Skip to main content

A Review of the Determinant Factors of Technology Adoption

  • Conference paper
  • First Online:
Applied Informatics and Cybernetics in Intelligent Systems (CSOC 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1226))

Included in the following conference series:

Abstract

Technology adoption has been researched extensively in the literature. New or modified models are emerging with different variables but not much re-search has been done to find out the variable that has been consistent in most model and which ones has been added to modify models. Eighty papers published in seventy-three journals and seven conference proceedings between the years 1992–2019 were reviewed. This study was conducted with three objectives in mind (1) to highlight the mostly used factors from the reviewed literature, (2) to investigate technology adoption factors that were found significant and non-significant from an analytic point of view, (3) to identify factors to be used as core factors of a generic adoption model. Results of the study revealed that the identified factors are mostly derived from TRA, TAM, TPB, MPCU, DOI, SCT, UTAUT and its extensions.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Abramson, J., Dawson, M., Stevens, J.: An examination of the prior use of e-learning within an extended technology acceptance model and the factors that influence the behavioral intention of users to use m-learning. SAGE Open 5(4), 2158244015621114 (2015)

    Article  Google Scholar 

  2. AbuShanab, E., Pearson, J.M.: Internet banking in Jordan: the unified theory of acceptance and use of technology (UTAUT) perspective. J. Syst. Inf. Technol. 9(1), 78–97 (2007)

    Article  Google Scholar 

  3. Agarwal, R., Prasad, J.: Are individual differences germane to the acceptance of new information technologies? Decis. Sci. 30(2), 361–391 (1999)

    Article  Google Scholar 

  4. Aghaunor, L., Fotoh, X.: Factors affecting ecommerce adoption in Nigerian banks (2006)

    Google Scholar 

  5. Ajzen, I.: The theory of planned behavior. Organ. Behav. Hum. Decis. Process. 50(2), 179–211 (1991)

    Article  Google Scholar 

  6. Alalwan, A.A., et al.: Examining factors influencing Jordanian customers’ intentions and adoption of internet banking: extending UTAUT2 with risk. J. Retail. Consum. Serv. 40, 125–138 (2018)

    Article  Google Scholar 

  7. Alambaigi, A., Ahangari, I.: Technology Acceptance Model (TAM) as a predictor model for explaining agricultural experts behavior in acceptance of ICT. Int. J. Agric. Manage. Dev. 6(2), 235–247 (2016)

    Google Scholar 

  8. Al-Azawei, A., Lundqvist, K.: Learner differences in perceived satisfaction of an online learning: an extension to the technology acceptance model in an Arabic sample. Electron. J. e-Learn. 13(5), 408–426 (2015)

    Google Scholar 

  9. Alharbi, S., Drew, S.: Using the technology acceptance model in understanding academics’ behavioural intention to use learning management systems. Int. J. Adv. Comput. Sci. Appl. 5(1), 143–155 (2014)

    Google Scholar 

  10. Al-hawari, M.A., Mouakket, S.: The influence of technology acceptance model (TAM) factors on students’e-satisfaction and e-retention within the context of UAE e-learning. Educ. Bus. Soc. Contemp. Middle East. Issue 3(4), 299–314 (2010)

    Article  Google Scholar 

  11. Alkhalifah, A.: A research methodology to explore the adoption of e-government. Int. J. Comput. Eng. Inf. Technol. 9(9), 216 (2017)

    Google Scholar 

  12. Al-Qeisi, K., Dennis, C., Abbad, M.: How viable is the UTAUT model in a non-Western context? Int. Bus. Res. 8(2), 204–219 (2015)

    Article  Google Scholar 

  13. Alrawabdeh, W.: Environmental factors affecting mobile commerce adoption-an exploratory study on the Telecommunication firms in Jordan. Int. J. Bus. Soc. Sci. 5(8) (2014)

    Google Scholar 

  14. Alshamaila, Y., Papagiannidis, S., Li, F.: Cloud computing adoption by SMEs in the north east of England: a multi-perspective framework. J. Enterp. Inf. Manage. 26(3), 250–275 (2013)

