Search results

With more cities aiming to achieve climate neutrality, identifying the funding to support these plans is essential. The purpose of this paper is to exploit the present of a structured green bonds framework in Sweden to investigate the typology of abatement projects Swedish municipalities invested in and understand their effectiveness.

Marginal abatement cost curves of the green bond measures are constructed by using the financial and abatement data provided by municipalities on an annual basis.

The results highlight the economic competitiveness of clean energy production, measured in abatement potential per unit of currency, even when compared to other emerging technologies that have attracted the interest of policymakers. A comparison with previous studies on the cost efficiency of carbon capture storage reveals that clean energy projects, especially wind energy production, can contribute to the reduction of emissions in a more efficient way. The Swedish carbon tax is a good incentive tool for investments in clean energy projects.

The improvement concerning previous applications is twofold: the authors expand the financial considerations to include the whole life-cycle costs, and the authors consider all the greenhouse gases. This research constitutes a prime in using financial and environmental data produced by local governments to assess the effectiveness of their environmental measures.

In construction projects, engineering variations are very common and create breeding grounds for opportunistic claims. This study investigates the complementary effect between an inspection mechanism and a reputation system in deterring opportunistic claims, considering an employer with limited inspection accuracy and a contractor, which can be either reputation-concerned or opportunistic.

This paper applies a signaling game to investigate the complementary effect between the employer's inspection and a reputation system in deterring the contractor's possible opportunistic claim, considering the information-flow influence of claiming prices.

This study finds that in the exogenous-inspection-accuracy case, the employer does not always inspect the claim. A more stringent reputation system complements a less accurate inspection only when the inspection cost is lower than a threshold, but may decline the employer's surplus or social welfare. In the optimal-inspection-accuracy case, the employer always inspects the claim. However, only a sufficiently stringent reputation system can guarantee the effectiveness of an optimal inspection in curbing opportunistic claims. A more stringent reputation system has a value-stepping effect on the employer's surplus but may unexpectedly impair social welfare, whereas a higher inspection cost efficiency always reduces social welfare.

This article contributes to the project management literature by combing the signaling game theory with the reputation theory and thus embeds the problem of inspection mechanism design into a broader socio-economic framework.

This study attempts to comprehensively analyze the cost Malmquist productivity index of conventional and Islamic banks in Saudi Arabia, the largest dual banking sector in the world, during the COVID-19 pandemic.

This study employs the novel approach of cost Malmquist productivity index, which focuses on production costs, to measure the change in cost productivity so that the actual impact of the COVID-19 pandemic could be captured.

The Saudi Central Bank has successfully mitigated the impact of the COVID-19 epidemic on the Saudi banking sector by implementing several policies and services. This success is reflected in the large positive shift in the production frontier of Saudi banks. Moreover, it was found that Islamic Saudi banks were by far more productive than conventional Saudi banks during the COVID-19 pandemic. However, the total cost productivity index (CMPCH) of Islamic Saudi banks starts to decline sharply in the last quarter of 2022 compared to conventional Saudi banks, indicating that Islamic banks in Saudi Arabia are suffering the most from the tighter monetary policy recently implemented by the Saudi Central Bank.

The results provide insights for policymakers and investors on how different types of banks respond differently to economic crises and monetary policy changes. Targeted support measures may be needed to ensure all banks remain productive and efficient.

To the author’s knowledge, this is the first study to use this innovative methodology to assess the impact of COVID-19 on bank performance in a dual banking sector.

The present study assesses the commercial bank profit efficiency and its relationship to banking sector competition in a rapidly growing emerging economy, India from 2009 to 2019 using stochastic frontier analysis (SFA).

Lerner indices, conventional and efficiency-adjusted, quantify competition. Two SFA models are employed to calculate alternative profit efficiency (inefficiency) scores: the two-step time-decay approach proposed by Battese and Coelli (1992) and the recently developed single-step pairwise difference estimator (PDE) by Belotti and Ilardi (2018). In the first step of the BC92 framework, profit inefficiency is calculated, and in the second step, Tobit and Fractional Regression Model (FRM) are utilized to evaluate profit inefficiency correlates. PDE concurrently solves the frontier and inefficiency equations using the maximum likelihood process.

The results suggest that foreign banks are less profit efficient than domestic equivalents, supporting the “home-field advantage” hypothesis in India. Further, increasing competition drives bank managers to make riskier lending and investment choices, decreasing bank profit efficiency. However, this effect varies depending on bank ownership and size.

Literature on the competition bank efficiency link is conspicuously scant, with a focus on technical and cost efficiency. Less is known regarding the influence of competition on bank profit efficiency. The article is one of the first to examine commercial bank profit efficiency and its relationship to banking sector competition. Additionally, the study work represents one of the first applications of the FRM presented by Papke and Wooldridge (1996) and the PDE provided by Belotti and Ilardi (2018).

