IMPLEMENTING ISO 14001 AND ENVIRONMENTAL PERFORMANCE EVALUATION: A LOGISTIC REGRESSION MODEL

Full text

Saleh Alsulamy
Ibrahim Falqi
Mohamed Mansour 
Shaik Dawood
Abdullah Alshehri
https://doi.org/10.21278/TOF.453018320
ISSN 1333-1124
eISSN 1849-1391
IMPLEMENTING ISO 14001 AND ENVIRONMENTAL
PERFORMANCE EVALUATION: A LOGISTIC REGRESSION MODEL
Summary
Due to the growing popularity of environmental management systems and the ongoing
debate among practitioners and researchers concerning the influence of environmental
management systems on environmental performance, there is a need to assess how the
implemented environmental management systems impact the environment. The current study
examines the relationship between the guidelines provided by the ISO 14031 and ISO 14001
standards from three aspects, namely, utilizing information and data, planning for
environmental performance and reviewing and improving environmental performance. This
study will utilize a binary logistic regression to model and analyse the link between ISO
14001 and ISO 14031 using a 7-point Likert scale questionnaire. A total of 590 companies
operating within the Saudi Arabia industrial sector were invited to take part in the study. The
collection of data using questionnaires lasted from January to March 2019, and the results
were analysed and compared with those of related studies. The model included a dependent
variable representing whether the company is certified or not for ISO 14001 and 13
independent variables representing the main ISO 14031 guidelines. The research findings
revealed that the developed model predicts 92.8% of the values, and the remaining 7.2% of
the values are not covered. Thirteen independent variables were positively correlated with the
dependent variable, indicating that the company is certified. The results of this study
contribute significantly to the determination of the relationship between environmental
performance and ISO 14001 certification.
Key words: binary logistic regression, environmental performance evaluation,
environmental management systems, ISO 14031, ISO 14001
1. Introduction
Recently, there has been great interest in adopting environmental management systems
(EMSs), mainly the international standard ISO 14001, due to their role in enhancing the
public image, relationships with stakeholders, and environmental performance (EP) of
industrial companies [1-3]. In particular, an increasing number of companies have
implemented the standards to manage their EP [4]. By 2018, a total of 317,646 companies in
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Implementing ISO 14001 and S. Alsulamy, I. Falqi, M. Mansour
Environmental Performance Evaluation: S. Dawood, A. Alshehri
A Logistic Regression Model
174 different countries were certified according to ISO 14001 [5]. Nonetheless, a common
reproach is that the ISO 14001 certification does not measure the company’s actual EP [6].
Concurrently, many companies tend to concentrate on implementing environmental
management strategies such as environmental performance evaluation (EPE) without the need
for implementing an EMS [7, 8]. This raises concerns about how an EMS relates to
environmental efficiency in practice. One way of determining the link between the EP and the
certification of ISO 14001 is tracing the development of environmental indicators to assess if
environmental auditors have achieved an improvement or not. Another approach to assess this
relationship is to compare EP of ISO 14001-certified and uncertified companies. For example,
Morrow and Rondinelli [9] evaluated how the EMS adoption and certification impacts EP
using a case study of five gas and domestic energy companies in Germany. The study findings
revealed that the companies that are ISO 14001 certified showed improvements in EP.
Despite the positive effect of the ISO 14001 certification, there are also various criticisms,
with the most common reproach being that the actual EP is not measured by the certification
of ISO 14001[10]. Similarly, Rondinelli and Vastag [11] ascertain that ISO 14001
certification neither measures nor ensures improved EP. The authors also add that ISO 14001
certification does not guarantee regulatory compliance of a certified facility.
A significant number of scholars strive to examine how implementing EMS influences
the EP, benefits, and expenses of a company. However, previous research results on the
impact of ISO 14001 certification of a company on the EP are still inconsistent. Based on a
49% response rate out of 354 companies surveyed, Enroth and Zackrisson [12] concluded that
the ISO 14001 certification had a positive effect on EP. Senior managers’ commitment to an
ISO 14001-certified company will have a positive impact by improving the environmental
mindfulness of the staff and changing organisational behavior based on the environmental
concerns, thus increasing the chances of attaining desired EP [13]. Moreover, studies have
brought to light that companies with an ISO 14001-certified EMS have stronger chances of
achieving a higher EP [14, 15]. Aiyub et al. [16] examined EP of 59 companies in the UK that
were already ISO 14001 certified. The researchers revealed that the companies improved their
procedures and documentation, achieved increased awareness among the staff, improved their
reputation, and enhanced their structures following the ISO 14001 certification. The authors
further revealed that the standard continued to face problems relating to expenditures and time
available during the implementation procedures and upholding the standard. Bansal and
Bogner [17] argued that certified companies reduced their electricity, lubricants, and natural
gas use while producing lesser amounts of contaminated water and solid waste.
