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INTERNATIONAL & COMPARATIVE EDUCATION | RESEARCH ARTICLE
Increasing demand for transnational education
in Ghana: A structural equation analysis of the
causation
Received: 23 January 2019 Eric O. Owusu-Kumih
1*, Fred Boateng2 and Frank Loglo3
Accepted: 07 September 2019
First Published: 18 September 2019 Abstract: Transnational Education (TNE) has often been used as a measure to
*Corresponding author: Eric support the economic sustainability of the institution of higher learning involved.
O. Owusu-Kumih, Ghana Technology But in recent times, TNE has become one of the important avenues for Higher
University College, PMB 18, Koforidua,
Ghana Education Institutions (HEI) to improve their International Students Portfolios
E-mail: eowus-kumih@gtuc.edu.gh. without bringing the students to the HEI host-country. But the bigger question is on
Reviewing editor: the demand side of transnational education; what is pushing students in developing
Sheng-Ju Chan, National Chung
Cheng University, Chiayi City, countries to enroll in Transnational Education (TNE) Programmes, modules or aca-
Taiwan, Province of China demic units? This research paper used the structural equation model to analyse
Additional information is available at a surveyed sample data drawn from a TNE active students’ population of 2720 in
the end of the article
Ghana; representing five universities in Ghana which had either an articulation or
franchise TNE agreements with one or more of UK universities. The results showed
a causation of both signaling and human capital development factors; however,
a much stronger path relationship along the TNE Diploma acquisition, human
capital development and high earnings. The research posits that the value of the
TNE Diplomas in Ghana is in its ability to deliver the requisite knowledge, ability and
ABOUT THE AUTHOR PUBLIC INTEREST STATEMENT
This article is one of several Transnational This paper discusses the reason why students
Education research themes of this authorship preferred getting a foreign university education in
team. The team comprises of Dr Eric O. Owusu- their home countries through a transnational
Kumih (Director, Ghana Technology University education arrangements (TNE). Under the TNE,
College, Koforidua Campus) is a Transnational students are enrolled into a local university
Education and Educational Enterprise Strategist, (receiving institution) to pursue a foreign degree
Frank Senyo Loglo (Director, Ghana Technology from an international university (Sending institu-
University College, Ho Campus), is an expert in tion) through a TNE. Three theoretical arguments
flexible learning in higher education with special were put to test. The first was that, an employee
emphasis on open education, non-traditional who has TNE qualification potentially earns more
and lifelong learning, and Dr Fred Boateng, an salary. The second was that, students prefer to
expert in Higher Education Management and attain the foreign degree in their home countries
Administration. The current research focus of because, a TNE qualification from a reputable
this team is towards the development of international university convinces employer that
a Transnational Education Management you are capable of performing (TNE Qualification
Capability Maturity Model for uptake by receiving Signals performance). The third theory tested was
HEI’s in developing countries. that, students preferred to enrol into foreign uni-
versities locally because the programme con-
tents, tuition delivery and the learning
experiences teach the students to perform, and
indeed they are able to perform better on the job
(i.e. Human Capital Development Theory). The
results from the research showed some very
interesting linkages between the theories.
© 2019 The Author(s). This open access article is distributed under a Creative Commons
Attribution (CC-BY) 4.0 license.
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skills to its graduates. Again, the results showed that the inherent “signals” of good
education of which TNE arrangements provides, adds to its overall appeal.
Subjects: Higher Education; International & Comparative Education; Education &
Development
Keywords: Human capital development; returns to education signaling; structural
equation model; transnational education
1. Introduction
Many researchers have suggested that, globalization seems to be the driving force behind the
increasing trends of internationalization of higher education. This according to them have resulted
in the increases in global movement of people through exchange Programmes, flow of ideas and
capital flight particularly towards the wealthier countries (Akinlo, 2004; Bhandari & Blumenthal,
2011; Knight, 2008; Montgomery, 2010; Ndulu, 2004). Indeed, a recent national and worldwide
surveys of university Transnational Education (Henceforth, TNE) priorities and rationales show that
establishing an international profile or global standing is becoming more important that reaching
international standards of excellence (International Associations of Universities, 2010).
