隨著電信業者的利潤邊際逐漸下滑，3.5G行動電話寬頻上網服務(HSPA)因能促進多元的行動加值服務，而被認為是現階段可增加業者營收的方法。本論文目的是要探索影響3.5G行動電話寬頻上網服務消費者使用行為的因素，使用結構化方程模式(SEM)來探索各因素間的因果關係，及估計這些因素對3.5G行動電話寬頻上網服務消費者態度及實際使用行為的影響效果係數，其問卷調查施行期間是2009年7月至9月間。
本研究以科技接受模式及計劃行為理論為基礎，並結合了消費者知覺產品涉入、知覺品質、知覺成本、知覺有趣而發展了一個混合模型來模式化及研究消費者對HSPA服務的接受度。研究結果顯示消費者知覺有用、知覺品質、知覺產品涉入對消費者態度有顯著正向直接影響，相反的，知覺品質對消費者態度有顯著負向直接影響，並且消費者知覺有用、知覺產品涉入及消費者態度對實際使用行為有顯著正向直接影響；其次，消費者知覺品質對消費者知覺有用及知覺有趣有顯著正向直接影響，消費者知覺品質對消費者知覺成本有顯著負向直接影響，消費者知覺有趣對消費者知覺有用有顯著正向直接影響。
另一項重要結論是消費者知覺產品涉入對消費者知覺有用、知覺品質、知覺有趣、消費者態度及消費者實際使用有顯著正向直接影響，尤其消費者知覺產品涉入對消費者實際使用的直接及間接總和影響效果是最大，扮演最重要的角色，其次才是消費者知覺有用，因此了解消費者產品涉入對於了解消費者與產品的關係及發展有效的行銷策略是很重要的。本研究結果對3G業者發展其行動網際網路服務或其他新的服務都具有寶貴的參考價值!

As profit margin gradually declines, 3.5G High Speed Packet Access (HSPA) service which facilitates diversified mobile services to consumers with its higher bandwidth than previously is expected to increase revenue of telecommunication operators. This paper aims to explore the factors influencing the subscribers to use HSPA service. I used structural equation model (SEM) to examine the causal relationship among the variables and estimate the effect of the factors on consumers’ attitude toward using 3.5G HSPA service and the actual use of 3.5G HSPA service. The survey data used in this study was collected in Taiwan from July through September in 2009.
I developed a hybrid model which was based on technology acceptance model and theory of planned behavior and incorporated four additional variables perceived cost, perceived quality, perceived enjoyment, and perceived product involvement to model user acceptance in the HSPA context. The empirical results show that perceived usefulness, perceived quality and perceived product involvement has significantly positive effect on consumers’ attitude. Contrarily, perceived cost has significantly negative effect on consumers’ attitude toward HSPA service. And, perceived usefulness, perceived product involvement and consumers’ attitude has significantly positive effect on actual use of HSPA service. In addition, perceived quality has significantly positive effect on perceived usefulness and perceived enjoyment toward HSPA service. Moreover, perceived quality has significantly negative effect on perceived cost toward using HSPA service. Perceived enjoyment has significantly positive effect on perceived usefulness toward HSPA service.
Another important conclusion is that perceived product involvement has significantly positive effects on perceived usefulness, perceived enjoyment, perceived quality, attitude and actual usage of HSPA service. Especially, perceived product involvement plays an important role and has the biggest total of direct and indirect effect on actual use, followed by perceived usefulness. Understanding consumers’ product involvement is important to realize the consumer-product relationship and develop the effective marketing strategies toward HSPA service. The research results provide valuable references for 3G operators to develop their marketing strategy of mobile Internet service or other new services.

1.Ahire, S.L., Golhar, D.Y., and Waller, M.A. (1996), “Development and validation of TQM implementation constructs”, Decision Sciences, Vol. 27, No. 1, pp. 23-56.
2.Ajzen, I. and Fishbein, M. (1980), Understanding Attitudes and Predicting Social Behavior, Prentice Hall, Englewood Cliffs, NJ, USA.
3.Ajzen, I. (1991), “The Theory of Planned Behavior,” Organizational Behavior and Human Decision Process, Vol. 50, pp. 179-211.
4.Ajzen, I. and Madden, T.J. (1986) “Prediction of goal-directed behavior: attitudes, intentions, and perceived behavioral control”, Journal of Experimental Social Psychology, 22, 453–474.
5.Anderson, J.C., Gerbing, D.W. (1988), “Structural equation modeling in practice: A review and recommended two-step approach”, Psychological Bulletin, Vol. 103, No. 3, pp. 411–423.
6.Bagozzi, R.P. and Yi, Y. (1988), “On the evaluation of structural equation models”, Academy of Marketing Science, Vol. 16, No. 1, pp. 74-94.
7.BBC (2006), “Personalized adverts on 3.5G HSPA”, available at: http://news. bbc. co.uk/2/hi/technology/6180509.stm
8.BBC (2007), “3.5G HSPA predicted to be a hit”, available at: http://news.bbc.co.uk /2/hi/technology/6639249.stm
9.Beatty, S.E. and Smith, S.M. (1987), "External search effort: an investigation cross several product categories", Journal of Consumer Research, Vol. 14, No. 1, pp. 83-95.
