a classification model for prediction of certification motivations

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A classification model for prediction of certification motivations

from the contents of ISO 9001 audit reportsPaulo Sampaioa∗, Pedro Saraivab and António Guimarães Rodriguesc

aProduction and Systems Department, University of Minho, 4710-057, Braga, Portugal;bChemical Engineering Department, University of Coimbra, 3030-290, Coimbra, Portugal;cProduction and Systems Department, University of Minho, 4710-057, Braga, Portugal

ISO 9001 certification motivations can be classified into two main categories: (1)internal motivations; and (2) external motivations. Internal motivations are relatedwith genuine organisational improvement goals (productivity, internal communication,process performance), while external motivations are mainly related to promotional

and marketing issues (customer and market pressures, market share). Some companiesthat become certified mostly upon the basis of external motivations define their maingoal as ‘obtaining registration’, and thus typically adopt a limited view over thescope of quality management systems implementation and certification.

Based upon a detailed review of 100 ISO 9001 audit reports, we performed a detailedstatistical comparison between both types of motivations driving companies in theircertification efforts, explored their differences and similarities and derived a statisticallybased classification model that was able to predict, for a particular organisation, whatkind of predominant motivation lead to its certification, from information that can beretrieved from the contents of the corresponding ISO 9001 audit reports.

Keywords: ISO 9001; quality management; classification model; statistics

Introduction

According to the ISO 9001 related literature, a company becomes certified based mainly

upon either internal motivations and/or external motivations. Internal motivations arepresent in those companies that are really committed to the continuous improvement of

their internal processes, and therefore aim to achieve effective organisational improve-

ments. External motivations, on the other hand, are related mostly to promotional and

marketing issues, customers and market pressures and market share enlargement goals.

Even though all organisations present both kinds of motivations to some extent, only

one is usually the most predominant and determines the organisations’ decision to

become ISO 9001 certified. The implementation and certification of a quality managementsystem should be both an important organisational improvement tool – an internal motiv-

ation, as well as a marketing and competitive advantage for certified companies – an

external motivation (Sampaio, Saraiva, & Guimarães Rodrigues, 2009). However, the

motivation for doing so is usually dominated by one or the other of both factors mentioned.

In this paper we will illustrate some key results derived from a detailed analysis of a

sample of 100 ISO 9001 audit reports that correspond to Portuguese certified companies.

This sample was randomly selected from a group of companies certified by the leading Por-

tuguese certification body – Associação Portuguesa de Certificação (APCER, 2006). The

∗Corresponding author. Email: [email protected]

Total Quality Management

Vol. 21, No. 12, December 2010, 1279–1298


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analyzed audit report, for each company, was in all cases related to the last ISO 9001 audit

performed, regardless of its nature (certification/registration, surveillance or follow-upaudits).

With such a detailed analysis conducted on all of the reports, in what we believe to be a

pioneering contribution in this field, as far as Portugal is concerned, we tried to examine

and explore the following issues:

(1) Most common non conformities (NC) raised in ISO 9001 audits.

(2) Standard clauses with more non conformities.

(3) Number of non conformities by activity sector.

(4) Number of non conformities by auditor.

(5) Number of non conformities by company, and its relationship with company size.

Our main research question was: ‘Can we predict a firm’s motivation in obtaining ISO

9001 certification form its audit report?’. Thus, based upon the previous results, we

proposed, developed and tested a statistical classification model, aimed at predicting the

companies ISO 9001 certification main motivation from their audit report profiles. Sucha model has as its major inputs variables that can be identified and derived from an ISO

9001 audit report, and allowed us to identify and predict if a particular company faced

quality management system implementation and ISO 9001 certification as a real commit-

ment – internal motivations, or, on the other hand, if the company became certified mostly

because of promotional and marketing issues – external motivations.

Quantitative results allowed us to evaluate the classification performance of our model,

both through training and testing sets of data, confirming its statistical significance and

validity. We used the Statistical Package for Social Sciences 15.0 (SPSS) to perform

the statistical analyses.

APCER is a private Portuguese organisation dedicated to the certification of manage-ment systems, services, products and people as a method of guaranteeing quality and

promoting the competitive advantage of organisations, whether public or private, national

or international.

In Portugal, APCER is the clear market leader. More than 4500 certificates of confor-

mity have been issued since its foundation, including the certification of organisations in

Spain, Morocco, Mozambique, Angola, Brazil and China (Macao).

APCER is the only Portuguese certification entity representing the international

network IQNet – The International Certification Network, which bestows immediate

international recognition on organisations certified by APCER.

