Drawing on population ecology and net political theory, the purpose of this paper is to examine the influence of representative institutions (net political benefits (NPB)) on the…
Abstract
Purpose
Drawing on population ecology and net political theory, the purpose of this paper is to examine the influence of representative institutions (net political benefits (NPB)) on the success rates of privatization adopted by state‐owned enterprises (SOEs) and their learning curve during the process.
Design/methodology/approach
The author focuses on the privatization of SOEs listed in China and employs the social network with event‐history analysis (survival analysis).
Findings
The results demonstrate that the determinants of NPB increase the success rates of privatization, while SOEs learn from two perspectives: private firms and other privatizing SOEs within the same population. In particular, I suggest that the outdegree market location network centrality before privatization, as well as the indegree centrality after privatization, both positively moderate the relationship between learning processes and the success rate of SOEs' privatization.
Practical implications
More precisely, institutions are factors that not only determine the choice of SOEs to privatization, but also the success of this strategy. The finding will encourage government administrations, both central and local, to promote the development of institutions in order to facilitate market transactions. Implications for firms might be the learning mechanisms discovered in this research. When firms adopted the strategy of privatization, they could choose two sources of learning: private firms within the same industry and others who were implementing the same strategy.
Originality/value
The contribution of the paper is, its synthesis of institutional and ecological perspectives and research on privatization in emerging markets and organization learning in networking. Theoretically, the author extends the privatization literature into a worldly context with a combination of institution and organization learning theory. Empirically, with the application of network perspective and survival analysis, the author uncovered and carefully examined the learning mechanism in the process of privatization.
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Zhixin Wang, Peng Xu, Bohan Liu, Yankun Cao, Zhi Liu and Zhaojun Liu
This paper aims to demonstrate the principle and practical applications of hyperspectral object detection, carry out the problem we now face and the possible solution. Also some…
Abstract
Purpose
This paper aims to demonstrate the principle and practical applications of hyperspectral object detection, carry out the problem we now face and the possible solution. Also some challenges in this field are discussed.
Design/methodology/approach
First, the paper summarized the current research status of the hyperspectral techniques. Then, the paper demonstrated the development of underwater hyperspectral techniques from three major aspects, which are UHI preprocess, unmixing and applications. Finally, the paper presents a conclusion of applications of hyperspectral imaging and future research directions.
Findings
Various methods and scenarios for underwater object detection with hyperspectral imaging are compared, which include preprocessing, unmixing and classification. A summary is made to demonstrate the application scope and results of different methods, which may play an important role in the application of underwater hyperspectral object detection in the future.
Originality/value
This paper introduced several methods of hyperspectral image process, give out the conclusion of the advantages and disadvantages of each method, then demonstrated the challenges we face and the possible way to deal with them.
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Yang Li, Zhaojun Yang, Fei Chen and Jin Zhao
This paper aims to investigate the effects of air inlet flow rate on the bearing cavity and operating conditions during the oil-air lubrication.
Abstract
Purpose
This paper aims to investigate the effects of air inlet flow rate on the bearing cavity and operating conditions during the oil-air lubrication.
Design/methodology/approach
A model of oil-air lubrication of rolling bearings is established using computational fluid dynamics numerical simulation. Moreover, temperature and vibration experiments are carried out for comparisons and validation.
Findings
Results suggest that the velocity and pressure distributions of the oil-air flow inside the chamber are not uniform. Moreover, the uniform decreases with increasing air inlet flow rate. The non-uniform oil distribution inside the bearing significantly influences the bearing temperature rise and lubrication effect. Furthermore, the decrease in pressure uniformity enhances the vibration intensity and increases the amplitude of the vibration acceleration by more than 40 per cent. Increasing the air inlet flow rate improves lubrication and cooling efficiency but produces intense vibrations.
Originality/value
A method of establishing rolling bearings model under oil-air lubrication is presented in the paper. The effect of air inlet flow rate on flow uniform under oil-air lubrication has been researched insightfully. The results provide a useful reference to improve the oil-air lubrication system and enhance the operational stability of the motorized spindle.
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Meijuan Li, Jiarong Zhang and Zijie Shen
Three-parameter interval grey numbers (TPIGNs) have been extensively studied as an extended form of interval numbers. However, most existing TPIGN multi-attribute decision-making…
Abstract
Purpose
Three-parameter interval grey numbers (TPIGNs) have been extensively studied as an extended form of interval numbers. However, most existing TPIGN multi-attribute decision-making methods only consider the similarity of positions, ignore the similarity of developmental directions and focus primarily on static evaluation. To address these limitations, in this study, the authors propose a dynamic technique for order preference by similarity to an ideal solution (TOPSIS) based on modified Jaccard similarity and angle similarity for TPIGNs.
