User Intention Research Articles

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.

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.

Optimizing the Utilization of Generative Artificial Intelligence (AI) in the AEC Industry: ChatGPT Prompt Engineering and Design

Generative Artificial Intelligence (AI) holds significant potential for revolutionizing the Architecture, Engineering, and Construction (AEC) industry by automating complex tasks such as construction scheduling, hazard recognition, resource leveling, information retrieval from BIM, etc. However, realizing this potential requires a strategic approach to ensure effective utilization and maximum benefit. This paper presents guidelines for prompt design and engineering to elicit desired responses from ChatGPT, a Generative AI tool, in AEC applications. Key steps include understanding user intent, leveraging model capabilities, and optimizing prompt structures. By following these guidelines, stakeholders in the AEC industry can harness the power of Generative AI to improve construction scheduling processes, increase project efficiency, and ultimately drive innovation and growth in the industry. Several illustrative examples on construction scheduling and hazard recognition are provided to demonstrate the methodology proposed in this research. It is concluded that Generative AI, when effectively utilized, significantly enhances project scheduling and hazard recognition capability in the AEC industry with minimal error.

Modeling the critical success factors for continuous usage intention of foodservice mobile apps

ABSTRACT Foodservice apps are gaining popularity and have garnered significant scholarly and industry interest, yet there is a limited understanding of the critical factors driving their continuous usage. Insights about critical success factors can help organizations anticipate this industry’s future. To address this gap, the study has developed a hierarchical model to evaluate the relationships among critical success factors affecting the continuous usage intention of foodservice apps. A five-level hierarchical structure is established using modified Total Interpretive Structural Modeling and Matrice d’Impacts Croisés Multiplication Appliquée à un Classement analysis. The analysis identifies and categorizes eight critical success factors through an extensive literature review verified by consulting experts. The findings have shown varying levels of influence and dependence among the critical success factors. Information quality, food quality, app design, and perceived healthiness were the most significant factors driving the intention for continuous usage. The study’s practical implications are significant for foodservice managers and app developers, suggesting that prioritizing factors like information quality, app design, and perceived healthiness can enhance user engagement and long-term app usage.

How loneliness leads to the conversational AI usage intention: the roles of anthropomorphic interface, para-social interaction

How loneliness leads to the conversational AI usage intention: the roles of anthropomorphic interface, para-social interaction

Promoting Sustainable Transportation: Factors Influencing Battery Electric Vehicle Adoption Across Age Groups in Thailand

Battery Electric Vehicles (BEVs) are a crucial innovation for achieving sustainable transportation and reducing greenhouse gas emissions, which are major contributors to global warming and climate change. While previous studies have explored attitudes towards BEV technology acceptance, few have examined the interplay of external factors such as government measures and adoption barriers in promoting sustainable mobility. This study addresses this gap by investigating the roles of government policies, usage obstacles and innovation diffusion in stimulating BEV purchase intentions, while applying the Innovative Diffusion Theory (IDT). Data from 3632 respondents in Thailand were analyzed using structural equation modeling (SEM) to examine causal relationships between factors. The results indicate that government policies supporting BEV users enhance innovation diffusion in society, leading to increased adoption intentions. Furthermore, effective policies help mitigate barriers to BEV usage, further encouraging adoption. The study also reveals that causal relationships of BEV usage intentions vary across age groups, highlighting the need for targeted approaches in promoting sustainable transportation. These findings contribute to the development of evidence-based policy recommendations to accelerate BEV adoption, supporting Thailand’s Carbon Neutrality goals and broader sustainable development objectives. By elucidating the complex dynamics of BEV adoption, this research provides valuable insights for policymakers and stakeholders working towards a more sustainable and environmentally friendly transportation sector.

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

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.

