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

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.

Keywords:

• Innovation
• sustainable development
• absorptive capacity
• construction industry
• survey
• regression analysis

Notes

1. The NACE‐code system is based on the European standard for industry classifications. NACE means ‘Nomenclature Générale des Activités Economiques dans l'Union Européenne’ (General Name for Economic Activities in the European Union). The first four digits of the code are the same in all European countries.

2. Statistics Sweden is a central government authority for official statistics and other government statistics and in this capacity also has the responsibility for coordinating and supporting the Swedish system for official statistics.

3. The optimal analysis would have been using principal components analysis and structural equation modeling (SEM). However, owing to the nature of our data, a mix of a scale and sum variables, this was not a viable option.

4. The regression coefficient, b, is the average amount the dependent variable increases when the independent variable increases one unit.

5. In initial regression analyses we included interaction terms in order to test for moderating effects. However, none of the interaction terms proved to be significant predictors and are not included in the regression analyses presented in the table.

6. As indicated by the regression coefficient R².