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Journal ArticleDOI

A comparative analysis of value management practices between consumer and construction based firms

30 Aug 2019-Problems and perspectives in management (LLC "CPC "Business Perspectives")-Vol. 17, Iss: 3, pp 280-292

TL;DR: In this paper, the use of value management tools in non-management and non-business domains appears to be high, as exemplified by numerous studies conducted on the subject matter in the construction based disciplines, but understanding how such essential tool works in the consumer based domains seems lacking.

AbstractThe use of value management tools in non-management and non-business domains appears to be high, as exemplified by numerous studies conducted on the subject matter in the construction based disciplines, but understanding how such essential tool works in the consumer based domains seems lacking, this study becomes relevant in this regard. The aim of the study therefore is to understand how consumer based and construction based firms differ with regard to the use, focus and control of value management on a firm-by-firm basis, locational basis and on the basis of industrial typology. The researchers adopted a survey research design using a 16-item questionnaire instrument administered to 509 respondents across 10 firms: 5 being consumer and the other 5 being construction based firms. The formulated hypotheses were tested using Kruskal-Wallis and Mann Whitney’s U-test for non-parametric comparisons. The results obtained showed that consumer based firms ranked higher than construction based firms, both on a firm-by-firm (CSB = 256.9, CTB = 247.4, p@0.005; CSB=264.6, CTB = 234.3, p@0.011) for focus and control, respectively, and on an industrial type (CSB = 267.65, CTB = 235.93, p@0.017; CSB = 268.71, CTB = 234.33, p@0.009; CSB = 269.21, CTB = 233.58, p@0.007; CSB = 268.38, CTB = 234.83, p@0.011) comparison basis on actual usage, perceived usage, focus and control of value management, respectively. For the locational difference, there were no statistical significance. The study concludes that there is a case for a multidisciplinary study of value management as it appears more present in consumer than construction based firms. more

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06 May 2013
TL;DR: In this paper, the authors present a discussion of whether, if, how, and when a moderate mediator can be used to moderate another variable's effect in a conditional process analysis.
Abstract: I. FUNDAMENTAL CONCEPTS 1. Introduction 1.1. A Scientist in Training 1.2. Questions of Whether, If, How, and When 1.3. Conditional Process Analysis 1.4. Correlation, Causality, and Statistical Modeling 1.5. Statistical Software 1.6. Overview of this Book 1.7. Chapter Summary 2. Simple Linear Regression 2.1. Correlation and Prediction 2.2. The Simple Linear Regression Equation 2.3. Statistical Inference 2.4. Assumptions for Interpretation and Statistical Inference 2.5. Chapter Summary 3. Multiple Linear Regression 3.1. The Multiple Linear Regression Equation 3.2. Partial Association and Statistical Control 3.3. Statistical Inference in Multiple Regression 3.4. Statistical and Conceptual Diagrams 3.5. Chapter Summary II. MEDIATION ANALYSIS 4. The Simple Mediation Model 4.1. The Simple Mediation Model 4.2. Estimation of the Direct, Indirect, and Total Effects of X 4.3. Example with Dichotomous X: The Influence of Presumed Media Influence 4.4. Statistical Inference 4.5. An Example with Continuous X: Economic Stress among Small Business Owners 4.6. Chapter Summary 5. Multiple Mediator Models 5.1. The Parallel Multiple Mediator Model 5.2. Example Using the Presumed Media Influence Study 5.3. Statistical Inference 5.4. The Serial Multiple Mediator Model 5.5. Complementarity and Competition among Mediators 5.6. OLS Regression versus Structural Equation Modeling 5.7. Chapter Summary III. MODERATION ANALYSIS 6. Miscellaneous Topics in Mediation Analysis 6.1. What About Baron and Kenny? 6.2. Confounding and Causal Order 6.3. Effect Size 6.4. Multiple Xs or Ys: Analyze Separately or Simultaneously? 6.5. Reporting a Mediation Analysis 6.6. Chapter Summary 7. Fundamentals of Moderation Analysis 7.1. Conditional and Unconditional Effects 7.2. An Example: Sex Discrimination in the Workplace 7.3. Visualizing Moderation 7.4. Probing an Interaction 7.5. Chapter Summary 8. Extending Moderation Analysis Principles 8.1. Moderation Involving a Dichotomous Moderator 8.2. Interaction between Two Quantitative Variables 8.3. Hierarchical versus Simultaneous Variable Entry 8.4. The Equivalence between Moderated Regression Analysis and a 2 x 2 Factorial Analysis of Variance 8.5. Chapter Summary 9. Miscellaneous Topics in Moderation Analysis 9.1. Truths and Myths about Mean Centering 9.2. The Estimation and Interpretation of Standardized Regression Coefficients in a Moderation Analysis 9.3. Artificial Categorization and Subgroups Analysis 9.4. More Than One Moderator 9.5. Reporting a Moderation Analysis 9.6. Chapter Summary IV. CONDITIONAL PROCESS ANALYSIS 10. Conditional Process Analysis 10.1. Examples of Conditional Process Models in the Literature 10.2. Conditional Direct and Indirect Effects 10.3. Example: Hiding Your Feelings from Your Work Team 10.4. Statistical Inference 10.5. Conditional Process Analysis in PROCESS 10.6. Chapter Summary 11. Further Examples of Conditional Process Analysis 11.1. Revisiting the Sexual Discrimination Study 11.2. Moderation of the Direct and Indirect Effects in a Conditional Process Model 11.3. Visualizing the Direct and Indirect Effects 11.4. Mediated Moderation 11.5. Chapter Summary 12. Miscellaneous Topics in Conditional Process Analysis 12.1. A Strategy for Approaching Your Analysis 12.2. Can a Variable Simultaneously Mediate and Moderate Another Variable's Effect? 12.3. Comparing Conditional Indirect Effects and a Formal Test of Moderated Mediation 12.4. The Pitfalls of Subgroups Analysis 12.5. Writing about Conditional Process Modeling 12.6. Chapter Summary Appendix A. Using PROCESS Appendix B. Monte Carlo Confidence Intervals in SPSS and SAS

