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Emotional labour (EL) is a complex phenomenon that has received increasing attention in recent years due to its impact on employee’s well-being and job satisfaction. For a comprehensive understanding of the evolving field of EL, it is important to extract different research trends, new developments and research directions in this domain. The study aims to reveal 13 prominent research topics based on the topic modelling analysis.

Using latent Dirichlet allocation (LDA) method, topic modelling is done on 1,462 journal research papers published between 1999 and 2023, extracted from the Scopus database using the keyword “EL”.

The analysis identifies several emerging trends in EL research, including emotional regulation training and job redesign. Similarly, the topics like EL strategies, cultural differences and EL, EL in hospitality, organizational support and EL, EL and gender and psychological well-being of nursing workers are popular research topics in this domain.

The findings provide valuable insights into the current state of EL research and can provide a direction for future research as well as assist organizations to design practices aimed at improving working conditions for employees in various industries.

Topic modelling on emotional labor is done. The paper identifies specific topics or clusters related to emotional labor, quantifies these topics using topic modeling, adds empirical rigor, and allows for comparisons across different contexts.

Sustainable human resource management (SuHRM), which aims to achieve positive environmental, social and economic outcomes at the same time, has gained prominence across industries. However, the challenges of implementing SuHRM across industries are largely under-studied. The purpose of this study is to identify the grey areas in the field of SuHRM by using an unsupervised learning algorithm on the abstracts of 607 papers published in prominent journals from 1995 to 2023. Most of the articles have been published post-2018.

The analysis of the data (abstracts of the selected articles) has been done using topic modelling via latent Dirichlet algorithm (LDA).

The output from topic modelling-LDA reveals nine primary focus areas of SuHRM research – the link between SuHRM and employee well-being; job satisfaction; challenges of implementing SuHRM; exploring new horizons in SuHRM; reaping the benefits of using SuHRM as a strategic tool; green HRM practices; link between SuHRM and organisational performance; link between corporate social responsible and HRM.

The insights gained from this study along with the discussions on each topic will be extremely beneficial for researchers, academicians, journal editors and practitioners to channelise their research focus. No other study has used a smart algorithm to identify the research clusters of SuHRM.

By utilizing topic modeling techniques, the study offers a novel approach to analyzing and understanding trends and patterns in HRM research related to sustainability. The significance of the paper would be in its potential to shed light on emerging areas of interest and provide valuable implications for future research and practice in Sustainable HRM.

The purpose of this paper is to attempt to highlight the importance of Gandhi's philosophy for creating socially responsible systems for holistic development.

It is a qualitative analysis of Gandhi's writings and interpretation of his ideas for the creation of socially responsible systems which will result in holistic development.

The analysis of Gandhian philosophy brings about a realization that focusing on unidimensional development is detrimental to society. There is a need to integrate various elements of human civilization for the overall happiness of mankind. Gandhi identified the inter‐linkages between various aspects of human life and insisted on creating harmony between them. The authors find that he was against compartmentalization of self and society. Hence, the paper talks about holistic management and the role that organization, social systems and individuals can play in its achievement. His philosophy provides a backdrop against which socially responsible systems can be created.

Though Gandhi's contribution in politics has been widely acknowledged, there is very little work on integration of his philosophy to all walks of life. The paper is original in the sense that instead of dividing his philosophy into narrow segments, it tries to establish the inter‐linkage between the various dimensions of human life.

The Covid-19 prediction process is more indispensable to handle the spread and death occurred rate because of Covid-19. However early and precise prediction of Covid-19 is more difficult because of different sizes and resolutions of input image. Thus these challenges and problems experienced by traditional Covid-19 detection methods are considered as major motivation to develop JHBO-based DNFN.

The major contribution of this research is to design an effectual Covid-19 detection model using devised JHBO-based DNFN. Here, the audio signal is considered as input for detecting Covid-19. The Gaussian filter is applied to input signal for removing the noises and then feature extraction is performed. The substantial features, like spectral roll-off, spectral bandwidth, Mel-frequency cepstral coefficients (MFCC), spectral flatness, zero crossing rate, spectral centroid, mean square energy and spectral contract are extracted for further processing. Finally, DNFN is applied for detecting Covid-19 and the deep leaning model is trained by designed JHBO algorithm. Accordingly, the developed JHBO method is newly designed by incorporating Honey Badger optimization Algorithm (HBA) and Jaya algorithm.

The performance of proposed hybrid optimization-based deep learning algorithm is estimated by means of two performance metrics, namely testing accuracy, sensitivity and specificity of 0.9176, 0.9218 and 0.9219.

The JHBO-based DNFN approach is developed for Covid-19 detection. The developed approach can be extended by including other hybrid optimization algorithms as well as other features can be extracted for further improving the detection performance.

The proposed Covid-19 detection method is useful in various applications, like medical and so on.

Developed JHBO-enabled DNFN for Covid-19 detection: An effective Covid-19 detection technique is introduced based on hybrid optimization–driven deep learning model. The DNFN is used for detecting Covid-19, which classifies the feature vector as Covid-19 or non-Covid-19. Moreover, the DNFN is trained by devised JHBO approach, which is introduced by combining HBA and Jaya algorithm.

The present study examines the mediating role of teleworking and the moderating role of resilience in explaining the relationship between perceived stress and psychological well-being of working mothers in India. Conservation of resource theory (COR) is taken to support the present study.

The data of 326 respondents has been collected from working mothers in various sectors of Delhi NCR region of India. Confirmatory factor analysis was used for construct validity, and SPSS Macro Process (Hayes) was used for testing the hypotheses.

The results of the study found an inverse association between perceived stress and psychological well-being. Teleworking acted as a partial mediator and resilience proved to be a significant moderator for teleworking-well-being relationship.

The study is based at Delhi NCR of India, and future studies may be based on a diverse population within the country to generalize the findings in different cultural and industrial contexts. The present work is based only on the psychological well-being of the working mothers, it can be extended to study the organizational stress for both the genders and other demographic variables.

The study extends the research on perceived stress and teleworking by empirically testing the association between perceived stress and psychological well-being in the presence of teleworking as a mediating variable. The findings suggest some practical implications for HR managers and OD Practitioners. The organizations must develop a plan to support working mothers by providing flexible working hours and arranging online stress management programs for them.

Although teleworking is studied previously, there is a scarcity of research examining the impact of teleworking on psychological well-being of working mothers in Asian context. It would help in understanding the process that how teleworking has been stressful for working mothers and also deliberate the role of resilience in the relationship between teleworking and psychological well-being due to perceived stress, as it seems a ray of hope in new normal work situations.

This study aims to determine the impact of personality traits and social experience on Indonesian youngsters’ intention to purchase impulsively from social commerce (SC) platforms. Furthermore, latent state-trait, personal traits and social impact were used to determine the factors influencing this impulsive behavior.

This is a theoretical research model with data obtained from 658 Indonesian youngsters between the ages of 18 and 24. The data were prepared using exploratory and confirmatory factors with the structural equation modeling (SEM) approach used to analyze the direct, indirect and moderating effects.

The result showed that hedonic motivation is the most influential personality trait construct that directly determines youngsters’ purchasing intention, followed by perceived behavior control. Furthermore, their constructs from social experience, namely, subjective norms and peer communication, significantly have an indirect effect on the dependent variable through mediator hedonic motivation and perceived behavior control.

Preliminary studies neglected the social interaction process used by youngsters’ in the impulsive purchase of the SC context. Therefore, this research postulated the associated factors by involving their interplay between personal traits and social experience.