Journal of Higher Education Policy and Management
ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/cjhe20
Academic workloads: what does a manager need
to consider?
Beth R. Crisp
To cite this article: Beth R. Crisp (2022) Academic workloads: what does a manager need
to consider?, Journal of Higher Education Policy and Management, 44:6, 547-562, DOI:
10.1080/1360080X.2022.2064404
To link to this article: https://doi.org/10.1080/1360080X.2022.2064404
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Published online: 13 Apr 2022.
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JOURNAL OF HIGHER EDUCATION POLICY AND MANAGEMENT
2022, VOL. 44, NO. 6, 547–562
https://doi.org/10.1080/1360080X.2022.2064404
RESEARCH ARTICLE
Academic workloads: what does a manager need to consider?
Beth R. Crisp
School of Health and Social Development, Deakin University, Geelong, Victoria, Australia
ABSTRACT
KEYWORDS
Utilising Bacchi’s framework ‘What’s the problem represented to be?’
the literature on managing academic workloads was analysed to
explore why workload allocation models are deemed essential in
the contemporary university and the assumptions which underpin
workload allocations. Whether due to the need for efficient use of
scarce resources or ensuring equity for staff, workload allocation
models are promoted as an accurate measure of each individual’s
workload and as a measure of accountability. The literature privileges
the views of staff whose workloads are ‘managed’ but unable to do
their work within their contracted hours. Universities justify long
hours of unpaid overtime as a norm of professionalism. Critical issues
for managers are identified, and suggestions to enable them to be
more effective in the process of workload allocation are presented.
Academic workloads;
accountability; professional
development; university
managers; workload
allocation models; scoping
review
Introduction
As a social work educator it is in my professional DNA to not only know what to do but
to understand the implications of my actions and the assumptions that underlie the
choice I make (Green Lister, 2012). While I have encountered senior academics who have
regarded their move to management as stepping out of professional practice, Kenny
(2009, p. 639) has noted the importance of ‘professional academic leadership’ whose role
it is ‘facilitate the contribution of academics to effective policy and strategy . . . [and]
protection of the integrity of academic work’. Yet, despite being organisations with
specialist expertise in educating professionals, universities are very poor at providing
the training that senior academic staff require to undertake their jobs (Evans, 2017).
As Deputy Head of a school with approximately 100 academic staff from a range of
disciplines, I am tasked with implementing the Faculty’s workload allocation model.
Having worked in several universities in Australia and the United Kingdom, my experi­
ence has been that workload allocation is almost always controversial (see also Kenny,
Fluck, & Jetson, 2012). Moreover, the imperfections of workload models are one of the
few things that academics can be relied on to reach a consensus.
Workload management often seems to be reduced to the administrative tasks asso­
ciated with compliance. However, as academic practice, it is about making the best use of
scarce staff resources to ensure that the needs of the institution and the aspirations of staff
CONTACT Beth R. Crisp
beth.crisp@deakin.edu.au
School of Health and Social Development, Deakin University,
Waterfront Campus, Locked Bag 20001, Geelong, Victoria 3220, Australia
Supplemental data for this article can be accessed here
© 2022 Association for Tertiary Education Management and the LH Martin Institute for Tertiary Education Leadership and Management
548
B. R. CRISP
members are addressed. However, while this clearly requires a high level of expertise,
most academic managers are lucky to receive anything more than cursory training
around the workload allocation models they are expected to implement or the systems
used in their organisation to manage workloads (Ladyshewsky & Flavell, 2011; Watson,
King, Dekeyser, Baré, & Baldock, 2015), let alone be exposed to any theoretical or
philosophical underpinnings of the models they are expected to implement and oversee.
Indeed, I receive daily notifications of masterclasses or management training in my
inbox; however, none includes the suggestion that there might be benefits to increasing
my expertise in academic workload management.
A not uncommon response by senior academics requiring further knowledge of some
aspect of their work is to engage in a programme of self-guided reading (Ladyshewsky &
Flavell, 2011). This led to a decision to undertake a scoping review once I realised there
was a considerable amount of literature that might benefit others like me whose academic
practice includes workload management. Following an analysis of the literature, this
paper concludes with the ways I am implementing the findings in my professional
practice.
