Contents |
Authors:
Maria Olearova, ORCID: https://orcid.org/0000-0001-9086-7975 Ph.D., University of Presov in Presov, Slovakia Radovan Bacik, ORCID: https://orcid.org/0000-0002-5780-3838 Ph.D., Professor, University of Presov in Presov, Slovakia Beata Gavurova, ORCID: https://orcid.org/0000-0002-0606-879X Ph.D., Professor, Technical University of Kosice, Slovakia Martin Rigelsky, ORCID: https://orcid.org/0000-0003-1427-4689 Ph.D., University of Presov in Presov, Slovakia
Pages: 99-110
Language: English
DOI: https://doi.org/10.21272/mmi.2023.1-09
Received: 21.05.2022
Accepted: 01.03.2023
Published: 31.03.2023
Download: |
Views: |
Downloads: |
|
|
|
Abstract
There is no consensus in the academic community on whether modern technology positively impacts people’s lives or, on the contrary, whether its use has more negative consequences. Given the universal nature of cell phones, the limitless possibilities of use, and their wide-ranging functionalities, it is reasonable to believe that these devices have been responsible for changing people’s time management. However, different research approaches make it very difficult to confirm or reject hypotheses that consider associations between cell phone use and time use regarding the different activities in a unified way. This fact suggests that there is still a vast scope in research for further exploring and pursuing how technologies, their development, and their uses are able to permeate the everyday working and social life of the population. Based on this, the present paper aims to assess the relationship between the mobile communication device use and time-use change in a sample of Organisation for Economic Co-operation and Development (OECD) countries. This analysis used 3 cell phone use indicators and 12 time-bound indicators by using data for the year 2020. The most significant finding was the confirmation of the assumption arising from the application of regression analysis that the frequency of use of cell phones is not significantly related to the changes in the time structure. However, some significant relationships emerged in the models specified for women. This study also discovered that the most apparent difference was observed in unpaid and paid time throughout the day. The paper provides relevant findings which can be beneficial in many aspects. For example, in the business world, they can help manage business activities, improve performance measurement, or improve managerial decisions related to workflow optimization. The findings provide an understanding not only of the population’s well-being but also of the ICT sector state and, ultimately, of all the characteristics of the sustainable development of the countries. In addition, the contribution of this study is also possible in designing more effective decisions by policymakers. In the article, we discuss the study’s results, outline some practical implications, and suggest potential avenues for further research on this issue.
Keywords: IC technologies, mobile broadband, OECD countries, sustainability, time use.
JEL Classification: M10, M20, M30.
Cite as: Olearova, M., Bacik, R., Gavurova, B., & Rigelsky, M (2023). Identifying the Relationship Between the Use of Mobile Technologies and Time: A Study Based on a Sample of OECD Member Countries Marketing and Management of Innovations, 1, 99-110. https://doi.org/10.21272/mmi.2023.1-09
This work is licensed under a Creative Commons Attribution 4.0 International License
References
- Adam, B. (1995). Timewatch: The Social Analysis of Time. Cambridge: Polity Press. [Google Scholar]
- Andone, I., Blaszkiewicz, K., Eibes, M., Trendafilov, B., Montag, C., & Markowetz, A. (2016). How Age and Gender Affect Smartphone Usage. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing Adjunct (pp. 9-12). Universität Ulm. [Google Scholar] [CrossRef]
- Anshari, M., Alas, Y., Hardaker, G., Jaidin, J. H., Smith, M., & Ahad, A. D. (2016). Smartphone habit and behavior in Brunei: Personalization, gender, and generation gap. Computers in human behavior, 64, 719-727. [Google Scholar] [CrossRef]
