Financial Constraint on R&D Activities in Vietnamese Universities – an Empirical Research

Abstract

R&D is one of the most important roles of universities. Many previous studies examined the impact of financial factor on university R&D activities but reached no consensus view. This article contributes to the current literature by exploring how financial factor and other factors influence R&D activities in Vietnamese universities. The author employed a survey dataset from the Association of Vietnam universities and colleges to check whether unfavourable financial condition hindered university R&D activities. Using structural equation modelling, the author found empirical evidence that financial constraint could hamper R&D productivity. On the other hand, favourable conditions in management, communication, infrastructure and human resources were found to improve R&D activities. This led to some policy suggestions to improve R&D activities in Vietnam higher education institutions.

Full text

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Organizations and Markets in Emerging Economies ISSN 2029-4581 eISSN 2345-0037
2020, vol. 11, no. 1(21), pp. 222–243 DOI: hps://doi.org/10.15388/omee.2020.11.32
Financial Constraint on R&D Activities
in Vietnamese Universities – an Empirical
Research
Nguyen Dang TUE
Hanoi University of Science and Technology, Vietnam
tue.nguyendang@hust.edu.vn; nguyendangtue@gmail.com
hps://orcid.org/0000-0002-8640-6428
Abstract. R&D is one of the most important roles of universities. Many previous studies examined
the impact of nancial factor on university R&D activities but reached no consensus view. is article
contributes to the current literature by exploring how nancial factor and other factors inuence R&D
activities in Vietnamese universities. e author employed a survey dataset om the Association of
Vietnam universities and colleges to check whether unfavourable nancial condition hindered university
R&D activities. Using structural equation modelling, the author found empirical evidence that nancial
constraint could hamper R&D productivity. On the other hand, favourable conditions in management,
communication, inastructure and human resources were found to improve R&D activities. is led to
some policy suggestions to improve R&D activities in Vietnam higher education institutions.
Keywords: nancial constraint; barriers; R&D; university; SEM
1. Background
Universities play many important roles in the modern society. Universities can function
as communities dedicated to learning and personal development, sources of expertise
and vocational identity or sites for knowledge evaluation and application (Vallance,
2016). Among these roles, university’s performing research and development (R&D)
to evaluate and apply new knowledge is one of the most essential (Watson et al., 2011).
According to OECD (2015), research and development (R&D) consists of creative
and systematic work undertaken to increase the knowledge stock and devise new ap-
plications of available knowledge. University’s R&D activities help discover, explicate
and assess new knowledge, ideas, and technologies. Knowledge generated by R&D is
Received: 14/4/2019. Accepted: 17/3/2020
Copyright © 2020 Nguyen Dang TUE. Published by Vilnius University Press. is is an Open Access article distributed un-
der the terms of the Creative Commons Aribution Licence, which permits unrestricted use, distribution, and reproduction
in any medium, provided the original author and source are credited.
Contents lists available at Vilnius University Press

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Financial Constraint on R&D Activities in Vietnamese Universities – an Empirical Research
the basis of sustainable growth (Gibbs, 2009). University’s R&D activities also foster
professional excellence, which is vital for beer higher education and training. Publi-
cations and intellectual properties from R&D activities not only strengthen a universi-
ty’s academic reputation but also promote its industry involvement. Indeed, previous
studies found consistent positive relationship between universities’ R&D result and
their commercialization activities. For example, Perkmann et al. (2011) used a dataset
covering all UK universities and found that in technology-oriented disciplines, depart-
mental faculty research results positively related to industry involvement. e higher
rank a department was in terms of research quality, the more likely its members would
get involved in industry collaboration. Likewise, Manseld (1995) and Balconi and
Laboranti (2006) showed that industry involvement was strongly complementary with
excellent scientic research in technology-oriented disciplines.
Despite its importance, there was no rm conclusion about which factors can inu-
ence university R&D activities. Especially, previous lines of research did not explicitly
examine nancial constraint as a barrier for R&D activities.
In Vietnam, academic research is still largely undertaken at research institutes instead
of universities due to a legacy of the old Soviet-based system (Australian Government,
2018). Vietnam research sector remains relatively underdeveloped and underfunded
by international comparison (World Bank, 2008). e number of research publications
by Vietnamese scholars is far below that of other countries in the South East Asia such
as ailand (Trines, 2017). For example, Vietnam’s four leading universities each gen-
erated 15-30 times fewer publications than ailand’s two most prestigious universities
(Pham, 2010). In scientic disciplines such as medicine and agriculture where labora-
tory investment is indispensable, there was lack of resources to facilitate research and
publication (Pham, 2010). Harman and Le (2010) reviewed Vietnamese publication
rates and found that university research productivity level was low. e number of ar-
ticles published was 0.36 per sta member in national universities and 0.09 in regional
universities. Vietnamese university academics had lile time available for research due
to high student teaching load and had access to very limited funding (Welsh, 2010).
Furthermore, Vietnamese government have put into implementation various poli-
cies to renovate higher education system and institutions in recent years. e govern-
ment scaled back various regulations and at the same time extended the autonomy of
higher education institutions in terms of training, scientic research, organization, per-
sonnel, nance and international cooperation. In 2014, Vietnam ministry of Education
and Training approved a list of 233 universities to participate in a pilot program and
awarded them more autonomy to improve university capacity and capability (Resolu-
tion 77). e autonomy in the Resolution covered university governance, university -
nancing, curriculum design and R&D activities. However, together with more autono-
my, nancial support from government budget to these universities severely decreased.
Consequently, many universities in autonomy program had to face diculties in diver-
sifying their sources of revenues, which predominantly came from tuition fees. ey