Literature review

ISO 9001 certification motivations and benefits

ISO 9001 certification motivations can be classified according to one of two main

categories: internal and external motivations. Internal motivations are related to the goal

of achieving organisational improvement, while external motivations are mainly related

to promotional and marketing issues, customer pressures and market share gains (Brown,

van der Wiele, & Loughton, 1998; Bryde & Slocock, 1998; Buttle, 1997; Corbett, Luca,

& Pan, 2003; Douglas, Coleman, & Oddy, 2003; Escanciano, Fernandéz, & Vasquez,

2001; Gonzaléz, 2001; Gotzamani & Tsiotras, 2002; Gustafsson, Klefsjo, Berggren, &

Granfors-Wellemets, 2001; Jones, Arndt, & Kustin, 1997; Lee & Palmer, 1999; Lipovatz,

Stenos, & Vaka, 1999; Llopis & Tarı́, 2003; Magd & Curry, 2003; Mo & Chan, 1997;

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ISO 9001 certification is frequently used mostly as a marketing tool (Poksinska et al.,

2002). Jones et al. (1997) defined two organisation types, according to their main purpose

for achieving certification: the ‘non-development companies’, which are those whose

primary reason for seeking quality certification is driven by the mentality of ‘achieving

a certificate’; and the ‘developmental companies’, which are those that adopt quality

certification because of their belief in the internal benefits that can derive from it.

In more detail, Magd and Curry (2003) concluded that the most important reasons for

certification, among Egyptian companies, were the following: ‘improve the efficiency of

the quality system’; ‘pressures from competitors/foreign partners’; ‘to maintain/increasemarket share’; ‘to meet government demands’ and ‘to comply with customers’ require-

ments’. Some companies also stated that without ISO 9001 certification they cannot

achieve a significant number of contracts (Douglas et al., 2003). Corbett et al. (2003),

based in an international survey, concluded that the main motivations for ISO 9001

certification are as follows: ‘quality improvements’; ‘improvements in corporate

image’; ‘marketing advantage’; and ‘customer pressure’. Concerning US companies,

one of the most important underlying reasons for becoming certified is the existence of commercial relationships with European markets (Bhuiyan & Alam, 2004).

Like the motivations, ISO 9001 certification benefits can be also classified into exter-

nal and internal categories (Bhuiyan & Alam, 2004; Brown et al., 1998; Buttle, 1997;

Casadesús, Giménez, & Heras, 2001; Casadesús, Heras, & Arana 2004; Coleman &

Douglas, 2003; Corbett et al., 2003; Douglas et al., 2003; Escanciano et al., 2001; Escan-

ciano, Fernández, & Vasquez, 2002; Gotzamani & Tsiotras, 2002; Gustafsson et al., 2001;

Halis & Oztas 2002; Leung, Chan, & Lee, 1999; Liebesman, 2002; Lipovatz et al., 1999;

Magd & Curry, 2003; Mo & Chan, 1997; Poksinska et al., 2002; Ragothaman & Korte,

1999; Staines, 2000; Stevenson & Barnes, 2001; Torre et al., 2001; van der Wiele,

Iwaarden, Williams, & Dale, 2005).Casadesús et al. (2001) proposed a classification for ISO 9001 benefits based upon the

perceived benefits obtained, suggesting four organisation types: ‘companies with high

internal benefits (HIB)’; ‘companies with moderate internal benefits (MIB)’; ‘companies

with high external benefits (HEB)’ and ‘companies with moderate external benefits

(MEB)’ (Table 1).

Although ‘product quality improvements’ are often quoted as an important ISO 9001

benefit, such an improvement may not be the direct result of a Quality Management

System implementation (Withers & Ebrahimpour, 2001).

Table 1. Most commonly stated ISO 9001 certification benefits reported in the literature.

External benefits Internal benefits

B access to new markets;B corporate image improvement;B market share improvement;B ISO 9000 certification as a marketing tool;B customer relationship improvements;B customer satisfaction;B customer communication improvements.

B productivity improvements;B product defect rate decreases;B quality awareness improvements;B definition of the personnel responsibilities

and obligations;B delivery times improvements;B internal organisation improvements;B nonconformities decreases;B customers complaints decreases;

B internal communication improvements;B product quality improvement;B competitive advantage improvement;

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There is a consensual opinion that ISO 9001 benefits are related to company certification

motivations, i.e. when companies become certified based upon internal motivations the

derived benefits are fulfilled on a more global dimension. On the other hand, when companies

implement ISO 9001 based mostly on external motivations, improvements obtained are then

mainly of an external nature (Brown et al., 1998; Corbett et al., 2003; Gotzamani & Tsiotras,

2002; Jones et al., 1997; Llopis & Tarı́, 2003; Poksinska et al., 2002; Williams, 2004).

