The Use of the Time Diary Method to Explore Academic Time Management: Insights from an
Australian University
Dr Branka Krivokapic-Skoko, School of Management and Marketing, Charles Sturt University,
Bathurst, 2795 NSW, Australia
bkrivokapic@csu.edu.au
Dr Roderick Duncan, School of Finance and Accounting, Charles Sturt University, Bathurst, 2795
NSW, Australia
rduncan@csu.edu.au
Dr. Kerry Tilbrook, School of Management and Marketing, Charles Sturt University, Bathurst, 2795
NSW, Australia
ktilbrook@csu.edu.au
Abstract
Academics and universities have an interest in tracking the tasks and workloads of academics in the
areas of teaching, research and administration, but do academics and their employers know how
many hours a week an academic engages in particular tasks? We discuss the on-going development
of an electronic time diary tool to measure an academic’s teaching, research and administrative tasks.
Our preliminary findings suggest that time spent communicating with students is now a significant
portion of an academic workday. Academics work long hours interrupted by the demands of students
as customers coupled with increasing accountability and compliance within universities. We find that
academics value aspects of their work which foster self-direction and creativity in both teaching and
research activities.
Keywords: academics and time management; higher education management; time diaries
1. Introduction
The introduction of learning technologies such as subject websites, forums and emails has resulted in
changes in the tasks expected of academics in running their subjects. Changes in the student cohorts
entering universities have meant a change in teaching practices as the new generation of students
have higher expectations of communication with academics. University student profiles are changing
because:

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current students are less likely to be pursuing their studies uninterrupted by paid work
obligations
changes to admissions standards means that universities are admitting students with varied
levels of preparation for university and arguably without adequate remediation interventions in
place, and
introduction of the new VLE (virtual learning environment) has contributed to a climate where
students expect academics to be contacable24/7 (Anderson et al. 2002; Tennant M,
McMullen C & Kaczynski D (2010).
These fundamental workplace changes give rise to several questions that universities and academics
have strong interests in answering. What tasks are academics doing to run their subjects? How long
are academics spending on those tasks to run their subjects? The answers given by past generations
of academics may have little relevance in today’s universities.
The aim of this research was to assess the use of the time by academics, so in the next stage of this
research we can develop and pilot a time diary tool for measuring academics’ activities and use of
time across teaching, research and administration. This tool would assist academics in their own time
management, help academic leaders manage their academic staff and enable universities as
institutions to better leverage their resources to achieve quality outcomes in teaching and research.
2. Academics’ activities and use of time
Previous researchers have given some emphasis to the impact of new technologies on academics’
activities and time (Marginson & Considine 2000; Anderson et al. 2002; Hull 2006; Blackmore &
Sachs 2007). While the educational possibilities of informational and communication technologies for
online learning, collaborative teaching and other new forms of teaching are enormous, one of the
downsides of these new technologies is the way that new technologies impacts academic workloads.
As Tennant et al. (2010, p. 8) argued in their book on teaching and learning in higher eduction,
dealing responsibly with students’ emails takes a large amount of time, much more than student
contact in the so-called “pre-electronic” era. Accurately measuring the tasks and time required for
teaching a subject can assist universities in evaluating their teaching practices and also in juggling
other academic tasks such as scholarship and research. Equally, an opportunity for academics to
reflect on their own use of time will support academics in evaluating their own teaching practices and
professional activities.
While information technology has many advantaged for the academics it has also the potential to
greatly expand the workload and the time needed to be spent on a variety of the new activities. The
preparation of computer-enabled learning takes more time for academics due to the increased need
for individual learning and training to master these techniques (Anderson et al. 2002; Hill 2006). Time
spent dealing with student emails has also increased greatly with the proliferation of means of
electronic communication. Yet this is invisible work (Anderson et al. 2002, p.13; Soliman 1999) which
is very difficult to capture in existing academic workload documents. Eighty-four percent of
respondents in the Anderson et al (2002) survey agreed that time spent on email communication was
increasing. There is also an expectation by students that academics can answer their enquiries 24/7,
which is part of the overall trend of commodification of the higher education industry with students
portrayed as ‘customers’ (Slaughter & Leslie 1997; Marginson & Considine 2000) leading to the
syndrome of the ‘24 hour professor’ (Chronicle of Higher Education, May 31, 2002, cited in Anderson
et al. 2002, p.18).
