Can business humanize its stakeholders? And if so, how does this relate to moral consideration for stakeholders? In this paper we compare two business orientations that are relevant for current business theory and practice: a stakeholder orientation and a profit orientation. We empirically investigate the causal relationships between business orientation, humanization, and moral consideration. We report the results of six experiments, making use of different operationalizations of a stakeholder and profit orientation, different stakeholders, and different participant samples. Our findings support (...) the prediction that individual stakeholders observing a stakeholder-oriented firm see the firm’s other stakeholders as more human than individual stakeholders observing a profit-oriented firm. This humanization, in turn, increases individual stakeholders’ moral consideration for the firm’s other stakeholders. Our findings underscore the importance of humanization for stakeholders’ moral consideration for each other. This paper contributes to a deeper understanding of the firm as a moral community of stakeholders. Specifically, we move away from a focus on managers, and how they can make business more moral. Instead we direct attention to stakeholders, and how business can make these stakeholders more moral. (shrink)

Background: Mental healthcare needs person-tailored interventions. Experience Sampling Method (ESM) can provide daily life monitoring of personal experiences. This study aims to operationalize and test a measure of momentary reward-related Quality of Life (rQoL). Intuitively, quality of life improves by spending more time on rewarding experiences. ESM clinical interventions can use this information to coach patients to find a realistic, optimal balance of positive experiences (maximize reward) in daily life. rQoL combines the frequency of engaging in a relevant context (a (...) ‘behavior setting’) with concurrent (positive) affect. High rQoL occurs when the most frequent behavior settings are combined with positive affect or infrequent behavior settings co-occur with low positive affect. Methods: Resampling procedures (Monte Carlo experiments) were applied to assess the reliability of rQoL using various behavior setting definitions under different sampling circumstances, for real or virtual subjects with low-, average- and high contextual variability. Furthermore, resampling was used to assess whether rQoL is a distinct concept from positive affect. Virtual ESM beep datasets were extracted from 1058 valid ESM observations for virtual and real subjects. Results: Behavior settings defined by Who-What contextual information were most informative. Simulations of at least 100 ESM observations are needed for reliable assessment. Virtual ESM beep datasets of a real subject can be defined by Who-What-Where behavior setting combinations. Large sample sizes are necessary for reliable rQoL assessments, except for subjects with low contextual variability. rQoL is distinct from positive affect. Conclusion: rQoL is a feasible concept. Monte Carlo experiments should be used to assess the reliable implementation of an ESM statistic. Future research in ESM should asses the behavior of summary statistics under different sampling situations. This exploration is especially relevant in clinical implementation, where often only small datasets are available. (shrink)

Vaccination policy is an ethically challenging domain of public policy. It is a matter of collective importance that reaches into the most private sphere of citizens and unavoidably conflicts with individual-based ethics. Policy makers need to walk a tight rope in order to complement utilitarian public health values with individual autonomy rights, protection of privacy, non-discrimination and protection of the worst-off. Whether vaccination is voluntary or compulsory, universal or targeted, every option faces complex ethical hurdles because of the interdependence of (...) humans in infectious disease matters. In this article, we explore the following three policy questions. (i) Ethically, which policy measures should be addressed when vaccination coverage is insufficient in a population? Information campaigns, legal compulsion, or the use of financial incentives can all be effective, but also controversial policy options. (ii) Is it ethical to target vaccination programs at certain risk-groups? If such measures are necessary, we argue that policymakers will often have to decide which is more important to uphold: non-discrimination or the protection of privacy. And (iii), what is the ethical significance of adverse herd immunity effects? Some vaccination programs will improve average population health, but will at the same time increase the risk of severe morbidity and mortality for individuals in the worst-off groups of society. (shrink)

We apply an online optimization process based on machine learning to the production of Bose-Einstein condensates. BEC is typically created with an exponential evaporation ramp that is optimal for ergodic dynamics with two-body s-wave interactions and no other loss rates, but likely sub-optimal for real experiments. Through repeated machine-controlled scientific experimentation and observations our ’learner’ discovers an optimal evaporation ramp for BEC production. In contrast to previous work, our learner uses a Gaussian process to develop a statistical model of the (...) relationship between the parameters it controls and the quality of the BEC produced. We demonstrate that the Gaussian process machine learner is able to discover a ramp that produces high quality BECs in 10 times fewer iterations than a previously used online optimization technique. Furthermore, we show the internal model developed can be used to determine which parameters are essential in BEC creation and which are unimportant, providing insight into the optimization process of the system. (shrink)