Pánik próbája a mérés
Avagy önvezető technológiák elfogadásának valós idejű vizsgálata neurotudományi mérésekkel
DOI:
https://doi.org/10.14267/VEZTUD.2022.07.05Keywords:
self-driving vehicles, technology adoption, intention to use, neuroeconomicsAbstract
There is a broad international research interest in the study of consumer acceptance of self-driving technology. Most researchers use questionnaires based on different versions of TAM and UTAUT models to investigate this topic. However, the vast majority of respondents fill out the questionnaires, without any first-hand experience of self-driving technology. Addressing this limitation, the authors offered their participants a short test drive as passengers in a self-driving vehicle. In addition to the questionnaires, in the course of these trials they collected real-time electroencephalography (EEG) and eye movement data from each participant. A linear regression model revealed high explanatory power (97%), when physiological measurements were combined with a follow-up UTAUT-2 questionnaire. The results suggest that when surveys are combined with in real-time in-situ measurements, explanatory variables for technology adoption relate to experience and emotion. Neuroscientific measures may play an important role in detecting the latter.
Downloads
References
Abdur-Rahim, J., Morales, Y., Gupta, P., Umata, I., Watanabe, A., Even, J., Suyama, T., & Ishii, S. (2016). Multi-Sensor Based State Prediction for Personal Mobility Vehicles. PLOS ONE, 11(10), e0162593 1-29. https://doi.org/10.1371/journal.pone.0162593
Ajzen, I. (1991). The theory of planned behaviour. Organizational Behavior and Human Decision Processes, 50(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-T
Alvino, L. (2018). Consumer Neuroscience: New directions in predicitng consumers’ behavior and their preferences for product characteristics (PhD thesis). Molise, IT: University of Molise.
Ambrus I. (2019). Az autonóm járművek és a büntetőjogi felelősségre vonás akadályai. In Mezei Kitti (szerk.), A bűnügyi tudományok és az informatika (pp. 9-26). Budapest–Pécs: PTE ÁJK–MTA TK.
Arakawa, T., Hibi, R. & Taka-A, F. (2019). Psychological assessment of a driver’s mental state in autonomous vehicles. Transportation Research: Part A, 124, 587- 610. https://doi.org/10.1016/j.tra.2018.05.003
Ariely, D., & Berns, G. S. (2010). Neuromarketing: The hope and hype of neuroimaging in business. Science and Society, 11, 284-292. https://doi.org/10.1038/nrn2795
Baccarella, C.V., Wagner, T. F., Scheiner, C. W., Maier, L. & Voigt, K-I. (2020). Investigating consumer acceptance of autonomous technologies: the case of self-driving automobiles. European Journal of Innovation Management, 24(4), 1210-1323. https://doi.org/10.1108/EJIM-09-2019-0245
Banyár, J. (2019). Az önvezető autók lehetséges hatásai az életmódra és a gazdaságra. Polgári Szemle, 4(6), 132–152. https://doi.org/10.24307/psz.2019.1210
Barnett, S. B. & Cerf, M. (2017). A Ticket for your Thoughts: Method for Predicting Content Recall and Sales Using Neural Similarity of Moviegoers. Journal of Consumer Research, 44(1), 160-181. https://doi.org/10.1093/jcr/ucw083
Braun, R. (2020). A digitális (auto)mobilitás évtizedei. Vezetéstudomány, 51(1), 46-54. https://doi.org/10.14267/VEZTUD.2020.01.04
Bruce, A. S., Bruce, J. M., Black, W. M., Lepping, R. J., Henry, J. M., Cherry, J. B. C., Martin, L. E., Papa, V. B., Devis, A. M., Brooks, W. M. & Savage, C. R. (2014). Branding and a Child’s Brain: an fMRI study of neural responses to logos. Social Cognitive and Affective Neuroscience, 9(1), 188-122. https://doi.org/10.1093/scan/nss109
Cisler, D., Greenwood, P. M., Roberts, D. M., McKendrick, R., & Baldwin, C. L. (2019). Comparing the relative strenghts of EEG and low-cost physiological devices in modelling attention allocation in semi-autonomous vehicles. Frontiers in Human Neuroscience, 13(109), https://doi.org/10.3389/fnhum.2019.00109
Cohen, T., Stilgoe J., Stares S., Akyelken N., Cavoli C., Day J., Dickinson J., Fors V., Hopkins D., Lyons G., Marres N., Newman J., Reardon L., Sipe N., Tennant C., Wadud Z. & Wigley, E. (2020). A constructive role for social science in the development of automated vehicles. Transportation Research Interdisciplinary Perspectives, 6, 100133. https://doi.org/10.1016/j.trip.2020.100133
Cohen, T., Stilgoe, J. & Cavoli, C. (2018). Reframing the governance of automotive automation: insights from UK stakeholder workshops. Journal of Responsible Innovation, 5, 1-23. https://10.1080/23299460.2018.1495030
Csizmadia, P. (2017). Everett Rogers innovációs elmélete és annak felhasználási lehetőségei az egészségfejlesztésben. Egészségfejlesztés, 5(4), 50-58. https://doi.org/10.24365/ef.v58i4.208
Csizmadia, Z. (2019). Az autonóm, önvezető technológiák elterjedésének társadalmi következményei – Kérdések, dilemmák és szempontok. Tér Gazdaság Ember, 1, 59-86.
