Abstract:
Purpose – Artificial intelligence (AI) has transformed marketing operations, creating new benchmarks for
operational productivity, customer interaction and sales growth. This study investigates factors that affect the
adoption of AI among marketing professionals, focusing on developing benchmarking archetypes and assessing
the moderating impact of technology resistance (TR).
Design/methodology/approach – Data from 353 marketing professionals across diverse sectors in Sri Lanka
was analyzed using a dual-method approach. The UTAUT2 model guided hypotheses tested with PLS-SEM to
establish generalizable benchmarks, while fuzzy-set qualitative comparative analysis(fsQCA) was employed to
identify distinct adoption archetypes serving as configurational benchmarks.
Findings – All the UTAUT2 factors significantly influence AI adoption, with TR as a substantial barrier. The
fsQCA revealed seven distinct benchmarking archetypes, with behavioral intention, effort expectancy,
facilitating conditions, hedonic motivation and price value emerging as core conditionsfor high adoption, while
performance expectancy, social influence and habit functioning as peripheral factors.
Practical implications – The research provides diagnostic benchmarking tools that organizations can use to
assesstheir AIreadiness, identify implementation pathways aligned with their contextual characteristics,reduce
technology resistance and enhance marketing efficiencies.
Originality/value – This study advances benchmarking literature by identifying both generalizable adoption
drivers and distinct configurational archetypes for AI implementation in marketing while establishing
technology resistance as a critical moderating variable.
