Acceptability and Readiness of Fast-Food Personnel Toward Artificial Intelligence Financial Tools for Internal Control
Vince Ledren A. Deleste | Joshua James Zar D. Batallones | Jeo Francis S. Bijare | Francine I. De La Cruz | Kristian Jerund G. Germia | Ace Gerome M. Niño
Discipline: business studies
Abstract:
Despite the growing integration of artificial intelligence (AI) tools in financial operations, there is
a limited understanding of how fast-food employees perceive and adapt to such technologies. This study
aimed to assess the level of acceptability of artificial intelligence (AI) financial tools for internal control and
their impact on the readiness of fast-food personnel, as well as the differences and relationships between
acceptability and readiness. Using a quantitative-correlational research design, the study examined fastfood
personnel through the Technology Acceptance Model (TAM) and Technology Readiness Index (TRI).
Results revealed that both the willingness to adopt and readiness of the fast-food personnel were high,
indicating a positive perception of AI financial tools. Moreover, no significant differences in acceptability
were found when participants were grouped by age and job position; however, a considerable difference
emerged when participants were grouped by sex in terms of ease of use, suggesting that males and females
perceive AI financial tools differently. Regarding readiness, significant differences were observed in
optimism and innovativeness when grouped according to sex, indicating that sex influences an
individual’s level of preparedness. Lastly, a powerful and significant positive relationship was found
between the respondents’ level of acceptability and readiness, implying that readiness and acceptability
influence each other, suggesting that openness to AI tools and the capacity to engage with them are
mutually reinforcing. These findings offer practical insights for organizational training programs and
digital transformation strategies in the fast-food sector. The study recommends that owners and
management provide proper formal training for personnel and identify key areas for improvement. Future
research should also explore other factors that may affect acceptability and readiness.
References:
- Albofera, Q. K. L., Digan, D. A., Torres, J. R., & Quezada, R. J. C. (2024). Technology acceptance among college students living in remote areas. American Journal of Multidisciplinary Research and Innovation, 3(4), 138–147. https://doi.org/10.54536/ajmri.v3i4.2898
- Anand, A. (2022). What is a survey? Analytics steps. Retrieved from https://tinyurl.com/365xjy63
- Bhandari, P. (2023). Correlational research | When & how to use. Scribbr. Retrieved from https://tinyurl.com/4sxtfxs8
- Deloitte. (2024). New Deloitte survey on Gen AI adoption. Retrieved from https://tinyurl.com/mr2dchub
- Domingo, M. A., Galeon, K. S., Pastor, D. A., & Toribio, J. B. (2022). University students’ levels of anxiety, readiness, and acceptance for e-learning during the COVID-19 pandemic. Social Sciences, Humanities and Education Journal (SHE Journal), 4(1), 73–86. Retrieved from https://tinyurl.com/32anp5fj
- Ethan, H. (2023). The limitations of manual data processing: Overcoming human error and inefficiency | Insightvity. Insightvity. https://tinyurl.com/4w7se3y2
- Gaganao, R. D., Discar, R. N., & Fabillar, I. N. L. (2022). E-learning readiness of teachers in the new normal education: The case of national high schools in Eastern Samar. International Journal of Evaluation and Research in Education (IJERE), 11(3), 1040. https://doi.org/10.11591/ijere.v11i3.22542
- Gfrerer, A., Hutter, K., Füller, J., & Ströhle, T. (2020). Ready or not: Managers’ and employees’ different perceptions of digital readiness. California Management Review, 63(2), 23–48. https://doi.org/10.1177/0008125620977487
- Golzar, J., Noor, S., & Tajik, O. (2022). Convenience sampling. International Journal of Education Language Studies, 1(2). https://doi.org/10.22034/ijels.2022.162981
- Goswami, A., & Dutta, S. (2016). Gender differences in technology usage—A literature review. Open Journal of Business and Management, 04(01), 51–59. https://doi.org/10.4236/ojbm.2016.41006
- Heale, R., & Twycross, A. (2015). Validity and reliability in quantitative studies. Evidence-Based Nursing, 18(3), 66–67. https://doi.org/10.1136/eb-2015-102129
- Joseph, G. V., Thomas, K. A., & Nero, A. (2021). Impact of technology readiness and techno stress on teacher engagement in higher secondary schools. Digital Education Review, 40, 51–65. https://doi.org/10.1344/der.2021.40.51-65
