The more the better? Data disclosure between the conflicting priorities of privacy concerns, information sensitivity and personalization in e-commerce

Gouthier, Matthias H. J.; Nennstiel, Carina; Kern, Nora; Wendel, Lars

doi:10.1016/j.jbusres.2022.04.034

Cited by 26 publications

(10 citation statements)

References 80 publications

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“…A plausible explanation for the rejection of H9a is that users' trust is highly relevant to their willingness to disclose personal data (Duan & Deng, 2022; Kim et al, 2019). However, full disclosure of data does not necessarily lead to personalized service, for example if the algorithms underlying the robot comprise only mechanical AI rather than feeling or thinking AI (Cloarec et al, 2022; Gouthier et al, 2022; Huang & Rust, 2018). Users with previous experience of privacy invasion in particular pay more attention to privacy risks than personalization benefits (Li et al, 2020), making the effect of trust on NPC less salient.…”

Section: Discussion Conclusion Limitations and Future Researchmentioning

confidence: 99%

“…Despite the fruitful and diverse studies in the literature, there remain three main gaps in research on the personalization–privacy paradox (see Table 1). First, this paradox has been examined in multiple research contexts, such as smart home services (Lavado‐Nalvaiz et al, 2022; Zhang et al, 2023), social network system use (Cloarec et al, 2022; Li et al, 2020; Ying et al, 2023), e‐commerce (Chen et al, 2022; Gouthier et al, 2022), mobile app use (Albashrawi & Motiwalla, 2019; Duan & Deng, 2022; Lei et al, 2022), online/offline retail and advertising (Ameen et al, 2022; Chen et al, 2019; Zeng et al, 2021), and Internet of Things (IoT) services (Kim et al, 2019; Xie & Lei, 2022). Although personalization and privacy are important determinants of whether medical AI is to be implemented in a given context (Guo et al, 2016), with the exception of Liu and Tao (2022), little attention has been paid to the personalization–privacy paradox in the context of AI medical diagnosis acceptance (Research Gap 1).…”

Section: Introductionmentioning

confidence: 99%

“…Third, studies on the personalization–privacy paradox have mainly concentrated on the direct effects of personalization services and privacy risks (e.g., Albashrawi & Motiwalla, 2019; Chen et al, 2022; Gouthier et al, 2022; Ying et al, 2023; Zeng et al, 2021; Zhang et al, 2023). It is well acknowledged that the personalization–privacy paradox may be reconciled by leveraging contextual consideration.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Reconciling the personalization–privacy paradox via DoctorBots: The roles of service robot acceptance model elements and technology anxiety

Shi,

Lu,

Zhou

2023

J of Consumer Behaviour

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The role of artificial intelligence (AI)‐based DoctorBots in improving healthcare and the medical industry is expected to increase significantly in the coming decades. However, contrary to the general view that AI can gradually replace human work, user acceptance of DoctorBots has become an obstacle to the development of AI medical diagnosis. Building on the service robot acceptance model (sRAM), this study investigates the potential of the functional, socioemotional, and relational elements of DoctorBots to reconcile the personalization–privacy paradox, thus enhancing user acceptance. Via two scenario‐based experiments with 398 participants, this study reveals that the negative influence of the personalization–privacy paradox on user acceptance is exacerbated when users' technology anxiety is high. In addition, an online survey of 400 DoctorBot users indicates that ease of use, subjective social norms, social presence, and rapport are effective in addressing both nonpersonalization (NPC) and privacy concerns (PVC). These findings suggest that the healthcare industry can leverage DoctorBots to implement self‐diagnosis. Specifically, DoctorBots' functional elements are effective in mitigating users' NPC, and their relational elements are effective in extenuating users' PVC.

