Dan Long, Zi-yao Xia and Wang-bin Hu
The purpose of this paper is to bridge the obvious gap presented in research on antecedents of effectuation by building a research model from the perspectives of effectuation and…
Abstract
Purpose
The purpose of this paper is to bridge the obvious gap presented in research on antecedents of effectuation by building a research model from the perspectives of effectuation and entrepreneurial opportunity.
Design/methodology/approach
This paper examines the effects of patterns of opportunity discovery and the innovativeness of entrepreneurial opportunity on the decision-making process of effectuation in new venture creation. Eight hypotheses are put forward and examined by hierarchical multiple logistic regression. The data in this paper are based on the first two rounds of survey data from Chinese Panel Study of Entrepreneurial dynamics.
Findings
The empirical results show that patterns of opportunity discovery have significant positive effects (at least partially) on effectuation. Namely, entrepreneurs employing fortuitous discovery tend to use available means and leverage contingency. And with lower innovativeness of opportunity, entrepreneurs are more likely to use affordable loss and leverage contingency.
Research limitations/implications
The study is limited to each dimension of effectuation based on the single-item measure, which cannot completely reflect the effectual construct. More research should to be done to improve measures of effectuation.
Practical implications
The findings are useful for entrepreneurs to make effective decisions whether to choose effectuation in the face of different patterns of opportunity discovery. Besides, it provides the advice on how to cope with the innovativeness of opportunity and seize entrepreneurial opportunities to entrepreneurs.
Originality/value
This paper first systematically studies the effects of entrepreneurial opportunity on effectuation, making up for the obvious gap of research on antecedents of effectuation.
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Jinqiang Wang, Yaobin Lu, Si Fan, Peng Hu and Bin Wang
The purpose of the research is to explore how small and medium enterprises (SMEs) in central China achieve intelligent transformation through the use of artificial intelligence…
Abstract
Purpose
The purpose of the research is to explore how small and medium enterprises (SMEs) in central China achieve intelligent transformation through the use of artificial intelligence (AI). Because of unequal resource allocation, constraints on the intelligent transformation of SMEs in central China are different from those in economically and technologically well-developed coastal provinces. Hence, the authors focus on SMEs in central China to identify drivers of and barriers to intelligent transformation.
Design/methodology/approach
The interview data were collected from 66 SMEs across 20 industries in central China. To verify the validity of the data collection method, the authors used two methods to control for retrospective bias: multi-level informants and enterprises' AI project application materials (Wei and Clegg, 2020). The final data were validated without conflicts. Next, the authors cautiously followed a two-step approach recommended by Venkatesh et al. (2010) and used NVivo 11.0 to analyze the collected text data.
Findings
SMEs in central China are enthusiastic about intelligent transformation while facing both internal and external pressures. SMEs need to pay attention to both internal (enterprise development needs, implementation cost, human resources and top management involvement) and external factors (external market pressure, convenience of AI technology and policy support) and their different impacts on intelligent transformation. However, constrained by limited resources, SMEs in central China have been forced to take a step-by-step intelligent transformation strategy based on their actual needs with the technological flexibility method in the short term.
Originality/value
Considering the large number of SMEs and their importance in promoting China's economic development and job creation (SME Bureau of MIIT, 2020), more research on SMEs with limited resources is needed. In the study, the authors confirmed that enterprises should handle “social responsibility” carefully because over-emphasizing it will hinder intelligent transformation. However, firms should pay attention to the role of executives in promoting intelligent transformation and make full use of policy support to access more resources.
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Dan Song, Zhaohua Deng and Bin Wang
As more firms adopted AI-related services in recent years, AI service failures have increased. However, the potential costs of AI implementation are not well understood…
Abstract
Purpose
As more firms adopted AI-related services in recent years, AI service failures have increased. However, the potential costs of AI implementation are not well understood, especially the effect of AI service failure events. This study examines the influences of AI service failure events, including their industry, size, timing, and type, on firm value.
Design/methodology/approach
This study will conduct an event study of 120 AI service failure events in listed companies to evaluate the costs of such events.
Findings
First, AI service failure events have a negative impact on the firm value. Second, small firms experience more share price declines due to AI service failure events than large firms. Third, AI service failure events in more recent years have a more intensively negative impact than those in more distant years. Finally, we identify different types of AI service failure and find that there are order effects on firm value across the service failure event types: accuracy > safety > privacy > fairness.
Originality/value
First, this study is the initial effort to empirically examine market reactions to AI service failure events using the event study method. Second, this study comprehensively considers the effect of contextual influencing factors, including industry type, firm size and event year. Third, this study improves the understanding of AI service failure by proposing a novel classification and disclosing the detailed impacts of different event types, which provides valuable guidance for managers and developers.
