Customer Intention to Use AI Technology on Beauty Industry

Authors

  • Yustikarani Julianti Pambudi Prodi Perdagangan Internasional Wilayah ASEAN dan RRT, Politeknik APP Jakarta
  • I Putu Wahyu Dwinata JS Fakultas Ekonomi dan Bisnis, Universitas Mahasaraswati

DOI:

https://doi.org/10.12695/ajtm.2023.16.2.5

Keywords:

AI Technology, Intention To Use, Technology Acceptance Model, Technology Readiness, Subjective Norm

Abstract

Artificial intelligence has emerged as one of the biggest disruptive technologies of the new era and will continue to evolve in the future. AI takes an important role in marketing processes, and the use of AI in marketing is massively increasing and can be easily found. Beauty industry is one of the industries where most companies develop their AI technology to attract customers. This innovation utilizes AI technology in providing solutions to problems related to makeup and/or skincare as experienced by the customers. This study aims to analyze the relationship between technology readiness, technology acceptance model and subjective norm as potential factors in encouraging the customer to use AI technology in the beauty industry. The data was collected using a google form questionnaire. The sampling technique for this research was purposive random sampling. A total of 155 responses were collated and only 127 responses are qualified with the criteria ‘ever using AI Technology in the beauty industry’. The results showed that while perceived usefulness had no significant impact on the intention to utilize AI technology, perceived comfort and subjective standards did. Additionally, the perceived advantages and ease of use are affected differently by each element of technology readiness.

Downloads

Download data is not yet available.

References

Acheampong, P., Zhiwen, L., Antwi, H. A., Otoo, A. A. A., Mensah, W. G., & Sarpong, P. B. (2017). Hybridizing an Extended Technology Readiness Index with Technology Acceptance Model (TAM) to Predict E-Payment Adoption in Ghana. American Journal Of Multidisciplinary Research, 5(2), 172–184.

Aji, H. M., Berakon, I., & Riza, A. F. (2021). The effects of subjective norm and knowledge about riba on intention to use e-money in Indonesia. Journal of Islamic Marketing, 12(6), 1180–1196. https://doi.org/https://doi.org/10.1108/JIMA-10-2019-0203

Ajzen, I. (2020). The theory of planned behavior: Frequently asked questions. Human Behavior and Emerging Technologies, 2(4), 314–324. https://doi.org/https://doi.org/10.1002/hbe2.195
Ardiansah, M. N., Chariri, A., Rahardja, S., & Udin, U. (2020). The effect of electronic payments security on e-commerce consumer perception: An extended model of technology acceptance. Management Science Letters, 1(1), 1473–1480. https://doi.org/https://doi.org/10.5267/j.msl.2019.12.020

Bakirtaş, H., & Akkaş, C. (2020). Technology Readiness and Technology Acceptance of Academic Staffs. International Journal of Management Economics and Business, 16(4), 1–12. https://doi.org/https://doi.org/10.17130/ijmeb.853629

Bilgihan, A., Barreda, A., Okumus, F., & Nusair, K. (2016). Consumer perception of knowledge-sharing in travel-related Online Social Networks. Tourism Management, 52(1), 287–296. https://doi.org/https://doi.org/10.1016/j.tourman.2015.07.002

Blut, M., & Wang, C. (2020). Technology readiness: A meta-analysis of conceptualizations of the construct and its impact on technology usage. Journal of the Academy of Marketing Science, 48(4), 649–669. https://doi.org/https://doi.org/10.1007/s11747-019-00680-8

Bughin, J., Seong, J., Manyika, J., Chui, M., & Joshi, R. (2018). Notes From the AI Frontier Modeling the Impact of AI on the World Economy. McKinsey Global Institute.

