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Journal of Humanities, Arts and Social Science

ISSN Print: 2576-0556 Downloads: 415706 Total View: 3323132
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Article Open Access http://dx.doi.org/10.26855/jhass.2023.09.036

Acceptance of Digital Technology Among Male and Female University Students: With a Focus on STEM Students

Angela Schorr1,*, Alexander Gorovoj2

1Media Psychology Lab, University of Siegen, Siegen, Germany.

2Fraunhofer IAO, Stuttgart, Germany.

*Corresponding author: Angela Schorr

Published: October 31,2023

Abstract

locusThe acceptance of digital technologies is an important, cross-disciplinary indicator for the scientific and professional development of university students, especially STEM students. The study is based on Davis’ Technology Acceptance Model and Ajzens’ Theory of Planned Behavior. To find out whether and how both genders still differ in this area, students from three university faculties (education, economic sciences, natural sciences; N=428) were surveyed. The digital technology acceptance scale and scales recording the test subjects’ personal media biography, digital skills, self-efficacy, performance goal orientations, control beliefs, and stress were applied. As a result, female and male students differ as far as digital technologies are concerned: In the total group, female students exhibit significantly lower values in digital technology acceptance, computer affinity and digital media self-efficacy. The separate analysis of STEM students yields encouraging results: In STEM, the profiles of both genders match in almost all points. Nevertheless, the regression models for predicting digital technology acceptance show that while the attitudes, skills and social support perceptions recorded naturally connect with male STEM students’ digital technology acceptance, only the variable digital media self-efficacy predicts the criterion among female STEM students. For future research, it is recommended to focus the research field on measures to promote the sustainable development of study and career-related interests among female STEM students during their studies.

References

Agudo-Peregrina, Á. F., Hernández-García, Á., & Pascual-Miguel, F. J. (2014). Behavioral intention, use behavior and the acceptance of electronic learning systems: Differences between higher education and lifelong learning. Computers in Human Behavior, 34, 301-314.

https://doi.org/10.1016/j.chb.2013.10.035.

Ajzen, I. (2020). The theory of planned behavior: Frequently asked questions. Human Behavior and Emerging Technologies, 2(4), 314-324. https://doi.org/10.1002/hbe2.195.

Al-Alaumie, A. (2013). Enhanced technology acceptance model to explain and predict learners’ behavioral intentions in learning management. PhD thesis. University of Bedfordshire. http://hdl.handle.net/10547/323773.

Ammenwerth, E. (2019). Technology acceptance models in health informatics: TAM and UTAUT. Studies in Health Technology & Informatics, 263, 64-71. https://doi.org/10.3233/shti190111.

Arnett, J.J. (2007). Emerging adulthood: What is it, and what is it good for? Child Development Perspectives, 1(2), 68-73. https://doi.org/10.1111/j.1750-8606.2007.00016.x.

Arthur, W. B. (2009). The nature of technology: What it is and how it evolves. New York, N.Y.: Simon & Schuster.

Bandura, A. (1997). Self-efficacy: The exercise of control. New York: W.H. Freeman.

Basilotta-Gómez-Pablos, V., Matarranz, M., Casado-Aranda, L. A., & Otto, A. (2022). Teachers’ digital competencies in higher education: a systematic literature review. International Journal of Educational Technology in Higher Education, 19(1), 1-16. https://doi.org/10.1186/s41239-021-00312-8.

Brinkmann, R. D. (2014). Angewandte Gesundheitspsychologie. Hallbergmoos: Pearson Verlag.

Button, S. B., Mathieu, J. E., & Zajac, D. M. (1996). Goal orientation in organizational research: A conceptual and em-pirical foundation. Organizational Behavior and Human Decision Processes, 67(1), 26-48. 

https://doi.org/10.1006/obhd.1996.0063.

Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd Ed.). Hillsdale, N. J.: Lawrence Erlbaum Associates Publishers.

Cohen, S., Kamarck, T. & Mermelstein, R. (1983). A Global Measure of Perceived Stress. Journal of Health and Social Behavior, 24(4), 385. https://doi.org/10.2307/2136404.

Davis, F. D. (2015). On the relationship between HCI and technology acceptance research. In Human-Computer Interaction and Management Information Systems: Foundations (pp. 409-415). New York: Routledge.