    Article  Google Scholar 

  15. Alshehri, M., Drew, S., AlGhamdi, R.: Analysis of citizens acceptance for e-government services: applying the UTAUT model (2013). arXiv preprint arXiv:1304.3157

  16. Alsheikh, L., Bojei, J.: Determinants affecting customer’s intention to adopt mobile banking in Saudi Arabia. Int. Arab J. e-Technol. 3(4), 210–219 (2014)

    Google Scholar 

  17. Amadin, F., Obienu, A.: Intention to use university e-mail system based on modified utaut model: perspectives of University of Benin Postgraduate Students. In: Proceedings of the 6th iSTEAMS Multidisciplinary Cross_Border Conference, University of Professional Studies, Accra Ghana (2016)

    Google Scholar 

  18. Armitage, C.J., Conner, M.: Efficacy of the theory of planned behaviour: a meta-analytic review. Br. J. Soc. Psychol. 40(4), 471–499 (2001)

    Article  Google Scholar 

  19. Attuquayefio, S., Addo, H.: Using the UTAUT model to analyze students’ ICT adoption. Int. J. Educ. Develop. using Inf. Commun. Technol. 10(3), 75–86 (2014)

    Google Scholar 

  20. Baharin, A.T., et al.: Evaluating effectiveness of IDEWL using technology acceptance model. Procedia Soc. Behav. Sci. 171, 897–904 (2015)

    Article  Google Scholar 

  21. Beldad, A.D., Hegner, S.M.: Expanding the technology acceptance model with the inclusion of trust, social influence, and health valuation to determine the predictors of German users’ willingness to continue using a fitness app: a structural equation modeling approach. Int. J. Hum. Comput. Interact. 34(9), 882–893 (2018)

    Article  Google Scholar 

  22. Bresciani, S., Eppler, M.: Extending tam to information visualization: a framework for evaluation. Electron. J. Inf. Syst. Eval. 18(1), 46–58 (2015)

    Google Scholar 

  23. Chang, C.-C., Yan, C.-F., Tseng, J.-S.: Perceived convenience in an extended technology acceptance model: mobile technology and English learning for college students. Australas. J. Educ. Technol. 28(5), 809–826 (2012)

    Google Scholar 

  24. Chen, H., et al.: An extended technology acceptance model for mobile social gaming service popularity analysis. Mobile Inf. Syst. 2017, 1–12 (2017)

    Google Scholar 

  25. Chiu, C.-Y., Chen, S., Chen, C.-L.: An integrated perspective of TOE framework and innovation diffusion in broadband mobile applications adoption by enterprises. Int. J. Manage. Econ. Soc. Sci. 6(1), 14–39 (2017)

    Google Scholar 

  26. Chong, A.Y.-L., et al.: Factors affecting the adoption level of c-commerce: an empirical study. J. Comput. Inf. Syst. 50(2), 13–22 (2009)

    Google Scholar 

  27. Chong, S., Bauer, C.: A model of factor influences on Electronic Commerce adoption and diffusion in small-and medium-sized enterprises. In: PACIS Proceedings, p. 23 (2000)

    Google Scholar 

  28. Dadayan, L., Ferro, E.: When technology meets the mind: a comparative study of the technology acceptance model. In: Wimmer, M.A., Traunmüller, R., Grönlund, Å., Andersen, K.V. (eds.) Electronic Government. Lecture Notes in Computer Science, vol. 3591. Springer, Heidelberg (2005). https://doi.org/10.1007/11545156_1

    Chapter  Google Scholar 

  29. Daniel, E.M., Grimshaw, D.J.: An exploratory comparison of electronic commerce adoption in large and small enterprises. J. Inf. Technol. 17(3), 133–147 (2002)

    Article  Google Scholar 

  30. Davis, F.D.: Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 1989, 319–340 (1989)

    Article  Google Scholar 

  31. Davis, F.D., Bagozzi, R.P., Warshaw, P.R.: User acceptance of computer technology: a comparison of two theoretical models. Manage. Sci. 35(8), 982–1003 (1989)

    Article  Google Scholar 

  32. Diop, E.B., Zhao, S., Van Duy, T.: An extension of the technology acceptance model for understanding travelers’ adoption of variable message signs. PLoS ONE 14(4), e0216007 (2019)

