Conceptions of God, normative convictions and socially responsible business conduct: An explorative study among executives

The case for socially responsible business conduct is often made from an economical or ethical perspective with the organization as level of analysis. This paper focuses on the relationship between the religious belief of corporate decision-makers and socially responsible business conduct. Based on in-depth interviews with twenty Dutch executives from different religious backgrounds, we find much inductive evidence of a relationship between their conception of God, norms and values and business conduct. We also find that executives with a monotheistic conception of God display a stronger orientation toward socially responsible business conduct than executives with a pantheistic conception of God.Socially responsible business conduct; religion; values; ethics; leadership

Evaluation of Dutch election programs: The impact of parameter uncertainty

Shortly before the national elections in the Netherlands, the Netherlands Bureau for Economic Policy Analysis (CPB) evaluates the economic effects of the policy proposals in election programs. This paper investigates the sensitivity of this analysis to the uncertainty of parameter estimates in the economic models that are used. For this purpose, a Monte Carlo analysis of five election programs is performed, using a core version of one of the CPB models. We find that the range of projected outcomes is surprisingly small. Nonetheless, caution remains needed, especially when the estimated effects of different parties are roughly similar.Model simulation; policy evaluation; parameter uncertainty; CGE models

Motives of Socially Responsible Business Conduct

The social and ecological challenges that governments face have raised their interest in socially responsible business conduct (SRBC). In this article we analyze the motives of executives to perform SRBC. We distinguish three types of motives: financial, ethical and altruistic motives. We test the hypotheses on a sample of 473 executives. The estimation results show that SRBC is driven by a combination of intrinsic and extrinsic motives, but that the intrinsic motives are stronger than the extrinsic motive.intrinsic motivation;extrinsic motivation;corporate social responsibility;socially responsible business conduct

Who learns better Bayesian network structures: Accuracy and speed of structure learning algorithms

Three classes of algorithms to learn the structure of Bayesian networks from data are common in the literature: constraint-based algorithms, which use conditional independence tests to learn the dependence structure of the data; score-based algorithms, which use goodness-of-fit scores as objective functions to maximise; and hybrid algorithms that combine both approaches. Constraint-based and score-based algorithms have been shown to learn the same structures when conditional independence and goodness of fit are both assessed using entropy and the topological ordering of the network is known [1]. In this paper, we investigate how these three classes of algorithms perform outside the assumptions above in terms of speed and accuracy of network reconstruction for both discrete and Gaussian Bayesian networks. We approach this question by recognising that structure learning is defined by the combination of a statistical criterion and an algorithm that determines how the criterion is applied to the data. Removing the confounding effect of different choices for the statistical criterion, we find using both simulated and real-world complex data that constraint-based algorithms are often less accurate than score-based algorithms, but are seldom faster (even at large sample sizes); and that hybrid algorithms are neither faster nor more accurate than constraint-based algorithms. This suggests that commonly held beliefs on structure learning in the literature are strongly influenced by the choice of particular statistical criteria rather than just by the properties of the algorithms themselves.CEG and JMG were supported by the project MULTI-SDM (CGL2015-66583-R, MINECO/FEDER)

The Probabilistic Backbone of Data-Driven Complex Networks: An example in Climate

Correlation Networks (CNs) inherently suffer from redundant information in their network topology. Bayesian Networks (BNs), on the other hand, include only non-redundant information (from a probabilistic perspective) resulting in a sparse topology from which generalizable physical features can be extracted. We advocate the use of BNs to construct data-driven complex networks as they can be regarded as the probabilistic backbone of the underlying complex system. Results are illustrated at the hand of a global climate dataset

Evaluation of Dutch election programs: The impact of parameter uncertainty

Shortly before the national elections in the Netherlands, the Netherlands Bureau for Economic Policy Analysis (CPB) evaluates the economic effects of the policy proposals in election programs. This paper investigates the sensitivity of this analysis to the uncertainty of parameter estimates in the economic models that are used. For this purpose, a Monte Carlo analysis of five election programs is performed, using a core version of one of the CPB models. We find that the range of projected outcomes is surprisingly small. Nonetheless, caution remains needed, especially when the estimated effects of different parties are roughly similar

Conceptions of God, normative convictions and socially responsible business conduct: An explorative study among executives

The case for socially responsible business conduct is often made from an economical or ethical perspective with the organization as level of analysis. This paper focuses on the relationship between the religious belief of corporate decision-makers and socially responsible business conduct. Based on in-depth interviews with twenty Dutch executives from different religious backgrounds, we find much inductive evidence of a relationship between their conception of God, norms and values and business conduct. We also find that executives with a monotheistic conception of God display a stronger orientation toward socially responsible business conduct than executives with a pantheistic conception of God

Evaluation of Dutch election programs: The impact of parameter uncertainty

Shortly before the national elections in the Netherlands, the Netherlands Bureau for Economic Policy Analysis (CPB) evaluates the economic effects of the policy proposals in election programs. This paper investigates the sensitivity of this analysis to the uncertainty of parameter estimates in the economic models that are used. For this purpose, a Monte Carlo analysis of five election programs is performed, using a core version of one of the CPB models. We find that the range of projected outcomes is surprisingly small. Nonetheless, caution remains needed, especially when the estimated effects of different parties are roughly similar

Interactive virtual 3D image reconstruction to assist renal surgery in patients with fusion anomalies of the kidney

Objective: Renal fusion anomalies are rare and usually present as horseshoe kidneys or crossed fusion ectopia. The complex renal anatomy seen in patients with these anomalies can present a challenge. Pre-operative planning is therefore paramount in the surgical management of these cases. Herein we report the use of interactive virtual three-dimensional (3D) reconstruction to aid renal surgery in patients with fusion anomalies of the kidney. / Materials and Methods: A total of seven cases were performed between May 2016 and October 2020. 3D reconstruction was rendered by Innersight Labs using pre-operative computed tomography (CT) scans. / Results: Five patients had malignant disease and two patients had benign pathology. Robotic and open operations were performed in four and three patients, respectively. / Conclusion: The use of 3D reconstruction in the cases reported in this series allowed for the identification of variations in renal vasculature, and this informed the choice of operative approach. / Oxford Centre for Evidence-Based Medicine Evidence Level: 4