Investor decision making: an investigation of the modality effect

References

• Babbie, E. (1998). The practice of social research (8th ed.). CA, USA: Wadsworth.
   Google Scholar
• Baddeley, A., Hitch, G., & Allen, R. (2021). A multicomponent model of working memory. In R. Logie, V. Camos & N. Cowan (Eds.), Working memory: The state of the science (pp. 10–36). Oxford University Press.
   Google Scholar
• Baddeley, A. (1992). Working memory. Science, 255(5044), 556–559. https://doi.org/10.1126/science.1736359
   PubMed Web of Science ®Google Scholar
• Bernard, T., & Flitman, A. (2002). Using repertory grid analysis to gather qualitative data for information systems research. ACIS 2002 proceedings (pp. 98).
   Google Scholar
• Campbell, D.T. (1955). The informant in quantitative research. American Journal of Sociology, 60(4), 339–342. https://doi.org/10.1086/221565
   Web of Science ®Google Scholar
• Carlucci, D., Renna, P., Materi, S., & Schiuma, G. (2020). Intelligent decision-making model based on minority game for resource allocation in cloud manufacturing. Management Decision, 58(11), 2305–2325. https://doi.org/10.1108/MD-09-2019-1303
   Web of Science ®Google Scholar
• Castro-Alonso, J., & Sweller, J. (2020). The modality effect of cognitive load theory. In S. Nazir & A. Teperi (Eds.), Advances in human factors in training, education, and learning sciences. (pp. 75–84). Springer-Verlag.
   Google Scholar
• Cheung, M., & Liao, Z. (2009). Investing in real-world equity markets with anAHP-based decision framework. Journal of Decision Systems, 18(2), 149–183. https://doi.org/10.3166/jds.18.149-163
   Google Scholar
• Coshall, J.T. (2000). Measurement of tourists’ images: The repertory grid approach. Journal of Travel Research, 39(1), 85–89. https://doi.org/10.1177/004728750003900111
   Google Scholar
• De Koning, B.B., Rop, G., & Paas, F. (2020). Learning from split-attention materials: Effects of teaching physical and mental learning strategies. Contemporary Educational Psychology, 61(4), 101873. https://doi.org/10.1016/j.cedpsych.2020.101873
   Google Scholar
• Easterby-Smith, M. (1980). The design, analysis and interpretation of repertory grids. International Journal of Man-Machine Studies, 13(1), 3–24. https://doi.org/10.1016/S0020-7373(80)80032-0
   Google Scholar
• Fransella, F., Bell, R., & Bannister, D. (2004). A manual for repertory grid technique. John Wiley & Sons.
   Google Scholar
• Fu, Q., & Lv, J. (2020). Research on application of cognitive-driven human-computer interaction. American Scientific Research Journal for Engineering, Technology, and Sciences, 64(1), 9–27. https://asrjetsjournal.org/index.php/American_Scientific_Journal/article/view/5510
   Google Scholar
• Gibson, B. (1992). Financial information for decision making: An alternative small firm perspective. The Journal of Entrepreneurial Finance, 1(3), 221–232. https://digitalcommons.pepperdine.edu/jef/vol1/iss3/3/
   Google Scholar
• Ginns, P. (2005). Meta-analysis of the modality effect. Learning and Instruction, 15(4), 313–331. https://doi.org/10.1016/j.learninstruc.2005.07.001
   Web of Science ®Google Scholar
• Hankinson, G. (2004). Repertory grid analysis: An application to the measurement of destination images. International Journal of Nonprofit and Voluntary Sector Marketing, 9(2), 145–153. https://doi.org/10.1002/nvsm.241
   Google Scholar
• Harter, S.L., Erbes, C.R., & Hart, C.C. (2004). Content analysis of the personal constructs of female sexual abuse survivors elicited through repertory grid technique. Journal of Constructivist Psychology, 17(1), 27–43. https://doi.org/10.1080/10720530490250679
   Web of Science ®Google Scholar
• Hilton, D. (2001). The psychology of financial decision-making: Applications to trading, dealing, and investment analysis. The Journal of Psychology and Financial Markets, 2(1), 37–53. https://doi.org/10.1207/S15327760JPFM0201_4
   Google Scholar
• Hinkle, D.N. (1965). The change of personal constructs from the viewpoint of a theory of construct implications. The Ohio State University.
