I, Chatbot: Modeling the determinants of users’ satisfaction and continuance intention of AI-powered service agents

Research on driving factors of consumer purchase intention of artificial intelligence creative products based on user behavior

With the continuous advancement of artificial intelligence (AI) technology, AIGC (AI-generated content) has increasingly permeated various sectors, leading to a significant transformation in the design industry. This study aims to explore user purchase intention and the influencing factors of AI-generated cultural and creative products, thereby formulating strategies to enhance user satisfaction. Based on the stimulus-organism-response theory, the theory of planned behavior, the value adoption model, the innovation diffusion theory, and the unified theory of acceptance and use of technology 2, a comprehensive model is constructed. The model also incorporates external variables such as perceived value (PV), perceived price (PP), social influence, hedonic motivation (HM), and cultural experience (CE). Additionally, self-innovation is considered as a key moderator to explore its role in moderating the relationships between PV, PP, and user perceived behavioral control. Using 526 valid samples, this study employs structural equation modeling to conduct exploratory factor analysis and confirmatory factor analysis, and further verifies the importance of variables through artificial neural networks. The findings indicate that behavioral attitude, HM, PP, PV, and generative quality are the primary factors influencing user purchase intention. In the decision-making process, users not only consider the price and quality of the products but also place significant importance on the pleasurable experience and cultural uniqueness they offer. This study extends the theoretical application of AIGC in the field of cultural and creative consumption, enriches the user behavior research model, and provides practical insights for companies to optimize AI-generated cultural products, enhance user experience, and improve market acceptance.

Consumer Autonomy in Generative AI Services: The Role of Task Difficulty and AI Design Elements in Enhancing Trust, Satisfaction, and Usage Intention

As generative AI services become increasingly integrated into consumer decision making, concerns have grown regarding their influence on consumer autonomy-the extent to which individuals retain independent control over AI-assisted decisions. Although these services offer efficiency and convenience, they can simultaneously constrain consumer decision making, potentially impacting trust, satisfaction, and usage intention. This study investigates the role of perceived consumer autonomy in shaping consumer responses, specifically examining how task difficulty (Study 1) and AI service design elements-explainability, feedback, and shared responsibility (Study 2)-influence autonomy perceptions and subsequent consumer outcomes. Using two scenario-based experiments involving a total of 708 participants, the results reveal that perceived autonomy significantly enhances consumer trust, particularly in contexts involving high task difficulty. Among the tested AI design interventions, shared responsibility emerged as most effective in enhancing perceived autonomy, trust, satisfaction, and long-term engagement, whereas explainability and feedback alone showed limited impact. These findings underscore the importance of designing AI services that actively support consumer agency through user-involved decision-making frameworks rather than relying solely on passive informational transparency. Theoretical implications for consumer autonomy in AI interactions are discussed, along with practical recommendations for designing consumer-centered AI services.

AI, How Much Shall I Tell You? Exchange and Communal Consumer-AI Relationships and the Willingness to Disclose Personal Information

Personal information is an important resource for the optimal functioning of AI and technology. Starting from the different theories that define human relationships and the way information is exchanged within them, we investigate the way in which communal and exchange relationships are formed between consumers and AI and the way they influence consumers' willingness to disclose personal information to AI. With the help of structural equation modeling, we prove empirically that attachment to AI rather develops communal relationships compared to exchange relationships between consumers and AI. Communal relationships have a stronger influence on both enjoyment and self-disclosing behavior, while exchange relationships do not trigger a self-disclosing behavior unless there is enjoyment. Furthermore, attachment to AI alone does not influence self-disclosing behavior unless a communal relationship is developed. Our structural equation model emphasized the complex nature of relationships between consumers and AI and has important implications for the way AI will be optimally integrated in business processes and society.

AI Chatbots for Psychological Health for Health Professionals: Scoping Review

Background

Health professionals face significant psychological burdens including burnout, anxiety, and depression. These can negatively impact their well-being and patient care. Traditional psychological health interventions often encounter limitations such as a lack of accessibility and privacy. Artificial intelligence (AI) chatbots are being explored as potential solutions to these challenges, offering available and immediate support. Therefore, it is necessary to systematically evaluate the characteristics and effectiveness of AI chatbots designed specifically for health professionals.

Objective

This scoping review aims to evaluate the existing literature on the use of AI chatbots for psychological health support among health professionals.

Methods

Following Arksey and O'Malley's framework, a comprehensive literature search was conducted across eight databases, covering studies published before 2024, including backward and forward citation tracking and manual searching from the included studies. Studies were screened for relevance based on inclusion and exclusion criteria, among 2465 studies retrieved, 10 studies met the criteria for review.

Results

Among the 10 studies, six chatbots were delivered via mobile platforms, and four via web-based platforms, all enabling one-on-one interactions. Natural language processing algorithms were used in six studies and cognitive behavioral therapy techniques were applied to psychological health in four studies. Usability was evaluated in six studies through participant feedback and engagement metrics. Improvements in anxiety, depression, and burnout were observed in four studies, although one reported an increase in depressive symptoms.

Conclusions

AI chatbots show potential tools to support the psychological health of health professionals by offering personalized and accessible interventions. Nonetheless, further research is required to establish standardized protocols and validate the effectiveness of these interventions. Future studies should focus on refining chatbot designs and assessing their impact on diverse health professionals.

Analysing how AI-powered chatbots influence destination decisions

This study aims to explore the role of destination chatbots as innovative tools in travel planning, focusing on their ability to enhance user experiences and influence decision-making processes. Based on the Technology Acceptance Model, Enterprise Content Management, and Information Systems Security models, the study examines the psychological, emotional, and technological factors that drive user satisfaction, continued use, and intention to visit a destination. Understanding these factors is crucial for improving chatbot design and optimizing their implementation in the tourism industry. A total of 312 responses were collected from university students who regularly engage in tourism-related activities. The survey employed a structured questionnaire with items measuring information quality, user satisfaction, perceived enjoyment, usefulness, and behavioral intentions using a 7-point Likert scale. Structural equation modelling [SEM] was used to analyze the relationships between constructs, allowing us to evaluate the validity and reliability of the model. The results reveal that information quality positively enhances user satisfaction, perceived enjoyment, and perceived usefulness. Moreover, perceived enjoyment and usefulness are critical psychological and emotional drivers influencing users' decision to continue utilizing chatbots. Additionally, the analysis highlights the intention to continue using destination chatbots as a strong predictor of tourists' intention to visit the destination. The findings contribute to the theoretical understanding of technology acceptance and user behavior in tourism, while providing practical insights for destination managers and developers to enhance chatbot features and improve traveler engagement.

