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Attitude Moderates the Relation between Frequency of Media Multitasking in the Classroom and Delay Discounting

Abstract

The present study examined whether the attitude toward texting in the classroom moderates the relation between delay discounting and frequency of texting in the classroom. College students completed a survey to assess their attitude and frequency of texting in the classroom. Based on this information, students were stratified into four groups: Appropriate-Frequent, Appropriate-Infrequent, Inappropriate-Frequent, and Inappropriate-Infrequent. The groups were then compared on the degree of delay discounting of hypothetical monetary reinforcers. In the delay-discounting task, participants made repeated choices between $1,000 available after a delay and an equal or lesser amount of money available immediately. The results showed that the attitude toward texting in the classroom moderates the relation between the degree of delay discounting and the frequency of texting in the classroom. Among students who perceive texting in the classroom as inappropriate, those who frequently text in the classroom showed greater rates of delay discounting than those who infrequently text in the classroom, whereas there was no difference in students who perceive texting in the classroom as appropriate. Impulsive characteristics of texting in the classroom from a behavioral economic perspective are discussed.

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Data Availability

The datasets used and analyzed during the current study are available from the corresponding author on request.

Notes

  1. 1.

    According to Iacobucci, Posavac, Kardes, Schneider, and Popovich (2015), the use of a median-split procedure can be justified if there is no multicollinearity between independent variables and a study focuses on group differences (p. 662). Because the point-biserial correlation coefficient between the frequency and the attitude of TIC (upper and lower halves were coded as 1 and 0, respectively, for both frequency and attitude data) is nonsignificant (r = -.13, p = .20) and the primary focus of the present study is the difference among groups (in particular the comparison between the Inappropriate-Frequent group and the other groups), a median-split procedure should be justifiable in the present study. To illustrate the details about the frequency and attitude data, however, the descriptive statistics that indicate the distributions are provided for both frequency and attitude data, respectively: skewness: .43 (SE = .20) and -.22 (SE = .20); kurtosis: -.85 (SE = .40) and -.63 (SE = .40); and range: 1–5 and 1–5.

  2. 2.

    It is important to note that the conventional AUC (Myerson et al., 2001) emphasizes the indifference points at larger delays, whereas AUClogd (Borges et al., 2016) emphasizes those at smaller delays. If an AUC measure is used as a nontheoretical, descriptive measure of discounting (e.g., regardless of its relation to the k value of the hyperbolic equation), the choice between conventional AUC and AUClogd should essentially be a matter of preference, in particular if conventional AUC and AUClogd produce the same results. If, however, the two AUC measures produce different results (as in the case of the present data set), the best approach would be to present both results and leave the interpretation to the readers.

  3. 3.

    According to Faraway (2014), running simple main effects even when the interaction is not statistically significant is justifiable because a nonsignificant interaction does not exclude the possibility that an interaction effect exists in the population. This may be particularly important given the power to detect interactions is generally low (Aiken & West, 1991).

References

  1. Abel, J. I., Buff, C. L., & Abel, J. P. (2012). Can they defer the cellular lure? College students’ self-control and cell phone usage. Review of Business Research, 12, 101–106 Retrieved from http://rbr-journal.org/domains/RBR-JOURNAL/.

    Google Scholar 

  2. Aiken, L. S., & West, S. G. (1991). Multiple regression: Testing and interpreting interactions. Thousand Oaks, CA: Sage.

  3. Amlung, M., Vedelago, L., Acker, J., Balodis, I., & MacKillop, J. (2017). Steep delay discounting and addictive behavior: A meta-analysis of continuous associations. Addiction, 112, 51–62. https://doi.org/10.1111/add.13535.

    Article  PubMed  Google Scholar 

  4. Atchley, P., & Warden, A. C. (2012). The need of young adults to text now: Using delay discounting to assess informational choice. Journal of Applied Research in Memory & Cognition, 1, 229–234. https://doi.org/10.1016/j.jarmac.2012.09.001.

