Vlerick Repository
The Vlerick Repository is a searchable open-access publication database, containing the complete archive of research output written by Vlerick Business School faculty and researchers.
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Item From analytics to empathy and creativity: Charting the AI revolution in marketing practice and education(Sage publications, 2024)The rapid advancement of artificial intelligence (AI) increasingly demands an understanding of its impact on marketing practice and education. Our hybrid literature review synthesizes 312 peer-reviewed articles on AI in marketing and consumer behavior, using scientometrics and the TCCM (Theory, Context, Characteristics, Methodology) framework. We identify five research areas: human–AI interaction in services, natural language processing (NLP) and computer vision for consumer insights, AI for e-commerce and decision support, marketing automation and creativity, and AI ethics. AI’s evolution is marked by a transition from analytical to empathetic and intuitive technologies like affective computing and generative AI. We highlight the changing dynamics between humans and AI, AI integration in marketing practices and education, and the transformed AI-enhanced marketing workplace. We underscore the significance of ethical considerations, the well-being of users, and the integration of generative AI tools. This review provides a comprehensive guide for forthcoming research, practical applications, and educational advancements in AI-enhanced marketing.Item Automatic selection of the best performing control point approach for project control with resource constraints(Elsevier, 2025)During project execution, the actual project progress shows deviations from the baseline schedule due to uncertainty. To complete the project timely, project monitoring is performed at discrete control points to identify project opportunities/problems and take possible corrective actions. These control points affect the quality of project monitoring and corrective actions, but little guidance is available on identifying situations where the control points pay off the most in terms of project duration. This paper proposes new control point approaches considering the risk, the complexity of the network, and subnetwork information to determine the timing of project monitoring and action taking. Moreover, new parameters are proposed to model more realistic project characteristics. Subsequently, a classification model is developed to select the best performing control point approach given project characteristics. An extensive computational experiment is conducted on a set of 3,810 artificial projects with diverse project characteristics to evaluate the performance of the classification model and further validate it on empirical project data. The computational results indicate that the classification model outperforms the average performance of any proposed control point approaches. The results also show that the resource variability that indicates the resource usage deviations between project activities is the primary driver for detecting the best control point approach for projects with resource constraints.Item Resource allocation models and heuristics for the multi-project scheduling with global resource transfers and local resource constraints(Pergam, 2025)The transfer times and costs of global resources between different projects and the choice of transfer modes significantly affect the multi-project scheduling. This paper investigates four versions of the resource-constrained multi-project scheduling problem with global resource transfers and local resource constraints based on four realistic transfer scenarios, in which the global resource transfer times and costs are considered with a single transfer mode or multiple transfer modes. Three classes of heuristics with huge amount of priority rules are adapted and tested for the new problems. The schedule generation schemes of each class of heuristics are improved from two aspects. On the one hand, resource availability checks are divided into global and local phases due to their different characteristics. On the other hand, resource transfer rules and transfer mode rules are introduced to deal with resource transfer and transfer mode issues, respectively. The three class of heuristics are tested on well-known datasets of the multi-project problem, which are extended with transfer data using a transfer time/cost generation procedure. The numerical experiments first evaluate the performance of a set of priority rules, then effectively apply the priority rule heuristics in the genetic algorithm, and finally compare the performance of the priority rule heuristics with CPLEX on small-scale instances. Additionally, a multi-project case study verifies the applicability and good performance of priority rules that perform well in numerical experiments. Furthermore, the best performing rules are used by two machine learning methods in literature to automatically select the most promising ones.Item Linking outcomes to costs: A unified measure to advance value-based healthcare(Pergamon Elsevier Science Ltd, 2025)Guided by the Value-Based Healthcare framework, the healthcare sector increasingly aims to maximize patient value by improving the quality of care while containing costs, which requires aligning the interests of patients, health providers and payers. This study addresses the need for advanced patient value measurement techniques to navigate this complex balance by introducing a four-step framework that combines Data Envelopment Analysis (DEA) and Time-Driven Activity-Based Costing. The framework starts by defining Decision-Making Units and specifying the treatment pathway (Step 1), followed by selecting the relevant inputs (i.e., costs) and outputs (i.e., health outcomes) (Step 2). Next, the DEA model is tailored to fit the specific medical context (Step 3), ultimately translating the value equation into unified, individual value scores that rank patients by perceived value (Step 4). Unlike traditional healthcare evaluations, the multiple health outcomes are connected to granular costing information without relying on monetary values or subjective weighting. Using real-life data from a case study focused on psoriasis, we demonstrate that value assessments significantly differ when considering a comprehensive set of health outcomes, rather than relying on a single primary outcome or treating costs and outcomes separately. These holistic value scores are used to pinpoint inefficiencies on an individual level, analyse patterns of health improvements through cluster analysis, and assess the impact of contextual variables on value creation using econometric analysis. Our results revealed the complex interplay between outcomes and costs by identifying factors like the presence of comorbidities, which had no direct influence on costs or outcomes, as overall value driver. In summary, this research proposes an intuitive metric for value benchmarks across time, health providers and treatments, ultimately contributing to the effective delivery of personalized and value-based healthcare.Item Synchromodal replenishment under non-stationary demand: an illustrative case study(2024)Synchromodal replenishment aligns transport mode decisions with inventory replenishment needs. We present a case study considering the simultaneous use of road and rail transport to replenish a distribution center in Belgium from a supplier in Spain, aiming for a modal shift from road to sustainable rail transport. Product demand is non-stationary, meaning the demand distribution changes over time. Although the underlying demand distribution is not directly observable, demand observations provide partial information.- We apply the synchromodal replenishment policy proposed in Yee et al. (2024) that combines a committed, stable rail order with flexible short-term orders on rail and road. The short-term orders are based on inventory levels and partial information about the non-stationary demand. The case study demonstrates the value of adding short-term flexibility to rail orders to induce a modal shift. Our analysis shows how the proposed policy improves the modal shift compared to a benchmark policy without flexible rail orders. The retailer can reduce the carbon footprint of its replenishments without compromising service levels or costs. We also show how offering the flexible rail option increases the rail operator’s revenues. These findings highlight the potential of synchromodal replenishment with flexible rail orders to facilitate a modal shift.