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had to struggle in stabilising their operation with much less nancial support from the
state budget (Pham, 2010). Many policy makers and academics argued that the cut in
state budget subsidy might seriously hurt those universities’ income and consequently
worsen their R&D activities.
In this context, the discussions about whether lack of nancial resources is a major ob-
stacle to university R&D and which factors are the main determinants of university R&D
activities are drawing aention from both policy makers and university managers. is
study hence explores factors aecting R&D activities in Vietnamese universities with the
emphasis on the nancial factor. Using the data collected by the Association of Vietnam
universities and colleges, the author applied the structural equation modelling method to
examine how infrastructure, management, communication, human resources and nan-
cial constraint may inuence R&D activities. e results showed that various factors aect
R&D productivity of university, and the nancial factor was a major R&D constraint.
is research enriched the current literature with the following main points. First,
it would be the rst of its kind to examine nancial constraint on R&D activities in
Vietnamese science and technology universities. Next, it conrmed the signicance
of factors aecting university R&D activities such as infrastructure, human resources,
management and communication. Lastly, it could serve to assist R&D policy makers to
revise current policies and devise new policy measures to help Vietnamese universities
promote their R&D activities.
e structure of this article is as follows. Part 1 presents background of the study.
Part 2 reviews some related literature and summarizes variables used in previous re-
search. Part 3 presents data and model. Part 4 presents ndings and discussions. Finally,
Part 5 gives some conclusion remarks.
2. Literature review
Many previous studies were dedicated to determining factors aecting R&D activities.
ey referred to R&D activities as various terms such as academic productivity, scien-
tic yield, publication rate, research results, etc.
Finkelstein (1984) suggested 7 main factors aecting faculty publication rate, which
all related to faculty’s ability and characteristics. Creswell (1985) divided university
research result determinants into two groups of individual traits (e.g., faculty’s time for
research, academic exchange with colleagues) and institutional characteristics (e.g.,
size and reputation of the university). Similarly, Dundar & Lewis (1998) divided de-
terminants of research activities outcomes into individual and environmental groups.
Individual group comprised characteristics and experience of university lecturers,
while environmental group included those related to university characteristics such as
the number of professors and the size of the faculty. Uncles (2000) argued that there
were at least three impediments to research productivity including inadequate training,
sub-optimal concentrations of research activity, and competing commitments.

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Financial Constraint on R&D Activities in Vietnamese Universities – an Empirical Research
Brocato (2001) used data obtained from U.S. universities and divided research re-
sult determinants into groups of factors related to psychological and demographic char-
acteristics of individuals and factors related to university and faculty. Chan et al. (2001)
ranked research productivity among the 97 Asia-Pacic universities using a set of 17
nance journals in the 1990s and found that management factors such as motivation
and the degree of research emphasis played an important role in determining research
productivity. Ynalvez & Shrum (2011) found that publication productivity signicant-
ly linked to professional network factors, but there was no evidence of any association
with scientic collaboration.
Beerkens (2013) empirically examined the eect of management on academic re-
search productivity. e results suggest that management practices had consistent pos-
itive eect on research productivity. Universities with a more intensive management
approach achieved both higher absolute level and faster growth in R&D productivity.
Dhillon et al. (2015) studied the research outcomes of a faculty of Universiti Te-
knologi Malaysia and detected three groups of factors that aected research results in-
cluding the individual factor, environmental factor and behaviour factor. Banal-Estanol
et al. (2015) analysed the channels through which degree of industry collaboration
aected research output using a panel dataset of engineering department researchers
in UK universities. e ndings indicated that the relationship between collaboration
degree and publication rates was curvilinear, i.e. the eect of collaboration depended
on the degree of collaboration. e number of publications increased both with the
presence of research funding and with the fraction of funding in collaboration with in-
dustry, but only up to around 30–40%. Ibegbulam and Jacintha (2016) analysed the
contributors to high publication output among librarians in Nigerian University librar-
ies and the barriers to research and publication among librarians. ey showed that lack
of a research grant and a tight work schedule hindered research. Sahoo et al. (2017) ex-
amined research productivity in Indian management schools by developing a compos-
ite indicator of research productivity and using the directional-benet-of-doubt model.
ey found that faculty members who had their doctoral degrees from foreign schools
were more productive than those who had similar degrees from Indian schools. Re-
search of Ghabban et al. (2018) found empirical evidence supporting the role of knowl-
edge sharing in improving scholarly publication performance. Most recently, Nafukho
et al. (2019) found that individual characteristics (e.g., gender, rank, terminal degree,
and experience) and institutional characteristics (e.g., number of undergraduate stu-
dents enrolled, percentage of PhD students enrolled and funding allocated for research
function) inuenced faculty research productivity.
To be brief, dierent authors utilised dierent sets of factors aecting R&D activ-
ities. Table 1 summarizes the most frequently mentioned factors including infrastruc-
ture, communication, human resources and management.
Financial factors were included in many studies as major determinants of R&D ac-
tivities from various points of view.

