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Neurotics Can't Focus: An in situ Study of Online Multitasking in the Workplace



In HCI research, attention has focused on understanding external influences on workplace multitasking. We explore instead how multitasking might be influenced by individual factors: personality, stress, and sleep. Forty information workers' online activity was tracked over two work weeks. The median duration of online screen focus was 40 seconds. The personality trait of Neuroticism was associated with shorter online focus duration and Impulsivity-Urgency was associated with longer online focus duration. Stress and sleep duration showed trends to be inversely associated with online focus. Shorter focus duration was associated with lower assessed productivity at day's end. Factor analysis revealed a factor of lack of control which significantly predicts multitasking. Our results suggest that there could be a trait for distractibility where some individuals are susceptible to online attention shifting in the workplace. Our results have implications for information systems (e.g. educational systems, game design) where attention focus is key.
Neurotics Can't Focus:
An in situ Study of Online Multitasking in the Workplace
Gloria Mark1, Shamsi T. Iqbal2, Mary Czerwinski2, Paul Johns2, Akane Sano3
1Department of Informatics
2Microsoft Research
3Media Lab
University of California, Irvine
One Microsoft Way
Massachusetts Institute of Technology
Redmond, WA 98052 USA
Cambridge, MA 02139 USA
In HCI research, attention has focused on understanding
external influences on workplace multitasking. We explore
instead how multitasking might be influenced by individual
factors: personality, stress, and sleep. Forty information
workers' online activity was tracked over two work weeks.
The median duration of online screen focus was 40 seconds.
The personality trait of Neuroticism was associated with
shorter online focus duration and Impulsivity-Urgency was
associated with longer online focus duration. Stress and
sleep duration showed trends to be inversely associated
with online focus. Shorter focus duration was associated
with lower assessed productivity at day's end. Factor
analysis revealed a factor of lack of control which
significantly predicts multitasking. Our results suggest that
there could be a trait for distractibility where some
individuals are susceptible to online attention shifting in the
workplace. Our results have implications for information
systems (e.g. educational systems, game design) where
attention focus is key.
Author Keywords
Multitasking; focus; information work; personality; stress;
sleep; productivity
ACM Classification Keywords
H.5.3 [Information Interfaces and Presentation (e.g., HCI)]:
Group and Organization Interfaces; K.4.m [Computers and
Society]: Miscellaneous.
In today's information workplace, the surfeit of digital
resources continually compete for people's attention. While
switching among multiple online activities may benefit
productivity, it can also distract people from the task-at-
hand [5, 19]. Multitasking, the switching of attention
among different activities, can be triggered internally (e.g.,
through boredom) or by external sources (e.g. email
notifications). When a person switches between different
activities frequently, their duration of focus on any one
activity reduces as a consequence. In HCI, a fair amount of
attention has been given to examining external influences
on interruptions [5, 6, 15, 19] and to frequency of activity
switching, e.g., [5, 15]. However, research has not explored
individual characteristics to help understand multitasking
behavior in the workplace.
It is not clear whether multitasking is an efficient behavior.
In the workplace, activities were found to shift every three
minutes, on average, including online work and interactions
with people [15]. Evidence shows that switching attention
between different tasks results in a 50% longer time to
finish those tasks, compared to focusing on one task
through to completion before starting the next one [13].
Some research suggests that cognitive differences could be
an explanation for why some people multitask more than
others. Heavy multitaskers were found to have less ability
to filter out interference from environmental stimuli, which
makes them more susceptible to distractions [27].
Individual differences also exist in people's ability to
control attention [20]. Laboratory studies have investigated
types of attention, such as selective or divided attention, or
vigilance. However, to our knowledge, few studies have
examined factors related to individual personality and
situations that affect online focus duration in the real world
setting of the workplace, cf [25]. We feel this is important
given the extent to which digital media is used in
information work.
In this paper we contribute to understanding multitasking
behavior with empirical results that show that Neuroticism
and Impulsivity relate to shorter online focus. A factor
analysis revealed that a proposed trait described as lack of
control may explain focus duration. This study is part of a
larger project, HealthSense, tracking workplace behavior.
