Sustainable development early warning and financing risk management of resource-based industrial clusters using optimization algorithms

PurposeThe purpose is to analyze and discuss the sustainable development (SD) and financing risk assessment (FRA) of resource-based industrial clusters under the Internet of Things (IoT) economy and promote the application of Machine Learning methods and intelligent optimization algorithms in FRA.Design/methodology/approachThis study used the Support Vector Machine (SVM) algorithm that is analyzed together with the Genetic Algorithm (GA) and Ant Colony Optimization (ACO) algorithm. First, Yulin City in Shaanxi Province is selected for case analysis. Then, resource-based industrial clusters are studied, and an SD early-warning model is implemented. Then, the financing Risk Assessment Index System is established from the perspective of construction-operation-transfer. Finally, the risk assessment results of Support Vector Regression (SVR) and ACO-based SVR (ACO-SVR) are analyzed.FindingsThe results show that the overall sustainability of resource-based industrial clusters and IoT industrial clusters is good in the Yulin City of Shaanxi Province, and the early warning model of GA-based SVR (GA-SVR) has been achieved good results. Yulin City shows an excellent SD momentum in the resource-based industrial cluster, but there are still some risks. Therefore, it is necessary to promote the industrial structure of SD and improve the stability of the resource-based industrial cluster for Yulin City.Originality/valueThe results can provide a direction for the research on the early warning and evaluation of the SD-oriented resource-based industrial clusters and the IoT industrial clusters, promoting the application of SVM technology in the engineering field.

Evaluate the sustainable marketing strategy to optimal online leasing of new energy vehicles under the background big data economy

PurposeUnder the big data background, there are many influencing factors for investors of new energy vehicles (NEV), and government subsidies promote the sustainable development of the new energy vehicle industry. Therefore, the purpose of the study is to provide solutions for the sustainable development of NEV.Design/methodology/approachThe sustainable marketing strategy of NEV in China is put forward. This paper first analyzes the subsidy policy effect of NEV under the background of big data. It then establishes the online optimal leasing strategy under multiple strategy choices and the online leasing strategy of multiple vehicles under the inflation market.FindingsWith the fixed cost of NEV in each lease period, the optimal competition ratio of online decision-makers will continue to decrease with the increase of the difference between prepaid funds and government subsidies. In the decision-making of renting and purchasing multiple vehicles, the general strategy competition ratio is 2.922, while the optimal competition ratio of the online renting and purchasing strategy proposed by the research is 2.723.Research limitations/implicationsThe research is limited by the limited data and information collected, so the optimal decision-making model has some limitations. The authors need to find more representative data to optimize the model.Practical implicationsAs an emerging industry, NEV have developed rapidly in recent years. Based on the online algorithm and competitive ratio theory, this paper solves the decision-making problem of operators and gives the optimal strategy to promote the green development of the new energy vehicle industry.Originality/valueThis paper proposes the optimal strategy for online investors of new energy vehicle operators by combining online algorithm and competitive ratio theory. The numerical analysis results of the optimal online model under multi strategy selection show that with the same difference between prepaid funds and government subsidies, the time point will be delayed and the time point will be advanced as the cost of leasing NEV in each period increases.

Innovational duality and sustainable development: finding optima amidst socio-ecological policy trade-off in post-COVID-19 era

PurposeThis study aims to analyze the socio-ecological policy trade-off caused by technological innovations in the post-COVID-19 era. The study outcomes are utilized to design a comprehensive policy framework for attaining sustainable development goals (SDGs).Design/methodology/approachStudy is done for 100 countries over 1991–2019. Second-generation estimation method is used. Innovation is measured by total factor productivity, environmental quality is measured by carbon dioxide (CO2) emissions and social dimension is captured by unemployment.FindingsInnovation–CO2 emissions association is found to be inverted U-shaped and innovation–unemployment association is found to be U-shaped.Research limitations/implicationsThe study outcomes show the conflicting impact of technological innovation leading to policy trade-off. This dual impact of innovation is considered during policy recommendation.Practical implicationsThe policy framework recommended in the study shows a way to address the objectives of SDG 8, 9 and 13 during post-COVID-19 period.Social implicationsPolicy recommendations in the study show a way to internalize the negative social externality exerted by innovation.Originality/valueThis study contributes to the literature by considering the policy trade-off caused by innovation and recommending an SDG-oriented policy framework for the post-COVID-19 era.

