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This study aims to explore the relationship between chief executive officer (CEO) power and stock price crash risk in India. Furthermore, it seeks to analyse how insider trades may moderate the impact of CEO power on stock price crash risk.

A study of 236 companies from the S&P BSE 500 Index (2014–2023) have been analysed through pooled ordinary least square (OLS) regression in the baseline analysis. To enhance the results' reliability, robustness checks include alternative methodologies, such as panel data regression with fixed-effects, binary logistic regression and Bayesian regression. Additional control variables and alternative crash risk measure have also been utilised. To address potential endogeneity, instrumental variable techniques such as two-stage least squares (IV-2SLS) and difference-in-difference (DiD) methodologies are utilised.

Stakeholder theory is supported by results revealing that CEO power proxies like CEO duality, status and directorship reduce one-year ahead stock price crash risk and vice versa. Insider trades are found to moderate the link between select dimensions of CEO power and stock price crash risk. These findings persist after addressing potential endogeneity concerns, and the results remain consistent across alternative methodologies and variable inclusions.

This study significantly advances research on stock price crash risk, especially in emerging economies like India. The implications of these findings are crucial for investors aiming to mitigate crash risk, for corporations seeking enhanced governance measures and for policymakers considering the economic and welfare consequences associated with this phenomenon.

The purpose of this study is to investigate the impact of e-service quality and e-trust on customer e-satisfaction and, subsequently, on customer e-loyalty towards a website in the online shopping environment of Pakistan.

The research employed a quantitative approach and utilised structural equation modelling to investigate the relationship between e-service quality and e-trust on consumers’ e-satisfaction and e-loyalty. The data were collected from 250 individuals who actively use online shopping websites to purchase products in Pakistan.

The findings revealed that e-service quality and e-trust offered on e-commerce websites significantly impacted customer e-loyalty. However, it was found that both e-service quality and e-trust do not have a significant impact on customer e-satisfaction. In addition, the findings showed that customer e-satisfaction positively impacts e-loyalty.

Overall, these findings emphasise the importance of e-service quality, e-trust and customer e-satisfaction and their role in cultivating customer loyalty within the context of the online shopping environment in Pakistan.

This study contributes to the existing literature on online shopping in Pakistan by exploring the factors influencing consumer behaviour in this context. The findings add to the academic understanding of consumer behaviour and provide valuable insights for e-commerce businesses in Pakistan.

The purpose of the paper is to predict mapping of areas vulnerable to flooding in the Ourika watershed in the High Atlas of Morocco with the aim of providing a useful tool capable of helping in the mitigation and management of floods in the associated region, as well as Morocco as a whole.

Four machine learning (ML) algorithms including k-nearest neighbors (KNN), artificial neural network, random forest (RF) and x-gradient boost (XGB) are adopted for modeling. Additionally, 16 predictors divided into categorical and numerical variables are used as inputs for modeling.

The results showed that RF and XGB were the best performing algorithms, with AUC scores of 99.1 and 99.2%, respectively. Conversely, KNN had the lowest predictive power, scoring 94.4%. Overall, the algorithms predicted that over 60% of the watershed was in the very low flood risk class, while the high flood risk class accounted for less than 15% of the area.

There are limited, if not non-existent studies on modeling using AI tools including ML in the region in predictive modeling of flooding, making this study intriguing.

Municipalities have the potential to become models of the circular economy (CE). This paper aims to examine the impact of the municipal council’s characteristics on municipal CE disclosure and promotion.

This paper is based on the resource dependence and upper echelons theories. For a sample of the 100 largest cities in Canada, a mixed methodology is used to code and analyze data and test the hypotheses.

Municipal councillors’ education and experience related to the environment or sustainability are both likely to affect CE disclosure, and their sector membership (public or private) moderates the relationship between CE disclosure and councillors’ experience. This experience may be reinforced by membership in the private sector, which has applied CE principles more extensively than the public sector has. Municipal councils with a greater number of councillors from the private sector appear to perform better in matters of transparency and to disclose more CE information on their public websites.

