Cited by 954Impact of Technology on AdolescentsCyberloafing and Workplace BehaviorChild Development and Digital Technology

How “phubbing” becomes the norm: The antecedents and consequences of snubbing via smartphone

Open access
Chotpitayasunondh, Varoth, Douglas, Karen M.·2016·Computers in Human Behavior

Abstract

Smartphones allow people to connect with others from almost anywhere at any time. However, there is growing concern that smartphones may actually sometimes detract, rather than complement, social interactions. The term “phubbing” represents the act of snubbing someone in a social setting by concentrating on one’s phone instead of talking to the person directly. The current study was designed to examine some of the psychological antecedents and consequences of phubbing behavior. We examined the contributing roles of Internet addiction, fear of missing out, self-control, and smartphone addiction, and how the frequency of phubbing behavior and of being phubbed may both lead to the perception that phubbing is normative. The results revealed that Internet addiction, fear of missing out, and self-control predicted smartphone addiction, which in turn predicted the extent to which people phub. This path also predicted the extent to which people feel that phubbing is normative, both via (a) the extent to which people are phubbed themselves, and (b) independently. Further, gender moderated the relationship between the extent to which people are phubbed and their perception that phubbing is normative. The present findings suggest that phubbing is an important factor in modern communication that warrants further investigation.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.chb.2016.05.018

Cites

17 of 17 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • The most commonly referenced cut-off point for determining when smartphone use becomes problematic was based on the work of Kwon, Lee, et al. (2013) and their Smartphone Addiction Scale.

    “The most commonly referenced cut-off point for determining when smartphone use becomes problematic was based on the work of Kwon, Lee, et al. (2013) and their Smartphone Addiction Scale (Chotpitayasunondh & Douglas, 2016;Contractor et al., 2017;Enez;Enez Darcin et al., 2016;Hartanto & Yang, 2016).”

    From Antecedents and consequences of problematic smartphone use: A systematic literature review of an emerging research area (2021) · cites this paper as Chotpitayasunondh & Douglas, 2016

    The Characteristics section reports a cut-off for smartphone addiction, and the SAS-SV section says the short scale was developed from the original Smartphone Addiction Scale. The References identify Kwon, Lee, et al. (2013) with the original scale, but the paper uses the short version and does not say its cut-off is the most commonly referenced. (LLM, conf=0.88)

    Experimental — accuracy not yet measured
  • Other studies found that PSU resulted in reduced mindfulness and phubbing, where smartphone users pay attention to their phone instead of engaging with the person in front of them.

    “Yet other studies found that PSU resulted in reduced mindfulness (Chotpitayasunondh & Douglas, 2016;Lu et al., 2011;Vaghefi et al., 2017;Volkmer & Lermer, 2019) and phubbing, where smartphone users pays attention to their phone instead of engaging with the person in front of them.”

    From Antecedents and consequences of problematic smartphone use: A systematic literature review of an emerging research area (2021) · cites this paper as Chotpitayasunondh & Douglas, 2016

    The Discussion reports that smartphone addiction predicted phubbing behavior, and the Introduction defines phubbing as focusing on a phone instead of another person. The paper does not report reduced mindfulness, although the citation appears alongside that part of the sentence (searched: mindfulness, mindful). (LLM, conf=0.86)

    Experimental — accuracy not yet measured
  • Research on gender differences suggests that males have a more instrumental view of smartphone use, whereas females use the smartphone more to facilitate social interaction.

    “Research on gender differences suggests that males have a more instrumental view of smartphone use, whereas females use the smartphone more to facilitate social interaction (Anshari, Alas, & Sulaiman, 2019;Chotpitayasunondh & Douglas, 2016;Hong et al., 2012;Lee, Lee, et al., 2014;Van Deursen et al., 2015).”
  • Gender moderates the relationship between the extent to which people are phubbed and their perception that phubbing is normative.

    “Furthermore, gender was found to moderate the relationship between the extent to which people are phubbed and their perception that phubbing is normative (Chotpitayasunondh & Douglas, 2016).”