Erauskin-Tolosa et al. [18] showed a positive relationship between corporate EP and the
certification of the EU Eco-Management and Audit Scheme (EMAS) after reviewing a total
of 53 scholarly studies that analysed 182,926 companies. Melnyk et al. [19] revealed that
companies that happen to possess an official EMS such as ISO 14001 consider the EMS
impact beyond pollution reduction and consider a significant positive effect on various
dimensions of operations performance. Testa et al. [20] hypothesized that implementing an
environmental management system within energy-intensive industries impacts the long- and
short-term goals of EP. The authors further elucidated that a different impact of EMAS and
ISO14001 on EP may occur. Qi et al. [21] investigated how corporate EP is impacted by
internalization. The authors concluded that internalization mediates the relationship between
the EP and ISO 14001 certification. Heras‐Saizarbitoria et al. [22] also examined the impact
of the motivation sources that result in companies adopting EMSs, on the systems’ outcomes.
Their results revealed that companies enjoy more significant device benefits upon the
adoption of EMS.
Some studies document that the ISO 14001 adoption negatively affects EP. On the other
hand, other studies have not established a link between ISO 4001 implementation and EPE.
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S. Alsulamy, I. Falqi, M. Mansour Implementing ISO 14001 and
S. Dawood, A. Alshehri Environmental Performance Evaluation:
A Logistic Regression Model
Ammenberg [23] examined if EMS improved EP carrying out a case study of 26 small
enterprises from various fields. The author hypothesized that an ISO 14001 certification does
not guarantee improved EP, although it could result in a reduction in environmental pollution.
Welch et al. [24] explored the impact of ISO 14001 and EMAS on business development and
EP. The authors established some positive impacts of ISO 14001 and EMAS on the aspects of
EP, while there were neither effects nor adverse effects on the EP parameters. These findings
were consistent with those of Ammenberg [10]. Zobel [25] examined changes in the
performance of 115 Swedish manufacturing companies from 6 different environmental sectors
for a period of 12 years. Among these companies, some implemented EMS while others had
not. The authors established that there existed no statistically significant relationship between a
change in EPE and ISO 14001 certification. Aravind and Christmann [26] argued that ISO
14001 certified companies may not have implemented standard requirements sufficiently and
the quality of the implementation standard impacted the EP of the facilities. The authors
further revealed that the EP of the uncertified and certified companies was not substantially
different from the low-quality implementers and the overall sample, whereas high-quality
implementers performed better compared to their uncertified counterparts. Barla [27] showed
how the ISO 14001 implementation could have unclear effects on environmental management
results and practices. Heras-Saizarbitoria et al. [28] also investigated financial projections of
the ISO 14001 certification in Spain. The authors did not establish any evidence to show that
the ISO 14001 certification resulted in improved performance.
Consequently, a general inference cannot be made as to whether EMSs have beneficial
impacts on corporate EP. Additionally, while measuring the EP of industry projects, MEPI
[29] revealed that there is no statistically significant relationship between EP and EMS
certification. The researcher included six industrial sectors of six EU nations. Montabon et al.
[14] concluded that some of the companies that were EMS certified performed even worse
than those that were not EMS certified. However, the research did not measure the rate at
which EP changes between EMS non-certified and EMS certified. The primary goal of the
National Database on Environmental Management System of the Environmental Law Institute
and the University of North Carolina in the Chapel Hill NDEMS [30] was to examine how
ISO 14001 certification impacts a company’s environmental and economic performance. The
project is yet to arrive at some conclusions concerning how EMS impacts EP. Moreover,
although some scholars have reported a lack of relationship between EP and ISO 14001 [31],
there is still no agreement between researchers concerning the definition of EPE [32] and the
interrelationship between EPE and adopting an EMS. As such, examining how EMS
influences the EPE of a company and vice versa remains a subject of considerable interest
among scholars and researchers.
The current study examines the relationship between EPE and ISO 14001 certification
using a case study of companies that were ISO 14001 certified by 2018. The companies of
interest are those located in Saudi Arabia. A questionnaire was used for data collection. Due
to the representation of the dependent (Y) and independent variables in the current study being
binary, 1 shows that the company is ISO 14001 certified and 0 indicates that the company is
not certified. It is, therefore, appropriate to use one of the regression models for modelling
this situation. In this context, we have established that the Binary Logistic Regression (BLR)
is the most common; hence, it was chosen as the modelling approach in this study. The model
computes odds ratios (ORs) for one response compared to the other odds to support the
decision-making process [33].
