Over the past decade, ethical leadership has increasingly become one of the most popular topics in the areas of leadership and business ethics. As a result, there now exists a substantial body of empirical research addressing ethical leadership issues, but the findings reported by this body of research are highly fragmented. The topic has advanced to the stage where a review and synthesis of existing literature can provide great value and help move the scholarly conversation forward. The primary purposes of (...) this article are to review empirical findings from the ethical leadership literature utilizing a framework consisting of the antecedents, mediators, moderators and outcomes of ethical leadership, and suggest a set of interesting research opportunities, thereby facilitating future investigation. We base our synthesis on a review of 62 empirical studies on ethical leadership that were published between 2005 and mid-2015. (shrink)

Italian Sounding—i. e., the Italian appearance of a product or service brand irrespective of its country of origin—represents a global market phenomenon affecting a wide range of economic sectors, particularly the agro-food sector. Although its economic impact has been repeatedly stressed from different points of view, systematic scientific knowledge regarding its social–psychological bases is lacking. Three studies carried out in three different countries address this literature gap. Different consumer groups are targeted regarding major product categories pre-selected categories, which are the (...) major Italian food goods within the specific country according to piloting. In each study, the main independent variable has been manipulated by presenting real product images, whose “Italianness” degree is effectively manipulated by the main study variable across three or four levels. Main hypotheses are tested via a survey with the specific product images administered to samples in Italy, China, and the USA. Across the three studies, results show that Made in Italy products, compared to the other ones, are advantaged in terms of the main dependent variables: reputation profile, general reputation, attitude, and willingness to pay. Moreover, Italian Sounding products are endowed with corresponding significant advantages when compared to the Generic Foreign by non-Italian samples. Results reveal the specific social–psychological profile of Italian Sounding products in terms of either weaknesses or strengths when compared to both Made in Italy products and Generic Foreign ones, differently in the eyes of Italian and non-Italian consumers across different countries. Finally, consistently across the three studies, the extent to which a food product is perceived to be Italian increases consumers' WTP for that product, and this effect is consistently mediated by the product's reputation. (shrink)

Electromechanical actuators are more and more widely used as actuation devices in flight control system of aircrafts and helicopters. The reliability of EMAs is vital because it will cause serious accidents if the malfunction of EMAs occurs, so it is significant to detect and diagnose the fault of EMAs timely. However, EMAs often run under variable conditions in realistic environment, and the vibration signals of EMAs are nonlinear and nonstationary, which make it difficult to effectively achieve fault diagnosis. This paper (...) proposed a fault diagnosis method of electromechanical actuators based on variational mode decomposition multifractal detrended fluctuation analysis and probabilistic neural network. First, the vibration signals were decomposed by VMD into a number of intrinsic mode functions. Second, the multifractal features hidden in IMFs were extracted by using MFDFA, and the generalized Hurst exponents were selected as the feature vectors. Then, the principal component analysis was introduced to realize dimension reduction of the extracted feature vectors. Finally, the probabilistic neural network was utilized to classify the fault modes. The experimental results show that this method can effectively achieve the fault diagnosis of EMAs even under diffident working conditions. Simultaneously, the diagnosis performance of the proposed method in this paper has an advantage over that of EMD-MFDFA method for feature extraction. (shrink)

Because they are key components of aircraft, improving the safety, reliability and economy of engines is crucial. To ensure flight safety and reduce the cost of maintenance during aircraft engine operation, a prognostics and health management system that focuses on fault diagnosis, health assessment, and life prediction is introduced to solve the problems. Predicting the remaining useful life is the most important information for making decisions about aircraft engine operation and maintenance, and it relies largely on the selection of performance (...) degradation features. The choice of such features is highly significant, but there are some weaknesses in the current algorithm for RUL prediction, notably, the inability to obtain tendencies from the data. Especially with aircraft engines, extracting useful degradation features from multisensor data with complex correlations is a key technical problem that has hindered the implementation of degradation assessment. To solve these problems, deep learning has been proposed in recent years to exploit multiple layers of nonlinear information processing for unsupervised self-learning of features. This paper presents a deep learning approach to predict the RUL of an aircraft engine based on a stacked sparse autoencoder and logistic regression. The stacked sparse autoencoder is used to automatically extract performance degradation features from multiple sensors on the aircraft engine and to fuse multiple features through multilayer self-learning. Logistic regression is used to predict the remaining useful life. However, the hyperparameters of the deep learning, which significantly impact the feature extraction and prediction performance, are determined based on expert experience in most cases. The grid search method is introduced in this paper to optimize the hyperparameters of the proposed aircraft engine RUL prediction model. An application of this method of predicting the RUL of an aircraft engine with a benchmark dataset is employed to demonstrate the effectiveness of the proposed approach. (shrink)

