Determinants Of Usage Intention Research Articles

Game psychotherapy intervention based on entertainment interactive robots for preventing depression in university students

This study designs an entertainment interactive robot system for gaming psychotherapy intervention and evaluates its effectiveness in preventing depression in students. In terms of the architecture and hardware of the entertainment interactive robot system, the robot adopts high-performance processors and sensors to achieve the ability to quickly respond and accurately perceive user actions. In terms of software, a mathematical model was established based on the structure and kinematic principles of the robot to describe its motion. Preprocess user gesture data by analyzing the features and patterns of user gestures, extracting key action information, and using machine learning algorithms or deep learning models to classify and recognize gestures to determine user intent and action types. Evaluate action recognition and effectiveness, and verify the accuracy and reliability of the gesture interaction module through comparison and testing with actual user gestures. The study designed an entertainment interactive robot assisted game that provides a fun and interactive experience to attract students’ attention and stimulate positive emotions. Through game design and behavioral pattern evaluation, the research team evaluated the preventive effect of entertainment interactive robot assisted games on depression and proposed optimization strategies.

Influence of Perceived Value on Clients' Behavioural Intention in Online Psychological Counseling

This study employed quantitative research methods to survey 400 customers in China who had used online psychological counseling. Subsequently, SmartPLS4.0 was used to conduct structural equation modeling to test the comprehensive relationship between variables. The research demonstrates the role of emotional experience, perceived usefulness, and ease of use in determining user intent. Conversely, there is a negative correlation with perceived cost. The findings suggest that cost plays a significant role in user decision-making when it comes to online psychological counseling. These results provide valuable insight into how users perceive such services and can guide their development.

COVID-19 Emergency and Changes in the Usage Intention of Mobile Banking Services in Pakistani Peri-Urban Areas

This study demonstrates the usage intention of mobile banking services by Pakistanis living in peri-urban areas of Punjab in the time of COVID-19 emergency by adapting the technology acceptance model (TAM) and technology model of readiness (TR). Understanding the relationships between the determinants of usage intention helps the management of Pakistani banks to run the gamut in its mobile banking services in Pakistani outskirts even when the COVID-19 pandemic is over. To test this relationship, data was collected (n=392) from the four largest cities of Punjab by using convenience sampling techniques. The data was analyzed by hierarchical regression using SPSS 19. The results indicate that the proposed model verified the effect of Positive TR (Optimism, Innovation) and hostile TR (Insecurity, Discomfort), perceived usefulness and behavioral intention towards the use of mobile banking, especially in the period of COVID-19 pandemic. It appears that the COVID-19 pandemic has changed the psychological processes of people living in peri-urban (Lahore, Faisalabad and Multan) and outskirts to use mobile banking services continuingly. This research examined the perception of people living in the peri-urban areas of Punjab, Pakistan, to use mobile banking services for initially applying the Technology Readiness acceptance model (TRAM) with distinct roles of Technology readiness. Furthermore, mediating effects of perceived usefulness are determined between TR and Behavioral intention regarding the use of mobile banking.

Identifying the Antecedents of University Students’ Usage Behaviour of Fitness Apps

The purpose of the study is to explore the antecedents of university students’ fitness application usage behaviours by combining the theory of planned behaviour and the technology acceptance model. An anonymous questionnaire survey was adopted to address the objectives of the study. Purposive and snowball sampling was used to select eligible students from six universities in Zhanjiang City. An online survey was used to collect data from 634 eligible subjects, and partial least squares structural equation modelling was used to analyse the collected data. The results indicated that the students’ perceived usefulness (β = 0.17, p < 0.05) and perceived ease of use (β = 0.32, p < 0.05) concerning the application and their attitude (β = 0.31, p < 0.05) toward it significantly influenced their usage intentions. Furthermore, perceived usefulness (β = 0.11, p < 0.05) and perceived ease of use (β = 0.38, p < 0.05) fully mediated the relationship between subjective norms and usage intentions. However, subjective norms and perceived behavioural control did not enhance the students’ intentions to use fitness applications. That is, students’ attitudes and fitness application design are the determinants of usage intention. Accordingly, improving students’ fitness applications usage intention requires strategies that involve customised services, social networking, and collaboration with schools; this would further increase students’ engagement in physical exercise.

