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Towards a comprehensive understanding of blockchain technology adoption in various industries in developing and emerging economies: a systematic review
The fast growth and wide range of applications of blockchain (BC) technology in various industries is irrefutable. Generally, BC technology is still in at an infant stage but it has generated significant interests in many sectors and industries. Nonetheless, despite an uptake of interest on the application of BC technology, the extent of its adoption in various industries in many countries remains partially understood. This paper aims to assess the current status of research on adoption of BC technology in various industries, particularly in developing and emerging economies. This study systematically reviewed the applied theories and models, adoption factors considered in each study, benefits, barriers and challenges of BC adoption intention in different industries from 86 articles published in the past five years from 2019 to end of June 2023. Findings showed several popular adoption models such as the Technology Acceptance Model, Unified Theory of Acceptance and Use of Technology and Task Technology Fit in the reviewed articles. Benefits, barriers and challenges were evident from each of the industries, implying the need to further understand BC adoption and application in these industries. This review also identifies a few research gaps and provides recommendations for future researches.
Journal Article
Information technology
In this book, check out many stories from the world of Information Technology, including breaking headlines about psychology's impact on social media technology, 3D Printing, engineering new batteries, and math models to break secret codes.
Book
Virtual Knowledge
Today we are witnessing dramatic changes in the way scientific and scholarly knowledge is created, codified, and communicated. This transformation is connected to the use of digital technologies and the virtualization of knowledge. In this book, scholars from a range of disciplines consider just what, if anything, is new when knowledge is produced in new ways. Does knowledge itself change when the tools of knowledge acquisition, representation, and distribution become digital? Issues of knowledge creation and dissemination go beyond the development and use of new computational tools. The book, which draws on work from the Virtual Knowledge Studio, brings together research on scientific practice, infrastructure, and technology. Focusing on issues of digital scholarship in the humanities and social sciences, the contributors discuss who can be considered legitimate knowledge creators, the value of \"invisible\" labor, the role of data visualization in policy making, the visualization of uncertainty, the conceptualization of openness in scholarly communication, data floods in the social sciences, and how expectations about future research shape research practices. The contributors combine an appreciation of the transformative power of the virtual with a commitment to the empirical study of practice and use.The hardcover edition does not include a dust jacket.
eBook
Big Data, Little Data, No Data
\"Big Data\" is on the covers ofScience, Nature, theEconomist, andWiredmagazines, on the front pages of theWall Street Journaland theNew York Times.But despite the media hyperbole, as Christine Borgman points out in this examination of data and scholarly research, having the right data is usually better than having more data; little data can be just as valuable as big data. In many cases, there are no data -- because relevant data don't exist, cannot be found, or are not available. Moreover, data sharing is difficult, incentives to do so are minimal, and data practices vary widely across disciplines.Borgman, an often-cited authority on scholarly communication, argues that data have no value or meaning in isolation; they exist within a knowledge infrastructure -- an ecology of people, practices, technologies, institutions, material objects, and relationships. After laying out the premises of her investigation -- six \"provocations\" meant to inspire discussion about the uses of data in scholarship -- Borgman offers case studies of data practices in the sciences, the social sciences, and the humanities, and then considers the implications of her findings for scholarly practice and research policy. To manage and exploit data over the long term, Borgman argues, requires massive investment in knowledge infrastructures; at stake is the future of scholarship.
eBook
Self-Selected or Mandated, Open Access Increases Citation Impact for Higher Quality Research
Articles whose authors have supplemented subscription-based access to the publisher's version by self-archiving their own final draft to make it accessible free for all on the web (\"Open Access\", OA) are cited significantly more than articles in the same journal and year that have not been made OA. Some have suggested that this \"OA Advantage\" may not be causal but just a self-selection bias, because authors preferentially make higher-quality articles OA. To test this we compared self-selective self-archiving with mandatory self-archiving for a sample of 27,197 articles published 2002-2006 in 1,984 journals. METHDOLOGY/PRINCIPAL FINDINGS: The OA Advantage proved just as high for both. Logistic regression analysis showed that the advantage is independent of other correlates of citations (article age; journal impact factor; number of co-authors, references or pages; field; article type; or country) and highest for the most highly cited articles. The OA Advantage is real, independent and causal, but skewed. Its size is indeed correlated with quality, just as citations themselves are (the top 20% of articles receive about 80% of all citations).
The OA advantage is greater for the more citable articles, not because of a quality bias from authors self-selecting what to make OA, but because of a quality advantage, from users self-selecting what to use and cite, freed by OA from the constraints of selective accessibility to subscribers only. It is hoped that these findings will help motivate the adoption of OA self-archiving mandates by universities, research institutions and research funders.
Journal Article
Practical Hamiltonian learning with unitary dynamics and Gibbs states
We study the problem of learning the parameters for the Hamiltonian of a quantum many-body system, given limited access to the system. In this work, we build upon recent approaches to Hamiltonian learning via derivative estimation. We propose a protocol that improves the scaling dependence of prior works, particularly with respect to parameters relating to the structure of the Hamiltonian (e.g., its locality k). Furthermore, by deriving exact bounds on the performance of our protocol, we are able to provide a precise numerical prescription for theoretically optimal settings of hyperparameters in our learning protocol, such as the maximum evolution time (when learning with unitary dynamics) or minimum temperature (when learning with Gibbs states). Thanks to these improvements, our protocol has practical scaling for large problems: we demonstrate this with a numerical simulation of our protocol on an 80-qubit system.
Journal Article