Chatterjee, S., Ghosh, S. K., Chaudhuri, R., & Nguyen, B. (2019). Are CRM systems ready for AI integration?: A conceptual framework of organizational readiness for effective AI-CRM integration. The Bottom Line, 32(2), 144–157. https://doi.org/https://doi.org/10.1108/BL-02-2019-0069

Chen, S.-C., Jong, D., & Lai, M.-T. (2014). Assessing the Relationship between Technology Readiness and Continuance Intention in an E-Appointment System: Relationship Quality as a Mediator. Journal of Medical Systems, 38(9), 76. https://doi.org/. https://doi.org/10.1007/s10916-014-0076-3

Cheng, E. W. L. (2019). Choosing between the theory of planned behavior (TPB) and the technology acceptance model (TAM). Educational Technology Research and Development, 67(1), 21–37. https://doi.org/https://doi.org/10.1007/s11423-018-9598-6

Chi, T. (2018). Understanding Chinese consumer adoption of apparel mobile commerce: An extended TAM approach. Journal of Retailing and Consumer Services, 44(1), 274–284. https://doi.org/https://doi.org/10.1016/j.jretconser.2018.07.019

Chotijah, U., & Retrialisca, F. (2020). Analysis of Information Technology Readiness in Furniture Business in Indonesia. Indonesian Journal of Information Systems, 3(1), 14–22. https://doi.org/https://doi.org/10.24002/ijis.v3i1.3470

Chung, T. S., Rust, R. T., & Wedel, M. (2009). My Mobile Music: An Adaptive Personalization System for Digital Audio Players. Marketing Science, 28(1), 52–68. https://doi.org/https://doi.org/10.1287/mksc.1080.0371

Danurdoro, K., & Wulandari, D. (2016). The Impact of Perceived Usefulness, Perceived Ease of Use, Subjective Norm, and Experience Toward Student’s Intention to Use Internet Banking. Jurnal Ekonomi Dan Ekonomi Studi Pembangunan, 8(1), 17–22. https://doi.org/https://doi.org/10.17977/um002v8i12016p017

Davenport, T., Guha, A., & Grewal, D. (2021). How to Design an AI Marketing Strategy. Harvard Business Review, 1(1), 13–20.
Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48(1), 24–42. https://doi.org/https://doi.org/10.1007/s11747-019-00696-0

Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319. https://doi.org/https://doi.org/10.2307/249008
Edelman, D., & Abraham, M. (2022). Customer Experience in the Age of AI. Harvard Business Review, 3(4), 15–25.

Erdoğmuş, N., & Esen, M. (2011). An Investigation of the Effects of Technology Readiness on Technology Acceptance in e-HRM. Procedia - Social and Behavioral Sciences, 24(1), 487–495. https://doi.org/https://doi.org/10.1016/j.sbspro.2011.09.131

Godoe, P., & Johansen, T. S. (2012). Understanding adoption of new technologies: Technology readiness and technology acceptance as an integrated concept. Journal of European Psychology Students, 3(3), 38. https://doi.org/https://doi.org/10.5334/jeps.aq

Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/https://doi.org/10.1108/EBR-11-2018-0203

Hamid, A. A., Razak, F. Z. A., Bakar, A. A., & Abdullah, W. S. W. (2016). The Effects of Perceived Usefulness and Perceived Ease of Use on Continuance Intention to Use E-Government. Procedia Economics and Finance, 35(1), 644–649. https://doi.org/https://doi.org/10.1016/S2212-5671(16)00079-4

Hansen, J. M., Saridakis, G., & Benson, V. (2018). Risk, trust, and the interaction of perceived ease of use and behavioral control in predicting consumers’ use of social media for transactions. Computers in Human Behavior, 80(1), 197–206. https://doi.org/https://doi.org/10.1016/j.chb.2017.11.010

Hasbullah, N. A., Osman, A., Abdullah, S., Salahuddin, S. N., Ramlee, N. F., & Soha, H. M. (2016). The Relationship of Attitude, Subjective Norm and Website Usability on Consumer Intention to Purchase Online: An Evidence of Malaysian Youth. Procedia Economics and Finance, 35(1), 493–502. https://doi.org/https://doi.org/10.1016/S2212-5671(16)00061-7

Huang, M.-H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49(1), 30–50. https://doi.org/https://doi.org/10.1007/s11747-020-00749-9

Jeyaraj, A., Rottman, J. W., & Lacity, M. C. (2006). A Review of the Predictors, Linkages, and Biases in IT Innovation Adoption Research. Journal of Information Technology, 21(1), 1–23. https://doi.org/https://doi.org/10.1057/palgrave.jit.2000056

Joo, Y. J., Park, S., & Lim, E. (2021). Factors Influencing Preservice Teachers’ Intention to Use Technology. Educational Technology & Society, 13(1), 48–59.

