1
Do taste and quality perception influence
2
consumer preferences for wood?
3
An econometric model with latent variables
4
Sandrine Costa, Serge Garcia, Liette Ibanez, LEF
5
November 2009
6
Abstract:
7
The aim of our article is to understand consumer preferences for wood and competitive ma-
8
terials. Assuming that a wood product is composed of various attributes, we consider that
9
consumers’ choices are not guided by observed characteristics but, instead, by the quality per-
10
ception consumers have of these attributes. An econometric model integrating this factor by
11
means of latent variables allows us to analyze the influence of individual characteristics and in-
12
formation (external sources and/or experience) on product quality perception. We also include
13
consumers’ attitudes in the model by supposing that the importance consumers place on at-
14
tributes accurately describes their tastes. Data on material choices (wood vs. vinyl) for window
15
frames validate our assumptions.
16
Keywords: quality perception, information, taste, latent variable model, wood.
17
JEL Classification: C25, D12, L15
1
18
1
Introduction
19
Benefits derived from wood is a key factor in the competition between wood and non-wood
20
products. However, these benefits are not necessarily directly observable by consumers when
21
they decide to purchase a product. Indeed consumers might possess different knowledge and
22
possess different information sources. As a consequence their perception of benefits derived from
23
the product varies. The aim of our article is to understand in what extent demand for wood
24
products is determined by perception, experience and information on wood characteristics. To do
25
so, we build an econometric model of purchase decision that captures the impact of information
26
acquisition on consumers preferences.
27
A consumer’s decision to purchase an article depends on his/her product preferences. Gen-
28
erally, a product is considered to be composed of several attributes (Lancaster, 1966). In Lan-
29
caster’s approach, consumers attach importance to attributes rather than the product itself.
30
However, the quality level of each product attribute is supposed to be identical across all con-
31
sumers, as well as known by all consumers. Lancaster argues that the utility derived from a
32
product is equal to the sum of utilities derived from all the product’s attributes.
33
Our analysis is inspired by McFadden (1986) who integrated psychological factors into con-
34
sumer choice decisions. We consider that consumer preferences for a product are determined by
35
two “non usual” factors. First, we assume that consumers can be differentiated by the impor-
36
tance they place on product attributes in their purchase decision. We refer to these observed
37
variables as attribute weights or tastes. Tastes reflect individual needs and values, and explain
38
consumer heterogeneity. Second, the quality level of each attribute is an endogenous and non-
39
observed variable. We assume that each consumer forms a quality perception for each attribute
40
and might judge the quality level of attributes differently for differentiated products. As noted
2
41
by Ben-Akiva et al. (2002, p.448), “Perceptions are the individual’s beliefs or estimates of the
42
levels of attributes of the alternatives”. Along this article, we will speak of quality perception
43
as the estimated quality level by consumers on product attributes.
44
A consumer’s quality perception is highly influenced by the information he or she possesses,
45
and the way information influences the quality perception depends on the type of product
46
attribute considered. For experience attributes (Nelson, 1974), previous purchase and/or con-
47
sumption might be enough to evaluate the real level of quality whereas for credence attributes
48
other types of information could be more successful. In reality, consumers can access various
49
sources of information, such as advertising, information disclosure by consumers groups, direct
50
contact with professionals, or product purchase.
51
An important literature focuses on consumer preferences and competition between wood and
52
non-wood products (or species) with an application to structural framing materials (Eastin et
53
al., 2001; Garth et al., 2004; Shook et al., 2007), furniture markets (Forbes et al., 2001a, 2001b;
54
Wu and Vlosky, 2000; Olah et al., 2003), and floor covering (Jonsson, 2005, 2006). These studies
55
base their approach on the Lancaster’s model of product attributes. Among these studies, Shook
56
et al. (2007) use discrete choice modeling to analyze the substitution between North-American
57
softwood timber species, in the context of trade disputes between the U.S. and Canada. Jonsson
58
(2006) uses Customer Satisfaction Modeling and latent variables to assess customer needs in the
59
context of floor covering. However, both papers do not take into account that consumers have
60
biased knowledge and information on product characteristics.
61
Environmental considerations become more and more important in public policy making. For
62
example, in France the government aims to increase the wood share in the French construction
63
sector by 25 percent (in value terms). Wood use in production has a certain global advantage
3
64
as it reduces environmental damages (renewal resource, carbon dioxide sequestration, etc.) in
65
comparison to other materials such as plastic or steal. So far, various policies to encourage the
66
use of wood have been implemented, such as mandatory increase in the share of wood-material
67
in public procurements, architect training programs to use wood, consumer information via TV
68
commercials and advertisements in newspapers. The aim of our work is to set up a framework to
69
analyze the purchase decision with respect to knowledge, experience and information on product
70
characteristics. From a policy point of view the framework could help to implement targeted
71
information policies or educational programs.
72
Our data concern hypothetical purchase choices for specific materials (vinyl and wood) in
73
the French window market. We assume that consumers do not necessarily place the same
74
values on these window materials. A window is composed of various attributes, such as acoustic
75
and thermal insulation, product life, and environmental characteristics. Since most window
76
attributes are not observable before purchase, consumers possess imperfect information and
77
assess the quality of materials based on their information sources and experience. The assessment
78
of the quality of each attribute depends on the material, and thus influences the global quality
79
perception for each material. These perceived qualities are introduced into the model as latent
80
variables. Our methodology is general and can be applied to any situation with discrete choice
81
behavior where products are characterized by several attributes for which quality is not directly
82
observable and where indicators exist, that is, observable variables related to information and
83
product experience that might determine quality perception.
84
This paper is organized as follows. First, we present the assumptions of our model for
85
latent variables (quality perceptions) and discuss the modifications made with respect to the
86
existing literature. Second, the econometric methodology is described. A two-step estimation
4
87
procedure is adopted: we first estimate the latent variable model, and then the predicted latent
88
variables are introduced into a discrete choice model. Third, data on material choices in the
89
French window market are used to show that endogenous quality levels of product attributes
90
provide a meaningful explanation of the choice decision of consumers. Finally, we conclude
91
that consumers have different quality perceptions of materials depending on the information
92
they possess on product attributes and we discuss the importance of information campaigns to
93
modify purchase decisions.
94
2
95
The purpose of this paper is to construct an econometric model that explains consumers’ pur-
96
chase decisions by simultaneously integrating perceived product quality (latent variables) and
97
tastes (observable variables). This methodology allows to better understand consumer prefer-
98
ences for quality supposing that individual beliefs (on quality) are based on private information
99
and experience.
