Search results
1 – 10 of over 53000Kun-Huang Huarng and Tiffany Hui-Kuang Yu
The use of linear regression analysis is common in the social sciences. The purpose of this paper is to show the advantage of a qualitative research method, namely, structured…
Abstract
Purpose
The use of linear regression analysis is common in the social sciences. The purpose of this paper is to show the advantage of a qualitative research method, namely, structured qualitative analysis (SQA), over the linear regression method by using different characteristics of data.
Design/methodology/approach
Data were gathered from a study of online consumer behavior in Taiwan. The authors changed the content of the data to have different sets of data. These data sets were used to demonstrate how SQA and linear regression works individually, and to contrast the empirical analyses and empirical results from linear regression and SQA.
Findings
The linear regression method uses one equation to model different characteristics of data. When facing a data set containing a big and a small size of different characteristics, linear regression tends to provide an equation by modeling the characteristics of the big size data and subsuming those of the small size. When facing a data set containing similar sizes of data with different characteristics, linear regression tends to provide an equation by averaging these data. The major concern is that the one equation may not be able to reflect the data of various characteristics (different values of independent variables) that result in the same outcome (the same value of dependent variable). In contrast, SQA can identify various variable combinations (multiple relationships) leading to the same outcome. SQA provided multiple relationships to represent different sizes of data with different characteristics so it created consistent empirical results.
Research limitations/implications
Two research methods work differently. The popular linear regression tends to use one equation to model different sizes and characteristics of data. The single equation may not be able to cover different behaviors but may lead to the same outcome. Instead, SQA provides multiple relationships for different sizes of data with different characteristics. The analyses are more consistent and the results are more appropriate. The academics may re-think the existing literature using linear regression. It would be interesting to see if there are new findings for similar problems by using SQA. The practitioners have a new method to model real world problems and to understand different possible combinations of variables leading to the same outcome. Even the relationship obtained from a small data set may be very valuable to practitioners.
Originality/value
This paper compared online consumer behavior by using two research methods to analyze different data sets. The paper offered the manipulation of real data sets to create different data sizes of different characteristics. The variations in empirical results from both methods due to the various data sets facilitate the comparison of both methods. Hence, this paper can serve as a complement to the existing literature, focusing on the justification of research methods and on limitations of linear regression.
Details
Keywords
Vivekanand Venkataraman, Syed Usmanulla, Appaiah Sonnappa, Pratiksha Sadashiv, Suhaib Soofi Mohammed and Sundaresh S. Narayanan
The purpose of this paper is to identify significant factors of environmental variables and pollutants that have an effect on PM2.5 through wavelet and regression analysis.
Abstract
Purpose
The purpose of this paper is to identify significant factors of environmental variables and pollutants that have an effect on PM2.5 through wavelet and regression analysis.
Design/methodology/approach
In order to provide stable data set for regression analysis, multiresolution analysis using wavelets is conducted. For the sampled data, multicollinearity among the independent variables is removed by using principal component analysis and multiple linear regression analysis is conducted using PM2.5 as a dependent variable.
Findings
It is found that few pollutants such as NO2, NOx, SO2, benzene and environmental factors such as ambient temperature, solar radiation and wind direction affect PM2.5. The regression model developed has high R2 value of 91.9 percent, and the residues are stationary and not correlated indicating a sound model.
Research limitations/implications
The research provides a framework for extracting stationary data and other important features such as change points in mean and variance, using the sample data for regression analysis. The work needs to be extended across all areas in India and for various other stationary data sets there can be different factors affecting PM2.5.
Practical implications
Control measures such as control charts can be implemented for significant factors.
Social implications
Rules and regulations can be made more stringent on the factors.
Originality/value
The originality of this paper lies in the integration of wavelets with regression analysis for air pollution data.
Details
Keywords
Reviews the three sales forecasting models most commonly applied inretail site evaluation: multiple regression analysis; multiplediscriminant analysis; gravity models. Discusses…
Abstract
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.
Details
Keywords
Michael D. Hausfeld, Gordon C. Rausser, Gareth J. Macartney, Michael P. Lehmann and Sathya S. Gosselin
In class action antitrust litigation, the standards for acceptable economic analysis at class certification have continued to evolve. The most recent event in this evolution is…
Abstract
In class action antitrust litigation, the standards for acceptable economic analysis at class certification have continued to evolve. The most recent event in this evolution is the United States Supreme Court’s decision in Comcast Corp. v. Behrend, 133 S. Ct. 1435 (2013). The evolution of pre-Comcast law on this topic is presented, the Comcast decision is thoroughly assessed, as are the standards for developing reliable economic analysis. This article explains how economic evidence of both antitrust liability and damages ought to be developed in light of the teachings of Comcast, and how liability evidence can be used by economists to support a finding of common impact for certification purposes. In addition, the article addresses how statistical techniques such as averaging, price-dispersion analysis, and multiple regressions have and should be employed to establish common proof of damages.
