Pattern of smartphone internet use among international students in a university in Bangkok, Thailand
Keywords:Internet use, Smartphones, International students
Purpose - Forty-five percent of the world population used the Internet in 2016. Sixty percent of the Thai population uses the Internet. University students predominately contribute to the high usage trend. The study aimed to determine the pattern of smartphone Internet use among international students in a university in Bangkok, Thailand.
Design/methodology/approach - A cross-sectional study was carried among 351 international students aged 18 to 54 through a self-administered online questionnaire developed by the researcher. Validity and reliability of questionnaire were checked and deemed acceptable for use (Cronbach’s alpha = 0.89; average IOC = 0.96). Regarding Internet usage pattern, this study examined the following: Internet-related activities, the situation of use, place of most access, frequency, and amount of time spent. Purposive sampling was employed. Descriptive statistics and a chi-square test were used in data assessment.
Findings - More than 90% of males reported engaging in social networking and information seeking. More than 90% of females reported to engage in social networking, information seeking, and text messaging. Online shopping was found to be significantly associated with gender (p = .001). Roughly 70% of males used their smartphones when doing homework; more than 50% of females used their smartphones while eating. More than 70% of males and females said to mostly accessed the Internet through their smartphones at home. Over 70% of males and females go on the Internet more than five times per day. Nearly 30% of males spent three to four hours on the Internet during the weekends; while one-third of females spent seven hours or more during the weekends. More than one-third of males and females spent three to four hours during the weekdays.
Originality/value - Results revealed subtle differences between male and female Internet usage pattern. Females spent more time on the Internet through the smartphone than males. However, there were no significant differences in Internet usage pattern between males and females except online shopping.
2. Roberts JA, Yaya LH, Manolis C. The invisible addiction: cell-phone activities and addiction among male and female college students. J Behav Addict. 2014 Dec; 3(4): 254-65. doi: 10.1556/JBA.3.2014.015
3. Electronic Transactions Development Agency [ETDA]. Thailand internet user profile 2016. ETDA; 2016.
4. van Deursen AJAM, Bolle CL, Hegner SM, Kommers PAM. Modeling habitual and addictive smartphone behavior: The role of smartphone usage types, emotional intelligence, social stress, self-regulation, age, and gender. Comput Human Behav. 2015; 45: 411-20. doi: 10.1016/j.chb.2014.12.039
5. Lepp A, Barkley JE, Sanders GJ, Rebold M, Gates P. The relationship between cell phone use, physical and sedentary activity, and cardiorespiratory fitness in a sample of U.S. college students. Int J Behav Nutr Phys Act. 2013 Jun 21; 10(1): 79. doi: 10.1186/1479-5868-10-79
6. Shan Z, Deng G, Li J, Li Y, Zhang Y, Zhao Q. Correlational analysis of neck/shoulder pain and low back pain with the use of digital products, physical activity and psychological status among adolescents in Shanghai. PLoS One. 2013; 8(10): e78109. doi: 10.1371/journal.pone.0078109
7. Kim J, Hwang Y, Kang S, Kim M, Kim TS, Kim J, et al. Association between exposure to smartphones and ocular health in adolescents. Ophthalmic Epidemiol. 2016 Aug; 23(4): 269-76. doi: 10.3109/09286586.2015.1136652
8. Kim SE, Kim JW, Jee YS. Relationship between smartphone addiction and physical activity in Chinese international students in Korea. J Behav Addict. 2015 Sep; 4(3): 200-5. doi: 10.1556/2006.4.2015.028
9. Kim HJ, Min JY, Kim HJ, Min KB. Accident risk associated with smartphone addiction: a study on university students in Korea. J Behav Addict. 2017 Dec; 6(4): 699-707. doi: 10.1556/2006.6.2017.070
10. Kubey RW, Lavin MJ, Barrows JR. Internet use and collegiate academic performance decrements: Early findings. J Commun. 2001 Jun; 51(2): 366-82. doi: DOI 10.1111/j.1460-2466.2001.tb02885.x
11. Naz A, Khan W, Daraz U, Hussain M. The malevolence of technology: an investigation into the various socio-economic impacts of excessive cell phone use among university students (A case study of University of Malakand, KPK Pakistan). International Journal of Academic Research in Business and Social Sciences. 2011 Oct; 1(3): 321-6.
12. Krejcie RV, Morgan DW. Determining sample size for research activities. Educ Psychol Meas. 1970; 30(3): 607-10.
13. Cheon J, Lee S, Crooks SM, Song J. An investigation of mobile learning readiness in higher education based on the theory of planned behavior. Comput Educ. 2012 Nov; 59(3): 1054-64. doi: 10.1016/j.compedu.2012.04.015
14. Oulasvirta A, Rattenbury T, Ma LY, Raita E. Habits make smartphone use more pervasive. Pers Ubiquitous Comput. 2012 Jan; 16(1): 105-14. doi: 10.1007/s00779-011-0412-2
15. Hakoama M, Hakoyama S. The impact of cell phone use on social networking and development among college students. The American Association of Behavioral and Social Sciences Journal. 2011; 15: 1-20.
16. Geser H. Are girls (even) more addicted? Some gender patterns of cell phone usage. Zurich: Institute of Sociology, University of Zurich; 2006.
17. Junco R, Merson D, Salter DW. The effect of gender, ethnicity, and income on college students' use of communication technologies. Cyberpsychol Behav Soc Netw. 2010 Dec; 13(6): 619-27. doi: 10.1089/cyber.2009.0357