Perceived Internet privacy concerns on social networks in Europe

Cecere, Grazia; Guel, Fabrice Le; Soulié, Nicolas

doi:10.1016/j.techfore.2015.01.021

Cited by 55 publications

(36 citation statements)

References 30 publications

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“…Differences in cultural dimensions are associated with differences in privacy concerns in some domains. For example, high power distance has been shown to be related to higher levels of concerns over social network services (SNS) privacy, and higher levels of individualism have been found to be related to lower levels of privacy concerns (Cecere, Le Guel, & Soulié, 2015). Differences in these dimensions may also have implications for how members of a society accept government surveillance or how they enact measures to protect their privacy.…”

Section: National Culturementioning

confidence: 99%

Cultural factors and the role of privacy concerns in acceptance of government surveillance

Thompson

McGill

Bunn

et al. 2020

Asso for Info Science & Tech

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Though there is a tension between citizens' privacy concerns and their acceptance of government surveillance, there is little systematic research in this space, and less still in a cross cultural context. We address the research gap by modeling the factors that drive public acceptance of government surveillance, and by exploring the influence of national culture. The research involved an online survey of 242 Australian and Sri Lankan residents. Data was analyzed using PLS, revealing that privacy concerns around initial collection of citizens' data influenced levels of acceptance of surveillance in Australia but not Sri Lanka, whereas concerns about secondary use of data did not influence levels of acceptance in either country. These findings suggest that respondents conflate surveillance with the collection of data and may not consider subsequent secondary use. We also investigate cultural differences, finding that societal collectivism and power distance significantly affect the strength of the relationships between privacy concerns and acceptance of surveillance, on the one hand, and adoption of privacy protections, on the other. Our research also considers the role of trust in government, and perceived need for surveillance. Findings are discussed with their implications for theory and practice.

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Section: National Culturementioning

confidence: 99%

Cultural factors and the role of privacy concerns in acceptance of government surveillance

Thompson

McGill

Bunn

et al. 2020

Asso for Info Science & Tech

View full text Add to dashboard Cite

show abstract

“…Panian (2010) states that establishing and enforcing policies and processes around the management of data should be the foundation of effective data governance practice as using big data for data science often raises ethical concerns. For example, automatic data collection may cause privacy infringements (Cecere et al 2015;van den Broek and van Veenstra 2018), such as in the case of cameras used to track traffic on highways, which often record personally identifiable data such as number plates or faces of persons in the vehicles.…”

Section: The Role Of Data Governance With Regards To the Organizationmentioning

confidence: 99%

Trusted Decision-Making: Data Governance for Creating Trust in Data Science Decision Outcomes

Brous¹,

Janssen

2020

Administrative Sciences

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Organizations are increasingly introducing data science initiatives to support decision-making. However, the decision outcomes of data science initiatives are not always used or adopted by decision-makers, often due to uncertainty about the quality of data input. It is, therefore, not surprising that organizations are increasingly turning to data governance as a means to improve the acceptance of data science decision outcomes. In this paper, propositions will be developed to understand the role of data governance in creating trust in data science decision outcomes. Two explanatory case studies in the asset management domain are analyzed to derive boundary conditions. The first case study is a data science project designed to improve the efficiency of road management through predictive maintenance, and the second case study is a data science project designed to detect fraudulent usage of electricity in medium and low voltage electrical grids without infringing privacy regulations. The duality of technology is used as our theoretical lens to understand the interactions between the organization, decision-makers, and technology. The results show that data science decision outcomes are more likely to be accepted if the organization has an established data governance capability. Data governance is also needed to ensure that organizational conditions of data science are met, and that incurred organizational changes are managed efficiently. These results imply that a mature data governance capability is required before sufficient trust can be placed in data science decision outcomes for decision-making.

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“…For example, Panian [43] states that establishing and enforcing policies and processes around the management of data should be the foundation of effective data governance practice as using big data for data science often raises ethical concerns. Automatic data collection may cause privacy infringements [44,45] such as cameras used to track traffic on highways which often record personally identifiable data such as number plates or faces of persons in the vehicles. Data governance processes should ensure that these personally identifiable features are removed before data is shared or used for purposes other than legally allowed.…”

Section: Literature Backgroundmentioning

confidence: 99%

Data Governance as Success Factor for Data Science

Brous

Janssen

Krans

2020

Lecture Notes in Computer Science

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More and more, asset management organizations are introducing data science initiatives to support predictive maintenance and anomaly detection. Asset management organizations are by nature data intensive to manage their assets like bridges, dykes, railways and roads. For this, they often implement data lakes using a variety of architectures and technologies to store big data and facilitate data science initiatives. However, the decision-outcomes of data science models are often highly reliant on the quality of the data. The data in the data lake therefore has to be of sufficient quality to develop trust by decision-makers. Not surprisingly, organizations are increasingly adopting data governance as a means to ensure that the quality of data entering the data lake is and remains of sufficient quality, and to ensure the organization remains legally compliant. The objective of the case study is to understand the role of data governance as success factor for data science. For this, a case study regarding the governance of data in a data lake in the asset management domain is analyzed to test three propositions contributing to the success of using data science. The results show that unambiguous ownership of the data, monitoring the quality of the data entering the data lake, and a controlled overview of standard and specific compliance requirements are important factors for maintaining data quality and compliance and building trust in data science products.

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Perceived Internet privacy concerns on social networks in Europe

Cited by 55 publications

References 30 publications

Cultural factors and the role of privacy concerns in acceptance of government surveillance

Cultural factors and the role of privacy concerns in acceptance of government surveillance

Trusted Decision-Making: Data Governance for Creating Trust in Data Science Decision Outcomes

Data Governance as Success Factor for Data Science

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