ArticlePDF Available

Productivity: Time of Day, Day of week, and Morningness Effects

Productivity: Time of Day, Day of
week, and Morningness Effects
Kristin Lee Sotak1, Tianna R. Moxley1, Samira Todd1,
Shannon Yearwood1, and Gregory J. Privitera2
1SUNY Oswego, Department of Marketing and Management,
School of Business, 7060 NY-104, Oswego, NY 13126. 2Saint
Bonaventure University, Department of Psychology, 3261 W State
St, St Bonaventure, NY 14778
Kristin Lee Sotak is a Professor of Management at SUNY Oswego,
Gregory J. Privitera is a Professor of Psychology at St. Bonaventure
University, and Tianna R. Moxley, Samira Todd, and Shannon
Yearwood are students at SUNY Oswego.
Corresponding author: Kristin Lee Sotak, Department of Marketing
and Management, School of Business, SUNY Oswego, 7060
Route 104 Oswego, NY 13126, USA. Phone: 607-341-3781. E-mail:
Student productivity is thought to fluctuate based on factors that
include days of the week and individual differences. The purpose
of the present study is to examine the effects of personality, day
86 The BRC Academy Journal of Education Vol. 8, No. 1
of week, time of day, and morningness/eveningness on produc-
tivity. To extend previous research, the present study evaluated
productivity as well as the interactions between variables, thereby
allowing for assessment of interaction effects. A survey research
design was used in which daily surveys were administeredfive
times a day (9am-12pm, 12pm-3pm, 3pm-6pm, 6pm-9pm, and
9pm-12am) for ten weekdays, Monday through Friday. Results
show that productivity is lowest on Friday’s and that this effect
was independent of morningness (i.e., no interaction effect) at
all times of day.
Keywords: Productivity, day of the week, time of day, morning-
ness/eveningness, individual difference, day of the week, time of
day, morningness/eveningness, individual difference.
Productivity is a subject of interest for numerous purposes in the personal,
academic, and work spheres of modern society. CEOs crunch numbers
and develop ideas to improve profit, academic board advisors implement
methods to improve attendance and grades, and students plan when and
how to study to improve academic performance. This is an increasingly
important factor nowadays, where we have more things to accomplish
yet less time to do so, and productivity has never been more relevant for
the modern-day student. In fact, academic performance is a major stressor
for those enrolled in academic institutions. In recent years there has been
a 30% rise in college students seeking counseling, 61% due to anxiety
and 28% due specifically to academic performance (Winerman, 2017).
One of the most talked about subjects in college is time management and
effective studying (Jackson, 2009). Both of these factors are considered
integral to student success. Many colleges not only offer tips for how
to live productive student lives on their websites but also integrate time
management skills into their freshman courses to minimize stress and
Student Productivity Effects 87
maximize efficiency (Purdue University Global, 2018; “Time management
tips,” 2019).
Recently the United States has had to face a rather bleak statistic:
while education costs may be rising, learning has stagnated, with no
significant gain in literacy of 17-year-olds since 1971, and there has been
a complete plateau of math skills since the 1990s (National Center for
Education Statistics, 2013). This has led to a significant decrease in the
United States’ education sector, and the implications for future society
are grim (Rothwell, 2016). Only about half of the population born in
1980 is economically better off than their parents. Compare this to the
90% born in 1940 and we conclude that there has been a 40% decrease
in stability in one or two generations (Chetty, Grusky, Hell, Hendren,
Manduca, & Narang, 2016). As the economic and societal implications
of these statistics loom over educational institutions, it may be time
to consider the impact of productivity from not only an academically
task-oriented perspective but from a learning perspective. Results from
studies of student productivity may not only be used to improve our
understanding of work quality and time efficiency in education but may
be implemented in public policy to maximize the quality of learning.
These real-world applications may just be the impetus to a complete
restructuring of how we teach, learn, and retain information. They may
not only greatly improve the quality of our educational institutions but
may enhance the current prognosis of the nation’s economic status and
overall quality of life.
The focus of the current study is to investigate the effects of time of
day, day of week, individual differences in morningness/eveningness,
and the interaction of these variables on productivity. While there is a
great deal of previous literature analyzing the effect of various factors on
individual productivity, nearly all of these factors are tested independently
of one another. The purpose of the current study is to not only establish
the significance of the effect of these factors specifically on academic
88 The BRC Academy Journal of Education Vol. 8, No. 1
productivity but also to analyze the interaction between these various
factors and fill in any gaps left by previous research.
Literature Review
Day of the Week and Productivity
Societal and cultural stereotypes concerning days of the week are
commonplace and are an integral factor of our seven-day week. They give
rise to sayings such as “Monday Blues” (negative emotions associated
with Monday), “hump day” (half way through the work week), and TGIF
(thank god it’s Friday). Though these stereotypes are mainly a result of
our social interactions and structure, they still have a major effect on our
moods and productivity. Mondays are said to be emotional and productive
“low points” of the week due to several factors such as recovering from
weekend activities and the shift from pleasurable activities to demanding
activities (Areni, 2008; Larsen & Kasimatis, 1990).
In some instances, it has even been found that Monday’s are a high-point
for incivility in the workplace (Nicholson & Griffin, 2017) and behavioral
infractions committed by children in school (Challen, 2016). This suggests
that negative moods are peaking on Mondays unanimously among age
groups and that these negative moods are reducing productivity due to
the conflicts that arise from the low affective states. Supporting this, it
has been found that Mondays are the lowest days of productivity during
the seven-day week (Bryson & Forth, 2007). Other studies have suggested
that, in addition to the emotional aspect of Monday’s, they are the lowest
productive days of the week due to the amount of work that is incoming
and the time it takes for individuals to re-adjust to their routine after
spending the weekend engaging in social activities. Fridays were also
found to be low on the productivity scale as well, being the second least
productive day of the week next to Monday due to focus being on nearing
weekend activities rather than important, task-related activities at hand
(Dresner, Yao & Zhu, 2010). Students are likely to experience these same
Student Productivity Effects 89
mood effects, whether or not they are members of the workforce, due
to the similarities between the structure and functioning of academic
institutions and labor institutions. We expect that these low affective
states will result in low Monday and Friday productivity in students.
