ISO Certification And Quality Management: Impact On Industry Performance

Types of ISO standards and their significance

Discuss about the Internal and External Institutional Pressure.

Each and every industry (manufacturing as well as service industries) trying to satisfy the consumers’ needs. Industries seek to admire the complete quality management through various operations. Certification is one of the proof that industry is walking on their path of quality. Certification is the written assurance by an external international organization that product or service provided by an industry meets the specific necessary requirements.

ISO is an independent, non-governmental international organization with a membership of 161 national standards bodies. There are many standards including ISO 13845 for medical device, ISO 22000 for Food safety management, ISO 31000 for risk management, ISO 50001 for energy management etc.  ISO 9000 provides the guidance and tool for industries who want to ensure that their product or service quality is improved consistently by satisfying the customer’s all needs and requirements.

Quality management is as wide research area among the academics and practitioners.  In that most of the deals with the determinants of adoption of ISO certification and its impact on financial indicators. (Christmann & Taylor, 2006; Du, Yin, & Zhang, 2016; Fikru, 2014a, 2014b, 2016; Nakamura, Takahashi, & Vertinsky, 2001; Pekovic, 2010; Wu, Chu, & Liu, 2007).

We have data of 5717 industries out of which 5257 are non-certified and 460 are certified. This 5717 firms compromises industry sectors like storage and transportation, telecommunication, computer service, software, business services, Research and Development, specialized technology services and technology exchange and promotion. Data also contains foundation year of firm, number of employees with their education level, sales of firm, profit of firm, Total asset of the firm, Equity of the firm, Total capital of firm, 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 firm.

We analyzed the data for certified and non-certified firm. We consider the number of employee, number of masters and doctors, bachelors, diploma holders, number of employees attended high school, number of employees with junior high school or below, total asset of the company, Equity of the company, Total capital, Capital from government, Capital from overseas, Capital from other sources and Age of the company are considered to be impact variables for adopting the ISO 9000 certification. We consider sales of the company, profit of the company, Return on sale, Return on assets, overseas investment as a performance variable.

Factors affecting adoption of ISO certification

We present some frequency distributions and descriptive statistics for the variables under study for certified and non-certified industries. We study the association between certification status with industry type and overseas investment. We test the mean impact variables for certified and non-certified industries. We studied the correlation between performance variable. We test the mean of performance variable certified and non-certified industries.

In today’s world, industries with ISO Certification considered to be faithful industry. There are variety of variables that can be affect the determinant of ISO certification. In literature, there is huge amount of research is done in connection with determinants of firm participation in ISO Certification and its impact on firm performance in all over the world. (Christmann & Taylor, 2006; Du, Yin, & Zhang, 2016; Fikru, 2014a, 2014b, 2016; Nakamura, Takahashi, & Vertinsky, 2001; Pekovic, 2010; Wu, Chu, & Liu, 2007). Fikru(2016) concludes that foreign ownership, plant size and business communications through company website are vital for the adoption of standards in Africa. Pekovic (2014) finds  that firm size, corporate status and previous experience with similar standards, export and customer satisfaction are important role in the ISO 9000 certification for both manufacturing and service sectors in French. Terziovski, et. al.  (2003) conclude that there is a positive and significant relationship between the manager’s motives for adopting ISO 9000 certification and business performance.

For any data analysis, use of different statistical techniques is becomes compulsory. There are many statistical tools and techniques available but the selection of proper tools and techniques is very important for the analysis of data. We summarized the data using frequency distribution and descriptive statistics. We used chi-square test to study the association between certification status with industry type and overseas investment. We used t-test for determining the determinants of adoption of ISO 9000.  We used one way ANOVA for the significance of impact variables for certified firms by considering the type of industry as a level. We used t-test for testing the significance of performance variables for certified and non-certified firms. We did the data analysis using SPSS software.

Table 1 summarizes the frequency distribution of certification status, industry type and FDI status. Out of 5717 firms 460 (8.046%) are certified and 5257 (91.954%) are non-certified. There are 2722 are Business Service industries which accumulated 47.6 % of total industries. From the 5717 firms 21% are specialized technology services industries. About 3% industries invested in overseas investment.

