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姓名 鄭筱儒(Hsiao-Ru Cheng)  查詢紙本館藏   畢業系所 企業管理學系
論文名稱 科技接受意圖之前置因素探討:以數位匯流為例
(Investigating the Antecedent of Technology Acceptance Intentions: The Case of Digital Convergence)
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摘要(中) 科技接受領域隨著時代演進,已有相當多的研究從各種不同觀點延伸討論科技接受模型的前置因素。然而科技發展日新月異,僅以少數幾個前置因素進行討論無法完整契合解釋新科技接受行為意圖。本研究以全面性的觀點,彙總文獻影響科技接受意圖之前置因素,並以數位匯流的數位有線電視為例進行實證。本研究利用網路問卷方式進行調查,共回收有效問卷362份,以結構方程式進行本研究假設之驗證。研究結果發現知覺效益、社會因素以及知覺控制對於消費者使用數位有線電視的態度具有正向顯著影響。沉入成本對於消費者使用數位有線電視的態度具有負向顯著影響,而消費者態度又會影響到使用者的意圖。在調節效果的部分,沉入成本對態度的影響會受到性別的不同而有顯著的差異,研究結果顯示女性消費者在沉入成本對態度的影響結果是顯著的,而男性消費者的結果是不顯著的,表示對於女性消費者而言,過去所投資的資源能否運用在數位匯流產品上會影響個人的行為態度與意圖。
摘要(英) Interesting in technology acceptance model has grown dramatically in recent years, there are tremendous researches discussing the causes of technology acceptance model from different perspectives. However, science and technology change with each passing day, results on the relationships among few causes related to technology acceptance behavior, as well as the intention, are often incomplete. A comprehensive understanding of new technology acceptance behavioral intention thus remains elusive. Hence, we develop a model that employs the comprehensive perspective as predictors of technology acceptance model and test the proposed model in the context of a longitudinal field study of 362 users of digital cable TV via internet. Our results indicate that perceived benefits, social factor and perceived control are positively related to the attitude of consumer of using digital cable TV, sunk cost is negatively related to the attitude of consumer of using digital cable TV, further, the attitude of consumer has significant effect on the intention of consumer. This research also confirms moderating effects of gender in the relationship between sunk cost and attitude. The effect of sunk cost on attitude is significant on female consumer, yet, it is not significant on male consumer side. For female, it shows that the past investments on the products of digital convergence influence individual’s behavior and intention.
關鍵字(中) ★ 數位匯流
★ 科技接受
★ 購買意圖
★ 彙總分析
關鍵字(英) ★ Digital Convergence
★ Technology Acceptance
★ Purchase Intention
★ Meta-Analysis
論文目次 中文摘要 i
Abstract ii
誌謝辭 iii
目錄 iv
圖目錄 vi
表目錄 vii
第一章 緒論 1
1-1 研究背景與研究動機 1
1-2 研究目的 3
1-3 研究流程 4
第二章 文獻探討 5
2-1 理性行為理論 5
2-2 計畫行為理論 7
2-3 科技接受模式 8
2-4 整合型科技接受模式 9
2-5 科技接受意圖的前置因素 11
第三章 研究方法 18
3-1 前置因素彙整及操作型定義 18
3-2 問卷設計 20
3-3 研究對象與資料蒐集 25
3-4 統計分析方法 25
第四章 研究結果 27
4-1 敘述性統計分析 27
4-2 因素分析與彙整模型假設 29
4-2-1 因素分析 29
4-2-2 彙整模型假設 36
4-3 信度與效度分析 39
4-3-1 信度分析 39
4-3-2 效度分析 39
4-4 結構方程式分析與調節效果 41
4-4-1 配適度分析 41
4-4-2 模型驗證結果 41
4-4-3 調節效果 43
第五章 結論與建議 44
5-1 研究結論 44
5-2 實務意涵 46
5-3 研究限制與建議 48
參考文獻 49
附錄一、問卷 58
附錄二、數位匯流 64
參考文獻 中文文獻
[1] 張玉山. (2003). 數位匯流趨勢下電子通訊產業之管制變革與應有取向之研究.
