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IS THERE A DIFFERENCE PERFORMANCE BETWEEN INDUSTRY BASE
SMEs IN THE SARBAGITA BALI? : A COMPARATIVE APPROACH
Ni Nyoman Yuliarmi
1*
,
Ni Putu Martini Dewi
2
, Surya Dewi Rustariyuni
3
Department of Developmental Economics, Faculty of Economics and Business,
Udayana University, Bali, Indonesia
1,2,3
[email protected]
1
, [email protected].id
2
, [email protected]
3
PAPER INFO ABSTRACT
Received: 01-02-2023
Revised: 25-02-2023
Approved: 15-03-2023
This study seeks to: 1) analyze differences in social capital, human resources, and
financing sources of small and medium enterprises (SMEs) based on the industry
in the Sarbagita Area, Bali Province; 2) analyze performance differences among
SMEs based on the industry in the Sarbagita Area, Bali Province. By using a
location-stratified random sampling technique to determine the number of
samples and accidental sampling to determine respondents, this study used 203
SME business units. We used both primary and secondary data sources to
generate quantitative and qualitative data. Data was ge rated by non-behavior
observations, structured interviews, and in-depth interviews. The study then
analyzed the data using the ANOVA analysis. The results show that: 1) the
manufacturing and trade industries have different social capital; there is a
difference in human resources between the trade and service Industries; the
manufacturing and service industries have different financing sources, especially
for internal financing sources while other financing sources do not exhibit
different significant difference; 2) SMEs in the manufacturing and trade
industries have different performance. Based on the results, this study suggests
that: 1) SMEs need to rely on not only internal financing sources because external
financing is sufficiently available with affordable interest rates; 2) SME owners
need to enhance their networks with their fellow entrepreneurs or with their
suppliers to improve their business performance.
Keywords: Social Capital; Human Capital; Financing; and Performance.
INTRODUCTION
Small and Medium Enterprises (SMEs) contribute significantly to the Indonesian economy
because they absorb the labor force, create job opportunities, and survive during economic crises.
Further, SMEs in leading sectors contribute significantly to the economic sector by increasing
Gross Domestic Product (GDP), reducing unemployment and poverty rates, and promoting tourist
activities (Sitharam & Hoque, 2016). However, SMEs still suffer various problems, mainly
limited business and managerial skills, low-quality human resources, and limited financing
sources (especially from banks), limited access to information, and lack of innovation.
In this respect, SMEs can use external financing sources such as cooperatives, Village
Credit Institution (LPD-Lembaga Perkreditan Desa), state-owned and private banks, and even
friends or relatives. They can also combine internal financing sources with external ones.
However, entrepreneurs also often experience information asymmetry and moral hazard problems
in financing their businesses (Momtaz, 2021). Thus, SME owners need to initiate mutual trust to
reduce moral risk as a reflection of social capital. Social capital likely reduces concerns about
difficulties in accessing capital from financial institutions (Chua et al., 2011). Similarly, Bosse
(2009) holds that social capital is crucial in receiving loans. Further, personal, financial, and
relational factors are key variables to predict the dynamics of small firms’ growth (Kozan et al.,
2012).
Besides social capital, human capital, such as education level and age, can also affect
individuals’ likelihood to receive loans. Human capital, such as education and experience,
significantly affects firm performance in both the manufacturing and service Industries
Is There A Difference Performance Between Industry Base SMEs In The Sarbagita Bali?
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304
(Okafor, 2012). Meanwhile, Ha L. C. (2016) find that working experience is the most significant
predictor of firm performance.
Financial, human, and social capital enables firms to access financial resources (Atsan,
2016). Further, entrepreneurs’ social capital enhances financial, marketing, production, and
information access (Fornoni et al., 2012). Improved human capital significantly increases firm
performance (Al-Sharafat, 2017). Also, SMEs that develop more networks with financial
institutions are likely to have financing access (Kurniawan, 2014). Based on these arguments, the
research problem of this study is “ Are there differences in SMEs’ social capital, human resources,
financing sources, and performance based on industry type (manufacturing, trade, and service) in
the Sarbagita Area, Bali Province?”
Literature review
Social capital is information, trust, and mutual norms within individuals’ social networks
Woolcock in (Korte & Lin, 2013). Further, social capital also refers to trust, care for others, and
willingness to comply with existing norms of a certain community and to receive sanctions when
disobeying the norms according to Bowles and Grintis (2001) in (Arjona, 2017). Social capital
offers economic value for individuals (Engbers et al., 2017) and communities (Engbers et al.,
2017; Oh et al., 2014) who invest in it. Strong social networks facilitate entrepreneurial spirits
(Bouncken et al., 2018). Social capital refers to interpersonal resources that can be accessed by
individuals through strong and weak social networks (Beaudoin, 2011). Referring to (Renko,
Autio, & Tontti, 2002; Tsai, 2006), capital from networks, social norms, and trust is equally
important with financial and human capital in preserving the creation process of firm value, such
as organizations’ innovating performance.
Empirically find that SMEs in China exhibit the reciprocal relationship between the micro-
macro managerial values, social capital, and firm performance (Wu & Leung, 2005). In particular,
they measure social capital with trust and firm performance with overall performance and
improved competitiveness. Social capital as a social network also mediates the impact of
internationalization on SMEs’ performance. Hanka & Engbers (2017) establish that social capital
develops the economy. Fatoki (2011) empirically finds the significantly positive relationship
between social capital, human capital, and financial capital with SMEs’ performance (Fatoki,
2011). The findings are consistent with the human capital theory of Schultz (1961) and Becker
(1964) that argue that investments in human capital improve human performance. The results are
also consistent with Hisrich and Drnovsek (2002) who argue that experience and education
positively affect new firms’ performance (Hisrich & Drnovsek, 2002). Their results are consistent
with Ojokuku, R.M & Sajuyigbe, A.S. (2015) who observe that the human resource development
variable significantly affects SMEs’ performance. In a similar vein (Ojokuku & Sajuyigbe, 2015),
Tessema (2014) documents that human capital investments increase firm performance (Tessema,
2014). Meanwhile, Bartocho demonstrates that financial resources significantly affect
employees’ performance which in turn plays a key role in organizational performance (Jerotich
& Bartocho, 2016).
Empirically show that internal financing sources positively affect performance, while
external financing sources also positively affect performance, albeit insignificantly (Palacios et
al., 2016). Conclude that business financing sources such as commercial loans, retained earnings
financings, and trade financing significantly affect SMEs’ financial performance (Manini et al.,
2016). Indicate that financial resources (such as personal savings, and formal and informal
financing sources) significantly affect business performance (Oladele et al., 2014). Their
statistical results show that formal financing sources are the most significant independent
variables in explaining SMEs’ performance in Ado-Ekiti metropolitan city. Biney, C. (Biney,
2018) demonstrates that SMEs that receive venture capital financing exhibit better performance
in terms of sales and employee growth. Government-owned external financing sources (SFI) play
the main role in improving SMEs’ technical efficiency and export performance. However, only a
few Thai manufacturing SMEs actively seek external financing from these institutions. In this
respect, foreign commercial banks actively help improve SMEs’ technical efficiency.
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RESEARCH METHOD
The study used a comparative research design by using latent variables, namely SMEs’
performance, social capital, human capital, and financing sources. We used the Likert scale
that ranged from one to five and reflected respondents’ perceptions from strongly disagree to
strongly agree. This study was conducted in the Sarbagita Area, Bali Province which consists
of Denpasar City, Badung Regency, Gianyar Regency, and Tabanan Regency. We then used the
product-moment correlation to test the validity of our instrument. The correlation value above
0.3 indicates that the research instrument is valid. Meanwhile, the reliability test relies on the
internal consistency method (Cronbach’s Alpha value). Cronbach’s Alpha which is greater than
0.6 implies that the research instrument is reliable.
By using the location-stratified random sampling technique to determine the number of
samples and the accidental sampling to determine respondents, we generated 203 business units.
The study collected the data through non-behavior observations, structured interviews, and in-
depth interviews. We then quantitatively analyze the data by using descriptive statistics and
running the inferential analysis with the Anova analysis.
RESULTS AND DISCUSSION
As indicated by Table 1, the ANOVA results demonstrate the differences in social capital
from the norms, trust, and network indicators based on industry. Meanwhile, Table 2 displays the
results of the Post Hoc Test with the Tukey HSD method to identify which Industries exhibit
differences in the indicators of social capital. The detailed ANOVA and Post Hoc Test analysis will
be discussed in more in details the following parts.
Differences in Social Capital based on Industry
Our Anova analysis produces the F-test value of 3.659 (sig = 0.027 < 0.05). As shown by
Table 1, the Post Hoc Test with the Tukey HSD method produces the results suggest that there
are differences in social capital among SMEs in the manufacturing and trade Industries as
measured with the norms indicator. For the trust indicator, the ANOVA analysis suggests that
there are differences in social capital based on industry, as indicated by the F-test value of 2.754
(sig = 0.066< 0.010). The Post Hoc Test with the Tukey HSD method indicates that there are
differences in social capital between SMEs in the trade and service Industries for the trust
indicator (sig = 0.059 < 0.10) (Table 1).
Table 1
ANOVA Analysis – Differences in Social Capital (Norms, Trust, and Networks)
Based on Industry
ANOVA
Norms
Sum of Squares
Mean Square
Sig.
Between Groups
2.142
1.071
.027
Within GrouPs
58.545
.293
Total
60.687
1) Trust
Sum of Squares
Mean Square
Sig.
Between Groups
1.012
.506
.066
Within Groups
36.747
.184
Total
37.759
Networks
Sum of Squares
Mean Square
Sig.
Between Groups
.033
.017
.902
Within Groups
32.492
.162
Total
32.525
However, for the networks indicator, our ANOVA analysis suggests that SMEs in the three
Industries do not exhibit significant differences in social capital, as indicated by the F-test value
of 0.103 (sig=0.902>0.05) (Table 1). The Post Hoc Test documents that for the networks
indicator, SMEs in these three Industries do not exhibit statistically significant differences in
Is There A Difference Performance Between Industry Base SMEs In The Sarbagita Bali?
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306
social capital (significance value is higher than 0.05) (Table 2).
The processing and trade business sectors differ in social capital norms in terms of
respondents’ perceived appreciation of the instruments used. We use two instruments to measure
norms, namely hard work, and honesty. Both processing and trade business sectors consider
hardworks and honesty norms as the indicators of social capital very important, as indicated by
their perceived appreciation values rrangingbetween four (agree) to five (fully agree) on the
statement that hard works and honesty are very important norms. Processing firms interact not
only with their raw material suppliers but also with their customers and peers. Meanwhile, trading
firms mostly interact with their suppliers and distributors.
Table 2
The Post Hoc Test (Tukey HSD) of the Differences in Social Capital (Norms, Trust, and
Networks) Based on Industry
Multiple Comparisons
Dependent Variable: Norms
Tukey HSD
(I) Industry
(J) Industry
Mean Difference (I-J)
Std. Error
Sig.
95% Confidence Interval
Lower Bound
Upper Bound
Manufacturing
Trade
.25196
*
.09315
.020
.0320
.4719
Service
.13337
.09534
.343
-.0918
.3585
Trade
Manufacturing
-.25196
*
.09315
.020
-.4719
-.0320
Service
-.11860
.09124
.397
-.3340
.0968
Service
Manufacturing
-.13337
.09534
.343
-.3585
.0918
Trade
.11860
.09124
.397
-.0968
.3340
Dependent Variable: Trust
Tukey HSD
(I) Industry
(J) Industry
Mean Difference (I-J)
Std. Error
Sig.
95% Confidence Interval
Lower Bound
Upper Bound
Manufacturing
Trade
-.11051
.07380
.294
-.2848
.0638
Service
.05549
.07554
.743
-.1229
.2339
Trade
Manufacturing
.11051
.07380
.294
-.0638
.2848
Service
.16599
.07229
.059
-.0047
.3367
Service
Manufacturing
-.05549
.07554
.743
-.2339
.1229
Trade
-.16599
.07229
.059
-.3367
.0047
Dependent Variable: Networks
Tukey HSD
(I) Industry
(J) Industry
Mean Difference (I-J)
Std. Error
Sig.
95% Confidence Interval
Lower Bound
Upper Bound
Manufacturing
Trade
.02757
.06940
.917
-.1363
.1914
Service
.00181
.07103
1.000
-.1659
.1695
Trade
Manufacturing
-.02757
.06940
.917
-.1914
.1363
Service
-.02577
.06797
.924
-.1863
.1347
Service
Manufacturing
-.00181
.07103
1.000
-.1695
.1659
Trade
.02577
.06797
.924
-.1347
.1863
The processing and trading firm respondents differ in their appreciation of norms. In this
respect, 34% of the processing firm respondents fully agree that hard works are crucial in running
the business, whereas 58% of the trading firm respondents fully agree with this statement.