Attributes and quality perception in product choice
100
In the economic literature on transportation, latent preferences have been introduced into
101
mode choice models (Ben-Akiva et al., 2002). For instance, Johansson et al. (2006) argue
102
that people have different attitudes towards environmental considerations, safety, comfort, con-
103
venience and flexibility, and that this has an important impact on the consumer’s choice of
104
transportation. The introduction of attitudinal and behavioral indicator variables makes it
105
possible to construct a more precise choice model. Marketing studies attempt to establish the
106
link between perceived value and consumer behavior. Indeed, the perception of product quality
107
depends, in part, on the messages and information transmitted to consumers. Depending on
108
the amount and quality of information, consumers might react differently in terms of consump5
109
tion behavior. Swait and Sweeney (2000) showed that the utility of buying a specific product
110
is conditional on the type of consumer. They proposed segmenting consumer behavior into
111
groups possessing specific behavior characteristics. Ben-Akiva et al. (2002) proposed a general
112
methodology for including latent variables such as attitudes and perceptions in choice models.
113
The methodology integrates a discrete choice model and a latent variable model, each one con-
114
sisting of structural and measurement equations. The basic assumption in their methodology is
115
that observable explanatory variables simultaneously explain utility and latent variables.
116
We adopted a similar model but also integrated substantial modifications. The set up of
117
our model is outlined in Figure 1. Observed variables are represented in rectangles, whereas
118
unobservable (or latent) variables are represented in ellipses. Solid arrows represent structural
119
equations which correspond to cause-and-effect relationships. The relationships between observ-
120
able indicators and latent variables are represented by dashed arrows.
121
122
[Figure 1 here]
123
124
We can see that all observed explanatory variables do not affect the same latent endogenous
125
variables. In particular, variables related to information possessed by consumers, which we
126
call V 1 , only explain quality perception of products Z ∗ and not utility U ∗ directly, contrary to
127
Ben-Akiva et al. (2002). Quality perception is a determinant of consumer utility and based
128
on quality indicators I. As with classical choice models, utility is also explained by individual
129
characteristic variables referred to as V 2 and V 3 in Figure 1 (see Section 4 for a description of
130
variables). Therefore, consumer preferences, which simultaneously depend on tastes for different
131
attributes and product quality perception, determine consumer product choices.
6
132
Moreover, we suppose in our model that consumer heterogeneity is observable, contrary to the
133
framework of Ben-Akiva et al. (2002), and more generally to models that focus only on consumer
134
tastes through latent classes (for a review of the literature, see Greene 2001). The observed
135
variable is the weight consumers give to attributes in their purchase decision, referred to as W
136
in Figure 1. We assume that the weight that consumers give to attributes accurately describes
137
their tastes. The assumption refers to location or differentiation models (Hotelling, 1929; Mussa
138
and Rosen, 1979) where consumers are differentiated by their preferences for product quality
139
generally represented by their revenue, taste or travel distance.
140
In our study, we want to focus on preferences for product characteristics and exclude the
141
product price from their demand decision. Thus, in our survey, respondents were requested
142
to make a product choice considering that products are sold at identical prices. Technically,
143
product choices depend on the difference between perceived utility. In the case where prices
144
are assumed to be identical, product price is no longer an explanatory variable for the product
145
choice.[1] We should notice that, in reality, for a same standard window frame, prices vary
146
considerably as they are sold with different services. However, the price interval for wooden
147
and plastic window frames do largely overlap. The consumer’s choice is then only based on
148
the technical properties of products, and price need not to be considered in our econometric
149
set up. This assumption allows us to concentrate mainly on non-directly observable product
150
attributes for which consumers might have different quality perceptions. The model aims to
151
link the consumer’s knowledge about product attributes with his or her quality perception and
152
tries to see in what way information campaigns could orientate the consumer’s choices.
7
153
3
154
The integrated model described in the previous section is composed of two parts: a standard
155
discrete choice model and a latent variable model that is defined to carry out the treatment and
156
the prediction of latent perception variables. The latent variable model (or structural equation
157
model, SEM) consists of structural latent variable equations and measurement equations. The
158
discrete choice model is composed of a structural equation and a decision rule equation.
159
3.1
160
The latent variable model has been popularized by the LISREL program – Linear Structural
161
Relationship (Jöreskog 1979; Jöreskog and Sörbom 1982). The latent variables are modeled
162
by specifying a structural model and a measurement model. In general, the structural latent
163
variable equations specify the relationship between endogenous and exogenous latent variables.
164
However, it is also possible, as in our analysis, to include exogenous observed variables as part
165
of these structural equations. The measurement equations specify the relationship between the
166
measured indicators and the latent variables.[2]
167
168
Econometric methodology
The latent variable model
Assuming a linear specification, the structural latent variable equations written in matrix
form are:
169
Z ∗ = V 1 β + ǫZ ,
170
where Z ∗ is a vector of latent (or non observed) variables of quality perception and beliefs. V 1
171
is a vector of (observed) explanatory variables related to the information held by the consumer,
172
β is the matrix of unknown parameters to be estimated, and ǫZ is the error vector i.i.d. (0, ΣZ ).
173
As mentioned above, unlike the study of Ben-Akiva et al. (2002), V 1 are specific regressors of
8
(1)
174
175
Z ∗.
The set of measurement equations is defined as follows:
176
I = Z ∗ γ + ǫI ,
177
where I is a vector of measured indicators of perceived quality, γ is the matrix of unknown
178
parameters to be estimated, and ǫI is the measurement error vector i.i.d. (0, ΣI ).
179
(2)
Our latent variable model is detailed in Appendix A.2.
180
3.2
The discrete choice model
181
The decision rule consists of a stated choice depending on the latent utility U ∗ :
y=
182



 1 if
U ∗ = Uwood − Uvinyl ≥ 0


 0 otherwise,
(3)
183
where y = 1 if the respondent chooses wood as the material for his/her windows, and y = 0
184
if he/she prefers vinyl. U ∗ is the latent utility difference between utility for wood (Uwood ) and
185
utility for vinyl (Uvinyl ).
186
As in many empirical studies, the structural utility function is assumed to be linear:
187
U ∗ = XαX + Z ∗ αZ + W αW + ǫU .
188
Utility depends on observed explanatory variables (X), including socio-demographic variables
189
(V 2 ) and behavioral variables related to housing (V 3 ) in our analysis. However, utility also
190
depends on latent variables of quality perception (Z ∗ ) and taste factors (W ). W is a vector of
9
(4)
191
(observed) weights allocated to the commodity characteristics, and encompasses consumer tastes
192
and attitudes. The introduction of such variables is the other important difference with respect
193
to the model of Ben-Akiva et al. (2002). αX , αZ , αW are the parameter vectors associated
194
with these variables. Assuming that the distribution of the error term ǫU is standard normal,
195
we estimate a Probit model that represents the respondent’s choice of material:
P (y = 1) = Φ(XαX + Z ∗ αZ + W αW ),
196
(5)
197
where Φ(.) is the standard normal cumulative distribution function.