Kamal, S. A., Shafiq, M., & Kakria, P. (2020). Investigating acceptance of telemedicine services through an extended technology acceptance model (TAM). Technology in Society, 60(1), 101–112. https://doi.org/https://doi.org/10.1016/j.techsoc.2019.101212

Kamble, S., Gunasekaran, A., & Arha, H. (2019). Understanding the Blockchain technology adoption in supply chains-Indian context. International Journal of Production Research, 57(7), 2009–2033. https://doi.org/https://doi.org/10.1080/00207543.2018.1518610

Kim, H., Kim, T., & Shin, S. W. (2009). Modeling roles of subjective norms and eTrust in customers’ acceptance of airline B2C eCommerce websites. Tourism Management, 30(2), 266–277. https://doi.org/https://doi.org/10.1016/j.tourman.2008.07.001

Koivisto, K., Makkonen, M., Frank, L., & Riekkinen, J. (2016). Extending the Technology Acceptance Model with Personal Innovativeness and Technology Readiness: A Comparison of Three Models. BLED 2016 : Proceedings of the 29th Bled EConference "Digital Economy., 1–10.

Kok, J. N., Boers, E. J., Kosters, W. A., & Van der Putten, P. (2009). Artificial Intelligence: Definition, Trends, Techniques and Cases. Encyclopedia of Life Support Systems (EOLSS), 5(1), 1–12.

Lin, J. C., & Hsieh, P. (2006). The role of technology readiness in customers’ perception and adoption of self‐service technologies. International Journal of Service Industry Management, 17(5), 497–517. https://doi.org/https://doi.org/10.1108/09564230610689795

Longoni, C., Bonezzi, A., & Morewedge, C. K. (2019). Resistance to Medical Artificial Intelligence. Journal of Consumer Research, 46(4), 629–650. https://doi.org/https://doi.org/10.1093/jcr/ucz013

Murphy, J., Gretzel, U., & Pesonen, J. (2019). Marketing robot services in hospitality and tourism: The role of anthropomorphism. Journal of Travel & Tourism Marketing, 36(7), 784–795. https://doi.org/. https://doi.org/10.1080/10548408.2019.1571983

Nagy, S., & Hajdu, N. (2021). Consumer Acceptance of the Use of Artificial Intelligence in Online Shopping: Evidence From Hungary. Amfiteatru Economic, 23(56), 155–174. https://doi.org/https://doi.org/10.24818/EA/2021/56/155

Negnevitsky, M. (2005). Artificial intelligence: A guide to intelligent systems (2nd ed.). USA: Addison-Wesley.

Nugroho, A., Najib, M., & Simanjuntak, M. (2018). Factors Affecting Consumer Interest In Electronic Money Usage With Theory Of Planned Behavior (TPB). Journal of Consumer Sciences, 3(1), 15. https://doi.org/https://doi.org/10.29244/jcs.3.1.15-27

Nugroho, M. A., & Fajar, M. A. (2017). Effects of Technology Readiness Towards Acceptance of Mandatory Web-Based Attendance System. Procedia Computer Science, 124(1), 319–328. https://doi.org/https://doi.org/10.1016/j.procs.2017.12.161

Parasuraman, A. (2000). Technology Readiness Index (Tri): A Multiple-Item Scale to Measure Readiness to Embrace New Technologies. Journal of Service Research, 2(4), 307–320. https://doi.org/https://doi.org/10.1177/109467050024001

Pillai, R., Sivathanu, B., & Dwivedi, Y. K. (2020). Shopping intention at AI-powered automated retail stores (AIPARS). Journal of Retailing and Consumer Services, 57(1), 102–107. https://doi.org/https://doi.org/10.1016/j.jretconser.2020.102207

Purba, J. T. (2018). Strategic Innovation Through Technology Readiness And Acceptance In Implementing ICT For Corporate Sustainability. 12th International Annual Symposium on Management, March, 14th 2015 in Makassar Sulawesi, Indonesia. https://doi.org/https://doi.org/10.13140/RG.2.2.15744.53764

Russell, S., & Norvig, P. (2016). Artificial Intelligence : A Modern Approach (3rd ed.). Pearson Education Limited.