Method
The study took the form of a scoping review, whose purpose is to scope a body of
literature rather than exhaustively explore a known body of literature to answer one or
more specific questions. It can be undertaken either as a standalone piece of research
or to establish research questions for subsequent studies (Munn et al., 2018). Unsure
where literature on such a specific topic as the management of academic workloads
might be found, I began with a search using the term ‘academic workload model’ in
Google Scholar, restricting my interest to literature published since 2000 in the
English language. This initial screen on 19 May 2021 identified a group of 11 articles
and one conference paper that seemed to relevant to my interests. These were
retrieved, read and their reference lists scrutinised for other potentially relevant
publications. Using the above keyword term, I then searched journals that had
published these articles for additional relevant material. I continued this process
until saturation when no new articles were identified. By the end of August 2021,
a total of 59 journal articles, three conference papers and two published reports had
been retrieved and examined.
Whether they are legal documents formally recognised as part of an enterprise
agreement or are adopted by workgroups despite having no formal status, workload
allocation models guide how work is allocated within an organisation or part thereof
(Kenny & Fluck, 2012). From the standpoint that policies are created to address pro­
blems, whether real or perceived, Carol Bacchi’s (2009) framework ‘What’s the problem
represented to be?’ was initially developed as a structured approach to policy analysis.
However, it has since been used in various ways, including the study of organisational
procedures (Bletsas & Beasley, 2012). Bacchi’s framework, which utilises six key ques­
tions to explore explicit statements and implicit assumptions, and critiques and con­
sequences of these, provided an approach for me as a manager to interrogate the
literature on academic workload models and identify issues that managers of academic
workload allocations need to understand.
JOURNAL OF HIGHER EDUCATION POLICY AND MANAGEMENT
549
Only documents retrieved that contributed to answering Bacchi’s six questions are
included in the following analysis. After all the documents were read, 11 journal articles
failed to provide insight into any of Bacchi’s questions. Hence, the analysis presented
below is based on 48 journal articles plus all of the reports (2) and conference papers (3)
retrieved. Two thirds of the documents concerned work in Australia (22), UK (13) and
comparative studies from both these countries (2). Other documents related to academic
work in New Zealand and South Africa (4 each), Ireland and the US (2 each) and single
papers from Ethiopia, France, Norway and Sweden. Methodologies included standar­
dised surveys or semi-structured interviews with academics, case studies of academic
units, literature reviews and scholarly essays. Further details are provided in supplemen­
tary data for this paper.
Academic workloads: a ‘What’s the problem represented to be?’ analysis
What’s the ‘problem’ that led to workload allocation models?
Bacchi’s first question concerns the aims that a policy or procedure seeks to address; in
this instance why formal workload allocation models exist in many universities.
However, as Hull (2006) has previously noted, the reasons why these models are deemed
to be required cluster around either ensuring efficient use of scarce resources or ensuring
equity for staff.
The hours available for allocation are finite, limited by the number of staff and the
hours they are contracted to work (Paewai, Meyer, & Houston, 2007). Poor processes can
lead to staff being allocated work far in excess of their contracted hours (Barrett &
Barrett, 2007). Universities have a duty of care to staff to ensure they are not setting
unrealistic work expectations (Hull & Harris, 2006), thereby placing staff at risk of
damaging their physical and/or mental health (Wolf, 2010). Moreover, within an aca­
demic unit, there are strategic or core aspects of work which must be done. Hence,
workload allocation models can signal organisational priorities: the tasks managers must
ensure are allocated. Strategic directions can also be signalled by models regarding of how
work is allocated (Barrett & Barrett, 2010). For example, an expectation that academic
staff are research active might include explicit allocations of time for both teaching and
research (Ringwood et al., 2005).
Academic workloads are perceived to be fair if the expectations can be met within the
total annual workload (Bitzer, 2007). However, this does not simply mean that an annual
allocation should ensure sufficient hours in the year to undertake work. Importantly,
there must be enough hours in the working week to ensure time-critical tasks, such as
course development, teaching and assessment, can occur within the required timeframes.
However, even if all work in a unit is allocated, the allocations may not be equitable if
there is no consistent interpretation of how the model should be implemented (Dekeyser,
Watson, & Baré, 2016). Equitable workloads are those that are judged fair in comparison
to colleagues/peers, not just concerning the number of hours of work allocated, but also
in terms of opportunities for tasks which bring accolades and rewards, such as promo­
tion, or time for scholarly endeavours including research (Bitzer, 2007; Wolf, 2010).
