Araştırma Makalesi
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Otonom Araç Teknolojisi ve Teknoloji Kabulü: Tüketicilerin Sürücüsüz Araçlara Yönelik Tutumları ve Gelecekte Kullanım Niyeti Üzerinde Teknolojik Hazırlığın Rolü

Yıl 2024, Cilt: 36 Sayı: 1, 383 - 407, 28.03.2024
https://doi.org/10.35234/fumbd.1385541

Öz

Otomotivdeki gelişmeler ve yapay zeka teknolojilerinin ilerlemesiyle otomotiv sektörüne giren otonom (sürücüsüz) arabalar, pazarlama alanında hızla kendine yer bulmaktadır. Pazarda hızla gelişmesine rağmen, tüketicilerin kaygılarını ve otonom araç kullanma isteklerini etkileyen faktörler mevcuttur. Bu faktörlerin keşfedilebilmesi için tüketicilerin bu teknolojiye hazır olup olmadıkları ve hangi yönleriyle bu teknolojiye hazır oldukları araştırılması gereken konulardır. Bu durumun bir sonucu olarak tüketicilerin otonom araç kullanmaya hazır olmaları, kullanıma yönelik tutumları ve gelecekte kullanma niyetleri önem taşımaktadır. Bu çalışma tüketicilerin otonom araç kullanımına yönelik tutum ve niyetlerini etkileyen faktörleri ortaya çıkarmayı amaçlamaktadır. Araştırma verileri çevrimiçi anket yöntemiyle toplanmıştır. Araştırmada kolayda örnekleme yöntemi kullanılmıştır. Araştırma modeli Smart PLS kullanılarak yapısal eşitlik modellemesi ile test edilmiştir. Araştırma sonucunda rahatsızlık ve güvensizlik boyutlarının tüketicilerin kullanıma yönelik tutumlarını önemli ölçüde ve olumsuz yönde etkilediği tespit edilmiştir. İyimserlik, yenilikçilik ve antropomorfizm boyutlarının tüketicilerin kullanıma yönelik tutumlarını anlamlı ve pozitif yönde etkilediği, kullanıcıların kullanıma yönelik tutumlarının ise kullanım niyetlerini anlamlı ve pozitif yönde etkilediği tespit edilmiştir. Araştırma sonuçları, otonom otomobilleri piyasaya süren markaların iyimserlik, yenilikçilik ve antropomorfizm boyutlarında iyileştirmelere önem vermesi ve tüketicilerin rahatsızlık ve güvensizliğini ortadan kaldıracak iyileştirmeler yapması gerektiğini göstermektedir.

Etik Beyan

Bu çalışmanın, özgün bir çalışma olduğunu; çalışmanın hazırlık, veri toplama, analiz ve bilgilerin sunumu olmak üzere tüm aşamalarından bilimsel etik ilke ve kurallarına uygun davrandığımızı; bu çalışma kapsamında elde edilmeyen tüm veri ve bilgiler için kaynak gösterdiğimizi ve bu kaynaklara kaynakçada yer verdiğimizi; kullanılan verilerde herhangi bir değişiklik yapmadığımızı, çalışmanın Committee on Publication Ethics (COPE)' in tüm şartlarını ve koşullarını kabul ederek etik görev ve sorumluluklara riayet ettiğimizi beyan ederiz. Herhangi bir zamanda, çalışmayla ilgili yaptığımızı bu beyana aykırı bir durumun saptanması durumunda, ortaya çıkacak tüm ahlaki ve hukuki sonuçlara razı olduğumu bildiririm.

