Young undergraduate college students are often described as digital natives, presumed to prefer living and working in completely digital information environments. In reality, their world is part-paper/part-digital, in constant transition among successive forms of digital storage and communication devices. Studying for a degree is the daily work of these young people, and effective management of paper and digital academic materials and resources contributes crucially to their success in life. Students must also constantly manage their work against deadlines to meet their course and university requirements. This study, following the Personal Information Management (PIM) paradigm, examines student academic information management under these various constraints and pressures. A total of 41 18- to 22-year-old students were interviewed and observed regarding the content, structure, and uses of their immediate working environment within their dormitory rooms. Students exhibited remarkable creativity and variety in the mixture of automated and manual resources and devices used to support their academic work. The demands of a yearlong procession of assignments, papers, projects, and examinations increase the importance of time management activities and influence much of their behavior. Results provide insights on student use of various kinds of information technology and their overall planning and management of information associated with their studies.
Web 2.0 creates a new world of collaboration. Many online communities of practice have provided a virtual Internet platform for members to create, collaborate, and contribute their expertise and knowledge. To date, we still do not fully understand how members evaluate their knowledge-sharing experiences, and how these evaluations affect their decisions to continue sharing knowledge in online communities of practice. In this study, we examined why members continue to share knowledge in online communities of practice, through theorizing and empirically validating the factors and emergent mechanisms (post-knowledge-sharing evaluation processes) that drive continuance. Specifically, we theorized that members make judgments about their knowledge-sharing behaviors by comparing their normative expectations of reciprocity and capability of helping other members with their actual experiences. We empirically tested our research model using an online survey of members of an online community of practice. Our results showed that when members found that they receive the reciprocity they expected, they will feel satisfied. Likewise, when they found that they can help other members as they expected, they will feel satisfied and their knowledge self-efficacy will also be enhanced. Both satisfaction and knowledge self-efficacy further affect their intention to continue sharing knowledge in an online community of practice. We expect this study will generate interest among researchers in this important area of research, and that the model proposed in this article will serve as a starting point for furthering our limited understanding of continuance behaviors in online communities of practice.
Knowledge sharing is a difficult task for most organizations, and there are many reasons for this. In this article, we propose that the nature of the knowledge shared and an individual's social network influence employees to find more value in person‐to‐person knowledge sharing, which could lead them to bypass the codified knowledge provided by a knowledge management system (KMS). We surveyed employees of a workman's compensation board in Canada and used social network analysis and hierarchical linear modeling to analyze the data. The results show that knowledge complexity and knowledge teachability increased the likelihood of finding value in person‐to‐person knowledge transfer, but knowledge observability did not. Contrary to expectations, whether the knowledge was available in the KMS had no impact on the value of person‐to‐person knowledge transfer. In terms of the social network, individuals with larger networks tended to perceive more value in the person‐to‐person transfer of knowledge than those with smaller networks.
Images contained in scientific publications are widely considered useful for educational and research purposes, and their accurate indexing is critical for efficient and effective retrieval. Such image retrieval is complicated by the fact that figures in the scientific literature often combine multiple individual subfigures (panels). Multipanel figures are in fact the predominant pattern in certain types of scientific publications.The goal of this work is to automatically segment multipanel figures—a necessary step for automatic semantic indexing and in the development of image retrieval systems targeting the scientific literature. We have developed a method that uses the image content as well as the associated figure caption to: (1) automatically detect panel boundaries; (2) detect panel labels in the images and convert them to text; and (3) detect the labels and textual descriptions of each panel within the captions. Our approach combines the output of image‐content and text‐based processing steps to split the multipanel figures into individual subfigures and assign to each subfigure its corresponding section of the caption. The developed system achieved precision of 81% and recall of 73% on the task of automatic segmentation of multipanel figures.
