Tackling the rational decision-making in ethical consumption

Main Finding

The chapter’s systematic review finds that rational decision-making interacts with psychological and contextual factors to produce an enduring ethical consumption intention–behaviour gap. Three interconnected thematic clusters explain this gap: (1) multifaceted reasons for the intention–behaviour gap (cost–benefit rationalisations, cultural norms, neutralisation tactics), (2) psychological drivers (guilt, pride, habit, brand strategies) that shape ethical choices, and (3) collaborative promotion strategies (community initiatives, corporate reformulations, cultural narratives). Current models overemphasise pure cost–benefit rationality and underrepresent emotional, cultural, and neutralisation dynamics; the authors propose integrating bounded rationality, mixed-culture evidence, and emotional triggers to better predict and close the gap (RP1, RP2).

Key Points

• Three thematic clusters:
  • Cluster 1 (Reasons for intention–behaviour gap): financial/pragmatic trade-offs, cognitive limits, cultural/institutional barriers, and neutralisation strategies (e.g., denying responsibility/harm).
  • Cluster 2 (Psychological factors): emotions (guilt/pride), habits, moral intuitions, perceived consumer effectiveness, and brand/communication strategies; these interact with rational cost–benefit calculations.
  • Cluster 3 (Promoting ethical consumption): community-based initiatives, corporate strategies (including greenwashing risks), and cultural narratives to shift norms.
• Main research gaps:
  • Overreliance on rational cost–benefit models (e.g., Theory of Planned Behaviour) without sufficient incorporation of emotions, moral intuition, and cultural context.
  • Lack of empirical validation of neutralisation strategies and of bounded-rationality mechanisms across cultures.
  • Sparse mixed-culture and longitudinal evidence on how interventions (price framing, social-norm appeals, emotional triggers) reconcile individual rationality with collective responsibility.
• Proposed research propositions:
  • RP1: Integrate mixed-culture insights and bounded rationality with contextual triggers (price framing, social-norm appeals) to close the gap.
  • RP2: Build integrated models combining psychological triggers with rational decision processes to better predict ethical purchases.

Data & Methods

• Method: Systematic literature review following PRISMA 2020.
• Data source: Scopus search restricted to English-language peer-reviewed journal articles.
• Search: used wildcard "rational*" and related terms (rational decision making, reasoned action, cognitive reasoning, rational choice) combined with "ethical consumption".
• Screening: initial 61 records → screened abstracts → final inclusion of 23 articles.
• Analytic approach:
  • Bibliographic coupling and science mapping using VOSviewer to detect intellectual clusters.
  • Thematic content analysis to extract mechanisms, gaps, and propositions.
• Sample composition: 23 documents distributed across three clusters (8, 9, and 6 items).
• Limitations: single database (Scopus), English-only selection, small final sample (23 papers), focus on published literature (potential publication bias).

Implications for AI Economics

The chapter’s findings offer several actionable implications for AI economics research and applications that model or intervene in ethical consumption:

• Modeling consumer behaviour

  • Move beyond classical utility-maximisation agents: incorporate bounded rationality, satisficing, and emotional-state variables (guilt, pride, habit) into economic and AI predictive models.
  • Use hybrid models (e.g., combining theory of planned behaviour components with affective-state features) to improve forecasts of ethical purchase uptake.
  • Implement agent-based models with heterogeneous agents who use neutralisation tactics and respond to cultural norms to simulate market-level impacts of policy or firm interventions.

• Data-driven empirical work

  • Use NLP to detect neutralisation language and moral rationalisations in social media, product reviews, and survey open-texts; quantify when and how neutralisation predicts behavioural rollback.
  • Leverage cross-cultural corpora and transfer-learning methods to test RP1 across diverse contexts and avoid cultural bias in models.
  • Combine causal inference (A/B tests, instrumental variables) and ML to validate which interventions (price framing, social-norm nudges, emotional messaging) causally close the intention–behaviour gap.

• Design of interventions and platforms

  • Deploy recommender systems and price/promotion algorithms that integrate moral signals and social-norm cues (while monitoring fairness and privacy) to nudge ethical choices.
  • Use personalized nudging that balances rational incentives (price/quality) with emotional triggers (pride, social recognition); test for heterogeneous treatment effects to avoid backfire or manipulation.
  • Build detection tools for greenwashing using supervised learning on firm disclosures and third-party certifications; align marketplace algorithms to promote verifiable ethical options.

• Policy and welfare considerations

  • Evaluate welfare implications of algorithmic nudges (transparency, autonomy, distributional effects). AI economists should study whether algorithmic interventions disproportionately influence certain demographic or cultural groups.
  • Model externalities of firm-level algorithmic marketing (e.g., targeted green claims) and regulate marketplace recommender incentives to prevent rent-seeking that widens the intention–behaviour gap.

• Empirical research agenda for AI economics

  • RP-driven projects: (a) ML-augmented cross-cultural experiments testing RP1 (price framing + social norms under bounded rationality), (b) integrated predictive models per RP2 that combine affective, normative, and rational features, validated by field experiments.
  • Use panel data and sequence models to track transitions from intention to purchase over time and to identify intervention timing that overcomes habit and convenience constraints.

Overall, integrating psychological dynamics, cultural heterogeneity, and neutralisation phenomena into AI-enabled economic models and interventions will improve predictions and policy designs for promoting consistent ethical consumption while raising important ethical and regulatory questions about algorithmic nudging and marketplace incentives.