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Neuromanagement decision making in facial recognition biometric authentication as a mobile payment technology in retail, restaurant, and hotel business models

Abstract

Research background: With growing evidence of biometric identification techniques as authentication, there is a pivotal need for comprehending contactless payments by use of facial recognition algorithms in retail, restaurant, and hotel business models.

Purpose of the article: In this research, previous findings were cumulated showing that harnessing facial recognition payment applications as software-based contactless biometric algorithms results in remarkably qualitative enhancement in purchasing experience.

Methods: Throughout March and November 2021, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was carried out, with search terms including "facial recognition payment technology", "facial recognition payment system", "facial recognition payment application", "face recognition-based payment service", "facial authentication for mobile payment transactions", and "contactless payment through facial recognition algorithms". As the analyzed research was published between 2017 and 2021, only 187 articles satisfied the eligibility criteria. By removing questionable or unclear findings (limited/nonessential data), results unsubstantiated by replication, too general content, or having quite similar titles, 38, mainly empirical, sources were selected. The Systematic Review Data Repository was harnessed, a software program for the gathering, processing, and analysis of data for our systematic review. The quality of the selected scholarly sources was assessed by employing the Mixed Method Appraisal Tool.

Findings & value added: Harnessing facial recognition payment applications as software-based contactless biometric algorithms results in remarkably qualitative enhancement in purchasing experience. Subsequent attention should be directed to whether perceived value and trust shape customers? adoption of biometric recognition payment devices.

Keywords

neuromanagement decision making, facial recognition, biometric authentication, mobile payment technology, retail, restaurant, hotel business

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References

  1. Adams, D., & Krulicky, T. (2021). Artificial intelligence-driven big data analytics, real-time sensor networks, and product decision-making information systems in sustainable manufacturing Internet of Things. Economics, Management, and Financial Markets, 16(3), 81?93. doi: 10.22381/emfm16320215. DOI: https://doi.org/10.22381/emfm16320215
    View in Google Scholar
  2. Adams, D., Novak, A., Kliestik, T., & Potcovaru, A.-M. (2021). Sensor-based big data applications and environmentally sustainable urban development in Internet of Things-enabled smart cities. Geopolitics, History, and International Relations, 13(1), 108?118. doi: 10.22381/GHIR131202110. DOI: https://doi.org/10.22381/GHIR131202110
    View in Google Scholar
  3. Aljanabi, M. A., Hussain, Z. M., & Lu, S. F. (2018). An entropy-histogram approach for image similarity and face recognition. Mathematical Problems in Engineering, 9801308. doi: 10.1155/2018/9801308. DOI: https://doi.org/10.1155/2018/9801308
    View in Google Scholar
  4. Andrejevic, M., & Selwyn, N. (2020). Facial recognition technology in schools: critical questions and concerns. Learning, Media and Technology, 45, 115?128. doi: 10.1080/17439884.2020.1686014. DOI: https://doi.org/10.1080/17439884.2020.1686014
    View in Google Scholar
  5. Androniceanu, A. (2019). Social responsibility, an essential strategic option for a sustainable development in the field of bio-economy. Amfiteatru Economic, 21(52), 503?519. doi: 10.24818/EA/2019/52/503. DOI: https://doi.org/10.24818/EA/2019/52/503
    View in Google Scholar
  6. Androniceanu, A., Kinnunen, J., & Georgescu, I. (2020). E-Government clusters in the EU based on the Gaussian mixture models. Administratie si Management Public, 35, 6?20. doi: 10.24818/amp/2020.35-01. DOI: https://doi.org/10.24818/amp/2020.35-01
    View in Google Scholar
  7. Androniceanu, A. (2021). Transparency in public administration as a challenge for a good democratic governance. Administratie si Management Public, 36, 149?164. doi: 10.24818/amp/2021.36-09. DOI: https://doi.org/10.24818/amp/2021.36-09
    View in Google Scholar