Data analysis using statistical techniques

Table 1: Frequency Distribution of Certification status, industry type and FDI Status

Variable	Perticular	Frequency	Percent
Certification Status	not certified	5257	91.954
certified	460	8.046
Total	5717	100.000
Industry Type	storage and transportation	392	6.857
Telecommunication	184	3.218
computer services	365	6.384
software	390	6.822
Business services	2722	47.612
Research and Development	222	3.883
Specialized technology services	1200	20.990
Technology exchange and promotion	242	4.233
Total	5717	100.000
FDI Status	NO FDI	5546	97.009
with FDI	171	2.991
Total	5717	100.000

Table 2 shows the proportion of certified firm for different industry type. From Table 2, we observed that 19% software, 18% Specialized technology services, 15% Research and Development, 8% computer services, 5% Technology exchange and promotion (5%), 4% storage and transportation, 3% Business services and 2% Telecommunication firms are certified.

Table 2: Proportion of certified firm for different industry type

Industry Type	Proportion	No. of Observation
software	0.19	390
Specialized technology services	0.18	1200
Research and Development	0.15	222
computer services	0.08	365
Technology exchange and promotion	0.05	242
storage and transportation	0.04	392
Business services	0.03	2722
Telecommunication	0.02	184
Total	0.08	5717

In this section we studied the association of certification status with industry type and FDI status. 

We test whether there is any association between the certification status and industry type. Here our null hypothesis is that there is no significant association between the certification status and industry type and alternative hypothesis is that there is significant association between the certification status and industry type. Table 3a shows the contingency table for certification status and industry type. Table 3b shows the output of chi-square test of association. The Pearson chi-square significance i.e. P-value < 0.05, suggest that there is significant association between the certification status and industry type

Table 3a: Contingency table for certification status and industry type

		certification status	Total
		not certified	certified
Industry type	storage and transportation	378	14	392
Telecommunication	180	4	184
computer services	335	30	365
software	314	76	390
Business services	2650	72	2722
Research and Development	189	33	222
Specialized technology services	981	219	1200
Technology exchange and promotion	230	12	242
Total	5257	460	5717

Table 3b: Chi-Square test for association between the certification status and industry type

Chi-Square Tests
	Value	df	Asymp. Sig. (2-sided)
Pearson Chi-Square	3.815E2a	7	.000
Likelihood Ratio	361.226	7	.000
Linear-by-Linear Association	.963	1	.327
N of Valid Cases	5717
a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 14.80.

We test whether there is any association between the certification status and FDI status. Here our null hypothesis is that there is no significant association between the certification status and FDI status and alternative hypothesis is that there is significant association between the certification status and FDI status.. Table 4a displays the contingency table for certification status and FDI status. Table 4b shows the output of chi-square test of association. The Pearson chi-square significance i.e. P-value=0.723  > 0.05, suggest that there is no significant association between the certification status and FDI status.

Table 3a: Contingency table for certification status and FDI status

		certification dummy	Total
		not certified	certified
FDI dummy	NO FDI	5101	445	5546
with FDI	156	15	171
Total	5257	460	5717

Table 3b: Chi-Square test for association between the certification status and FDI status

	Value	df	Asymp. Sig. (2-sided)	Exact Sig. (2-sided)	Exact Sig. (1-sided)
Pearson Chi-Square	.125a	1	.723
Continuity Correctionb	.045	1	.832
Likelihood Ratio	.122	1	.727
Fisher’s Exact Test				.669	.402
Linear-by-Linear Association	.125	1	.723
N of Valid Casesb	5717
a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 13.76.
b. Computed only for a 2×2 table

Table 4 presented the descriptive statistics for the impact variables. One can easily see the differences in the impact variables for certified and non-certified firms. By comparing values in Table 4, we can see that mean and median of impact variables are more for certified firms than non-certified firms.

In Table 5, we represents the Pearson’s correlation coefficient and it’s significance for impact variables. Most of the correlation are significant and positive.

