Don’t Disturb Me! Understanding the Impact of Interruptions on Knowledge Work: an Exploratory Neuroimaging Study

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Abstract

As we become more and more connected, the number of technology interruptions are increasing as well. The mechanisms by which a technology interruption takes attention away and ongoing task performance decreases need more investigation. Through neuroimaging, this paper explores how technologies can interrupt concentration, focus and attention of knowledge workers. Subjects were given reading tasks and subjected to a series of randomly timed audio interruptions. Using an electroencephalogram (EEG) measurement device, we recorded their brain waves. Consistent with the literature, we found interruptions significantly increased task completion time and decreased task performance. Neuroimaging analysis showed activity in the frontal lobe, temporal lobe and insular cortex of the participants due to interruptions. The paper also investigates differences due to gender and age. The results suggest application developers should consider underlying mechanisms of processing interruptions.

Keywords

Interruption NeuroIS Neuroimaging Electroencephalogram (EEG) Knowledge work 

Notes

Acknowledgements

We thank the associate editor and two anonymous reviewers for their useful feedback that improved this paper. Many thanks to Dr. James E. Cane for sharing the readings/paragraphs adopted in our experiment. We also thank Vijay Singh and Nandan Moza for their assistance during the experiment.

An earlier version of this paper was presented at 49th Hawaii International Conference on System Sciences (HICSS), 2016 (Kalgotra et al. 2016).