  8. Bacalu, F. (2021). Digital policing tools as social control technologies: data-driven predictive algorithms, automated facial recognition surveillance, and law enforcement biometrics. Analysis and Metaphysics, 20, 74?88. doi: 10.22381/a m2020215. DOI: https://doi.org/10.22381/AM2020215
    View in Google Scholar
  9. Bailey, L. (2021). The digital fabric of reproductive technologies: fertility, pregnancy, and menstrual cycle tracking apps. Journal of Research in Gender Studies, 11(2), 126?138. doi: 10.22381/JRGS11220219. DOI: https://doi.org/10.22381/JRGS11220219
    View in Google Scholar
  10. Balica, R. (2019). Automated data analysis in organizations: sensory algorithmic devices, intrusive workplace monitoring, and employee surveillance. Psychosociological Issues in Human Resource Management, 7(2), 61?66. doi: 10.22381/PIHRM72201910. DOI: https://doi.org/10.22381/PIHRM72201910
    View in Google Scholar
  11. Barbu, C. M., Florea, D. L., Dabija, D. C., & Barbu, M. C. R. (2021). Customer experience in fintech. Journal of Theoretical and Applied Electronic Commerce Research, 16, 1415?1433. doi: 10.3390/jtaer16050080. DOI: https://doi.org/10.3390/jtaer16050080
    View in Google Scholar
  12. Bennett, A. (2021). Autonomous vehicle driving algorithms and smart mobility technologies in big data-driven transportation planning and engineering. Contemporary Readings in Law and Social Justice, 13(1), 20?29. doi: 10.2238 1/CRLSJ13120212. DOI: https://doi.org/10.22381/CRLSJ13120212
    View in Google Scholar
  13. Birtus, M., & Lăzăroiu, G. (2021). The neurobehavioral economics of the COVID-19 pandemic: Consumer cognition, perception, sentiment, choice, and decision-making. Analysis and Metaphysics, 20, 89?101. doi: 10.22381/am2020216. DOI: https://doi.org/10.22381/AM2020216
    View in Google Scholar
  14. Blackburn, E., & Pera, A. (2021). Autonomous vehicle interaction control software, big geospatial data analytics, and networked driverless technologies in smart sustainable urban transport systems. Contemporary Readings in Law and Social Justice, 13(2), 121?134. doi: 10.22381/CRLSJ13220219. DOI: https://doi.org/10.22381/CRLSJ13220219
    View in Google Scholar
  15. Blake, R., & Frajtova Michalikova, K. (2021). Deep learning-based sensing technologies, artificial intelligence-based decision-making algorithms, and big geospatial data analytics in cognitive Internet of Things. Analysis and Metaphysics, 20, 159?173. doi: 10.22381/am20202111. DOI: https://doi.org/10.22381/AM20202111
    View in Google Scholar
  16. Blake, R., Michalkova, L., & Bilan, Y. (2021). Robotic wireless sensor networks, industrial artificial intelligence, and deep learning-assisted smart process planning in sustainable cyber-physical manufacturing systems. Journal of Self-Governance and Management Economics, 9(4), 48?61. doi: 10.22381/jsme 9420214. DOI: https://doi.org/10.22381/jsme9420214
    View in Google Scholar
  17. Burke, S., & Zvarikova, K. (2021). Urban Internet of Things systems and data monitoring algorithms in smart and environmentally sustainable cities. Geopolitics, History, and International Relations, 13(2), 135?148. doi: 10.2238 1/GHIR132202110. DOI: https://doi.org/10.22381/GHIR132202110
    View in Google Scholar
  18. Campbell, E., Novak, A., & Novak Sedlackova, A. (2021). Algorithm-driven sensing devices and connected vehicle data in smart transportation networks. Contemporary Readings in Law and Social Justice, 13(1), 91?100. doi: 10.223 81/CRLSJ13120219. DOI: https://doi.org/10.22381/CRLSJ13120219
    View in Google Scholar
  19. Cham, T.-H., Cheah, J.-H., Cheng, B.-L., & Lim, X.-J. (2021). I am too old for this! Barriers contributing to the non-adoption of mobile payment. International Journal of Bank Marketing. Advance online publication. doi: 10.1108/IJBM-06-2021-0283. DOI: https://doi.org/10.1108/IJBM-06-2021-0283
    View in Google Scholar
  20. Chapman, D. (2021). Environmentally sustainable urban development and Internet of Things connected sensors in cognitive smart cities. Geopolitics, History, and International Relations, 13(2), 51?64. doi: 10.22381/GHIR13220214. DOI: https://doi.org/10.22381/GHIR13220214
    View in Google Scholar
  21. Church, K. W. (2018). Emerging trends: artificial intelligence, China and my new job at Baidu. Natural Language Engineering, 24, 641?647. doi: 10.1017/S13 51324918000189. DOI: https://doi.org/10.1017/S1351324918000189
    View in Google Scholar
  22. Ciftci, O., Choi, E.-K. (C.), & Berezina, K. (2020). Customer intention to use facial recognition technology at quick-service restaurants. e-Review of Tourism Research, 17, 753?763.