Multitasking can be viewed at different levels of
granularity. From a broad perspective, multitasking refers
to switching among different tasks or projects. At a more
fine-grained level of analysis, multitasking can indicate
switching of attention among different activities, which
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could be within the same task. This finer-grained
perspective can be used as a lens to understand how people
shift their attention when working online. Models of
attentional resource allocation describe that people have
limited attentional resources, and investing resources for
some activities leaves fewer resources available to apply to
other activities [35]. Based on a review of the literature, we
expect that the following personality and physiological
factors can affect resource use, reflected in online focus.
Personality: Neuroticism. In laboratory studies, higher
Neuroticism relates to lower performance in selective
attention tasks [30]. Neuroticism is a personality trait,
assumed to be invariant (and to have a biological basis), is
characterized by anxiety and is a response to emotional
stimuli [9]. Neurotics are more prone to stress, report more
daily problems, and tend to reanalyze prior events over and
over in their minds [17]. Because Neuroticism is considered
an invariant trait, the effects of an experience in one domain
(e.g. home life) can carry over into another domain (e.g. the
workplace) [12]. Investing mental resources, e.g., in
reevaluating prior events, could result in fewer attentional
resources available to focus on the current activity. The
relationship of Neuroticism and attention has not been
investigated in the workplace. We expect that Neuroticism
might become manifest in the workplace as a shorter
duration of focus on any computer screen.
H1: Neuroticism is related to a shorter focus duration.
Personality: Impulsivity. Impulsivity could also influence
multitasking. While considered a personality trait,
impulsivity is viewed as a heterogeneous construct (see [8]
for a review). Generally speaking, people who are
impulsive lack resources to restrain themselves from
actions [8]. In the workplace, people who are impulsive
may not be able to resist distractions, e.g., checking email
or the Internet, leading to attention shifting and thus shorter
focus duration on any single activity. We draw on the
model of impulsiveness of Whiteside et al. [34] based on a
factor analysis of previous models, which identified four
impulsivity factors considered to be distinct psychological
processes. We feel the most relevant factors for
understanding workplace multitasking and attentional focus
are the factors of Urgency and (Lack of) Perseverance.
Urgency refers to the tendency to act on strong impulses
(e.g. one cannot control cravings). (Lack of) Perseverance
refers to one's inability to remain on a task until completion.
We feel that less relevant to our fine-grained perspective of
multitasking are the factors of Lack of Premeditation,
which refers to the inability to carefully plan longer term
action (e.g. before going on a trip to Hawaii); and
sensation-seeking, the tendency to seek adventure.
H2a: Impulsivity-urgency is related to shorter focus
H2b: Impulsivity-(lack of) perseverance is related to
shorter focus duration.
Stress. Stress can also impact online focus duration.
Lazarus [23] defines stress as when external and internal
demands exceed a person's available resources. Theories of
the effect of stress on attention explain that stress uses up
available attentional resources [2]. Some amount of stress
can prolong focus and inhibit attending to irrelevant
information, or distractions [18]. However, alternative
views (capacity-resource view; thought suppression)
explain that stress makes it difficult for people to
selectively focus since stress depletes resources that inhibit
the ability to filter out distractions [1], and to suppress
irrelevant information [33]. In laboratory studies, stress has
been associated with impaired selective attentional
performance, e.g. [32], which is consistent with views that
stress depletes attentional resources. In the workplace,
psychological stress has been linked to reduced efficiency,
decreased performance capacity, and low motivation [10].
Thus, we expect stress to shorten focus.
H3: Stress is related to shorter focus duration.
Physical well-being: sleep. A consistent finding of sleep
deprivation is that it affects psychomotor vigilance [14, 24],
even when sleep loss is relatively minor [31]. Laboratory
studies show that subjects who were sleep deprived showed
deficits in switching between different cognitive tasks, yet
no deficits were found with repetitive tasks [2]. This
suggests that sleep loss impairs cognitive control which
affects the ability to filter out irrelevant stimuli, i.e. to
ignore distractions. Thus, less sleep could impair the ability
to filter out distractions, leading to shorter focus duration.
These results however, are all from laboratory studies. It is
not clear whether these results on sleep duration would
apply to people's behavior in the workplace. We expect that
the effects of sleep duration would be manifest through
shorter durations of focus when working online.
H4: Less sleep is associated with shorter focus duration.
Focus duration and productivity. What might be a
consequence of focus duration? People who switch between
different tasks take longer to finish them, as opposed to
performing tasks in sequential order [13]. Multitasking is
reported to contribute to cognitive overload [29], which
could also negatively impact productivity. For instance, in a
hospital setting, multitasking resulted in gaps in information
flow and errors [22]. We expect that information workers
who shift their online focus more frequently over the course
of the day should feel less productive at the end of the day.
