跳到主要內容

臺灣博碩士論文加值系統

(44.201.97.138) 您好!臺灣時間:2024/09/08 03:59
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

: 
twitterline
研究生:艾沙立
研究生(外文):M. ASHARI FITRA RACHMANNULLAH
論文名稱:公眾使用自主渡輪的意願:計畫行為理論的 延伸
論文名稱(外文):The Public’s Intention to Use Autonomous Ferries: An Extension of Theory Planned Behaviour
指導教授:林泰誠林泰誠引用關係
指導教授(外文):LIRN, TAIH-CHERNG
口試委員:呂錦山邱榮和王蕙芝桑國忠林泰誠
口試委員(外文):LU, CHIN-SHANCHIU, RONG-HERWANG, HUI-CHIHSHANG, KUO-CHUNGLIRN, TAIH-CHERNG
口試日期:2024-04-19
學位類別:博士
校院名稱:國立臺灣海洋大學
系所名稱:航運管理學系
學門:運輸服務學門
學類:運輸管理學類
論文種類:學術論文
論文出版年:2024
畢業學年度:112
語文別:英文
論文頁數:83
中文關鍵詞:計劃行為理論感知知識結構方程模型自主渡輪
外文關鍵詞:Theory of Planned BehaviourPerceived KnowledgeStructural Equation ModellingAutonomous Ferry
ORCID或ResearchGate:https://orcid.org/0000-0001-7915-9679
相關次數:
  • 被引用被引用:0
  • 點閱點閱:10
  • 評分評分:
  • 下載下載:3
  • 收藏至我的研究室書目清單書目收藏:0
先進技術在海事產業的應用帶來了渡輪運輸的各種創新。自主渡輪的引入有可能徹底改變渡輪市場。自主渡輪秉承可持續發展的精神,預計在運營過程中將比傳統渡輪更具成本效益和安全性。因此,一些國家的渡輪運營商已開始在各自的水道上運行自主渡輪,以促進河岸間的乘客運輸。這項創新技術超乎想象地有能力改變傳統的渡輪運營商業模式。自主渡輪的出現為市場帶來了顛覆性的機會,影響了收入模式、價值主張和現有渡輪業務實踐中的價值鏈重整。然而,鑑於自主渡輪是為乘客通勤而設計的,因此有必要考慮潛在乘客的觀點。

本研究深入探討了自主渡輪的發展軌跡,特別側重於了解公眾對其的看法。目的是制定政策並解決自主渡輪早期在現有航線實施時遭遇的實際問題,這些政策可作為對政府或自主渡輪運營商的建議。該研究的對象是印尼民眾,這些民眾認識到渡輪對該國的物流和乘客運輸至關重要。問卷通過便利抽樣和社交媒體在線分發,共收到 500 名受訪者的回應。該研究檢視了將感知知識擴展到計劃行為理論(TPB),以理解公眾對自主渡輪使用意圖的看法。結構方程模型(SEM)用於檢驗提出的假設。

結果表明,主觀規範和態度顯著影響印尼人使用自主渡輪的意圖。此外,感知知識的擴展通過態度變數間接影響意圖,但未能直接影響意圖。此外,男性和女性在使用自主渡輪的意圖方面沒有顯著差異。

根據這些結果,本研究建議在自主渡輪引入的初期階段,制定旨在提高對於自駕船的認知和參與度的管理和營銷策略。此外,本研究還指出自身的研究限制並對這個不斷發展的領域中的未來研究機會提出建議。
The application of advanced technology in the maritime industry have brought forth
various innovations in ferry transportation. The introduction of autonomous ferries in the maritime transportation service industry holds the potential to revolutionize the ferry market. Embracing the spirit of sustainability, autonomous ferries are anticipated to offer cost-efficiency and enhanced safety compared to traditional ferries during operation. As a result, ferry operators in several countries have begun operating them in their respective waterways to facilitate passenger transportation across riverbanks. This innovative technology possesses the capacity to reshape the conventional business model of ferry operations beyond imagination. The advent of autonomous ferries presents opportunities for disrupting market, affecting revenue models, value propositions, and value chain configurations within existing ferry business practices. However, given that autonomous ferries are designed for passenger commuting, it is imperative to consider the perspective of potential passengers.

