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Copyright of Revista Iberoamericana de Ciencia, Tecnologia y Sociedad is the property of Centro de Estudios sobre Ciencia, Desarrollo y Educacion Superior and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Intelligent voice assistants, such as Apple Siri and Amazon Alexa, are widely used nowadays. These task-oriented dialogue systems require a semantic parsing module in order to process user utterances and understand the action to be performed. This semantic parsing component was initially implemented by rule-based or statistical slot-filling approaches for processing simple queries; however, the appearance of more complex utterances demanded the application of shift-reduce parsers or sequence-to-sequence models. Although shift-reduce approaches were initially considered the most promising option, the emergence of sequence-to-sequence neural systems has propelled them to the forefront as the highest-performing method for this particular task. In this article, we advance the research on shift-reduce semantic parsing for task-oriented dialogue. We implement novel shift-reduce parsers that rely on Stack-Transformers. This framework allows to adequately model transition systems on the transformer neural architecture, notably boosting shift-reduce parsing performance. Furthermore, our approach goes beyond the conventional top-down algorithm: we incorporate alternative bottom-up and in-order transition systems derived from constituency parsing into the realm of task-oriented parsing. We extensively test our approach on multiple domains from the Facebook TOP benchmark, improving over existing shift-reduce parsers and state-of-the-art sequence-to-sequence models in both high-resource and low-resource settings. We also empirically prove that the in-order algorithm substantially outperforms the commonly used top-down strategy. Through the creation of innovative transition systems and harnessing the capabilities of a robust neural architecture, our study showcases the superiority of shift-reduce parsers over leading sequence-to-sequence methods on the main benchmark. [ABSTRACT FROM AUTHOR]

Internet-of-Things technologies may help luxury companies deliver their offerings in a different way. However, their actual effects on consumers remain hazy. This paper sheds light on the impact that consumers' openness to technological innovations may have on perceived usefulness of Voice Assistants (VAs) in the context of luxury tourism. In two studies using luxury yachts and luxury hotels as research settings, we operationalised consumers' openness to technological innovations as risk propensity and openness to change, respectively, and examined their impact on consumers' perceived usefulness of VAs, considering the moderating role of status consumption orientation. Results showed that a greater level of openness to technological innovations leads consumers to perceive VAs as more useful, and this effect is mitigated when consumers exhibit greater status consumption orientation. [ABSTRACT FROM AUTHOR]

Voice Assistants (VAs), driven by Artificial Intelligence (AI), are gaining prominence and becoming increasingly relevant in various domains. Their distinctiveness lies in the incorporation of human-like attributes, setting them apart from conventional technologies and firmly situating them within the domain of Anthropomorphic Technologies (AT). This study aimed to comprehensively examine the impact of customers' gender, the gender of Anthropomorphic Voice Assistant (AVA), and the kind of relationship customers perceived to form with AVA on customers' attitudes toward AVA using an experimental design. [ABSTRACT FROM AUTHOR]

Speaking in a second/foreign language, especially in English, is one of the most anxiety-provoking tasks for language learners. Anxiety provoked while speaking in a second language distresses the learners and further affects their oral proficiency in English. This article focuses on investigating the presence of anxiety among (n = 86) first-year Arts and Science students of a reputed university in India through Foreign Language Speaking Anxiety (FLSA) scale. The researchers tested the presence of anxiety pre and post-usage of Intelligent Personal Assistant (IPA) in Second Language (SL) classroom through the FLSA scale to deduce the impact of IPA-assisted language learning on speaking anxiety. Besides quantitative analysis, an in-depth interview was conducted with (n = 10) students to shed further insights on the significance of the study through qualitative inputs. Students who were interviewed voiced that they felt less anxious while conversing with IPA, and it paves way for ample practice and makes them feel comfortable while speaking in SL. The results revealed that the students had moderate to serious levels of anxiety in second language classrooms and the usage of IPA has significantly reduced their FLSA. [ABSTRACT FROM AUTHOR]

Purpose: The growing integration of artificial intelligence (AI) assistants and voice assistants provides a platform for AI to enter consumers' everyday lives. As these voice assistants become ubiquitous, their widespread adoption underscores the need to understand how to create voice assistants that can naturally interact with and support users. Grounded in the stereotype content model from social psychology, this study aims to investigate the influence of perceived humanness and personality on building trust and continuous usage intentions in voice assistants. Specifically, a fresh perspective examining the determining factors that shape personality trait perceptions of competence and warmth in voice assistants is proposed. Design/methodology/approach: An online survey of 457 participants and structural equation modeling is conducted to validate the research model. Findings: Anthropomorphism, social presence and interactivity drive perceived warmth, whereas performance and effort expectations drive perceived competence. Perceived competence and perceived warmth together positively affect users' trust in voice assistants, leading to a higher likelihood of continuous usage intentions. Originality/value: This research provides profound theoretical contributions to the emerging field of human-AI interaction and offer practical implications for marketers aiming to leverage voice assistant personalities to build trusted and long-lasting interactions. [ABSTRACT FROM AUTHOR]