Companies that sought quality certification for ‘developmental reasons’ had experi-

enced more internal benefits from certification (Jones et al., 1997). Brown et al. (1998)

argued that companies driven by internal reasons to seek certification have a more positive

perception about improvements achieved. The manager that sees certification as an

opportunity to improve internal processes and systems, rather than simply wanting to get

a certificate on the wall, will get broader positive results from ISO 9001 certification.

Gotzamani and Tsiotras (2002) stated that companies seeking ISO 9001 certification

mainly based upon external motivations will also achieve mostly external benefits, while

those that seek certification based on true quality improvement will get benefits mainly

in terms of internal operations improvement (Poksinska et al., 2002; Williams, 2004).Llopis and Tarı́ (2003) suggest that companies more concerned about internal reasons

are those that:

. Obtain higher profits deriving from the implementation of a quality system.

. Reach a greater practical implementation of quality management principles.

. Are most likely to progress towards total quality management.

Logistic regression model

Regression methods have become an integral component of any data analysis concernedwith describing the relationship between a response variable and one or more explanatory

variables. It is often the case that the outcome variable is discrete, taking on two or more

possible values.

The main goal of a logistic regression model is to find the best fitting and most parsi-

monious model to describe the relationship between an outcome (dependent or response

variable) and a set of independent variables (Hosmer & Lameshow, 1989).

According to Hosmer and Lameshow (1989), the difference between a logistic

regression model and a linear regression model is that the outcome variable in logistic

regression is binary. This difference is reflected both in the choice of a parametric

model and in the underlying assumptions. Once this difference is accounted for, the

methods employed in an analysis using logistic regression follow the same general

principles used in linear regression.

In any regression problem the key quantity to be predicted is the mean value of the

outcome variable, given the value of the independent one(s). According to Hosmer and

Lameshow (1989), this quantity is called the conditional mean and is expressed as

E(Y | x), where Y denotes the outcome variable and x denotes a value of the independent

one(s). In linear regression we assume that this mean may be expressed as an equation

linear in x, such as:

E (Y x) = b 0 + b 1 x (1)

According to Equation (1), E (Y | x) can take any value as x ranges from 21 to +1.



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zero and less than or equal to one. The change in E (Y | x) per-unit change in x becomes

progressively smaller as the conditional mean gets closer to zero or one.

According to Cox (1970) there are two primary reasons for choosing the logistic

distribution. These are: (1) from a mathematical point of view, it is an extremely flexible

and easily used function; and (2) it lends itself to a biologically meaningful interpretation.

Assuming that p (x) ¼ E (Y | x), the logistic regression model form is as follows:

p ( x) =exp(b 0 + b 1 x1 + ...+ b i xi)

1 + exp(b 0 + b 1 x1 + ...+ b i xi)(2)

Performing the logit transformation in terms of p (x), the logistic regression model is

defined as:

g( x) = ln(p ( x)

1 − p ( x)) = b 0 + b 1 x1 + ...+ b i xi (3)

The importance of the logit transformation (2) is that g(x) has many of the desirable

properties of a linear regression model. The logit is linear in its parameters, may be continu-ous and range form 21 to +1, depending on the range of x (Hosmer & Lameshow, 1989).

One of the most important differences between the linear regression model and the

logistic one is that in the linear regression model we assume that an observation of

the outcome variable may be expressed as y ¼ E (Y | x) + 1. The quantity 1 is called the

error and expresses an observation’s deviation from the conditional mean. The most

common assumption is that 1 follows a normal distribution with mean zero and some

variance that is constant across levels of the independent variable. It follows that the

conditional distribution of the outcome variable given x will be normal with a mean of

E (Y | x), and a variance that is constant. When the outcome variable is dichotomous, we

may express the value of the outcome variable given x as y¼

p (x) + 1. Here the quantity1 may assume one of two values (Hosmer & Lameshow, 1989):

. If y ¼ 1, then 1 ¼ 1 2 p (x) with probability p (x).

. If y ¼ 0, then 1 ¼ 2p (x) with probability 1 2 p (x).

Thus, 1 has a distribution with mean zero and variance equal to p (x)[1 2 p (x)]. The

conditional distribution of the outcome variable follows a binomial distribution with

probability given by the conditional mean, p (x).

