Research Report: ISO 9000 Adoption Determinants and Firm Performance

This report analyzes the determinants of ISO 9000 adoption and its subsequent impact on firm performance, utilizing data from 5717 industries, of which 460 are ISO 9000 certified. The study compares certified and non-certified firms across various factors including industry type, company age, employee education levels, and financial metrics such as sales, profit, assets, and return on investment. The research employs frequency distribution, descriptive statistics, t-tests, and ANOVA to identify significant differences and correlations. Key findings reveal that software, specialized technology services, and R&D firms exhibit higher certification rates. The report concludes that the number of employees, particularly those with higher education, and company age are crucial determinants of ISO 9000 adoption. Furthermore, significant differences were observed in sales, profit, assets, equity, and returns between certified and non-certified firms, suggesting a positive impact of ISO 9000 certification on financial performance.

Determinants of the adoption of ISO9000 and its impact on firm
performance
Executive Summary
In this study, we have data of 5717 industries out of which 460 are certified (5257 not
certified). We analyzed the certified and non-certified firms. Data is collected for both
certified and non-certified firms with respective to industry type, foundation year of industry,
number of employees with education level (master/Doctor, bachelor, diploma holder, High
school, Junior high school and below), sales of company, profit of company, Total asset of
the company, Equity of the company, Total capital, Capital from government, Capital from
overseas, Capital from other sources, Return on sales, Return on assets, Percentage of
overseas investment in the total investment, Overseas investment and Age of the company.
In this study we determine the determinants of ISO 9000. We analyzed the certified
and non-certified industries with respective to industry type. We considered the number of
employees with education level and age of the company are the important variable for
predicting the status of certification. We studied the impact of certification on the
performance of the industry. We considered sales of company, profit of company, Total asset
of the company, Equity of the company, Total capital, Capital from government, Capital from
overseas, Capital from other sources, Return on sales, Return on assets, Percentage of
overseas investment in the total investment, Overseas investment variables as a performance
variables.
We test whether the proportion of certified firm is same for all the industry type or not. We
study whether the number of employees with education level and age of the company are the
significantly different for certified firms or not. We test whether the performance variables
are significantly different for certified and non-certified firms.
Page 1 of 21

We used the frequency distribution, descriptive statistics for the study variables. We observed
the proportion of certified firm is same for all the industry type or not. We used t-test for
determining the determinants of adoption of ISO 9000. We used t-test for testing the
significance of performance variables for certified and non-certified firms. We used one way
ANOVA for the significance of performance variables for certified firms by considering the
type of industry as a level.
We observed that 19% software, 18% Specialized technology services, 15% Research
and Development firms are certified. Certification adoption was very little in the
Telecommunication (2%), Business services (3%), storage and transportation (4%),
Technology exchange and promotion (5%) and computer services (8%).
We conclude that mean of number of employee, number of masters and doctors,
bachelors, diploma holders and number of employees attended high school for certified firm
is more than non-certified firms. Mean age of certified firm is also more than mean age of
non-certified firms. We can say that as the number of employees, masters and doctors,
bachelors, diploma holder, attended high school and age of company in years are the
important variables for the certified firms and are to be considered as the main determinant.
We observe that mean of Sales of the company, Profit of the company, Total asset of the
company, Equity of the company, Total capital, Capital from other sources, Return on sales
and Return on assets for certified and non-certified firm are significantly different whereas
mean of Capital from government, Capital from overseas and Percentage of overseas
investment in the total investment are not significantly different for certified and non-certified
firm.
Page 2 of 21

