Secondly, there should be some policies to strengthen links between
businesses and partners, especially universities in training human resources and
cooperating technology innovation. Businesses need to be aware of the need to
associate and coordinate with partners in product development. The role of
research institutions and research universities need to be emphasized because this
is a high concentration of scientific content. Firms should actively seek, contact
these partners for agreement and cooperation; At the same time, building a good
and long-term cooperative relationship between businesses and organizations.
The coordination between the actors in the innovation system including
universities and businesses play an important role, so it is necessary to have
policies to promote cooperation between universities and firms together.
Government levels play an intermediary role in connecting universities and
businesses by regularly organizing technology fairs, conferences and seminars
with the participation of universities and businesses to serve more effectively the
demand for technology supply and demand
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this field such as Drucker (1985);
Eveleens (2010).
2.1.2. Determinant of innovation research
There are evidence in literature proved that innovation might be affected by
both internal and external factors namely Avermaete et al. (2004); Hussen and
Çokgezen (2019); Lim (2017).
Research on innovation could also be categorized in term of sources for
innovation, especially innovation idea. Some researchers believed that
innovation could only generated by the firms themselves or they follow
resources-based view (RBV). However, there are also other line of the literature
believed in knowledge-based view (KBV) when they study innovation.
Table 2.1: List of main innovation research categorized by theory
Follow resources-based view
(RBV)
Knowledge-based view
(KBV)
Research Barney (1991); Bates and
Flynn (1995); Tarafdar and
Gordon (2007); Terziovski
(2010); Bakar and Ahmad
(2010)
Cohen & Levinthal (1990);
Grant (1996b); Nonaka and
Takeuchi (1995); Nonaka
and Peltokorpi (2006); Lu et
al. (2008); Quintane et al
(2011); De Castro et al.
(2011); Isaksson et al. (2016)
Source: Collected by the author
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2.1.3. Product innovation research
Studies on product innovation seems to be the most popular in literature as
it started very early and various of them available on academic sources (Bakar &
Ahmad, 2010; Barasa, Knoben, Vermeulen, Kimuyu, & Kinyanjui, 2017;
Chakrabarti, 1974; Cooper & Kleinschmidt, 1986).
2.2. Research on Innovation in Vietnam
Studies on innovation in Vietnam starts from early 2000s, however, most of
the studies is about innovation in agriculture field (Chairatana & Sinh, 2003;
Martin, Castella, Anh, Eguienta, & Hieu, 2004; Spielman & Kelemework, 2009;
Van Linh, 2001). The reason behinds it might be because Vietnam had just
opened its economy and agriculture still played the main role.
Recent years, innovation is getting much attention in macro level and other
fields as well. Some examples are: N. N. Anh et al. (2011); R.-J. Lin et al. (2013);
N. N. Thang, Quang, and Son (2013); P. T. T. Anh (2014); Tuan et al. (2016);
Voeten (2016); Vân et al. (2018); D. K. Nguyen, Phong, and Hui (2019); Son,
Cung, Thang, and Phong (2019)
CHAPTER 3: THEORY FRAMEWORK
3.1. Definition of Innovation
3.2. Types of Innovation
Radical innovation
Incremental innovation
Product innovation
Process Innovation
Marketing innovation
Organizational innovation
3.3. Determinants of Innovation
3.3.1. Following Resources Based view theory
3.3.2. Following Knowledge based view theory
3.3.3. Conclusion
Although the RBV struggles that a firm’s inner resources are necessary in
nourishing competitive advantage. Giving a brief review of the relevant
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academic literature we can see an expanding trend on resource application
research that recommend value can solitary be obtained from resources by
using them in a cleverer mode than the rivals (Barasa et al., 2017). Hence,
knowing what information available from outside and learning from that seems
to be a major influence on firm innovative outcome.
This is also in line with KBV that the role of knowledge becomes more and
more essential. Knowledge is believed to be a at most important resource of firms
that could influence firm accomplishment (Argote et al., 2000, Oerlemans and
Knoben, 2010, Agarwal and Shah, 2014). Current studies also concentrate on the
effect of knowledge at nation level and highlight that firms could lessen poverty
by leading to the knowledge based-economy (Cooke, 2001, Godin, 2006, Lehrer,
2018). In addition, in Vietnam, right from the 90s of the twentieth century, the
Communist Party of Vietnam determined: "The revolution of modern science and
technology is taking place strongly, attracting all different countries". Most
recently, the Party has determined to associate industrialization and
modernization with the development of the knowledge-based economy:
"Promote industrialization and modernization with the development of the
knowledge-based economy and environmental protection." (Document of the
11th National Congress of the Communist Party of Vietnam), in which the Party
affirms, scientific and technological development is really a fundamental driver
of the process of rapid and sustainable development. Thus, it can be seen from
the beginning, our Party attaches great importance to creating motivation for the
formation and development of the knowledge economy.
