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1 – 4 of 4Jianbo Song, Wencheng Cao and Yuan George Shan
This study uses data from the Chinese banking sector to explore the relationship between green credit and risk-taking in commercial banks. It also examines whether the level of…
Abstract
Purpose
This study uses data from the Chinese banking sector to explore the relationship between green credit and risk-taking in commercial banks. It also examines whether the level of regional green development acts as a moderator regarding this relationship.
Design/methodology/approach
Using a dataset composed of annual observations from 57 Chinese commercial banks between 2008 and 2021, this study employs both piecewise and curvilinear models.
Findings
Our results indicate that when the scale of green credit is low (<0.164), it increases the risk-taking of commercial banks. Conversely, when the scale of green credit is high (>0.164), it reduces the risk-taking of commercial banks. Moreover, this nonlinear relationship impact exhibits bank heterogeneity. Furthermore, the results show that the level of regional green development and local government policy support negatively moderate the relationship between green credit and commercial bank risk-taking. Furthermore, we find that green credit can directly enhance the net interest margin of commercial banks.
Originality/value
This study is the first to provide evidence of a nonlinear relationship between green credit and risk-taking in commercial banks, and it identifies the significant roles of regional green development level and local government policy support in the Chinese context.
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Keywords
Zengrui Zheng, Kainan Su, Shifeng Lin, Zhiquan Fu and Chenguang Yang
Visual simultaneous localization and mapping (SLAM) has limitations such as sensitivity to lighting changes and lower measurement accuracy. The effective fusion of information…
Abstract
Purpose
Visual simultaneous localization and mapping (SLAM) has limitations such as sensitivity to lighting changes and lower measurement accuracy. The effective fusion of information from multiple modalities to address these limitations has emerged as a key research focus. This study aims to provide a comprehensive review of the development of vision-based SLAM (including visual SLAM) for navigation and pose estimation, with a specific focus on techniques for integrating multiple modalities.
Design/methodology/approach
This paper initially introduces the mathematical models and framework development of visual SLAM. Subsequently, this paper presents various methods for improving accuracy in visual SLAM by fusing different spatial and semantic features. This paper also examines the research advancements in vision-based SLAM with respect to multi-sensor fusion in both loosely coupled and tightly coupled approaches. Finally, this paper analyzes the limitations of current vision-based SLAM and provides predictions for future advancements.
Findings
The combination of vision-based SLAM and deep learning has significant potential for development. There are advantages and disadvantages to both loosely coupled and tightly coupled approaches in multi-sensor fusion, and the most suitable algorithm should be chosen based on the specific application scenario. In the future, vision-based SLAM is evolving toward better addressing challenges such as resource-limited platforms and long-term mapping.
Originality/value
This review introduces the development of vision-based SLAM and focuses on the advancements in multimodal fusion. It allows readers to quickly understand the progress and current status of research in this field.
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Matthew Ikuabe, Clinton Aigbavboa, Chimay Anumba and Ayodeji Emmanuel Oke
Through its advanced computational capabilities, cyber–physical systems (CPS) proffer solutions to some of the cultural challenges plaguing the effective delivery of facilities…
Abstract
Purpose
Through its advanced computational capabilities, cyber–physical systems (CPS) proffer solutions to some of the cultural challenges plaguing the effective delivery of facilities management (FM) mandates. This study aims to explore the drivers for the uptake of CPS for FM functions using a qualitative approach – the Delphi technique.
Design/methodology/approach
Using the Delphi technique, the study selected experts through a well-defined process entailing a pre-determined set of criteria. The experts gave their opinions in two iterations which were subjected to statistical analyses such as the measure of central tendency and interquartile deviation in ascertaining consensus among the experts and the Mann–Whitney U test in establishing if there is a difference in the opinions given by the experts.
Findings
The study’s findings show that six of the identified drivers of the uptake of CPS for FM were attributed to be of very high significance, while 12 were of high significance. Furthermore, it was revealed that there is no significant statistical difference in the opinions given by experts in professional practice and academia.
Practical implications
The study’s outcome provides the requisite insight into the propelling measures for the uptake of CPS for FM by organisations and, by extension, aiding digital transformation for effective FM delivery.
Originality/value
To the best of the authors’ knowledge, evidence from the literature suggests that no study has showcased the drivers of the incorporation of CPS for FM. Hence, this study fills this gap in knowledge by unravelling the significant propelling measures of the integration of CPS for FM functions.
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Mohammad Iranmanesh, Morteza Ghobakhloo, Behzad Foroughi, Mehrbakhsh Nilashi and Elaheh Yadegaridehkordi
This study aims to explore and ranks the factors that might determine attitudes and intentions toward using autonomous vehicles (AVs).
Abstract
Purpose
This study aims to explore and ranks the factors that might determine attitudes and intentions toward using autonomous vehicles (AVs).
Design/methodology/approach
The “technology acceptance model” (TAM) was extended by assessing the moderating influences of personal-related factors. Data were collected from 378 Vietnamese and analysed using a combination of “partial least squares” and the “adaptive neuro-fuzzy inference system” (ANFIS) technique.
Findings
The findings demonstrated the power of TAM in explaining the attitude and intention to use AVs. ANFIS enables ranking the importance of determinants and predicting the outcomes. Perceived ease of use and attitude were the most crucial drivers of attitude and intention to use AVs, respectively. Personal innovativeness negatively moderates the influence of perceived ease of use on attitude. Data privacy concerns moderate positively the impact of perceived usefulness on attitude. The moderating effect of price sensitivity was not supported.
Practical implications
These findings provide insights for policymakers and automobile companies' managers, designers and marketers on driving factors in making decisions to adopt AVs.
Originality/value
The study extends the AVs literature by illustrating the importance of personal-related factors, ranking the determinants of attitude and intention, illustrating the inter-relationships among AVs adoption factors and predicting individuals' attitudes and behaviours towards using AVs.
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