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For fixed-wing micro air vehicles, the attitude determination is usually produced by the horizon/Global Navigation Satellite System (GNSS) in which the GNSS provides yaw estimates, while roll and pitch are computed using horizon sensors. However, the attitude determination has been independently obtained from the two sensors, which will result in insufficient usage of data. Also, when implementing attitude determination algorithms on embedded platforms, the computational resources are highly restricted. This paper aims to propose a computationally efficient linear Kalman filter to solve the problem.

The observation model is in the form of a least-square optimization composed by GNSS and horizontal measurements. Analytical quaternion solution along with its covariance is derived to significantly speed up on-chip computation.

The reconstructed attitude from Horizon/GNSS is integrated with quaternion kinematic equation from gyroscopic data that builds up a fast linear Kalman filter. The proposed filter does not involve coupling effects presented in existing works and will be more robust encountering bad GNSS measurements.

Electronic systems are designed on a real-world fixed-wing plane. Experiments are conducted on this platform that show comparisons on the accuracy and computation execution time of the proposed method and existing representatives. The results indicate that the proposed algorithm is accurate and much faster computation speed in studied scenarios.

Grounded on the value-based adoption model and innovation diffusion theory, this study examined consumer purchase decisions of mobile Global Positioning System (GPS) navigation apps. In addition, this study also investigated the moderating role that perceived availability of free substitutes (PAFS) plays in the relationship between perceived value and purchase intention. The paper aims to discuss these issues.

Data collected from 219 mobile users were analyzed against the research model using the partial least squares approach.

The results showed that compatibility, relative advantage, perceived enjoyment, perceived cost (positively), and complexity (negatively) influenced these users’ value perceptions and purchase decisions. Furthermore, PAFS significantly weakened the positive relationship between perceived value and purchase intentions.

Based on these findings, the authors provide practical suggestions for mobile app developers to increase mobile app sales. This study also helps advance knowledge of mobile internet marketing.

This study is a pioneering effort in explaining consumer purchase intentions in the context of mobile GPS navigation app.

Based on the literature on technology readiness, online learning readiness, and mobile computer anxiety, the purpose of this paper is to develop and validate a mobile learning readiness (MLR) scale which can be used to assess individuals’ readiness to embrace m-learning systems.

Based on previous literature, this study conceptualizes the construct of MLR and generates an initial 55-item MLR scale. A total of 319 responses are collected from a three-month internet-based survey. Based on the sample data, this study provides an empirical validation of the MLR construct and its underlying dimensionality, and develops a generic MLR scale with desirable psychometric properties, including reliability, content validity, criterion-related validity, convergent validity, discriminant validity, and nomological validity.

This study develops and validates a 19-item MLR scale with three dimensions (i.e. m-learning self-efficacy, optimism, and self-directed learning). A tentative norm of the MLR scale is presented, and the scale’s theoretical and practical applications are also discussed.

This study is a pioneering effort to develop and validate a MLR scale. The results of this study are helpful to researchers in building m-learning theories and to educators in assessing and promoting individuals’ acceptance of m-learning systems.

This paper aims to use the Laplace Adomian decomposition method (LADM) to investigate the effects of thermal convection, thermal conduction, surface emissivity and thermal radiation on the heat dissipated by a continuously moving plate undergoing thermal processing.

In performing the analysis, it is assumed that the thermal conductivity and surface emissivity of the plate are both temperature-dependent. The accuracy of the LADM solutions is confirmed by comparing the results obtained for the temperature distribution within the plate with those reported in the literature based on the differential transformation method.

It is shown that the heat dissipated from the plate reduces as the Peclet number increases. By contrast, the dissipated heat increases as any one of the non-dimensionalized parameters of the system, i.e. Nc, Nr and B, increases. In addition, the temperature drop along the length of the plate reduces as parameter A increases owing to a more rapid heat transfer.

The results provide a useful source of reference for the choice of suitable materials and cooling fluids in a variety of practical applications.

This article aims at a Unified Theory of Acceptance and Use of Technology (UTAUT) model framework that was used to investigate the impact of a 16-h smartphone training program on the correlations among different constructs of smartphone use in a sample of older adults.

A total of 208 participants aged 60–78 (mean: 65.4) years completed a questionnaire that collected information on demographic variables and the frequency and duration of smartphone use as well as the answers to questions on the six UTAUT constructs of performance expectancy, effort expectancy, social influence, facilitating conditions, and behavioral intention and usage behavior. The data were analyzed using partial least squares analysis.

This study was the first to compare post-training changes in the correlations among UTAUT constructs. The results revealed significant post-training changes in all construct correlations. Behavioral intention and facilitating conditions were shown to significantly impact usage behavior both before and after training and performance expectancy was shown to impact behavioral intention before training. After training, both effort expectancy and social influence were found to impact behavioral intention significantly. Moreover, the impact of facilitating conditions on usage behavior was significantly increased after training.

To date, no study published in the literature has investigated the impact of technological training on the technology-use intentions and behaviors of older adults. The findings of this study suggest that, for older adults, the results of the acceptance and use model for smartphones change significantly and positively between pre-smartphone training and post-smartphone training time points. The findings support that technology training has a positive impact on smartphone use in older adults.

