Розробка моделі використання "розумного будинку" іт-фахівцями: роль машинного навчання

Автор(и)

DOI:

https://doi.org/10.15587/1729-4061.2022.265657

Ключові слова:

РСАДПЗ, Інтернет речей, відносна перевага, розумний будинок, модель прийняття технологій

Анотація

Моделі використання "розумного будинку" переважають у розвинених країнах, тоді як в країнах, що розвиваються вони все ще відсутні. У контексті "розумного будинку" широко використовується модель прийняття технологій (МПТ), інші теорії прийняття технологій маловивчені. Метою даного дослідження є вивчення досвіду використання "розумного будинку" фахівцями з інформаційних технологій (ІТ) в Раді співробітництва арабських держав Перської затоки (РСАДПЗ). У роботі використовуються теорії існування і показано позитивний вплив відносної переваги, зручності, доступності та вартості на намір використовувати "розумний будинок". Крім того, висловлено припущення, що намір використовувати, а також сприятливі умови безпосередньо впливають на фактичне використання "розумного будинку". В якості посередника між наміром використовувати і фактичним використанням були запропоновані знання з машинного навчання. Дані були зібрані у ІТ-фахівців РСАДПЗ за допомогою цілеспрямованої вибірки. Аналіз проводився методом аналізу моментних структур (АМОС). Результати показали, що зручність, доступність та відносна перевага мають позитивний вплив, тоді як вартість негативно впливає на намір використовувати "розумний будинок". Намір використовувати і сприятливі умови позитивно вплинули на фактичне використання. Знання в області машинного навчання мали позитивний вплив на намір використовувати і фактичне використання. Особам, що приймають рішення, рекомендується розширити переваги використання "розумного будинку" на основі Інтернету речей та створити індивідуальний план, що дозволяє використовувати "розумний будинок" на всіх рівнях. Знання в області машинного навчання мають велике значення для використання "розумного будинку". В цьому відношенні, у розширенні використання "розумного будинку" важливу роль відіграють спеціалізовані курси.

