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This paper proposes that the trade-off between medical benefits and privacy concerns among mHealth users extends to their disclosure intentions, manifested as individuals simultaneously holding intentions to tend to disclose in the near future and to reduce disclosure in the distant future. Consequently, this paper aims to explore the privacy decision-making process of mHealth users from the perspective of a dual trade-off.

This paper constructs the model using the privacy calculus theory and the antecedent-privacy concern-outcome framework. It employs the construal level theory to evaluate the impact of privacy calculus on two types of disclosure intentions. The study empirically tests the model using a data sample of 386 mHealth users.

The results indicate that perceived benefits positively affect both near-future and distant-future disclosure intentions. In contrast, perceived risks just negatively affect distant-future disclosure intention. Additionally, perceived benefits, near-future and distant-future disclosure intentions positively affect disclosure behavior. The findings also reveal that privacy management perception positively affects perceived benefits. Personalized services and privacy invasion experience positively affect perceived benefits and risks, while trust negatively affects perceived risks.

This paper considers the trade-off in the privacy calculus phase as the first trade-off. On this basis, this trade-off will extend to the disclosure intention. The individuals’ two times of trade-offs between privacy concerns and medical benefits constitute the dual trade-off perspective. This paper first uses this perspective to explore the privacy decision-making process of mHealth users. This paper employs the construal level theory to effectively evaluate the impact of privacy calculus on both disclosure intentions in mHealth, extending the theory’s applicability. Moreover, we introduce antecedents of privacy calculus from the perspectives of platform, society, and individuals, enhancing the study’s realism. The research findings provide a basis for mHealth platforms to better cater to users’ privacy needs.

Sharing co-owned information on social network platforms has become a common and inevitable phenomenon. However, due to the uniqueness of co-owned information, the privacy calculus theory based on a single information owner cannot explain co-owned information disclosure. Therefore, this study tries to investigate the underlying mechanism of users’ co-owned information disclosure from a collective privacy calculus perspective.

Through a survey of 740 participants, covariance-based structural equation modeling (CB-SEM) was used to verify the proposed model and hypotheses.

The results show that personal benefit, others’ benefit and relationship benefit promote users’ co-owned information disclosure by positively affecting personal distributive fairness and others’ distributive fairness perception. Meanwhile, personal privacy risk and others’ privacy risk prevent users’ co-owned information disclosure by negatively affecting personal distributive fairness and others’ distributive fairness perception. Besides, others’ information ownership perception enhances the positive effect of others’ distributive fairness perception on co-owned information disclosure intention. Furthermore, others’ information ownership strengthens the mediating role of others’ distributive fairness.

The findings of this study enrich the research scope of information disclosure and privacy calculus theory and help social network platform developers design collective privacy protection functions.

This study develops a collective privacy calculus model to understand users’ co-owned information disclosure on social network platforms, confirming the mediating role of collective distributive fairness and the moderating role of others’ information ownership perception in the process of collective privacy calculus.

Interest in the intersection of sustainability and supply chain resiliency has grown in recent years by managers and scholars. However, examples of how sustainability can improve resiliency are rare. The purpose of this article is to address this dearth of evidence by investigating how sustainable strategies and practices can create more robust supply chains that are resilient to global disruptions. A strategic model of resiliency is introduced based on the supply chain management practices at Dr. Bronner’s, a rapidly growing personal care product company. The case study data explores the relationship between sustainability and resiliency and reveals a potential pathway for companies to merge the two.

Privacy is a sensitive issue in business because it involves how a platform uses consumer personal data. In terms of consumer rights, personal information needs to be protected in the privacy policy (PP). This study describes several aspects of the PP that consumers need to pay attention to, especially points prone to misuse of personal information.

This research used a taxonomy of consumer privacy concerns in e-commerce to reveal general and specific privacy concerns. The privacy calculus theory was also applied to explore consumer rationalization using (1) consumer knowledge about PP, (2) subjective perception, and (3) proximity to the PP features. Furthermore, the netnographic approach was used to combine the interrelation between technology and social construction. A sample of 378 young consumers in several major cities in Indonesia participated online and offline. Semi-structured interviews were also conducted to gain more in-depth comprehension.

The results showed that most young consumers have sufficient basic knowledge of the important points of PP. Furthermore, they tend not to read the PP because it is long and cumbersome, and therefore do not wish to expend much cognitive effort on it.

This study provides several results that can be utilized by policymakers or e-commerce companies to pay more attention to PPs for young groups. In addition, e-commerce companies can increase the knowledge of the privacy situation of Internet users in general.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-11-2022-0740

The purpose of this paper is to investigate the thermoelastic interactions in a homogeneous, transversely isotropic infinite medium with a spherical cavity in the context of two temperature Lord-Shulman (2TLS) generalized theory of thermoelasticity considering Eringen’s nonlocal theory and memory dependent derivative (MDD). Memory-dependent derivative is found to be better than fractional calculus for reflecting the memory effect which leads us to the current investigation.

The governing field equations of the problem are solved analytically using the eigenvalue approach in the transformed domain of Laplace when the cavity’s boundary is being loaded thermomechanically. Using MATLAB software the numerical solution in real space-time domain is obtained by Stehfest method.

