Skip to main content Skip to main navigation menu Skip to site footer
Articles
DOI: 10.14254/1795-6889.2025.21-3.11
Submitted: 2025-03-21
Published: 2025-12-30

Social influence as a determinant of mobile application acceptance across generations: A Polish perspective

University of Szczecin
Hue University
social influence mobile application acceptance of technology behavioural intention generations human–technology interaction

Abstract

The widespread diffusion of mobile devices has made mobile applications an integral part of everyday life across age groups. Although prior research has consistently identified social influence (SI) as an important determinant of technology acceptance, its role across different generations remains insufficiently examined in the context of mobile applications. This study investigates the impact of social influence on behavioural intention to use mobile applications among four generational cohorts: Baby Boomers, Generation X, Generation Y, and Generation Z. The analysis is based on data from a nationwide CAWI survey conducted in Poland (N = 2,400; 600 respondents per generation). Reliability analysis, exploratory factor analysis, linear regression, and comparative statistical tests were applied. The results show that social influence is a significant predictor of behavioural intention in all generational groups. However, no statistically significant differences are observed in the strength of the SI–BI relationship across generations. At the same time, significant differences emerge in perceived levels of social influence, with Baby Boomers reporting higher mean values than younger cohorts. The findings highlight generational differences in the perceived relevance of social influence rather than in its behavioural impact, contributing to research on human–technology interaction.

Metrics

Metrics Loading ...

