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This study aims to investigate the spatial distribution of housing prices and identify the affecting factors (independent variable) on the cost of residential units (dependent variable).

The method of the present study is descriptive-analytical and has an applied purpose. The used statistical population in this study is the residential units’ price in Tehran in 2021. For this purpose, the average per square meter of residential units in the city neighborhoods was entered in the geographical information system. Two techniques of ordinary least squares regression and geographically weighted regression have been used to analyze housing prices and modeling. Then, the results of the ordinary least squares regression and geographically weighted regression models were compared by using the housing price interpolation map predicted in each model and the accurate housing price interpolation map.

Based on the results, the ordinary least squares regression model has poorly modeled housing prices in the study area. The results of the geographically weighted regression model show that the variables (access rate to sports fields, distance from gas station and water station) have a direct and significant effect. Still, the variable (distance from fault) has a non-significant impact on increasing housing prices at a city level. In addition, to identify the affecting variables of housing prices, the results confirm the desirability of the geographically weighted regression technique in terms of accuracy compared to the ordinary least squares regression technique in explaining housing prices. The results of this study indicate that the housing prices in Tehran are affected by the access level to urban services and facilities.

Identifying factors affecting housing prices helps create sustainable housing in Tehran. Building sustainable housing represents spending less energy during the construction process together with the utilization phase, which ultimately provides housing at an acceptable price for all income deciles. In housing construction, the more you consider the sustainable housing principles, the more sustainable housing you provide and you take a step toward sustainable development. Therefore, sustainable housing is an important planning factor for local authorities and developers. As a result, it is necessary to institutionalize an integrated vision based on the concepts of sustainable development in the field of housing in the Tehran metropolis.

The purpose of this study is to quantify and evaluate the efficiency of knowledge services public libraries (PLKS) in China and to analyze the factors affecting the efficiency of PLKS. The focus of this study is to explore the characteristics and changing trends of PLKS and how to improve the efficiency of PLKS. The conclusions drawn in this paper are expected to provide important references for academic research and practical exploration in related fields.

This study constructs a three-stage process of PLKS. On this basis, the efficiency is analyzed by using the Super-NSBM model, and the influencing factors of PLKS are analyzed by using OLS. Finally, through the comprehensive interpretation of the measured data and the existing social development conditions, the authors put forward relevant recommendations.

In the first stage, the PLKS in China meets the basic requirements, but there is an obvious waste of resources in the second stage. In addition, China’s public libraries show obvious regional differences in knowledge service efficiency. Finally, it was found that economic and technological factors can increase the efficiency of PLKS, but the increase in the number of university students will reduce the efficiency of PLKS.

In this study, the authors provide an exhaustive deconstruction and explanation of PLKS, which leads to the proposal of a three-stage PLKS efficiency conversion process. Furthermore, the authors identify a diverse set of quantifiable metrics that are readily accessible for open collection. Notably, the authors correlated the efficiency of PLKS with the influencing factors, exploring PLKS from a new perspective and enriching the research related to PLKS.

This paper aims to compare the efficiency of spatial and nonspatial hedonic price models in capturing housing submarkets dynamics for cities in developing countries. This study expects to contribute to a better understanding of the housing price determinants from both nonspatial and spatial perspectives. In addition, this paper fills a gap in the literature on the study of housing prices from a spatial perspective in Latin American cities.

This study uses a comparative analysis between an ordinary least squares regression and a geographical weighted regression, GWR. The study also assesses the performance of two distinct data sources: the city’s cadastral records and a real estate sales web portal.

The results suggest that compared to the traditional regression model, the spatial regression models are more effective at capturing housing market variations on a fine scale. Moreover, they reveal interesting findings on the spatial varying, sometimes contradictory effects of some housing attributes on housing prices in different areas of the city, suggesting the potential impact from segregation.

The availability of data on housing prices and characteristics in Latin American cities is fragmented and complex. The level of detail, granularity and coverage is not consistent over time. For this reason, this study combines and compares data sets from official and unofficial sources in an effort to close this gap. Likewise, the socioeconomic variables that come from the census must be carefully analyzed, knowing the historical context in which they were constructed, what they represent and their interpretation.

This paper suggests that despite the improvement on the spatial models, the selection of a specific one should always be based on the diagnosis of it as it highly depends on the data used and the objectives of the study.

This study enriches the limited body of literature on spatial hedonic price models of housing in Latin American cities. It also shed light on the importance of spatial approaches to identify complex housing submarkets.

This study investigates the association between corporate sustainability disclosures and firm performance and value.

This study collected data from 694 manufacturing companies operating in 34 countries between 2007 and 2019, yielding 6,181 firm-year observations. This study employs a dual-model framework to analyze the influence of environmental, social, and governance (ESG) performance on return on assets (ROA), return on equity (ROE), and Tobin's Q ratio. Two sets of control variables, firm- and country-specific, were incorporated to account for potential confounding factors. To validate the robustness of the findings, we utilized a battery of econometric techniques, including traditional ordinary least squares (OLS), firm-fixed effects, quantile regression, and instrumental variables-generalized method of moments (IV-GMM), applied to both the pooled and firm-fixed effects models.