The main goal of this study was to generate a regression model that relates to the EPE
guidelines defined within the ISO 14031 standard for implementing the international ISO
14001 standard and EMS. The following research question guided the study: Do the ISO
14031 guidelines for EPE significantly predict the ISO 14001 certification status of industrial
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Implementing ISO 14001 and S. Alsulamy, I. Falqi, M. Mansour
Environmental Performance Evaluation: S. Dawood, A. Alshehri
A Logistic Regression Model
companies? The null hypothesis was that there exists no statistically significant relationship
between EPE and ISO 14001 certification. Contrary, the alternative hypothesis was that there
is a statistically significant relationship between EPE and ISO 14001 certification. The
statistical analysis is based on a two-tail test and a level of significance (α) equal to 0.05. To
answer the research question, the preceding section describes the methodological approach
adopted in this study. The interactions among independent variables were neglected in the
current study.
The remaining four sections of this paper present the following. The adopted materials
and methods are illustrated in Section 2. Section 3, which covers the results, utilizes the
solution methodology to assess the developed BLR model’s accuracy built upon the judgment
and the opinions of industrial specialists. Section 4 includes a discussion of the results. Lastly,
section 5 provides a comprehensive summary of the research.
2. Materials and methods
A qualitative design was selected since this paper is concerned with experiences with
ISO 14001 and ISO 14031. The study employed a BLR to analyse the gathered binary data
[34]. Comprehensive advantages and disadvantages of BLR with other classification methods
are discussed in [35]. To examine the relationship between the variables, Cohen’s standard
was utilized [36]. Besides, Holm correlations were used to examine the correlations [37]. For
ordinal data, the researcher used the R language to analyse and describe the collected data
[38]. The reference level of the variables was set as equal to 0. The collinearity check was
done using variance inflation factor (VIF) values [39]. The overall model fit was measured
based on the values of χ
2
and McFadden R
2
. The model coefficient calculations were carried
according to the Omnibus likelihood ratio test, and the confidence intervals were estimated
based on the ORs for a 95% interval [39].
Companies in the Saudi Arabian industrial sector were selected for data collection due to
the efforts of Saudi companies to achieve ISO 14001 certification and to understand the
importance of working with environmental laws [40]. The Saudi industrial sector includes
more than 30 industrial fields, such as construction, agriculture, fishing, etc. According to a
survey of certifications conducted by ISO for management system standards, the total number
of certified companies in Saudi Arabia is 362, distributed among 31 industrial fields in 497
sites [5, 41]. The collection of data using a questionnaire was ideal since quantitative research
permits a greater degree of flexibility in terms of sources of data [42]. Also, the questionnaire
was an ideal tool for collecting data since participants had sufficient time to think and respond.
Further, the questionnaire was able to cover an extensive geographic area as compared with
other survey techniques. The questionnaire was designed to examine the answers provided by
company managers at two different levels: top managers and executive managers.
In this study, a random sample consisting of 900 companies was generated, representing
industrial fields and company details, to identify the sample size. The set of candidate
companies included certified and uncertified companies from all industrial fields. An email
was sent to the managers of candidate companies between January and March 2019. The
filled-out questionnaires were received, collected, and prepared for processing using the
RapidMiner software [43]. Data cleansing was done by removing low-quality, duplicate,
incomplete, and highly correlated records [44]. For validity purposes, a feedback call for a set
of 150 companies was done to ensure that the collected data had the required accuracy. In
total, 596 questionnaires out of the 900 received were found to be complete and appropriate
for statistical analysis. The valid set included 235 certified and 361 uncertified companies and
covered 30 industrial fields, as shown in Table 1. The table presents data on the type of
industrial sector and the number of certified and uncertified companies that responded and
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S. Alsulamy, I. Falqi, M. Mansour Implementing ISO 14001 and
S. Dawood, A. Alshehri Environmental Performance Evaluation:
A Logistic Regression Model
sent a correctly filled out questionnaire. Table 1 shows that the largest sector participating in
the study was construction, with 46 certified and 60 uncertified companies, from a total of
106 out of 596 companies. Moreover, the sample consists of 9% small-sized, 31% medium-
sized, and 60% large-sized enterprises based on the Organisation for Economic Co-operation
and Development's Working Body for small and medium-sized enterprises and the European
Commission DG XXIII classification for business enterprises about the total number of
employees. In a micro-sized enterprise, the employee number ranges from 1 to 9, in a small-
sized enterprise the employee number ranges from 10 to 99, in a medium-sized enterprise the
employee number ranges from 100 to 499, and in a large-sized enterprise the employee
number is more than 500.
