A Systematic Review of the Technological Factors Affecting the Adoption of Advanced IT with Specific Emphasis on Building Information Modeling

  • Mohamed Ghayth Elghdban
  • Nurhidayah Binti Azmy
  • Adnan Bin Zulkiple
  • Mohammed A. Al-SharafiEmail author
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 295)


Despite the sensitivity of the architecture, engineering, and construction (AEC) industry to the changes in demographic factors, economic activity, and social development, the industry is consistently progressing and becoming more successful. Organizations in the AEC industry are lagging in the adoption of advanced IT. For instance, Building Information Modelling (BIM) is among the top technologies utilized by the industry. BIM is extensively known as one of the innovations of IT to have emerged within the AEC industry. Although BIM processes require organization-wide adoption, few studies focused on the technological factors influencing BIM adoption at the organizational level in the AEC industry. Therefore, the present study aims to further enrich the literature of such studies through a systematic literature review (SLR). The main objective of this SLR is to analyze the current studies that involved the technological factors that influence organizations to adopt and use advanced IT, such as BIM. Therein, 78 up-to-date studies retrieved from two primary databases (Scopus and Web of Science) were critically analyzed in the period between 2015 and October 2019. The review identified 42 technological factors that may affect BIM adoption in the AEC industry. The outcome of this SLR will add significantly to the existing literature in IT adoption in general, and BIM adoption practically.


IT adoption Technological factors Building information modeling Architecture Engineering And construction (AEC) industry 


  1. 1.
    World Economic Forum: Shaping the Future of Construction: A Breakthrough in Mindset and Technology (2016)Google Scholar
  2. 2.
    Murray, M.: Rethinking construction: the egan report (1998), pp. 178–195. Blackwell Science, Oxford, UK (2003)Google Scholar
  3. 3.
    Mitropoulos, P., Tatum, C.B.: Technology adoption decisions in construction organizations. J. Prof. Nurs. 30(4), 292–299 (1999)Google Scholar
  4. 4.
    Lee, H.W., Oh, H., Kim, Y., Choi, K.: Quantitative analysis of warnings in building information modeling (BIM). Autom. Constr. 51(C), 23–31 (2015)Google Scholar
  5. 5.
    Eastman, C.M.: BIM Handbook: A Guide to Building Information Modeling for Owners, Managers, Designers, Engineers and Contractors, vol. 12, no. 3 (2011)Google Scholar
  6. 6.
    Baharuddin, H.E.A., Othman, A.F., Adnan, H., Ismail, W.N.W.: BIM training: the impact on BIM adoption among quantity surveyors in government agencies. In: IOP Conference Series: Earth and Environmental Science, vol. 233, no. 2, p. 022036. IOP Publishing (2019)Google Scholar
  7. 7.
    Gerges, M., Austin, S., Mayouf, M., Ahiakwo, O., Jaeger, M., Saad, A.: An investigation into the implementation of building information modeling in the Middle East. J. Inf. Technol. Constr. 22(2), 1–15 (2017)Google Scholar
  8. 8.
    NBS: International BIM Report 2016—The International Picture, p. 24. NBS (2016)Google Scholar
  9. 9.
    Howard, R., Restrepo, L., Chang, C.-Y.: Addressing individual perceptions: an application of the unified theory of acceptance and use of technology to building information modelling. Int. J. Proj. Manage. 35(2), 107–120 (2017)Google Scholar
  10. 10.
    Kim, S., Park, C.H., Chin, S.: Assessment of BIM acceptance degree of Korean AEC participants. KSCE J. Civ. Eng. 20(4), 1163–1177 (2016)Google Scholar
  11. 11.
    Acquah, R., Eyiah, A.K., Oteng, D.: Acceptance of building information modelling: a survey of professionals in the construction industry in Ghana. J. Inf. Technol. Constr. 23, 75–91 (2018)Google Scholar
  12. 12.
    Xu, H., Feng, J., Li, S.: Users-orientated evaluation of building information model in the Chinese construction industry. Autom. Constr. 39, 32–46 (2014)Google Scholar
  13. 13.