Csizmadia Z. & Rechnitzer J. (szerk.) (2021). Az önvezető járművek világa. Budapest: Akadémiai Kiadó.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 137-340. https://doi.org/10.2307/249008
EC (2019). Autonomous driving in European transport. Official Journal of the European Union, C(411), 2-12.
Fishbein, M. & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Boston, USA: Addison Wesley.
Ford, J. B. (2019). What do we know about neuromarketing. Journal of Advertising Research, 59(3), 257-258. https://doi.org/10.2501/JAR-2019-031
Gyimesi, Á. (2019). Az autonóm gépjárművek hatása a kormányzati költségvetésre és foglalkoztatásra. Tér Gazdaság Ember, 1, 137-158.
Gyollai, Á., Simor, P., Köteles, F., & Demetrovics, Z. (2011). Psychometric properties of the Hungarian ver- sion of the original and the short form of the Positive and Negative Affect Schedule (PANAS). Neuropsy- chopharmacologia Hungarica, 13(2), 73-79.
Harmon‐J., E., & Gable, P. A. (2018). On the role of asymmetric frontal cortical activity in approach and withdrawal motivation: An updated review of the evidence. Psychophysiology, 55(1), 1-23. https://doi.org/10.1111/psyp.12879
Hartikainen, K. M. (2021). Emotion-Attention Interaction in the Right Hemisphere. Brain Sciences, 11(8), 1-19. https://doi.org/10.3390/brainsci11081006
Hochman, M., Parmet, Y., & Oron-G, T. (2020). Pedestrian’s understanding of a fully autonomous vehicle’s intent to stop: A learning effect over time. Frontiers in Psychology, 11, 585280. https://doi.org/10.3389/fpsyg.2020.585280
Jun, G., & Smitha, K. G. (2016). EEG based stress level identification. In 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 003270– 003274. https://doi.org/10.1109/SMC.2016.7844738
Kapser, S., & Abdelrahman, M. (2020). Acceptance of autonomous delivery vehicles for last-mile delivery in Germany – Extending UTAUT-2 with risk perceptions. Transportation Research Part C, 111, 210-225. https://doi.org/10.1016/j.trc.2019.12.016
Kaur, K. & Rampersad, G. (2018). Trust in driverless cars: Investigating the key factors influencing the adoption of driverless cars. Journal of Engineering and Technology Management, 48, 87-96. https://doi.org/10.1016/j.jengtecman.2018.04.006
Kecskés, G. (2020). Az autonóm járművek jogi kérdéseinek nemzetközi kontextusa, különös tekintettel a környezetjogi vetületekre. Állam- és Jogtudomány, 61(4), 52-64.
Keszey, T. & Zsukk, J. (2017). Az új technológiák fogyasztói elfogadása. A magyar és nemzetközi szakirodalom áttekintése és kritikai értékelése. Vezetéstudomány, 48(10), 38-47. https://doi.org/10.14267/VEZTUD.2017.10.05
Keszey, T. (2020). Behavioural intention to use autonomous vehicles: Systematic review and empirical extension. Transportation Research Part C, 119, 1-16. https://doi.org/10.1016/j.trc.2020.102732
Khusbaba, R. N., Wise, C., Kodagoda, S., Louviere, J., Kahn, B. E. & Townsend, C. (2013). Consumer neuroscience: Assembling the brain response to marketing stimuli using electroencephalogram (EEG) and eye tracking. Expert Systems with Applications, 40(9), 3803-3812. https://doi.org/10.1016/j.eswa.2012.12.095
Kim, T.-Y., Ko, H., & Kim, S.-H. (2020). Data Analysis for Emotion Classification Based on Bio-Information in Self-Driving Vehicles. Journal of Advanced Transportation, 1–11. https://doi.org/10.1155/2020/8167295
Koul, S. & Eydgahi, A. (2018). Utilizing technology acceptance model (TAM) for driverless car technology adoption. Journal of Technology Management & Innovation, 13(4), 37-46. https://doi.org/10.4067/S0718-27242018000400037
KPMG (2018). Autonomous Vehicles Readiness Index. Assessing countries’ openness and preparedness for autonomous vehicles.
