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研究生: 葉涵菲
Yeh, Han-Fei
論文名稱: 以UTAUT2推衍影響消費者接受整合奈米影像感測器智慧裝置之關鍵要素
A UTAUT2 Based Derivation of Key Factors Influencing the Customers Acceptance of Nano-Sensor Integrated Smart Devices
指導教授: 郭金國
Kuo, Chin-Guo
學位類別: 碩士
Master
系所名稱: 工業教育學系
Department of Industrial Education
論文出版年: 2018
畢業學年度: 106
語文別: 英文
論文頁數: 138
中文關鍵詞: 高像素智慧手機影像奈米感測第2代整合型科技接受理論決策實驗室分析法結構方程模式
英文關鍵詞: High-Pixel Smart-phone, nanosensor, UTAUT2, SEM, DEMATEL
DOI URL: http://doi.org/10.6345/THE.NTNU.DIE.045.2018.E01
論文種類: 學術論文
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  • 奈米科技的是全世界先進國家取得未來競爭優勢的關鍵要素,趕上奈米科技的狂潮,是台灣產業永續經營的契機。奈米科技的發展,可以滿足元件微小化的需求。奈米科技的應用多元,而典型的應用之一,為奈米影像感測器,奈米影像感測器與消費性電子平台整合,亦為潮流之一,少有學者探討,確為消費電子廠商訂定行銷策略之關鍵要素。因此,本研究擬導入第2代整合型科技接受理論 (Unified Theory of Acceptance and Use of Technology 2, UTAUT2)為基礎,預測影響整合奈米感測器消費電子設備消費行為之因素; 本研究以將結合決策實驗室分析法 (Decision Making Trial and Evaluation Laboratory, DEMATEL)先行分析各構面和準則之間的關係,並以德菲法之專家小組(expert panel)決定德菲法研究的最終結果。因此專家小組成員的遴選以了解先進奈米製程經驗的人士參與。後以結構方程模式(Structural Equation Modeling, SEM)檢定前述DEMATEL推導之影響關係之假設顯著。且分析架構之可行性,結構方程模式(Structural Equation Modeling, SEM) 推衍UTAUT2 各變數對消費者接受整合奈米感測器消費電子設備之關聯程度與顯著性,並以台北市使用高像素手機之工作經驗人士為對象,實證本研究架構之可行性,從以上實證結果來看,一般用戶對於高像素手機是否是先進奈米製程製做較為無感,但一般用戶對於產品娛樂性與產品性價比較為重視,而專家用戶對於先進奈米製程改善暗電流之雜訊呈現有感,故此研究對於專用戶和一般用戶所呈現的數據結果可提供之後想探討相關研究方向時策略之依據.

    關鍵字: 高像素智慧手機、影像奈米感測、第2代整合型科技接受理論、決策實驗室分析法、結構方程模式

    The key factor for the world's advanced countries to gain competitive leadership in the future is nanotechnology. If you can catch up with the field of nanotechnology, it is an opportunity for the sustainable development of Taiwan's industry.Because meet the demand of components miniaturization. Only the integration of consumer electronics devices with nano-meter sensors will affect consumers' acceptance of emerging technology products. Few scholars have discussed the key elements in setting marketing strategies for consumer electronics manufacturers. Therefore, based on the introduction of the second generation of UTAUT2, this study predicts the factors influencing the consumer behavior of integrated nano-sensors in consumer electronic devices and Structural Equation Modeling deduced the correlation degree and significance of the variations of UTAUT2 to consumer acceptance. Using the work experience person who use the High-Pixel smart phone product in Taipei City as Object, demonstration of the feasibility of this research framework. Use high-pixel mobile phones in Taipei. For the work experience, the feasibility of this research structure is demonstrated. From the above empirical results, the general user is not interested in whether the high-pixel mobile phone is advanced nano-process, but the general user is interested in product entertainment and product cost. More emphasis, and expert users have a sense of the noise improvement of the advanced nanometer process to improve the dark current. Therefore, the research results for the data presented by the user and the general user can provide the basis for the strategy after discussing the relevant research direction.
    Keywords: High-Pixel Smart-phone,nanosensor,UTAUT2,SEM,DEMATEL

