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1 – 10 of 811Ammar Chakhrit, Mohammed Bougofa, Islam Hadj Mohamed Guetarni, Abderraouf Bouafia, Rabeh Kharzi, Naima Nehal and Mohammed Chennoufi
This paper aims to enable the analysts of reliability and safety systems to evaluate the risk and prioritize failure modes ideally to prefer measures for reducing the risk of…
Abstract
Purpose
This paper aims to enable the analysts of reliability and safety systems to evaluate the risk and prioritize failure modes ideally to prefer measures for reducing the risk of undesired events.
Design/methodology/approach
To address the constraints considered in the conventional failure mode and effects analysis (FMEA) method for criticality assessment, the authors propose a new hybrid model combining different multi-criteria decision-making (MCDM) methods. The analytical hierarchy process (AHP) is used to construct a criticality matrix and calculate the weights of different criteria based on five criticalities: personnel, equipment, time, cost and quality. In addition, a preference ranking organization method for enrichment evaluation (PROMETHEE) method is used to improve the prioritization of the failure modes. A comparative work in which the robust data envelopment analysis (RDEA)-FMEA approach was used to evaluate the validity and effectiveness of the suggested approach and simplify the comparative analysis.
Findings
This work aims to highlight the real case study of the automotive parts industry. Using this analysis enables assessing the risk efficiently and gives an alternative ranking to that acquired by the traditional FMEA method. The obtained findings offer that combining of two multi-criteria decision approaches and integrating their outcomes allow for instilling confidence in decision-makers concerning the risk assessment and the ranking of the different failure modes.
Originality/value
This research gives encouraging outcomes concerning the risk assessment and failure modes ranking in order to reduce the frequency of occurrence and gravity of the undesired events by handling different forms of uncertainty and divergent judgments of experts.
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Abdul Rauf, Daniel Efurosibina Attoye and Robert H. Crawford
Recently, there has been a shift toward the embodied energy assessment of buildings. However, the impact of material service life on the life-cycle embodied energy has received…
Abstract
Purpose
Recently, there has been a shift toward the embodied energy assessment of buildings. However, the impact of material service life on the life-cycle embodied energy has received little attention. We aimed to address this knowledge gap, particularly in the context of the UAE and investigated the embodied energy associated with the use of concrete and other materials commonly used in residential buildings in the hot desert climate of the UAE.
Design/methodology/approach
Using input–output based hybrid analysis, we quantified the life-cycle embodied energy of a villa in the UAE with over 50 years of building life using the average, minimum, and maximum material service life values. Mathematical calculations were performed using MS Excel, and a detailed bill of quantities with >170 building materials and components of the villa were used for investigation.
Findings
For the base case, the initial embodied energy was 57% (7390.5 GJ), whereas the recurrent embodied energy was 43% (5,690 GJ) of the life-cycle embodied energy based on average material service life values. The proportion of the recurrent embodied energy with minimum material service life values was increased to 68% of the life-cycle embodied energy, while it dropped to 15% with maximum material service life values.
Originality/value
The findings provide new data to guide building construction in the UAE and show that recurrent embodied energy contributes significantly to life-cycle energy demand. Further, the study of material service life variations provides deeper insights into future building material specifications and management considerations for building maintenance.
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Manpreet Kaur, Amit Kumar and Anil Kumar Mittal
In past decades, artificial neural network (ANN) models have revolutionised various stock market operations due to their superior ability to deal with nonlinear data and garnered…
Abstract
Purpose
In past decades, artificial neural network (ANN) models have revolutionised various stock market operations due to their superior ability to deal with nonlinear data and garnered considerable attention from researchers worldwide. The present study aims to synthesize the research field concerning ANN applications in the stock market to a) systematically map the research trends, key contributors, scientific collaborations, and knowledge structure, and b) uncover the challenges and future research areas in the field.
Design/methodology/approach
To provide a comprehensive appraisal of the extant literature, the study adopted the mixed approach of quantitative (bibliometric analysis) and qualitative (intensive review of influential articles) assessment to analyse 1,483 articles published in the Scopus and Web of Science indexed journals during 1992–2022. The bibliographic data was processed and analysed using VOSviewer and R software.
Findings
The results revealed the proliferation of articles since 2018, with China as the dominant country, Wang J as the most prolific author, “Expert Systems with Applications” as the leading journal, “computer science” as the dominant subject area, and “stock price forecasting” as the predominantly explored research theme in the field. Furthermore, “portfolio optimization”, “sentiment analysis”, “algorithmic trading”, and “crisis prediction” are found as recently emerged research areas.
Originality/value
To the best of the authors’ knowledge, the current study is a novel attempt that holistically assesses the existing literature on ANN applications throughout the entire domain of stock market. The main contribution of the current study lies in discussing the challenges along with the viable methodological solutions and providing application area-wise knowledge gaps for future studies.
