Author List: Rai, Arun; Patnayakuni, Ravi;
Journal of Management Information Systems, 1996, Volume 13, Issue 2, Page 205-234.
The adoption rate of computer-aided software engineering (CASE) technology continues to be low among information systems departments (ISDs). Some ISDs have reported significant hurdles in propagating CASE usage, while documenting the advantages of the technology. We construct and empirically test a theoretical model to explain CASE adoption behavior. Factors considered include need pull (environmental instability of the ISD and performance gap of the ISD), technology push (internal experimentation and learning from external information sources), and the adoption context (top-management support for the IS function, CASE championship, training availability, and job/role rotation). A national survey of 2,700 ISDs resulted in 405 usable responses for the data analysis. Our analysis suggests a reasonable fit between the model and the data. The results indicate that the need-pull factors do not directly promote CASE adoption behavior. Performance deficit promotes CASE championship behavior while negatively affecting other elements of the adoption context. The instability of ISDs, where the very existence of the ISD may be in question, negatively affects all elements of the adoption context. Learning about CASE from external information sources directly promotes CASE adoption. Both technology push factors positively affect all four elements of the adoption context. Of the contextual elements, CASE training availability, CASE championship, and job/role rotation positively affect CASE adoption behavior. Top management support does not affect CASE adoption behavior, which suggests that such support may be more critical for postadoption stages of the diffusion process.
Keywords: CASE technology;information systems implementation;information technology diffusion;software engineering innovations
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#49 0.194 adoption diffusion technology adopters innovation adopt process information potential innovations influence new characteristics early adopting set compatibility time initial current
#173 0.164 effect impact affect results positive effects direct findings influence important positively model data suggest test factors negative affects significant relationship
#82 0.158 case study studies paper use research analysis interpretive identify qualitative approach understanding critical development managerial elements exploring points positivist presents
#58 0.062 internal external audit auditing results sources closure auditors study control bridging appears integrity manager effectiveness auditor controls facilitating boundaries potential
#75 0.059 behavior behaviors behavioral study individuals affect model outcomes psychological individual responses negative influence explain hypotheses expected theories consequences impact theory
#261 0.057 software development maintenance case productivity application tools systems function tool engineering projects effort code developed applications analysis estimation methodology methods
#42 0.051 perceived results study field individual support effects microcomputer pressure external usefulness test psychological obligations characteristics variables indicate existence availability investigating