Evidence map›Paper›PMID 42718628›Full record

ArticleFrontiers in nutrition2026

Digital technology innovation and agricultural industry chain resilience: implications for food security.

Jie Chen, Jiayi Xu, Xintong Xiong, Yuqi Zhao, Huibin Yao

Abstract read
In one paragraph

Article in Frontiers in nutrition, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Jie ChenSchool of Economics and Management, Changsha University of Science and Technology, Changsha, Hunan, China.
Jiayi XuSchool of Economics and Management, Changsha University of Science and Technology, Changsha, Hunan, China.
Xintong XiongSchool of Economics and Management, Changsha University of Science and Technology, Changsha, Hunan, China.
Yuqi ZhaoSchool of Cross-Border E-Commerce, Yango University, Fuzhou, Fujian, China.
Huibin YaoSchool of Economics, Hunan University of Finance and Economics, Changsha, Hunan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Food security is increasingly threatened by climate shocks, market volatility, and geopolitical disruptions. A resilient agricultural industry chain is critical to ensuring a stable food supply, yet empirical evidence on how firm-level digital technology innovation enhances such resilience remains scarce. Using micro-level data from Chinese agricultural firms over the period 2010-2025, which comprise 69 listed agricultural firms with 714 firm-year unbalanced panel observations, this study examines the impact of digital technology innovation on agricultural industry chain resilience. The results show that digital technology innovation significantly improves chain resilience, a finding that remains robust across multiple tests. Mediation analysis confirms that this effect operates through firm operational efficiency and market expectations. Heterogeneity analysis is conducted along two dimensions, namely regional heterogeneity and firm heterogeneity. Significant positive effects are found in eastern regions, urban agglomerations, and non-old-industrial bases from the regional perspective, and in non-state-owned, large-scale, and mature enterprises from the firm perspective. These findings provide empirical evidence for food-security-oriented policies that promote agricultural digitalization while addressing structural weak links in the food system.

Indexed as

agricultural industry chain resiliencedigital technology innovationfood securityheterogeneitymediating effect

Identifiers

PMID42718628
PMCPMC13553335

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.