The Role of Individual Capabilities in Maximizing the Benefits for Students Using GenAI Tools in Higher Education

Although the adoption and benefits of GenAI (Generative Artificial Intelligence) tools among higher education students have been widely explored in existing studies, less is known about how individual capabilities influence the use of these tools. Drawing on the Information System Success Model (ISSM) and the Expectation-Confirmation Model (ECM), this study examines how students' capabilities, including critical thinking, self-directed learning ability, and AI literacy, impact the quality of information obtained from GenAI tools. Additionally, it explores the relationships among information quality, student satisfaction, and the intention to continue using GenAI tools in higher education. Survey data from 1448 GenAI tools users in Chinese universities reveal that students with stronger capabilities tend to extract higher-quality information, which in turn fosters their satisfaction with GenAI tools and the intention to continue using these tools. The findings highlight the crucial role of individual capabilities in maximizing the potential of GenAI tools, and it emphasizes the need to cultivate students' critical thinking, self-directed learning ability, and AI literacy to achieve sustainable success in the GenAI era. Theoretically, this study extends the ISSM and ECM by exploring the impact of students' capabilities and the mediating role of user satisfaction between information quality and the intention to continue using GenAI tools. Practically, this study provides implications for educators and policymakers to enhance students' capabilities, thus maximizing the potential benefits of GenAI tools in higher education.

User avoidance behavior in pharmaceutical e-commerce intelligent customer service: a stressor-strain-outcome perspective

Introduction

This study explores the implementation of Intelligent Customer Service (ICS) in pharmaceutical e-commerce, aiming to enhance user acceptance and service efficiency while addressing the psychological factors influencing user behavior. It expands the boundaries of technology acceptance research by focusing on ICS use and avoidance in high-risk environments.

Method

A total of 418 valid questionnaires were collected from participants, ensuring data quality through rigorous screening. The study employed SPSS for data normality tests and SmartPLS for structural equation modeling to analyze the relationships between emotional stress, system overload, and user avoidance behavior.

Results

The findings indicate that system overload, information overload, and service overload significantly contribute to user emotional stress, which in turn drives avoidance behavior. The analysis revealed strong explanatory power (R 2 values ranging from 0.450 to 0.586) and confirmed the mediating role of emotional stress in the relationship between overload factors and user avoidance.

Discussion

This research highlights the critical role of emotional stress in user interactions with ICS, suggesting that pharmaceutical e-commerce companies must refine their ICS design to meet diverse user needs and reduce cognitive burdens. By leveraging big data and establishing robust feedback mechanisms, companies can enhance user experience and loyalty. The study also identifies limitations in demographic representation and suggests future research should incorporate qualitative methods for a deeper understanding of user behavior.

Characteristics of ChatGPT users from Germany: Implications for the digital divide from web tracking data

A major challenge of our time is reducing disparities in access to and effective use of digital technologies, with recent discussions highlighting the role of AI in exacerbating the digital divide. We examine user characteristics that predict usage of the AI-powered conversational agent ChatGPT. We combine behavioral and survey data in a web tracked sample of N = 1376 German citizens to investigate differences in ChatGPT activity (usage, visits, and adoption) during the first 11 months from the launch of the service (November 30, 2022). Guided by a model of technology acceptance (UTAUT-2), we examine the role of socio-demographics commonly associated with the digital divide in ChatGPT activity and explore further socio-political attributes identified via stability selection in Lasso regressions. We confirm that lower age and higher education affect ChatGPT usage, but do not find that gender or income do. We find full-time employment and more children to be barriers to ChatGPT activity. Using a variety of social media was positively associated with ChatGPT activity. In terms of political variables, political knowledge and political self-efficacy as well as some political behaviors such as voting, debating political issues online and offline and political action online were all associated with ChatGPT activity, with online political debating and political self-efficacy negatively so. Finally, need for cognition and communication skills such as writing, attending meetings, or giving presentations, were also associated with ChatGPT engagement, though chairing/organizing meetings was negatively associated. Our research informs efforts to address digital disparities and promote digital literacy among underserved populations by presenting implications, recommendations, and discussions on ethical and social issues of our findings.

From Acceptance to Dependence: Exploring Influences of Smart Healthcare on Continuous Use Intention of Mobile Health Services Among Older Adults with Chronic Illnesses in China

With the acceleration of the aging process in China, chronic diseases have become one of the main health threats for older adults, creating significant pressure on society and the healthcare system. As information technology and artificial intelligence advance rapidly, smart health services have become readily accessible. However, utilization rates among the older adults, especially those with chronic illnesses, remain low, preventing them from fully benefiting from these advanced technologies. The value of mobile health (mHealth) services can only be realized through sustained use. Therefore, this study empirically investigates the continuous use intention of mHealth services from the perspective of older adults with chronic illnesses, integrating the Technology Acceptance Model (TAM) and Value-Based Adoption Model (VAM). A total of 372 questionnaires were collected from various cities in China, and data were analyzed using SPSS 24.0 and Partial Least Squares Structural Equation Modeling (PLS-SEM). Results indicate that perceived ease of use (β = 0.155, p = 0.004; β = 0.116, p = 0.027) and perceived usefulness (β = 0.175, p = 0.001; β = 0.151, p = 0.004) have a significant positive impact on attitude and perceived value. Perceived enjoyment significantly influences attitude (β = 0.147, p = 0.010), while perceived risk (β = -0.189, p < 0.001; β = -0.281, p < 0.001) and perceived cost (β = -0.155, p = 0.003; β = -0.130, p = 0.022) have a significant negative impact on attitude and perceived value. Both attitude (β = 0.357, p < 0.001) and perceived value (β = 0.314, p < 0.001) positively impact continuous intention. In total, only one of the twelve hypotheses was not supported. This study not only provides strong evidence for the effectiveness of the integrated TAM and VAM model in the mHealth field but also offers theoretical insights and practical recommendations for product optimization and promotion to mHealth service providers and designers.

Compulsive ChatGPT usage, anxiety, burnout, and sleep disturbance: A serial mediation model based on stimulus-organism-response perspective

The limited understanding of the detrimental repercussions stemming from the adoption of artificial intelligence, exemplified by ChatGPT, on users' mental health issues underscores the urgency of our current research endeavor. In response to this knowledge gap, our study employs the stimulus-organism-response (SOR) framework and implements a serial mediation model to probe into the impacts of compulsive ChatGPT usage on users' mental health outcomes. This model serves as a powerful analytical lens, allowing us to unravel the relationships between the stimulus (compulsive ChatGPT usage), organism (anxiety and burnout), and response (sleep disturbance). Using a cross-sectional survey design, we collected data from 2602 ChatGPT users in Vietnam via purposive sampling and utilized structural equation modeling to assess the hypothesis model. The findings confirm that compulsive ChatGPT usage directly correlates with heightened anxiety, burnout, and sleep disturbance. Moreover, compulsive usage indirectly contributes to sleep disturbance through anxiety and burnout, demonstrating a significant serial mediation effect. This expanded understanding, derived from a sizable and diverse user base, positions our research at the forefront of unraveling the intricate dynamics between AI adoption and mental well-being.

