Adoption of International Standards and its Impact on Firm-level Performance in Southeast Asia: Effect of Self-Motivation and Supply Chain Requirement

1. 9th GLOBELICS International Conference
15-17 November 2011, Buenos Aires, Argentina
Adoption of International Standards and its Impact on Firm-level Performance in
Southeast Asia: Effect of Self-Motivation and Supply Chain Requirement
TOMOHIRO MACHIKITA
Inter-disciplinary Studies Center, Institute of Developing Economies
3-2-2 Wakaba, Mihama-ku, Chiba-shi, Chiba, 261-8545, Japan
machi@ide.go.jp
YASUSHI UEKI
Bangkok Research Center, Institute of Developing Economies
16F, 161 Rajadamri Road, Pathumwan, Bangkok 10330, Thailand
yasushi_ueki@ide-jetro.org
Tel: +66-2-253-6441(ext. 204)
Fax: +66-2-254-1447
1

2. Abstract
This paper attempts to provide new evidence on adoption and impact of international
standards by investigating firm-level dataset constructed by the questionnaire survey
conducted in Indonesia, the Philippines, Thailand and Vietnam in 2009. This paper focuses
on: (1) what outcomes can be expected from the adoption of international standards; (2)
whether differences in the outcomes exist between firms voluntarily adopted standards and
those adopted upon requirements from their customers; and (3) what kind of a firm is
required by its customer to adopt and actually adopted international standards. The empirical
results show the adoption is significantly correlated with outcome indicators and profit.
Although there are not considerable differences in outcomes between firms voluntarily
adopted and those adopted upon customer’s requirement, voluntarily-adopted firms tend to
decrease inventories and increase profits. Firms voluntarily adopted are likely to ship cargos
daily and practice JIT with their customers and provide training programs to their employees.
On the other hand, firms adopted standards facing customers’ request have better engineering
knowledge. This implies that organizational mechanisms fostering intrinsic motivations and
capabilities of employees may enable firms to adopt complex management practices like JIT
in addition to international standards under silent supply chain pressure and enhance
profitability.
Keywords: ISO; supply chain, process improvement; Southeast Asia.
JEL classification: L25, M11, O31, O33
2

3. 1. Introduction
Management systems within firms have been getting complex as they face more requests
from various stakeholders. Their corporate customers and final consumers require higher-
quality, stable and safe products. Employees demand safer and healthier work environments.
The society requires firms not only to pursue more profits and efficiencies but also to take
more responsibilities for confirming to social norms as corporate citizen. Such changes in
public’s attitudes toward private firms compel governments strengthen social regulations to
discourage antisocial corporate behaviors.
All of these pressurize firms, especially multinational companies (MNCs), to introduce
standardized quality, environment or other management systems. They are also forced to, for
example, use environmentally-friendly parts, materials and other inputs and produce green
products, considering the whole product life cycle. Effects of these managerial requirements
for a firm reach not only in-house departments but also the whole supply chain of the firm.
This is because the whole production process of a product cannot be complete within the firm.
Therefore, the firm requires its suppliers to adopt standard management systems.
In reality, in their procurement policy or purchasing guidelines, MNCs explicitly or
implicitly set the adoption of international standards as a condition to become their suppliers.
For example, suppliers of Toshiba are “expected to establish a quality assurance system in
accordance with the ISO 9000 family of standards,” and “encouraged to adopt ISO 14001-
based environmental management systems and to promote third-party certification”
according to Toshiba Group Procurement Policy.1 Taiwanese Foxconn requires suppliers of
printed circuit boards (PCBs) to be certified under ISO 14001. Foxconn Technology Group
(2010) reports that 74% of the PCB suppliers have obtained ISO 14001 certificate and the
firm aims that 100% PCBs suppliers be certified ISO 14001 standards by 2010. Foxconn also
asks suppliers to conduct green house gas (GHG) inventory and reduction according to the
international standard ISO 14064.
Reflecting MNCs’ strategies and the agglomeration of export-oriented manufacturing
industries, firms in East Asia are main adopters of the international standards developed by
the International Organization for Standardization (ISO). Far East accounts for 37.4% of the
ISO 9001, 50.3% of the ISO 14001, and 47.9% of the ISO/TS 16949 certificates that have
been issued in the world up to 2009 (ISO 2010). It can be said that the adoption of
1
Toshiba (http://www.toshiba.co.jp/procure/en/policy/index.htm), accessed on May 22, 2011. In Toyota Green Purchasing Guideline,
suppliers are requested to: acquire ISO 14001 certification or maintain the certification if suppliers have already obtained certification; and
fill out the ISO 14001 Certification Survey Form every year. More detailed information are available at Toyota’s website
(http://www.toyota-global.com/sustainability/environmental_responsibility/basic_stance_on_the_environment/pdf/p4_5.pdf), accessed on
May 22, 2011.
3

4. international standards are pre-requisite for firms in East Asia to participate in international
value chains, although the ISO standards are voluntary notionally.
On the other hand, the costs for adopting and maintaining ISO international standards are
heavy burdens for firms in developing countries, especially small and medium sized
enterprises (SMEs). There are also complaints from firms, especially local SMEs, who cannot
recognize benefits from ISO standards, even though they have made a substantial investment
in acquiring international standards according to customers’ requests.
This paper attempts to contribute to the literature by providing new evidence from firms
in Southeast Asia. Our focuses are placed on the following three issues: (1) what outcomes
can be expected from the adoption of international standards; (2) whether differences in the
outcomes exist between firms voluntarily adopted standards and those adopted upon
requirements from their customers and (3) what kind of a firm is required by its customer to
adopt international standards and actually adopted them. Probit estimations are mainly
applied to examine these issues. Firm-level dataset was constructed by the questionnaire
survey conducted in Indonesia, the Philippines, Thailand and Vietnam in 2009.
The result of binary and ordered probit estimations for whole sample show positively
significant relationships between the adoption of international standards and (1) the outcomes
such as improvements in process control, decrease in inputs, and development of markets,
and (2) profits. Although there are not considerable differences in outcomes between firms
voluntarily adopted and those adopted upon customer’s requirement, voluntarily-adopted
firms tend to decrease inventories and increase profits. Firms adopted standards without
customers’ request ship cargos daily, practice JIT with their customers and have MNC-
experienced top management. Firms adopted standards facing customers’ request have higher
percentage of engineers who finished technical college and higher educations.
This paper is structured as follows. The second section briefly reviews literatures to raise
the issues relevant to Southeast Asia. The third section explains the data using tables and
figures to observe the current situation in Southeast Asia, using descriptive statistics.
Econometric methods are applied from the forth section. The forth section examines the
relationship between the adoption of international standards and firm-level performances.
The fifth section investigates the difference between firms adopted upon customers’ request
and those adopted voluntarily. The sixth section focuses on firm-level characteristics that
affect the adoption of international standards. The seventh section summarizes empirical
findings and discusses implications.
4

5. 2. The Relationships between International Standards and Process Improvements
Firms in Southeast Asia have been facing harder competition in the more liberalized
market and regional economic integration. Even firms in the region whose competitiveness
used to depend on cheap labors cannot be economically sustainable without pursuing
continuous improvements and innovations. Firms are also demanded by diversified
stakeholders to take more social responsibilities.
To response to these changes in business environments and requirements, firms need to
achieve process and product improvements. Internal efforts at the firm level are indispensable
to achieve these. Collaborations with external entities are getting more importance because
the processes for producing a product are not completed within a firm. Literature on
innovation emphasize that external sources of information are crucial for firms in Southeast
Asia where indigenous firms do not have sufficient capabilities to conduct in-house research
and development (R&D) (Machikita, Ueki 2011a).
Therefore, mechanisms to facilitate communication within a firm and between firms will
affect performances of intra and inter-firm collaborations. Face-to-face communication is one
of the means to smooth exchanges information, especially tacit knowledge. Empirical
evidences suggest face-to-face communications are significantly important for firms in
Southeast Asia to transfer technologies and knowledge through supply chains (Machikita,
Ueki 2011b).
Organizational forms that motivate employees intrinsically may also influence creation
and transfer of tacit knowledge that sustain competitive advantages. It is necessary to balance
between intrinsic and extrinsic motivations to generate and sustain distinctive competence
(Osterloh, Frey 2000; Osterloh, Frost, Frey 2002).
Codification and standardization are another effective approach to facilitate treatment,
accumulation and dispersion of knowledge, learning and creation of new knowledge. From
this point of view, international standards such as ISO 9000 and 14000 series are a common
language (Franceschini, Galetto, Maisano, Mastrogiacomo 2010). The costly tacit-knowledge
codification and documentation processes embedded in international standards can provide
opportunities for communication and assessment of existing business processes. Such whole
system of international standards may result in stabilizing processing and innovations
(Bénézech, Lambert, Lanoux, Lerch, Loos-Baroin 2001).
There are extensive literatures concerning international standards. The literature review
by Sampaio, Saraiva, and Rodrigues (2009) identifies eight major research questions on ISO
9000 including: certification market evolution; certification motivations and benefits, barriers
5

