Emerging information technology acceptance model for the development of smart construction system
The potential of emerging information technology has been proposed by many researchers and practitioners in the construction industry, including smart construction. Meanwhile, emerging information technology acceptance and use is one of the major subjects for current smart construction study and practice. Furthermore, although there are many potential applications for and benefits of emerging information technology in the development of smart construction system, the current issue is that it is unclear why this technology is adopted, and that the factors that enhance its implementation are unknown. Therefore, an emerging information technology acceptance model (EITAM) was proposed, and our hypotheses were tested by structural equation modeling (SEM) based on an open-ended questionnaire survey. This study identified the factors that affect emerging information technology acceptance from engineering construction technology and innovation professionals. The EITAM evaluation results can be used to develop an emerging information technology acceptance strategy that is suitable for continual smart construction promotion. Finally, this study can provide guidance to smart construction developers to establish an effective technological integration plan.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Adams, D. A.; Nelson, R. R.; Todd, P. A. 1992. Perceived usefulness, ease of use, and usage of information technology: A replication, MIS Quarterly 16(2): 227–247. https://doi.org/10.2307/249577
Agarwal, R.; Prasad, J. 1997. The role of innovation characteristics and perceived voluntariness in the acceptance of information technologies, Decision Sciences 28(3): 557–582. https://doi.org/10.1111/j.1540-5915.1997.tb01322.x
Aggelidis, V. P.; Chatzoglou, P. D. 2009. Using a modified technology acceptance model in hospitals, International Journal of Medical Informatics 78(2): 115–126. https://doi.org/10.1016/j.ijmedinf.2008.06.006
Aibinu, A. A.; Al-lawati, A. M. 2010. Using PLS-SEM technique to model construction organizations’ willingness to participate in e-bidding, Automation in Construction 19(6): 714–724. https://doi.org/10.1016/j.autcon.2010.02.016
Anderson, J. C.; Gerbing, D. W. 1988. Structural equation modeling in practice: A review and recommended two-step approach, Psychological Bulletin 103(3): 411–423. https://doi.org/10.1300/J079v24n03_06
Bach, M. P.; Čeljo, A.; Zoroja, J. 2016. Technology acceptance model for business intelligence systems: Preliminary research, Procedia Computer Science 100: 995–1001. https://doi.org/10.1016/j.procs.2016.09.270
Bach, M. P.; Zoroja, J.; Čeljo, A. 2017. An extension of the technology acceptance model for business intelligence systems: Project management maturity perspective, International Journal of Information Systems & Project Management 5(2): 5–21. https://doi.org/10.12821/ijispm050201
Bagai, A.; Al-khalidi, H. R.; Sherwood, M. W.; Muñoz, D.; Roettig, M. L.; Jollis, J. G.; Granger, C. B. 2014. Regional systems of care demonstration project: Mission: Lifeline STEMI Systems Accelerator: Design and methodology, American Heart Journal 167(1): 15–21. https://doi.org/10.1016/j.ahj.2013.10.005
Barclay, D.; Thompson, R; Higgins, C. 1995. The partial least squares (PLS) approach to causal modeling: Personal computer adoption and use as an illustration, Technology Studies 2(2): 285–309. https://doi.org/10.1017/CBO9781107415324.004
Barua, A.; Kriebel, C. H.; Mukhopadhyay, T. 1989. A new approach to measuring the business value of information technologies, Gsia Working Papers 77(19): 22–30. https://doi.org/10.1145/1017359.1017366
Beglaryan, M.; Petrosyan, V.; Bunker, E. 2017. Development of a tripolar model of technology acceptance: Hospital-based physicians’ perspective on HER, International Journal of Medical Informatics 102: 50–61. https://doi.org/10.1016/j.ijmedinf.2017.02.013
Bhattacherjee, A.; Sanford, C. 2006. Influence processes for information technology acceptance: An elaboration likelihood model, MIS Quarterly 30(4): 805–825. https://doi.org/10.2307/25148755
Bollen, K. A. 1989. Structural equations with latent variables. New York: John Wiley & Sons. https://doi.org/10.1002/9781118619179
Chen, C. F.; Xu, X.; Arpan, L. 2017. Between the technology acceptance model and sustainable energy technology acceptance model: Investigating smart meter acceptance in the United States, Energy Research & Social Science 25: 93–104. https://doi.org/10.1016/j.erss.2016.12.011
Chin, J.; Lin, S. C. 2016. A behavioral model of managerial perspectives regarding technology acceptance in building energy management systems, Sustainability 8: 641. https://doi.org/10.3390/su8070641
Clausen, L. 1999. The role of demonstration projects in construction innovation processes: Methodological considerations, in Conference on the Future of Construction Research, 1999, Luleå University of Technology, Luleå, Sweden, 1–15.