10.Beatty, Sharon E., Lynn R.K., and Pamela H. (1988), “The involvement- commitment model: Theory and implication”, Journal of Business Research, Vol. 16, pp. 149-167.
11.Bel, L. T. and Widows, R. (1999), “Product knowledge and product involvement as moderators of the effects of information on purchase decisions: A case study using the perfect information frontier approach”, The Journal of Consumer Affairs, Vol. 33, No. 1.
12.Bentler, P.M. (1990), “Comparative fit indexes in structural models”, Psychological Bulletin, Vol. 107, No. 2, pp238-246.
13.Blechar, J., Constantiou, I.D., and Damsgaard, J. (2006), “Exploring the influence of reference situations and reference pricing on mobile service user behaviour”, European Journal of Information Systems, Vol. 15, pp. 285-291.
14.Boston.com (2006), “Japan launches digital TV for cell phones”, available at: http://www.boston.com/business/technology/articles/2006/04/01/japan_launches_digital_tv_for_cell_phones/
15.Browne, M.W. and Cudeck, R. (1993), “Alternative ways of assessing model fit”, in K.A. Bollen and J.S. Long (Eds.), Testing Structural Equation Models, pp. 136–162, Thousand Oaks, CA: Sage.
16.Bruner, G.C. and Kumar, A. (2005), “Explaining consumer acceptance of handheld Internet devices”, Journal of Business Research, Vol. 58, pp. 553-558.
17.Cacioppo, J.T., Petty, R.E., and Morris, K.J. (1983), “Effects of need for cognition on message evaluation, recall and persuasion”, Journal of Personality and Social Psychology, Vol. 45, pp. 805-818.
18.Cai, L.A., Feng, R., and Breiter, D. (2004), “Tourist purchase decision involvement and information references”, Journal of Vacation Markering, pp. 138-149.
19.Carmines, E.G. and McIver, J.P. (1981), “Analyzing models with unobserved variables: Analysis of covariance structures”, In G.W. Bohrnstedt and E.F. Borgatta (eds.), Social Measurement: Current Issues. Newbury Park, CA: Sage, pp. 65–110.
20.Cellular-news (2007), “BenQ selects chips for Taiwan 3.5G HSPA trials”, available at: http://www.cellular-news.com/story/23091.php
21.Celsi, R.L. and Oslon, J.C. (1988), “The role of involvement in attention and comprehension processes”, Journal of Consumer Research, Vol. 15, pp. 210-224.
22.Chau, P.Y.K. and Hu, P.J.H. (2001), “Information technology acceptance by individual professionals: A model comparison approach”, Decision Sciences, Vol. 32, pp. 699–719.
23.Chau, P.Y.K. and Hu, P.J.H. (2002), “Investigating healthcare professionals’ decisions on telemedicine technology acceptance: An empirical test of competing theories”, Information and Management, Vol. 39, pp. 297–311.
24.Cheong, J.H. and Park, M.C. (2005), “Mobile Internet acceptance in Korea”, Internet Research, Vol. 15, No. 2, pp. 125-140.
25.Churchill, G.A. and Iacobucci, D. (2002), Marketing Research: Methodology Foundations, 9th edition. South-Western, USA.
26.Crawford, I.M. and Lomas, R.A. (1980), “Factor analysis – a tool for data reduction”, European Journal of Marketing, Vol. 14, No. 7, pp. 414-21.
27.Cronbach, L.J. (1951), “Coefficient alpha and the internal structure of tests”, Psychometrika, Vol. 16, No. 3, pp. 297-334.
28.Dabholkar, P.A. and Bagozzi, R.P. (2002), “An attitudinal model of technology-based self-service: Moderating effects of consumer traits and situational factors”, Journal of the Academy of Marketing Science, Vol. 30, No. 3, pp. 184-201.
29.Davis, F.D. (1989), “Perceived usefulness, perceived ease of use, and user acceptance of information technology”, MIS Quarterly, pp. 319-340.
30.Davis, F.D., Bagozzi, R.P. and Warshaw, P.R. (1989), “User acceptance of computer technology: A comparison of two theoretical models”, Management Science, Vol. 35, No. 8, pp. 982-1003.
31.Davis, F.D., 1993, User acceptance of information technology: System characteristics, user perceptions, and behavioral impacts, International Journal of Man Machine Studies, 38(3), 475-487.
32.Delone, W. and McLean, E. (2003), “The Delone and McLean model of information systems success: A ten-year update”, Journal of Management Information Systems, Vol. 19, No. 4, pp. 9-30.
33.Digitimes (2007), “Taiwan market: PTS kicks off 3.5G HSPA trial broadcast”, available at: http://www.digitimes.com/displays/a20070904PD201.html
34.Dodds, W.B., Monroe, K.B., and Grewal, D. (1991), “Effects of price, brand, and store information on buyers’ product evaluations”, Journal of Marketing Research, Vol. 28, No. 2, pp. 195-219.
