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Journal ArticleDOI

Agile manufacturing: Relation to JIT, operational performance and firm performance

TL;DR: In this paper, a structural model incorporating agile manufacturing as the focal construct is theorized and tested using data collected from production and operations managers working for large U.S. manufacturers, the model is assessed following a structural equation modeling methodology.
About: This article is published in Journal of Operations Management.The article was published on 2011-05-01. It has received 377 citations till now. The article focuses on the topics: Agile manufacturing.
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TL;DR: This study provides a practical guideline for evaluating and using PLS and uses examples from the operations management literature to demonstrate how the specific points in this guideline can be applied.
Abstract: The partial least squares (PLS) approach to structural equation modeling (SEM) has been widely adopted in business research fields such as information systems, consumer behavior, and marketing. The use of PLS in the field of operations management is also growing. However, questions still exist among some operations management researchers regarding whether and how PLS should be used. To address these questions, our study provides a practical guideline for using PLS and uses examples from the operations management literature to demonstrate how the specific points in this guideline can be applied. In addition, our study reviews and summarizes the use of PLS in the recent operations management literature according to our guideline. The main contribution of this study is to present a practical guideline for evaluating and using PLS that is tailored to the operations management field.

1,002 citations


Cites background or methods from "Agile manufacturing: Relation to JI..."

  • ...14 model operational performance as a formative construct if one follows the guidelines set by Jarvis et al. (2003) and Diamantopoulos and Winklhofer (2001)....

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  • ...In the OM literature, operational performance is modeled as reflective constructs in some studies (e.g., Cao and Zhang, 2011; Inman et al., 2011)....

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  • ...some studies (e.g., Cao and Zhang, 2011; Inman et al., 2011)....

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Journal ArticleDOI
TL;DR: In this article, the authors present a practical guideline for evaluating and using PLS that is tailored to the operations management field and use examples from operations management literature to demonstrate how the specific points in this guideline can be applied.

925 citations

Journal ArticleDOI
TL;DR: This study investigates the quantitative relationship between knowledge sharing, innovation and performance and develops a research model positing that knowledge sharing not only have positive relationship with performance directly but also influence innovation which in turn contributes to firm performance.
Abstract: Highlights?Exploring the effect Knowledge sharing (KS) have on innovation and firm performance. ?Confirming the mediating role of innovation between KS and performance. ?finding that explicit KS impacts innovation speed more than financial performance. ?finding that tacit KS impacts innovation quality more than operational performance. This study investigates the quantitative relationship between knowledge sharing, innovation and performance. Based on the literature review, we develop a research model positing that knowledge sharing not only have positive relationship with performance directly but also influence innovation which in turn contributes to firm performance. This model is empirically tested using data collected from 89 high technology firms in Jiangsu Province of China. It is found that both explicit and tacit knowledge sharing practices facilitate innovation and performance. Explicit knowledge sharing has more significant effects on innovation speed and financial performance while tacit knowledge sharing has more significant effects on innovation quality and operational performance.

812 citations

Journal ArticleDOI
TL;DR: In this paper, the authors investigated the impact of knowledge sharing on firm performance and the mediating role of intellectual capital (IC) and found that tacit knowledge sharing significantly contributes to all three components of IC, namely human, structural and relational capital, while explicit KS only has a significant influence on human and structural capital.
Abstract: Purpose – The aim of this paper is to investigate the impact of knowledge sharing (KS) on firm performance and the mediating role of intellectual capital (IC). Design/methodology/approach – A research model was developed based on prior KS and IC studies. A survey was administered to a sample of high technology firms in China and 228 usable responses were collected. Structural equation modeling (SEM) was employed to test the research model. Findings – Tacit KS significantly was found to contribute to all three components of IC, namely human, structural and relational capital, while explicit KS only has a significant influence on human and structural capital. Human, structural and relational capital, enhance both operational and financial performance of firms. The effect of KS on firm performance is mediated by IC. Explicit KS has a greater effect on financial performance than operational performance, whereas tacit KS has a greater impact on operational performance than financial performance. Research limit...

361 citations


Cites methods from "Agile manufacturing: Relation to JI..."

  • ...The financial performance measurement was based on Bowersox et al. (2000), Inman et al. (2011), and Vaccaro et al. (2010)....

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Journal ArticleDOI
TL;DR: In this article, the authors examined the relationship between supply chain agility and cost efficiency and customer effectiveness across various environmental situations, and provided evidence to managers that deploying resource to enhance FSCA can positively impact the firm's bottom line.