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 62

  1. Axelrod, R. (1986). An evolutionary approach to norms. American Political Science Review, 80(04), 1095e1111. http://dx.doi.org/10.2307/1960858. · DOI
  2. Baron, N. S., & Campbell, E. M. (2012). Gender and mobile phones in cross-national context. Language Sciences, 34(1), 13e27. http://dx.doi.org/10.1016/ j.langsci.2011.06.018. · DOI
  3. Bechara, A., & Van Der Linden, M. (2005). Decision-making and impulse control after frontal lobe injuries. Current Opinion in Neurology, 18(6), 734e739. http:// dx.doi.org/10.1097/01.wco.0000194141.56429. · DOI
  4. Beranuy, M., Oberst, U., Carbonell, X., & Chamarro, A. (2009). Problematic internet and mobile phone use and clinical symptoms in college students: the role of emotional intelligence. Computers in Human Behavior, 25(5), 1182e1187. http:// dx.doi.org/10.1016/j.chb.2009.03.001. · DOI · Paper page
  5. Berg, J., Dickhaut, J., & McCabe, K. (1995). Trust, reciprocity, and social history. Games and Economic Behavior, 10(1), 122e142. http://dx.doi.org/10.1006/ game.1995.1027. · DOI · Paper page
  6. Borsari, B., & Carey, K. B. (2003). Descriptive and injunctive norms in college drinking: a meta-analytic integration. Journal of Studies on Alcohol, 64(3), 331e341. http://dx.doi.org/10.15288/jsa.2003.64.331. · DOI · Paper page
  7. Campbell, S. W., & Kwak, N. (2010). Mobile communication and civic life: linking patterns of use to civic and political engagement. Journal of Communication, 60(3), 536e555. http://dx.doi.org/10.1111/j.1460-2466.2010.01496.x. · DOI
  8. Carbonell, X., Oberst, U., & Beranuy, M. (2013). The cell phone in the twenty-first century: a risk for addiction or a necessary tool. Principles of Addiction, 1, 901e909. http://dx.doi.org/10.1016/B978-0-12-398336-7.00091-7. · DOI
  9. Cheok, A. D., Sreekumar, A., Lei, C., & Thang, L. N. (2006). Capture the flag: mixed- reality social gaming with smart phones. Pervasive Computing, IEEE, 5(2), 62e69. http://dx.doi.org/10.1109/MPRV.2006. · DOI
  10. Cummiskey, M. (2011). There's an app for that smartphone use in health and physical education. Journal of Physical Education, Recreation & Dance, 82(8), 24e30. http://dx.doi.org/10.1080/07303084.2011.10598672. · DOI
  11. Dalbudak, E., Evren, C., Aldemir, S., Coskun, K. S., Ugurlu, H., & Yildirim, F. G. (2013). Relationship of internet addiction severity with depression, anxiety, and alex- ithymia, temperament and character in university students. Cyberpsychology, Behavior and Social Networking, 16(4), 272e278. http://dx.doi.org/10.1089/ cyber.2012.0390. · DOI
  12. Davey, S., & Davey, A. (2014). Assessment of smartphone addiction in indian ado- lescents: a mixed method study by systematic-review and meta-analysis approach. International Journal of Preventive Medicine, 5(12), 1500e1511.
  13. Dittmar, H., Long, K., & Meek, R. (2004). Buying on the internet: gender differences in on-line and conventional buying motivations. Sex Roles, 50(5e6), 423e444. http://dx.doi.org/10.1023/B: SERS.0000018896.35251. · DOI
  14. Do, T. M. T., & Gatica-Perez, D. (2013). Human interaction discovery in smartphone proximity networks. Personal and Ubiquitous Computing, 17(3), 413e431. http:// dx.doi.org/10.1007/s00779-011-0489-7. · DOI
  15. Echeburua, E., & de Corral, P. (2010). Addiction to new technologies and to online social networking in young people: a new challenge. [Adiccion a las nuevas tecnologias y a las redes sociales en jovenes: un nuevo reto] Adicciones, 22(2), 91e95.
  16. Falaki, H., Mahajan, R., Kandula, S., Lymberopoulos, D., Govindan, R., & Estrin, D. (2010). Diversity in smartphone usage. In Proceedings of the 8th international conference on mobile systems, applications, and services (pp. 179e194). http:// dx.doi.org/10.1145/1814433.1814453. · DOI
  17. Falk, A., & Fischbacher, U. (2006). A theory of reciprocity. Games and Economic Behavior, 54(2), 293e315. http://dx.doi.org/10.1016/j.geb.2005.03.001. · DOI · Paper page