Introduction
Complete customer satisfaction is the motto of the most of the industries
(manufacturing as well as service industries). There are many industries who claims that they
fulfill all the customer’s needs. Each consumer have different approach to select his or her
service provider. Certification is one of the most common approach of the customer for
selecting the service provider. Certification is nothing but the provision by an external body
of written assurance (in terms of certificate) that the product, service or system in question
meets specific requirements.
ISO is an independent, non-governmental international organization with a
membership of 161 national standards bodies. Through its members, it brings together
experts to share knowledge and develop voluntary, consensus-based, market relevant
International Standards that support innovation and provide solutions to global challenges.
The ISO 9000 series of quality management systems standard has been widely applied all
over the world since its introduction in 1987. By the end of 2013, ISO 9000 had been adopted
by over 1,129,000 facilities in 189 countries. Both academics and practitioners are interest in
understanding the determinants of adoption of ISO 9000, and the impact of the adoption of
ISO 9000 on firm financial performance (Christmann & Taylor, 2006; Du, Yin, & Zhang,
2016; Fikru, 2014a, 2014b, 2016; Nakamura, Takahashi, & Vertinsky, 2001; Pekovic, 2010;
Wu, Chu, & Liu, 2007).
In this study, we have data of 5717 industries out of which 460 are certified (5257 not
certified). We analyzed the certified and non-certified firms. Data is collected for both
certified and non-certified firms with respective to industry type, foundation year of industry,
number of employees with education level (master/Doctor, bachelor, diploma holder, High
school, Junior high school and below), sales of company, profit of company, Total asset of
the company, Equity of the company, Total capital, Capital from government, Capital from
Page 3 of 21

overseas, Capital from other sources, Return on sales, Return on assets, Percentage of
overseas investment in the total investment, Overseas investment and Age of the company.
In this study we determine the determinants of ISO 9000. We analyzed the certified
and non-certified industries with respective to industry type. We considered the number of
employees with education level and age of the company are the important variable for
predicting the status of certification. We studied the impact of certification on the
performance of the industry. We considered sales of company, profit of company, Total asset
of the company, Equity of the company, Total capital, Capital from government, Capital from
overseas, Capital from other sources, Return on sales, Return on assets, Percentage of
overseas investment in the total investment, Overseas investment variables as a performance
variables.
We test whether the proportion of certified firm is same for all the industry type or
not. We study whether the number of employees with education level and age of the company
are the significantly different for certified firms or not. We test whether the performance
variables are significantly different for certified and non-certified firms.
Literature Review
In today’s world certification is an important concern for all the industries. Numerous
empirical studies have examined the determinants of firm participation in ISO Certification in
developed as well as developing countries. (Christmann & Taylor, 2006; Du, Yin, & Zhang,
2016; Fikru, 2014a, 2014b, 2016; Nakamura, Takahashi, & Vertinsky, 2001; Pekovic, 2010;
Wu, Chu, & Liu, 2007). For example, Fikru(2016) finds that foreign ownership, plant size
and business communications through company website are important for the adoption of
standards in Africa. DeCanio and Watkins (1998) conclude that firm size, earnings and
insider shareholders are significant determinants of voluntary participation of US firms in the
Green Lights programme. Fikru (2014a) finds that international connectivity, sources of
Page 4 of 21

finance such as credit from local banks or local customers, and manager’s human capital as
significant determinants of international standards.
Research Methodology
The use of different statistical tools and techniques is becomes mandatory for
analysis. The selection of proper tools and techniques is very important for the analysis of
data. We used the frequency distribution, descriptive statistics for the study variables. We
observed the proportion of certified firm is same for all the industry type or not. We used t-
test for determining the determinants of adoption of ISO 9000. We used t-test for testing the
significance of performance variables for certified and non-certified firms. We used one way
ANOVA for the significance of performance variables for certified firms by considering the
type of industry as a level. We have mostly used SPSS software for analysis.
Data Analysis and Results
Table 1 summarizes the frequency distribution of certified and non-certified firms. Out of
5717 firms 460 (8%) are certified and 5257 (92%) are non-certified. Table 2 gives the
frequency distribution of industry type. Business Service accumulated 47.6 % and
Specialized technology services accumulated 21% of total 5717 industries.
Table 1: Frequency Distribution of Certification dummy
Frequency Percent Cumulative Percent
not certified 5257 92.0 92.0
certified 460 8.0 100.0
Total 5717 100.0
Page 5 of 21

