Multitasking is prevalent during computer-mediated work. Users tend to switch between multiple ongoing computer-based tasks either due to a personal decision to break from the current task (self-interruption) or due to an external interruption, such as an electronic notification. To examine how different types of multitasking, along with subjective task difficulty, influence performance, we conducted a controlled experiment using a custom-developed multitasking environment. A total of 636 subjects were randomly assigned into one of the three conditions: discretionary, where they were allowed to decide when and how often to switch tasks; mandatory, where they were forced to switch tasks at specific times; and sequential, where they had to perform tasks in sequence, without switching. The experimental environment featured a primary problem-solving task and five secondary tasks. The results show that when the primary task was considered difficult, subjects forced to multitask had significantly lower performance compared with not only the subjects who did not multitask but also the subjects who were able to multitask at their discretion. Conversely, when the primary task was considered easy, subjects forced to multitask had significantly higher performance than both the subjects who did not multitask and the subjects who multitasked at their discretion.
The study compares the effects of different types of multitasking and subjective task difficulty with an experiment.
It uses a custom-developed multitasking environment with three conditions.
Compares performance scores of mandatory, discretionary and no multitasking.
Those forced to multitask performed the worst when the task was deemed difficult.
However, when the task was deemed easy, those forced to multitask performed the best.
Multitasking is a prevalent behavior when using personal computers or mobile platforms. Both at home and in the workplace, people are frequently switching tasks to check their email, their social networking site, or another website. Studies report that computer users have multiple applications open, and switch between them frequently (Crook and Barrowcliff, 2001; Czerwinski et al., 2004). In fact, managing one's email alone involves a lot of multitasking (Bellotti et al., 2005). Multitasking is defined as the performance of several tasks at once (Rubinstein et al., 2001). However, depending on how tasks and times are defined, several interpretations are possible (Benbunan-Fich et al., 2011). Some researchers reserve the term multitasking for simultaneously conducted activities (Meyer and Kieras, 1997), while others define it in terms of task switching (Czerwinski et al., 2004). More encompassing definitions accommodate all possible cases. For example, Salvucci and Taatgen (2011) defined a multitasking continuum based on the average time spent on one task before switching to another. At one extreme, there are tasks that involve highly frequent and sometimes imperceptible switching, such as talking while driving. At the other extreme, there are tasks that involve longer spans between switches, such as writing a paper and reading email.
Prior research has identified two different drivers of multitasking: external interruptions and internal decisions to stop ongoing tasks (Gonzalez and Mark, 2004; Mark et al., 2005; Miyata and Norman, 1986). An external interruption occurs when an event in the environment forces a user to switch tasks, while an internal interruption comes from one's self, i.e. self-initiated, when a user decides to switch tasks at his/her discretion (Miyata and Norman, 1986). Self-initiated interruptions occur just as often as external interruptions (Gonzalez and Mark, 2004).
While multitasking has been examined in the human–computer interaction (HCI) literature, there is still ample opportunity to extend research on this topic (McCrickard et al., 2003c). There are at least two areas where additional research might be fruitful. One is the study of voluntary task switching. The work of Payne et al. (2007) established that people switch away from tasks that are no longer rewarding. Related research studies by Janssen et al. (2011) and Duggan et al. (2013) have incorporated explicit payoff structures (rewards) to investigate in more depth the determinants of voluntary task interleaving. The second area that could benefit from additional research is the study of how multitasking affects performance. The existing literature in this regard is somewhat fragmented. Some studies have examined how users' performance is impacted when receiving external interruptions (Bailey and Konstan, 2006; McFarlane, 2002; Speier et al., 2003). Other studies have focused on the relation between discretionary multitasking and the resulting performance. The findings suggest that although some amount of multitasking may not be detrimental for performance (Davidson, 2011; Palladino, 2007), intensive multitasking, characterized by high frequency switching and a large number of ongoing tasks, tends to degrade performance (Adler and Benbunan-Fich, 2012; Bailey and Konstan, 2006; Hembrooke and Gay, 2003).
The present study seeks to systematically compare different types of multitasking and its effects on performance. Using a laboratory experiment, three types of multitasking resulting from mandatory interruptions (Bailey and Iqbal, 2008), discretionary self-interruptions (Payne et al., 2007) and sequential execution where all tasks are performed in succession (Salvucci and Bogunovich, 2010) are compared. These alternative multitasking scenarios are examined in conjunction with subjective task difficulty to investigate the effects on performance. An analysis of the differences in performance among these scenarios will enable HCI researchers to have a better understanding of the positive or negative impacts of multitasking.
