Adoption of mobile telephony and multiattribute utility model
Privzemanje mobilne telefonije in večkriterijsko odločanje
Tomaž Turk
University of Ljubljana
Faculty of economics
Kardeljeva pl. 17
1101 Ljubljana
SLOVENIA
[email protected]
Abstract: The adoption process of high-technology products, such as information technology and
mobile telephony (information and communication technologies, ICT), is the main focus of this paper.
Firstly we describe the framework of factors which influence the adoption process, as proposed by Heres
and Mante-Meijer, in the light of expected utility. This framework helps to explain motivation and
feasibility of users' actions during the adoption process, but as a general framework it lacks a better
insight into relationships between factors. One possible tool for exploration is the multiattribute utility
approach. The paper explains the concept of utility maximization on the individual level. Empirical
cross-section models for utility estimation, which are based on the proposed framework, are then
developed and empirically tested with a dataset about mobile telephony usage, which was collected in
nine countries in Europe. The analysis is aimed to provide a starting set of parameters for a discreteoutcome microsimulation model.
Povzetek: V članku opisujemo dejavnike, ki vplivajo na odločitve posameznikov o nakupu različnih
rešitev s področja informacijske tehnologije in mobilne telefonije. Najprej opisujemo okvirni model,
predlagan s strani raziskovalcev J. Heresa and E. Mante-Meijer, ki naj bi pomagal pri razumevanju teh
dejavnikov, predvsem motiviranost in izvedljivost posameznikovih ukrepov pri nakupu in uporabi
informacijske tehnologije. Predlagan okvirni model je preveč splošen, da bi pojasnil povezave med
različnimi dejavniki. V tem članku v predlagani okvir vključujemo večkriterijski model koristnosti ter
napravimo primer empirične raziskave vplivov različnih dejavnikov na posameznikovo odločitev o
nakupu mobilnega telefona, na podlagi podatkov, ki smo jih pridobili v devetih evropskih državah.
Analiza naj bi ponudila osnovne parametre za parametrični mikrosimulacijski model.
-1-
1. INTRODUCTION
Today, stimulating adoption of mobile telephony and other ICTs (information-communication
technologies - products and services) is the most critical mission not only for the ICT companies, but
also for governments and other non-profit organisations. The proposition of Heres and Mante-Meijer
(2001) suggests that different effects, which influence the consumers' adoption process, can be grouped
into two categories. The first category considered is the context of the factor (whether it is person- or
product-related). On the other hand motivation and feasibility of the user's action seem to give a picture
of dynamics in the adoption process. In searching for possible connections among sets of factors in the
framework, it seems that the important thread or the "common denominator" can be the concept of
multiattribute utility as the basis of consumers' decisions.
In this paper we introduce one possible way to estimate the relevance of different factors for utility of
ICTs. Nowadays, our approach is common in econometrics and marketing research (see, for instance,
Franses and Paap, 2001). We show how utility behind people-related factors can be determined and
empirically estimated on the basis of EURESCOM P903 dataset (see Mante-Meijer, 2001 and
Concejero, 2001). Our analysis is focused on the users' decision wether to become a mobile phone owner
or not.
It should be emphasized that utility is purely a scientific construct, which helps us to understand a
decision-making process. In economic analysis it is often convenient to summarize a consumer's
behavior by means of a utility function, which is constructed by means of preference ordering. It can be
shown that if the preference ordering is complete, reflexive, transitive, and continuous, then it can be
represented by a continuous utility function. A utility function is often a very convenient way to describe
preferences, but it should not be given any psychological interpretation. The only relevant feature of a
utility function is its ordinal character (Varian, 1992). We can say that the utility function shows
preferences, but it cannot explain them.
On the other hand, the multi-attribute utility models can be used to estimate the importance of variables,
which influence the utility (and preference ordering). The formal background to this approach lies in the
field of Bayesian decision theory. With multi-attribute utility models one can show how factors
influence the decision process regarding not only adoption, but also the decisions that are behind usage
patterns (Roberts and Urban, 1988).
The utility approach can explain the actual choices between different substitute products or services.
