‘Okay google, what about my privacy?’: User's privacy perceptions and acceptance of voice based digital assistants

A Multilayer Architecture towards the Development and Distribution of Multimodal Interface Applications on the Edge

Today, Smart Assistants (SAs) are supported by significantly improved Natural Language Processing (NLP) and Natural Language Understanding (NLU) engines as well as AI-enabled decision support, enabling efficient information communication, easy appliance/device control, and seamless access to entertainment services, among others. In fact, an increasing number of modern households are being equipped with SAs, which promise to enhance user experience in the context of smart environments through verbal interaction. Currently, the market in SAs is dominated by products manufactured by technology giants that provide well designed off-the-shelf solutions. However, their simple setup and ease of use come with trade-offs, as these SAs abide by proprietary and/or closed-source architectures and offer limited functionality. Their enforced vendor lock-in does not provide (power) users with the ability to build custom conversational applications through their SAs. On the other hand, employing an open-source approach for building and deploying an SA (which comes with a significant overhead) necessitates expertise in multiple domains and fluency in the multimodal technologies used to build the envisioned applications. In this context, this paper proposes a methodology for developing and deploying conversational applications on the edge on top of an open-source software and hardware infrastructure via a multilayer architecture that simplifies low-level complexity and reduces learning overhead. The proposed approach facilitates the rapid development of applications by third-party developers, thereby enabling the establishment of a marketplace of customized applications aimed at the smart assisted living domain, among others. The supporting framework supports application developers, device owners, and ecosystem administrators in building, testing, uploading, and deploying applications, remotely controlling devices, and monitoring device performance. A demonstration of this methodology is presented and discussed focusing on health and assisted living applications for the elderly.

Dataset of concerns for privacy information practices and consumer behavior in online market: A survey in Vietnam

This dataset presents survey results on concerns for consumer privacy information practises in business, online trust; and purchase intention in the online marketplace in Vietnam. The raw data was collected via an online questionnaire of 467 respondents aged 18 and over recruited randomly. The survey included questions on demographic attributes as well as ratings and rankings for various statements related to privacy information concerns, such as Collection, Unauthorized secondary use (internal), Improper Access, Error; consumer online trust; and purchase intention when shopping online. The de-identified dataset is available in CSV format, including the question/statement text, collection method details, and coded response values. This novel dataset further investigates the impact of privacy information concerns on consumer behaviors in an emergent Southeast Asian e-commerce market. As one of the first collections of empirical data focused distinctly on perspectives within Vietnam, this dataset has high reuse potential for research on information privacy attitudes, responses, and needs within the country and in comparison to regional/global trends.

Developing trustworthy artificial intelligence: insights from research on interpersonal, human-automation, and human-AI trust

The rapid advancement of artificial intelligence (AI) has impacted society in many aspects. Alongside this progress, concerns such as privacy violation, discriminatory bias, and safety risks have also surfaced, highlighting the need for the development of ethical, responsible, and socially beneficial AI. In response, the concept of trustworthy AI has gained prominence, and several guidelines for developing trustworthy AI have been proposed. Against this background, we demonstrate the significance of psychological research in identifying factors that contribute to the formation of trust in AI. Specifically, we review research findings on interpersonal, human-automation, and human-AI trust from the perspective of a three-dimension framework (i.e., the trustor, the trustee, and their interactive context). The framework synthesizes common factors related to trust formation and maintenance across different trust types. These factors point out the foundational requirements for building trustworthy AI and provide pivotal guidance for its development that also involves communication, education, and training for users. We conclude by discussing how the insights in trust research can help enhance AI's trustworthiness and foster its adoption and application.

Investigating the acceptance intentions of online shopping assistants in E-commerce interactions: Mediating role of trust and effects of consumer demographics

Online shopping has various advantages, such as convenience, easy access to information, a greater variety of products or services, discounts, and lower prices. However, the absence of salespeople's personalized assistance decreases the online customer experience. Business-to-consumer e-commerce companies are increasingly implementing online shopping assistants (OSAs), interactive and automated tools used to assist customers without salespeople's assistance. However, no comprehensive model of OSA acceptance in e-commerce exists, including constructs from multiple information system disciplines, sociopsychology, and information security. This study aims to fill these gaps by empirically investigating consumers' intention to accept OSAs from a functional, social, relational, and security perspective. It identifies OSA acceptance factors in e-commerce through an extensive literature review and expert opinion. A research model is proposed after identifying structural relationships among the study's variables from the literature. The study employs partial least squares-structural equation modeling (PLS-SEM) to validate the proposed model empirically. The results indicate that anthropomorphism, attitude, ease of use, enjoyment, privacy, trust, and usefulness are crucial determinants of acceptance variables. There are significant moderating effects of respondents' gender and education on OSA acceptance. The study's results have substantial implications for academia, extending and validating the Technology Acceptance Model (TAM) for OSA acceptance in e-commerce. The study will help e-commerce marketers develop optimal adoption strategies when implementing OSAs on social media platforms.