Time of the Day and Productivity
In addition to days of the week, the time of the day has equally important
effects on productivity. The implications of this factor are hard-hitting,
especially taking into account the fact that the majority of secondary
educational schedules in the United States start before 8:30 am (Center
for Disease Control, 2015), which vehemently disregards the American
Academy of Pediatrics (AAP) recommendation that middle schools and
high schools should start no earlier than 8:30 am (Owens, 2014.) Contrary
to the AAP’s rulings, however, productivity and learning have been
found to improve earlier in the day, with specific course GPAs improving
when they are moved to earlier class times (Pope, 2016). It should be
noted that in Pope’s (2016) study gender, education of parents, age, and
regular performance of the students were taken into account, but that the
morningness/eveningness of the student or teachers were not tested for.
Therefore, it has not been cited whether or not the students tested were
of morning type, which could have placed them at a higher cognitive
functioning level early in the morning due to increased attention and
responsiveness, or if the teachers were morning types, which would
have theoretically improved their teaching abilities due to their increased
cognitive abilities as well. Due to this, it cannot be stated that earlier
class times would improve all students’ GPAs nor that it would improve
an individual student’s GPAs an equal amount.
As for time’s effect on behavior, fewer behavioral incidents are reported
during the morning, with the rate of incidents rising throughout the day
and peaking just before children are released from school (Challen, 2016).
This may be an important factor related to the results of the previous
study where the morning courses received better grades than afternoon
courses. Behavioral incidents were not taken into account when testing
90 The BRC Academy Journal of Education Vol. 8, No. 1
for GPAs and it appears they may be correlated in some way to grades,
with infractions sharing an inverse relationship with good marks. This
may suggest that better grades are had when there are fewer social
disturbances occurring, and may as well represent a shift in focus from
school-related activities to social activities. Morningness/Eveningness
were not tested for and therefore any correlations between behavioral
incidents and morning/evening types were not recorded. It can be inferred
from both studies that academically related task-oriented productivity
is higher in the morning than it is in the afternoon or evening. To fill
this gap, we took care to not only examine time of day affects and
morningness/eveningness affects but examined their interaction. We
expect that morning types will be more productive in the morning
(9am-12 pm) and that evening types will be more productive in the
evening (6pm-12am).
Personality and Performance
Other studies have explored the effect of another influential individual
difference: personality. Previous research shows that personality is related
to the quality of work produced and that it can even significantly affect
academic performance in students (Sanches, Rejano, & Rodriguez, 2001).
Often considered the most influential and used commonly in studies of
personality are the “Big-Five” personality traits, comprised of Neuroti-
cism, Openness, Agreeableness, Extraversion, and Conscientiousness
(Goldberg, 1990). Across multiple studies, results have concurrently
suggested that individuals with high scores of conscientiousness have
high productivity. Likewise, those scoring high on the neuroticism scale
score low on productivity. (Cubel et al, 2016; Neal et al, 2012; Mishra &
Jha, 2015). These traits seem to not only dictate what personality types are
most productive, but also under what circumstances various personality
types perform the best (Barrick & Mount, 1991). For example, individuals
scoring high with openness work best and are most productive when
alone. This suggests that the trait of openness inhibits cooperation. Indi-
viduals scoring high on extraversion, inversely, worked less proficiently
Student Productivity Effects 91
while alone (Neal et al, 2012). These implications are significant when
applied institutions. Students testing higher for openness may perform
better academically if they are taught in smaller, more-personal groups
and were assigned to complete work individually. Inversely, those testing
high for extraversion may be placed in groups to learn and complete
multi-person projects. The implications are that 1.) individual differences
are important for understanding productivity, and 2.) the interaction
between individual differences and the circumstances under which indi-
viduals work are also important for understanding productivity. In the
next section, we discuss another important individual difference for
productivity, morningness/eveningness, and its interaction with time
of day.
Individual differences play an integral part in individual productivity.
Previous research (Escribano, Díaz-Morales, Delgado, & Collado, 2012;
Beşoluk, 2011; Mishra & Jha, 2015) has explored the effect of a significantly
influential individual difference: Morningness/Eveningness. Individual
productivity fluctuates throughout the day. Studies suggest that this is
due to the interaction between an individual's morningness (morning
type/evening type) and the time of day it is. This has proved particularly
relevant for students who, pre-secondary education, have academic
schedules that start early in the day and end during the mid-to-late
afternoon. Evening types have been found to have worse academic
performance than Morning types on average (Escribano et al, 2012).
This has been attributed to the fact that Morning types, when attending
school, are learning at their peak-cognitive time range, while Evening
types are learning at their lowest (Escribano et al, 2012). That is, morning
types are completing work and learning the academic curriculum when
they are more awake, alert, and primed to understand new information.
For evening types, this period of alertness takes place after school has
already been let out. This distinction has been particularly important
during exams, nearly all of which take place during the morning. Morning
92 The BRC Academy Journal of Education Vol. 8, No. 1
types were found to perform significantly better on high school entrance
exams that took place during morning hours, while evening types
performed significantly worse (Beşoluk, 2011.) Contrary to both of
these studies, Mishra & Jha (2015) found that there was no significant
effect of Morningness/Eveningness on the productivity of students.
However, it should be noted that Mishra and Jha (2015) did not take
into account the time of day the work was completed and therefore it is
significantly different than the previous studies. It is quite possible that
Morning types and Evening types have an equal level of productivity
when the total work completed is tallied at the end of the day; the
low productivity levels of morning individuals during evenings and the
low productivity levels of evening individuals during mornings may
result in equal overall productivity over the course of a day. In this
instance, morning types may have completed their work in the morning
and evening types in the evening. Time of day was not held constant,
and therefore is not an accurate predictor of the correlation between
Morningness and academic performance in highly structured institutions.
In the current study, we test the specific interaction between time of
day and morningness/eveningness. Furthermore, we divided the day into
discrete time periods. Morning ranged from 9am to noon, early afternoon
was from noon to 3pm, late afternoon was from 3pm to 6pm, evening
was 6pm to 9pm, and late evening was 9pm to midnight. We expect that
there will be no overall difference in productivity among individuals
varying on the morningness scale, but that morning types will be more
productive in the morning (9am-12pm) and that evening types will be
more productive in the evening (6pm-12am).
This Study: Summary and research questions
As previously mentioned, while there is a great deal of literature analyzing
the effect of various factors on individual productivity nearly all of these
factors are tested independently of one another. The purpose of the
current study is to measure these variables as well as their interactions
Student Productivity Effects 93
to fill in any gaps left by previous research. We recruited undergraduate
students to be tested for Morningness and to complete productivity
surveys daily. These surveys were completed five times a day, one
survey for each discrete time period (morning 9am-12pm, early afternoon
12pm-3pm, late afternoon 3pm-6pm, early evening 6pm-9pm, evening
9pm-12am) for ten weekdays (Monday-Friday). In designing the study,
we took extreme care to ask questions that would not only examine the
main effects of morningness, the day of the week, and time of day but to
also ask questions that would examine the interactions of these factors.
