IRAF-BRB: An explainable AI framework for enhanced interpretability in project risk assessment

In high-stakes project risk assessment, balancing predictive accuracy with interpretability is critical to fostering stakeholder trust and supporting well-informed decision-making. This study presents the Interpretable Risk Assessment Framework with Belief Rule-Based Systems (IRAF-BRB), an Explainable AI (XAI) framework specifically designed to improve transparency, accountability, and accuracy in risk assessment. IRAF-BRB combines Interpretive Structural Modeling (ISM) to map and analyze interdependencies among risk factors with an optimized Belief Rule-Based (BRB) model. A modified Differential Evolution Covariance Matrix Self-Adaptation (DECMSA) algorithm is employed to enhance the predictive power of the BRB model while preserving interpretability, ensuring that stakeholders can both trust and understand the model’s outputs. By transforming complex risk data into intuitive visualizations, the IRAF-BRB framework enables project managers to identify key risk drivers and anticipate cascading effects, leading to proactive risk mitigation. Experimental results demonstrate that IRAF-BRB reduces Mean Squared Error (MSE) to 4.09 e − 4 in predicting risk levels for high-rise construction projects, outperforming traditional BRB models such as Differential Evolution-based BRB (DE-BRB) ( 8.29 e − 4 ) and Particle Swarm Optimization-based BRB (PSO-BRB) ( 2.53 e − 3 ) . The statistical significance of these results was confirmed via a two-sample t-test ( p < 0.05 ) , establishing IRAF-BRB as a reliable and effective tool for accurate and interpretable risk assessment

Courting the sharks: The effects of CEO narcissistic admiration and rivalry on new venture funding

We draw from the social psychology literature to introduce an alternative conceptualization of executive narcissism—narcissistic admiration and rivalry. In the context of CEOs pitching to investors, we theorize how narcissistic CEOs may use distinct behavioral strategies to pursue status, thereby shaping investor sentiment and ultimately affecting investors’ funding decisions. Using Shark Tank data, we find evidence that narcissistic admiration and rivalry are associated with opposing patterns in new venture funding, as shaped by investor sentiment. Specifically, CEO narcissistic admiration is positively associated with new venture funding by increasing investor sentiment, whereas CEO narcissistic rivalry is negatively associated with new venture funding by decreasing investor sentiment. These results highlight the need to separate narcissistic admiration and rivalry in executive narcissism research and illustrate the underlying mechanisms through which executive narcissism shapes organizational outcomes. Overall, this study provides new insights into two pathways of executive narcissism and offers evidence consistent with the idea that executive narcissism matters in entrepreneurial contexts

De l’analyse à l’empathie et à la créativité : La révolution de l’IA dans la pratique et l’enseignement du marketing

Les progrès rapides de l’intelligence artificielle (IA) exigent de comprendre son impact sur la pratique et l’enseignement du marketing. En adoptant une approche scientométrique et le cadre TCCM (Théorie, Contexte, Caractéristiques, Méthode), notre analyse hybride de la littérature existante synthétise 312 articles sur l’IA dans le domaine du marketing et du comportement du consommateur. Nous identifions cinq domaines de recherche : l’interaction entre l’homme et l’IA dans les services, le traitement du langage naturel ( natural language processing, NLP) et la vision par ordinateur pour la compréhension du consommateur, l’IA pour l’e-commerce et l’aide à la décision, l’automatisation du marketing et la créativité, et l’éthique de l’IA. L’évolution de l’IA est marquée par une transition des technologies analytiques vers des technologies empathiques et intuitives telles que l’informatique affective et l’IA générative. Nous mettons en évidence l’évolution de la dynamique entre les humains et l’IA, l’intégration de l’IA dans les pratiques de marketing et l’éducation, et la transformation du lieu de travail marketing amélioré par l’IA. Nous soulignons l’importance des considérations éthiques, du bien-être des utilisateurs et de l’intégration des outils d’IA générative. Cette étude constitue un guide pour les recherches à venir, les applications pratiques et les progrès éducatifs dans le domaine du marketing et l’IA

