Search results

In the time of coronavirus disease 2019 (COVID-19) epidemic, the effective leadership is what all the organisations are now requiring. Retaining and satisfying the employees in these tough times has become very difficult. In view of this, the present study attempts to investigate three objectives: first, to find out the direct effect of effective leadership on job satisfaction and organisational citizenship behaviour (OCB); second, to examine the relationship between job satisfaction and OCB and, third, to investigate whether effective leadership positively moderate and mediate the link between job satisfaction and OCB among managerial employees of private manufacturing firms of Chhattisgarh state.

Correlational research design was applied in the present study. Cluster sampling was used to finalise sample region, and simple random technique was applied to collect primary responses. Employees working at the managerial positions were chosen as participants in the present study. About 530 questionnaires were sent to the participants in which 400 responses were found useable for analysis.

The results explained a significant relation of effective leadership with job satisfaction and OCB. In addition, job satisfaction also revealed a positive correlation with OCB. The moderating and mediating effect of effective leadership in the link between job satisfaction and OCB was also noted in significant association.

Private sector enterprises were economically harmed by COVID-19's sudden arrival. This forced corporations to minimise expenses by cutting staff, production and operations. Employees felt alone, needed assistance and guidance. This research demonstrates how effective leadership may reconnect workers and boost organisational performance.

Without competent and talented employees, no organisation can grow and sustain for a long time. It becomes essential for every organisation to retain and satisfy the employees to achieve their predetermined organisational goals. The present study examines the mediating effect of workplace spirituality dimensions (i.e. meaningful work, compassion, transcendence, mindfulness and sense of community) in the link between job satisfaction and organisational citizenship behaviour (OCB) among managerial employees of selected manufacturing firms of Chhattisgarh state.

Correlational research design was incorporated. Employees working at managerial positions at different private manufacturing firms of Chhattisgarh state were chosen as a sample for the present study. Regression analysis and confirmatory factor analysis tools were used to analyse the primary data collected from 400 respondents.

The results revealed that all the dimensions of workplace spirituality, i.e. meaningful work, compassion, transcendence, mindfulness and sense of community, were found statistically significant and partially mediated between job satisfaction and OCB among managerial employees of Chhattisgarh. The authors discussed the results thoroughly and provided avenues for the future research.

The findings of the present research study will assist all private organisations in rethinking their employee retention and satisfaction strategies, since the presence of workplace spirituality in the organisation has a significant and beneficial impact on its environment. The current research will assist organisations in creating circumstances for OCB for employee via the introduction of workplace spirituality.

Creating spirituality in the current situation, where Covid-19 has suddenly affected all organisations around the world, would be extremely beneficial in terms of employee retention and satisfaction, which would eventually aid in the development of an environment conducive to citizenship behaviour at the workplace. However, the role of workplace spirituality as a mediator in the link between job satisfaction and OCB is innovative and has received little attention in the research community.

This study aims to provide a measurement model, and the underlying constructs and items, for Logistics 4.0 in manufacturing companies. Industry 4.0 technology for logistics processes has been termed Logistics 4.0. Logistics 4.0 and its elements have seen varied conceptualizations in the literature. The literature has mainly focused on conceptual and theoretical studies, which supports the notion that Logistics 4.0 is a relatively young area of research. Refinement of constructs and building consensus perspectives and definitions is necessary for practical and theoretical advances in this area.

Based on a detailed literature review and practitioner focus group interviews, items of Logistics 4.0 for manufacturing enterprises were further validated by using a large-scale survey with practicing experts from organizations located in Central Europe, the Northeastern United States of America and Northern Thailand. Exploratory and confirmatory factor analyses were used to define a measurement model for Logistics 4.0.

Based on 239 responses the exploratory and confirmatory factor analyses resulted in nine items and three factors for the final Logistics 4.0 measurement model. It combines “the leveraging of increased organizational capabilities” (factor 1) with “the rise of interconnection and material flow transparency” (factor 2) and “the setting up of autonomization in logistics processes” (factor 3).

Practitioners can use the proposed measurement model to assess their current level of maturity regarding the implementation of Logistics 4.0 practices. They can map the current state and derive appropriate implementation plans as well as benchmark against best practices across or between industries based on these metrics.

Logistics 4.0 is a relatively young research area, which necessitates greater development through empirical validation. To the best of the authors knowledge, an empirically validated multidimensional construct to measure Logistics 4.0 in manufacturing companies does not exist.

This research explores the application of machine learning to optimize pricing strategies in the aftermarket sector, particularly focusing on parts with no assigned values and the detection of outliers. The study emphasizes the need to incorporate technical features to improve pricing accuracy and decision-making.

The methodology involves data collection from web scraping and backend sources, followed by data preprocessing, feature engineering and model selection to capture the technical attributes of parts. A Random Forest Regressor model is chosen and trained to predict prices, achieving a 76.14% accuracy rate.

The model demonstrates accurate price prediction for parts with no assigned values while remaining within an acceptable price range. Additionally, outliers representing extreme pricing scenarios are successfully identified and predicted within the acceptable range.

This research bridges the gap between industry practice and academic research by demonstrating the effectiveness of machine learning for aftermarket pricing optimization. It offers an approach to address the challenges of pricing parts without assigned values and identifying outliers, potentially leading to increased revenue, sharper pricing tactics and a competitive advantage for aftermarket companies.