[2] 劉幼琍. (2004). 世界重要國家有線電視數位化策略之比較分析暨我國有線電視全面數位化可行策略研析: 行政院新聞局有線廣播電視事業發展基金委託研究計畫.
[3] 資策會(2011),數位匯流發展方案及相關推動策略簡介
英文文獻
[1] Agarwal, R., and Karahanna, E. (2000). Time flies when you’re having fun: cognitive absorption and beliefs about information technology usage. MIS Quarterly, 24(4), 665-694.
[2] Aguilar‐Luzón, M.d.C., García‐Martínez, J.M.Á., Calvo‐Salguero, A., and Salinas, J.M. (2012). Comparative Study Between the Theory of Planned Behavior and the Value–Belief–Norm Model Regarding the Environment, on Spanish Housewives’ Recycling Behavior. Journal of Applied Social Psychology, 42(11), 2797-2833.
[3] Ahuja, M.K., and Thatcher, J.B. (2005). Moving beyond intentions and toward the theory of trying: effects of work environment and gender on post-adoption information technology use. MIS Quarterly, 29(3), 427-459.
[4] Ajzen, I. (1985). From intentions to actions: A theory of planned behavior: Springer.
[5] Ajzen, I., & Madden, T.J. (1986). Prediction of goal-directed behavior: Attitudes, intentions, and perceived behavioral control. Journal of Experimental Social Psychology, 22(5), 453-474.
[6] Al-Natour, S., Benbasat, I., and Cenfetelli, R. (2011). The adoption of online shopping assistants: perceived similarity as an antecedent to evaluative beliefs. Journal of the Association for Information Systems, 12(5), 2.
[7] Babrow, A.S., Black, D.R., and Tiffany, S.T. (1990). Beliefs, attitudes, intentions, and a smoking-cessation program: a planned behavior analysis of communication campaign development. Health Communication, 2(3), 145-163.
[8] Bagozzi, R.P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74-94.
[9] Bandura, A. (1978). Self-efficacy: Toward a unifying theory of behavioral change. Advances in Behaviour Research and Therapy, 1(4), 139-161.
[10] Bang, H.‐K., Ellinger, A.E., Hadjimarcou, J., and Traichal, P. A. (2000). Consumer concern, knowledge, belief, and attitude toward renewable energy: An application of the reasoned action theory. Psychology & Marketing, 17(6), 449-468.
[11] Bearden, W.O., Netemeyer, R.G., and Teel, J.E. (1989). Measurement of consumer susceptibility to interpersonal influence. Journal of Consumer Research, 473-481.
[12] Brockner, J., Rubin, J.Z., Fine, J., Hamilton, T.P., Thomas, B., and Turetsky, B. (1982). Factors affecting entrapment in escalating conflicts: The importance of timing. Journal of Research in Personality, 16(2), 247-266.
[13] Brown, S.A., Dennis, A.R., and Venkatesh, V. (2010). Predicting collaboration technology use: Integrating technology adoption and collaboration research. Journal of Management Information Systems, 27(2), 9-54.
[14] Brown, S. A., and Venkatesh, V. (2005). Model of adoption of technology in households: A baseline model test and extension incorporating household life cycle. MIS Quarterly, 399-426.
[15] Burnham, T.A., Frels, J.K., and Mahajan, V. (2003). Consumer switching costs: a typology, antecedents, and consequences. Journal of the Academy of Marketing Science, 31(2), 109-126.
[16] Chen, Z., and Dubinsky, A.J. (2003). A conceptual model of perceived customer value in e‐commerce: A preliminary investigation. Psychology and Marketing, 20(4), 323-347.
[17] Cheng, Y.‐M. (2011). Antecedents and consequences of e‐learning acceptance. Information Systems Journal, 21(3), 269-299.
[18] Commission, European. (1997). Green paper on the convergence of the telecommunications, media and information technology sectors, and the implications for regulation: Towards an information society approach: Office for Official Publications of the European Communities.
