在高齡化的趨勢下，龐大的高齡者照護需求以及照護人力的短缺，促進了醫療照護設備及居家照護系統的開發，使用智慧家庭產品來輔助生活也日趨盛行。然而，過去少有對於智慧家庭接受度相關的研究，故本研究目的是探討高齡者對於智慧家庭的使用意願，以Davis (1993) 所提出的科技接受模式做為理論架構，並加入社會影響及使用教學兩個變數來做為高齡者使用行為意願之影響因素。

本研究之對象為台灣科技大學與台北大學EMBA的學生，本研究問卷透過網路方式進行問卷，以SPSS與LISREL統計軟體進行分析。SPSS用來針對樣本敘述性統計分析與量表信度之檢驗，LISREL則用以執行結構方程式(Structural Equation Model, SEM)，用於量表信度效度檢驗與假說檢定。

研究結果顯示，測量信度α值在0.7以上，符合要求水準，實證性因素分析中，也具有適度之收斂效度及區辨效度。結構模型分析顯示，對於高齡者使用智慧家庭意願之4個影響因素中，知覺易用因素、知覺有用性、社會影響及使用教學，呈現正向顯著影響使用意願，說明這4個因素是影響智慧家庭消費者使用意願之決定因素，與本研究假說的方向相吻合。最後根據研究結果與結論，提出理論與實務管理意涵的探討。

With the trend of aging society, the tremendous healthcare demand for the elderly and the shortage of care workers have promoted the development of medical care equipment and home care systems. Using smart home products to assist life has also become increasingly popular. However, there have been few studies related to the acceptance of smart home products in the past. Therefore, the purpose of this study is based on technology acceptance model proposed by Davis (1993) as a theoretical framework and added social influence and user training factor to explore elderly behavioral intention to use smart homes products.

The respondents of this study are mainly EMBA students from the National Taiwan University of Science and Technology and National Taipei University. The questionnaires were distributed through a network, and we used SPSS and LISREL statistical software packages to analyze research data. SPSS is used to test the descriptive statistics analysis and test the reliability. LISREL is used to perform Structural Equation Model (SEM), which is used to test reliability, validity test and hypothesis verification.

The results of this study show that α, the reliability, is above 0.7, which meets the requirement. In the factor analysis, there are also adequate convergent validity and discriminant validity. The structural model analysis shows that among the four factors, perceived usefulness, perceived ease of use, social influence, and user training have positively affected the intention to use for elderly. It shows that these four factors are the decisive factors that influence the acceptance of consumers of smart home, and they are consistent with the research hypothesis. Based on these hypothesis, the theoretical and practical management implications are discussed.

Agarwal, P. & Prasad, J. (1997). The role of innovation characteristics and perceived voluntariness in the acceptance of information technologies. Decision Sciences, 28, pp. 557-582.
Ajzen, I. & Madden, T.J. (1986). Prediction of goal-directed behavior: Attitudes, intentions, and perceived behavioral control . Journal of Experimental Social Psychology, 22, pp. 453-474.
Ajzen, I. (1991). Theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, pp. 179-211.
Arning, K. & Ziefle, M. (2007). Understanding age differences in PDA acceptance and performance. Computers in Human Behavior, 23 (6), pp. 2904-2927.
Bandura, A. (1977). Self-efficacy: toward a unifying theory of behavioral change. Psychol Rev, 84, pp. 191-215.
Chan, M. (2009, 10). Smart homes — Current features and future perspectives. Maturitas, 64, pp. 90-97.
Chan, M., Campo, E., Esteve, D. & Fourniols, JY. (2009). Smart homes — current features and future perspectives. Maturitas, 64, pp. 90-97.
Chen, K. & Chan, A.H.S. (2014). Gerontechnology acceptance by elderly Hong Kong Chinese: a senior technology acceptance model (STAM). Journal Ergonomics, 57, pp. 635-652.
Chen, K. A. (2011). A review of technology acceptance by older adults. Gerontechnology, 10, pp. 1-12.
Chen, L., Nugent, C. & Wang, H. (2012). A knowledge-driven approach to activity recognition in smart homes. IEEE Transactions on Knowledge and Data Engineering, 24 (6), pp. 961-974.