    Article  Google Scholar 

  33. Dumpit, D.Z., Fernandez, C.J.: Analysis of the use of social media in higher education institutions (heis) using the technology acceptance model. Int. J. Educ. Technol. High. Educ. 14(1), 1–16 (2017). https://doi.org/10.1186/s41239-017-0045-2

    Article  Google Scholar 

  34. El-Gohary, H.: Factors affecting E-Marketing adoption and implementation in tourism firms: an empirical investigation of Egyptian small tourism organisations. Int. J. Educ. Technol. High. Educ. 33(5), 1256–1269 (2012)

    Google Scholar 

  35. Fathema, N., Shannon, D., Ross, M.: Expanding the Technology Acceptance Model (TAM) to examine faculty use of Learning Management Systems (LMSs) in higher education institutions. J. Online Learn. Teach. 11(2), 210–232 (2015)

    Google Scholar 

  36. Fishbein, M.: A theory of reasoned action: some applications and implications. In: Nebraska Symposium on Motivation. Nebraska Symposium on Motivation (1980)

    Google Scholar 

  37. Fishbein, M., Ajzen, I.: Understanding attitudes and predicting social behavior (1980)

    Google Scholar 

  38. Gagnon, M.P., et al.: Using a modified technology acceptance model to evaluate healthcare professionals’ adoption of a new telemonitoring system. Telemed. e-Health 18(1), 54–59 (2012)

    Article  Google Scholar 

  39. Ghane, F., et al.: The role of social influence and innovation characteristics in the adoption of Integrated Pest Management (IPM) practices by paddy farmers in Iran. In: International Conference on Social Science and Humanity-IPEDR, Singapore (2011)

    Google Scholar 

  40. Giannakos, M.N., Vlamos, P.: Educational webcasts’ acceptance: empirical examination and the role of experience. Br. J. Edu. Technol. 44(1), 125–143 (2013)

    Article  Google Scholar 

  41. Godoe, P., Johansen, T.: Understanding adoption of new technologies: technology readiness and technology acceptance as an integrated concept. J. Eur. Psychol. Stud. 3(1), 38–52 (2012)

    Google Scholar 

  42. Gunbatar, M.S.: Examination of undergraduate and associate degree students’ computer programming attitude and self-efficacy according to thinking style, gender and experience. Contemp. Educ. Technol. 9(4), 354–373 (2018)

    Article  Google Scholar 

  43. Gupta, B., Dasgupta, S., Gupta, A.: Adoption of ICT in a government organization in a developing country: an empirical study. J. Strateg. Inf. Syst. 17(2), 140–154 (2008)

    Article  Google Scholar 

  44. He, Y., Chen, Q., Kitkuakul, S.: Regulatory focus and technology acceptance: perceived ease of use and usefulness as efficacy. Cogent Bus. Manage. 5(1), 1459006 (2018)

    Article  Google Scholar 

  45. Horton, R.P., et al.: Explaining intranet use with the technology acceptance model. J. Inf. Technol. 16(4), 237–249 (2001)

    Article  Google Scholar 

  46. Hsieh, H.-F., Shannon, S.E.: Three approaches to qualitative content analysis. Qual. Health Res. 15(9), 1277–1288 (2005)

    Article  Google Scholar 

  47. Hussin, H., Noor, R.M.: Innovating business through e-commerce: exploring the willingness of Malaysian SMEs. In: Proceedings of the Second International Conference on Innovation in IT. Citeseer (2005)

    Google Scholar 

  48. Ibrahim, A., Adu-Gyamfi, M., Kassim, B.A.: Factors affecting the adoption of ICT by administrators in the university for development studies tamale: empirical evidence from the UTAUT model. Int. J. Sustain. Manage. Inf. Technol. 4(1), 1 (2018)

    Google Scholar 

  49. Isiyaku, D.D., Ayub, A.F.M., Abdulkadir, S.: Empirical modeling of information communication technology usage behaviour among business education teachers in tertiary colleges of a developing country. S. Afr. J. Educ. 35(4), 1–14 (2015)

