Motivation and emotion/Book/2019/Multi-tasking and productivity

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Multi-tasking and productivity:
What is the relationship between multi-tasking and productivity?

Overview[edit | edit source]

Figure 1. Using a computer while talking on the phone.

According to Lau (2017), the practice of multi-tasking can often be viewed as the act of engaging in more than one task or activity within a specified time frame. However, the act of multi-tasking may often be referred to as a distraction (Aagaard, 2018).

The following are examples of multi-tasking: undertaking two projects in the same time period (Leonard, 2008) or playing music in the background when studying (Thompson, Schellenberg & Letnic, 2011). Difficult multi-tasking, however, is also possible such as when a teacher manages a classroom whilst speaking to a student who has a problem (Brante, 2009) or when parsing two sentences at the same time because it puts a strain on people’s declarative knowledge (Carrier, Rosen, Cheever & Lim, 2015).

On the other hand, productivity, according to Tangen (2005), is associated with creating or generating value. The act of multitasking showed reductions in productivity in several studies whilst other studies did identify that it also enhanced productivity (Otto, Wahl, Lefort, Frei, 2012). For instance, multi-tasking may lead to enhanced productivity in particular fields, such as the military and the health care industry because multi-tasking enables people in these industries to rescue more people and to save lives (Le, Haller, Langer & Courvoisier, 2012). Otto et al. (2012) states that multi-tasking can have the ability to improve productivity if people are able to master it.

Among the important reasons why individuals may be drawn to multi-tasking [awkward expression?] is based on the idea that more tasks can be completed if there is only a short amount of time to complete the tasks (Baron, 2010 as cited in Le et al., 2012). Although different studies show different results, this chapter focuses on the negative effects of multi-tasking on productivity. An interesting research article that tackles the benefits of multi-tasking can be found here instead.

This book chapter first tackles the disadvantages of multi-tasking which is followed by discussions regarding two important theories that focus on the relationship between multi-tasking and productivity. These two theories are the 'Memory-for-goals' theory and the 'Cognitive Theory of Multimedia Learning Approach'. The negative effects of multi-tasking on student productivity is also explored.

Disadvantages of multi-tasking[edit | edit source]

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Divided Attention[edit | edit source]

When individuals focus only on one primary task/activity, their full attention is solely focused on that specific task but if an additonal[spelling?] task is performed, their attention is now divided between two tasks which may decrease their performance (Adler, Adepu, Bestha & Gutstein, 2015). For instance, according to Watson et al. (2016), there are people who believe that they can be more productive by using their mobile phones while driving in a safe manner. In Watson et al. (2016)'s study, they found that inattentional blindness can emerge by utilising mobile phones while driving which reduces their productivity due to the large amount of memory errors (Watson et al., 2016). Furthermore, it was also identified that people who use mobile phones and driving at the same time may depend more on reconstructive process in their memory system because of divided attention which lowers their productivity since they are more prone to errors (Watson et al., 2016).

Motivation and Self-Efficacy[edit | edit source]

Conversely, Calderwood, Ackerman & Conklin (2014) found that students’ motivation and self-efficacy levels were lower when they repeatedly multi-task and when they spend greater time multi-tasking in their study with 60 university students as participants. On average, the students garnered 35 distractions in 25 minutes by working on an assignment while engaging in media multi-tasking (Calderwood et al., 2014). This finding may be important to be aware of because both motivation and self-efficacy are regarded as crucial in the attainment of goal-directed behaviour and particularly in academic efficiency (Calderwood et al., 2014).

As a final note, multitasking may also hinder the process of transferring information into individuals' short-term memory and long-term memory systems (Edwards & Gronlund, 1998 as cited in Judd, 2013). More information regarding this finding can be found here.