"Chatting with ChatGPT": Analyzing the factors influencing users' intention to Use the Open AI's ChatGPT using the UTAUT model

Open AI's ChatGPT has emerged as a popular AI language model that can engage in natural language conversations with users. Based on a qualitative research approach using semistructured interviews with 32 ChatGPT users from India, this study examined the factors influencing users' acceptance and use of ChatGPT using the unified theory of acceptance and usage of technology (UTAUT) model. The study results demonstrated that the four factors of UTAUT, along with two extended constructs, i.e. perceived interactivity and privacy concerns, can explain users' interaction and engagement with ChatGPT. The study also found that age and experience can moderate the impact of various factors on the use of ChatGPT. The theoretical and practical implications of the study were also discussed.

Increasing acceptance of medical AI: The role of medical staff participation in AI development

BACKGROUND

Medical artificial intelligence (AI) in varying degrees has exerted significant influence on many medical fields, especially in the midst of the COVID-19 pandemic. However, little is known regarding how to address the reluctance of medical staff to use AI technology. While recent research has highlighted the importance of medical staff participation in the development of AI, the current understanding of influence of medical staff participation on acceptance of AI is limited.

OBJECTIVES

To provide insights into the mechanism that how medical staff participation impacts on the medical staff's acceptance of AI and to examine the moderating effect of speciesism.

METHODS

This study was conducted from 6th August to 3rd September. Data was collected from doctors and nurses and a total of 288 valid questionnaires were obtained. Smart PLS 3.2.8 was used as partial least square (PLS) software to validate the research model.

RESULTS

The study determined that medical staff participation had a significant impact on acceptance of medical AI-IDT (β = 0.35, p ≤ 0.001) and acceptance of medical AI-ADT (β = 0.44, p ≤ 0.001). The results also show that AI self-efficacy and AI anxiety have significant mediating effects and speciesism has significant moderating effects among the theoretical model.

CONCLUSIONS

This study provides insights into ways to explore influence factors of acceptance of AI based on user participation perspective. The results indicate that medical staff participation enhances acceptance of medical AI through the cognitive path (i.e., AI self-efficacy) and the affective path (i.e., AI anxiety). These results have practical implications for how organizations assist the staff to accommodate themselves to AI technology in the future.

Development of a scale for capturing psychological aspects of physical-digital integration: relationships with psychosocial functioning and facial emotion recognition

The present work aims at developing a scale for the assessment of a construct that we called "physical-digital integration", which refers to the tendency of some individuals not to perceive a clear differentiation between feelings and perceptions that pertain to the physical or digital environment. The construct is articulated in four facets: identity, social relationships, time-space perception, and sensory perception. Data from a sample of 369 participants were collected to evaluate factor structure (unidimensional model, bifactor model, correlated four-factor model), internal consistency (Cronbach's α, McDonald's ω), and correlations of the physical-digital integration scale with other measures. Results showed that the scale is valid and internally consistent, and that both the total score and the scores at its four subscales are worthy of consideration. The physical-digital integration scores were found to be differently associated with digital and non-digital behaviors, individuals' ability to read emotions in the facial expressions of others, and indicators of psychosocial functioning (anxiety, depression, and satisfaction with social relationships). The paper proposes a new measure whose scores are associated with several variables that may have relevant consequences at both individual and social levels.

Algorithms in the court: does it matter which part of the judicial decision-making is automated?

Artificial intelligence plays an increasingly important role in legal disputes, influencing not only the reality outside the court but also the judicial decision-making process itself. While it is clear why judges may generally benefit from technology as a tool for reducing effort costs or increasing accuracy, the presence of technology in the judicial process may also affect the public perception of the courts. In particular, if individuals are averse to adjudication that involves a high degree of automation, particularly given fairness concerns, then judicial technology may yield lower benefits than expected. However, the degree of aversion may well depend on how technology is used, i.e., on the timing and strength of judicial reliance on algorithms. Using an exploratory survey, we investigate whether the stage in which judges turn to algorithms for assistance matters for individual beliefs about the fairness of case outcomes. Specifically, we elicit beliefs about the use of algorithms in four different stages of adjudication: (i) information acquisition, (ii) information analysis, (iii) decision selection, and (iv) decision implementation. Our analysis indicates that individuals generally perceive the use of algorithms as fairer in the information acquisition stage than in other stages. However, individuals with a legal profession also perceive automation in the decision implementation stage as less fair compared to other individuals. Our findings, hence, suggest that individuals do care about how and when algorithms are used in the courts.