References

  1. Altmann, E. M., Trafton, J. G., & Hambrick, D. Z. (2014). Momentary interruptions can derail the train of thought. Journal of Experimental Psychology.General, 143(1), 215–226.  https://doi.org/10.1037/a0030986.CrossRefGoogle Scholar
  2. Aral, S., Brynjolfsson, E., & Alstyne, M. (2012). Information, technology, and information worker productivity. Information Systems Research, 23(3/2/2), 849–867.  https://doi.org/10.1287/isre.1110.0408.CrossRefGoogle Scholar
  3. Bailey, B. P., & Konstan, J. A. (2006). On the need for attention-aware systems: Measuring effects of interruption on task performance, error rate, and affective state. Computers in Human Behavior, 22(4), 685–708.  https://doi.org/10.1016/j.chb.2005.12.009.CrossRefGoogle Scholar
  4. Baron, R. S. (1986). Distraction-conflict theory: progress and problems. Advances in Experimental Social Psychology, 19(1986), 1–39.Google Scholar
  5. Basoglu, K. A., Fuller, M. A., & Sweeney, J. T. (2009). Investigating the effects of computer mediated interruptions: an analysis of task characteristics and interruption frequency on financial performance. International Journal of Accounting Information Systems, 10(4), 177–189.  https://doi.org/10.1016/j.accinf.2009.10.003.CrossRefGoogle Scholar
  6. Bhattacherjee, A. (2001). Understanding information systems continuance: an expectation-confirmation model. MIS Quarterly, 351–370.Google Scholar
  7. Bobrov, P., Frolov, A., Cantor, C., Fedulova, I., Bakhnyan, M., & Zhavoronkov, A. (2011). Brain-computer interface based on generation of visual images. PLoS One, 6(6), e20674.CrossRefGoogle Scholar
  8. Brinkley, I. (2009). Knowledge workers and knowledge work: A knowledge economy programme report. London: Work Foundation.Google Scholar
  9. Brocke, J., & Liang, T.-p. (2014). Guidelines for neuroscience studies in information systems research. Journal of Management Information Systems, 30(4), 211–233.  https://doi.org/10.2753/MIS0742-1222300408.CrossRefGoogle Scholar
  10. Cane, J., Cauchard, F., & Weger, U. (2012). The time-course of recovery from interruption during reading: eye movement evidence for the role of interruption lag and spatial memory. The Quarterly Journal of Experimental Psychology, 65(7), 1397.  https://doi.org/10.1080/17470218.2012.656666.CrossRefGoogle Scholar
  11. Carmichael, L., & Dearborn, W. F. (1947). Reading and visual fatigue. Boston: Houghton Mifflin.Google Scholar
  12. Clapp, W. C., Rubens, M. T., & Gazzaley, A. (2010). Mechanisms of working memory disruption by external interference. Cerebral cortex (New York, N.Y.: 1991), 20(4), 859–872.  https://doi.org/10.1093/cercor/bhp150.CrossRefGoogle Scholar
  13. Clapp, W. C., Rubens, M. T., Sabharwal, J., Gazzaley, A., & Raichle, M. E. (2011). Deficit in switching between functional brain networks underlies the impact of multitasking on working memory in older adults. Proceedings of the National Academy of Sciences of the United States of America, 108(17), 7212–7217.  https://doi.org/10.1073/pnas.1015297108.CrossRefGoogle Scholar
  14. Daselaar, S. M., Rombouts, S. A. R. B., Veltman, D. J., Raaijmakers, J. G. W., Lazeron, R. H. C., & Jonker, C. (2001). Parahippocampal activation during successful recognition of words: a self-paced event-related fMRI study. NeuroImage, 13(6), 1113–1120.  https://doi.org/10.1006/nimg.2001.0758.CrossRefGoogle Scholar
  15. Delorme, A., & Makeig, S. (2004). EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods, 134(1), 9–21.CrossRefGoogle Scholar
  16. Dimoka, A., & Davis, F. D. (2008). Where does TAM reside in the brain? The neural mechanisms underlying technology adoption. ICIS 2008 Proceedings, 169.Google Scholar
  17. Dimoka, A., Pavlou, P. A., & Davis, F. D. (2011). Research commentary-NeuroIS: the potential of cognitive neuroscience for information systems research. Information Systems Research, 22(4), 687–702.CrossRefGoogle Scholar
  18. Dimoka, A., Banker, R. D., Benbasat, I., Davis, F. D., Dennis, A. R., Gefen, D., et al. (2012). On the use of Neuropyhsiological tools in IS research: developing a research agenda for NeuroIS. MIS Quarterly, 36(3), 679.Google Scholar
  19. Dux, P. E., Tombu, M. N., Harrison, S., Rogers, B. P., Tong, F., & Marois, R. (2009). Training improves multitasking performance by increasing the speed of information processing in human prefrontal cortex. Neuron, 63(1), 127–138.  https://doi.org/10.1016/j.neuron.2009.06.005.CrossRefGoogle Scholar
  20. Eals, M., & Silverman, I. (1994). The hunter-gatherer theory of spatial sex differences: Proximate factors mediating the female advantage in recall of object arrays. Ethology and Sociobiology, 15(2), 95–105.CrossRefGoogle Scholar
  21. Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009). Statistical power analyses using G* power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41(4), 1149–1160.CrossRefGoogle Scholar