This study aims to investigate how smartphone voice assistants (SVAs) impact brand engagement among Malaysian university students, who are leading in digital consumption trends. The study aims to investigate how brand characteristics such as appeal, attachment, perceived utility, and usability impact consumer behaviour by utilising Brand Identity and Technology Acceptance models. Analysed using structural equation modelling (SEM) in AMOS, data from 500 students experienced with voice assistants was collected through a survey. The results show strong connections in all studied relationships, emphasising the crucial importance of various brand attributes in promoting engagement. Usability and perceived utility are essential factors, indicating that businesses should focus on creating products that are both functional and user-friendly. The study provides distinctive perspectives on improving student engagement and brand partnerships in a competitive environment. It highlights the necessity for more research to help voice assistant professionals and academics enhance consumer-brand connections, especially among the digitally adept youth population in Malaysia. [ABSTRACT FROM AUTHOR]

Voice assistants (VAs), such as Alexa, Siri, or Google Now, are becoming increasingly ubiquitous. Consequently, the potential societal impacts of such systems are gaining relevance in public and academic discourses. Investigating the effects of VA voice features on user perception of VAs is one central aspect of research. Here, the potential effects of VAs on the perception of gender roles and the attribution of gender stereotypes stand out. Following the Computers as Social Actors paradigm, it is assumed that gendered voices have the potential to reproduce existing gender stereotypes. Yet, recently, the implementation of gender-ambiguous voices has been suggested to mitigate such effects and promote more diverse technology design. In this study, we set out to inquire about the relationship between gendered VA voice (male, female, gender-ambiguous) and gender stereotype activation. In two online surveys with an experimental manipulation (Study 1: N = 140, Study 2: N = 397), we test stereotype activation as a function of VA voice, user gender, and conversation topic. Empirical findings are mixed, with no effects for conversation topic but indications of small effects of VA voice. Implications for future research are discussed. [ABSTRACT FROM AUTHOR]

In an era where user experience reigns supreme, an unexpected element is subtly influencing our interactions with technology--"Cuteness." However, when discussing the design of digital products such as AIVAs, is cuteness merely a matter of appearance? This paper aims to unveil the complex psychological mechanisms and their impact on usage intention hidden behind the seemingly harmless allure of cuteness. Through an experimental study involving 284 participants and utilizing a moderated serial mediation model via PLS-SEM, this research reveals how aesthetic design differences influence usage intentions. The findings disclose three key insights: (1) A positive relationship between cuteness and usage intention is confirmed; (2) Social presence, performance expectancy and customer value act as serial mediations between cuteness and usage intention; (3) Perceived risk moderates the impact of cuteness on usage intentions by influencing social presence, performance expectancy, hedonic value and functional value. This contributes theoretical insights and practical guidance for the sustainable development and success of AIVAs. [ABSTRACT FROM AUTHOR]

This study integrates two lines of research: technologies as tools and technologies as social beings, under the theoretical framework of dynamic systems, to investigate the reciprocal dynamics between functional use and relational use of artificial intelligence (AI) voice assistants, and the mediating roles of self-disclosure and privacy concerns. A two-wave longitudinal survey was conducted among 354 AI voice assistant users across 2 months. Factor analysis results supported the conceptualization and operationalization of functional use and relational use of voice assistants. Results from the cross-lagged panel model confirmed that functional use and relational use reinforced themselves over time, respectively. Relational use increased subsequent functional use, and relational use reinforced itself through self-disclosure. Surprisingly, functional use did not increase subsequent relational use; instead, longitudinal mediation analysis showed that functional use reduced subsequent relational use due to the lack of self-disclosure. Furthermore, while self-disclosure increased subsequent privacy concerns, privacy concerns did not reduce subsequent self-disclosure. [ABSTRACT FROM AUTHOR]

The widespread use of voice assistants (VAs) creates a pressing need to understand what drives consumers to use different VAs. Existing studies have commonly focused on the net effects of antecedents that explain why consumers adopt or continue using VAs, ignoring the complexity of consumer behavior and the combinatorial effects of multiple antecedents. Our study proposes that consumer intention to continue using VAs does not depend on a single characteristic of products or consumers but on specific configurations of such characteristics. By integrating human–technology interaction and media richness theories, we suggest that consumers with distinct psychometric profiles and learning styles may evaluate humanlike and technological attributes of VAs differently. Our study shows that the complex interconnectedness between different VA attributes and consumer characteristics can provide a holistic understanding of why some consumers continue or stop using VAs. The results advance the media richness literature by offering novel insights into multimodality in consumer–technology interactions by examining consumer evaluations of single and multimodal VAs (e.g., smart speakers vs. touchscreen smart speakers). Our study provides templates for managers to effectively design VAs aligned with their segmentation and targeting strategies. [ABSTRACT FROM AUTHOR]

