Towards Technology Acceptance: a Bayesian Network of soft requirements, the case of the NHS COVID-19 Test and Trace App

Luis Garcia Paucar; Nelly Bencomo; Alistair Sutcliffe

doi:10.1109/REW53955.2021.00026

Towards Technology Acceptance: a Bayesian Network of soft requirements, the case of the NHS COVID-19 Test and Trace App

Luis Garcia Paucar, Nelly Bencomo, Alistair Sutcliffe

Research output: Chapter in Book/Published conference output › Conference publication

Abstract

Context: With the growing importance and complexity of software-based systems in relevant domain areas such as healthcare, education and e-government, acceptance of software products is essential.Problem / Motivation: We require to understand, model, and predict decisions taken by end users regarding the adoption and utilization of software products, where soft factors (such as human values, motivations and attitudes) need to be taken into account.Idea: In this paper, we address this need by using a novel probabilistic approach that allows the prediction of end users’ decisions and ranks soft factors importance in taking these decisions.Solution and Early Results: We implement a computational Bayesian network to model hidden states and their relationships to the dynamics of technology acceptance. The model has been applied in the healthcare domain using the NHS COVID-19 Test and Trace app (COVID-19 app). We found that soft factors such as Fear of infection and Altruism were important for the COVID-19 app acceptance. The results are reported as part of a two stage-validation of the model.

Original language	English
Title of host publication	Proceedings - 29th IEEE International Requirements Engineering Conference Workshops, REW 2021
Editors	Tao Yue, Mehdi Mirakhorli
Publisher	IEEE
Pages	146-152
Number of pages	7
ISBN (Electronic)	978-1-6654-1898-0
ISBN (Print)	978-1-6654-1899-7
DOIs	https://doi.org/10.1109/REW53955.2021.00026
Publication status	Published - 27 Oct 2021
Event	2021 IEEE 29th International Requirements Engineering Conference Workshops (REW) - Notre Dame, IN, USA Duration: 20 Sept 2021 → 24 Sept 2021

Publication series

Name	Proceedings of the IEEE International Conference on Requirements Engineering
Volume	2021-September
ISSN (Print)	1090-705X
ISSN (Electronic)	2332-6441

Conference

Conference	2021 IEEE 29th International Requirements Engineering Conference Workshops (REW)
Period	20/09/21 → 24/09/21

Bibliographical note

Funding Information:
This work has been partially funded by the Leverhulme Trust Research Fellowship (Grant No. RF-2019-548/9) and the EPSRC Research Project Twenty20Insight (Grant No. EP/T017627/1).

Keywords

Bayesian inference
Technology acceptance
probabilistic models
reasoning tools
soft requirements

Access to Document

10.1109/REW53955.2021.00026

Cite this

Paucar, L. G., Bencomo, N., & Sutcliffe, A. (2021). Towards Technology Acceptance: a Bayesian Network of soft requirements, the case of the NHS COVID-19 Test and Trace App. In T. Yue, & M. Mirakhorli (Eds.), Proceedings - 29th IEEE International Requirements Engineering Conference Workshops, REW 2021 (pp. 146-152). (Proceedings of the IEEE International Conference on Requirements Engineering; Vol. 2021-September). IEEE. https://doi.org/10.1109/REW53955.2021.00026

Paucar, Luis Garcia ; Bencomo, Nelly ; Sutcliffe, Alistair. / Towards Technology Acceptance: a Bayesian Network of soft requirements, the case of the NHS COVID-19 Test and Trace App. Proceedings - 29th IEEE International Requirements Engineering Conference Workshops, REW 2021. editor / Tao Yue ; Mehdi Mirakhorli. IEEE, 2021. pp. 146-152 (Proceedings of the IEEE International Conference on Requirements Engineering).