The goal of this research is to evaluate the effect of ad rank on the performance of keyword advertising campaigns. We examined a large‐scale data file comprised of nearly 7,000,000 records spanning 33 consecutive months of a major US retailer's search engine marketing campaign. The theoretical foundation is serial position effect to explain searcher behavior when interacting with ranked ad listings. We control for temporal effects and use one‐way analysis of variance ( ANOVA ) with T amhane's T 2 tests to examine the effect of ad rank on critical keyword advertising metrics, including clicks, cost‐per‐click, sales revenue, orders, items sold, and advertising return on investment. Our findings show significant ad rank effect on most of those metrics, although less effect on conversion rates. A primacy effect was found on both clicks and sales, indicating a general compelling performance of top‐ranked ads listed on the first results page. Conversion rates, on the other hand, follow a relatively stable distribution except for the top 2 ads, which had significantly higher conversion rates. However, examining conversion potential (the effect of both clicks and conversion rate), we show that ad rank has a significant effect on the performance of keyword advertising campaigns. Conversion potential is a more accurate measure of the impact of an ad's position. In fact, the first ad position generates about 80% of the total profits, after controlling for advertising costs. In addition to providing theoretical grounding, the research results reported in this paper are beneficial to companies using search engine marketing as they strive to design more effective advertising campaigns.
This article presents a new Parsimonious Citer‐Based Measure for assessing the quality of academic papers. This new measure is parsimonious as it looks for the smallest set of citing authors (citers) who have read a certain paper. The Parsimonious Citer‐Based Measure aims to address potential distortion in the values of existing citer‐based measures. These distortions occur because of various factors, such as the practice of hyperauthorship. This new measure is empirically compared with existing measures, such as the number of citers and the number of citations in the field of artificial intelligence (AI). The results show that the new measure is highly correlated with those two measures. However, the new measure is more robust against citation manipulations and better differentiates between prominent and nonprominent AI researchers than the above‐mentioned measures.
Digital Rights Movement explores several aspects of the ongoing conflict between digital content owners and providers and users. As owners seek more leverage through reformed laws and enhanced distribution technologies, users have taken the obvious course of likewise employing technologies to attempt restoration of lost rights. “Technological resistance is the logical response” (p. 13) and Postigo views the push-back as a form of technological resistance. In this book Postigo explores the legislative processes that offered owners and providers this leverage and the responses of various user-stakeholders seeking to reclaim lost rights of fair use and free speech. Fair use not in a strict legal sense but in a more societal sense based on the social contract between information owners and information users and free speech in the use of code as speech as protest. Recounting several major episodes in this ongoing conflict (the Sklyarov, Bunner, and Reimerdes litigations as well as the iTunes portability hack and more recent efforts), Postigo argues that the fair rights movement in digital content is no less a movement than those of the past. Technology is viewed “not only as artifact, but as action, collective action. . .straddle[ing] law, culture, protest and participation: they occupy those domains both physically and meaningfully” (p. 15). Except for those who study legislative history related to copyright law or those around back when Al Gore invented the internet, the discussion in Chapters 2 and 3 can offer an important backdrop for understanding the present state of policy-making in the copyright arena. The perpetual mantra of the content owners of pirates on our shores or barbarians at our gate and the reticence of many users and public interest groups to believe such pronouncements while at the same bemoaning the loss of the information commons and access to information. Recent proposed legislation such as SOPA (Stop Online Piracy Act) and PIPA (Protect Intellectual Property Act) have a context and that context is found in the forerunners of the Digital Millennium Copyright Act (DMCA), the so-called Green Paper (draft version), and the White Paper (final version) of a report: Intellectual Property and the National Information Infrastructure: The Report of the Working Group on Intellectual Property Rights (1995). Even though the Working Group sought public input, in Postigo’s view its vision was shortsighted. Recommendation regarding use of technological protection measures the process appeared outcome-determinative, never contemplating the unintended effects of new technologies, the impact on evolving fair uses, and the constitutional implications for free speech (p. 26). True enough, but at the time no one foresaw what internet commerce might look like in 2010 or will in 2015. Rather than conceive of new business models, the content industry followed a familiar script of retrenchment, looking to stronger legal and, in this instance, technological protection measures to control content distribution and use. Postigo recounts this in perhaps more detail than the casual reader needs at times. However, those versed