Yan Yu, Qingsong Tian and Fengxian Yan
Fewer researchers have investigated the climatic and economic drivers of land-use change simultaneously and the interplay between drivers. This paper aims to investigate the…
Abstract
Purpose
Fewer researchers have investigated the climatic and economic drivers of land-use change simultaneously and the interplay between drivers. This paper aims to investigate the nonlinear and interaction effects of price and climate variables on the rice acreage in high-latitude regions of China.
Design/methodology/approach
This study applies a multivariate adaptive regression spline to characterize the effects of price and climate expectations on rice acreage in high-latitude regions of China from 1992 to 2017. Then, yield expectation is added into the model to investigate the mechanism of climate effects on rice area allocation.
Findings
The results of importance assessment suggest that rice price, climate and total agricultural area play an important role in rice area allocation, and the importance of temperature is always higher than that of precipitation, especially for minimum temperature. Based on the estimated hinge functions and coefficients, it is found that total agricultural area has strong nonlinear and interaction effects with climate and price as forms of third-order interaction. However, the order of interaction terms reduces to second order after absorbing the expected yield. Additionally, the marginal effects of driven factors are calculated at different quantiles. The total area shows a positive and increasing marginal effect with the increase of total area. But the positive impact of price on the rice area can only be observed when price reached 50% or higher quantiles. Climate variables also show strong nonlinear marginal effects, and most climatic effects would disappear or be weakened once absorbing the expected rice yield. Expected yield is an efficient mechanism to explain the correlation between crop area and climate variables, but the impact of minimum temperature cannot be completely modeled by the yield expectation.
Originality/value
To the best of the authors’ knowledge, this is the first study to examine the nonlinear response of land-use change to climate and economic in high-latitude regions of China using the machine learning method.
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The purpose of this paper is to predict the daily accuracy improvement for the Jakarta Islamic Index (JKII) prices using deep learning (DL) with small and big data of symmetric…
Abstract
Purpose
The purpose of this paper is to predict the daily accuracy improvement for the Jakarta Islamic Index (JKII) prices using deep learning (DL) with small and big data of symmetric volatility information.
Design/methodology/approach
This paper uses the nonlinear autoregressive exogenous (NARX) neural network as the optimal DL approach for predicting daily accuracy improvement through small and big data of symmetric volatility information of the JKII based on the criteria of the highest accuracy score of testing and training. To train the neural network, this paper employs the three DL techniques, namely Levenberg–Marquardt (LM), Bayesian regularization (BR) and scaled conjugate gradient (SCG).
Findings
The experimental results show that the optimal DL technique for predicting daily accuracy improvement of the JKII prices is the LM training algorithm based on using small data which provide superior prediction accuracy to big data of symmetric volatility information. The LM technique develops the optimal network solution for the prediction process with 24 neurons in the hidden layer across a delay parameter equal to 20, which affords the best predicting accuracy based on the criteria of mean squared error (MSE) and correlation coefficient.
Practical implications
This research would fill a literature gap by offering new operative techniques of DL to predict daily accuracy improvement and reduce the trading risk for the JKII prices based on symmetric volatility information.
Originality/value
This research is the first that predicts the daily accuracy improvement for JKII prices using DL with symmetric volatility information.
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Qiang Zhang, Xiaofeng Li, Yundong Ma and Wenquan Li
In this paper, the C80 special coal gondola car was taken as the subject, and the load test data of the car body at the center plate, side bearing and coupler measured on the…
Abstract
Purpose
In this paper, the C80 special coal gondola car was taken as the subject, and the load test data of the car body at the center plate, side bearing and coupler measured on the dedicated line were broken down to generate the random load component spectrums of the car body under five working conditions, namely expansion, bouncing, rolling, torsion and pitching according to the typical motion attitude of the car body.
Design/methodology/approach
On the basis of processing the measured load data, the random load component spectrums were equivalently converted into sinusoidal load component spectrums for bench test based on the principle of pseudo-damage equivalence of load. Relying on the fatigue and vibration test bench of the whole railway wagon, by taking each sinusoidal load component spectrum as the simulation target, the time waveform replication (TWR) iteration technology was adopted to create the drive signal of each loading actuator required for the fatigue test of car body on the bench, and the drive signal was corrected based on the equivalence principle of measured stress fatigue damage to obtain the fatigue test loads of car body under various typical working conditions.
