Skip to main content

Differential Associations between Risky Cell-Phone Behaviors and Discounting Types

Abstract

As cell-phone use increases, the probability for individuals to engage in risky behaviors while using cell phones also increases. Previous studies have shown significant positive associations between increasing risky behaviors while using cellphones (i.e., texting while driving [TWD] and using smartphones while walking [USWW]) and increased delay discounting rates. These participant samples were obtained either in the United States or Japan and did not measure both risky behaviors. The present study measured both self-reported TWD and USWW for 456 U.S. college participants to determine whether these risky cell-phone behaviors were associated with each other, and/or with increased delay and social discounting rates. Results indicated that self-reported TWD and USWW were significantly positively associated with each other (r = 0.42). Neither TWD nor USWW were associated with delay discounting rates. However, increased TWD was significantly related to increasing social discounting rates. USWW was not significantly associated with increased social discounting rates. These results show differential associations between risky behaviors while using cell phones and different discounting measures (delay and social). In addition, USWW rates were about two times larger for U.S. participants, relative to a previous Japanese sample, suggesting that culture and perceived risk may affect subsequent associations with discounting rates.

This is a preview of subscription content, access via your institution.

Figure 1.
Figure 2.

Data Availability

The datasets from the current study are available from the corresponding author on reasonable request.

Notes

  1. 1.

    An AUCord analysis showed the same results as using log transformed s-values. Social discounting increased as TWD increased, H = 7.79, p = 0.02, η2 = 0.02. In addition, the only difference between groups was for participants that never engaged in TWD and TWD > 2, with the latter group socially discounting at a higher rate than the former, z = 2.77, p = 0.006, η2 = 0.05.

  2. 2.

    An AUCord analysis showed a similar trend for increased social discounting as USWW increased, H = 4.73, p = 0.09.

References

  1. 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.

    Article  Google Scholar 

  2. Barlow, P., Reeves, A., McKee, M., Galea, G., & Stuckler, D. (2016). Unhealthy diets, obesity and time discounting: a systematic literature review and network analysis. Obesity Reviews, 17, 810–819.

    Article  Google Scholar 

  3. Białaszek, W., Ostaszewski, P., Green, L., & Myerson, J. (2019). On four types of devaluation of outcomes due to their costs: Delay, probability, effort, and social discounting. The Psychological Record, 69(3), 415–424.

    Article  Google Scholar 

  4. Bickel, W. K., & Mueller, E. T. (2009). Toward the study of trans-disease processes: A novel approach with special reference to the study of co-morbidity. Journal of Dual Diagnosis, 5(2), 131–138.

    Article  Google Scholar 

  5. 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.

    Article  Google Scholar 

  6. Bradstreet, M. P., Higgins, S. T., Heil, S. H., Badger, G. J., Skelly, J. M., Lynch, M. E., & Trayah, M. C. (2012). Social discounting and cigarette smoking during pregnancy. Journal of Behavioral Decision Making, 25(5), 502–511.

    Article  Google Scholar 

  7. Epstein, L. H., Richards, J. B., Saad, F. G., Paluch, R. A., Roemmich, J. N., & Lerman, C. (2003). Comparison between two measures of delay discounting in smokers. Experimental & Clinical Psychopharmacology, 11, 131–138.

    Article  Google Scholar 

  8. 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.

    Article  Google Scholar 

  9. 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.

    Article  Google Scholar 

  10. 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.

    Article  Google Scholar 

  11. Hayashi, Y., Russo, C. T., & Wirth, O. (2015). Texting while driving as impulsive choice: A behavioral economic analysis. Accident Analysis & Prevention, 83, 182–189.

    Article  Google Scholar 

  12. Horvath, J., Mundinger, C., Schmitgen, M. M., Wolf, N. D., Sambataro, F., Hirjak, D., … Wolf, R. C. (2020). Structural and functional correlates of smartphone addiction. Addictive Behaviors, 105, 106334.

  13. Igaki, T., Romanowich, P., & Yamagishi, N. (2019). Using smartphones while walking is associated with delay but not social discounting. The Psychological Record, 69(4), 513–524.

    Article  Google Scholar 

  14. Johnson, M. W., & Bickel, W. K. (2008). An algorithm for identifying nonsystematic delay-discounting data. Experimental & Clinical Psychopharmacology, 16, 264–274.

    Article  Google Scholar 

  15. Jones, B., & Rachlin, H. (2006). Social discounting. Psychological Science, 17(4), 283–286.

    Article  Google Scholar 

  16. Kaplan, B. A., Amlung, M., Reed, D. D., Jarmolowicz, D. P., McKerchar, T. L., & Lemley, S. M. (2016). Automating scoring of delay discounting for the 21- and 27-item monetary choice questionnaires. The Behavior Analyst, 39(2), 293–304.

    Article  Google Scholar 

  17. Kirby, K. N., Petry, N. M., & Bickel, W. K. (1999). Heroin addicts have higher discount rates for delayed rewards than non-drug-using controls. Journal of Experimental Psychology: General, 128(1), 78–87.