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abstract = "Context: With the growing importance and complexity of software-based systems in relevant domain areas such as healthcare, education and e-government, acceptance of software products is essential.Problem / Motivation: We require to understand, model, and predict decisions taken by end users regarding the adoption and utilization of software products, where soft factors (such as human values, motivations and attitudes) need to be taken into account.Idea: In this paper, we address this need by using a novel probabilistic approach that allows the prediction of end users{\textquoteright} decisions and ranks soft factors importance in taking these decisions.Solution and Early Results: We implement a computational Bayesian network to model hidden states and their relationships to the dynamics of technology acceptance. The model has been applied in the healthcare domain using the NHS COVID-19 Test and Trace app (COVID-19 app). We found that soft factors such as Fear of infection and Altruism were important for the COVID-19 app acceptance. The results are reported as part of a two stage-validation of the model.",

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Paucar, LG, Bencomo, N & Sutcliffe, A 2021, Towards Technology Acceptance: a Bayesian Network of soft requirements, the case of the NHS COVID-19 Test and Trace App. in T Yue & M Mirakhorli (eds), Proceedings - 29th IEEE International Requirements Engineering Conference Workshops, REW 2021. Proceedings of the IEEE International Conference on Requirements Engineering, vol. 2021-September, IEEE, pp. 146-152, 2021 IEEE 29th International Requirements Engineering Conference Workshops (REW), 20/09/21. https://doi.org/10.1109/REW53955.2021.00026

Towards Technology Acceptance: a Bayesian Network of soft requirements, the case of the NHS COVID-19 Test and Trace App. / Paucar, Luis Garcia; Bencomo, Nelly; Sutcliffe, Alistair.
Proceedings - 29th IEEE International Requirements Engineering Conference Workshops, REW 2021. ed. / Tao Yue; Mehdi Mirakhorli. IEEE, 2021. p. 146-152 (Proceedings of the IEEE International Conference on Requirements Engineering; Vol. 2021-September).

Research output: Chapter in Book/Published conference output › Conference publication

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AU - Paucar, Luis Garcia

AU - Bencomo, Nelly

AU - Sutcliffe, Alistair

N1 - Funding Information: This work has been partially funded by the Leverhulme Trust Research Fellowship (Grant No. RF-2019-548/9) and the EPSRC Research Project Twenty20Insight (Grant No. EP/T017627/1).

PY - 2021/10/27

Y1 - 2021/10/27

N2 - Context: With the growing importance and complexity of software-based systems in relevant domain areas such as healthcare, education and e-government, acceptance of software products is essential.Problem / Motivation: We require to understand, model, and predict decisions taken by end users regarding the adoption and utilization of software products, where soft factors (such as human values, motivations and attitudes) need to be taken into account.Idea: In this paper, we address this need by using a novel probabilistic approach that allows the prediction of end users’ decisions and ranks soft factors importance in taking these decisions.Solution and Early Results: We implement a computational Bayesian network to model hidden states and their relationships to the dynamics of technology acceptance. The model has been applied in the healthcare domain using the NHS COVID-19 Test and Trace app (COVID-19 app). We found that soft factors such as Fear of infection and Altruism were important for the COVID-19 app acceptance. The results are reported as part of a two stage-validation of the model.

AB - Context: With the growing importance and complexity of software-based systems in relevant domain areas such as healthcare, education and e-government, acceptance of software products is essential.Problem / Motivation: We require to understand, model, and predict decisions taken by end users regarding the adoption and utilization of software products, where soft factors (such as human values, motivations and attitudes) need to be taken into account.Idea: In this paper, we address this need by using a novel probabilistic approach that allows the prediction of end users’ decisions and ranks soft factors importance in taking these decisions.Solution and Early Results: We implement a computational Bayesian network to model hidden states and their relationships to the dynamics of technology acceptance. The model has been applied in the healthcare domain using the NHS COVID-19 Test and Trace app (COVID-19 app). We found that soft factors such as Fear of infection and Altruism were important for the COVID-19 app acceptance. The results are reported as part of a two stage-validation of the model.

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Paucar LG, Bencomo N, Sutcliffe A. Towards Technology Acceptance: a Bayesian Network of soft requirements, the case of the NHS COVID-19 Test and Trace App. In Yue T, Mirakhorli M, editors, Proceedings - 29th IEEE International Requirements Engineering Conference Workshops, REW 2021. IEEE. 2021. p. 146-152. (Proceedings of the IEEE International Conference on Requirements Engineering). doi: 10.1109/REW53955.2021.00026

Towards Technology Acceptance: a Bayesian Network of soft requirements, the case of the NHS COVID-19 Test and Trace App

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

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