Adoption of unmanned, cashierless retail technologyin Croatia: A study on student perceptions

Abstract

Objective: The objective of the article is to examine the technology acceptance of unmanned, cashierless technology. Since 2015, several startups have developed a new technology innovation called unmanned, cashierless technology, which has been steadily spreading globally over the past nine years. This study presents an analysis of user acceptance of this innovation among students in higher education institutions in Croatia.

Research Design & Methods: We examined factors influencing attitudes towards cashless transactions within the framework of the unified theory of technology acceptance and use (UTAUT2). We developed seven hypotheses based on previous literature and research models. We conducted the research through an online survey of Croatian students (n=406). We applied variance-based structural equitation modelling (PLS-SEM) to analyse the primary database.

Findings: The new trend in smart retail could help retailers to find a new way to improve their competitiveness. Based on our results, most UTAUT2 predictors such as performance expectancy, effort expectancy, social influence, hedonic motivation, and price sensitivity significantly influence behavioural intention.

Implications & Recommendations: This study offers implications for existing research on the new technology acceptance and contributes to relevant literature on customer behaviour. Given the importance of customer perception to improve business performance, the current study has some implications for marketers and retailers.

Contribution & Value Added: Investigating the adoption of unmanned, cashless technology, particularly among Generation Z, is an important and actual topic. This research can guide stakeholders and policymakers who are planning to introduce this cashierless technology. Based on the factors analysed, we can identify important and less important factors influencing consumers’ intentions. In this way, we can identify certain preferences of the target group analysed and use it as a basis for targeting them (e.g., in a campaign) when opening new stores.

Keywords

smart retail, cshierless stores, unmanned stores, technology acceptance and use, UTAUT2, PLS-SEM

References

1. Adapa, S., Fazal-e-Hasan, S.M., Makam, S.B., Azeem, M.M., & Mortimer, G. (2020). Examining the anteced-ents and consequences of perceived shopping value through smart retail technology. Journal of Retailing and Consumer Services, 52, 101901. https://doi.org/10.1016/j.jretconser.2019.101901
2. Alalwan, A.A., Dwivedi, Y.K., & Rana, N.P. (2017). Factors influencing adoption of mobile banking by Jordani-an bank customers: Extending UTAUT2 with trust. International Journal of Information Management, 37(3), 99-110. https://doi.org/10.1016/j.ijinfomgt.2017.01.002
3. Alavi, M. (2013). Structural equation modelling (SEM) in health sciences education researches: an overview of the method and its application. Iranian Journal of Medical Education, 13(6), 519-530. Retrieved from http://ijme.mui.ac.ir/article-1-2228-en.html on May 1, 2023.
4. Alkhatib, S., Kecskés, P., & Keller, V. (2023). Green marketing in the digital age: A systematic literature re-view. Sustainability, 15(16), 12369. https://doi.org/10.3390/su151612369
5. Ameri, A., Khajouei, R., Ameri, A., & Jahani, Y. (2020). Acceptance of a mobile-based educational application (LabSafety) by pharmacy students: An application of the UTAUT2 model. Education and Information Technologies, 25(1), 419-435. https://doi.org/10.1007/s10639-019-09965-5
6. Androniceanu, A. (2024). Artificial intelligence in administration and public management. Administratie si Management Public, 42, 99-114. https://doi.org/10.24818/amp/2023.42-06
7. Androniceanu, A., Sabie, O.M., Georgescu, I., & Drugău-Constantin, A.L. (2023). Main factors and causes that are influencing the digital competences of human resources. Administratie si Management Public, 41, 26-53. https://doi.org/10.24818/amp/2023.41-02