Wearable neurofeedback acceptance model for students’ stress and anxiety management in academic settings

This study investigates the technology acceptance of a proposed multimodal wearable sensing framework, named mSense, within the context of non-invasive real-time neurofeedback for student stress and anxiety management. The COVID-19 pandemic has intensified mental health challenges, particularly for students. Non-invasive techniques, such as wearable biofeedback and neurofeedback devices, are suggested as potential solutions. To explore the acceptance and intention to use such innovative devices, this research applies the Technology Acceptance Model (TAM), based on the co-creation approach. An online survey was conducted with 106 participants, including higher education students, health researchers, medical professionals, and software developers. The TAM key constructs (usage attitude, perceived usefulness, perceived ease of use, and intention to use) were validated through statistical analysis, including Partial Least Square-Structural Equation Modeling. Additionally, qualitative analysis of open-ended survey responses was performed. Results confirm the acceptance of the mSense framework for neurofeedback-based stress and anxiety management. The study contributes valuable insights into factors influencing user intention to use multimodal wearable devices in educational settings. The findings have theoretical implications for technology acceptance and practical implications for extending the usage of innovative sensors in clinical and educational environments, thereby supporting both physical and mental health.

Adoption of SPACE-learning management system in education era 4.0: an extended technology acceptance model with self-efficacy

The COVID-19 pandemic has caused constraints on education that takes place face-to-face; thus, the learning process has been conducted online. With this emergence, an application-based learning tool called a Learning Management System (LMS) was created to cater to the requirements of distant students. Therefore, this study explores how SPACE-LMS is used in the Teacher Professional Education Program (TPEP) and how it interacts with the Technology Acceptance Model (TAM) and self-efficacy. This study collected data from TPEP participants in the province of West Sumatra-Indonesia from the 2022 cohort, and 245 individuals actively participated. As the quantitative method, Partial Least Square-Structural Equation Modeling (PLS-SEM) analysis with SmartPLS software was used to test and predict the conceptual model. The empirical findings demonstrate that the readiness of adopting SPACE influences perceived ease of use (PEU) and perceived usefulness (PU). Self-efficacy (SE) influences PEU and intention to use (ITU); PEU influences PU; PEU influences attitudes toward users (ATT); and PU influences ATT. Interestingly, PU had no impact on the ITU. The desire of TPEP students to use the SPACE-LMS is also influenced by their sense of self-efficacy. Thus, this study has both theoretical and practical implications.

Assessing the impact of consumption values on satisfaction and usage intentions of millet-based food products

PurposeThe purpose of this study is to assess the impact of consumption values on customer satisfaction and intention to consume millet-based foods. This study examines both tangible and intangible values of the millet-based food to the customer.Design/methodology/approachBased on consumption value theory (CVT), the present study evaluates the impact of consumption value factors such as emotional, epistemic, health, price, prestige and taste/quality values on satisfaction and intention to use millet-based foods and develops a unique research model. Later, leveraging a dataset comprising more than 410 responses from Pune city in India, a rigorous empirical examination of the proposed model was conducted employing SmartPLS 4.0 software.FindingsThe study's finding unveils both the significant and insignificant impacts of various consumption values, both from the tangible and intangible consumption value perspectives on satisfaction and intention to consume millet-based foods. The study also provides a validated food consumption model which can be used for other similar food consumption behaviour of the customers.Research limitations/implicationsThis study provides insights as to how different consumption values impact the satisfaction of the customers for the millet-based food. This study illuminates additional aspects of the tangible and intangible factors impacting the satisfaction level of customers to consume millet-based products. Also, this is a cross-sectional study, and the respondents are only based in Pune, India. Thus, the study results cannot be generalised.Originality/valueThis study employs CVT to explore the unexplored impact of consumption values on satisfaction and intention to use millet-based foods, which is unexplored in the past literature. Moreover, this study develops a theoretical model with high explanatory power, and the research model adds value to the existing body of literature from areas such as food preference, consumer behaviour and value consumption insights.

