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The study aims to determine the impact of sustainable manufacturing on environmental performance through government regulation and eco-innovation in Indonesian small and medium-sized enterprises (SMEs).

The results indicate sustainable manufacturing plays a significant role in SMEs' environmental performance and regulations, and eco-innovation can moderate it. It also reveals that government regulation has a positive and significant effect on environmental performance. Moreover, eco-innovation has a positive and significant effect on environmental performance.

The findings of this study indicate that SMEs can embrace sustainable manufacturing practices and achieve their long-term sustainability goals by adhering to regulations, collaborating with stakeholders and implementing eco-friendly innovations.

This research uncovers ground-breaking perspectives on the evolution of scientific knowledge about the impact of eco-innovation, regulatory measures and sustainable manufacturing practices on the environmental performance of SMEs.

The objective of this work is to demonstrate the relationships between the two main processes of research and development (R&D) activities: the knowledge generation phase (KPP) and the knowledge commercialization, or transfer, phase (KCP), in a sector that is intensive in this type of activity, such as the pharmaceutical sector. In addition, within the framework of the general objective of this work, the authors propose two other objectives: (1) make advances in network efficiency measurement models, and (2) determine the factors associated with efficiency in the KPP and in the KCP in companies of the pharmaceutical sector in Spain.

A Network Data Envelopment Analysis (NDEA) model (Färe and Grosskopf, 2000) with categorical variables (Lee et al., 2020; Yeh and Chang, 2020) has been applied, and a sensitivity analysis of the obtained results has been performed through a DEA model of categorical variables, in accordance with the work of Banker and Morey (1986), to corroborate the results of the proposed model. The sample is made up of 77 companies in the pharmaceutical sector in Spain.

The results obtained point to a greater efficiency of pharmaceutical companies in the KPP, rather than in the KCP. Furthermore, the study finds that 1) alliances between companies have been the accelerating factors of efficiency in the KCP (but patents have slowed this down the most); 2) the quality of R&D and the number of R&D personnel are the factors that most affect efficiency in the KPP; and 3) the quality of R&D again, the benefits obtained and the position in the market are the factors that most affect efficiency in the KCP.

The authors have not found studies that show whether the efficiency obtained by R&D-intensive companies in the KPP phase is related to better results in terms of efficiency in the KCP phase. No papers have been found that analyse the role of alliances between R&D-intensive companies and patents, as agents that facilitate efficiency in the KCP phase, covering the gap in the research on both problems. Notwithstanding, this work opens up a research path which is related to the improvement of network efficiency models (since it includes categorical variables) and the assessment of the opinions of those who are responsible for R&D departments; it can be applied to decision-making on the aspects to improve efficiency in R&D-intensive companies.

Research based on Bangladesh–US trade data examines the Heckscher–Ohlin model and the Rybczynski hypothesis in this study.

Ordinary least square (OLS) techniques are used in this study, which relies on data from the NBER International Trade and Geography Data and the UN Comtrade Database for the years 2018 and 2008.

The research shows that trade between the United States and Bangladesh follows Heckscher–Ohlin and Rybcyzinski's trade predictions. According to the study, since labor is in plentiful supply in Bangladesh, Bangladesh's labor-based sectors have a higher US labor-to-capital import shares than US capital-based industries. As Bangladesh has not changed significantly from a labor-based country since 2008, it retains the same pattern even though the share of US unskilled labor-based sectors imported from Bangladesh decreased in 2018.

The findings of this study have a wide range of implications for both trade theory and policy debates between Bangladesh and the United States.

The purpose of this study is to analyze and forecast the financial sustainability and resilience of commercial banks of Bangladesh in response to the negative effects of COVID-19 pandemic.

Eighteen publicly listed commercial banks of Dhaka Stock Exchange (DSE) have been taken as a sample for this study. To measure the riskiness of banks' credit portfolio, nine industries of DSE have been considered to determine probable loss of revenue arising from the COVID-19 pandemic shock. Moreover, two commonly used multiple-criteria-decision-making (MCDM) tools namely TOPSIS method and HELLWIG method have been used for analyzing the data.

Based on the performance scores under TOPSIS and HELLWIG method, banks are categorized into three groups (six banks each) namely top resilient, moderate resilient and low resilient. It is found that EBL and DBBL are the most resilient banks, and ONEBANK is the worst resilient bank in Bangladesh in managing the COVID-19 pandemic shock.

This study concludes that banks with low capital adequacy, low liquidity ratio, low performance and higher NPLs are more vulnerable to the shocks caused by the COVID-19 pandemic. The management of commercial banks should emphasize on maintaining higher capital base and reducing default loans.

Resilience of the Bangladeshi banking sector under any adverse economic event has been examined by only using stress testing approach. This study is empirical evidence where both TOPSIS and HELLWIG MCDM methods have been used to make the result conclusive.

For over a century, traditional Japanese cotton crepe fabrics have been popular for men’s underwear in the humid summer. Now, consumer demand is for crepe fabrics that are more attractive, reflecting a shift in use from underwear to women’s dresses. The purpose of this paper is to clarify how the structures of the crepe and its constituent yarns affect the physical properties, handle and silhouette formability of crepe fabrics for dresses.

Three plain-weave gray fabrics were finished by four different processes to change their crepe structures. The mechanical and surface properties of the fabrics were measured using the Kawabata evaluation system for fabrics. The primary hand values and silhouette formability of the fabrics were calculated using conversion equations based on the physical properties. The handle of the crepe fabrics and the aesthetic appearance of flared collars made of them were assessed by female students using the semantic differential method.

