scholarly journals Heterogeneous Analysis of the Nexus between Big Data Analytics and Value Co-Creation: Insight from Selected Service Businesses in Ghana

2021 ◽  
Vol 25 ◽  
pp. 533-543
Author(s):  
Samuel Kofi Otchere ◽  
Hongyun Tian ◽  
Cephas Paa Kwasi Coffie ◽  
Frank Hammond
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Structural Equation ◽  
Big Data Analytics ◽  
Equation Model ◽  
Data Teams ◽  
Strategic Decisions ◽  
Data Volume ◽  
Using Data ◽  
Service Businesses

The study examines big datas role analytics ininterfirm value co-creation in selected service businesses in Ghana by focusing specifically on the heterogenous big data characteristics. This is in response to the ongoing debate on value co-creation and big data nexus. Using data from 61 purposively sampled service businesses in Accra-Ghana, the structural equation model provides that; big data analytics positively influences interfirm value co-creation in the selected businesses. Further, aside from big data volume, all the other big data characteristics (velocity, veracity, value, and variety) also individually influence value co-creation. Therefore, this challenges businesses of all sizes to invest in the application of big data for strategic decisions like value co-creation. Again, although businesses should be willing to collaborate with others, big data teams should be fully equipped for optimal analytics.

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.

Big data analytics capabilities and knowledge management: impact on firm performance

2019 ◽  
Vol 57 (8) ◽  
pp. 1923-1936 ◽  
Author(s):  
Alberto Ferraris ◽  
Alberto Mazzoleni ◽  
Alain Devalle ◽  
Jerome Couturier
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
Data Analytics ◽  
Structural Equation ◽  
Positive Impact ◽  
Big Data Analytics ◽  
Integrated Approach ◽  
Content Type ◽  
Decision Making Processes ◽  
External Sources

Purpose Big data analytics (BDA) guarantees that data may be analysed and categorised into useful information for businesses and transformed into big data related-knowledge and efficient decision-making processes, thereby improving performance. However, the management of the knowledge generated from the BDA as well as its integration and combination with firm knowledge have scarcely been investigated, despite an emergent need of a structured and integrated approach. The paper aims to discuss these issues. Design/methodology/approach Through an empirical analysis based on structural equation modelling with data collected from 88 Italian SMEs, the authors tested if BDA capabilities have a positive impact on firm performances, as well as the mediator effect of knowledge management (KM) on this relationship. Findings The findings of this paper show that firms that developed more BDA capabilities than others, both technological and managerial, increased their performances and that KM orientation plays a significant role in amplifying the effect of BDA capabilities. Originality/value BDA has the potential to change the way firms compete through better understanding, processing, and exploiting of huge amounts of data coming from different internal and external sources and processes. Some managerial and theoretical implications are proposed and discussed in light of the emergence of this new phenomenon.

Classification of Pests for Rice Crop Using Big Data Analytics

2019 ◽  
Vol 8 (3) ◽  
pp. 27-31
Author(s):  
R. P. L. Durgabai ◽  
P. Bhargavi ◽  
S. Jyothi
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Andhra Pradesh ◽  
Big Data Analytics ◽  
Rice Crop ◽  
Strategic Decisions ◽  
High Productivity ◽  
Almost All ◽  
Big Data Technology

Data, in today’s world, is essential. The Big Data technology is rising to examine the data to make fast insight and strategic decisions. Big data refers to the facility to assemble and examine the vast amounts of data that is being generated by different departments working directly or indirectly involved in agriculture. Due to lack of resources the pest analysis of rice crop is in poor condition which effects the production. In Andhra Pradesh rice is cultivated in almost all the districts. The goal is to provide better solutions for finding pest attack conditions in all districts using Big Data Analytics and to make better decisions on high productivity of rice crop in Andhra Pradesh.

Performance-based evaluation of academic libraries in the big data era

2020 ◽  
pp. 016555152091851 ◽  
Author(s):  
A Y M Atiquil Islam ◽  
Khurshid Ahmad ◽  
Muhammad Rafi ◽  
Zheng JianMing
Keyword(s):  
Big Data ◽  
Technology Acceptance ◽  
Academic Libraries ◽  
Data Analytics ◽  
Structural Equation ◽  
Big Data Analytics ◽  
Data Practices ◽  
Ease Of Access ◽  
Acceptance Model ◽  
Performance Based Evaluation

The concept of big data has been extensively considered as a technological modernisation in organisations and educational institutes. Thus, the purpose of this study is to determine whether the modified technology acceptance model (MTAM) is viable for evaluating the performance of librarians in the use of big data analytics in academic libraries. This study used an empirical research method for collecting data from 211 librarians working in Pakistan’s universities. On the basis of the findings of the MTAM analysis by structural equation modelling, the performances of the academic libraries were comprehended through the process of big data. The main influential components of the performance analysis in this study were the big data analytics capabilities, perceived ease of access and the usefulness of big data practices in academic libraries. Subsequently, the utilisation of big data was significantly affected by skills, perceived ease of access and the usefulness of academic libraries. The results also suggested that the various components of the academic libraries lead to effective organisational performance when linked to big data analytics.

scholarly journals Decision support model to adopt big data analytics in higher education systems

2021 ◽  
Vol 8 (6) ◽  
pp. 67-78
Author(s):  
Adel Alkhalil ◽  
Keyword(s):  
Higher Education ◽  
Big Data ◽  
Data Analytics ◽  
Structural Equation ◽  
Data Science ◽  
Big Data Analytics ◽  
Equation Modeling ◽  
Systematic Research ◽  
Education Systems ◽  
Higher Education Systems