We used two sample t-test for testing whether there is significant difference between mean of impact variables for certified and non-certified firms. Table 6 represents the independent two sample t test for the impact variables for certified and non-certified firms. From table 6, Levene’s Test for Equality of Variances suggest that impact variables other than number of employees having education below high school and other does not have same variance for certified firm and non-certified firm. According to the two sample t-test, impact variable (other than number of employees having education below high school and other, capital from government (state) and capital from overseas have statistically different mean for certified and non-certified firms.

Table 4: Descriptive Statistics of impact variables for certified and non-certified firms

Variable	Certification Status	Mean	Median	StDev	Minimum	Maximum	Q1	Q3
Employee number	Non-Certified	40.096	21	65.257	11	969	15	38
Certified	100.53	55	129.46	11	913	29	110.75
Number of employees with master or doctor	Non-Certified	1.102	0	4.713	0	161	0	0
Certified	4.541	1	11.735	0	136	0	4
Number of employees with bachelor	Non-Certified	9.489	4	23.266	0	530	0	10
Certified	38.83	19.5	55.59	0	388	8	41.5
Number of employees with diploma	Non-Certified	11.337	6	20.95	0	490	3	12
Certified	31	16	44.44	0	343	8	35
Number of employees with high school education	Non-Certified	11.626	5	28.711	0	600	1	11
Certified	19.52	6	51.92	0	689	2	18
Number of employees with junior high school or below	Non-Certified	6.537	0	25.923	0	568	0	4
Certified	6.64	0	26.65	0	245	0	2
Total asset of the company	Non-Certified	14981	3700	53575	1000	978548	2000	8654
Certified	33525	10089	63491	1000	544060	4334	30655
Equity of the company	Non-Certified	6899	1500	30642	-1367	877989	743	3639
Certified	16773	5252	33693	31	303459	2266	15221
Total capital	Non-Certified	4372	1000	16972	10	402110	500	2626
Certified	9267	3500	18155	30	184000	1858	10000
Capital from government	Non-Certified	1138	0	11427	0	402110	0	0
Certified	1919	0	9188	0	140100	0	0
Capital from overseas	Non-Certified	330	0	4651	0	150000	0	0
Certified	569	0	3953	0	49806	0	0
Capital from other sources	Non-Certified	2904	1000	11278	0	400000	380	2000
Certified	6779	3000	15922	0	184000	1000	6000
Age of the company	Non-Certified	7.3884	6	6.8154	2	61	3	9
Certified	10.28	8	9.144	2	56	5	13

Table 5: Pearson’s correlation coefficient and its significance value

	Employee Number	Number of employees with master or doctor	Number of employees with bachelor	Number of employees with diploma	Number of employees with high school education	Number of employees with junior high school or below	Total asset of the company	Equity of the company	Total capital	Capital from government	Capital from overseas	Capital from other sources
Number of employees with master or doctor	0.393
0
Number of employees with bachelor	0.638	0.577
0	0
Number of employees with diploma	0.697	0.237	0.533
0	0	0
Number of employees with high school education	0.72	0.05	0.1	0.293
0	0	0	0
Number of employees with junior high school or below	0.559	-0.006	-0.016	0.07	0.461
0	0.652	0.227	0	0
Total asset of the company	0.337	0.224	0.275	0.262	0.205	0.122
0	0	0	0	0	0
Equity of the company	0.303	0.253	0.278	0.216	0.181	0.087	0.864
0	0	0	0	0	0	0
Total capital	0.272	0.169	0.244	0.195	0.173	0.083	0.686	0.787
0	0	0	0	0	0	0	0
Capital from government	0.141	0.102	0.128	0.147	0.087	-0.002	0.427	0.544	0.672
0	0	0	0	0	0.89	0	0	0
Capital from overseas	0.09	0.095	0.078	0.051	0.075	0.014	0.235	0.315	0.311	0.054
0	0	0	0	0	0.289	0	0	0	0
Capital from other sources	0.225	0.111	0.201	0.123	0.139	0.117	0.497	0.501	0.69	0	0.01
0	0	0	0	0	0	0	0	0	0.997	0.447
Age of the company	0.207	0.132	0.19	0.13	0.126	0.082	0.133	0.111	0.094	0.073	0.008	0.063
0	0	0	0	0	0	0	0	0	0	0.541	0

Here we studied whether the impact variables which significantly different for certified and non-certified firms have same mean for all industry type or not. We used one way ANOVA for this purpose.