6. and drawbacks; impacts on organizational and financial performance; and ISO 9001 and total
quality management (TQM).
Empirical studies in the related literature have shown mixed results of the effect of
international standards on business performances. (Sharma 2005). Quazi, Hong, and Meng
(2002) confirm no significant effect of the ISO 9000 certification on quality management
practices and quality results of firms in Singapore. Employing panel data reported by OECD
nations, Clougherty and Grajek (2008) find ISO diffusion have no effect in developed nations,
but enhance inward FDI and exports in developing nations. These findings suggest that
attributes of companies that are closely related to firms’ capabilities may affect the impact of
international standards on business performances.
Motivations for firms to adopt international standards are also one of the main issues in
the literature as a factor that may affect benefits of international standards (Heras-
Saizarbitoria, Landín, Molina-Azorín 2011). Sun and Cheng (2002) investigate Norwegian
manufacturing companies. They find that customers’ demand and pressure encourage SMEs
to practice quality management, while large firms implement it due to mainly internal
benefits. They also insist that SMEs’ performance improvement is marginally correlated with
ISO 9000 certification, however no significant correlation can be identified for the large
company. As surveyed by Heras-Saizarbitoria, Arana, and San Miguel (2010), not only
internal benefits but also external factors including customer pressures motivate firms to
adopt international standards. Actually ISO 9000 has been diffused along supply chains
(Neumayer, Perkins 2005; Corbett 2006). Arimura, Darnall, and Katayama (2011) found
government programs that encourage voluntary adoption of environmental management
systems may promote Japanese facilities to require their suppliers to undertake specific
environmental practices.
These related literatures provide important implications to consider the diffusion and
benefits of international standards in Southeast Asia. In the manufacturing sector in Southeast
Asia where MNCs and large firms take leadership in the governance of value chains,
customers’ request may be a considerably powerful motive for firms to adopt international
standards. In practical, some MNCs have purchasing policies that stipulate potential suppliers
to adopt specific international standards. Even if international standards are voluntary, they
are substantially obligatory in some cases.
Although tons of ISO certifications are issued in East Asia, the topics related to
international standards, has not been investigated sufficiently. The necessity of empirical
studies, above all for Southeast Asia, is growing because governments and private companies
6

7. in developed countries as well as developing countries require their partners to fulfill stricter
standards. As observed by Machikita and Ueki (2010) that examined the relationship between
the adoption of ISO standards and the geographical structure of production networks, such
new business environments may have considerable influences on the structure of East Asian
production networks that are a basic infrastructure for export-driven economic development
in the region.
The worst affected entities by such changes in business environments would be
indigenous firms and SMEs in developing countries that do not have sufficient capacities to
satisfy one standard after another. Therefore, the investigation in the following sections takes
into careful consideration differences in firm-level characteristics such as nationality, size,
sector, and so force.
3. The Data
3.1. The sample
The dataset used in this paper was developed from the Survey on Fostering Production
and Science & Technology Linkages to Stimulate Innovation in ASEAN (hereafter ERIA
Establishment Survey 2009). The original questionnaire was designed by the authors and
their collaborators to capture firm-level production networks and collaborative efforts for
innovation. The establishments participated in the survey were asked details on not only their
own characteristics including their sources of information used for process and product
innovation activities but also attributes of their main customer and supplier and cooperative
activities with them. These unique characteristics differentiate our dataset from existing large
sample survey on the adoption of the ISO standards reported by the ISO and governments in
some countries that are not necessarily enable to associate ISO standards with firm-level
business performances.
The data was collected by mail and interviews conducted during November 2009 –
January 2010 in five industrial districts in four countries in Southeast Asia: JABODETABEK
(Jakarta, Bogor, Depok, Tangerang and Bekasi) in Indonesia; CALABARZON (Cavite,
Laguna, Batangas, Rizal, and Quezon) in the Philippines; Bangkok and surrounding area in
Thailand; and Hanoi and Ho Chi Minh City area in Vietnam. A total of 864 establishments
agreed to participate in the survey including: 183 establishments in Indonesia; 203
establishments in the Philippines; 178 establishments in Thailand; and 300 establishments in
Vietnam. The establishments responded to the survey primarily involve in manufacturing.
7

8. 3.2. Characteristics of the Establishments
Tables 1 and 2 summarize characteristics of the responded establishments. The average
age of the responded establishments (variable Age) is 16.8 years old. Some 63.9% of them are
categorized as SMEs that employ 199 or less workers (SME). About 67.5% of them are 100%
locally-owned (Local). The high proportion of local firms differentiates the dataset from
other firm surveys that often focus on MNCs.
Reflecting the industrial structure in developing countries, the sample includes firms
whose main activities are: Food including beverages and tobacco (11.1%); Textile including
apparel and leather (10.6%); Electronics including computers and parts (11.8%); and Other
machines including machinery industries other than electronics (21.1%). Chemicals including
plastic and rubber products (12.8%) are also an important sector, although fewer
establishments produce other basic materials such as non-metallic mineral products (Non-
metal: 1.5%) and iron and steel (Iron: 4.7%).
3.3. Adoption of International Standards
Table 1 also shows that 43.3% of the respondents (Request) are required by their main
customers to adopt international standards (ISO9000, ISO14000, etc.) and 50.3% of them
(Standards) have adopted any of them.
Table 3 describes the influence of firm characteristics to the requirement from the
customer and the adoption of international standards. There are statistically significant
differences between MNCs/joint ventures (JVs) and local firms and between large firms and
SMEs. Higher proportion of MNCs/JVs: (1) was requested by their customers to adopt
international standards; (2) has adopted international standards; (3) adopted upon customer’s
request; and (4) adopted without customer’s request.
For example, among the MNCs/JVs, 60.5% of them were required to adopt international
standards and 70.5% of them have adopted any of them. These percentages for local firms are
35.0% and 40.7% respectively, which are significantly smaller than the percentage for
MNCs/JVs. Among the firms adopted international standards, (1) 81.8% of MNCs/JVs were
requested adoption by their partners while 57.4% of local firms received such request, and (2)
53.2% of MNCs/JVs have adopted without customer’s requirement although 31.7% of local
firms have done without it.
Table 3 also presents that firms tend to be motivated by their customers’ requirement to
adopt international standards rather than their own voluntary initiatives, irrespective of
8

9. nationality and size of the respondent firms. Table 4 observes this finding from a different
point of view by providing the evidence that the proportion of the firms to whom their
customers required the adoption of international standards is 58.9% for the firms adopted
standards and 27.5% for those not adopted.
Table 4 reflects the possible relationship between the adoption of international standards
and business performance and whether the difference in motives may affect the impact of the
adoption on business performances. To measure performances, firms were asked annual
change in profit (Profit) that is measured on a five-point Likert scale ranging from 1
(substantial decrease) to 5 (substantial increase). They were also asked 11 questions about
their achievements in 2007-2009, which correspond to 11 dummy variables for outcome
(Defect, Inventory, Material, Labor, Quality, Flexibility, Lead-time, Domestic market,
Foreign market, Pollution, Regulation) listed in Table 1 and Appendix Table A1 as dependent
variables. These 11 dummy variables for outcome are aggregated into the variable Outcomes
that can ranges from 0 to 11.
Table 4 shows that the establishments certified international standards have had better
outcomes and increased profits with higher possibility than non-certified ones. These findings
are obvious from Figures 1 and 2. Table 4 also suggests that there are not significant
differences in the performances between establishments adopted internationals standards with
and without customer’s requests. But there are exceptions. The establishments obtained
certifications without requirements from customers have decreased inventories of products
and increased profits. On the other hand, those responded to customers’ requirements have
achieved better performances in decreasing defective products, reducing labor inputs,
reducing environmental impacts caused by factory operations and meeting regulatory
requirements on products.
9

10. Table 1: Summary Statistics I
Variable Obs Mean Std. Dev. Min Max
Dependent variable
Defect 864 0.727 0.446 0 1
Inventory 864 0.580 0.494 0 1
Material 864 0.506 0.500 0 1
Labor 864 0.334 0.472 0 1
Quality 864 0.838 0.369 0 1
Flexibility 864 0.752 0.432 0 1
Lead-time 864 0.503 0.500 0 1
Domestic market 864 0.606 0.489 0 1
Foreign market 864 0.350 0.477 0 1
Pollution 864 0.612 0.488 0 1
Regulation 864 0.825 0.380 0 1
Outcomes 864 6.634 2.814 0 11
Profit 849 3.356 1.004 1 5
Independent variable
Standards 864 0.503 0.500 0 1
Request 864 0.433 0.496 0 1
Control variable
SME 864 0.639 0.481 0 1
Local 864 0.675 0.469 0 1
Food 864 0.111 0.314 0 1
Textile 864 0.106 0.309 0 1
Chemicals 864 0.128 0.335 0 1
Non-metal 864 0.015 0.122 0 1
Iron 864 0.047 0.213 0 1
Electronics 864 0.118 0.323 0 1
Other machines 864 0.211 0.408 0 1
Indonesia 864 0.212 0.409 0 1
Philippines 864 0.235 0.424 0 1
Thailand 864 0.206 0.405 0 1
Vietnam 864 0.347 0.476 0 1
Source: ERIA Establishment Survey 2009.
10

11. Table 2: Summary Statistics II
Variable Obs Mean Std. Dev. Min Max
Independent variable
Foreign-owned customer 864 0.203 0.402 0 1
JV customer 864 0.161 0.368 0 1
Capital tie with customer 864 0.406 0.491 0 1
SME customer 864 0.473 0.500 0 1
Ship a few times in a day 864 0.113 0.317 0 1
Ship once in a day 864 0.141 0.348 0 1
Ship a few times in a week 864 0.328 0.470 0 1
Ship once in a week 864 0.176 0.381 0 1
Ship once in a month 864 0.093 0.290 0 1
JIT with customer 864 0.553 0.497 0 1
Dispatch engineer to customer 864 0.541 0.499 0 1
Customer dispatches engineer 864 0.432 0.496 0 1
Customer dispatches trainer 864 0.319 0.467 0 1
Customer dispatches trainee 864 0.242 0.428 0 1
R&D 864 0.501 0.500 0 1
OJT 864 0.590 0.492 0 1
OFF-JT 864 0.465 0.499 0 1
Top has master/Ph.D. 864 0.284 0.451 0 1
Top is engineer 864 0.578 0.494 0 1
Top is MNC-experienced 864 0.459 0.499 0 1
0-20% of engineers 864 0.219 0.414 0 1
20-40% of engineers 864 0.066 0.248 0 1
40-60% of engineers 864 0.063 0.242 0 1
60-80% of engineers 864 0.168 0.374 0 1
80-100% of engineers 864 0.332 0.471 0 1
Age 833 16.796 13.922 0 181
Source: ERIA Establishment Survey 2009.
11

