User Intention Research Articles

What Drives Continuance Usage Intention Toward Virtual Reality Games? Gamer-Avatar Identification and Experiential Consumption Perspectives

The rapid growth of the virtual reality (VR) gaming industry has underscored the importance of psychological and experiential factors in maintaining sustained engagement. Based on the self-determination theory and experiential consumption perspective (ECP), this research proposed a conceptual framework incorporating gamer-avatar identification and experiential factors as key determinants of VR games continuance usage intention and considered the moderating role of uncertainty avoidance. We gathered 385 valid responses from Chinese VR gamers. The research model, analyzed via AMOS and SPSS, showed that gamer-avatar identification significantly affects continuance usage intention, with immersion and perceived authenticity serving as crucial mediators. In addition, uncertainty avoidance moderates these relationships. In contrast, perceived value did not mediate this relationship, challenging traditional consumer behavior theories.

Disciplinary differences in undergraduate students' engagement with generative artificial intelligence

The rapid development of generative artificial intelligence (GenAI) technologies has sparked widespread discussions about their potential applications in higher education. However, little is known about how students from various disciplines engage with GenAI tools. This study explores undergraduate students' GenAI knowledge, usage intentions, and task-specific engagement across academic disciplines. Using a disciplinary categorization framework, we examine how the hard/soft and pure/applied dimensions relate to students' interactions with GenAI. We surveyed 193 undergraduates from diverse disciplines at a university in Singapore. The questionnaire assessed students' GenAI knowledge, usage intentions, and engagement with GenAI for cognitive and routine tasks against their disciplinary background. The results indicate substantial disciplinary disparities in the level of engagement of students with GenAI. Compared to pure fields, applied fields (both hard and soft) consistently exhibit higher levels of GenAI knowledge and utilization intentions. Furthermore, the engagement of GenAI in routine tasks is relatively consistent across disciplines; however, there are substantial disparities in cognitive tasks, with applied fields exhibiting higher engagement. These results suggest that the practical orientation of applied fields drives GenAI adoption and utilization in academic settings. The study emphasizes considering disciplinary differences to better integrate GenAI into higher education and calls for tailored approaches that align with each field's unique epistemological and methodological traditions to balance GenAI's practical benefits with the preservation of core disciplinary knowledge and skills.

SoftBoMI: a non-invasive wearable body-machine interface for mapping movement of shoulder to commands

Objective.Customized human-machine interfaces for controlling assistive devices are vital in improving the self-help ability of upper limb amputees and tetraplegic patients. Given that most of them possess residual shoulder mobility, using it to generate commands to operate assistive devices can serve as a complementary approach to brain-computer interfaces.Approach.We propose a hybrid body-machine interface prototype that integrates soft sensors and an inertial measurement unit. This study introduces both a rule-based data decoding method and a user intent inference-based decoding method to map human shoulder movements into continuous commands. Additionally, by incorporating prior knowledge of the user's operational performance into a shared autonomy framework, we implement an adaptive switching command mapping approach. This approach enables seamless transitions between the two decoding methods, enhancing their adaptability across different tasks.Main results.The proposed method has been validated on individuals with cervical spinal cord injury, bilateral arm amputation, and healthy subjects through a series of center-out target reaching tasks and a virtual powered wheelchair driving task. The experimental results show that using both the soft sensors and the gyroscope exhibits the most well-rounded performance in intent inference. Additionally, the rule-based method demonstrates better dynamic performance for wheelchair operation, while the intent inference method is more accurate but has higher latency. Adaptive switching decoding methods offer the best adaptability by seamlessly transitioning between decoding methods for different tasks. Furthermore, we discussed the differences and characteristics among the various types of participants in the experiment.Significance.The proposed method has the potential to be integrated into clothing, enabling non-invasive interaction with assistive devices in daily life, and could serve as a tool for rehabilitation assessment in the future.

Grounding with Structure: Exploring Design Variations of Grounded Human-AI Collaboration in a Natural Language Interface

Selecting an effective utterance among countless possibilities that match a user's intention poses a challenge when using natural language interfaces. To address the challenge, we leveraged the principle of least collaborative effort in communication grounding theory and designed three grounded conversational interactions: 1) a grounding interface allows users to start with a provisional input and then invite a conversational agent to complete their input, 2) a multiple grounding interface presents multiple inputs for the user to select from, and 3) a structured grounding interface guides users to write inputs in a structure best understood by the system. We compared our three grounding interfaces to an ungrounded control interface in a crowdsourced study (N=80) using a natural language system that generates small programs. We found that the grounding interfaces reduced cognitive load and improved task performance. The structured grounding interface further reduced speaker change costs and improved technology acceptance, without sacrificing the perception of control. We discuss the implications of designing grounded conversational interactions in natural language systems.

