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A Bayesian Network-based model to understand the role of soft requirements in technology acceptance: the Case of the NHSCOVID-19 Test and Trace App

Garcia-Paucar, Luis and Bencomo, Nelly and Sutcliffe, Alistair and Sawyer, Pete (2022) 'A Bayesian Network-based model to understand the role of soft requirements in technology acceptance: the Case of the NHSCOVID-19 Test and Trace App.', 37th Annual ACM Symposium on Applied Computing (ACM SAC'2022) Brno, Czech Republic, 25-29 April 2022.

Abstract

Soft requirements (such as human values, motivations, and personal attitudes) can strongly influence technology acceptance. As such, we need to understand, model and predict decisions made by end users regarding the adoption and utilization of software products, where soft requirements need to be taken into account. Therefore, we address this need by using a novel Bayesian network approach that allows the prediction of end users’ decisions and ranks soft requirements’ importance when making these decisions. The approach offers insights that help requirements engineers better understand which soft requirements are essential for particular software to be accepted by its target users. We have implemented a Bayesian network to model hidden states and their relationships to the dynamics of technology acceptance. The model has been applied to the healthcare domain using the NHS COVID-19 Test and Trace app (COVID-19 app). Our findings show that soft requirements such as Responsibility and Trust (e.g. Trust in the supplier/brand) are relevant for the COVID-19 app acceptance. However, the importance of soft requirements is also contextual and time-dependent. For example, Fear of infection was an essential soft requirement, but its relevance decreased over time. The results are reported as part of a two stage-validation of the model.

Item Type:Conference item (Paper)
Full text:(AM) Accepted Manuscript
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Status:Peer-reviewed
Publisher Web site:https://doi.org/10.1145/3477314.3507147
Publisher statement:© ACM 2022. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in SAC '22: Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing, https://doi.org/10.1145/3477314.3507147
Date accepted:16 December 2021
Date deposited:17 January 2022
Date of first online publication:06 May 2022
Date first made open access:13 September 2022

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