Computers in Human Behavior 28 (2012) 1526–1532

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Computers in Human Behavior

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Mindful multitasking: The relationship between mindful flexibilityand media multitasking

Amanda Ie ⇑, Chiara S. Haller, Ellen J. Langer, Delphine S. CourvoisierDepartment of Psychology, Harvard University, Cambridge, MA 02138, United States

a r t i c l e i n f o a b s t r a c t

Article history:Available online 20 April 2012

Keywords:Media multitaskingTask switchingMindfulnessMindful flexibilityCognitive flexibilityCreativity

0747-5632/$ - see front matter � 2012 Elsevier Ltd. Ahttp://dx.doi.org/10.1016/j.chb.2012.03.022

⇑ Corresponding author. Tel.: +1 408 876 0329.E-mail address: aie@wjh.harvard.edu (A. Ie).

With the insatiable demand for and corresponding burgeoning of electronic devices enabling individualsto accomplish many things simultaneously, effective multitasking may be a necessity in today’s world.The present study was concerned with the improvement of media multitasking by increasing mindfulflexibility through a state mindfulness induction (Langer, 1989). Seventy-five participants were random-ized into one of three state mindfulness induction groups (High Mindful, Low Mindful, No Treatment). Mul-titasking performance was assessed via a dual-task paradigm that involved composing an essay on acomputer and solving anagrams sent via a browser-based chat program. Results revealed that higher traitmindfulness was related to greater tolerance of ambiguity, a greater tendency to adopt a Heuristic thanAlgorithmic thinking style, greater complexity in thinking style, greater positive affect, and less negativeaffect. Although the state mindfulness inductions did not differentially affect multitasking performanceacross the three groups, trait mindfulness predicted the performance of individuals in the No Treatmentgroup. Overall, the study suggested that younger individuals and individuals who have a dispositionaltendency to remain implicitly or explicitly aware of multiple perspectives of a situation are better atmedia multitasking.

� 2012 Elsevier Ltd. All rights reserved.

1. Introduction

Multitasking (i.e., the act of rapidly switching from one task toanother or juggling multiple task at the same time – e.g., Tugend,2008; Wallis, 2006) is assumed to increase productivity and incertain fields, such as the military (e.g., Shanker & Richtel, 2011)and healthcare (e.g., Chisholm, Collison, Nelson, & Cordell, 2000;Laxmisan et al., 2007), successful multitasking translates to livessaved. One of the major reasons for people’s desire or propensityto multitask is the perception of a shortage of time and an acceler-ation of the pace of daily life, also known as the ‘‘time squeeze’’(Southerton & Tomlinson, 2005); in feeling pressed for time, indi-viduals multitask in order to get more done (Baron, 2010).

The rapid switching between tasks (Jersild, 1927; Monsell,2003; Rogers & Monsell, 1995) suggests that successful multitask-ing may be dependent on cognitive flexibility, defined as the abilityto adjust cognitive processing strategies to match novel and unex-pected conditions (Bora et al., 2008; Cañas, Quesada, Antoli, &Fajardo, 2003; Scott, 1962). The ability to rapidly adapt to the pres-ent environment, or task at hand, may enable better performanceoverall. For instance, switching from writing a research proposalto responding to an incoming chat message and back again may

ll rights reserved.

be easier if one could flexibly switch to and from a writing-a-pro-posal processing mode to an engaging-in-a-conversation process-ing mode.

Langer (1989) referred to cognitive flexibility in her social psy-chological concept of mindfulness, which she defined as an activestate of consciousness characterized by the practice of drawing no-vel distinctions, going beyond premature cognitive commitments,and being aware of alternative perspectives (see Langer, 1989,1992; Langer, Blank, & Chanowitz, 1978; Langer & Moldoveanu,2000). Brown and Langer (1990) noted that ‘‘cognitive flexibilityis the essence of mindfulness’’ and more importantly, distin-guished intelligent flexibility from mindful flexibility. Intelligent flex-ibility refers to the ability to flexibly choose among cognitivestrategies and to select the optimal strategy to solve a problem.Thus, intelligent flexibility assumes a linear, one-to-one correspon-dence between problem and solution. In contrast, mindful flexibil-ity refers to the implicit awareness that a problem can be viewedfrom multiple perspectives. Therefore, mindful flexibility is not in-tently linear since it assumes that there is no absolute, optimal fitbetween problem and solution. Since successful multitasking per-formance may be dependent on cognitive flexibility, multitaskingperformance may potentially be improved by enhancing mindfulflexibility, as for example, through the elevation of individuals’state mindfulness (as defined by Langer (1989)). Although studieshave shown that intelligence is related to intelligent flexibility

A. Ie et al. / Computers in Human Behavior 28 (2012) 1526–1532 1527

(e.g., Colzato, van Wouwe, Lavender, & Hommel, 2006; but seeFriedman et al., 2006), intelligence is not assumed to play a largerole in mindful flexibility, as defined by Brown and Langer.