H5: Shorter focus duration is related to lower assessed
productivity at the end of the day.
Forty volunteers (20 females, 20 males) in a large high tech
U.S. corporation, who responded to an ad, were observed in
situ in their real work environment for about 12 business
days. Their job roles involved information work and were
varied: administrative support, engineering, and
management. Participants were compensated with $250.
Participants' computer activity at work was logged during
all business hours automatically via custom-built Windows
Activity Logging software. This logging software tracks
every open application, which window is in the foreground,
and whether the user is interacting with that window (with
mouse, keyboard, touch, etc.). We measured the total
duration of all applications, defined as the number of
seconds that each application was in the foreground
window, ending when the user either changed windows or
the computer had no keyboard or mouse activity for a
period of five minutes. As participants at times might not be
using their computer for various reasons (e.g., while at a
meeting), we used only those hours of data when the
computer was used (i.e., when logged data showed that
computer duration was greater than zero for that hour).
Focus Duration (online) was measured by dividing total
daily logged computer duration by the number of computer
screen switches. As discussed, we take a fine-grained
perspective on multitasking to view shifting attention
online. We feel that screen switches are a reasonable proxy
for attention duration with computer work.
At the beginning of the study, a general survey was given.
Neuroticism was measured as part of the Big 5 personality
inventory [26]. Impulsivity was measured by the UPPS
Impulsive Behavior Scale [34], using the dimensions of
Urgency and Perseverance. Stress was measured by the
Perceived Stress Scale (PSS) [3], a widely-deployed global
measure of the degree to which people perceive stress in
their lives. While these scales could be related to a greater
or lesser degree, we felt that a thorough examination of our
hypothesized variables would tell a more complete story.
Our goal was to discover several lines of converging
evidence that particular factors were significantly at play.
Productivity was measured by six items in the daily end of
day survey, asking about accomplishment, efficiency,
satisfaction, effectiveness, quality, and overall productivity
(e.g., “How efficient do you feel you were today in
performing your work?”). Responses were measured on a
Likert scale, with 1=not at all, and 7=extremely. The item
dimensions were highly correlated (with correlations
ranging from .68 to .94), so we combined them additively
to construct an index measure of Productivity.
Sleep was measured by the Fitbit Flex actigraph which
participants wore 24 hours a day.
Analyses. Focus Duration was our dependent variable for
H1-H4. For H5, Focus Duration was the independent
variable and end-of-day Productivity assessment was the
dependent variable. For the analyses of daily data, we used
only full weekday days of window logging (the time of the
study setup sometimes resulted in partial days of data
collection), and used only days when computer usage was
greater than zero. For analyzing Neuroticism, Impulsivity,
and Stress, we analyzed the data using regression analysis
in SPSS. For analyzing sleep and productivity, which were
daily data, i.e., multiple daily measures per person, we used
Linear Mixed-Effects Models (LMM) in SPSS. This uses
random and fixed effects to account for the repeated
measures within subjects.
Table 1 presents the average duration of focus on any
computer screen for our 40 participants. Averaged over all
workdays per person, and for all applications and online
sites, the median duration of focus is about 40 seconds.
Participants had slightly longer focus on email clients, and
productivity software (Word, Powerpoint, Excel, Visual
Studio, etc.) but had a shorter focus when using
communication software (e.g., Skype, IM, Lync). The SD is
also fairly small. In Table 1 we further divide switching
behavior into switching between applications (e.g., between
Word and email) and switching within applications (e.g.,
opening up different word documents or switching Internet
sites). Switches occur more often between different
applications which could suggest more of a context shift
than switching within applications. Thus, the data shows
that our participants' online activity is characterized by
fairly short durations of focus on their computer screens.
Personality traits and focus
Results of our hypotheses tests are shown in Table 2 with p-
values adjusted by the Holm-Bonferroni sequential
adjustment [16]. The results support H1: the higher the
Neuroticism, the shorter the focus duration, explaining
13.4% of the variance of online focus duration.
We found support for H2a: the trait of Impulsivity-Urgency
is associated with shorter focus duration, explaining 16.5%
of the variance. We reject H2b: Impulsivity-Lack of
Perseverance was not significantly associated with focus.
Stress and focus
We found weak support for H4: a strong trend showed that
higher Stress was related to shorter focus duration,
explaining 10.5% of the variance.
