Business and Economics

How Local Is Local Competition? An Analysis of Spatial Competition in the Retail Market

Ari PramonoHarmen Oppewal

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Analyse the competition,

where ..... • The demand is distributed over space

and ....• The consumer is moving across the space

and therefore…• The patronage is restricted by the consumer’s

spatio-temporal constraints

Objectives

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Research Questions.....

1. What is the actual role of spatial structure (i.e., clustering and spatial differentiation) in determining the spatial substitutability of alternatives?

2. What about the role of the non-spatial attributes?

Brand NFRThe format….

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Research Questions (cont’d)

3. How do consumers’ different decision process modes affect the spatial substitutability of alternatives?

PLANNED (CHOICE) MODE

• coordinated with regular activity• patronage is based on past information, experimentation and habitation

• Compensatory decision on a predetermined choice set

Ad-HOC (SEARCH) MODE

• Spontaneous decision• Sequential exposure of alternatives

(Kitamura and Sperling, 1987)

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Conceptual Model

Spatial Competition

Distance

Contiguity

Non spatial differentation

Spatio temporal Limitation(Peak hours – Off Peak)

Behavior modes(Choice – Search)

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Modelling Steps

Modelling Individual

‘on-the fly’ Choice

OD-Demand Modelling

Perceived Substitutability modeling

Competition Modelling

𝑸𝒌= 𝑴𝒐𝒅.𝑷ሺ𝒌ȁ�𝒐,𝒅ሻ𝒎𝒅=𝟏

𝒏𝒐=𝟏 𝑴𝒐𝒅=𝑻𝒐.𝑨.𝑾𝒅𝜷.𝒆−∅.𝒅𝒊𝒔𝒕𝒐𝒅

𝐶𝑖𝑗 = σ σ 𝑴𝒐,𝒅.𝑪𝑺ሺ𝒊,𝒋ȁ�𝒐,𝒅ሻ𝒏𝒅=𝟏𝒏𝒐=𝟏 σ σ 𝑴𝒐,𝒅𝒏𝒐=𝟏𝒏𝒅=𝟏

Cij = α + β1.distanceij+β2.contiguityij+β3.Quality differenceij

CS(i,j)=1 if and j are most likely to be considered simultaneously, and 0 if not

MODELLING THE COMPETITION OF GAS STATION

Study Example

28th February 2011Presentation title 7

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Survey Method : Interception of 287 motorist at sampled gas stations Data : Revealed Choice Stated Choice (Hypothetical situation ) Trip data (O-D, Tank Level)

Study Area : City of Bandung, Indonesia

MNL Planned Choice Search

Coeff t-stat Coeff t-stat OTF t-stat

Cons 1.447837 5.73 1.536715 5.98 -0.1018341 -0.2

cons2 -12.72077 -0.02 -15.99286 -0.02 -1.544937 -0.6

Detour -0.0006438 -9.24 -0.0005171 -6.14 -0.0002847 -4.84

Quality 0.0622467 1.82 0.2163645 3.59 0.001141 0.04

log (dominance) -0.7883585 -5.03 -0.7952468 -5.07 -1.202707 -4.61

detour x off peak -0.0003127 -2.25 -0.0002737 -2.01 -0.0001101 -1.17

quality x off peak -0.0362741 -0.66 -0.0110604 -0.2 0.0186984 2.03

dominance x off

peak

0.1240502 0.49 0.12658 0.5 -0.1208099 -0.29

detour x low tank 0.0001003 0.92 0.0000681 0.66 -0.0000653 -0.59

quality x low tank 0.0161144 0.3 0.0009624 0.2 -0.023841 -3.54

dominance x low

tank

0.3653381 1.52 0.3698403 1.53 0.650738 1.44

threshold (𝜃) 18.08971 3.27 1.076813 1.82 𝜃 x quality -0.1988119 -3.19 -0.0045611 -0.69

FOS 1.092104 3.82

FOS x tank 0.2120251 0.44

FOS x off peak -0.885995 -2.03

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Importance of Station Quality only in two -stage

Location is more important in search

In off-peak hours motorists are less concerned about location

In off-peak hours Quality becomes slightly more important in search mode

But if the tank is low ... they don’t care about quality

Evidence of Feasibility Set

Motorists stick to their “mental map”(especially in peak hours)

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Step-1: On-the-fly Choice Modelling

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Step-2 : OD-Demand Modeling

Sales Map Demand at origin Map

• The sales is more evenly distributed than the demand• The demand from the high concentrated areas are not realized locally, but

in the outlet closer to the central city.• The gas station sales is more affected by commuter rather than local

market

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Step-3 : Perceived Subsitutatibility Model (sample result)

Competing stations (Planned Choice)Competing stations (Ad Hoc Search)

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Choice Search

Peak Off Peak Peak Off Peak

constant -0.10856a -0.04139a 0.01962 0.01471

-5.60 -4.72 1.15 1.15

distance (km) -0.00021 -0.00011 -0.00030b -0.00023b

(dij) -1.16 -1.31 -1.85 -1.85

Contiguity -0.00006 0.00003 -0.00001 -0.00001

(adjij) -0.09 0.09 -0.01 -0.01

Mean Quality

Index

0.00139a 0.00053a -0.00011 -0.00009

(Qij) 6.20 5.26 -0.58 -0.58

Step-4 : Spatial Competition Modelling

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Conclusions Substitutability among spatially distributed firms in on-the fly situations

is spatio-temporally dynamic and heavily affected by the consumers’ behavioural modes.

The spatial configuration of the competing firms plays different roles in determining the firm’s substitutability (or competition) depending on the behavioural modes of the consumers.

In Choice mode, the firms’ spatial substitutability patterns are less contingent on the spatial structure (proximity and contiguity) and more depending on the non-spatial attractiveness.

In the situation where consumers are more likely to conduct Ad-hoc Search behaviour, the firm’s spatial substitutability patterns will be more affected by the spatial structure. Therefore the highest competition will come from the local (closest) rivals.