Big data analytics capabilities and performance: Evidence from a moderated multi-mediation model

2019 ◽  
Vol 149 ◽  
pp. 119781 ◽  
Author(s):  
Riccardo Rialti ◽  
Lamberto Zollo ◽  
Alberto Ferraris ◽  
Ilan Alon
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Mediation Model ◽  
And Performance

scholarly journals Big Data Analytics in E-commerce for the U.S. and China Through Literature Reviewing

2021 ◽  
Vol 9 (1) ◽  
pp. 16-44
Author(s):  
Weiqing Zhuang ◽  
Morgan C. Wang ◽  
Ichiro Nakamoto ◽  
Ming Jiang
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Web Of Science ◽  
Big Data Analytics ◽  
Theoretical Research ◽  
Analysis Method ◽  
Theoretical Comparison ◽  
Statistical Analysis Method ◽  
And Performance ◽  
The U.S

Abstract Big data analytics (BDA) in e-commerce, which is an emerging field that started in 2006, deeply affects the development of global e-commerce, especially its layout and performance in the U.S. and China. This paper seeks to examine the relative influence of theoretical research of BDA in e-commerce to explain the differences between the U.S. and China by adopting a statistical analysis method on the basis of samples collected from two main literature databases, Web of Science and CNKI, aimed at the U.S. and China. The results of this study help clarify doubts regarding the development of China’s e-commerce, which exceeds that of the U.S. today, in view of the theoretical comparison of BDA in e-commerce between them.

A bibliometric analysis of research on Big Data analytics for business and management

2019 ◽  
Vol 57 (8) ◽  
pp. 1993-2009 ◽  
Author(s):  
Lorenzo Ardito ◽  
Veronica Scuotto ◽  
Manlio Del Giudice ◽  
Antonio Messeni Petruzzelli
Keyword(s):  
Big Data ◽  
Citation Analysis ◽  
Bibliometric Analysis ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Theoretical Development ◽  
Firm Resources ◽  
Content Type ◽  
Subjective Assessments ◽  
And Performance

Purpose The purpose of this paper is to scrutinize and classify the literature linking Big Data analytics and management phenomena. Design/methodology/approach An objective bibliometric analysis is conducted, supported by subjective assessments based on the studies focused on the intertwining of Big Data analytics and management fields. Specifically, deeper descriptive statistics and document co-citation analysis are provided. Findings From the document co-citation analysis and its evaluation, four clusters depicting literature linking Big Data analytics and management phenomena are revealed: theoretical development of Big Data analytics; management transition to Big Data analytics; Big Data analytics and firm resources, capabilities and performance; and Big Data analytics for supply chain management. Originality/value To the best of the authors’ knowledge, this is one of the first attempts to comprehend the research streams which, over time, have paved the way to the intersection between Big Data analytics and management fields.

scholarly journals A Survey on Security Models for Data Privacy in Big Data Analytics

2018 ◽  
Vol 7 (S1) ◽  
pp. 87-89
Author(s):  
Avula Satya Sai Kumar ◽  
S. Mohan ◽  
R. Arunkumar
Keyword(s):  
Big Data ◽  
Access Control ◽  
Data Analytics ◽  
Data Privacy ◽  
Big Data Analytics ◽  
Private Key ◽  
Access Structures ◽  
Security Models ◽  
Attribute Based Encryption ◽  
And Performance

As emerging data world like Google and Wikipedia, volume of the data growing gradually for centralization and provide high availability. The storing and retrieval in large volume of data is specialized with the big data techniques. In addition to the data management, big data techniques should need more concentration on the security aspects and data privacy when the data deals with authorized and confidential. It is to provide secure encryption and access control in centralized data through Attribute Based Encryption (ABE) Algorithm. A set of most descriptive attributes is used as categorize to produce secret private key and performs access control. Several works proposed in existing based on the different access structures of ABE algorithms. Thus the algorithms and the proposed applications are literally surveyed and detailed explained and also discuss the functionalities and performance aspects comparison for desired ABE systems.