2. RELATED WORK
2.1 Mandatory multitasking
In HCI, multitasking has been examined from the perspective of external interruptions and the role of notification systems (McCrickard et al., 2003a,b; McFarlane, 2002; McFarlane and Latorella, 2002; Oulasvirta and Saariluoma, 2004, 2006; Trafton et al., 2003). Interruptions tend to have negative effects on performance (Oulasvirta and Saariluoma, 2004, 2006). In particular, being interrupted with a secondary task can impact performance on the primary task because of the extra time and effort needed to recall the primary task when it is resumed.
Interruptions are a frequent occurrence in computer-mediated work (Iqbal and Horvitz, 2007). Bailey and Konstan (2006) found that when interrupted, users needed more time to finish their primary task, made more errors in both tasks and had more annoyance and anxiety than those who were not interrupted. A substantial body of research has examined the disruptive effects of interruptions and has documented that increased complexity in the interrupting task leads to slower resumption times (Hodgetts and Jones, 2006) and lower primary task accuracy (Gillie and Broadbent, 1989). Cades and colleagues found that interruption complexity, defined by the number of mental operators required to complete a task, reduces the opportunity for rehearsal in the primary task, leading to an increase in the disruptiveness of the interruption (Cades et al., 2007, 2010).
Of the four different types of interruptions (immediate, negotiated, mediated and scheduled) identified by McFarlane (2002), immediate is the most detrimental for performance. The other types are not as detrimental because the user has some level of control, as he/she can decide whether or not to respond immediately (negotiated), a middle agent determines whether the interruption will occur (mediated) or the interruptions occur at predetermined intervals (scheduled).
Receiving interruptions during a task and being forced to respond at that moment is disruptive and causes users to lose their thought process and control during the performance of a task (Altmann and Trafton, 2002). Bogunovich and Salvucci (2011) discuss the concept of cognitive load interruptibility and argue that forced interruptions are less disruptive when the cognitive load is low. A key determinant of cognitive load is the level of difficulty of a task, which can be assessed through an objective measure of task complexity or through a subjective perception of complexity (Maynard and Hakel, 1997). From an objective perspective, task difficulty can be determined by task designers based on an estimation of the amount of mental resources required to complete a task. In contrast, subjective task difficulty refers to the perception that some tasks seem harder due to an intuitive sense of difficulty (Cades et al., 2008).
The impact of interruptions are contingent upon the level of difficulty of the task being performed (Gillie and Broadbent, 1989). For example, Speier et al. (2003) found that interruptions helped improve performance on simple tasks but hurt performance on more complicated tasks. When users are interrupted during complex tasks, their cognitive ability is impaired and task performance suffers. During a complex task, a distraction can interrupt the user's concentration and therefore can cause negative results (Altmann and Trafton, 2002). However, during simple tasks, where users do not have to invest a substantial amount of cognitive resources on the task at hand, interruptions can actually help them focus their attention (Speier et al., 2003), thereby improving their performance.
2.2 Discretionary multitasking
Multitasking also occurs when users decide at their own volition to interrupt the current task to pursue another one. Jin and Dabbish (2009) identified seven categories of internal interruptions. These categories explain why a user would switch to another task: adjustment, break, routine, wait, inquiry, trigger and recollection. A user may need to take a breakwhen frustrated or tired, or multitask due to a trigger or recollection when recalling a related or completed new task. People also multitask due to routine, such as checking one's email out of habit, or they may multitask due to necessary adjustments of the working environment. Other causes of multitasking include a wait, which involves filling downtime during a task, or an inquiry to receive necessary information that will help complete the task.
Discretionary multitasking has also been examined in the psychology literature. Payne et al. (2007) conducted a set of experiments designed to investigate different types of multitasking. In their second experiment, participants were performing two similar computer-based tasks and were allowed to switch between these tasks at will. The results of this experiment indicated that people switched either because tasks were no longer rewarding or because they finished a sub-goal and decided to take a break from the current task by attending to another. In fact, when given a choice, people prefer to switch at low cognitive load points (Bogunovich and Salvucci, 2011) because workload decreases upon the completion of a sub-task and the disruptive effects of interruptions are minimized at natural breaking points (Bailey and Iqbal, 2008).