    Tsai, M.-H., Kang, S.-C., Hsieh, S.-H.: Lessons learnt from customization of a BIM tool for a design-build company. J. Chin. Inst. Eng. 37(2), 189–199 (2014)Google Scholar
  14. 14.
    Davis, F.D.: A technology acceptance model for empirically testing new end-user information systems: theory and results (1986)Google Scholar
  15. 15.
    Davis, F.D.: Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 13(3), 319 (1989)Google Scholar
  16. 16.
    Schifter, D.E., Ajzen, I.: Intention, perceived control, and weight loss: an application of the theory of planned behavior. J. Pers. Soc. Psychol. 49(3), 843 (1985)Google Scholar
  17. 17.
    Ajzen, I.: The theory of planned behavior. Organ. Behav. Hum. Decis. Process. 50(2), 179–211 (1991)Google Scholar
  18. 18.
    Venkatesh, V., Morris, M.G., Davis, G.B., Davis, F.D.: User acceptance of information technology: toward a unified view. MIS Q. 425–478 (2003)Google Scholar
  19. 19.
    Rogers, E.M.: Diffusion of Innovations. The Free Press, New York (1995)Google Scholar
  20. 20.
    Wu, Y.W., Hsu, I.T., Lin, H.Y.: Using TAM to explore vocational students’ willingness to adopt a web-based BIM cost estimating system. Adv. Mater. Res. 1079(1080), 1098–1102 (2015)Google Scholar
  21. 21.
    Batarseh, S., Kamardeen, I.: The impact of individual beliefs and expectations on BIM adoption in the AEC industry, vol. 1, pp. 466–475 (2017)Google Scholar
  22. 22.
    Al-Emran, M., Mezhuyev, V., Kamaludin, A.: Technology acceptance model in m-learning context: a systematic review. Comput. Educ. 125, 389–412 (2018)Google Scholar
  23. 23.
    Al-Emran, M., Mezhuyev, V., Kamaludin, A., Shaalan, K.: The impact of knowledge management processes on information systems: a systematic review. Int. J. Inf. Manage. 43, 173–187 (2018)Google Scholar
  24. 24.
    Junior, C.H., Oliveira, T., Yanaze, M.: The adoption stages (evaluation, adoption, and routinisation) of ERP systems with business analytics functionality in the context of farms. Comput. Electron. Agric. 156, 334–348 (2019)Google Scholar
  25. 25.
    Bhuyan, S., Dash, M.: Exploring cloud computing adoption in private hospitals in India: an investigation of DOI and TOE model. J. Adv. Res. Dyn. Control Syst. 10(8), 443–451 (2018)Google Scholar
  26. 26.
    AL-Shboul, M.A.: Towards better understanding of determinants logistical factors in SMEs for cloud ERP adoption in developing economies. Bus. Process Manag. J. 25(5), 889–907 (2018).
  27. 27.
    Martins, R., Oliveira, T., Thomas, M.A.: An empirical analysis to assess the determinants of SaaS diffusion in firms. Comput. Hum. Behav. 62, 19–33 (2016)Google Scholar
  28. 28.
    Yang, Z., Sun, J., Zhang, Y., Wang, Y.: Understanding SaaS adoption from the perspective of organizational users: a tripod readiness model. Comput. Hum. Behav. 45, 254–264 (2015)Google Scholar
  29. 29.
    Safari, F., et al.: The adoption of software-as-a-service (SaaS): ranking the determinants. J. Enterp. Inf. Manage. 28(3), 400–422 (2015)Google Scholar
  30. 30.
    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)Google Scholar
  31. 31.
    Rosli, K., Yeow, P.H.P., Siew, E.-G.: Adoption of audit technology among audit firms. In: 24th Australasian Conference on Information Systems (ACIS) (2013)Google Scholar
  32. 32.
    Chong, A.Y.L., Chan, F.T.S.: Structural equation modeling for multi-stage analysis on Radio Frequency Identification (RFID) diffusion in the health care industry. Expert Syst. Appl. 39(10), 8645–8654 (2012)Google Scholar
  33. 33.
    Henderson, D., Sheetz, S.D., Trinkle, B.S.: The determinants of inter-organizational and internal in-house adoption of XBRL: a structural equation model. Int. J. Account. Inf. Syst. 13(2), 109–140 (2012)Google Scholar
  34. 34.
