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To maximize the network total profit by calculating the difference between costs and revenue (first objective function). To maximize the positive impact on the environment by integrating GSCM factors in RL (second objective function). To calculate the efficiency of disassembly centers by SDEA method, which are selected as suppliers and maximize the total efficiency (third objective function). To evaluate the resources and total efficiency of the proposed model to facilitate the allocation resource process, to increase resource efficiency and to improve the efficiency of disassembly centers by Inverse DEA.

The design of a closed-loop logistics network for after-sales service for mobile phones and digital cameras has been developed by the mixed-integer linear programming method (MILP). Development of MILP method has been performed by simultaneously considering three main objectives including: total network profit, green supply chain factors (environmental sustainability) and maximizing the efficiency of disassembly centers. The proposed model of study is a six-level, multi-objective, single-period and multi-product that focuses on electrical waste. The efficiency of product return centers is calculated by SDEA method and the most efficient centers are selected.

The results of using the model in a case mining showed that, due to the use of green factors in network design, environmental pollution and undesirable disposal of some electronic waste were reduced. Also, with the reduction of waste disposal, valuable materials entered the market cycle and the network profit increased.

(1) Design a closed-loop reverse logistics network for after-sales services; (2) Introduce a multi-objective multi-echelon mixed integer linear programming model; (3) Sensitivity analysis use Inverse-DEA method to increase the efficiency of inefficient units; (4) Use the GSC factors and DEA method in reverse logistics network.

Assessing inputs and outputs is a significant aspect of taking decisions while there are complex and multistage processes in many examinations. Due to the presence of interval performance measures in various real-world studies, the purpose of this study is to address the changes of interval inputs of two-stage processes for the perturbations of interval outputs of two-stage systems, given that the overall efficiency scores are maintained.

Actually, an interval inverse two-stage data envelopment analysis (DEA) model is proposed to plan resources. To illustrate, an interval two-stage network DEA model with external interval inputs and outputs and also its inverse problem are suggested to estimate the upper and lower bounds of the entire efficiency and the stages efficiency along with the variations of interval inputs.

An example from the literature and a real case study of the banking industry are applied to demonstrate the introduced approach. The results show the proposed approach is suitable to estimate the resources of two-stage systems when interval measures are presented.

To the best of the authors’ knowledge, there is no study to estimate the fluctuation of imprecise inputs related to network structures for the changes of imprecise outputs while the interval efficiency of network processes is maintained. Accordingly, this paper considers the resource planning problem when there are imprecise and interval measures in two-stage networks.

This paper explores the connection between capital structure and financial performance within European listed firms. The primary objective is to demonstrate an inverse U-shaped relationship between these two variables and pinpoint an optimal debt-equity mix.

In this study, we adopt a dynamic modeling approach to investigate the relationship between a firm’s capital structure and financial performance. Drawing on well-established theories and prior empirical studies, our model examines 3,121 dividend-paying firms from 41 European countries over 14 years, from 2008 to 2021. To enhance the reliability of our findings, we employ two distinct estimation techniques: the fixed effect model (FE) and the system generalized method of moments (System-GMM).

This study reveals an inverse U-shaped relationship between the firm’s financial performance, measured by the return on equity (ROE) and its capital structure (total liability to total assets ratio). Furthermore, an optimal capital structure of about 29% is determined for all firms in the sample, and about 21%, 28% and 41% industry-specific capital structure for manufacturing, real estate and wholesale trade, respectively.

This paper contributes to existing knowledge by empirically determining an optimal capital structure for listed firms across various industries in Europe, which very few studies have attempted to do in the past. An optimal capital structure is an invaluable benchmark for managers and other stakeholders, informing their decision-making.

This study aims to explore the empirical relationship between public–private investment (PPI) in energy and environmental quality.

The authors hypothesize that PPI can reduce pollution emissions and test this hypothesis by sampling the 20-year data of emerging and growth-leading economies (EAGLE) and adopting two estimation techniques named panel estimated generalized least square and fully modified ordinary least square models.