Copyright of Multilingua is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Digital Voice Assistants (DVAs) have become a ubiquitous technology in today's home and childhood environments. Inspired by (Bernstein and Crowley, J Learn Sci 17:225–247, 2008) original study (n = 60, age 4–7 years) on how children's ontological conceptualizations of life and technology were systematically associated with their real-world exposure to robotic entities, the current study explored this association for children in their middle childhood (n = 143, age 7–11 years) and with different levels of DVA-exposure. We analyzed correlational survey data from 143 parent–child dyads who were recruited on 'Amazon Mechanical Turk' (MTurk). Children's ontological conceptualization patterns of life and technology were measured by asking them to conceptualize nine prototypical organically living and technological entities (e.g., humans, cats, smartphones, DVAs) with respect to their biology, intelligence, and psychology. Their ontological conceptualization patterns were then associated with their DVA-exposure and additional control variables (e.g., children's technological affinity, demographic/individual characteristics). Compared to biology and psychology, intelligence was a less differentiating factor for children to differentiate between organically living and technological entities. This differentiation pattern became more pronounced with technological affinity. There was some evidence that children with higher DVA-exposure differentiated more rigorously between organically living and technological entities on the basis of psychology. To the best of our knowledge, this is the first study exploring children's real-world exposure to DVAs and how it is associated with their conceptual understandings of life and technology. Findings suggest although psychological conceptualizations of technology may become more pronounced with DVA-exposure, it is far from clear such tendencies blur ontological boundaries between life and technology from children's perspective. [ABSTRACT FROM AUTHOR]

Purpose of Review: The present overview addresses the importance of voice in human-technology interactions within the sexual realm. It introduces empirical evidence within two key domains: (a) sexualized interactions involving artificial entities and (b) communication about sexuality-related health. Moreover, the review underscores existing research gaps. Recent Findings: Theories and first empirical studies underline the importance of voice within sexualized interactions with voice assistants or conversational agents as social cues. However, research on voice usage in sexual health-related contexts reveals contradicting results, mainly because these technologies ask users to vocalize potentially sensitive topics. Summary: Although the utilization of voice in technology is steadily advancing, the question of whether voice serves as the optimal medium for social interactions involving sexually related artificial entities and sexual health-related communication remains unanswered. This uncertainty stems from the fact that certain information must be conveyed verbally, which could also be communicated through alternative means, such as text-based interactions. [ABSTRACT FROM AUTHOR]

This paper develops a critical perspective on the use of conversational agents (CAs) with children at home. Drawing on interviews with eleven parents of pre-school children living in Norway, we illustrate the ways in which parents resisted the values epitomised by CAs. We problematise CAs' attributes in light of parents' ontological perceptions of what it means to be human and outline how their attitudes correspond to Bourdieu's [1998a. Acts of Resistance. New York: New Press] concept of acts of resistance. For example, parents saw artificial conversation designed for profit as a potential threat to users' autonomy and the instant gratification of CAs as a threat to children's development. Parents' antecedent beliefs map onto the ontological tensions between human and non-human attributes and challenge the neoliberal discourse by demanding freedom and equality for users rather than productivity and economic gain. Parents' comments reflect the belief that artificial conversation with a machine inappropriately and ineffectively mimics a nuanced and intimate human-to-human experience in service of profit motives. [ABSTRACT FROM AUTHOR]

Internet-based cognitive behavioral therapy (iCBT) offers a scalable, cost-effective, accessible, and low-threshold form of psychotherapy. Recent advancements explored the use of conversational agents such as chatbots and voice assistants to enhance the delivery of iCBT. These agents can deliver iCBT-based exercises, recognize and track emotional states, assess therapy progress, convey empathy, and potentially predict longterm therapy outcome. However, existing systems predominantly utilize categorical approaches for emotional modeling, which can oversimplify the complexity of human emotional states. To address this, we developed a transformer-based model for dimensional text-based emotion recognition, fine-tuned with a novel, comprehensive dimensional emotion dataset comprising 75,503 samples. This model significantly outperforms existing state-of-the-art models in detecting the dimensions of valence, arousal, and dominance, achieving a Pearson correlation coefficient of r = 0.90, r = 0.77, and r = 0.64, respectively. Furthermore, a feasibility study involving 20 participants confirmed the model’s technical effectiveness and its usability, acceptance, and empathic understanding in a conversational agent-based iCBT setting, marking a substantial improvement in personalized and effective therapy experiences. [ABSTRACT FROM AUTHOR]