DBOS: A Dialog-Based Object Query System for Hospital Nurses.

Due to the advance of indoor positioning technology, it is now possible to trace mobile medical equipment (such as electrocardiography machines, patient monitors, and so on) being moved around a hospital ward. With the support of an object tracking system, nurses can easily locate and find a device, especially when they prepare for a shift change or a medical treatment. As nurses usually face high workloads, it is highly desirable to provide nurses with a user-friendly search interface integrated into a popular mobile app that they use daily. For this, DBOS, a dialog-based object query system, is proposed, which simulates a real conversation with users via the Line messaging app’s chatbot interface. A hybrid method that combines cosine similarity (CS) and term frequency–inverse document frequency (TF-IDF) is used to determine user intent. The result is returned to the user through Line’s interface. To evaluate the applicability of DBOS, 70 search queries given by a head nurse were tested. DBOS was compared with CS, TF-IDF, and Facebook Wit.ai respectively. The experiment results show that DBOS outperforms the abovementioned methods and can achieve a 92.8% accuracy in identifying user intent.

Adoption of telemedicine applications among Saudi citizens during COVID-19 pandemic: An alternative health delivery system

This study aims to investigate individual behavior towards adoption of telemedicine application based on wireless sensor network applications (WSN) during COVID-19 pandemic. Therefore, the research model extends the unified theory of acceptance and use of technology with the task technology fit model, awareness and self-efficacy to determine user intention to adopt wireless sensor network application for medical assistance. A research survey was conducted towards Saudi citizens in voluntary setting. In response, 348 valid responses were retrieved. Sample size was computed with prior-power analysis using G-power software. The research model is empirically tested with latest statistical approach namely structural equation modeling (SEM). Findings of the structural model indicate that wireless sensor network application was jointly predicted by performance expectancy, social influence, effort expectancy, facilitating condition, task technology fit, awareness and self-efficacy and explained substantial variance R2 79.5% in individual behavior to adopt wireless sensor network application. The effect size analysis (f2) revealed that task technology fit and facilitating condition were the most important factors to determine user intention to adopt wireless sensor network application. The current study contributes to theory by extending the unified theory of acceptance and use of technology with task technology fit model, self-efficacy and awareness and enrich information system literature in the context of user intention to adopt wireless sensor network application. Practically, this study suggest that health consultant should focus on technology fitness including wireless devices and the number of facilities to operate wireless sensor network which in turn boost user confidence towards adoption of wireless sensor network application for health consultation. This study develops a unique research model that inspects user behavior towards adoption of wireless sensor network technology for better health care system. The findings of this study answer how to monitor COVID-19 while using of wireless sensor network applications. Moreover, this study is useful for clinical management through virtual environment and guides health advisors, software developers and physicians to design user friendly telemedicine application by using wireless sensor network technology.

Multi-turn intent determination and slot filling with neural networks and regular expressions

Intent determination and slot filling are two prominent research areas related to natural language understanding (NLU). In a multi-turn NLU system, contextual information from dialogue history is exploited to mitigate the ambiguity of user utterance. State-of-the-art models employ memory networks to encode dialogue context, which is used by neural networks for determining user intent and associated slots. However, these methods rely on a large amount of labelled data, whereas we often have limited labelled data. To address this problem, we propose a multi-task learning model based on neural networks and regular expressions (REs), to jointly perform intent determination and slot filling tasks. The proposed model integrates neural networks with REs to encode domain knowledge and handle cases with a limited amount of labelled data in an end-to-end trainable manner. More specifically, the model employs a pre-trained BERT model to obtain contextual word representations of user utterances. These representations are utilized by a memory network to encode multi-turn information which is shared by the tasks. Furthermore, the convolutional neural network (CNN) and the recurrent neural network (RNN) are applied to contextual word representations and dialogue context for intent determination and slot filling tasks, respectively. These neural networks are then combined with REs which encode domain knowledge about a particular intent or slot value. Finally, the two neural networks are trained simultaneously by minimizing the joint loss. Extensive experiments on Key-Value Retrieval and Frames datasets show that the proposed model outperforms baseline methods in both tasks while requiring modest human effort.

