Main Article Content
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.
Keywords
AI Technology
Intention To Use
Technology Acceptance Model
Technology Readiness
Subjective Norm
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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Copyright @2017. This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (http://creativecommons.org/licenses/by-nc-sa/4.0/) which permits unrestricted non-commercial used, distribution and reproduction in any medium
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
References
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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
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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
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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
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Edelman, D., & Abraham, M. (2022). Customer Experience in the Age of AI. Harvard Business Review, 3(4), 15–25.
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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
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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
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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
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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
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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
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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
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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