1.1. Factors related to mindful flexibility and multitasking performance

The purpose of this study was to examine if inducing a state ofmindful flexibility impacts media multitasking (Foehr, 2006) per-formance above and beyond the tendency to multitask and factorslinked to mindful flexibility, such as trait mindfulness, intoleranceof ambiguity, thinking style, complexity, and affective state. Ophir,Nass, and Wagner (2009) found that individuals with a tendency tomultitask while using various media forms (e.g., magazines, televi-sion, computers) were less able to filter out irrelevant stimuli andthus paradoxically exhibited worse task switching performancethan light media multitaskers. Trait mindfulness is the dispositionaltendency to remain implicitly or explicitly aware of multiple per-spectives of a situation (Brown & Langer, 1990; Langer, 1997).Intolerance of ambiguity refers to one’s tendency to interpret anill-defined situation as a threat or a source of discomfort (Budner,1962; Grenier, Barrette, & Ladouceur, 2005). In contrast, toleranceof ambiguity has been positively related to openness to experience(McCrae, 1996), which in turn has been associated with flexibility(McCrae, Costa, & Piedmont, 1993). An individual’s thinking style isdefined as a preferred way of organizing and processing informa-tion (Riding, Glass, & Douglas, 1993). Specifically, Algorithmic ver-sus Heuristic thinking styles (Groner & Groner, 1990; Haller &Courvoisier, 2010) distinguish individuals who prefer to tackleproblems by obeying a well-defined sequence of operations andtend to approach novel situations using mechanical and familiartactics from those who prefer using shortcuts that lead to an esti-mate rather than a predefined solution. Heuristic-oriented individ-uals are thus more likely to mindfully approach problems in anovel manner as the situation demands. Complexity is the propen-sity to switch between different poles of a thinking style dimension(Haller & Courvoisier, 2010; Haller, Courvoisier, & Cropley, 2010). Itmight be that more complex individuals may more flexibly switchbetween thinking styles as the situation demands and thus bemore mindful of their environment. Inductions of positive affectivestates have been shown to promote cognitive flexibility, reduceperseveration, increase attentional breadth, and increase distracti-bility (Dreisbach & Goschke, 2004; Fiedler, 2001; Rowe, Hirsh, &Anderson, 2007). Positive affect has also been linked to creativityacross a diverse range of settings (see Ashby, Velentin, & Turken,2002). Negative affective states, in contrast, have been associatedwith a constriction of attentional focus, avoidance of mistakes,and a more focused or analytic mode of processing (Derryberry &Rothbart, 1988; Fiedler, 2001). Thus, baseline positive affectivestates might influence mindful flexibility.

1.2. The present research

Based on the previously discussed research, we set out to testthree main hypotheses:

H1: Since trait mindfulness, intolerance of ambiguity, thinkingstyle, complexity, positive affect, and negative affect are theo-retically related to the construct of mindful flexibility, these fac-tors should be correlated with each other.H2: Media multitasking performance will differ depending onwhether individuals received an induction of high state mind-fulness, an induction of low state mindfulness, or No Treatment.H3: The interaction between state mindfulness and trait mind-fulness will predict media multitasking performance above andbeyond other factors linked to mindful flexibility.

2. Method

2.1. Participants

Seventy-five participants (45 female) between the ages of 18and 50 years (M = 24.1, SD = 6.8) were recruited from the HarvardUniversity Psychology Study Pool. Participants were compensatedwith either 1.0-h of study pool credit or $10. Since the computer-ized multitasking exercise required participants to toggle betweentwo windows, compose an essay, and solve anagrams, individualswere eligible to participate only if they had at least 1 year of com-puter experience and were fluent in English.

