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Dyadic interactions are significant for human life. Most body sensor networks-based research studies focus on daily actions, but few works have been done to recognize affective actions during interactions. The purpose of this paper is to analyze and recognize affective actions collected from dyadic interactions.

A framework that combines hidden Markov models (HMMs) and k-nearest neighbor (kNN) using Fisher kernel learning is presented in this paper. Furthermore, different features are considered according to the interaction situations (positive situation and negative situation).

Three experiments are conducted in this paper. Experimental results demonstrate that the proposed Fisher kernel learning-based framework outperforms methods using Fisher kernel-based approach, using only HMMs and kNN.

The research may help to facilitate nonverbal communication. Moreover, it is important to equip social robots and animated agents with affective communication abilities.

The presented framework may gain strengths from both generative and discriminative models. Further, different features are considered based on the interaction situations.

Due to the complex mechanism during walking, human gait takes plenty of information reflecting human motion. The method of quantitative measurement of gait makes a profound influence in many fields, such as clinical medicine, biped robot control strategy and so on. The purpose of this paper is to present a gait analysis system based on inertial measurement unit (IMU) and combined with body sensor network (BSN).

The authors placed two wireless inertial nodes on the left and right ankles, so that the acceleration and angular velocity could be obtained from both sides at the same time. By using the kinematical model of the human gait, many methods such as time series analysis, pattern recognition and numerical analysis, are introduced to fuse the inertial data and estimate the sagittal gait parameters.

The gait parameters evaluation gains a practical precision, especially in the gait phase detection and the process of how the two feet cooperate with each other has been analyzed to learn about the mechanism of biped walking.

The gait analysis procedure is off line, so that the system ensures sampling at a high rate.

This gait analysis system can be utilized to measure quantitative gait parameters. Further, the coordination of dual gait pattern is presented. Last but not least, the system can also be put into capturing and analyzing the motion of other parts of the body.

The purpose of this paper is to develop a health monitoring system that can measure human vital signs and recognize human activity based on body sensor network (BSN).

The system is mainly composed of electrocardiogram (ECG) signal collection node, blood oxygen signal collection node, inertial sensor node, receiving node and upper computer software. The three collection nodes collect ECG signals, blood oxygen signals and motion signals. And then collected signals are transmitted wirelessly to receiving node and analyzed by software in upper computer in real-time.

Experiment results show that the system can simultaneously monitor human ECG, heart rate, pulse rate, SpO₂ and recognize human activity. A classifier based on coupled hidden Markov model (CHMM) is adopted to recognize human activity. The average recognition accuracy of CHMM classifier is 94.8 percent, which is higher than some existent methods, such as supported vector machine (SVM), C4.5 decision tree and naive Bayes classifier (NBC).

The monitoring system may be used for falling detection, elderly care, postoperative care, rehabilitation training, sports training and other fields in the future.

First, the system can measure human vital signs (ECG, blood pressure, pulse rate, SpO₂, temperature, heart rate) and recognizes some specific simple or complex activities (sitting, lying, go boating, bicycle riding). Second, the researches of using CHMM for activity recognition based on BSN are extremely few. Consequently, the classifier based on CHMM is adopted to recognize activity with ideal recognition accuracies in this paper.

To understand the effectiveness of electronic word of mouth, the purpose of this paper is to examine how high- vs low-knowledge consumers judge and attribute the credibility of positive and negative online reviews by drawing upon accessibility–diagnosticity theory and attribution theory.

This study conducts an observation-based study in an online forum and a 2 (review valence) × 2 (consumer knowledge) between-participants factorial experiment to examine the proposed hypotheses.

High-knowledge consumers elicit less perceived credibility and make more non-product-relevant attribution than low-knowledge consumers in negative online reviews. Consumer attribution is also found to mediate the effects of the review valence by consumer knowledge interaction on review credibility.

This study adds to extant research by examining how consumer knowledge plays a key role in determining consumer perception of online review credibility. This study also advances the understanding of different casual inferences about online reviews between high- and low-knowledge consumers.

On the basis of social identity theory, this paper aims to predict and test the influence of abusive supervision on service employees’ proactive customer service performance (PCSP) in the hotel industry.

Data were collected from 198 service employee-coworker dyads from 12 hotels in China. Previously developed and validated measures of abusive supervision, organizational identification, collectivism and PCSP were used and found to be highly reliable in this study.

Time-lagged data from 12 hotels in China reveal that abusive supervision negatively influences service employees’ PCSP, through organizational identification. In addition, employees’ collectivistic value orientation also strengthens the negative relationship between abusive supervision and organizational identification. These findings have several theoretical and managerial implications, especially for hospitality context.

First, the study suggests that hotels should design supervisors’ selection, training and monitoring to reduce mistreatment, which could be highly costly to employees’ identification and hence proactive behaviors. In addition, hotel supervisors are encouraged to learn to regulate their emotions by developing emotional management skills and interpersonal skills. Second, because collectivists are more likely to be affected by abusive supervisors, organizations should pay special attention to them by allocating more supportive resources, providing psychological comfort and expert counseling. Finally, hotels and managers should seek to meet individuals’ basic needs by fostering positive relationships between supervisors and employees, offering favorable treatment and connecting an organization’s goals with employees’ individual values. By doing so, employees’ organizational identification will be enhanced and hence contribute to PCSP.