This study investigates the factors that build resistance and attitude towards AI voice assistants (AIVA). A theoretical model is proposed using the dual-factor framework by integrating status quo bias factors (sunk cost, regret avoidance, inertia, perceived value, switching costs, and perceived threat) and Technology Acceptance Model (TAM; perceived ease of use and perceived usefulness) variables. The study model investigates the relationship between the status quo factors and resistance towards adoption of AIVA, and the relationship between TAM factors and attitudes towards AIVA. A sample of four hundred and twenty was analysed using structural equation modeling to investigate the proposed hypotheses. The results indicate an insignificant relationship between inertia and resistance to AIVA. Perceived value was found to have a negative but significant relationship with resistance to AIVA. Further, the study also found that inertia significantly differs across gender (male/female) and age groupings. The study's framework and results are posited as adding value to the extant literature and practice, directly related to status quo bias theory, dual-factor model and TAM. [ABSTRACT FROM AUTHOR]

Copyright of HMD: Praxis der Wirtschaftsinformatik is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Introduction: Voice assistants (VAs) have the potential to uphold and enhance the quality of life for older adults. However, the extent to which older adults accept and benefit from VAs may be relatively modest. Methods: This study developed a comprehensive model combined with product and personal characteristics to explain the acceptance of VAs among older adults, using semi-structured interviews (Study 1) and questionnaires (Study 2). Results: Results revealed that in terms of product characteristics, perceived usefulness and perceived enjoyment significantly affect behavior intention. Regarding personal characteristics of older adults, technological self-efficacy and dispositional resistance to change significantly affect behavior intention. However, no direct impact of perceived ease of use and perceived trust on behavior intention. Additionally, perceived enjoyment influenced both perceived ease of use and perceived usefulness. Discussion: Results suggested the significant role of technology self-efficacy and dispositional resistance to change in predicting the acceptance of VAs among older adults. Our newly developed model offers valuable insights for tailoring VAs to this demographic during design and implementation. [ABSTRACT FROM AUTHOR]

In recent years, user experience (UX) has gained importance in the field of interactive systems. To ensure its success, interactive systems must be evaluated. As most of the standardized evaluation tools are dedicated to graphical user interfaces (GUIs), the evaluation of voice-based interactive systems or voice user interfaces is still in its infancy. With the help of a well-established evaluation scale, the System Usability Scale (SUS), two prominent, widely accepted voice assistants were evaluated. The evaluation, with SUS, was conducted with 16 participants who performed a set of tasks on Amazon Alexa Echo Dot and Google Nest Mini. We compared the SUS score of Amazon Alexa Echo Dot and Google Nest Mini. Furthermore, we derived the confidence interval for both voice assistants. To enhance understanding for usability practitioners, we analyzed the Adjective Rating Score of both interfaces to comprehend the experience of an interface's usability through words rather than numbers. Additionally, we validated the correlation between the SUS score and the Adjective Rating Score. Finally, a paired sample t-test was conducted to compare the SUS score of Amazon Alexa Echo Dot and Google Nest Mini. This resulted in a huge difference in scores. Hence, in this study, we corroborate the utility of the SUS in voice user interfaces and conclude by encouraging researchers to use SUS as a usability metric to evaluate voice user interfaces. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to establish and empirically investigate a research model examining the effect of four dimensions of the technology readiness index – optimism, innovativeness, discomfort and insecurity – on customer engagement that further influences purchase intention in the context of online shopping through artificial intelligence voice assistants (AI VAs). Design/methodology/approach: Data were collected in India from 429 customers in a self-administered online survey. Data analysis uses the structural equation modelling technique. Findings: Technology readiness dimensions, e.g. optimism, innovativeness, discomfort and insecurity, are critical factors driving customer engagement. Customer engagement further results in purchase intention in online shopping through AI VAs. Research limitations/implications: This study adds to the literature by understanding how customers' technology readiness levels drive engagement and purchase intention. However, this study includes customer engagement as a unidimensional construct. Further research can consist of customer engagement as a multidimensional construct. Practical implications: The findings offer guidelines for e-retailers to enhance customer engagement that matches their personality traits, thereby strengthening their purchase intention through AI VAs. Originality/value: The research contributes to the literature by empirically investigating a research model, revealing optimism, innovativeness, discomfort and insecurity as crucial parameters for customer engagement and purchase intention. [ABSTRACT FROM AUTHOR]