    View in Google Scholar
  23. Ciftci, O., Choi, E.-K. (C.), & Berezina, K. (2021). Let?s face it: are customers ready for facial recognition technology at quick-service restaurants? International Journal of Hospitality Management, 95, 102941. doi: 10.1016/j.i jhm.2021.102941. DOI: https://doi.org/10.1016/j.ijhm.2021.102941
    View in Google Scholar
  24. Ciobanu, A, Androniceanu, A., & Lăzăroiu, G. (2019). An integrated psycho-sociological perspective on public employees? motivation and performance. Frontiers in Psychology, 10, 36. doi: 10.3389/fpsyg.2019.00036. DOI: https://doi.org/10.3389/fpsyg.2019.00036
    View in Google Scholar
  25. Dang, V. T., Nguyen, N., Nguyen, H. V., Nguyen, H., Van Huy, L., & Tran, V. T. (2021). Consumer attitudes toward facial recognition payment: an examination of antecedents and outcomes. International Journal of Bank Marketing. Advance online publication. doi: 10.1108/IJBM-04-2021-0135. DOI: https://doi.org/10.1108/IJBM-04-2021-0135
    View in Google Scholar
  26. Daragmeh, A., Lentner, C., & Sági, J. (2021). FinTech payments in the era of COVID-19: factors influencing behavioral intentions of ?Generation X? in Hungary to use mobile payment. Journal of Behavioral and Experimental Finance, 32, 100574. doi: 10.1016/j.jbef.2021.100574. DOI: https://doi.org/10.1016/j.jbef.2021.100574
    View in Google Scholar
  27. De Keyser, A., Bart, Y., Gu, X., Liu, S. Q., Robinson, S. G., & Kannan, P. K. (2021). Opportunities and challenges of using biometrics for business: Developing a research agenda. Journal of Business Research, 136, 52?62. doi: 10.1016/j.jbusres.2021.07.028. DOI: https://doi.org/10.1016/j.jbusres.2021.07.028
    View in Google Scholar
  28. Du, M. (2018). Mobile payment recognition technology based on face detection algorithm. Concurrency and Computation: Practice and Experience, 30, e4655. doi: 10.1002/cpe.4655. DOI: https://doi.org/10.1002/cpe.4655
    View in Google Scholar
  29. Elloumi, W., Cauchois, C., & Pasqual, C. (2021). Will face recognition revolutionise the shopping experience? Biometric Technology Today, 3, 8?11. doi: 10.1016/S0969-4765(21)00036-9. DOI: https://doi.org/10.1016/S0969-4765(21)00036-9
    View in Google Scholar
  30. Feng, W., Zhou, J., Dan, C., Peiyan, Z., & Li, Z. (2017). Research on mobile commerce payment management based on the face biometric authentication. International Journal of Mobile Communications, 15, 278?305. DOI: https://doi.org/10.1504/IJMC.2017.083463
    View in Google Scholar
  31. Ford, C. (2021). Technologically-mediated emotional and social experiences: intimate data sharing by algorithm-based fertility apps. Journal of Research in Gender Studies, 11(2), 87?99. doi: 10.22381/JRGS11220216. DOI: https://doi.org/10.22381/JRGS11220216
    View in Google Scholar
  32. Galbraith, A., & Podhorska, I. (2021). Artificial intelligence data-driven Internet of Things systems, robotic wireless sensor networks, and sustainable organizational performance in cyber-physical smart manufacturing. Economics, Management, and Financial Markets, 16(4), 56?69. doi: 10.22381/emfm16420 214. DOI: https://doi.org/10.22381/emfm16420214
    View in Google Scholar
  33. Gibson, P., & Macek, J. (2021). Sustainable industrial big data, automated production processes, and cyber-physical system-based manufacturing in smart networked factories. Journal of Self-Governance and Management Economics, 9(3), 22?34. doi: 10.22381/jsme9320212. DOI: https://doi.org/10.22381/jsme9320212
    View in Google Scholar
  34. Griffin, K., & Krastev, V. (2021). Smart traffic planning and analytics, autonomous mobility technologies, and algorithm-driven sensing devices in urban transportation systems. Contemporary Readings in Law and Social Justice, 13(2), 65?78. doi: 10.22381/CRLSJ13220215. DOI: https://doi.org/10.22381/CRLSJ13220215
    View in Google Scholar
  35. Hamilton, S. (2021). Real-time big data analytics, sustainable Industry 4.0 wireless networks, and Internet of Things-based decision support systems in cyber-physical smart manufacturing. Economics, Management, and Financial Markets, 16(2), 84?94. doi: 10.22381/emfm16220215. DOI: https://doi.org/10.22381/emfm16220215
    View in Google Scholar