Analysis of willingness to use health management APP for female college students: application of UTAUT model based on Fogg theory.

As the development process of medical industry informatization has entered the stage of smart healthcare, health management applications (apps) have played an important role in improving people's health and preventing diseases, especially among female college students. This study combines the UTAUT model and the Fogg behavioral model (FBM) as a theoretical framework to investigate the factors affecting female college students' willingness to use health management apps. A survey was conducted with 624 female college students regarding their usage of AI health management mobile applications. The analysis reveals that social influence (β = 0.497, p < 0.001), performance expectancy (β = 0.268, p < 0.001), effort expectancy (β = 0.359, p < 0.001), and facilitating conditions (β = 0.603, p < 0.001) positively predict attitude; social influence (β = 0.36, p < 0.001) and effort expectancy (β = 0.183, p < 0.001) positively predict perceived risk, while facilitating conditions negatively predict perceived risk (β = -0.108, p < 0.01). Additionally, performance expectancy (β = 0.231, p < 0.001), effort expectancy (β = 0.285, p < 0.001), facilitating conditions (β = 0.25, p < 0.01), and attitude (β = 0.291, p < 0.05) positively predict an individual's intention to use such applications, which in turn affects actual behavior (β = 0.804, p < 0.001). This study develops a comprehensive theoretical framework to explore the psychological and social factors influencing female college students' utilization of health management applications. The findings underscore the significant roles of social influence, performance expectancy, effort expectancy, and facilitating conditions in shaping user attitudes and intentions. These insights offer valuable guidance for formulating effective interventions to enhance the adoption of these applications.

The Service Robot Customer Experience (SR-CX): A Matter of AI Intelligences and Customer Service Goals

This research aims to generate a nuanced understanding of service robot customer experiences (SR-CX). Specifically, this work (1) empirically explores the impact of different AI intelligences (mechanical, thinking, and feeling AI) on SR-CX (i.e., SR-CX strength and SR-CX dimensionality) and its downstream impact on important service outcomes (i.e., overall service experience and service usage intentions), and (2) considers the moderating role of consumer service goals (hedonic versus utilitarian). Drawing on insights from two field studies and two online experiments, this research demonstrates that SR-CX—which is impacted differently by varying AI intelligences—affects service outcomes. Specifically, more sophisticated AI intelligences lead to enhanced service outcomes for customers with hedonic service goals across settings by strengthening SR-CX and triggering a more extensive set of SR-CX dimensions. This pattern, however, is less clear for customers driven by utilitarian service goals. For these customers, the role of SR-CX strength and SR-CX dimensionality varies across settings. These findings, which empirically support the importance of SR-CX, may help organizations to strategically leverage robots with different intelligence levels along service journeys of customers with different service goals.

VRTree: Example‐Based 3D Interactive Tree Modeling in Virtual Reality

AbstractWe present VRTree, an example‐based interactive virtual reality (VR) system designed to efficiently create diverse 3D tree models while faithfully preserving botanical characteristics of real‐world references. Our method employs a novel representation called Hierarchical Branch Lobe (HBL), which captures the hierarchical features of trees and serves as a versatile intermediary for intuitive VR interaction. The HBL representation decomposes a 3D tree into a series of concise examples, each consisting of a small set of main branches, secondary branches, and lobe‐bounded twigs. The core of our system involves two key components: (1) We design an automatic algorithm to extract an initial library of HBL examples from real tree point clouds. These HBL examples can be optionally refined according to user intentions through an interactive editing process. (2) Users can interact with the extracted HBL examples to assemble new tree structures, ensuring the local features align with the target tree species. A shape‐guided procedural growth algorithm then transforms these assembled HBL structures into highly realistic, finegrained 3D tree models. Extensive experiments and user studies demonstrate that VRTree outperforms current state‐of‐the‐art approaches, offering a highly effective and easy‐to‐use VR tool for tree modeling.

