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“Smart devices think you're “too lazy” to opt out of privacy defaults” was the headline of a recent news report indicating that individuals might be too lazy to stop disclosing their private information and therefore to protect their information privacy. In current privacy research, privacy concerns and self-disclosure are central constructs regarding protecting privacy. One might assume that being concerned about protecting privacy would lead individuals to disclose less personal information. However, past research has shown that individuals continue to disclose personal information despite high privacy concerns, which is commonly referred to as the privacy paradox. This study introduces laziness as a personality trait in the privacy context, asking to what degree individual laziness influences privacy issues.

After conceptualizing, defining and operationalizing laziness, the authors analyzed information collected in a longitudinal empirical study and evaluated the results through structural equation modeling.

The findings show that the privacy paradox holds true, yet the level of laziness influences it. In particular, the privacy paradox applies to very lazy individuals but not to less lazy individuals.

With these results one can better explain the privacy paradox and self-disclosure behavior.

The state might want to introduce laws that not only bring organizations to handle information in a private manner but also make it as easy as possible for individuals to protect their privacy.

Based on a literature review, a clear research gap has been identified, filled by this research study.

The collection and use of personal medical information for mobile health (mHealth) service raise significant privacy concerns. In this context, this study aims to explore the privacy paradox and its impact from the perspective of paradox resolution.

Based on social support theory and privacy calculus theory, this study first studies the effect of social support on perceived benefits, and explores the moderating effect of perceived health status on the privacy trade-off process. Secondly, the study examines the path of “privacy concerns – disclosure intention – disclosure behavior” to verify the existence of the privacy paradox. Following this, based on rational choice theory, the rationality degree is introduced as a moderating variable to investigate both its impact on the central route and the strength of this impact on the privacy paradox.

Empirical results show that informational support and emotional support influence perceived benefits significantly. Perceived benefits significantly influence privacy concerns, and perceived health status has a significant positive moderating effect. The authors further find that there is a privacy paradox within the mHealth context, and the privacy paradox is moderated negatively by rationality degree. The findings indicate that the impact strength of the privacy paradox will decrease with increases in rationality degree.

The findings indicate that it is crucial to evaluate the privacy paradox and its impact from the perspective of paradox resolution.

This study offers a complete comprehension of the privacy paradox in mHealth and provides several valuable recommendations for enhancing both mHealth services and privacy controls.

There is always a need to discover how a paradox between a customer’s desire for a more personalized experience and their privacy and security concerns would shape their intention to continue using contactless payment methods. However, personalization–privacy paradox has not been well-covered over the area of contactless payment. Therefore, this study aims to empirically examine the impact of personalization–privacy paradox on the customers’ continued intention (CIN) to use contactless payment.

/methodology/approach – The empirical part of the current study was conducted in Saudi Arabia by collecting the primary data using online questionnaire from a convenience sample size of 297 actual users of contactless payment methods.

Based on structural equation modeling, personalization and privacy invasion were approved to significantly impact perceived value of information disclosure (PVD). Strong causal associations were confirmed between perceived severity, structural assurance and response cost with privacy invasion. Finally, both PVD and privacy invasion significantly predict CIN.

There are other important factors (i.e. technology interactivity, technology readiness, social influence, trust, prior experience, etc.) were not tested in the current study. Therefore, future studies would pay more attention regarding the impact of these factors. The current study data were also collected using a convenience sample of actual users of contactless payment methods. Therefore, there is a concern regarding the generalizability of the current study results to other kind of customers who have not used contactless payment.

This study has integrated both personalization–privacy paradox and protection motivation theory in one model. The current study holds value in providing a new and complete picture of the inhibitors and enablers of customers’ CIN to use contactless payment, including new types of inhibitors. Furthermore, personalization–privacy paradox has not been fully examined over the related area of Fintech and contactless payment in general. Therefore, this study was able to extend the theoretical horizon personalization–privacy paradox to new area (i.e. contactless payment) and new cultural context (Saudi Arabia).

This paper aims to demonstrate how building competency in privacy can be used to transform the corporate strategy to generate a sustainable competitive advantage. A novel framework is presented as a guide to redesigning strategy by striking a balance between customer expectation and organizational objectives. In doing so, the paper offers four possible outcomes of accommodation, accumulation, association and affiliation, providing illustrations to each scenario for strategy formulation.

The relationship between privacy paradox and corporate strategy was examined through a qualitative research study. The author conducted 30 in-depth interviews on grounded theory methodology investigating customer insights on the nature and extent of privacy protection associated with e-commerce and organizational approach.

The customers are dissatisfied with existing data security strategies adopted by firms in protecting privacy. The over-reliance on systems has negatively influenced the communication between the organization and customers, leading to a possible competitive disadvantage. The firms need to redesign privacy strategy shifting from a system-driven approach to providing personalized service.

