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This study aims to validate a model for estimating platoon delay due to pedestrian crossing for use in Kuwait City.

The model was modified slightly for the scenario used in Kuwait, in which the presence of raised crosswalk meant that all incoming traffic would slow down automatically. Using video footage to observe the site, several variables were collected, and a model was used to calculate the delays suffered by the vehicles because of pedestrian crossing. The model was validated using the actual footage and manual observation to measure the delays.

The model showed a good match fit to the observed data, as the average delays differed by 22.5% between the two methods. Following the comparison, a sensitivity analysis was made on three variables: the acceleration rate, deceleration rate, as well as the pedestrian walking time. The analysis has shown that deceleration rate has approximately twice the effect on the model than the acceleration rate has. It has also shown that the pedestrian walking time has a major effect on the model, in an almost one-to-one correlation. A 50% change of the pedestrian walking time is associated with approximately 50% change in the model’s output delay.

A model for estimating platoon delay because of pedestrian crossing was validated for use in Kuwait City. The model was modified slightly for the scenario used in Kuwait, in which the presence of raised crosswalk meant that all incoming traffic would slow down automatically.

We propose a risk factor for idiosyncratic entropy and explore the relationship between this factor and expected stock returns.

We estimate a cross-sectional model of expected entropy that uses several common risk factors to predict idiosyncratic entropy.

We find a negative relationship between expected idiosyncratic entropy and returns. Specifically, the Carhart alpha of a low expected entropy portfolio exceeds the alpha of a high expected entropy portfolio by −2.37% per month. We also find a negative and significant price of expected idiosyncratic entropy risk using the Fama-MacBeth cross-sectional regressions. Interestingly, expected entropy helps us explain the idiosyncratic volatility puzzle that stocks with high idiosyncratic volatility earn low expected returns.

We propose a risk factor of idiosyncratic entropy and explore the relationship between this factor and expected stock returns. Interestingly, expected entropy helps us explain the idiosyncratic volatility puzzle that stocks with high idiosyncratic volatility earn low expected returns.

Salesperson performance is accepted as a relevant factor of retailing success. However, scarce studies reveal the relationship between sales performance and brand relationship. The purpose of this study is both, from one side, to empirically demonstrate the impact of salesperson brand attachment (SBA) on sales performance and, on the other side, to identify the mediators of this relationship in small retailing.

A survey was conducted with a sample of 206 small retailers from different sectors of an emerging country. The proposed model was tested using partial least squares–structural equation modeling (PLS-SEM) in SmartPLS3.

The results demonstrated that SBA is relevant to driving sales performance through two relevant paths – one following SBA–satisfaction–performance and one path following the SBA–commitment–performance. The model was able to explain 63% of the outcome performance.

Regarding small retailers, where the owners, employees and managers have higher levels of interaction than the large national retail chains, the marketing executives must invest in improving the attachment to the brand and create emotional bonds and cognition between marketers and the brand. They must develop strategies to promote job satisfaction and organizational commitment because they determine performance.

Despite the relevance of small businesses for economies worldwide and the importance of salesperson brand relationships, no study has been developed to demonstrate the impacts of such relationships on salesperson performance in retailing. Furthermore, in addition to the central role of organizational commitment in the sales research, to the best of the authors’ knowledge, this is the first study to explore how to mediate the relationship between brand attachment and sales performance.

El rendimiento del vendedor se acepta como un factor relevante para el éxito del comercio minorista. Sin embargo, los estudios que revelan la relación entre el resultado de las ventas y la relación con la marca son escasos. El propósito de este estudio es, por un lado, demostrar empíricamente el impacto del apego a la marca del vendedor (SBA) en el resultado de las ventas y, por otro lado, identificar los mediadores de esta relación en el comercio minorista.

Se realizó una encuesta con una muestra de 206 vendedores de pequeños minoristas provenientes de diferentes sectores de un país emergente. El modelo estructural se analizó mediante ecuaciones estructurales basada en mínimos cuadrados (PLS-SEM) utilizando SmartPLS3.