Watch That Attitude! Examining the Role of Attitude in the Technology Acceptance Model through Meta-Analytic Structural Equation Modelling

The Technology Acceptance Model (TAM), proposed by Fred Davis in 1986, is a foundational framework for understanding technology adoption, emphasizing Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) as key determinants of Intention to Use (ITU). While Attitude Toward Using (ATU) was initially central to TAM, it was later omitted in iterations like TAM2 and UTAUT. This paper revisits the role of ATU within TAM, employing One-Step Meta-Analytic Structural Equation Modelling (OSMASEM) to analyze educational technology acceptance. The findings reveal that ATU significantly mediates the relationship between PEOU, PU, and ITU, enhancing the explanatory power of TAM. By comparing models with and without ATU (TAM-O and TAM-R), the findings demonstrate that incorporating ATU provides a more comprehensive understanding of user behavior, particularly in voluntary use contexts. The study underscores the importance of attitudes in technology adoption and suggests that future TAM iterations should consider ATU to improve predictive accuracy. This research contributes to theoretical advancements in TAM and offers practical insights for enhancing technology acceptance in educational settings.

Changes in Technology Acceptance of Smart Care Beds Among Long-Term Care Workers in Korea.

This study investigates the changing perceptions of Korean care workers regarding a robotic bed designed to assist with repositioning and prevent pressure ulcers. With the primary aim of assessing the technology acceptance among care workers using the robotic bed to solve the problem of a shortage of care workers in Korea, we sought to examine the possibility of applying care robots in the field. A total of 20 long-term care workers participated in the experiment, and their attitudes were measured before and after using the robot. Frequency analysis and paired t-tests were conducted using Stata 17 to analyze the data. The results show significant changes in the perceived ease of use (PEOU), facilitating conditions (FCs), and gerontology anxiety (GA), with the PEOU increasing by 19.87%, FC increasing by 20.63%, and GA decreasing by 17.2%. However, there was no significant change in the perceived usefulness (PU) and intention to use (IU). The results showing that the perception of technology acceptance changed significantly mean that the use of the care robot means that there is a high possibility of positive perceptions in Korean care settings when care robots are distributed in the field in the future, considering that the experimental environment was limited due to the early stage of development of care robots. This study highlights the need for practical demonstrations and thorough training to improve technology acceptance among care workers before the application of care technology in the long-term care environment in South Korea.

The Distinction Between Search and Discovery Systems : A Scholarly Analysis

This comprehensive analysis explores the fundamental distinctions between search and discovery systems in the context of information retrieval and user interaction. The article examines the underlying principles, methodologies, and user experiences associated with each system, elucidating their unique roles in the modern digital landscape. By comparing key aspects such as user intent, interaction models, algorithmic foundations, and evaluation metrics, the article highlights the complementary strengths of these paradigms. The research also investigates emerging hybrid approaches that combine elements of both search and discovery, addressing evolving user expectations and the challenges of information overload. Through this in-depth exploration, the article contributes to the ongoing discourse on the future of information access and informs the development of more effective, user-centric systems capable of meeting diverse information needs in an increasingly complex digital ecosystem.

Psychometric Properties of the Automated Insulin Delivery: Benefits and Burdens Scale for Adults with Type 1 Diabetes.

Objective: To evaluate the psychometric properties of a patient-reported outcome measure, the Automated Insulin Delivery-Benefits and Burdens Scale (AID-BBS), which was designed to assess benefits and burdens of AID use in adults with type 1 diabetes (T1D). The measure was hypothesized to have validity, reliability, and clinical utility for predicting likelihood of continued use of an AID system. Research Design and Methods: A total of 217 adults with T1D (ages from 18 to 82 years) who were enrolled in an AID system research trial completed AID-BBS items at study midpoint (6 weeks) and at the end of the trial (13 weeks). Data were collected on pre-post glycemic outcomes. Participants completed other patient-reported psychosocial outcome measures (e.g., emotional well-being, diabetes distress, attitudes toward diabetes technology, diabetes treatment satisfaction) at Week 13. Likelihood of continued device use was assessed with three items at 13 weeks. Results: Exploratory factor analysis supported a one-factor structure for each subscale (15-item benefit and 9-item burden subscale) when evaluated separately. Convergent, discriminant, and predictive validity, internal consistency, and test-retest reliability were supported. Benefit and burden subscales at week 6 predicted usage intention above and beyond device impact on glycemic outcomes, also controlling for baseline glycemic outcomes. Conclusion: Findings support the AID-BBS as a psychometrically valid, reliable, and useful instrument for assessing burdens and benefits associated with AID system use in adults with T1D. The measure can be used to help health care providers set realistic expectations and proactively address modifiable burdens. Clinical Trial Registration Number: NCT04200313.

