Multi-Device Use: Understanding the Motivations behind Switching between Multiple Devices during a Task

References

• Abdul-Muhmin, A. G. (2010). Repeat purchase intentions in online shopping: The role of satisfaction, attitude, and online retailers’ performance. Journal of International Consumer Marketing, 23(1), 5–20. doi:10.1080/08961530.2011.524571
   Google Scholar
• Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. doi:10.1016/0749-5978(91)90020-T
   Web of Science ®Google Scholar
• Au, N., Ngai, E. W., & Cheng, T. (2002). A critical review of end-user information system satisfaction research and a new research framework. Omega, 30(6), 451–478. doi:10.1016/S0305-0483(02)00054-3
   Web of Science ®Google Scholar
• Bagozzi, R. P., Yi, Y., & Phillips, L. W. (1991). Assessing construct validity in organizational research. Administrative Science Quarterly, 36(3). doi:10.2307/2393203
   Web of Science ®Google Scholar
• Bansal, H. S., Taylor, S. F., & James, Y. S. (2005). “Migrating” to new service providers to new service providers: Toward a unifying framework of consumers’ switching behaviors. Journal of the Academy of Marketing Science, 33(1), 96–115. doi:10.1177/0092070304267928
   Web of Science ®Google Scholar
• Beck, A., Emery, G., & Greenberg, R. (2005). Anxiety disorders and phobias: A cognitive perspective. New York, USA: Basic Books.
   Google Scholar
• Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351–370. doi:10.2307/3250921
   Web of Science ®Google Scholar
• Burger, A. (2013). 2013 U.S. college explorer: Connecting with increasingly connected college students. Retrieved from http://www.telecompetitor.com/2013-u-s-college-explorer-connecting-with-increasingly-connected-college-students/
   Google Scholar
• Campbell, D. J. (1988). Task complexity: A review and analysis. Academy of Management Review, 13(1), 40–52. doi:10.5465/amr.1988.4306775
   Web of Science ®Google Scholar
• Colle, H. A., & Reid, G. B. (1997). A framework for mental workload research and applications using formal measurement theory. International Journal of Cognitive Ergonomics, 14, 303–333.
   Google Scholar
• Comrey, A. L. (1973). A first course in factor analysis. New York, NY: Academic Press.
   Google Scholar
• Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. doi:10.2307/249008
   Web of Science ®Google Scholar
• Davis, F. D. (1993). User acceptance of information technology: System characteristics, user perceptions and behavioral impacts. International Journal of Man-Machine Studies, 38(3), 475–487. doi:10.1006/imms.1993.1022
   Google Scholar
• Dearman, D., & Pierce, J. S. (2008). It’s on my other computer!: Computing with multiple devices. The 26 annual SIGCHI Conference on Human Factors in Computing Systems, Florence, Italy.
   Google Scholar
• DeLone, W. H., & McLean, E. R. (1992). Information systems success: The quest for the dependent variable. Information Systems Research, 3(1), 60–95. doi:10.1287/isre.3.1.60
   Web of Science ®Google Scholar
• Dishaw, M. T., & Strong, D. M. (1999). Extending the technology acceptance model with task-ìtechnology fit constructs. Information & Management, 36(1), 9–21. doi:10.1016/S0378-7206(98)00101-3
   Web of Science ®Google Scholar
• Doll, W. J., & Torkzadeh, G. (1988). The measurement of end-user computing satisfaction. MIS Quarterly, 12(2), 259–274. doi:10.2307/248851
   Web of Science ®Google Scholar
• Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of Marketing Research, 18(1), 382–388. doi:10.1177/002224378101800313
   Web of Science ®Google Scholar
• Gefen, D. (2000). E-commerce: The role of familiarity and trust. Omega, 28(6), 725–737. doi:10.1016/S0305-0483(00)00021-9
   Web of Science ®Google Scholar
• Goodhue, D. L. (1995). Understanding user evaluations of information systems. Management Science, 41(12), 1827–1844. doi:10.1287/mnsc.41.12.1827
   Web of Science ®Google Scholar