Background: Patient-reported outcome (PRO) measures provide valuable evidence in clinical trials; however, poor compliance with PRO measures is a notable and long-standing problem, resulting in missing data that potentially impact the interpretation of trial results. Interactive, patient-centric platforms may increase participants' motivation to complete PRO measures over the course of a clinical trial. Thus, the aim of this study was to evaluate and optimize the usability of 3 popular consumer technologies—a traditional app-based interface, a chatbot interface, and a speech-operated interface—that may be used to improve user engagement and compliance with PRO measures. Methods: Participants aged 18–75 years from the general United States population tested the usability of 3 ePRO platforms: a traditional app-based interface using Datacubed Health Platform (Datacubed), a web-based chatbot interface using the Orbita platform, and a speech-operated Alexa interface using an Alexa Skill called "My Daily Wellness." The usability of these platforms was tested with 2 PRO measures: the EQ-5D-5 L and the SF-12v2 Health Survey (SF-12v2), Daily recall. Using a crossover design, 3 cohorts of participants tested each ePRO platform daily for 1 week. After testing, interviews were conducted regarding the participants' experience with each platform. Results: A total of 24 adults participated in the study. The mean age of participants was 45 years (range, 21–71 years), and half were female (n = 12; 50%). Overall, participants prioritized speed, ease of use, and device portability in selecting their preferred platform. The Datacubed app met these criteria and was the preferred platform among most participants (n = 20; 83%). Participants also suggested various modifications to the platforms, such as programmable notifications, adjustable speed, and additional daily reminders. Conclusions: These data demonstrate the importance of speed, ease of use, and device portability, features that are currently incorporated in the Datacubed app, in ePRO platforms used in future clinical trials. Additionally, the usability of ePRO platforms may be optimized by adding programmable notifications, adjustable speed, and increased daily reminders. The results of this study may be used to enhance the usability and patient centricity of these platforms to improve user compliance and engagement during clinical trials. [ABSTRACT FROM AUTHOR]

Voice assistants based on Artificial Intelligence (AI) are computer programs that use natural language processing (NLP) and machine learning (ML) algorithms to recognize and respond to voice commands and queries in a human-like way. These voice assistants use speech recognition technology to understand spoken commands and questions, process them, and provide relevant responses. The objective of this study is to examine the intellectual framework and effectiveness of voice assistants and to analyze the literature produced by renowned researchers in terms of authors, keywords, and major organizations. This review seeks to offer valuable insights into the growing body of literature on virtual assistants. In this article, the authors have used the bibliometric method to systematically summarize the current state of voice assistant research. They analyzed 563 articles related to Voice assistants from the Scopus database between 2002 and 2023 using the R-Studio 'Biblioshiny' tool. The analysis includes an examination of the core journals, articles, authors, institutions, and relevant countries to determine the most influential voice assistants literature. This study also provides a detailed understanding of Co-occurrence network, Co-citation network and Co-word analysis. The authors have also used thematic maps to identify the different research topics of voice literature and have grouped them into four clusters, including Motor themes, Niche themes, Emerging themes, and Basic themes. The article concludes by discussing the limitations of the study and highlighting future research directions. [ABSTRACT FROM AUTHOR]

This study draws upon Behavioral Reasoning Theory and the Technology Acceptance Model to investigate consumer engagement with AI‐powered voice assistants. The study creates a theoretical model to examine the effects of reasons for and reasons against using voice assistants. This research exemplifies attitudes towards using voice assistants and willingness to provide personal information as key constructs. The current study tests data from 491 voice assistant users via mTurk, and we utilize a multimethod analysis scheme including the partial least squares technique and the fuzzy set qualitative comparative analysis approach to provide an assessment of the proposed model. Findings indicated that while privacy cynicism has a negative impact upon the attitude towards using voice assistants, the countervailing values of trust, perceived usefulness, and ease of use have off‐setting positive impact. The study also highlights the moderating role of habit on the behavioral mechanisms driving consumer engagement via willingness to provide privacy information. This research advances the emerging literature on voice assistants with respect to privacy‐related factors driving consumer engagement. [ABSTRACT FROM AUTHOR]

Although Voice Assistants are ubiquitously available for some years now, the interaction is still monotonous and utilitarian. Sound design offers conceptual and methodological research to design auditive interfaces. Our work aims to complement and supplement voice interaction with sonic overlays to enrich the user experience. Therefore, we followed a user-centered design process to develop a sound library for weather forecasts based on empirical results from a user survey of associative mapping. After analyzing the data, we created audio clips for seven weather conditions and evaluated the perceived combination of sound and speech with 15 participants in an interview study. Our findings show that supplementing speech with soundscapes is a promising concept that communicates information and induces emotions with a positive affect for the user experience of Voice Assistants. Besides a novel design approach and a collection of sound overlays, we provide four design implications to support voice interaction designers. [ABSTRACT FROM AUTHOR]

