... This paper discusses the experience of the authors, based upon a collection of experiments, in terms of a framework for organizing sets of related studies. With such a framework, experiments can be viewed as part of common families of studies, rather than being isolated events. Common families of studies can contribute to important and relevant hypotheses that may not be suggested by individual experiments. A framework also facilitates building knowledge in an incremental manner through the replication of experiments within families of studies. To support the framework, this paper discusses the experiences of the authors in carrying out empirical studies, with specific emphasis on persistent problems encountered in experimental design, threats to validity, criteria for evaluation, and execution of experiments in the domain of software engineering.

When on the move, cognitive resources are reserved partly for passively monitoring and reacting to contexts and events, and partly for actively constructing them. The Resource Competition Framework (RCF), building on the Multiple Resources Theory, explains how psychosocial tasks typical of mobile situations compete for cognitive resources and then suggests that this leads to the depletion of resources for task interaction and eventually results in the breakdown of fluent interaction. RCF predictions were tested in a semi-naturalistic field study measuring attention during the performance of assigned Web search tasks on mobile phone while moving through nine varied but typical urban situations. Notably, we discovered up to eight-fold differentials between micro-level measurements of attentional resource fragmentation, for example from spans of over 16 seconds in a laboratory condition dropping to bursts of just a few seconds in difficult mobile situations. By calibrating perceptual sampling, reducing resource usage for tasks of secondary importance, and resisting the impulse to switch tasks before finalization, participants compensated for the resource depletion. The findings are compared to previous studies in office contexts. The work is valuable in many areas of HCI dealing with mobility. ACM Classification Keywords: H5.m. Information interfaces and presentation (e.g., HCI): Miscellaneous

The aim of this research essay is to examine the structural nature of theory in information systems. Despite the importance of theory, questions relating to its form and structure are neglected in comparison with questions relating to epistemology. The essay addresses issues of causality, explanation, prediction and generalization that underlie an understanding of theory. A taxonomy is proposed that classifies information systems theories with respect to the manner in which four central goals are addressed: analysis, explanation, prediction and prescription. Five interrelated types of theory are distinguished: (i) theory for analysing; (ii) theory for explaining, (iii) theory for predicting; (iv) theory for explaining and predicting; and (v) theory for design and action. Examples illustrate the nature of each theory type. The applicability of the taxonomy is demonstrated by classifying a sample of journal articles. The paper contributes by showing that multiple views of theory exist and by exposing the assumptions underlying different viewpoints. In addition, it is suggested that the type of theory under development can influence the choice of an epistemological approach. Support is given for the legitimacy and value of each theory type. The building of integrated bodies of theory that encompass all theory types is advocated.

Abstract: The case for combining research methods generally, and more specifically that for combining qualitative and quantitative methods, is strong. Yet, research designs that extensively integrate both fieldwork (e.g. case studies) and survey research are rare. Moreover, some journals tend tacitly to specialize by methodology thereby encouraging purity of method. The multi-method model of research while not new, has not been appreciated. In this respect it is useful to articulate and describe its usage through example. By reference to a recently completed study of IS consultant engagement success factors this paper presents an analysis of the benefits of integrating case study and survey research methods. The emphasis is on the qualitative case study method and how it can compliment more quantitative survey research. Benefits are demonstrated through specific examples from the reference study.

The canonical particle swarm algorithm is a new approach to optimization, drawing inspiration from group behavior and the establishment of social norms. It is gaining popularity, especially because of the speed of convergence and the fact it is easy to use. However, we feel that each individual is not simply influenced by the best performer among his neighbors. We thus decided to make the individuals “fully informed. ” The results are very promising, as informed individuals seem to find better solutions in all the benchmark functions.

The influence of teammates ' shared mental models on team processes and performance was tested using 56 undergraduate dyads who "flew " a series of missions on a personal-computer-based flight-combat simulation. The authors both conceptually and empirically distinguished between teammates' task- and team-based mental models and indexed their convergence or "sharedness " using individually completed paired-comparisons matrices analyzed using a network-based algorithm. The results illustrated that both shared-team- and task-based mental models related positively to subsequent team process and performance. Furthermore, team processes fully mediated the relationship between mental model convergence and team effectiveness. Results are discussed in terms of the role of shared cognitions in team effectiveness and the applicability of different interventions designed to achieve such convergence. Increased technology has contributed to the complexity of many tasks performed in the workplace, making it difficult for employ-ees to complete their work independently. In response to the technological advances, many organizations have adopted a team approach to work. Teams are viewed as being more suitable for complex tasks because they allow members to share the workload, monitor the work behaviors of other members, and develop and contribute expertise on subtasks. An abundance of research has been conducted on the factors that contribute to high team perfor-mance (for reviews, see Gist, Locke, & Taylor, 1987; Salas, Dickinson, Converse, & Tannenbaum, 1992). One variable that has recently received much theoretical attention concerns the in-

Human-computer interaction is multidisciplinary, drawing paradigms and techniques from both the natural sciences and the design disciplines. HCI cannot be considered a pure natural science because it studies the interaction between people and artificially-created artifacts, rather than naturally-occurring phenomena, which violates several basic assumptions of natural science. Similarly, HCI cannot be considered a pure design discipline because it strives to independently verify design decisions and processes, and borrows many values from scientists. The purpose of this paper is to provide a simple framework that describes how the research and design models underlying HCI can be integrated. We explore the relationships among these approaches in the context of a particular research site, CENA, the Centre d' tudes de la Navigation Arienne, and illustrate how the various disciplines can contribute to a complex design problem: improving the interface to the French air traffic control syste...

Organizations are attempting to leverage their knowledge resources by employing knowledge management (KM) systems, a key form of which are electronic knowledge repositories (EKRs). A large number of KM initiatives fail due to reluctance of employees to share knowledge through these systems. Motivated by such concerns, this study formulates and tests a theoretical model to explain EKR usage by knowledge contributors. The model employs social exchange theory to identify cost and benefit factors affecting EKR usage, and social capital theory to account for the moderating influence of contextual factors. The model is validated through a large-scale survey of public sector organizations. The results reveal that knowledge self-efficacy and enjoyment in helping others significantly impact EKR usage by knowledge contributors. Contextual factors (generalized trust, pro-sharing norms, and identification) moderate the impact of codification effort, reciprocity, and organizational reward on EKR usage, respectively. It can be seen that extrinsic benefits (reciprocity and organizational reward) impact EKR usage contingent on particular contextual factors whereas the effects of intrinsic benefits (knowledge self-efficacy and enjoyment in helping others) on EKR usage are not moderated by contextual factors. The loss of knowledge power and image do not appear to impact EKR usage by knowledge contributors. Besides contributing to theory building in KM, the results of this study inform KM practice.

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This chapter is about methods for managing and analyzing qualitative data. By qualitative data we mean text: newspapers, movies, sitcoms, e-mail traffic, folktales, life histories. We also mean narratives—narratives about getting divorced, about being sick, about surviving hand-to-hand combat, about selling sex, about trying to quit smoking. In fact, most of the archaeologically recoverable information about human thought and human behavior is text, the good stuff of social science. Scholars in content analysis began using computers in the 1950s to do statistical analysis of texts (Pool, 1959), but recent advances in technology are changing the economics of the social