adoption of b2c e-commerce by city centre retailers: the relevance of place, product and...
TRANSCRIPT
Adoption of b2c e-commerce by city centre retailers:
The relevance of place, product and organisation.
Oedzge Atzema & Jesse WeltevredenUrban & Regional research
centre Utrecht (URU)
ICT: Mobilizing persons, places and spaces,
November 4-7 2004, Doorn
Outline Presentation
• Main objectives
• City centres
• Consumer data
• Data Collection
• Results
• Conclusions
OutlineOutline
Objectives
City centres
Consumer data
Data collection
Results
Conclusions
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.
Outline
ObjectivesObjectives
City centres
Consumer data
Data collection
Results
Conclusions
City centresOutline
Objectives
City centresCity centres
Consumer data
Data collection
Results
Conclusions
# 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 Consumer data (1)data (1)
Data collection
Results
Conclusions
(2) Bought online from whom? (Based on respondents’ last 3 online purchases)
# Organisation type (Top 150) Share (%)
1 Dotcoms (e.g., Amazon.com) 33.9
2 Catalogue Retailers• With physical outlets (e.g., ECI)
• Without physical outlets (e.g., Neckermann)
19.1(4.1)
(15.1)
3 Traditional retailers• Independent retailers
• Multiple retailers (e.g., Hunkemöller)
14.0(3.0)
(11.0)
4 Service providers/Manufacturers• With physical outlets (e.g., Vodafone)
• Without physical outlets (e.g., Dell)
12.0(1.4)
(10.5)
5 Online Auctions (e.g., E-bay) 9.0
Other/Unknown 11.9
Total (N= 5,254 online purchases) 100
Outline
Objectives
City centres
Consumer Consumer data (2)data (2)
Data collection
Results
Conclusions
(3) Impact of online buying on purchases in various city centre stores (N= 2,010)
0% 1% 2% 3% 4% 5% 6% 7% 8% 9% 10% 11% 12% 13% 14%
JewellerFlorist
Pet shopOptician
DIY storeGift shop
Perfume storeFurniture shop
Houshold goodsShoe storeDrug store
SupermarketSex shop
Sporting GoodsToy store
2nd hand shopUnderwearPhoto/Film
Telecom shopBrown & White
Department StoreClothing
Software storeTravel agency
Computer storeBook store
CD shop
Less purchases More purchases
Outline
Objectives
City centres
Consumer Consumer data (3)data (3)
Data collection
Results
Conclusions
Data Collection (1)
1. Examination of the retail composition of 8 city centres (October-November, 2003) (N= 3,369 shops);
2. Searching for a retailer’s 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 Data collection collection
(1)(1)
Results
Conclusions
• 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): OperationalisationOutline
Objectives
City centres
Consumer data
Data Data collection collection
(2)(2)
Results
Conclusions
Logistic regression of active website and online sales adoption using a product classification (part 1)
Website Online sales
B (s.e.) B (s.e.)
Place: Large, high fun 0 0
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 area 0 0
Non pedestrian area 0.133 (0.099) -0.013 (0.145)
Product: Convenience goods 0 0
Experience type 1 0.783*** (0.156) -1.461*** (0.202)
Experience type 2 1.361*** (0.136) -0.789*** (0.454)
Search goods 1.812*** (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)Results (1.1)
Conclusions
Website Online sales
B (s.e.) B (s.e.)
Organisation: Independent, 1 outlet 0 0
Independent, > 1 outlet 0.387*** (0.128) -0.088 (0.273)
Chain, < 30 outlets 1.606*** (0.136) 0.668*** (0.217)
Chain, > 29 outlets 3.610*** (0.222) 1.054*** (0.205)
Franchise, < 50 outlets 2.807*** (0.193) 0.656*** (0.229)
Franchise, > 49 outlets 3.970*** (0.262) 1.012*** (0.213)
Nagelkerke R square 0.401 0.174
No. cases 2,909 1,661
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)Results (1.2)
Conclusions
Logistic regression of active website and online sales adoption using a sector classification (part I)
Website Online sales
B (s.e.) B (s.e.)
Place: Large, high fun 0 0
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 area 0 0
Non pedestrian area 0.085 (0.102) -0.013 (0.145)
Organisation: Independent, 1 outlet 0 0
Independent, > 1 outlet 0.410*** (0.133) -0.112 (0.308)
Chain, < 30 outlets 1.544*** (0.139) 0.278 (0.244)
Chain, > 29 outlets 3.485*** (0.227) 0.586*** (0.226)
Franchise, < 50 outlets 2.730*** (0.193) 0.474** (0.238)
Franchise, > 49 outlets 3.749*** (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)Results (2.1)
Conclusions
Website Online sales
B (s.e.) B (s.e.)
Sectors: Clothing & Accessories 0 0
Food & Drinks -0.597*** (0.174) 1.127*** (0.251)
Footwear & Leather g. -0.092 (0.188) -0.911** (0.418)
Health & Personal care 0.208 (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 goods 0.635*** (0.204) 1.147*** (0.308)
Furniture & DIY 0.803*** (0.161) -1.204** (0.481)
Arts & Antiquities 0.829*** (0.209) -0.603 (0.625)
Toys & Sporting goods 0.906*** (0.237) 0.934*** (0.270)
Media goods 1.117*** (0.206) 2.562*** (0.249)
Consumer electronics 1.669*** (0.271) 1.710*** (0.217)
Nagelkerke R square 0.404 0.265
No. cases 2,816 1,582
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)Results (2.2)
Conclusions
Conclusions
• 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
Results
ConclusionsConclusions
End of Presentation