Kaynakça

  • Yiğit E, Oner AE, Yöntem O. Otonom Araçların Otomotiv Sektörüne Etkileri ve Beraberinde Getirdiği Yenilikler. Avrupa Bilim ve Teknoloji Dergisi,2020 181-186.
  • Khayyam H, Javadi B, Jalili M, Jazar R N. Artificial intelligence and internet of things for autonomous vehicles. Nonlinear Approaches in Engineering Applications: Automotive Applications of Engineering Problems, 2020 39-68.
  • Tekin A T, Özkale L, Gültekin-Karakaş D. The Turkish automotive industry in the era of digital technologies and autonomous cars. In Proceedings of the International Symposium for Production Research 2019 (pp. 319-327). Springer International Publishing.
  • Alharbi A, Sohaib O. Technology readiness and cryptocurrency adoption: pls-sem and deep learning neural network analysis. Ieee Access, 2021, 9, 21388-21394. https://doi.org/10.1109/access.2021.3055785
  • Lim H S M, Taeihagh A. Algorithmic decision-making in AVs: Understanding ethical and technical concerns for smart cities. Sustainability, 2019, 11(20), 5791.
  • Dokic J, Müller B, Meyer G. European roadmap smart systems for automated driving. European Technology Platform on Smart Systems Integration, 2015, 39.
  • Shi E, Gasser T M, Seeck A, Auerswald R. The principles of operation framework: A comprehensive classification concept for automated driving functions. SAE International Journal of Connected and Automated Vehicles, 2020, 3(12-03-01-0003), 27-37.
  • Rojas Rueda D, Nieuwenhuijsen M J, Khreis H, Frumkin H. Autonomous vehicles and public health. Annu Rev Public Health. 2020, 2(41), 329-45.
  • Schwarting W, Alonso–Mora J, Rus D. Planning and decision-making for autonomous vehicles. Annual Review of Control Robotics and Autonomous Systems, 2018, 1(1), 187-210. https://doi.org/10.1146/annurev-control-060117-105157
  • Tastan Y, Kaymaz H. Otonom Araçların Önündeki Zorluklar. International Journal of Advances in Engineering and Pure Sciences, 2021, 33(2), 195-209.
  • Kaur K, Rampersad G. Trust in driverless cars: Investigating key factors influencing the adoption of driverless cars. Journal of Engineering and Technology Management, 2018, 48, 87-96.
  • Schaefer K E, Straub E R. Will passengers trust driverless vehicles? Removing the steering wheel and pedals. In 2016 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA). 2016, pp. 159-165. IEEE.
  • Du H, Zhu G, Zheng J. Why travelers trust and accept self-driving cars: An empirical study. Travel behaviour and society, 2021, 22, 1-9.
  • Hulse L, Xie H, Galea E. Perceptions of autonomous vehicles: relationships with road users, risk, gender and age. Safety Science, 2018, 102, 1-13. https://doi.org/10.1016/j.ssci.2017.10.001
  • Moody J, Bailey N, Zhao J. Public perceptions of autonomous vehicle safety: An international comparison. Safety science, 2020, 121, 634-650.
  • Kyriakidis M, Happee R, de Winter J C. Public opinion on automated driving: Results of an international questionnaire among 5000 respondents. Transportation research part F: traffic psychology and behaviour, 2015, 32, 127-140.
  • Cunningham M L, Regan M A, Ledger S A, Bennett J M. To buy or not to buy? Predicting willingness to pay for automated vehicles based on public opinion. Transportation research part F: traffic psychology and behaviour, 2019, 65, 418-438.
  • Nair G S, Bhat C R. Sharing the road with autonomous vehicles: Perceived safety and regulatory preferences. Transportation research part C: emerging technologies, 2021, 122, 102885.
  • Pyrialakou V D, Gkartzonikas C, Gatlin J D, Gkritza K. Perceptions of safety on a shared road: Driving, cycling, or walking near an autonomous vehicle. Journal of safety research, 2020, 72, 249-258.
  • Martínez-Díaz M, Soriguera F. Autonomous vehicles: theoretical and practical challenges. Transportation Research Procedia, 2018, 33, 275-282.
  • Rajasekhar M V, Jaswal A K. Autonomous vehicle: The future of automobiles. In 2015 IEEE International Transportation Electrification Conference (ITEC), 2020, pp. 1-6. IEEE.
  • Yeong D J, Velasco-Hernandez G, Barry J, Walsh J. Sensor and sensor fusion technology in autonomous vehicles: A review. Sensors, 2021, 21(6), 2140.
  • Gökaşar İ, Dündar S. Sürücüsüz taşıtların trafik akım hızına etkisinin yapay sinir ağları ile incelenmesi. Akıllı Ulaşım Sistemleri ve Uygulamaları Dergisi, 2018, 1(2), 56-71.
  • Harrington R, Senatore C, Scanlon J, Yee R. The role of infrastructure in an automated vehicle future. Bridge, 2018, 40(06).
  • Thrun S, Montemerlo M, Dahlkamp H, Stavens D, Aron A, Diebel J, Mahoney P. Stanley: the Robot That Won the Darpa Grand Challenge., 2007, 1-43. https://doi.org/10.1007/978-3-540-73429-1_1
  • Urmson C, Anhalt J, Bagnell D, Baker C, Bittner R, Clark M, Ferguson D. Autonomous Driving In Urban Environments: Boss and The Urban Challenge., 2009, 1-59. https://doi.org/10.1007/978-3-642-03991-1_1
  • Parasuraman A, Colby C. An updated and streamlined technology readiness index. Journal of Service Research, 2014, 18(1), 59-74. https://doi.org/10.1177/1094670514539730
  • Parasuraman A. Technology readiness index (tri). Journal of Service Research, 2000, 2(4), 307-320. https://doi.org/10.1177/109467050024001
  • Parasuraman A, Colby C L. An updated and streamlined technology readiness index: TRI 2.0. Journal of service research, 2015, 18(1), 59-74.
  • Sani A, Pusparini N, Budiyantara A, Irwansyah I, Hindardjo A. Investigating readiness attitude toward using mobile payment systems through technology acceptance model. Jurnal Riset Informatika, 2021, 3(3), 211-218. https://doi.org/10.34288/jri.v3i3.233
  • Wahyuni A, Juraida A, Anwar A. Readiness factor identification bandung city msmes use blockchain technology. Jurnal Sistem Dan Manajemen Industri, 2021, 5(2), 53-62. https://doi.org/10.30656/jsmi.v5i2.2787
  • Lam S, Chiang J, Parasuraman A. The effects of the dimensions of technology readiness on technology acceptance: an empirical analysis. Journal of Interactive Marketing, 2008, 22(4), 19-39. https://doi.org/10.1002/dir.20119
  • Chen S, Chen H, Chen M. Determinants of satisfaction and continuance intention towards self‐service technologies. Industrial Management & Data Systems, 2009, 109(9), 1248-1263. https://doi.org/10.1108/02635570911002306
  • Pradhan M, Oh J, Lee H. Understanding travelers’ behavior for sustainable smart tourism: a technology readiness perspective. Sustainability, 2018, 10(11), 4259. https://doi.org/10.3390/su10114259
  • Dzulkifli F, Wahyuni E, Wicaksono G. Analisis kesiapan pengguna lective menggunakan metode technology readiness index (tri). Jurnal Repositor, 2020, 2(7), 923. https://doi.org/10.22219/repositor.v2i7.676
  • Yieh K, Chen J, Wei M. The effects of technology readiness on customer perceived value: an empirical analysis. Journal of Family and Economic Issues, 2012, 33(2), 177-183. https://doi.org/10.1007/s10834-012-9314-3
  • Shim H, Han S, Ha J. The effects of consumer readiness on the adoption of self-service technology: moderating effects of consumer traits and situational factors. Sustainability, 2020, 13(1), 95. https://doi.org/10.3390/su13010095
  • Moxley J, Czaja S. The factors influencing older adults’ decisions surrounding adoption of technology: quantitative experimental study. Jmir Aging, 2022, 5(4), e39890. https://doi.org/10.2196/39890
  • Kim M, Son M. What determines consumer attitude toward green credit card services? a moderated mediation approach. Sustainability, 2021, 13(19), 10865. https://doi.org/10.3390/su131910865
  • Lee W, Lim Z, Tang L, Yahya N, Varathan K, Ludin S. Patients' technology readiness and whealth literacy. Cin Computers Informatics Nursing, 2021, 40(4), 244-250. https://doi.org/10.1097/cin.0000000000000854
  • Blut M, Wang C. Technology readiness: a meta-analysis of conceptualizations of the construct and its impact on technology usage. Journal of the Academy of Marketing Science, 2019, 48(4), 649-669. https://doi.org/10.1007/s11747-019-00680-8
  • Csuka S, Martos T, Kapornaky M, Sallay V. Attitudes toward technologies of the near future: the role of technology readiness in a hungarian adult sample. International Journal of Innovation and Technology Management, 2019, 16(06). https://doi.org/10.1142/s0219877019500469
  • Lara J, Novaes A, Afonso B, Tissot-Lara T. Chinese technology: a study of the image and the desire for possession, using the technology readiness index – tri scale. International Journal of Innovation, 2022, 10(4), 638-665. https://doi.org/10.5585/iji.v10i4.21638
  • Atkinson K, Westeinde J, Ducharme R, Wilson S, Deeks S, Crowcroft N, Wilson K. Can mobile technologies improve on-time vaccination? a study piloting maternal use of immunizeca, a pan-canadian immunization app. Human Vaccines & Immunotherapeutics, 2016, 12(10), 2654-2661. https://doi.org/10.1080/21645515.2016.1194146
  • Bakirtaş H, Akkaş C. Technology readiness and technology acceptance of academic staffs. International Journal of Management Economics and Business, 2020, 16(4). https://doi.org/10.17130/ijmeb.853629
  • Kayser L, Rossen S, Karnoe A, Elsworth G, Vibe-Petersen J, Christensen J, Osborne R. Development of the multidimensional readiness and enablement index for health technology (readhy) tool to measure individuals’ health technology readiness: initial testing in a cancer rehabilitation setting. Journal of Medical Internet Research, 2019, 21(2), e10377. https://doi.org/10.2196/10377
  • Thorsen I, Rossen S, Glümer C, Midtgaard J, Ried-Larsen M, Kayser L. Health technology readiness profiles among danish individuals with type 2 diabetes: cross-sectional study. Journal of Medical Internet Research, 2020, 22(9), e21195. https://doi.org/10.2196/21195
  • Atkinson K, Ducharme R, Westeinde J, Wilson S, Deeks S, Pascali D, Wilson K. Vaccination attitudes and mobile readiness: a survey of expectant and new mothers. Human Vaccines & Immunotherapeutics, 2015, 11(4), 1039-1045. https://doi.org/10.1080/21645515.2015.1009807
  • Lai Y, Lee J. Integration of technology readiness index (tri) into the technology acceptance model (tam) for explaining behavior in adoption of bim. Asian Education Studies, 2020, 5(2), 10. https://doi.org/10.20849/aes.v5i2.816
  • Ramadhani S, Suroso A, Ratono J. Consumer attitude, behavioral intention, and watching behavior of online video advertising on youtube. Jurnal Aplikasi Manajemen, 2020, 18(3), 493-503. https://doi.org/10.21776/ub.jam.2020.018.03.09
  • Shim H, Han S, Ha J. The effects of consumer readiness on the adoption of self-service technology: moderating effects of consumer traits and situational factors. Sustainability, 2020, 13(1), 95. https://doi.org/10.3390/su13010095
  • Chen M, Lin N. Incorporation of health consciousness into the technology readiness and acceptance model to predict app download and usage intentions. Internet Research, 2018, 28(2), 351-373. https://doi.org/10.1108/intr-03-2017-0099
  • Matarirano O, Yeboah A, Gqokonqana O. Readiness of students for multi-modal emergency remote teaching at a selected south african higher education institution. International Journal of Higher Education, 2021, 10(6), 135. https://doi.org/10.5430/ijhe.v10n6p135
  • Mahmood A, Imran M, Adil K. Modeling individual beliefs to transfigure technology readiness into technology acceptance in financial institutions. Sage Open, 2023, 13(1), 215824402211497. https://doi.org/10.1177/21582440221149718
  • Li N, Oyler D, Zhang M, Yıldız Y, Kolmanovsky I, Girard A. Game theoretic modeling of driver and vehicle interactions for verification and validation of autonomous vehicle control systems. Ieee Transactions on Control Systems Technology, 2018, 26(5), 1782-1797. https://doi.org/10.1109/tcst.2017.2723574
  • Xiao Y, Liu Z. Accident liability determination of autonomous driving systems based on artificial intelligence technology and its impact on public mental health. Journal of Environmental and Public Health, 2022, 1-12. https://doi.org/10.1155/2022/2671968
  • Muhammad T, Kashmiri F, Yan H, Wang T, Lu H. A cellular automata model for heterogeneous traffic flow incorporating micro autonomous vehicles. Journal of Advanced Transportation, 2022, 1-21. https://doi.org/10.1155/2022/8815026
  • Muhammad T, Kashmiri F, Naeem H, Xin Q, Chia-Chun H, Lu H. Simulation study of autonomous vehicles’ effect on traffic flow characteristics including autonomous buses. Journal of Advanced Transportation, 2020, 1-17. https://doi.org/10.1155/2020/4318652
  • Tan L, Ma C, Xu X, Xu J. Choice behavior of autonomous vehicles based on logistic models. Sustainability, 2019, 12(1), 54. https://doi.org/10.3390/su12010054
  • Asadi-Shekari Z, Saadi I, Cools M. Applying machine learning to explore feelings about sharing the road with autonomous vehicles as a bicyclist or as a pedestrian. Sustainability, 2022, 14(3), 1898. https://doi.org/10.3390/su14031898
  • Azevedo C, Marczuk K, Raveau S, Soh H, Adnan M, Basak K, Ben‐Akiva M. Microsimulation of demand and supply of autonomous mobility on demand. Transportation Research Record Journal of the Transportation Research Board, 2016, 2564(1), 21-30. https://doi.org/10.3141/2564-03
  • Wu Z, Zhou H, Xi H, Wu N. Analysing public acceptance of autonomous buses based on an extended tam model. Iet Intelligent Transport Systems, 2021, 15(10), 1318-1330. https://doi.org/10.1049/itr2.12100
  • Zhang S, Jing P, Xu G. The acceptance of independent autonomous vehicles and cooperative vehicle-highway autonomous vehicles. Information, 2021, 12(9), 346. https://doi.org/10.3390/info12090346
  • Golbabaei F, Yigitcanlar T, Paz A, Bunker J. Individual predictors of autonomous vehicle public acceptance and intention to use: a systematic review of the literature. Journal of Open Innovation Technology Market and Complexity, 2020, 6(4), 106. https://doi.org/10.3390/joitmc6040106
  • Huang T. Psychological factors affecting potential users’ intention to use autonomous vehicles. Plos One, 2023, 18(3), e0282915. https://doi.org/10.1371/journal.pone.0282915
  • Si H, Tan G, Zuo H. A deep coordination graph convolution reinforcement learning for multi-intelligent vehicle driving policy. Wireless Communications and Mobile Computing, 2022, 1-13. https://doi.org/10.1155/2022/9665421
  • Girdhar M, Hong J, Moore J. (Cybersecurity of autonomous vehicles: a systematic literature review of adversarial attacks and defense models. Ieee Open Journal of Vehicular Technology, 2023, 4, 417-437. https://doi.org/10.1109/ojvt.2023.3265363
  • Ma Y, Wang Z, Yang H, Yang L. Artificial intelligence applications in the development of autonomous vehicles: a survey. Ieee/Caa Journal of Automatica Sinica, 2020, 7(2), 315-329. https://doi.org/10.1109/jas.2020.1003021
  • Meidute-Kavaliauskiene I, Yildiz B, Çiğdem Ş, Činčikaitė R. Do people prefer cars that people don’t drive? a survey study on autonomous vehicles. Energies, 2021, 14(16), 4795. https://doi.org/10.3390/en14164795
  • Erskine M, Brooks S, Greer T, Apigian C. From driver assistance to fully-autonomous: examining consumer acceptance of autonomous vehicle technologies. Journal of Consumer Marketing, 2020, 37(7), 883-894. https://doi.org/10.1108/jcm-10-2019-3441
  • Becker F, Axhausen K. Literature review on surveys investigating the acceptance of automated vehicles. Transportation, 2017, 44(6), 1293-1306. https://doi.org/10.1007/s11116-017-9808-9
  • Gabor B. Assessing self-driving vehicle awareness in Hungarian rejecting groups. Deturope - The Central European Journal of Tourism and Regional Development, 2022, 14(3), 129-143. https://doi.org/10.32725/det.2022.025
  • Nasır S, Özçelik S. Sürücüsüz araçlara yönelik tüketici tutumları. Avrasya Sosyal ve Ekonomi Araştırmaları Dergisi, 2017, 4(12), 590-603.
  • Yiğit E, Öner A E, Yöntem O. Otonom Araçların Otomotiv Sektörüne Etkileri ve Beraberinde Getirdiği Yenilikler. Avrupa Bilim ve Teknoloji Dergisi, (Özel Sayı), 2020, 181-186.
  • Kocagöz E, İğde Ç S, Çetindağ G. Elektrikli ve akıllı, yerli ve milli: Türkiye’nin Otomobili Girişim Grubu’nun tanıttığı araçlara yönelik tüketicilerin ilk değerlendirmeleri. Erciyes Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 2020, (49), 55-74.
  • Aylak B, Oral O, Yazici K. Yapay zeka ve makine öğrenmesi tekniklerinin lojistik sektöründe kullanımı. El-Cezeri Fen Ve Mühendislik Dergisi. 2020, https://doi.org/10.31202/ecjse.776314
  • Şener E. Autonomous-shared vehicle management system. Politeknik Dergisi, 2023, 26(1), 81-92. https://doi.org/10.2339/politeknik.931490
  • Semiz H, Öztürk E. Karayolu taşımacılığında otonom sürüşe geçiş sürecinde türkiye’nin ihtiyaç duyacağı mevzuat değişiklikleri. Akıllı Ulaşım Sistemleri ve Uygulamaları Dergisi, 2023, 6(1), 1-21. https://doi.org/10.51513/jitsa.1141649
  • Ecevit M. Son adım teslimat yöntemi olan otonom teslimat araçlarının tüketiciler tarafından kabulü: teknolojiye hazırlığın düzenleyici rolü. Akıllı Ulaşım Sistemleri Ve Uygulamaları Dergisi, 2023, 6(1), 166-183. https://doi.org/10.51513/jitsa.1256291
  • Oğuz A, Aydemir M. Yapay potansiyel alan ile otonom araçların kavşak geçiş önceliğinin belirlenmesi. European Journal of Science and Technology. 2022, https://doi.org/10.31590/ejosat.1040657
  • Özçevik Y, Solmaz Ö, Baysal E, Ökten M. A real-time simulation environment architecture for autonomous vehicle design. Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, 2023, 38(3), 1867-1878. https://doi.org/10.17341/gazimmfd.1030482