Tennant et al. (2010, p. 135) argue that academics now struggle with controlling the impact
technology is having upon the work day and upon personal time and that “technology has made
academics hyper-accessible. Many academics feel they now have to be hyper-responsible.” As
Buckholdt and Miller (2009. p 3) argued “the idea of 40 hour work week has become even less
relevant in a society organized by information technology”. Some other changes in the education and
training environment, like the ones listed below, have led to work intensification for academics
(Dearlove, 1998; Jarvis, 2001; Ogbonna & Harris, 2004; Kinman & Jones, cited in Hull, 2006; Baty,
2005b, cited in Hull, 2006; Blackmore & Sachs, 2007, Jensen and Morgan 2009):

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

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Technological duress - the introduction of new IT systems that academics are expected to
master, such as Interact and MSI;
Devolution of administration - the transfer of tasks that had traditionally been done by
administration to academics, such as managing travel expenses under ProMaster and
entering subject outlines under MSI;
Multiplication of campuses - daily travel between campuses is a feature of the workload of
some academics;
Uncoordinated escalation of bureaucratic process - the creation of entirely new systems
and requirements for academics, such as entering publications in the centralised research
output data base and completing annual performance management reports;
Increasing partnerships with outside organisations which require academic administrative
support;
Casualisation - full-time academics find themselves supporting casual staff in tasks that
casuals are unable or untrained to perform, such as drawing up exams or engaging with
administration;
Increasing external accountability pressures from government funding bodies e.g. ERA and
quality assurance requirements coupled with the requirement to generate self-funding from
entrepreneurial activities with insufficient infrastructure.
In many surveys of academic staff in the United States, Britain, Australia and New Zealand there was
an overwhelming picture of work intensification, higher stress levels and growing disillusionment with
the changing content of academic work which seems part of a global trend (OECD 1998). In analysing
how new technologies are transforming academics McShane (July 2006) also identified that apart
from significant increase in the workload coupled with the introduction of the new technology related
tasks students now expect academics to available almost 24/7. A survey 1,100 academics from 99
UK universities (Kinman and Jones, 2004) revealed high levels of academic stress, where nearly a
half of the respondents reported that that they were under strain, over two thirds find the work
stressful and almost 80% believe the status of profession is in decline. McLean (2006) and
Pachnowski and Jurcyk (2003) emphasised the level of stress and dissatisfaction among distance
educators at USA universities, caused by ‘round the clock’ nature of distance education.
Some commentators also discuss the seeming mismatch between the aspirations of academics and
the managerial expectations of the universities (Marginson & Considine 2000). All universities
potentially face the same risk of producing high levels of job dissatisfaction, alienation and
disengagement for academics (Anderson et al. 2002; Coaldrake & Stedman 1998; Ogbonna & Harris
2004).
There is very little research on work intensification that actually measures this in a quantitative way.
Most existing research is in survey form which gives an interesting account of respondent’s
perceptions, attitudes and experiences but very little evidence in terms of a detailed time-breakdown
of these tasks. Many survey results discuss an increase in numbers of hours worked (Anderson et al.
2002; Coaldrake & Stedman 2000; Jensen & Morgan 2009; Vardi 2009) but the individual tasks are
not analysed in detail. Correspondingly, time management for academic leaders and heads of schools
is mentioned as important but it is not really quantified or treated in any depth (Ramsden 1998). One
of the problems is how difficult it is to measure the inter-relationships and interdependencies of these
learning and teaching tasks and their overlaps. Another problem is the subjectivities in terms of
academics’ responses to this allocation of work and their subsequent activities. These activities are
valued according to personal preferences (Wolf 2010) and as such are difficult to evaluate as they
also entail other complex questions such as staffing levels and administrative processes (Vardi 2009).
As early as 1999, a survey by McInnis concluded that “we are perhaps at a critical point for the
academic profession where the amount of hours worked, and the diffusion and fragmentation of tasks
seriously threatens the quality of both research and teaching” (McInnis 1999, p. 63, cited in Anderson
et al. 2002, p. 10). Perhaps this is even more critical with ever increasing demands for accountability
and devolution of academic workload from centres/senior executive, placing academics increasingly
in the position of harried middle-managers trapped between the demands of management and the
students they teach.
