Search results

1 – 10 of 59
Article
Publication date: 28 February 2024

Ibraheem Saleh Al Koliby, Mohammed A. Al-Hakimi, Mohammed Abdulrahman Kaid Zaid, Mohammed Farooque Khan, Murad Baqis Hasan and Mohammed A. Alshadadi

Although green entrepreneurial orientation (GEO) has received much attention, it is unclear whether it affects technological green innovation (GI). Therefore, this study aims to…

Abstract

Purpose

Although green entrepreneurial orientation (GEO) has received much attention, it is unclear whether it affects technological green innovation (GI). Therefore, this study aims to understand how GEO affects technological GI, with its dimensions green product innovation (GPRODI) and green process innovation (GPROCI), as well as to explore whether resource orchestration capability (ROC) moderates the relationships between them.

Design/methodology/approach

Based on a cross-sectional survey design, data were gathered from 177 managers of large manufacturing firms in Yemen and analysed using partial least squares structural equation modelling via SmartPLS software.

Findings

The results revealed that GEO positively affects both GPRODI and GPROCI, with a higher effect on GPROCI. Importantly, ROC does, in fact, positively moderate the link between GEO and GPRODI.

Research limitations/implications

This research adds to knowledge by combining GEO, ROC and technological GI into a unified framework, considering the perspectives of the resource-based view and the resource orchestration theory. However, the study’s use of cross-sectional survey data makes it impossible to infer causes. This is because GEO, ROC and technological GI all have effects on time that this empirical framework cannot account for.

Practical implications

The findings from this research provide valuable insights for executives and decision makers of large manufacturing companies, who are expected to show increasing interest in adopting ROC into their organisations. This suggests that environmentally-conscious entrepreneurial firms can enhance their GI efforts by embracing ROC.

Social implications

By adopting the proposed framework, firms can carry out their activities in ways that do not harm environmental and societal well-being, as simply achieving high economic performance is no longer sufficient.

Originality/value

Theoretically, the results offer an in-depth understanding of the role of GEO in the technological GI domain by indicating that GEO can promote GPRODI and GPROCI. In addition, the results shed new light on the boundaries of GEO from the perspective of resource orchestration theory. Furthermore, the findings present important insights for managers aiming to enhance their comprehension of leveraging GEO and ROC to foster technological GI.

Article
Publication date: 22 March 2024

Mohd Mustaqeem, Suhel Mustajab and Mahfooz Alam

Software defect prediction (SDP) is a critical aspect of software quality assurance, aiming to identify and manage potential defects in software systems. In this paper, we have…

Abstract

Purpose

Software defect prediction (SDP) is a critical aspect of software quality assurance, aiming to identify and manage potential defects in software systems. In this paper, we have proposed a novel hybrid approach that combines Gray Wolf Optimization with Feature Selection (GWOFS) and multilayer perceptron (MLP) for SDP. The GWOFS-MLP hybrid model is designed to optimize feature selection, ultimately enhancing the accuracy and efficiency of SDP. Gray Wolf Optimization, inspired by the social hierarchy and hunting behavior of gray wolves, is employed to select a subset of relevant features from an extensive pool of potential predictors. This study investigates the key challenges that traditional SDP approaches encounter and proposes promising solutions to overcome time complexity and the curse of the dimensionality reduction problem.

Design/methodology/approach

The integration of GWOFS and MLP results in a robust hybrid model that can adapt to diverse software datasets. This feature selection process harnesses the cooperative hunting behavior of wolves, allowing for the exploration of critical feature combinations. The selected features are then fed into an MLP, a powerful artificial neural network (ANN) known for its capability to learn intricate patterns within software metrics. MLP serves as the predictive engine, utilizing the curated feature set to model and classify software defects accurately.

Findings

The performance evaluation of the GWOFS-MLP hybrid model on a real-world software defect dataset demonstrates its effectiveness. The model achieves a remarkable training accuracy of 97.69% and a testing accuracy of 97.99%. Additionally, the receiver operating characteristic area under the curve (ROC-AUC) score of 0.89 highlights the model’s ability to discriminate between defective and defect-free software components.