According to Hosmer and Lameshow (1989), when the outcome variable is dichoto-

mous it is important to point out that:

. The conditional mean of the regression equation must be formulated to be boundedbetween zero and one.

. The binomial distribution describes the distribution of the errors and will be the

statistical distribution upon which the analysis is based.. The principles that guide an analysis using linear regression will also guide logistic

regression.

Analysis and discussion of results

Audit reports exploratory data analysis

The first results are derived from the contents and exploratory data analysis we performed

on over 100 ISO 9001 audit reports, randomly selected from the database of the Portuguese



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. More than 80% of the companies are of a Small or Medium size.

. Overall, 510 Non Conformities (NC) were identified.

. The clauses 8.2 – Monitoring and measurement; 4.2 – Documentation requirements;

7.5 – Production and service provision; and 7.4 – Purchasing were responsible for

50% of the total number of NC identified.. Forty-three percent of the NC belong to Chapter 7 – Product realisation.. Nine major NC were identified.

A more detailed overview of the NC, as they are related with the different clauses and

sections of the ISO 9001 standard, is presented below.

a) Non conformities per ISO 9001 clause

According to Figure 1, approximately 50% of NC belong to clauses 8.2; 4.3; 7.5;

and 7.4. We can also state that the number of NC in Chapter 7 has a preponderant

weight over the total number of NC – 43%. The second position belongs to clause4.2 – Documentation requirements. The highest number of NC was verified in clause

8.2 – Monitoring and measurement, which by itself alone is responsible for 13% of

the total number of NC.

On a research project performed over 227 USA companies, Liebesman (2002) ident-

ified clause 4.2 – Documentation requirements as being the one with the highest number

of NC (23%), followed by clauses 5.1 and 5.4, both with a score of 10%. In our case, as

shown above, clauses 5.1 and 5.4 do not seem to have a significant weight in the total

number of non conformities that were identified.

Ritterbeck (2007), in an analysis performed on over 100 AS 9100 audit reports, ident-

ified the following top five NC: (1) 8.2.2 – Internal audit (15%); (2) 7.5.1 – Control of

production and service provision (11%); (3) Corrective action (10%); (4) 7.6 – Control

of monitoring and measuring devices (10%); (5) 8.3 – Control of nonconforming

product (8%). There seems to be a quite different profile of NC when comparing the

results of AS 9100 versus ISO 9001 audit reports.



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f) Non conformities statistical distribution by company

According to Figure 3, we can see that the majority of the analyzed companies do have anumber of NC ranging between 0 and 7. The number of companies with more than seven

NC di d l

Table 3. Average number of non conformities in each activity sector.

EACNumber of companies NC

Average NC/Company

3 Food products, beverages and tobacco 2 22 11.0016 Concrete, cement, lime, plaster, etc. 2 17 8.5012 Chemicals, chemical products and fibres 7 49 7.0029 Wholesale and retail trade; repairs of motor

vehicles. . .8 51 6.38

18 Machinery and equipment 12 72 6.0014 Rubber and plastic products 3 17 5.6717 Basic metal and fabricated metal products 10 53 5.3023 Manufacturing not elsewhere classified 5 24 4.8019 Electrical and optical equipment 5 23 4.6028 Construction 12 55 4.5835 Other services 7 32 4.57

31 Transport, storage and communication 2 9 4.504 Textiles and textile products 9 39 4.336 Wood and wood products 4 17 4.252 Mining and quarrying 1 3 3.00

22 Other transport equipment 1 3 3.0015 Non-metallic mineral products 7 20 2.8627 Water supply 1 2 2.0038 Health and social work 2 2 1.0013 Pharmaceuticals 1 0 0.00

Table 2. Clauses with the highest number of NC in each of the ISO 9001 chapters.

Clause NC

4.2 Documentation requirements 645.6 Management review 24

6.2 Human resources 347.5 Product and service provision 648.2 Monitoring and measurement 66

Total 252

Table 4. Average number of non conformities versus company size.

Company size NC (Average)

Micro (≤9 workers) 4.83Small (10 – 49) 5.03Medium (50 – 249) 5.04Large (≥250) 5.23



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Classification model

Based on a detailed analysis of the 100 audit reports, we have developed a classification

model aimed at predicting the companies ISO 9001 certification motivations from their

audit report contents. Such a model has as its major inputs critical variables that can be

identified from an audit report and allow us to identify if a company faces ISO 9001

certification as a real commitment or, on the other hand, if it became certified mostly

because of promotional or marketing issues. Quantitative results will allow us to evaluate

the classification performance of this model, both through training and testing sets of data

(see Figure 4).