Unfortunately, the estimation process for this problem cannot be adequately illustrated using the
mentioned dataset, but we believe that the research approach would be basically the same as in our
example.
2. UTILITY AND ICT PRODUCTS
The proposed framework (Heres and Mante-Meijer, 2001) suggests that factors that influence adoption
process can be grouped according to two dimensins (see Figure 1). How can wants and possibilities
regarding ICT products and services be regarded in the view of utility? Let's say that a user is making the
decision whether to buy an ICT product or not. Characteristics of a product are reviewed agaist the
user’s needs. Those characteristics can influence the product's utility in two different ways. Firstly, a
characteristic can be favourable or preferred (e.g. functionality), while others can be unwanted or
negative (e.g. complexity).
-2-
Wants
Possibilities
Person
Personal preferences and
lifestyle
Personal abilities
ICT –
person
Person wants to adopt the ICT:
perceived need or necessity
Person has the possibility to
adopt the ICT:
perceived easiness to adopt ICT
ICT
ICT functionalities
ICT constraints
Figure 1: Adoption of ICT, a two-dimensional framework (Heres and Mante-Meijer, 2001).
It is rather a tempting idea that we should choose this criteria to classify characteristics and divide them
between wants and possibilities. Is this feasible? A characteristic can have a mixed influence, because of
the relationships with other characteristics. Consider, for example, the product's price. Normally, we can
think about the price to be unwanted or negative (the higher the price, the lower the utility). If the
product can be classified as luxury for a certain group of users, then a relatively high price can influence
utility in a positive direction, so the price as a characteristic of a product can belong to wants.
An important thing in the utility concept is the understanding of utility maximization. Maximization is a
constant process and includes every decision, which is made by a person. It includes different decisions,
such as:
-
whether to buy an ICT product or not, or to subscribe to a service or not
-
which brand of an ICT product or a service to buy
-
which ICT product or service to use in a particular situation
-
which bundle of products and services to buy
-
the amount of products to buy and services to use.
The third type of questions is important for the adoption process, more precisely for the actual usage of a
product, and for domestication. The fourth and fifth type of questions are relativizations of the utility of
a product in question. The utility of a product is always compared to the utilities of a broad set of
products, which a person uses to satisfy his needs every day, including food, clothes, etc.
From that point of view we can explain, for instance, how the income of a user influences his decisions.
If he considers the idea about buying an ICT product, then its price is important. If his income is rather
low, the utility of food, for instance, is higher than the utility of an ICT product, which he sees as non
essential. If his income is average or high, the utility of an ICT product is higher in comparisson to the
utility of unnaturally high ammounts of food.
3. UTILITY AND USERS' DECISIONS
If a consumer should choose among two similar ICT products, we can mathematically represent his
decision process as:
U1 > U2
-3-
where U1 is utility of the first product and U2 is utility of the second product. In this case, where the
utility of the first product is greater than the utility of the second product, the consumer chooses the first
product. Similar, when consumer wants to replace an old product for a new one, he considers their
utilities and he might buy a new one, if he finds:
Unew ICT > Uold ICT
We can state that the consumer adopts a specific ICT as a tool when utility gained from the usage of a
new tool is higher than utility emerging from the usage of the existing (old) tool. When a user doesn't
own a product (he considers to buy one), he tries to get some information about it and this way he tries
to estimate the utility he could gain with it. The above formula becomes
U'new ICT > Uold ICT
U'new ICT is expected utility, estimated according to the user's knowledge about new ICT and his
experiences. How does a user estimate the utility? He considers different characteristics of it:
UA = kpUp(pA) + knUn(nA) + kwUw(wA)
(1)
where A is particular product, p, n and w are characteristics, or attributes of A, and k are weights. With
weights we can express different importances of particular attributes for different people. Someone
considers a colour of an ICT, while for another user it is more important for an ICT product to have an
infrared communication port. The overall utility is a sum of partial utilities, which are derived from
different attributes. Every attribute has its own utility function.
It is also interesting to consider different courses of actions, which users make with ICT products. For
instance, if a user has a mobile phone and also a stationary one, which one will he use to call a friend?
