Purpose This paper aims to answer the question of how, why and when abusive supervision affects employee creativity. Drawing on the conservation of resources theory, this paper examines the direct and indirect (via psychological distress) effects of abusive supervision on employee creativity. It further investigates the boundary conditions imposed by employees’ perceived distributive and procedural justice in the relationships between abusive supervision, psychological distress and employee creativity. Design/methodology/approach The study uses multi-sourced and time-lagged data collected in three waves from a survey of employees-supervisor dyads working in the Chinese manufacturing sector. In the first wave, the authors received data from 347 employees on perceived abusive supervision and perceived distributive and procedural justice. In the second wave, 320 employees shared their perceptions of psychological distress at work. In the third wave, the authors received ratings for employee creativity from the direct supervisors of 300 employees. The data were analyzed using bootstrapped moderated mediation procedures. Findings The findings revealed a significant negative influence of abusive supervision on employee creativity both directly and indirectly in the presence of perceived psychological distress. However, distributive and procedural justice was found to mitigate the negative impact of abusive supervision on employee creativity. Practical implications Abusive supervision has adverse consequences for employees’ creativity because it affects their psychological health. HR and top management should prioritize addressing abusive supervision first and foremost to boost employee creativity in the workplace. Managers should give employees opportunities for participation and foster a climate of fairness in the organization to mitigate the harmful consequences of abusive supervision. Originality/value To the best of the authors’ knowledge, this is the first empirical study that examines the psychological distress-based mechanism in the relationship between abusive supervision and creativity while considering the interactive effects of distributive and procedural justice. It addresses an important research gap in the literature by proposing that organizational perceived distributive and procedural justice can mitigate the detrimental effects of abusive supervision.

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The design and field programmable gate array implementation of a single-input fuzzy (SIF) proportionalintegral-derivative (PID) control scheme applied to DC-DC buck converters are presented. The SIF logic reduces the number of fuzzy rules and therefore the hardware resource occupancy without control property degradation compared with the double-input fuzzy (DIF) controller, which is realised by transforming the two-dimension rule tables into onedimension rule vectors using signed distance method and genetic algorithm. The adopted signed distance method requires the two-dimension rule tables to be of Toeplitz structure, which is realised by establishing the inference rules through analysing the system response curve. The fuzzy logic regulates the PID parameters based on the conditions of the power converters. As a result, the SIF-PID controller is superior to the conventional PID controller and is similar to DIF-PID controller in terms of the control performance. The proposed controller has been validated with simulation and experimental results.

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U.S. state educational testing programs administer tests to track student progress and hold schools accountable for educational outcomes. Methods from item response theory, especially Rasch models, are usually used to equate different forms of a test. The most popular method for estimating Rasch models yields inconsistent estimates and relies on ad hoc adjustments to obtain good approximations. Furthermore, psychometricians have paid little attention to the estimation of effective standard errors for Rasch models, especially under complex sample designs. This article presents a computationally efficient, statistically consistent estimator for Rasch models, based on a nonparametric marginal maximum likelihood approach, along with complete, designconsistent estimators of the standard error, based on the full information matrix and including covariance terms among items, covariances between items, and parameters of the distribution of the latent trait. Simulations support the consistency of the estimators in both simple random samples and more realistic multistage samples. Index terms: Rasch model, standard error, nonparametric marginal maximum likelihood, equating, item response theory State educational testing programs periodically administer tests to students to track individual student progress and to hold teachers, schools, or districts accountable for educationally meaningful outcomes. Operationally, running these programs requires the periodic release of items, both to avoid their accidental or malicious disclosure and to inform the public of the types of tasks students are expected to perform. Replacement items, and even whole replacement test forms, must be linked to the same underlying scale as the tests or items that they replace.In the United States, state testing programs rely on a very limited set of statistical measurement models to link test items to a common scale. Perhaps the most common measurement models belong to the Rasch family of statistical models. Several characteristics of Rasch models (discussed briefly below) render them particularly attractive for programs that will receive public scrutiny.Within the United States, current practice in the estimation of Rasch models lags behind the statistical research on the subject in two ways. First, the most popular method and software for estimating Rasch models in U.S. state testing programs yield inconsistent estimates and rely on ad hoc adjustments to obtain good approximations (see Wright & Douglas, 1977). Second, psychometricians have paid remarkably little attention to the estimation of effective standard errors (SEs) for Rasch models, especially under the complex sample designs typically found in state testing programs.This article presents a computationally efficient, statistically consistent estimator for Rasch models, along with complete, design-consistent estimators of the SE. The proposed estimator is based on a nonparametric marginal maximum likelihood approach (NPMML) to estimation. The SE developed here is complete in that it is based...