12. Table 3: Adoption of International Standards
MNC/JV Local Large SME
Percent (1) Percent (2) diff Percent (1) Percent (2) diff
Customer required the adoption 60.5% 35.0% (***) 54.2% 37.1% (***)
Adoption of international standards 70.5% 40.7% (***) 67.9% 40.4% (***)
Observations 281 583 312 552
Adoption of international standards
Upon customer's request 81.8% 57.4% (***) 79.3% 59.5% (***)
No. of Observations 170 204 169 205
Voluntarily 53.2% 31.7% (***) 54.5% 29.1% (***)
No. of Observations 111 379 143 347
Notes: diff=Percent(1)-Percent(2), H0: diff=0. (***) indicates H0 (Ha: diff>0) are significant at 1% level.
Source: ERIA Establishment Survey 2009.
Table 4: Adoption of International Standards and Performances
Adopted
Not Adopted Adopted Not requested Requested
Percent (1) Percent (2) diff Percent (1) Percent (2) diff
Customer required the adoption 27.5% 58.9% ***
(1) Decrease defective goods 67.6% 77.7% *** 72.6% 81.3% **
(2) Decrease inventories 50.8% 65.1% *** 69.8% 61.7% (**)
(3) Decrease raw materials 41.0% 60.0% *** 59.2% 60.5%
(4) Reduce labor input 30.8% 36.1% ** 31.8% 39.1% *
(5) Improve quality of goods 81.8% 85.7% * 83.2% 87.5%
(6) Improve flexibility of production 68.8% 81.6% *** 79.3% 83.2%
(7) Reduce lead-time 42.0% 58.6% *** 62.0% 56.3%
(8) Enter/Increase domestic market 52.9% 68.3% *** 70.4% 66.8%
(9) Enter/Increase foreign market 23.5% 46.2% *** 49.2% 44.1%
(10) Reduce environmental impacts 50.6% 71.7% *** 67.0% 75.0% **
(11) Meet regulatory requirements 0.7% 0.9% *** 87.7% 92.2% *
(12) Number of outcomes 5.8 7.4 *** 7.3 7.5
Observations 429 435 179 256
Increase profit 3.2 3.5 *** 3.7 3.3 (***)
Observations 419 430 179 256
Notes: diff=Percent(1)-Percent(2), H0: diff=0. ***, **,* indicate H0 (Ha: diff<0) are significant at 1%, 5%,
10% level respectively. (***), (**) indicate H0 (Ha: diff>0) are significant at 1% and 5% level respectively.
Source: ERIA Establishment Survey 2009.
12

13. Figure 1: Adoption of International Standards and Outcomes
Not Adopted Adopted
20
15
Percent
10
5
0
0 2 4 6 8 10 0 2 4 6 8 10
Number of Outcomes
Adoption of International Standards
Note: The variable Outcomes is an aggregate total of 11 dummy variables for
outcome, thus ranges from 0 to 11.
Source: ERIA Establishment Survey 2009.
Figure 2: Adoption of International Standards and Change in Profit
Not Adopted Adopted
50
45
40
35
30
Percent
25
20
15
10
5
0
1 2 3 4 5 1 2 3 4 5
Annual Change in Profit
1(Substantial decrease) - 3(almost same) - 5(Substantial increase)
Adoption of International Standards
Note: The annual change in Profit is measured on a five-point Likert scale ranging
from 1 (substantial decrease) to 5 (substantial increase).
Source: ERIA Establishment Survey 2009.
13

14. 4. Adoption of International Standards and Performance
4.1. Empirical Strategy
The descriptive statistics in the former section implies the relationship between the
adoptions of international standards and business performances. Because rigorous statistical
methods should be applied to examine it, the following model is developed:
yi = α + β∗Standardsi + γ∗xi + ui. (1)
The dependent variable y is one of the following performance indicators: a dummy
variable for outcome (Defect, Inventory, Material, Labor, Quality, Flexibility, Lead-time,
Domestic market, Foreign market, Pollution, Regulation); Outcomes that can ranges from 0
to 11; and a five-point Likert-type variable Profit.
The independent variables are Standards and control variables x, both of which are
binary. The variable Standardsi is coded 1 if firm (i) has adopted international standards and 0
otherwise. A set of the binary variables xi are control variables for size (SME), nationality
(Local), main business activity (Food, Textile, Chemicals, Non-metal, Iron, Electronics,
Other machines), and location where firm (i) is located (Indonesia, Philippines, Vietnam).
Details of the dependent, independent and control variables are listed in Appendix Table A1.
We applied binary probit estimations when the dependent variable is binary and ordered
probit estimations when the dependent variable is Outcomes or Profit. As there are 13
performance indicators, 13 estimations are implemented using the whole sample as a baseline.
Then the same estimations are carried out using restricted samples to check robustness of the
estimated coefficients on Standards and influence of firm characteristics to the relationship
between Standards and performance.
4.2. Results
Table 5 shows the result of baseline estimations using the whole sample. The adoption of
international standards has positively significant relationships with all performance indicators
except the improvement in quality of goods and services in the column (5). Among the
control variables, SMEs are less likely to develop markets, improve quality and pollution
controls control and increase profits than large firms. Significant differences between
MNCs/JVs and local firms are not identified in all performance indicators except the
development of or new entry into foreign market and compliance with regulatory
requirements on products.
14

15. The equation (1) is estimated by using sub-samples to observe robustness of the
coefficients on Standards estimated using the whole sample, or whether there are significant
differences according to characteristics of the establishments. Table 6 reports only the
estimated coefficients on Standards and robust standard errors. The far-left column indicates
criteria for restricting the sample, which is chosen from the control variables in the equation
(1). The figures in the first row for “Whole” are the same as the coefficients on and robust
standard errors for Standards in Table 5.
Table 6 makes it obvious that there are differences among firm groups in the
significances of the estimated coefficients. If the sample is restricted to one of the sector, the
number of significant coefficients on Standards decreases considerably. For example, the
coefficient for Food sector is positively significant only when the dependent variable is
Inventory and Pollution and negatively significant in the regression of Quality.
There are differences between local firms and MNCs/JVs. Local firm adopted
international standards tend to have decreased labor inputs and increased profit, while
MNCs/JVs have decreased defective products and inventories and improved flexibilities. Not
so many differences were observed between SMEs and large firms. Higher proportion of the
SMEs conforming to international standards have developed or entered into foreign markets
although large firms have increased profit.
Dissimilarities are also observed among local firms and among MNCs/JVs. Local SMEs
adopted international standards have realized better achievements than local large firms.
Except Defect and Flexibility, the significant coefficients for the sample restricted to local
SMEs are same as the results based on the whole sample. In contrast, the coefficient on
Standards is significant in only four of the 13 regressions for large local firms. Multinational
SMEs have more significant coefficients than large MNCs. But the foreign-owned SMEs
with certifications have not increased profit even although large certified MNCs have realized
it. The differences between local and foreign-owned SMEs exist in: decreases in inventory,
materials and labor input and increase in profit that local SMEs have attained; and decrease
in defective products and improvement in quality and flexibility of production or service
provision that have achieved by foreign-owned SMEs.
It can be considered local large firms can not recognize benefits from obtaining
certifications of international standards. Compared with local large firms, local SMEs
adopted international standards tend to develop domestic and foreign markets and increase
profit. Relative to large local or foreign-owned firms, local or foreign-owned SMEs have had
succeeded in developing or entering foreign market.
15