358 citations

References
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Journal ArticleDOI
TL;DR: This article seeks to make theorists and researchers aware of the importance of not using the terms moderator and mediator interchangeably by carefully elaborating the many ways in which moderators and mediators differ, and delineates the conceptual and strategic implications of making use of such distinctions with regard to a wide range of phenomena.
Abstract: In this article, we attempt to distinguish between the properties of moderator and mediator variables at a number of levels. First, we seek to make theorists and researchers aware of the importance of not using the terms moderator and mediator interchangeably by carefully elaborating, both conceptually and strategically, the many ways in which moderators and mediators differ. We then go beyond this largely pedagogical function and delineate the conceptual and strategic implications of making use of such distinctions with regard to a wide range of phenomena, including control and stress, attitudes, and personality traits. We also provide a specific compendium of analytic procedures appropriate for making the most effective use of the moderator and mediator distinction, both separately and in terms of a broader causal system that includes both moderators and mediators.

80,095 citations

Book
27 May 1998
TL;DR: The book aims to provide the skills necessary to begin to use SEM in research and to interpret and critique the use of method by others.
Abstract: Designed for students and researchers without an extensive quantitative background, this book offers an informative guide to the application, interpretation and pitfalls of structural equation modelling (SEM) in the social sciences. The book covers introductory techniques including path analysis and confirmatory factor analysis, and provides an overview of more advanced methods such as the evaluation of non-linear effects, the analysis of means in convariance structure models, and latent growth models for longitudinal data. Providing examples from various disciplines to illustrate all aspects of SEM, the book offers clear instructions on the preparation and screening of data, common mistakes to avoid and widely used software programs (Amos, EQS and LISREL). The book aims to provide the skills necessary to begin to use SEM in research and to interpret and critique the use of method by others.

42,102 citations

Journal ArticleDOI
01 Jan 1973
TL;DR: In this paper, a six-step framework for organizing and discussing multivariate data analysis techniques with flowcharts for each is presented, focusing on the use of each technique, rather than its mathematical derivation.
Abstract: Offers an applications-oriented approach to multivariate data analysis, focusing on the use of each technique, rather than its mathematical derivation. The text introduces a six-step framework for organizing and discussing techniques with flowcharts for each. Well-suited for the non-statistician, this applications-oriented introduction to multivariate analysis focuses on the fundamental concepts that affect the use of specific techniques rather than the mathematical derivation of the technique. Provides an overview of several techniques and approaches that are available to analysts today - e.g., data warehousing and data mining, neural networks and resampling/bootstrapping. Chapters are organized to provide a practical, logical progression of the phases of analysis and to group similar types of techniques applicable to most situations. Table of Contents 1. Introduction. I. PREPARING FOR A MULTIVARIATE ANALYSIS. 2. Examining Your Data. 3. Factor Analysis. II. DEPENDENCE TECHNIQUES. 4. Multiple Regression. 5. Multiple Discriminant Analysis and Logistic Regression. 6. Multivariate Analysis of Variance. 7. Conjoint Analysis. 8. Canonical Correlation Analysis. III. INTERDEPENDENCE TECHNIQUES. 9. Cluster Analysis. 10. Multidimensional Scaling. IV. ADVANCED AND EMERGING TECHNIQUES. 11. Structural Equation Modeling. 12. Emerging Techniques in Multivariate Analysis. Appendix A: Applications of Multivariate Data Analysis. Index.

37,124 citations

Journal ArticleDOI
TL;DR: This chapter discusses Structural Equation Modeling: An Introduction, and SEM: Confirmatory Factor Analysis, and Testing A Structural Model, which shows how the model can be modified for different data types.
Abstract: I Introduction 1 Introduction II Preparing For a MV Analysis 2 Examining Your Data 3 Factor Analysis III Dependence Techniques 4 Multiple Regression Analysis 5 Multiple Discriminate Analysis and Logistic Regression 6 Multivariate Analysis of Variance 7 Conjoint Analysis IV Interdependence Techniques 8 Cluster Analysis 9 Multidimensional Scaling and Correspondence Analysis V Moving Beyond the Basic Techniques 10 Structural Equation Modeling: Overview 10a Appendix -- SEM 11 CFA: Confirmatory Factor Analysis 11a Appendix -- CFA 12 SEM: Testing A Structural Model 12a Appendix -- SEM APPENDIX A Basic Stats

23,353 citations

Journal ArticleDOI
TL;DR: In this paper, the authors identify six categories of self-reports and discuss such problems as common method variance, the consistency motif, and social desirability, as well as statistical and post hoc remedies and some procedural methods for dealing with artifactual bias.

14,482 citations