- Bacik, R., Fedorko, R., Nastisin, L., & Gavurova, B. (2018). Factors of communication mix on social media and their role in forming customer experience and brand image. Management & Marketing. Challenges for the Knowledge Society, 13(3), 1108-1118. [Google Scholar] [CrossRef]
- Bae, S.M. (2017). The relationship between the type of smartphone use and smartphone dependence of Korean adolescents: National survey study. Children and Youth Services Review, 81, 207–211. [Google Scholar] [CrossRef]
- Bianchi, A., & Phillips, J. G. (2005). Psychological predictors of problem mobile phone use. Cyberpsychology and Behavior, 8(1), 39-51. [Google Scholar] [CrossRef]
- Boumosleh, J., & Jaalouk, D. (2017). Depression, anxiety, and smartphone addiction in university students-A cross sectional study. PLoS ONE, 12(8). [Google Scholar] [CrossRef]
- Castells, M. (2010). The Information Age: Economy, Society and Culture. Volume I: The Rise of the Network Society. 2-nd edition. [Google Scholar]
- Chen, B., Liu, F., Ding, S., Ying, X., Wang, L., & Wen, Y. (2017). Gender differences in factors associated with smartphone addiction: a cross-sectional study among medical college students. BMC psychiatry, 17(1), 1-9. [Google Scholar] [CrossRef]
- Choudhary, P., & Velaga, N. R. (2019). Effects of phone use on driving performance: A comparative analysis of young and professional drivers. Safety science, 111, 179-187. [Google Scholar] [CrossRef]
- Cisco (2020). Cisco Annual Internet Report (2018–2023) White Paper. Retrieved from [Link]
- Demirci, K., Akgönül, M., & Akpinar, A. (2015). Relationship of smartphone use severity with sleep quality, depression, and anxiety in university students. Journal of behavioral addictions, 4(2), 85-92. [Google Scholar] [CrossRef]
- Deng, T., Kanthawala, S., Meng, J., Peng, W., Kononova, A., Hao, Q., … & David, P. (2019). Measuring smartphone usage and task switching with log tracking and self-reports. Mobile Media & Communication, 7(1), 3-23. [Google Scholar] [CrossRef]
- Derks, D., & Bakker, A. B. (2014). Smartphone use, work–home interference, and burnout: A diary study on the role of recovery. Applied Psychology, 63(3), 411-440. [Google Scholar] [CrossRef]
- Derks, D., & Bakker, A. B. (2014). Smartphone use, work–home interference, and burnout: A diary study on the role of recovery. Applied Psychology, 63(3), 411-440. [Google Scholar] [CrossRef]
- Donker, T., Petrie, K., Proudfoot, J., Clarke, J., Birch, M. R., & Christensen, H. (2013). Smartphones for smarter delivery of mental health programs: a systematic review. Journal of medical Internet research, 15(11), e247. [Google Scholar] [CrossRef]
- Ehrlen, V. (2021). Tracking oneself for others: Communal and self-motivational value of sharing exercise data online. Leisure Studies, 40(4), 545-560. [Google Scholar] [CrossRef]
- Eriksson, N., Rosenbröijer, C. J., & Fagerstrøm, A. (2018). Smartphones as decision support in retail stores–The role of product category and gender. Procedia Computer Science, 138, 508-515. [Google Scholar] [CrossRef]
- Farman, J. (2020). Mobile interface theory: Embodied space and locative media. Routledge. [Google Scholar]
- Floros, C., Cai, W., McKenna, B., & Ajeeb, D. (2021). Imagine being off-the-grid: Millennials’ perceptions of digital-free travel. Journal of Sustainable Tourism, 29(5), 751-766. [Google Scholar] [CrossRef]
- Furst, R. T., Evans, D. N., & Roderick, N. M. (2018). Frequency of college student smartphone use: impact on classroom homework assignments. Journal of Technology in Behavioral Science, 3, 49-57. [Google Scholar] [CrossRef]
- Goggin, G. (2011). Ubiquitous apps: Politics of openness in global mobile cultures. Digital creativity, 22(3), 148-159. [Google Scholar] [CrossRef]
- Hahn, K. H., & Kim, J. (2013). Salient antecedents of mobile shopping intentions: Media dependency, fashion/brand interest and peer influence. Journal of Global Fashion Marketing, 4(4), 225-246. [Google Scholar] [CrossRef]
- Han, J. S., & Patterson, I. (2007). An analysis of the influence that leisure experiences have on a person’s mood state, health and wellbeing. Annals of Leisure Research, 10(3-4), 328-351. [Google Scholar] [CrossRef]