2.2. Procedure

Upon arrival at the laboratory, participants provided informedconsent and filled out a computerized pre-experimental question-naire (described below). Participants were then randomly assignedto one of three groups who were given the Low Mindfulness exer-cises, High Mindfulness exercises, or No Treatment. Participantswere given 20 min to complete the exercises, except those in theNo Treatment control group who proceeded immediately to themultitasking exercise. The experimenter was blind to participants’assignation to the high or low mindful group and instructions werestandardized (see below).

Next, participants engaged in the multitasking exercise, whichinvolved composing an essay and solving 12 anagrams in 12 min.The essay prompt was: You are in the following scenario: You arein your car on your way to an important interview and you get stuckin the snow. Anagrams were sent via the browser-based GoogleTalk chat program to increase ecological validity, especially in lightof the fact that the majority of participants were college students(cf. Case & King, 2003; Shiu & Lenhart, 2004). To minimize thepreparation effect (Monsell, 2003), viz., the reduction in the aver-age switch cost as a result of having time to prepare for an upcom-ing task, anagrams were administered at specific time points thatwere unpredictable to the participants. A first anagram was sentas an example.

2.3. Materials

2.3.1. Pre-experimental questionnairesThe Positive and Negative Affect Scale (PANAS; Watson, Clark, &

Tellegen, 1988) is a 20-item scale that assesses levels of positiveand negative affect. In the present study, participants reported towhat extent they felt (e.g., alert) ‘‘at this present moment’’ on afive-point scale (Very slightly or not at all = 1; Extremely = 5). Theinternal consistency of the ‘‘present moment’’ version of the PANAShas been shown to be good, with a > .85 for positive and for nega-tive affect (Watson et al., 1988).

The Langer Mindfulness Scale (LMS; Bodner & Langer, 2001) is a21-item questionnaire that assesses dispositional mindfulness.Participants rated the extent to which they agreed with the itemson a seven-point scale (Strongly disagree = 1; Strongly agree = 7).The LMS has been shown to have a good internal consistency(a > .80; Haigh, Moore, Kashdan, & Fresco, 2011).

The Intolerance of Ambiguity Scale (IAS; Budner, 1962) is a 16-item inventory that assesses one’s tendency to feel comfortable insituations where variables or outcomes are poorly defined. Itemsare rated on a seven-point scale (Strongly disagree = 1; Stronglyagree = 7). While the scale’s internal consistency is low, with aranging from .39 to .62 (Budner, 1962), the scale has been deemedto have the best documented and most consistent degree of con-struct validity (Sidanius, 1988), and has been the most widely cited

1528 A. Ie et al. / Computers in Human Behavior 28 (2012) 1526–1532

and used measure of intolerance of ambiguity (Grenier et al.,2005).

The Heuristic Questionnaire (Groner & Groner, 1990) is a 30-item inventory that assesses Algorithmic versus Heuristic thinkingstyle. Items are rated on a four-point scale (Not at all = 0; Com-pletely = +3 for Heuristic items and �3 for Algorithmic items).The scale’s internal consistency has been shown to be good(a > .75; Haller & Courvoisier, 2010).

Complexity in thinking style was calculated based on scores ofthe Heuristic Questionnaire. Complexity depends on both the var-iability within individuals and the difficulty of each questionnaireitem. Thus, for the single dimension of thinking style, complexitywas calculated as follows (Haller & Courvoisier, 2010, p. 151):ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiXn

i¼1

ðxij � �x:j � ð�xi: � �x::ÞÞ2

n� 1

s

where xij refers to the individual value of a person j on an item i, �x:jrefers to the mean of an individual j over all items, �xi: refers to themean of all individuals for a specific item i, �x:: refers to the globalmean of all individuals over all items, and n refers to the numberof the Heuristic Questionnaire items. In other words, complexityis reflected in the difference between an individual’s response to aspecific item and the individual’s thinking style mean ðxij � �x:jÞwhile the difficulty of each questionnaire item (viz., the degree ofclustering of responses) is reflected in the difference between thesample mean for a specific item and the global mean ð�xi: � �x::Þ.