Measurement and performance evaluation of triple band differential integrated extraoral rectifying antenna for data transfer and RF energy harvesting for tongue drive system

Wearable assistive devices are vitally important for tetraplegic individuals to provide valuable insights into their intended directives tailored to tongue motions in wireless healthcare industries. The flexible differentially driven extraoral antenna and rectenna measurement system are developed to enable differential sensing and monitoring of the set of unique tongue gestures for extraoral tongue drive system (eTDS) applications in three frequency bands of Industrial, Scientific, and Medical (ISM) (915.0 MHz, 2400 MHz, and 5800 MHz). The performance analysis is carried out using the heterogeneous human head model. The differential rectifier is coplanarly integrated with the differential extraoral antenna on the same 0.254 mm thin and 9.5 mm wider Rogers RT/ Duroid 6010 LM substrate. The footprint of the fabricated differential rectenna is 0.135 \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{\\varvec{\\lambda\\:}}_{\\varvec{g}}\ imes\\:$$\\end{document} 0.082 \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{\\varvec{\\lambda\\:}}_{\\varvec{g}}$$\\end{document}\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:\ imes\\:$$\\end{document} 0.002 \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{\\varvec{\\lambda\\:}}_{\\varvec{g}}$$\\end{document} where the planar size of differential rectifier is 15.75\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:\\:\ imes\\:$$\\end{document} 2.5 mm2. The fabricated systems are situated closely to an artificial head model. The maximum conversion efficiency achieved at 2400 MHz and 5800 MHz is 83.45% and 74.8%, respectively, for 11 dBm of RF input power. Further, the link analysis, including interfacing circuit losses, was carried out theoretically at 915 MHz. Thus, the proposed differential extraoral systems can be employed to acquire and transmit the user intentions in eTDS inspired healthcare applications.

Cybernic robot hand-arm that realizes cooperative work as a new hand-arm for people with a single upper-limb dysfunction

A robot hand-arm that can perform various tasks with the unaffected arm could ease the daily lives of patients with a single upper-limb dysfunction. A smooth interaction between robot and patient is desirable since their other arm functions normally. If the robot can move in response to the user’s intentions and cooperate with the unaffected arm, even without detailed operation, it can effectively assist with daily tasks. This study aims to propose and develop a cybernic robot hand-arm with the following features: 1) input of user intention via bioelectrical signals from the paralyzed arm, the unaffected arm’s motion, and voice; 2) autonomous control of support movements; 3) a control system that integrates voluntary and autonomous control by combining 1) and 2) to thus allow smooth work support in cooperation with the unaffected arm, reflecting intention as a part of the body; and 4) a learning function to provide work support across various tasks in daily use. We confirmed the feasibility and usefulness of the proposed system through a pilot study involving three patients. The system learned to support new tasks by working with the user through an operating function that does not require the involvement of the unaffected arm. The system divides the support actions into movement phases and learns the phase-shift conditions from the sensor information about the user’s intention. After learning, the system autonomously performs learned support actions through voluntary phase shifts based on input about the user’s intention via bioelectrical signals, the unaffected arm’s motion, and by voice, enabling smooth collaborative movement with the unaffected arm. Experiments with patients demonstrated that the system could learn and provide smooth work support in cooperation with the unaffected arm to successfully complete tasks they find difficult. Additionally, the questionnaire subjectively confirmed that cooperative work according to the user’s intention was achieved and that work time was within a feasible range for daily life. Furthermore, it was observed that participants who used bioelectrical signals from their paralyzed arm perceived the system as part of their body. We thus confirmed the feasibility and usefulness of various cooperative task supports using the proposed method.

Measuring instability in chronic human intracortical neural recordings towards stable, long-term brain-computer interfaces.