Role of Synchronous, Moderated, and Anonymous Peer Support Chats on Reducing Momentary Loneliness in Older Adults: Retrospective Observational Study

BACKGROUND

Older adults have a high rate of loneliness, which contributes to increased psychosocial risk, medical morbidity, and mortality. Digital emotional support interventions provide a convenient and rapid avenue for additional support. Digital peer support interventions for emotional struggles contrast the usual provider-based clinical care models because they offer more accessible, direct support for empowerment, highlighting the users' autonomy, competence, and relatedness.

OBJECTIVE

This study aims to examine a novel anonymous and synchronous peer-to-peer digital chat service facilitated by trained human moderators. The experience of a cohort of 699 adults aged ≥65 years was analyzed to determine (1) if participation, alone, led to measurable aggregate change in momentary loneliness and optimism and (2) the impact of peers on momentary loneliness and optimism.

METHODS

Participants were each prompted with a single question: "What's your struggle?" Using a proprietary artificial intelligence model, the free-text response automatched the respondent based on their self-expressed emotional struggle to peers and a chat moderator. Exchanged messages were analyzed to quantitatively measure the change in momentary loneliness and optimism using a third-party, public, natural language processing model (GPT-4 [OpenAI]). The sentiment change analysis was initially performed at the individual level and then averaged across all users with similar emotion types to produce a statistically significant (P<.05) collective trend per emotion. To evaluate the peer impact on momentary loneliness and optimism, we performed propensity matching to align the moderator+single user and moderator+small group chat cohorts and then compare the emotion trends between the matched cohorts.

RESULTS

Loneliness and optimism trends significantly improved after 8 (P=.02) to 9 minutes (P=.03) into the chat. We observed a significant improvement in the momentary loneliness and optimism trends between the moderator+small group compared to the moderator+single user chat cohort after 19 (P=.049) and 21 minutes (P=.04) for optimism and loneliness, respectively.

CONCLUSIONS

Chat-based peer support may be a viable intervention to help address momentary loneliness in older adults and present an alternative to traditional care. The promising results support the need for further study to expand the evidence for such cost-effective options.

Expectancy Violations and Discontinuance Behavior in Live-Streaming Commerce: Exploring Human Interactions with Virtual Streamers

Virtual streamers, as a typical application of AI-enabled digital humans, are increasingly being utilized in live-streaming commerce due to technological advancements and industry innovations. Although virtual streamers present several benefits, there is potential for adverse effects when they do not align with consumer expectations. Drawing upon expectancy violations theory, this study developed a theoretical model to explore whether and how consumers' expectation violations during human-virtual streamer interactions affect consumers' discontinuance behavior. Through an online questionnaire survey of 307 Chinese consumers with prior experience interacting with virtual streamers, this study used a partial least squares structural equation model to analyze the research model. The empirical results indicated that professionalism expectation violation, empathy expectation violation, and responsiveness expectation violation positively influenced consumers' distrust and dissatisfaction, which subsequently led to discontinuance behavior. This study contributes to the literature on live-streaming commerce, human-AI interaction, and expectancy violation theory. Furthermore, the findings offer valuable insights for practitioners in the field of live-streaming commerce by enabling them to formulate preventive or remedial strategies to mitigate potential negative outcomes when implementing virtual streamers.

Is AI chatbot recommendation convincing customer? An analytical response based on the elaboration likelihood model

The integration of artificial intelligence (AI) technology in e-commerce has currently stimulated scholarly attention, however studies on AI and e-commerce generally relatively few. The current study aims to evaluate how artificial intelligence (AI) chatbots persuade users to consider chatbot recommendations in a web-based buying situation. Employing the theory of elaboration likelihood, the current study presents an analytical framework for identifying factors and internal mechanisms of consumers' readiness to adopt AI chatbot recommendations. The authors evaluated the model employing questionnaire responses from 411 Chinese AI chatbot consumers. The findings of present study indicated that chatbot recommendation reliability and accuracy is positively related to AI technology trust and have negative effect on perceived self-threat. In addition, AI technology trust is positively related to intention to adopt chatbot decision whereas perceived self-threat negatively related to intention to adopt chatbot decision. The perceived dialogue strengthens the significant relationship between AI-tech trust and intention to adopt chatbot decision and weakens the negative relationship between perceived self-threat and intention to adopt AI chatbot decisions.

An integration of expectation confirmation model and information systems success model to explore the factors affecting the continuous intention to utilise virtual classrooms

Virtual classrooms have recently gained significant consideration in educational institutes and universities due to their potential to encourage and support students' learning activities. Although recent research has focused extensively on online learning, virtual classrooms and the factors affecting their continuous use have garnered little attention, especially in Arab Gulf countries such as Saudi Arabia. Thus, this study integrates the expectation confirmation model and the information systems success model to assess the factors affecting students' continuous intention to utilise virtual classrooms in higher education. We examined the effects of information quality, service quality, system quality, confirmation, perceived usefulness, and satisfaction on the continuous intention to utilise virtual classrooms. Data were collected from 441 students and analysed using structural equation modelling "SEM". SEM is a powerful multivariate approach used increasingly in empirical investigation for evaluating and testing casual relationships. The results revealed that the proposed model demonstrated high explanatory power in explaining students' continuous intention to utilise virtual classrooms (R2 = 0. 86). Additionally, information quality had a significant effect on confirmation and an insignificant effect on perceived usefulness. System quality affected perceived usefulness and confirmation. Contrary to our expectations, service quality had a significant negative effect on perceived usefulness and confirmation. Additionally, perceived usefulness and confirmation affected students' satisfaction with using virtual classrooms, and satisfaction affected students' continuous intention to utilise virtual classrooms. This study contributes to the literature by offering a holistic integrated model that increases the understanding of the factors influencing students' continuous intention to utilise virtual classrooms, hence aiding in increasing their utilisation. Furthermore, it provides practical implications for enhancing students' continuous intention to utilise virtual classrooms. Virtual classroom developers must focus on improving the system quality of virtual classrooms. According to our results, higher system quality led the students to perceive virtual classrooms as useful and confirmed their favourable experiences with virtual classrooms. Additionally, providing students with high information quality in virtual classrooms would enhance their confirmation experiences, leading to the continuous intention to utilise virtual classrooms.