We first hypothesize that Fridays and Mondays are the least productive
days of the week due to low affective states coming social weekend
activities. This has been unanimously supported across research, though
the reasons for these effects has only been suggested and have not been
studied in any refinement and therefore we explored the meanings behind
these effects. Second, we hypothesized that morningness does not affect
overall productivity. This was found in Mishra and Jha’s (2015) study
but refuted in others (Escribano et al, 2011). Finally, we hypothesize
that morning types are more productive early in the day and evening
types are more productive later in the day due to their independent peak
cognitive abilities. It seems that there has been some discrepancy in the
results of previous studies researching this particular topic and, while
many assumptions were made, there were no studies testing evening
type productivity during various periods of the day and specifically
comparing it to morning type productivity during various periods of
the day. Therefore, we compared time periods and morning/evening
types as an interaction.
Participants and Procedures
Undergraduate students in Upstate New York were recruited to participate
in the current study for extra credit. The study was approved by the
94 The BRC Academy Journal of Education Vol. 8, No. 1
institution’s internal review board (IRB), and participants were informed
of their rights and the nature of the study and signed a copy of the consent
form before participating. Daily surveys were sent out and completed five
times a day (9am-12pm, 12pm-3pm, 3pm-6pm, 6pm-9pm, and 9pm-12am)
for ten weekdays, Monday through Friday. Participants also completed
a one-time survey on demographic information and the morningness
trait. To increase data quality, careless response items (Meade & Craig,
2012) were randomly included throughout the surveys. For example, we
included questions such as, “Please respond Moderately for this survey
question.” If participants did not select this option, we assumed they
were carelessly responding and deleted their data for that particular
survey. We also screened data to ensure surveys were completed in the
time period they were supposed to. The original sample size consisted
of 204 participants (5,354 total observations). After screening for data to
make sure surveys were completed on time, the sample size dropped to
202 (5,151 observations). A total of 1.6% of the daily survey data were
discarded due to participants failing careless response items, resulting in
200 participants (5,067 observations). Last, we merged the daily surveys
(50 in total) with the one-time survey. The one-time survey had an original
sample size of n = 176; however, seven participants were deleted due to
careless responding (n = 169). From these participants, however, not all
completed the daily surveys. Therefore, the final sample size was n = 162
(4,646 observations). There were 80 females, 82 males and the average
age was 20.38 years (SD = 1.86). One-hundred and seven participants
were white, 43 were Asian, four were Black/African American, five were
Hispanic, and the remainder of the sample identified as Other.
The 13-item morningness scale (Smith et al, 1989) was used to measure
the individual difference trait, morningness/eveningness. Questions from
this survey include, “If you had to rise at 6:00 a.m., what do you think
it would be like?” and “How long a time does it usually take before you
“recover your senses” in the morning after rising from a night’s sleep?”
Student Productivity Effects 95
All questions were multiple choice with various possible responses. The
scale was reliable (alpha = .85). A median split was used to differentiate
low- and high-morning individuals, where the median level was 2.31.
Last, a three-item measure of productivity (alpha = 0.91) was created for
this survey. The three items were, “To what extent were you productive?”
“To what extent did you get a lot of work done?” and “To what extent did
you make good use of your time?” Participants could respond on a 5-point
scale ranging from “Very slightly or not at all,” to “A little”, “Moderately,”
“Quite a bit,” or “Extremely.” Due to the fact that participants were
being survey five times in one day, survey fatigue was a concern (Dalal,
Lam, Weiss, Welch, & Hulin, 2009). To decrease the likelihood of this
negatively impacting the data, we chose to use this three-item measure
of productivity.
Data Analysis
Data were analyzed in R (R Core Team, 2016) with the nlme package
(Pinheiro et al., 2016) using a multilevel model due to the mixed design
and to manage the violation of the assumption of independence in
observations (Kenny & Judd, 1986). Time of day and day of the week
were both repeated measures, and morningness was a between-subjects
measure. A multilevel model was used over an analysis of variance
because multilevel models better handle sphericity and are more flexible
(Field, Miles, & Field, 2012). The multilevel model was built by adding one
predictor at a time to the model, where the model with the recently added
predictor was compared to the previous model using a log likelihood
ratio with the anova() function in R. Significant log likelihood ratio tests
resulted in the added predictor being kept in the model.
The first model was the null model, which only included an intercept
that was allowed to vary, and was used to calculate the intraclass
correlation coefficient (ICC; Bliese, 2016). The ICC was .34, meaning that
a large portion of the variance in the dependent variable, productivity,
could be explained by both individual participants but also the time of
day and the day of the week. We proceeded by building into the null
96 The BRC Academy Journal of Education Vol. 8, No. 1
model the day of the week variable. Compared to the null model, this
second model was significant . Next, we added the time of day variable
to the second model, which was also significant . To this model, we
added the morningness individual difference variable, which was again
significant . Lastly, when the time of day and morningness interaction
was added to the model, it was not significant , which suggested adding
this component to the model did not significantly improve the model.
Results from the final model are reported in Table 1. Compared to Friday,
all other days of the week were significantly more productive (t(510) =
6.38, 7.20, 6.90, 6.11, p < .0001, effect sizes r = .27, .30, .29, .26). Compared
to 9am to noon, noon to 3pm was more productive (t(2148) = 2.21, p < .05,
effect size r = .05), but compared to 9am to noon, 9pm to midnight was
less productive (t(2148) = -2.79, p < .01, effect size r = .06). Relative to
those high in morningness, those low in morningness reported being
less productive (t(160) = -3.75, p < .001, effect size r = .28). Last, the lack
of significance in the log likelihood ratio test for the model with the
added interaction effect can be interpreted as individuals who are high
on morningness are not more productive than those low on morningness
at any time of day.
The aim of this study was to investigate whether student productivity
fluctuate based on time of day, day of week, and morningness-eveningness
personality. Results showed that, compared to Friday, all other days of
the week were more productive. Furthermore, results did not support
the hypothesis that Monday was a non-productive day. Contrary to
expectations, morningness individuals were more productive, overall,
compared to eveningness individuals. Last, we found no interaction
between morningness-eveningness, personality, and time of day.