    Google Scholar 

  50. Julianto, I.P., Yasa, I.N.P.: The analysis of technology acceptance model (TAM) on the use of accounting information system. In: International Conference on Tourism, Economics, Accounting, Management, and Social Science, TEAMS 2018. Atlantis Press (2019)

    Google Scholar 

  51. Kijsanayotin, B., Pannarunothai, S., Speedie, S.M.: Factors influencing health information technology adoption in Thailand’s community health centers: applying the UTAUT model. Int. J. Med. Informatics 78(6), 404–416 (2009)

    Article  Google Scholar 

  52. Lee, J., et al.: Adoption of Internet technologies in small businesses. In: PACIS Proceedings, p. 71 (2001)

    Google Scholar 

  53. Liao, S., et al.: The adoption of virtual banking: an empirical study. Int. J. Inf. Manage. 19(1), 63–74 (1999)

    Article  Google Scholar 

  54. Lu, C.-T., Huang, S.-Y., Lo, P.-Y.: An empirical study of on-line tax filing acceptance model: integrating TAM and TPB. Afr. J. Bus. Manage. 4(5), 800–810 (2010)

    Google Scholar 

  55. Lu, J., Yao, J.E., Yu, C.-S.: Personal innovativeness, social influences and adoption of wireless Internet services via mobile technology. J. Strateg. Inf. Syst. 14(3), 245–268 (2005)

    Article  Google Scholar 

  56. Maduku, D.K.: Behavioral intention towards mobile banking usage by South African retail banking clients. Invest. Manage. Financ. Innov. 11(3), 37–51 (2014)

    Google Scholar 

  57. Maduku, D.K.: Understanding behavioural intention towards e-books use: does gender really matter. In: Proceedings of 31st International Business Research Conference (2015)

    Google Scholar 

  58. Marques, A., et al.: Medical records system adoption in European hospitals. Electron. J. Inf. Syst. Eval. 14(1), 89 (2011)

    Google Scholar 

  59. Masrom, M.: Technology acceptance model and e-learning. Technology 21(24), 81 (2007)

    Google Scholar 

  60. Mathieson, K.: Predicting user intentions: comparing the technology acceptance model with the theory of planned behavior. Inf. Syst. Res. 2(3), 173–191 (1991)

    Article  Google Scholar 

  61. Phung, T.M., Thi, L., Pham, M., Do, N.H.: Effect of eWOW and social influence on product adoption intention. In: Proceedings of 11th International Days of Statistics and Economics (2017)

    Google Scholar 

  62. Moghavvemi, S., et al.: The entrepreneur’s perception on information technology innovation adoption: an empirical analysis of the role of precipitating events on usage behavior. Innovation 14(2), 231–246 (2012)

    Article  Google Scholar 

  63. Nasri, W.: Factors influencing the adoption of internet banking in Tunisia. Int. J. Bus. Manage. 6(8), 143–160 (2011)

    Article  Google Scholar 

  64. Nchunge, D.M., Sakwa, M., Mwangi, W.: User’s perception on ICT adoption for education support in schools: a survey of secondary school teacher’s in Thika district Kenya. Int. J. Humanit. Soc. Sci. 2(10), 17–29 (2012)

    Google Scholar 

  65. Oliveira, T., Martins, M.F.: Understanding e-business adoption across industries in European countries. Ind. Manage. Data Syst. 110(9), 1337–1354 (2010)

    Article  Google Scholar 

  66. Park, N., et al.: Factors influencing smartphone use and dependency in South Korea. Comput. Hum. Behav. 29(4), 1763–1770 (2013)

    Article  Google Scholar 

  67. Parker, D., et al.: Intention to commit driving violations: An application of the theory of planned behavior. J. Appl. Psychol. 77(1), 94 (1992)

    Article  Google Scholar 

  68. Rahi, S., Ghani, M.A.: The role of UTAUT, DOI, perceived technology security and game elements in internet banking adoption. World J. Sci. Technol. Sustain. Develop. 15(4), 338–356 (2018)

    Article  Google Scholar 

  69. Rahi, S., Ghani, M., Ngah, A.: A structural equation model for evaluating user’s intention to adopt internet banking and intention to recommend technology. Accounting 4(4), 139–152 (2018)