The empirical analysis vows that PPI has an inverse relationship with CO₂ emissions, corroborating the sustainable development driving role of PPI. In addition, the empirical outcomes suggest a negative/positive role of energy imports and economic growth. Meanwhile, foreign direct investment is negatively linked with CO₂ emissions, corroborating the pollution halo hypothesis in the case of EAGLE. However, financial development shows a positive relationship with CO₂ emissions.

This study offers an important policy outlay regarding the pollution mitigation role of PPI in EAGLE. The environmental sustainability in underlying economies can be achieved by enhancing the magnitude of public–private cooperation in energy investment. The empirical analysis supplements cutting-edge empirical evidence regarding PPI as a driver of important sustainable development goal (SDG), i.e. environmental sustainability.

To the best of the authors’ knowledge, this study is the first study that examines how one can achieve an important SDG regarding environmental sustainability through PPI in energy.

The study aims to test prospect theory (PT) predictions in the cryptocurrency (CC) market. It proposes a new asset pricing model that explores the potential of prospect theory value (PTV) as a significant predictor of CC returns.

The study comprehensively analyses a large sample set of 1,629 CCs, representing more than 95% of the CC market. The study uses a portfolio analysis approach, employing univariate and bivariate sorting techniques with equal-weighted and value-weighted portfolios. The study also employs ordinary least squares (OLS) regression, panel data methods and quantile regression (QR) to estimate the models.

This study demonstrates an average inverse relationship between PTV and CC returns. However, this relationship exhibits asymmetry across different quantiles, indicating that investor reactions vary based on market conditions. Moreover, PTV provides more robust predictions for smaller CCs characterized by high volatility and illiquidity. Notably, the findings highlight the dominant role of the probability weighting (PW) component in PT for predicting CC behaviors, suggesting a preference for lottery-like characteristics among CC investors.

The study is one of the early studies on CC price dynamics from the PT perspective. The study is the first to apply a QR approach to analyze the cross-section of CCs using a PT-based asset pricing model. The results shed light on CC investors' decision-making processes and risk perception, offering valuable insights to regulators, policymakers and market participants. From a practical perspective, a trading strategy centered around the PTV effect can be implemented.

The purpose of this study is to analyze the nonlinear effects of corporate philanthropy on the responses of both internal and external stakeholders as well as its impact on corporate financial performance.

Based on the stakeholder theory, the authors developed a conceptual model to examine the nonlinear effects of corporate philanthropy on company performance. For the empirical analysis, data from 397 company-years was analyzed using a using a Heckman two-stage model. The robustness of the findings was also confirmed through panel regression analysis.

The study revealed a linear relationship between corporate reputation and corporate philanthropy, whereas job satisfaction exhibited a nonlinear relationship with corporate philanthropy.

This research bridges the gap in extant literature by scrutinizing the nonlinear associations between corporate philanthropy and financial performance. Additionally, it addresses an emerging scholarly demand to uncover the “dark side” of corporate philanthropy through an investigation into its adverse impacts on employee satisfaction. Moreover, the study augments existing understandings of stakeholder theory and corporate philanthropy, positing that the influence of corporate philanthropy, as conceptualized through stakeholder theory, hinges on perceived fairness in multilateral relationships.

In the current competitive era of industrialization, a significant level of innovation is necessary to meet the growing competition. There are many economic forces that determine the pace of innovation within a country. Among others, this study aims to focus on exploring the relevant role of corruption control (CC) in determining the innovation level.

For empirical analysis, the authors sample the 24 years of data (1996–2019) of Asian economies and use the fully modified ordinary least square (OLS) and dynamic OLS models to check the regression among variables. The selection of both techniques is based upon the empirical suggestions offered by unit root testing and the Johansen cointegration test.

The empirical findings infer the positive and statistically significant role of CC in boosting innovation. Strengthening the corruption-free environment encourages innovation activities within the country. In addition, foreign direct investment has a negative relationship with CC while financial development, economic growth, export volume and government subsidies positively determine the innovation level.

Based on empirical analysis, it is suggested that the policy officials should do more focus on CC to enhance the competitiveness of the country through more innovation.

