AI-Augmented marketing decision-making and competitive performance

A resource-based view of capability orchestration

Bibliographic Data

ID21282452
AuthorsRenu Sharma (0000-0003-2378-7056, Princess Nourah bint Abdulrahman University, corresponding author)
Year2026
Volume268
Pages107351
Publication date2026-08-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueActa Psychologica (JOURNAL)
Journal identifiersISSN: 0001-6918 • E-ISSN: 1873-6297
PublisherElsevier BV (PUBLISHER)
DOI10.1016/j.actpsy.2026.107351
PMID42413417
OpenAlexW7167578468
LanguageEN
References cited35

Artificial intelligence (AI) is increasingly embedded in marketing decision-making, yet it remains unclear whether AI itself constitutes a source of sustained competitive advantage. Drawing on Resource-Based Theory and the dynamic capabilities perspective, this study examines whether AI capability maturity is associated with competitive performance through complementary organizational and socio-cognitive mechanisms. Using survey data from 312 marketing and digital leaders and analyzing the data through partial least squares structural equation modeling (PLS-SEM), the findings indicate that AI capability maturity is positively associated with Human-AI Integration, which in turn is positively associated with Marketing Agility and Competitive Performance. A significant serial mediation effect suggests that the relationship between AI capability maturity and competitive performance operates primarily through layered capability development rather than technology possession alone. Furthermore, Data Governance Quality positively moderates the relationship between Human-AI Integration and Marketing Agility, highlighting governance as an important enabling condition. The study extends Resource-Based Theory to intelligent systems by demonstrating that AI-enabled value creation is associated with complementary integration and adaptive capabilities rather than technological resources alone. From a psychological perspective, the findings highlight the importance of trust calibration, interpretability, reliance behavior, decision confidence, and Human-AI collaboration in AI-assisted decision-making. Managerially, the results suggest that organizations should invest not only in AI technologies but also in integration routines, governance mechanisms, and agile marketing processes to maximize the potential benefits associated with AI-enabled decision-making

Competitive advantage · Industrial marketing · Orchestration · Work (physics) · Big Data and Business Intelligence · Competitive and Knowledge Intelligence · Customer churn and segmentation

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