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作者:MATOVU ASHIRAF MUHMOODU
作者(英文):MATOVU ASHIRAF MUHMOODU
論文名稱:Predicting User Behavioural intention to Use Battery Electric Motorcycle (BEM): A perspective from UTAUT Model
論文名稱(英文):Predicting User Behavioural intention to Use Battery Electric Motorcycle (BEM): A perspective from UTAUT Model
指導教授:彭玉樹
指導教授(英文):Yu- Shu, Peng
口試委員:欒錦榮
廖明坤
口試委員(英文):Chin-jung Luan
Ming-Kung Liao
學位類別:碩士
校院名稱:國立東華大學
系所名稱:國際企業學系
學號:610533016
出版年(民國):108
畢業學年度:107
語文別:英文
論文頁數:64
關鍵詞(英文):UTAUT ModelEffort ExpectancyPerformance ExpectancySocial InfluenceFacilitating ConditionsRange AnxietyEnvironmental ConcernBehavioral Intention to UseBattery Electric Motorcycle
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There is a growing demand and use of electric locomotive. In recent years, electric vehicles have started dominating the automobile market. On the other hand, Battery Electric Motorcycle (BEM) is gaining significant interest. In the context of BEM, gaps were observed with regards to its behavioral intention to use. Thus, this study attempted to fill these gaps by predicting the behavioral intention to use BEM using Unified Theory of Acceptance and Use of Technology (UTAUT) model. The study collected data from 225 respondents. The study employed Statistical Packages for Social Science (SPSS) and AMOS to run regression analysis to test the hypotheses. The study found that performance expectancy, effort expectancy, social influence and facilitation conditions have a significant and positive effect on behavioral intention to use BEM. However, the study did not find the moderating effect of range anxiety and environmental concern in the relationship between the core constructs of UTAUT and behavioral intention to use.
1.Introduction 1
1.1 Background 1
1.2 Research Questions and Objectives 2
1.3 Research Objectives 2
2. Literature Review 5
2.1 Unified Theory of Acceptance and Use of Technology (UTAUT) 5
2.2 Relationship between Performance Expectancy and Behavioural Intention to Use 8
2.3 Relationship between Effort Expectancy and Behavioral Intention to Use 8
2.4 Relationship between Social Influence and Behavioral intention to Use 8
2.5 Relationship between Facilitating Conditions and Behavioural Intention to Use 9
2.6 Relationship between Range Anxiety and Behavioral Intention to Use 9
2.7 Relationship between Environmental Concern and Behavioral Intention to Use 10
2.8 Relationship Amongst the Core Constructs of UTAUT, Range Anxiety and Behavioral Intention to Use. 10
2.9 Relationship Amongst the Core Constructs of UTAUT, Environmental Concern and Behavioral Intention to Use. 11
3. Methodology 12
3.1 Research Framework and Hypotheses 12
3.2 Measures 15
3.3 Data Collection Procedure and Sample Characteristics 17
3.4 Data Analysis Method 17
4. Data Analysis 20
4.1 Descriptive Statistics 20
4.2 Validity and Reliability Analysis 22
4.3 Hypothesis Testing 28
4.4 Moderated Regression Analysis 33
5. Conclusion and Suggestions 38
5.1 Research Conclusion 38
5.2 Research and Managerial Implications 39
5.3 Limitation and Areas for Further Research 41
References 43
Appendix 1: English Questionnaire 50
Appendix 2: Chinese Questionnaire 53

Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modelling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411-423.
Ayele, A. A., & Sreenivasarao, D. (2013). A Case study of acceptance and use of electronic library services in Universities based on SO-UTAUT model. International Journal of Innovative Research in Computer and Communication Engineering, 1(4), 903-911.
Bagozzi, R. P. (1992). The self-regulation of attitudes, intentions, and behaviour. Social Psychology Quarterly, 55(2), 178-204.
Bajpai, S., & Bajpai, R. (2014). The goodness of measurement: Reliability and validity. International Journal of Medical Science and Public Health, 3(2), 112-115.
Bentler, P. M., & Wu, E. J. C. (1993). EQS/ Windows User’s Guide. Los Angeles, CA: BMDP Statistical Software.
Bialocerkowski, A. E., & Bragge, P. (2008). Measurement error and reliability testing: Application to rehabilitation. International Journal of Therapy and Rehabilitation, 15(10), 422-427.