Analysis of sustainable E-money usage in a developing country: Perspective from the development of the Philippines

Emerging technologies and their applications are steadily gaining traction and diversifying across various domains, including the financial sector. E-money represents one such extension, facilitating cashless transactions through mobile applications which has recently bloomed in the Philippines. This research sought to offer valuable insights into the role of digital currency in developing emerging economies by integrating the UTAUT2 model with the Sustainability Theory of Planned Behavior (STPB). This study aimed to assist developing nations in gauging their communities' attitudes towards e-money from a sustainability standpoint. The study involved the analysis of responses from 638 valid participants, employing Structural Equation Modeling (SEM). The outcomes revealed that effort expectancy (β: 0.398) significantly influenced e-money adoption, as per the integrated UTAUT2 and STPB framework, followed by other UTAUT2 domains. Subsequently, attitude (β: 0.368) and subjective norm (β: 0.273) followed in terms of their impact, alongside perceived behavioral control (β: 0.26) and the sustainability domains. These findings shed light on users' perception of e-money as a valuable addition to their daily lives, primarily due to its transactional convenience. Users also believe that e-money enhances their ability to achieve personal goals, giving them control over their time and convenience. As a result, these study findings are pertinent for comprehending user intentions, especially within the context of the continuous advancement of technology.

The Impact of Logistics Robot Delivery Service Quality on Usage Intention: Focusing on the Mediating Effect of Playfulness

The Impact of Logistics Robot Delivery Service Quality on Usage Intention: Focusing on the Mediating Effect of Playfulness

Decoding Users’ Continuance Intentions towards Digital Financial Platforms in the Indian Economy

Indian financial sector has seen a dramatic change in recent years, with digital finance unlocking new possibilities for financial inclusion. This paper investigates the factors influencing the continuous usage intention and advocacy intention, regarding digital finance, in the Indian economy. Utilizing the Stimulus-Organism-Response (S-O-R) model, the study examines how ease of use, credibility and ubiquity (stimulus) could influence user trust, satisfaction, and engagement (organism), ultimately affecting their intention to use and recommend the service (response) continuously. Non-probability convenience sampling technique was used to select the study sample. Based on the results, features of digital financial platforms such as ease of use, credibility and ubiquity, were found to have a significant impact on user trust, satisfaction, and engagement. These factors partially mediate the stimulus and response variables. The research findings provide Digital Financial Service Providers (DFSPs) with practical guidance on creating and implementing user-centric platforms that create engagement and trust. User experience should be prioritized and making sure that platform should be accessible, easy to use and compatible with a variety of devices.

Research on the influence of perceived quality on users' continuance usage intention of online live streaming class platforms: the mediating role of flow experience and the moderating impact of perceived usefulness.

With the penetration of the "Internet+" into social life, the digitization of education has become a trend and national demand. Alongside the rise of the "Everyone Live Streaming" era, online live streaming class platforms have rapidly grown due to favorable user experiences. Grounded in the Expectation Confirmation Theory, this study employed questionnaire surveys to investigate the influence mechanism of perceived quality, flow experience, and perceived usefulness on individuals' continuance usage intention of online live streaming class platforms. Through the analysis of 773 survey responses, this study reveals several key findings. Firstly, perceived quality significantly and positively affects users' intention to continue using the online live streaming class platforms. Secondly, flow experience partially mediates the relationship between perceived quality and continuance usage intention, underscoring its significance in user decision-making. Additionally, Perceived usefulness negatively moderates the relationship between perceived quality and flow experience, demonstrating varied effects. On the one hand, online live streaming platforms should actively invest in improving the quality of user perception, as this can lead to a better user flow experience and continued willingness to use. On the other hand, they should also pay more attention to the user's flow experience, which has a direct impact on the user's propensity to continue to use. Besides, the perceived usefulness of the online live class by the user should also be taken seriously.

Are public disease outbreaks still relevant to tourists’ decisions about using shared mobility?