    Article  Google Scholar 

  5. Audrain-McGovern, J., Rodriguez, D., Epstein, L. H., Cuevas, J., Rodgers, K., & Wileyto, E. P. (2009). Does delay discounting play an etiological role in smoking or is it a consequence of smoking? Drug & Alcohol Dependence, 103, 99–106. https://doi.org/10.1016/j.drugalcdep.2008.12.019.

    Article  Google Scholar 

  6. Becker, M. W., Alzahabi, R., & Hopwood, C. J. (2012). Media multitasking is associated with symptoms of depression and social anxiety. Cyberpsychology, Behavior, & Social Networking, 16, 132–135. https://doi.org/10.1089/cyber.2012.0291.

    Article  Google Scholar 

  7. Bickel, W. K., Jarmolowicz, D. P., Mueller, E. T., & Gatchalian, K. M. (2011). The behavioral economics and neuroeconomics of reinforcer pathologies: Implications for etiology and treatment of addiction. Current Psychiatry Reports, 13, 406–415. https://doi.org/10.1007/s11920-011-0215-1.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Billieux, J., Maurage, P., Lopez-Fernandez, O., Kuss, D. J., & Griffiths, M. D. (2015). Can disordered mobile phone use be considered a behavioral addiction? An update on current evidence and a comprehensive model for future research. Current Addiction Reports, 2, 156–162. https://doi.org/10.1007/s40429-015-0054-y.

    Article  Google Scholar 

  9. Bjornsen, C. A., & Archer, K. J. (2015). Relations between college students’ cell phone use during class and grades. Scholarship of Teaching & Learning in Psychology, 1, 326–336. https://doi.org/10.1037/stl0000045.

    Article  Google Scholar 

  10. Bolkan, S., & Griffin, D. J. (2017). Students’ use of cell phones in class for off-task behaviors: The indirect impact of instructors’ teaching behaviors through boredom and students’ attitudes. Communication Education, 66, 313–329. https://doi.org/10.1080/03634523.2016.1241888.

    Article  Google Scholar 

  11. Borges, A. M., Kuang, J., Milhorn, H., & Yi, R. (2016). An alternative approach to calculating Area-Under-the-Curve (AUC) in delay discounting research. Journal of the Experimental Analysis of Behavior, 106, 145–155. https://doi.org/10.1002/jeab.219.

    Article  PubMed  Google Scholar 

  12. Calamaro, C. J., Mason, T. B. A., & Ratcliffe, S. J. (2009). Adolescents living the 24/7 lifestyle: Effects of caffeine and technology on sleep duration and daytime functioning. Pediatrics, 123, e1005–e1010. https://doi.org/10.1542/peds.2008-3641.

    Article  PubMed  Google Scholar 

  13. Chiesa, A., & Serretti, A. (2014). Are mindfulness-based interventions effective for substance use disorders? A systematic review of the evidence. Substance Use & Misuse, 49, 492–512. https://doi.org/10.3109/10826084.2013.770027.

    Article  Google Scholar 

  14. Chung, H. J., Weyandt, L. L., & Swentosky, A. (2014). The physiology of executive functioning. In S. Goldstein & J. A. Naglieri (Eds.), Handbook of executive functioning (pp. 13–27). New York, NY: Springer. https://doi.org/10.1007/978-1-4614-8106-5_1.

    Chapter  Google Scholar 

  15. Ellis, Y., Daniels, B. W., & Jauregui, A. (2010). The effect of multitasking on the grade performance of business students. Research in Higher Education Journal, 8, 1–10 http://aabri.com/rhej.html.

    Google Scholar 

  16. Faraway, J. J. (2014). Linear models with R (2nd ed.). Boca Raton, FL: CRC Press.

  17. Gingerich, A. C., & Lineweaver, T. T. (2014). OMG! Texting in class = u fail :( Empirical evidence that text messaging during class disrupts comprehension. Teaching of Psychology, 41, 44–51. https://doi.org/10.1177/0098628313514177.