This paper presents a novel framework the privacy strategy matrix (PSM), introducing privacy as a strategic expedient in transforming corporate strategy facilitating privacy protection as a metaphor for differentiation. PSM framework provides a standard to evaluate the effectiveness of the corporate strategy in managing privacy manifesting a path toward deriving a sustainable competitive advantage.

Online privacy research has seen a focus on user behavior over the last decade, partly to understand and explain user decision-making and seeming inconsistencies regarding users' stated preferences. This article investigates the level of modeling that contemporary approaches rely on to explain said inconsistencies and whether drawn conclusions are justified by the applied modeling methodology. Additionally, it provides resources for researchers interested in using computational modeling.

The article uses data from a pre-existing literature review on the privacy paradox (N = 179 articles) to identify three characteristics of prior research: (1) the frequency of references to computational-level theories of human decision-making and perception in the literature, (2) the frequency of interpretations of human decision-making based on computational-level theories, and (3) the frequency of actual computational-level modeling implementations.

After excluding unrelated articles, 44.1 percent of investigated articles reference at least one theory that has been traditionally interpreted on a computational level. 33.1 percent of all relevant articles make statements regarding computational properties of human cognition in online privacy scenarios. Meanwhile, 5.1 percent of all relevant articles apply formalized computational-level modeling to substantiate their claims.

The findings highlight the importance of formal, computational-level modeling in online privacy research, which has so far drawn computational-level conclusions without utilizing appropriate modeling techniques. Furthermore, this article provides an overview of said modeling techniques and their benefits to researchers, as well as references for model theories and resources for practical implementation.

This study serves two purposes: (1) to evaluate the effects of organizational ambidexterity by examining how the balanced and the combined sales–service configurations of chatbots differ in their abilities to enhance customer experience and patronage and (2) to apply information boundary theory to assess the contingent role that chatbot sales–service ambidexterity can play in adapting to customers' personalization–privacy paradox.

An online survey of artificial intelligence chatbots users was conducted, and a mixed-methods research design involving response surface analysis and polynomial regression was adopted to address the research aim.

The results of polynomial regressions on survey data from 507 online customers indicated that as the benefits of personalization decreased and the risk to privacy increased, the inherently negative (positive) effects of imbalanced (combined) chatbots' sales–service ambidexterity had an increasing (decreasing) influence on customer experience. Furthermore, customer experience fully mediated the association of chatbots' sales–service ambidexterity with customer patronage.

First, this study enriches the literature on frontline ambidexterity and extends it to the setting of human–machine interaction. Second, the study contributes to the literature on the personalization–privacy paradox by demonstrating the importance of frontline ambidexterity for adapting to customer concerns. Third, the study examines the conduit between artificial intelligence (AI) chatbots' ambidexterity and sales performance, thereby helping to reconcile the previously inconsistent evidence regarding this relationship.

This investigation aims to examine the differences and similarities between activity tracking technology users from two regions (the USA and Germany) in their intended privacy-related behavior. The focus lies on data handling after hypothetical discontinuance of use, data protection and privacy policy seeking, and privacy concerns.

The data was collected through an online survey in 2019. In order to identify significant differences between participants from Germany and the USA, the chi-squared test and the Mann–Whitney U test were applied.

The intensity of several privacy-related concerns was significantly different between the two groups. The majority of the participants did not inform themselves about the respective data privacy policies or terms and conditions before installing an activity tracking application. The majority of the German participants knew that they could request the deletion of all their collected data. In contrast, only 35% out of 68 participants from the US knew about this option.

This study intends to raise awareness about managing the collected health and fitness data after stopping to use activity tracking technologies. Furthermore, to reduce privacy and security concerns, the involvement of the government, companies and users is necessary to handle and share data more considerably and in a sustainable way.

This study sheds light on users of activity tracking technologies from a broad perspective (here, participants from the USA and Germany). It incorporates not only concerns and the privacy paradox but (intended) user behavior, including seeking information on data protection and privacy policy and handling data after hypothetical discontinuance of use of the technology.

This paper aims to explore the overall research question “How can artificial intelligence (AI) influence consumer information disclosure?”. It considers how anthropomorphism of AI, personalisation and privacy concerns influence consumers’ attitudes and encourage disclosure of their private information.

This research draws upon the personalisation-privacy paradox (PPP) and privacy calculus theory (PCT) to address the research question and examine how AI can influence consumer information disclosure. It is proposed that anthropomorphism of AI and personalisation positively influence consumer attitudes and intentions to disclose personal information to a digital assistant, while privacy concerns negatively affect attitude and information disclosure.

This paper develops a conceptual model based on and presents seven research propositions (RPs) for future research.

Building upon PPP and PCT, this paper presents a view on the benefits and drawbacks of AI from a consumer perspective. This paper contributes to literature by critically reflecting upon on the question how consumer information disclosure is influenced by AI. In addition, seven RPs and future research areas are outlined in relation to privacy and consumer information disclosure in relation to AI.