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e rst line of research explored universities’ nancial resources for doing research
and how research fund was distributed. Grimpe (2012) studied scientists’ strategies for
obtaining project-based research funding in the presence of multiple funding oppor-
tunities using data of scientists at German universities and public research institutes.
e results indicated that scientist productivity determined the chance of obtaining
foundation and industry grants. Hicks (2012) found that complex, dynamic perfor-
mance-based research funding systems compromised important values such as equi-
ty or diversity and enhanced control by professional elites. Laudel and Gläser (2014)
analysed projects funded by the European Research Council (ERC) and argued that
important research for the progress of a eld could be dicult to fund with common
project grants. e predominance and standardization of grant funding reduced the
chances of unconventional projects across all disciplines. Wu (2015) used a Chinese
longitudinal panel dataset of the projects sponsored by the National Natural Science
Foundation to investigate the distribution of scientic funding across universities and
research disciplines. e author found that the inequality of funding distribution de-
creased following generalized Pareto distribution and geometric distribution function.
Another line of research determines whether more nancial resources can boost
R&D activities. Many authors found positive relationships between the two. Defazio
et al. (2009) examined how funding conditional on collaboration requirement aected
collaborative behaviour and researcher productivity using data of 294 researchers in
39 EU research networks over a 15-year period. e authors found a positive impact
of funding and collaboration on research productivity. Specically, in the post-funding
period, there was evidence that funding opportunities promoted collaboration, which
in turn enhanced research productivity.
Bolli & Somogyi (2011) analysed the impact of private and public third-party funds
on the productivity of Swiss university departments and public research institutions.
e authors found that public donors focused on publications, while private donors fos-
tered technology transfer. Both private and public third-party funding improved publi-
cation productivity, while private funding mainly fostered technology transfer produc-
tivity. Ubfal & Maoli (2011) evaluated the impact of research grants on the amount
of collaboration among scientic researchers by comparing collaboration indicators for
researchers with nancially supported projects against those of a control group who did
not receive the grant. e results showed a positive and statistically signicant eect of
the grants on both the total number of dierent co-authors and a measure of research-
ers’ integration into the scientic community. Fedderke and Goldschmidt (2014) eval-
uated whether a substantial increase in public funding to researchers was associated
with a material dierence in their productivity. ey compared performance measures
of researchers who obtained substantial funding against those with similar scholarly
standing but did not receive such grant. e results showed that substantial funding
improved researcher performance, but such increase was conditional on the quality and
disciplines of the researchers. Muscio et al. (2013) used nancial data for the whole

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Financial Constraint on R&D Activities in Vietnamese Universities – an Empirical Research
population of Italian university departments engaged in research in the engineering and
physical sciences to estimate a set of probit and tobit panel data models to answer the
questions whether and to what extent government funding aected the external fund-
ing options available to universities. ey found evidence that government funding to
universities played a role as a complement to funding from research contracts and con-
sulting and helped promote universities’ industry collaboration. Callaert et al. (2015)
found a positive and signicant relationship between budget from university-industry
collaboration activities and the university’s scientic yield. Research of Banal-Estanol
et al. (2015) also found that the availability of nancial resources was key to success of
applied research programs.
Nevertheless, some researchers found a negative relationship between funding and
R&D activities.
TABLE 1. Factors aecting R&D activities in previous research
Factors Research
1Infrastructure/institutional
ability
Creswell, 1985; Dundar & Lewis, 1998; Brocato, 2001;
Dhillon et al., 2015; Nafukho et al., 2019
2Management Chan et al., 2001; Beerkens, 2013; Ibegbulam & Jacintha,
2016
3 Communication Uncles, 2000; Ynalvez & Shrum, 2011; Banal-Estanol et al.,
2015; Dhillon et al., 2015; Ghabban et al., 2018
4Human resources/faculty’s
ability
Finkelstein, 1984; Creswell, 1985; Dundar & Lewis, 1998;
Uncles, 2000; Brocato, 2001; Dhillon et al., 2015; Sahoo et
al., 2017; Nafukho et al., 2019
5Financial
factor
Positive
impact
Defazio et al., 2009; Bolli & Somogyi, 2011; Ubfal & Maf-
oli, 2011; Fedderke & Goldschmidt, 2014
Negative
impact
Auranen & Nieminen, 2010; Toole & Czarnitzki, 2007;
Goldfarb, 2008; Bolli et al., 2016
Source: the author’s summary of previous literature.
Research of Toole and Czarnitzki (2007) revealed that academics receiving grant
from a small business innovation research program were more productive than their
colleagues. However, their publication productivity diminished aer geing the fund.
Goldfarb (2008) analysed data collected from 221 NASA funded university researchers
and found that those who were constantly funded by the NASA experienced a reduction
in academic productivity. Auranen and Nieminen (2010) analysed whether competitive
funding systems were more ecient in producing scientic publications from a mac-
ro-level. e results showed that there were signicant dierences in the competitive-
ness of funding systems, but no straightforward connection between nancial incentives
and the eciency of university research activity. Similarly, Bolli et al. (2016) estimated

