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A Bayesian multivariate probit analysis of Korean firms' information system adoption.
- Source :
- Industrial Management & Data Systems; 2011, Vol. 111 Issue 9, p1465-1480, 16p
- Publication Year :
- 2011
-
Abstract
- Purpose – The purpose of this paper is to reveal the core determinants and adoption patterns of the major enterprise information systems. Design/methodology/approach – This study incorporated the core representative and meaningful explanatory variables in the major previous literatures and analyzes the core determinants of businesses' adoption of the essential information systems and the substitutionary patterns among them, using a Bayesian multivariate probit model, which is based on McFadden's random utility model and capable of handling multiple response data. Findings – It was found that not only factors from the classical technological diffusion viewpoint but also factors such as organizational tools and strategic behaviors play an important role in firms' adoption of information systems. Specifically, epidemic effect generally outweighs size effect, and putting more effort into the intensity of information strategy planning is more influential than the hiring of a professional chief information officer. On the other hand, such variables as age of the firm, labor intensity, and number of PCs per person generally have no significant impacts. Finally, a relatively strong complementary relationship exists between enterprise resource planning and customer relationship management adoption, and between e-buy and groupware adoption. Originality/value – The results presented in this paper have important implications for firms on a minimal budget that want to maximize their productivity through the adoption of information systems. They also provide important information for government policymakers whose job it is to design strategies for the successful deployment of information systems. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 02635577
- Volume :
- 111
- Issue :
- 9
- Database :
- Complementary Index
- Journal :
- Industrial Management & Data Systems
- Publication Type :
- Academic Journal
- Accession number :
- 67671078
- Full Text :
- https://doi.org/10.1108/02635571111182791