How to increase consumer intention to use Chatbots? An empirical analysis of hedonic and utilitarian motivations on social presence and the moderating effects of fear across generations

As chatbots become more advanced and popular, marketing research has paid enormous attention to the antecedents of consumer adoption of chatbots. This has become increasingly relevant because chatbots can help mitigate the fear and loneliness caused by the global pandemic. Therefore, unlike previous work that focused on design factors, we theorize that social presence serves a mediating role between consumer motivations (i.e., hedonic and utilitarian) and intention to use a chatbot service based on self-determination theory. Our results from a structural equation model (n  = 377) indicate that hedonic (but not utilitarian) motivation significantly affects chatbots’ social presence, ultimately influencing intention to use the chatbot service. We also found that fear of COVID-19 amplifies the effect of social presence on intention to use the chatbot service. In this dynamic, we found an additional moderated moderation effect of generational cohorts (i.e., baby boomers and Generations X, Y, and Z) in experiencing different levels of fear of COVID-19. Overall, our findings emphasize the importance of motivation-matching features for consumer adoption of chatbot services. Our findings also indicate that marketers may utilize the fear element to increase adoption of chatbot services, especially when targeting the young generations (e.g., Generation Z).

Exploring the antecedents of customers’ willingness to use service robots in restaurants

This study examines the willingness of customers to accept, and their intention to recommend, the services provided by service robots in restaurants. A mixed-methods research approach was taken to evaluate a theoretical model based on behavioural reasoning theory (BRT). The results demonstrated the important influence of positive attitudes and objections to the use of service robots on consumers’ willingness to use service robots, as well as their intention to recommend restaurants that use them. Among the main aspects that affect attitudes and objections, we found hedonic perceptions, perceived safety, interaction quality perception and anthropomorphism.

Application and extension of the UTAUT2 model for determining behavioral intention factors in use of the artificial intelligence virtual assistants

Virtual Assistants, also known as conversational artificial intelligence, are transforming the reality around us. These virtual assistants have challenged our daily lives by assisting us in the different dimensions of our lives, such as health, entertainment, home, and education, among others. The main purpose of this study is to develop and empirically test a model to predict factors that affect users' behavioral intentions when they use intelligent virtual assistants. As a theoretical basis for investigating behavioral intention of using virtual assistants from the consumers' perspective, researchers employed the extended Unified Theory of Acceptance and Use of Technology (UTAUT2). For this research paper, seven variables were analyzed: performance expectancy, effort expectancy, facilitating conditions, social influence, hedonic motivation, habit, and price/value. In order to improve consumer behavior prediction, three additional factors were included in the study: perceived privacy risk, trust, and personal innovativeness. Researchers carried out an online survey with 304 responses. The obtained sample was analyzed with Structural Equation Modeling (SEM) through IBM SPSS V. 27.0 and AMOS V 27.0. The main study results reveal that factors, such as habit, trust, and personal innovation, have a significant impact on the adoption of virtual assistants. However, on the other side, performance expectancy, effort expectancy, facilitating conditions, social influence, hedonic motivation, price/value, and perceived privacy risk were not significant factors in the users' intention to adopt this service. This research paper examines the effect of personal innovation, security, and trust variables in relation to the use of virtual assistants. It contributes to a more holistic understanding of the adoption of these intelligent devices and tries to fill the knowledge gap on this topic, as it is an emerging technology. This investigation also provides relevant information on how to successfully implement these technologies.

Effect of e-service quality on customer engagement behavior in community e-commerce

Customer engagement behavior is a critical success factor for community e-commerce. While many community e-commerce websites are currently improving service quality to enhance customer engagement behavior, little is known about how such e-services affect customer engagement behavior. Building upon the stimulus-organism-response (SOR) model, this study developed a research model to explain how e-service quality of community e-commerce platform affects customer engagement behavior through customer trust and perceived risk. The research model was empirically evaluated by surveying 326 customers who have shopped through the community e-commerce platforms. The results indicate that e-service quality (system design, intelligent fulfillment, security assurance, and interactive service) positively affects customer engagement behavior. Besides, customer trust and perceived risk play a mediating role between e-service quality and customer engagement behavior. This study offers recommendations to managers on how to build an attractive community e-commerce platform to stimulate customer engagement behavior.