Kurdi, B., Lozano, S., & Banaji, M. R. (2017). Introducing the Open Affective Standardized Image Set (OASIS). Behavior Research Methods, 49(2), 457–470. https://doi.org/10.3758/s13428-016-0715-3
Lados, M. & Tóth, M., L. (2019). Autonóm járművek az okos városokban. Tér Gazdaság Ember, 1, 159-174.
Lee, J., & Yang, J. H. (2020). Analysis of Driver’s EEG Given Take-Over Alarm in SAE Level 3 Automated Driving in a Simulated Environment. International Journal of Automotive Technology, 21(3), 719–728. https://doi.org/10.1007/s12239-020-0070-3
Leicht, T., Chtourou, A. & Youssef, K. B. (2018). Consumer innovativeness and intentioned autonomous car adoption. Journal of High Technology Management Research, 29, 1-11. https://doi.org/10.1016/j.hitech.2018.04.001
Liu, P., Xu, Z., & Zhao, X. (2019). Road test of self-driving vehicles: Affective and cognitive pathways in acceptance formation. Transportation Research: Part A, 124, 354-369. https://doi.org/10.1016/j.tra.2019.04.004
Luck, S. J. (2014). An introduction to the event-related potential technique (Second edition). Cambridge, USA: The MIT Press.
Lukovics, M., Udvari, B., Zuti, B., & Kézy, B. (2018). Az önvezető autók és a felelősségteljes innováció. Közgazdasági Szemle, 65(9), 949-974. https://doi.org/10.18414/KSZ.2018.9.949
Madarász, N. & Szikora, P. (2018): Önvezető autók társadalmi elfogadottsága napjainkban. In. Csiszárik-Kocsir Á. & Garai-Fodor M. (szerk.), Vállalkozásfejlesztés a XXI. században (pp. 159-171). Budapest: Óbudai Egye- tem, Keleti Károly Gazdasági Kar.
Madigan, R., Louw, T., Wilbrink, M., Schieben, A. & Merat, N. (2017). What influences the decision to use automated public transport? Using UTAUT to understand public acceptance of automated road transport systems. Transportation Research Part F: Traffic Psychology and Behaviour, 50, 55-64. https://doi.org/10.1016/j.trf.2017.07.007
Majó-Petri, Z. & Huszár, S. (2020): Autonóm járművek, önvezető autók: mit gondol a közönség? Közlekedéstudományi Szemle, 70(1), 66-75. https://doi.org/10.24228/KTSZ.2020.1.2
Minguillon, J., Lopez-G., M. A., & Pelayo, F. (2016). Stress Assessment by Prefrontal Relative Gamma. Frontiers in Computational Neuroscience, 10, 1-9. https://doi.org/10.3389/fncom.2016.00101
Miskolczi, M., Ásványi, K., Jászberényi, M., & Kökény, L. (2021). Hogyan döntsön a mesterséges intelligencia? Az önvezető autók morális kérdései. Magyar Tudomány, 182(3), 342–352. https://doi.org/10.1556/2065.182.2021.3.6
Moták, L., Neuville, E., Chambres, P., Marmoint, F., Monéger, F., Coutarel, F. & Izaute, M. (2017). Antecedent variables of intentions to use an autonomous shuttle: Moving beyond TAM and TPB? European Review of Applied Psychology, 67(5), 269-278. https://doi.org/10.1016/j.erap.2017.06.001
Müller, J. M. (2019). Comparing Technology Acceptance for Autonomous Vehicles, Battery Electric Vehicles, and Car Sharing—A Study across Europe, China, and North America. Sustainability, 11(16), https://doi.org/10.3390/su11164333
Navarro, J., Francois, M., & Mars, F. (2016). Obstical avoidance under automated steering: Impact on driving and gaze behaviours. Transportation Research Part F: Traffic Psychology and Behaviour, 43, 315-324. https://doi.org/10.1016/j.trf.2016.09.007
Nordhoff, S., Louw, T., Innamaa, S. & Lehtonen, E. (2020). Using the UTAUT-2 model to explain public acceptance of conditionally automated (L3) cars: A questionaire study among 9,188 car drivers from eight European countires. Transportation Research Part F: Traffic Psychology and Behavior, 74, 280-297. https://doi.org/10.1016/j.trf.2020.07.015
Panagiotopoulos, I. & Dimitrakopoulos, G. (2018). An empirical investigation on consumers’ intentions towards autonomous driving. Transportation Research Part C: Emerging Technologies, 95, 773-784. https://doi.org/10.1016/j.trc.2018.08.013
Park, C. (2018). Using Electroencepalography and structured data collection techniques to measure passenger emotional response in human-auonomous vehicle interactions. Florida, USA: Florida Atlantic University.