8. Andrzejewski, M., & Dunal, P. (2021). Artificial intelligence in the curricula of postgraduate stud-ies in financial management: Survey results. International Entrepreneurship Review, 7(4), 89-93. https://doi.org/10.15678/IER.2021.0704.07
9. Arpaci, I., Karatas, K., Kusci, I., & Al-Emran, M. (2022). Understanding the social sustainability of the Metaverse by integrating UTAUT2 and big five personality traits: A hybrid SEM-ANN approach. Technol-ogy in Society, 71, 102120. https://doi.org/10.1016/j.techsoc.2022.102120
10. Beh, P.K., Ganesan, Y., Iranmanesh, M., & Foroughi, B. (2021). Using smartwatches for fitness and health monitoring: the UTAUT2 combined with threat appraisal as moderators. Behaviour & Information Tech-nology, 40(3), 282-299. https://doi.org/10.1080/0144929X.2019.1685597
11. Becker, J.M., Klein, K., & Wetzels, M. (2012). Hierarchical latent variable models in PLS-SEM: guidelines for using reflective-formative type models. Long Range Planning, 45(5-6), 359-394. https://doi.org/10.1016/j.lrp.2012.10.001
12. Brown, S.A., & Venkatesh, V. (2005). Model of adoption of technology in households: A baseline model test and extension incorporating household life cycle. MIS Quarterly, 29(3), 399-426. https://doi.org/10.2307/25148690
13. Civelek, M., Krajčík, V., & Ključnikov, A. (2023). The impacts of dynamic capabilities on SMEs’ digital trans-formation process: The resource-based view perspective. Oeconomia Copernicana, 14(4), 1367-1392. https://doi.org/10.24136/oc.2023.019
14. Ćuzović, S., Mladenović, S.S., & Ćuzović, D. (2017). The Impact of Retail Formats on the Development of Food Retailing. Entrepreneurial Business and Economics Review, 5(1), 11-26. https://doi.org/10.15678/EBER.2017.050101
15. 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/10.1007/s11747-019-00696-0
16. D’souza, D.J., Joshi, H.G. & Prabhu, R. (2021). Assessment of Consumers Acceptance of E-Commerce to Pur-chase Geographical Indication Based Crop Using Technology Acceptance Model (TAM). AGRIS on-line Pa-pers in Economics and Informatics, 13(3), 25-33. https://doi.org/10.7160/aol.2021.130303
17. Dhingra, S., & Gupta, S. (2020). Behavioural intention to use mobile banking: An extension of UTAUT2 model. International Journal of Mobile Human Computer Interaction (IJMHCI), 12(3), 1-20. https://doi.org/10.4018/IJMHCI.2020070101
18. Dias, T., Gonçalves, R., Lopes da Costa, R., F. Pereira, L., & Dias, Álvaro. (2023). The impact of artificial intelli-gence on consumer behaviour and changes in business activity due to pandemic effects. Human Tech-nology, 19(1), 121-148. https://doi.org/10.14254/1795-6889.2023.19-1.8
19. Hair Jr, J.F., Sarstedt, M., Hopkins, L., & Kuppelwieser, V.G. (2014). Partial least squares structural equation modelling (PLS-SEM) An emerging tool in business research. European Business Review, 26(2), 106-121. https://doi.org/10.1108/EBR-10-2013-0128
20. Gelencsér, M., Szabó-Szentgróti, G., Kőmüves, Zs.S., & Hollósy-Vadász, G. (2023). The Holistic Model of La-bour Retention: The Impact of Workplace Wellbeing Factors on Employee Retention. Administrative Sci-ences, 13(5), 121. https://doi.org/10.3390/admsci13050121
21. Griethuijsen, R.A.L.F., Eijck, M.W., Haste, H., Brok, P.J., Skinner, N.C., & Mansour, N., et al. (2014). Global patterns in students’ views of science and interest in science. Research in Science Education, 45(4), 581-603. https://doi.org/10.1007/s11165-014-9438-6
22. Gumasing, J.J., Prasetyo, Y.T., Persada, S.F., Ong, A.K.S., Young, M.N., Nadlifatin, R., & Redi, A.A.N.P. (2022). Using Online Grocery Applications during the COVID-19 Pandemic: Their Relationship with Open Innova-tion. Journal of Open Innovation: Technology, Market, and Complexity, 8(2), 93, https://doi.org/10.3390/joitmc8020093
23. Hair, J.F., Anderson, R.E., Babin, B.J., & Black, W.C. (2010). Multivariate Data Analysis: A Global Perspective. Pearson Education.
