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.

Big Data Analytics in the Agribusiness Supply Chain Management

2021 ◽  
Vol 9 (3) ◽  
pp. 32-42
Author(s):  
Marisol Valencia-Cárdenas ◽  
Jorge Anibal Restrepo-Morales ◽  
Francisco Javier Día-Serna
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Structural Equation Model ◽  
Inventory Management ◽  
Latent Variables ◽  
Structural Equation ◽  
Big Data Analytics ◽  
Equation Model ◽  
Chain Management

Importance and impact of the systems related to Agribusiness and Agri-food, are increasing around the world and demand a paramount attention. Collaboration in the inventory management is an integral part of the supply chain management, related to proactive integration among the chain actors facilitating production and supply, in especial in the agroindustrial sector of the Departamento de Antioquia, Colombia. This research establishes the main relationships between latent variables as collaboration, technology, models, optimization and inventory management, based on a literature review and applying a Structural Equation Model to a survey data of a sample of agribusiness companies. The results show that Available Technologies associated with Big Data, generates improvement of Collaboration Strategies, improving also Forecasting and Optimization; besides, Inventory Planning and Collaboration are related to Available Technologies associated with Big Data. A Poisson regression model and a Structural Equation Model estimations detect that the increasing strategies of technologies and Big Data are favorable to apply collaboration in the supply chain management, increasing possibilities to the enterprise competitiveness.

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.

Analyzing the post-harvest supply chain enablers of vertical integration for rural employability and marketability

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rahul Priyadarshi ◽  
Srikanta Routroy ◽  
Girish Kant
Keyword(s):  
Factor Analysis ◽  
Supply Chain ◽  
Vertical Integration ◽  
Rural Areas ◽  
Structural Equation ◽  
Equation Modeling ◽  
Value Addition ◽  
Content Type ◽  
Post Harvest ◽  
The Impact

Purpose The purpose of this study is to analyze the post-harvest supply chain enablers (PHSCEs) for vertical integration to enhance rural employability, farmer profitability and rural produce marketability (i.e. market prospects) in the post-harvest supply chain (PHSC). The impact of vertical integration is also explored for various commercial produces. Design/methodology/approach A structural equation modeling (SEM) of PHSCEs for vertical integration was developed to enhance market prospects, rural employability and farmer profitability. The impact of business-to-business (B2B) and business-to-customer market prospects are explored in various dimensions for stakeholders such as farmers, manufacturers (processors), distributors and retailers. The fuzzy technique for order of preference by similarity to ideal solution (F-TOPSIS) was used to prioritize these PHSCEs to improve market prospects and rural employability. Findings The PHSCEs are clustered into three groups, namely, initiatives at the strategic frontier, initiatives at the tactical frontier and concerns for rural employability via vertical integration using exploratory factor analysis, confirmatory factor analysis and SEM to prove the null hypothesis. With F-TOPSIS results, the availability of warehousing was found to be the most crucial enabler when observing the PHSCEs from the initiatives’ perspective. The technology adaptability and availability, institute for training and research and information infrastructure and information visibility were found to be the key PHSCEs when observed from PHSC stakeholders’ perspectives. Research limitations/implications The implementation of this study will improve the rural produce marketability, rural employability, B2B marketing (i.e. effective distribution) and subsequent value chains with the practice of vertical integration for fresh produce at the rural level. Practical implications The outcomes of this study have a key role in developing the rural regions and improving rural livelihoods via value addition. The awareness of commercial cultivation and value addition in rural areas needs to be improved. This will help farmers to earn better revenues with improved market prospects in comparison to the revenues obtained from the cultivation of staple/conventional crops. Originality/value In an era of cold chains and food processing, this study aims to disseminate awareness about value addition for commercial and fresh produces at the rural level. The implication of this study will improve rural produce marketability, rural employability and farmer profitability at the rural level with the level of vertical integration.

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 impact of applying knowledge in the technological pillars of Industry 4.0 on supply chain performance

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Anurak Sawangwong ◽  
Poti Chaopaisarn
Keyword(s):  
Factor Analysis ◽  
Supply Chain ◽  
Organizational Performance ◽  
Cyber Security ◽  
Industry 4.0 ◽  
Structural Equation ◽  
Equation Modeling ◽  
Supply Chain Performance ◽  
Content Type ◽  
The Impact

