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We investigate the effects of management-employee similarity on mistreated employees’ propensities to engage in legal and organizational claiming, to quit, and to not seek a remedy in ongoing employment relationships.

We test hypotheses generated by the similarity-attraction and similarity-betrayal paradigms using Tobit regression and data from vignette-based employee surveys.

Mistreated employees with same-sex supervisors are more likely to initiate legal claims and to quit than those with opposite-sex supervisors, but less likely to initiate legal claims and to quit when they have a same-race supervisor than when they have a different-race supervisor. The effects of management-employee similarity on mistreated employees’ remedy-seeking responses exhibit asymmetries by gender and by race. The presence of same-race supervisors or other managers appears to diminish the greater reluctance of nonwhite employees, compared to white employees, to use organizational claiming mechanisms.

We know of no prior published research that has investigated the determinants of employees’ propensities to engage in multiple forms of remedy seeking, as well as the propensity to not seek a remedy, in response to plausibly illegal mistreatment not involving dismissal.

This study's purpose is to investigate the antecedents of an employee's remedy‐seeking behavioral intentions in response to wrongful dismissal.

Hypotheses generated by two paradigms are tested, the similarity‐attraction and the similarity‐betrayal paradigms, using Tobit regression and data from a scenario‐based survey of employees.

Consistent with the similarity‐attraction paradigm, the management team's racial and deep‐level similarity to the employee both were negatively related to the employee's propensity to consult a lawyer. Consistent with the similarity‐betrayal paradigm, the employee's propensity to consult a lawyer increased with the supervisor's deep‐level similarity to the employee; among men, the propensity to complain to regulatory agencies increased with the management team's gender similarity and the propensity to not seek a remedy declined with the supervisor's gender similarity.

Limitations of the study include the use a single‐source, cross‐sectional, convenience sample; the small size and heterogeneity of the non‐white sub‐sample; and the limited number of control variables. Future research should explore whether the findings are robust when tested using alternative types of data; alternative wrongful dismissal scenarios; a more extensive set of controls for organizational, job, and personal characteristics; and larger, more diverse sub‐samples of non‐whites.

Organizations should manage dismissals in a manner that encourages employees to favor internal remedy‐seeking over external remedy‐seeking options.

This study is the first to investigate the antecedents of a wrongfully dismissed employee's propensity to engage in internal as well as external remedy‐seeking and to explore the effects of management's similarity to the employee on the employee's remedy‐seeking actions.

Traditionally, hiring indefinite duration contract employees has been the dominant method used by U.S. organizations to staff their labor needs. Indefinite duration contract employees, hereafter referred to as “regular” employees, have three defining characteristics: (1) they are hired directly as employees of the organization whose work they perform; (2) the duration of the employment relationship is unspecified, with a mutual expectation that it will continue as long as it is mutually satisfactory; and (3) the employment relationship provides ongoing – as opposed to intermittent – work. When their demand for labor increases, organizations staffed exclusively by regular employees can respond by having their employees work overtime or by hiring additional regular employees. Conversely, when their demand for labor decreases, such organizations can either maintain “inventories” of excess regular employees or reduce labor inputs by laying-off or reducing the work hours of regular employees.

The campaign for striker replacement legislation, which began in the late 1980s and had effectively ended by the mid-1990s, was the most important political battle over labor legislation since the defeat of the Labor Law Reform Bill in 1978. Striker replacement was the AFL-CIO’s top legislative priority in the early 1990s and, coming quickly after the passage of NAFTA, which labor had opposed, the defeat of its campaign solidified organized labor’s reputation for failure in legislative battles. As yet, however, the political campaign for striker replacement legislation has attracted surprisingly little attention from industrial relations scholars.

Trust is one of the main pillars of many communication and interaction domains. Computing is no exception. Fog computing (FC) has emerged as mitigation of several cloud computing limitations. However, selecting a trustworthy node from the fog network still presents serious challenges. This paper aims to propose an algorithm intended to mitigate the trust and the security issues related to selecting a node of a fog network.

The proposed model/algorithm is based on two main concepts, namely, machine learning using fuzzy neural networks (FNNs) and the weighted weakest link (WWL) algorithm. The crux of the proposed model is to be trained, validated and used to classify the fog nodes according to their trust scores. A total of 2,482 certified computing products, in addition to a set of nodes composed of multiple items, are used to train, validate and test the proposed model. A scenario including nodes composed of multiple computing items is designed for applying and evaluating the performance of the proposed model/algorithm.

The results show a well-performing trust model with an accuracy of 0.9996. Thus, the end-users of FC services adopting the proposed approach could be more confident when selecting elected fog nodes. The trained, validated and tested model was able to classify the nodes according to their trust level. The proposed model is a novel approach to fog nodes selection in a fog network.

Certainly, all data could be collected, however, some features are very difficult to have their scores. Available techniques such as regression analysis and the use of the experts have their own limitations. Experts might be subjective, even though the author used the fuzzy group decision-making model to mitigate the subjectivity effect. A methodical evaluation by specialized bodies such as the security certification process is paramount to mitigate these issues. The author recommends the repetition of the same study when data form such bodies is available.

The novel combination of FNN and WWL in a trust model mitigates uncertainty, subjectivity and enables the trust classification of complex FC nodes. Furthermore, the combination also allowed the classification of fog nodes composed of diverse computing items, which is not possible without the WWL. The proposed algorithm will provide the required intelligence for end-users (devices) to make sound decisions when requesting fog services.