3. A time dairy method
A time diary approach, which is also referred to as a ‘micro behaviour’ approach to survey research
(Robinson & Bostrom 1994), can be used for collecting self-reports of an individual’s daily behaviour
(Robinson 1999). It is widely recognised (Kitterød 2001) that studies based on time diaries are one of
most reliable and valid data sources concerning time use. By using the time diaries it will be possible
to report activities as they naturally and sequentially occur in the daily work of academics, and
therefore explore actual behaviour. Time diaries also allow participants to record experiences of their
daily activities, and Bolger, Davis & Rafaeli (2003) documented therapeutic outcomes from this selfreflective data collection process.
Using time diaries has been endorsed by researchers in educational leadership and management
(Morrison 2007). Time diaries have been used to examine the ways in which heads of schools and
faculties manage the time for professional activities (Earley & Fletcher-Campbell 1989). A time diary
method has been used to explore internet use (Ishii 2004; Collopy 1994; Nie & Hillygus 2002) and has
been seen as an ideal method to detect the new and unanticipated activities, such as the use of new
communication technologies (Robinson & Bostrom 1994). This approach has a potential for
professional development and reflective practice on the use of the academics’ time. Another
advantage of the time diary method is that it can measure simultaneous usage patterns, particularly in
the context of understanding academics’ use of the internet in their teaching, research and
administrative tasks.
The ways in which academics manage their time for profession activities was explored by the use of
the time dairy method in the empirical context of a regionally based Australian university. Combined
with reflective statements from the diaries and the focus groups, the time diary records were
collected, aggregated, collated and analysed in order to get a better understanding of the workday
experiences. The project used a seven-day work schedule instrument (Harms & Gershuny 2009) and
purposive rather than random samples of the participants, as has been done in the vast majority of
the time diary studies (Bolger et al. 2003; Pentland et al. 1999; Nie & Hillygus 2002).
Research participants were fulltime academics from different faculties across Charles Sturt University,
NSW, who were asked to make a continuous recording of their daily working activities. As the vast
majority of the time dairy studies, this research has involved purposive rather than random sample of
the participants, and the academics volunteered to take part in this research. Because participant
motivation in his case was high and the researcher – participant rapport was obviously good was and
already established we expected a low rate of faked compliances (Amie et al (2006). A fixed-time
schedule was used, where the spacing of intervals was an hour, which is considered to capture the
change process in daily work activities (Bolger et al. 2003). A continuous recording allowed
participants to determine freely the time when one activity ends and another begins. The first set of
the diaries was open-ended and once the participants identified and captured specific types of
activities we developed pre-coded diaries (Harms & Gershuny 2009). We used the random-hour
technique (Pentland et al. 1999) in which research participants were reporting on a smaller segment
of the day. The participants were requested to maintain a schedule of entries every 3 hours as a
minimum. Initially, we decided to employ paper and pencil diaries which is still the most commonly
used approach in time diary research (Bolger et al. 2003). Because this method places substantial
time demands on the participants, participants were given incentives in the form of small individual
research funds.
The time diary sheet to be filled out (a shortened version is contained in Appendix 1) was a 24 hour
time diary and included the following main categories of daily activities:






Communicating with students
Subject administration
Subject preparation
Subject delivery
Research
Service and general administration
These activities were further broken down on the time sheets so that the research activity included
hours spent on grant preparation, reviewing articles for journals, filling out ethics forms, research
meetings with co-authors and other activities associated with research.
Both main activities and possible parallel activities are captured in time diaries. Parallel tasks (often
called secondary activities), such as searching the internet for appropriate video clips to use in
teaching while answering student emails, were recorded. As academics often do tasks
simultaneously, registration of the parallel activities provided the most complete picture of academics’
time use. Where multiple activities were reported in an hour, the academic’s time was assumed to
have been split equally between the reported activities.
Apart from capturing the amount of time academic tend to spend on different daily activities we also
use the time diary method to explore the subjective evaluations of the academic’s daily experience. A
three unit measurement scale was used to self-record the level of effectiveness and emotional
satisfaction felt by the academic at the end of a working day. Exploratory regression analysis was
used for empirical assessment of the relationships between the amount of time spent on particular
activities and reported levels of efficiency and emotional satisfaction.
