TAM-VS: A Technology Acceptance Model for Video Surveillance

Krempel, Erik; Beyerer, Jürgen

doi:10.1007/978-3-319-06749-0_6

Cited by 11 publications

(21 citation statements)

References 9 publications

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“…As for the video surveillance solution, the potential problems probably outweigh the benefits. Indeed, the security and privacy issues and the feeling of being controlled, that may be experienced by people with disabilities, could compromise the whole system's acceptance and decrease the feeling of independence (Krempel and Beyerer, 2014).…”

Section: Discussionmentioning

confidence: 99%

Smart Co-housing for People With Disabilities: A Preliminary Assessment of Caregivers’ Interaction With the DOMHO System

et al. 2021

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Millions of people with motor and cognitive disabilities face hardships in daily life due to the limited accessibility and inclusiveness of living spaces which limit their autonomy and independence. The DOMHO project deals with these fundamental issues by leveraging an innovative solution: a smart co-housing apartment. Besides, the project aims at exploiting the well know effects of co-housing on individuals’ health and well-being in combination with ambient assisted living technologies. The present study focused on the interaction of caregivers with the control application of an integrated smart system. Participants performed different tasks, fill out a questionnaire, and were interviewed. Performance and usability of the user interface, trust in technology, privacy, and attitudes towards home automation were explored. A series of guidelines for domotic technology control interfaces design was identified, and a high level of trust in these advanced tools was shown. Caregivers considered smart technologies as a work aid and a means for enhancing autonomy and life quality for users with disabilities.

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Section: Discussionmentioning

confidence: 99%

Smart Co-housing for People With Disabilities: A Preliminary Assessment of Caregivers’ Interaction With the DOMHO System

et al. 2021

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“…What metrics are used to demonstrate artefact utility and improvement over previous artefacts? (7) What new knowledge is added to the knowledge base and in what form (e.g., peerreviewed literature, meta-artefacts, new theory, new method)? (8) Has the research question been satisfactorily addressed?…”

Section: Environmentmentioning

confidence: 99%

“…face recognition or violence detection, latterly also privacy and transparency related work has been made. 7 While this research helps us to design systems that combine functionality and privacy, only little understanding is present how the people under surveillance will react to the new systems; average citizens do not understand technological details and they are unable to distinguish between systems with varying privacy protection. 7 Privacy in surveillance is a special concern when, for example, using drones and surveillance cameras, with automated border control, and when collecting and analysing big data.…”

Section: Privacy Surveillance and Privacy By Designmentioning

confidence: 99%

“…7 While this research helps us to design systems that combine functionality and privacy, only little understanding is present how the people under surveillance will react to the new systems; average citizens do not understand technological details and they are unable to distinguish between systems with varying privacy protection. 7 Privacy in surveillance is a special concern when, for example, using drones and surveillance cameras, with automated border control, and when collecting and analysing big data. In addition, the impact of new surveillance technologies on the fundamental rights of asylum seekers and refugees as well as the increased responsibility this more effective situational awareness brings (under international refugee law and the Search and Rescue regime: duty to render assistance) have all been debated by numerous scholars.…”

Section: Privacy Surveillance and Privacy By Designmentioning

confidence: 99%

“…Many surveys for understanding the acceptance of surveillance were made in special places (airports, public transport and shopping malls), but their outcome depends on recently happened events, e.g., a terrorist attack or a reported misuse of a video sequence and the underlying factors are not considered and no generic model for the acceptance exists. 7 Privacy by design (PbD) is an approach to systems engineering approach intended to ensure privacy protection from the earliest stages of a project and to be taken into account throughout the whole engineering process. The PbD concept is closely related to the concept of privacy enhancing technologies (PET) published in 1995.…”

Section: Privacy Surveillance and Privacy By Designmentioning

confidence: 99%

See 2 more Smart Citations

Design Science Research towards Privacy by Design in Maritime Surveillance ICT Systems

Rajamäki¹

2019

ISIJ

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Maritime surveillance is essential for creating maritime awareness. When open source intelligence (OSINT) is becoming a part of it, privacy in surveillance will be a special concern. However, processing of personal data in surveillance is regulated by the General Data Protection Regulation (GDPR) and/or by the Directive 2016/680 on the protection of natural persons with regard to the processing of personal data by competent authorities for the purposes of the prevention, investigation, detection or prosecution of criminal offences or the execution of criminal penalties, and on the free movement of such data. In both of these regulations, Privacy by Design (PbD) approach is mandatory. GDPR encourages applying a Data Protection Impact Assessment (DPIA) to identify and minimize data protection risks as the initial step of any new project. This design science research shows how PbD and DPIA are adapted in the MARISA project and tries to be a step towards new meta-artefacts and useful methods for the design and validation of privacy requirements engineering approaches into maritime surveillance ICT systems.

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User Behavior Assessment Towards Biometric Facial Recognition System: A SEM-Neural Network Approach

Hizam

Ahmed

Fahad

et al. 2021

Advances in Intelligent Systems and Computing

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A smart home is grounded on the sensors that endure automation, safety, and structural integration. The security mechanism in digital setup possesses vibrant prominence and the biometric facial recognition system is novel addition to accrue the smart home features. Understanding the implementation of such technology is the outcome of user behavior modeling. However, there is the paucity of empirical research that explains the role of cognitive, functional, and social aspects of end-user's acceptance behavior towards biometric facial recognition systems at homes. Therefore, a causal research survey was conducted to comprehend the behavioral intention towards the use of a biometric facial recognition system. Technology Acceptance Model (TAM) was implied with Perceived System Quality (PSQ) and Social Influence (SI) to hypothesize the conceptual framework. Data was collected from 475 respondents through online questionnaires. Structural Equation Modeling (SEM) and Artificial Neural Network (ANN) were employed to analyze the surveyed data. The results showed that all the variables of the proposed framework significantly affected the behavioral intention to use the system. The PSQ appeared as the noteworthy predictor towards biometric facial recognition system usability through regression and sensitivity analyses. A multi-analytical approach towards understanding the technology user behavior will support the efficient decision-making process in Human-centric computing.

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TAM-VS: A Technology Acceptance Model for Video Surveillance

Cited by 11 publications

References 9 publications

Smart Co-housing for People With Disabilities: A Preliminary Assessment of Caregivers’ Interaction With the DOMHO System

Smart Co-housing for People With Disabilities: A Preliminary Assessment of Caregivers’ Interaction With the DOMHO System

Design Science Research towards Privacy by Design in Maritime Surveillance ICT Systems

User Behavior Assessment Towards Biometric Facial Recognition System: A SEM-Neural Network Approach

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