Proceedings of 8th Asian Business Research Conference
1 - 2 April 2013, Bangkok, Thailand, ISBN: 978-1-922069-20-7
What makes profits of low-technology SMEs grow?
Gaku Funabashi*
This paper examines factors for the growth of profit in low-technology small
and medium enterprises (SMEs) using firm-level data for the Indonesian
manufacturing sector. The foremost measure to evaluate businesses is
profit. Yet, most studies that examine the growth of SMEs in low- and
middle-income countries use employment numbers for evaluation because
of the difficulty in obtaining financial data from small firms. Results from
linear regression estimation indicate that there is no substantial association
between profit growth and firm characteristics, sectors or location. However,
these results also show that new product development and quality
improvement significantly impact profit growth. Moreover, an in-depth
analysis of sales quantity and price changes for each product revealed that
a more significant variable, unit raw material cost reduction, was induced by
efforts for developing existing and new products, but not vice versa.
JEL Codes: O17 and P42
1. Introduction
Governments in many low- and middle-income countries understand the important role of
small and medium enterprises (SMEs) for the country’s economic development. SMEs are
expected to contribute to industrial growth by promoting greater economic dynamism
(Wren and Storey, 2002). As a result, there is a strong and positive association between
SME growth and GDP per capita growth (Thurik and Wennekers, 2004; Bech, DemirgucKunt, and Levine, 2005). Moreover, SMEs provide individuals with a source of
independent revenue (Coad and Tamvada, 2008; Ayyagari, Demirguc-Kunt and
Maksimovic, 2011), thus, SMEs are expected to reduce poverty by increasing
employment. However, many SMEs are vulnerable and often have difficulty surviving due
to the lack of resources (Auster and Aldrich, 1984; Acs, 1999). This is why SME support is
considered a critical policy in many countries.
Numerous attempts have been made to show factors in the growth of SMEs and, thereby,
provide implications to support policies. The majority of studies into small firm growth in
developing countries use employment numbers as a measure of growth. There are two
main reasons, first, because of the expected role of SMEs as providers of employment,
high-growth SMEs can simultaneously demonstrate labour productivity and employment
increases (Smallbone, Leig, and North, 1995). However, productivity can be damaged by
SMEs having more than enough staff. If profits decrease because of low labour
productivity, businesses are not sustainable. Therefore, number of employees is not the
most important measure to evaluate businesses. As Penrose (1959) clearly stated,
enterprises are motivated by profit. To be sustainable, enterprises need to increase profit
first and then invest for future opportunities. Continuation of such an operation makes it
possible to secure a firm basis of enterprise development. Only enterprises with a rigid
operational and financial base can contribute to creating employment opportunities and
establishing sustainability in industrial growth.
* Gaku Funabashi, Ponton Partners Inc., Japan, Email : funabashi@ponton.jp
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Proceedings of 8th Asian Business Research Conference
1 - 2 April 2013, Bangkok, Thailand, ISBN: 978-1-922069-20-7
Another reason for using employment information for evaluating SME growth is that it is
often extremely difficult to obtain reliable financial data from small firms in low- and
middle-income countries (McPherson, 1996; Mead and Liedholm, 1998; Bigsten and
Gebreeyesus, 2007). Hence only few studies have so far investigated small firm growth
through actual profit data. This is particularly the case in studies into low-technology
informal firms. Some researchers examine the impact of programmes in terms of profit
changes. For example, Karlan and Valdivia (2011) studied sales of training participants in
good and bad months for micro-finance borrowers in Peru. Additionally, Mano et al. (2011)
showed how the profits of management training participants changed in Ghana. However,
these researchers did not examine the factors for the profit change.
Literature examining profit changes focuses on the impact of support programmes and not
the factors for growth, while those that discussed factors for SME growth did not
investigate actual profit information. This paper examines whether factors for the growth in
profits of low-technology SMEs fit the conclusions in existing literature regarding SME
growth using unique firm-level data of Indonesian SMEs. This paper also attempts to
uncover what influences significant factors through an in-depth analysis of quantity and
price changes for each product.
As a result of linear regression estimation, I could not conclude that firm characteristics,
sector and location had a statistically significant impact on profit. However, I found that
new product development and quality improvement of existing products positively
impacted growth. Moreover, a more significant variable, unit raw material cost reduction,
was induced by efforts for developing existing and new products, but not vice versa.
The remainder of the paper is organized as follows. Section 2 reviews existing literature
and provide hypotheses. Section 3 presents our model and data, and section 4 discusses
the results of estimations. Section 5 presents policy implications and conclusions.
2. Literature Review
In this section, I review existing literature that studies factors for the growth of SMEs and
provide hypotheses for examining factors for the profit growth of SMEs.
First, I extract critical factors for SME growth in low- and middle-income countries from
existing literature and establish a base framework for the analysis. Table 1 is a summary
of such growth factors besides the external business environment. I categorized factor
variables into four issues, the characteristics of enterprises including (1) owner gender (2)
intentions of operations (3) the sectors to which SMEs belong and (4) their locations.
2.1. Firm characteristics
As described in Jovanovic’s (1982) model, many articles conclude that young and smaller
enterprises grew faster than well established and large ones (in addition to the studies
shown in Table 1 see, for example, Andersson et al., 2004; Ayyagari, Demirguc-Kunt and
Maksimovic, 2011). In contrast, formality had a positive impact on growth (Storey, 1994;
Capp, Elstrold and Jones, 2005). Registered enterprises tend to grow faster than informal
firms. Access to finance influences advancing businesses (Beck and Demirguc-Kunt,
2006; Karlan and Morduch, 2009). Access to finance also had a second effect in that it
induced SMEs to keep accounting records, which are essential for analyzing own
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Proceedings of 8th Asian Business Research Conference
1 - 2 April 2013, Bangkok, Thailand, ISBN: 978-1-922069-20-7
businesses (Karlan and Valdivia, 2011; Mano et al., 2011). Individual entrepreneur
characteristics include owner gender, education and prior experience. Although female
entrepreneurs have a positive impact in high-income countries (Kalleberg and Leicht,
1991), they were often disadvantaged in low- and middle-income countries because of
women’s risk-averse characteristics and low social status in those countries (McPherson,
1996; Mead and Liedholm, 1998). Higher education level of owners is related to better
performance (Bosma et al., 2004; Rauch, Frese and Utsch, 2005) as is prior experience of
owners (Oviatt and McDougall, 2005; Elfenbein et al., 2010).
Table 1. Factors for SME Growth
McPherson
(1996)
(1) Firm
characteristics
a) Age
b) Size
c) Formality
d) Access to
finance
e) Owner
gender
(2) Operation
–
–
Mead &
Liedholm
(1998)
–
–
Tybout
(2000)
–
Sleuwaegen
& Goedhuys
(2002)
Biggs &
Shah
(2006)
Bigsten &
Gebreeyesus
(2007)
–
–
+
+
+
Nichter &
Goldmark
(2009)
Berry
et al.
(2002)*
–
+
+
–
–
–
a) Product
+
development
b) Process
+
+
+
+
+
improvement
c) Internationa
+
+
-lization
d) Inter-firm
+
+
+
cooperation
(3) Sector
x
x
(4) Location
x
Notes: (+) Positive relationship between the element and enterprise growth; (–) Negative relationship between the
element and enterprise growth; (x) relationship exists positively or negatively depending on the case. *Literature
discussed cases in Indonesia.
For Indonesian cases, Indarti and Langeberg (2004) showed the same results as the
above literature in terms of firm size, access to finance and owner characteristics. Tan and
Batra (1995) also argue about the positive impact of access to finance on firm growth,
although they conclude that larger firms grew faster.
2.2. Operations
Fundamentally, all research results discuss the positive impact of product development
and process improvement on profit growth. The more that SMEs develop new products
and increase productivity using higher technology, the faster enterprises grow and
increase their chances of survival (Madsen and Servais, 1997; Crick and Jones, 2000;
Melitz, 2003; Wagner, 2005; Bernard et al., 2007).
