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Online shopping is expected to continue without slowing down because of the advantages that it presents to consumers in the digitalising world. This study aims to determine the factors regarding the social and environmental indicators and the demographical and economical factors that affect the online shopping tendencies of households in Turkey. The results of this research can be used to review the online shopping strategies by the decision-makers.

In this study, the cross-sectional data acquired from the Household Budget Research survey, which has been conducted by the Turkish Statistical Institute between 2015 and 2018, is used. In this data set consisting of 11,491 in 2015, 12,096 in 2016, 12,166 in 2017 and 11,828 in 2018, a total of 47,581 data from the households that are 15-year-old and older are used. To determine the factors affecting the online shopping behaviour of households, binary logistic regression and binary probit regression analyses are applied. As a result of these analyses, it has been decided that the most suitable model is the binary probit regression model.

According to the analysis results, it has been detected that factors such as educational status, age, marital status, employment status, income, life assurance ownership, credit card usage, automobile ownership and the year of the survey affect the online shopping behaviour of households.

In this study, factors affecting the tendency to do online shopping, which has gained big importance particularly with the COVID-19 pandemic, are determined. In Turkey, households’ tendency to do online shopping is affected by the demographical and economical factors and by the factors related to the social and environmental indicators. Determination of the effects of these factors has been a guide for the decision-makers and policymakers in explaining the tendency to shop online and creating a competitive advantage.

In this study, data consisting of a total of 47,581 observations, which has acquired from the Household Budget Research survey conducted by the Turkish Statistical Institute between 2015 and 2018, are used by applying a weighting process, and no study that is as comprehensive and inclusive as this study has been found in the literature, e-commerce that has become prevalent with the help of technological progress and changing habits in the past years is continuing to become prevalent more increasingly particularly after COVID-19 pandemic. Therefore, the value of this study underlies its contribution to e-commerce awareness.

Artificial neural network (ANN) models, part of the discipline of machine learning and artificial intelligence, are becoming more popular in the marketing literature and in marketing practice. This paper aims to provide a series of tests between ANN models and competing predictive models.

A total of 46 pairs of models were evaluated in an objective model-building environment. Either logistic regression or multiple regression models were developed and then were compared to ANN models using the same set of input variables. Three sets of B2B data were used to test the models. Emphasis also was placed on evaluating small samples.

ANN models tend to generate model predictions that are more accurate or the same as logistic regression models. However, when ANN models are compared to multiple regression models, the results are mixed. For small sample sizes, the modeling results are the same as for larger samples.

Like all marketing research, this application is limited by the methods and the data used to conduct the research. The findings strongly suggest that, because of their predictive accuracy, ANN models will have an important role in the future of B2B marketing research and model-building applications.

ANN models should be carefully considered for potential use in marketing research and model-building applications by B2B academics and practitioners alike.

The research contributes to the B2B marketing literature by providing a more rigorous test on ANN models using B2B data than has been conducted before.

The purpose of this paper is to discuss and assess the structural characteristics (conceptual utility) of the most popular classification and predictive techniques employed in customer relationship management and customer scoring and to evaluate their classification and predictive precision.

A sample of customers' credit rating and socio‐demographic profiles are employed to evaluate the analytic and classification properties of discriminant analysis, binary logistic regression, artificial neural networks, C5 algorithm, and regression trees employing Chi‐squared Automatic Interaction Detector (CHAID).

With regards to interpretability and the conceptual utility of the parameters generated by the five techniques, logistic regression provides easily interpretable parameters through its logit. The logits can be interpreted in the same way as regression slopes. In addition, the logits can be converted to odds providing a common sense evaluation of the relative importance of each independent variable. Finally, the technique provides robust statistical tests to evaluate the model parameters. Finally, both CHAID and the C5 algorithm provide visual tools (regression tree) and semantic rules (rule set for classification) to facilitate the interpretation of the model parameters. These can be highly desirable properties when the researcher attempts to explain the conceptual and operational foundations of the model.

Most treatments of complex classification procedures have been undertaken idiosyncratically, that is, evaluating only one technique. This paper evaluates and compares the conceptual utility and predictive precision of five different classification techniques on a moderate sample size and provides clear guidelines in technique selection when undertaking customer scoring and classification.

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.