To scale EPEs in the Saudi industry, ISO 14031 was utilized. A questionnaire that was
scaled as either 0 or 1, as listed in [3], was developed based on the requirements of the ISO
14031 standard and emailed to the selected companies. The questionnaire constructs
comprised 13 items characterized by three descriptor variables: utilizing information and data,
planning for EPEs and reviewing and improving EPEs [45]. Additionally, there was a
question that examined whether the company was ISO 14001 certified or not, which
represented the value of the predicted variable (Y). Variable (Y) was coded as a binary
variable, with 0 indicating that the company was not certified and 1 indicating that it was ISO
14001 certified. The descriptor variables included items 5, 6, and 2, respectively. The items
belonging to the first measurement were coded as “P1” to “P5”. The items belonging to the
second measurement were coded as “D1” to “D6”. Lastly, items belonging to the third
measurement were coded as “R1” and “I1”. The respondents ranked the constructs of the ISO
14031 implementation level on a “0 – 1” scale, where the rating of 1 indicated strong
agreement with the statement. This approach showed the respondents’ views and opinions on
the degree of implementation in their company [46, 47].
Table 1 Response rates of certified and uncertified companies and their industrial fields
Sr.
N
o. Industrial sector
# of certified
companies
# of uncertified
companies Total
n % n % n %
1 Agriculture, fishing, and forestry 1 0.43 3 0.83 4 0.67
2 Basic metal and fabricated metal products 20 8.51 30 8.31 50 8.39
3 Chemicals, chemical products, and fibres 11 4.68 20 5.54 31 5.20
4 Concrete, cement, lime, plaster, etc. 6 2.55 10 2.77 16 2.68
5 Construction 46 19.57 60 16.62 106 17.79
6 Education 2 0.85 8 2.22 10 1.68
7 Electrical and optical equipment 7 2.98 8 2.22 15 2.52
8 Electricity supply 2 0.85 4 1.11 6 1.01
9 Engineering services 25 10.64 35 9.70 60 10.07
10 Financial intermediation, real estate, and renting 5 2.13 10 2.77 15 2.52
11 Food products, beverage, and tobacco 13 5.53 18 4.99 31 5.20
12 Health and social work 1 0.43 5 1.39 6 1.01
13 Hotels and restaurants 1 0.43 3 0.83 4 0.67
14 Information technology 2 0.85 5 1.39 7 1.17
15 Machinery and equipment 9 3.83 15 4.16 24 4.03
16 Manufacture of coke and refined fuel products 7 2.98 8 2.22 15 2.52
17 Manufacture of wood and wood products 1 0.43 5 1.39 6 1.01
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Implementing ISO 14001 and S. Alsulamy, I. Falqi, M. Mansour
Environmental Performance Evaluation: S. Dawood, A. Alshehri
A Logistic Regression Model
Sr.
N
o. Industrial sector
# of certified
companies
# of uncertified
companies Total
n % n % n %
18 Non-metallic mineral products 2 0.85 3 0.83 5 0.84
19 Other services 2 0.85 4 1.11 6 1.01
20 Other social services 1 0.43 5 1.39 6 1.01
21 Pharmaceuticals 1 0.43 3 0.83 4 0.67
22 Printing companies 1 0.43 5 1.39 6 1.01
23 Public administration 9 3.83 15 4.16 24 4.03
24 Pulp, paper, and paper products 1 0.43 5 1.39 6 1.01
25 Recycling 15 6.38 20 5.54 35 5.87
26 Rubber and plastic products 4 1.70 8 2.22 12 2.01
27 Textiles and textile products 6 2.55 10 2.77 16 2.68
28 Transport, storage, and communication 5 2.13 8 2.22 13 2.18
29 Water supply 17 7.23 17 4.71 34 5.70
30
Wholesale and retail trade, repairs of motor
vehicles, motorcycles and personal and household
goods
2 0.85 4 1.11 6 1.01
Total 235 100 361 100 596 100
3. Results
The collected data were formulated as a BLR model with one dependent variable and
13 independent variables. The t response variable (Y) represents the status of the company
regarding ISO 14001 certification. It takes the level of 1 when the company is ISO 14001-
certified and the level of 0 if the company is not certified. The explanatory variables
represent the guidelines outlined in ISO 14031 (see the measuring tool given in [3]). The
model was developed and evaluated based on an α of 0.05 in terms of fit measures, model
coefficients, and predictive measures.