    摘要 i Abstract ii Table of Contents iii List of Table v List of Figure viii Chapter 1 Introduction 1 1.1 Research Background and Motivations 1 1.2 Research Purposes 9 1.3 Research Process Thesis Structure 10 1.4 Research Limitations 11 1.5 Research Framework 12 1.6 Thesis Structure 12 Chapter 2 Literature Review 15 2.1 Innovation Diffusion Theory (IDT) 15 2.2 Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) 18 2.3 Nano Sensor 22 2.4 TAM 27 Chapter 3 Research Methods 31 3.1 Modified Delphi Method 31 3.2 Decision Making Trial and Evaluation Laboratory 33 3.3 Structural Equation Modeling(SEM) 38 Chapter 4 Empirical Study 49 4.1 Background and Related Factors 50 4.2 Dimensions and Criteria Definition by Modified Delphi Method 52 4.3 Establish the Causal Relationship between Dimensions and Criteria by DEMATEL 62 4.4 The Empirical study result of PLS Method 83 Chapter 5 Discussion 97 5.1 Implications of Management 97 5.2 Research Methods and Dimension discussion 111 Chapter 6 Conclusions 115 Reference 119

    Adhikari, S. (2016). Nonlocal Mechanics Based Computational Methods for Nano-mechanical Sensors (Keynote Address). Procedia Technology, 23(Supplement C), 7-19.

    Agag, G., & El-Masry, A. A. (2016). Understanding consumer intention to participate in online travel community and effects on consumer intention to purchase travel online and WOM: An integration of innovation diffusion theory and TAM with trust. Computers in Human Behavior, 60, 97-111.

    Al-Gahtani, S. S., Hubona, G. S., & Wang, J. (2007). Information technology (IT) in Saudi Arabia: Culture and the acceptance and use of IT. Information & Management, 44(8), 681-691.

    Amabile, T. M. (1997). Motivating creativity in organizations: On doing what you love and loving what you do. California management review, 40(1), 39-58.

    Asadi, E., Askari, H., Behrad Khamesee, M., & Khajepour, A. (2017). High frequency nano electromagnetic self-powered sensor: Concept, modelling and analysis. Measurement, 107(Supplement C), 31-40.

    Bagozzi, R. P. (1980). Performance and satisfaction in an industrial sales force: An examination of their antecedents and simultaneity. the Journal of Marketing, 65-77.

    Baptista, G., & Oliveira, T. (2015). Understanding mobile banking: The unified theory of acceptance and use of technology combined with cultural moderators. Computers in Human Behavior, 50, 418-430.

    Bass, F. M. (1969). A New Product Growth for Model Consumer Durables. Management Science, 15(5), 215-227.

    Becker, J.-M., Klein, K., & Wetzels, M. (2012).Hierarchical Latent Variable Models in PLS-SEM: Guidelines for Using Reflective-Formative Type Models. Long Range Planning, 45(5), 359-394.

    Boksberger, P. E., & Melsen, L. (2011). Perceived value: a critical examination of definitions, concepts and measures for the service industry. Journal of Services Marketing, 25(3), 229-240.

    Bollen, K. A., & Stine, R. A. (1992). Bootstrapping goodness-of-fit measures in structural equation models. Sociological Methods & Research, 21(2), 205-229.

    Bozeman, B., Larédo, P., & Mangematin, V. (2007). Understanding the emergence and deployment of “nano” S&T. Research Policy, 36(6), 807-812.

    Canfarotta, F., Rapini, R., & Piletsky, S. (2018). Recent advances in electrochemical sensors based on chiral and nano-sized imprinted polymers. Current Opinion in Electrochemistry, 7(Supplement C), 146-152.

    Chang, E.-C., & Tseng, Y.-F. (2013). Research note: E-store image, perceived value and perceived risk. Journal of Business Research, 66(7), 864-870.

    Chiu, Y.-J., Chen, H.-C., Tzeng, G.-H., & Shyu, J. Z. (2006). Marketing strategy based on customer behaviour for the LCD-TV. International journal of management and decision making, 7(2-3), 143-165.

    Chong, A. Y.-L. (2013). Mobile commerce usage activities: The roles of demographic and motivation variables. Technological Forecasting and Social Change, 80(7), 1350-1359.

    Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2013). Applied multiple regression/correlation analysis for the behavioral sciences: Routledge.

    Csikszentmihalyi, M. (1992). A reponse to the Kimiecik & Stein and Jackson papers. Journal of Applied Sport Psychology, 4(2), 181-183.

    Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340.

    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.

    Dawson, M. D., & Brucker, P. S. (2001). The utility of the Delphi method in MFT research. American Journal of Family Therapy, 29(2), 125-140.

    Dodds, W. B., Monroe, K. B., & Grewal, D. (1991). Effects of price, brand, and store information on buyers' product evaluations. Journal of marketing research, 307-319.

    Escobar-Rodríguez, T., & Carvajal-Trujillo, E. (2013). Online drivers of consumer purchase of website airline tickets. Journal of Air Transport Management, 32(Supplement C), 58-64.

    Fornell, C., & Larcker, D. F. (1981). SEM with unobservable variables and measurement error: Algebra and statistics. Journal of marketing research, 18(3), 382-388.

    Francesco, P., Mathieu, P., & Sénéchal, D. (2012). Conformal field theory: Springer Science & Business Media.

    Fuksa, M. (2013). Mobile Technologies and Services Development Impact on Mobile Internet Usage in Latvia. Procedia Computer Science, 26, 41-50.

    Gabus, A., & Fontela, E. (1972). World problems. An Invitation to Further Thought within the Framework of DEMATEL, BATTELLE Institute, Geneva Research Centre, Geneva.

    Gharbi, J.-E. (2008). Determinants and consequences of the website perceived value. Journal of Internet Banking and Commerce, 13(1).

    Gribel, L. (2018). Drivers of Wearable Computing Adoption: An Empirical Study of Success Factors Including IT Security and Consumer Behaviour-Related Aspects. University of Plymouth.

    Hair Jr, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2016). A primer on partial least squares structural equation modeling (PLS-SEM): Sage Publications.

    Henker, S., Mayr, C., Schlüssler, J., Schüffny, R., Ramacher, U., & Heittmann, A. (2007). Active pixel sensor arrays in 90/65nm CMOS-technologies with vertically stacked photodiodes. Paper presented at the Proc. IEEE International Image Sensor Workshop IIS07.

    Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing New challenges to international marketing (pp. 277-319): Emerald Group Publishing Limited.

    Hoehle, H., Scornavacca, E., & Huff, S. (2012). Three decades of research on consumer adoption and utilization of electronic banking channels: A literature analysis. Decision Support Systems, 54(1), 122-132.

    Holden, R. J., & Karsh, B.-T. (2010). The technology acceptance model: its past and its future in health care. Journal of biomedical informatics, 43(1), 159-172.

    Hori, S., & Shimizu, Y. (1999). Designing methods of human interface for supervisory control systems. Control engineering practice, 7(11), 1413-1419.

    Houston, S., & Bolding Jr, J. (1974). Part, partial, and multiple correlation in commonality analysis of multiple regression models. Multiple Linear Regression Viewpoints, 5, 36-40.

    Howell, R. (1989). A Prototype Robotic Arm for Use by Severely Orthopedically Handicapped Students. Final Report.

    Huang, C.-Y., Shyu, J. Z., & Tzeng, G.-H. (2007). Reconfiguring the innovation policy portfolios for Taiwan's SIP Mall industry. Technovation, 27(12), 744-765.

    Huang, C.-Y., Tzeng, G.-H., & Ho, W.-R. J. (2011). System on chip design service e-business value maximization through a novel MCDM framework. Expert Systems with Applications, 38(7), 7947-7962.

    Igbaria, M., Guimaraes, T., & Davis, G. B. (1995). Testing the determinants of microcomputer usage via a structural equation model. Journal of management information systems, 11(4), 87-114.

    Jeng, D. J.-F., & Tzeng, G.-H. (2012). Social influence on the use of clinical decision support systems: revisiting the unified theory of acceptance and use of technology by the fuzzy DEMATEL technique. Computers & Industrial Engineering, 62(3), 819-828.

    Jones, J., & Hunter, D. (1995). Consensus methods for medical and health services research. BMJ: British Medical Journal, 311(7001), 376-380.

