A proposition of an emerging technologies expectations model: an example of student attitudes towards blockchain
Abstract
The paper proposes an Emerging Technologies Expectations Model (ETEM) that aims at explaining the differences in perception of new technologies as well as the expectations towards them. These Expectations, classified into Technology Evolution, Technology Revolution, Social Evolution and Social Revolution are explained by Knowledge and Usage that in turn are shaped by Information Sources. The Information Sources factor, which influences both Expectations and Knowledge, and the Usage factor both play an important role in the model. The application of this model was illustrated using blockchain as an example of an emerging technology, and data from a survey conducted among IT university students in Cracow, Poland. The proposed model contributes to filling the research gap concerning a comprehensive explanation of people’s expectations towards emerging technologies, considering the way people absorb knowledge and undertake the usage of technology based on various information sources. It also provides practical implications, since the knowledge of the factors that can influence people’s expectations towards emerging technologies might be useful in shaping these expectations.
First published online 15 November 2021
Keyword : technology adoption model, emerging technology, blockchain, young people’s expectations, students’ sources of knowledge
How to Cite
Dymek, D., Grabowski, M., & Paliwoda-Pękosz, G. (2022). A proposition of an emerging technologies expectations model: an example of student attitudes towards blockchain. Technological and Economic Development of Economy, 28(1), 101–130. https://doi.org/10.3846/tede.2021.15702
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Dymek, D., Grabowski, M., & Paliwoda-Pękosz, G. (2020). Students expectations towards new technologies: A case of blockchain. In AMCIS 2020 Proceedings, 1. https://aisel.aisnet.org/amcis2020/global_dev/global_dev/1
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Iansiti, M., & Lakhani, K. (2017). The truth about blockchain. Harvard Business Review, 95(1), 118–127. https://www.hbs.edu/faculty/Pages/item.aspx?num=52100
IT Governance Institute. (2007). COBIT 4.1. IT Governance Institute. Rolling Meadows. https://www.worldcat.org/title/cobit-41/oclc/807062082
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Kinai, A., Markus, I., Oduor, E., & Diriye, A. (2017). Asset-based lending via a secure distributed platform. In ICTD ’17, ACM International Conference Proceeding Series, Part F1320 (pp. 17–20). Association for Computing Machinery. https://doi.org/10.1145/3136560.3136594
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Lai, P. C. (2017). The literature review of technology adoption models and theories for the novelty technology. JISTEM-Journal of Information Systems and Technology Management, 14(1), 21–38. https://www.scielo.br/j/jistm/a/D3NXPz5WF4gQX9cSdLKQv6D/?lang=en&format=pdf
Lee, J. Y., Paik, W., & Joo, S. (2012). Information resource selection of undergraduate students in academic search tasks. Information Research: An International Electronic Journal, 17(1), 511. http://informationr.net/ir/17-1/paper511.html
Lemieux, V. L. (2016). Trusting records: Is blockchain technology the answer? Records Management Journal, 26(2), 110–139. https://doi.org/10.1108/RMJ-12-2015-0042
Lin, C. H., Shih, H. Y., & Sher, P. J. (2007). Integrating technology readiness into technology acceptance: The TRAM model. Psychology & Mar-keting, 24(7), 641–657. https://doi.org/10.1002/mar.20177
Lowry, P. B., Gaskin, J., & Moody, G. D. (2015). Proposing the multi-motive information systems continuance model (MISC) to better explain end-user system evaluations and continuance intentions. Journal of the Association for Information Systems, 16(7), 515–579. https://doi.org/10.17705/1jais.00403
MacKenzie, D., & Wajcman, J. (Eds.) (1999). The social shaping of technology (2nd ed.). Open University Press. https://www.worldcat.org/title/social-shaping-of-technology/oclc/39713267
Magruk, A. (2011). Innovative classification of technology foresight methods. Technological and Economic Development of Economy, 17(4), 700–715. https://doi.org/10.3846/20294913.2011.649912
Mandel, M. J. (2000, February 14). The risk that boom will turn to bust. Business Week, 3668, 120–122.
Mazambani, L., & Mutambara, E. (2019). Predicting FinTech innovation adoption in South Africa: The case of cryptocurrency. African Journal of Economic and Management Studies, 11(1), 30–50. https://doi.org/10.1108/AJEMS-04-2019-0152
Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system. https://bitcoin.org/en/bitcoin-paper
OECD, & SOEC. (1997). Proposed guidelines for collecting and interpreting technological innovation data: Oslo manual. Organisation for Eco-nomic Co-operation and Development & Statistical Office of the European Communities. https://doi.org/10.1787/9789264192263-en
Oliver, R. L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research, 17(4), 460–469. https://doi.org/10.1177/002224378001700405
Paliwoda-Pękosz, G., Dymek, D., & Grabowski, M. (2021). Adoption of emerging information technologies through the lenses of knowledge acquisition. In AMCIS 2021 Proceedings. https://aisel.aisnet.org/amcis2021/adopt_diffusion/adopt_diffusion/6/
Parasuraman, A. (2000). Technology Readiness Index (TRI) a multiple-item scale to measure readiness to embrace new technologies. Journal of Service Research, 2(4), 307–320. https://doi.org/10.1177/109467050024001
Parasuraman, A., & Colby, C. L. (2015). An updated and streamlined technology readiness index: TRI 2.0. Journal of Service Research, 18(1), 59–74. https://doi.org/10.1177/1094670514539730
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