AI‐based voice assistants are some of the most popular consumer products of artificial intelligence technology. This study investigates the influence of the reasoning process on overall consumer attitudes and adoption intentions towards AI‐based voice assistants. Behavioural reasoning theory was applied to hypothesize relationships between values, context‐specific reasons, attitudes, and adoption intention. Partial least squares structural equation modelling was used to test the research model and hypotheses using data from 550 potential consumers in India. This research suggests a positive influence of consumers' 'reasons for' on their attitude and adoption intentions towards AI‐based voice assistants, as well as a favourable impact of consumers' values on 'reasons for'. This study discovered that 'reasons for' and 'reasons against' are not only opposites but are entirely autonomous and have an independent impact on consumers' attitudes towards AI‐based voice assistants. This research improved our understanding of AI‐based product adoption by identifying the paramount role of consumers' values and the 'reasons for' and 'reasons against' in shaping their attitudes and adoption intentions towards AI‐based voice assistants. [ABSTRACT FROM AUTHOR]

There is an emerging interest in examining user attitudes towards voice assistants (VAs); however, there is limited research on how user attitudes are formulated in different contexts. Drawing from the stereotype content models, the current study attempts to investigate how users perceive and evaluate voice assistants (VAs) in different contexts (i.e., functional vs. social tasks) based on warmth, competence and trustworthiness. Study 1 (N = 123) employs a within‐subjects design to examine how task type (functional vs. social) affects user perceptions and attitudes towards a VA (i.e., Google Assistant). Study 2 (N = 116) and Study 3 (N = 61) examine the boundary effect of perceived psychological power and ease of use. The findings show that attitude is significantly more positive in functional tasks (vs. social), and this effect is mediated by perceived competence. This indirect effect is also significantly moderated by perceived ease of use. Perceived warmth does not mediate the effect of social tasks on attitude, and trust in VAs is a direct outcome of functional tasks. Taken together, this study contributes to both theory and practice in many ways. Specifically, the findings are the first to demonstrate a direct effect of task type on consumer perceptions and attitudes. Additionally, the findings indicate that user evaluations of VAs are still dominated by user perceptions of the competence of the VAs. [ABSTRACT FROM AUTHOR]

With the nascent rise of the voice intelligence industry, consumer engagement is evolving. The expected shift from navigating digital environments by a "click" of a mouse or a "touch" of a screen to "voice commands" has set digital platforms for a race to become leaders in voice-based services. The Commission's inquiry into the consumer IoT sector revealed that the development of the market for general-purpose voice assistants is spearheaded by a handful of big technology companies, highlighting the concerns over the contestability and growing concentration in these markets. This contribution posits that voice assistants are uniquely positioned to engage in dynamically personalized steering – hypernudging – of consumers toward market outcomes. It examines hypernudging by voice assistants through the lens of abuse of dominance prohibition enshrined in article 102 TFEU, showcasing that advanced user influencing, such as hypernudging, could become a vehicle for engaging in a more subtle anticompetitive self-preferencing. [ABSTRACT FROM AUTHOR]

Voice assistants (VAs) are devices that utilize AI, machine learning, and NLP to facilitate users to perform diverse tasks verbally. VAs also have a unique feature that allows them to be "always on" so that every sound generated in their background can be analyzed and start interacting with users when they recognize their wake-up command, for instance, "Hey Siri" or "Okay Google", which implies that VAs have to be listening to users at all time. This raises the issue of privacy in the form of perceived surveillance. This study aims to assess how perceived surveillance affects the continuance usage intention of VAs in Indonesia with the addition of personal information disclosure as a mediator. Surveillance effect model was utilized to measure perceived surveillance. The model was calculated using PLS-SEM based on online survey data (N=222) distributed over social media. It was revealed that perceived surveillance affects the continuance usage intention of VAs negatively and is partially mediated by personal information disclosure. The result also affirmed that trust, perceived risk, and prior negative experiences are predictors of perceived surveillance. Therefore, VA companies should be mindful of how their customers' continuance usage intention is affected by how much perceived surveillance they feel. [ABSTRACT FROM AUTHOR]

The use of voice-controlled virtual assistants (VAs) is significant, and user numbers increase every year. Extensive use of VAs has provided the large, cash-rich technology companies who sell them with another way of consuming users' data, providing a lucrative revenue stream. Whilst these companies are legally obliged to treat users' information "fairly and responsibly," artificial intelligence techniques used to process data have become incredibly sophisticated, leading to users' concerns that a lack of clarity is making it hard to understand the nature and scope of data collection and use. There has been little work undertaken on a self-contained user awareness tool targeting VAs. PrivExtractor, a novel web-based awareness dashboard for VA users, intends to redress this imbalance of understanding between the data "processors" and the user. It aims to achieve this using the four largest VA vendors as a case study and providing a comparison function that examines the four companies' privacy practices and their compliance with data protection law. As a result of this research, we conclude that the companies studied are largely compliant with the law, as expected. However, the user remains disadvantaged due to the ineffectiveness of current data regulation that does not oblige the companies to fully and transparently disclose how and when they use, share, or profit from the data. Furthermore, the software tool developed during the research is, we believe, the first that is capable of a comparative analysis of VA privacy with a visual demonstration to increase ease of understanding for the user. [ABSTRACT FROM AUTHOR]

