Decision-Making Processes in Consumer Behavior and Choice

Consumer decision making encompasses the cognitive, emotional and contextual factors that guide individuals from problem recognition through to choice and post-purchase evaluation. Early models emphasised rational comparison of product attributes and utility maximisation, but contemporary research highlights the interplay of heuristics, experiential imagery and social influences. Digital environments and recommendation algorithms have introduced novel choice architectures, altering perceptions of variety and personal control. At the same time, the complexity of modern products—characterised by heterogeneous and interrelated features—shapes expectations of usability and capability, thereby influencing adoption rates. Cultural norms, time pressure and self-efficacy further modulate search effort, choice satisfaction and post-decision regret. Understanding these processes is of global significance, informing marketing strategies, consumer policy and the design of fair and transparent recommendation systems.

Research from Nature Portfolio

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Research from all publishers

Investigations into AI-driven recommendation agents reveal that consumers respond positively to large option sets when suggestions are generated by a language model. Empirical studies demonstrate that choice overload effects differ depending on whether options are presented by a human advisor or an artificial system, with AI suggestions often lowering perceived decision difficulty and yielding higher preference under extensive assortments.

Research on product categorisation distinguishes attribute-based grouping, which clusters items by shared features, from benefit-based schemes that organise around consumer goals. Across laboratory and field settings, benefit-oriented displays enhance mental imagery of product use, elevate anticipated consumption value and boost purchase likelihood. These effects are moderated by individual imagery ability and the presence of external imagery cues.

Work on product complexity analyses how feature heterogeneity (the diversity of functions) and feature interrelatedness (the coherence of functions) affect attitudes and purchase intentions. Findings indicate that emphasising interrelatedness fosters perceptions of capability and supports adoption, while high heterogeneity may undermine expected usability. Marketers can therefore manage complexity by de-emphasising disparate features and highlighting integrated functionality.

Decision-Making Processes in Consumer Behavior and Choice publication trend

The graph below shows the total number of articles in decision-making processes in consumer behavior and choice across all publications each year (not limited to Nature Index journals).

Technical terms

Choice overload: A phenomenon where an excessive number of options leads to decision difficulty, reduced satisfaction or choice deferral.

Benefit-based categorization: Grouping products according to the consumer problems they solve, enhancing mental imagery and perceived value.

Attribute-based categorization: Organising products by shared physical or functional features, facilitating straightforward comparison.

Feature heterogeneity: The extent to which a product’s functions are diverse and varied, affecting perceived usability.

Feature interrelatedness: The degree to which a product’s functions are coherent and complementary, influencing perceived capability.

Recommendation agent: A system—human or artificial—that suggests products or services based on consumer data and algorithms.

References

1. Decisions with ChatGPT: Reexamining choice overload in ChatGPT recommendations. Journal of Retailing and Consumer Services (2023).
2. The effects of benefit-based (vs. attribute-based) product categorizations on mental imagery and purchase behavior. Journal of Retailing (2024).
3. How product complexity affects consumer adoption of new products: The role of feature heterogeneity and interrelatedness. Journal of the Academy of Marketing Science (2023).