    Article  Google Scholar 

  18. Kuhlmann, T., Dantlgraber, M., & Reips, U. D. (2017). Investigating measurement equivalence of visual analogue scales and Likert-type scales in internet-based personality questionnaires. Behavior Research Methods, 49(6), 2173–2181.

    Article  Google Scholar 

  19. Kwon, M., Lee, J. Y., Won, W. Y., Park, J. W., Min, J. A., Hahn, C., … Kim, D. J. (2013). Development and validation of a smartphone addiction scale (SAS). PloS One, 8(2), e56936.

  20. Li, L., Shults, R. A., Andridge, R. R., Yellman, M. A., Xiang, H., & Zhu, M. (2018). Texting/emailing while driving among high school students in 35 states, United States, 2015. Journal of Adolescent Health, 63, 701–708.

    Article  Google Scholar 

  21. Lin, Y.-H., Chang, L.-R., Lee, Y.-H., Tseng, H.-W., Kuo, T. B. J., & Chen, S.-H. (2014). Development and validation of the Smartphone Addiction Inventory (SPAI). PloS One, 9(6), e98312.

    Article  Google Scholar 

  22. Lopez-Fernandez, O. (2017). Short version of the Smartphone Addiction Scale adapted to Spanish and French: Towards a cross-cultural research in problematic mobile phone use. Addictive Behaviors, 64, 275–280.

    Article  Google Scholar 

  23. Madden, G. J., & Bickel, W. K. (2010). Impulsivity: The behavioral and neurological science of discounting. Washington, DC: American Psychological Association.

    Book  Google Scholar 

  24. Martin, R. J., Cox, M. J., Chaney, B. H., & Knowlden, A. P. (2018). Examination of associations between risky driving behaviors and hazardous drinking among a sample of college students. Traffic Injury Prevention, 19(6), 563–568.

    Article  Google Scholar 

  25. 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: Vol. 5. The effect of delay and intervening events on reinforcement value (pp. 55–73). Hillsdale, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  26. National Center for Statistics and Analysis. (2019, April). Distracted driving in fatal crashes, 2017. (Traffic Safety Facts Research Note. Report No. DOT HS 812 700). Washington, DC: National Highway Traffic Safety Administration.

  27. National Safety Council. (2015). Annual estimate of cell phone crashes 2013. Retrieved from http://www.nsc.org/DistractedDrivingDocuments/CPK/Attributable-Risk-Summary.pdf

  28. Pew Research Center: Internet & Technology. (2019). Mobile fact sheet. Retrieved from https://www.pewresearch.org/internet/fact-sheet/mobile/.

  29. Povolotskiy, R., Gupta, N., Leverant, A. B., Kandinov, A., & Paskhover, B. (2019). Head and neck injuries associated with cell phone use. JAMA Otolaryngology: Head & Neck Surgery. https://doi.org/10.1001/jamaoto.2019.3678.

  30. Rachlin, H., & Jones, B. A. (2008). Social discounting and delay discounting. Journal of Behavioral Decision Making, 21(1), 29–43.

    Article  Google Scholar 

  31. Rachlin, H., & Locey, M. (2011). A behavioral analysis of altruism. Behavioural Processes, 87(1), 25–33.

    Article  Google Scholar 

  32. Rachlin, H., Raineri, A., & Cross, D. (1991). Subjective probability and delay. Journal of the Experimental Analysis of Behavior, 55, 233–244.

    Article  Google Scholar 

  33. 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(2), 253–261.

    Article  Google Scholar 

  34. Romanowich, P., & Igaki, T. (2017). Effects of reward magnitude, tobacco, and alcohol use on social discounting in Japan and United States college students. The Psychological Record, 67, 345–353.

    Article  Google Scholar 

  35. 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(1), 94–107.

    Article  Google Scholar 

  36. Wainwright, K., Green, B. E., & Romanowich, P. (2018). The relationship between delay, social discounting, and body mass index in university students. The Psychological Record, 68, 441–449.

    Article  Google Scholar 

  37. Yi, R., Carter, A. E., & Landes, R. D. (2012). Restricted psychological horizon in active methamphetamine users: Future, past, probability, and social discounting. Behavioural Pharmacology, 23, 358–366.

    Article  Google Scholar 

  38. Young, K. S. (2004). Internet addiction: A new clinical phenomenon and its consequences. American Behavioral Scientist, 48(4), 402–415.

    Article  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Paul Romanowich.

Ethics declarations

On behalf of all authors, the corresponding author states that there is no conflict of interest. Internal research funding to the corresponding author was used to conduct data collection. Institutional Review Board acceptance was obtained before data collection. All participants were informed of their rights as human participants in a psychology experiment before completing any tasks or measures.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Funding from a UTSA INTRA Grant to Paul Romanowich was used for data collection and analysis.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Romanowich, P., Igaki, T., Yamagishi, N. et al. Differential Associations between Risky Cell-Phone Behaviors and Discounting Types. Psychol Rec 71, 199–209 (2021). https://doi.org/10.1007/s40732-020-00446-7

Download citation

Keywords

  • Delay discounting
  • Impulsiveness
  • Sharing
  • Social discounting
  • Texting