  36. Haseeb, M., Hussain, H. I., Kot, S., Androniceanu, A., & Jermsittiparsert, K. (2019). Role of social and technological challenges in achieving a sustainable competitive advantage and sustainable business performance. Sustainability, 11(14), 3811. doi: 10.3390/su11143811. DOI: https://doi.org/10.3390/su11143811
    View in Google Scholar
  37. Hurley, D., & Popescu, G. H. (2021). Medical big data and wearable Internet of Things healthcare systems in remotely monitoring and caring for confirmed or suspected COVID-19 patients. American Journal of Medical Research, 8(2), 78?90. doi: 10.22381/ajmr8220216. DOI: https://doi.org/10.22381/ajmr8220216
    View in Google Scholar
  38. Ionescu, L. (2020). Digital data aggregation, analysis, and infrastructures in fintech operations. Review of Contemporary Philosophy, 19, 92?98. doi: 10.22381/RC P19202010. DOI: https://doi.org/10.22381/RCP19202010
    View in Google Scholar
  39. Johnson, E., & Nica, E. (2021). Connected vehicle technologies, autonomous driving perception algorithms, and smart sustainable urban mobility behaviors in networked transport systems. Contemporary Readings in Law and Social Justice, 13(2), 37?50. doi: 10.22381/CRLSJ13220213. DOI: https://doi.org/10.22381/CRLSJ13220213
    View in Google Scholar
  40. Kassick, D. (2019). Workforce analytics and human resource metrics: algorithmically managed workers, tracking and surveillance technologies, and wearable biological measuring devices. Psychosociological Issues in Human Resource Management, 7(2), 55?60. doi: 10.22381/PIHRM7220199. DOI: https://doi.org/10.22381/PIHRM7220199
    View in Google Scholar
  41. Kim, M., Kim, S., & Kim, J. (2019). Can mobile and biometric payments replace cards in the Korean offline payments market? Consumer preference analysis for payment systems using a discrete choice model. Telematics and Informatics, 38, 46?58. doi: 10.1016/j.tele.2019.02.003. DOI: https://doi.org/10.1016/j.tele.2019.02.003
    View in Google Scholar
  42. Konhäusner, P., Shang, B., & Dabija, D.-C. (2021). Application of the 4Es in online crowdfunding platforms: a comparative perspective of Germany and China. Journal of Risk and Financial Management, 14, 49. doi: 10.3390/ jrfm14020049. DOI: https://doi.org/10.3390/jrfm14020049
    View in Google Scholar
  43. Kostka, G., Steinacker, L., & Meckel, M. (2021). Between security and convenience: facial recognition technology in the eyes of citizens in China, Germany, the United Kingdom, and the United States. Public Understanding of Science. Advance online publication. doi: 10.1177/09636625211001555. DOI: https://doi.org/10.1177/09636625211001555
    View in Google Scholar
  44. Kovacova, M., & Lăzăroiu, G. (2021). Sustainable organizational performance, cyber-physical production networks, and deep learning-assisted smart process planning in Industry 4.0-based manufacturing systems. Economics, Management, and Financial Markets, 16(3), 41?54. doi: 10.22381/emfm16320 212.
    View in Google Scholar
  45. Lai, X., & Rau, P.-L. P. (2021). Has facial recognition technology been misused? A public perception model of facial recognition scenarios. Computers in Human Behavior, 124, 106894. doi: 10.1016/j.chb.2021.106894. DOI: https://doi.org/10.1016/j.chb.2021.106894
    View in Google Scholar
  46. Lau, A. (2020). New technologies used in COVID-19 for business survival: Insights from the Hotel Sector in China. Information Technology & Tourism, 22, 497?504. doi: 10.1007/s40558-020-00193-z. DOI: https://doi.org/10.1007/s40558-020-00193-z
    View in Google Scholar
  47. Lăzăroiu, G., Kliestik, T., & Novak, A. (2021). Internet of Things smart devices, industrial artificial intelligence, and real-time sensor networks in sustainable cyber-physical production systems. Journal of Self-Governance and Management Economics, 9(1), 20?30. doi: 10.22381/jsme9120212. DOI: https://doi.org/10.22381/jsme9120212
    View in Google Scholar
  48. Lăzăroiu, G., & Harrison, A. (2021). Internet of Things sensing infrastructures and data-driven planning technologies in smart sustainable city governance and management. Geopolitics, History, and International Relations, 13(2), 23?36. doi: 10.22381/GHIR13220212. DOI: https://doi.org/10.22381/GHIR13220212
    View in Google Scholar
  49. Levy, K., & Barocas, S. (2018). Refractive surveillance: monitoring customers to manage workers. International Journal of Communication, 12, 1166?1188. doi: 1932?8036/20180005.