16. Table 5: Relationship between the Adoption of International Standards and Performance (Whole Sample)
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13)
Binary Ordered Ordered
domestic foreign
defect inventory material labor quality flexibility lead-time pollution regulation outcomes profit
market market
Standards 0.105*** 0.094** 0.178*** 0.071* 0.013 0.109*** 0.184*** 0.135*** 0.165*** 0.225*** 0.144*** 0.534*** 0.156*
(0.034) (0.037) (0.037) (0.036) (0.028) (0.032) (0.038) (0.037) (0.035) (0.036) (0.027) (0.079) (0.081)
SME -0.027 -0.025 -0.061 0.040 -0.085*** -0.019 -0.021 -0.067* -0.121*** -0.074* -0.030 -0.193** -0.209**
(0.034) (0.038) (0.039) (0.036) (0.026) (0.033) (0.040) (0.038) (0.037) (0.038) (0.027) (0.077) (0.083)
Local -0.038 -0.052 0.028 -0.042 -0.030 -0.040 0.057 -0.018 -0.132*** -0.002 -0.051* -0.117 -0.017
(0.038) (0.041) (0.043) (0.040) (0.030) (0.035) (0.044) (0.042) (0.041) (0.043) (0.029) (0.085) (0.091)
Food 0.017 -0.070 -0.055 -0.035 0.020 0.020 -0.086 -0.017 -0.127** -0.067 0.057 -0.149 0.079
(0.054) (0.063) (0.064) (0.058) (0.043) (0.053) (0.065) (0.065) (0.056) (0.065) (0.040) (0.120) (0.130)
Textile -0.007 -0.033 0.031 0.031 -0.017 -0.063 -0.137** -0.111* -0.006 -0.094 -0.095* -0.190 -0.423***
(0.057) (0.064) (0.065) (0.063) (0.048) (0.058) (0.065) (0.067) (0.062) (0.065) (0.054) (0.136) (0.136)
Chemicals 0.003 0.039 0.082 0.027 -0.004 0.010 0.008 -0.065 -0.045 0.010 -0.032 0.041 -0.244*
(0.054) (0.059) (0.059) (0.060) (0.043) (0.049) (0.062) (0.061) (0.055) (0.061) (0.047) (0.131) (0.132)
Non-metal -0.162 0.030 0.152 0.138 -0.068 -0.197 -0.041 0.120 0.049 -0.014 -0.079 -0.026 0.482
(0.143) (0.142) (0.144) (0.164) (0.116) (0.137) (0.146) (0.121) (0.131) (0.149) (0.120) (0.290) (0.389)
Iron 0.051 0.021 0.106 0.156* -0.017 0.000 -0.165* -0.219** -0.227*** -0.035 -0.085 -0.190 0.212
(0.074) (0.088) (0.086) (0.090) (0.062) (0.071) (0.085) (0.086) (0.055) (0.088) (0.076) (0.160) (0.209)
Electronics -0.089 0.044 -0.007 0.001 0.008 0.082* -0.045 -0.014 -0.028 -0.159** -0.019 -0.077 -0.173
(0.062) (0.063) (0.064) (0.060) (0.046) (0.049) (0.066) (0.066) (0.059) (0.065) (0.051) (0.129) (0.132)
Other machines 0.016 0.083* 0.085 0.047 0.038 0.080** 0.128** 0.006 -0.050 0.032 -0.001 0.156 -0.173
(0.045) (0.050) (0.052) (0.051) (0.034) (0.039) (0.054) (0.053) (0.048) (0.053) (0.038) (0.106) (0.111)
Indonesia 0.214*** 0.012 -0.072 0.063 0.087*** 0.203*** 0.449*** 0.329*** 0.033 0.211*** 0.116*** 0.688*** 0.214**
(0.032) (0.054) (0.055) (0.053) (0.030) (0.031) (0.041) (0.038) (0.055) (0.044) (0.027) (0.111) (0.105)
Philippines 0.239*** 0.111** 0.136** 0.256*** 0.076** 0.146*** 0.328*** 0.149*** 0.052 0.293*** 0.085*** 0.797*** -0.605***
(0.031) (0.051) (0.053) (0.053) (0.031) (0.035) (0.049) (0.048) (0.055) (0.041) (0.030) (0.127) (0.118)
Vietnam 0.219*** 0.216*** -0.027 -0.114** 0.060* 0.113*** 0.309*** 0.356*** 0.110** 0.045 0.050 0.506*** 0.999***
(0.035) (0.046) (0.050) (0.046) (0.031) (0.037) (0.049) (0.040) (0.051) (0.048) (0.032) (0.101) (0.103)
Observations 864 864 864 864 864 864 864 864 864 864 864 864 849
Pseudo R2 0.069 0.055 0.051 0.076 0.032 0.064 0.105 0.099 0.088 0.093 0.082 0.036 0.111
Notes: Marginal effect for binary probit estimation. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
16

17. Table 6: Effects of the Adoption of International Standards on Performance
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13)
Binary Ordered Ordered
domestic foreign
defect inventory material labor quality flexibility lead-time pollution regulation outcomes profit
market market
<Sample>
Whole 0.105*** 0.094** 0.178*** 0.071* 0.013 0.109*** 0.184*** 0.135*** 0.165*** 0.225*** 0.144*** 0.534*** 0.156*
(0.034) (0.037) (0.037) (0.036) (0.028) (0.032) (0.038) (0.037) (0.035) (0.036) (0.027) (0.079) (0.081)
Local 0.015 0.059 0.178*** 0.072* -0.042 0.064 0.146*** 0.102** 0.166*** 0.202*** 0.095*** 0.421*** 0.211**
(0.040) (0.045) (0.045) (0.043) (0.035) (0.040) (0.046) (0.044) (0.041) (0.043) (0.033) (0.094) (0.094)
MNC/JV 0.320*** 0.142** 0.178** 0.047 0.156*** 0.207*** 0.245*** 0.248*** 0.120* 0.272*** 0.266*** 0.747*** 0.008
(0.068) (0.068) (0.072) (0.072) (0.053) (0.060) (0.069) (0.074) (0.070) (0.073) (0.058) (0.153) (0.168)
SME 0.085** 0.098** 0.179*** 0.072 0.007 0.083** 0.161*** 0.129*** 0.180*** 0.219*** 0.168*** 0.527*** 0.082
(0.042) (0.047) (0.047) (0.046) (0.038) (0.041) (0.048) (0.047) (0.042) (0.045) (0.033) (0.102) (0.103)
Large 0.142** 0.112* 0.164** 0.078 0.029 0.143** 0.272*** 0.150** 0.076 0.242*** 0.111** 0.568*** 0.249*
(0.061) (0.065) (0.067) (0.063) (0.038) (0.058) (0.064) (0.066) (0.067) (0.065) (0.047) (0.139) (0.144)
Local SME 0.028 0.094* 0.208*** 0.095* -0.037 0.042 0.138** 0.120** 0.183*** 0.219*** 0.128*** 0.480*** 0.213*
(0.049) (0.054) (0.053) (0.052) (0.044) (0.049) (0.055) (0.053) (0.049) (0.052) (0.038) (0.115) (0.113)
Local large -0.029 0.137 0.160* 0.018 -0.015 0.107 0.250*** 0.078 0.054 0.191** 0.032 0.371* 0.197
(0.080) (0.094) (0.091) (0.071) (0.062) (0.078) (0.089) (0.083) (0.091) (0.089) (0.061) (0.193) (0.184)
SME MNC/JV 0.299*** 0.149 0.112 0.019 0.221** 0.257*** 0.248** 0.259** 0.181* 0.200** 0.333*** 0.718*** -0.384
(0.098) (0.099) (0.102) (0.097) (0.086) (0.085) (0.098) (0.114) (0.101) (0.101) (0.083) (0.217) (0.245)
Large MNC/JV 0.359*** 0.065 0.168 0.080 0.128 0.132 0.254** 0.187* 0.029 0.374*** 0.226** 0.672*** 0.433*
(0.102) (0.102) (0.112) (0.105) (0.078) (0.091) (0.101) (0.110) (0.106) (0.116) (0.088) (0.226) (0.244)
Indonesia 0.172** 0.015 0.113 0.012 0.018 0.004 0.173** 0.132* 0.223*** 0.329*** 0.191*** 0.552*** 0.183
(0.077) (0.088) (0.087) (0.082) (0.064) (0.078) (0.085) (0.078) (0.081) (0.076) (0.052) (0.192) (0.194)
Philippines 0.170*** 0.112 0.092 0.078 0.077 0.163*** 0.149* 0.186** 0.157* 0.104* 0.149*** 0.536*** -0.184
(0.058) (0.079) (0.079) (0.082) (0.049) (0.061) (0.082) (0.082) (0.083) (0.061) (0.049) (0.186) (0.180)
Thailand 0.174** 0.044 0.210** 0.108 0.049 0.117 0.232*** 0.151* 0.181** 0.330*** 0.307*** 0.678*** -0.099
(0.087) (0.095) (0.086) (0.079) (0.075) (0.085) (0.066) (0.084) (0.081) (0.081) (0.072) (0.181) (0.206)
Vietnam -0.011 0.160*** 0.234*** 0.064 -0.032 0.129** 0.176*** 0.120** 0.133** 0.188*** 0.053 0.540*** 0.509***
(0.050) (0.055) (0.059) (0.045) (0.042) (0.051) (0.060) (0.052) (0.057) (0.060) (0.046) (0.127) (0.132)
17

18. (Continued)
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13)
Binary Ordered Ordered
domestic foreign
defect inventory material labor quality flexibility lead-time pollution regulation outcomes profit
market market
Food 0.084 0.233* 0.122 0.128 -0.198** -0.047 0.183 0.039 0.184 0.211* 0.049 0.400 0.075
(0.101) (0.125) (0.133) (0.121) (0.101) (0.113) (0.130) (0.131) (0.115) (0.113) (0.049) (0.292) (0.256)
Textile 0.201** 0.241* 0.195 0.123 -0.068 0.097 0.165 0.053 0.321*** 0.297*** 0.255*** 0.673*** 0.665**
(0.088) (0.126) (0.122) (0.126) (0.066) (0.116) (0.124) (0.129) (0.119) (0.110) (0.081) (0.241) (0.275)
Chemicals 0.065 0.032 0.281*** -0.086 0.074 0.192** -0.002 0.177* 0.077 0.123 0.176** 0.389* 0.045
(0.087) (0.101) (0.096) (0.095) (0.076) (0.090) (0.109) (0.101) (0.097) (0.098) (0.079) (0.210) (0.217)
Electronics 0.117 0.122 0.286** 0.079 0.068 0.141* 0.350*** 0.049 0.128 0.226* 0.193** 0.650*** -0.094
(0.109) (0.107) (0.115) (0.099) (0.080) (0.083) (0.101) (0.110) (0.110) (0.128) (0.088) (0.252) (0.264)
Other machines 0.106 0.109 0.104 0.055 -0.002 0.061 0.126 0.132 0.147* 0.195** 0.094 0.454*** 0.440**
(0.072) (0.077) (0.083) (0.083) (0.048) (0.070) (0.083) (0.081) (0.079) (0.079) (0.061) (0.169) (0.190)
Notes: Only estimated coefficients are reported. Marginal effect for binary probit estimation. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
18