    Article  Google Scholar 

  70. Ramdani, B., Kawalek, P., Oswaldo, L.: Knowledge management and enterprise systems adoption by SMEs. J. Enterp. Inf. Manage. 22(1/2), 10–24 (2009)

    Article  Google Scholar 

  71. Rogers, E.M.: Diffusion of preventive innovations. Addict. Behav. 27(6), 989–993 (2002)

    Article  Google Scholar 

  72. Sachs, G.: Profiles in Innovation: Virtual & Augmented Reality - Understanding the race for the next computing platform. Goldman Sachs Group Inc. (2016)

    Google Scholar 

  73. Sahin, I.: Detailed review of Rogers’ diffusion of innovations theory and educational technology-related studies based on Rogers’ theory. Turk. Online J. Educ. Technol. (TOJET) 5(2), 14–23 (2006)

    Google Scholar 

  74. Sánchez, R.A., Hueros, A.D.: Motivational factors that influence the acceptance of Moodle using TAM. Comput. Hum. Behav. 26(6), 1632–1640 (2010)

    Article  Google Scholar 

  75. Shah Alam, S., Ali, M.Y., Jani, M.F.M.: An empirical study of factors affecting electronic commerce adoption among SMEs in Malaysia. J. Bus. Econ. Manage. 12(2), 375–399 (2011)

    Article  Google Scholar 

  76. Shahroom, A., Hussin, N.: Industrial revolution 4.0 and education. Int. J. Acad. Res. Bus. Soc. Sci. 8(9), 314–319 (2018)

    Google Scholar 

  77. Southey, G.: The theories of reasoned action and planned behaviour applied to business decisions: a selective annotated bibliography. J. New Bus. Ideas Trends 9(1), 43–50 (2011)

    MathSciNet  Google Scholar 

  78. Straub, E.T.: Understanding technology adoption: theory and future directions for informal learning. Rev. Educ. Res. 79(2), 625–649 (2009)

    Article  Google Scholar 

  79. Šumak, B., et al.: Factors affecting acceptance and use of Moodle: an empirical study based on TAM. Informatica 35(1), 91–100 (2011)

    Google Scholar 

  80. Tan, K.S., Eze, U.C.: An empirical study of internet-based ICT adoption among Malaysian SMEs. Commun. IBIMA 1(1), 1–12 (2008)

    Google Scholar 

  81. Tan, M., Teo, T.S.: Factors influencing the adoption of Internet banking. J. Assoc. Inf. Syst. 1(1), 5 (2000)

    Google Scholar 

  82. Taylor, S., Todd, P.A.: Understanding information technology usage: a test of competing models. Inf. Syst. Res. 6(2), 144–176 (1995)

    Article  Google Scholar 

  83. Teo, T.S., Pok, S.H.: Adoption of WAP-enabled mobile phones among Internet users. Omega 31(6), 483–498 (2003)

    Article  Google Scholar 

  84. Thong, J.Y.: An integrated model of information systems adoption in small businesses. J. Manage. Inf. Syst. 15(4), 187–214 (1999)

    Article  Google Scholar 

  85. 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. Manage. 29(1), 131–151 (2018)

    Article  Google Scholar 

  86. Van den Berg, J., Van der Lingen, E.: An empirical study of the factors affecting the adoption of mobile enterprise applications. S. Afr. J. Ind. Eng. 30(1), 124–146 (2019)

    Google Scholar 

  87. Van Slyke, C., Belanger, F., Comunale, C.L.: Factors influencing the adoption of web-based shopping: the impact of trust. ACM SIGMIS Database Database Adv. Inf. Syst. 35(2), 32–49 (2004)

    Article  Google Scholar 

  88. Van Slyke, C., Belanger, F., Hightower, R.: Understanding gender-based differences in consumer e-commerce adoption. In: Proceedings of the 2005 Southern Association of Information Systems Conference (2005)

    Google Scholar 

  89. Van Slyke, C., et al.: The influence of culture on consumer-oriented electronic commerce adoption. J. Electron. Commer. Res. 11(1) (2010)

