Psychological determinants of users’ adoption and word-of-mouth recommendations of smart voice assistants

Cancer patients' acceptance of virtual reality interventions for self-emotion regulation

This study investigates the acceptability of Virtual reality (VR) technology for emotional regulation among cancer patients. Drawing from extensive literature, we enhanced external variables across user characteristics, product impact factors, and social environment influences, creating the "Theoretical Model of Cancer Patients' Acceptance of VR Intervention for Self-Emotion Regulation." Surveying 489 Chinese cancer patients validated the model's strong reliability through SPSS AMOS analysis. The acceptance of VR intervention for self-emotional regulation among cancer patients was assessed, revealing that the average scores across all 13 dimensions exceeded 3. This indicates that cancer patients hold a positive attitude toward VR-based emotional regulation interventions. Perceived usefulness, usage attitude, social norms, immersion, and personal innovation correlated positively with behavioral intention, while technological anxiety and perceived risk showed negative correlations. Findings support 15 hypotheses, offering theoretical backing for VR technology in emotional regulation for cancer patients. These insights provide medical institutions with valuable data on patient attitudes, facilitating the development of targeted treatment approaches.

Connecting self-esteem to problematic AI chatbot use: the multiple mediating roles of positive and negative psychological states

The emergence of AI chatbot products has ushered in a new era of human-AI interaction, yet scholars and practitioners have expressed concerns about their use due to potential addictive and adverse effects. Currently, the understanding of problematic AI chatbot use (PACU) remains incomplete and inconclusive. Despite previous findings that indicate negative outcomes associated with the use of AI products, limited studies have explored the underlying factors that drive the complex process leading to the formation of PACU. Furthermore, while existing literature highlights how personal traits influences problematic IT use via evoked psychological states, it largely overlooks that the positive psychological experience may also have a potential influence on problematic outcomes. Incorporating flow experience into the compensatory internet use theory, this study presents a multiple mediation model to investigate how social anxiety, escapism, and AI chatbot flow influence the relationship between self-esteem and PACU. We examine the model using Partial Least Squares Structural Equation Modeling (PLS-SEM) with cross-sectional data collected from 563 online users who have engaged with AI chatbots. Our findings indicate that users with low self-esteem are more likely to conduct problematic behavior when using AI chatbots. This relationship can be mediated by social anxiety, escapism and AI chatbot flow. This study sheds light on how self-esteem negatively affects PACU, unraveling the underlying psychological processes experienced by users with low self-esteem in their interactions with AI chatbots. Also, we provide practical insights for online users and practitioners to mitigate the potential negative impacts of AI product usage.

Emotional design for pro-environmental life: Visual appeal and user interactivity influence sustainable consumption intention with moderating effect of positive emotion

As the public pursues sustainable consumption patterns, pro-environmental mobile applications are being developed to promote green lifestyles. Ant Forest has attracted a large number of users to participate due to its gamified interactive design. The objectives of this study are to understand the process by which visual appeal and user interactivity in pro-environmental mobile applications influence the user's sustainable consumption intention and to analyse the moderating effect of the user's positive emotion on this process. The researchers constructed a theoretical framework based on the Stimulus-Organism-Response (S-O-R) theory in this study and used the Partial Least Squares Structural Equation Modelling (PLS-SEM) for quantitative analysis with an online questionnaire survey of 586 Ant Forest users. Findings for this study include: 1) Visual appeal shows a large positive effect on the perceived usefulness of Ant Forest; 2) Ant Forest's perceived usefulness shows a significant positive effect on the user's sustainable consumption intention; 3) Ant Forest's perceived usefulness fully mediates the relationship between visual appeal and the user's sustainable consumption intention; 4) Positive emotion moderates the relationship between perceived usefulness and sustainable consumption intention. This study improves the application of the S-O-R theory in emotional design research, while emphasising the importance of optimising the visual appeal of design elements and guiding users' positive emotions in the development of pro-environmental mobile applications. The findings of this study highlight the importance of incorporating positive emotions into visual design for designers and developers of pro-environmental applications, further clarifying the focus of emotional design.

Understanding Users' Acceptance of Artificial Intelligence Applications: A Literature Review

In recent years, with the continuous expansion of artificial intelligence (AI) application forms and fields, users' acceptance of AI applications has attracted increasing attention from scholars and business practitioners. Although extant studies have extensively explored user acceptance of different AI applications, there is still a lack of understanding of the roles played by different AI applications in human-AI interaction, which may limit the understanding of inconsistent findings about user acceptance of AI. This study addresses this issue by conducting a systematic literature review on AI acceptance research in leading journals of Information Systems and Marketing disciplines from 2020 to 2023. Based on a review of 80 papers, this study made contributions by (i) providing an overview of methodologies and theoretical frameworks utilized in AI acceptance research; (ii) summarizing the key factors, potential mechanisms, and theorization of users' acceptance response to AI service providers and AI task substitutes, respectively; and (iii) proposing opinions on the limitations of extant research and providing guidance for future research.