• Goodhue, D. L. (1998). Development and measurement validity of a task‐technology fit instrument for user evaluations of information system. Decision Sciences, 29(1), 105–138. doi:10.1111/deci.1998.29.issue-1
   Web of Science ®Google Scholar
• Goodhue, D. L., Klein, B. D., & March, S. T. (2000). User evaluations of IS as surrogates for objective performance. Information & Management, 38(2), 87–101. doi:10.1016/S0378-7206(00)00057-4
   Web of Science ®Google Scholar
• Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS Quarterly, 19(2), 213–236. doi:10.2307/249689
   Web of Science ®Google Scholar
• Google. (2012). The new multi-screen world: Understanding cross-platform consumer behavior. Retrieved from http://services.google.com/fh/files/misc/multiscreenworld_final.pdf
   Google Scholar
• Hackbarth, G., Grover, V., & Yi, M. Y. (2003). Computer playfulness and anxiety: Positive and negative mediators of the system experience effect on perceived ease of use. Information & Management, 40(3), 221–232. doi:10.1016/S0378-7206(02)00006-X
   Web of Science ®Google Scholar
• Heinssen, R. K., Jr, Glass, C. R., & Knight, L. A. (1987). Assessing computer anxiety: Development and validation of the computer anxiety rating scale. Computers in Human Behavior, 3(1), 49–59. doi:10.1016/0747-5632(87)90010-0
   Google Scholar
• Hsin, H.-C. (2010). Task-technology fit and user acceptance of online auction. International Journal of Human-Computer Studies, 68(1), 69–89. doi:10.1016/j.ijhcs.2009.09.010
   Web of Science ®Google Scholar
• Johnson, E. J., & Payne, J. W. (1985). Effort and accuracy in choice. Management Science, 31(4), 395–414. doi:10.1287/mnsc.31.4.395
   Web of Science ®Google Scholar
• Jokela, T., Ojala, J., & Olsson, T. (2015). A diary study on combining multiple information devices in everyday activities and tasks. The 33rd Annual ACM Conference on Human Factors in Computing Systems, Seoul, Republic of Korea.
   Google Scholar
• Kane, S. K., Karlson, A. K., Meyers, B. R., Johns, P., Jacobs, A., & Smith, G. (2009). Exploring cross-device web use on PCs and mobile devices. In GrossJan, T., Kotzé G. P., Oestreicher, L., Palanque, P., Prates, R. O., & Winckler, M. Human-Computer Interaction-INTERACT 2009 (pp. 722–735). Uppsala, Sweden: Springer.
   Google Scholar
• Kokini, C. M., Lee, S., Koubek, R. J., & Moon, S. K. (2012). Considering context: The role of mental workload and operator control in users’ perceptions of usability. International Journal of Human-Computer Interaction, 28(9), 543–559. doi:10.1080/10447318.2011.622973
   Web of Science ®Google Scholar
• Komiak, S. Y. X., & Benbasat, I. (2006). The effects of personalization and familiarity on trust and adoption of recommendation agents. MIS Quarterly, 30(4), 941–960. doi:10.2307/25148760
   Web of Science ®Google Scholar
• Koufaris, M. (2002). Applying the technology acceptance model and flow theory to online consumer behavior. Information Systems Research, 13(2), 205–223. doi:10.1287/isre.13.2.205.83
   Web of Science ®Google Scholar
• Kuo, F.-Y., Chu, T.-H., Hsu, M.-H., & Hsieh, H.-S. (2004). An investigation of effort–accuracy trade-off and the impact of self-efficacy on web searching behaviors. Decision Support Systems, 37(3), 331–342. doi:10.1016/S0167-9236(03)00032-0
   Web of Science ®Google Scholar
• Lansdown, T. C., Brook-Carter, N., & Kersloot, T. (2004). Distraction from multiple in-vehicle secondary tasks: Vehicle performance and mental workload implications. Ergonomics, 47(1), 91–104. doi:10.1080/00140130310001629775
   PubMed Web of Science ®Google Scholar
• Lee, -C.-C., Cheng, H. K., & Cheng, -H.-H. (2007). An empirical study of mobile commerce in insurance industry: Task-technology fit and individual differences. Decision Support Systems, 43(1), 95–110. doi:10.1016/j.dss.2005.05.008
   Web of Science ®Google Scholar
• Levin, M. (2014). Designing multi-device experiences: An ecosystem approach to user experiences across devices. Sebastopol, CA: O’Reilly Media, Inc.
