Influence of Users’ Privacy Risks Literacy on the Intention to Install a Mobile Application

Santo, Alessio De; Gaspoz, Cédric

doi:10.1007/978-3-319-16486-1_33

Cited by 9 publications

(4 citation statements)

References 17 publications

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“…The PCM (r) and its ACM (V) were the inputs for Stage 2, which adopted a weighted least squares (WLS) estimation to fit the proposed models [142]. To fit the hypothesized model, Stage 2 of TSSEM analysis uses a covariance matrix generated by weighting the elements in the PCM using the ACM.…”

Section: Inputs and Outputs Of Stagementioning

confidence: 99%

“…Producing Model-Fit Statistics. TSSEM Stage 2 produces the standard fit indices and parameter estimates [142]. Table 4 (PMT models) and Table 5 (EPPM models) detail the results of the analyses by reporting the estimated path coefficients and their significance as well as the R 2 values for each dependent variable and the fit indices for assessing model fit, using the generally accepted thresholds for CFI, TLI, RMSEA, and SRMR.…”

Section: Inputs and Outputs Of Stagementioning

confidence: 99%

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Examining the Differential Effectiveness of Fear Appeals in Information Security Management Using Two-Stage Meta-Analysis

Lowry,

Moody,

Parameswaran

et al. 2023

Journal of Management Information Systems

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Section: Inputs and Outputs Of Stagementioning

confidence: 99%

Section: Inputs and Outputs Of Stagementioning

confidence: 99%

Examining the Differential Effectiveness of Fear Appeals in Information Security Management Using Two-Stage Meta-Analysis

Lowry,

Moody,

Parameswaran

et al. 2023

Journal of Management Information Systems

View full text Add to dashboard Cite

show abstract

“…Imgraben et al (2014) found that users lack awareness of potential negative consequences associated with risky behaviour such as jailbreaking phones and then loading unverified apps on phones. A lack of knowledge and awareness concerning threats and consequences related to data protection, app downloading, backups and network connectivity were also indicated (Alsaleh et al, 2017;Barth et al, 2019;Egelman et al, 2014;Etaher and Weir, 2016;Gkioulos et al, 2017;Imgraben et al, 2014;De Santo and Gaspoz, 2015;Serrano-Tellería, 2018;Watson and Zheng, 2017).…”

Section: Knowledge and Awarenessmentioning

confidence: 99%

A systematic literature review of the factors affecting smartphone user threat avoidance behaviour

Butler

2020

ICS

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Purpose There is a need for behavioural research within the smartphone context to better understand users’ behaviour, as it is one of the reasons for the proliferation of mobile threats. This study aims to identify the human factors that affect smartphone users’ threat avoidance behaviour. Design/methodology/approach A structured literature review (SLR) was applied to answer the research question. A total of 27 sources were analysed, from which 16 codes emerged. After synthesis, six themes transpired. Findings Six factors were identified as drivers and/or challenges of smartphone users’ threat avoidance behaviour, namely, knowledge and awareness, misconceptions and trust, cost and benefit considerations, carelessness, perceived measure effectiveness and the user’s perceived skills and efficacy. Research limitations/implications The results can encourage and provide a starting point for further research on human behaviour to improve smartphone user behaviour. Practical implications The mobile industry should focus on eradicating common misconceptions and undue trust in mobile security that is prevalent among smartphone users and make cost effective and usable interventions available. Training and awareness programs should be updated to include the factors that were identified in this study to affect smartphone users’ threat avoidance behaviour. In addition to improving users’ declarative knowledge concerning available smartphone measures and tools, procedural knowledge should also be improved to ensure proper use of available protective measures. Users should realise the importance of staying updated with evolving smartphone technology and associated threats. Originality/value This study acknowledges and supports the notion that addressing human behaviour is crucial in the fight against mobile threats. It addresses the need for behavioural research to analyse the factors that drive smartphone user behaviour. Furthermore, it uses and documents the use of a SLR, a research technique often unfamiliar among information security researchers.

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“…So too does the attention people tend to pay to specific transfer settings. In digital contexts, consumers have become used to pressing install without paying attention to notices and permissions (De Santo and Gaspoz 2015; Jensen, Potts, and Jensen 2005; see cases 2 and 14). Notably, lack of salience is characteristic of information but can also happen for goods, such as in case 16, where the good is hidden among other goods.…”

Section: The 3r Interdependent Privacy Protection Frameworkmentioning

confidence: 99%

Your Data Is My Data: A Framework for Addressing Interdependent Privacy Infringements

Kamleitner¹,

Mitchell²

2019

Journal of Public Policy & Marketing

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Everyone holds personal information about others. Each person’s privacy thus critically depends on the interplay of multiple actors. In an age of technology integration, this interdependence of data protection is becoming a major threat to privacy. Yet current regulation focuses on the sharing of information between two parties rather than multiactor situations. This study highlights how current policy inadequacies, illustrated by the European Union General Data Protection Regulation, can be overcome by means of a deeper understanding of the phenomenon. Specifically, the authors introduce a new phenomenological framework to explain interdependent infringements. This framework builds on parallels between property and privacy and suggests that interdependent peer protection necessitates three hierarchical steps, “the 3Rs”: realize, recognize, and respect. In response to observed failures at these steps, the authors identify four classes of intervention that constitute a toolbox addressing what can be done by marketers, regulators, and privacy organizations. While the first three classes of interventions address issues arising from the corresponding 3Rs, the authors specifically advocate for a fourth class of interventions that proposes radical alternatives that shift the responsibilities for privacy protection away from consumers.

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Influence of Users’ Privacy Risks Literacy on the Intention to Install a Mobile Application

Cited by 9 publications

References 17 publications

Examining the Differential Effectiveness of Fear Appeals in Information Security Management Using Two-Stage Meta-Analysis

Examining the Differential Effectiveness of Fear Appeals in Information Security Management Using Two-Stage Meta-Analysis

A systematic literature review of the factors affecting smartphone user threat avoidance behaviour

Your Data Is My Data: A Framework for Addressing Interdependent Privacy Infringements

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