- Hassan, R. (2009). Empires of Speed: Time and the Acceleration of Politics and Society. Leiden: Brill Academic Publishers. [Google Scholar]
- Haug, S., Castro, R. P., Kwon, M., Filler, A., Kowatsch, T., & Schaub, M. P. (2015). Smartphone use and smartphone addiction among young people in Switzerland. Journal of behavioral addictions, 4(4), 299-307. [Google Scholar] [CrossRef]
- Herrschel, T., & Dierwechter, Y. (2018). Smart transitions in city regionalism: territory, politics and the quest for competitiveness and sustainability. Routledge. [Google Scholar]
- Ho, R. C., Zhang, M. W., Tsang, T. Y., Toh, A. H., Pan, F., Lu, Y., … & Mak, K. K. (2014). The association between internet addiction and psychiatric co-morbidity: a meta-analysis. BMC psychiatry, 14(1), 1-10. [Google Scholar] [CrossRef]
- Hong, F. Y., Chiu, S. I., & Huang, D. H. (2012). A model of the relationship between psychological characteristics, mobile phone addiction and use of mobile phones by Taiwanese university female students. Computers in human behavior, 28(6), 2152-2159. [Google Scholar] [CrossRef]
- Irimiás, A., Csordás, T., Kiss, K., & Michalkó, G. (2021). Aggregated roles of smartphones in young adults’ leisure and well-being: a diary study. Sustainability, 13(8), 4133. [Google Scholar] [CrossRef]
- Jeong, S. H., Kim, H., Yum, J. Y., & Hwang, Y. (2016). What type of content are smartphone users addicted to?: SNS vs. games. Computers in human behavior, 54, 10-17. [Google Scholar] [CrossRef]
- Klimova, B., & Valis, M. (2018). Smartphone applications can serve as effective cognitive training tools in healthy aging. Frontiers in aging neuroscience, 9, 436. [Google Scholar] [CrossRef]
- Klopfer, E., & Squire, K. (2008). Environmental Detectives—the development of an augmented reality platform for environmental simulations. Educational technology research and development, 56, 203-228. [Google Scholar] [CrossRef]
- Kossek, E. E., & Lautsch, B. A. (2012). Work–family boundary management styles in organizations: A cross-level model. Organizational Psychology Review, 2(2), 152-171. [Google Scholar] [CrossRef]
- Krithika, M., & Vasantha, S. (2013). The mobile phone usage among teens and young adults impact of invading technology. International Journal of Innovative Research in Science, Engineering and Technology, 2(12), 7259-7265. [Google Scholar]
- Kumar, J. D., & Arulchelvan, S. (2018). The attitude towards smartphones and its influence on process, social and compulsive usage. Athens Journal of Mass Media and Communications, 4(4), 301-318. [Google Scholar] [CrossRef]
- Kuss, D. J., & Griffiths, M. D. (2017). Social networking sites and addiction: Ten lessons learned. International journal of environmental research and public health, 14(3), 311. [Google Scholar] [CrossRef]
- Kuss, D. J. (2013). Internet gaming addiction: current perspectives. Psychology research and behavior management, 125-137. [Google Scholar]
- Kuss, D.J., Griffiths, M.D. & Binder, J.F. (2013a). Internet addiction in students: prevalence and risk factors. Computers in Human Behavior, 29(3), 959–966. [Google Scholar] [CrossRef]
- Kuss, D.J., van Rooij, A., Shorter, G.W., Griffiths, M.D. & van de Mheen, D. (2013b). Internet addiction in adolescents: prevalence and risk factors. Computers in Human Behavior, 29(5), 1987–1996. [Google Scholar] [CrossRef]
- Kwon, M., Kim, D.J., Cho, H., & Yang, S. (2013). The smartphone addiction scale: Development and validation of a short version for adolescents. PLoS One, 8(12), e83558. [Google Scholar] [CrossRef]
- Lanaj, K., Johnson, R.E. & Barnes, C.M. (2014). Beginning the workday yet already depleted? Consequences of late-night smartphone use and sleep. Organizational Behavior and Human Decision Processes, 124(1), 11–23. [Google Scholar] [CrossRef]
- Lee, E.J. & Kim, H.S. (2018). Gender Differences in Smartphone Addiction Behaviors Associated With Parent–Child Bonding, Parent–Child Communication, and Parental Mediation Among Korean Elementary School Students. Journal of Addictions Nursing, 29(4), 244. [Google Scholar] [CrossRef]