The development of the knowledge-based economy in Vietnam is a major
strategic change: transforming the economy from resource-based to primarily
based on human knowledge and creative capacity. With available resources,
using new knowledge and new technologies to make more, better and more
efficient. Natural resources are restricted, the capacity to create of human being
is limitless.
Therefore, the author would also want to test the relationship between
knowledge sources and innovation in the context of Vietnam to see if it is in line
with previous studies.
8
3.4. Knowledge sources
A number of authors have emphasized the vital role of knowledge in
building and sustaining innovation (Schulze and Hoegl, 2008, Quintane et al.,
2011, Leonard-Barton, 1995). In addition, innovation is defined as a knowledge-
based commodity. Hence, firms need to have knowledge to innovate and thus to
profit from innovation (Lundvall, 1992, Lundvall, 1988). Therefore, this study
also focuses on the impact of knowledge on innovation. In doing so, it uses three
different sources of knowledge: internal knowledge sources, collaborative
knowledge sources and regional knowledge sources.
3.4.1. Internal Knowledge Sources
3.4.2. Collaborative Knowledge Sources
3.4.3. Regional Knowledge Sources
3.5. Hypotheses
As explained above, different sources of knowledge can have a different
effect on firm-level innovation. Following this line of thought, the author
hypothesizes that in a transition country like Vietnam, with a weak innovation
system, knowledge even plays a more vital role compared to advanced
economies. Below, the author develop hypotheses that link the different
knowledge sources to innovation.
3.5.1. Internal knowledge sources
Hypothesis 1a: The stronger a firm’s internal R&D, the higher the likelihood
that that firm produces a product innovation.
Hypothesis 1b: The longer time the top manager of a firm working in this
sector, the higher the likelihood that that firm produces a product innovation.
3.5.2. Collaborative knowledge sources
Hypothesis 2a: The stronger a firm’s collaborative knowledge gained from
inside the supply chain, the higher the likelihood that that firm produces product
innovation.
Hypothesis 2b: The stronger a firms’ collaborative knowledge gained from
outside the supply chain, the higher the likelihood that that firm produces a
product innovation.
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3.5.3. Regional knowledge sources
Hypothesis 3a: The stronger the knowledge base of the region a firm is
located in the higher the likelihood that that firm produces a product innovation
Hypothesis 3b: The higher the population of the region a firm is located in
the higher the likelihood that that firm produces a product innovation
3.6. Research model
Figure 3.1: Research model with all the variables
Source: The author composed and designed
CHAPTER 4: METHODOLOGY AND DATA COLLECTION
4.1. Research context
Although Vietnam has grown astonishingly in the past three decades, the
transition to a flourishing and modern economy has only just begun. Vietnam has
per capita income still accounting for only a little part of the global average, the
country is attempting to maintain its fast-growing path and follow the direction
of other successful East Asian economies to join the ranks of high-middle income
countries for the past half-decade. Even though Vietnam has every potential to
Internal Knowledge sources
- Internal R& D
- Managerial Experience
Collaborative Knowledge Sources
- Knowledge from inside the supply chain
(from customers, competitors, suppliers)
- Knowledge from outside the supply chain
(Universities, Research institutes)
Regional Knowledge Sources
- Knowledge benefit from the position of the
firm even when firms do not enter in any
collaboration
Innovation outcome
- Product innovation
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achieve this goal, victory is not by itself. The population is aging rapidly, which
moderate labor productivity. Moreover, slow investment growth is weighing on
Vietnam's medium-term growth potential. How to cope with the resistance of
domestic structures? According to the political report of the 10th Central
Committee of the Party at the 11th National Congress of the Party, Vietnam needs
to steer the external environment changing, where global trade structures are
shifting, breakthrough technology, rapid innovation, and Climate change is
shaping opportunities and creating new risks for the country.
