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The research aims to understand how smart speakers are perceived by their actual and potential users, their attitude towards smart speakers and consequently their intention to use them.

The authors apply a structural equation modelling (SEM) approach to test the research hypotheses through data coming from a structured questionnaire.

The results show that the higher the importance attributed to usefulness and ease of use, the higher the positive attitude that in turn positively affects the intention to use smart speakers. A significant relationship also emerged between task technology fit and attitude towards smart speakers, as well as between perceived enjoyment and attitude towards smart speakers. Perceived privacy risk, innovativeness and social attraction have been found to not significantly impact attitudes towards smart speakers.

Although several academic studies have focused on various aspects of smart technologies, only a few studies discuss the factors that push consumers to use smart speakers for activities related to commercial transactions. Therefore, looking at the rapid rise of smart speakers for daily tasks and the gradual acceptance of voice interaction with digital tools, the authors proposed a study about Italian users' intention to use smart speakers. Specifically, to fill the gap in the existing literature, the authors applied a SEM approach to identify utilitarian and hedonic benefits that motivate the use of these devices.

Artificially intelligent (AI) assistant-enabled smart speaker not only can provide assistance by navigating the massive amount of product and brand information on the internet but also can facilitate two-way conversations with individuals, thus resembling a human interaction. Although smart speakers have substantial implications for practitioners, the knowledge of the underlying psychological factors that drive continuance usage remains limited. Drawing on social response theory and the technology acceptance model, this study aims to elucidate the adoption process of smart speakers.

A field survey of 391 smart speaker users were obtained. Partial least squares structural equation modeling was used to analyze the data.

Media richness (social cues) and parasocial interactions (social role) are key determinants affecting the establishment of trust, perceived usefulness and perceived ease of use, which, in turn, affect attitude, continuance usage intentions and online purchase intentions through AI assistants.

AI assistant-enabled smart speakers are revolutionizing how people interact with smart products. Studies of smart speakers have mainly focused on functional or technical perspectives. This study is the first to propose a comprehensive model from both functional and social perspectives of continuance usage intention of the smart speaker and online purchase intentions through AI assistants.

The purpose of this paper is to investigate the effects of anthropomorphism and identify factors related to adopting voice shopping on smart speakers.

Progress in partial least squares structural equation modeling (PLS-SEM) approach is used to test the proposed research framework regarding anthropomorphism and user perceptions on voice shopping via smart speakers. Individuals' responses to questions about attitude and intention to use voice shopping via smart speakers were collected and analyzed.

The results showed that anthropomorphism had a positive influence on satisfaction, which, in turn, had a positive impact on intention to adopt voice shopping, and customers had positive opinions regarding smart speakers.

This study only reflects a younger perspective on smart speaker voice shopping. This study identified the characteristics of smart speakers that increase customers' intention to purchase, which can be used to formulate sales strategies and management guidelines.

This research provided a new perspective to enable practitioners to promote smart speakers for voice shopping. Smart speaker manufacturers can utilize the findings of this research to improve the system design of smart speakers to further facilitate voice shopping.

Unlike previous studies, which focused on product attributes of smart speakers or voice shopping experiences, this study provided a clear picture of how the anthropomorphic feature of smart speakers affects customers' intention to adopt voice shopping.

The main objective of this chapter is broadening the understanding of anthropomorphic artificial intelligence (AI) (e.g. avatars, humanoid robots, chatbots) in both physical and digital environments. The chapter strives to demonstrate how organisations can curate relationship marketing and enhance customer experience by employing anthropomorphic AI. To achieve this, the chapter extends existing understanding in three ways. First, it explains the interconnectivity between relationship marketing and customer experience. Second, it presents anthropomorphic AI along with its different characteristics and technologies. Third, it offers some real-life uses cases and examples of such AI drawing from practical insights into five selected industries. Overall, the chapter provides some food of thoughts concerning the successful application and deployment of anthropomorphic AI in marketing practices.

This study explores the role of smart voice assistants (SVAs) as purchase recommenders, a phenomenon the authors term “word of voice” (WOV) communication. By integrating human–computer interaction (HCI) literature and electronic word of mouth (eWOM) research, the authors examine what makes consumers trust in SVA-transmitted WOV communication following their initial interactions with their SVAs during a purchase process (i.e. post-trust); and the authors propose that consumers' perceptions of their SVAs' smart capabilities (i.e. cognitive, emotional and social) are critically important for building this trust. Moreover, the study explores the influence of post-trust on consumers' adherence to WOV communication, measured by three types of behavioural intentions.

Data from a survey of 202 United States (US)-based SVA users who employ them to obtain purchase recommendations were collected and analysed. They confirmed the validity of the measurement scales and provided input for the partial least squares modelling (PLS-SEM).

The results demonstrated that post-trust in WOV communication partially or totally mediates the effect of smart capabilities on consumer adherence to WOV communication; identified the key role of cognitive, emotional and social smart capabilities for building consumers' post-trust in WOV and demonstrated the influence of this trust on behavioural intentions.

