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The perseverative cognition framework suggests that observing ostracism has negative implications for observers due to affective rumination and that a proactive personality might make observers more vulnerable to this effect.

Data from 49 team leaders and 218 team members were obtained through a three-wave survey in China. Path analysis was used to examine the theoretical model.

The results indicate that observing ostracism increased turnover intention and reduced task performance and that these relationships were mediated by affective rumination. Furthermore, these effects were stronger for observers with high proactive personality.

Workplace ostracism harms employees; however, its effects on observers remain underexplored. This paper extends research on the effects of ostracism by revealing that ostracism is not only harmful to the well-being of its victims but also adversely affects the work-related attitudes and behaviors of observers, especially those with proactive personality.

Organizations should be aware of the harmful effects of workplace ostracism on observers, and take actions to inhibit workplace ostracism as well as reduce the negatives impacts.

The results reveal the cognitive mechanism of affective rumination, in which observing workplace ostracism affects observers' behaviors and attitudes, highlighting the importance of observing effect of workplace ostracism.

Efficiency of a commercial bank affects both its competitiveness and the role it plays in the process of economic development. Although great efforts have been exerted in developing the various aspects of banking efficiency, there seems to be a lack of research on examining the impact of the bank efficiency from the employee wage perspective. The mechanism of how employee wage affects commercial bank efficiency and the relationship between the two were analyzed in this paper. Based on the growing body of research on efficiency in banking, the aim of this paper is to examine if competitiveness of employee wages at any commercial bank has any impact on the bank efficiency score.

The method used was quantitative analysis, which was based on comparing the evaluated efficiencies of the banks with employee wages published in the bank reports. The empirical data in this paper were based on 16 Chinese listed commercial banks from 2004 to 2012. The per capita wage of commercial banks was selected as the wage indicator, and the efficiency value obtained by the slack-based measure (SBM) model was selected as the efficiency indicator. According to the calculated data, the Tobit regression model was built to analyze the relationship between employee wage and commercial bank efficiency.

The research results show that employee wage is the key variable that influences the efficiency of Chinese commercial banks, and the inverted U-shaped relationship between employee wage and commercial banks efficiency shows up.

The wage structure data of the composition of basic pay and bonus were not available at the time of conducting the research. Per capita wages were used instead to reflect the employee wage levels of Chinese banks.

This study can provide some help for the banking industry by analyzing the wage levels from the perspective of efficiency and also further enriches the theoretical system of the relationship between employee wage and bank efficiency.

The purpose of this paper is to promote a wider understanding of the importance of distinguishing between presenteeism behavior and its motivation and between the avoidance and approach dimensions of motivation, and to rectify the neglect of presenteeism’s antecedents (in particular, situational ones). It develops a theoretical model that explains how situational antecedents affect presenteeism – conventionally defined as attending work while ill.

An ordinary least-squares regression-based path analysis is employed to analyze the findings of a sample of 277 employees in service organizations in southwestern China.

Findings demonstrate that the situational factor, leader–member exchange (LMX), is positively related to the approach dimension of presenteeism motivation and that of workload moderates the positive link between presenteeism motivation and behavior, such that employees who experience higher workload more frequently display presenteeism behavior.

Findings suggest that managers should be prudent when developing relationships with their subordinates and consider the ways in which they may most effectively encourage employees to support their organization.

This is the first study to consider LMX and workload as situational antecedents of presenteeism motivation and behavior.

The magnetometer measurement update plays a key role in correcting yaw estimation in fusion algorithms, and hence, the yaw estimation is vulnerable to magnetic disturbances. The purpose of this study is to improve the ability of the fusion algorithm to deal with magnetic disturbances.

In this paper, an adaptive measurement equation based on vehicle status is derived, which can constrain the yaw estimation from drifting when vehicle is running straight. Using this new measurement, a Kalman filter-based fusion algorithm is constructed, and its performance is evaluated experimentally.

The experiments results demonstrate that the new measurement update works as an effective supplement to the magnetometer measurement update in the present of magnetic disturbances, and the proposed fusion algorithm has better yaw estimation accuracy than the conventional algorithm.

