Artificial intelligence-driven photovoltaic building materials industry: Greenization and digitization innovation conversion of photovoltaic technology based on a novel interval fuzzy field theory decision-making model

Article type: Research Article

Authors: Zhang, Nan^a | Yin, Jiayi^b | Zhang, Ning^{a; *} | Sun, Tongtong^b | Yin, Shi^{b; c; *} | Wan, Lijun^d

Affiliations: [a] College of Humanities and Social Sciences, Hebei Agricultural University, Baoding, China | [b] College of Economics and Management, Hebei Agricultural University, Baoding, China | [c] School of Economics and Management, Harbin Engineering University, Harbin, China | [d] School of Management, Suqian University, Suqian, Jiangsu, China

Correspondence: [*] Corresponding author. Ning Zhang and Shi Yin, E-mail: shyshi0314@163.com; https://orcid.org/0000-0001-6885-7412.

Abstract: Digital technologies, such as big data, the Internet, and artificial intelligence, are rapidly advancing. Photovoltaic building materials enterprises (PBMEs) have been leveraging digital transformation to enhance their technological innovation capabilities and gain a competitive edge. In the global context of transitioning towards a low-carbon economy, the deep integration of digital technology offers a new solution for the green transformation of PBMEs. The synergy between green traction digitalization and digitalization enables green practices, making collaborative integration crucial for the far-reaching development of PBMEs. Within the framework of China’s “double carbon” policy, domestic PBMEs are experiencing exponential growth, where digital green innovation (DGI) has become their primary objective. In this DGI context, selecting the right partners is the first step that significantly impacts the efficiency and effectiveness of DGI implementation. Therefore, the purpose of this study is to assist PBMEs in selecting high-quality partners, promoting the DGI process, enhancing technological innovation capabilities, and gaining a competitive advantage. To achieve this, the paper proposes constructing a theoretical framework for evaluating the DGI cooperation ability of PBMEs using the theory of ecological reciprocity. Based on this framework, an evaluation index system is established to assess the DGI cooperation ability of potential partners The interval intuitionistic fuzzy evaluation method, combined with a double combination weighting approach, is employed to evaluate the DGI ability of selected partners. Furthermore, by applying field theory, a dynamic selection model for strategic alliance partners is developed to aid PBMEs in selecting high-quality partners for DGI and facilitating the DGI process. The research findings indicate that: i) The evaluation standard framework for DGI cooperation ability of PBMEs encompasses “symbiosis,” “mutualism,” and “regeneration,” along with the crucial environmental element of mutual trust. ii) The evaluation method based on double combination weighting effectively assesses the comprehensive DGI capabilities of selected PBME partners. The application of field theory enables scientific and effective dynamic partner selection for PBMEs through resource complementarity. iii) The proposed framework and partner selection model can be employed in real partner selection scenarios for PBMEs, allowing them to choose high-quality partners, enhance their DGI capabilities, and attain practical selection outcomes. This paper presents novel partner selection model that integrates decision rules and resource complementarity, enabling PBMEs to efficiently select DGI partners from a pool of potential candidates and improve their innovation efficiency. The utilization of the double combination weighting method and field theory in the partner selection paradigm of D extends the theoretical foundation, while the establishment of the DGI capability evaluation index system for PBME partners contributes to empirical applications.

Keywords: Photovoltaic building materials enterprises, digital green innovation, partner selection, double combination weighting, field theory

DOI: 10.3233/JIFS-234838

Journal: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6411-6437, 2024