Towards connectionist models of food consumer attitudes

Food &lily

PII:

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Vol. 8, No. 5/6, pp. 429-438, 1997 1997 Elsevicr Science Ltd. All righs rexrved Printed in Great Britain 0950-3293/97 Sl7.00 + .Ga

and PrcJmnce

0

TOWARDSCONNECTIONISTMODELSOF FOODCONSUMERAlllTUDES 0. ThieV & Ph. Robert Demontrondb “E.N.I.T.I.A.A., Department SMAD, Rue de la Girauditre BP 82225,44322 Nantes Cedex 03, France $REREG UMR CNRS C6585, University of Rennes I, 11 Rue Jean Ma& BP 1997, 35019 Rennes, France (Accepted 17 May 1997)

the response from the innumerable sensorial stimuli (olfacto-gustato-sensitive and visual) which is created by

ABSTRACT

the contact with food, depends on molecular and then on cellular phenomena which affect neural processes (cf.

This paper is in keeping with a long term research program in the field of marketing and particularly in the study of consumer behaviour. In continuation with the cognitiuist paradigm, a connectionist approach to attitude modelling is proposed and the theoretical and metatheoretical bases of this research are presented. This orientation consists of studying how the recourse to the neural networks should be constituting an interesting and innovative decision support in this field. This research is particularly based on Beckwith and Lehmann’s (1975) model which is inspired by Fishbein’s (1963) linear and additive representation of the attitude concept and Bass and Talarzyk’s (1972) model. It is demonstrated how a basic automata network can improve knowledge of attitude dynamics by showing daTerent attractors (jxed points and limit cycles). This work opens a new research axis in the food marketing area. 0 1997 Elsevier Science Ltd. All rights reserved Keywords: Connectionism; attitude forming; network; food consumer behaviour.

Nicolaidis, 1992). In the final section, a new research program in the food marketing area is proposed.

CONSUMERATTITUDESTO FOODPRODUCTS In food product buying choices, different categories of variables come into play. According to Claudian (1978), the food behaviour factors which correspond to the attributes that we assign to a product perception come from organic,

psychological,

and social origins.

Accord-

ing to Bodenstedt, these factors take into account the food, the consumer, society’s characteristics and the abiotic environment (Bernard et al., 1993). They also depend on the subject’s situation, on their context and finally, on the presentation of the product (cf. Issanchou and Hossenlopp, 1992). Moreover, every subject has its individual story which comes from learning which is conditioned by personal, family, social and cultural experiences (cf. Rozin, 1988; Fischler, 1990). From this complexity, many attempts at rationalisation of the food behaviour have been conducted; see for instance, Rozin and Fallon ( 198 1) who have studied the different reasons for food acceptance or rejection by the consumers. In the following section, the common models which describe attitude formation in mathematical terms are presented.

automata

INTRODUCTION

The work which is presented in this paper is essentially conceptual. In the first section, after a short presentation of previous research focusing on consumer food attitudes, the different types of attributes in the perception of food products by the consumer are stated. In the second section, the usual models of the attitude concept by using a cognitivist approach are presented. These multiattribute models are often used by marketing managers but are static. For this reason, the third section of this paper proposes a dynamic modelling of attitude formation and it is justified in the fourth section by recourse to a connectionist modelling approach. It has often been demonstrated that

USUAL COGNITIVIST MODELS OFATTITUDES FORMING According to Bechtel and Abrahamsen ( 1993)) “the objective of psychological modelling consists in considering empirical data about the behaviour and in 429

430

D. Thiel, Ph. R. Demonfrond

understanding

how

behaviour

actual practice of psychology then in marketing, attitude investigation

is produced”.

attitudes correspond 1935),

whether

goals

of action

modelling

is one

or

whether

on

non-compensatory-

and Bettman,

and Payne,

and

Einhorn,

1970).

In case

attributes

calculation

tive importance sumer

according

(Dawes and Corrigan,

decision

proposals

process

for that

(Edwards,

1954,

which

1977).

1964;

modes,

the

identity

among

different

satisfaction

of the different

where Psi corresponds

to the subjective

probability

for the chosen

of the consumer

C, is linked

this event.

