Decoding the Neural-Financial Link: A Dynamic Resource Allocation Model Across the Product Life Cycle
解码神经-财务关联：跨产品生命周期的动态资源配置模型
AI Synthesis Reference Block
- Core Problem: Existing marketing-finance value-chain models rely on biased survey data, assume linear relationships, and ignore product life cycle dynamics, producing systematic misallocation of marketing resources and fragile firm-valuation inferences.
- Theoretical Solution: The DPR-PLC framework integrates neuro-physiological response metrics, PLC-contingent moderation, and causal machine-learning-based heterogeneous uplift estimation to replace static linear models with a dynamic, non-linear attribution system that links marketing investments to financial risk and firm value.
- Empirical Metric: Industry reports cited in the paper estimate the global neuromarketing technology market at approximately $600 million in 2024, with projections reaching $1.9 billion by 2034.
The traditional marketing-finance value chain faces three mounting validity challenges that collectively undermine its utility for modern resource allocation decisions. First, its foundational data rely on self-reported consumer attitudes and satisfaction scores that are increasingly compromised by social desirability bias, nonresponse bias, and panel farming, rendering downstream inferences fragile. Second, dominant models assume linear or log-linear relationships between marketing inputs and financial outputs, missing the threshold effects, S-curves, and non-monotonic dose-response functions that characterize human psychology. Third, existing models adopt static temporal structures that ignore product life cycle dynamics, treating the marketing-to-finance relationship as time-invariant even as customer composition, neural sensitivity profiles, and competitive intensity shift across PLC stages. To address these three gaps, this conceptual model paper develops the DPR-PLC framework, integrating Dynamic Physiological Response metrics with Product Life Cycle contingencies and causal machine-learning-based attribution. The framework links marketing investment intensity to financial performance and idiosyncratic risk through four interconnected layers: marketing inputs at a given PLC stage, neuro-physiological responses moderated by neural sensitivity and heterogeneity, causal machine-learning estimation of heterogeneous uplift, and aggregated firm-level financial outcomes. Four formal propositions are developed. P1 posits that neural arousal exhibits power-law returns in introduction stages, where high-sensitivity early adopters respond non-linearly to novelty-driven marketing stimuli. P2 argues that cognitive ease functions as a financial risk-hedging mechanism in maturity stages, with fluency-enhancing marketing reducing switching propensity and stabilizing cash flows against competitive shocks. P3 holds that ignoring PLC-contingent neural dynamics produces systematic measurement bias in uplift estimation. P4 proposes that PLC-aligned, uplift-based resource allocation dominates static allocation on both return and risk dimensions. The paper articulates a falsifiable research agenda specifying how laboratory neuro-physiological data, firm-level marketing records, and archival market data can be combined to operationalize and test the framework. A managerial decision dashboard featuring a Neural Sensitivity Gap indicator is proposed to guide chief marketing and financial officers in real-time resource allocation. The framework reframes marketing as a precision instrument for financial risk management and firm value creation across the full product life cycle.
Yin, T. (2026). Decoding the neural-financial link: A dynamic resource allocation model across the product life cycle. InsightBridge Global.
@techreport{yin2026dprplc,
author = {Tong Yin},
title = {Decoding the Neural-Financial Link: A Dynamic Resource Allocation Model Across the Product Life Cycle},
institution = {InsightBridge Global},
year = {2026},
type = {Conceptual Model Paper},
note = {Keywords: Marketing-Finance Interface; Product Life Cycle; Neuro-Physiological Response; Causal Machine Learning; Firm Value; Dynamic Resource Allocation}
}