    Kim, G., Moon, J.-H., Moh, C.-Y., & Lim, J.-g. (2015). A microfluidic nano-biosensor for the detection of pathogenic Salmonella. Biosensors and Bioelectronics, 67(Supplement C), 243-247.

    Kim, W., Kwak, Y. H., Park, K. B., Kim, J., Choi, S., Ju, B.-K., & Kim, K. (2017). Development of a touch sensor capable of touch and touch load recognition. Sensors and Actuators A: Physical, 264(Supplement C), 298-307.

    Koufaris, M. (2002). Applying the technology acceptance model and flow theory to online consumer behavior. Information systems research, 13(2), 205-223.

    Kuo, Y.-F., Wu, C.-M., & Deng, W.-J. (2009). The relationships among service quality, perceived value, customer satisfaction, and post-purchase intention in mobile value-added services. Computers in Human Behavior, 25(4), 887-896.

    Lauhon, L. J., Gudiksen, M. S., Wang, D., & Lieber, C. M. (2002). Epitaxial core–shell and core–multishell nanowire heterostructures. Nature, 420(6911), 57-61.

    Lieber, C. M. (2003). Nanoscale science and technology: building a big future from small things. MRS bulletin, 28(7), 486-491.

    Lim, T.-C. (2016). Nanosensors: theory and applications in industry, healthcare and defense Florida, USA: CRC Press.

    Lim, T.-C., & Ramakrishna, S. (2006). A conceptual review of nanosensors. Zeitschrift für Naturforschung A, 61(7-8), 402-412.

    Liou, J. J., Tzeng, G.-H., & Chang, H.-C. (2007). Airline safety measurement using a hybrid model. Journal of air transport management, 13(4), 243-249.

    Lu, J., Yao, J. E., & Yu, C.-S. (2005). Personal innovativeness, social influences and adoption of wireless Internet services via mobile technology. The Journal of Strategic Information Systems, 14(3), 245-268.

    Lu, J., Yu, C.-S., Liu, C., & Yao, J. E. (2003). Technology acceptance model for wireless Internet. Internet Research, 13(3), 206-222.

    Lu, Y., Zhou, T., & Wang, B. (2009). Exploring Chinese users’ acceptance of instant messaging using the theory of planned behavior, the technology acceptance model, and the flow theory. Computers in human behavior, 25(1), 29-39.

    Lyons, M. (1971). Techniques for using ordinal measures in regression and path analysis. Sociological methodology, 3, 147-171.

    Macedo, I. M. (2017). Predicting the acceptance and use of information and communication technology by older adults: An empirical examination of the revised UTAUT2. Computers in Human Behavior, 75(Supplement C), 935-948.

    Magni, M., Taylor, M. S., & Venkatesh, V. (2010). ‘To play or not to play’: A cross-temporal investigation using hedonic and instrumental perspectives to explain user intentions to explore a technology. International journal of human-computer studies, 68(9), 572-588.

    Manski, C. F. (1993). Identification of Endogenous Social Effects: The Reflection Problem. The Review of Economic Studies, 60(3), 531-542.

    Mathieson, K. (1991). Predicting user intentions: comparing the technology acceptance model with the theory of planned behavior. Information systems research, 2(3), 173-191.

    Murry Jr, J. W., & Hammons, J. O. (1995). Delphi: A versatile methodology for conducting qualitative research. The Review of Higher Education, 18(4), 423-436.

    Oh, J., & Yoon, S.-J. (2014). Validation of Haptic Enabling Technology Acceptance Model (HE-TAM): Integration of IDT and TAM. Telematics and Informatics, 31(4), 585-596.

    Pedhazur, E. J. (1982). Multiple regression in behavioral research: Explanation and prediction . Fort Worth, TX: Holt, Rinehart and Winston. Pedhazur2Multiple Regression in Behavioral Research: Explanation and Prediction1982.

    Petrick, J. F. (2002). Development of a multi-dimensional scale for measuring the perceived value of a service. Journal of leisure research, 34(2), 119-134.

    Pilke, E. M. (2004). Flow experiences in information technology use. International journal of human-computer studies, 61(3), 347-357.

    Raman, A., & Don, Y. (2013). Preservice teachers' acceptance of learning management software: An application of the UTAUT2 model. International Education Studies, 6(7), 157-164.