Diekman, A. B., Steinberg, M., Brown, E. R., Belanger, A. L., & Clark, E. K. (2017). A goal congruity model of role entry, engagement, and exit: Understanding communal goal processes in STEM gender gaps. Personality and Social Psychology Review, 21(2), 142-175.

Dweck, C. S., & Leggett, E. L. (1988). A social-cognitive approach to motivation and personality. Psychological Review, 95(2), 256-273. https://doi.org/10.1037/0033-295X.95.2.256.  

Eisinga, R., Grotenhuis, M. T., & Pelzer, B. (2013). The reliability of a two-item scale: Pearson, Cronbach, or Spear-man-Brown? International Journal of Public Health, 58(4), 637-642. https://doi.org/10.1007/s00038-012-0416-3.

Förtsch, S. M., & Gärtig-Daugs, A. (2020). Trust yourself: You have the IT-Factor! Career coaching for female computer scientists. International Journal of Gender, Science and Technology, 11(3), 490-527. Retrieved from 

https://genderandset.open.ac.uk/index.php/genderandset/article/view/660.

Frazier, P., Gabriel, A., Merians, A., & Lust, K. (2019). Understanding stress as an impediment to academic performance. Journal of American College Health, 67(6), 562-570. https://doi.org/10.1080/07448481.2018.1499649.

Golden-Kreutz, D. M., Browne, M. W., Frierson, G. M., & Andersen, B. L. (2004). Assessing stress in cancer patients: A second-order factor analysis model for the Perceived Stress Scale. Assessment, 11(3), 216-223. 

https://doi.org/10.1177/1073191104267398.

Gorovoj, A., & Schorr, A. (2020). Zur wahren Bedeutung von Einstellungs- und Persönlichkeitsfaktoren für die Akzeptanz Digitaler Medien (On the role of attitude and personality factors for the acceptance of digital media). Gesellschaft für Arbeitswissenschaft (Ed.). Digitaler Wandel, Digitale Arbeit, Digitaler Mensch? Dokumentation des 66. Arbeitswissenschaftlichen Kongresses, No.35, B.20.3, 1-6.

Granić, A., & Marangunić, N. (2019). Technology acceptance model in educational context: A systematic literature review. British Journal of Educational Technology, 50(5), 2572-2593. https://doi.org/10.1111/bjet.12864.

Graves, B. S., Hall, M. E., Dias-Karch, C., Haischer, M. H., & Apter, C. (2021). Gender differences in perceived stress and coping among college students. PloS one, 16(8), e0255634. https://doi.org/10.1371/journal.pone.0255634.

Heerwegh, D., De Wit, K., & Verhoeven, J. C. (2016). Exploring the self-reported ICT skill levels of undergraduate science students. Journal of Information Technology Education: Research, 14, 19-47. https://doi.org/10.28945/2334.

Hsia, J.W., Chang, C.C., & Tseng, A.H. (2014). Effects of individuals' locus of control and computer self-efficacy on their e-learning acceptance in high-tech companies. Behavior & Information Technology, 33, 51-64.

https://link.springer.com/article/10.1007/s12528-015-9103-8.

Jerusalem, M., & Schwarzer, R. (2003). SWE-Skala zur Allgemeinen Selbstwirksamkeitserwartung.psycharchives.org.

Johnson VA, Beehr TA. Making use of professional development: Employee interests and motivational goal orientations. Journal of Vocational Behavior, 84, 99-108. https://doi.org/10.1016/j.jvb.2013.12.003.

Kelly, R., Garr, O. M., Leahy, K., & Goos, M. (2020). An investigation of university students and professionals’ professional STEM identity status. Journal of Science Education and Technology, 29(4), 536-546. 

https://doi.org/10.1007/s10956-020-09834-8.

Klebl, M. (2014). Lernen mit Fehlern: Kontrollüberzeugungen bei Fehlfunktionen in kooperativen webbasierten Arbeitsumgebungen. Gesellschaft für Medien in der Wissenschaft, 

http://2014.gmw-online.de/wp-content/uploads/533.pdf.

Kleinbeck, U. (2010). Handlungsziele. In Heckhausen, H. (Ed.). Motivation und Handeln (pp. 285-307). Berlin: Springer Verlag. https://doi.org/10.1007/978-3-642-12693-2_11.