  • Uçarlı A, İlçi V, Par K, Peker A. Otonom araçlarda çoklu gnss uydu sistemleri kullanımının konum doğruluğuna etkisinin araştırılması. Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi. 2022, https://doi.org/10.28948/ngumuh.1082124
  • Akkaya S, Özbay H. Otonom araçların akıllı ulaşım politikaları üzerindeki etkileri. Akıllı Ulaşım Sistemleri Ve Uygulamaları Dergisi, 2022, 5(2), 200-210. https://doi.org/10.51513/jitsa.1160891
  • Vandecasteele B, Geuens M. Motivated consumer innovativeness: concept, measurement, and validation. International Journal of Research in Marketing, 2010, 27(4), 308-318. https://doi.org/10.1016/j.ijresmar.2010.08.004
  • Soo S. Customers’ intention to use robot-serviced restaurants in korea: relationship of coolness and mci factors. International Journal of Contemporary Hospitality Management, 2020, 32(9), 2947-2968. https://doi.org/10.1108/ijchm-01-2020-0046
  • Jansson J. Consumer eco-innovation adoption: assessing attitudinal factors and perceived product characteristics. Business Strategy and the Environment, 2011, 20(3), 192-210. https://doi.org/10.1002/bse.690
  • Kim H, Fiore A, Niehm L, Jeong M. Psychographic characteristics affecting behavioral intentions towards pop‐up retail. International Journal of Retail & Distribution Management, 2010, 38(2), 133-154. https://doi.org/10.1108/09590551011020138
  • Esfahani M, Reynolds N. Impact of consumer innovativeness on really new product adoption. Marketing Intelligence & Planning, 2021, 39(4), 589-612. https://doi.org/10.1108/mip-07-2020-0304
  • Hirunyawipada T, Paswan A. Consumer innovativeness and perceived risk: implications for high technology product adoption. Journal of Consumer Marketing, 2006, 23(4), 182-198. https://doi.org/10.1108/07363760610674310
  • Shams R, Brown M, Alpert F. A model and empirical test of evolving consumer perceived brand innovativeness and its two-way relationship with consumer perceived product innovativeness. Australasian Marketing Journal (Amj), 2020, 28(4), 171-180. https://doi.org/10.1016/j.ausmj.2020.04.006
  • Albarrán I, Molina J, Gijón C. Perception of artificial intelligence in spain. Telematics and Informatics, 2021, 63, 101672. https://doi.org/10.1016/j.tele.2021.101672
  • Sohaib O, Hussain W, Asif M, Ahmad M, Mazzara M. A pls-sem neural network approach for understanding cryptocurrency adoption. Ieee Access, 2020, 8, 13138-13150. https://doi.org/10.1109/access.2019.2960083
  • Pires P J, da Costa Filho B A, da Cunha J C. Technology readiness index (TRI) factors as differentiating elements between users and non users of internet banking, and as antecedents of the technology acceptance model (TAM). In ENTERprise Information Systems: International Conference, CENTERIS 2011, Vilamoura, Portugal, October 5-7, 2011, Proceedings, Part II (pp. 215-229). Springer Berlin Heidelberg.
  • Karayaman S. İyimserlik ve Değişime Direncin Endüstri 4.0 Uyum Yeteneği Üzerindeki Etkisi. Sosyal, Beşeri Ve İdari Bilimler Dergisi, 2023, 6(10), 1329–1347. https://doi.org/10.26677/TR1010.2023.1317
  • Sinha M, Majra H, Hutchins J, Saxena R. Mobile payments in india: the privacy factor. The International Journal of Bank Marketing, 2019, 37(1), 192-209. https://doi.org/10.1108/ijbm-05-2017-0099
  • Waytz A, Heafner J, Epley N. The mind in the machine: anthropomorphism increases trust in an autonomous vehicle. Journal of Experimental Social Psychology, 2014, 52, 113-117. https://doi.org/10.1016/j.jesp.2014.01.005
  • Cheng P, Meng F, Yao J. Driving with agents: investigating the influences of anthropomorphism level and physicality of agents on drivers' perceived control, trust, and driving performance. Frontiers in Psychology, 2022, 13. https://doi.org/10.3389/fpsyg.2022.883417
  • Tian Y, Wang X. A study on psychological determinants of users' autonomous vehicles adoption from anthropomorphism and utaut perspectives. Frontiers in Psychology, 2022, 13. https://doi.org/10.3389/fpsyg.2022.986800
  • Niu D, Terken J, Eggen B. Anthropomorphizing information to enhance trust in autonomous vehicles. Human Factors and Ergonomics in Manufacturing & Service Industries, 2018, 28(6), 352-359. https://doi.org/10.1002/hfm.20745
  • Sonmez F, Nart S. Antropomorfizm: Kavramın Tarihi, Teoriler Ve Tüketici Davranışları Bağlamında Bir Literatür İncelemesi. İnönü Üniversitesi Uluslararası Sosyal Bilimler Dergisi, 2022, 11(2), 580-613.
  • Kamran H. Pazarlamada Yapay Zekânın Kullanımı: Yapay Zekâ Pazarlama Araçlarının Tüketici Kabulüne Ilişkin Bir Araştırma (Doctoral dissertation, 2021, Bursa Uludag University (Turkey)
  • Kuo K, Liu C, Ma C. An investigation of the effect of nurses’ technology readiness on the acceptance of mobile electronic medical record systems. BMC Medical Informatics and Decision Making, 2013, 13(1). https://doi.org/10.1186/1472-6947-13-88
  • Shin S, Lee W. The effects of technology readiness and technology acceptance on nfc mobile payment services in korea. Journal of Applied Business Research (Jabr), 2014, 30(6), 1615. https://doi.org/10.19030/jabr.v30i6.8873
  • Yaykın H A, Tolay E. Teknolojik Hazır Bulunuşluğun Algılanan Çalışan Performansı Üzerindeki Etkisi: Otomotiv Sektöründe Bir Araştırma. Journal of Business in The Digital Age, 2023, 6(Özel Sayı), 57-65.
  • Lima E, Hopkins T, Gurney E, Shortall O, Lovatt F, Davies P, Kaler J. Drivers for precision livestock technology adoption: a study of factors associated with adoption of electronic identification technology by commercial sheep farmers in england and wales. Plos One, 2018, 13(1), e0190489. https://doi.org/10.1371/journal.pone.0190489
  • Roy S, Balaji M, Quazi A, Quaddus M. Predictors of customer acceptance of and resistance to smart technologies in the retail sector. Journal of Retailing and Consumer Services, 2018, 42, 147-160. https://doi.org/10.1016/j.jretconser.2018.02.005
  • Chen M, Lin N. Incorporation of health consciousness into the technology readiness and acceptance model to predict app download and usage intentions. Internet Research, 2018, 28(2), 351-373. https://doi.org/10.1108/intr-03-2017-0099
  • Şekkeli Z H. Dijital Dönüşüme Dair Algıların Teknolojiye Hazir Olma ve Kabul Modeli (TRAM) ile Analizi: Kahramanmaraş Sütçü İmam Üniversitesi MYO Öğrencileri Üzerinde Ampirik Bir Çalışma. Bilge Uluslararası Sosyal Araştırmalar Dergisi, 2022, 6(2), 78-89.
  • Anayat S, Rasool G, Pathania A. Examining the context‐specific reasons and adoption of artificial intelligence‐based voice assistants: a behavioural reasoning theory approach. International Journal of Consumer Studies, 2023, 47(5), 1885-1910. https://doi.org/10.1111/ijcs.12963
  • Wagner G, Raymond L, Paré G. Understanding prospective physicians’ intention to use artificial intelligence in their future medical practice: configurational analysis. Jmir Medical Education, 2023, 9, e45631. https://doi.org/10.2196/45631
  • Dwivedi Y, Rana N, Jeyaraj A, Clement M, Williams M. Re-examining the unified theory of acceptance and use of technology (utaut): towards a revised theoretical model. Information Systems Frontiers, 2017, 21(3), 719-734. https://doi.org/10.1007/s10796-017-9774-y
  • Teo T, Zhou M, Noyes J. Teachers and technology: development of an extended theory of planned behavior. Educational Technology Research and Development, 2016, 64(6), 1033-1052. https://doi.org/10.1007/s11423-016-9446-5
  • Chin J, Do C, Kim M. How to increase sport facility users’ intention to use ai fitness services: based on the technology adoption model. International Journal of Environmental Research and Public Health, 2022, 19(21), 14453. https://doi.org/10.3390/ijerph192114453
  • Ho Y, Alam S, Masukujjaman M, Lin C, Susmit S, Susmit, S. Intention to adopt ai-powered online service among tourism and hospitality companies. International Journal of Technology and Human Interaction, 2022, 18(1), 1-19. https://doi.org/10.4018/ijthi.299357
  • Liang Y, Lee S, Workman J. Implementation of artificial intelligence in fashion: are consumers ready?. Clothing and Textiles Research Journal, 2019, 38(1), 3-18. https://doi.org/10.1177/0887302x19873437
  • Li K, Li Y, Franklin T. Preservice teachers’ intention to adopt technology in their future classrooms. Journal of Educational Computing Research, 2016, 54(7), 946-966. https://doi.org/10.1177/0735633116641694
  • Cosmo L, Piper L, Vittorio A. The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing, 2021, (1-2), 83-102. https://doi.org/10.1007/s43039-021-00020-1
  • Levay K, Freese J, Druckman J. The demographic and political composition of mechanical turk samples. Sage Open, 2016, 6(1), 215824401663643. https://doi.org/10.1177/2158244016636433
  • Sonnenschein S, Stites M, Ross A. Home learning environments for young children in the u.s. during covid-19. Early Education and Development, 2021, 32(6), 794-811. https://doi.org/10.1080/10409289.2021.1943282
  • Kock N, Hadaya P. Minimum sample size estimation in pls‐sem: the inverse square root and gamma‐exponential methods. Information Systems Journal, 2016, 28(1), 227-261. https://doi.org/10.1111/isj.12131
  • Savalei V. A comparison of several approaches for controlling measurement error in small samples. Psychological Methods, 2019, 24(3), 352-370. https://doi.org/10.1037/met0000181
  • Siahaan A, Thiodore J. Analysis influence of consumer behavior to purchase organic foods in Jakarta, 2022, https://doi.org/10.2991/absr.k.220101.009
  • Gursoy D, Chi O H, Lu L, Nunkoo R. Consumers acceptance of artificially intelligent (AI) device use in service delivery. International Journal of Information Management, 2019, 49, 157-169.
  • Rese A, Schreiber S, Baier D. Technology acceptance modeling of augmented reality at the point of sale: can surveys be replaced by an analysis of online reviews?. Journal of Retailing and Consumer Services, 2014, 21(5), 869-876.
  • Taylor S, Todd P A. Understanding information technology usage: A test of competing models. Information systems research, 1995, 6(2), 144-176.
  • Ringle C M, Wende S, Becker J-M. SmartPLS 4. Oststeinbek: SmartPLS GmbH, 2022, http://www.smartpls.com.
  • Hair J, Risher J, Sarstedt M, Ringle C. When to use and how to report the results of pls-sem. European Business Review, 2019, 31(1), 2-24. https://doi.org/10.1108/ebr-11-2018-0203
  • Yuan K. Comments on the article “marketing or methodology? exposing the fallacies of pls with simple demonstrations” and pls-sem in general. European Journal of Marketing, 2023, 57(6), 1618-1625. https://doi.org/10.1108/ejm-07-2021-0472
  • Magno F, Cassia F, Ringle C. A brief review of partial least squares structural equation modeling (pls-sem) use in quality management studies. The TQM Journal. 2022, https://doi.org/10.1108/tqm-06-2022-0197
  • Buditjahjanto I. Analyzing factors of gui simulation as learning media toward students' learning outcomes. Journal of Technology and Science Education, 2022, 12(1), 83. https://doi.org/10.3926/jotse.1317
  • Khmeleva G, Kurnikova M, Nedelka E, Tóth B. Determinants of sustainable cross-border cooperation: a structural model for the hungarian context using the pls-sem methodology. Sustainability, 2022, 14(2), 893. https://doi.org/10.3390/su14020893
  • Hair Jr J, Hult G, Ringle C, Sarstedt M, Danks N, Ray S. An introduction to structural equation modeling. 2021, 1-29. https://doi.org/10.1007/978-3-030-80519-7_1
  • Prybutok G, Ta A, Liu X, Prybutok V. An integrated structural equation model of ehealth behavioral intention. International Journal of Healthcare Information Systems and Informatics, 2020, 15(1), 20-39. https://doi.org/10.4018/ijhisi.2020010102
  • Taber K S. The use of Cronbach’s alpha when developing and reporting research instruments in science education. Research in Science Education, 2018, 48, 1273-1296.
  • Eldrandaly K A, Naguib S M, Hassan M M. A model for measuring geographic information systems success. Journal of Geographic Information System, 2015, 7(4), 328.
  • Hair J F, Black W C, Babin B J, Anderson R E. Multivariate data analysis 2014, pp. 1–734. Eng: Pearson Education Limited.
  • Fornell C, Larcker D F. Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of Marketing Research, 1981, 18(3), 382–388.
  • Henseler J. Partial least squares path modeling; Advanced Methods for Modeling Markets, 2017, ss. 361-381. Springer.
  • Garson G D. Partial least squares. Regression and structural equation models. Statistical Publishing Associates. 2016.
  • Oemar H, Prasetyaningsih E, Bakar S, Djamaludin D, Septiani A. Awareness and intention to register halal certification of micro and small-scale food enterprises. F1000research, 2023, 11, 170. https://doi.org/10.12688/f1000research.75968.3
  • Razavi-Termeh S, Sadeghi-Niaraki A, Choi S. Spatial modeling of asthma-prone areas using remote sensing and ensemble machine learning algorithms. Remote Sensing, 2021, 13(16), 3222. https://doi.org/10.3390/rs13163222
  • Faisal C, Fernandez-Lanvin D, Andrés J, Gonzalez-Rodriguez M. Design quality in building behavioral intention through affective and cognitive involvement for e-learning on smartphones. Internet Research, 2020, 30(6), 1631-1663. https://doi.org/10.1108/intr-05-2019-0217
  • Ioannou A, Tussyadiah I. Privacy and surveillance attitudes during health crises: acceptance of surveillance and privacy protection behaviours. Technology in Society, 2021, 67, 101774. https://doi.org/10.1016/j.techsoc.2021.101774
  • Vargas P, González F, Landi V, Jurado J, Delgado-Bermejo J. Sexual dimorphism and breed characterization of creole hens through biometric canonical discriminant analysis across ecuadorian agroecological areas. Animals, 2019, 10(1), 32. https://doi.org/10.3390/ani10010032
  • Reddy C, Hamann R, Urban B. Country-level entrepreneurship: crowding out the population’s need for autonomy. Acta Commercii, 2015, 15(1). https://doi.org/10.4102/ac.v15i1.292
  • Khokhar A. What decides women entrepreneurship in india?. Journal of Entrepreneurship and Innovation in Emerging Economies, 2019, 5(2), 180-197. https://doi.org/10.1177/2393957519862465
  • Ye M, Hao F, Shahzad M, Kamran H. How green organizational strategy and environmental csr affect organizational sustainable performance through green technology innovation amid covid-19. Frontiers in Environmental Science, 2022, 10. https://doi.org/10.3389/fenvs.2022.959260
  • Fam S, Loh S, Musa H, Yanto H, Khoo L, Yong D. Overall equipment efficiency (oee) enhancement in manufacture of electronic components & boards industry through total productive maintenance practices. Matec Web of Conferences, 2018, 150, 05037. https://doi.org/10.1051/matecconf/201815005037
  • Garg N, Talukdar A, Ganguly A, Kumar C. Knowledge hiding in academia: an empirical study of indian higher education students. Journal of Knowledge Management, 2021, 25(9), 2196-2219. https://doi.org/10.1108/jkm-10-2020-0783
  • Park K, Koh C. Effect of change management capability in real-time environment: an information orientation perspective in supply chain management. Behaviour and Information Technology, 2014, 34(1), 94-104. https://doi.org/10.1080/0144929x.2014.945961
  • Otieno F, Gachohi J, Gikuma-Njuru P, Kariuki P, Oyas H, Canfield S, Blackburn J. Modeling the potential future distribution of anthrax outbreaks under multiple climate change scenarios for kenya. International Journal of Environmental Research and Public Health, 2021, 18(8), 4176. https://doi.org/10.3390/ijerph18084176
  • Luque-Vílchez M, Mesa-Pérez E, Husillos J, Larrinaga C. The influence of pro-environmental managers’ personal values on environmental disclosure. Sustainability Accounting Management and Policy Journal, 2019, 10(1), 41-61. https://doi.org/10.1108/sampj-01-2018-0016
  • Cohen J. Statistical power analysis for the behavioral sciences. 1988, 2nd Edition, Lawrence Erlbaum Associates, USA
  • Ali F, Amin M, Cobanoglu C. An integrated model of service experience, emotions, satisfaction, and price acceptance: An empirical analysis in the Chinese hospitality industry. Journal of Hospitality Marketing & Management, 2016, 25(4), 449-475.
  • Payre W, Cestac J, Delhomme P. Intention to use a fully automated car: attitudes and a priori acceptability. Transportation Research Part F Traffic Psychology and Behaviour, 2014, 27, 252-263. https://doi.org/10.1016/j.trf.2014.04.009
  • Staufenbiel T, König C. A model for the effects of job insecurity on performance, turnover intention, and absenteeism. Journal of Occupational and Organizational Psychology, 2010, 83(1), 101-117. https://doi.org/10.1348/096317908x401912
  • Nordhoff S, Winter J, Kyriakidis M, Arem B, Happee R. Acceptance of driverless vehicles: results from a large cross-national questionnaire study. Journal of Advanced Transportation, 2018, 1-22. https://doi.org/10.1155/2018/5382192
  • Salonen A, Haavisto N. Towards autonomous transportation. passengers’ experiences, perceptions and feelings in a driverless shuttle bus in finland. Sustainability, 2019, 11(3), 588. https://doi.org/10.3390/su11030588
  • Cugurullo F, Acheampong R, Guériau M, Dusparić I. The transition to autonomous cars, the redesign of cities and the future of urban sustainability. Urban Geography, 2020 42(6), 833-859. https://doi.org/10.1080/02723638.2020.1746096