19. If the influences of sub-sampling are paid attentions to, the coefficient on Standards is
the most robust significant in the regressions of Pollution and Outcomes. For each dependent
variable, 18 estimations were attempted. Only the estimation using the sample of Chemicals
does not have a significant marginal effect of Standards on Pollution. When the dependent
variable is Outcomes and the sample is restricted to the Food related sector, the estimated
coefficient Standards is not significant. Other relatively robust coefficients were obtained
from the regressions of Lead-time, Regulation, Domestic market, and Foreign market. From
these analyses, firms adopted international standards may have better management systems to
meet environmental and other regulatory requirements. They also show better results in
market developments.
5. Comparison Between Customer-requested and Voluntary Adoption
5.1. Empirical Strategy
Table 4 attempted to show how difference in motives to adopt international standards
may affect performances. To examine this question by regression analyses, firstly we
investigate which one or both may really matter for firms who achieved better performance:
requests from customers to adopt international standards that put pressure on firms to make
improvements or actual adoption of them. For this purpose implemented are estimations of
the following model that is based on the equation (1):
yi = α + β∗Requesti + γ∗xi + ui. (2).
The variables in equation (2) are the same as those in equation (1) except replacing
independent variable Standards with Request.
Then the equation (1) is estimated again by using the data only for the firms adopted
international standards. To investigate the impact of requirements from customers on
performances of the firms adopted international standards, the certified firms are categorized
into two groups: firms (1) requested and (2) not requested. As already discussed in the
previous section, firm characteristics may or may not affect results of the estimation. The
same methodology as Table 6 is applied to the two groups to get a better understanding on the
impacts.
5.2. Results
Table 7 provides results of the regression of performance indicators on the requirement
19

20. from main customer to adopt international standards modeled as the equation (2). The
coefficient on Request is positively significant only in the three regressions of Defect at the
1% significant level, Pollution at the 5% level and Regulation at the 10% level. Compared to
the significant coefficients on Standards in Table 5, the number of the significant coefficient
on Request is small. From these findings it can be considered that adoption of international
standards will have substantial impacts on management system and performance.
Tables 8, 9 and 10 show estimation results of the equation (1) with the sample restricted
to the firms that have adopted international standards. To investigate the influence of
difference in motives to performances, the sample is divided into the firms who adopted
international standards having a requirement from their main customers to adopt them and
those who adopted them without such requirement from their main customers.
Table 8 presents the estimation result for the firms adopted international standards upon
the requirement from the customers. The coefficients on Standards are significant at the 1%
and 5% levels except in the regressions of Inventory, Labor, Quality, and Profit. The
coefficients on control variables for size, nationality and industries are not as constantly
significant as Standards.
Table 9 presents the estimation result for the firms adopted international standards
without the requirement from the customers. The coefficients on Standards are significant in
all of the regressions except of Defect, and Quality. Although the coefficients on Electronics
are negative, the coefficients on other control variables for size, nationality and industries are
not persistently significant.
To see the robustness of the coefficients on Standards shown in Tables 8 and 9 or
whether there are significant differences in the significant coefficients according to
characteristics of the firms and presence/absence of customers’ requirement, the equation (1)
is estimated restricting the sample in the same way as conducted in Table 6. Because of the
constraint of the number of the observations, the sample was restricted to local firms,
MNCs/JVs, SMEs and large firms. The upper portion of Table 10 contains only the estimated
coefficients on Standards and robust standard errors for the adopted firms required by their
customers, while the lower portion of Table 10 tabulates figures for the adopted firms without
requirement from their customers.
The coefficient on Standards in the regression of Material, Pollution, and Outcomes is
significant irrespective of presence or absence of the requirement from customers. The
coefficient is also relatively robust in the regression of Lead-time and Regulation.
20

21. Table 7: Relationship between Customer’s Request for Adopting International Standards and Performance (Whole Sample)
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13)
Binary probit Ordered probit
domestic foreign
defect inventory material labor quality flexibility lead-time pollution regulation outcomes profit
market market
Request 0.090*** -0.045 0.003 0.010 0.030 0.025 -0.015 0.021 -0.010 0.089** 0.045* 0.076 0.001
(0.032) (0.037) (0.037) (0.035) (0.027) (0.032) (0.038) (0.037) (0.036) (0.036) (0.027) (0.075) (0.078)
SME -0.040 -0.051 -0.096** 0.026 -0.085*** -0.039 -0.060 -0.093** -0.153*** -0.110*** -0.057** -0.290*** -0.240***
(0.033) (0.037) (0.038) (0.036) (0.025) (0.032) (0.039) (0.037) (0.037) (0.036) (0.027) (0.075) (0.083)
Local -0.041 -0.079* -0.010 -0.055 -0.027 -0.056 0.015 -0.043 -0.168*** -0.033 -0.067** -0.215** -0.051
(0.037) (0.041) (0.042) (0.040) (0.029) (0.034) (0.044) (0.041) (0.041) (0.041) (0.029) (0.086) (0.088)
Food 0.007 -0.081 -0.072 -0.043 0.019 0.011 -0.103 -0.031 -0.139*** -0.085 0.048 -0.196* 0.060
(0.056) (0.063) (0.063) (0.057) (0.043) (0.053) (0.063) (0.065) (0.054) (0.064) (0.042) (0.119) (0.129)
Textile -0.018 -0.063 -0.011 0.015 -0.015 -0.086 -0.176*** -0.139** -0.045 -0.129** -0.126** -0.292** -0.460***
(0.058) (0.065) (0.065) (0.062) (0.047) (0.059) (0.062) (0.066) (0.060) (0.065) (0.058) (0.137) (0.137)
Chemicals 0.003 0.049 0.094 0.029 -0.005 0.015 0.023 -0.057 -0.031 0.020 -0.025 0.072 -0.234*
(0.054) (0.058) (0.059) (0.060) (0.043) (0.049) (0.061) (0.061) (0.055) (0.060) (0.047) (0.131) (0.132)
Non-metal -0.134 0.028 0.162 0.141 -0.061 -0.181 -0.033 0.131 0.059 0.008 -0.066 0.019 0.488
(0.140) (0.141) (0.137) (0.162) (0.114) (0.142) (0.149) (0.120) (0.133) (0.147) (0.121) (0.303) (0.389)
Iron 0.050 0.030 0.111 0.156* -0.021 0.002 -0.153* -0.213** -0.221*** -0.036 -0.081 -0.178 0.215
(0.073) (0.087) (0.085) (0.090) (0.063) (0.071) (0.085) (0.086) (0.058) (0.088) (0.074) (0.155) (0.209)
Electronics -0.085 0.058 0.013 0.007 0.007 0.089* -0.021 0.000 -0.006 -0.138** -0.006 -0.026 -0.158
(0.062) (0.063) (0.064) (0.060) (0.046) (0.048) (0.066) (0.065) (0.061) (0.064) (0.050) (0.131) (0.131)
Other machines 0.018 0.097** 0.101** 0.053 0.035 0.087** 0.145*** 0.017 -0.031 0.043 0.010 0.195* -0.159
(0.046) (0.050) (0.052) (0.051) (0.034) (0.039) (0.053) (0.053) (0.048) (0.051) (0.037) (0.105) (0.111)
Indonesia 0.221*** 0.009 -0.068 0.064 0.089*** 0.205*** 0.440*** 0.330*** 0.034 0.215*** 0.122*** 0.683*** 0.216**
(0.032) (0.054) (0.055) (0.053) (0.029) (0.031) (0.042) (0.038) (0.055) (0.044) (0.028) (0.111) (0.105)
Philippines 0.241*** 0.100** 0.124** 0.253*** 0.079*** 0.143*** 0.310*** 0.142*** 0.039 0.286*** 0.086*** 0.753*** -0.613***
(0.032) (0.051) (0.053) (0.054) (0.031) (0.036) (0.050) (0.048) (0.054) (0.041) (0.032) (0.126) (0.117)
Vietnam 0.240*** 0.212*** -0.015 -0.107** 0.066** 0.123*** 0.309*** 0.363*** 0.115** 0.075 0.071** 0.539*** 1.005***
(0.035) (0.047) (0.051) (0.047) (0.032) (0.037) (0.049) (0.040) (0.051) (0.048) (0.032) (0.102) (0.104)
Observations 864 864 864 864 864 864 864 864 864 864 864 864 849
Pseudo R2 0.067 0.051 0.032 0.073 0.033 0.053 0.086 0.088 0.069 0.066 0.050 0.025 0.109
Notes: Marginal effect for binary probit estimation. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
21

