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Cited by 216

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

An absorptive capacity model for green innovation and performance in the construction industry

Gluch, Pernilla, Gustafsson, Mathias, Thuvander, Liane·2009·Construction Management and Economics

Abstract

Applying the model of absorptive capacity (ACAP), antecedents, predictors and moderators for green innovation and performance in the construction industry are investigated. The aim is to identify mechanisms that influence green innovation and environmental performance in a construction company. Data come from a questionnaire survey assessing environmental attitudes, management and performance within the Swedish construction industry. For data analysis, linear regression analysis was used. From testing the ACAP theory and model, it was concluded that it has a promising potential in explaining mechanisms behind green innovation and performance. The application of ACAP has resulted in a revised ACAP model, green ACAP. Findings indicate that organizations can affect their capacity to absorb green innovations and improve their business performance by focusing on three predictors of green business advantage: acquisition, assimilation and transformation. As such, the green ACAP can serve as a framework for focused efforts within the construction industry.

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Metadata

Authors
Gluch, P., Gustafsson, M., & Thuvander, L.
Venue
Construction Management and Economics
Published
2009-05
Volume
27
Issue
5
Pages
451-464
ISSN
  • 0144-6193 (ISSN-L)
  • 1466-433X
OpenAlex
W2046797609
Cited by
216
References
2
FWCI
11.44
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: An absorptive capacity model for green innovation and performance in the construction industry. https://app.cochat.ai/papers/10.1080/01446190902896645
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Gluch, P., Gustafsson, M., & Thuvander, L. (2009). An absorptive capacity model for green innovation and performance in the construction industry. Construction Management and Economics. https://doi.org/10.1080/01446190902896645

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.