Search (9 results, page 1 of 1)

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Abstract

Online health question-and-answer consultation with physicians is becoming a common phenomenon. However, it is unclear how users identify the most satisfying reply. Based on the dual-process theory of knowledge adoption, we developed a conceptual model and empirical method to study which factors influence adoption of a reply. We extracted 6 variables for argument quality (Ease of understanding, Relevance, Completeness, Objectivity, Timeliness, Structure) and 4 for source credibility (Physician's online experience, Physician's offline expertise, Hospital location, Hospital level). The empirical results indicate that both central and peripheral routes affect user's adoption of a response. Physician's offline expertise negatively affects user's adoption decision, while physician's online experience positively affects it; this effect is positively moderated by user involvement.

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Abstract

The second edition of the best-selling guide to referencing electronic information and citing the complete range of electronic formats includes text-based information, electronic journals and discussion lists, Web sites, CD-ROM and multimedia products, and commercial online documents

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Date

28. 1.2006 19:21:22

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Abstract

Numerous studies have been conducted on the information interaction behavior of search engine users. Few studies have considered information interactions in the domain of academic search. We focus on conversion behavior in this domain. Conversions have been widely studied in the e-commerce domain, e.g., for online shopping and hotel booking, but little is known about conversions in academic search. We start with a description of a unique dataset of a particular type of conversion in academic search, viz. users' downloads of scientific papers. Then we move to an observational analysis of users' download actions. We first characterize user actions and show their statistics in sessions. Then we focus on behavioral and topical aspects of downloads, revealing behavioral correlations across download sessions. We discover unique properties that differ from other conversion settings such as online shopping. Using insights gained from these observations, we consider the task of predicting the next download. In particular, we focus on predicting the time until the next download session, and on predicting the number of downloads. We cast these as time series prediction problems and model them using LSTMs. We develop a specialized model built on user segmentations that achieves significant improvements over the state-of-the art.

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Date

20. 1.2015 18:30:22

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Abstract

Libraries have been integrating active server pages (ASP) with Web-based databases for searching and retrieving electronic information for the past five years; however, a literature review reveals that a more complete description of modifying data through the Web interface is needed. At the Texas A&M University Libraries, a Web database of Internet links was developed using ASP, Microsoft Access, and Microsoft Internet Information Server (IIS) to facilitate use of online resources. The implementation of the Internet Links database is described with focus on its data management functions. Also described are other library applications of ASP technology. The project explores a more complete approach to library Web database applications than was found in the current literature and should serve to facilitate reference service.

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Abstract

Purpose The purpose of this paper is to develop a content analysis framework and from that derive a process model of knowledge construction in the context of virtual product user communities, organization sponsored online forums where product users collaboratively construct knowledge to solve their technical problems. Design/methodology/approach The study is based on a deductive and qualitative content analysis of discussion threads about solving technical problems selected from a series of virtual product user communities. Data are complemented with thematic analysis of interviews with forum members. Findings The research develops a content analysis framework for knowledge construction. It is based on a combination of existing codes derived from frameworks developed for computer-supported collaborative learning and new categories identified from the data. Analysis using this framework allows the authors to propose a knowledge construction process model showing how these elements are organized around a typical "trial and error" knowledge construction strategy. Practical implications The research makes suggestions about organizations' management of knowledge activities in virtual product user communities, including moderators' roles in facilitation. Originality/value The paper outlines a new framework for analysing knowledge activities where there is a low level of critical thinking and a model of knowledge construction by trial and error. The new framework and model can be applied in other similar contexts.

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Abstract

In this paper, we study the location-aware targeted influence maximization problem in social networks, which finds a seed set to maximize the influence spread over the targeted users. In particular, we consider those users who have both topic and geographical preferences on promotion products as targeted users. To efficiently solve this problem, one challenge is how to find the targeted users and compute their preferences efficiently for given requests. To address this challenge, we devise a TR-tree index structure, where each tree node stores users' topic and geographical preferences. By traversing the TR-tree in depth-first order, we can efficiently find the targeted users. Another challenge of the problem is to devise algorithms for efficient seeds selection. We solve this challenge from two complementary directions. In one direction, we adopt the maximum influence arborescence (MIA) model to approximate the influence spread, and propose two efficient approximation algorithms with math formula approximation ratio, which prune some candidate seeds with small influences by precomputing users' initial influences offline and estimating the upper bound of their marginal influences online. In the other direction, we propose a fast heuristic algorithm to improve efficiency. Experiments conducted on real-world data sets demonstrate the effectiveness and efficiency of our proposed algorithms.

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Abstract

While there have been a growing number of studies on makerspaces in different disciplines, little is known about how young people interact with information in makerspaces. This study aimed to unpack how young people (middle and high schoolers) sought, used, and shared information in voluntary free-choice library makerspace activities. Qualitative methods, including individual interviews, observations, photovoice, and focus groups, were used to elicit 21 participants' experiences at two library makerspaces. The findings showed that young people engaged in dynamic practices of information seeking, use, and sharing, and revealed how the historical, sociocultural, material, and technological contexts embedded in makerspace activities shaped these information practices. Information practices of tinkering, sensing, and imagining in makerspaces were highlighted. Various criteria that young people used in evaluating human sources and online information were identified as well. The study also demonstrated the communicative and collaborative aspects of young people's information practices through information sharing. The findings extended Savolainen's everyday information practices model and addressed the gap in the current literature on young people's information behavior and information practices. Understanding how young people interact with information in makerspaces can help makerspace facilitators and information professionals better support youth services and facilitate makerspace activities.