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AI as a Habit Architect: A Theoretical Model of Adaptive Reinforcement in Digital Marketing Cover

AI as a Habit Architect: A Theoretical Model of Adaptive Reinforcement in Digital Marketing

Open Access
|May 2026

Figures & Tables

Figure 1.

Literature identification, screening, and selection process

Note: Four additional sources (2025) were incorporated following peer-review to strengthen contemporary contextual positioning.

Source: own study.

Table 1.

Illustrative mapping of key studies across conceptual dimensions

Author (Year)ContextAI TechniqueGamification MechanismEthical DimensionAnalytical Contribution
Hamari et al. (2014)Digital platformsBehavioral analyticsPoints & reward systemsEngagement intensityEmpirical evidence of gamification effects
Mittelstadt et al. (2016)Algorithmic systemsMachine learningPersonalised targetingTransparency, fairnessMapping of algorithmic ethical risks
Floridi et al. (2021)AI governanceML optimizationAdaptive system designExplicability, proportionalityStructural AI ethics framework
Knutas et al. (2018)Personalised gamificationAlgorithm-based personalizationAdaptive feedbackBehavioral calibrationDesign of personalised gamification processes

[i] Source: own study.

Figure 2.

Adaptive reinforcement architecture in AI-driven gamification

Source: own study.

DOI: https://doi.org/10.2478/ijcm-2026-0008 | Journal eISSN: 2449-8939 | Journal ISSN: 2449-8920
Language: English
Page range: 75 - 90
Published on: May 6, 2026
Published by: Jagiellonian University
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2026 Magdalena Brzozowska-Woś, published by Jagiellonian University
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.