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Determinants of Users’ Intention to Use IoT: A Conceptual Framework

  • Nura Muhammad BabaEmail author
  • Ahmad Suhaimi Baharudin
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1073)

Abstract

The Internet of things (IoT) is realised as a potentially effective means of integrating multiple technologies to improve the quality of people’s life and offering interesting and advantageous new services to individuals. However, it has emerged that consumers’ acceptance of IoT is currently low despite its huge economic potentials and impacts, as well as high investment from the private and public sectors. Yet, few studies have investigated the perspectives of the users on IoT. Specifically, only a few empirical researches had examined the determinants of IoT service adoption from the user’s perspective and research model were still not fully developed. Hence, there is a dearth of empirical research on IoT adoption in Malaysia. Therefore, this research aims to develop an integrative model of factors influencing users’ acceptance of IoT. The research applied an integrated model from the combine theories of technology acceptance model (TAM), and the theory of perceived risk. The research hope to provide useful insight into the key driving factors with regard to understanding consumers’ behavioural intention to use the IoT.

Keywords

IoT TAM Behavioural intention 

Notes

Acknowledgement

This research is supported by Universiti Sains Malaysia through the USM Bridging Grant 2017 [A/C Number: 304.PKOMP.6316001].

References

  1. 1.
    Internet Live Stats: Internet Users (2017). http://www.internetlivestats.com/internet-users/malaysia/. Accessed 5 Mar 2017
  2. 2.
    Statista: Number of connected devices worldwide 2012–2020 (2017). https://www.statista.com/statistics/471264/iot-number-of-connected-devices-worldwide/. Accessed 6 Mar 2017
  3. 3.
    Cisco: Internet of Things: Connected Means Informed, p. 3. Cisco (2016)Google Scholar
  4. 4.
    Fantana, N., et al.: IoT applications–value creation for industry. In: Internet of Things: Converging Technologies for Smart Environments and Integrated Ecosystems, p. 153 (2013)Google Scholar
  5. 5.
    Lim, J.H.: Antedecents and outcome of Internet of Things adoption: a perspective of public listed companies on main market board of Bursa Malaysia. Universiti Sains Malaysia (2015)Google Scholar
  6. 6.
    Verizon: State of the Market: Internet of Things 2016. Verizon.com (2016)Google Scholar
  7. 7.
    Lund, D., et al.: Worldwide and regional Internet of Things (IoT) 2014–2020 forecast: a virtuous circle of proven value and demand. Technical Report, International Data Corporation (IDC) (2014)Google Scholar
  8. 8.
    Gao, L., Bai, X.: A unified perspective on the factors influencing consumer acceptance of Internet of Things technology. Asia Pac. J. Mark. Logist. 26(2), 211–231 (2014)CrossRefGoogle Scholar
  9. 9.
    Chong, A.Y.-L., et al.: Predicting RFID adoption in healthcare supply chain from the perspectives of users. Int. J. Prod. Econ. 159, 66–75 (2015)CrossRefGoogle Scholar
  10. 10.
    Gubbi, J., et al.: Internet of Things (IoT): a vision, architectural elements, and future directions. Future Gener. Comput. Syst. 29(7), 1645–1660 (2013)CrossRefGoogle Scholar
  11. 11.
    Rose, K., Eldridge, S., Chapin, L.: The Internet of Things: an overview, pp. 1–50. The Internet Society (ISOC) (2015)Google Scholar
  12. 12.
    Botta, A., et al.: Integration of cloud computing and Internet of Things: a survey. Future Gener. Comput. Syst. 56, 684–700 (2016)CrossRefGoogle Scholar
  13. 13.
    ISO: Internet of Things: Preliminary Report 2014, Switzerland, pp. 1–11 (2015)Google Scholar
  14. 14.
    Mimos: National Internet of Things Strategic Roadmap: A Summary, Malaysia, pp. 1–24 (2015)Google Scholar
  15. 15.