To investigate the feasibility of using artificial neural networks for conceptual design of bracings systems for tall steel buildings.

Database of 234 design examples has been developed using commercially available detailed design software. These examples represent building up to 20 storeys. Feed forward back‐propagation neural network is trained on these examples. The results obtained from the artificial neural network are evaluated by re‐substitution, hold‐out and ten‐fold cross‐validation techniques.

Results indicate that artificial neural network would give a performance of 97.91 percent (ten‐fold cross‐validation). The performance of this system is benchmarked by developing a binary logistic regression model from the same data. Performance of the two models has been compared using McNemar's test and receiver operation characteristics curves. Artificial neural network shows a better performance. The difference is found to be statically significant.

The developed model is applicable only to steel building up to 20 storeys. The feasibility of using artificial neural networks for conceptual design of bracings systems for tall steel buildings more than 20 storeys has not been investigated.

Implementation of the broad methodology outlined for the use of neural networks can be accomplished by conducting short training courses. This will provide personnel with flexibility in addressing buildings‐specifics bracing conditions and limitations.

In tall building design a lot of progress has been made in the development of software tools for numerical intensive tasks of analysis, design and optimization, however, professional software tools are not available to help the designer to choose an optimum building configuration at the conceptual design stage. The presented research provides a methodology to investigate the feasibility of using artificial neural networks for conceptual design of bracings systems for tall buildings. It is found that this approach for the selection of bracings in tall buildings is a better and cost effective option compared with database generated on the basis of expert opinion. It also correctly classifies and recommends the type of trussed bracing system.

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.

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.

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 study aims to apply a systematic statistical approach, including several plot indexes, to diagnose the goodness of fit of a logistic regression model, and then to detect the outliers and influential observations of the data from experimental data.

The proposed statistical approach is applied to analyze some experimental data on internal solitary wave propagation.

A suitable logistic regression model in which the relationship between the response variable and the explanatory variables is found. The problem of multicollinearity is tested. It was found that certain observations would not have the problem of multicollinearity. The P‐values for both the Pearson and deviance χ² tests are greater than 0.05. However, the Pearson χ² value is larger than the degrees of freedom. This finding indicates that although this model fits the data, it has a slight overdispersion. After three outliers and influential observations (cases 11, 27, and 49) are removed from the data, and the remaining observations are refitted the goodness‐of‐fit of the revised model to the data is improved.

A comparison of the four predictive powers: R², max‐rescaled R², the Somers' D, and the concordance index c, shows that the revised model has better predictive abilities than the original model.

The goodness‐of‐fit and prediction ability of the revised logistic regression model are more appropriate than those of the original model.

The purpose of this study is to estimate the determinants of household food insecurity in the Gauteng City-Region, South Africa. This is motivated by the fact that food insecurity remains a key challenge at the household level in South Africa. Furthermore, the Gauteng Province has been rapidly urbanising due to a migrant influx, both locally and internationally. The findings will assist the country in achieving its mandate on the local economic development policy, Agenda 2063 and the Sustainable Development Goals 1 and 2.

The study adopted a quantitative cross-section design, utilising the binary logistic regression technique, drawing on the Gauteng City-Region Observatory Quality of Life 2020/2021 data, consisting of 13,616 observations, randomly drawn from nine municipalities in Gauteng City-Region.

The main findings of the study highlight unemployment, health status, education, household size, indigency and income as the main determinants of food insecurity in Gauteng City-Region. Policies towards sustainable urban agriculture, improving access to education, increasing employment and income, and health for all can help improve the food insecurity status of households in the Gauteng City-Region.

Further studies would require an in-depth assessment of household coping mechanisms, as well as the influence of household income (notably government social grants) and access to credit on household food security status, to better understand the dynamics of food security in the Gauteng City-Region.

Determinants of food insecurity should be considered when developing and implementing policies to reduce food insecurity in urban municipalities.

The study is of interest as it interdicts food insecurity issues, which have an effect on socio-economic well-being.

The study adds value by providing evidence on the determinants of food insecurity in an urban setting in a developing country. Gauteng is the richest of all provinces in South Africa and is also at the receiving end of internal and international migration. Factors affecting food insecurity have changed in the nine cities. This compromises nutrition safety and calls for targeted policy interventions.