The purpose of the present study was to determine whether a behavioral economic framework of demand analysis can be used to characterize text-message dependency. To this end, we developed a novel hypothetical task using likelihood measures to quantify demand for social interaction through text messaging. Participants completed the hypothetical demand task in which they rated their likelihood of paying an extra charge, ranging from $0.10 to $80, to continue text messaging after reaching their monthly limit. The demand for social interaction from text messaging was more intense and less elastic for the participants with higher levels of text-message dependency compared to those with lower levels of text-message dependency. The results of this proof-of-concept study support the utility of behavioral economic demand analysis for characterizing text-message dependency. In addition, the greater intensity and lesser elasticity of the demand for social interaction shown by text-dependent participants suggests that text-message dependency can be characterized by both excessiveness and persistence of the behavior, similar to other impulsivity related problems.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
Tax calculation will be finalised during checkout.
Availability of Data and Materials
The datasets used and analyzed during the current study are available from the corresponding author on request.
Amlung, M. T., Acker, J., Stojek, M. K., Murphy, J. G., & MacKillop, J. (2012). Is talk “cheap”? An initial investigation of the equivalence of alcohol purchase task performance for hypothetical and actual rewards. Alcoholism: Clinical and Experimental Research, 36, 716–724. https://doi.org/10.1111/j.1530-0277.2011.01656.x.
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.
Bickel, W. K., Johnson, M. W., Koffarnus, M. N., MacKillop, J., & Murphy, J. G. (2014). The behavioral economics of substance use disorders: Reinforcement pathologies and their repair. Annual Review of Clinical Psychology, 10, 641–677. https://doi.org/10.1146/annurev-clinpsy-032813-153724.
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.
Billieux, J., van Rooij, A. J., Heeren, A., Schimmenti, A., Maurage, P., Edman, J., et al. (2017). Behavioural addiction open definition 2.0: Using the open science framework for collaborative and transparent theoretical development. Addiction, 112, 1723–1724. https://doi.org/10.1111/add.13938.
Blessington, G. P., & Hayashi, Y. (2020). Gender as a moderating variable between delay discounting and text dependency in college students. The Psychological Record, 70, 99–108. https://doi.org/10.1007/s40732-019-00373-2.
Broadbent, J., & Dakki, M. A. (2015). How much is too much to pay for internet access? A behavioral economic analysis of internet use. Cyberpsychology, Behavior, & Social Networking, 18, 457–461. https://doi.org/10.1089/cyber.2014.0367.
Cyders, M. A., & Coskunpinar, A. (2011). Measurement of constructs using self-report and behavioral lab tasks: Is there overlap in nomothetic span and construct representation for impulsivity? Clinical Psychology Review, 31, 965–982. https://doi.org/10.1016/j.cpr.2011.06.001.
Drouin, M., Kaiser, D. H., & Miller, D. A. (2012). Phantom vibrations among undergraduates: Prevalence and associated psychological characteristics. Computers in Human Behavior, 28, 1490–1496. https://doi.org/10.1016/j.chb.2012.03.013.
Ellis, D. A. (2019). Are smartphones really that bad? Improving the psychological measurement of technology-related behaviors. Computers in Human Behavior, 97, 60–66. https://doi.org/10.1016/j.chb.2019.03.006.
Epstein, L. H., Salvy, S. J., Carr, K. A., Dearing, K. K., & Bickel, W. K. (2010). Food reinforcement, delay discounting and obesity. Physiology & Behavior, 100, 438–445. https://doi.org/10.1016/j.physbeh.2010.04.029.
Epstein, L. H., Paluch, R. A., Carr, K. A., Temple, J. L., Bickel, W. K., & MacKillop, J. (2018). Reinforcing value and hypothetical behavioral economic demand for food and their relation to BMI. Eating Behaviors, 29, 120–127. https://doi.org/10.1016/j.eatbeh.2018.03.008.
Ferraro, F. R., Winger, A., Kreiger, M., Langseth, M., Weivoda, L., Palmiscno, J., … Wulff, T. (2012). Text-message dependence, impulsivity, and executive function. In M. A. Cyders (Ed.), Psychology of impulsivity (pp. 233–236). Nova Science.
Ferraro, F. R., Holfeld, B., Frankl, S., Frye, N., & Halvorson, N. (2015). Texting/iPod dependence, executive function and sleep quality in college students. Computers in Human Behavior, 49, 44–49. https://doi.org/10.1016/j.chb.2015.02.043.
