Search results
1 – 10 of 539Xiaozeng Xu, Yikun Wu and Bo Zeng
Traditional grey models are integer order whitening differential models; these models are relatively effective for the prediction of regular raw data, but the prediction error of…
Abstract
Purpose
Traditional grey models are integer order whitening differential models; these models are relatively effective for the prediction of regular raw data, but the prediction error of irregular series or shock series is large, and the prediction effect is not ideal.
Design/methodology/approach
The new model realizes the dynamic expansion and optimization of the grey Bernoulli model. Meanwhile, it also enhances the variability and self-adaptability of the model structure. And nonlinear parameters are computed by the particle swarm optimization (PSO) algorithm.
Findings
Establishing a prediction model based on the raw data from the last six years, it is verified that the prediction performance of the new model is far superior to other mainstream grey prediction models, especially for irregular sequences and oscillating sequences. Ultimately, forecasting models are constructed to calculate various energy consumption aspects in Chongqing. The findings of this study offer a valuable reference for the government in shaping energy consumption policies and optimizing the energy structure.
Research limitations/implications
It is imperative to recognize its inherent limitations. Firstly, the fractional differential order of the model is restricted to 0 < a < 2, encompassing only a three-parameter model. Future investigations could delve into the development of a multi-parameter model applicable when a = 2. Secondly, this paper exclusively focuses on the model itself, neglecting the consideration of raw data preprocessing, such as smoothing operators, buffer operators and background values. Incorporating these factors could significantly enhance the model’s effectiveness, particularly in the context of medium-term or long-term predictions.
Practical implications
This contribution plays a constructive role in expanding the model repertoire of the grey prediction model. The utilization of the developed model for predicting total energy consumption, coal consumption, natural gas consumption, oil consumption and other energy sources from 2021 to 2022 validates the efficacy and feasibility of the innovative model.
Social implications
These findings, in turn, provide valuable guidance and decision-making support for both the Chinese Government and the Chongqing Government in optimizing energy structure and formulating effective energy policies.
Originality/value
This research holds significant importance in enriching the theoretical framework of the grey prediction model.
Highlights
The highlights of the paper are as follows:
A novel grey Bernoulli prediction model is proposed to improve the model’s structure.
Fractional derivative, fractional accumulating generation operator and Bernoulli equation are added to the new model.
The proposed model can achieve full compatibility with the traditional mainstream grey prediction models.
Energy consumption in Chongqing verifies that the performance of the new model is much better than that of the traditional grey models.
The research provides a reference basis for the government to formulate energy consumption policies and optimize energy structure.
A novel grey Bernoulli prediction model is proposed to improve the model’s structure.
Fractional derivative, fractional accumulating generation operator and Bernoulli equation are added to the new model.
The proposed model can achieve full compatibility with the traditional mainstream grey prediction models.
Energy consumption in Chongqing verifies that the performance of the new model is much better than that of the traditional grey models.
The research provides a reference basis for the government to formulate energy consumption policies and optimize energy structure.
Details
Keywords
Elif Ozturk, Hande Bahar Turker and V. Aslihan Nasir
Collaborating with consumers during new product development can provide companies with significant benefits and competitive advantages. Although several studies have been…
Abstract
Purpose
Collaborating with consumers during new product development can provide companies with significant benefits and competitive advantages. Although several studies have been conducted on the design of co-innovation platforms, there is still a need for a more comprehensive understanding of the co-innovation phenomenon. To address this gap, this research aims to identify the critical success factors of co-innovation platforms and provide an extensive analysis of the variables that determine their effectiveness.
Design/methodology/approach
This study presents a systematic literature review of co-innovation platforms based on an analysis of 89 articles published in 50 scholarly journals in the disciplines of information systems, marketing and business, covering the years from 2006 to 2022.
Findings
The review synthesizes the current state of scientific knowledge and groups prior studies thematically as critical success factors of co-innovation platforms. As a result, eight success factors have been identified in terms of quantity and quality of contributions. These factors include product involvement, perceived fairness, sense of community, interactive environment, employee involvement, participant diversity, assessment structure and task design.
Originality/value
The study consolidates existing research about the critical success of co-innovation platforms. It also provides a research framework that incorporates a diverse set of variables that can be used to assess co-innovation performance in future studies.