The emerging big data analytics and IoT in supply chain management: a systematic review

2018 ◽  
Vol 25 (2) ◽  
pp. 141-156 ◽  
Author(s):  
Arun Aryal ◽  
Ying Liao ◽  
Prasnna Nattuthurai ◽  
Bo Li
Keyword(s):  
Systematic Review ◽  
Big Data ◽  
Supply Chain ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Research Approach ◽  
Network Systems ◽  
Content Type ◽  
And Performance ◽  
Over Time

Purpose The purpose of this study is to provide insights into the way in which understanding and implementation of disruptive technology, specifically big data analytics and the Internet of Things (IoT), have changed over time. The study also examines the ways in which research in supply chain and related fields differ when responding to and managing disruptive change. Design/methodology/approach This study follows a four-step systematic review process, consisting of literature collection, descriptive analysis, category selection and material evaluation. For the last stage of evaluating relevant issues and trends in the literature, the latent semantic analysis method was adopted using Leximancer, which allows more rapid, reliable and consistent content analysis. Findings The empirical analysis identified key research trends in big data analytics and IoT divided over two time-periods, in which research demonstrated steady growth by 2015 and the rapid growth was shown afterwards. The key finding of this review is that the main interest in recent big data is toward overlapping customer service, support and supply chain network, systems and performance. Major research themes in IoT moved from general supply chain and business information management to more specific context including supply chain design, model and performance. Originality/value In addition to providing more awareness of this research approach, the authors seek to identify important trends in disruptive technologies research over time.

scholarly journals Study on Big Data Frameworks

2021 ◽  
pp. 491-499
Author(s):  
Adriano Fernandes ◽  
Jonathan Barretto ◽  
Jonas Fernandes
Keyword(s):  
Big Data ◽  
Execution Time ◽  
Data Analytics ◽  
Processing Time ◽  
Performance Metrics ◽  
Big Data Analytics ◽  
Apache Hadoop ◽  
And Performance ◽  
General Collection ◽  
Apache Storm

Big data analytics is becoming more and more popular every day as a tool for evaluating large volumes of data on demand. Apache Hadoop, Spark, Storm, and Flink are four of the most widely used big data processing frameworks. Although all four architectures support big data analysis, they vary in how they are used and the infrastructure that supports it. This paper defines a general collection of main performance metrics, which include Processing Time, CPU Use, Latency, Execution Time, Performance, Scalability, and Fault-tolerance, and contrasting the four big data architectures against these KPIs in a literature review. When compared to Apache Hadoop and Apache Storm frameworks for non-real-time results, Spark was found to be the winner over multiple KPIs, including processing time, CPU usage, Latency, Execution time, and Scalability. In terms of processing time, CPU consumption, latency, execution time, and performance, Flink surpassed Apache Spark and Apache Storm architectures.

scholarly journals Achieving the Success of Sustainability Development Projects through Big Data Analytics and Artificial Intelligence Capability

2020 ◽  
Vol 12 (3) ◽  
pp. 949 ◽  
Author(s):  
Haili Zhang ◽  
Michael Song ◽  
Huanhuan He
Keyword(s):  
Artificial Intelligence ◽  
United States ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
The United States ◽  
Sustainable Growth ◽  
Development Projects ◽  
Research Model ◽  
And Performance

There has been increased interest in studying how big data analytics capability (BDAC) and artificial intelligence capability (AIC) lead to sustainable innovation and performance. Yet, few studies have investigated how these two emerging capabilities affect the success of sustainability development projects through the mediating effects of the sustainability design and commercialization processes. Based on Day and Wensley’s theoretical framework for diagnosing competitive superiority, we propose a research model to investigate how sustainability design and commercialization mediate the relationships between two emerging capabilities and sustainable growth and performance. To test the proposed research model, we collected empirical data from 905 sustainability development projects from China and the United States. This study makes theoretical and managerial contributions to sustainable development theory. The study findings reveal several interesting results. First, BDAC and AIC not only increase the proficiency of sustainability design and commercialization but also directly enhance sustainable growth and performance. Second, sustainability design and commercialization mediate the positive effects of BDAC and AIC on sustainable growth and performance. Finally, the empirical analyses uncovered several cross-national differences. For sustainability design, BDAC is more important than AIC in the United States, while AIC is more important than BDAC in China.

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