Unveiling the e-Servicescape of ChatGPT: Exploring User Psychology and Engagement in AI-Powered Chatbot Experiences

This study explores the psychological motivations that drive ChatGPT users to embrace and sustain the use of such technology based on the fundamental notion of the environmental psychology theory, including servicescapes. To do so, this study delves into the influence of ChatGPT's e-servicescapes on users' emotional states and intention to engage with ChatGPT for decision-making processes. This study conducted an online survey among ChatGPT users in the United States. Structural equation modeling revealed that negative emotions were significantly influenced by various e-servicescape sub-dimensions, including security, visual appeal, entertainment value, originality of design, and social factors. Positive emotions, on the other hand, were influenced by factors such as visual appeal, customization, interactivity, and relevance of information. Both positive and negative emotions significantly affected user satisfaction, which, in turn, shaped their behavioral intention to engage with ChatGPT. This study contributes to the understanding of digital environmental psychology and chatbots by extending the notion of e-servicescapes to the context of AI-based services. It underscores the significance of e-servicescapes in shaping user experiences and provides valuable insights for business scholars and marketing practitioners.

Investigating influencing factors of learning satisfaction in AI ChatGPT for research: University students perspective

This study investigates the determinants of ChatGPT adoption among university students and its impact on learning satisfaction. Utilizing the Technology Acceptance Model (TAM) and incorporating insights from interaction learning, collaborative learning, and information quality, a structural equation modeling approach was employed. This research collected valuable responses from 262 students at King Faisal University in Saudi Arabia through the use of self-report questionnaires. The data's reliability and validity were assessed using confirmation factor analysis, followed by path analysis to explore the hypotheses in the proposed model. The results indicate the pivotal roles of interaction learning and collaborative learning in fostering ChatGPT adoption. Social interaction played a significant role, as researchers engaging in conversations and knowledge-sharing expressed increased comfort with ChatGPT. Information quality was found to substantially influence researchers' decisions to continue using ChatGPT, emphasizing the need for ongoing improvement in the accuracy and relevance of content provided. Perceived ease of use and perceived usefulness played intermediary roles in linking ChatGPT engagement to learning satisfaction. User-friendly interfaces and perceived utility were identified as crucial factors affecting overall satisfaction levels. Notably, ChatGPT positively impacted learning motivation, indicating its potential to enhance student engagement and interest in learning. The study's findings have implications for educational practitioners seeking to improve the implementation of AI technologies in university students, emphasizing user-friendly design, collaborative learning, and factors influencing satisfaction. The study concludes with insights into the complex interplay between AI-powered tools, learning objectives, and motivation, highlighting the need for continued research to comprehensively understand these dynamics.

Using Artificial Intelligence to Label Free-Text Operative and Ultrasound Reports for Grading Pediatric Appendicitis

PURPOSE

Data science approaches personalizing pediatric appendicitis management are hampered by small datasets and unstructured electronic medical records (EMR). Artificial intelligence (AI) chatbots based on large language models can structure free-text EMR data. We compare data extraction quality between ChatGPT-4 and human data collectors.

METHODS

To train AI models to grade pediatric appendicitis preoperatively, several data collectors extracted detailed preoperative and operative data from 2100 children operated for acute appendicitis. Collectors were trained for the task based on satisfactory Kappa scores. ChatGPT-4 was prompted to structure free text from 103 random anonymized ultrasound and operative records in the dataset using the set variables and coding options, and to estimate appendicitis severity grade from the operative report. A pediatric surgeon then adjudicated all data, identifying errors in each method.

RESULTS

Within the 44 ultrasound (42.7%) and 32 operative reports (31.1%) discordant in at least one field, 98% of the errors were found in the manual data extraction. The appendicitis grade was erroneously assigned manually in 29 patients (28.2%), and by ChatGPT-4 in 3 (2.9%). Across datasets, the use of the AI chatbot was able to avoid misclassification in 59.2% of the records including both reports and extracted data approximately 40 times faster.

CONCLUSION

AI chatbot significantly outperformed manual data extraction in accuracy for ultrasound and operative reports, and correctly assigned the appendicitis grade. While wider validation is required and data safety concerns must be addressed, these AI tools show significant promise in improving the accuracy and efficiency of research data collection.

LEVELS OF EVIDENCE

Level III.

What Contributes to Student Language Learning Satisfaction and Achievement with Learning Management Systems?

Learning management systems (LMSs) have received substantial global attention and have undergone extensive research, with most discussions focusing on users' acceptance and continuation of LMS use in the higher education sector. However, research is scarce in terms of identifying the factors that are advantageous to K-12 students' learning and satisfaction when using LMSs for language learning. This study aims to examine the impacts of internal and contextual factors on secondary students' learning satisfaction and English achievement when using LMSs. Data were collected from 289 students through an online survey. The results of the structural equation modeling showed that satisfaction had the most significant impact on English achievement. Furthermore, both internal and contextual factors, including technology self-efficacy, interest, task value, teacher support, and technology facilitation, positively impacted learning satisfaction with LMSs. In addition, teacher support exerted the strongest impact on satisfaction, followed by interest and technology self-efficacy. However, only internal factors, such as interest and task value, were positively associated with English achievement. Neither teacher support nor technology facilitation significantly impacted English performance. Given the increasing availability of LMS usage, the findings of this study can facilitate the more effective implementation of LMSs in China and globally. The study contributes to the theory and practice of LMSs use in K-12 English education. The limitations and implications of the study were discussed as well.

Decoding Gen Z: AI's influence on brand trust and purchasing behavior

This study focuses on the role of AI in shaping Generation Z's consumer behaviors across fashion, technology, beauty, and education sectors. Analyzing responses from 224 participants, our findings reveal that AI exposure, attitude toward AI, and AI accuracy perception significantly enhance brand trust, which in turn positively impacts purchasing decisions. Notably, flow experience acts as a mediator between brand trust and purchasing decisions. These insights underscore the critical role of AI in developing brand trust and influencing purchasing choices among Generation Z, offering valuable implications for marketers in an increasingly digital landscape.

Factors Predicting Intentions of Adoption and Continued Use of Artificial Intelligence Chatbots for Mental Health: Examining the Role of UTAUT Model, Stigma, Privacy Concerns, and Artificial Intelligence Hesitancy

Background: Artificial intelligence-based chatbots (AI chatbots) can potentially improve mental health care, yet factors predicting their adoption and continued use are unclear. Methods: We conducted an online survey with a sample of U.S. adults with symptoms of depression and anxiety (N = 393) in 2021 before the release of ChatGPT. We explored factors predicting the adoption and continued use of AI chatbots, including factors of the unified theory of acceptance and use of technology model, stigma, privacy concerns, and AI hesitancy. Results: Results from the regression indicated that for nonusers, performance expectancy, price value, descriptive norm, and psychological distress are positively related to the intention of adopting AI chatbots, while AI hesitancy and effort expectancy are negatively associated with adopting AI chatbots. For those with experience in using AI chatbots for mental health, performance expectancy, price value, descriptive norm, and injunctive norm are positively related to the intention of continuing to use AI chatbots. Conclusions: Understanding the adoption and continued use of AI chatbots among adults with symptoms of depression and anxiety is essential given that there is a widening gap in the supply and demand of care. AI chatbots provide new opportunities for quality care by supporting accessible, affordable, efficient, and personalized care. This study provides insights for developing and deploying AI chatbots such as ChatGPT in the context of mental health care. Findings could be used to design innovative interventions that encourage the adoption and continued use of AI chatbots among people with symptoms of depression and anxiety and who have difficulty accessing care.

