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Based on the cognition–affect–conation pattern, this study explores the factors that affect the intention to use facial recognition services (FRS). The study adopts the driving factor perspective to examine how network externalities influence FRS use intention through the mediating role of satisfaction and the barrier factor perspective to analyze how perceived privacy risk affects FRS use intention through the mediating role of privacy cynicism.

The data collected from 478 Chinese FRS users are analyzed via partial least squares-based structural equation modeling (PLS-SEM).

The study produces the following results. (1) FRS use intention is motivated directly by the positive affective factor of satisfaction and the negative affective factor of privacy cynicism. (2) Satisfaction is affected by cognitive factors related to network externalities. Perceived complementarity and perceived compatibility, two indirect network externalities, positively affect satisfaction, whereas perceived critical mass, a direct network externality, does not significantly affect satisfaction. In addition, perceived privacy risk generates privacy cynicism. (3) Resistance to change positively moderates the relationship between privacy cynicism and intention to use FRS.

This study extends knowledge on people's use of FRS by exploring affect- and cognitive-based factors and finding that the affect-based factors (satisfaction and privacy cynicism) play fully mediating roles in the relationship between the cognitive-based factors and use intention. This study also expands the cognitive boundaries of FRS use by exploring the functional condition between affect-based factors and use intention, that is, the moderating role of resistance to use.

With the upgrade of natural language interaction technology, the simulation extension of intelligent voice assistants (IVAs) and the uncertainty of products and services have received more and more attention. However, most of the existing research focuses on investigating the application of theories to explain consumer behavior related to intention to use and adopt IVAs, while ignoring the impact of its privacy issues on consumer resistance. This article especially examines the negative impact of artificial intelligence-based IVAs’ privacy concerns on consumer resistance, and studies the mediating effect of perceived creepiness in the context of privacy cynicism and privacy paradox and the moderating effect of anthropomorphized roles of IVAs and perceived corporate social responsibility (CSR) of IVAs’ companies. The demographic variables are also included.

Based on the theory of human–computer interaction (HCI), this study addresses the consumer privacy concerns of IVAs, builds a model of the influence mechanism on consumer resistance, and then verifies the mediating effect of perceived creepiness and the moderating effect of anthropomorphized roles of IVAs and perceived CSR of IVAs companies. This research explores underlying mechanism with three experiments.

It turns out that consumers’ privacy concerns are related to their resistance to IVAs through perceived creepiness. The servant (vs. partner) anthropomorphized role of IVAs is likely to induce more privacy concerns and in turn higher resistance. At the same time, when the company’s CSR is perceived high, the impact of the concerns of IVAs’ privacy issues on consumer resistance will be weakened, and the intermediary mechanism of perceiving creepiness in HCI and anthropomorphism of new technology are further explained and verified. The differences between different age and gender are also revealed in the study.

The research conclusions have strategic reference significance for enterprises to build the design framework of IVAs and formulate the response strategy of IVAs’ privacy concerns. And it offers implications for researchers and closes the research gap of IVAs from the perspective of innovation resistance.

This conceptual contribution is based on the observation that digital inequalities literature has not sufficiently considered digital footprints as an important social differentiator. The purpose of the paper is to inspire current digital inequality frameworks to include this new dimension.

Literature on digital inequalities is combined with research on privacy, big data and algorithms. The focus on current findings from an interdisciplinary point of view allows for a synthesis of different perspectives and conceptual development of digital footprints as a new dimension of digital inequality.

Digital footprints originate from active content creation, passive participation and platform-generated data. The literature review shows how different social groups may experience systematic advantages or disadvantages based on their digital footprints. A special emphasis should be on those at the margins, for example, users of low socioeconomic background.

By combining largely independent research fields, the contribution opens new avenues for studying digital inequalities, including innovative methodologies to do so.

Mobile Applications (App) privacy has become a prominent social problem. Compared with privacy concerns, this study examines a relatively novel concept of privacy fatigue and explores its effect on the users’ intention to disclose their personal information via mobile Apps. In addition, the personality traits are proposed as antecedents that will induce the personal perception of privacy fatigue and privacy concerns differently.

Data were collected from 426 respondents. Structure equation modeling was used to test the hypotheses.

The findings describe that App users’ intention toward personal information disclosure is determined by privacy fatigue and privacy concerns, but the former has a greater impact. With minor exceptions, the two factors are also influenced by different personality traits. Specifically, neuroticism has positive effects on privacy fatigue, but agreeableness and extraversion have presented the opposite results on the two variables.

This research is very scarce to examine the joint effects of privacy fatigue, privacy concerns and personality traits on App users’ disclosing intention. In doing so, these results will be of benefit to App providers and platform managers and can be the basis for a variety of follow-up studies.

While previous research just focuses on privacy concerns, this study explores the critical roles of privacy fatigue and opens up a new avenue of emotion-attitude analysis that can further increase the specificity and richness of users’ privacy research. Additionally, implications for personality traits as antecedents in the impact of App users’ privacy emotions and attitudes are discussed.

Many internet users exhibit signs of privacy helplessness and entirely give up online privacy management. However, we know little about what privacy helplessness is, when users are likely to experience it and its implications for privacy behavior. The objectives of this study were twofold: (a) the conceptual explication of privacy helplessness as a novel construct in privacy research and (b) the development of a theoretical model that specifies the antecedents and consequences of privacy helplessness.

A research model of privacy helplessness that contains three subcomponents of privacy helplessness, five antecedents and one outcome was developed. The model was empirically examined based on survey data collected from 589 Facebook users in the USA.

