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Despite its practical importance, the emotion of awe has received scant research attention in the organizational literature. To facilitate future scholarship on this important topic, the authors explore and compare the elicitors of awe at work in three countries representing two culture clusters, including the USA and Canada (the Anglo cluster) and China (the Confucian Asia cluster).

The authors gathered responses from 163 working adults from the USA and Canada and 126 working adults from China using open-response survey, and analyzed each response following the guidance of grounded theory.

Across cultures, there are 10 common elicitors of awe, including virtue of organization, ability and achievement of organization, beauty of workplace, virtue of colleagues, ability and achievement of colleagues, dedication of colleagues, charisma of colleagues, status and power of colleagues, personal growth and achievement and perceived meaningfulness. Looking within cultures, the authors found two awe elicitors that are specific to China: status and power of organization, and work content.

Organizations and supervisors wishing to induce the emotion of awe would be well-advised to pay attention to the design of their workplace, as well as their attitudes and behaviors toward employees, customers and the general public.

This study contributes to the emotion literature, organizational literature and cross-cultural literature by demonstrating elicitors of awe in the workplace across the Anglo cluster and the Confucian Asia cluster.

Despite its prevalence in organizations, passive leadership has received scant research attention compared to more active forms of leadership. The purpose of this paper is to demonstrate the significant impact that passive leadership can have on two core constructs in the organizational sciences – trust and perceptions of fairness.

Data were collected through self-report questionnaires (n=192), using a three-wave survey methodology in order to provide temporal separation between the predictor, mediator, and outcome variables.

Passive leadership was negatively associated with cognition-based trust. Further, passive leadership exerted a negative indirect effect on employee perceptions of justice, through cognition-based trust.

Although this research was explicitly interested in employee perceptions, the use of self-report measures may be considered a study limitation.

This study underscores that passive leadership may represent a serious impediment to effective supervisor-employee relations. Employees do not trust supervisors who display a passive leadership style and a lack of trust can lead employees to perceive the supervisor as unfair.

This study contributes new insights into the psychological effects of passive leadership behavior by demonstrating the potential role of passive leadership in shaping employee perceptions of trust and fairness.

This paper aims to present a new kinematics mapping method based on dynamic equivalent points. In teleoperation, this method enables a robotic (follower) arm to mimic human (leader) arm postures and avoid obstacles in a human-like manner.

The information of the human arm is extracted based on the characteristics of human arm motion, and the concept of equivalent points is introduced. Then, an equivalent point is determined to transform the robotic arm with a nonhuman-like kinematic structure into an anthropomorphic robotic arm. Based on this equivalent point, a mapping method is developed to ensure that the two arms are similar. Finally, the similarity between the human elbow angle and robot elbow angle is further improved by using this method and an augmented Jacobian matrix with a compensation coefficient.

Numerical simulations and physical prototype experiments are conducted to verify the effectiveness and feasibility of the proposed method. In environments with obstacles, this method can adjust the position of the equivalent point in real time to avoid obstacles. In environments without obstacles, the similarity between the human elbow angle and robot elbow angle is further improved at the expense of the end-effector accuracy.

This study presents a new kinematics mapping method, which can realize the complete mapping between the human arm and heterogeneous robotic arm in teleoperation. This method is versatile and can be applied to various mechanical arms with different structures.

Drawing upon the conservation of resources theory, this study aims to investigate whether, how and when salespeople’s substantive attribution of the organization’s corporate social responsibility (CSR) affects value-based selling (VBS). The authors argue that salespeople’s substantive CSR attribution increase value-based selling through two mechanisms (i.e. by lowering emotional exhaustion and increasing empathy), and treatment by customers can increase or decrease the strength of these relationships.

B2B salespeople working in various industries in China were recruited through snowball sampling to participate in the study. There were 462 volunteers (57.58% women; aged 30–55; tenure ranging from six months to 15 years) who provided valid self-report questionnaires.

Hierarchical multiple regression supported the association between salespeople’s substantive CSR attribution and VBS. The results showed that salespeople’s emotional state (i.e. emotional exhaustion and empathy) mediated the association between substantive CSR attribution and VBS. As expected, salespeople’s experiences of customer incivility weakened the mediating effect of emotional exhaustion; contrary to expectations, customer-initiated interpersonal justice weakened the mediation effect of empathy.

This study makes a unique contribution to the existing marketing literature by first investigating the role of salespeople’s attribution of CSR motives in facilitating their VBS, which answers the call to identify factors that predict VBS. In addition, to the best of the authors’ knowledge, the authors are the first to test salespeople’s emotions as a mechanism of the link between their CSR attributions and selling behaviors.

Entity relation extraction is an important research direction to obtain structured information. However, most of the current methods are to determine the relations between entities in a given sentence based on a stepwise method, seldom considering entities and relations into a unified framework. The joint learning method is an optimal solution that combines relations and entities. This paper aims to optimize hierarchical reinforcement learning framework and provide an efficient model to extract entity relation.

This paper is based on the hierarchical reinforcement learning framework of joint learning and combines the model with BERT, the best language representation model, to optimize the word embedding and encoding process. Besides, this paper adjusts some punctuation marks to make the data set more standardized, and introduces positional information to improve the performance of the model.

Experiments show that the model proposed in this paper outperforms the baseline model with a 13% improvement, and achieve 0.742 in F1 score in NYT10 data set. This model can effectively extract entities and relations in large-scale unstructured text and can be applied to the fields of multi-domain information retrieval, intelligent understanding and intelligent interaction.

The research provides an efficient solution for researchers in a different domain to make use of artificial intelligence (AI) technologies to process their unstructured text more accurately.