Virtual resonance: analyzing IPA usage intensity under COVID-19's isolating canopy

The widespread adoption of smartphones coupled with advancements in artificial intelligence has significantly propelled the use of intelligent personal assistants (IPAs). These digital assistants have become indispensable for many users, particularly during the COVID-19 pandemic. Employing coviance-based structural equation modeling (CB-SEM) and analyzing data from 260 participants, this study explores the key factors influencing IPA usage intensity. Contrary to expectations, affective risk perception showed no significant impact on either IPA usage or parasocial interaction during the pandemic. In stark contrast, cabin fever syndrome significantly influenced both IPA usage and parasocial interaction, underscoring the role of environmental and psychological stressors in shaping technology use. Furthermore, loneliness was found to significantly enhance parasocial interaction with IPAs, though it did not affect usage intensity. The findings highlight a substantial connection between parasocial interaction and IPA usage intensity, suggesting that users who engage in human-like interactions with IPAs tend to use them more extensively. These insights not only deepen our understanding of how IPAs are utilized during health crises but also point to potential directions for developing IPAs that are more responsive to users' emotional and social needs.

Effects of ChatGPT's AI capabilities and human-like traits on spreading information in work environments

The rapid proliferation and integration of AI chatbots in office environments, specifically the advanced AI model ChatGPT, prompts an examination of how its features and updates impact knowledge processes, satisfaction, and word-of-mouth (WOM) among office workers. This study investigates the determinants of WOM among office workers who are users of ChatGPT. We adopted a quantitative approach, utilizing a stratified random sampling technique to collect data from a diverse group of office workers experienced in using ChatGPT. The hypotheses were rigorously tested through Structural Equation Modeling (SEM) using the SmartPLS 4. The results revealed that system updates, memorability, and non-language barrier attributes of ChatGPT significantly enhanced knowledge acquisition and application. Additionally, the human-like personality traits of ChatGPT significantly increased both utilitarian value and satisfaction. Furthermore, the study showed that knowledge acquisition and application led to a significant increase in utilitarian value and satisfaction, which subsequently increased WOM. Age had a positive influence on WOM, while gender had no significant impact. The findings provide theoretical contributions by expanding our understanding of AI chatbots' role in knowledge processes, satisfaction, and WOM, particularly among office workers.

Application of the extended value-belief-norm (VBN) theory to understand consumers' intention to use autonomous delivery vehicles (ADVs)

Emissions from transportation fuel vehicles are polluting the environment more than ever before. Currently, autonomous delivery vehicles (ADVs) are welcomed by logistics service providers and lots of consumers in general for their significant contribution in reducing environmental pollution and increasing delivery efficiency. However, there is a lack of in-depth research on consumers' willingness to adopt ADVs, despite the fact that ADVs are innovative delivery method in the field of last-mile delivery. The purpose of the study is to expand the existing knowledge of consumers' adoption behaviors by developing and testing the extended Value-Belief-Norm (VBN) theory in autonomous delivery literature. In particular, the study adds subjective norms and COVID-19 risk into the original VBN model as a theoretical framework to gain a comprehensive understanding of the consumer decision-making processes with respect to their intentions to use ADVs. Structural equation modeling is conducted using data from a web-based survey of 561 consumers through an online sample platform. The results of the study demonstrated that both subjective norms and COVID-19 risk are all related with consumers' intention to use ADVs. The casual chain hypotheses of the extend VBN theory mentioned in our study are all supported. The results of the study make significant theoretical and managerial contributions to the field of last-mile delivery and the adoption behaviors of emerging technologies.

How to increase consumer intention to use Chatbots? An empirical analysis of hedonic and utilitarian motivations on social presence and the moderating effects of fear across generations

As chatbots become more advanced and popular, marketing research has paid enormous attention to the antecedents of consumer adoption of chatbots. This has become increasingly relevant because chatbots can help mitigate the fear and loneliness caused by the global pandemic. Therefore, unlike previous work that focused on design factors, we theorize that social presence serves a mediating role between consumer motivations (i.e., hedonic and utilitarian) and intention to use a chatbot service based on self-determination theory. Our results from a structural equation model (n  = 377) indicate that hedonic (but not utilitarian) motivation significantly affects chatbots’ social presence, ultimately influencing intention to use the chatbot service. We also found that fear of COVID-19 amplifies the effect of social presence on intention to use the chatbot service. In this dynamic, we found an additional moderated moderation effect of generational cohorts (i.e., baby boomers and Generations X, Y, and Z) in experiencing different levels of fear of COVID-19. Overall, our findings emphasize the importance of motivation-matching features for consumer adoption of chatbot services. Our findings also indicate that marketers may utilize the fear element to increase adoption of chatbot services, especially when targeting the young generations (e.g., Generation Z).