[19] Compeau, D.R., and Higgins, C.A. (1995). Application of social cognitive theory to training for computer skills. Information Systems Research, 6(2), 118-143.
[20] Cothran, T. (2011). Google Scholar acceptance and use among graduate students: A quantitative study. Library and Information Science Research, 33(4), 293-301.
[21] Darley, W.K., and Smith, R.E. (1995). Gender differences in information processing strategies: An empirical test of the selectivity model in advertising response. Journal of Advertising, 41-56.
[22] Davis, F.D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 319-340.
[23] Davis, F.D., Bagozzi, R.P., and Warshaw, P.R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace1. Journal of Applied Social Psychology, 22(14), 1111-1132.
[24] Ithiel de Sola Pool. (1983). Technologies of Freedom: Belknap Press.
[25] Doney, P.M., and Cannon, J.P. (1997). An examination of the nature of trust in buyer-seller relationships. The Journal of Marketing, 35-51.
[26] Dowling, G.R., and Staelin, R. (1994). A model of perceived risk and intended risk-handling activity. Journal of consumer research, 119-134.
[27] Economides, N. (1996). The economics of networks. International journal of industrial organization, 14(6), 673-699.
[28] Fishbein, M., and Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Reading (MA): Addison-Wesley.
[29] Flannery, B.L., and May, D.R. (2000). Environmental ethical decision making in the US metal-finishing industry. Academy of Management Journal, 642-662.
[30] Fornell, C., and Larcker, D.F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of Marketing Research, 382-388.
[31] Goodhue, D.L., and Thompson, R.L. (1995). Task-technology fit and individual performance. MIS Quarterly, 213-236.
[32] Ha, I., Yoon, Y., and Choi, M. (2007). Determinants of adoption of mobile games under mobile broadband wireless access environment. Information and Management, 44(3), 276-286.
[33] Hair, J.F., Anderson, R.E., Tatham, R.L., and Black, W.C. (1998). Multivariate data analysis, 5th. NY: Prentice Hall International.
[34] Hair, J.F., Bush, R.P., and Ortinau, D.J. (2006). Marketing research: McGraw-Hill/Irwin.
[35] Hardgrave, B.C., Davis, F.D., and Riemenschneider, C.K. (2003). Investigating determinants of software developers’ intentions to follow methodologies. Journal of Management Information Systems, 20(1), 123-152.
[36] Hoffman, L.R., and Kleinman, G.B. (1994). Individual and Group in Group Problem Solving The Valence Model Redressed. Human Communication Research, 21(1), 36-59.
[37] Hsieh, J.J., Rai, A., and Keil, M. (2008). Understanding digital inequality: Comparing continued use behavioral models of the socio-economically advantaged and disadvantaged. Mis Quarterly, 32(1), 97-126.
[38] Hsu, C.-L., and Lin, J.C.-C. (2008). Acceptance of blog usage: The roles of technology acceptance, social influence and knowledge sharing motivation. Information and Management, 45(1), 65-74.
[39] Hsu, C.-L., and Lu, H.-P. (2004). Why do people play on-line games? An extended TAM with social influences and flow experience. Information and Management, 41(7), 853-868.
[40] Hu, L.‐t., and Bentler, P.M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55.
[41] Igbaria, M., Iivari, J., and Maragahh, H. (1995). Why do individuals use computer technology? A Finnish case study. Information & Management, 29(5), 227-238.
[42] Im, I., Hong, S., and Kang, M.S. (2011). An international comparison of technology adoption: Testing the UTAUT model. Information and Management, 48(1), 1-8.
[43] ITU-T, Recommendation Z, and Recommendation, Z. (1996). 120: Message sequence chart (MSC). ITU-T, Geneva, 27.
[44] Jain, H., Ramamurthy, K., Ryu, H.-S., and Yasai-Ardekani, M. (1998). Success of data resource management in distributed environments: an empirical investigation. MIS Quarterly, 1-29.