Cronbach, L. 1951. Coefficient alpha and the internal structure of tests, Psychometrika 16(3): 297–334. https://doi.org/10.1007/BF02310555
Dave, L. 2009. The cost of capital, corporation finance and the theory of investment: a refinement, Applied Economics Letters 16(10): 1017–1019. https://doi.org/10.1080/17446540802345448
Davis, F. D. 1989. Perceived usefulness, perceived ease of use and user acceptance of information technology, MIS Quartely 13(3): 319–340. https://doi.org/10.2307/249008
Davis, F. D.; Bagozzi, R. P.; Warshaw, P. R. 1989. User acceptance of computer technology: A comparison of two theoretical models, Management Science 35(8): 982–1003. https://doi.org/10.2307/249008
Dinev, T.; Hu, Q. 2007. The centrality of awareness in the formation of user behavioral intention toward protective information technologies, Journal of the Association for Information Systems 8(7): 386–408. https://doi.org/10.17705/1jais.00133
Ford, D. N.; Pena, F. 1994. Design of a proactive cost feedback system for construction project management, in Proceedings of the 1st Congress on Computing in Civil Engineering, 1994, Washington DC, USA, 1365–1372.
Fornell, C.; Larcker, D. F. 1981. Evaluating structural equation models with unobservable and measurement error, Journal of Marketing Research 18: 39–51. https://doi.org/10.2307/3151312
Froese, T. M. 2010. The impact of emerging information technology on project management for construction, Automation in Construction 19(5): 531–538. https://doi.org/10.1016/j.autcon.2009.11.004
Ghazizadeh, M.; Lee, J. D.; Boyle, L. N. 2012. Extending the technology acceptance model to assess automation, Cognition Technology & Work 14(1): 39–49. https://doi.org/10.1007/s10111-011-0194-3
Hair, J. F., Jr.; Black, W. C.; Babin, B. J.; Anderson, R. E. 2009. Multivariate data analysis: A global perspective. 7th ed. Upper Saddle River: Prentice Hall.
Hamner, M.; Qazi, R. 2009. Expanding the technology acceptance model to include additional factors such as personal utility, Government Information Quarterly 26: 128–136. https://doi.org/10.1016/j.giq.2007.12.003
Harborne, P.; Hendry, C. 2009. Pathways to commercial wind power in the US, Europe and Japan: The role of demonstration projects and field trials in the innovation process, Energy Policy 37(9): 3580–3595. https://doi.org/10.1016/j.enpol.2009.04.027
Hayduk, L. A.; Littvay, L. 2012. Should researchers use single indicators, best indicators, or multiple indicators in structural equation models, BMC Medical Research Methodology 12(1): 159. https://doi.org/10.1186/1471-2288-12-159
He, Z.; Chen, X.; Lv, T. J. 2013. Research into consumers’ user acceptance willingness of mobile advertising. Berlin Heidelberg: Springer. https://doi.org/10.1007/978-3-642-38442-4_126
Holden, R. J.; Karsh, B. 2010. The technology acceptance model: Its past and its future in health care, Journal of Biomedical Informatics 43(1): 159–172. https://doi.org/10.1016/j.jbi.2009.07.002
Jokonya, O. 2015. Validating technology acceptance model (TAM) during IT adoption in organizations, in IEEE International Conference on Cloud Computing Technology & Science, 30 November – 3 December 2015, Vancouver, BC, Canada, 509–516. https://doi.org/10.1109/CloudCom.2015.56
Kakoli, B.; Soumava, B. 2010. User acceptance of information technology across cultures, International Journal of Intercultural Information Management 2(3): 553–561. https://doi.org/10.1504/IJIIM.2010.037862
Klitkou, A.; Coenen, L.; Andersen, P. D.; Fevolden, A.; Hansen, T. 2013. Role of demonstration projects in innovation: transition to sustainable energy and transport, in The 4th International Conference on Sustainability Transitions (IST 2013), 19–21 June 2013, Zürich, Switzerland, 638–664.