Los resultados mostraron que el SBA es esencial para impulsar el resultado de las ventas a través de dos caminos relevantes: uno siguiendo el SBA-satisfacción-rendimiento y la otra dirección siguiendo el SBA-compromiso-rendimiento. El modelo fue capaz de explicar el 63% del rendimiento.

Con respecto a los pequeños minoristas, donde los propietarios, empleados y gerentes tienen niveles más altos de interacción que las grandes cadenas minoristas nacionales, los ejecutivos de marketing deben invertir en mejorar el apego a la marca y crear vínculos emocionales y cognitivos entre los vendedores y la marca. Deben desarrollar estrategias para promover la satisfacción laboral y el compromiso organizacional ya que determinan el rendimiento.

A pesar de la relevancia de las pequeñas empresas para las economías de todo el mundo y la importancia de las relaciones de marca en los vendedores, no se ha desarrollado ningún estudio para demostrar los impactos de tales relaciones en el rendimiento de los vendedores en el comercio minorista. Además, aparte del papel central del compromiso organizacional en la investigación en ventas, este es el primer estudio que explora cómo media la relación entre el apego a la marca y el resultado de las ventas.

This paper aims to examine how the regional variable in country conflict modeling affects forecast accuracy and identifies a methodology to further improve the predictions.

This paper uses statistical learning methods to both evaluate the quantity of data for clustering countries along with quantifying accuracy according to the number of clusters used.

This study demonstrates that increasing the number of clusters for modeling improves the ability to predict conflict as long as the models are robust.

This study investigates the quantity of clusters used in conflict modeling, while previous research assumes a specific quantity before modeling.

Despite large bodies of research related to the impacts of e-commerce on last-mile logistics and sustainability, there has been limited effort to evaluate urban freight using an equity lens. Therefore, this study proposes a modeling framework that enables researchers and planners to estimate the baseline equity performance of a major e-commerce platform and evaluate equity impacts of possible urban freight management strategies. The study also analyzes the sensitivity of various operational decisions to mitigate bias in the analysis.

The model adapts empirical methodologies from activity-based modeling, transport equity evaluation, and residential freight trip generation (RFTG) to estimate person- and household-level delivery demand and cargo van traffic exposure in 41 U.S. Metropolitan Statistical Areas (MSAs).

Evaluating 12 measurements across varying population segments and spatial units, the study finds robust evidence for racial and socio-economic inequities in last-mile delivery for low-income and, especially, populations of color (POC). By the most conservative measurement, POC are exposed to roughly 35% more cargo van traffic than white populations on average, despite ordering less than half as many packages. The study explores the model’s utility by evaluating a simple scenario that finds marginal equity gains for urban freight management strategies that prioritize line-haul efficiency improvements over those improving intra-neighborhood circulations.

Presents a first effort in building a modeling framework for more equitable decision-making in last-mile delivery operations and broader city planning.

The Asian banking system has been appreciated with many distinct qualities including consistent in profitability. Many studies have examined the profitability of Asian banking sector from diverse perspectives. However, studies on bank profitability in connection to the capital structure, operating efficiency and non-interest income are only a few. This study investigates the influence of capital structure as estimated by leverage ratio and long-term debt, operating efficiency and non-interest income on the profitability of the banking industry in 28 countries of Asia.

This paper utilizes fixed effect regression model by involving panel data with sample of 492 banks from 28 countries of Asia for the time span of 15 years from 2004 to 2018.

The results confirm that an increase in total debt ratio increases the profit margin of the bank as supported by the agency cost theory, suggesting that the debt financing increases the profitability of the firm. In addition, the findings reveal that lowering the operating expenses and managing of costs effectively can boost the profitability of bank. Furthermore, non-interest income plays a vital role when the interest rates are lower. Hence the study suggests that a careful investment in this sector can generate income as well as increase the profit margin of the banking arena.