The user compares utility of different products and also his present means of satisfaction (old
technology, skills and methods).
4. HOW TO ESTIMATE UTILITY?
The utility concept can explain the reasoning behind users' decisions. If we can explain the reasoning, is
it also possible to predict users' behavior?
Both tasks request an empirical estimation of utility functions and weights, according to the model (1).
The utility functions can be estimated for a group of users with statistical tests, while weights are
different for different users. The utility functions can be estimated on the basis of samples for relatively
small groups of users with specific characteristics (e.g. older persons, high educated persons).
Nevertheless, there can be some other complications concerning statistical analysis. For instance, the
utility functions for different attributes are not necessarily independent from one another. The variables
in a chosen model should be selected carefully. The framework can give a good starting point in
understanding the relationships between different characteristics. One possible approach to the analysis
of decision-making is the development of decision "simulations" or what-if analysis and to compare the
results to the actual decisions in the real world.
In what-if analysis one needs to define utility functions and variables, which measure characteristics of
the products. The utility function of a particular characteristic can be defined on a strictly theoretical
basis, but the empirical evidence should be the final judge of the quality of such analysis. Reliable
-4-
results can be achieved and models can become more confident, if utility function definitions are
developed through empirical studies. One can observe and try to estimate the dependence of utility from
the variables, which describe the product's characteristics. According to the proposed framework, the
utility of a product or service can be observed also from the variables, which describe persons (their
social status, knowledge etc.). The problem is, that utility cannot be measured directly. Moreover, the
model (1) can be further empirically explored only on the level of an individual user. The rest of the
paper deals with the cross-sectional approach in estimating the importance of different factors on the
utility.
5. THE LATENT VARIABLE APPROACH
Categorical data collected within surveys is considered mostly as inherently categorical and is modelled
as such. In this approach there are direct one-to-one correspondences between population parameters of
interest and sample statistics. The focus is on estimating population parameters that correspond to their
sample analogs.
A somewhat different view on categorical data is also possible. The key to this approach is the existence
of a continuous unobserved or latent variable, underlying an observed categorical variable. When the
latent variable crosses a threshold, the observed categorical variable takes on a different value. It is more
convenient to think of the sample data as actual realizations of population quantities, which are
unobservable.
In the view of the utility concept and users' decision making, the observed response categories may
reflect the actual choices made by individuals in a sample. Underlying each choice at the population
level is the utility, which represents the difference between the costs and the benefits of a particular
choice made by an individual decision maker (Powers and Xie, 2000).
Consider a binomial dependent variable, which describes some users' decision (e.g. whether to adopt an
ICT product or not). It is better to replace the usual linear model
Yi = 0 + 1xi + i
where  is distributed normally
Yi ~ N(0 + 1xi + i , )
with a model with the Bernoulli distribution
Yi ~ BIN(1, F(0 + 1xi + i ))
(2)
where the function F has the property that it maps 0 + 1xi + i onto the interval (0, 1). One now focuses
on the probability that, for example, Yi = 1, given the outcome of 0 + 1xi + i. This can be written also
as
Pr[Yi = 1|Xi] = 1 - Pr[Yi = 0|Xi]
Xi collects the intercept and observed independent variables xi, while Yi denotes a random variable with
realization yi (1 or 0), which takes values conditional on the values of xi (Franses and Paap, 2001).
-5-
The latent variable (in our case utility) is continuous in nature. In case of a single explanatory variable
we can describe it as
yi*   0   1 xi   i
(3)1
Utility yi* is mapped onto the binomial variable yi according to the rule
Yi  1 if yi*  0
Yi  0 if yi*  0
This interpretation can be seen as "cost-benefit" approach to the understanding of decision-making.
Threshold value of utility can be chosen as equal to zero2. The user explores the expected utility gained
from using a particular service; when this value is positive, the user prefers the service, and the value of
observed variable Y for user i is set to 1.
Let us consider the user i who is making a choice between two products (or two brands of a product).
