Pengaruh GAAIS terhadap Personal Innovativeness: Sikap pada Lintas Generasi

https://doi.org/10.35912/rambis.v4i2.3436
Published: Dec 6, 2024

Abstract:

Purpose: To see the attitude of AI towards personal innovativeness and to see the differences in attitudes between generation Z and previous generations

Research methodology: Respondents in this study are 1,293 people consisted of entrepreneurs and professional workers covering generation Z and previous generations.

Results: GAAIS has influence of 9.5% on personal innovativeness, bus the data showa no statistically significant difference in attitude towards AI between cross-generational.

Limitations: The analysis in this study still requires the addition of more in-depth variables such as personality, trust, social and subject norms to be able to predict attitudes towards AI in more depth.

Contribution: This study is expected to provide deeper insight into the influence of attitudes towards AI on personal innovativeness, considering that in the future AI will be close on society.

Keywords:
1. Cross-Generational
2. GAAIS
3. Personal Innovativeness
Authors:
1 . Vincentia Renata Kusuma
2 . Yoke Pribadi Kornarius
3 . Angela Caroline
4 . Triningtyas Elisabeth Putri Gusti
5 . Agus Gunawan
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How to Cite
Kusuma, V. R., Kornarius, Y. P. ., Caroline, A., Gusti, T. E. P. ., & Gunawan, A. (2024). Pengaruh GAAIS terhadap Personal Innovativeness: Sikap pada Lintas Generasi . Reviu Akuntansi, Manajemen, Dan Bisnis, 4(2), 145–155. https://doi.org/10.35912/rambis.v4i2.3436
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Issue & Section
Vol. 4 No. 2 (2024): Desember
Articles
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References

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    Chan, C. K., & Lee, K. K. (2023). The AI generation gap: Are Gen Z students more interested in adopting generative AI such as ChatGPT in teaching and learning than their Gen X and millennial generation teachers? Smart Learning Environment, 10(60). doi:10.1186/s40561-023-00269-3

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    Verganti, R., Vendraminelli, L., & Lansiti, M. (2020). Innovation and Design in the Age of Artificial Intelligence. Journal of Product Innovation Management, 37(3), 212-227. doi:10.1111/jpim.12523

    Wörndl, W., Koo, C., & Stienmetz, J. L. (Eds.). (2021). Information and Communication Technologies in Tourism 2021: Proceedings of the ENTER 2021 ETourism Conference, January 19–22, 2021. Switzerland: Springer International Publishing.

