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The purpose of this paper is to evaluate the effect of main parameters and their interactions on the workers' Lifting Index in a steel rolling mill.

NIOSH (National Institute for Occupational Safety and Health) lifting equation has been used to evaluate the risk of lifting tasks with respect to low back injury under varying load (10, 15, 20 kg), frequency (2, 3, 4 lifts/min), and twisting angle (0, 30, 45 degree).

The level of importance of the parameters on lifting index at origin and destination has been determined using analysis of variance (ANOVA).The analysis draws on lifting parameters and uses both main effects and interactions to describe the variation in Lifting Index and to identify the social influence associated with back injury. The interactions between object weight and twisting angle and object weight and lifting frequency turn out to be significant (p<0.05), whereas the interaction between twisting angle and lifting frequency is less significant (p=0.061).

The study includes a specific location (steel rolling mills located in Jammu region of India) only.

The findings suggest that focus should be made on all lifting parameters, rather than sole emphasis on the load to be lifted.

The paper supports the view that load, twisting angle and lifting frequency greatly influence the physical stressfulness of the task. It is suggested that the workplace should be designed for negligible twisting and moderate lifting frequency, so as to have minimum Lifting Index.

Public service motivation (PSM), among actual and prospective employees in the public sector, lead to positive work outcomes. However, there is limited integration of PSM with existing motivational theories, especially self-determination theory (SDT). The impact of destructive leadership styles on PSM is also understudied. The study aims to fill the gap.

The study used a cross-sectional survey of 693 public sector officials employed in Indian Railways to test the hypotheses. PLS-SEM was used for the analysis. The study found that fulfillment of basic psychological needs (BPN) at work leads to improved PSM while BPN partially mediates the positive/negative effect of constructive/destructive leadership styles like servant/autocratic leadership on employees' PSM.

Autocratic/servant leadership was negatively/positively and significantly related to PSM and to all three BPN needs. Servant leadership was found to be positively and significantly related to all three BPN needs, while the BPN needs partially mediated the relationship between servant leadership and PSM.

The study identifies servant leadership as a driver of PSM and suggests that managers employed in public sector establishments should be trained to be less autocratic and more attuned to subordinates' needs. The baneful impact of autocratic leadership on employee PSM is highlighted. The cross-sectional nature of study makes it susceptible to common-method bias. The sample was limited to a single country. Future longitudinal and experimental studies based on samples drawn from multiple countries can yield more robust results.

The study advances the integration of PSM with SDT by identifying PSM with introjected motivation. It is also the first study to link destructive leadership styles with a reduction in employees' PSM. It identifies a counterintuitive, negative relationship between competence need satisfaction and PSM in the Indian public sector.

The mechanisms through which superiors’ leadership styles and subordinates’ internal cognitions affect subordinates’ actual behaviour and attitudes are relatively unexplored in most contexts. This paper aims to bridge the gap by exploring the mediating effect of teachers’ cognitions (psychological empowerment) in the relationship between principals’ leadership style (empowering leadership) and teachers’ behaviour (job crafting) and attitudes (work alienation and organizational commitment).

Partial least squares structural equation modelling (PLS-SEM) was used on data obtained from 624 teachers of primary classes in Indian private schools.

Psychological empowerment partially mediates the relationship between empowering leadership and job crafting and job crafting partially mediates the relationship between empowering leadership and work alienation and affective commitment. Work alienation partially mediates the relationship between job crafting and affective commitment. Empowering leadership has a direct effect on job crafting.

Due to the lack of longitudinal data, causality cannot be established. Also, there are concerns about the factor structure of scales.

Principals demonstrating empowering leadership can help teachers become more proactive and feel more empowered, less alienated and more committed. More proactive teachers and less alienated teachers are more likely to engage in self-initiated professional development and collaboration, thereby improving the teaching-learning process. Though this study was done in the school context, it is believed that the findings can plausibly apply to managers/leaders who work with complex, ambiguous work and knowledge workers.

First, the study extends the research on job crafting by studying the relationship between leadership style (empowering leadership) and job crafting. Second, the identification of the mechanisms through which leaders (principals) can help subordinates (teachers) find meaning in work (reduction in alienation) and develop commitment is an original contribution.

The purpose of this paper is to deal with the performance modeling and optimization for the stock preparation unit of a paper plant using genetic algorithm. It provides the optimum unit availability level for different combinations of failure and repair rates of the subsystems of the stock preparation unit of the paper plant concerned.

In this paper, efforts have been made to develop performance models based on real situations for the stock preparation unit. The performance in terms of availability has been evaluated on the basis of Markov birth‐death process. After that, the performance optimization using genetic algorithm is done, which gives the optimum unit availability levels for different combinations of failure and repair rates of the subsystems of stock preparation units for enhancing the overall performance of the paper plant.

The effect of genetic algorithm parameters, namely number of generations, population size and crossover probability on the unit performance i.e. availability has been analyzed and discussed with the concerned paper plant management. It is found that these results are highly beneficial to the maintenance engineers for the purpose of effective maintenance planning to enhance the overall performance (availability) of the stock preparation unit of the paper plant.

Most of the researchers have confined their work to the development and analysis of theoretical models which has little practical significance. To fulfill this deficiency, efforts have been made in the present work to develop a model based on real situations for the stock preparation unit.

Artificial intelligence (AI)-based human capital management (HCM) software solutions represent a potentially effective way to leverage and streamline a bank’s human resources. However, despite the attractiveness of AI-based HCM solutions to improve banks’ effectiveness, to the best of the authors’ knowledge, there are no current studies that identify critical success factors (CSFs) for adopting AI-based HCM in the banking sector. This study aims to fill this gap by investigating CSFs for adopting AI-based HCM software solutions in the banking sector.

Full consistency method methodology and technology–organization–environment, economic and human framework are used for categorizing and ranking CSFs.

The study identifies the technological and environmental dimensions as the most and least important dimensions for AI-based HCM adoption in banks. Among specific CSFs, compatible technology facilities, sufficient privacy and security and relative advantages of technology over competing technologies were identified as the most important. Implementation of AI-based HCM solutions requires significant outlays of resources, both human and financial, for banks.

The study provides bank administrators a set of objective parameters and criterion to evaluate the feasibility of adopting a particular AI-based HCM solution in banks.