Leveraging machine learning for strategic performance management

This dissertation investigates the use of machine learning (ML) in strategic performance management. While ML applications have been widely explored in financial accounting, their use in management accounting remains relatively underexamined. This research aims to fill this gap by demonstrating how ML can provide in different stages of strategic performance management, including identifying strategic groups, performance measurement and resource allocation. The first chapter explores the potential of ML algorithms to mitigate cognitive biases that managers face when analyzing performance data and making strategic resource allocation decisions. Through a computer-simulated business game, this study compares the effectiveness of ML-based budget allocation against human decision-making. The findings indicate that ML algorithms significantly outperform human participants in optimizing budget allocations, leading to improved organizational value creation. However, the results also highlight the complementary nature of ML and human strategic reasoning. While ML efficiently processes large datasets and uncovers complex, nonlinear relationships, human expertise is needed to align the resource allocation with broader strategic objectives. The second chapter applies an unsupervised learning approach to develop a more nuanced classification of business strategies in the airline industry. Existing research typically categorizes airlines into either focused or full-service strategies. However, recent industry trends suggest that some airlines are adopting hybrid strategies that blend elements of both approaches. Using fuzzy clustering, this study identifies such hybrid airlines and evaluates their performance relative to pure strategic positions. The results reveal that hybrid airlines often achieve superior financial performance, but only when they effectively manage their capacity utilization. If they fail to leverage their increased complexity into a better use of their capacity, the benefits dissapear. The third chapter leverages supervised learning techniques to examine the relationship between nonfinancial performance measures and profitability in the airline industry. By applying ML methods, this study takes an exploratory approach to identify key performance indicators that predict airline profitability, taking into account interactions and nonlinearities. The findings suggest that operational efficiency measures, such as load factors, labor productivity, and fuel consumption , are the strongest predictors of financial success. Moreover, the study uncovers interaction effects, such as the moderating impact of capacity utilization on service failures and a U-shaped relationship between customer complaints and profitability. These results highlight the importance of considering both direct and indirect effects of performance metrics in strategic decision-making. By integrating ML techniques into strategic performance management, this dissertation contributes to the management accounting literature by showcasing ML's ability to uncover hidden patterns, enhance decision-making, and optimize resource allocation

Year Report 2024 (research in the energy sector)

The 2024 Year Report for the PhD project between an energy business partner and Vlerick Business School highlights the research activities executed in 2024. Early in the year, a large dataset of customer service interactions was developed, linking conversation transcripts to customer satisfaction and recommendation scores. Using advanced Natural Language Processing (NLP) techniques, the study extracted emotional and linguistic features to analyze their impact on customer perceptions. A key focus was the application of machine learning models for Emotion Recognition in Conversations (ERC) and the integration of multimodal approaches combining textual and audio features. Mid-year, research expanded into assessing service quality through Large Language Models (LLMs) and conducting statistical analyses on the effects of emotional alignment in service interactions. The findings were consolidated into an academic manuscript, which underwent rigorous peer review and was ultimately accepted for publication in a high-impact journal. Toward the end of the year, work progressed on refining multimodal emotion recognition models and developing predictive frameworks for customer satisfaction and recommendation outcomes. Future research will explore the trade-offs between different modeling approaches and their applications in real-world service environments

