Abstract Today, firms depend on technology transfer to establish competitive advantage in the global market place. Since the failure rates in TT projects are surprisingly high, special attention should be given to the identification of factors leading to ineffectiveness of TT projects and factors facilitating TT effectiveness. One of the most important factors affecting TT success is the organization’s competence in dealing with the newly transferred technologies. Every organization must somehow modify its structure, resources and culture to adapt the new technologies. In this paper, we first identified all the significant indicators representing organizational competence and TT effectiveness in Iranian firms. Then we measured the effect of each of organizational competence indicators on TT effectiveness in Iranian electrical manufacturing firms.

Abstract A critical step to develop artificial neural networks that has considerable effect on network performance is designing architecture of neural networks. In designing the architecture of networks, generally, the number of hidden layers, number of neurons in each layer and transfer functions are determined. Most researchers often use trial and error approach and/or ignore interactive effects between the factors of design. In this research, a model is presented based on the design of experiment (DOE) for optimal architecture of neural networks. The proposed model was applied to determine the optimal architecture of neural network for forecasting the monthly consumption of gas oil of Iran. To evaluate the effectiveness of the proposed model, using the common method of trial and error was used and advantages of the proposed model were shown. In addition, to compare the performance of neural networks by statistical methods, two models based on regression and ARIMA were designed. Comparison of the forecasting results obtained by neural networks and the statistical methods proved that the proposed model produced better forecasts in all performance criteria.

Abstract “Selecting right Person for the right job” in the organizations seems to be the most important managerial issue. Traditionally, this is realized through a “simple Job- Person Match”, which is an “Individual view”. This methodology, by some trends and paradigm shift in human resource management (i.e. Team Working), should be evolved to a “Group viwe”. In this study, “interpersonal interactions” is added to the traditional one. First, the suggested model for personnel selection and placement is formulated into a Quadratic mathematical form and then a Hopfield neural network has been used to solve it by using Matlab software, a well known and validated software for neural nets. The results show, there is a significant difference between the mean of solutions by traditional view (based on individual level) and the mean of solutions by the new suggensted one (based on group level): t(19) = - 10.966, P-Value=0.000.

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