Whether Vietnam can continue to maintain previous growth or not, this is
extremely important. Although growth is not the goal that the government are
aiming for, growth is a necessary condition for the wider development. It is the
basis needed for creating jobs, alleviating poverty and mobilizing resources to
invest in health, education and other social goals. Vietnam have some advantages
that we should not forget which are a geographic location, an open economy, a
young and largely labor force in rural areas, and a high level of domestic savings.
Therefore, the country has conditions for high and sustainable growth.
Nonetheless, in order to take advantage of these fundamentals, the government
needs to regularly focus on policy and institutional reforms targeted at creating
increased productivity, effective investment in human capital and physical
capital, sustainable and efficient use of natural resources.
Recently, the big trends are global transforming forces, shaping the future
world by profound influence on businesses, society, economy, culture and human
life. Hence, Vietnam should be able to identify success, analyze and has action
to face those major trends to build a long-term dynamic development strategy
that contributes to successful economic development due to the fact that Vietnam
is likely to be the country most affected by major global trends.
Vietnam is one of the most open countries in the world, with trade to GDP
ratio reaching nearly 200% and FDI inflow accounting for about 7% of GDP
in 2018 (GSO, 2019). Regarding geopolitical position and trade structure,
Vietnam may be more vulnerable to climate change and US-China trade
tensions. Therefore, The Central Theoretical Council (2019) suggest four
major global trends that Vietnam needs to consider in the next two decades
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which are population movement, disruptive technology, the rise of China and
climate change.
We could see the vital role of innovation especially breakthrough technology
which could be defined as emerging technologies that cause a change in costs or
access to products or services, or quickly change the way we collect information,
produce or interact operative (Lientz and Rea, 2016) . Vietnam are currently in the
midst of the 4th industrial revolution, characterized by rapid technological diffusion,
multi-technology convergence, and emergence of global platforms. Breakthrough
technology is often based on technology and digital products, but they can go beyond
the connection and potential of the internet. This includes modern production
methods such as robotics, artificial intelligence, and the internet of things. They also
include advances in nanotechnology and biotechnology - and new production lines
such as batteries, drones, solar panels, self-driving cars, and exotic materials (Lientz
and Rea, 2016).
The vast opportunities will be associated with innovation and technological
breakthroughs that contribute to expanding access to global markets, creating
new goods and services, transforming production processes, changing business
models, and make a significant impact on development.
The economic and social transformation that innovation and especially
breakthrough technology could bring is that they could accelerate development
of a country. If a country cannot compete in the future global economy, they will
be left behind. Hence, to take advantage of the potential of new business models,
ways of providing services, and shifting the source of competitiveness, Vietnam
need to have a multi-sector and multi-pronged approach to increase opportunities
especially foster innovation and technology for the country.
Assessing innovation policy in Vietnam
Even though the Government has had several preferential policies, typically
the National Technological Innovation Fund established under the Decision
1342/QD-TTg dated 5/5 8/2013 has the function of preferential loans, loan interest
support, capital support for enterprises transferring technology research and
innovation. However, the sponsorship of such policies and funds for technology
innovation is still limited. Research results of Luong Minh Huan and Nguyen Thi
12
Thuy Duong (2016) show that enterprises rarely access capital from the state budget
to implement technology improvement activities, despite the chapter’s government
funds and funds for this issue. In addition, the government development fund for
enterprises require that local authorities must have reciprocal capital while the
locality has no reciprocal capital, so there is no fund. This makes the policy seems
not to be useful as it should be.
4.2. Methodology approach
Descriptive approach aims to examine and screen variables in the original
research model. While econometric approach can verify the logic and rules of
observations in the study, helping verify the hypotheses mentioned above.
To measure the dependent variable, the author uses a dummy variable that
takes the value of “1” if a firm has introduced any new or significantly improved
innovative product and “0” if otherwise. Hence, a binary logistic regression
model is chosen for analysing the data.
4.2.1. Logistic regression
4.2.2. Innovation research used logit regression
4.3. Data collection
The data used in this study is from two main sources: (1) The World Bank
Enterprise Survey (ES) conducted between November 2014 and April 2016 and
(2) the Innovation Capabilities Survey (ICS) conducted from October 2016 to
February 2017. The ES is an ongoing project covering over 155,000 firms in 148
countries, collecting data based on firms’ experiences and enterprises’ perception
of the business environment and investment climate. This firm-level survey
comprises non-agricultural formal, private-sector firms. The ICS in this study is
a follow-up and complementary to the ES. Respondents are randomly selected
from the ES sample. For Vietnam, 300 manufacturing firms have been included
in the sample. The ICS focuses on innovative activities and innovative
capabilities of manufacturing firms. The standardized questionnaires have been
translated into local languages and back translated into English to check its
accuracy.