This study delves into the ongoing development of autonomous ferries, particularly
focusing on understanding the public's perspective on them. The aim is to develop policies and address practical concerns for the early implementation of autonomous ferries into existing routes, which can serve as recommendations to the government or autonomous ferry operators. The study targets Indonesian individuals, recognizing that ferries are crucial for logistics and passenger movement in the country. The questionnaire was distributed online using convenience sampling and social media, garnering responses from a total of 500 respondents. The study examines the extension of perceived knowledge to the Theory of Planned Behaviour (TPB) to comprehend the public's intent regarding autonomous ferries using intention. Structural Equation Modelling (SEM) was conducted to test the proposed hypotheses.

The results indicate that subjective norm and attitude significantly influence the intention to use autonomous ferries among Indonesian individuals. Moreover, the extension of perceived knowledge indirectly affects intention through the attitude variable but fails to directly influence intention. Additionally, there is no significant difference between males and females regarding their intention to use autonomous ferries.

In light of these results, the study recommends managerial and marketing strategies aimed at raising awareness and engagement with autonomous ferries during the initial stages of introduction. Additionally, the study identifies limitations and suggests opportunities for future research in this evolving field
Acknowledgment i
摘要 ii
Abstract iii
List of Table v
List of Figure vi
Table of Content vii
Chapter 1 Introduction 1
1.1. Research Background and Motivation 1
1.2. Research Problem 3
1.2.1. Purpose 4
1.2.2. Research Questions 4
1.3. Research Structure 5
Chapter 2 Literature Review 6
2.2. Ferry Transportation 6
2.3. Autonomy in Ship Technology 10
2.4. Knowledge Perception of the Autonomous Ferry 13
2.5. Theories in Adopting an Innovation 15
2.5.1. Diffusion of Innovation Theory 16
2.5.2. Theory of Acceptance Model 17
2.5.3. Theory of Planned Behaviour 18
2.6. Applying an Extended Theory of Planned Behaviour in Adoption of Autonomous Ferry 20
Chapter 3 Methodology 22
3.1. Proposed Model and Hypotheses Development 22
3.1.1. Subjective Norm Concerning Intention to Use Autonomous Ferry 23
3.1.2. Attitude Towards Intention to Use Autonomous Ferry 24
3.1.3. Perceived Behaviour Control in Intention to Use Autonomous Ferry 24
3.1.4. Perceived Knowledge 25
3.2. Survey Design and Sampling 26
Chapter 4 Data Analysis 28
4.1. Respondent’s Data Analysis 28
4.2. Measurement Model Analysis 33
4.3. Structural Model Analysis and Hypothesis Test 35
4.4. Discussion of Findings 37
4.5. Theoretical and Practical Implication 39
Chapter 5 Conclusion and Suggestion 41
5.1. Conclusion 41
5.2. Limitation and Future Research 42
References 44
Appendix A 62
Appendix B 64
Appendix C 68
Abdullah, S. I. N. W., Samdin, Z., Ho, J. A., & Ng, S. I. (2020). Sustainability of marine parks: Is knowledge–attitude–behaviour still relevant? Environment, Development and Sustainability, 22, 7357–7384.
Adu-Gyamfi, G., Song, H., Nketiah, E., Obuobi, B., Adjei, M., & Cudjoe, D. (2022). Determinants of adoption intention of battery swap technology for electric vehicles. Energy, 251.
Ahmad, A., Madi, Y., Abuhashesh, M., Nusairat, N. M., & Masa’deh, R. (2020). The knowledge, attitude, and practice of the adoption of green fashion innovation. Journal of Open Innovation: Technology, Market, and Complexity 2020, Vol. 6, Page 107, 6, 107.
Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In Action control (pp. 11–39).
Ajzen, I. (1991). The theory of planned behavior. In Organizational Behavior and Human Decision Processes (Vol. 50, pp. 438–459).
Ajzen, I. (2012). The theory of planned behavior. In Handbook of Theories of Social Psychology: Volume 1 (Vol. 50, pp. 438–459).
Ajzen, I., Fishbein, M., & Heilbroner, R. L. (1980). Understanding attitudes and predicting social behavior (Vol. 278). Prentice-hall Englewood Cliffs, NJ.
Ajzen, I., Joyce, N., Sheikh, S., & Cote, N. G. (2011). Knowledge and the prediction of behavior: The role of information accuracy in the theory of planned behavior. Basic and Applied Social Psychology, 33, 101–117.
Alba, J. W., & Hutchinson, J. W. (2000). Knowledge calibration: What consumers know and what they think they know. Journal of Consumer Research, 27, 123–156.
Allal, A. A., Mansouri, K., Youssfi, M., & Qbadou, M. (2018). Toward energy saving and environmental protection by implementation of autonomous ship. 2018 19th IEEE Mediterranean Electrotechnical Conference (MELECON).