Біографія автора

Baraa Sharef, Ahlia University

Doctor of Information Sciences and Technologies, Assistant Professor

Department of Information Technology

Посилання

  1. Marikyan, D., Papagiannidis, S., Alamanos, E. (2019). A systematic review of the smart home literature: A user perspective. Technological Forecasting and Social Change, 138, 139–154. doi: https://doi.org/10.1016/j.techfore.2018.08.015
  2. Sovacool, B. K., Furszyfer Del Rio, D. D. (2020). Smart home technologies in Europe: A critical review of concepts, benefits, risks and policies. Renewable and Sustainable Energy Reviews, 120, 109663. doi: https://doi.org/10.1016/j.rser.2019.109663
  3. Almusaylim, Z. A., Zaman, N. (2018). A review on smart home present state and challenges: linked to context-awareness internet of things (IoT). Wireless Networks, 25 (6), 3193–3204. doi: https://doi.org/10.1007/s11276-018-1712-5
  4. Awad, S. R., Sharef, B. T., Salih, A. M., Malallah, F. L. (2022). Deep learning-based iraqi banknotes classification system for blind people. Eastern-European Journal of Enterprise Technologies, 1 (2 (115)), 31–38. doi: https://doi.org/10.15587/1729-4061.2022.248642
  5. Liang, T., Zeng, B., Liu, J., Ye, L., Zou, C. (2018). An Unsupervised User Behavior Prediction Algorithm Based on Machine Learning and Neural Network For Smart Home. IEEE Access, 6, 49237–49247. doi: https://doi.org/10.1109/access.2018.2868984
  6. Hargreaves, T., Wilson, C., Hauxwell-Baldwin, R. (2017). Learning to live in a smart home. Building Research & Information, 46 (1), 127–139. doi: https://doi.org/10.1080/09613218.2017.1286882
  7. Maleki, M., Manshouri, N., Kayikcioglu, T. (2021). Brain-computer Interface Systems for Smart Homes - A Review Study. Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering), 14 (2), 144–155. doi: https://doi.org/10.2174/2352096513999200727175948
  8. Miandashti, F. J., Izadi, M., Shirehjini, A. A. N., Shirmohammadi, S. (2020). An Empirical Approach to Modeling User-System Interaction Conflicts in Smart Homes. IEEE Transactions on Human-Machine Systems, 50 (6), 573–583. doi: https://doi.org/10.1109/thms.2020.3017784
  9. Ousghir, S., Daoud, M. (2022). Exploratory study on innovation management in startups, an attempt to design it through the business model. Eastern-European Journal of Enterprise Technologies, 1 (13 (115)), 20–26. doi: https://doi.org/10.15587/1729-4061.2022.251845
  10. Marufuzzaman, M., Tumbraegel, T., Rahman, L. F., Sidek, L. M. (2021). A machine learning approach to predict the activity of smart home inhabitant. Journal of Ambient Intelligence and Smart Environments, 13 (4), 271–283. doi: https://doi.org/10.3233/ais-210604
  11. Machorro-Cano, I., Alor-Hernández, G., Paredes-Valverde, M. A., Rodríguez-Mazahua, L., Sánchez-Cervantes, J. L., Olmedo-Aguirre, J. O. (2020). HEMS-IoT: A Big Data and Machine Learning-Based Smart Home System for Energy Saving. Energies, 13 (5), 1097. doi: https://doi.org/10.3390/en13051097
  12. Babichenko, A., Kravchenko, Y., Babichenko, J., Lysachenko, I., Krasnikov, I., Velma, V. (2022). Design of an intelligent system to control the technological system of ammonia production secondary condensation. Eastern-European Journal of Enterprise Technologies, 1 (2 (115)), 105–115. doi: https://doi.org/10.15587/1729-4061.2022.252383
  13. Khudov, H., Makoveichuk, O., Misiuk, D., Pievtsov, H., Khizhnyak, I., Solomonenko, Y. et. al. (2022). Devising a method for processing the image of a vehicle’s license plate when shooting with a smartphone camera. Eastern-European Journal of Enterprise Technologies, 1 (2 (115)), 6–21. doi: https://doi.org/10.15587/1729-4061.2022.252310
  14. Galchonkov, O., Nevrev, A., Shevchuk, B., Baranov, N. (2022). Determination of the influence of the choice of the pruning procedure parameters on the learning quality of a multilayer perceptron. Eastern-European Journal of Enterprise Technologies, 1 (9 (115)), 75–83. doi: https://doi.org/10.15587/1729-4061.2022.253103