Denoting the products as A and B his evaluation of expected utilities can be written as
uA,i = A + Axi +A,i
uB,i = B + Bxi +B,i
(4)
The user prefers brand A if the utility of A exceeds that of B, as in
Pr[Yi = 1|Xi] = Pr[uA,i > uB,i|Xi]
= Pr[A - B + (A - B)xi > A,i - B,i |Xi]
= Pr[i ≤ 0 + 1xi|Xi]
where i equals A,i - B,i, 0 equals A - B and 1 is A - B. Note that individual parameters in (4)
cannot be identified, one can only identify the differences between them. Parameters 0 and 1 can be
seen as measurements of the effect of xi on the choice for brand A relative to brand B, which is in
accordance with the understanding of the utility concept.
We now have two interpretations of the equation (3). The first interpretation is helpful if we consider
different factors, which influence the decision process, including user-related. On the other hand, the
1
In practice normal distribution is chosen for the Probit model and logistic distribution is chosen for the
Logit model. In general, we can say that the probability of observing Yi = 1 given Xi is equal to the
cumulative distribution function of i, evaluated at Xi, or
Pr[Yi = 1|Xi] = F(Xi)
For Probit, we have F(Xi) = (Xi), and for Logit F(Xi) = (Xi). For further reference see e.g.
Franses, Paap (2001) and Powers, Xie (2000).
2
In parameter estimation the threshold value is normally set to 0 due to identification. If the threshold
value were , the intercept parameter in (3) would change to 0 - .
-6-
second possible interpretation can be used when there are two or more products or brands of the same
product to compare. In the next section we will show how it is possible to estimate the utility of
becoming an owner of an ICT product, considering factors related to users.
6. UTILITY AND USERS' RELATED FACTORS
From the description of the framework we can derive some groups of users related factors, which we can
include in the model (3). We can identify groups such as
a) professional and financial status
b) social network
c) education
d) mobility
e) ICT orientation
f) age.
In estimating utility one should be cautious in interpreting the results. We are not measuring the impact
of a particular factor per sé, but its indirect impact to the expected utility. With financial status we aren't
trying to measure the fact that, for instance, rich people have better opportunities to buy an ICT product,
but whether the expected utility of ICT product is, on average, higher or lower for those with greater
income.
The basis for our work is data collected during the EURESCOM P903 "Cross Cultural Attitudes to ICTs
in Everyday Life" project. The findings of this project are based on a mix of qualitative and quantitative
research, the former involving 36 focus group discussions in six countries, the latter involving a survey
conducted in year 2000 in nine European countries, with 1000 respondents each. Further methodological
facts about collected datasets, analyses and results can be found in Mante-Meijer (2001) and Concejero
(2001).
The factors described above have been used to choose a set of 12 variables from the P903 dataset:
-
MICT - respondent is owner of a mobile phone (1 - true or 0 - false)
-
INCCAT - income category of a household (total household income before tax and other deductions)
-
INCOME - real monthly household equivalence income (before tax) in hundreds of Euros3
-
ACTIVE - reported professional position of respondent as "active" (1 - true or 0 - false)
-
NETSIZE - size of the social network (number of persons)
-
HOUSIZE - household size (number of persons)
3
Survey dataset includes variable INCCAT, which puts a household into one of the income categories
(or classes). Different classes were defined for each country. We have derived the INCOME variable
from those grouped data with class marks (in the dataset this variable is called "monthly gross household
income in Euros"). Next we have calculated income per "consumption unit" according to the OECD
methodology. This way we took into account the number of household members (i.e. "monthly gross
household equivalence income in Euros"). We have then used gross domestic product per capita to
relativise the family economic situation across different countries of residence. This way we were able to
compare INCOME level between households in different countries, regardless of the different economic
situation in each particular country ("real monthly gross household equivalence income in Euros").
-7-
-
EDUC - education, respondent (1 - incomplete or primary, 2 - secondary level, 3 - tertiary level)
-
WTRIP - work related trips in the last year (number of trips)
-
VTRIP - vacation or weekend trips in the last year (number of trips)
-
INT_EASY - "The Internet is easy to use" (from 1 - agree to 5 - disagree)
-
MOB_FUN - "Using a mobile telephone is fun" (from 1 - agree to 5 - disagree)
-
AGE - age of the respondent in years.