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.
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.
Hayashi, Y., Friedel, J. E., Foreman, A. M., & Wirth, O. (2019a). A behavioral economic analysis of demand for texting while driving. The Psychological Record, 69, 225–237. https://doi.org/10.1007/s40732-019-00341-w.
Hayashi, Y., Friedel, J. E., Foreman, A. M., & Wirth, O. (2019b). 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.
Higgins, S. T., Reed, D. D., Redner, R., Skelly, J. M., Zvorsky, I. A., & Kurti, A. N. (2017). Simulating demand for cigarettes among pregnant women: A low-risk method for studying vulnerable populations. Journal of the Experimental Analysis of Behavior, 107, 176–190. https://doi.org/10.1002/jeab.232.
Hursh, S. R. (1980). Economic concepts for the analysis of behavior. Journal of the Experimental Analysis of Behavior, 34, 219–238. https://doi.org/10.1901/jeab.1980.34-219.
Hursh, S. R., & Silberberg, A. (2008). Economic demand and essential value. Psychological Review, 115, 186–198. https://doi.org/10.1037/0033-295X.115.1.186.
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.
Igarashi, T., Motoyoshi, T., Takai, J., & Yoshida, T. (2008). No mobile, no life: Self-perception and text-message dependency among Japanese high school students. Computers in Human Behavior, 24, 2311–2324. https://doi.org/10.1016/j.chb.2007.12.001.
Jacobs, E. A., & Bickel, W. K. (1999). Modeling drug consumption in the clinic using simulation procedures: Demand for heroin and cigarettes in opioid-dependent outpatients. Experimental & Clinical Psychopharmacology, 7, 412–426. https://doi.org/10.1037/1064-1222.214.171.1242.
Kaplan, B. A., & Reed, D. D. (2018). Happy hour drink specials in the Alcohol Purchase Task. Experimental & Clinical Psychopharmacology, 26, 156–167. https://doi.org/10.1037/pha0000174.
Koffarnus, M. N., & Kaplan, B. A. (2018). Clinical models of decision making in addiction. Pharmacology Biochemistry & Behavior, 164, 71–83. https://doi.org/10.1016/j.pbb.2017.08.010.
Koffarnus, M. N., Franck, C. T., Stein, J. S., & Bickel, W. K. (2015). A modified exponential behavioral economic demand model to better describe consumption data. Experimental & Clinical Psychopharmacology, 23, 504–512. https://doi.org/10.1037/pha0000045.
Kruger, D. J., & Djerf, J. M. (2017). Bad vibrations? Cell phone dependency predicts phantom communication experiences. Computers in Human Behavior, 70, 360–364. https://doi.org/10.1016/j.chb.2017.01.017.
Lepp, A., Barkley, J. E., & Karpinski, A. C. (2014). The relationship between cell phone use, academic performance, anxiety, and satisfaction with life in college students. Computers in Human Behavior, 31, 343–350. https://doi.org/10.1016/j.chb.2013.10.049.
Liese, B. S., Benau, E. M., Atchley, P., Reed, D., Becirevic, A., & Kaplan, B. (2019). The Self-perception of Text-message Dependency Scale (STDS): Psychometric update based on a United States sample. American Journal of Drug & Alcohol Abuse, 45, 42–50. https://doi.org/10.1080/00952990.2018.1465572.
Lu, X., Watanabe, J., Liu, Q., Uji, M., Shono, M., & Kitamura, T. (2011). Internet and mobile phone text-messaging dependency: Factor structure and correlation with dysphoric mood among Japanese adults. Computers in Human Behavior, 27, 1702–1709. https://doi.org/10.1016/j.chb.2011.02.009.
Lu, X., Katoh, T., Chen, Z., Nagata, T., & Kitamura, T. (2014). Text messaging: Are dependency and excessive use discretely different for Japanese university students? Psychiatry Research, 216, 255–262. https://doi.org/10.1016/j.psychres.2013.12.024.
MacKillop, J., Murphy, J. G., Ray, L. A., Eisenberg, D. T. A., Lisman, S. A., Lum, J. K., & Wilson, D. S. (2008). Further validation of a cigarette purchase task for assessing the relative reinforcing efficacy of nicotine in college smokers. Experimental & Clinical Psychopharmacology, 16, 57–65. https://doi.org/10.1037/1064-12126.96.36.199.