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a simultaneous two-stage stochastic frontier model and found that international public
funds decreased the productivity of the best performing universities.
In analysing previous literature, it was not conclusive whether the nancial factor
positively or negatively aected R&D activities (Table 1). Besides, lile research ex-
plicitly examined nancing as a constraint factor together with other R&D determi-
nants. is research includes nancial constraint into a comprehensive framework to
answer the question whether it can be a hindrance to R&D activities.
3. Model and data
3.1. Conceptual model
Based on literature review of previous studies presented in Section 2, the author pro-
posed a structural model in which ve factors are assumed to aect R&D activities of
the universities. Infrastructure, communication, human resources and management are
included as motivating factors, while the nancial factor is included in the model as a
constraint.
Because most of previous studies were accommodated for universities in developed
countries, the author implemented a small qualitative study to amend the measures.
15 higher education experts associated with the Association of Vietnam universities
and colleges were interviewed to propound evaluation measures. ese experts pro-
posed at minimum 3 aspects for each factor’s evaluation. e proposed measures were
then summarized, arranged and ltered for repetition and unsuitability. Next, the list of
proposed measures was emailed to the experts to give importance score for each item.
ese items were retained if they met the conventional threshold average score of 6.5
out of 10. In the last step, a trial survey was conducted to evaluate the reliability of the
developed items.
Figure 1 presents the conceptual model.
e author thus aempted to validate the following ve hypotheses:
H1: Inastructure favourable condition positively relates to university R&D activities.
H2: Management favourable condition positively relates to university R&D activities.
H3: Communication favourable condition positively relates to university R&D activities.
H4: Human resource favourable condition positively relates to university R&D activities.
H5: Financial constraint negatively relates to university R&D activities.
e factors were evaluated based on the answers of questions in a 5-level Likert with
the value of 1 equivalent to “totally agree” and the value of 5 equivalent to “totally dis-
agree”.

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Nguyen Dang Tue.
Financial Constraint on R&D Activities in Vietnamese Universities – an Empirical Research
FIGURE 1. Structural equation conceptual model
According to Banal-Estanol et al. (2015), research and development activities are
dicult to measure empirically and even more dicult to compare across institutions
and time. Besides, currently there is no ocial statistics about university R&D activities
in Vietnam. In this study, the author measured R&D activities based on respondents’
opinions about whether the university R&D achieved its target, matched the university
ability, increased in the period of 5 years and was well applicable in the industry.
e management factor was measured based on the answers to the questions about
the internal regulation, support activities, etc. of the university for R&D activities. e
communication factor was measured by the view of respondents on the maer such as
whether the university set up good connection with the industry, whether the faculty
exchanged information frequently to each other. Similarly, the human resources factor
was evaluated based on the respondents’ opinions about the questions whether the uni-
versity faculty had adequate research skills, ability, etc. Finally, the nancial constraint
measure was evaluated based on the questions about whether R&D projects could not
be completed due to lack of nancial sources, whether the university department lacked
ability to aract nancial sources for R&D activities. Appendix 1 presents the details of
the questions used for factor measure evaluation.

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For robustness check, the values of the constructs were taken average to aggregate
data at university level. First the values of each item composing the measures in the
conceptual model (namely, infrastructure, human resources, communication, manage-
ment, nancial constraint and R&D activities) were taken average to create general in-
dices for the measures. en, the calculated index values obtained from respondents of
each university were taken average by equal weights to create the index value for each
university. It means there are 115 values of each index variable. Each index is a continu-
ous variable with values ranging from 1 to 5.
A simple OLS regression was conducted in the form:






  

(1)
where:
INF – Index for university infrastructure;
HUM – Index for university human resource;
COM – Index for university communication;
MGT – Index for university management;
FIN – Index for university nancial constraint;
Xj – A vector of control variables including university student number, years in op-
eration, university location dummy (1 if the university locates in a big city, 0
otherwise), private ownership dummy (1 if the university is a private university,
0 otherwise);
εi an error term.
3.2. Data
is research used data from a survey conducted by the Association of Vietnam Univer-
sities and Colleges on 115 science and technology universities in Vietnam. A university
was chosen for this survey if it had at least 40% of its training programs in science and
technology (List of universities in the survey can be found in Appendix 2). e Asso-
ciation carried out the survey in May and June 2018 through direct and indirect chan-
nels. Lecturers and high level managers from targeted universities were asked to ll out
questionnaire answer sheets that they received in a national conference organized in
Hanoi in May 2018 (i.e. direct channel) or in mails sent to them at the same period (i.e.
indirect channel). e respondents expressed each individual’s opinions about their
universities’ R&D activities and the factors aecting their universities’ R&D activities.
e total number of valid questionnaire answers was 632, which accounted for 75.5%
of the total number of distributed questionnaires.
For control variables for the robustness check regression, data about the number of
university students, the location of the universities, years in operation and whether the
universities are private were all collected from Annual Handbook for University Enrol-
ment (2018) published by the Ministry of Education and Training.

