e-Resilience of British retail centres Dr L.Dolega & Dr A. Singleton Email: [email protected] www.cdrc.ac.uk 

Content  Research problem  Forces impacting town centre performance  E-resilience project  Concept  Methods  Outputs  Next steps 

 The vulnerability of UK retail centres to online sales  Extent to which retail centres are exposed to populations with variable engagement in online retail  Impact of online sales on town centres performance and their evolving function  Other forces shaping town centres configuration A Research problem 

Forces impacting town centre performance 

Competition from out-of-centre large retail developments  ‘Free for all’ approach  ‘Town centre first’ policies  Adaptive response of major retailers 

Shock of the economic crisis  Impact on town centres structure  Increase in vacant outlets  Comparison retail impacted most  Leisure services most resilient  Factors enhancing resilience of town centres  North-south divide  Centre size  High proportion of services  Retail diversity  Corporate foodstore entry/presence 

Changing demographics and consumer culture  Ageing society  Decreasing household size  Increased demand for ‘value for money’  Progressive rise of convenience culture  Impact of convenience culture on UK high streets  Rapid growth of convenience stores (all types of retailers)  Potential adverse impact on small specialist retailers 

Rapid growth of online sales  Online sales reached 12% of total sales in the UK  Amazon - 8th biggest retailer in the UK  Transformation of major retailers into ‘bricks & clicks’  Impact on the traditional high street 

The e-resilience project 

 A better understanding of the drivers of retail centres performance and future challenges  Of which use of the Internet engagement is widely attributed  Estimation of catchment areas for town centres  Capturing multiple dimensions of town centre attractiveness A Business Need 

E-resilience concept  E-resilience measures the vulnerability of British retail centres to the impacts of growing online sales  More specifically e-resilience is about the balance between retail supply at local level and the exposure of particular centres to online shopping 

Dimensions of e-resilience  Connectivity - available infrastructure to get online  Behaviour - propensity to use internet for shopping  Demographics (ethnicity, age, gender, disability)  Retail supply - attractiveness, accessibility & convenience 

Research methodology  Defining retail centres and delineating their primary/secondary catchment areas  Estimating consumers engagement with information and communication technologies at small area level  Creating a framework for measuring e-resilience  Conducting sensitivity analyses on retail centre catchments and their e-resilience 

Estimating town centre catchments  Catchment area estimation techniques  Simple methods – buffers, drive distance/time  Spatial interaction approach – gravity and probabilistic models  Major components of the model  Attractiveness  Distance/decay parameter  Study area  Catchment models for regional/national scale 

The Huff probability model 

Retail catchments English Regions Primary catchment Secondary catchment ´ Catchments for: 1. England 2. North West 3. Southampton 1 2 3 

Town centres classification  Multidimensional measure of town centres attractiveness  15 supply-related factors & 1300 observations  Six distinctive clusters  Major shopping and leisure hubs  Second tier (clone) town centres  Specialist market towns  Neighbourhood centres  Shopping parades 714 167 1191 652 811 168 649 116 962 326 210 780 670 128 900 587 883 586 303 1243 601 1143 353 345 1073 804 1061 735 152 1004 946 858 248 998 243 217 810 1270 81 1241 1226 400 366 887 269 831 211 1233 1052 909 628 209 1123 1258 976 645 408 708 241 379 284 220 666 332 92 1012 939 509 213 1230 578 1122 1082 197 1262 665 776 607 541 844 222 785 73 1074 1065 1166 195 1162 1041 1220 124 1268 119 115 694 493 148 1202 643 478 1112 1000 458 72 778 554 1025 621 120 1015 516 250 471 686 60 947 715 605 1263 926 191 433 75 235 52 1261 779 923 472 703 46 1149 1036 482 786 138 1038 821 511 25 363 347 362 334 1184 915 736 8 1032 837 492 438 1235 465 870 341 1217 874 1195 1210 94 992 925 136 921 127 589 594 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244 423 1126 945 302 41 1069 731 1100 1267 614 1200 467 999 16 890 328 1117 1185 424 485 4 1011 881 949 367 336 763 1070 70 1137 549 533 108 564 78 529 473 280 464 251 1098 864 937 969 801 758 756 1042 585 1024 484 2 508 495 617 950 368 959 221 273 1204 77 823 437 1188 1167 619 354 781 632 476 704 569 1110 286 903 466 256 264 682 1078 474 285 523 1088 182 174 386 175 631 526 755 74 725 983 339 1245 229 351 1063 125 86 1240 817 917 696 1250 1142 706 777 626 677 442 1006 352 315 990 265 1111 765 638 678 325 216 840 184 50 295 146 219 627 1 -1396.84 -897.89 -398.95 100.00 Observations Similarity Dendrogram Ward Linkage, Euclidean Distance 

Internet Use & Engagement patterns 

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Next steps  Validation of catchment models with customers’ insight data  Estimation of ‘e-resilience’ patterns for the UK  Providing the characteristics of ‘e-resilient catchments  Updating town centres boundaries 

Any questions?