Customer Perception and Awareness of Green Banking Practices

The governments, business firms, policymakers, advocacy groups, and even the public recently are hotly debating on the issues of environmentally friendly practices. In this context, being a part of 'going green', green banking, which plays an important role in environmental sustainability, has been a buzzword in the global baking industry. This study identifies how the customers perceive the emerging concept of green banking initiatives of banks and also analyzes the factors that influence such practices of the customers. Using a structured questionnaire, the primary data were collected from 403 commercial banks of Kathmandu valley, Nepal. The awareness index was prepared, and the binary logit model was applied for the econometric analysis. This study observed that the customers are positive towards the environmentally friendly practices of banks and ready to adopt the green banking practices. The research implies that in order to promote environmental sustainability, banks and financial institutions should be able to educate the customers about green banking practices and their benefits.

Analysis of Factors Influencing Credit Access of Vietnamese Informal Labors in the Time of COVID-19 Pandemic

Credit is considered as an essential tool to make informal labor’s income better. In order to improve quality of their life, the state should have some supports them in credit access. This study analyzes factors causing credit access of informal labors to be changed in the time of COVID-19 pandemic. Using survey data collected from 2020 VHSSL (2019–2020), this approach has two models including a binary logit model and a multinomial logit model (MLM). The results revealed that the positive factors including education, material, collateral, credit size, credit source, credit debt which are likely to affect to credit access, however age, family size, ethnicity, interest, paid money are negative. Besides, it also concludes that quality of life of informal labor is considerably influenced by credit access, collateral, credit source, credit debt from the observed samples. Additionally, this paper recommends some policies to enhance informal labor’s access to credit and their quality of life.

Analysing pedestrian–vehicle conflicts behaviour at urban pedestrian crossings

Pedestrian safety has become a serious problem with the rapid growth of motorised vehicle in transportation system in developing counties. Pedestrians often respond differently to changes in surrounding and traffic conditions. A study was undertaken to investigate pedestrians’ gap acceptance and the parameters affecting their risk-taking behaviours based on time-to-collision and post-encroachment-time indexes. Three signalised intersections and two midblock crossings were selected in Qazvin, Iran. A total of 752 pedestrians were examined by video recording and field observation, and pedestrians’ gap acceptance behaviour was estimated by using binary logit model. Results showed that the average time to collision and post-encroachment time were 4.27 s and 1.44 s, respectively. In addition, the presence of children alongside the older pedestrians led to a less risk-taking crossing. Additionally, pedestrian risk-taking was reduced by increasing both time indexes. Rainy weather also reduced pedestrians’ risk-taking behaviour. Elasticity analysis indicated that parameters such as pedestrians’ conflict with vehicles at the first or second half of the crossings, walking with a child, speed of the approaching vehicle, the crossing type and running while crossing were the most important factors in pedestrian risk-taking.

Studying the Role of Personality Traits on the Evacuation Choice Behavior Pattern in Urban Road Network in Different Severity Scales of Natural Disaster

The study of evacuation behavior in response to disaster is necessary for emergency traffic management. As decision-making is not exclusively dependent on observable variables, in this research, it is attempted to study the evacuation choice behavior pattern in emergency response to earthquake disaster by considering both physical and behavioral factors. Personality traits are measured as behavioral latent factors by confirmatory factor analysis (CFA) of the short form of NEO-Five-Factor Inventory (NEO-FFI). A revealed preference survey with more than 700 samples was conducted in Qazvin city (Iran) which was based on real-life earthquake experience and the stated preference survey was conducted for six designated scenarios with different severities and times of earthquakes. Analysis of evacuation behavior is conducted by 3 types of discrete choice models (traditional binary logit model (TBLM), hybrid binary logit model (HBLM), and random parameters/mixed binary logit model (MBLM)). First, TBLM is estimated to study the effect of observable variables on response of people to earthquake disaster. Then, by adding the personality traits to modeling structure and constructing HBLM, the correct prediction percentage of the model increased. This study also considers heterogeneous mixtures of population in terms of income, family size, and five factors of personality traits by MBLM. The MBLM captures the heterogeneous responses of the respondent. By considering these variables as random parameters, the Log Likelihood function and pseudo square (ρ2) of the model increased.

Studying the Role of Behavioral Characteristics in Individuals Travel Choice Behavior in Response to Earthquake Disaster Using Discrete Choice Models

The study of decision-making in response to earthquake disaster including evacuation and destination choice behavior is necessary for emergency planners in order to provide emergency traffic management and solve evacuation problems in transportation network. In this research, it is attempted to study the pattern of choice behavior in Qazvin transport network by considering physical and behavioral factors simultaneously. The required data were collected through a questionnaire from 546 inhabitants of Qazvin based on the stated preferences (SP) survey for six designated earthquake scenarios at different severities and times. The effects of socioeconomic factors, trip daily characteristics, and behavioral factors on decisions to evacuate and choose destinations are studied in this research. The binary logit model is applied for analyzing the evacuation decision, and the multinomial logit model is applied for the destination choice in response to the earthquake disaster. The binary logit model analysis with 73% correct prediction indicates that socioeconomic characteristics including age, marital status, family size, and car ownership have positive effects on evacuation choice. Furthermore, the analysis of behavioral factors indicates that moderate religious belief decreases the likelihood of evacuation, while following previous experiences, trust in acquired trainings, and following decisions made by others moderately increase the likelihood of evacuation. Also, socioeconomic and behavioral factors are the determinants of the destination decision, and the results indicate that individuals following previous experiences highly are more likely to choose gas station and relative’s home destination versus going to urban open spaces.