An empirical assessment of a modified artificially intelligent device use acceptance model—From the task-oriented perspective

Artificial intelligence (AI) is a cutting-edge technology that has been widely applied in tourism operations. To enhance tourists' experience, many tourism suppliers introduced AI devices to interact with tourists. Previous studies classified AI devices as task- and social- oriented based on their functions; however, current models that explain customers' intention to use AI devices did not reflect the discrepancy between the two different types. Therefore, this paper attempts to fill this gap by proposing a theoretical model for the use of task-oriented AI devices. Based on the multi-stage appraisal framework and the Structural Equation Modeling analysis, this paper presents the following findings: (1) utilitarian motivation, interaction convenience, and task-technology fit are the factors appraised in the first stage; (2) perceived competence and flow experience are the factors appraised in the second stage; (3) utilitarian motivation, interaction convenience, and task-technology fit are positively associated with perceived competence. (4) Perceived competence positively influences flow experience, which further affects customers' switching intention from task-oriented AI devices to human service; (5) the serial mediating effect of perceived competence and flow experience between the stimulus mentioned in the first appraisal stage and the switching intention is confirmed. This study reveals the underlying psychological mechanism when customers use task-oriented AI devices, and it provides a theoretical framework for task-oriented AI device adoption.

Analyzing technology acceptance and perception of privacy in ambient assisted living for using sensor-based technologies

People increasingly use various technologies that enable them to ease their everyday lives in different areas. Not only wearable devices are gaining ground, but also sensor-based ambient devices and systems are increasingly perceived as beneficial in supporting users. Especially older and/or frail persons can benefit from the so-called lifelogging technologies assisting the users in different activities and supporting their mobility and autonomy. This paper empirically investigates users’ technology acceptance and privacy perceptions related to sensor-based applications implemented in private environments (i.e., passive infrared sensors for presence detection, humidity and temperature sensors for ambient monitoring, magnetic sensors for user-furniture interaction). For this purpose, we designed an online survey entitled “Acceptance and privacy perceptions of sensor-based lifelogging technologies” and collected data from N = 312 German adults. In terms of user acceptance, statistical analyses revealed that participants strongly agree on the benefits of such sensor-based ambient technologies, also perceiving these as useful and easy to use. Nevertheless, their intention to use the sensor-based applications was still rather limited. The evaluation of privacy perceptions showed that participants highly value their privacy and hence require a high degree of protection for their personal data. The potential users assessed the collection of data especially in the most intimate spaces of domestic environments, such as bathrooms and bedrooms, as critical. On the other hand, participants were also willing to provide complete data transparency in case of an acute risk to their health. Our results suggest that users’ perceptions of personal privacy largely affect the acceptance and successful adoption of sensor-based lifelogging in home environments.

Adoption of AI-Enabled Tools in Social Development Organizations in India: An Extension of UTAUT Model

Social development organizations increasingly employ artificial intelligence (AI)-enabled tools to help team members collaborate effectively and efficiently. These tools are used in various team management tasks and activities. Based on the unified theory of acceptance and use of technology (UTAUT), this study explores various factors influencing employees' use of AI-enabled tools. The study extends the model in two ways: a) by evaluating the impact of these tools on the employees' collaboration and b) by exploring the moderating role of AI aversion. Data were collected through an online survey of employees working with AI-enabled tools. The analysis of the research model was conducted using partial least squares (PLS), with a two-step model - measurement and structural models of assessment. The results revealed that the antecedent variables, such as effort expectancy, performance expectancy, social influence, and facilitating conditions, are positively associated with using AI-enabled tools, which have a positive relationship with collaboration. It also concluded a significant effect of AI aversion in the relationship between performance expectancy and use of technology. These findings imply that organizations should focus on building an environment to adopt AI-enabled tools while also addressing employees' concerns about AI.

Culling the FLoC: Market forces, regulatory regimes and Google’s (mis)steps on the path away from targeted advertising

This paper analyzes the short history of Google’s AI-driven data collation and marketing technology, Federated Learning of Cohorts (FLoC), which was designed to replace third-party cookies, the technology at the heart of “surveillance capitalism.” Using publicly available data such as patents, investor calls, public filings, github accounts, and presentations, this paper explores FLoCs and its immediate replacements, The Topics API and FLEDGE, and contests claims that Google’s new marketing technologies are both ‘privacy-centric’ and as effective as surveillance-driven targeted advertising. The paper argues that Google’s parent company, Alphabet is starting on a path away from being an advertising and information company to being an “AI-first” company, and sees FLoC as one (mis)step on this path. The paper shows how an combination of interacting factors – corporate ideology, market forces, regulatory responses, and internal cultural conflict – are driving this transformation, but concludes that surveillance will continue to be at the heart of any AI-first economy.