Al-Zoubi, M. I. (2013). Predicting e-business adoption through integrating the constructs of the rogers’s diffusion of innovation theory combined with technology-organization-environment model. International Journal of Advanced Computer Research, 3, 2277–7970.
Awa, H. O., Ukoha, O., & Emecheta, B. C. (2016). Using T-O-E theoretical framework to study the adoption of ERP solution. Cogent Business and Management, 3, 1–23.
Baird, A. J. (2000). The Japan coastal ferry system. Maritime Policy & Management, 27, 3–16.
Bansal, P., Kockelman, K. M., & Singh, A. (2016). Assessing public opinions of and interest in new vehicle technologies: An Austin perspective. Transportation Research Part C: Emerging Technologies, 67, 1–14.
Bimbraw, K. (2015). Autonomous cars: past, present and future. 2015 12th International Conference on Informatics in Control, Automation and Robotics (ICINCO), 191–198.
Borhan, M. N., Ibrahim, A. N. H., & Miskeen, M. A. A. (2019). Extending the theory of planned behaviour to predict the intention to take the new high-speed rail for intercity travel in Libya: Assessment of the influence of novelty seeking, trust and external influence. Transportation Research Part A: Policy and Practice, 130, 373–384.
BPS-Statistics Indonesia. (2019). Sea transportation statistics. (Subdirectorate of Transportation Statistics, Ed.). Jakarta: BPS-Statistics Indonesia.
Bruzzone, A. (2012). Guidelines for ferry transportation services. Washington, D.C.: National Academies Press. https://doi.org/10.17226/14644
Buckley, L., Kaye, S. A., & Pradhan, A. K. (2018). Psychosocial factors associated with intended use of automated vehicles: A simulated driving study. Accident Analysis and Prevention, 115, 202–208.
Burton-Jones, A., & Hubona, G. S. (2006). The mediation of external variables in the technology acceptance model. Information & Management, 43, 706–717.
Byrne, B. M. (2016). Structural equation modeling with AMOS. Structural Equation Modeling With AMOS. Routledge.
Calantone, R. J., Chan, K., & Cui, A. S. (2006). Decomposing product innovativeness and its effects on new product success. Journal of Product Innovation Management, 23, 408–421.
Charness, N., Yoon, J. S., Souders, D., Stothart, C., & Yehnert, C. (2018). Predictors of attitudes toward autonomous vehicles: The roles of age, gender, prior knowledge, and personality. Frontiers in Psychology, 9.
Chen, C. Der, Fan, Y. W., & Farn, C. K. (2007). Predicting electronic toll collection service adoption: An integration of the technology acceptance model and the theory of planned behavior. Transportation Research Part C: Emerging Technologies, 15, 300–311.
Cheng, E. W. L. (2019). Choosing between the theory of planned behavior (TPB) and the technology acceptance model (TAM). Educational Technology Research and Development, 67, 21–37.
Choi, J. K., & Ji, Y. G. (2015). Investigating the importance of trust on adopting an autonomous vehicle. International Journal of Human-Computer Interaction, 31, 692–702.
Chuttur, M. (2009). Overview of the technology acceptance model: origins , developments and future directions. Sprouts: Working Papers on Information Systems, 9, 1–23.
Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16, 297–334.
Czaja, S. J., Charness, N., Fisk, A. D., Hertzog, C., Nair, S. N., Rogers, W. A., & Sharit, J. (2006). Factors predicting the use of technology: Findings from the Center for Research and Education on Aging and Technology Enhancement (CREATE). Psychology and Aging, 21, 333–352.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35, 982–1003.
Dieplinger, M., & Fürst, E. (2014). The acceptability of road pricing: Evidence from two studies in Vienna and four other European cities. Transport Policy, 36, 10–18.
Ding, Y., Li, R., Wang, X., & Schmid, J. (2022). Heterogeneity of autonomous vehicle adoption behavior due to peer effects and prior-AV knowledge. Transportation, 49, 1837–1860.
Donald, I. J., Cooper, S. R., & Conchie, S. M. (2014). An extended theory of planned behaviour model of the psychological factors affecting commuters’ transport mode use. Journal of Environmental Psychology, 40, 39–48.
Drucker, P. F. (1985). Innovation and entrepreneurship practice and principles.
Du, H., Liu, D., Sovacool, B. K., Wang, Y., Ma, S., & Li, R. Y. M. (2018). Who buys new energy vehicles in China? Assessing social-psychological predictors of purchasing awareness, intention, and policy. Transportation Research Part F: Traffic Psychology and Behaviour, 58, 56–69.
