A Consumer-Centric Framework for Measuring Product Obsolescence Using User-Generated Content and Large Language Models: Evidence From IoT Devices
Why this work is in the frame
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Bibliographic record
Abstract
Identifying product obsolescence factors is essential for guiding sustainable design and extending product longevity. Unlike prior studies, this research leverages online consumer reviews to explore product obsolescence factors. First, ChatGPT-4o, an advanced pre-trained large language model (LLM), is utilized to identify these factors. User-generated content (UGC) time series-based product obsolescence indexes are then defined to quantify each factor's impact, offering a UGC-based complement to earlier methods that depended on expert judgment, supplier input, or survey data. By leveraging real-time customer insights, this approach aligns with Industry 4.0 principles, offering a UGC-based method that can support engineering managers to proactively address product obsolescence. It integrates factors' relative importance, determined through frequency–analytic hierarchy process (Freq-AHP), with their severity impact on consumers, assessed using the Robustly optimized Bidirectional Encoder Representations from Transformers approach (RoBERTa). This is further supported by a robustness check, where small perturbations were applied to sentiment intensities and all indices recalculated, confirming the aggregated obsolescence index remained stable across all product categories. This study focuses on consumer IoT devices, an area underexplored in existing literature, analyzing 47,695 online consumer reviews across nine product categories and selecting 4,771 online obsolescence-related reviews for detailed analysis. Findings reveal nineteen key factors and demonstrate a fundamental shift in obsolescence, indicating that product obsolescence of consumer IoT devices is increasingly driven by adaptability, interoperability, and digital resilience rather than physical durability. These insights demonstrate the potential of the proposed approach to inform product obsolescence mitigation strategies and guide more resilient, user-centered design in IoT ecosystems.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it