Lee, S.; Yu, J.; Jeong, D. 2013. BIM acceptance model in construction organizations, Journal of Management in Engineering 31(3): 0401404801. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000252
Legris, P.; Ingham, J.; Collerette, P. 2003. Why do people use information technology? A critical review of the technology acceptance model, Information & Management 40(3): 191–204. https://doi.org/10.1016/S0378-7206(01)00143-4
Liang, G. 2017. Discussion on the application of intelligent information technology in construction engineering, China Computer & Communication 18: 31–36 (in Chinese).
Lin, W. T.; Chuang, C. H.; Choi, J. H. 2010. A partial adjustment approach to evaluating and measuring the business value of information technology, International Journal of Production Economics 127(1): 158–172. https://doi.org/10.1016/j.ijpe.2010.05.007
Ma, J.; Jin, L. 2011. On the role that the construction of the demonstration projects plays in promoting the content building in vocational colleges, Value Engineering 30(7): 275–276 (in Chinese).
Malhotra, Y.; Galletta, D. F. 1999. Extending the technology acceptance model to account for social influence: theoretical bases and empirical validation, in Proceedings of the 32nd Hawaii International Conference on System Sciences (HICSS-32), 5–8 January 1999, Hawaii, 1–14. https://doi.org/10.1109/HICSS.1999.772658
Marangunić, N.; Granić, A. 2015. Technology acceptance model: a literature review from 1986 to 2013, Universal Access in the Information Society 14(1): 81–95. https://doi.org/10.1007/s10209-014-0348-1
Mavaahebi, M.; Nagasaka, K. 2013. A neural network and expert systems based model for measuring business effectiveness of information technology investment, American Journal of Industrial & Business Management 3(2): 245–254. https://doi.org/10.4236/ajibm.2013.32030
McCloskey, D. W. 2008. The importance of ease of use, usefulness, and trust to online consumers: An examination of the technology acceptance model with older consumers, Journal of Organizational & End User Computing 18(3): 47–65. https://doi.org/10.4018/joeuc.2006070103
Michel, C.; Bobillier-chaumon, M. E.; Sarnin, P. 2014. Technology acceptance model: analyse of the value build through the user experience, in Proceedings of the 8th International Conference on Partitioned Global Address Space Programming Models, 6–10 October 2014, Eugene, OR, USA, 130–137. https://doi.org/10.1145/2671470.2671489
Mooney, J. G.; Gurbaxani, V.; Kraemer, K. L. 1996. A process oriented framework for assessing the business value of information technology, Acm Sigmis Database: The Database for Advances in Information Systems 27(2): 68–81. https://doi.org/10.1145/243350.243363
Morell, J. A. 1994. Standards and the market acceptance of information technology: An exploration of relationships, Computer Standards & Interfaces 16(4): 321–329. https://doi.org/10.1016/0920-5489(94)90057-4
Mortenson, M. J.; Vidgen, R. 2016. A computational literature review of the technology acceptance model, International Journal of Information Management 36(6): 1248–1259. https://doi.org/10.1016/j.ijinfomgt.2016.07.007
Nikou, S. A.; Economides, A. A. 2017. Mobile-based assessment: Integrating acceptance and motivational factors into a combined model of Self-Determination Theory and technology acceptance, Computers in Human Behavior 68: 83–95. https://doi.org/10.1016/j.chb.2016.11.020
Niu, Y.; Lu, W.; Chen, K.; Huang, G. G.; Anumba, C. 2016. Smart construction objects, Journal of Computing in Civil Engineering 30(4): 040150701. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000550
Niu, Y.; Lu, W.; Liu, D.; Chen, K.; Xue, F. 2017. A smart construction object (SCO)-Enabled proactive data management system for construction equipment management, in ASCE International Workshop on Computing in Civil Engineering, 2017, 130–138. https://doi.org/10.1061/9780784480830.017
Nofsinger, J. R. 2017. The psychology of investing. London: Routledge.
Nunnalyy, J. C. 1978. Psychometric theory. New York: McGraw-Hill.