    Rogers, E. M. (2003). Diffusion of innovations. Free Press. New York, 551.

    Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American psychologist, 55(1), 68-78.

    Schumacker, R., & Lomax, R. (1996). A Beginner's Guide to Structural Equation Modeling (Lawrence Erl baum Associates, Mahwah, New Jersey).

    Selin, C. (2007). Expectations and the Emergence of Nanotechnology. Science, Technology, & Human Values, 32(2), 196-220.

    Specht, D. A. (1975). On the evaluation of causal models. Social Science Research, 4(2), 113-133.

    Swait, J., & Sweeney, J. C. (2000). Perceived value and its impact on choice behavior in a retail setting. Journal of Retailing and Consumer Services, 7(2), 77-88.

    Teo, T. S., Lim, V. K., & Lai, R. Y. (1999). Intrinsic and extrinsic motivation in Internet usage. Omega, 27(1), 25-37.

    Thompson, R. L., Higgins, C. A., & Howell, J. M. (1991). Personal computing: toward a conceptual model of utilization. MIS quarterly, 125-143.

    Tzeng, G.-H., Chiang, C.-H., & Li, C.-W. (2007). Evaluating intertwined effects in e-learning programs: A novel hybrid MCDM model based on factor analysis and DEMATEL. Expert Systems with Applications, 32(4), 1028-1044.

    Tzeng, G.-H., & Huang, C.-Y. (2012). Combined DEMATEL technique with hybrid MCDM methods for creating the aspired intelligent global manufacturing & logistics systems. Annals of Operations Research, 197(1), 159-190.

    Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204.

    Venkatesh, V., & Morris, M. G. (2000). Why don't men ever stop to ask for directions? Gender, social influence, and their role in technology acceptance and usage behavior. MIS quarterly, 115-139.

    Venkatesh, V., Thong, J. Y., & 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, H.-Y., & Wang, S.-H. (2010). Predicting mobile hotel reservation adoption: Insight from a perceived value standpoint. International Journal of Hospitality Management, 29(4), 598-608.

    Wani, T. A., & Ali, S. (2015). Innovation diffusion theory. Journal of General Management Research, 3(2), 101-118.

    Wei, P.-L., Huang, J.-H., Tzeng, G.-H., & Wu, S.-I. (2010). Causal modeling of web-advertising effects by improving SEM based on DEMATEL technique. International Journal of Information Technology & Decision Making, 9(5), 799-829.

    Wejinya, U. C., Shen, Y., Xi, N., Chiu Lai, K. W., & Zhang, J. (2008). An efficient approach of handling and deposition of micro and nano entities using sensorized microfluidic end-effector system. Sensors and Actuators A: Physical, 147(1), 6-16.

    Wolfe, L. M. (1977). An Introduction to Path Analysis. Multiple Linear Regression Viewpoints, 8(1), 36-61.

    Wright, S. (1921). Correlation and causation. Journal of agricultural research, 20(7), 557-585.

    Wright, S. (1934). The method of path coefficients. The annals of mathematical statistics, 5(3), 161-215.

    Wright, S. (1960). Path coefficients and path regressions: alternative or complementary concepts? Biometrics, 16(2), 189-202.

    Yang, J. L., & Tzeng, G.-H. (2011). An integrated MCDM technique combined with DEMATEL for a novel cluster-weighted with ANP method. Expert Systems with Applications, 38(3), 1417-1424.

    Yin, J., Santos, V. J., & Posner, J. D. (2017). Bioinspired flexible microfluidic shear force sensor skin. Sensors and Actuators A: Physical, 264(Supplement C), 289-297.

    Zhang, X. (2018). Frugal innovation and the digital divide: Developing an extended model of the diffusion of innovations. International Journal of Innovation Studies.

    Zhao, L., Lu, Y., Zhang, L., & Chau, P. Y. (2012). Assessing the effects of service quality and justice on customer satisfaction and the continuance intention of mobile value-added services: An empirical test of a multidimensional model. Decision Support Systems, 52(3), 645-656.

    Zhou, T., & Lu, Y. (2011). Examining mobile instant messaging user loyalty from the perspectives of network externalities and flow experience. Computers in Human Behavior, 27(2), 883-889.

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