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The purpose of this paper is to define the concept of guanxi deviant behaviour (GDB) initially on the basis of a theoretical study of guanxi, guanxi behaviour and workplace deviant behaviour and to analyse the influence of GDB and the relationship between GDB and counterproductive work behaviour.

This study collects interview data from 30 enterprise executives, summarises relevant literature from four major databases (two in English and two in Chinese) and applies a grounded theory methodology to refine and further define the core category of GDB, and the main category is interpreted and validated using triangulation.

The three dimensions of GDB are guanxi bribery behaviour, irregular connected transaction behaviour and guanxi allied behaviour. There are links amongst the three dimensions, no dimension has an independent existence and that is not the end of the GDB issue. Generally, the occurrence of a kind of GDB can be construed to be a preparation for the implementation of another kind, and the latter is the real purpose of the perpetrators.

This paper is expected to attract the attention of managers and improve the ability of recognising, preventing and punishing GDB.

This study not only enriches organisational behaviour theory but also enhances the awareness of, and insights into, the negative effects of guanxi.

This paper aims to clarify the factors that affect the formation of organizational human capital competitive advantage (OHCCA) and construct its structural dimensions.

This research method adopted grounded theory using 20 interviews of managers from 10 companies. Relevant literature was reviewed to conduct open coding, Axial coding and selective coding to ensure OHCCA concept and dimensions.

Studies have shown that OHCCA formation of results from investment and collaboration of three levels: organization, teams and departments and employees. OHCCA formation is composed of three dimensions of organizational human capital investment: planning, practice and stock.

This research enriches the organizational human capital and competitive advantage theories.

The practical significance is to provide theoretical and practical guidance for organizations in creating OHCCAs.

This research is the first to propose and define the OHCCA concept and construct a three-dimensional structure model. Furthermore, this research has revealed the leading factors that affect OHCCA's formation process.

The time series forecasting is an essential methodology which can be used for analysing time series data in order to extract meaningful statistics based on the information obtained from past and present. These modelling approaches are particularly complicated when the available resources are limited as well as anomalous. The purpose of this paper is to propose a new hybrid forecasting approach based on unbiased GM(1,1) and artificial neural network (UBGM_BPNN) to forecast time series patterns to predict future behaviours. The empirical investigation was conducted by using daily share prices in Colombo Stock Exchange, Sri Lanka.

The methodology of this study is running under three main phases as follows. In the first phase, traditional grey operational mechanisms, namely, GM(1,1), unbiased GM(1,1) and nonlinear grey Bernoulli model, are used. In the second phase, the new proposed hybrid approach, namely, UBGM_BPNN was implemented successfully for forecasting short-term predictions under high volatility. In the last stage, to pick out the most suitable model for forecasting with a limited number of observations, three model-accuracy standards were employed. They are mean absolute deviation, mean absolute percentage error and root-mean-square error.

The empirical results disclosed that the UNBG_BPNN model gives the minimum error accuracies in both training and testing stages. Furthermore, results indicated that UNBG_BPNN affords the best simulation result than other selected models.

The authors strongly believe that this study will provide significant contributions to domestic and international policy makers as well as government to open up a new direction to develop investments in the future.

The new proposed UBGM_BPNN hybrid forecasting methodology is better to handle incomplete, noisy, and uncertain data in both model building and ex post testing stages.