User Acceptance of Digital Turkish Lira (DTL): Investigation in the Framework of Technology Acceptance Model (TAM) and Planned Behaviour Theory (PBT)

Yıl 2022, Cilt: 30 Sayı: 54, 357 - 376, 25.10.2022

Yavuz Toraman

https://doi.org/10.17233/sosyoekonomi.2022.04.19

Cited By: 3

Öz

The increase in technological devices has made the digitalisation of many products and services compulsory. One of the essential results of this digitalisation is the Digital Turkish Lira (DTL). The current study examines the factors affecting the acceptance and intention to use DTL. In this context, the acceptance of use by users has been tried to be explained by structural equation modelling based on TAM and TPB in the literature. The proposed research model was analysed with the Smart PLS 3 package program. When the hypothesis results are examined, firstly, the variables within the scope of TPB have a positive effect on the intention to use. In contrast, the other variables positively impact the intention to use over the ease of use within the scope of TAM. It has been determined that users find DTL more reliable than cryptocurrency /digital money. The relationship between DTL and the Central Bank of the Republic of Turkey can be shown as the reason for this.

Anahtar Kelimeler

Digital Turkish Lira (DTL), E-Commerce, Metaverse, Blockchain, Cryptocurrency, Last Mile Delivery

Dijital Türk Lirasının (DTL) Kullanım Kabulü: Teknoloji Kabul Modeli (TKM) ve Planlı Davranış Teorisi (PDT) Çerçevesinde İncelenmesi

Öz

Teknolojik cihazların yaygınlaşması birçok ürün ve hizmetin dijitalleşmesini zorunlu hale getirmiştir. Bu dijitalleşmenin önemli sonuçlarından biri de Dijital Türk Lirasıdır (DTL). Mevcut çalışma DTL’nin kullanım kabulünü, niyetini etkileyen faktörlerin incelenmesi üzerine kurulmuştur. Bu bağlamda kullanıcıların kullanım kabulü literatürdeki TKM ve PDT’ ye dayandırılarak yapısal eşitlik modellemesiyle açıklanmaya çalışılmıştır. Önerilen araştırma modeli Smart PLS 3 paket programı ile analiz edilmiştir. Hipotez sonuçları incelendiğinde ilk olarak PDT kapsamındaki değişkenler kullanma niyetini olumlu etkilerken, TKM kapsamındaki güven kullanma kolaylığı üzerinden diğer değişkenler ise fayda üzerinden kullanma niyetini pozitif etkilemiştir. Kullanıcıların kripto/dijital paralara göre DTL’yi daha güvenilir bulduğu tespit edilmiştir. DTL ile Türkiye Cumhuriyet Merkez Bankası arasındaki ilişki bunun nedeni olarak gösterilebilir.

Anahtar Kelimeler

Dijital Türk Lirası (DTL), E-Ticaret, Metaverse, Blokzincir, Kripto Para, Son Adım Teslimat