Fahruddin, H. E. (2021, December 1). Tourism faces PPKM level 3 during Christmas, New Year holiday. Retrieved March 23, 2024, from https://www.thejakartapost.com/paper/2021/11/30/tourism-faces-ppkm-level-3-during-christmas-new-year-holiday.html
Fishbein, M., & Ajzen, I. (1977). Belief, attitude, intention, and behavior: An introduction to theory and research.
Focus Taiwan. (2023, April 18). New cargo-passenger vessel to operate Taiwan-Matsu route. Retrieved February 23, 2024, from https://ocacnews.net/article/337778
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and Measurements Error. Journal of Marketing Research, XVIII, 39–50.
Garver, M. S., & Mentzer, J. T. (1999). Logistics research methods: employing structural equation modeling to test for construct validity. Journal of Business Logistics, 20, 33–57.
Goerlandt, F., & Pulsifer, K. (2022). An exploratory investigation of public perceptions towards autonomous urban ferries. Safety Science, 145, 105496.
Gupta, N., Fischer, A. R. H., & Frewer, L. J. (2012). Socio-psychological determinants of public acceptance of technologies: A review. Public Understanding of Science, 21, 782–795.
Haboucha, C. J., Ishaq, R., & Shiftan, Y. (2017). User preferences regarding autonomous vehicles. Transportation Research Part C: Emerging Technologies, 78, 37–49.
Hair, J.F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate data analysis with readings (5nd ed.). Prentice-Hall, Upper Saddle River.
Hair, Joseph F., Black, W. C., Babin, B. J., & Anderson, R. E. (2014). Multivariate data analysis.
Ham, M., Jeger, M., & Frajman Ivković, A. (2015). The role of subjective norms in forming the intention to purchase green food. Economic Research-Ekonomska Istraživanja, 28, 738–748.
Han, H. (2015). Travelers’ pro-environmental behavior in a green lodging context: Converging value-belief-norm theory and the theory of planned behavior. Tourism Management, 47, 164–177.
Heath, Y. Y., & Gifford, R. (2002). Extending the theory of planned behavior: Predicting the use of public transportation. Journal of Applied Social Psychology, 32, 2154–2189.
Herno Della, R., & Rachmannullah, A. F. (2021). Perspektif kepuasan penumpang dalam kualitas pelayanan kapal feri: Studi kasus pelabuhan penyeberangan Merak-Bakauheni. Cantilever: Jurnal Penelitian Dan Kajian Bidang Teknik Sipil, 10, 1–9.
Horiuchi, S., & Jin, T. (1982). Human Behavior. In National Bureau of Standards, Special Publication (Vol. 55, pp. 26–30).
Hsu, W. K. K., Chen, J. W., Huynh, N. T., & Lin, Y. Y. (2022). Risk assessment of navigation safety for ferries. Journal of Marine Science and Engineering, 10.
IMO. (2019). Autonomous shipping. Retrieved December 13, 2019, from http://www.imo.org/en/MediaCentre/HotTopics/Pages/Autonomous-shipping.aspx
Jackson, D. L. (2003). Revisiting sample size and number of parameter estimates: Some support for the N:q hypothesis. Structural Equation Modeling, 10, 128–141.
Jan Rødseth, Ø. (2017). Definitions for autonomous merchant ships NFAS Norwegian forum for autonomous ships document information title definition for autonomous merchant ships.
japan-guide.com. (2018). Japanese ferries (Domestic routes). Retrieved September 3, 2018, from https://www.japan-guide.com/e/e2355.html
Jing, P., Huang, H., Ran, B., Zhan, F., & Shi, Y. (2019). Exploring the factors affecting mode choice intention of autonomous vehicle based on an extended theory of planned behavior-A case study in China. Sustainability (Switzerland), 11, 1–20.
Joe, T., Bessant, J., & Pavitt, K. (2005). Managing innovation: integrating technological, market and organizational change. John Wiley & Sons.
Jones, M. L. (2007). Hofstede-culturally questionable? Oxford Business & Economics Conference.
Joseph, R. (2006). Calculated risks: The toxicity and human health risks of chemicals in our environment. News.Ge.
Kaplan, S. (1991). Beyond rationality: Clarity-based decision making. Environment, Cognition, and Action: An Integrated Approach, 171–190.
Kassaw, C., Pandey, · Digvijay, Pandey, D., & Abdul, A. P. J. (2020). COVID-19 pandemic related to anxiety disorder among communities using public transport at Addis Ababa, Ethiopia, March 2020: Cross-sectional Study Design. Human Arenas.