What is the relationship between productivity and multi-tasking?[edit | edit source]

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Memory-for-goals theory[edit | edit source]

The Memory-for-goals theory suggests that the original task will involve more time to accomplish if interrupting tasks are present (Chen & Yan, 2016). The Memory-for-goals theory views a goal in terms of a mental image that indicates a motive to succeed in a task or to perform a certain action which could either be mental or physical (Altmann & Trafton, 2002 as cited in Adler & Benbunan-Fich, 2012). This theory also provides an explanation regarding the performance of executing many tasks or multitasking performance via a ‘goal-activation process’ (Adler & Benbunan-Fich, 2012). Activation in this area is the process of the different goals climbing upwards where they turn into the centre of attention (Adler & Benbunan-Fich, 2012). Therefore, the goal that was activated the latest or recently becomes the one responsible for behaviour whereas the older goals are delayed (Altmann & Trafton, 2002 as cited in Adler & Benbunan-Fich, 2012). This also means that when an individual takes a long period of time from delaying a certain goal, the certain goal will also be harder to retrieve (Foroughi, Werner, Nelson & Boehm-Davis, 2014).

Adler & Benbunan-Fich (2012)'s study sought to determine how multitasking can affect productivity and accuracy using the Memory-for-goals theory. The study involved 205 participants whereby 102 of them were in the experimental group (multitasking group) whilst the rest were in the control group (non-multitasking group) (Adler & Benbunan-Fich, 2012). The participants were handed six tasks wherein each task had a correct answer (Adler & Benbunan-Fich, 2012). Those who belonged in the control group can only complete all tasks in sequence but those in the experimental group had to accomplish these six tasks which were presented in six tabs wherein they had to switch from one tab to another simultaneously (Adler & Benbunan-Fich, 2012). The results revealed that people who were multi-tasking in a medium level showed a good level of productivity but the participants who had a low or high level of multi-tasking had the lowest productivity (Adler & Benbunan-Fich, 2012). This study is reflective of the Memory-for-goals theory because the action of switching tasks translates to attention being shifted from the previous goal and onto the new goal (Adler & Benbunan-Fich, 2012). The strength of this study is the large sample size and how each group had approximately the same number of participants. However, according to Adler & Benbunan-Fich (2012), the weakness of their study was the participants' age group because they only recruited college students and did not include other age groups.

Another study was conducted in line with the Memory-for-goals theory. In Foroughi et al. (2014)'s study, all 54 university students had to outline/plan three essays by writing it and afterwards they were also required to type the three essays online but ‘interruption tasks’ were purposely implemented during both the writing and outlining process to determine if multi-tasking would have an effect on their essays. The interruption tasks entailed answering a set of questions that were not related to their essay by writing them on a paper (Foroughi et al., 2014). The participants were divided into two groups wherein each group had a different set of conditions in terms of the time at which the interruptions were introduced and the time they had to finish their essay (Foroughi et al., 2014). Both groups also had to do these two tasks but with the absence of the interruptions which means there were a total of three conditions in this study (Foroughi et al., 2014). In both groups, the quality of the essays declined as opposed to when they had no interruptions (Foroughi et al., 2014). Furthermore, the word count in the essays also decreased in the writing process but not during the outlining process (Foroughi et al., 2014). The strength of this study was their usage of a non-interrupted condition to compare the results; however, it would have been more beneficial to have a larger sample size and if the study analysed how the quality of the essays differed amongst the age of the participants in multi-tasking because there have been several studies which found that there exists differences in multi-tasking capability in different age groups (Zwarun & Hall, 2014).

Cognitive theory of multimedia learning (CTML)[edit | edit source]

CTML states that individuals are surrounded by words and pictures but they are owned by two separate information processing channels (Chen & Yan, 2016). Learning significantly entails a substantial amount of cognitive processing in both of these channels; however, learners only have a limited capacity (Chen & Yan, 2016). The CTML explains that when people are in the process of both capturing and utilising images and words concurrently, their working memory becomes restricted (Mayer & Moreno, 2003).

