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1 – 10 of 38Abdulmohsen S. Almohsen, Naif M. Alsanabani, Abdullah M. Alsugair and Khalid S. Al-Gahtani
The variance between the winning bid and the owner's estimated cost (OEC) is one of the construction management risks in the pre-tendering phase. The study aims to enhance the…
Abstract
Purpose
The variance between the winning bid and the owner's estimated cost (OEC) is one of the construction management risks in the pre-tendering phase. The study aims to enhance the quality of the owner's estimation for predicting precisely the contract cost at the pre-tendering phase and avoiding future issues that arise through the construction phase.
Design/methodology/approach
This paper integrated artificial neural networks (ANN), deep neural networks (DNN) and time series (TS) techniques to estimate the ratio of a low bid to the OEC (R) for different size contracts and three types of contracts (building, electric and mechanic) accurately based on 94 contracts from King Saud University. The ANN and DNN models were evaluated using mean absolute percentage error (MAPE), mean sum square error (MSSE) and root mean sums square error (RMSSE).
Findings
The main finding is that the ANN provides high accuracy with MAPE, MSSE and RMSSE a 2.94%, 0.0015 and 0.039, respectively. The DNN's precision was high, with an RMSSE of 0.15 on average.
Practical implications
The owner and consultant are expected to use the study's findings to create more accuracy of the owner's estimate and decrease the difference between the owner's estimate and the lowest submitted offer for better decision-making.
Originality/value
This study fills the knowledge gap by developing an ANN model to handle missing TS data and forecasting the difference between a low bid and an OEC at the pre-tendering phase.
Yadong Liu, Nathee Naktnasukanjn, Anukul Tamprasirt and Tanarat Rattanadamrongaksorn
Bitcoin (BTC) is significantly correlated with global financial assets such as crude oil, gold and the US dollar. BTC and global financial assets have become more closely related…
Abstract
Purpose
Bitcoin (BTC) is significantly correlated with global financial assets such as crude oil, gold and the US dollar. BTC and global financial assets have become more closely related, particularly since the outbreak of the COVID-19 pandemic. The purpose of this paper is to formulate BTC investment decisions with the aid of global financial assets.
Design/methodology/approach
This study suggests a more accurate prediction model for BTC trading by combining the dynamic conditional correlation generalized autoregressive conditional heteroscedasticity (DCC-GARCH) model with the artificial neural network (ANN). The DCC-GARCH model offers significant input information, including dynamic correlation and volatility, to the ANN. To analyze the data effectively, the study divides it into two periods: before and during the COVID-19 outbreak. Each period is then further divided into a training set and a prediction set.
Findings
The empirical results show that BTC and gold have the highest positive correlation compared with crude oil and the USD, while BTC and the USD have a dynamic and negative correlation. More importantly, the ANN-DCC-GARCH model had a cumulative return of 318% before the outbreak of the COVID-19 pandemic and can decrease loss by 50% during the COVID-19 pandemic. Moreover, the risk-averse can turn a loss into a profit of about 20% in 2022.
Originality/value
The empirical analysis provides technical support and decision-making reference for investors and financial institutions to make investment decisions on BTC.
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Monica Puri Sikka, Alok Sarkar and Samridhi Garg
With the help of basic physics, the application of computer algorithms in the form of recent advances such as machine learning and neural networking in textile Industry has been…
Abstract
Purpose
With the help of basic physics, the application of computer algorithms in the form of recent advances such as machine learning and neural networking in textile Industry has been discussed in this review. Scientists have linked the underlying structural or chemical science of textile materials and discovered several strategies for completing some of the most time-consuming tasks with ease and precision. Since the 1980s, computer algorithms and machine learning have been used to aid the majority of the textile testing process. With the rise in demand for automation, deep learning, and neural networks, these two now handle the majority of testing and quality control operations in the form of image processing.
Design/methodology/approach
The state-of-the-art of artificial intelligence (AI) applications in the textile sector is reviewed in this paper. Based on several research problems and AI-based methods, the current literature is evaluated. The research issues are categorized into three categories based on the operation processes of the textile industry, including yarn manufacturing, fabric manufacture and coloration.
Findings
AI-assisted automation has improved not only machine efficiency but also overall industry operations. AI's fundamental concepts have been examined for real-world challenges. Several scientists conducted the majority of the case studies, and they confirmed that image analysis, backpropagation and neural networking may be specifically used as testing techniques in textile material testing. AI can be used to automate processes in various circumstances.
Originality/value
This research conducts a thorough analysis of artificial neural network applications in the textile sector.