Details
Keywords
Constructing and evaluating behavioral science models is a complex process. Decisions must be made about which variables to include, which variables are related to each other, the…
Abstract
Constructing and evaluating behavioral science models is a complex process. Decisions must be made about which variables to include, which variables are related to each other, the functional forms of the relationships, and so on. The last 10 years have seen a substantial extension of the range of statistical tools available for use in the construction process. The progress in tool development has been accompanied by the publication of handbooks that introduce the methods in general terms (Arminger et al., 1995; Tinsley & Brown, 2000a). Each chapter in these handbooks cites a wide range of books and articles on specific analysis topics.
Eugene F. Stone-Romero and Patrick J. Rosopa
Mediating effects are often tested using hierarchical multiple regression (HMR) procedures. Typical of the HMR-based strategies is the very frequently cited and widely used…
Abstract
Mediating effects are often tested using hierarchical multiple regression (HMR) procedures. Typical of the HMR-based strategies is the very frequently cited and widely used procedure described by Baron and Kenny (1986). Unfortunately, there are several important problems with it. More specifically, as we demonstrate below, it: (a) is of virtually no value for buttressing claims of mediating effects for data from non-experimental research; (b) produces erroneous inferences about the existence of mediating effects for misspecified mediation models; and (c) is incapable of providing credible evidence of such effects in a large proportion of cases, even for properly specified mediation models. We detail a number of important implications of our analyses.
Oluwatoyin Esther Akinbowale, Polly Mashigo and Mulatu Fekadu Zerihun
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…
Abstract
Purpose
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.
Design/methodology/approach
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).
Findings
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).
Practical implications
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.
Originality/value
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.
Details
Keywords
The introductory chapter includes how to design-in good practices in theory, data collection procedures, analysis, and interpretations to avoid these bad practices. Given that bad…
Abstract
The introductory chapter includes how to design-in good practices in theory, data collection procedures, analysis, and interpretations to avoid these bad practices. Given that bad practices in research are ingrained in the career training of scholars in sub-disciplines of business/management (e.g., through reading articles exhibiting bad practices usually without discussions of the severe weaknesses in these studies and by research courses stressing the use of regression analysis and structural equation modeling), this editorial is likely to have little impact. However, scholars and executives supporting good practices should not lose hope. The relevant literature includes a few brilliant contributions that can serve as beacons for eliminating the current pervasive bad practices and for performing highly competent research.
Details
Keywords
Enrico Laghi, Michele Di Marcantonio, Valentina Cillo and Niccolo Paoloni
This study aims to validate a direct method to measure relational capital through the estimation of corporate brands. Considering the influence of relational capital management in…
Abstract
Purpose
This study aims to validate a direct method to measure relational capital through the estimation of corporate brands. Considering the influence of relational capital management in leading performance and brand development, we consider brand value as a proxy for relational capital. The main research goal is to extend the previous literature on intellectual capital, financial performance and brand management by elaborating and testing an original approach for valuating corporate brands using regression analysis on multiples based on firm-specific accounting data and market information.
Design/methodology/approach
The authors propose two econometric models, for both listed and non-listed companies, which consider brand valuations made by primary consulting entities (Interbrand, Brand Finance, BrandZ, European Brand Institute) and multiples derived from accounting and market data of firms. Models were tested on a sample of nonfinancial firms for the period from 2006 to 2019, distinguishing between IAS/IFRS-based and US GAAP-based reporting standards.
Findings
The empirical results show that the identified set of market and accounting multiples proved to be significant information for estimating the value of brands within the IAS/IFRS framework, while a lower explanatory power was assessed for US GAAP firms. Furthermore, the empirical evidence confirm that the direct, relative approach based on multiples is more accurate for valuating listed firms than non-listed firms. Robustness analysis demonstrates that findings do not change significantly when the reference datasets and the main assumptions of the models are altered.
Research limitations/implications
The statistical significance of the analysis is limited by the non-objective nature of brand value estimates. The use of additional sources for brand valuations might allow for the further assessment of the robustness of the relationships identified.
Practical implications
Due to their efficacy and ease of use, the proposed models represent valid practical tools for managers, investors, analysts and professional evaluators.
Originality/value
This work contributes to the existing literature through the identification of significant, stable relationships between brand values and the main economic, financial and asset characteristics of firms; the identification of those relationships would allow for the extension of the multiples approach also to the evaluation of brands.
Details
Keywords
Serkan Akinci, Erdener Kaynak, Eda Atilgan and Şafak Aksoy
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…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Research limitations/implications
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.
Originality/value
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.
Details