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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.

With the expansion of artificial intelligence (AI) technology in everyday life, it is critical to discuss how and why consumers respond in certain ways to AI agents. However, few studies have examined the mechanisms underlying users’ responses to these agents. This study aims to identify such mechanisms and discuss how users form loyalty toward AI agents. Specifically, this study addresses interactivity with AI voice assistants as a key determinant of user loyalty, presenting user perceptions of the human-likeness of AI voice assistants and communication self-efficacy as sequential mediators.

We investigate the effects of human-likeness and communication self-efficacy on the relationship between interactivity and loyalty to AI voice assistants by developing a sequential mediation model. To estimate the empirical model, data were collected through an online survey with 330 respondents.

The results indicate that interactivity influences loyalty directly and positively. In addition, interactivity affects loyalty indirectly sequentially through human-likeness and communication self-efficacy.

By uncovering the psychological mechanisms underlying users’ loyalty to AI voice assistants, this study provides new academic and managerial insights that have not been clearly identified in the current literature.

The present study investigates the customers' behavioural intention to use voice-based artificial intelligence (AI) to find the appropriate hotels and resorts in an emerging nation. This study determines the influences of information quality, system quality, privacy, and novelty value on attitude and behavioural intention to use voice-based artificial intelligence to obtain the appropriate information and find the location of the hotels and resorts.

This study used a purposive sampling method for collecting data from the respondents, who are customers of the hotels and resorts in Bangladesh. A self-administered survey questionnaire was used to obtain responses from 378 respondents. After collecting the data, the reliability and validity of the constructs and hypotheses were tested via partial least squares structural equation modelling (PLS-SEM).

The findings of the study indicate that information quality, system quality, privacy and novelty value have a positive and significant impact on attitude and behavioural intention to use voice-based AI assistant services in an emerging nation. However, system quality does not significantly influence behavioural intention to use voice-based AI assistant but it has an indirect significant influence on behavioural intention through the mediation effect of attitude.

The study’s findings provide essential guidelines for practitioners to understand the impacts of information quality, system quality, privacy, and novelty value on attitude and behavioural intention to use voice-based artificial intelligence to find the appropriate hotels and resorts to meet customers' needs and expectations.

This study contributes to the existing literature on technology adoption by highlighting the interconnectedness of various factors influencing users' behavioural intentions. The study’s focus on an emerging nation provides a valuable theoretical contribution. It highlights that user perceptions and attitudes towards technology adoption may differ from those in developed nations due to unique contextual factors.

The present study aimed to explore the consequences of perceived authenticity in artificial intelligence (AI) assistants and develop a serial-mediation architecture specifying causation of loyalty in human–AI relationships. It intended to assess the predictive power of the developed model based on a training-holdout sample procedure. It further attempted to map and examine the predictors of loyalty, strengthening such relationship.

Partial least squares structural equation modeling (PLS-SEM) based on bootstrapping technique was employed to examine the higher-order effects pertaining to human–AI relational intricacies. The sample size of the study comprised of 412 AI assistant users belonging to millennial generation. PLS-Predict algorithm was used to assess the predictive power of the model, while importance-performance analysis was executed to assess the effectiveness of the predictor variables on a two-dimensional map.

A positive relationship was found between “Perceived Authenticity” and “Loyalty,” which was serially mediated by “Perceived-Quality” and “Animacy” in human–AI relational context. The construct “Loyalty” remained a significant predictor of “Emotional-Attachment” and “Word-of-Mouth.” The model possessed high predictive power. Mapping analysis delivered contradictory result, indicating “authenticity” as the most significant predictor of “loyalty,” but the least effective on performance dimension.

The findings of the study may assist marketers to understand the relevance of AI authenticity and examine the critical behavioral consequences underlying customer retention and extension strategies.

The study is pioneer to introduce a hybrid AI authenticity model and establish its predictive power in explaining the transactional and communal view of human reciprocation in human–AI relationship. It exclusively provided relative assessment of the predictors of loyalty on a two-dimensional map.

Scholars have highlighted personal interactions between employees and their leaders in an increasingly distributed and hybrid work environment as an essential mechanism that engages employees toward organizational goals. Enhanced employee engagement significantly contributes to sustained organizational performance and growth. While Artificial Intelligence (AI) applications in the HR domain are increasing, research to understand the implication of AI-based virtual assistants on enabling trust and managing human resources is, at best, limited.

Drawing on the social response theory and the social exchange theory, and based on a multi-source, time-lagged field study spanning over ten months, we investigated the impact of AI-based virtual assistants on employee attitudes, namely perception of fairness and employee engagement.