  22. Frei, E., Gamma, A., Pascual-Marqui, R., Lehmann, D., Hell, D., & Vollenweider, F. X. (2001). Localization of MDMA-induced brain activity in healthy volunteers using low resolution brain electromagnetic tomography (LORETA). Human Brain Mapping, 14(3), 152–165.  https://doi.org/10.1002/hbm.1049.CrossRefGoogle Scholar
  23. González, V. M., & Mark, G. (2005). Managing currents of work: Multi-tasking among multiple collaborations (pp. 143–162). Dordrecht: Springer Netherlands.Google Scholar
  24. Grandhi, S., & Jones, Q. (2010). Technology-mediated interruption management. International Journal of Human-Computer Studies, 68(5), 288–306.CrossRefGoogle Scholar
  25. Gupta, A., & Sharda, R. (2008). SIMONE: a simulator for interruptions and message overload in network environments. International Journal of Simulation and Process Modelling, 4(3–4), 237–247.CrossRefGoogle Scholar
  26. Gupta, A., Li, H., & Sharda, R. (2013). Should I send this message? Understanding the impact of interruptions, social hierarchy and perceived task complexity on user performance and perceived workload. Decision Support Systems, 55(1), 135–145.CrossRefGoogle Scholar
  27. Holmes, A. P., Blair, R. C., Watson, J. D., & Ford, I. (1996). Nonparametric analysis of statistic images from functional mapping experiments. Journal of Cerebral Blood Flow & Metabolism, 16(1), 7–22.  https://doi.org/10.1097/00004647-199601000-00002.CrossRefGoogle Scholar
  28. Kalgotra, P., Sharda, R., & McHaney, R. (2016). Understanding the impact of interruptions on knowledge work: An exploratory neuroimaging study. 2016 49th Hawaii International Conference on System Sciences (HICSS), Koloa, HI, 2016, pp. 658–667.Google Scholar
  29. Kalgotra, P., Luse, A., & Sharda, R. (2017). Take control of interruptions in your life: lessons from routine activity theory of criminology. In Proceedings of the 50th Hawaii International Conference on System Sciences (pp. 5668–5677).Google Scholar
  30. Khushaba, R. N., Wise, C., Kodagoda, S., Louviere, J., Kahn, B. E., & Townsend, C. (2013). Consumer neuroscience: assessing the brain response to marketing stimuli using electroencephalogram and eye tracking. Expert Systems with Applications, 40(9), 3803.  https://doi.org/10.1016/j.eswa.2012.12.095.CrossRefGoogle Scholar
  31. Kubicki, S., Herrmann, W., Fichte, K., & Freund, G. (1979). Reflections on the topics: EEG frequency bands and regulation of vigilance. Pharmakopsychiatrie, Neuro-Psychopharmakologie, 12(2), 237–245.CrossRefGoogle Scholar
  32. Lang, A. (2000). The limited capacity model of mediated message processing. Journal of Communication, 50(1), 46–70.CrossRefGoogle Scholar
  33. Li, H., Gupta, A., Luo, X., & Warkentin, M. (2011). Exploring the impact of instant messaging on subjective task complexity and user satisfaction. European Journal of Information Systems, 20(2), 139–155.CrossRefGoogle Scholar
  34. Lin, B. C., Kain, J. M., & Fritz, C. (2013). Don’t interrupt me! An examination of the relationship between intrusions at work and employee strain. International Journal of Stress Management, 20(2), 77.CrossRefGoogle Scholar
  35. Mäntylä, T., Psykologiska, I., Stockholms, U., & Samhällsvetenskapliga, F. (2013). Gender differences in multitasking reflect spatial ability. Psychological Science, 24(4), 514–520.CrossRefGoogle Scholar
  36. McFarlane, D. C. (1997). Interruption of people in human-computer interaction: A general unifying definition of human interruption and taxonomy (NRL Formalreport NRL/FR/5510–97–9870). Washington, DC: Naval Research Laboratory.Google Scholar
  37. Mencl, W. E., Pugh, K. R., Shaywitz, S. E., Shaywitz, B. A., Fulbright, R. K., Constable, R. T., et al. (2000). Network analysis of brain activations in working memory: Behavior and age relationships. Microscopy Research and Technique, 51(1), 64–74.  https://doi.org/10.1002/1097-0029(20001001)51:1<64::AID-JEMT7>3.0.CO;2-D.CrossRefGoogle Scholar
  38. Minas, R. K., Potter, R. F., Dennis, A. R., Bartelt, V., & Bae, S. (2014). Putting on the thinking cap: using NeuroIS to understand information processing biases in virtual teams. Journal of Management Information Systems, 30(4), 49–82.  https://doi.org/10.2753/MIS0742-1222300403.CrossRefGoogle Scholar
  39. Mirz, F., Ovesen, T., Ishizu, K., Johannsen, P., Madsen, S., Gjedde, A., & Pedersen, C. B. (1999). Stimulus-dependent central processing of auditory stimuli: a PET study. Scandinavian Audiology, 28(3), 161–169.  https://doi.org/10.1080/010503999424734.CrossRefGoogle Scholar
  40. Misulis, K. E. (2013). Atlas of EEG, seizure semiology, and management. Oxford: Oxford University Press.CrossRefGoogle Scholar