Can Monetary Incentive Regulate Boarding Passenger Distribution on the Metro Station Platform?

To equilibrate the passenger distribution on the metro platform and carriage, a monetary incentive policy was explored in this paper; a discount on travel fare was provided to motivate metro passengers to queue for boarding in the noncrowded areas on the platform. The congested state is evaluated combined with the passenger distribution in the upcoming metro carriage. The utility of metro passengers and companies caused by the monetary incentive policy was analyzed, and the binary logit model was used to relate the utility to the passenger’s willingness to move from crowded areas to noncrowded ones. With data acquired from the questionnaire survey, a regression analysis was employed to explain the variation in passengers’ willingness to move as a function of discount level as well as personal and trip characteristics. The regression results show that effect of incentive discount is greater on female passengers and elderly passengers. A 10% discount can motivate most passengers aged over 40, and a 30% discount works on most female passengers. According to the different levels of passenger sensitivity, a particular discount can be determined to motivate a specific proportion of passengers to move and achieve the regulation of passenger distribution on the metro station platform and metro carriage.

Determinants of Smallholders’ Food Security Status in Kalu District, Northern Ethiopia

This study analyzed the determinants of rural households’ food security in the Kallu district of the Amhara region, Northern Ethiopia. The study used primary data collected from 395 randomly selected rural households. The study employed descriptive statistics and a binary logit model to estimate the status and determinants of smallholders’ food security, respectively. Of the total sample households, 47.30% are food insecure. The binary logit model results showed that sex and education level of the household head, livestock ownership, credit access, and technology adoption have positive and significant effects on food security, while age and market distance are negatively associated with the probability to be food secure. The results suggest that improving access to marketing and financial services will contribute to improving the food security status of smallholders.

Factors Influencing Soil Erosion Management Practices in Ejersa Lafo District, West Showa Zone, Oromia, Regional State, Ethiopia

Soil erosion by water is the major Agricultural problem, which results in reduction of agricultural productivity. This study focused on farmer perception of soil erosion and factors influence soil erosion management practices. A cross sectional data was used to assess information about soil erosion perception and factors influence soil erosion management practices. The Ejersa Lafo district was purposively selected from the districts of West Shoa Zones. A total of 142 sample respondents were randomly selected. Interview questionnaire, focus group discussion and survey of field observation were used as data collection tools. Data were analyzed using descriptive statistics and econometric model. Result from descriptive statistics show that majority of the respondents perceived soil erosion problem and its consequence on farmland. Both biological and physical soil erosion management practices in the area. Binary logit model estimation result shows that education level, landholding size, slopes of the land and land ownership type were mostsignificant to soil erosion management practices in the area. Therefore, taking these factors into account in setting appropriate soil erosion management measures may help policy makers and farmers to couple with erosion problem. The study recommended a need for the government to enforce effective policies to control and prevent soil erosion problem. Keywords: Binary logit model: Management practices: Soil Erosion

A Logit Model for Bivariate Binary Responses

This article provides a bivariate binary logit model and statistical inference procedures for parameter estimation and hypothesis testing. The bivariate binary logit (BBL) model is an extension of the binary logit model that has two correlated binary responses. The BBL model responses were formed using a 2 × 2 contingency table, which follows a multinomial distribution. The maximum likelihood and Berndt–Hall–Hall–Hausman (BHHH) methods were used to obtain the BBL model. Hypothesis testing of the BBL model contains the simultaneous test and the partial test. The test statistics of the simultaneous test and the partial test were determined using the maximum likelihood ratio test method. The likelihood ratio statistics of the simultaneous test and the partial test were approximately asymptotically chi-square distributed with 3p degrees of freedom. The BBL model was applied to a real dataset, and the BBL model with the single covariate was better than the BBL model with multiple covariates.

Determinants of Zakat Payment to Zakat Institution

The Zakat, one of the five pillars of Islam, is a financial resource for Islamic states in addition to taxes. Given the importance of Zakat funds for the economy and society, Zakat collection and distribution institutions have been established. This chapter was interested in the behavior of individuals in setting up a Zakat institution in the Tunisian context. The authors have studied the factors that affect the intention to pay Zakat to Zakat institutions. To do this, a questionnaire was administered to 100 people. The authors obtain three types of information: attitude towards Zakat, attitude towards institutions, and information related to the intention to pay Zakat to Zakat institutions. The authors have estimated by the binary logit model the relationship between the intention to pay Zakat to Zakat institutions and four factors: trust, quality of service, proximity, and the level of understanding of the Zakat principles. Three of them are significant.