For many years, there have been two competing theories: human capital theory and the
signaling hypothesis. Human capital theory contends that higher education increases the produc-
tivity of students, which explains, inter alia, why graduates earn more than non-graduates (Becker,
1994; Becker & Woessmann, 2009; Hartog & Van den Brink, 2007; Nafukho, Hairston, & Brooks,
2004). Signaling theory contends that higher education identifies high productivity individuals
without necessarily raising their future productivity to employers. This too explains why graduates
earn more, on average, than non-graduates (Chevalier, Harmon, Walker, & Zhu, 2004; Groot &
Oosterbeek, 1994; Kjelland, 2008).
Most literature suggest that internationalization is largely adopted as a measure to support the
economic sustainability of the higher education institutions involved. But the bigger problem is on the
demand side of transnational education Ghana; what is pushing students in Ghana to enroll in
transnational education programmes, modules or academic units? Some researchers such as Ilieva,
Beck and Waterstone (2014). Knight (2015) worked on the various aspect of this problem but placed
emphasis on the universities from a developed country. However, much of the research works con-
ducted or the debates have been the benefits or otherwise generated by the home country (sending
institution). Indeed, much of the recent criticisms of the TNE have been on the sending institution’s
fixation on the revenue-making venture as well as the rise in its ranking metrifications (HE ranking
status) (Knight &McNamara, 2015). This research paper attempts to contribute in filling the knowledge
gap that exist as far as the receiving TNE country is concern. This research focuses on the returns TNE
brings to the students who enroll and complete the TNE programmes.
2. Theoretical framework
Reference Knight (2015) and Knight (2013) defined the TNE and internationalization of higher educa-
tion as “the process of integrating an international, intercultural or global dimension into the purpose,
functions or delivery of post-secondary education”. Again, in one of his most recent article, Knight
(2015) stated that the internationalization at the national, sector, and institutional levels is defined as
the “process of integrating an international, intercultural, or global dimension into the purpose,
functions or delivery of postsecondary education”. The internationalization of higher education is
considered to be a response to, and even a product of globalization resulting in an intensification of
the global/local flows of people, ideas, and capital in higher education institutions, particularly in
wealthier countries (Bhandari & Blumenthal, 2011; Knight, 2008; Montgomery, 2010). Reference Wals
and Jickling (2002) discussed sustainability with specific reference to higher education and reiterated
that the term is often problematic in that it has multiple meanings. However (Thompson, Peteraf,
Gamble, Strickland, & Jain, 2013) attributed sustainability in relation to competitive advantage. To
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them, gaining a competitive advantage alone was not enough, but the ability to build a system that is
able to sustain a competitive advantage over time is the key difference between a market leader and
a follower. However, Knight (2015) as well as McNamara and Knight (2014) suggested that the right
partnership among international universities give them a much-needed competitive advantage.
Internationalization of higher education is currently nuanced by transnational education (TNE).
Many would-be international students now pursue foreign degrees in their home countries at a local
higher education institution through an articulation or franchise arrangements with a foreign
university.
Reference Hoey (2016) suggested that proper and a rationally sensitive ethical consideration in
any international partnership arrangements ensure that the outcomes are beneficial to all con-
cerned, including the communities and student bodies of the countries in which the partnered
institutions are located. Also, Pashby and de Oliveira Andreotti (2016) analysed a situation within
a larger project context focusing on ethics and internationalization in higher education. The
concluded that internationalization is occurring at a fast pace and encompasses overlapping and
contradictory aims largely framed by market imperatives. And that, ethics were imperative to
success of any prospective deal. Their work also suggested that institutions of higher education are
increasingly promoting sustainability.
Reference Taylor (2016), on the other hand, developed a nine-stage model which was to serve as
a guideline for universities seeking international partnerships. The model anticipated that both partners
needed to build a sustainable stage in the model process. Reference Lindsay and Antoniou (2016) also
concluded that culture is an important consideration in any international partnership arrangement with
UK universities however, its relevance is a facade in relation to the extent to which the UK degree is
cherished by the local populations. This was a shape contrast in comparison to work done by Bodycott
and Lai (2012) whomaintained that culture still held sway towards decisions people make with regards
to UK university. Based on their findings of the null cultural effects, Lindsay and Antoniou (2016) implied
that it was no surprise that some institutions implore state regulators to aid or hinder UK universities
entry. However, according to them the potential market and local desire for a UK degree can outweigh
difficulties with national legislation, although clearly the harmonization of legislation aids market entry
and reduces risks.
2.1. The returns to education
A strong relationship between education and labour income has been observed repeatedly in
empirical studies. (Kirby & Riley, 2008; Mincer, 1958; Walker & Zhu, 2001). Indeed, multi-discipline
development researchers have seemingly concluded that education have a positive effect on
earning of an individual and improves economic growth of a society (Becker, 1994; Becker &
Woessmann, 2009; Chevalier et al., 2004; Groot & Oosterbeek, 1994; Hartog & Van Den Brink,
2007; Kjelland, 2008; Nafukho et al., 2004).
Returns to investment in education based on human capital theory have been estimated since
the late 1950s. In the 50s-plus year history of the estimates of returns to investment in education,
there have been several reviews of the empirical results in attempts to establish patterns. Many
more estimates from a wide variety of countries, and estimates based on new econometric
techniques reaffirm the importance of human capital theory. In the human capital perspective,
over education may result from a deliberate choice because the low-level job is a good investment
opportunity. Reference Kroch and Sjoblom (1993) tested the prediction that over schooled workers
are more likely to move to higher-level occupations (measured from wage regression coefficient
weighted sums of schooling, experience and required training). The prediction is borne out, but the
same result of increased likelihood of upward occupational mobility is found for under-schooled
workers. Again, Sicherman (1991) also found that overeducated workers are more likely to change
occupation when moving firm and that, undereducated workers are more likely to move to
a different job in the same occupation. One such piece of evidence is given by Alba-Ramirez
(1993); who concluded that the overeducated have faster earnings growth with tenure.
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Signaling theory contends that HE identifies high productivity individuals without necessarily
raising their future productivity to employers. This too explains why graduates earn more, on
average, than non-graduates (Chevalier et al., 2004; Groot & Oosterbeek, 1994; Kjelland, 2008).
Within the paucity of available literature, TNE has shown to indirectly influence future earnings
of graduates of the receiving institution. Researchers Schreiber, Nora, Stage, Barlow, and King
(2006) concluded that, graduates from some receiving TNE countries get opportunity to emigrate,
work abroad and earn a higher income. Comparatively, graduates with domestic university quali-
fications will struggle to get employed if they were to cross borders. Again, Lien (2008) also found
that, the key motivation of students enrolling in a TNE programme is to improve their professional
skills for career advancement. This means that the potential to develop the prerequisite skills of
employment which will ultimately affect their performance and progression was the mean ratio-
nale. The latter study above confirms human capital development influences the students’ choice
of TNE while the former study affirms that signaling of a TNE prospects affects the students’
decisions to enroll. The two arguments eventually influence earnings or incomes.
From the above, the following hypotheses are therefore made:
2.2. Hypothesis statements
H1- Preference for TNE Diplomas has a direct effect on Higher Earning
H2- Preference for TNE Diplomas has an indirect effect on Higher Earning through signalling as
a mediator
H3- Preference for TNE Diplomas has an indirect effect on Higher Earning through HC development
as a mediator
3. Methodology
The study used the structural equation model to analyse the surveyed sample data drawn from a TNE
active students’ population of 2720 in Ghana. This represents a combined TNE student population of five
universities in Ghana which have at least one TNE arrangement with a foreign university. Questionnaire
administered through the email was preferred as data collection instrument because it was efficient and
economical. A total of 300 questionnaireswere emailed to student sampled proportionally, 231were filled
and returned. The responseswere subsequently used for the analysis; giving a response rate of 77%. Data
were analyzed by SPSS version 20 and also by SMART-PLS version 3 for the Structural Equation Model
(SEM). SEM is an agglomeration of statistical models which seeks to clarify and explain the relationships
between multiple latent variables (Hu & Bentler, 1999). SEM was employed in this study because
researchers are able to examine the interrelationships between multiple dependents with their indepen-
dent constructs at the same time (Hu & Bentler, 1999; Byrne, 2008). Again, SEM gives the researcher the
opportunity to examine both the measurement and structural components of a proposed model. This is
achieved by testing the relationships among different kinds of independent and dependent constructs all
within the same time (Fornell & Larcker, 1981; Gefen, Straub, & Boudreau, 2000; Hu & Bentler, 1999;
Tabachnick & Fidell, 2001; Byrne, 2008). Because of these important attributes, SEM analytical techniques
havebecomevery popularmethodof empirical analysis in every discipline (Henseler, Hubona, &Ray, 2016;
Hoyle, 1995; Hu & Bentler, 1999; Shah & Goldstein, 2006). This makes the SEM most suitable for this
research study because this study has multiple independent-dependent relationships. For this research
study, the variance-based partial least square SEM (PLS SEM) technique was used.