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Ruchi Kejriwal, Monika Garg and Gaurav Sarin
Stock market has always been lucrative for various investors. But, because of its speculative nature, it is difficult to predict the price movement. Investors have been using both…
Abstract
Purpose
Stock market has always been lucrative for various investors. But, because of its speculative nature, it is difficult to predict the price movement. Investors have been using both fundamental and technical analysis to predict the prices. Fundamental analysis helps to study structured data of the company. Technical analysis helps to study price trends, and with the increasing and easy availability of unstructured data have made it important to study the market sentiment. Market sentiment has a major impact on the prices in short run. Hence, the purpose is to understand the market sentiment timely and effectively.
Design/methodology/approach
The research includes text mining and then creating various models for classification. The accuracy of these models is checked using confusion matrix.
Findings
Out of the six machine learning techniques used to create the classification model, kernel support vector machine gave the highest accuracy of 68%. This model can be now used to analyse the tweets, news and various other unstructured data to predict the price movement.
Originality/value
This study will help investors classify a news or a tweet into “positive”, “negative” or “neutral” quickly and determine the stock price trends.
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Ewa Sońta-Drączkowska and Agnieszka Krogulec
This study seeks to illuminate the managerial tensions inherent in implementing scaled agile (on the organizational, top management, middle management and team levels) and to…
Abstract
Purpose
This study seeks to illuminate the managerial tensions inherent in implementing scaled agile (on the organizational, top management, middle management and team levels) and to frame these challenges within the broader context of project management.
Design/methodology/approach
The study adopts a grounded theory approach and delves into a qualitative dataset sourced from 34 interviews with subject matter experts actively engaged in scaling agile initiatives within large organizations spanning various industries. Additionally, the data have been enriched through a comprehensive literature review of the existing body of knowledge on scaling agile.
Findings
As a result of our investigation, we propose a framework of managerial tensions in scaling agile in large corporate settings and a series of research propositions and questions that may contribute significantly to the body of knowledge surrounding the phenomenon of “deprojectification” and propose agenda for the future studies in the field of project management.
Research limitations/implications
The study also carries significant managerial implications. Firstly, based on the insights from the practice of scaling agile in large corporate setting, management can build awareness of the challenges inherent of transitioning to agile practices. This may help to anticipate the possible problems and proactively develop strategies how to address them. Secondly, management can be instructed about contingencies inherent in scaling agile, along with the potential disfunctions and side effects (unintended outcomes) that may emerge during the transition process. Thirdly, project management practitioners can gain insights on how scaling agile may cause shifts in the approach to managing projects, project team management and competencies that need to be developed to cope with environments where various approaches to managing projects coexist.
Practical implications
These insights can aid in the agile transition process, beginning with directing managerial attention toward contextual factors and progressing through potential challenges at the organizational, top management, middle management and team levels. Furthermore, the study highlights possible dysfunctionalities and side effects of scaling agile, shedding light on the “dark side” of agile.
Originality/value
The study contributes to the expansion of the empirical database on the implementation of agile practices in large organizational settings. It plays a role in defining and delineating the phenomenon of scaling agile within the context of project management and outlines a research agenda for future project management studies. Additionally, our study adds to the ongoing discourse surrounding the “deprojectification” effect that can occur during the scaling of agile. Lastly, it establishes connections between project management and software development literature regarding the implementation of agile at scale.
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Behzad Foroughi, Elaheh Yadegaridehkordi, Mohammad Iranmanesh, Teerachart Sukcharoen, Morteza Ghobakhlo and Mehrbakhsh Nilashi
Customers increasingly use food delivery applications (FDAs) to place orders. Despite the popularity of FDAs, limited research has investigated the drivers of the continuance…
Abstract
Purpose
Customers increasingly use food delivery applications (FDAs) to place orders. Despite the popularity of FDAs, limited research has investigated the drivers of the continuance intention to use FDAs. This study aims to uncover the drivers of the continuance intention to use FDAs by integrating the “technology continuance theory” (TCT) with perceived task-technology fit, perceived value and perceived food safety.
Design/methodology/approach
Data were collected from 398 individuals in Thailand and evaluated using “partial least squares” (PLS) and “fuzzy-set qualitative comparative analysis” (fsQCA).
Findings
The PLS results supported the significance of all direct relationships, except the effects of perceived ease of use on attitude and perceived usefulness on continuance intention. Accordingly, perceived food safety positively moderated the impact of perceived ease of use on attitudes. The fsQCA uncovered seven solutions with various combinations of factors that predicted high continuance intention.
Practical implications
This study enables food delivery apps to develop effective strategies for retaining users and sustaining financial performance.
Originality/value
This research contributes to the literature by investigating the factors underlying the continuous use of FDAs with a new PLS-fsQCA technique and applying TCT in a new technological context, FDAs and enriching it by adding three variables: perceived task-technology fit, perceived value and perceived food safety.