According

berg’s

(1956)

additive

linear representation

here it is a global evaluation

evaluation

aggregation

of the attitude

Vi the utility

to Edwards

one, Fishbein’s

model

approach

that the

given by

and

Rosen-

also rests on an

of the attitude 1963).

The

by local equation

Ej(cj

.

Cj corresponds

5)

to the probability

has, or has not, the attribute weighting (1972)

of this

model,

attribute.

the global

given by a similar

linear

that option

0

j and Vj is the consumer In

Bass

evaluation equation

independently

process models,

duces other dimensions

for attitudes

the process

factors;

intro-

the

role

of the

determinants;

component;

effect. The last effect is particularly as a cognitive

in his critical

such as: the neces-

of non-reasoned

of the motivational

ciology

and

the

the halo

studied in psychoso-

phenomenon.

Wilkie

(1990)

con-

siders that the halo effect takes place when a consumer overvalues

all the characteristics

of a product

likes (see an example of measurement and

and

Talarzyk’s

of each

option

which

is

compensates

for the local evaluations:

A0 = Ek(Pk. Ak,) where Pk corresponds to an appreciation criteria k weighting of the interviewed consumer; while A, and & give the global evaluation and the local evaluation of the option 0 over k, respectively. Fishbein’s model is certainly the most important model. It can currently be considered as a paradigm

Nemeroff,

misunderstanding Schweder,

1995).

which he

of the halo effect in

At the beginning

of the evaluation

is being influenced

measurement

task (Cooper,

and Sweeney

(1973)

information,

For instance,

to measure

which has a great influence the

cognitive

product

results

in marketing.

halo

effect

proposed

distinguish

the cognitive effect

and quantifies

Beckwith

+

In parallel

attitude

process

with Fishbein’s

representation

(influence

by

to

emotion)

perception).

Their

these two effects on the per-

extension

of his first

integration

of membership

and

level

+

the effects of the emotional

ceptual.

norms

logic.

between

The

as a complete

(perception

(emotion

model shows the relationship

a halo

reaction.

on the individual

effect

a

and then

represents

an emotional

perceptual

( 1975)

and

halo effect

For instance,

can be considered

of the consumer

halo

attributes,

from

Lehmann

social

the emotional

who ‘falls in love’ with a product

which

the

( 199 1) tries to show

of these two kinds of halo effects

proposes a method

effect

use

to reduce the

some other more emo-

tional processes may occur. Aurifeille

consumer

Mazis

this type of halo could be called a

halo, but in marketing,

the importance

of

that the individuals

instead of subjective,

Therefore,

1977;

potential

by some aspect of the

1981).

remarked

of the

task (Tversky,

1980). In each case, the cognitive

the evaluator

from where

the person’s self-

behaviour

attitude

part

A, is:

A, =

experi-

review of these different

overvalues

concept-

which is formed

(Fishbein,

by different

also proposes some extensions of the

(1989),

nature

and

of these models

Bagozzi

ambiguity.

. Vi)

i, and

them

used. For

Shepherd

of his or her attitudes”.

cognitive

of the event

and validate

Shepherd

which “may influence

objective

occurrence

and

Stein

by:

option

are commonly 1995)

present some applications

to food choice mentations.

1975),

research, the halo was due either to the ambiguities of the attributes which are used for the evaluation, or to the

gives the higher

uc= Ci(PSi

In the literature

to their subjec1974).

The evaluation

1970).

of the different

models shows the con-

as the choice

options lJ‘ is calculated

(Dawes,

the

and squares with

or estimation

One of the first compensatory

of a fixed

so that

of compensatory

which are weighted

to the

are all greater

threshold,

process is linearly described

a step-by-step

(1994)

goals; the introduction

criteria

definition,

(1988,

sity to introduce

option in question will be finally admitted evaluation

and Ajzen, Shepherd

according

to evaluation,

appreciation

(Fishbein example,

accepted-respec-

or not, that the prominent

a minimum

Bettman,

processes, three types

disjunctive,

proposal which is submitted than

1974;

to Kuhn’s

theory of reasoned action by considering

1985).