  15. Dong, X., Chang, Y., Wang, Y., Yan, J. (2017). Understanding usage of Internet of Things (IOT) systems in China. Information Technology & People, 30 (1), 117–138. doi: https://doi.org/10.1108/itp-11-2015-0272
  16. Park, E., Cho, Y., Han, J., Kwon, S. J. (2017). Comprehensive Approaches to User Acceptance of Internet of Things in a Smart Home Environment. IEEE Internet of Things Journal, 4 (6), 2342–2350. doi: https://doi.org/10.1109/jiot.2017.2750765
  17. Mital, M., Chang, V., Choudhary, P., Papa, A., Pani, A. K. (2018). Adoption of Internet of Things in India: A test of competing models using a structured equation modeling approach. Technological Forecasting and Social Change, 136, 339–346. doi: https://doi.org/10.1016/j.techfore.2017.03.001
  18. de Boer, P. S., van Deursen, A. J. A. M., van Rompay, T. J. L. (2019). Accepting the Internet-of-Things in our homes: The role of user skills. Telematics and Informatics, 36, 147–156. doi: https://doi.org/10.1016/j.tele.2018.12.004
  19. Nakashydze, L., Gil’orme, T. (2015). Energy security assessment when introducing renewable energy technologies. Eastern-European Journal of Enterprise Technologies, 4 (8 (76)), 54–59. doi: https://doi.org/10.15587/1729-4061.2015.46577
  20. Pronoza, P., Kuzenko, T., Sablina, N. (2022). Implementation of strategic tools in the process of financial security management of industrial enterprises in Ukraine. Eastern-European Journal of Enterprise Technologies, 2 (13 (116)), 15–23. doi: https://doi.org/10.15587/1729-4061.2022.254234
  21. Poltorak, A., Khrystenko, O., Sukhorukova, A., Moroz, T., Sharin, O. (2022). Development of an integrated approach to assessing the impact of innovative development on the level of financial security of households. Eastern-European Journal of Enterprise Technologies, 1 (13 (115)), 103–112. doi: https://doi.org/10.15587/1729-4061.2022.253062
  22. Shachak, A., Kuziemsky, C., Petersen, C. (2019). Beyond TAM and UTAUT: Future directions for HIT implementation research. Journal of Biomedical Informatics, 100, 103315. doi: https://doi.org/10.1016/j.jbi.2019.103315
  23. Hong, A., Nam, C., Kim, S. (2020). What will be the possible barriers to consumers’ adoption of smart home services? Telecommunications Policy, 44 (2), 101867. doi: https://doi.org/10.1016/j.telpol.2019.101867
  24. Arthanat, S., Chang, H., Wilcox, J. (2020). Determinants of information communication and smart home automation technology adoption for aging-in-place. Journal of Enabling Technologies, 14 (2), 73–86. doi: https://doi.org/10.1108/jet-11-2019-0050
  25. Balta-Ozkan, N., Davidson, R., Bicket, M., Whitmarsh, L. (2013). Social barriers to the adoption of smart homes. Energy Policy, 63, 363–374. doi: https://doi.org/10.1016/j.enpol.2013.08.043
  26. Pal, D., Funilkul, S., Charoenkitkarn, N., Kanthamanon, P. (2018). Internet-of-Things and Smart Homes for Elderly Healthcare: An End User Perspective. IEEE Access, 6, 10483–10496. doi: https://doi.org/10.1109/access.2018.2808472
  27. Cvitić, I., Peraković, D., Periša, M., Gupta, B. (2021). Ensemble machine learning approach for classification of IoT devices in smart home. International Journal of Machine Learning and Cybernetics, 12 (11), 3179–3202. doi: https://doi.org/10.1007/s13042-020-01241-0
  28. Lytvyn, V., Vysotska, V., Demchuk, A., Demkiv, I., Ukhanska, O., Hladun, V. et. al. (2019). Design of the architecture of an intelligent system for distributing commercial content in the internet space based on SEO-technologies, neural networks, and Machine Learning. Eastern-European Journal of Enterprise Technologies, 2 (2 (98)), 15–34. doi: https://doi.org/10.15587/1729-4061.2019.164441
  29. Tsoy, A., Titlov, O., Granovskiy, A., Koretskiy, D., Vorobyova, O., Tsoy, D., Jamasheva, R. (2022). Improvement of refrigerating machine energy efficiency through radiative removal of condensation heat. Eastern-European Journal of Enterprise Technologies, 1 (8 (115)), 35–45. doi: https://doi.org/10.15587/1729-4061.2022.251834