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Financial Constraint on R&D Activities in Vietnamese Universities – an Empirical Research
4. Findings
4.1. Reliability and validity
Before CFA analysis, the author conducted a standard EFA analysis to arrange the fac-
tor groups. Table 2 presents the nal constructs. Six unidimensional scales were uti-
lized in the model including infrastructure, management, communication, human re-
sources, nancial constraint and R&D activities. e result of CFA analysis for each
factor showed that the model achieved overall t to the actual data. e factor loadings
of items in each factor were larger than 0,5 indicating convergent validity of the con-
structs. e Cronbach’s Alpha and composite reliability coecients were all larger than
0.7. e AVE values were all larger than 0.50. erefore, it can be concluded that the
constructs are reliable.
TABLE 2. Reliability, convergent validity and model t index
Constructs
(Number of Items)
Mean
(Variance)
Range of
loadings CR AVE Cronbach’s
Alpha
Infrastructure (5) 1.621 (077) 0.596-0.826 0.845 0.526 0.799
Management (7) 1.444(0.003) 0.656-0.832 0.890 0.537 0.860
Communication (5) 2.606 (0.025) 0.851-0.899 0.944 0.771 0.931
Human resources (5) 1.577(0.001) 0.733-0.863 0.882 0.600 0.855
Financial constraint (4) 1.249(0.001) 0.685-0.832 0.842 0.574 0.774
R&D outcomes (4) 2.936 (0.088) 0.746-0.892 0.903 0.701 0.875
Model
t index
Chi-square/df = 2.94; CFI = 0.919; TLI = 0.911; IFI = 0.919;
RMSEA = 0.055
Source: Author’s calculation
e result data analysis of the nal model showed that the model achieved overall t
to the actual data: the ratio of Chi-square/df was 2.94, which was smaller than 3. CFI
(0.919), TLI (0.911) and IFI (0.919) are all larger than 0.9, while RMSEA (0.055) was
smaller than 0.08.
4.2. Structural model and hypotheses test
Table 3 presents results of the estimated equations.
e structural model results matched the conceptual framework where all the coe-
cients had the expected signs. All hypotheses were accepted. H1 and H2 were accepted
at the 10% condence level, H3 at the 5% condence level, H4 and H5 at the 1% con-
dence level. e documented positive signs for coecient estimates of infrastructure,
management, communication and human resources factors imply that more favoura-
ble conditions in infrastructure, management, communication and human resources

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would improve the university R&D activities. On the other hand, nancial constraint
coecient estimate had a negative sign and the largest absolute value implying, nance
was a substantial deterrent to university R&D activities.
TABLE 3. SEM model results
Variables Coecient
estimates
Standard
error p values Hypo-
thesis Conclusion
Infrastructure 0.134 0.07 0.055 H1 Accepted at 10%
condence level
Management 0.151 0.079 0.056 H2 Accepted at 10%
condence level
Communication 0.09 0.037 0.014 H3 Accepted at 5%
condence level
Human resources 0.256 0.07 0.000 H4 Accepted at 1%
condence level
Financial con-
straint -0.907 0.12 0.000 H5 Accepted at 1%
condence level
Source: Author’s calculation
4.3. Robustness check
Table 4 shows that about 59% of the universities in this study are based in big cities of
Vietnam, 29% of them are private, and the average number of students is about 2269.
TABLE 4. Summary statistics of explanatory variables for robustness check
Variables Mean SD Min Max
Infrastructure index 1.62 0.28 1.20 2.65
Management index 1.44 0.22 1.00 2.18
Communication index 2.63 0.45 1.42 3.50
Human Resources index 1.58 0.28 1.20 2.65
Financial constraint index 1.27 0.28 1.20 2.65
Number of Students 2269 1506 140 7340
Location (Dummy) 0.59 0.49 0 1
Private (Dummy) 0.29 0.45 0 1
Years in operation 26.37 21.02 4 117
R&D Activity index 2.93 0.35 1.8 3.55
Source: Author’s calculation

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Financial Constraint on R&D Activities in Vietnamese Universities – an Empirical Research
Following the method described in 3.1, the author calculates index values for Infra-
structure, Management, Communication, Human Resources, Financial constraint and
R&D Activity based on respondents’ answers.
Table 4 shows that the average values of infrastructure, management and human
resources index for universities under investigation are small, which shows that infra-
structure, management and human resources factors are adequate, according to the re-
spondents. Communication index has the mean value of 2.63, which shows that this
factor is just mediocre among universities under study. Financial constraint index has
the mean value of 1.27, which shows that it is a major concern in most universities.
R&D Activity Index has rather a high value of 2.93 brought about by the fact that many
respondents tend to disagree when answering the R&D activity evaluation questions,
showing that R&D activity result is not quite satisfactory in Vietnam universities in the
research.
e result for robustness check is presented in Table 5.
TABLE 5. Factors aecting R&D results – OLS regressions with index value
Variables Coecient Robust
standard errors Coecient Robust
standard errors
Infrastructure -0.020 0.135 -0.007 0.138
Management 0.127 0.151 0.172 0.162
Communication 0.018 0.065 -0.005 0.068
Human Resources 0.222* 0.121 0.224* 0.122
Financial constraint -0.371*** 0.066 -0.455*** 0.084
Number of Students  0.000 0.000
Location  -0.015 0.063
Private  0.029 0.077
Years in operation  0.001 0.002
Observations 115 115
R-squared 0.2379 0.2575
*, **, *** mean statistically signicant levels at 10%, 5% and 1%, respectively.
Source: Author’s calculation
In the table, regressions without control variables and with control variables are
presented. e OLS results were consistent with the results obtained by SEM meth-
od where most of the coecients have the same signs except for infrastructure index.
However, only human resources index and nancial constraint index coecients are
statistically signicant.

