Supply chain dynamics, big data capability and product performance

Purpose: The purpose of this study is to examine big data capability's impact on product improvement and explore supply chain dynamics including relationship building and knowledge sharing as important contribution to big data capability. Design/methodology/approach: The research model is tested with survey data. Data analysis results empirically support the proposed model and the hypothesized relationships between the concepts. Findings: First, the hypothesis testing results of this study show that big data capability directly enhances product improvement. Second, this study shows that supply chain relationship building and knowledge sharing are positively related to the development of big data capability. Research limitations/implications: In supply chain management, there are multiple factors, besides relationship building, that serve as conditioners to knowledge sharing's effect on product performance. We only examined the role of relationship building in this area. Practical implications: Findings from this research encourage firms to take advantage of their supply chain resources to develop a big data capability that positively contributes to firm performance. Originality/value: The contribution lies in that it brings to light this step that connects big data capabilities and market and financial performance, which is missing in prior research. This study contributes to the literature by identifying supply chain management activities, more specifically, supply chain relationship building and knowledge sharing, as antecedents to big data capability. This helps to extend this emergent enterprise of big data research to a new area and points to new directions for future research.