Table 7: One way ANOVA for comparing the mean of significant impact variable of different industry type

Variable	Sources of Variation	Sum of Squares	df	Mean Square	F	Sig.
employee number	Between Groups	283984.333	7	40569.190	2.475	.017
Within Groups	7409316.241	452	16392.293
Total	7693300.574	459
master and doctor	Between Groups	1228.644	7	175.521	1.280	.258
Within Groups	61975.571	452	137.114
Total	63204.215	459
bachelor	Between Groups	68308.120	7	9758.303	3.267	.002
Within Groups	1350026.654	452	2986.785
Total	1418334.774	459
diploma	Between Groups	35520.322	7	5074.332	2.633	.011
Within Groups	870967.669	452	1926.920
Total	906487.991	459
high school	Between Groups	88663.439	7	12666.206	4.984	.000
Within Groups	1148719.298	452	2541.414
Total	1237382.737	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 other	Between Groups	6.049E9	7	8.642E8	3.541	.001
Within Groups	1.103E11	452	2.441E8
Total	1.164E11	459
age of company in years	Between Groups	2741.858	7	391.694	4.969	.000
Within Groups	35632.965	452	78.834
Total	38374.824	459

From Table 7, we can say that all the significant impact variable (except masters and doctors) are significantly different from each other for different industries.

We used two sample t-test for testing the mean of performance variable for certified and non-certified firms. Table 8a and Table 8b demonstrate the results. Table 8a shows the summary statistics for performance variable. We can see that mean of sale and revenue is more for certified firms than other from Table 8b.  By observing the significance value of independent two sample t-test, we claim that mean of all the performance variable are statistically different for certified and non-certified firms.

Table 8a: Summary Statistics for performance variables

	certification dummy	N	Mean	Std. Deviation	Std. Error Mean
sales	certified	460	2.96E4	55356.924	2581.030
not certified	5257	1.01E4	29617.093	408.482
profit	certified	460	4384.67	9237.036	430.679
not certified	5257	1865.26	6911.274	95.321
return on sales	certified	460	.1538	.12417	.00579
not certified	5257	.1943	.12344	.00170
return on asset	certified	460	.1668	.16839	.00785
not certified	5257	.2285	.21102	.00291

Table 8b: Independent two sample t test for performance 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	16365.950	22551.625
Equal variances not assumed			7.446	482.255	.000	19458.787	2613.154	14324.214	24593.361
profit	Equal variances assumed	82.963	.000	7.271	5715	.000	2519.411	346.491	1840.158	3198.664
Equal variances not assumed			5.712	504.965	.000	2519.411	441.101	1652.791	3386.031
return on sales	Equal variances assumed	.953	.329	-6.750	5715	.000	-.04053	.00600	-.05230	-.02876
Equal variances not assumed			-6.716	541.462	.000	-.04053	.00603	-.05238	-.02867
return on asset	Equal variances assumed	38.491	.000	-6.102	5715	.000	-.06169	.01011	-.08151	-.04187
Equal variances not assumed			-7.368	592.837	.000	-.06169	.00837	-.07814	-.04525

We observed that there is strong association of industry type and certification status. There is very little certification adoption in the Telecommunication (2%),  Business services (3%), storage and transportation (4%) and Technology exchange and promotion (5%).

There is significant difference in mean of certified and non-certified firms for number of employee, number of masters and doctors, bachelors, diploma holders, number of employees attended high school, total asset of the company, Equity of the company, Total capital, Capital from other sources and Age of the company. We observed that mean sale and revenue is more and significant in certified firms than non-certified firms.

Company needs to focused on the significant impact variables for adoption of ISO certification. For example recruit the more employee with higher education level

In this study, we focused on determinants on adoption of ISO certification where data is collected for some variables. This are not only the variables which determines the determinants of adoption of ISO. Other variables like turn over, geographical area, number of training to the employees etc. This may impact on adoption of ISO.

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

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

Terziovski, M., Power, D., & Sohal, A. S. (2003). The longitudinal effects of the ISO 9000 certification process on business performance. European Journal of operational research, 146(3), 580-595.

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

Data Analysis and Results