198
3.3
199
As in the case of Johansson et al. (2006), we chose to estimate the interest parameters in two
200
steps. First, the latent variable model, equations (1) and (2), and the associated parameters
201
are estimated. This particularity allows us to compute the predicted values of latent variables
202
(perception variables). The framework for modeling and estimation of the variable latent model
203
is based on the LISREL program. This is a hybrid technique that encompasses aspects of
204
confirmatory factor analysis, path analysis and regression. The estimation of our model is
205
described in Appendix A.2. Second, we transfer the predicted perception variables into the
206
choice model, equations (3) and (4), and the resulting model is then estimated, conditional on
207
the perception variables (as well as other explanatory variables V 2 and V 3 , and taste factors
208
W ).
Estimation method
209
We decided to adopt a two-step method and to specifically analyze the latent variable model
210
for several reasons. First, SEM and associated programs are powerful tools that make it possible
211
to take into account interactions, correlated independent variables and error terms, measure10
212
ment errors, multiple latent variables each measured by multiple indicators. Second, a two-step
213
method allows a good adjustment of the latent variable model. Finally, it allows us to specifically
214
study the reliability of indicators for latent perception and to better understand how information
215
influences quality perception for materials, which is the focus of our paper.
216
The two-step estimation procedure that consists in transferring the fitted latent variables into
217
the utility function may pose two problems. First, as noted in Ben-Akiva et al. (2002), the fitted
218
latent variables may introduce measurement error in an explanatory variable of the second-step
219
regression. If the measurement error is uncorrelated with the observed (fitted) measure of the
220
latent variable but correlated with the latent variable itself, then the two-step estimation pro-
221
duces consistent estimators of all regression parameters (Wooldridge, 2002). This is the usual
222
assumption of applied econometricians when dealing with generated regressors (Pagan, 1984).
223
In the opposite case (correlation with the fitted latent variable), this results in inconsistent esti-
224
mates of the parameters. However, if the variance of the latent variable’s random error is small,
225
then a sufficiently large sample size may reduce the measurement error and result in acceptable
226
parameter estimates. We might suppose that generated regressors do not pose problems of in-
227
consistency as usually done. However, we will still check in the empirical application that the
228
variance of the error for the latent variable is sufficiently small.
229
Second, the presence of a generated regressor generally produces invalid standard errors and
230
test statistics because these latter ignore the sampling variation in the estimated parameter
231
associated with the generated regressor (Wooldridge, 2002). Two alternative solutions are com-
232
monly used in this case : deriving the asymptotic distribution of the two-step estimator for an
233
equation with a generated regressors or using bootstrap standard errors. We will use this latter
234
solution.
11
235
4
Application: Choice of materials (wood vs.
vinyl) in the
French window frame market
236
237
4.1
Data
238
All the data used here are from a specific survey on consumer tastes and beliefs about windows.
239
The survey was conducted by a marketing agency[3] in November 2003, and concerned a set of
240
968 consumers representative of the French population drawn from a stratified sample frame.
241
940 participants remained after eliminating individuals with missing data. The survey was done
242
by phone. Respondents were informed that the survey aimed to better understand consumer
243
behavior towards window purchase and was used for scientific (and non commercial) purposes.
244
No other information concerning windows were given.
245
The survey included six types of data:
246
• knowledge that individuals possess about attributes and their quality (variables referred
247
to as V 1 ). Knowledge was supposed either to be obtained through experience. Experience
248
was derived from the window material (wood, vinyl, aluminum, etc.) in their place of
249
residence, window age, how they would classify the insulation (e.g., thermal, acoustic)
250
and brightness of their home. Or knowledge is also supposed to be dependent on private
251
external information sources. Respondents were asked whether they felt informed about
252
the windows and about materials or whether they had been informed on windows recently.
253
• Socio-demographic characteristics of individuals (variables referred to as V 2 ): age, gender,
254
255
socio-professional group (SPG), family composition.
• Lodging characteristics of individuals (variables referred to as V 3 ): type of housing (e.g.,
12
256
house, apartment), building style of their housing (classic, modern, typical), importance
257
(with a five-point Likert scale) attached to the home.
258
• Consumer tastes for product attributes (weight W ): Table 2 describes the importance
259
consumers attribute to window attributes into their product choice. We considered several
260
attributes of a window: thermal insulation, acoustic insulation, maintenance, product life,
261
aesthetics, environmental properties, fire resistance and safety. The choice of the attributes
262
was based on interviews with architects and confirmed by scientific studies (Eastin et al.,
263
2004).
264
• Quality perception of product attributes (quality indicators I), which was analyzed for all
265
attributes and for each material (wood and vinyl). Table 1 describes consumers’ perception
266
for window attributes according to the material (wood or vinyl).
267
268
• Stated choice y based on the question: “Suppose that wooden windows and vinyl windows
are sold at the same price, which material would you choose?”
269
Table A.6 in Appendix A.1 presents summary statistics and a description of variables V 1 ,
270
V 2 , V 3 and y. All answers concerning judgments are rated on a 1 to 5 scale (Likert scale) where
271
1 corresponds to a low appreciation and 5 to a high one, and 3 indicates indifference.
272
273
[Table 1 here]
274
275
Table 1 shows that consumers value differently window characteristics for wood and vinyl.
276
In general, vinyl windows are perceived to have a (very) high thermal and acoustic insulation.
277
Vinyl windows are considered to be easier to maintain than wooden windows. On the opposite,
13
278
wooden windows are perceived to be highly aesthetic and environmental-friendly. Respondents
279
seemed to underestimate the thermal and acoustic value of wood. Thus, beliefs about technical
280
properties of wood are sometimes false, which is less frequently the case for vinyl. One of
281
the explanations for this result could be the experience of respondents with rather old (not
282
necessarily well maintained) wooden windows which, in most cases, are not double glazed. The
283
basic assumption of our model on equal prices (i.e., respondents were supposed to consider the
284
vinyl window price to be equal to the wooden window price) seems to be adequate. Consumers
285
value wooden and vinyl windows similarly. Moreover in both cases, the price is not a good
286
indicator of the quality of a window (only significantly different from zero for the vinyl windows),
287
see Table 3. We will come back to this assumption in the estimation of the choice model.
288
Table 2 also gives precious information. First of all, respondents did not consider price to
289
be the most important criteria in a window purchase. Second, environmental considerations are
290
least important in consumers purchase decisions. Characteristics that are perceived to be very
291
good for vinyl windows, such as thermal and acoustic insulation or maintenance, are the most
292
important factors when buying a window.
293
294
[Table 2 here]
295
4.2
Results
296
4.2.1
297
The latent variable model consists of a structural model and a measurement model. Both models
298
are simultaneously estimated by a LISREL-type structural equation modeling program.[4] The
299
first part of the latent variable model is composed of two latent variables Z1∗ (quality perception
The latent variable model
14
300
of wood) and Z2∗ (quality perception of vinyl) explained by variables V 1 (see in the upper part
301
of Table 3 for variables retained in the empirical application). The second part of the latent
302
variable model deals with the measurement equations (see the lower part of Table 3). Ten qual-
303
ity indicators are used to predict latent perceived quality variables. A χ2 statistic built on the
304
discrepancy function gives a good indication of the model fit. For our model, the test statistic
305
is equal to 2081.75 with 319 degrees of freedom. Its p-value is less than 0.0001. The goodness
306
of fit index (GFI) is close to 1 (with a value of 0.8416). Moreover, the root mean square error
307
of approximation (RMSEA) is equal to 0.0767. These measures indicate that the model fits the
308
data well.