However, issues of gender, race or other forms of disadvantage were not discussed in the
reviewed literature regarding notions of fairness. This is despite there being considerable
literature documenting that female academics are frequently expected to do
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B. R. CRISP
a disproportionate amount of ‘academic housework’ (Heijstra, Steinthorsdóttir, &
Einarsdóttir, 2017; Macfarlane & Burg, 2019), that gendered expectations impede their
career trajectories (see Eggins, 2017; White & O’Connor, 2017), and that such expecta­
tions are further magnified for women from racial minorities (Nzinga-Johnson, 2013).
Informal work allocation methods are increasingly seen as problematic and potentially
discriminatory (Graham, 2015). Rather than favouring staff who are best able to deflect
requests that they take on particular tasks, it is claimed that transparent and fair models
provide an instrument for promoting equity by identifying staff who are under- or overload (Vardi, 2009). Furthermore, the processes for ensuring equity must also be timeefficient:
. . . if one had no formulaic allocations whatsoever, and treated every workload scenario as
one to be thought about from scratch, then things would be unmanageable in another way,
requiring long workload management meetings to be held to explore such matters.
Moreover, if there was any concern to treat like cases alike, more meetings would have to
be held in which workload managers satisfied themselves that they were exercising their
discretion in a way equivalent to other workload managers in similar scenarios. (DavsonGalle, 2010, p. 67)
What are the assumptions which underlie this representation of the ‘problem’?
Having identified why workload allocations models are deemed necessary,
Bacchi’s second question considers the assumptions around these. The literature suggests
that one or more of the following assumptions are present when workload allocations are
considered: that models provide an accurate measure of an individual’s workload; the
model is effectively implemented; and that workload planning can occur on an annual
basis. Each of these will be considered in turn.
Although often promoted as accurate, critics claim academic work is difficult to
quantify and that workload allocation models typically underestimate the true time
required to undertake specific tasks (Boyd, 2014; Kenny et al., 2012). Furthermore, it
has been claimed that ‘all models, it would seem, omit some key variable, or misspecify variables, or use inappropriate rules of weighting and exceptions. The conse­
quence is a perception of winners and losers, and complaints of unfairness’ (Wolf,
2010, p. 247).
Little research has been done on the time academics actually require to perform key
components of their work (Kennedy, Laurillard, Horan, & Charlton, 2015; Kenny &
Fluck, 2019). Consequently, it is unsurprising that a case study of four academic units in
one New Zealand university found huge discrepancies in the amount of time allowed for
teaching-related tasks. While some tasks such as coordination may depend on the
number of students enrolled, critically, there were differences in tasks irrespective of
the number of students. For example, lecture preparation was allocated between 1 and
11 hours per lecture and tutorial preparation between 0.5 and 4 hours per tutorial
(Houston, Meyer, & Paewai, 2006).
Workload models typically assume that all staff require the same amount of time to
undertake similar tasks. Yet, there may be differences between disciplines regarding the
preparation and delivery of learning materials (Ringwood et al., 2005). For example, in
a discipline where the content does not substantially change from year to year, lecture
JOURNAL OF HIGHER EDUCATION POLICY AND MANAGEMENT
551
material may be reused with minor changes. In contrast, in disciplines where the knowl­
edge or content is not static, there may be little or no ability to recycle lectures (Dekeyser,
Watson, & Baré, 2014). In a similar vein, it has been proposed that the assessment of
work integrated learning is far more time consuming than traditional assessment tasks:
however, workload models may not recognise this (Bilgin, Rowe, & Clark, 2017). It has
also been suggested that experts require much less time than novices to undertake some
tasks (Boyd, 2014; Gregory & Lodge, 2015; Kenny & Fluck, 2018; Wolf, 2010).
Conversely, what appears to be the same task, such as a research allocation, may have
quite different expectations of the professoriate who are expected to provide leadership to
a discipline in addition to running research programmes, whereas junior researchers may
be primarily tasked with doing their own research (Evans, 2017; Macfarlane, 2011).
Similarly, teaching may require differential allocations of time depending on the mode
of delivery. It is claimed that staff spend more time in online courses than teaching in
traditional classroom settings (Sammons & Ruth, 2007), and that learning new technol­
ogies require time that tends not to be factored into workloads (Gous & Roberts, 2015).