    View in Google Scholar
  50. Li, Y., Wang, Y., Hao, S., & Zhao, X. (2019). Intelligent terminal face spoofing detection algorithm based on deep belief network. Journal of Electronic Imaging, 28, 043024. doi: 10.1117/1.JEI.28.4.043024. DOI: https://doi.org/10.1117/1.JEI.28.4.043024
    View in Google Scholar
  51. Liu, D., & Tu, W. (2021). Factors influencing consumers? adoptions of biometric recognition payment devices: combination of initial trust and UTAUT model. International Journal of Mobile Communications, 19, 345?363. DOI: https://doi.org/10.1504/IJMC.2021.114324
    View in Google Scholar
  52. Liu, Y.-l., Yan, W., & Hu, B. (2021). Resistance to facial recognition payment in China: the influence of privacy-related factors. Telecommunications Policy, 45, 102155. doi: 10.1016/j.telpol.2021.102155. DOI: https://doi.org/10.1016/j.telpol.2021.102155
    View in Google Scholar
  53. Lott, D. (2018). Biometrics: modernising customer authentication for financial services and payments. Journal of Payments Strategy & Systems, 12, 371?382.
    View in Google Scholar
  54. Mihăilă, R., & Brani?te, L. (2021). Digital semantics of beauty apps and filters: big data-driven facial retouching, aesthetic self-monitoring devices, and augmented reality-based body-enhancing technologies. Journal of Research in Gender Studies, 11(2), 100?112. doi: 10.22381/JRGS11220217. DOI: https://doi.org/10.22381/JRGS11220217
    View in Google Scholar
  55. Mircică, N. (2020). Restoring public trust in digital platform operations: machine learning algorithmic structuring of social media content. Review of Contemporary Philosophy, 19, 85?91. doi: 10.22381/RCP1920209. DOI: https://doi.org/10.22381/RCP1920209
    View in Google Scholar
  56. Mitchell, A. (2021). Autonomous vehicle algorithms, big geospatial data analytics, and interconnected sensor networks in urban transportation systems. Contemporary Readings in Law and Social Justice, 13(1), 50?59. doi: 10.2238 1/CRLSJ13120215. DOI: https://doi.org/10.22381/CRLSJ13120215
    View in Google Scholar
  57. Mitchell, K., Grupac, M., & Zauskova, A. (2021). Ethical management and implementation of COVID-19 immunity passports and vaccination certificates: Lawfulness, fairness, and transparency. Linguistic and Philosophical Investigations, 20, 45?54. doi: 10.22381/LPI2020213. DOI: https://doi.org/10.22381/LPI2020213
    View in Google Scholar
  58. Monajemi, M. (2018). Privacy regulation in the age of biometrics that deal with a new world order of information. University of Miami International and Comparative Law Review, 25, 371?408.
    View in Google Scholar
  59. Moriuchi, E. (2021). An empirical study of consumers? intention to use biometric facial recognition as a payment method. Psychology & Marketing. Advance online publication. doi: 10.1002/mar.21495. DOI: https://doi.org/10.1002/mar.21495
    View in Google Scholar
  60. Morrison, M. (2021). The datafication of fertility and reproductive health: Menstrual cycle tracking apps and ovulation detection algorithms. Journal of Research in Gender Studies, 11(2), 139?151. doi: 10.22381/JRGS112202110. DOI: https://doi.org/10.22381/JRGS112202110
    View in Google Scholar
  61. Morrison, M., & Lăzăroiu, G. (2021). Cognitive Internet of Medical Things, big healthcare data analytics, and artificial intelligence-based diagnostic algorithms during the COVID-19 pandemic. American Journal of Medical Research, 8(2), 23?36. doi: 10.22381/ajmr8220212. DOI: https://doi.org/10.22381/ajmr8220212
    View in Google Scholar
  62. Nica, E., Miklencicova, R., & Kicova, E. (2019). Artificial intelligence-supported workplace decisions: big data algorithmic analytics, sensory and tracking technologies, and metabolism monitors. Psychosociological Issues in Human Resource Management, 7(2), 31?36. doi: 10.22381/PIHRM7220195. DOI: https://doi.org/10.22381/PIHRM7220195
    View in Google Scholar
  63. Nica, E. (2021). Urban big data analytics and sustainable governance networks in integrated smart city planning and management. Geopolitics, History, and International Relations, 13(2), 93?106. doi: 10.22381/GHIR13220217. DOI: https://doi.org/10.22381/GHIR13220217
    View in Google Scholar
