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After absorbing their predecessors’ ideas about consumption theory, Marxist scholars have developed a distinctive consumption theory. They have analyzed the phenomena of consumption alienation and information overload from various angles and by focusing on various dimensions. A mastery of Marxist consumption theory helps one correctly understand the impact of consumer information overload on decisions related to social development and purchasing and enables one to properly evaluate the relationship between production and consumption. Marxist consumption theory regards consumption as the core component of social reproduction and emphasizes its dialectical unity with the production, distribution, and exchange of goods.
The explosive expansion of e-commerce has profoundly reshaped the consumer purchase journey, creating digital storefronts where product variety and information depth far exceed those found offline (R. Sharma et al., 2023). Faced with hundreds—or even thousands—of alternative items, specifications, and customer reviews, shoppers routinely encounter a level of informational complexity that can exceed their cognitive limits (Jabr & Rahman, 2022). This information overload is widely recognized as a driver of postponed purchases, hesitancy and, in extreme cases, outright decision paralysis (Chavan, 2024). A clearer grasp of how individuals manage this burden is therefore essential for platform designers and marketers who seek to streamline the user experience, sustain satisfaction, and ultimately lift conversion rates (Zhang et al., 2023).
The phenomenon of information overload on modern e-commerce platforms is essentially an extension of the capitalist mode of production into the digital space. Although prior studies have addressed overload in e-commerce, most have done so from a single vantage point. Research on cognitive overload concentrates on the sheer number of alternatives or attributes (Ali, 2025), whereas work on emotional overload highlights frustration, anxiety, and other affective reactions to excessive detail (Olevskyi, 2022). How these cognitive and affective strains interact—and how their combination shapes downstream decisions—remains insufficiently explored (Jacob et al., 2024). As a consequence, prevailing models either isolate one facet of overload or omit the nuanced interplay between cognition and emotion that typifies real-world shopping contexts.
The extant research has two major limitations. First, it regards information overload as a single-dimensional variable, neglecting its multilayered structure, which is composed of information characteristics (quantity, complexity), system features (push mechanism, interface design), and subjective factors (cognitive style, emotional state). Second, it lacks the transformation of Marxist consumption theory into the philosophy of technology, thereby failing to reveal the power relations that underlie algorithmic recommendations. Filling this gap poses three principal challenges. First, consumer behavior under conditions of overload is inherently multifactorial; any explanatory model must capture both the quantity of information and the quality of the emotional response it triggers (Belabbes et al., 2023). Second, e-commerce platforms and user cohorts differ markedly across sectors, implying that a one-size-fits-all solution lacks external validity. Third, increased model realism often comes at the cost of computational tractability, demanding a careful trade-off between parsimony and predictive power.
To confront these issues, we introduce the Multilayered Information Overload and Consumer Decision Behavior Model (MIO-CD), which is based on Marxist consumption theory. The framework integrates three complementary overload dimensions—quantitative, qualitative, and emotional—to deliver a holistic account of online decision processes. By explicitly modeling how these facets converge, MIO-CD predicts key behavioral outcomes, such as purchase delays, repurchase intentions, and hesitation episodes, with higher fidelity than single-dimension approaches.
Methodologically, the present study combines large-scale text and numerical data—ranging from review corpora to transaction logs—with Marxist consumption theory advanced sentiment analysis and machine learning pipelines. These techniques allow real-time estimation of overload levels and their behavioral consequences. A comprehensive experimental campaign benchmarks MIO-CD against established baselines, revealing consistent performance gains in accuracy and interpretability.