However, the extent to which teaching online increases academic workloads is not
enumerated in any of the articles included in this review. One study suggested that
effective training in using learning management systems can reduce teacher hours in the
online environment. Nevertheless, the difference, if any, in workloads between trained
online teachers and those in face-to-face classrooms is unknown (Haggerty, 2015).
Although no model is perfect, some are better than others (Dekeyser et al., 2014;
Robertson & Germov, 2015). The choice of model can make a huge difference to the
number of hours allocated to resourcing a course (Dekeyser et al., 2016). The simplest
workload formulas tend to lack the level of nuance required when staff do not have the
same responsibilities (Vardi, 2009). Workload models only include selected key tasks and
do not account for many others which may be required, leaving staff who are theoretically
on-load, but in reality, are overloaded (Miller, 2019; Vardi, 2009). For example, alloca­
tions for professional development associated with new technologies is minimal or absent
in many models (Gregory & Lodge, 2015; Haggerty, 2015). Non-standard (Boyd, 2014) or
newly emerging (Mœglin & Vidal, 2015) roles are particularly at risk of being poorly
understood and hence not adequately accounted for.
A survey of academics at the University of South Africa found they rated as important
38 of 40 discrete tasks, which together constitute academic work (Bezuidenhout, 2015).
Such specificity can have its own difficulties in determining separate allocations (Vardi,
2009) and even models with a high degree of task delineation may miss some tasks. For
example, managing staff was not listed among the academic tasks Bezuidenhout (2015)
identified, although managing large teams of sessional staff increasingly common
(Sammons & Ruth, 2007). It is therefore common to bundle together groups of tasks
Items in workload models as proxy indicators rather than absolute measures of each of
many tasks (Franco-Santos, Rivera, & Bourne, 2014). Such bundling assumes that while
each staff member may require more or less time than would be notionally allocated to
separate tasks, the bundled allocation is a good approximation of the total time required.
Following on from the assumption that workload allocation models provide an
accurate measure of workloads, is the assumption that the model is effectively imple­
mented, which includes ensuring strategic priorities are addressed (Robertson &
Germov, 2015). This starts with a commitment from management that implementation
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B. R. CRISP
of the model will occur fairly and transparently (Kenny, 2018). This, in turn, requires all
departments, schools or faculties, and all academic staff within these organisational units,
to have a workload allocated in accordance with the selected model (DowlingHetherington, 2016).
Fair and transparent implementation across administrative units makes assumptions
about similarities that may not be valid. In particular, there needs to be recognition of the
specific context and issues where the model is to be adopted (Robertson & Germov,
2015). For instance, academics in different institutions or departments with ostensibly
the same workload could in fact have different amounts of work that needs to be done if
there is disparate administrative support (Stensaker, Frølich, & Aamodt, 2020):
Challenges due to information, context, or management constraints do not exhaust the
causes of academics’ concerns about workloads. Even if an academic were to accept that the
measures, choices and allocations of a workload system are as good as they can be, he or she
might nevertheless judge the system, and parts of the system or its operation, favourably or
unfavourably from his or her subjective point of view. (Wolf, 2010, p. 248)
Thus, a further requirement for effective implementation is that academic staff are
consulted rather about their work allocation. Ideally, consultation occurs at the relevant
organisational level of unit or department in the development of the model and then
with individual staff in annual workload planning meetings (Barrett & Barrett, 2007).
An element of personal choice may encourage a staff member to take on a role they
consider less favourably on the basis that there is time set aside for tasks which closely
align with their own interests (Wolf, 2010). Finally, in respect to implementation,
a workload allocation model can empower staff who are unhappy with their allocation
to have it reviewed, particularly if they believe their workload to be too high (Kenny,
2018).
With a robust model that is effectively implemented, an assumption may emerge that
workload planning only needs to occur at the beginning of each year. However staff
resignations or long-term absences (such as extended sick leave or maternity leave) may
occur at short notice at any time throughout the year. Larger than expected enrolment
numbers can also create unanticipated gaps that require filling at short notice. Annual
workloads that allow little room for change can limit universities making timely
responses to emerging new needs (Franco-Santos et al., 2014). While temporary staff
may be able to cover some situations, institutional rules on what tasks may or may not be
done by short-term academic staff may require changes to workloads of ongoing staff.
How has this representation come about?