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Section: Discussion Conclusion Limitations and Future Researchmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Reconciling the personalization–privacy paradox via DoctorBots: The roles of service robot acceptance model elements and technology anxiety

Shi,

Lu,

Zhou

2023

J of Consumer Behaviour

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“…The resultant tensions can be viewed as trade-offs that service firms need to navigate in order to be profitable yet ethical or as a “utilitarian perspective weighing the benefits and costs” (Hermann 2022, p. 44). The privacy literature developed the “privacy calculus” to address such trade-offs from the consumer perspective and suggests that consumers weigh the benefits and costs, which then determine their privacy responses (Gouthier et al 2022; Smith, Dinev, and Xu 2011). Likewise, we argue that service firms weigh the benefits and costs of good CDR to determine how much they will engage in CDR.…”

Section: Why Do Service Firms Engage In Poor Cdr Practices?mentioning

confidence: 99%

Corporate Digital Responsibility in Service Firms and Their Ecosystems

Wirtz

Hartley

Tarbit

2022

Journal of Service Research

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Digitization, artificial intelligence, and service robots carry serious ethical, privacy, and fairness risks. Using the lens of corporate digital responsibility (CDR), we examine these risks and their mitigation in service firms and make five contributions. First, we show that CDR is critical in service contexts because of the vast streams of customer data involved and digital service technology’s omnipresence, opacity, and complexity. Second, we synthesize the ethics, privacy, and fairness literature using the CDR data and technology life-cycle perspective to understand better the nature of these risks in a service context. Third, to provide insights on the origins of these risks, we examine the digital service ecosystem and the related flows of money, service, data, insights, and technologies. Fourth, we deduct that the underlying causes of CDR issues are trade-offs between good CDR practices and organizational objectives (e.g., profit opportunities versus CDR risks) and introduce the CDR calculus to capture this. We also conclude that regulation will need to step in when a firm’s CDR calculus becomes so negative that good CDR is unlikely. Finally, we advance a set of strategies, tools, and practices service firms can use to manage these trade-offs and build a strong CDR culture.

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“…Research methods used in privacy researchKeith et al, 2013;Sutanto et al, 2013;Menard and Bott, 2020;Visentin et al, 2021;Gouthier et al, 2022) Literature review 16(Michael and Clarke, 2013;Cheung, 2014;Arunkumar et al, 2015;Smith and Kollars, 2015;Pennekamp et al, 2017;Bhuyan et al, 2017;Fong, 2017;Li et al, 2017;Asuquo et al, 2018;Tovino, 2019;Khan et al, 2019;Liu et al, 2019;Nurgalieva et al, 2020;Benjumea et al, 2020;Maseeh et al, 2021;Scarpi et al, 2022) Conceptual 21(Konidala et al, 2012;Lian et al, 2013;Bal et al, 2015;Chen et al, 2015;Cottrill and Vonu Thakuriah, 2015;Liu and Simpson, 2016;Choi et al, 2016;Alepis and Patsakis, 2017;Krupp et alTheories used in consumer privacy in smartphones…”

mentioning

confidence: 99%

Consumer privacy in smartphones: a systematic literature review

R.,

Shome,

Shankar

et al. 2023

JCM

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Purpose This study aims to provide a systematic review of consumer privacy literature in the context of smartphones and undertake a comprehensive analysis of academic research on this evolving research area. Design/methodology/approach This review synthesises antecedents, consequences and mediators reported in consumer privacy literature and presents these factors in a conceptual framework to demonstrate the consumer privacy phenomenon. Findings Based on the synthesis of constructs reported in the existing literature, a conceptual framework is proposed highlighting antecedents, mediators and outcomes of experiential marketing efforts. Finally, this study deciphers overlooked areas of consumer privacy in the context of smartphone research and provides insightful directions to advance research in this domain in terms of theory development, context, characteristics and methodology. Originality/value This study significantly contributes to consumer behaviour literature, specifically consumer privacy literature.

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The more the better? Data disclosure between the conflicting priorities of privacy concerns, information sensitivity and personalization in e-commerce

Cited by 26 publications

References 80 publications

Reconciling the personalization–privacy paradox via DoctorBots: The roles of service robot acceptance model elements and technology anxiety

Reconciling the personalization–privacy paradox via DoctorBots: The roles of service robot acceptance model elements and technology anxiety

Corporate Digital Responsibility in Service Firms and Their Ecosystems

Consumer privacy in smartphones: a systematic literature review

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