   Google Scholar
• Hirschberg, J., & Manning, C.D. (2015). Advances in natural language processing. Science, 349(6245), 261–266. https://doi.org/10.1126/science.aaa8685
   PubMed Web of Science ®Google Scholar
• Horvitz, E., & Rutledge, G. (1991). Time-dependent utility and action under uncertainty. Proceedings of the seventh uncertainty conference (pp. 151–158).
   Google Scholar
• Hoy, M.B. (2018). Alexa, Siri, Cortana, and More: An introduction to voice assistants. Medical Reference Services Quarterly, 37(1), 81–88. https://doi.org/10.1080/02763869.2018.1404391
   PubMedGoogle Scholar
• Hunter, M.G. (1997). The use of RepGrids to gather interview data about information systems analysts. Information Systems Journal, 7(1), 67–81. https://doi.org/10.1046/j.1365-2575.1997.00005.x
   Web of Science ®Google Scholar
• Hwang, Y., & Jeong, S.-H. (2021). The role of user control in media multitasking effects. Media Psychology, 24(1), 79–108. https://doi.org/10.1080/15213269.2019.1659152
   Web of Science ®Google Scholar
• Inan, F.A., Crooks, S.M., Cheon, J., Ari, F., Flores, R., Kurucay, M., & Paniukov, D. (2015). The reverse modality effect: Examining student learning from interactive computer‐based instruction. British Journal of Educational Technology, 46(1), 123–130. https://doi.org/10.1111/bjet.12129
   Web of Science ®Google Scholar
• Kahneman, D., & Thaler, K. (2006). Anomalies: Utility maximization and experienced utility. Journal of Economics Perspectives, 20(1), 221–234. https://doi.org/10.1257/089533006776526076
   Web of Science ®Google Scholar
• Kahneman, D., Wakker, P., & Sarin, R. (1997). Back to Bentham? Explorations of experienced utility. The Quarterly Journal of Economics, 112(2), 375–406. https://doi.org/10.1162/003355397555235
   Web of Science ®Google Scholar
• Kelly, G.A. (1955). The psychology of personal constructs. Volume 1: A theory of personality. WW Norton and Company.
   Google Scholar
• Kelly, G.A. (1977). Personal construct theory and the psychotherapeutic interview. Cognitive Therapy and Research, 1(4), 355–362. https://doi.org/10.1007/BF01663999
   Google Scholar
• Kelly, G.A. (2017). A brief introduction to personal construct theory. Costruttivismi, 4(1), 3–25. https://doi.org/10.23826/2017.01.003.025
   Google Scholar
• Koć‐Januchta, M.M., Höffler, T.N., Eckhardt, M., & Leutner, D. (2019). Does modality play a role? Visual-verbal cognitive style and multimedia learning. Journal of Computer Assisted Learning, 35(6), 747–757. https://doi.org/10.1111/jcal.12381
   Web of Science ®Google Scholar
• Kundisch, D., & Dzoienziol, J. (2008). Decision support for financial planning. Journal of Decision Systems, 17(2), 175–209. https://doi.org/10.3166/jds.17.175-209
   Google Scholar
• Lam, K., Liu, T., & Wong, W. (2010). A pseudo-Bayesian model in financial decision making with implications to market volatility, under- and overreaction. European Journal of Operational Research, 203(1), 166–175. https://doi.org/10.1016/j.ejor.2009.07.005
   Web of Science ®Google Scholar
• Leahy, W., & Sweller, J. (2011). Cognitive load theory, modality of presentation and the transient information effect. Applied Cognitive Psychology, 25(6), 943–951. https://doi.org/10.1002/acp.1787
   Web of Science ®Google Scholar
• Liu, T.-X., Lin, Y.-C., Hsu, C.-Y., Hsu, C.-Y., & Paas, F. (2021). Learning from animations and computer simulations: Modality and reverse modality effects. British Journal of Educational Technology, 52(1), 304–317. https://doi.org/10.1111/bjet.12996
   Web of Science ®Google Scholar
• Loh, F., Vomhoff, V., Wamser, F., Metzger, F., & Hoßfeld, T. (2019). Traffic measurement study on video streaming with the Amazon echo show. Proceedings of the 4th Internet-QoE workshop on QoE-based analysis and management of data communication networks, Internet-QoE’19 (pp. 31–36). Association for Computing Machinery.
   Google Scholar
• Low, R., & Sweller, J. (2005). The modality principle in multimedia learning. In The Cambridge handbook of multimedia learning (Vol. 147, pp. 158).
