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This study explores features that impact consumers' purchase intention through artificial intelligence (AI), because it is believed that through artificial intelligence, consumers' intention to purchase grows significantly, especially in the retail sector, whereby retailers provide lucrative offers to motivate consumers. The study develops a theoretical framework based on media-richness theory to investigate the role of perceived anthropomorphism toward an intention to purchase products using AI.

The study is based on cross-sectional data through an online survey. The data have been analyzed using PLS-SEM and SPSS PROCESS macro.

The results show that consumers tend to demand anthropomorphized products to gain a better shopping experience and, therefore, demand features that attract and motivate them to purchase through artificial intelligence via mediating variables, such as perceived animacy and perceived intelligence. Moreover, trust in artificial intelligence moderates the relationship between perceived anthropomorphism and perceived animacy.

The study investigates and concludes with managerial and academic insights into consumer purchase intention through artificial intelligence in the retail and marketing sector.

The development of mobile technology has changed the traditional financial industry and banking sector. While traditional banks have adopted artificial intelligence (AI) techniques to deepen the development of mobile banking applications (apps), the current literature lacks research on the use of AI-based constructs to explore users' mobile banking app adoption intentions. To fill this gap, based on stimulus-organism-response (SOR) theory, two AI feature constructs as stimuli are considered, namely, perceived intelligence and anthropomorphism. This study then develops a research model to investigate how intelligence and anthropomorphism affect task-technology fit (TTF), perceived cost, perceived risk and trust (organism), which in turn influence users' AI mobile banking app adoption (response).

This study used a convenience nonprobability sampling approach; a total of 451 responses were collected to examine the model. The partial least squares technique was utilized for data analysis.

The results show that intelligence and anthropomorphism increase users' willingness to adopt mobile banking apps through TTF and trust. However, higher levels of anthropomorphism enhance users' perceived cost. In addition, both intelligence and anthropomorphism have insignificant effects on perceived risk. The results provide theoretical contributions for AI-based mobile banking app adoption and offer practical guidance for bank planning to use AI to retain users.

Based on SOR theory, this study reveals that as features, AI-enabled intelligence and anthropomorphism help us further understand users' perceptions regarding cost, risk, TTF and trust in the context of AI-enabled app adoption intentions.

The purpose of this study is to develop a framework for the perceived intelligence of VAs and explore the mechanisms of different dimensions of the perceived intelligence of VAs on users’ exploration intention (UEI) and how these antecedents can collectively result in the highest level of UEI.

An online survey on Amazon Mechanical Turk is employed. The model is tested utilizing the structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) approach from the collected data of VA users (N = 244).

According to the SEM outcomes, perceptual, cognitive, emotional and social intelligence have different mechanisms on UEI. Findings from the fsQCA reinforce the SEM results and provide the configurations that enhanced UEI.

This study extends the conceptual framework of perceived intelligence and enriches the literature on anthropomorphism and users’ exploration. These findings also provide insightful suggestions for practitioners regarding the design of VA products.

Retailers increasingly endeavour to implement artificial intelligence (AI) innovations, such as humanoid social robots (HSRs), to enhance customer experience. This paper investigates the interactive effect of HSR intelligence and consumers' speciesism on their perceptions of retail robots as sales assistants.

Three online experiments testing the effects of HSRs' intellectual intelligence on individuals' perceived competence and, consequently, their decision to shop at a retail store that uses HSRs as sales assistants are reported. Furthermore, the authors examine whether speciesism attenuates these effects such that a mediation effect is likely to be observed for individuals low in speciesism but not for those with high levels of speciesism. Data for all studies were collected on Prolific and analysed with SPSS to perform a logistic regression and PROCESS 4.0 (Hayes, 2022) for the mediation and moderated-mediation analysis.