Autonomous Vehicle Technology and Technology Acceptance: The Role of Technological Readiness on Consumers' Attitudes Towards Driverless Cars and Intention to Use in the Future

Yıl 2024, Cilt: 36 Sayı: 1, 383 - 407, 28.03.2024
https://doi.org/10.35234/fumbd.1385541

Öz

Autonomous (driverless) cars, which have entered the automotive industry with the developments in automotive and the advancement of artificial intelligence technologies, are rapidly finding a place in the marketing field. At this point, there are factors affecting consumers' concerns and willingness to use autonomous vehicles. In order to discover these factors, the readiness of consumers and the aspects in which they are ready for this technology are issues that need to be investigated. As a result of this situation, consumers' readiness to use autonomous vehicles, their attitudes toward using them, and their intentions to use them in the future are essential. This study aims to reveal the factors affecting consumers' attitudes and intentions towards using autonomous cars. Research data was collected via an online survey method. The convenience sampling method was used in the research. The research model was tested by structural equation modeling using Smart PLS. As a result of the research, it was found that discomfort and distrust dimensions significantly and negatively affected consumers' attitudes towards usage. It was found that the dimensions of optimism, innovativeness, and anthropomorphism significantly and positively affected consumers' attitudes toward use, and users' attitudes towards use significantly and positively affected their intention to use. The research results show that brands that put autonomous cars on the market should give importance to improvements in the dimensions of optimism, innovation, and anthropomorphism and should make improvements that will eliminate consumers' discomfort and insecurity.