26,130 citations

Journal ArticleDOI
TL;DR: In this paper, the authors show that the limit distribution is normal if n, n$ go to infinity in any arbitrary manner, where n = m = 8 and n = n = 8.
Abstract: Let $x$ and $y$ be two random variables with continuous cumulative distribution functions $f$ and $g$. A statistic $U$ depending on the relative ranks of the $x$'s and $y$'s is proposed for testing the hypothesis $f = g$. Wilcoxon proposed an equivalent test in the Biometrics Bulletin, December, 1945, but gave only a few points of the distribution of his statistic. Under the hypothesis $f = g$ the probability of obtaining a given $U$ in a sample of $n x's$ and $m y's$ is the solution of a certain recurrence relation involving $n$ and $m$. Using this recurrence relation tables have been computed giving the probability of $U$ for samples up to $n = m = 8$. At this point the distribution is almost normal. From the recurrence relation explicit expressions for the mean, variance, and fourth moment are obtained. The 2rth moment is shown to have a certain form which enabled us to prove that the limit distribution is normal if $m, n$ go to infinity in any arbitrary manner. The test is shown to be consistent with respect to the class of alternatives $f(x) > g(x)$ for every $x$.

9,469 citations

"A comparative analysis of value man..." refers methods in this paper

  • ...…which is the Kruskal-Wallis H Test for independent samples with more than two groups (Feir-Walsh & Toothaker, 1974), this is for individual firm and location, while we used the Mann-Whitney’s U Test for the sample with just two groups (Mann & Whitney, 1947; B. Zhang & Y. Zhang, 2009)....


Journal ArticleDOI
Paul G. Curran1
TL;DR: Different perspectives are integrated into a review and assessment of current techniques, an introduction of new techniques, and a generation of recommendations for practical use to detect and remove Invalid data in self-report data collections.
Abstract: Self-report data collections, particularly through online measures, are ubiquitous in both experimental and non-experimental psychology. Invalid data can be present in such data collections for a number of reasons. One reason is careless or insufficient effort (C/IE) responding. The past decade has seen a rise in research on techniques to detect and remove these data before normal analysis (Huang, Curran, Keeney, Poposki, & DeShon, 2012; Johnson, 2005; Meade & Craig, 2012). The rigorous use of these techniques is valuable tool for the removal of error that can impact survey results (Huang, Liu, & Bowling, 2015). This research has encompassed a number of sub-fields of psychology, and this paper aims to integrate different perspectives into a review and assessment of current techniques, an introduction of new techniques, and a generation of recommendations for practical use. Concerns about C/IE responding are a factor any time self-report data are collected, and all such researchers should be well-versed on methods to detect this pattern of response.

310 citations

"A comparative analysis of value man..." refers background in this paper

  • ...During the pilot study, a total of 563 respondents were targeted, but 509 responded and returned, and although there were a couple of missing cases, these were infinitesimal and did not warrant a total rejection (Curran, 2016)....


15 Jun 2002

200 citations