   Google Scholar
• Mayer, R.E., & Anderson, R.B. (1992). The instructive animation: Helping students build connections between words and pictures in multimedia learning. Journal of Educational Psychology, 84(4), 444. https://doi.org/10.1037/0022-0663.84.4.444
   Web of Science ®Google Scholar
• Mayer, R.E., & Moreno, R. (1998). A split-attention effect in multimedia learning: Evidence for dual processing systems in working memory. Journal of Educational Psychology, 90(2), 312. https://doi.org/10.1037/0022-0663.90.2.312
   Web of Science ®Google Scholar
• Mayer, R.E. (1997). Multimedia learning: Are we asking the right questions? Educational Psychologist, 32(1), 1–19. https://doi.org/10.1207/s15326985ep3201_1
   Web of Science ®Google Scholar
• McAvoy, J. (2006). Evaluating the evaluations: Preconceptions of Project Post-Mortems. Electronic Journal of Information Systems Evaluation, 9(2), 65–72. https://academic-publishing.org/index.php/ejise/article/view/320
   Google Scholar
• Merigo, J., & Gil-Lafuente, A. (2010). New decision-making techniques and their application in the selection of financial products. Information Sciences, 180(1), 2085–2094. https://doi.org/10.1016/j.ins.2010.01.028
   Google Scholar
• Moreno, R., & Mayer, R.E. (1999). Cognitive principles of multimedia learning: The role of modality and contiguity. Journal of Educational Psychology, 91(2), 358. https://doi.org/10.1037/0022-0663.91.2.358
   Web of Science ®Google Scholar
• Moreno, R. (2006). Does the modality principle hold for different media? A test of the method‐affects‐learning hypothesis. Journal of Computer Assisted Learning, 22(3), 149–158. https://doi.org/10.1111/j.1365-2729.2006.00170.x
   Web of Science ®Google Scholar
• Mousavi, S.Y., Low, R., & Sweller, J. (1995). Reducing cognitive load by mixing auditory and visual presentation modes. Journal of Educational Psychology, 87(2), 319. https://doi.org/10.1037/0022-0663.87.2.319
   Web of Science ®Google Scholar
• Nguyen, M.H., Bol, N., & Lustria, M.L.A. (2020). Perceived active control over online health information: Underlying mechanisms of mode tailoring effects on Website attitude and information recall. Journal of Health Communication, 25(4), 271–282. https://doi.org/10.1080/10810730.2020.1743797
   PubMed Web of Science ®Google Scholar
• O’Hare, J. (2007). Retail investor remedies under rule 10b-5. University of Cincinatti Law Review, 76(2), 521. https://digitalcommons.law.villanova.edu/wps/art89
   Web of Science ®Google Scholar
• Parthasarathy, S., & Sundaram, S. (2021). Detecting expressions with multimodal transformers. 2021 IEEE Spoken Language Technology workshop (SLT) (pp. 636–643).
   Google Scholar
• Penney, C.G. (1989). Modality effects and the structure of short-term verbal memory. Memory & Cognition, 17(4), 398–422. https://doi.org/10.3758/BF03202613
   PubMed Web of Science ®Google Scholar
• Pradhan, A., Lazar, A., & Findlater, L. (2020). Use of intelligent voice assistants by older adults with low technology use. ACM Transactions on Computer-Human Interaction, 27(4), 1–27. https://doi.org/10.1145/3373759
   Web of Science ®Google Scholar
• Rogers, B., & Ryals, L. (2007). Using repertory grid to access the underlying realities in key account relationships. International Journal of Market Research, 49(5), 595–612. https://doi.org/10.1177/147078530704900506
   Web of Science ®Google Scholar
• Rummer, R., Schweppe, J., Fürstenberg, A., Seufert, T., & Brünken, R. (2010). Working memory interference during processing texts and pictures: Implications for the explanation of the modality effect. Applied Cognitive Psychology: The Official Journal of the Society for Applied Research in Memory and Cognition, 24(2), 164–176. https://doi.org/10.1002/acp.1546
   Web of Science ®Google Scholar
• Schüler, A., & Mayer, M.G. (2020). Illustrations before text reduce visuospatial working memory load during text processing. Discourse Processes, 57(8), 627–658. https://doi.org/10.1080/0163853X.2020.1750931
   Web of Science ®Google Scholar
• Sharma, R., Pavlović, V.I., & Huang, T.S. (2002). Toward multimodal human–computer interface. In A. Bovik, C. Chen, & D. Goldgof (Eds.), Advances in image processing and understanding: A Festschrift for Thomas S Huang (pp. 349–365). NJ, USA: World Scientific.https://doi.org/10.1142/5042
   Google Scholar
• Shi, W., Chen, W.-N., Gu, T., Jin, H., & Zhang, J. (2021). Handling uncertainty in financial decision making: A clustering estimation of distribution algorithm with simplified simulation. IEEE Transactions on Emerging Topics in Computational Intelligence, 5(1), 42–56. https://doi.org/10.1109/TETCI.2020.3013652
   Google Scholar
• Shrivastava, A., & Jain, J. (2020). Natural language processing: A literature survey. International Journal of Communication and Information Technology 2, 1(2), 15–20. https://www.computersciencejournals.com/ijcit/article/13/1-2-5-364.pdf
   Google Scholar
• Stanek, S., Sroka, H., & Twardowski, Z. (2007). A decision support system in diagnosing the financial condition of an organization. Journal of Decision Systems, 16(2), 173–196. https://doi.org/10.3166/jds.16.173-196
   Google Scholar
• Stewart, V., Stewart, A., & Fonda, N. (1981). Business applications of repertory grid. McGraw-Hill London.
