Using “Yams” for Enterprise Knowledge Sharing among Knowledge Workers from the Perspective of a Task Categorisation-Knowledge Sharing Systems Fit

Part 2: Key Competencies, Learning and Life TransitionsInternational audienceEmerging digital technologies play a key role in the development of enterprises. Their uses demand a transition on the part of knowledge workers, however. Web 2.0 is an emerging communication technology that supports collaborative knowledge sharing in corporate learning paradigms, changing tailor-made, expensive and high learning curve digital systems to simple but well-accepted ones [1, 2]. These platforms revolutionise how participants share, communicate and create knowledge in a corporate setting [3]. The use of Web 2.0 to support Knowledge Sharing (KS) has been extensively investigated [4, 5]. Studies that use a task-technology fit model on systems such as decision support [6] and eLearning [7] demonstrate that a good fit between tasks and digital technologies is able to improve performance of knowledge workers. This research reports the outcomes on the fit between task categorization and knowledge sharing systems. The task categories and Web 2.0 functions used in knowledge sharing practices were consistent. The outcomes highlighted that intuitive design, ease of use and a low learning curve were able to elicit both tacit and explicit organizational knowledge. Text analysis demonstrated that new knowledge was created, exchanged and shared. The study concluded that knowledge sharing activity and the fit between Web 2.0 functions and task categories were consistent and significant

Die Collectio Francofurtana: eine französische Decretalensammlung Analyse beruhend auf Vorarbeiten von Walther Holtzmann

What makes an e-commerce company successful? In 2011 24% of venture capital in the US went into Internet companies adding up to a total of $6.9 billion (PwC & NVCA, 2011). With such high stakes the question of e-commerce success is more topical than ever. Google, one of the biggest e-commerce companies in the world, despite huge successful products like Google Search, has also seen failures. In this paper, we explore factors associated with successful and unsuccessful adoption of Google products using a literature study in conjunction with qualitative analysis of the Google Search, Google Health, and Google Plus products. Our research identifies key success factors for user adoption of Google products and predicts that Google Plus in its present form will lead to failure. The study shows that perceived compatibility, perceived usefulness, information quality, balancing risks with trust, and finally social pressure are important success factors for Google. Despite limiting the examination to Google products, results can serve as a guideline for other e-commerce venture

The effects of transparency on trust in and acceptance of a content-based art recommender

The increasing availability of (digital) cultural heritage artefacts offers great potential for increased access to art content, but also necessitates tools to help users deal with such abundance of information. User-adaptive art recommender systems aim to present their users with art content tailored to their interests. These systems try to adapt to the user based on feedback from the user on which artworks he or she finds interesting. Users need to be able to depend on the system to competently adapt to their feedback and find the artworks that are most interesting to them. This paper investigates the influence of transparency on user trust in and acceptance of content-based recommender systems. A between-subject experiment (N = 60) evaluated interaction with three versions of a content-based art recommender in the cultural heritage domain. This recommender system provides users with artworks that are of interest to them, based on their ratings of other artworks. Version 1 was not transparent, version 2 explained to the user why a recommendation had been made and version 3 showed a rating of how certain the system was that a recommendation would be of interest to the user. Results show that explaining to the user why a recommendation was made increased acceptance of the recommendations. Trust in the system itself was not improved by transparency. Showing how certain the system was of a recommendation did not influence trust and acceptance. A number of guidelines for design of recommender systems in the cultural heritage domain have been derived from the study’s results