309
310
[Table 3 here]
311
Estimation results of latent variable equations
One objective of this paper was to bet-
312
ter understand how quality beliefs are formed and, more precisely, if knowledge on windows
313
and materials (obtained either through experience or by external information sources) changes
314
quality beliefs. In the upper part of Table 3, we report the estimation results for the two latent
315
variables, quality perception of wood and quality perception of vinyl.[5] These results allows
316
us to know which factors (proxies of information) positively or negatively influence the quality
317
perception for both materials.
318
First, recent information received by individuals on windows and conveyed by way of adver-
319
tising or by store advisors for instance, has a negative impact on their perception of wood.[6]
320
Second, the degree of information respondents feel to possess both about windows and materials
321
has a significant impact on the perceived quality of window materials. On the one hand, the
322
more that individuals feel to be informed about windows, the greater the quality perception
15
323
of wood. On the other hand, the coefficient associated with the information on materials is
324
significantly positive for the perceived quality of vinyl.
325
Third, another way that consumers acquire information is through experience. In the survey,
326
we collected data on the type of windows (i.e., type of material) in their lodging as well as their
327
perception of brightness and performance of insulation in their house. We created variables by
328
crossing both questions in order to simulate experience and perception on materials. Estimation
329
results show a significant impact of these variables on perceived quality. So a combination of the
330
material of the windows in their home with the observed level of insulation and brightness in
331
their home, influences respondents’ opinions on the quality perception of materials. For instance,
332
good or very good thermal and acoustic insulation associated with wooden windows at home
333
improves the perceived quality of wood and decreased the perception of vinyl. In the same
334
way, poor brightness associated with wooden windows increase the perception of vinyl, whereas
335
this has no impact on the perception of wood. Also, Do-It-Yourselfers (DIY) who might have
336
a higher probability to manipulate materials than non-DIY, have a better opinion of wooden
337
windows, whereas this has no effect on the perception of vinyl.
338
Finally, we have checked the accuracy of the predicted latent perception variables before
339
injecting them in the choice model. Indeed, as indicated in the description of the estimation
340
method, the two-step procedure may result in inconsistency of parameters due to the introduc-
341
tion of measurement errors by using fitted latent variables as regressors. However, the estimated
342
variances of the latent variable random errors are small (0.42 and 0.53, respectively, for perceived
343
wood quality and perceived vinyl quality). Thus, we may expect that an individual’s true value
344
of the latent variable is not too far off from its expected value.
16
345
Estimation results of measurement equations The measurement model allows us to kwow
346
which indicator variables are the best predictors for the (latent) quality perception and con-
347
tribute to the construction of the latent preferences. Hence, results concerning the measurement
348
equations indicate the level of reliability and validity of the indicators of the quality perception
349
(in the lower part of Table 3).
350
The estimated factor loadings in the measurement equations are all positive and significantly
351
different from zero at the 1% level of significance, excepted for the price indicators. The results
352
confirm that the question of the global quality of windows is the best indicator of perceived
353
quality among all indicators, with R2 equal to 0.61 and 0.71, respectively, for perceived wood
354
and vinyl quality. Moreover, thermal insulation seems to be the second best indicator (0.56
355
and 0.73, respectively, for wood and vinyl), followed by acoustic insulation, maintenance and
356
product life. Fire resistance, safety and environmental considerations are weak indicators for
357
the perception of window quality. Finally, the dependence between quality perception and price
358
indicators is low: Correlation coefficients between the price indicator and the latent variable
359
(quality perception) turned out to be very small, 0.0017 and 0.0193, respectively, for wood and
360
vinyl. This confirms that consumers did not integrate price into their judgment of materials.
361
4.2.2
362
In the discrete choice model, we estimated the probability of choosing wood (y = 1) rather than
363
vinyl (y = 0) as material for windows. We must remember that respondents were requested
364
to make a product choice considering that products are sold at identical prices. Several tests
365
already showed that respondents had taken into account this recommendation. To eliminate
366
any ambiguity, we included the price indicators for wooden and vinyl windows into the choice
The choice model
17
367
model. None of the variables are significantly different from zero. So we may conclude that
368
respondents have correctly answered the questionnaire and focuse on other explanatory variables
369
to analyze consumer preferences. Our model includes, along with “usual” explanatory variables,
370
the predicted latent variables (i.e., predicted quality perceptions of wood and vinyl) coming
371
from the latent variable model, and taste (or attitudes) on the basis of attribute weight. Table 5
372
presents the results of the choice model estimation.
373
Before analyzing parameter estimates in Table 5, we made several specification tests on our
374
model. We had to make sure that the integrated model described in Figure 1 is an appropriate
375
specification for explaining the role of information in the choice decision. In particular, a latent
376
variable model was combined with the choice model in order to take non-observed perception
377
variables into account that could influence consumer choice. Moreover, our model supposes that
378
the preferences and tastes are different between consumers, and that the weights consumers
379
put on attributes (assumed to represent the tastes of consumers) are observed factors that affect
380
their utility. We report the results of likelihood ratio (LR) tests on these assumptions in Table 4.
381
382
[Table 4 here]
383
384
We first tested a model without attribute weights. There are nine restrictions such that
385
coefficients associated with the weights for the nine defined attributes of a window (including
386
price) are equal to zero. This model is not accepted at the 1% level, revealing the importance of
387
tastes in the choice decision. Second, the hypothesis that the two parameters of the predicted
388
latent variables are jointly equal to zero is also rejected at the 1% level. This result confirms
389
that the integration of the latent variable model is necessary and that the quality perception
18
390
variables provide a meaningful explanation of the choice decision of consumers. We also tested
391
for the null hypothesis that both coefficients associated with weights and coefficients associated
392
with latent variables are equal to zero. The test statistic is also greater than the critical value
393
implying the rejection of this hypothesis. Finally, we carry out the standard LR test for the
394
global goodness-of-fit testing the null hypothesis that all coefficients excepted the contant term
395
are zero. This hypothesis is also highly rejected. The integrated model appears to be the most
396
accurate specification.[7]
397
398
[Table 5 here]
399
400
Several conclusions can be drawn from the estimates in Table 5. First, as seen by the results
401
of previous tests, quality perception is important in consumers’ purchase decision. Indeed,
402
estimated parameters associated with each of two latent variables, quality perception of wood
403
and quality perception of vinyl, are highly significantly different from zero (at the 1% level).