ABSTRACT This research note investigates how potential public health crises affect shared mobility use by tourists. Using mixed methods, it extends the Protection Motivation Theory and Usage Situation Theory by exploring how trust and risk management influence usage intentions among current users and non-users of shared mobility services. Its qualitative analysis reveals the importance of tailored communication and transparency from service providers to foster trust and encourage adoption. Its quantitative ones indicate that experienced users emphasise coping mechanisms, whilst non-users are more swayed by perceived threats. Although COVID-19 restrictions have eased, work of this kind will remain relevant as the tourism sector adapts to enduring changes and prepares for potential future crises. By offering strategies to enhance shared mobility resilience during health emergencies, this study provides valuable guidance for tourism, hospitality and mobility stakeholders navigating an uncertain time.

Service Quality and its Impact on Usage Intention in Malaysian Rail Transportation

Service Quality and its Impact on Usage Intention in Malaysian Rail Transportation

Efficient Session-based Recommendation with Contrastive Graph-based Shortest Path Search

Session-based recommendation aims to predict the next item based on a set of anonymous sessions. Capturing user intent from a short interaction sequence imposes a variety of challenges since no user profiles are available and interaction data is naturally sparse. Recent approaches relying on graph neural networks (GNNs) for session-based recommendation use global item relations to explore collaborative information from different sessions. These methods capture the topological structure of the graph and rely on multi-hop information aggregation in GNNs to exchange information along edges. Consequently, graph-based models suffer from noisy item relations in the training data and introduce high complexity for large item catalogs. We propose to explicitly model the multi-hop information aggregation mechanism over multiple layers via shortest-path edges based on knowledge from the sequential recommendation domain. Our approach does not require multiple layers to exchange information and ignores unreliable item-item relations. Furthermore, to address inherent data sparsity, we are the first to apply supervised contrastive learning by mining data-driven positive and hard negative item samples from the training data. Extensive experiments on four different datasets show that the proposed approach outperforms almost all of the state-of-the-art methods.

How does perceived security influence mobile wallet users' behavior? A SEM investigation

PurposeThe digital disruptions have provided alternative methods of monetary transactions. Despite the digital wave, cash as a payment option has regained its position. The purpose of this research is to investigate behavioral intentions of mobile wallet (m-wallet) users. The paper explores the dynamics of perception, behavioral intention motivation and satisfaction of m-wallet users.Design/methodology/approachThe authors have used a self-administered questionnaire for data collection. A total of 506 responses were analysed using confirmatory factor analysis in conjunction with Structural Equation Modeling, ensuring the validity and reliability of the insights into the behavioral dynamics of m-wallet users.FindingsThe research highlights the direct impact of perceived security on m-wallet users' perceptions, which subsequently influence both direct and indirect behavioral intentions. Moreover, satisfaction emerged as a significant determinant directly shaping behavioral intentions.Originality/valueThis study contributes significantly to the existing literature by offering a comprehensive understanding of the factors driving m-wallet adoption and usage intentions, thereby equipping stakeholders and policymakers with the necessary tools to devise effective strategies to promote mobile payment technologies in North India. The study employs a multifaceted model that incorporates six key elements, providing a comprehensive understanding of the complex interrelationships among these variables.

Understanding the key driver of e-government services continuance usage intention: An integrated model of Expectation Confirmation Model and Technology Acceptance Model

Continuous usage is crucial for ensuring the longevity of technological advancements. The success of e-government is contingent upon its ongoing use, rather than its initial acceptance. Nevertheless, there has been a dearth of scholarly research on the ongoing use of e-government services. The objective of this study was to identify the primary factors that influences the continued use of e-government services in Indonesia. The research model was created by integrating both Expectation Confirmation Model and Technology Acceptance Model, two theories that are frequently employed in the adoption of technology. The data was obtained by administering an online survey to 217 Indonesian citizens who had previously utilized the Online Citizen Aspiration and Complaints Service (LAPOR) e-Government services. The results indicate that perceived ease of use had a substantial impact on citizen satisfaction and perceived usefulness. In contrast to previous research conducted in the context of e-Government, it was found that perceived usefulness did not have a significant correlation with the intention to continue using the system. The most significant predictor of continued intention to use was citizen satisfaction. Surprisingly, satisfaction was more significantly influenced by perceived ease of use than perceived usefulness. The implications of these findings are elaborated upon.