How the dimensions of culture influence supply chain collaboration: an explanatory sequential mixed-methods investigation

Abstract

PurposeThis study aims to demonstrate how integration is achieved in an explanatory sequential mixed-methods design by assessing the effect of collaborative cultural dimensions on supply chain collaboration amongst firms in Ghana's downstream petroleum sector. Specifically, the study examined how collectivism, long-term orientation, power symmetry, as well as uncertainty avoidance influence supply chain collaboration. Besides, it also demonstrates how integration is achieved in an explanatory sequential mixed-methods design.Design/methodology/approachUsing an explanatory sequential mixed-methods design, the study employed a partial least squares structural equation modelling (PLS-SEM) analysis of quantitative data (N = 166), followed by a thematic analysis of eight semi-structured interviews to explain how and why the dimensions of collaborative culture impact supply chain collaboration.FindingsThe quantitative findings suggest that three out of the four dimensions of culture significantly predict supply chain collaboration. Integrating the quantitative and qualitative findings suggests convergence between the results of the quantitative and qualitative phases of the study as the qualitative results compliment the quantitative findings and offer more nuanced understanding of the cultural mechanisms responsible for successful supply chain collaborations.Practical implicationsThe findings provide managers in the downstream petroleum sector with insights into how and why the dimensions of collaborative culture influence supply chain collaboration. These managers should, therefore, build corporate cultures characterized with high levels of long-term orientation, power symmetry and uncertainty avoidance.Originality/valueOwing to the role of culture in successful supply chain collaborations, this study, through a mixed-methods design, links the dimensions of collaborative culture with supply chain collaboration in the downstream petroleum sector. Moreover, it demonstrates how integration and complementarity are achieved at the study design, methods, as well as the interpretation and reporting levels of an explanatory sequential mixed-methods design.

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“…(2015), studies that employ PLS structural equation modeling should report R 2 and Q 2 . While the R 2 explains the variance of dependent variables predicted by independent variables, the Q 2 explains a model achieves predictive relevance if Q 2 values are > 0 (Acquah, Naude, & Soni, 2021; Hair et al., 2019). As presented in Exhibit 5, the model achieves robust predictions of the dependent variables and has predictive relevance since all Q 2 were > 0.…”

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