  64. Nica, E., & Stehel, V. (2021). Internet of Things sensing networks, artificial intelligence-based decision-making algorithms, and real-time process monitoring in sustainable Industry 4.0. Journal of Self-Governance and Management Economics, 9(3), 35?47. doi: 10.22381/jsme9320213. DOI: https://doi.org/10.22381/jsme9320213
    View in Google Scholar
  65. Nica, E., Stan, C. I., Lu?an (Petre), A. G., & Oa?a (Geambazi), R.-?. (2021). Internet of Things-based real-time production logistics, sustainable industrial value creation, and artificial intelligence-driven big data analytics in cyber-physical smart manufacturing systems. Economics, Management, and Financial Markets, 16(1), 52?62. doi: 10.22381/emfm16120215. DOI: https://doi.org/10.22381/emfm16120215
    View in Google Scholar
  66. Norfolk, L., & O?Regan, M. (2021). Biometric technologies at music festivals: an extended technology acceptance model. Journal of Convention & Event Tourism, 22, 36?60. doi: 10.1080/15470148.2020.1811184. DOI: https://doi.org/10.1080/15470148.2020.1811184
    View in Google Scholar
  67. Novak, A., Bennett, D., & Kliestik, T. (2021). Product decision-making information systems, real-time sensor networks, and artificial intelligence-driven big data analytics in sustainable Industry 4.0. Economics, Management, and Financial Markets, 16(2), 62?72. doi: 10.22381/emfm16220213. DOI: https://doi.org/10.22381/emfm16220213
    View in Google Scholar
  68. Olsen, M. (2019). Using data analytics in the management of employees: digital means of tracking, monitoring, and surveilling worker activities. Psychosociological Issues in Human Resource Management, 7(2), 43?48. doi: 10.22381/PIHRM7220197. DOI: https://doi.org/10.22381/PIHRM7220197
    View in Google Scholar
  69. Olssen, M. (2021). The rehabilitation of the concept of public good: reappraising the attacks from liberalism and neo-liberalism from a poststructuralist perspective. Review of Contemporary Philosophy, 20, 7?52. doi: 10.22381/RC P2020211. DOI: https://doi.org/10.22381/RCP2020211
    View in Google Scholar
  70. Palm, M. (2018). Then press enter: digital payment technology and the history of telephone interface. Cultural Studies, 32, 582?599. doi: 10.1080/09502386.201 7.1384034. DOI: https://doi.org/10.1080/09502386.2017.1384034
    View in Google Scholar
  71. Pantano, E. (2020). Non-verbal evaluation of retail service encounters through consumers? facial expressions. Computers in Human Behavior, 111, 106448. doi: 10.1016/j.chb.2020.106448. DOI: https://doi.org/10.1016/j.chb.2020.106448
    View in Google Scholar
  72. Pelau, C., Dabija, D.-C., & Ene, I. (2021). What makes an AI device human-like? The role of interaction quality, empathy and perceived psychological anthropomorphic characteristics in the acceptance of artificial intelligence in the service industry. Computers in Human Behavior, 122, 106855. doi: 10.1016/ j.chb.2021.106855. DOI: https://doi.org/10.1016/j.chb.2021.106855
    View in Google Scholar
  73. Peters, M. A. (2022). A post-marxist reading of the knowledge economy: open knowledge production, cognitive capitalism, and knowledge socialism. Analysis and Metaphysics, 21, 7?23. doi: 10.22381/am2120221. DOI: https://doi.org/10.22381/am2120221
    View in Google Scholar
  74. Platt, C. (2021). Public legitimacy of vaccine passports: ethical and regulatory issues raised by COVID-19 immunity certificates. Linguistic and Philosophical Investigations, 20, 135?144. doi: 10.22381/LPI20202112. DOI: https://doi.org/10.22381/LPI20202112
    View in Google Scholar
  75. Poole, D. (2017). Identity and verification in the digital age: where we are today and what the future could hold. Journal of Payments Strategy & Systems, 10, 383?388.
    View in Google Scholar
  76. Popescu, C. K., Oa?a (Geambazi), R.-?., Geambazi, P., & Alexandru, B. (2021a). Real-time process monitoring, Industry 4.0 wireless networks, and cognitive automation in cyber-physical system-based manufacturing. Journal of Self-Governance and Management Economics, 9(1), 53?63. doi: 10.22381/jsme 9120215. DOI: https://doi.org/10.22381/jsme9120215
    View in Google Scholar
  77. Popescu, G. H., Petreanu, S., Alexandru, B., & Corpodean, H. (2021b). Internet of Things-based real-time production logistics, cyber-physical process monitoring systems, and industrial artificial intelligence in sustainable smart manufacturing. Journal of Self-Governance and Management Economics, 9(2), 52?62. doi: 10.22381/jsme9220215.