    Google Scholar 

  90. Venkatesh, V., Davis, F.D.: A theoretical extension of the technology acceptance model: four longitudinal field studies. Manage. Sci. 46(2), 186–204 (2000)

    Article  Google Scholar 

  91. Venkatesh, V., et al.: User acceptance of information technology: toward a unified view. MIS Q. 2003, 425–478 (2003)

    Article  Google Scholar 

  92. Wang, Y., Meister, D., Wang, Y.: Reexamining relative advantage and perceived usefulness: an empirical study. Int. J. Inf. Commun. Technol. Educ. 7(1), 46–59 (2011)

    Article  Google Scholar 

  93. Weng, F., et al.: A TAM-based study of the attitude towards use intention of multimedia among school teachers. Appl. Syst. Innov. 1(3), 36 (2018)

    Article  Google Scholar 

  94. Yucel, U., Gulbahar, Y.: Technology acceptance model: a review of the prior predictors. J. Fac. Educ. Sci. 46(1), 89–109 (2013)

    Google Scholar 

  95. Yuen, A., Ma, W.: Exploring teacher acceptance of E-learning technology. Asia Pac. J. Teach. Educ. 36(3), 229–243 (2008)

    Article  Google Scholar 

  96. Zahir, M., Gharleghi, B.: Adoption of internet banking in maldives, the most important determinants. Asian Soc. Sci. 11(2), 181–189 (2015)

    Google Scholar 

  97. Zhang, S., Zhao, J., Tan, W.: Extending TAM for online learning systems: an intrinsic motivation perspective. Tsinghua Sci. Technol. 13(3), 312–317 (2008)

    Article  Google Scholar 

  98. Zhang, Y., Wildemuth, B.M.: Qualitative analysis of content. In: Applications of Social Research Methods to Questions in Information Library Science, vol. 308, p. 319 (2009)

    Google Scholar 

  99. Zhu, K., et al.: Innovation diffusion in global contexts: determinants of post-adoption digital transformation of European companies. Eur. J. Inf. Syst. 15(6), 601–616 (2006)

    Article  Google Scholar 

  100. Zhu, K., Kraemer, K., Xu, S.: Electronic business adoption by European firms: a cross-country assessment of the facilitators and inhibitors. Eur. J. Inf. Syst. 12(4), 251–268 (2003)

    Article  Google Scholar 

  101. Zhu, K., Kraemer, K.L.: Post-adoption variations in usage and value of e-business by organizations: cross-country evidence from the retail industry. Inf. Syst. Res. 16(1), 61–84 (2005)

    Article  Google Scholar 

  102. Hsu, C.-L., Lu, H.-P.: Why do people play on-line games? An extended TAM with social influences and flow experience. Inf. Manage. Sci. 41(7), 853–868 (2004)

    Google Scholar 

  103. Kuan, K.K., Chau, P.Y.: A perception-based model for EDI adoption in small businesses using a technology–organization–environment framework. Inf. Manage. Sci. 38(8), 507–521 (2001)

    Google Scholar 

  104. Nassuora, A.B.: Understanding factors affecting the adoption of m-commerce by consumers. J. Appl. Sci. 13(6), 913–918 (2013)

    Article  Google Scholar 

  105. Muriithi, P., Horner, D., Pemberton, L.: Factors contributing to adoption and use of information and communication technologies within research collaborations in Kenya. Inf. Technol. Develop. 22(Suppl. 1), 84–100 (2016)

    Article  Google Scholar 

  106. Mosweu, O., Bwalya, K.J.: A multivariate analysis of the determinants for adoption and use of the Document Workflow Management System in Botswana’s public sector. S. Afr. J. Libr. Inf. Sci. 84(2), 27–38 (2018)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Kayode Emmanuel Oyetade .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Oyetade, K.E., Zuva, T., Harmse, A. (2020). A Review of the Determinant Factors of Technology Adoption. In: Silhavy, R. (eds) Applied Informatics and Cybernetics in Intelligent Systems. CSOC 2020. Advances in Intelligent Systems and Computing, vol 1226. Springer, Cham. https://doi.org/10.1007/978-3-030-51974-2_26

Download citation

Publish with us

Policies and ethics