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Mobile phones have become one of the most favored devices to maintain social connections as well as logging digital information about personal lives. The privacy of the metadata being generated in this process has been a topic of intense debate over the last few years, but most of the debate has been focused on stonewalling such data. At the same time, such metadata is already being used to automatically infer a user’s preferences for commercial products, media, or political agencies. The purpose of this paper is to understand the predictive power of phone usage features on individual privacy attitudes.

The present study uses a mixed-method approach, involving analysis of mobile phone metadata, self-reported survey on privacy attitudes and semi-structured interviews. This paper analyzes the interconnections between user’s social and behavioral data as obtained via their phone with their self-reported privacy attitudes and interprets them based on the semi-structured interviews.

The findings from the study suggest that an analysis of mobile phone metadata reveals vital clues to a person’s privacy attitudes. This study finds that multiple phone signals have significant predictive power on an individual’s privacy attitudes. The results motivate a newer direction of automatically inferring a user’s privacy attitudes by leveraging their phone usage information.

An ability to automatically infer a user’s privacy attitudes could allow users to utilize their own phone metadata to get automatic recommendations for privacy settings appropriate for them. This study offers information scientists, government agencies and mobile app developers, an understanding of user privacy needs, helping them create apps that take these traits into account.

The primary value of this paper lies in providing a better understanding of the predictive power of phone usage features on individual privacy attitudes.

This study aims to explain how hotel guests form attitudes toward facial recognition technology in Singapore by integrating technology acceptance model (TAM), privacy calculus theory and personal innovativeness.

A self-administered online questionnaire was developed with measurements adopted from past research. Guests who stayed in four- or five-star hotels in Singapore were recruited via systematic random sampling. Structural equation modeling was conducted to examine the proposed integrated models.

Results showed that hotel guests performed calculative cognitive processes, weighing the benefits and risks of using facial recognition check-in system. Contradictory to the past research which suggested that trust activates both perceived risk and benefits, this study demonstrated that trust independently directed consumer attention on the benefits gained while risk perception was triggered by privacy concern. Furthermore, the current study revealed that the ease of use of facial recognition check-in system could possibly backfire.

The research indicates that the effort to adopt new technology in the hotel industry is promising in view of the growing millennials and Generation Z population who are digital natives. Furthermore, the current study highlights ways to elevate institutional trust and divert consumers’ attention from risk perception to enhance their positive attitude and behavior toward accepting facial recognition check-in system.

This study integrated TAM with privacy calculus theory and personal innovativeness in examining the acceptance of facial recognition check-in system in the hotel industry in Singapore. This study is also the first, to the best of the authors’ knowledge, to investigate the relationships among privacy concern, perceived risk, institutional trust and perceived benefits, as well as their effects on consumers’ attitudes and behavior toward the biometric system.

Since its inception in 2009, the growth of real-time bidding (RTB) advertising has been dramatic. Yet, there is a dearth of research in the information system (IS) literature despite the potential for negative e-commerce outcomes. The purpose of this paper is to identify salient antecedents of users’ attitude toward RTB advertisements.

A research model was constructed and tested with data from 437 respondents. SmartPLS 3.0, a partial least square (PLS) structural equation modeling (SEM) tool, was used to evaluate the research model and test the hypotheses.

The findings indicate that user attitude is determined by opposing influences from the cognitive and affective attributes of an RTB advertisement. A surprise is found to elicit greater perception of advertisement personalization, timeliness and relevance, as well as privacy and intrusiveness concerns. While RTB advertisement relevance appears to lessen the effect of advertisement intrusiveness, privacy concern is exacerbated when the advertisement is more personalized. The authors discuss the implications of this study for click-through intentions and e-commerce.

At this point in the evolution of RTB advertising, the findings indicate that the surprise generated by the appearance of an RTB advertisement is not currently a “bad” surprise. In addition, the formation of positive user attitude toward RTB is complex because cognitive factors interact with users' concerns to strengthen or weaken the negative effects. The authors also demonstrate that attitude and stimulus–organism–response (S–O–R) theories are useful theoretical bases for the development of causal models to predict RTB attitude and click-through intentions.

The present study investigates the customers' behavioural intention to use voice-based artificial intelligence (AI) to find the appropriate hotels and resorts in an emerging nation. This study determines the influences of information quality, system quality, privacy, and novelty value on attitude and behavioural intention to use voice-based artificial intelligence to obtain the appropriate information and find the location of the hotels and resorts.

