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Higher Education
https://doi.org/10.1007/s10734-024-01333-8
University autonomy isapredictor ofEnglish medium
instruction inEuropean higher education
PeterWingrove1 · BeatriceZuaro1 · MarionNao1 · DoganYuksel1 ·
LeventeLittvay2,3 · AnnaKristinaHultgren1
Accepted: 8 October 2024
© The Author(s) 2024
Abstract
Despite extensive research into English as a Medium of Instruction (EMI) in higher edu-
cation, few if any studies have explored the role of higher education autonomy in driv-
ing EMI. This paper tests the novel hypothesis that university autonomy—spearheaded
across European higher education through neoliberally predicated ‘steering at a distance’
reforms—predicts EMI. The data are multilevel with higher education institutions (HEIs)
nested inside education systems. The University Autonomy Scorecards (Pruvot & Ester-
mann, 2017) operationalise university autonomy at the education-system level (n = 26) and
measure four dimensions of autonomy: academic, financial, staffing, and organisational.
We include ‘overall autonomy’ as the average. The European Tertiary Education Regis-
ter provides HEI-level data (n = 1815), which we combine with a count of English-taught
degree programmes (ETPs) to measure EMI, provided by Study Portals, the largest online
portal of degree programmes. We conduct multilevel regression to analyse the relation-
ships between autonomy and EMI. The results showed that overall autonomy predicts EMI
in public universities (p = 0.002). Increasing overall autonomy by one point above the mean
increases the likelihood of offering EMI by 9.5%. Academic, staffing, and organisational
autonomy predict EMI in public universities, whereas financial autonomy does not. The
first to quantify a relationship between university autonomy and EMI, this study offers new
insights into how EMI comes about. By revealing a previously obscured interconnected-
ness between language shift and higher education governance, the study demonstrates the
value added of an interdisciplinary approach and proposes a new line of inquiry for future
research.
Keywords Steering at a distance· University autonomy· English medium instruction·
Higher education· Multilevel regression
Introduction
The use of English to teach academic subjects mostly in higher education contexts, com-
monly referred to as ‘English-medium instruction’ (EMI), has been described as an
‘unstoppable train’ (Macaro, 2015). Studies which have investigated the presence of EMI
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in European higher education have reported rapid growth since the turn of the millennium,
estimating up to tenfold growth in English-taught degree programmes (ETPs) in Europe
between 2001 and 2014 (Wächter & Maiworm, 2014) and claiming up to 50-fold growth
in English-taught bachelor’s programmes between 2009 and 2017 (Sandström & Neghina,
2017).1
To date, explanations for the growth of EMI in European higher education have largely
pointed to The Bologna Process and internationalisation (Dimova etal., 2015; Hultgren,
2024; Macaro, 2018; Wilkinson & Gabriëls, 2021). In this paper, we propose a novel
explanation, namely that EMI is driven by a set of neoliberal reforms that have granted
higher education institutions (HEIs) greater autonomy and accountability. Whilst govern-
ance reforms are an established area of interest in fields such as higher education studies,
public administration, political science, organisational studies, etc. (Dougherty & Natow,
2020; Kelchen, 2019), little attention has been paid to the relationship between governance
and language of instruction.
One reason the relationship between governance and EMI has not been addressed in the
literature is that political science and public administration are rarely concerned with lan-
guage. Meanwhile, applied linguistics rarely engages substantially with higher education
governance (Macaro & Aizawa, 2022). To bridge this gap, we approach the research ques-
tion of what drives EMI through an interdisciplinary lens that bridges applied linguistics
and political science and related fields. A second reason this topic has not been addressed
concerns the availability of data which measure EMI. Major databases, such as Eurostat
and OECD, typically collect data on education trends concerning participation, graduation,
staffing, and finance, but language of instruction is a blind spot.
The question of what drives English is important for ethical, legal, and political rea-
sons. An ethical concern is that whilst converging on a common language has benefits for
international communication, defaulting to English can disadvantage non-native speakers,
resulting in negative experiences for lecturers and students (Block, 2022). Furthermore,
the primacy of English can result in ‘domain loss’: the erosion of other languages in aca-
demic and professional domains. In Italy, domain loss became a legal concern when lec-
turers at the Polytechnic University of Milan brought a case against the university when
it decided to teach all MA and PhD degree programmes exclusively in English (Murphy
& Zuaro, 2021). The case reached the constitutional court, and the university ultimately
lost the appeal as teaching exclusively in English was deemed unconstitutional, threaten-
ing the primacy of the Italian language. A more recent development in the Netherlands has
seen MPs vote on an education bill to require two-thirds of content for standard bachelor’s
degrees to be in Dutch.2 This pushback may have come about as the Netherlands has been
leading the way in EMI since the 1980s (Wilkinson Gabriëls, 2021) with 29% of bach-
elor’s degrees and 75% of master’s degrees taught exclusively in English by 2022 (VSNU,
1 Wächter and Maiworm (2014, p.131) caution that a tenfold increase is likely an over-estimation as their
earlier studies in 2001 and 2007 likely undercounted ETPs compared to 2014. Likewise, a 50-fold increase
in English-taught bachelor’s programmes is likely an over-estimation, as the data in the Sandström and
Neghina (2017) study were collected from the data company Study Portals, which was founded in 2009.
It is unlikely that Study Portals managed to capture all ETPs in Europe within the first few years of their
founding. A more modest estimate, based on a comparison with Wächter and Maiworm’s data, suggests
approximately an eightfold increase in English-taught bachelors between 2009 and 2017.
2 This bill is yet to be commented on in the academic literature, but is reported here: https:// www. thegu ard-
ian. com/ educa tion/ 2023/ jun/ 20/ nethe rlands- seeks- curbs- on- engli sh- langu age- unive rsity- cours es accessed
January 2024.
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2023). In response to the bill, the Universities of the Netherlands Association spokesperson
argued that ‘this national control … interferes with the autonomy of universities’2. In both
Italy and the Netherlands, EMI in higher education developed unchecked until it ran into
legal or political resistance.
In this paper, we hope to bring some clarity to the connection between EMI and higher
education autonomy, which has roots in neoliberal reform. Since the 1980s and onwards,
most European countries have sought to reform their higher education systems towards
granting institutions greater autonomy while putting into place more accountability,
referred to as ‘steering at a distance’ (Kickert, 1995). The rationale behind such ‘steering at
a distance’ approaches to governance is both economic and ideological (Eurydice, 2000).