Analyzing ChatGPT adoption drivers with the TOEK framework

With the rapid advancements in AI technology and its growing impact on various aspects of daily life, understanding the factors that influence users' adoption intention becomes essential. This study focuses on the determinants affecting the adoption intention of ChatGPT, an AI-driven language model, among university students. The research extends the Technology-Organization-Environment (TOE) framework by integrating the concept of knowledge application. A cross-sectional research design was employed, gathering data through a survey conducted to university students. Structural equation modeling was used to analyze the data, aimed at examining the relationships between key determinants influencing adoption intention. The findings of this research indicate that factors such as network quality, accessibility, and system responsiveness contribute to satisfaction. Furthermore, satisfaction, organizational culture, social influence, and knowledge application significantly affect adoption intention. These findings offer both theoretical and practical implications.

"Chatting with ChatGPT": Analyzing the factors influencing users' intention to Use the Open AI's ChatGPT using the UTAUT model

Open AI's ChatGPT has emerged as a popular AI language model that can engage in natural language conversations with users. Based on a qualitative research approach using semistructured interviews with 32 ChatGPT users from India, this study examined the factors influencing users' acceptance and use of ChatGPT using the unified theory of acceptance and usage of technology (UTAUT) model. The study results demonstrated that the four factors of UTAUT, along with two extended constructs, i.e. perceived interactivity and privacy concerns, can explain users' interaction and engagement with ChatGPT. The study also found that age and experience can moderate the impact of various factors on the use of ChatGPT. The theoretical and practical implications of the study were also discussed.

Modeling users' satisfaction and visit intention using AI-based chatbots

AI-based chatbots are an emerging technology disrupting the tourism industry. Although chatbots have received increasing attention, there is little evidence of their impact on tourists' decisions to visit a destination. This study evaluates the key attributes of chatbots and their effects on user satisfaction and visit intention. We use structural equation modeling with covariance procedures to test the proposed model and its hypotheses. The results showed that informativeness, empathy, and interactivity are critical attributes for satisfaction, which drive tourists' intention to visit a destination.

The adoption of conversational assistants in the banking industry: is the perceived risk a moderator?

The world has noticed tremendous growth in information technology, particularly the Internet of Things and artificial intelligence. Nowadays, a lot of people rely on conversational assistants (CAs) and other intelligent virtual objects to check account balances, communicate more quickly, make payments, and manage their financial assets with banks or other financial institutions. This study scrutinizes how consumers espouse and utilize conversational assistants in banking amenities. To provide empirical evidence and generalize sample results in a larger context, a quantitative research approach has been utilized. A structured questionnaire was prepared, which generates 181 participants. The questionnaire was selected for its suitability in systematically capturing consumers' perceptions and intentions. According to the findings of partial least square structural equation modeling (PLS-SEM), perceived ease of use (PEOU), perceived enjoyment (PE), and perceived trust (PT) have significant impacts on users' intentions to use conversational assistants, however, perceived usefulness (PU) does not have any significant effects. Furthermore, the relationship between PEOU and intention is significantly and negatively moderated by perceived risk (PR). By enabling stakeholders to create strategies that improve customer experience and unleash the full potential of conversational assistants in banking services, these findings help to better understand consumer behavior.

Exploring chatbot trust: Antecedents and behavioural outcomes

An awareness about the antecedents and behavioural outcomes of trust in chatbots can enable service providers to design suitable marketing strategies. An online questionnaire was administered to users of four major banking chatbots (SBI Intelligent Assistant, HDFC Bank's Electronic Virtual Assistant, ICICI bank's iPal, and Axis Aha) in India. A total of 507 samples were received of which 435 were complete and subject to analysis to test the hypotheses. Based on the results, it is found that the hypothesised antecedents, except interface, design, and technology fear factors, could explain 38.6% of the variance in the banking chatbot trust. Further, in terms of behavioural outcomes chatbot trust could explain, 9.9% of the variance in customer attitude, 11.4% of the variance in behavioural intention, and 13.6% of the variance in user satisfaction. The study provides valuable insights for managers on how they can leverage chatbot trust to increase customer interaction with their brand. By proposing and testing a novel conceptual model and examining the factors that impact chatbot trust and its key outcomes, this study significantly contributes to the AI marketing literature.

Health persuasion through emoji: How emoji interacted with information source to predict health behaviors in COVID-19 situation

By providing health information through visual communication, public health organizations can effectively guide and persuade people to adopt healthy behaviors, which is critical in the context of public health crises. In this study, drawing upon congruity theory and the premise of visual communication, we examined how information source and emoji may shape people's preventive and self-protective behaviors through perceived fear (PF) and perceived controllability (PC). Using a convenience sample of 210 participants, we conducted a 2 (emoji: with versus without) × 2 (information source: official versus unofficial) between-subject experiment. The results indicated that, compared with nonuse, the use of emoji in information resulted in higher PF, stronger preventive behavioral intention (PBI), and lower PC. In addition, a strong interaction effect was observed between emoji and the source of information on PBI. When emoji were added to health information released by an unofficial organization, the text outperformed that from an official agency in persuading people to adopt preventive behaviors. Furthermore, we determined that PF mediated the effect of emoji on PBI, but only for unofficial information sources. These results provide a reference for enhancing the effectiveness of health information including visual cues, such as emoji.

Chatbots, virtual-try-on (VTO), e-WOM: modeling the determinants of attitude’ and continued intention with PEEIM as moderator in online shopping

Purpose

The purpose of this study is to develop an empirical model by understanding the relative significance of interactive technological forces, such as chatbots, virtual try-on technology (VTO) and e-word-of-mouth (e-WOM), to improve interactive marketing experiences among consumers. This study also validates the moderating role of the perceived effectiveness of e-commerce institutional mechanism (PEEIM) as a moderator between attitude and continued intention.

Design/methodology/approach

Data were collected through personal visits and an online survey. The link to the survey questionnaire was shared on different social media platforms and social networking sites. A total of 362 responses obtained in the online and offline modes were considered for this study.

Findings

e-WOM emerged as the strongest predictor of attitude, followed by chatbots and VTO. The results of this study revealed that PEEIM did not moderate the relationship between attitude and continued intention.

