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Cited by 50Technology Use by Older AdultsTechnology Adoption and User BehaviourInnovative Human-Technology Interaction

Factors influencing the elderly’s behavioural intention to use smart home technologies in Saudi Arabia

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Maswadi, Kholoud, Ghani, Norjihan Abdul, Hamid, Suraya·2022·PLOS ONE
DOI 10.1371/journal.pone.0272525PMC PMC9426941
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Abstract

In recent years, smart home technologies have offered opportunities for elderly people to manage their daily health-related activities. Despite the advancement in smart home technology (SHT), the level of end-user acceptance among elderly people is still low. This study proposes an SHT framework by examining the determinants of elderly behavioural intention (BI) to use smart home technologies by extending the Unified Theory of Acceptance and Use of Technology (UTAUT) model. This study uses the quantitative approach to survey about 486 elderly people in Saudi Arabia, and it applies the Partial Least Square Structural Equation Model (PLS-SEM) technique to perform the data analysis. Findings reveal that culture influence and technology awareness are significant factors in determining the BI to use SHT among elderly people. The study also finds that attitude mediates the relationships between performance expectancy, effort expectancy, and behavioural intention. We find that region and education moderate the relationships between culture influence, technology awareness, and behavioural intention. This study theoretically extends the UTAUT theory by including external constructs: culture influence, technology awareness, attitude, education, and region.

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  • Applying a combination of assessment methods, either direct and indirect methods or triangulation, instead of a single assessment method, can lead to very highly effective learning experiences for students.

    “In this result, it was observed that applying a combination of assessment methods, either a combination of direct and indirect methods or the triangulation method, instead of a single assessment method, can lead to very highly effective learning experiences for students (Bellido-García et al., 2022;Maswadi et al., 2022).”

    From A meta-analysis to explore the role of design thinking in enhancing creativity as learning outcomes in STEM education (2026) · cites this paper as Maswadi et al., 2022

    Section 4.4 discusses a statistical test for bias in survey data, and Section 5.1 reports measurement-validity results. Neither addresses student learning experiences or whether combining direct and indirect assessment methods or triangulation improves them. Searches for those assessment terms and for student learning experiences found no relevant passage in our copy. (LLM, conf=0.77)

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  1. [1]B. Al-Shargabi and O. Sabri, “Internet of Things: An exploration study of opportunities and challenges,” in 2017 International Conference on Engineering & MIS (ICEMIS), 2017, pp. 1–4.
  2. [2]Mashal I. and Shuhaiber A., “ What makes Jordanian residents buy smart home devices?,” Kybernetes, vol. 48, no. 8, 2019, doi: 10.1108/k-01-2018-0008 · DOI
  3. [3]Gross C., Siepermann M., and Lackes R., “ The Acceptance of Smart Home Technology,” in Lecture Notes in Business Information Processing, 2020, vol. 398 LNBIP, doi: 10.1007/978-3-030-61140-8_1 · DOI
  4. [4]P. Visutsak and M. Daoudi, “The smart home for the elderly: Perceptions, technologies and psychological accessibilities: The requirements analysis for the elderly in Thailand,” in ICAT 2017—26th International Conference on Information, Communication and Automation Technologies, Proceedings, 2017, vol. 2017-December, doi: 10.1109/ICAT.2017.8171625 · DOI
  5. [7]Venkatesh V., Morris M. G., Davis G. B., and Davis F. D., “ User acceptance of information technology: Toward a unified view,” MIS Q. Manag. Inf. Syst., vol. 27, no. 3, 2003, doi: 10.2307/30036540 · DOI · Paper page
  6. [8]Taylor S. and Todd P. A., “ Understanding information technology usage: A test of competing models,” Inf. Syst. Res., vol. 6, no. 2, pp. 144– 176, 1995.