Internationalization, meaning exporting, also has a positive influence on growth (Clerides
et al., 1998; Clercq et al., 2005; Wolff and Pett, 2006). Inter-firm cooperation also key to
growth since it allows for market expansion. Agreements with buyers decreased the risks
and costs associated with entering new markets by providing a guaranteed flow of orders
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Proceedings of 8th Asian Business Research Conference
1 - 2 April 2013, Bangkok, Thailand, ISBN: 978-1-922069-20-7
and critical information about market requirements (Aitken et al., 1997; Aw, 2002).
Operations became smoother in foreign markets as relationships with intermediary
enterprises progressed from mere marketing to a network of enterprises (Crick and
Spence, 2005; Zain and Ng, 2006). Furthermore, internationalization and inter-firm
cooperation are closely linked. Multinational corporations took an important role in the
export of components from SMEs (Acs et al., 1997; Ghauri, Lutz and Tesfom, 2003).
Internationalization is also important because it often linked companies with foreign
learning channels, and firms with foreign technical assistance had higher productivity
levels (World Bank, 1993; Levy, 1994).
For Indonesian cases, Sandee and Rietveld (2001) and Berry, Rodriguez and Sandee
(2001) argue that the positive effects of better quality products and process improvement
in export markets can lead to a growth in demand. Weijland (1992) and Geenhuizen et al.
(2010) discuss the importance of middlemen for expanding markets and various trade
networks that link rural industries with distant markets. Tambunan (2008) shows that firms
in clusters secure access to a wider market when those clusters have external networks
linked to large enterprises.
2.3. Sector and location
Sector characteristics often impact the structure of enterprises and investments (Storey,
1994). Enterprises in certain industries such as the chemical industry are usually larger in
size and are more capital intensive than others. Hence sector characteristics could
influence the growth of enterprises in terms of economies of scale. A considerable number
of studies focus only on Indonesia, including Sandee and Rietveld (2001) and Sato (2000),
and discuss the growth of small businesses in a specific area of Central Java. However,
little attention has been given to the comparison of different regions in the same country.
In fac, 58% of the population and more than 60% of SMEs are concentrated in Java (BPS,
2010) in Indonesia. Thus, operations of SMEs in Java may differ from those elsewhere in
the country.
Considering all of the above from existing literature, my hypotheses are;
Hypothesis 1: Firm age, size and female ownership have negative impacts, while formality
and access to finance have positive impacts on SME profit growth.
Hypothesis 2: Product development, process improvement, internationalization (in other
words, market expansion) and inter-firm cooperation have positive impacts on SME
profit growth.
Hypothesis 3: There are certain sectors and locations in which SMEs profit grows faster.
3. Methodology and Model
I now introduce the model and data used to investigate the above mentioned hypotheses.
A survey was conducted to collect data from SMEs that participated in trainings
implemented by public support institutions in Indonesia.
3.1. Model
I applied the linear regression estimation model using firm-level data for the manufacturing
sector in Indonesia. The dependent variable was the growth rate of gross profits between
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Proceedings of 8th Asian Business Research Conference
1 - 2 April 2013, Bangkok, Thailand, ISBN: 978-1-922069-20-7
one month before and three months after training. Gross profits in this paper are
calculated as sales revenue less the costs of raw materials. Some enterprises could have
extraordinarily high sales revenue, even though their number of employees was small. In
most of these cases, the cost of raw materials was also high. Hence I used gross profit
instead of sales revenue. Moreover, in the apparel industry several enterprises are
subcontractors for whom raw materials are provided free of charge. However, material
costs are already deducted when the price is set by manufacturers at a higher level in the
supply chain. Thus, sales revenue equals gross profits. I was able to compare the profits
of all sample enterprises at the same level using gross profits.
There is a question about the time-frame of the survey used to examine the impact of
variables on SME growth. Karlan and Valdivia (2011) compared sales data for several
points of time over a two-year period. Mano et al. (2011) studied changes one year after
training. Tan (2009) argues that a much longer period is necessary to observe changes,
and made the survey ten years after the programme was conducted. In general,
management decisions by SME owners are made in a shorter period than those made by
directors of large enterprises. Even though enormous uncertainties exist in decision
making, entrepreneurs are encouraged to take action before decisions make complete
sense (Busenitz and Barney, 1997). Therefore, the smaller enterprises, the more their
ways of managing organizations are influenced by the information they receive just before
making decisions. If I can simultaneously provide an opportunity that induces actions by
SMEs, it is possible to examine development of firms over only a short period of time.
