5,900 research outputs found
Crowdsourcing Strategizing: A View From the Top
Crowdsourcing strategizing is the application of crowdsourcing for organizational strategy development. While crowdsourcing is experiencing popularity in application and discussion, the concept is not new. However, literature on the value of crowdsourcing strategizing is not widespread in academic or business works. This qualitative case study explored crowdsourcing strategizing in Richmond, Virginia metro area nonprofits. The study was conducted to explore the lack of understanding on the value of crowdsourcing strategizing, with a focus on leaderships perspective of value. The results showed that nonprofit leaders found value in the crowdsourced data gathered through crowdsourcing strategizing
Digitalization and Development
This book examines the diffusion of digitalization and Industry 4.0 technologies in Malaysia by focusing on the ecosystem critical for its expansion. The chapters examine the digital proliferation in major sectors of agriculture, manufacturing, e-commerce and services, as well as the intermediary organizations essential for the orderly performance of socioeconomic agents.
The book incisively reviews policy instruments critical for the effective and orderly development of the embedding organizations, and the regulatory framework needed to quicken the appropriation of socioeconomic synergies from digitalization and Industry 4.0 technologies. It highlights the importance of collaboration between government, academic and industry partners, as well as makes key recommendations on how to encourage adoption of IR4.0 technologies in the short- and long-term.
This book bridges the concepts and applications of digitalization and Industry 4.0 and will be a must-read for policy makers seeking to quicken the adoption of its technologies
Die unsicheren Kanäle
Zeitgenössische IT-Sicherheit operiert in einer Überbietungslogik zwischen Sicherheitsvorkehrungen und Angriffsszenarien. Diese paranoid strukturierte Form negativer Sicherheit lässt sich vom Ursprung der IT-Sicherheit in der modernen Kryptografie über Computerviren und -würmer, Ransomware und Backdoors bis hin zum AIDS-Diskurs der 1980er Jahre nachzeichnen. Doch Sicherheit in und mit digital vernetzten Medien lässt sich auch anders denken: Marie-Luise Shnayien schlägt die Verwendung eines reparativen, queeren Sicherheitsbegriffs vor, dessen Praktiken zwar nicht auf der Ebene des Technischen angesiedelt sind, aber dennoch nicht ohne ein genaues Wissen desselben auskommen
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
The regulation of digital platforms: the case of pagoPA
How can EU regulation affect innovation. Digital revolution: How big data have changed the world and the legal landscape. The regulation of digital platforms in Europe. Digital revolution: How distributed ledger technologies are changing the world and the legal landscape. Regulation of digital payments: the case of pagopa
The Politics of Platformization: Amsterdam Dialogues on Platform Theory
What is platformization and why is it a relevant category in the contemporary political landscape? How is it related to cybernetics and the history of computation? This book tries to answer such questions by engaging in multidisciplinary dialogues about the first ten years of the emerging fields of platform studies and platform theory. It deploys a narrative and playful approach that makes use of anecdotes, personal histories, etymologies, and futurable speculations to investigate both the fragmented genealogy that led to platformization and the organizational and economic trends that guide nowadays platform sociotechnical imaginaries
Perceptions and Practicalities for Private Machine Learning
data they and their partners hold while maintaining data subjects' privacy. In this thesis I show that private computation, such as private machine learning, can increase end-users' acceptance of data sharing practices, but not unconditionally. There are many factors that influence end-users' privacy perceptions in this space; including the number of organizations involved and the reciprocity of any data sharing practices. End-users emphasized the importance of detailing the purpose of a computation and clarifying that inputs to private computation are not shared across organizations. End-users also struggled with the notion of protections not being guaranteed 100\%, such as in statistical based schemes, thus demonstrating a need for a thorough understanding of the risk form attacks in such applications. When training a machine learning model on private data, it is critical to understand the conditions under which that data can be protected; and when it cannot. For instance, membership inference attacks aim to violate privacy protections by determining whether specific data was used to train a particular machine learning model.
Further, the successful transition of private machine learning theoretical research to practical use must account for gaps in achieving these properties that arise due to the realities of concrete implementations, threat models, and use cases; which is not currently the case
- …