The Influence of AI and AR Technology in Personalized Recommendations on Customer Usage Intention: A Case Study of Cosmetic Products on Shopee
Source abstract
Abstract
With the rapid growth of Augmented Reality (AR) in e-commerce, it is necessary to conduct in-depth studies related to the integration of AR with Artificial Intelligence (AI) technology to improve personalization in product recommendations. AI and AR are expected to work together, where AI systems analyze consumer data to provide more suitable product recommendations, while AR helps consumers visualize these recommendations in the real world. This research explores the influence of AI and AR technology integration in personalized recommendations on customer usage intention, with a focus on the cosmetics industry on the e-commerce platform Shopee. Adopting the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB), this research uses quantitative methods with the try-on feature of cosmetic products as the main focus. With a total number of 387 respondents, the results of the PLS-SEM analysis revealed factors impacting the intention to use, such as perceived ease of use, perceived usefulness, and users’ perceived trust, on the intention to use personalized recommendations. This research contributes to the understanding of AI–AR technology acceptance in the context of online shopping, particularly in the cosmetics sector.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Technology Adoption and User Behaviour · Consumer Retail Behavior Studies · Blockchain Technology in Education and Learning
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Computer Science, University of Indonesia, Depok 16424, Indonesia
Funding
Faculty of Computer Science, University of Indonesia
References (76)
Show source-provided references
1. Use of E-Commerce in The World of Business10.33050/sabda.v2i1.273 ↗
2. The Role of E-Commerce in the World Economy10.17762/pae.v58i2.3149 ↗
3. Are You Satisfied with Shopee? A Study of Customer Satisfaction Towards Shopee10.32535/ijthap.v6i2.2342 ↗
4. (2023, December 20). E-Commerce Dengan Pengunjung Terbanyak Kuartal I 2023. Available online: https://databoks.katadata.co.id/datapublish/2023/05/03/5-e-commerce-dengan-pengunjung-terbanyak-kuartal-i-2023.
5. Influence of augmented reality on shopping behavior10.1108/MD-02-2022-0136 ↗
6. Arghashi, V. (2022). Shopping with augmented reality: How wow-effect changes the equations!. Electron. Commer. Res. Appl., 54.10.1016/j.elerap.2022.101166 ↗
7. Jayaswal, P., and Parida, B. (2023). The role of augmented reality in redefining e-tailing: A review and research agenda. J. Bus. Res., 160.10.1016/j.jbusres.2023.113765 ↗
8. Dethe, H.S., and Joy, E. (2023, January 26–28). Revolutionizing E-commerce with 3D Visualization: An Experimental Assessment of Behavioural Shopper Responses to Augmented Reality in Online Shopping. Proceedings of the 2023 4th International Conference for Emerging Technology (INCET), Belgaum, India.10.1109/INCET57972.2023.10170472 ↗
9. The playground effect: How augmented reality drives creative customer engagement10.1016/j.jbusres.2020.05.002 ↗
10. Joshi, A., and Jain, S. (2022, January 26–27). An Approach of Augmented Reality in Field of Furniture Shopping. Proceedings of the 2022 Fourth International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT), Mandya, India.10.1109/ICERECT56837.2022.10060853 ↗
11. Can’t touch this: The impact of augmented reality versus touch and non-touch interfaces on perceived ownership10.1007/s10055-018-0335-6 ↗
12. Turban, E., Outland, J., David, K., Lee, J.K., Liang, T.-P., and Turban, D.C. (2018). Electronic Commerce 2018: A Managerial and Social Networks Perspective, Springer.10.1007/978-3-319-58715-8 ↗
13. Taking a look at different types of e-commerce
14. A Review Paper on E-Commerce
15. Analysis of E-Commerce Management Policies Based on The Current Situation of Development
16. Implementation of Augmented Reality Shopping in E-Commerce to Increase Customer’s Purchase Intention10.21512/emacsjournal.v5i2.9954 ↗
17. Augmented reality: A review10.1080/02763869.2012.670604 ↗
18. Could the Implementation of Augmented Reality Influence Consumers’ Purchase Intention in E-Commerce?10.26905/jmdk.v11i1.9561 ↗
19. Sato, K., Soejima, T., Saito, M., Sasage, R., Kamioka, N., Goto, N., and Kawai, Y. (2021, January 9–11). Development of Augmented Reality-Based Application to Search for Electric Appliances and Furniture. Proceedings of the 2021 IEEE 3rd Global Conference on Life Sciences and Technologies (LifeTech), Nara, Japan.10.1109/LifeTech52111.2021.9391825 ↗
