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Adoption of b2c e-commerce by city centre retailers: The relevance of place, product and organisation. Oedzge Atzema & Jesse Weltevreden Urban & Regional.

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Presentation on theme: "Adoption of b2c e-commerce by city centre retailers: The relevance of place, product and organisation. Oedzge Atzema & Jesse Weltevreden Urban & Regional."— Presentation transcript:

1 Adoption of b2c e-commerce by city centre retailers: The relevance of place, product and organisation. Oedzge Atzema & Jesse Weltevreden Urban & Regional research centre Utrecht (URU) ICT: Mobilizing persons, places and spaces, November 4-7 2004, Doorn

2 Outline Presentation Main objectives City centres Consumer data Data Collection Results Conclusions Outline Objectives City centres Consumer data Data collection Results Conclusions

3 Main Objectives To investigate the factors that determine the adoption of online shopping by consumers; To investigate the impact of consumers' online shopping behaviour on their physical shopping behaviour in city centres (this presentation); To investigate the factors that determine the adoption of b2c e-commerce by city centre retailers (this presentation); To investigate the effects of retailers Internet strategy on their organisation and city centre stores. OutlineObjectives City centres Consumer data Data collection Results Conclusions

4 City centres Outline Objectives City centres Consumer data Data collection Results Conclusions

5 # Top 10 Internet (N = 5,678 purchases) % Top 10 City Centre (N = 5,695 p.) % 1 Books 12.6 Upper wear 23.4 2 Upper wear 8.8 Shoes 10.7 3 Videos & DVDs 8.6 Personal care 7.4 4 Theatre tickets etc. 8.3 Groceries 5.7 5 CDs 7.0 Books 4.8 6 Computer hardware 6.6 Underwear 4.8 7 Bus/Train/Airline tickets 5.5 Cosmetics etc. 3.9 8 Used merchandise 5.5 Videos & DVDs 3.8 9 Travel 4.1 Theatre tickets etc. 3.5 10 Underwear 3.7 Presents & Gifts 3.4 (1) 10 Most popular products on the Internet and in the city centre (based on respondents last 3 purchases) Outline Objectives City centres Consumer data (1) Data collection Results Conclusions

6 (2) Bought online from whom? (Based on respondents last 3 online purchases) #Organisation type (Top 150)Share (%) 1Dotcoms (e.g., Amazon.com)33.9 2Catalogue Retailers With physical outlets (e.g., ECI) Without physical outlets (e.g., Neckermann) 19.1 (4.1) (15.1) 3Traditional retailers Independent retailers Multiple retailers (e.g., Hunkemöller) 14.0 (3.0) (11.0) 4Service providers/Manufacturers With physical outlets (e.g., Vodafone) Without physical outlets (e.g., Dell) 12.0 (1.4) (10.5) 5Online Auctions (e.g., E-bay)9.0 Other/Unknown11.9 Total (N= 5,254 online purchases)100 Outline Objectives City centres Consumer data (2) Data collection Results Conclusions

7 (3) Impact of online buying on purchases in various city centre stores (N= 2,010) Outline Objectives City centres Consumer data (3) Data collection Results Conclusions

8 Data Collection (1) 1.Examination of the retail composition of 8 city centres (October-November, 2003) (N= 3,369 shops); 2.Searching for a retailers Website via a Search Engine (November, 2003); 3.Brief Interviews about Web presence and promotion of Website (December-February, 2004) (N= 3,274 shops, Response of 97.2%); 4.Analysing the Internet strategy of each retailer (March, 2004). Outline Objectives City centres Consumer data Data collection (1) Results Conclusions

9 Shop level approach; B2c e-commerce adoption 2 stages: active website & online sales; Place: 4 types of city centres; and pedestrian vs. non pedestrian areas; Product: 4 product categories; and 12 main sectors; Organisation: 6 types. Data Collection (2): Operationalisation Outline Objectives City centres Consumer data Data collection (2) Results Conclusions