References

  1. Ajzen, I., & Fishbein, M. (2002). Understanding attitudes and predicting social behavior (Transferred to digital print on demand). Prentice-Hall.
  2. Alam, M. Z., Hu, W., & Barua, Z. (2018). Using the UTAUT model to determine factors affecting acceptance and use of mobile health (mHealth) services in Bangladesh. Journal of Studies in Social Sciences, 17(2), 137–172.
  3. Alkailani, M., & Nusairat, N. (2022). What motivates Jordanians to adopt mobile commerce? An empirical study of the most relevant factors. International Journal of Data and Network Science, 6(2), 487–496. https://doi.org/10.5267/j.ijdns.2021.12.005 DOI: https://doi.org/10.5267/j.ijdns.2021.12.005
  4. Blok, M., Van Ingen, E., De Boer, A. H., & Slootman, M. (2020). The use of information and communication technologies by older people with cognitive impairments: From barriers to benefits. Computers in Human Behavior, 104, 106173. https://doi.org/10.1016/j.chb.2019.106173 DOI: https://doi.org/10.1016/j.chb.2019.106173
  5. Boontarig, W., Chutimaskul, W., Chongsuphajaisiddhi, V., & Papasratorn, B. (2012). Factors influencing the Thai elderly intention to use smartphone for e-Health services. 2012 IEEE Symposium on Humanities, Science and Engineering Research, 479–483. https://doi.org/10.1109/SHUSER.2012.6268881 DOI: https://doi.org/10.1109/SHUSER.2012.6268881
  6. Boudreaux, E. D., Waring, M. E., Hayes, R. B., Sadasivam, R. S., Mullen, S., & Pagoto, S. (2014). Evaluating and selecting mobile health apps: Strategies for healthcare providers and healthcare organizations. Translational Behavioral Medicine, 4(4), 363–371. https://doi.org/10.1007/s13142-014-0293-9 DOI: https://doi.org/10.1007/s13142-014-0293-9
  7. Boyd, D. M., & Ellison, N. B. (2007). Social Network Sites: Definition, History, and Scholarship. Journal of Computer-Mediated Communication, 13(1), 210–230. https://doi.org/10.1111/j.1083-6101.2007.00393.x DOI: https://doi.org/10.1111/j.1083-6101.2007.00393.x
  8. Carter, S., & Yeo, A. C.-M. (2016). Mobile apps usage by Malaysian business undergraduates and postgraduates: Implications for consumer behaviour theory and marketing practice. Internet Research, 26(3), 733–757. https://doi.org/10.1108/IntR-10-2014-0273 DOI: https://doi.org/10.1108/IntR-10-2014-0273
  9. Chaouali, W., Ben Yahia, I., & Souiden, N. (2016). The interplay of counter-conformity motivation, social influence, and trust in customers’ intention to adopt Internet banking services: The case of an emerging country. Journal of Retailing and Consumer Services, 28, 209–218. https://doi.org/10.1016/j.jretconser.2015.10.007 DOI: https://doi.org/10.1016/j.jretconser.2015.10.007
  10. Cimperman, M., Makovec Brenčič, M., & Trkman, P. (2016). Analyzing older users’ home telehealth services acceptance behavior—Applying an Extended UTAUT model. International Journal of Medical Informatics, 90, 22–31. https://doi.org/10.1016/j.ijmedinf.2016.03.002 DOI: https://doi.org/10.1016/j.ijmedinf.2016.03.002
  11. DataReportal – Digital 2025: Poland. (2025). https://datareportal.com/reports/digital-2025-poland?rq=digital%20report%202025%20poland
  12. DataReportal – Global overview. (2025). https://datareportal.com/reports/digital-2025-global-overview-report?utm_source=Global_Digital_Reports&utm_medium=Report&utm_campaign=Digital_2025&utm_content=Report_Promo
  13. Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319. https://doi.org/10.2307/249008 DOI: https://doi.org/10.2307/249008
  14. Faraj, S., Pachidi, S., & Sayegh, K. (2018). Working and organizing in the age of the learning algorithm. Information and Organization, 28(1), 62–70. https://doi.org/10.1016/j.infoandorg.2018.02.005 DOI: https://doi.org/10.1016/j.infoandorg.2018.02.005
  15. Garavand, A., Samadbeik, M., Nadri, H., Rahimi, B., & Asadi, H. (2019). Effective Factors in Adoption of Mobile Health Applications between Medical Sciences Students Using the UTAUT Model. Methods of Information in Medicine, 58(04/05), 131–139. https://doi.org/10.1055/s-0040-1701607 DOI: https://doi.org/10.1055/s-0040-1701607
  16. Hair, J. F. (Ed.). (2010). Multivariate data analysis: A global perspective (7. ed., global ed). Pearson.
  17. Hoque, R., & Sorwar, G. (2017). Understanding factors influencing the adoption of mHealth by the elderly: An extension of the UTAUT model. International Journal of Medical Informatics, 101, 75–84. https://doi.org/10.1016/j.ijmedinf.2017.02.002 DOI: https://doi.org/10.1016/j.ijmedinf.2017.02.002
  18. Huang, C.-Y., & Kao, Y.-S. (2015). UTAUT2 based predictions of factors influencing the technology acceptance of phablets by DNP. Mathematical Problems in Engineering, 2015, 1–23. https://doi.org/10.1155/2015/603747 DOI: https://doi.org/10.1155/2015/603747
  19. Juaneda-Ayensa, E., Mosquera, A., & Sierra Murillo, Y. (2016). Omnichannel customer behavior: Key drivers of technology acceptance and use and their effects on purchase intention. Frontiers in Psychology, 7. https://doi.org/10.3389/fpsyg.2016.01117 DOI: https://doi.org/10.3389/fpsyg.2016.01117