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Inma Rodríguez-Ardura, Antoni Meseguer-Artola, Doaa Herzallah and Qian Fu
There is an ongoing challenge to map the efficacy of e-retailing strategies in building both value co-creation opportunities for online customers and customer value for companies…
Abstract
Purpose
There is an ongoing challenge to map the efficacy of e-retailing strategies in building both value co-creation opportunities for online customers and customer value for companies. Based on the service-dominant (S-D) logic, an integrative model is provided that connects the impact of convenience and personalisation strategies (CPSs) on an e-retailer's performance – by offering co-creation opportunities and customer engagement.
Design/methodology/approach
The survey instrument is validated and the model is tested with data from active online customers using a novel methodology that blends artificial neural network (ANN) analysis with partial least squares (PLS) in both the measurement model and the path analysis.
Findings
The findings robustly support the model and yield evidence of the contribution of CPSs in effective value propositions, the interface between the S-D logic and customer engagement, and the direct effect of customer engagement on tangible forms of value for companies.
Originality/value
This study is the first scholarly effort to provide a comprehensive understanding of how and why CPSs can maximise customer value for the e-retailer, while simultaneously testing the customer value/engagement interface with a new blended ANN-PLS method.
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Geming Zhang, Lin Yang and Wenxiang Jiang
The purpose of this study is to introduce the top-level design ideas and the overall architecture of earthquake early-warning system for high speed railways in China, which is…
Abstract
Purpose
The purpose of this study is to introduce the top-level design ideas and the overall architecture of earthquake early-warning system for high speed railways in China, which is based on P-wave earthquake early-warning and multiple ways of rapid treatment.
Design/methodology/approach
The paper describes the key technologies that are involved in the development of the system, such as P-wave identification and earthquake early-warning, multi-source seismic information fusion and earthquake emergency treatment technologies. The paper also presents the test results of the system, which show that it has complete functions and its major performance indicators meet the design requirements.
Findings
The study demonstrates that the high speed railways earthquake early-warning system serves as an important technical tool for high speed railways to cope with the threat of earthquake to the operation safety. The key technical indicators of the system have excellent performance: The first report time of the P-wave is less than three seconds. From the first arrival of P-wave to the beginning of train braking, the total delay of onboard emergency treatment is 3.63 seconds under 95% probability. The average total delay for power failures triggered by substations is 3.3 seconds.
Originality/value
The paper provides a valuable reference for the research and development of earthquake early-warning system for high speed railways in other countries and regions. It also contributes to the earthquake prevention and disaster reduction efforts.
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Temidayo Oluwasola Osunsanmi, Timothy O. Olawumi, Andrew Smith, Suha Jaradat, Clinton Aigbavboa, John Aliu, Ayodeji Oke, Oluwaseyi Ajayi and Opeyemi Oyeyipo
The study aims to develop a model that supports the application of data science techniques for real estate professionals in the fourth industrial revolution (4IR) era. The present…
Abstract
Purpose
The study aims to develop a model that supports the application of data science techniques for real estate professionals in the fourth industrial revolution (4IR) era. The present 4IR era gave birth to big data sets and is beyond real estate professionals' analysis techniques. This has led to a situation where most real estate professionals rely on their intuition while neglecting a rigorous analysis for real estate investment appraisals. The heavy reliance on their intuition has been responsible for the under-performance of real estate investment, especially in Africa.
Design/methodology/approach
This study utilised a survey questionnaire to randomly source data from real estate professionals. The questionnaire was analysed using a combination of Statistical package for social science (SPSS) V24 and Analysis of a Moment Structures (AMOS) graphics V27 software. Exploratory factor analysis was employed to break down the variables (drivers) into meaningful dimensions helpful in developing the conceptual framework. The framework was validated using covariance-based structural equation modelling. The model was validated using fit indices like discriminant validity, standardised root mean square (SRMR), comparative fit index (CFI), Normed Fit Index (NFI), etc.
Findings
The model revealed that an inclusive educational system, decentralised real estate market and data management system are the major drivers for applying data science techniques to real estate professionals. Also, real estate professionals' application of the drivers will guarantee an effective data analysis of real estate investments.
Originality/value
Numerous studies have clamoured for adopting data science techniques for real estate professionals. There is a lack of studies on the drivers that will guarantee the successful adoption of data science techniques. A modern form of data analysis for real estate professionals was also proposed in the study.
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This study aims to examine the relationship between internal and external factors and job satisfaction, and between job satisfaction and auditors’ performance.
