Najah Al-shanableh
The adoption of big data analytics in Jordanian SMEs: An extended technology organization environment framework with diffusion of innovation and perceived usefulness
Al-shanableh, Najah; Alzyoud, Mazen; Alomar, Saleh; Kilani, Yousef; Nashnush, Eman; Al-Hawary, Sulieman; Al-Momani, Ala’a
Authors
Mazen Alzyoud
Saleh Alomar
Yousef Kilani
Dr Eman Nashnush E.B.Nashnush@salford.ac.uk
Lecturer
Sulieman Al-Hawary
Ala’a Al-Momani
Abstract
While many small and medium enterprises (SMEs)recognize the benefits of Big Data Analytics (BDA) for digital transformation, they face challenges in implementing this technology, highlighting the need for more research on its adoption by SMEs. The objective of this study is to amalgamate the Technology Organization Environment (TOE) framework with the Diffusion of Innovation (DOI) theory, aiming to dissect the factors that sway BDA adoption in Jordanian SMEs. Additionally, the study delves into how perceived usefulness impacts this adoption process. Utilizing structural equation modeling, the study examined data from 388 managers in Jordan. The study validates all its hypotheses, revealing that variables like relative advantage, compatibility, complexity, top management support, competitive pressure, and security influence perceived usefulness, which subsequently has a positive impact on BDA adoption. This research presents a range of theoretical and practical insights.
Citation
Al-shanableh, N., Alzyoud, M., Alomar, S., Kilani, Y., Nashnush, E., Al-Hawary, S., & Al-Momani, A. (2024). The adoption of big data analytics in Jordanian SMEs: An extended technology organization environment framework with diffusion of innovation and perceived usefulness. International journal of data and network science (Print), 8(2), 753-764. https://doi.org/10.5267/j.ijdns.2024.1.003
Journal Article Type | Article |
---|---|
Acceptance Date | Jan 8, 2024 |
Publication Date | 2024 |
Deposit Date | Sep 3, 2024 |
Publicly Available Date | Sep 17, 2024 |
Journal | International Journal of Data and Network Science |
Print ISSN | 2561-8148 |
Electronic ISSN | 2561-8156 |
Peer Reviewed | Peer Reviewed |
Volume | 8 |
Issue | 2 |
Pages | 753-764 |
DOI | https://doi.org/10.5267/j.ijdns.2024.1.003 |
Files
Published Version
(833 Kb)
PDF
You might also like
EBNO: Evolution of cost‐sensitive Bayesian networks
(2019)
Journal Article
Cost-Sensitive Bayesian Network Learning Using Sampling
(2014)
Book Chapter
Learning cost-sensitive Bayesian networks via direct and indirect methods
(2016)
Journal Article
Downloadable Citations
About USIR
Administrator e-mail: library-research@salford.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2025
Advanced Search