  7. [11]Alanazi M. H. and Soh B., “ Behavioral Intention to Use IoT Technology in Healthcare Settings,” Eng. Technol. Appl. Sci. Res., vol. 9, no. 5, 2019, doi: 10.48084/etasr.3063 · DOI
  8. [12]Li W., Yigitcanlar T., Erol I., and Liu A., “ Motivations, barriers and risks of smart home adoption: From systematic literature review to conceptual framework,” Energy Res. Soc. Sci., vol. 80, p. 102211, 2021.
  9. [13]Sokullu R., Akkaş M. A., and Demir E., “ IoT supported smart home for the elderly,” Internet of Things, vol. 11, p. 100239, 2020.
  10. [14]Al-Husamiyah A. and Al-Bashayreh M., “ A comprehensive acceptance model for smart home services,” Int. J. Data Netw. Sci., vol. 6, no. 1, pp. 45– 58, 2022.
  11. [15]Shuhaiber A., “ The role of perceived control, enjoyment, cost, sustainability and trust on intention to use smart meters: An empirical study using SEM-PLS,” in Advances in Intelligent Systems and Computing, 2018, vol. 746, doi: 10.1007/978-3-319-77712-2_74 · DOI
  12. [16]Al Hubaishi H. and Ali A., “ The Effect of Provided Education Services on Meeting Residents’ Satisfaction in the United Arab Emirates, the Case of Ajman Emirate,” Creat. Educ., vol. 13, no. 4, pp. 1511– 1528, 2022.
  13. [17]Pal D., Funilkul S., Vanijja V., and Papasratorn B., “ Analyzing the Elderly Users’ Adoption of Smart-Home Services,” IEEE Access, vol. 6, 2018, doi: 10.1109/ACCESS.2018.2869599 · DOI
  14. [18]Hvalič‐Touzery S., Dolničar V., and Prevodnik K., “ Factors influencing informal carers’ acceptance of assistive telecare systems in the pre‐and post‐implementation phase: A scoping study,” Health Soc. Care Community, 2022. doi: 10.1111/hsc.13840 35574935 PMC9541532 · DOI
  15. [19]Ghorayeb A., Comber R., and Gooberman-Hill R., “ Older adults’ perspectives of smart home technology: Are we developing the technology that older people want?,” Int. J. Hum. Comput. Stud., vol. 147, p. 102571, 2021.
  16. [20]Espino-Rodríguez T. F. and Taha M. G., “ Supplier innovativeness in supply chain integration and sustainable performance in the hotel industry,” Int. J. Hosp. Manag., vol. 100, p. 103103, 2022.
  17. [21]Al Aufa B., Renindra I. S., Putri J. S., and Nurmansyah M. I., “ An application of the Unified Theory of Acceptance and Use of Technology (UTAUT) model for understanding patient perceptions on using hospital mobile application,” Enferm. Clin., vol. 30, 2020, doi: 10.1016/j.enfcli.2020.06.025 · DOI
  18. [24]Pal D., Triyason T., Funilkul S., and Chutimaskul W., “ Smart Homes and Quality of Life for the Elderly: Perspective of Competing Models,” IEEE Access, vol. 6, 2018, doi: 10.1109/ACCESS.2018.2798614 · DOI
  19. [25]Rodrigues M. J., Postolache O., and Cercas F., “ Physiological and behavior monitoring systems for smart healthcare environments: A review,” Sensors (Switzerland), vol. 20, no. 8. 2020, doi: 10.3390/s20082186 32290639 PMC7218909 · DOI
  20. [26]Dorronzoro-Zubiete E., Rivera-Romero O., Giunti G., and Sevillano J. L., “ Smart home applications for cognitive health of older adults,” in Smart Home Technologies and Services for Geriatric Rehabilitation, Elsevier, 2022, pp. 123– 140.
  21. [27]Bleja J., Grossmann U., and Langer H., “ A collaborative system business model for ambient assisted living systems,” 2018, doi: 10.1109/IDAACS-SWS.2018.8525684 · DOI
  22. [28]McCurry J., “ Video games for the elderly: an answer to dementia or a marketing tool,” Guard., vol. 3, 2006.