    Ifinedo, P.: An empirical analysis of factors influencing internet/e-business technologies adoption by SMEs in Canada. Int. J. Inf. Technol. Decis. Making 10(04), 731–766 (2011)Google Scholar
  35. 35.
    Wang, Y.M., Wang, Y.S., Yang, Y.F.: Understanding the determinants of RFID adoption in the manufacturing industry. Technol. Forecast. Soc. Change 77(5), 803–815 (2010)Google Scholar
  36. 36.
    Doolin, B., Al Haj Ali, E.: Adoption of mobile technology in the supply chain. Int. J. eB. Res. 4(4), 1–15 (2008)Google Scholar
  37. 37.
    Zhu, K., Dong, S., Xu, S.X., Kraemer, K.L.: Innovation diffusion in global contexts: determinants of post-adoption digital transformation of European companies. Eur. J. Inf. Syst. 15(6), 601–616 (2006)Google Scholar
  38. 38.
    Hassan, H., Tretiakov, A., Whiddett, D.: Factors affecting the breadth and depth of e-procurement use in small and medium enterprises. J. Organ. Comput. Electron. Commer. 27(4), 304–324 (2017)Google Scholar
  39. 39.
    Azmi, A., Sapiei, N.S., Mustapha, M.Z., Abdullah, M.: SMEs’ tax compliance costs and IT adoption: the case of a value-added tax. Int. J. Account. Inf. Syst. 23, 1–13 (2016)Google Scholar
  40. 40.
    Wang, Y.M., Wang, Y.C.: Determinants of firms’ knowledge management system implementation: an empirical study. Comput. Hum. Behav. 64, 829–842 (2016)Google Scholar
  41. 41.
    Alharbi, F., Atkins, A., Stanier, C.: Understanding the determinants of cloud computing adoption in Saudi healthcare organisations. Complex Intell. Syst. 2(3), 155–171 (2016)Google Scholar
  42. 42.
    Gangwar, H., Date, H., Ramaswamy, R.: Developing a cloud-computing adoption framework. Glob. Bus. Rev. 16(4), 632–651 (2015)Google Scholar
  43. 43.
    Van Huy, L., Rowe, F., Truex, D., Huynh, M.Q.: An empirical study of determinants of e-commerce adoption in SMEs in Vietnam. J. Glob. Inf. Manage. 20(3), 23–54 (2012)Google Scholar
  44. 44.
    Haberli, C., Oliveira, T., Yanaze, M.: Understanding the determinants of adoption of enterprise resource planning (ERP) technology within the agrifood context: the case of the Midwest of Brazil. Int. Food Agribusiness Manage. Rev. 20(5), 729–746 (2017)Google Scholar
  45. 45.
    Ali, O., Soar, J., Yong, J., McClymont, H., Angus, D.: Collaborative cloud computing adoption in Australian regional municipal government: an exploratory study. In: 2015 IEEE 19th International Conference on Computer Supported Cooperative Work in Design (CSCWD), pp. 540–548. IEEE (2015)Google Scholar
  46. 46.
    Agrawal, K.P.: Investigating the determinants of Big Data Analytics (BDA) adoption in emerging economies. Acad. Manage. Proc. 2015(1), 11290 (2016)Google Scholar
  47. 47.
    MacLennan, E., Van Belle, J.P.: Factors affecting the organizational adoption of service-oriented architecture (SOA). Inf. Syst. eB. Manage. 12(1), 71–100 (2014)Google Scholar
  48. 48.
    Hwang, B.N., Huang, C.Y., Wu, C.H.: A TOE approach to establish a green supply chain adoption decision model in the semiconductor industry. Sustainability 8(2), 168 (2016)Google Scholar
  49. 49.
    Awa, H.O., Ojiabo, O.U.: A model of adoption determinants of ERP within T-O-E framework. Inf. Technol. People 29(4), 901–930 (2016)Google Scholar
  50. 50.
    Alam, M.G.R., Masum, A.K.M., Beh, L.S., Hong, C.S.: Critical factors influencing decision to adopt human resource information system (HRIS) in hospitals. PLoS One 11(8) (2016)Google Scholar
  51. 51.
