Factors driving business intelligence adoption: an extended technology-organization-environment framework

Abstract

Business intelligence (BI) is a vital component for businesses of all scales, offering actionable insights crucial for timely decision-making. This technology has become integral across diverse enterprises. Recognizing the factors influencing BI adoption is imperative, and this article employs the organization, complexity, knowledge, technology, user perception and experience, economic, environmental, and social (OCKTUEES) framework to identify key aspects. Building upon the TOE framework, it pinpoints significant variables, emphasizing the importance of factors like user perception and experience, technology, social, economical, and environmental. Employing structural equation modelling on primary data yields actionable insights to address BI adoption challenges. Analysis reveals the user perception and experience, technology, social, economic, and environmental as the top factors. However, the organization appears vulnerable, necessitating a mitigation strategy for successful BI adoption. The study predicts insignificant variables requiring mitigation, such as high costs, inadequate resources, organizational size, security and privacy concerns, risk of open-source adoption, and perception of analytics impacting jobs. This research aids those navigating the BI implementation journey.

Publication DOI: https://doi.org/10.11591/ijeecs.v34.i3.pp1893-1903
Divisions: College of Business and Social Sciences > Aston Business School > Operations & Information Management
College of Business and Social Sciences
College of Business and Social Sciences > Aston Business School
Aston University (General)
Additional Information: This is an open access article under the CC BY-SA license.
Uncontrolled Keywords: Adoption framework,BI drivers,Business intelligence,Organizational adoption,Technology adoption,TOE,Signal Processing,Information Systems,Hardware and Architecture,Computer Networks and Communications,Control and Optimization,Electrical and Electronic Engineering
Publication ISSN: 2502-4760
Last Modified: 25 Apr 2025 07:12
Date Deposited: 23 Apr 2025 15:35
Full Text Link:
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
https://ijeecs. ... icle/view/36653 (Publisher URL)
PURE Output Type: Article
Published Date: 2024-06
Accepted Date: 2024-03-04
Authors: Subramaniam, Radhakrishnan
Palakeel, Prashobhan
Arunmozhi, Manimuthu (ORCID Profile 0000-0003-4909-4880)
Sridharan, Manikandan
Marimuthu, Uthayakumar

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