Although the e-reader marketplace has become increasingly significant in recent years, the current lack of understanding of factors that drive consumers' intention to use e-readers suggests there is much to be learned in this area. This study develops and tests a variation on the Technology Acceptance Model in order to investigate factors influencing consumers' intention to use e-readers. A survey of 234 consumers revealed that perceived usefulness and subjective norm positively influence intention to use, whereas there was little evidence to support the significance of image, cost, or ease of use. Findings have both theoretical and practical implications. Results provide empirical support for some existing TAM relationships, and a basis from which to examine additional factors in order to further develop knowledge affecting intention to use e-readers. Confirmation of the influence of perceived usefulness and subjective norm has implications for practitioners, particularly in terms of product design and marketing.

Automated database design tools employ knowledge-based systems technology in order to provide intelligent support to humans during the process of database analysis and design. However, the level to which these tools can simulate the diagnostic capabilities of human designers when performing a design task remains in question. Human designers employ what might be called "knowledge of the real world" in carrying their design activities; such knowledge is employed by only a few automated database design tools. Therefore, in recent years, there have been a number of attempts to develop tools that are capable of exploiting such real-world knowledge. It has been claimed that the use of such knowledge has the potential to increase the diagnostic performance of automated database design tools. However, to date, little if any formal exploration and validation of this claim has taken place. This paper presents our activities in exploring and validating the implications for exploiting three approaches facilitating the use and exploitation of real-world knowledge in the diagnostic performance of database design tools. The results obtained have demonstrated that the improvement of certain aspects of diagnostic performance has been achieved. However, the extent to which these aspects have been attained and subsequently statistically validated varies.