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1 – 10 of 88Biyanka Ekanayake, Alireza Ahmadian Fard Fini, Johnny Kwok Wai Wong and Peter Smith
Recognising the as-built state of construction elements is crucial for construction progress monitoring. Construction scholars have used computer vision-based algorithms to…
Abstract
Purpose
Recognising the as-built state of construction elements is crucial for construction progress monitoring. Construction scholars have used computer vision-based algorithms to automate this process. Robust object recognition from indoor site images has been inhibited by technical challenges related to indoor objects, lighting conditions and camera positioning. Compared with traditional machine learning algorithms, one-stage detector deep learning (DL) algorithms can prioritise the inference speed, enable real-time accurate object detection and classification. This study aims to present a DL-based approach to facilitate the as-built state recognition of indoor construction works.
Design/methodology/approach
The one-stage DL-based approach was built upon YOLO version 4 (YOLOv4) algorithm using transfer learning with few hyperparameters customised and trained in the Google Colab virtual machine. The process of framing, insulation and drywall installation of indoor partitions was selected as the as-built scenario. For training, images were captured from two indoor sites with publicly available online images.
Findings
The DL model reported a best-trained weight with a mean average precision of 92% and an average loss of 0.83. Compared to previous studies, the automation level of this study is high due to the use of fixed time-lapse cameras for data collection and zero manual intervention from the pre-processing algorithms to enhance visual quality of indoor images.
Originality/value
This study extends the application of DL models for recognising as-built state of indoor construction works upon providing training images. Presenting a workflow on training DL models in a virtual machine platform by reducing the computational complexities associated with DL models is also materialised.
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Mahesh Babu Purushothaman and Kasun Moolika Gedara
This pragmatic research paper aims to unravel the smart vision-based method (SVBM), an AI program to correlate the computer vision (recorded and live videos using mobile and…
Abstract
Purpose
This pragmatic research paper aims to unravel the smart vision-based method (SVBM), an AI program to correlate the computer vision (recorded and live videos using mobile and embedded cameras) that aids in manual lifting human pose deduction, analysis and training in the construction sector.
Design/methodology/approach
Using a pragmatic approach combined with the literature review, this study discusses the SVBM. The research method includes a literature review followed by a pragmatic approach and lab validation of the acquired data. Adopting the practical approach, the authors of this article developed an SVBM, an AI program to correlate computer vision (recorded and live videos using mobile and embedded cameras).
Findings
Results show that SVBM observes the relevant events without additional attachments to the human body and compares them with the standard axis to identify abnormal postures using mobile and other cameras. Angles of critical nodal points are projected through human pose detection and calculating body part movement angles using a novel software program and mobile application. The SVBM demonstrates its ability to data capture and analysis in real-time and offline using videos recorded earlier and is validated for program coding and results repeatability.
Research limitations/implications
Literature review methodology limitations include not keeping in phase with the most updated field knowledge. This limitation is offset by choosing the range for literature review within the last two decades. This literature review may not have captured all published articles because the restriction of database access and search was based only on English. Also, the authors may have omitted fruitful articles hiding in a less popular journal. These limitations are acknowledged. The critical limitation is that the trust, privacy and psychological issues are not addressed in SVBM, which is recognised. However, the benefits of SVBM naturally offset this limitation to being adopted practically.
Practical implications
The theoretical and practical implications include customised and individualistic prediction and preventing most posture-related hazardous behaviours before a critical injury happens. The theoretical implications include mimicking the human pose and lab-based analysis without attaching sensors that naturally alter the working poses. SVBM would help researchers develop more accurate data and theoretical models close to actuals.
Social implications
By using SVBM, the possibility of early deduction and prevention of musculoskeletal disorders is high; the social implications include the benefits of being a healthier society and health concerned construction sector.
Originality/value
Human pose detection, especially joint angle calculation in a work environment, is crucial to early deduction of muscoloskeletal disorders. Conventional digital technology-based methods to detect pose flaws focus on location information from wearables and laboratory-controlled motion sensors. For the first time, this paper presents novel computer vision (recorded and live videos using mobile and embedded cameras) and digital image-related deep learning methods without attachment to the human body for manual handling pose deduction and analysis of angles, neckline and torso line in an actual construction work environment.