Kaye, S. A., Lewis, I., Buckley, L., & Rakotonirainy, A. (2020). Assessing the feasibility of the theory of planned behaviour in predicting drivers’ intentions to operate conditional and full automated vehicles. Transportation Research Part F: Traffic Psychology and Behaviour, 74, 173–183.
Kaye, S. A., Lewis, I., Forward, S., & Delhomme, P. (2020). A priori acceptance of highly automated cars in Australia, France, and Sweden: A theoretically-informed investigation guided by the TPB and UTAUT. Accident Analysis and Prevention.
Kinantya, P. (2022, April 19). Rencana implementasi sarana angkutan umum autonomous berbasis energi listrik di wilayah ibu kota negara baru. Retrieved October 29, 2022, from https://baketrans.dephub.go.id/berita/rencana-implementasi-sarana-angkutan-umum-autonomous-berbasis-energi-listrik-di-wilayah-ibu-kota-negara-baru
Kline, R. B. (2015). Principles and practices of structural equation modelling. Methodology in the social sciences (4th ed.).
Koufteros, X. A. (1999). Testing a model of pull production: a paradigm for manufacturing research using structural equation modeling. Journal of Operations Management, 17, 467–488.
Kurniawan, A., Hutapea, G., Hardianto, S., Suhartana, I. K., Yuliani, A., Putra, T. P., Siahaan, W. J., Hidayat, K., Humang, W. P., Paotonan, C., & Paroka, D. (2022). Finding a new home: rerouting of ferry ships from Merak–Bakauheni to east Indonesian trajectories. Sustainability, 15, 630.
Lai, M. F., & Lo, H. K. (2004). Ferry service network design: optimal fleet size, routing, and scheduling. Transportation Research Part A: Policy and Practice, 38, 305–328.
Laurinen, M. (2016). Remote and autonomous ships: The next steps. AAWA: Advanced Autonomous Waterborne Applications.
Lawson, C., & Weisbrod, R. (2005). Ferry transport: The realm of responsibility for ferry disasters in developing nations. Journal of Public Transportation, 8, 17–31.
Legris, P., Ingham, J., & Collerette, P. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information & Management, 40, 191–204.
Levander, O. (2017). Autonomous ships on the high seas. IEEE Spectrum, 54, 26–31.
Li, M., & Zhao, J. (2019). Gaining acceptance by informing the people? Public knowledge, attitudes, and acceptance of transportation policies. Journal of Planning Education and Research, 39, 166–183.
Liao, C., Yu, H., & Zhu, W. (2020). Perceived knowledge, coping efficacy and consumer consumption changes in response to food recall. Sustainability, 12, 2696.
Liao, F., Molin, E., Timmermans, H., & van Wee, B. (2019). Consumer preferences for business models in electric vehicle adoption. Transport Policy, 73, 12–24.
Liljamo, T., Liimatainen, H., & Pöllänen, M. (2018). Attitudes and concerns on automated vehicles. Transportation Research Part F: Traffic Psychology and Behaviour, 59, 24–44.
Lin, S. H., Lee, H. C., Chang, C. Ter, & James Fu, C. (2020). Behavioral intention towards mobile learning in Taiwan, China, Indonesia, and Vietnam. Technology in Society, 63, 101387.
Lirn, T.-C., Rachmannullah, A. F., & Della, R. H. (2023). Autonomous ship: Review of concept, definition, and challenging. In AIP Conference Proceedings (Vol. 2689).
Lu, C.-S., & Tseng, P.-H. (2012). Identifying crucial safety assessment criteria for passenger ferry services. Safety Science, 50, 1462–1471.
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.
Maritime Port Bureau. (2024, February 21). The maritime port bureau has achieved extraordinary results in 2023 and looks forward to achieving four major goals in 2024. Retrieved February 23, 2024, from https://www.motcmpb.gov.tw/En/Information/Detail/d790658f-53ab-4a10-8147-7e4f2117203e?SiteId=2&NodeId=10014
Marsh, H. W., Hau, K.-T., & Wen, Z. (2009). Structural equation modeling in search of golden rules: Comment on hypothesis-testing approaches to setting cut off values for fit indexes and dangers in overgeneralizing Hu and Bentler’s (1999) findings.
Mathew, S. (2021, May 27). Ferry planned for south Taiwan-Penghu route to transport 80 cars, 4 buses. Retrieved February 23, 2024, from https://www.taiwannews.com.tw/en/news/4211310
Mathieson. (1991). Comparing the technology acceptance model with the theory of planned behaviour. Information Systems Research, 2, 3, 173–191.
