An important prerequisite for the success of any online service is ensuring that customers' experience – via the interface – satisfies both sensory and functional needs. Developing interfaces that are responsive to customers' needs requires a perspective on interface design as well as a deep understanding of the customers themselves. Drawing upon research in consumer behavior concerning consumer beliefs about technology, we deploy an alternative way to describe customers based on psychographic characteristics. Technology readiness (TR), a multidimensional psychographic construct, offers a way to segment online customers based upon underlying positive and negative technology beliefs. The core premise of this study is that the beliefs form the foundation for expectations of how things should work and how specific online service interfaces are evaluated by customers. At the same time, usability evaluations of specific online services might be contingent on contextual factors, specifically the type of site (hedonic vs. utilitarian) and access method (Web vs. wireless Web). The aspects of usability examined here are those incorporated into the usability metric and instrument based on the Microsoft Usability Guidelines (MUG). The results of an empirical study with 160 participants indicate that (i) TR customer segments vary in usability requirements, and (ii) usability evaluations of specific online service interfaces are influenced by complex interactions among site type, access method, and TR segment membership. As organizations continue to expand their online service offerings, our findings can assist managers in formulating and evolving appropriate Web and wireless Web design and marketing strategies.

This paper describes efforts to develop a pedagogical environment that seeks to influence the learning experiences of students as mobile applications end users, developers, and decision makers. Specifically, via a collaborative effort involving industry sponsors, university technology services, and multiple academic units engaged in information technology education, a graduate-level course called Mobile Applications Development (MAD) was created. The core innovativeness of MAD lies in its delivery structure as a problem-based learning course—centered on emerging technologies like mobile technology—that brings together students with diverse backgrounds from different academic units across the campus. MAD culminates in an industry-sponsored competition, where student teams present their mobile solution to a panel of expert judges from industry and higher education. Via MAD and the associated competitions, students, faculty, and institutional partners can explore the opportunities and challenges associated with mobile technologies. This paper discusses how problem-based learning principles guided the design and implementation of MAD. A multiperspective assessment of the success of MAD is offered. Finally, key lessons learned and guidance to assist other educators are also offered.

Geospatio–temporal conceptual models provide a mechanism to explicitly represent geospatial and temporal aspects of applications. Such models, which focus on both “what” and “when/where,” need to be more expressive than conventional conceptual models (e.g., the ER model), which primarily focus on “what” is important for a given application. In this study, we view conceptual schema comprehension of geospatio–temporal data semantics in terms of matching the external problem representation (that is, the conceptual schema) to the problem-solving task (that is, syntactic and semantic comprehension tasks), an argument based on the theory of cognitive fit. Our theory suggests that an external problem representation that matches the problem solver’s internal task representation will enhance performance, for example, in comprehending such schemas. To assess performance on geospatio–temporal schema comprehension tasks, we conducted a laboratory experiment using two semantically identical conceptual schemas, one of which mapped closely to the internal task representation while the other did not. As expected, we found that the geospatio–temporal conceptual schema that corresponded to the internal representation of the task enhanced the accuracy of schema comprehension; comprehension time was equivalent for both. Cognitive fit between the internal representation of the task and conceptual schemas with geospatio–temporal annotations was, therefore, manifested in accuracy of schema comprehension and not in time for problem solution. Our findings suggest that the annotated schemas facilitate understanding of data semantics represented on the schema.

In this paper, we summarize the discussions of the panel on “Advances in Data Modeling Research,” held at the Americas Conference on Information Systems (AMCIS) in 2005. We focus on four primary areas where data modeling research offers rich opportunities: spatio-temporal semantics, genome research, ontological analysis and empirical evaluation of existing models. We highlight past work in each area and also discuss open questions, with a view to promoting future research in the overall data modeling area.

A database design-support environment supports a data analyst in eliciting, articulating, specifying and validating data-related requirements. Extant design-support environments—based on conventional conceptual models—do not adequately support applications that need to organize data based on time (e.g., accounting, portfolio management, personnel management) and/or space (e.g., facility management, transportation, logistics). For geo-spatio-temporal applications, it is left to database designers to discover, design and implement—on an ad-hoc basis—the temporal and geospatial concepts that they need to represent the miniworld. To elicit the geo-spatio-temporal data semantics, we characterize guiding principles for augmenting the conventional conceptual database design approach, present our annotation-based approach, and illustrate how our proposed approach can be instantiated via a proof-of-concept prototype. Via a proof-of-concept database design-support environment, we exemplify our annotation-based approach, and show how segregating ‘‘what’’ from ‘‘when/where’’ via annotations satisfies ontologic- and cognition-based requirements, dovetails with existing database design methodologies, results in upward-compatible conceptual as well as XML schemas, and provides a straightforward mechanism to extend extant design-support environments.