Numerical results for the different thermophysical quantities are presented in graphs and the effects of delay time parameter, non-local parameter and two temperature parameters are studied thereafter. The outcomes of this study convince that the displacement u, conductive temperature ϕ, thermodynamic temperature θ are concave upward whereas radial stress τ_rr is concave downward for every choice of delay time parameter ω, two temperature parameter η and non-local parameter “ζ”. As a specific instance of our findings, the conclusions of an equivalent problem involving integer order thermoelasticity theory can be obtained, and the corresponding results of this article can be readily inferred for isotropic materials.

The novelty of this research lies in the adoption of generalized thermoelastic theory with memory dependent derivative and Eringen’s nonlocality for analyzing the thermoelastic interactions in an infinite body with spherical cavity by employing eigenvalue approach. It has applications to many thermo-dynamical systems.

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.

From the buying club’s perspective, the transfer of a player can be interpreted as an investment from which the club expects uncertain future benefits. This paper aims to develop a decision-oriented approach for the valuation of football players that could theoretically help clubs determine the subjective value of investing in a player to assess its potential economic advantage.

We build on a semi-investment-theoretical risk-value model and elaborate an approach that can be applied in imperfect markets under uncertainty. Furthermore, we illustrate the valuation process with a numerical example based on fictitious data. Due to this explicitly intended decision support, our approach differs fundamentally from a large part of the literature, which is empirically based and attempts to explain observable figures through various influencing factors.

We propose a semi-investment-theoretical valuation approach that is based on a two-step model, namely, a first valuation at the club level and a final calculation to determine the decision value for an individual player. In contrast to the previous literature, we do not rely on an econometric framework that attempts to explain observable past variables but rather present a general, forward-looking decision model that can support managers in their investment decisions.

This approach is the first to show managers how to make an economically rational investment decision by determining the maximum payable price. Nevertheless, there is no normative requirement for the decision-maker. The club will obviously have to supplement the calculus with nonfinancial objectives. Overall, our paper can constitute a first step toward decision-oriented player valuation and for theoretical comparison with practical investment decisions in football clubs, which obviously take into account other specific sports team decisions.

Understanding the mechanical and thermal behavior of materials is the goal of the branch of study known as fractional thermoelasticity, which blends fractional calculus with thermoelasticity. It accounts for the fact that heat transfer and deformation are non-local processes that depend on long-term memory. The sphere is free of external stresses and rotates around one of its radial axes at a constant rate. The coupled system equations are solved using the Laplace transform. The outcomes showed that the viscoelastic deformation and thermal stresses increased with the value of the fractional order coefficients.

The results obtained are considered good because they indicate that the approach or model under examination shows robust performance and produces accurate or reliable results that are consistent with the corresponding literature.

This study introduces a proposed viscoelastic photoelastic heat transfer model based on the Moore-Gibson-Thompson framework, accompanied by the incorporation of a new fractional derivative operator. In deriving this model, the recently proposed Caputo proportional fractional derivative was considered. This work also sheds light on how thermoelastic materials transfer light energy and how plasmas interact with viscoelasticity. The derived model was used to consider the behavior of a solid semiconductor sphere immersed in a magnetic field and subjected to a sudden change in temperature.

This study introduces a proposed viscoelastic photoelastic heat transfer model based on the Moore-Gibson-Thompson framework, accompanied by the incorporation of a new fractional derivative operator. In deriving this model, the recently proposed Caputo proportional fractional derivative was considered. This work also sheds light on how thermoelastic materials transfer light energy and how plasmas interact with viscoelasticity. The derived model was used to consider the behavior of a solid semiconductor sphere immersed in a magnetic field and subjected to a sudden change in temperature.

This study aims to understand the extent of privacy concerns regarding voice-activated personal assistants (VAPAs) on Twitter. It investigates three key areas: (1) the effect of privacy-related press coverage on public sentiment and discussion volume; (2) the comparative negativity of privacy-focused conversations versus general conversations; and (3) the specific privacy-related topics that arise most frequently and their impact on sentiment and discussion volume.

A dataset of 441,427 tweets mentioning Amazon Alexa, Google Assistant, and Apple Siri from July 1, 2019 to June 30, 2021 were collected. Privacy-related press coverage has also been monitored. Sentiment analysis was conducted using the dictionary-based software LIWC and VADER, whereas text mining packages in R were used to identify privacy-related issues.

Negative privacy-related news significantly increases both negativity and volume in Twitter conversations, whereas positive news only boosts volume. Privacy-related tweets were notably more negative than general tweets. Specific keywords were found to either increase or decrease the sentiment and discussion volume. Additionally, a temporal evolution in sentiment, with general attitudes toward VAPAs becoming more positive, but privacy-specific discussions becoming more negative was observed.

This research augments the existing online privacy literature by employing text mining methodologies to gauge consumer sentiments regarding privacy concerns linked to VAPAs, a topic currently underexplored. Furthermore, this research uniquely integrates established theories from privacy calculus and social contract theory to deepen our analysis.

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.