Morris, V., Amlung, M., Kaplan, B. A., Reed, D. D., Petker, T., & MacKillop, J. (2017). Using crowdsourcing to examine behavioral economic measures of alcohol value and proportionate alcohol reinforcement. Experimental & Clinical Psychopharmacology, 25, 314–321. https://doi.org/10.1037/pha0000130.
Murphy, J. G., & MacKillop, J. (2006). Relative reinforcing efficacy of alcohol among college student drinkers. Experimental & Clinical Psychopharmacology, 14, 219–227. https://doi.org/10.1037/1064-12188.8.131.52.
Nederhof, A. J. (1985). Methods of coping with social desirability bias: A review. European Journal of Social Psychology, 15, 263–280. https://doi.org/10.1002/ejsp.2420150303.
Pew Research Center. (2019). Mobile fact sheet. Retrieved from http://www.pewinternet.org/fact-sheet/mobile/.
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.
Roberts, J. A., Yaya, L. H. P., & Manolis, C. (2014). The invisible addiction: Cell-phone activities and addiction among male and female college students. Journal of Behavioral Addictions, 3, 254–265. https://doi.org/10.1556/JBA.3.2014.015.
Sánchez-Martínez, M., & Otero, A. (2008). Factors associated with cell phone use in adolescents in the community of Madrid (Spain). CyberPsychology & Behavior, 12, 131–137. https://doi.org/10.1089/cpb.2008.0164.
Snider, S. E., LaConte, S. M., & Bickel, W. K. (2016). Episodic future thinking: Expansion of the temporal window in individuals with alcohol dependence. Alcoholism: Clinical & Experimental Research, 40, 1558–1566. https://doi.org/10.1111/acer.13112.
Stein, J. S., Koffarnus, M. N., Snider, S. E., Quisenberry, A. J., & Bickel, W. K. (2015). Identification and management of nonsystematic purchase-task data: Towards best practice. Experimental & Clinical Psychopharmacology, 23, 377–386. https://doi.org/10.1037/pha0000020.
Stein, J. S., Wilson, A. G., Koffarnus, M. N., Daniel, T. O., Epstein, L. H., & Bickel, W. K. (2016). Unstuck in time: Episodic future thinking reduces delay discounting and cigarette smoking. Psychopharmacology, 233, 3771–3778. https://doi.org/10.1007/s00213-016-4410-y.
Stein, J. S., Tegge, A. N., Turner, J. K., & Bickel, W. K. (2018). Episodic future thinking reduces delay discounting and cigarette demand: An investigation of the good-subject effect. Journal of Behavioral Medicine, 41, 269–276. https://doi.org/10.1007/s10865-017-9908-1.
Sze, Y. Y., Stein, J. S., Bickel, W. K., Paluch, R. A., & Epstein, L. H. (2017). Bleak present, bright future: Online episodic future thinking, scarcity, delay discounting, and food demand. Clinical Psychological Science, 5, 683–697. https://doi.org/10.1177/2167702617696511.
Teeters, J. B., Meshesha, L. Z., Dennhardt, A. A., & Murphy, J. G. (2019). Elevated demand and proportionate substance-related reinforcement are associated with driving after cannabis use. Canadian Journal of Addiction, 10, 42. https://doi.org/10.1097/CXA.0000000000000062.
Weinstock, J., Mulhauser, K., Oremus, E. G., & D’Agostino, A. R. (2016). Demand for gambling: Development and assessment of a gambling purchase task. International Gambling Studies, 16, 316–327. https://doi.org/10.1080/14459795.2016.1182570.
Zvorsky, I., Nighbor, T. D., Kurti, A. N., DeSarno, M., Naudé, G., Reed, D. D., & Higgins, S. T. (2019). Sensitivity of hypothetical purchase task indices when studying substance use: A systematic literature review. Preventive Medicine, 128, 105789. https://doi.org/10.1016/j.ypmed.2019.105789.
Conflict of Interest
The authors declare that they have no conflict of interest.
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
Informed consent was obtained from all individual participants included in the study.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Hayashi, Y., Blessington, G.P. Excessive Valuation of Social Interaction in Text-Message Dependency: A Behavioral Economic Demand Analysis. Psychol Rec 71, 237–245 (2021). https://doi.org/10.1007/s40732-020-00418-x
- Text-message dependency
- Text messaging
- Demand analysis
- Behavioral economics
- College students