Tytuł artykułu
Treść / Zawartość
Pełne teksty:
Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
We present a novel study concerning the attitudes of road transport enterprises towards a broad application of telematics in operational management in road transportation. The study aims to assess telematics application in road transport and its changes over time while showing the factors most likely to determine the systems’ use. Unobserved categories defined in the technology acceptance model (TAM) are adjusted to measure perceived usefulness, perceived ease of use, and attitudes toward using telematics systems by road transport managers. The study is based on 323 transport enterprises analyzed in two waves in 2020 and 2021. The use of two different time points is motivated by an observed increase in the digitalization of transport documents caused by the COVID-19 pandemic. The empirical findings support the TAM’s usefulness in evaluating IT in transport business management. The findings also reveal that the significantly increased telematics use in 2020 was observed while it was endured. The results are checked for robustness and used for simulations. The study compares managers’ behaviors over time and simulates the effect of individual (observed) variables on unobserved TAM categories.
Czasopismo
Rocznik
Tom
Strony
5--17
Opis fizyczny
Bibliogr. 27 poz.
Twórcy
autor
- Nicolaus Copernicus University in Torun, Department of Logistics; Gagarina 13A, 87-100 Torun, Poland
autor
- Nicolaus Copernicus University in Torun, Department of Econometrics and Statistics; Gagarina 13A, 87-100 Torun, Poland
autor
- Nicolaus Copernicus University in Torun, Department of Economics; Gagarina 13A, 87-100 Torun, Poland
Bibliografia
- 1. Adian, I. & et al. Small and medium enterprises in the pandemic impact, responses and the role of development finance. Policy Research Working Paper. 2020. No. 9414. World Bank Group. Available at: https://openknowledge.worldbank.org/handle/10986/34552?show=full#:~:text=10.1596/1813%2D9450%2D9414.
- 2. Clampit, A.J. & Lorenz, M.P. & Gamble, J.E. & Lee, J. Performance stability among small and medium-sized enterprises during COVID-19: A test of the efficacy of dynamic capabilities. International Small Business Journal: Researching Entrepreneurship. 2022. Vol. 40(3). P. 403-419. DOI: 10.1177/02662426211033270.
- 3. Osińska, M. & Zalewski, W. Vulnerability and resilience of the road transport industry in Poland to the COVID‑19 pandemic crisis. Transportation. 2021. Vol. 50. P. 331-354. DOI: 10.1007/s11116-021-10246-9.
- 4. Gartner Raport. Magic quadrant for Transportation Management Systems. 30 March. 2021. Available at: https://www.gartner.com/en/documents/4000019.
- 5. Davis, F. A technology acceptance model for empirically testing new end-user information systems: theory and results. Cambridge, MA: MIT Press. 1985.
- 6. Grant, D.B. & Trautrims, A. & Wong, C.Y. Sustainable logistics and supply chain management. Kogan Page, London; Philadelphia. 2015.
- 7. Dillon, A. & Morris, M. From “can they” to “will they?”: extending usability evaluation to address acceptance. In: Hoadley, E. & Benbasat, I. (eds). Proceedings of the Fourth Americas Conference on Information Systems (AMCIS). Baltimore: Univ. of Baltimore. 1998. Available at: http://aisel.aisnet.org/amcis1998?utm_source=aisel.aisnet.org%2Famcis1998%2F325&utm_medium=PDF&utm_campaign=PDFCoverPages.
- 8. Davis, F.D. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly.1989. Vol. 13(3). P. 319-340. DOI: 10.2307/249008.
- 9. Venkatesh, V. & Davis, F.D. A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science. 2000. Vol. 46(2). P. 186-204. DOI: 10.1287/mnsc.46.2.186.11926.
- 10.Venkatesh, V. & Bala, H. Technology acceptance model 3 and a research agenda on interventions. Decision Science. 2008. Vol. 39(2). P. 273-312. DOI: 10.1111/j.1540-5915.2008.00192.x.
- 11.Venkatesh, V. & Morris, M.G. & Davis, F.D. & Davis, G.B. User acceptance of information technology: toward a unified view. MIS Quarterly. 2003. Vol. 27(3). P. 425-478. DOI: 10.2307/30036540.
- 12.Hsiao, C.H. & Yang, C. The intellectual development of the technology acceptance model: A co-citation analysis. International Journal of Information Management. 2011. Vol. 31(2). P. 128-136. DOI: 10.1016/j.ijinfomgt.2010.07.003.
- 13.Chow, M. & Herold, D.K. & Choo, T.M. & Chan, K. Extending the technology acceptance model to explore the intention to use Second Life for enhancing healthcare education. Computers and Education. 2012. Vol. 59(4). P. 1136-1144. DOI: 10.1016/j.compedu.2012.05.011.
- 14.Venkatesh, V. & Thong, J. & Xu, X. Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Quarterly. 2012. Vol. 36(1). P. 157-178. DOI: 10.2307/41410412.
- 15.Rosel, J. & Plewis, I. Longitudinal data analysis with structural equations. Methodology: European Journal of Research Methods for the Behavioral and Social Sciences. 2008. Vol. 4(1). P. 37-50. DOI: 10.1027/1614-2241.4.1.37.
- 16.Cortina, J.M. What is coefficient alpha? An examination of theory and applications. Journal of Applied Psychology.1993. Vol. 78(1). P. 98-104. DOI: 10.1037/0021-9010.78.1.98.
- 17.Fornell, C. & Larcker, D.F. Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research. 1981. Vol. 18(1). P. 39-50. DOI: 10.2307/3151312.
- 18.Bollen, K.A. Structural Equations with Latent Variables. 1989. Wiley, New York.
- 19.Pearl, J. Causality. 2000. New York, Cambridge University Press.
- 20.Byrne, B.M. Structural equation modelling with AMOS. 2010. Routledge, New York. DOI.org/10.4324/9780203805534.
- 21.Akaike, H. A. Bayesian extension of the minimum AIC procedure of autoregressive model fitting. Biometrika.1979. Vol. 66. P. 237-242. DOI: 10.2307/2335654.
- 22.Browne, M.W. & Cudeck, R. Alternative ways of assessing model fit. In: Bollen K. & Long J. (eds). Testing Structural Equation Models. Sage; Newbury Park. 1993. P. 136-162.
- 23.Żurek, M. Inklinacje behawioralne na rynkach kapitałowych w świetle modeli SEM, Wydawnictwo Naukowe Uniwersytetu Mikołaja Kopernika. Toruń. 2016. [In English: Behavioral inclinations in financial markets in the light of SEMs. Torun: NCU Publishing House].
- 24.Efron, B. & Tibshirani, R. Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy. Statistical Science. 1986. Vol. 1(1). P. 54-75. DOI: 10.1214/SS%2F1177013815.
- 25.Arbuckle, J.L. IBM SPSS Amos 22 User’s Guide. 2013. IBM Corp.
- 26.Heinbach, C. & Kammler, F. & Thomas, O. Exploring design requirements of fleet telematics systems supporting road freight transportation: a digital service side perspective. Conference Paper, 17th International Conference on Wirtschaftsinformatik. 2022, Nürnberg, Germany.
- 27.Heinbach, C. & Beinke, J. & Kammler, F. & Thomas, O. Data-driven forwarding: a typology of digital platforms for road freight transport management. Electronic Markets. 2022. Vol. 32. P. 808-828. DOI: 10.1007/s12525-022-00540-4.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-65d2cf59-fdbe-432d-a179-1ac757c9c186