Some descriptive statistics based on this set of variables are given in Table 1.
MICT
INCOME
ACTIVE
NETSIZE
HOUSIZE
EDUC
WTRIP
VTRIP
INT_EASY
MOB_FUN
AGE
Valid N
Factor
Group
N
a)
a)
b)
b)
c)
d)
d)
e)
e)
f)
9079
7021
9079
9079
9079
8419
5072
8294
7166
8235
9051
2955
Range
90,51
537.00
5.00
220.00
250.00
79.00
Minimum Maximum
1.00
1,80
0.00
0.00
1.00
1.00
0.00
0.00
1.00
1.00
15.00
2.00
92,31
1.00
537.00
6.00
3.00
220.00
250.00
5.00
5.00
94.00
Mean
1.38
12,84
0.56
60.74
2.63
1.98
2.76
4.83
2.31
3.07
42.64
Std.
Variance
Deviation
0.49
0.24
7,08
50,19
0.49
0.25
47.33 2.240.26
1.31
1.73
0.71
0.50
11.96
143.07
10.13
102.67
1.21
1.47
1.32
1.73
17.10
292.42
Table 1: Descriptive statistics of the variables used in our analysis, collected during the EURESCOM P903 project. Variable INCCAT (factor group a) is defined in different ways depending
on the country of the respondent, so it is not shown in this table. For reference see further
analysis and Mante-Meijer (2001) and Concejero (2001).
7. ESTIMATION AND RESULTS
Firstly we estimated three different models on the basis of sample data for respondents in all nine
countries. The first model includes variables ACTIVE, EDUC, WTRIP, MOB_FUN and AGE. From the
Table 2 we can see that the estimated odds of owning a mobile phone for professionally active people
are exp(0.644) = 1.9 times that of professionally inactive. Stated differently, the odds of having a mobile
phone for inactive people are 1 / exp(0.644) = 0.53.
For continuous measures such as AGE we can use the term multiplicative effect to express the effect of
changes in AGE on the odds of owning the mobile phone. For example, for a person, that is one year
older, the odds of owning a mobile phone are exp(-0.026) = 0.97 times lesser. Similarly, for a person,
that is one year younger, the odds of owning a mobile phone are 1 / exp(-0.026) = 1.03 times greater.
-8-
Const
INCOME
ACTIVE
NETSIZE
HOUSIZE
EDUC
WTRIP
MOB_FUN
AGE
Log L
2
D.F.
Model 1
Std.
B
Error Exp(B)
1.528 0.1756
0.644 0.0814 1.9035
0.350 0.0524 1.4190
0.028 0.0061 1.0285
-0.276 0.0283 0.7589
-0.026 0.0025 0.9746
3,337.035
484.548
5
Model 2
Std.
B
Error Exp(B)
1.285 0.1811
0.659 0.0818 1.9323
0.004 0.0008 1.0044
0.330 0.0526 1.3906
0.025 0.0060 1.0258
-0.285 0.0286 0.7521
-0.025 0.0026 0.9754
4,883.643
517.225
6
Model 3
Std.
B
Error Exp(B)
1.2751 0.2113
0.0406 0.0069 1.0414
0.5003 0.0984 1.6492
0.0052 0.0009 1.0052
0.2366 0.0618 1.2669
0.0225 0.0067 1.0228
-0.3125 0.0331 0.7316
-0.0279 0.0030 0.9725
3,770.448
470.976
7
Table 2: Logit estimates for binomial variable MICT (owner = 1) for individual-level data. All
beta coefficients are statistically significant at 0.001.
The model seems promising, according to the values of Log L and 2 tests. Even more important are the
estimated coefficients. Their values and signs are as expected, for instance:
-
the odds of having a mobile phone for professionally active persons are > 1.0
-
the odds of owning a mobile phone increase with education
-
for a one trip increase in the number of work related trips in the last year, the odds of having a
mobile phone increase by 1.02 times
-
for ICT-prone persons the odds of having a mobile phone are higher (1 / exp(-0.276) = 1.31),4
-
it is more likely to own a mobile phone for younger persons.
