Digital Exclusion and Relative Digital Deprivation: Exploring Factors and Moderators of Internet Non-Use in the UK
View/ Open
ueno_et_al_2023 (889.6Kb)
Download
Publication date
2023-12Keyword
Digital divideDigital poverty
Digital exclusion
Resource and Appropriation Theory
Relative Digital Deprivation Theory
Covid-19
Rights
(c) 2023 The Authors. This is an Open Access article distributed under the Creative Commons CC-BY license (http://creativecommons.org/licenses/by/4.0/)Peer-Reviewed
YesOpen Access status
openAccessAccepted for publication
2023-10-15
Metadata
Show full item recordAbstract
This paper investigates internet non-use in the UK. We apply Resource and Appropriation Theory (RAT), identifying main factors associated with internet non-use in the UK: (1) older age, (2) lower socio-economic classification, (3) disability, (4) less education/qualifications, and (5) lower housing tenure. We extend RAT by exploring magnifying effects of disadvantages, particularly, moderating effects of gender, housing tenure, urban/rural, North/South divide, and ethnicity. Internet non-users tend to be in lower-paid jobs, which impacts productivity even more during than before Covid, closing the loop of the RAT vicious circle. A thread runs through the results on the importance of attitudes and motivation. Accordingly, we recommend interventions based on Relative Digital Deprivation Theory. Once an individual understands that they suffer digital inequality, they are more likely to change attitudes and behavior to reduce inequality. If encouraged by family and friends, they may then view internet non-use as fixable and worth fixing, potentially becoming internet users.Version
Published versionCitation
Ueno A, Dennis C and Dafoulas GA (2023) Digital Exclusion and Relative Digital Deprivation: Exploring Factors and Moderators of Internet Non-Use in the UK. Technological Forecasting and Social Change. 197: 122935.Link to Version of Record
https://doi.org/10.1016/j.techfore.2023.122935Type
Articleae974a485f413a2113503eed53cd6c53
https://doi.org/10.1016/j.techfore.2023.122935
Scopus Count
Collections
Related items
Showing items related by title, author, creator and subject.
-
Digital Watermarking of Images towards Content Protection.Jiang, Jianmin; Ipson, Stanley S.; Nasir, Ibrahim A. (University of BradfordSchool of Computing, Informatics & Media, 2010-09-20)With the rapid growth of the internet and digital media techniques over the last decade, multimedia data such as images, video and audio can easily be copied, altered and distributed over the internet without any loss in quality. Therefore, protection of ownership of multimedia data has become a very significant and challenging issue. Three novel image watermarking algorithms have been designed and implemented for copyright protection. The first proposed algorithm is based on embedding multiple watermarks in the blue channel of colour images to achieve more robustness against attacks. The second proposed algorithm aims to achieve better trade-offs between imperceptibility and robustness requirements of a digital watermarking system. It embeds a watermark in adaptive manner via classification of DCT blocks with three levels: smooth, edges and texture, implemented in the DCT domain by analyzing the values of AC coefficients. The third algorithm aims to achieve robustness against geometric attacks, which can desynchronize the location of the watermark and hence cause incorrect watermark detection. It uses geometrically invariant feature points and image normalization to overcome the problem of synchronization errors caused by geometric attacks. Experimental results show that the proposed algorithms are robust and outperform related techniques found in literature.
-
From content-based to semantic image retrieval. Low level feature extraction, classification using image processing and neural networks, content based image retrieval, hybrid low level and high level based image retrieval in the compressed DCT domain.Jiang, Jianmin; Ipson, Stanley S.; Mohamed, Aamer S. S. (University of BradfordDepartment of Electronic Imaging and Media Communication, 2010-09-29)Digital image archiving urgently requires advanced techniques for more efficient storage and retrieval methods because of the increasing amount of digital. Although JPEG supply systems to compress image data efficiently, the problems of how to organize the image database structure for efficient indexing and retrieval, how to index and retrieve image data from DCT compressed domain and how to interpret image data semantically are major obstacles for further development of digital image database system. In content-based image, image analysis is the primary step to extract useful information from image databases. The difficulty in content-based image retrieval is how to summarize the low-level features into high-level or semantic descriptors to facilitate the retrieval procedure. Such a shift toward a semantic visual data learning or detection of semantic objects generates an urgent need to link the low level features with semantic understanding of the observed visual information. To solve such a -semantic gap¿ problem, an efficient way is to develop a number of classifiers to identify the presence of semantic image components that can be connected to semantic descriptors. Among various semantic objects, the human face is a very important example, which is usually also the most significant element in many images and photos. The presence of faces can usually be correlated to specific scenes with semantic inference according to a given ontology. Therefore, face detection can be an efficient tool to annotate images for semantic descriptors. In this thesis, a paradigm to process, analyze and interpret digital images is proposed. In order to speed up access to desired images, after accessing image data, image features are presented for analysis. This analysis gives not only a structure for content-based image retrieval but also the basic units ii for high-level semantic image interpretation. Finally, images are interpreted and classified into some semantic categories by semantic object detection categorization algorithm.
-
Visualising the Crucible of Shetland’s Broch Building. The role of digital documentation and legacy data in supporting the research, active conservation and presentation of Shetland’s heritageWilson, Andrew S.; Wilson, Lyn; Dockrill, Stephen; Turner, V.E.; Bond, Julie; Sou, Li Z. (University of BradfordSchool of Archaeological and Forensic Sciences, Faculty of Life Sciences, 2021)