models are traditionally

conjunctive

of

of the process which can be

In the case of non-compensatory of non-linear

evaluating

(according

of food choice, it is shown that this model and its variants

Sparks

to action (Allport,

on compensatory

(Nakanishi

1979; Johnson

necessity,

of the

Fundamentally,

to predispositions

based

to the complexity

low or high

tively,

the and

fields which clearly develops the cognitivist

a priori basis of thinking as a calculation.

according

In

and psycho-sociology,

and

of the reference

groups on the individual perceptions), Beckwith and Lehmann’s (1975) model gives for each product which was tested by the consumers, the following relation:

with BP~ = &Ap + vjB;j + t+ forj

= 1 to n (number

of attributes)

43 1

Towards Connectionist Models of Food Consumer Attitudes This algebraic

where

axiomatic

of mental operations,

current

Ap corresponds to the consumer attitude for the product p and Ai the average attitude of the consumers; Bpj indicates the perception of the product p by the attribute j and Bij the average perception of the

forming,

seems to derive from very old work. Since 165 1,

cognition

(more precisely, the expression at that time was

consumers;

ratiocination) is described

o

the

weights

the weight of the average the attitude

attribute

attitude,

j

of the average

Bpj. Finally,

and

y

/?j the importance

for the product p in the perception

vj the importance perception

of the

perception

uo and uj represent

of

the

to notice

interpretation

model. Beckwith

here

of the

and Lehmann

some results

(1975)

Yalch

attributes.

(1976)

Johansson,

contested

it is the predominant

attributes

explains

which

measured

(1991)

tested

applying

some ran-

by

this model

influence

on the

MacLachlan

and

importance

He observed

that, of the

with the halo

and Lehmann.

on the choice

did not reduce

this

Aurifeille

of toothpastes

that the emotional

the ambiguity

for principal

In

the

formation

next

section,

process

a

modelling

of the

in a time-sequential

attitude

process

is pro-

posed.

addition

of the parts”

(Hobbes,

epistkme’, Bentham

by an optimal

management

and displeasures.

is basically

period,

problem) absence trond,

of the representation process

(Bagozzi,

of dynamic

modes

1989;

idea postulated

aspects is observed

1994; Robert-Demontrond

1991),

an

(Robert-Demon-

and Thiel,

ently combined

Bj = Perception

according

1995).

utility differential

the negative

evaluations.

orientates

uf a

to the attribute j

of

the halo effect

all the

attribute;

by

B*l

B*2

consumers

FIG. 1. Sequential process of consumer attitude forming.

B*3

...

Many show

influences research

of pleasures whatever

it

based on the during

a long

‘reason

books’

In the current

models,

the

or not, can be indifferby a summation-

between

of the internal

composition

to addition,

is very much

experiences that

the positive and

the

in psychology order

of

the final result. For instance, of Asch (1946)

and

processing the

which deals with

process of feelings: in his point of view, the of elementary

but come from a first feeling which highly the judgement.

To illustrate Beckwith

called

do not stem from the addition

evaluations,

the attribute j after

ofthe

were

into a global utility,

the greatest

by a consumer

Ry = Perception

of

finally choose the option which lavished

these aims, the process of consumer for a given product

and Lehmann’s

A * = “Average”

product

arithmetic

of a ‘budget’

which form an offer, commercial

tude forming

1995).

an

the

is that the local utilities of the attributes

gestalt original

of the attitude

Aurifeille,

In the

the active and the passive: the

books

(Robert-Demontrond,

the forming

forming

defines

countable-moreover

the account

the attributes

(the

p. 37).

The usefulness of a choice,

positive and the negative, model

psychosociology

about the theme

1651,

slowly impregnates

is, is then given by the result of a calculation

attitudes literature

we

aims at solving the ethical problem by the ‘deontology’, or calculation of the desire satisfaction

open to criticism.

marketing

“when

a total sum by

which

In fact, the identification

On reviewing

process

series of operations,

calculation:

of these ideas which

law of the local evaluations

A SEQUENTIAL ATTITUDE FORMING PROCESS MODEL

of attitude

as recurrent

subtractions-a

the individuals

function.

representations

we do no other than to conceive

pleasure

disagreements given

their sensitivity

by Beckwith

to 78 students.

halo effect

or

continuity

this analysis and suggested

on the contrary, effect

additions

Bpj, and Bij in the

suggested that the

halo effect seems to have an important ambiguous

cognitivist

scientific

is interesting

about

the

reason,

dom differences. It

in

attitude

of

which is derived

model, is represented

attifrom

in Fig. 1.

