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Article
Publication date: 4 December 2024

Shatha Siefan, Jiju Antony, Ahmad Mayyas, Mohammed Omar, Anupama Prashar, Guilherme Tortorella, Monika Foster and Maher Maalouf

This study investigates the adoption and effects of operational excellence methodologies on sustainable performance within the sector, drawing insights from 18 global quality…

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Abstract

Purpose

This study investigates the adoption and effects of operational excellence methodologies on sustainable performance within the sector, drawing insights from 18 global quality management experts.

Design/methodology/approach

Utilizing a phenomenological approach alongside constant comparison, classical content and taxonomy analysis, qualitative data from semi-structured interviews are rigorously examined. The study examines the challenges and outcomes of implementing lean, six sigma and lean six sigma, particularly in the financial, social and environmental realms. By integrating academic research with real-world applications, this research identifies challenges and opportunities across diverse service industries, with the aim of informing best practices for practitioners.

Findings

The findings highlight a significant impact on financial outcomes, with lean six sigma implementations predominantly enhancing financial performance. However, perceptions differ regarding sustainability and the acknowledgment of such impact. In terms of social performance, opinions vary from consistently positive impact to a dual effect – both positive and negative. Regarding environmental impact, perspectives range from limited to significant positive outcomes. Additionally, quantitative analysis of operational measures underscores a noteworthy emphasis on financial performance, with a grand average of 4.23. Social performance marginally surpasses environmental performance, with averages of 3.01 and 2.95, respectively.

Originality/value

The critical role of the service sector in modern economies highlights the imperative for enhancing operational efficiency and sustainability. The findings highlight the importance of proactively integrating lean six sigma principles into the operational frameworks of service organizations to optimize both operational and sustainable performance.

Details

International Journal of Quality & Reliability Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0265-671X

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Gender and Tourism
Type: Book
ISBN: 978-1-80117-322-3

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Article
Publication date: 2 January 2023

Jitendra Sharma and Bibhuti Bhusan Tripathy

Supplier evaluation and selection is an essential (multi-criteria decision-making) MCDM process that considers qualitative and quantitative factors. This research work attempts to…

404

Abstract

Purpose

Supplier evaluation and selection is an essential (multi-criteria decision-making) MCDM process that considers qualitative and quantitative factors. This research work attempts to use a MCDM technique based on merging fuzzy Technique for Order Preference by Similarity to Ideal Solution (F-TOPSIS) and Quality Function Deployment (QFD) ideas. The study attempts to find the supplier's attributes (HOWs) to accomplish its goals after determining the product's characteristics to suit the company's needs (WHATs).

Design/methodology/approach

The proposed research methodology comprises the following four steps: Step 1: Determine the product purchase requirements (“WHATs”) and those pertinent to supplier evaluation (“HOWs”). In Step 2, the relative importance of the “WHAT-HOW” correlation scores is determined and also the resulting weights of “HOWs”. In Step 3, linguistic evaluations of possible suppliers in comparison to subjective criteria are given to the decision-makers. Step 4 combines the QFD and F-TOPSIS techniques to select suppliers.

Findings

A fuzzy MCDM method based on fusing and integrating fuzzy information and QFD is presented to solve the drawbacks of conventional decision-making strategies used in supplier selection. Using the F-TOPSIS method, fuzzy positive ideal solution (FPIS) and fuzzy negative ideal solution (FNIS), the relative closeness coefficient values for all alternatives are computed. The suppliers are ranked by relating the closeness of coefficient values. This method permits the combination of ambiguous and subjective data expressed as fuzzy-defined integers or linguistic variables.

Originality/value

QFD and TOPSIS, two widely used approaches, are combined in this article to rank and evaluate suppliers based on the traits that the suppliers choose to prioritize. This study demonstrates that the method employed could address multiple-criteria decision-making scenarios in a computationally efficient manner. The effectiveness and applicability of the method are illustrated using an example.

Details

The TQM Journal, vol. 35 no. 8
Type: Research Article
ISSN: 1754-2731

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Article
Publication date: 26 November 2024

Aneel Manan, Zhang Pu, Jawad Ahmad and Muhammad Umar

Rapid industrialization and construction generate substantial concrete waste, leading to significant environmental issues. Nearly 10 billion metric tonnes of concrete waste are…

30

Abstract

Purpose

Rapid industrialization and construction generate substantial concrete waste, leading to significant environmental issues. Nearly 10 billion metric tonnes of concrete waste are produced globally per year. In addition, concrete also accelerates the consumption of natural resources, leading to the depletion of these natural resources. Therefore, this study uses artificial intelligence (AI) to examine the utilization of recycled concrete aggregate (RCA) in concrete.