Sleep and focus
Eight outliers were removed from the daily sleep variable.
For this two week study, we found a strong trend that the
less one sleeps, the longer is the focus duration. Thus, we
reject H4. However, this surprising result could be
explained by deadlines. In the workplace, when people have
deadlines, they may sleep less the night before and become
highly focused on work to meet the deadline. Participants
All computer usage
Email usage
Productivity SW usage
Communication SW usage
Daily switches within apps
Daily switches betw apps
Table 1. Avg. online screen focus duration (sec.) and switches.
were asked at the end of the day how much deadlines
influenced their work that day with a 7-point Likert scale
item. We found that the more deadlines influenced work,
the longer the focus duration: F(1,285)=4.32, p<.04. A
significant sleep by deadline interaction shows that the
combination of less sleep with more influence of deadlines,
the longer the focus: F(1,289)=3.74, p<.05. However, we
are unable to draw conclusions on how sustainable this
would be over a longer period of time as our two-week
study duration limited us from investigating that question.12
Productivity and focus
Using the daily data, three outliers were removed from
focus duration. Productivity assessment was the dependent
variable, and Focus Duration was the independent variable,
controlling for Neuroticism, Impulsivity-Urgency, Stress,
and Sleep. Using LMM, we found a significant relationship,
supporting H5. The Variance Inflation Factor was less than
5, indicating that multicollinearity was not a problem.
Factor analysis
We conducted a factor analysis on our observed variables
(including deadlines). Factor analysis enables a researcher
to uncover a structure of unobserved variables among
correlated variables, by explaining the variability through
latent factors [21]. With factor analysis, each variable is
primarily associated with a distinct factor. We used a
Varimax rotation with a Kaiser normalization. A scree plot
revealed that two factors should be used, accounting for
62.4% of the variance (Table 3). We interpret the first
factor as "lack of control" since Neuroticism and Stress may
be responsible for a lack of control in suppressing thoughts
(e.g. in reanalyzing prior events) and Impulsivity-Urgency
refers to a lack of impulse control. The second factor loaded
onto the single variable of deadlines, which we interpret as
"time pressure", i.e., a situational explanation. We next
regressed Focus Duration on these two factors: F(2,
37)=5.51, p<.008, adj. R2=.19. The results show that lack
1 As there is no standard method for determining an R2 in LMM
models [7] we ran a linear model with fixed effects alone to get an
R2 value. Not including random effects will underestimate the
variance explained as it provides a lower bound.
Lack of Control (Neur, Imp-Urgency, Stress)
Time Pressure (Deadlines)
Table 3. Regression model for Focus Duration, based on the
two factors identified through factor analysis.
of control is a significant factor that can explain online
attention duration (Table 3).
We found that in information work, online focus is
characterized by short durations, with only a median span
of 40 seconds. Our results build on previous in situ
descriptive studies of multitasking [5, 15] by suggesting
that there may be an inherent trait of distractibility, cf [11],
uncovered by a factor we call lack of control. As
personality is difficult to change, the factor of "lack of
control" could represent an invariant trait that makes people
susceptible to online distractions. Given all the potential
distractions that digital media presents to information
workers, our results suggest that inherent traits could make
some people more susceptible to distractions.
A potential consequence of multitasking is that shorter
focus duration positively correlates with lower assessed
productivity at days' end. This is consistent with interviews
that describe that switching activities has a cost, e.g., in
doing redundant work [15]. The result provides empirical
support for costs in information work though more research
is needed to uncover potential underlying factors.
Our results of personality effects have implications for
educational systems and game design, where personality is
found to influence use, e.g. [28]. System design could adapt
to a user depending on one's pattern for focusing attention.
Also, virtual agents could adapt interruptions and
messaging according to a person’s ability to focus.
As our study was only done in one workplace, we can only
generalize to similar workplaces that are high tech, with
educated workers (like our participants). Because
personality traits are assumed invariant, the relationship of
personality and focus duration appears causal (i.e., it is not
likely that one's focus duration changes one's Neuroticism
or Impulsivity trait). However, there could be underlying
variables that influence these relationships which all
warrant further exploration. Nevertheless, this research is a
first step at investigating individual differences that can
influence online focus; we hope that this research can spark
further investigation to unpack explanations for
multitasking and attention focus in the workplace.