Determining User Intent of Partly Dynamic Shoulder Tasks in Individuals With Chronic Stroke Using Pattern Recognition.

Stroke remains the leading cause of long-term disability in the US. Although therapy can achieve limited improvement of paretic arm use and performance, weakness and abnormal muscle synergies-which cause unintentional elbow, wrist, and finger flexion during shoulder abduction-contribute significantly to limb disuse and compound rehabilitation efforts. Emerging wearable exoskeleton technology could provide powered abduction support for the paretic arm, but requires a clinically feasible, robust control scheme capable of differentiating multiple shoulder degrees-of-freedom. This study examines whether pattern recognition of sensor data can accurately identify user intent for 9 combinations of 1- and 2- degree-of-freedom shoulder tasks. Participants with stroke (n = 12) used their paretic and non-paretic arms, and healthy controls (n = 12) used their dominant arm to complete tasks on a lab-based robot involving combinations of abduction, adduction, and internal and external rotation of the shoulder. We examined the effect of arm (paretic, non-paretic), load level (25% vs 50% maximal voluntary torque), and dataset (electromyography, load cell, or combined) on classifier performance. Results suggest that paretic arm, lower load levels, and using load cell or EMG data alone reduced classifier accuracy. However, this method still shows promise. Further work will examine classifier-user interaction during active control of a robotic device and optimization/minimization of sensors.

An Extended Decomposed Theory of Planned Behaviour to Predict the Usage Intention of the Electric Car: A Multi-Group Comparison

An Extended Decomposed Theory of Planned Behaviour (DTPB) is developed that integrates emotions towards car driving and electric cars as well as car driving habits of the DTPB, and is empirically validated in a Belgian sample (n = 1023). Multi-group comparisons explore how the determinants of usage intention are different between groups of consumers differing in environmentally-friendly behaviour, environmental concern, innovativeness and personal values. Besides attitudes, media, perceived complexity, compatibility and relative advantage, emotions towards the electric car and reflective emotions towards car driving have a strong effect on usage intention. Car driving habits and perceived behavioural control (facilitators and constraints) do not substantially affect usage intention. Only people differing in personal values show a different motivational structure for a number of important drivers of usage intention.

Determinants of Usage Intention of LINE Users in Taiwan

With the huge user base, LINE has become an important medium of mobile communications in the world. This study attempts to explore the factors influencing usage intention for LINE users. Drawing on the theory of planned behavior (TPB), this study adds three antecedents, including perceived enjoyment, perceived critical mass, and self-efficacy into the TPB model, and further examines the moderating effect of frequent users on the causal relationships. A structural equation modeling is used and 458 LINE users in Taiwan are investigated. The results reveal that perceived enjoyment and perceived critical mass are positively associated with behavioral attitude. Also, self-efficacy is positively associated with perceived behavioral control. Moreover, behavioral attitude, subjective norms, and perceived behavioral control are positively associated with usage intention, and the causal relationships are significantly varied between frequent and infrequent users.

Failure‐resilient real‐time processing of health streams

SummaryThe ability to analyze streaming data in real time is vital in systems that process data from health sensors. These systems need to build and maintain state, as well as preserve this state during system failures. In this work, we introduce a fault‐tolerance scheme designed for the Granules stream processing system. We work with two distinct health stream datasets: thorax extension and electroencephalogram (EEG) signal analysis. We have developed a monitoring program to track trends in the thorax extension dataset and a classification system for the EEG dataset, which allows us to determine user intent from EEG signals. Using these two motivating applications, we have explored several approaches to fault tolerance through replication, developing a hybrid approach that is particularly suited to health streams. Copyright © 2014 John Wiley & Sons, Ltd.

