Understanding the Followers of Toxic Leaders: Toxic Illusio and Personal Uncertainty

Toxic leaders are one of the main threats to the wellbeing of people in the workplace and in society in general, and followers play a critical role in constructing and maintaining toxic leaders. In this narrative review, we draw on Bourdieu's concept of illusio and incorporate it with the social and cognitive psychology approaches in an attempt to frame the dynamic system that sustains toxic leadership through continued support of the followers. More specifically, as we introduce the illusio perspective in a process‐relational context to the toxic leadership discussion, we (i) address the allure of toxic leaders as an incentive for followers to join the toxic illusio as a way to cope with their high personal uncertainty and (ii) illustrate the mechanisms and processes that motivate followers of toxic leaders to remain in the toxic illusio once they join. In this context, we also briefly discuss and differentiate between the ethical and moral dimensions of toxic leadership.     

Assessment of acceptance sampling plans using posterior distribution for a dependent process

In this study, performance of single acceptance sampling plans by attribute is investigated by using the distribution of fraction nonconformance (i.e. lot quality distribution) for a dependent production process. It is the aim of this study to demonstrate that, in order to emphasize consumer risk (i.e. the risk of accepting a bad lot), it is better to evaluate a sampling plan based upon its performance as assessed by the posterior distribution of fractions nonconforming in accepted lots. Similarly, it is the desired posterior distribution that sets the basis for designing a sampling plan. The prior distribution used in this study is derived from a Markovian model of dependence.     

Dependence bias in conventional p-charts and its correction with an approximate lot quality distribution

In this study, the variance of mean fraction nonconformance under the exact lot quality distribution with first order dependence is examined. The extent of the validity of binomial assumption with respect to the exact distribution in constructing the p-charts for dependent production processes is investigated and it is shown that neglect of dependence for small lot sizes may cause bias in the width of the area between the control limits. An approximate lot quality distribution with great versatility is proposed and is used for correcting the bias due to dependence in p-charts.