Originality/value

Using the self-determination theory and behavioral reasoning theory as theoretical frameworks, this study is an initial endeavor in the online shopping context to empirically validate interactive forces like chatbots, VTO, e-WOM and PEEIM as moderators together to arrive at a holistic framework. These forces, in turn, act as significant contributors to online shopping satisfaction.

Interacting with chatbots later in life: A technology acceptance perspective in COVID-19 pandemic situation

Introduction

Within the technological development path, chatbots are considered an important tool for economic and social entities to become more efficient and to develop customer-centric experiences that mimic human behavior. Although artificial intelligence is increasingly used, there is a lack of empirical studies that aim to understand consumers' experience with chatbots. Moreover, in a context characterized by constant population aging and an increased life-expectancy, the way aging adults perceive technology becomes of great interest. However, based on the digital divide (unequal access to technology, knowledge, and resources), and since young adults (aged between 18 and 34 years old) are considered to have greater affinity for technology, most of the research is dedicated to their perception. The present paper investigates the way chatbots are perceived by middle-aged and aging adults in Romania.

Methods

An online opinion survey has been conducted. The age-range of the subjects is 40-78 years old, a convenience sampling technique being used (N = 235). The timeframe of the study is May-June 2021. Thus, the COVID-19 pandemic is the core context of the research. A covariance-based structural equation modelling (CB-SEM) has been used to test the theoretical assumptions as it is a procedure used for complex conceptual models and theory testing.

Results

The results show that while perceived ease of use is explained by the effort, the competence, and the perceive external control in interacting with chatbots, perceived usefulness is supported by the perceived ease of use and subjective norms. Furthermore, individuals are likely to further use chatbots (behavioral intention) if they consider this interaction useful and if the others' opinion is in favor of using it. Gender and age seem to have no effect on behavioral intention. As studies on chatbots and aging adults are few and are mainly investigating reactions in the healthcare domain, this research is one of the first attempts to better understand the way chatbots in a not domain-specific context are perceived later in life. Likewise, judging from a business perspective, the results can help economic and social organizations to improve and adapt AI-based interaction for the aging customers.

Understanding citizen perceptions of AI in the smart city

Artificial intelligence (AI) is embedded in a wide variety of Smart City applications and infrastructures, often without the citizens being aware of the nature of their “intelligence”. AI can affect citizens’ lives concretely, and thus, there may be uncertainty, concerns, or even fears related to AI. To build acceptable futures of Smart Cities with AI-enabled functionalities, the Human-Centered AI (HCAI) approach offers a relevant framework for understanding citizen perceptions. However, only a few studies have focused on clarifying the citizen perceptions of AI in the context of smart city research. To address this gap, we conducted a two-phased study. In the pre-study, we explored citizen perceptions and experiences of AI with a short survey (N = 91). Second, scenario-based interviews (N = 7) were utilized to gain in-depth insights of citizen perceptions of AI in the Smart City context. Five central themes were recognized: (1) I don’t like them monitoring me, (2) I want maximum gain for minimum effort, (3) I don’t want AI to mimic people, (4) I’ll avoid using AI if I consider the risk too high, and (5) I don’t need to be concerned about AI. These offer an idea of human-centered requirements worth considering while designing AI applications for future Smart Cities.

A Multi-Industry Analysis of the Future Use of AI Chatbots

Artificial intelligence (AI) chatbots are set to be the defining technology of the next decade due to their ability to increase human capability at a low cost. However, more research is required to assess individuals’ behavioural intentions to use this technology when it becomes publicly available. This study applied an extended Technology Acceptance Model (TAM), with additional predictors of trust and privacy concerns, to assess individuals’ behavioural intentions to use AI chatbots across three industries: mental health care, online shopping, and online banking. These services were selected due to the current popularity of regular chatbots in these fields. Participants ( $N=360$ , 202 females) aged between 17 and 85 years ( $M=38.17$ , $\text{SD}=17.66$ ) completed a 71-item online, cross-sectional survey. As hypothesised, perceived usefulness and trust were significant positive predictors of behavioural intentions across all three behaviours. However, the influence of the perceived ease of use and privacy concerns on behavioural intentions differed across the three behaviours. These findings highlight that the combination of predictors within the extended TAM have different influences on behavioural intentions to use AI chatbots for mental health care, online shopping, and online banking. This research contributes to the literature by demonstrating that the influence of the variables in one field cannot be generalised across all uses of AI chatbots.

Association of artificial intelligence use and the retention of elderly caregivers: A cross-sectional study based on empowerment theory

AIM

The purpose of this study is to investigate how the use of artificial intelligence is associated with the retention of elderly caregivers.

BACKGROUND

The turnover of elderly caregivers is high and increasing. Elderly care institutions are beginning to use artificial intelligence to support caregivers in their work, and the use of technology is critical to staff retention. Empowerment of elderly caregivers has been neglected by managers and researchers.

METHODS

This cross-sectional study involved 511 elderly caregivers in 25 elderly institutions. Six validated standardized scales were used for data collection, and the software SPSS and SmartPLS were used for data analysis.

RESULTS

The quality of artificial intelligence has a significant positive effect on empowerment. Artificial intelligence psychological empowerment (β = .355, p < .001) and artificial intelligence structural empowerment (β = .375, p < .001) both had positive effects on retention intention, and the jointly explained variance (R² ) was 42.6%.

CONCLUSIONS

The results show that a significant relationship exists between artificial intelligence empowerment and retention intention. Elderly caregivers with more structural empowerment have higher retention intention.

IMPLICATIONS FOR NURSING MANAGEMENT

Artificial intelligence suppliers need to pay attention to the role of product quality in elderly care services, continuously improve artificial intelligence quality, and strengthen the application and routine maintenance of artificial intelligence technologies. Elderly care institution managers should pay special attention to artificial intelligence structural empowerment (such as artificial intelligence-related education and training, learning and development opportunities, and resource support).

Riaz M, Nauman S, Ashfaq M. Browsing or buying: A serial mediation analysis of consumer’s online purchase intentions in times of COVID-19 pandemic

The role of digitization and globalization have changed consumers’ online buying behaviors, specifically in the times of the COVID-19 pandemic crisis. This seriously influences the online retail industry in developing countries that are already struggling to move toward digital trading through e-business. Pakistan being a developing country is no exception, and it is, therefore, pertinent to examine factors that contribute to digital trading. Employing theories of reasoned action and the technology acceptance model, this study aims to investigate how personal innovativeness and perceived usefulness impact consumers’ online purchase intentions through a serial mediational model. The data were collected through an online survey from 410 respondents. Structural Equation Modeling (SEM) was used to test the proposed model. This study showed significant results for the direct effect of personal innovativeness and perceived usefulness on online purchase intentions as well as the indirect serial effect via internet browsing and attitude toward online purchasing. The study results have some important practical implications for selling firms, especially in the times of COVID-19. The study suggests that online retailers should be more responsive to the aforementioned factors to facilitate consumers to spend more time browsing, which influences consumers’ interest and intention to make online purchases. As the social distancing and lockdown approaches were implemented in Pakistan and other parts of the world, the trend toward online purchases has increased. Due to this shift in the overall purchasing behavior of consumers and the potential for strong growth in e-commerce, organizations need to consider the post-COVID situation to expand their business in an online platform for addressing the future pandemic crisis.