Merriam-Webster. (n.d.). Ferry. Retrieved February 20, 2024, from https://www.merriam-webster.com/dictionary/ferry
Min, S., So, K. K. F., & Jeong, M. (2019). Consumer adoption of the Uber mobile application: Insights from diffusion of innovation theory and technology acceptance model. Journal of Travel and Tourism Marketing, 36, 770–783.
Ministry of Transportation. (2015). Strategic plan ministry of transport 2015-2019.
Ministry of Transportation. (2020). Indonesian transportation statistic 2020 Vol.1. Ministry of Transportation. Pustikom-Kementrian Perhubungan.
MOL. (2022). MOL and partners set world records for time and distance in autonomous navigation with sea trial using large commercial car ferry - Follows successful trial of coastal containership in autonomous sailing - | Mitsui O.S.K. Lines. Retrieved June 8, 2022, from https://www.mol.co.jp/en/pr/2022/22017.html
Moons, I., & De Pelsmacker, P. (2015). An extended decomposed theory of planned behaviour to predict the usage intention of the electric car: A multi-group comparison. Sustainability, 7, 6212–6245.
Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2, 192–222.
Moták, L., Neuville, E., Chambres, P., Marmoiton, F., Monéger, F., Coutarel, F., & Izaute, M. (2017). Antecedent variables of intentions to use an autonomous shuttle: Moving beyond TAM and TPB? Revue Europeenne de Psychologie Appliquee, 67, 269–278.
Mun, Y. Y., Jackson, J. D., Park, J. S., & Probst, J. C. (2006). Understanding information technology acceptance by individual professionals: Toward an integrative view. Information & Management, 43, 350–363.
Munim, Z. H. (2019). Autonomous ships: a review, innovative applications and future maritime business models. Supply Chain Forum: An International Journal, 20, 266–279.
MUNIN. (n.d.). The autonomous ship | MUNIN. Retrieved December 13, 2019, from http://www.unmanned-ship.org/munin/about/the-autonomus-ship/
Nordhoff, S., De Winter, J., Kyriakidis, M., Van Arem, B., & Happee, R. (2018). Acceptance of driverless vehicles: Results from a large cross-national questionnaire study. Journal of Advanced Transportation, 2018.
Odonkor, S. T., & Adams, S. (2020). An assessment of public knowledge, perception and acceptance of nuclear energy in Ghana. Journal of Cleaner Production, 269.
OECD. (2017). Public acceptance and emerging production technologies. In The Next Production Revolution (pp. 277–298). OECD.
Panagiotopoulos, I., & Dimitrakopoulos, G. (2018). An empirical investigation on consumers’ intentions towards autonomous driving. Transportation Research Part C: Emerging Technologies, 95, 773–784.
Park, C. W., Gardner, M. P., & Thukral, V. K. (1988). Self-perceived knowledge: Some effects on information processing for a choice task. Source: The American Journal of Psychology (Vol. 101). Retrieved from http://www.jstor.orgURL:http://www.jstor.org/stable/1423087
Park, E., & Ohm, J. Y. (2014). Factors influencing the public intention to use renewable energy technologies in South Korea: Effects of the fukushima nuclear accident. Energy Policy, 65, 198–211.
Park, W., Mothersbaugh, D. L., & Feick, L. (1994). Consumer knowledge assessment. Journal of Consumer Research, 21, 71.
Parkins, J. R., Rollins, C., Anders, S., & Comeau, L. (2018). Predicting intention to adopt solar technology in Canada: The role of knowledge, public engagement, and visibility. Energy Policy, 114, 114–122.
Payre, W., Cestac, J., & Delhomme, P. (2014). Intention to use a fully automated car: Attitudes and a priori acceptability. Transportation Research Part F: Traffic Psychology and Behaviour, 27, 252–263.
Pricilia, S. E. (2018). Model mitigasi risiko operasi pada industri penyeberangan: studi kasus lintasan penyeberangan Ketapang - Gilimanuk. Institut Teknologi Sepuluh Nopember. Retrieved from http://repository.its.ac.id/id/eprint/50521
Rachmannullah, A. F. (2019). The study on the determinants of ferry subsidy status in Indonesia (Master Thesis). 航運管理學系. 國立臺灣海洋大學, 基隆市. Retrieved from https://hdl.handle.net/11296/2x5s4a
Rachmannullah, A. F., Lirn, T. C., & Shang, K. C. (2024). Exploring market segmentation for autonomous ferries. Journal of Marine Science and Technology, 32, 1–15.