This material is based upon work supported by the NSF
under grant #1218705.
adj R2
H1: Neuroticism
1, 38
H2a: Impuls-Urgency
1, 38
H2b: Impul-Lack Persev
1, 38
H3: Stress
1, 38
H4: Sleep
1, 280
H5: Productivity regressed
on focus duration
1, 231
Table 2. Results of linear regression of Focus Duration based
on each predictor variable for H1-H5. *p-values are
adjusted with the Holm's method [16].
1. John A. Bargh. 1992. The ecology of automaticity:
Toward establishing the conditions needed to produce
automatic processing effects. American Journal of
Psychology, 105, 181199.
2. Eran Chajut and Daniel Algom. 2003. Selective
attention improves under stress: implications for
theories of social cognition. Journal of personality and
social psychology 85.2: 231.
3. Sheldon Cohen, Tom Kamarck, and Robin
Mermelstein. 1983. A global measure of perceived
stress. Journal of health and social behavior 1983:
4. Allessandro Couyoumdjian, Stefano Sdoia, Daniela
Tempesta, Giuseppe Curcio, Elisabetta Rastellini,
Luigi De Gennaro, and Michele Ferrara. 2010. The
effects of sleep and sleep deprivation on task-switching
performance. Journal of Sleep Research, 19, 6470.
5. Mary Czerwinski, Eric Horvitz and Susan Wilhite.
2004. A diary study of task switching and
interruptions. in Proceedings CHI'04, 175-182.
6. Laura Dabbish, and Robert E. Kraut. 2004. Controlling
interruptions: awareness displays and social motivation
for coordination. Proceedings of the 2004 ACM
conference on Computer supported cooperative work,
7. Lloyd J. Edwards, Keith E. Muller, Russell D.
Wolfinger, Bahjat F. Qaqish, and Oliver
Schabenberger. 2008. An R2 statistic for Fixed Effects
in the Linear Mixed Model. Stat Med. 2008 (27(29)).
8. John L. Evenden. 1999. Varieties of
impulsivity. Psychopharmacology, 146(4), 348-361.
9. Hans J. Eysenck, Hans J. and Michael W.
Eysenck. 1987. Personality and individual differences.
10. Kerry Fairbrother and James Warn. 2003. Workplace
dimensions, stress and job satisfaction. Journal of
managerial psychology 18.1: 8-21.
11. Sophie Forster and Nilli Lavie. 2015. Establishing the
attention-distractibility trait. Psychological science.
Dec. 14. 0956797615617761.
12. Michael R. Frone, Marcia Russell, and M. Lyme
Cooper. 1994. Relationship between job and family
satisfaction: Causal or noncausal covariation? Journal
of Management. 20:565579.
13. Richard Gendreau. 2007. The new techno culture in the
workplace and at home. Journal of American Academy
of Business, Cambridge, 11(2), 191-196.
14. Namni Goel, Hengyi Rao, Jeffrey S. Durmer, and
David F. Dinges. 2009. Neurocognitive consequences
of sleep deprivation. Seminars in Neurology, 29, 320
15. Victor M. Gonzalez and Gloria Mark. 2004. "Constant,
Constant, Multi-tasking Craziness”: Managing
Multiple Working Spheres. Proceedings CHI'04, 113-
16. Sture Holm. 1979. A simple sequentially rejective
multiple test procedure. Scandinavian journal of
statistics, 6, 65-70.
17. Briana N Horwitz, Gloria Luong, and Susan T.
Charles. 2008. Neuroticism and extraversion share
genetic and environmental effects with negative and
positive mood spillover in a nationally representative
sample. Personality and individual differences, 45(7),
18. Pascal Huguet, Marie P. Galvaing, Jean M. Monteil,
and Florence Dumas. 1999. Social presence effects in
the Stroop task: Further evidence for an attentional
view of social facilitation. Journal of Personality and
Social Psychology, 77, 10111025.
19. Shamsi T. Iqbal and Eric Horvitz. 2007. Disruption and
Recovery of Computing Tasks: Field Study, Analysis
and Directions. in Proceedings of CHI'07, 677-686.
20. Steven W.Keele, and Harold L. Hawkins. 1982.
Explorations of Individual Differences Relevant to
High Level Skill. Journal of Motor Behavior, 14(1), 3-
21. Derrick N. Lawley, and Albert E. Maxwell. 1971.
Factor Analysis as a Statistical Method. New York:
American Elsevier Pub. Co.
