117 research outputs found

    15 Years of Enterprise Architecting at HICSS: Revisiting the Critical Problems

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    The Enterprise Architecture (EA) minitrack has been a mainstay of HICSS for the past 15 years. The methodology, tools, and processes of enterprise architecting have evolved during that period. In 2005, Kaisler and Armour identified some critical challenges in modeling, management, and maintenance for EA that needed attention to ensure a viable technical discipline. Over 15 years, we have accepted 93 papers for presentation. Reviewing these papers and drawing up on our experience over the past 15 years, we conclude that some progress has been made, some challenges remain to be addressed, and some new challenges have emerged. This paper revises existing challenges and identifies additional challenges to be addressed in the next 10 years

    The Trajectory of IT in Healthcare at HICSS: A Literature Review, Analysis, and Future Directions

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    Research has extensively demonstrated that healthcare industry has rapidly implemented and adopted information technology in recent years. Research in health information technology (HIT), which represents a major component of the Hawaii International Conference on System Sciences, demonstrates similar findings. In this paper, review the literature to better understand the work on HIT that researchers have conducted in HICSS from 2008 to 2017. In doing so, we identify themes, methods, technology types, research populations, context, and emerged research gaps from the reviewed literature. With much change and development in the HIT field and varying levels of adoption, this review uncovers, catalogs, and analyzes the research in HIT at HICSS in this ten-year period and provides future directions for research in the field

    Digitalisation and Enterprise Knowledge (net)Working

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    Social media and emerging mobile technologies have forever changed the landscape of human interaction. Furthermore, they already play a pivotal role also in enterprises as a part of the organisational Knowledge Management System. Almost all large organisations have already implemented at least one Enterprise Social Media tool since they enable collaboration, provide easy access to information, and are available at reasonable costs. The effects of the decoupling of the real and the virtual world (as a result of Social Media use) on the construct knowledge and on knowledge management are still not sufficiently investigated. Against this background, the paper presents an exploratory approach of the development of a specific morphological tableau as an instrument for the analysis of employees’ behavior in context of knowledge management related ESM use. Furthermore, the application of the tableau is exemplary illustrated and further research steps are explained

    Crowd sourcing challenges assessment index for disaster management

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    Emergency agencies (EA) rely on inter-agency approaches to information management during disasters. EA have shown a significant interest in the use of cloud-based social media such as Twitter and Facebook for crowd-sourcing and distribution of disaster information. While the intentions are clear, the question of what are its major challenges are not. EA have a need to recognise the challenges in the use of social media under their local circumstances. This paper analysed the recent literature, 2010 Haiti earthquake and 2010-11 Queensland flood cases and developed a crowd sourcing challenges assessment index construct specific to EA areas of interest. We argue that, this assessment index, as a part of our large conceptual framework of context aware cloud adaptation (CACA), can be useful for the facilitation of citizens, NGOs and government agencies in a strategy for use of social media for crowd sourcing, in preventing, preparing for, responding to and recovering from disasters. © (2012) by the AIS/ICIS Administrative Office All rights reserved

    High-performance Diagnosis of Sleep Disorders: A Novel, Accurate and Fast Machine Learning Approach Using Electroencephalographic Data

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    While diagnosing sleep disorders by physicians using electroencephalographic data is protracted and inaccurate, we report promising results from a novel, fast and reliable machine learning approach. Our approach only needs an electroencephalographic recording snippet of 10 minutes instead of eight hours to correctly classify the disorder with an accuracy of over 90 percent. The Rapid Eye Movement sleep behavior disorder can lead to secondary diseases like Parkinson or Dementia. Therefore, it is important to classify the disorder fast and with a high level of accuracy - which is now possible with our approach

    Development of a Machine Learning Based Algorithm To Accurately Detect Schizophrenia based on One-minute EEG Recordings

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    While diagnosing schizophrenia by physicians based on patients' history and their overall mental health is inaccurate, we report on promising results using a novel, fast and reliable machine learning approach based on electroencephalography (EEG) recordings. We show that a fine granular division of EEG spectra in combination with the Random Forest classifier allows a distinction to be made between paranoid schizophrenic (ICD-10 F20.0) and non-schizophrenic persons with a very good balanced accuracy of 96.77 percent. We evaluate our approach on EEG data from an open neurological and psychiatric repository containing 499 one-minute recordings of n=28 participants (14 paranoid schizophrenic and 14 healthy controls). Since the fact that neither diagnostic tests nor biomarkers are available yet to diagnose paranoid schizophrenia, our approach paves the way to a quick and reliable diagnosis with a high accuracy. Furthermore, interesting insights about the most predictive subbands were gained by analyzing the electroencephalographic spectrum up to 100 Hz

    A Typology of Digital Offerings

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    This paper develops a typology of digital offerings to shed light on the distinct characteristics of this emerging digital phenomenon. Drawing on Roman contract law, the typology focuses on digital rights offered (selling, leasing, partnering, and agencing) and digital assets involved (tangible and intangible). These two dimensions lead to eight archetypes that we illustrate through the diverse Amazon portfolio of digital offerings. The typology sets out to shape the scholarly discourse around digital offering research and practice and to provide a foundation from which the characteristics and mechanisms of digital offering value appropriation can be further understood and operationalized. Ultimately, by rejecting the traditional service vs. product distinction and instead accounting for offering variations based on the intrinsic merits of digital offerings, we are embracing a digital terminology rather than attempting to transfer the terminology of the physical world to the digital realm

    Institutionalizing Analytic Data Sharing in SME Ecosystems – A Role-Based Perspective

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    There is a variety of reasons that sharing data among Small and Medium-Sized Enterprises (SMEs) carries business potential, particularly for analyti-cal applications. But outside a few niche domains, the number of success stories for data sharing is rather modest. Based on a qualitative study and first experiences from a research project with pilot im-plementations, we argue that this is mainly due to a lack of an institutionalized governance structure: Founding a separate legal entity for data sharing and analysis can address core concerns regarding sharing valuable data assets. However, this requires a well-calibrated set of defined roles for the in-volved partners. Based on our results we propose a first concept on delineating and mapping out those roles
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