How technological emergence, saturation, and rejuvenation are re-shaping the e-commerce landscape and disrupting consumption? A time series analysis
Bibliographic record
Abstract
Technological advancements in the ICT sector have enabled the development of online platforms that have drastically transformed the e-commerce landscape. The rise of the sharing economy is a key in point. Academics claim that this change is causing a noticeable shift in consumers’ habits from traditional towards collaborative and sharing activities due to environmental, social, and economic motives. In this article, we test the empirical validity of this hypothesis; namely, we test whether or not the recent change in e-commerce landscape is causing a permanent transition in US consumption over the last two decades by fitting a nonlinear smooth transition regression model to the cycle of US consumption with a constructed exogenous regime-driving variable that captures the various aspects of digital technology. The econometric analysis confirms the existence of a non-permanent regime switch in consumption. In particular, we show that the emergence, saturation, and rejuvenation of digital technology are causing consumption to switch between two stationary regimes. Consumption oscillates smoothly, but frequently, between both regimes in the early period (2000-2006) due to the emergence of new technologies and in the recent period (2017-2019) due to technological rejuvenation. It persists, however, in the mid period (2007-2016) due technological saturation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".