scholarly journals Developing countries organizations’ readiness for Big Data analytics

2017 ◽  
Vol 15 (1) ◽  
pp. 260-270 ◽  
Author(s):  
Billy Mathias Kalema ◽  
Motau Mokgadi
Keyword(s):  
Developing Countries ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Management Support ◽  
Top Management Support ◽  
Support Organization ◽  
Skilled Manpower ◽  
Analytical Processing ◽  
Ict Infrastructure

Regardless of the nature, size, or business sector, organizations are now collecting burgeoning various volumes of data in different formats. As much as voluminous data are necessary for organizations to draw good insights needed for making informed decisions, traditional architectures and existing infrastructures are limited in delivering fast analytical processing needed for these Big Data. For success organizations need to apply technologies and methods that could empower them to cost effectively analyze these Big Data. However, many organizations in developing countries are constrained with limited access to technology, finances, infrastructure and skilled manpower. Yet, for productive use of these technologies and methods needed for Big Data analytics, both the organizations and their workforce need to be prepared. The major objective for this study was to investigate developing countries organizations’ readiness for Big Data analytics. Data for the study were collected from a public sector in South Africa and analyzed quantitatively. Results indicated that scalability, ICT infrastructure, top management support, organization size, financial resources, culture, employees’ e-skills, organization’s customers’ and vendors are significant factors for organizations’ readiness for Big Data analytics. Likewise strategies, security and competitive pressure were found not to be significant. This study contributes to the scanty literature of Big Data analytics by providing empirical evidence of the factors that need attention when organizations are preparing for Big Data analytics.

Adoption of Big Data Analytics in Supply Chain Management

2021 ◽  
Vol 14 (2) ◽  
pp. 88-107
Author(s):  
Amin Khalil Alsadi ◽  
Thamir Hamad Alaskar ◽  
Karim Mezghani
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Top Management ◽  
Management Support ◽  
Chain Management ◽  
Top Management Support ◽  
Intention To Adopt

Supported by the literature on big data, supply chain management (SCM), and resource-based theory (RBT), this study aims to evaluate the organizational variables that influence the intention of Saudi SCM professionals to adopt big data analytics (BDA) in SCM. A survey of 220 supply chain respondents revealed that both top management support and data-driven culture have a high significant influence on their intention to adopt BDA. However, the firm entrepreneurial orientation showed no significant effect. Also, the findings revealed that supply chain connectivity positively moderates the link between top management support and intention. This study contributes to the practical field, offering valuable insights for decision makers considering BDA adoption in SCM. It also contributes to the literature by helping minimize the research gap in BDA adoption in the Saudi context.

Understanding the Determinants of Big Data Analytics Adoption

2022 ◽  
pp. 1549-1577
Author(s):  
Surabhi Verma ◽  
Sushil Chaurasia
Keyword(s):  
Big Data ◽  
Significant Influence ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Relative Advantage ◽  
Technology Readiness ◽  
Management Support ◽  
Competitive Pressure ◽  
Top Management Support ◽  
Data Environment

This article aims to empirically investigate the factors that affects the adoption of big data analytics by firms (adopters and non-adopters). The current study is based on three feature that influence BDA adoption: technological context (relative advantage, complexity, compatibility), organizational context (top management support, technology readiness, organizational data environment), and environmental context (competitive pressure, and trading partner pressure). A structured questionnaire-based survey method was used to collect data from 231 firm managers. Relevant hypotheses were derived and tested by partial least squares. The results indicated that technology, organization and environment contexts impact firms' adoption of big data analytics. The findings also revealed that relative advantage, complexity, compatibility, top management support, technology readiness, organizational data environment and competitive pressure have a significant influence on the adopters of big data analytics, whereas relative advantage, complexity and competitive pressure have a significant influence on the non-adopters of big data analytics.

Understanding the Determinants of Big Data Analytics Adoption

2019 ◽  
Vol 32 (3) ◽  
pp. 1-26 ◽  
Author(s):  
Surabhi Verma ◽  
Sushil Chaurasia
Keyword(s):  
Big Data ◽  
Significant Influence ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Relative Advantage ◽  
Technology Readiness ◽  
Management Support ◽  
Competitive Pressure ◽  
Top Management Support ◽  
Data Environment

This article aims to empirically investigate the factors that affects the adoption of big data analytics by firms (adopters and non-adopters). The current study is based on three feature that influence BDA adoption: technological context (relative advantage, complexity, compatibility), organizational context (top management support, technology readiness, organizational data environment), and environmental context (competitive pressure, and trading partner pressure). A structured questionnaire-based survey method was used to collect data from 231 firm managers. Relevant hypotheses were derived and tested by partial least squares. The results indicated that technology, organization and environment contexts impact firms' adoption of big data analytics. The findings also revealed that relative advantage, complexity, compatibility, top management support, technology readiness, organizational data environment and competitive pressure have a significant influence on the adopters of big data analytics, whereas relative advantage, complexity and competitive pressure have a significant influence on the non-adopters of big data analytics.

Big Data Analytics in Developing Economies

2021 ◽  
pp. 149-166
Author(s):  
Forgor Lempogo ◽  
Ezer Osei Yeboah-Boateng ◽  
William Leslie Brown-Acquaye
Keyword(s):  
Developing Countries ◽  
Big Data ◽  
Data Analytics ◽  
Data Science ◽  
Developing World ◽  
Developing Economies ◽  
Big Data Analytics ◽  
It Infrastructure ◽  
Developed Economies ◽  
The Past

In a world increasingly driven by data, most developed economies are leveraging big data to achieve greater feats in various sectors of their economies. From advertisement, commerce, healthcare, and energy to defense, big data has given new insights into the huge volume of data accumulated over the past few decades that is helping reshape our knowledge and understanding of these sectors. Unfortunately, the same cannot be said about the state of big data in the developing world, where investments in IT infrastructure are dangerously low, keeping huge proportions of the population offline. This chapter discussed the challenges that exist in developing countries, which affect the smooth take-off of big data and data science as well as recommendations as to how countries and companies in the developing world can overcome these challenges to harness the benefits and opportunities presented by this technology.

A Survey of Big Data Analytics Systems

2014 ◽  
pp. 392-418 ◽  
Author(s):  
M. Baby Nirmala
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Processing System ◽  
Full Potential ◽  
Analytical Processing ◽  
Tools And Techniques ◽  
New Generation ◽  
Analytical Platforms

In this emerging era of analytics 3.0, where big data is the heart of talk in all sectors, achieving and extracting the full potential from this vast data is accomplished by many vendors through their new generation analytical processing systems. This chapter deals with a brief introduction of the categories of analytical processing system, followed by some prominent analytical platforms, appliances, frameworks, engines, fabrics, solutions, tools, and products of the big data vendors. Finally, it deals with big data analytics in the network, its security, WAN optimization tools, and techniques for cloud-based big data analytics.

scholarly journals Smarter big data analytics for traffic applications in developing countries

2019 ◽  
pp. 11-27
Author(s):  
Tran Vu Pham ◽  
Quang Tran Minh ◽  
Hong-Linh Truong ◽  
Hoa Dam
Keyword(s):  
Developing Countries ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics
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