E-Commerce Revolution: How the Pandemic Reshaped the US Consumer Shopping Habits: A PACF and ARIMA Approach
Notice bibliographique
Résumé
Accelerated digital transformations and the evolution of consumer behavior in recent years underscore the need for a systemic perspective in marketing analytics to better comprehend the complex interplay between technology, data, and the profound changes triggered by global events, such as the COVID-19 pandemic. The COVID-19 pandemic has catalyzed a massive shift toward digitalization and transformed e-commerce from an option to a necessity for both businesses and consumers. This paper analyzes the total store and non-store sales, as well as total e-commerce sales, of the US retail trade across six main business categories and nine subcategories from the first quarter of 2018 to the first quarter of 2024. The data was divided into three time spans, corresponding to pre-, during, and post-COVID-19 pandemic periods, to examine the changing behavior of US consumers over time for different business categories. The statistical and econometric methods employed are the partial autocorrelation function (PACF), autocorrelation function, autoregressive integrated moving average model, inferential statistics, and regression model. The results indicate that the pandemic significantly increased non-store retailer sales compared to the pre-pandemic period, underscoring the importance of e-commerce. When physical stores reopened, e-commerce sales did not decline to pre-pandemic levels. The PACF analysis showed seasonality and lagged correlations. Thus, the pandemic-induced buying behaviors of US consumers continue to influence current sales patterns. The pandemic was more than just a temporary disruption, which permanently changed the retail sector. Retailers that quickly adapted to online models gained a competitive edge, whereas US consumers became accustomed to the convenience and flexibility of e-commerce. The behavior of US consumers adapted not only in response to immediate needs during the pandemic but also led to longer-term shifts in spending patterns, with each category reacting uniquely based on product type and perceived necessity. The analysis of how the COVID-19 pandemic transformed consumer behavior in the US reveals several important implications for both consumers and trade policymakers. First, the long-lasting and structural shift toward e-commerce is confirmed, representing a fundamental change in the dynamics of demand and supply. For consumers, the convenience, flexibility, and accessibility of digital channels have moved beyond mere situational advantages to become a behavioral norm. This shift has empowered consumers by giving them greater access to price comparisons, more diverse options, and increased informational transparency. Additionally, the data shows the emergence of hybrid consumption models: essential goods are mainly purchased online, while purchases of branded clothing, electronics, furniture, luxury items, and similar products continue to favor the traditional retail experience.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».