The World Bank uses stratified random sampling as the sampling
methodology, which means that all population units are grouped within
13
homogeneous groups and simple random samples are selected within each group.
This method helps to obtain unbiased estimates from different subdivisions of the
population with some known level of precision as well as obtain unbiased
estimates for the whole population. In most cases, stratified sampling is more
precise and may produce a smaller bound on the error of estimation than using a
simple sampling method. The strata for the surveys are firm size, business sector
and geographic region within a country.
The data for this study is merged from the most recent version of the ES and
the ICS conducted in Vietnam. Unsurprisingly, the data contain missing
observations, hence our analyses will use fewer observations than the full sample.
The data is analyzed by using logit and tobit models. Logit models will be
applied when the dependent variable is binary (process innovation present
yes/no) whereas tobit models will be utilized for censored dependent variables
(percentage of sales from new products).
4.4. Variables
Table 4.1: Variable measurement
Variable Measurement Source Questio
n No
Reference
Innovation
(Avermaet
e et al.,
2004);
(Baumann
and
Kritikos,
2016)
Product
innovation
Firm introduced any new
product or service: "1" Yes
"0" No
ICS
H3a,
H3b,
H3c
Internal
Knowledge
sources
(Díaz et al.,
2016)
Manager
experience
Top manager’s number of
working experience year in
ES B7
14
this sector
Internal R&D
Dummy variable: "1" Yes "0"
No
ICS B01
Collaborative
knowledge
sources
(Caloghiro
u et al.,
2004)
Inside the supply
chain
Innovation developed with
competitors, customers, and
supplier: "1" if b1b is Yes, 2
if b1b and b1c or b1j is Yes
and “3” if all b1b, b1c and
b1j are Yes, "0" none of the
three is “Yes”
ICS
Competitors Information or ideas from
competitors: "1" Yes "0" No ICS B1b
Suppliers Information or ideas from
suppliers: "1" Yes "0" No ICS B1c
Customers
Information or ideas from
customers’ feedback: "1" Yes
"0" No
ICS B1j
Outside the
supply chain
Information or ideas from
universities and research
institutes: "1" Yes "0" No
ICS B1e
Regional
Knowledge
sources
(Barasa et
al., 2017)
Regional R&D
(log)
% of firms conducting
internal R&D within a region
using mean of the internal
R&D over the 4 regions in
Vietnam
ICS B1
Firm location
City with population of less
than 50.000 equal “1”
City with population from
ES
A3
15
50.000 to less than 250.000
equal “2”
City with population from
250.000 to less than 1 million
equal “3”
City with population over 1
million equal "4"
Control variables
Age
Number of years since
establishment ES B5
Size Number of permanent, full
time employees
ES L1
Source: The author composed and designed
CHAPTER 5: RESEARCH RESULTS
5.1. General description of the ES and ICS sample
5.1.1. Distribution of firms by sector and region
5.1.2 Descriptive statistics
5.1.3. Innovation
5.1.3.1. Product innovation
5.1.3.2. Innovation activities
5.1.3.3. Sources of information for innovation
5.1.3.4. Barriers to innovation
5.2. Descriptive statistics of the sample merged from ES and ICS
5.3. Knowledge Sources and Product Innovation
A binary logistic regression model is used for the hypotheses. Model 1 is a
baseline model, in which the author included only control variables to evaluate the
independent variables explanatory value. The author added internal knowledge
sources in Model 2. Model 3 tests the effect of collaborative knowledge sources.
Model 4 includes the regional knowledge sources. Model 5 assesses the effects of all
independent variables simultaneously. Given that the AIC/BIC indicate that model 5
16
has the best model fit, the results is mainly interpreted based on this model. Table 5.1
and 5.2 report all the results of the models. In order to check for multicollinearity, it
is required to calculate VIFs. The mean of VIF is 1.26, which is well below 10 as are
all individual VIFs. As such, multicollinearity is not an issue in this study’s data.
Our results show that the control variables (firm age and size) have no
significant association with firms’ likelihood to innovate. With regard to the
direct effect of internal knowledge sources on innovation, this study finds that
internal R&D is positively and statistically significant correlated with firm
innovation. Hypothesis 1a is supported: a firm’s likelihood to innovate increases
when there is an increase in internal R&D. On the other hand, for hypothesis 1b
the result is positive, but not significant. Therefore, the study could not say if
managerial experience of firms in Vietnam has any relationship with innovation.
With regard to the relationship between collaborative knowledge sources
and product innovation the results confirm that a firm’s collaborative knowledge
gained from inside the supply chain (customers, suppliers, competitors) is
positively related to with product innovation of that firm. On the other hand, it
shows no significant relationship between collaboration with universities or
research institutes and innovation. Khanna and Palepu (2005) mention that in
developed economies, firms can rely on a variety of institutions to minimize
market failure, while firms in emerging markets are, for example, confronted
with institutional voids, i.e. weak linkages between firms and universities and/or
research institutes. As such, hypothesis 2a is strongly supported, while there is
no support for hypothesis 2b.
Table 5.1: Logistic Regression Results of each individual knowledge
source models
Model 2 Model 3 Model 4
DV= Product
Innovation == 1
DV= Product
Innovation == 1
DV= Product
Innovation == 1
B SE P>|z| B SE P>|z| B SE P>|z|
Age (log) -0.05 0.23 0.83 -0.00 0.31 0.99 0.04 0.24 0.86
Size (log) -0.11 0.17 0.49 -0.11 0.16 0.50 -0.02 0.14 0.91
Manager 0.02 0.02 0.42
17
experience
Internal R&D 2.06*** 0.24 0.00
Inside supply
chain knowledge 1.04*** 0.30 0.00
Outside supply
chain knowledge
0.22 0.21 0.30
Regional R&D 0.01 0.02 0.64
Firm Location 0.13 0.09 0.13
Constant -0.64 0.75 0.40 -
1.71***
0.32 0.00 -1.21 1.09 0.27
Observations 284.00 284.00 284.00
Prob> Chi2 0.00 0.00 0.00
Pseudo R-Square 0.09 0.26 0.00
AIC 344.77 281.04 377.82
BIC 355.72 291.98 388.77
For regional knowledge sources the study finds no significant relationship
between regional R&D, product innovation. Hence, the author could not accept
hypothesis 3a. However, the location of firms is significant positively correlated
with product innovation. Firms in a city with larger populations are likely to
produce more product innovation than their counterparts in less crowded cities.
That might be explained by the fact that in big cities more facilities and
infrastructure are available for firms to utilize. Moreover, in densely populated
cities, firms might be able to find more suitable personnel who bring new
knowledge (Glaeser and Mare, 2001). As such, hypothesis 3b is accepted.
Table 5.2: Logistic Regression Results of the all independent variables
simultaneously model
Model 5
DV= Product Innovation ==
1
B SE P>|z|
Age (log) -0.09 0.25 0.73
Size (log) -0.13 0.19 0.50
18
Manager experience 0.03 0.02 0.20
Internal R&D 1.46*** 0.21 0.00
Inside supply chain
knowledge
1.09*** 0.34 0.00
Outside supply chain
knowledge
0.03 0.39 0.94
Regional R&D -0.03 0.03 0.26
Firm Location 0.27* 0.15 0.07
Constant -2.63*** 0.32 0.00
Observations 284.00
Prob> Chi2 0.00
Pseudo R-Square 0.31
AIC 261.56
BIC 272.51
As mentioned in our theoretical background several studies from the open
innovation literature, suggest that specific sources of knowledge might be more
relevant than others and that over-search of external knowledge might be
detrimental to innovation (Laursen and Salter, 2006, Bayona-Saez et al., 2017,
Hung and Chou, 2013). To probe the existence of such effects in our setting, the
author performed two additional explorative analyses. Specifically, the author
checked the impact of the knowledge sources separately and tested for an inverted
U-shape effect. The results of these analyses are reported in Table 5.15. With
regard to the former, this study found that only knowledge from customers is
positively and significantly associated with product innovation. This is in line
with the study of Doloreux and Lord-Tarte (2013), which emphasizes that the
tastes of customers are paramount and customer ideas are a highly important
source of information for product development. With regard to the latter, the
results in Table 5.3, which are plotted in Figure 5.1, demonstrate an inverted U-
shape relationship between knowledge from inside the supply chain and product
innovation. Moreover, the author tested whether the point estimate for the highest
value of knowledge use differs significantly from the second highest score and
find a significant different (p=0.003). This indicates that the downward sloping
19
part of the inverted U-shape is statistically significant and that this study truly
finds negative relationship
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