The second model includes the variable, which gives the size of the social network of the respondent
(NETSIZE). The model shows that the odds of having a mobile phone for each new person in the social
network increase by exp(0.004) = 1.004 times. It is important to note, however, that other variables in
the model have almost the same coefficients as in the first model. The values of the Log L and 2 tests
are higher for the second model.
In the third model the real monthly household equivalence income (before tax) in hundreds of Euros has
been added (INCOME). From the values of Log L and 2 tests we can see that the model is not more
significant than the second. Based on the results of the third model, we can say that, on average, a one
hundred Euro increase in real monthly household equivalence income rises the odds of having a mobile
phone by exp(0.04) = 1.04 times.
Some other combinations of variables are possible, but we have found such models to be inadequate. We
determined, for example, that household size (HOUSIZE) is not significant, if we use real monthly
household equivalence income to measure the economic situation of a household. On the other hand, if
we use real monthly household income for that purpose, which is just a "lump sum" of funds for all
4
Note that the scales for variables MOB_FUN and INT_EASY are defined in descending order from 1 agree to 5 - disagree.
-9-
members of a household, HOUSIZE becomes significant. We believe the former way of estimation is
more consistent.
1,0
Probability
0,9
1,0
Probability
0,9
0,8
0,8
0,7
0,7
0,6
0,6
0,5
0,5
0,4
0,4
0,3
0,3
0,2
0,2
Situation a)
Situation b)
Situation c)
0,1
Situation a)
Situation b)
Situation c)
0,1
0,0
0,0
0
50
100
150
200
250
300
350
400
450
500
Size of the social network (persons)
1,0
Probability
0,9
15
25
35
45
55
65
75
85
Age (years)
1,0
Probability
0,9
0,8
0,8
0,7
0,7
0,6
0,6
0,5
0,5
0,4
0,4
0,3
0,3
0,2
0,2
Situation a)
Situation b)
Situation c)
0,1
0,1
0,0
0,0
0
25
50
75
100
Situation a)
Situation b)
Situation c)
125
150
175
200
Work related trips (times in the last year)
0
10
20
30
40
50
60
70
80
90
100
Real monthly household equivalence income (before tax) in hundreds of Euros
Figure 2: Probability of owning a mobile phone by the size of the social network, age, work
related trips and real monthly household equivalence income in hundreds of Euros.
Some other variables are also redundant, for instance the variables INT_EASY and MOB_FUN indicate
the same factor, so we excluded INT_EASY from the three models presented in Table 2. Whether there
is a redundancy problem with WTRIP and VTRIP (the number of work related trips in the last year and
the number of vacation and weekend trips in the last year), we cannot say on the basis of those results
(see also concluding remarks). We found that the variable VTRIP does not appear to be significant.
- 10 -
Const
INCCAT
ACTIVE
NETSIZE
EDUC
WTRIP
VTRIP
INT_EASY
MOB_FUN
AGE
Log L
2
D.F.
Czech Republic (n = 389)
Std.
B
Sig.
Exp(B)
Error
1.3978
0.4609 0.0024
0.0551
0.0193 0.0043 1.0566
0.0300
0.0100 0.0026 1.0304
-0.2058
0.0983 0.0364 0.8140
-0.0306
0.0100 0.0021 0.9699
471.440
49.142
4
Germany (n = 880)
B
Const
INCCAT
ACTIVE
NETSIZE
EDUC
WTRIP
VTRIP
INT_EASY
MOB_FUN
AGE
Log L
-59.2888
0.2053
0.5149
-0.6595
-0.0321
2
D.F.
Std. Error
12.2273
0.0402
0.1713
0.0827
0.0053
860.129
214.977
4
Sig.
0.0000
0.0000
0.0027
0.0000
0.0000
Exp(B)
1.2278
1.6734
0.5171
0.9684
Denmark (n = 730)
Std.
B
Sig.
