Upon graduation, students make the decision to either become an entrepreneur or an employee. Numerous studies have thus investigated personal and environmental factors that impact this decision. As cognitive styles have become more and more important in determining individual and organisational behaviour, and as they are presumed to provide new valuable insights over and above other personal factors, they provide the ideal focus to further explore this career choice. In this article, we aim to explore how creating, planning, and knowing cognitive style relate to entrepreneurial attitudes, intentions, and career choices. Using the Theory of Planned Behaviour, in a first sample, we investigate the direct and indirect impact that cognitive styles have on entrepreneurial intention through attitudes. In our second sample, we look at how career preferences for entrepreneurship or a more traditional career as an employee are affected by cognitive styles. Using structural equation modelling analysis, this study finds evidence for the importance of creating cognitive style on entrepreneurial outcomes. Additionally, we find evidence for the relationship between planning cognitive style and wanting to be an employee. Knowing style does not lead to either preference. This paper extends the current knowledge on cognitive styles and entrepreneurship by analysing the impact of other cognitive styles than the predominantly used innovative styles and by also exploring its impact on important antecedents of entrepreneurial intentions, such as entrepreneurial attitude and career preferences.

The classical spare part demand forecasting literature studies methods to forecast intermittent demand. The majority of these methods do not consider the underlying demand generating factors. Demand for spare parts originates from the part replacements of the installed base of machines, which are either done preventively or upon breakdown of the part. This information from service operations, which we refer to as installed base information, can be used to forecast future spare part demand. In this paper we review the literature on the use of such installed base information for spare part demand forecasting to asses (1) what type of installed base information can be useful; (2) how this information can be used to derive forecasts; (3) what is the value of using installed base information to improve forecasting; and (4) what are the limits of the currently existing methods. The latter serve as motivation for future research.

This paper analyses the project completion time distribution in a Markovian PERT network. Several techniques to obtain exact or numerical expressions for the project completion time distribution are evaluated, with the underlying assumption that the activity durations are exponentially distributed random variables. We show that some of the methods advocated in the project scheduling literature are unable to solve standard datasets from the literature. We propose a framework to analyse the applicability, accuracy and sensitivity of different methods to compute project makespan distributions. An alternative data generation process is proposed to benchmark the different methods and the influence of project dataset parameters on the obtained results is extensively assessed.

The goal of this paper is to investigate the impact of different solution representations, as part of a metaheuristic approach, on net present value optimization in project scheduling. We specifically consider the discrete time/cost trade-off problem with net present value optimization and apply three payment models from literature. Each of these models determines the timing and size of cash flows from the contractor’s viewpoint. The contribution of this paper to literature is twofold. First, we include cash flow distribution variants in the payment models, to also distinguish between different manners in which value is created and costs are incurred, as part of a general model for the contractor’s cash flow management. This general model is developed in order to explicitly include the progress of activities in the determination of the timing and size of payments to the contractor, which is currently lacking in literature. Second, we employ an iterated local search framework to compare different solution representations and their corresponding local search and repair heuristics. The goal is to unambiguously show that the choice of a solution representation deserves a fair amount of attention, alongside the selection of appropriate diversification and intensification operators, even though this is not always the case in literature. Each part of the proposed algorithm is validated on a large dataset of test instances, generated to allow for a broad comparison of the solution representations. Our results clearly quantify the statistically significant differences between three types of representations for the project scheduling problem under study.

Monitoring the performance of projects in progress and controlling their expected outcome by taking corrective actions is a crucial task for any project manager. Project control systems are in use to quantify the project performance at a certain moment in time, and allow the project manager to predict the expected outcome if no action is taken. Consequently, these systems serve as mechanism that provide warning signals that tell the project manager when it is time to take corrective actions to bring the expected project outcome back on track. In order to trust these generated warning signals, the project manager has to set limits on the provide performance metrics that serve as thresholds for these actions. This paper gives an overview of different approaches discussed in the literature to control projects using such actions thresholds. First and foremost, the paper discusses three classes of actions thresholds,ranging from very easy-to-use rules-of-thumb to more advanced statistical project control methodologies. Each of these tools have been the subject to research studies, each of which aim at showing their power to predict project problems during its progress. In addition, the paper will emphasize the fundamental different between statistical project control using tolerance limits and statistical process control for projects. Finally, three different quality metrics to evaluate the performance of such control methods are presented and discussed.

This paper investigates how the risk governance practices of European financial institutions quantitatively cluster on the corporate governance characteristics of the corporation, particularly ownership structure and board independence. Using hand-collected data on a sample of 54 banks and 33 insurance companies, we find that financial institutions with powerful owners (i.e., those with >20% ownership) have a lower chief risk officer (CRO) presence and lower risk committee presence. In addition, state-controlled institutions and institutions with more independent boards have more independent risk committees.

If the closed-form formula for the probability density function is not available, implementing the maximum likelihood estimation is challenging. We introduce a simple, fast, and accurate way for the estimation of numerous distributions that belong to the class of tempered stable probability distributions. Estimation is based on the Method of Simulated Quantiles (Dominicy and Veredas (2013)). MSQ consists of matching empirical and theoretical functions of quantiles that are informative about the parameters of interest. In the Monte Carlo study we show that MSQ is significantly faster than Maximum Likelihood and the estimates are almost as precise as MLE. A Value at Risk study using 13 years of daily returns from 21 world-wide market indexes shows that MSQ estimates provide as good risk assessments as with MLE.

Few high-growth firms (HGFs) are able to maintain high-growth over time. The purpose of this paper is to find out why only a small number of firms become persistent HGFs, explicitly focusing on the role of the founding entrepreneur in this process.

Research at the family firm–Corporate Social Responsibility (CSR) nexus lacks agreement about whether family firms are more or less socially responsible than their non‐family counterparts, which leads discussion relating to the bright and dark side of socioemotional wealth (SEW). We add to this ongoing debate in two different ways. First, we build on family firm heterogeneity and argue for a gray side to SEW, located between the bright and dark sides that is dependent upon the kind of family firm ownership. Second, we assume that prior research on a diverse set of CSR behaviors may, to some extent, explain the contradicting results; thus, we propose going back a step and focusing on management’s attention to CSR as an important antecedent of CSR behavior. By analyzing the letters to the shareholders of German HDAX firms from 2003 to 2012, this study finds that family ownership positively affects management’s attention to CSR, mainly driven by founders and family foundations. The research adds to our understanding of the family firm–CSR nexus by scrutinizing the role SEW plays in management’s attention to CSR when it comes to family firm heterogeneity.

To achieve therapeutic innovation in oncology, already expensive novel medicines are often concomitantly combined to potentially enhance effectiveness. While this aggravates the pricing problem, comparing effectiveness of novel yet expensive (concomitant) treatments is much needed for healthcare decision-making to deliver effective but affordable treatments. This study reviewed published clinical trials and real-world studies of targeted and immune therapies. In total, 48 studies compared and/or combined multiple novel products on breast, colorectal, lung and melanoma cancers. To a great extent, products evaluated in each study were owned by one manufacturer. However, cross-manufacturer assessments are also needed. Next to costs and intensive market competition, the absence of a regulatory framework enforcing real-world multiproduct studies prevents these from being conducted. Trusted third parties could facilitate such real-world studies, for which appropriate and efficient data access is needed.