So how has it happened that workload allocation models are now considered essential in
most universities despite acknowledgement that such models are flawed? Bacchi’s third
question considers how we have arrived at this point.
Universities are often regarded as expensive public enterprises that should be scruti­
nised to ensure they provide ‘value for money’ (Burgess, Lewis, & Mobbs, 2003, p. 217).
In particular, there are often concerns that universities are over-staffed and not effectively
or efficiently managing their staff (Dowling-Hetherington, 2016). Staffing costs account
for much of the budget of higher education institutions (Graham, 2016). Moreover,
JOURNAL OF HIGHER EDUCATION POLICY AND MANAGEMENT
553
caricatures of academics as ‘gentlemen’ without a real job (Bryson, 2004) fuel
a perception that universities are unable to manage and account for these expensive
staff resources (Graham, 2015).
In a context where their key funder, the state, does not trust university managements to
manage their resources (Tight, 2010), workload allocation models provide a mechanism for
universities to demonstrate that their staffing costs are not excessive. Claims of increased
efficiency have also underpinned many universities moving from having small single
discipline organisational units to large multidisciplinary schools with dozens, if not
hundreds, of staff. Without a formal model, it would be impossible for heads of these
large academic units to be aware of individual staff loads (Barrett & Barrett, 2007).
A common formula for measuring workloads also enables a head to delegate the manage­
ment of workload allocation to a number of staff with the expectation of a consistent
approach being adopted by each of these managers (Barrett & Barrett, 2008).
At the same time, subjecting all academic staff to the rigours of accounting for their
time according to a flawed model might also be construed as the academic workforce not
being trusted to do what is required. This is particularly so when the expectations on
academics are constantly increasing (Bezuidenhout, 2015). For example:
Academics are under increasing pressure to publish high quality research in preferred
journals; to apply for grants, to demonstrate research impact and to build external links
with industry and community. In addition, growing student numbers, wider student
diversity, changes in student expectations as ‘paying customers’, the ‘employability’ agenda,
and pressure to adopt new and innovative teaching methodologies have all impacted on the
nature and burden of the academic workload. (Hornibrook, 2012, p. 29)
These increased expectations are typically accompanied by growing compliance require­
ments, which further add to workloads (Paewai et al., 2007).
What is left unproblematic? Where are the silences?
While the introduction of workload allocation models is advantageous for university
management, and advantageous for some academics but disadvantageous for others,
Bacchi’s fourth question asks whether there are other stakeholders and issues which need
to be taken into account. Groups that potentially have a legitimate stake in workload
allocation, but which tend to be disregarded, include 1) students and 2) the individual
staff who are responsible for negotiating workloads with academic staff.
As noted previously, the need for fairness and transparency are often claimed as why
workload allocation models are introduced. (Hull, 2006). But fairness to whom? While
the need for equity between teaching staff is acknowledged, workload allocation systems
should also be fair to students, particularly when students are paying fees to study at
university. Arguably a model that is fair to students is one in which teaching staff actually
have a sufficient allocation to administer, prepare, and deliver courses, and have an
assessment regime which is appropriate to the unit of study (Hornibrook, 2012).
However, unless universities receive negative feedback on the delivery of teaching
programs, there may be little incentive to address the issue of fairness to students
(Stensaker et al., 2020).
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B. R. CRISP
Students also need to be able to ask questions of those teaching them. Massification of
university systems has resulted in increasing numbers of students but staff numbers have
not increased proportionately. Consequently, individual staff have to service more
students yet maintain their teaching quality (Akalu, 2016). Workload models which
presume minimal time is required for student consultation either fail to provide students
with the support they need to succeed in their studies or discriminate against staff who
provide additional support to students (Ruth, 2006).
Increasing use of online delivery may also have changed student expectations that
academic staff will be responsive to their queries throughout the week and not just during
set class hours. Students in online settings also tend to call on academic staff for technical
queries even though this is not built into academic workloads, and academic staff who are
end-users of systems are not necessarily equipped to deal with highly technical issues
(Berge, 2008). However, estimates of the extent of increased support provided to students
are absent from the reviewed literature.
In addition to students, another voice missing from discussions about workload
allocations is that of staff who find themselves administering academic workloads.
The literature on academic workloads tends to present university management and
staff with workloads as distinct groupings (for example, Kenny & Fluck, 2014).