in copyright, such as this reviewer, appreciated the discussion of the controversy and debate surrounding the proposed formulation of the word “transmission” in the draft report (the Green Paper), the problems of first sale, and, more important, fair use in the digital and internet context. The Green Paper also introduced users to the concept that came to fruition in the DMCA: prohibitions on circumventing technologies that control access to digital works protected by copyright. The danger, of course, is that without access use can be prohibited or at least curtailed. In reviewing and comparing the process from Green Paper to White Paper to DMCA, Postigo makes several important observations. First, there were a variety of viewpoints on what the National Information Infrastructure (NNI) could become. Users hoped for a place where “copyright and intellectual property can be reimagined rather than reinforced” (p. 42). Second, the objections of users to the prohibitions on circumventing technological protection measures were ignored. Policy makers listened to the loudest voice, that of the content owners and providers. Even at this early stage support against such prohibitions was mounting. A number of legal scholars entered the debate and argued that in addition to stifling innovation, the use of such technologies coupled with legal prohibitions against circumvention “were counter to user experience with digital media” (p. 42) and would counteract the potential benefit from a distributed network such as the NII. If there is some criticism, the impact of licenses is perhaps overlooked. Not only does technology inhibit uses perceived to be fair but the End User License Agreement accompanying much content is just as restrictive, although far less obvious to the user, as most click-to-agree without ever reading the terms. Postigo observes that the various judicial pronouncements regarding the ultimate purpose of copyright law were ignored or twisted by the Green and © 2013 ASIS&T
In a recent presentation at the 17th International Conference on Science and Technology Indicators, Schneider (2012) criticised the proposal of Bornmann, de Moya Anegon, and Leydesdorff (2012) and Leydesdorff and Bornmann (2012) to use statistical tests in order to evaluate research assessments and university rankings. We agree with Schneider's proposal to add statistical power analysis and effect size measures to research evaluations, but disagree that these procedures would replace significance testing. Accordingly, effect size measures were added to the Excel sheets that we bring online for testing performance differences between institutions in the Leiden Ranking and the SCImago Institutions Ranking.
Online videos provide a novel, and often interactive, platform for the popularization of science. One successful collection is hosted on the TED (Technology, Entertainment, Design) website. This study uses a range of bibliometric (citation) and webometric (usage and bookmarking) indicators to examine TED videos in order to provide insights into the type and scope of their impact. The results suggest that TED Talks impact primarily the public sphere, with about three-quarters of a billion total views, rather than the academic realm. Differences were found among broad disciplinary areas, with art and design videos having generally lower levels of impact but science and technology videos generating otherwise average impact for TED. Many of the metrics were only loosely related, but there was a general consensus about the most popular videos as measured through views or comments on YouTube and the TED site. Moreover, most videos were found in at least one online syllabus and videos in online syllabi tended to be more viewed, discussed, and blogged. Less-liked videos generated more discussion, although this may be because they are more controversial. Science and technology videos presented by academics were more liked than those by nonacademics, showing that academics are not disadvantaged in this new media environment.
Given the importance of cross‐disciplinary research (CDR), facilitatingCDReffectiveness is a priority for many institutions and funding agencies. There are a number ofCDRtypes, however, and the effectiveness of facilitation efforts will require sensitivity to that diversity. This article presents a method characterizing a spectrum ofCDRdesigned to inform facilitation efforts that relies on bibliometric techniques and citation data. We illustrate its use by theToolbox Project, an ongoing effort to enhance cross‐disciplinary communication inCDRteams through structured, philosophical dialogue about research assumptions in a workshop setting.Toolbox Project workshops have been conducted with more than 85 research teams, but the project's extensibility to an objectively characterized range ofCDRcollaborations has not been examined. To guide wider application of theToolbox Project, we have developed a method that uses multivariate statistical analyses of transformed citation proportions from published manuscripts to identify candidate areas ofCDR, and then overlays information from previousToolbox participant groups on these areas to determine candidate areas for future application. The approach supplies 3 results of general interest:A way to employ small data sets and familiar statistical techniques to characterize CDR spectra as a guide to scholarship on CDR patterns and trends.A model for using bibliometric techniques to guide broadly applicable interventions similar to the Toolbox.A method for identifying the location of collaborative CDR teams on a map of scientific activity, of use to research administrators, research teams, and other efforts to enhance CDR projects.