4. Empirical results
Figure 2 below shows both the constructed inner model and outer model with indicators to both
the endogenous and exogenous latent variables. This figure is giving no numeric dimensions
because the PLS path Algorithm had not been run. The indicators for the latent variables are as
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follows: TNE Diplomas (TNE1, TNE2, TNE3, TNE4); Signaling Factors (SIG1, SIG2, SIG3, SIG4); Human
Capital Development Factors (HCD1, HCD2, HCD3, HCD4); High Earnings or high Compensation (HE1,
HE2, HE3, HE4). Some of the indicators express reflective scales while others are formative and are
shown in Figure 1 below:
4.1. Assessment of the measurement model
Assessing a measurement model provided an opportunity for a well-thorough testing of the reliability
and validity of the latent constructs and its indicator variables. It is possible to establish an approx-
imate model fit when one conducts a tests of model fit. An approximate model fit criteria helps
researcher’s answer the question of how big is the inconsistencies that exist between the model-
implied and empirical correlationmatrix. If it is established that the inconsistency is substantial, then it
becomes a problem which has to be addressed by the researcher. According to Kiker & Santos (1991),
the only approximate model fit criterion which is currently being used for PLS path modelling is the
standardized root mean square residual (SRMR); even that it is used cautiously. A value of 0 for SRMR
would indicate a perfect fit and generally, an SRMR value less than 0.05 and over generously 0.08
would be seen as a generally acceptable fit (Henseler et al., 2016). Again, Kiker & Santos (1991) cited
past researchers and suggested that the normed fit index (NFI) is also used as another determinant for
the approximate model fit criterion. However, the values for the composite models are still being
determine but for the factormodel, any value above 0.90 is acceptable (Henseler et al., 2016). Because
the PLS-basedModel-fit is still in the early stages,most researchers cautioned its application especially
using the critical threshold values given above as the benchmark for evaluating, reporting and
comparing models (Hu & Bentler, 1999). In spite of these cautions, these researchers have given
a report on the approximate model fit (Saturated Model) in Table 1 below:
4.2. Assessing reliability and validity of construct
The measures of composite reliability in the PLS software have three determinants; namely the
Composite Reliability, Dillon-Goldstein’s rho A and the Cronbach’s Alpha. In practice, the signifi-
cance of Cronbach’s Alpha in determining reliability of PLS has been relegated to the lower
boundaries in recent literature since its use is seen to underestimate the true reliability of the
Figure 1. Conceptual/hypothe-
sized path model.
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Figure 2. Path model of both R2
values (nodes) and the path
coefficient with a highlighted
effect.
Table 1. Summary of the values for the fit
Indicators Meaning Saturated Model
SRMR Standardized Root Mean Square 0.070
Residual
d_ULS Squared Euclidean Distance 0.670
d_G1 Geodesic Distance 1 0.349
d_G2 Geodesic Distance 2 0.306
χ2 Chi-Square 428.658
NFI Normed Fit Index 0.783
constructs (Hair, Hult, Ringle, & Sarstedt, 2017; Sijtsma, 2009). For reliability to pass, all the
measured values are expected to be greater than 0.70 (Dijkstra & Henseler, 2015). Again,
researcher have been cautioned to ensure that the factor measurement is also free from systema-
tic errors and systematic measurement error. This means that attempt should be made at ensur-
ing a convergent validity, this could be done by the use of the average variance extracted (AVE)
which was suggested by Nunnally and Bernstein (1994). They suggested an AVE value of 0.5 and
above as the acceptable. There are two other criteria which have also been found to be informative
about discriminant validity. These are the Fornell-Larcker criterion which was proposed by Nunnally
and Bernstein (1994) and HTMT proposed by Kiker and Santos (1991). The Fornell-Larcker criterion
says that a “factor’s AVE should be higher than its squared correlations with all other factors in the
model”. For a variance-based structural equation model such as partial least square, researcher to
be able to concisely discriminate between two factors, the HTMT value must be lesser than 0.90;
the latter is recommended in most recent studies (Hair, et al., 2017). Finally, it is highly recom-
mended that researchers evaluate the variance inflation factor (VIF) of the indicators of the latent
variables. If the VIF values are found to be extremely higher than one (i.e. above 4) then it means
that multicollinearity might be playing a significant role. The values of the reliability and validity
after the SmartPLS runs are given in Tables 2–5 below;
Table 2 shows that all the dataset for the constructs passed the reliability with Cronbach’s
Alpha, rho A and Composite Reliability in respect of High Earnings (0.822; 0.827; 0.882), Human
Capital Development Factor (0.861, 0.862, 0.906), Signaling Factor (0.814; 0.818; 0.877), TNE
Diplomas (0.830; 0.836; 0.887). The Average Variance Extracted (AVE) was used to measure the
construct validity. The values measured for each variable are as follows: High Earnings (0.652),
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Table 2. Construct reliability and validity values
Constructs Cronbach’s rho_A Composite Average
Alpha Reliability Variance
Extracted (AVE)
High Earnings 0.822 0.827 0.882 0.652
Human Capital 0.861 0.862 0.906 0.706
Development Factor
Signaling Factor 0.814 0.818 0.877 0.642
TNE Diplomas 0.830 0.836 0.887 0.663
Human Capital Development Factor (0.706), Signaling Factor (0.642) and TNE Diplomas (0.663).
