Purpose
To perceive the reasons for misusing the internet at work, an activity known as cyberloafing, efforts were made to find a new approach to reduce this negative behavior among employees. Thus, this study aims to identify the antecedents of job embeddedness (JE) and their effects on cyberloafing among the employees of public universities in eastern Iran.
Design/methodology/approach
To this end, the antecedents of JE were first extracted by reviewing the literature in this regard. In the next step, the opinions of the expert team were taken into account to select five variables. Subsequently, the conceptual model and hypotheses were presented and tested through structural equation modeling. A 57-item questionnaire was then distributed among the employees of eastern Iranian universities, who were selected through random stratified sampling. Finally, the data collected from of 510 questionnaires were analyzed.
Findings
According to the findings, it can be argued that JE with its five antecedents of family support, work support, job flexibility, work practices and task significance is able to significantly reduce cyberloafing. Moreover, the full mediating role of JE was confirmed.
Originality/value
Cyberloafing is a term describing the actions of employees who use their internet access at work for personal purposes pretending to do legitimate work. It has been turned into a serious challenge in developing countries such as Iran. It is, therefore vital to identify its factors and antecedents to diminish the counterproductive behavior in the workplace.
<p style='text-indent:20px;'>Redundancy allocation problem (RAP) is a common technique for increasing the reliability of systems. In this paper, a new model for the RAP is introduced that takes into account the warm standby and mixed strategy, the model dynamics, and the type of the strategy in redundancy allocation problems. A recursive formula is first obtained for the reliability function in the dynamic warm standby and mixed redundancy strategies that leverages the success mode analysis and works for any arbitrary failure-time distribution. Failure rates for warm standby units change before and after their replacement with a damaged unit, and, therefore, the reliability function in warm standby varies with time (i.e., the model is dynamic). Although dynamic models are commonplace in practice, they are more challenging to assess than static models, which have been mainly considered in the literature. An optimization problem is then formulated to select the best redundancy strategy and redundancy levels. Genetic algorithm and particle swarm optimization are leveraged to solve the problem. Finally, the efficiency of the presented method is verified through a numerical example. The experimental results verify that the proposed model for RAP significantly improves the system reliability, which can be of vital importance for system designers.</p>
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