24. Hair, Joe F., Ringle, C.M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139-152. https://doi.org/10.2753/MTP1069-6679190202
25. Henseler, J., Hubona, G., & Ray, P.A. (2016). Using PLS path modelling in new technology research: updated guidelines. Industrial Management & Data Systems, 116(1), 2-20. https://doi.org/10.1108/IMDS-09-2015-0382
26. Henseler, J., Ringle, C.M., & Sarstedt, M. (2015). A New Criterion for Assessing Discriminant Validity in Vari-ance-Based Structural Equation Modelling. Journal of the Academy of Marketing Science, 43, 115-35. https://doi.org/10.1007/s11747-014-0403-8
27. Hopalı, E. (2023). An Analysis of Customer Attitudes Towards Technology Driven Products: A Case Study in the Retail Industry (Doctoral dissertation, Marmara Universitesi (Turkey). Retrieved from https://www.proquest.com/openview/4c2a8acb5298bf499158b7c9ef341871/1?pq-origsite=gscholar&cbl=2026366&diss=y on March 20, 2024.
28. Hunady, J., Pisár, P., Vugec, D.S., & Bach, M.P. (2022). Digital Transformation in European Union: North is leading, and South is lagging behind. International Journal of Information Systems and Project Manage-ment, 10(4), 58-81. https://doi.org/10.12821/ijispm100403
29. Hsu, S.L. (2022). The Study of consumers’ intention to purchase in unmanned stores. Journal of Economic and Management, 18(1), 27. Retrieved from https://jem.fcu.edu.tw/assets/uploads/files/manuscript20220324090829285010.pdf on March 20, 2024.
30. Indrawati, & Putri, D.A. (2018). Analyzing Factors Influencing Continuance Intention of E-Payment Adoption Using Modified UTAUT 2 Model, 6th International Conference on Information and Communication Technology (ICoICT), Bandung, Indonesia, 2018, 167-173, https://doi.org/10.1109/ICoICT.2018.8528748
31. Ingalagi, S.S., Mutkekar, R.R., & Kulkarni, P.M. (2021). Artificial Intelligence (AI) adaptation: Analysis of de-terminants among Small to Medium-sized Enterprises (SME’s). In IOP Conference Series: Materials Sci-ence and Engineering, 1049(1), 012017. IOP Publishing. https://doi.org/10.1088/1757-899X/1049/1/012017
32. Ives, B., Cossick, K., & Adams, D. (2019). Amazon Go: disrupting retail?. Journal of Information Technology Teaching Cases, 9(1), 2-12. https://doi.org/10.1177/2043886918819092
33. Jajic, I., Khawaja, S., Hussain Qureshi, F., & Pejić Bach, M. (2022). Augmented reality in business and econom-ics: Bibliometric and topics analysis. Interdisciplinary Description of Complex Systems: INDECS, 20(6), 723-744. https://doi.org/10.7906/indecs.20.6.5
34. Jung, S.D., Claire, S., & Kim, S. (2024). Gen Z consumers’ expectations for smart convenience stores in the USA, South Korea, and Japan. Young Consumers, 25(3), 400-420. https://doi.org/10.1108/YC-10-2022-1623
35. Kapser, S., & Abdelrahman, M. (2020). Acceptance of autonomous delivery vehicles for last-mile delivery in Germany–Extending UTAUT2 with risk perceptions, Transportation Research Part C: Emerging Technolo-gies, 111, 210-225, https://doi.org/10.1016/j.trc.2019.12.016
36. Kilani, A.A.Z., Kakeesh, D.F., Al-Weshah, G.A., & Al-Debei, M.M. (2023). Consumer post-adoption of e-wallet: An extended UTAUT2 perspective with trust, Journal of Open Innovation: Technology, Market, and Com-plexity, 9(3), 100113. https://doi.org/10.1016/j.joitmc.2023.100113
37. Kliestik, T., Nica, E., Durana, P., & Popescu, G.H. (2023). Artificial intelligence-based predictive maintenance, time-sensitive networking, and big data-driven algorithmic decision-making in the economics of Indus-trial Internet of Things. Oeconomia Copernicana, 14(4), 1097-1138. https://doi.org/10.24136/oc.2023.033
38. Knežević, B., Naletina, D., & Damić, M. (2016). The Changing Structure of Retail Industry: Case Studies on Competitive Advantage of Small Companies in Croatia. Entrepreneurial Business and Economics Review, 4(4), 171-187. https://doi.org/10.15678/EBER.2016.040411
39. Knežević, B., Škrobot, P., & Žmuk, B. (2021). Position and role of social supermarkets in food supply chains. Business Systems Research: International journal of the Society for Advancing Innovation and Research in Economy, 12(1), 179-196. https://doi.org/10.2478/bsrj-2021-0012
40. Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration (ijec), 11(4), 1-10. https://doi.org/10.4018/ijec.2015100101
41. Kolková, A., & Ključnikov, A. (2022). Demand forecasting: AI-based, statistical and hybrid models vs practice-based models – the case of SMEs and large enterprises. Economics and Sociology, 15(4), 39-62. https://doi.org/10.14254/2071-789X.2022/15-4/2
42. Korkmaz, H., Fidanoglu, A., Ozcelik, S., & Okumus, A. (2022). User acceptance of autonomous public transport systems: Extended UTAUT2 model. Journal of Public Transportation, 24, 100013. https://doi.org/10.5038/2375-0901.23.1.5
43. Kwon, J., & Ahn, J. (2023). Effects of perceived values on affective and conative attitudes in cashierless store services. Inter-national Journal of Quality and Service Sciences, 15(3/4), 259-272. https://doi.org/10.1108/IJQSS-11-2022-0118
44. Leguina, A. (2015). A primer on partial least squares structural equation modeling (PLS-SEM). International Journal of Research & Method in Education, 38(2), 220-221. https://doi.org/10.1080/1743727X.2015.1005806
45. Lewandowska, A., Berniak-Woźny, J., & Ahmad, N. (2023). Competitiveness and innovation of small and medium enterprises under Industry 4.0 and 5.0 challenges: A comprehensive bibliometric analysis. Equi-librium. Quarterly Journal of Economics and Economic Policy, 18(4), 1045-1074. https://doi.org/10.24136/eq.2023.033
46. Lieonov, S., Hlawiczka, R., Boiko, A., Mynenko, S., & Garai-Fodor, M. (2022). Structural modelling for as-sessing the effectiveness of system for countering legalization of illicit money. Journal of International Studies, 15(3), 215-233. https://doi.org/10.14254/2071-8330.2022/15-3/15
47. Lin, C.Y. (2022). Understanding consumer perceptions and attitudes toward smart retail services. Journal of Services Marketing, 36(8), 1015-1030. https://doi.org/10.1108/JSM-09-2020-0407
48. Lowry, P.B., & Gaskin J. (2014). Partial least squares (PLS) structural equation modeling (SEM) for building and testing behavioral causal theory: When to choose it and how to use it. IEEE Transactions on Profes-sional Communication, 57(2), 123-146, https://doi.org/10.1109/TPC.2014.2312452
49. Mathew, A.O., Chowdhury, S., Devpura, S., & Lingappa, A.K. (2023). Factors Influencing Technology Ac-ceptance of Drones for Last‐Mile Food Deliveries: An Adaptation of the UTAUT2 Model. Human Behavior and Emerging Technologies, 2023(1), 7399080. https://doi.org/10.1155/2023/7399080
50. Meet, R.K., Kala, D., & Al-Adwan, A.S. (2022). Exploring factors affecting the adoption of MOOC in Genera-tion Z using extended UTAUT2 model. Education and Information Technologies, 27(7), 10261-10283. https://doi.org/10.1007/s10639-022-11052-1
51. Memon, A.H., & Rahman, I.A. (2013). Analysis of cost overrun factors for small scale construction projects in Malaysia using PLS-SEM method. Modern Applied Science, 7(8), 78. https://doi.org/10.5539/mas.v7n8p78
52. Milaković, I.K., & Mihić, M. (2016). Predictors and effect of consumer price sensitivity: The case of Croatia. In Forum on Economics and Business, 19(129), 3-26, Hungarian Economists' Society of Romania. Retrieved from http://193.231.19.17/kozgazdaszforum.ro/admin/upload/328_KF2016_4_cikk1.pdf on April 2, 2024.