22. Table 8: Relationship between Customer-requested Adoption of International Standards and Performance
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13)
Binary Ordered Ordered
domestic foreign
defect inventory material labor quality flexibility lead-time pollution regulation outcomes profit
market market
Standards 0.114** 0.094 0.214*** 0.062 -0.012 0.142*** 0.219*** 0.192*** 0.171*** 0.195*** 0.147*** 0.603*** 0.072
(0.055) (0.062) (0.059) (0.060) (0.038) (0.053) (0.060) (0.061) (0.055) (0.060) (0.046) (0.126) (0.130)
SME -0.039 0.004 -0.064 0.051 -0.108*** -0.043 0.018 -0.035 -0.088 -0.100* -0.012 -0.198* -0.096
(0.047) (0.057) (0.057) (0.054) (0.035) (0.044) (0.060) (0.056) (0.054) (0.053) (0.033) (0.112) (0.120)
Local -0.064 -0.180*** -0.000 -0.074 -0.063* -0.057 0.030 -0.028 -0.097 -0.021 -0.075** -0.226* -0.184
(0.051) (0.060) (0.064) (0.059) (0.037) (0.048) (0.066) (0.061) (0.061) (0.061) (0.037) (0.126) (0.126)
Food -0.062 -0.021 -0.201* -0.194** -0.051 0.015 -0.080 -0.075 -0.162* -0.285*** -0.032 -0.433** 0.210
(0.089) (0.109) (0.109) (0.084) (0.077) (0.079) (0.115) (0.113) (0.092) (0.105) (0.072) (0.187) (0.206)
Textile 0.001 0.093 -0.029 0.046 0.040 0.029 -0.247** 0.023 0.231* -0.153 -0.153 -0.023 -0.043
(0.099) (0.114) (0.119) (0.123) (0.061) (0.087) (0.109) (0.138) (0.127) (0.131) (0.105) (0.234) (0.267)
Chemicals 0.022 0.053 0.024 -0.136* 0.013 -0.036 -0.077 -0.165* -0.144* -0.146 -0.083 -0.245 -0.145
(0.070) (0.086) (0.089) (0.077) (0.051) (0.070) (0.093) (0.089) (0.074) (0.094) (0.068) (0.184) (0.174)
Non-metal 0.105 0.360 -0.145 0.218 0.116 -0.196 0.715 1.115
(0.286) (0.281) (0.268) (0.232) (0.327) (0.288) (0.610) (1.298)
Iron 0.027 0.162 0.118 0.316** -0.089 -0.084 -0.170 -0.150 -0.282*** -0.175 -0.135 -0.181 0.154
(0.104) (0.116) (0.127) (0.125) (0.096) (0.111) (0.129) (0.127) (0.075) (0.134) (0.115) (0.293) (0.297)
Electronics -0.030 0.194** -0.012 0.091 0.041 0.116** 0.076 0.086 -0.026 -0.143 -0.018 0.181 0.082
(0.078) (0.080) (0.092) (0.091) (0.051) (0.056) (0.090) (0.086) (0.085) (0.093) (0.059) (0.182) (0.194)
Other machines 0.091* 0.195*** 0.085 0.032 0.014 0.123** 0.144* 0.011 -0.046 0.018 0.047 0.293* -0.016
(0.055) (0.069) (0.077) (0.076) (0.045) (0.049) (0.080) (0.078) (0.071) (0.077) (0.041) (0.155) (0.164)
Indonesia 0.194*** -0.027 -0.124 0.192** 0.081** 0.138*** 0.473*** 0.334*** 0.103 0.234*** 0.097*** 0.739*** 0.235
(0.039) (0.082) (0.080) (0.082) (0.032) (0.044) (0.058) (0.055) (0.081) (0.052) (0.028) (0.163) (0.150)
Philippines 0.211*** 0.053 0.145* 0.304*** 0.090*** 0.146*** 0.340*** 0.084 0.116 0.280*** 0.082** 0.823*** -0.807***
(0.041) (0.076) (0.076) (0.075) (0.035) (0.047) (0.070) (0.072) (0.077) (0.053) (0.034) (0.176) (0.171)
Vietnam 0.236*** 0.131* 0.011 -0.030 0.056 0.084* 0.180** 0.340*** 0.075 0.092 0.054 0.498*** 0.857***
(0.041) (0.072) (0.076) (0.073) (0.036) (0.048) (0.077) (0.056) (0.075) (0.063) (0.033) (0.155) (0.151)
Observations 371 374 371 374 371 374 374 371 374 374 371 374 368
Pseudo R2 0.123 0.0846 0.0810 0.119 0.0932 0.111 0.148 0.123 0.0975 0.130 0.152 0.0618 0.0996
Notes: The sample is restricted to firms adopted upon cutomers’ request. Marginal effect for binary probit estimation. Robust standard errors in parentheses. *** p<0.01, **
p<0.05, * p<0.1.
22

23. Table 9: Relationship between Voluntary Adoption of International Standards and Performance
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13)
Binary Ordered Ordered
domestic foreign
defect inventory material labor quality flexibility lead-time pollution regulation outcomes profit
market market
Standards 0.069 0.125** 0.178*** 0.090* 0.004 0.088** 0.188*** 0.107** 0.168*** 0.239*** 0.126*** 0.519*** 0.227**
(0.047) (0.050) (0.051) (0.050) (0.039) (0.044) (0.053) (0.051) (0.050) (0.050) (0.035) (0.107) (0.109)
SME -0.014 -0.070 -0.069 0.038 -0.065* 0.003 -0.036 -0.096* -0.142*** -0.049 -0.051 -0.197* -0.323***
(0.050) (0.052) (0.055) (0.050) (0.037) (0.048) (0.056) (0.053) (0.053) (0.055) (0.040) (0.110) (0.113)
Local 0.024 0.075 0.058 -0.010 0.030 -0.007 0.102 0.010 -0.176*** 0.049 -0.004 0.025 0.149
(0.056) (0.059) (0.059) (0.056) (0.046) (0.051) (0.062) (0.058) (0.057) (0.061) (0.045) (0.126) (0.132)
Food 0.076 -0.086 0.031 0.049 0.074 0.046 -0.077 0.013 -0.115* 0.057 0.138*** 0.023 -0.022
(0.069) (0.081) (0.080) (0.075) (0.050) (0.067) (0.082) (0.081) (0.069) (0.080) (0.043) (0.157) (0.170)
Textile -0.012 -0.102 0.049 0.039 -0.022 -0.100 -0.111 -0.165** -0.088 -0.040 -0.072 -0.238 -0.611***
(0.071) (0.078) (0.078) (0.074) (0.060) (0.072) (0.079) (0.078) (0.069) (0.077) (0.065) (0.167) (0.167)
Chemicals -0.020 0.042 0.141* 0.180** -0.021 0.048 0.061 0.019 0.045 0.139* 0.004 0.277 -0.329*
(0.079) (0.082) (0.079) (0.085) (0.064) (0.067) (0.086) (0.084) (0.079) (0.078) (0.063) (0.185) (0.197)
Non-metal -0.273* -0.026 0.091 0.093 -0.083 -0.205 -0.098 0.032 0.037 0.070 -0.096 -0.205 0.243
(0.163) (0.173) (0.178) (0.188) (0.141) (0.166) (0.172) (0.154) (0.138) (0.168) (0.145) (0.329) (0.381)
Iron 0.068 -0.074 0.107 0.037 0.050 0.082 -0.159 -0.262** -0.179** 0.079 -0.034 -0.180 0.314
(0.104) (0.122) (0.117) (0.119) (0.075) (0.089) (0.112) (0.116) (0.080) (0.114) (0.097) (0.172) (0.288)
Electronics -0.168* -0.160* -0.017 -0.164** -0.056 0.017 -0.209** -0.109 -0.017 -0.222** -0.042 -0.404** -0.397**
(0.096) (0.096) (0.092) (0.067) (0.078) (0.084) (0.091) (0.098) (0.085) (0.092) (0.082) (0.179) (0.167)
Other machines -0.066 -0.007 0.094 0.064 0.051 0.032 0.107 0.012 -0.055 0.027 -0.056 0.045 -0.283*
(0.070) (0.074) (0.073) (0.070) (0.048) (0.061) (0.075) (0.074) (0.065) (0.073) (0.061) (0.147) (0.152)
Indonesia 0.221*** 0.030 -0.050 -0.034 0.086* 0.239*** 0.433*** 0.337*** -0.037 0.186*** 0.111** 0.644*** 0.250*
(0.050) (0.076) (0.077) (0.068) (0.046) (0.044) (0.060) (0.055) (0.075) (0.069) (0.045) (0.159) (0.147)
Philippines 0.251*** 0.118 0.111 0.169** 0.049 0.130** 0.301*** 0.189*** -0.025 0.275*** 0.061 0.703*** -0.436***
(0.049) (0.073) (0.078) (0.077) (0.050) (0.053) (0.073) (0.067) (0.076) (0.065) (0.051) (0.185) (0.163)
Vietnam 0.239*** 0.281*** -0.059 -0.204*** 0.076 0.147*** 0.382*** 0.375*** 0.069 0.012 0.051 0.510*** 1.133***
(0.055) (0.065) (0.072) (0.060) (0.048) (0.055) (0.069) (0.059) (0.071) (0.071) (0.050) (0.141) (0.146)
Observations 490 490 490 490 490 490 490 490 490 490 490 490 481
Pseudo R2 0.0609 0.0774 0.0392 0.0921 0.0262 0.0568 0.109 0.0992 0.112 0.0829 0.0648 0.0292 0.131
Notes: The sample is restricted to firms adopted voluntarily. Marginal effect for binary probit estimation. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, *
p<0.1.
23