A knowledge map of digital library (DL) research shows the semantic organization of DL research topics and also the evolution of the field. The research reported in this article aims to find the core topics and subtopics of DL research in order to build a knowledge map of the DL domain. The methodology is comprised of a four-step research process, and two knowledge organization methods (classification and thesaurus building) were used. A knowledge map covering 21 core topics and 1,015 subtopics of DL research was created and provides a systematic overview of DL research during the last two decades (1990–2010). We argue that the map can work as a knowledge platform to guide, evaluate, and improve the activities of DL research, education, and practices. Moreover, it can be transformed into a DL ontology for various applications. The research methodology can be used to map any human knowledge domain; it is a novel and scientific method for producing comprehensive and systematic knowledge maps based on literary warrant.
This article compares doctoral students' and faculty members' referencing behavior through the analysis of a large corpus of scientific articles. It shows that doctoral students tend to cite more documents per article than faculty members, and that the literature they cite is, on average, more recent. It also demonstrates that doctoral students cite a larger proportion of conference proceedings and journal articles than faculty members and faculty members are more likely to self‐cite and cite theses than doctoral students. Analysis of the impact of cited journals indicates that in health research, faculty members tend to cite journals with slightly lower impact factors whereas in social sciences and humanities, faculty members cite journals with higher impact factors. Finally, it provides evidence that, in every discipline, faculty members tend to cite a higher proportion of clinical/applied research journals than doctoral students. This study contributes to the understanding of referencing patterns and age stratification in academia. Implications for understanding the information‐seeking behavior of academics are discussed.
This article offers important background information about a new product, the Book Citation Index (BKCI), launched in 2011 by Thomson Reuters. Information is illustrated by some new facts concerning The BKCI's use in bibliometrics, coverage analysis, and a series of idiosyncrasies worthy of further discussion. The BKCI was launched primarily to assist researchers identify useful and relevant research that was previously invisible to them, owing to the lack of significant book content in citation indexes such as the Web of Science. So far, the content of 33,000 books has been added to the desktops of the global research community, the majority in the arts, humanities, and social sciences fields. Initial analyses of the data from The BKCI have indicated that The BKCI, in its current version, should not be used for bibliometric or evaluative purposes. The most significant limitations to this potential application are the high share of publications without address information, the inflation of publication counts, the lack of cumulative citation counts from different hierarchical levels, and inconsistency in citation counts between the cited reference search and the book citation index. However, The BKCI is a first step toward creating a reliable and necessary citation data source for monographs — a very challenging issue, because, unlike journals and conference proceedings, books have specific requirements, and several problems emerge not only in the context of subject classification, but also in their role as cited publications and in citing publications.
Semantic similarity is vital to many areas, such as information retrieval. Various methods have been proposed with a focus on comparing unstructured text documents. Several of these have been enhanced with ontology; however, they have not been applied to ontology instances. With the growth in ontology instance data published online through, for example, Linked Open Data, there is an increasing need to apply semantic similarity to ontology instances. Drawing on ontology-supported polarity mining (OSPM), we propose an algorithm that enhances the computation of semantic similarity with polarity mining techniques. The algorithm is evaluated with online customer review data. The experimental results show that the proposed algorithm outperforms the baseline algorithm in multiple settings.
Journal of the American Society for Information Science and TechnologyVolume 64, Issue 3 p. 650-650 LETTER TO THE EDITOR The problem of percentile rank scores used with small reference sets Lutz Bornmann, Lutz Bornmann [email protected] Division for Science and Innovation Studies, Administrative Headquarters of the Max Planck Society, Hofgartenstraße 8, 80539 Munich, GermanySearch for more papers by this author Lutz Bornmann, Lutz Bornmann [email protected] Division for Science and Innovation Studies, Administrative Headquarters of the Max Planck Society, Hofgartenstraße 8, 80539 Munich, GermanySearch for more papers by this author First published: 20 February 2013 https://doi.org/10.1002/asi.22720Citations: 5Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. References Hyndman, R.J., & Fan, Y.N. (1996). Sample quantiles in statistical packages. American Statistician, 50(4), 361–365. Leydesdorff, L. (in press). Accounting for the uncertainty in the evaluation of percentile ranks. Journal of the American Society for Information Science and Technology. Leydesdorff, L., Bornmann, L., Mutz, R., & Opthof, T. (2011). Turning the tables in citation analysis one more time: principles for comparing sets of documents. Journal of the American Society for Information Science and Technology, 62(7), 1370–1381. Rousseau, R. (2012). Basic properties of both percentile rank scores and the I3 indicator. Journal of the American Society for Information Science and Technology, 63(2), 416–420. doi: 10.1002/asi.21684. Schreiber, M. (2012). Inconsistencies of recently proposed citation impact indicators and how to avoid them. Retrieved February 20, from http://arxiv.org/abs/1202.3861 Sheskin, D. (2007). Handbook of parametric and nonparametric statistical procedures ( 4th ed.). Boca Raton, FL, USA: Chapman & Hall/CRC. Citing Literature Volume64, Issue3March 2013Pages 650-650 ReferencesRelatedInformation
