Artificial Intelligence Utilization and Perceived Firm Performance in Chinese Logistics Firms: The Roles of Innovation Capability and Logistics Efficiency
Artificial intelligence (AI) is used in logistics, but the mechanisms linking AI utilization to firm performance remain insufficiently differentiated. Drawing on the information technology business value perspective and dynamic capabilities theory, this study examines whether managers’ perceptions of logistics-oriented AI utilization are associated with perceived firm performance through innovation capability and logistics efficiency, with managerial support treated as a secondary boundary condition. Cross-sectional survey data from 254 middle- and senior-level managers in Chinese logistics firms were analyzed using IBM SPSS Statistics 27 and IBM SPSS Amos 29 (IBM Corp., Armonk, NY, USA), and the PROCESS macro version 4.2 (Andrew F. Hayes, Calgary, AB, Canada), with Model 83 and 5000 bootstrap samples. Perceived AI utilization was positively associated with innovation capability, logistics efficiency, and perceived firm performance. Both mediators showed significant indirect effects, and their sequential indirect effect was also significant. The two individual indirect effects did not differ significantly, but both exceeded the sequential indirect effect. The proposed sequential, reverse-sequence, and parallel-mediation models produced identical fit indices, whereas the restricted direct-effects model showed weaker fit. Neither the AI utilization–managerial support interaction nor the moderated mediation indices was significant. Exploratory item-level analyses showed differentiated associations for demand forecasting and order allocation and for AI infrastructure; the pattern remained stable among 194 respondents involved in AI- or digital transformation-related activities. Innovation capability and logistics efficiency appear to function as complementary mechanisms, with a smaller capability-to-process pathway. Their relative ordering cannot be determined from the cross-sectional data. As the data are self-reported, the findings represent associations among managerial perceptions rather than objective causal effects. Sustainability implications are limited to operational efficiency because environmental outcomes were not directly measured.