Negotiation intelligence: Onderhandelen omdenken

Onderhandelingen bepalen de toekomst van partnerships, regeringen, akkoorden en leiderschap. In een snel veranderende wereld vallen we vaak terug op compromissen, machtsstrijd en kortetermijndenken. Om de toekomst vorm te geven, moeten we onderhandelen herdenken en onderhandelingsintelligentie (NQ®) ontwikkelen voor transformatie van onszelf, onze relaties en resultaten. Negotiation Intelligence biedt een grensverleggende onderhandelingsfilosofie, een beproefd samenwerkingsmodel en een groeipad naar duurzame gedragsverandering voor elke onderhandelaar. In dialoog met 24 maatschappelijke koplopers en aan de hand van krachtige beelden, presenteert dit boek een nieuwe kaart voor onderhandelen, die ieder van ons in staat stelt een verschil te maken als gamechangers en impactondernemers: Peter Adriaenssens, Bas Beerens, Peter Blom, Hans Bourlon, Edward Boute, Hanan Challouki, Wim Dejonghe, Jos Delbeke, Petra De Sutter, Frans de Waal, Dirk De Wachter, Dirk Frimout, Katleen Gabriels, Marc Herremans, Louis Jonckheere, Stefaan Lauwers, Roberto Martinez, Kim Swyngedouw, Sophie Vandebroek, Herman Van Rompuy, Saskia Van Uffelen, Koen Vanmechelen, Annelies Verlinden en Ann Wauters

2016 49th Hawaii International Conference on System Sciences (HICSS)

Contributing digital resources (CDR) is an emerging process that enables companies to capitalize on their digital resource investments by positioning them as value propositions. These value propositions can then be used to meet the needs of integrating companies. CDR can help companies to adapt based on changing market conditions by integrating digital resources from contributing companies instead of developing these resources themselves. An example of such CDR is the collaboration between Stripe and AirWallet, where Stripe has positioned their payment processing digital resource to AirWallet, so that it can be integrated into their mobile wallet platform to facilitate seamless payment experiences. This paper explores how value is created when companies contribute digital resources to meet the needs of other companies. Using resource-based view theory, we conceptualize the dimensions of value by describing the value proposition positioning and leveraging strategy. Through four illustrative case studies, we examine contributions of digital resources in collaborations, proposing a theoretical framework and testing it empirically

Integrating Forecasting and Inventory Decisions Using Machine Learning

Can inventory ordering decisions be improved by integrating forecasting and inventory decisions using machine learning? That is the question addressed in this study of three large Belgian companies in the food industry. Van der Haar, Sagaert, and Boute investigate the performance of methods that predict optimal order quantities directly, instead of !rst forecasting and then calculating optimal inventory quantities. Their results show that using an integrated approach can lead to substantial cost savings for smoother time series, yet the opposite holds when applying it to erratic and lumpy time series

AI can make the relative-valuation process sess subjective

This article presents a new methodology for relative valuation that incorporates artificial intelligence. The methodology uses AI to review historical data—such as revenues, earnings, and debt levels—to detect patterns and relationships related to historical valuations that traditional methods might miss. By integrating AI into the relative-valuation process, organizations can transform a traditionally subjective art into a more rigorous, transparent, and data-driven science. The process not only enhances valuation accuracy but also builds confidence among stakeholders by clearly showing the rationale behind each valuation decision. As a case study, the authors apply their methodology to Mastercard, analyze the results, and then recommend four key steps that companies should take if they are looking to integrate AI into valuatio

How strategic is your sustainability strategy? Really?

Sustainability has become an important topic on the strategic agenda of most firms. Business is facing great demands from all sorts of stakeholders today due to the world’s enormous societal challenges: climate change and its consequences (like water scarcity), social inequality and injustice, poverty, depletion of natural resources, to name just a few. And people are expecting businesses to play a bigger role in addressing these societal problems. Firms have responded by setting up numerous sustainability initiatives – often captured under the label of ESG (Environment, Social, and Governance). But for many firms, the journey towards becoming more sustainable is a tough one. Despite good intentions, the implementation of corporate sustainability programmes has been slow at best, and sloppy and ineffective at worst. We believe that a major reason firms struggle to transform towards sustainability is that these sustainability programmes are insufficiently embedded in the company’s core strategy. In this paper, we analyse why this is a problem and what managers can do about it. More specifically, we propose a new approach to managing your sustainability initiatives, one that is more grounded in strategy