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Enregistrement W162657275

Effects of Atmospherics on Revenue Generation in Small Business Restaurants

2006· article· en· W162657275 sur OpenAlex

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Notice bibliographique

RevueJournal of business & entrepreneurship · 2006
Typearticle
Langueen
DomaineBusiness, Management and Accounting
ThématiqueConsumer Retail Behavior Studies
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésRevenueYield managementRevenue managementRevenue assuranceBusinessService (business)Revenue centerMarketingMarginal revenueTotal revenueRevenue modelSales managementAdvertisingFinance
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

ABSTRACT Atmospheric variables such as interior layout and music stimulate behavioral responses from customers in service settings. This study examined the extent to which these cues affect revenue generation in 153 full-service small business restaurants. The results demonstrate that both interior layout and music are significant predictors of revenue generation and thus they may offer an important collateral strategy to restaurant revenue management. INTRODUCTION An important segment of the economy, full-service restaurants, had sales of $144.6 billion in 2002 (U.S. Census Bureau, 2002). With a continuing rise in American meals being eaten away from home, these sales are expected to grow (National Restaurant Association, 2005). Among restaurants, seven out of ten are single-unit; i.e., independent operations (National Restaurant Association, 2005). Kimes, Chase, Choi, Lee, and Ngonzi (1998) developed a framework for applying revenue management in restaurants. Conceived in the airline industry as yield management, revenue management involves the management of demand and pricing in order to maximize sales revenues (Cross, 1997). It has been shown to increase sales revenue by as much as 7% for airlines (Marraorstein, Rossomme, & Sarel, 2003). Revenue management is now used in a range of industries such as communication, hotels, and shipping (McGill & Van Ryzin, 1999). The restaurant revenue management framework developed by Kimes et al. (1998) suggests demand-managing strategies that present customers with cues to affect revenue generation. Indeed, case study evidence shows that restaurant revenue management, used in conjunction with adjustments to table top mix (e.g., a four-top is a table that seats four), increases sales by 5% (Kimes, 2004). Atmospheric cues are a potential collateral strategy to restaurant revenue management. Based on a stimulus-organism-response (SOR) framework, atmospherics involves the use of stimuli such as interior layout and music to elicit behavioral responses. For example, studies show that atmospheric cues can result in faster shopping traffic flow and an increase in the time and money customers spend in a retail store (Areni & Kim, 1993; Milliman, 1982). Atmospheric stimuli such as interior layout and music help to create ambiance in a restaurant setting. However, there is no large-sample empirical evidence on the effects of these variables on revenue generation in small business restaurants. Evidence of the effects of atmospheric cues would extend the restaurant revenue management literature and contribute to restaurant managers' understanding of collateral revenue management practices. Thus, the purpose of this study was to examine whether the use of atmospheric cues, such as interior layout and music, in small business restaurants has significant effects on revenue generation. In the following section, literatures on restaurant revenue management and atmospherics are reviewed to derive a hypothesis. The hypothesis was tested with a survey of small business restaurant managers. Results are presented, and implications for revenue management in small business restaurants are discussed. LITERATURE REVIEW Revenue management manages demand in order to maximize sales revenues from a business's existing capacity (Cross, 1997; Kimes & Chase, 1998). There are several conditions that facilitate the practice of revenue management in a business. First, the outputs of the business should be perishable (Weigand, 1999). For example, airlines have a perishable product (i.e., a flight on a given date and time to a given destination flies only once). Second, a business should have primarily fixed capacity (Weatherford & Bodily, 1992). For example, airlines have fixed capacity in their investment in a fleet of airplanes. Given fixed capacity, one means that any business can use to seek to improve its profitability is to increase the amount of revenue that is generated from that fixed capacity. …

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,039
Score d'incertitude au seuil0,968

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,002
Études des sciences et des technologies0,0000,000
Communication savante0,0000,001
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,025
Tête enseignante GPT0,218
Écart entre enseignants0,192 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle