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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

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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

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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.

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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.

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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

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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

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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.

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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

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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.

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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.

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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.

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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.

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Figure 1: Adoption of International Standards and Outcomes

05

10

15

20

0 2 4 6 8 10 0 2 4 6 8 10

Not Adopted Adopted

Percent

Number of OutcomesAdoption 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

05

10

15

20

25

30

35

40

45

50

1 2 3 4 5 1 2 3 4 5

Not Adopted Adopted

Percent

Annual Change in Profit1(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.

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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.

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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.

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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

defect inventory material labor quality flexibility lead-time domestic

market

foreign

market pollution regulation outcomes profit

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.

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

defect inventory material labor quality flexibility lead-time domestic

market

foreign

market pollution regulation outcomes profit

<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)

18

(Continued)

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13)

Binary Ordered Ordered

defect inventory material labor quality flexibility lead-time domestic

market

foreign

market pollution regulation outcomes profit

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.

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

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.

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

defect inventory material labor quality flexibility lead-time domestic

market

foreign

market pollution regulation outcomes profit

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.

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

defect inventory material labor quality flexibility lead-time domestic

market

foreign

market pollution regulation outcomes profit

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.

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

defect inventory material labor quality flexibility lead-time domestic

market

foreign

market pollution regulation outcomes profit

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.

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

defect inventory material labor quality flexibility lead-time domestic

market

foreign

market pollution regulation outcomes profit

<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.

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

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.

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.

28

Table 11: Factors Influential to the Adopted of International Standards

(1) (2) (3) (4) (5) (6)

Required

by

customer

Adopted

Adopted

upon

request

Voluntarily

adopted

Turned

down

request

No request

& Not

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)

29

(Continue)

(1) (2) (3) (4) (5) (6)

Required

by

customer

Adopted

Adopted

upon

request

Voluntarily

adopted

Turned

down

request

No request

& Not

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.

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.

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

32

benefits of ISO 14001?” International Journal of Operations & Production Management,

31(2): 192-216.

International Organization for Standardization (ISO) (2010) The ISO Survey of Certifications

2009, Geneva: ISO.

Machikita, T., Y. Ueki (2010) “Spatial Architecture of the Production Networks in Southeast

Asia,” ERIA Discussion Paper No. 2010-01.

Machikita, T., Y. Ueki (2011a) “Linked Versus Non-linked Firms in Innovation: The Effects

of Economies of Network in Agglomeration in East Asia,” International Journal of

Institutions and Economies 3(1): 77-102.

Machikita, T., Y. Ueki (2011b) “The Impacts of Face-to-face and Frequent Interactions on

Innovation: Evidence from Upstream-Downstream Relations,” forthcoming in

International Journal of Institutions and Economies.

Neumayer E., R. Perkins (2005) “Uneven geographies of organizational practice: Explaining

the cross-national transfer and diffusion of ISO 9000,” Economic Geography 81(3): 237-

259.

Osterloh, M., B.S. Frey (2000) “Motivation, knowledge transfer, and organizational forms,”

Organization Science 11(5):538-550.

Osterloh, M., J. Frost, B.S. Frey (2002) “The dynamics of motivation in new organizational

forms,” International Journal of the Economics of Business 9(1):61-77.

Quazi, H.A., C.W. Hong, C.T. Meng (2002) “Impact of ISO 9000 certification on quality

management practices: A comparative study,” Total Quality Management 13(1):53-67.

Sampaio, P., P. Saraiva, A.G. Rodrigues (2009) “ISO 9001 certification research: questions,

answers and approach,” International Journal of Quality & Reliability Management 26(1):

38-58.

Sharma, D.S. (2005) “The association between ISO 9000 certification and financial

performance,” The International Journal of Accounting 40(2): 151–172.

Sun, H., T. Cheng (2002) “Comparing Reasons, Practices and Effects of ISO 9000

Certification and TQM Implementation in Norwegian SMEs and Large Firms,”

International Small Business Journal 20(4): 421–442.

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Appendix

Table A1: List of Variables I

Variable Definition

Dependent variable

Defect = 1 if the respondent firm has decreased defective products during 2007-

2009.

Inventory = 1 if the respondent firm has decreased inventories of products during

2007-2009.

Material = 1 if the respondent firm has reduced raw materials and energy during

2007-2009.

Labor = 1 if the respondent firm has reduced labor input during 2007-2009.

Quality = 1 if the respondent firm has improved quality of goods or services

during 2007-2009.

Flexibility = 1 if the respondent firm has improved flexibility of production or

service provision during 2007-2009.

Lead-time = 1 if the respondent firm has reduced lead-time to introduce a new

product or service during 2007-2009.

Domestic market = 1 if the respondent firm has entered new markets or increase market

share in the domestic market during 2007-2009.

Foreign market = 1 if the respondent firm has entered new markets aboard or increase

exports during 2007-2009.

Pollution = 1 if the respondent firm has reduced environmental impacts caused by

factory operations during 2007-2009..

Regulation = 1 if the respondent firm has met regulatory requirements on products

during 2007-2009.

Outcomes Sum of the 11 dummy variables listed above.

Profit

= 5 if annual sales of the respondent firm has increased substantially; = 4

if increased; = 3 if almost same; = 2 if decreased; =1 if decreased

substantially.

Independent variable

Standards = 1 if the respondent firm has adopted international standards (ISO9000,

ISO14000, or others).

Request = 1 if main customer requires the respondent firm to adopt international

standards (ISO9000, ISO14000, etc.).

Control variable

SME = 1 if the respondent firm employs 199 or less workers.

Local = 1 if the respondent firm is 100% locally owned.

Food = 1 if the main activity of the respondent firm is food, beverages or

tobacco.

Textile = 1 if the main activity of the respondent firm is textiles, apparel or

leather.

Chemicals = 1 if the main activity of the respondent firm is chemicals, plastics or

rubber products.

Non-metal = 1 if the main activity of the respondent firm is non-metallic mineral

products.

Iron = 1 if the main activity of the respondent firm is iron or steel.

Electronics = 1 if the main activity of the respondent firm is computers, other

electronics and parts.

Other machines = 1 if the main activity of the respondent firm is machinery other than

electronics.

Indonesia = 1 if the main activity of the respondent firm is located in Indonesia.

Philippines = 1 if the main activity of the respondent firm is located in the

Philippines.

Thailand = 1 if the main activity of the respondent firm is located in Thailand.

Vietnam = 1 if the main activity of the respondent firm is located in Vietnam.

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Table A2: List of Variables II

Variable Definition

Independent variable

Foreign-owned customer = 1 if main customer is 100% foreign owned.

JV customer = 1 if main customer is a joint venture.

Capital tie with customer = 1 if the respondent firm has a capital tie-up with main customer.

SME customer = 1 if main customer employ 199 or less workers.

Ship a few times in a day = 1 if the respondent firm ships cargos a few times in a day.

Ship once in a day = 1 if the respondent firm ships cargos once in a day.

Ship a few times in a week = 1 if the respondent firm ships cargos a few times in a week.

Ship once in a week = 1 if the respondent firm ships cargos once in a week.

Ship once in a month = 1 if the respondent firm ships cargos once in a month.

JIT with customer = 1 if the respondent firm has adopted JIT with main customer.

Dispatch engineer to customer = 1 if the respondent firm dispatches engineers to main customer.

Customer dispatches engineer = 1 if main customer dispatches engineers to the respondent firm.

R&D = 1 if the respondent firm conducts R&D.

OJT = 1 if the respondent firm has an OJT training program.

OFF-JT = 1 if the respondent firm has an Off-JT training program.

Top has master/Ph.D. = 1 if the top management of the respondent has a master or Ph.D. degree.

Top is engineer = 1 if the top management of the respondent is/was an engineer.

Top is MNC-experienced = 1 if the top management of the respondent has an experience working

for a MNC/JV.

0-20% of engineers = 1 if 0-20% of the engineers of the respondent are technical college

graduates or higher.

20-40% of engineers = 1 if 20-40% of the engineers of the respondent are technical college

graduates or higher.

40-60% of engineers = 1 if 40-60% of the engineers of the respondent are technical college

graduates or higher.

60-80% of engineers = 1 if 60-80% of the engineers of the respondent are technical college

graduates or higher.

80-100% of engineers = 1 if 80-100% of the engineers of the respondent are technical college

graduates or higher.

Age Age of the respondent firm