53. Minh, T.N., Phamthi, V., & Minh, D.N. (2022). A Recipe for Success, Necessary Dimensions of Operations Management: A Case Study based on Walmart's Triumphs. ENTRENOVA-ENTerprise REsearch InNOVA-tion, 8(1), 406-420. https://doi.org/10.54820/entrenova-2022-0034
54. Mitchell, K.M., Holtz, B.E., & McCarroll, A.M. (2022). Assessing college students' perceptions of and intentions to use a mobile app for mental health. Telemedicine and e-Health, 28(4), 566-574. https://doi.org/10.1089/tmj.2021.0106
55. Moraru, M. (2021). How Retailers Support Consumers during the Pandemic: Kaufland Case Study. ENTRENO-VA-ENTerprise REsearch InNOVAtion, 7(1), 178-189. https://doi.org/10.54820/ZTII7228
56. Morosan, C., & DeFranco, A. (2016). It's about time: Revisiting UTAUT2 to examine consumers’ intentions to use NFC mobile payments in hotels. International Journal of Hospitality Management, 53, 17-29. https://doi.org/10.1016/j.ijhm.2015.11.003
57. Nikolopoulou, K., Gialamas, V., & Lavidas, K. (2021). Habit, hedonic motivation, performance expectancy and technological pedagogical knowledge affect teachers’ intention to use mobile internet. Computers and Education Open, 2, 100041.
58. Nordhoff, S., Louw, T., Innamaa, S., Lehtonen, E., Beuster, A., Torrao, G., Bjorvatne, A., Kessel, T., Malin, F., Happee, R., & Merat, N. (2020). Using the UTAUT2 model to explain public acceptance of conditionally automated (L3) cars: A questionnaire study among 9,118 car drivers from eight European countries. Transportation Research Part F: Traffic Psychology and Behaviour, 74, 280-297. https://doi.org/10.1016/j.trf.2020.07.015
59. Senyo, P.K., & Osabutey, E.L. (2020). Unearthing antecedents to financial inclusion through FinTech innova-tions. Technovation, 98, 102155. https://doi.org/10.1016/j.technovation.2020.102155
60. Palau-Saumell, R., Forgas-Coll, S., Sánchez-García, J., & Robres, E. (2019). User acceptance of mobile apps for restaurants: An expanded and extended UTAUT-2. Sustainability, 11(4), 1210. https://doi.org/10.3390/su11041210
61. Park, S. (2023). Study on the Distribution Environmental Characteristics of Unmanned Stores. Journal of Distribution Science, 21(3), 101-111. http://doi.org/10.15722/jds.21.03.202303.101
62. Payne, J., Lloyd, C., & Jose, S.P. (2023). ‘They tell us after they've decided things’: A cross‐country analysis of unions and digitalisation in retail. Industrial Relations Journal, 54(1), 3-19. https://doi.org/10.1111/irj.12390
63. Poncin, I., Garnier, M., Mimoun, M.S.B., & Leclercq, T. (2017). Smart technologies and shopping experience: Are gamification interfaces effective? The case of the Smartstore. Technological Forecasting and Social Change, 124, 320-331. https://doi.org/10.1016/j.techfore.2017.01.025
64. Ponte, D., & Bonazzi, S. (2023). Physical supermarkets and digital integration: acceptance of the cashierless concept. Tech-nology Analysis & Strategic Management, 35(9), 1178-1190. https://doi.org/10.1080/09537325.2021.1994942
65. Qi, C. (2019). Is checkout- free store a flash in the pan? Factors influencing Hong Kong people’s adoption intention of checkout-free stores. ACIS 2019 Proceedings. 11. Retrieved from https://aisel.aisnet.org/acis2019/11 on March 6, 2024.