Details
Keywords
Yongsheng Zhao, Jiaqing Luo, Ying Li, Caixia Zhang and Honglie Ma
The combination of improved PSO (IPSO) algorithm and artificial neural network (ANN) model for intelligent monitoring of the bearing performance of the hydrostatic turntable.
Abstract
Purpose
The combination of improved PSO (IPSO) algorithm and artificial neural network (ANN) model for intelligent monitoring of the bearing performance of the hydrostatic turntable.
Design/methodology/approach
This paper proposes an artificial neural network model based on IPSO algorithm for intelligent monitoring of hydrostatic turntables.
Findings
The theoretical model proposed in this paper improves the accuracy of the working performance of the static pressure turntable and provides a new direction for intelligent monitoring of the static pressure turntable. Therefore, the theoretical research in this paper is novel.
Originality/value
Theoretical novelties: an ANN model based on the IPSO algorithm is designed to monitor the load-bearing performance of a static pressure turntable intelligently; this study show that the convergence accuracy and convergence speed of the IPSO-NN model have been improved by 52.55% and 10%, respectively, compared to traditional training models; and the proposed model could be used to solve the multidimensional nonlinear problem in the intelligent monitoring of hydrostatic turntables.
Peer review
The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-03-2024-0081/
Details
Keywords
Yanchao Sun, Jiayu Li, Hongde Qin and Yutong Du
Autonomous underwater vehicle (AUV) is widely used in resource prospection and underwater detection due to its excellent performance. This study considers input saturation…
Abstract
Purpose
Autonomous underwater vehicle (AUV) is widely used in resource prospection and underwater detection due to its excellent performance. This study considers input saturation, nonlinear model uncertainties and external ocean current disturbances. The containment errors can be limited to a small neighborhood of zero in finite time by employing control strategy. The control strategy can keep errors within a certain range between the trajectory followed by AUVs and their intended targets. This can mitigate the issues of collisions and disruptions in communication which may arise from AUVs being in close proximity or excessively distant from each other.
Design/methodology/approach
The tracking errors are constrained. Based on the directed communication topology, a cooperative formation control algorithm for multi-AUV systems with constrained errors is designed. By using the saturation function, state observers are designed to estimate the AUV’s velocity in six degrees of freedom. A new virtual control algorithm is designed through combining backstepping technique and the tan-type barrier Lyapunov function. Neural networks are used to estimate and compensate for the nonlinear model uncertainties and external ocean current disturbances. A neural network adaptive law is designed.
Findings
The containment errors can be limited to a small neighborhood of zero in finite time so that follower AUVs can arrive at the convex hull consisting of leader AUVs within finite time. The validity of the results is indicated by simulations.
Originality/value
The state observers are designed to approximate the speed of the AUV and improve the accuracy of the control method. The anti-saturation function and neural network adaptive law are designed to deal with input saturation and general disturbances, respectively. It can ensure the safety and reliability of the multiple AUV systems.
Details
Keywords
This paper explores whether fintech paves the way for the transition to carbon neutrality in the context of China’s climate policy uncertainty (CCPU) and the influence of the…
Abstract
Purpose
This paper explores whether fintech paves the way for the transition to carbon neutrality in the context of China’s climate policy uncertainty (CCPU) and the influence of the ocean carbon sink market.
Design/methodology/approach
We apply a novel wavelet analysis technique to investigate the time-frequency dependence between the CCPU index, the CSI (China Securities Index) Fintech Theme Index (CFTI) and the Carbon Neutral Concept Index (CNCI).
Findings
The empirical results show that CCPU and CFTI have a detrimental effect on CNCI in high-frequency bands. Furthermore, in low-frequency domains, the development of CFTI can effectively promote the realization of carbon neutrality.
Practical implications
Our findings show that information from the CCPU and CFTI can be utilized to forecast the movement of CNCI. Therefore, the government should strike a balance between fintech development and environmental regulation and, hence, promote the use of renewable energy to reduce carbon emissions, facilitating the orderly and regular development of the ocean carbon sink market.
Originality/value
The development of high-quality fintech and positive climate policy reforms are crucial for achieving carbon neutrality targets and promoting the growth of the marine carbon sink market.
