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This study investigates the impact of corporate social responsibility (CSR) on corporate innovation. We selected the listed nonfinancial firms of South Asian Economies. The sample of the study comprised a total of 426 listed manufacturing firms of South Asian Countries for period spans 10 years from 2012 to 2021. In this study, descriptive statistics, multicollinearity diagnostic tests, correlation analysis and two-step dynamic panel system generalized method of moments (GMM) were applied to analyze the data. CSR measured with three proxies' social indicators, environmental indicators, and CSR composite index of social and environmental indicators. However, corporate innovation is captured with number of citations received in a year and number of patents filed in the year. Overall, findings of the study using all measures of CSR shows that CSR significantly and positively related with corporate innovation. Our results find support for CSR-innovation view with all measures of CSR. The findings suggest that the current study is helpful for managers, regulators, policymakers, and researchers. For managers, the study helps them to make the CSR and innovation decision. The policymakers should take appropriate innovative decision while considering factors such as CSR. This study can also be extended by considering this study for developed and emerging economies sample.

This study aims to examine the mediating role of students’ knowledge of sustainable development (SD) in the relationship between green campus initiatives by higher education institutions (HEIs) and student proactivity.

The research, with a quantitative approach, was carried out with students linked to HEIs belonging to the Sustainable Campus Network – Portugal (RCS-PT).

It was concluded that communications of HEI SD initiatives, green campus operations and approach to SD in the classroom have a positive and significant effect on students’ proactivity toward SD. It was also concluded that SD-oriented student knowledge is a mediator in these relationships.

An underlying argument is that students with SD-oriented knowledge engage in proactive behaviors, taking the best HEI initiatives as a precedent among students of the HEIs belonging to the RCS-PT.

HEI initiatives can be drivers for proactive student behaviors regarding SD. Thus, this study brings guidance to university leaders and other stakeholders. The findings can also be useful for those involved in planning SD-oriented actions in HEIs.

Strengthen the role of higher education as co-creators of change by promoting the principles of SD in future professionals. Education is a strong instrument for behavioral change, so HEIs play a fundamental role here having a direct impact on society.

This research sought to expand the dialogue about SD in HEIs, especially in achieving sustainable development goals, intertwined with the idea of participation and engagement of students.

The purpose of this study is to analyse cyberfraud in the South African banking industry using a multiple regression approach and develop a predictive model for the estimation and prediction of financial losses due to cyberfraud.

To mitigate the occurrence of cyberfraud, this study uses the multiple regression approach to correlate the relationship between financial loss and cyberfraud activities. The cyberfraud activities in South Africa are classified into three, namely, digital banking application, online and mobile banking fraud. Secondary data that captures the rate of cyberfraud occurrences within these three major categories with their resulting financial losses were used for the multiple regression analysis that was carried out in the Statistical Package for Social Science (SPSS, 2022 environment).

The results obtained indicate that the South African financial institutions still incur significant financial losses due to cyberfraud perpetration. The two main independent variables used to estimate the magnitude of financial loss in the South Africa’s banking industry are online (internet) banking fraud (X2) and mobile banking fraud (X3). Furthermore, a multiple regression model equation was developed for the prediction of financial loss as a function of the two independent variables (X2 and X3).

This study adds to the literature on cyberfraud mitigation. The findings may promote the combat against cyberfraud in the South Africa’s financial institutions. It may also assist South Africa’s financial institutions to predict the financial loss that financial institutions can incur over time. It is recommended that South Africa’s financial institutions pay attention to these two key variables and mitigate any associated risks as they are crucial in determining their profitability.

Existing literature indicated significant financial losses to cyberfraud perpetration without establishing any relationship between the magnitude of losses incurred and the prevalent forms of cyberfraud. Thus, the novelty of this study lies in the analysis of cyberfraud in the South African banking industry using a multiple regression approach to link financial losses to the perpetration of the prevalent forms of cyberfraud. It also develops a predictive model for the estimation and projection of financial losses.

This study aims to explore the holistic context of organisational staff retention in small, medium and large organisations. It also aims to identify the factors affecting the retention of organisations of different sizes.

The study implements an empirical test of a model created during previous research with the participation of 511 employees. The responses to the online questionnaire and the modelling were analysed using the partial least squares structural equation modelling method. The models were tested for internal consistency reliability, convergent and discriminant validity, multicollinearity and model fit.

