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The purpose of this paper is to examine two different approaches in the prediction of the economic recession periods in the US economy.

A logit regression was applied and the prediction performance in two out‐of‐sample periods, 2007‐2009 and 2010 was examined. On the other hand, feed‐forwards neural networks with Levenberg‐Marquardt error backpropagation algorithm were applied and then neural networks self‐organizing map (SOM) on the training outputs was estimated.

The paper presents the cluster results from SOM training in order to find the patterns of economic recessions and expansions. It is concluded that logit model forecasts the current financial crisis period at 75 percent accuracy, but logit model is useful as it provides a warning signal three quarters before the current financial crisis started officially. Also, it is estimated that the financial crisis, even if it reached its peak in 2009, the economic recession will be continued in 2010 too. Furthermore, the patterns generated by SOM neural networks show various possible versions with one common characteristic, that financial crisis is not over in 2009 and the economic recession will be continued in the USA even up to 2011‐2012, if government does not apply direct drastic measures.

Both logistic regression (logit) and SOMs procedures are useful. The first one is useful to examine the significance and the magnitude of each variable, while the second one is useful for clustering and identifying patterns in economic recessions and expansions.

The purpose of this paper is to examine the factors influencing Indonesian farmers’ use of the warehouse receipt system (WRS) and their choice of private and public warehouses.

Primary data were collected through questionnaires administered to 500 farmers in two districts, Subang and Cianjur, in West Java Province in Indonesia. Binary logit regression was employed to examine the factors influencing farmers’ use of the WRS. Binary and bivariate probit regressions were employed to determine the factors influencing farmers’ choice of private and public warehouses.

The empirical results of the binary logit regression revealed that age, land ownership, selling price, the use of the warehouse receipt as collateral security and the availability of transportation facility positively influenced farmers’ use of the WRS, whereas education, income, farm profit and participation in farmers’ group negatively influenced farmers’ use of the WRS. The results of the binary probit regressions revealed that profit, availability of insurance and processing facility positively influenced the farmers’ decision to use the private WRS, whereas education, production, selling price and distance from the farm to the warehouse negatively influenced farmers’ decision to use the private WRS. Age, education, selling price and distance from the farm to the warehouse positively influenced the farmers’ decision to use the public WRS, whereas production and availability of processing facility negatively influenced the decision of farmers to use the public WRS.

This study highlights the importance of education and government assistance regarding the provision of facilities and price indemnified insurance for successful implementation of the WRS.

This study provides an empirical contribution to the existing literature on the development of WRS in Indonesia. In terms of methods of analysis, previous studies used purely qualitative and descriptive methods, whereas this study employed econometric techniques (i.e. binary logit, binary probit and bivariate probit regressions) to examine the WRS in Indonesia. In addition, whereas previous studies explored the WRS in general, this study investigated the farmers’ use of the public and private warehouses in addition to exploring the WRS in general. Finally, the finding that the average annual profit of non-users was significantly higher than that of the users of the WRS is striking, and this could be attributed to the current challenges of the implementation of the WRS, including high transportation and warehouse rental costs.

The purpose of this study is to examine tax stamp evasion at Twifu Atti-Morkwa and Hemang Lower Denkyira districts in the central region of Ghana.

A cross-sectional survey design was adopted to sample 305 micro-taxpayers through the use of multi-stage sampling technique. Primary data were collected from the micro-taxpayers using structured interview. Binary and multinomial logit regression models were used to regress the tax stamp evasion on economic and non-economic factors.

The study found that the likelihood of micro taxpayers to evade tax stamp is predicted by age, application of sanctions, guilt feeling, transportation cost to tax office and rate of tax audit. Thus, the study found partial support for expected utility, planned behaviour and attributory theories in explaining tax evasion behaviour of micro-taxpayers.

There are several measures of addressing tax evasion behaviour of micro taxpayers. Evasion behaviour can be deterred by enforcement strategies such as application of sanctions and regular tax audit, establishment of more tax offices in the districts and writing normative messages on the faces of tax stamp stickers.

