Big Data Analytics and Discrete Choice Model for Enterprise Credit Risk Early Warning Algorithm

A business credit risk early warning algorithm based on big data analysis and discrete selection model is presented to address the issues of poor sample fitting performance, long warning time, and low warning accuracy that plague the traditional enterprise credit risk early warning algorithm. A-share listed enterprises in China were chosen as the credit data source for screening the samples based on big data analysis. After screening, financial failure firms were coupled, and paired samples were created. The credit risk variables, which included financial and corporate governance characteristics, were chosen based on the created samples. The enterprise financial risk submodel and the nonfinancial risk submodel were built based on the enterprise credit risk variables, and the financial and nonfinancial index scores of enterprise customers were evaluated separately to develop a discrete choice model of enterprise credit risk. The algorithm’s sample fitting performance was employed to achieve early warning of corporate credit risk. The algorithm based on big data analytics and discrete choice model is compared to the traditional method in order to verify its validity. The findings of the experiment reveal that the algorithm’s sample fitting performance is superior to the traditional one, making it more suitable for enterprise credit risk early warning. The proposed model depicts 85% accuracy.

Travel Mode Choice Modeling: Predictive Efficacy between Machine Learning Models and Discrete Choice Model

Background: A complex travel behaviour among users is intertwined with many factors. Traditionally, the exploration in travel mode choice modeling has been dominated by the Discrete Choice model, nonetheless, owing to the advancement in computational techniques, machine learning has gained traction in understanding travel behavior. Aim: This study aims at predicting users’ travel model choice by means of machine learning models against a conventional Discrete Choice Model, i.e., Binary Logistic Regression. Objective: To investigate the comparison between machine learning models, namely Neural Network, Random Forest, Decision Tree, and Support Vector Machine against the Discrete Choice Model (Binary Logistic Regression) in the prediction of travel mode choice amongst Kuantan City. Methodology: The dataset was collected in Kuantan City, Malaysia, through the Revealed/Stated Preferences (RP/SP) Survey. The data collected was split into a ratio of 80:20 for training and testing before evaluating them between the aforesaid models. The hyperparameters of the models were set to default. The performance of the models is evaluated based on classification accuracy. Results: It was shown in the present study that the Neural Network Model is able to attain a higher prediction accuracy as compared to Binary Logistic Regression (Discrete Choice Model) in classifying mode choice of Kuantan users either to choose public transport or private vehicles as daily transportation. Feature importance technique is crucial for identifying the significant features in modelling travel mode choice. It is demonstrated that the Neural Network Model can yield exceptional classification of mode choice up to 73.4% and 72.4% of training and testing data, respectively, by considering the features identified via the feature importance technique, suggesting the viability of the proposed technique in supporting an informed decision. Conclusion: The findings highlight the strengths and limitations of the Machine Learning Technique as well as the Discrete Choice Model in modeling travel mode choice. It was shown that Machine Learning models have the capability to provide better prediction that could assist the urban transportation planning among policymakers. Meanwhile, it could be also demonstrated that the Discrete Choice Model (Binary Logistic Regression) is helpful in getting a better understanding in expressing the inference relationship between variables for improvising the future transportation system.

The Value of Auditor Industry Specialization: Evidence from a Structural Model

This study investigates the value of auditor industry specialization. In the first step, we use a discrete choice model to derive the first-order demand for auditor industry specialization. Our results reveal that clients have a general preference for auditor industry specialization, relating to both audit firm and audit office specialization. Further, we observe that specializations at the audit firm and audit office level are substitutes. We also find that larger and more complex clients have a stronger demand for industry specialization at the audit office level. In the second step, we use the results from the discrete choice model to quantify the value of auditor industry specialist for clients. We find that the overall value of industry specialization aggregated across all clients is 5.2 million USD (0.36% of audit fees) and that industry specialization at the firm (office) level is decisive for auditor choice in 4% (6%) of all cases.

Differences in Commuting Modes and Residential Locations as Factors of Turnover Intention of Healthcare Workers of Ambulatory Clinics

Organisations require novel perspectives for achieving a stable workforce. One of such perspectives is having healthy employees, through timely medical care in ambulatory clinics. But when healthcare providers exhibit turnover intentions, and ultimately turnover behaviour, the purpose for such facilities is defeated. The study sought to understand if healthcare workers’ commuting modes and the differences in their residential locations affect their turnover intentions. These variables, which are yet to be investigated in the turnover literature, were examined within the assumptions of discrete choice model. Therefore, 137 healthcare workers of 11 ambulatory clinics, randomly selected from operating clinics in Marina, Lagos Island, were surveyed using purposive sampling method. The results suggest that the differences in residential locations did not produce statistically significant differences in turnover intention. However, there were significant differences in turnover intentions of participants who drive their cars and those who commute with public transport ( F (1, 131) = 9.14, p < 0.01). Further result negates the constant travel time hypothesis and the discrete choice model. The recommendations are focused on coordinated transport schedules, decentralised congested economic activities and polycentric city planning policies. These will enhance dispersed commuting.

Effects of High-Speed Rail on Sustainable Development of Urban Tourism: Evidence from Discrete Choice Model of Chinese Tourists’ Preference for City Destinations

In China, the opening of high-speed rails (HSR) brings significant changes to the source-destination spatial distance, the accessibility of destinations, and the spatial structure of tourist flows in each region, exerting varied HSR effects on different types of cities. Against this backdrop, it is meaningful to deeply explore tourists’ preference for city destinations in the light of HSR effects. The exploration could contribute greatly to the planning, marketing, management, and sustainable development of urban tourism. This paper takes Xiangtan and Yueyang as typical cases of the diffusion effect and the corridor effect of HSR. Firstly, the factors affecting destination choice were identified, and the attribute levels were configured, forming multiple virtual alternatives. Next, questionnaire surveys were carried out to collect tourists’ selections between each pair of alternatives. Further, a discrete choice model was constructed to assign a weight to each factor, reflecting its importance to tourists’ decision-making regarding their destination selection and to disclose the law of tourists’ preferences for destinations. The results showed that (1) Under the HSR diffusion effect, the top three factors affecting tourists’ preference for destinations in Xiangtan are convenience, connection time, and popularity; under HSR corridor effect, the top three factors affecting the tourists’ preference for destinations in Yueyang are reputation, convenience, and leisure and reception facilities (LRFs). (2) The destination preference is closely associated with personal features like gender, income, occupation, and fellow travelers. Tourists with different personal features give different attention to the various influencing factors. The research findings provide a reference for the sustainable development of urban tourism.