Search (10 results, page 1 of 1)

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Abstract

This review introduces a comprehensive model of the cognitive process and mechanisms of individual sensemaking to provide a theoretical basis for: - empirical studies that improve our understanding of the cognitive process and mechanisms of sensemaking and integration of results of such studies; - education in critical thinking and sensemaking skills; - the design of sensemaking assistant tools that support and guide users. The paper reviews and extends existing sensemaking models with ideas from learning and cognition. It reviews literature on sensemaking models in human-computer interaction (HCI), cognitive system engineering, organizational communication, and library and information sciences (LIS), learning theories, cognitive psychology, and task-based information seeking. The model resulting from this synthesis moves to a stronger basis for explaining sensemaking behaviors and conceptual changes. The model illustrates the iterative processes of sensemaking, extends existing models that focus on activities by integrating cognitive mechanisms and the creation of instantiated structure elements of knowledge, and different types of conceptual change to show a complete picture of the cognitive processes of sensemaking. The processes and cognitive mechanisms identified provide better foundations for knowledge creation, organization, and sharing practices and a stronger basis for design of sensemaking assistant systems and tools.

Date

22. 8.2014 16:55:39

Type

a

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Abstract

Many scientific fields share common interests for research and education. Yet, very often, these fields do not communicate to each other and are unaware of the work in other fields. Understanding the commonalities and differences among related fields can broaden our understanding of the interested phenomena from various perspectives, better utilize resources, enhance collaboration, and eventually move the related fields forward together. In this article, we present a conceptual framework, namely the Information-Model or I-model, to describe various aspects of information related fields. We consider this a timely effort in light of the evolutions of several information related fields and a set of questions related to the identities of these fields. It is especially timely in defining the newly formed Information Field from a community of twenty some information schools. We posit that the information related fields are built on a number of other fields but with their own unique foci and concerns. That is, core components from other fundamental fields interact and integrate with each other to form dynamic and interesting information related fields that all have to do with information, technology, people, and organization/society. The conceptual framework can have a number of uses. Besides providing a unified view of these related fields, it can be used to examine old case studies, recent research projects, educational programs and curricula concerns, as well as to illustrate the commonalities and differences with the information related fields.

Type

a

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Abstract

External information plays an important role in group decision-making processes, yet research about external information support for Group Support Systems (GSS) has been lacking. In this study, we propose an approach to build a concept space to provide external concept support for GSS users. Built on a Web mining algorithm, the approach can mine a concept space from the Web and retrieve related concepts from the concept space based on users' comments in a real-time manner. We conduct two experiments to evaluate the quality of the proposed approach and the effectiveness of the external concept support provided by this approach. The experiment results indicate that the concept space mined from the Web contained qualified concepts to stimulate divergent thinking. The results also demonstrate that external concept support in GSS greatly enhanced group productivity for idea generation tasks.

Type

a

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Abstract

In the information systems (IS) field, research interest in attitude has fluctuated over the past decades given the inconsistent and inconclusive findings on attitude's effects on behavioral intention (BI) to use information and communication technology (ICT). This study addresses the conceptual, operational, and temporal dynamics of attitude that may have caused the inconsistent and inconclusive results. A longitudinal study was conducted to validate our hypotheses. The results show that: (a) The attitude that significantly influences BI needs to be at a particular specificity with BI on two aspects, the same evaluation target and the same evaluation time, where the time specificity can supersede the target specificity; (b) the relationships among attitudes and intention remain the same if they are measured at the same time, regardless of use stages; (c) the two types of attitudes show different long-lasting effects over time; (d) omitting important mediating factors in a research model may generate misleading messages; and (e) attitudes alone can explain a large amount of variances in BI. The results can help explain the reasons behind inconsistent findings in the literature, inspire additional research efforts, and suggest bringing attitudes back to information systems research due to their theoretical and practical importance.

Type

a

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Abstract

As the information field (IField) becomes more recognized by different constituencies for education and research, the need to better understand its intellectual characteristics becomes more compelling. Although there are various conceptualizations of the IField, to date, in-depth studies based on empirical evidence are scarce. This article reports a study that fills this gap. We focus on the first five ISchools in the ICaucus as a proxy to represent the IField. The intellectual characteristics are depicted by two independent sets of data on tenure track faculty as knowledge contributors: their intellectual heritages and the intellectual substance in their journal publications. We use a critical analysis method to examine doctoral training areas and 3 years of journal publications. Our results indicate that (a) the IField can be better conceptualized with empirical support by a four-component model that includes People, Information, Technology, and Management, as predicted by the I-Model (Zhang & Benjamin, 2007); (b) the ISchools' faculty members are diverse, interdisciplinary, and multidisciplinary as shown by their intellectual heritages, by their research foci, by journals in which they publish, by the contexts within which they conduct research, and by the levels of analysis in research investigations; (c) the five ISchools share similarities while evincing differences in both faculty heritages and intellectual substances; (d) ISchool tenure track faculty members do not collaborate much with each other within or across schools although there is great potential; and (e) intellectual heritages are not good predictors of scholars' intellectual substance. We conclude by discussing the implications of the findings on IField identity, IField development, new ISchool formation and existing ISchool evolution, faculty career development, and collaboration within the IField.