However, many mid-level staff who administer workloads are also subject to these
measures themselves and may experience conflict between their role as a manager
and beliefs as to the rights of workers, particularly for those who are members of
a union (Graham, 2016). Furthermore, what they have learnt about managing
workloads may reflect on their own experiences of having their workloads managed,
particularly when it comes to balancing compliance with considering how best to
use the skills of each team member and how to best develop their potential
(Ladyshewsky & Flavell, 2011). Moreover, managers who are workload managed
may well account for most managers believing their staff consider their workload
model as ungenerous or allocating insufficient time to do required tasks (Watson
et al., 2015).
What effects are produced by this representation?
Bacchi’s fifth question concerns the impact of the way a problem has been represented.
In this instance, there may be a lack of clarity about the reasons for implementing
workload allocation systems perceptions that workload allocations often underestimate
the actual time required to do the work, and academic of staff to do the work required
without a formal allocation system. Not surprisingly, this context can lead to staff
dissatisfaction.
Universities are expected to foster creativity (Franco-Santos et al., 2014) and
hence have promoted ‘the idea of the professional as a self-managing individual’
(Burgess et al., 2003, p. 218). Furthermore, the promise of autonomy and flexibility
are important factors in attracting and retaining people to work in academia
(Bellamy, Morley, & Watty, 2003). Hence, the introduction of workload models
potentially leaves academics feeling demoralised because they cannot be trusted
(Lazarsfeld Jensen & Morgan, 2009) and resenting being micro-managed (Woelert
& Yates, 2015).
JOURNAL OF HIGHER EDUCATION POLICY AND MANAGEMENT
555
Without some measure of workload the intrinsic motivation of many academic staff
can be taken advantage of by the greedy institution (Franco-Santos et al., 2014). However,
a workload allocation model that is unrealistic and does not acknowledge how much time
is required to undertake tasks is also dependent on the good-will of staff to work long
hours (Kinman & Jones, 2003). Indeed, it has been claimed that
If universities venture down the path of allocating the time academics put into their work, it
cannot be done dishonestly. While the detailed atomisation of the complex academic role is not
our aim, the time associated with genuinely important activities or roles must be acknowledged
within an individual’s workload to be credible. Attempts to hide important academic service, or
other academic tasks, in order to reduce costs due to budget pressures, will destroy trust and
could be counterproductive in the longer run for universities. (Kenny & Fluck, 2019, p. 29)
Overwork can readily lead to loss of physical and/or mental health (Kinman & Jones,
2003; Melin, Astvik, & Bernhard-Oettel, 2014) and impact family life, including con­
tributing to relationship breakdowns (Kinman & Jones, 2003). Yet, common mechanisms
for coping with excessive workloads involve strategies that are potentially healththreatening, such as missing lunch, bringing work home at nights and weekends, doing
work while on holiday and not taking sick leave when unwell (Melin et al., 2014). Some
staff, such as those on short-term contracts, might feel unable to formally challenge
unjust workloads (Bryson, 2004). In contrast, tenured academic staff may prioritise
aspects of their work they believe to be most important, while deprioritising less
important work such as attending certain meetings, taking on more work (Melin et al.,
2014), or developing new lectures for existing curricula (Dowling-Hetherington, 2016).
How has this representation of the ‘problem’ been promoted and defended?
As academic workload models have been implemented in many universities, the initial
reason for their requirement to ensure that finite working hours are fairly distributed
between staff ostensibly appears to have been met. However, given that working several
hours a week more than contracted is the norm for academics (Cannizzo & Osbaldiston,
2016; Houston et al., 2006; Kinman & Jones, 2003), how do universities account for and
defend these additional working hours of their staff? Addressing this discrepancy between
contracted and actual hours worked question is at the heart of Bacchi’s sixth question.
University managements do not consider staff working long hours problematic since
that is what professionals do (Lyons & Ingersoll, 2010). In fact, workload allocation
processes may be viewed by management as a mechanism for transferring accountability
for ensuring work is done to individual staff members (Franco-Santos et al., 2014; Kenny,
2017), particularly if it can be demonstrated that working long hours is a choice that
individual academics make. For example, it has been suggested that
Given the self-directed nature of much academic work, staff have a responsibility to reflect
upon the impact of their individual choices about the work they undertake, including
consideration of work priorities, the exceeding of requirements, and their engagement
with work allocation consultative processes (Robertson & Germov, 2015, p. 513)
A UK study found that three-quarters of university academics described their view as
‘challenging or exciting’ rather than ‘dull or routine’ (Bryson, 2004, p. 45). Hence long
working hours may be sanctioned by high levels of work satisfaction (Wolf, 2010). This is
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B. R. CRISP
particularly so when longer working hours are associated with more research outputs
(Cannizzo & Osbaldiston, 2016) and for other tasks where the line between work and
non-work is thin (Wolf, 2010).