El papel del antropomorfismo, la personalización y los problemas de privacidad y perspectivas para la investigación futura

Resumen

Este artículo explora la pregunta general de investigación “¿Cómo puede influir la inteligencia artificial (IA) en la divulgación de información por parte de los consumidores? Se analiza cómo el antropomorfismo de la IA, la personalización y la preocupación por la privacidad influyen en la actitud de los consumidores y fomentan la revelación de su información privada.

Esta investigación se basa en la paradoja de la personalización y la privacidad y en la teoría del cálculo de la privacidad para abordar la pregunta de investigación y examinar cómo la IA puede influir en la revelación de información de los consumidores. Se propone que el antropomorfismo de la IA y la personalización influyen positivamente en las actitudes de los consumidores y en su intención de revelar información personal a un asistente digital, mientras que la preocupación por la privacidad afecta negativamente a la actitud y a la revelación de información.

Este artículo desarrolla un modelo conceptual basado en siete propuestas de investigación para el futuro.

Basándose en la paradoja de la personalización y la privacidad y en la teoría del cálculo de la privacidad, este artículo presenta un punto de vista sobre los beneficios e inconvenientes de la IA desde la perspectiva del consumidor. Este artículo contribuye a la literatura al reflexionar de forma crítica sobre la cuestión de cómo influye la IA en la revelación de información del consumidor. Además, se esbozan siete propuestas de investigación y futuras áreas de investigación en relación con la privacidad y la divulgación de información del consumidor en relación con la IA.

鼓励消费者分享他们的秘密？拟人化、个性化和隐私问题的作用以及未来研究的途径

摘要

本文探讨了 “人工智能如何影响消费者的信息披露？"这一总体研究问题。它考虑了人工智能（AI）的拟人化、个性化和隐私问题是如何影响消费者的态度并鼓励他们披露私人信息的。

本研究借鉴了个性化-隐私悖论和隐私计算理论来解决研究问题, 并研究人工智能如何影响消费者信息披露。本文提出, 人工智能的拟人化和个性化对消费者向数字助理披露个人信息的态度和意图有积极影响, 而隐私问题对态度和信息披露有消极影响。

本文在此基础上建立了一个概念模型, 并为未来的研究提出了七个研究命题。

在个性化-隐私悖论和隐私计算理论的基础上, 本文从消费者的角度提出了对人工智能的好处和坏处的看法。本文通过对消费者信息披露如何受到人工智能影响的问题进行批判性反思, 对文献做出了贡献。此外, 本文概述了与人工智能相关的隐私和消费者信息披露方面的七个研究命题和未来研究领域。

Considering that users’ information privacy concerns may affect the development of e-commerce, the purpose of this paper is to explore what drives internet users’ willingness to provide personal information; further, the paper examines how extrinsic rewards moderate the relationship between users’ information privacy concerns and willingness to provide personal information.

Data collected from 345 valid internet users in the context of electronic commerce were analyzed using the partial least squares approach.

The result showed that agreeableness, risk-taking propensity and experience of privacy invasion were three main antecedents of information privacy concerns among the seven individual factors. Additionally, information privacy concerns did not significantly affect users’ willingness to provide personal information in the privacy calculation mechanism; however, extrinsic rewards directly affected users’ disclosure intention. The authors found that extrinsic rewards had not moderated the relationship between users’ information privacy concerns and their willingness to provide personal information.

This study is an exploratory effort to develop and validate a model for explaining why internet users were willing to provide personal information. The results of this study are helpful to researchers in developing theories of information privacy concerns and to practitioners in promoting internet users’ willingness to provide personal information in an e-commerce context.

This paper aimed to explain why individuals still tend to disclose their privacy information even when privacy risks are high and whether individuals disclose or withhold information following the same logic.

This study develops a configurational decision tree model (CDTM) for precisely understanding individuals' decision-making process of privacy disclosure. A survey of location-based social network service (LBSNS) users was conducted to collect data, and fuzzy-set qualitative comparative analysis (fsQCA) was adopted to validate the hypotheses.

This paper identified two configurations for high and low disclosure, respectively, and found that the benefits and the risks did not function independently but interdependently, and the justice would play a crucial role when both the benefits and the risks were high. Furthermore, the authors found that there were asymmetric mechanisms for high disclosure and low disclosure, and males focused more on perceived usefulness, while females concerned more about perceived enjoyment, privacy risks and perceived justice.

This paper further extends privacy calculus model (PCM) and deepens the understanding of the privacy calculus process from a configurational perspective. In addition, this study also provides guidance for future research on how to adopt the configurational approach with qualitative comparative analysis (QCA) to revise and improve relevant theories for information systems (IS) behavioral research.