   Google Scholar
• Tabbers, H.K., Martens, R.L., & Van Merrienboer, J.J. (2001). The modality effect in multimedia instructions [Paper presentation]. Proceedings of the annual meeting of the cognitive science society Edinburgh, UK. In J. Moore, & K. Stenning . NJ, USA: Lawrence Erlbaum Associates.
   Google Scholar
• Tabbers, H.K., Martens, R.L., & Van Merriënboer, J.J. (2004). Multimedia instructions and cognitive load theory: Effects of modality and cueing. British Journal of Educational Psychology, 74(1), 71–81. https://doi.org/10.1348/000709904322848824
   PubMed Web of Science ®Google Scholar
• Tan, F.B., & Hunter, M.G. (2002). The repertory grid technique: A method for the study of cognition in information systems. MIS Quarterly, 26(1), 39–57. https://doi.org/10.2307/4132340
   Web of Science ®Google Scholar
• Triantafyllidis, E., Mcgreavy, C., Gu, J., & Li, Z. (2020). Study of multimodal interfaces and the improvements on teleoperation. IEEE Access, 8(1), 78213–78227. https://doi.org/10.1109/ACCESS.2020.2990080
   Google Scholar
• Van Merrienboer, J.J., & Ayres, P. (2005). Research on cognitive load theory and its design implications for e-learning. Educational Technology Research and Development, 53(3), 5–13. https://doi.org/10.1007/BF02504793
   Web of Science ®Google Scholar
• Wang, X., Lin, L., Han, M., & Spector, J.M. (2020). Impacts of cues on learning: Using eye-tracking technologies to examine the functions and designs of added cues in short instructional videos. Computers in Human Behavior, 107, 106279. https://doi.org/10.1016/j.chb.2020.106279
   Web of Science ®Google Scholar
• Wang, X. (2020). Financial decision making under uncertainty: Psychological coping methods. In T. Zaleskiewicz & J. Traczyk (Eds.), Psychological perspectives on financial decision making (pp. 187–201). NY, USA: Springer.
   Google Scholar
• Watson, R.T., & Webster, J. (2020). Analysing the past to prepare for the future: Writing a literature review a roadmap for release 2.0. Journal of Decision Systems, 29(3), 129–147. https://doi.org/10.1080/12460125.2020.1798591
   Web of Science ®Google Scholar
• Webster, J., & Watson, R.T. (2002). Analyzing the past to prepare for the future: Writing a literature review. MIS Quarterly 26(2) , xiii–xxiii. http://www.jstor.org/stable/4132319
   Web of Science ®Google Scholar
• Wen, P., Opoku-Baah, C., Park, M., & Blake, R. (2020). Judging relative onsets and offsets of audiovisual events. Vision, 4(1), 17. https://doi.org/10.3390/vision4010017
   Google Scholar
• Xu, M., Fu, Y., Yu, J., Zhu, P., Shen, M., & Chen, H. (2020). Source information is inherently linked to working memory representation for auditory but not for visual stimuli. Cognition, 197(4), 104160. https://doi.org/10.1016/j.cognition.2019.104160
   PubMedGoogle Scholar
• Zopoundis, C., & Doumpos, M. (2002). Multi-criteria decision aid in financial decision making: Methodologies and literature review. Journal of Multi-criteria Decision Analysis, 11(4), 167–186. https://doi.org/10.1002/mcda.333
   Google Scholar