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Today's leading businesses have understood the role of 'social' in their everyday activity. Online social networks (OSN) and social media have melt and become an essential part of every firm's concern. Brand advocates are the new leading triggers for company's success in online social networks and are responsible for the long-term engagement between a firm and its customers. But what can it be said about this impressive crowd of customers that are gravitating around a certain brand advocacy or a certain community? Are they as responsive to a certain message as one might think? Are they really impressed by the advertising campaigns? Are they equally reacting to a certain comment or news? How they process the everyday grey knowledge that is circulating in OSN? In fact, how impressionable they are and which are the best ways a company can get to them? To give an answer to these questions, the present paper uses grey systems theory for spreading the customer's crowd into groups based on how impressionable they are by commercials, ads, videos and comments in OSN. [ABSTRACT FROM AUTHOR]

The article presents the model of successors' competencies in family firms. The model was created on the basis of empirical data collected in Poland and analysed with the use of Grey Clustering Analysis. At the first i.e. an empirical stage of the studies, a list of competences was identified which are estimated as significant for the successors. At the second stage, on the basis of significance assessment of the competences distinguished by the successors, a list of distinguished competences according to their significance level in a succession process was sorted out. At the third stage, using the basis of grey systems theory, the competences were classified according to their relation of similarities. As the result the successors' competency model was proposed. [ABSTRACT FROM AUTHOR]

Investments are accompanied by risks. How investors choose the right investment tools to assist in the selection of investment targets is a topic worth exploring. Therefore, this study aimed to develop an investment decision-making process to deal with this issue. Firstly, we proposed a globalized modified grey relational analysis to select the representative indicators including the financial indicators and risk measurement indicators. Then we combined financial and risk evaluation indicators, and divided companies into low, moderate and high-risk groups through the grey clustering analysis. Finally, Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR) combined with the grey entropy weighting method was applied to business performance evaluation and sorting of each grouping. In order to verify this study, a combination of 21 financial ratios and four risk indicators was utilized in order to verify the evaluation and decision-making process in the operating performance of 62 listed opto-electronics companies in Taiwan. The results of ranking the operating performance for each group can be made available to company managers as a reference in order to enhance competitiveness and business performance. The results can also be used as the basis for decision-making to aid investors who are facing many investment portfolios.

Investments are accompanied by risks. How investors choose the right investment tools to assist in the selection of investment targets is a topic worth exploring. Therefore, this study aimed to develop an investment decision-making process to deal with this issue. Firstly, we proposed a globalized modified grey relational analysis to select the representative indicators including the financial indicators and risk measurement indicators. Then we combined financial and risk evaluation indicators, and divided companies into low, moderate and high-risk groups through the grey clustering analysis. Finally, Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR) combined with the grey entropy weighting method was applied to business performance evaluation and sorting of each grouping. In order to verify this study, a combination of 21 financial ratios and four risk indicators was utilized in order to verify the evaluation and decision-making process in the operating performance of 62 listed opto-electronics companies in Taiwan. The results of ranking the operating performance for each group can be made available to company managers as a reference in order to enhance competitiveness and business performance. The results can also be used as the basis for decision-making to aid investors who are facing many investment portfolios. [ABSTRACT FROM PUBLISHER]

This paper takes 30 provinces in China as the research objects, and by using the data of CDM projects from 2005 to 2012, the characteristics of regional differences have been analyzed. According to the Q-Q-S evaluation principles, the measure index system is firstly proposed from the dimensions of quantity, quality and reduction scale. Then grey incidence and grey clustering analysis methods are used to do the further research; at last the regional differences and characteristics have been discussed. The conclusions are as follows: (1) the comprehensive development levels of CDM projects -in different provinces in China are relatively weak; (2) the development level in quantity, quality and reduction scale dimensions of CDM projects varies with the provinces obviously; (3) the development of CDM projects in eastern, central and western regions presents a strong disequilibrium. The eastern and western regions have the advantages in quality, reduction scale and the comprehensive development of CDM projects. However, the level of the central region is relatively low. [ABSTRACT FROM AUTHOR]

Abstract: Deng proposed grey clustering analysis (GCA) in 1987. Later, Jin presented a new method in 1993, called grey relational clustering (GRC) method that combined grey relational analysis with clustering. However, the GRC method cannot use a tree diagram to make appropriate classification decisions without re-computation. This study thus attempts to combine GRC and hierarchical clustering analysis. Given the existence of an excess of medical resources in the Taipei area, this study attempts to understand the degree of concentration of medical resources in this area. Specifically, this study applies a geographical information system (GIS) to present the geographical distribution of hospitals in Taipei. Additionally, a new-type of cluster analysis, known as hierarchical grey relation clustering analysis, is used to analyze the distribution of hospitals and understand how they compete with one another. The analytical results demonstrate that hierarchical grey relation clustering analysis is a suitable method of analyzing geographical position. Tree diagrams can help policymakers make appropriate classification decisions without re-computation. The study results can inform hospitals of their competitors and help them to develop appropriate responses. Additionally, the analytical results can also provide a reference to government or hospital policymakers to help them position hospitals in areas, thus achieving a better distribution of medical resources in Taipei. [Copyright &y& Elsevier]

With respect to increasing the evaluated sensitivity, this study employs the Grey Clustering Analysis (GCA) to refine the Grey Relational Analysis (GRA) not only to detect the slight fluctuation in the ten economies through evaluating the 7-year major ten nation-performance competitive indicators in the ten economies comprised of two developed economies (USA and Japan), four booming economies (Four Asia Tigers) and four emerging economies (BRIC) but also, further, to create the innovative Grey-Clustering Macroeconomic Assessment Model (GCMAM). According to the measured results of the Grey Clustering Analysis (GCA), the comprehensive 7-year nation-performance of top 5 ranking originally are Singapore, USA, Japan, Hong Kong and Taiwan. However, through cross-employ comparison of the Grey Clustering Analysis (GCA) and Grey Relational Analysis (GILA), Singapore, USA, China, Brazil and Korea have been the new top 5 economies through the evaluation of the innovative Grey-Clustering Macroeconomic Assessment Model (GCMAM). [ABSTRACT FROM AUTHOR]