Neuromuscular Interfacing: Establishing an EMG-Driven Model for the Human Elbow Joint

Assistive devices aim to mitigate the effects of physical disability by aiding users to move their limbs or by rehabilitating through therapy. These devices are commonly embodied by robotic or exoskeletal systems that are still in development and use the electromyographic (EMG) signal to determine user intent. Not much focus has been placed on developing a neuromuscular interface (NI) that solely relies on the EMG signal, and does not require modifications to the end user's state to enhance the signal (such as adding weights). This paper presents the development of a flexible, physiological model for the elbow joint that is leading toward the implementation of an NI, which predicts joint motion from EMG signals for both able-bodied and less-abled users. The approach uses musculotendon models to determine muscle contraction forces, a proposed musculoskeletal model to determine total joint torque, and a kinematic model to determine joint rotational kinematics. After a sensitivity analysis and tuning using genetic algorithms, subject trials yielded an average root-mean-square error of 6.53° and 22.4° for a single cycle and random cycles of movement of the elbow joint, respectively. This helps us to validate the elbow model and paves the way toward the development of an NI.

The Effects of Personality Traits on User Acceptance of Mobile Commerce

Existing research on user acceptance of mobile commerce has found that technological perceptions—such as perceived usefulness, perceived ease of use, and perceived compatibility—had significant effects on user behavior. However, the effects of personality traits have seldom been examined. The purpose of this research is to examine the effects of five personality traits of extraversion, agreeableness, openness to experience, conscientiousness, and neuroticism on user adoption of mobile commerce. Partial least squares was employed to conduct data analysis. The results show that extraversion has a strong effect on trust, whereas neuroticism has significantly negative effects on trust and perceived usefulness, both of which determine user intention to adopt mobile commerce.

Intention to Use Biometric Systems

This study set out to investigate the critical factors that determine user intention to use a biometric system. We integrated previous research in the technology acceptance and biometric engineering literatures and identified six important factors: Perceived System Security, Perceived False Acceptance rates, Perceived False Rejection rates, Perceived System Invasiveness, System Trust and Facilitating Conditions. Eleven hypotheses were developed regarding the six factors to formulate direct and indirect influences of the six factors in a biometric intention-to-use model. The results support the eleven hypotheses and validate the model, which has an average variance explained of 31.1%. The contributions of the paper are discussed and future research directions are suggested.

Resistance to change and the adoption of digital libraries: An integrative model

AbstractIn this paper we extend earlier work on the role of the personality trait of resistance to change (RTC) in the adoption of digital libraries. We present an integrative study, drawing on a number of research streams, including IT adoption, social psychology, and digital‐library acceptance. Using structural equation modeling, we confirm RTC as a direct antecedent of effort expectancy. In addition, we also find that by affecting computer anxiety and result demonstrability, RTC acts as an indirect antecedent to both effort expectancy and performance expectancy, which in turn determine user intention to adopt digital library technology. Implications for research and practice are discussed.

Technology acceptance model for wireless Internet

Wireless Internet via mobile devices (WIMD) is leading the world into another spectrum of communications and means of conducting day‐to‐day business and life activities. Full bloom of wireless Internet services depends on user acceptance, as well as technology improvement. This paper develops a technology acceptance model for wireless Internet via mobile devices (TAM for wireless Internet), a conceptual framework to explain the factors influencing user acceptance of WIMD. By revising the technology acceptance model (TAM) to represent some unique features of the wireless system under study, TAM for wireless Internet proposes that constructs such as individual differences, technology complexity, facilitating conditions, social influences, and wireless trust environment determine user‐perceived short and long‐term usefulness, and ease of using WIMD. These, in turn, determine user intention and willingness to adopt WIMD. Twelve propositions are developed to promote and facilitate future empirical research relating to WIMD.