Roberts. (2018). Infomaritime.eu - Autonomous ships timeline (1970 - 2018). Retrieved December 13, 2019, from http://infomaritime.eu/index.php/2018/06/08/timeline-development-of-autonomous-ships/
Robertson, R. D., Meister, S. R., & Vanlaar, W. G. (2016). Automated vehicles: driver knowledge, attitudes and practices, 37p. Retrieved from http://www.tirf.ca/publications/PDF_publications/Automated Vehicles Driver Knowledge Attitudes and Practices-9.pdf
Roche, M. Y., Mourato, S., Fischedick, M., Pietzner, K., & Viebahn, P. (2010). Public attitudes towards and demand for hydrogen and fuel cell vehicles: A review of the evidence and methodological implications. Energy Policy, 38, 5301–5310.
Rogers, E. M. (1983). Diffusion of innovation (Third Edit). New York: The Free Press.
Rogers, E. M. (1993). The diffusion of innovations model. Nato Asi Series D Behavioural and Social Sciences, 70, 9.
Rogers, E. M., Singhal, A., & Quinlan, M. M. (2014). Diffusion of innovations. In An integrated approach to communication theory and research (pp. 432–448). Routledge.
Rolls-Royce. (2016). Autonomous ships : The next step. Marine Ship Intelligence, 1–8.
Rolls-Royce. (2018). Rolls-Royce and Finferries demonstrate world’s first fully autonomous ferry.
Schönknecht, R., & Bertholdt, J. (1983). Ships and shipping of tomorrow. Cornell Maritime Press.
Schreiber, J. B., Stage, F. K., King, J., Nora, A., & Barlow, E. A. (2006). Reporting structural equation modeling and confirmatory factor analysis results: A review. Journal of Educational Research. Routledge.
Schwartz, N. E. (1976). Nutrition knowledge, attitudes and practices of canadian public health nurses. Journal of Nutrition Education, 8, 28–31.
Shaaban, K., & Maher, A. (2020). Using the theory of planned behavior to predict the use of an upcoming public transportation service in Qatar. Case Studies on Transport Policy, 8, 484–491.
Shan, S. (2021, July 30). Contract for new ferry to serve Penghu signed. Retrieved February 23, 2024, from https://www.taipeitimes.com/News/taiwan/archives/2021/07/30/2003761720
Shang, Y. Y., Chia, H. C., Hsin, Y. C., & Tze, C. Lou. (2007). Optimal scheduling models for ferry companies under alliances. Journal of Marine Science and Technology, 15, 53–66. Retrieved from http://www.airitilibrary.com/Publication/alDetailedMesh?DocID=15633470-201003-201004010077-201004010077-11-20
Shih, Y. Y., & Fang, K. (2004). The use of a decomposed theory of planned behavior to study Internet banking in Taiwan. Internet Research, 14, 213–223.
Ship and Ocean Industries R&D Center. (2020). Maritime autonomous surface ship (MASS) - 財團法人船舶暨海洋產業研發中心. Retrieved September 2, 2020, from https://www.soic.org.tw/en/ship-engineering/自動駕駛船舶/
Ship Technology News. (2020). Eight countries to promote maritime autonomous ship development. Retrieved September 2, 2020, from https://www.ship-technology.com/news/eight-countries-maritime-autonomous-ship-development/
Simon, A. (2014). ReVolt – next generation short sea shipping - DNV GL. Retrieved December 22, 2019, from https://www.dnvgl.com/news/revolt-next-generation-short-sea-shipping-7279
Singh, V., Singh, V., & Vaibhav, S. (2020). A review and simple meta-analysis of factors influencing adoption of electric vehicles. Transportation Research Part D: Transport and Environment, 86.
Sun, K. K., He, S. Y., & Thøgersen, J. (2022). The purchase intention of electric vehicles in Hong Kong, a high-density Asian context, and main differences from a Nordic context. Transport Policy, 128, 98–112.
Sun, Y., Bhattacherjee, A., & Ma, Q. (2009). Extending technology usage to work settings: The role of perceived work compatibility in ERP implementation. Information and Management, 46, 351–356.
Tabachnick, B. G., Fidell, L. S., & Ullman, J. B. (2007). Using multivariate statistics (Vol. 5). Pearson Boston, MA.
Talebian, A., & Mishra, S. (2018). Predicting the adoption of connected autonomous vehicles: A new approach based on the theory of diffusion of innovations. Transportation Research Part C: Emerging Technologies, 95, 363–380.
Tanya, K. (2008). Attitude : A concept analysis. Nursing Forum, 43, 144–150.