Data science or specifically data analytics systems have become an emerging trend in information technology and have attracted many organizations, including higher education. Higher Education Systems (HES) involve very active entities (students, faculty members, researchers, employers) who generate and require large volumes of data that go beyond the structured data stored in the house. The collection, analysis, and visualization of such big data present a huge challenge for HES. Big data analysis could be the solution to this challenge. However, the rationale and decision process for the adoption of big data analytics can be difficult. Such a knowledge-driven process requires a multitude of technical and organizational aspects that must be accounted for to ensure informed decisions are made. Existing research and development indicates that the decision to adopt, although systematic research with a theoretical background is rare and none of the existing studies have considered diffusion of innovation (DOI) theory. This paper aims to support HES, by providing a systematic analysis of the determinants for the decision to adopt big data analytics. An integrated framework referred to as the Technology Organization Environment (TOE) framework is proposed. The proposed framework is validated using structural equation modeling. Eleven determinants are confirmed that influence the TOE-driven framework for data analytics in HES. The result is expected to contribute to on-going research that attempts to address the complex and multidimensional challenge that relates to data science and analytics implementation in HES.

Big data analytics adoption model for small and medium enterprises

2020 ◽  
Vol 11 (4) ◽  
pp. 483-513 ◽  
Author(s):  
Parisa Maroufkhani ◽  
Wan Khairuzzaman Wan Ismail ◽  
Morteza Ghobakhloo
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Structural Equation ◽  
Small And Medium Enterprises ◽  
Big Data Analytics ◽  
Mediation Effect ◽  
Unified Framework ◽  
Content Type ◽  
Medium Enterprises ◽  
The Relationship

Purpose Big data analytics (BDA) is recognized as a turning point for firms to improve their performance. Although small- and medium-sized enterprises (SMEs) are crucial for every economy, they are lagging far behind in the usage of BDA. This study aims to provide a single and unified model for the adoption of BDA among SMEs with the integration of the technology–organization–environment (TOE) model and resource-based view. Design/methodology/approach A survey of 112 manufacturing SMEs in Iran was conducted, and the data were analysed using structural equation modelling to test the model of this study. Findings The results offer evidence of a BDA mediation effect in the relationship between technological, organizational and environmental contexts, and SMEs performance. The findings also demonstrated that technological and organizational elements are the more significant determinants of BDA adoption in the context of SMEs. In addition, the result of this study confirmed that BDA adoption could enhance the financial and market performance of SMEs. Practical implications Providing a single unified framework of BDA adoption for SMEs enables them to appreciate the importance of most influential elements (technology, organization and environment) in the adoption of BDA. Also, this study may encourage SMEs to be more willing to use BDA in their businesses. Originality/value Although there are studies on BDA adoption and firm performance among large companies, there is a lack of empirical research on SMEs, in particular, based on the TOE model. SMEs differ from large companies in terms of the availability of resources and size. Therefore, this study aimed to initiate a conceptual framework of BDA adoption for SMEs to assist them to be able to take advantage of the adoption of such technology.

Big Data and Its Role in Facilitating the Visualization of Financial Analytics

2018 ◽  
pp. 704-722
Author(s):  
Iman Raeesi Vanani ◽  
Maziar Shiraj Kheiri
Keyword(s):  
Big Data ◽  
Global Economy ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Accounting Measurement ◽  
Use Of Data ◽  
Accounting Environment ◽  
Using Data ◽  
And Function ◽  
Support Decision Making

The business use of data analytics is growing rapidly in the accounting environment. Similar to many new systems that involve accounting information, data analytics has fundamentally changed task based processes particularly those tasks that provide inference, prediction and assurance to decision makers. Big Data analytics is the process of inspecting, cleaning, transforming, and modeling Big Data to discover and communicate useful information and patterns, suggest conclusions, and support decision making. Big Data now pervades every sector and function of the global economy. These essays focus on the uses and challenges of Big Data in accounting (measurement) and auditing (assurance). The objective of this chapter is to examine how Big Data analytics will impact the accounting and auditing environment. This is important to practitioners as well as academics because they will be using data analytics in accounting and auditing tasks and will need to have an in-depth familiarity with financial analytics to effectively accomplish these tasks and make effective and efficient decisions.

scholarly journals Usefulness and Impact of Big Data in Libraries: An Opportunity to Implement Embedded Librarianship

2021 ◽  
Author(s):  
Subhajit Panda
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Information Overload ◽  
Big Data Analytics ◽  
Library Services ◽  
Data Practices ◽  
Data Life Cycle ◽  
Data Volume ◽  
Primary Focus

The concept of Big Data has been extensively considered as a technological modernisation in Library & Information centres. According to IDC, data volume is set to increase exponentially and envisages a data volume of over 160 zettabytes by the year 2025. Size is the first, and at times, the only dimension that leaps out at the mention of Big Data. Big Data is defined as information overload due to the volume, velocity, variety, variability & veracity of the data which must be processed to get value and better visualisation. Big Data contains the answer to several valuable questions related to patterns, trends & associations of user behaviour. It plays a major role in helping libraries to clearly understand the changing user needs, accordingly, reshape & restructure their services & procedures. The primary focus of this study was to explore the concept of Big Data in a library environment, steps to introduce Big Data in libraries and the use of Big Data in providing library services using the concept of data life cycle developed by DataONE. The main influential components to perform this study was the capabilities of Big Data analytics, the need & usefulness of Big Data practices, its significant utilisation in libraries and discuss some globally taken practical initiatives. The study highlights the important role of Big Data analytics capabilities to uncover new challenges of information utilisation, consequently helps a librarian to fulfil his role as an Embedded Librarian, both in theoretical & practical terms.

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