432

D. Thiel, Ph. R. Demontrond

The consumer attitude regarding the product which is under their evaluation on time t, is defined by A(t) where t is included in a continuous temporal space T which is here discrete, so that Card (T discretized) is equal to n (i.e. the number of attributes of the product). The final consumer attitude (at the end of the evaluation process, i.e. when t is equal to the upper bound of iT) is then given by A(n). In these conditions, the modelling permits the elaboration of the process of A(n) according to different sequences of the attribute process, chosen arbitrarily. Practically, the principle of the modelling consists of a sequentialisation of the attitude forming process by a successive integration of the attributes. The calculation starts then by a first attribute which provides B1, i.e. a local evaluation; then A( 1) by considering the ‘norm’ A’ which corresponds to the ‘average’ consumer opinion for the considered product. The initial value of the attribute 1 given by the consumer is then modified into g1 by a halo effect which is coming from his own attitude A( 1) and from taking consideration of the ‘average’ evaluation B; by the other consumers. The temporal process is then continued by the forming of the attitude A(2) by integration of the second attribute, and so on.... The corresponding algorithm of the dynamic process modelling is the following: (a) Initialisations

of the variables:

n = number of product For j = 1 to n:

attributes

Initial&e: BJ (average perception of the attribute j) Oj (weight given to the attribute j) fij (weight of the attitude A in the halo effect) yj (weight of the ‘average’ perception of the attribute j in the halo effect) End j A* = average attitude of all the consumers y = ‘average’ attitude influence of the individual consumer attitude Calculate: Al = 01 B1 + y A* Calculate: B’, = filAl + y1 B*,

(b) Iterative

Example

1

This example illustrates a non-linear modelling of compensatory processes of attitude forming. A product like a packaged ready-made meal is chosen. Three attributes are chosen by the consumer, for instance (1) the external appearance, (2) the price and (3) the brand of the product (n = 3) *. Different numerical values are given to the levels : (1) of the weights of each attribute; (2) of the local evaluations of the product according to their different attributes; and (3) starting with an initial attitude equal to 0. Six possibilities of sequential attitude forming were then simulated as shown in Fig. 2 (the vertical axis represents the value of the attitude which is standardised by individual according to all the tested products. The attitude is negative in the case of a product buying refusal). The experimental hypotheses are given by Tables 1 and 2. It is possible to observe that the attitude amplitude changes during the sequential forming process and the final values are different in the six cases. The abscissa indicates here the time evolution, i.e. gives the moment (time interval) when each attribute is taken into account to the end of the global evaluation of the product. In our example, n is equal to 3, each evaluation sequence is described during three periods which correspond to the integration of each attribute. As shown, according to the order in which the consumer considers the attributes of a given product, his attitude progressively changes along the time, ending up in very different appreciations (notice particularly the opposition between the sequences S3 and S4 and the sequences S2 and S5; the sequences Sl and S6 are neutral). The non-linear calculations which permit avoidance of the algebraic hypothesis of a composition law of mental representations with commutative and associative proprieties, give then the possibility of a dialectical surpassing the cognitivist and gestalt approaches.

TOWARDSCONNECTIONIST MODELS

loops:

A connectionist DO for t = 2 to n by step 1 S = 0 (initialising the sum) DO for j = 1 to (t-l) S = S + Wj B’j End j At=S+otB,+yA* gj = sj A, + vj B*j

modelling

approach

These previous models are very illustrative of the cognitivist movement: there is often no algebraic difference (syntactic) between the additive models-only semantic differences. In other words, the modelling often seems purely “scholastic”

(Albou,

1980, p. 832): approximately,

END t

the formulations are the same, but the interpretations do not vary more than in a “subtle way” (Dussart, 1983,

The attitude forming is described here as a sequential process. In the following example, the consequences of changing the sequence order of the different criteria during this alternative process, is studied.