This study used a purposive sampling method for collecting data from the respondents, who are customers of the hotels and resorts in Bangladesh. A self-administered survey questionnaire was used to obtain responses from 378 respondents. After collecting the data, the reliability and validity of the constructs and hypotheses were tested via partial least squares structural equation modelling (PLS-SEM).

The findings of the study indicate that information quality, system quality, privacy and novelty value have a positive and significant impact on attitude and behavioural intention to use voice-based AI assistant services in an emerging nation. However, system quality does not significantly influence behavioural intention to use voice-based AI assistant but it has an indirect significant influence on behavioural intention through the mediation effect of attitude.

The study’s findings provide essential guidelines for practitioners to understand the impacts of information quality, system quality, privacy, and novelty value on attitude and behavioural intention to use voice-based artificial intelligence to find the appropriate hotels and resorts to meet customers' needs and expectations.

This study contributes to the existing literature on technology adoption by highlighting the interconnectedness of various factors influencing users' behavioural intentions. The study’s focus on an emerging nation provides a valuable theoretical contribution. It highlights that user perceptions and attitudes towards technology adoption may differ from those in developed nations due to unique contextual factors.

The COVID-19 pandemic has had far-reaching effects on society and will continue to be a subject of study for researchers in the years to come. Businesses have implemented technologies that reduce reliance on physical currencies, such as e-commerce sites and contactless payments. This study aims to examine the users’ attitudes and behaviors toward mobile payments. The focus is on identifying the most effective techniques and approaches that businesses can use to encourage user adoption of mobile payments.

This study uses survey data from 396 active mobile payment users across the mid-west region of the USA to test the proposed hypothesis. The snowball sampling approach is used to sample the participants for the data collection. This study uses partial least squares structural equation modeling to test the ten hypotheses proposed in this study.

This study finds that organizational commitment and privacy customization can significantly overcome users’ protective attitudes toward mobile payments during the pandemic. In addition, providing users with privacy customization options can significantly encourage self-disclosure, which is crucial for transaction authentication and fraud detection.

Envisioned in the backdrop of the COVID pandemic, this is one of the earliest studies investigating the role of privacy customization, self-disclosure and organizational commitment on mobile payment adoption.

Studies on the coronavirus disease 2019 (COVID-19) tracing apps have mostly focused on how to optimize adoption and continuous use, but did not consider potential long-term effects of their introduction. This study aims to analyse whether the characteristics of the recent introduction of tracing apps may negatively impact individuals' attitudes and intentions to adopt future tracking technology.

In an online experiment across three countries (Australia, Germany, UK), the authors measured how perceived benefits of COVID-19 tracing apps as well as specific government and campaign-related factors affect privacy concerns, attitude towards future tracking apps and intention to adopt. The authors manipulated the type of provider (governmental vs private) and the type of beneficiaries of the future tracking technology app (the individual alone or also the public) as determinants of adoption.

The authors find that privacy concerns towards the COVID-19 tracing apps negatively impact attitude and intention to adopt future tracking apps. Future adoption is more likely if the app is provided by the government, whereas additional benefits to the public do not positively stimulate adoption. Second, the study analyzed different factors, including perceptions on governments and the app introduction, as well as perceived benefits.

Taking the introduction of COVID-19 apps in different countries as a basis, the authors link both perceived benefits and contextual factors to privacy concerns, attitudes towards and intention to adopt the related technology in the future. The authors hereby clarify the responsibility of governmental actors who conduct large-scale technology introductions for the future diffusion of related technologies.

This paper aims to investigate the extent of personal privacy concerns expressed by university students in Vietnam while using TikTok, the influence of peer interactions and social norms on privacy attitudes and behaviors and the strategies used by university students in Vietnam to mitigate privacy risks on TikTok.

A qualitative approach using semi-structured interviews was used to gather data on the following: the degree to which Vietnamese university students express concerns about their personal privacy while using TikTok; how peer interactions and social norms influence privacy attitudes and behaviors; and the strategies these students use to mitigate privacy risks on the platform.

Findings indicate that although university students exhibit a relatively low level of concern regarding their personal privacy on TikTok, peer influences and societal norms significantly shape their attitudes and behaviors on the platform. Moreover, students use several strategies to mitigate privacy risks, such as selective content sharing and refraining from engaging with unknown links.

These insights provide valuable implications for the development of targeted interventions and educational initiatives aimed at fostering a more privacy-conscious TikTok user community among university students in Vietnam.