Again, Table 3 shows the results for the discriminant validity using the Fornell-Larcker Criterion.
From that table, all the variables passed the Fornell-Larcker Criterion. This is because the
factor’s AVE was significantly higher than its squared correlations with all other factors in
the model. Table 4 also shows the results for the Heterotrait-Monotrait Ratio (HTMT) which is
also used for measuring discriminant validity of the latent variables within the construct. From
the table above all the values were within the acceptable range which is that for a construct to
be deemed to have been valid, its HTMT values should be lesser than 0.90. Finally, Table 5 also
shows the results for the Inner VIF Values of which the variance inflation factor (VIF) of the
indicators of the latent variables as well as of the variables themselves are shown in the tables.
From the table, all the values are much closer to Figure 1 and it is the Authors' Own Construct
for the Conceptual/Hypothesized Path Model ; this means that multicollinearity has not been
established making the data valid, reliable and the results acceptable.
4.3. Assessment of the structural model
The structural model was assessed using the endogenous (Variable Variance Explain—R2), Direct
Effects (Path coefficient absolute size, sign; Significance p-value; confidence interval; effect size),
Indirect Effect (coefficient absolute size, sign; Significance p-value; confidence interval) and the
Total Effects (Coefficient (absolute size, sign; Significance p-value; confidence interval). In order to
Table 3. Fornell-Larcker criterion values for discriminant validity
High Earnings Human Capital Signaling TNE Diploma
Dev’t Factors Factors
High Earnings 0.807
Human Capital 0.668 0.840
Development Factor
Signaling Factor 0.608 0.441 0.801
TNE Diplomas 0.565 0.413 0.401 0.814
Table 4. Heterotrait-monotrait ratio (HTMT) values for discriminant validity
High Earnings Human Capital Signaling TNE Diploma
Dev’t Factors Factors
High Earnings
Human Capital 0.788
Development Factor
Signaling Factor 0.743 0.523
TNE Diplomas 0.674 0.488 0.485
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Table 5. Inner VIF values of collinearity statistics (VIF) toward establishing discriminant
validity
High Earnings Human Capital Signaling Factor TNE Diplomas
Dev’t Factor
High Earnings
Human Capital 1.353
Dev’t Factor
Signaling Factor 1.337
TNE Diplomas 1.299 1.000 1.000
measure the significant effects, researchers should determine how substantial the effect are by
assessing the effect size f2; if the values are above 0.02, 0.15, and 0.35. The effects could be
regarded as weak, moderate and strong, respectively (Cohen, 1988). The R2 ranges from zero (0) to
one (1) whiles 1 defines the perfect predictor. To test the significance of the values for the path
coefficient, a bootstrapping would have to be done with a sample of 5000. A T-Statistics of above
1.96 and a P-Value of less than 0.05 would show that the path is statistically significant and
supported (Ringle, Wende & Becker, 2015). Figure 2 shows the values for both R2 and F2 while Table
6 and Table 7 below sum up the discussed criteria for construct model assessment.
4.4. Discussion of the results
This research work in Ghana confirms decades’ old global realism and empirical facts that there was
a strong relationship between education and labour compensations or earnings; and that, the higher
you rise on the educational ladder the higher your paycheck rises (Kirby & Riley, 2008; Mincer, 1958;
Walker & Zhu, 2001). For emphasis, this research focused on higher educational diplomas attained
through a transnational education in Ghana either delivered directly or indirectly through a local
assignee. The results above show a strong direct, indirect and a reverse effect on high earnings. In
simple terms, graduates of TNE have high propensity of an upward adjustment of their salaries either
through an increase in work-related responsibilities which comes along with an adjusted compensa-
tion or by just presenting the attained certificate to HR for recognition and a corresponding salary
adjustments. This is consistent with the work done by both Schreiber et al. (2006) and Lien (2008) who
concluded that, TNE diplomas increase the employability of the graduates both locally and abroad;
consequently, up to their earnings.