In terms of task difficulty, Czerwinski et al. (2004) found that complex tasks were more difficult for subjects to resume. However, given a set of tasks, the level of difficulty affects which tasks subjects decide to pursue, the order in which these are executed and the extent to which they are interleaved (Yeung, 2010). When faced with multiple tasks, people can strategically control their allocation of attention to maximize their payoffs and meet specific performance goals (Duggan et al., 2013; Janssen and Brumby, 2010; Janssen et al., 2011).
2.3 Sequential task completion
While conceptually different, both discretionary and mandatory multitasking are theoretically important, particularly when compared with sequential task performance, which is free from interruptions. The most important difference is that the user controls the pace and timing of self-interruptions in discretionary multitasking, but does not control them in the mandatory interruption scenario. The sequential scenario, where tasks are performed consecutively and without interruptions, serves as a control condition to systematically compare different types of multitasking. In sequential execution (also called serial or mono-tasking), only one task is executed at a time from beginning to end. Although multiple tasks are completed in a time frame, there is no task interference and no switching. Thus, this mode is widely used to establish a baseline condition for performance.
2.4 Performance effects and task complexity
The relation between multitasking and performance can be explained from the perspective cognitive skills/abilities or with other factors, such as personality traits or psychological states. The level of arousal is one of the factors that has been used to explain the effects of multitasking on task performance (Oswald et al., 2007). Complex tasks produce higher levels of mental workloads and lead to higher arousal than easier tasks. Therefore, the level of difficulty of a task imposes mental workload demands on the performer that interacts differently with task interruptions. At low levels of workload, performance is compromised due to inattention and lack of stimulation, while at high levels, performance also suffers due to the cognitive inability to deal with overload. Optimum performance is in the middle, where there is the right combination of workload and attention. This inverted-U relationship between workload and performance is known as the Yerkes–Dodson law (Yerkes and Dodson, 1908). According to this law, easy tasks produce low levels of arousal, and performance can improve when the user faces additional stimuli (Teigen, 1994). Therefore, receiving interruptions during an easy task may help performance. In contrast, because difficult tasks already require substantial cognitive resources for their performance, extra interruptions further increase the overload, and performance is impaired (Altmann and Trafton, 2002).
Prior research has examined performance differences considering objective task difficulty (Payne et al., 2007; Speier et al., 2003), usually in the context of a single multitasking scenario. In discretionary switching, Payne et al. (2007) found that time allocation is sensitive to the level of difficulty of each task as participants seek to optimize performance. In an interruption scenario, Speier et al.'s (2003) comparative study of simple and complex tasks found that interruptions during a task helped performance with simple tasks, but hurt the execution of more complicated ones.
Complex tasks require more cognitive effort than easier tasks and task performance is impacted. However, the same task can be difficult for one person and easier for another. Maynard and Hakel (1997) indicate that task performance depends not only on objective task complexity but on subjective perceptions of task difficulty as well. As a result, we propose that depending on the subjective difficulty of the task at hand, performance will be affected differently as a result of discretionary or mandatory interruptions. Furthermore, performance will be impacted for both the primary task and the interrupting tasks.
Deciding to take a break or being forced to take a break can affect a user's performance in different ways. The negative effects of mandatory interruptions are due to the cognitive costs associated with switching between ongoing tasks at times that are beyond the control of the user (Altmann and Trafton, 2002). When people multitask at their discretion, they can decide when and how often to switch among ongoing tasks. During a complex task, receiving unplanned interruptions can impact performance more than planning an interruption at a suitable breaking point. Therefore, we propose:
H1a. During a task they consider harder, those who are forced to multitask will perform worse on all tasks than those who do not multitask.
H1b. During a task they consider harder, those who are forced to multitask will perform worse on all tasks than those who multitask at their discretion.
In contrast, during an easier task, less cognitive resources are used. Based on the Yerkes–Dodson law, easy tasks are associated with low arousal levels. Any increase in the level of arousal can improve performance. For example, an unexpected interruption will raise the levels of arousal, and performance will improve.
Performance of those who are forced to multitask will also be different from performance of those who choose to multitask at their discretion. When multitasking at one's discretion a user may choose to switch tasks after a sub-goal has been completed (Payne et al., 2007), depending upon their priorities (Janssen and Brumby, 2010; Janssen et al., 2012). Because the user can decide when to switch and it is not unexpected, these self-interruptions are known and do not increase arousal. Given that receiving unexpected interruptions can provide greater stimulation, those forced to multitask can improve their performance more than those who multitask at their discretion. Based on these arguments, we formulate the following hypotheses:
H2a. During a task they consider easier, those who are forced to multitask will perform better in all tasks than those who do not multitask.