Economically, HEIs have faced pressure due to massification and a growing number of
students pursuing university studies (Trow, 2006), necessitating more cost-effective modes
of financing. Ideologically, steering at a distance reforms are premised on a neoliberal
philosophy, traceable to the Reagan/Thatcher era in the 1980s, where a widespread dis-
trust of etatism emerged alongside an associated belief that public sector institutions were
inefficient and needed reform (Krüger etal., 2018; Capano & Pritoni, 2020). Steering at a
distance reforms thus aimed to make HEIs more dynamic, competitive, and cost-effective
(Bleiklie, 2018; Ferlie etal., 2008) by granting them greater autonomy and incentivising
them to be proactive (Capano & Pritoni, 2020; Krüger etal., 2018).
Although ‘autonomy’ is a notoriously elusive concept, it is generally understood as inde-
pendence from the state (Capano & Pritoni, 2020; Krüger etal., 2018). However, although
government control has loosened in some areas, it has tightened in others (Enders etal.,
2013; Euridyce, 2000) because the granting of autonomy has often been combined with the
introduction of accountability mechanisms, such as ‘performance indicators’ (Minassians,
2015), ‘governance by numbers’ (Shore & Wright, 2015; Supiot, 2017), and ‘big data gov-
ernance’ (Beerkens, 2021). In these modes of governance, the government sets targets for
HEIs and then assumes (or delegates) the role of monitoring and assessing compliance,
as captured in the rise of the ‘audit culture’ (Shore & Wright, 2015) and the ‘evaluative
state’ (Neave, 2012). There has been a shift, in other words, from ‘government to gov-
ernance’ (Eurydice, 2000; Krüger etal., 2018; Capano & Pritoni, 2020), with state power
devolving in three directions (Pierre & Peters, 2020): upwards to supranational actors such
as the OECD and the European Union; downwards to provinces, local governments, and
HEIs themselves; and outwards to international higher education organisations such as the
European Association for Quality Assurance in Higher Education, the Academic Coopera-
tion Association, and not least the European University Association, whose data on auton-
omy we draw on in this study. With all this monitoring in place, some have argued that
‘autonomy’ has decreased, and the role of the nation-state has changed rather than lessened
(Westerheijden etal., 2010). In our terms, autonomy in this context can be understood as a
form of regulated autonomy, as opposed to a model of libertarian autonomy, which would
occur in the absence of any government involvement whatsoever.
Methodologically, this paper uses multilevel regression to model the relationship
between institutional autonomy and EMI in a cross-sectional study. EMI is measured at
the level of HEIs in terms of English-taught degree programmes (ETPs). Our data includes
1815 HEIs, nested within 26 European education systems, with data on ETPs collected
from Study Portals, the largest online portal of ETPs. Autonomy is measured at the level of
education systems (which in most cases constitutes a country) by reference to the Univer-
sity Autonomy Scorecards (Pruvot & Estermann, 2017).
These scorecards measure institutional autonomy in European higher education
across four dimensions: academic, financial, staffing, and organisational autonomy.
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Higher Education
Additionally, scores consider negotiations between institutions and the state on the use
of accountability mechanisms. Taking the interplay of autonomy and accountability
mechanisms into consideration aligns the scorecards more closely with ‘regulated’
autonomy in the context of steering at a distance reforms.
We theorise that autonomy is linked to EMI via constituting a part of, or a proxy
for, the competitive environment brought about by steering at a distance reforms. It is
within the globally competitive environment that HEIs are incentivised to transition
from being primarily nationally to internationally oriented. This international competi-
tion necessitates a common language, English, which is adopted due to its status as a
global lingua franca. Take the example of performance-based funding targets on enrol-
ment (Kelchen, 2019). The targets themselves are an accountability mechanism and the
universities are granted sufficient autonomy to meet these goals as they see fit. Univer-
sities can then use their given autonomy to implement EMI and EMI-facilitating poli-
cies, such as establishing new degree programmes in English, recruiting international
staff, and setting internationally competitive tuition fees, which would enable them to
meet enrolment (and other) targets.
From this perspective, autonomy may be a component in a causal chain that leads to
EMI, moderating the effect of globalisation pressures in an English as a lingua franca
environment on the provision of EMI. Our measure of autonomy, whilst perhaps a
proxy for an internationally competitive environment, may also be a part of it, which
enables universities to offer EMI as part of an internationalisation strategy. Although
this study only investigates association not causation, we can speculate on the nature
of this relationship as potentially causal, with perhaps global economic forces driving
EMI, but autonomy facilitating it. Moreover, it is worth pointing out that autonomy
itself, whether defined as regulated or libertarian, enables institutions to act within
their own interests, which may or may not align with those of the state, or even their
own faculty, as exemplified in the case of the Polytechnic of Milan.
Therefore, in this study, we investigate the relationship between autonomy and the
ownership structure of universities in terms of ETPs. We suggest that, as steering at a
distance reforms regulate the public sector, the effect of autonomy within an education
system can be expected to affect public universities directly and private universities
indirectly, potentially through competition with public universities. Prior theory sug-
gests that competition between public and private universities may drive EMI (Macaro,
2018). This operates within a global market, where HEIs compete for students and
staff. Whilst this complex interplay is hard to capture, we anticipate more EMI-prac-
tising public universities in systems which grant them greater institutional autonomy.
We test the following hypotheses. In public universities, is English-medium instruc-
tion predicted by:
1. Overall autonomy?
2. Academic autonomy?
3. Financial autonomy?
4. Staffing autonomy?
5. Organisational autonomy?
Here, ‘overall autonomy’ is the arithmetic mean of the four dimensions. This paper
is structured as follows: data and measurements, results, discussion, and conclusion.
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Data andmeasurements
Overview
This study combines multiple data sources into a two-level hierarchical data structure. In
total, 1815 HEIs (meso-level) are nested inside 26 education systems (macro-level). Our
meso-level data combines data on ETPs from Study Portals, the largest online portal of
ETPs; with the European Tertiary Education Register (ETER; Lepori etal., 2023), a com-
prehensive database of European HEIs. Our macro-level data is comprised of the university
autonomy scorecards provided by the European University Association (Pruvot & Ester-
mann, 2017) and the Education First English Proficiency Index (EF EPI) (Education First,
2018). We base our analysis on the 2017 edition of the autonomy scorecards with ETPs
commencing in the academic year 2018, L1 ETER data from 2018, and the EPI report
from 2018. Therefore, this is a cross-sectional empirical study on the effect of autonomy on
commencing ETPs in the following year.
Meso‑level data
As previously noted, our meso-level data (L1) are comprised of institutional data from
ETER and data on ETPs from Study Portals. By joining Study Portals data (HEIs which
offer EMI) with ETER (a comprehensive database of European HEIs), we avoid selecting
on the dependent variable. In other words, if we only look at HEIs which offer EMI, this
would overestimate the prevalence and success of EMI programmes by only looking at
successful cases. Moreover, both databases cover the European Higher Education Area,
which enables us to run our analyses across the entire region, rather than combining data
from national databases, which likely vary in collection techniques and definition of EMI.
ETER provides a comprehensive database of European HEIs alongside meso-level pre-
dictors (L1). Our meso-level predictors comprise three classification indicators relevant to
running EMI programmes: institutional control, institution size, and education intensity.
We summarise these details from the ETER handbook (Lepori, 2023) as follows. Insti-
tutional control is a binary categorical variable, 0 if the HEI is private and mostly funded
by private sources, and 1 if the institution is under public control or mostly funded by the
state. Institution size is a categorical variable based on the number of full-time equivalent
(FTE) academic staff. In cases of missingness, head count (HC) of academic staff is used to
estimate FTE academic staff. There are four levels to institution size: (1) below 100 FTEs;
(2) below 500 FTEs; (3) below 1500 FTEs; and (4) more or equal to 1500 FTEs. Education
intensity is the number of ISCED 5–7 students divided by academic staff and sorted into
four categories: (1) below 10 students per academic staff member; (2) below 25; (3) below
50; and (4) 50 or above. One strength of using ETER in our analysis is that these variables
are consistently defined and measured across the countries in our study.
We include institutional control as our hypotheses concern the effect of autonomy on
public HEIs in terms of ETPs. Institution size is included as our dependent variable is a
count of ETPs not a percentage and therefore we are required to control for size. Education
intensity accounts for the fact that institutions differ in pedagogic focus.
We joined data from Study Portals on the number of English-taught bachelor’s and mas-
ter’s programmes commencing in a given academic year to ETER. The datasets were joined
manually using detailed information on HEI name and location present in both datasets.
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As our hypotheses concern the effect of autonomy on HEIs in terms of EMI, represented
by both bachelor’s and master’s ETPs, we analyse a combined response variable of the
sum of master’s and bachelor’s ETPs. The exploration of multiple dependents divided by
level or discipline is beyond the scope of a single paper. As this measure comes from an
online portal, strictly speaking, this measure is a count of internationally advertised ETPs,
which differs from some prior studies which collected data from institutional surveys (e.g.
Wächter & Maiworm, 2008). The trade-offs between both measures are discussed in the
‘Limitations’ section.
The HEIs represented in Study Portals data are a subset of ETER HEIs. ETER HEIs
without programmes listed on Study Portals are allocated zero ETPs as default. Therefore,
each institution starts at zero and we count each time an ETP is advertised. Figure1 visual-
ises these data, with green dots representing the count of ETPs. Note that regions without
autonomy scorecards are not represented.
Macro‑level data
As noted in the overview, our macro-level (L2) data are comprised of 26 education sys-
tems from two sources: the university autonomy scorecards and the English Proficiency
Index. All education systems represent a single country with two exceptions: Germany and
Belgium. Due to the unique systems in these countries, three regional education systems
(Brandenburg, Hessen, and North Rhine-Westphalia) are represented from Germany and
Fig. 1 EMI in universities nested within European education systems in 2018
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Higher Education
two regional education systems (Flanders and Wallonia-Brussels Federation) are repre-
sented from Belgium. However, we maintain a two-level hierarchical structure.
The scorecards operationalise university autonomy and enable us to test our hypotheses.
Autonomy is measured across four dimensions, each determined by indicators (Table1).
The scorecards measure each dimension on a scale of 0–100; however, ostensibly scores
range from 32 to 100, with average dimension scores ranging from 59 to 69. Scores are
calculated by starting from 100 and deducting points based upon restrictions to indicators,
which are weighted by importance. ‘Overall autonomy’ is the arithmetic mean of all four
dimensions.
The 2017 edition of the autonomy scorecards acts as a predictor for ETPs commencing
in 2018. These programmes can most likely be traced to prior years in terms of administra-
tion, which puts their inception in 2017 or perhaps earlier. This likely varies between insti-
tutions and is unknowable from the perspective of a large-scale generalisable study. Like-
wise, the time taken for macro-level policies to effect meso-level outcomes is not measured
by our study. Instead, this study provides a generalisable look at the state of autonomy as
it stands in 2017 and whether this predicts commencing ETPs in the following year. Pin-
pointing when events occurred and how they are related is beyond the remit of our predic-
tive modelling.
The English Proficiency Index captures the average level of English ability for each
level 2 group through test scores. In the 2018 edition, scores are measured between 0 and
100 based upon test data from over 1,300,000 test-takers worldwide. This measure acts as
a control with two competing hypotheses: greater English language competence predicts
more ETPs as the population is more amenable to EMI. Alternatively, lower English lan-
guage ability predicts more ETPs as English language skills are in demand. In either case,
we have theoretical reasons that include EPI as a relevant predictor.
Table 1 Dimensions of university autonomy (Pruvot & Estermann, 2017)
Academic Financial
- Capacity to decide on overall student numbers - Length and type of public funding
- Ability to select students - Capacity to keep surplus
- Ability to introduce programmes - Capacity to borrow money
- Ability to terminate programmes - Ability to own buildings
- Ability to choose language of instruction - Ability to charge tuition fees for national/
EU students
- Capacity to select QA mechanisms and providers - Ability to charge tuition fees for non-EU
students
- Ability to design content of degree programmes
Staffing Organisational
- Ability to decide on recruitment procedures - Selection procedure for the executive head
- Ability to decide on salaries - Selection criteria for the executive head
- Ability to decide on dismissals - Dismissal of the executive head
- Ability to decide on promotions - Term of office of the executive head
- Note: indicators pertain to senior academic and administra-
tive staff
- Inclusion and selection of external mem-
bers in governing bodies
- Capacity to decide on academic structures
- Capacity to create legal entities
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Data imputation
We conducted data imputation to address missingness in the data. We used predictive mean
matching offered by the mice package (v3.16.0; van Buuren & Groothuis-Oudshoorn,
2011) in R to impute data for our L1 variables in the ETER database. Prior studies have
suggested the value of imputation with this dataset (Bruni etal., 2021). For our L2 varia-
bles, we imputed data taking the average between subsequent and prior years. For instance,
we imputed EPI scores for Estonia and Latvia in 2018, as they were not included in that
edition.
Analysis
We test our hypotheses concerning the 2017 edition of the autonomy scorecards using data
on ETPs commencing the following academic year. The two most recent autonomy score-
cards were published in 2017 and 2023; however, the ETER project presents data from
2010 to 2020, requiring us to base our analysis on the 2017 scorecard.
