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Firms that invest simultaneously in digital tools, green practices and talent see sizable supply‑chain efficiency gains, and most of the improvement—about 84.6%—works through technological innovation; effects are larger for SOEs, non‑high‑tech firms and Eastern China, while high‑tech firms face integration frictions.

The Influence Mechanism of New Quality Productivity Forces on Supply Chain Efficiency: Technological Innovation as a Mediating Variable

Yawei Wang, Bohang Li · March 13, 2026 · Sustainability

openalex correlational medium evidence 7/10 relevance DOI Source PDF

A composite measure of digital, green, and talent capacities (NQPF) is positively associated with firm-level supply‑chain efficiency among Shanghai and Shenzhen A‑share firms (2012–2022), with technological innovation mediating roughly 84.6% of the effect.

Amidst global economic volatility and technological disruption, enhancing supply chain efficiency remains critical, yet the fragmented theoretical frameworks, opaque mediation mechanisms, and neglected contextual heterogeneity in understanding how new quality productivity forces (NQPFs) drive this transformation constitute a critical research gap. This study empirically examines NQPF’s impact on supply chain efficiency and its underlying mechanisms, addressing three core problems: first, the lack of a holistic NQPF framework integrating digital, green, and talent dimensions; second, insufficiently explored mediating roles of technological innovation; and third, unaddressed heterogeneity across ownership types, industries, and regions. Using 2012–2022 panel data from Shanghai and Shenzhen A-share listed companies, we employ robustness tests, mediating effect models, and heterogeneity analyses. The results reveal that the NQPF significantly improves supply chain efficiency, primarily through technological innovation—which accounts for 84.6% of the variance—acting via technological innovation, management restructuring, and digital transformation. Crucially, heterogeneity analysis reveals stronger effects in state-owned enterprises, nonhigh-tech industries, and Eastern China, whereas high-tech sectors face integration challenges. Our originality lies in three aspects: integrating the NQPF’s multiple dimensions into a unified theoretical framework; empirically clarifying the “black-box” mediation of innovation; and providing granular evidence for differentiated regional/industrial policies to bolster supply chain resilience.

Summary

Main Finding

New quality productivity forces (NQPFs) — a composite of digital, green, and talent capacities — significantly increase firm-level supply chain efficiency among Shanghai and Shenzhen A‑share listed companies (2012–2022). The effect operates overwhelmingly through technological innovation (explaining 84.6% of the mediated effect), with additional pathways via management restructuring and digital transformation. Effects are heterogeneous: stronger in state‑owned enterprises (SOEs), non‑high‑tech industries, and Eastern China; high‑tech sectors show integration challenges.

Key Points

Definition/conceptual advance: NQPF integrates digital, green, and talent dimensions into a single framework for assessing quality-driven productivity that shapes supply‑chain outcomes.
Main quantitative result: NQPF has a positive and statistically robust effect on supply chain efficiency; technological innovation is the dominant mediating channel (84.6% of the mediation).
Additional mechanisms: management restructuring and digital transformation also contribute to translating NQPF into efficiency gains.
Heterogeneity:
- Ownership: stronger effects for SOEs than private firms.
- Industry: stronger effects in non‑high‑tech industries; high‑tech industries face "integration" frictions that dampen gains.
- Region: stronger effects in Eastern China compared with other regions.
Original contributions:
Unified, multi‑dimension NQPF framework linking digital, green, and talent forces to supply‑chain outcomes.
Empirical unpacking of the “black box” mediating role of technological innovation.
Granular evidence supporting differentiated regional/industrial policy prescriptions to improve resilience.

Data & Methods

Data: Panel of Shanghai and Shenzhen A‑share listed firms, 2012–2022.
Empirical strategy:
- Baseline regressions estimating the relationship between firm‑level NQPF and supply chain efficiency.
- Robustness checks (alternative specifications and sensitivity analyses) to validate main results.
- Mediating‑effect models to decompose indirect pathways; technological innovation identified as the principal mediator (84.6%).
- Heterogeneity analyses by ownership type (SOE vs. non‑SOE), industry (high‑tech vs. non‑high‑tech), and region (Eastern vs. other).
Note: paper reports proportion of mediated effect and conducts multiple robustness exercises; specific variable constructions and instruments (if any) are described in the full paper.