The findings show that the level of speciesism moderates the relationship between HSR intellectual intelligence and perceived competence such that an effect is found for low but not for high HSR intelligence. When HSR intellectual intelligence is low, individuals with higher levels of speciesism (vs low) rate the HSR as less competent and display lower HSR acceptance (i.e. customers' decision to shop using retail robots as sales assistants).

This research responds to calls in research to adopt a human-like perspective to understand the compatibility between humans and robots and determine how personality traits, such as a person's level of speciesism, may affect the acceptance of AI technologies replicating human characteristics (Schmitt, 2019). To the best of the authors' knowledge, the present research is the first to examine the moderating role of speciesism on customer perceptions of non-human retail assistants (i.e. human-like and intelligent service robots). This study is the first to showcase that speciesism, normally considered a negative social behaviour, can positively influence individuals' decisions to engage with HSRs.

An important issue for researchers and managers of organizations is the understanding of user-perceived values of collective intelligence (UPVoCI) in online social networks (OSNs) with the purpose of helping organizations identify the values that cause internet users and members of OSNs to share information and knowledge during they participate in collective intelligence (co-intelligence) activities. However, the development of measurement instruments and predictive models and rules for predicting UPVoCI are inadequate. The paper aims to discuss these issues.

A novel measurement scale was developed to measure UPVoCI using a user-oriented research strategy that is based on qualitative and quantitative research methods. This work also identified critical indicators and constructed predictive models and rules for forecasting UPVoCI by multivariate statistical methods and data mining.

A 17-item scale of UPVoCI was developed and 17 measurement items were associated with two major dimensions, which are the user-perceived social value of co-intelligence and the user-perceived problem-solving value of co-intelligence. Ten critical indicators of UPVoCI that are important in predicting UPVoCI and 12 rules for predicting UPVoCI were identified and a refined model for predicting UPVoCI was constructed.

The results in this work allow organizations to determine the perceived value of members of OSNs and the benefits of their participating in co-intelligence activities, as a basis for adjusting user-oriented online co-intelligence and service strategies with the goal of improving collaborative innovation performance.

This work systematically developed a novel scale for measuring UPVoCI in OSNs and constructed new models and rules for predicting UPVoCI in OSNs.

The current study aims to examine the relationship between emotional intelligence and perceived organizational justice, and how the interplay between them influences turnover intentions.

A sample of 368 employees from a financial organization was used to test the research model and hypotheses. Archival information regarding participants’ actual turnover was also acquired.

Emotional intelligence was positively related to perceived organizational justice and negatively related to turnover intentions. Furthermore, perceived organizational justice fully mediated the relationship between emotional intelligence and turnover intentions. The archival data concerning employees’ actual turnover was significantly related to the self‐report turnover intentions.

Emotional intelligence training may be a powerful tool that organizations and human resource managers can employ to enhance perceived organizational justice and reduce employees’ turnover.

This research broadens the scope through which the contribution of emotional intelligence in the workplace can be viewed. Findings from the current study advance our understanding of the process by which emotional intelligence affects employees’ perceptions and attitudes.

The purpose of this paper is to answer “Why do Chinese consumers use IOT systems?” and “Do consumers’ cognitive and affect experiences moderate the relationship between psychological perception factors and perceived usefulness?”

In-depth interview with consumers and experts had been conducted and then the perceived psychological factors that influence perceived usefulness had been summarized. Based on a survey test of 337 smart home users, this study applies partial least squares technique analysis to test the research model.

The research results show that perceived psychological factors (perceived ease of use, perceived intelligence, perceived convenience and perceived privacy risk) have significant effect on the intention and behavior of IOT systems usage through perceived usefulness. Cognitive experience moderates the relationship between perceived ease of use and perceived usefulness, perceived privacy risk and perceived usefulness; affect experience moderates the relationship between perceived ease of use and perceived usefulness.

The current study mainly tested the antecedents of consumers’ usage of IOT systems, and the outcome of using the system was not investigated. Future research can examine the outcome (e.g. satisfaction, perceived value) by using the expectation-confirmation theory.