When a Chatbot Smiles at You: The Psychological Mechanism Underlying the Effects of Friendly Language Use by Product Recommendation Chatbots

Based on the computers are social actors theory and social presence theory, the current study investigates the psychological mechanism by which the use of friendly language by a personalized product recommendation chatbot influences product attitudes. Results indicated that the effect of the friendly chatbot on more positive product attitudes was sequentially mediated by social presence and user satisfaction. Previous experience with product recommendation chatbots was found to moderate the serial mediation effects. Furthermore, the current study found that a friendly chatbot led to higher rates of contact information disclosure by consumers. Theoretical and practical implications are discussed.

Enhancing Marketing Provision through Increased Online Safety That Imbues Consumer Confidence: Coupling AI and ML with the AIDA Model

To enhance the effectiveness of artificial intelligence (AI) and machine learning (ML) in online retail operations and avoid succumbing to digital myopia, marketers need to be aware of the different approaches to utilizing AI/ML in terms of the information they make available to appropriate groups of consumers. This can be viewed as utilizing AI/ML to improve the customer journey experience. Reflecting on this, the main question to be addressed is: how can retailers utilize big data through the implementation of AI/ML to improve the efficiency of their marketing operations so that customers feel safe buying online? To answer this question, we conducted a systematic literature review and posed several subquestions that resulted in insights into why marketers need to pay specific attention to AI/ML capability. We explain how different AI/ML tools/functionalities can be related to different stages of the AIDA (Awareness, Interest, Desire, and Action) model, which in turn helps retailers to recognize potential opportunities as well as increase consumer confidence. We outline how digital myopia can be reduced by focusing on human inputs. Although challenges still exist, it is clear that retailers need to identify the boundaries in terms of AI/ML’s ability to enhance the company’s business model.

Language Use in Conversational Agent-Based Health Communication: Systematic Review

Background

Given the growing significance of conversational agents (CAs), researchers have conducted a plethora of relevant studies on various technology- and usability-oriented issues. However, few investigations focus on language use in CA-based health communication to examine its influence on the user perception of CAs and their role in delivering health care services.

Objective

This review aims to present the language use of CAs in health care to identify the achievements made and breakthroughs to be realized to inform researchers and more specifically CA designers.

Methods

This review was conducted by following the protocols of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 statement. We first designed the search strategy according to the research aim and then performed the keyword searches in PubMed and ProQuest databases for retrieving relevant publications (n=179). Subsequently, 3 researchers screened and reviewed the publications independently to select studies meeting the predefined selection criteria. Finally, we synthesized and analyzed the eligible articles (N=11) through thematic synthesis.

Results

Among the 11 included publications, 6 deal exclusively with the language use of the CAs studied, and the remaining 5 are only partly related to this topic. The language use of the CAs in these studies can be roughly classified into six themes: (1) personal pronouns, (2) responses to health and lifestyle prompts, (3) strategic wording and rich linguistic resources, (4) a 3-staged conversation framework, (5) human-like well-manipulated conversations, and (6) symbols and images coupled with phrases. These derived themes effectively engaged users in health communication. Meanwhile, we identified substantial room for improvement based on the inconsistent responses of some CAs and their inability to present large volumes of information on safety-critical health and lifestyle prompts.

Conclusions

This is the first systematic review of language use in CA-based health communication. The results and limitations identified in the 11 included papers can give fresh insights into the design and development, popularization, and research of CA applications. This review can provide practical implications for incorporating positive language use into the design of health CAs and improving their effective language output in health communication. In this way, upgraded CAs will be more capable of handling various health problems particularly in the context of nationwide and even worldwide public health crises.

Factors affecting business school students’ performance during the COVID-19 pandemic: A moderated and mediated model

The COVID-19 pandemic has directly influenced higher education by converting traditional face-to-face (F2F) learning to blended learning (BL). Because of this dramatic change in the academic environment, it is essential to evaluate student views and preferences and how the BL setting affects their academic performance. Therefore, the current research aims to investigate the relationship between Unified Theory of Acceptance and Use of Technology (UTAUT) constructs (performance expectancy, effort expectancy, and facilitating condition) and student academic performance through student attitude. We also examined the moderating role of trust in technology between UTAUT constructs and student performance. The study uses a sample of 1050 business management university students for mediation/moderation analysis using the Hayes Bootstrap technique. The results demonstrate that student attitude mediates the relationship between UTAUT constructs and student academic performance, with trust in technology strengthening the relationship. The study offers implications for universities and policymakers.

The role of institutional factors and cognitive absorption on students' satisfaction and performance in online learning during COVID 19

With the rise of the Covid-19 pandemic, there has been a severe negative impact on all aspects of life, whether it be a job, business, health, education, etc. As a result, institutions, schools, colleges and universities are being shut down globally to control the spread of Covid-19. Due to this reason, the mode of education has a dramatic shift from on-campus to online learning with virtual teaching using digital technologies. This sudden shift has elevated the stress level among the students because they were not mentally prepared for it, and hence their academic performance has been adversely affected. So, there needs to figure out the underlying process to make online learning more productive. Thus, to obtain this objective, the present study has integrated the modified Technology Acceptance Model (TAM), Task Technology Fit Model (TTF), DeLone and McLean Model of Information Systems Success (DMISM) and Unified Theory of Acceptance and Use of Technology (UTAUT) model. A sample of 404 students was obtained, where 202 students were from the top ten public sector universities, and 202 were from the top ten private sector universities of Punjab. Structural Equation Modelling (SEM) was used to analyze the hypothesized framework using AMOS. The results reveal that institutional factors positively impact students' performance mediated by user satisfaction and task technology fit. Similarly, institutional factors affect performance through mediation by user satisfaction and actual usage in sequence. Cognitive absorption was used as a moderator between institutional factors and user satisfaction. In the end, theoretical and practical inferences have also been discussed.

Use of Health Care Chatbots Among Young People in China During the Omicron Wave of COVID-19: Evaluation of the User Experience of and Satisfaction With the Technology

BACKGROUND

Long before the outbreak of COVID-19, chatbots had been playing an increasingly crucial role and gaining growing popularity in health care. In the current omicron waves of this pandemic when the most resilient health care systems at the time are increasingly being overburdened, these conversational agents (CA) are being resorted to as preferred alternatives for health care information. For many people, especially adolescents and the middle-aged, mobile phones are the most favored source of information. As a result of this, it is more important than ever to investigate the user experience of and satisfaction with chatbots on mobile phones.

