IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Self-Service Business Intelligence Adoption in Business Enterprises: The Effects of Information Quality, System Quality, and Analysis Quality

Self-Service Business Intelligence Adoption in Business Enterprises: The Effects of Information Quality, System Quality, and Analysis Quality
View Sample PDF
Author(s): Mohammad Daradkeh (Yarmouk University, Jordan)and Radwan Moh'd Al-Dwairi (Yarmouk University, Jordan)
Copyright: 2018
Pages: 23
Source title: Operations and Service Management: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-5225-3909-4.ch050

Purchase


Abstract

Despite the growing popularity of self-service business intelligence (SSBI) tools, empirical research that investigates their acceptance by business professionals is still scarce. This paper presents and tests an integrated model of the antecedents of users' acceptance of SSBI tools in business enterprises. The proposed model is developed based on the technology acceptance model (TAM) and incorporating information and system quality from DeLone and McLean IS success model. It also includes an important factor from the business intelligence literature called analysis quality. To test the model, data were collected through a questionnaire survey from 331 business users working in a variety of industries in Jordan. Data were analysed using structural equation modeling (SEM) techniques. The results demonstrated that the three quality factors– information quality, system quality and analysis quality – are key antecedents of perceived usefulness and ease of use, which in turn were found to be strong predictors of users' intention to use SSBI tools. The findings of this study provide several implications for research and practice, and thus should help in the design and deployment of more user-accepted SSBI tools.

Related Content

Sonal Linda. © 2024. 24 pages.
Yasmin Yousaf Mossa, Peter Smith, Kathleen Ann Bland. © 2024. 40 pages.
Ugochukwu Okwudili Matthew, Jazuli Sanusi Kazaure, Charles Chukwuebuka Ndukwu, Godwin Nse Ebong, Andrew Chinonso Nwanakwaugwu, Ubochi Chibueze Nwamouh. © 2024. 29 pages.
Shruti Jose, Priyakrushna Mohanty. © 2024. 20 pages.
Richa Srishti. © 2024. 15 pages.
Aleksei Alipichev, Liudmila Nazarova, Yana Chistova. © 2024. 21 pages.
Mustafa Öztürk Akcaoğlu, Burcu Karabulut Coşkun. © 2024. 18 pages.
Body Bottom