This study provides a useful insight into the key driving factors in consumers’ intention and behavior of using IOT systems. The previous studies over IOT systems have not observed consumers’ perception of IOT systems, but in fact IOT systems are being applied to more and more personal users.

The purpose of this paper is to study the influence of perceived control on product preferences of consumers under self-threat.

By conducting four experiments, this paper manipulated the participants’ self-threat and three sources of perceived control – namely, the controllability of the threat itself, the internal and external locus of control of the individual and priming the existing positive or negative experience of the individuals. After the manipulations, the participants’ product preferences for self-growth goods or hedonic goods were measured.

The findings of this research indicated that when consumers have a higher perceived control of the threats they are facing, they are more likely to adopt problem-focused coping strategies and show a preference for self-growth goods, which can help them resolve their threats. However, when consumers have a lower perceived control on the threats they are facing, they are more likely to adopt emotion-focused coping strategies and show a preference for hedonic goods, which can help them adjust their emotions.

This paper systematically confirms the interactive effect of perceived control and self-threat on consumers’ product preferences, as well as the meditation role of problem-focused coping and emotion-focused coping. It also provides insights for marketers to know what factors may affect consumers’ preferences for products in self-threat situations, thus contributing to marketing practices.

This study aims to understand a customer-purchase mechanism in the artificial intelligence (AI)-powered chatbot context based on the elaboration likelihood model (ELM) and technology acceptance model (TAM). The first objective is to examine how to boost chatbot adoption. The second objective is to investigate the role of information characteristics, technology-related characteristics and attitude toward AI in purchase intention.

Data was collected from a sample of 492 users in Vietnam, who are potential customers of chatbots for purchase. Structural equation modeling was applied for data analysis.

Results illustrate that chatbot adoption is significantly influenced by information credibility, technology-related factors (i.e. interactivity, relative advantage and perceived intelligence), attitude toward AI and perceived usefulness. Moreover, information quality and persuasiveness motivate information credibility. Information credibility and attitude toward AI are the essential motivations for perceived usefulness. Finally, chatbot adoption and information credibility determine purchase intention.

The results are insightful for practitioners to envisage the importance of chatbot use for customer purchase in the AI scenario. Additionally, this research offers a framework to practitioners for shaping customer engagement in chatbots.

The value of this work lies in the incorporation of technology-related characteristics into the two well-established theories, the ELM and TAM, to identify the importance of AI and its applications (i.e. chatbots) for purchase and to understand the formation of perceived usefulness and chatbot use through information credibility and attitude toward AI.

Due to the popularity of mobile devices and the development of artificial intelligence (AI), AI-powered mobile fitness applications (MFAs) have entered people's daily lives. However, the extant literature lacks empirical investigations that explore users' continuance usage intentions regarding AI-powered MFAs. To fill this research gap, this paper employs goal-setting theory to establish a research model for exploring how AI-enabled features (i.e. intelligence and anthropomorphism) affect users' perceptions of goal difficulties and goal specificities, which in turn affect their MFA continuance usage intentions.

This paper uses a survey method to analyze the research model, and a total of 223 responses are collected. The partial least squares (PLS) technique is utilized for data analysis.

The results show that intelligence and anthropomorphism affect the continuance usage intention of MFA users through their goal difficulty and specificity. Both intelligence and anthropomorphism positively influence goal specificity, whereas they negatively affect goal difficulty. In addition, goal specificity increases users' MFA continuance usage intention, whereas goal difficulty decreases users' continuance usage intention. The findings of this study provide theoretical contributions for AI technology adoption research and offer practical strategies for firms to retain MFA users.

Based on goal-setting theory, this study reveals that as two primary AI features of contemporary mobile fitness apps, intelligence and anthropomorphism, can increase comprehension of users' perceptions regarding goal difficulty and specificity in the context of users' continuance usage intentions toward AI-powered MFAs.