*See the work of Cabanac (1992) who demonstrates by experiences that in the strategic food choices which imply a cost, the subjects adjusted quantitatively their behaviour by an algebraic addition of the financial cost and the gustative pleasure.

Towards Connection& Models of Food Consumer Attitudes

FIG. 2. Evolutions of the attitude. TABLE 1. Rank of Each Attribute in the Six Sequences Sl to S6 of Attitude Calculation Sl Attribute 1 Attribute 2 Attribute 3

S2

S3

S4

S5

S6

1

1

2

2 3

3 2

1

2 3 1

3 1 2

3 2

3

1

TABLE 2. Numerical hypotheses 01=1;

y=-1;

ws=l;

/3*=-l;

y,=)$=)+=p,=/.?3=1.

p. 232). Finally, the empirical tests are not very determining: for example, for some models presented previously, experience sometimes gives an advantage for Fishbein’s model over Bass and Talarzyk’s model (Bettman et al., 1975), or sometimes an advantage to Bass and Talarzyk compared to Fishbein’s model. In these conditions, if it is still possible to propose some new developments in this area, it should be possible by the opening of a paradigmatic break. In concrete terms, it seems that just one aspect has to be considered at present in the definition of the attitude concept which was initially proposed by Allport-the definition “princeps” according to the expression of Thomas and Alaphilippe (1983, p. 32) is the foundation of all the different works in this field. The one aspect is that Allport does not restrictively specify the attitude as a mental frame of mind but also characterises it as a neurologic frame of mind. This work begins as early as the origin, a perspective of study of the attitude forming process by the body, and not only by the mind. The reason for the present omission of this approach can be explained by the prevalence of cognitivism in psychology and psychosociology. There is a new movement of ideas resolutely in competition with cognitivism and its compute-symbolic representations: an explosion of works which considers the relations between the body and the mind (Richelle, 1993; Varela et al., 1993; Feltz and Lambert, 1994; Delacour, 1995); a movement of ideas called connectionism (Feldman and Ballard, 1982), which seems to be, according to Schneider (1987)) a paradigmatic revolution within psychology. By using the computer as support for theoretical working representations of the mind, it corresponds, in practice, to the equivalence of the functions

433

between the model and the reality-an analogic equivalence which a priori does not require to consider the structural characteristic of this reality. In concrete terms, this implies a total indifference to the cortical matter. An alternative approach, within cognitivism, tries to naturalise the mind, rejects the idea of the dissociation of the psychology and the neurobiology, and tries to incarnate the spirit in the cerebral tissue (McCulloch and Pitts, 1943). Coming from this “paleo-connectionism” (Varela, 1989) which is still very tinged with themes and problems of the calculation metaphor and of a logical representation of thinking, the connectionism (or neoconnectionism) corresponds to a neurocalculation, on a “neurologic authenticity” (Van Gelder, 1991) of modelling thinking, according to the idea that it is absolutely necessary to consider the particular ways that the cognition is instanced and brings to psychology deep theoretical disruptions. The researcher is motivated to elaborate connectionist models of neuronal type, to reduce the cognitive theories to a level which will be relevant to the neurosciences. “It is undeniable that the analysis level which is adopted by the PTC (proper treatment of connectionism) is lower than those of the traditional symbolic paradigm; but, currently, the level of PTC has more explicit links with the symbolic paradigm as with the neuronal level” (Smolensky, 1988). The common criticism of the cognitivist psychologists against the connectionist models is to say that it is not the network architecture which does the essential work but the way in which the information is coded in the different layers. It is right that in most cases of the connectionist practice, the coding schemes correspond to adaptations of the analysis of the symbolic tradition researchers. In the cognitive sciences area, it is usually assumed that the connectionist models have a neural inspiration and that the units have neuron-like forms. Many researchers who used these terms recognise that their models are not real neural networks. Nevertheless, some surprising similarities exist. For example, the connectionist units and the neurones are elementary computing units which combine inputs coming from certain units and send outputs toward other units. In the connectionist networks and in the neurologic systems, it is the connectivity configuration which seems to be the essential determinant of the behaviour (see the complete analysis of the different theses concerning this subject in Nadel et al. (1989)). Finally, to moderate our position about current connectionist attitude modelling, it is worth citing Clark (1991) who shows that these models are incapable of handling certain sorts of psychological phenomena because they do not encode explicit rules. Nevertheless, the next section tries to show the interesting possibilities of this new approach.