  23. [29]Dey J., “ Healthy Space for Ageing: Embedding Smart Home Technology,” Indian J. Gerontol., vol. 36, no. 1, pp. 98– 112, 2022.
  24. [30]Broekens J., Heerink M., and Rosendal H., “ Assistive social robots in elderly care: a review,” Gerontechnology, vol. 8, no. 2, pp. 94– 103, 2009.
  25. [31]Maswadi K., Ghani N. B. A., and Hamid S. B., “ Systematic Literature Review of Smart Home Monitoring Technologies Based on IoT for the Elderly,” IEEE Access, vol. 8, 2020, doi: 10.1109/ACCESS.2020.2992727 · DOI
  26. [32]Shin J., Park Y., and Lee D., “ Who will be smart home users? An analysis of adoption and diffusion of smart homes,” Technol. Forecast. Soc. Change, vol. 134, 2018, doi: 10.1016/j.techfore.2018.06.029 · DOI
  27. [33]Al-Shargabi B., Sabri O., and Aljawarneh S., “ The adoption of an e-learning system using information systems success model: a case study of Jazan University,” PeerJ Comput. Sci., vol. 7, p. e723, 2021. doi: 10.7717/peerj-cs.723 34712797 PMC8507483 · DOI
  28. [34]Maswadi K., Ghani N. A., Hamid S., and Rasheed M. B., “ Human activity classification using Decision Tree and Naive Bayes classifiers,” Multimed. Tools Appl., vol. 80, no. 14, pp. 21709– 21726, 2021.
  29. [35]Marikyan D., Papagiannidis S., and Alamanos E., “ A systematic review of the smart home literature: A user perspective,” Technol. Forecast. Soc. Change, vol. 138, 2019, doi: 10.1016/j.techfore.2018.08.015 · DOI
  30. [36]Alaa M., Zaidan A. A., Zaidan B. B., Talal M., and Kiah M. L. M., “ A review of smart home applications based on Internet of Things,” Journal of Network and Computer Applications, vol. 97. 2017, doi: 10.1016/j.jnca.2017.08.017 · DOI
  31. [37]Wilson C., Hargreaves T., and Hauxwell-Baldwin R., “ Benefits and risks of smart home technologies,” Energy Policy, vol. 103, 2017, doi: 10.1016/j.enpol.2016.12.047 · DOI · Paper page
  32. [38]Al-Ruithe M., Benkhelifa E., and Hameed K., “ Current State of Cloud Computing Adoption—An Empirical Study in Major Public Sector Organizations of Saudi Arabia (KSA),” in Procedia Computer Science, 2017, vol. 110, doi: 10.1016/j.procs.2017.06.080 · DOI
  33. [40]Guhr N., Werth O., Blacha P. P. H., and Breitner M. H., “ Privacy concerns in the smart home context,” SN Appl. Sci., vol. 2, no. 2, pp. 1– 12, 2020.
  34. [41]Sequeiros H., Oliveira T., and Thomas M. A., “ The impact of IoT smart home services on psychological well-being,” Inf. Syst. Front., pp. 1– 18, 2021.