404
As expected, a higher quality perception of wood leads to a greater probability of choosing this
405
material for windows. In the same way, a lower quality perception of vinyl also implies a choice
406
of wood rather than vinyl.
407
Second, tastes have a significant effect on the material choice for three of the nine char-
408
acteristics of a window: thermal insulation, aesthetics and maintenance. For these indicators,
409
quality beliefs differ considerably between wooden windows and vinyl windows (see Table 1).
410
Thermal insulation has a negative effect on the choice for wooden windows. Respondents per-
411
ceived wood to be less performing for thermal insulation than vinyl (see Table 1). Furthermore,
412
we can see in the lower part of Table 3 that the thermal insulation property highly influences
19
413
the quality perception of either wooden or vinyl windows. Moreover, if aesthetics is important
414
or very important factor in the window choice, wooden windows will be preferred over vinyl
415
ones. Indeed, people generally believe that wooden windows are more attractive than vinyl
416
windows (see Table 1). Vinyl windows are generally considered to be easier to maintain than
417
wooden windows. As a result, when people attach importance to maintenance, they will choose
418
vinyl windows rather than wooden windows. It should be observed that both the parameters of
419
aesthetics and maintenance indicators are significantly different from zero at the 1% level, while
420
the one of thermal insulation is significantly different at the 5% level. Individuals do not attach
421
same importance to window characteristics. Tastes for window characteristics however could be
422
altered by educational programs. For instance, civic education on environmental responsibilities
423
could raise tastes for environmental considerations and could modify consumer purchase choice
424
in favor of wood.
425
Third, other variables such as lodging characteristics or socio-economic factors also influences
426
the choice of window material. For example, consumers living in standard or modern houses
427
will opt less easily for wooden windows than consumers of traditional (typical) livings. Vinyl
428
is more frequently chosen by owners rather than tenants. Older people seem to have a higher
429
probability to purchase wooden windows than younger people. An explication of this result
430
might be the fact that they have more spare-time to maintain. The region a person lives in is
431
also an explaining factor but not the socio-professional group (SPG). However, a high income
432
level for the consumer explains the choice of wood rather than vinyl. A family with children
433
will have a slight preference for wood, whereas this preference is definitely evident for singles.
434
Surprisingly, ecologically-minded consumers tend to prefer vinyl windows rather than wooden
435
windows, maybe because they think that harvesting and thus wood as a material is harmful for
20
436
forests.
437
Our results show that quality beliefs that consumers possess about windows and materials
438
highly influence their consumption choice. In the case of windows, quality perception is deter-
439
mined by the consumer’s level of knowledge, based on either (external) information or experience.
440
The choice model shows that the consumption decision is highly dependent on consumer quality
441
perception. This means that information campaigns might be an efficient tool for increasing the
442
market share of wood: Either by informing people on real performances of wood (for example,
443
wood possesses the same thermal and acoustic performance as vinyl), or by trying to change
444
tastes, i.e. purchase criteria (for example, by educating the citizen on the importance of the
445
sustainability of materials used in house construction).
446
5
447
We set up an econometric model where the quality level of product attributes is an endoge-
448
nous variable and depends on the information level of consumers, in the case of window choice.
449
We showed that the introduction of quality perceptions and tastes for different attributes sig-
450
nificantly improves the results of a “classical” discrete choice model. This result is not very
451
surprising since several attributes of windows are not observable before purchase. The situation
452
of imperfect information in purchase decisions means that consumers do not assess the objective
453
quality level of attributes in the same way. Therefore, depending on the information they possess
454
on attributes, they form different quality perceptions.
Conclusion and perspectives
455
The quality perception of attributes is a determining factor in the purchase decision, espe-
456
cially for the attributes for which consumers have high preferences. This means that if produc-
457
ers can favorably influence the consumer’s perception, demand might increase. For example,
21
458
a better perception of thermal insulation (which is not observable before purchase) positively
459
influences global quality perception and could thus highly influence purchase decisions. Thus,
460
if wood producers could persuade people that wooden windows insulate as effectively as vinyl
461
ones –or even better– (by doing better marketing of their products), consumers might switch
462
their consumption towards wooden windows. With the model we aimed to show that consumer
463
preferences are heterogeneous and based on imperfect information. Indeed, consumers do not
464
necessarily possess correct information on properties of products. The perception consumers
465
have on these properties is a very important factor in their purchase decision. Consequently, we
466
might think that information campaigns can easily modify consumer purchase choices. A logic
467
extension should be to test whether information provision positively (or negatively) influences
468
consumer preferences.
469
In our application to windows, we use stated choice data because data on observed choices
470
were much more difficult to collect on the window market. It could be useful to apply this
471
approach to observed choices and stated preferences (taste indicators and quality perception
472
of products). Studying consumer beliefs about product quality could also be possible with
473
experimental data. More sophisticated analyses of the impact of different kinds of information
474
available (e.g., general information before going into the store, information available in the store
475
on the product) could also enrich the analysis and provide interesting insights for policy-making.
22
476
References
477
Ben-Akiva, M., J. Walker, A.T. Bernardino, D.A. Gopinath, T. Morikawa, and A. Poly-
478
doropoulou. 2002. “Integration of Choice and Latent Variable Models.” in Perpetual Motion:
479
Travel Behaviour Research Opportunities and Application Challenges, (Elsevier Science, Ed.
480
Mahmassani), Chapter 21, 431-470.
481
482
483
484
485
486
487
488
Browne, M.W. 1984. “Asymptotically Distribution-Free Methods for the Analysis of Covariance
Structures.” British Journal of Mathematical and Statistical Psychology 37(1):62–83.
Eastin, I., S. Shook, and S. Fleishman. 2001. “Material substitution in the U.S. residential
construction industry, 1994 versus 1998.” Forest Products Journal 51(9):30–37.
Forbes, C.L., L.G. Jahn, and P.A. Araman. 2001a. “An Investigation of Hardwood Plywood
Markets. Part 1. Archituctural Woodworkers.” Forest Products Journal 51(3):17–24.
—. 2001b. “An Investigation of Hardwood Plywood Markets. Part 2. Fixture Manufacturers.”
Forest Products Journal 51(6):25–31.
489
Garth, J., I. Eastin, and J. Edelson. 2004. “Material Substitution Trends in Residential Con-
490
struction 1995, 1998 and 2001.” Working paper 93, Center for International Trade in Forest
491
Products, Washington, 64 p.
492
493
494
495
Greene, W. 2001. “Fixed and Random Effects in Nonlinear Models.” Working papers, New York
University, Leonard N. Stern School of Business, Department of Economics.
Johansson, M.V., T. Heldt, and P. Johansson. 2006. “The Effects of Attitudes and Personality
Traits on Mode Choice.” Transportation Research Part A 40(6):507–525.
23
496
Jonsson, R. 2005. “The end consumer’s choice of floorcovering in the Netherlands and the United
497
Kingdom: a comparative pilot study of substitute competition.” Journal of Wood Science
498
51(2):154–160.
