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The impact of ownership traits on acquisition behavior and performance
No full textContext This doctoral thesis delves into examining ownership traits and their impact on corporate acquisition behavior, with a primary emphasis on family-owned and private equity-owned firms. The research investigates the decision-making process of these block holders when investing in firms, particularly the dilemma they face in choosing between monitoring efforts to maximize shareholder value and pursuing private motives. The studies build on the determining factors that influence each type of owner's inclination towards value maximization, considering their unique identity, correlated private motives, and the relative significance of these motives compared to potential financial gains they might forego in pursuing private benefits. Aim To investigate how shareholder identity affects the acquisition behavior and performance of firms. Studies Chapter one explores how private family firms select their targets, focusing on diversification, internationalization, and innovation in their acquisition strategies. Using Behavioral Agency Theory, the study argues that these firms tend to prioritize social and emotional wealth (SEW) over purely financial gains. The paper hypothesizes that this tendency leads family firms to approach target selection cautiously, for instance, leading towards an aversion to acquiring highly innovative or cross-border targets. In chapter two, the focus shifts to the influence of private equity (PE) investors on publicly listed firms, particularly in the context of Mergers & Acquisitions (M&A). The chapter suggests that the motivation to create value through M&A decisions depends on whether the PE firm's investment occurs before or after the firm's initial public offering (IPO). Chapter three addresses the agency cost associated with surplus capital in the PE industry and its implications for buy-and-build strategies. As the industry accumulates an increasing pool of unallocated capital, PE firms face mounting pressure to deploy it judiciously. The study contends that implementing buy-and-build strategies, which involve making add-on acquisitions in portfolio companies, can effectively channel excessive levels of unutilized capital. Findings The first chapter underscores that family-owned firms prioritize SEW over financial gains in target selection for acquisitions, resulting in a conservative approach. This preference is particularly pronounced when led by family CEOs and founders. In the second chapter, the study reveals that post-IPO PE investors enhance M&A performance, especially with greater board representation. In contrast, pre-IPO investors are associated with lower returns, particularly when they exert stronger board influence and the firm's stock liquidity is lower. Finally, the third chapter addresses the pressure faced by PE firms to efficiently deploy surplus capital, introducing the buy-and-build strategy as a response. It finds that this pressure can lead to more investments in add-on acquisitions and a larger propensity for those deals to be unrelated to the platform's core industry. Moreover, buyout returns tend to be lower for buyout targets owned by pressured PE firms
The use of continuous action representations to scale deep reinforcement learning for inventory control
No full textAbstract Accepted by: M. Zied Babai Deep reinforcement learning (DRL) can solve complex inventory problems with a multi-dimensional state space. However, most approaches use a discrete action representation and do not scale well to problems with multi-dimensional action spaces. We use DRL with a continuous action representation for inventory problems with a large (multi-dimensional) discrete action space. To obtain feasible discrete actions from a continuous action representation, we add a tailored mapping function to the policy network that maps the continuous outputs of the policy network to a feasible integer solution. We demonstrate our approach to multi-product inventory control. We show how a continuous action representation solves larger problem instances and requires much less training time than a discrete action representation. Moreover, we show its performance matches state-of-the-art heuristic replenishment policies. This promising research avenue might pave the way for applying DRL in inventory control at scale and in practice.(Research Foundation - Flanders
Constructive heuristics for selecting and scheduling alternative subgraphs in resource-constrained projects
No full textIn this paper, we investigate two constructive heuristics based on existing and newly developed priority rules (PRs) for the resource-constrained project scheduling problem with alternative subgraphs (RCPSP-AS). The RCPSP-AS deals with scheduling the selected activities from work packages that can be executed in different ways, resulting in a selection and a scheduling subproblem. The inclusion of alternatives in the project structure implies that even moderate-sized projects become very large, motivating the use of PR-based approaches. In the existing literature, many PRs were already developed for the scheduling subproblem, however, no studies have focused on specific PRs for the selection subproblem. Therefore, we examine the performance of previously developed PRs for the RCPSP-AS and observe that employing a unique PR for each subproblem decreases the project makespan. Based on this knowledge, we develop two constructive heuristics based on well-suited PRs. In the first constructive heuristic, distinct PRs are selected based on the project properties, while several schedules according to different PRs are generated in the second constructive heuristic. Our experiments show that project managers should consider the project properties and select the appropriate selection PRs accordingly in order to minimise the project makespan in the RCPSP-AS
Value in psoriasis (IRIS) trial: implementing value-based healthcare in psoriasis management – a 1-year prospective clinical study to evaluate feasibility and value creation
No full textINTRODUCTION: Currently, the healthcare sector is under tremendous financial pressure, and many acknowledge that a dramatic shift is required as the current system is not sustainable. Furthermore, the quality of care that is delivered varies strongly. Several solutions have been proposed of which the conceptual framework known as value-based healthcare (VBHC) is further explored in this study for psoriasis. Psoriasis is a chronic inflammatory skin disease, which is associated with a high disease burden and high treatment costs. The objective of this study is to investigate the feasibility of using the VBHC framework for the management of psoriasis.