Baran (2013)'s study aimed to discover the applicability of the CTML. A total of 1033 faculty staff participated in the study from 70 universities and their age ranged from 21 to 69 years of age and the average age was 37.4 (Baran, 2013). The influence of multi-tasking four different technological types: internet usage, talking on phone, listening to music/sounds and watching television on academic work were assessed through a survey (Baran, 2013). In the study, multi-tasking between internet usage and academic work received the most negative feedback followed by talking on phone and watching television wherein they believed it delayed their academic work (Baran, 2013). However, listening to music did not result to a delay in academic work according to the participants (Baran, 2013). Furthermore, the results also showed that as a result of multi-tasking, participants who are 43 years old and below had more delays in their academic work than participants who are aged 44 and above (Baran, 2013). The study therefore shows that the younger participants are, their usage of technology increases which, in turn, leads to more academic delay as a result of multi-tasking (Baran, 2013). This finding may be relatable to the point mentioned above (Zwarun & Hall, 2014) regarding age differences in multi-tasking capability. In regards to the CTML, according to Baran (2013), internet usage receiving the most negative feedback may be due to the complexity of the information offered by the internet because the internet contains both auditory and visual content which entails interactivity. On the other hand, Baran (2013) also added that it is possible that listening to music did not prompt an academic delay amongst the participants because the verbal content found in the academic work and the auditory content found in music does not result in attention being divided. The weakness of this study, however, is the use of a survey method compared to the study of Adler & Benbunan-Fich (2012) and Foroughi et al. (2014) who directly measured the participants' productivity. Conversely, the strength of this study is their inclusion of different types of technological modes of multitasking to identify how each type differs from the other in terms of how they affect the participants' academic work.

Negative effects of multi-tasking on students[edit | edit source]

Figure 2. Students listening to a lecture.

There have been studies which support the belief that technology can be beneficial for academic purposes but there are also some that claim these technologies can negatively affects students’ learning (Fried, 2008 as cited in Wood et al., 2012). For instance, students who study while messaging their peers and friends through instant messaging applications may not completely understand the academic material they are reading due to the distractions arising from multitasking (Bowman, Levine, Waite & Gendron, 2010 as cited in Wood et al., 2012). Students who partake in multi-tasking can also be prone to performance reductions (Junco & Cotton, 2011 as cited in Wood et al., 2012). Furthermore, multi-tasking associated with technology is often viewed as non-challenging amongst students (Carrier, Cheever, Rosen, Benitez, & Chang, 2009 as cited in Wood et al., 2012). Wood et al. (2012)'s study comprised of 154 participants who were placed in one of the conditions wherein four conditions involved multi-tasking with technology such as Facebook, MSN or mobile texting and three conditions that required multi-tasking but with no technology. All of the participants had to multi-task whilst listening to three lectures proceeded by a quiz on each lectures to measure their academic performance (Wood et al., 2012). The results showed that Facebook and MSN particularly led to poorer academic performance compared to the other distractions (Wood et al., 2012). The strength of the study was requiring the participants to listen to more than one lecture to allow longer time of multi-tasking. However, Wood et al. (2012) acknowledged that it would have been beneficial to study demographics such as the participants’ gender and their association with multi-tasking and academic performance.

Conclusion[edit | edit source]

As previously mentioned, it is clear that multi-tasking has negative effects on people's productivity through their memory systems such as what the CTML stated whereas for the Memory-for-goals theory, multi-tasking also had negative effects on how people achieve goals[awkward expression?]. In addition, academic performance may also be affected as a result of multi-tasking with technology. Both the Memory-for-goals theory and the Cognitive Theory of Multimedia Learning are theories that further expand and explain how multi-tasking can produce negative effects. However, as briefly mentioned, there is also research which supports the idea that multi-tasking can have benefits on people's productivity. Overall, based on the information presented, it is apparent that multi-tasking can have consequences on people's productivity.

See Also[edit | edit source]

References[edit | edit source]

Aagaard, J. (2018). Multitasking as distraction: A conceptual analysis of media multitasking research. Theory & Psychology, 29(1), 87-99. https://doi:10.1177/0959354318815766

Adler, R., & Benbunan-Fich, R. (2012). Juggling on a high wire: Multitasking effects on performance. International Journal Of Human-Computer Studies, 70(2), 156-168.https://doi:10.1016/j.ijhcs.2011.10.003

Adler, R., Adepu, S., Bestha, A., & Gutstein, Y. (2015). Remind me: Minimizing negative effects of multitasking. Procedia Manufacturing, 3, 5459-5466. https://doi:10.1016/j.promfg.2015.07.680