24. Table 10: Comparison between Customer-requested and Voluntary Adoption of International Standards and Performance
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13)
Binary Ordered Ordered
domestic foreign
defect inventory material labor quality flexibility lead-time pollution regulation outcomes profit
market market
<Requested>
Whole 0.114** 0.094 0.214*** 0.062 -0.012 0.142*** 0.219*** 0.192*** 0.171*** 0.195*** 0.147*** 0.603*** 0.072
(0.055) (0.062) (0.059) (0.060) (0.038) (0.053) (0.060) (0.061) (0.055) (0.060) (0.046) (0.126) (0.130)
Local 0.093 0.124 0.252*** 0.112* -0.039 0.138** 0.126 0.140* 0.190*** 0.169** 0.101* 0.557*** 0.219
(0.069) (0.078) (0.074) (0.067) (0.057) (0.070) (0.078) (0.078) (0.063) (0.076) (0.057) (0.159) (0.158)
MNC/JV 0.192* 0.048 0.189* -0.081 0.043 0.140 0.377*** 0.399*** 0.153 0.220** 0.242*** 0.702*** -0.215
(0.101) (0.098) (0.102) (0.117) (0.062) (0.088) (0.092) (0.116) (0.104) (0.101) (0.087) (0.217) (0.247)
SME 0.096 0.075 0.198** 0.069 -0.009 0.144** 0.199** 0.243*** 0.129* 0.189** 0.191*** 0.601*** -0.117
(0.071) (0.081) (0.077) (0.075) (0.059) (0.070) (0.080) (0.079) (0.069) (0.079) (0.061) (0.160) (0.171)
Large 0.121 0.091 0.210** 0.039 0.006 0.132 0.309*** 0.138 0.172 0.196** 0.107 0.637*** 0.343
(0.095) (0.101) (0.103) (0.101) (0.039) (0.083) (0.094) (0.103) (0.105) (0.099) (0.066) (0.226) (0.226)
<Voluntary>
Whole 0.069 0.125** 0.178*** 0.090* 0.004 0.088** 0.188*** 0.107** 0.168*** 0.239*** 0.126*** 0.519*** 0.227**
(0.047) (0.050) (0.051) (0.050) (0.039) (0.044) (0.053) (0.051) (0.050) (0.050) (0.035) (0.107) (0.109)
Local -0.065 0.081 0.162*** 0.067 -0.059 0.032 0.208*** 0.099* 0.178*** 0.228*** 0.082** 0.401*** 0.273**
(0.056) (0.059) (0.059) (0.058) (0.047) (0.052) (0.060) (0.059) (0.056) (0.057) (0.042) (0.123) (0.127)
MNC/JV 0.491*** 0.202* 0.244** 0.102 0.252*** 0.239*** 0.111 0.049 0.058 0.322*** 0.307*** 0.750*** 0.125
(0.101) (0.110) (0.115) (0.107) (0.088) (0.092) (0.115) (0.117) (0.115) (0.118) (0.085) (0.240) (0.234)
SME 0.052 0.164*** 0.181*** 0.086 0.016 0.078 0.165** 0.082 0.233*** 0.250*** 0.166*** 0.573*** 0.293**
(0.060) (0.062) (0.065) (0.064) (0.052) (0.056) (0.066) (0.065) (0.061) (0.061) (0.042) (0.142) (0.141)
Large 0.143* 0.133 0.164* 0.120 0.003 0.089 0.248*** 0.124 -0.001 0.283*** 0.083 0.458*** 0.125
(0.086) (0.095) (0.092) (0.082) (0.058) (0.085) (0.092) (0.088) (0.092) (0.094) (0.073) (0.171) (0.187)
Notes: Only estimated coefficients are reported. Marginal effect for binary probit estimation. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
24

25. Distinct differences between adopted firms with and without customers’ requirement are
observed when the dependent variable is Inventory and Profit. When Standards is regressed
on Inventories using the whole sample of adopted firms and the sample limited to adopted
MNCs and SMEs, the coefficient on Standards is significant at the 5%, 10%, and 1% level
respectively. In contrast the coefficient is not significant for the firms adopted upon
customers’ request. In the same manner, when Standards is regressed on Profit using the
whole sample of adopted firms and the sample limited to adopted local firms and SMEs, the
coefficient on Standards is significant at the 5% level. But the coefficient is not significant for
the firms adopted upon customers’ request. There results imply that firms voluntarily adopted
international standards tend to establish better inventory control and make profits than firms
passively adopted them.
On the other hand, the firms accepted customers’ requirement may have better
achievement in the development of domestic market. There are differences in the significance
of estimated coefficients for MNCs/JVs and SMEs. The coefficient on Standards is
significant at the 1% level for MNCs/JVs and SMEs adopted international standards required
by their customers but not significant for these two groups adopted them without the
requirement.
6. The Mechanisms for Adopting International Standards
6.1. Empirical Strategy
The regressions above indicate that the adoption of international standards will have
positive impacts on firm-level performance and whether firms have adopted them with or
without requirements from their main customers will have different performances. It is
important to understand characteristics of the firms who are required from their main
customers to adopt international standards and actually adopted them. To explore this issue,
the following model is developed:
yi = α + β1∗Customeri + β2∗Capacityi +γ∗xi + ui. (3).
The independent variables Customer are characteristics of and relationships with the
main customer of firm (i). The variables Customer include customer’s nationality (MNC or
JV), capital tie with customer, size of customer, frequency of shipping, JIT with customer,
and human exchange. The variables Capacity are factors influential to capabilities and
decisions of firm (i) such as implementation of R&D, training programs for employees, top
25

26. management’s backgrounds, and academic background of engineers. Age of firm (i) may also
be correlated with the capability and probability of adopting international standards. Details
on these variables are listed in Table 2 and Appendix Table A2. The control variables x in
equation (3) are the same as those in equation (1).
The following six binary variables are defied as dependent variable: Required by
customer that is coded 1 if the firm (i) is required by its main customer to adopt international
standards and 0 otherwise; Adopted that is coded 1 if the firm (i) has adopted them
irrespective of presence or absence of such requirement; Adopted upon request that takes the
value 1 if the firm (i) has adopted them, responding to the requirement from its main
customer; Voluntarily adopted that is equal to 1 if the firm (i) has adopted them even though
it is not being required to adopt them by its main customer; Turn down request that is coded 1
if the firm (i) is required and has not adopted them; and No request & Not adopted that is
coded 1 if the firm (i) is not required and has not adopted them. Binary probit estimations are
applied to these regressions.
6.2. Results
Table 11 summarizes the results of binary probit estimation. From the columns (1) to (6),
capital tie with customer and monthly shipping of products are not relevant to the
requirement from the customer and the adoption of international standards. From the columns
(1) and (5), the firms that have SME customers are less likely to be required the adoption by
such SME customers, so that they have fewer opportunities to turn down requirements.
From the column (1), firms that have foreign-owned customers are likely to be requested
by their customers. But such firms do not necessarily adopt them as implied by the column
(3). On the other hand, the significant coefficient on Foreign-owned customer in the column
(4) suggests firms that have foreign-owned customers are more likely to adopt them without
requirement.
The firms voluntarily adopted tend to have better production and logistic controls that
enable to make daily and weekly shipments as the significant coefficients on Ship a few times
in a day, Ship once in a day, and Ship a few times in a week are shown in the column (4). The
coefficients on JIT with customer are significant for all of the regressions. As in the column
(5), establishments performing JIT with their main customers are associated with a higher
probability of refusing customer’s request and negatively correlated to Adopted upon request
and No request & Not adopted. Thus firms with strong ability enough to develop a JIT system
will make decisions on their own account, even if they are requested by their customers.
26

27. The similar implication to logistics capability can be derived from engineer exchange.
The establishments that dispatch engineers to their customers tend to be requested but less
likely to adopt them upon customers’ request. In contrast, those accept engineers from their
customers are more likely to adopt upon request. Thus if it is assumed that more capable
firms dispatch their engineers, the establishments who dispatch engineers to their customers
may not adopt upon requests from less capable customers or may be able to turn down
requirements from the customers without fears to lose businesses. In the same way, the
establishments whose customers with stronger abilities dispatch engineers may have no other
choice to adopt upon request.
Among other variables relevant to firms’ capability, training programs for employees are
influential to the establishments voluntarily adopt international standards as the positive
coefficients on OJT and OFF-JT significant at the 5% level in the column (4) are shown. The
variable OJT is negatively correlated with the group Adopted upon request (column (3)). Top
management’s backgrounds and academic background of engineers are also important: Top is
MNC-experienced and 20-40% of engineers are positively significant at the 5% and 10%
level respectively for the regression of Adopted upon request; and Top is MNC-experienced is
positively significant at the 5% level for the regression of Voluntarily adopted.
In sum, the establishments adopted upon a request has not enough capacity to perform
JIT, learn from customers through engineer exchange with the customers, do not have OJT
programs, and have a top management with engineering background and have higher
percentage of engineers who finished technical college and higher educations. Such better
engineering knowledge allows them to respond to customers’ requirement. The
establishments voluntarily adopt international standards have foreign-owned customers, a
better production logistics control enable to make daily and weekly shipments and JIT,
training programs that stimulate employees’ willingness to make improvements, and top
managements who understand from working experiences in MNCs the importance of
management systems that conform to international standards. The establishments that can
refuse a requirement from their customers have bargaining abilities against the customers,
especially SME customers, which are backed up by technological and managerial edges on
the customers. The establishments without request and adoption have a lack of such
relationships with customers and capabilities. It can be said, by comparing to the firms
requested the adoption, that the establishments without request and adoption need to improve
logistics, create training programs, and dispatch engineers to learn from customers.
27