Abstract
Purpose
This study aims to examine the relationship between internal and external factors and job satisfaction, and between job satisfaction and auditors’ performance.
Design/methodology/approach
This research used deductive approach. Data was gathered from 83 auditors in the Saudi Organisation for Certified Public Accountants (SOCPA) database. By implementing the partial least squares-structural equation modelling (PLS-SEM) technique, the suggested hypotheses were examined.
Findings
The results show that internal factors, i.e., achievement, advancement, recognition and growth, significantly impact job satisfaction. Subsequently, the external factors, i.e., company policies, relationship with a peer and relationship with supervisor, significantly impact job satisfaction. In contrast, work security has no relationship with job satisfaction. Furthermore, job satisfaction is a significant driver for auditors' performance.
Research limitations/implications
This research sheds light on the relationships between internal and external factors, job satisfaction and auditors' performance in the Saudi context. It would be interesting to investigate these relationships in a different setting, such as a different country, time or industry. Future studies should broaden the sample frame to include different types of employees to obtain more generalisable results.
Practical implications
This study may help managers of auditing departments formulate appropriate strategies and design effective programs to increase the level of job satisfaction between auditors by enhancing such factors, which will lead to improving the auditors' performance.
Originality/value
This research provide an empirical evidence to support the theoretical assumptions of Herzberg's which is much needed.
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Chin Ann Chong, Lee Peng Ng and I-Chi Chen
This study evaluates the moderating role of work-based social supports (i.e. supervisor support and co-worker support) in the relationship between job insecurity and job burnout…
Abstract
Purpose
This study evaluates the moderating role of work-based social supports (i.e. supervisor support and co-worker support) in the relationship between job insecurity and job burnout among hospitality employees in Malaysia. Besides, the direct effect between job insecurity and job burnout is examined.
Design/methodology/approach
The cross-sectional data of this study were based on a total of 220 self-administered questionnaires that have been completed by hospitality employees from three different states in Malaysia. Respondents were recruited based on a snowball sampling approach. The data were collected during the COVID-19 pandemic, which was from October 2020 to January 2021.
Findings
Partial least square-structural equation modeling (PLS-SEM) was performed via SmartPLS software. The finding confirmed that job insecurity significantly intensifies employees' job burnout. Supervisor support and co-worker support were found to moderate the link between job insecurity and burnout. As anticipated, the relationship between job insecurity and job burnout increased when supervisor support is low. But high co-worker support was found to strengthen the impact of job insecurity on job burnout instead of the reverse.
Originality/value
This study supplements the existing literature by clarifying which sources of work-based social support (i.e. co-worker support or supervisor) is more salient in alleviating the adverse impact of job insecurity on job burnout during the COVID-19 pandemic among hospitality employees in Malaysia.
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Karen-Ann M. Dwyer, Niamh M. Brennan and Collette E. Kirwan
This rich descriptive study examines auditors' client risk assessment (i.e. “key audit matters”/critical audit matters) disclosures in expanded audit reports of 328 Financial…
Abstract
Purpose
This rich descriptive study examines auditors' client risk assessment (i.e. “key audit matters”/critical audit matters) disclosures in expanded audit reports of 328 Financial Times Stock Exchange (FTSE) 350 companies. The study compares auditor-identified client risks with corporate risk disclosures identified in audit committee reports, in terms of number and type of risks. The research also compares variation in auditor-identified client risks between individual Big 4 audit firms. In addition, the study examines auditor ranking of their client risks disclosed.
Design/methodology/approach
The study manually content analyses disclosures in audit reports and audit committee reports of a sample of 328 FTSE-350 companies with 2015 year-ends.
Findings
Audit committees identify more risks than auditors (23% more risks). However, auditor-identified client risks and audit-committee-identified risks are similar (80% similar), as are auditor-identified client risks between the individual Big 4 audit firms. Only ten (3%) audit reports rank the importance of auditor-identified client risks.
Research limitations/implications
Sample is restricted to one year, one jurisdiction, large-listed companies and companies audited by Big 4 auditors.
Practical implications
The study provides important insights for regulators, auditors and users of financial statements by identifying influences on disclosure of auditor-identified client risks.
Originality/value
The paper mobilises institutional theory to interpret the findings. The findings suggest that auditor-identified client risks in expanded audit reports may demonstrate mimetic behaviour in terms of similarity with audit-committee-identified risks and similarity between individual Big 4 audit firms. The study provides important insights for regulators, auditors and users of financial statements by identifying influences on disclosure of auditor-identified client risks.
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