Introduction to ISO 9000
“International Certifiable Management Standards” have been introduced as the mechanism of governance for firm self-regulation of issues regarding corporate social responsibility that are influential if and only if proficient industries comply with the needs of standards. The research report aims to have certified components as per “Customer preferences”, “Customer monitoring” and “Expected sections” by customers (Christmann & Taylor, 2006).
About the implementations of ISO 9000, the “National Bureau of Statistics of China” carried out an economic census of the various firms in 2008. The data collected from the census is analysed in this research report. The key features of the internal strategy of the industries are found to be –
- Innovation
- Cost reduction
- Quality improvement (Pekovic, 2010)
The research report might make stake-holders and policy-makers enough capable to “Better and effectively” formulate regulations and acts. However, it may affect the success of the business industries in both service and manufacturing sectors (Pekovic, 2010). The ISO 9000 is relied on the profitability variables namely the return on equity, return on assets and sales. According to Javorcik & Sawada, (2018), there is a long term relationship between the environmental and economic performance that uses the multivariate regression analysis to define the positive relationship between the fixed and random effects models.
The research study relates large scale Chinese manufacturing organizations with each other. The objectives of such types of empirical collaboration are –
- To obtain “ISO 9000” environmental certification
- To accomplish environmental targets in case of decision-making
- To become an early adopter of “ISO 9000” (Nakamura, Takahashi & Vertinsky, 2001)
ISO 9000 is an international standard of quality management that has been widely implemented across the world since its introduction in 1987. By the end of 2013, ISO 9000 had been considered by more than 1129000 facilities in 189 countries. The adoption of ISO 9000 on the financial performance of firms is the interesting fact towards practitioners and academics simultaneously.
This innovative study with the help of routines and operational processes enhance small and medium enterprises that would elongate “International market”. The maximization of professional benefits operates the context of multiple institutions (Du, Yin & Zhang, 2016). According to an empirical approach, the determinants of ISO 9000 are generally different from each other. ISO 9000 certification varies absurdly among various manufacturing firms and services.
The identification and characteristics of the manufacturing industries obtains evidences about the “Corporate Status”, “Firm size” and “Experience of similar standards” featuring especially external strategies. “International environment” and “Quality standards” drives the pressure from international markets and importers. It is a notable fact that, export and customer satisfaction takes a vital responsibility towards ISO 9000 certification across both “Service sector” and “Manufacturing sector”.
On the other hand, the employees of small enterprise and organisation is the part of internal capabilities. The expenditure invests the measurement in property, equipment and assets. The matured industries also build up resources and capabilities create policy management of the decision (Fikru, 2014). The matured industries are rebounded by the profitability since it helps in benefitting the application of the strategy focus, innovation and cooperation’s (Pantouvakis & Karakasnaki, 2016). Innovations forms the basis of improving efficiency which requires adequate investment in leveraging the focus and cooperation’s (Lakhal, 2014). Operating profit, revenue and equity helps in leveraging the ownership reformation which would help in deepening the operations with improved quality development.
Determinants of ISO 9000 implementation across different sectors
It is an exploratory data analysis (Wiersma & Jurs, 2005). Most of the variables undertaken in this data sample is quantitative in nature. Some nominal variables are changed to numerical variables such as “Certification” and “DFIdummy”. Also, some nominal variables are present in this data set that are “Year”, “Industry2” and “Industry4”. Therefore, the data is analysed by quantitative method. Mainly two types of analysis are executed here that are descriptive statistics and inferential statistics. Descriptive statistics involve the summary statistics of the only numerical variables only and frequency statistics include the frequency distributions of the categorical variables. On the other hand, inferential statistics involve the association, correlation and links of several variables.
Descriptive statistics help to find out the actual pattern and behaviour of numerical data sets (Liu, Parelius & Singh, 1999). Measures of central tendency and measures of dispersion are the key factors of descriptive statistics.
The average revenue is found to be 11698.57 (SD = 32873.61). The highest revenue is observed to be 869176 and minimum revenue is 1000. The average profit is observed to be only 2067.97 (SD = 7158.42). The highest operating profit is found to be 296176 and lowest operating profit is only 17. The mean amount of total assets is observed as 16473.1 (SD = 54666.56). The highest amount of total asset is 978548 and lowest amount of total asset is 1000. The average amount of equity is 7693.77 (SD = 31012.08). The highest amount of equity amount for any observation is 877989 and lowest amount of equity amount for any observation is (-1367).
The mean amount of total capital is 4765.67 (SD = 17120.18). The range of total capital is 402100 with minimum capital 10 and maximum capital 402110. The total capital has three partitions.
- The average capital from the government is 1201.122 (SD = 11264.94). The range of amount of capital from government is 402110 with minimum capital 0 and maximum capital 402110.
- The average capital from the overseas is 348.87 (SD = 4598.81). The range of amount of capital from overseas is 150000 with minimum capital 0 and maximum capital 150000.
- The average capital from other sources is 3125.68 (SD = 11765.55). The range of amount of capital from other sources is 400000 with minimum capital 0 and maximum capital 40000.
The average of return on sales is 0.1911 (SD = 0.124). The return on sales is ranges from 0.0133 to 0.5058 with a range of 0.4925. The average of return on assets is 0.2236 (SD = 0.2086). The return on assets lies in the interval of 0.0145 to 1.0226 with a range 1.008. The mean of the percentage of overseas investment in the total investment of all observations are 2.45%. The percentage of overseas investment in the in all the observations ranges 0 to 1.