Design/methodology/approach

An extensive database of 583 data points are collected from the literature for predictive modeling. Four machine learning algorithms, namely artificial neural network (ANN), random forest (RF), ridge regression (RR) and least adjacent shrinkage and selection operator (LASSO) regression (LR), in predicting simultaneously concrete compressive and tensile strength were evaluated. The dataset contains 10 independent variables and two dependent variables. Statistical parameters, including coefficient of determination (R2), mean square error (MSE), mean absolute error (MAE) and root mean square error (RMSE), were employed to assess the accuracy of the algorithms. In addition, K-fold cross-validation was employed to validate the obtained results, and SHapley Additive exPlanations (SHAP) analysis was applied to identify the most sensitive parameters out of the 10 input parameters.

Findings

The results indicate that the RF prediction model performance is better and more satisfactory than other algorithms. Furthermore, the ANN algorithm ranks as the second most accurate algorithm. However, RR and LR exhibit poor findings with low accuracy. K-fold cross-validation was successfully applied to validate the obtained results and SHAP analysis indicates that cement content and recycled aggregate percentages are the effective input parameter. Therefore, special attention should be given to sensitive parameters to enhance the concrete performance.

Originality/value

This study uniquely applies AI to optimize the use of RCA in concrete production. By evaluating four machine learning algorithms, ANN, RF, RR and LR on a comprehensive dataset, this study identities the most effective predictive models for concrete compressive and tensile strength. The use of SHAP analysis to determine key input parameters and K-fold cross-validation for result validation adds to the study robustness. The findings highlight the superior performance of the RF model and provide actionable insights into enhancing concrete performance with RCA, contributing to sustainable construction practice.

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Article
Publication date: 14 December 2020

Hasan Mahmud Reza, Towhid Hasan, Marjia Sultana and Md. Omar Faruque

Diabetes mellitus is becoming a growing concern worldwide. Hence, the purpose of this study is to assess the magnitude of poor glycemic control and to identify the determinants of…

157

Abstract

Purpose

Diabetes mellitus is becoming a growing concern worldwide. Hence, the purpose of this study is to assess the magnitude of poor glycemic control and to identify the determinants of poor glycemic control among diabetic patients attending a tertiary care hospital in Bangladesh.

Design/methodology/approach

This cross-sectional study was conducted among 732 diabetes patients seeking care at the outpatient department of Bangladesh Institute of Health Sciences Hospital, Dhaka, Bangladesh. Information, including glycemic status, was collected from patients’ medical records using a structured questionnaire.

Findings

About 87.6% of the patients were found to have poor glycemic control (glycosylated hemoglobin = 7%). Variables that were significant in bivariate analysis were put into a multivariate model where the factors associated with poor glycemic control were patients aged 41–60 years (odds ratio (OR)=2.26; 95% confidence interval (CI): 1.19–4.32, p = 0.013), suffering from diabetes for > 7 years (OR = 1.84; 95% CI: 1.12–2.99, p = 0.015), using insulin (OR = 2.34; 95% CI: 1.23–4.47; p = 0.010) or diet alone (OR = 0.20; 95% CI: 0.05–0.80, p = 0.023) as a type of diabetes treatment and proper use of medicine (OR = 0.37; 95% CI: 0.17–0.82, p = 0.015).

Originality/value

The high prevalence of poor glycemic control among diabetic patients is evident; therefore, strategic management and proper attention focusing on the predictors of poor glycemic control are necessary to reduce the long-term complications of diabetes.

Details

Nutrition & Food Science , vol. 51 no. 6
Type: Research Article
ISSN: 0034-6659

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Article
Publication date: 21 December 2021

Laouni Djafri

This work can be used as a building block in other settings such as GPU, Map-Reduce, Spark or any other. Also, DDPML can be deployed on other distributed systems such as P2P…

453

Abstract

Purpose

This work can be used as a building block in other settings such as GPU, Map-Reduce, Spark or any other. Also, DDPML can be deployed on other distributed systems such as P2P networks, clusters, clouds computing or other technologies.