Student Productivity Effects 97
It is not surprising that, compared to other days of the week, Friday
was a less productive day. With weekend activities approaching, students
may “check-out” from academics and begin to focus on weekend social
activities (Areni, 2008; Larsen & Kasimatis, 1990). However, contrary
to expectations, Monday was not less productive compared to other
days of the week. In addition to the analyses reported in the Methods
section, we ran an ANOVA with post hoc analyses that also showed
Monday was no more or less productive compared to other days of the
week. Though this may seem surprising, contrarian findings may be
explained by misconceptions and stereotypes of Monday. Though we
hear terms such as “Monday Blues” and “Thank God It’s Friday!” it is
possible that Mondays are not as dreadful as we think. For example,
Croft and Walker (2001) asked participants to report how they were
feeling each day of the week. Then, they were asked to recall how they
thought they felt those days. Results showed that reported moods and
recalled moods were different. In other words, participants reported
feeling better or worse than they thought they felt on those days. In fact,
Monday was recalled as the worst day compared to other days, even
though how participants actually reported feeling was not as “blue” as
we typically think. To further support this notion, Dai, Milkman, and
Riis (2014) found support for their fresh start theory. According to this
theory, beginnings of time periods, such as new years, Mondays, and
birthdays are significant temporal landmarks that allow people to start
over. This is why people typically set new year’s resolutions – they see
it as a new time period and opportunity to start fresh and become a new,
better self. The researchers found this effect for Mondays. It is possible
that students, too, see Mondays as fresh starts that make them motivated
and productive. Last, Mondays may simply be productive days due to the
new assignments that are given and other assignments that are due that
week. Because students and individuals seek to reduce the discrepancy
created by ideal states and current states (Locke & Latham, 1991), they
are motivated to be productive and get things done.
98 The BRC Academy Journal of Education Vol. 8, No. 1
Interestingly, we found that those high on morningness were more
productive, on average, compared to those low on morningness (i.e. those
high on eveningness). There are a few possible explanations for this. First,
it is possible that society and how it is structured allows for morning
individuals to be more productive. Classes start at 8am, workdays are
typically 9-5, and stores typically open in the morning and close sometime
in the evening around 6pm. For morning individuals, this aligns well
with their cycle; however, for evening individuals, these time periods do
not necessarily align with theirs. Whereas the early bird (i.e., morning
individuals) get the worm, evening individuals are still warming up.
Furthermore, it is possible that evening individuals are just as productive
as morning individuals, but we were not able to capture this because we
did not survey participants during another significant time period: 12am
– 3pm. Though parents and faculty may encourage students to get to
bed at a decent hour (West, 2017; Mathis, n.d.) many individuals do not
adhere to this and prefer to work during late hours. For example, notable
CEOs and businesspeople, such as Jeff Bezos of Amazon, Aaron Levie of
Box, and the NBA’s Dallas Mavericks owner Mark Cuban are known to
work at all hours of the night (Smith, 2016). Cuban stays up until 2am and
both Bezos and Levie have been known to work until 3am. This may also
explain the why we did not find an interaction between time of day and
morningness-eveningness individual differences. It is possible that if we
included this time period we would have found different results. In fact,
it is worth noting the interaction was close to marginally significant (p
= .1032). Based on these results, those low on morningness (i.e., evening
individuals) were most productive from 9pm-midnight. It is possible this
productive time period continues through the early hours of the morning
from midnight to 3am but we were not able to capture it. Last, though
we did not make specific hypotheses about time of day, we did note that
the most productive times appeared to be 12pm – 3pm. This may be a
period when most individuals – whether high or low on morningness
– appear to be more or less productive.
Student Productivity Effects 99
The current study is unique in that it collected data over time using 50
surveys. Additionally, we used careless response items (Meade & Craig,
2012) to screen data for poor quality. If participants did not provide
correct answers to these questions, then, these survey data were removed
from the data set before analysis. However, there are limitations worth
mentioning. First, our study is limited in generalizability (Shaddisk, Cook,
& Campbell, 2002). Participants in this study were undergraduate students
from a respectable university in Northeastern USA. Findings may not
generalize to other samples, such as graduate, non-traditional college
students, or low/high ranked academic programs (Piffer, Ponzi, Sapienza,
Zingales, & Maestripieri, 2014). Furthermore, results may not be able to be
generalized to international educational institutions. Another limitation
of this study is that all scores originated from one common source
the participants – which may lead to common method bias (Podsakoff,
MacKenzie, Lee, & Podsakoff, 2003). As an alternative, productivity
could have been measured by other individuals, such as classmates in
a group project or faculty members. Last, the measure of productivity
was a self-report, subjective measure rather than an objective measure.
This is a concern, considering research shows that self-report, explicit
measures differ from objective, implicit measures (Fineman, 1977). As an
example from a study in the creativity literature, students were asked
in a task to self-report their level of creativity. They were also given a
task that required them to create something creative (an alien) and to
creatively come up with as many uses for a brick as possible (Goncalo,
Flynn, & Kim, 2010). This task allowed for a more objective measure of
creativity. Results showed that for narcissistic individuals (i.e., those who
have a grandiose self-perception and who feel entitled), their self-report
measures of creativity did not converge with actual objective measures of
creativity, which were assessed by independent raters. The implications
in the current study are that the measure used for the dependent variable,
productivity, may affect results. More objective measures may better
measure productivity compared to self-reports. However, our findings
100 The BRC Academy Journal of Education Vol. 8, No. 1
are still important, as we still learn when students feel they are most and
least productive throughout the day and week.
Future Research
Future research in this field could extend surveys to the weekend, allowing
for further assessment on the relationship between days of the week
and productivity. Surveys could also be given in 24-hour cycles (i.e.,
from midnight to 3am, 3am to 6am, and 6am to 9am). This is particularly
important given flexible work schedules, overnight work shifts, and
when students may be active. In our student sample, approximately 18%
(29/162) students reported that they would go to bed between 1:45 am
and 3am if they were entirely free to plan their evening. Considering this,
there seems to be a significant number of students who are active and
possibly productive in the early hours of the morning. Having this data
would also allow us to better investigate the relationship and interaction
between time of day and morningness. Future research would benefit
from not only collecting data during more hours throughout the day, but
also by collecting data from other students. For example, online academic
programs cater to individuals with atypical schedules. It is possible that
these individuals are most productive late at night or early in the morning,
when they are free from distractions. Future research would benefit from
using objective measures of productivity. For example, instead of students
reporting how productive they thought they were, they could report
what they accomplished. Last, research would benefit from looking at
other important constructs for students, such as critical thinking and
creativity. These are buzz words that employers of students often seek.
Though it may seem intuitive that morning individuals would be most
creative in the morning and evening individuals would be most creative
in the evening, research finds the opposite is true (Wieth & Zacks, 2011).
This is due to the fact that when you are not at your peak cognitive level
you are prone to distractions and mistakes.