- Kaushik, M. K., & Agrawal, D. (2021). Influence of technology readiness in adoption of e-learning. International Journal of Educational Management, 35(2), 483–495. https://doi.org/10.1108/ijem-04-2020-0216
- Latif, D. V., Arsalan, S., & Hussain, H. I. (2021). The effect of gender in the implementation of self-ordering machine in a fast food restaurant. Turkish Journal of Computer and Mathematics Education Vol. 12 No.11 (2021), 1392–1396. https://doi.org/10.17762/turcomat.v12i11.6051
- Mordorintelligence. (2024). Philippines' foodservice market size. Market Research Company - Mordor IntelligenceTM. Retrieved from https://tinyurl.com/578wf8d5
- Morris, M. G., Venkatesh, V., & Ackerman, P. L. (2005). Gender and age differences in employee decisions about new technology: An extension to the theory of planned behavior. IEEE Transactions on Engineering Management, 52(1), 69–84. https://doi.org/10.1109/TEM.2004.839967
- Na, T.-K., Lee, S.-H., & Yang, J.-Y. (2021). Moderating effect of gender on the relationship between technology readiness index and consumers’ continuous use intention of self-service restaurant kiosks. Information, 12(7), 280. https://doi.org/10.3390/info12070280
- PricewaterhouseCoopers. (2022). PwC’s global economic crime and fraud survey 2022. PwC. Retrieved from https://tinyurl.com/mr45ft3w
- PricewaterhouseCoopers. (2024). Asia Pacific CEO Survey 2024_Territory snapshot (Philippines). PwC. Retrieved from https://tinyurl.com/k6zrpb5k
- Quah, W. B., Che Abu Bakar, A. F., & Mohd Yusop, N. (2021). Determining the relationship between self-directed learning readiness and acceptance of E-learning among culinary students. Journal of Tourism, Hospitality & Culinary Arts, 13(1), 37–45. https://ir.uitm.edu.my/id/eprint/67502/
- Rusli, N. M. B. R., Samah, R. S., & Kamaruddin, R. K. (2023). Technology readiness index of paddy farmers in MADA, KADA, and IADA BLS, Malaysia. Journal of Economics and Sustainability, 5(1), 27–42. https://doi.org/10.32890/jes2023.5.1.3
- Singh, S. (2023). What is descriptive research? Definition, methods, types, and examples | Researcher. Life. Retrieved from https://tinyurl.com/4tv8ybnx
- Sreekumar, D. (2023). What is quantitative research? Definition, methods, types, and examples | Researcher. Life. Retrieved from https://tinyurl.com/jhw4p93z
- Su, Y.-S., Lai, C.-C., Wu, T.-K., & Lai, C.-F. (2022). The effects of applying an augmented reality English teaching system on students’ STEAM learning perceptions and technology acceptance. Frontiers in Psychology, 13, 996162. https://doi.org/10.3389/fpsyg.2022.996162
- Taber, K. S. (2018). The use of Cronbach’s alpha when developing and reporting research instruments in science education. Research in Science Education, 48(6), 1273–1296. https://doi.org/10.1007/s11165-016-9602-2
- Taherdoost, H. (2016). Validity and reliability of the research instrument; How to test the validation of a questionnaire/survey in a research. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3205040
- Tarhini, A., Hone, K., & Liu, X. (2014). Measuring the moderating effect of gender and age on E-learning acceptance in England: A structural equation modeling approach for an extended technology acceptance model. Journal of Educational Computing Research, 51(2), 163–184. https://doi.org/10.2190/EC.51.2.b
- Taylor, S., & Todd, P. (1995). Assessing its usage: The role of prior experience. MIS Quarterly, 19(4), 561. https://doi.org/10.2307/249633
- Tsai, Y.-R. (2015). Applying the technology acceptance model (TAM) to explore the effects of a course management system (CMS)-assisted EFL writing instruction. CALICO Journal, 32(1), 153–171. https://doi.org/10.1558/calico.v32i1.25961
- Ubah, A. E. (2023). Investigating the impact of artificial intelligence tools in finance. Retrieved from https://docs.neu.edu.tr/library/9618305364.pdf
- Venkatesh, Morris, Davis, G., & Davis, F. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425. https://doi.org/10.2307/30036540
- 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. https://doi.org/10.2307/3250981
- Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315. https://doi.org/10.1111/j.1540-5915.2008.00192.x
- Wang, Y., Wu, M., & Wang, H. (2009). Investigating the determinants of age and gender differences in the acceptance of mobile learning. British Journal of Educational Technology, 40(1), 92–118. https://doi.org/10.1111/j.1467-8535.2007.00809.x
- Williams, K. (2024). What are questionnaires? Benefits, types, and examples. SurveySparrow. Retrieved October 3, 2024, from https://surveysparrow.com/blog/questionnaires/
Full Text:
Note: Kindly Login or Register to gain access to this article.
ISSN 2984-8385 (Online)
ISSN 2984-8288 (Print)