ESG performance drivers and corporate growth: a life-cycle-based fsQCA–PSM study of China’s construction and manufacturing enterprises

This study adopts a multi-perspective TOE framework and integrates Necessary Condition Analysis (NCA), fuzzy-set Qualitative Comparative Analysis (fsQCA), and Propensity Score Matching (PSM) to investigate how technological, organizational, and environmental factors jointly influence ESG performance and firm growth. Using a sample of construction and manufacturing firms in China, we find: (1) ESG improvements result from the synergy of multiple factors. Four causal paths are identified: H1a (digital transformation + R&D investment + marginal firm size), H1b (digital transformation + financial performance + government subsidies + marginal industry competition), H2 (R&D investment + financial performance + marginal firm size), and H3 (financial performance + industry competition). These form three configuration types: internal–external collaboration, proactive development, and self-reliance under external pressure. (2) The configurations display causal asymmetry, with a single-factor-driven model linked to poor ESG outcomes. (3) The impact on growth varies: the self-reliance model consistently promotes growth; the other two hinder it during early stages but enhance it in maturity. In the decline phase, only internal–external collaboration remains effective. This research advances ESG understanding and informs strategies for enterprise growth.

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Publication Details

Journal
Journal of Asian Architecture and Building Engineering
Published
2025-06-13
DOI
https://doi.org/10.1080/13467581.2025.2517911
Citations
9
Primary Topic
International Business and FDI
Type
article
Field-Weighted Citation Impact
20.04
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article

ESG performance drivers and corporate growth: a life-cycle-based fsQCA–PSM study of China’s construction and manufacturing enterprises

Li Ding, Han Yan, Shenglin Ma
9 citations
Journal of Asian Architecture and Building Engineering
International Business and FDI
20.04
article

ESG performance drivers and corporate growth: a life-cycle-based fsQCA–PSM study of China’s construction and manufacturing enterprises

Li Ding, Han Yan, Shenglin Ma
article en
9 citations

Abstract

This study adopts a multi-perspective TOE framework and integrates Necessary Condition Analysis (NCA), fuzzy-set Qualitative Comparative Analysis (fsQCA), and Propensity Score Matching (PSM) to investigate how technological, organizational, and environmental factors jointly influence ESG performance and firm growth. Using a sample of construction and manufacturing firms in China, we find: (1) ESG improvements result from the synergy of multiple factors. Four causal paths are identified: H1a (digital transformation + R&D investment + marginal firm size), H1b (digital transformation + financial performance + government subsidies + marginal industry competition), H2 (R&D investment + financial performance + marginal firm size), and H3 (financial performance + industry competition). These form three configuration types: internal–external collaboration, proactive development, and self-reliance under external pressure. (2) The configurations display causal asymmetry, with a single-factor-driven model linked to poor ESG outcomes. (3) The impact on growth varies: the self-reliance model consistently promotes growth; the other two hinder it during early stages but enhance it in maturity. In the decline phase, only internal–external collaboration remains effective. This research advances ESG understanding and informs strategies for enterprise growth.

Journal of Asian Architecture and Building Engineering
Hebei University of Economics and Business (CN), North University of China (CN), Heilongjiang Bayi Agricultural University (CN)
Openalex Percentile: Top 1%
International Business and FDI
20.04
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ESG performance drivers and corporate growth: a life-cycle-based fsQCA–PSM study of China’s construction and manufacturing enterprises — Li Ding, Han Yan, et al. · Journal of Asian Architecture and Building Engineering (2025) | TGRS Research Map | TGRS