Tao, C. C., & Fan, C. C. (2017). A modified decomposed theory of planned behaviour model to analyze user intention towards distance-based electronic toll collection services. Promet - Traffic - Traffico, 29, 85–97.
Taylor, S., & Todd, P. (1995). Decomposition and crossover effects in the theory of planned behavior: A study of consumer adoption intentions. International Journal of Research in Marketing, 12, 137–155.
The Maritime Executive. (2020). Demonstration of autonomous and remote-controlled ship operations. Retrieved May 4, 2021, from https://www.maritime-executive.com/article/demonstration-of-autonomous-and-remote-controlled-ship-operations
The Nippon Foundation. (2021). 5th Demonstration test of fully autonomous ship navigation successfully completed | The Nippon Foundation. Retrieved September 28, 2022, from https://www.nippon-foundation.or.jp/en/news/articles/2022/20220301-67775.html
Tornatzky, L. G., & Klein, K. J. (1982). Innovation characteristics and innovation adoption-implementation: A meta-analysis of finding. IEEE Transactions on Engineering Management, EM-29, 28–45.
Veal, R., Tsimplis, M., & Serdyc, A. (2019). The legal status and operation of unmanned maritime vehicles. Ocean Development and International Law, 50, 23–48.
Wang, S., Fan, J., Zhao, D., Yang, S., & Fu, Y. (2016). Predicting consumers’ intention to adopt hybrid electric vehicles: using an extended version of the theory of planned behavior model. Transportation, 43, 123–143.
Wang, S., Wang, J., Li, J., Wang, J., & Liang, L. (2018). Policy implications for promoting the adoption of electric vehicles: Do consumer’s knowledge, perceived risk and financial incentive policy matter? Transportation Research Part A: Policy and Practice, 117, 58–69.
Wang, S., Wang, J., Lin, S., & Li, J. (2019). Public perceptions and acceptance of nuclear energy in China: The role of public knowledge, perceived benefit, perceived risk and public engagement. Energy Policy, 126, 352–360.
Wang, X., Yuen, K. F., Wong, Y. D., & Teo, C. C. (2019). Consumer participation in last-mile logistics service: an investigation on cognitions and affects. International Journal of Physical Distribution and Logistics Management, 49, 217–238.
Wang, Y., Cheng, L., & Li, S. (2019). Study on the influence of subway passengers’ non-adaptive behavior based on Knowledge-Attitude-Practice theory. In 2019 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS) (pp. 72–76). IEEE.
Weisbrod, R. E., & Lawson, C. T. (2003). Ferry systems: Planning for the revitalization of U.S. cities. Journal of Urban Technology, 10, 47–68.
Wergeland, T. (2012). Ferry passenger markets. In The Blackwell Companion to Maritime Economics (pp. 161–183). Wiley.
Wu, I. L., & Chiu, M. L. (2015). Organizational applications of IT innovation and firm’s competitive performance: A resource-based view and the innovation diffusion approach. Journal of Engineering and Technology Management - JET-M, 35, 25–44.
Wu, X. (2018). Role of workplace charging opportunities on adoption of plug-in electric vehicles – Analysis based on GPS-based longitudinal travel data. Energy Policy, 114, 367–379.
Xia, Y., & Yang, Y. (2018). RMSEA, CFI, and TLI in structural equation modeling with ordered categorical data: The story they tell depends on the estimation methods.
Yuen, K. F., Chua, G., Wang, X., Ma, F., & Li, K. X. (2020). Understanding public acceptance of autonomous vehicles using the theory of planned behaviour. International Journal of Environmental Research and Public Health, 17, 1–19.
Yuen, K. F., Thi, D., Huyen, K., Wang, X., & Qi, G. (2020). Factors influencing the adoption of shared autonomous vehicles. International Journal of Environmental Research and Public Health.
Yuen, K. F., Wang, X., Ng, L. T. W., & Wong, Y. D. (2018). An investigation of customers’ intention to use self-collection services for last-mile delivery. Transport Policy, 66, 1–8.
Zhang, T., Tan, H., Li, S., Zhu, H., & Tao, D. (2019). Public’s acceptance of automated vehicles: the role of initial trust and subjective norm. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 63, 919–923.
Zhang, Y., & Li, L. (2020). Intention of Chinese college students to use carsharing: An application of the theory of planned behavior. Transportation Research Part F: Traffic Psychology and Behaviour, 75, 106–119.
Zhao, P., & Gao, Y. (2022). Public transit travel choice in the post COVID-19 pandemic era: An application of the extended Theory of Planned behavior. Travel Behaviour and Society, 28, 181–195.

QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
無相關期刊