In total, 1815 HEIs are nested within 26 education systems. As our data and hypotheses
are multilevel, we use multilevel regression to test the relationship between institutional
autonomy and EMI. Our dependent variable is zero-inflated and over-dispersed. Therefore,
we fit zero-inflated negative binomial models.3 Models that ignore zero-inflation or treat
it as overdispersion can bias parameter estimates (Harrison, 2014). Zero-inflated negative
binomial (ZINB) models effectively run two models simultaneously: a logistic regression
(the zero-inflation model), which estimates the probability of observing excess zeros; and
a negative binomial regression (the conditional model), which models the count of ETPs.
The zero-inflation model predicts the probability of observing excess zeros. That is, zeros
not due to the count distribution in our conditional model (‘sampling zeros’), but zeros which
occur for structural reasons (‘excess zeros’), which would indicate HEIs not practicing EMI
at all. For instance, a daily count of alcohol use would contain sampling zeros on days when
individuals do not drink alcohol. However, teetotal individuals would have structural zeros for
all observations. Similarly, some institutions may not practice EMI at all, whereas others may
practice EMI but still have sampling zeros. By fitting a ZINB model, we avoid biasing our esti-
mates and we are able to (1) predict EMI versus non-EMI institutions (zero-inflation) and (2)
predict the count of ETPs in EMI-practicing institutions (conditional).
Concerning interpretation, a negative estimate in the zero-inflation model indicates a
reduction in the likelihood of observing excess zeros, i.e. an increased likelihood of observ-
ing EMI. The conditional model is the opposite: a negative estimate predicts a reduction in
the number of ETPs in EMI-practising HEIs.
Our model building process started with intercept-only models, and we added theoreti-
cally relevant predictors which increased model fit. The result of our model building pro-
cess is formulated as follows:
Zero-inflation model:
3 We compared Poisson and negative binomial models and their zero-inflated and hurdle counterparts. We
also included variants in terms of negative binomial family, nbinom1 and nbinom2 from the glmmTMB
package (Brooks etal., 2017). We found that the zero-inflated negative binomial model (nbinom2) fit the
data best in terms of Akaike information criterion (AIC) and satisfied model assumptions concerning zero-
inflation, dispersion, collinearity, and convergence.
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Level 1:
logit(ETPs)ij =𝛽0j+𝛽1j(Public)ij +rij
Level 2:
𝛽0j=𝛾00 +𝛾01(Autonomy)j+𝛾02(EPI)j
Conditional model:
Level 1:
count(ETPs)ij =𝛽0j+𝛽1j(Public)ij +𝛽2j(Controls)ij +eij
Level 2:
𝛽0j=𝛾00 +𝛾01(Autonomy)j+𝛾02(EPI)j+u0j
Correlation:
In the zero-inflation model, the dependent variable is the log-odds of observing excess
zeros for the ith HEI within the jth education system. β0j represents the intercept. ‘Public’
indicates whether the HEI is public (1) or private (0). At level 2, ‘autonomy’ represents the
autonomy scorecards, either as overall autonomy or as specific dimensions. The English
Proficiency Index is denoted by EPI.
In the conditional model, the dependent variable is the predictive log of the expected
count of ETPs. This contains ‘controls’ relevant to the count distribution: institution size
and education intensity. As ‘public’ forms the lower-level variable in a cross-level interac-
tion, it is included as a random slope (Heisig & Schaeffer, 2019). We centred our continu-
ous variables. The correlation parameter, ρij, accounts for dependence between the condi-
tional and zero-inflation models.
Results
Overall autonomy
Starting with hypothesis 1: is overall autonomy a predictor of English-medium instruction
in public universities? In Table2, the main effect of overall autonomy shows the effect
of autonomy on private HEIs (i.e. assuming ‘public’ is 0) and the interaction term shows
how the effect of autonomy differs between public and private HEIs. In the logistic model,
where a negative coefficient predicts fewer excess zeros (i.e. more EMI-practising HEIs),
the effect of being a public university is negative and non-significant (coefficient = − 1.59,
standard error = 0.902, p = 0.078), suggesting that there is no difference between public
HEIs and private HEIs, everything else held constant. The fact that this result is close to
significance suggests that public HEIs may be more likely to offer EMI, but we cannot
reject the null hypothesis that there is no effect.
We find that the effect of overall autonomy (on private HEIs) is positive and non-signif-
icant (coefficient = 0.050, standard er ror = 0.043, p = 0.247), suggesting there is no effect.
However, the interaction shows that the effect of autonomy is dependent on whether the
HEI is public or private. Here we find a negative and significant result (coefficient = − 0.15,
standard er ror = 0.049, p = 0.002). Therefore, there is no difference between public or pri-
vate HEIs, except when we increase overall autonomy, in which case we find that public
𝛽1j=𝛾10 +𝛾11(Autonomy)j
𝛽1j=𝛾10 +𝛾11(Autonomy)j+u1j
𝛽2j=𝛾20
𝜌ij(eij,rij)
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HEIs are more likely to offer EMI compared to private HEIs. This confirms our hypothesis
that overall autonomy is a significant predictor of EMI in public universities.
Concerning effect size, the odds ratio (OR) for the effect of overall autonomy within
public universities4 suggests that increasing overall autonomy by one point above the
mean, whilst controlling for all other variables, increases the likelihood of public uni-
versities offering EMI by 9.5%. If we increase overall autonomy by ten points, the likeli-
hood of offering EMI increases by 63%. We visualise the predicted probabilities in Fig.2,
which shows that as overall autonomy increases, the likelihood of observing excess zeros
decreases.
Table 2 Overall autonomy as a predictor of ETPs
Observations: 1815; groups: 26
Significance: ***p > 0.001; **p > 0.01; *p > 0.05
Overall autonomy
Zero-inflation fixed effects (logistic) Coef SE OR p
Level1
Intercept − 0.114 0.677 0.892 0.866
Public − 1.590 0.902 0.204 0.078
Level2
English proficiency − 0.125 0.068 0.882 0.064
Overall autonomy 0.050 0.043 1.051 0.247
Cross-level interaction
Public × overall autonomy − 0.150 0.049 0.861 0.002**
Conditional fixed effects (negative binomial) Coef SE IRR p
Level1
Intercept − 4.497 0.555 0.011 < 0.001***
Size 1.535 0.108 4.642 < 0.001***
Education intensity 0.320 0.121 1.377 0.008**
Public − 0.224 0.453 0.799 0.621
Level2
English proficiency − 0.021 0.050 0.979 0.678
Overall autonomy 0.017 0.035 1.017 0.628
Cross-level interaction
Public × overall autonomy 0.010 0.037 1.010 0.798
Conditional random effects (negative binomial) Var SD Corr
Intercept 0.714 0.845
Public slope 1.094 1.046 − 0.59
Model fit R2(Cond.) R2(Marg.) AIC
0.49 0.35 2530
4 As the interaction concerns a continuous variable and a categorical variable, we sum the coefficients for
the continuous variable and the interaction and exponent the result. This differs from the odds ratio for the
interaction term alone.