Again, the results showed that the relationship is also indirect through two mediating
factors; Signaling and Human Capital Development (HCD). The results showed that all the
mediating factors had a statistically significant effect on earnings. In fact, the inner model
(R2) in Figure 2 showed that Signaling and Human Capital Development alone could explain
more than 62% of the variance in the earnings of a TNE Diploma holder; this is extremely
significant to industry watchers. Again, early study conducted by Lien (2008) has been sup-
ported by this results. In the previous work, the researcher Lien (2008) established that, the key
TNE students’ enrolment motivations were to develop their professional skills and prepare
Table 6. Results for the path coefficient of construct model
High Earning Human Capital Signaling Factor TNE Dip
Dev’t Factor
High Earnings
Human Capital Dev’t 0.419
Factor
Signaling Factor 0.318
TNE Diplomas 0.265 0.413 0.401
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Table 7. Summary of the structural model and paths (T-STATS & P- VALUES)
Orig. Sampl(O) SamplMean (M) Stdard Deviat T Stats (|O/STDEV|) P-Values Path
(STDEV) Supported/Not
Supported
Human Capital Dev’t Factor -> 0.419 0.411 0.085 4.906 0.00 Path Supported
High Earnings
Signaling Factor -> High Earnings 0.318 0.321 0.075 4.228 0.00 Path Supported
TNE Diplomas -> High Earnings 0.265 0.263 0.073 3.657 0.00 Path Supported
TNE Diplomas -> Human Capital 0.413 0.410 0.096 4.311 0.00 Path Supported
Dev’t Factor
TNE Diplomas -> Signaling Factor 0.401 0.398 0.097 4.122 0.00 Path Supported
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themselves for future career progression. Which means that, students understand that, their
potential future earning heavily depends on their performance after acquiring the TNE certifi-
cates. The situation is not the same at the other end of the model. The inner models of both
Signaling and HCD were weak with TNE Diplomas had a weak influence on the variance of
16.1% and 17.1%, respectively. This means TNE Diplomas have very little explanations on the
variances of both Signaling and HCD while other factors which are yet to be determined have
a significant influencing role on them. Again, a reverse effect seen in the relationship between
TNE Diplomas and high earnings could be as the results of very expensive tuition fees rate, only
those who are already on high earnings could afford to pay and complete such programmes in
Ghana. Finally, the path coefficient expressed is significant for all the paths however, HCD to
high earnings was the highest.
5. Conclusion and recommendations
This research posits that the value of the TNE Diplomas in Ghana is in its ability to deliver the
requisite knowledge, ability and skills to its students so that they could perform at their
workplaces of which a performance-adduced compensation would be merited. The inherent
signals of good education it brings adds to its overall appeal. But whether education endows an
individual with human capital, or acts merely as a signal of existing human capital, the fact
remains that wages are an increasing function of educational attainment. In other words, an
individual’s decision to pursue a TNE diploma depends on nothing more than the established
positive correlation between education and earnings, upon which both signaling theory and
human capital theory depend.
5.1. Implications and recommendations
This research posits an overall good outlook for TNE in Ghana. This means that the demand for TNE
will continue to increase as long as students continuously receive the expected learning outcomes
and earnings in the labour market. To this end, management of higher education institutions in
receiving countries as well as the policy makers within the educational space in those countries must
put in place a requisite enactment to help guide the growth of TNE which is increasingly addressing
the skills gap in developing countries.
Because data collection was set within a successfully managed TNE arrangements in Ghana, this
research is therefore, limited to only TNE arrangements where the host institutions originate from the
United Kingdom. This study recommends that future study could focus on gathering data from all TNE
receiving institutions in Ghana. Secondly, this research recommends that future research attention could
be devoted to establishing the real impact of the TNE on teaching and learning at the receiving
institution.
Funding causation, Eric O. Owusu-Kumih, Fred Boateng & Frank
The authors received no direct funding for this research. Loglo, Cogent Education (2019), 6: 1668705.
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