The Media Use Questionnaire (Ophir et al., 2009) assesses one’stendency to multitask while using media forms. The questionnaireaddresses 12 media forms (e.g., print media such as newspapers,television, music, phone, instant messaging, web surfing). Thequestionnaire allows for the calculation of a Media Multitasking In-dex (MMI) that reflects the mean number of media a person simul-taneously uses when engaging in other media, and is calculated asfollows (Ophir et al., 2009, p. 15586):

MMI ¼X11

i¼1

mi � hi

htotal

where mi represents the number of other media typically usedwhile using primary medium i, hi is the number of hours per weekreportedly spent using primary medium i, and htotal is the totalnumber of hours per week spent with all primary media. To calcu-late mi, numeric values were assigned to each of the matrix re-sponses (Never = 0, A little of the time = .33, Some of the time = .67,Most of the time = 1) and responses were summed for each primarymedium. Although Ophir et al. did not provide information on theinternal consistency of the questionnaire, they reported that theMMI produced a relatively normal distribution and was positivelycorrelated with the total hours of media use, even when the totalhours of media use was accounted for in the calculation of the MMI.

2.3.2. Low and High Mindfulness exercisesThe Low and the High Mindfulness exercises were intended to be

opposites of each other. The Low Mindfulness exercises reinforcedthe notion that there are preconstructed, rigid categories whereasthe High Mindfulness exercises encouraged the creation of noveldistinctions and flexibility. Thus, the former served to strengthenthe rigid boundaries between categories whereas the latter servedto emphasize the permeability of categories. Participants were gi-ven sample responses for each exercise. The Low and High Mindful-ness exercises were the same with respect to the stimuli used (i.e.,scenarios, nouns, object pairs) and the number of questions, butdiffered with respect to the specific questions that were asked.

2.3.2.1. Low Mindfulness exercises. There were three categories ofLow Mindfulness exercises that targeted areas such as adopting a

single perspective and solidifying categorical boundaries. In thefirst exercise, participants were given three ambiguous scenarios(e.g., Jonathan took $5000 from the teller) and were asked to provideanswers to six specific questions that did not require the genera-tion of alternative explanations for the same event. The secondexercise presented participants with three nouns (rain, wine, hon-esty). Participants were asked to provide six answers for each nounthat did not require the generation of novel categories. In the finalexercise, participants were presented with three object pairs(lampshade-drape, brick-shoe, ruler-pair of scissors) and were askedto provide two ways in which the object pairs were dissimilar toeach other, list one common use of each object, and indicatewhether people in general commonly use each object.

2.3.2.2. High Mindfulness exercises. There were three categories ofHigh Mindfulness exercises that targeted areas such as making no-vel distinctions and increasing mindful flexibility. In the first exer-cise, participants were presented with the same three ambiguousscenarios and were asked to provide three positive and three neg-ative explanations for the target person’s actions. In the secondexercise, participants were presented with the three nouns andwere asked to provide three reasons for why each noun could beconsidered to be ‘‘good’’ and three reasons for why the noun couldbe considered to be ‘‘bad.’’ Finally, participants were presentedwith the three object pairs and were asked to indicate two waysin which the object pairs were similar and to describe two noveluses for each object.

2.3.3. Multitasking exerciseThe multitasking exercise consisted of typing an essay and solv-

ing 12 anagrams in 12 min. Since the two tasks draw on commonperceptual (i.e., visually attending to the computer screen), cogni-tive (i.e., verbal), and motor (i.e., typing) resources, switching be-tween tasks will degrade performance for one or both tasks(Salvucci & Taatgen, 2008).

2.3.3.1. Anagrams. Twelve anagrams were selected based on theirrelevance to the essay prompt. The first author and a researchassistant categorized eight of the anagrams (window, escape, shovel,unload, pushing, frustrated, melting, shivering) as moderately to veryrelated while the remaining four (peanut, table, stairs, blinking)were categorized as least related.

2.3.3.2. Essay ratings. Six judges blind to the experimental groupsscored the essays using the Creativity Solution Diagnosis Scale(CSDS; e.g., Cropley & Cropley, 2008; Haller, Courvoisier, & Cropley,2011). Cropley and Cropley argued that the CSDS could be used bya single non-expert assessor, is based on relatively closely definedindicators of creativity rather than intuitions, and yields a profile ofstrengths and weaknesses rather than an undifferentiated globalassessment. The CSDS evaluated the essays on five dimensions(24 total items) on a five-point scale (Absent = 0; Pronounced = 4).