Understanding mobile users' information adoption behaviour: an extension of the information adoption model

PurposeMobile messaging applications (MMAs) have surpassed top social media platforms. Recent and rapid use of MMAs has made it extremely difficult to ignore the existence of customer-to-customer (C2C) mobile information. This study, therefore, aims to expand the knowledge of customers' adoption behaviour of such information.Design/methodology/approachThrough applying and utilizing social support theory (SST) and the information adoption model (IAM), this study introduces a holistic theoretical model, explaining customers' adoption of information derived from MMAs and exploring the antecedents of IAM. Based on the data collected from 305 UK MMA users, this study empirically tests the research model using structural equation modelling estimation.FindingsThe results of this study reveal that social support is a key antecedent of information quality and credibility and support IAM in terms of its ability to explain MMAs' information adoption.Practical implicationsThe insights are valuable for businesses and marketers to understand customers' mobile communications and be socially support-oriented while developing marketing communication strategies.Originality/valueThe study integrates SST and IAM to improve the understanding of customers' information adoption behaviour. It is the first attempt that establishes that social support is a key antecedent of IAM.

The role of digital technologies to unleash a green recovery: pathways and pitfalls to achieve the European Green Deal

PurposeThe article aims to concern identification and development of pathways for a green recovery process post pandemic taking into account the role of digital technologies for unleashing the policies planned within the European Green Deal (EGD).Design/methodology/approachThe study is based on a systematic literature review (SLR). The electronic databases Scopus and Web of Science (WoS) were surveyed. The authors followed the SLR guidelines laid down by Tranfield et al. (2003) and the Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) framework and 65 articles were found eligible after thorough reading and inclusion in the analysis.FindingsThe article presents an innovative framework containing the digital technologies and their roles in enabling the achievement of the EGD policies and the barriers to their adoption.Originality/valueThe proposed framework would guide organizations and policymakers' decisions to pursue a pathway in which a green recovery is possible, mainly after the consequences of the current pandemic, considering the pitfalls of the journey. The article is original as it provides an up-to-date guidance toward an emerging theme, which is a green recovery economy including a net-zero carbon worldwide target.

Evaluate the sustainable reuse strategy of the corporate financial management based on the big data model

PurposeThe purposes are to explore corporate financial management optimization in the context of big data and provide a sustainable financial strategy for corporate development.Design/methodology/approachFirst, the shortcomings of the traditional financial management model are analyzed under the background of big data analysis. The big data analytic technology is employed to extract financial big data information and establish an efficient corporate financial management model. Second, the deep learning (DL) algorithm is applied to implement a corporate financial early-warning model to predict the potential risks in corporate finance, considering the predictability of corporate financial risks. Finally, a corporate value-centered development strategy based on sustainable growth is proposed for long-term development.FindingsThe experimental results demonstrate that the financial early-warning model based on DL has an accuracy of 90.7 and 88.9% for the two-year financial alert, which is far superior to the prediction effect of the traditional financial risk prediction models.Originality/valueThe obtained results can provide a reference for establishing a sustainable development pattern of corporate financial management under the background of big data.

The mediating effect of supply chain flexibility on the relationship between supply chain integration and supply chain performance