    Ahuja, R., Jain, M., Sawhney, A., Arif, M.: Adoption of BIM by architectural firms in India: technology–organization–environment perspective. Archit. Eng. Des. Manage. 12(4), 311–330 (2016)Google Scholar
  52. 52.
    Al Isma’ili, S., Li, M., Shen, J., He, Q.: Cloud computing adoption determinants: an analysis of Australian SMEs. In: Pacific Asia Conference on Information Systems 2016 Proceedings, pp. 1–17 (2016)Google Scholar
  53. 53.
    Lai, H.M., Lin, I., Tseng, L.T.: High-Level Managers’ Considerations for RFID Adoption in Hospitals: An Empirical Study in Taiwan. J Med Syst 38(2), 1–17 (2014)Google Scholar
  54. 54.
    Chauhan, S., Jaiswal, M., Rai, S., Motiwalla, L., Pipino, L.: Determinants of adoption for open-source office applications: a plural investigation. Inf. Syst. Manage. 35(2), 80–97 (2018)Google Scholar
  55. 55.
    Gangwar, H.: Understanding the determinants of big data adoption in India. Inf. Resour. Manage. J. 31(4), 1–22 (2018)Google Scholar
  56. 56.
    Awa, H.O., Ojiabo, O.U., Orokor, L.E.: Integrated technology-organization-environment (T-O-E) taxonomies for technology adoption. J. Enterp. Inf. Manage. 30(6), 893–921 (2017)Google Scholar
  57. 57.
    Lin, H.F., Lin, S.M.: Determinants of e-business diffusion: a test of the technology diffusion perspective. Technovation 28(3), 135–145 (2008)Google Scholar
  58. 58.
    Zhai, C.: Research on post-adoption behavior of B2B e-marketplace in China. In: 2010 International Conference on Management and Service Science, MASS 2010, no. 1 (2010)Google Scholar
  59. 59.
    Mangula, I.S., Van De Weerd, I., Brinkkemper, S.: The adoption of software-as-a-service: an Indonesian case study. In: Proceedings—Pacific Asia Conference on Information Systems, PACIS 2014 (2014)Google Scholar
  60. 60.
    Chen,Y.,Yin,Y., Browne, G.J., Li, D.: Adoption of building informationmodeling in Chinese construction industry: The technology organization environment framework. Eng. Constr. Archit. Manage. 26(9), 1878–1898 (2019)Google Scholar
  61. 61.
    AlBar, A.M., Hoque, M.R.: Factors affecting cloud ERP adoption in Saudi Arabia: an empirical study. Inf. Dev. 35(1), 150–164 (2019)Google Scholar
  62. 62.
    Khan, M.J., Mahmood, S.: Assessing the determinants of adopting component-based development in a global context: a client-vendor analysis. IEEE Access 6, 79060–79073 (2018)Google Scholar
  63. 63.
    Hsu, C.L., Lin, J.C.C.: Factors affecting the adoption of cloud services in enterprises. Inf. Syst. eB. Manage. 14(4), 791–822 (2016)Google Scholar
  64. 64.
    Simamora, B.H., Sarmedy, J.: Improving services through adoption of cloud computing at PT XYZ in Indonesia. J. Theor. Appl. Inf. Technol. 73(3), 395–404 (2015)Google Scholar
  65. 65.
    Senyo, P.K., Effah, J., Addae, E.: Preliminary insight into cloud computing adoption in a developing country. J. Enterp. Inf. Manage. 29(4), 505–524 (2016)Google Scholar
  66. 66.
    Yoon, T.E., George, J.F.: Why aren’t organizations adopting virtual worlds? Comput. Hum. Behav. 29(3), 772–790 (2013)Google Scholar
  67. 67.
    Tarhini, A., Al-Gharbi, K., Al-Badi, A., AlHinai, Y.S.: An analysis of the factors affecting the adoption of cloud computing in higher educational institutions. Int. J. Cloud Appl. Comput. 8(4), 49–71 (2018)Google Scholar
  68. 68.
    Ajjan, H., Kumar, R.L., Subramaniam, C.: Understanding differences between adopters and nonadopters of information technology project portfolio management. Int. J. Inf. Technol. Decis. Making 12(06), 1151–1174 (2013)Google Scholar
  69. 69.