The empirical analysis robust the findings of existing literature in an alternative data set and offers innovative views regarding the role of other factors in boosting the innovation level.

This article analyses the recent inverse transition from goodwill impairment to goodwill amortisation implemented in Spain in 2016. The authors contribute to the existing literature by describing their differing impact over goodwill and impairment figures and testing the impact of goodwill on balances over stock prices.

First, using a database with all Spanish non-financial firms with positive goodwill on their balance sheets, the authors describe the impact of the regulatory change over goodwill and impairment figures. Second, focussing on listed firms only, the authors study the impact of financial reporting of goodwill and impairment on stock prices.

Average goodwill per company and the share of goodwill over total assets significantly reduced after 2016, but the results cannot be easily extrapolated to listed firms due to lack of data. When testing the impact of potentially inflated goodwill balances on prices, the authors find that investors kept overvaluing firms with inflated goodwill balances also with the amortisation method.

The lack of data for listed firms with goodwill in Spain makes it difficult to obtain statistically sound evidence, the results could be biased by the cultural traits of the country and related to the intensity of enforcement and monitoring.

This might suggest that the effects of the impairment method linger, so the authors conform to the interpretation that the systematic amortisation paired with a periodic impairment test may lead to accounting that better reflects the underlying economics of goodwill.

To the best of the authors' knowledge, there are no recent articles that analyse this new “turn-around” requiring again the systematic amortisation of goodwill.

This study aims to empirically explore several factors that encourage muzakki (zakat payers) to pay their zakat through institutions by elaborating on their extrinsic and intrinsic motivations as the composite factors regarding the attitude and intention improvement of muzakki. This study specifically studies zakat payment via digital means and categorizes the muzakki groups into two (urban and suburban) to be considered in the results.

Overall, this study gathers the data from 298 muzakki using a partial least squares technique the multigroup analysis to compare the analysis.

This study found that different sociodemographic aspects will result in varied performances of motivation in using technology between the two groups. Furthermore, positive preference aspects, such as muzakki’s attitude, can be a catalyst in improving their motivation to pay zakat through institutions.

The findings of this study can be used as a foundation to improve the technology-based services that will be more accessible and reachable. Provision of technical follow-ups regarding the utilization of technology, including community-based digital platform socializations, availability of online customer service that will respond to muzakki’s needs and synergy between stakeholders, are the primary obligations that a zakat institution must fulfill.

As far as the researchers are concerned, the studies focusing on the motivational factors and attitude of muzakki as an intervention in paying zakat via institutions are limited in numbers, especially studies on digital payment. In this study, however, classifying the groups into two will help gain a deeper understanding of this topic.

The purpose of this paper is to investigate the relationship between tax avoidance and earnings persistence in the light of a developing economy, with the main focus on China.

In the analysis, the author conducts a survey on the tax avoidance situation of Chinese listed companies from 2012 to 2020. Then, a multivariate regression analysis is performed in order to analyse the relationship between corporate tax avoidance and earnings persistence.

The findings of the present study show that tax avoidance has a significant positive effect on earnings persistence. However, when the degree of tax avoidance is high, the “risk effect” of tax avoidance exceeds the “value effect”, and tax avoidance will reduce the persistence of earnings. This conclusion is even more prominent when the company is non-state-owned. Further research shows the increase of institutional investors’ shareholding ratio can improve “value effect” of tax avoidance, lessen “risk effect” of tax avoidance, and positively affect the relationship between tax avoidance and earnings persistence.

This study provides evidence for investors to understand the dual effect of tax avoidance on earnings persistence. The results may have implications for regulatory bodies. They can provide a better understanding of the corporate governance role of institutional investors in curbing opportunistic tax avoidance.

This study enriches the research on tax avoidance effects by analysing the impact of tax avoidance on earnings persistence. This study also compensates for the shortcomings of analysing earnings persistence mainly from the perspective of tax differences in the past, and promotes the study of the corporate governance effects of institutional investors under different levels of tax avoidance.