Data-driven capabilities, supply chain integration and competitive performance

2019 ◽  
Vol 121 (11) ◽  
pp. 2708-2729 ◽  
Author(s):  
Muhammad Irfan ◽  
Mingzheng Wang
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Developing Country ◽  
Structural Equation ◽  
Supply Chain Integration ◽  
Data Driven ◽  
Equation Modeling ◽  
Competitive Performance ◽  
Shared Resources ◽  
Content Type

Purpose The purpose of this paper is to analyze the effects of data-driven capabilities on supply chain integration (SCI) and competitive performance of firms in the food and beverages (F & B) industry in Pakistan. Design/methodology/approach The authors adopt the structural equation modeling approach to test the proposed hypotheses using AMOS 23. Survey data were collected from 240 firms in the F & B industry in Pakistan. Findings The results revealed that SCI (i.e. internal integration (II) and external integration (EI)) significantly mediates the effect of data-driven capabilities (i.e. flexible information technology resources and data assimilation) on a firm’s competitive performance. In addition to the direct effects, II also has an indirect effect on competitive performance through EI. Practical implications The study has several implications for managers in the context of big data application in food supply chain management (FSCM) in a developing country context. The study posits that firms can achieve excellence in performance by governing data-driven supply chain operations. The study also has implications for distributors and importers in the F & B industry. The cloud-based sharing of data can improve the operational performance of channel members while reducing their overall cost of operations. In practice, food franchises largely get the advantage of shared resources of their suppliers in managing orders, payments, inventory and after-sales services. Originality/value The study is novel and deepens the understanding about the use of big data in FSCM keeping in view the industry trends and stakeholder’s priorities in a developing country context.

Big data-driven supply chain and performance: a resource-based view

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shampy Kamboj ◽  
Shruti Rana
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Small And Medium Enterprises ◽  
Data Driven ◽  
Equation Modeling ◽  
Firm Age ◽  
Content Type ◽  
Sustainable Performance ◽  
Medium Enterprises

PurposeThe main objective of this paper is to study the role of supply chain performance (SCP) as a mediator between big data-driven supply chain (BDDSC) and firm sustainable performance. In addition, the role of firm age as a moderator between BDDSC and SCP as well as between SCP and firm sustainable performance has also been explored.Design/methodology/approachThe 200 managers of medium or senior level positions in micro, small and medium enterprises (MSMEs) located at Delhi-NCR have been contacted. Further, collected data have been confirmed with confirmatory factor analysis (CFA). In this paper, structure equation modeling (SEM) has been employed to empirically check the proposed hypotheses and their relationships.FindingsThe findings confirmed that SCP mediates the link between BDDSC and firm sustainable performance. Additionally, firm age moderates the association between BDDSC and SCP as well as between SCP and firm sustainable performance.Research limitations/implicationsThe role of SCP and firm age between BDDSC and sustainable performance have been examined in the context of MSMEs in Delhi-NCR and thereby limit the generalization of results to other industries and country contexts.Originality/valueThe present study adds to the existing literature via recognizing the blackbox using SCP and firm age to comprehend BDDSC and firm sustainable performance relationship.

Supply chain integration and its impact on sustainability

2018 ◽  
Vol 118 (9) ◽  
pp. 1749-1765 ◽  
Author(s):  
Mingu Kang ◽  
Ma Ga (Mark) Yang ◽  
Youngwon Park ◽  
Baofeng Huo
Keyword(s):  
Supply Chain ◽  
Structural Equation ◽  
Management Practices ◽  
Social Performance ◽  
Supply Chain Integration ◽  
Equation Modeling ◽  
Content Type ◽  
New Perspective ◽  
And Performance ◽  
And Function

Purpose The purpose of this paper is to examine the role of supply chain integration (SCI) in improving sustainability management practices (SMPs) and performance. Design/methodology/approach Based on data collected from 931 manufacturing firms in multiple countries and regions, the authors conducted a structural equation modeling analysis to test the proposed hypotheses. Findings The findings suggest that supplier and customer integration are vital enablers for both intra- and inter-organizational SMPs. The results also reveal that both intra- and inter-organizational SMPs are significantly and positively associated with sustainability performance (i.e. economic, environmental and social performance) and function as complements to jointly enhance environmental and social performance. Originality/value This study incorporates SCI into the sustainability literature, providing a new perspective on sustainability and supply chain management research.

