Evaluation of small and medium-sized enterprises’ sustainable development with hesitant fuzzy linguistic group decision-making method

摘要

With the rapid development of the economy, the competition among enterprises is increasingly intensifying and the internal contradictions of the enterprise are prominent, which pose many challenges to the sustainable development of small- and medium-sized enterprises (SMEs). Therefore, the scientific assessment of an enterprise’s sustainable development capability is important, as it reflects the current development status and the potential future opportunities. Hesitant fuzzy linguistic preference relations (HFLPRs), a combination of linguistic term sets and hesitant fuzzy preference relations are useful in solving group decision-making (GDM) problems. HFLTS can tackle situations in which decision makers (DMs) consider multiple potential linguistic terms at the same time than a single term for an indicator, alternative, variable, etc., to express their preferences without the use of numerical values. This paper introduces a new GDM approach under the hesitant fuzzy linguistic environment based on a multiplicative consistency adjustment algorithm and a Charnes-Cooper-Rhodes Data Envelopment Analysis (CCR-DEA) model to assess the sustainable development of SMEs. First, the concepts of HFLPR and multiplicative consistency, including the consistency index and consistency checking approach, are reviewed. Then, a new consistency-improving method for achieving an acceptable HFLPR is introduced, which ensures that each HFLPR satisfies the requirements for multiplicative consistency. After this transformation, an innovative CCR-DEA model is developed using the input–output technique to determine the final ranking of alternatives and to achieve an optimal decision-making solution. Finally, the hesitant fuzzy linguistic decision-making method is intended to be used to assess the economic growth of SMEs. The advantages and applicability of the proposed approach are determined by comparative analysis.