Determinants of E-Commerce Demand, Travel Behavior, and Travel-Related Expenditures Among U.S. Households
Why this work is in the frame
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Bibliographic record
Abstract
This research investigates how rapid growth in e-commerce and consumer travel-related expenditure influences household travel demand, their mode choice, and transportation spending behavior in the United States. The time frame of the dataset focuses on the post-COVID-19 era. It uses the data from the 2022 U.S. National Household Travel Survey (NHTS) and the first quarter of 2019 - 2024 American Consumer Expenditure Survey (ACES). NHTS, a national data source that allows analysis of people's characteristics and household travel, including daily non-commercial travel across all modes. ACES collects detailed information on consumers' incomes and purchasing habits. For travel-related expenditures, the survey reports spending amounts by travel mode and trip-related categories. The research examines the interaction and factors influencing online shopping and consumer purchasing habits, along with their corresponding mode-choice behavior, using Negative Binomial (NB), Zero-Inflated Binomial Regression (ZINB), a Joint Bayesian Bivariate model, and a Log-Transformed Cobb-Douglas Demand function. The results from online shopping reveal that several socio-demographic variables, such as household members, car ownership, number of workers, adults, and young children in the households, education, and income, significantly influence the frequency of online shopping and mode preference. The results also show that households with more drivers make fewer online purchases, whether for goods, food, or groceries. However, larger households tend to make more online purchases. Due to COVID-19, individuals who order online less are more likely to use public transit or ride share and less likely to drive. Also, individuals who use public transit more often than before the COVID-19 pandemic is less likely to have goods delivered to them. Regarding the ACES, the result shows that transportation and lodging expenditure consistently accounted for the largest share of total trip spending, followed by food, entertainment, and alcoholic beverages. Public transportation use and gas and oil expenditures emerged as the most significant predictors of total trip spending. Another result suggests that during the COVID-19 pandemic, spending on public transportation decreased as a proportion of total trip expenditures, but spending on leased autos, trucks, and vans increased, likely due to safety concerns. People who spend more on vehicle insurance tend to reduce their travel activity and trip-related spending in response to uncertainty at the start of the pandemic in 2020. This research provides insight into how e-commerce and consumer spending behavior would affect mode choice, travel demand, and transportation expenditure, and how policyholders can use it as a resource to improve transportation policy.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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