OBJECTIVE

The objective of this study was twofold: (1) Informed by Deneche and Warren's evaluation framework, Zhu et al's measures of variables, and the theory of consumption values (TCV), we designed a new assessment model for evaluating the user experience of and satisfaction with chatbots on mobile phones, and (2) we aimed to validate the newly developed model and use it to gain an understanding of the user experience of and satisfaction with popular health care chatbots that are available for use by young people aged 17-35 years in southeast China in self-diagnosis and for acquiring information about COVID-19 and virus variants that are currently spreading.

METHODS

First, to assess user experience and satisfaction, we established an assessment model based on relevant literature and TCV. Second, the chatbots were prescreened and selected for investigation. Subsequently, 413 informants were recruited from Nantong University, China. This was followed by a questionnaire survey soliciting the participants' experience of and satisfaction with the selected health care chatbots via wenjuanxing, an online questionnaire survey platform. Finally, quantitative and qualitative analyses were conducted to find the informants' perception.

RESULTS

The data collected were highly reliable (Cronbach α=.986) and valid: communalities=0.632-0.823, Kaiser-Meyer-Olkin (KMO)=0.980, and percentage of cumulative variance (rotated)=75.257% (P<.001). The findings of this study suggest a considerable positive impact of functional, epistemic, emotional, social, and conditional values on the participants' overall user experience and satisfaction and a positive correlation between these values and user experience and satisfaction (Pearson correlation P<.001). The functional values (mean 1.762, SD 0.630) and epistemic values (mean 1.834, SD 0.654) of the selected chatbots were relatively more important contributors to the students' positive experience and overall satisfaction than the emotional values (mean 1.993, SD 0.683), conditional values (mean 1.995, SD 0.718), and social values (mean 1.998, SD 0.696). All the participants (n=413, 100%) had a positive experience and were thus satisfied with the selected health care chatbots. The 5 grade categories of participants showed different degrees of user experience and satisfaction: Seniors (mean 1.853, SD 0.108) were the most receptive to health care chatbots for COVID-19 self-diagnosis and information, and second-year graduate candidates (mean 2.069, SD 0.133) were the least receptive; freshmen (mean 1.883, SD 0.114) and juniors (mean 1.925, SD 0.087) felt slightly more positive than sophomores (mean 1.989, SD 0.092) and first-year graduate candidates (mean 1.992, SD 0.116) when engaged in conversations with the chatbots. In addition, female informants (mean 1.931, SD 0.098) showed a relatively more receptive attitude toward the selected chatbots than male respondents (mean 1.999, SD 0.051).

CONCLUSIONS

This study investigated the use of health care chatbots among young people (aged 17-35 years) in China, focusing on their user experience and satisfaction examined through an assessment framework. The findings show that the 5 domains in the new assessment model all have a positive impact on the participants' user experience and satisfaction. In this paper, we examined the usability of health care chatbots as well as actual chatbots used for other purposes, enriching the literature on the subject. This study also provides practical implication for designers and developers as well as for governments of all countries, especially in the critical period of the omicron waves of COVID-19 and other future public health crises.

The Effects of Chatbot Service Recovery With Emotion Words on Customer Satisfaction, Repurchase Intention, and Positive Word-Of-Mouth

This study sought to examine the effect of the quality of chatbot services on customer satisfaction, repurchase intention, and positive word-of-mouth by comparing two groups, namely chatbots with and without emotion words. An online survey was conducted for 2 weeks in May 2021. A total of 380 responses were collected and analyzed using structural equation modeling to test the hypothesis. The theoretical basis of the study was the SERVQUAL theory, which is widely used in measuring and managing service quality in various industries. The results showed that the assurance and reliability of chatbots positively impact customer satisfaction for both groups. However, empathy and interactivity positively affect customer satisfaction only for chatbots with emotion words. Responsiveness did not have an impact on customer satisfaction for both groups. Customer satisfaction positively impacts repurchase intention and positive word-of-mouth for both groups. The findings of this study can serve as a priori research to empirically prove the effectiveness of chatbots with emotion words.

The Terms of "You(s)": How the Term of Address Used by Conversational Agents Influences User Evaluations in French and German Linguaculture

Background: Conversational agents (CAs) are a novel approach to delivering digital health interventions. In human interactions, terms of address often change depending on the context or relationship between interlocutors. In many languages, this encompasses T/V distinction—formal and informal forms of the second-person pronoun “You”—that conveys different levels of familiarity. Yet, few research articles have examined whether CAs' use of T/V distinction across language contexts affects users' evaluations of digital health applications.

Methods: In an online experiment (N = 284), we manipulated a public health CA prototype to use either informal or formal T/V distinction forms in French (“tu” vs. “vous”) and German (“du” vs. “Sie”) language settings. A MANCOVA and post-hoc tests were performed to examine the effects of the independent variables (i.e., T/V distinction and Language) and the moderating role of users' demographic profile (i.e., Age and Gender) on eleven user evaluation variables. These were related to four themes: (i) Sociability, (ii) CA-User Collaboration, (iii) Service Evaluation, and (iv) Behavioral Intentions.

Results: Results showed a four-way interaction between T/V Distinction, Language, Age, and Gender, influencing user evaluations across all outcome themes. For French speakers, when the informal “T form” (“Tu”) was used, higher user evaluation scores were generated for younger women and older men (e.g., the CA felt more humanlike or individuals were more likely to recommend the CA), whereas when the formal “V form” (“Vous”) was used, higher user evaluation scores were generated for younger men and older women. For German speakers, when the informal T form (“Du”) was used, younger users' evaluations were comparable regardless of Gender, however, as individuals' Age increased, the use of “Du” resulted in lower user evaluation scores, with this effect more pronounced in men. When using the formal V form (“Sie”), user evaluation scores were relatively stable, regardless of Gender, and only increasing slightly with Age.

Conclusions: Results highlight how user CA evaluations vary based on the T/V distinction used and language setting, however, that even within a culturally homogenous language group, evaluations vary based on user demographics, thus highlighting the importance of personalizing CA language.

The impact of reusable tableware packaging combined with environmental propaganda on consumer behaviour in online retail

With the development of the takeaway industry, the demand for disposable cutlery is increasing, posing a heavy burden on the environment. Helping reusable tableware increase market share is important because it helps preserve the natural environment while making commercial gains. Given the additional cost to consumers of using reusable tableware in many settings, this article examines the impact of incorporating environmental propaganda into packaging design on consumer behaviour. The results show that the new packaging with high environmental propaganda satisfaction improves consumers' brand loyalty, purchase intention and continuance intention. The packaging of low environmental propaganda satisfaction may have negative effects and should be used with caution.