Kaynakça

• Aboelmaged, M. (2021), “E-waste recycling behaviour: An integration of recycling habits into the theory of planned behaviour”, Journal of Cleaner Production, 278, 124182.
• Adrian, T. & T. Mancini-Griffoli (2019), “The Rise of Digital Money”, Annual Review of Financial Economics, 13, 1-20.
• Agustina, D. (2019), “Extension of Technology Acceptance Model (Etam): Adoption of Cryptocurrency Online Trading Technology”, Jurnal Ekonomi, 24(2), 272-287.
• Ajzen, I. & S. Sheikh (2013), “Action versus inaction: Anticipated affect in the theory of planned behavior”, Journal of Applied Social Psychology, 43(1), 155-162.
• Ajzen, I. (1991), “The Theory of Planned Behavior”, Organizational Behavior and Human Decision Processes, 50(2), 179-211.
• Assaker, G. (2020), “Age and gender differences in online travel reviews and user-generated-content (UGC) adoption: extending the technology acceptance model (TAM) with credibility theory”, Journal of Hospitality Marketing & Management, 29(4), 428-449.
• Auer, R. et al. (2020), “Covid-19, Cash, and the Future of Payments”, BIS Bulletin, 3, 1-7.
• Bech, M.L. & R. Garratt (2017), “Central bank cryptocurrencies”, BIS Quarterly Review September.
• BKM (2021), İstatistikler, <https://bkm.com.tr/istatistik>, 14.11.2021.
• Chandra, S. et al. (2010), “Evaluating the role of trust in consumer adoption of mobile payment systems: An empirical analysis”, Communications of the Association for Information Systems, 27(1), 562-588.
• Dalvi-Esfahani, M. et al. (2020), “Students’ green information technology behavior: Beliefs and personality traits”, Journal of Cleaner Production, 257, 120406.
• Daoud, J.I. (2017), “Multicollinearity and regression analysis”, in: Journal of Physics: Conference Series, IOP Publishing, 949(1), 012009.
• Davis, F.D. (1989), “Perceived usefulness, perceived ease of use, and user acceptance of information technology”, MIS Quarterly, 13(3), 319-339.
• Davis, F.D. et al. (1989), “User acceptance of computer technology: A comparison of two theoretical models”, Management Science, 35(8), 982-1003.
• Di Pierro, M. (2017), “What is the blockchain?”, Computing in Science & Engineering, 19(5), 92-95.
• Ethereum (2021), <https://ethereum.org/en/developers/docs/smart-contracts>, 20.11.2021.
• Fussell, S.G. & D. Truong (2021), “Using virtual reality for dynamic learning: an extended technology acceptance model”, Virtual Reality, 26, 249-267.
• Gao, L. et al. (2017), “Application of the extended theory of planned behavior to understand individual’s energy saving behavior in workplaces”, Resources, Conservation and Recycling, 127, 107-113.
• Gefen, D. (2000), “E-commerce: the role of familiarity and trust”, Omega, 28(6), 725-737.
• Giampietri, E. et al. (2018), “A Theory of Planned behaviour perspective for investigating the role of trust in consumer purchasing decision related to short food supply chains”, Food Quality and Preference, 64, 160-166.
• Hair, J.F. et al. (2010), Multivariate Data Analysis, 7th ed., Pearson Education.
• Hair, J.F. et al. (2017), A Primer on Partial Least Squares Structural Equation Modelling (PLS-SEM), Los Angeles: Sage Publication. Second Edition.
• Hansen, J.M. et al. (2018), “Risk, trust, and the interaction of perceived ease of use and behavioral control in predicting consumers’ use of social media for transactions”, Computers in Human Behavior, 80, 197-206.
• Jarvenpaa, S.L. & D.E. Leidner (1999), “Communication and trust in global virtual teams”, Organization Science, 10(6), 791-815.
• Kamble, S. et al. (2019), “Understanding the Blockchain technology adoption in supply chains-Indian context”, International Journal of Production Research, 57(7), 2009-2033.
• Karahanna, E. et al. (1999), “Information technology adoption across time: a cross-sectional comparison of pre-adoption and post-adoption beliefs”, MIS Quarterly, 23(2), 183-213.
• Kong, F. et al. (2021), “Technology acceptance model of mobile social media among Chinese college students”, Journal of Technology in Behavioral Science, 6(2), 365-369.
• Kurtuluş, K. (2010), Araştırma Yöntemleri, Türkmen Kitabevi, 2010.
• Manis, K.T. & D. Choi (2019), “The virtual reality hardware acceptance model (VR-HAM): Extending and individuating the technology acceptance model (TAM) for virtual reality hardware”, Journal of Business Research, 100, 503-513.
• Min, S. et al. (2019), “Consumer adoption of the Uber mobile application: Insights from diffusion of innovation theory and technology acceptance model”, Journal of Travel & Tourism Marketing, 36(7), 770-783.
• Morris, M.G. & V. Venkatesh (2000), “Age Differences In Technology Adoption Decisions Implications For A Changing Work Force”, Personnel Psychology, 53(2), 375-403.
• Nakamoto, S. (2008), “Bitcoin: A peer-to-peer electronic cash system”, Decentralized Business Review, 21260.