    View in Google Scholar
  78. Riley, A., & Nica, E. (2021). Internet of Things-based smart healthcare systems and wireless biomedical sensing devices in monitoring, detection, and prevention of COVID-19. American Journal of Medical Research, 8(2), 51?64. doi: 10.22381/ajmr8220214. DOI: https://doi.org/10.22381/ajmr8220214
    View in Google Scholar
  79. Riley, C., Vrbka, J., & Rowland, Z. (2021). Internet of Things-enabled sustainability, big data-driven decision-making processes, and digitized mass production in Industry 4.0-based manufacturing systems. Journal of Self-Governance and Management Economics, 9(1), 42?52. doi: 10.22381/jsme 912 0214. DOI: https://doi.org/10.22381/jsme9120214
    View in Google Scholar
  80. Robinson, R., Zvarikova, K., & Sosedova, J. (2021). Restricting human rights and increasing discrimination through COVID-19 vaccination certificates: Necessity, benefits, risks, and costs. Linguistic and Philosophical Investigations, 20, 115?124. doi: 10.22381/LPI20202110. DOI: https://doi.org/10.22381/LPI20202110
    View in Google Scholar
  81. Seng, S., Al-Ameen, M. N., & Wright, M. (2021). A first look into users? perceptions of facial recognition in the physical world. Computers & Security, 105, 102227. doi: 10.1016/j.cose.2021.102227. DOI: https://doi.org/10.1016/j.cose.2021.102227
    View in Google Scholar
  82. Siekelova A., Kliestik T., Svabova L., Androniceanu A., & Schönfeld J. (2017). Receivables management: the importance of financial indicators in assessing the creditworthiness. Polish Journal of Management Studies, 15(2), 217?228. doi: 10.17512/pjms.2017.15.2.20. DOI: https://doi.org/10.17512/pjms.2017.15.2.20
    View in Google Scholar
  83. Stehel, V., Bradley, C., Suler, P., & Bilan, S. (2021). Cyber-physical system-based real-time monitoring, industrial big data analytics, and smart factory performance in sustainable manufacturing Internet of Things. Economics, Management, and Financial Markets, 16(1), 42?51. doi: 10.22381/emfm1612 0214. DOI: https://doi.org/10.22381/emfm16120214
    View in Google Scholar
  84. Suler, P., Palmer, L., & Bilan, S. (2021). Internet of Things sensing networks, digitized mass production, and sustainable organizational performance in cyber-physical system-based smart factories. Journal of Self-Governance and Management Economics, 9(2), 42?51. doi: 10.22381/jsme9220214. DOI: https://doi.org/10.22381/jsme9220214
    View in Google Scholar
  85. Townsend, J. (2021). Interconnected sensor networks and machine learning-based analytics in data-driven smart sustainable cities. Geopolitics, History, and International Relations, 13(1), 31?41. doi: 10.22381/GHIR13120213. DOI: https://doi.org/10.22381/GHIR13120213
    View in Google Scholar
  86. Turner, D., & Pera, A. (2021). Wearable Internet of Medical Things sensor devices, big healthcare data, and artificial intelligence-based diagnostic algorithms in real-time COVID-19 detection and monitoring systems. American Journal of Medical Research, 8(2), 132?145. doi: 10.22381/ajmr822 02110. DOI: https://doi.org/10.22381/ajmr82202110
    View in Google Scholar
  87. Valaskova, K., Ward, P., & Svabova, L. (2021). Deep learning-assisted smart process planning, cognitive automation, and industrial big data analytics in sustainable cyber-physical production systems. Journal of Self-Governance and Management Economics, 9(2), 9?20. doi: 10.22381/jsme9220211. DOI: https://doi.org/10.22381/jsme9220211
    View in Google Scholar
  88. Venkatesan, R., Princy, B. A., Kumar, V. D. A., Raghuraman, M., Gupta, M. K., Kumar, A., Kumar, A., & Kumar Khan, A. (2021). Secure online payment through facial recognition and proxy detection with the help of TripleDES encryption. Journal of Discrete Mathematical Sciences and Cryptography, 24(8), 2195?2205. doi: 10.1080/09720529.2021.2011096. DOI: https://doi.org/10.1080/09720529.2021.2011096
    View in Google Scholar
  89. Wade, K., Vrbka, J., Zhuravleva, N. A., & Machova, V. (2021). Sustainable governance networks and urban Internet of Things systems in big data-driven smart cities. Geopolitics, History, and International Relations, 13(1), 64?74. doi: 10.22381/GHIR13120216. DOI: https://doi.org/10.22381/GHIR13120216
    View in Google Scholar
  90. Wallace, S., & Lăzăroiu, G. (2021). Predictive control algorithms, real-world connected vehicle data, and smart mobility technologies in intelligent transportation planning and engineering. Contemporary Readings in Law and Social Justice, 13(2), 79?92. doi: 10.22381/CRLSJ13220216. DOI: https://doi.org/10.22381/CRLSJ13220216
