Search results

With China’s economic growth slowing down and the growth rate of fiscal revenue decreasing, the pressure on local government debts is further increasing. Under this background, it is of great significance to clarify the relation between local government debts and China’s economic growth in order to give full play to the positive role of local debts in stabling growth. The paper aims to discuss this issue.

Therefore, this paper explores the impact of Chinese local government debt on economic growth from theoretical and empirical aspects, respectively, and compares the regional differences between different debts and economic growth dynamics.

In the theoretical model part, this paper constructs a three-sector dynamic game model, under the two circumstances of whether local government is subject to debt constraints, and examines the relation between local government debt and economic growth and other variables through numerical simulation. Research shows that when the government is not constrained by debt, there is an inverted “U” relation between government debt and economic growth. When the government is constrained by debt, the economic growth rate gradually decreases as the government debt increases.

In the theoretical analysis part, this paper tries to estimate the amount of local debts under different calibers and examines the impact of different types of local government debts on China’s economic growth and their regional differences. The results show that excessive accumulation of government hidden debts in the eastern region is not conducive to economic growth, while explicit debts in the central and western regions significantly contribute to local economic growth. The results of empirical analysis are basically consistent with the predictions of the theoretical model.

I aimed to obtain a deeper insight into the link between supplier involvement in product development (SIPD), supplier relationship resilience and company performance.

To collect data, a survey among 500 Polish manufacturing companies was conducted. I used quantitative methods (structural equation modeling) to test several research hypotheses referring to a single supplier–customer relationship. Thanks to the use of multi-construct measurement of SIPD and supplier relationship resilience, the study provides detailed research results on the topic.

Collaborative practices implemented during SIPD increase procurement flexibility and decrease redundancy in the relationship with the involved supplier. Communication during SIPD increases supplier flexibility and procurement flexibility. Increased supplier flexibility and increased procurement flexibility in the relationship with the involved supplier as well as collaborative practices during SIPD positively impact company performance. I confirmed the indirect effect between communication during SIPD and company performance when the mediators are supplier flexibility and procurement flexibility. Decreased redundancy in relationship with involved supplier does not impact company performance.

Supply chain managers need to rethink SIPD practice to effectively ensure supply chain resilience (SCRES), especially in the face of the contemporary global crisis and black swans affecting the supplier base. My article provides important managerial insights into drivers of SCRES and company performance.

To the best of my knowledge, this research is among the first to conclude that SIPD does not have an unequivocally positive or direct impact on supplier relationship resilience. The research fills the gap by analyzing the impact of SIPD on two main SCRES elements. The study examines supplier relationship resilience, understood as flexibility and redundancy elements, in a single supplier–buyer relationship perspective. Thus, the presented considerations go beyond the traditional understanding of flexibility and redundancy in supplier relationship management, that is through the prism of double or multi sourcing and having back up-suppliers.

The contribution of the banking industry to the financial crisis of 2007/8 has raised public concerns about the financial soundness of banks around the world with many countries still suffering the backlogs of this crisis. The continuous emergence of such crises at both national and international levels increases governments', bank regulators' and financial market participants' need for reliable tools to assess the financial soundness of banks. In this context, this study investigates the financial soundness of the Kazakh banking sector, which is ranked by the World Bank as the first in the world in terms of the percentage of nonperforming loans (NPL) to total gross loans in 2012.

Using data about all Kazakh banks over the period January 01, 2008 to January 01, 2014, the study identifies a number of accounting indicators that influence the financial soundness of banks using principal component analysis (PCA). Then, it uses the outcomes of the PCA in a cluster analysis and groups the Kazakh banks into sound, risky and unsound banks at two points in time: January 01, 2008 and January 01, 2014. This methodology was further tested against a ranking system of banks and proved to be more reliable in detecting risky banks.

Fifteen financial ratios were initially selected as accounting indicators for the assessment of bank financial soundness. Using PCA, twelve indicators were isolated, which explain five principal components of capital adequacy, return on assets, profitability, asset quality, liquidity and leverage. Then using the “k-means” method, the results suggest a structure of the Kazakh banking sector on January 01, 2008 that includes two groups of banks: sound and risky banks. On January 01, 2014, this structure of the banking system has changed to include three groups of banks: sound, risky and unsound banks. Thus, in 2014 a new group of banks has emerged, i.e. financially unsound banks.

The proposed cluster-based methodology has proven to be a reliable tool to detect the financial soundness of Kazakh banks, which makes us advocate its employability for bank monitoring and supervision purposes.

This study is the first to employ a cluster-based methodology to assess the financial soundness of a banking sector. This methodology can be used at a micro-level to determine the structure of a banking sector. Also, it can be used to monitor any changes in the structure of a banking sector and provide early warning signals about the financial health of banks.