    Venkatesh, V., et al.: User acceptance of information technology: toward a unified view. MIS Q. 27, 425–478 (2003)CrossRefGoogle Scholar
  16. 16.
    Venkatesh, V., Davis, F.D., Morris, M.G.: Dead or alive? The development, trajectory and future of technology adoption research. J. Assoc. Inf. Syst. 8(4), 267–286 (2007)Google Scholar
  17. 17.
    Hsu, C.-L., Lin, J.C.-C.: An empirical examination of consumer adoption of Internet of Things services: network externalities and concern for information privacy perspectives. Comput. Hum. Behav. 62, 516–527 (2016)CrossRefGoogle Scholar
  18. 18.
    Chong, A.Y.-L., Chan, F.T., Ooi, K.-B.: Predicting consumer decisions to adopt mobile commerce: cross country empirical examination between China and Malaysia. Decis. Support Syst. 53(1), 34–43 (2012)CrossRefGoogle Scholar
  19. 19.
    Kim, K.J., Shin, D.-H.: An acceptance model for smart watches: implications for the adoption of future wearable technology. Internet Res. 25(4), 527–541 (2015)CrossRefGoogle Scholar
  20. 20.
    Alolayan, B.: Do I really have to accept smart fridges? An empirical study (2014)Google Scholar
  21. 21.
    Dahlberg, T., et al.: Past, present and future of mobile payments research: a literature review. Electron. Commer. Res. Appl. 7(2), 165–181 (2008)CrossRefGoogle Scholar
  22. 22.
    Tu, M.: An exploratory study of Internet of Things (IoT) adoption intention in logistics and supply chain management: a mixed research approach. Int. J. Logist. Manag. 29(1), 131–151 (2018)CrossRefGoogle Scholar
  23. 23.
    Kamble, S.S., et al.: Modeling the Internet of Things adoption barriers in food retail supply chains. J. Retail. Consum. Serv. 48, 154–168 (2019)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Hsu, C.-L., Lin, J.C.-C.: Exploring factors affecting the adoption of Internet of Things services. J. Comput. Inf. Syst. 58(1), 49–57 (2018)Google Scholar
  25. 25.
    Mani, Z., Chouk, I.: Drivers of consumers’ resistance to smart products. J. Mark. Manag., 1–22 (2016)Google Scholar
  26. 26.
    Kowatsch, T., Maass, W.: Critical privacy factors of internet of things services: an empirical investigation with domain experts. In: Knowledge and Technologies in Innovative Information Systems, pp. 200–211. Springer (2012)Google Scholar
  27. 27.
    Shin, D.H.: Conceptualizing and measuring quality of experience of the internet of things: exploring how quality is perceived by users. Inf. Manag. 54(8), 998–1011 (2017)CrossRefGoogle Scholar
  28. 28.
    Liew, C.S., et al.: Factors influencing consumer acceptance of Internet of Things technology. In: Handbook of Research on Leveraging Consumer Psychology for Effective Customer Engagement, p. 168. IGI Global (2016)Google Scholar
  29. 29.
    Mital, M., et al.: Adoption of Internet of Things in India: a test of competing models using a structured equation modeling approach. Technol. Forecast. Soc. Change (2017)Google Scholar
  30. 30.
    Dong, X., Chang, Y., Wang, Y., Yan, J.: Understanding usage of Internet of Things (IOT) systems in China: cognitive experience and affect experience as moderator. Inf. Technol. People 30(1), 117–138 (2017)CrossRefGoogle Scholar
  31. 31.
    Balaji, M., et al.: User acceptance of IoT applications in retail industry. In: Technology Adoption and Social Issues: Concepts, Methodologies, Tools, and Applications, pp. 1331–1352. IGI Global (2018)Google Scholar
  32. 32.
    Jayashankar, P., et al.: IoT adoption in agriculture: the role of trust, perceived value and risk. J. Bus. Ind. Mark. 33(6), 804–821 (2018)CrossRefGoogle Scholar
  33. 33.
    Abushakra, A., Nikbin, D.: Extending the UTAUT2 model to understand the entrepreneur acceptance and adopting Internet of Things (IoT). In: International Conference on Knowledge Management in Organizations. Springer (2019)Google Scholar
  34. 34.
    Lee, W., Shin, S.: An empirical study of consumer adoption of Internet of Things services. Int. J. Eng. Technol. Innov. 9(1), 1 (2019)Google Scholar
