Antecedents and enablers of supply chain agility and its effect on performance: a dynamic capabilities perspective

Abstract

This paper investigates the fundamental building blocks of supply chain agility, which are conceptualised as supply- and demand-side competence. While the former refers to production and supply management related activities, the latter refers to distribution and demand management related activities. The model further assesses the influence of supply chain agility on operational performance, as well as its mediating role in the relationship between supply- and demand-side competence and performance. Within this framework, process compliance, i.e. how well supply chain management processes are internally executed by the firm's employees, is viewed as an enabler (moderator) on the relationship between supply chain competencies and supply chain agility. Theoretical substantiation is provided by the resource-based view of the firm augmented with the dynamic capabilities perspective. The model is tested with data from 121 supply chain management professionals. Implications for both academic theory development and supply chain and production management practice are provided.

Keywords:

• supply chain agility
• supply-side competence
• demand-side competence
• process compliance
• operational performance
• resource-based view of the firm
• dynamic capabilities
• survey

Notes

1. Authors contributed equally and are listed in alphabetical order.

2. The standardised coefficients provided by PLS were converted to unstandardised coefficients by multiplying the standardised coefficients by the standard deviation of the dependent variable (Y) and dividing them by the standard deviation of the independent variable (X). Please refer to Table 5 for the standardised coefficients (Std. Beta), standard deviation of the dependent variable (Y), standard deviation of the independent variable (X), and the standard error.

3. The pseudo F test is similar to that employed to test nested models in stepwise linear regression. The f ² statistic is computed based on the differences in R ². The f ² value is calculated by dividing ( – ) by (1 – ). The pseudo F statistic is calculated by multiplying f ² by (n − k − 1), with a = 1 and (n − k) degrees of freedom where n is the sample size and k is the number of independent constructs in the model.