The paper aims to solve the problem of personnel intrusion identification within the limits of high-speed railways. It adopts the fusion method of millimeter wave radar and camera to improve the accuracy of object recognition in dark and harsh weather conditions.

This paper adopts the fusion strategy of radar and camera linkage to achieve focus amplification of long-distance targets and solves the problem of low illumination by laser light filling of the focus point. In order to improve the recognition effect, this paper adopts the YOLOv8 algorithm for multi-scale target recognition. In addition, for the image distortion caused by bad weather, this paper proposes a linkage and tracking fusion strategy to output the correct alarm results.

Simulated intrusion tests show that the proposed method can effectively detect human intrusion within 0–200 m during the day and night in sunny weather and can achieve more than 80% recognition accuracy for extreme severe weather conditions.

(1) The authors propose a personnel intrusion monitoring scheme based on the fusion of millimeter wave radar and camera, achieving all-weather intrusion monitoring; (2) The authors propose a new multi-level fusion algorithm based on linkage and tracking to achieve intrusion target monitoring under adverse weather conditions; (3) The authors have conducted a large number of innovative simulation experiments to verify the effectiveness of the method proposed in this article.

This study aims to explore the connection between demographic traits and the choice of attraction patterns among international repeat tourists.

The study employed a questionnaire survey to collect data from 1550 international repeat tourists who visited Tanzania between November 2022 and July 2023. Convenient sampling was employed as tourists were selected from the three international airports of Tanzania, namely Kilimanjaro International Airport, Julius Nyerere International Airport, and Abeid Aman Karume International Airport. A multinomial logistic regression model was used to examine the impact of socio-demographic characteristics on the selection of attraction patterns among international repeat tourists.

The study revealed that demographic factors, including age, marital status, income level, occupation, and education level, exhibit statistically significant correlations with preferences for distinct attraction patterns. This significance was established through a p-value of less than 0.05 for all the aforementioned variables.

This study is primarily focused on international repeat tourists, thereby limiting insights into the preferences of domestic tourists. To better inform strategies aimed at attracting a larger domestic tourist base, future research may prioritize the investigation of choice of attractions patterns among domestic tourists in relation to their demographic characteristics.

This study contributes to the nuanced understanding of international tourist behavior by unraveling the extent to which demographic traits impact tourists’ choices of attraction patterns, thereby providing insights crucial for effective marketing strategies, improved visitor experiences, and sustainable tourism development strategies.

This study aims to contribute to the field of computer systems for sustainability research. It proposes a framework for sustainability awareness using online social networks (OSNs) by analyzing major research streams of the current state of knowledge and different bibliometric variables, and identifies a future research agenda in the field.

The preferred reporting items for systematic review and meta-analysis (PRISMA) methodology, content analysis and bibliometric tools were employed to identify, select, collect, synthesize, analyze and evaluate all research published on sustainability awareness using OSNs to provide complete insight into this research area.

This study proposed a framework comprising four categories for sustainability awareness using OSNs. These four categories are: the key factors to success, analysis of existing tools, proposal of new methods, approaches and theoretical frameworks, and case examples. In addition, this study synthesized the future research challenges for each category of the proposed framework.

Fostering sustainability awareness and sustainable behavior using OSNs is a growing area of research that seeks cultural change in society to achieve sustainable development. Through OSNs, people can discover and become aware of the consequences of unsustainable practices and habits in society, and learn how to develop sustainable behavior.

The peer review history for this article is available at https://publons.com/publon/10.1108/OIR

This study is the maiden attempt to understand the effect of specific human resource practices (HRPs) on employee retention (ER) with the mediation of job satisfaction (JS) and moderation of work experience (WE) and job hopping (JH) in the context of the textile industry.

This study adopted a quantitative methodology and applied quota sampling to gather data from employees (n = 365) of leading textile companies in India. The conceptual model and hypotheses were tested with the help of Partial Least Squares-Structural Equation Modelling (PLS-SEM).

The findings of a path analysis revealed that compensation and performance appraisal (CPA) have the highest impact on JS followed by employee work participation (EWP). On the other hand, EWP had the highest impact on ER followed by grievance handling (GRH). The study revealed that JS significantly mediates between HRPs like CPA and ER. During Multi-group analysis (MGA) it was found that the importance of EWP and health and safety (HAS) was more in employee groups with higher WE, but it was the opposite in the case of CPA. In the case of JH behavior, the study observed that EWP leads to JS in loyal employees. Similarly, JS led to ER, and the effect was more pronounced for loyal employees.

In the context of the Indian textile industry, this work is the first attempt to comprehend how HRPs affect ER. Secondly, it confirmed that JS is not a guaranteed mediator between HRPs and ER, it could act as an insignificant, partial or full mediator. Additionally, this study establishes the moderating effects of WE and JH in the model through multigroup analysis.