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In the negative binomial model, a positive coefficient predicts a greater number of ETPs
in EMI-practising HEIs. Here we find non-significant results concerning whether the insti-
tution is public or private (coefficient = − 0.224, standard error = 0.453, p = 0.621), t he
effect of overall autonomy (on private HEIs) (coefficient = 0.017, standard error = 0.035,
p = 0.628), and how the effect of autonomy differs between public and private in the inter-
action (coefficient = 0.01, standard error = 0.037, p = 0.798). The directionality of the effect
Fig. 2 Predicted probabilities of non-EMI HEIs (excess zeros) by overall autonomy
Fig. 3 Predicted count of ETPs by overall autonomy
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suggests that increasing overall autonomy may result in an increased number of ETPs in
public EMI-practising institutions, although the evidence is not strong enough to reject the
null hypothesis that there is no effect.
As a measure of effect size, the incident rate ratio (IRR) suggests that the number of
ETPs will be 2.7% larger in public HEIs that have one more point of overall autonomy
compared to public HEIs of an equal size, of equal pedagogic focus, and with an equal
level of English ability within the country. An increase of ten points of overall autonomy
above the mean suggests that the number of offered ETPs will be 30.1% larger. We visu-
alised the predicted count of ETPs in Fig.3, which shows (with great uncertainty) that as
overall autonomy increases, the predicted count of ETPs increases in public HEIs.
Dimensions ofautonomy
Now that we have established that overall autonomy is a predictor of EMI in public uni-
versities, does this hold concerning the individual dimensions of autonomy? In this sec-
tion, we show that academic, staffing, and organisational autonomy predict EMI, but
Fig. 4 Predicted probabilities of non-EMI HEIs (excess zeros) by dimension of autonomy
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Higher Education
financial autonomy does not (zero-inflation models). We find insignificant results concern-
ing whether EMI-offering institutions offer more EMI (conditional models). We visualise
the predicted probabilities of excess zeros in Fig.4 and the predicted count of ETPs in
Fig.5.
Academic autonomy concerns the pedagogic activities of the university. Table3 shows
that academic autonomy is a significant predictor of EMI in public HEIs compared to
private HEIs in our zero-inflation model (coefficient = − 0.123, standard error = 0.039,
p = 0.002). We find an insignificant result concerning the effect of being a public HEI.
Therefore, being a public or private HEI does not predict an increased probability of offer-
ing EMI; however, when we increase academic autonomy, offering EMI becomes more
likely in public HEIs. Interestingly, we find a significant result concerning the main effect
of academic autonomy (on private HEIs) suggesting that increasing academic autonomy
increases the likelihood of private HEIs not offering EMI. This suggests that increasing
academic autonomy has opposite effects on private versus public HEIs. In terms of effect
size, the odds ratio suggests that increasing academic autonomy one point above the mean
increases the likelihood of public HEIs offering EMI by 3.7%, which increases to 31.1% for
Fig. 5 Predicted count of ETPs by dimension of autonomy
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Higher Education
a ten-point increase. The IRR for the interaction in the conditional model suggests that the
number of ETPs will be 0.9% larger in public HEIs with a one-point increase of academic
autonomy, and 1.5% larger for a ten-point increase.
Financial autonomy concerns the financial freedom of the university. Table4 shows that
financial autonomy is not a predictor of EMI in public HEIs compared to private HEIs
(coefficient = − 0.041, standard error = 0.064, p = 0.518). However, the directionality of the
effect suggests that increasing financial autonomy may increase the likelihood of public
HEIs offering EMI compared to private HEIs, although we cannot reject the null hypoth-
esis that there is no effect. In terms of effect size, the odds ratio suggests that increasing
financial autonomy by one point above the mean increases the likelihood of public HEIs
offering EMI by 2.9%, which increases to 25.7% for a ten-point increase. The IRR for the
interaction in our conditional model suggests − 0.4% fewer ETPs in public HEIs for a one-
point increase in financial autonomy above the mean, and − 4.4% fewer for a ten-point
increase. Importantly, all these results are insignificant.
Table 3 Academic autonomy as a predictor of ETPs
Observations: 1815; groups: 26
Significance: ***p > 0.001; **p > 0.01; *p > 0.05
Academic autonomy
Zero-inflation fixed effects (logistic) Coef SE OR p
Level1
Intercept − 0.804 0.777 0.447 0.301
Public − 0.355 0.731 0.701 0.627
Level2
English proficiency − 0.209 0.066 0.811 0.002**
Academic autonomy 0.086 0.035 1.089 0.015*
Cross-level interaction
Public × academic autonomy − 0.123 0.039 0.884 0.002**
Conditional fixed effects (negative binomial) Coef SE IRR p
Level1
Intercept − 4.584 0.542 0.010 < 0.001***
Size − 0.077 0.434 4.558 < 0.001***
Education intensity 1.517 0.109 1.372 0.009**
Public 0.316 0.120 0.926 0.860
Level2
English proficiency − 0.017 0.050 0.983 0.732
Academic autonomy 0.036 0.024 1.037 0.135
Cross-level interaction
Public × academic autonomy − 0.027 0.026 0.973 0.285
Conditional random effects (negative binomial) Var SD Corr
Intercept 0.800 0.894
Public slope 1.310 1.145 − 0.67
Model fit R2(Cond.) R2(Marg.) AIC
0.491 0.352 2534
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Higher Education
Staffing autonomy concerns the recruitment, promotion, dismissal, and salaries of sen-
ior administrative and senior academic staff. Table5 shows that staffing autonomy is a sig-
nificant predictor of EMI in public HEIs compared to private HEIs (coefficient = − 0.145,
standard error = 0.052, p = 0.005). We find a significant result concerning the main effect of
staffing autonomy (on private HEIs) suggesting that increasing staffing autonomy decreases
the likelihood of private HEIs offering EMI. In terms of effect size, the odds ratio suggests
that increasing staffing autonomy by one point above the mean increases the likelihood
of public HEIs offering EMI by 1.4%, which increases to 23.4% for a ten-point increase.
The IRR for the interaction in our conditional model predicts 2.1% more ETPs in public
HEIs for a one-point increase in staffing autonomy, and 23.4% more ETPs for a ten-point
increase.