The protocol for filling out the time dairies was given to the participants in February 2011, and
subsequently one focus group was held in April 2011 in order to obtain comments on the initial data
collecting, after which the categorization in the time diary of some activities were expanded and other
activities were contracted so as to better match the daily experiences of the participating academics.
During the first two weeks of the research project we ran a pilot test on the small group of academics.
We maintained ongoing contacts with the participants by acknowledging that filling out the time diary
sheets was time-consuming and that the project relied on the good will of the academics involved.
4. Results
An initial database of time diaries was compiled during the testing of the paper version of the diary
tool. Eight academics from three Faculties - Business, Science and Arts - from Charles Sturt
University filled out 278 daily diary sheets. All eight academics held positions as Lecturers and Senior
Lecturers with both teaching and research responsibilities. None of the eight had significant university
leadership roles. Of the 278 diary sheets submitted, 265 sheets revealed information about
academics’ use of time, and 223 sheets revealed information about academics’ self-evaluations of
their emotional response to their work and also the work effectiveness of their days. Table 1 reports
summary statistics on the 265 sheets revealing academics’ use of time.
Average hours of a working day reported by the academics were 9.27 which included both weekdays
and weekends. Academics reported working very long days in some periods as well as a significant
number of weekends. There were large variations in activities across days with the reported standard
deviations of activities being greater than the values of the average hours for those activities. The
diary sheets revealed few days with large blocks of time devoted to single activities, rather academics
split their hours and days across multiple activities.
As a proportion of total working hours reported by the academics, the delivery of subjects - teaching
of lectures or tutorials, conducting labs or delivering seminars - took the least amount of time at only 9
percent of academic time. The activity which consumed the largest proportion of academic time was
research at 26 percent of reported hours, followed by service and general administration at 21
percent. Direct communication with students through electronic or non-electronic means consumed
16 percent of academics’ reported hours.
Table 1: Summary of daily activities
Activity
Hours per day
Average (Hours)
Std. Dev. (Hours)
Communicating with
students
1.49
1.91
Percentage of
Average Day (%)
16.0
Subject administration
1.12
1.89
13.4
Subject preparation
1.38
2.02
14.8
Subject delivery
0.81
1.57
9.0
Research
2.47
3.26
25.8
Service and general
administration
1.99
2.56
21.0
Total
9.27
2.79
100.0
The participating academics were asked to rate their day emotionally on a scale of “Good”, “OK” and
“Poor”, as well as rating how effective they believed their day was. There was a high degree of
correlation between the two ratings with a correlation of 0.44 between the emotional rating and the
effectiveness rating of the day. Academics were more likely to give a higher positive affect and
effectiveness ratings on reported weekends than on weekdays. Possibly this difference between
weekdays and weekends was due to fewer interruptions and more self-directed activities on the
weekends.
A probit regression was conducted on the emotion and effectiveness ratings using the STATA 11
(StataCorp 2009) to ascertain the activities which made the participating academics more likely to
indicate a “Good” emotional rating for that day. Table 2 reports the results of the probit analysis on the
proportion of each activity for that day and the self-assessed effectiveness rating the academic
indicated for that same day. There are positive and statistically significant relationships between the
probability of an academic indicating a “Good” day and the proportion of the day spent on subject
administration and also on research. None of the other activities had a statistically significant
relationship with the emotional rating for the day given by the academic, although the proportion of the
day devoted to communicating to students and to service and general administration had a negative
but statistically insignificant impact on the probability of given a “Good” rating for the day. The
effectiveness rating of the day given by the academic had no statistically significant relationship with
the activities undertaken in the day.
Table 2: Results of a probit analysis on the probability of an academic reporting a “Good” day
emotionally
Dependent variable: Probability of experiencing a “Good” day emotionally as an academic
Proportion of day spent on:
Coefficient
Standard Error
Communicating with students
-0.76
0.70
Subject administration
1.97**
0.56
Subject preparation
0.55
0.57
Subject delivery
0.58
0.64
Research
1.19**
0.45
Effectiveness rating of day
1.46**
0.20
**p<.01
Number of observations: 220; Pseudo-R2: 0.31
Note: Proportion of day spent on service and general administration was dropped as an explanatory variable as
time variables sum to one for the day.
5. Discussions and reflections
The results from the analysis of the pilot time diary data have confirmed for these academics many of
the findings made in earlier surveys of academics (Anderson et al. 2002; McInnis 1999; Jensen &
Morgan 2009). Academics have long workweeks with highly fragmented working days spent meeting
the demands from students and administration while attempting to perform their core tasks of
teaching, research and service.