Table 2: Frequency Distribution of Industry Type
Frequency Percent Cumulative Percent
storage and transportation 392 6.9 6.9
Telecommunication 184 3.2 10.1
computer services 365 6.4 16.5
software 390 6.8 23.3
Business services 2722 47.6 70.9
Research and Development 222 3.9 74.8
Specialized technology services 1200 21.0 95.8
Technology exchange and promotion 242 4.2 100.0
Total 5717 100.0
We observed the proportion of certified firm accordingly to the industry type and reported in
Table 3. From Table 3, one can observed that 19% software, 18% Specialized technology
services, 15% Research and Development firms are certified. Cerification adoption was very
little in the Telecommunication (2%), Business services (3%), storage and transportation
(4%), Technology exchange and promotion (5%) and computer services (8%).
Table 3: Proportion of Certified firms industry type wise
industry type Proportion No. of Observation Std. Deviation
storage and transportation .04 392 .186
Telecommunication .02 184 .146
computer services .08 365 .275
software .19 390 .397
Business services .03 2722 .161
Research and Development .15 222 .357
Specialized technology services .18 1200 .386
Technology exchange and promotion .05 242 .218
Total .08 5717 .272
To determine the determinant of adoption of ISO 9000, we used the logistic
regression. Here certification dummy is our response variable which has dichotomous scale.
(0 for non-certified and 1 for certified). We used employee number, education levels and
company age as an independent variables for fitting the logistic model to the certification
Page 6 of 21

dummy variable. First we observed the descriptive statistics of the predictor variables
(independent variables) education levels and company’s age for certified and non-certified
firms.
Table 4a: Descriptive Statistics for predictor variables (independent variables)
education levels and company’s age for certified firms.
N Minimum Maximum Mean Std. Deviation
employee number 460 11 913 100.53 129.464
master and doctor 460 0 136 4.54 11.735
bachelor 460 0 388 38.83 55.588
diploma 460 0 343 31.00 44.440
high school 460 0 689 19.52 51.921
other 460 0 245 6.64 26.653
age of company in years 460 2 56 10.28 9.144
Valid N (listwise) 460
Table 4b: Descriptive Statistics for predictor variables (independent variables)
education levels and company’s age for certified firms.
N Minimum Maximum Mean Std. Deviation
employee number 5257 11 969 40.10 65.257
master and doctor 5257 0 161 1.10 4.713
bachelor 5257 0 530 9.49 23.266
diploma 5257 0 490 11.34 20.950
high school 5257 0 600 11.63 28.711
other 5257 0 568 6.54 25.923
age of company in years 5257 2 61 7.39 6.815
Valid N (listwise) 5257
Page 7 of 21

By comparing Table 4a and 4b, we can see that mean of number of employee, number of
masters and doctors, bachelors, diploma holders and number of employees attended high
school for certified firm is more than non-certified firms. Mean age of certified firm is also
more than mean age of non-certified firms.
Table 5 represents the Pearson’s correlation coefficient and it’s significance. All the predictor
variable are positively correlated with response variable and significant expect the number of
employees having education below high school and other. From Table 5, one can observe the
correlation between the predictor variables.
Page 8 of 21

Table 5: Correlation matrix of response and predictor variable
certification
dummy employee number master and doctor bachelor diploma high school other
age of company in
years
certification
dummy
Pearson Correlation 1 .221** .164** .281** .220** .069** .001 .111**
Sig. (2-tailed) .000 .000 .000 .000 .000 .935 .000
N 5717 5717 5717 5717 5717 5717 5717 5717
employee
number
Pearson Correlation .221** 1 .393** .638** .697** .720** .559** .207**
Sig. (2-tailed) .000 .000 .000 .000 .000 .000 .000
N 5717 5717 5717 5717 5717 5717 5717 5717
master and
doctor
Pearson Correlation .164** .393** 1 .577** .237** .050** -.006 .132**
Sig. (2-tailed) .000 .000 .000 .000 .000 .652 .000
N 5717 5717 5717 5717 5717 5717 5717 5717
bachelor Pearson Correlation .281** .638** .577** 1 .533** .100** -.016 .190**
Sig. (2-tailed) .000 .000 .000 .000 .000 .227 .000
N 5717 5717 5717 5717 5717 5717 5717 5717
diploma Pearson Correlation .220** .697** .237** .533** 1 .293** .070** .130**
Sig. (2-tailed) .000 .000 .000 .000 .000 .000 .000
N 5717 5717 5717 5717 5717 5717 5717 5717
high school Pearson Correlation .069** .720** .050** .100** .293** 1 .461** .126**
Sig. (2-tailed) .000 .000 .000 .000 .000 .000 .000
N 5717 5717 5717 5717 5717 5717 5717 5717
other Pearson Correlation .001 .559** -.006 -.016 .070** .461** 1 .082**
Sig. (2-tailed) .935 .000 .652 .227 .000 .000 .000
N 5717 5717 5717 5717 5717 5717 5717 5717
age of
company in
years
Pearson Correlation .111** .207** .132** .190** .130** .126** .082** 1
Sig. (2-tailed) .000 .000 .000 .000 .000 .000 .000
N 5717 5717 5717 5717 5717 5717 5717 5717
**. Correlation is significant at the 0.01 level (2-tailed).
Page 9 of 21