The paper examines the profitability of bank by including impact of leverage ratio and long-term debt as a measure of capital structure along with the influence of operational efficiency and non-interest income which contributes to the understanding of the existing literature.

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.

This study aims to examine the role of hunger, environmental, economic, landfill and water shortage concerns as significant dimensions of consumer social awareness marketing in socially responsible plate food consumption.

To carry out their purpose, the authors validate the hypothesized model empirically through data from 1,536 households using structural equation modeling (SEM). In particular, the construct measures of the structural model have been tested by confirmatory factor analysis (CFA).

The outcome the authors came up with is coherent with the hypothesized model, and it proves a positive relationship of the five dimensions identified on consumer awareness. Moreover, the study results show the crucial role of landfill and water shortage concerns in measuring consumer awareness.

These findings may be of interest to practitioners, academics and policymakers for socially responsible food consumption guidance and training for planning consumer awareness programs. More in detail, this study offers the indication that the dimensions of the social consumer awareness construct are differing from commercial consumer awareness.

Even though several previous studies have addressed the concept of consumer awareness concerning product and service purchase decisions, this is one of the first research studies on consumer awareness as a multidimensional construct in social marketing studies domain.

Few models have attempted to explain intrapreneurial behavior from the perspective of competencies. Therefore, the purpose of this paper is to contribute along this line by developing and validating a scale to measure intrapreneurial competencies for a Costa Rican organizational context.

A three stage process was followed. The first stage considered literature review, expert judgment, cognitive interviews, and back-translation. In the second stage, the questionnaire was administered to a sample of 543 university professionals who worked mainly in private organizations in Costa Rica. The third stage led to evaluate of the proposed scale’s psychometric properties, including, exploratory factor analysis procedure performing by SPSS 19; confirmatory factor analysis procedures by means of structural equation modeling using EQS 6.2 version and finally, a linear regression model to obtain evidence of external criterion-related validity, performed by SPSS 19.

This study provides evidence of five sub-dimensions of employee attributes, i.e., “opportunity promoter”, “proactivity”, “flexibility”, “drive”, and “risk taking” that constitute a higher-level construct called intrapreneurial competencies. The scale provided evidence of convergent, discriminant, and criterion-related validity – the latter, using an employee innovative behavior scale.

The model offers a first step to continue studies that aim at developing a robust model of intrapreneurial competencies. This potential predictive capacity of an instrument of this nature would be useful for the business sector, particularly as a diagnostic instrument to strengthen processes of staff development in areas that promote the development of innovation and the creation of new businesses for the company.

The proposed model has been aimed to predict stock market signals by designing an accurate model. In this sense, the stock market is analysed by the technical analysis of Japanese Candlestick, which is combined by the following meta heuristic algorithms: support vector machine (SVM), meta-heuristic algorithms, particle swarm optimization (PSO), imperialist competition algorithm (ICA) and genetic algorithm (GA).

In addition, among the developed algorithms, the most effective one is chosen to determine probable sell and buy signals. Moreover, the authors have proposed comparative results to validate the designed model in this study with the same basic models of three articles in the past. Hence, PSO is used as a classification method to search the solution space absolutelyand with the high speed of running. In terms of the second model, SVM and ICA are examined by the time. Where the ICA is an improver for the SVM parameters. Finally, in the third model, SVM and GA are studied, where GA acts as optimizer and feature selection agent.

Results have been indicated that, the prediction accuracy of all new models are high for only six days, however, with respect to the confusion matrixes results, it is understood that the SVM-GA and SVM-ICA models have correctly predicted more sell signals, and the SCM-PSO model has correctly predicted more buy signals. However, SVM-ICA has shown better performance than other models considering executing the implemented models.

In this study, the authors to analyze the data the long length of time between the years 2013–2021, makes the input data analysis challenging. They must be changed with respect to the conditions.

In this study, two methods have been developed in a candlestick model, they are raw based and signal-based approaches which the hit rate is determined by the percentage of correct evaluations of the stock market for a 16-day period.