{
  "abstract": "In the digital era, digital literacy has emerged as a critical determinant of employment quality in the labor market. Using nationally representative data from the 2022 China Family Panel Studies (CFPS), this study constructs multidimensional indices of digital literacy and employment quality. Ordinary least squares (OLS) regressions, robustness checks, and moderation models are employed to examine the relationships between these variables and the underlying mechanisms. The findings suggest that digital literacy is significantly associated with employment quality. This relationship is positively moderated by return expectations and negatively moderated by human capital, indicating a stronger association among low-skilled workers with high return expectations. Regional heterogeneity reveals the most significant associations in western China and urban agglomerations—attributable to higher marginal returns on digital skills in underdeveloped regions and denser digital economies in clustered zones. Digital literacy emerges as a pivotal correlate of employment quality, particularly for vulnerable groups and lagging regions. Policy interventions should prioritize (1) inclusive, tiered digital training programs targeting low-skilled workers; (2) regionally differentiated strategies; and (3) leveraging urban agglomerations as hubs for cross-regional resource sharing. These approaches can help narrow digital divides and advance sustainable, equitable, and high-quality employment in the digital era.",
  "abstract_shown": true,
  "affiliations": [
    "College of Economics and Management, Yuzhang Normal University, Nanchang, China",
    "Modernization Development Research Center of Nanchang Metropolitan Area, Yuzhang Normal University, Nanchang, China",
    "College of Economics and Management, Jiangxi Agricultural University, Nanchang, China"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Xiaoyun Wu"
    },
    {
      "affs": [
        1
      ],
      "name": "Danting Cao"
    },
    {
      "affs": [
        2
      ],
      "name": "Beihe Wu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0wr8je58b2tep.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0358194",
  "fulltext_url": null,
  "id": "0wr8je58b2tep",
  "issns": [
    "1932-6203"
  ],
  "landing_url": "https://doi.org/10.1371/journal.pone.0358194",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1710|Digital literacy in education"
    ],
    [
      "plos",
      "/Biology and life sciences/Neuroscience/Cognitive science/Cognitive psychology/Academic skills/Literacy"
    ],
    [
      "plos",
      "/Biology and life sciences/Psychology/Cognitive psychology/Academic skills/Literacy"
    ],
    [
      "plos",
      "/Computer and information sciences/Computer networks/Internet"
    ],
    [
      "plos",
      "/Physical sciences/Mathematics/Statistics/Statistical methods/Instrumental variable analysis"
    ],
    [
      "plos",
      "/Physical sciences/Physics/Thermodynamics/Entropy"
    ],
    [
      "plos",
      "/Research and analysis methods/Mathematical and statistical techniques/Statistical methods/Instrumental variable analysis"
    ],
    [
      "plos",
      "/Social sciences/Economics/Human capital"
    ],
    [
      "plos",
      "/Social sciences/Economics/Labor economics/Employment"
    ],
    [
      "plos",
      "/Social sciences/Economics/Labor economics/Employment/Careers"
    ],
    [
      "plos",
      "/Social sciences/Economics/Labor economics/Labor markets"
    ],
    [
      "plos",
      "/Social sciences/Psychology/Cognitive psychology/Academic skills/Literacy"
    ]
  ],
  "openalex": "W7214004814",
  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0358194&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-22",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/0wr8je58b2tep.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:43Z",
      "native_id": "10.1371/journal.pone.0358194",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0358194"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "plos:neuroscience",
      "subfield": "neuroscience"
    },
    {
      "field": "mathematics",
      "origin": "plos:statistical methods",
      "subfield": "statistics"
    },
    {
      "field": "physics",
      "origin": "plos:thermodynamics",
      "subfield": "statistical-nonlinear"
    }
  ],
  "title": "The relationship between digital literacy and employment quality: Evidence from a Chinese Household Survey",
  "title_text": "The relationship between digital literacy and employment quality: Evidence from a Chinese Household Survey",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:43Z",
  "url": "https://research.draconitic.com/paper/0wr8je58b2tep",
  "venue": "PLOS One",
  "versions": []
}