Kaynakça

  • Yiğit E, Oner AE, Yöntem O. Otonom Araçların Otomotiv Sektörüne Etkileri ve Beraberinde Getirdiği Yenilikler. Avrupa Bilim ve Teknoloji Dergisi,2020 181-186.
  • Khayyam H, Javadi B, Jalili M, Jazar R N. Artificial intelligence and internet of things for autonomous vehicles. Nonlinear Approaches in Engineering Applications: Automotive Applications of Engineering Problems, 2020 39-68.
  • Tekin A T, Özkale L, Gültekin-Karakaş D. The Turkish automotive industry in the era of digital technologies and autonomous cars. In Proceedings of the International Symposium for Production Research 2019 (pp. 319-327). Springer International Publishing.
  • Alharbi A, Sohaib O. Technology readiness and cryptocurrency adoption: pls-sem and deep learning neural network analysis. Ieee Access, 2021, 9, 21388-21394. https://doi.org/10.1109/access.2021.3055785
  • Lim H S M, Taeihagh A. Algorithmic decision-making in AVs: Understanding ethical and technical concerns for smart cities. Sustainability, 2019, 11(20), 5791.
  • Dokic J, Müller B, Meyer G. European roadmap smart systems for automated driving. European Technology Platform on Smart Systems Integration, 2015, 39.
  • Shi E, Gasser T M, Seeck A, Auerswald R. The principles of operation framework: A comprehensive classification concept for automated driving functions. SAE International Journal of Connected and Automated Vehicles, 2020, 3(12-03-01-0003), 27-37.
  • Rojas Rueda D, Nieuwenhuijsen M J, Khreis H, Frumkin H. Autonomous vehicles and public health. Annu Rev Public Health. 2020, 2(41), 329-45.
  • Schwarting W, Alonso–Mora J, Rus D. Planning and decision-making for autonomous vehicles. Annual Review of Control Robotics and Autonomous Systems, 2018, 1(1), 187-210. https://doi.org/10.1146/annurev-control-060117-105157
  • Tastan Y, Kaymaz H. Otonom Araçların Önündeki Zorluklar. International Journal of Advances in Engineering and Pure Sciences, 2021, 33(2), 195-209.
  • Kaur K, Rampersad G. Trust in driverless cars: Investigating key factors influencing the adoption of driverless cars. Journal of Engineering and Technology Management, 2018, 48, 87-96.
  • Schaefer K E, Straub E R. Will passengers trust driverless vehicles? Removing the steering wheel and pedals. In 2016 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA). 2016, pp. 159-165. IEEE.
  • Du H, Zhu G, Zheng J. Why travelers trust and accept self-driving cars: An empirical study. Travel behaviour and society, 2021, 22, 1-9.
  • Hulse L, Xie H, Galea E. Perceptions of autonomous vehicles: relationships with road users, risk, gender and age. Safety Science, 2018, 102, 1-13. https://doi.org/10.1016/j.ssci.2017.10.001
  • Moody J, Bailey N, Zhao J. Public perceptions of autonomous vehicle safety: An international comparison. Safety science, 2020, 121, 634-650.
  • Kyriakidis M, Happee R, de Winter J C. Public opinion on automated driving: Results of an international questionnaire among 5000 respondents. Transportation research part F: traffic psychology and behaviour, 2015, 32, 127-140.
  • Cunningham M L, Regan M A, Ledger S A, Bennett J M. To buy or not to buy? Predicting willingness to pay for automated vehicles based on public opinion. Transportation research part F: traffic psychology and behaviour, 2019, 65, 418-438.
  • Nair G S, Bhat C R. Sharing the road with autonomous vehicles: Perceived safety and regulatory preferences. Transportation research part C: emerging technologies, 2021, 122, 102885.
  • Pyrialakou V D, Gkartzonikas C, Gatlin J D, Gkritza K. Perceptions of safety on a shared road: Driving, cycling, or walking near an autonomous vehicle. Journal of safety research, 2020, 72, 249-258.
  • Martínez-Díaz M, Soriguera F. Autonomous vehicles: theoretical and practical challenges. Transportation Research Procedia, 2018, 33, 275-282.
  • Rajasekhar M V, Jaswal A K. Autonomous vehicle: The future of automobiles. In 2015 IEEE International Transportation Electrification Conference (ITEC), 2020, pp. 1-6. IEEE.
  • Yeong D J, Velasco-Hernandez G, Barry J, Walsh J. Sensor and sensor fusion technology in autonomous vehicles: A review. Sensors, 2021, 21(6), 2140.
  • Gökaşar İ, Dündar S. Sürücüsüz taşıtların trafik akım hızına etkisinin yapay sinir ağları ile incelenmesi. Akıllı Ulaşım Sistemleri ve Uygulamaları Dergisi, 2018, 1(2), 56-71.
  • Harrington R, Senatore C, Scanlon J, Yee R. The role of infrastructure in an automated vehicle future. Bridge, 2018, 40(06).
  • Thrun S, Montemerlo M, Dahlkamp H, Stavens D, Aron A, Diebel J, Mahoney P. Stanley: the Robot That Won the Darpa Grand Challenge., 2007, 1-43. https://doi.org/10.1007/978-3-540-73429-1_1
  • Urmson C, Anhalt J, Bagnell D, Baker C, Bittner R, Clark M, Ferguson D. Autonomous Driving In Urban Environments: Boss and The Urban Challenge., 2009, 1-59. https://doi.org/10.1007/978-3-642-03991-1_1
  • Parasuraman A, Colby C. An updated and streamlined technology readiness index. Journal of Service Research, 2014, 18(1), 59-74. https://doi.org/10.1177/1094670514539730
  • Parasuraman A. Technology readiness index (tri). Journal of Service Research, 2000, 2(4), 307-320. https://doi.org/10.1177/109467050024001
  • Parasuraman A, Colby C L. An updated and streamlined technology readiness index: TRI 2.0. Journal of service research, 2015, 18(1), 59-74.
  • Sani A, Pusparini N, Budiyantara A, Irwansyah I, Hindardjo A. Investigating readiness attitude toward using mobile payment systems through technology acceptance model. Jurnal Riset Informatika, 2021, 3(3), 211-218. https://doi.org/10.34288/jri.v3i3.233
  • Wahyuni A, Juraida A, Anwar A. Readiness factor identification bandung city msmes use blockchain technology. Jurnal Sistem Dan Manajemen Industri, 2021, 5(2), 53-62. https://doi.org/10.30656/jsmi.v5i2.2787
  • Lam S, Chiang J, Parasuraman A. The effects of the dimensions of technology readiness on technology acceptance: an empirical analysis. Journal of Interactive Marketing, 2008, 22(4), 19-39. https://doi.org/10.1002/dir.20119
  • Chen S, Chen H, Chen M. Determinants of satisfaction and continuance intention towards self‐service technologies. Industrial Management & Data Systems, 2009, 109(9), 1248-1263. https://doi.org/10.1108/02635570911002306
  • Pradhan M, Oh J, Lee H. Understanding travelers’ behavior for sustainable smart tourism: a technology readiness perspective. Sustainability, 2018, 10(11), 4259. https://doi.org/10.3390/su10114259
  • Dzulkifli F, Wahyuni E, Wicaksono G. Analisis kesiapan pengguna lective menggunakan metode technology readiness index (tri). Jurnal Repositor, 2020, 2(7), 923. https://doi.org/10.22219/repositor.v2i7.676
  • Yieh K, Chen J, Wei M. The effects of technology readiness on customer perceived value: an empirical analysis. Journal of Family and Economic Issues, 2012, 33(2), 177-183. https://doi.org/10.1007/s10834-012-9314-3
  • Shim H, Han S, Ha J. The effects of consumer readiness on the adoption of self-service technology: moderating effects of consumer traits and situational factors. Sustainability, 2020, 13(1), 95. https://doi.org/10.3390/su13010095
  • Moxley J, Czaja S. The factors influencing older adults’ decisions surrounding adoption of technology: quantitative experimental study. Jmir Aging, 2022, 5(4), e39890. https://doi.org/10.2196/39890
  • Kim M, Son M. What determines consumer attitude toward green credit card services? a moderated mediation approach. Sustainability, 2021, 13(19), 10865. https://doi.org/10.3390/su131910865
  • Lee W, Lim Z, Tang L, Yahya N, Varathan K, Ludin S. Patients' technology readiness and whealth literacy. Cin Computers Informatics Nursing, 2021, 40(4), 244-250. https://doi.org/10.1097/cin.0000000000000854
  • Blut M, Wang C. Technology readiness: a meta-analysis of conceptualizations of the construct and its impact on technology usage. Journal of the Academy of Marketing Science, 2019, 48(4), 649-669. https://doi.org/10.1007/s11747-019-00680-8
  • Csuka S, Martos T, Kapornaky M, Sallay V. Attitudes toward technologies of the near future: the role of technology readiness in a hungarian adult sample. International Journal of Innovation and Technology Management, 2019, 16(06). https://doi.org/10.1142/s0219877019500469
  • Lara J, Novaes A, Afonso B, Tissot-Lara T. Chinese technology: a study of the image and the desire for possession, using the technology readiness index – tri scale. International Journal of Innovation, 2022, 10(4), 638-665. https://doi.org/10.5585/iji.v10i4.21638
  • Atkinson K, Westeinde J, Ducharme R, Wilson S, Deeks S, Crowcroft N, Wilson K. Can mobile technologies improve on-time vaccination? a study piloting maternal use of immunizeca, a pan-canadian immunization app. Human Vaccines & Immunotherapeutics, 2016, 12(10), 2654-2661. https://doi.org/10.1080/21645515.2016.1194146
  • Bakirtaş H, Akkaş C. Technology readiness and technology acceptance of academic staffs. International Journal of Management Economics and Business, 2020, 16(4). https://doi.org/10.17130/ijmeb.853629
  • Kayser L, Rossen S, Karnoe A, Elsworth G, Vibe-Petersen J, Christensen J, Osborne R. Development of the multidimensional readiness and enablement index for health technology (readhy) tool to measure individuals’ health technology readiness: initial testing in a cancer rehabilitation setting. Journal of Medical Internet Research, 2019, 21(2), e10377. https://doi.org/10.2196/10377
  • Thorsen I, Rossen S, Glümer C, Midtgaard J, Ried-Larsen M, Kayser L. Health technology readiness profiles among danish individuals with type 2 diabetes: cross-sectional study. Journal of Medical Internet Research, 2020, 22(9), e21195. https://doi.org/10.2196/21195
  • Atkinson K, Ducharme R, Westeinde J, Wilson S, Deeks S, Pascali D, Wilson K. Vaccination attitudes and mobile readiness: a survey of expectant and new mothers. Human Vaccines & Immunotherapeutics, 2015, 11(4), 1039-1045. https://doi.org/10.1080/21645515.2015.1009807
  • Lai Y, Lee J. Integration of technology readiness index (tri) into the technology acceptance model (tam) for explaining behavior in adoption of bim. Asian Education Studies, 2020, 5(2), 10. https://doi.org/10.20849/aes.v5i2.816
  • Ramadhani S, Suroso A, Ratono J. Consumer attitude, behavioral intention, and watching behavior of online video advertising on youtube. Jurnal Aplikasi Manajemen, 2020, 18(3), 493-503. https://doi.org/10.21776/ub.jam.2020.018.03.09
  • Shim H, Han S, Ha J. The effects of consumer readiness on the adoption of self-service technology: moderating effects of consumer traits and situational factors. Sustainability, 2020, 13(1), 95. https://doi.org/10.3390/su13010095
  • Chen M, Lin N. Incorporation of health consciousness into the technology readiness and acceptance model to predict app download and usage intentions. Internet Research, 2018, 28(2), 351-373. https://doi.org/10.1108/intr-03-2017-0099
  • Matarirano O, Yeboah A, Gqokonqana O. Readiness of students for multi-modal emergency remote teaching at a selected south african higher education institution. International Journal of Higher Education, 2021, 10(6), 135. https://doi.org/10.5430/ijhe.v10n6p135
  • Mahmood A, Imran M, Adil K. Modeling individual beliefs to transfigure technology readiness into technology acceptance in financial institutions. Sage Open, 2023, 13(1), 215824402211497. https://doi.org/10.1177/21582440221149718
  • Li N, Oyler D, Zhang M, Yıldız Y, Kolmanovsky I, Girard A. Game theoretic modeling of driver and vehicle interactions for verification and validation of autonomous vehicle control systems. Ieee Transactions on Control Systems Technology, 2018, 26(5), 1782-1797. https://doi.org/10.1109/tcst.2017.2723574