Design/methodology/approach
First, the authors extend Jaccard similarity to a TPIGN environment to represent positional similarity. A simple example is provided to illustrate the limitations of the traditional Jaccard similarity. Then, the authors introduce an angle similarity measure to represent developmental directional similarity. Finally, a dynamic TOPSIS model is constructed by incorporating time-series data into conventional two-dimensional static data. Stage weights are obtained by an objective function designed to maximize the amount and minimize the fluctuation of decision information. A quadratic weighting method is adopted to derive the overall evaluation value of alternatives.
Findings
To evaluate the effectiveness of the proposed method, this study takes the pre-assessment of ice disaster and the selection of cooperative enterprises as examples. The authors then provide the results of comparative and sensitivity analyses, which demonstrate the effectiveness and flexibility of the proposed method.
Originality/value
The proposed TOPSIS method in a TPIGN environment can take a more holistic and dynamic perspective for decision-making, which helps mitigate the limitations of single-perspective or static evaluations.
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Wenqi Mao, Kexin Ran, Ting-Kwei Wang, Anyuan Yu, Hongyue Lv and Jieh-Haur Chen
Although extensive research has been conducted on precast production, irregular component loading constraints have received little attention, resulting in limitations for…
Abstract
Purpose
Although extensive research has been conducted on precast production, irregular component loading constraints have received little attention, resulting in limitations for transportation cost optimization. Traditional irregular component loading methods are based on past performance, which frequently wastes vehicle space. Additionally, real-time road conditions, precast component assembly times, and delivery vehicle waiting times due to equipment constraints at the construction site affect transportation time and overall transportation costs. Therefore, this paper aims to provide an optimization model for Just-In-Time (JIT) delivery of precast components considering 3D loading constraints, real-time road conditions and assembly time.
Design/methodology/approach
In order to propose a JIT (just-in-time) delivery optimization model, the effects of the sizes of irregular precast components, the assembly time, and the loading methods are considered in the 3D loading constraint model. In addition, for JIT delivery, incorporating real-time road conditions in the transportation process is essential to mitigate delays in the delivery of precast components. The 3D precast component loading problem is solved by using a hybrid genetic algorithm which mixes the genetic algorithm and the simulated annealing algorithm.
Findings
A real case study was used to validate the JIT delivery optimization model. The results indicated this study contributes to the optimization of strategies for loading irregular precast components and the reduction of transportation costs by 5.38%.
Originality/value
This study establishes a JIT delivery optimization model with the aim of reducing transportation costs by considering 3D loading constraints, real-time road conditions and assembly time. The irregular precast component is simplified into 3D bounding box and loaded with three-space division heuristic packing algorithm. In addition, the hybrid algorithm mixing the genetic algorithm and the simulated annealing algorithm is to solve the 3D container loading problem, which provides both global search capability and the ability to perform local searching. The JIT delivery optimization model can provide decision-makers with a more comprehensive and economical strategy for loading and transporting irregular precast components.
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Wendy Kesuma, Irwan Adi Ekaputra and Dony Abdul Chalid
This paper investigates whether individual investors are attentive to stock splits and whether higher split ratios (stronger private information signals) reduce the disposition…
Abstract
Purpose
This paper investigates whether individual investors are attentive to stock splits and whether higher split ratios (stronger private information signals) reduce the disposition effect.
Design/methodology/approach
This study employs stock split events and transaction data in the Indonesia Stock Exchange (IDX) from January 2004 to December 2017. The authors measure individual investors' attention using buy-initiated trades. To test the effect of split signal on disposition effect, the authors regress individual investors' sell-initiated trades on past stock returns.
Findings
Unlike Birru (2015), the authors find that individual investors are attentive to stock splits, especially when stock split ratios are high. In turn, stock splits tend to weaken the disposition effect. The higher the stock split ratios, the weaker the disposition effect.
Research limitations/implications
This study has a limitation in that the authors exclude all stock splits with dividend events around the split date. These stock splits cover 37% of all splits in Indonesia.
Practical implications
Practically, individual investors should look for stock-related information to reduce disposition bias.
Originality/value
To the best of authors’ knowledge, this study is the first to test individual investors' attention on stock splits based on their buy-initiated trades. This study is also the first to test the impact of stock split ratios on the disposition effect reduction. This study's findings enrich the scant literature on individual investors' attention and how to reduce their disposition effect bias.