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The purpose of this paper is to examine the Shariah governance mechanisms of takaful insurance and their impact on its financial performance.
Abstract
Purpose
The purpose of this paper is to examine the Shariah governance mechanisms of takaful insurance and their impact on its financial performance.
Design/methodology/approach
The effect of Shariah governance mechanisms on financial performance is analyzed over 2012–2018 on a sample of 11 takaful listed insurances in the Middle East region. Using multiple regression models, four hypotheses addressing Shariah governance mechanisms are tested.
Findings
The findings generally reveal that Shariah governance has an impact on the financial performance of takaful insurance. The Shariah Supervisory Board (SSB) size, the members’ reputation and their qualifications are the main determinants of financial performance for listed takaful insurance.
Research limitations/implications
This paper includes two main limitations that may affect the accuracy of the finding. First, the results are restricted to the Middle East region and may not be generalized to other regions. Second, the sample is dominated by UAE, i.e. 4 takaful insurances out of 11.
Practical implications
Both Shariah governance and regular governance have an impact on the financial performance of takaful insurance. Yet, the effect of Shariah governance is more robust. To improve its financial performance, takaful insurance should expand the size of the SSB, hiring reputable scholars and recruit doctors in Islamic economics.
Originality/value
This research studies takaful insurance, unlike the majority of other works that have focused on Islamic banks.
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Xiaoying Li, Xiujuan Jin, Heng Li, Lulu Gong and Deyang Zhou
Considering the substantial benefits derived from the use of Building Information Modeling (BIM) in construction projects, governments and its related sectors have introduced…
Abstract
Purpose
Considering the substantial benefits derived from the use of Building Information Modeling (BIM) in construction projects, governments and its related sectors have introduced mandatory policies requiring the use of BIM. However, little is known about the impact of mandatory policies on BIM-based project performance. Therefore, the purpose of this paper is to provide a systematical understanding on the impact of policy interventions on the implementation practice of innovative technologies.
Design/methodology/approach
This paper utilizes the propensity score matching and difference in differences (PSM-DID) method to investigate the impact of policy interventions on BIM-based project performance. Using the panel data collected from 2015 to 2021 in the Hong Kong construction industry, this paper explores the impact of the first mandatory BIM policy on the BIM-based project performance of three key stakeholders.
Findings
The subjective BIM performance and BIM return on investment (ROI) have significantly improved after implementing the mandatory BIM policy. The promotion effect of mandatory BIM policy on BIM-based project performance gradually increases over time. Moreover, the promotion effect of mandatory BIM policy on BIM performance shows significant heterogeneity for different stakeholders and organizations of different sizes.
Originality/value
This study examined the impact of policy interventions on BIM-based project performance. The research findings can provide a holistic understanding of the potential implications of innovative mandatory policy in performance improvement and offer some constructive suggestions to policymakers and industry practitioners to promote the penetration of BIM in the construction industry.
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Wang Zhang, Lizhe Fan, Yanbin Guo, Weihua Liu and Chao Ding
The purpose of this study is to establish a method for accurately extracting torch and seam features. This will improve the quality of narrow gap welding. An adaptive deflection…
Abstract
Purpose
The purpose of this study is to establish a method for accurately extracting torch and seam features. This will improve the quality of narrow gap welding. An adaptive deflection correction system based on passive light vision sensors was designed using the Halcon software from MVtec Germany as a platform.
Design/methodology/approach
This paper proposes an adaptive correction system for welding guns and seams divided into image calibration and feature extraction. In the image calibration method, the field of view distortion because of the position of the camera is resolved using image calibration techniques. In the feature extraction method, clear features of the weld gun and weld seam are accurately extracted after processing using algorithms such as impact filtering, subpixel (XLD), Gaussian Laplacian and sense region for the weld gun and weld seam. The gun and weld seam centers are accurately fitted using least squares. After calculating the deviation values, the error values are monitored, and error correction is achieved by programmable logic controller (PLC) control. Finally, experimental verification and analysis of the tracking errors are carried out.
Findings
The results show that the system achieves great results in dealing with camera aberrations. Weld gun features can be effectively and accurately identified. The difference between a scratch and a weld is effectively distinguished. The system accurately detects the center features of the torch and weld and controls the correction error to within 0.3mm.