Coal is the basic energy and essential resource in China, which is crucial to the economic lifeline and energy security of the country. Coal mining has been ever exposed to potential safety risks owing to the complex geologic environment. Effective safety supervision is a vital guarantee for safe production in coal mines. This paper aims to explore the impacts of the internet+ coal mine safety supervision (CMSS) mode that is being emerged in China.

In this study, the key factors influencing CMSS are identified by social network analysis. They are used to develop a multiple linear regression model of law enforcement frequency for conventional CMSS mode, which is then modified by an analytical hierarchy process to predict the law enforcement frequency of internet+ CMSS mode.

The regression model demonstrated high accuracy and reliability in predicting law enforcement frequency. Comparative analysis revealed that the law enforcement frequency in the internet+ mode was approximately 40% lower than the conventional mode. This reduction suggests a potential improvement in cost-efficiency, and the difference is expected to become even more significant with an increase in law enforcement frequency.

To the best of the authors’ knowledge, this is one of the few available pieces of research which explore the cost-efficiency of CMSS by forecasting law enforcement frequency. The study results provide a theoretical basis for promoting the internet+ CMSS mode to realize the healthy and sustainable development of the coal mining industry.

This paper aims to investigate the short- and long-run influence of core banking solutions (CBSs) on productive efficiency and identify the presence of potential network externalities arising from CBS adoption. This paper further examines the differential behaviour of long-term effects across the banking structure.

This study uses a panel data set of Indian commercial banks from 2005 to 2021. Economic efficiency is quantified using VRS-based DEA programming algorithms. Productivity changes are measured through an input-oriented, DEA-based Malmquist productivity index. Short- and long-run effects are examined through a finite autoregressive distributed lag model, estimated through a pooled mean-group estimator.

Findings suggest that CBS adoption negatively correlates with cost structure until the first year of adoption. Nevertheless, significant benefits are visible from the third year. Furthermore, such associations are highly susceptible to the industry structure. CBS results in higher incremental benefits for private banks vis-à-vis state-owned banks. Large banks receive significant and quicker productivity improvements from CBS vis-à-vis small banks. Bank age guides CBS–performance associations, highlighting that mature banks may face the issue of legacy infrastructure in CBS adoption. The resultant networking externalities are significant as they enhance the attractiveness of the network, which subsequently augments inter-branch and inter-bank communications.

To the best of the authors’ knowledge, this study is the first to recognise the stickiness of one of the most homogeneously adopted technological innovations in the Indian banking sector. The presence of a conjoint technological network has the potential to enhance the service delivery process and ensure superior returns for Indian banks.

This study explores how strategic human resource practices enhance the competitive capability of differentiation and cost-effectiveness by leveraging knowledge resources in Indian IT/software organizations. It examines the mediating effect of knowledge management (KM) processes in the relationship between strategic HR practices, competitive differentiation and cost-effectiveness capabilities.

An online questionnaire survey collected data from 380 knowledge workers in 25 IT/software and consultancy firms. The authors checked data reliability and validity by conducting exploratory factor analysis in SPSS and confirmatory factor analysis in AMOS. The authors evaluated hypotheses using path analysis in structural equational modeling in AMOS.

Strategic HR practices significantly and positively affect KM processes and competitive capabilities-differentiation and cost-efficiency. Both strategic HR practices and KM processes have a closer association with differentiation than cost-effectiveness. Knowledge management processes significantly and positively mediate between strategic HR practices and competitive capabilities. The mediation is more substantial in predicting differentiation than cost-effectiveness.

It is a cross-sectional study with a constrained capacity to predict accurate causal inferences; The authors call for future studies with longitudinal design and objective measures. Further studies are required to explore the impact of various strategic HR configurations on KMP to understand how different routes stimulate a particular competitive strategy. This conceptual framework can be validated across different industry types and sizes.

This study provides practical insights to HR and knowledge managers regarding devising HR and KM processes to accomplish the goals of differentiation and cost-effective, competitive strategies. This study highlights that leveraging human capital for effective KM is crucial for gaining a competitive advantage.

The paper adds to the strategic HR and KM literature by exploring the mediating role of KM processes in enabling strategic HR processes to enhance differentiation and cost-effective, competitive strategies. It provides original empirical evidence from knowledge-intensive IT/software consultancies, particularly in India's emerging economy. It indicates the current state of HR practices adopted for optimum utilization of knowledge resources and the importance of differentiation strategy for Indian knowledge-intensive IT/software firms.