scholarly journals Impact of big data and predictive analytics capability on supply chain sustainability

2018 ◽  
Vol 29 (2) ◽  
pp. 513-538 ◽  
Author(s):  
Shirish Jeble ◽  
Rameshwar Dubey ◽  
Stephen J. Childe ◽  
Thanos Papadopoulos ◽  
David Roubaud ◽  
...  
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Theoretical Model ◽  
Performance Measures ◽  
Structural Equation ◽  
Predictive Analytics ◽  
Equation Modeling ◽  
Sustainable Supply Chain ◽  
Content Type ◽  
The Impact

PurposeThe purpose of this paper is to develop a theoretical model to explain the impact of big data and predictive analytics (BDPA) on sustainable business development goal of the organization.Design/methodology/approachThe authors have developed the theoretical model using resource-based view logic and contingency theory. The model was further tested using partial least squares-structural equation modeling (PLS-SEM) following Peng and Lai (2012) arguments. The authors gathered 205 responses using survey-based instrument for PLS-SEM.FindingsThe statistical results suggest that out of four research hypotheses, the authors found support for three hypotheses (H1-H3) and the authors did not find support forH4. Although the authors did not find support forH4(moderating role of supply base complexity (SBC)), however, in future the relationship between BDPA, SBC and sustainable supply chain performance measures remain interesting research questions for further studies.Originality/valueThis study makes some original contribution to the operations and supply chain management literature. The authors provide theory-driven and empirically proven results which extend previous studies which have focused on single performance measures (i.e. economic or environmental). Hence, by studying the impact of BDPA on three performance measures the authors have attempted to answer some of the unresolved questions. The authors also offer numerous guidance to the practitioners and policy makers, based on empirical results.

A structural equation model for big data adoption in the healthcare supply chain

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Dindayal Agrawal ◽  
Jitender Madaan
Keyword(s):  
Factor Analysis ◽  
Big Data ◽  
Supply Chain ◽  
Latent Variables ◽  
Structural Equation ◽  
Equation Model ◽  
Equation Modeling ◽  
Data Governance ◽  
Social Perspective ◽  
Content Type

PurposeThe purpose of this study is to examine the barriers to the implementation of big data (BD) in the healthcare supply chain (HSC).Design/methodology/approachFirst, the barriers concerning BD adoption in the HSC were found by conducting a detailed literature survey and with the expert's opinion. Then the exploratory factor analysis (EFA) was employed to categorize the barriers. The obtained results are verified using the confirmatory factor analysis (CFA). Structural equation modeling (SEM) analysis gives the path diagram representing the interrelationship between latent variables and observed variables.FindingsThe segregation of 13 barriers into three categories, namely “data governance perspective,” “technological and expertise perspective,” and “organizational and social perspective,” is performed using EFA. Three hypotheses are tested, and all are accepted. It can be concluded that the “data governance perspective” is positively related to “technological and expertise perspective” and “organizational and social perspective” factors. Also, the “technological and expertise perspective” is positively related to “organizational and social perspective.”Research limitations/implicationsIn literature, very few studies have been performed on finding the barriers to BD adoption in the HSC. The systematic methodology and statistical verification applied in this study empowers the healthcare organizations and policymakers in further decision-making.Originality/valueThis paper is first of its kind to adopt an approach to classify barriers to BD implementation in the HSC into three distinct perspectives.

scholarly journals Understanding supply chain analytics capabilities and agility for data-rich environments

2019 ◽  
Vol 39 (6/7/8) ◽  
pp. 887-912 ◽  
Author(s):  
Samuel Fosso Wamba ◽  
Shahriar Akter
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Structural Equation ◽  
Equation Modeling ◽  
Future Research ◽  
Chain Management ◽  
Content Type ◽  
Supply Chain Agility ◽  
The Usa