Park, S. C.; Hong, W. K.; Kim, S.; Wang, X. 2014. Mathematical model of hybrid precast gravity frames for smart construction and engineering, Mathematical Problems in Engineering 6: 1–14. https://doi.org/10.1155/2014/916951
Sepasgozaar, S. M. E.; Shirowzhan, S.; Wang, C. 2017. A scanner technology acceptance model for construction projects, Procedia Engineering 180: 1237–1246. https://doi.org/10.1016/j.proeng.2017.04.285
Smyth, H. 2010. Construction industry performance improvement programmes: The UK case of demonstration projects in the ‘Continuous Improvement’ programme, Construction Management & Economics 28(3): 255–270. https://doi.org/10.1080/01446190903505948
Sternberg, R. J.; Lubart, T. I. 2010. An investment theory of creativity and its development, Human Development 34(1): 1–31. https://doi.org/10.1159/000277029
Tsai, C. Y.; Wang, C. C.; Lu, M. T. 2011. Using the technology acceptance model to analyze ease of use of a mobile communication system, Social Behavior & Personality 39(1): 65–69. https://doi.org/10.2224/sbp.2011.39.1.65
Tsui, H. C. 2012. Advertising, quality, and willingness-to-pay: Experimental examination of signaling theory, Journal of Economic Psychology 33(6): 1193–1203. https://doi.org/10.1016/j.joep.2012.08.011
Turner, M.; Kitchenham, B.; Brereton, P. Charters, S.; Budgen, D. 2010. Does the technology acceptance model predict actual use? A systematic literature review, Information & Software Technology 52(5): 463–479. https://doi.org/10.1016/j.infsof.2009.11.005
Venkatesh, V.; Bala, H. 2008. Technology acceptance model 3 and a research agenda on interventions, Decision Sciences 39(2): 273–315. https://doi.org/10.1111/j.1540-5915.2008.00192.x
Venkatesh, V.; Davis, F. D. 2000. A theoretical extension of the technology acceptance model: Four longitudinal field studies, Management Science 46: 186–204. https://doi.org/10.1287/mnsc.220.127.116.1126
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): 425–478. https://doi.org/10.2307/30036540
Wang, N.; Linag, H.; Zhong, W.; Xue, Y.; Xiao, J. 2012. Resource structuring or capability building? An empirical study of the business value of information technology, Journal of Management Information Systems 29(2): 325–367. https://doi.org/10.2753/MIS0742-1222290211
Weng, X. 2003. The progress of investing psychology theories of America, Advances in Psychological Science 11(3): 262–266 (in Chinese).
Wu, C. 2015. The design of management information system for construction project cost, in International Conference on Education Technology, Management and Humanities Science, 2015, 1261–1264. https://doi.org/10.2991/etmhs-15.2015.277
Wu, Y.; Wang, Y. 2016. A study of smart construction and information management models of AEC projects in China, International Journal of Simulation: Systems, Science and Technology 17(21): 2.1–2.8.
Xiong, B.; Skitmore, M.; Xia, B. 2015. A critical review of structural equation modeling applications in construction research, Automation in Construction 49: 59–70. https://doi.org/10.1016/j.autcon.2014.09.006
Xue, X.; Shen, Q.; Fan, H.; Li, H.; Fan, S. 2012. IT supported collaborative work in A/E/C projects: A ten-year review, Automation in Construction 21(1): 1–9. https://doi.org/10.1016/j.autcon.2011.05.016
Yang, T. H.; Zheng, Q. H.; Wang, Y.; Wang, S. F. 2012. Fuzzy fault tree analysis of power project safety risk for the smart construction, in International Conference on Management Science & Engineering, 2012, 43(5): 372–376. https://doi.org/10.1109/ICMSE.2012.6414208
Zain, M.; Rose, R. C.; Adbullah, I.; Masrom, M. 2008. The relationship between information technology acceptance and organizational agility, Science & Technology Progress & Policy 42(6): 829–839 (in Chinese).
Zhong, R. Y.; Peng, Y.; Xue, F.; Fang, J.; Zou, W. W.; Luo, H.; Thomas, N. S.; Lu, W. S.; Sheng, G. Q. P.; Huang, G. Q. 2017. Prefabricated construction enabled by the Internet-of-Things, Automation in Construction 76: 59–70. https://doi.org/10.1016/j.autcon.2017.01.006