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The objective of this article is to determine the usage and application of logistic regression analysis in the marketing literature by comparing the market positioning of prominent marketing journals.

In order to identify the logistic regression applications, those journals having “marketing” term in their titles and indexed by the social citation index (SSCI) were included. As a result, the target population consisted of 12 journals fulfilling the criteria set. However, only eight of these that were accessible by the researchers were included in the study.

The classification of marketing articles from the chosen prominent marketing journals were made by journal title, article topic, target population, data collection method, and study location has mapped the position of logistic regression in the marketing literature.

The sample journal coverage was limited with 12 marketing journals indexed in SSCI. In some of the journals utilized, the accessibility was limited by the electronic database year coverage. Due to this limitation, the researchers could not reach the exact number of articles using logistic regression.

The results of this study could highlight what is researched with logistic regression about marketing problems and may shed light on solving different problems on marketing topics for the future.

Citizen trust in police is important in terms of citizen consent to government policies and of police achieving their organizational goals. In the previous study, improvements in police policy, organizational operation and policing activities were developed to clarify which factors influence trust in police and how trust can be improved. This research raises the question, would changes in trust in police have an impact on trust in government? In this paper, this research question is discussed theoretically and the causal relationship analyzed empirically by applying OLS, ordered logistic, 2SLS and logistic regressions.

The basic analysis methods are to apply the OLS and the ordered logistic regression. OLS regression analysis is an analytical method that minimizes an error range of a regression line. The assumptions for OLS are: linearity, independence, equilibrium, extrapolation and multicollinearity issues. These problems were statistically verified and analyzed, in order to confirm the robustness of the analysis results by comparing the results of the ordered logistic regression because of the sequence characteristic of the dependent variable. The data to be used in this study is the Asia Barometer Survey in 2013.

Trust in police and citizen perception of safety are analyzed as important factors to increase trust in the government. The effects of trust in police are more significant than the effects of control variables, and the direction and strength of the results are stable. The effect of trust in police on trust in government is strengthened by the perception of safety (IV). In addition, OLS, ordered logistic regression analysis, which analyzed trust in central government and local government, and logistic regression analysis categorized by trust and distrust show the stability.

This paper has implications in terms of theoretical and empirical analysis of the relationship between trust in police and trust in government. In addition, the impact of perception of safety on trust in police can be provided to police officers, policymakers and governors who are seeking to increase trust in government. This paper is also meaningful in that it is the microscopic research based on the citizens' survey. One of the limitations of macroscopic research is that it does not consider the individual perceptions of citizens.

The results of this paper can confirm the relationship of the virtuous cycle, which is perception of safety – trust in police – trust in government. The police will need to provide security services to improve citizens' perception of safety and make great efforts to create safer communities and society. Trust in police formed through this process can be an important component of trust in government. By making citizens feel safer and achieving trust in police, ultimately, trust in government will be improved.

The police perform one of the essential roles of government and are one of the major components of trust in government, but the police sector has been neglected compared to the roles of the economic and political sectors. These influences of macro factors are too abstract to allow specific policy directions to be suggested. If we consider trust in police, and factors that can improve trust in government, we can suggest practical policy alternatives.

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.

To present a decision support system (DSS) enabling the analysis of the cost‐effectiveness of direct‐shipping long‐haul road transport policies, including full truck load (FTL) and less than truck load (LTL) modes, and to select the optimal carrier.

Analytical estimation of transportation costs is provided in a framework including an interactive computer procedure and a dedicated database structure capable of characterizing the logistics system.

Main criticalities of manual logistic planning are: sub‐optimal selection of carrier and excessive use of LTL transport, while the optimal FTL vs LTL trade‐off is not fully explored in practice.

This is an analysis tool of user‐defined scenarios and does not provide the automatic synthesis of shipments planning. Admittedly, this model does not attempt to optimize the shipping strategy, but to quantitatively assess the effects of the adopted decisions.

Alternative shipping policies can be compared to perform what‐if analyses and explore the outcome of alternative decisions (FTL vs LTL shipping modes) even in terms of transportation expenditures. Allows rapid selection of the optimal motor carrier and assesses the extra cost due to a sub‐optimal choice. Gives the experienced manager a framework for critical assessment of shipping decisions, suggesting improvement areas for cost reduction.

With respect to other software tools for carrier selection provides explicit analysis of extra costs incurred by manual planning, thus becoming a strategic tool for logistic decision making. Furthermore, enables managerial insights to be gained and makes manual planning more effective.

The objective of this paper is to develop a model that can predict financial distress amongst public listed companies in Malaysia using the logistic regression analysis.

The logistic regression analysis used in this paper is geared towards developing a model that can predict financial distress amongst public listed companies in Malaysia.

The results prove that five financial ratios have been found to be significant and useful for corporate failure prediction in Malaysia. The overall predictive accuracy is 91.5 percent and this demonstrates that the logistic regression analysis used is a reliable technique for financial distress prediction. In addition, the predictive accuracy of the model in this paper is higher than that of previous studies, which utilised discriminant analysis rather than the method adopted in this research.

The economic crisis mostly began to affect Malaysia's economic standing in July 1997 causing many companies to fall into financial distress, as they were unable to cope with the unexpected downturn. A financial distress prediction model is therefore required to act as a predictor of Malaysian public listed companies' well‐being prior to a financial crisis and to gauge the warning signals of the onset of a downturn in order to strategize their survival techniques during this phase. This study focuses on public listed companies in Malaysia, thus the model adopted is tailored to suit the given context.

The cost of supporting multi‐million‐pound military systems and equipment during the “in‐service” phase is now often well in excess of two‐thirds of the total cost of ownership. The management approach used to predict, budget, validate and control overall support costs is known as integrated logistic support (ILS). Explains the military approach to “designing for support”, how this is integrated into the operational requirement, and the benefits which accrue. This leads to an analytical process known as logistic support analysis (LSA), which is integrated into a dynamic “supportability database” known as the logistic support analysis records (LSAR). It then becomes the definitive repository for information on all support activities, including provisioning and technical documentation, and continues “live” in the in‐service phase “supporting the design”. Under a US Department of Defense initiative, using electronic data interchange (EDI), known as CALS ‐ which was computer‐aided acquisition and logistics support, and has now been redefined as “continuous acquisition and life‐cycle support” ‐ the UK Ministry of Defence is currently fielding an Interim Defence Standard 00‐60, which it is promoting as a contender for a NATO and ISO Standard on ILS. The final edition will be a standard for contracting for ILS, provisioning, technical documentation and CALS using EDI.

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.

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.

Purpose – The purpose of this chapter is to examine methodological trends in emerging market strategy research and to provide a comprehensive review of methods of assessing group variation in comparative studies.

Methodology/approach – This comprises a systematic review of the methodology of emerging market research over the past 10 years, followed by methodological best practices for comparative studies involving emerging and mature markets, with exemplars from the past research.

Findings – Despite previous calls for more comparative studies in emerging market research, most of the literature is reporting on single-country studies. There is some confusion in terminology and the methods used in this area of strategy research. Increased attention to the “East” calls for a reevaluation of methods utilized in comparative studies. The methods described in this chapter present best practices for comparative research.

Social implications – More comparative studies would substantially expand our understanding of the differences between the emerging and developed markets, and the potential impact of emerging markets on global economy. Rigorous research methods extend validity and generalizability of the studies.

Originality/value – This chapter is the first study to date to analyze the methodological trends of the entire field of emerging market research over the span of 10 years and to provide systematic methodological recommendations tailored to analyzing variation in comparative studies.

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.