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This article verified the construct of servant leadership and validated a measure developed in Western culture. Results from exploratory factor analysis (EFA) (N=285) produced a five-factor model – altruistic calling, emotional healing, persuasive mapping, wisdom, and community stewardship with less items than the original measure. Confirmatory factor analysis (CFA) (N=304) indicated that the 5-factor servant leadership model fits the data best. Correlation analysis of the supervisor-subordinate paired sample (N=209 dyads) showed that servant leadership has more common features with transformational leadership and less with paternalistic leadership; the predictive power of servant leadership was roughly equivalent to that of transformational leadership but higher than that of paternalistic leadership when predicting criterion variables such as overall satisfaction and deviance behavior. Our results totally demonstrated that the revised servant leadership scale in Chinese culture has higher reliability and validity, which could be used for subsequent studies as an effective instrument.

The first section of this volume deals with leadership qualities in the global environment. In particular, we are pleased to have scholars share their leading-edge research in terms of the following leadership characteristics and qualities: personality, competency, the ability to scan environment in search of useful information, the ability to anticipate and manage errors, and integrity and ethical leadership.

Soon Ang (Ph.D. Minnesota) is Goh Tjoei Kok Distinguished Chair of Management and Head, Division of Strategy, Management & Organization at the Nanyang Business School, Nanyang Technological University, Singapore. Her research interests are in cultural intelligence, global leadership, and outsourcing. She has published extensively in Academy of Management Journal, Journal of Applied Psychology, Information Systems Research, Organization Science, Management Science, MIS Quarterly, and Social Forces, and serves on editorial boards including Management Science, Organization Science, Applied Psychology, Decision Science, Information System Research, MIS Quarterly, etc. She has pioneered and coauthored two books on cultural intelligence (Stanford University Press) and coedited the Handbook of Cultural Intelligence (ME Sharpe). She was recently awarded the prestigious Distinguished International Alumni Award by the University of Minnesota for her academic leadership and scholarship record.

The large volume of big data makes it impractical for traditional clustering algorithms which are usually designed for entire data set. The purpose of this paper is to focus on incremental clustering which divides data into series of data chunks and only a small amount of data need to be clustered at each time. Few researches on incremental clustering algorithm address the problem of optimizing cluster center initialization for each data chunk and selecting multiple passing points for each cluster.

Through optimizing initial cluster centers, quality of clustering results is improved for each data chunk and then quality of final clustering results is enhanced. Moreover, through selecting multiple passing points, more accurate information is passed down to improve the final clustering results. The method has been proposed to solve those two problems and is applied in the proposed algorithm based on streaming kernel fuzzy c-means (stKFCM) algorithm.

Experimental results show that the proposed algorithm demonstrates more accuracy and better performance than streaming kernel stKFCM algorithm.

This paper addresses the problem of improving the performance of increment clustering through optimizing cluster center initialization and selecting multiple passing points. The paper analyzed the performance of the proposed scheme and proved its effectiveness.

This paper aims to examine the unique Chinese context by analyzing the city labels (e.g. smart city and eco city) used by Chinese local governments at or above the provincial capital level to represent themselves (adopted city labels) and the developmental pathways they actually pursued (adopted developmental pathways).

The authors compared the city brand choices to those anticipated based on their geographic and economic contexts (predicted city labels and developmental pathways) as well as the directives outlined in national planning documents (imposed city labels and developmental pathways). The authors identified ten main categories of city labels used to designate themselves and establish the frequency of their use based on municipal plan documents, economic and geographic data and national plan documents and policy reports, respectively.

The authors discovered that both local economic development and geographic factors, as well as top-down administrative influences, significantly impact city branding strategies in the 38 Chinese cities studied. When these models fall short in predicting adopted city labels and pathways, it is often because cities favor a service-oriented reputation over a manufacturing-focused one, and they prefer diverse, multifaceted industrial images to uniform ones.

The originality and value of this paper lie in its contribution to the academic literature on city branding by developing a predictive model for brand development at the municipal level, with explicit attention to the national-local nexus. The paper’s approach differs from existing research in the first cluster of city branding by not addressing issues of stakeholder involvement or adoption and implementation processes. Additionally, the paper’s focus on the political power dynamics at the national level and urban governance details at the municipal level provides a unique perspective on the topic. Overall, this paper provides a valuable contribution to the field of city branding by expanding the understanding of brand development and its impact on the socioeconomic environment.

The purpose of this study is to develop a modified parameter identification method and a novel measurement method to calibrate a 3 degrees-of-freedom (3-DOF) parallel tool head. This parallel tool head is a parallel mechanism module in a five-axes hybrid machine tool. The proposed parameter identification method is named as the Modified Singular Value Decomposition (MSVD) method. It aims to overcome the difficulty of choosing the algorithm parameter in the regularization identification method. The novel measurement method is named as the vector projection (VP) method which is developed to expand the measurement range of self-made measurement implements.

Newton Iterative Algorithm based on Least Square Method is analyzed by using the Singular Value Decomposition method. Based on the analysis result, the MSVD method is proposed. The VP method transforms the angle measurement into the displacement measurement by taking full advantage of the ability that the 3-DOF parallel tool head can move in the X – Y plane.

The kinematic calibration approach is verified by calibration simulations, a Rotation Tool Center Point accuracy test and an experiment of machining an “S”-shaped test specimen.

The kinematic calibration approach with the MSVD method and VP method could be successfully applied to the 3-DOF parallel tool head and other 3-DOF parallel mechanisms.

Automatic interpretation of Nominal Compounds is a crucial issue for many applications, for example, sentence understanding, machine translation, question-answering system and so forth. Many automatic interpretation models of Nominal Compounds use the strategies based on verbs or rules to obtain the interpretation of compounds. However, the performances of these models are still limited. The purpose of this paper is to propose an effective approach for automatic interpretation of Chinese nominal compounds.

The authors propose a top-down and bottom-up model based on rules and large-scale corpus for automatic interpretation of Nominal Compounds.

Experimental results demonstrate that the proposed model outperforms the state-of-the-art automatic interpretation model.

The paper is an up-to-date study of automatic interpretation for Nominal Compounds. It can help people understand the meaning of Nominal Compounds in reading. With a better understanding of Nominal Compounds, we can discover more hidden knowledge in them.