PurposeSupply chain integration (SCI) and flexibility (SCF) are recognised as crucial business practices and capability in the global competitive market. However, limited research has paid attention to study the relationship between SCI, SCF and their impact on supply chain performance (SCP). Therefore, the purpose of this paper is to establish a relationship between integration, flexibility and performance.Design/methodology/approachThe structural equation modelling technique was used to analyse the 187 data collected from Indian organisations through the survey methodology.FindingsFindings indicate that external integration contributes significantly to realise SCF and SCP. Sourcing and logistics flexibility also help to improve the SCP. The mediation analysis showed that the association of customer and supplier integration with SCP is partially and fully mediated by logistics flexibility, respectively. This study suggests that integration influences the SCP when the firm has a strong association with downstream partners and enough capability for logistics flexibility.Research limitations/implicationsThe study has collected cross-sectional data to analyse the relationship between SCI, SCF and SCP. However, as integration requires an effort of the years, longitudinal data and industry-specific studies may provide comprehensive views to validate the results of this study.Originality/valueBuilding on relational view theory and dynamic capability theory, the study has proposed the SCP assessment framework based on the relationship between SCI and SCF.

Study on the application of big data techniques for the third-party logistics using novel support vector machine algorithm

PurposeThis paper aims to effectively explore the application effect of big data techniques based on an α-support vector machine-stochastic gradient descent (SVMSGD) algorithm in third-party logistics, obtain the valuable information hidden in the logistics big data and promote the logistics enterprises to make more reasonable planning schemes.Design/methodology/approachIn this paper, the forgetting factor is introduced without changing the algorithm's complexity and proposed an algorithm based on the forgetting factor called the α-SVMSGD algorithm. The algorithm selectively deletes or retains the historical data, which improves the adaptability of the classifier to the real-time new logistics data. The simulation results verify the application effect of the algorithm.FindingsWith the increase of training times, the test error percentages of gradient descent (GD) algorithm, gradient descent support (SGD) algorithm and the α-SVMSGD algorithm decrease gradually; in the process of logistics big data processing, the α-SVMSGD algorithm has the efficiency of SGD algorithm while ensuring that the GD direction approaches the optimal solution direction and can use a small amount of data to obtain more accurate results and enhance the convergence accuracy.Research limitations/implicationsThe threshold setting of the forgetting factor still needs to be improved. Setting thresholds for different data types in self-learning has become a research direction. The number of forgotten data can be effectively controlled through big data processing technology to improve data support for the normal operation of third-party logistics.Practical implicationsIt can effectively reduce the time-consuming of data mining, realize the rapid and accurate convergence of sample data without increasing the complexity of samples, improve the efficiency of logistics big data mining, reduce the redundancy of historical data, and has a certain reference value in promoting the development of logistics industry.Originality/valueThe classification algorithm proposed in this paper has feasibility and high convergence in third-party logistics big data mining. The α-SVMSGD algorithm proposed in this paper has a certain application value in real-time logistics data mining, but the design of the forgetting factor threshold needs to be improved. In the future, the authors will continue to study how to set different data type thresholds in self-learning.

Multi-objective robust optimization for multi-stage-multi-product agile closed-loop supply chain under uncertainty in the context of circular economy

PurposeA circular economy (CE) is an economic system that tries to eliminate waste and continually use resources. Due to growing environmental concerns, supply chain (SC) design should be based on the CE considerations. In addition, responding and satisfying customers are the challenges managers constantly encounter. This study aims to improve the design of an agile closed-loop supply chain (CLSC) from the CE point of view.Design/methodology/approachIn this research, a new multi-stage, multi-product and multi-period design of a CLSC network under uncertainty is proposed that aligns with the goals of CE and SC participants. Recycling of goods is an important part of the CLSC. Therefore, a multi-objective mixed-integer linear programming model (MILP) is proposed to formulate the problem. Besides, a robust counterpart of multi-objective MILP is offered based on robust optimization to cope with the uncertainty of parameters. Finally, the proposed model is solved using the e-constraint method.FindingsThe proposed model aims to provide the strategic choice of economic order to the suppliers and third-party logistic companies. The present study, which is carried out using a numerical example and sensitivity analysis, provides a robust model and solution methodology that are effective and applicable in CE-related problems.Practical implicationsThis study shows how all upstream and downstream units of the SC network must work integrated to meet customer needs considering the CE context.Originality/valueThe main goal of the CE is to optimize resources, reduce the use of raw materials, and revitalize waste by recycling. In this study, a comprehensive model that can consider both SC design and CE necessities is developed that considers all SC participants.