Student Productivity Effects 101
Implications for educators
The implications of our work are that it corroborates the importance of
offering a variety of options for students regarding days and times for
classes to promote productivity. Moreover, the results show that higher
education institutions should consider more options for classes inasmuch
as certain days of the week are more productive than others. For example,
we found that productivity was lowest on Friday. However, Monday
was not particular low in productivity. Based on this result, it may
behoove institutions to offer compressed workweeks for students. In an
organizational setting, there are different types of compressed workweeks.
One such workweek is the 4/10 schedule, where employees work four 10-
hour days instead of five 8-hour days. In organizations, there are many
benefits to the compressed workweeks, including productivity (Facer et
al., 2009; Wadsworth & Facer, 2016). Given that students report lower
levels of productivity on Fridays, it may also be worth considering offer
courses on Tuesday/Thursday and Monday/Wednesday schedules rather
than or in addition to Monday/Wednesday/Friday classes. Concomitantly,
one study found attendance for a Monday/Wednesday/Friday class was
lowest on Fridays compared to Mondays and Wednesdays (Choudhury,
2018). More importantly, this low attendance factor resulted in lower
performance for students; compressed workweeks for students would
help address these negative effects.
Another alternative to consider is the 9/80 schedule that works over
the course of two weeks. In this schedule, employees work 4 9-hour days
and one 8-hour day (typically a Friday) one week, and then the following
week they work 4 9-hour days and then have the last day off (typically
a Friday). In fact, in educational settings, some institutions are trying
this out as an alternative to support a more active learning environment
as opposed to traditional classes comprised of lecture. As an example,
Coker College in South Carolina experimented with hour-long classes
on Monday and Wednesday and then a 2-hour long class every-other
Friday (Flaherty, 2017). With this schedule, some faculty reported being
102 The BRC Academy Journal of Education Vol. 8, No. 1
able to do more hands-on learning in their classes in that they were able
to use activities to engage and excite students. Given the results from
our study and those from other scholars, implementing a compressed
workweek for student may be beneficial.
In our study, we found students were least productive on Fridays; however,
students were no more or less productive on Monday compared to other
days of the week. Furthermore, we found morningness students were more
productive, on average, compared to eveningness students. Last, there
was no interaction between time of day and morningness-eveningness.
Though future research is warranted to better understand how these
variables affect productivity, our research stresses the importance of
how individual differences and scheduling may affect students’ academic
productivity and therefore performance.
Areni, C. S. (2008). (Tell me why) I don’t like Mondays: Does an
overvaluation of future discretionary time underlie reported weekly
mood cycles? Cognition & Emotion, 22(7), 1228–1252. doi: https://doi.
Barrick, M. R., & Mount, M. K. (1991). The big five personality dimensions
and jobpe rformance: a meta-analysis. Personnel Psychology, 44(10),
1-26. doi:
Beşoluk, Ş. (2011). Morningness–eveningness preferences and university
entrance examination scores of high school students. Personality &
Individual Differences, 50(2), 248–252. doi:
Beşoluk, S., Önder, I., & Deveci, I. (2011). Morningness-eveningness pref-
erences and academic achievement of University students. Chronobi-
ology International, 28(2), 118-125. doi: 10.3109/07420528.2010.540729
Student Productivity Effects 103
Bryson, A., & Forth, J. (2007). Are there day of the week productivity
affects? Manpower Human Resources Lab.
Bliese, P. (2016). Multilevel modeling in R [PDF]. Retrieved from: https://
Challen, A. (2013). Day and time patterns in school behavior [PDF]. Re-
trieved from:
Choudhury, I. (2018). Thank God it’s Friday: Student Attendance in Classes
Just before Weekend. Paper presented at the ASEE Gulf-Southwest
Section Annual Conference, The University of Texas at Austin.
Center for Disease Control. (2015). Results from the school health policies
and practices study 2014 [PDF]. Retrieved from: https://www.cdc.
Chetty, R., Grusky, D., Hell, M., Hendren, N., Manduca, R., & Narang,
J. (2016) The fading american dream: trends in absolute income
mobility since 1940 [PDF]. Retrieved from: http://www.equality-of-
Croft, G. P., & Walker, A. E. (2001). Are the Monday blues all in the mind?
The role of expectancy in the subjective experience of mood. Journal
of Applied Social Psychology, 31(6), 1133-1145. doi:
Cubel, M., Nuevo‐Chiquero, A., Sanchez‐Pages, S. & Vidal‐Fernandez,
M. (2016). Do personality traits affect productivity? Evidence from
the Laboratory. The Economic Journal, 126(592), 654-681. doi:10.1111/
Dai, H., Milkman, K. L., Riis, J. (2014). The fresh start effect: temporal
landmarks motivate aspirational behavior. Management Science, 60
(10), 2381-2617. doi:
Dalal, R. I., Lam, H., Weiss, H. M., Welch, E. R., & Hulin, C. L.
(2009). A within-person approach to work behavior and performance:
concurrent and lagged citizenship-counterproductivity associations,
and dynamic relationships with affect and overall job performance.
The Academy of Management Journal 52(5):1051-1066. doi: 10.5465/
104 The BRC Academy Journal of Education Vol. 8, No. 1
Dresner, M., Yao, Y., & Zhu, K. (2010). Performance variations in order
fulfillment across days of the week: how IT-enabled procurement
may help. Available at SSRN: or
Escribano, C., Diaz-Morales, J. F., Delgado, P., & Collado, M. J. (2012).
Morningness/eveningness and school performance among Spanish
adolescents: further evidence. Learning and Individual Differences,
22(3), 409–413. doi:
Facer, R. L., Wadsworth, L. L., & Arbon, C. A. (2009). Cities leading the
way: The use of alternative work schedules. In 2009 municipal year-
book (pp. 28-33). Washington, DC: International City-County Man-
agement Association Press.
Field, A., Miles, J., & Field, Z. (2012). Discovering statistics using R. Thou-
sand Oaks, CA: SAGE.
Fineman, S. (1977). The achievement motive construct and its
measurement: where are we now? British Journal of Psychology, 68,
1-22. doi:
Flaherty, C. (2017, October 5). Breaking out of the M-W-F routine.
Inside Higher Ed. Retrieved from https://www.insidehighered.
Goldberg, R. (1990). An alternative description of personality: The Big-
Five factor structure. Journal of Personality and Social Psychology,
59(6), 1216-1229. doi:
Goncalo, J. A., Flynn, F. J., & Kim, S. H. (2010). Are two narcissists
better than one? The link between narcissism, perceived creativity,
and creative performance. Personality and Social Psychology Bulletin,
31(10), 1484-1495. doi:
Jackson, V. P. (2009). Time management: a realistic approach. Journal
of the American College of Radiology, 6(6), 434 - 436. doi: https://doi.
