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The privacy paradox refers to the situation where users of online services continue to disclose personal information even when they are concerned about their privacy. One recent study of Facebook users published in Internet Research concludes that laziness contributes to the privacy paradox. The purpose of this study is to challenge the laziness explanation. To do so, we adopt a cognitive dispositions perspective and examine how a person’s external locus of control influences the privacy paradox, beyond the trait of laziness.

A mixed method approach is adopted. We first develop a research model which hypothesises the moderating effects of both laziness and external locus of control on privacy issues. We quantitatively test the research model through a two-phase survey of 463 Facebook users using the Hayes PROCESS macro. We then conduct a qualitative study to verify and develop the findings from the quantitative phase.

The privacy paradox holds true. The findings confirm the significant influence of external locus of control on the privacy paradox. While our quantitative findings suggest laziness does not affect the association between privacy concerns and self-disclosure, our qualitative data does provide some support for the laziness explanation.

Our study extends existing research by showing that a person’s external locus of control provides a stronger explanation for the privacy paradox than the laziness perspective. As such, this study further reveals the boundary conditions on which the privacy paradox exists for some users of social networking sites, but not others. Our study also suggests cognitive dissonance coping strategies, which are largely absent in prior investigations, may influence the privacy paradox.

This paper aims to propose a solution for dissolving the “privacy paradox” in social networks, and explore the feasibility of adopting a synergistic mechanism of “deep-learning algorithms” and “differential privacy algorithms” to dissolve this issue.

To validate our viewpoint, this study constructs a game model with two algorithms as the core strategies.

The “deep-learning algorithms” offer a “profit guarantee” to both network users and operators. On the other hand, the “differential privacy algorithms” provide a “security guarantee” to both network users and operators. By combining these two approaches, the synergistic mechanism achieves a balance between “privacy security” and “data value”.

The findings of this paper suggest that algorithm practitioners should accelerate the innovation of algorithmic mechanisms, network operators should take responsibility for users’ privacy protection, and users should develop a correct understanding of privacy. This will provide a feasible approach to achieve the balance between “privacy security” and “data value”.

These findings offer some insights into users’ privacy protection and personal data sharing.

Drawing on the personalization–privacy paradox and guided by means–end analysis, this study explores how consumers balance their concerns for privacy and the benefits of smart home device personalization and the role that trust plays in the process. More specifically, this study aims to investigate how perceptions of smart device personalization and privacy concerns are shaped by consumers’ experiences and the role of trust in the deliberation process.

In-depth interviews were conducted across diverse demographic groups of smart device users to shed light on the balancing act between personalization and privacy.

The study found that product experience, ownership type, perceived value of convenience and control and quality of life via “smart things” are key motivators for product usage. The benefits of tailored recommendations and high relevance are balanced against the risks of echo chamber effects and loss of control. The results also show the role of active involvement in the privacy calculus and trust level. The study points to the significance of an ecosystem-based service/business model in gaining consumer confidence when they balance between personalization and privacy.

Although many studies have explored trust, privacy concerns and personalization in an artificial intelligence (AI)-related context, few have addressed trust in the context of both smart devices and the personalization–privacy paradox. As such, this study adds to the existing literature by incorporating the concept of trust and addressing both privacy concerns and personalization in the AI context.

This review study aims to explore the prevalent paradoxes in digital transformation (DTN) of business and provide insights on how businesses can effectively navigate them.

The study conducts a systematic literature review, utilizing findings from a bibliometric analysis. A sample of 229 articles published in top-tier journals, retrieved from the Scopus database, is reviewed to identify nine clusters representing different sectors and paradoxes in DTN.

The review identifies and summarizes studies addressing the paradoxes that arise during DTN in various sectors. Scholars have analyzed the growing need for digital innovations and the benefits they bring, but this study aggregates high-quality research to address the gap in understanding prevalent paradoxes.

This study provides valuable insights into the paradoxes of DTN and offers guidance to businesses on effectively managing these challenges. It contributes to the existing literature by consolidating and presenting key research findings in this domain.

In the paradox of personalized services and privacy risks, what factors influence users’ decisions is considered an interesting issue worth exploring. The current study aims to empirically explore privacy behavior of social media users by developing a theoretical model based on privacy calculus theory.

Privacy risks, conceptualized as natural risks and integrated risks, were proposed to affect the intention of privacy disclosure and protection. The model was validated through a hybrid approach of structural equation modeling (SEM)-artificial neural network (ANN) to analyze the data collected from 527 effective responses.

The results from the SEM analysis indicated that social interaction and perceived enjoyment were strong determinants of perceived benefits, which in turn played a dominant role in the intention to disclose the privacy in social media. Similarly, trust and privacy invasion experience were significantly related to perceived risks that had the most considerable effect on users’ privacy protection intention. And the following ANN models revealed consistent relationships and rankings with the SEM results.

This study broadened the application perspective of privacy calculus theory to identify both linear and non-linear effects of privacy risks and privacy benefits on users’ intention to disclose or protect their privacy by using a state-of-the-art methodological approach combining SEM and ANN.

While research on privacy concerns is rich in understanding and depth, there is still not a clear understanding of why people express having privacy concerns, but do not behave consistently with their concern. We propose that this misalignment derives from a diverse set of privacy boundaries, depending on the user. This research builds on prior Communication Privacy Management Theory research to further define individual privacy boundaries. Beyond that, we evaluate the relationship between the privacy boundaries people set, and their ability to protect themselves.