The paper proposes a new adaptive measurement equation for yaw estimation based on vehicle status. And, using this measurement, the fusion algorithm can not only reduce the weight of disturbed sensor measurement but also utilize the character of vehicle running to deal with magnetic disturbances. This strategy can also be used in other orientation estimation fields.

Financial services industry is increasingly showing interest in automated financial advisors, or robo-advisors, with the aim of democratizing access to financial advice and stimulating investment behavior among populations that were previously less active and less served. However, the extent to which consumers trust this technology influences the adoption of rob-advisors. The resemblance to a human, or anthropomorphism, can provide a sense of social presence and increase trust.

In this paper, we conduct an experiment (N = 223) to test the effect of anthropomorphism (low vs medium vs high) and gender (male vs female) of the robo-advisor on social presence. This perception, in turn, enables consumers to evaluate personality characteristics of the robo-advisor, such as competence, warmth, and persuasiveness, all of which are related to trust in the robo-advisor. We separately conduct an experimental study (N = 206) testing the effect of gender neutrality on consumer responses to robo-advisory anthropomorphism.

Our results show that consumers prefer human-alike robo-advisors over machinelike or humanoid robo-advisors. This preference is only observed for male robo-advisors and is explained by perceived competence and perceived persuasiveness. Furthermore, highlighting gender neutrality undermines the positive effect of robo-advisor anthropomorphism on trust.

We contribute to the body of knowledge on robo-advisor design by showing the effect of robot’s anthropomorphism and gender on consumer perceptions and trust. Consequently, we offer insightful recommendations to promote the adoption of robo-advisory services in the financial sector.

The sources of magnetic sensors errors are numerous, such as currents around, soft magnetic and hard magnetic materials and so on. The traditional methods mainly use explicit error models, and it is difficult to include all interference factors. This paper aims to present an implicit error model and studies its high-precision training method.

A multi-level extreme learning machine based on reverse tuning (MR-ELM) is presented to compensate for magnetic compass measurement errors by increasing the depth of the network. To ensure the real-time performance of the algorithm, the network structure is fixed to two ELM levels, and the maximum number of levels and neurons will not be continuously increased. The parameters of MR-ELM are further modified by reverse tuning to ensure network accuracy. Because the parameters of the network have been basically determined by least squares, the number of iterations is far less than that in the traditional BP neural network, and the real-time can still be guaranteed.

The results show that the training time of the MR-ELM is 19.65 s, which is about four times that of the fixed extreme learning algorithm, but training accuracy and generalization performance of the error model are better. The heading error is reduced from the pre-compensation ±2.5° to ±0.125°, and the root mean square error is 0.055°, which is about 0.46 times that of the fixed extreme learning algorithm.

MR-ELM is presented to compensate for magnetic compass measurement errors by increasing the depth of the network. In this case, the multi-level ELM network parameters are further modified by reverse tuning to ensure network accuracy. Because the parameters of the network have been basically determined by least squares, the number of iterations is far less than that in the traditional BP neural network, and the real-time training can still be guaranteed. The revised manuscript improved the ELM algorithm itself (referred to as MR-ELM) and bring new ideas to the peers in the magnetic compass error compensation field.

The three-axis magnetic sensors are mostly calibrated by scalar method such as ellipsoid fitting and so on, but these methods cannot completely determine the 12 parameters of the error model. A two-stage calibration method based on particle swarm optimization (TSC-PSO) is proposed, which makes full use of the amplitude invariance and direction invariance of Earth’s magnetic field vector.

The TSC-PSO designs two-stage fitness function. Stage 1: design a fitness function of the particle swarm by the amplitude invariance of the Earth’s magnetic field to obtain a preliminary error matrix G and the bias error B. Stage 2: further design the fitness function of the particle swarm by the invariance of the Earth’s magnetic field to obtain a rotation matrix R, thereby determining the error matrix uniquely.

The proposed TSC-PSO can completely determine 12 unknown parameters in error model and further decrease the maximum fluctuation error of the Earth’s magnetic field amplitude and the absolute error of heading.