Furthermore, the longer a survey period, the more SMEs are influenced by changes in
external business environments, such as economic conditions. I avoided influences of
changes in external business environments by using data from a shorter survey period.
Based on the above ideas, I administered a pre-survey to 28 randomly selected
enterprises in seven cities on four different islands between March and June 2008.
According to the pre-survey results, 78% of firms showed positive changes in improving
product quality and 57% increased sales revenues and profits with only three to four
months after training. Thus, I used period of three months after training to view changes in
the survey.
Our independent variables consisted of four categories: enterprise characteristics,
operations, sectors and locations. Variables of enterprise characteristic include the year of
establishment, the number of employees, a dummy variable for registration, a dummy
variable for a new loan and a dummy variable for female ownership. The dummy for
registration was one if an enterprise was already registered, zero otherwise. The dummy
for a new loan was one if an enterprise acquired a loan from a financial institution after
training, zero otherwise. I used gross profits from which interest payments were not yet
deducted. Hence costs for raising capital from external sources do not influence profit in
this model. The dummy for female ownership was one if an owner was a woman, zero
otherwise.
Variables related to the operation are dummy variables for new product development, the
changes of unit raw material cost, market stages reached by sample firms and a dummy
variable for acquiring new business partners. The dummy for new product development
was one if SMEs added new product lines or improved product quality after training; zero
otherwise. Since most technology is imported from more advanced countries, I did not
expect to find much innovation-oriented research and development by SMEs in low- and
middle-income countries. However, I observed technical efforts to modify imported
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Proceedings of 8th Asian Business Research Conference
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technology to differentiate local products (Biggs, 1995). In this paper, this type of
modification is also considered new product development.
The change of unit raw material costs is calculated as (RMC t2/NS t2) – (RMC t1/NS t1),
where RMC is monthly raw material cost, NS monthly net sales, t1 one month before
training, t2 three months after training. If sample firms could reduce unit costs of raw
material, it was negative. Introducing new production facilities can also influence process
improvement. However, this is not included in this model due to its high collinearity with
access to finance. I examined not only internationalization but also the impact of market
reached, including domestic markets, considering the fact that Indonesia is a huge country
with many islands. I set up three stages of sales market expansion in which local
government boundaries are characterized. Stage 1 (province): selling products in the
same province, Stage 2 (other provinces): selling products in other provinces and Stage 3
(foreign country): selling products in foreign countries. The sales market expands with
each new stage. Stage 1 is denoted as unity, Stage 2 = 2 and Stage 3 = 3. The dummy for
a business partner was used to see the impact of inter-firm cooperation. Business
partners in this paper mean intermediaries who sell products in outer markets. If sample
firms acquired business partners after training, it was one, zero otherwise.
The dummy variable of each sector shows the industry to which each firm belongs. These
are the food processing, textile, footwear, ceramic, metal processing, wooden furniture,
handicraft and chemical industries. I also included a dummy variable for location. If a
sample enterprise produced products in Java, it was one, zero otherwise.
3.2. Data
In this paper, I used data from local SMEs including informal household industries and
registered SMEs to estimate the above models.
Table 2. Summary of Sample Enterprises (N = 98)
Variable
Mean
Std. Dev.
Min.
Max.
Number of employees
19.5
41.4
1
278
Number of product lines
3.2
2.6
1
12
Net sales per month (rupiah)
165 mil.
659 mil.
215,000
4.9 bil.
Gross profit per month per employee (rupiah)
15.9 mil.
129 mil.
23,182
1.3 bil.