As long as academic careers remain desirable, universities may have little incentive to
review their workload allocation practices and address questions of exploitation (Kinman
& Jones, 2003). Individual staff who feel aggrieved by their workload may be able to make
a case for reduced responsibilities (Houston et al., 2006) and heads of academic units
typically have some discretion to alter workloads for individuals who appear to have been
treated unfairly by the model (Barrett & Barrett, 2007). However, this places the onus on
individual staff and managers to remedy unfair situations on a case-by-case basis (Boyd,
2014). Furthermore, workload models are rarely evaluated (Graham, 2015), so assump­
tions that workload management is ethical practice remain untested (Boyd, 2014). An
equitable distribution of work that is exploitative is not the same as fair workload
allocations (Vardi, 2009).
Discussion
The origins of this paper were a desire to understand more about workload allocation
models to guide my work as a university manager. I discovered that knowledge around
workload allocation models tends to be limited and ‘does not provide a comprehensive
research-base for clear guidelines with known consequences’ (Houston et al., 2006, p. 27).
Moreover, the role of managers in workload allocation is not necessarily clear, resulting
in managers, even within the same institution, having very different understandings of
their role (Graham, 2016). For more than a century there has been debate on how
academic workloads should be determined (Yuker, 1975); however, the literature is silent
about equipping and supporting individual managers to administer workloads
(Hornibrook, 2012). Consequently, it is hardly surprising that most managers do not
receive any training in implementing workload models other than the specific details of
the university’s policy or tools to manage the process (Watson et al., 2015). Furthermore,
lack of training around workload management is consistent with a lack of training in
many vital aspects of academic management. This is justified by the short-term nature of
many university managerial appointments (Barrett & Barrett, 2008).
The predominance of literature in this review emerging from Australia and the United
Kingdom is noteworthy. Workload allocation has been a concern of the national aca­
demic staff unions in both countries (Barrett & Barrett, 2008), and several of the
Australian studies were published in Australian Universities Review, a journal auspiced
by the National Tertiary Education Union. Including papers and reports from New
Zealand and South Africa, resulted in 46 of the 53 documents reviewed coming from
countries that are part of the British Commonwealth. Higher education in these countries
is predominantly in publicly funded institutions, and the labour force is highly unionised.
Academic language across these countries is often similar and would account for the
initial search term ‘academic workload model’ being found in literature originating in
these countries.
As one of the reviewers of an earlier version of this paper suggested, the use of the
search term ‘faculty workload model’ may well have resulted in far more than two papers
being sourced from the US. However, given that most of the literature reviewed was
JOURNAL OF HIGHER EDUCATION POLICY AND MANAGEMENT
557
obtained by reviewing the reference lists of retrieved documents, it is surprising that not
more American literature was found. This raises two distinct possibilities. Either
American literature has not focused on the management of faculty workloads, or scholars
from Commonwealth countries are either unaware or do not find relevant American
literature on this topic. As such, the findings reported in this paper may have less
applicability outside Commonwealth countries.
However, several issues emerge from the literature, which those managing academic
workloads need to consider if they are to do more than muddle through the process. The
first is that workload negotiations occur in a context in which ‘academic staff and
administrators have to deal with competing interests and tasks in an increasingly com­
plex and demanding environment’ (Melin et al., 2014, p. 290). This environment includes
a complex array of incentives and constraints that will differ for each staff member (Wolf,
2010) including ‘respecting the autonomous culture of academe’ (Boyd, 2014, p. 323), the
right not to be exploited by being required to work more than their contracted hours
(Bitzer, 2007), and the aspirations of staff members. However, the literature is silent as to
how a manager should consider this complex array of competing factors and ensure a fair
outcome for both staff members and the organisation.