  35. [42]Shuhaiber A. and Mashal I., “ Understanding users’ acceptance of smart homes,” Technol. Soc., vol. 58, 2019, doi: 10.1016/j.techsoc.2019.01.003 · DOI
  36. [43]Pal D., Funilkul S., Charoenkitkarn N., and Kanthamanon P., “ Internet-of-Things and Smart Homes for Elderly Healthcare: An End User Perspective,” IEEE Access, vol. 6. pp. 10483– 10496, 2018, doi: 10.1109/ACCESS.2018.2808472 · DOI
  37. [45]Yang H., Lee H., and Zo H., “ User acceptance of smart home services: An extension of the theory of planned behavior,” Ind. Manag. Data Syst., vol. 117, no. 1, 2017, doi: 10.1108/IMDS-01-2016-0017 · DOI · Paper page
  38. [47]Kim S., Lee K.-H., Hwang H., and Yoo S., “ Analysis of the factors influencing healthcare professionals’ adoption of mobile electronic medical record (EMR) using the unified theory of acceptance and use of technology (UTAUT) in a tertiary hospital,” BMC Med. Inform. Decis. Mak., vol. 16, no. 1, 2015, doi: 10.1186/s12911-016-0249-8 26831123 PMC4736616 · DOI
  39. [48]Ashraf A., Liu J. H., and Rauf Q., “ Aging population perception and post adoption behavior about the usability of smart home technology of pakistani culture,” 2020, doi: 10.1145/3379247.3379248 · DOI
  40. [49]Shuhaiber A. and Mashal I., “ Understanding users’ acceptance of smart homes,” Technol. Soc., vol. 58, 2019, doi: 10.1016/j.techsoc.2019.01.003 · DOI
  41. [50]Fernando N., Loke S. W., and Rahayu W., “ Mobile cloud computing: A survey,” Futur. Gener. Comput. Syst., vol. 29, no. 1, 2013, doi: 10.1016/j.future.2012.05.023 · DOI
  42. [51]Özsungur F., “ A research on the effects of successful aging on the acceptance and use of technology of the elderly,” Assist. Technol., vol. 34, no. 1, pp. 77– 90, 2022. doi: 10.1080/10400435.2019.1691085 31710261 · DOI
  43. [52]C.-K. Tu and H. Liu, “The Moderating Effects of Subjective Well-being on the Elderly’s Acceptance and Use of Gerontechnology: An Extended UTAUT Model,” in 2021 the 5th International Conference on Management Engineering, Software Engineering and Service Sciences, 2021, pp. 118–124.
  44. [53]Pirzada P., Wilde A., Doherty G. H., and Harris-Birtill D., “ Ethics and acceptance of smart homes for older adults,” Informatics Heal. Soc. Care, vol. 47, no. 1, pp. 10– 37, 2022. doi: 10.1080/17538157.2021.1923500 34240661 · DOI
  45. [54]Hoque R. and Sorwar G., “ Understanding factors influencing the adoption of mHealth by the elderly: An extension of the UTAUT model,” Int. J. Med. Inform., vol. 101, pp. 75– 84, 2017. doi: 10.1016/j.ijmedinf.2017.02.002 28347450 · DOI · Paper page
  46. [55]Sorwar G., Aggar C., Penman O., Seton C., and Ward A., “ Factors that predict the acceptance and adoption of smart home technology by seniors in Australia: a structural equation model with longitudinal data,” Informatics Heal. Soc. Care, pp. 1– 15, 2022. doi: 10.1080/17538157.2022.2069028 35473463 · DOI
  47. [56]Vitman A., Iecovich E., and Alfasi N., “ Ageism and social integration of older adults in their neighborhoods in Israel,” Gerontologist, vol. 54, no. 2, pp. 177– 189, 2014. doi: 10.1093/geront/gnt008 23463803 · DOI
  48. [58]Bu J., Yin J., Yu Y., and Zhan Y., “ Identifying the daily activity spaces of older adults living in a high-density urban area: a study using the smartphone-based global positioning system trajectory in shanghai,” Sustainability, vol. 13, no. 9, p. 5003, 2021.
  49. [59]Lee C. and Coughlin J. F., “ PERSPECTIVE: Older adults’ adoption of technology: an integrated approach to identifying determinants and barriers,” J. Prod. Innov. Manag., vol. 32, no. 5, pp. 747– 759, 2015.