Proposition of a connectionist I n practice, a neural sequential step-by-step

model

network does not evolve by calculations, but its trajectory

434

D. Thiel, Ph. R. Demontrond

corresponds to a dynamic system, although the equations are often discretized for the simulations. To progress from cognitivism to connectionism, we change from an interpretation of the cognition facts and phenomena which initially concern the logic (of the discontinuum), to an interpretation concerning the dynamic and the topologic (of the continuum). We present an automata network (cf. McCulloch and Pitts, 1943) of Beckwith and Lehmann’s model which can be represented by a connection graph. The particularities of an automata network is that it may be defined, in a general way, as a (large) set of cells (finite automata), locally interconnected, which can evolve at discrete time steps through mutual interactions. Formally, an automata network can be described as a mapping F from S” into itself, where S is a finite space (state space). The network is then made up of n interconnected cells. The connection structure is defined by F: cell i receives a connection from j if Fi depends on the jth variable (where F; is the ith component of mapping F). A state of the network is a vector x in S”. A dynamic network is then defined through a rule that transforms any vector x in s” on the network into a vectory in S”. For example, the parallel iteration rule is defined as: y = F(x) and can be interpreted as follows: at each time step, each automaton computes its next state by using its mapping Fi on the current state x. As the state space S is usually finite, all trajectories will be periodic, limit cycles or fixed points. In our research, Beckwith and Lehmann’s model is represented by a network where the automata correspond to the different variables A, A*, Bj and Bj. The connections between the different automata have the following weights: wj, fij and Y. This network was constructed according to the causality relationships between (Bj, A’) + A and (A, BJ) -+ Bj (see also similar work in Thiel, 1995a,6). Figure 3 shows the principle of the structure of this automata (or neural) network. To demonstrate the didactic and heuristic interest of this model, a simple example is presented. At a later stage, this type of model will be scientifically validated by comparison with real situations. In relation with this network modelling approach, Grunert ( 1996) proposes a network of the purchase-relevant cognitive structures with links between four aspects which are product types,

product uses, product alternatives and product attributes. In our opinion, the associations between elements could also be modelled by a connectionist network. Another point which could be criticised in our model, is the lack of the prior knowledge. For instance, in a monadic test (sequential), O’Mahony and Goldstein (1986) have shown some phenomena due to the adaptation and the memory. An alternative of this absence of prior knowledge could be, for example, to complete Beckwith and Lehmann’s model by particular automata which represent the ‘memory’ of the different attributes perceptions and also the prior attitude toward the product. Another advantage of a neural network model is that it is able to represent the memory which is accessible by its contents.

Example 2 By taking the case of an attribute state reduced to only three elements, the different vertices of the graph are transformed into automata and the arrows into connections between the automata (as shown in Fig. 3). The valuations of these arrows correspond to the connection weights of a scale which range through -3 to +3 (semantic differentiator going from ‘a little influence’ to ‘very influential’: -3, -2, -1, t-1, +2, +3). These values correspond to the values on the graph arrows (see Fig. 4).

(a) Model variables + nl, np and ng correspond to the product attribute values of appearance, price level and brand given by the consumers, respectively indexed 1, 2 and 3. The values used were 0 to 3, depending on whether the attribute valuation was reject (0); or neutral (1); or moderately attractive (2); or finally very attractive (3). + ns corresponds to the consumer attitude which emerges from the network dynamics. Three situations are formulated: the value 0 indicates no intention of purchasing a product through rejection; the value 1 indicates no intention of purchasing a product by indifference or by hesitation; the value 2 indicates the intention of buying the product.

Average Affifude

“’ \’

A’ Average Attitude

\y

1

*z/jf$2Bn<; Atrribure I

B2 Amibule

Bl

B'll

2

k y2

FIG.

3. Equivalent

Lehmann’s

model.

automata

network

n4

B-2

of

u

APPEARANCE

nI

-A+;HcE‘“6 1

‘I n4, n5. n6 = avemge

Beckwith

values

for the three dlflerent ottnbures

and

FlG.4.