Park, C., Shahrdar, S., & Nojoumian, M. (2018). EEG- Based Classification of Emotional State Using an Autonomous Vehicle Simulator. In 2018 IEEE 10th Sensor Array and Multichannel Signal Processing Workshop (SAM) (pp. 297–300). IEEE. https://doi.org/10.1109/SAM.2018.8448945
Páthy, Á. (2021). Kényelem és félelem – Az önvezető járművek várható előnyeinek és hátrányainak megítélése. In Csizmadia, Z. & Rechnitzer, J. (szerk.), Az önvezető járművek világa. Akadémiai Kiadó, Budapest. https://doi.org/10.1556/9789634546290
Pelsőci, B., L., Nagy, Á. & Gáti, M. (2021). Az értékesítés digitális átalakulása – Az egyéni és szervezeti technológiaelfogadást meghatározó tényezők empirikus elemzése. Vezetéstudomány, 52(10), 14-27. https://doi.org/10.14267/VEZTUD.2021.10.02
Pozharliev, R. I. (2017). Social Neuromarketing: The role of social context in measuring advertising effectiveness (PhD Thesis). Rotterdam: Erasmus University.
Raue, M., D’Ambrosio, L. A., Ward, C., Lee, C., Jacquillat, C. & Coughlin, J. F. (2019). The influence of feelings while driving regular cars on the perception and acceptance of self-driving cars. Risk Analysis, 39(2), 358-374. https://doi.org/10.1111/risa.13267
Rogers, E. (2003). Diffusion of Innovations. New York, USA: Simon and Schuster.
Seet, M., Harvy, J., Bose, R., Dragomir, A., Bezerianos, A., & Thakor, N. (2022). Differential Impact of Autonomous Vehicle Malfunctions on Human Trust. IEEE Transactions on Intelligent Transportation Systems, 23(1), 548–557. https://doi.org/10.1109/TITS.2020.3013278
Semenova, V. (2020). Technológiaadaptációs elméletek a blokklánc-technológia elterjedésének vizsgálatakor a funkcionalista és interpretatív paradigmák keretében. Vezetéstudomány, 51(11), 26-38. https://doi.org/10.14267/VEZTUD.2020.11.03
Smahó, M. (2021): Autonóm járművek a jövő városában. In Csizmadia, Z. & Rechnitzer, J. (szerk.), Az önvezető járművek világa. Budapest: Akadémiai Kiadó. https://doi.org/10.1556/9789634546290
Smyth, J., Chen, H., Donzella, V. & Woodman, R. (2021). Public acceptance of driver state monitoring for automated vehicles: Applying the UTAUT framework.
Transportation Research Part F: Psychology and Behaviour, 83, 179-191. https://doi.org/10.1016/j.trf.2021.10.003
Stephenson, A. C., Eimontaite, I., Caleb-S., P., Morgan, P. L., Khatun, T., Davis, J., & Alford, C. (2020). Effects of an unexpected event on older adults’ autonomic arousal and eye fixation during autonomous driving. Frontiers in Psychology, 11, 571961. https://doi.org/10.3389/fpsyg.2020.571961
Strauch, C., Mühl, K., Patro, K., Grabmaier, C., Reithinger, S., Baumann, M. & Huckauf, A. (2019). Real autonomous driving from a pessenger’s perspective: Two experimental investigations using gaze behaviour and trust ratings in field and simulator. Transportation Research: Part F, 66, 15-28. https://doi.org/10.1016/j.trf.2019.08.013
Sun, L., Peräkylä, J., & Hartikainen, K. M. (2017). Frontal Alpha Asymmetry, a Potential Biomarker for the Effect of Neuromodulation on Brain’s Affective Circuitry— Preliminary Evidence from a Deep Brain Stimulation Study. Frontiers in Human Neuroscience, 11, 1-9. https://doi.org/10.3389/fnhum.2017.00584
Szemerédi, E. (2019). Autonóm járművek – Biztonság, használat és észlelt hasznosság. Tér Gazdaság Ember, 1, 111-136.