Kenny, D. A., & Judd, C. M. (1986). Consequences of violating the
independence assumption in analysis of variance. Psychological
Student Productivity Effects 105
Bulletin, 99(3), 422-431. Doi:
Larsen, R. J., & Kasmatis, M. (1990). Individual differences in entertainment
of mood to the weekly calendar. Journal of Personality and Social
Psychology, 58(1), 164-171.
Locke, E. A., Latham, G. P. (1991). A theory of goal setting & task per-
formance. The Academy of Management Review, 16(2), 480-483. doi:
Mathis, M. (n.d.). Strategies for dealing with sleepy students [Article].
Retrieved from:
Meade, A. W., & Craig, S. B. (2012). Identifying careless responses in
survey data. Psychological Methods, 17(3), 437-455. doi: http://dx.doi.
Mishra, S., & Jha, M. (2015). Relationship between neuroticism, morn-
ingness-eveningness preference and academic performance. Indian
Journal of Health & Wellbeing, 6(7), 672–675.
National Center for Chronic Disease Prevention and Health Promotion.
(2018). Schools start too early [Article]. Retrieved from: https://www.
National Center for Education Statistics. (2013). The Nation’s Report
Card: Trends in Academic Progress 2012 [PDF]. Retrieved from:
Neal, A., Yeo, G., Koy, A. & Xiao, T. (2012). Predicting the form and
direction of work role performance from the Big 5 model of personality
traits. Journal of Organizational Behavior, 33(2), 175-192. doi: https://
Nicholson, T., & Griffin, B. (2017). Thank goodness it’s Friday: weekly
pattern of workplace incivility. Anxiety, Stress & Coping, 30(1), 1–
14. doi:
106 The BRC Academy Journal of Education Vol. 8, No. 1
Owens, J. A., Rhoda, A., Carskadon, M., Millman, R., & Wolfson, A.
(2014.) School start times for adolescents. Pediatrics, 134(3), 642-649.
doi: 10.1542/peds.2014-1697
Piffer, D., Ponzi, D., Maestripieri, D., Sapienza, P., & Zingales, L. (2014).
Morningness-eveningness and intelligence among high-achieving US
students: night owls have higher GMAT scores than early morning
types in a top-ranked MBA program. Intelligence, 47, 107-112. doi:
Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D., & R Core Team 2014. nlme:
Linear and Nonlinear Mixed Effects Models. R package version 3.1-118,
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003).
Common method biases in behavioral research: A critical review of the
literature and recommended remedies. Journal of Applied Psychology,
88(5), 879-903. doi:
Pope, N. (2016). How the time of day affects productivity: evidence from
school schedules. Review of Economics and Statistics, 98(1), 1-11. doi:
Purdue University Global. (2018). Time management for busy college
students [Blog]. Retrieved
R Core Team 2014. R: A language and environment for statistical com-
puting. R Foundation
for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, http://
Rothwell, J. (2016). The declining productivity of education [Article]. Re-
trieved from:
Sánchez, M. M., Rejano, E. I., & Rodríguez, Y. T. (2001). Personality
and academic productivity in University students. Social Behavior &
Personality: An International Journal, 29(3), 299–305. doi: https://doi-
Student Productivity Effects 107
Shaddish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and
quasi-experimental designs for generalized causal inference. Boston:
Houghton Mifflin.
Smith, J. (2016). The insane work ethic of Mark Cuban, Jeff Bezos, and 14
other powerful leaders. Inc [Article]. Retrieved from:
Smith, C. S., Reilly, C., & Midkiff, K. (1989). Evaluation of three circadian
rhythm questionnaires with suggestions for an improved measure of
morningness. Journal of Applied Psychology, 74(5), 728-738.
Time management tips. (2019). Retrieved from: https://
U.S. Bureau of Labor Statistics. (2016). American time use survey [Chart].
Retrieved from:
Wadsworth, L. L., & Facer, R. L. (2016). Work–Family Balance and Alter-
native Work Schedules: Exploring the Impact of 4-Day Workweeks
on State Employees. Public Personnel Management, 45(4), 382-404.
DOI: 10.1177/0091026016678856
West, K. (2017) 10 benefits of an early bedtime for your child [Article].
Retrieved from:
Wieth, M. B., Zacks, R. T. (2011). Time of day effects on problem solving:
when the non-optimal is optimal. Thinking & Reasoning, 17(4), 387-401.
Winerman, L. (2017). By the numbers: stress on campus. American
Psychological Association, 48(8). Retrieved from:
American Program Bureau (2017). Speaking to the World. Retrieved
September 3, 2017 from
108 The BRC Academy Journal of Education Vol. 8, No. 1
Armatas, C., & Papadopoulos, T. (2013). Approaches to work-integrated
learning and engaging industry in vocational ICT courses: Evaluation
of an Australian pilot program.International Journal of Training Re-
search,11(1), 56-68. doi:10.5172/ijtr.2013.11.1.56
Ballard, J. A. (2008). Extending the classroom: The telephonic visit.De-
cision Sciences Journal of Innovative Education, 6(1), 173-177.
Bass, B. L., Drake, T. R., & Linney, K. D. (2007). Impact of an intimate re-
lationships class on unrealistic relationship beliefs.Journal of Family
and Consumer Sciences,99(1), 52-59.
Bronson, G., & Stern, M. (2011). Constructing incrementally reinforced
excel project sets using the S.M.A.R.T. management goal-setting ap-
proach. International Journal of Management and Information Sys-
tems,15(1), 9-14.
Butler, R. D. (1997). Using Gender Balance to Enhance Teaching Effec-
tiveness.Business Communication Quarterly,60(3), 93-96.
Chesnut, R., & Tran-Johnson, J. (2013). Impact of a Student Leadership
Development Program.American Journal of Pharmaceutical Educa-
tion,77(10), 1-9.
Chou, C. C. (2001). Formative evaluation of synchronous CMC systems
for a learner-centered online course.Journal of Interactive Learning
Research,12(2-3), 173-192.
Cox, J. (2017). Classroom Management: Guest Speakers Support Learning., K-12 News, Lessons & Shared Resources by Teachers,
For Teachers. Retrieved September 3, 2017 from http://www.teachhub.
Elstad, E., Lejonberg, E., & Christophersen, K. (2017). Student evaluation
of high-school teaching: Which factors are associated with teachers'
perception of the usefulness of being evaluated? Journal for Educa-
tional Research Online,9(1), 99-117.
