Despite the proliferation of corporate social responsibility (CSR) studies, it is accruing academic interest since there still remains a lot to be further explored. The purpose of the study is to examine whether/how CSR perception affect employee/intern thriving at work and its mediator through perceived external prestige in the hospitality industry. Data from 501 hospitality industry employees and interns in China were collected using a quantitative survey consisting of 35 questions. Statistical findings showed that CSR perception and thriving at work were positively related. Additionally, perceived external prestige partially mediated the connection between CSR perception and thriving at work. Furthermore, the study found that hotel interns generally exhibited lower levels of CSR perception and thriving at work compared with frontline or managerial staff. The study underscores the importance of collaborative efforts between hotel practitioners and university educators to enhance CSR perception and promote thriving among hotel interns. By prioritizing the improvement of CSR perception and thriving at work, the hotel sector can potentially mitigate workforce shortages and reduce high turnover rates.

A method for studying the resilience of energy and socio-ecological systems is considered; it integrates approaches developed at the International Institute of Applied Systems Analysis and the Melentyev Institute of Energy Systems (MESI) of the Siberian Branch of the Russian Academy of Sciences. The article discusses in detail the methods of using intelligent information technologies, in particular semantic technologies and knowledge engineering (cognitive probabilistic modeling), which the authors propose to use in assessing the risks of natural and man-made threats to the resilience of the energy sector and social and ecological systems. More attention is paid to the study and adaptation of the integral indicator of quality of life, which makes it possible to combine these interdisciplinary studies.

Drawing on the theoretical framework of Job Demands-Resources (JD-R), our study aims to consider how workplace antecedents of perceived quiet firing (also known as involuntary attrition), perceived co-worker support, and experience (tenure at an organization) may influence quiet quitting behavior. Data were collected via questionnaire responses from 209 workers in India who had graduated from university within the last 7 years. The findings show that (1) perceived quiet firing is positively associated with quiet quitting; (2) perceived co-worker support is negatively associated with quiet quitting; (3) experience moderates the positive association between perceived quiet firing and quiet quitting in such a way that the relationship is weaker as one’s tenure at an organization increases; and (4) experience does not moderate the negative association between perceived co-worker support and quiet quitting. The study’s contributions come from understanding how the interplay of demands (i.e., perceived quiet firing) and resources (i.e., perceived co-worker support and experience) determine quiet quitting behaviors in the workplace. Additionally, the temporal dimension of experience facilitates the acquisition of organizational-specific knowledge and resources. In contrast, perceptions of co-worker support appear specific to a given point in time. Policy implications come from providing guidance to organizations on how to reduce quiet quitting behaviors by ensuring that the resources available to employees exceed the demands placed on them.

Among contemporary computational techniques, Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) are favoured because of their capacity to tackle non-linear modelling and complex stochastic datasets. Nondeterministic models involve some computational intricacies when deciphering real-life problems but always yield better outcomes. For the first time, this study utilized the ANN and ANFIS models for modelling power generation/electric power output (EPO) from databases generated in a combined cycle power plant (CCPP). The study presents a comparative study between ANNs and ANFIS to estimate the power output generation of a combined cycle power plant in Turkey. The inputs of the ANN and ANFIS models are ambient temperature (AT), ambient pressure (AP), relative humidity (RH), and exhaust vacuum (V), correlated with electric power output. Several models were developed to achieve the best architecture as the number of hidden neurons varied for the ANNs, while the training process was conducted for the ANFIS model. A comparison of the developed hybrid models was completed using statistical criteria such as the coefficient of determination (R²), mean average error (MAE), and average absolute deviation (AAD). The R² of 0.945, MAE of 3.001%, and AAD of 3.722% for the ANN model were compared to those of R² of 0.9499, MAE of 2.843% and AAD of 2.842% for the ANFIS model. Even though both ANN and ANFIS are relevant in estimating and predicting power production, the ANFIS model exhibits higher superiority compared to the ANN model in accurately estimating the EPO of the CCPP located in Turkey and its environment.

COVID-19 has led to abrupt changes in work norms and practices. Despite receding pandemic restrictions, the popularity of remote or hybrid work has not subsided. As employees around the world continue to call for more flexibility and autonomy in the way they work, human resource leaders must continuously consider and evaluate decisions based on ever-changing sentiment, balancing the interests of employees and employers alike. In this perspective article, we review the current state of work in the Southeast Asian region, focusing on Malaysia, Singapore, Indonesia, the Philippines, and Thailand, and present preliminary results from a region-wide mental health assessment that was conducted in late 2022. We argue for the continuation of hybrid work in the region and elaborate on the mental health risks that come with remote working.