Catherine, N., Mayoka, K., Geofrey, K. M., Moya, M. B., & Aballo, G. (2017). Effort expectancy, performance expectancy, social influence and facilitating conditions as predictors of behavioural intentions to use ATMS with Fingerprint Authentication in Ugandan Banks Global Journal of Computer Science and Technology: E Network, Web & Security, 17(5) 5-21.
Chao, Cheng-Min. (2019). Factors Determining the Behavioural Intention to Use Mobile Learning: An Application and Extension of the UTAUT Model. Frontiers in Psychology. 10. 10.3389/fpsyg.2019.01652.
Chen, H. S., Tsai, B. K., & Hsieh, C. M. (2017). Determinants of consumers’ purchasing intentions for the hydrogen-electric motorcycle. Sustainability, 9(8), 1-12.
Davis, F. D.; Bagozzi, R. P.; Warshaw, P. R. (1989), "User acceptance of computer technology: A comparison of two theoretical models", Management Science, 35 (8): 982–1003.
Drost, E. A. (2011). Validity and reliability in social science research. Education Research and Perspectives, 38(1) 105-124.
Egbue O., & Long S. (2012). Barriers to widespread adoption of electric vehicles: an analysis of consumer attitudes and perceptions. Energy Pol 2012; 48(2012),7 17-729.
Elliott, A. C., & Wright, I. C. (1999). Customer-needs information in the new product development process: an empirical study, Proceedings of International Conference on Engineering Design, ICED99, Munich, 3 August, 1559–1564.
European Commission (2012). Transport: Clean transport. Retrieved from: http://ec.europa.eu/transport/themes/urban/vehicles/road/electric_en.htm.Google Scholar
Fehrenbacher, D. D., & Choo, K. K. R. (2018). On the potential of socioeconomic characteristics to understand the adoption of information technology. In Proceedings of the 18th International Conference on Electronic Business, ICEB, Guilin, China, December 2-6, 785-790.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.
Franke, T., Rauh, N., Unther, M. G. Trantow, M. & Krems, J. F. (2016). Which Factors Can Protect Against Range Stress in Everyday Usage of Battery Electric Vehicles? Toward Enhancing Sustainability of Electric Mobility Systems. Human Factors: The Journal of the Human Factors and Ergonomics Society, 58(1), 13–26.
Fumo, N., & Rafe Biswas, M. A. (2015). Regression analysis for prediction of residential energy consumption. Renewable and Sustainable Energy Reviews, 47, 332–343. doi:10.1016/j.rser.2015.03.035
Ghalandari, K. (2012). The Effect of Performance Expectancy, Effort Expectancy, Social Influence and Facilitating Conditions on Acceptance of E-Banking Services in Iran: the Moderating Role of Age and Gender. Middle-East Journal of Scientific Research 12 (6): 801-807.
Ha, H. Y. & Janda, S. (2012). Predicting consumer intentions to purchase energy-efficient products. Journal of Consumer Marketing, 29(7), 461–469. https:// doi.org/10.1108/07363761211274974.
Hair, J. F. Jr., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate Data Analysis (7th Ed.). Upper Saddle River, NJ: Prentice-Hall Inc.
Hartmann, F. G. H., & Moers, F. 1999. Testing contingency hypotheses in budgetary research:
Hartwick, J., & Barki, H. (1994). Explaining the role of user participation in Information System use,” Management Science 40(4), 40-465. https://doi.org/10.1016/j.jclepro.2017.05.154.
Hayes, A. F. (2013). Introduction to Mediation, Moderation and Conditional Process Analysis: A Regression-based Approach, New York, NY: Guilford Press.
Heale, H., & Twycross, W. (2015). Validity and reliability in quantitative studies. Evidence Based Nursing, 18(3), 66-67.
Helm, R., & Mark, A. (2012). Analysis and evaluation of moderator effects in regression models: State of art, alternatives, and empirical example. Review of Managerial Science, 6(4): 307- 332.
Hofstede, G. (2011). Dimensionalizing cultures: The Hofstede model in context. Online Readings in Psychology and Culture, 2(1). https://doi.org/10.9707/2307-0919.1014
Im, I., Hong, S., & Kang, M. S. (2011). An international comparison of technology adoption Testing the UTAUT models. Information & Management 48 (2011) 1–8.