The academic workweek is 37.5 hours per week which is supposedly 7.5 hours per workday. The
participants in the pilot study were found to be working far longer weeks, including weekends and
hours well outside the core day. If the time spent communicating with students is considered part of
the teaching tasks of academics, in a typical day academics were found to be working 6.8 hours per
day to perform their teaching and general administration roles. Consequently, academics can only
meet their research target by working a longer day of 9.3 hours per day.
The academics in the study worked very fragmented days, because the academics frequently
reported engaging in three to six different tasks in a one hour period. The inability of academics to
devote large blocks of time to individual tasks and the constant interruption of activities is similar to
the “vicious work-time cycle” in software engineers’ workdays found by Perlow (1999), where the
fragmentation of activities significantly reduced the productivity of the engineers. Communication with
students, either directly through conversation, phone calls and emails or indirectly through electronic
forum posting and announcements, was found to consume 1.5 hours per day or 16% of the
academic’s total time.
We found that the academics in the study were more likely to give a positive emotional rating for the
day if they were engaged in subject administration or research tasks for that day. Academics working
on weekends were also more likely to give a positive emotional value for the day. A possible
explanation for these findings is that academics are happier when they are engaged in self-directed
tasks, such as conducting their own research or grading papers.
Usage of the time diary method raised the question about reliability of the data collection process,
such as the extent to which participants comply with instructions, particularly if they were completing
daily time dairies retrospectively. As Collopy (1996) noted there are biases in retrospective selfreports of time use for work –related tasks because there is a tendency for respondents to overstate
the amount of time they spend on a particular task. Self-assessment of the time spent on daily
activities academics spend on research, teaching, administration may be overstated and this has to
be considered in discussing the findings from this research. Obviously, there is the retrospection error
occurring when the paper and pen diary is not at hand (Bolger et al. 2003). We believe that because
of the highly-motivated participants (academics are increasingly concerned and critical of the
workload policies at Australian universities) the data collection was reasonably accurate. For future
research on this topic, electronic diaries (Green 2006) can be used to record the time of entering the
data into daily records therefore verifying compliance of the participants. Also, the experience
sampling method (ESM) which is a signal-contingent method of data collection (Hektner et al. 2007)
may be used to address this problem. Although ESM is an effective research tool for analysing time
use it is more difficult and expensive to administer, and therefore it was not considered practical to be
used in this project. Nonetheless, being aware of the strength of ESM in assessing the quality of
experience at work we will use ESM in future studies to assess academics’ subjective experiences
during workdays.
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Appendix 1: Sample time diary for partial day
Faculty:
Day:
Communicating with students
By email
By phone/Skype
By forums/announcements
By face-to-face
Subject administration
Marking assignments
Marking exams
Entering gradesheet
Setting up Interact
Setting up MSI
Moderation
Subject co-ordination
Subject preparation
Developing lectures
Developing tutorials
Developing exams
Developing online content
Developing materials
Subject delivery
Delivering lectures/seminars
Delivering tutorials/labs
Research
Research administration
Reviewing/refereeing
Reading literature
Writing
Grant preparation
Meetings for research
Thinking/planning/general research
Supervision of PhD/DBA students
Service and general admin
Committee attendance
Course/discipline administration
Community/professional engagement
Professional development/training
General admin/internet/email
Travel for ____________
Conversations with colleagues
Other (include category)
How was your day emotionally?
How effective were you today?
Good
Good
5am
10am
6am
7am
8am
9am
Ok
Ok
11am
12noon
Poor
Poor
1pm
2pm
3pm
Table 2: Results of a probit analysis on the probability of an academic reporting a “Good” day
emotionally
Dependent variable: Probability of experiencing a “Good” day emotionally as an academic
Proportion of day spent on:
Coefficient
Standard Error
Communicating with students
-0.76
0.70
Subject administration
1.97**
0.56
Subject preparation
0.55
0.57
Subject delivery
0.58
0.64
Research
1.19**
0.45
Effectiveness rating of day
1.46**
0.20
**p<.01
Number of observations: 220
Pseudo-R2: 0.31
Note: Proportion of day spent on service and general administration was dropped as an explanatory
variable as time variables sum to one for the day.