Comparing the fabrics made from the same gray fabric, the piqué crepe fabrics showed larger Hari (anti-drape) and Shari (crispness) than the others. The subjective hand value of softness was closely related to fabric thickness. The assessors preferred the fine piqué crepe fabrics over the wide piqué fabrics regarding both the tactile feeling of the fabrics and the aesthetic appearance of the flared collars. The attractiveness of the flared collars was dominated by the shear stiffness of the fabrics.

The fine piqué crepe fabric made from fine yarns produced a more preferable handle. The fine piqué fabric made from thicker yarns produced flared collars with silhouettes that are more attractive. This indicates that the fine piqué structure is a positive feature that makes the fabric suitable for various types of dresses.

The Chinese fund market has witnessed significant developments in recent years. However, although there has been a range of studies assessing fund performance in developed industries, the rapidly developing fund industry in China has received very little attention. This study aims to examine the performance of open-end securities investment funds investing in Chinese domestic equity during the period May 2003 to September 2020. Specifically, applying a non-parametric bootstrap methodology from the literature on fund performance, the authors investigate the role of skill versus luck in this rapidly evolving investment funds industry.

This study evaluates the performance of Chinese equity securities investment funds from 2003–2020 using a bootstrap methodology to distinguish skill from luck in performance. The authors consider unconditional and conditional performance models.

The bootstrap methodology incorporates non-normality in the idiosyncratic risk of fund returns, which is a major drawback in “conventional” performance statistics. The evidence does not support the existence of “genuine” skilled fund managers. In addition, it indicates that poor performance is mainly attributable to bad stock picking skills.

The authors find that the top-ranked funds with positive abnormal performance are attributed to “good luck” not “good skill” while the negative abnormal performance of bottom funds is mainly due to “bad skill.” Therefore, sensible advice for most Chinese equity investors would be against trying to “pick winners funds” among Chinese securities investment funds but it would be recommended to avoid holding “losers.” At the present time, investors should consider other types of funds, such as index/tracker funds with lower transactions. In addition, less risk-averse investors may consider Chinese hedge funds [Zhao (2012)] or exchange-traded fund [Han (2012)].

The paper makes several contributions to the literature. First, the authors examine a wide range (over 50) of risk-adjusted performance models, which account for both unconditional and conditional risk factors. The authors also control for the profitability and investment risks in Fama and French (2015). Second, the authors select the “best-fit” model across all risk-adjusted models examined and a single “best-fit” model from each of the three classes. Therefore, the bootstrap analysis, which is mainly based on the selected best-fit models, is more precise and robust. Third, the authors reduce the possibility that findings may be sample-period specific or may be a survivor (upward) biased. Fourth, the authors consider further analysis based on sub-periods and compare fund performance in different market conditions to provide more implications to investors and practitioners. Fifth, the authors carry out extensive robustness checks and show that the findings are robust in relation to different minimum fund histories and serial correlation and heteroscedasticity adjustments. Sixth, the authors use higher frequency weekly data to improve statistical estimation.

This paper aims to examine the impact of corporate social responsibility (CSR) on financial performance (FP) of Indian steel industry in terms of value-added (VAM), profitability (PM), market (MM) and growth measures (GM).

It is an empirical study using secondary data of 40 companies for 14 years collected from CSR/annual reports/official websites of the companies and Prowess database. The panel regression analysis, MANOVA and univariate ANOVA have been conducted to examine the impact of CSR on FP.

The result indicates a positive impact of CSR on FP in terms of VAM, PM and GM, thereby indicating that more investments in CSR will generate wealth for shareholders, enhance profitability and sales. Moreover, this study shows no noticeable relationship between CSR and MM.

This study contributes to the literature on the CSR–FP relationship and also has implications for managers, investors and other stakeholders. Companies with higher CSR rating create a brand image, attract proficient employees, get greater profit, loyal customers and have less possibility of bribery and corruption. This study may result in being influential to companies confined not only to this sector but also reaching to the others, thus inspiring them to contribute their share of profit for the welfare of society.

To the best of the authors' knowledge, it is the first comprehensive study to examine the impact of CSR on FP of Indian steel industry by considering four dimensions for measuring FP. It provides evidence about the relationship between CSR and FP.

Disaster management and humanitarian logistics (HT) play crucial roles in large-scale events such as earthquakes, floods, hurricanes and tsunamis. Well-organized disaster response is crucial for effectively managing medical centres, staff allocation and casualty distribution during emergencies. To address this issue, this study aims to introduce a multi-objective stochastic programming model to enhance disaster preparedness and response, focusing on the critical first 72 h after earthquakes. The purpose is to optimize the allocation of resources, temporary medical centres and medical staff to save lives effectively.

This study uses stochastic programming-based dynamic modelling and a discrete-time Markov Chain to address uncertainty. The model considers potential road and hospital damage and distance limits and introduces an a-reliability level for untreated casualties. It divides the initial 72 h into four periods to capture earthquake dynamics.

Using a real case study in Istanbul’s Kartal district, the model’s effectiveness is demonstrated for earthquake scenarios. Key insights include optimal medical centre locations, required capacities, necessary medical staff and casualty allocation strategies, all vital for efficient disaster response within the critical first 72 h.

This study innovates by integrating stochastic programming and dynamic modelling to tackle post-disaster medical response. The use of a Markov Chain for uncertain health conditions and focus on the immediate aftermath of earthquakes offer practical value. By optimizing resource allocation amid uncertainties, the study contributes significantly to disaster management and HT research.