Meanwhile, 56% of the processing firm respondents and 59% of the trading firm respondent fully
agree that the honesty norm is crucial in business. The emphasis on hard work and honesty
instruments of the norms leads to different norms between the processing and trading firms. The
trade and service firm respondents also differ in their appreciation of the trust indicator of social
capital because they have a different emphasis on trust. Further analysis suggests the different
descriptive values of the instruments used. We use two instruments to measure trust, namely
protecting product quality and preserving trust from transacting partners. Respondents are
considered to appreciate the trust indicator of social capital if they agree or fully agree with the
instruments. About 50% and 46% of the trading firm and service firm respondents fully agree on
the importance of protecting product quality. In a similar vein, 60% and 49% of the trading firm
Is There A Difference Performance Between Industry Base SMEs In The Sarbagita Bali?
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and service firm respondents fully agree on the importance of preserving trust from transacting
partners. Next, we analyzed differences in social capital from the three indicators (norms, trust,
and networks) based on industry. As displayed by Table 3, the results inform that there are
statistical differences in social capital based on industry as indicated by the F-test value of 2.483
(sig.= 0.086<0.10).
Table 3
ANOVA Analysis – Differences in Social Capital Based on Industry
ANOVA
Social Capital
Sum of Squares
Df
Mean Square
F
Between Groups
.662
2
.331
2.483
Within Groups
26.670
200
.133
Total
27.333
202
As displayed in Table 4, we then ran the Post Hoc Test with the Tukey HSD method to
identify further differences in social capital based on industry. The analysis results in a
significance value of 0.070 < 0.10.
Table 4
The Post Hoc Test (Tukey HSD) of the Differences in Social Capital Based on Industry
Multiple Comparisons
Dependent Variable: Modal Sosial
Tukey HSD
(I) Industry
(J) Industry
Mean
Difference (I-J)
Std. Error
Sig.
95% Confidence Interval
Lower Bound
Upper Bound
Manufacturing
Trade
.13977
.06287
.070
-.0087
.2882
Service
.06759
.06435
.546
-.0844
.2195
Trade
Manufacturing
-.13977
.06287
.070
-.2882
.0087
Service
-.07218
.06158
.471
-.2176
.0732
Service
Manufacturing
-.06759
.06435
.546
-.2195
.0844
Trade
.07218
.06158
.471
-.0732
.2176
Differences in Human Resources Based on Industry
From the human resources perspective, the analysis shows that there are differences in
human resources based on industry (see Table 5), as indicated by the F-test value of 2.626 (sig.
= 0.075<0.10).
Table 5
ANOVA Analysis – Differences in Human Resources Based on Industry
ANOVA
Human Resources
Sum of Squares
Mean Square
F
Sig.
Between Groups
.986
.493
2.626
.075
Within Groups
37.531
.188
Total
38.517
Further, the Post Hoc Test with the Tukey HSD method demonstrates that there are differences
in human resources between the trade and service industries (sig. = 0.068<0.10) (Table 6).
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Table 6
The Post Hoc Test (Tukey HSD) of the Differences in Human Resources Based on
Industry
Multiple Comparisons
Dependent Variable: SDM
Tukey HSD
(I) Industry
(J) Industry
Mean Difference
(I-J)
Std. Error
Sig.
95% Confidence Interval
Lower Bound
Upper Bound
Manufacturing
Trade
-.11116
.07458
.298
-.2873
.0650
Service
.05208
.07634
.774
-.1282
.2323
Trade
Manufacturing
.11116
.07458
.298
-.0650
.2873
Service
.16324
.07305
.068
-.0093
.3357
Service
Manufacturing
-.05208
.07634
.774
-.2323
.1282
Trade
-.16324
.07305
.068
-.3357
.0093
Differences in Financing Sources Based on Industry
SMEs in manufacturing, trade, and service industries exhibit varying financing sources,
such as LPD, state-owned banks, private banks, cooperatives, and friends/ relatives. The ANOVA
analysis finds that there are differences in financing sources based on the industry with the F-test
value of 2.540 (sig. 0.081<0.10) (Table 7).
Table 7
ANOVA Analysis – Differences in Financing Sources Based on Industry
ANOVA
Financing Sources
Sum of Squares
Df
Mean Square
F
Between Groups
5.056
2
2.528
2.540
Within Groups
199.042
200
.995
Total
204.099
202
Further analysis with the Post Hoc Test with the Tukey HSD method shows that there are
differences in financing sources between SMEs in the manufacturing and trade industries (sig.
0.095< 0.10) (Table 8).
Table 8
The Post Hoc Test (Tukey HSD) of the Differences in Financing Sources Based on
Industry
Multiple Comparisons
Dependent Variable: Financing Sources
Tukey HSD
(I) Industry
(J) Industry
Mean Difference
(I-J)
Std. Error
Sig.
95% Confidence Interval
Lower Bound
Upper Bound
Manufacturing
Trade
-.35833
.17176
.095
-.7639
.0472
Service
-.06789
.17580
.921
-.4830
.3472
Trade
Manufacturing
.35833
.17176
.095
-.0472
.7639
Service
.29044
.16823
.198
-.1068
.6877
Service
Manufacturing
.06789
.17580
.921
-.3472
.4830
Trade
-.29044
.16823
.198
-.6877
.1068
Differences in Performance Based on Industry
We measured business performance with the instruments of business innovation, sales, and
the ability to preserve customer loyalty. Our ANOVA analysis shows that there are differences
in business performance based on industry as indicated by the F-test value of 3.659 (sig. 0.027<
0.05) (Table 9).
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309 Return: Study of Management, Economic and Bussines, Vol. 2(3), March 2023
Table 9
ANOVA Analysis – Differences in Performance Based on Industry
ANOVA
Performance
Sum of Squares
Df
Mean Square
F
Between Groups
2.142
2
1.071
3.659
Within Groups
58.545
200
.293
Total
60.687
202
The Post Hoc Test with the Tukey HSD suggests that there are differences in performance
between SMEs in the manufacturing and trade industries (sig. 0.020 < 0.05) (Table 4.10).
Table 10
The Post Hoc Test (Tukey HSD) of the Differences in Performance Based on Industry
Multiple Comparisons
Dependent Variable: Performance
Tukey HSD
(I) Industry
(J) Industry
Mean
Difference (I-J)
Std. Error
Sig.
95% Confidence Interval
Lower Bound
Upper Bound
Manufacturing
Trade
.25196
*
.09315
.020
.0320
.4719
Service
.13337
.09534
.343
-.0918
.3585
Trade
Manufacturing
-.25196
*
.09315
.020
-.4719
-.0320
Service
-.11860
.09124
.397
-.3340
.0968
Service
Manufacturing
-.13337
.09534
.343
-.3585
.0918
Trade
.11860
.09124
.397
-.0968
.3340
*. The mean difference is significant at the 0.05 level.
We measure business performance with three indicators, namely enhancing innovation,
increasing sales, and maintaining customers. The analysis demonstrates that only about 4% of
MSME owners do not significantly enhance their innovation. Next, only about 19% of the
respondents indicate no significant increase in sales volume. Further, about 8% of the respondents
mention that they maintain their existing customers moderately well, and 92% of the respondents
consider maintaining good relationships with existing customers crucial. Hence, it is crucial to
maintain existing customers to buy products from the respondents. By focusing on these three
indicators of MSMEs’ business performance, the respondents from the processing, trade, and
service industries will potentially preserve their business performance. Although these three
business sectors (processing, trade, and service) exhibit statistical differences in business
performance, respondents from these three business sectors understand that preserving these
indicators of business performance is very important.
CONCLUSION
To answer the research objectives, the study concludes the following: 1) there are
differences in social capital between SMEs in the manufacturing and trade Industries; there are
differences in human resources between SMEs in the trade and service Industries; there are
differences in financing sources between SMEs in the manufacturing and service industries in
terms of internal financing sources, while there are no differences for other financing sources; 2)
SMEs in the manufacturing and trade industries exhibit different performance.
Although a quantitative approach through Anova analysis has been able to answer the
objectives of the problem, a qualitative in-depth analysis should be able to better explain the
differences in performance that occur. These limitations can later be used as new ideas in
developing this research further. In addition, including social variables such as culture and
customs will enrich the results of this study further.
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