Organizational investment in information systems is often large and risky given the variety of information requirements placed on systems today. To make more informed decisions and to meet the challenge of developing systems that satisfy these demands, system developers need to achieve a better understanding of factors that ultimately lead to system usage. To enhance this understanding, we posit a holistic framework to examine several constructs suggested in the literature that lead to the behavioral intention to use an information system. Our framework includes situational involvement, intrinsic involvement, argument for change, perceived usefulness, ease of use, prior usage, and attitude constructs. We extend the Davis, Bagozzi, and Warshaw Technology Acceptance Model (TAM), which is founded on the Theory of Reasoned Action. A diverse sample from indushy is used to test our model. Structural equation modeling is used to examine the entire pattern of intercorrelations among the constructs and to test related propositions. A hierarchical s t r u c m is used to compare the explanatory ability of TAM with our extension. Our model explains a large portion of the covariance among the constructs that lead to a user's behavioral intention to use an information system and compares favorably with TAM. The results indicate that (1) the direct effect of situational involvement on behavioral intention as well as attitude is significant in the negative direction, (2) attitude seems to play a mediating role, and (3) intrinsic involvement plays a significant role in shaping perceptions. Finally, we conclude that the user involvement construct needs to be separated into its psychological as well as its participative components for developers to understand its impact on the systems development process.
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SUMMARYEstimation of the parameters of the stable laws is considered for samples of size at least 50. The modulus of the difference between the empirical and theoretical characteristic functions is weighted and integrated over the real line; this function of the sample values and the parameters is then minimized with respect to the parameters via a gradient search routine nested within a sequential search. Extensive simulation experiments validate the effectiveness of the estimation procedure over the entire parameter space. Application of the procedure to stock market price behaviour is made.
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