Metrics for the architectural quality of Internet businesses are essential in gauging the success and failure of e-commerce. This study proposes six dimensions of architectural metrics for Internet businesses: internal stability, external security, information gathering, order processing, system interface, and communication interface. The metrics are based on the three constructs that have been used to evaluate buildings in the real world. The structural construct indicates that Internet businesses need to be stable internally and secure externally. The functional construct implies that Internet businesses should provide convenient functions in the information-gathering and order-processing phases. Finally, the representational construct indicates that they need to provide a pleasant interface both to the system and to those using it. For each of the six metrics, we have constructed questionnaires to measure the perceived level of architectural quality and identified feature lists that may be closely related to the perceived quality level. Large-scale empirical studies were conducted both to validate the proposed metrics and to explore their relevance across four Internet business domains. The validity of the metrics has been obtained in three ways. First, the content validity of the metrics was assured by pretests and pilot survey. Second, the results from the confirmatory factor analysis showed that the metrics had high convergent and discriminant validities. Finally, the reliability coefficients were found to be high enough to establish the reliability of the proposed metrics. The relevance of the metrics has been explored in two ways. Structural equation models were used to test the causal relations between the three constructs and user satisfaction, as well as customer loyalty, in four domains. Correlation analyses were used to explore the relations between the perceived architectural quality and objective design features in four domains. This paper ends with the implications and limitations of the study results.
In order to understand diagrammatic reasoning with multiple diagrams, this study proposes a theoretical framework that focuses on the cognitive processes of perceptual and conceptual integration. The perceptual integration process involves establishing interdependence between relevant system elements that have been dispersed across multiple diagrams, while the conceptual integration process involves generating and refining hypotheses about a system by combining higher-level information inferred from the diagrams. This study applies a diagrammatic reasoning framework of a single diagram to assess the usability of multiple diagrams as an integral part of a system development methodology. Our experiment evaluated the effectiveness and usability of design guidelines to aid problem solving with multiple diagrams. The results of our experiment revealed that understanding a system represented by multiple diagrams involves a process of searching for related information and of developing hypotheses about the target system. The results also showed that these perceptual and conceptual integration processes were facilitated by incorporating visual cues and contextual information in the multiple diagrams as representation aids. Visual cues indicate which elements in a diagram are related to elements in other diagrams; the contextual information indicates how the individual datum in one diagram is related to the overall hypothesis about the entire system.
Our theoretical framework views programming as search in three problem spaces: rule, instance, and representation. The main objectives of this study are to find out how programmers change representation while working in multiple problem spaces, and how representation change increases the difficulty of programming tasks. Our theory of programming indicates that programming is similar to the way scientists discover and test theories. That is, programmers generate hypotheses in the rule space and test these hypotheses in the instance space. Moreover, programmers change their representations in the representation space when rule development becomes too difficult or alternative representations are available. We conducted three empirical studies with different programming tasks: writing a new program, understanding an existing program, and reusing an old program. Our results indicate that considerable cognitive difficulties stem from the need to change representations in these tasks. We conclude by discussing the implications of viewing programming as a scientific discovery for the design of programming environments and training methods.
Information retrieval using probabilistic techniques has attracted significant attention on the part of researchers in information and computer science over the past few decades. In the 1980s, knowledge-based techniques also have made an impressive contribution to "intelligent" information retrieval and indexing. More recently, information science researchers have turned to other, newer artificial intelligence-based inductive learning techniques including neural networks, symbolic learning, and genetic algorithms. The newer techniques have provided great opportunities for researchers to experiment with diverse paradigms for effective information processing and retrieval. In this article we first provide an overview of newer techniques and their usage in information science research. We then present in detail the algorithms we adopted for a hybrid Genetic Algorithms and Neural Nets based system, called GANNET. GANNET performed concept (keyword) optimization for user-selected documents during information retrieval using the genetic algorithms. It then used the optimized concepts to perform concept exploration in a large network of related concepts through the Hopfield net parallel relaxation procedure. Based on a test collection of about 3,000 articles from DIALOG and an automatically created thesaurus, and using Jaccard's score as a performance measure, our experiment showed that GANNET improved the Jaccard's scores by about 50 percent and it helped identify the underlying concepts (keywords) that best describe the user-selected documents.