Conci, M., Pianesi, F. & Zancanaro, M. (2009). Useful, social and enjoyable: Mobile phone adoption by older people. Lecture Notes in Computer Science, 5726, pp. 63-76.
Cook, D. (2012). Learning setting-generalized activity models for smart spaces. IEEE Intelligent Systems, 27 (1), pp. 32-38.
Council, N. D. (2014, 11 1). Demographic Forecasting (Year 103 to 150). Retrieved from https://www.ndc.gov.tw/News_Content.aspx
Davis F.D., Bagozzi, R.P. & Warshaw, P.R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology, 22, pp. 1111-1132.
Davis, F. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13 (3), pp. 319-340.
Davis, F. (1993). User acceptance of information technology: system characteristics, user perceptions, and behavioral impacts. International Journal of Man Machine Studies, 38, pp. 475-487.
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), pp. 982-1003.
Dwivedi, Y.K., Rana, N.P., Chen, H. & Williams M.D. (2011). A meta-analysis of the unified theory of acceptance and use of technology (UTAUT). M. Nüttgens, A. Gadatsch, K. Kautz, I. Schirmer, N. Blinn (Eds.), Governance and sustainability in information systems. Managing the transfer and diffusion of IT: Springer, Berlin, pp. 155-170.
Escobar-Rodríguez, T., Monge-Lozano, P. & Romero-Alonso, M. (2012). Acceptance of e-prescriptions and automated medication-management systems in hospitals: An extension of the Technology Acceptance Model. Journal of Information Systems, 26(1), pp. 77-96.
Fishbein, M. & Ajzen, I. (1975). Belief, Attitude, Intentions and Behavior: An Introduction to Theory and Research. Boston: Addison–Wesley.
Fornell, C. & David F. L. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(February), pp. 39-50.
Fornell, C. & David F. L. (1981, 2). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. . Journal of Marketing Research, 18, pp. 39–50.
Goodhue, D. L. & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS Quarterly, 19(2), pp. 213–236.
Gupta, S. & Bostrom, R. (2006). Using peer-to-peer technology for collaborative knowledge management: Concepts, frameworks and research issues. Knowledge Management Research & Practice, 4, pp. 189-196.
Guthrie, J.P. & Schwoerer, C.E. (1994). Individual and contextual influences on self‐assessed training needs. Journal of Organizational Behavior, 15, pp. 405-422.
Hair, J.F., Anderson, R.E., Tatham, R.L. & Black, W.C. (1995). Multivariate Data Analysis with Reading, 4th Edition. Prentice Hall, Englewood Cliffs: NJ.
Hair, J.F., Anderson, R.E., Tatham, R.L. & Black, W.C. (1995). Multivariate Data Analysis with Reading, 4th Edition. Englewood Cliffs, NJ: Prentice Hall.
Harms, E. (2015). Smart home–good things come to those who wait. Proceedings of the 8th International Conference on Energy Efficiency in Domestic Appliances and Lighting (EEDAL'15). Switzerland.
Hartwick, J. & Barki, H. (1994). Explaining the role of user participation in information system use. Management Science, 40, pp. 440-465.
Herath, T. & Rao, H.R. (2009). Encouraging information security behaviors: role of penalties, pressures and perceived effectiveness. Decision Support Systems, 47 (2), pp. 154-165.
Hoque, R. & Sorwar, G. (2017, 5). Understanding factors influencing the adoption of MHealth by the elderly: an extension of the UTAUT model. Int. J. Med. Inf., 101, pp. 75-84.
Huang, J., Cheng, X.Q., Shen, H.W., Zhou, T. & Jin, X . (2012). Exploring social influence via posterior effect of word-of-mouth recommendations. Proceedings of the 5th ACM International Conference on Web Search and Data Mining (pp. 573-582). New York: ACM Press.
Hwangbo, H., Yoon, S.H., Jin, B.S., Han, Y.S. & Ji, Y.G. (2013). A study of pointing performance of elderly users on smartphones. International Journal of Human-Computer Interaction, 29, pp. 604-618.
Igbaria, M. (1990). End-user computing effectiveness: A structural equation model. Omega, 18(6), pp. 637-652.
Igbaria, M., Zinatelli, N., Cragg, P. & Cavaye, A. L. M. (1997). Personal computing acceptance factors in small firms: A structural equation model. MIS Quart, 21(3), pp. 279-305.
