JoWUA

Abstract

Technological Innovation Capability (TIC) is an intricate concept which defines the essence of a firm’s influence in the long run. It is associated with multiple quantitative and qualitative criteria, and various types of uncertainty can be seen while measuring these criteria. Therefore, to address this issue, a Belief Rule-Based Expert System (BRBES) can be employed with the capability of handling multiple criteria and their associated uncertainties in an integrated framework. In this article, two web-based BRBESs, namely conjunctive BRBES, and disjunctive BRBES, have been developed which are capable of reading data and producing web-based output by taking uncertainties into consideration. Then a comparison has been performed between them to determine the reliability of TIC evaluation. The results show that the performance of conjunctive BRBES is promising than disjunctive BRBES for technological innovation capability evaluation. In addition, a new learning mechanism, namely Belief Rule-Based Adaptive Particle Swarm Optimization (BRBAPSO), has been developed to support learning in BRBES and a comparison between trained conjunctive and trained disjunctive BRBES has also been carried out to evaluate TIC, where trained conjunctive BRBES is found effective than trained disjunctive BRBES.