Work-in-Progress
CHI 2012, May 5–10, 2012, Austin, Texas, USA
The Effects of Positive and Negative Self-Interruptions in Discretionary Multitasking Rachel F. Adler
Abstract
The Graduate Center
Human multitasking is often the result of self-initiated interruptions in the performance of an ongoing task. Compared to externally induced interruptions, selfinterruptions have not received enough research attention. To address this gap, this paper develops a detailed classification of self-interruptions rooted in positive and negative feelings of task progress based on responses subjects provided after completing a multitasking laboratory experiment. The results suggest that multitasking due to negative feelings is associated with more self-interruptions than those triggered by positive feelings and that more selfinterruptions may produce lower accuracy in all tasks. Therefore, negative internal triggers of selfinterruptions seem to unleash a downward spiral that ultimately affects performance.
City University of New York 365 Fifth Avenue New York, NY 10016 USA
[email protected] Raquel Benbunan-Fich Baruch College City University of New York One Bernard Baruch Way New York, NY 10010 USA
[email protected]
Keywords Multitasking; Interruptions; Self-interruptions; Performance Copyright is held by the author/owner(s). CHI’12, May 5–10, 2012, Austin, Texas, USA.
ACM Classification Keywords
ACM 978-1-4503-1016-1/12/05.
H.5.2 [Information Interfaces and Presentation (e.g., HCI)]: User Interfaces – User-centered design;
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Introduction Multitasking is commonly defined as undertaking multiple tasks at the same time [13]. Usually, multitasking consists of interleaving independent tasks in the same time period and switching among them. While this interspersing contributes to the illusion of productivity as more tasks are performed in a period of time, many studies show that performance degrades when attention is divided [1], particularly during complex tasks [15]. Despite the potential negative consequences, multitasking is prevalent in everyday life [2]. Since multitasking is characteristic of modern computer usage, Human-Computer Interaction researchers have begun exploring its causes or triggers and consequences in more depth. In multitasking situations, different tasks are combined in the same timeframe. The decision to abandon an ongoing task and undertake another is due to two different types of interruptions: external and internal [7,10,14]. The former refers to external notifications or environmental cues, while the latter points out to internal decisions to stop an ongoing task, due to personal thought processes or choices. The focus of this research is on internally-motivated interruptions, which have been called self-interruptions [9] to emphasize that the decision to pause occurs in the absence of external triggers. Despite the frequency of self-interruptions [4], with a few notable exceptions [12], the existing literature has not devoted enough attention to the determinants of these types of interruptions. In contrast, there is a plethora of studies investigating the consequences of external interruptions [1,6,11,15]. There are also a few studies [5,8,9] integrating both types of interruptions in
a single classification scheme. For example, [9] provide a classification on self-interruptions that includes environmental causes. Our study seeks to contribute to the extant literature by developing a refined classification of only internal self-interruptions and investigating its performance consequences. Flow Theory Conceptually, self-interruptions can be conceived as self-imposed disruptions in the flow of work. According to [3], flow is the mental state that occurs when a person is completely immersed in a particular task, with total focus and involvement. In order to achieve this flow state, there must be a balance between the person’s skills and the level of challenge provided by a task. This balance results in the feeling of “optimal experience” in the performance of a task. Under these circumstances, self-interruptions are less likely to occur. When people face tasks that are not challenging enough, this feeling of optimal experience is not achieved and people may need additional stimulation afforded by new tasks, hence engaging in what we call positive self-interruptions. In contrast, when the ongoing task is too difficult for the level of skill, people experience negative feelings that are likely to trigger self-interruptions. These self-interruptions can provide a necessary break to clear their heads. In both cases, positive or negative discrepancies, self-interruptions indicate that a state of flow was not achieved.
Research Methods To investigate the reasons that trigger human multitasking, a laboratory experiment was conducted using an experimental environment implemented
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through a custom-developed application in Microsoft Visual C++. Participants had to answer several problem-solving tasks. The system featured a main task and five mini-tasks; all presented in different tabs (see Figure 1). Each task had a correct solution and a time limit for its completion. The primary task was a Sudoku problem of medium difficulty. The goal of Sudoku is to fill in all the boxes in a 9x9 grid, so that each column, row and 3x3 box have the numbers 1 through 9 without repetitions. In addition, there were five secondary tasks of shorter duration, one textual (form words with given letters), two visual (identify the shape that does not fit the pattern) and two numeric series (find the number that completes the sequence). Participants were able to multitask at their discretion by clicking on the corresponding tab at any moment.