  18. Goldstein, R. Z., & Volkow, N. D. (2011). Dysfunction of the prefrontal cortex in addiction: Neuroimaging findings and clinical implications. Nature Reviews Neuroscience, 12, 652–669. https://doi.org/10.1038/nrn3119.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Hadar, A., Hadas, I., Lazarovits, A., Alyagon, U., Eliraz, D., & Zangen, A. (2017). Answering the missed call: Initial exploration of cognitive and electrophysiological changes associated with smartphone use and abuse. PLoS One, 12(7), e0180094. https://doi.org/10.1371/journal.pone.0180094.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Hayashi, Y., & Blessington, G. P. (2018). A behavioral economic analysis of media multitasking: Delay discounting as an underlying process of texting in the classroom. Computers in Human Behavior, 86, 245–255. https://doi.org/10.1016/j.chb.2018.04.049.

    Article  Google Scholar 

  21. Hayashi, Y., & Blessington, G. P. (2020). Excessive valuation of social interaction in text-message dependency: A behavioral economic demand analysis. The Psychological Record. Advance online publication. https://doi.org/10.1007/s40732-020-00418-x.

  22. Hayashi, Y., Fessler, H. J., Friedel, J. E., Foreman, A. M., & Wirth, O. (2018). The roles of delay and probability discounting in texting while driving: Toward the development of a translational scientific program. Journal of the Experimental Analysis of Behavior, 110, 229–242. https://doi.org/10.1002/jeab.460.

    Article  PubMed  Google Scholar 

  23. Hayashi, Y., Foreman, A. M., Friedel, J. E., & Wirth, O. (2018). Executive function and dangerous driving behaviors in young drivers. Transportation Research Part F: Traffic Psychology & Behaviour, 52, 51–61. https://doi.org/10.1016/j.trf.2017.11.007.

    Article  Google Scholar 

  24. Hayashi, Y., Friedel, J. E., Foreman, A. M., & Wirth, O. (2019). A cluster analysis of text message users based on their demand for text messaging: A behavioral economic approach. Journal of the Experimental Analysis of Behavior, 112, 273–289. https://doi.org/10.1002/jeab.554.

    Article  PubMed  Google Scholar 

  25. Hayashi, Y., Miller, K., Foreman, A. M., & Wirth, O. (2016). A behavioral economic analysis of texting while driving: Delay discounting processes. Accident Analysis & Prevention, 97, 132–140. https://doi.org/10.1016/j.aap.2016.08.028.

    Article  Google Scholar 

  26. Hayashi, Y., & Nenstiel, J. N. (2019). Media multitasking in the classroom: Problematic mobile phone use and impulse control as predictors of texting in the classroom. Current Psychology. Advance online publication. https://doi.org/10.1007/s12144-019-00395-7.

  27. Hayashi, Y., Rivera, E. A., Modico, J. G., Foreman, A. M., & Wirth, O. (2017). Texting while driving, executive function, and impulsivity in college students. Accident Analysis & Prevention, 102, 72–80. https://doi.org/10.1016/j.aap.2017.02.016.

    Article  Google Scholar 

  28. Hayashi, Y., Russo, C. T., & Wirth, O. (2015). Texting while driving as impulsive choice: A behavioral economic analysis. Accident Analysis & Prevention, 83, 182–189. https://doi.org/10.1016/j.aap.2015.07.025.

    Article  Google Scholar 

  29. Iacobucci, D., Posavac, S. S., Kardes, F. R., Schneider, M. J., & Popovich, D. L. (2015). Toward a more nuanced understanding of the statistical properties of a median split. Journal of Consumer Psychology, 25, 652–665. https://doi.org/10.1016/j.jcps.2014.12.002.

    Article  Google Scholar 

  30. Igaki, T., Romanowich, P., & Yamagishi, N. (2019). Using smartphones while walking is associated with delay but not social discounting. The Psychological Record, 69, 513–524. https://doi.org/10.1007/s40732-019-00348-3.

    Article  Google Scholar 

  31. Johnson, M. W., & Bickel, W. K. (2008). An algorithm for identifying nonsystematic delay-discounting data. Experimental & Clinical Psychopharmacology, 16, 264–274. https://doi.org/10.1037/1064-1297.16.3.264.