Error
-40.1078 6.2894 0.0000
0.2101 0.0307 0.0000
-0.3784 0.0671 0.0000
-0.0186 0.0054 0.0006
680.854
103.618
3
Italy (n = 675)
Std.
B
Sig.
Error
1.8637 0.2883 0.0000
0.9535 0.1746 0.0000
-0.3645 0.0759 0.0000
-0.0256 0.0056 0.0000
518.095
103.321
3
Norway (n = 912)
B
Const
INCCAT
ACTIVE
NETSIZE
EDUC
WTRIP
VTRIP
INT_EASY
MOB_FUN
AGE
Log L
2
D.F.
-203.8837
0.2935
0.0030
-0.1796
-0.0199
Std. Error
42.6792
0.0607
0.0016
0.0648
0.0048
938.878
75.689
4
Sig.
0.0000
0.0000
0.0550
0.0056
0.0000
Exp(B)
1.3411
1.0030
0.8356
0.9803
B
1.1693
0.8786
0.2861
-0.3395
-0.0235
Exp(B)
1.2338
0.6849
0.9816
Exp(B)
2.5947
0.6945
0.9748
Spain (n = 675)
Std.
Sig. Exp(B)
Error
0.4512 0.0096
0.1786 0.0000 2.4075
0.1447 0.0480 1.3312
0.0770 0.0000 0.7121
0.0057 0.0000 0.9768
606.486
107.252
4
France (n = 813)
Std.
B
Sig.
Error
-65.5902 20.9271 0.0017
0.1706
0.0517 0.0010
0.0068
0.0023 0.0039
-0.4453
0.0699 0.0000
-0.0476
0.0057 0.0000
911.125
176.196
4
Netherlands (n = 596)
Std.
B
Sig.
Error
-93.9712 38.7664 0.0153
0.1614
0.0644 0.0121
0.0052
0.0024 0.0288
-0.4480
0.0716 0.0000
-0.0310
0.0070 0.0000
589.787
93.380
4
B
-134.7092
0.1529
0.0431
-0.3499
-0.0394
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1.1861
1.0068
0.6406
0.9535
Exp(B)
1.1752
1.0052
0.6389
0.9695
UK (n = 562)
Std.
Sig. Exp(B)
Error
45.9098 0.0033
0.0509 0.0027 1.1652
0.0200 0.0308 1.0441
0.0766 0.0000 0.7047
0.0059 0.0000 0.9614
587.238
111.638
4
Table 3: Logit estimates for binomial variable MICT (owner = 1) for individual-level data for
each country participating in EURESCOM P903.
According to the results of Model 3 we can write
Exp(B)
logit[Pr(MICT = 1)] = 1.28 + 0.04 INCOME + 0.5 ACTIVE + 0.005 NETSIZE + ··· (5)
··· + 0.24 EDUC + 0.02 WTRIP - 0.32 MOB_FUN - 0.03 AGE
The marginal effects for continuous measures can be shown graphically. One can plot the predicted
probability for a range of values of the continuous variable, keeping other variables fixed at some
arbitrary value. Results from Model 3 are presented in Figure 2, keeping variables at these arbitrary
chosen values:
-
INCOME at its mean value 12,84 hundreds of Euros,
-
NETSIZE at its mean value 60.74 persons,
-
AGE at value 30 years and
-
WTRIP at its mean value 2.76 trips.
Graphs are plotted for three different hypothetic cases:
a) a professionally active person with secondary level education, who would answer the question
MOB_FUN with "neither agree nor disagree" (situation a);
b) a professionally inactive person with secondary level education, who would answer the question
MOB_FUN with "neither agree nor disagree" (situation b); 5
c) a professionally active person with tertiary level education, who would answer the question
MOB_FUN with "neither agree nor disagree" (situation c).
We also performed a slightly different analyses for each participating country (see Table 3). To illustrate
the differences in explaining the results we changed the calculated variable INCOME with categorical
variable INCCAT, which derives directly from the questionnaire. For each participating country the
definition of classes for the INCCAT variable is different (see Mante-Meijer, 2001 and Concejero,
2001). We found, for example, that on average, if a respondent's household in Denmark increases its
income and moves to the next income class, the odds of having a mobile phone for a respondent increase
by exp(0.21) = 1.23 times.