Purpose Big data-driven supply chain analytics capability (SCAC) is now emerging as the next frontier of supply chain transformation. Yet, very few studies have been directed to identify its dimensions, subdimensions and model their holistic impact on supply chain agility (SCAG) and firm performance (FPER). Therefore, to fill this gap, the purpose of this paper is to develop and validate a dynamic SCAC model and assess both its direct and indirect impact on FPER using analytics-driven SCAG as a mediator. Design/methodology/approach The study draws on the emerging literature on big data, the resource-based view and the dynamic capability theory to develop a multi-dimensional, hierarchical SCAC model. Then, the model is tested using data collected from supply chain analytics professionals, managers and mid-level manager in the USA. The study uses the partial least squares-based structural equation modeling to prove the research model. Findings The findings of the study identify supply chain management (i.e. planning, investment, coordination and control), supply chain technology (i.e. connectivity, compatibility and modularity) and supply chain talent (i.e. technology management knowledge, technical knowledge, relational knowledge and business knowledge) as the significant antecedents of a dynamic SCAC model. The study also identifies analytics-driven SCAG as the significant mediator between overall SCAC and FPER. Based on these key findings, the paper discusses their implications for theory, methods and practice. Finally, limitations and future research directions are presented. Originality/value The study fills an important gap in supply chain management research by estimating the significance of various dimensions and subdimensions of a dynamic SCAC model and their overall effects on SCAG and FPER.

The effects of strategic sourcing on supply chain strategies

2020 ◽  
Vol 13 (2) ◽  
pp. 129-148
Author(s):  
Sibel Yildiz Çankaya
Keyword(s):  
Supply Chain ◽  
Structural Equation ◽  
Mail Survey ◽  
Equation Modeling ◽  
Competitive Performance ◽  
Cross Sectional ◽  
Content Type ◽  
Strategic Sourcing ◽  
Plant Level ◽  
Lean Supply Chain

Purpose This study aims to examine the effects of strategic sourcing (SS) on lean supply chain (LSC) and agile supply chain (ASC) strategies and investigate the role of these concepts on development of competitive performance. Design/methodology/approach A proposed research model and hypotheses are tested by using cross-sectional e-mail survey data collected from the manufacturing firms operating in Turkey. SS is conceptualized as a second-order factor. Structural equation modeling is used to test the proposed hypotheses. Findings This study reached the conclusion that SS affects LSC and ASC strategies positively. Additionally, it is seen that these concepts are effective in improvement of competitive performance. Practical implications The results are important in terms of emphasizing the significance of SS in improvement of the lean and agile nature of the supply chain. Originality/value This study contributes to the literature by providing empirical evidence regarding the relationships among SS, supply chain strategies and competitive performance. Research limitations/implications This study was carried out on the plant level where one person from each organization responded to the survey.

scholarly journals Environmental scanning, supply chain integration, responsiveness, and operational performance

2019 ◽  
Vol 39 (5) ◽  
pp. 787-814 ◽  
Author(s):  
Wantao Yu ◽  
Roberto Chavez ◽  
Mark Jacobs ◽  
Chee Yew Wong ◽  
Chunlin Yuan
Keyword(s):  
Supply Chain ◽  
Structural Equation ◽  
Business Environment ◽  
Supply Chain Integration ◽  
Operational Performance ◽  
Equation Modeling ◽  
Environmental Scanning ◽  
Content Type ◽  
Processing Theory ◽  
The Relationship

Purpose It remains unclear how environmental scanning (ES) can generate firm performance through supply chain management (SCM) practices. The purpose of this paper is to investigate the effects of ES on operational performance through supply chain integration (SCI) and supply chain responsiveness (SCR). Design/methodology/approach The scanning–interpretation–action–performance (SIAP) model and organization information processing theory (OIPT) are used to explain the ES–SCI–SCR–performance (S–I–A–P) relationships, which were tested by structural equation modeling of survey data of 329 manufacturing firms in China. Findings The results indicate that ES has a significant positive effect on SCI and SCR. SCI is significantly and positively related to SCR. SCR partially mediates the relationship between ES and operational performance, and fully mediates the relationship between SCI and operational performance. Practical implications Supply chain managers should collaborate with senior executives to obtain signals from ES activities, as input for building SCI and SCR and use SCI as a joint interpretation mechanism of ES signals for developing SCR to reap operational advantages in the rapidly changing business environment. Originality/value Strategic management academics and practitioners have explicitly emphasized the importance of ES in developing strategic plans but are unsure about the role of SCM in creating operational advantages through ES. Using the SIAP model, this study theorizes and demonstrates how SCI and SCR transform signals from ES into operational performance. In doing so, a more precise application of OIPT is explicated in the supply chain context.

