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Focusing on the corporations in China and aiming to figure out the significant connection between organizational justice perception and job satisfaction from Chinese setting, this study aimed to examine the effects of organizational justice upon job satisfaction of the full-time and part-time employees in the state owned enterprise (SOEs) and primate Chinese companies.

The study adopted the questionnaire to investigate more than 300 employees, and the empirical data of this paper is based on statistical analysis, such as confirmatory factor analysis, correlational and regression analysis.

The paper arrives at the conclusion that in SOEs, the employees’ perception about procedural justice was higher than distributive justice. While in private enterprises, the procedural justice and interactive justice were tested to have similar coefficients. The relationship between organizational justice and job satisfaction differed between full-time employees and part-time employees.

This study opens a new window for understanding how organizational justice influences employees’ job satisfaction in Chinese context, taking a further step to explore the different impacts of organizational justice on job satisfaction among different types of employees.

This paper collected data from both SOE and private companies in China, increasing the external validity of the findings. Meanwhile, the authors observed consistent findings with the studies in Western Society, which increase the generalization of our findings as well. The findings highlight the value of integrating literatures on organizational justice and job satisfaction.

The purpose of this investigation is to identify, evaluate, integrate and summarize relevant and qualified papers through conducting a systematic literature review (SLR) on the application of recommender systems (RSs) to suggest a scholarly publication venue for researcher's paper.

To identify the relevant papers published up to August 11, 2018, an SLR study on four databases (Scopus, Web of Science, IEEE Xplore and ScienceDirect) was conducted. We pursued the guidelines presented by Kitchenham and Charters (2007) for performing SLRs in software engineering. The papers were analyzed based on data sources, RSs classes, techniques/methods/algorithms, datasets, evaluation methodologies and metrics, as well as future directions.

A total of 32 papers were identified. The most data sources exploited in these papers were textual (title/abstract/keywords) and co-authorship data. The RS classes in the selected papers were almost equally used. DBLP was the main dataset utilized. Cosine similarity, social network analysis (SNA) and term frequency–inverse document frequency (TF–IDF) algorithm were frequently used. In terms of evaluation methodologies, 24 papers applied only offline evaluations. Furthermore, precision, accuracy and recall metrics were the popular performance metrics. In the reviewed papers, “use more datasets” and “new algorithms” were frequently mentioned in the future work part as well as conclusions.

Given that a review study has not been conducted in this area, this paper can provide an insight into the current status in this area and may also contribute to future research in this field.