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Although numerous signal modalities are available for emotion recognition, audio and visual modalities are the most common and predominant forms for human beings to express their emotional states in daily communication. Therefore, how to achieve automatic and accurate audiovisual emotion recognition is significantly important for developing engaging and empathetic human–computer interaction environment. However, two major challenges exist in the field of audiovisual emotion recognition: (1) how to effectively capture representations of each single modality and eliminate redundant features and (2) how to efficiently integrate information from these two modalities to generate discriminative representations.

A novel key-frame extraction-based attention fusion network (KE-AFN) is proposed for audiovisual emotion recognition. KE-AFN attempts to integrate key-frame extraction with multimodal interaction and fusion to enhance audiovisual representations and reduce redundant computation, filling the research gaps of existing approaches. Specifically, the local maximum–based content analysis is designed to extract key-frames from videos for the purpose of eliminating data redundancy. Two modules, including “Multi-head Attention-based Intra-modality Interaction Module” and “Multi-head Attention-based Cross-modality Interaction Module”, are proposed to mine and capture intra- and cross-modality interactions for further reducing data redundancy and producing more powerful multimodal representations.

Extensive experiments on two benchmark datasets (i.e. RAVDESS and CMU-MOSEI) demonstrate the effectiveness and rationality of KE-AFN. Specifically, (1) KE-AFN is superior to state-of-the-art baselines for audiovisual emotion recognition. (2) Exploring the supplementary and complementary information of different modalities can provide more emotional clues for better emotion recognition. (3) The proposed key-frame extraction strategy can enhance the performance by more than 2.79 per cent on accuracy. (4) Both exploring intra- and cross-modality interactions and employing attention-based audiovisual fusion can lead to better prediction performance.

The proposed KE-AFN can support the development of engaging and empathetic human–computer interaction environment.

The concept of ubiquitous assimilation in e-commerce, denoting the seamless integration of technologies into customer shopping experiences, has played a pivotal role in aiding e-satisfaction and, consequently, fostering patronage intention. Among these, text-based chatbots are significant innovations. In light of this, the paper aims to develop a conceptual framework and comprehend the patronage behaviour of artificial intelligence-enabled chatbot users by using chatbot usability cues and to determine whether the social presence and flow theories impact e-satisfaction, which leads to users’ patronage intention. The current research provides insights into online travel agencies (OTAs), a crucial segment within the travel and tourism sector. Given the significance of building a loyal clientele and cultivating patronage in this industry, these insights are of paramount importance for achieving sustained profitability and growth.

The research framework primarily focused on the factors that precede e-satisfaction and patronage intention among chatbot users, which include social presence, flow, perceived anthropomorphism and need for human interaction. The researchers collected the data by surveying 397 OTA chatbot users by using an online questionnaire. The data of this cross-sectional study were analysed using covariance-based structural equation modelling.

Findings reveal that e-satisfaction is positively linked with patronage intention and the variables of social presence and flow impact e-satisfaction along with chatbot usability cues. There were direct and indirect relations between chatbot usability and e-satisfaction. Moreover, the personal attributes, “need for human interaction” and, “perceived anthropomorphism” were found to moderate relations between chatbot usability cues, social presence and flow.

The impact of chatbot’s usability cues/attributes on e-satisfaction, along with perceived attributes – social presence and flow in the realm of OTAs contributes to the human–chatbot interaction literature. Moreover, the interacting effects of perceived anthropomorphism and the need for human interaction are unique in the current contextual relations.

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.

This study presents one of the earliest empirical investigations on how brand chatbots' anthropomorphic design and social presence communication strategies may improve consumer evaluation outcomes via the mediators of parasocial interaction and perceived dialogue.

This study employs a 2 (high vs. low social presence communication) by 2 (anthropomorphic vs. non-anthropomorphic bot profile) between-subject experimental design to evaluate how chatbots' high social presence communication and anthropomorphic profile design may enhance perceptions of parasocial interactions and dialogue with the chatbot, which in turn drive user engagement, interaction satisfaction and attitude toward the represented brand.

The influences of chatbots' high social presence communication on consumer engagement outcomes are mediated by perceived parasocial interaction and dialogue. Additionally, chatbots' anthropomorphic profile design can boost the positive effects of social presence communication via the psychological mediators.