As was already stated, a company can become ISO 9001 certified based upon two mainmotivation categories: internal motivations and external motivations. The aim of our

model is to predict the companys’ main motivation for ISO 9001 certification from a

set of input variables that can be found from audit reports.

Data gathering

During the first phase of our model development, we have collected different opinions

about each company, from people that we consider to be relevant concerning a fair

evaluation of the company main motivations for achieving certification (Figure 5). We

asked each of them to classify the corresponding companies according to their opinion

concerning the main ISO 9001 certification motivation.

We exhaustively analyzed all of the 100 audit reports and classified each one of the

Figure 3. Histogram for the number of non conformities found in each company.



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reports profiles. The classification protocol used to classify those companies was based on

the identification of common patterns (variables) that could be defined as inputs to classify

companies according to their ISO 9001 predominant motivation.As already stated, we have also collected the corresponding opinion from other people

who have specific knowledge about the given companies. For that purpose, we have inter-

viewed each certified company Process Manager and two Coordinator Managers (from the

companies’ certification body), as well as the Audit Team Members. The Process Manager

is the person who is responsible for the management of the certification process at the cer-

tification body and is close to, and has a detailed knowledge of, each one of the sampled

companies. We contacted all APCER’s Process Managers, who classified the companies

they were responsible for. The Coordinator Manager is the person who manages a team

of Process Managers, and does not have specific knowledge about a large set of compa-

nies. Generally, the Coordinator Manager is someone with wide experience in ISO

9001 audits. The last interviewed group were the audit teams that were involved in the

sampled companies’ last audits. In the Process and Coordinators Managers groups the

response rate was 100%. However, in the auditors group we have only reached a response

rate of 78%. Overall, this means that a total of 92 auditors contributed information

regarding the main motivations associated with this set of ISO 9001 certified companies.

After collecting all the responses, we assigned to each company its final classification

(observed value, y) – corresponding to either Internal Motivations (y ¼ 1) or External

Motivations (y ¼ 2). In order to define each company classification, we computed the

average of the classifications attributed by the interviewed people (Research Team,

Process Manager; Coordinator Manager and Auditors) to each of the 100 sampled

companies.After categorising all the companies that belong to our sample, we found that for 48

i l l ifi i f i h 1 I l M i i 2 E l

Figure 5. Information gathering as for companies’ certification motivations.

Figure 4. Classification model structure.



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Motivations, was given by the different people that were involved in such a classification

(Research Team, Process Manager; Coordinator Manager and Auditors). The remaining

52 companies, however, did not reach the same single answer from all the relevant

people surveyed. Thus, our gold standard for the classification problem was comprised

of those 48 companies where a predominant ISO 9001 motivation was found to be

consensual.

Together with the classification of each one of the sampled companies, we also asked

to interview people to identify companies’ specific characteristics (variables) that they

considered to classify the main ISO 9001 motivation category of each one of the certified

companies. After collecting all of the information, we initially identified a set of 13

prediction variables (model inputs), x, built from our analysis and exchanges of opinions

with surveyed people. Most of the variables were available and extracted from the audit

reports (Table 5).

Model development In order to develop our classification model, we used binary logistic regression, because

this kind of model produces quite good results in situations where the dependent variable

is nominal and the independent variables are nominal or continuous (Kleinbaum, Kupper,

& Muller, 1988). The binary logistic regression model describes the relationship between

the dependent nominal variable and the set of independent variables, used for that purpose

in the logit transformation (Hosmer & Lemeshow, 1989), as stated before.

We developed two different models, one with our gold standard (Model 1), and the

other including all the companies in the sample (Model 2). However, in this second

model we excluded 10 companies from the training set of data, because they presented

an observed average value of 1.50, meaning that there is a tie between internal and external

Table 5. Variables description.

Variable Description

x1 Did the company become certified based on customers, market pressure, or promotionalaspects?

x2 How many non conformities were identified in the last ISO 9001 audit? x3 Did the company present non conformities related to the development of its quality

management system or that interfere with a continuous improvement philosophy? x4 Did the company present non conformities related to other past audits that were not yet

corrected?

x5 Was ISO 9001 certification used by the company in order to get public funds or to achievepublic contracts?

x6 Did the company have major non conformities in the last ISO 9001 audit? x7 Is top management involved and committed with the quality management system? x8 Is the quality management system implemented only to fulfil the minimum ISO 9001

requirements? x9 Was the quality management system implemented and certified in order to improve the

company’s internal processes and internal organisation? x10 Was ISO 9001 certification imposed by the company headquarters? x11 What kind of relationship does the company have with the certification body Process

Manager? x12 Did the company present in the last ISO 9001 audit non conformities related to statutory

and regulatory requirements? x13 Do the human resources present knowledge and competences related with the quality

management system?