The findings are contradictory: there is a negative relationship between sustainability disclosure and operating performance and return on equity, but a positive relationship between sustainability disclosure and firm value. The negative correlation is consistent with agency theory and the positive correlation is consistent with the legitimacy and shareholder theories. These results are robust to performance measures and estimation methods.

Short-term profit shouldn't deter sustainability. It boosts legitimacy, reputation, efficiency, and long-term market value. Investors must look beyond profitability ratios, embracing ESG metrics. Firms should see sustainability as strategic investment, not cost. Patience pays off: long-term gains await. Regulation can guide balanced growth, prioritizing both shareholders and societal well-being.

This study is the first to adopt a firm’s fixed-effect quantile regression, which provides deep insights into the role of sustainability disclosure in meeting stakeholders’ expectations.

This paper examines the impact of leadership behaviors on startup's entrepreneurship in Vietnamese southern areas in the time of Covid-19.

The paper uses OLS regression model to evaluate the impact of leadership behaviors on startup's entrepreneurship in Vietnamese southern areas. Besides, the paper also uses AHP method to identify the factors that influence leadership behaviors in startups in the context of economic shock like the Covid-19 pandemic.

Results reveal that the transformational leadership behavior has a positive correlation with startup's entrepreneurship while the transactional and laissez-faire leadership behaviors are negatively related to startup's entrepreneurship. In addition, using AHP method, certain determinants of leadership behaviors in startups have been specified; among which, the most profound influencing factors are respectively hi-tech use (0.081), training policy (0.079) and check (0.78). In contrast, the factors with negligible impacts include recruitment policy (0.004) and culture (0.037) in startups in the context of economic shock like the Covid-19 pandemic.

This research only evaluates this impact in the Covid-19 times. In the future, comparisons between the time after the Covid-19 at traditional enterprises and state enterprises are to be done to clarify the difference of this impact.

By using OLS model and AHP model with the data collected from 209 start-ups, the paper examines the impact of leadership behaviors on start-up's entrepreneurship in the time context of economic shock like the Covid-19 pandemic and propose some recommendations to enhance entrepreneurship in startups in the developing countries.

This study is a step forward in the entrepreneurship research branch that investigates the entrepreneurship issue from the perspectives of social and interpersonal processes. With the identification of the leadership behaviors’ role in shaping start-up's entrepreneurship come highly applicable implications towards the emergence of more effective entrepreneurial startups in the time of Covid-19 and this prevalent digital context.

With the identification of the leadership behaviors' role in shaping startup's entrepreneurship come highly applicable implications toward the emergence of more effective entrepreneurial startups in the time of Covid-19 and this prevalent digital context. This study is conducted to analyze the impact of leadership behaviors on startup's entrepreneurship in the southern areas of Viet Nam having the most startups but affected the most seriously by the Covid-19 pandemic, which has not research on this topic for startups in a developing country like Vietnam in the context of an economic shock.

This paper explores if the firm’s strategic orientation can be associated with differences in cost behavior activity.

Using Compustat data from 1979 to 2012, the archival study examines if there are differences between differentiation and cost leadership strategies on the firm’s cost stickiness.

The main finding provides evidence that firms pursuing a differentiation strategy exhibit greater cost stickiness, on average, as compared to firms pursuing a cost leadership strategy. This relationship is moderated by the optimistic or pessimistic expectations of managers for future sales.

This paper contributes to the literature on cost management by explaining how strategic positioning affects firms’ cost behavior using the framework of asymmetric cost behavior. The study encompasses both product and period costs and documents the impact on earnings.

This paper aims to determine whether audit opinions in Morocco, an emerging market, are value relevant to the stock market, through the investigation of the market reaction to the issuance of modified audit opinions (MAOs).

The event study approach is used. The data are derived from the financial reports of listed companies on the Casablanca Stock Exchange over a period of 10 years from 2010 to 2019.

This paper does not find evidence that the market reacts to the issuance of MAOs when grouped together. When partitioning the sample by types, there is an evidence of a stock market reaction to qualified audit opinions and the qualified audit opinions with observation paragraph when they are combined with a negative variation of earnings per share. Examination of the impact of different natures of qualifications shows no consistent results and that the market does not distinguish between natures of qualifications.

These results may be due to the fact that some investors have information about the audit opinion long before it is made public, due to privileged access to audit opinions, or that investors underestimate audit opinions relative to other financial indicators.

This study contributes to the existing literature by investigating an emerging market, not previously tested, after the introduction of several regulatory reforms in Moroccan market aimed at enhancing transparency in financial reporting. It refines the market reaction models used in previous studies by using both ordinary least squares and the Scholes–Williams techniques that correct for the effect of thin trading on the market index. In addition, special attention is given to studying the market reaction to each type of MAOs and to each natures of qualifications.