van der Heiden, R. M. A., Janssen, C. P., Donker, S. F., Hardeman, L. E. S., Mans, K., & Kenemans, J. L. (2018). Susceptibility to audio signals during autonomous driving. Plos One. https://doi.org/10.1371/journal.pone.0201963
Venkatesh, V. & Bala, H. (2008). Technology Acceptance Model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273-315. https://doi.org/10.1111/j.1540-5915.2008.00192.x
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204. https://doi.org/10.1287/mnsc.46.2.186.11926
Venkatesh, V., Morris, M. G., Davis, G. B. & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478. https://doi.org/10.2307/30036540
Venkatesh, V., Thong, J. Y. L. & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157-178. https://doi.org/10.2307/41410412
Venkatraman, V., Dimoka, A., Pavlou, P. A., Vo, K., Hampton, W., Bollinger, B., Hershfield, H. E., Ishihara, M. & Winer, R. S. (2015). Predicting Advertising Success Beyond Traditional Measures: New Insights from Neurophysiological Methods and Market Response Modeling. Journal of Marketing Research, 52(4), 436-452. https://doi.org/10.1509/jmr.13.0593
Vorster, I. A. (2015). The Influence Of Sonic Logos In Television Advertisements: A Neuromarketing Perspective. Stellenbosch, RSA: Stellenbosch University.
Wintersberger, P., Riener, A., & Frison, A. K. (2016). Automated Driving System, Male, or Female Driver: Who’d You Prefer? Comparative Analysis of Passengers’ Mental Conditions, Emotional States & Qualitative Feedback. In Proceedings of the 8th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (pp. 51–58). New York: Association for Computing Machinery. https://doi.org/10.1145/3003715.3005410
Wu, J., Liao, H., Wang, J. W. & Chen T. (2019). The role of environmental concern in the public acceptance of autonomous electric vehicles: A survey from China. Transportation Research Part F: Traffic Psychology and Behaviour, 60, 37-46. https://doi.org/10.1016/j.trf.2018.09.029
Xu, Z., Jiang, Z., Wang, G., Wang, R., Li, T., Liu, J., Zhang, Y., & Liu, P. (2021). When the automated driving system fails: Dynamics of public responses to automated vehicles. Transportation Research Part C: Emerging Technologies, 129, 103271. https://doi.org/10.1016/j.trc.2021.103271
Yang, L., Rui, M., Zhang, H., Wei, G., & Jiang, S. (2018). Driving behavior recognition using EEG data from a simulated car-following experiment. Accident Analysis & Prevention, 116, 30-40. https://doi.org/10.1016/j.aap.2017.11.010
Yi W., T., & Mohd A., S. A. (2020). Electroencephalogram (EEG) stress analysis on alpha/beta ratio and theta/ beta ratio. Indonesian Journal of Electrical Engineering and Computer Science, 17(1), 175. https://doi.org/10.11591/ijeecs.v17.i1.pp175-182
Yoon, C., Gutchess, A. H., Feinberg, F. & Polk, T. A. (2006). A functional magnetic resonance imaging study of neural dissociations between brand and personal judgments. Journal of Consumer Research, 33(1), 31-40. https://doi.org/10.1086/504132
Zhang, S., Jing, P. & Xu, G. (2021). The Acceptance of Independent Autonomous Vehicles and Cooperative Vehicle-Highway Autonomous Vehicles. Information, 12(9), 346. https://doi.org/10.3390/info12090346
Zoellick, J. C., Kuhlmey, A., Schenk, L., Schindel, D., & Blüher, S. (2019). Amused, accepted, and used? Attitudes and emotions towards automated vehicles, their relationships, and predictive value for usage intention. Transportation Research Part F: Traffic Psychology and Behaviour, 65, 68–78. https://doi.org/10.1016/j.trf.2019.07.009
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 Vezetéstudomány / Budapest Management Review
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors assign copyright to Vezetéstudomány / Budapest Management Review. Authors are responsible for permission to reproduce copyright material from other sources.