H2b. During a task they consider easier, those who are forced to multitask will perform better in all tasks than those who multitask at their discretion.
4. MATERIAL AND METHODS
Six hundred and thirty-six subjects (334 male and 302 female) were recruited from a large urban college in the Northeast USA. About half (307) received $10 monetary compensation and the other half (329) received course credit. Subjects performed the computer-based experiment in a laboratory setting. Participants were equally distributed in the three conditions (212 subjects in each).
We developed an experimental multitasking environment in Microsoft Visual C++. In this environment, we conducted a controlled experiment where participants had to perform a primary task and a set of secondary tasks. Participants were randomly assigned into one of three multitasking conditions: discretionary,mandatory and sequential.
Discretionary: In the discretionary condition, all tasks were presented at once, in different tabs and subjects were able to choose when to complete each task. Subjects in this condition were allowed to switch tasks at any point. The interface kept track of when subjects were switching and how often.
Mandatory: In the mandatory condition, the secondary tasks appeared while subjects were in the middle of completing the primary task. In this condition, subjects were interrupted at different intervals of time with pop-up windows that forced them to complete other tasks. The interrupting task had to be completed before the user was able to resume the primary task. In this instance, one of the visual exercises covers the screen and subjects have to answer as many answers as they can before time for the time for this task expires. Once the time limit was reached, subjects were brought back to the primary task screen.
Sequential: In the sequential condition, the secondary tasks were displayed as pop-up windows only after the primary task was completed (i.e. the total allotted time on task had elapsed).
The experimental environment presented six game-like tasks for participants in all three conditions. The primary task was a Sudoku puzzle.1 The goal of a Sudoku puzzle is to fill in all the boxes in a 9×9 grid, so that each column, row and 3×3 box have the numbers 1–9 without any of those numbers being repeated.
There were five secondary tasks: one textual task, two visual tasks and two number series tasks. The textual task consisted of unscrambling a series of letters to find up to 20 words. The visual tasks required subjects to select the shape that best fit the pattern. Subjects were shown four shapes and had to choose the shape that did not belong. There were ten visual multiple-choice problems and there were two of these visual tasks (i.e. two sets of ten visual exercises).2 The Number Series tasks involved subjects guessing the missing number in the series of numbers presented. Subjects had two number series exercises to complete, each with ten questions.3
Sudoku was chosen as the primary task as it requires more time and concentration to complete than the secondary tasks. In addition, when subjects are performing other tasks and return to the primary task, they need to remember their thought process. While multitasking may not be as disruptive when dealing with multiple tasks on different modalities, such as one auditory task and a separate visual task, having multiple tasks in the same modality is more disruptive (Wickens, 2002). Although the chosen tasks were unique in that they used different skills (visual, textual or numeric), they all required significant cognitive resources for their successful performance.
The goal was to implement tasks that required different skills and durations in order to emulate an actual computer usage session. Generally, users work on a primary task, which requires more time and concentration. They might be interrupted by an instant message alert, which will not require as much time or thought to respond to. Or, perhaps they receive an email message that requires a little more time than the IM alert, but less than their primary task. Our experiment tried to mimic this by providing different types of tasks with different durations.
The time to complete each task was limited and determined based on prior pilot testing. The time for each task was intentionally shorter than the time subjects needed to complete the task in order to avoid subjects being idle. For the primary task (Sudoku), the maximum time limit was set to 18 min. For the secondary tasks, the time for the word task was set to 1.5 min, while the time for the two visual and two numeric series tasks was set to 48 s. Since time allotted for each task was the same for every subject, we were able to compare the performance results across all the three different conditions, ruling out the potential influence of time on task.
Upon arrival to the lab, each subject was randomly assigned to one of the three conditions and given specific instructions according to their condition. After signing a consent form, subjects started to use the multitasking environment. They were presented with a pre-test questionnaire, which included demographic questions (i.e. age and gender) as well as questions about usage of and comfort with a computer, and prior experience with Sudoku. After this questionnaire session, participants had a practice round of Sudoku to familiarize themselves with the Sudoku as well as the interface for this task. Next, the system presented a reminder of the game instructions and the tasks were presented according to the condition (one at a time in sequential, all at once in discretionary or through interruptions in the mandatory condition). Once the time for all the tasks expired, subjects were brought to the post-test questionnaire. The results of the tasks and questionnaires were automatically written to a unique log file generated for each participant.
