Online Privacy and Information Disclosure by Consumers

Ichihashi, Shota

doi:10.1257/aer.20181052

Cited by 149 publications

(56 citation statements)

References 33 publications

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“…The working definition of privacy concern in this study is user's risk perception of their own private information during their use of short-form video APP, and privacy protection behavior means the user's response behavior after perceiving the risk of privacy. Since stepping into the Internet era, many scholars have conducted many researches on the privacy of users and drawn many meaningful conclusions (Ichihashi, 2020;Johnson et al, 2020). Chen et al (2017) found that the concern of network privacy has an impact on the privacy protection behavior of users.…”

Section: Privacy Concern and Privacy Protection Behaviormentioning

confidence: 99%

Examining the factors influencing college students’ continuance intention to use short-form video APP

Mou

2021

AJIM

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PurposeThe purpose of this study is to explore the effects of recommendation algorithm, product reputation, new product novelty, privacy concern and privacy protection behavior on users’ satisfaction and continuance intention to use short-form video application (APP).Design/methodology/approachBased on the existing theories, the research model of this study was developed and 445 valid data were collected through a questionnaire survey. The partial least squares structural equation modeling (PLS-SEM) was employed for data analysis to test the research model and hypotheses.FindingsThe results reveal that the recommendation algorithm has a significant positive effect on user satisfaction, new product novelty and privacy concern. The influence of recommendation algorithm on privacy concern is negatively moderated by product reputation. Privacy concern has a significant and positive impact on privacy protection behavior, and privacy protection behavior has a significant and positive impact on user satisfaction. New product novelty also has significant impact on user satisfaction.Originality/valueThis study is one of the earliest studies to incorporate recommendation algorithm as a construct into the college students’ continuance intention to use short-form video APP. The influence of reputation as a moderator variable on the relationship between algorithm and privacy concerns is also investigated.

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Section: Privacy Concern and Privacy Protection Behaviormentioning

confidence: 99%

Examining the factors influencing college students’ continuance intention to use short-form video APP

Mou

2021

AJIM

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“…Static models about privacy have been studied in Gradwohl (2018), who analyzes the problem of full implementation when agents prefer to protect their privacy, in Gilboa‐Freedman and Smorodinsky (2018), who axiomatize preferences over privacy and in Bird and Neeman (2020), who study how to regulate what information a firm can use when interacting with a consumer so as to maximize the consumer's welfare 5 . Finally, in the two‐sided market setting, Hidir and Vellodi (2018) and Ichihashi (2019) have considered letting the consumer directly communicate to the seller via cheap talk or Bayesian persuasion. In our model, however, the consumer reveals information about his private type by responding to the seller's mechanism.…”

Section: Related Literaturementioning

confidence: 99%

Bayesian privacy

Eilat

Eliaz

2021

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Modern information technologies make it possible to store, analyze, and trade unprecedented amounts of detailed information about individuals. This has led to public discussions on whether individuals' privacy should be better protected by restricting the amount or the precision of information that is collected by commercial institutions on their participants. We contribute to this discussion by proposing a Bayesian approach to measure loss of privacy in a mechanism. Specifically, we define the loss of privacy associated with a mechanism as the difference between the designer's prior and posterior beliefs about an agent's type, where this difference is calculated using Kullback–Leibler divergence, and where the change in beliefs is triggered by actions taken by the agent in the mechanism. We consider both ex post (for every realized type, the maximal difference in beliefs cannot exceed some threshold κ) and ex ante (the expected difference in beliefs over all type realizations cannot exceed some threshold κ) measures of privacy loss. Applying these notions to the monopolistic screening environment of Mussa and Rosen (1978), we study the properties of optimal privacy‐constrained mechanisms and the relation between welfare/profits and privacy levels.

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“…One could think, for example, about situations where information allows producers to help consumers make better choices (Ichihashi, 2019) or where data is used to construct credit ratings that could potentially improve the allocation of credit (Pagano and Jappelli, 1993).…”

Section: Policymentioning

confidence: 99%

Privacy as a Public Good: A Case for Electronic Cash

Garratt

Oordt

2019

SSRN Journal

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Privacy is a feature inherent to the use of cash for payments. With steadily increasing market shares of commercial digital payments platforms, privacy in payments may no longer be attainable in the future. In this paper, we explore the potential welfare impact of reductions in privacy in payments in a dynamic framework. In our framework, firms may use data collected through payments to price discriminate among future customers. A public good aspect of privacy in payments arises because individual customers do not bear the full cost of failing to protect their privacy. As a consequence, they may suboptimally choose not to preserve their privacy in payments. When left to market forces alone, the use of privacy-preserving means of payments, such as cash, may decline faster than is optimal. Bank topics: Digital currencies and fintech; Bank notes; Payment clearing and settlement systems; Central bank research JEL codes: E42, G28 "This [central bank digital] currency could satisfy public policy goals, such as (i) financial inclusion, (ii) security and consumer protection; and to provide what the private sector cannot: (iii) privacy in payments."

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Online Privacy and Information Disclosure by Consumers

Cited by 149 publications

References 33 publications

Examining the factors influencing college students’ continuance intention to use short-form video APP

Examining the factors influencing college students’ continuance intention to use short-form video APP

Bayesian privacy

Privacy as a Public Good: A Case for Electronic Cash

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