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Xianglu Hua, Lingyu Hu, Reham Eltantawy, Liangqing Zhang, Bin Wang, Yifan Tian and Justin Zuopeng Zhang
Achieving sustainability and sustainable performance has emerged as a critical area of focus for both academic research and practice. However, this pursuit faces challenges…
Abstract
Purpose
Achieving sustainability and sustainable performance has emerged as a critical area of focus for both academic research and practice. However, this pursuit faces challenges, particularly concerning the inadequacy of supply chain information. To address this issue, our study employs the organizational information processing theory to explore how adopting blockchain technology enables firms to learn from and collaborate with their supply chain partners, ultimately facilitating their sustainable performance even in the presence of organizational inertia.
Design/methodology/approach
Underpinned by the organizational information processing theory and drawing data from 220 manufacturing firms in China, we use structural equation modeling to test our conceptual model.
Findings
Our results demonstrate that blockchain technology adoption can significantly enhance sustainable performance. Furthermore, supply chain learning acts as a mediator between blockchain technology adoption and sustainable performance, while organizational inertia plays a negative moderating role between blockchain technology adoption and supply chain learning.
Originality/value
These findings extend the existing literature on blockchain technology adoption and supply chain management, offering novel insights into the pivotal role of blockchain in fostering supply chain learning and achieving sustainable performance. Our study provides valuable practical implications for managers seeking to leverage blockchain technology to enhance sustainability and facilitate organizational learning.
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Jiang Wu, Xiao Huang and Bin Wang
To better understand the success of an open source software (OSS) project, this study aims to examine the role of social dependency networks (i.e. social and technical…
Abstract
Purpose
To better understand the success of an open source software (OSS) project, this study aims to examine the role of social dependency networks (i.e. social and technical dependencies) in online communities.
Design/methodology/approach
This study focuses on dependencies using three network metrics – degree centrality, betweenness centrality and closeness centrality – in developer and module networks. A longitudinal analysis from the projects hosted at Sourceforge.net is conducted to examine the effects of social and technical networks on the success of OSS projects. To address our research questions, we have constructed research models to investigate the social network effects in developer networks, the technical network effects in module networks, and the social-technical network effects in both types of networks.
Findings
The results reveal nonlinear relationships between degree centrality in both social and technical networks and OSS success, highlighting the importance of a moderate level of degree centrality in team structure and software architecture. Meanwhile, a moderate level of betweenness centrality and a lower level of closeness centrality between developers lead to a higher chance of OSS project success.
Originality/value
This study is the first attempt to consider the network metrics in both module networks of the technical sub-system and developer networks of the social sub-system to better understand their influences on project success.
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Shuai Yang, Bin Wang, Junyuan Tao, Zhe Ruan and Hong Liu
The 6D pose estimation is a crucial branch of robot vision. However, the authors find that due to the failure to make full use of the complementarity of the appearance and…
Abstract
Purpose
The 6D pose estimation is a crucial branch of robot vision. However, the authors find that due to the failure to make full use of the complementarity of the appearance and geometry information of the object, the failure to deeply explore the contributions of the features from different regions to the pose estimation, and the failure to take advantage of the invariance of the geometric structure of keypoints, the performances of the most existing methods are not satisfactory. This paper aims to design a high-precision 6D pose estimation method based on above insights.
Design/methodology/approach
First, a multi-scale cross-attention-based feature fusion module (MCFF) is designed to aggregate the appearance and geometry information by exploring the correlations between appearance features and geometry features in the various regions. Second, the authors build a multi-query regional-attention-based feature differentiation module (MRFD) to learn the contribution of each region to each keypoint. Finally, a geometric enhancement mechanism (GEM) is designed to use structure information to predict keypoints and optimize both pose and keypoints in the inference phase.
Findings
Experiments on several benchmarks and real robot show that the proposed method performs better than existing methods. Ablation studies illustrate the effectiveness of each module of the authors’ method.
Originality/value
A high-precision 6D pose estimation method is proposed by studying the relationship between the appearance and geometry from different object parts and the geometric invariance of the keypoints, which is of great significance for various robot applications.
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Bin Wang, Fanghong Gao, Le Tong, Qian Zhang and Sulei Zhu
Traffic flow prediction has always been a top priority of intelligent transportation systems. There are many mature methods for short-term traffic flow prediction. However, the…
Abstract
Purpose
Traffic flow prediction has always been a top priority of intelligent transportation systems. There are many mature methods for short-term traffic flow prediction. However, the existing methods are often insufficient in capturing long-term spatial-temporal dependencies. To predict long-term dependencies more accurately, in this paper, a new and more effective traffic flow prediction model is proposed.
