Andrew Burton-Jones

Reconceptualizing System Usage: An Approach and Empirical Test

Although DeLone, McLean, and others insist that system usage is a key variable in information systems research, the system usage construct has received little theoretical scrutiny, boasts no widely accepted definition, and has been operationalized by a diverse set of unsystematized measures. In this article, we present a systematic approach for reconceptualizing the system usage construct in particular nomological contexts. Comprising two stages, definition and selection, the approach enables researchers to develop clear and valid measures of system usage for a given theoretical and substantive context. The definition stage requires that researchers define system usage and explicate its underlying assumptions. In the selection stage, we suggest that system usage be conceptualized in terms of its structure and function. The structure of system usage is tripartite, comprising a user, system, and task, and researchers need to justify which elements of usage are most relevant for their study. In terms of function, researchers should choose measures for each element (i.e., user, system, and/or task) that tie closely to the other constructs in the researchers nomological network. To provide evidence of the viability of the approach, we undertook an empirical investigation of the relationship between system usage and short-run task performance in cognitively engaging tasks. The results support the benefits of the approach and show how an inappropriate choice of usage measures can lead researchers to draw opposite conclusions in an empirical study. Together, the approach and the results of the empirical investigation suggest new directions for research into the nature of system usage, its antecedents, and its consequences.

Toward a deeper understanding of system usage in organizations: a multilevel perspective

The objective of this paper is to contribute to a deeper understanding of system usage in organizations by examining its multilevel nature. Past research on system usage has suffered from a levels bias, with researchers studying system usage at single levels of analysis only (e.g., the individual, group, or organizational level). Although single-level research can be useful, we suggest that studying organizations one level at a time will ultimately lead to an unnatural, incomplete, and very disjointed view of how information systems are used in practice. To redress this situation, we draw on recent advances in multilevel theory to present system usage as a multilevel construct and provide an illustration for what it takes for researchers to study it as such. The multilevel perspective advanced in this article offers rich opportunities for theoretical and empirical insights and suggests a new foundation for in-depth research on the nature of system usage, its emergence and change, and its antecedents and consequences.

A semiotic metrics suite for assessing the quality of ontologies

A suite of metrics is proposed to assess the quality of an ontology. Drawing upon semiotic theory, the metrics assess the syntactic, semantic, pragmatic, and social aspects of ontology quality. We operationalize the metrics and implement them in a prototype tool called the Ontology Auditor. An initial validation of the Ontology Auditor on the DARPA Agent Markup Language (DAML) library of domain ontologies indicates that the metrics are feasible and highlights the wide variation in quality among ontologies in the library. The contribution of the research is to provide a theory-based framework that developers can use to develop high quality ontologies and that applications can use to choose appropriate ontologies for a given task.

From Use to Effective Use: A Representation Theory Perspective

Information systems must be used effectively to obtain maximum benefits from them. However, despite a great deal of research on when and why systems are used, very little research has examined what effective system use involves and what drives it. To move from use to effective use requires understanding an information systems nature and purpose, which in turn requires a theory of information systems. We draw on representation theory, which states that an information system is made up of several structures that serve to represent some part of the world that a user and other stakeholders must understand. From this theory, we derive a high-level framework of how effective use and performance evolve, as well as specific models of the nature and drivers of effective use. The models are designed to explain the effective use of any information system and offer unique insights that would not be offered by traditional views, which tend to consider information systems to be just another tool. We explain how our theory extends existing research, provides a rich platform for research on effective use, and how it contributes back to the theory of information systems from which it was derived.