Baran, B. (2013). The effect of multitasking to faculty members' academic works. Educational Sciences: Theory & Practice, 1-17. https://doi:10.12738/estp.2013.4.1718

Bowman, L., Levine, L., Waite, B., & Gendron, M. (2010). Can students really multitask? An experimental study of instant messaging while reading. Computers & Education, 54(4), 927-931. https://doi:10.1016/j.compedu.2009.09.024

Brante, G. (2009). Multitasking and synchronous work: Complexities in teacher work. Teaching And Teacher Education, 25(3), 430-436. https://doi:10.1016/j.tate.2008.09.015

Calderwood, C., Ackerman, P., & Conklin, E. (2014). What else do college students “do” while studying? An investigation of multitasking. Computers & Education, 75, 19-29. https://doi:10.1016/j.compedu.2014.02.004

Carrier, L., Rosen, L., Cheever, N., & Lim, A. (2015). Causes, effects, and practicalities of everyday multitasking. Developmental Review, 35, 64-78. https://doi:10.1016/j.dr.2014.12.005

Chen, Q., & Yan, Z. (2016). Does multitasking with mobile phones affect learning? A review. Computers In Human Behavior, 54, 34-42. https://doi:10.1016/j.chb.2015.07.047

Foroughi, C., Werner, N., Nelson, E., & Boehm-Davis, D. (2014). Do interruptions affect quality of work?. Human Factors: The Journal Of The Human Factors And Ergonomics Society, 56(7), 1262-1271. https://doi:10.1177/0018720814531786

Judd, T. (2013). Making sense of multitasking: Key behaviours. Computers & Education, 63, 358-367. https://doi:10.1016/j.compedu.2012.12.017

Lau, W. (2017). Effects of social media usage and social media multitasking on the academic performance of university students. Computers In Human Behavior, 68, 286-291. https://doi:10.1016/j.chb.2016.11.043

Le, A., Haller, C., Langer, E., & Courvoisier, D. (2012). Mindful multitasking: The relationship between mindful flexibility and media multitasking. Computers In Human Behavior, 28(4), 1526-1532. https://doi:10.1016/j.chb.2012.03.022

Leonard, K. (2008). A cross-cultural investigation of temporal orientation in work organizations: A differentiation matching approach. International Journal Of Intercultural Relations, 32(6), 479-492. https://doi:10.1016/j.ijintrel.2008.04.007

Otto, S., Wahl, K., Lefort, C., & Frei, W. (2012). Exploring the impact of multitasking in the workplace. Journal of Business Studies Quarterly, 3(4), 154–162. Retrieved from http://search.proquest.com/docview/1036929617/

Mayer, R., & Moreno, R. (2003). Nine ways to reduce cognitive load in multimedia learning. Educational Psychologist, 38(1), 43-52. https://doi:10.1207/s15326985ep3801_6

Tangen, S. (2005). Demystifying productivity and performance. International Journal Of Productivity And Performance Management, 54(1), 34-46. https://doi:10.1108/17410400510571437

Thompson, W., Schellenberg, E., & Letnic, A. (2011). Fast and loud background music disrupts reading comprehension. Psychology Of Music, 40(6), 700-708. https://doi:10.1177/0305735611400173

Watson, J., Memmott, M., Moffitt, C., Coleman, J., Turrill, J., Fernández, Á., & Strayer, D. (2016). On working memory and a productivity illusion in distracted driving. Journal Of Applied Research In Memory And Cognition, 5(4), 445-453. https://doi:10.1016/j.jarmac.2016.06.008

Wood, E., Zivcakova, L., Gentile, P., Archer, K., De Pasquale, D., & Nosko, A. (2012). Examining the impact of off-task multi-tasking with technology on real-time classroom learning. Computers & Education, 58(1), 365-374. https://doi:10.1016/j.compedu.2011.08.029

Zwarun, L., & Hall, A. (2014). What’s going on? age, distraction, and multitasking during online survey taking. Computers In Human Behavior, 41, 236-244. https://doi:10.1016/j.chb.2014.09.041

External Links[edit | edit source]