We describe the latent semantic indexing subspace signature model ( LSISSM ) for semantic content representation of unstructured text. Grounded on singular value decomposition, the model represents terms and documents by the distribution signatures of their statistical contribution across the top‐ranking latent concept dimensions. LSISSM matches term signatures with document signatures according to their mapping coherence between latent semantic indexing ( LSI ) term subspace and LSI document subspace. LSISSM does feature reduction and finds a low‐rank approximation of scalable and sparse term‐document matrices. Experiments demonstrate that this approach significantly improves the performance of major clustering algorithms such as standard K ‐means and self‐organizing maps compared with the vector space model and the traditional LSI model. The unique contribution ranking mechanism in LSISSM also improves the initialization of standard K ‐means compared with random seeding procedure, which sometimes causes low efficiency and effectiveness of clustering. A two‐stage initialization strategy based on LSISSM significantly reduces the running time of standard K ‐means procedures.
Expertise retrieval has attracted significant interest in the field of information retrieval. Expert finding has been studied extensively, with less attention going to the complementary task of expert profiling, that is, automatically identifying topics about which a person is knowledgeable. We describe a test collection for expert profiling in which expert users have self-selected their knowledge areas. Motivated by the sparseness of this set of knowledge areas, we report on an assessment experiment in which academic experts judge a profile that has been automatically generated by state-of-the-art expert-profiling algorithms; optionally, experts can indicate a level of expertise for relevant areas. Experts may also give feedback on the quality of the system-generated knowledge areas. We report on a content analysis of these comments and gain insights into what aspects of profiles matter to experts. We provide an error analysis of the system-generated profiles, identifying factors that help explain why certain experts may be harder to profile than others. We also analyze the impact on evaluating expert-profiling systems of using self-selected versus judged system-generated knowledge areas as ground truth; they rank systems somewhat differently but detect about the same amount of pairwise significant differences despite the fact that the judged system-generated assessments are more sparse.
Journal of the American Society for Information Science and TechnologyVolume 64, Issue 2 p. 433-433 IN MEMORIAM In Memoriam: Yale Mitchell Braunstein, 1945–2012 Michael K. Buckland, Michael K. Buckland University of California, BerkeleySearch for more papers by this author Michael K. Buckland, Michael K. Buckland University of California, BerkeleySearch for more papers by this author First published: 04 December 2012 https://doi.org/10.1002/asi.22832Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume64, Issue2February 2013Pages 433-433 RelatedInformation
In this article, we report on the status of graphs in 21 scientific agricultural journals indexed in Thomson Reuters' Web of Knowledge. We analyze the authors' use of graphs in this context in relation to the quality of these journals as measured by their 2-year impact factors. We note a substantial variability in the use of graphs in this context: For one journal, 100% of the papers include graphs, whereas for others only about 50% of them include graphs. We also show that higher impact agricultural journals publish more papers with graphs and that there are more graphs in these papers than in those in journals with lower impact factors (r = +0.40).
In this paper we present results from an investigation of religious information searching based on analyzing log files from a large general-purpose search engine. From approximately 15 million queries, we identified 124,422 that were part of 60,759 user sessions. We present a method for categorizing queries based on related terms and show differences in search patterns between religious searches and web searching more generally. We also investigate the search patterns found in queries related to 5 religions: Christianity, Hinduism, Islam, Buddhism, and Judaism. Different search patterns are found to emerge. Results from this study complement existing studies of religious information searching and provide a level of detailed analysis not reported to date. We show, for example, that sessions involving religion-related queries tend to last longer, that the lengths of religion-related queries are greater, and that the number of unique URLs clicked is higher when compared to all queries. The results of the study can serve to provide information on what this large population of users is actually searching for.