We run the independent two sample t test for comparing the mean number of
employee, masters and doctors, bachelors, diploma holder, attended high school, other and
age of company in years. Table 6a shows the mean, standard deviation and standard error for
each variable for certified and non-certified firms.
Table 6a: Group Statistics
certification
dummy N Mean Std. Deviation Std. Error Mean
employee number not certified 5257 40.10 65.257 .900
certified 460 100.53 129.464 6.036
master and doctor not certified 5257 1.10 4.713 .065
certified 460 4.54 11.735 .547
bachelor not certified 5257 9.49 23.266 .321
certified 460 38.83 55.588 2.592
diploma not certified 5257 11.34 20.950 .289
certified 460 31.00 44.440 2.072
high school not certified 5257 11.63 28.711 .396
certified 460 19.52 51.921 2.421
other not certified 5257 6.54 25.923 .358
certified 460 6.64 26.653 1.243
age of company in years not certified 5257 7.39 6.815 .094
certified 460 10.28 9.144 .426
Table 6b represents the independent two sample t test for the considered variables. Table 6b
also shows the Levene's Test for Equality of Variances for all the variables. Levene's Test for
Equality of Variances suggest that there is significant differences between the variance of
considered variable for the certified and non-certified firms. So we assume that unequal
variances assumption for the independent two sample t test. SPSS gives the output for both
the assumptions under equal as well as unequal variance assumption. From Table 6b, we
conclude that mean of number of employees, masters and doctors, bachelors, diploma holder,
attended high school and age of company in years are significantly different for the certified
Page 10 of 21

and non-certified firms. Mean of number of employees, masters and doctors, bachelors,
diploma holder, attended high school and age of company in years for the certified firm is
more than non-certified firms. We can say that as the number of employees, masters and
doctors, bachelors, diploma holder, attended high school and age of company in years are the
important variables for the certified firms and are to be considered as the main determinant.
Page 11 of 21

Table 6b: Independent Samples Test for considered variables
Levene's Test for Equality
of Variances t-test for Equality of Means
F Sig. t df
Sig.
(2-
tailed)
Mean
Difference
Std. Error
Difference
95% Confidence Interval of the
Difference
Lower Upper
employee
number
Equal variances assumed 279.851 .000 -17.133 5715 .000 -60.434 3.527 -67.349 -53.520
Equal variances not assumed -9.902 479.615 .000 -60.434 6.103 -72.426 -48.442
master and
doctor
Equal variances assumed 242.202 .000 -12.606 5715 .000 -3.439 .273 -3.974 -2.904
Equal variances not assumed -6.242 472.041 .000 -3.439 .551 -4.522 -2.357
bachelor Equal variances assumed 527.366 .000 -22.094 5715 .000 -29.341 1.328 -31.945 -26.738
Equal variances not assumed -11.235 473.170 .000 -29.341 2.612 -34.473 -24.210
diploma Equal variances assumed 292.164 .000 -17.050 5715 .000 -19.658 1.153 -21.918 -17.398
Equal variances not assumed -9.396 477.010 .000 -19.658 2.092 -23.769 -15.547
high school Equal variances assumed 54.455 .000 -5.203 5715 .000 -7.898 1.518 -10.873 -4.922
Equal variances not assumed -3.220 483.860 .001 -7.898 2.453 -12.717 -3.078
other Equal variances assumed .497 .481 -.081 5715 .935 -.103 1.263 -2.579 2.374
Equal variances not assumed -.079 537.809 .937 -.103 1.293 -2.643 2.438
age of
company in
years
Equal variances assumed 35.312 .000 -8.460 5715 .000 -2.892 .342 -3.562 -2.222
Equal variances not assumed -6.624 504.609 .000 -2.892 .437 -3.750 -2.034
Page 12 of 21