Organisational autonomy concerns the leadership structure of the university. Table 6
shows that organisational autonomy is a significant predictor of EMI in public HEIs
Table 4 Financial autonomy as a predictor of ETPs
Observations: 1815; groups: 26
Significance: ***p > 0.001; **p > 0.01; *p > 0.05
Financial autonomy
Zero-inflation fixed effects (logistic) Coef SE OR p
Level1
Intercept − 2.615 1.681 0.073 0.120
Public 1.375 1.536 3.957 0.371
Level2
English proficiency − 0.258 0.064 0.773 < 0.001***
Financial autonomy 0.011 0.061 1.011 0.851
Cross-level interaction
Public × financial autonomy − 0.041 0.064 0.960 0.518
Conditional fixed effects (negative binomial) Coef SE IRR p
Level1
Intercept − 5.157 0.491 0.006 < 0.001***
Size 1.558 0.109 4.747 < 0.001***
Education intensity 0.315 0.120 1.370 0.009**
Public 0.304 0.381 1.355 0.425
Level2
English proficiency 0.010 0.042 1.011 0.801
Financial autonomy − 0.021 0.022 0.980 0.349
Cross-level interaction
Public × financial autonomy 0.016 0.024 1.016 0.499
Conditional random effects (negative binomial) Var SD Corr
Intercept 0.942 0.971
Public slope 0.870 0.933 − 0.59
Model fit R2(Cond.) R2(Marg.) AIC
0.480 0.347 2537
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Higher Education
compared to private HEIs (coefficient = − 0.093, standard er ror = 0.034, p = 0.007). The effect
of organisational autonomy (on private HEIs) is also significant, suggesting that increasing
organisational autonomy predicts fewer private HEIs offering EMI. In terms of effect size,
the odds ratio for the interaction suggests that increasing organisational autonomy by one
point above the mean predicts a 3.5% increase in the likelihood of public HEIs offering EMI,
which increases to 48.2% for a ten-point increase. The interaction in the conditional model is
insignificant, with the IRR suggesting a one-point increase of organisational autonomy above
the mean predicting 0.2% more ETPs, which increases to 8.4% for a ten-point increase.
We can further our exploration by visualising the predicted probabilities of excess zeros
by differing levels of autonomy between public and private HEIs (Fig.4) and likewise the
predicted count of ETPs (Fig.5). Figure4 shows how increases in academic, staffing, and
organisational autonomy decrease the likelihood of observing excess zeros in public HEIs
Table 5 Staffing autonomy as a predictor of ETPs
Observations: 1815; groups: 26
Significance: ***p > 0.001; **p > 0.01; *p > 0.05
Staffing autonomy
Zero-inflation fixed effects (logistic) Coef SE OR p
Level1
Intercept − 1.630 1.001 0.196 0.103
Public 0.467 0.814 1.595 0.567
Level2
English proficiency − 0.267 0.083 0.765 0.001**
Staffing autonomy 0.131 0.052 1.139 0.013*
Cross-level interaction
Public × staffing autonomy − 0.145 0.052 0.865 0.005**
Conditional fixed effects (negative binomial) Coef SE IRR p
Level1
Intercept − 4.675 0.534 0.009 < 0.001***
Size 1.531 0.110 4.622 < 0.001***
Education intensity 0.346 0.122 1.414 0.004
Public − 0.072 0.403 0.930 0.858
Level2
English proficiency − 0.032 0.052 0.968 0.538
Staffing autonomy 0.045 0.030 1.046 0.134
Cross-level interaction
Public × staffing autonomy − 0.024 0.030 0.977 0.422
Conditional random effects (negative binomial) Var SD Corr
Intercept 1.056 1.028
Public slope 0.594 0.771 − 0.61
Model fit R2(Cond.) R2(Marg.) AIC
0.495 0.363 2533
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Higher Education
(i.e. observing EMI) and increase the likelihood of observing excess zeros in private HEIs
(i.e. observing non-EMI). This difference is most stark concerning academic autonomy and
staffing autonomy.
In Fig.5, the weakness of our modelling in comparison to our zero-inflated models is
apparent here, where we can see broad trends, which hint at an effect of increasing auton-
omy associated with a greater count of ETPs in public HEIs, but not strongly enough to
reject the null hypothesis in each case that there is no effect.
Discussion
Our results open up discussion questions: why is overall autonomy a predictor with the
largest effect size? Why are academic, staffing, and organisational autonomy predictors of
EMI, but financial autonomy is not? Why are our models better at predicting EMI versus
non-EMI and worse at predicting the count of ETPs in EMI-practising HEIs? Key to this
discussion is the role of institutional autonomy as a central component of ‘steering at a dis-
tance’ reforms in European higher education and the implications the current findings have
for theorisations on the rise of English as the language of instruction in European higher
education.
Overall autonomy was found to be a predictor of EMI (p = 0.002). Our model predicts
that an increase of one point of overall autonomy above the mean increases the likelihood
of public universities offering EMI by 9.5%, and ten points increases the likelihood by
63%. These predicted values are higher than those for the individual dimensions, suggest-
ing that HEIs require freedom in multiple dimensions in order to offer EMI. Importantly,
this finding provides statistical evidence for theories in linguistics that link the rise of
English with neoliberalism. This is in light of a recent ‘political economy turn’ in applied
linguistics (Block, 2017), focussing on English language teaching and learning within the
context of neoliberalism (e.g. Block etal., 2013; Bori, 2018), where the English language
itself is a form of capital (Petrovic& Yazan, 2021). This can be framed within a broader
context whereby English has had a free ride on the back of global capitalism, going back
as far as the 1600s (O’Regan, 2021). The recent turn towards neoliberalism is the modern
extension of this history and steering at a distance reforms within higher education are but
one component of this complex behemoth.
Higher education expansion, which includes developing programmes, hiring staff,
recruiting students, and generating revenue, involves endogenous processes: as it expands,
it affects factors which affect expansion. The provision of EMI is situated within this eco-
system and this research does not make claims as to which event, the granting of autonomy
and the provision of EMI, precedes another. While this study explored the extent to which
granting HEIs autonomy increases their likelihood of offering ETPs, neither of these pro-
cesses occurs in isolation but in tandem with myriads of other social processes.
There is, however, evidence from a case study in the Netherlands that illustrated how
granting universities autonomy can encourage the provision of EMI. In a case university
in the Netherlands, EMI was introduced only a couple of years after the country had gone
through a steering at a distance reform in 1985. There is interview data that the enhanced
autonomy and added accountability mechanisms that were put into place with the reform
did incentivise the university management to introduce EMI at master’s level (Hultgren &
Wilkinson, 2022). Elsewhere in Europe, however, where the rise of EMI is more recent,
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Higher Education
it is arguably more difficult to pinpoint which event precedes another and it is likely that
‘steering at a distance’ reforms and ‘EMI’ flow back and forth across countries in tandem
with other globalisation processes.