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Aer controlling for university characteristics, nancial constraint index still has
signicant eect on university R&D activity index. e eect of nancial constraint in-
dex is even higher (i.e. larger absolute coecient values) aer controlling for university
characteristics. It thus consolidates the result from SEM model that nancial constraint
does negatively aect university R&D activity.
5. Discussions
e results in the previous section lead to several implications as follows.
First, the research results imply that nancial constraint is a major obstacle of uni-
versity R&D. is is consistent with previous reports and studies in which Vietnam
is shown not to have built yet a complete and synchronous nancial mechanism for
science and technology activities to aract enough nancial resources (Bui, 2014). At
the same time, the existing nancial resources have not been allocated and used eec-
tively as expected (Nguyen, 2015). Financial resources for research mainly focus on
research institutes, creating a separation between research and teaching. e limitation
of funding for science and technology research at universities has limited the active par-
ticipation of lecturers in scientic research. As a result, the research capacity of lecturers
and students is not fully promoted, the next generation of researchers has not received
adequate training. is led to the decline of the quality of human resources in science
and technology research and the eectiveness of science and technology research over
time (Bui, 2016).
Second, given potentially large social returns of university R&D, policy makers
should aribute more emphasis to the role that funding can play as a motivation to help
university aract more external nancial sources such as those from donors and compa-
nies through collaboration activities. ese gains should be more explicitly considered in
designing policy instruments and in estimating their rate of return. ere is growing po-
litical pressure on universities to intensify their interaction with industry and to enlarge
their own research funding options, in a context characterized by increasing constraints
on public spending on higher education. Universities in Vietnam and other countries
are facing the decreasing trend of government funds to nance their operational and
research expenditures. erefore, it indicated a menace to university R&D activities and
required universities to nd other nancial sources to compensate for this reduction.
ird, results from this research can guide universities in R&D activities improve-
ment. Besides making sure that nancial source is adequate, university should also pay
aention to improving their infrastructure such as laboratory and experiment equip-
ment. University should as well care about maintaining good communication among
lecturers while at the same time upgrading its R&D management. In addition, uni-
versity managers should not neglect R&D ability of the faculty. In other words, policy
makers and university managers should launch new initiatives that generate university
nancial income and at the same time improve other factors aecting university R&D.

235
Nguyen Dang Tue.
Financial Constraint on R&D Activities in Vietnamese Universities – an Empirical Research
One of the examples is royalty-sharing arrangements, which can stimulate researchers’
eorts and ultimately improve university R&D activities (Arqué-Castells et al., 2016).
Other program interventions that encourage academic researchers to collaborate with
industry could also be benecial. ese programs not only facilitate the transfer of
knowledge and accelerate the exploitation of new inventions, but also increase academ-
ic research output (Banal-Estanol et al., 2015).
Fourth, as it is shown in the ndings, nancial constraint coecient estimate had a
negative sign and the largest absolute value. It indicates that nancial sources may be
a precondition for other factors to eectuate to allow Vietnam science and technolo-
gy universities to aain notable R&D outcomes in the context (Vietnam) where many
science and technology universities are state-owned and hence lack funding for R&D
activities. Taking the above into consideration, the relation between nancial constraint
and other factors should be exhaustively studied in future research.
6. Conclusions
is research yielded some preliminary conclusions, which should be useful for theo-
ry, practice and policymaking. e evidence from the data suggested that the nancial
factor was the most important factor inuencing R&D activities in universities. e
author found supportive evidence of a signicant negative eect of nancial constraint
on university R&D activities. Compared with previous studies, this research bolstered
empirical evidence about positive impact of favourable conditions of infrastructure,
communication, human resources and management on R&D activities.
is article extends the current literature in two key points. First, it is one of the
rst studies to include the nancial factor as a constraint to R&D activities. e model
explicitly included nancial constraint beside other potential factors that aected R&D
activities. Second, it is one of the rst empirical studies about the impact of various de-
terminants on R&D activities in Vietnam universities. Vietnam, being in the process of
transition from a planned economy to a market economy, has an institutional context
and level of economic development very dierent from the developed countries where
most previous studies were conducted. e author used a large, comprehensive data-
set including all Vietnam science and technology universities, which provided a rather
broad insight into the country’s higher education.
Nevertheless, this study suers several limitations that readers should take into ac-
count when considering its results and implications.
First, the author had to limit the analysis to R&D activities evaluated by opinions
of university managers and lecturers. e research examines R&D activities from their
specic viewpoints in a short period. Managers and lecturers themselves may give bi-
ased estimates about what the university can and has accomplished in terms of R&D
activities. Future research should use other objective R&D measures and approaches
from a dierent viewpoint to gain a more comprehensive picture of the problem area.