First, scarcely any study has focused on negative types of leadership styles and how they affect employees’ PCSP. The authors address the research gap by extending the antecedent scope of PCSP to dark side management and provide empirical evidence about the suppressing effects of abusive supervision on PCSP. Second, the focus on organizational identification provides a new extension for social identity theory in application for incurring employees’ proactive behaviors. Third, this study provides a novel contribution by suggesting that the level of collectivism an employee holds can exacerbate the salience of abusive supervision.

The purpose of this paper is to draw on the theory of servant leadership to examine the influence of chief executive officer (CEO) servant leadership on firm performance in the hospitality industry. It examined the mediating role of the service climate and the moderating role of competitive intensity in the relationship between CEO servant leadership and firm performance.

Multiple regression was used to analyze multi-wave, multi-source data from 92 hotels in China. A moderated path analysis was used to test the moderating effects.

CEO servant leadership positively influenced firm performance via the service climate in the hospitality industry. Competitive intensity strengthened the direct effect of the service climate on firm performance, and the indirect effect of CEO servant leadership on firm performance via service climate.

The findings offer managerial insights into CEO succession, service management and human resource management.

This is one of the first studies to investigate how and when CEO servant leadership might shape firm outcomes in the hospitality industry. Theoretically, the findings enrich our understanding of how CEO leadership might shape firm-level outcomes.

Man has been seeking an ideal existence for a very long time. In this existence, justice, love, and peace are no longer words, but actual experiences. How ever, with the American preemptive invasion and occupation of Afghanistan and Iraq and the subsequent prisoner abuse, such an existence seems to be farther and farther away from reality. The purpose of this work is to stop this dangerous trend by promoting justice, love, and peace through a change of the paradigm that is inconsistent with justice, love, and peace. The strong paradigm that created the strong nation like the U.S. and the strong man like George W. Bush have been the culprit, rather than the contributor, of the above three universal ideals. Thus, rather than justice, love, and peace, the strong paradigm resulted in in justice, hatred, and violence. In order to remove these three and related evils, what the world needs in the beginning of the third millenium is the weak paradigm. Through the acceptance of the latter paradigm, the golden mean or middle paradigm can be formulated, which is a synergy of the weak and the strong paradigm. In order to understand properly the meaning of these paradigms, however, some digression appears necessary.

WeChat is the largest acquaintance social networking platform in China, in which users can view and reshare web pages shared by friends. This paper aims to analyze the spatio-temporal dynamics of web pages diffused in WeChat and advice on commercials.

A large number of web pages diffused in WeChat are collected and exclusively divided into four categories according to their titles, including advertisements, news bulletins, holiday greetings and emotional essays. For each web page, an information cascade (tree structure) is constructed to describe the diffusion trace. Based on the categories, the spatio-temporal popularity is characterized; the topological, temporal and spatial properties are examined; and the spatio-temporal diffusion velocity is explored.

Through comparative analysis, different categories of pages show diversity. For spatio-temporal popularity, there is no significant difference in cascade size; holiday greetings usually last for a relatively short time on average; emotional essays are more likely to spread to more provinces. For topological, temporal and spatial characteristics, the diffusion process of advertisements is more likely to be broadcasting than other categories; news bulletins and holiday greetings have an obvious bursty; the number of viewing behavior decreases from east to west in general. For spatio-temporal diffusion velocity, emotional essays diffuse the fastest in topological and spatio-temporal dimensions.

These findings contribute to promoting products and providing support for data driven modeling of information diffusion and human activity in spatio-temporal dimensions.

This study aims to explore the dimensions and sub-factors of Airbnb hosts’ affinity-seeking strategies. It also aims to build a conceptual framework of hosts’ affinity-seeking strategies and their impact on Airbnb guests’ feelings of affection toward their host and/or the homestay and their behavioral intentions.

Based on 150,161 Inside Airbnb online reviews in three major US cities, this study uses semantic analysis to explore processes through which hosts’ affinity-seeking strategies are constructed.

A conceptual framework is proposed to identify two dimensions (warmth and competency) and their sub-factors (“presenting friendly attitudes,” “showing personality traits,” “providing service and help” and “promoting social interaction and sharing”) of Airbnb homestay hosts’ affinity-seeking strategies. The framework shows a positive relationship between these strategies and guests’ affection and behavior in response.

The research findings provide valuable insights to hosts for improving their affinity and strengthening their competitive advantages. They also offer guidance to destination management organizations on how to build a positive destination image.

To the best of the authors’ knowledge, this study is the first to conceptualize Airbnb homestay hosts’ affinity-seeking strategies. It contributes to the literature by incorporating social cognition theory and service theory in the analysis of these strategies.