3.1 Assumption check
The result of Kendall’s tau B correlation analysis depicted in Table 2 showed that
there was not a statistically significant correlation among the model’s independent variables
at p < .05. The relationship was positive, weak and not reaching the level of statistical
significance. From the research findings, the researcher failed to reject the null hypothesis
and concluded that the variables under investigation were positively correlated. The
researcher performed a BLR to assess whether the explanatory variables had a significant
impact on the likelihood of observing the Y classification of the ISO 14001-certified
company. The reference classification for Y was 0. A total number of 596 companies were
included in this analysis: 235 in the ISO 14001-certified company category and 361 in the
ISO 14001-uncertified company category. Table 3 presents the multicollinearity check in
terms of VIFs for each predictor in the model and tolerance. The table indicates that the VIF
values are below 5, which indicated that there was no multicollinearity in the model [48].
The constant-only model was tested and it was significant: Wald(1) = 26.23, p < 0.001. This
model classified all cases as working, which correctly classified 60.6% of the data by
chance.
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S. Alsulamy, I. Falqi, M. Mansour Implementing ISO 14001 and
S. Dawood, A. Alshehri Environmental Performance Evaluation:
A Logistic Regression Model
Table 2 Kendall's Tau B - correlation matrix
P1 P2 P3 P4 P5 D1 D2 D3 D4 D5 D6 R1 I1
P1 — 0.23
***
0.29
***
0.24
***
0.32
***
0.26
***
0.19
***
0.26
***
0.17
***
0.18
***
0.26
***
0.29
***
0.29
***
P2 — 0.14
***
0.10
**
0.24
***
0.16
***
0.27
***
0.27
***
0.21
***
0.26
***
0.17
***
0.32
***
0.16
***
P3 — 0.10
*
0.23
***
0.17
***
0.20
***
0.27
***
0.29
***
0.18
***
0.24
***
0.18
***
0.31
***
P4 — 0.28
***
0.18
***
0.22
***
0.15
***
0.27
***
0.26
***
0.25
***
0.10
*
0.15
***
P5 — 0.21
***
0.35
***
0.32
***
0.30
***
0.28
***
0.34
***
0.19
***
0.25
***
D1 — 0.28
***
0.29
***
0.28
***
0.17
***
0.17
***
0.25
***
0.31
***
D2 — 0.31
***
0.25
***
0.23
***
0.27
***
0.10
***
0.32
***
D3 — 0.18
***
0.24
***
0.34
***
0.20
***
0.28
***
D4 — 0.10
***
0.21
***
0.12
**
0.31
***
D5 — 0.28
***
0.15
***
0.18
***
D6 — 0.15
***
0.24
***
R1 — 0.13
**
I1 —
Note: * p < .05, ** p < .01, *** p < .001
Table 3 Model assumption checks: VIFs for independent variables
Variable P1 P2 P3 P4 P5 D1 D2 D3 D4 D5 D6 R1 I1
VIF 1.17 1.35 1.34 1.27 1.38 1.23 1.14 1.2 1.24 1.15 1.2 1.4 1.18
Tolerance 0.85 0.74 0.75 0.79 0.73 0.82 0.87 0.84 0.81 0.87 0.83 0.7 0.85
3.2 Model fit
The significance level (α) of 0.05 was used to evaluate the model. The overall model
was significant, χ2(13) = 606, p < 0.001, which suggested that the explanatory variables of the
model significantly impacted the odds of observing an ISO 14001-certified company category
of Y. To determine the model fit, McFadden’s R-squared (R²McF), Cox and Snell’s
R-squared (R²CS), and Nagelkerke’s R-squared (R²N) values were calculated, where the
values greater than 0.2 indicated the model had an excellent fit [49]. The R²McF, R²CS, and
R²N values calculated for this model were 0.76, 0.63, and 0.86, respectively, and χ2 was equal
to 606 with a degree of freedom (df) equal to 13 and a p < 0.001, as depicted in Table 4. The
table shows the χ2, df, and p-values for independent variables. The Omnibus likelihood ratio
tests shown in Table 5 indicate the significance of the model coefficients. The p-values are
< 0.05. The calculation of the p-values, χ2, and df aimed to define each of the independent
variable’s significance, as presented in Table 6. The table shows that the 13 variables are
significant, with dfs equal to 1 and p-values less than 0.001. Since the p-values are < 0.05
(Table 6), it is important to incorporate the variables in the model. As such, one would not
need to eliminate any variables from the model.