- 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. Proceedings 32nd Annual ACM Conference on Human Factors in Computing Systems. Canada: Toronto. Pp. 2327-2336.[Google Scholar]
- Li, L. & Lin, T.T.C. (2017). Examining how dependence on smartphones at work relates to Chinese employees’ workplace social capital, job performance, and smartphone addiction. Information Development, 34 (5), 489-503. [Google Scholar] [CrossRef]
- Liebherrad, M., Schubertb, P., Antonsad, S., Montagc, C. & Brandad, M. (2020). Smartphones and attention, curse or blessing? – A review on the effects of smartphone usage on attention, inhibition, and working memory. Computers in Human Behavior Reports, 1, 100005. [Google Scholar] [CrossRef]
- Lin, Y.H., Lin, Y.C., Lee, Y.H., Lin, P.H.., Lin, S.H., Chang, L.R. & Kuo, T.B.J. (2015). Time distortion associated with smartphone addiction: identifying smartphone addiction via a mobile application (App). Journal of Psychiatric Research, 65,139-145. [Google Scholar] [CrossRef]
- Lopez-Fernandez, O., et al., 2017. Use and misuse of mobile technologies in young adulthood: a European cross-cultural empirical study. Journal of Behavioral Addictions, 6(2), 168–177. [Google Scholar] [CrossRef]
- Martínez-Sánchez, I., Goig-Martínez, R.M., Álvarez-Rodríguez, J. & Fernández-Cruz, M. (2020). Factors Contributing to Mobile Phone Dependence Amongst Young People—Educational Implications. Sustainability, 12(6), 2554. [Google Scholar] [CrossRef]
- McDaniel, B.T. & Coyne, S.M. (2016). Technoference”: The interference of technology in couple relationships and implications for women’s personal and relational well-being. Psychology of Popular Media, 5(1), 85–98. [Google Scholar] [CrossRef]
- Michelson, W. (2005). Time use: Expanding the explanatory power of the social sci-ences. London: Paradigm.
- Montag, C., Błaszkiewicz, K., Lachmann, B., Sariyska, R., Andone, I., Trendafilov, B. & Markowetz, A., (2015b). Recorded behavior as a valuable resource for diagnostics inmobile phone addiction: Evidence from Psychoinformatics. Behavioral Sciences, 5(4), 434–442. [Google Scholar] [CrossRef]
- Morahan-Martin, J. (2004). Women and the Internet: Promise and Perils. CyberPsychology & Behavior, 3(5), 683-691. [Google Scholar] [CrossRef]
- Mullan, K. & Wajcman, J. (2019). Have Mobile Devices Changed Working Patterns in the 21st Century? A Time-diary Analysis of Work Extension in the UK. Work, Employment and Society, 33(1) 3–20. [Google Scholar] [CrossRef]
- Nadolu, B. & Nadolu, D. (2020). Homo Interneticus—The Sociological Reality of Mobile Online Being. Sustainability, 12(5), 1800. [Google Scholar] [CrossRef]
- OECD (2021a). Information and Communication Technology – Broadband database. Retrieved from [Link]
- OECD (2021b). Time Use – Time Use. Retrieved from [Link]
- Oulasvirta, A., Rattenbury, T., Ma, L. & Raita, E. (2012). Habits make smartphone use more pervasive. Personal and Ubiquitous Computing, 16(1), 105–114. [Google Scholar] [CrossRef]
- Ozkaya, H., Serdar, M., Acar, H., Pekgor, S. & Arica, G.S. (2020). E valuation of the frequency/addiction of smartphone use and its effect on sleep quality in university students. Annals of Medical Research, 27(2), 657-63. [Google Scholar] [CrossRef]
- Panova, T. & Lleras, A. (2016). Avoidance or boredom: negative mental health outcomes associated with use of information and communication technologies depend on users’ motivations. Computers in Human Behavior, 58, 249–258. [Google Scholar] [CrossRef]
- Pantzar, M. & Shove, E. (2010). Temporal rhythms as outcomes of social practices:A speculative discussion. Ethnologia Europaea, 40(1), 19-29. [Google Scholar] [CrossRef]
- Pantzar, M. (2010). Future shock: Discussing the changing temporal architecture of daily life. Journal of Futures Studies, 14(4), 1–22. [Google Scholar]
- Park, S., Yoon, H., Koo, C. & Lee, W.S. (2021). Role of the Leisure Attributes of Shared Bicycles in Promoting Leisure Benefits and Quality of Life. Sustainability, 13(2), 739. [Google Scholar] [CrossRef]
- Perlow, L. (2012). Sleeping with Your Smartphone. Boston, MA: Harvard Business Review Press. [Google Scholar]
- Rosa, H. (2013). Social Acceleration: A New Theory of Modernity. New York: Columbia University Press.