Student Productivity Effects 109
Farruggio, P. (2009). Bilingual Education: Using a Virtual Guest Speaker
and Online Discussion to Expand Latino Preservice Teachers' Con-
sciousness.Multicultural Education,17(1), 33-37.
Gapp, R., & Fisher, R. (2006). Achieving excellence through innova-
tive approaches to student involvement in course evaluation within
the tertiary education sector.Quality Assurance in Education,14(2),
Fay, N., Page, A. C., Serfaty, C., Tai, V., & Winkler, C. (2008). Speaker
overestimation of communication effectiveness and fear of negative
evaluation: Being realistic is unrealistic.Psychonomic Bulletin & Re-
view,15(6), 1160-5.
Hew, K. F., Chen, Q., & Tang, Y. (2018). Understanding student engage-
ment in large-scale open online courses: A machine learning facili-
tated analysis of Student’s reflections in 18 highly rated MOOCs.In-
ternational Review of Research in Open and Distance Learning,19(3),
Jones, P., Jones, A., Skinner, H., & Packham, G. (2013). Embedding Enter-
prise: A Business School Undergraduate Course with an Enterprise
Focus.Industry and Higher Education,27(3), 203-213.
Kamboj, P., & Singh, S. K. (2015). Effectiveness of Selected Teaching
Strategies in Relation to the Learning Styles of Secondary School Stu-
dents in India.Interchange: A Quarterly Review of Education, 46(3),
Keysar, B., & Henly, A. S. (2002). Speakers' overestimation of their
effectiveness.Psychological Science,13(3), 207-212.
Kim, E., & Vail, C. (2011). Improving Preservice Teachers' Perspectives on
Family Involvement in Teaching Children with Special Needs: Guest
Speaker versus Video.Teacher Education and Special Education,34(4),
Kubal, T., Meyler, D., Stone, R. T., & Mauney, T. T. (2003). Teaching diver-
sity and learning outcomes: bringing lived experience into the class-
room.Teaching Sociology,31(4), 441-455.
110 The BRC Academy Journal of Education Vol. 8, No. 1
Latham, G.P. & Yukl, G.A. (1975). A review of research on the application
of goal setting in organizations. The Academy of Management Journal,
Milanowski, A. T. (2005). Split roles in performance evaluation--A field
study involving new teachers.Journal of Personnel Evaluation in Ed-
ucation,18(3), 153-169.
Moore, J., Lovell, C. D., McGann, T., & Wyrick, J. (1998). Why involve-
ment matters: A review of research on student involvement in the
collegiate setting.College Student Affairs Journal,17(2), 4-17.
Murray, G. L., & Bollinger, D. J. (2001). Developing cross-cultural
awareness: Learning through the experiences of others.TESL Canada
Journal/Revue TESL Du Canada,19(1), 62-72.
Nebesniak, A. L., & Heaton, R. M. (2010). Student confidence & stu-
dent involvement.Mathematics Teaching in the Middle School,16(2),
Persaud, N. (2014). Questionable content of an industry-supported med-
ical school lecture series: A case study.Journal of Medical Ethics,40(6),
Richard, A. J., & Montoya, I. D. (1994). Working together: Contractual re-
lations for the management consultant.Journal of Management Con-
sulting,8(2), 29-34.
Roehl, A. (2013). Bridging the field trip gap: Integrating web-based video
as a teaching and learning partner in interior design education.Jour-
nal of Family and Consumer Sciences,105(1), 42-46.
Smith, R; Pettinga, D; & Bowman, D. (2012). Measuring the Effective-
ness of a New Career Development Plan Curriculum for Freshman
Business College Students. Journal of the Academy of Business Edu-
Sortedahl, C. K., & Imhoff, H. (2016). Perspectives from the Field: Bring-
ing Nurse Leaders into the Classroom.Nursing Education Perspectives
(National League for Nursing),37(2), 113-114. doi:10.5480/14-1385.
Student Productivity Effects 111
Stanford University School of Business (2017). Guest Speakers. Retrieved
September 3, 2017 from
Strickland, K., Gray, C., & Hill, G. (2012). The use of podcasts to enhance
research-teaching linkages in undergraduate nursing students.Nurse
Education in Practice,12(4), 210-214.
Wood, L. (2011). Global marine protection targets: How S.M.A.R.T are
they? Environmental Management,47(4), 525-535.
Zorek, J. A., B.A., Katz, N. L., & Popovich, N. G. (2011). Guest speakers
in a professional development seminar series. American Journal of
Pharmaceutical Education,75(2), 1-28.
Citation Information
Kristin Lee Sotak, Tianna R. Moxley, Samira Todd, Shannon Yearwood,
and Gregory J. Privitera. “Productivity: Time of Day, Day of week, and
Morningness Effects.” The BRC Academy Journal of Education 8, no. 1
(2020): 85–111.
Web Appendix
A web appendix for this case is available at:
... Due to the repeated measurements (same participant under different chamber conditions), univariable linear mixed-effect models were used to assess the association between cognitive test results (response times and error rates) and CO 2 concentrations (600, 1500 and 2100 ppm). Potential confounding factors (Table S5, Supplementary Material) that were considered in this analysis were: gender [53][54][55][56][57], age [54,55,[57][58][59][60], education [53,57], first language [61], weekday [62,63], test time durations (41.5 ± 4.6 min), meal [64], caffeine drink [65], exercise [66], sleep hours [67], thermal sensation [68], perceived air quality [69], perceived lighting quality [70], perceived noise level [70,71], acute health symptoms [72][73][74][75], personal impacts, menstrual period [76], clothing level, perceived difficulty level [77], exit, TVOC concentration [15,78] and PM levels [79]. Factors that were significantly associated (p-value <0.05) with at least five cognitive performance outcomes (response times and error rates of BARS tests) across the three conditions were then included in multivariable mixed-effect models to correct for confounding. ...