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Second, the results of this research are non-experimental and should be interpret-
ed with caution. e methods used in this paper will give biased estimates if there are
dierences in R&D outcomes across universities due to unobserved factors that are
not xed over time. In other words, further work is needed to test the robustness of the
results with regard to the heterogeneity of universities and their sta characteristics,
and the changes over time, and to control for problems related to endogeneity between
these characteristics. Future research should apply other theories to examine the viabil-
ity of long-term research results.
ird, this research is based on a survey covering only science and technology uni-
versities in Vietnam. Research in the future may seek to cover all universities in Viet-
nam to give a broader picture of R&D activities in Vietnamese higher education system.
Besides, data obtained from university R&D activities such as number of researchers,
number of research projects, number of patents or total value of grants should be com-
bined with data from this survey to allow for more inclusive analysis.
Universities may also have various R&D activities and subsidize them by various
nancial sources. However, the discussion provided here cannot describe the full range
of complexities that mark university R&D activities and their evolution over time. In-
stead, the author aimed to provide a concise account of the impact rather than all possi-
ble outcomes. Further research is needed to examine the specic sources of nance and
other determinants in promoting various kinds of R&D activities. With more compre-
hensive and homogeneous information, it could be possible to compare between the
eects of determinants on a specic type of R&D activities.
7. Acknowledgement
is article presents part of the research B-2017-41 funded by the Ministry of Edu-
cation and Training, Vietnam. e author would like to thank Economic Research Cen-
tre, Graduate School of Economics, Nagoya University, Association of Vietnam Univer-
sities and Colleges and Professor Eiji Mangyo for their kind support and sponsorship.
Appendices
Appendix 1: Scales, Items and Measures Included in the Survey Questionnaire
Infrastructure
Your university’s R&D infrastructure is adequate
Your university’s R&D infrastructure is up-to-date
Your university’s R&D infrastructure is constantly upgraded
Your university’s R&D infrastructure is fully integrated
Your university’s data and information sources for R&D are profuse

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Nguyen Dang Tue.
Financial Constraint on R&D Activities in Vietnamese Universities – an Empirical Research
Management
Your university creates favourable conditions for R&D activities
Your university frequently organises R&D related competitions
Your university periodically publishes information about R&D activities
Your university has special rewards to faculty sta having excellent R&D results
Your university’s R&D funding procedure is simple
Your university supports faculty in completing application to get external R&D sources
Your university’s R&D funding procedure is public to all related personnel
Communication
Faculty sta have good communication with related professional network
Faculty sta have frequent academic communication with each other
Information about external sources for R&D activities is widely available
Your university frequently organizes R&D workshops/symposia/conferences
Human resources
e number of faculty is large enough to conduct R&D activities
Your university faculty sta is well trained to conduct R&D activities
Your university’s faculty sta has good R&D skills
Your university’s faculty sta follows ethical principles in R&D activities
Your university’s faculty sta has good reputation in doing R&D activities
Financial constraint
R&D projects cannot be implemented due to lack of university funding
Financial source from university is inadequate to complete R&D activities
University lacks agents to aract funding for R&D activities
Faculty cannot acquire enough funding for R&D activities
Your university’s faculty sta is not trained how to search for appropriate source of funding for
R&D activities
R&D activity results
R&D results meet the targets set by your university
R&D results adequately match your university’s capability
e number of good publications published by your university tended to increase in the last 5
years
Outcomes of your university’s R&D activities are well applied in the industry
Appendix 2: List of Vietnamese science and technology universities included in the
Survey
1 Trường Đại học Công nghệ ông tin và Truyền thông - Đại học ái Nguyên
2 Trường Đại học Khoa học - Đại học ái Nguyên
3 Trường Đại học Kỹ thuật Công nghiệp - Đại học ái Nguyên
4 Trường Đại học Nông Lâm - Đại học ái Nguyên
5 Trường Đại học Y Dược - Đại học ái Nguyên
6 Trường Đại học Bách khoa Đà Nẵng - Đại học Đà Nẵng
7 Trường Đại học Khoa học - Đại học Huế

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8 Trường Đại học Nông Lâm - Đại học Huế
9 Trường Đại học Y Dược - Đại học Huế
10 Trường Đại học Tây Bắc
11 Trường Đại học Y Dược Cần ơ
12 Trường Đại học Dầu khí Việt Nam
13 Trường Đại học Kiến trúc Hà Nội
14 Trường Đại học Tài nguyên và Môi trường ành phố Hồ Chí Minh
15 Trường Đại học Quảng Bình
16 Trường Đại học Tài nguyên và Môi trường Hà Nội
17 Trường Đại học Khánh Hòa
18 Trường Đại học ăng Long
19 Trường Đại học Hoa Sen
20 Trường Đại học Trà Vinh
21 Trường Đại học Lạc Hồng
22 Trường Đại học Sài Gòn
23 Trường Đại học Dân lập Hải Phòng
24 Trường Đại học FPT
25 Trường Đại học Giao thông Vận tải ành phố Hồ Chí Minh
26 Trường Đại học Tây Nguyên
27 Trường Đại học Hồng Đức
28 Trường Đại học Hàng hải Việt Nam
29 Trường Đại học Lâm nghiệp Việt Nam
30 Trường Đại học Đồng áp
31 Trường Đại học Đà Lạt
32 Trường Đại học Y Phạm Ngọc ạch
33 Trường Đại học ủ Dầu Một
34 Trường Đại học Sư phạm Kỹ thuật Hưng Yên
35 Trường Đại học Xây dựng
36 Trường Đại học Mở ành phố Hồ Chí Minh
37 Trường Đại học Công nghệ Giao thông Vận tải
38 Trường Đại học Điện lực
39 Trường Đại học Công nghiệp ực phẩm ành phố Hồ Chí Minh
40 Trường Đại học Nông Lâm ành phố Hồ Chí Minh
41 Trường Đại học Dược Hà Nội
42 Trường Đại học Vinh
43 Trường Đại học Công nghiệp Hà Nội
44 Trường Đại học Công nghệ ành phố Hồ Chí Minh
45 Trường Đại học Nguyễn Tất ành