Jöreskog, K.G. & Sörbom D. (1993). LISREL 8: Structural Equation Modeling with the SIMPLIs Command Language. Mooresville: IL.
Jöreskog, K.G. & Sörbom D. (1993). LISREL 8: Structural Equation Modeling with the SIMPLIs Command Language,Scientific Software. Mooresville, IL.: Scientific Software International.
Kallweit, K.., Spreer, P. & Toporowski, W. (2014). Why do customers use self-service information technologies in retail? The mediating effect of perceived service quality. J. Retail.Consum. Serv., 21 (3), pp. 268-276.
Kansal, P. (2016). Perceived risk and technology acceptance model in self-service banking: A study on the nature of mediation. South Asian Journal of Management, 23(2), pp. 51-71.
Kelman, H. (1958). Compliance, Identification, and Internalization: Three Processes of Attitude Change. Journal of Conflict Resolution, 2, pp. 51-60.
Liu, A.T., Donaldson, M.G., Ahamed, Y., Graf, P., Cook, W.L., Close, J., Lord, S.R. & Khan, K.M. (2008). Otago home-based strength and balance retraining improves executive functioning in older fallers: a randomized controlled trial. J. Am. Geriatr. Soc., 56, pp. 1821-1830.
Lu H.P. & Lee M.R. (2011). Experience differences and continuance intention of blog sharing. Behavior & Information Technology, 31 (11), pp. 1081-1095.
Luora, T.T., Lu, H.P., Yu, H. & Lu, Y. (2015). Exploring the critical quality attributes and models of smart homes. Maturitas, 82, pp. 377-386.
Martins, C., Oliveira, T. & Popovič, A. (2014). Understanding the Internet banking adoption: A unified theory of acceptance and use of technology and perceived risk application. International Journal of Information Management, 34, pp. 1-13.
Martins, C., Oliverira, T. & Popovic, A. (2014). Understanding the Internet banking adoption: A unified theory of acceptance and use of technology and perceived risk application. International Journal of Information Management, 34 (1), pp. 1-13.
Martins, C., Oliveira, T. & Popovič, A. (2014). Understanding the Internet banking adoption: A unified theory of acceptance and use of technology and perceived risk application. International Journal of Information Management, 34, pp. 1-13.
Mathieson, K. (1991). Predicting User Intentions: Comparing the Technology Acceptance Model with the Theory of Planned Behavior. Information Systems Research, 2, pp. 173-191.
Melenhorst, A.S., Rogers, W.A. & Bouwhuis, D.G. (2006). Older adults’ motivated choice for technological innovation: Evidence for benefit-driven selectivity. Psychology and Aging 21(1), pp. 190-195.
Mitzner, T.L., Boron, J.B., Bailey Fausset, C., Adams, A.E., Charness, N. & Czaja, S.J. (2010). Older adults talk technology: Technology usage and attitudes. Computers in Human Behavior, 26 (6), pp. 1710-1721.
Moore, G. & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation . Information Systems Research, 2, pp. 192-222.
Oh, J.C. & Yoon, S.J. (2014). Predicting the use of online information services based on a modified UTAUT model. Behaviour & Information Technology, 33 (7), pp. 716-729.
Paidi, V., Xie, S., Coffie, R., Moran, B., Heikman, S., Keller, S., Chini, A., DenBaar, S. P., Mishra, U. K., Long, S. & Rodwell, M. J. W. (2003). High Linearity and High Efficiency of Class-B Power Amplifiers in GaN HEMT Technology. IEEE Tran. Microwave Theory and Tech, 51(2), pp. 643-652.
Pallapa, G. &. (2014). Adaptive and context-aware privacy preservation exploiting user interactions in smart environmentsG Pallapa, SK Das, M Di Francesco, T Aura. Pervasive and Mobile Computing, 12, pp. 232-243.
Renko, S. & Druzijanic, M. (2014). Perceived usefulness of innovative technology in retailing: consumers' point of view. J. Retail. Consum. Serv., 21 (5), pp. 836-843.
Robles, R.J & Kim, T. (2010). Applications, systems and methods in smart home technology: a review. Int. J. Adv. Sci. Technology, 15, pp. 37-48.
Romero, D., Maldonado, L., Fuentes, L. & Prine, L. (2015). Association of reproductive health training on intention to provide services after residency: the family physician resident survey. Fam Med. 2015;47, pp. 22-30.