Exploring the antecedents of customers’ willingness to use service robots in restaurants

This study examines the willingness of customers to accept, and their intention to recommend, the services provided by service robots in restaurants. A mixed-methods research approach was taken to evaluate a theoretical model based on behavioural reasoning theory (BRT). The results demonstrated the important influence of positive attitudes and objections to the use of service robots on consumers’ willingness to use service robots, as well as their intention to recommend restaurants that use them. Among the main aspects that affect attitudes and objections, we found hedonic perceptions, perceived safety, interaction quality perception and anthropomorphism.

The cuter, the better? The impact of cuteness on intention to purchase AI voice assistants: A moderated serial-mediation model

Due to the lockdown, more and more people are used to communicating with AI voice assistants during the post-COVID era. This study investigates the relationship between the perceived cuteness of AI voice assistants and the intention to purchase via a moderated serial-mediation model. We tested a PLS-SEM model with 284 survey data from an online experiment. The findings indicate that: (1) different cuteness appearances lead to different levels of perceived cuteness; (2) perceived cuteness positively affects intention to purchase; (3) the positive direct effect is serial mediated by social presence and user engagement; (4) the serial mediation effect is negatively moderated by perceived risk of service failure, which means the positive influence of perceived cuteness on intention to purchase is weakened at a high level of perceived risk. Our research has both theoretical and managerial contributions, which also reminds enterprises to grasp the cuteness degree of the product.

Application and extension of the UTAUT2 model for determining behavioral intention factors in use of the artificial intelligence virtual assistants

Virtual Assistants, also known as conversational artificial intelligence, are transforming the reality around us. These virtual assistants have challenged our daily lives by assisting us in the different dimensions of our lives, such as health, entertainment, home, and education, among others. The main purpose of this study is to develop and empirically test a model to predict factors that affect users' behavioral intentions when they use intelligent virtual assistants. As a theoretical basis for investigating behavioral intention of using virtual assistants from the consumers' perspective, researchers employed the extended Unified Theory of Acceptance and Use of Technology (UTAUT2). For this research paper, seven variables were analyzed: performance expectancy, effort expectancy, facilitating conditions, social influence, hedonic motivation, habit, and price/value. In order to improve consumer behavior prediction, three additional factors were included in the study: perceived privacy risk, trust, and personal innovativeness. Researchers carried out an online survey with 304 responses. The obtained sample was analyzed with Structural Equation Modeling (SEM) through IBM SPSS V. 27.0 and AMOS V 27.0. The main study results reveal that factors, such as habit, trust, and personal innovation, have a significant impact on the adoption of virtual assistants. However, on the other side, performance expectancy, effort expectancy, facilitating conditions, social influence, hedonic motivation, price/value, and perceived privacy risk were not significant factors in the users' intention to adopt this service. This research paper examines the effect of personal innovation, security, and trust variables in relation to the use of virtual assistants. It contributes to a more holistic understanding of the adoption of these intelligent devices and tries to fill the knowledge gap on this topic, as it is an emerging technology. This investigation also provides relevant information on how to successfully implement these technologies.

A Mind in Intelligent Personal Assistants: An Empirical Study of Mind-Based Anthropomorphism, Fulfilled Motivations, and Exploratory Usage of Intelligent Personal Assistants

Intelligent personal assistants (IPAs) own anthropomorphic features which enable users’ perception of anthropomorphism. Adopting the perspective of mind-based anthropomorphism, the purpose of this paper is to investigate how mind-based anthropomorphism influences users’ exploratory usage of IPAs. Based on the notion that anthropomorphism can satisfy people’s sociality and effectance motivation, we hypothesize that mind-based anthropomorphism can enhance people’s social connection with IPAs and IPA self-efficacy, which can in turn influence their exploratory usage of IPAs. Questionnaires were developed and distributed to users who had experience in smart speaker-based IPAs on Wenjuanxing and 551 valid questionnaires were collected to test the research model. The results revealed that cognitive and affective anthropomorphism exerted common and differential impacts on IPA self-efficacy and social connection. Cognitive anthropomorphism versus affective anthropomorphism had stronger influences on IPA self-efficacy, while affective anthropomorphism had stronger impacts on social connection. Both IPA self-efficacy and social connection enhanced users’ intentions to explore IPAs. This study enriches previous studies on IPA adoption or post-adoption by investigating exploratory usage which captures how users are deeply engaged with IPAs.