91.95% facilities are not certified (dummy = 0) and rest of the 8.05% facilities are certified (dummy = 1). Among the undertaken industries, a highest percentages (47.61%) of industries are involved in “Business Services”, followed by “Specialized technology services” (20.99%). From the graph of appendix, it could be interpreted that most of the considered age of the firms is 2 years. The newly developed firms are much in percentages.
Inferential helps to find interpretation and predictions about the data sets. Not only that, inferential statistics has a vital role in testing of hypothesis where various tests like ANOVA, T-test and Chi-square test are executed.
The hypothesis is stated as:
Null hypothesis: The averages of considered variables are equal (Heiberger & Neuwirth, 2009).
Relationship between environmental and economic performance
Alternative hypothesis: There exists at least one inequality in the averages of considered variables.
As the p-value of the F-statistic (=129.75) is 0.0, hence, we reject the null hypothesis at 5% significant level. Therefore, it is 95% evident that the three variables “Capital from government”, “Capital from overseas” and “Capital from other sources” do not have equal averages.
As the p-value of the F-statistic (=2.3722) is 0.0, hence, we reject the null hypothesis at 5% significant level. Therefore, it is 95% evident that the five variables regarding the number of “Masters or Doctors”, “Bachelor”, “Diploma” and “High school education” and “Junior high school or below” do not have equal averages.
The correlation co-efficient between Sales of the company and Profit of the company is positive and strong (r = 0.7622) (Sedgwick, 2012). The correlation between return of sales and return of assets is positive and moderate (r = 0.5554).
The hypothesis is stated as:
Null hypothesis: The difference of considered two numerical variables is equal to 0.
Alternative hypothesis: The difference of considered two numerical variables is unequal to 0.
The two sample t-test verifies the equality of means of two quantitative variables. The two variables considered here are “Return on Sales” (ROS) and “Return on assets” (ROA) with mean 0.191 and 0.224 respectively. The difference of averages of two quantitative variables is hypothesised as 0. The two-tailed p–value with t-statistic (= -10.1215) is found to be 0.0. Hence, the null hypothesis of equality of averages of ROS and ROA is rejected with 5% significant level (Keselman et al., 2004). Therefore, it could be concluded that averages of ROS and ROA are not equal.
The hypothesis is stated as:
Null hypothesis: The considered two categorical factors are independent to each other.
Alternative hypothesis: The considered two categorical factors are not independent to each other as there exists association between these two variables.
Here, two categorical variables are “Count of certification” and “FDI dummy”. As per, chi-square test table, the chi-square test statistic is 0.125 with 1 degrees of freedom. The p-value of the Chi-square test statistic is 0.723 (>0.05). Therefore, the null hypothesis cannot be rejected with 95% confidence. Hence, it could be interpreted that with the absence of association, the “Certification” and “FDIdummy” are independent to each other (Moore, 1976).
In Socio-economic circumstances, the organizational pressures hamper businesses in the growing countries. Governments in growing countries would respond to bureaucratic regulation for businesses. It affects the adoption of international certification (Wu, Chu & Liu, 2007). The study empirically tests the preferences and likeliness to adopt ISO 9000. The research study finds that as per analysis of several attributes of the data, the incorporation of ISO 9000 is essential in China. The study concludes about a crucial impact on operating profit on generated revenues. The revenue generation could have been greater if ISO 9000 would be incorporated for the undertaken companies. However, FDI do not gets effected by certification.
Capitals gained from government, overseas and other sources are not equal; especially capital from other sources is lesser than other types of sources. These would have been greater for ISO 9000 certification also. If revenue and profit enhances, the certification process also becomes fruitful. Return on sales is less than Return of assets. The trust and confidence of the organizational managers towards certification would have provide a better performance. The managers can undertake the steps of reducing and improving the debt to capital. This includes improving the sales profitability with better administration of inventory and restructuring of debt. The most logical steps to improve the capital is to reduce the debt to capital ratio by increasing the sales revenues and profitability (Monika et al., 2017). This can be done by raising the prices and reducing the costs.
Exploratory data analysis
The research did not took into account any information regarding preference or unlikeliness towards certification. Regression models would have been more information provider in this platform. A questionnaire regarding response of the company managers about their point of views towards ISO 9000 would have been more promising. More number of samples not only from China, but also from other countries should be collected for further analysis (Price & Murnan, 2004). The literature could have also explored the areas of local and domestic company level to gain a better understanding of the ISO cortication (Campos et al., 2015).
References:
Campos, L. M., de Melo Heizen, D. A., Verdinelli, M. A., & Miguel, P. A. C. (2015). Environmental performance indicators: a study on ISO 14001 certified companies. Journal of Cleaner Production, 99, 286-296.
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.
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
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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
Heiberger, R. M., & Neuwirth, E. (2009). One-way anova. In R through excel (pp. 165-191). Springer, New York, NY.
Javorcik, B., & Sawada, N. (2018). The ISO 9000 certification: Little pain, big gain?. European Economic Review, 105, 103-114.
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Lakhal, L. (2014). The relationship between ISO 9000 certification, TQM practices, and organizational performance. Quality Management Journal, 21(3), 38-48.
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Monika, M., Aswini, P. M., & Parthasarathy, K. (2017). Effectiveness of in Service Training on ISO 18001 Certification among Industrial Employees.
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