Literature has already analysed the relation between a property’s time on market (TOM) and other housing characteristics, but few to none include the property’s energy performance certificates (EPC) and none make a comparison between the selling and rental market. This paper aims to address these gaps by studying the relationship between TOM, price and EPC in both markets.

By introducing a combination of alternative tests, this study confirms a causal relation between TOM and price in the cross-sectional data. This allows this study to use a two-stage least square model and analyse 392,498 Flemish sale and rental properties transacted between 2019 and 2023.

The results indicate that both sale and rental properties with higher prices increase the TOM by 4–6 days, and this effect is even stronger in the selling market when the value-added tax is included. This study also finds that EPC labels have a complex relation with the time on market. A-labelled properties tend to increase the transaction time between 10 and 54 days, but B- and C-labelled properties decrease TOM between 20 and 30 days. In addition, the poorer performing labels (E and F) react differently across markets because of market-specific policies.

This paper provides novel insights by studying the relationship between TOM and EPC while also considering TOM’s endogenous relationship with the price. We control for these relationships in both the selling and rental market.

The novel coronavirus (COVID-19) has caused financial stress and limited their lending agility, resulting in more non-performing loans (NPLs) and lower performance during the II wave of the coronavirus crisis. Therefore, it is essential to identify the risky factors influencing the financial performance of Indian banks spanning 2018–2022.

Our sample consists of a balanced panel dataset of 75 scheduled commercial banks from three different ownership groups, including public, private and foreign banks, that were actively engaged in their operations during 2018–2022. Factor identification is performed via a fixed-effects model (FEM) that solves the issue of heterogeneity across different with banks over time. Additionally, to ensure the robustness of our findings, we also identify the risky drivers of the financial performance of Indian banks using an alternative measure, the pooled ordinary least squares (OLS) model.

Empirical evidence indicates that default risk, solvency risk and COVAR reduce financial performance in India. However, high liquidity, Z-score and the COVID-19 crisis enhance the financial performance of Indian banks. Unsystematic risk and systemic risk factors play an important role in determining the prognosis of COVID-19. The study supports the “bad-management,” “moral hazard” and “tail risk spillover of a single bank to the system” hypotheses. Public sector banks (PSBs) have considerable potential to achieve financial performance while controlling unsystematic risk and exogenous shocks relative to their peer group. Finally, robustness check estimates confirm the coefficients of the main model.

This study contributes to the knowledge in the banking literature by identifying risk factors that may affect financial performance during a crisis nexus and providing information about preventive measures. These insights are valuable to bankers, academics, managers and regulators for policy formulation. The findings of this paper provide important insights by considering all the risk factors that may be responsible for reducing the probability of financial performance in the banking system of an emerging market economy.

The empirical analysis has been done with a fresh perspective to consider unsystematic risk, systemic risk and exogenous risk (COVID-19) with the financial performance of Indian banks. Furthermore, none of the existing banking literature explicitly explores the drivers of the I and II waves of COVID-19 while considering COVID-19 as a dependent variable. Therefore, the aim of the present study is to make efforts in this direction.

This paper proposes an analysis of occupational accidents in Italy at the regional level. For this purpose, our panel is composed of 20 regions over the 2010–2019 time span.

We apply different econometric estimation techniques (pooled OLS model, panel fixed and random effects models and semiparametric fixed model) using INAIL and ISTAT data. Our models investigate workplace accidents at the regional level by accounting for socioeconomic, labour market and productive system variables and controlling for possible underreporting bias.

Overall results reveal the existence of a relevant under-notification phenomenon of accidents at work with respect to moderate accidents, that is higher especially for the southern regions of Italy. However, when considering as outcome variable an alternative set of more severe workplace accidents our model specification remains highly jointly statistically significant. Among our main findings, the analysis shows that worker skills (blue collar) strongly affect the regional pattern of workplace accidents, i.e. an increase of 1% of low paid employees generates about an increase of 1.8 severe workplace accidents per thousand workers. Moreover, we provide evidence that the size of the firm is inversely related to the occupational accident rates. Finally, our results highlight a nonlinear relationship between GDP and occupational accidents for the Italian regional context, confirmed by the high statistical significance of the quadratic term in all the estimated linear models and by the semi-parametric analysis.

A first element of originality of our study consists of investigating the macro determinants of occupation accidents at a regional Italian level. Second, the empirical literature (Boone and Van Ours, 2006) highlights the possible bias of underreporting behaviours on nonfatal accidents in contrast to fatal accidents that are always reported. From this perspective, we have identified a few analyses (namely, Boone et al., 2011) considering different accident sets characterised by different severity degrees. Thus, this paper contributes to the literature considering five alternative subsets of accidents stratified by degree of severity (i.e. moderate, severe, moderate plus severe, severe plus fatal and total accident rates) to test for possible underreporting bias affecting our econometric model.