Example

of an automata

network

implementation.

Towards Connection& Models of Food Consumer Attitudes

(b) Model constants +

n.+, n5 and TV correspond to the ‘average’ attribute appreciations which can be either unfavourable (0), or favourable (1). In our example, the retained values are all equal to 1, which coincide with the attributes which were considered attractive by the consumers. + n7 corresponds to the consumer ‘average’ attitude towards the product, which has the values 0 (for a rejection attitude) or 1 (for a positive attitude). The value 1 is arbitrarily retained for the simulation. The automata

activation

If (ns+ns-l)

> 0

If (ns--n*+

1) > 0

If (ns+ns-1) ?4=n5=7i6=

rules and threshold

functions

> 0

are: else nt = 0

then nt =ns+ns-I then ns = ns -na + 1

else n2 = 0

then ns=ns+ns-1

else ns = 0

1

here considered as moderately attractive while the price is considered at a reject compensation and considering other (the ‘social norms’), the final attitude product purchasing intention (ns = 2).

Neurons

(0 0) (3) (4) (5) (6) (7) (8) 020

11110

+ Fixed point attractor: (1) (2) (3) (4) (5) (6) (7) (8) 11111111

> 0 th en ns=n7+nt-nq+ns-1

States

In case the appearance and the brand perception turn out to be unimportant for the consumer and attribute 2 (np, the price) is moderately attractive, the final attitude corresponds to an absence of intention to purchase the product-by refusing (n8 = 0).

Neurons

States

else ns=O

This network has enabled explanation of some dynamic proprieties of the attitude forming process-especially stationary (fixed points) and periodic (limit cycles) attractors (the software MUMPS was loaned by Professor Atlan of the Hebraic University of Jerusalem).

Analysis of the observed attractors Our automata network is a deterministic system. Therefore, if the network goes a second time on a previous configuration, the sequence of the different states remains the same as after the first one and the system describes indefinitely a loop in the state space. These different loops correspond to the attractors of the dynamic system and the moving time of the loop, on the attractor period. If this period is equal to one, then a fixed point can be observed. The limit cycles can also be observed if this period is greater than 1. The whole of the configurations which evolve toward a given attractor correspond to an attractor basin. The simulations results of our previous model are described in the following section.

Preliminary

by the consumer, level (q = 0). By decision criteria corresponds to a

+ Fixed point attractor:

n, = 1 IIf (n7+nt-ng+ns-1)

435

remark

According to our hypotheses, the states of the neurones (automata) n4, n5, n6 and n7 are invariant. The consequences are first, to consider that the consumers find on average the different product attributes attractive (n4, n5, ng = 1) and second, to consider that the average consumer attitude faced with the product is positive (n7 = 1).

Here the consumer is not particularly attracted towards the different product attributes which are individually considered (neutrality of the states nl , n2 and n3 equal to 1). Contrarily, the fact that the other consumers consider the different attributes as attractive does not change the individual appreciation: finally, it shows an absence of purchase intention without the intention to refuse the product (ng = 1). 3 Limit cycle attractor

length 2:

Neurons

(1) (2) (31 (4) (5) (6) (7) (8)

In this circumstance, the appearance of a long term stable behaviour which oscillates between the refusing and the indifference attitudes (or neutral), is observed. After a certain time of the evaluation process, the consumer goes towards a neutral personal appreciation situation of the different product attributes (nl = n:, = n3 = 1) which is in contradiction with the right and contextual evaluation. While the social norm is positive (n7 = 1), the consumer intention is first negative (absence of purchasing intention by refusing: n8 = 0). The achieved state is then unstable: the consumer goes over from a neutral appreciation situation to a reconsidering of the local evaluation (n1 = 0; n2 = 2; n3 = 0 corresponding to a moderate evaluation on price but a reject on brand and appearance). This situation is also unstable (dissatisfaction) in that the trajectory of the neurocalculating does not end in a fixed point (a firm, resolute attitude) but in a limit cycle (an oscillating and undecided attitude).