We considered sales of company, profit of company, Total asset of the company, Equity of
the company, Total capital, Capital from government, Capital from overseas, Capital from
other sources, Return on sales, Return on assets, Percentage of overseas investment in the
total investment, Overseas investment variables as a performance variables. We firstly
summarizes the descriptive statistics for the performance variable for certified and non-
certified firms in the Table 7a and Table 7b respectively.
Table 7a: Descriptive Statistics for performance variables for the certified firms
N Minimum Maximum Mean Std. Deviation
sales 460 1000 629516 2.96E4 55356.924
profit 460 18 75004 4384.67 9237.036
asset 460 1000 544060 3.35E4 63491.066
equity 460 31 303459 1.68E4 33693.403
capital paid 460 30 184000 9267.01 18155.238
capital from state 460 0 140100 1919.22 9187.625
capital from overseas 460 0 49806 569.00 3953.449
capital from other 460 0 184000 6778.79 15922.061
return on sales 460 .01 .50 .1538 .12417
return on asset 460 .01 .95 .1668 .16839
percentage of FDI 460 .00 1.00 .0252 .14910
Valid N (listwise) 460
From Table 7a and Table 7b, we can observed the differences in summary statistic of the
performance variables for certified and non-certified firms. Now we are interested in testing
whether there is significant differences in the performance variables for certified and non-
certified firms. We run the
Page 13 of 21

Table 7b: Descriptive Statistics for performance variables for the non-certified firms
N Minimum Maximum Mean Std. Deviation
sales 5257 1000 869176 1.01E4 29617.093
profit 5257 17 296176 1865.26 6911.274
asset 5257 1000 978548 1.50E4 53574.923
equity 5257 -1367 877989 6899.36 30642.061
capital paid 5257 10 402110 4371.79 16971.715
capital from state 5257 0 402110 1138.29 11427.342
capital from overseas 5257 0 150000 329.61 4650.860
capital from other 5257 0 400000 2903.90 11278.126
return on sales 5257 .01 .51 .1943 .12344
return on asset 5257 .01 1.02 .2285 .21102
percentage of FDI 5257 .00 1.00 .0244 .14940
Valid N (listwise) 5257
We run the independent two sample t test for comparing the mean of Sales of the company,
Profit of the company, Total asset of the company, Equity of the company, Total capital,
Capital from government, Capital from overseas, Capital from other sources, Return on sales
Return on assets, Percentage of overseas investment in the total investment for certified and
non-certified firm.
Table 8 represents the independent two sample t test for the performance variables. Table 8
also shows the Levene's Test for Equality of Variances for all the performance variables.
Levene's Test for Equality of Variances suggest that there is significant differences between
the variance of performance variable for the certified and non-certified firms. So we assume
that unequal variances assumption for the independent two sample t test. SPSS gives the
output for both the assumptions under equal as well as unequal variance assumption. From
Table 8, we conclude that mean of Sales of the company, Profit of the company, Total asset
of the company, Equity of the company, Total capital, Capital from other sources, Return on
Page 14 of 21

sales and Return on assets for certified and non-certified firm are significantly different
whereas mean of Capital from government, Capital from overseas and Percentage of
overseas investment in the total investment are not significantly different for certified and
non-certified firm.
We now test whether there is any significant difference in the performance variable in
the certified firms of different industry type. We used one way ANOVA as an analysis tool
for testing this claim. Table 9 summarizes the ANOVA table for performance variable in the
certified firms of different industry type. From Table 9, we can conclude that Sales of the
company, Profit of the company, Capital from government and Return on assets are
significantly different for different type of industries which are certified
Page 15 of 21