  1. Alkawsi, G., Ali, N., & Baashar, Y. (2021). The Moderating Role of Personal Innovativeness and Users Experience in Accepting the Smart Meter Technology. Applied Science, 11, 3297. doi:10.3390/app11083297
  2. Bendbya, H., Davenport, T. H., & Pachidi, S. (2020). Artificial Intelligence in Organizations: Current State and Future Opportunities. MIS Quarterly Executive, 19(4), 9-21. doi:10.2139/ssrn.3741983
  3. Boonprasert, M. (Ed.). (2021). Apheit International Journal (Vol. 10). Thailand: The Association of Private Higher Education Institutions of Thailand under the Patronage of Her Royal Highness Princess Maha Chakri Sirindhorn (APHEIT).
  4. Borges, A. F., Laurindo, F. J., Spínola, M. M., Gonçalves, R. F., & Mattos, C. A. (2020). The Strategic Use of Artificial Intelligence in the Digital Era: Systematic Literature Review and Future Research Directions. International Journal of Information Management, (). doi:10.1016/j.ijinfomgt.2020.102225
  5. Calvo-Porral, C., & Pesqueira-Sanchez, R. (2019). Generational differences in technology behaviour: comparing millennials and Generation X. Kybernetes, 49(11), 2755-2772. doi:https://doi.org/10.1108/K-09-2019-0598
  6. Chan, C. K., & Lee, K. K. (2023). The AI generation gap: Are Gen Z students more interested in adopting generative AI such as ChatGPT in teaching and learning than their Gen X and millennial generation teachers? Smart Learning Environment, 10(60). doi:10.1186/s40561-023-00269-3
  7. Corporation, I. (2022). IBM Global AI Adoption Index 2022. United States of America. Retrieved from https://www.ibm.com/downloads/cas/GVAGA3JP
  8. Darma, B. (2021). Statistika Penelitian Menggunakan SPSS. Yogyakarta: GUEPEDIA.
  9. Francis, T., & Hoefel, F. (2018). ‘True Gen’: Generation Z and its Implications for Companies. Mckinsey&Company.
  10. Hill, R. (2017). Embracing Digital: Key Considerations for Publishers, Marketers and Customers. Information Service & Use, 37(3), 349-354. doi:10.3233/ISU-170845
  11. Huang, M.-H., Rush, R., & Maksimovic, V. (2019). The Feeling Economy: Managing in the Next Generation of Artificial Intelligence (AI). California Management Review, 61(4), 1-23. doi:https://doi.org/10.1177/0008125619863436
  12. Jiyoung, P., & Sang, W. E. (2022). Who Likes Artificial Intelligence? Personality Predictors of Attitudes toward Artificial Intelligence. The Journal of Psychology, 156(1), 68-94. doi:10.1080/00223980.2021.2012109
  13. Kaya, F., Aydin, F., Schepman, A., Roadway, P., Yetisensoy, O., & Kaya, M. D. (2022). The Roles of Personality Traits, AI Anxiety, and Demographic Factors in Attitudes toward Artificial Intelligence. International Journal Of Human–Computer Interaction, 40(2), 497-514. doi:https://doi.org/10.1080/10447318.2022.2151730
  14. Lichtenthaler, U. (2019). Extremes of acceptance: employee attitudes toward artificial intelligence. Journal of Business Strategy, 41(5), 39-45. doi:https://doi.org/10.1108/JBS-12-2018-0204
  15. Liu, J., Chang, H., Forrest, J. Y.-L., & Yang, B. (2020). Influence of artificial intelligence on technological innovation: Evidence from the panel data of china's manufacturing sectors. Technological Forecasting & Social Change, 158. doi:10.1016/j.techfore.2020.120142
  16. Mohammad, M. M., Poursaberi, R., & Salahshoor, M. R. (2018). Evaluating the Adoption of Evidence-Based Practice Using Rogers’s Diffusion of Innovation Theory: A Model Testing Study. Health Promotion Perspective, 8(1), 25-32. doi:10.15171/hpp.2018.03
  17. N.N., M., & Dixit, Y. (2020). IoT, Big Data and Artificial Intelligence in Agriculture and Food Industry. IEEE Internet of Things Journal, PP99, 1-1. doi:10.1109/JIOT.2020.2998584
  18. Sahoo, R. (2021). Researching Children and Childhoods in India. New Delhi: School of Open Learning.
  19. Schepman, A., & Roadway, P. (2020). Initial validation of the general attitudes towards Artificial Intelligence Scale. Computers in Human Behavior Reports, 1. doi:https://doi.org/10.1016/j.chbr.2020.100014
  20. Schepman, A., & Roadway, P. (2023). The General Attitudes towards Artificial Intelligence Scale (GAAIS): Confirmatory Validation and Associations with Personality, Corporate Distrust, and General Trust. International Journal of Human–Computer Interaction, 39(13), 2724-2741. doi:10.1080/10447318.2022.2085400
  21. Silvia, V. (2021). Statistika Deskriptif . Yogyakarta: Andi Offset. Retrieved July 15, 2024
  22. Vargo, S. L., & Akaka, M. A. (2020). Rethinking the Process of Diffusion in Innovation: A Service-Ecosystems and Institutional Perspective. Journal of Business Research, 116, 526-534. doi:https://doi.org/10.1016/j.jbusres.2020.01.038
  23. Vargo, S. L., Wieland, H., & Akaka, M. A. (2015). Innovation Through Institutionalization: A service Ecosystems Perspective. Industrial Marketing Management, 44, 63-72. doi:https://doi.org/10.1016/j.indmarman.2014.10.008
  24. Verganti, R., Vendraminelli, L., & Lansiti, M. (2020). Innovation and Design in the Age of Artificial Intelligence. Journal of Product Innovation Management, 37(3), 212-227. doi:10.1111/jpim.12523
  25. Wörndl, W., Koo, C., & Stienmetz, J. L. (Eds.). (2021). Information and Communication Technologies in Tourism 2021: Proceedings of the ENTER 2021 ETourism Conference, January 19–22, 2021. Switzerland: Springer International Publishing.