Originality/value
An adaptive correction system based on a passive light vision sensor is designed which corrects the field-of-view distortion caused by the camera’s position deviation. Differences in features between scratches and welds are distinguished, and image features are effectively extracted. The final system weld error is controlled to 0.3 mm.
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Janya Chanchaichujit, Sreejith Balasubramanian and Vinaya Shukla
The purpose of this study is to identify and analyze the barriers associated with the adoption of Industry 4.0 technologies in agricultural supply chains.
Abstract
Purpose
The purpose of this study is to identify and analyze the barriers associated with the adoption of Industry 4.0 technologies in agricultural supply chains.
Design/methodology/approach
The study initially identified thirteen barriers by conducting a literature review and semi-structured interviews with key stakeholders. Subsequently, these barriers were validated and modeled using an integrated Fuzzy Delphi-ISM approach. Finally, MICMAC analysis was employed to categorize the barriers into distinct clusters.
Findings
The results provide considerable insights into the hierarchical structure and complex interrelationships between the barriers as well the driving and dependence power of barriers. Lack of information about technologies and lack of compatibility with traditional methods emerged as the two main barriers which directly and indirectly influence the other ones.
Research limitations/implications
The robust hybrid Fuzzy Delphi and ISM techniques used in this study can serve as a useful model and benchmark for similar studies probing the barriers to Industry 4.0 adoption. From a theoretical standpoint, this study expands the scope of institutional theory in explaining Industry 4.0 adoption barriers.
Practical implications
The study is timely for the post-COVID-19 recovery and growth of the agricultural sector. The findings are helpful for policymakers and agriculture supply chain stakeholders in devising new strategies and policy interventions to prioritize and address Industry 4.0 adoption barriers.
Originality/value
It is the first comprehensive, multi-country and multi-method empirical study to comprehensively identify and model barriers to Industry 4.0 adoption in agricultural supply chains in emerging economies.
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Rosylin Mohd Yusof, Zaemah Zainuddin, Hafirda Akma Bt Musaddad, Siti Latipah Harun and Mohd Aamir Adeeb Abdul Rahim
This paper aims to propose a model for democratization of Islamic home financing to tackle the issue of sustainability of homeownership affordability.
Abstract
Purpose
This paper aims to propose a model for democratization of Islamic home financing to tackle the issue of sustainability of homeownership affordability.
Design/methodology/approach
A conceptual framework and fractional equity model (FEM) are developed to incorporate big data analytics, artificial intelligence and blockchain technology in an ecosystem for affordability and sustainability of homeownership via the proposed financing model. In addition, the FEM adopts the simulation approach to show its validity in terms of liquidity when compared with traditional home financing. In this regard, this paper is focused on developing and demonstrating the feasibility of a new financing model, rather than testing specific hypotheses or relationships. This is to propose the democratization model for Islamic Home Financing that will not benefit the prospective home buyers without compromising the profitability of the financial institutions.
Findings
The findings indicate that the proposed end-to-end solution within the financing ecosystem can lead to more efficient matching market between the buyers and sellers of houses, reduced transaction costs, greater transparency and enhanced efficiency which in the end could lead to lower costs of owning homes and sustained financial resilience among house owners. The findings indicate that the FEM model is able to increase homeownership with more elements of liquidity, marketability and sustainability for homebuyers.
Research limitations/implications
This research highlights the potential of big data and blockchain technology in democratizing Islamic home financing and evidence that the transfer of ownership is possible through tokenization. However, this will require a mature financing environment to adapt the technology for practical application.
Practical implications
The model proposes a solution to propagate shared prosperity among stakeholders such as the house buyers/owners, sellers, investors as well the government agencies. The proposed FEM model provides alternative home financing that is more marketable, flexible and sustainable for households/buyers and financiers.
Social implications
It is hoped that with the proposed financing ecosystem to promote affordability and sustainability of homeownership via big data analytics, artificial intelligence and blockchain technology can lead to greater financial resilience for homeowners which can then be translated to enhanced well-being, increased productivity and can further promote economic growth.
Originality/value
This research is a concept paper based on academic research and industry collaboration with a technology provider.
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Nourhen Sallemi and Ghazi Zouari
The purpose of this study is to examine the impact of board characteristics (board size, board independence and duality) on the performance of takaful insurance providers with…
Abstract
Purpose
The purpose of this study is to examine the impact of board characteristics (board size, board independence and duality) on the performance of takaful insurance providers with distinguishable muamalah contracts (wakalah and hybrid) moderated by ownership concentration.
