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Despite extensive research on personality and leader emergence, very little is known about the process by which employees become or emerge as leaders based on their performance. Integrating functional leadership theory and a behavior perspective, the authors aim to explore the parallel multiple behavioral mediators in the conscientiousness–leader emergence link.

By integrating a field survey study and two experimental studies, the authors use parallel multiple mediation analysis to explore the mechanisms by which conscientiousness leads to high levels of leader emergence.

Conscientiousness is positively associated with employee leader emergence. Employee functional behaviors are positively associated with leader emergence. The authors consistently found that the effect of conscientiousness on leader emergence is primarily explained by increases in task- and change-oriented behaviors but not relations-oriented behaviors.

Organizations can design relevant training programs to cultivate and enhance employees' functional behavior, as the study findings suggest that an effective way to translate employees' conscientiousness into their leader emergence is to improve their task- and change-oriented behaviors.

This research highlights the consistent and important role of employees' functional behaviors in the form of task- and change-oriented behaviors linking conscientiousness to leader emergence.

This paper throws light on some of the nature of artificial intelligence (AI) development, which will serve as a starter for helping to advance its development.

This work reveals the evolutions and trends of AI from four dimensions: research, output, influence and competition through leveraging academic knowledge graph with 130,750 AI scholars and 43,746 scholarly articles.

The authors unearth that the “research convergence” phenomenon becomes more evident in current AI research for scholars' highly similar research interests in different regions. The authors notice that Pareto's principle applies to AI scholars' outputs, and the outputs have been increasing at an explosive rate in the past two decades. The authors discover that top works dominate the AI academia, for they attracted considerable attention. Finally, the authors delve into AI competition, which accelerates technology development, talent flow, and collaboration.

The work aims to throw light on the nature of AI development, which will serve as a starter for helping to advance its development. The work will help us to have a more comprehensive and profound understanding of the evolutions and trends, which bridge the gap between literature research and AI development as well as enlighten the way the authors promote AI development and its strategy formulation.

The purpose of this paper is to improve the accuracy of short time load forecasting to ensure the economical and safe operation of power systems. The traditional neural network applied in time series like load forecasting, easily plunges into local optimum and has a complicated learning process, leading to relatively slow calculating speed. On the basis of existing literature, the authors carried out studies in an effort to optimize a new recurrent neural network by wavelet analysis to solve the previous problems.

The main technique the authors applied is referred to as echo state network (ESN). Detailed information has been acquired by the authors using wavelet analysis. After obtaining more information from original time series, different reservoirs can be built for each subsequence. The proposed method is tested by using hourly electricity load data from a southern city in China. In addition, some traditional methods are also applied for the same task, as contrast.

The experiment has led the authors to believe that the optimized model is encouraging and performs better. Compared with standard ESN, BP network and SVM, the experimental results indicate that WS‐ESN improves the prediction accuracy and has less computing consumption.

The paper develops a new method for short time load forecasting. Wavelet decomposition is employed to pre‐process the original load data. The approximate part associated with low frequencies and several detailed parts associated with high frequencies components give expression to different information from original data. According to this, suitable ESN is chosen for each sub‐sequence, respectively. Therefore, the model combining the advantages of both ESN and wavelet analysis improves the result for short time load forecasting, and can be applied to other time series problem.

This study examines the dynamic relationships of visit behavior in the multiple channels [personal computer (PC) and mobile channels] on online store sales performance.

The empirical data were from an online store for the period between August 14, 2015 and May 15, 2016. The data consisted of consumer visit behavior and online store sales performance. Vector autoregression with an exogenous variables model was adopted to investigate the dynamic relationships.

The empirical results show significant relationships between visit behavior metrics (number of visitors, average number of visits per visitor and average length of each visit) in the two channels and online store sales performance. The number of visitors through the PC and mobile channels strongly and positively affects online store sales performance both in the short term and in the longer term. Moreover, the number of visitors in the PC channel has the strongest influence on sales performance metrics, followed by the number of visitors and the average number of visits in the mobile channel. The PC channel's visit behavior metrics explain a larger proportion of the sales performance variance than that in the mobile channel.

The previous literature on consumer behavior in multichannel marketing mainly focuses on channel selection or migration, and examines the different factors affecting channel choice behavior. Little is known about the impacts of visit behavior in the multiple channels. This study adopts the heuristic-systematic information processing theory to unveil the impacts of visit behavior metrics in the PC and mobile channels on online store sales performance.

Online product ratings play an important role in the decision-making process of consumers, which are not only sources of information used by consumers to understand the function and quality of a product or service but also sources of information used to find desirable products. The purpose of this paper is to develop a decision-based method for supporting the purchase decisions of consumers based on not only the online product ratings but also the actual product attributes.

First, two types of utility values are designed to measure the preference of the consumer based on either online ratings or actual product attributes. Then, the traditional TOPSIS method is adopted to achieve a comprehensive value by integrating the two types of utility values so that all of the alternative products can be ranked. Further, a product selection support system prototype is designed and developed to support the purchase decisions of consumers.

To help consumers select desirable products efficiently, it is necessary to develop a product selection method based on the online ratings of alternative products and consumer expectations.

The research shows that the proposed method can not only support consumers’ purchase decisions based on a large number of online product ratings but also help manufacturers to find out consumers’ demands or requirements on products so as to facilitate the design of new products or the improvement of products. On the basis of the proposed method, the developed system prototype is helpful for consumers to select desirable products.

To support the purchase decisions of consumers, a new decision-based method for selecting desirable online products is proposed.