Intracortical brain-computer interfaces (iBCIs) enable people with tetraplegia to gain intuitive cursor control from movement intentions. To translate to practical use, iBCIs should provide reliable performance for extended periods of time. However, performance begins to degrade as the relationship between kinematic intention and recorded neural activity shifts compared to when the decoder was initially trained. In addition to developing decoders to better handle long-term instability, identifying when to recalibrate will also optimize performance. We propose a method, "MINDFUL", to measure instabilities in neural data for useful long-term iBCI,without needing labels of user intentions. Longitudinal data were analyzed from two BrainGate2 participants with tetraplegia as they used fixed decoders to control a computer cursor spanning 142 days and 28 days, respectively. We demonstrate a measure of instability that correlates with changes in closed-loop cursor performance solely based on the recorded neural activity (Pearson r = 0.93 and 0.72, respectively). This result suggests a strategy to infer online iBCI performance from neural data alone and to determine when recalibration should take place for practical long-term use.

Wearable Biosensor Smart Glasses Based on Augmented Reality and Eye Tracking.

With the rapid development of wearable biosensor technology, the combination of head-mounted displays and augmented reality (AR) technology has shown great potential for health monitoring and biomedical diagnosis applications. However, further optimizing its performance and improving data interaction accuracy remain crucial issues that must be addressed. In this study, we develop smart glasses based on augmented reality and eye tracking technology. Through real-time information interaction with the server, the smart glasses realize accurate scene perception and analysis of the user's intention and combine with mixed-reality display technology to provide dynamic and real-time intelligent interaction services. A multi-level hardware architecture and optimized data processing process are adopted during the research process to enhance the system's real-time accuracy. Meanwhile, combining the deep learning method with the geometric model significantly improves the system's ability to perceive user behavior and environmental information in complex environments. The experimental results show that when the distance between the subject and the display is 1 m, the eye tracking accuracy of the smart glasses can reach 1.0° with an error of no more than ±0.1°. This study demonstrates that the effective integration of AR and eye tracking technology dramatically improves the functional performance of smart glasses in multiple scenarios. Future research will further optimize smart glasses' algorithms and hardware performance, enhance their application potential in daily health monitoring and medical diagnosis, and provide more possibilities for the innovative development of wearable devices in medical and health management.

Adaptive and transparent decision-making in autonomous robots through graph-structured world models

ABSTRACT The growing ubiquity of autonomous robots across different fields necessitates that agents adapt to diverse tasks and ensure transparency and intelligibility in their decision-making processes. This study presents a novel framework that combines a graph-structured world model with large language models (LLMs) to address these requirements. First, a latent space is created to capture the reachability between distinct states. Next, a graph is constructed within this latent space by clustering an offline dataset that effectively captures the complex dynamics of the environment. Subsequently, LLMs are employed to redefine the reward function and relabel the dataset, thereby establishing a well-defined Markov decision process based on the previously learned graph. This relabeling process ensures that the agent's decision space aligns with user intentions. Consequently, a predictive world model is obtained, offering insights into potential future states and facilitating graph-based planning. Moreover, by inputting the planned path of the agent in the graph-structured world model into LLMs, natural language explanations can be generated to provide transparency in the decision-making process. Experiments on the D4RL benchmark validated the effectiveness of our approach in long-horizon planning, its adaptability to different user tasks, and its inherent explainability.

Framework to enable and test conversational assistant for APIs and RPAs

AbstractIn the realm of business automation, conversational assistants are emerging as the primary method for making automation software accessible to users in various business sectors. Access to automation primarily occurs through application programming interface (APIs) and robotic process automation (RPAs). To effectively convert APIs and RPAs into chatbots on a larger scale, it is crucial to establish an automated process for generating data and training models that can recognize user intentions, identify questions for conversational slot filling, and provide recommendations for subsequent actions. In this paper, we present a technique for enhancing and generating natural language conversational artifacts from API specifications using large language models (LLMs). The goal is to utilize LLMs in the “build” phase to assist humans in creating skills for digital assistants. As a result, the system does not need to rely on LLMs during conversations with business users, leading to efficient deployment. Along with enabling digital assistants, our system employs LLMs as proxies to simulate human interaction and automatically evaluate the digital assistant's performance. Experimental results highlight the effectiveness of our proposed approach. Our system is deployed in the IBM Watson Orchestrate product for general availability.