Municipalities could use the findings to foster their transition to CE by implementing a CE-related training plan for their councillors. A CE-dedicated section on their websites could improve transparency and inform and educate residents about CE.

The public sector could learn from the private sector’s best practices regarding CE.

This paper contributes to the literature by providing empirical evidence of the transparency and engagement of municipalities toward CE. The authors extend the resource dependence and upper echelons theories to a new context, that of public organizations.

This research aims to better understand the factors and determinants that shape the job satisfaction of European family business owners.

The study is based on a unique sample of 11,362 European family business owners surveyed within the European Union Labour Force Survey (EU LFS) framework, and the main findings were obtained by estimating ordered logistic regression models.

The authors show that only 26.8% of European family business owners are women, which underlines the gender imbalance in family business ownership, and the authors' results also report that their job satisfaction is significantly lower compared to males. The authors also find the highest job satisfaction amongst family business owners with master-level degrees and point out several interesting statistically significant differences across the industry focus of the family business.

This research contributes to the body of knowledge on the job satisfaction of family business owners by conducting a large-scale study based on a statistically representative sample of European respondents.

The purpose of this paper is to investigate the vehicle-based sensor effect and pavement temperature on road condition assessment, as well as to compute a threshold value for the classification of pavement conditions.

Four sensors were placed on the vehicle’s control arms and one inside the vehicle to collect vibration acceleration data for analysis. The Analysis of Variance (ANOVA) tests were performed to diagnose the effect of the vehicle-based sensors’ placement in the field. To classify road conditions and identify pavement distress (point of interest), the probability distribution was applied based on the magnitude values of vibration data.

Results from ANOVA indicate that pavement sensing patterns from the sensors placed on the front control arms were statistically significant, and there is no difference between the sensors placed on the same side of the vehicle (e.g., left or right side). A reference threshold (i.e., 1.7 g) was computed from the distribution fitting method to classify road conditions and identify the road distress based on the magnitude values that combine all acceleration along three axes. In addition, the pavement temperature was found to be highly correlated with the sensing patterns, which is noteworthy for future projects.

The paper investigates the effect of pavement sensors’ placement in assessing road conditions, emphasizing the implications for future road condition assessment projects. A threshold value for classifying road conditions was proposed and applied in class assignments (I-17 highway projects).

Using an integrated theoretical model, this study aims to examine the moderating role of gamification in influencing intentions to use mobile payment applications in Bahrain.

The current examination happens to be the first approximation in the context of Bahrain wherein an extended TPB-based model integrating variables from TAM and UTAUT2 is used along with gamification and situational influence to examine the intentions to use m-payment applications.

The findings revealed that among the variates of the TPB, AT and PB significantly affect the intentions (IN) to use m-payment applications in Bahrain, but SN fails to affect intentions, similarly SI also fails to affect intentions thereby proving that the pandemic fails to drive the intention of the population under study toward using m-payment applications. However, when the application offers gamification (GM) features, SI significantly affects intentions through GM, thus experience along with situation drives intentions and this becomes the major theoretical contribution of the study.

This examination offers useful practical implications in the form of the findings revealing that GM affects intentions to use m-payment applications and that GM moderates the relationship between perceived risk (PR) and IN, as well as SI and IN, which can be used by the service providers to improve the user experience and achieve better acceptance of their application.

The novelty of the study lies in testing the integrated theoretical model in the context of a GCC nation, Bahrain.

A stylized fact in finance literature is the belief in positive relationship between ex ante return and risk. Hence, a rational investor, by utility preference axiom can only consider committing fund in asset which promises commensurate higher return for higher risk. Questions have been asked as to whether this holds true across securities, sectors and markets. Empirical evidence appears less convincing, especially in developing markets. Accordingly, the author investigates the nature of reward for taking risk in the Nigerian Capital Market within the context of individual assets and markets.

The author employed ex post design to collect weekly stock prices of firms listed on the Premium Board of Nigerian Stock Exchange for period 2014–2022 to attempt to answer research questions. Data were analyzed using a unique M Vec TGarch-in-Mean model considered to be robust in handling many assets, and hence portfolio management.