The present study contributes by examining the employment of SVAs as recommenders during the purchase process; the authors term this type of communication WOV. It analyses consumers with experience of using SVAs in their purchase processes, revealing that post-trust in WOV communication is the psychological mechanism that explains how the smart capabilities of SVAs determine consumer adherence to the recommendations they receive.

This study aims to examine users’ affective relationships with smart voice assistants (SVAs) and aims to analyze how these relationships explain user engagement behaviors toward the brands of SVAs. Drawing on relational cohesion theory, it proposes that cohesion between users and SVAs influences brand engagement behaviors, that is, continuing purchasing other products of the brand, providing knowledge to the brand and referring the brand.

Data from a survey of 717 US regular SVA users confirm the validity of the measurement scales and provide the input for the covariance-based structural equation modeling.

The results demonstrate that frequent user-SVA interactions evoke positive emotions, which encourage cohesive relationships. Pleasured-satisfaction and interest emerge as strong emotions. Moreover, relational cohesion between users and SVAs promotes engagement with the brand of the assistant.

This paper applies an interpersonal approach in a context that, to date, has been examined from a predominantly technological perspective. It shows that users develop positive emotions toward smart technologies through their interactions, and establishes the importance of building affective relationships. To the best of the authors’ knowledge, this is the first study to analyze cohesion between users and smart technologies and to examine the effect of this cohesion on user engagement with the brand.

This study aims to examine the influence of the derived attributes of embedded artificial intelligence-mobile smart speech recognition (AI-MSSR) technology, namely perceived usefulness, perceived ease of use (PEOU) and perceived enjoyment (PE) on consumer purchase intention (PI) through the chain relationships of attitudes to AI and consumer smart experience, with the moderating effect of consumer innovativeness and Generation (Gen) X and Gen Y in fashion retail.

The study employed a quantitative survey strategy, drawing a sample of 836 respondents from Sri Lanka and India representing Gen X and Gen Y. The data analysis was carried out using smart partial least squares structural equation modelling (PLS-SEM).

The findings show a positive relationship between the perceived attributes of MSSR and consumer PI via attitudes towards AI (AAI) and smart consumer experiences. In addition, consumer innovativeness and Generations X and Y have a moderating impact on the aforementioned relationship. The theoretical and managerial implications of the study are discussed with a note on the research limitations and further research directions.

To multiply the effects of embedded AI-MSSR and consumer PI in fashion retail marketing, managers can develop strategies that strengthen the links between awareness, knowledge of the derived attributes of embedded AI-MSSR and PI by encouraging innovative consumers, especially Gen Y consumers, to engage with embedded AI-MSSR.

This study advances the literature on embedded AI-MSSR and consumer PI in fashion retail marketing by providing an integrated view of the technology acceptance model (TAM), the diffusion of innovation (DOI) theory and the generational cohort perspective in predicting PI.

Based on the post-acceptance model of information system continuance (PAMISC), this study investigates the influence of the early-stage users' personal traits (specifically personal innovativeness and technology anxiety) and ex-post instrumentality perceptions (specifically price value, hedonic motivation, compatibility and perceived security) on social diffusion of smart technologies measured by the intention to recommend artificial intelligence-based voice assistant systems (AIVAS) to others.

Survey data from 400 US AIVAS users were collected and analyzed with Statistical Product and Service Solutions (SPSS) 18.0 and the partial least square technique using advanced analysis of composites (ADANCO) 2.1.

AIVAS technology is presently at the early stage of market penetration (about 25% of market penetration in the USA). A survey of AIVAS technology users reveals that personal innovativeness is directly and indirectly (through confirmation and continuance) associated with a stronger intention to recommend the use of the device to others. Confirmation is associated with all four ex-post instrumentality perceptions (hedonic motivation, compatibility, price value and perceived security). Among the four, however, only hedonic motivation and compatibility are significant predictors of satisfaction, which lead to use continuance and, eventually, intention to recommend. Finally, technology anxiety is found to be indirectly (but not directly) associated with a lower intention to recommend.

This is the first study conducted on the early-stage AIVAS users that evaluates the influence of both personal traits and ex-post instrumentality perceptions on users' intention for continuance and recommendation to others.

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 paper aims to categorize users of voice assistants and analyze decision-making conflicts to predict intention to adopt voice commerce (v-commerce).

This exploratory study used expert survey-based data collection founded on data saturation.

This study identifies three forms of voice systems based on senses aroused (screen first, voice only and voice first) and four profiles of voice users (passive resistant, hedonistic adopter, utilitarian adopter and active resistant), each with a different appraisal of the benefits and costs of v-commerce adoption and the experiences (positive or negative) felt during the shopping experience. This study proposes a conceptual model to predict intention to adopt v-commerce depending on voice-system and -user characteristics.

Learning from this study can help improve the marketing strategies and actions put in place by voice-assistant brands and advertisers by providing insights for adapting product recommendation algorithms to meet the needs of the identified profiles.

This paper provides an answer to the limits of classical approaches based on “one-size-fits-all” strategy by showing how voice-assistant users have different profiles that span a gradient of advance in technology adoption.