Improving purchasing performance through big data analytics-driven purchasing ambidexterity: a knowledge-based dynamic capabilities view

Purpose Drawing on the knowledge-based dynamic capabilities view, this study aims to examine how big data analytics (BDA) use for strategic purchasing (BDAUSP) contributes to purchasing ambidexterity and subsequently enhances purchasing performance. It further investigates the differential associations of exploratory and exploitative purchasing with purchasing innovation and operational performance (OP), alongside the moderating role of data-driven culture (DDC). Design/methodology/approach Structural equation modeling is used to test the hypotheses with 354 valid survey responses from Chinese manufacturing firms. Findings BDAUSP is positively associated with both exploratory and exploitative purchasing. Exploratory purchasing shows a stronger link to purchasing innovation performance (IP) than to OP; exploitative purchasing shows the reverse. DDC is negatively associated with purchasing ambidexterity but positively moderates BDAUSP’s associations with both exploratory and exploitative purchasing. Practical implications Managers should invest in BDA infrastructure to scan multitier supplier networks, thereby achieving purchasing ambidexterity. They should also establish separate performance systems for purchasing OP and IP, and align a DDC with the maturity of BDA, fostering a culture in which data supports rather than replaces managerial judgment. Originality/value This study demonstrates that the purchasing function requires an ambidextrous approach to knowledge configuration to leverage insights acquired through BDAUSP from supply networks. It also identifies differentiated performance pathways of exploratory versus exploitative purchasing. Uncovering DDC’s paradoxical dual role, it provides novel insights into how culture and technology coevolve, clarifying boundary conditions under which BDAUSP fosters purchasing ambidexterity.