The study found that idea of risk-expected return trade-off is perhaps more general than as depicted by traditional finance literature. The regression revealed that conditional variance and covariance risks reveal minimal or no differences in sign and sizes of coefficients. However, standard errors were also found to be large suggesting somewhat inconclusive evidence of existence of defined incentive structure for taking additional risk in the market.

In terms of choice of methodology and outcomes, this research adds substantial value to body of knowledge. The adapted multivariate model used in this paper is a rare approach especially for management of portfolios in developing markets. Remarkably, the research found empirical evidence that positive risk-expected return trade-off, as known in mainstream literature, is not supported especially using a typical developing country data.

This paper aims to identify the level of contribution of different levels of education to remaining in unemployment as well as the transition from unemployment to employment in Egypt.

In this paper, transition probabilities matrix differentiated by gender, age groups, educational levels, marital status and place of residence based on worker flows across employment, unemployment and out of labor force states during the period 2012–2018 using Egypt Labor Market Panel Survey of 2018. The results point to the highly static nature of the Egyptian labor market. Employment and the out of labor force states are the least mobile among labor market states. This is because employment state is very desirable and the out of labor force is the largest labor market states, especially for females. Also, this study examines the impact of different educational levels separately on remaining in unemployment and transition from unemployment to employment state using eight binary logistic regression models.

The main results of transitions from unemployment to employment are relatively large for males, elder-age, uneducated workers as well as workers who are not married and urban residents, and the results of the logistic regression models consistent with the transition probabilities matrix results, except for few cases. Based on the above findings, there is enough evidence to accept the null hypothesis that no education has a positive significant impact to transition unemployed individuals from unemployment to employment, while less than intermediate as well as higher education have a negative significant impact to transition unemployed individuals from unemployment to employment.

This paper proposes to address the problem of the unemployment among highly educated which is much higher compared with illiterates and try to understand the impact of different levels of education separately on the transition from unemployment to employment, to help the policymakers to eradicate the gap between education and the demand of the labor market in Egypt.

The study examined the personal information management (PIM) challenges encountered by faculty in six universities in Ghana, their information refinding experiences and the perceived role of memory. The study tested the hypothesis that faculty PIM performance will significantly differ when the differences in the influence of personal factors (age, gender and rank) on their memory are considered.

The study was guided by a sample survey design. A questionnaire designed based on themes extracted from earlier interviews was used to collect quantitative data from 235 faculty members from six universities in Ghana. Data analysis was undertaken with a discrete multivariate Generalized Linear Model to investigate how memory intermediates in the relationship between age, gender and rank, and, refinding of stored information.

The paper identified two subfunctions of refinding (Refinding 1 and Refinding 2) associated with self-confidence in information re-finding, and, memory (Memory 1 and Memory 2), associated with the use of complimentary frames to locate previously found and stored information. There were no significant multivariate effects for gender as a stand-alone variable. Males who were aged less than 39 could refind stored information irrespective of the memory class. Older faculty aged 40–49 who possess Memory 1 and senior lecturers who possess Memory 2 performed well in refinding information. There was a statistically significant effect of age and memory; and rank and memory.

This study was limited to faculty in Ghana, whereas the study itself has implications for demographic differences in PIM.

Identifying how memory mediates the role of personal factors in faculty refinding of stored information will be necessary for the efforts to understand and design systems and technologies for enhancing faculty capacity to find/refind stored information.

Understanding how human memory can be augmented by technology is a great PIM strategy, but understanding how human memory and personal factors interplay to affect PIM is more important.

PIM of faculty has been extensively examined in the literature, and limitations of memory has always been identified as a constraint. Human memory has been augmented with technology, although the outcome has been very minimal. This study shows that in addition to technology augmentation, personal factors interplay with human memory to affect PIM. Discrete multivariate Generalized Linear Model applied in this study is an innovative way of addressing the challenges of assimilating statistical methodologies in psychosocial disciplines.