Sectoral GVC embeddedness and organizational technology adoption. An analysis across sectors in 28 European countries

Purpose This study aims to investigate to what extent the adoption of technology by organizations depends on the characteristics of the sector in which they operate. The characteristics under study are the participation in Global Value Chains (GVCs) at the sectoral level. Using the term GVC embeddedness emphasizes that it is about an organization’s membership in a sector. Conducting this research is intended to contribute to the technology-organization-environment (TOE) model, which dominates research on the technology adoption by organizations. While the technology and the organization components of the model are well understood, this is less so for the environment component that is central in the present study. Design/methodology/approach The research was conducted by combining two datasets. The European Company Survey of 2019 provides data about technology adoption by organizations, as it includes information about whether organizations use robots, data analytics to improve processes for the production of goods and services and data analytics to monitor employee performance. At the sectoral level, these data are combined with data from the Trade in Value Added from the Organisation for Economic Co-operation and Development. It was possible to match these data on 15 sectors for 28 countries. Over 20,000 companies were included in the analyses. Logistic multi-level modeling was applied to analyze the nested data with binary outcomes. Findings GVC embeddedness is positively associated with technology adoption. GVC participation at the sectoral level explains a large share of variation in the use of robots. While it also explains the use of data analytics to improve production processes and employee monitoring, it does so to a lesser extent. For these two kinds of technologies, forward participation matters more than backward participation, suggesting that pressure from foreign buyers may explain the use of these technologies. Originality/value This study aims to provide the following novel insights to the literature. First, it aims to expand the TOE model by further investigating the environment component of that model. Secondly, it aims to integrate GVC research and research on technology adoption at the organizational level. And, thirdly, it adds novel insights into research concerning the technology-adoption by adding the role of external factors at the sectoral level.

Authors

Institutions

Publication Details

Journal
Innovation & Management Review
Published
2025-09-04
DOI
https://doi.org/10.1108/inmr-06-2023-0094
Primary Topic
Firm Innovation and Growth
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Sectoral GVC embeddedness and organizational technology adoption. An analysis across sectors in 28 European countries

Ferry Koster, Alissa Lysanne van Zijl
Innovation & Management Review
Firm Innovation and Growth
article

Sectoral GVC embeddedness and organizational technology adoption. An analysis across sectors in 28 European countries

Ferry Koster, Alissa Lysanne van Zijl
article en

Abstract

Purpose This study aims to investigate to what extent the adoption of technology by organizations depends on the characteristics of the sector in which they operate. The characteristics under study are the participation in Global Value Chains (GVCs) at the sectoral level. Using the term GVC embeddedness emphasizes that it is about an organization’s membership in a sector. Conducting this research is intended to contribute to the technology-organization-environment (TOE) model, which dominates research on the technology adoption by organizations. While the technology and the organization components of the model are well understood, this is less so for the environment component that is central in the present study. Design/methodology/approach The research was conducted by combining two datasets. The European Company Survey of 2019 provides data about technology adoption by organizations, as it includes information about whether organizations use robots, data analytics to improve processes for the production of goods and services and data analytics to monitor employee performance. At the sectoral level, these data are combined with data from the Trade in Value Added from the Organisation for Economic Co-operation and Development. It was possible to match these data on 15 sectors for 28 countries. Over 20,000 companies were included in the analyses. Logistic multi-level modeling was applied to analyze the nested data with binary outcomes. Findings GVC embeddedness is positively associated with technology adoption. GVC participation at the sectoral level explains a large share of variation in the use of robots. While it also explains the use of data analytics to improve production processes and employee monitoring, it does so to a lesser extent. For these two kinds of technologies, forward participation matters more than backward participation, suggesting that pressure from foreign buyers may explain the use of these technologies. Originality/value This study aims to provide the following novel insights to the literature. First, it aims to expand the TOE model by further investigating the environment component of that model. Secondly, it aims to integrate GVC research and research on technology adoption at the organizational level. And, thirdly, it adds novel insights into research concerning the technology-adoption by adding the role of external factors at the sectoral level.

Innovation & Management Review
Erasmus University Rotterdam (NL)
HORIZON EUROPE Framework Programme
Openalex Percentile: Top 68%
Firm Innovation and Growth
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.