66. Ray, A., Jana, S., & Rana, N.P. (2023). An NLP-based Mixed-method Approach to Explore the Impact of Grati-fications and Emotions on the Acceptance of Amazon Go. Asia Pacific Journal of Information Systems, 33(3), 541-572. https://doi.org/10.14329/apjis.2023.33.3.541
67. Ringle, C.M., Wende, S., & Becker, J-M. (2022). SmartPLS 4. Oststeinbek: SmartPLS. Retrieved from https://www.smartpls.com on March 6, 2024.
68. Roshchyk, I., Oliinyk, O., Mishchuk, H., & Bilan, Y. (2022). IT Products, E-Commerce, and Growth: Analysis of Links in Emerging Market. Transformations in Business & Economics, 21(1), 209-227.
69. Schmitz, A., Díaz-Martín, A.M., & Guillén, M.J.Y. (2022). Modifying UTAUT2 for a cross-country comparison of telemedicine adoption. Computers in Human Behavior, 130, 107183. https://doi.org/10.1016/j.chb.2022.107183
70. Shoheib, Z., & Abu-Shanab, E.A. (2022). Adapting the UTAUT2 model for social commerce context. Interna-tional Journal of E-Business Research (IJEBR), 18(1), 1-20. Retrieved from https://www.igi-global.com/pdf.aspx?tid=293293&ptid=277523&ctid=4&oa=true&isxn=9781799893783 on March 6, 2024.
71. Schögel, M., & Lienhard, S.D. (2020). Cashierless Stores-The New Way to the Customer? Marketing Review St. Gallen: St. Gallen, Switzerland. Retrieved from www.alexandria.unisg.ch/server/api/core/bitstreams/30932a88-0593-4cd8-92af-32a9d9851db5/content on March 5, 2024.
72. Selter, J.L., Fota, A., Wagner, K., & Schramm-Klein, H. (2023). Aspects driving customers' intention to use automated purchasing processes. International Journal of Retail & Distribution Management, 51(9/10), 1158-1173. https://doi.org/10.1108/IJRDM-10-2022-0397
73. Sieja, M., & Wach, K. (2023). Revolutionary artificial intelligence or rogue technology? The promises and pitfalls of ChatGPT. International Entrepreneurship Review, 9(4), 101-115. https://doi.org/10.15678/IER.2023.0904.07
74. Stočes, M., Vaněk, J., Jarolímek, J., Novák, V., Masner, J., Šimek, P., Kánská, E., Havránek, M., Kubata, K. & Voral, V. (2023). Agriculture Data Platform – Institutional Data Repository – Selected Aspects. AGRIS on-line Papers in Economics and Informatics, 15(4), 127-133. https://doi.org/10.7160/aol.2023.150409
75. Sun, W., Shin, H.Y., Wu, H., & Chang, X. (2023). Extending UTAUT2 with knowledge to test Chinese consum-ers' adoption of imported spirits flash delivery applications. Heliyon, 9(5), e16346, https://doi.org/10.1016/j.heliyon.2023.e16346
76. Szabó-Szentgróti, E., Konczos-Szombathelyi, M., Rámháp, S., & Kézai, P.K. (2023a). How Consumers Accept Unmanned Smart Stores?–Introducing a Proposed Technology Acceptance Model. Chemical Engineering Transactions, 107, 373-378. https://doi.org/10.3303/CET23107063
77. Szabó-Szentgróti, E., Rámháp, S., & Kézai, P.K. (2023b). Systematic Review of Cashierless Stores (Just Walk Out Stores) Revolutionizing The Retail. Management & Marketing, 18(s1), 427-448. https://doi.org/10.2478/mmcks-2023-0023
78. Śmigielska, G., & Stefańska, M. (2017). Innovative Positioning as a Marketing Tool of Retailers on the Food Market. Entrepreneurial Business and Economics Review, 5(1), 77-90. https://doi.org/10.15678/EBER.2017.050105
79. Ton, A.D., Szabó-Szentgróti, G., & Hammerl, L. (2022a). Competition within Cross-Functional Teams: A Struc-tural Equation Model on Knowledge Hiding. Social Sciences, 11(1), 30. https://doi.org/10.3390/socsci11010030