Table 4 Model fit measures and pseudo R2
Model Pseudo R2 Overall model test
R
²
McF
R
²
CS
R
²
N
χ² df p
1 0.76 0.63 0.86 606 13 <0.001
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Implementing ISO 14001 and S. Alsulamy, I. Falqi, M. Mansour
Environmental Performance Evaluation: S. Dawood, A. Alshehri
A Logistic Regression Model
Table 5 Model coefficients: Omnibus likelihood ratio tests
Predictor P1 P2 P3 P4 P5 D1 D2 D3 D4 D5 D6 R1 I1
χ
2
30.34 26.86 21.2 16.85 35.56 14.69 16.66 11.65 9.3 8.9 15.46 37.73 27.66
Table 6 presents the unstandardized regression weight (B), the degree to which the
unstandardized regression weight can vary by B, standard error (SE), the 95% confidence
interval (CI) for ORs (95% CI), Wald χ
2
, p, and OR for independent variables. The “P1”
coefficient was significant, B = 2.04, OR = 7.72, p < 0.001, signifying that if “P1” increases by
one unit the odds of observing the ISO 14001-certified company category of Y increase by
nearly 672%. The regression coefficient for “D1” was significant, B = 1. 49, OR = 4.46,
p < 0.001, signifying that if “D1” increases by one unit, the odds of observing the ISO 14001-
certified company category of Y increase by nearly 346 %. The regression coefficient for “I1”
was significant, B .1.98, OR = 7.26, p < 0.001, signifying that if “I1” increases by one unit, the
odds of observing the ISO 14001-certified company category of Y increase by nearly 626 %.
Table 6 Logistic model coefficients with P1, P4, D2, D5, I1, P2, P5, D3, D6, P3, D1, D4, and R1 predicting Y
Variable B SE 95.0% CI χ
2
p OR
(Intercept) −11.45 1.19 [−13.77, −9.12] 92.90 <0.001
P1 2.04 0.40 [1.26, 2.83] 26.02 <0.001 7.72
P2 2.05 0.43 [1.21, 2.89] 22.82 <0.001 7.74
P3 1.83 0.43 [0.99, 2.66] 18.29 <0.001 6.21
P4 1.62 0.42 [0.80, 2.44] 15.07 <0.001 5.06
P5 2.32 0.43 [1.47, 3.17] 28.52 <0.001 10.13
D1 1.49 0.41 [0.69, 2.29] 13.41 <0.001 4.46
D2 1.55 0.39 [0.77, 2.32] 15.36 <0.001 4.70
D3 1.34 0.41 [0.54, 2.14] 10.79 0.001 3.82
D4 1.21 0.41 [0.41, 2.02] 8.74 0.003 3.36
D5 1.16 0.40 [0.38, 1.94] 8.50 0.004 3.18
D6 1.53 0.41 [0.73, 2.32] 14.22 <0.001 4.62
R1 2.42 0.44 [1.56, 3.28] 30.39 <0.001 11.23
I1 1.98 0.41 [1.19, 2.78] 23.91 <0.001 7.26
Estimates represent the log odds of “Y = 1” vs. “Y = 0”.
Table 7 Model predictive measures: Classification table, Y
Observed Predicted % Correct
Y = 0 Y = 1
ISO 14001-uncertified company, Y = 0 340 21 94.2
ISO 14001-certified company, Y = 1 22 213 90.6
The cut-off value is set to 0.5.
According to the percentage of the explained deviation, the most appropriate logit
model expression is defined in Equations (1) and (2). These equations depict the results of the
fitted BLR model to show the relationship between the dependent variable (Y) and the 13
explanatory variables extracted from ISO 14031. The equation of the fitted model is as
follows:
exp( )
(1 exp( ))
eta
Yeta


(1)
where Y represents the probability of event 1, i.e., the company has an ISO 14001 certification
and the linear function of eta is equal to:
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S. Alsulamy, I. Falqi, M. Mansour Implementing ISO 14001 and
S. Dawood, A. Alshehri Environmental Performance Evaluation:
A Logistic Regression Model
11.45 2.04 1 2.05 2 1.83 3 1.62 4 2.32 5 1.49 1
1.55 2 1.34 3 1.21 4 1.16 5 1.53 6 2.42 1 1.98 1
PPPPet P D
D
DD D RI
a
D

 

(2)
The logit model utilizing the 13 independent variables was also applicable for assessing
EP for industrial companies, irrespective of whether they have ISO 14001 certification, by
deciding the values of the binary variables. For instance, if the values of the 13 independent
variables from P1 to I1 are (1,1,1,0,1,1,1,1,1,1,1,0,1), then the eta values are equal to 7.05 and
Y to 1.00. This value shows that the probability is 1.00 that the company is ISO 14001
certified. On the contrary, values of (0,0,0,1,0,1,0,1,0,1,0,1,0) correspond to eta equals -3.41
and Y equals 0.03. This value signifies that the probability that the company is ISO 14001
certified is equivalent to 0.03.
4. Discussion
BLR modelling was utilized to prove a statistically significant relationship between
industrial companies’ ISO 14001 certification status and ISO 14031 guidelines for the EPE.
