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This study investigates the relationship between corporate social responsibility (CSR) and financial performance of the banking sector from the prospective of emerging countries.

This study obtained balance sheet and income statement data for 173 banks in 20 emerging countries from the Bankscope database from 2005–2018. The CSR-related data were taken from the Thomson Reuters ASSET4 database. Moreover, macroeconomic controls such as GDP per capita, inflation, and financial development are attained from the GFDD. The series of institutional quality indices (Political Stability, Rule of Law, Control of Corruption, Government Effectiveness, and Regulatory Quality) is obtained from the WGI. At the same time, national culture and bank regulation are attained from Hofstede Insights and Barth et al. (2013). We used the panel fixed-effects model in our baseline estimations, while 2SLS and GMM were applied to control for endogeneity.

The finding shows that CSR activities significantly improve bank performance, but the effect varies across the bank. Only environmentally friendly activities have shown a significant positive relationship with banking performance for CSR dimensions. However, the social and government dimensions did not significantly affect bank performance. Moreover, a sound institutional and regulatory environment and national norms play an important role in the nexus of CSR activities and bank performance.

This study provides empirical evidence that sheds light on CSR and bank performance in an emerging market context.

This study aims to address the timely question of whether Bitcoin exhibited a safe haven property against the major Australian stock indices during the first and second waves of the COVID-19 pandemic in Australia and whether such property is similar or different in one year time from the first wave of the COVID-19.

The authors used the bivariate Dynamic Conditional Correlation, Generalized Autoregressive Conditional Heteroskedasticity model, on the five-day returns of Bitcoin and Australian stock indices for the sample period between 23 April, 2011 and 19 April, 2021.

The results show that Bitcoin offered weak safe haven and hedging benefits when combined in a portfolio with S&P/ASX 200 Financials index, S&P/ASX 200 Banks index or S&P/ASX 300 Banks index. In regard to the S&P/ASX All Ordinaries Gold index, the authors found Bitcoin a risky candidate with inconsistent safe haven and hedging benefits. Against S&P/ASX 50 index, S&P/ASX 200 index and S&P/ASX 300 index, Bitcoin was nothing more than a diversifier. The outset of the second COVID-19 wave, which was comparatively more severe than the first, is also reflected in the results with considerably higher correlations.

There is a lack of in-depth empirical evidence on the safe haven capabilities of Bitcoins for various Australian stock indices during the first and second waves of the COVID-19 pandemic. The study bridges this void in research.

Because of the growing financial market integration, China’s stock market’s volatility spillover effect has gradually increased. Traditional strategies do not capture stock volatility in dependence and dynamic conditions. Therefore, this study aims to find an effective stochastic model to predict the volatility spillover effect in the dynamic stock markets.

To assess the time-varying dynamics and volatility spillover, this study has used an integrated approach of dynamic conditional correlation model, copula and extreme-value theory. A daily log-returns of three leading indices of Pakistan Stock Exchange (PSX) and Shanghai Stock Exchange (SSE) from the period of 2009 to 2019 is used in the modeling of value-at-risk (VaR) for volatility estimation. The Student’s t copula has been selected based on maximum likelihood estimation and Akaike’s information criteria values of all the copulas using the goodness-of-fit test.

The model results show stronger dependency between all major portfolios of PSX and SSE, with the parametric value of 0.98. Subsequently, the results of dependence structure positively estimate the spillover effect of SSE over PSX. Furthermore, the back-testing results show that the VaR model performs well at 99% and 95% levels of confidence and gives more accurate estimates upon the maximum level of confidence.

This study is helpful for the investment managers to manage the risk associated to portfolios under dependence conditions. Moreover, this study is also helpful for the researchers in the field of financial risk management who are trying to improve the returns by addressing the issues of volatility estimations.

This study contributes to the body of knowledge by providing a practical model to manage the volatility spillover effect in dependence conditions between as well as across the financial markets.

The pandemic's health and social issues have significantly altered the character and manner of teaching and learning in higher education across the country. The use of technology to replace or integrate face-to-face learning with online learning has become a necessary requirement for promoting and continuing learning processes. Furthermore, integrating technology is a goal of Sustainable Development Goal 4 (SDG 4) to make teaching and learning more innovative and sophisticated. This paper is based on a systematic review grounded in a synthesis of research papers and documents analyzing the current status of teachers' pedagogy through online learning modes in the context of Pakistan.

Through content analyses of academic studies in higher education and reflection on the online teaching experiences, this study discusses how students' learning is associated with teachers' teaching approaches in the modern era of digitalization and innovation.

The review and analysis suggest that online teaching is not viewed as an innovative phenomenon; rather, teachers simply teach their traditionally designed face-to-face courses through the use of technology. The paper suggests that transforming teachers' pedagogical insight to make online learning sustainable is an urgent need for higher education.

The analysis provides a basis for consideration of teacher learning and quality education (SDG #4) to fulfill the nation’s agenda for sustainable development. The analysis helps educators and administrators in higher education institutions reflect on their policies and practices that have short- and long-term effects on students' learning outcomes.

The purpose of this research is to primarily understand how crypto traders can use the Bitcoin as a hedge or safe haven asset to reduce their losses from crypto trading. The study also aims to provide insights to crypto investors (portfolio managers) who wish to maintain a crypto portfolio for the medium term and can use the Bitcoin to minimize their losses. The findings of this research can also be used by policymakers and regulators for accommodating the Bitcoin as a medium of exchange, considering its safe haven nature.

