Cause and Effect Relationships of Internal and External Technological Learning Mechanisms in the Iranian Renewable Energy Firms

In developing countries, firms usually have limited capital, infrastructure, and institutions for technological learning. Hence, knowledge of firms about external and internal learning mechanisms as well as using other firm’s experiences lead to reduced cost and time of technological learning. Present paper contributes to renewable energy firm’s knowledge by analyzing cause and effect relationships of internal and external technological learning mechanisms. For this purpose, internal and external technological learning mechanisms were extracted by literature review. Then, cause and effect relationships of learning mechanisms in Iranian renewable energy firms were investigated by the DEMATEL method. The results indicate that firms should use a combination of internal and external learning mechanisms to enhance their technological capability. Moreover, intramural research and development, trial and error, reverse engineering of competitor’s products, imitation of competitors, and licensing agreement have been the most frequent technological learning mechanisms used by Iranian renewable energy firms. These are the issues that need to be addressed by renewable energy firms and policymakers in developing countries.

A New Model for Determining Weight Coefficients of Criteria in MCDM Models: Full Consistency Method (FUCOM)

In this paper, a new multi-criteria problem solving method—the Full Consistency Method (FUCOM)—is proposed. The model implies the definition of two groups of constraints that need to satisfy the optimal values of weight coefficients. The first group of constraints is the condition that the relations of the weight coefficients of criteria should be equal to the comparative priorities of the criteria. The second group of constraints is defined on the basis of the conditions of mathematical transitivity. After defining the constraints and solving the model, in addition to optimal weight values, a deviation from full consistency (DFC) is obtained. The degree of DFC is the deviation value of the obtained weight coefficients from the estimated comparative priorities of the criteria. In addition, DFC is also the reliability confirmation of the obtained weights of criteria. In order to illustrate the proposed model and evaluate its performance, FUCOM was tested on several numerical examples from the literature. The model validation was performed by comparing it with the other subjective models (the Best Worst Method (BWM) and Analytic Hierarchy Process (AHP)), based on the pairwise comparisons of the criteria and the validation of the results by using DFC. The results show that FUCOM provides better results than the BWM and AHP methods, when the relation between consistency and the required number of the comparisons of the criteria are taken into consideration. The main advantages of FUCOM in relation to the existing multi-criteria decision-making (MCDM) methods are as follows: (1) a significantly smaller number of pairwise comparisons (only n − 1), (2) a consistent pairwise comparison of criteria, and (3) the calculation of the reliable values of criteria weight coefficients, which contribute to rational judgment.