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Online shopping is expected to continue without slowing down because of the advantages that it presents to consumers in the digitalising world. This study aims to determine the factors regarding the social and environmental indicators and the demographical and economical factors that affect the online shopping tendencies of households in Turkey. The results of this research can be used to review the online shopping strategies by the decision-makers.

In this study, the cross-sectional data acquired from the Household Budget Research survey, which has been conducted by the Turkish Statistical Institute between 2015 and 2018, is used. In this data set consisting of 11,491 in 2015, 12,096 in 2016, 12,166 in 2017 and 11,828 in 2018, a total of 47,581 data from the households that are 15-year-old and older are used. To determine the factors affecting the online shopping behaviour of households, binary logistic regression and binary probit regression analyses are applied. As a result of these analyses, it has been decided that the most suitable model is the binary probit regression model.

According to the analysis results, it has been detected that factors such as educational status, age, marital status, employment status, income, life assurance ownership, credit card usage, automobile ownership and the year of the survey affect the online shopping behaviour of households.

In this study, factors affecting the tendency to do online shopping, which has gained big importance particularly with the COVID-19 pandemic, are determined. In Turkey, households’ tendency to do online shopping is affected by the demographical and economical factors and by the factors related to the social and environmental indicators. Determination of the effects of these factors has been a guide for the decision-makers and policymakers in explaining the tendency to shop online and creating a competitive advantage.

In this study, data consisting of a total of 47,581 observations, which has acquired from the Household Budget Research survey conducted by the Turkish Statistical Institute between 2015 and 2018, are used by applying a weighting process, and no study that is as comprehensive and inclusive as this study has been found in the literature, e-commerce that has become prevalent with the help of technological progress and changing habits in the past years is continuing to become prevalent more increasingly particularly after COVID-19 pandemic. Therefore, the value of this study underlies its contribution to e-commerce awareness.

The purpose of this paper is to propose predictive models of speculative revaluation attacks, which would facilitate currency risk hedging in emerging and developed countries.

The purpose of this paper is achieved using the methodology of multiple triangulation. Paper combines different theoretical perspectives (three generations of speculative attack models), two sources of data (emerging countries and developed countries) and three methods (logit regression, probit regression and artificial neural networks, ANN) for identification of leading indicators and forecasting of speculative attacks. Combination of multiple observations (data), underlying theories and methods allowed achieving least biased results.

A list of leading indicators of speculative revaluation attacks was generated based on previous researches and three generations of speculative attacks' models. Qualitative and quantitative differences of speculative revaluation attacks in emerging and developed countries were identified. The decision matrix of currency risk hedging in the context of speculative devaluation and revaluation attacks was proposed.

Although the sample of this researcher includes a wide range of countries (65 in total), their separation into developed and emerging countries is arbitrary (in the course of 35 years some countries have changed the status from emerging towards developed). The initial list of leading indicators is limited, includes mostly economic variables. It could be improved by encompassing political variables, credit ratings, consumer and business confidence indices.

Developed predictive models of speculative revaluation attacks may significantly reduce important element of risk – uncertainty – and, consequently, the cost of financial hedging.

This paper is one of the first public attempts to apply alternative methodology of ANN for forecasting speculative attacks. The results showed that latter method is more accurate than probit and logit regressions. Also, to the author's best knowledge, this is a first public attempt to separately analyse the phenomenon of speculative revaluation attacks.

The built environment is responsible for approximately 40% of the world’s energy consumption, 30% of raw material use, 25% of solid waste, 25% of water use, 12% of land use and 33% of greenhouse gas emissions. Thus, environmental improvement and decarbonization are becoming increasingly critical objectives for the construction industry. Sustainable construction can be achieved through several practices, including: considering life-cycle assessment, circular construction, resource efficiency and waste management and providing eco-efficient materials, reducing energy demands and consumption and incorporating low-carbon technologies and renewable energy sources. To achieve sustainable construction goals, it is critical to educate the future workforce about decarbonization, circular construction and how to overcome the challenges involved in transitioning to sustainable construction. This study aims to understand the gap in student knowledge related to decarbonization and circular construction and the importance of incorporating these topics in civil engineering and construction management curricula.

This study surveyed 120 undergraduate and graduate students at one of the largest minority-serving institutions in the USA to understand the gap in student knowledge related to decarbonization and circular construction as well as the importance of incorporating these topics in civil engineering and construction management curricula. The authors conducted several statistical measures to assess the consistency, reliability and adequacy of the sample size, including the Kaiser–Meyer–Olkin measure of sampling adequacy, the normality test to evaluate the appropriateness of using an ordered probit regression analysis and a multicollinearity test to observe the correlation between independent variables. The data was analyzed using ordered probit regression analysis to investigate the need for a curriculum that serves in educating students about decarbonization and circular construction.

The results of this research highlight the gaps in students’ knowledge pertaining to sustainable practices and the importance of providing future construction workforce with such knowledge to tackle global inevitable challenges.