• Nuryyev, G. et al. (2020), “Blockchain technology adoption behavior and sustainability of the business in tourism and hospitality SMEs: An empirical study”, Sustainability, 12(3), 1256.
• Özdamar, K. (2004), Paket Programlar ile İstatistiksel Veri Analizi, Eskişehir: Kaan Kitabevi.
• Pavlou, P.A. (2002), “What Drives Electronic Commerce? A Theory Of Planned Behavior Perspective”, in: Academy of Management Proceedings, 2002(1), 1-6.
• Pavlou, P.A. (2003), “Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model”, International Journal of Electronic Commerce, 7(3), 101-134.
• Plouffe, C.R. et al. (2001), “Richness versus parsimony in modeling technology adoption decisions-understanding merchant adoption of a smart card-based payment system”, Information Systems Research, 12(2), 208-222.
• Queiroz, M.M. et al. (2021), “Blockchain adoption in operations and supply chain management: empirical evidence from an emerging economy”, International Journal of Production Research, 59(20), 6087-6103.
• Rafique, H. et al. (2020), “Investigating the acceptance of mobile library applications with an extended technology acceptance model (TAM)”, Computers & Education, 145, 103732.
• Rogers, E.M. (2002), “Diffusion of preventive innovations”, Addictive Behaviors, 27(6), 989-993.
• Sagnier, C. et al. (2020), “User acceptance of virtual reality: an extended technology acceptance model”, International Journal of Human-Computer Interaction, 36(11), 993-1007.
• Salloum, S.A. et al. (2019), “Exploring students’ acceptance of e-learning through the development of a comprehensive technology acceptance model”, IEEE Access, 7, 128445-128462.
• Schierz, P.G. et al. (2010), “Understanding consumer acceptance of mobile payment services: An empirical analysis”, Electronic Commerce Research and Applications, 9(3), 209-216.
• Soliman, M. (2021), “Extending the theory of planned behavior to predict tourism destination revisit intention”, International Journal of Hospitality & Tourism Administration, 22(5), 524-549.
• Sukendro, S. et al. (2020), “Using an extended Technology Acceptance Model to understand students’ use of e-learning during Covid-19: Indonesian sport science education context”, Heliyon, 6(11), e05410.
• Sveriges Riksbank (2018), The Riksbank’s E‐Krona Project Report 2, Stockholm, <https://www.riksbank.se/globalassets/media/rapporter/e-krona/2018/the-riksbanks-e kronaproject-report-2.pdf>, 15.11.2021.
• Şimşek, O.M. et al. (2021), “The impact of interpersonal cognitive distortions on satisfaction with life and the mediating role of loneliness”, Sustainability, 13(16), 9293.
• Tama, R.A.Z. et al. (2021), “Assessing farmers’ intention towards conservation agriculture by using the Extended Theory of Planned Behavior”, Journal of Environmental Management, 280, 111654.
• TCMB (2021), <https://www.tcmb.gov.tr/wps/wcm/connect/TR/TCMB+TR/Main+Menu/Duyurular/Basin/2021/DUY2021-40>, 15.11.2021.
• Toraman, Y. (2021), “E-Para ve Tokenler (Dijital Türk Akçesi) İle Borçlanma: Dijital Türk Lirası (DTL) Üzerine Bir Çalışma”, Bilge Uluslararası Sosyal Araştırmalar Dergisi, 5(2), 124-134.
• Toraman, Y. (2022), “User Acceptance of Metaverse: Insights from Technology Acceptance Model (TAM) and Planned Behavior Theory (PBT)”, EMAJ Emerging Markets Journal, 12(1), 67-75.
• Venkatesh, V. & F.D. Davis (1996), “A model of the antecedents of perceived ease of use: Development and test”, Decision Sciences, 27(3), 451-481.
• Venkatesh, V. & M.G. Morris (2000), “Why don't men ever stop to ask for directions? Gender, social influence, and their role in technology acceptance and usage behavior”, MIS Quarterly, 24(1), 115-139.
• Warkentin, M. & C. Orgeron (2020), “Using the security triad to assess blockchain technology in public sector applications”, International Journal of Information Management, 52, 102090.
• Wüst, K. & A. Gervais (2018), “Do you need a blockchain?”, in: 2018 Crypto Valley Conference on Blockchain Technology (CVCBT) (45-54), IEEE.
• Yanagawa, N. & H. Yamaoka (2019), Digital Innovation, Data Revolution and Central Bank Digital Currency (No. 19-E-2), Bank of Japan.
• Yang, C.S. (2019), “Maritime shipping digitalization: Blockchain-based technology applications, future improvements, and intention to use”, Transportation Research Part E: Logistics and Transportation Review, 131, 108-117.
• Yang, H. et al. (2017), “User acceptance of smart home services: an extension of the theory of planned behavior”, Industrial Management & Data Systems, 117(1), 68-89.
• Yorulmaz, M. & S. Alnıpak (2020), “Yönetici düzeyindeki gemi adamlarının elektronik seyir teknolojileri kullanımının teknoloji kabul modeli ile incelenmesi”, OPUS International Journal of Society Researches, 16(29), 1928-1954.