Knowledge creation: revisiting the 'ba' humbug people and 'latent' knowledge in organizational learning

Knowledge management theory has struggled with the concept of `knowledge creation'. Since the seminal article of Nonaka in 1991, an industry has grown up seeking to capture the knowledge in the heads and hearts of individuals so as to leverage them for organizational learning and growth. But the process of Socialization, Externalization, Combination and Internalization (SECI) outlined by Nonaka and his colleagues has essentially dealt with knowledge transfer rather than knowledge creation. This paper attempts to fill the gap in the process - from Nonaka's own addition of the need for "ba" to Snowden's suggestion of that we consider "Cynefin" as a space for knowledge creation. Drawing upon a much older theoretical frame - work the Johari Window developed in group dynamics, this paper suggests an alternative concept - latent knowledge - and introduces a different model for the process of knowledge creation.

Forecasting overseas visitors into the United Kingdom using continuous time and autoregressive fractional integrated moving average models with discrete data

This paper applies Gaussian estimation methods to continuous time models for modelling overseas visitors into the UK. The use of continuous time modelling is widely used in economics and finance but not in tourism forecasting. Using monthly data for 1986–2010, various continuous time models are estimated and compared to autoregressive integrated moving average (ARIMA) and autoregressive fractionally integrated moving average (ARFIMA) models. Dynamic forecasts are obtained over different periods. The empirical results show that the ARIMA model performs very well, but that the constant elasticity of variance (CEV) continuous time model has the lowest root mean squared error (RMSE) over a short period.

Do human resource practices enhance organizational commitment in SMEs with low employee satisfaction?

This paper considers a large matched employee–employer data set to estimate a model of organizational commitment. In particular, it focuses on the role of firm size and management formality to explain organizational commitment in British small and medium-sized enterprises (SMEs) with high and low levels of employee satisfaction. It is shown that size ‘in itself’ can explain differences in organizational commitment, and that organizational commitment tends to be higher in organizations with high employee satisfaction compared with organizations of similar size with low employee satisfaction. Crucially, the results suggest that formal human resource (HR) practices can be used as important tools to increase commitment and thus, potentially, effort and performance within underperforming SMEs with low employee satisfaction. However, formal HR practices commonly used by large firms may be unnecessary in SMEs which benefit from high employee satisfaction and positive employment relations within a context of informality.

Volatility and VaR Forecasting in the Madrid Stock Exchange

This paper provides an empirical study to assess the forecasting performance of a wide range of models for predicting volatility and VaR in the Madrid Stock Exchange. The models performance was measured by using different loss functions and criteria. The results show that FIAPARCH processes capture and forecast more accurately the dynamics of IBEX-35 returns volatility. It is also observed that assuming a heavy-tailed distribution does not improve models ability for predicting volatility. However, when the aim is forecasting VaR, we find evidence of that the Student’s t FIAPARCH outperforms the models it nests the lower the target quantile.