Findings
The fatigue test results on the test bench were substantially close to the measured test results on the line. According to the results, the relative error between the fatigue damage of the car body on the test bench and the measured damage on the line was within the range of −16.03%–27.14%.
Originality/value
The bench test results basically reproduced the fatigue damage of the key parts of the car body on the line.
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Dr Ray Y. Zhong, Professor Kim Tan and Professor Gopalakrishnan Bhaskaran
Urban MICE competitiveness research consists of two clusters, one that is public-statistics-based and another that is questionnaire-based. Supply-side research on urban MICE…
Abstract
Purpose
Urban MICE competitiveness research consists of two clusters, one that is public-statistics-based and another that is questionnaire-based. Supply-side research on urban MICE competitiveness is rare. Based on the findings of Chen (2014) and other scholars, the purpose of this paper is to design counterpart statistical indicators to empirically analyze CMCA member cities.
Design/methodology/approach
After calculating the standardized Z value of the original statistical data for 17 CMCA member cities, the authors conducted confirmatory factor analysis for the first-level principal components, based on which hierarchical clustering was performed; then, regression analysis was conducted with the MICE profit factor as the dependent variable and the cost factor, tight support factor and facilitating factor as the independent variables to support publishing articles.
Findings
The confirmatory factor analysis showed that the urban MICE competitiveness indicators from the supply-side perspective include the profit factor, cost factor, tight support factor and facilitating factor.
Research limitations/implications
On the basis of research findings from the demand perspective and the literature review, the authors constructed an urban MICE competitiveness indicator system from the perspective of the supply side and conducted principal component analysis. However, because of the inaccessibility of panel data, the current data were only sufficient to conduct the research. If panel data could be acquired, further research could be conducted to perfect the current indicator system for urban MICE competitiveness.
Practical implications
The findings suggest that tourism total income, tourism foreign exchange income, inbound tourist number, number of exhibitions, exhibition area, number of UFI member cities and number of ICCA member cities were the main reason for the gap between different cities’ competitiveness and the reform focus for improving urban MICE competitiveness. The cost factor had a significantly negative influence on urban MICE competitiveness, implying that the higher the average hotel room price and revenue per available room, the less competitive the MICE host city is.
Social implications
The tight support factor exerts a significant positive influence on urban MICE competitiveness from the supply-side perspective, while the cost factor exerts a significant negative influence. The findings suggest that the tourism total income, tourism foreign exchange income, inbound tourist number, number of exhibitions, exhibition area, number of UFI member cities and number of ICCA member cities were the main reason for the gap between different cities’ competitiveness and the reform focus for improving urban MICE competitiveness. The cost factor had a significantly negative influence on urban MICE competitiveness, implying that the higher the average hotel room price and revenue per available room, the less competitive the MICE host city is.
Originality/value
The research bridge the empirical statistics and the questionnaire-based perception study on urban MICE tourism image, and advance to construct an empirical statistics based indicator system for urban MICE tourism image.
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Fuzhao Chen, Zhilei Chen, Qian Chen, Tianyang Gao, Mingyan Dai, Xiang Zhang and Lin Sun
The electromechanical brake system is leading the latest development trend in railway braking technology. The tolerance stack-up generated during the assembly and production…
Abstract
Purpose
The electromechanical brake system is leading the latest development trend in railway braking technology. The tolerance stack-up generated during the assembly and production process catalyzes the slight geometric dimensioning and tolerancing between the motor stator and rotor inside the electromechanical cylinder. The tolerance leads to imprecise brake control, so it is necessary to diagnose the fault of the motor in the fully assembled electromechanical brake system. This paper aims to present improved variational mode decomposition (VMD) algorithm, which endeavors to elucidate and push the boundaries of mechanical synchronicity problems within the realm of the electromechanical brake system.