  50. [61]Furszyfer Del Rio D. D., Sovacool B. K., and Griffiths S., “ Culture, energy and climate sustainability, and smart home technologies: A mixed methods comparison of four countries,” Energy Clim. Chang., vol. 2, 2021, doi: 10.1016/j.egycc.2021.100035 · DOI
  51. [62]Vincenzo J. L., Patton S. K., Lefler L. L., McElfish P. A., Wei J., and Curran G. M., “ A qualitative study of older adults’ facilitators, barriers, and cues to action to engage in falls prevention using health belief model constructs,” Arch. Gerontol. Geriatr., vol. 99, p. 104610, 2022. doi: 10.1016/j.archger.2021.104610 34954649 PMC9344858 · DOI
  52. [63]Coughlan T., Brown M., Mortier R., Houghton R. J., Goulden M., and Lawson G., “ Exploring acceptance and consequences of the Internet of Things in the home,” 2012, doi: 10.1109/GreenCom.2012.32 · DOI
  53. [64]Fritz R. L., Corbett C. L., Vandermause R., and Cook D., “ The influence of culture on older adults’ adoption of smart home monitoring,” Gerontechnology, vol. 14, no. 3, 2016, doi: 10.4017/gt.2016.14.3.010.00 · DOI
  54. [65]Mutahar A. M., Daud N. M., Ramayah T., Isaac O., and Aldholay A. H., “ The effect of awareness and perceived risk on the technology acceptance model (TAM): mobile banking in Yemen,” Int. J. Serv. Stand., vol. 12, no. 2, pp. 180– 204, 2018.
  55. [66]Alaeddin O. and Altounjy R., “ Trust, technology awareness and satisfaction effect into the intention to use cryptocurrency among generation Z in Malaysia,” Int. J. Eng. Technol., vol. 7, no. 4.29, pp. 8– 10, 2018.
  56. [67]Aldossari M. Q. and Sidorova A., “ Consumer Acceptance of Internet of Things (IoT): Smart Home Context,” J. Comput. Inf. Syst., vol. 60, no. 6, 2020, doi: 10.1080/08874417.2018.1543000 · DOI
  57. [68]Al-Emran M., Al-Maroof R., Al-Sharafi M. A., and Arpaci I., “ What impacts learning with wearables? An integrated theoretical model,” Interact. Learn. Environ., pp. 1– 21, 2020.
  58. [71]Almetere E. S., Kelana B. W. Y., and Mansor N. N. A., “ Using UTAUT model to determine factors affecting internet of things acceptance in public universities,” Int. J. Acad. Res. Bus. Soc. Sci, vol. 10, pp. 142– 150, 2020.
  59. [73]Lemieux J. and McAlister L., “ Handling missing values in marketing data: A comparison of techniques,” MSI Reports, vol. 2, pp. 41– 60, 2005.
  60. [74]Mohamed A. H. H. M., Tawfik H., Al-Jumeily D., and Norton L., “ MoHTAM: A technology acceptance model for mobile health applications,” in 2011 Developments in E-systems Engineering, 2011, pp. 13– 18.
  61. [75]Barton S. M., “ Social capital framework in the adoption of E-learning,” Int. J. E-Learning Corp. Gov. Heal. High. Educ., vol. 12, no. 2, 2013.
  62. [76]Petrić B. and Czárl B., “ Validating a writing strategy questionnaire,” System, vol. 31, no. 2, 2003, doi: 10.1016/S0346-251X(03)00020-4 · DOI
  63. [77]Wallace L. S., Blake G. H., Parham J. S., and Baldridge R. E., “ Development and content validation of family practice residency recruitment questionnaires,” Fam. Med., vol. 35, no. 7, 2003. 12861461
  64. [78]Hassan Z. A., Schattner P., and Mazza D., “ Doing A Pilot Study: Why Is It Essential?,” Malaysian Fam. physician Off. J. Acad. Fam. Physicians Malaysia, vol. 1, no. 2–3, 2006. PMC4453116 27570591
  65. [80]Lim J. S. and Zhang J., “ Adoption of AI-driven personalization in digital news platforms: An integrative model of technology acceptance and perceived contingency,” Technol. Soc., vol. 69, p. 101965, 2022.