This research fills a critical gap in the existing literature by focusing on the influence of social norms and peer influences on privacy attitudes and behaviors on social media platforms. While prior studies have explored various factors impacting privacy concerns on social media, the role of social dynamics has been understudied. Moreover, the research specifically addresses the lack of investigation into privacy concerns on TikTok, a platform rapidly gaining popularity among younger demographics.

This paper presents an initial development of a personal data attitude (PDA) measurement instrument based on established psychometric principles. The aim of the research was to develop a reliable measurement scale for quantifying and comparing attitudes towards personal data that can be incorporated into cybersecurity behavioural research models. Such a scale has become necessary for understanding individuals’ attitudes towards specific sets of data, as more technologies are being designed to harvest, collate, share and analyse personal data.

An initial set of 34 five-point Likert-style items were developed with eight subscales and administered to participants online. The data collected were subjected to exploratory and confirmatory factor analyses and MANOVA. The results are consistent with the multidimensionality of attitude theories and suggest that the adopted methodology for the study is appropriate for future research with a more representative sample.

Factor analysis of 247 responses identified six constructs of individuals’ attitude towards personal data: protective behaviour, privacy concerns, cost-benefit, awareness, responsibility and security. This paper illustrates how the PDA scale can be a useful guide for information security research and design by briefly discussing the factor structure of the PDA and related results.

This study addresses a genuine gap in research by taking the first step towards establishing empirical evidence for dimensions underlying personal data attitudes. It also adds a significant benchmark to a growing body of literature on understanding and modelling computer users’ security behaviours.

Social media has created a new level of interconnected communication. However, the use of online platforms brings about various ways in which a user’s personal data can be put at risk. This study aims to investigate what drives the disclosure of personal information online and whether an increase in awareness of the value of personal information motivates users to safeguard their information.

Fourteen university students participated in a mixed-methods experiment, where responses to Likert-type scale items were combined with responses to interview questions to provide insight into the cost–benefit analysis users conduct when disclosing information online.

Overall, the findings indicate that users are able to disregard their concerns due to a resigned and apathetic attitude towards privacy. Furthermore, subjective norms enhanced by fear of missing out (FOMO) further allows users to overlook potential risks to their information in order to avoid social isolation and sanction. Alternatively, an increased awareness of the personal value of information and having experienced a previous privacy violation encourage the protection of information and limited disclosure.

This study provides insight into privacy and information disclosure on social media in South Africa. To the knowledge of the researchers, this is the first study to include a combination of the theory of planned behaviour and the privacy calculus model, together with the antecedent factors of personal valuation of information, trust in the social media provider, FOMO.

Many online transactions and digital services depend on consumers’ willingness to take privacy risks, such as when shopping online, joining social networks, using online banking or interacting with e-health platforms. Their decisions depend on not only how much they would suffer if their data were revealed but also how uncomfortable they feel about taking such a risk. Such an aversion to risk is a neglected factor when evaluating the value of privacy. The aim of this paper is to propose an empirical method to measure both privacy risk aversion and privacy worth and how those affect privacy decisions.

The authors let individuals play privacy lotteries and derive a measure of the value of privacy under risk (VPR) and empirically test the validity of this measure in a laboratory experiment with 148 participants. Individuals were asked to make a series of incentivized decisions on whether to incur the risk of revealing private information to other participants.

The results confirm that the willingness to incur a privacy risk is driven by a complex array of factors, including risk aversion, self-reported value for private information and general attitudes to privacy (derived from surveys). The VPR does not depend on whether there is a preexisting threat to privacy. The authors find qualified support for the existence of an order effect, whereby presenting financial choices prior to privacy ones leads to less concern for privacy.

Attitude to risk in the domain of privacy decisions is largely understudied. In this paper, the authors take a first step toward closing this empirical and methodological gap by offering (and validating) a method for the incentivized elicitation of the implicit VPR and proposing a robust and meaningful monetary measure of the level of aversion to privacy risks. This measure is a crucial step in designing and implementing the practical strategies for evaluating privacy as a competitive advantage and designing markets for privacy risk regulations (e.g. through cyber insurances).

The present study advances research on the economics of consumer privacy – one of the most controversial topics in the digital age. In light of the proliferation of privacy regulations, the mentioned method for measuring the VPR provides an important instrument for policymakers’ informed decisions regarding what tradeoffs consumers consider beneficial and fair and where to draw the line for violations of consumers’ expectations, preferences and welfare.

The authors present a novel method to measure the VPR that takes account of both the value of private information to consumers and their tolerance for privacy risks. The authors explain how this method can be used more generally to elicit attitudes to a wide range of privacy risks involving exposure of various types of private information.