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