Rust, R. T., & Huang, M.-H. (2021). Engaged to a Robot? The Role of AI in Service. Journal of Service Research, 24(1), 30–41. https://doi.org/https://doi.org/10.1177/1094670520902266

Schepers, J., & Wetzels, M. (2007). A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects. Information & Management, 44(1), 90–103. https://doi.org/https://doi.org/10.1016/j.im.2006.10.007

Sher, P. J., Lin, C.-H., & Shih, H.-Y. (2007). Integrating technology readiness into technology acceptance: The TRAM model. Psychology and Marketing, 24(7), 641–657. https://doi.org/https://doi.org/10.1002/mar.20177

Shin, S., & Lee, W. (2014). The Effects Of Technology Readiness And Technology Acceptance On Nfc Mobile Payment Services In Korea. Journal of Applied Business Research (JABR), 30(6), 16–15. https://doi.org/https://doi.org/10.19030/jabr.v30i6.8873

Tahar, A., Riyadh, H. A., Sofyani, H., & Purnomo, W. E. (2020). Perceived Ease of Use, Perceived Usefulness, Perceived Security and Intention to Use E-Filing: The Role of Technology Readiness. The Journal of Asian Finance, Economics and Business, 7(9), 537–547. https://doi.org/https://doi.org/10.13106/JAFEB.2020.VOL7.NO9.537

Teo, A. C., Tan, G. W. H., Cheah, C. M., Ooi, K. B., & Yew, K. T. (2012). Can the demographic and subjective norms influence the adoption of mobile banking? International Journal of Mobile Communications, 10(6), 578. https://doi.org/https://doi.org/10.1504/IJMC.2012.049757

Teo, T. (2009). The Impact of Subjective Norm and Facilitating Conditions on Pre-Service Teachers’ Attitude toward Computer Use: A Structural Equation Modeling of an Extended Technology Acceptance Model. Journal of Educational Computing Research, 40(1), 89–109. https://doi.org/https://doi.org/10.2190/EC.40.1.d

Tseng, J., & Tzou, H. (2022). How AI and AR can help beauty industry. IEEE Consumer Technology Society, 1(1), 1–8. https://ctsoc.ieee.org/images/CTSOC-NCT-2022-01-FA.pdf

Tsikriktsis, N. (2004). A Technology Readiness-Based Taxonomy of Customers: A Replication and Extension. Journal of Service Research, 7(1), 42–52. https://doi.org/https://doi.org/10.1177/1094670504266132

Verma, S., Sharma, R., Deb, S., & Maitra, D. (2021). Artificial intelligence in marketing: Systematic review and future research direction. International Journal of Information Management Data Insights, 1(1), 100–120. https://doi.org/https://doi.org/10.1016/j.jjimei.2020.100002

Vrublevskaia, O. (2021). Effectiveness And Universality Of Artificial Intelligence Implementation In Marketing. Lab University of Applied Sciences, 60(1), 1-12`.

Walczuch, R., Lemmink, J., & Streukens, S. (2007). The effect of service employees’ technology readiness on technology acceptance. Information & Management, 44(2), 206–215. https://doi.org/https://doi.org/10.1016/j.im.2006.12.005

Wirth, N. (2018). Hello marketing, what can artificial intelligence help you with? International Journal of Market Research, 60(5), 435–438. https://doi.org/https://doi.org/10.1177/1470785318776841

Zhong, Y., Oh, S., & Moon, H. C. (2021). Service transformation under industry 4.0: Investigating acceptance of facial recognition payment through an extended technology acceptance model. , 64, . Technology in Society, 64(1), 101–115. https://doi.org/https://doi.org/10.1016/j.techsoc.2020.101515

Zhuang, X., Hou, X., Feng, Z., Lin, Z., & Li, J. (2021). Subjective norms, attitudes, and intentions of AR technology use in tourism experience: The moderating effect of millennials. Leisure Studies, 40(3), 392–406. https://doi.org/https://doi.org/10.1080/02614367.2020.1843692

Downloads

Submitted

2023-02-11

Accepted

2024-01-08

Published

2024-01-08

How to Cite

Pambudi, Y. J., & Dwinata JS, I. P. W. (2024). Customer Intention to Use AI Technology on Beauty Industry. The Asian Journal of Technology Management (AJTM), 16(2), 136–151. https://doi.org/10.12695/ajtm.2023.16.2.5

Issue

Section

Articles