actual computer usage session. Subjects were provided with instructions both as a hand-out and on the computer interface itself. Subjects were informed that Sudoku was the main task and that their performance was calculated considering all tasks. Upon completion of the tasks, the final screen of the interface provided space to answer an open ended question asking why each person decided to switch tasks. The two dependent variables were actual performance scores and actual number of switches. Performance scores for each task were calculated as the number of correct responses a subject entered divided by the total number of possible responses. Secondary tasks’ scores were calculated in a similar fashion. Total performance was computed as the average of the scores obtained in all of the assigned tasks, in order to examine the overall performance consequences associated with multitasking. The number of self-interruptions was calculated as the number of tab switches. For example, a subject performing all six tasks sequentially (without multitasking) would have five switches. Therefore, the number of self-interruptions was calculated as the total number of switches (tab clicks) minus five.
Figure 1. Tab-Based Multitasking Environment
Sudoku was chosen as the primary task since it requires more mental concentration than the secondary tasks. The goal was to implement tasks of different skills and duration to emulate what happens in an
Results A total of 212 participants were recruited from the undergraduate student population of a large urban college in the Northeast of the U.S. Analysis of the post-test questionnaire responses indicated that 26 subjects were not aware that they were free to switch tasks at will. Data from these subjects was removed from the sample.
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Two independent coders classified the responses to the open ended question about the reasons for switching. Based on Flow Theory, their responses were categorized into negative and positive triggers. Reasons that indicated unchallenging tasks were classified as positive, while those indicating difficulty with the ongoing task were classified as negative. The intercoder reliability of this classification calculated with the percentage of agreement was 91%. The discrepancies between the coders were solved by discussing the differences and reaching agreement in defining each classification. After the coding was finalized, twenty answers were vague or could not be placed in the typology and were coded as ‘Other.’ Participants with this code were removed from further analysis, yielding a final sample of 166 (85 male and 81 female). A fraction of the sample (67 participants) reported that they did not choose to multitask, even though they were aware of this possibility. The rest of the participant’s reasons for multitasking are grouped into positive and negative, as shown in Figure 2. About one third of the sample (33%), switched tasks for negative reasons, while 45 subjects, about 26%, did so for positive reasons. Negative reasons include Obstruction, Exhaustion, and Frustration. Obstruction occurs when a user experiences a temporary block in the performance of a task. An example of a subject’s response was “When I couldn’t figure out what I was doing wrong.” Exhaustion occurs when a person experiences cognitive fatigue with the current task, for example “When I needed to clear my head, I would switch to another task”. Frustration occurs when user feels the task is too
difficult, for example “I switched because the Sudoku was hard”. Positive triggers for multitasking seek to make the experience more enjoyable for participants and include: Reorganization, Exploration, and Stimulation. Reorganization occurs when users decide to restructure the order of the tasks to improve their performance, for example “To finsih [sic] the easiest one first.” Exploration occurs when users multitask in order to view other open tasks, for example: “Just to see the actual content of each task”. Stimulation occurs when the user considers the task too easy, for example “I switched to avoid getting bored.”
Triggers Count Negative Reasons Obstruction 30 Exhaustion 14 Frustration 10 Positive Reasons Reorganization 31 Exploration 8 Stimulation 6 Reported No Self-Interruptions Focus-Task-Strategy 67 Total 166
Percent 18% 8% 6% 19% 5% 4% 40% 100%
Figure 2. Reasons for Self-Interruptions
Data resulting from the coding of the qualitative answers indicates that 67 participants (about 40%) reported no self-interruptions, due to their deliberate strategy to focus on one task at a time. When comparing their answers to their pattern of activities, only about half (33) actually had 0 self-interruptions (i.e. no switches). The other half did have minimal self-
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interruptions according to their count of task switches automatically collected by the application. For the entire sample, we checked whether the amount of self-interruptions varied depending on the nature of the trigger, with an ANOVA using the three group classifications (positive, negative, and no selfinterruptions). As expected, the results indicate that subjects who reported no self-interruptions had the lowest number of switches (1.46). However, those who experienced negative feelings interrupted their work more often than those in the positive category (5.26 vs. 3.76). A post-hoc analysis via a Duncan test shows that the average number of switches is significantly different among the three groups (F=15.35, p