20. Artificial intelligence in retail—A systematic literature review10.1108/FS-10-2021-0210 ↗
21. Devagiri, J.S., Paheding, S., Niyaz, Q., Yang, X., and Smith, S. (2022). Augmented Reality and Artificial Intelligence in industry: Trends, tools, and future challenges. Expert Syst. Appl., 207.10.1016/j.eswa.2022.118002 ↗
22. Artificial intelligence in E-Commerce: A bibliometric study and literature review10.1007/s12525-022-00537-z ↗
23. Vullam, N., Vellela, S.S., Venkateswara, R., Rao, M.V., Basha, S.K.K., and Roja, D. (2023, January 4–6). Multi-Agent Personalized Recommendation System in E-Commerce based on User. Proceedings of the 2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023, Salem, India.10.1109/ICAAIC56838.2023.10140756 ↗
24. Li, S. (2023). Big Data-Based Recommendation Algorithm in E-commerce Personalized Marketing Shujun. Wuhan International Conference on e-Business at AIS Electronic Library (AISeL), Springer.10.1007/978-3-031-32299-0_6 ↗
25. Quantifying uncertainty in PLS-SEM-based mediation analyses10.1057/s41270-023-00231-9 ↗
26. Exploring predictors of innovation performance of SMEs: A PLS-SEM approach10.1108/ER-02-2022-0078 ↗
27. Evaluating Four Factors of Students’ Career Adaptabilities (Caa) Using Pls-Sem Approach
28. Perceived usefulness, perceived ease of use, and user acceptance of information technology10.2307/249008 ↗
29. Technology Acceptance Model: A Survey of Literature
30. The Technology Acceptance Model: Its past and its future in health care10.1016/j.jbi.2009.07.002 ↗
31. Examining a Theory of Planned Behavior (TPB) and Technology Acceptance Model (TAM) in Internetpurchasing Using Structural Equation Modeling
32. The Theory of Planned Behavior10.1016/0749-5978(91)90020-T ↗
33. Ruiz-Herrera, L.G., Valencia-Arias, A., Gallegos, A., Benjumea-Arias, M., and Flores-Siapo, E. (2023). Technology acceptance factors of e-commerce among young people: An integration of the technology acceptance model and theory of planned behavior. Heliyon, 9.10.1016/j.heliyon.2023.e16418 ↗
34. Perceived usefulness, ease of use, and usage of information technology: A replication10.2307/249577 ↗
35. Satoh, Y., Shiogama, H., Hanasaki, N., Pokhrel, Y., Boulange, J.E.S., Burek, P., Gosling, S.N., Grillakis, M., Koutroulis, A., and Schmied, H.M. (2021). A quantitative evaluation of the issue of drought definition: A source of disagreement in future drought assessments. Environ. Res. Lett., 16.10.1088/1748-9326/ac2348 ↗
36. Kitchenham, B. (2004). Procedures for Performing Systematic Reviews, Keele University.
37. Keele, S. (2023, December 20). Guidelines for Performing Systematic Literature Reviews in Software Engineering; Technical Report, Technical Report, Ver. 2.3, EBSE Technical Report; EBSE. Available online: https://www.researchgate.net/profile/Barbara-Kitchenham/publication/302924724_Guidelines_for_performing_Systematic_Literature_Reviews_in_Software_Engineering/links/61712932766c4a211c03a6f7/Guidelines-for-performing-Systematic-Literature-Reviews-in-Software-Engineering.pdf.
38. Wang, C., Ahmad, S.F., Ayassrah, A.Y.B.A., Awwad, E.M., Irshad, M., Ali, Y.A., Al-Razgan, M., Khan, Y., and Han, H. (2023). An empirical evaluation of technology acceptance model for Artificial Intelligence in E-commerce. Heliyon, 9.10.1016/j.heliyon.2023.e18349 ↗
39. Tong, T., and Xiong, Y. (2022). Modeling Intention-Based Critical Determinants of E-Commerce Utilization: Emerging Business Models and Transformation in the Digital World. Front. Psychol., 13.10.3389/fpsyg.2022.889147 ↗
40. Satisfaction with Online Commercial Group Chat: The Influence of Perceived Technology Attributes, Chat Group Characteristics, and Advisor Communication Style10.1016/j.jretai.2007.03.004 ↗
41. Kim, S.S. (2020). Purchase intention in the online open market: Do concerns for E-commerce really matter?. Sustainability, 12.10.3390/su12030773 ↗
42. Innovation and Evaluation of Technology Acceptance Model (Tam) in S-Commerce: A Case of Social Media Platforms in Ghana10.4236/jcc.2022.103007 ↗
43. Impact of AR and VR in Industrial Revolution10.22214/ijraset.2022.47442 ↗
44. The partial least squares approach to causal modeling: Personal computer adoption and use as an illustration
45. Chin, W.W. (1998). The partial least squares approach for structural equation modeling. Modern Methods for Business Research, Lawrence Erlbaum Associates Publishers. Methodology for Business and Management.