2.3.3.3. Composite multitasking score. To compute a composite mul-titasking score, the CSDS ratings averaged across six judges, totalnumber of anagrams correctly solved, and the average speed takento solve an anagram were first transformed into z-scores and thensummed.

2.3.4. Post-experimental questionnairesParticipants rated the difficulty level of and how much they

liked the Low and High Mindfulness exercises (Low and High Mindfulgroups only), the essay task, and the anagram task on seven-pointscales (Very easy = 1; Very difficult = 7; and Dislike extremely = 1;Like extremely = 7, respectively).

Table 1Participant characteristics.

Variable High mindful Low mindful No treatment p Effect size

Sex 17 F, 8 M 14 F, 11 M 14 F, 11 M .61 0.12Age in years M (SD)* 22.04a (3.67) 26.12a (7.31) 24.12 (8.06) .046 0.19MMI M (SD)* 3.45 (1.66) 2.82b (1.73) 4.09b (1.35) .02 0.28LMS M (SD) 114.86 (12.40) 114.83 (17.59) 109.04 (13.31) .28 0.09IAS M (SD) 51.40 (9.04) 49.72 (9.74) 51.88 (8.11) .67 0.13TS-M M (SD) 0.21 (0.26) 0.29 (0.37) 0.24 (0.31) .71 0.13TS-C M (SD) 1.25 (0.12) 1.28 (0.18) 1.26 (0.16) .87 0.15PANAS-P1 M (SD) 24.77 (6.02) 27.17 (8.47) 25.76 (7.61) .55 0.11PANAS-N1 M (SD) 13.23 (3.13) 13.92 (4.47) 13.60 (3.35) .82 0.15

Note: MMI = Media Multitasking Index; LMS = Langer Mindfulness Scale; IAS = Intolerance of Ambiguity Scale; TS-M = Thinking Style Mean; TS-C = Thinking Style Complexity;PANAS-P1 = pre-experimental positive affect; PANAS-N1 = pre-experimental negative affect. Means sharing a common subscript are statistically different at a = .05 accordingto Fisher’s LSD procedure.* p < .05.

Table 2Psychometric properties of the major study variables.

Variable na M SD ab Range Skew

Potential Actual

MMI 75 3.43 1.62 .51 0–11 0–6.8 �0.16LMS 75 112.80 14.41 .91 21–147 80–146 �0.38IAS 75 51.00 8.91 .61 16–112 25–71 �0.06TS-M 75 0.24 0.32 .79 �1.50 to 1.50 �0.47 to 0.97 �0.05PANAS-P1 71 25.93 7.43 .90 10–50 15–46 0.59PANAS-N1 71 13.59 3.67 .79 10–50 10–30 1.60

a The variation in sample size (n) is due to the individuals choosing not to respond to specific items.b Cronbach’s alpha based on standardized items.

Table 3Correlations among pre-experimental questionnaire measures.

Variable IAS TS-M TS-C PANAS-P1 PANAS-N1

LMS �.35** .63** .54** .35** �.31**

IAS – �.54** �.43** �.01 .13TS-M – .80** .30** �.25*

TS-C – .23 �.23PANAS-P1 – �.16

* p < .05.** p < .01.

A. Ie et al. / Computers in Human Behavior 28 (2012) 1526–1532 1529

2.4. Data analysis

All statistical analyses were carried out using SPSS 18.0. Groupallocation was examined using Pearson’s chi-squared test for cate-gorical variables and analysis of variance (ANOVA) for continuousvariables. The reliabilities were assessed using Cronbach’s alpha.A multivariate analysis of covariance (MANCOVA using Pillai’strace) was conducted to examine group differences in the percep-tions of the Low/High Mindfulness exercises, the essay task, and theanagrams task.

To assess whether the factors that were assumed to comprisethe construct of mindful flexibility were related to each other,Pearson correlation coefficients were performed among these vari-ables. A four-step hierarchical multiple regression analysis wasperformed to examine whether media multitasking performancediffered among the High Mindful, Low Mindful, and No Treatmentgroups, and to assess whether the interaction between state andtrait mindfulness predicted media multitasking performanceabove and beyond other factors linked to mindful flexibility. Inthe first step, age and multimedia use were entered. In the secondstep, all centered variables measured in the pre-experimentalquestionnaire were entered. Next, the dummy-coded groups (HighMindful and Low Mindful; the No Treatment group served as the

reference) were entered to test whether including the state mind-fulness induction in the model would result in a significant changein the amount of variance explained. In the final step, the interac-tion of group and trait mindfulness was entered.