Conceptualizing Systems for Understanding: An Empirical Test of Decomposition Principles in Object-Oriented Analysis

During the early phase of systems development, systems analysts often conceptualize the domain under study and represent it in one or more conceptual models. One of the most important, yet elusive roles of conceptual models is to increase analysts understanding of a domain. In this paper, we evaluate the ability of the good decomposition model (GDM) (Wand and Weber 1990) to explain the degree to which conceptual models communicate meaning about a domain to analysts. We address the question, Do unified modeling language (UML) analysis diagrams that manifest better decompositions increase analysts understanding of a domain? GDM defines five conditions (minimality, determinism, losslessness, weak coupling, and strong cohesion) deemed necessary to decompose a domain in such a way that the resulting model communicates meaning about the domain effectively. In our evaluation, we operationalized each of these conditions in a set of UML diagrams and tested participants understanding of those diagrams. Our results lend support to GDM across measures of actual understanding. However, the impact on participants perceptions of their understanding was equivocal.

Minimizing Method Bias Through Programmatic Research

Researchers have long known that research methods influence construct measurements and that this influence, or method bias, can lead to false conclusions. Despite much work in the methodological literature on specific aspects of method bias, such as common method bias and self-report bias, the meaning of method bias remains unclear, and there is no comprehensive approach for dealing with it. This paper offers a clear definition of method bias, proposes a more comprehensive approach for dealing with it, and describes a demonstration exercise applying the approach in an empirical study of how individual system use and task performance relate. The demonstration suggests that the approach is feasible and illustrates how it can help researchers test theories and identify new research opportunities.

CONQUER: A Methodology for Context-Aware Query Processing on the World Wide Web

A major impediment to accurate information retrieval from the World Wide Web is the inability of search engines to incorporate semantics in the search process. This research presents a methodology, CONQUER (CONtext-aware QUERy processing), that enhances the semantic content of Web queries using two complementary knowledge sources: lexicons and ontologies. The methodology constructs a semantic net using the original query as a seed, and refines the net with terms from the two knowledge sources. The enhanced query, represented by the refined semantic net, can be executed by search engines. This paper describes the methodology and its implementation in a prototype. An empirical evaluation shows that queries suggested by the prototype produce more relevant results than those obtained by the original queries. The research, thus, provides a successful demonstration of the use of existing knowledge sources to enhance the semantic content of Web queries. The paper concludes by identifying potential uses of such enhancements of search technology in organizational contexts.

A heuristic-based methodology for semantic augmentation of user queries on the web

As the World Wide Web continues to grow, so does the need for effective approaches to processing users’ queries that retrieve the most relevant information. Most search engines provide the user with many web pages, but at varying levels of relevancy. The Semantic Web has been proposed to retrieve and use more semantic information from the web. However, the capture and processing of semantic information is a difficult task because of the well-known problems that machines have with processing semantics. This research proposes a heuristic-based methodology for building context aware web queries. The methodology expands a user’s query to identify possible word senses and then makes the query more relevant by restricting it using relevant information from the WordNet lexicon and the DARPA DAML library of domain ontologies. The methodology is implemented in a prototype. Initial testing of the prototype and comparison to results obtained from Google show that this heuristic based approach to processing queries can provide more relevant results to users, especially when query terms are ambiguous and/or when the methodology’s heuristics are invoked.

Modeling the user acceptance of e-mail

The technology acceptance model (TAM) predicts whether users will ultimately use software applications based upon causal relationships among belief and attitudinal constructs that influence usage behavior. Electronic mail, or email, is a collaborative technology available to virtually all members of an organization, and typically, there are alternative email applications available for use. This study applies TAM to assess the user acceptance and voluntary usage of a particular email application, cc:mail, in two different organizations. The results largely validate TAM, although the findings suggest that certain external variables, namely length of time since first use, and level of education, directly affect email usage behavior apart from their influence as mediated through the perceived usefulness (PU) and perceived ease of use (PEOU) constructs.

The Role of User Profiles in Context-Aware Query Processing for the Semantic Web

Many queries processed on the World Wide Web do not return the desired results because they fail to take into account the context of the query and information about user’s situation and preferences. In this research, we propose the use of user profiles as a way to increase the accuracy of web pages returned from the Web. A methodology for creating, representing, and using user profiles is proposed. A frame-based representation captures the initial user’s profile. The user’s query history and post query analysis is intended to further update and augment the user’s profile. The effectiveness of the approach for creating and using user profiles is demonstrated by testing various queries.