    Article  Google Scholar 

  32. Loh, K. K., & Kanai, R. (2014). Higher media multi-tasking activity is associated with smaller gray-matter density in the anterior cingulate cortex. PLoS One, 9(9), e106698. https://doi.org/10.1371/journal.pone.0106698.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Loree, A. M., Lundahl, L. H., & Ledgerwood, D. M. (2014). Impulsivity as a predictor of treatment outcome in substance use disorders: Review and synthesis. Drug & Alcohol Review, 34, 119–134. https://doi.org/10.1111/dar.12132.

    Article  Google Scholar 

  34. MacKillop, J., Amlung, M. T., Few, L. R., Ray, L. A., Sweet, L. H., & Munafò, M. R. (2011). Delayed reward discounting and addictive behavior: A meta-analysis. Psychopharmacology, 216, 305–321. https://doi.org/10.1007/s00213-011-2229-0.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Magen, H. (2017). The relations between executive functions, media multitasking and polychronicity. Computers in Human Behavior, 67, 1–9. https://doi.org/10.1016/j.chb.2016.10.011.

    Article  Google Scholar 

  36. Mazur, J. E. (1987). An adjusting procedure for studying delayed reinforcement. In M. L. Commons, J. E. Mazur, J. A. Nevin, & H. Rachlin (Eds.), Quantitative analyses of behavior: The effect of delay and of intervening events on reinforcement value (Vol. 5; pp. 55–73). Hillsdale, NJ: Lawrence Erlbaum Associates.

  37. Monsell, S. (2003). Task switching. Trends in Cognitive Sciences, 7, 134–140. https://doi.org/10.1016/S1364-6613(03)00028-7.

    Article  Google Scholar 

  38. Myerson, J., Green, L., & Warusawitharana, M. (2001). Area under the curve as a measure of discounting. Journal of the Experimental Analysis of Behavior, 76, 235–243. https://doi.org/10.1901/jeab.2001.76-235.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Olmsted, N. M., & Terry, C. P. (2014). Who’s texting in class? A look at behavioral and psychological predictors. Psi Chi Journal of Psychological Research, 19, 183–190. https://doi.org/10.24839/2164-8204.JN19.4.183.

    Article  Google Scholar 

  40. Petry, N. M., & Madden, G. J. (2010). Discounting and pathological gambling. In G. J. Madden & W. K. Bickel (Eds.), Impulsivity: The behavioral and neurological science of discounting (pp. 273–294). Washington, DC: American Psychological Association.

    Chapter  Google Scholar 

  41. Rachlin, H., Raineri, A., & Cross, D. (1991). Subjective probability and delay. Journal of the Experimental Analysis of Behavior, 55, 233–244. https://doi.org/10.1901/jeab.1991.55-233.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Redner, R., & Hirst, J. (2020). Preliminary evaluation of delay discounting and cell phone use in the college classroom. The Psychological Record. Advance online publication. https://doi.org/10.1007/s40732-020-00405-2.

  43. Reed, D. D., Becirevic, A., Atchley, P., Kaplan, B. A., & Liese, B. S. (2016). Validation of a novel delay discounting of text messaging questionnaire. The Psychological Record, 66, 253–261. https://doi.org/10.1007/s40732-016-0167-2.

    Article  Google Scholar 

  44. Reid, R. C., McKittrick, H. L., Davtian, M., & Fong, T. W. (2012). Self-reported differences on measures of executive function in a patient sample of pathological gamblers. International Journal of Neuroscience, 122, 500–505. https://doi.org/10.3109/00207454.2012.673516.

    Article  Google Scholar 

  45. Rosen, L. D., Carrier, L. M., & Cheever, N. A. (2013). Facebook and texting made me do it: Media-induced task-switching while studying. Computers in Human Behavior, 29, 948–958. https://doi.org/10.1016/j.chb.2012.12.001.

    Article  Google Scholar 

  46. Rubinstein, J. S., Meyer, D. E., & Evans, J. E. (2001). Executive control of cognitive processes in task switching. Journal of Experimental Psychology. Human Perception & Performance, 27, 763–797.