This study helps explains the tax evasion behaviour of micro-taxpayers of a developing economy like Ghana using a special type of tax design meant to capture such taxpayers in the tax bracket. To the best of our knowledge, the study is unique in terms of the means of measuring tax evasion and the methodologies used.

The role of energy efficiency and particularly energy performance certificates (EPCs) has emerged as a topical and important aspect of real estate markets. Various studies have been carried out investigating the perceived capitalisation effects of energy efficiency on property prices. There, however, remains divergence of opinion whether the capitalisation effect is truly in existence with extant research showing differing magnitudes of effects, if any. To date, no study (that the authors are aware of) has investigated the nature of the transition between EPC bands and price effects. The purpose of this study is to add to the research of the energy efficiency of housing to examine the nature of the likelihood of property characteristics being associated with higher EPC scores and value.

This research undertakes a suite of methodological tests to investigate the more latent relationships between EPC bands and pricing behaviour using 3,797 achieved sales prices within the Belfast housing market. Binary logit regression models are specified in conjunction with a Polytomous Universal Model to examine the likelihood of EPC bands falling within a particular property type and the likelihood of any pricing effects.

The findings show the differing property types to comprise very distinct and complex relationships in terms of price and EPC banding. The binary logit model estimations for both terrace properties and apartments reveal an increased likelihood to obtain higher EPC scores, with the semi-detached sector displaying a “mixed effect” with detached property revealing decreased probability of having superior energy performance and decreased likelihood of having poorer energy performance. The ordinal model estimations indicate that sales price comprises no relationship with energy performance, inferring that there is no increased probability of an increase in sales price with higher EPC rating.

This research offers new insights and focus on achieving a better understanding of the nexus between energy performance and property characteristics using alternative modelling approaches. This provides more exploratory insights into the complex relationships and offers awareness for policy discourse in terms of targeting properties which will tend to be poorer in energy efficiency.

Fast food (FF) consumption increased dramatically through the 1990s in the USA, accounting for nearly 35.5 percent of total away‐from‐home expenditures in 1999. Given dramatic changes in food consumption, and heightened public concern about health and obesity, there is a considerable need for research to understand better the factors affecting US FF consumption. This paper aims to fill this gap.

In this paper, logistic regression is applied to analyze the socioeconomic and demographic factors influencing the likelihood of consuming FF using United States Department of Agriculture data from the Continuing Survey of Food Intakes by Individuals from 1994 to 1996 and the Supplemental Children's Survey of 1998.

In general, the expected likelihood of FF consumption increases until around 20‐30 years of age and then decreases; increases as household income grows until about $50,000‐60,000 and then decreases; and decreases as household size grows. Further, males from the Midwest and South regions that live outside central cities in Metropolitan Statistical Areas have the highest likelihood of consuming FF.

While much literature has addressed key questions about expenditure on food away from home, this study complements previous work by focusing on food items consumed from FF facilities in the 1990s. In addition, the results find highly significant and important (statistically and economically) interactions between the likelihood of FF consumption and age, income, and household size.

Based on a survey of 897 farm households, the purpose of this paper is to build a framework using cluster analysis to explain how farmers make decisions on joining group guarantee, and analyzes factors influencing their decisions using multinomial and binary Logit regressions.

The approach of combining cluster analysis with Logit regression is an innovative approach to survey assessment. In addition, by design the authors have identified the four mutually exclusive groups of borrowers combining Group Guarantee membership and actual formal borrowing.

An extremely important observation according to the data is that most farmers appear to be part of group guarantees only because they have to in order to get access to formal credit products. 87.21 percent of the people who belong to groups and utilize the formal credit products belong to this category because their lenders have made participation in groups compulsory for access to credit. This may ration farmers’ willingness to even apply for credit. It also indicates a preference on the part of older and more risk-averse respondents to avoid participation in group guarantees. Out of financial characteristics the total loan holdings appears to be the only significant indicator of participation in group guarantees. Furthermore the results indicate that informal and formal credit appear to be replaceable for farmers.

