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The purpose of this paper is to investigate the double-edged effects of supervisor bottom-line mentality (BLM) on subordinates' work-related behaviors (work performance and knowledge hiding) and the moderating role of subordinate gender.

The theoretical model was tested using a sample of 218 three-wave multi-source data collected from employees of five firms in southern China.

The results revealed that supervisor BLM is positively associated with subordinate BLM. Although subordinate BLM can enhance their work performance, it can also lead to knowledge hiding toward coworkers. Furthermore, these indirect effects are moderated by subordinate gender.

Managers should pay more attention to the potential positive and negative consequences of supervisor BLM and intervene to mitigate the negative impact of BLM.

This study is among the first to examine how supervisor BLM can be a mixed blessing and elicit both positive and negative behaviors from their subordinates. Moreover, by illuminating how subordinate gender moderates the relationship between supervisor BLM and subordinates' work-related behaviors, we enrich and extend the BLM literature.

Library intelligence institutions, which are a kind of traditional knowledge management organization, are at the frontline of the big data revolution, in which the use of unstructured data has become a modern knowledge management resource. The paper aims to discuss this issue.

This research combined theme logic structure (TLS), artificial neural network (ANN), and ensemble empirical mode decomposition (EEMD) to transform unstructured data into a signal-wave to examine the research characteristics.

Research characteristics have a vital effect on knowledge management activities and management behavior through concentration and relaxation, and ultimately form a quasi-periodic evolution. Knowledge management should actively control the evolution of the research characteristics because the natural development of six to nine years was found to be difficult to plot.

Periodic evaluation using TLS-ANN-EEMD gives insights into journal evolution and allows journal managers and contributors to follow the intrinsic mode functions and predict the journal research characteristics tendencies.

The purpose of this paper is to examine the different strategies implemented by a number of successful Chinese firms currently striving to build global brands in order to improve their export capabilities. A particular emphasis is put on the transfer of marketing technology for brand engineering in order to achieve this goal.

The analysis uses case study methodology to understand what many prominent Chinese exporting firms have achieved, and develops a theory about their general strategy. Five firms have been chosen: Lenovo, Haier, Cosco, Tsingtao, Geely. Aside from these five, information is also given on the branding strategy of Li Ning and Suntech Power. A great part of the information collected is coming from “desk research”, except for Haier, Lenovo and Tsingtao for which personal contacts and visits took place in 2005 and 2006.

The findings suggest that some of the most successful Chinese firms in the field of development of brand image either use some marketing tools, such as increasing their communication spending, improving quality control, emphasizing their corporate social responsibility visibility, or by seeking a partnership through mergers/acquisition with successful foreign brands. A basic global branding model has been defined as consistent with Chinese firms’ experience.

The study was limited to seven firms to be considered among the most successful Chinese businesses. It does not intend to be perceived as statistically representative. The period of observation of the effect of the strategy which was implemented was short and during a time of booming Chinese economy. It was impossible to isolate the extraneous variables linked to the economic or competitive situation, knowing that they could affect the observations on the firms that were studied.

Though the entry strategies on the Chinese market as well as inbound foreign direct investments have been the object of a great number of publications, the outbound strategies of Chinese exporting firms, as well as the impact of technology transfer, has been covered less frequently. Therefore, this paper can have value for candidates for the improvement of global branding.

Accurate and timely cost prediction is critical to the success of construction projects which is still facing challenges especially at the early stage. In the context of rapid development of machine learning technology and the massive cost data from historical projects, this paper aims to propose a novel cost prediction model based on historical data with improved performance when only limited information about the new project is available.

The proposed approach combines regression analysis (RA) and artificial neural network (ANN) to build a novel hybrid cost prediction model with the former as front-end prediction and the latter as back-end correction. Firstly, the main factors influencing the cost of building projects are identified through literature research and subsequently screened by principal component analysis (PCA). Secondly the optimal RA model is determined through multi-model comparison and used for front-end prediction. Finally, ANN is applied to construct the error correction model. The hybrid RA-ANN model was trained and tested with cost data from 128 completed construction projects in China.

The results show that the hybrid cost prediction model has the advantages of both RA and ANN whose prediction accuracy is higher than that of RA and ANN only with the information such as total floor area, height and number of floors.

(1) The most critical influencing factors of the buildings’ cost are found out by means of PCA on the historical data. (2) A novel hybrid RA-ANN model is proposed which proved to have the advantages of both RA and ANN with higher accuracy. (3) The comparison among different models has been carried out which is helpful to future model selection.