46. Hinton, P., McMurray, I., and Brownlow, C. (2004). SPSS Explained, Routledge. [1st ed.].10.4324/9780203642597 ↗
47. Sarstedt, M., Ringle, C.M., and Hair, J.F. (2017). Partial Least Squares Structural Equation Modeling. Handbook of Market Research, Springer International Publishing.10.1007/978-3-319-05542-8_15-1 ↗
48. Impact of competitiveness on salespeople’s commitment and performance10.1016/j.jbusres.2011.10.026 ↗
49. Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research10.1108/EBR-10-2013-0128 ↗
50. A new criterion for assessing discriminant validity in variance-based structural equation modeling10.1007/s11747-014-0403-8 ↗
51. A meta-analysis of the technology acceptance model10.1016/j.im.2006.05.003 ↗
52. Customers’ Intention and Adoption of Telebanking in Jordan10.1080/10580530.2016.1155950 ↗
53. Trust and risk in consumer acceptance of e-services10.1007/s10660-015-9205-4 ↗
54. Understanding the mobile payment technology acceptance based on valence theory: A case of restaurant transactions10.1108/IJCHM-04-2016-0192 ↗
55. A Meta-analysis of Online Trust Relationships in E-commerce10.1016/j.intmar.2017.01.001 ↗
56. The effects of online service quality of e-commerce Websites on user satisfaction10.1108/EL-10-2013-0193 ↗
57. The role of technology readiness in individuals’ intention to use health and fitness applications: A comparison between users and non-users10.1108/APJML-09-2019-0534 ↗
58. What drives smartwatch purchase intention? Perspectives from hardware, software, design, and value10.1016/j.tele.2017.10.002 ↗
59. Understanding and Predicting Electronic Commerce Adoption: An Extension of the Theory of Planned Behavior10.25300/MISQ/2021/16266 ↗
60. (2023, December 20). Target Audience: Decorative Cosmetics Users in Indonesia|Statista. Available online: https://www.statista.com/study/125831/decorative-cosmetics-users-in-indonesia/.
61. Customer Loyalty as an Impact of Perceived Usefulness to Grab Users, Mediated by Customer Satisfaction and Moderated by Perceived Ease of Use10.21512/bbr.v12i1.6293 ↗
62. The Impact of Perceived Usefulness and Perceived Ease-of-Use Toward Repurchase Intention in the Indonesian E-Commerce Industry10.25124/jmi.v19i3.2412 ↗
63. Sherifi, D. (2023, December 14). Perceived Usefulness and Perceived Ease of Use Impact on Patient Portal Use. Available online: https://api.semanticscholar.org/CorpusID:79876699.
64. Impact of Perceived Security on Trust, Perceived Risk, and Acceptance of Online Banking in Iran10.1080/15332861.2016.1191052 ↗
65. The Impact of Perceived Security and Perceived Trust on the Use of m-Payment Applications in Saudi Arabia10.18517/ijaseit.12.6.16540 ↗
66. Almaiah, M.A., Al-Otaibi, S., Shishakly, R., Hassan, L., Lutfi, A., Alrawad, M., Qatawneh, M., and Abu Alghanam, O. (2023). Investigating the Role of Perceived Risk, Perceived Security and Perceived Trust on Smart m-Banking Application Using SEM. Sustainability, 15.10.3390/su15139908 ↗
67. In Automatic We Trust: Investigating the Impact of Trust, Control, Personality Characteristics, and Extrinsic and Intrinsic Motivations on the Acceptance of Autonomous Vehicles10.1080/10447318.2019.1572353 ↗
68. Dawwas, A., and Al-Haddad, S. (2023, December 15). The Impact of Locus of Control on Innovativeness. Available online: https://api.semanticscholar.org/CorpusID:199463023.
69. The influence of leadership styles on new product development performance: The moderating effect of product innovativeness10.1108/APJML-05-2019-0290 ↗
70. Applying the Technology Acceptance Model to Social Networking Sites (SNS): Impact of Subjective Norm and Social Capital on the Acceptance of SNS10.1080/10447318.2012.756333 ↗
71. Effects of Perceived Control on Usage Intention toward Digital Finance Service: Moderating Role of Privacy Concern
72. Public’s acceptance of automated vehicles: The role of initial trust and subjective norm10.1177/1071181319631183 ↗
73. Shuhaiber, A.H. (2023, December 19). The Role of Perceived Control, Enjoyment, Cost, Sustainability and Trust on Intention to Use Smart Meters: An Empirical Study Using SEM-PLS. In WorldCIST. Available online: https://api.semanticscholar.org/CorpusID:13782821.
74. Metaverse as a driver for customer experience and value co-creation: Implications for hospitality and tourism management and marketing10.1108/IJCHM-05-2022-0631 ↗
75. Carignani, F., Gesualda, I., and Francesco, B. (2023, January 29–30). How management uses AI in the museum field: From chatbots towards chatGTP. Proceedings of the Intervento Presentato al Convegno Sinergie-SIMA 2023 Conference, Bari, Italy.
76. Belenioti, Z.C. (2022). COVID-19 resilience via digital cultural heritage; digital life in museums and festivals during the anthropause. Int. J. Technol. Mark., 16.10.1504/IJTMKT.2022.10049149 ↗