  • Xiao Y, Liu Z. Accident liability determination of autonomous driving systems based on artificial intelligence technology and its impact on public mental health. Journal of Environmental and Public Health, 2022, 1-12. https://doi.org/10.1155/2022/2671968
  • Muhammad T, Kashmiri F, Yan H, Wang T, Lu H. A cellular automata model for heterogeneous traffic flow incorporating micro autonomous vehicles. Journal of Advanced Transportation, 2022, 1-21. https://doi.org/10.1155/2022/8815026
  • Muhammad T, Kashmiri F, Naeem H, Xin Q, Chia-Chun H, Lu H. Simulation study of autonomous vehicles’ effect on traffic flow characteristics including autonomous buses. Journal of Advanced Transportation, 2020, 1-17. https://doi.org/10.1155/2020/4318652
  • Tan L, Ma C, Xu X, Xu J. Choice behavior of autonomous vehicles based on logistic models. Sustainability, 2019, 12(1), 54. https://doi.org/10.3390/su12010054
  • Asadi-Shekari Z, Saadi I, Cools M. Applying machine learning to explore feelings about sharing the road with autonomous vehicles as a bicyclist or as a pedestrian. Sustainability, 2022, 14(3), 1898. https://doi.org/10.3390/su14031898
  • Azevedo C, Marczuk K, Raveau S, Soh H, Adnan M, Basak K, Ben‐Akiva M. Microsimulation of demand and supply of autonomous mobility on demand. Transportation Research Record Journal of the Transportation Research Board, 2016, 2564(1), 21-30. https://doi.org/10.3141/2564-03
  • Wu Z, Zhou H, Xi H, Wu N. Analysing public acceptance of autonomous buses based on an extended tam model. Iet Intelligent Transport Systems, 2021, 15(10), 1318-1330. https://doi.org/10.1049/itr2.12100
  • Zhang S, Jing P, Xu G. The acceptance of independent autonomous vehicles and cooperative vehicle-highway autonomous vehicles. Information, 2021, 12(9), 346. https://doi.org/10.3390/info12090346
  • Golbabaei F, Yigitcanlar T, Paz A, Bunker J. Individual predictors of autonomous vehicle public acceptance and intention to use: a systematic review of the literature. Journal of Open Innovation Technology Market and Complexity, 2020, 6(4), 106. https://doi.org/10.3390/joitmc6040106
  • Huang T. Psychological factors affecting potential users’ intention to use autonomous vehicles. Plos One, 2023, 18(3), e0282915. https://doi.org/10.1371/journal.pone.0282915
  • Si H, Tan G, Zuo H. A deep coordination graph convolution reinforcement learning for multi-intelligent vehicle driving policy. Wireless Communications and Mobile Computing, 2022, 1-13. https://doi.org/10.1155/2022/9665421
  • Girdhar M, Hong J, Moore J. (Cybersecurity of autonomous vehicles: a systematic literature review of adversarial attacks and defense models. Ieee Open Journal of Vehicular Technology, 2023, 4, 417-437. https://doi.org/10.1109/ojvt.2023.3265363
  • Ma Y, Wang Z, Yang H, Yang L. Artificial intelligence applications in the development of autonomous vehicles: a survey. Ieee/Caa Journal of Automatica Sinica, 2020, 7(2), 315-329. https://doi.org/10.1109/jas.2020.1003021
  • Meidute-Kavaliauskiene I, Yildiz B, Çiğdem Ş, Činčikaitė R. Do people prefer cars that people don’t drive? a survey study on autonomous vehicles. Energies, 2021, 14(16), 4795. https://doi.org/10.3390/en14164795
  • Erskine M, Brooks S, Greer T, Apigian C. From driver assistance to fully-autonomous: examining consumer acceptance of autonomous vehicle technologies. Journal of Consumer Marketing, 2020, 37(7), 883-894. https://doi.org/10.1108/jcm-10-2019-3441
  • Becker F, Axhausen K. Literature review on surveys investigating the acceptance of automated vehicles. Transportation, 2017, 44(6), 1293-1306. https://doi.org/10.1007/s11116-017-9808-9
  • Gabor B. Assessing self-driving vehicle awareness in Hungarian rejecting groups. Deturope - The Central European Journal of Tourism and Regional Development, 2022, 14(3), 129-143. https://doi.org/10.32725/det.2022.025
  • Nasır S, Özçelik S. Sürücüsüz araçlara yönelik tüketici tutumları. Avrasya Sosyal ve Ekonomi Araştırmaları Dergisi, 2017, 4(12), 590-603.
  • Yiğit E, Öner A E, Yöntem O. Otonom Araçların Otomotiv Sektörüne Etkileri ve Beraberinde Getirdiği Yenilikler. Avrupa Bilim ve Teknoloji Dergisi, (Özel Sayı), 2020, 181-186.
  • Kocagöz E, İğde Ç S, Çetindağ G. Elektrikli ve akıllı, yerli ve milli: Türkiye’nin Otomobili Girişim Grubu’nun tanıttığı araçlara yönelik tüketicilerin ilk değerlendirmeleri. Erciyes Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 2020, (49), 55-74.
  • Aylak B, Oral O, Yazici K. Yapay zeka ve makine öğrenmesi tekniklerinin lojistik sektöründe kullanımı. El-Cezeri Fen Ve Mühendislik Dergisi. 2020, https://doi.org/10.31202/ecjse.776314
  • Şener E. Autonomous-shared vehicle management system. Politeknik Dergisi, 2023, 26(1), 81-92. https://doi.org/10.2339/politeknik.931490
  • Semiz H, Öztürk E. Karayolu taşımacılığında otonom sürüşe geçiş sürecinde türkiye’nin ihtiyaç duyacağı mevzuat değişiklikleri. Akıllı Ulaşım Sistemleri ve Uygulamaları Dergisi, 2023, 6(1), 1-21. https://doi.org/10.51513/jitsa.1141649
  • Ecevit M. Son adım teslimat yöntemi olan otonom teslimat araçlarının tüketiciler tarafından kabulü: teknolojiye hazırlığın düzenleyici rolü. Akıllı Ulaşım Sistemleri Ve Uygulamaları Dergisi, 2023, 6(1), 166-183. https://doi.org/10.51513/jitsa.1256291
  • Oğuz A, Aydemir M. Yapay potansiyel alan ile otonom araçların kavşak geçiş önceliğinin belirlenmesi. European Journal of Science and Technology. 2022, https://doi.org/10.31590/ejosat.1040657
  • Özçevik Y, Solmaz Ö, Baysal E, Ökten M. A real-time simulation environment architecture for autonomous vehicle design. Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, 2023, 38(3), 1867-1878. https://doi.org/10.17341/gazimmfd.1030482
  • Uçarlı A, İlçi V, Par K, Peker A. Otonom araçlarda çoklu gnss uydu sistemleri kullanımının konum doğruluğuna etkisinin araştırılması. Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi. 2022, https://doi.org/10.28948/ngumuh.1082124
  • Akkaya S, Özbay H. Otonom araçların akıllı ulaşım politikaları üzerindeki etkileri. Akıllı Ulaşım Sistemleri Ve Uygulamaları Dergisi, 2022, 5(2), 200-210. https://doi.org/10.51513/jitsa.1160891
  • Vandecasteele B, Geuens M. Motivated consumer innovativeness: concept, measurement, and validation. International Journal of Research in Marketing, 2010, 27(4), 308-318. https://doi.org/10.1016/j.ijresmar.2010.08.004
  • Soo S. Customers’ intention to use robot-serviced restaurants in korea: relationship of coolness and mci factors. International Journal of Contemporary Hospitality Management, 2020, 32(9), 2947-2968. https://doi.org/10.1108/ijchm-01-2020-0046
  • Jansson J. Consumer eco-innovation adoption: assessing attitudinal factors and perceived product characteristics. Business Strategy and the Environment, 2011, 20(3), 192-210. https://doi.org/10.1002/bse.690
  • Kim H, Fiore A, Niehm L, Jeong M. Psychographic characteristics affecting behavioral intentions towards pop‐up retail. International Journal of Retail & Distribution Management, 2010, 38(2), 133-154. https://doi.org/10.1108/09590551011020138
  • Esfahani M, Reynolds N. Impact of consumer innovativeness on really new product adoption. Marketing Intelligence & Planning, 2021, 39(4), 589-612. https://doi.org/10.1108/mip-07-2020-0304
  • Hirunyawipada T, Paswan A. Consumer innovativeness and perceived risk: implications for high technology product adoption. Journal of Consumer Marketing, 2006, 23(4), 182-198. https://doi.org/10.1108/07363760610674310
  • Shams R, Brown M, Alpert F. A model and empirical test of evolving consumer perceived brand innovativeness and its two-way relationship with consumer perceived product innovativeness. Australasian Marketing Journal (Amj), 2020, 28(4), 171-180. https://doi.org/10.1016/j.ausmj.2020.04.006
  • Albarrán I, Molina J, Gijón C. Perception of artificial intelligence in spain. Telematics and Informatics, 2021, 63, 101672. https://doi.org/10.1016/j.tele.2021.101672
  • Sohaib O, Hussain W, Asif M, Ahmad M, Mazzara M. A pls-sem neural network approach for understanding cryptocurrency adoption. Ieee Access, 2020, 8, 13138-13150. https://doi.org/10.1109/access.2019.2960083
  • Pires P J, da Costa Filho B A, da Cunha J C. Technology readiness index (TRI) factors as differentiating elements between users and non users of internet banking, and as antecedents of the technology acceptance model (TAM). In ENTERprise Information Systems: International Conference, CENTERIS 2011, Vilamoura, Portugal, October 5-7, 2011, Proceedings, Part II (pp. 215-229). Springer Berlin Heidelberg.
  • Karayaman S. İyimserlik ve Değişime Direncin Endüstri 4.0 Uyum Yeteneği Üzerindeki Etkisi. Sosyal, Beşeri Ve İdari Bilimler Dergisi, 2023, 6(10), 1329–1347. https://doi.org/10.26677/TR1010.2023.1317
  • Sinha M, Majra H, Hutchins J, Saxena R. Mobile payments in india: the privacy factor. The International Journal of Bank Marketing, 2019, 37(1), 192-209. https://doi.org/10.1108/ijbm-05-2017-0099
  • Waytz A, Heafner J, Epley N. The mind in the machine: anthropomorphism increases trust in an autonomous vehicle. Journal of Experimental Social Psychology, 2014, 52, 113-117. https://doi.org/10.1016/j.jesp.2014.01.005
  • Cheng P, Meng F, Yao J. Driving with agents: investigating the influences of anthropomorphism level and physicality of agents on drivers' perceived control, trust, and driving performance. Frontiers in Psychology, 2022, 13. https://doi.org/10.3389/fpsyg.2022.883417
  • Tian Y, Wang X. A study on psychological determinants of users' autonomous vehicles adoption from anthropomorphism and utaut perspectives. Frontiers in Psychology, 2022, 13. https://doi.org/10.3389/fpsyg.2022.986800
  • Niu D, Terken J, Eggen B. Anthropomorphizing information to enhance trust in autonomous vehicles. Human Factors and Ergonomics in Manufacturing & Service Industries, 2018, 28(6), 352-359. https://doi.org/10.1002/hfm.20745
  • Sonmez F, Nart S. Antropomorfizm: Kavramın Tarihi, Teoriler Ve Tüketici Davranışları Bağlamında Bir Literatür İncelemesi. İnönü Üniversitesi Uluslararası Sosyal Bilimler Dergisi, 2022, 11(2), 580-613.
  • Kamran H. Pazarlamada Yapay Zekânın Kullanımı: Yapay Zekâ Pazarlama Araçlarının Tüketici Kabulüne Ilişkin Bir Araştırma (Doctoral dissertation, 2021, Bursa Uludag University (Turkey)
  • Kuo K, Liu C, Ma C. An investigation of the effect of nurses’ technology readiness on the acceptance of mobile electronic medical record systems. BMC Medical Informatics and Decision Making, 2013, 13(1). https://doi.org/10.1186/1472-6947-13-88
  • Shin S, Lee W. The effects of technology readiness and technology acceptance on nfc mobile payment services in korea. Journal of Applied Business Research (Jabr), 2014, 30(6), 1615. https://doi.org/10.19030/jabr.v30i6.8873
  • Yaykın H A, Tolay E. Teknolojik Hazır Bulunuşluğun Algılanan Çalışan Performansı Üzerindeki Etkisi: Otomotiv Sektöründe Bir Araştırma. Journal of Business in The Digital Age, 2023, 6(Özel Sayı), 57-65.
  • Lima E, Hopkins T, Gurney E, Shortall O, Lovatt F, Davies P, Kaler J. Drivers for precision livestock technology adoption: a study of factors associated with adoption of electronic identification technology by commercial sheep farmers in england and wales. Plos One, 2018, 13(1), e0190489. https://doi.org/10.1371/journal.pone.0190489
  • Roy S, Balaji M, Quazi A, Quaddus M. Predictors of customer acceptance of and resistance to smart technologies in the retail sector. Journal of Retailing and Consumer Services, 2018, 42, 147-160. https://doi.org/10.1016/j.jretconser.2018.02.005