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Proceedings of 8th Asian Business Research Conference
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Table 3. Distribution of Sample Enterprises (N = 98)
Number of Employees (%)
1-4
Location (%)
Industry (%)
(micro)
29
Java
45
Food processing
36
5-19 (small)
49
Sumatra
26
Textiles
18
20-99 (medium)
19
Kalimantan
10
Footwear
16
Sulawesi
18
Ceramics
8
Metalworking
7
Wooden furniture
6
Handicrafts
2
101- (large)
3
Maluku
Registration (%)
1
Year of establishment (%)
Registered
50
-1999
50
Chemical
2
Not registered
50
2000-
50
Others
4
Target enterprises for our survey were those who participated in management skills and
production technologies training conducted by the Japan International Cooperation
Agency together with the Indonesian Ministry of Industry. I expected that the training
worked as a trigger to induce decisions and actions by owners. SMEs that participated in
training may not be a random sample, and these companies may be more motivated and
aggressive than average firms (Storey, 2004). However, they are more suitable for
levelling the technology of sample firms. Owners of SMEs decided whether they would
participate in training after checking the curriculum. Technology levels of training are
relatively low since the programmes are mainly designed for home industries. Enterprises
which already obtained technological support from foreign enterprises or achieved a
certain technology level would not participate. Thus, I could expect that the technology
levels of participant firms were more or less the same.
I collected data for the analysis from a survey conducted between March and July 2009.
Participants were asked to return to training venues. Survey participants calculated profits
using a prepared questionnaire. A survey trainer explained how to calculate and fill out the
questionnaire so that everyone understood the questions correctly. The trainer recalculated the profits for all respondents to avoid errors from participant miscalculations. In
Indonesia there is a season in which people consume goods more than they do at any
other time. This is the period just before holidays after Ramadan, a fasting month. This
period of time fell in August in the year I conducted the survey, and the period for
examining changes did not include this season. Hence the trend of consumption did not
change largely during the survey period. In total, 163 of a total of 330 training participants
participated in this survey. Some participants were from the same firm, in which case I
used only one response. Some responses were incomplete and thus eliminated.
Eventually, data from 98 firms were used for estimation. Tables 2 and 3 summarize the
sample’s descriptive statistics.
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4. Results and discussion
In this section, I examine and discuss the estimation.
As shown in Table 4, I could not find any significance in the size, age, formality of firms,
access to finance and gender of owners in terms of enterprise characteristics. These
results are not consistent with existing literature. Even informal businesses are capable of
increasing profit by changing the way they operate. As Capp, Elstrold and Jones (2005)
stated, informal firms tend to be sub-skilled and produce sub-standard products. However,
there is still a way these firms can increase profits. It is also true that credit from financial
institutions is not a critical factor. Many sample firms are home industries using simple
equipment for production. Hence large investment is not required to develop new products
or improve production process technologies, at least in the short run.
For variables related to the firm operation, product development and change of unit raw
material cost were statistically significant at the 1% significance level. Reducing unit costs
had the largest impact on profit increase. On the other hand, the market stage reached
and business partner were not statistically significant. This means that it is possible for an
individual firm to increase profits even though it remains only within a provincial-level
market. Although many studies discuss the positive influence of internationalization, the
potential of a local market should not be underestimated.
Food processing, metal processing and wooden furniture were statistically significant at
the 5% significance level with a negative correlation. These industries can be
disadvantaged due to the degree of labour intensity or trends in consumer behaviour. At
the same time, the coefficients of all other industries were also negative, although they
were not statistically significant. Hence it is difficult to conclude that a certain industry has
a clear positive or negative impact. This is the same for the location dummy, Java does
not have either a positive or negative impact compared to other islands.
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Proceedings of 8th Asian Business Research Conference
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Table 4. Estimation Results
Dependent variable
Growth rate of gross profit
Independent variable
Coefficient
−0.1
(0.37)
0.6
(1.45)
−29.9
(30.5)
−8.6
(37.5)
−36.4
(38.5)
79.5***
(29.5)
−270.1***
(101.2)
−0.5
(23.0)
40.0
(28.7)
Number of employee
Year of establishment
Registration
New loan
Female owner
Product development
Unit raw material cost
Market reached
Business partner
N
98
Independent variable
Coefficient
−161.3**
Food processing
(73.1)
−67.4
(54.6)
−27.7
(5.0)
−179.6**
(88.6)
−200.0**
(86.0)
−18.1
(99.3)
−135.9
(114.3)
−129.5
(82.2)
Textile
Footwear
Metal processing
Wooden furniture
Handicraft
Chemical
Java
R sq
0.246
Adjusted R sq
0.086
Notes: Standard errors are in parentheses. ***, ** and * show statistical significance of the coefficients at the
1%, 5% and 10% levels, respectively. The ceramic industry was omitted because of collinearity.