  18. Forgays, D. K., Hyman, I., & Schreiber, J. (2014). Texting everywhere for everything: gender and age differences in cell phone etiquette and use. Computers in Human Behavior, 31, 314e321. http://dx.doi.org/10.1016/j.chb.2013.10.053. · DOI
  19. Frangos, C. C., Frangos, C. C., & Sotiropoulos, I. (2012). A meta-analysis of the reli- ability of young's internet addiction test. In , vol. 1. Proceedings of the world congress on engineering (pp. 368e371).
  20. Gummesson, E. (2004). From one-to-one to many-to-many marketing. In Service excellence in Management: Interdisciplinary contributions, proceedings from the QUIS 9 symposium (pp. 16e25). Sweden: Karlstad University Karlstad.
  21. Habuchi, I. (2005). Accelerating reflexivity. In M. Ito, D. Okabe, & M. Matsuda (Eds.), Personal, portable, pedestrian. Mobile phones in Japanese life (pp. 165e182).
  22. Ha, J. H., Chin, B., Park, D., Ryu, S., & Yu, J. (2008). Characteristics of excessive cellular phone use in Korean adolescents. CyberPsychology & Behavior, 11(6), 783e784. http://dx.doi.org/10.1089/cpb.2008.0096. · DOI · Paper page
  23. Ha, Y., & Hwang, W. J. (2014). Gender differences in internet addiction associated with psychological health indicators among adolescents using a national web- based survey. International Journal of Mental Health and Addiction, 12(5), 660e669. http://dx.doi.org/10.1007/s11469-014-9500-7. · DOI
  24. Hakoama, M., & Hakoyama, S. (2012). Young adults' evaluations of cell phone manners. The American Association of Behavioral and Social Sciences Journal, the AABSS Journal, 16, 140e154.
  25. Hong, F., Chiu, S., & Huang, D. (2012). A model of the relationship between psy- chological characteristics, mobile phone addiction and use of mobile phones by Taiwanese university female students. Computers in Human Behavior, 28(6), 2152e2159. http://dx.doi.org/10.1016/j.chb.2012.06.020. · DOI · Paper page
  26. Huang, F., Lee, Y., & Hwang, S. (2009). E-shopping behavior and user-web interac- tion for developing a useful green website. Human-computer interaction, 446e454. http://dx.doi.org/10.1007/978-3-642-02574-7_50. · DOI
  27. Jang, M. H., & Ji, E. S. (2012). Gender differences in associations between parental problem drinking and early adolescents' internet addiction. Journal for Spe- cialists in Pediatric Nursing : JSPN, 17(4), 288e300. http://dx.doi.org/10.1111/ j.1744-6155.2012.00344.x. · DOI
  28. Jenaro, C., Flores, N., G omez-Vela, M., Gonz alez-Gil, F., & Caballo, C. (2007). Prob- lematic internet and cell-phone use: psychological, behavioral, and health correlates. Addiction Research & Theory, 15(3), 309e320. http://dx.doi.org/ 10.1080/16066350701350247. · DOI · Paper page
  29. Jones, T. (2014). Cell phone use while walking across campus: an observation and survey. Elon Journal of Undergraduate Research in Communications, 5(1). Retrieved from http://www.studentpulse.com/a?id¼975.
  30. Karada g, E., Tosuntas ¸, S ¸. B., Erzen, E., Duru, P., Bostan, N., S ¸ahin, B. M., … Babada g, B. (2015). Determinants of phubbing, which is the sum of many virtual addictions: a structural equation model. Journal of Behavioral Addictions, 1e15. http:// dx.doi.org/10.1556/2006.4.2015.005. · DOI · Paper page
  31. Karjaluoto, H., Riquelme, H. E., & Rios, R. E. (2010). The moderating effect of gender in the adoption of mobile banking. International Journal of Bank Marketing, 28(5), 328e341. http://dx.doi.org/10.1108/02652321011064872. · DOI
  32. Keysar, B., Converse, B. A., Wang, J., & Epley, N. (2008). Reciprocity is not give and take: asymmetric reciprocity to positive and negative acts. Psychological Science, 19(12), 1280e1286. http://dx.doi.org/10.1111/j.1467-9280.2008.02223.x. · DOI
  33. Khan, M. (2008). Adverse effects of excessive mobile phone use. International Journal of Occupational Medicine and Environmental Health, 21(4), 289e293. http://dx.doi.org/10.2478/v10001-008-0028-6. · DOI
  34. Kim, E. J., Namkoong, K., Ku, T., & Kim, S. J. (2008). The relationship between online game addiction and aggression, self-control and narcissistic personality traits. European Psychiatry, 23(3), 212e218. http://dx.doi.org/10.1016/ j.eurpsy.2007.10.010. · DOI
