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1 – 5 of 5Shicheng Huang, Yaqi Wang, Xiaoya Gong and Fumin Deng
This paper aims to explore the underlying mechanisms and boundary conditions through which equipment manufacturing enterprises can capture market value from digital…
Abstract
Purpose
This paper aims to explore the underlying mechanisms and boundary conditions through which equipment manufacturing enterprises can capture market value from digital transformation, with a specific focus on the roles of knowledge search and knowledge recombination.
Design/methodology/approach
This study uses a double fixed-effects model to test the hypotheses, using a unique data set of “firm-year” observations from 739 publicly listed equipment manufacturing companies in China, spanning the period from 2018 to 2022.
Findings
Digital transformation drives market value creation in equipment manufacturing enterprises through both breakthrough knowledge recombination (BKR) and progressive knowledge recombination (PKR). In addition, the analysis of marginal conditions reveals that diversified knowledge search serves as a substitute for digital transformation in promoting BKR, while also positively moderating the relationship between digital transformation and PKR.
Originality/value
Grounded in the knowledge-based view theoretical framework, this study introduces the novel concepts of BKR and PKR and systematically examines how digital transformation impacts market value in equipment manufacturing enterprises.
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Shicheng Chen, Daniel Roy Eyers, Jonathan Gosling and Yuan Huang
Whilst there has been much research examining risk management in construction supply chains, there is a relative dearth of knowledge concerning small and medium-sized enterprises…
Abstract
Purpose
Whilst there has been much research examining risk management in construction supply chains, there is a relative dearth of knowledge concerning small and medium-sized enterprises (SMEs) in this context. SMEs are considered vulnerable economic agents due to their financial constraints and reduced viability compared to large firms. This study aims to fill this gap by providing a comprehensive review, identifying key challenges in the research and generating a future research agenda.
Design/methodology/approach
A structured literature review was conducted in this study, resulting in the identification of 106 articles that relate to construction SME risks. Thematic analysis was then employed to determine the supply chain risk themes. Additionally, VOSviewer was employed to depict content frequency and, most recently, trends based on the timeline.
Findings
This paper uncovers eight distinct supply chain risks pertinent to construction SMEs, arranging these into three themes from the standpoint of supply chain risk management. Moreover, it identifies six gaps in the existing body of research on construction SMEs and puts forth prospective research directions and questions to address each of these identified gaps.
Originality/value
The practical significance of this study is to provide SMEs in the construction industry with a comprehensive framework for identifying and categorizing risks related to management and strategy, operations and processes and sustainability. With this framework, SMEs can systematically assess potential risks at all stages of a project.
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Han Shen, Chengyi Song, Mimi Li and Qian Jiang
SNS, namely social networking sites, has become one of the most effective and fast channels of information diffusion and dissemination. As an influential way of online marketing…
Abstract
SNS, namely social networking sites, has become one of the most effective and fast channels of information diffusion and dissemination. As an influential way of online marketing, SNS has been increasingly used by tourism organizations and enterprises to shape their destination image. On the basis of previews literature of destination image and SNS, this paper used the text analysis software ROST Content Mining (ROST CM) System to do a case study of the SNS destination marketing of Singapore on Chinese market. The authors analyze the text related to Singapore tourism on the major SNS in mainland China: Renren, Sina Weibo, and Douban, through word frequency analysis and the social semantic network, to summarize the destination image of Singapore on SNS. The paper also focuses on the difference of image building by official and individual SNS. Results found by this paper can be used by the relevant tourism organizations and enterprises to improve their destination marketing and image building on SNS channels.
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Hao He, Dongfang Yang, Shicheng Wang, Shuyang Wang and Xing Liu
The purpose of this paper is to study the road segmentation problem of cross-modal remote sensing images.
Abstract
Purpose
The purpose of this paper is to study the road segmentation problem of cross-modal remote sensing images.
Design/methodology/approach
First, the baseline network based on the U-net is trained under a large-scale dataset of remote sensing imagery. Then, the cross-modal training data are used to fine-tune the first two convolutional layers of the pre-trained network to achieve the adaptation to the local features of the cross-modal data. For the cross-modal data of different band, an autoencoder is designed to achieve data conversion and local feature extraction.
Findings
The experimental results show the effectiveness and practicability of the proposed method. Compared with the ordinary method, the proposed method gets much better metrics.
Originality/value
The originality is the transfer learning strategy that fine-tunes the low-level layers for the cross-modal data application. The proposed method can achieve satisfied road segmentation with a small amount of cross-modal training data, so that is has a good application value. Still, for the similar application of cross-modal data, the idea provided by this paper is helpful.
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Yali Wang, Jian Zuo, Min Pan, Bocun Tu, Rui-Dong Chang, Shicheng Liu, Feng Xiong and Na Dong
Accurate and timely cost prediction is critical to the success of construction projects which is still facing challenges especially at the early stage. In the context of rapid…
Abstract
Purpose
Accurate and timely cost prediction is critical to the success of construction projects which is still facing challenges especially at the early stage. In the context of rapid development of machine learning technology and the massive cost data from historical projects, this paper aims to propose a novel cost prediction model based on historical data with improved performance when only limited information about the new project is available.
Design/methodology/approach
The proposed approach combines regression analysis (RA) and artificial neural network (ANN) to build a novel hybrid cost prediction model with the former as front-end prediction and the latter as back-end correction. Firstly, the main factors influencing the cost of building projects are identified through literature research and subsequently screened by principal component analysis (PCA). Secondly the optimal RA model is determined through multi-model comparison and used for front-end prediction. Finally, ANN is applied to construct the error correction model. The hybrid RA-ANN model was trained and tested with cost data from 128 completed construction projects in China.
Findings
The results show that the hybrid cost prediction model has the advantages of both RA and ANN whose prediction accuracy is higher than that of RA and ANN only with the information such as total floor area, height and number of floors.
Originality/value
(1) The most critical influencing factors of the buildings’ cost are found out by means of PCA on the historical data. (2) A novel hybrid RA-ANN model is proposed which proved to have the advantages of both RA and ANN with higher accuracy. (3) The comparison among different models has been carried out which is helpful to future model selection.
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