چه خبره؟ سن، حواس پرتی، انجام همزمان چندکار در طول انجام نظرسنجی آنلاین
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|38801||2014||9 صفحه PDF||سفارش دهید||8024 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Computers in Human Behavior, Volume 41, December 2014, Pages 236–244
Abstract Nearly 6000 adults from 7 countries participated in an online survey about what other activities they engaged in while taking the survey and how distracted they felt. Younger people were more likely than older ones to engage in electronic and non-electronic multitasking. Engaging in a wider range of tasks was associated with feeling more distracted. However, once the variety of tasks was taken into account, interruptions associated with checking or talking on one’s phone made participants feel less distracted. The relationship between age, multitasking, and feeling distraction was curvilinear, with middle-aged respondents being more affected by multitasking than either younger or older survey takers. The findings suggest that people of all ages are often deliberate multitaskers who choose their distractions intentionally, at least some of the time. This bodes well for researchers seeking to administer online surveys, because it suggests that survey takers will set themselves up with the type and amount of distractions they are comfortable with. The finding that a high degree of electronic multitasking may decrease the perception of distraction should be followed by experiments verifying if this perception corresponds to actual task performance.
1. Introduction As portable, network-connected devices such as tablet computers and smart phones become more prevalent, media multitasking has become a subject of increasing interest. Media multitasking refers to engaging in multiple tasks within the same time period, where at least one task involves a form of mediated communication. Devices like laptops, tablets, and smart phones make it easier for individuals to switch back and forth between tasks on one device (e.g., reading a text message while playing a video game on one’s phone), across multiple media devices (e.g., watching a television while updating one’s social networking status on a mobile phone), or between mediated and non-mediated environments (e.g., reading email while cooking dinner) (Jeong & Fishbein, 2007). A commonly expressed concern, both in the academic literature (Bowman et al., 2010 and Waite et al., 2009) and the popular press (e.g., Richtel, 2010 and Stross, 2012) is that multitasking negatively affects concentration, engagement, and task performance. Individuals who are multitasking are thought to be less efficient and less thorough in completing the tasks that they are engaged in, although there is some speculation that these effects may affect different age groups unequally (e.g., Carrier, Cheever, Rosen, Benitez, & Chang, 2009). This possibility has wide-ranging implications for contexts where the ability to focus matters, such as education, where multitasking could inhibit academic success (Levine et al., 2007 and Waite et al., 2009), or consumption of entertainment media, where it could interfere with the ability to become involved. Another context in which the effects of multitasking might be consequential is in the case of online survey-taking. This form of research administration has become increasingly popular in both industry and academia (Groves, 2011). Yet little attention has been given to how often research participants combine computer-based surveys with other activities, when such multitasking might be most common, or how participants’ attention and performance might be affected by it. The current study therefore sought to address these questions. It investigated the relationship between the amount and type of multitasking computer users engage in while responding to a survey using a large, international sample. It examined the relationship between different forms of multitasking and the participants’ subjective sense of being distracted from the task. It also examined the relationship between multitasking and the participants’ age, given previous research suggesting possible differences across generations. In pursuing these objectives, it helps identify correlates of multitasking, provides valuable information for those interested in how individuals combine activities online, and assists researchers in designing and interpreting their survey research.
نتیجه گیری انگلیسی
4. Results 4.1. Preliminary analysis The proportions of participants who reported taking part in each of the multitasking activities are reported in Table 2. Only a small percentage reported taking part in each one of the activities within the approximately 30 min that it took to complete the survey. Nevertheless, the numbers were not trivial. Almost one in four engaged in some sort of electronic multitasking. These tasks ranged from ones that might provide only minimal distraction, such as hearing a mobile phone ping to indicate a new message (reported by 17%), to those that are likely to be quite disruptive, such as moving away from the browser screen on the computer to complete another task (13%). Table 2. Percentage of participants reporting different types of multitasking. Percentage Environmental distractions Background music 17 Background video 7 Background conversation 9 Non-electronic multitasking Left computer 16 Direct conversation 6 Electronic multitasking At least one instance of electronic multitasking 29 Heard a noise notifying me that I had received an instant message, voice mail, text or email 17 I left the browser screen the survey is on to do another task on my computer, tablet, or phone 13 Read some sort of written message, such as a text, tweet, social networking update, or email 12 Wrote or responded to written message, such as text, tweet, status update or email 8 I talked on the phone or participated in a voice chat 8 Table options 4.2. Setting and distraction RQ1 asked whether there was a relationship between where the survey was completed and how distracted participants felt while taking it. There was a significant difference in the mean level of distraction among those who completed the survey at home, at work, or in a public place, F(2, 5759) = 40.07, p < .001, partial η2 = .01. The means indicated that participants who completed the survey at home were the least distracted (M = 4.25, SD = 2.93), followed by those who completed it at work (M = 5.15, SD = 3.20), and those who completed it in some other public place like a library or café (M = 6.21, SD = 3.82). 