Privacy controls as an information source to reduce data poisoning in artificial intelligence-powered personalization

Cloarec, Julien

doi:10.1016/j.jbusres.2022.07.045

Cited by 24 publications

(4 citation statements)

References 83 publications

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“…Another crucial concern to address is the issue of data poisoning (Cloarec, 2022). Data poisoning constitutes an adversarial attack where a malicious actor manipulates or inserts tainted data into the training dataset used to develop a machine learning model, resulting in bias within the model's outcomes (Roselli et al, 2019).…”

Section: The Case For Responsible and Trustworthy Aimentioning

confidence: 99%

How to promote AI in the US federal government: Insights from policy process frameworks

Khan,

Shoaib,

Arledge

2024

Government Information Quarterly

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Section: The Case For Responsible and Trustworthy Aimentioning

confidence: 99%

How to promote AI in the US federal government: Insights from policy process frameworks

Khan,

Shoaib,

Arledge

2024

Government Information Quarterly

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“…Data is critical to the operation of marketers and is also a core of business today (Cloarec, 2022). The effect of data-driven analysis has become a popular topic of supply chain management since the rapid development of information technology (Brinch, 2018; Zhang et al ., 2022).…”

Section: Literature Reviewmentioning

confidence: 99%

Decisions of pricing and delivery-lead-time in dual-channel supply chains with data-driven marketing using internal financing and contract coordination

Tang

Huang

2023

IMDS

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PurposeThe purpose of this research is to investigate how to introduce a financing scheme to tackle the manufacturer's capital constraint problem, discuss the effects of data-driven marketing (DDM) quality, cross-channel-return (CCR) rate and financing interest rate on the members' pricing and delivery-lead-time decisions and optimal performances, and analyzes `how to achieve the coordination within a dual-channel supply chain (DSC) by contract coordination.Design/methodology/approachThis work establishes a DSC model with DDM, and the offline retailer can provide internal financing to the capital-constrained online manufacturer. The demand under the price is determined based on DDM quality, customer channel preference and delivery lead time. Then, combined with the Stackelberg game, the optimal pricing and delivery-lead-time decisions are discussed under the inconsistent and consistent pricing strategies with decentralized and centralized systems. Furthermore, it designs a manufacturer-revenue sharing contract to coordinate the members under the two pricing strategies.Findings(1) The increase of DDM quality will reduce the delivery-lead-time under the inconsistent or consistent pricing strategy and will push the selling prices; (2) The growth of the CCR rate will raise selling prices and extend the delivery-lead-time under the decentralized decision; (3) Under price competition, the offline selling price is higher than the online selling price when customers prefer the offline channel and vice versa; (4) The retailer and the manufacturer can achieve a win-win situation through a manufacturer-revenue sharing contract.Originality/valueThis paper contributes to the studies related to DSC by investigating pricing and delivery-lead-time decisions based on DDM, CCR, internal financing and supply chain contract and proposes some managerial implications.

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“…The evolution of digital marketing has precipitated a nuanced clash between personalization and privacy, underscored by the rapid advancement in data technologies and artificial intelligence (Cloarec 2022; Cloarec et al, 2023; Forbes, 2022). This dynamic landscape has fostered a paradox wherein personalization efforts, though aimed at enhancing user engagement, simultaneously stoke consumer privacy concerns.…”

Section: Introductionmentioning

confidence: 99%

Transformative privacy calculus: Conceptualizing the personalization‐privacy paradox on social media

Cloarec,

Meyer‐Waarden,

Munzel

2024

Psychology and Marketing

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The rapid evolution of digital marketing underscores a critical tension between personalization and privacy, exacerbated by advances in data technologies and artificial intelligence. This study delves into the personalization‐privacy paradox, emphasizing the dichotomy of consumer behavior—desiring customized interactions while guarding personal data. We explore how happiness with the internet (HWI) influences users' willingness to disclose personal information on social media, employing social exchange theory as our conceptual framework. Our research develops and tests a conceptual model that investigates the psychological mechanisms driving information‐sharing behaviors on social media, including the moderating roles of trust beliefs and information collection concerns. By examining the mediating effect of posting frequency on the relationship between HWI and information disclosure for personalization, our findings contribute to understanding the complex interplay between happiness, trust and privacy concerns, coined as transformative privacy calculus. Our study enriches social exchange and privacy calculus theories, providing valuable implications for marketers aiming to navigate the balance between personalization and privacy, suggesting strategies to enhance user engagement without compromising privacy standards.

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Privacy controls as an information source to reduce data poisoning in artificial intelligence-powered personalization

Cited by 24 publications

References 83 publications

How to promote AI in the US federal government: Insights from policy process frameworks

How to promote AI in the US federal government: Insights from policy process frameworks

Decisions of pricing and delivery-lead-time in dual-channel supply chains with data-driven marketing using internal financing and contract coordination

Transformative privacy calculus: Conceptualizing the personalization‐privacy paradox on social media

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