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motivations derived from the different people that classified these particular companies.

In model 2, those companies with a value smaller than 1.50 were classified as having

Internal Motivations, while, on the other hand, companies with an observed value

higher than 1.50 were categorized as having External Motivations.

For both Tables 6 and 7 we have:

. b̂ – Estimated parameter for each variable in the logit model.

. SE ( b̂ ) – Standard error of the estimated parameter.

. Wald – Wald statistic, which is obtained by comparing the square of the maximum

likelihood estimate of the slope parameter, b , to an estimate of its variance. The

Wald statistic follows a x 2 statistical distribution with one degree of freedom.. Exp (b ) – Represents the odds ratio, which is defined as the ratio of the odds of an

event occurring in one group to the odds of it occurring in another group, or to a

sample-based estimate of that ratio, which can be computed based on b̂ .. p-value.

We would like to point out that x7, x9 and x11 are categorical variables, with three poss-ible response levels. However, for Model 1, variables x10 and x12 were not analyzed,

because they have assumed a constant value for all the sampled companies.

According to Table 6, we can verify that for Model 1 only x1; x2; x3; x4 and x13 should

be considered in our classifier ( p values smaller than 0,05). Concerning Model 2, the

variables to be kept for classification purposes are the following: x1; x2; x3; x4, x5; x7(2);

x8; x9(2); x13.

As a result of this initial univariate analysis, we identified which of the 13 variables

present a significant relationship with the independent variable. Our next step consisted

of the development of a multivariate logistic regression model, using the previously

selected variables. For that purpose, we use a Backward Stepwise procedure as a variableselection technique, leading to the final models described in Tables 8 and 9.

The next step comprised an evaluation and validation of these models, through the use

of a receiver operating characteristic (ROC) curve methodology and the Pyramid Popu-

lation graphic, in order to evaluate the models performance. Concerning the Pyramid

Table 6. Binary logistic regression univariate scores (Model 1).

Variable b̂ SE ( b̂ ) Wald Exp( b̂ ) p value

x1 2.959 0.887 11.124 19.286 0.001

x2 0.532 0.170 9.853 1.703 0.002 x3 2.923 1.140 6.578 18.600 0.010 x4 2.457 0.887 7.666 11.667 0.006 x5 21.961 40192.970 0.000 3.0E + 09 1.000 x6 21.111 17300.440 0.000 1.0E + 09 0.999 x7(1) 219.123 28420.702 0.000 0.000 0.999 x7(2) 222.845 28420.702 0.000 0.000 0.999 x8 23.059 12118.637 0.000 1.0E + 10 0.998 x9(1) 221.673 12118.637 0.000 0.000 0.999 x9(2) 242.406 14634.645 0.000 0.000 0.998 x10 2 2 2 2 2 x11(1) 222.072 23205.412 0.000 0.000 0.999

x11(2) 2

42.406 46410.839 0.000 0.000 0.999 x12 2 2 2 2 2 x13 22.269 0.786 8.331 0.103 0.004



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Population graphics, we used a cut value of 0.5 for the predicted probability ( ˆ y). Therefore,

all companies that presented a predicted probability smaller than 0.5 were classified as

having Internal Motivations, while those companies that presented a predicted probability

higher than 0.5 were classified as having External Motivations. The software predicted

probability of 1 corresponds to an observed value of 2 (External Motivations), and the pre-

dicted probability of 0 corresponds to an observed value of 1 (Internal Motivations).

As illustrated in Figure 6, the results obtained with Model 1 are better than those for

Model 2, as should be expected, since companies used to develop Model 1 were thosecompanies with an exact observed value. These results are also reinforced with the

d l ROC A if f Fi 7 M d l 1 d

Table 7. Binary logistic regression univariate models (Model 2).

Variable b̂ SE ( b̂ ) Wald Exp( b̂ ) p value

x1 1.504 0.457 10.845 4.500 0.001 x2 0.374 0.100 13.981 1.454 0.000

x3 2.134 0.809 6.954 8.448 0.008 x4 1.771 0.622 8.109 5.875 0.004 x5 2.207 1.103 4.003 9.091 0.045 x6 21.104 13761.628 0.000 1.0E + 09 0.999 x7(1) 20.944 1.185 0.635 0.389 0.426 x7(2) 22.632 1.114 5.575 0.072 0.018 x8 2.193 0.682 10.327 8.960 0.001 x9(1) 21.014 0.726 1.952 0.363 0.162 x9(2) 23.997 0.876 20.840 0.018 0.000 x10 21.524 28420.722 0.000 2.0E + 09 0.999 x11(1) 221.560 200096.496 0.000 0.000 0.999 x11(2) 242.406 44937.111 0.000 0.000 0.999

x12 21.497 40192.970 0.000 2.0E + 09 1.000 x13 21.360 0.476 8.144 0.257 0.004

Table 9. Binary logistic regression multivariate model (Model 2).