Köller, O., & Baumert, J. (1998). Ein deutsches Instrument zur Erfassung von Zielorientierungen bei Schülerinnen und Schülern. Diagnostica, 44(4), 173-181.

Krampen, G. (1980). IPC-Fragebogen zu Kontrollüberzeugungen. Göttingen: Hogrefe Verlag.

Kuhlmann, S., Piel, M., & Wolf, O. T. (2005). Impaired memory retrieval after psychosocial stress in healthy young men. Journal of Neuroscience, 25(11), 2977-2982. https://doi.org/10.1523/JNEUROSCI.5139-04.2005.

Lai, P. C. (2017). The literature review of technology adoption models and theories for the novelty technology. JISTEM-Journal of Information Systems and Technology Management, 14, 21-38.

http://dx.doi.org/10.4301/S1807-17752017000100002. 

Leaper, C., & Starr, C. R. (2019). Helping and hindering undergraduate women’s STEM motivation: Experiences with STEM encouragement, STEM-related gender bias, and sexual harassment. Psychology of Women Quarterly, 43(2), 165-183. https://doi.org/10.1177/0361684318806302.

Lehman, K. J., Sax, L.J., & Zimmerman, H.B. (2017). Women planning to major in computer science: who are they and what makes them unique? Computer Science Education, 26, 277-298. 

http://dx.doi.org/10.1080/08993408.2016.1271536.

Lehman, K. J., Newhouse, K. N., Sundar, S., & Sax, L. J. (2023). Nevertheless, they persisted: Factors that promote per-sistence for women and racially/ethnically minoritized students in undergraduate computing. Computer Science Education, 33(2), 260-285. https://doi.org/10.1080/08993408.2022.2086401.

Levenson, H. (1981). Differentiating among internality, powerful others, and chance. Research with the locus of control construct, 1, 15-63. https://doi.org/10.1016/B978-0-12-443201-7.50006-3.

Lloyd, A., Gore, J., Holmes, K., Smith, M., & Fray, L. (2018). Parental influences on those seeking a career in STEM: The primacy of gender. International Journal of Gender, Science and Technology, 10(2), 308-328. Retrieved from https://genderandset.open.ac.uk/index.php/genderandset/article/view/510.

Mehta, C. M., & Wilson, J. (2020). Gender segregation and its correlates in established adulthood. Sex Roles, 83(3), 240-253. https://doi.org/10.1007/s11199-019-01099-9.

Mun, Y. Y., & Hwang, Y. (2003). Predicting the use of web-based information systems: Self-efficacy, enjoyment, learning goal orientation, and the technology acceptance model. International Journal of Human-Computer Studies, 59(4), 431-449. https://doi.org/10.1016/S1071-5819(03)00114-9.

Granić, A., & Marangunić, N. (2019). Technology acceptance model in educational context: A systematic literature re-view. Opoku, M. O., & Francis, E. K. (2019). Relevance of the technology acceptance model (TAM) in information management research: a review of selected empirical evidence. Research Journal of Business and Management, 7(1), 34-44. http://dx.doi.org/10.17261/Pressacademia.2020.1186.

Orvis, K. A., Horn, D. B., & Belanich, J. (2009). An examination of the role individual differences play in video-game-based training. Military Psychology, 21(4), 461-481. https://doi.org/10.1080/08995600903206412.

Park, I., Kim, D., Moon, J., Kim, S., Kang, Y., & Bae, S., (2022). Searching for New Technology Acceptance ModBritish Journal of Educational Technology, 50(5), 2572-2593. https://doi.org/10.1111/bjet.12864.

Graves, B. S., Hall, M. E., Dias-Karch, C., Haischer, M. H., & Apter, C. (2021). Gender differences in perceived stress and coping among college students. PloS one, 16(8), e0255634. https://doi.org/10.1371/journal.pone.0255634.

Heerwegh, D., De Wit, K., & Verhoeven, J. C. (2016). Exploring the self-reported ICT skill levels of undergraduate science students. Journal of Information Technology Education: Research, 14, 19-47. https://doi.org/10.28945/2334.

Hsia, J.W., Chang, C.C., & Tseng, A.H. (2014). Effects of individuals' locus of control and computer self-efficacy on their e-learning acceptance in high-tech companies. Behavior & Information Technology, 33, 51-64.

https://link.springer.com/article/10.1007/s12528-015-9103-8.

