Evaluating the use of mixed reality in CSI training through the integration of the task-technology fit and technology acceptance model
Evaluating the use of mixed reality in CSI training through the integration of the task-technology fit and technology acceptance model
Despite the emerging literature on adopting Mixed Reality headsets in crime scene investigation ¬ , it is still debatable on how to employ these headsets and its application for training purposes in higher education and police academies. Hence, this research presents a novel hybrid theoretical framework that combines the Task-technology Fit and Technology Acceptance Model variables and the most prominent features of MR headsets—immersion, interactivity and mobility. The main objective is to explore young investigators’ behavioural intention to adopt MR headsets and their applications for investigation training practices. To validate the developed model, a questionnaire survey was the primary method used to collect data from 160 police academy students using the partial least squares-structural equation modelling technique. The empirical results revealed that task technology fit has a positive impact on the perceived usefulness of MR headset applications and no significant positive impact on the perceived ease of use applications of MR devices. On the contrary, individual technology fit has a positive impact on the perceived ease of use and no significant positive effects were found regarding the perceived usefulness of investigation training purposes. Furthermore, the results indicated that the mobility of MR wearable devices positively influences the perceived ease of use and the perceived usefulness for crime scene practices. The study also addresses the theoretical contributions and practical implications of these outcomes.
114732-114752
Albeedan, Meshal
f65cf782-ad33-4b80-89ac-9d6c2a810f6b
Kolivand, Hoshang
37df27c1-e301-4a2c-8298-d2e0fa486126
Hammady, Ramy
9d5ff940-2d85-44e7-b001-222ae2feb935
11 October 2023
Albeedan, Meshal
f65cf782-ad33-4b80-89ac-9d6c2a810f6b
Kolivand, Hoshang
37df27c1-e301-4a2c-8298-d2e0fa486126
Hammady, Ramy
9d5ff940-2d85-44e7-b001-222ae2feb935
Albeedan, Meshal, Kolivand, Hoshang and Hammady, Ramy
(2023)
Evaluating the use of mixed reality in CSI training through the integration of the task-technology fit and technology acceptance model.
IEEE Access, 11, .
(doi:10.1109/ACCESS.2023.3323949).
Abstract
Despite the emerging literature on adopting Mixed Reality headsets in crime scene investigation ¬ , it is still debatable on how to employ these headsets and its application for training purposes in higher education and police academies. Hence, this research presents a novel hybrid theoretical framework that combines the Task-technology Fit and Technology Acceptance Model variables and the most prominent features of MR headsets—immersion, interactivity and mobility. The main objective is to explore young investigators’ behavioural intention to adopt MR headsets and their applications for investigation training practices. To validate the developed model, a questionnaire survey was the primary method used to collect data from 160 police academy students using the partial least squares-structural equation modelling technique. The empirical results revealed that task technology fit has a positive impact on the perceived usefulness of MR headset applications and no significant positive impact on the perceived ease of use applications of MR devices. On the contrary, individual technology fit has a positive impact on the perceived ease of use and no significant positive effects were found regarding the perceived usefulness of investigation training purposes. Furthermore, the results indicated that the mobility of MR wearable devices positively influences the perceived ease of use and the perceived usefulness for crime scene practices. The study also addresses the theoretical contributions and practical implications of these outcomes.
Text
Evaluating_the_Use_of_Mixed_Reality_in_CSI_Training_Through_the_Integration_of_the_Task-Technology_Fit_and_Technology_Acceptance_Model
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Accepted/In Press date: 5 October 2023
Published date: 11 October 2023
Identifiers
Local EPrints ID: 500347
URI: http://eprints.soton.ac.uk/id/eprint/500347
ISSN: 2169-3536
PURE UUID: dc8280c2-4b8e-4d93-bbf2-9be036bb81c5
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Date deposited: 25 Apr 2025 17:02
Last modified: 22 Aug 2025 02:49
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Contributors
Author:
Meshal Albeedan
Author:
Hoshang Kolivand
Author:
Ramy Hammady
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