Two models were tested by organisation size, which revealed a total of 62 significant correlations between the latent variables tested. Identical correlations were present in both models in 22 cases. After testing the hypotheses, critical variables (nature of work, normative commitment, benefits, co-workers and organisational commitment) were identified that determine employees’ organisational commitment and intention to leave, regardless of the size of the organisation.

As a result of this research, the models developed are suitable for identifying differences in organisational staffing levels, but there is as yet no empirical evidence on the use of the scales for homogeneous groups of employees.

The results show that employees’ normative commitment and organisational commitment are critical factors for retention. Of the satisfaction factors examined, the nature of work, benefits and co-workers have a significant impact on retention in organisations, so organisational retention measures should focus on improving satisfaction regarding these factors.

The readers of the journal would appreciate the work, which highlights the significance of employee psychology and retention for organisational success.

The study is based on primary data and, to the best of the authors’ knowledge, is one of the few studies that take a holistic approach to organisational staff retention in the context of the moderating effect of organisational size. This study contributes to a comprehensive understanding of the phenomenon of employee retention and in contrast to previous research, examines the combined effect of several factors.

To improve the loan repayment performance (LRP) of microfinance banks (MFBs) in Pakistan, this study aims to look at the direct impact of multiple borrowing (MB) on LRP and client-business performance (CBP), as well as the direct impact of CBP on LRP. The moderating function of pandemic factors in the relationship between MB and CBP, as well as the mediating effect of CBP in the association between MB and LRP, was also investigated in this study.

A questionnaire was used to obtain data from 531 lower-level workers of microfinance institutions (MFIs) for the study. The respondents were chosen using stratified sampling, which divided the target population into four influential groups: lending officers in agriculture, lending officers in businesses, lending officers in gold loans and lending officers in salary loans. In this study, a two-stage structural equation modeling approach was used, including a measurement model (outer model) and a structural model (inner model). The validity and reliability of the questionnaire were investigated using the measurement model (outer model), whereas PLS-SEM bootstrapping was performed to test the hypothesis and find the relationship among different underpinning constructs by using the structural model (inner model).

The outcomes of this study demonstrate that MB has a direct impact on CBP, and that CBP has a direct impact on LRP. MB, on the contrary, had no direct and significant impact on LRP in this study. The idea that CBP mediates the relationship between MB and LRP, as well as the moderating effect of pandemic factors on the relationship between MB and CBP, is supported by this research.

Until now, the influence of MB on LRP via the mediating role of CBP and the moderating role of a pandemic factor in the setting of Pakistani MFBs has received little attention. During the COVID-19 pandemic, this research also aids MFBs in better understanding MB and its impact on LRP. Furthermore, based on the findings of this study, Pakistani MFIs can enhance their LRP by implementing new lending regulations, particularly with reference to MB and the COVID-19 pandemic.

In recent years, the global consumption of craft beer witnesses remarkable growth. This growth is attributed to the evolving demographics of beer consumers, particularly the emergence of a new generation known as Digitarians or Generation Z. This study aims to analyze the key determinants influencing craft beer consumption among Digitarians.

An online questionnaire is administered, and a total of 296 completed responses are included in the statistical analysis. The methodology uses logistic regressions combined with a backward selection process and variance inflation factor analysis to address multicollinearity. The logistic regressions are conducted in three steps to delve into the research objective and gain insights into the behavior of young consumers. The stepwise backward selection aids in obtaining robust coefficients as a variable selection tool.

The results shed light on how Digitarians’ preferences for craft beer are influenced by various factors, including self-perceived knowledge, alcohol content, gender, food pairings, environment and companionship.

To the best of the authors’ knowledge, this paper contributes novel insights by being the first study to explore the significance of craft beer choices among Digitarians, identifying the role of several predictors in their consumption patterns.

The purpose of this study paper is to highlight certain related barriers and issues of housing affordability and examine the factors that influence housing affordability in Malaysia.

This study used panel data including several variables, namely, household expense, population, home financing, interest rate, inflation rate (IF) and rental rate (RR). The regression models of panel data, namely, the ordinary least square model, the fixed effects model and the random effects model, were evaluated for their suitability.

The findings revealed that RR and IF have a positive and significant impact towards housing affordability. The results provide strong evidence that RR as alternative in determining the home affordability as it helped in reducing the cost and the financing duration period of houses while at the same time increasing the level of capability of homeownership. Meanwhile, the level of IF has positive and significant impact towards housing affordability because it will cause a drop or increase in the purchasing power of households, as well as a decline or increase in the capability to own a house.