This study advances the interactive marketing literature by focusing on an emerging interactive technology, chatbots. Additionally, distinct from prior chatbot studies that focused on the utilitarian use of chatbots for online customer support, this study not only examines which factors of chatbot communication and profile design may drive chatbot effectiveness but also examines the mechanism underlying the messaging and design effects on consumer engagement. The findings highlight the mediating role of interpersonal factors of parasocial interaction and perceived dialogue.

The purpose of this paper is to examine the effect of flow experience on users’ social commerce intention.

Based on the 287 valid responses collected from a survey, structural equation modeling was used to examine the research model.

The results indicated that social interaction, which includes human–computer interaction and human–human interaction, has a significant effect on the flow experience, which in turn affects social purchase and social sharing intention.

The results imply that companies need to facilitate social interaction to improve users’ experience and promote their social commerce intention.

Although prior research has examined social commerce user behaviour from multiple perspectives such as trust, perceived value and technological perceptions, it has focused on the effect of cognitive beliefs and neglected the effect of affective beliefs such as flow experience. This research tries to fill the gap.

As voice search has progressively become a new way of information acquisition and human–computer interaction, this paper aims to explore the users' voice search behavior in human–vehicle interaction.

This study employed mixed research methods, including questionnaires and interviews. A total of 151 Amazon MTurk volunteers were recruited to complete a questionnaire based on their most recent and impressive voice search experience. After the questionnaire, this paper conducted an online interview with the participants.

This paper studied users' voice search behavior characteristics in the context of the human–vehicle interaction and analyzed the voice search content, search need, search motivation and user satisfaction. In addition, this paper studied the barriers and suggestions for voice search in human–vehicle interaction through a content analysis of the interviews.

This paper's analysis of users' barriers and suggestions has a specific reference value for optimizing the voice search interaction system and improving the service.

This study is exploratory research that seeks to identify users' voice search needs and tasks and investigate voice search satisfaction in human–vehicle interaction context.

In recent developments in Artificial Intelligence (AI) and especially in robotics we can observe a tendency towards building intelligent artefacts that are meant to be social, to have ‘human social’ characteristics like emotions, the ability to conduct dialogue, to learn, to develop personality, character traits, and social competencies. Care, entertainment, pet and educational robots are conceptualised as friendly, understanding partners and credible assistants which communicate ‘naturally’ with users, show emotions and support them in everyday life. Social robots are often designed to interact physically, affectively and socially with humans in order to learn from them. To achieve this goal, roboticists often model the human‐robot interaction on early caregiver‐infant interactions. In this paper I want to analyse prominent visions of these ‘socio‐emotional’ machines as well as early prototypes and commercial products with regard to the human‐machine interface. By means of this I will ask how feminist critiques of technology could be applied to the field of social robotics in which concepts like sociality or emotion are crucial elements while, at the same time, these concepts play an important role in feminist critiques of technology.

Service robotics, a branch of robotics that entails the development of robots able to assist humans in their environment, is of growing interest in the hospitality industry. Designing effective autonomous service robots, however, requires an understanding of Human–Robot Interaction (HRI), a relatively young discipline dedicated to understanding, designing, and evaluating robotic systems for use by or with humans. HRI has not yet received sufficient attention in hospitality robotic design, much like Human–Computer Interaction (HCI) in property management system design in the 1980s. This article proposes a set of introductory HRI guidelines with implementation standards for autonomous hospitality service robots.

A set of key user-centered HRI guidelines for hospitality service robots were extracted from 52 research articles. These are organized into service performance categories to provide more context for their application in hospitality settings.

Based on an extensive literature review, this article presents some HRI guidelines that may drive higher levels of acceptance of service robots in customer-facing situations. Deriving meaningful HRI guidelines requires an understanding of how customers evaluate service interactions with humans in hospitality settings and to what degree those will differ with service robots.

Robots are challenging assumptions on how hospitality businesses operate. They are being increasingly deployed by hotels and restaurants to boost productivity and maintain service levels. Effective HRI guidelines incorporate user requirements and expectations in the design specifications. Compilation of such information for designers of hospitality service robots will offer a clearer roadmap for them to follow.