  • Chen M, Lin N. Incorporation of health consciousness into the technology readiness and acceptance model to predict app download and usage intentions. Internet Research, 2018, 28(2), 351-373. https://doi.org/10.1108/intr-03-2017-0099
  • Şekkeli Z H. Dijital Dönüşüme Dair Algıların Teknolojiye Hazir Olma ve Kabul Modeli (TRAM) ile Analizi: Kahramanmaraş Sütçü İmam Üniversitesi MYO Öğrencileri Üzerinde Ampirik Bir Çalışma. Bilge Uluslararası Sosyal Araştırmalar Dergisi, 2022, 6(2), 78-89.
  • Anayat S, Rasool G, Pathania A. Examining the context‐specific reasons and adoption of artificial intelligence‐based voice assistants: a behavioural reasoning theory approach. International Journal of Consumer Studies, 2023, 47(5), 1885-1910. https://doi.org/10.1111/ijcs.12963
  • Wagner G, Raymond L, Paré G. Understanding prospective physicians’ intention to use artificial intelligence in their future medical practice: configurational analysis. Jmir Medical Education, 2023, 9, e45631. https://doi.org/10.2196/45631
  • Dwivedi Y, Rana N, Jeyaraj A, Clement M, Williams M. Re-examining the unified theory of acceptance and use of technology (utaut): towards a revised theoretical model. Information Systems Frontiers, 2017, 21(3), 719-734. https://doi.org/10.1007/s10796-017-9774-y
  • Teo T, Zhou M, Noyes J. Teachers and technology: development of an extended theory of planned behavior. Educational Technology Research and Development, 2016, 64(6), 1033-1052. https://doi.org/10.1007/s11423-016-9446-5
  • Chin J, Do C, Kim M. How to increase sport facility users’ intention to use ai fitness services: based on the technology adoption model. International Journal of Environmental Research and Public Health, 2022, 19(21), 14453. https://doi.org/10.3390/ijerph192114453
  • Ho Y, Alam S, Masukujjaman M, Lin C, Susmit S, Susmit, S. Intention to adopt ai-powered online service among tourism and hospitality companies. International Journal of Technology and Human Interaction, 2022, 18(1), 1-19. https://doi.org/10.4018/ijthi.299357
  • Liang Y, Lee S, Workman J. Implementation of artificial intelligence in fashion: are consumers ready?. Clothing and Textiles Research Journal, 2019, 38(1), 3-18. https://doi.org/10.1177/0887302x19873437
  • Li K, Li Y, Franklin T. Preservice teachers’ intention to adopt technology in their future classrooms. Journal of Educational Computing Research, 2016, 54(7), 946-966. https://doi.org/10.1177/0735633116641694
  • Cosmo L, Piper L, Vittorio A. The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing, 2021, (1-2), 83-102. https://doi.org/10.1007/s43039-021-00020-1
  • Levay K, Freese J, Druckman J. The demographic and political composition of mechanical turk samples. Sage Open, 2016, 6(1), 215824401663643. https://doi.org/10.1177/2158244016636433
  • Sonnenschein S, Stites M, Ross A. Home learning environments for young children in the u.s. during covid-19. Early Education and Development, 2021, 32(6), 794-811. https://doi.org/10.1080/10409289.2021.1943282
  • Kock N, Hadaya P. Minimum sample size estimation in pls‐sem: the inverse square root and gamma‐exponential methods. Information Systems Journal, 2016, 28(1), 227-261. https://doi.org/10.1111/isj.12131
  • Savalei V. A comparison of several approaches for controlling measurement error in small samples. Psychological Methods, 2019, 24(3), 352-370. https://doi.org/10.1037/met0000181
  • Siahaan A, Thiodore J. Analysis influence of consumer behavior to purchase organic foods in Jakarta, 2022, https://doi.org/10.2991/absr.k.220101.009
  • Gursoy D, Chi O H, Lu L, Nunkoo R. Consumers acceptance of artificially intelligent (AI) device use in service delivery. International Journal of Information Management, 2019, 49, 157-169.
  • Rese A, Schreiber S, Baier D. Technology acceptance modeling of augmented reality at the point of sale: can surveys be replaced by an analysis of online reviews?. Journal of Retailing and Consumer Services, 2014, 21(5), 869-876.
  • Taylor S, Todd P A. Understanding information technology usage: A test of competing models. Information systems research, 1995, 6(2), 144-176.
  • Ringle C M, Wende S, Becker J-M. SmartPLS 4. Oststeinbek: SmartPLS GmbH, 2022, http://www.smartpls.com.
  • Hair J, Risher J, Sarstedt M, Ringle C. When to use and how to report the results of pls-sem. European Business Review, 2019, 31(1), 2-24. https://doi.org/10.1108/ebr-11-2018-0203
  • Yuan K. Comments on the article “marketing or methodology? exposing the fallacies of pls with simple demonstrations” and pls-sem in general. European Journal of Marketing, 2023, 57(6), 1618-1625. https://doi.org/10.1108/ejm-07-2021-0472
  • Magno F, Cassia F, Ringle C. A brief review of partial least squares structural equation modeling (pls-sem) use in quality management studies. The TQM Journal. 2022, https://doi.org/10.1108/tqm-06-2022-0197
  • Buditjahjanto I. Analyzing factors of gui simulation as learning media toward students' learning outcomes. Journal of Technology and Science Education, 2022, 12(1), 83. https://doi.org/10.3926/jotse.1317
  • Khmeleva G, Kurnikova M, Nedelka E, Tóth B. Determinants of sustainable cross-border cooperation: a structural model for the hungarian context using the pls-sem methodology. Sustainability, 2022, 14(2), 893. https://doi.org/10.3390/su14020893
  • Hair Jr J, Hult G, Ringle C, Sarstedt M, Danks N, Ray S. An introduction to structural equation modeling. 2021, 1-29. https://doi.org/10.1007/978-3-030-80519-7_1
  • Prybutok G, Ta A, Liu X, Prybutok V. An integrated structural equation model of ehealth behavioral intention. International Journal of Healthcare Information Systems and Informatics, 2020, 15(1), 20-39. https://doi.org/10.4018/ijhisi.2020010102
  • Taber K S. The use of Cronbach’s alpha when developing and reporting research instruments in science education. Research in Science Education, 2018, 48, 1273-1296.
  • Eldrandaly K A, Naguib S M, Hassan M M. A model for measuring geographic information systems success. Journal of Geographic Information System, 2015, 7(4), 328.
  • Hair J F, Black W C, Babin B J, Anderson R E. Multivariate data analysis 2014, pp. 1–734. Eng: Pearson Education Limited.
  • Fornell C, Larcker D F. Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of Marketing Research, 1981, 18(3), 382–388.
  • Henseler J. Partial least squares path modeling; Advanced Methods for Modeling Markets, 2017, ss. 361-381. Springer.
  • Garson G D. Partial least squares. Regression and structural equation models. Statistical Publishing Associates. 2016.
  • Oemar H, Prasetyaningsih E, Bakar S, Djamaludin D, Septiani A. Awareness and intention to register halal certification of micro and small-scale food enterprises. F1000research, 2023, 11, 170. https://doi.org/10.12688/f1000research.75968.3
  • Razavi-Termeh S, Sadeghi-Niaraki A, Choi S. Spatial modeling of asthma-prone areas using remote sensing and ensemble machine learning algorithms. Remote Sensing, 2021, 13(16), 3222. https://doi.org/10.3390/rs13163222
  • Faisal C, Fernandez-Lanvin D, Andrés J, Gonzalez-Rodriguez M. Design quality in building behavioral intention through affective and cognitive involvement for e-learning on smartphones. Internet Research, 2020, 30(6), 1631-1663. https://doi.org/10.1108/intr-05-2019-0217
  • Ioannou A, Tussyadiah I. Privacy and surveillance attitudes during health crises: acceptance of surveillance and privacy protection behaviours. Technology in Society, 2021, 67, 101774. https://doi.org/10.1016/j.techsoc.2021.101774
  • Vargas P, González F, Landi V, Jurado J, Delgado-Bermejo J. Sexual dimorphism and breed characterization of creole hens through biometric canonical discriminant analysis across ecuadorian agroecological areas. Animals, 2019, 10(1), 32. https://doi.org/10.3390/ani10010032
  • Reddy C, Hamann R, Urban B. Country-level entrepreneurship: crowding out the population’s need for autonomy. Acta Commercii, 2015, 15(1). https://doi.org/10.4102/ac.v15i1.292
  • Khokhar A. What decides women entrepreneurship in india?. Journal of Entrepreneurship and Innovation in Emerging Economies, 2019, 5(2), 180-197. https://doi.org/10.1177/2393957519862465
  • Ye M, Hao F, Shahzad M, Kamran H. How green organizational strategy and environmental csr affect organizational sustainable performance through green technology innovation amid covid-19. Frontiers in Environmental Science, 2022, 10. https://doi.org/10.3389/fenvs.2022.959260
  • Fam S, Loh S, Musa H, Yanto H, Khoo L, Yong D. Overall equipment efficiency (oee) enhancement in manufacture of electronic components & boards industry through total productive maintenance practices. Matec Web of Conferences, 2018, 150, 05037. https://doi.org/10.1051/matecconf/201815005037
  • Garg N, Talukdar A, Ganguly A, Kumar C. Knowledge hiding in academia: an empirical study of indian higher education students. Journal of Knowledge Management, 2021, 25(9), 2196-2219. https://doi.org/10.1108/jkm-10-2020-0783
  • Park K, Koh C. Effect of change management capability in real-time environment: an information orientation perspective in supply chain management. Behaviour and Information Technology, 2014, 34(1), 94-104. https://doi.org/10.1080/0144929x.2014.945961
  • Otieno F, Gachohi J, Gikuma-Njuru P, Kariuki P, Oyas H, Canfield S, Blackburn J. Modeling the potential future distribution of anthrax outbreaks under multiple climate change scenarios for kenya. International Journal of Environmental Research and Public Health, 2021, 18(8), 4176. https://doi.org/10.3390/ijerph18084176
  • Luque-Vílchez M, Mesa-Pérez E, Husillos J, Larrinaga C. The influence of pro-environmental managers’ personal values on environmental disclosure. Sustainability Accounting Management and Policy Journal, 2019, 10(1), 41-61. https://doi.org/10.1108/sampj-01-2018-0016
  • Cohen J. Statistical power analysis for the behavioral sciences. 1988, 2nd Edition, Lawrence Erlbaum Associates, USA
  • Ali F, Amin M, Cobanoglu C. An integrated model of service experience, emotions, satisfaction, and price acceptance: An empirical analysis in the Chinese hospitality industry. Journal of Hospitality Marketing & Management, 2016, 25(4), 449-475.
  • Payre W, Cestac J, Delhomme P. Intention to use a fully automated car: attitudes and a priori acceptability. Transportation Research Part F Traffic Psychology and Behaviour, 2014, 27, 252-263. https://doi.org/10.1016/j.trf.2014.04.009
  • Staufenbiel T, König C. A model for the effects of job insecurity on performance, turnover intention, and absenteeism. Journal of Occupational and Organizational Psychology, 2010, 83(1), 101-117. https://doi.org/10.1348/096317908x401912
  • Nordhoff S, Winter J, Kyriakidis M, Arem B, Happee R. Acceptance of driverless vehicles: results from a large cross-national questionnaire study. Journal of Advanced Transportation, 2018, 1-22. https://doi.org/10.1155/2018/5382192
  • Salonen A, Haavisto N. Towards autonomous transportation. passengers’ experiences, perceptions and feelings in a driverless shuttle bus in finland. Sustainability, 2019, 11(3), 588. https://doi.org/10.3390/su11030588
  • Cugurullo F, Acheampong R, Guériau M, Dusparić I. The transition to autonomous cars, the redesign of cities and the future of urban sustainability. Urban Geography, 2020 42(6), 833-859. https://doi.org/10.1080/02723638.2020.1746096
Toplam 159 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Otonom Araç Sistemleri, Otomotiv Mühendisliği (Diğer)
Bölüm MBD
Yazarlar

