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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.

The housing market in Kenya continues to experience an excessive imbalance between supply and demand. This imbalance renders the housing market volatile, and stakeholders lose repeatedly. The purpose of the study was to forecast housing prices (HPs) in Kenya using simple and complex regression models to assess the best model for projecting the HPs in Kenya.

The study used time series data from 1975 to 2020 of the selected macroeconomic factors sourced from Kenya National Bureau of Statistics, Central Bank of Kenya and Hass Consult Limited. Linear regression, multiple regression, autoregressive integrated moving average (ARIMA) and autoregressive distributed lag (ARDL) models regression techniques were used to model HPs.

The study concludes that the performance of the housing market is very sensitive to changes in the economic indicators, and therefore, the key players in the housing market should consider the performance of the economy during the project feasibility studies and appraisals. From the results, it can be deduced that complex models outperform simple models in forecasting HPs in Kenya. The vector autoregressive (VAR) model performs the best in forecasting HPs considering its lowest root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and bias proportion coefficient. ARIMA models perform dismally in forecasting HPs, and therefore, we conclude that HP is not a self-projecting variable.

A model for projecting HPs could be a game changer if applied during the project appraisal stage by the developers and project managers. The study thoroughly compared the various regression models to ascertain the best model for forecasting the prices and revealed that complex models perform better than simple models in forecasting HPs. The study recommends a VAR model in forecasting HPs considering its lowest RMSE, MAE, MAPE and bias proportion coefficient compared to other models. The model, if used in collaboration with the already existing hedonic models, will ensure that the investments in the housing markets are well-informed, and hence, a reduction in economic losses arising from poor market forecasting techniques. However, these study findings are only applicable to the commercial housing market i.e. houses for sale and rent.

While more research has been done on HP projections, this study was based on a comparison of simple and complex regression models of projecting HPs. A total of five models were compared in the study: the simple regression model, multiple regression model, ARIMA model, ARDL model and VAR model. The findings reveal that complex models outperform simple models in projecting HPs. Nonetheless, the study also used nine macroeconomic indicators in the model-building process. Granger causality test reveals that only household income (HHI), gross domestic product, interest rate, exchange rates (EXCR) and private capital inflows have a significant effect on the changes in HPs. Nonetheless, the study adds two little-known indicators in the projection of HPs, which are the EXCR and HHI.

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.

Graduate students often come to statistics courses with varying levels of motivation and previous academic preparation. Within the statistics education literature, there is a growing consensus to guide instructors who want to help their students gain the requisite statistical knowledge so they can conduct their own research and report their results accurately. Recommendations from the literature include using real data, showing worked-out example problems, and providing immediate feedback to allow students to reflect on the correct and incorrect decisions they made in their analyses. This chapter describes the use of expert decision models (EDMs) in two graduate-level statistics courses – multiple regression and structural equation modeling. Decision-Based learning is an effective way to support graduate students’ developing thinking about statistics. In both courses, the students encounter the EDM through a series of assignments which guides students through the process of specifying a statistical model, running that model in Statistical Package for the Social Sciences or Mplus, and interpreting the results. These assignments use real datasets whenever possible and are designed to expose students to various issues they may experience in their research (missing data, violations of assumptions, etc.) and to illustrate how an expert would have adapted to those issues to complete the analysis. The EDM, with its just-in-time, just-enough instruction, helps students navigate these obstacles through guided practice and allows them to develop the conditional knowledge to handle issues that will arise as they carry out their own research.

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.

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.

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.

This study aims to investigate the valuation accuracy of Malaysian initial public offerings (IPOs) by using price-multiple methods.

Cross-sectional data including 467 IPOs listed on the Malaysian stock exchange were used for the period of 2000–2017. This study used univariate ordinary least square (OLS) regression to analyse the relationship between IPOs’ price-multiples and comparable firms’ price-multiples. The test of valuation accuracy was conducted via computing valuation errors by segregating the sample into two groups: fixed-price IPOs and book-built IPOs. Furthermore, multiple OLS regression was used to examine the influence of IPO valuation on underpricing.

The findings of the results suggested that IPOs price-to-earnings (P/E), price-to-book (P/B) and price-to-sales (P/S) multiples were positively related to the median P/E, P/B and P/S multiples of five comparable firms matched by industry and revenues. The P/S multiple was shown to be the most significant valuation method, specifically in book-built IPOs. The findings indicated that those firms that had a lower valuation in comparison to the comparable firms were inclined to underprice their IPOs to allure investors to subscribe IPOs. In addition, book-built IPOs that had fair valuations were inclined to generate higher initial returns for investors.

The findings of this study observed implications for underwriters in avoiding the mis-valuation issue by considering the book-building mechanism.

This study attempted to explore the suitability of the valuation method to value IPOs in Malaysia.

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.