Ryan, R.M., Lynch, M.F., Vansteenkiste, M. & Deci, E.L . (2011). Motivation and autonomy in counseling, psychotherapy, and behavior change: a look at theory and practice. Couns. Psychol., 39 (2), pp. 193-260.
Schillewaert, N., Ahearne, M.J., Frambach, R.T. & Moenaert, R.K. (2005). The adoption of information technology in the sales force. Industrial Marketing Management, 34, pp. 323-336.
Schwoerer, C. E., May, D. R., Hollensbe, E. C. & Mencl, J. (2005). General and specific self-efficacy in the context of a training intervention to enhance performance expectancy. Human Resource Development Quarterly, 16(1), pp. 111-129.
Sumak, B., Hericko, M. & Pusnik, M. (2011). A meta-analysis of e-learning technology acceptance: the role of user types and e-learning technology types. Computers in Human Behavior, 27 (6), pp. 2067-2077.
Sun, H. & Zhang, P. (2006). The role of moderating factors in user technology acceptance. Int J Hum Comput Stud, 64, pp. 53-78.
Taylor, S. & Todd, PA. (1995). Assessing IT Usage: The Role of Prior Experience. MIS Quarterly, 19, pp. 561-570.
Teo, T. (2009). Is there an attitude problem? reconsidering the role of attitude in the TAM. British Journal of Educational Technology, 40 (6), pp. 1139-1141.
Thompson, R.L., Higgins, C.A. & Howell, J.M. (1991). Personal Computing: Towards a Conceptual Model of Utilization. MIS Quarterly, 15, pp. 125-143.
Tsai, H.T. & Bagozzi, R.P. (2014). Contribution behavior in virtual communities: Cognitive, emotional, and social influences. MIS Quarterly, 38 (1), pp. 143-163.
Venkatesh, V. & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39 (2), pp. 273-315.
Venkatesh, V. & Davis F.D. (2000). A theoretical extension of the technology acceptance model: four longitudinal field studies. Management Science, 46 (2), pp. 186-204.
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), pp. 425-478.
Verdegem, P. & Marez, L. De. (2011). Rethinking determinants of ICT acceptance: towards an integrated and comprehensive overview. Technovation, 31 (8), pp. 411-423.
Wagner, N., Hassanein, K. & Head, M. (2010). Computer use by older adults: A multi-disciplinary review. Computers in Human Behavior, 26 (5), pp. 870-882.
Wang, Y.L., Meister, D.B. & Gray, P.H. (2013). Social influence and knowledge management systems use: Evidence from panel data. MIS Quarterly, 37 (1), pp. 299-313.
Warren, S.J., Donlinger, M.J. & Barab, S.A. (2008). A move towards PBL writing: effects of a digital learning environment designed to improve elementary student writing. Journal of Research on Technology in Education, 41, pp. 113-140.
Wu, K., Zhao, Y., Zhu, Q., Tan, X. & Zheng, H. (2011). A meta-analysis of the impact of trust on technology acceptance model: Investigation of moderating influence of subject and context type. International Journal of Information Management, 31, pp. 572-581.
Yusif, S., Soar, J. & Hafeez-Baig, A. (2016). Older people, assistive technologies, and the barriers to adoption: a systematic review. Int. J. Med. Inform, 94, pp. 112-116.
Zajicek, M. (2001). Interface design for older adults. In Proceedings of the EC/NSF Workshop on Universal Accessibility of Ubiquitous Computing: Providing for the Elderly, pp. 60-65.
Zhou, T. (2012). Examing Location-Based Services Usage from the Perspectives of Unified Theory of Acceptance and Use of Technology and Privacy Risk. Journal of Electronic Commerce Research, 13(2), pp. 135-144.
Zhou, T., Lu, Y. & Wang, B. (2010). Integrating TTF and UTAUT to explain mobile banking user adoption. Computers in Human Behavior, 26, pp. 760-767.
Zhuang, H., Sun, Y., Tang, J., Zhang, J. & Sun, X. (2013). Influence maximization in dynamic social networks. Data Mining (ICDM),2013 IEEE 13th International Conference on, (pp. 1313-1318).
吳統雄. (1990). 電話調查：理論與方法，第二版. 台灣: 聯經出版社.