The results of exploratory and confirmatory factor analyses showed that privacy helplessness is adequately assessed by a three-factor model with affective, cognitive and motivational components. The results of structural equation modeling indicated that these three aspects of privacy helplessness are uniquely predicted by five theoretical factors: (a) prior experience of privacy risks, (b) personal mastery, (c) perceived costs of adaptive privacy actions, (d) perceived rewards of privacy inactions and (e) perceived vulnerability. Furthermore, it was found that helplessness as motivational deficits (and cognitive helplessness via this) impedes adaptive privacy actions, while cognitive helplessness promotes adaptive privacy actions when they do not result in motivational deficits.

This study pioneers investigation in understanding key constituents, attributes and processes underlying privacy helplessness. First, the present study developed the first theory-derived, successively validated measurement model of privacy helplessness. Second, this research proposed a theoretical model of privacy helplessness, specifying antecedents and consequences of privacy helplessness.

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.

The advent of extreme automation from new technologies such as artificial intelligence portends a massive increase in unemployment. The psychological impact of this threat on the workforce is critically important. This paper aims to examine the functioning of individuals' anxiety buffers in response to this threat.

A two-stage mixed-methods design is used. In stage 1, qualitative data are gathered through semi-structured interviews. In stage 2, quantitative data are collected through two experiments to assess the psychological impact of exposure to the threat.

Exposure to the threat of extreme automation reduces self-esteem, faith in the worldview and attachment security. When self-esteem and attachment security are under attack, they are ineffective as anxiety buffers, and anxiety levels increase. Additionally, there is a distal effect such that during a period of distraction, the threatened anxiety buffers are reinforced and return to their normal levels.

This study is limited to a homogenous culture in which work is highly salient. Future research should include other cultures, other methods of exposure and further examine the distal effects.

The study examines the previously underexplored issue of individuals' psychological response to the impending changes in the workforce because of technological advancements.

This study aims to identify and explore different risk typologies associated with consumer acceptance of purchase recommendations from voice assistants (VAs).

Drawing on components of perceived risk, consumer trust theory, and consumption value theory, a research model was proposed and tested using structural equation modeling (SEM) with data from 482 voice shoppers.

The results reveal that, unlike risks associated with physical harm, privacy breaches, and security threats, a variety of other concerns—including financial, psychological, social, performance-related risks, time loss, and the overall perceived risks—significantly influence consumers' willingness to accept VAs purchase recommendations. The effect is mediated by trust in VA purchase recommendations and their perceived value. Different types of risk affect various consumption values, with functional value being the most influential. The model explains 58.6% of the variance in purchase recommendation acceptance and significantly elucidates the variance in all consumption values.

This study contributes crucial knowledge to understanding consumer decision-making processes as they increasingly leverage AI-powered voice-based dialogue platforms for online purchasing. It emphasizes recognizing diverse risk typologies associated with VA purchase recommendations and their impact on consumer purchase behavior. The findings offer insights for marketing managers seeking to navigate the challenges posed by consumers' perceived risks while leveraging VAs as an integral component of modern shopping environments.

In today’s digital world, data-driven digital artefacts pose challenges for education, as many students lack an understanding of data and feel powerless when interacting with them. This paper aims to address these challenges and introduces the data awareness framework. It focuses on understanding data-driven technologies and reflecting on the role of data in everyday life. The paper also presents an empirical study on young school students’ data awareness.

The study involves a teaching unit on data awareness framed by a pre- and post-test design using a questionnaire on students’ awareness and understanding of and reflection on data practices of data-driven digital artefacts.

The study’s findings indicate that the data awareness framework supports students in understanding data practices of data-driven digital artefacts. The findings also suggest that the framework encourages students to reflect on these data practices and think about their daily behaviour.

Students learn a model about interactions with data-driven digital artefacts and use it to analyse data-driven applications. This approach appears to enable students to understand these artefacts from everyday life and reflect on these interactions. The work contributes to research on data and artificial intelligence literacies and suggests a way to support students in developing self-determination and agency during interactions with data-driven digital artefacts.

Artificial intelligence (AI) is used by banking services primarily to automate systems; however, this ecosystem does not work in emerging markets because human intervention is needed, and there are concerns related to infrastructure. There is plenty of research on AI-mediated banking services, but the existing discussions are cumbersome, and studies on AI's service features in banking for emerging markets are limited. Furthermore, the ongoing discussions are centred on developed markets where automation in banking services is noteworthy and accepted. Through this paper, the authors emphasise the relevance of AI mediation in emerging markets and the possible role of strategising AI in banking services for personalised experiences.

The authors' article followed an exploratory, inductive approach through in-depth interviews and thematic analysis. In total, 36 financial experts were interviewed, and the relevant perspectives were analysed to develop the research process and framework for a personalised banking experience.

The authors' paper introduced five key themes and presented those themes accordingly. The first theme details the importance of AI-mediated banking and the skills necessary for operational capacity. The second theme is on the relevance of AI-mediated banking awareness amongst users. The third is about channelling the importance of AI-driven interfaces through managers and employees. Fourth, the authors emphasised the relevance of human intervention due to users' demographic patterns. The fifth theme led to a discussion on personalised AI-mediated banking services.

The authors recommend that managers understand the relevance of quality service amongst users. The authors' paper discusses the relevance of AI and human intervention in banking services; however, the process for seamless, personalised banking experiences is not provided. Thus, this paper encourages managers to build a banking ecosystem that delivers a seamless banking experience through AI.

The authors' paper highlights the importance of human intervention in AI-driven banking by introducing personalised service experience elements and highlighting the role of customer experience in AI-driven banking services in emerging markets.