Fatih Bilici 0000-0003-4803-0463

İbrahim Kürşad Türkoğlu 0000-0003-4627-4894

Yayımlanma Tarihi 28 Mart 2024
Gönderilme Tarihi 3 Kasım 2023
Kabul Tarihi 19 Mart 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 36 Sayı: 1

Kaynak Göster

APA Bilici, F., & Türkoğlu, İ. K. (2024). Autonomous Vehicle Technology and Technology Acceptance: The Role of Technological Readiness on Consumers’ Attitudes Towards Driverless Cars and Intention to Use in the Future. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 36(1), 383-407. https://doi.org/10.35234/fumbd.1385541
AMA Bilici F, Türkoğlu İK. Autonomous Vehicle Technology and Technology Acceptance: The Role of Technological Readiness on Consumers’ Attitudes Towards Driverless Cars and Intention to Use in the Future. Fırat Üniversitesi Mühendislik Bilimleri Dergisi. Mart 2024;36(1):383-407. doi:10.35234/fumbd.1385541
Chicago Bilici, Fatih, ve İbrahim Kürşad Türkoğlu. “Autonomous Vehicle Technology and Technology Acceptance: The Role of Technological Readiness on Consumers’ Attitudes Towards Driverless Cars and Intention to Use in the Future”. Fırat Üniversitesi Mühendislik Bilimleri Dergisi 36, sy. 1 (Mart 2024): 383-407. https://doi.org/10.35234/fumbd.1385541.
EndNote Bilici F, Türkoğlu İK (01 Mart 2024) Autonomous Vehicle Technology and Technology Acceptance: The Role of Technological Readiness on Consumers’ Attitudes Towards Driverless Cars and Intention to Use in the Future. Fırat Üniversitesi Mühendislik Bilimleri Dergisi 36 1 383–407.
IEEE F. Bilici ve İ. K. Türkoğlu, “Autonomous Vehicle Technology and Technology Acceptance: The Role of Technological Readiness on Consumers’ Attitudes Towards Driverless Cars and Intention to Use in the Future”, Fırat Üniversitesi Mühendislik Bilimleri Dergisi, c. 36, sy. 1, ss. 383–407, 2024, doi: 10.35234/fumbd.1385541.
ISNAD Bilici, Fatih - Türkoğlu, İbrahim Kürşad. “Autonomous Vehicle Technology and Technology Acceptance: The Role of Technological Readiness on Consumers’ Attitudes Towards Driverless Cars and Intention to Use in the Future”. Fırat Üniversitesi Mühendislik Bilimleri Dergisi 36/1 (Mart 2024), 383-407. https://doi.org/10.35234/fumbd.1385541.
JAMA Bilici F, Türkoğlu İK. Autonomous Vehicle Technology and Technology Acceptance: The Role of Technological Readiness on Consumers’ Attitudes Towards Driverless Cars and Intention to Use in the Future. Fırat Üniversitesi Mühendislik Bilimleri Dergisi. 2024;36:383–407.
MLA Bilici, Fatih ve İbrahim Kürşad Türkoğlu. “Autonomous Vehicle Technology and Technology Acceptance: The Role of Technological Readiness on Consumers’ Attitudes Towards Driverless Cars and Intention to Use in the Future”. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, c. 36, sy. 1, 2024, ss. 383-07, doi:10.35234/fumbd.1385541.
Vancouver Bilici F, Türkoğlu İK. Autonomous Vehicle Technology and Technology Acceptance: The Role of Technological Readiness on Consumers’ Attitudes Towards Driverless Cars and Intention to Use in the Future. Fırat Üniversitesi Mühendislik Bilimleri Dergisi. 2024;36(1):383-407.