The proposed TSC-PSO provides an effective solution for three-axis magnetic sensor error compensation, which can greatly reduce the price of magnetic sensors and be used in the fields of Earth’s magnetic survey, drilling and Earth’s magnetic integrated navigation.

The proposed TSC-PSO has significantly improved the magnetic field amplitude and heading accuracy and does not require additional heading reference. In addition, the method is insensitive to noise and initialization conditions, has good robustness and can converge to a global optimum.

Chatbots are increasingly engaged in service marketing. Some academics and managers think using anthropomorphism chatbots will improve positive attitudes and behaviors in the customer journey, but at a high degree of anthropomorphism, consumers may experience negative emotions such as fear and disgust due to the feeling that the robots resemble humans too much, which is known as the uncanny valley effect. Therefore, the authors aim to explore whether chatbot anthropomorphism will promote or limit the development of the customer journey and explore the moderating factors and the antecedent factors affecting consumers' perceptions of chatbot anthropomorphism.

The authors collected 72,782 unique data points from 42 articles and 82 samples using a meta-analysis. Based on the stimuli-organism-response (SOR) model, the impact of anthropomorphic chatbots on the consumer journey was discussed.

The authors’ findings show that chatbot anthropomorphism positively impacts the customer journey but not their negative attitudes. Further moderator analysis reveals that the impact depends on service result, chatbot gender and sample source. The chatbot anthropomorphism is significantly influenced by social presence cues, emotional message cues and mixed cues.

This research contributes to the chatbot anthropomorphism literature and offers guidance for managers on whether and how to enhance chatbot anthropomorphism to facilitate the customer journey and improve service sustainability.

The economic reforms which turned the centrally planned economy to a market economy have profoundly changed the tripartite relationship between the state, work unit, and citizen in urban China and brought significant changes to the institutional care provision for young children. The aim of this paper is to investigate the changes to the institutional care since 1980, with particular emphasis on the most recent years from mid‐1990s, and explore how the institutional care has changed over the recent decades without a clear institutional basis.

The analysis draws on second‐hand materials from published literature, a range of longitudinal national and local statistics and policy documents, and also on first‐hand information which was collected in Beijing from in‐depth interviews with key informants and case studies of different kinds of kindergartens.

The paper finds that the previous work‐unit based public care system has changed to a much more complicated care mix in which the roles of the state, employer, community, market and the informal sector of the family in terms of provision and funding have all changed significantly.

The findings of this paper may help to inform appropriate policy responses in Chinese child care provision. The study suggests that formal care provision should be expanded towards universal access regardless of people's income and employment status in China.

The paper questions and complicates the “state withdrawal” representation of social welfare change and argues that it is not “the state” but “the work unit and community organization” retreat from public care provision. It also argues that the change in the role of the state has been multifaceted, and not a simple one‐directional movement of marketization in which the state retreated from welfare provision in entirety.

Lenders in online peer-to-peer (P2P) lending platforms are always non-experts and face severe information asymmetry. This paper aims to achieve the goals of gaining high returns with risk limitations or lowering risks with expected returns for P2P lenders.

This paper used data from a leading online P2P lending platform in America. First, the authors constructed a logistic regression-based credit scoring model and a linear regression-based profit scoring model to predict the default probabilities and profitability of loans. Second, based on the predictions of loan risk and loan return, the authors constructed linear programming model to form the optimal loan portfolio for lenders.

The research results show that compared to a logistic regression-based credit scoring method, the proposed new framework could make more returns for lenders with risks unchanged. Furthermore, compared to a linear regression-based profit scoring method, the proposed new framework could lower risks for lenders without lowering returns. In addition, comparisons with advanced machine learning techniques further validate its superiority.

Unlike previous studies that focus solely on predicting the default probability or profitability of loans, this study considers loan allocation in online P2P lending as an optimization research problem using a new framework based upon modern portfolio theory (MPT). This study may contribute theoretically to the extension of MPT in the specific context of online P2P lending and benefit lenders and platforms to develop more efficient investment tools.