Given the food challenges that society is facing, we draw upon recent developments in the study of how food reputation affects food preferences and food choices, providing here a starting standard point for measuring every aspect of food reputation in different cultural contexts across the world. Specifically, while previous attempts focused either on specific aspects of food or on measures of food features validated in one language only, the present research validates the Food Reputation Map (FRM) in Italian, English and (...) Chinese over 2250 participants worldwide. Here we successfully measure food reputation across twenty-three specific indicators, further grouped into six synthetic indicators of food reputation. Critically, results show that: a) the specific measurement tool of food reputation can vary across cultural contexts, and that b) people’s reputation of food products or categories changes significantly across different cultural contexts. Therefore, in order to understand people’s food preferences and consumption, it is important to take into account the repertoire of cultural differences that underlies the contexts of analysis: the three context-specific versions of the FRM presented here effectively deal with this issue and provide reliable context-specific insights on stakeholders’ interests, perspectives, attitudes and behaviors related to food perceptions, assessment and consumption, which can be effectively leveraged to foster food sustainability. (shrink)

Authentic leadership has appeared as a significant field of research. Building on social exchange theory that explicates how individuals mutually mechanize reciprocation and eventually establish a trust-based relationship, we postulated a positive relationship between authentic leadership and followers' organizational citizenship behaviors (OCB). Based on a two-wave time-lagged design, the data were obtained from 270 employees working in the private banking sector of Pakistan. We found that authentic leadership is positively associated with subordinate’s OCB, as well as, leads to a higher (...) level of both affective and cognitive-based followers' trust. The results also indicated that both affective and cognitive-based trust positively mediated the relationship between authentic leadership and OCB. The implication for theory and practice are discussed. (shrink)

The fast-paced lifestyle resulting from China's rapid economic development has caused more than 500 million sudden cardiac deaths in 2020. Online first-aid education for the public is considered a key potential solution to avoid such incidents and would have great practical value. This study focuses on understanding the impact of past first-aid experience on the intention for online learning of first-aid knowledge and skills from the perspective of individual psychological factors based on the construction level and prosociality theories. More specifically, (...) it tests the mediating roles of psychological distances and prosociality, which reflect psychological status, in the relationship between experience and learning intention along with a series of associated demographic variables. Primary data collected via a survey are analyzed through regression analysis. The results show that first-aid experience has a significant positive impact on online first-aid learning intention. In addition, psychological distances from first-aid events, and prosociality, play mediating roles in the relationship between first-aid experience and learning intention. Thus, this study contributes to understanding first-aid learning intention by revealing the impact of the individual factors of psychological distances and prosociality. (shrink)

Studies show that research and development may not always benefit entrepreneurial firms. This paper focuses on the double-edged effect of R&D activities on attracting institutional investment in entrepreneurial firms. Based on a panel dataset of 700 listed entrepreneurial firms in ChiNext, we document: an inverted-U relationship between R&D intensity and future institutional investment, which we argue is evidence that institutional investors are concerned about R&D overinvestment; an inverted-U relationship between R&D capitalization and future institutional investment, which we argue shows suspicion (...) of the institutional investors towards high R&D capitalization. Furthermore, by splitting institutional investors into venture capitals and non-venture capitals, we confirm that VCs have higher acceptance of both R&D intensity and capitalization as VCs have more expertise to alleviate a certain level of risks. (shrink)

Due to fresh foods' unique characteristics, where quality, freshness, and perishability are the main concerns, consumers are more inclined to choose offline channels for purchasing foods. However, it is not well-understood how these behaviors are affected by the adverse external environment, e.g., smog pollution. Fine particulate matters on smog days would irritate the respiratory tract and pose health risks to people, triggering negative emotions such as sadness and depression. People tend to stay in a clean indoor environment on smog days. (...) An adverse external environment is causing a gradual change in people's habits and emotions. Still, its impact on shopping behaviors is a complex process in need of further study. The study fills this gap by examining the impact of smog pollution on customer channel choice. Based on data from an e-commerce retailer that operates in both online and offline channels. We find that the degree of smog pollution has a significant positive effect on online channel purchasing at aggregated store-, product-, and individual- levels; moreover, the retailer's in-store interactive activities would restrain this positive relationship; variation of product pricing and customers' healthy eating tendency would pronounce the positive association between smog and online purchasing. These results can serve as a reference for retailers to adjust channel strategies in the face of harsh external conditions. (shrink)

本书是一部故事哲学书,由一百多个饱含禅意的故事和精粹的哲学点评构成,分为智慧、人生、爱情、事业、方法、功戒、包容七个部分。.

