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In recent years, there has been a tremendous increase in the number of applicants seeking placements in undergraduate architecture programs. It is important during the selection phase of admission at universities to identify new intakes who possess the capability to succeed. Admission variable (i.e. prior academic achievement) is one of the most important criteria considered during the selection process. This paper aims to investigates the efficacy of using data mining techniques to predict the academic performance of architecture students based on information contained in prior academic achievement.

The input variables, i.e. prior academic achievement, were extracted from students’ academic records. Logistic regression and support vector machine (SVM) are the data mining techniques adopted in this study. The collected data were divided into two parts. The first part was used for training the model, while the other part was used to evaluate the predictive accuracy of the developed models.

The results revealed that SVM model outperformed the logistic regression model in terms of accuracy. Taken together, it is evident that prior academic achievement is a good predictor of academic performance of architecture students.

Although the factors affecting academic performance of students are numerous, the present study focuses on the effect of prior academic achievement on academic performance of architecture students.

The developed SVM model can be used as a decision-making tool for selecting new intakes into the architecture program at Nigerian universities.

Proper planning for the response phase of humanitarian relief can significantly prevent many financial and human losses. To this aim, a multi-objective optimization model is proposed in this paper that considers different types of injured people, different vehicles with determining capacities and multi-period logistic planning. This model can be updated based on new information about resources and newly identified injured people.

The main objective function of the proposed model in this paper is minimizing the unsatisfied prioritized injured people in the network. Besides, the total transportation activities of different types of vehicles are considered as another objective function. Therefore, these objectives are optimized hierarchically in the proposed model using the Lexicographic method. This method finds the best value for the first objective function. Then, it tries to optimize transportation activities as the second objective function while maintaining the optimality of the first objective function.

The performances of the proposed model were analyzed in different cases and its robust approach for different problems was shown within the framework of a case study. Besides, the sensitivity analysis of results shows the logical behavior of the proposed model against various factors.

The proposed methodology can be applied to find the best response plan for all crises.

In this paper, we have tried to use a multi-objective optimization model to guide and correct response programs to deal with the occurred crisis. This is important because it can help emergency managers to improve their plans.

The purpose of this paper is to assess the capacity of two methodological approaches – discrete choice and survival analysis models – to investigate the relationship between socio-economic characteristics and turnover in a retailing company. A comparison of the estimation results under each model and their interpretation is carried out. The study provides a guide to determine, assess and interpret the effects of different driving factors behind turnover.

The authors use a data set containing information about 1,199 workers followed up between January 2007 and December 2009. First, not distinguishing voluntary and involuntary resignation, a binary logistic regression model and a Cox proportional hazards (PH) model for univariate survival data are set up and estimated. Second, distinguishing voluntary and involuntary resignation, a multinomial logistic regression model and a Cox PH model for competing risk data are set up and estimated.

When no distinction is made, the results point that wage and age exert a negative effect on turnover. Risk of resignation is higher for male, single, not married and Spanish nationals. When the distinction is made, previous results hold for voluntary turnover: wage, age, gender, marital status and nationality are significant. However, when explaining involuntary turnover, all variables except wage lose explaining power. The survival analysis approach is better suited as it measures risk of resignation in a longitudinal way. Discrete choice models only study the risk at a particular cut-off point (24 months in case of this study).

This paper is a systematic application, evaluation and comparison of four different statistical models for analysing employee turnover in a single firm. This work is original because no systematic comparison has been done in the context of turnover.

This research article aims to understand the role of brand image, service quality and price (charge) in revitalising functional mass brands into prestigious mass brands.

The empirical research framework was developed by synthesising the past literature on masstige marketing and brand extension. Data was collected using a survey questionnaire from 396 respondents availing M-Wallet. Structural equation modelling was used to validate the brand revitalization attributes; further, the binary logistic regression model examined the effect of revitalization attributes on the chance of increasing customer's perception of masstige.

The exploratory study suggested brand image, service quality and value for money pricing as essential attributes to revitalize mass brands into masstige brands; furthermore, path analysis validated the positive effects of these attributes on the perception of masstige. The proposed binary logistic regression model suggested brand image as sensitive attributes, increasing the odds ratio by 9.39 times in favour of perceiving brand as masstige followed by the perceived service quality that is 5.87 times. The prediction capability of the proposed binary logistic regression model is found to be 96%.

The methodology of this study provides the basis for future researchers to advance research on masstige. This study will assist the marketers of mass brands to make better marketing decisions related to how masstige image can be sustained or a new or less known brand can be revitalized into a prestigious brand.

This study is the first to provide empirical evidence of how the mass brand can be revitalised as masstige brands by considering image, quality and price attributes.

In this paper, we propose new performance measure model in logistic industry. New model has been learned by key points of PZB model and advanced structure of MBNQA which has cause measure points and effect measure points. The structure of new performance measure model is cubic model which is reflected with time. We try to verify this model apply advance logistic company.

The purpose of this paper is to attempt to outline the standard profile of the typical wine consumer, by identifying some relevant features that can influence his/her purchasing choices. Therefore, the purpose of the research is to identify the pre-eminent attributes for wine consumers and the different levels of importance that consumers ascribe to the attributes identified at the time of purchase.

In order to collect the necessary data, an ad hoc questionnaire was utilized. The questionnaire, which was anonymous, was directly distributed with the face-to-face method. In total, 1,500 copies of the questionnaire had been prepared. The data collected were processed through the use of the binary logistic regression model and the ordinal logistic regression model. The first binary logistic regression model allows to evaluate the dependence of the dichotomous variable on some potential predictors. The ordinal logistic regression model, known in literature as a cumulative model of proportional quotas, is generally appropriate for situations in which the ordinal response variable has discrete categories.

