| The use of business-to-business (B2B) information technology is one of the fastest growing areas in electronic commerce. There is tremendous optimism regarding Internet B2B and its advantages. Millions are being invested in development.; B2B technologies have not always been successful, however. Despite the tremendous confidence in traditional electronic data interchange (EDI), it did not penetrate the small and medium enterprise (SME) sector. This was a critical failure, as SMEs are the essential suppliers and/or distributors in all industries.; This study looks at small business supplier adoption of Internet B2B. It uses EDI adoption research and examines Internet B2B from the small business user's point of view. It examines adoption in Hawaii, a geographically isolated economy, and develops policy implications regarding IT adoption.; Working from the theories of diffusion of innovation, network externalities, critical mass, inter-organizational relationships, and general systems, a dynamic simulation model is developed. Several experts examine and validate the model. Scenarios are used to forecast adoption behavior under different circumstances.; The analysis reveals that small suppliers will adopt Internet B2B technologies more rapidly and at a higher rate than traditional EDI due to improved expected intangible and tangible benefit to cost comparisons. Small suppliers in Hawaii will adopt at lower rates than small suppliers nationally. The model tests the effects of two trends, outside online competition and prime contracting. Outside online competition will boost adoption in Hawaii, whereas an increase in prime contracting will result in lower adoption rates.; One of the critical insights the model produces is that independent variables have different effects at different times during the innovation decision process. Partner pressure is important for awareness. Expected tangible and intangible benefit-to-cost comparisons fuel the adoption decision. Post-adoption tangible and intangible benefit-to-cost comparisons determine whether or not the pool of adopters remains constant or drops off.; This study incorporates major diffusion theories in a dynamic model that reveals new key insights regarding the diffusion process. An understanding of variable interaction, the critical timing of variable influence, and the importance of adopter feedback will enable researchers and policy makers to understand, facilitate, and forecast technology adoption behaviors. |