This study aims to explore the relationship between transformational leadership and employee voice behavior and the role of relational identification and work engagement as mediators in the same.

This study uses structural equation modeling to analyze the data from a questionnaire survey of 251 Taiwanese hospitality industry employees.

The findings demonstrate that transformational leadership has significant relationships with relational identification, work engagement and employee voice behavior and that relational identification and work engagement sequentially mediate between transformational leadership and employee voice behavior.

The results of this study provide insights into the intervening mechanisms linking leaders’ behavior with employees’ voices, while also highlighting the potential importance of relational identification in organizations, especially concerning the enhancement of employees’ work engagement and voice.

The findings reveal the mechanisms by which supervisors’ transformational leadership encourages employees to voice their suggestions, providing empirical evidence of the sequential mediation of relational identification and work engagement. The results help clarify the psychological process by which leaders influence their followers.

The purpose of this paper is to understand what drives customers’ behavioral loyalty and explore the relationship between intentional and behavioral loyalty in the context of e-tailing.

Based on the theory of reasoned action and the recency-frequency-monetary value model, this study proposes a research model to explore the relationships among satisfaction, switching cost, intentional loyalty (i.e. word of mouth (WOM) and repurchase intention), and behavioral loyalty (i.e. purchase frequency and monetary value). Data collected from 266 respondents in the context of e-tailing are tested against the research model using a partial least squares (PLS) approach.

The results indicate that both satisfaction and switching cost are positively related to intentional loyalty (i.e. WOM and repurchase intention), and that the relationship of satisfaction with intentional loyalty outweighs that of switching cost. Additionally, while repurchase intention significantly associates with purchase frequency and monetary value, a relatively small portion of the variance in both purchase frequency and monetary value are explained. More importantly, WOM is unrelated to both purchase frequency and monetary value. The insignificance of WOM and the low predictability of repurchase intention indicate that the relationship between intentional and behavioral loyalty is weak in e-tailing context.

This study provided empirical evidence to support the weak relationship between intentional and behavioral customer loyalty in the context of e-tailing. The findings provide several important theoretical and practical implications for e-tailing customer relationship management.

Student motivation underpins the challenge of learning, made more complex by the move to online education. While emotions are integral to students' motivation, research has, to date, overlooked the dualistic nature of emotions that can cause stress. Using approach-avoidance conflict theory, the authors explore this issue in the context of novel online students' responses to a fully online class.

Using a combination of critical incident technique and laddering, the authors implemented the big data method of sentiment analysis (SA) which results in approach tables with 1,318 tokens and avoid tables with 1,090 tokens. Using lexicon-based SA, the authors identify tokens relating to approach, avoid and mixed emotions.

The authors implemented the big data method of SA which results in approach tables with 1,318 tokens and avoid tables with 1,090 tokens. Using lexicon-based SA, the authors identify tokens relating to approach, avoid and mixed emotions. These ambivalent emotions provide an opportunity for teachers to rapidly diagnose and address issues of student engagement in an online learning class.

Results demonstrate the practical application of SA to unpack the role of emotions in online learner motivation.

The purposes of this paper are (1) to explore the overall development of AI technologies and applications that have been demonstrated to be fundamentally important in the healthcare industry, and their related commercialized products and (2) to identify technologies with promise as the basis of useful applications and profitable products in the AI-healthcare domain.

This study adopts a technology-driven technology roadmap approach, combined with natural language processing (NLP)-based patents analysis, to identify promising and potentially profitable existing AI technologies and products in the domain of AI healthcare.

Robotics technology exhibits huge potential in surgical and diagnostics applications. Intuitive Surgical Inc., manufacturer of the Da Vinci robotic system and Ion robotic lung-biopsy system, dominates the robotics-assisted surgical and diagnostic fields. Diagnostics and medical imaging are particularly active fields for the application of AI, not only for analysis of CT and MRI scans, but also for image archiving and communications.

This study is a pioneering attempt to clarify the interrelationships of particular promising technologies for application and related products in the AI-healthcare domain. Its findings provide critical information about the patent activities of key incumbent actors, and thus offer important insights into recent and current technological and product developments in the emergent AI-healthcare sector.

The purpose is to forward systems theory one more step towards social theory and integrate problem-solving and theory-building, and search for the integration and unity of science by revealing the nature and role of critical systems thinking (CST).

This article describes relations between systems theory and social theory in three parts. First, it examines the links of systems methodologies with three social science approaches as well as the role of CST. Second, the focus of theory and the form of explanation are discussed from critical social science (CSS) perspective. Third, the direction of theorizing of a CST-based systems theory is investigated.

First, CST is a hidden assumption of system dynamics (SD)/systems thinking (ST). Second, systems theory is positioned in CSS. Third, CST integrates traditional and soft systems methodologies (SSM), and connects systems science and social science. Fourth, this article reveals hidden links between systems approaches and three corresponding social science approaches. Fifth, the theoretical focus of a CST-based systems theory could be formal/structure theory and/or substantive/content theory. Sixth, the form of explanation could be structural/mechanismic explanation combining causal and interpretive explanations. Seventh, a CST-based systems theory may adopt abduction, which complements a defect in deduction and induction in a difficulty of nonlinearity.

It illustrates a graph of the competing approaches in systems science corresponding to paradigms in social science.