Design/methodology/approach

In the age of Big Data, all companies want to benefit from large amounts of data. These data can help them understand their internal and external environment and anticipate associated phenomena, as the data turn into knowledge that can be used for prediction later. Thus, this knowledge becomes a great asset in companies' hands. This is precisely the objective of data mining. But with the production of a large amount of data and knowledge at a faster pace, the authors are now talking about Big Data mining. For this reason, the authors’ proposed works mainly aim at solving the problem of volume, veracity, validity and velocity when classifying Big Data using distributed and parallel processing techniques. So, the problem that the authors are raising in this work is how the authors can make machine learning algorithms work in a distributed and parallel way at the same time without losing the accuracy of classification results. To solve this problem, the authors propose a system called Dynamic Distributed and Parallel Machine Learning (DDPML) algorithms. To build it, the authors divided their work into two parts. In the first, the authors propose a distributed architecture that is controlled by Map-Reduce algorithm which in turn depends on random sampling technique. So, the distributed architecture that the authors designed is specially directed to handle big data processing that operates in a coherent and efficient manner with the sampling strategy proposed in this work. This architecture also helps the authors to actually verify the classification results obtained using the representative learning base (RLB). In the second part, the authors have extracted the representative learning base by sampling at two levels using the stratified random sampling method. This sampling method is also applied to extract the shared learning base (SLB) and the partial learning base for the first level (PLBL1) and the partial learning base for the second level (PLBL2). The experimental results show the efficiency of our solution that the authors provided without significant loss of the classification results. Thus, in practical terms, the system DDPML is generally dedicated to big data mining processing, and works effectively in distributed systems with a simple structure, such as client-server networks.

Findings

The authors got very satisfactory classification results.

Originality/value

DDPML system is specially designed to smoothly handle big data mining classification.

Details

Data Technologies and Applications, vol. 56 no. 4
Type: Research Article
ISSN: 2514-9288

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Article
Publication date: 9 February 2022

Hemant Harishchandra Kore and Saroj Koul

The study identifies the challenges of developing the “electric vehicle (EV)” charging infrastructure in India, having an ambitious target of 30% EV adoption by 2030.

1170

Abstract

Purpose

The study identifies the challenges of developing the “electric vehicle (EV)” charging infrastructure in India, having an ambitious target of 30% EV adoption by 2030.

Design/methodology/approach

First, a systematic literature review determined EV adoption and challenges in the EV charging infrastructure development globally and specifically in India. Secondly, a focussed group study in which 10 domain experts were consulted to identify additional challenges in India's EV adoption involving EV charging infrastructure.

Findings

Accordingly, 11 significant challenges of EV charging infrastructure development in India have been identified–seven through the comparative analysis of the literature review and four from the focussed group study. Secondary data provides insight into the situation around developed countries and in developing countries, specifically in India. Finally, the Government of India's measures and priorities to facilitate such a development are emphasised.

Research limitations/implications

The study can help policymakers/researchers understand the gaps and align measures to address the challenges. A focussed group study may have its limitations due to the perception of the experts.

Originality/value

The systematic literature review of 43 articles using comparative analysis and subsequently a focussed group study of experts to verify and add challenges has made the study unique.

Details

Management of Environmental Quality: An International Journal, vol. 33 no. 3
Type: Research Article
ISSN: 1477-7835

Keywords

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Article
Publication date: 8 May 2017

Abubakar Idris Hassan, Mohd Nazri Baharom and Rozita Abdul Mutalib

The purpose of this paper is to examine the social capital factors of career advancement of female academic staff in Nigerian universities.

785

Abstract

Purpose

The purpose of this paper is to examine the social capital factors of career advancement of female academic staff in Nigerian universities.

Design/methodology/approach

A measurement and structural analysis were conducted for the three independent variables and a dependent variable on 20 public universities. Data were collected using a structured self-administered questionnaire. The dependent variable was female academic staff career advancement and the independent variables were mentoring, networking and government machinery. Using stratified random sampling, 532 academic staff were selected as the study respondents. They represented sampling criteria such as federal and state universities.

Findings

Structural modeling analysis showed that social capital variables, specifically mentoring, networking and government machinery variables, were significant contributors to the career advancement of the female academic staff in Nigerian universities.

Practical implications

This study creates an insight into the knowledge of career advancement among female academic staff in public universities. These institutions dominate the university system in Nigeria and serve as the main avenue for university education in the country. At the level of higher institution, HRD is significant, particularly in creating awareness among academic staff about their career planning and aspirations, the role that the perceived environmental factors play in their advancement to higher positions in the university and how they should further utilize those factors.

Originality/value

The paper examines social capital factors (limited to mentoring, networking and government machinery) that are of concern to managing the career advancement of female academic staff in public universities.

Details

Journal of Management Development, vol. 36 no. 4
Type: Research Article
ISSN: 0262-1711

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