   Google Scholar
• Lin, T. T. (2019). Why do people watch multiscreen videos and use dual screening? investigating users’ polychronicity, media multitasking motivation, and media repertoire. International Journal of Human–Computer Interaction. doi:10.1080/10447318.2018.1561813
   Web of Science ®Google Scholar
• Lin, T.-C., & Huang, -C.-C. (2008). Understanding knowledge management system usage antecedents: An integration of social cognitive theory and task technology fit. Information & Management, 45(6), 410–417. doi:10.1016/j.im.2008.06.004
   Web of Science ®Google Scholar
• Lindell, M. K., & Whitney, D. J. (2001). Accounting for common method variance in cross-sectional research designs. Journal of Applied Psychology, 86(1), 114–121. doi:10.1037/0021-9010.86.1.114
   PubMed Web of Science ®Google Scholar
• Ling, C., Stegman, A., Barhbaya, C., & Shehab, R. (2017). Are two better than one? a comparison between single-and dual-monitor work stations in productivity and user’s windows management style. International Journal of Human–Computer Interaction, 33(3), 190–198. doi:10.1080/10447318.2016.1231392
   Web of Science ®Google Scholar
• Liu, Y., Lee, Y., & Chen, A. N. (2011). Evaluating the effects of task–individual–technology fit in multi-DSS models context: A two-phase view. Decision Support Systems, 51(3), 688–700. doi:10.1016/j.dss.2011.03.009
   Web of Science ®Google Scholar
• Lohmöller, J.-B. (1989). Latent variable path modeling with partial least squares. Heidelberg: Physica-Verlag.
   Google Scholar
• Madden, T. J., Ellen, P. S., & Ajzen, I. (1992). A comparison of the theory of planned behavior and the theory of reasoned action. Personality and Social Psychology Bulletin, 18(1), 3–9. doi:10.1177/0146167292181001
   Web of Science ®Google Scholar
• Maruping, L. M., & Agarwal, R. (2004). Managing team interpersonal processes through technology: A task-technology fit perspective. Journal of Applied Psychology, 89(6), 975. doi:10.1037/0021-9010.89.6.975
   PubMed Web of Science ®Google Scholar
• Maynard, D. C., & Hakel, M. D. (1997). Effects of objective and subjective task complexity on performance. Human Performance, 10(4), 303–330. doi:10.1207/s15327043hup1004_1
   Web of Science ®Google Scholar
• Miller, G. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. The Psychological Review, 63, 81–97. doi:10.1037/h0043158
   PubMed Web of Science ®Google Scholar
• Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(3), 192–222. doi:10.1287/isre.2.3.192
   Web of Science ®Google Scholar
• Networks, B. (2013). The impact of BYOD in education. Retrieved from http://www.bradfordnetworks.com/resources/whitepapers/the-impact-of-byod-in-education/
   Google Scholar
• Nguyen, N. T., & Lee, H. (2019). Understanding usability challenges in shifting between multiple devices during a task. IEEE International Conference on Pervasive Computing and Communications Workshops. Kyoto, Japan.
   Google Scholar
• Oliver, R. L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research, 17(4), 460–469. doi:10.1177/002224378001700405
   Web of Science ®Google Scholar
• Oulasvirta, A., & Sumari, L. (2007). Mobile kits and laptop trays: Managing multiple devices in mobile information work. The SIGCHI Conference on Human Factors in Computing Systems. San Jose,  California, USA.
   Google Scholar
• Pagani, M. (2006). Determinants of adoption of high speed data services in the business market: Evidence for a combined technology acceptance model with task technology fit model. Information & Management, 43(7), 847–860. doi:10.1016/j.im.2006.08.003
   Web of Science ®Google Scholar
• Parkes, A. (2013). The effect of task–individual–technology fit on user attitude and performance: An experimental investigation. Decision Support Systems, 54(2), 997–1009. doi:10.1016/j.dss.2012.10.025
   Web of Science ®Google Scholar
• Payne, J. W., Bettman, J. R., & Johnson, E. J. (1993). The adaptive decision maker. New York, NY: Cambridge University Press.
   Google Scholar
• Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. doi:10.1037/0021-9010.88.5.879
   PubMed Web of Science ®Google Scholar
• Posselt, T., & Gerstner, E. (2005). Pre‐sale vs. Post‐sale e‐satisfaction: Impact on repurchase intention and overall satisfaction. Journal of Interactive Marketing, 19(4), 35–47. doi:10.1002/dir.20048
   Google Scholar
• Rashid, U., Nacenta, M. A., & Quigley, A. (2012). The cost of display switching: A comparison of mobile, large display and hybrid UI configurations. The International Working Conference on Advanced Visual Interfaces, Naples, Italy.
   Google Scholar
• Recarte, M. A., & Nunes, L. M. (2003). Mental workload while driving: Effects on visual search, discrimination, and decision making. Journal of Experimental Psychology Applied, 9(2), 119–133. doi:10.1037/1076-898X.9.2.119
   PubMed Web of Science ®Google Scholar
• Rosenberg, M. J., & Hovland, C. I. (1960). Attitude organization and change: An analysis of consistency among attitude components. Oxford, England: Yale University. Press.
