The impact of supply chain integration on performance: A contingency and configuration approach

Abstract: Facing uncertain environments, firms have strived to achieve greater supply chain collaboration to leverage the resources and knowledge of their suppliers and customers. The objective of the study is to uncover the nature of supply chain collaboration and explore its impact on firm performance based on a paradigm of collaborative advantage. Reliable and valid instruments of these constructs were developed through rigorous empirical analysis. Data were collected through a Web survey of U.S. manufacturing firms in various industries. The statistical methods used include confirmatory factor analysis and structural equation modeling (i.e., LISREL). The results indicate that supply chain collaboration improves collaborative advantage and indeed has a bottom-line influence on firm performance, and collaborative advantage is an intermediate variable that enables supply chain partners to achieve synergies and create superior performance. A further analysis of the moderation effect of firm size reveals that collaborative advantage completely mediates the relationship between supply chain collaboration and firm performance for small firms while it partially mediates the relationship for medium and large firms.

Abstract: Globalisation of supply chains makes their management and control more difficult. Blockchain technology, as a distributed digital ledger technology which ensures transparency, traceability, and sec...

Abstract: This paper extends prior supply chain research by building and empirically testing a theoretical model of the contingency effects of environmental uncertainty (EU) on the relationships between three dimensions of supply chain integration and four dimensions of operational performance. Based on the contingency and organizational information processing theories, we argue that under a high EU, the associations between supplier/customer integration, and delivery and flexibility performance, and those between internal integration, and product quality and production cost, will be strengthened. These theoretical propositions are largely confirmed by multi-group and structural path analyses of survey responses collected from 151 of Thailand’s automotive manufacturing plants. This paper contributes to operations management contingency research and provides theory-driven and empirically proven explanations for managers to differentiate the effects of internal and external integration efforts under different environmental conditions. © 2011 Elsevier B.V. All rights reserved.

Abstract: Supply chain integration (SCI) among internal functions within a company, and external trading partners within a supply chain, has received increasing attention from academicians and practitioners in recent years. SCI consists of internal integration of different functions within a company and external integration with trading partners. While both supply chain internal and external integration have been studied extensively, our understanding of what influences SCI and the relationship between internal and external integration is still very limited. This paper argues that external integration with customers and suppliers is simultaneously influenced by internal integration and relationship commitment to customers and suppliers. Internal integration enables external integration because organizations must first develop internal integration capabilities through system-, data-, and process-integration, before they can engage in meaningful external integration. At the same time, before external integration can be successfully implemented, organizations must have a willingness to integrate with external supply chain partners, which is demonstrated by their relationship commitment. We propose and test a model that specifies the relationship between internal integration, relationship commitment, and external integration, using data collected from manufacturing firms in China. The results show that internal integration and relationship commitment improve external integration independently, and their interactive effect on external integration is not significant. However, internal integration has a much greater impact on external integration than relationship commitment. We also examine the model for companies with different ownerships, and the results indicate that for Chinese controlled companies where there is a strong collectivism culture and more reliance on “Guanxi” (relationship), relationship commitment has a significant impact on external integration with suppliers and customers. This is in stark contrast to foreign controlled companies, characterized by a more individualistic culture and more reliance on technological capabilities, where no significant relationship between relationship commitment and external integration could be found. The model is also tested across different industries and different regions in China, providing useful insights for Chinese companies in particular. This study makes significant contributions to the SCI literature by simultaneously studying the effects of internal integration and relationship commitment on external integration, and providing several future research directions.

Abstract: Purpose – The purpose of this research is to explore the resilience domain, which is important in the field of supply chain management; it investigates the effects relational competencies have for resilience and the effect resilience, in turn, has on a supply chain ' s customer value. Design/methodology/approach – The research is empirical in nature and employs a confirmatory approach that builds on the relational view as a primary theoretical foundation. It utilizes survey data collected from manufacturing firms from three countries, which is analyzed using structural equation modeling. Findings – It is found that communicative and cooperative relationships have a positive effect on resilience, while integration does not have a significant effect. It is also found that improved resilience, obtained by investing in agility and robustness, enhances a supply chain ' s customer value. Practical implications – Some findings contrast the expectations derived from theory. Particularly, practitioners can learn t...

Abstract: This article examines the adequacy of the “rules of thumb” conventional cutoff criteria and several new alternatives for various fit indexes used to evaluate model fit in practice. Using a 2‐index presentation strategy, which includes using the maximum likelihood (ML)‐based standardized root mean squared residual (SRMR) and supplementing it with either Tucker‐Lewis Index (TLI), Bollen's (1989) Fit Index (BL89), Relative Noncentrality Index (RNI), Comparative Fit Index (CFI), Gamma Hat, McDonald's Centrality Index (Mc), or root mean squared error of approximation (RMSEA), various combinations of cutoff values from selected ranges of cutoff criteria for the ML‐based SRMR and a given supplemental fit index were used to calculate rejection rates for various types of true‐population and misspecified models; that is, models with misspecified factor covariance(s) and models with misspecified factor loading(s). The results suggest that, for the ML method, a cutoff value close to .95 for TLI, BL89, CFI, RNI, and G...

Abstract: The statistical tests used in the analysis of structural equation models with unobservable variables and measurement error are examined. A drawback of the commonly applied chi square test, in addit...

Abstract: Interest in the problem of method biases has a long history in the behavioral sciences. Despite this, a comprehensive summary of the potential sources of method biases and how to control for them does not exist. Therefore, the purpose of this article is to examine the extent to which method biases influence behavioral research results, identify potential sources of method biases, discuss the cognitive processes through which method biases influence responses to measures, evaluate the many different procedural and statistical techniques that can be used to control method biases, and provide recommendations for how to select appropriate procedural and statistical remedies for different types of research settings.

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.

Abstract: In this article, we provide guidance for substantive researchers on the use of structural equation modeling in practice for theory testing and development. We present a comprehensive, two-step modeling approach that employs a series of nested models and sequential chi-square difference tests. We discuss the comparative advantages of this approach over a one-step approach. Considerations in specification, assessment of fit, and respecification of measurement models using confirmatory factor analysis are reviewed. As background to the two-step approach, the distinction between exploratory and confirmatory analysis, the distinction between complementary approaches for theory testing versus predictive application, and some developments in estimation methods also are discussed.