PurposeThe purpose of the study is to investigate the impact of technological pillars of Industry 4.0 based on knowledge to adopt the supply chain performance of Thai small and medium-sized enterprises (SMEs) 4.0. In addition, to increase knowledge and understanding of how to apply knowledge in technology 4.0 to improve the efficiency of supply chains and organizations.Design/methodology/approachAn integrated model was developed from applying knowledge in five technological pillars of Industry 4.0 such as Internet of things (IoTs), cloud computing, big data and analytics, additive manufacturing and cyber-security. The bibliometric analysis was used to find the relationship between the technological pillars of Industry 4.0 and the literature review. The survey questionnaires were sent to Thai SME 4.0 (manufacturing aspect). Of these, 240 useable responses were received, resulting in a response rate of 65.84%, after then, the exploratory factor analysis (EFA), confirmatory factor analysis (CFA), structural equation modeling (SEM) and validity were used to evaluate the model through IBM SPSS 21 and AMOS 22.FindingsEFA showed the four groups of the technological pillars of Industry 4.0, such as support human, automation, real-time and security. These groups positively impact supply chain performance (increase delivery reliability, increase resource efficiency, decrease costs in the supply chain and reduce delivery time). Another important finding is that supply chain performance positively impacts organizational performance in profitability, return on investment (ROI) and sale growth.Originality/valueThis study is a model development to support the supply chain performance and increase understanding related to applying knowledge in technology 4.0 that remains unclear for SME 4.0.

Performance evaluation of higher education system amid COVID-19: a threat or an opportunity?

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Bhaskar B. Gardas ◽  
Nima Jafari Navimipour
Keyword(s):  
Higher Education ◽  
Factor Analysis ◽  
Structural Equation Modeling ◽  
Human Resource Development ◽  
Latent Variables ◽  
Structural Equation ◽  
Equation Modeling ◽  
Educational Performance ◽  
Content Type ◽  
Confirmatory Factor

PurposeCOVID-19 is moving the world towards a significant number of structural changes, and this pandemic is influencing each individual, society and industry at large. The present empirical research intends to identify the constructs (latent variables) caused mainly due to the outbreak of COVID-19 and analyze their influence on the education system's performance.Design/methodology/approachA pilot study was carried out with 105 responses to gain deeper insights into the factor structure and validate the scale. Then, the exploratory factor analysis was applied to explore five factors. Later on, the confirmatory factor analysis was employed to check the model's unidimensionality, validity and reliability. Finally, structural equation modeling (SEM) was used to explore the factors influencing educational performance.FindingsFour hypotheses were tested, out of which two were supported, i.e. “compatibility with online mode” and “new opportunities” were found to influence educational performance significantly.Practical implicationsThis investigation aims to provide vital information to the ministry of human resource development and educationists/academicians to understand the influence of the higher education system's factors. Also, it offers some strategies and plans to improve the higher educational systems performance in similar situations.Originality/valueThe previous studies did not identify and analyze the factors that influence the educational system's performance; especially, amid COVID-19 using the exploratory, confirmatory factor analyses and structural equation modeling approach.

Indian students’ attitude toward educational debt: scale development and validation

2019 ◽  
Vol 27 (4) ◽  
pp. 361-383 ◽  
Author(s):  
Jagdish Kaur ◽  
Sangeeta Arora
Keyword(s):  
Factor Analysis ◽  
Structural Equation Model ◽  
Structural Equation ◽  
Equation Model ◽  
Equation Modeling ◽  
Economic Empowerment ◽  
Content Type ◽  
Educational Debt ◽  
The Impact ◽  
Students Satisfaction

Purpose This paper aims to develop, refine and validate a multidimensional scale for measuring students’ attitude toward educational debt for higher studies in Punjab (India) and the impact of this attitude on the satisfaction of students. Design/methodology/approach The study uses interview and survey approach. The sample comprises 417 students from four public and four private universities of Punjab (India). Exploratory factor analysis and confirmatory factor analysis have been used to develop and validate students’ attitude toward education loan scale (Morgado et al., 2017). Further, structural equation modeling (SEM) has been used to analyze the impact of factors of students’ attitude on their satisfaction. Findings The scale has been tested for both reliability and validity. Analysis has revealed six factors of students’ attitude toward educational debt, namely, economic empowerment, social empowerment, utility, procedural requirements, risk and stress. These, six independent variables and one dependent variable, i.e. students’ satisfaction, were entered into structural equation model. The structural equation model shows that procedural requirements, economic empowerment and utility have a positive, whereas stress has a negative and significant impact on the students’ satisfaction. Practical implications Education financing is a gigantic problem nowadays due to the high cost of self-financing courses in Punjab. To make higher education accessible to all students, education loan plays a vital role. Thus, the attitude of students is of great importance to policymakers to bring reforms in education loan scheme. Originality/value To the best of the authors’ knowledge, this study is the foremost study for developing a validated tool to measure the students’ attitude toward educational debt in India.