22. Archana Laxmisan, Forogh Hakimzada, Osman R.
Sayan, Robert A. Green, Jiajie Zhang, and Vimla L.
Patel. 2007. The multitasking clinician: decision-
making and cognitive demand during and after team
handoffs in emergency care. International journal of
medical informatics, 76(11), 801-811.
23. Richard S.Lazarus, Psychological stress in the
workplace. 1995. Occupational stress: A handbook 1:
24. Julian Lim and David F. Dinges. 2008. Sleep
deprivation and vigilant attention. Annals of the New
York Academy of Sciences, 1129, 305322.
25. Gloria Mark, Shamsi T. Iqbal, Mary Czerwinski, and
Paul Johns. 2014. Bored Tuesdays and focused
afternoons: The rhythm of attention and online activity
in the workplace. Proceedings of CHI’14, ACM Press,
26. Robert R. McCrae and Paul T. Costa. 1999. The five
factor theory of personality. in Handbook of
Personality: Theory and Research, L.A. Pervin, O.P.
Johns, NY: Guilford, 139-153.
27. Eyal Ophir, Clifford Nass, and Anthony D. Wagner.
2009. Cognitive control in media
multitaskers. Proceedings of the National Academy of
Sciences 106.37: 15583-15587.
28. Rita Orji, Julita Vassileva, and Regan L. Mandryk.
2014. Modeling the efficacy of persuasive strategies
for different gamer types in serious games for health."
User Modeling and User-Adapted Interaction 24, no.
5: 453-498.
29. Joshua S. Rubinstein, David E. Meyer, and Jeffrey E.
Evans. 2001. Executive control of cognitive processes
in task switching. Journal of Experimental Psychology:
Human Perception and Performance 27.4: 763.
30. Blażej Szymura, and Edward Nęcka. 2005. Three
superfactors of personality and three aspects of
attention." Advances in personality psychology: 75-90.
31. Hans PA Van Dongen, Greg Maislin, Janet M.
Mullington, and David F. Dinges. 2003. The
cumulative cost of additional wakefulness: Dose-
response effects on neurobehavioral functions and
sleep physiology from chronic sleep restriction and
total sleep deprivation. Sleep, 26, 117126.
32. Kavita Vedhara, J. Hyde, Iain Gilchrist, Michelle
Tytherleigh, and Sue Plummer. 2000. Acute stress,
memory, attention and cortisol.
Psychoneuroendocrinology 25, 6: 535-549.
33. Richard M.Wenzlaff, and Daniel M. Wegner. 2000.
Thought suppression. Annual Review of Psychology,
51, 5991.
34. Stephen P. Whiteside and Donald R. Lynam. 2001.
The five factor model and impulsivity: Using a
structural model of personality to understand
impulsivity. Personality and individual
differences, 30(4), 669-689.
35. Christopher D.Wickens. 1980. The structure of
attentional resources. Attention and performance VIII,
... Prior works have also shown that both the features of interruptions (Brumby et al., 2019;Katidioti et al., 2016) and those of the people being interrupted (Kurapati et al., 2017;Mark et al., 2016a) influence interruption outcomes. Among the attributes of interruptions, interruption sources are closely related to emotional experience (Fletcher et al., 2018;Seipp, 2019). ...
... Considering the personal characteristics of individual knowledge workers, researchers found that personality (Mark et al., 2016a), cognitive ability (Drews & Musters, 2015), and multitasking ability (Kurapati et al., 2017) influence interruption results. Given the frequency of interruptions in the digital age, the role of multitasking ability becomes salient. ...
Full-text available
The pervasive use of information and communication technology (ICT) typically causes constant interruptions. Digital interruptions permeating work-nonwork boundaries can lead to well-being problems, which are particularly common for knowledge workers. In this study, we examine the effect of work-nonwork interruptions on emotional experience to manage interruptions better and improve ICT users’ well-being. A pilot study was conducted using an experience sampling method (ESM) for ten days (216 observations) with an interview of eight participants, along with a larger ESM study lasting for a full week among 34 knowledge workers (999 observations). We examined the discrete effect of interruptions on positive and negative affect and explored their association with daily aggregated effects on emotional exhaustion. The results showed that variations in the sources and types of interruptions (extrinsic vs. intrinsic and work vs. nonwork, respectively) influenced value orientation, which in turn influenced emotional experience. More specifically, hedonic value was found to be a motivating factor and utilitarian value a hygiene factor in determining the emotional consequences of interruptions. As various degrees of both utilitarian and hedonic values are interwoven in an interruption, this value orientation explains the consequences of interruptions better than only considering work and nonwork features. This finding suggests that emotional exhaustion caused by work-nonwork conflict fundamentally refers to the utilitarian-hedonic conflict. The findings of this study extend the literature on the effect of work-nonwork interruptions on emotional experiences and provide guidelines on interruption management to maintain positive and avoid negative effects of digital interruptions.