The results returned by the elaboration of the binary logistic regression model refer to the influence of the variables sex, age, educational status and income on the “wine consumption” result, which is a dichotomous variable. The only variables found to be statistically significant are gender and educational status. The most significant variables that emerged from the implementation of the ordinary logistic regression model are gender, brand, choice based on price, place of production, harvest and certification. The analysis carried out has shown that with reference to wine as a product, it is essential to focus on several attributes, among which there are of course quality and brand.

Although field experiments are extremely useful for testing behavioral hypotheses, they are often limited by a small sample. Future research in this area might focus on the knowledge level of sustainable wine of the consumer. In relation to the knowledge of the characteristics of the wine, it is possible to estimate the willingness to pay a surplus for a wine produced with sustainable methods by the consumer and the possible level of price premium.

The originality of the research lies mainly in a deeper knowledge of wine consumption trends. This information is useful to better define the wine market and to allow, especially to small businesses, to establish effective marketing strategies in relation to the real preferences of consumers and the decision-making process of choice put in place by them. In order to achieve this, the influence of all the variables on the “satisfaction of wine consumption” result was evaluated. The strength of this paper is the use of an adequate statistical approach based on the use of models, typical of inferential statistics, to reach conclusions that can be extended to the entire population of wine growers.

The purpose of this paper is to present application of recency, frequency and monetary value (RFM) approach to predict customer insolvency using telecommunication data corresponding to RFM of late payments. The study tackles a serious problem that telecommunication companies often face and shows the ways to deal with it.

Based on a real telecom customer data, RFM approach was tested against decision trees and logistic regression models. Proposed models were evaluated with lift measure, area under the receiver operating characteristic and the ability to detect significant amount of money owed by insolvent customers.

The main findings from the research are twofold: RFM approach offers a viable alternative for customer insolvency classification. The proposed models perform well and all of them can capture significant amount of money owed by insolvent customers what is of high importance for the revenue assurance.

In comparison to previous studies proposed research presents novelty in the following areas. First, it deals with RFM applied to insolvency data (previous studies dealt with direct marketing data). Second, with these three variables it is possible to act as an early warning system for predicting the risk level and probable anomalies as quickly as it is possible (data retrieval and computational time is reduced). Third, RFM approach was tested against decision trees and logistic regression and the quality of the models was also assessed three months after the estimation.

Introduces the concept of service‐oriented agile logistics and presents a generic apparatus for the design of such systems. An analysis of future communication‐based logistics leads to the logistic control and engineering system SERVICES. The logistic system is conceived as a “society” of interacting “self‐responsible intelligent service‐producing actors”, where services or functions are taken as the system‐base. This means that, instead of working with process‐modules, the development of a service‐oriented information‐system primarily works with service‐modules that program the operational interaction between client, service‐producer and possibly sub‐contracted service‐producers. From this the supporting execution‐control in the context of the service‐producing units can be deduced. A case study of a high‐performance deep‐sea container terminal is given. This shows that the function‐programming system of SERVICES is generic, adequate and effective and that it favours distributed control.

The purpose is to explore the differences and similarities between fraudulent financial reporting detection and business failure prediction (BFP) models, especially in terms of which explanatory variables and methodologies are most effective.

In total, 52 financial variables were identified from previous studies as potentially significant. A number of Taiwanese firms experienced financial distress or were accused of fraudulent reporting in 2005. Data on these firms and their contemporaries were obtained from the Taiwan Economic Journal data bank and Taiwan Stock Exchange Corporation. Financial variables were calculated for the years 2003 and 2004. Three well‐known data mining algorithms were applied to build detection/prediction models for this sample: logistic regression, neural networks, and classification trees.

Many of the variables are effective at both detecting fraudulent financial reporting and predicting business failures. In terms of overall accuracy, logistic regression outperforms the other two algorithms for detecting fraudulent financial reporting. Whether logistic regression or a decision tree is best for BFP depends on the relative opportunity cost of misclassifying failing and healthy firms.

The financial factors used to detect fraudulent reporting are helpful for predicting business failure.

This study attempts to identify fundamental determinants of bond ratings for non-financial and financial firms. Further the study aims to develop a parsimonious bond rating model and compare its efficacy across statistical and range of machine learning methods in the Indian context. The study is motivated by the insufficiency of prior work in the Indian context.

The authors identify the critical determinants of non-financial and financial firms using multinomial logistic regression. Various machine learning and statistical methods are employed to identify the optimal bond rating prediction model. The data cover 8,346 bond issues from 2009 to 2019.

The authors find that industry concentration, sales, operating leverage, operating efficiency, profitability, solvency, strategic ownership, age, firm size and firm value play an important role in rating non-financial firms. Operating efficiency, profitability, strategic ownership and size are also relevant for financial firms besides additional determinants related to the capital adequacy, asset quality, management efficiency, earnings quality and liquidity (CAMEL) approach. The authors find that random forest outperforms logit and other machine learning methods with an accuracy rate of 92 and 91% for non-financial and financial firms.

The study identifies important determinants of bond ratings for both non-financial and financial firms. The study interalia finds that the random forest technique is the most appropriate method for bond ratings predictions in India.

Better bond ratings may mitigate corporate defaults.

Unlike prior literature, the study identifies determinants of bond ratings for both non-financial and financial firms. The study also experiments with modern machine learning techniques besides the traditional statistical approach for model building in case of relatively under researched market.