Purpose: Extant research has shown that workplace ostracism (WO) elicits counterproductive work behaviors, such as employee silence (ES), culminating in reduced job performance. However, lesser is known about the factors that buffer against this underlying linkage. With an emphasis on conservation of resource (COR) theory and social identity theory, this study investigates the hitherto unexplored moderating roles of moral identification (MI) and organizational identification (OI) in the relationship between WO and ES.Design/methodology/approach: The study employed a time-lagged design to collect multi-source data from 250 employees working in the service sector organizations in Pakistan. Data are analyzed in SMARTPLS (v 3.3.3) to assess the measurement model and the structural model.Findings: Results reveal that WO is positively correlated with ES and negatively correlated with job performance. At the same time, ES mediates the negative relationship between WO and job performance. In addition, MI and OI buffer against the positive connection between WO and ES. The positive association between WO and ES is less pronounced at high levels of MI and OI and vice versa.Practical implications: The findings indicate that there is potential value in developing MI and OI, for which several interventions are discussed.Originality/value: This study is one of the few efforts to outstretch the boundary conditions of ES. Moreover, this is the first study to investigate the role of identity-based perspective in the relationship between WO and ES.

This study finds that eco-preneurship and green technology initiatives have the potential to improve the environmental quality of selected East Asian economies in the short term, but in the long term, the impact is negative, and there is a need for a better understanding of their effects on greenhouse gas emissions. The pressing need to address the negative impact of human activity on the environment has given rise these initiatives worldwide. However, there is a lack of research on the effectiveness of these initiatives in East Asian economies. In this study, we examine the short-term and long-term effects of eco-preneurship and green technology on greenhouse gas emissions in China and Japan. The results show that in the short term, both linear and non-linear estimations of eco-preneurship have a significant impact on reducing greenhouse gas emissions in China and Japan. Similarly, short-term linear estimations are significant in both China and Japan, but non-linear estimates are only significant in Japan. The same is true for positive shocks related to green technology in China and Japan, although the estimated coefficient for negative shocks is negative only in Japan. Taken together, these results indicate that eco-preneurship and green technology can improve the environmental quality of these selected economies in East Asia to some extent.

Education is the cultivation of people to promote and guarantee the development of society. Education reforms can play a vital role in the development of a country. However, it is crucial to continually monitor the educational model’s performance by forecasting the outcome’s progress. Machine learning‐based models are currently a hot topic in improving the forecasting research area. Forecasting models can help to analyse the impact of future outcomes by showing yearly trends. For this study, we developed a hybrid, forecasting time‐series model by long short‐term memory (LSTM) network and self‐attention mechanism (SAM) to monitor Morocco’s educational reform. We analysed six universities’ performance and provided a prediction model to evaluate the best‐performing university’s performance after implementing the latest reform, i.e., from 2015–2030. We forecasted the six universities’ research outcomes and tested our proposed methodology’s accuracy against other time‐series models. Results show that our model performs better for predicting research outcomes. The percentage increase in university performance after nine years is discussed to help predict the best‐performing university. Our proposed algorithm accuracy and performance are better than other algorithms like LSTM and RNN.