Type

a

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Abstract

Modeling multidimensional relevance in information retrieval (IR) has attracted much attention in recent years. However, most existing studies are conducted through relatively small-scale user studies, which may not reflect a real-world and natural search scenario. In this article, we propose to study the multidimensional user relevance model (MURM) on large scale query logs, which record users' various search behaviors (e.g., query reformulations, clicks and dwelling time, etc.) in natural search settings. We advance an existing MURM model (including five dimensions: topicality, novelty, reliability, understandability, and scope) by providing two additional dimensions, that is, interest and habit. The two new dimensions represent personalized relevance judgment on retrieved documents. Further, for each dimension in the enriched MURM model, a set of computable features are formulated. By conducting extensive document ranking experiments on Bing's query logs and TREC session Track data, we systematically investigated the impact of each dimension on retrieval performance and gained a series of insightful findings which may bring benefits for the design of future IR systems.

Type

a

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Abstract

Affect factors have gained researchers' attention in a number of fields. The Information Systems (IS) literature, however, shows some gaps and inconsistencies regarding the role of affect factors in human-computer interaction. Building upon prior research, this study aims at a better understanding of affect factors by clarifying their relationships with each other and with other primary user acceptance factors. Two affect variables that are different in nature were examined: computer playfulness (CP) and perceived enjoyment (PE). We theoretically clarified and methodologically verified their mediating effects and causal relationships with other primary factors influencing user technology acceptance, namely perceived ease of use (PEOU), perceived usefulness (PU), and behavioral intention (BI). Quantitative data were analyzed using R.M. Baron and D. Kenny's (1986) method for mediating effects and P.R. Cohen, A. Carlsson, L. Ballesteros, and R.S. Amant's (1993) path analysis method for causal relationships. These analyses largely supported our hypotheses. Results from this research indicate that a PE -> PEOU causal direction is favored, and PEOU partially mediates PE's impacts on PU whereas PE fully mediates CP's impact on PEOU. With the increased interest in various affect factors in user technology acceptance and use, our study sheds light on the role of affect factors from both theoretical and methodological perspectives. Practical implications are discussed as well.

Type

a

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Abstract

Throughout an information search, a user needs to make sense of the information found to create an understanding. This requires cognitive effort that can be demanding. Building on prior sensemaking models and expanding them with ideas from learning and cognitive psychology, we examined the use of cognitive mechanisms during individual sensemaking. We conducted a qualitative user study of 15 students who searched for and made sense of information for business analysis and news writing tasks. Through the analysis of think-aloud protocols, recordings of screen movements, intermediate work products of sensemaking, including notes and concept maps, and final reports, we observed the use of 17 data-driven and structure-driven mechanisms for processing new information, examining individual concepts and relationships, and detecting anomalies. These cognitive mechanisms, as the basic operators that move sensemaking forward, provide in-depth understanding of how people process information to produce sense. Meaningful learning and sensemaking are closely related, so our findings apply to learning as well. Our results contribute to a better understanding of the sensemaking process-how people think-and this better understanding can inform the teaching of thinking skills and the design of improved sensemaking assistants and mind tools.

Type

a

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Abstract

This study compares the 45 "Best Paper" award articles with nonaward articles published in the Journal of Association for Information Science and Technology (JASIST) to observe the differences in citations. The results show that, in most cases, the citations of the award articles are more numerous than the median, belonging to the Top-50% stratum. Only 15.6% of the award articles have the status of being the most-cited article of the year in which the article was published; 24.4% belong to the Top-5% stratum of the publication year; 44.4% belong to the Top-10% stratum of the publication year; and 73.3% belong to the Top-25% stratum of the publication year. Surprisingly, from 2000 to 2012, none of the award articles made it to the Top-10% stratum, apart from the year 2004; the least-cited award article received only three citations during a 5-year period. The results show a wide range of citations among the Best JASIST Papers. This study also observes that the number of articles changed little from 1969 to 1995 but grew rapidly from 1996 to 2012. Suggestions for possible ways to better meet the challenges of the journal's growth in size and scope in selecting award articles are provided.

Type

a

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