Table 8: Independent Samples Test for considered variable
Levene's Test for
Equality of Variances t-test for Equality of Means
F Sig. t df
Sig. (2-
tailed) Mean Difference
Std. Error
Difference
95% Confidence Interval of
the Difference
Lower Upper
sales Equal variances assumed 203.848 .000 -12.334 5715 .000 -19458.787 1577.673 -22551.625 -16365.950
Equal variances not assumed -7.446 482.255 .000 -19458.787 2613.154 -24593.361 -14324.214
profit Equal variances assumed 82.963 .000 -7.271 5715 .000 -2519.411 346.491 -3198.664 -1840.158
Equal variances not assumed -5.712 504.965 .000 -2519.411 441.101 -3386.031 -1652.791
asset Equal variances assumed 55.638 .000 -7.006 5715 .000 -18543.423 2646.909 -23732.369 -13354.478
Equal variances not assumed -6.078 517.802 .000 -18543.423 3051.112 -24537.504 -12549.343
equity Equal variances assumed 42.889 .000 -6.572 5715 .000 -9873.213 1502.347 -12818.383 -6928.044
Equal variances not assumed -6.069 527.600 .000 -9873.213 1626.816 -13069.046 -6677.381
capital
paid
Equal variances assumed 22.963 .000 -5.898 5715 .000 -4895.218 829.974 -6522.283 -3268.154
Equal variances not assumed -5.574 531.608 .000 -4895.218 878.260 -6620.504 -3169.932
capital
from state
Equal variances assumed 4.586 .032 -1.426 5715 .154 -780.928 547.678 -1854.585 292.730
Equal variances not assumed -1.711 590.730 .088 -780.928 456.449 -1677.387 115.532
capital
from
overseas
Equal variances assumed 4.331 .037 -1.071 5715 .284 -239.396 223.602 -677.742 198.950
Equal variances not assumed -1.227 576.160 .220 -239.396 195.173 -622.733 143.940
capital
from other
Equal variances assumed 51.611 .000 -6.800 5715 .000 -3874.894 569.819 -4991.955 -2757.834
Equal variances not assumed -5.109 500.104 .000 -3874.894 758.491 -5365.116 -2384.673
return on
sales
Equal variances assumed .953 .329 6.750 5715 .000 .04053 .00600 .02876 .05230
Equal variances not assumed 6.716 541.462 .000 .04053 .00603 .02867 .05238
return on
asset
Equal variances assumed 38.491 .000 6.102 5715 .000 .06169 .01011 .04187 .08151
Equal variances not assumed 7.368 592.837 .000 .06169 .00837 .04525 .07814
percentag
e of FDI
Equal variances assumed .038 .846 -.105 5715 .916 -.00077 .00726 -.01500 .01347
Equal variances not assumed -.106 542.836 .916 -.00077 .00725 -.01501 .01348
Page 16 of 21

Page 17 of 21

Table 9: ANOVA table for performance variable in the certified firms of different
industry type
Sum of Squares df Mean Square F Sig.
sales Between Groups 2.826E10 7 4.037E9 1.324 .237
Within Groups 1.378E12 452 3.049E9
Total 1.407E12 459
profit Between Groups 9.809E8 7 1.401E8 1.659 .117
Within Groups 3.818E10 452 8.447E7
Total 3.916E10 459
asset Between Groups 1.160E11 7 1.657E10 4.320 .000
Within Groups 1.734E12 452 3.837E9
Total 1.850E12 459
equity Between Groups 2.650E10 7 3.786E9 3.460 .001
Within Groups 4.946E11 452 1.094E9
Total 5.211E11 459
capital paid Between Groups 1.024E10 7 1.463E9 4.688 .000
Within Groups 1.411E11 452 3.121E8
Total 1.513E11 459
capital from
state
Between Groups 9.740E8 7 1.391E8 1.665 .116
Within Groups 3.777E10 452 8.356E7
Total 3.875E10 459
capital from
overseas
Between Groups 3.900E8 7 5.572E7 3.713 .001
Within Groups 6.784E9 452 1.501E7
Total 7.174E9 459
capital from
other
Between Groups 6.049E9 7 8.642E8 3.541 .001
Within Groups 1.103E11 452 2.441E8
Total 1.164E11 459
return on sales Between Groups .402 7 .057 3.892 .000
Within Groups 6.674 452 .015
Total 7.077 459
return on
asset
Between Groups .230 7 .033 1.163 .323
Within Groups 12.785 452 .028
Total 13.015 459
percentage of
FDI
Between Groups .419 7 .060 2.763 .008
Within Groups 9.785 452 .022
Total 10.204 459
Page 18 of 21

