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The purpose of the study is to investigate the relationship between workplace incivility (WIN) and ineffectual employee silence (IES) through rationalized knowledge-hiding (RKH) and regulation of emotion, drawing on the conservation of resources (COR) and social exchange theory (SET).

The study employed a cross-sectional design and used the partial least squares (PLS)-structural equational modeling (SEM) algorithm to test the reliability, validity of the measurement and hypotheses using a sample of 252 information technology (IT) professionals.

The results demonstrate that experienced WIN and RKH behavior significantly exacerbate IES. On the other side, the regulation of emotion decreases the negative influence of WIN and aids in the reduction of IES.

The study suggests that organizations should take appropriate measures to alleviate WIN, which might prevent concealing information/knowledge, IES and encourage employees to practice regulation of emotion.

The study significantly contributes to the relationship between uncivil behavior and ES and expands the knowledge on the mediating roles of RKH and regulation of emotion.

The study aims to focus on data envelopment analysis for assessing the microfinance institutions (MFIs) efficiency over the footings of its undesirable output, i.e. non-performing loans (NPLs). The attention is not only to evaluate the efficiency but also to identify the variable wise inefficiencies incorporating the quality of the portfolio.

The paper assessed MFI efficiency using three different methods of treatment of undesirable output to portray the significant difference. It also has used an advanced methodological model, i.e. weighted Russell directional distance model (WRDDM), under the non-radial assumption that allowed us to find the variable-wise inefficiency contribution. The study also investigated the efficiency differences concerning ownership, including all sizes of MFIs.

The study findings evidence the fall in efficiency score as NPL integrated, and it is found to be statistically significant. In the context of inefficiency assessment, among all input and output variables, total employees and operating expenses, portfolio quality inefficiencies are the leading causes of MFI inefficiencies. Undesirable output inefficiency accounts for almost one-third part of the total inefficiencies and remaining due to input inefficiencies. It is significant to draw attention that there is no improvement in undesirable output inefficiency. By contrast, input inefficiencies retained gains for two years and gradually showed a decreasing trend throughout 2015–2017.

The authors have used balanced panel data of 72 Indian MFIs for five years' period from 2013–2017 whose complete data were available in the Microfinance Information Exchange.

The paper has focused on identifying the inefficiencies that are needed to be focused on to attain efficiency. It could provide vital information to the managers, policymakers in identifying the causes of inefficiencies, which is crucial to improve for long-term sustainability. It will be a roadmap for benchmarking, strategy building and policy-making processes.

The findings of the study help in finding the benchmarking information for the inefficient decision-making units to identify the target units that need particular attention to focus. These practices could give a positive outcome, not only for institutions but also for the MFI clients.

The study provides an insight in to variable-wise inefficiency measurement using advanced model WRDDM in Indian context MFIs.

This study aims to understand the impact of negative supervisor gossip on job performance among South Indian hotel employees. The focus is not just on the direct influence, but also on the mediating role of feedback-seeking behaviour (FSB) and the moderating effects of the agreeableness trait.

Through purposive sampling, data was garnered from South Indian hotel employees. Comprehensive analyses were performed using partial least squares structural equation modelling.

The analysis shows that FSB plays a mediating role in the positive relationship between negative supervisor gossip and job performance. In addition, the influence of gossip on FSB and subsequent job performance was more pronounced for employees with high agreeableness.

This research underscores the complex interplay between negative supervisor gossip and job performance, revealing that such gossip can catalyze FSB process in employees. It suggests that under certain conditions, negative gossip can be transformed into a constructive force that enhances job performance, challenging traditional perceptions of gossip in the workplace.

The findings underscore the importance of understanding the effects of workplace dynamics, like supervisor gossip, on employee behaviour and performance. Recognizing the influence of individual personality traits, such as agreeableness, can guide management strategies for fostering a productive work environment.

This research sheds light on the intricate interplay between negative supervisor gossip, FSB and agreeableness, offering a novel perspective on their combined impact on job performance. It not only enriches the existing literature on workplace communication but also broadens the understanding of the role of personality traits in shaping employee responses and outcomes.

