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The purpose of this study is to analyse cyberfraud in the South African banking industry using a multiple regression approach and develop a predictive model for the estimation and prediction of financial losses due to cyberfraud.

To mitigate the occurrence of cyberfraud, this study uses the multiple regression approach to correlate the relationship between financial loss and cyberfraud activities. The cyberfraud activities in South Africa are classified into three, namely, digital banking application, online and mobile banking fraud. Secondary data that captures the rate of cyberfraud occurrences within these three major categories with their resulting financial losses were used for the multiple regression analysis that was carried out in the Statistical Package for Social Science (SPSS, 2022 environment).

The results obtained indicate that the South African financial institutions still incur significant financial losses due to cyberfraud perpetration. The two main independent variables used to estimate the magnitude of financial loss in the South Africa’s banking industry are online (internet) banking fraud (X2) and mobile banking fraud (X3). Furthermore, a multiple regression model equation was developed for the prediction of financial loss as a function of the two independent variables (X2 and X3).

This study adds to the literature on cyberfraud mitigation. The findings may promote the combat against cyberfraud in the South Africa’s financial institutions. It may also assist South Africa’s financial institutions to predict the financial loss that financial institutions can incur over time. It is recommended that South Africa’s financial institutions pay attention to these two key variables and mitigate any associated risks as they are crucial in determining their profitability.

Existing literature indicated significant financial losses to cyberfraud perpetration without establishing any relationship between the magnitude of losses incurred and the prevalent forms of cyberfraud. Thus, the novelty of this study lies in the analysis of cyberfraud in the South African banking industry using a multiple regression approach to link financial losses to the perpetration of the prevalent forms of cyberfraud. It also develops a predictive model for the estimation and projection of financial losses.

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.

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.

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.

This paper aims to discuss the utilization of artificial neural networks (ANNs) and multiple regression method for estimating surface roughness in milling medium density fiberboard (MDF) material with a parallel robot.

In ANN modeling, performance parameters such as root mean square error, mean error percentage, mean square error and correlation coefficients (R²) for the experimental data were determined based on conjugate gradient back propagation, Levenberg–Marquardt (LM), resilient back propagation, scaled conjugate gradient and quasi-Newton back propagation feed forward back propagation training algorithm with logistic transfer function.

In the ANN architecture established for the surface roughness (Ra), three neurons [cutting speed (V), feed rate (f) and depth of cut (a)] were contained in the input layer, five neurons were included in its hidden layer and one neuron was contained in the output layer (3-5-1).Trials showed that LM learning algorithm was the best learning algorithm for the surface roughness. The ANN model obtained with the LM learning algorithm yielded estimation training values R² (97.5 per cent) and testing values R² (99 per cent). The R² for multiple regressions was obtained as 96.1 per cent.

The result of the surface roughness estimation model showed that the equation obtained from the multiple regressions with quadratic model had an acceptable estimation capacity. The ANN model showed a more dependable estimation when compared with the multiple regression models. Hereby, these models can be used to effectively control the milling process to reach a satisfactory surface quality.

The purpose of this paper is to explore the effects of human interaction versus interactions with technology in overall customer satisfaction with banking services, specifically, tellers versus Automated Teller Machines (ATMs) vs online transactions. All types of interactions are important in services, yet their level of importance is changing as the environment change.

Two studies were conducted through surveys with students who had a bank checking account; six item measures were used to examine human interaction, interaction with an ATM, interaction with an online banking service and overall satisfaction with the specific bank. Multiple regression analyses were conducted to determine the effects of the interactions on overall satisfaction.

The findings from the two studies show that while the human encounter was more important before online banking became so prevalent, the convenience of online banking has displaced the importance of human interaction. However, there were gender differences in that males, more than females, remain influenced by teller transactions.

The study utilizes student samples, which could be biased. However, students are also users of banking services so they represent a traditional target market for financial service firms.

The results are informative for managers when planning and implementing new online services in the financial industry.

This paper draws together research on interpersonal interactions and technological interactions to examine the effects on overall satisfaction. Given the proliferation of technological advances, understanding how these technologies impact customer satisfaction is vital.

The purpose of this study is to develop a multiple regression model that can be used to predict the number of patrons that seek assistance at the reference desk of the library. This will facilitate the scheduling of the reference desk librarians.

A multiple regression model is developed, where the dependent variable in the regression model is the number of patrons that seek assistance at the reference desk of the library and the predictor variables (independent variables) are the door count and the semester under study. Data were gathered at the University of Tennessee at Chattanooga for an entire year. Using these data, a multiple regression model was formulated and tested for significance. Then, the model was used for forecasting the required staff at the reference desk for a period for which data was available.

The regression model, with the addition of daily variations, proved to be a good predictor of the number of patrons seeking assistance. Hence, the staffing need was estimated. Overall, the regression model with the added daily index proved to be a very good predictor.

It is crucial to be able to predict the number of clients at the reference desk that seek assistance per day. With the use of a sample of data, it was possible to predict the number of clients seeking assistance at the reference desk.

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.

Destination image researchers have investigated and found that several factors have influences on destination image. Some of these factors are related to perceivers (e.g. sociodemographics and behavior); some are related to the destination of concern (e.g. promotional materials); some are independent of both perceivers and the destination (e.g. news coverage and movies); and some are methodological choices made by researchers while measuring destination image (e.g. qualitative methods vs. quantitative methods). However, there has been a lack of studies measuring the relative magnitude of the influence posed by different factors. The purpose of this study is to investigate the relative impact of factors influencing destination image, by analyzing the large scale and longitudinal dataset of the Michigan Regional Travel Market Survey, applying multiple regression analysis. The nature of this secondary dataset allowed the inclusion of a set of sociodemographic and travel behavior variables as well as one variable related to the survey methodology. Only few of those selected variables (i.e. age, race and visitation) were found to be robust. Implications and future research suggestions are provided.