Uses and gratifications (U&G) is a media use paradigm from mass communications research that guides the assessment of consumer motivations for media usage and access. It has been used previously in research and decision making related to the promotion of emerging radio and television media. Recent adaptations of U&G research to the Internet are incomplete and have not identified important new Internet-specific gratifications. This paper empirically derives dimensions of consumer Internet use and usage gratifications among customers of a prominent Internet Service Provider (ISP). Results describe three key dimensions related to consumer use of the Internet, including process and content gratifications as previously found in studies of television, as well as an entirely new social gratification that is unique to Internet use. All three dimensions of gratification are relevant to managing the Internet as a commercial medium, and measures developed from the gratification profiles identified here can serve as trait-valid scales in future Internet and e-commerce research.

Lack of consent to a request for donation was the primary cause of the gap between the number of potential donors and the number of actual donors. Since potential and actual donors are highly concentrated in larger hospitals, resources invested to improve the process of obtaining consent in larger hospitals should maximize the rate of organ recovery. The performance of organ-procurement organizations can be assessed objectively through the comparison of the number of actual donors with the number of potential donors in the given service area.

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Specifically,we discuss an application of the back error propagation network for making bankruptcy prediction decisions. Results of simulations with one and two hidden layers with varying nodes are presented. It is observed that the configuration with two hidden layers had a superior classification accuracy compared to the one with a single hidden layer. Based on the initial results it appears that neural network algorithms can be investigated further as potential models for bankruptcy prediction.