International Energy Agency (2012). Hybrid and electric vehicles: The electric drive captures the imagination. Retrieved from http://www.ieahev.org/assets/1/7/IA-HEV_2011_annual_report_web.pdf
Ivan, K. W. (2015). Factors influencing the behavioural intention towards full electric vehicles: an empirical study in Macau. Sustainability, 7(9), 12564-12585.
Jensen, A. F., Cherchi, E., & Mabit. S. L. (2013). On the stability of preferences and attitudes before and after experiencing an electric vehicle. Transport Res Part D Transport Environ, 2013(25), 24-32.
Jöreskog, K. G., & Sörbom, D. S. (1993). A Guide to the Program and Application. Chicago, IL: SPSS Inc.,
Khazaei, H., & Khazaei, A. (2016). Electric vehicles and factors that influencing their adoption moderating effects of driving experience and voluntariness of use (conceptual framework). Journal of Business and Management, 18(12), 60–65.
Kim, H. & Damhorst, M. R. (1998). Environmental concern and apparel consumption. Clothing and Textiles Research Journal, 16(3), 126-133.
Lee, Ji-Hwan and Kim, Soo Wook and Song, Chi Hoon,(2010) The Effects of Trust and Perceived Risk on Users’ Acceptance of ICT Services (October 1, 2010). KAIST Business School Working Paper Series No. 2010-007. Available at SSRN: https://ssrn.com/abstract=1703213 or http://dx.doi.org/10.2139/ssrn.1703213
Ma, L., Zhang, X., Ding, X., & Wang, G. (2018). Bike sharing and users’ subjective well-being: An empirical study in China. Transportation Research Part A: A Policy and Practice, 118(2018) 14–24.
Maillet, É., Mathieu, L., & Sicotte, C. (2015). Modelling factors explaining the acceptance, actual use and satisfaction of nurses using an Electronic Patient Record in acute care settings: An extension of the UTAUT. International Journal of Medical Informatics, 84(1), 36–47. doi:10.1016/j.ijmedinf.2014.09.004
Maruping, L. M., Bala, H., Venkatesh, V., & Brown, S. A. (2016). Going beyond intention: Integrating behavioral expectation into the unified theory of acceptance and use of technology. Journal of the Association for Information Science and Technology, 68(3), 623–637. doi:10.1002/asi.23699
Mohajan, H. (2017). Two criteria for good measurements in research: Validity and reliability. Annals of Spiru Haret University, 17(3), 58-82.
Omer, M., Klomsri, T., Tedre, M., Popova, I., Klingberg-Allvin, M. et al. (2015) E-learning opens the door to the global community. Novice users’ experiences of e-learning in a Somali University. Journal of Online Learning and Teaching, 11(2)
Osswald, S., Wurhofer, D., Trösterer, S., Beck, E., & Tscheligi, M. (2012). Predicting Information Technology Usage in the Car: Towards a Car Technology Acceptance Model. Proceedings of the 4th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, Portsmouth, October 17-19, 51–58.
Rana, N. P., Williams, M. D., Dwivedi, Y. K., & Williams, J. (2011). Theories and theoretical models for examining the adoption of e-government services. e-Service Journal, 8(2), 26–55.
Rauh, N. Franke, T., & Rems, J. F. (2015). “Understanding the impact of electric vehicle driving experience on range anxiety,”Human Factors, 57, 177–187.
Razak, M. I. M., Yusof, A. M., Mashahadi, F., Alias, Z., & Othman, M. Z. (2014). Intention to Purchase Hybrid Cars in Malaysia: An overview. International Journal of Economics. Commerce and Management, 2(10), 1–13.
Rokka, J., & Uusitalo, L. (2008). Preference for green packaging in consumer choices do consumer’s care? International Journal of consumer studies, 32(5) 516-525
Russell, W., & Joan, W. (1978). Environmental concern: The development of a measure. Environment and Behavior, 10(1), 3–15. https://doi.org/10.1177/ 0013916578101001
Schumacker, R. E., & Lomax, R. G. (2010). A Beginners’ Guide to Structural Equation Modelling. New York, NY: Routledge.
Sierzchula, W., Bakker, S., Maat, K., & van Wee, B. (2014). The influence of financial incentives and other socio-economic factors on electric vehicle adoption. Energy Policy, 68, 183–194.