Originality/value

Experimental implementations using machine learning-based techniques with feature reduction are conducted to validate the proposed solutions. The goal is to enhance SDP’s accuracy, relevance and efficiency, ultimately improving software quality assurance processes. The confusion matrix further illustrates the model’s performance, with only a small number of false positives and false negatives.

Details

International Journal of Intelligent Computing and Cybernetics, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1756-378X

Keywords

Article
Publication date: 19 March 2024

Thao-Trang Huynh-Cam, Long-Sheng Chen and Tzu-Chuen Lu

This study aimed to use enrollment information including demographic, family background and financial status, which can be gathered before the first semester starts, to construct…

Abstract

Purpose

This study aimed to use enrollment information including demographic, family background and financial status, which can be gathered before the first semester starts, to construct early prediction models (EPMs) and extract crucial factors associated with first-year student dropout probability.

Design/methodology/approach

The real-world samples comprised the enrolled records of 2,412 first-year students of a private university (UNI) in Taiwan. This work utilized decision trees (DT), multilayer perceptron (MLP) and logistic regression (LR) algorithms for constructing EPMs; under-sampling, random oversampling and synthetic minority over sampling technique (SMOTE) methods for solving data imbalance problems; accuracy, precision, recall, F1-score, receiver operator characteristic (ROC) curve and area under ROC curve (AUC) for evaluating constructed EPMs.

Findings

DT outperformed MLP and LR with accuracy (97.59%), precision (98%), recall (97%), F1_score (97%), and ROC-AUC (98%). The top-ranking factors comprised “student loan,” “dad occupations,” “mom educational level,” “department,” “mom occupations,” “admission type,” “school fee waiver” and “main sources of living.”

Practical implications

This work only used enrollment information to identify dropout students and crucial factors associated with dropout probability as soon as students enter universities. The extracted rules could be utilized to enhance student retention.

Originality/value

Although first-year student dropouts have gained non-stop attention from researchers in educational practices and theories worldwide, diverse previous studies utilized while-and/or post-semester factors, and/or questionnaires for predicting. These methods failed to offer universities early warning systems (EWS) and/or assist them in providing in-time assistance to dropouts, who face economic difficulties. This work provided universities with an EWS and extracted rules for early dropout prevention and intervention.

Details

Journal of Applied Research in Higher Education, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2050-7003

Keywords

Article
Publication date: 6 February 2024

Reyhan Sabri and Belgin Sakallı

Places of worship have historically been maintained using traditional building management techniques, including regular monitoring, upkeep and maintenance provided by their…

Abstract

Purpose

Places of worship have historically been maintained using traditional building management techniques, including regular monitoring, upkeep and maintenance provided by their religious communities. This paper examines the conservation issues arising after the forced displacement of the traditional custodians, which is a significant concern in conflict-ridden environments.

Design/methodology/approach

As a unique example of a long-term conflict, the divided Cyprus provides this research with illustrative cases to derive the data. The research employs content analysis of official documents, physical observations and interviews with conservation professionals.

Findings

This research demonstrates the human and environmental factors impacting the conservation of the material fabric and the use-related challenges stemming from the intangible significance of the religious legacy belonging to displaced communities. It highlights the urgency to formulate more effective mechanisms and regulatory frameworks to address vulnerability issues promptly.

Originality/value

Preservation problems on religious heritage buildings arising from the loss of traditional custodians after conflicts are an under-researched area in conservation literature. Drawing on research that was conducted several decades after the displacement of Cypriot communities, this paper reveals new insights into the magnitude of the conservation problems and the use-related complexities that need to be addressed to formulate mutually acceptable solutions for a sustainable future.

