Reporting of research findings is often selective. This threatens the validity of the published body of knowledge if the decision to report depends on the nature of the results. The evidence derived from studies on causes and mechanisms underlying selective reporting may help to avoid or reduce reporting bias. Such research should be guided by a theoretical framework of possible causal pathways that lead to reporting bias. We build upon a classification of determinants of selective reporting that we recently developed in a systematic review of the topic. The resulting theoretical framework features four clusters of causes. There are two clusters of necessary causes: (A) motivations (e.g. a preference for particular findings) and (B) means (e.g. a flexible study design). These two combined represent a sufficient cause for reporting bias to occur. The framework also features two clusters of component causes: (C) conflicts and balancing of interests referring to the individual or the team, and (D) pressures from science and society. The component causes may modify the effect of the necessary causes or may lead to reporting bias mediated through the necessary causes. Our theoretical framework is meant to inspire further research and to create awareness among researchers and end-users of research about reporting bias and its causes.
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Randomised controlled trials are strongly advocated to evaluate the effects of intervention programmes on household energy saving behaviours. While randomised controlled trials are the ideal, in many cases, they are not feasible. Notably, many intervention studies rely on voluntary participation of households in the intervention programme, in which case random selection and random assignment are seriously challenged. Moreover, studies employing randomised controlled trials typically do not study the underlying processes causing behaviour change. Yet, the latter is highly important to improve theory and practice. We propose a systematic approach to causal inference based on graphical causal models to study effects of intervention programmes on household energy saving behaviours when randomised controlled trials are not feasible. Using a simple example, we explain why such an approach not only provides a formal tool to accurately establish effects of intervention programmes, but also enables a better understanding of the processes underlying behaviour change.
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Hospitality organizations are particularly vulnerable to changes in demand caused by disruptive events such as natural catastrophes, geopolitical events, and pandemic diseases. Nevertheless, the development of organizational resilience by hospitality organizations has remained under-explored. The ongoing digitalization trend provides a unique opportunity for hospitality organizations to combine the adoption of digitalization tools with the development of data analytic capability as a way to anticipate disruptive events and mitigate their impact on operations and performance. Through a cross-sectional survey design and using Partial Least Square Structural Equation Modeling, the present study demonstrates that hospitality organizations can improve their organizational resilience by developing data analytic capability. This can be achieved by (1) investing in the digital tools and IT infrastructure that allows them to sense their environment and (2) adapting their organizational infrastructure to quickly be able to use this information in decision-making. A limitation of the study lies in the use of cross-sectional data which limits temporal causality inferences.