... However, there are people who express a preference for multitaskingso-called polychronicityas opposed to performing only a single task at a time, and who do not necessarily feel stressed in dual-or multitasking situations (Poposki & Oswald, 2010). Moreover, multitasking performance is associated with a variety of further factors such as personality characteristics, as for instance lower multitasking performance has been found in neurotic than in non-neurotic people (Mark et al., 2016). Furthermore, sex and age play a major role, and better multitasking performance has been found in younger and female than in elderly or male participants (Crews & Russ, 2020). ...
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In the age of digitization, multitasking requirements are ubiquitous, especially in the workplace. Multitasking (MT) describes the activity of performing multiple (at least two) tasks at the same time. Dual tasking (DT) refers to the sequential switching between two tasks. The aim of our systematic review and meta-analysis was first to investigate whether physiological stress systems become activated in response to or during MT/DT and, secondly, whether this (re-)activity is higher compared to single tasking. We focused on the Sympathetic Nervous System (SNS), the Parasympathetic Nervous System (PNS), the hypothalamic-pituitary adrenal (HPA) axis, and the immune system. The systematic review has been pre-registered with PROSPERO (CRD42020181415). A total of twenty-five articles were identified as eligible, in which n = 26 studies were reported, with N = 1,142 participants. Our main findings are that SNS activity is significantly higher and PNS activity is significantly lower during MT/DT than during single tasking. Only two studies were found, in which HPA axis (re-)activity was surveyed. No eligible study was identified in which immune system (re-)activity was investigated. This is the first systematic synthesis of the literature base showing that stress system activity is increased during MT/DT in comparison to single-tasking.
... They focus on the negative side of issues (Sheikh et al. 2019) and are likely to experience worries and stress at work. Prior studies found that individuals who score high on neuroticism tend to have a lower ability to focus on tasks for an extended period of time because they are worriers (Mark et al. 2016). They stress about decisions they have made and replay conversations in their mind. ...
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Employees' nonwork use of information technology (IT), or cyberslacking, is of growing concern due to its erosion of job performance and other negative organizational consequences. Research on cyberslacking antecedents has drawn on diverse theoretical perspectives, resulting in a lack of cohesive explanation of cyberslacking. Further, prior studies generally overlooked IT-specific variables. To address the cyberslacking problems in organizations and research gaps in the literature, we used a combination of a literature-based approach and a qualitative inquiry to develop a model of cyberslacking that includes a 2x2 typology of antecedents. The proposed model was tested and supported in a three-wave field study of 395 employees in a Fortune-100 US organization. For research, this work organizes antecedents from diverse research streams and validates their relative impact on cyberslacking, thus providing a cohesive theoretical explanation of cyberslacking. This work also incorporates contextualization (i.e., IT-specific factors) into theory development and enriches IS literature by examining the nonwork aspects of IT use and their negative consequences to organizations. For practice, the results provide practitioners with insights into nonwork use of IT in organizations, particularly on how they can take organizational action to mitigate cyberslacking and maintain employee productivity.
The goal of the project described here was to extend the immersive experience of the user in the Virtual Land Bridge. Such extensions would facilitate the use of the software system to contribute to the development of individual student learning plans over several different age categories. The principal strategy was to modify the multi-agent planning system to allow for the generation of local tactical behavior of the agents in the virtual world. This resulted in the addition of a new layer to the MAS planning system, producing a hybrid system that utilized global knowledge monolithically in the Pathfinder portion and local tactical knowledge in the VR layer. The resultant hybrid system was designed to produce improved caribou agent decision making and movement that could scale up to support large herds on the order of a hundred thousand and more. This new system is shown to increase user immersion that can contribute to the development of student learning profiles.
