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EMAC 2024 Annual


Real-Time Data Processing and Retailer’s Performance Under Demand Uncertainty
(A2024-119360)

Published: May 28, 2024

AUTHORS

Ruiqing Cao, Stockholm School of Economics; Catherine (Sijia) Ma, Tilburg University

ABSTRACT

Retail firms are increasingly adopting various technologies to collect real-time information on consumers, products, and channels. Stream processing is the up-to-date technology for retailers to extract values from the real-time information. However, harnessing the effectiveness of stream processing and developing dynamic capabilities therein, especially under demand uncertainty, has become an emerging challenge for traditional retailers. This study focuses on investigating the impact of stream processing on firm performance under demand uncertainty. We found that stream processing can significantly impact firms’ profitability, especially in periods with volatile demand shocks. More importantly, we emphasize the key role of complementary data architecture in the effect of stream processing as a potential mechanism. This study contributes to the literature by providing deeper insights into the economic value of stream processing and the importance of architectural support as a key contingency. It also provides managerial implications for firms seeking to develop dynamic capabilities under demand uncertainty.