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Perceptions of employment histories are important insofar as they influence future job prospects. Critically, in light of the current pandemic, wherein many individuals are likely to have unanticipated employment gaps and/or temporary work experiences, this exploratory study aims to seek a better understanding of the signal associated with temporary employment histories, which is particularly germane to individuals' employment trajectories and a successful labour market recovery.

Drawing primarily on signalling theory and using a simulated hiring decision experiment, the authors examined the perceptions of temporary employment histories, as well as the period effect of COVID-19, a major exogenous event, on the attitudes of fictitious jobseekers with standard, temporary and unemployment histories.

The authors find that prior to COVID-19 unemployed and temporary-work candidates were perceived less favourably as compared to applicants employed in a permanent job. During the COVID-19 pandemic, assessments of jobseekers with temporary employment histories were less critical and the previously negative signal associated with job-hopping reversed. This study’s third wave of data, which were collected post-COVID, showed that such perceptions largely dissipated, with the exception for those with a history of temporary work with different employers.

The paper serves as a reminder to check, insofar as possible, preconceived biases of temporary employment histories to avoid potential attribution errors and miss otherwise capable candidates.

This paper makes a unique and timely contribution by focussing and examining the differential effect of economic climate, pivoted by the COVID-19 pandemic, on perceptions of temporary employment histories.

Create and share a MATLAB library that performs data augmentation algorithms for audio data. This study aims to help machine learning researchers to improve their models using the algorithms proposed by the authors.

The authors structured our library into methods to augment raw audio data and spectrograms. In the paper, the authors describe the structure of the library and give a brief explanation of how every function works. The authors then perform experiments to show that the library is effective.

The authors prove that the library is efficient using a competitive dataset. The authors try multiple data augmentation approaches proposed by them and show that they improve the performance.

A MATLAB library specifically designed for data augmentation was not available before. The authors are the first to provide an efficient and parallel implementation of a large number of algorithms.

With the rapid development of short videos in China, the public has become accustomed to using short videos to express their opinions. This paper aims to solve problems such as how to represent the features of different modalities and achieve effective cross-modal feature fusion when analyzing the multi-modal sentiment of Chinese short videos (CSVs).

This paper aims to propose a sentiment analysis model MSCNN-CPL-CAFF using multi-scale convolutional neural network and cross attention fusion mechanism to analyze the CSVs. The audio-visual and textual data of CSVs themed on “COVID-19, catering industry” are collected from CSV platform Douyin first, and then a comparative analysis is conducted with advanced baseline models.

The sample number of the weak negative and neutral sentiment is the largest, and the sample number of the positive and weak positive sentiment is relatively small, accounting for only about 11% of the total samples. The MSCNN-CPL-CAFF model has achieved the Acc-2, Acc-3 and F1 score of 85.01%, 74.16 and 84.84%, respectively, which outperforms the highest value of baseline methods in accuracy and achieves competitive computation speed.

This research offers some implications regarding the impact of COVID-19 on catering industry in China by focusing on multi-modal sentiment of CSVs. The methodology can be utilized to analyze the opinions of the general public on social media platform and to categorize them accordingly.

This paper presents a novel deep-learning multimodal sentiment analysis model, which provides a new perspective for public opinion research on the short video platform.

Drawing on the challenge–hindrance stressor framework and the “too-much-of-a-good-thing” principle, this study examined the curvilinear effects of two emic social challenge stressors (guanxi beliefs and participative decision-making (PDM)) and the moderating effect of an etic social hindrance stressor (perceived organizational politics) on Hong Kong and United States nurses’ job satisfaction.

A quantitative survey method was implemented, with the data provided by 355 Hong Kong nurses and 116 United States nurses. Structural equation modeling was used to examine the degree of measurement equivalence across Hong Kong and US nurses. The proposed model and the research questions were tested using nonlinear structural equation modeling analyses.

The results show that while guanxi beliefs only showed an inverted U-shaped relation on Hong Kong nurses’ job satisfaction, PDM had an inverted U-shaped relation with both Hong Kong and United States nurses’ job satisfaction. The authors also found that Hong Kong nurses experienced the highest job satisfaction when their guanxi beliefs and perceived organization politics were both high.

The results add to the comprehension of the nuances of the often-held assumption of linearity in organizational sciences and support the speculation of social stressors-outcomes linkages.

Managers need to recognize that while the nurturing and development of effective relationships with employees via social interaction are important, managers also need to be aware that too much guanxi and PDM may lead employees to feel overwhelmed with expectations of reciprocity and reconciliation to such an extent that they suffer adverse outcomes and become dissatisfied with their jobs.

First, the authors found that influences of guanxi beliefs and PDM are not purely linear and that previous research may have neglected the curvilinear nature of their influences on job satisfaction. Second, the authors echo researchers’ call to consider an organization’s political context to fully understand employees’ attitudes and reactions toward social interactions at work. Third, the authors examine boundary conditions of curvilinear relationships to understand the delicate dynamics.

The aim of this study is to analyze the differences between non-standard forms of employment (FoE) (i.e. dependent self-employment/business-to-business/B2B contract and contract of mandate) in terms of investing in the development of future competencies by employees and employers. This study also examined additional factors which influence these investments.

To collect data, the computer-assisted telephone interview technique was used. 200 employees from different companies located in Poland participated in this study, wherein each of the above-mentioned FoEs (i.e. dependent self-employment and contract of mandate) was represented by 100 people. The Chi-Square test and multivariate logistic regression analysis were used in the statistical analyses.

In the case of only 2 out of 14 competencies, there were statistically significant differences between the two groups of respondents: the employers financed training courses for B2B employees more frequently than for mandate contract workers. Moreover, in only one case there was a statistically significant difference: the self-employed financed training courses themselves more often than mandate contract workers. This study revealed an important impact of other variables such as respondents’ age, education level, parental status and industry on the training activities undertaken by employers and employees.

Although the issue of developing future competencies is important, there is little research examining this problem in the context of people who work in non-standard FoE. Moreover, previous research primarily focused on identifying differences between people working under employment contracts and the self-employed. This article fills these research gaps as well as shows that more factors should be considered in the research models to get a deeper insight into the problem of non-standard FoEs.