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This study aims to investigate the relationship between project management competency, job motivation and performance in people engaged in electronic commerce (e-commerce). A questionnaire was developed for e-commerce workers in various professional aspects including business strategy planning, content management and design, sales services, marketing business sales, host settings, analysis and management. A total of 299 valid questionnaires were retrieved. Multiple regression analysis was applied to the testing and analysis on important competences for project management and the factors influencing job performance.

In this study, the related indexes are measured mainly through appropriate questionnaire design and the questionnaires were mainly distributed among employees and managers of the e-commerce enterprises. Tables 5–1 provide the personal profiles of the e-commerce personnel. A total of 350 questionnaires were distributed and 320 questionnaires were returned. After the 320 questionnaires were sorted and categorized, 21 incompletely-filled and invalid questionnaires were excluded, for a total of 299 valid questionnaires that were returned. Based on the quantitative data obtained from the returned valid questionnaires, files were created and statistical analyzes were conducted by the computerized statistical software statistical product and service solutions 21. According to the research question and nature of this study, the present study mainly adopted statistical methods, including descriptive statistical analysis, reliability analysis, Pearson’s correlation analysis and regression analysis.

The results suggested that the project management competency of those engaged in e-commerce had a positive influence on their work performance. The capabilities in the management of integration, scope and procurement were significantly important factors identified in this study. In addition, the internal and external motivations of those engaged in e-commerce had a positive influence on work performance, and thus, facilitated their influence on the project management competency. The results and statistical analysis could be a reference in e-commerce-related business management and serve as the basis for evaluation of the training of project management competencies for those engaged in e-commerce and further improvements of human capitals and corporate competitive advantages.

This study used literature on project management competence and job performance as a foundation; previous studies argue that project management competence has a positive correlation with job performance. Empirical results reveal that among the e-commerce personnel, most dimensions of project management competence are significantly correlated with job performance. This study reveals that stakeholder management competence, the newly introduced 10th dimension of project management competence, is also significantly correlated with job performance. Therefore, study results reveal that project management competence has a significant positive correlation with job performance. In this study, the two constructs of internal motivation and external motivation in job motivation are introduced for use as disturbing factors. Empirical results reveal that internal motivation and external motivation have a significant positive disturbing effect with respect to the influence of cost management competence and human resource management competence on job performance. Hackman and Oldham (1975) contend that the jobs calling for a variety of skills can boost the job motivation of employees. Study results reveal that job motivation is of great importance to the influence of project management competence on job performance and can be used as the basis for improving job performance.

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.

Meta-regression is widely used and misused today in meta-analyses in psychology, organizational behavior, marketing, management, and other social sciences, as an approach to the identification and calibration of moderators, with most users being unaware of serious problems in its use. The purpose of this paper is to describe nine serious methodological problems that plague applications of meta-regression.

This paper is methodological in nature and is based on well-established principles of measurement and statistics. These principles are used to illuminate the potential pitfalls in typical applications of meta-regression.

The analysis in this paper demonstrates that many of the nine statistical and measurement pitfalls in the use of meta-regression are nearly universal in applications in the literature, leading to the conclusion that few meta-regressions in the literature today are trustworthy. A second conclusion is that in almost all cases, hierarchical subgrouping of studies is superior to meta-regression as a method of identifying and calibrating moderators. Finally, a third conclusion is that, contrary to popular belief among researchers, the process of accurately identifying and calibrating moderators, even with the best available methods, is complex, difficult, and data demanding.

This paper provides useful guidance to meta-analytic researchers that will improve the practice of moderator identification and calibration in social science research literatures.

Today, many important decisions are made on the basis of the results of meta-analyses. These include decisions in medicine, pharmacology, applied psychology, management, marketing, social policy, and other social sciences. The guidance provided in this paper will improve the quality of such decisions by improving the accuracy and trustworthiness of meta-analytic results.

This paper is original and valuable in that there is no similar listing and discussion of the pitfalls in the use of meta-regression in the literature, and there is currently a widespread lack of knowledge of these problems among meta-analytic researchers in all disciplines.

Shalev's (2007) critique of the use of multiple regression in comparative research brings together and synthesizes a variety of previous critiques, ranging from those focusing on foundational issues (e.g., the persistent problem of limited diversity), to estimation issues (e.g., the unrealistic assumption of correct model specification), to narrow technical issues (e.g., the difficulty of deriving valid standard errors for regression coefficients in pooled cross-sectional time-series models). Broadly speaking, these concerns can be described as epistemological, theoretical, and methodological, respectively. While the distinctions among these three are not always clear-cut, the tripartite scheme provides a useful way to map the different kinds of critiques that may be directed at the use of regression analysis in comparative research. In the first half of this essay we build upon Shalev's discussion to clarify the conditions under which regression analysis may be epistemologically, theoretically, or methodologically inappropriate for comparative research. Our goal is to situate Shalev's specific critiques of the use of multiple regression in comparative work within the context of social research in general.

The purpose of this paper is to identify significant factors of environmental variables and pollutants that have an effect on PM_2.5 through wavelet and regression analysis.

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 PM_2.5 as a dependent variable.

It is found that few pollutants such as NO₂, NO_x, SO₂, benzene and environmental factors such as ambient temperature, solar radiation and wind direction affect PM_2.5. The regression model developed has high R² value of 91.9 percent, and the residues are stationary and not correlated indicating a sound model.

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 PM_2.5.

Control measures such as control charts can be implemented for significant factors.

Rules and regulations can be made more stringent on the factors.

The originality of this paper lies in the integration of wavelets with regression analysis for air pollution data.

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.

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.

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.

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.

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.

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.

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.

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.