Assessing innovativeness of manufacturing firms using an intuitionistic fuzzy based MCDM framework

摘要

Purpose - The purpose of this paper is to provide a framework for assessing the overall innovativeness of manufacturing firms using a multi-attribute group decision-making methodology. Design/methodology/approach - This study identifies the indicators of firms' innovativeness from the literature. The concept of neutrosophic numbers has been used to assign different importance weights to individual decision makers to account for the differences in their educational backgrounds and practical experience. An intuitionistic fuzzy based TOPSIS procedure is adapted for ranking the candidate firms based on their performance on identified criteria. The implementation of the proposed methodology is demonstrated through an explanatory example. Sensitivity analysis is carried out to judge the robustness of the proposed framework. Findings - The proposed framework provides an efficient and reliable tool to subjectively evaluate and compare the innovativeness of manufacturing firms. The sensitivity analysis shows that the methodology is robust enough to absorb the noise factors/errors/variations, etc. Research limitations/implications - Motivated by this work, future studies can consider developing an integrated innovativeness index for evaluation of innovativeness of manufacturing firms. The concept of interval valued intuitionistic fuzzy and neutrosophic sets can be utilized to reduce the margin of perceptual errors even further. Practical implications - The study will provide the firms with a framework for benchmarking their innovative performance. The firms can analyze their current performance and reconfigure their resources and capabilities suitably to improve their competitive position. Originality/value - This study is one of the few attempts that have been made to articulate a firm level innovativeness assessment tool for manufacturing firms operating in an industry sector. Advanced concepts of fuzzy and neutrosophic sets have been utilized to eliminate the chances of bias/perceptual errors that most often affect the quality of decisions in today's dynamic and uncertain decision-making environment.