    View in Google Scholar
  91. Watkins, D. (2021). Real-time big data analytics, smart industrial value creation, and robotic wireless sensor networks in Internet of Things-based decision support systems. Economics, Management, and Financial Markets, 16(1), 31?41. doi: 10.22381/emfm16120213. DOI: https://doi.org/10.22381/emfm16120213
    View in Google Scholar
  92. Welch, H. (2021). Algorithmically monitoring menstruation, ovulation, and pregnancy by use of period and fertility tracking apps. Journal of Research in Gender Studies, 11(2), 113?125. doi: 10.22381/JRGS11220218. DOI: https://doi.org/10.22381/JRGS11220218
    View in Google Scholar
  93. Wingard, D. (2019). Data-driven automated decision-making in assessing employee performance and productivity: designing and implementing workforce metrics and analytics. Psychosociological Issues in Human Resource Management, 7(2), 13?18. doi: 10.22381/PIHRM7220192. DOI: https://doi.org/10.22381/PIHRM7220192
    View in Google Scholar
  94. Woods, M., & Miklencicova, R. (2021). Digital epidemiological surveillance, smart telemedicine diagnosis systems, and machine learning-based real-time data sensing and processing in COVID-19 remote patient monitoring. American Journal of Medical Research, 8(2), 65?77. doi: 10.22381/ajmr8220215. DOI: https://doi.org/10.22381/ajmr8220215
    View in Google Scholar
  95. Woodward, B., & Kliestik, T. (2021). Intelligent transportation applications, autonomous vehicle perception sensor data, and decision-making self-driving car control algorithms in smart sustainable urban mobility systems. Contemporary Readings in Law and Social Justice, 13(2), 51?64. doi: 10.223 81/CRLSJ13220214. DOI: https://doi.org/10.22381/CRLSJ13220214
    View in Google Scholar
  96. Wójtowicz, A., & Chmielewski, J. (2017). Technical feasibility of context-aware passive payment authorization for physical points of sale. Personal and Ubiquitous Computing, 21, 1113?1125. doi: 10.1007/s00779-017-1035-z. DOI: https://doi.org/10.1007/s00779-017-1035-z
    View in Google Scholar
  97. Xu, F. Z., Zhang, Y., Zhang, T., & Wang, J. (2021). Facial recognition check-in services at hotels. Journal of Hospitality Marketing & Management, 30(3), 373?393. doi: 10.1080/19368623.2020.1813670. DOI: https://doi.org/10.1080/19368623.2020.1813670
    View in Google Scholar
  98. Yang, T., Zhao, X., Wang, X., & Lv, H. (2020). Evaluating facial recognition web services with adversarial and synthetic samples. Neurocomputing, 406, 378?385. doi: 10.1016/j.neucom.2019.11.117. DOI: https://doi.org/10.1016/j.neucom.2019.11.117
    View in Google Scholar
  99. Yang, Y. (2020). Research on brush face payment system based on internet artificial intelligence. Journal of Intelligent & Fuzzy Systems, 38, 21?28. doi: 10.3233/JIFS-179376. DOI: https://doi.org/10.3233/JIFS-179376
    View in Google Scholar
  100. Zhang, W. K., & Kang, M. J. (2019). Factors affecting the use of facial-recognition payment: an example of Chinese consumers. IEEE Access, 7, 154360?154374. doi: 10.1109/ACCESS.2019.2927705. DOI: https://doi.org/10.1109/ACCESS.2019.2927705
    View in Google Scholar
  101. Zhang, H., Li, D., Ji, Y., Zhou, H., Wu, W., & Liu, K. (2020). Toward new retail: a benchmark dataset for smart unmanned vending machines. IEEE Transactions on Industrial Informatics, 16, 7722?7731. doi: 10.1109/TII.201 9.2954956. DOI: https://doi.org/10.1109/TII.2019.2954956
    View in Google Scholar
  102. Zhao, F., Li, J., Zhang, L., Li, Z., & Na, S.-G. (2020). Multi-view face recognition using deep neural networks. Future Generation Computer Systems, 111, 375?380. doi: 10.1016/j.future.2020.05.002. DOI: https://doi.org/10.1016/j.future.2020.05.002
    View in Google Scholar
  103. 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. Technology in Society, 64, 101515. doi: 10.1016/j.techsoc.2020.101515. DOI: https://doi.org/10.1016/j.techsoc.2020.101515
    View in Google Scholar
  104. Zhi, H., & Liu, S. (2019). Face recognition based on genetic algorithm. Journal of Visual Communication and Image Representation, 58, 495?502. doi: 10.1016/j. jvcir.2018.12.012. DOI: https://doi.org/10.1016/j.jvcir.2018.12.012
    View in Google Scholar

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