Variables INT_EASY and MOB_FUN appear as significant in all nine models, but we have included
just one of them for the same reasons as listed in the general model. We think that using MOB_FUN is
accurate and the significance for almost all countries confirms this, but the significance of INT_EASY is
higher for Italy and Spain.
NETSIZE appears to be statistically significant in France, Netherlands and Norway. Their coefficients
are positive but rather low in absolute value. VTRIP is statistically significant only for the Czech
Republic and UK. It's also interesting that HOUSIZE is excluded from all nine models due to
insignificance. This is consistent with our findings in the Model 3, as stated above.
8. CONCLUDING REMARKS AND FUTURE WORK
We have shown that the estimated coefficients could be a good starting point for an explanation and
understanding of the reasoning behind the decision process, that occurs when users are making choices
5
It seems odd to compute the probability with regard to the number of work related trips for inactive
persons. The fact is that the number of work related trips was measured for the period of last year. In that
time interval the interviewed person could have become retired or inactive in some other way.
- 12 -
about ICT product adoption and usage. It is possible to use estimated coefficients for "what-if" analyses
and for deducting how people behave on average. Very plain what-if analyses can be conducted from
plots (as those presented on Figure 1), more complex ones using models like (5). If we collected enough
data for people with certain characteristics, we would be able to go further and research more detailed
questions for smaller groups of people. We have made a step further in this direction with calculations
for people living in different European countries. A tempting idea is also to investigate the situation in
Slovenia and other central-european countries.
It is important to emphasize the fact that our analysis is dealing with user-related factors. When ICT- and
people-ICT-related factors are also taken into account, one can develop more detailed models which
describe reasoning behind choosing between different brands of ICT products, different services, etc.
Our analysis can be widened with close examination of the variables included in the EURESCOM P903
dataset, especially with regard to their connections. We believe that different variables are categories,
which can be measured, and that they can reveal latent factors (take, for instance, the problem of
similarity of INT_EASY and MOB_FUN, WTRIP and VTRIP). One possible approach to researching
connections between variables in the search for common factors can be variables clustering.
With the framework (Heres and Mante-Meijer, 2001) and the utility approach one can look deep into
relationships among several factors, which are important for users' decision making. Empirical
estimation can give ideas about the importance of different factors. Based on that knowledge,
simulations and what-if analyses can be made, showing the most probable outcomes when designing
new ICT products and services.
9. REFERENCES
1. Concejero, P. (Ed): ICT Uses in Everyday Life. Checking it out with the people. ICT Markets and
users In Europe (detailed documents). Volume 2: Quantitative report. Project P903, EURESCOM,
2001.
2. Franses, P.H. and Paap, R.: Quantitative models in marketing research. Cambridge : Cambridge
University Press, 2001. ISBN 0-521-80166-4.
3. Heres, J., Mante-Meijer, E.: Abstract - Stimulating adoption of mobile telephony: a proposed
framework. Paper presented at the E-Usages 3rd International Conference on Uses & Services in
Telecommunications, Paris, 2001.
4. Mante-Meijer, E. (Ed): ICT Uses in Everyday Life. Checking it out with the people. ICT Markets
and users In Europe (detailed documents). Volume 1: Set-up of the research and qualitative report.
Project P903, EURESCOM, 2001.
5. McFadden, D.: Conditional logit analysis of qualitative choice behaviour. Published in: Zarembka,
P. (ed.): Frontiers in econometrics. Academic Press, New York, 1973.
6. Powers, D.A. and Xie, Y.: Statistical methods for categorical data analysis. Academic Press, New
York, 2000. ISBN 0-12-563736-5.
7. Roberts, J.H. and Urban, G.L.: Modelling multiattribute utility, risk and belief dynamics for new
consumer durable brand choice. Management Science 34, (1988), 167-185.
8. Varian, H.R.: Microeconomic Analysis. W. W. Norton & Company, New York, 1992. ISBN: 0-39395735-7.
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