The designs of most chatbots and voice assistants provide features for the agent to understand what the user said, but not the equivalent features for the user to understand the agent. Although the agents in the study suffered from many under-trained intents and a considerable lack of knowledge in the case of open-domain inquiries, the NCF dialog patterns nonetheless appeared to help users, and agents, manage the interaction, and move on. Keywords: Conversational user interfaces; conversational UX design; Conversation Analysis; chatbots; voice assistants EN Conversational user interfaces conversational UX design Conversation Analysis chatbots voice assistants 168 193 26 04/20/23 20230701 NES 230701 1. Two versions of tutorials were given to the users who are the full-featured agents and the reduced features agents, respectively, since each agent uses different interaction mechanics. Similarly, only one participant in all 23 conversations asked the buddy agent about its hobbies I first i , whereas the full-featured agent brought up its hobby, Splendor, in 13 of the 15 conversations. [Extracted from the article]

Widespread, and growing, use of artificial intelligence (AI)–enabled voice assistants (VAs) creates a pressing need to understand what drives VA evaluations. This article proposes a new framework wherein perceptions of VA artificiality and VA intelligence are positioned as key drivers of VA evaluations. Building from work on signaling theory, AI, technology adoption, and voice technology, the authors conceptualize VA features as signals related to either artificiality or intelligence, which in turn affect VA evaluations. This study represents the first application of signaling theory when examining VA evaluations; also, it is the first work to position VA artificiality and intelligence (cf. other factors) as key drivers of VA evaluations. Further, the paper examines the role of several theory-driven and/ or practice-relevant moderators, relating to the effects of artificiality and intelligence on VA evaluations. The results of these investigations can help firms suitably design their VAs and suitably design segmentation strategies. [ABSTRACT FROM AUTHOR]

In July 2022 the European Council gave final approval to new regulation of digital markets. This specifically addresses the main concerns raised by the business behaviour of operators of core services in their gatekeeping positions. The list of core services was extended during revisions. In this article, I address the question of whether the inclusion of virtual assistants into the list of core services was the right decision. Overall, this paper argues that (i) virtual assistants as gatekeepers for consumption should be listed among core services, (ii) some of the Digital Markets Act's obligations need to be adopted to fit the specifics of virtual assistants, (iii) there are two relevant dimensions of power which should be considered in competition policy and regulation analysis: market power on virtual assistants' market and the ecosystem of related markets (cross-market integration criterion), (iv) the growth of new gatekeepers should be prevented, among other means by stricter merger control. [ABSTRACT FROM AUTHOR]

This study investigates the impact of attitude and trust on the behavioral intention of adopting artificial intelligence (AI)-based voice-assistant services through the integration of parasocial relationship theory (PSR) and human–computer interaction (HCI) theory. Employing a deductive approach, partial least squares structural equation modeling (PLS-SEM) was performed to assess the proposed model based on 295 participants by using a purposive sampling method. Findings revealed that the functional components significantly impacted users' attitudes, while social attributes, such as social cognition and electronic word of mouth (e-WOM), were revealed to be critical constituents in driving users' trust. Moreover, technology self-efficacy (TSE) showed a moderating impact on the relationship between trust and behavioral intention to use voice-assistant services. Based on the findings, the study provides robust and meaningful insights for managers and designers to foster trust and form a positive attitude among the users. Additionally, the outcome of the study adds to the body of research on AI-based technology adoption and interactions by investigating the antecedents of trust and attitudes towards voice-assistant services. [ABSTRACT FROM AUTHOR]

Despite various attempts to prevent food waste and motivate conscious food handling, household members find it difficult to correctly assess the edibility of food. With the rise of ambient voice assistants, we did a design case study to support households' in situ decision-making process in collaboration with our voice agent prototype, Fischer Fritz. Therefore, we conducted 15 contextual inquiries to understand food practices at home. Furthermore, we interviewed six fish experts to inform the design of our voice agent on how to guide consumers and teach food literacy. Finally, we created a prototype and discussed with 15 consumers its impact and capability to convey embodied knowledge to the human that is engaged as sensor. Our design research goes beyond current Human-Food Interaction automation approaches by emphasizing the human-food relationship in technology design and demonstrating future complementary human-agent collaboration with the aim to increase humans' competence to sense, think, and act. [ABSTRACT FROM AUTHOR]