Sudoku performance was calculated as the number of correct answers as a percentage of the total answers required. Specifically, in the Sudoku task there were 49 empty spaces that needed to be filled out with the appropriate numbers. The score was the number of correct values entered divided by the total number of squares that had to be filled during the session (49).
Secondarytask performance was computed by averaging the performance scores of all five secondary tasks. For the word task, there were 20 acceptable words that could be generated from unscrambling the letters. The percent correct is the number of correct responses out of 20. The same method was applied to calculate the visual and number series tasks' scores.
Overall performance was calculated as the average of all six tasks (Sudoku and secondary tasks).
Subjective task difficulty: While the Sudoku puzzle chosen was from an online selection of puzzles in the easy category, not all subjects may find it easy. Therefore, we measured subjective task difficulty in the post-test questionnaire by asking the subjects to rate the level of difficulty of the primary task (from1=easy to5=hard).
Table 1 presents the basic statistics of the demographic and skills characteristics (age, computer skills and Sudoku experience) of the sample.
Descriptive statistics (n=636).
To ensure that randomization worked and to rule out alternative explanations, the demographic characteristics of participants were first checked for possible pre-existing differences among conditions. None of the continuous pre-test questionnaire variables showed a systematic variation. Separate ANOVAs were performed using age (MeanDiscretionary=21.98; MeanSequential=22.53; MeanMandatory=22.80; F(2,632)=1.71 ns), computer skills (MeanDiscretionary=3.71; MeanSequential=3.70; MeanMandatory=3.70; F(2,633)=0.02 ns) and Sudoku experience (MeanDiscretionary=1.54; MeanSequential=1.57; MeanMandatory=1.53; F(2,633)=0.04 ns) as dependent variables. A separate χ2 analysis was conducted for gender. The results showed that male and female participants were equally distributed across conditions (χ2=1.68; P=0.43 ns). The demographic variables (age, gender, computer skills and Sudoku experience) were similarly distributed across conditions. In the subsequent statistical analyses, the previous experience with Sudoku will be used as a control given its potential effect to explain differences in Sudoku performance.
To examine whether the experimental condition had any influence on the subjective task difficulty, we compared subjective task difficulty across conditions and found no variation. Thus, the subjects' subjective task difficulty is independent of the multitasking condition to which they were assigned. In particular, Sudoku level of difficulty (mean=3.22; 1.38 SD and median=3), was not significantly different across conditions (F(2,633)=0.54 ns).
5.1 Test of hypotheses
In order to formally test our hypotheses, we examined whether there was an interaction effect between subjective task difficulty and experimental condition. To perform the analyses, we divided the sample into three categories: those who found the primary task (Sudoku) to be difficult (i.e. rated its difficulty with 1 or 2), those who found it easier (i.e. gave a rating of 4 or 5) and those who were neutral (i.e. chose the medium level 3). Three models were run, one for each dependent variable (overall performance, Sudoku performance and secondary task performance).
The results of each model are reported in Table 2. The name of the corresponding dependent variable is listed at the top of the table. The top portion shows the means of the nine conditions (3 modes, 3 levels of difficulty). The bottom portion of the table shows the F for the entire model and the corresponding percentage of variance explained (R2). The main effects for the explanatory variables are listed below, indicating in each case the F-statistic, its significance and the eta square (η2) to indicate the strength of the association or effect size.
Comparison of performance by multitasking condition and task difficulty.
|Multitasking condition||Subjective Sudoku difficulty||Overall performance||Sudoku performance||Secondary task performance|
To Multitask or Not to Multitask
In today’s business world, companies are having to do more with less and employees are being asked to work harder and for longer hours. A majority of people in the office spend their time bouncing back and forth between tasks, believing their multitasking is making them more efficient. New studies, however, have found that multitasking is no longer a skill to brag about, but to worry about. These studies suggest that multitasking causes us to actually make more mistakes, retain less information, and change the way our brain works, leaving everyone wondering “to multitask or not to multitask?”