Variable b SE (b ) Wald Exp(b ) p value

Constant 23.053 1.437 4.513 0.047 0.034 x1 1.437 0.719 3.989 4.206 0.046 x2 0.312 0.145 4.610 1.366 0.032 x3 2.222 1.266 3.079 9.223 0.079 x4 2.140 0.862 6.163 8.498 0.013 x5 2.445 1.433 2.913 11.532 0.088

x9(1) 0.390 1.008 0.150 1.477 0.699 x9(2) 21.862 1.118 2.773 0.155 0.096

Table 8. Binary logistic regression multivariate model (Model 1).

Variable b SE (b ) Wald Exp(b ) p value

Constant 28.049 2.913 7.634 0.000 0.006 x1 4.635 1.876 6.106 102.900 0.013 x2 0.780 0.353 4.888 2.182 0.027 x3 5.763 2.657 4.706 318.428 0.030 x4 3.437 1.570 4.794 31.090 0.029



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Figure 6. Pyramid Population for estimated values in both Model 1 and Model 2.



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the curve of 0.978 and Model 2 presents an area of 0.926, with only 2% of the sampled

companies not being correctly classified under Model 1.

We have also analyzed other possible alternative solutions for Model 2, with the aim of

finding a model which enables us to classify companies and predict their ISO 9001 motiv-

ations by only making use of information that is readily available and can be directly

extracted from audit reports. For that purpose, we have tested the following models,

based upon Model 2, but with different sets of independent variables being considered

– Model 2 alternatives, as shown in Table 10.

In Model 2a we have excluded x9 from the set of predictors, leading to an area under

the ROC curve of 0.897. Concerning Model 2b, we have excluded x5, and found that x2 and

x9(1) were not significant at a 0.05 significance level. Regarding Model 2c, we have

excluded x5 and x9 from the underlying model. The ROC curve values, for the two

previous models, are, respectively, 0.912 and 0.882.

Model performance evaluation

Given the several classification models available, we tried to evaluate which was the best

for predicting the companies ISO 9001 certification motivations from audit reports. For

that purpose, we have used a non parametric methodology developed by Braga, Costa

and Oliveira (2003, 2004), that tries to perform a global and partial ROC curves compari-

son. This methodology should be used when the different ROC curves cross each other. If

the curves do not cross each other, then the best model – the model with the best perform-

ance, is the one that is closer to the top left corner of the ROC space. However, since the

ROC curves of the models developed cross each other, as we can see in Figure 8, one needs

to make a more detailed comparison.

The first output obtained from an application of the methodology developed by Braga

et al. (2003, 2004) is related to: (1) the differences between the different ROC curves

areas; and (2) the number of crossings between each curve. As can be verified from

Table 11, the difference between Model 1 and Model 2 ROC curves areas is 0.05145

and Model 1 and Model 2 curves cross each other 11 times.

Table 12 represents the so called extensions measures. As we can verify, in 75.2% of the

ROC space Model 1 has a better performance than Model 2. On the other hand, Model 2 has

a better performance than Model 1 in 18.4% of the ROC space. In the remaining 6.4% of the

space both models have the same performance. According to Table 12, we can see that

Table 10. p values and ROC curves for Model 2 alternatives.

p value

Variables Model 2a Model 2b Model 2c

x1 0.017 0.007 0.002 x2 0.002 0.056 0.003 x3 0.009 0.017 x4 0.003 0.034 0.006 x5 0.034 N/A N/A x9(1) N/A 0.569 N/A x9(2) N/A 0.002 N/A

ROC 0.897 0.912 0.882



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The last result is related to the application of a bootstrap test, in order to indentify

statistical significant differences between the ROC curves. According to Table 13, we

can verify that only for the case of Model 1 were we able to find significant statistical

differences, when compared with the remaining models.