  35. Kolyesnikova, N., Dodd, T. H., & Wilcox, J. B. (2009). Gender as a moderator of reciprocal consumer behavior. Journal of Consumer Marketing, 26(3), 200e213. http://dx.doi.org/10.1108/07363760910954136. · DOI
  36. Kwon, M., Kim, D. J., Cho, H., & Yang, S. (2013a). The smartphone addiction scale: development and validation of a short version for adolescents. PloS One, 8(12), e83558. http://dx.doi.org/10.1371/journal.pone.0083558. · DOI · Paper page
  37. Lee, Y., Chang, C., Lin, Y., & Cheng, Z. (2014). The dark side of smartphone usage: psychological traits, compulsive behavior and technostress. Computers in Hu- man Behavior, 31, 373e383. http://dx.doi.org/10.1016/j.chb.2013.10.047. · DOI
  38. Lee, Y. H., Ko, C. H., & Chou, C. (2015). Re-visiting internet addiction among Taiwanese students: a cross-sectional comparison of students' expectations, online gaming, and online social interaction. Journal of Abnormal Child Psy- chology, 43(3), 589e599. http://dx.doi.org/10.1007/s10802-014-9915-4. · DOI · Paper page
  39. Lee, U., Lee, J., Ko, M., Lee, C., Kim, Y., Yang, S., … Song, J. (2014). Hooked on smartphones: an exploratory study on smartphone overuse among college students. In Proceedings of the 32nd Annual ACM conference on human factors in computing systems (pp. 2327e2336). http://dx.doi.org/10.1145/ 2556288.2557366. · DOI
  40. Lewis, M. A., & Neighbors, C. (2004). Gender-specific misperceptions of college student drinking norms. Psychology of Addictive Behaviors, 18(4), 334. http:// dx.doi.org/10.1037/0893-164X.18.4.334. · DOI
  41. Lipscomb, T. J., Totten, J. W., Cook, R. A., & Lesch, W. (2007). Cellular phone etiquette among college students. International Journal of Consumer Studies, 31(1), 46e56. http://dx.doi.org/10.1111/j.1470-6431.2005.00483.x. · DOI
  42. Malouf, E. T., Schaefer, K. E., Witt, E. A., Moore, K. E., Stuewig, J., & Tangney, J. P. (2014). The brief self-control scale predicts jail inmates' recidivism, substance dependence, and post-release adjustment. Personality & Social Psychology Bulletin, 40(3), 334e347. http://dx.doi.org/10.1177/0146167213511666. · DOI
  43. Marks, G., & Miller, N. (1987). Ten years of research on the false-consensus effect: an empirical and theoretical review. Psychological Bulletin, 102(1), 72. http:// dx.doi.org/10.1037/0033-2909.102.1.72. Mehroof, M., & Griffiths, M. D. (2010). Online gaming addiction: the role of sensa- tion seeking, self-control, neuroticism, aggression, state anxiety, and trait anxiety. Cyberpsychology, Behavior, and Social Networking, 13(3), 313e316. http://dx.doi.org/10.1089/cyber.2009.0229. · DOI
  44. Misra, S., Cheng, L., Genevie, J., & Yuan, M. (2014). The iPhone effect the quality of in-person social interactions in the presence of mobile devices. Environment and Behavior, 1e24. http://dx.doi.org/10.1177/0013916514539755. · DOI
  45. Mok, J. Y., Choi, S. W., Kim, D. J., Choi, J. S., Lee, J., Ahn, H., … Song, W. Y. (2014). Latent class analysis on internet and smartphone addiction in college students. Neuropsychiatric Disease and Treatment, 10, 817e828. http://dx.doi.org/10.2147/ NDT.S59293. · DOI
  46. Nakhaie, M. R., Silverman, R. A., & LaGrange, T. C. (2000). Self-control and social control: an examination of gender, ethnicity, class and delinquency. Canadian Journal of Sociology/Cahiers Canadiens De Sociologie, 35e59. http://dx.doi.org/ 10.2307/3341910. · DOI
  47. Oulasvirta, A., Rattenbury, T., Ma, L., & Raita, E. (2012). Habits make smartphone use more pervasive. Personal and Ubiquitous Computing, 16(1), 105e114. http:// dx.doi.org/10.1007/s00779-011-0412-2. · DOI · Paper page
  48. Park, N., Kee, K. F., & Valenzuela, S. (2009). Being immersed in social networking environment: facebook groups, uses and gratifications, and social outcomes. CyberPsychology & Behavior, 12(6), 729e733. http://dx.doi.org/10.1089/ cpb.2009.0003. · DOI
  49. Park, C., & Park, Y. R. (2014). The conceptual model on smart phone addiction among early childhood. International Journal of Social Science and Humanity, 4, 147e150. http://dx.doi.org/10.7763/IJSSH.2014.V4.336. · DOI