[45] Jarvenpaa, S.L., Tractinsky, N., and Saarinen, L. (1999). Consumer Trust in an Internet Store: A Cross‐Cultural Validation. Journal of Computer‐Mediated Communication, 5(2).
[46] Jones, M.A., Mothersbaugh, D.L., and Beatty, S.E. (2002). Why customers stay: measuring the underlying dimensions of services switching costs and managing their differential strategic outcomes. Journal of Business Research, 55(6), 441-450.
[47] Judd, B.R., and Weissenberger, S. (1982). A systematic approach to nuclear safeguards decision-making. Management Science, 28(3), 289-302.
[48] Kaiser, H.F. (1974). An index of factorial simplicity. Psychometrika, 39(1), 31-36.
[49] Karahanna, E., and Straub, D.W. (1999). The psychological origins of perceived usefulness and ease-of-use. Information and Management, 35(4), 237-250.
[50] Kauffman, R.J., McAndrews, J., and Wang, Y.-M. (2000). Opening the “black box” of network externalities in network adoption. Information Systems Research, 11(1), 61-82.
[51] Keil, M., Mann, J., and Rai, A. (2000). Why software projects escalate: an empirical analysis and test of four theoretical models 1, 2. Mis Quarterly, 24(4), 631-664.
[52] Kim, H.-W., and Kankanhalli, At. (2009). Investigating user resistance to information systems implementation: A status quo bias perspective. Mis Quarterly, 33(3), 567-582.
[53] Kim, J. (2006). Toward an understanding of Web‐based subscription database acceptance. Journal of the American Society for Information Science and Technology, 57(13), 1715-1728.
[54] Kim, S., and Garrison, G. (2010). Understanding users’ behaviors regarding supply chain technology: Determinants impacting the adoption and implementation of RFID technology in South Korea. International Journal of Information Management, 30(5), 388-398.
[55] Kwon, O., and Wen, Y. (2010). An empirical study of the factors affecting social network service use. Computers in Human Behavior, 26(2), 254-263.
[56] Laukkanen, T. (2007). Internet vs mobile banking: comparing customer value perceptions. Business Process Management Journal, 13(6), 788-797.
[57] Lee, H.-Y., Ahn, H., and Han, I. (2007). VCR: Virtual community recommender using the technology acceptance model and the user’s needs type. Expert Systems with Applications, 33(4), 984-995.
[58] Lee, M.K.O., Cheung, C.M.K., and Chen, Z. (2005). Acceptance of Internet-based learning medium: the role of extrinsic and intrinsic motivation. Information and Management, 42(8), 1095-1104.
[59] Lee, Y.-C. (2006). An empirical investigation into factors influencing the adoption of an e-learning system. Online Information Review, 30(5), 517-541.
[60] Liker, J.K., and Sindi, A.A. (1997). User acceptance of expert systems: a test of the theory of reasoned action. Journal of Engineering and Technology management, 14(2), 147-173.
[61] Lin, H.-F. (2011). An empirical investigation of mobile banking adoption: the effect of innovation attributes and knowledge-based trust. International Journal of Information Management, 31(3), 252-260.
[62] Loiacono, E.T., Watson, R.T., and Goodhue, D.L. (2007). WebQual: an instrument for consumer evaluation of web sites. International Journal of Electronic Commerce, 11(3), 51-87.
[63] Lou, H., Luo, W., and Strong, D. (2000). Perceived critical mass effect on groupware acceptance. European Journal of Information Systems, 9(2), 91-103.
[64] Lu, H., and Lin, J.C.-C. (2002). Predicting customer behavior in the market-space: a study of Rayport and Sviokla’s framework. Information & Management, 40(1), 1-10.
[65] Lu, Y., Deng, Z., and Wang, B. (2010). Exploring factors affecting Chinese consumers’ usage of short message service for personal communication. Information Systems Journal, 20(2), 183-208.
[66] Lutz, R.J. (1977). An experimental investigation of causal relations among cognitions, affect, and behavioral intention. Journal of Consumer Research, 197-208.
[67] Lynne, G.D., Franklin, C.C., Hodges, A. and Rahmani, M. (1995). Conservation technology adoption decisions and the theory of planned behavior. Journal of economic psychology, 16(4), 581-598.