499
500
—. 2006. “Increasing the competitiveness of wood in material substitution: a method for assessing and prioritizing customer needs.” Journal of Wood Science 52(2):154–162.
501
Jöreskog, K.G. 1979. “Structural Equation Models in Social Science.” in Karl G. Jöreskog and
502
Dag Sörbom (eds), Advances in Factor Analysis and Structural Equation Models, ch. 4, Cam-
503
bridge, MA: Alt Books.
504
505
506
507
508
509
510
511
Jöreskog, K.G., and D. Sörbom. 1982. “Recent Developments in Structural Equation Modeling.”
Journal of Marketing Research 19(4):404–465, Special Issue on Causal Modeling.
Lancaster, K.J. 1966. “A New Approach to Consumer Theory.” Journal of Political Economy
74(2):132–157.
McFadden, D. 1986. “The Choice Theory Approach to Market Research.” Marketing Science
5(4):275–297, Special Issue on Consumer Choice Models.
Muthén, B. 1984. “A General Structural Equation Model with Ordered Categorical, and Continuous Latent Variables Indicators.” Psychometrica 49(1):115–132.
512
Nelson, P. 1974. “Advertising as Information.” Journal of Political Economy 82(4):729–754.
513
Olah, D., R. Smith, and B. Hansen. 2003. “Wood material use in the U.S. cabinet industry 1999
514
515
516
to 2001.” Forest Products Journal 53(1):25–31.
Pagan, A. 1984. “Econometric Issues in the Analysis of Regressions with Generated Regressors.”
International Economic Review 25(1):221–247.
24
517
Shook, S.R., J.A. Soria, and D.J. Nalle. 2007. “Examination of North American softwood lumber
518
species substitution using discrete choice preferences and disaggregated end-use markets.”
519
Canadian Journal of Forest Research 37(12):2521–2540.
520
521
522
523
524
525
Swait, J., and J.C. Sweeney. 2000. “Perceived Value and its Impact on Choice Behavior in a
Retail Setting.” Journal of Retailing and Consumer Services 7(2):77–88.
Wooldridge, J.M. 2002. Econometric Analysis of Cross Section and Panel Data. The MIT Press,
Cambridge, Massachusetts.
Wu, Q., and R.P. Vlosky. 2000. “Panel Products: A Perspective from Furniture and Cabinet
Manufacturers in the Southern United States.” Forest Products Journal 50(9):45–50.
526
Zhang, G., and M.W. Browne. 2006. “Bootstrap Fit Testing, Confidence Intervals, and Standard
527
Error Estimation in the Factor Analysis of Polychoric Correlation Matrices.” Behaviormetrika
528
33(1):61–74.
25
529
A
Appendix
530
A.1
531
[Table A.6 here]
532
A.2
533
The latent variable model is a structural equation model composed of two structural equations
534
(A.1) and (A.2), corresponding to the two materials and two sets of measurement equations
535
(A.3) and (A.4), expressing relationships among latent variables Z ∗ and manifest variables or
536
indicators I. The complete system can be written as follows:
Descriptive Statistics
Estimation of the latent variable model
537
Z1∗ = β1 V 1 + ǫZ1
(A.1)
538
Z2∗ = β2 V 1 + ǫZ2
(A.2)
539
I1 = γ1 Z1∗ + ǫI1
(A.3)
I2 = γ2 Z2∗ + ǫI2 ,
(A.4)
540
541
542
where V 1 is a (8 × 1) vector of explanatory variables, β1 and β2 are the associated (1 × 8) vectors
543
of parameters to be estimated, and ǫZ1 and ǫZ2 are i.i.d (0, ΣZ1 ) and (0, ΣZ2 ), respectively. I1
544
and I2 are (10 × 1) vectors of indicators (see Table 1 for definitions), γ1 and γ2 are the (10 × 1)
545
vectors of unknown parameters to be estimated,[8] and ǫI1 and ǫI2 are the measurement errors
546
respectively i.i.d (0, ΣI1 ) and (0, ΣI2 ). The covariance of the error terms in the structural latent
547
equations and in the measurement equations are equal to zero.
548
Structural equation modeling is a multivariate technique using covariance structure analysis.
549
We call θ the parameter vector to be estimated, which includes β1 , β2 , γ1 , γ2 and the variances
26
550
of matrix ΣZ1 , ΣZ2 , ΣI1 and ΣI2 . The parameter vector θ is estimated iteratively by an algo-
551
rithm that minimizes the difference between the sample covariance matrix S and the estimated
552
b θ̂).
covariance matrix of estimated parameters Σ(
553
Maximum likelihood estimation (MLE) is the most common method used to estimate struc-
554
tural parameters of this model. Multivariate normal distribution and continuous variables are
555
some of the key assumptions of MLE. However, the manifest variables are often not continu-
556
ous (dichotomous or polytomous variables). This is the case in our model. In particular, the
557
measured indicators and some observed variables are based on a five-point Likert scale (ordinal
558
variables). In this case, the assumption of multivariate normality of data does not hold. The
559
parameter estimates of the latent variable model are still convergent, but the estimated standard
560
errors are underestimated and the fit measures based on χ2 values are not good.
561
In practice, when the number of Likert categories is 4 or higher and skew and kurtosis
562
are within normal limits, use of MLE may be justified. However, as reported by Zhang and
563
Browne (2006), another approach can be to estimate tetrachoric and polychoric correlations,[9]
564
respectively, from the dichotomous and ordinal variables, and to use generalized least squares
565
(GLS) where the weight matrix is the inverse of the matrix of tetrachoric/polychoric correlations
566
(instead of Pearson correlations, Muthén, 1984). When the model is correctly specified, GLS
567
gives asymptotically valid test statistics and standard error estimates (Browne, 1984). The
568
discrepancy function to minimize is written as F = F (S, Σ(θ)). For GLS, this function is:
569
570
FGLS =
1
tr(W −1 (S − Σ)2 ),
2
where W is the weight matrix and tr indicates the trace of a matrix.
27
571
Notes
572
573
[1] In the empirical application, different tests were carried out to make sure that the as-
574
sumption was verified, i.e. that consumers did not integrate price into their judgements. In the
575
questionnaire, respondents were asked to give a price appreciation, for wooden as well as plastic
576
window frames. First, we showed that the price estimation was a bad indicator for product
577
quality. Second, respondents did not judge differently the price levels of wooden and plastic
578
frames. Finally, price indicators were not significant explanatory variables in the global choice
579
model.
580
581
[2] In structural equation modeling, non-observed variables are called latent variables and
observed variables are called manifest variables.
582
[3] ED Institut, marketing studies institute, www.edinstitut.com.