This study applies the cross-quantilogram (CQ) approach introduced by Han et al. (2016) to examine the safe-haven property of the Bitcoin against the other selected crypto assets. This method is robust for estimating bivariate volatility spillover between two markets given unusual distributions and extreme observations. The CQ method is capable of calculating the magnitude of the shock from one market to another under different quantiles. Additionally, this method is suitable for fat-tailed distributions. Finally, the method allows anticipating long lags to evaluate the strength of the relationship between two variables in terms of durations and directions simultaneously.

The Bitcoin acts as a weak safe haven asset for a majority of new crypto assets for the entire study period. These results hold even during greed and fear sentiments in the crypto market. The Bitcoin has the ability to protect crypto assets from sharp downturns in the crypto market and hence gives crypto traders some respite when trading in a highly volatile asset class.

This study is the first attempt to show how the Bitcoin can act as a true matriarch/patriarch for crypto assets and protect them during market turmoil. This study presents a clear and concise representation of this relationship via heatmaps constructed from CQ analysis, depicting the quantile dependence association between the Bitcoin and other crypto assets. The uniqueness of this study also lies in the fact that it assesses the protective properties of the Bitcoin not only for the entire sample period but also specifically during periods of greed and fear in the crypto market.

The purpose of this paper is to evaluate the prevalence and antibiograms of bacteria isolated from various fresh fruit juices at a local market in Faisalabad.

Fresh fruit juice samples (n=125) were randomly collected using aseptic technique. Each sample (10 mL) was serially diluted with 90 mL of sterile peptone water, from 1×10⁻¹ to 1×10⁻⁵. Each dilution was then used to inoculate nutrient agar by surface spread plating. Aerobic colony counts (ACCs) were determined by colony counting. The isolates were sub-cultured on blood and MacConkey agar. Preliminary identification was achieved on the basis of colony morphology and culture characteristic, and confirmed by API® 20E, 20NE, and API® Staph testing. Antimicrobial susceptibility testing was carried out using the Kirby-Bauer disk diffusion assay, per CLSI 2015 guidelines.

The mean ACC ranged from 2.0×10⁶ CFU/mL to 4.93×10⁶ CFU/mL, with the highest ACC determined for orange juice. Overall, 153 polymicrobial were identified in 125 samples; 103 of these were Gram-negative rods (GNR) and 28 were Gram-positive cocci (GPC). Escherichia coli (n=38), Klebsiella pneumoniae (n=32) and Pseudomonas aeruginosa (n=24) were the predominant GNR; Staphylococcus aureus (n=28) was the predominant GPC. Antibiogram analysis revealed that all GNR were resistant to ampicillin. However, most E. coli isolates were resistant to ceftazidime (72.4 percent of isolates), and ceftriaxone and cefepime (68.9 percent), while most K. pneumoniae isolates were resistant to cefepime (72 percent) and ceftriaxone (64 percent). All S. aureus isolates were resistant to penicillin, while most (64 percent) were resistant to piperacillin; the most effective drugs against bacteria were vancomycin and imipenem.

The findings suggest that the local government regulatory food and public health authorities should take immediate emergency measures. Appropriate surveillance studies and periodic monitoring of food items should be regularly performed to safeguard public health.

The current study revealed the prevalence of multidrug-resistant bacteria in freshly prepared fruit juices sold by local street vendors.

The purpose of this paper is to analyze the effects of data-driven capabilities on supply chain integration (SCI) and competitive performance of firms in the food and beverages (F & B) industry in Pakistan.

The authors adopt the structural equation modeling approach to test the proposed hypotheses using AMOS 23. Survey data were collected from 240 firms in the F & B industry in Pakistan.

The results revealed that SCI (i.e. internal integration (II) and external integration (EI)) significantly mediates the effect of data-driven capabilities (i.e. flexible information technology resources and data assimilation) on a firm’s competitive performance. In addition to the direct effects, II also has an indirect effect on competitive performance through EI.

The study has several implications for managers in the context of big data application in food supply chain management (FSCM) in a developing country context. The study posits that firms can achieve excellence in performance by governing data-driven supply chain operations. The study also has implications for distributors and importers in the F & B industry. The cloud-based sharing of data can improve the operational performance of channel members while reducing their overall cost of operations. In practice, food franchises largely get the advantage of shared resources of their suppliers in managing orders, payments, inventory and after-sales services.

The study is novel and deepens the understanding about the use of big data in FSCM keeping in view the industry trends and stakeholder’s priorities in a developing country context.

The purpose of this study is to verify all factors that promote renewable energy (RE) consumption. Past studies have shown that financial development (FD) and economic growth (EG) are the major drivers toward RE development, while oil prices had mixed outcomes in different regions by different studies.

Global warming effects have been the major reason of the transition by nations from fossil fuel use to RE sources that are considered as friendly to the environment. This research uses the fixed effects and random effects techniques, to ascertain the factors which impact RE development. The generalized linear model is also used to check the robustness of the Fixed Effects and Random Effects models’ results, while the Kao, Pedroni and Westerlund tests are used to check cointegration in the specified model.

The major findings of this study show the importance of EG and FD in promoting RE development. Oil prices, inflation rate and public sector credit present a negative effect on RE development, while foreign direct investment does not significantly impact RE development.

This research recommends the use of FD in promoting RE sources, as well as the stabilization of oil prices and consumer prices.

This research is important because it specifies the three proxies of FD, together with foreign direct investment inflation rate, EG and oil prices, in modeling RE. By investigating the impact of oil prices on RE in the emerging seven economies, this research becomes one of the few studies done in this region, as per the authors’ knowhow.