Design/methodology/approach
The VMD algorithm plays a pivotal role in the preliminary phase, employing mode decomposition techniques to decompose the motor speed signals. Afterward, the error energy algorithm precision is utilized to extract abnormal features, leveraging the practical intrinsic mode functions, eliminating extraneous noise and enhancing the signal’s fidelity. This refined signal then becomes the basis for fault analysis. In the analytical step, the cepstrum is employed to calculate the formant and envelope of the reconstructed signal. By scrutinizing the formant and envelope, the fault point within the electromechanical brake system is precisely identified, contributing to a sophisticated and accurate fault diagnosis.
Findings
This paper innovatively uses the VMD algorithm for the modal decomposition of electromechanical brake (EMB) motor speed signals and combines it with the error energy algorithm to achieve abnormal feature extraction. The signal is reconstructed according to the effective intrinsic mode functions (IMFS) component of removing noise, and the formant and envelope are calculated by cepstrum to locate the fault point. Experiments show that the empirical mode decomposition (EMD) algorithm can effectively decompose the original speed signal. After feature extraction, signal enhancement and fault identification, the motor mechanical fault point can be accurately located. This fault diagnosis method is an effective fault diagnosis algorithm suitable for EMB systems.
Originality/value
By using this improved VMD algorithm, the electromechanical brake system can precisely identify the rotational anomaly of the motor. This method can offer an online diagnosis analysis function during operation and contribute to an automated factory inspection strategy while parts are assembled. Compared with the conventional motor diagnosis method, this improved VMD algorithm can eliminate the need for additional acceleration sensors and save hardware costs. Moreover, the accumulation of online detection functions helps improve the reliability of train electromechanical braking systems.
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Abderahman Rejeb, Karim Rejeb and Suhaiza Zailani
This study aims to address the noted gap in comprehensive overviews detailing the developmental trajectory of Islamic finance (IF) as an interdisciplinary academic field.
Abstract
Purpose
This study aims to address the noted gap in comprehensive overviews detailing the developmental trajectory of Islamic finance (IF) as an interdisciplinary academic field.
Design/methodology/approach
The study introduces a unique approach using the combined methodologies of co-word analysis and main path analysis (MPA) by examining a broad collection of IF research articles.
Findings
The investigation identifies dominant themes and foundational works that have influenced the IF discipline. The data reveals prominent areas such as Shariah governance, financial resilience, ethical dimensions and customer-centric frameworks. The MPA offers detailed insights, narrating a journey from the foundational principles of IF to its current challenges and opportunities. This journey covers harmonizing religious beliefs with contemporary financial models, changes in regulatory landscapes and the continuous effort to align with broader socioeconomic aspirations. Emerging areas of interest include using new technologies in IF, standardizing global Islamic banking and assessing its socioeconomic effects on broader populations.
Originality/value
This study represents a pioneering effort to map out and deepen the understanding of the IF field, highlighting its dynamic evolution and suggesting potential avenues for future academic exploration.
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Mohammed Alotaibi and Imdadullah Hidayat-ur-Rehman
This study aims to empirically analyze the factors influencing users’ intention to use chatbots for airline ticket consultation. It seeks to introduce a comprehensive framework…
Abstract
Purpose
This study aims to empirically analyze the factors influencing users’ intention to use chatbots for airline ticket consultation. It seeks to introduce a comprehensive framework based on the technology acceptance model (TAM) that integrates key factors alongside traditional TAM constructs to understand what drives behavioral intention to use chatbots in the context of airline ticket consultation.
Design/methodology/approach
The study uses the partial least squares-structural equation modeling (PLS-SEM) approach to validate the proposed model empirically. Data were collected through a survey questionnaire distributed to potential users in Saudi Arabia, with 393 valid responses from a total of 409 received being included in the analysis.
Findings
The empirical analysis confirms the significance of perceived usefulness and user satisfaction as direct determinants of behavioral intention. Additionally, it reveals that factors such as perceived ubiquitous access, perceived completeness, perceived accuracy, perceived unbiased response and perceived convenience have both direct and indirect significant impacts on the behavioral intention to use chatbots for airline ticket consultation.