Table 5. Consequences of Product Development (N = 98)
Product
develop
-ment
Change of
product lines
Done
Increase 36
Same
20
Decrease 3
Not
Same
34
Decrease 5
Total
98
Change of gross
profit
Increase
Decrease
Increase
Decrease
Increase
Decrease
Increase
Decrease
Increase
Decrease
33
3
15
5
3
0
25
9
1
4
98
Sales quantity increase
without
lowering price
22* (67%)
0 ( 0%)
12 (80%)
0 ( 0%)
3 (100%)
0 ( 0%)
13 (52%)
0 ( 0%)
0 ( 0%)
1 (25%)
51
with higher price
10* (30%)
0 ( 0%)
5 (33%)
0 ( 0%)
1 (33%)
0 ( 0%)
5 (20%)
0 ( 0%)
0 ( 0%)
1 (25%)
22
* Sales quantity of existing products; sales of new products are not included.
Now I examine how two statistically significant variables −product development and unit
raw material cost− influence profit increases. Table 5 shows the consequences of product
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development by sample enterprises. More firms that worked on product development
could increase sales quantity without lowering prices than could firms that had not
developed products. It is irrelevant whether firms actually increased the number of product
lines. Even firms with the same or a fewer number of product lines could sell more without
lowering prices. Moreover, approximately one third of firms sold more quantity than before
training even with higher prices. These results lead to the conclusion that the firms with
the same or fewer number of product lines improved the quality of their existing products
and consumers positively accepted quality changes and purchased products in larger
quantity.
Table 6. Examples of Successful Sample Firms Conducted Product Development
Firm
Location
No. of
emplo
-yees
Monthly
revenue
(mil Rp)
Gross
profit
increase
(%)
No of
product
change
(%)
Unit raw
material
change
(% points)
Solok
W. Sumatra
3
1.6
241
100
0.10
Makassar
S. Sulawesi
8
12.4
54
50
−0.01
Sector
Product*
Food
Process
-ing
Food
Process
-ing
Processed
fish
(1 line)
Processed
coconut
(2 lines)
C
Textiles
Clothes
(7 lines)
Garut
W. Java
15
8.3
21
0
0.04
D
Textiles
Clothes
(1 lines)
Majalaya
W. Java
55
87.5
260
−50
−0.23
E
Footwear
Shoes
(6 lines)
Bantul
C. java
7
19.3
36
0
−0.01
F
Ceramics
Pottery
(5 lines)
Purwakarta
W. Java
9
5.8
250
120
−0.13
G
Metal
working
Knives, pans
(7 lines)
Bedagai
N. Sumatra
6
18.2
38
0
0.03
H
Wooden
furniture
Rattan chairs
(5 lines)
Margasari Ulu
S. Kalimantan
90
11.4
122
40
0.05
A
B
Notes: N: North, S: South, E: East, W: West, C: Central
* The number of product lines one month before training
Table 6 shows examples of successful firms that increased sales quantity with higher
prices as a result of product development. Half the firms added new product lines, but the
other half increased gross profits with the same or fewer number of product lines. For
example, Firms C and G increased gross profits even with higher unit raw material costs
for the same number of product lines. The only way to achieve it is to increase revenue
from existing products by selling a larger quantity or raising prices. It seems reasonable
that these firms improved the quality of their existing products either way.
The second variable with statistical significance was unit raw material cost. Unless sales
decrease by a large amount, firms that could reduce unit material costs naturally increase
gross profit. Table 7 shows curriculum distribution of training in which sample enterprises
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participated. More than half of the curriculum focused on production technology for
product development and introduced low-technology, but scientifically proven ways.