80. Ton, A.D., Hammerl, L., & Szabó-Szentgróti, G. (2022b). Using Smartphones to Prevent Cross-Functional Team Knowledge Hiding: The Impact of Openness & Neuroticism. International Journal of Interactive Mobile Technologies (IJIM), 16(11), 162-177. https://doi.org/10.3991/ijim.v16i11.30503
81. Triantafillidou, A., Siomkos, G., & Papafilippaki, E. (2017). The effects of retail store characteristics on in-store leisure shopping experience. International Journal of Retail & Distribution Management, 45(10), 1034-1060. https://doi.org/10.1108/IJRDM-07-2016-0121
82. Tseng, T.H., Lin, S., Wang, Y.S., & Liu, H.X. (2022). Investigating teachers’ adoption of MOOCs: the perspective of UTAUT2. Interactive Learning Environments, 30(4), 635-650. https://doi.org/10.1080/10494820.2019.1674888
83. Türegün, N. (2019). Impact of technology in financial reporting: The case of Amazon Go. Journal of Corporate Accounting & Finance, 30(3), 90-95. http://doi.org/10.1002/jcaf.22394
84. Thomas, M. (2023). Croatian Supermarket Ignites Retail Revolution with Region's First Cashierless SMART Store. Retrieved from https://www.thedubrovniktimes.com/news/croatia/item/15535-croatian-supermarket-ignites-retail-revolution-with-region-s-first-cashierless-smart-store on January 10, 2023.
85. Venkatesh, V., Morris, M.G., Davis, G.B., & Davis, F.D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478. http://doi.org/10.2307/30036540
86. Venkatesh, V., Thong, J.Y., & Xu, X. (2012). Consumer acceptance and use of information technology: extend-ing the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157-178. http://doi.org/10.2307/41410412
87. Vitezić, V., & Perić, M. (2024). The role of digital skills in the acceptance of artificial intelligence, Journal of Business & Industrial Marketing, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/JBIM-04-2023-0210
88. Wei, M.F., Luh, Y.H., Huang, Y.H., & Chang, Y.C. (2021). Young generation’s mobile payment adoption behav-ior: Analysis based on an extended UTAUT model. Journal of Theoretical and Applied Electronic Com-merce Research, 16(4), 618-637. https://doi.org/10.3390/jtaer16040037
89. Wong, J.W., & Yap, K.H.A. (2024). Factors influencing the adoption of artificial intelligence in accounting among micro, small medium enterprises (MSMEs). Quantum Journal of Social Sciences and Humanities, 5(1), 16-28. https://doi.org/10.55197/qjssh.v5i1.323
90. Xu, J., Hu, Z., Zou, Z., Zou, J., Hu, X., Liu, L., & Zheng, L. (2020). Design of smart unstaffed retail shop based on IoT and artificial intelligence. IEEE Access, 8, 147728-147737. http://doi.org/10.1109/ACCESS.2020.3014047
91. Zarco, C., Giráldez-Cru, J., Cordón, O., & Liébana-Cabanillas, F. (2024). A comprehensive view of biometric payment in retailing: A complete study from user to expert. Journal of Retailing and Consumer Services, 79. https://doi.org/10.1016/j.jretconser.2024.103789
92. Zhu, X., Zhang, P., Wei, Y., Li, Y., & Zhao, H. (2019). Measuring the efficiency and driving factors of urban land use based on the DEA method and the PLS-SEM model—A case study of 35 large and medium-sized cit-ies in China. Sustainable Cities and Society, 50, 101646. https://doi.org/10.1016/j.scs.2019.101646