Details
Keywords
Yongbin Lv, Ying Jia, Chenying Sang and Xianming Sun
This study investigates the causal relationship and mechanisms between the development of digital finance and household carbon emissions. Its objective is to explore how digital…
Abstract
Purpose
This study investigates the causal relationship and mechanisms between the development of digital finance and household carbon emissions. Its objective is to explore how digital finance can influence the carbon footprint at the household level, aiming to contribute to the broader understanding of financial innovations' environmental impacts.
Design/methodology/approach
The research combines macro and micro data, employing input-output analysis to utilize data from the China Household Finance Survey (CHFS) for the years 2013, 2015, 2017, and 2019, national input-output tables, and Energy Statistical Yearbooks. This approach calculated CO2 emissions at the household level, including the growth rate of household carbon emissions and per capita emissions. It further integrates the Peking University Digital Financial Inclusion Index of China (PKU-DFIIC) for 2012–2018 and corresponding urban economic data, resulting in panel data for 7,191 households across 151 cities over four years. A fixed effects model was employed to examine the impact of digital finance development on household carbon emissions.
Findings
The findings reveal that digital finance significantly lowers household carbon emissions. Further investigation shows that digital transformation, consumption structure upgrades, and improved household financial literacy enhance the restraining effect of digital finance on carbon emissions. Heterogeneity analysis indicates that this mitigating effect is more pronounced in households during the nurturing phase, those using convenient payment methods, small-scale, and urban households. Sub-index tests suggest that the broadening coverage and deepening usage of digital finance primarily drive its impact on reducing household carbon emissions.
Practical implications
The paper recommends that China should continue to strengthen the layout of digital infrastructure, leverage the advantages of digital finance, promote digital financial education, and facilitate household-level carbon emission management to support the achievement of China's dual carbon goals.
Originality/value
The originality of this paper lies in its detailed examination of the carbon reduction effects of digital finance at the micro (household) level. Unlike previous studies on carbon emissions that focused on absolute emissions, this research investigates the marginal impact of digital finance on relative increases in emissions. This method provides a robust assessment of the net effects of digital finance and offers a novel perspective for examining household carbon reduction measures. The study underscores the importance of considering heterogeneity when formulating targeted policies for households with different characteristics.
Details
Keywords
Min Qin, Shuqin Li, Fangtong Cai, Wei Zhu and Shanshan Qiu
With the proliferation of ideas submitted by users in firm-built online user innovation communities, community managers are faced with the problem of user idea overload. The…
Abstract
Purpose
With the proliferation of ideas submitted by users in firm-built online user innovation communities, community managers are faced with the problem of user idea overload. The purpose of this paper is to explore the influencing factors on the idea adoption to identify high quality ideas, and then propose a method to quickly filter high value ideas.
Design/methodology/approach
The authors collected more than 110,000 data submitted by Xiaomi community users and analyzed the factors affecting idea adoption using a multinomial logistic regression model. In addition, the authors also used BP neural network to predict the idea adoption process.
Findings
The empirical results show that idea semantics, number of likes, number of comments, number of related posts, the existence of pictures and self-presentation have positive impact on idea adoption, while idea length and idea timeliness had negative impact on idea adoption. In addition, this paper calculates the idea evaluation value through the idea adoption process predicted by neural network and the mean value of idea term frequency inverse document frequency (TF-IDF).
Originality/value
This empirical study expands the theoretical perspective of idea adoption research by using dual-process theory and enriches the research methods in the field of idea adoption research through the multinomial logistic regression method. Based on our findings, firms can quickly identify valuable ideas and effectively alleviate the information overload problem of online user innovation communities.
Details
Keywords
Meng Zhu and Xiaolong Xu
Intent detection (ID) and slot filling (SF) are two important tasks in natural language understanding. ID is to identify the main intent of a paragraph of text. The goal of SF is…
Abstract
Purpose
Intent detection (ID) and slot filling (SF) are two important tasks in natural language understanding. ID is to identify the main intent of a paragraph of text. The goal of SF is to extract the information that is important to the intent from the input sentence. However, most of the existing methods use sentence-level intention recognition, which has the risk of error propagation, and the relationship between intention recognition and SF is not explicitly modeled. Aiming at this problem, this paper proposes a collaborative model of ID and SF for intelligent spoken language understanding called ID-SF-Fusion.