Details

Journal of Cultural Heritage Management and Sustainable Development, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2044-1266

Keywords

Article
Publication date: 4 May 2023

Zeping Wang, Hengte Du, Liangyan Tao and Saad Ahmed Javed

The traditional failure mode and effect analysis (FMEA) has some limitations, such as the neglect of relevant historical data, subjective use of rating numbering and the less…

Abstract

Purpose

The traditional failure mode and effect analysis (FMEA) has some limitations, such as the neglect of relevant historical data, subjective use of rating numbering and the less rationality and accuracy of the Risk Priority Number. The current study proposes a machine learning–enhanced FMEA (ML-FMEA) method based on a popular machine learning tool, Waikato environment for knowledge analysis (WEKA).

Design/methodology/approach

This work uses the collected FMEA historical data to predict the probability of component/product failure risk by machine learning based on different commonly used classifiers. To compare the correct classification rate of ML-FMEA based on different classifiers, the 10-fold cross-validation is employed. Moreover, the prediction error is estimated by repeated experiments with different random seeds under varying initialization settings. Finally, the case of the submersible pump in Bhattacharjee et al. (2020) is utilized to test the performance of the proposed method.

Findings

The results show that ML-FMEA, based on most of the commonly used classifiers, outperforms the Bhattacharjee model. For example, the ML-FMEA based on Random Committee improves the correct classification rate from 77.47 to 90.09 per cent and area under the curve of receiver operating characteristic curve (ROC) from 80.9 to 91.8 per cent, respectively.

Originality/value

The proposed method not only enables the decision-maker to use the historical failure data and predict the probability of the risk of failure but also may pave a new way for the application of machine learning techniques in FMEA.

Details

Data Technologies and Applications, vol. 58 no. 1
Type: Research Article
ISSN: 2514-9288

Keywords

Executive summary
Publication date: 29 January 2024

CYPRUS: New UN envoy will find positions have hardened

Details

DOI: 10.1108/OXAN-ES284873

ISSN: 2633-304X

Keywords

Geographic
Topical
Article
Publication date: 30 January 2024

Amani Fathi Jamal, Sam El Nemar and Georgia Sakka

This research explores the link between job redesign and skilling in three Lebanese service provider industries, aiming to understand how these factors affect organizational…

148

Abstract

Purpose

This research explores the link between job redesign and skilling in three Lebanese service provider industries, aiming to understand how these factors affect organizational agility, a crucial factor for efficiency and effectiveness and promote long-term interventions through job redesign, upskilling and reskilling.

Design/methodology/approach

This study employed two surveys, one for personnel (employees) and one for human aid managers (HR managers). These surveys collected data from 384 employees and 67 HR managers. The study utilized a work design questionnaire (WDQ), skilling application evaluation and the change acceptance model and testing to evaluate job redesign, skilling application effectiveness, technology acceptance and change readiness.

Findings

It was revealed that there is a significant and positive relationship between job redesign and the application of skilling programs. This relationship was shown to enhance organizational agility, with a particular focus on employees' technology acceptance and readiness for change. The integrated framework that combines job redesign, upskilling and reskilling was empirically tested and found to enable organizations to build their agility. The study also identified challenges and offered solutions for implementation, emphasizing the importance of employee responsiveness.

Practical implications

This research emphasizes the need for organizations to adapt job designs and enhance employee skills to enhance organizational agility, recommending a structured approach that combines job redesign and skill development efforts.

Originality/value

This research integrates job redesign, upskilling and reskilling in Lebanese service provider industries, contributing to organizational change and workforce development. It emphasizes technology acceptance and readiness for change.

Details

EuroMed Journal of Business, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1450-2194

Keywords

Article
Publication date: 19 December 2023

Guilherme Dayrell Mendonça, Stanley Robson de Medeiros Oliveira, Orlando Fontes Lima Jr and Paulo Tarso Vilela de Resende

The objective of this paper is to evaluate whether the data from consignors, logistics service providers (LSPs) and consignees contribute to the prediction of air transport…

Abstract

Purpose

The objective of this paper is to evaluate whether the data from consignors, logistics service providers (LSPs) and consignees contribute to the prediction of air transport shipment delays in a machine learning application.