  30. Huang, H., Yu, H. (2019). Compact and Fast Machine Learning Accelerator for IoT Devices. Springer, 149. doi: https://doi.org/10.1007/978-981-13-3323-1
  31. Aliiev, E., Paliy, A., Kis, V., Paliy, A., Petrov, R., Plyuta, L. et. al. (2022). Establishing the influence of technical and technological parameters of milking equipment on the efficiency of machine milking. Eastern-European Journal of Enterprise Technologies, 1 (1 (115)), 44–55. doi: https://doi.org/10.15587/1729-4061.2022.251172
  32. Nykyforov, А., Antoshchenkov, R., Halych, I., Kis, V., Polyansky, P., Koshulko, V. et. al. (2022). Construction of a regression model for assessing the efficiency of separation of lightweight seeds on vibratory machines involving measures to reduce the harmful influence of the aerodynamic factor. Eastern-European Journal of Enterprise Technologies, 2 (1 (116)), 24–34. doi: https://doi.org/10.15587/1729-4061.2022.253657
  33. Zahorulko, A., Zagorulko, A., Kasabova, K., Liashenko, B., Postadzhiev, A., Sashnova, M. (2022). Improving a tempering machine for confectionery masses. Eastern-European Journal of Enterprise Technologies, 2 (11 (116)), 6–11. doi: https://doi.org/10.15587/1729-4061.2022.254873
  34. Petrakov, Y., Korenkov, V., Myhovych, A. (2022). Technology for programming contour milling on a CNC machine. Eastern-European Journal of Enterprise Technologies, 2 (1 (116)), 55–61. doi: https://doi.org/10.15587/1729-4061.2022.255389
  35. Alarifi, A., Tolba, A. (2019). Optimizing the network energy of cloud assisted internet of things by using the adaptive neural learning approach in wireless sensor networks. Computers in Industry, 106, 133–141. doi: https://doi.org/10.1016/j.compind.2019.01.004
  36. Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13 (3), 319. doi: https://doi.org/10.2307/249008
  37. 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. doi: https://doi.org/10.2307/30036540
  38. Shin, D.-H., Jin Park, Y. (2017). Understanding the Internet of Things ecosystem: multi-level analysis of users, society, and ecology. Digital Policy, Regulation and Governance, 19 (1), 77–100. doi: https://doi.org/10.1108/dprg-07-2016-0035
  39. Khayer, A., Talukder, Md. S., Bao, Y., Hossain, Md. N. (2020). Cloud computing adoption and its impact on SMEs’ performance for cloud supported operations: A dual-stage analytical approach. Technology in Society, 60, 101225. doi: https://doi.org/10.1016/j.techsoc.2019.101225
  40. Sivathanu, B. (2018). Adoption of internet of things (IOT) based wearables for healthcare of older adults – a behavioural reasoning theory (BRT) approach. Journal of Enabling Technologies, 12 (4), 169–185. doi: https://doi.org/10.1108/jet-12-2017-0048
  41. Pinochet, L. H. C., Lopes, E. L., Srulzon, C. H. F., Onusic, L. M. (2018). The influence of the attributes of “Internet of Things” products on functional and emotional experiences of purchase intention. Innovation & Management Review, 15 (3), 303–320. doi: https://doi.org/10.1108/inmr-05-2018-0028
  42. de Oliveira, G. A. A., de Bettio, R. W., Freire, A. P. (2016). Accessibility of the smart home for users with visual disabilities: an evaluation of open source mobile applications for home automation. Proceedings of the 15th Brazilian Symposium on Human Factors in Computing Systems. doi: https://doi.org/10.1145/3033701.3033730
  43. Agustina, R., Suprianto, D., Ariyanto, R. (2021). Technology Acceptance Model Analysis of User Behavioral Intentions on IoT Smart Board Devices. 2021 1st Conference on Online Teaching for Mobile Education (OT4ME). doi: https://doi.org/10.1109/ot4me53559.2021.9638937
  44. Sivarethinamohan, R., Sujatha, S. (2021). Upskilling and Curating the Potentials of IoT Enabled Smart Cities: Use Cases and Implementation Strategies. Artificial Intelligence in Industrial Applications, 67–78. doi: https://doi.org/10.1007/978-3-030-85383-9_5