Green HRM practices in the hospitality sector are now receiving growing interest. However, the extent to which these practices contribute towards employee non-green workplace outcomes remains largely unknown. This study explores the relationships among green HRM practices, happiness at work, employee resilience, and feedback-seeking behaviour.

The study employs two-wave data from a sample of 306 five-star hotel employees in India. Using partial least square-structural equation modelling, the relationships are tested.

The study’s results demonstrate that green HRM practices positively impact happiness at work, employee resilience, and feedback-seeking behaviour. Additionally, the relationship between green HRM practices and feedback-seeking behaviour and employee resilience is mediated by happiness at work.

Drawing on the Job Demands-Resources Theory, Social Exchange Theory, and Broaden and Build theory, this paper proposes that green HRM practices can contribute to happiness at work, employee resilience, and feedback-seeking behaviour.

To establish a positive connection between green HRM practices and employee outcomes, organizations must recognize the vital role played by happiness at work as a mediator. This means that organizations must implement green HRM practices and ensure their positive impact on employee happiness at work.

The originality of this research lies in its holistic approach to green HRM outcomes, suggesting that the benefits of these practices extend beyond environmental impacts to influence the psychological and behavioural dimensions of employees.

Despite the prevalence of uncivil behaviors across families and past studies attributing work stressors to suicidal ideation (SI), there is no conclusive evidence of the interactive effect of family incivility (FI) aggravating SI. Hence, the purpose of this study is to explore the association between FI and SI through emotional exhaustion (EE) in the workplace and regulation of emotion.

A time lag (T1 and T2) study is applied for primary data collection using a survey questionnaire. The partial least squares–structural equational modeling algorithm tests reliability, validity and hypotheses.

Experiencing FI exacerbates SI, while the regulation of emotion and EE mediate the association between FI and SI.

Professionals are advised to adopt regulation of emotion that fosters desirable behavior and shields targets from FI and EE, minimizing the intensity of SI.

This study significantly adds to how FI and EE aggravate SI and contribute to the body of knowledge on the regulation of emotion in stress and coping mechanisms.

The purpose of this study is to compare the classification learning ability of our algorithm based on boosted support vector machine (B-SVM), against other classification techniques in predicting the credit ratings of banks. The key feature of this study is the usage of an imbalanced dataset (in the response variable/rating) with a smaller number of observations (number of banks).

In general, datasets in banking sector are small and imbalanced too. In this study, 23 Scheduled Commercial Banks (SCBs) have been chosen (in India), and their corresponding corporate ratings have been collated from the Indian subsidiary of reputed global rating agency. The top management of the rating agency provided 12 input (quantitative) variables that are considered essential for rating a bank within India. In order to overcome the challenge of dataset being imbalanced and having small number of observations, this study uses an algorithm, namely “Modified Boosted Support Vector Machines” (MBSVMs) proposed by Punniyamoorthy Murugesan and Sundar Rengasamy. This study also compares the classification ability of the aforementioned algorithm against other classification techniques such as multi-class SVM, back propagation neural networks, multi-class linear discriminant analysis (LDA) and k-nearest neighbors (k-NN) classification, on the basis of geometric mean (GM).

The performances of each algorithm have been compared based on one metric—the geometric mean, also known as GMean (GM). This metric typically indicates the class-wise sensitivity by using the values of products. The findings of the study prove that the proposed MBSVM technique outperforms the other techniques.

This study provides an algorithm to predict ratings of banks where the dataset is small and imbalanced. One of the limitations of this research study is that subjective factors have not been included in our model; the sole focus is on the results generated by the models (driven by quantitative parameters). In future, studies may be conducted which may include subjective parameters (proxied by relevant and quantifiable variables).

Various stakeholders such as investors, regulators and central banks can predict the credit ratings of banks by themselves, by inputting appropriate data to the model.

In the process of rating banks, the usage of an imbalanced dataset can lessen the performance of the soft-computing techniques. In order to overcome this, the authors have come up with a novel classification approach based on “MBSVMs”, which can be used as a yardstick for such imbalanced datasets. For this purpose, through primary research, 12 features have been identified that are considered essential by the credit rating agencies.