583
[4] We used the CALIS procedure (Covariance Analysis of Linear Structural Equations) from
584
the SAS system for Windows (version 9.1) to estimate parameters and test the appropriateness
585
of the linear structural equation model using covariance structure analysis. Since we had a set of
586
linear structural equations to describe our model, we used the LINEQS statement. In particular,
587
it is possible to specify variances and covariances in the model, to choose between different
588
estimation methods and to enter correlation matrices instead of raw data. We constructed
589
tetrachoric/polychoric correlation matrices using the FREQ procedure of SAS.
590
591
592
593
[5] In order to have the best predictions of the latent variables (i.e., minimizing the variance
of their random error), we only retained statistically significant factors.
[6] Respondents were asked if they had received recently information (either through ads or
in stores) on windows.
28
594
[7] Significant gains with respect to simpler models can also be measured in terms of cor-
595
rectly predicted observations and pseudo R2 . For the integrated model, 67% of observations
596
are correctly predicted by the model, and R2 is equal to 0.18. Without latent variables, 63% of
597
observations are correctly predicted by the model, and R2 falls to 0.13. If weights and latent
598
variables are not included in the model, the percentage of correctly predicted observations is
599
60%, and the pseudo R2 is 0.08.
600
601
[8] For each material and thus for each vector of indicators I1 and I2 , the parameter associated
with the indicator called “global quality” is set to 1.
602
[9] Tetrachoric and polychoric correlations extrapolate what the categorical variable distri-
603
butions would be if continuous, adding tails to the distribution. As such, it is an estimate based
604
on the assumption of an underlying continuous bivariate normal distribution.
29
Figure 1: Integrated choice and latent variable model
Information V 1
Individual characteristics
V 2 and V 3
Taste, weight of attributes
W
beliefs, quality perception
Z∗
Utility U ∗
Quality indicators
I
Stated preferences
y
30
Table 1: Quality perception of product attributes for wood and vinyl (indicators I)
Appreciation
(Lickert scale)
Very low
(=1)
Low
(=2)
Indifferent
(=3)
High
(=4)
Very high
(=5)
Mean
Std.
Question: What do you think about this attribute for the wooden (vinyl) window?
Wood
I11
I12
I13
I14
I15
I16
I17
I18
I19
I1p
Global quality
Thermal insulation
Acoustic insulation
Maintenance
Product life
Aesthetics
Environment
Fire resistance
Safety
Price
4.04%
3.72%
4.04%
17.7%
3.62%
1.06%
5.74%
35.5%
15.1%
5.43%
15.5%
17.8%
18.7%
53.8%
25.4%
3.09%
17.0%
39.7%
36.1%
16.3%
2.55%
9.57%
14.0%
2.45%
8.62%
1.28%
14.9%
7.66%
12.3%
42.0%
50.2%
39.3%
38.1%
18.8%
35.9%
21.2%
32.6%
13.4%
26.9%
28.5%
27.7%
29.7%
25.1%
7.23%
26.4%
73.4%
29.8%
3.72%
9.57%
7.77%
3.82
3.73
3.61
2.44
3.56
4.63
3.64
2.10
2.80
3.17
1.12
1.17
1.17
1.19
1.23
0.76
1.23
1.14
1.26
0.97
2.66%
1.70%
1.49%
1.06%
1.17%
5.85%
7.02%
21.2%
11.4%
3.30%
7.02%
5.21%
5.00%
2.87%
6.60%
18.4%
27.9%
35.6%
25.0%
16.4%
7.77%
12.1%
18.0%
6.17%
18.2%
5.11%
24.0%
16.9%
17.2%
41.3%
40.4%
38.1%
38.3%
28.9%
34.6%
44.9%
29.9%
21.7%
35.4%
30.5%
42.1%
42.9%
37.2%
61.0%
39.5%
25.7%
11.2%
4.57%
11.0%
8.52%
4.12
4.15
4.05
4.46
4.05
3.66
3.10
2.53
3.10
3.25
1.00
0.94
0.94
0.82
0.97
1.20
1.14
1.18
1.22
0.94
Vinyl
I21
I22
I23
I24
I25
I26
I27
I28
I29
I2p
Global quality
Thermal insulation
Acoustic insulation
Maintenance
Product life
Aesthetics
Environment
Fire resistance
Safety
Price
Notes: N = 940.
31
Table 2: Taste for product attributes (weight W )
(Lickert scale)
Not
important
(=1)
Few
important
(=2)
Indifferent
(=3)
Rather
important
(=4)
Very
important
(=5)
Mean
Std.
4.82
4.62
4.62
4.49
4.14
4.53
4.11
4.45
4.50
0.56
0.86
0.75
0.86
1.24
0.87
1.15
1.00
0.86
Question: When you choose a window, how important is this attribute?
Thermal insulation
Acoustic insulation
Product life
Aesthetics
Fire resistance
Maintenance
Environment
Safety
Price
0.64%
1.28%
0.53%
0.32%
5.00%
1.17%
3.72%
2.77%
1.17%
1.38%
5.43%
3.94%
7.23%
12.8%
5.96%
11.4%
6.38%
4.57%
0.32%
0.85%
1.49%
0.43%
2.45%
0.64%
4.04%
1.91%
3.40%
Notes: N = 940.
32
11.0%
15.1%
21.0%
27.1%
23.3%
23.0%
31.5%
20.6%
24.7%
86.7%
77.3%
73.1%
64.9%
56.5%
69.3%
49.4%
68.3%
66.2%
Table 3: Estimation results of the latent variable model
Z1∗ (Wood)
Latent variable equations
(two equations, one per column)
Coef.
t-stat
Z2∗ (Vinyl)
Coef.
t-stat
0.0798
4.05
−0.7467
−0.7488
0.3621
0.7054
−10.79
−9.81
3.05
4.61
(Explanatory) Variables V 1
Received information (recently)
Informed about windows (feeling)
Informed about materials (feeling)
Do-it-yourselfers
Very good thermal insulation × Home wooden windows
Good acoustic insulation × Home wooden windows
Poor Brightness × Home wooden windows
Purchase of windows × Home wooden windows
−0.1190
0.0927
−1.83
4.37
0.0569
0.9322
0.4728
3.51
13.50
6.76
0.9154
5.92
2
R
0.38
Measurement equations
(two times ten equations, one per row)
Coef.
t-stat
1.0000
0.9293
0.8434
0.7378
0.9699
0.5278
0.5291
0.4198
0.5409
0.0390
–
19.75
18.92
15.82
20.15
18.31
10.50
9.30
12.84
1.02
0.20
Coef.
t-stat
R2
(Explained) Indicators I
I11
I12
I13
I14
I15
I16
I17
I18
I19
I1p
Global quality
Thermal insulation
Acoustic insulation
Maintenance
Product life
Aesthetics
Environment
Fire
Safety
Price
I21
I22
I23
I24
I25
I26
I27
I28
I29
I2p
Global quality
Thermal insulation
Acoustic insulation
Maintenance
Product life
Aesthetics
Environment
Fire
Safety
Price
Fit function=2.2170
Goodness-of-fit statistics
0.60
0.56
0.47
0.32
0.55
0.43
0.15
0.18
0.35
0.00
1.0000
0.9187
0.8401
0.6566
0.6532
0.8150
0.4788
0.3174
0.4868
0.1427
χ2 (319)
2081.75 (¡0.0001)
GFI
0.8416
Notes: N = 940. GFI=Goodness of fit index, RMSEA=Root mean square error approximation.
