A FORMATIVE MODEL FOR MEASURING CUSTOMER SATISFACTION WITH A DEGREE COURSE

Dipartimento di Economia Politica e Aziendale

Università degli Studi di Milano

via Conservatorio, 7

20122 Milano

tel. ++39/02/50321501

fax ++39/02/50321450

E Mail: [email protected]

Pubblicazione depositata presso gli Uffici Stampa della Procura della Repubblica e della Prefettura di Milano

A

G

NICOLINI – F

DE BATTISTI

1

A Formative Model for Measuring Customer Satisfaction with a

Degree Course

Giovanna Nicolini - Francesca De Battisti

Dipartimento di Economia Politica e Aziendale

Università degli Studi di Milano

[email protected]

[email protected]

1. Introduction

Customer satisfaction with a service/product (p/s) can be measured through a survey

of the actual perception of the users or otherwise comparing their actual perception with

their expectations. More appropriately in the first case “quality” is considered, in the

second “customer satisfaction” (CS) (Cronin et al.1992,1994). Therefore to measure the

CS we have to compare the evaluations of the user with his expectations connected to

an ideal p/s. For some kinds of p/s such expectations are typically “subjective”, they

have to be gathered ad hoc; for others they can be suggested by the provider the p/s

referring to an optimum p/s; in this way the expectations are collected in an “objective”

way.

This paper proposes an index founded on objective expectations, with the aim of

measuring the CS of a service such as “a university degree course” (DC) provided by

the Italian universities. In fact the “quality of university teaching” has been tested for

many years now submitting a questionnaire to the students attending the courses and

present at the end of each course; the questionnaire is divided into four dimensions:

particular organisation of the teaching

,

characteristics of the teaching

,

characteristics

of the exercises

,

general organisation of the teaching within the overall degree course

.

A different number of attributes are associated to each dimension, with a different

question linked to each attribute, and the student answers using an item scale.

Afterwards we substitute items for scores, with

m

as the minimum value and

M

the

maximum, the same for all the questions. By means of an item scale the student tells his

personal satisfaction, while the expected satisfaction will be considered objectively the

same for all the questions and equal to the maximum value

M

on the same scale. It

follows that the level of satisfaction, for each question, can be defined by a function

measuring the differences between the scores observed and the maximum

M

. The

function chosen

G

is similar to that proposed by Minkowsky with parameter

λ

. And

since such a function is concerned with a single question, we have to use a model to

combine them in an overall CS measure.

To this end formative models are proposed; SERVQUAL is an example, it is a global

measure obtained by the weighted mean of the dimension indexes, which are simple

means of the differences between the scores observed and those expected by each

2

individual. In SERVQUAL the problem of weighting is concerned only with the

dimensions and is often solved by asking the interviewed to give a weight to the

dimensions themselves (with the constraint that the sum of weights is equal to one).

The index proposed in this paper is within the logic of the formative models, but

nonetheless presents some particulars regarding: a) a second level of aggregation, b) a

different number of respondents. In fact, in this case the means of the dimensions do not

lead to a CS global measure (as in SERVQUAL), but only to a partial measure

regarding the teaching; it is the combination of these latter measures that lead to the

overall CS index for DC. Lastly the different teaching courses are attended by a

different number of students and moreover often item non-response is verified.

While point a) is easily solved, point b) requires careful examination to establish

opportune weights for the different levels.

In paragraph 2 the index

G

is briefly set out, in paragraph 3 the method for

calculating the overall index is shown and lastly, in paragraph 4, the variability within

and between the intermediate levels (dimensions and teaching) is analysed to obtain the

overall variability measure.

2. Distance Indexes

G

Let

n

be a population size of individuals giving their personal satisfaction with an

attribute

y

of a service dimension; let

y

, with

k

=

1

,...,

K

;

m

≤

y

≤

M

, a score on an

evaluation scale and

n

, with

∑

n

=

n

, the number of individuals choosing

y

. To

obtain a function of the differences between the values

y

and the maximum value

M

we use the measures of distance proposed by Minkowski:

λ 1 k λ K 1 k k * λ

y

M

n

n

1

G

















−

=

∑

.

(1)

The index (1) can be normalised taking it to its maximum value (Fabbris, 2000):

λ λ λ λ λ λ λ λ 1 1 1 1 * *

1

)

(

)

(

_



_











−

−

=

























−

−

=

=

∑

∑

f

M

y

m

M

n

m

M

n

M

y

G

Max

G

G

,

(2)

where

f

=

n

n

.

We observe 0

≤

G

≤

1; in fact,

G

is equal to zero when all the units

of the population are gathered at the maximum value, it is equal to one when all the

units are gathered at the maximum distance from the optimum and take on increasing

values because of their gap from the optimum. Moreover such an index can be used

with any score scale and it is independent of the type of scale chosen (Fabbris, 2000).

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