Online charitable giving is prevalent, and how to attract individuals' attention to donate is essential for charities. Little is known about the interaction effect of empathy (donor) and vulnerability (receiver) on donate intention. To bridge this gap, this study aims to investigate whether the influence of empathy on charitable giving would be moderated by receivers' vulnerability, and if yes, what is the mechanism.

Five experiments were conducted in the context of charitable giving with 1,303 participants to test our hypotheses.

When empathetic individuals confronted high vulnerable receivers, they were less likely to donate; otherwise, they were more likely to donate when they confronted low vulnerable receivers, and this interaction effect was mediated by concern about self.

The present research identifies a novel moderator of the effect of empathy on charitable giving and elucidates the underlying mechanism of concern about self. Based on these findings, the authors provide actionable implications for charities by demonstrating the interaction effect of empathy and vulnerability on donate intention.

This paper aims to comprehensively clarify the research status of thermal transport of supercritical aviation kerosene, with particular interests in the effect of cracking on heat transfer.

A brief review of current research on supercritical aviation kerosene is presented in views of the surrogate model of hydrocarbon fuels, chemical cracking mechanism of hydrocarbon fuels, thermo-physical properties of hydrocarbon fuels, turbulence models, flow characteristics and thermal performances, which indicates that more efforts need to be directed into these topics. Therefore, supercritical thermal transport of n-decane is then computationally investigated in the condition of thermal pyrolysis, while the ASPEN HYSYS gives the properties of n-decane and pyrolysis products. In addition, the one-step chemical cracking mechanism and SST k-ω turbulence model are applied with relatively high precision.

The existing surrogate models of aviation kerosene are limited to a specific scope of application and their thermo-physical properties deviate from the experimental data. The turbulence models used to implement numerical simulation should be studied to further improve the prediction accuracy. The thermal-induced acceleration is driven by the drastic density change, which is caused by the production of small molecules. The wall temperature of the combustion chamber can be effectively reduced by this behavior, i.e. the phenomenon of heat transfer deterioration can be attenuated or suppressed by thermal pyrolysis.

The issues in numerical studies of supercritical aviation kerosene are clearly revealed, and the conjugation mechanism between thermal pyrolysis and convective heat transfer is initially presented.

The purpose of this paper is to numerically study the influence of wall conduction on the heat transfer of supercritical n-decane in the active regenerative cooling channels.

A horizontally placed rectangular pipe with a solid zone and another one without a solid zone were used. A drastic variation of thermo-physical properties was emphatically addressed. After the verification of mesh and turbulence models comparing with the experimental results, a mesh number of 4.5 M and the low Reynolds number SST k-ω turbulence model were chosen. The solution of the governing equations and the acquisition of the numerical results were executed by the commercial software FLUENT 2020 R1.

The numerical results indicate that there is a heat transfer deterioration (HTD) potential for the upper wall, lower wall and sidewall with the decrease of mass flux. Due to wall conduction, the distribution of the fluid temperature at spanwise-normal planes becomes uniform and this feature also takes advantage of the relatively uniform transverse velocity. For the streamwise-normal planes, the low fluid temperature appears close to the upper wall at the region near the sidewall and vice versa for the region near the centre. Undoubtedly, the secondary flow at the cross-section plays a crucial role in this process and the relatively cool mainstream is affected by the vortices.

This study warns that the wall conduction must be considered in the practical design and thermal optimization due to the sensibility of thermo-physical properties to the heat flux. The secondary flow caused by the buoyancy force (gravity) plays a significant role in the supercritical heat transfer and mixed convection heat transfer should be further studied.

The purpose of this paper is to measure Chinese industries’ eco-efficiency during 2006-2013. The Chinese industry attained rapid achievement in recent decades, but meanwhile, overconsumption of energy and environmental pollution have become serious problems. To solve these problems, many research studies used the data envelopment analysis (DEA) to measure the Chinese industry’s eco-efficiency. However, because the target set by these works is usually the furthest one for a province to be efficient, it may hardly be accepted by any province.

This paper builds a new “closest target method” based on an additive DEA model considering the undesirable outputs. This method is a mixed-integer programming problem which can measure the ecological efficiency of provinces and more importantly guide the province to perform efficiently with minimum effort.

The results show that the eco-efficiency of Chinese provinces increased at the average level, but the deviations remained at a larger value. Compared to the “furthest” target methods, the targets by the approach proposed by this study are more acceptable for a province to improve its performance on both economy and environment counts.

This study is the first attempt to introduce the closest targets concept to measure the eco-efficiency and set the target for each provincial industry in China.

Purpose – This study explores the probability of expropriation of minority shareholders by controlling shareholders in the form of CEO compensation under an imperfect governance institution by using a novel Chinese dataset over 2001–2010.

Design/methodology/approach – We use a direct method to gauge controlling shareholders’ tunneling and expropriation of minority shareholders, and we present a simple model to link corporate governance and the degree of entrenchment by the largest shareholder. We use both Logit and Probit models to predict the likelihood of tunneling and use two-stage least square (2SLS) regression to address the endogeneity issues.

Findings – There are significant deterioration effects between controlling shareholder's tunneling and firm performance. Firms with more tunneling activities typically have larger controlling ownership, greater evidence of state control, less balance of power among large shareholders, and weaker board characteristics.

Research limitations/implications – The positive relationship between controlling shareholders’ tunneling and executive compensation implies that the controlling shareholder might divert personal benefits from the public firms at the expense of minority shareholders.

Originality/value – We focus on the effects of corporate governance restructuring on executive compensation and controlling shareholders’ tunneling in the Chinese context, and we also investigate whether these effects are stronger with the involvement of state ownership. We empirically address the issues between executive compensation and expropriation of minority shareholders.