  41. Monsell, S. (2003). Task switching. Trends in Cognitive Sciences, 7(3), 134–140.  https://doi.org/10.1016/S1364-6613(03)00028-7.CrossRefGoogle Scholar
  42. Nichols, T. E., & Holmes, A. P. (2002). Nonparametric permutation tests for functional neuroimaging: A primer with examples. Human Brain Mapping, 15(1), 1–25.  https://doi.org/10.1002/hbm.1058.CrossRefGoogle Scholar
  43. O'Conaill, B., & Frohlich, D. (1995). Timespace in the workplace: dealing with interruptions. Paper presented at the Conference companion on Human factors in computing systems.Google Scholar
  44. Otten, L. J., Henson, R. N., & Rugg, M. D. (2001). Depth of processing effects on neural correlates of memory encoding. Brain, 124(2), 399–412.CrossRefGoogle Scholar
  45. Oulasvirta, A., & Saariluoma, P. (2006). Surviving task interruptions: Investigating the implications of long-term working memory theory. International Journal of Human - Computer Studies, 64(10), 941–961.  https://doi.org/10.1016/j.ijhcs.2006.04.006.CrossRefGoogle Scholar
  46. Pascual-Marqui, R. D. (1999). Review of methods for solving the EEG inverse problem. International Journal of Bioelectromagnetism, 1(1), 75–86.Google Scholar
  47. Pascual-Marqui, R. D. (2002). Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details. Methods and Findings in Experimental and Clinical Pharmacology, 24(Suppl D), 5–12.Google Scholar
  48. Pascual-Marqui, R. D. (2007). Discrete, 3D distributed, linear imaging methods of electrical neuronal activity. Part 1: Exact, zero error localization. arXiv:0710.3341 [math-ph].Google Scholar
  49. Pascual-Marqui, R. D., & Biscay-Lirio, R. (1993). Spatial resolution of neuronal generators based on EEG and MEG measurements. International Journal of Neuroscience, 68(1–2), 93–105.CrossRefGoogle Scholar
  50. Pirini, M., Mancini, M., Farella, E., & Chiari, L. (2011). EEG correlates of postural audio-biofeedback. Human Movement Science, 30(2), 249–261.  https://doi.org/10.1016/j.humov.2010.05.016.CrossRefGoogle Scholar
  51. Ren, D., Zhou, H., & Fu, X. (2009). A deeper look at gender difference in multitasking: Gender-specific mechanism of cognitive control. Paper presented at the Natural Computation, 2009. ICNC'09. Fifth International Conference on.Google Scholar
  52. Riedl, R., Banker, R. D., Benbasat, I., Davis, F. D., Dennis, A. R., Dimoka, A., et al. (2010a). On the foundations of NeuroIS: reflections on the Gmunden retreat 2009. Communications of the Association for Information Systems, 27, 243.Google Scholar
  53. Riedl, R., Hubert, M., & Kenning, P. (2010b). Are there neural gender differences in online trust?: An fMRI study on the perceived trusworthiness of eBay offers. Management Information Systems, 34(2), 397–428.CrossRefGoogle Scholar
  54. Sakai, K., & Passingham, R. E. (2004). Prefrontal selection and medial temporal lobe reactivation in retrieval of short-term verbal information. Cerebral cortex (New York, N.Y.: 1991), 14(8), 914–921.  https://doi.org/10.1093/cercor/bhh050.CrossRefGoogle Scholar
  55. Speier, C., Vessey, I., & Valacich, J. S. (2003). The effects of interruptions, task complexity, and information presentation on computer-supported decision-making performance. Decision Sciences, 34(4), 771–797.  https://doi.org/10.1111/j.1540-5414.2003.02292.x.CrossRefGoogle Scholar
  56. Spira, J. B., & Feintuch, J. B. (2005). The cost of not paying attention: How interruptions impact knowledge worker productivity. New York: Basex New York.Google Scholar
  57. Stytsenko, K., Jablonskis, E., & Prahm, C. (2011). Evaluation of consumer EEG device Emotiv EPOC. Paper presented at the MEi: CogSci Conference 2011, Ljubljana.Google Scholar
  58. Talairach, J., & Tournoux, P. (1988). Co-planar stereotaxic atlas of the human brain 3-dimensional proportional system: An approach to cerebral imaging. Stuttgart: Thieme Medical Publishers.Google Scholar
  59. Trafton, J. G., Altmann, E. M., Brock, D. P., & Mintz, F. E. (2003). Preparing to resume an interrupted task: effects of prospective goal encoding and retrospective rehearsal. International Journal of Human - Computer Studies, 58(5), 583–603.  https://doi.org/10.1016/S1071-5819(03)00023-5.CrossRefGoogle Scholar
  60. Wickens, C. D., & McCarley, J. S. (2007). Applied attention theory. Boca Raton: CRC press.CrossRefGoogle Scholar
  61. Xu, X., Ma, W. W. K., & See-To, E. W. K. (2010). Will mobile video become the killer application for 3G mobile internet? A model of media convergence acceptance. Information Systems Frontiers, 12(3), 311–322.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Graduate School of ManagementClark UniversityWorcesterUSA
  2. 2.Spears School of BusinessOklahoma State UniversityStillwaterUSA
  3. 3.Department of ManagementKansas State UniversityManhattanUSA

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