Besides, the findings revealed that none of the guidelines that were represented by the
explanatory variables could be removed from the model. Thus, the observed heterogeneity in
prior studies concerned with an evaluation of the relationship was explained by using
different measurement tools, performance indicators, and industrial categories. Additionally,
the 13 indicators of EP as outlined in ISO 14031 were ranked per their impact on the ISO
14001 certification status based on the values of the correlation coefficients.
The findings of this study agree with those of other scholars who have identified a
statistically significant relationship between EPE and the implementation of ISO 14001. Such
scholars include [4, 8, 12-24] and they disagree with the voices calling for negative effects,
such as in [9, 12, 25-29, 31, 32, 37, 45, 50]. Additionally, this study demonstrates that there is
a relationship, which rejects the claim of MEPI [29] that there does not exist any statistically
significant relationship between EPE and the implementation of EMS. The fact that we found
a relationship between EPE and the company’s status in terms of being environmentally
certified or not according to one of the international EMS requirements indicates that the
existence of such a relationship may be useful in the application of ISO 14031 as a
requirement for obtaining ISO 14001 or other EMSs, in addition to setting technical EPE
criteria for different economic activities.
The adopted BLR approach suggests a positive relationship among EPE indicators and
ISO 14001 requirements, allowing practitioners to direct their engineering attention and focus
on reviewing and improving EPE, planning for EPE, and using data and information based on
the average coefficient performance of the independent variables of each dimension.
Additionally, the model estimates high positive regression coefficients for four variables,
ranging from 2.04 to 2.42: Periodical EPE reviews (R1), the data on the selection and use of
environmental indicator is a useful form (P5), EPE planning based on the interested parties’
views and EP criteria (P2), and the EPE planning based on the controlling/significant
environmental indicators (P1). The relevance of these variables shows the need to design and
plan ways of improving the indicator value of the environmental condition. This reflects the
importance of EMS implementation on the EP of industrial companies. As the environmental
management research moves from observational studies and environmental impact
assessment design to directly test the contribution of companies to environmental pollution,
there is a growing need to develop and transition to a modernized set of tools, such as
sustainable EMSs, new environmental protection technologies, the development of new EPE
models, and the inclusion of environmental regulations in the design process of products and
industrial systems.
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Environmental Performance Evaluation: S. Dawood, A. Alshehri
A Logistic Regression Model
This research represents a scientific basis for judging the relationship between EPE or
ISO 14031 guidelines and the implementation of EMSs such as ISO 14031, EMAS, or any
other international standard compared to other techniques that depend mainly on descriptive
statistics measures. This approach satisfies the decision-making process to a certain level of
significance. It opens research directions to explore environmental management measures and
to direct technical measures or indicators. The type of industry can be included in the
proposed model as a single independent variable as well as the type of respondent and other
controlling variables. These features suggest that our overall approach could be used by many
investigators because the independent variables of the model apply to all industrial companies
regardless of their size and industrial specialization category.
Although identifying a BLR model represents an extraordinary step in investigating the
relationship between EPE and EMS ISO 14001, much additional work needs to be done. For
instance, in the current study, the model assumed the interactions between the explanatory
variables. Therefore, a further study needs to be conducted to examine the relationship
between explanatory variables. Additionally, the model only focused on general ISO 14031
guidelines that relate to operational, management and general indicators of the environmental
conditions. As such, a further study is needed to include specific technical and engineering
environmental indicators as part of the environmental condition indicators. Moreover,
limitations include evaluating only EPE for Saudi industrial companies; increasing the
sample size could have beneficial effects on the model results. Furthermore, the estimates of
the model coefficients are based on the environmental managers’ views and opinions; thus,
they are subject to confounding and bias, which may have also impacted the estimated values
of the model. The study investigates the relationship between EPE and ISO 14001 and it is
concluded that EPE depends on various variables, both of which are exogenous and
endogenous, rather than EMS and ISO 14001 certification. Indeed, the economic
performance may lead to some latent variables working as drivers influencing EPE. So, a
possibility always remains that certified firms have EPE which is different in other ways.
However, the used methods determine the direct effect of the ISO 14001 certification on
EPE. A limitation also exists as there is no distinction made between the companies that
received an ISO 14001 certification in 2018 and those which had been certified for a longer
period. It is believed that the relationship could vary based on the point in time the company
was ISO 14001 certified.
Our approach has implications for modelling because it represents the EPE construct in
general. Each measurement variable can include another detailed sub-variable. Increasing the
level of the variable detail may change the results of the study. The research methodology can
be implemented by companies to define the gap between the actual EPEs of their businesses.