Exploring the consequences of judgmental and quantitative forecasting on firms' competitive performance in supply chains

2020 ◽  
Vol 27 (5) ◽  
pp. 1717-1737
Author(s):  
Reza Salehzadeh ◽  
Reihaneh Alsadat Tabaeeian ◽  
Farahnaz Esteki
Keyword(s):  
Supply Chain ◽  
Supply Chains ◽  
Structural Equation ◽  
Three Dimensions ◽  
Equation Modeling ◽  
Supply Chain Performance ◽  
Manufacturing Companies ◽  
Competitive Performance ◽  
Content Type ◽  
Forecasting Methods

PurposeThe purpose of this study is to examine the impacts of different forecasting methods (judgmental, quantitative and mixed forecasting) on firms' supply chains and competitive performance.Design/methodology/approachWorking with three groups of manufacturing companies, we explore the consequences of judgmental, quantitative and mixed forecasting methods on firms' competitive performance in supply chains. The validity of constructs and path relationships was examined using structural equation modeling (SEM).FindingsOur findings indicate that supply chain efficiency influences both cost reduction and customer satisfaction. In addition, the three dimensions of supply chain performance are shown to be direct antecedents of competitive performance. Our empirical results reveal that although all studied forecasting methods meaningfully influence supply chain performance, the mixed method, compared to the other two methods, has greater capabilities to enhance supply chain performance.Originality/valueThis research provides originality and insight into supply chain practices through forecasting methods to improve competitive performance.

The impacts of intra-organizational structural elements on supply chain integration

2019 ◽  
Vol 119 (5) ◽  
pp. 1031-1045
Author(s):  
Yanming Zhang ◽  
Xiande Zhao ◽  
Baofeng Huo
Keyword(s):  
Supply Chain ◽  
Structural Equation ◽  
Supply Chain Integration ◽  
Three Dimensions ◽  
Equation Modeling ◽  
Structural Elements ◽  
Content Type ◽  
Positive Effects ◽  
Supplier Integration ◽  
Human Goodness

PurposeFollowing resource-based view, the purpose of this paper is to investigate the effects of three intra-organizational structural elements on supply chain integration (SCI).Design/methodology/approachBased on data collected from ten countries, this study employs the structural equation modeling method to test the proposed model.FindingsThe results demonstrate that teamwork culture is positively related to three dimensions of SCI. Organizational commitment has positive effects on internal and customer integration (CI), whereas it has no significant effect on supplier integration (SI). Human goodness is only positively related to internal integration, but has no significant effect on SI or CI.Originality/valueThis study contributes to both structural elements literature and SCI enabler literature by operationalizing three human-related components of structural elements and empirically investigating relationships between intra-organizational structural elements and SCI.

Understanding big data-driven supply chain and performance measures for customer satisfaction

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ramadas Thekkoote
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Customer Satisfaction ◽  
Performance Measures ◽  
Data Driven ◽  
Equation Modeling ◽  
Supply Chain Performance ◽  
Content Type ◽  
Different Dimensions ◽  
The Impact

PurposeSupply chain analytics with big data capability are now growing to the next frontier in transforming the supply chain. However, very few studies have identified its different dimensions and overall effects on supply chain performance measures and customer satisfaction. The aim of this paper to design the data-driven supply chain model to evaluate the impact on supply chain performance and customer satisfaction.Design/methodology/approachThis research uses the resource-based view, emerging literature on big data, supply chain performance measures and customer satisfaction theory to develop the big data-driven supply chain (BDDSC) model. The model tested using questionnaire data collected from supply chain managers and supply chain analysts. To prove the research model, the study uses the structural equation modeling technique.FindingsThe results of the study identify the supply chain performance measures (integration, innovation, flexibility, efficiency, quality and market performance) and customer satisfaction (cost, flexibility, quality and delivery) positively associated with the BDDSC model.Originality/valueThis paper fills the significant gap in the BDDSC on the different dimensions of supply chain performance measures and their impacts on customer satisfaction.

Sign in / Sign up

Export Citation Format

Share Document