According to the results obtained, we can conclude that Model 1 has the best perform-

ance, in order to predict why companies become ISO 9001 certified, if based mostly on

internal motivations or on external motivations. However, one must remember that

Model 1 was developed based on companies that reached a precise observed value, i.e.

companies for which the evaluation teams did share a complete consensus about their

dominant ISO 9001 motivation.Next, we further evaluated the model performance over two different data sets. One set

Figure 8. Different Alternative Model ROC curves.

Table 11. ROC curve areas and number of crossings.

Model 10.97754

0.05145 0.08010 0.06554 0.09595

11 Model 20.92607

0.02862 0.01407 0.04447

9 0 Model 2a0.89744

20.01455 0.01585

3 1 1 Model 2b

0.91200

0.03040

7 0 8 5 Model 2c0,88160

Table 12. Model extension measures.

Model 1 75.2% 79.6% 81.6% 83.6%18.4% Model 2 41.6% 45.6% 52.4%14.0% 0,0% Model 2a 39.6% 35.6%9.2% 18.8% 28.0% Model 2b 42.0%10.0% 0.0% 11.6% 32.8% Model 2c



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categorisation value (n ¼ 48). For those companies, we have performed an internal modelvalidation, using cross validation. We have also tested the model performance over the

group of companies with a non exact consensual observed dependent value (n ¼ 42).

We have performed such additional validations under two different situations. First, we

have validated the model using all the variables that composed it ( x1; x2; x3; x4), and

second using only variables that can be found directly and easily extracted from audit

reports ( x2; x3; x4).

According to Table 14, we can verify that the model performance is better with x1, but

there is not a significant difference between the two described situations. As expected, the

percentage of wrong classifications was smaller with our gold standard data, when

compared with the results obtained with the application of the model over companieswithout a consensual dependent variable definition.

Therefore, the final mathematical expressions for the models developed are as follows:

p ( x) = e(−8,049+4,635 x1+0,780 x2+5,763 x3+3,437 x4)

1 + e(−8,049+4,635 x1+0,780 x2+5,763 x3+3,437 x4) (4)

p ( x) = e(−5,156+0,574 x2+3,851 x3+2,807 x4)

1 + e(−5,156+0,574 x2+3,851 x3+2,807 x4) (5)

with (4) representing the model with x1 and (5) the model without x1.

Conclusions

According to the literature, ISO 9000 certification motivations can be classified into two

main categories: internal motivations and external motivations. Internal motivations are

related to genuine organisational improvement goals (productivity, internal communi-

cation, internal processes performance improvement), while external ones are mainly

related to promotional and marketing issues (customer and market pressures, market

share improvement). Some companies that become certified based mostly upon external

motivations defined their main goal as ‘obtaining registration’, and thus are of a very

Table 13. Bootstrap test.

Model 1 0.000 0.000 0.000 0.000Sig. Model 2 0.973 0.875 0.124Sig. No Sig. Model 2a 0.999 0.999Sig. No Sig. No Sig. Model 2b 0.991

Sig. No Sig. No Sig. No Sig. Model 2c

Table 14. Model 1 additional validation.

Model 1 With x1 Without x1

Precise y (n ¼ 48) ROC Curve 0.978 0.940Internal validation/Cross validation % of wrong classifications 12.50 16.67Not precise y (n ¼ 42) % of wrong classifications 26.20 31.00



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Although these two groups of motivations are present in the majority of the companies,

usually only one is the most important and predominant.

Issues related to ISO 9001 certification motivations have been already deeply and

exhaustively analyzed in the quality management literature. However, the use of statistical

quantitative methodologies of classification, in order to categorize companies according to

their main ISO 9001 motivation, corresponds to a new contribution to the quality manage-

ment and ISO 9000 standards literature. With our research we were able to develop classi-

fication methodologies which allow one to classify companies, according to their dominant

ISO 9000 motivation, using information gathered from their audit report profiles.

Based upon a sample of 100 ISO 9001 certified companies from Portugal, that we used

to develop our classification methodologies, we have performed a detailed statistical

analysis that provides the following main results:

. Overall, 510 NC were identified.

. Fifty percent of the non conformities belong to ISO 9001 clauses 8.2, 4.2, 7.5 and

7.4.. Chapter 7 – Product realisation, is the ISO 9001 chapter with the highest number of

non conformities.. There was no evidence of a relationship between the number of non conformities and

company sizes.

From the same sets of data, together with opinions collected from auditors and other

experts, we were able to derive statistically sound and valid classifiers, which allow us

to predict if a given company follows mostly internal or external motivations from infor-

mation contained in the corresponding audit reports.

Acknowledgements

The authors acknowledge financial support provided by Fundação para a Ciência e a Tec-

nologia (FCT) through research grant (BD/16032/2004).

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