28. Table 11: Factors Influential to the Adopted of International Standards
(1) (2) (3) (4) (5) (6)
Required Adopted Turned No request
Voluntarily
by Adopted upon down & Not
adopted
customer request request adopted
Foreign-owned customer 0.191*** 0.176*** -0.020 0.168*** 0.012 -0.191***
(0.059) (0.058) (0.043) (0.056) (0.037) (0.046)
JV customer 0.208*** 0.052 -0.073* 0.122** 0.065 -0.131***
(0.058) (0.059) (0.038) (0.055) (0.043) (0.047)
Capital tie with customer -0.064 0.000 0.030 -0.035 -0.025 0.031
(0.045) (0.047) (0.031) (0.037) (0.026) (0.043)
SME customer -0.096** 0.017 0.041 -0.044 -0.047* 0.044
(0.043) (0.044) (0.033) (0.036) (0.026) (0.040)
Ship a few times in a day 0.179** 0.179** 0.043 0.147* 0.036 -0.190***
(0.075) (0.072) (0.065) (0.077) (0.051) (0.049)
Ship once in a day 0.132* 0.308*** 0.099 0.197** -0.047 -0.227***
(0.077) (0.060) (0.062) (0.078) (0.036) (0.044)
Ship a few times in a week 0.097 0.107* -0.001 0.110* -0.009 -0.088*
(0.063) (0.059) (0.045) (0.057) (0.035) (0.050)
Ship once in a week 0.127* 0.047 -0.007 0.053 0.061 -0.095*
(0.070) (0.070) (0.050) (0.064) (0.046) (0.056)
Ship once in a month -0.008 0.064 0.085 -0.021 0.023 -0.073
(0.084) (0.088) (0.071) (0.071) (0.052) (0.071)
JIT with customer 0.226*** 0.083* -0.057* 0.158*** 0.058** -0.157***
(0.042) (0.046) (0.032) (0.036) (0.025) (0.042)
Dispatch engineer to customer 0.179*** -0.032 -0.101** 0.059 0.071** -0.070
(0.053) (0.056) (0.041) (0.044) (0.032) (0.053)
Customer dispatches engineer -0.008 0.141*** 0.066* 0.049 -0.048* -0.081*
(0.051) (0.049) (0.037) (0.042) (0.029) (0.046)
R&D 0.052 0.073* 0.013 0.056 0.004 -0.063
(0.042) (0.043) (0.031) (0.036) (0.026) (0.039)
OJT 0.129*** -0.003 -0.066* 0.079** 0.036 -0.033
(0.044) (0.046) (0.035) (0.039) (0.025) (0.042)
OFF-JT 0.079* 0.103** -0.007 0.101** -0.031 -0.072*
(0.045) (0.046) (0.034) (0.039) (0.027) (0.042)
Top has master/Ph.D. 0.010 0.122** 0.048 0.053 -0.036 -0.071
(0.048) (0.048) (0.037) (0.041) (0.027) (0.044)
Top is engineer 0.006 0.087* 0.063** 0.011 0.004 -0.077*
(0.047) (0.046) (0.032) (0.038) (0.028) (0.044)
Top is MNC-experienced 0.025 0.047 -0.027 0.071** -0.041 -0.009
(0.044) (0.045) (0.033) (0.036) (0.027) (0.042)
20-40% of engineers 0.015 0.170** 0.152* 0.002 0.000 -0.170***
(0.084) (0.081) (0.082) (0.068) (0.048) (0.061)
40-60% of engineers 0.042 0.140* 0.061 0.087 -0.033 -0.081
(0.090) (0.084) (0.074) (0.085) (0.042) (0.073)
60-80% of engineers -0.123** -0.016 0.055 -0.065 -0.042 0.072
(0.061) (0.067) (0.056) (0.051) (0.031) (0.064)
80-100% of engineers -0.035 0.101* 0.055 0.030 -0.055* -0.022
(0.058) (0.058) (0.047) (0.051) (0.031) (0.054)
Age 0.000 0.005*** 0.002 0.002* -0.002* -0.003*
(0.001) (0.002) (0.001) (0.001) (0.001) (0.002)
28

29. (Continue)
(1) (2) (3) (4) (5) (6)
Required Adopted Turned No request
Voluntarily
by Adopted upon down & Not
adopted
customer request request adopted
SME -0.050 -0.132*** -0.052 -0.054 0.014 0.121***
(0.045) (0.046) (0.034) (0.039) (0.028) (0.041)
Local -0.070 -0.153*** -0.044 -0.094** 0.025 0.111**
(0.051) (0.053) (0.043) (0.045) (0.031) (0.047)
Food -0.005 -0.116 0.025 -0.144*** 0.109** -0.015
(0.071) (0.072) (0.054) (0.047) (0.053) (0.064)
Textile -0.147** -0.158** -0.084* -0.070 -0.058* 0.219***
(0.067) (0.073) (0.044) (0.058) (0.031) (0.073)
Chemicals 0.004 0.017 -0.034 0.052 -0.041 0.047
(0.066) (0.067) (0.044) (0.060) (0.034) (0.064)
Non-metal -0.287*** -0.021 0.113 -0.108 0.250
(0.100) (0.158) (0.152) (0.092) (0.153)
Iron 0.101 0.024 -0.046 0.085 0.019 -0.037
(0.097) (0.102) (0.058) (0.098) (0.061) (0.087)
Electronics 0.100 0.072 -0.011 0.090 0.007 -0.094
(0.074) (0.076) (0.050) (0.068) (0.044) (0.065)
Other machines 0.074 0.064 -0.037 0.093* -0.022 -0.032
(0.059) (0.059) (0.040) (0.053) (0.033) (0.053)
Indonesia -0.142** 0.013 0.003 0.012 -0.101*** 0.161**
(0.062) (0.068) (0.051) (0.057) (0.025) (0.067)
Philippines -0.170*** -0.070 0.017 -0.052 -0.072** 0.197***
(0.062) (0.070) (0.054) (0.052) (0.030) (0.069)
Vietnam -0.173** 0.062 0.181*** -0.074 -0.037 0.018
(0.070) (0.074) (0.065) (0.058) (0.038) (0.069)
Observations 833 833 833 833 820 833
Pseudo R2 0.209 0.236 0.117 0.262 0.0868 0.254
Notes: Marginal effect for binary probit estimation. Robust standard errors in parentheses. *** p<0.01, **
p<0.05, * p<0.1.
29

30. 7. Concluding Remarks
This paper attempts to provide new evidence on the adoption and impact of international
standards by using unique firm-level dataset that was constructed by the questionnaire survey
conducted in Indonesia, the Philippines, Thailand and Vietnam in 2009. Among a huge
research questions discussed by related literature, this paper focuses on the following three
issues:
(1) Which performance indicator has a significant relationship with the adoption of
international standards?
(2) Whether there are differences in the performance between firms adopted international
standards voluntarily and those adopted upon requirements from their customers?
(3) Searching factors influential to adopt international standards.
The empirical results from probit estimations show significant relationships between the
adoption and performance indicators. In particular, firms adopted international standards are
more likely to reduce environmental impacts caused by factory operations and meet
regulatory requirements on products. They also take advantage of international standards to
develop domestic and overseas markets. But the relationship between the adoption and profit
is not robust.
Differences between firms adopted international standards upon a requirement from their
main customers and adopted voluntarily are the most obvious in terms of inventory
management and profit. The latter group of the firms voluntarily adopted international
standards tend to establish better inventory control and make profits than the former group of
the firms passively adopted them.
Reflecting these empirical findings, firms adopted standards without customers’ request
ship out cargos frequently, practice JIT with their customers and have top management
experienced in MNCs, in addition to providing training programs to their employees. Such
firms have better organizational characteristics that foster self-motivations and create and
share tacit knowledge among employees.
On the other hand, firms adopted standards upon a requirement from customers have
better engineering capabilities. The firms that can decline a requirement from their customers
have bargaining abilities backed up by technological and managerial edges on the customers.
The uncertified firms not even be required the adoption of international standards should
review logistic management, set up training programs, and dispatch engineers to learn from
customers.
30

31. Acknowledgements
This paper is one of the results of the research project titled “Fostering Production and
Science & Technology Linkages to Stimulate Innovation in ASEAN,” which was organized
by the Economic Research Institute for ASEAN and East Asia (ERIA) in fiscal year 2009.
This project was executed by the Institute of Developing Economies (IDE) in close
cooperation with the Center for Strategic and International Studies (CSIS), Indonesia, the
Philippine Institute for Development Studies (PIDS), Sirindannhorn International Institute of
Technology, Thammasat University (SIIT), and the Industry Policy and Strategy Institute
(IPSI). The authors would like to express their deep and sincere gratitude to Hidetoshi
Nishimura, Fukunari Kimura and So Umezaki for their assistance to the research project. The
views expressed in this paper are those of the authors and do not necessarily reflect the views
of the organizations.
References
Arimura, T., N. Darnall, H. Katayama (2011) “Is ISO14001 a gateway to more advanced
voluntary action? The case of green supply chain management,” Journal of Environmental
Economics and Management 61(2): 170–182.
Bénézech, D., G. Lambert, B. Lanoux, C. Lerch, J. Loos-Baroin (2001) “Completion of
knowledge codification: an illustration through the ISO 9000 standards implementation
process,” Research Policy, 30(9): 1395-1407.
Clougherty, J.A., M. Grajek (2008) “The impact of ISO 9000 diffusion on trade and FDI: A
new institutional analysis,” Journal of International Business Studies 39(4): 613-633.
Corbett, C.J. (2006) “Global diffusion of ISO 9000 certification through supply chain,”
Manufacturing & Service Operations Management 8(4): 330-350.
Foxconn Technology Group (2010) 2009 Corporate Social & Environmental Responsibility
Report.
Franceschini, F., M. Galetto, D. Maisano, L. Mastrogiacomo (2010) “Clustering of European
countries based on ISO 9000 certification diffusion,” International Journal of Quality &
Reliability Management, 27(5): 558-575.
Heras-Saizarbitoria, I., G. Arana, E. San Miguel (2010) “An analysis of the main drivers for
ISO 9001 and other isomorphic metastandards,” Review of International Comparative
Management, 11(4): 562-574.
Heras-Saizarbitoria, I., G.A. Landín, J.F. Molina-Azorín (2011) “Do drivers matter for the
31