Implications for AI Economics

AI as a central component of NQPF: the finding that technological innovation (which includes AI adoption and related R&D) accounts for the vast majority of NQPF’s effect implies that AI-driven innovation is a key lever for supply‑chain efficiency.
Policy targeting:
- Prioritize AI/digital upskilling (talent) and green/AI complementarities to maximize supply‑chain returns, especially outside high‑tech sectors where marginal gains are larger.
- Address integration frictions in high‑tech firms (organizational, interoperability, or legacy systems) to unlock latent AI benefits.
- Regional policy differentiation: support diffusion of AI capabilities from Eastern China to other regions through infrastructure and talent mobility programs.
Firm strategy:
- Invest holistically across digital tools (including AI), green practices, and human capital rather than single‑axis investments; coordinate these investments to amplify innovation-mediated gains.
- Combine AI adoption with management restructuring to realize operational and organizational efficiency.
Research agenda:
- Deeper causal identification of AI‑specific effects within the technological innovation channel (e.g., instruments, natural experiments).
- Micro‑level studies on how particular AI applications (demand forecasting, procurement optimization, dynamic routing) map onto supply‑chain efficiency gains across firm types.
- Generalizability tests beyond listed firms and outside China to assess external validity and contextual moderators.

Assessment

Paper Typecorrelational Evidence Strengthmedium — The paper uses a decade-long firm-level panel, multiple robustness checks, mediation models, and heterogeneity analyses, which produce consistent and informative associations; however, it lacks clear exogenous variation or instrumental strategies to rule out reverse causality and omitted variable bias, and the composite NQPF index may introduce measurement ambiguity. Methods Rigormedium — Appropriate econometric approaches (panel regressions, mediated-effects decomposition, extensive robustness and heterogeneity checks) are applied and reported, but key threats remain: potential endogeneity of NQPF, limited discussion of measurement error in the composite index, and no convincing quasi-experimental identification to move from correlation to causation. SampleFirm-year panel of Shanghai and Shenzhen A-share listed companies from 2012 to 2022; firm-level constructed NQPF index (digital, green, talent dimensions), firm-level outcome measure of supply-chain efficiency, and covariates including ownership (SOE vs non-SOE), industry classification (high-tech vs non-high-tech), and region (Eastern vs other). Exact sample size and variable construction are described in the full paper. Themesproductivity innovation IdentificationPanel regressions with firm and year controls, robustness checks, and mediation analysis decomposing indirect effects; no exogenous variation, instruments, or natural experiment reported to secure causal identification. GeneralizabilityRestricted to publicly listed A‑share firms in Shanghai and Shenzhen — excludes SMEs and unlisted firms, China-specific institutional context; results may not generalize to other countries with different ownership and regulatory environments, Time window 2012–2022 may miss post-2022 rapid AI diffusion dynamics, Composite NQPF index and its components may be context-dependent and not directly comparable across settings, Heterogeneous effects (SOE vs private, regional differences, industry) limit simple extrapolation to all firms or sectors

Claims (9)

Claim	Direction	Confidence	Outcome	Details
New quality productivity forces (NQPF) significantly improve supply chain efficiency. Firm Productivity	positive	high	supply chain efficiency	statistically significant improvement in supply chain efficiency 0.3
Technological innovation is the primary mediating mechanism through which NQPF affects supply chain efficiency, accounting for 84.6% of the effect. Firm Productivity	positive	high	proportion of NQPF effect on supply chain efficiency mediated (variance explained by mediation)	technological innovation mediates 84.6% of the NQPF effect 0.3
NQPF affects supply chain efficiency via multiple mechanisms: technological innovation, management restructuring, and digital transformation. Firm Productivity	positive	medium	supply chain efficiency (through identified mediators)	0.18
The positive effect of NQPF on supply chain efficiency is stronger in state-owned enterprises (SOEs) than in non-state firms. Firm Productivity	positive	medium	supply chain efficiency (effect size of NQPF by ownership type)	effect stronger in state-owned enterprises (SOEs) than non-state firms 0.18
NQPF has stronger positive effects on supply chain efficiency in non-high-tech industries; high-tech sectors face integration challenges that weaken the effect. Firm Productivity	mixed	medium	supply chain efficiency (differential NQPF effect by industry type)	stronger positive effects in non-high-tech industries 0.18
NQPF’s positive impact on supply chain efficiency is stronger in Eastern China compared with other regions. Firm Productivity	positive	medium	supply chain efficiency (regional variation in NQPF effect)	stronger effects in Eastern China 0.18
This study develops a unified NQPF theoretical framework integrating digital, green, and talent dimensions. Research Productivity	positive	medium	theoretical comprehensiveness (qualitative framework integration)	0.18
The paper empirically clarifies the previously opaque ('black-box') mediation role of technological innovation between NQPF and supply chain efficiency. Firm Productivity	positive	medium	degree of mediation (technological innovation mediating NQPF → supply chain efficiency)	empirical clarification of mediation role (see reported 84.6%) 0.18
Findings provide granular evidence to support differentiated regional and industrial policies aimed at strengthening supply chain resilience. Governance And Regulation	positive	medium	policy relevance inferred from heterogeneity in NQPF effects on supply chain efficiency	0.18