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Sanaz Khalaj Rahimi and Donya Rahmani
The study aims to optimize truck routes by minimizing social and economic costs. It introduces a strategy involving diverse drones and their potential for reusing at DNs based on…
Abstract
Purpose
The study aims to optimize truck routes by minimizing social and economic costs. It introduces a strategy involving diverse drones and their potential for reusing at DNs based on flight range. In HTDRP-DC, trucks can select and transport various drones to LDs to reduce deprivation time. This study estimates the nonlinear deprivation cost function using a linear two-piece-wise function, leading to MILP formulations. A heuristic-based Benders Decomposition approach is implemented to address medium and large instances. Valid inequalities and a heuristic method enhance convergence boundaries, ensuring an efficient solution methodology.
Design/methodology/approach
Research has yet to address critical factors in disaster logistics: minimizing the social and economic costs simultaneously and using drones in relief distribution; deprivation as a social cost measures the human suffering from a shortage of relief supplies. The proposed hybrid truck-drone routing problem minimizing deprivation cost (HTDRP-DC) involves distributing relief supplies to dispersed demand nodes with undamaged (LDs) or damaged (DNs) access roads, utilizing multiple trucks and diverse drones. A Benders Decomposition approach is enhanced by accelerating techniques.
Findings
Incorporating deprivation and economic costs results in selecting optimal routes, effectively reducing the time required to assist affected areas. Additionally, employing various drone types and their reuse in damaged nodes reduces deprivation time and associated deprivation costs. The study employs valid inequalities and the heuristic method to solve the master problem, substantially reducing computational time and iterations compared to GAMS and classical Benders Decomposition Algorithm. The proposed heuristic-based Benders Decomposition approach is applied to a disaster in Tehran, demonstrating efficient solutions for the HTDRP-DC regarding computational time and convergence rate.
Originality/value
Current research introduces an HTDRP-DC problem that addresses minimizing deprivation costs considering the vehicle’s arrival time as the deprivation time, offering a unique solution to optimize route selection in relief distribution. Furthermore, integrating heuristic methods and valid inequalities into the Benders Decomposition approach enhances its effectiveness in solving complex routing challenges in disaster scenarios.
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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.
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Kalidas Das and Pinaki Ranjan Duari
Several graphs, streamlines, isotherms and 3D plots are illustrated to enlighten the noteworthy fallouts of the investigation. Embedding flow factors for velocity, induced…
Abstract
Purpose
Several graphs, streamlines, isotherms and 3D plots are illustrated to enlighten the noteworthy fallouts of the investigation. Embedding flow factors for velocity, induced magnetic field and temperature have been determined using parametric analysis.
Design/methodology/approach
Ternary hybrid nanofluids has outstanding hydrothermal performance compared to classical mono nanofluids and hybrid nanofluids owing to the presence of triple tiny metallic particles. Ternary hybrid nanofluids are considered as most promising candidates in solar energy, heat exchangers, electronics cooling, automotive cooling, nuclear reactors, automobile, aerospace, biomedical devices, food processing etc. In this work, a ternary hybrid nanofluid flow that contains metallic nanoparticles over a wedge under the prevalence of solar radiating heat, induced magnetic field and the shape factor of nanoparticles is considered. A ternary hybrid nanofluid is synthesized by dispersing iron oxide (Fe3O4), silver (Ag) and magnesium oxide (MgO) nanoparticles in a water (H2O) base fluid. By employing similarity transformations, we can convert the governing equations into ordinary differential equations and then solve numerically by using the Runge–Kutta–Fehlberg approach.
Findings
There is no fund for the research work.
Social implications
This kind of study may be used to improve the performance of solar collectors, solar energy and solar cells.
Originality/value
This investigation unfolds the hydrothermal changes of radiative water-based Fe3O4-Ag-MgO-H2O ternary hybrid nanofluidic transport past a static and moving wedge in the presence of solar radiating heating and induced magnetic fields. The shape factor of nanoparticles has been considered in this study.
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Martin Gutmann, Erik Jentges and Douglas MacKevett
The purpose of this paper is to describe an innovative approach to overcoming a common dilemma in designing negotiation simulations – that of situating a simulation in a real-life…
Abstract
Purpose
The purpose of this paper is to describe an innovative approach to overcoming a common dilemma in designing negotiation simulations – that of situating a simulation in a real-life or fictitious context. This binary choice, which the authors call the negotiation designer’s dilemma, has profound implications for the types of learning activities and outcomes that can be integrated into the overall learning experience. As a way of overcoming the trade-offs inherent in this dilemma, the authors developed what they term hybrid simulations, which blend elements of fact and fiction in its contextual design in a particular way.
Design/methodology/approach
The authors were part of a negotiation simulation design team that used Design Thinking to understand the negotiation designer’s dilemma and to prototype and test a corresponding solution.
Findings
This paper demonstrates the benefits, potential applications and the how-to of hybrid simulations within the context of two such simulations the authors have designed at two different Swiss business schools. This paper concludes by discussing the potential and limitations for the application of hybrid simulations, as well as areas of potential further development.
Originality/value
The concept of a hybrid negotiation is a novel design trick that can be used in a variety of negotiation simulation contexts.
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