Note(s): This table reports two-stage regression analyses on whether enterprise digital development promotes management forecast accuracy and in turn, increases capacity utilization. Columns (1) and (2) report the first-stage regression estimates for the effect of digital development on forecast accuracy. The dependent variable is forecast accuracy, Forecast, which takes the value of 1 if the management forecast is close to changes in EBITDA to proxy the forecast accuracy and otherwise it is 0. The key independent variables are digitization development proxies, DigitalW and DigitalS. Control variables include Size, Leverage, ROA, CFO, TFP, Employee, Age, BSize, IndR, SH1, SOE and HHI. Columns (3) and (4) report the second-stage regression estimates for the effect of digtial-related forecast accuracy on capacity utilization. The dependent variable is capacity utilization, CU. The key independent variables, $ForecastWˆ$ (⁠$ForecastSˆ$⁠) are the predicted DigitalW (DigitalS) based on the first-stage regression, capturing the part of forecast accuracy that can be explained by digital development. Control variables are the same as those in the first-stage regression. For brevity, we do not report the coefficients of the control variables. Standard errors are all heteroscedastic, robust, clustered at the firm level.  Appendix 2 presents variable definitions. Continuous variables are winsorized at the 1 and 99% levels. ∗, ∗∗ and ∗∗∗ represent significance levels of 10, 5 and 1%, respectively

Source(s): Authors’ own work