  66. [82]Hair J. F., Black W. C., Babin B. J., and Anderson R. E., “ Multivariate Data Analysis,” Vectors. 2010, doi: 10.1016/j.ijpharm.2011.02.019 · DOI
  67. [83]Hair J. F., Ringle C. M., and Sarstedt M., “ PLS-SEM: Indeed a silver bullet,” J. Mark. Theory Pract., vol. 19, no. 2, 2011, doi: 10.2753/MTP1069-6679190202 · DOI · Paper page
  68. [84]Creswell J. W., “ Educational research: Planning, conducting, and evaluating quantitative and qualitative research,” Educational Research, vol. 4. 2012.
  69. [85]Fornell C. and Larcker D. F., “ Evaluating Structural Equation Models with Unobservable Variables and Measurement Error,” J. Mark. Res., vol. 18, no. 1, 1981, doi: 10.2307/3151312 · DOI · Paper page
  70. [87]Nunnally J. and Bernstein I., “ Psychometric Theory, 3rd edn, 1994,” McGraw-Hill, New York, vol. 3, 1994.
  71. [89]Hair J., Black W., Babin B., and Anderson R., “ Multivariate Data Analysis: A Global Perspective,” in Multivariate Data Analysis: A Global Perspective, vol. 7th, 2010.
  72. [90]Petter S., Straub D., and Rai A., “ Specifying formative constructs in information systems research,” MIS Q. Manag. Inf. Syst., vol. 31, no. 4, 2007, doi: 10.2307/25148814 · DOI · Paper page
  73. [91]Williams C. K., Wynn D., Madupalli R., Karahanna E., and Duncan B. K., “ Explaining users’ security behaviors with the security belief model,” J. Organ. End User Comput., vol. 26, no. 3, 2014, doi: 10.4018/joeuc.2014070102 · DOI
  74. [92]Bagozzi R. P. and Yi Y., “ On the evaluation of structural equation models,” J. Acad. Mark. Sci., vol. 16, no. 1, 1988, doi: 10.1007/BF02723327 · DOI · Paper page
  75. [93]Gefen D., Straub D., and Boudreau M.-C., “ Structural Equation Modeling and Regression: Guidelines for Research Practice,” Commun. Assoc. Inf. Syst., vol. 4, 2000, doi: 10.17705/1cais.00407 · DOI · Paper page
  76. [94]Gefen D. and Straub D., “ A Practical Guide To Factorial Validity Using PLS-Graph: Tutorial And Annotated Example,” Commun. Assoc. Inf. Syst., vol. 16, 2005, doi: 10.17705/1cais.01605 · DOI · Paper page
  77. [95]Chinn W. W., “ The Partial Least Squares Approach to Structural Equation Modelling,” Mod. Methods Bus. Res., vol. 29, no. 2, 1998.
  78. [98]Chand S., Sajjad M. Z., and Usman R. M., “ Evaluating the suitability of two parametric wind speed distributions: a case study from Pakistan,” Model. Earth Syst. Environ., vol. 7, no. 3, pp. 1683– 1691, 2021.
  79. [99]Cimperman M., Brenčič M. M., and Trkman P., “ Analyzing older users’ home telehealth services acceptance behavior—applying an Extended UTAUT model,” Int. J. Med. Inform., vol. 90, pp. 22– 31, 2016. doi: 10.1016/j.ijmedinf.2016.03.002 27103194 · DOI
  80. [100]Chou Y.-H. D., Li T.-Y. D., and Ho C.-T. B., “ Factors influencing the adoption of mobile commerce in Taiwan,” Int. J. Mob. Commun., vol. 16, no. 2, pp. 117– 134, 2018.
  81. [101]Presthus W. and O’Malley N. O., “ Motivations and barriers for end-user adoption of bitcoin as digital currency,” Procedia Comput. Sci., vol. 121, pp. 89– 97, 2017.
  82. [102]A.-E. Bennani and R. Oumlil, “Factors fostering IT acceptance by nurses in Morocco: Short paper,” in IEEE 7th International Conference on Research Challenges in Information Science (RCIS), 2013, pp. 1–6.