A survey was conducted to assess how private individuals find twenty items. Along with measuring the sensitivity of information, we collected responses on the Online Privacy Information Literacy test to measure differences in sensitivity based on privacy knowledge. 285 participant’s responses were evaluated using exploratory factor analysis and K-means clustering.

We identify five different groups of privacy indicators. Our findings also suggest that users have limited understanding of how to keep data private, even if they have high privacy concerns.

We contribute to theory by offering guidance on how to better apply theoretical understanding, based on our results. More explicitly, we offer analysis that suggests boundary conditions might be absent from current theoretical understanding. Practically, we offer guidance for understanding privacy differences, which is important to understanding how to implement privacy protection laws.

The advancement of enterprise social networks (ESNs) facilitates information sharing but also presents the challenge of managing information boundaries. This study aims to explore the factors that influence the information-control behavior of ESN users when continuously sharing information.

This study specifies the information-control behaviors in the “wall posts” channel and applies communication privacy management (CPM) theory to analyze the effects of the individual-specific factor (disposition to value information), context-specific factors (work-relatedness and information richness) and risk-benefit ratio (public benefit and public risk). Data on actual information-control behaviors extracted from ESN logs are examined using multilevel mixed-effects logistic regression analysis.

The study's findings show the direct effects of the individual-specific factor, context-specific factors and risk-benefit ratio, highlighting interactions between the individual motivation factor and ESN context factors.

This study reshapes the relationship of CPM theory boundary rules in the ESN context, extending information-control research and providing insights into ESNs' information-control practices.

The widespread application of smart technologies in services not only brings efficiency and convenience to consumers but also inevitably comes with negative effects. Therefore, this article aims to illustrate the impact of privacy invasion on consumers' intention to use smart services. Using distrust as a mediating variable, compare two different modes of interaction between voice and text, and study the positive impact of privacy commitment. This study aims to provide recommendations for smart service providers to make the consumer experience better.

This paper adopts an experimental approach, with data collection and hypothesis analysis by designing four different experiments.

The results show that the negative impact of privacy invasion on consumers' intention to use smart services is moderated by privacy commitments and interaction modes. This article verifies the mediating effect of distrust on consumers' intention to use when privacy invasion occurs and verifies the moderating effect of the interaction modes by comparing voice interaction with text interaction and demonstrates that text interaction mode will attenuate the mediating role of distrust in the path in privacy invasion. Besides, it also indicates that privacy commitments can moderate the relationship between privacy invasion’s effect on distrust and intention to use.

Focusing on privacy invasion, this study explores consumers' intention to use smart services, compares the two interaction modes of voice and text to explore their moderating effects, deeply explores consumer psychology and studies the mediating role of distrust and the moderating role of privacy commitment.

The purpose of this study was to investigate the roles of employee experience and top management commitment in the relationship between human resource (HR) records management culture and HR records privacy control in organisations in Ghana.

Structural equation modelling was used in analysing the data. Following the specification of the model, three main types of analyses were carried out. They were reflective measurement model analyses to test reliability and validity; formative measurement model analyses to test redundancy, collinearity, significance and relevance of the lower-order constructs; and structural model analyses to ascertain the explanatory and predictive powers of the model, significance of the hypotheses and their effect sizes.

The study confirmed that communication, privacy awareness and training and risk assessment are dimensions of HR records management culture. Concerning the hypotheses, it was established that HR records management culture is related to HR records privacy control. Also, the study showed that employee experience positively moderated the relationship HR records management culture has with HR records privacy control. However, top management commitment negatively moderated the relationship HR records management culture has with HR records privacy control.

Organisations committed to the privacy control of HR records need to ensure the retention of their employees, as the longer they stay with the organisation, the more they embody the HR records management culture which improves the privacy control of HR records. For top management commitment, it should be restricted to providing strategic direction for HR records privacy control, as the day-to-day influence of top management commitment on the HR records management culture does not improve the privacy control of HR records.

This study demonstrates that communication, privacy awareness and training and risk assessment are dimensions of HR record management culture. Also, the extent of employee experience and top management commitment required in the relationship between HR records management culture and HR records privacy control is revealed.

A wealth of studies have identified numerous antecedents to online self-disclosure. However, the number of competing theoretical perspectives and inconsistent findings have hampered efforts to obtain a clear understanding of what truly influences online self-disclosure. To address this gap, this study draws on the antecedent-privacy concern-outcome (APCO) framework in a one-stage meta-analytical structural equation modeling (one-stage MASEM) study to test a nomological online self-disclosure model that assesses the factors affecting online self-disclosure.

Using the one-stage MASEM technique, this study conducts a meta-analysis of online self-disclosure literature that comprises 130 independent samples extracted from 110 articles reported by 53,024 individuals.

The results reveal that trust, privacy concern, privacy risk and privacy benefit are the important antecedents of online self-disclosure. Privacy concern can be influenced by general privacy concern, privacy experience and privacy control. Furthermore, moderator analysis indicates that technology type has moderating effects on the links between online self-disclosure and some of its drivers.

First, with the guidance of the APCO framework, this study provides a comprehensive framework that connects the most relevant antecedents underlying online self-disclosure using one-stage MASEM. Second, this study identifies the contextual factors that influence the effectiveness of the antecedents of online self-disclosure.