Design/methodology/approach
This paper proposes a new and more effective traffic flow prediction model, named channel attention-based spatial-temporal graph neural networks. A graph convolutional network is used to extract local spatial-temporal correlations, a channel attention mechanism is used to enhance the influence of nearby spatial-temporal dependencies on decision-making and a transformer mechanism is used to capture long-term dependencies.
Findings
The proposed model is applied to two common highway datasets: METR-LA collected in Los Angeles and PEMS-BAY collected in the California Bay Area. This model outperforms the other five in terms of performance on three performance metrics a popular model.
Originality/value
(1) Based on the spatial-temporal synchronization graph convolution module, a spatial-temporal channel attention module is designed to increase the influence of proximity dependence on decision-making by enhancing or suppressing different channels. (2) To better capture long-term dependencies, the transformer module is introduced.
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Faguo Liu, Qian Zhang, Tao Yan, Bin Wang, Ying Gao, Jiaqi Hou and Feiniu Yuan
Light field images (LFIs) have gained popularity as a technology to increase the field of view (FoV) of plenoptic cameras since they can capture information about light rays with…
Abstract
Purpose
Light field images (LFIs) have gained popularity as a technology to increase the field of view (FoV) of plenoptic cameras since they can capture information about light rays with a large FoV. Wide FoV causes light field (LF) data to increase rapidly, which restricts the use of LF imaging in image processing, visual analysis and user interface. Effective LFI coding methods become of paramount importance. This paper aims to eliminate more redundancy by exploring sparsity and correlation in the angular domain of LFIs, as well as mitigate the loss of perceptual quality of LFIs caused by encoding.
Design/methodology/approach
This work proposes a new efficient LF coding framework. On the coding side, a new sampling scheme and a hierarchical prediction structure are used to eliminate redundancy in the LFI's angular and spatial domains. At the decoding side, high-quality dense LF is reconstructed using a view synthesis method based on the residual channel attention network (RCAN).
Findings
In three different LF datasets, our proposed coding framework not only reduces the transmitted bit rate but also maintains a higher view quality than the current more advanced methods.
Originality/value
(1) A new sampling scheme is designed to synthesize high-quality LFIs while better ensuring LF angular domain sparsity. (2) To further eliminate redundancy in the spatial domain, new ranking schemes and hierarchical prediction structures are designed. (3) A synthetic network based on RCAN and a novel loss function is designed to mitigate the perceptual quality loss due to the coding process.
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Bin Wang, Wanbin Chen, Shan Gao and Dezhi Wang
This paper aims to prepare a composite film on LY12 aluminum (Al) alloy by immersing in dodecyl phosphate and cerium nitrate solution by self-assembling methods. The effect of…
Abstract
Purpose
This paper aims to prepare a composite film on LY12 aluminum (Al) alloy by immersing in dodecyl phosphate and cerium nitrate solution by self-assembling methods. The effect of dipping sequence in dodecyl phosphate and cerium nitrate solution on the corrosion resistance of the composite film is studied.
Design/methodology/approach
The corrosion resistance of the dodecyl phosphate/cerium composite film is investigated by electrochemical measurement and film composition analysis.
Findings
The dipping sequence in dodecyl phosphate and cerium nitrate solutions has a significant impact on the corrosion resistance of the composite film. It shows best corrosion resistance by first dipping in dodecyl phosphate and then dipping in cerium nitrate solution.
Originality/value
The research shown in this work lays a scientific basis of the film preparation for industrial applications in the future.
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Shan Gao, Bin Wang, Xinjie Yao and Quan Yuan
This paper aims to characterize the surface film formed on Alloys 800 and 690 in chloride and thiosulfate-containing solution at 300°C.
Abstract
Purpose
This paper aims to characterize the surface film formed on Alloys 800 and 690 in chloride and thiosulfate-containing solution at 300°C.
Design/methodology/approach
Alloy 800 and 690 were immersed in chloride and thiosulfate-containing solution at 300°C up to five days, and then the surface film was analyzed by scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), transmission electron microscopy (TEM) and energy dispersive X-ray spectrometers (EDX).
Findings
Through static immersion experiments in a high-temperature and high-pressure water environment, the alloy samples covered by surface film after five days of immersion were obtained. The morphology of the surface film was characterized at both horizontal and cross-sectional scales using SEM and focused ion beam-TEM techniques. It was observed that due to the influence of the quartz lining, the surface film primarily exhibited a bilayered structure. The first layer contained a significant amount of SiO2, with a higher content of metal hydroxides compared to metal oxides. The second layer was predominantly composed of Fe, Ni and Cr, with a higher content of metal oxides compared to metal hydroxides.
Originality/value
The results showed that the materials of the lining of the autoclave could significantly influence the film composition of the tested material, which should be paid attention when analyzing the corrosion mechanism at high temperature.