Originality/value
This research advances theoretical understanding and holds practical implications for designing and implementing effective chatbot services. By investigating the complex interplay of these factors, the study makes substantive contributions to both theoretical advancements and practical applications in the field, particularly in enhancing the user experience and acceptance of chatbots for airline ticket consultations.
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The purpose of this study is to identify the determinants of success in peer-to-peer (P2P) lending campaigns, especially amid global financial disruptions like the COVID-19…
Abstract
Purpose
The purpose of this study is to identify the determinants of success in peer-to-peer (P2P) lending campaigns, especially amid global financial disruptions like the COVID-19 pandemic. Addressing a notable gap in current research, we explore how factors such as firm uncertainty, loan characteristics (interest rates and maturity) and venture quality (human, social and intellectual capital) influence P2P lending effectiveness. Using multiple regression analysis on data from 523 projects on the October platform, our study aims to enhance the understanding and operational efficiency of P2P platforms, contributing to a more resilient financial ecosystem.
Design/methodology/approach
This study employs a quantitative research design using multiple regression analysis to examine the impact of specific variables on the success of P2P lending campaigns. Data were collected from 523 concluded P2P lending projects on the October platform, spanning from 2015 to 2021. Variables of interest include the level of uncertainty of the firm, loan characteristics such as interest rate and maturity and the quality of the venture assessed through human, social and intellectual capital. This method allows for a robust analysis of the factors contributing to the success of P2P lending within a dynamic financial context.
Findings
The findings of this study reveal that the success of P2P lending campaigns is significantly influenced by the level of uncertainty of the firm, the interest rate of the loan and the quality of the venture. Specifically, higher uncertainty in firms correlates negatively with campaign success, while competitive interest rates positively impact funding outcomes. Furthermore, ventures that demonstrate robust human capital, particularly those with management teams that possess diverse skills and high qualifications, tend to attract more funding. These results underscore the critical role of strategic financial and human resource planning in enhancing the effectiveness of P2P lending platforms.
Originality/value
This study contributes uniquely to the literature by integrating multiple variables – firm uncertainty, loan characteristics and venture quality – into a comprehensive analysis of success factors in P2P lending. It addresses the scarcity of research examining the combined effects of these factors, particularly in the context of global financial disruptions like the COVID-19 pandemic. By focusing on a specific European platform during a dynamic period, this research provides new insights into how P2P lending can adapt to and thrive amid financial crises. The findings offer valuable guidance for both practitioners and policymakers aiming to optimize P2P lending practices in uncertain economic landscapes.
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The Spanish airport system contains several regional airports within an amenity distance and alternative travel modes. Profitable airports cross-subsidise small airports, which…
Abstract
Purpose
The Spanish airport system contains several regional airports within an amenity distance and alternative travel modes. Profitable airports cross-subsidise small airports, which are not required for regional development or connectivity. Airports are government-owned and centralised-managed by Spanish Airports and Air Navigation (AENA, for its Spanish acronym). This study aims to analyse the probability of an under-used public infrastructure and the AENA’s managerial ability as per the financial sustainability of the network in the long term.
Design/methodology/approach
The national regulatory framework determines the airports’ environment. Six airports revealed unobserved heterogeneity, avoiding model misspecification. The framework is defined through proxies of the singularities of the Spanish framework: public investments and geographical specifications. The stochastic frontier analysis model follows two time-varying specifications, accounting for airports’ environmental factors, to ensure the robustness of the results to differ from the inefficiency caused by AENA and external factors.
Findings
Airports’ infrastructure capacity and traffic are not correlated; regional airports become a financial burden for the system unless they specialise or differentiate. Proxies defining the airports’ context are relevant. Because airports do not compete for airlines and passengers, there are too many regional airports with little traffic, resulting in disused public infrastructure that falls far short of improving connectivity and regional development.
Originality/value
This study contributes to paying attention to the characteristics of the regulatory framework, such as management strongly centralised in AENA, airport charges decided by the owner, lack of competition and lack of an independent regulatory entity. Another original contribution considers reliable capital measures (airports’ infrastructure).