Impact of Visual Feedback Configurations in a Task-oriented Immersive Virtual Reality Mirror Therapy.

Mirror Therapy is a form of upper limb therapy in stroke, which consists on showing the mirrored symmetric movement of the unimpaired side using a mirror placed in the medial sagittal plane. The illusion of movement corresponding to the impaired extremity could facilitate neuroplasticity, and assists patients in regaining certain lost motor functions. Recent studies have shown the potential and benefit of translating this effective therapy in an immersive virtual reality (VR) environment, using head mounted display and hand tracking systems. This work investigates the feasibility to use myoelectric control in an immersive VR environment for the mirror therapy to accomplish a funtional task. Surface electromyography sensors were used to measure muscle activation and detect user intention to perform grasping actions in two visual feedback configurations: unimanual and bimanual. Even though in both conditions the virtual mirrored hand is controlled by the healthy hand, the latter creates the illusion that two functional hands cooperate for grasping. A total of 18 healthy subjects participated in the evaluation of the environment, control method and a comparison between the two configurations. This includes self-rating surveys and performance metrics. Results from the Simulator Sickness Questionnaire showed negligible adverse symptoms related to the use of the VR-application proposed. Positive outcomes from the System Usability Scale and Presence Questionnaire affirmed its feasible as a form of therapy for the rehabilitation of hemiparetic stroke patients. Although the adapted Box and Blocks Test proved immediate training-induced learning, performance measures revealed no significant distinctions between visual feedback configurations.

Advancing Session-Based Recommendations with Atten-Mixer+: Dynamic and Adaptive Multi-Level Intent Mining

Session-based recommendation (SBR) systems, traditionally reliant on complex graph neural networks (GNNs), often face challenges with marginal performance improvements despite increased model complexity. In this paper, we dissect the classical GNN-based SBR models and empirically find that the sophisticated GNN propagations might be redundant, given the readout module plays a significant role in GNN-based models. Based on this observation, we introduce Atten-Mixer+, an advanced iteration of our previously developed Multi-Level Attention Mixture Network (Atten-Mixer). Atten-Mixer+ forgoes GNN propagation in favor of a dynamic and adaptive readout process, tailored to the unique characteristics of each session. Different from the vanilla version, Atten-Mixer+ features the Adaptive Intent Scaler (AIS) layer, which dynamically determines the depth of multi-level user intent analysis, and a soft allocation approach for generating user intent queries across entire user interaction sequences. This innovative design allows Atten-Mixer+ to capture a nuanced and comprehensive understanding of user behaviors, overcoming the limitations of fixed-length analysis. Empirical evaluations on benchmark datasets highlight Atten-Mixer+'s superior efficiency and effectiveness, marking a significant step forward in the predictive accuracy of session-based recommendation systems.

Voice Unbound: The Impact of Localization and Experience on Continuous Personal Voice Assistant Usage and Its Drivers

This research explores the key factors driving the continuous use of Personal Voice Assistants (PVAs), particularly emphasising the roles of localization and experience in shaping these behaviours. Data has been gathered from 1041 PVA users through an online survey. By integrating psychological (Attitude, Enjoyment, and Personal Innovativeness), technological (Perceived Ease of Use and Usefulness), and social (Subjective Social Norms) factors, the study uncovers the complex mechanisms that influence users’ continued usage intentions towards PVAs. It also examines how localization and experience moderate these relationships, offering key insights for developers and marketers to optimise users’ continuous usage and expand market reach by effectively targeting diverse consumer segments. Further, qualitative insights reveal user needs and barriers, offering a deeper understanding of user expectations. The study also highlights several promising avenues for future research to further examine evolving dynamics between users and PVAs.