Let’s drive environmentally friendly: A perspective from asymmetrical modelling by using fuzzy set qualitative comparative analysis

This study intends to examine the complexity of the factors that influence truck drivers’ intentions to adopt eco-driving behaviour. A total of 198 truck drivers from the third-party logistics service provides participated in the study survey. An asymmetrical analytical approach through fuzzy set qu...

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Veröffentlicht in:Current psychology (New Brunswick, N.J.) N.J.), 2023-11, Vol.42 (31), p.27275-27293
Hauptverfasser: Abbasi, Ghazanfar Ali, Quan, Lai Shu, Kumar, Kavigtha Mohan, Iranmanesh, Mohammad
Format: Artikel
Sprache:eng
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Zusammenfassung:This study intends to examine the complexity of the factors that influence truck drivers’ intentions to adopt eco-driving behaviour. A total of 198 truck drivers from the third-party logistics service provides participated in the study survey. An asymmetrical analytical approach through fuzzy set qualitative comparative analysis (fsQCA) examined the casual recipes of the factors effecting driver’s intention to adopt eco-driving behaviour. This research also applied symmetrical analysis by using partial least squares structural equation modelling (PLS-SEM), to compare the analysis with the findings from fsQCA. Results revealed three models of factors that lead to an intention to adopt eco-driving behaviour. The findings have profound practical and theoretical implications for the growth of new theories in transportation and environmental sustainability. Highlights • Symmetrical analysis revealed that both perceived knowledge and subjective norm had insignificant associations with attitude and intentions. • Whereas, asymmetrical analysis, compared to symmetrical analysis revealed that both subjective norm and attitude were found as the necessary component to achieve proposed outcome. • fsQCA yielded 76% of variance whereas symmetrical analysis by using PLS-SEM yielded 61.7% of proposed model’s explanatory capacity, validating the use of asymmetrical methods.
ISSN:1046-1310
1936-4733
DOI:10.1007/s12144-022-03813-5