The findings of this study contribute to sustainable construction bodies of knowledge by advocating for a reformed curriculum to prepare the future workforce and adopt less carbonized, more circular approaches within the engineering and construction industry.

Studies on employee volunteerism have inconsistency in results. This study aims to examine whether employee volunteerism contributes to financial performance, and if employee volunteerism creates community benefit for firm legitimacy.

The data covers 80 companies (640 firm-year observations) and uses the Indian stock market for the period 2010-2017 as a testing ground. Canonical correlation analysis, panel regression and panel probit regression are used in this study.

The first findings of the study show employee volunteerism through employee skill contribution, number of hours spent on volunteerism, employee cash contribution and employee material contribution provide the substantive contributions to community benefit and financial performance and also contribute a possible positive reflection on employee commitment. The second findings show that return on asset and return on equity do not improve the practice of employee volunteerism. However, the stock price return (SPR) improves the practice of employee volunteerism. The third findings show that the engagement of third-party assurance (TPA) improves the practice of employee volunteerism. Finally, TPA and SPR are more likely to cause a firm to undertake employee volunteerism.

The research study is limited to large firms on the Indian stock market that submit sustainability reports.

An implication from the study suggests that the critical driver of employee volunteerism is employee skill contribution, and firms stand to benefit if well managed.

TPA and financial performance contribute an increase in employee volunteerism, and therefore deepens the scholarly debate on employee volunteerism. Employee volunteerism–community benefit nexus creates a new dimension to the theory of legitimacy for firms in an emerging economy.

This study aims to investigate the factors that drive housing loan default in India based on unique micro-level data drawn from a public sector bank's credit files with a national presence in India. The authors address endogeneity in the loan to value ratio (LTV) while deciphering the drivers of default.

The study uses a probit regression approach to analyze the relationship between the probability of default and the explanatory variables. The authors introduce two instrumental variables to address the issue of endogeneity. The authors also add state-level demographic and several other control variables, including an indicator variable that captures the recent regulatory change. The authors’ analysis is based on 102,327 housing loans originated by the bank between January 2001 and December 2017.

The authors find that addressing the endogeneity issue is essential to specify default drivers, especially LTV, correctly. The nature of employment, gender, socio-religious category and age have a distinct bearing on housing loan defaults. Apart from the LTV ratio, the other key determinants of default are the interest rate, frequency of repayment, prepayment options and the loan period. The findings suggest that the population classification of branch location plays a significant role in loan default. The authors find that an increase in per capita income and an increase in the number of employed people in the state, which reflects borrowers' ability to pay by borrowers, reduce the probability of default. The change in the regulatory classification of loan assets by the Reserve Bank of India did not bear the main results.

The non-availability of the house price index in analyzing the default dynamics in the Indian housing finance market for the period covered under the study has constrained our analysis. The applicability of the equity theory of default, strategic default, borrowers' characteristics and personality traits are potential research areas in the Indian housing finance market.

The study's findings are expected to provide valuable inputs to the banks and the housing finance companies to explore and formulate appropriate strategic options in lending to this sector. It has highlighted various vistas of tailor-making housing loan product offerings by the commercial banks to ensure and steady and healthy growth of their loan portfolio. It has also highlighted the regulatory and policy underpinnings to ensure the healthy growth of the Indian housing finance market.

The study provides a fresh perspective on the default drivers in the Indian housing finance market based on micro-level data. In our analysis, the authors find clear evidence of endogeneity in LTV and argue that any attempts to decipher the default drivers of housing loans without addressing the issue of endogeneity may lead to faulty interpretation. Therefore, this study is unique in recognizing endogeneity and has gone deeper in identifying the default drivers in the Indian housing market not addressed by earlier papers on the Indian housing market. The authors also control for the regulatory changes in the Indian housing finance market and include state-level control variables like per capita GDP and the number of workers per thousand to capture the borrowers' ability to pay characteristics.

Aims to distinguish among different levels of default risk for residential mortgage loans and to examine the significant factors for the different levels of default risk. Classifies the sample into default and non‐default groups and analyses the original mortgage loan data by factor and cluster analyses based on borrower characteristics, property characteristics and microeconomic variables in order to derive risk classifications from various likelihoods of default. Furthermore, applies logit, probit and discriminant analyses to examine the significant factors for all three clusters. The empirical results show that the three clusters may be ranked as follows, in order of risk, from the least to greatest likelihood of default: the owner‐occupied housing buyer, invester group and young buyer clusters. In addition, the factor “borrower’s education level” has negative impact for all three clusters.

This paper examines factors that are associated with the level of a firm's environmental disclosure in the footnotes of its annual report financial statements and its 10-K report filed with the Securities and Exchange Commission (SEC). The levels of environmental disclosure are measured using the Wiseman scale (Wiseman, 1982). An N-chotomous probit analysis is utilized where the level of disclosure is the dependent variable, and the independent variables are firm characteristics including: (1) institutional blockholder stock ownership, (2) amount of foreign concentration, (3) earnings volatility, (4) profitability, (5) leverage, (6) future need for debt financing, (7) firm size, and (8) industry membership.