    Xu, W., Ou, P., Fan, W.: Antecedents of ERP assimilation and its impact on ERP value: a TOE-based model and empirical test. Inf. Syst. Front. 19(1), 13–30 (2017)Google Scholar
  70. 70.
    Ilin, V., Ivetić, J., Simić, D.: Understanding the determinants of e-business adoption in ERP-enabled firms and non-ERP-enabled firms: a case study of the Western Balkan Peninsula. Technol. Forecast. Soc. Change 125, 206–223 (2017)Google Scholar
  71. 71.
    Puklavec, B., Oliveira, T., Popovič, A.: Understanding the determinants of business intelligence system adoption stages an empirical study of SMEs. Ind. Manage. Data Syst. 118(1), 236–261 (2018)Google Scholar
  72. 72.
    Chandra, S., Kumar, K.N.K.N., Road, H., Kumar, K.N.K.N., Road, H.: Exploring factors influencing organizational adoption of augmented reality in e-commerce: empirical analysis using technology–organization–environment model. J. Electron. Commer. Res. 19(3), 237–265 (2018)Google Scholar
  73. 73.
    Alkhalil, A., Sahandi, R., John, D.: An exploration of the determinants for decision to migrate existing resources to cloud computing using an integrated TOE-DOI model. J. Cloud Comput. 6(1) (2017)Google Scholar
  74. 74.
    Wei, J., Lowry, P.B., Seedorf, S.: The assimilation of RFID technology by Chinese companies: a technology diffusion perspective. Inf. Manage. 52(6), 628–642 (2015)Google Scholar
  75. 75.
    Chana, F.T.S., Chong, A.Y.L.: Determinants of mobile supply chain management system diffusion: a structural equation analysis of manufacturing firms. Int. J. Prod. Res. 51(4), 1196–1213 (2013)Google Scholar
  76. 76.
    Sila, I., Dobni, D.: Patterns of B2B e-commerce usage in SMEs. Ind. Manage. Data Syst. 112(8), 1255–1271 (2012)Google Scholar
  77. 77.
    Wu, X., Subramaniam, C.: Understanding and predicting radio frequency identification (RFID) adoption in supply chains. J. Organ. Comput. Electron. Commer. 21(4), 348–367 (2011)Google Scholar
  78. 78.
    Rouhani, S., Ashrafi, A., Ravasan, A.Z., Afshari, S.: Business intelligence systems adoption model. J. Organ. End User Comput. 30(2), 43–70 (2018)Google Scholar
  79. 79.
    Ammar, A., Ahmed, E.M.: Factors influencing Sudanese microfinance intention to adopt mobile banking. Cogent Bus. Manage. 3(1), 1–20 (2016)Google Scholar
  80. 80.
    Hsu, P.F., Ray, S., Li-Hsieh, Y.Y.: Examining cloud computing adoption intention, pricing mechanism, and deployment model. Int. J. Inf. Manage. 34(4), 474–488 (2014)Google Scholar
  81. 81.
    Cao, Q., Baker, J., Wetherbe, J., Gu, V.: Organizational adoption of innovation: identifying factors that influence RFID adoption in the healthcare industry. In: European Conference on Information Systems 2012, pp. 5–15 (2012)Google Scholar
  82. 82.
    Troshani, I., Rampersad, G., Plewa, C.: Organisational adoption of e-business: the case of an innovation management tool at a university and technology transfer office. Int. J. Netw. Virtual Organ. 9(3), 265 (2011)Google Scholar
  83. 83.
    Cao, Y., Ajjan, H., Hong, P., Le, T.: Using social media for competitive business outcomes: an empirical study of companies in China. J. Adv. Manage. Res. 15(2), 211–235 (2018)Google Scholar
  84. 84.
    Ifinedo, P.: Internet/e-business technologies acceptance in Canada’s SMEs: an exploratory investigation. Internet Res. 21(3), 255–281 (2011)Google Scholar
  85. 85.
    Zhang, H., Xiao, J.: Assimilation of social media in local government: an examination of key drivers. Electron. Libr. 35(3), 427–444 (2017)MathSciNetGoogle Scholar
  86. 86.
