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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.

Reviews the three sales forecasting models most commonly applied in retail site evaluation: multiple regression analysis; multiple discriminant analysis; gravity models. Discusses the important issues involved in the development and application of these methods – including their respective strengths and weaknesses. Key points are that there is no “black box” method and that in the real world of retailing the methods reduce, but do not remove, the need for practical, subjective analysis.

The purpose of this paper is to estimate long-term currency exchange rate and also identify the key factors for decision makers in the currency exchange market. The study is expected to aid decision makers to take positions in the dynamic Forex market.

This study is based on quantitative and fundamental analysis of statistically oriented regression models. The trend of quarterly exchange rates is investigated using 110 variables including economic elements, interest rate and other currencies. This research is based on the same information that banks’ dealers use for the analysis. Ordinary least squares linear regression also known as “least squared errors regression” was used to estimate the value of the dependent variable.

The study concludes that “only Australian economic data” or “only the US economic data” cannot fully reflect the trend of AUD/USD. EUR influences AUD relatively larger than the other main market currencies. Six-month Australian interest rate itself affects AUD/USD trend much more than the six-month interest difference between AUD and USD.

The results indicate that the economic autoregressive moving average model can be used to predict future exchange rate using primary factors identified and not from the generic market or economic view. This helps adjust to the general, common (and possibly wrong) views when making a buy or sell decision.

This is one of the first studies in the context using the information of bank dealers for AUD/USD. This study is highly relevant in the current context, given the significant growth in Forex trade.

Over the last twenty years the financial markets of Hong Kong have developed rapidly. Although empirical studies on the behaviour of the Hong Kong stock market abound, much controversy over the efficiency of the market still exists. Some recent studies have shown that the market is inefficient in the “weak” form. Therefore one can justify employing the “fundamental approach” for stock price forecasting This study explores the use of multiple regression techniques to forecast stock price index. The results show that unemployment rate, trade balance, consumer price index and money supply are all significant in leading the stock price index. However, the regression models are still short of sufficient power to effectively predict change of direction of the index. Further enhancement of the models is needed if they are to be of real use for investment purposes.

Aims to commend SEM (structural equation modeling) that excels beyond multiple regression, which is a popular statistical technique to test the relationships of independent and dependent variables, in expanding the explanatory ability and statistical efficiency for parsimonious model testing with a single comprehensive method. SEM is employed to find the real “best fitting” model. This article also presents an incremental approach to SEM, which is a procedural design and sounds workable for testing simple models and presents an example to test a parsimonious model of MBA knowledge and skills transfer using SEM and multiple regression. The results indicate that only one significant relationship can be justified by multiple regression. SEM, on the other hand, has helped to develop new relationships based on the modification indexes, which are also theoretically accepted. Finally, three relationships are shown to be significant and the “best fitting” structural model has been established.

The purpose of the work is to model disproportions in development of regional economy of Russia and to determine perspectives and recommendations for overcoming them and achieving the balance of the economy. The applied methods are based on Popkova's methodology of calculation of “underdevelopment whirlpools,” which allows conducting dynamic modeling of disproportions in development of regional economy. The research is performed in three consecutive stages. At the first stage, the dynamic model of development of the Russia's regional economy is compiled with the help of the methodology of “underdevelopment whirlpools” in federal districts of the Russian Federation based on GDP per capita. At the second stage, the key factors of emergence of disproportions in development of the Russia's regional economy are determined and models of multiple regression of development of the Russia's regional economy are compiled. At the third stage, target parameters of the determined factors are set for reducing the “underdevelopment whirlpools” in the Russia's regional economy by automatized solution of the optimization task with application of the simplex method and recommendations for overcoming the disproportions in development of the Russia's regional economy are compiled. As a result, it is concluded that regional economy of Russia is not well-balanced, as it has deep structural disproportions. These disproportions are caused by insufficient attention to peculiarities of regional economic systems during development and implementation of regional strategies of state management of economy. For more precise accounting of the influence of the key factors of appearance of disproportions and highly-effective management of them for overcoming the “underdevelopment whirlpools,” the algorithm of overcoming the disproportions in development of the Russia's regional economy is developed by the authors, which envisages various managerial measures depending on peculiarities of each Russian region.

When researchers find an association between two variables, it is useful to evaluate the role of other constructs in this association. While assessing these mediation effects, it is important to determine if results are equal for different groups. It is possible that the strength of a mediation effect may differ for males and females, for example – such an effect is known as moderated mediation. The paper aims to discuss these issues.

Participants were 2,532 adolescents from diverse ethnic/racial backgrounds and equally distributed across gender. The goal of this study was to investigate parental respect as a potential mediator of the relationship between gender and delinquency and mental health, and to determine whether observed mediation is moderated by gender.

Parental respect mediated the association between gender and both delinquency and mental health. Specifically, parental respect was a protective factor against delinquency and mental health problems for both females and males.

Demonstrated the process of estimating models in Lavaan, using two approaches (i.e. single group regression and multiple group regression model), and including covariates in both models.

The authors demonstrate the process of estimating these models in Lavaan, using two approaches, a single group regression model and a multiple group model, and the authors demonstrate how to include covariates in these models.

The purpose of this paper is to address and overcome the problem that a single prediction model cannot accurately fit a data sequence with large fluctuations.

Initially, the grey linear regression combination model was put forward. The Discrete Grey Model (DGM)(1,1) model and the multiple linear regression model were then combined using the entropy weight method. The grain yield from 2010 to 2015 was forecasted using DGM(1,1), a multiple linear regression model, the combined model and a GM(1,N) model. The predicted values were then compared against the actual values.

The results reveal that the combination model used in this paper offers greater simulation precision. The combination model can be applied to the series with fluctuations and the weights of influencing factors in the model can be objectively evaluated. The simulation accuracy of GM(1,N) model fluctuates greatly in this prediction.

The combined model adopted in this paper can be applied to grain forecasting to improve the accuracy of grain prediction. This is important as data on grain yield are typically characterised by large fluctuation and some information is often missed.

This paper puts the grey linear regression combination model which combines the DGM(1,1) model and the multiple linear regression model using the entropy weight method to determine the results weighting of the two models. It is intended that prediction accuracy can be improved through the combination of models used within this paper.

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.

This study aims to examine the relationship between an ownership structure with multiple large shareholders and corporate social responsibility (CSR) with regard to Chinese-listed companies.

Multiple regression analysis was used on 4,940 samples of 884 listed companies in China for the period 2009–2017, to empirically test the influence of an ownership structure on enterprises’ fulfillment of social responsibility. Moreover, the propensity score matching–difference in differences and Heckman two-stage approaches were used for the robustness of the regression results.

The results show that ownership structures with multiple large shareholders can promote social responsibility. The check-and-balance ability of non-controlling large shareholders, corporate information transparency and corporate system environment moderate the relationship between multiple large shareholders and CSR engagement.

This paper complements prior studies on the ownership structure of multiple large shareholders. The findings enrich the literature on corporate governance and CSR. The results also reveal information about the situational factors, helping identify the mechanism through which the ownership structure of multiple large shareholders affects CSR.