  35. 35.
    The Acquity Group: Igniting Growth in Consumer Technology, United States (2016)Google Scholar
  36. 36.
    Accenture: The Internet of Things: The Future of Consumer Adoption, USA, pp. 1–12 (2014)Google Scholar
  37. 37.
    Arpaci, I.: Understanding and predicting students’ intention to use mobile cloud storage services. Comput. Hum. Behav. 58, 150–157 (2016)CrossRefGoogle Scholar
  38. 38.
    Keong, W.E.Y.: The determinants of mobile shopping mall apps adoption intention in Malaysia: an empirical investigation. In: 2016 11th International Conference on Computer Science & Education (ICCSE). IEEE (2016)Google Scholar
  39. 39.
    Hew, T.-S., et al.: Predicting drivers of mobile entertainment adoption: a two-stage SEM-artificial-neural-network analysis. J. Comput. Inf. Syst. 56(4), 352–370 (2016)Google Scholar
  40. 40.
    Chiyangwa, T.B., Alexander, P.T.: Rapidly co-evolving technology adoption and diffusion models. Telematics Inform. 33(1), 56–76 (2016)CrossRefGoogle Scholar
  41. 41.
    Ramayah, T., et al.: Explaining the adoption of Internet stock trading in Malaysia: comparing models. Asian J. Technol. Innov. 22(1), 131–151 (2014)CrossRefGoogle Scholar
  42. 42.
    Makki, A.M., Ozturk, A.B., Singh, D.: Role of risk, self-efficacy, and innovativeness on behavioral intentions for mobile payment systems in the restaurant industry. J. Foodserv. Bus. Res. 19(5), 454–473 (2016)CrossRefGoogle Scholar
  43. 43.
    Hsu, C.-L., Lin, J.C.-C.: Exploring factors affecting the adoption of Internet of Things services. J. Comput. Inf. Syst., 1–9 (2016)Google Scholar
  44. 44.
    Chuah, S.H.-W., et al.: Wearable technologies: the role of usefulness and visibility in smartwatch adoption. Comput. Hum. Behav. 65, 276–284 (2016)CrossRefGoogle Scholar
  45. 45.
    Khalilzadeh, J., Ozturk, A.B., Bilgihan, A.: Security-related factors in extended UTAUT model for NFC based mobile payment in the restaurant industry. Comput. Hum. Behav. 70, 460–474 (2017)CrossRefGoogle Scholar
  46. 46.
    Ooi, K.-B., Tan, G.W.-H.: Mobile technology acceptance model: an investigation using mobile users to explore smartphone credit card. Expert Syst. Appl. 59, 33–46 (2016)CrossRefGoogle Scholar
  47. 47.
    Zailani, S., et al.: Determinants of RFID adoption in Malaysia’s healthcare industry: occupational level as a moderator. J. Med. Syst. 39(1), 172 (2015)CrossRefGoogle Scholar
  48. 48.
    Moore, G.C., Benbasat, I.: Development of an instrument to measure the perceptions of adopting an information technology innovation. Inf. Syst. Res. 2(3), 192–222 (1991)CrossRefGoogle Scholar
  49. 49.
    Davis, F.D.: Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 13, 319–340 (1989)CrossRefGoogle Scholar
  50. 50.
    Davis, F.D., Bagozzi, R.P., Warshaw, P.R.: User acceptance of computer technology: a comparison of two theoretical models. Manag. Sci. 35(8), 982–1003 (1989)CrossRefGoogle Scholar
  51. 51.
    Al-Momani, A.M., Mahmoud, M.A., Sharifuddin, M.: Modeling the adoption of internet of things services: a conceptual framework. IJAR 2(5), 361–367 (2016)Google Scholar
  52. 52.
    Di Pietro, L., et al.: The integrated model on mobile payment acceptance (IMMPA): an empirical application to public transport. Transp. Res. Part C: Emerg. Technol. 56, 463–479 (2015)CrossRefGoogle Scholar
  53. 53.
    Schierz, P.G., Schilke, O., Wirtz, B.W.: Understanding consumer acceptance of mobile payment services: an empirical analysis. Electron. Commer. Res. Appl. 9(3), 209–216 (2010)CrossRefGoogle Scholar
  54. 54.
    Shin, S., Lee, W.-J.: The effects of technology readiness and technology acceptance on NFC mobile payment services in Korea. J. Appl. Bus. Res. 30(6), 1615 (2014)CrossRefGoogle Scholar
  55. 55.
