An Analysis of Economic Impact on IoT Industry under GDPR

Seo, Junwoo; Kim, Kyoungmin; Park, Mookyu; Park, Moosung; Lee, Kyung-Ho

doi:10.1155/2018/6792028

Cited by 19 publications

(9 citation statements)

References 13 publications

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“…Further investigation into the reason for the slower operation speed was found to be caused by network instability. Excluding the system's own factors, from the test results, the system running speed is relatively stable and reliable [18].…”

Section: System Testmentioning

confidence: 99%

Economic Information Analysis and Its Application in Decision-Making under the Background of Big Data

Chen

Sanweng

2021

Mobile Information Systems

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In the Internet age, data information is expanding at an alarming rate, and at the same time, it is showing a trend of diversification and fragmentation. How to make reasonable use of this intricate information and effectively use it in the work to give full play to the value of big data (BD) seems urgent. Based on this, this paper studies the economic information analysis and decision-making system under the background of BD. This article first analyzes the development status of our country’s industrial economic decision-making technology, then studies the service role of BD on macroeconomic decision-making in industrial development, then conducts an in-depth study of industrial economic information analysis methods and trends, and designs an industrial economic information model. Finally, this article tested the running speed and interactive function of the system. The test results show that the running speed of the system is very fast, and the interactive function is very accurate.

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Section: System Testmentioning

confidence: 99%

Economic Information Analysis and Its Application in Decision-Making under the Background of Big Data

Chen

Sanweng

2021

Mobile Information Systems

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“…Accordingly, by building the necessary capabilities to address the privacy paradox (i.e. aligning the rational, irrational and structural privacy conditions with the customers' expectations), companies could gain a strategic advantage in the market today (Li et al 2019;Seo et al 2018). These capabilities have already been linked to higher trust and customer satisfaction (Dehghanpouri et al, 2020;Eastlick et al 2006;Featherman et al 2010;Wu et al 2012), willingness to share data (Dinev and Hart 2006;Hui et al 2007;Morlok 2016), damage control for data breaches (Malhotra and Malhotra 2011) and even firm performance .…”

Section: Why Should Companies Care?mentioning

confidence: 99%

Addressing the privacy paradox on the organizational level: review and future directions

Gotsch

Schögel

2021

Manag Rev Q

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The discrepancy between informational privacy attitudes and actual behaviour of consumers is called the “privacy paradox”. Researchers across disciplines have formulated different theories on why consumers’ privacy concerns do not translate into increased protective behaviour. Over the past two decades multiple differing explanations for the paradox have been published. However, authors generally agree that companies are in a strong position to reduce consumers’ paradoxical behaviour by improving their customers’ informational privacy. Hence, this paper aims at answering the question: How can companies address the privacy paradox to improve their customers’ information privacy? Reviewing a sample of improvement recommendations from 138 papers that explore 41 theories in total, we determined that companies can generally align their privacy practices more closely with customers’ expectations across 4 inter-connected managerial processes: (1) strategic initiatives, (2) structural improvements, (3) human resource management, and (4) service development. The findings of this systematic literature review detail how companies can address both the rational and irrational nature of the privacy decision-making process. Furthermore, we propose a dynamic model able to identify weaknesses and strengths in companies’ privacy orientation.

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“…Even if there is a growing body of literature supporting privacy by design, only few papers adopt an ex post analysis. Seo, Kim, Park, Park, and Lee (2018) examine the impacts and consequences of GDPR on an IoT industry and they perform a cost analysis. The outcome is that those costs are extremely high.…”

Section: Related Workmentioning

confidence: 99%

“…From the aforementioned literature, it is straightforward to see that privacy regulations are conflictual with business interests. GDPR or similar privacy regulations create friction, complexity, and litigations to IoT ecosystems (Seo, Kim, Park, Park, & Lee, 2018; Shovon, Roy, Shil, & Atik, 2019). Even if there is a growing body of literature supporting privacy by design , only few papers adopt an ex post analysis.…”

Section: Related Workmentioning

confidence: 99%

Privacy Accountability and Penalties for IoT Firms

Ciardiello

Liddo

2020

Risk Analysis

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Internet of things (IoT) business partnership are formed by technological partners and traditional manufacturers. IoT sensors and devices capture data from manufacturers' products. Data enforce product/service innovation thanks to data sharing among companies. However, data sharing among firms increases the risk of data breaches. The latter is due to two phenomena: information linkage and privacy interdependency. Data Protection Authorities (DPA) protect data users' rights and fine firms if there is an infringement of privacy laws. DPA sanction the responsible for the infringement of privacy laws. We present two different business scenarios: the first occurs when each firm is a data owner; the second occurs when only the manufacturer is the data owner. For both scenarios, we present two fair penalty schemes that suggest the following: total amount of the fine; and how to share the fine among participants. Penalties critically vary at how innovation networks are structured in IoT industries. Our penalties provide incentives to data sharing since they redistribute firms' responsibility against data breaches. Our penalties may mitigate the risk on the manufacturer if is the unique responsible for data handling.

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An Analysis of Economic Impact on IoT Industry under GDPR

Cited by 19 publications

References 13 publications

Economic Information Analysis and Its Application in Decision-Making under the Background of Big Data

Economic Information Analysis and Its Application in Decision-Making under the Background of Big Data

Addressing the privacy paradox on the organizational level: review and future directions

Privacy Accountability and Penalties for IoT Firms

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