Tell me what that means to you: Small-story narratives in technology adoption
Tell me what that means to you: Small-story narratives in technology adoption
Technology adoption is often predicted based on little information such as the Perceived ease-of-use and the Perceived usefulness of the technology. Related constructs such as Attitude to use, Behavioral intention to use and External variables cannot be easily operationalised and so are often ignored. However, technology characteristics themselves fail to represent other factors such as potential adopter attitudes and how they react to the opportunities offered by the technology to meet their needs. In a series of three studies, qualitative methods were used to identify, validate and then exploit narrative themes. Based on the short narratives of potential adopters discussing their experiences with a set of cybersecurity tools, we are developing a small-story narrative framework to capture how they respond to the technology contextualised directly within their professional environment. Akin to concepts from adoption frameworks in healthcare intervention studies, we conclude that adopter’s personal response to a technology and how they make sense of it in their environment becomes evident in the narratives they create.
Grounded theory, Mixed methods, Narrative analysis, Qualitative methods, Small-story narratives, Technology acceptance, Technology adoption
274-289
Pickering, Brian
225088d0-729e-4f17-afe2-1ad1193ccae6
Phillips, Stephen
47610c30-a543-4bac-a96a-bc1fce564a59
Surridge, Michael
3bd360fa-1962-4992-bb16-12fc4dd7d9a9
16 June 2022
Pickering, Brian
225088d0-729e-4f17-afe2-1ad1193ccae6
Phillips, Stephen
47610c30-a543-4bac-a96a-bc1fce564a59
Surridge, Michael
3bd360fa-1962-4992-bb16-12fc4dd7d9a9
Pickering, Brian, Phillips, Stephen and Surridge, Michael
(2022)
Tell me what that means to you: Small-story narratives in technology adoption.
Kurosu, Masaaki
(ed.)
In Human-Computer Interaction. Theoretical Approaches and Design Methods - Thematic Area, HCI 2022, Held as Part of the 24th HCI International Conference, HCII 2022, Proceedings.
vol. 13302 LNCS,
Springer.
.
(doi:10.1007/978-3-031-05311-5_19).
Record type:
Conference or Workshop Item
(Paper)
Abstract
Technology adoption is often predicted based on little information such as the Perceived ease-of-use and the Perceived usefulness of the technology. Related constructs such as Attitude to use, Behavioral intention to use and External variables cannot be easily operationalised and so are often ignored. However, technology characteristics themselves fail to represent other factors such as potential adopter attitudes and how they react to the opportunities offered by the technology to meet their needs. In a series of three studies, qualitative methods were used to identify, validate and then exploit narrative themes. Based on the short narratives of potential adopters discussing their experiences with a set of cybersecurity tools, we are developing a small-story narrative framework to capture how they respond to the technology contextualised directly within their professional environment. Akin to concepts from adoption frameworks in healthcare intervention studies, we conclude that adopter’s personal response to a technology and how they make sense of it in their environment becomes evident in the narratives they create.
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e-pub ahead of print date: 16 June 2022
Published date: 16 June 2022
Additional Information:
Funding Information:
This work was supported by the EU H2020 project CyberKit4SME (Grant agreement: 883188); and in part by the FogProtect project, a Horizon 2020 Research and Innovation Action to secure sensitive data along the computing continuum (under grant agreement No. 871525).
Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Keywords:
Grounded theory, Mixed methods, Narrative analysis, Qualitative methods, Small-story narratives, Technology acceptance, Technology adoption
Identifiers
Local EPrints ID: 467974
URI: http://eprints.soton.ac.uk/id/eprint/467974
ISSN: 0302-9743
PURE UUID: 5434086f-ee8c-4cd2-bd67-0bb9f0f213fc
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Date deposited: 26 Jul 2022 17:22
Last modified: 17 Mar 2024 03:23
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Author:
Stephen Phillips
Author:
Michael Surridge
Editor:
Masaaki Kurosu
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