The usage of AI-based virtual assistants is associated directly with employee engagement and indirectly through employees’ perceptions of fairness. While employees’ past performance moderates the relationship between perceived fairness and employee engagement, the interaction effect becomes non-significant with AI-based virtual assistants.

Our study contributes to the emerging literature on AI-based virtual assistants in HRM and employee engagement. The virtual assistants’ use to enhance employee engagement emerges as an opportunity for task substitution and augmentation. Our study demonstrates that AI-based virtual assistants can enhance employee engagement and help build perceptions of fairness among employees.

With the emerging importance of AI, there is an increasing interest in explaining human-computer interactions and their effect on employee engagement. Our study is among the early empirical studies examining the implications of AI-based virtual assistants on employee outcomes.

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.

An online survey of 457 participants and structural equation modeling is conducted to validate the research model.

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.

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.

This study social based on cognitive theory (SCT), aims to better understand how transformational leadership affects team-level knowledge sharing and absorptive ability in the construction industry. It also examines the moderating influence of the AI-based virtual assistant on the indirect relationship between transformational leadership and team innovation through knowledge sharing and absorptive ability at the team level.

This study used a simple random sample approach to gather data from several small and medium-sized construction firms in Anhui Province, China. A total of 407 respondents, including 89 site engineers and 321 team members, provided their responses on a five-point Likert scale questionnaire.

The findings showed that AI-based virtual assistants significantly moderated the direct and indirect association between transformational leadership and knowledge sharing, and subsequently with team innovation. Unexpectedly, the findings showed that AI-based virtual assistant did not moderate the direct relationship between transformational leadership and team-level absorptive capacity.

This study adds a fresh perspective to the literature on construction management by examining team innovation driven by transformational leadership through an underlying mechanism. It is unique in that it uses the team adaptation theory to investigate the understudied relationship between transformational leadership and team innovation in the construction industry.

Extending the technology acceptance model (TAM) to a new context, the purpose of this paper is to propose an integrative model of the brand of artificial intelligence-enabled voice assistants (AI-EVA) and customers’ perceptions and behavioral intentions of using AI-EVA in hotels. Moderating effects of construal levels and hotel scales were examined.

This paper adopted a mixed method approach. A qualitative and phenomenological methodology was adopted in Study 1 to explore hotel customers’ experience with AI-EVA. Study 2 applied experimental design to investigate the effects of the brand of AI-EVA and construal level on customers’ perceptions and behavioral intentions of using AI-EVA. Based on Studies 1 and 2 results, Study 3 examined how the brand of AI-EVA and hotel scale affect customers’ perceptions and behavioral intentions of using AI-EVA during hotel stays.

This research found that customers perceive a higher level of perceived usefulness, perceived ease of use and anthropomorphism when AI-EVA is branded (vs off-brand). Perceived usefulness positively affects customers’ intention to use and to spread positive word-of-mouth. Anxiety of using AI-enabled devices and privacy concerns inhibit customers’ intention to use AI-EVA. Anthropomorphism increases customers’ willingness to spread positive word-of-mouth. Construal level moderates the effect of the brand of AI-EVAs on perceived ease of use and anthropomorphism. Hotel scale moderates the effect of brand on perceived usefulness.

This paper is one of the first attempts to uncover and integrate different factors underlying customers’ perceptions of using AI-EVA in an extended TAM in hotel settings. This paper provides an integrative model extending the TAM to a new context by deploying a mixed-method approach across three studies.

This study aims to determine how the attitudes toward artificial intelligence (AI) of religious tourists affect their AI self-efficacy and their engagement in AI. This study specifically intends to investigate the mediating role of AI self-efficacy in the relationship between attitudes toward AI and the engagement in AI of religious tourists. This study also seeks to identify the role of AI assistant use as a moderator in the relationship between attitudes toward AI and AI self-efficacy.

The data used in this study was gathered from a sample of 282 religious tourists who had just visited Karbala, central Iraq. Purposive sampling, which comprises a focused and systematic approach to data collection, was used after carefully assessing the distinctive characteristics and properties of the research population.

The results showed that attitudes to AI had a noticeable impact on AI self-efficacy, which, in turn, exerted a positive impact on engagement with AI. In addition, the use of AI assistants acted to positively moderate AI self-efficacy in terms of mediating the link between attitudes to AI and AI engagement.

The distinctive focus on religious tourists adds an original perspective to the existing literature, shedding light on how their attitudes towards AI impact not only their self-efficacy but also their engagement in dealing with AI. In addition, this study delves into the moderating role of AI assistant use, introducing a unique factor in understanding the complex interplay between attitudes, self-efficacy, and engagement in the context of religious tourism. The selection of Karbala, central Iraq, as this study site further adds originality, providing insights into a specific religious and cultural context.