METHODS AND ANALYSIS: This is a prospective clinical study in which new patients attending the psoriasis clinic (PsoPlus) of the Ghent University Hospital will be followed up during a period of 1 year. The main outcome is to determine the value created for psoriasis patients. The created value will be considered as a reflection of the evolution of the value score (ie, the weighted outputs (outcomes) divided by weighted inputs (costs)) obtained using data envelopment analysis. Secondary outcomes are related to comorbidity control, outcome evolution and treatment costs. In addition, a bundled payment scheme will be determined as well as potential improvements in the treatment process. A total of 350 patients will be included in this trial and the study initiation is foreseen on 1 March 2023.
ETHICS AND DISSEMINATION: This study has been approved by the Ethics Committee of the Ghent University Hospital. The findings of this study will be disseminated by various means: (1) publication in one or more peer-reviewed dermatology and/or management journals, (2) (inter)national congresses, (3) via the psoriasis patient community and (4) through the research team's social media channels.
TRIAL REGISTRATION NUMBER: NCT05480917
The Longitudinal Effect of Digitally Administered Feedback on the Eco-Driving Behavior of Company Car Drivers
No full textIn the global fight against climate change, stimulating eco-driving could contribute to the reduction of CO2 emissions. Company car drivers are a main target in this challenge as they represent a significant market share and are typically not motivated financially to drive more fuel efficiently (and thus more eco-friendly). As this target group has received little previous research attention, we examine whether digitally administered feedback and coaching systems can trigger such company car owners to drive eco-friendly. We do so by using respondents (employees of a financial services company (N = 327)) that voluntarily have a digital device (‘dongle’) installed in their company car, which monitors and records driving behavior-related variables. In a longitudinal real-life field study, we communicate eco-driving recommendations (e.g., avoid harsh braking, accelerate gently, etc.) to the respondent drivers via a digital (computer) interface. Over a 21-week time frame (one block of seven weeks before the intervention, seven weeks of intervention, and seven weeks after the intervention), we test whether eco-driving recommendations in combination with personalized, graphical ‘eco-score index evolution’ feedback increase eco-driving behavior. We also experimentally evaluate the impact of adding social comparison elements to the feedback (e.g., providing feedback on a person’s eco-driving performance compared to that of the same car brand users). Structural Equation Modeling (in MPlus 8.4) is used to analyze data. Our results show that digitally administered personal performance feedback increases eco-driving behavior both during and after the feedback intervention. However, we do not observe increased effects when social comparison information is added to the feedback. As this latter element is surprising, we conclude with a reflection on possible explanations and suggest areas for future research. We contribute to the sustainable eco-driving literature by researching an understudied group: company car drivers. More specifically, we contribute by demonstrating the effectiveness of digitally administered personal performance feedback on eco-driving for this group and by observing and reflecting on the (in)effectiveness of feedback containing social comparison information
From Fit and Forget to Flex or Regret in Distribution Grids: Dealing With Congestion in European Distribution Grids
No full textThe support of decentralized energy resources under the Fit for 55 package and the REPowerEU plan places distribution grid users and distribution system operators (DSOs) at the center of the future European energy system. Also, the interaction between both types of agents is gaining importance for two reasons. First, DSOs face challenges connecting these new grid users to their network, leading to an increased need for grid investments and congestion management measures. Second, engaging these new grid users can bring opportunities for DSOs to manage their network and its possible congestion more efficiently
Fostering flexibility in distribution networks through empirical studies, regulatory analyses, and mathematical programs
No full textThe rise of congestion issues in European distribution grids due to the rapid uptake of distributed energy resources highlights the need for advancements in distribution network operation and planning. Recognizing this need, the Clean Energy Package introduced several provisions to support distribution system operators in leveraging the potential of the flexibility that is increasingly present in their networks. In this context, the main objective of this dissertation is to gain scientific knowledge and empirical information to foster the use of flexibility in distribution networks through empirical studies, regulatory analyses and mathematical programs. More specifically, the following three topics are covered: developments in distribution network planning, practical experiences with flexibility tools, and open issues when designing flexibility markets. The first part of this dissertation examines developments in distribution network planning and finds that currently, no single approach exists across European distribution system operators. Furthermore, the level of transparency and developing a robust methodology for flexibility remain open issues in distribution network planning. The second part explores the regulatory toolbox for flexibility and examines practical implementations of dynamic distribution network tariffs and flexible