  50. Pelaprat, E., & Brown, B. (2012). Reciprocity: understanding online social relations. First Monday, 17(10). http://dx.doi.org/10.5210/fm.v17i10.3324. · DOI
  51. Przybylski, A. K., Murayama, K., DeHaan, C. R., & Gladwell, V. (2013). Motivational, emotional, and behavioral correlates of fear of missing out. Computers in Human Behavior, 29(4), 1841e1848. http://dx.doi.org/10.1016/j.chb.2013.02.014. · DOI · Paper page
  52. Raento, M., Oulasvirta, A., & Eagle, N. (2009). Smartphones an emerging tool for social scientists. Sociological Methods & Research, 37(3), 426e454. http:// dx.doi.org/10.1177/0049124108330005. · DOI · Paper page
  53. Roberts, J. A., Yaya, L. H., & Manolis, C. (2014). The invisible addiction: cell-phone activities and addiction among male and female college students. Journal of Behavioral Addictions, 3(4), 254e265. http://dx.doi.org/10.1556/JBA.3.2014.015. · DOI · Paper page
  54. Ross, L. (1977). The intuitive psychologist and his shortcomings: distortions in the attribution process. Advances in Experimental Social Psychology, 10, 173e220. http://dx.doi.org/10.1016/S0065-2601(08)60357-3. · DOI · Paper page
  55. Ross, L., Greene, D., & House, P. (1977). The "false consensus effect": an egocentric bias in social perception and attribution processes. Journal of Experimental Social Psychology, 13(3), 279e301. http://dx.doi.org/10.1016/0022-1031(77)90049-X. · DOI · Paper page
  56. Sunstein, C. R. (1996). Social norms and social roles. Columbia Law Review, 903e968. http://dx.doi.org/10.2307/1123430. · DOI
  57. Takao, M., Takahashi, S., & Kitamura, M. (2009). Addictive personality and prob- lematic mobile phone use. Cyberpsychology & Behavior : The Impact of the Internet, Multimedia and Virtual Reality on Behavior and Society, 12(5), 501e507. http://dx.doi.org/10.1089/cpb.2009.0022. · DOI
  58. Turner, M., Love, S., & Howell, M. (2008). Understanding emotions experienced when using a mobile phone in public: the social usability of mobile (cellular) telephones. Telematics and Informatics, 25(3), 201e215. http://dx.doi.org/ 10.1016/j.tele.2007.03.001. · DOI
  59. Walsh, S. P., White, K. M., Hyde, M. K., & Watson, B. (2008). Dialling and driving: factors influencing intentions to use a mobile phone while driving. Accident Analysis & Prevention, 40(6), 1893e1900. http://dx.doi.org/10.1016/ j.aap.2008.07.005. · DOI
  60. Walsh, S. P., White, K. M., & Young, R. M. (2008). Over-connected? A qualitative exploration of the relationship between Australian youth and their mobile phones. Journal of Adolescence, 31(1), 77e92. http://dx.doi.org/10.1016/ j.adolescence.2007.04.004. · DOI
  61. Young, K. S. (1998). Caught in the net: How to recognize the signs of internet addic- tioneand a winning strategy for recovery. New York, NY: John Wiley & Sons.
  62. Zhang, K. Z., Chen, C., & Lee, M. K. (2014). Understanding the role of motives in smartphone addiction. In Proceedings of the 18th pacific Asia conference on in- formation systems.

Cited by

Sorted by citations

Authors
Chotpitayasunondh, V., & Douglas, K. M.
Affiliations
University of Kent (GB)
Corresponding author
Chotpitayasunondh, Varoth
Venue
Computers in Human Behavior
Publisher
Elsevier BV
Published
2016-10
Volume
63
Pages
9-18
ISSN
0747-5632 (ISSN-L)
OpenAlex
W2352711343
Cited by
954
References
17
Citations by year
2026: 106 · 2025: 159 · 2024: 121 · 2023: 137 · 2022: 104 · 2021: 115 · 2020: 98 · 2019: 59 · 2018: 39 · 2017: 9 · 2016: 5
FWCI
106.75
Type
journal article
Language
en
Open access
Locations
  • Computers in Human Behavior (publishedVersion, cc-by)
  • Kent Academic Repository (University of Kent) (submittedVersion)
Topics
  • Social Sciences › Sociology and Political Science › Impact of Technology on Adolescents
  • Social Sciences › Demography › Cyberloafing and Workplace Behavior
  • Social Sciences › Education › Child Development and Digital Technology
Keywords
phubbing, smartphone addiction, internet addiction, fear of missing out, self-control, perceived social norms, gender differences, social interaction, smartphone use

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review