- Saidon, J., Musa, R., Harun, M.H.M. & Adam, A.A. (2016). The conceptual framework of pathological smartphone Use (PSU). Procedia Economics and Finance, 37, 426–431. [Google Scholar] [CrossRef]
- Samaha, M. & Hawi, N.S. (2016). Relationships among smartphone addiction, stress, academic performance, and satisfaction with life. Computers in Human Behavior, 57, 321-325. [Google Scholar] [CrossRef]
- Schatzki, T. (2009). Timespace and the organization of social life. In: Shove, E., Trentman, F., Wilks, R., (Eds.). Time, consumption and everyday life: Practice, materiality and culture (35-48). Oxford: Berg. [Google Scholar] [CrossRef]
- Schrock, A. R. (2015). Communicative affordances of mobile media: Portability, availability, locatability, and multimediality. International Journal of Communication, 9(1), 1229–1246. [Google Scholar]
- Silk, M., Millington, B., Rich, E. & Bush, A. (2016). (Re-)thinking digital leisure. Leisure Studies, 35(6), 712–723. [Google Scholar] [CrossRef]
- Southerton, D. (2006). Analyzing the temporal organization of daily life: Social constraints, practices and their allocation. Sociology, 40(3), 435-454. [Google Scholar] [CrossRef]
- Stefko, R., Bacík, R., Fedorko, R., Horváth, J., Propper, M. & Gavurová, B. (2017). Gender differences in the case of work satisfaction and motivation. Polish Journal of Management Studies, 16(1), 215-225. [Google Scholar] [CrossRef]
- Stefko, R., Dorcák, P. & Pollák, F. (2011). Shopping on the internet from the point of view of customers. Polish Journal of Management Studies, 4(2), 214-222. [Google Scholar]
- Taywade, A. & Khubalkar, R. (2019). Gender differences in smartphone usage patterns of adolescents. International Journal of Indian Psychology, 7(4), 516-523. [Google Scholar] [CrossRef]
- Van Deursen, A.J.A.M., Bolle, C.L., Hegner, S.M. & Kommers, P.A.M. (2015). Modeling habitual and addictive smartphone behaviour. The role of smartphone usage types, emotional intelligence, social stress, self-regulation, age and gender. Computers in Human Behavior, 45, 411-420. [Google Scholar] [CrossRef]
- Venkatesh, V. & Morris, M.G. (2000). Why Don’t Men Ever Stop to Ask for Directions? Gender, Social Influence, and Their Role in Technology Acceptance and Usage Behavior. MIS Quarterly, 24(1), 115-139. [Google Scholar] [CrossRef]
- Wajcman, J. (2015). Pressed for Time: The Acceleration of Life in Digital Capitalism. Chicago, IL: Chicago University Press. [Google Scholar]
- Wang, L. & Lee, J.H. (2021). The impact of K-beauty social media influencers, sponsorship, and product exposure on consumer acceptance of new products. Fashion and Textiles, 8(15). [Google Scholar] [CrossRef]
- Wang, P., Chiu, D., Ho, K. & Lo, P. (2016). Why read it on your mobile device? Change in reading habit of electronic magazines for university students. The Journal of Academic Librarianship, 42(6), 664–669. [Google Scholar] [CrossRef]
|