Sustainable services are often regarded as sustainable strategies and operations producing goods and services that satisfy customer needs and significantly improve social and environmental performance. To be sustainable, service providers must satisfy consumers' needs or otherwise they will become redundant and economically irrelevant. This paper presents the results of an empirical study on the determinants of customer satisfaction and loyalty in life-insurance services in Vietnam based on a database collected through a questionnaire survey of 1476 customers during 2017. A path analysis technique is applied to test the proposed framework on the direct and indirect relationship between variables. The results of statistical analysis indicate that customer satisfaction in life-insurance services is significantly explained by such factors as corporate image, service quality and perceived value. Our findings suggest that a life-insurance service provider should focus on enhancing service quality and corporate image in order to obtain customer satisfaction that leads to customer loyalty.
This paper proposes a novel approach to recognize activities based on sensor-placement feature selection. The method is designed to address a problem of multisensor fusion information of wearable sensors which are located in different positions of a human body. Precisely, the approach can extract the best feature set that characterizes each activity regarding a body-sensor location to recognize daily living activities. We firstly preprocess the raw data by utilizing a low-pass filter. After extracting various features, feature selection algorithms are applied separately on feature sets of each sensor to obtain the best feature set for each body position. Then, we investigate the correlation of the features in each set to optimize the feature set. Finally, a classifier is applied to an optimized feature set, which contains features from four body positions to classify thirteen activities. In experimental results, we obtain an overall accuracy of 99.13% by applying the proposed method to the benchmark dataset. The results show that we can reduce the computation time for the feature selection step and achieve a high accuracy rate by performing feature selection for the placement of each sensor. In addition, our proposed method can be used for a multiple-sensor configuration to classify activities of daily living. The method is also expected to deploy to an activity classification system-based big data platform since each sensor node only sends essential information characterizing itself to a cloud server.
In this paper, we propose an adaptive step-estimation method to estimate the distance traveled for arm-swinging activities at three level-walking speeds, i.e., low, normal, and high speed. The proposed method is constructed based on a polynomial function of the pedestrian speed and variance of walking acceleration. We firstly apply a low-pass filter with 10 Hz cut-off frequency for acceleration data. Then, we analyze the acceleration data to find the number of steps in each sample. Finally, the traveled distance is calculated by summing all step lengths which are estimated by the proposed method during walking. Applying the proposed method, we can estimate the walking distance with an accuracy rate of 95.35% in a normal walking speed. The accuracy rates of low and high walking speeds are 94.63% and 94.97%, respectively. Furthermore, the proposed method outperforms conventional methods in terms of accuracy and standard deviation at low, normal, and high speeds.
Human activity recognition and pedestrian dead reckoning are an interesting field because of their importance utilities in daily life healthcare. Currently, these fields are facing many challenges, one of which is the lack of a robust algorithm with high performance. This paper proposes a new method to implement a robust step detection and adaptive distance estimation algorithm based on the classification of five daily wrist activities during walking at various speeds using a smart band. The key idea is that the non-parametric adaptive distance estimator is performed after two activity classifiers and a robust step detector. In this study, two classifiers perform two phases of recognizing five wrist activities during walking. Then, a robust step detection algorithm, which is integrated with an adaptive threshold, peak and valley correction algorithm, is applied to the classified activities to detect the walking steps. In addition, the misclassification activities are fed back to the previous layer. Finally, three adaptive distance estimators, which are based on a non-parametric model of the average walking speed, calculate the length of each strike. The experimental results show that the average classification accuracy is about 99%, and the accuracy of the step detection is 98.7%. The error of the estimated distance is 2.2–4.2% depending on the type of wrist activities.
This study examined whether the adverse link between passive social network usage (PSNU) and life satisfaction was mediated by both envy and self-esteem (serial- and parallel-mediation models) based on social comparison theory (Festinger, 1954). In addition, according to social role theory (Eagly, 1987), sex was used as a moderator variable to moderate several recommended pathways related to envy. A total of 590 Vietnamese university students voluntarily participated in this survey study. The findings revealed that envy and self-esteem both mediated the relationship between PSNU and life satisfaction. The moderating effect of sex was significant in envy-related pathways as well as in the association between PSNU and life satisfaction. Specially, most of these pathways worked for women but not for men, implying that envy is a relatively critical emotion for women in social network environment. The results supported the hypothesized theories. The limitations and implications of these results are discussed.
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