In time and accurate detection of cancer can save the life of the person affected. According to the World Health Organization (WHO), breast cancer occupies the most frequent incidence among all the cancers whereas breast cancer takes fifth place in the case of mortality numbers. Out of many image processing techniques, certain works have focused on convolutional neural networks (CNNs) for processing these images. However, deep learning models are to be explored well.

In this work, multivariate statistics-based kernel principal component analysis (KPCA) is used for essential features. KPCA is simultaneously helpful for denoising the data. These features are processed through a heterogeneous ensemble model that consists of three base models. The base models comprise recurrent neural network (RNN), long short-term memory (LSTM) and gated recurrent unit (GRU). The outcomes of these base learners are fed to fuzzy adaptive resonance theory mapping (ARTMAP) model for decision making as the nodes are added to the F_2ˆa layer if the winning criteria are fulfilled that makes the ARTMAP model more robust.

The proposed model is verified using breast histopathology image dataset publicly available at Kaggle. The model provides 99.36% training accuracy and 98.72% validation accuracy. The proposed model utilizes data processing in all aspects, i.e. image denoising to reduce the data redundancy, training by ensemble learning to provide higher results than that of single models. The final classification by a fuzzy ARTMAP model that controls the number of nodes depending upon the performance makes robust accurate classification.

Research in the field of medical applications is an ongoing method. More advanced algorithms are being developed for better classification. Still, the scope is there to design the models in terms of better performance, practicability and cost efficiency in the future. Also, the ensemble models may be chosen with different combinations and characteristics. Only signal instead of images may be verified for this proposed model. Experimental analysis shows the improved performance of the proposed model. This method needs to be verified using practical models. Also, the practical implementation will be carried out for its real-time performance and cost efficiency.

The proposed model is utilized for denoising and to reduce the data redundancy so that the feature selection is done using KPCA. Training and classification are performed using heterogeneous ensemble model designed using RNN, LSTM and GRU as base classifiers to provide higher results than that of single models. Use of adaptive fuzzy mapping model makes the final classification accurate. The effectiveness of combining these methods to a single model is analyzed in this work.

The scope of this research is to investigate performing loan efficiency for fifty European banks during the period 2008–2017.

The study is structured as a two-stage analysis of performing loan efficiency and its driving factors. In the first stage of the proposed methodology “Data Envelopment Analysis” is used to estimate performing loan efficiency for each bank included in the sample. A bootstrap statistical procedure enhances the findings. In the second stage, the impact of other factors on the efficiency scores of loan performance using tobit regression is investigated.

The results are consistent with the findings of the individual banks' financial analyses. According to the findings of DEA implementation, the evaluated banks may enhance their cost efficiency by 39% on average. In addition, the results indicate that loan efficiency performance improves after 2015, coinciding with the business cycle's upward trend. The tobit regression is employed in the second stage to examine the influence of bank-related and macroeconomic factors on banks' loan management efficiency. According to the findings of the tobit regression, three factors, namely the capital adequacy ratio, GDP per capita and managerial inefficiency, have a substantial influence on performing loan efficiency.

This research investigates the effectiveness of European economic policy in protecting the European banking system from the consequences of the sovereign debt crisis in several euro area members. The results highlight the distance of the Eurozone from the level of the ‘optimal currency area’.

This paper investigates the effect of financial development on bank profitability. The authors examine whether financial development is an important determinant of bank profitability.

The ordinary least square and the generalized method of moments regression methods were used to analyze the impact of financial development on the profitability of the Nigerian banking sector.

The authors find a significant negative relationship between the financial system deposits to GDP ratio and the non-interest income of Nigerian banks. This indicates that higher financial system deposits to GDP depresses the non-interest income of Nigerian banks. The result implies that the larger the size of the Nigerian financial system, the lower the profitability of banks in Nigeria. Also, the authors observe that bank concentration, nonperforming loans, cost efficiency and the level of inflation are significant determinants of the profitability of Nigerian banks.

It is recommended that regulators should establish market-enabling policies that encourage new banks to emerge in the banking industry. The entry of new banks can lead to increase in financial system deposits and credit supply for economic growth. Regulators also need to understand the role of Nigerian banks in promoting financial development and find ways to collaborate with banks towards financial sector development. Another implication of the findings for asset managers is that asset managers will need to take into account the prevailing level of financial development, particularly the size of the financial system, in their asset pricing and investment decisions. This will ensure that investors get value for their investments in Nigeria. The financial implication of the study is that the level of financial development in Nigeria can improve the finance-growth linkages in Nigeria through the efficient allocation of credit and capital to crucial sectors of the Nigerian economy to spur growth in those sectors.

Evidence dealing with how financial development affects the profitability of the banking sector in African countries is scarce in the literature, and is completely absent for Nigeria. This paper addresses this research gap.