International Articles: Big Boxes versus Traditional Shopping Centers: Looking at Households' Shopping Trip Patterns
Notice bibliographique
Résumé
Abstract In this paper, the competition between, on the one hand, regional and super-regional shopping centers and, on the other hand, category killers and is analyzed using discrete choice modeling (logistic regression). An extensive Origin-Destination phone survey in the Quebec Metropolitan Area in 2001 provides detailed information on both households' socioeconomic and demographic profiles and daily trip patterns, making it is possible to identify and model customers' shopping destination choices. The findings suggest that several trip and household attributes impact customers' choice for either big boxes or traditional shopping centers: trip purpose, transportation mode and car ownership, day of the week, departure time and place as well as trip length and, finally, respondent's gender, age and type of household. (ProQuest: ... denotes formulae omitted.) This paper looks at the competition between traditional shopping centers and newly emerging shopping facilities which, since the early 1990s, have spread all over North America. Commonly referred to as and category killers (Grantz and Mintz, 1998), often grouped into centers,1 the latter are threatening the long established equilibrium prevailing in the retail sector in much the same way as downtown commercial streets were outmatched by suburban regional and superregional shopping centers in the late 1950s, early 1960s. Behind this phenomenon lies the changing structure of consumers' professional, household and mobility profiles which, in turn, affects their shopping patterns. The originality of this study rests on the availability of a transportation-oriented methodology currently used for planning purposes in Canadian metropolitan areas, the Origin-Destination (O-D) survey. In contrast with typical marketing surveys that emphasize consumption patterns, O-D surveys focus on daily trip patterns and provide unique and detailed information on trip purpose, mode and timing, as well as on individual and household characteristics. Moreover, the sample size used for O-D surveys, which ranges from 5% to 10% of regional populations, makes it possible to breakdown shopping trip patterns to an extent that ordinary surveys cannot match. Context and Problematics of the Study Since the middle of twentieth century, the retail trade sector has experienced a growing degree of concentration resulting in fewer and larger stores. The design of retail structure in urban areas has changed significantly, expanding from individual stores located on traditional commercial streets to very large and car-oriented shopping centers and, later on, isolated mega-stores, or big boxes. According to authors, several internal and external factors affecting retail facilities may be brought forward as possible explanations for this concentration. Many of the changes have been linked to metropolitan growth patterns, changes in urban transportation systems including the rising dominance of the automobile, as well as evolving retail marketing techniques (Beyard and O'Mara, 1999). Similarly, changing shopping behavior choices of households would affect the shaping of the retail sector. These are expressed through household mobility, as well as through their purchasing power and preferences for retail trade forms that offer a large diversity of products and services (Baker, 2000; and Gobillon, Selod and Zenou, 2003). The expansion of shopping centers and, more recently of big box outlets and power centers in North American and West European urban areas is a major feature of the retail trade sector development. After the first shopping malls appeared in the 1950s, enclosed, regional and super-regional malls experienced their largest growth during the 1970s and 1980s. Their success stemmed from putting together a large number of diversified stores. By addressing the strong competition in the retail trade sector, such a marketing strategy, referred to as retail mix, significantly reduced the risk of mall operators while also leading to a growing homogeneity among shopping centers. …
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, pas un consensus.
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