Design/methodology/approach

The research database contained 2,244 air freight intercontinental shipments to 4 automotive production plants in Latin America. Different algorithm classes were tested in the knowledge discovery in databases (KDD) process: support vector machine (SVM), random forest (RF), artificial neural networks (ANN) and k-nearest neighbors (KNN).

Findings

Shipper, consignee and LSP data attribute selection achieved 86% accuracy through the RF algorithm in a cross-validation scenario after a combined class balancing procedure.

Originality/value

These findings expand the current literature on machine learning applied to air freight delay management, which has mostly focused on weather, airport structure, flight schedule, ground delay and congestion as explanatory attributes.

Details

International Journal of Physical Distribution & Logistics Management, vol. 54 no. 1
Type: Research Article
ISSN: 0960-0035

Keywords

Open Access
Article
Publication date: 5 October 2023

Babitha Philip and Hamad AlJassmi

To proactively draw efficient maintenance plans, road agencies should be able to forecast main road distress parameters, such as cracking, rutting, deflection and International…

Abstract

Purpose

To proactively draw efficient maintenance plans, road agencies should be able to forecast main road distress parameters, such as cracking, rutting, deflection and International Roughness Index (IRI). Nonetheless, the behavior of those parameters throughout pavement life cycles is associated with high uncertainty, resulting from various interrelated factors that fluctuate over time. This study aims to propose the use of dynamic Bayesian belief networks for the development of time-series prediction models to probabilistically forecast road distress parameters.

Design/methodology/approach

While Bayesian belief network (BBN) has the merit of capturing uncertainty associated with variables in a domain, dynamic BBNs, in particular, are deemed ideal for forecasting road distress over time due to its Markovian and invariant transition probability properties. Four dynamic BBN models are developed to represent rutting, deflection, cracking and IRI, using pavement data collected from 32 major road sections in the United Arab Emirates between 2013 and 2019. Those models are based on several factors affecting pavement deterioration, which are classified into three categories traffic factors, environmental factors and road-specific factors.

Findings

The four developed performance prediction models achieved an overall precision and reliability rate of over 80%.

Originality/value

The proposed approach provides flexibility to illustrate road conditions under various scenarios, which is beneficial for pavement maintainers in obtaining a realistic representation of expected future road conditions, where maintenance efforts could be prioritized and optimized.

Details

Construction Innovation , vol. 24 no. 1
Type: Research Article
ISSN: 1471-4175

Keywords

Article
Publication date: 14 September 2023

Cheng Liu, Yi Shi, Wenjing Xie and Xinzhong Bao

This paper aims to provide a complete analysis framework and prediction method for the construction of the patent securitization (PS) basic asset pool.

Abstract

Purpose

This paper aims to provide a complete analysis framework and prediction method for the construction of the patent securitization (PS) basic asset pool.

Design/methodology/approach

This paper proposes an integrated classification method based on genetic algorithm and random forest algorithm. First, comprehensively consider the patent value evaluation model and SME credit evaluation model, determine 17 indicators to measure the patent value and SME credit; Secondly, establish the classification label of high-quality basic assets; Then, genetic algorithm and random forest model are used to predict and screen high-quality basic assets; Finally, the performance of the model is evaluated.

Findings

The machine learning model proposed in this study is mainly used to solve the screening problem of high-quality patents that constitute the underlying asset pool of PS. The empirical research shows that the integrated classification method based on genetic algorithm and random forest has good performance and prediction accuracy, and is superior to the single method that constitutes it.

Originality/value

The main contributions of the article are twofold: firstly, the machine learning model proposed in this article determines the standards for high-quality basic assets; Secondly, this article addresses the screening issue of basic assets in PS.

Details

Kybernetes, vol. 53 no. 2
Type: Research Article
ISSN: 0368-492X

Keywords

1 – 10 of 59