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The aim of this research is to determine the impact of a learning organisation (LO) on developing quality improvement practices (QIPs) and to propose knowledge-sharing (KS) as a moderator which is anticipated to support the beneficial effect of a LO on QIP. A further objective is to establish whether training provision raises the abilities of healthcare organisations (HCOs) to attain greater standards of QIP via a LO and KS.

A total of 240 responses, obtained from employees working at private HCOs in Jordan, comprised the valid dataset. Structural equation modeling (SEM) was utilised for data analysis. Multigroup analysis (MGA) was performed to compare the impact of workers who had or had not undergone training.

The findings indicated that QIP was significantly enhanced by LO. HCOs with the objective of converting a KS-moderated LO could attain improved QIP standards, but within the surveyed establishments, this effect was only appreciated at modest degrees. By performing MGA, no variations were identified in the impact of workers.

Managers should place more emphasis on training and learning within HCOs. However, respondents did indicate that their establishments had notable degrees of KS, suggesting a potential strength that could be used positively by the managerial hierarchy.

This study encompasses an original contribution to contemporary scholarship in the field of knowledge management and quality through its examination of the moderating effect of KS on LO and QIP.

This study aims to introduce a novel noise filter module designed for LiDAR simultaneous localization and mapping (SLAM) systems. The primary objective is to enhance pose estimation accuracy and improve the overall system performance in outdoor environments.

Distinct from traditional approaches, MCFilter emphasizes enhancing point cloud data quality at the pixel level. This framework hinges on two primary elements. First, the D-Tracker, a tracking algorithm, is grounded on multiresolution three-dimensional (3D) descriptors and adeptly maintains a balance between precision and efficiency. Second, the R-Filter introduces a pixel-level attribute named motion-correlation, which effectively identifies and removes dynamic points. Furthermore, designed as a modular component, MCFilter ensures seamless integration into existing LiDAR SLAM systems.

Based on rigorous testing with public data sets and real-world conditions, the MCFilter reported an increase in average accuracy of 12.39% and reduced processing time by 24.18%. These outcomes emphasize the method’s effectiveness in refining the performance of current LiDAR SLAM systems.

In this study, the authors present a novel 3D descriptor tracker designed for consistent feature point matching across successive frames. The authors also propose an innovative attribute to detect and eliminate noise points. Experimental results demonstrate that integrating this method into existing LiDAR SLAM systems yields state-of-the-art performance.

Becoming a learning organisation (LO) is an aspiration for every organisation as it offers internal capabilities, a competitive advantage and synergy gains to organisational members. Scholars across the globe have tried to examine the outcomes of LO at various organisational levels. Still, the existing literature is fragmented, and there is no systematic understanding of the multi-level outcomes of LO. Therefore, this study aims to synthesise, analyse and categorise the scientific literature into various levels of outcomes of LO to provide a conceptual framework for use by future researchers and academicians.

The authors have performed bibliometric analysis using 603 research articles published in Scopus, entailing 1,345 authors from 77 countries, followed by a thematic cluster analysis using bibliographic coupling to understand the current research trends and to recommend a set of broad themes to provide direction for future researchers in this domain.

The results are largely descriptive and aim to capture a panoramic view of what has been written on the topic so far. The bibliometric analysis was conducted using different means like citation analysis, cluster analysis, and keyword analysis to reveal the most significant publications, notable authors, keywords, current research trends, and future research questions. Further, the bibliographic coupling led to the categorization of the outcomes of LO into the following four clusters (including sub-clusters): (1) Individual level learning outcomes (2) team-level learning outcomes, (3) organisational-wide learning outcomes and (4) inter-organisational learning outcomes.

Managers and practitioners (change agents) expect academicians and researchers to suggest a set of actions that integrates their learning efforts with business performance across diverse sectors and industries. So, future researchers may try and explain the findings of seminal studies identified in the most cited documents, to design choices and trade-offs that may address major hindrances in implementing the construct in true spirit. The researchers may collaborate with practitioners to study the outcomes of LO with a scientific and empirical lens. Finally, the study invites change agents and organisation development (OD) practitioners to document the outcomes of their efforts to create and leverage the outcomes of LO.

Researchers across the world have tried to examine the outcomes of LO at various levels in organisational setting including, measuring capabilities and attitudes at individual level, team capabilities and innovation, and organisational performance and sustainability, but still there is no tested conceptual framework which encompasses the various outcome levels of LO in one frame.