Design/methodology/approach
The sample consists of 30 takaful insurances. The authors divided it into two subsamples: 18 insurance companies using wakalah contracts provided by Southeast Asia and 12 insurance companies using hybrid contracts provided by the Gulf Cooperation Council over the period 2010–2020. For data analysis, the authors used the partial least squares path modeling method.
Findings
The results show that the larger the board of directors and the higher the number of independent directors, the greater the takaful performance in both the wakalah and hybrid subsamples. Nondual functions improve the takaful performance in both the wakalah and hybrid subsamples. The results also reveal that a highly concentrated ownership structure positively (negatively) moderates the relationship between board size and takaful performance in the wakalah (hybrid) subsamples. Moreover, highly concentrated ownership insignificantly (negatively) moderates the relationship between independent directors and takaful’s performance in the hybrid (wakalah) subsample. Furthermore, a highly concentrated ownership structure insignificantly (negatively) moderates the relationship between the nondual structure and takaful performance in the wakalah (hybrid) subsample.
Originality/value
This study contributes to the understanding of the moderating role of a highly concentrated ownership structure between the characteristics of the board of directors and the performance of takaful insurance, which applies wakalah and hybrid contracts. In addition, this study contributes to takaful insurance by determining the appropriate board characteristics that must be adopted to achieve oversight and improve performance. Regulators should appreciate this contribution to the formulation of suitable approaches for efficiently supervising takaful insurance activities.
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Herbert Sima, Henry F.L. Chung and Yulong Liu
Drawing on the organizational learning and relational governance literature, this study aims to advance a theoretical model to explain the export performance of emerging market…
Abstract
Purpose
Drawing on the organizational learning and relational governance literature, this study aims to advance a theoretical model to explain the export performance of emerging market export ventures.
Design/methodology/approach
This study selects quantitative methodology because the main objective of this study is to explore the role of export ventures’ performance (past) on guanxi networking, co-creation marketing strategies and present performance.
Findings
The empirical evidence suggests that guanxi networking and co-creation strategy can mediate the relationship between export venture performance in the preceding year and export venture performance in the following year. In addition, this study also provides some guidance for emerging market export ventures on how to build a strong guanxi networking and create opportunities for collaboration when the effect of export performance in the preceding year on current performance is absent.
Originality/value
The authors propose the inclusion of strategic guanxi networking-related factors (e.g. top executives’ ties with business-to-business customers, such as distributors in the host market) in the prior performance-current performance paradigm. The outcomes of this study also contribute to extant organizational learning theory research by integrating preceding performance research with the co-creation theory. The study offers new insights into organizational learning and relational governance from the emerging market perspective.
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Rajiv Gurung, Manesh Choubey and Runa Rai
Farmer producer organisations (FPOs) are considered as a strategy to improve the livelihoods of small farmers through economies of scale by providing collective strength to…
Abstract
Purpose
Farmer producer organisations (FPOs) are considered as a strategy to improve the livelihoods of small farmers through economies of scale by providing collective strength to farmers for improved access to production technology, value-addition services, high-quality inputs and marketing services for improving their incomes. This study investigates the impact of FPO membership on organic farming household's income in Northeast India.
Design/methodology/approach
This study uses field survey data collected from all four districts of Sikkim. Primary data were obtained from a survey of 560 organic farming households, 280 of which are FPO members and the rest 280 are non-members. Propensity score matching (PSM) is used to estimate the impact of FPO membership on net returns, return on investment (ROI) and profit margin.
Findings
Results show that the FPO members had, on average, Rs. 7,254–8,133 higher annual net returns, 4.6–4.8% higher ROI and 8–8.4% higher profit margin than the non-members. The findings confirm that FPO membership has a positive and significant impact on net returns, return on investment and profit margin. Also, heterogeneity analysis indicates that FPO membership has larger positive impact on relatively bigger farmers and female-headed households.
Research limitations/implications
As the study was based on a cross-sectional survey, the findings may be subjected to some limitations.
Originality/value
This study is based on a novel data set, collected specifically to examine the economic impact of FPO membership on organic farming in India.
Peer review
The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-06-2023-0451
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