  83. [104]Queiroz M. M. and Wamba S. F., “ Blockchain adoption challenges in supply chain: An empirical investigation of the main drivers in India and the USA,” Int. J. Inf. Manage., vol. 46, pp. 70– 82, 2019.
  84. [105]S. M. Barton, “Social and cultural factors that influence the uptake of e-learning: case studies in Malaysia, Indonesia, Turkey, Singapore and Australia,” Unpubl. PhD Thesis, RMIT Univ., 2010.
  85. [106]Zhao F. and Khan M. S., “ An Empirical Study of E-Government Service Adoption: Culture and Behavioral Intention,” Int. J. Public Adm., vol. 36, no. 10, 2013, doi: 10.1080/01900692.2013.791314 · DOI
  86. [107]Bankole F. O. and Bankole O. O., “ The effects of cultural dimension on ICT innovation: Empirical analysis of mobile phone services,” Telemat. Informatics, vol. 34, no. 2, 2017, doi: 10.1016/j.tele.2016.08.004 · DOI
  87. [108]Balta-Ozkan N., Boteler B., and Amerighi O., “ European smart home market development: Public views on technical and economic aspects across the United Kingdom, Germany and Italy,” Energy Res. Soc. Sci., vol. 3, pp. 65– 77, 2014.
  88. [109]Okonoko V. N. and Eruvwe U., “ Awareness of Information and Communication Technology-Based Information Resources in Library User Education Programmes in Colleges of Education in Southern Nigeria,” Inf. Impact J. Inf. Knowl. Manag., vol. 11, no. 4, pp. 156– 171, 2020.
  89. [110]Fjällbrant N., “ Why User Education and How Can Information Technology Help?,” IFLA J., vol. 16, no. 4, 1990, doi: 10.1177/034003529001600406 · DOI
  90. [111]Alalwan A. A., Dwivedi Y. K., Rana N. P., and Algharabat R., “ Examining factors influencing Jordanian customers’ intentions and adoption of internet banking: Extending UTAUT2 with risk,” J. Retail. Consum. Serv., vol. 40, pp. 125– 138, 2018.
  91. [112]Orlov L. M., “ Technology for aging: 2020 Market overview,” Aging Place Technol. Watch, pp. 2020– 2025, 2020.
  92. [114]Athanasopoulou A., de Reuver M., Nikou S., and Bouwman H., “ What technology enabled services impact business models in the automotive industry? An exploratory study,” Futures, vol. 109, 2019, doi: 10.1016/j.futures.2019.04.001 · DOI
  93. [115]Krasniqi X. and Hajrizi E., “ Use of IoT Technology to Drive the Automotive Industry from Connected to Full Autonomous Vehicles,” in IFAC-PapersOnLine, 2016, vol. 49, no. 29, doi: 10.1016/j.ifacol.2016.11.078 · DOI

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Authors
Maswadi, K., Ghani, N. A., & Hamid, S.
Affiliations
University of Malaya (MY)
Corresponding author
  • Maswadi, Kholoud
  • Hamid, Suraya
ORCID
Venue
PLOS ONE
Publisher
Public Library of Science (PLoS)
Published
2022-08-30
Volume
17
Issue
8
Pages
e0272525
ISSN
1932-6203 (ISSN-L)
DOI
10.1371/journal.pone.0272525
PMC
PMC9426941
OpenAlex
W4293579648
Cited by
50
References
8
Citations by year
2026: 10 · 2025: 16 · 2024: 16 · 2023: 8
FWCI
16.69
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS ONE (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Programme of the University of Malaya and the Ministry of Higher Education of Malaysia (IIRG006D-19SAH)
Topics
  • Social Sciences › Demography › Technology Use by Older Adults
  • Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
  • Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
Keywords
smart home technology, older adults, behavioral intention, technology acceptance, UTAUT, technology awareness, attitudes, performance expectancy, effort expectancy, Saudi Arabia
MeSH terms
Aged, Humans, Patient Acceptance of Health Care, Surveys and Questionnaires, Saudi Arabia, Technology, Intention

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