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Financial Constraint on R&D Activities in Vietnamese Universities – an Empirical Research
46 Trường Đại học Nha Trang
47 Trường Đại học Y tế Công cộng
48 Trường Đại học ủy lợi
49 Trường Đại học Mỏ - Địa chất
50 Trường Đại học Quy Nhơn
51 Trường Đại học Giao thông Vận tải
52 Học viện Công nghệ Bưu chính Viễn thông
53 Học viện nông nghiệp
54 Trường Đại học Công nghiệp ành phố Hồ Chí Minh
55 Trường Đại học Y Hà Nội
56 Trường Đại học Sư phạm Kỹ thuật ành phố Hồ Chí Minh
57 Trường Đại học Y Dược ành phố Hồ Chí Minh
58 Trường đại học Lê Quý Đôn (Học viện Kỹ thuật Quân sự)
59 Trường Đại học Cần ơ
60 Trường Đại học Duy Tân
61 Trường Đại học Bách khoa Hà Nội
62 Trường Đại học Công nghiệp Việt-Hung
63 Trường Đại học Công nghiệp Dệt May Hà Nội
64 Trường Đại học Công nghiệp Quảng Ninh
65 Trường Đại học Công nghiệp Việt Trì
66 Trường Đại học Điều dưỡng Nam Định
67 Trường Đại học Khoa học và Công nghệ Hà Nội
68 Trường Đại học Kiến trúc ành phố Hồ Chí Minh
69 Trường Đại học Kinh tế - Kỹ thuật Công nghiệp
70 Trường Đại học Kỹ thuật Y - Dược Đà Nẵng
71 Trường Đại học Kỹ thuật Y tế Hải Dương
72 Trường Đại học Mỹ thuật Công nghiệp
73 Trường Đại học Mỹ thuật ành phố Hồ Chí Minh
74 Trường Đại học Mỹ thuật Việt Nam
75 Trường Đại học Nông Lâm Bắc Giang
76 Trường Đại học Phạm Văn Đồng
77 Trường Đại học Sao Đỏ
78 Trường Đại học Sư phạm Kỹ thuật Nam Định
79 Trường Đại học Sư phạm kỹ thuật Vĩnh Long
80 Trường Đại học Hải Dương
81 Trường Đại học Xây dựng miền Trung
82 Trường Đại học Y Dược Hải Phòng
83 Trường Đại học Sư phạm Kỹ thuật Vinh

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84 Trường Đại học Bình Dương
85 Trường Đại học Chu Văn An
86 Trường Đại học Công nghệ Đông Á
87 Trường Đại học Công nghệ Đồng Nai
88 Trường Đại học Công nghệ Sài Gòn
89 Trường Đại học Công nghệ và Quản lý Hữu nghị
90 Trường Đại học Công nghệ Vạn Xuân
91 Trường Đại học Công nghiệp Vinh
92 Trường Đại học Đại Nam
93 Trường Đại học Hải Phòng
94 Trường Đại học Văn Lang
95 Trường Đại học Yersin Đà Lạt
96 Trường Đại học Đông Á
97 Trường Đại học Hòa Bình
98 Trường Đại học Kiến trúc Đà Nẵng
99 Trường Đại học Kinh doanh và Công nghệ Hà Nội
100 Trường Đại học Kinh tế - Công nghiệp Long An
101 Trường Đại học Kinh tế- Kỹ thuật Bình Dương
102 Trường Đại học Lương ế Vinh
103 Trường Đại học Ngoại ngữ Tin học ành phố Hồ Chí Minh
104 Trường Đại học Nguyễn Trãi
105 Trường Đại học Nam Cần ơ
106 Trường Đại học Phương Đông
107 Trường Đại học Quang Trung
108 Trường Đại học Quốc tế Bắc Hà
109 Trường Đại học Quốc tế Hồng Bàng
110 Trường Đại học ành Đô
111 Trường Đại học Võ Trường Toản
112 Trường Đại học Đồng Nai
113 Trường Đại học Kỹ thuật - Công nghệ Cần ơ
114 Trường Đại học Tiền Giang
115 Trường Đại học Lao động - Xã hội

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Financial Constraint on R&D Activities in Vietnamese Universities – an Empirical Research
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