[68] Madsen, M., and Gregor, S. (2000). Measuring human-computer trust. Paper presented at the Proceedings of Eleventh Australasian Conference on Information Systems.
[69] Mason, P. (1986). Brood parasitism in a host generalist, the shiny cowbird: II. Host selection. The Auk, 61-69.
[70] Mathieson, K. (1991). Predicting user intentions: comparing the technology acceptance model with the theory of planned behavior. Information systems research, 2(3), 173-191.
[71] McGrath, J.E., Kelly, J.R., and Machatka, D.E. (1984). The social psychology of time: Entrainment of behavior in social and organizational settings. Applied social psychology annual.
[72] Meyers-Levy, J. (1988). The influence of sex roles on judgment. Journal of consumer research, 522-530.
[73] Meyers-Levy, J., and Sternthal, B. (1991). Gender differences in the use of message cues and judgments. Journal of Marketing Research, 84-96.
[74] Moore, G.C., and Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information systems research, 2(3), 192-222.
[75] Negroponte, N., Tellaroli, S., Zellmeister, G., and Petit, C. (1995). A vida digital: Companhia das letras São Paulo.
[76] Nel, J., and Raleting, T. (2012). Gender differences in low-income non-users’ attitude towards Wireless Internet Gateway cellphone banking.
[77] Nunnally, J. (1978). Psychometric theory: New York: McGraw-Hill.
[78] Nysveen, H., Pedersen, P.E., and Thorbjørnsen, H. (2005). Explaining intention to use mobile chat services: moderating effects of gender. Journal of Consumer Marketing, 22(5), 247-256.
[79] Parasuraman, A. (2000). Technology Readiness Index (TRI) a multiple-item scale to measure readiness to embrace new technologies. Journal of Service Research, 2(4), 307-320.
[80] Pavlou, P.A., Liang, H., and Xue, Y. (2007). Understanding and mitigating uncertainty in online exchange relationships: A principal-agent perspective. Mis Quarterly, 31(1), 105-136.
[81] Ping, R.A. (1993). The effects of satisfaction and structural constraints on retailer exiting, voice, loyalty, opportunism, and neglect. Journal of Retailing, 69(3), 320-352.
[82] Polites, G.L., and Karahanna, E. (2012). Shackled to the status quo: the inhibiting effects of incumbent system habit, switching costs, and inertia on new system acceptance. MIS Quarterly, 36(1), 21-42.
[83] Polites, G.L., and Watson, R.T. (2009). Using social network analysis to analyze relationships among IS journals. Journal of the Association for Information Systems, 10(8), 595-636.
[84] Pura, M. (2005). Linking perceived value and loyalty in location-based mobile services. Managing Service Quality, 15(6), 509-538.
[85] Quinlan, S.L., Jaccard, J., and Blanton, H. (2006). A decision theoretic and prototype conceptualization of possible selves: Implications for the prediction of risk behavior. Journal of Personality, 74(2), 599-630.
[86] Ram, S., and Sheth, J.N. (1989). Consumer resistance to innovations: the marketing problem and its solutions. Journal of Consumer Marketing, 6(2), 5-14.
[87] Rice, R.E. (1987). Computer‐mediated communication and organizational innovation. Journal of Communication, 37(4), 65-94.
[88] Ring, P. S. and A. H. Van De Ven. (1994). Developmental processes of cooperative interorganizational relationships. Academy of management review, 90-118.
[89] Rogers, E.M. (1995). Diffusion of Innovation. 4th: New York: Free Press.
[90] Rosenstock, I.M. (1974). The health belief model and preventive health behavior. Health Education and Behavior, 2(4), 354-386.
[91] Ryan, M.J., and Bonfield, E.H. (1980). Fishbein’s intentions model: a test of external and pragmatic validity. The Journal of Marketing, 82-95.
[92] Samuelson, W., and Zeckhauser, R. (1988). Status quo bias in decision making. Journal of Risk and Uncertainty, 1(1), 7-59.