10 Logistic regression of active website and online sales adoption using a product classification (part 1) Table II: Logistic regression of active website and online sales adoption by city centre shops using a product classification * = p < 0.10; ** = p <0.05; *** = p <0.01 WebsiteOnline sales B (s.e.) Place:Large, high fun00 Medium, high fun-0.092 (0.125) -0.273 (0.174) Medium, medium fun-0.259* (0.135) -0.186 (0.181) Small, low fun-0.381*** (0.143) -0.463** (0.193) Pedestrian area00 Non pedestrian area0.133 (0.099) -0.013 (0.145) Product:Convenience goods00 Experience type 10.783*** (0.156) -1.461*** (0.202) Experience type 21.361*** (0.136) -0.789*** (0.454) Search goods1.812*** (0.191) 0.589*** (0.208) * = p < 0.10; ** = p < 0.05; *** = p < 0.01 Outline Objectives City centres Consumer data Data collection Results (1.1) Conclusions

11 WebsiteOnline sales B (s.e.) Organisation:Independent, 1 outlet00 Independent, > 1 outlet0.387*** (0.128) -0.088 (0.273) Chain, < 30 outlets1.606*** (0.136) 0.668*** (0.217) Chain, > 29 outlets3.610*** (0.222) 1.054*** (0.205) Franchise, < 50 outlets2.807*** (0.193) 0.656*** (0.229) Franchise, > 49 outlets3.970*** (0.262) 1.012*** (0.213) Nagelkerke R square0.4010.174 No. cases2,9091,661 Logistic regression of active website and online sales adoption using a product classification (part II) * = p < 0.10; ** = p < 0.05; *** = p < 0.01 Outline Objectives City centres Consumer data Data collection Results (1.2) Conclusions

12 Logistic regression of active website and online sales adoption using a sector classification (part I) Table II: Logistic regression of active website and online sales adoption by city centre shops using a product classification * = p < 0.10; ** = p <0.05; *** = p <0.01 WebsiteOnline sales B (s.e.) Place:Large, high fun00 Medium, high fun-0.093 (0.128) -0.273 (0.174) Medium, medium fun-0.289** (0.138) -0.186 (0.181) Small, low fun-0.401*** (0.147) -0.463** (0.193) Pedestrian area00 Non pedestrian area0.085 (0.102) -0.013 (0.145) Organisation:Independent, 1 outlet00 Independent, > 1 outlet0.410*** (0.133) -0.112 (0.308) Chain, < 30 outlets1.544*** (0.139) 0.278 (0.244) Chain, > 29 outlets3.485*** (0.227) 0.586*** (0.226) Franchise, < 50 outlets2.730*** (0.193) 0.474** (0.238) Franchise, > 49 outlets3.749*** (0.273) 0.750*** (0.224) * = p < 0.10; ** = p < 0.05; *** = p < 0.01 Outline Objectives City centres Consumer data Data collection Results (2.1) Conclusions

13 WebsiteOnline sales B (s.e.) Sectors:Clothing & Accessories00 Food & Drinks-0.597*** (0.174) 1.127*** (0.251) Footwear & Leather g.-0.092 (0.188) -0.911** (0.418) Health & Personal care0.208 (0.314) 1.860*** (0.272) Jewellery & Optical g.0.267 (0.211) 0.599* (0.336) Household & Luxury g.0.302 (0.248) -0.045 (0.467) Hobby goods0.635*** (0.204) 1.147*** (0.308) Furniture & DIY0.803*** (0.161) -1.204** (0.481) Arts & Antiquities0.829*** (0.209) -0.603 (0.625) Toys & Sporting goods0.906*** (0.237) 0.934*** (0.270) Media goods1.117*** (0.206) 2.562*** (0.249) Consumer electronics1.669*** (0.271) 1.710*** (0.217) Nagelkerke R square0.4040.265 No. cases2,8161,582 Logistic regression of active website and online sales adoption using a sector classification (part II) Outline Objectives City centres Consumer data Data collection Results (2.2) Conclusions

14 Some city centre retailers may already begin to feel the impact of changes in consumers shopping habits because of online shopping; For active website adoption organisation has the most explanatory value; For online sales sector type is more important; A high chance having a website need not coincide with a high likelihood of online sales adoption as well; Retailers not necessarily need to sell the same merchandise online as in their physical outlets; Location matters for both the adoption of an active website and online selling strategy. Outline Objectives City centres Consumer data Data collection ResultsConclusions

15 End of Presentation


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