  20. Karahanna, E., Straub, D. W., & Chervany, N. L. (1999). Information technology adoption across time: A cross-sectional comparison of pre-adoption and post-adoption beliefs. MIS Quarterly, 23(2), 183. https://doi.org/10.2307/249751 DOI: https://doi.org/10.2307/249751
  21. Kellogg, K. C., Valentine, M. A., & Christin, A. (2020). Algorithms at Work: The New Contested Terrain of Control. Academy of Management Annals, 14(1), 366–410. https://doi.org/10.5465/annals.2018.0174 DOI: https://doi.org/10.5465/annals.2018.0174
  22. Korjonen‐Kuusipuro, K., & Wojciechowski, A. (2023). Hopes for a better (techno) future. Human Technology, 19(1), 1–4. https://doi.org/10.14254/1795-6889.2023.19-1.1 DOI: https://doi.org/10.14254/1795-6889.2023.19-1.1
  23. Korjonen‐Kuusipuro, K., & Wojciechowski, A. (2022). The value of superdiverse human-technology entanglements. Human Technology, 18(1), 1–5. https://doi.org/10.14254/1795-6889.2022.18-1.1 DOI: https://doi.org/10.14254/1795-6889.2022.18-1.1
  24. Leonardi, P. M. (2021). COVID‐19 and the New Technologies of Organizing: Digital Exhaust, Digital Footprints, and Artificial Intelligence in the Wake of Remote Work. Journal of Management Studies, 58(1), 249–253. https://doi.org/10.1111/joms.12648 DOI: https://doi.org/10.1111/joms.12648
  25. Leonardi, P. M., & Treem, J. W. (2020). Behavioral Visibility: A new paradigm for organization studies in the age of digitization, digitalization, and datafication. Organization Studies, 41(12), 1601–1625. https://doi.org/10.1177/0170840620970728 DOI: https://doi.org/10.1177/0170840620970728
  26. Leonardi, P. M., & Vaast, E. (2017). Social Media and Their Affordances for Organizing: A Review and Agenda for Research. Academy of Management Annals, 11(1), 150–188. https://doi.org/10.5465/annals.2015.0144 DOI: https://doi.org/10.5465/annals.2015.0144
  27. Lewis, Agarwal, & Sambamurthy. (2003). Sources of influence on beliefs about Information technology use: An empirical study of knowledge workers. MIS Quarterly, 27(4), 657. https://doi.org/10.2307/30036552 DOI: https://doi.org/10.2307/30036552
  28. Lu, J., Yao, J. E., & Yu, C.-S. (2005). Personal innovativeness, social influences and adoption of wireless Internet services via mobile technology. The Journal of Strategic Information Systems, 14(3), 245–268. https://doi.org/10.1016/j.jsis.2005.07.003 DOI: https://doi.org/10.1016/j.jsis.2005.07.003
  29. Ma, Q., Chan, A. H. S., & Chen, K. (2016). Personal and other factors affecting acceptance of smartphone technology by older Chinese adults. Applied Ergonomics, 54, 62–71. Scopus. https://doi.org/10.1016/j.apergo.2015.11.015 DOI: https://doi.org/10.1016/j.apergo.2015.11.015
  30. Malik, I., & Mushquash, A. R. (2025). Acceptance of a mental health app (JoyPopTM) for postsecondary students: A prospective evaluation using the UTAUT2. Frontiers in Digital Health, 7, 1503428. https://doi.org/10.3389/fdgth.2025.1503428 DOI: https://doi.org/10.3389/fdgth.2025.1503428
  31. Martins, R. S., Quintana, A. C., & Gularte Quintana, C. (2021). Factors that influence the intention of using an app in higher education. Revista Catarinense Da Ciência Contábil, 20, e3193. https://doi.org/10.16930/2237-766220213193 DOI: https://doi.org/10.16930/2237-7662202131931
  32. Muñoz-Leiva, F., Climent-Climent, S., & Liébana-Cabanillas, F. (2017). Determinants of intention to use the mobile banking apps: An extension of the classic TAM model. Spanish Journal of Marketing - ESIC, 21(1), 25–38. Scopus. https://doi.org/10.1016/j.sjme.2016.12.001 DOI: https://doi.org/10.1016/j.sjme.2016.12.001
  33. Nah, F. F.-H., Siau, K., & Sheng, H. (2005). The value of mobile applications: A utility company study. Communications of the ACM, 48(2), 85–90. https://doi.org/10.1145/1042091.1042095 DOI: https://doi.org/10.1145/1042091.1042095
  34. Norman, D. A. (2013). The design of everyday things (Revised and expanded edition). Basic Books.
  35. Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory ((3rd ed.)). McGraw-Hill.
  36. Paternoster, R., Brame, R., Mazerolle, P., & Piquero, A. (1998). Using the correct statistical test for the equality of regression coefficients. Criminology, 36(4), 859–866. https://doi.org/10.1111/j.1745-9125.1998.tb01268.x DOI: https://doi.org/10.1111/j.1745-9125.1998.tb01268.x
  37. Payne, A., & Frow, P. (2017). Relationship marketing: Looking backwards towards the future. Journal of Services Marketing, 31(1), 11–15. https://doi.org/10.1108/JSM-11-2016-0380 DOI: https://doi.org/10.1108/JSM-11-2016-0380
  38. Peng, H., Ma, S., & Spector, J. M. (2019). Personalized adaptive learning: An emerging pedagogical approach enabled by a smart learning environment. Smart Learning Environments, 6(1), 9. https://doi.org/10.1186/s40561-019-0089-y DOI: https://doi.org/10.1186/s40561-019-0089-y