One of the complexities of workload allocation is that no two staff members may have the
same workload if different numbers of students are enrolled in the subjects taught by an
academic unit. In addition, some staff may have more administrative responsibilities than
others. There is nevertheless an expectation that the workloads of all staff are equitable.
While comprehensive models may assist in achieving this (Barrett & Barrett, 2007), it is still
necessary to ensure that the workload allocations are not unfair create or entrench discri­
mination (Boyd, 2014). For example, a staff member might perceive their teaching allocation
to be much lighter if it coincides with their research interests than if required to teach in an
area where they have little or no expertise (Wolf, 2010). Such a workload might seem
equitable on paper when compared with the total hours allocated to other staff. However,
while recognising that it is sometimes necessary for academics to teach outside their field of
expertise, it is not necessarily the most effective use of their talents (Kenny, 2009).
Fairness is typically assumed to exist when all staff have a full workload that can be
accomplished in the hours they are employed. This assumes that time allocations are
realistic expectations of the time required to undertake required tasks and is crucial for
ensuring staff with different responsibilities are not advantaged or disadvantaged by
workload allocation. However, if the evidence base underpinning time allocations is
flimsy or non-existent, especially in relation to online teaching, it may be more by luck
than good management when workloads are considered fair by all stakeholders.
Furthermore, changes in the way higher education operates, such as increasing use of
online teaching and increasing sophistication in the ways online programs are delivered
(Crisp, 2018) raise the prospect that time allocations that were once considered appro­
priate for online teaching might no longer be so.
If workload allocations are to be evidence-based, then the evidence base needs to
reflect how academics work now and not how they worked in the past. For example,
technological innovations are not only changing the way academics teach but also how
they do research (Weber, 2021) and carry out service roles such as managing staff (Field,
2015). Furthermore, presentation of research findings needs to be nuanced so that it is
clear what is included in a category such as ‘online teaching’. For instance, does this
558
B. R. CRISP
category relate to the time required for development and delivery of content only, or does
it include different forms of student engagement such as responding to student queries
either to the whole class on a discussion board or to individual emails or telephone calls?
The amount of administrative support received around teaching can also substantially
impact on academic workloads; However, it is not explicit in research as to how much
time academics require to undertake the work expected of them. For instance, as I write
this in early 2022 in Australia, large numbers of professional staff in universities are being
made redundant, and many academics are concerned about the additional work they will
have to do but which is not acknowledged nor been factored into their workloads.
Conclusion
As a manager who is expected to fairly administer complex work allocations based on
questionable evidence, undertaking this review has reinforced to me the invidious
position of middle managers in the university. Nevertheless, there are several things
I can do to improve this situation somewhat.
Since undertaking this review, rather than beginning the workload allocation process
with inputting some initial data for each staff member into the database and seeing what
variations are required to ensure equity, I have organised information sessions at which
the rationale for the model is explained and staff can ask questions. This addresses
concerns about a lack of transparency surrounding workload allocation processes
(Boyd, 2014). Once workloads have been drafted, I have been more conscious of the
need to review all allocations to ensure that discrimination is not being entrenched by
assuming that specific tasks are better undertaken by staff of a particular demographic
characteristic such as gender (Aiston, 2011).
Another essential point the critical role workload allocations can have in whether or
not strategic priorities are progressed for both individual staff and the organisation. As
a manager I need to consider how the workload model can lead to higher quality
educational experiences for students and/or build our unit’s research reputation
(Ringwood et al., 2005). Also, there are some activities that pay the costs of an academic
unit such as income for teaching or research. Additionally, other tasks do not directly
bring income but develop the capacity for these income-producing tasks to be under­
taken and are an investment in the future, such as capacity building or enhancing the
reputation of the unit’s work. As such, workload management of an academic unit is not
about ensuring that each staff member earns their income, but rather it is the unit earning
the required income (Bitzer, 2007).
Finally, as workload allocations will never be entirely accurate, it is crucial to develop
a shared understanding of what is ‘good enough’. This is not an excuse for complacency
by managers or the organisation. Rather, it involves an ongoing commitment to review­
ing and revising both the workload allocation model and the workload allocations for
individual staff.
Disclosure statement
No potential conflict of interest was reported by the author.
JOURNAL OF HIGHER EDUCATION POLICY AND MANAGEMENT
559
ORCID
Beth R. Crisp
http://orcid.org/0000-0001-7863-4482
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