本书从宇宙太空、万物之始、原子物理、人体生命、社会结构等多维度、多层面剖解世界与人生,辨物析理。.

TODIM is a well-known multiple-criteria decision-making which considers the bounded rationality of decision makers based on prospect theory. However, in the classical TODIM, the perceived probability weighting function and the difference of the risk attitudes for gains and losses are not consistent with the original idea of PT. Moreover, probabilistic hesitant fuzzy information shows its superiority in handling the situation that the DMs hesitate among several possible values with different possibilities. Hence, a novel TODIM with probabilistic hesitant fuzzy information is (...) proposed in this paper to simulate the perceptions of the DMs in PT. To show the advantages of the proposed method, a novel TODIM is combined with hesitant fuzzy information. Finally, a case study is carried out to demonstrate the feasibility of the proposed method, and a series of comparative analyses and the sensitivity analyses are used to show the stability of the proposed method. (shrink)

Angle is an important concept in geometry. Young children have difficulty separating angle size from other dimensions such as the length of angle sides, perhaps due to whole-object bias in word learning. The present study used the pre-test–training–post-test design to investigate the effectiveness of two ways of separating angle from angle size in 3–6-year-old Chinese preschoolers. A total of 228 children were given a pre-test and 219 of them failed the crucial test. 168 of the 219 children were present at (...) school during the training phase and were randomly assigned to three groups: the “toma” group, which received training to call the whole angle figure as “toma” and angle size as angle size; the “angle/angle size” group, which received the training of separating “angle” from “angle size”; and the control group, which used “angle size” alone to represent both the overall angle figure and angle size. Results showed that the “toma” group improved significantly more than the other two groups, the latter of which did not differ from each other. These results suggest that it is insufficient to have two separate words/phrases for children to learn to differentiate angle from angle size, perhaps due to their shared usage of the word angle. Instead, the use of a novel term is necessary and sufficient to improve learning. Implications for preschool education are discussed. (shrink)

Extreme Learning Machine is widely used in various fields because of its fast training and high accuracy. However, it does not primarily work well for Domain Adaptation in which there are many annotated data from auxiliary domain and few even no annotated data in target domain. In this paper, we propose a new variant of ELM called Discriminative Extreme Learning Machine with Cross-Domain Mean Approximation for unsupervised domain adaptation. It introduces Cross-Domain Mean Approximation into the hidden layer of ELM to (...) reduce distribution discrepancy between domains for domain bias elimination, which is conducive to train a high accuracy ELM on annotated data from auxiliary domains for target tasks. Linear Discriminative Analysis is also adopted to improve the discrimination of learned model and obtain higher accuracy. Moreover, we further provide a Discriminative Kernel Extreme Learning Machine with Cross-Domain Mean Approximation as the kernelization extension of DELM-CDMA. Some experiments are performed to investigate the proposed approach, and the result shows that DELM-CDMA and DKELM-CDMA could effectively extend ELM suitable for domain adaptation and outperform ELM and many other domain adaptation approaches. (shrink)

Existing research has identified the importance of role models in the imitation of cooperative behaviors. This Pre-Study attempted to explore the contagion effects of cooperative models. Drawing on goal contagion theory, we proposed that encountering cooperative models could catalyze participants’ cooperation when participants joined new groups without role models, and that moral elevation and calling would play a chain-mediating role in this process. To test the hypothesis, we designed a four-person public goods game consisting of two phases in which participants (...) were formed into teams with different people in each phase. We randomly assigned 108 participants to either a consistent contributor (CC) or control condition. The only difference was that participants in the CC condition encountered a cooperative role model (i.e., CC) in the first phase, while those in the control group did not. The results moderately supported all hypotheses. Briefly, our findings provide empirical evidence supporting the two processes of goal contagion theory: when individuals encounter a CC, they first make inferences about the CC’s goal, as reflected by moral elevation, and then adopt the model’s prosocial goals (i.e., calling), resulting in increased cooperative behaviors in new groups. These findings could extend our understanding of the contagion effect of cooperative modeling, but require high-powered replication studies before such conclusions can be drawn. (shrink)