The Use of Inside-Out and Outside-In Big Data Analytics on E-Platforms: Performance Impacts and Heterogeneity Analysis
Yuan Liu, Yuzhu Zheng, June Wei and Yang Yang
International Journal of Electronic Commerce,
Volume 27, Number 1, 2023, pp. 36-65.
Abstract:
Big data has brought unprecedented opportunities and challenges, prompting global firms to grow big data analytics (BDA) investments, especially in a turbulent business environment. However, there is insufficient empirical evidence in scholarly research on whether and how using BDA functions of various types creates business value. The current study divides BDA into inside-out and outside-in types and explores whether and how firms can create value by using functions of these two types of BDA. Then, the knowledge-based view (KBV) is applied as a theoretical foundation to investigate the independent and combined impacts of inside-out and outside-in BDA usage on firms’ sales performance. Furthermore, we build a quantile regression model to analyze the heterogeneity of independent and combined impacts among firms with different performance levels. The empirical study is based on a unique dataset collected on one of the largest electronic platforms (e-platforms) in China from 785 firms in 35 weeks. The results of the benchmark model based on two-way fixed effects show that both inside-out and outside-in BDA usage, as well as their interactions, are positively related to the sales performance of firms on e-platforms. The heterogeneity analysis indicates that inside-out (outside-in) BDA functions have a greater degree of impact on firms with lower (higher) sales performance. Through the theoretical and empirical analysis of the complex performance impacts of BDA usage, this study enriches the understanding of value creation in using multiple BDA functions and extends the theoretical account of KBV in the field of BDA.