    Shim, S., Lee, B., Kim, S.L.: Rival precedence and open platform adoption: an empirical analysis. Int. J. Inf. Manage. 38(1), 217–231 (2018)Google Scholar
  87. 87.
    Lin, H.F.: Understanding the determinants of electronic supply chain management system adoption: using the technology-organization-environment framework. Technol. Forecast. Soc. Change 86, 80–92 (2014)Google Scholar
  88. 88.
    Maditinos, D., Chatzoudes, D., Sarigiannidis, L.: Factors affecting e-business successful implementation. Int. J. Commer. Manage. 24(4), 300–320 (2016)Google Scholar
  89. 89.
    Rondović, B., Djuričković, T., Kašćelan, L.: Drivers of e-business diffusion in tourism: a decision tree approach. J. Theor. Appl. Electron. Commer. Res. 14(1), 30–50 (2019)Google Scholar
  90. 90.
    Wolf, M., Beck, R., König, W.: Environmental dynamics as driver of on-demand computing infrastructures—empirical insights from the financial services industry in UK. ECIS 1–14 (2012)Google Scholar
  91. 91.
    Ali, O., Soar, J., Shrestha, A.: Perceived potential for value creation from cloud computing: a study of the Australian regional government sector. Behav. Inf. Technol. 37(12), 1157–1176 (2018)Google Scholar
  92. 92.
    Sulaiman, H., Magaireh, A., Ramli, R.: Adoption of cloud-based e-health record through the technology, organization and environment perspective. Int. J. Eng. Technol. 7(4.35), 609 (2018)Google Scholar
  93. 93.
    Nam, D.W., Kang, D.W., Kim, S.: Process of big data analysis adoption: defining big data as a new IS innovation and examining factors affecting the process. In: Proceedings of Annual Hawaii International Conference on System Sciences, pp. 4792–4801. IEEE (2015)Google Scholar
  94. 94.
    Ramanathan, L., Krishnan, S.: An empirical investigation into the adoption of open source software in information technology outsourcing organizations. J. Syst. Inf. Technol. 17(2), 167–192 (2015)Google Scholar
  95. 95.
    Martins, R., Oliveira, T., Thomas, M., Tomás, S.: Firms’ continuance intention on SaaS use—an empirical study. Inf. Technol. People 32(1), 189–216 (2019)Google Scholar
  96. 96.
    Hossain, M.A., Standing, C., Chan, C.: The development and validation of a two-staged adoption model of RFID technology in livestock businesses. Inf. Technol. People 30(4), 785–808 (2017)Google Scholar
  97. 97.
    Indriasari, E., Wayan, S., Gaol, F.L.: Intelligent Information and Database Systems, vol. 7803. Springer International Publishing, Cham (2013)Google Scholar
  98. 98.
    Kim, D.J., Hebeler, J., Yoon, V., Davis, F.: Exploring determinants of semantic web technology adoption from IT professionals’ perspective: industry competition, organization innovativeness, and data management capability. Comput. Hum. Behav. 86, 18–33 (2018)Google Scholar
  99. 99.
    Cruz-Jesus, F., Pinheiro, A., Oliveira, T.: Understanding CRM adoption stages: empirical analysis building on the TOE framework. Comput. Ind. 109, 1–13 (2019)Google Scholar
  100. 100.
    Lin, H.F.: Contextual factors affecting knowledge management diffusion in SMEs. Ind. Manage. Data Syst. 114(9), 1415–1437 (2014)Google Scholar
  101. 101.
    Schwarz, C., Schwarz, A.: To adopt or not to adopt: a perception-based model of the EMR technology adoption decision utilizing the technology-organization-environment framework. J. Organ. End User Comput. 26(4), 57–79 (2014)Google Scholar

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© Springer Nature Switzerland AG 2021

Authors and Affiliations

  • Mohamed Ghayth Elghdban
    • 1
  • Nurhidayah Binti Azmy
    • 1
  • Adnan Bin Zulkiple
    • 1
  • Mohammed A. Al-Sharafi
    • 2
    Email author
  1. 1.Faculty of Engineering TechnologyUniversiti Malaysia PahangGambangMalaysia
  2. 2.Faculty of Computing, College of Computing and Applied SciencesUniversiti Malaysia PahangGambangMalaysia

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