Introduction to Intelligent Decision Support and Big Data for Logistics and Supply Chain Management Minitrack

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Using term clouds to represent segment-level semantic content of podcasts

Spoken audio, like any time-continuous medium, is notoriously difficult to browse or skim without support of an interface providing semantically annotated jump points to signal the user where to listen in. Creation of time-aligned metadata by human annotators is prohibitively expensive, motivating the investigation of representations of segment-level semantic content based on transcripts generated by automatic speech recognition (ASR). This paper examines the feasibility of using term clouds to provide users with a structured representation of the semantic content of podcast episodes. Podcast episodes are visualized as a series of sub-episode segments, each represented by a term cloud derived from a transcript generated by automatic speech recognition (ASR). Quality of segment-level term clouds is measured quantitatively and their utility is investigated using a small-scale user study based on human labeled segment boundaries. Since the segment-level clouds generated from ASR-transcripts prove useful, we examine an adaptation of text tiling techniques to speech in order to be able to generate segments as part of a completely automated indexing and structuring system for browsing of spoken audio. Results demonstrate that the segments generated are comparable with human selected segment boundaries

Giving and Following Recommendations on Video-on-Demand Services

This is an empirical paper about giving, receiving and following recommendations on Video-on-Demand (VoD) services, including results on gender-specific differences. Based upon a model for infor-mation behavior on VoD services, we applied an online survey and generated 1,258 valid question-naires from active VoD users. Participants receive recommendations from the systems once a week on average, but they follow them only occasionally. They give actively recommendations to other people sever-al times a month. Users do not receive recommenda-tions from other users as often as from the services (only several times a month); however, they follow those personal recommendations more often. The most important source for receiving personal rec-ommendations is face-to-face communication. Obvi-ously, VoD users follow personal recommendations from other people more than suggestions from algo-rithmically generated recommender systems. Besides, self-determined content selection following intrinsic motivation is important. The findings are of interest for research on digital and social media and for VoD services

Open Data Diffusion for Service Innovation: An Inductive Case Study on Cultural Open Data Services

Information Systems research on Open Data has been primarily focused on its contribution to e-government inquiries, government transparency, and open government. Recently, Open Data has been explored as a catalyser for service innovation as a consequence of big claims around the potential of such initiatives in terms of additional value that can be injected into the worldwide economy. Subsequently, the Open Data Services academic conversation was structured (Lindman et al. 2013a). The research project presented in this paper is an interpretive case study that was carried out to explore the factors that influence the diffusion of Open Data for new service development. This paper contributes to this debate by providing an interpretive inductive case study (Walsham 1995) of a tourism company that successfully turned several city authorities’ raw open datasets into a set of valuable services. Results demonstrate that 16 factors and 68 related variables are the most relevant in the process of diffusion of open data for new service development. Furthermore, this paper demonstrates the suitability of Social Constructionism and interpretive case study research to inductively generate knowledge in this field