    Article  Google Scholar 

  47. Sanbonmatsu, D. M., Strayer, D. L., Medeiros-Ward, N., & Watson, J. M. (2013). Who multi-tasks and why? Multi-tasking ability, perceived multi-tasking ability, impulsivity, and sensation seeking. PLoS One, 8(1), e54402. https://doi.org/10.1371/journal.pone.0054402.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Saville, B. K., Gisbert, A., Kopp, J., & Telesco, C. (2010). Internet addiction and delay discounting in college students. The Psychological Record, 60, 273–286. https://doi.org/10.1007/BF03395707.

    Article  Google Scholar 

  49. Schutten, D., Stokes, K. A., & Arnell, K. M. (2017). I want to media multitask and I want to do it now: Individual differences in media multitasking predict delay of gratification and system-1 thinking. Cognitive Research: Principles & Implications, 2, 1–10. https://doi.org/10.1186/s41235-016-0048-x.

    Article  Google Scholar 

  50. Smith, K. R., Lawyer, S. R., & Swift, J. K. (2018). A meta-analysis of nonsystematic responding in delay and probability reward discounting. Experimental & Clinical Psychopharmacology, 26, 94–107. https://doi.org/10.1037/pha0000167.

    Article  Google Scholar 

  51. Spinella, M. (2005). Self-rated executive function: Development of the executive function index. International Journal of Neuroscience, 115, 649–667. https://doi.org/10.1080/00207450590524304.

    Article  Google Scholar 

  52. Tang, Z., Zhang, H., Yan, A., & Qu, C. (2017). Time is money: The decision making of smartphone high users in gain and loss intertemporal choice. Frontiers in Psychology, 8(363). https://doi.org/10.3389/fpsyg.2017.00363

  53. van der Schuur, W. A., Baumgartner, S. E., Sumter, S. R., & Valkenburg, P. M. (2015). The consequences of media multitasking for youth: A review. Computers in Human Behavior, 53, 204–215. https://doi.org/10.1016/j.chb.2015.06.035.

    Article  Google Scholar 

  54. Wei, F.-Y. F., & Wang, Y. K. (2010). Students’ silent messages: Can teacher verbal and nonverbal immediacy moderate student use of text messaging in class? Communication Education, 59, 475–496. https://doi.org/10.1080/03634523.2010.496092.

    Article  Google Scholar 

  55. Wei, F.-Y. F., Wang, Y. K., & Klausner, M. (2012). Rethinking college students’ self-regulation and sustained attention: Does text messaging during class influence cognitive learning? Communication Education, 61, 185–204. https://doi.org/10.1080/03634523.2012.672755.

    Article  Google Scholar 

  56. Wentland, E. J. (1993). Survey responses: An evaluation of their validity. San Diego, CA: Academic Press.

  57. Williams, J. A., Berg, H., Gerber, H., Miller, M., Cox, D., Votteler, N., et al. (2011). “I get distracted by their being distracted”: The etiquette of in-class texting. Eastern Educational Journal, 40, 48–56. Retrieved from http://castle.eiu.edu/edjournal/.

    Google Scholar 

  58. Yi, R., Mitchell, S. H., & Bickel, W. K. (2010). Delay discounting and substance abuse-dependence. In G. J. Madden & W. K. Bickel (Eds.), Impulsivity: The behavioral and neurological science of discounting (pp. 191–211). Washington, DC: American Psychological Association.

    Chapter  Google Scholar 

  59. Zhou, Z., Zhu, H., Li, C., & Wang, J. (2014). Internet addictive individuals share impulsivity and executive dysfunction with alcohol-dependent patients. Frontiers in Behavioral Neuroscience, 8(288). https://doi.org/10.3389/fnbeh.2014.00288

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Hayashi, Y. Attitude Moderates the Relation between Frequency of Media Multitasking in the Classroom and Delay Discounting. Psychol Rec 71, 211–218 (2021). https://doi.org/10.1007/s40732-020-00443-w

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Keywords

  • Texting in the classroom
  • Delay discounting
  • Attitude-behavior discrepancy
  • Media multitasking
  • Behavioral economics
  • College students