4.3. Type of electronic multitasking and distraction RQ2 asked about the kinds of electronic media multitasking that were most strongly associated with distraction. To answer this, we conducted a regression analysis with the five individual measures reflecting how much each electronic multitasking activity took place. A summary of these results is reported in Table 3. Switching away from the browser to complete another task had an increasing effect on distraction the more often it was done. However, increases in the frequency of the other activities, which tend to involve interacting with a mobile phone (e.g., being notified of an incoming text, responding to one, or talking), were associated with decreases in subjective distraction (see Table 4). Table 3. Standardized betas showing impact of number of electronic multitasking activities and frequency of individual electronic multitasking activities on self-reported distraction.a β Range of tasks .37d Read some sort of written message, such as a text, tweet, social networking update, or email −.04b Heard a noise notifying me that I had received an instant message, voice mail, text or email −.11d Wrote or responded to written message, such as text, tweet, status update or email −.06c I talked on the phone or participated in a voice chat −.04b I left the browser screen the survey is on to do another task on my computer, tablet, or phone .06d a Note: Controlling for age, presence of environmental distractions, non-electronic multitasking. b p < .05. c p < .01. d p < .001. Table options Table 4. Hierarchical regression of age and multitasking on self-reported distraction. Model 1 β Model 2 β Model 3 β Model 4 β Model 5 β Model 6 β Age a 25–34 −.09c −.08c −.08c −.08c −.06c −.06b 35–44 −.17c −.15c −.15c −.14c −.11c −.11c 45–54 −.24 c −.21c −.21c −.20c −.15c −.15c 54–65 −.30 c −.27c −.26c −.24c −.18c −.18c 65 and over −.30 c −.26c −.25c −.24c −.19c −.19c Environmental distractions Background music .06c .05c .04b .02 .02 Background video .10c .09c .07c .05c .05c Background conversation .09c .10c .09c .07c .07c Non-electronic multitasking Direct conversation .13c .10c .07c .07c Left computer .24c .13c .17c Electronic multitasking Range of tasks .23c .37c Frequency −.16c ΔR2 .07b .02b .02b .05b .04c .004c a Note: The youngest age group, 18–24 year-olds, is the excluded, comparison variable. b p < .01. c p < .001. Table options 4.4. Amount of multitasking and distraction H1 predicted that multitasking would be associated with the participants’ self-reported distraction levels. A hierarchal regression analysis was carried out to investigate this. A set of dummy variables representing the respondents’ age was entered in the first block, followed by a block containing the three potential environmental distractions (background music, video, conversation). The two measures of non-electronic multitasking were next, followed by the multitasking variety index, which measured how many forms of electronic multitasking were reported by the participant. As noted in Table 5, each form of multitasking explained additional variance in self-reported distraction. In step two, the presence of background music, video, and conversation were each independently associated with an increase in feeling distracted, with video and conversation having the stronger relationships. Both engaging in conversation and leaving the computer to complete another task were associated with further increases in distraction. Most relevant to the debates about the implications of the increasing prevalence of smartphones and other personal electronic media, the analysis also indicated that the number of kinds of additional electronic tasks the participants reported was associated with an additional increase in self-reported distraction. These results provide support for H1. Table 5. Proportion of participants in each age range engaged in different types of multitasking. 18–24 25–34 35–44 45–54 55–64 65+ Background musica 23% 20% 16% 16% 16% 13% Background videoa 17% 11% 8% 5% 5% 3% Background conversationa 16% 16% 17% 11% 7% 5% Direct conversationa 9% 9% 8% 5% 4% 3% Left computer (at least once)a 22% 21% 17% 15% 11% 13% Any electronic multitaskinga 52% 40% 33% 25% 16% 17% a Differences between age groups significant at the p < .001 level according to chi-square analysis. Table options We also investigated whether the amount of multitasking contributed to distraction levels above and beyond the variety of activities engaged in. To do this, we entered into the regression equation the index representing a rough count of the number of times the participants engaged in all the electronic media multitasking activities. This variable significantly increased the variance explained by the model, albeit by a very modest amount. Unexpectedly, however, the direction of the relationship changed, indicating that increases in the number of times individuals engage in these activities were associated with a decrease in distraction once the range of different activities the participant engaged in was taken into account. 