This paper uses an interactive technology–business model to estimate the structural relationships and treatment effects between the AI integration index and the enterprise performance indicators under a smart-grid reform framework. From the point of view of enterprise management, based on the extent of correlation between variables, our results suggest that an energy utility manager should be aware that the conventional business model is less correlated with the digital performance index in the baseline period (as shown in the continuous SEM analysis) and that an enterprise holding the AI capability index can gain by including the digital investment variable in the business model (as confirmed in the Propensity Score analysis). Furthermore, we detect asymmetric effects of digital adoption and AI capability that for treated enterprises are higher than for non-treated enterprises suggesting that technology spillovers have not been substantially diffused among traditional utilities compared to early adopters. Empirical evidence result from this study shows that enterprise productivity could be sustained over time due to cumulative digital investment and organizational learning. Finally, the technology–business interactive model provided improved explanatory power compared to other models partly due to its taking into account structural heterogeneity and policy intervention effects often present in transition economies.
Saidov et al. (Wed,) studied this question.