Full-text available
Educational buildings frequently experience elevated CO2 concentrations with inadequate ventilation and high occupancy, sometimes exceeding building guideline levels. Some studies reported detrimental impacts on cognitive performance of indoor CO2 levels, while others did not. To generate further evidence, we conducted an experiment in an environmentally controlled chamber. Sixty-nine healthy university students were exposed individually for 70 min, in three separate sessions, to three CO2 conditions of 600, 1500 and 2100 ppm (crossover design). With fixed ventilation rates, pure CO2 was injected to achieve different exposure levels. A validated neurobehavioral BARS test battery was used to assess participants’ cognitive performance. Participants gave subjective ratings of indoor environment and reported any health symptom through questionnaires. Comparing elevated CO2 levels to 600 ppm, after adjusting for potential confounders, results showed significant improved performance, that is, responses were quicker in two out of ten tests, and no significant differences in accuracy for any test. Under 1500 ppm, participants rated the air quality significantly higher than at 600 ppm, but there were no differences at 2100 ppm. Differences were not significant on thermal sensation, perceived lighting quality, perceived noise level, or health symptoms for comparisons between conditions. Results indicate no clear link between pure CO2 levels below 2100 ppm and cognitive performance, perceived indoor environment quality and health symptoms. The findings are consistent with some prior studies, indicating that pure CO2 below 2100 ppm implies no harm in adults and should not be treated as a potential indoor pollutant in higher educational environments.
Full-text available
Student motivation in higher education is a popular topic, though there is virtually nothing known about how motivation changes over the week. Based on observations of student behaviour in the classroom and considering popularly used expressions in everyday life (e.g. TGIF, Motivation Monday), we investigated how motivation changed over the week and patterns that repeated weekly. Data were collected from undergraduate students over 56 consecutive days to allow detection of motivation cycles and fit trigonometric functions to the data via spectral analysis. We also examined how mood and motivation covaried over the week. Our results indicated (a) motivation follows a cosine function with a weekly cycle – motivation begins to increase on Sunday and is relatively high early-week, tapers off on Friday, and is lowest on Saturday, (b) weekly cycles of mood, and (c) cyclical covariation between mood and motivation. Implications for research, students, and universities are discussed.
Full-text available
p class="3">Although massive open online courses (MOOCs) have attracted much worldwide attention, scholars still understand little about the specific elements that students find engaging in these large open courses. This study offers a new original contribution by using a machine learning classifier to analyze 24,612 reflective sentences posted by 5,884 students, who participated in one or more of 18 highly rated MOOCs. Highly rated MOOCs were sampled because they exemplify good practices or teaching strategies. We selected highly rated MOOCs from Coursetalk, an open user-driven aggregator and discovery website that allows students to search and review various MOOCs. We defined a highly rated MOOC as a free online course that received an overall five-star course quality rating, and received at least 50 reviews from different learners within a specific subject area. We described six specific themes found across the entire data corpus: (a) structure and pace, (b) video, (c) instructor, (d) content and resources, (e) interaction and support, and (f) assignment and assessment. The findings of this study provide valuable insight into factors that students find engaging in large-scale open online courses.</p
Full-text available
This article offers communicative activities designed to enhance the cross-cultural awareness of Japanese university students whose language levels range from beginner to intermediate. Facilitating the development of cross-cultural awareness of foreign language students who have never lived in another culture or even visited one can be problematic. Although many educators have responded to the challenge with a knowledge-based approach, a recent study suggests a syllabus that emphasizes constructivist, process-oriented tasks would be more effective. In their efforts to implement the latter approach, the authors have devised activities that range from student-generated interviews of a guest speaker and e-mail exchanges with target language speakers to amini-video ethnography project that focuses on the cross-cultural experiences of others. The article outlines these activities and concludes with a brief evaluation of their effectiveness based on the learners' reactions.
Full-text available
While survey data supports a strong relationship between personality and labour market outcomes, the exact mechanisms behind this association remain unexplored. We take advantage of a controlled laboratory set-up to explore whether this relationship operates through productivity. Using a real-effort task, we analyse the impact of the Big Five personality traits on performance. We find that more neurotic subjects perform worse, and that more conscientious individuals perform better. These findings suggest that at least part of the effect of personality on labour market outcomes operates through productivity. In addition, we find evidence that gender and university major affect this relationship.
Aspiring to do better than one's parents The American dream promises that hard work and opportunity will lead to a better life. Although the specifics of what constitutes a better life vary from generation to generation, one constant is that children expect to do better—or at least to have a good chance at doing better—than their parents. Chetty et al. show that this dream did come true for children born in the middle of the 20th century, but only for half of children born in 1984 (see the Policy Forum by Katz and Krueger). A more even distribution of economic growth, rather than more growth, would allow more children to fulfill their dreams. Science , this issue p. 398 ; see also p. 382
In 2008, the State of Utah implemented a 4-day workweek for their employees. This article examines the impact on employees using a postimplementation survey. For employees on the 4-day schedule, there were no significant differences by gender on work–family balance or on the impact of the schedule. However, women did demonstrate slightly more positive attitudes toward the 4-day schedule. Employees with children at home reported lower work–family balance and greater impact of the 4-day schedule. In contrast, no difference in attitudes toward the 4-day schedule was found by age, although work–family balance differed among age groups. There were differences in work–family balance between employees on the 4-day schedule and those on traditional schedules; however, the more substantial factor was whether an employee selected his or her schedule. The current study highlights the importance of engaging employees when making significant organizational changes, such as transitioning from traditional work schedules to alternative schedules.
Background and objectives: Recent research has shown day-level differences in an individual's experience of uncivil behavior; however, it is unknown if that experience follows a consistent weekly change pattern. This study extends incivility theory and research by applying latent growth curve (LGC) modeling to diary study data to understand day-to-day changes in incivility. Design: The authors took a theory-driven approach, reviewing both mood and recovery theory that would support a decrease in incivility over the working week. Methods: Diary survey methodology was used, with a morning and evening survey completed on five consecutive workdays by 171 (73% of the 235 who initially volunteered, 95% of those who completed any surveys) employees in the legal industry. LGC analysis was used to identify patterns of experienced incivility, mood (both measured after work), and recovery (assessed the following morning). Results: Regardless of job demands and gender, a weekly pattern was identified with the likelihood of experiencing incivility (coded as 0 = none, 1 = some) decreasing from Monday to Friday by .78 each day (p < .001) in a relatively linear fashion with a slope factor of .34 (SE = 0.23; p > .05), indicating invariance between individuals. This weekly pattern was not explained by changes in mood or recovery. Conclusions: Results emphasize the impact of contextual factors such as time on workplace incivility and the need to consider weekly rhythms of other behaviors that are likely to affect employee well-being and productivity. Although limited to one week of data per person, the findings are likely to be relevant to studies of other forms of interpersonal mistreatment, such as social undermining and interpersonal conflict.
Sets of incrementally reinforced EXCEL spreadsheet cases have been constructed and used at Fairleigh Dickinson University over the past two years with a high degree of student involvement and participation. The construction of these cases is based on an adaptation of a management goal-setting technique, having the acronym S.M.A.R.T. This paper describes the application of this management goal-setting technique to the construction of spreadsheet case, and presents an example case illustrating its actual use.