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Enregistrement W3203531046

Smart cities and flagship stores: kitchen furniture

2021· article· en· W3203531046 sur OpenAlexaboutno aff
Aurelio Volpe, Donatella Cheri, Sara Banfi

Notice bibliographique

RevueCSIL reports · 2021
Typearticle
Langueen
DomaineBusiness, Management and Accounting
ThématiqueConsumer Retail Behavior Studies
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPer capitaSample (material)BusinessPopulationConsumption (sociology)Gross domestic productProduct (mathematics)Agricultural economicsMarket segmentationMarketingGeographyAdvertisingEconomicsEconomic growth
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

The GOAL of the Report 'Smart cities and flagship stores: kitchen furniture" is to provide: Kitchen companies with a tool to identify potential locations where to set their mono-brand stores, keeping into account potential synergies (for instance the presence of complementary brands) as well as an indicator of the cost of the area; The industry, in general, with an analysis on the medium-term trends affecting the main cities worldwide; The Report provides profiles of 85 cities worldwide with a selection of economic and demographic indicators (2013 and 2018), estimates of the potential market for kitchen furniture, in each city and the forecasts for the market development to the year 2023 (*). The study also offers an analysis of the geographical presence of a selected sample of 65 brands, each of which operates as a trend-setter in its own category. Each identified location is characterized by its type (store, multibrand store, shopping centre) and the cost of the area in which they are located. The aim is, thus, to provide a comprehensive view of the cities that a selection of international retailers entered. Finally, each profile presents a selection of kitchen furniture stores, in 82 out of the 85 selected cities. For each CITY PROFILE, the following data, indicators and forecasts are provided: Population and its rank within the sample, 2013, 2018 and 2023; Households and its rank within the sample, 2013, 2018 and 2023; Gross domestic product per capita and its rank within the sample, 2013, 2018 and 2023; Household’s consumption per capita and its rank within the sample, 2013, 2018 and 2023; Gross domestic product and its rank within the sample, 2013, 2018 and 2023; Household’s consumption and its rank within the sample, 2013, 2018 and 2023; Breakdown of households by the level of income, 2013, 2018 and 2023; Kitchen furniture demand and its growth rate, 2013, 2018 and 2023; Spatial analysis of the distribution of 50 brands within the city map; Spatial distribution of a selection of kitchen furniture stores. SELECTED CITIES group by geographic areas: Asia and Pacific: Melbourne, AU; Sydney, AU; Beijing, CN; Chengdu, CN; Chongqing, CN; Guangzhou, CN; Hangzhou, CN; Hong Kong, CN; Jinan, CN; Shanghai, CN; Tianjin, CN; Bangalore, IN; Mumbai, IN; Delhi, IN; Osaka, JP; Tokyo, JP; Seoul, KR; Kuala Lumpur-Klang Valley, MY; Auckland, NZ; Singapore, SG; Bangkok, TH; Ho Chi Minh City, VT. Eastern Europe outside the EU and Russia: Moscow, RU; Saint Petersburg, RU; Ankara, TR; Istanbul, TR; Kiev, UA. Europe: Vienna, AT; Brussels, BE; Prague, CZ; Copenhagen, DK; Helsinki, FI; Lyon, FR; Paris, FR; Berlin, DE; Frankfurt, DE; Munich, DE; Athens, GR; Budapest, HU; Dublin, IE; Milan, IT; Rome, IT; Amsterdam, NL; Oslo, NO; Warsaw, PO; Lisbon, PT; Bucharest, RO; Barcelona, ES; Madrid, ES; Stockholm, SE; Zurich, CH; London, UK; Manchester, UK. Middle East and Africa: Tel Aviv-Jaffa, IL; Doha, QA; Jedda, SA; Riyadh, SA; Cape Town, ZA; Abu Dhabi, AE; Dubai, AE. North America: Montreal, CA; Toronto, CA; Vancouver, CA; Mexico City, MX; Atlanta, US; Boston, US; Chicago, US; Dallas-Fort Worth, US; Detroit, US; Houston, US; Los Angeles, US; Miami, US; Minneapolis-Saint Paul, US; New York, US; Philadelphia, US; Phoenix, US; San Diego, US; San Francisco, US; Seattle, US; Washington, US. South America: Buenos Aires, AR; Rio de Janeiro, BR; Sao Paulo, BR; Santiago de Chile, CL; Bogota, CO; Lima, PE. Among the selected kitchen stores mentioned: 1000 Kuchnie, Al Meera Abu Dhabi, Architecs and Designers Bulding NY, Arredo 3 Mutfak, Binacci, Boffi Berlin, Bulthaup Berlin, Bulthaup Toronto, Bunnings, Cabinets and Beyond, Cabinets To Go, Casa Shopping, Chanintr Living, Da Vinci, Diacocina Madrid, Easy Home Beijing, Eggo, Eurokitchens, German Kitchen Center, Godrej Interio, Gruppo Cucine, HTH, International Market Center, Kaza Planejados, KIC ChongQing, Kitchen&Bath Shop, Kitchen Design Centre, Kitchen Innovation World Shanghai, Kitchen Works LA, Kuchnie Nolte, Kvik, La Cornue, Laura Ashley, Leicht Lisboa, Poggenpohl St Albans, Majestic Kitchens, Marquardt, Miacucina San Diego, Miami Design District, Modular Kitchen Delhi, Oppein Living, Panasonic Living Center, Poggenpohl Boston, Poliform Lyon, Porcelanosa Kitchen, Puustelli, ViA Hong Kong, Scavolini Detroit, Semel Kitchens, Shine Kitchen, Signature Interior, Stopino, theMart Chicago, TKI Amsterdam, Tulp Kitchens, Wuerfel Kuche Bangalore, Zahrani Kitchens. Among the kitchen brands mentioned: Al Meera Kitchens Arc Linea, Bertch, Bilotta, Boffi, Bulthaup, Crystal, Dada, De Wils, Dellanno, Dura Supreme, Elmwood, Eggersmann, Golden Home, Haecker, Hans Krug, Hanssem, Leicht, Lube, Marya, Mobalpa, Nobilia, Nolte, Oppein, Plain&Fancy, Poggenpohl, Poliform, Rutt, Scavolini, Siematic, Signature, Snaidero, Todeschini, Valcucine, Veneta Cucine, Wood Mode, WW Wood Products. Major Local markets monitored: Atlanta, Boston, Chicago, Dallas-Fort Worth, Detroit, Houston, Los Angeles, Miami, Minneapolis-Saint Paul, New York, Philadelphia, Phoenix, San Diego, San Francisco, Seattle, Washington. (*) Our economic and demographic indicator database is dated January 2020, therefore macroeconomic and sectorial estimations and forecasts were made before that date. The world has changed dramatically in the three months as the world has been put in a Great Lockdown. According to the IMF, 'the magnitude and speed of collapse in activity that has followed is unlike anything experienced in our lifetimes'. Up to the publication date of this report updates on forecasts up to 2023 havent be released.

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.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,028
Score d'incertitude au seuil0,087

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0020,005
Études des sciences et des technologies0,0000,000
Communication savante0,0020,002
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0260,014

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 Opus0,024
Tête enseignante GPT0,227
Écart entre enseignants0,203 · 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

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2021
Routes d'admission1
Résumé présentoui

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