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The purpose of this paper is to investigate the relationships among choice of risk management tools, perceived effectiveness of risk management tools, self-reported risk attitude, and farm and farmer characteristics.

A mail survey was used to collect information about utilization of risk management tools, perceived effectiveness of risk management tools, and factors that could influence choice of risk management tools by Illinois farmers. Cluster analysis, one-way ANOVA, χ2 tests of independence, and multinomial logistic regression were utilized to detect possible relationships among choice of risk management tools, perceived effectiveness of risk management tools, self-reported risk attitude, and farm and farmer characteristics.

Multinomial logistic regression analysis revealed that age and gross farm income (GFI) were the strongest predictors of the risk management tool utilization group to which an individual would be assigned. The number of risk management tools utilized decreased with age but increased with GFI. Neither self-reported risk attitude nor education was a significant independent variable in the multinomial logistic regression model, but both were strongly impacted by age. Younger farmers with higher GFI were the most likely users of hedging.

The results of this study provide support for the idea that farmers who are better able to generate revenue are better able to manage risk, but the direction of causality was not investigated.

Risk management service providers could benefit from this study as a benchmark for understanding their current and potential farmer clients’ risk management strategies.

This study used cluster analysis and multinomial logistic regression to address the complexity of decisions regarding multiple risk management tools. The number of tools utilized by individuals was investigated.

The purpose of this study is to understand the effect of price promotions, venue and place of residence on low‐risk, risky and high‐risk alcohol consumption behaviour of young women between 18 and 24 years of age who attend university in Australia, Wales and Germany.

The quantitative, self‐administered questionnaire collected data from a convenience sample of three universities in three OECD countries with high alcohol consumption being: a regional Australian university (n=305), a city Welsh university (n=354) and a rural German university (n=325).

First, the multinomial logistic regression results revealed that price promotions and venue influenced alcohol consumption in Wales alone while place of residence influenced alcohol consumption in Australia; however, price promotions, venue and place of residence had no effect on young women attending university in Germany. Second, the binomial logistic regression results for Wales reported a sensitivity to price promotions for all three alcohol consumption risk classifications; however, location was of little consequence to risky drinkers when compared to high risk drinkers. For Australia, the place of residence did not influence alcohol consumption for both risky and high‐risk drinkers.

The value of this study lies in the examination of three levels of alcohol consumption – low‐risk, risky and high‐risk – for the same cohort across three countries using the same test instrument and standard alcohol consumption metrics. As such, this study provides a more meaningful macro view of alcohol consumption; thus has the capacity to contribute to effectual intervention strategies.

The purpose of this study is to clarify the relationship between getting quality awards by companies and their financial performance in Iran's business.

In the first step, the relationship between awards scores and financial performance by canonical correlation analysis was examined. Then, binary and multinomial logistic regression was used to determine the degree of impact of each financial performance measure on getting quality awards. Finally, two forecasting functions were explored: the probability of achieving quality awards and the probability of achieving different levels of these awards.

Based on the analyzed data of 112 companies through canonical correlation analysis, there was a weak relationship between financial performance and getting quality awards. Also, by using logistic regression, no result was found to prove the impact of financial performance measures on getting Iran's national quality awards. It can be concluded that conceptually, deployment of excellence organizational models will not result in favorable outcomes, especially in the financial scope. Also, practically, excellence models have not been well deployed in Iranian companies, or these models do not fit to Iran's business environment. Organizational culture may not be consistent with quality.

Quality awards are given to qualified companies following the establishment of models of excellence such as the European Foundation for Quality Management (EFQM). The main novelty of this research is to clarify the relationship between getting quality awards by companies and their financial performance in Iran's business.

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.

Expanding the market share of safe food through a modern retail system has faced a lot of difficulties in Vietnam. Thus, a further understanding of consumer behaviour and loyalty towards such food is essential for food retailers. This study aims at exploring segments of consumer loyalty and its influential factors towards safe food brands in the country.

Based on a sample of 250 consumers buying safe food in Hanoi city in February 2019, two-step cluster and multinomial logistic regression analyses were applied.

The results show that four segments of brand loyalty were formed from the interaction between attitudinal and behavioural loyalty as the framework of Dick and Basu (1994), namely, true loyalty, spurious loyalty, latent loyalty and disloyalty. Notably, over 60% of the consumers were in latent loyalty and spurious loyalty, indicating variety-seeking behaviour, multi-brand loyalty or low recognition of the brand. Consumer satisfaction was the most vital motivating consumers to higher loyalty levels. Additionally, brand trust and brand familiarity played significant roles in developing true brand loyalty. An attractive selling store and friendly staff were also important in enhancing brand loyalty.

This study is one of the first to elicit consumer loyalty and identify factors driving the loyalty towards brands of safe food in a developing country like Vietnam.

This study aims to develop a regression-based machine learning model to predict housing price, determine and interpret factors that contribute to housing prices using different data sets available publicly. The significant determinants that affect housing prices will be first identified by using multinomial logistics regression (MLR) based on the level of relative importance. A comprehensive study is then conducted by using SHapley Additive exPlanations (SHAP) analysis to examine the features that cause the major changes in housing prices.

Predictive analytics is an effective way to deal with uncertainties in process modelling and improve decision-making for housing price prediction. The focus of this paper is two-fold; the authors first apply regression analysis to investigate how well the housing independent variables contribute to the housing price prediction. Two data sets are used for this study, namely, Ames Housing dataset and Melbourne Housing dataset. For both the data sets, random forest regression performs the best by achieving an average R² of 86% for the Ames dataset and 85% for the Melbourne dataset, respectively. Second, multinomial logistic regression is adopted to investigate and identify the factor determinants of housing sales price. For the Ames dataset, the authors find that the top three most significant factor variables to determine the housing price is the general living area, basement size and age of remodelling. As for the Melbourne dataset, properties having more rooms/bathrooms, larger land size and closer distance to central business district (CBD) are higher priced. This is followed by a comprehensive analysis on how these determinants contribute to the predictability of the selected regression model by using explainable SHAP values. These prominent factors can be used to determine the optimal price range of a property which are useful for decision-making for both buyers and sellers.

By using the combination of MLR and SHAP analysis, it is noticeable that general living area, basement size and age of remodelling are the top three most important variables in determining the house’s price in the Ames dataset, while properties with more rooms/bathrooms, larger land area and closer proximity to the CBD or to the South of Melbourne are more expensive in the Melbourne dataset. These important factors can be used to estimate the best price range for a housing property for better decision-making.

A limitation of this study is that the distribution of the housing prices is highly skewed. Although it is normal that the properties’ price is normally cluttered at the lower side and only a few houses are highly price. As mentioned before, MLR can effectively help in evaluating the likelihood ratio of each variable towards these categories. However, housing price is originally continuous, and there is a need to convert the price to categorical type. Nonetheless, the most effective method to categorize the data is still questionable.

The key point of this paper is the use of explainable machine learning approach to identify the prominent factors of housing price determination, which could be used to determine the optimal price range of a property which are useful for decision-making for both the buyers and sellers.

This study investigates the relationship between foreign ownership, earnings quality and overinvestment in Japanese zombie firms.

The study makes use of data from Japanese firms listed on the first section of the Tokyo Stock Exchange from 2009 to 2019. The study employs logistic and multinomial logistic models to test whether the overinvestment behavior of zombie firms is mitigated by foreign shareholdings and earnings quality.

The results show that (1) zombie firms tend to overinvest; (2) an increase in foreign ownership mitigates the overinvestment of zombie firms and (3) the mitigation of zombie firms' overinvestment by foreign ownership is stronger with higher earnings quality.

This study extends the discussion of earnings quality and investment efficiency to the zombie firm setting. Previous studies in accounting suggest that high earnings quality enhances firms' investment efficiency. The findings suggest that both a change in ownership structure and high-quality accounting information are necessary to mitigate the inefficiency of zombie firms.

Omni-channel shopping affords consumers a variety of delivery options to receive products based on their preferred times and locations. By considering consumers' contributions (physical, social and attentive efforts) in co-creating delivery services, this study investigates their preferences for parcel delivery.

A scenario-based questionnaire survey is conducted for data collection in Singapore (n = 483). Furthermore, a multinomial logistic regression is performed to assess consumers' choice mode of delivery among five alternatives, that is attended home delivery, unattended home delivery, automated self-collection locker, attended pickup point and click-and-collect.

Compared to attended home delivery, consumers who choose the alternatives are found to be more willing to contribute physical effort but less interested in responding attentively to informational updates. Efforts required for social interactions discourage consumers from choosing attended deliveries, prompting unattended alternatives (e.g. home delivery and self-collection) as more attractive choices. Additionally, socio-demographic factors and product value also influence consumers' preferences.

This study contributes to the literature by integrating the theoretical concept of consumer logistics into omni-channel studies, providing a new approach to examining consumers' channel behaviour. With detailed profiling that links product value and consumers' socio-demographics to their choice mode of delivery, the authors create practical insight into the optimal design of omni-channel distribution systems that best harness consumers' voluntary contributions.

The purpose of this paper is to apply the framework Price (2008) and HSBC Money Laundering Risk Procedures 2016 for estimating the risk contribution of each individual customer in Vietnamese banking system using the information from the survey in South East region in Vietnam in general and Ho Chi Minh city in specific.

Based on the collected data from the survey, the Money Laundering Risk Score (MLRS) is calculated for each customer who is using the services and products of Vietnamese commercial banks by the enhanced measurement model of Christopher Price, the ordinary least squares and three variations of logistic regression model.

This paper proposes an appropriate estimation of the money laundering risk (MLR) for personal customer using the most significant factors that affects MLR and suggests practical recommendations for commercial banking system.

This paper suggests an intuitive method to estimate the contribution of each customer factor on their MLRS.

The higher respondent’s group of age lead to the higher MLR occurred in the financial market. Follow the works of Wolfsberg et al. (2006), Sathan and Mahendhiran (2007), Usman Kemal (2014) and Reganati and Oliva (2017), this paper also confirms negative relationship between MLR with the respondent’s group of salary and the academic level. This indicated that the lower amount of money the respondents earn and lower academic level they were, the higher degree of MLR.

Evaluating a portfolio of agricultural loans has become an important issue in recent years primarily due to a large number of loan defaults. The purpose of this paper is to investigate the factors influencing credit repayment behavior of farmers in Karnataka.

The study is based on secondary data of 590 farmers collected from a private bank in the state of Karnataka, India. Binary logistic regression and multinomial regression analysis was carried out to estimate the probability of non-payment of a loan.

The results of the regression confirm a significant relationship between non-repayment of agricultural credit and characteristics of borrowers such as the age, years of banking relationship, yield of the crop, distance to bank branch, size and tenure of the loan, farm size and leverage and efficiency ratio.

The factors predicted by the model do certainly help in improving the decision-making process in agricultural lending. A rigorous assessment of family responsibilities, farm size, credit-to-asset ratio, interest burden on the farmers and farm income is suggested to reduce the probability of doubtful assets.

The studies that predict default risk in agricultural loan are limited in India. This is one of the few studies that estimate the determinants of substandard and doubtful categories of credit in a private sector bank.