Simsekoglu, Ö, & Nayum, A. (2019). Predictors of intention to buy a battery electric vehicle among conventional car drivers. Transportation Research Part F: Traffic Psychology and Behaviour, 60, 1–10. doi:10.1016/j.trf.2018.10.001
Smith, S. M., & Albaum, G. M. (2012). Basic Marketing Research: Volume 1, Handbook for Research Professionals, Provo, UT: Qualtrics Labs, Inc.
Social Influence and Facilitating Conditions on Acceptance of E-Banking Services in Iran: The Moderating Role of Age and Gender. Middle-East Journal of Scientific Research 12 (6): 801-807, 2012 ISSN 1990-9233
Taherdoost, H. (2016). Validity and reliability of the research instrument; How to test the validation of a questionnaire/ survey in a research. International Journal of Academic Research in Management (IJARM), 5(3), 28-36.
Tsai, Y. Y., Chao, C. M., Lin, H. M., & Cheng, B. W. (2018). Nursing staff intentions
to continuously use a blended e-learning system from an integrative
perspective. Quality & Quantity, 1-19
Vanneste, D., Vermeulen, B., & Declercq, A. (2013). Healthcare professionals’ acceptance of BelRAI, a web-based system enabling person-centred recording and data sharing across care settings with interRAI instruments: a UTAUT analysis. BMC Medical Informatics and Decision Making, 13(1). doi:10.1186/1472-6947-13-129
Vassileva, I., & Campillo, J. (2017). Adoption-barriers-for-electric-vehicles--Experiences-from-early- adopters from Sweden. Energy 120(2017) 632-641
Venkatesh, V. Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27, 425–478.
Venkatesh, V., & Davis, F. D. A. (2000). Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies, Management Science 45(2), 186-204.
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.
Wang, Z., Zhao, C., Yin, J., & Zhang, B. (2017). Purchasing intentions of Chinese citizens on new energy vehicles: How should one respond to current preferential policy? Journal of Cleaner Production, 161, 1000–1010.
Weinert, J., Ma, C., & Cherry, C. (2007). The transition to electric bikes in China: History and key reasons for rapid growth, Transportation 34(3), 301-318.
Wu, J. H., Wu, C.-W., Lee, C. T., & Lee, H. J. (2015). Green purchase intentions: An exploratory study of the Taiwanese electric motorcycle market. Journal of Business Research, 68(4), 829–833.
Wu, J. H., Wu, C.W., Lee, C.T., & Lee, H. J. (2015). Green purchase intentions: An exploratory study of the Taiwanese electric motorcycle market, Journal of Business Research 68(4), 829-833.
Wu, J., Liao, H., Wang, J., and Chen, J. (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 (2019) 37–46. doi:10.1016/j.trf.2018.09.029
Yang, K., & Forney, J. C. (2013). The moderating role of consumer technology anxiety in mobile shopping adoption: differential effects of facilitating conditions and social influences. Journal of Electronic Commerce Research, 14, 334–347.
Yuan, Q., Hao, W., Su, H., Bing, G., Gui, X., & Safikhani, A. (2018). Investigation on Range Anxiety and Safety Buffer of Battery Electric Vehicle Drivers. Journal of Advanced Transportation, 2018, 1–11. doi:10.1155/2018/8301209

Yusof, J. M, Singh, G. K. B., & Razak, R. A. (2013). Purchase Intention of Environment-Friendly Automobile. Procedia - Social and Behavioral Sciences 85 (2013) 400 – 410.
Zhang X, Wang K, Hao Y, Fan J. L, & Wei Y.M. (2013). The impact of government policy on preference for NEVs: The evidence from China. Energy Pol 2013; 61:382-393
Zhang, H., Song, X., Xia, T., Yuan, M., Fan, Z., Shibasaki, R., & Liang, Y. (2018). Battery electric vehicles in Japan: Human mobile behavior based adoption potential analysis and policy target response. Applied Energy, 220, 527–535. doi:10.1016/j.apenergy.2018.03.105
Zhou, Y., Wang, M., Hao, H., Johnson, L., & Wang, H. (2015). Plug-in electric vehicle market penetration and incentives: A global review. Mitigation and Adaptation Strategies for Global Change, 20(5), 777–795.
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