In general, however, the introduction of a neoliberal type of higher education govern-
ance opens up the sector for increased competition, marketisation, and commodification.
We suggest that part of this commodification involves the value placed upon EMI, enhanc-
ing the perceived value of a university degree, as students signal knowledge of their disci-
pline, English ability, and a connection to international communities of scholars. Whilst
this may be partly driven by students, the universities also benefit by attracting a wider tal-
ent pool of staff and students, increasing revenue, and climbing university rankings.
However, beyond overall autonomy, does the dimension matter? Our results suggest that
academic, staffing, and organisational autonomy are most important. Academic autonomy
Table 6 Organisational autonomy as a predictor of ETPs
Observations: 1815; groups: 26
Significance: ***p > 0.001; **p > 0.01; *p > 0.05
Organisational autonomy
Zero-inflation fixed effects (logistic) Coef SE OR p
Level1
Intercept − 0.526 0.727 0.591 0.470
Public − 0.756 0.820 0.470 0.357
Level2
English proficiency − 0.233 0.068 0.792 0.001***
Organisational autonomy 0.058 0.028 1.060 0.035*
Cross-level interaction
Public × organisational autonomy − 0.093 0.034 0.911 0.007**
Conditional fixed effects (negative binomial) Coef SE IRR p
Level1
Intercept − 4.608 0.564 0.010 < 0.001***
Size 1.521 0.110 4.576 < 0.001***
Education intensity 0.304 0.120 1.356 0.011*
Public − 0.062 0.390 0.940 0.874
Level2
English proficiency − 0.002 0.048 0.998 0.965
Organisational autonomy 0.009 0.027 1.009 0.751
Cross-level interaction
Public × organisational autonomy − 0.007 0.028 0.993 0.808
Conditional random effects (negative binomial) Var SD Corr
Intercept 1.262 1.123
Public slope 0.404 0.635 − 0.65
Model fit R2(Cond.) R2(Marg.) AIC
0.464 0.312 2536
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Higher Education
concerns the pedagogic activities of the university. Academic autonomy was found to be a
predictor of EMI (p = 0.002), with our model predicting that increasing academic auton-
omy by one point above the mean increases the likelihood of public HEIs offering EMI
by 3.7%. A ten-point increase suggests an increased likelihood of 31.1%. To see why aca-
demic autonomy is important, we can turn to the specific criteria in question. Academic
autonomy includes several key criteria for offering EMI, including ‘ability to choose the
language of instruction’, ‘the ability to introduce and terminate degree programmes’, ‘the
capacity to decide on overall student numbers’, and ‘the ability to select students’. Whilst
the ability to choose the language of instruction and the ability to introduce/terminate pro-
grammes are self-explanatory, we can discuss the relevance of autonomy over students and
admissions.
A key area of academic autonomy is related to selecting students and deciding upon
overall student numbers. The relevance of these areas coheres with our re-theorising of the
growth of EMI as linked to the internationally competitive environment brought about by
steering at a distance policies. Universities use English programmes as means to attract a
wider talent pool of international students. This coheres with Macaro’s (2018) suggestion
that competition for students between public and private universities may be driving EMI
growth. In this sense, public universities can only compete for students internationally if
they are given the autonomy to do so. If universities are limited in their ability to expand
the diversity and size of their student base, then ETPs are not as important. However, if
universities have more autonomy in this direction, then ETPs are a means to the end of
attracting a wider base of students and growing the size of the university.
Staffing autonomy involves freedom over recruitment, salaries, dismissals, and promo-
tions for senior academic and administrative staff. Staffing autonomy was found to be a
predictor of EMI (p = 0.005). Our model predicts that an increase of one point of staffing
autonomy above the mean increases the likelihood of public universities offering EMI by
1.4%, and a ten-point increase raises this likelihood to 13.2%. Autonomy over staffing deci-
sions is crucial for EMI because offering EMI requires the freedom to attract talent capable
of teaching in English. This necessitates autonomy in recruitment procedures and the abil-
ity to provide internationally competitive salaries.
Organisational autonomy concerns the leadership model of the university: the freedom
to choose who steers the ship and which direction to take it. Organisational autonomy was
found to be a significant predictor of EMI in public universities (p = 0.007), increasing
organisational autonomy by one point above the mean predicts an increased likelihood of
offering EMI of 3.5%, which raises to 48.2% for a ten-point increase. This dimension may
be particularly important as it concerns the elite participants who decide on whether to
adopt EMI. As noted in the introduction, the Polytechnic University of Milan decided to
teach all postgraduate degree programmes in English, against the wishes of the lecturers,
who brought a legal case against the university (Murphy & Zuaro, 2021). In this case, the
implementation of EMI was imposed by the leadership of the university. Our data supports
the view that a key aspect of autonomy required to offer EMI is freedom over the leader-
ship model of the university, which would grant them the ability to adopt EMI as a strate-
gic choice.
Financial autonomy did not predict EMI in public universities. The directionality of the
effect, the predicted probabilities in Fig.4, and the predicted count in Fig.5 all suggested
that increasing financial autonomy may predict more EMI-practising universities and more
ETPs, although we cannot rule out the null hypothesis that there is no relationship. Notably,
being financially autonomous does not imply being well funded, nor does autonomy over
tuition fees imply a greater number of international students. Whilst there are differences
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Higher Education
between the financial structures of public and private HEIs in terms of funding sources
and financial freedom, we do not find that financial autonomy is relevant to the provision
of EMI. In other words, assuming that private HEIs typically have greater autonomy over
their finances than public HEIs, our data suggests it is not the freedom over finances that
makes the difference in terms of EMI. However, there are likely other financial and eco-
nomic factors which affect EMI not captured by financial autonomy, including market
forces such as student demand. This may be a case of missing variable bias whereby finan-
cial autonomy could be relevant if other financial drivers are considered.
It is also worth discussing the fact that our models were better at predicting EMI ver-
sus non-EMI than predicting the number of ETPs in EMI-offering institutions. We high-
light two reasons why this might be the case. First, a face-value interpretation: measures
of autonomy are more relevant to whether institutions are able to offer EMI or not, but
less relevant to modelling more/less EMI within HEIs. Second, a data limitation: the
measure of ETPs is limited as programmes vary in size and HEIs may find that they are
able to offer one or two ETPs and then increase student numbers if they are faced with
increased demand. Increasing class size might be preferable to seeking approval for new
programmes, hiring new staff, and so on. Therefore, an institution may be able to offer
more EMI without increasing programme numbers. Using a measure of ‘percentage/num-
ber of students studying in EMI’ as a dependent may be better suited to investigating the
growth of EMI within institutions, rather than ETPs, as in the current study. This draws
attention to one of the key challenges of EMI research: the (un)availability of data on EMI
practices in higher education.
