TY - JOUR A1 - Yeung, Ching-man Au A1 - Noll, Michael G. A1 - Gibbins, Nicholas A1 - Meinel, Christoph A1 - Shadbolt, Nigel T1 - Spear spamming-resistant expertise analysis and ranking incollaborative tagging systems JF - Computational intelligence N2 - In this article, we discuss the notions of experts and expertise in resource discovery in the context of collaborative tagging systems. We propose that the level of expertise of a user with respect to a particular topic is mainly determined by two factors. First, an expert should possess a high-quality collection of resources, while the quality of a Web resource in turn depends on the expertise of the users who have assigned tags to it, forming a mutual reinforcement relationship. Second, an expert should be one who tends to identify interesting or useful resources before other users discover them, thus bringing these resources to the attention of the community of users. We propose a graph-based algorithm, SPEAR (spamming-resistant expertise analysis and ranking), which implements the above ideas for ranking users in a folksonomy. Our experiments show that our assumptions on expertise in resource discovery, and SPEAR as an implementation of these ideas, allow us to promote experts and demote spammers at the same time, with performance significantly better than the original hypertext-induced topic search algorithm and simple statistical measures currently used in most collaborative tagging systems. KW - collaborative tagging KW - expertise KW - folksonomy KW - HITS KW - ranking KW - spamming Y1 - 2011 U6 - https://doi.org/10.1111/j.1467-8640.2011.00384.x SN - 0824-7935 SN - 1467-8640 VL - 27 IS - 3 SP - 458 EP - 488 PB - Wiley-Blackwell CY - Hoboken ER - TY - JOUR A1 - Tiberius, Victor A1 - Weyland, Michael A1 - Mahto, Raj V. T1 - Best of entrepreneurship education? BT - a curriculum analysis of the highest-ranking entrepreneurship MBA programs JF - The international journal of management education N2 - Entrepreneurship education has gained widespread attention in both education practice and research over the past three decades. However, whereas research has a strong focus on its effects and many normative concepts exist, little is known about how entrepreneurship is actually taught. To address this research gap, we conduct a curriculum analysis of the 50 best programs in entrepreneurship, according to the 2018 Financial Times ranking “Top MBAs for Entrepreneurship 2018”. In particular, we examine their objectives, learning contents and teaching as well as assessment methods as four major dimensions of a graduate entrepreneurship curriculum. The results show that the programs are primarily business and management programs, with a comparatively small share of entrepreneurship itself. Entrepreneurship-specific goals are entrepreneurial attitudes and competences, such as entrepreneurial leadership, entrepreneurial mindset, entrepreneurial skills, opportunity creation, opportunity identification, and transforming uncertainty into opportunity. The learning contents also focus on business, management, and law, whereas the contents relating to entrepreneurship include entrepreneurial failure, entrepreneurial management, entrepreneurial thinking, and entrepreneurship in general. Teaching methods are mainly the ones usually found in higher education, with business plans and prototyping as additional entrepreneurial ones. Assessment methods do not differ from those in business and management education. KW - entrepreneurship education KW - curriculum analysis KW - ranking KW - best practice Y1 - 2022 U6 - https://doi.org/10.1016/j.ijme.2022.100753 SN - 1472-8117 SN - 2352-3565 VL - 21 IS - 1 PB - Elsevier CY - Amsterdam ER -