Analysis of factors impacting survivability of sustainable supply chain during COVID-19 pandemic: an empirical study in the context of SMEs

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Manish Mohan Baral ◽  
Rajesh Kumar Singh ◽  
Yiğit Kazançoğlu
Keyword(s):  
Supply Chain ◽  
Latent Variables ◽  
Structural Equation ◽  
Small And Medium Enterprises ◽  
Equation Modeling ◽  
Sustainable Supply Chain ◽  
Content Type ◽  
Sustainable Supply Chains ◽  
Business Environments ◽  
Medium Enterprises

PurposeNowadays, many firms are finding ways to enhance the survivability of sustainable supply chains (SUSSCs). The present study aims to develop a model for the SUSSCs of small and medium enterprises (SMEs) during the COVID-19 pandemic.Design/methodology/approachWith the help of exhaustive literature review, constructs and items are identified to collect the responses from different SMEs. A total of 278 complete responses are received and 6 hypotheses are developed. Hypotheses testing have been done using structural equation modeling (SEM).FindingsMajor constructs identified for the study are supply chain (SC) performance measurement under uncertainty (SPMU), supply chain cooperation (SCCO), supply chain positioning (SCP), supply chain administration (SCA), supply chain feasibility (SCF) and the SUSSCs. From statistical analysis of the data collected, it can be concluded that the considered latent variables contribute significantly towardsthe model fit.Research limitations/implicationsThe present study contributes to the existing literature on disruptions and survivability. The study can be further carried out in context to different countries and sectors to generalize the findings.Practical implicationsThe research findings will be fruitful for SMEs and other organizations in developing strategies to improve survivability during uncertain business environments.Originality/valueThe study has developed a model that shows that the identified latent variables and their indicators contribute significantly toward the dependent variable, i.e. survivability. It contributes significantly in bridging the research gaps existing in context to the survivability of SMEs.

Influence of intellectual capital and integration on operational performance: big data analytical capability perspectives

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Chun-Hsi Vivian Chen ◽  
Yu-Cheng Chen
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Intellectual Capital ◽  
Structural Equation ◽  
Operational Performance ◽  
Equation Modeling ◽  
Content Type ◽  
Intangible Resources ◽  
Analytical Capability ◽  
Internal Integration

Purpose In the digital economy, as artificial intelligence applications increase, big data analytical capability (BDAC) plays a crucial role, and intellectual capital is growing in importance. This study aims to examine the possible benefits and effects of intellectual capital, BDAC and integrations on operational performance. Design/methodology/approach This study collected data from firms in Asia, and a total of 257 senior managers completed a questionnaire. Confirmatory factor analysis and structural equation modeling (SEM) is used for statistical analysis. Findings Intellectual capital positively correlates with BDAC, and BDAC positively relates to internal integration but not to external integration. Furthermore, both internal integration and external integration positively correlate with operational performance. This study supports that internal integration is a significant mediator in the influence of BDAC on operational performance. Practical implications First, the authors provide empirical evidence that intelligent capital in intangible resources helps firms to build BDAC. Second, this study stresses the importance of BDAC in business, which enhances the integration of the whole supply chain and results in superior operational performance. Originality/value This is a first attempt from the perspective of intelligent capital and uses SEM to emphasize the relationships among BDAC, supply chain integration and performance based on unique and irreplaceable intangible resources, thus providing a new perspective on the contributing factors of BDAC.

Adoption of green practices throughout the supply chain: an empirical investigation

2019 ◽  
Vol 26 (6) ◽  
pp. 1650-1675 ◽  
Author(s):  
Kailash Choudhary ◽  
Kuldip Singh Sangwan
Keyword(s):  
Factor Analysis ◽  
Supply Chain ◽  
Supply Chains ◽  
Structural Equation ◽  
Structural Model ◽  
Performance Measure ◽  
Equation Modeling ◽  
Content Type ◽  
Green Practices ◽  
Research Propositions

Purpose There is a dichotomy in the actual and expected environmental performances of the Indian enterprises even though the Indian enterprises have aligned their businesses with intended nationally determined contributions (INDC) targets. The purpose of this paper is to analyze the supply chain of Indian enterprises to understand influences to adopt green practices throughout the supply chains, and how these green practices influence economic, operational and environmental performances to reveal the underlying currents explaining difference in actual and expected performance. Design/methodology/approach Five research propositions are developed based on the existing literature. Data are collected from 233 ceramic enterprises in India. Exploratory factor analysis has been done to test construct validity and correlation. Confirmatory factor analysis is used to check unidimensionality of constructs. Structural equation modeling is used to test the strength and direction of the relations between the constructs and to develop the model. Findings The findings of the study suggest that the Indian companies have aligned their businesses with INDC targets but they have not adopted the green practices in inbound and outbound supply chains; therefore, the actual environmental performance is not as expected. Other major finding is that the enterprise and government are not focusing on the informative pressure and instead the focus is on coercive techniques which are not yielding positive results. The statistical results show that the adoption of green practices led to the improvements in environmental and operational performances but reduction in economic performance. Originality/value This paper has analyzed green supply chain management pressure, practice and performance measure for Indian ceramic enterprises and proposed a structural model with their interrelation.

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