Voice assistants have become embedded in people's private spaces and domestic lives where they gather enormous amounts of personal information which is why they evoke serious privacy concerns. The paper reports the findings from a mixed-method study with 65 digital natives, their attitudes to privacy and actual and intended behaviour in privacy-sensitive situations and contexts. It also presents their recommendations to governments or organisations with regard to protecting their data. The results show that the majority are concerned about privacy but are willing to disclose personal data if the benefits outweigh the risks. The prevailing attitude is one characterised by uncertainty about what happens with their data, powerlessness about controlling their use, mistrust in big tech companies and uneasiness about the lack of transparency. Few take steps to self-manage their privacy, but rely on the government to take measures at the political and regulatory level. The respondents, however, show scant awareness of existing or planned legislation such as the GDPR and the Digital Services Act, respectively. A few participants are anxious to defend the analogue world and limit digitalization in general which in their opinion only opens the gate to surveillance and misuse. [ABSTRACT FROM AUTHOR]

Intelligent agents have great potential as facilitators of group conversation among older adults. However, little is known about how to design agents for this purpose and user group, especially in terms of agent embodiment. To this end, we conducted a mixed methods study of older adults' reactions to voice and body in a group conversation facilitation agent. Two agent forms with the same underlying artificial intelligence (AI) and voice system were compared: a humanoid robot and a voice assistant. One preliminary study (total n = 24) and one experimental study comparing voice and body morphologies (n = 36) were conducted with older adults and an experienced human facilitator. Findings revealed that the artificiality of the agent, regardless of its form, was beneficial for the socially uncomfortable task of conversation facilitation. Even so, talkative personality types had a poorer experience with the "bodied" robot version. Design implications and supplementary reactions, especially to agent voice, are also discussed. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to examine customers' willingness to engage in service interactions enabled by artificial intelligence (AI) controlled voice assistants (VA). Drawing on the tenets of dual-factor theory, this study measures the impact of both enablers and inhibitors – mediated by trust in Alexa – on customers' intentions to transact through VAs. Design/methodology/approach: Data from a survey of 290 users of VAs from Japan was collected through "Macromill". The authors used a covariance-based path analysis technique for data analysis after establishing the validity and reliability of the measures. Findings: The results of this study demonstrate that convenience and status-seeking act as enablers and positively influence trust in VAs, whereas risk barrier acts as an inhibitor and negatively influence trust in VAs. In turn, trust in VAs positively influences the intention to use VAs for transactional service interactions. This association is positively moderated by technology comfort. Originality/value: This study applies dual-factor theory to the context of VAs – a context that scholars have, to date, examined solely from a technology adoption perspective. For the first time, the authors adopt a dual-factor approach to identify a new set of antecedents for customers' intentions to use VAs for transactional service interactions. [ABSTRACT FROM AUTHOR]

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Intelligent personal assistants (IPAs), also known as smart speakers, are becoming part of everyday life in more and more households around the world. Phone and household IPAs are integrated in intimate home contexts and require connections to (social) media profiles, user accounts, and domestic appliances. Users can control their household with voice-activated commands in order to make life more convenient and efficient. Yet, IPAs also bring privacy and surveillance concerns about devices "listening in," the "platformization" of home life, and data security. Our exploratory mixed-methods study provides an in-depth and multidimensional account of users' privacy concerns around the emergence of IPAs in Dutch households. We differentiate between surveillance, security, and platform concerns, and our survey results indicate by which factors these are influenced. The focus group analysis highlights the role of conversation, recordability, locatability, control-ability, and assistance affordances. Our findings present a multidimensional and nuanced understanding of privacy concerns around household IPAs. We indicate how smart home technologies raise concerns about privacy, surveillance, device security, everyday behavior, and platform transparency, topics that demand urgent attention before the integration of IPAs will be fully normalized. [ABSTRACT FROM AUTHOR]

This paper reports the findings from an empirical study investigating the effectiveness of using intelligent voice assistants, Amazon Alexa in our case, to deliver a phishing training to users. Because intelligent voice assistants can hardly utilize visual cues but provide for convenient interaction with users, we developed an interaction-based phishing training focused on the principles of persuasion with examples on how to look for them in phishing emails. To test the effectiveness of this training, we conducted a between-subject study where 120 participants were randomly assigned in three groups: no training, interaction-based training with Alexa, and a facts-and-advice training and assessed a vignette of 28 emails. The results show that the participants in the interaction-based group statistically outperformed the others when detecting phishing emails that employed the following persuasion principles (and/or combinations of): authority, authority/scarcity, commitment, commitment/liking, and scarcity/liking. The paper discusses the implication of this result for future phishing training and anti-phishing efforts. [ABSTRACT FROM AUTHOR]