The most significant aspects to consider when analysing housing affordability in Malaysia are demand and supply. However, this study focuses on only five variables and only covers Malaysia. As a result, future researchers should analyse the study’s location, such as by region or district, and include additional variables from both the demand and supply sides. Homeownership of affordability requires a broader and more realistic definition in the current context of a more disruptive environment where technology such as fintech, blockchain and the internet of things acts as enablers for not only promoting homeownership but also ensuring homeownership sustainability. As a result, democratising Islamic home financing appears to be a viable option that requires rethinking, and further research is recommended.

The study proposes an end-to-end solution to promote homeownership levels by considering the level of RR as significant variables among stakeholders such as the house buyers/owners, sellers, investors as well the government agencies in influencing affordability in Malaysia.

This paper discusses the indicators of housing affordability index over the 21-year period of 2000–2020, covering all states in Malaysia. The comparison of affordability level can be seen through all states and by regions. Besides that, the findings revealed that RR and IF have a positive and significant impact towards housing affordability. RR is considered an essential variable in promoting homeownership in Malaysia and warrants further investigation towards policy implication. This paper also provides contribution on data on RR by states in Malaysia that can be used by policymakers to some extent.

The crux of this paper is to unveil efficient features and practical tools that can predict credit default.

Annual data of non-financial listed companies were taken from 2000 to 2020, along with 71 financial ratios. The dataset was bifurcated into three panels with three default assumptions. Logistic regression (LR) and k-nearest neighbor (KNN) binary classification algorithms were used to estimate credit default in this research.

The study’s findings revealed that features used in Model 3 (Case 3) were the efficient and best features comparatively. Results also showcased that KNN exposed higher accuracy than LR, which proves the supremacy of KNN on LR.

Using only two classifiers limits this research for a comprehensive comparison of results; this research was based on only financial data, which exhibits a sizeable room for including non-financial parameters in default estimation. Both limitations may be a direction for future research in this domain.

This study introduces efficient features and tools for credit default prediction using financial data, demonstrating KNN’s superior accuracy over LR and suggesting future research directions.

This paper examines the determinants of concurrent adoption of farm risk management strategies by rice growers in two different ecosystems of Telangana agriculture-irrigated and rainfed ecosystems.

The primary data have been collected from the rice growers in two different ecosystems, and after checking the variance inflation factor (VIF) for controlling multicollinearity, a multinomial logit model has been used to examine the determinants of concurrent adoption of coping strategies by rice growers.

The study finds that adopting one risk management strategy persuades farmers to embrace other strategies, reducing the risk in agriculture between the two ecosystems. Among the determinants, farmers' age, education, contact with extension services, irrigation sources, livestock income, total farm income, crop loss reasons, and crop insurance awareness significantly influence the adoption of various risk management measures. However, considerable heterogeneity is found among the driving forces across the rice ecosystems.

The major policy implications that can be drawn from the analysis are increased access to information through government-funded extension services and the provision of alternative risk management technologies, such as drought-resistant or flood-resistant seeds, farmers' field schools and increased provision of crop insurance, farmer-friendly agriculture extension services, and farm investment support, are critical for assisting farmers managing risks. In addition, however, there should be ecosystem-specific policies to tackle the ecosystem heterogeneity.

This paper is very timely and entails some relevant policy implications for the development of Indian agriculture.

This study aims to investigate the relationship between digital financial inclusion and economic growth in specific Asian countries, emphasizing the exploration of how digital financial inclusion dynamics impact gross domestic per capita income.

The study creates a digital financial inclusion composite index (DFII) by incorporating essential metrics from the Global Findex report. Economic growth is measured using Gross Domestic Product per capita income in its natural logarithmic form (LnPCI), with three control variables– employment-to-population ratio; population growth and inflation. The analysis utilizes a fixed-effect dummy variable model to examine the relationship, considering unobserved country-specific heterogeneity. 30 Asian countries have been selected for the study for the periods 2014, 2017 and 2021 based on their availability, as outlined in Table 4.

The research revealed a robust positive correlation between the Digital Financial Inclusion Index (DFII) and logarithmic GDP per capita income (LnPCI), indicating higher per capita income with enhanced digital financial inclusion. Employment and population exhibited minimal influence, whereas inflation had a notable negative effect on per capita income. Population growth showed a limited impact. The model demonstrated a high explanatory power for the dependent variable (high R-squared), and the residuals displayed low autocorrelation (Durbin–Watson of 1.96).

This study adds to the existing literature by examining the intricate connection between digital financial inclusion (DFI) and economic growth in 30 Asian countries, employing a comprehensive composite index for analysis.