily relate their own work to the field and more easily understand the scope of IF techniques and methods. Based on strengths and weaknesses of existing definitions, a definition is proposed that is argued to effectively fulfill the requirements that can be put on a definition of IF as a field of research. Although the proposed definition aims to be precise, it does not fully capture the richness and versatility of the IF field. To address that limitation, we highlight some topics to explore the scope of IF, covering the systems perspective of IF and its relation to ma-chine learning, optimization, robot behavior, opinion aggregation, and databases.