Purpose: The purpose of this study is to empirically investigate what aspects of service robot interactions with customers can lead to meaningful outcomes in the view of customers. The study examines functional and emotional elements of AI service robots in terms of meaningful outcomes.Design/methodology/approach: This study highlights AI service robots' meaningful outcomes as a viable research problem and proposes a research model utilizing the Stimulus-Organism-Response (SOR) framework. As an empirical approach, 260 datasets were collected from customers who have experience with AI service restaurants in China.Findings: The study examines the functional and emotional elements of AI-powered service robots on the attitude of and meaningful outcomes for customers. The results showed that the emotional (perceived friendliness and perceived coolness) and functional (perceived safety and robot competence) attributes of human–robot interactions (HRI) significantly affect the attitude toward using service robots. Second, the attitude toward using service robots significantly influences the experiential outcome and instrumental outcome of meaningful engagement.Research limitations/implications: This study highlights two elements (i.e. functional and emotional) of HRI effectiveness using two metrics: experiential and performance outcomes. Future studies should generalize the research findings of service robots in the current study using a larger quantity of data from various service fields.Originality/value: As the first empirical study highlighting the customer experience with service robots, this study opens up a feasible research direction for the service industry to pursue in terms of conducting HRI studies from the view of customers. It identifies a research model pursuant to customers' experience with HRI in creating meaningful outcomes and it theoretically extends the SOR model to the hospitality study, focusing on the HRI issue.

Purpose: 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.Design/methodology/approach: 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.Findings: 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.Originality/value: 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.

Purpose: As the center of the fourth industrial revolution, artificial intelligence (AI) has marked its presence in various disciplines including the education field in the form of AI-powered learning applications. The purpose of this study is to build a research model capturing the relationships among use contexts, user gratification, attitude, learning performance and continuous intention to use an AI-powered English learning application.Design/methodology/approach: Using the use and gratification theory, use contexts and the belief-attitude-intention theory, this paper uses a quantitative approach based on a survey method for data collection and structural equation modeling for analysis. A total of 478 students from an international university in Guangdong, China, participated in the survey after using Liulishuo for two weeks.Findings: The results showed that perceived use contexts affected all variables associated with gratifications-obtained and gratification-opportunities. With the exception of social integrativeness, all other gratification-based factors significantly affected attitude. The attitude in turn significantly influenced learning performance and continuous use intention.Originality/value: Mobile AI-powered learning applications are at the center of research on technology-enhanced learning in the age of media and technology convergence. The study is timely and contributes to the discussion of the roles of use context and gratifications on technology users’ attitudes and behavioral intentions.

Small and Medium-sized Enterprises (SMEs) are key contributors to Indonesia’s economy. E-commerce can assist SMEs in gaining a competitive edge and is expected to be the largest contributor to the country’s digital economy by 2030. However, only 22% of SMEs in Indonesia have adopted E-commerce. Hence this study aims to investigate the criteria that influence E-commerce adoption by Indonesian retail SMEs and select the best strategy using the Decision-Makers, Technological, Organizational, and Environmental (DTOE) Framework and the Diffusion of Innovation (DOI) theory. This study aims to fill the research gap by examining the essential factors in E-commerce adoption by Indonesian SMEs in the retail industry and their key strategy to increase utilization. The influence and priorities between criteria were calculated using the Decision-Making Trial and Evaluation Laboratory (DEMATEL) based Analytic Network Process (ANP) method. Furthermore, an E-commerce adoption strategy was selected using Complex Proportional Assessment (COPRAS) method. This research showed critical criteria for adopting E-commerce technology in retail SMEs, Decision Maker’s IT Knowledge, Innovativeness, and Complexity. In addition, developing or training Information Technology (IT) and E-commerce skills were identified as the best strategy to increase E-commerce adoption. This study can help raise the understanding of policymakers, E-commerce service providers, and retail SME decision-makers on influencing criteria in adopting the best strategy to be applied to increase this technology adoption.