[93] Sarker, S., Valacich, J.S., and Sarker, S. (2005). Technology adoption by groups: A valence perspective. Journal of the Association for Information Systems, 6(2), 3.
[94] Schilke, O., and Wirtz, B.W. (2012). Consumer acceptance of service bundles: An empirical investigation in the context of broadband triple play. Information and Management, 49(2), 81-88.
[95] Sheppard, B.H., Hartwick, J., and Warshaw, P.R. (1988). The theory of reasoned action: A meta-analysis of past research with recommendations for modifications and future research. Journal of consumer Research, 15(3), 325-343.
[96] Shin, D.-H. (2007). User acceptance of mobile Internet: Implication for convergence technologies. Interacting with Computers, 19(4), 472-483.
[97] Shin, D.H. (2009). Determinants of customer acceptance of multi-service network: An implication for IP-based technologies. Information & Management, 46(1), 16-22.
[98] Short, J., Williams, E., and Christie, B. (1976). The social psychology of telecommunications. New York, NY.
[99] Sung, Y., and Choi, S.M. (2010). “I won’t leave you although you disappoint me”: The interplay between satisfaction, investment, and alternatives in determining consumer–brand relationship commitment. Psychology and Marketing, 27(11), 1050-1073.
[100] Sweeney, J.C., and Soutar, G.N. (2001). Consumer perceived value: the development of a multiple item scale. Journal of retailing, 77(2), 203-220.
[101] Taylor, S., and Todd, P. (1995). Assessing IT usage: The role of prior experience. MIS Quarterly, 561-570.
[102] Taylor, S., and Todd, P.A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144-176.
[103] Teo, T.S.H., Lim, V.K.G., and Lai, R.Y.C. (1999). Intrinsic and extrinsic motivation in Internet usage. Omega, 27(1), 25-37.
[104] Tornatzky, L.G., and Klein, K.J. (1982). Innovation characteristics and innovation adoption-implementation: A meta-analysis of findings. Engineering Management, IEEE Transactions on,29(1), 28-45.
[105] Triandis, H.C. (1980). Values, attitudes, and interpersonal behavior. Paper presented at the Nebraska Symposium on Motivation. Nebraska Symposium on Motivation.
[106] Tseng, F.-M., and Lo, H.-Y.. (2011). Antecedents of consumers’ intentions to upgrade their mobile phones. Telecommunications Policy, 35(1), 74-86.
[107] Van der Heijden, H. (2004). User acceptance of hedonic information systems. MIS Quarterly, 695-704.
[108] Van Raaij, E.M., and Schepers, J.J.L. (2008). The acceptance and use of a virtual learning environment in China. Computers and Education, 50(3), 838-852.
[109] Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342-365.
[110] Venkatesh, V., Morris, M.G., Davis, G.B., and Davis, F.D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 425-478.
[111] Venkatesh, V., Speier, C., and Morris, M.G. (2002). User acceptance enablers in individual decision making about technology: Toward an integrated model. Decision Sciences, 33(2), 297-316.
[112] Voss, G.B., Parasuraman, A., and Grewal, D. (1998). The roles of price, performance, and expectations in determining satisfaction in service exchanges. The Journal of Marketing, 62(4), 46-61.
[113] Wang, H.-Y., and Wang, S.-H. (2010). User acceptance of mobile internet based on the Unified Theory of Acceptance and Use of Technology: Investigating the determinants and gender differences. Social Behavior and Personality: An International Journal, 38(3), 415-426.
[114] Xue, Y. (2009). Avoidance of information technology threats: a theoretical perspective. MIS Quarterly, 33(1), 71-90.
[115] Yang, K., and Jolly, L.D. (2008). Age cohort analysis in adoption of mobile data services: gen Xers versus baby boomers. Journal of Consumer Marketing, 25(5), 272-280.
[116] Zucker, L.G. (1986). Production of trust: Institutional sources of economic structure, 1840–1920. Research in organizational behavior.
指導教授 洪秀婉 審核日期 2013-6-26
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