  45. Al-Momani, A. M., Mahmoud, M. A., Ahmad, M. S. (2018). Factors that Influence the Acceptance of Internet of Things Services by Customers of Telecommunication Companies in Jordan. Journal of Organizational and End User Computing, 30 (4), 51–63. doi: https://doi.org/10.4018/joeuc.2018100104
  46. Karahoca, A., Karahoca, D., Aksöz, M. (2017). Examining intention to adopt to internet of things in healthcare technology products. Kybernetes, 47 (4), 742–770. doi: https://doi.org/10.1108/k-02-2017-0045
  47. Shuhaiber, A., Mashal, I. (2019). Understanding users’ acceptance of smart homes. Technology in Society, 58, 101110. doi: https://doi.org/10.1016/j.techsoc.2019.01.003
  48. Solangi, Z. A., Solangi, Y. A., Aziz, M. S. Abd., Asadullah. (2017). An empirical study of Internet of Things (IoT) – Based healthcare acceptance in Pakistan: PILOT study. 2017 IEEE 3rd International Conference on Engineering Technologies and Social Sciences (ICETSS). doi: https://doi.org/10.1109/icetss.2017.8324135
  49. Sohn, K., Kwon, O. (2020). Technology acceptance theories and factors influencing artificial Intelligence-based intelligent products. Telematics and Informatics, 47, 101324. doi: https://doi.org/10.1016/j.tele.2019.101324
  50. Chakraborty, S., Khayer, N., Ahmed, T. (2020). Assessing Critical Factors Affecting the Mass Adoption of IoT in Bangladesh. International Conference on Mechanical, Industrial and Energy Engineering. Available at: https://www.researchgate.net/publication/347889988_Assessing_Critical_Factors_Affecting_the_Mass_Adoption_of_IoT_in_Bangladesh
  51. Roma, P., Monaro, M., Muzi, L., Colasanti, M., Ricci, E., Biondi, S. et. al. (2020). How to Improve Compliance with Protective Health Measures during the COVID-19 Outbreak: Testing a Moderated Mediation Model and Machine Learning Algorithms. International Journal of Environmental Research and Public Health, 17 (19), 7252. doi: https://doi.org/10.3390/ijerph17197252
  52. Hwang, J., Park, S., Kim, I. (2020). Understanding motivated consumer innovativeness in the context of a robotic restaurant: The moderating role of product knowledge. Journal of Hospitality and Tourism Management, 44, 272–282. doi: https://doi.org/10.1016/j.jhtm.2020.06.003
  53. Rogers, E. (1995). Diffusion of innovations. Available at: https://web.stanford.edu/class/symbsys205/Diffusion%20of%20Innovations.htm
  54. Kayali, M., Alaaraj, S. (2020). Adoption of Cloud Based E-learning in Developing Countries: A Combination of DOI, TAM and UTAUT. International Journal of Contemporary Management and Information Technology, 1 (1), 1–7. Available at: https://ijcmit.com/wp-content/uploads/2020/11/Kayali-Alaaraj-2020-1d59ab79.pdf
  55. Lian, J.-W. (2015). Critical factors for cloud based e-invoice service adoption in Taiwan: An empirical study. International Journal of Information Management, 35 (1), 98–109. doi: https://doi.org/10.1016/j.ijinfomgt.2014.10.005
  56. Hair, J. F., Anderson, R. E., Babin, B. J., Black, W. C. (2010). Multivariate data analysis: A global perspective. Upper Saddle River (N.J.) : Pearson education, 800.
  57. 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 Professional Communication, 57 (2), 123–146. doi: https://doi.org/10.1109/tpc.2014.2312452
  58. Awang, Z. (2014). A Handbook on Structural Equation Modeling for Academicians and Practitioner. Bandar Baru Bangi, kuala lumpur, Malaysia: MPWS Rich Resources.
Developing a model of smart home usage among it specialists: the role of machine learning

##submission.downloads##

Опубліковано

2022-10-26

Як цитувати

Sharef, B. (2022). Розробка моделі використання "розумного будинку" іт-фахівцями: роль машинного навчання . Eastern-European Journal of Enterprise Technologies, 5(13 (119), 100–107. https://doi.org/10.15587/1729-4061.2022.265657

Номер

Розділ

Трансфер технологій: промисловість, енергетика, нанотехнології