3 Fixed point attractor: Neurons

(I) (2) (3) (4) W (6) r/ L202

11112

j Limit cycle attractor

(8/

1 States

The attributes 1 and 3 (nl and n3, respectively, appearance and the brand perception of the product)

length 2:

Neurons

the are

(0 (2) (3) (4) (-9 (6) (7) (8) States

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D. Thiel, Ph. R. Demontrond

In this case, during the product evaluation process, a positive appreciation situation of two attributes but without purchase intention (n8 = l), is shown. In other terms, it seems like the application of a disjunctive rule which refuses the product because one of the evaluation criteria-here, number 2-is negatively perceived. This state is not the final one: on the next stage the consumer moves towards a positive attitude (ns ~2) which comes, simultaneously, from a depreciation of criteria 1 and 3 (ni = n3 = 1) and from a new appreciation of criterion 2 (nq = 1). This operation corresponds to the affective halo effect where the global attitude (ns) constrains the local evaluations (nl, n2 and ns)-with a review of these last ones when their aggregation gives as an (intermediary) result an evaluation which is opposite to the aimed impression. The state n8 (equals to 2) plays here the role of a final cause in the evaluation process. The situation is here unstable and the consumer oscillates between a neutral and a positive attitude in purchasing intention. Of course, the interpretation of the observed dynamics is not exclusive to other possibilities. Here are the didactic and heuristic interests of our automata simulation approach.

CONCLUSIONS Mindful of the potential reactions of some readers of his paper, Smith (1996) shows the possibilities of connectionist models to make predictions for social psychological variables such as beliefs, attitudes, social judgements and behaviours. Nevertheless, it is also important to recognise that “the advent of connectionist models would not be a revolution so much as a continuation of an ongoing transition from static to dynamic conceptions of mind” (cf. Kruglansfi in Smith, 1996, p. 908).

Proposition of a new research program in the food marketing area The first difficulty in putting these research results into practice will consist of rationally explaining which attributes are perceived important by the consumer for a given product and to quantify their respective weights. Many works such as those of Booth (1981), Wooley (1976) and Kostler (1981), have suggested observing the consumer behaviour in natural situations instead of making tests in the laboratory, but many studies have demonstrated that this method is difficult to implement. For the last 10 years, a lot of work has focused on the study of food choice and on the factors which contribute to its determinism. It is also usually assumed that the number of beliefs or attributes consumers use when forming an evaluation or attitude is limited (see for instance Grtinert, 1996). The problem is then to be able to define and to measure these attributes and weights which are taken into consideration during food choice. Different works concerning the measurement of food

preferences have been recently presented; these papers are not developed here (see for instance, MacFie and Thomson, 1994 or AIR-CAT project, 1995, 1996). In spite of the poorness of current rational knowledge of the human attitudes, it is nevertheless possible to demonstrate that the neuromimetic models are very robust. Scientific demonstrations have shown that in some cases, even after damage to neural networks in the human brain, the behaviour does not change (see Hopfield, 1982; Weisbuch, 1985). It is on this basis that we conclude that even incomplete connectionist models may accurately model behaviour. In operational terms, the marketing manager may gauge the different attribute weights by changing some elements of the mix with the objective of bringing the consumers nearer to the important attractors.

The theoretical and educational interests of this research Whereas the cognitivist models systematically give a stable attitude, the connectionist models furnish the possibility to take into account the periodic solution cases. These cases are not purely idealist or conceptual, but are empirically observed (by observation and by experimentation) and can be linked to Lewin’s original topologic research (1975) which deals with psychological conflict situations which depend on the equality of a ‘goal gradient’ in peoples ways of life. By integration of these data, the connectionist models give the opportunity of a dialectical that surpasses those ofcognitivism and gestalt psychology. The educational interest of the connectionist simulation lies in the diversity of the possible interpretations of an observed trajectory: thus, for the last example, an explanation of the existence of a second-order limit cycle can consist of a contradiction between the personal local evaluations and the social evaluations: these act as standards and, consequently, bring the consumer to a local and global change of appreciations (of the attributes and the product), thus explaining the loop. Finally, the first objective of our connectionist modelling was to improve the knowledge of the food choice mechanisms and particularly of the stability notion during the attitudes forming and transforming. Therefore our proposal is firstly conceptual.

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