The impact of information sharing in supply chains on organisational performance: an empirical study
TL;DR: In this article, the authors investigated the relationship between the degree of information sharing and organisational performance and found that information sharing does not directly relate to the organizational performance, however, internal integration and costs-benefit sharing do not relate to information sharing.
Abstract: Information sharing has been cited as one of the major means to enhance supply chain performance It allows companies to better coordinate their activities with their supply chain partners that lead to increased performance This study conceptualises and assesses several factors that influence the degree of information sharing in supply chains, namely integrated information technologies, internal integration, information quality and costs–benefits sharing The relationship between the degree of information sharing and organisational performance is then tested Data from 150 manufacturing companies were collected and proposed relationships are examined using structural equation modelling The results show that integrated information technologies and information quality have positive influence on the intensity of information sharing However, internal integration and costs–benefits sharing do not relate to the intensity of information sharing This study finds that information sharing does not directly rela
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TL;DR: In this paper, the authors explore the potential impact of the fourth industrial environment on the performance improvements in business processes. But they do not consider the role of information technology (IT) in achieving performance improvements.
Abstract: Considering the crucial role Information Technology (IT) plays in achieving performance improvements in business processes, this paper aims to explore the potential impact of the fourth industrial ...
237 citations
TL;DR: In this article, the authors examined the relationship between employee commitment and supply chain integration dimensions to explain several performance measures, such as flexibility, delivery, quality, inventory and customer satisfaction.
149 citations
Cites background or result from "The impact of information sharing i..."
...Secondly, as already indicated, there are many prior studies that state that having INTI is a prerequisite for attaining adequate EI (Eng, 2006; Menon, 2012; Kim, 2013; Baihaqi & Sohal, 2013)....
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...The lack of INTI becomes the biggest obstacle to turning collaborative activities into operational efficiency (Baihaqi & Sohal, 2013)....
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...A significant positive correlation between INTI and EI has been reported in previous research (Gimenez & Ventura, 2005; Eng, 2006; Baihaqi & Sohal, 2013)....
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TL;DR: A review of the literature relating to the integration of big data with operations and supply chain management is reviewed in this paper, focusing on three key areas of the operations, namely manufacturing, procurement and logistics where big data has been applied.
Abstract: Operations and supply chain management encompasses a vast domain and hence provides a myriad of opportunities for huge voluminous data generated from various sources in real time. Such huge data having the requisite properties of big data can be utilised to gain critical and fundamental insights towards optimising the operations and supply chain and thus making effective and efficient decisions. In the recent years, research interest in big data has increased substantially and therefore researchers and practitioners have also tried to tap the capabilities of big data to optimise operations and supply chain management. In this paper, the literature relating to the integration of big data with operations and supply chain management is reviewed. In particular, reviewing past work is primarily focused on three key areas of the operations and supply chain management, namely manufacturing, procurement and logistics where big data has been applied. In addition to reviewing past literature, paper also pro...
136 citations
TL;DR: TISM based the proposed model evaluates the causality and illustrate factors with interpretation of relations via directed links in the form of Interpretive Matrix, and suggests that factors at the bottom level are crucial for sustainability focused chain to build its capability on risks and risk issues.
Abstract: Concern related to green and sustainability is growing from past few years in the research area of supply chain management. Collectively, these concerns involves a higher number of interacting factors, which further can multiply complexity by the decrease in visibility of the risks in supply chain operations and so add to its vulnerability. To make supply chain (SC) capable to bear simultaneously regular and risk condition, one requires proactive planning and flexibility in the decisions making. To provide supply chain designers a proactive decision model, this paper proposes to use a flexible decision approach, i.e. Interpretive Structural Modeling (ISM) for recognizing the combined interactions between factors influencing sustainable risk bearing SC. However, the interpretation of the interactive relationships represented by directed links for the identified factors relatively lacks in the ISM approach, and thus may distort the process of decision making. Therefore, in this study, ISM is extended to the Total Interpretive Structural Modeling (TISM) approach to overcome these issues in interpreting the directed links in the structural model for considered factors. Further, by using relationship analysis, we graphically categorize factors on the basis of their impact on performance. Finally, TISM based the proposed model evaluates the causality and illustrate factors with interpretation of relations via directed links in the form of Interpretive Matrix, and suggests that factors at the bottom level are crucial for sustainability focused chain to build its capability on risks and risk issues. The implications at managerial level and conclusions are presented in the end.