  39. Peng, S., Yang, A., Cao, L., Yu, S., & Xie, D. (2017). Social influence modeling using information theory in mobile social networks. Information Sciences, 379, 146–159. https://doi.org/10.1016/j.ins.2016.08.023 DOI: https://doi.org/10.1016/j.ins.2016.08.023
  40. Rogers, E. M. (2003). Diffusion of innovations (5th ed). Free Press.
  41. Salancik, G. R., & Pfeffer, J. (1978). A Social Information Processing Approach to Job Attitudes and Task Design. Administrative Science Quarterly, 23(2), 224. https://doi.org/10.2307/2392563 DOI: https://doi.org/10.2307/2392563
  42. San-Martín, S., Jiménez, N. H., & López-Catalán, B. (2016). The firms benefits of mobile CRM from the relationship marketing approach and the TOE model. Spanish Journal of Marketing - ESIC, 20(1), 18–29. https://doi.org/10.1016/j.reimke.2015.07.001 DOI: https://doi.org/10.1016/j.reimke.2015.07.001
  43. Schierz, P. G., Schilke, O., & Wirtz, B. W. (2010). Understanding consumer acceptance of mobile payment services: An empirical analysis. Electronic Commerce Research & Applications, 9(3), 209–216. DOI: https://doi.org/10.1016/j.elerap.2009.07.005
  44. Sirant, A., & Kucia, K. (2024). MarketHub—Przychody na rynku aplikacji w Polsce (2019-2027). https://markethub.pl/analiza-rynku-aplikacji-mobilnych/
  45. Soh, P. Y., Heng, H. B., Selvachandran, G., Anh, L. Q., Chau, H. T. M., Son, L. H., Abdel-Baset, M., Manogaran, G., & Varatharajan, R. (2024). Perception, acceptance and willingness of older adults in Malaysia towards online shopping: A study using the UTAUT and IRT models. Journal of Ambient Intelligence and Humanized Computing, 15(S1), 101–101. https://doi.org/10.1007/s12652-020-01718-4 DOI: https://doi.org/10.1007/s12652-020-01718-4
  46. Sözer, E. G. (2020). Determinants and outcomes of mobile app usage intention of gen Z: A cross category assessment. Beykoz Akademi Dergisi, 239–265. https://doi.org/10.14514/byk.m.26515393.2019.7/2.239-265 DOI: https://doi.org/10.14514/byk.m.26515393.2019.7/2.239-265
  47. Statista—Mobile Internet & Apps (p. https://www.statista.com/markets/424/topic/538/mobile-internet-apps/#statistic5). (2024).
  48. Sun, Y., Wang, N., Guo, X., & Peng, Z. (2013). Understanding the acceptance of mobile health services: A comparison and integration of alternative models. 14(2).
  49. Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144–176. https://doi.org/10.1287/isre.6.2.144 DOI: https://doi.org/10.1287/isre.6.2.144
  50. Tran, B. D., & Vu, D. H. (2024). Gen-Y Behavioral intention to adopt mobile tourism apps: Extending UTAUT2 with trust and security. International Journal of Data and Network Science, 8(4), 2173–2184. https://doi.org/10.5267/j.ijdns.2024.6.014 DOI: https://doi.org/10.5267/j.ijdns.2024.6.014
  51. Triandis, H. C. (1971). Attitude and Attitude Change. Wiley.
  52. Triandis, H. C. (1980). Values, attitudes, and interpersonal behavior. Nebraska Symposium on Motivation. Nebraska Symposium on Motivation, 27, 195–259.
  53. Vahdat, A., Alizadeh, A., Quach, S., & Hamelin, N. (2021). Would you like to shop via mobile app technology? The technology acceptance model, social factors and purchase intention. Australasian Marketing Journal, 29(2), 187–197. https://doi.org/10.1016/j.ausmj.2020.01.002 DOI: https://doi.org/10.1016/j.ausmj.2020.01.002
  54. Venkatesh, Morris, Davis, & Davis. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425. https://doi.org/10.2307/30036540 DOI: https://doi.org/10.2307/30036540
  55. Venkatesh, Thong, & Xu. (2012). Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly, 36(1), 157. https://doi.org/10.2307/41410412 DOI: https://doi.org/10.2307/41410412
  56. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926 DOI: https://doi.org/10.1287/mnsc.46.2.186.11926
  57. Viswanathan, V., Hollebeek, L. D., Malthouse, E. C., Maslowska, E., Jung Kim, S., & Xie, W. (2017). The dynamics of consumer engagement with mobile technologies. Service Science, 9(1), 36–49. https://doi.org/10.1287/serv.2016.0161 DOI: https://doi.org/10.1287/serv.2016.0161
  58. Webster, J., & Trevino, L. K. (1995). Rational and social theories as complementary explanations of communication media choices: Two policy-capturing studies. Academy of Management Journal, 38(6), 1544–1572. https://doi.org/10.2307/256843 DOI: https://doi.org/10.2307/256843
  59. Wills, M. J., El-Gayar, O. F., & Bennett, D. (2008). Examining healthcare professionals’ acceptance of electronic medical records using UTAUT. Issues In Information Systems, 9(2), 391–401. https://doi.org/10.48009/2_iis_2008_396-401 DOI: https://doi.org/10.48009/2_iis_2008_396-401

How to Cite

Kowalska, M., & Nguyen, T. H. N. . (2025). Social influence as a determinant of mobile application acceptance across generations: A Polish perspective. Human Technology, 21(3), 731–747. https://doi.org/10.14254/1795-6889.2025.21-3.11