Design/methodology/approach
ID-SF-Fusion uses Bidirectional Encoder Representation from Transformers (BERT) and Bidirectional Long Short-Term Memory (BiLSTM) to extract effective word embedding and context vectors containing the whole sentence information respectively. Fusion layer is used to provide intent–slot fusion information for SF task. In this way, the relationship between ID and SF task is fully explicitly modeled. This layer takes the result of ID and slot context vectors as input to obtain the fusion information which contains both ID result and slot information. Meanwhile, to further reduce error propagation, we use word-level ID for the ID-SF-Fusion model. Finally, two tasks of ID and SF are realized by joint optimization training.
Findings
We conducted experiments on two public datasets, Airline Travel Information Systems (ATIS) and Snips. The results show that the Intent ACC score and Slot F1 score of ID-SF-Fusion on ATIS and Snips are 98.0 per cent and 95.8 per cent, respectively, and the two indicators on Snips dataset are 98.6 per cent and 96.7 per cent, respectively. These models are superior to slot-gated, SF-ID NetWork, stack-Prop and other models. In addition, ablation experiments were performed to further analyze and discuss the proposed model.
Originality/value
This paper uses word-level intent recognition and introduces intent information into the SF process, which is a significant improvement on both data sets.
Details
Keywords
Yue Fang, Xin Bao, Baiqing Sun and Raymond Yiu Keung Lau
This paper aims to investigate the effect of CEO social media celebrity status on credit ratings and to determine whether potential threats on the CEO celebrity status negatively…
Abstract
Purpose
This paper aims to investigate the effect of CEO social media celebrity status on credit ratings and to determine whether potential threats on the CEO celebrity status negatively moderate the above association.
Design/methodology/approach
The authors collected tweets for 874 CEOs from 513 unique S&P 1500 firms. A panel data analysis was conducted on a panel with 4,235 observations from 2009 to 2020. We then tested the hypothesis with the ordinal logit model.
Findings
The empirical findings confirmed that CEO social media celebrity status is positively associated with corporate credit rating outcomes. Our path analyses revealed that CEOs with higher social media celebrity status have less incentive to conduct risk-taking behaviors and thus benefit credit ratings. When the rating agencies perceive potential threats to CEO celebrity status, including CEO myopia and CEO overconfidence, the association between CEO social media celebrity status and credit rating is weakened.
Practical implications
This study provides an in-depth understanding of CEO social media perception on credit ratings for firms' managers and capital market participants. Findings can help managers and firms improve their strategies for leveraging social media to release credit constraints. The debt market participants could adopt the CEO social media celebrity status and its concerned threats to setting debt contracts with an adequate price.
Originality/value
This is likely to be the first study that examines the effect of CEO social media celebrity status on credit ratings. The findings of this study also reveal that social media certificated celebrity CEOs tend to be capable of enhancing firm revenue and have lower risk-taking incentives, unlike mass media certificated celebrity CEOs.
Details
Keywords
Josephine Ofosu-Mensah Ababio, Eric B. Yiadom, John K.M. Mawutor, Joseph K. Tuffour and Edward Attah‐Botchwey
This study aims to use 67 developing countries to examine the role of financial inclusion as an “empowering tool” for renewable energy uptake and to improve environmental…
Abstract
Purpose
This study aims to use 67 developing countries to examine the role of financial inclusion as an “empowering tool” for renewable energy uptake and to improve environmental sustainability in developing countries.
Design/methodology/approach
Using a battery of econometric models, including the generalized method of moment-panel vector autoregression (GMM-PVAR), impulse response function, Granger causality, fully modified ordinary least squares and dynamic ordinary least squares, the study proposed and tested three hypotheses.
Findings
The results from various estimations indicate that financial inclusion has a positive effect on renewable energy consumption and environmental sustainability improvement in developing countries. The findings suggest that financial inclusion can improve environmental sustainability by increasing access to financing to fund renewable energy projects, support sustainable businesses and promote sustainable practices.
Originality/value
This study suggests that policymakers prioritize financial inclusion to promote renewable energy consumption and environmental sustainability. Policies should enhance access to financial services, offer financial incentives and subsidies, provide affordable loans through microfinance institutions and fintech companies and promote sustainable businesses and green technologies.
Details