Discussion and managerial advises
We observed that 19% software, 18% Specialized technology services, 15% Research
and Development firms are certified. Certification adoption was very little in the
Telecommunication (2%), Business services (3%), storage and transportation (4%),
Technology exchange and promotion (5%) and computer services (8%).
We conclude that mean of number of employee, number of masters and doctors,
bachelors, diploma holders and number of employees attended high school for certified firm
is more than non-certified firms. Mean age of certified firm is also more than mean age of
non-certified firms. We can say that as the number of employees, masters and doctors,
bachelors, diploma holder, attended high school and age of company in years are the
important variables for the certified firms and are to be considered as the main determinant.
We observe that mean of Sales of the company, Profit of the company, Total asset of
the company, Equity of the company, Total capital, Capital from other sources, Return on
sales and Return on assets for certified and non-certified firm are significantly different
whereas mean of Capital from government, Capital from overseas and Percentage of overseas
investment in the total investment are not significantly different for certified and non-certified
firm.
One may recruits more number of employees, masters and doctors, bachelors,
diploma holder, attended high school to adopt the ISO certification. This ISO certification
brings the industry or firm into new heights.
Limitations and directions for future research
This study is specially for determining determinants of the adoption of ISO
9000 standards only. There are many more standard e.g. ISO 14000 etc. for which
different study can be done. There may be more variables which affect the adoption of
ISO 9000 standards e.g. geographical area, turn over, salary paid to the employees,
Page 19 of 21

other benefits to the employee, customer satisfaction etc. One can study this type of
study by considering the given limitations. One can used advanced statistical tools
such as logistic regression, factor analysis for data analysis.
References
Boiral, O. (2003). ISO 9000: Outside the iron cage. Organization science, 14(6), 720-737.
Christmann, P., & Taylor, G. (2006). Firm self-regulation through international certifiable
standards: determinants of symbolic versus substantive implementation. Journal of
International Business Studies, 37(6), 863-878.
DeCanio, S. J., & Watkins, W. E. (1998). Investment in energy efficiency: do the
characteristics of firms matter?. Review of economics and statistics, 80(1), 95-107.
Du, Y. Z., Yin, J. L., & Zhang, Y. L. (2016). How innovativeness and institution affect ISO
9000 adoption and its effectiveness: evidence from small and medium enterprises in
China. Total Quality Management & Business Excellence, 27(11-12), 1315-1331.
doi:10.1080/14783363.2015.1075874
Fikru, M. G. (2014a). Firm Level Determinants of International Certification: Evidence from
Ethiopia. World Development, 64, 286-297. doi:10.1016/j.worlddev.2014.06.016
Fikru, M. G. (2014b). International certification in developing countries: The role of internal
and external institutional pressure. Journal of Environmental Management, 144, 286-
296. doi:10.1016/j.jenvman.2014.05.030
Fikru, M. G. (2016). Determinants of International Standards in sub-Saharan Africa: The role
of institutional pressure from different stakeholders. Ecological Economics, 130, 296-
307. doi:10.1016/j.ecolecon.2016.08.007
Nakamura, M., Takahashi, T., & Vertinsky, I. (2001). Why Japanese firms choose to certify:
A study of managerial responses to environmental issues. Journal of Environmental
Economics and Management, 42(1), 23-52. doi:10.1006/jeem.2000.1148
Page 20 of 21

Pekovic, S. (2010). The Determinants of ISO 9000 Certification: A Comparison of the
Manufacturing and Service Sectors. Journal of Economic Issues (Taylor & Francis
Ltd), 44(4), 895-914. doi:10.2753/JEI0021-3624440403
Wu, S. Y., Chu, P. Y., & Liu, T. Y. (2007). Determinants of a firm's ISO 14001 certification:
An empirical study of Taiwan. Pacific Economic Review, 12(4), 467-487.
doi:10.1111/j.1468-0106.2007.00365.x
Page 21 of 21