The profuse popularity of video conferencing has led to a simultaneous rise in the opportunity for the participants to multitask. Productive multitasking, such as taking notes, browsing for relevant information, etc., can help promote the cognitive attentiveness of participants. However, existing approaches of tagging inattentive participants solely based on their visual concentration on the meeting app fail to work in such instances. This paper proposes EmotiConf -- a novel real-time framework to monitor participants' attentiveness and a non-real-time framework for visual multitask detection without explicitly relying on their visual concentration. EmotiConf utilizes an unconventional observation where the emotional states of attentive participants, captured through their facial expressions, correlate and also correspond to the vocal expression of the speaker and the intent of the speech. Accordingly, EmotiConf develops a software wrapper to tag the inattentive participants while also characterizing visual multitasking instances performed by them. A thorough evaluation of EmotiConf confirms its usability with a high score of >80.
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While distractions using digital media have received attention in HCI, understanding engagement in workplace activities has been little explored. We logged digital activity and continually probed perspectives of 32 information workers for five days in situ to understand how attentional states change with context. We present a framework of how engagement and challenge in work relate to focus, boredom, and rote work. Overall, we find more focused attention than boredom in the workplace. Focus peaks mid-afternoon while boredom is highest in early afternoon. People are happiest doing rote work and most stressed doing focused work. On Mondays people are most bored but also most focused. Online activities are associated with different attentional states, showing different patterns at beginning and end of day, and before and after a mid-day break. Our study shows how rhythms of attentional states are associated with context and time, even in a dynamic workplace environment.
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Persuasive games for health are designed to alter human behavior or attitude using various Persuasive Technology (PT) strategies. Recent years have witnessed an increasing number of such games, which treat players as a monolithic group by adopting a one-size-fits-all design approach. Studies of gameplay motivation have shown that this is a bad approach because a motivational approach that works for one individual may actually demotivate behavior in others. In an attempt to resolve this weakness, we conducted a large-scale study on 1,108 gamers to examine the persuasiveness of ten PT strategies that are commonly employed in persuasive game design, and the receptiveness of seven gamer personalities (gamer types identified by BrianHex) to the ten PT strategies. We developed models showing the receptiveness of the gamer types to the PT strategies and created persuasive profiles, which are lists of strategies that can be employed to motivate behavior for each gamer type. We then explored the differences between the models and, based on the results, proposed two approaches for data-driven persuasive game design. The first is the one-size-fits-all approach that will motivate a majority of gamers, while not demotivating any player. The second is the personalized approach that will best persuade a particular type of gamer. We also compiled a list of the best and the worst strategies for each gamer type. Finally, to bridge the gap between game design and PT researchers, we map common game mechanics to the persuasive system design strategies.
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Contents: Facts and Theories of Adult Development. A Trait Approach to Personality. Measuring Personality. The Search for Growth or Decline in Personality. Cross-Cultural Perspectives on Personality and Aging. The Course of Personality Development in the Individual. Stability Reconsidered: Qualifications and Rival Hypotheses. A Different View: Ego Psychologies and Projective Methods. Adult Development as Seen through the Personal Interview. A Five-Factor Theory of Personality. The Influences of Personality on the Life Course.
In this book we attempt to give a factual account of the causes of criminality, a term we understand to mean the entire social behavior of people who violate the laws of their country. This is not a book on crime, which would have to be much more inclusive, taking into account sociological, economic, judicial, political, and other factors. We have concentrated on psychological causes of crime, not only because as psychologists we are more competent to deal with these factors, but also because we believe that they have been relatively neglected in recent years and require explicit statement and justification. This we have tried to provide. It should be noted that our concentration on psychological causes should not be interpreted to mean that other factors are not important, or do not require study.
Failures to focus attention will affect any task engagement (e.g., at work, in the classroom, when driving). At the clinical end, distractibility is a diagnostic criterion of attention-deficit/hyperactivity disorder (ADHD). In this study, we examined whether the inability to maintain attentional focus varies in the overall population in the form of an attention-distractibility trait. To test this idea, we administered an ADHD diagnostic tool to a sample of healthy participants and assessed the relationship between ADHD symptoms and task distraction. ADHD symptom summary scores were significantly positively associated with distractor interference in letter-search and name-classification tasks (as measured by reaction time), as long as the distractors were irrelevant (cartoon images) rather than relevant (i.e., compatible or incompatible with target names). Higher perceptual load during a task eliminated distraction irrespective of ADHD score. These findings suggest the existence of an attention-distractibility trait that confers vulnerability to irrelevant distraction, which can be remedied by increasing the level of perceptual load during the task.