Jerusalem, M., & Schwarzer, R. (2003). SWE-Skala zur Allgemeinen Selbstwirksamkeitserwartung.psycharchives.org.

Johnson VA, Beehr TA. Making use of professional development: Employee interests and motivational goal orientations. Journal of Vocational Behavior, 84, 99-108. https://doi.org/10.1016/j.jvb.2013.12.003.

Kelly, R., Garr, O. M., Leahy, K., & Goos, M. (2020). An investigation of university students and professionals’ professional STEM identity status. Journal of Science Education and Technology, 29(4), 536-546.

https://doi.org/10.1007/s10956-020-09834-8.

Klebl, M. (2014). Lernen mit Fehlern: Kontrollüberzeugungen bei Fehlfunktionen in kooperativen webbasierten Arbeitsumgebungen. Gesellschaft für Medien in der Wissenschaft,

http://2014.gmw-online.de/wp-content/uploads/533.pdf.

Kleinbeck, U. (2010). Handlungsziele. In Heckhausen, H. (Ed.). Motivation und Handeln (pp. 285-307). Berlin: Springer Verlag. https://doi.org/10.1007/978-3-642-12693-2_11.

Köller, O., & Baumert, J. (1998). Ein deutsches Instrument zur Erfassung von Zielorientierungen bei Schülerinnen und Schülern. Diagnostica, 44(4), 173-181.

Krampen, G. (1980). IPC-Fragebogen zu Kontrollüberzeugungen. Göttingen: Hogrefe Verlag.

Kuhlmann, S., Piel, M., & Wolf, O. T. (2005). Impaired memory retrieval after psychosocial stress in healthy young men. Journal of Neuroscience, 25(11), 2977-2982. https://doi.org/10.1523/JNEUROSCI.5139-04.2005.

Lai, P. C. (2017). The literature review of technology adoption models and theories for the novelty technology. JISTEM-Journal of Information Systems and Technology Management, 14, 21-38.

http://dx.doi.org/10.4301/S1807-17752017000100002.

Leaper, C., & Starr, C. R. (2019). Helping and hindering undergraduate women’s STEM motivation: Experiences with STEM encouragement, STEM-related gender bias, and sexual harassment. Psychology of Women Quarterly, 43(2), 165-183. https://doi.org/10.1177/0361684318806302.

Lehman, K. J., Sax, L.J., & Zimmerman, H.B. (2017). Women planning to major in computer science: who are they and what makes them unique? Computer Science Education, 26, 277-298.

http://dx.doi.org/10.1080/08993408.2016.1271536.

Lehman, K. J., Newhouse, K. N., Sundar, S., & Sax, L. J. (2023). Nevertheless, they persisted: Factors that promote persistence for women and racially/ethnically minoritized students in undergraduate computing. Computer Science Education, 33(2), 260-285. https://doi.org/10.1080/08993408.2022.2086401.

Levenson, H. (1981). Differentiating among internality, powerful others, and chance. Research with the locus of control construct, 1, 15-63. https://doi.org/10.1016/B978-0-12-443201-7.50006-3.

Lloyd, A., Gore, J., Holmes, K., Smith, M., & Fray, L. (2018). Parental influences on those seeking a career in STEM: The primacy of gender. International Journal of Gender, Science and Technology, 10(2), 308-328. Retrieved from https://genderandset.open.ac.uk/index.php/genderandset/article/view/510.

Mehta, C. M., & Wilson, J. (2020). Gender segregation and its correlates in established adulthood. Sex Roles, 83(3), 240-253. https://doi.org/10.1007/s11199-019-01099-9.

Mun, Y. Y., & Hwang, Y. (2003). Predicting the use of web-based information systems: Self-efficacy, enjoyment, learning goal orientation, and the technology acceptance model. International Journal of Human-Computer Studies, 59(4), 431-449. https://doi.org/10.1016/S1071-5819(03)00114-9.

How to cite this paper

Acceptance of Digital Technology Among Male and Female University Students: With a Focus on STEM Students

How to cite this paper: Angela Schorr, Alexander Gorovoj. (2023) Acceptance of Digital Technology Among Male and Female University Students: With a Focus on STEM Students. Journal of Humanities, Arts and Social Science7(9), 1901-1918.

DOI: http://dx.doi.org/10.26855/jhass.2023.09.036