Although information systems (IS) problem solving involves knowledge of both the IS and application domains, little attention has been paid to the role of application domain knowledge. In this study, which is set in the context of conceptual modeling, we examine the effects of both IS and application domain knowledge on different types of schema understanding tasks: syntactic and semantic comprehension tasks and schema-based problem-solving tasks. Our thesis was that while IS domain knowledge is important in solving all such tasks, the role of application domain knowledge is contingent upon the type of understanding task under investigation. We use the theory of cognitive fit to establish theoretical differences in the role of application domain knowledge among the different types of schema understanding tasks. We hypothesize that application domain knowledge does not influence the solution of syntactic and semantic comprehension tasks for which cognitive fit exists, but does influence the solution of schema-based problem-solving tasks for which cognitive fit does not exist. To assess performance on different types of conceptual schema understanding tasks, we conducted a laboratory experiment in which participants with high- and low-IS domain knowledge responded to two equivalent conceptual schemas that represented high and low levels of application knowledge (familiar and unfamiliar application domains). As expected, we found that IS domain knowledge is important in the solution of all types of conceptual schema understanding tasks in both familiar and unfamiliar applications domains, and that the effect of application domain knowledge is contingent on task type. Our findings for the EER model were similar to those for the ER model. Given the differential effects of application domain knowledge on different types of tasks, this study highlights the importance of considering more than one application domain in designing future studies on conceptual modeling.

Business rules are the basis of any organization. From an information systems perspective, these business rules function as constraints on a database helping ensure that the structure and content of the real world—sometimes referred to as miniworld—is accurately incorporated into the database. It is important to elicit these rules during the analysis and design stage, since the captured rules are the basis for subsequent development of a business constraints repository. We present a taxonomy for set-based business rules, and describe an overarching framework for modeling rules that constrain the cardinality of sets. The proposed framework results in various types constraints, i.e., attribute, class, participation, projection, co-occurrence, appearance and overlapping, on a semantic model that supports abstractions like classification, generalization/specialization, aggregation and association. We formally define the syntax of our proposed framework in Backus-Naur Form and explicate the semantics using first-order logic. We describe partial ordering in the constraints and define the concept of meta constraints, which can be used for automatic constraint consistency checking during the design stage itself. We demonstrate the practicality of our approach with a case study and show how our approach to modeling business rules seamlessly integrates into existing database design methodology. Via our proposed framework, we show how explicitly capturing data semantics will help bridge the semantic gap between the real world and its representation in an information system.

While many real-world applications need to organize data based on space (e.g., geology, geomarketing, environmental modeling) and/or time (e.g., accounting, inventory management, personnel management), existing conventional conceptual models do not provide a straightforward mechanism to explicitly capture the associated spatial and temporal semantics. As a result, it is left to database designers to discover, design, and implement—on an ad hoc basis—the temporal and spatial concepts that they need. We propose an annotation-based approach that allows a database designer to focus first on nontemporal and nongeospatial aspects (i.e., “what”) of the application and, subsequently, augment the conceptual schema with geospatiotemporal annotations (i.e., “when” and “where”). Via annotations, we enable a supplementary level of abstraction that succinctly encapsulates the geospatiotemporal data semantics and naturally extends the semantics of a conventional conceptual model. An overarching assumption in conceptual modeling has always been that expressiveness and formality need to be balanced with simplicity. We posit that our formally defined annotation-based approach is not only expressive, but also straightforward to understand and implement.

Granularities are integral to spatial and temporal data. A large number of applications require storage of facts along with their temporal and spatial context, which needs to be expressed in terms of appropriate granularities. For many real-world applications, a single granularity in the database is insufficient. In order to support any type of spatial or temporal reasoning, the semantics related to granularities needs to be embedded in the database. Specifying granularities related to facts is an important part of conceptual database design because underspecifying the granularity can restrict an application, affect the relative ordering of events and impact the topological relationships. Closely related to granularities is indeterminacy, i.e., an occurrence time or location associated with a fact that is not known exactly. In this paper, we present an ontology for spatial granularities that is a natural analog of temporal granularities. We propose an upward-compatible, annotation-based spatiotemporal conceptual model that can comprehensively capture the semantics related to spatial and temporal granularities, and indeterminacy without requiring new spatiotemporal constructs. We specify the formal semantics of this spatiotemporal conceptual model via translation to a conventional conceptual model. To underscore the practical focus of our approach, we describe an on-going case study. We apply our approach to a hydrogeologic application at the United States Geologic Survey and demonstrate that our proposed granularity-based spatiotemporal conceptual model is straightforward to use and is comprehensive.

The current revolution in interconnectivity and online availability of the earth science data has enabled hydrology end users to access a wide variety of the earth science data through the World Wide Web. However, these distributed data sources have various data formats and numerous spatial and temporal resolutions, which limits the usability of the available data. In this paper, we describe how we have applied semantic modeling and ontology to achieve context-based information integration. We are developing the Hydrology Decision Support System (HyDSS), a prototype state-of-the-art web-based decision support system that provides a comprehensive environment for information integration and analysis. It is aimed at supporting the entire decision making process of hydrological end-users; i.e., it helps in information collation and can provide an interface to third-party modeling and simulation tools.