641 research outputs found

    Dollar-Off or Percent-Off? Discount Framing, Construal Levels, and Advertising Appeals

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    In two studies, the authors reveal how consumers react to marketing messages when two commonly used promotional tactics – price discounts and advertising messages – are synergized. Building on construal level theory, Study 1 shows how dollar-off discount framings (“Buy 2, get $10 off”) trigger low-level construal, while percent-off discount framings (“Buy 2, Get 50% off”) activate high-level construal. Study 2 demonstrates that congruent levels are matched when dollar-off discount appeals are paired with attribute appeals and when percent-off appeals are paired with benefit appeals, leading to more effective marketing communications

    A Conceptual Framework of Information Requirements for Scientists Using Human Biological Samples

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    Introduction. This study was undertaken to develop an information requirement framework for scientists who use biological samples and related data in their research. Method. A self-reporting questionnaire completed by 137 respondents was used to collect data regarding demographics, bio-sample management, bio-sample use and requirements, data requirements, and work and research-related roles and activities. Analysis. Descriptive and TwoStep Cluster analyses were used to analyse the survey data necessary for developing a framework of information requirements. Results. Two groups of biomedical scientists (clinical group and basic scientist group) were formed by their distinct characteristics. A conceptual framework of information requirements for bio-sample researchers was formed. The study determined the following as core components: work roles, tasks, characteristics of data and bio-sample needs, factors affecting information seeking, and outcomes. Conclusions. This study will enable the system designer to understand bio-sample users by means of their information requirements resulted in the proposed framework. Future empirical studies should assess potential users, types of information required depending on their work-related roles, factors affecting information seeking, and the evaluation of information seeking effectiveness

    An exploratory study of user-centered indexing of published biomedical images

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    User-centered image indexing—often reported in research on collaborative tagging, social classification, folksonomy, or personal tagging—has received a considerable amount of attention [1-7]. The general themes in more recent studies on this topic include user-centered tagging behavior by types of images, pros and cons of user-created tags as compared to controlled index terms; assessment of the value added by user-generated tags, and comparison of automatic indexing versus human indexing in the context of web digital image collections such as Flickr. For instance, Golbeck\u27s finding restates the importance of indexer experience, order, and type of images [8]. Rorissa has found a significant difference in the number of terms assigned when using Flickr tags or index terms on the same image collection, which might suggest a difference in level of indexing by professional indexers and Flickr taggers [9]. Studies focusing on users and their tagging experiences and user-generated tags suggest ideas to be implemented as part of a personalized, customizable tagging system. Additionally, Stvilia and her colleagues have found that tagger age and image familiarity are negatively related, while indexing and tagging experience were positively associated [10]. A major question for biomedical image indexing is whether the results of the aforementioned studies, all of which dealt with general image collections, are applicable to images in the medical domain. In spite of the importance of visual material in medical education and the prevalence of digitized images in formal medical practice and education, medical students have few opportunities to annotate biomedical images. End-user training could improve the quality of image indexing and so improve retrieval. In a pilot assessment of image indexing and retrieval quality by medical students, this study compared concept completion and retrieval effectiveness of indexing terms generated by medical students on thirty-nine histology images selected from the PubMed Central (PMC) database. Indexing instruction was only given to an intervention group to test its impact on the quality of end-user image indexing

    Korean Migrant Youth Identity Work in the Transnational Social Field: A Link between Identity, Transnationalism, and New Media Literacy

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    Informed by the new understandings of space, culture, and identity in the fast-changing world where communication technology connects and compresses multiple spaces, this qualitative study examines how Korean migrant youth understand, negotiate, and articulate their complex identities across and beyond various borders. The research questions were: (1) What are the contexts in which migrant youth negotiate their identities? (2) How do youth understand and negotiate their sense of belonging? (3) How do youth’s cultural and literacy practices, particularly in new media, inform and shape their identities? Using an ethnographic case study design, I collected data from 32 survey participants and four core participants. Data included 32 surveys, 32 identity maps, 25 interview transcripts, 200 pages of field notes from observations, and 91 literacy documents across online and offline. A grounded theory approach and concepts of design and curatorship were used to analyze the data. Analysis demonstrated the intersections of conflict and flexibility, resistance and resilience, and vulnerability and agency in youths’ identity work. When youths’ identity was confined by the border-oriented discourses such as citizenship, race, and ethnicity, they expressed a sense of dissonance and felt that they were identified by who they are not. However, when they were able to cross national, linguistic, and cultural borders, they flexibly code-mixed and switched between languages, affiliated with audiences of diverse backgrounds, and positioned themselves resiliently. In this trans-bordering identity construction, new media played a crucial role by creating third spaces where youth could draw on their daily cultural practices, hybridizing diverse identity resources across contexts and audiences. New media served as a dialogic space for identity co-construction between youths and their audiences, an interactive learning platform, and a communicative medium for transnational relationships. Despite their relatively unsettled lives, the young migrants in this study behaved as agentive authors and designers of their identities with and in new media. Educational implications include the need to broaden the concept of literacy, to make connections between students’ lives and school curriculum, and to incorporate students’ voices in developing new pedagogy

    A Comparison of Intensive Care Unit Mortality Prediction Models through the Use of Data Mining Techniques

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    OBJECTIVES: The intensive care environment generates a wealth of critical care data suited to developing a well-calibrated prediction tool. This study was done to develop an intensive care unit (ICU) mortality prediction model built on University of Kentucky Hospital (UKH)\u27s data and to assess whether the performance of various data mining techniques, such as the artificial neural network (ANN), support vector machine (SVM) and decision trees (DT), outperform the conventional logistic regression (LR) statistical model. METHODS: The models were built on ICU data collected regarding 38,474 admissions to the UKH between January 1998 and September 2007. The first 24 hours of the ICU admission data were used, including patient demographics, admission information, physiology data, chronic health items, and outcome information. RESULTS: Only 15 study variables were identified as significant for inclusion in the model development. The DT algorithm slightly outperformed (AUC, 0.892) the other data mining techniques, followed by the ANN (AUC, 0.874), and SVM (AUC, 0.876), compared to that of the APACHE III performance (AUC, 0.871). CONCLUSIONS: With fewer variables needed, the machine learning algorithms that we developed were proven to be as good as the conventional APACHE III prediction
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