The purpose of this paper is to develop a novel interval Multi-Objective Optimization by a Ratio Analysis plus the Full Multiplicative Form(MULTIMOORA) with combination weights to evaluate the employment quality of college graduates, where the criteria are expressed by interval numbers and the weights of criteria are completely unknown.

Firstly, considering the subjective uncertainty of the weights of the criteria, the interval best worst method (I-BWM) was present to determine the subjective weights of the criteria. Secondly, by the improved interval number distance measure, an improved interval deviation maximization method (I-MDM) was introduced to detemine the objective weights. In the following, based on the I-BWM and the improved I-MDM, a combination weighting method that takes into account the subjective and objective weights is proposed. Finally, a multi-criteria decision-making method based on the interval MULTIMOORA with combination weights is present to evaluate the employment quality of college graduates, and then a comparative analysis with some of the existing distance measures of interval numberswas conducted to illustrate the flexibility.

According to the data of the Report on Employment Quality of Chinese College Graduats released by Mycos Research Institute in 2016–2020 and 2021–2022, the proposed method was used to evaluate the employment quality of college graduates during the period before and after the COVID-19 epidemic. The results verify that the method is more reasonable because the subjective and objective weights of the criteria can be fully considered. Finally, the feasibility and practicability of the proposed method are further verified by varying parameters.

Present an evaluation method on the employment quality of college graduates based on the Interval MULTIMOORA with combination weights considering the subjective and objective weights. And the proposed method is proved that it can provide a more reasonable evaluation results. At the same time, it is verified that the feasibility and the practicability of the proposed method are affected by varying parameters in the paper.

This paper investigates the probable differential impact of the confirmed cases of COVID-19 on the equities markets of G7 and Nordic countries to ascertain possible interdependencies, diversification and safe haven prospects in the era of the COVID-19 pandemic over the short-, intermediate- and long-term horizons.

The authors apply a unique methodology in a denoised frequency-domain entropy paradigm to the selected equities markets (Li et al. 2020).

The authors’ findings reinforce the operability of the entrenched market dynamics in the COVID-19 pandemic era. The authors divulge that different approaches to fighting the pandemic do not necessarily drive a change in the deep-rooted fundamentals of the equities market, specifically for the studied markets. Except for an extreme case nearing the end (start) of the short-term (intermediate-term) between Iceland and either Denmark or the US equities, there exists no potential for diversification across the studied markets, which could be ascribed to the degree of integration between these markets.

The authors’ findings suggest that politicians should pay closer attention to stock market fluctuations as well as the count of confirmed COVID-19 cases in their respective countries since these could cause changes to market dynamics in the short-term through investor sentiments.

The authors measure the flow of information from COVID-19 to G7 and Nordic equities using the entropy methodology induced by the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), which is a data-driven technique. The authors employ a larger sample period as a result of this, which is required to better comprehend the subtleties of investor behaviour within and among economies – G7 and Nordic geographical blocs – which largely employed different approaches to fighting the COVID-19 pandemic. The authors’ focus is on diverging time horizons, and the ICEEMDAN-based entropy would enable us to measure the amount of information conveyed to account for large tails in these nations' equity returns. Furthermore, the authors use a unique type of entropy known as Rényi entropy, which uses suitable weights to discern tailed distributions. The Shannon entropy does not account for the fact that financial assets have fat tails. In a pandemic like COVID-19, these fat tails are very strong, and they must be accounted for.

This study aims to explore supply chain resilience (SCR) and provides a unique resilience index. The work measures the resilience status of 37 organizations across 22 industries and provides insight into accessing the supply chain (SC) vulnerability in an uncertain environment.

This study involves measuring the resilience status of 37 organizations across 22 industries based on a subjective decision-making approach using fuzzy logic. Experts from industries rated the importance and level of implementation of 33 attributes of SCR, which are used to develop a fuzzy index of implementation that explains the resilience status of organizations.

A novel coexistent resilience index is computed based on mutualism to exhibit the proportion of contribution or learning of each attribute of an organization in an industry. The research will enhance the response plans and formation of strategic alliances for mutual coexistence by industry.

Evidence-based interpretations and suggestions are provided for each industry to enhance resilience through coexistence.

The work uniquely contributes to academic literature and SC strategy. The novel coexistent resilience index is computed based on mutualism, facilitating researchers to access SC resiliency.