    Oliveira, T., et al.: Mobile payment: understanding the determinants of customer adoption and intention to recommend the technology. Comput. Hum. Behav. 61, 404–414 (2016)CrossRefGoogle Scholar
  56. 56.
    Arpaci, I., Kilicer, K., Bardakci, S.: Effects of security and privacy concerns on educational use of cloud services. Comput. Hum. Behav. 45, 93–98 (2015)CrossRefGoogle Scholar
  57. 57.
    Salisbury, W.D., et al.: Perceived security and World Wide Web purchase intention. Ind. Manag. Data Syst. 101(4), 165–177 (2001)CrossRefGoogle Scholar
  58. 58.
    Chen, S.-C., Chen, H.-H., Chen, M.-F.: Determinants of satisfaction and continuance intention towards self-service technologies. Ind. Manag. Data Syst. 109(9), 1248–1263 (2009)CrossRefGoogle Scholar
  59. 59.
    Phonthanukitithaworn, C., Sellitto, C., Fong, M.W.: An investigation of mobile payment (m-payment) services in Thailand. Asia-Pac. J. Bus. Adm. 8(1), 37–54 (2016)CrossRefGoogle Scholar
  60. 60.
    Martins, C., Oliveira, T., Popovič, A.: Understanding the Internet banking adoption: a unified theory of acceptance and use of technology and perceived risk application. Int. J. Inf. Manag. 34(1), 1–13 (2014)CrossRefGoogle Scholar
  61. 61.
    Chan, S., Lu, M.: Understanding Internet Banking Adoption and Use Behavior: A Hong Kong Perspective (2004)Google Scholar
  62. 62.
    Cocosila, M., Archer, N.: Practitioner pre-adoption perceptions of Electronic Medical Record systems. Behav. Inf. Technol. 36(8), 827–838 (2017)CrossRefGoogle Scholar
  63. 63.
    Roy, S.K., et al.: Constituents and consequences of smart customer experience in retailing. Technol. Forecast. Soc. Change (2016)Google Scholar
  64. 64.
    Lim, N.: Consumers’ perceived risk: sources versus consequences. Electron. Commer. Res. Appl. 2(3), 216–228 (2003)CrossRefGoogle Scholar
  65. 65.
    Chang, H.H., Fu, C.S., Jain, H.T.: Modifying UTAUT and innovation diffusion theory to reveal online shopping behavior: familiarity and perceived risk as mediators. Inf. Dev. 32(5), 1757–1773 (2016)CrossRefGoogle Scholar
  66. 66.
    Flavián, C., Guinalíu, M.: Consumer trust, perceived security and privacy policy: three basic elements of loyalty to a web site. Ind. Manag. Data Syst. 106(5), 601–620 (2006)CrossRefGoogle Scholar
  67. 67.
    Curry, P.: Consumer risk: the importance of privacy and security while connected to Wi-Fi hotspots: does location matter? In: AMCIS (2011)Google Scholar
  68. 68.
    D’Alessandro, S., Girardi, A., Tiangsoongnern, L.: Perceived risk and trust as antecedents of online purchasing behavior in the USA gemstone industry. Asia Pac. J. Mark. Logist. 24(3), 433–460 (2012)CrossRefGoogle Scholar
  69. 69.
    Pavlou, P.A., Fygenson, M.: Understanding and predicting electronic commerce adoption: an extension of the theory of planned behavior. MIS Q. 30, 115–143 (2006)CrossRefGoogle Scholar
  70. 70.
    Yang, Q., et al.: Exploring consumer perceived risk and trust for online payments: an empirical study in China’s younger generation. Comput. Hum. Behav. 50, 9–24 (2015)CrossRefGoogle Scholar
  71. 71.
    Featherman, M.S., Pavlou, P.A.: Predicting e-services adoption: a perceived risk facets perspective. Int. J. Hum Comput Stud. 59(4), 451–474 (2003)CrossRefGoogle Scholar
  72. 72.
    Yu, J., et al.: User acceptance of media tablets: an empirical examination of perceived value. Telematics Inform. 34(4), 206–223 (2017)CrossRefGoogle Scholar
  73. 73.
    Shimp, T.A., Bearden, W.O.: Warranty and other extrinsic cue effects on consumers’ risk perceptions. J. Consum. Res. 9(1), 38–46 (1982)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Nura Muhammad Baba
    • 1
    • 2
    Email author
  • Ahmad Suhaimi Baharudin
    • 1
  1. 1.School of Computer ScienceUniversiti Sains Malaysia, USMGeorge TownMalaysia
  2. 2.Department of Office Technology and Management, School of Management StudiesKano State PolytechnicKanoNigeria

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