connection agreements. While incentives for efficient network usage are being integrated into both flexibility tools, open questions remain regarding the optimal design and compatibility of these tools. In this context, regulatory sandboxes can be an important regulatory instrument. The outcomes of existing sandbox projects reinforce the idea that regulatory sandboxes can be an effective instrument for examine innovations, such as flexibility, in a real environment. However, this potential can only be achieved when effectively implementing the administration, derogations, application process and reporting within sandbox frameworks. The third part qualitatively discusses open issues regarding the use cases, incentives, operational timeframes, rules and products, and roles and responsibilities in flexibility markets. Furthermore, a bilevel model was developed to evaluate strategic behavior in these markets. The derived characteristics of the price-setter and inc-dec games using flexibility and redispatch markets can be used to support regulators and system operators in detecting these games. Finally, a comparison of wind curtailment and imbalance data revealed that the counterbalancing costs associated with flexibility procurement can be significant, depending on the system's imbalance position. The influence of these counterbalancing costs on the use of flexibility in network planning was illustrated using a bilevel model and a stylized test case
Changing the landscape of psoriasis management : Value-based healthcare in psoriasis
No full textThe healthcare sector is under immense financial pressure, and many acknowledge that a dramatic shift is required as the current system is not sustainable. Worrisomely, an increase in healthcare spendings does not necessarily seem to equate to better health outcomes or quality of care. Several solutions have been proposed to tackle this issue of which the conceptual framework known as value-based healthcare (VBHC) is further explored in this dissertation for psoriasis. The VBHC framework is formulated on the premise that the healthcare sector should deliver integrated care and strive to maximize the value created – improving quality and performance of care whilst simultaneously using resources in a sustainable and transparent way. Value in this context is defined as the patient-relevant health outcomes achieved divided by the costs needed to achieve these outcomes. Psoriasis is a common chronic inflammatory skin disease that is associated with a high multidimensional disease burden. It is associated with numerous comorbidities and is known to reduce quality of life (QoL) and life expectancy. In recent years major advancements have been made in the treatment of psoriasis and currently a range of treatments is available that can help manage the disease. Despite the substantial burden of psoriasis, nontreatment and undertreatment remain common. Additionally, widespread treatment dissatisfaction exists among persons with psoriasis. The costs associated with managing psoriasis and its comorbidities present a substantial economic burden to the patient and to society. Considering these facts, psoriasis management could benefit from VBHC’s holistic approach. However, the potential of VBHC in dermatology has yet to be fully explored as the concept is relatively new to the field of dermatology and has only been sporadically described in dermatologic literature
New resource-constrained project scheduling instances for testing (meta-)heuristic scheduling algorithms
No full textThe resource-constrained project scheduling problem (RCPSP) is a well-known scheduling problem that has attracted attention since several decades. Despite the rapid progress of exact and (meta-)heuristic procedures, the problem can still not be solved to optimality for many problem instances of relatively small size. Due to the known complexity, many researchers have proposed fast and efficient meta-heuristic solution procedures that can solve the problem to near optimality. Despite the excellent results obtained in the last decades, little is known why some heuristics perform better than others. However, if researchers better understood why some meta-heuristic procedures generate good solutions for some project instances while still falling short for others, this could lead to insights to improve these meta-heuristics, ultimately leading to stronger algorithms and better overall solution quality. In this study, a new hardness indicator is proposed to measure the difficulty of providing near-optimal solutions for meta-heuristic procedures. The new indicator is based on a new concept that uses the σ distance metric to describe the solution space of the problem instance, and relies on current knowledge for lower and upper bound calculations for problem instances from five known datasets in the literature. This new indicator, which will be called the σ D indicator, will be used not only to measure the hardness of existing project datasets, but also to generate a new benchmark dataset that can be used for future research purposes. The new dataset contains project instances with different values for the σ D indicator, and it will be shown that the value of the σ distance metric actually describes the difficulty of the project instances through two fast and efficient meta-heuristic procedures from the literature
AI strategy to execution
No full textIn this book, we discuss the “strategy to the execution gap” a leader of an organization encounters while adopting Artificial Intelligence (AI) in that organization. The main focus is on value creation using AI and use of AI as competitive strategy. Although every organization across various industries is interested in integrating Artificial Intelligence into their business, a significant dilemma is the right AI strategy for their organization