Exploring factors influencing multitasking interaction with multiple smart devices Yubo Zhang a , Mao Mao a , Pei-Luen Patrick Rau a,⇑ , Pilsung Choe a , Lelkes Bela a , Feixiang Wang b a Department of Industrial Engineering, Tsinghua University, Beijing, China b Institute of Building Environment and Efficiency, China Academy of Building Research, Beijing, China article info Article history: Available online 19 July 2013 Keywords: Multitasking Smart devices Survey Factor analysis Interaction abstract The intention of this study is to investigate multitasking interaction with multiple smart devices and to unveil factors that play important roles in multitasking scenarios. A survey was carried out and 240 respondents participated whose scores ultimately demonstrated the degree of influence of various items on multitasking interaction with multiple smart devices. Then an exploratory factor analysis was con- ducted and a seven-factor model named MINDCOS was derived, including Motivation, Input, Navigation and control, Display screen, Cognitive workload, Output, Spatial distribution. The model was utilized to describe the scenario of multitasking interaction with multiple smart devices and the top three factors which illustrated the total variance the most were used to analyze related applications. Then the relation- ship between the factors and perceived behavior intention of multitasking interaction was tested by regression analysis. This study also found whether users had multitasking experience had a significant impact on their perceived influence of two factors which are Navigation and control and Output. Ó 2013 Elsevier Ltd. All rights reserved. 1. Introduction Traditionally multitasking human computer interaction hap- pens in the industrial field where one operator needs to interact with multiple machines simultaneously. For instance, in a nuclear power plant an operator faces a large panel with various instru- ments while monitoring a great deal of data and following stan- dard procedures to operate the panel. Another example is that of a pilot who needs to control multiple joysticks and must choose from many buttons to press one. The so-called multitasking was first introduced as polychronic time-use by Hall indicating combin- ing activities simultaneously (Rau, Gao, & Liang, 2008), which was opposite to monochronic time-use. Previous research found that time-use significantly influenced control strategy and performance in the industrial control process (Zhang, Goonetilleke, Plocher, & Max Liang, 2005). In such scenarios, multiple tasks share operators’ limited resources of attention and time. Thus, their performance may decrease while the possibility to make errors may increase, which significantly influences efficiency and safety. As for the tra- ditional multitasking scenarios in the industrial field which is usu- ally the interaction between a person and multiple monitors and instruments, there have been many studies focusing on resources allocation and performance (Sarno, 1995; Schneider & Detweiler, 1988; Wickens, Sandry, & Vidulich, 1983). With the development of smart devices, multitasking interaction has extended from traditional industrial environment to smart environment at home or office. It is very common and popular that owners of multiple smart devices such as tablet, smartphone, laptop, even smart TV engage in multitasking interac- tion with them. Based on a survey carried out by Nielsen Company, roughly 40% of tablet and smartphone owners in the US used their devices daily while watching TV (Nielsen, 2011). Another survey focusing on multi-screen users in US found that in 2011 there were about 34% of four-screen users, 32% of three-screen users and 28% of two-screen users 1 above 18 years old who were prone to using devices such as PC, smartphone and tablet while watching TV. Be- sides, 47% of the four-screen users would vote and purchase online with their mobile devices when seeing promotions on TV (iResearch, 2012). Another survey by the Hollywood Reporter showed that 79% of the respondents said that they always or sometimes visited Face- book while watching TV and 41% tweeted about the show they were watching and three quarters said that they posted about TV while watching live shows (Godley, 2012). Carrier, Cheever, Rosen, Benitez, and Chang (2009) identified 66 combinations of multitasking and found that large amounts of multitasking occurring across all gener- ations of persons in the US. According to previous survey, American teenagers from 8 to 18 years old spent 29% of the media use time in 0747-5632/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.chb.2013.06.042 ⇑ Corresponding author. Tel.: +86 10 62776664; fax: +86 10 62794399. E-mail address: rpl@mail.tsinghua.edu.cn (P.-L.P. Rau). 1 Four-screen users indicate users with TV, PC, smartphone and tablet; three-screen users indicate users with TV, PC and smartphone; two-screen users indicate users with TV and PC. Computers in Human Behavior 29 (2013) 2579–2588 Contents lists available at SciVerse ScienceDirect Computers in Human Behavior journal homepage: www.elsevier.com/locate/comphumbeh