The survey is confined only to the counties investigated. China is very diverse in its agricultural economies and many RCCs operate under different guidance and rules from those investigated here. Hence, while the authors can claim that the results are indicative, the authors cannot claim that they will hold generally.

Based on group guarantee loan mechanism and survey data analysis of 897 farm households, this paper analyzes influencing factors affecting farmers’ participation in group guarantees from microcosmic level, so as to provide some reference to further perfect micro credit operation mode and mechanism.

The results indicate that the Group Guarantee mechanism, while beneficial to some, may not hold global appeal for Chinese farmers. In the future RCCs may want to consider alternative approaches to loan security than placing the burden of guarantee on farmers’ family and friends.

The approach of combining cluster analysis with Logit regression is an innovative approach to survey assessment. In addition, by design the authors have identified the four mutually exclusive groups of borrowers combining Group Guarantee membership and actual formal borrowing.

The purpose of this paper is to examine the determinants of involuntary delisting rate for the Egyptian initial public offerings (IPOs) issued over the period 1992-2009.

A definition of survival time that considers the date when the new Egyptian listing rules were enforced to track delisting status for each IPO firm for five survival years is relied on. Binary logit regression analysis is used to identify these determinants. Total sample is divided into two subsamples: the first subsample covers the period from 1992 to 2004. It is used to estimate the logit equations and to predict delisting status of firms included in the second subsample, which covers the period from 2005 to 2009.

The probability of involuntary delisting decreases significantly with the increase in firm size, institutional ownership, assets growth rate, operating efficiency, offering size, initial returns and insider ownership. However, it increases significantly in IPO firms with high financial leverage. Based on the estimated logit regression equations, the status of the six firms included in the second subsample are correctly predicted.

The results provide several implications for investors, issuing firms and setters of listing rules.

This study uses new variables, such as firm type, institutional ownership and listing variables. In addition, several theories are tested and supported.

This study seeks to provide a systematic analysis of bounded rationality expressed by individual lenders in a Peer-to-peer (P2P) lending market.

26,383 personal loan listings collected from Moneyauction in Korea, were analyzed with binary logit regression. 6 hypothesis based on bounded rationality theory were constructed and tested. Binary logit regression was employed as both dependent variables have binary characteristics and can thus be assigned values equal to 0 or 1.

The results confirm that individual P2P lenders make their funding decisions based on bounded rationality, arousing from cognitive limitations, incomplete information, and time constraints.

By adopting the theory of bounded rationality, this study attempts to prepare the theoretical background for an explanation of the decision behavior of individual lenders in a P2P lending market.

The findings of this research emphasize the importance of the platform provider's role to facilitate the sustainable market growth of P2P lending as an alternative form of finance. As the rationality of individual lenders is bounded during their decision-making process according to the research findings, the platform provider must continuously adjust their decision criteria by referencing the cumulative loan repayment data.

This study attempts to identify for the first time the suboptimal decision making by individual lenders in a P2P lending market on the basis of bounded rationality theory.

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.

Considering the economic dimension of sustainability, the purpose of this paper is to analyze the risk of bankruptcy in the Pakistani firms of the non-financial sector from years 1995 to 2017.

Three techniques were used which include multivariate discriminant analysis (MDA), logit regression and multilayer perceptron artificial neural networks. The accounting data of firms were selected one year before the bankruptcy.

Findings were obtained by comparing and analyzing the methods which show that neural networks model outperforms in the prediction of bankruptcy. They further conclude that profitability and leverage indicators have the power of discrimination in bankruptcy prediction and the best variables to predict financial distress are also found and indicated.

Practically, this study may help the firms to better anticipate the risks of getting bankrupt by choosing the right method and to make effective decision making for organizational sustainability.

Three different techniques were used in this research to predict the bankruptcy of non-financial sector in Pakistan to make an effective prediction.