3. Results

The High Mindful group was significantly younger than the LowMindful group while the No Treatment group had a significantlyhigher MMI score than the Low Mindful group (Table 1). As a result,we statistically controlled for age and MMI score in all subsequentanalyses by including these variables as predictors in the models.Reliabilities were high for all scales except for the MMI and IAS (Ta-ble 2). Interrater reliability across the six CSDS ratings (sum of the24 CSDS scale items) from the six judges was high (a = .88).

There was a significant effect of group on the participants rat-ings of how difficult they found and how much they liked theLow/High Mindfulness exercises, essay task, and anagram task,V = 0.67, F(12,84) = 3.49, p < .001, g2

p ¼ :33. Variables related tomindful flexibility were highly correlated, except for positive andnegative affect (Table 3).

When group was the only predictor, the three groups did notsignificantly differ in their multitasking performances,F(2,70) = 1.23, p = 30, g2

p ¼ :03. Age and media use predicted animportant part of multitasking performance (26%; Table 4). How-ever, subjects’ characteristics still explained 6% of the variance. Inparticular, trait mindfulness was significantly associated with mul-titasking performance. The Low and High Mindful groups were notsignificantly different from the No Treatment control condition.However, group membership in general significantly predictedmultitasking performance (DR2 = .02). Finally, there was a signifi-cant interaction between group and trait mindfulness (Fig. 1).The slopes for the Low Mindful and High Mindful groups were notsignificant (p > .05 for both). For the No Treatment group, the stan-dardized beta coefficient was .27, p = .03.

Table 4Four-step hierarchical multiple regression analysis predicting multitasking performance (N = 71).

Variable DR2 F B SE B b

Step 1: covariates .26** 12.2**

Age �0.15 0.04 �.46**

MMI 0.10 0.15 .07

Step 2: pre-experimental measured variables .06 3.74**

LMS 0.07 0.03 .47*

IAS 0.01 0.03 .04TS-M 1.11 1.37 .15TS-C �2.97 2.49 �.20PANAS-P1 �0.05 0.03 �.16PANAS-N1 0.11 0.07 .17

Step 3: induced state mindfulness group .02 3.18**

Low mindful 0.62 0.60 .13High mindful 0.61 0.59 .13

Step 4: interactive function of trait mindfulnessand induced state mindfulness group

.07** 3.89**

LMS � low mindful �0.003 0.03 �.01LMS � high mindful �0.04 0.04 �.13

Note: All values are for the final model. For the final model: R2 = .42, F(11,59) = 3.89, p < .001.* p < .05.

** p < .01.

Fig. 1. Composite multitasking score as a function of trait mindfulness for allparticipants with separate regression lines for each group.

1530 A. Ie et al. / Computers in Human Behavior 28 (2012) 1526–1532

4. Discussion

The present study (1) established the relationships among thefactors thought to reflect the construct of mindful flexibility, (2)investigated whether media multitasking performance could beenhanced through increased state mindfulness, and (3) assessedthe predictive validity of the interactive function of state and traitmindfulness for media multitasking performance above and be-yond other factors linked to mindful flexibility. The results pro-vided insight as to what factors constitute the construct ofmindful flexibility and suggested that media multitasking perfor-mance could be predicted from trait mindfulness only for thosewhose state mindfulness was not manipulated.

Correlations among the factors thought to constitute mindfulflexibility corresponded to expectations. However, the correlationsof positive and negative affect with the other variables and witheach other were low and/or nonsignificant. This could be due tothe fact that positive affect and negative affect are not necessarilystrict opposites of each other—they are independent—and as such,

the strength of their association could be only moderate (seeCohen, Pham, & Andrade, 2008; Crawford & Henry, 2004).