33
–
27.29
22.87
21.26
17.75
18.62
10.69
7.12
12.39
3.79
RMSEA
0.0767
0.71
0.72
0.61
0.48
0.34
0.37
0.17
0.09
0.27
0.02
Table 4: Specification tests
Null hypothesis
34
ln Lr
LR statistics
Model without weights of attributes W
H0 : coef. related to W null
-554.71
52.20
χ20.01 (9) = 21.67
Reject H0
Model without quality perception Z ∗
H0 : coef. related to Ẑ1∗ and Ẑ2∗ null
-562.04
66.85
χ20.01 (2) = 9.21
Reject H0
Model without Z ∗ and W
H0 : coef. related to W , Ẑ1∗ and Ẑ2∗ null
-592.21
127.20
χ20.01 (11) = 24.72
Reject H0
Model with only the constant term
H0 : all coef. excepted the constant term null
-643.08
169.97
χ20.01 (34) = 56.06
Reject H0
Notes: N = 940. For the unrestricted (complete) model, ln Lu = −528.61.
Critical value
Decision
Table 5: Estimation results of the choice model
Estimated
Coef.
Variable
Bootstrap
Std. err.
Constant
I1p
I2p
Age
Not ecologically sensitivea
Not very ecologically sensitivea
Rather ecologically sensitivea
High income levela
Gives little importance to homea
Gives big importance to homea
SPG1
SPG3
SPG4
Reg1
Reg2
Reg3
Reg4
Single
Family
Woman
Owner
Modern
Classic
Rooms
1.7702∗∗∗
−0.0651
−0.0413
0.1700∗∗∗
−0.3326
−0.2458
−0.2832∗∗∗
0.7434∗∗∗
−1.0187∗
−0.2676∗∗∗
0.2668
0.0859
−0.1166
−0.2225
−0.3333∗∗
−0.2294∗
−0.1837
0.3844∗∗
0.1728
0.0741
−0.4208∗∗∗
−0.3059∗∗
−0.2935∗∗
0.0347∗
0.6738
0.0494
0.0535
0.0478
0.2718
0.1760
0.1091
0.2599
0.6119
0.1029
0.2021
0.1435
0.1560
0.1613
0.1417
0.1394
0.1766
0.1450
0.1098
0.0970
0.1182
0.1369
0.1170
0.0206
Quality perception of wood
Quality perception of vinyl
0.5268∗∗∗
−0.7616∗∗∗
0.1728
0.1271
Acoustic insulation
Thermal insulation
Product life
Aesthetics
Fire resistance
Maintenance
Environment
Safety
Price
0.0136
−0.2091∗∗
−0.0300
0.1985∗∗∗
−0.0208
−0.3165∗∗∗
0.0294
−0.0243
−0.0249
0.0639
0.1020
0.0688
0.0652
0.0466
0.0683
0.0506
0.0574
0.0628
Log-likelihood ln l
McFadden’s Pseudo R2
Correctly predicted
-528.61
0.1780
67%
Notes: Number of observations N = 940. Number of bootstrap
replications = 941. ***: significant at 1%, **: at 5%, *: at 10%.
a
Variables Ecological sensitivity, Finances and Home (defined in
Table A.6) initially built on a 1-5 scale have been tranformed
into dummies.
35
Table A.6: Descriptive statistics
Variable
Stated Choice y
Variables V
Description
Mean
Std.
Min
Max
=1 if wood is chosen
0.4330
0.4958
0.00
1.00
Assessment of insulation
Assessment of insulation
Assessment of brightness
=1 if windows in wood
=1 if windows in vinyl
=1 if recent change
=1 if recent information
Assessment of information
Assessment of information
Do-it-yourself rate
=1 if recent purchase
3.8553
3.8298
4.4319
0.5372
0.3128
0.0649
0.2543
3.0787
2.8819
3.3202
0.0840
1.3393
1.3514
0.9337
0.4989
0.4639
0.2465
0.4357
1.3152
1.3288
1.5007
0.2776
1.00
1.00
1.00
0.00
0.00
0.00
0.00
1.00
1.00
1.00
0.00
5.00
5.00
5.00
1.00
1.00
1.00
1.00
5.00
5.00
5.00
1.00
Importance to home
=1 if apartment
Number of rooms
=1 if owner (0 if tenant)
=1 if modern building
=1 if classic building
=1 if typical building
4.5489
0.3160
5.2883
0.6660
0.2287
0.5298
0.2298
0.7170
0.4651
3.1098
0.4719
0.4202
0.4994
0.4209
1.00
0.00
0.00
0.00
0.00
0.00
0.00
5.00
1.00
73.0
1.00
1.00
1.00
1.00
Class of ages
=1 if woman
=1 if single
=1 if single-parent family
=1 if couple
=1 if couple with kids
Ecologically-sensitive
Assessment of own finances
Farmer, craftsman, merchant
Executive, liberal profession
Intermediate profession
Employee
Manual laborer, service personnel
Unemployed, retired
City size
Paris region
Western region
North-Eastern region
South-Western region
South-Eastern region
3.2755
0.5840
0.1564
0.0372
0.2915
0.4957
3.9734
3.0021
0.0596
0.1074
0.1447
0.1106
0.1787
0.3989
2.8777
0.1660
0.2245
0.2426
0.1213
0.2457
1.2068
0.4931
0.3634
0.1894
0.4547
0.5002
1.0495
1.1611
0.2368
0.3098
0.3520
0.3139
0.3833
0.4899
1.4221
0.3722
0.4175
0.4289
0.3266
0.4308
1.00
0.00
0.00
0.00
0.00
0.00
1.00
1.00
0.00
0.00
0.00
0.00
0.00
0.00
1.00
0.00
0.00
0.00
0.00
0.00
5.00
1.00
1.00
1.00
1.00
1.00
5.00
5.00
1.00
1.00
1.00
1.00
1.00
1.00
5.00
1.00
1.00
1.00
1.00
1.00
1
Home thermal insulation
Home acoustic insulation
Home brightness
Home wooden windows
Home vinyl windows
Change of windows
Received information
Informed about windows
Informed about materials
Do-it-yourselfer
Purchase of windows
Variables V 2
Home
Apartment
Rooms
Owner
Modern
Classic
Typical
Variables V 3
Age
Woman
Single
Single parent
Couple
Family
Ecological sensitivity
Finances
SPG1
SPG2
SPG3
SPG4
SPG5
SPG6
City size
Reg1
Reg2
Reg3
Reg4
Reg5
Notes: Number of observations N = 940.
36