How Your Brain Multitasks
The prefrontal cortex of the brain begins working anytime you need to pay attention. This area of your brain helps keep your attention on a single goal and carry out the task by coordinating messages with other brain systems. Working on a single task means both sides of the prefrontal cortex are working together in harmony. Adding another task forces the left and right sides of the brain to work independently. Scientists at the Institut National de la Santé et de la Recherche Médicale (INSERM) in Paris discovered this when they asked study participants to complete two tasks at the same time while undergoing functional magnetic resonance imaging (fMRI). The results showed that the brain splits in half and causes us to forget details and make three times more mistakes when given two simultaneous goals.
It is important to note that multitasking while doing natural tasks like eating and walking are much easier than more complicated tasks like texting while driving. Those natural tasks place less of a demand on the prefrontal cortex, creating an easier switch between eating and walking to your next meeting. Not only does multitasking make us less productive, it may also be lowering our IQ and overall efficiency at work.
Multitasking Affects Your IQ
A study by the University of London found that participants who multitasked during cognitive tasks, experienced an IQ score decline similar to those who have stayed up all night. Some of the multitasking men had their IQ drop 15 points, leaving them with the average IQ of an 8-year-old child. The next time you find yourself in a meeting, trying to juggle listening to your boss and reading the day’s top stories, know that little information will be stored from either tasks when all is said and done.
Multitasking Affects Your Brain’s Efficiency
Being able to perform multiple tasks at work is believed to be a strength, yet a study in the Journal of Experimental Psychology: Human Perception and Performance (Vol. 27, No. 4) indicates that multitasking is less efficient because it takes extra time to shift mental gears every time a person switches between tasks. Joshua Rubinstein, PhD, of the Federal Aviation Administration, has proposed new models of cognitive control. The first, goal shifting, involves actively deciding to change tasks. Once you have decided to switch processes, your brain begins rule activation. This requires your brain to turn off the cognitive rules of the old task and turn on new rules for the next. This process can be seen in the workplace when someone switches from filling out financial excel sheets to writing emails. Their brain must first shift goals and decide that it is done with the math processes and ready to begin writing. Before they start writing, the brain turns off the math rules and activates language rules. The time it takes for the brain to fully switch processes and cognitive rules takes time and leads to inefficiency in the workplace.
How to Be Efficient Without Multitasking
Not multitasking at work can be difficult to stop, especially when there is a lot on your plate. Luckily, there are a few simple and conscious changes you can make to work even more efficiently. Rather than bouncing back and forth between tasks every other minute or so, dedicate chunks of time to a certain task. For example, spend 20 minutes reading the day’s news and then move on to your next assignment for 20 minutes, and so on.
A common workplace task that will challenge this strategy is checking email. Studies show the average professional spends about 23 percent of the day emailing. Inspired by that statistic, Gloria Mark of the University of California, Irvine, and her colleague Stephen Voida studied an office, cut off 13 employees from email for five days, strapped heart monitors to their chests, and tracked their computer use. The employees ended up being less stressed when cut off from email. They focused on one task for longer periods of time and switched screens less often, thereby minimizing multitasking, and working efficiently.
How to Combat Multitasking in Teams
Single-tasking on an individual level seems easy enough, but what happens when a team is involved? Multitasking with a group of coworkers creates a higher chance of miscommunication, missed deadlines, and poor work quality. If everyone in the group is distracted, there is little to no chance of coming together and producing the best work possible. To combat a sinking team, it’s important to remain collectively focused on one task, schedule blocks of time, and use less tools. Productivity will skyrocket if the group focuses their attention on one task. They will be able to come together and devote themselves towards the work. By creating blocks of time for different tasks you have a better chance staying productive and on schedule towards completion. Lastly, with fewer platforms being used, each member will have less transition time between tasks, keeping them in a productive mindset. Create a work model for your team based on organization, communication, and simplicity for the best productivity.
Key Takeaways for Management
- Be sensitive to challenges when multitasking
- Help employees prioritize work
- Let off the gas once in awhile. Allow for slow periods of time to give employees a break.
- Keep business transparent to help employees feel valued.
- Communicate expectations clearly.
- Face-to-face communication is more effective than email.
Next time you find yourself juggling simultaneous assignments at work, stop multitasking. Give your full attention to one project at a time and you will find your quality of work and efficiency increase greatly.
USC’s online Master of Science in Applied Psychology program is uniquely structured to explore human behavior in great depth to inform real-world business decisions that affect both organizational and consumer behavior.
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