133 citations
TL;DR: The findings enable the decision makers to appropriately choose the desired and drop undesired enablers in implementing the big data initiatives to improve the performance of OSCM.
Abstract: The purpose of this paper is to identify and analyse the interactions among various enablers which are critical to the success of big data initiatives in operations and supply chain management (OSCM).,Fourteen enablers of big data in OSCM have been selected from literature and consequent deliberations with experts from industry. Three different multi criteria decision-making (MCDM) techniques, namely, interpretive structural modeling (ISM), fuzzy total interpretive structural modeling (fuzzy-TISM) and decision-making trial and evaluation laboratory (DEMATEL) have been used to identify driving enablers. Further, common enablers from each technique, their hierarchies and inter-relationships have been established.,The enabler modelings using ISM, Fuzzy-TISM and DEMATEL shows that the top management commitment, financial support for big data initiatives, big data/data science skills, organizational structure and change management program are the most influential/driving enablers. Across all three different techniques, these five different enablers has been identified as the most promising ones to implement big data in OSCM. On the other hand, interpretability of analysis, big data quality management, data capture and storage and data security and privacy have been commonly identified across all three different modeling techniques as the most dependent big data enablers for OSCM.,The MCDM models of big data enablers have been formulated based on the inputs from few domain experts and may not reflect the opinion of whole practitioners community.,The findings enable the decision makers to appropriately choose the desired and drop undesired enablers in implementing the big data initiatives to improve the performance of OSCM. The most common driving big data enablers can be given high priority over others and can significantly enhance the performance of OSCM.,MCDM-based hierarchical models and causal diagram for big data enablers depicting contextual inter-relationships has been proposed which is a new effort for implementation of big data in OSCM.
87 citations
References
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Book•
27 May 1998TL;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
"The impact of information sharing i..." refers methods in this paper
...The non-significant paths were then subsequently deleted from the model in order to build a better competing model following the model trimming method proposed by Kline (2005)....
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Book•
28 Apr 1989
TL;DR: The General Model, Part I: Latent Variable and Measurement Models Combined, Part II: Extensions, Part III: Extensions and Part IV: Confirmatory Factor Analysis as discussed by the authors.
Abstract: Model Notation, Covariances, and Path Analysis. Causality and Causal Models. Structural Equation Models with Observed Variables. The Consequences of Measurement Error. Measurement Models: The Relation Between Latent and Observed Variables. Confirmatory Factor Analysis. The General Model, Part I: Latent Variable and Measurement Models Combined. The General Model, Part II: Extensions. Appendices. Distribution Theory. References. Index.
19,019 citations
TL;DR: This article used subjective estimates and extrapolations in an analysis of mail survey data from published studies for estimates of the magnitude of bias and found that the use of extrapolation led to substantial improvements over a strategy of not using extrapolation.
Abstract: Valid predictions for the direction of nonresponse bias were obtained from subjective estimates and extrapolations in an analysis of mail survey data from published studies For estimates of the magnitude of bias, the use of extrapolations led to substantial improvements over a strategy of not using extrapolations
11,245 citations
TL;DR: In this article, an index of factorial simplicity, employing the quartimax transformational criteria of Carroll, Wrigley and Neuhaus, and Saunders, was developed.
Abstract: An index of factorial simplicity, employing the quartimax transformational criteria of Carroll, Wrigley and Neuhaus, and Saunders, is developed. This index is both for each row separately and for a factor pattern matrix as a whole. The index varies between zero and one. The problem of calibrating the index is discussed.
10,346 citations
"The impact of information sharing i..." refers methods in this paper
...The Kaiser–Meyer–Olkin measure of sampling adequacy (Kaiser 1970, 1974) and the Barlett test of sphericity (Bartlett 1954) were used to assess the suitability of the sample for principal component analysis....
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Posted Content•
TL;DR: Valid predictions for the direction of nonresponse bias were obtained from subjective estimates and extrapolations in an analysis of mail survey data from published studies and the use of extrapolation led to substantial improvements over a strategy of not using extrapolation.
Abstract: Valid predictions for the direction of nonresponse bias were obtained from subjective estimates and extrapolations in an analysis of mail survey data from published studies. For estimates of the magnitude of bias, the use of extrapolations led to substantial improvements over a strategy of not using extrapolations.
9,589 citations
"The impact of information sharing i..." refers methods in this paper
...Non-response bias was tested by comparing the data from the companies who responded early with those who responded late (Armstrong and Overton 1977)....
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