Sessions for process improvement were not dominant in training. However the impact on
the unit raw material cost reduction was still large. Why was this possible? The level of
technology taught at the training was, again, relatively low so that participants could apply
it within a short period without making a large investment. Therefore many firms could
work on product development. I assume that many firms had to change the production
system itself to apply lessons learned in production technology training. As a result, the
curriculum aimed at product development also worked to improve the effectiveness of the
production process. For example, the course in Makassar in the food processing industry
included no classes on process improvement. Among sample firms from this course, 55%
worked on product development and all of them decreased unit raw material costs. On the
other hand, among participants of the course in Pontianak, which had no classes for
production technology, 75% reduced unit raw material costs but only 25% worked on
product development. Learning production technology for product development influenced
unit raw material cost reduction, but not vice versa.
Table 7. Curriculum Distribution of Trainings
Location
Bandung
Yogyakarta
Medan
Padang
Palembang
Bandar
Lampung
Pontianak
Banjar Baru
Makassar
Manado
Ambon
Average
Theme
Developing weaving design on natural material
Developing knitting design on natural material
Raw material preparation for earthenware and stone
ware body of pottery for plastic forming and slip casting
Technology of footwear manufacturing
Art paper making process from agriculture waste
Heat treatment process for metalworking quality
improvement
Quality improvement and diversification of fishery
products
Human resource competency of rubber goods
Food safety for MSMEs
Application and remedial of ISO9001-2000 quality
management system
Quality improvement of rattan knitting
Technology and management of various chocolate
products
Managerial and technological aspects on coconut
processing
Quality improvement of clove oil
Curriculum (%)*
Process
Product Others
20
66
14
24
60
16
4
74
22
8
3
9
76
86
86
16
11
5
5
68
27
4
8
54
54
42
38
78
0
22
16
0
67
65
17
35
6
72
22
5
14
72
64
23
22
*Process: process improvement skills; Product: production processing skills; Others: general management, site visit
Source: Compiled by author from ‘Minutes of Meeting between Japan International Cooperation Agency and Ministry of
Industry on the In-Country Training Programme on Technology and Management Improvement for Small and Medium
Enterprises’ (November 2008, January 2009)
5. Summary and conclusions
This paper discussed critical factors for profit growth of low-technology SMEs in Indonesia.
As a result of linear regression estimation, operations on product development and unit
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1 - 2 April 2013, Bangkok, Thailand, ISBN: 978-1-922069-20-7
material cost reduction had statistically significant impact on profit growth. These policy
implications from the results can be inferred.
For SMEs with relatively low-technology, efforts to develop new products work positively
on profit growth. Such actions also induce improvement of product quality, followed by
modification of production processes that eventually lead to the reduction of unit raw
material costs. Batra and Mahmood (2003) conclude that technical assistance that targets
specific sectors is more effective than courses in general management. I also recommend
that support for low-technology SMEs be designed for introducing production technology
that encourages SMEs to develop products rather than mere consulting or skill training on
management. It is hard to organize product development and production technology
training for a specific industry by private and non-governmental support institutions due to
the lack of technical skills and facilities. At the same time, the cost of searching and
acquiring new technology is much higher for small firms (Biggs, 2002). Hence public
testing and research institutes should take a role in co-organizing such supports.
Production technology training that I used as a trigger to induce actions in our survey is
effective, but costly. The number of SMEs that can participate in this training is also
limited. In case public and private sectors do not have enough budget for organizing
numerous training opportunities, exhibitions exchanging technology information for lowtechnology firms could also work.
The population sampled in this survey consists of participants in training programs in
Indonesia. Therefore, the results are conditional on the extent to which the sample reflects
the characteristics of Indonesian SMEs. It is necessary to examine factors for profit growth
of SMEs in other countries. Furthermore, this paper applied a survey period of three
months to see changes based on the results of a pre-survey. Moreover, the majority of
sample firms actually made prompt actions and achieved results within a short period.
However, it is still necessary to prove the mechanism for decision making and actions by
SMEs within a short period, theoretically before other studies apply a similar survey
period. Thus, it remains for future research.
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