The independent variables, which are EPE activities, are examined based on the guidelines
outlined in the ISO 14031 model [45]. The findings of this study will be greatly beneficial to
industry and society at large. Companies will benefit by improving their comprehension of
how implementing the guidelines defined by the ISO 14031 standards influences the ISO
14001 certification. These companies will utilise this information for their development and
as a marketing tool. Moreover, environmental authorities and customers will find it easier to
evaluate the EMSs of companies. Also, the following benefits are available to all companies
that will utilize the developed model: The measurement of how far the company’s EMS is
from its ISO 14001 certification enables the company to concentrate on improving the most
effective factors that influence EPE the most by determining coefficients of the model, and it
also enables practitioners who work in the field of environmental planning to add new
variables to the measurement variables of the developed model.
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S. Alsulamy, I. Falqi, M. Mansour Implementing ISO 14001 and
S. Dawood, A. Alshehri Environmental Performance Evaluation:
A Logistic Regression Model
5. Conclusions
This research belongs to studies that have comprehensively examined the relationship
between the implementation of ISO 14031 guidelines and EMS ISO 14001 standard. The
primary contribution of this research was the development of a BLR model that can be
utilized to define whether there is a relationship between the two variables, which has not
been well defined in the existing literature up to date. The developed model includes a binary
dependent variable and 13 binary independent variables. The regressor variable represents a
company’s status in terms of the ISO 14001 implementation. The independent variables
represent the guidelines for measuring EP in three areas: planning and preparation for EPE,
utilizing the collected data and information and reviewing and improving EPE. The developed
BLR model predicts 92.8% of the values, and the remaining 7.2% of the values are not
covered. Each of the 13 independent variables has a positive coefficient of correlation,
implying that the higher the corresponding value of the explanatory variable, the higher the
value of the dependent variable and the closer it is to the value (1); thus, the company is
certified. The most effective factor influencing the status of a company is a periodic review
with a coefficient of 2.42, using environmental condition indicator data with a coefficient of
2.32, EP criteria and the planning of the interested party with a coefficient of 2.05, planning
based on the control or significant environmental aspects with a coefficient of 2.04,
environmental review contributing to environmental conditions improvement with a
coefficient of 1.98, management performance indicator data selection and use with a
coefficient of 1.83, operational performance indicator data selection and use with a coefficient
of 1.62, systematic and regular environmental planning data collection with a coefficient of
1.55, documenting environmental reports with a coefficient of 1.53, the availability of
documented EPE procedures with a coefficient of 1.49, analysing EPE data with a coefficient
of 1.34, EPE benchmarking with a coefficient of 1.21, and then timely communication of the
EP information of the company with a coefficient of 1.16.
The developed BLR model was verified with respect to collinearity using the VIF and
the tolerance value. The model fit measures are deviance, overall model fit, R
²McF
, R
²CS
, R
²N
,
and χ². The model coefficients were tested with respect to the Omnibus test, and 95%
confidence interval for ORs. The predictive measures of the model were verified using the
classification table. A natural expansion of this study is to use other prediction and
classification statistical techniques such as Cox regression, multi-discriminate analysis models
and multinomial logistic regression models. Additionally, artificial intelligence models,
including gene expression and neural network programming methods, may be potential tools
to discover the relationship between the two standards. Industrial companies can utilize the
developed logistic regression model as a valuable tool for measuring whether the companies
themselves evaluate EP according to ISO 14031, even if they do not implement ISO 14001.
Acknowledgments
We thank the anonymous reviewers for their comments and suggestions. This study was
financially supported by King Khalid University [grant number R.G.P.1/220/41].
Conflict of interest
The authors declare that there is no conflict of interest.
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Submitted: 22.4.2020
Accepted: 27.8.2021
Saleh Alsulamy
Architecture and Planning Engineering
Department, College of Engineering,
King Khalid University, Abha 394, Saudi
Arabia; s.alsulamy@kku.edu.sa
Ibrahim Falqi
Civil Engineering Department, College
of Engineering, King Khalid University,
Abha 394, Saudi Arabia;
ifalqi@kku.edu.sa
Mohamed Mansour
*
Industrial Engineering Department,
College of Engineering, Zagazig
University, 44519 Zagazig, Egypt
Industrial Engineering Department,
College of Engineering, King Khalid
University, Abha 394, Saudi Arabia
Shaik Dawood
Industrial Engineering Department,
College of Engineering, King Khalid
University, Abha 394, Saudi Arabia
Abdullah Alshehri
Civil and Environmental Engineering
Department, College of Engineering,
Majmaah University, Majmaah 11952,
Saudi Arabia; a.m.alshehri@mu.edu.sa
*
Corresponding author:
momansor@kku.edu.sa
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