4.5. Age and multitasking H2 predicted that the age of the participants would be negatively associated with multitasking. Chi-square analyses were run examining the proportion of individuals in each age category who experienced distractions while taking the online survey. These analyses indicate that this hypothesis is supported. We first considered the possibility that younger people would have more background distractions present than older people while taking an online survey in the form of music, video, or background conversation. As shown in Table 5, age was associated with each of these forms of multitasking, with the proportion of respondents indicating that these potential distractions were present tending to decline with age, background music, χ2 (5, 5853) = 33.57, p < .001, Cramer’s V = .08; background video, χ2 (5, 5853) = 125.02, p < .001, Cramer’s V = .15; background conversation, χ2 (5, 5853) = 118.55, p < .001, Cramer’s V = .14. Further chi-square analyses indicated that the participants’ age was also related to their tendencies to report non-electronic multitasking (see Table 5), direct conversation, χ2 (5, 5853) = 56.44, p < .001, Cramer’s V = .10; leaving the computer, χ2 (5, 5853) = 55.66, p < .001, Cramer’s V = .10. Again, the percentage reporting these forms of multitasking showed a steady downward trend as the age category got older. Finally, we tested whether age was associated with reports of engaging in electronic media multitasking. We conducted a final chi-square comparing the proportions of respondents in each age category who reported at least one instance of electronic multitasking. It was significant, χ2 (5, 5853) = 359.52, p < .001, Cramer’s V = .25, with data suggesting it was less common among older participants (see Table 5). The proportions decreased steadily up to the “55–64” and “65 and over” age categories. We also carried out an ANOVA with the participants reporting at least one instance of electronic multitasking (n = 1683), which found age to be significantly associated with overall volume (range and frequency) of electronic multitasking they engaged in, F (5, 1677) = 15.80, p < .001, partial η2 = .05. Participants’ scores on the scale tended to decline with age, moving from a mean of 3.35 (SD = 2.81) among 18–24 year-olds, to 1.77 (SD = 2.01) among those over 64, lending additional support for H2. 4.6. Multitasking, distraction, and age H3 predicted that age would moderate the relationship between multitasking and distraction, with multitasking more strongly related to distraction among older users than younger ones. However, there is reason to suspect this moderated relationship, if it exists, might not be linear. The “digital generation” cohort effect, which predicts that those who have grown up with computers will be particularly comfortable with multitasking, suggests a jump in the strength of the association between multitasking and distraction between “Millennials,” who came of age beginning in the 90s, and older generations. Furthermore, research on cognitive changes in seniors indicates they differ from young adults, but provides little information about potential differences across adults who are between these age brackets. H3 was tested in a way that allowed us to identify non-linear patterns of moderation by first calculating the correlations between the amount of media multitasking and self-reported distraction within each age group, and then testing whether the correlations were significantly different from each other by converting them according to Fischer’s procedure and calculating the z-score ( Preacher, 2002). Although the correlation was significant within all age groups, the size of the correlations suggests a curvilinear relationship, as shown in Table 6. The correlation coefficients for the three youngest age groups were not significantly different from each other. However, the trend suggests an increase with age, with 34–45 year olds showing the strongest correlation in the sample, r = .38, p < .001. The next oldest age group, 45–54 year-olds, r = .23, p < .001, showed a relatively precipitous decrease in the strength of the correlation that was significantly different from that of next youngest group. The size of the relationship dropped again among the oldest age group, those 65 and older, r = .19, p < .001. The correlation among these participants was lower than among any other cohort. H3 was not supported, and it is overly simplistic to say that younger people can multitask with less effect on their concentration than older people. Rather, it was middle-aged people who felt the most distracted by their multitasking. Table 6. Correlations between amount of multitasking and self-reported distraction within age groups.a R 18–24 .29b,c,d 25–34 .33b 35–44 .38b 45–54 .23c,d 55–64 .28c 65+ .19d,e a Rows that do not share a superscript are significantly different at p = .05. Table options Examining the mean distraction scores for the different levels of multitasking across the different age groups sheds more light on this relationship (see Table 7). In general, the older one is, the less distracted one reports being, a pattern that also holds for those who did not multitask at all, albeit at lower levels. As the amount of multitasking engaged in increases, distraction scores tend to increase for younger participants more than older ones, although at the heaviest levels of multitasking, younger participants’ distraction scores often fall below those of their older high-multitasking counterparts. However, caution must be used in interpreting these findings given the relatively small number of participants in older age groups who were heavy multitaskers. Table 7. Mean (standard deviations) of self-reported distraction scoresa at select levels of electronic multitasking for different age groups. 18–24 25–34 35–44 45–54 55–64 65+ Avg 5.91(3.33) 5.23(3.31) 4.65(3.00) 4.10(2.84) 3.64(2.58) 3.40(2.37) 0 5.06(2.97) 4.46(2.97) 3.98(.10) 3.67(2.55) 3.38 2.37) 3.23(2.14) 1 5.93(3.27) 4.94(2.96) 4.78(.20) 4.82(2.84) 4.17(2.71) 4.00(2.87) 6 7.27(3.00) 7.73(4.20) 8.33(.79) 8.33(2.87) 3.33(1.53) 4.00(2.83) 10 14.00(.00) 7.46(3.95) 10.00(1.58) 5.00(3.91) 11.00(.00) – 15 4.00(.00) 7.75(4.43) 11.00(2.74) 2.00(.00) – 6.50(6.36) a A higher number represents feeling more distracted.