Although group membership in general significantly predictedmultitasking performance, the Low and High Mindful groups didnot significantly differ from the No Treatment control group. Thismight be because the Low and High Mindfulness exercises affectedstate mindfulness in the unintended directions, or because they didnot significantly influence state mindfulness at all. At the time ofthe study, there were no known measures or methods of assessingchanges in state mindfulness after a short 20-min intervention,thus we did not have a standard by which we could confirm thatthe High Mindfulness exercises resulted in high state mindfulnesswhile the Low Mindfulness exercises resulted in low state mindful-ness. We did find, however, that the High Mindfulness exerciseswere rated as more difficult than the Low Mindfulness exercises. Gi-ven that individuals are seldom in a mindful state, but rather, areoften on automatic pilot (e.g., Langer, Djikic, Pirson, Madenci, &Donohue, 2010; Nass & Moon, 2000), it is not surprising that theHigh Mindfulness exercises were judged as more difficult than theLow Mindfulness exercises. Future research should compare theefficacy of different state mindfulness inductions and shouldexamine ways by which changes in state mindfulness can bemeasured.

The regression analysis suggested that multitasking perfor-mance could be predicted from age and trait mindfulness. Youngerindividuals and those who have a greater tendency to be mindfulperformed better in the multitasking exercise used in the currentstudy. The former results are in line with Carrier, Cheever, Rosen,Benitez, and Chang (2009) finding that younger generations engagein greater amounts of instant messaging, a core component in themultitasking exercise used in the current study. Although theresearchers did not find generational differences in the perceiveddifficulty of combining tasks, it could be that younger individuals’repeated use of instant messaging in daily life (Case & King, 2003;Shiu & Lenhart, 2004) afforded them better multitasking skills forthe current context. Since instant messaging is a task that requiresfrequent attentional shifts, younger individuals’ repeated use ofinstant messaging fosters a dispositional tendency to be moremindful. Indeed, the negative correlation between age and traitmindfulness scores was trending toward significance,r(73) = �.22, p = .06. The finding that trait mindfulness predictedmultitasking performance only for those in the No Treatment group

A. Ie et al. / Computers in Human Behavior 28 (2012) 1526–1532 1531

might be attributed to the inefficacy of the Low Mindfulness andHigh Mindfulness exercises. As mentioned, it could be that the exer-cises affected state mindfulness in the unintended directions.

In sum, the results shed light on the factors that comprise theconstruct of mindful flexibility and also demonstrate that youngerindividuals and those who have a dispositional tendency to bemore mindful are better at media multitasking. While the interac-tive function of state and trait mindfulness predicted multitaskingperformance, it was not because the high state mindfulness induc-tion was effective at increasing mindful flexibility. Instead, traitmindfulness predicted multitasking performance only for thosewhose state mindfulness was not manipulated. This might havebeen due to the Low Mindfulness and High Mindfulness exerciseshaving unintended effects on state mindfulness.

4.1. Limitations and future directions

These findings represent a first step in research demonstratingthe relationship between mindfulness and media multitasking.While the ecological validity of the present study was enhancedthrough the use of tasks in which individuals often engage in out-side of the laboratory (i.e., word-processing while instant messag-ing), it is fair to say that solving anagrams are not the typical topicof conversation when individuals instant message. Also, when indi-viduals multitask, they typically engage in more than two activi-ties. Another limitation was the operationalization of statemindfulness; future studies should develop ways to measure statemindfulness, as defined by Langer (1989), so that the efficacy ofstate mindfulness inductions can be assessed. Finally, multitaskingperformance was assessed with a single task combination; othertask combinations may benefit differently from different levels ofor changes in trait and/or state mindfulness. Haigh et al. (2011)found that dispositionally mindful individuals were higher on po-sitive affect, curiosity, exploration, and absorption (factors that re-flect a flow-like engagement), thus future studies could considerevaluating whether encouraging a flow state (Csikszentmihalyi,1991) can improve media multitasking performance.

5. Conclusion

Effective media multitasking is crucial in today’s hectic andfast-paced world. Given the necessity of effective multitasking ina wide range of domains, it is an important endeavor to explorethe factors that contribute to successful multitasking. Our resultsalong with the proposed future research hope to bring a betterunderstanding of how to enhance multitasking performance. In amedia-rich world where we constantly feel the need to respondto the constant bombardment of e-mails, stay updated throughTweets, interact with others around us, all the while trying to thinkof the next greatest idea, fostering trait mindfulness may havevaluable practical importance.

Acknowledgments

We would like to thank the Swiss National Foundation for theaward PBBEP1_131566 that was granted to the second author.Thanks to Richard Hackman for his helpful comments and EvelynChow, Christine Armour, Christelle Ngnoumen, Asimina Lazaridou,and Jonah Friedman for assistance in completing the study.

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