Trust in a specific technology

McKnight, D. Harrison; Carter, Michelle; Thatcher, Jason Bennett; Clay, Paul

doi:10.1145/1985347.1985353

Cited by 618 publications

(368 citation statements)

References 64 publications

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“…Over time, it may be lost but also [20,30], though there will be a moderating effect in relation to security risk or privacy exposure [48]. We therefore hypothesise that:…”

Section: Trust In Hmnsmentioning

confidence: 96%

“…In a series of studies McKnight, Thatcher and their colleagues explored different constructs associated with trust in technology [30,31]. They found that trust in technology and a willingness to explore its use could be predicted from individual propensities to trust.…”

Section: Related Researchmentioning

confidence: 99%

“…Bringing together some of these constructs, this paper proposes updates to a recent model of trust in information technology [30,31] with a detailed exploration of selfefficacy as it relates to agency [3,18]. Since human-machine networks can be characterised by independently varying levels of agency [2], either or both may influence selfefficacy [18].…”

Section: Introductionmentioning

confidence: 99%

“…Any consequent perception of risk can undermine a willingness to engage in some online activities [28]. However, assuming scepticism can be kept low, regulation can reduce the negative effects of risk across a number of contexts, leading to an increased level of trust [29].Bringing together some of these constructs, this paper proposes updates to a recent model of trust in information technology [30,31] with a detailed exploration of selfefficacy as it relates to agency [3,18]. Since human-machine networks can be characterised by independently varying levels of agency [2], either or both may influence selfefficacy [18].…”

mentioning

confidence: 99%

“…On the other hand, for this exploratory study, we do not consider affect [35] or other motivators such as social identity or task [36] which may mediate behaviour in online networks. Similarly, we discount environmental trust as factors affecting both behaviour and self-efficacy [37].In the original study by Thatcher et al [31], the final set of constructs was based on an extensive literature review [30], and its validation via an opportunity sample of students and IT professionals to establish inter-construct dependencies and correlations. The intent to explore technology was seen to be dependent on social norms and computer self-efficacy, while the influence of trust in IT and in support personnel is mediated by the technology acceptance model (TAM) constructs of perceived usefulness and ease of use.…”

mentioning

confidence: 99%

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The Interplay Between Human and Machine Agency

Pickering

Engen

Walland

2017

Lecture Notes in Computer Science

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Abstract. Human-machine networks affect many aspects of our lives: from sharing experiences with family and friends, knowledge creation and distance learning, and managing utility bills or providing feedback on retail items, to more specialised networks providing decision support to human operators and the delivery of health care via a network of clinicians, family, friends, and both physical and virtual social robots. Such networks rely on increasingly sophisticated machine algorithms, e.g., to recommend friends or purchases, to track our online activities in order to optimise the services available, and assessing risk to help maintain or even enhance people's health. Users are being offered ever increasing power and reach through these networks by machines which have to support and allow users to be able to achieve goals such as maintaining contact, making better decisions, and monitoring their health. As such, this comes down to a synergy between human and machine agency in which one is dependent in complex ways on the other. With that agency questions arise about trust, risk and regulation, as well as social influence and potential for computer-mediated self-efficacy. In this paper, we explore these constructs and their relationships and present a model based on review of the literature which seeks to identify the various dependencies between them. IntroductionA definition of agency based on the notion of non-deterministic behaviours [1] fails to recognise the increasing variety and complexity of human-machine networks 1 (HMNs) [2], the intention of technology designers [3], and active intervention by bots within social networks [4,5]. The concept of agency is particularly problematic in human-machine interactions [6]. Machine or material agency may be seen as automation, which originally required some tolerance from human agents [7]. But this is no longer true: technology can actively support human activity [8], and manifests increasingly complex interaction types [9]. Machine and human agency may not be the same and yet equally valid [10]; machine agency may be just "perceived autonomy" [11]; and it 1 In the following we use human-machine network and network interchangeably.certainly enables human agency [12]. Indeed, agency may well be becoming a social and group construct where both humans and machines play a part [13,14]; and used effectively, agency may even lead to innovative review of working practice [15].The enabling contribution of machine agents within a network may have an effect on self-efficacy. Bandura's original definition of self-efficacy as an individual's belief in their ability to be able to achieve a given objective [16][17][18] has also been applied to technology [19,20] and its acceptance [21]. There are, however, constraints on the support and positive contribution of technology to human self-efficacy, not least in terms of anxiety and suspicion around technology use [22,23]. This may be further exacerbated by increasing machine animism: it may not always be obvious what machines ar...

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“…Over time, it may be lost but also [20,30], though there will be a moderating effect in relation to security risk or privacy exposure [48]. We therefore hypothesise that:…”

Section: Trust In Hmnsmentioning

confidence: 96%

Section: Related Researchmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

The Interplay Between Human and Machine Agency

Pickering

Engen

Walland

2017

Lecture Notes in Computer Science

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Trust in Virtual Online Environments

Järvenpää¹,

Cantú²,

Lim³

2017

The Wiley Blackwell Handbook of the Psychology of the Internet at Work

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Examining the determinants of acceptance and use of mobile contact tracing applications in Brazil: An extended privacy calculus perspective

Fox

Werff

Rosati

et al. 2021

Asso for Info Science & Tech

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Mobile contact tracing applications have emerged as a potential solution to track and reduce the transmission of viruses such as Covid‐19. These applications require the disclosure of potentially sensitive personal information thus generating understandable implications for personal privacy. This research aims to determine the factors driving acceptance of these applications, with acceptance represented by three distinct variables, namely usage intentions, willingness to disclose personal data, and willingness to rely on health advice. The study examines the influence of perceived privacy, social influence, and benefits on acceptance of contact tracing applications among a sample of 1,114 Brazilian citizens. The study leverages social contract theory to demonstrate the importance of perceived control and perceived surveillance in the formation of individuals' perceptions of privacy. Integrating privacy calculus theory with social contract theory to include reciprocity and social influence, our findings suggest that perceived privacy, reciprocal benefits, and social influence all positively influence individuals' intentions to download or continue the use of contact tracing applications, while intentions to disclose information are influenced by adoption intentions, perceived privacy, and reciprocal benefits and individuals' willingness to rely on contact tracing applications for health advice is influenced by reciprocal benefits and disclosure intentions.

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Trust in a specific technology

Cited by 618 publications

References 64 publications

The Interplay Between Human and Machine Agency

The Interplay Between Human and Machine Agency

Trust in Virtual Online Environments

Examining the determinants of acceptance and use of mobile contact tracing applications in Brazil: An extended privacy calculus perspective

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