Artificial intelligence-driven bioenergy system: digital green innovation partner selection of bioenergy enterprises based on interval fuzzy field model

Abstract

Purpose

The aim of this study is to (1) construct a standard framework for assessing the capability of bioenergy enterprises' digital green innovation partners; (2) quantify the choice of partners for digital green innovation by bioenergy enterprises; (3) propose based on a dual combination empowerment niche digital green innovation field model.

Design/methodology/approach

Fuzzy set theory is combined into field theory to investigate resource complementarity. The successful application of the model to a real case illustrates how the model can be used to address the problem of digital green innovation partner selection. Finally, the standard framework and digital green innovation field model can be applied to the practical partner selection of bioenergy enterprises.

Findings

Digital green innovation technology of superposition of complementarity, mutual trust and resources makes the digital green innovation knowledge from partners to biofuels in the enterprise. The index rating system included eight target layers: digital technology innovation level, bioenergy technology innovation level, bioenergy green level, aggregated digital green innovation resource level, bioenergy technology market development ability, co-operation mutual trust and cooperation aggregation degree.

Originality/value

This study helps to (1) construct the evaluation standard framework of digital green innovation capability based on the dual combination empowerment theory; (2) develop a new digital green innovation domain model for bioenergy enterprises to select digital green innovation partners; (3) assist bioenergy enterprises in implementing digital green innovation practices.

Citation

Yin, S., Gao, Z. and Mahmood, T. (2023), "Artificial intelligence-driven bioenergy system: digital green innovation partner selection of bioenergy enterprises based on interval fuzzy field model", Kybernetes, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/K-08-2023-1495