Intelligent Decision-Support Systems for Cross-Functional Workflow Optimization in Data-Driven Organizations

Abstract

This study investigated the quantitative relationships between Intelligent Decision-Support System (IDSS) capabilities and cross-functional workflow performance within a data-driven organizational context, with particular emphasis on the mediating role of cross-functional coordination and the moderating influence of data quality, workflow complexity, and organizational scale. Using a cross-sectional explanatory design, survey data from 238 respondents were integrated with 412 workflow-level cases extracted from organizational records. IDSS capabilities were operationalized through four dimensions: data integration capability, analytics intensity, automation degree, and feedback control strength. Workflow performance was measured objectively using cycle time, process variance proxies, bottleneck frequency, and rework rates. Descriptive results indicated relatively high levels of data integration capability (M = 4.18, SD = 0.62) and analytics intensity (M = 4.05, SD = 0.68), while automation degree showed lower maturity and higher dispersion (M = 3.62, SD = 0.81). Average workflow cycle time was 6.42 days (SD = 2.15), with a mean rework rate of 8.9% (SD = 4.2), highlighting substantial operational variability across cases. Regression analyses showed that IDSS capabilities explained a significant proportion of workflow performance variance beyond control variables, increasing explained variance in cycle time from R² = 0.19 to R² = 0.34 and in rework rate from R² = 0.14 to R² = 0.27. Data integration capability (β = −0.22, p < .001) and analytics intensity (β = −0.19, p < .001) demonstrated the strongest direct associations with reduced cycle time. Mediation analysis confirmed that cross-functional coordination partially mediated the relationship between IDSS capabilities and workflow performance, with significant indirect effects for both cycle time (β = −0.16, p < .001) and rework rate (β = −0.14, p < .001). Moderation results indicated that data quality (interaction β = −0.11, p = .006) and workflow complexity (interaction β = −0.09, p = .021) strengthened the IDSS–performance relationship, while organizational scale did not exhibit a significant moderating effect. Overall, the findings demonstrated that IDSS capabilities contributed to workflow optimization primarily through enhanced coordination and under conditions of high data quality and greater workflow complexity.