Search results

Lean management is a contemporary management system that firms adopt to boost their performance. Lean management can be integrated with human resources management to develop a new concept of lean human resources management (LHRM). This entails the implementation of several practices. However, the LHRM–performance paradigm remains underexplored in the literature. Hence, this study aims to examine the interrelationships between LHRM practices and the impacts of those practices on firm performance (FP).

Using two equal-sized samples (n = 250 each) of manufacturing firms in Jordan and Germany, this study proposes two structural equation models (i.e. a Jordanian and a German models) depicting the interrelationships between LHRM practices and the impacts of those practices on FP. After testing these models, a comparison between them is conducted, producing findings with theoretical and practical implications.

The main findings of this study indicate that the average implementation of LHRM practices among German manufacturing firms is at a higher level than the average implementation among Jordanian firms. The findings also support the proposed interrelationships between LHRM practices and the impact of those practices on FP for both the Jordanian and German models.

To the best of the authors’ knowledge, this study is among the first to highlight the proposed relationships, both in general and in the context of comparing developed and developing countries. Its findings have important implications that can enable manufacturing managers to benefit from the implementation of LHRM practices to enhance FP in different contexts. These findings provide valuable insights for human resource managers and decision-makers and open several avenues for future research.

The supply chain (SC) encompasses all actions related to meeting customer requests and transferring materials upstream to meet those demands. Organisations must operate towards increasing SC efficiency and effectiveness to meet SC objectives. Although most businesses expected the coronavirus disease 2019 (COVID-19) pandemic to severely negatively impact their SCs, they did not know how to model disruptions or their effects on performance in the event of a pandemic, leading to delayed responses, an incomplete understanding of the pandemic's effects and late deployment of recovery measures. Therefore, this study aims to consider the impact of implementing Bayesian network (BN) modelling to measure SC performance in the airline catering context.

This study presents a method for modelling and quantifying SC performance assessment for airline catering. In the COVID-19 context, the researchers proposed a BN model to measure SC performance and risk events and quantify the consequences of pandemic disruptions.

The study simulates and measures the impact of different triggers on SC performance and business continuity using forward and backward propagation analysis, among other BN features, enabling us to combine various SC perspectives and explicitly account for pandemic scenarios.

This study's findings offer a fresh theoretical perspective on the use of BNs in pandemic SC disruption modelling. The findings can be used as a decision-making tool to predict and better understand how pandemics affect SC performance.