The results indicate that higher foreign concentration, and to some extent, higher earnings volatility are associated with less environmental disclosure. These results provide evidence that firms with higher foreign concentration are more reluctant to disclose environmental information because they are under higher scrutiny from other countries and the international community. Additionally, it is probable that firms with a more volatile earnings process are less willing to disclose potential environmental costs and obligations because these additional expenditures can have an especially adverse effect during low-earnings periods.

The study assesses the relationship between institutional credit access and farmer satisfaction using contextual mediating and moderating variables. This study identifies various socioeconomic, service features and service quality determinants impacting institutional credit access.

The authors used the stratified random sampling method and selected 512 farmers from 40 villages in Maharashtra, India. Initially, the study employed probit regression analysis to identify the credit adoption determinants. Subsequently, the relationship between institutional credit and farmer satisfaction is identified through moderated-mediation analysis using the Statistical Package for the Social Sciences and Analysis of a Moment Structures (SPSS - AMOS model).

Probit model's results suggest that socioeconomic variables like education and bank distance; service quality variables like prompt service and employee behavior; and service characteristics variables like the interest rate, loan sanction time, repayment period, and documents for loan application significantly affect institutional credit adoption across the smallholders. Subsequently, the results of the moderating-mediation analysis show that working capital, perceived value and risk perception partially mediate the association between credit adoption and farmer satisfaction. The mediated effects are further moderated by farm advisory services and financial knowledge and skills.

The study is restricted in opportunity due to primary data, and it considers only farmers' perspectives to measure service quality and service features as constraints for institutional credit access.

The government, nongovernment organizations, civil societies and private institutions should provide sufficient financial knowledge and training to the farmers via extension services to utilize the borrowed capital effectively to bring economic welfare and mental satisfaction.

The existing literature rarely considered banking service quality and service features (demand side) variables as determinants of credit access. Further, the study brings novelty in examining how the capital management cognitive factors of the formal credit adopters influence the relationship between credit access and satisfaction.

This study aims to investigate the association between Muslim individuals’ portfolio allocation choice and Islamic religiosity (levels and dimensions), controlling for risk tolerance and sociodemographic factors.

The study uses primary data collected via survey questionnaires from a sample of 751 Muslim working individuals in Kuala Lumpur, Malaysia. Owing to the ordinal nature of the dependent variable, which reflects the levels of proportions of risky assets in portfolios, the data were analyzed using an ordered probit regression model.

The findings reveal that Islamic religiosity levels in general were insignificantly related to portfolio allocation, but that two dimensions of religiosity (virtue and obligation) significantly impact the allocations of risky assets in the portfolio. The higher the level of virtue, the lower the propensity to allocate risky assets into the portfolio. On the contrary, the higher the level of obligation, the higher the propensity to allocate risky assets in the portfolio. Meanwhile, individuals with higher risk tolerance, income and education levels show greater propensity to allocate risky assets in the portfolio.

The sample is restricted to Muslims in Kuala Lumpur; hence, the findings are not easily generalized to Muslim investors in general. Findings may differ between Muslims across the world, so future research needs to expand from a country specific to an international analysis. In addition, future studies could include other determinants of portfolio allocation, such as financial literacy.

The findings of this study may assist financial planners and policymakers to better understand the drivers of portfolio allocation among their Muslim clients.

While other studies have tended to focus on the impact of religiosity on the holdings of specific financial assets, such as Islamic bank accounts or Takaful, the present study explores the effect of Islamic religiosity dimensions on the allocations of risky assets in the portfolio. The study also develops an ordinal measure of portfolio allocation and makes a methodological contribution by using an ordered probit regression analysis.

Concern about the use of zero-tolerance policies for discipline has led to a search for alternatives such as training in early-warning signs of aggressive behavior and strategies for effective classroom management in schools. This chapter examines the effectiveness of the provision of alternatives to out-of-school suspensions (OSS) in reducing the use of exclusionary discipline for minor misbehavior and the school characteristics associated with these provisions.

This analysis uses the 2008 panel from the National School Survey on Crime and Safety to explore this question for approximately 1,000 high schools. The analysis is a probit regression analysis to examine the association between the provision of alternatives to OSS, school characteristics, and the use of OSS for low-level suspensions. This analytic approach provides wide generalizability for the findings, though it does also limit an ability to identify individual school- or student-level effects.

Findings based on probit regression analysis suggest that structural characteristics of schools – beyond student characteristics – are only somewhat related to variation in the use of OSS for low-level infractions and, on average, the availability of alternatives to OSS do not strongly decrease the frequency of OSS for lower-level infractions. These findings are important in the current era of discipline policy scrutiny where schools and policy-makers are searching for alternatives to traditional suspension practices in a limited empirical evidence base.

While these alternatives hold great promise, little is known about their effectiveness in addressing behavior problems and/or reducing OSS. More importantly, even less is known about the characteristics of schools likely to enact alternatives.