FIRM TECHNOLOGY ADOPTION MODEL (F-TAM) AMONG SME’S: AN INTERACTIVE ECO-SYSTEM PERSPECTIVE
Journal: IADIS INTERNATIONAL JOURNAL ON COMPUTER SCIENCE AND INFORMATION SYSTEMS (Vol.14, No. 1)Publication Date: 2019-01-01
Authors : Joshua Doe Rogier Van de Wetering Ben Honyenuga; Johan Versendaal;
Page : 70-91
Keywords : ;
Abstract
This paper seeks to test the Firm Technology Adoption Model (F-TAM) using data from a developing country context. The data for this current study were purposively collected from four hundred (400) SMEs in the Greater Accra Region of Ghana. We used partial least squares structural equation modeling (PLS-SEM) for our data analysis. Data revealed that, whereas employee factors can lead to firm adoption, firm factors of adoption do not lead to firm adoption if societal factors, characteristics of the innovation, and employee factors do not moderate the relationship between firm factors and firm adoption. Data also reveals that societal factors do not lead to firm adoption if employee factors do not mediate it. The theoretical contribution of this study is that it challenges the dominant idea in most of the earlier models that firm adoption of innovation will be realized, without reference to factors at other levels of adoption. This positioning of the F-TAM model is a significant departure from earlier models. For industry practitioners, these findings illustrate the essence of putting a premium on recruiting technologically savvy employees if the firm intends to adopt digital technologies.
Other Latest Articles
- RECOMMENDING THERAPEUTIC GAMES TARGETED TO THE INDIVIDUAL NEEDS OF ADULTS WITH AUTISM SPECTRUM DISORDER
- 5G TECHNOLOGY FOR MUSIC EDUCATION: A FEASIBILITY STUDY
- PERCEPTIONS OF TEACHERS TOWARD GAME-BASED PROGRAMMING TOOLS IN K-12 CLASSROOMS
- THE IMPACT OF WORKSHOP ACTIVITIES ON PARENTS’ CONCERNS ABOUT COMPUTER PROGRAMMING EDUCATION IN ELEMENTARY SCHOOL
- Possibilities for Developing and Implementing a Mobile Application for Recognizing the Shape of the Environment, Text, and Reading QR Codes Using the Android Camerax Framework and the Machine Learning KIT
Last modified: 2022-02-11 20:03:58