A further area of interest is English proficiency. EPI Score was found to be negative and
significant in our zero-inflation models for all four dimensions and was close to significance
for overall autonomy. In other words, EPI score typically predicts the offering of EMI.
This partially supports the position that EMI may be associated with countries with higher
English ability. However, there is the possibility of missing variable bias, where the effect
of EPI on EMI is moderated by other factors such as multilingualism, motivation, and the
demand for English skills.
Conclusion
Summary
This paper investigated the relationship between EMI in European higher education and
institutional autonomy, a key component of steering at a distance governance reforms.
The University Autonomy Scorecards operationalised institutional autonomy in the study,
which conceptualise autonomy in similar terms to how autonomy is framed within the con-
text of steering at a distance reforms. In our terms, we understand this as a form of ‘regu-
lated’ autonomy. Multilevel regression was used to test the relationship between university
autonomy and EMI, drawing on a comprehensive database of 1815 HEIs nested within 26
education systems.
We found that overall, academic, staffing, and organisational autonomy predicted the
probability of offering EMI in public HEIs, whereas financial autonomy did not. In cases
of significance, the odds ratio was greatest concerning overall autonomy. Interestingly, aca-
demic, staffing, and organisational autonomy are the dimensions which involve autonomy
over participants, broadly categorised as students (academic autonomy), academic and
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Higher Education
administrative staff (staffing autonomy), and senior management (organisational auton-
omy). Our findings are the first to quantify the link between governance and language of
instruction, an area which we identify as a blind-spot in higher education governance.
Limitations
The study offers a broad-brush account of the relationship between autonomy and EMI.
However, we acknowledge that there is ambiguity over the exact nature of this relationship,
particularly concerning the mechanism between autonomy and EMI. Autonomy may be a
moderator/mediator between globalisation pressures and EMI, or perhaps autonomy func-
tions as a proxy for other more crucial aspects of neoliberal reform.Moreover, our study
was based on the 2017 edition of the autonomy scorecards andtherefore provides a snap-
shot of Europe at that time and may be less applicable to current autonomyconditions in
Europe or indeed higher education contexts outside of Europe.Further research in the form
of detailed case studies could be conducted to trace how autonomy may or may not lead to
EMI and what conditions promote or hamper it (see, Hultgren etal., 2023; Thomas etal.,
2024). It would be interesting to see whether these processes are unique to the European
context, or if they replicate worldwide.
This study drew on internationally advertised ETPs as a measure of EMI. Prior stud-
ies have measured EMI using institution-level survey data (Wächter & Maiworm, 2008)
as well as internationally advertised ETPs (Sandström & Neghina, 2017; Wächter &
Maiworm, 2014). Both measures are limited in the fact that they may not capture all pro-
grammes that run, either due to not advertising programmes or survey non-response. How-
ever, Study Portals is the largest online portal of ETPs, which connects universities to
prospective students worldwide, which would only miss programmes which HEIs are not
actively advertising. Therefore, whilst Study Portals provides substantial up-to-date data
on ETPs, we may be missing programmes from institutions that are uninterested in inter-
national recruitment, preferring to recruit local students. As such, our measure is closer
to ‘internationally oriented’ EMI. This draws attention to one of the main challenges of
EMI research: the lack of good record keeping on language of instruction practices. We
hope that by drawing attention to this issue, more data will be generated which captures
language of instruction.
Implications
The gap that this research identified was the theorised link between institutional autonomy,
a key component of steering at a distance reforms, and EMI in European higher educa-
tion. To date, the presence of EMI in higher education has been attributed to The Bologna
Process, internationalisation, and the commodification of higher education. Our research
provides evidence for a novel explanation: that EMI can be attributed, at least in part, to
higher education autonomy. We are not claiming that institutional autonomy is the only
explanation. In fact, the link between institutional autonomy and EMI is underpinned by
other factors, such as massification, an increasingly mobile global population, and English
as a lingua franca. Our findings provide a generalisable context for research conducted on
higher education and EMI within Europe.
Underlying the practice of EMI are a range of ethical, legal, and political implications.
The current research may provide an evidence-base to inform political and legal disputes
over the implementation of EMI in contexts similar to the Polytechnic of Milan, where
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Higher Education
lecturers brought a legal case against the university, and in the Netherlands, which is cur-
rently considering legislation that restricts EMI in higher education. Our research suggests
that the liberalisation of higher education may pave the way for EMI and therefore efforts
to restrict EMI, by necessity, may have to restrict institutional autonomy. And conversely,
efforts towards the liberalisation of higher education can be expected to coincide with EMI
within the European context.
We hope that this research can highlight the importance of language of instruction for
those within higher education governance, whereby EMI may be an unintended conse-
quence of neoliberal reforms. The link between governance and linguistic outcomes is a
critically under-researched area of study, partially due to a lack of available data on lan-
guage of instruction. We would encourage those collecting data on higher education trends
to include data on language of instruction practices, either at the programme level or at
the level of student and staff participation. Moreover, researching the relationship between
governance and linguistic outcomes requires interdisciplinary collaboration to break new
ground; we hope that this article can pave the way for further research in this space.
Funding This work was supported by a UKRI Future Leaders Fellowship (grant number MR/T021500/1).
Declarations
Conflict of interest The authors declare no competing interests.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Com-
mons licence, and indicate if changes were made. The images or other third party material in this article
are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the
material. If material is not included in the article’s Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly
from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
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Authors and Aliations
PeterWingrove1 · BeatriceZuaro1 · MarionNao1 · DoganYuksel1 ·
LeventeLittvay2,3 · AnnaKristinaHultgren1
* Peter Wingrove
peter.wingrove@open.ac.uk; peterwingrove@gmail.com
Beatrice Zuaro
beatrice.zuaro@open.ac.uk
Marion Nao
marion.nao@open.ac.uk
Dogan Yuksel
dogan.yuksel@open.ac.uk
Levente Littvay
littvay.levente@tk.hun-ren.hu
Anna Kristina Hultgren
kristina.hultgren@open.ac.uk
1 Faculty ofWellbeing, Education andLanguage Studies, School ofLanguages andApplied
Linguistics, The Open University, MiltonKeynes, UK
2 HUN-REN Centre forSocial Sciences, Hungarian Academy ofScience Centre ofExcellence,
Budapest, Hungary
3 Democracy Institute inBudapest, Central European University, Budapest, Hungary
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
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3.
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