A Mind in Intelligent Personal Assistants: An Empirical Study of Mind-Based Anthropomorphism, Fulfilled Motivations, and Exploratory Usage of Intelligent Personal Assistants

Intelligent personal assistants (IPAs) own anthropomorphic features which enable users’ perception of anthropomorphism. Adopting the perspective of mind-based anthropomorphism, the purpose of this paper is to investigate how mind-based anthropomorphism influences users’ exploratory usage of IPAs. Based on the notion that anthropomorphism can satisfy people’s sociality and effectance motivation, we hypothesize that mind-based anthropomorphism can enhance people’s social connection with IPAs and IPA self-efficacy, which can in turn influence their exploratory usage of IPAs. Questionnaires were developed and distributed to users who had experience in smart speaker-based IPAs on Wenjuanxing and 551 valid questionnaires were collected to test the research model. The results revealed that cognitive and affective anthropomorphism exerted common and differential impacts on IPA self-efficacy and social connection. Cognitive anthropomorphism versus affective anthropomorphism had stronger influences on IPA self-efficacy, while affective anthropomorphism had stronger impacts on social connection. Both IPA self-efficacy and social connection enhanced users’ intentions to explore IPAs. This study enriches previous studies on IPA adoption or post-adoption by investigating exploratory usage which captures how users are deeply engaged with IPAs.

The effect of trust and its antecedents towards determining users' behavioral intention with voice-based consumer electronic devices

Advances in artificial intelligence (AI) have ushered in a new era of consumer electronic (CE) devices: the voice-based CE devices (VCED's). A striking feature that separates these from other CE devices are their anthropomorphic capabilities. While current CE research has given a strong focus on improving various technical and security aspects of the VCED's, not much efforts have been given to explore their diffusion and acceptance in the society. However, if the CE community is to progress then there is an urgent need to view these systems from a sociotechnical perspective and take the user perceptions into account for further product development. In this work we propose a novel research framework by incorporating Human Computer Interaction (HCI) theories and Para Social Relationship Theory for exploring the effect of trust on the behavioral intention of users towards VCED's, keeping in mind their human-like attributes. Data is analyzed using a Structural Equation Modelling approach from 675 users of VCED devices from two Asian countries. Results show that the functional aspects of performance and effort expectancy, and social aspects of presence and cognition affect the trust factor. Privacy concerns do not affect trust. Overall, the results suggest that users treat VCED's as social objects employing social rules while interaction that indicates a dual nature of anthropomorphic systems. Suitable suggestions are provided for CE researchers for future research.

Modifying UTAUT2 for a cross-country comparison of telemedicine adoption

The ongoing COVID19 pandemic has put digital health technologies in the spotlight. To gain a deeper understanding of patients’ usage intentions of virtual doctor appointments, the present research adapts the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) by integrating perceived security and perceived product advantage, two known barriers to successful telemedicine adoption. Applying age-stratified sampling, an online survey was distributed to 800 citizens in Germany and the United States of America. 710 completed and valid questionnaires were subsequently analyzed using SPSS and AMOS (versions 24).

Significant, direct, and positive effects of performance expectancy, hedonic motivation, perceived security, and perceived product advantage on the behavioral intention to use virtual doctor appointments were found. The analysis of the moderating variables, age and gender, showed significant differences in user's performance expectancy and effort expectancy, and perceived product advantage, respectively. With virtual health care models on the rise, these results are important for stakeholders such as policymakers, governments, employers, but also physicians, and insurance companies as they offer clear recommendations to design telemedicine adoption strategies to ensure successful patient engagement.

The SHAPES Smart Mirror Approach for Independent Living, Healthy and Active Ageing

The benefits that technology can provide in terms of health and support for independent living are in many cases not enough to break the barriers that prevent older adults from accepting and embracing technology. This work proposes a hardware and software platform based on a smart mirror, which is equipped with a set of digital solutions whose main focus is to overcome older adults’ reluctance to use technology at home and wearable devices on the move. The system has been developed in the context of two use cases: the support of independent living for older individuals with neurodegenerative diseases and the promotion of physical rehabilitation activities at home. Aspects such as reliability, usability, consumption of computational resources, performance and accuracy of the proposed platform and digital solutions have been evaluated in the initial stages of the pilots within the SHAPES project, an EU-funded innovation action. It can be concluded that the SHAPES smart mirror has the potential to contribute as a technological breakthrough to overcome the barriers that prevent older adults from engaging in the use of assistive technologies.