   Google Scholar
• Sher, V., Hatala, M., & Gašević, D. (2019). On multi-device use: Using technological modality profiles to explain differences in students’ learning. The 9th International Conference on Learning Analytics & Knowledge, Tempe, AZ.
   Google Scholar
• Shin, D.-H. (2016). Cross-platform users’ experiences toward designing interusable systems. International Journal of Human-Computer Interaction, 32(7), 503–514. doi:10.1080/10447318.2016.1177277
   Web of Science ®Google Scholar
• Shiv, B., & Huber, J. (2000). The impact of anticipating satisfaction on consumer choice. Journal of Consumer Research, 27(2), 202–216. doi:10.1086/314320
   Web of Science ®Google Scholar
• Song, G., & Ling, C. (2011). Users’ attitude and strategies in information management with multiple computers. International Journal of Human-Computer Interaction, 27(8), 762–792.
   Web of Science ®Google Scholar
• Speier, C., & Morris, M. G. (2003). The influence of query interface design on decision-making performance. MIS Quarterly, 27(3), 397–423. doi:10.2307/30036539
   Web of Science ®Google Scholar
• Speier, C., Valacich, J. S., & Vessey, I. (1999). The influence of task interruption on individual decision making: An information overload perspective. Decision Sciences, 30(2), 337–360. doi:10.1111/deci.1999.30.issue-2
   Web of Science ®Google Scholar
• 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. doi:10.1111/deci.2003.34.issue-4
   Web of Science ®Google Scholar
• Teunissen, S., De Graeff, A., Voest, E., & de Haes, J. (2007). Are anxiety and depressed mood related to physical symptom burden? A study in hospitalized advanced cancer patients. Palliative Medicine, 21(4), 341–346. doi:10.1177/0269216307079067
   PubMed Web of Science ®Google Scholar
• Thompson, R. L., Hlgglns, C. A., & Howell, J. M. (1991). Personal computing: Toward a conceptual model of utilization. MIS Quarterly, 15(1), 125–142. doi:10.2307/249443
   Web of Science ®Google Scholar
• Tse, D. K., & Wilton, P. C. (1988). Models of consumer satisfaction formation: An extension. Journal of Marketing Research, 25(2), 204–212. doi:10.1177/002224378802500209
   Web of Science ®Google Scholar
• Tungare, M., & Pérez-Quinones, M. A. (2009). Mental workload at transitions between multiple devices in personal information management. Personal Information Management Workshop, ASIST 2009, Vancouver, BC, Canada.
   Google Scholar
• Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. doi:10.2307/30036540
   Web of Science ®Google Scholar
• Venkatesh, V., Thong, J. Y., Chan, F. K., Hu, P. J. H., & Brown, S. A. (2011). Extending the two‐stage information systems continuance model: Incorporating UTAUT predictors and the role of context. Information Systems Journal, 21(6), 527–555. doi:10.1111/isj.2011.21.issue-6
   Web of Science ®Google Scholar
• Walljas, M., Segerstahl, K., Vaananen-Vainio-Mattila, K., & Oinas-Kukkonen, H. (2010). Cross-platform service user experience: A field study and an initial framework. The 12th International Conference on Human Computer Interaction with Mobile Devices and Services, Lisbon, Portugal.
   Google Scholar
• Wilson, G. F. (2002). An analysis of mental workload in pilots during flight using multiple psychophysiological measures. The International Journal of Aviation Psychology, 12(1), 3–18. doi:10.1207/S15327108IJAP1201_2
   Web of Science ®Google Scholar
• Wixom, B. H., & Todd, P. A. (2005). A theoretical integration of user satisfaction and technology acceptance. Information Systems Research, 16(1), 85–102. doi:10.1287/isre.1050.0042
   Web of Science ®Google Scholar
• Wood, R. E. (1986). Task complexity: Definition of the construct. Organizational Behavior and Human Decision Processes, 37(1), 60–82. doi:10.1016/0749-5978(86)90044-0
   Web of Science ®Google Scholar
• Yoo, Y. (2010). Computing in everyday life: A call for research on experiential computing. MIS Quarterly, 34(2), 213–231. doi:10.2307/20721425
   Web of Science ®Google Scholar
• Zigurs, I., & Buckland, B. K. (1998). A theory of task/technology fit and group support systems effectiveness. MIS Quarterly, 22(3), 313–334. doi:10.2307/249668
   Web of Science ®Google Scholar