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Record W3203531046

Smart cities and flagship stores: kitchen furniture

2021· article· en· W3203531046 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueCSIL reports · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Retail Behavior Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPer capitaSample (material)BusinessPopulationConsumption (sociology)Gross domestic productProduct (mathematics)Agricultural economicsMarket segmentationMarketingGeographyAdvertisingEconomicsEconomic growth
DOInot available

Abstract

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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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.384
Threshold uncertainty score0.649

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.227
Teacher spread0.203 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it