35.EE Times Asia (2006), “ESC-Taiwan to host summit on 3.5G HSPA”, available at:http://www.eetasia.com/ART_8800426489_499495_e191920e200607_no.HTM
36.eMarketer (2007), “Japan: Marketing to a mobile society”, available at: http://www.emarketer.com/Reports/All/Emarketer_2000381.aspx?src=report_head_info_reports
37.Erlandson, C. and Ocklind, P. (1998), “WAP—the wireless application protocol”, Ericsson Review, Vol. 75, No. 4, pp. 150–153.
38.Eroglu, S.A., Machleit, K.A., and Davis, L.M. (2003), "Empirical testing of a model of online store atmospherics and shopper responses", Psychology and Marketing, Vol. 20, No. 2, pp. 139- 150.
39.Fabrigar, L.R., Wegener, D.T., MacCallum, R.C., and Strahan, E.J. (1999), “Evaluating the use of exploratory factor analysis in psychological research”, Psychological Methods, Vol. 4, pp. 272-299.
40.Fang, X., Chan, S., Brzezinski, J., and Xu, S. (2005), “Moderating effects of task type on wireless technology acceptance”, Journal of Management Information Systems, Vol. 22, No. 3, pp. 123-157.
41.Fishbein, M., and Ajzen, I. (1975), Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research, Reading, MA: Addison-Wesley.
42.Hansen, F. (1981), “Hemispheral lateralization: Implications for unserstanding consumer behavior”, Journal of Consumer Research, Vol. 8, pp. 23-36.
43.Fornell, C.R. and Larcker, D.F. (1981), “Evaluating structural equation models with unobservable variables and measurement error”, Journal of Marketing Research, Vol. 18, No.1, pp. 39–50.
44.Ha, I., Yoon, Y., and Choi, M. (2007), “Determinants of adoption of mobile games under mobile broadband wireless access environment”, Information and Management, Vol. 44, No. 3, pp. 276-286.
45.Hair, J. F., Black, W. C., Babin, B. J., Anderson, R.E. and Tatham, R. L. (2006). Multivariate Data Analysis, 6th ed., Englewood Cliffs, NJ: Prentice Hall.
46.Hair, J.F., Anderson, R.E., Tatham, R.L. and Black, W.C. (1998), Multivariate Data Analysis, Englewood Cliffs, NJ: Prentice Hall.
47.Hamilton, S. and Chervany, N.L. (1981), “Evaluating information system effectiveness: Part I comparing evaluation approaches”, MIS Quarterly, Vol. 5, No. 1, pp. 55-69.
48.Heijden, H. (2003), “Factors influencing the usage of website: The case of generic portal in the Netherlands”, Information and Management, Vol. 40, No. 6, pp. 541-549.
49.Heinonen, K. and Pura, M. (2006), “Classifying mobile services”, available at: http:// project.hkkk.fi/helsinkimobility/papers/Mobile%20Services_2_2.pdf
50.Hellier, P.K., Geursen, G.M., Carr, R.A. and Rickard, J.A. (2003), “Customer repurchase intention: A general structural equation model”, European Journal of Marketing, Vol. 37, No. 11/12, pp. 1762-1800.
51.Hooley, G.J. (1980), “The multivariate jungle: The academic’s playground but the manager’s minefield”, European Journal of Marketing, Vol. 14, No. 7, pp. 379-86.
52.Houston, M.J. and Rothschild, M.L., (1978), “Conceptual and methodological perspectives on involvement,” Research Frontiers in Marketing, pp.184-187.
53.Hsu, C.L., Lu, H.P., and Hsu, H.H. (2007), “Adoption of the mobile Internet: An empirical study of multimedia message service (MMS)”, Omega, Vol. 35, pp. 715-726.
54.Hu, P.J., Chau, P.Y.K., Sheng, O.R.L., and Tam, K.Y. (1999) “Examining the technology acceptance model using physician acceptance of telemedicine technology”. Journal of Management Information Systems, Vol. 16, pp. 91–112.
55.Hung, S.Y., Cheng, Y.K., and Chia, M.C. (2003), “Critical factors of WAP services adoption: An empirical study”, Electronic Commerce Research and Applications, Vol. 2, pp. 42-60.
56.Igbaria, M., Parasuraman, S., and Baroudi, J.J. (1996), “A motivational model of microcomputer usage”, Journal of Management Information Systems, Vol. 13, No. 1, pp. 127-143.
57.Joreskog, K.G. and Sorbom, D. (1993), LISREL 8 User’s Reference Guide, Morrisville, IN: Scientific Software.
58.Kaiser, H.F. (1974), “An index of factorial simplicity', Psychometrika, Vol. 39, pp. 31-6.
59.Kim, H.W., Chan, H.C., and Gupta, S. (2007), “Value-based adoption of mobile Internet: An empirical investigation”, Decision Support Systems, Vol. 43, pp. 111-126.
60.Koufteros, X.A. (1999), “Testing a model of pull production: A paradigm for manufacturing research using structural equation modeling”, Journal of Operations Management, Vol. 17, pp. 467-488.
61.Krugman, H.E. (1965), “The impact of television advertising: Learning without involvement”, Public Opinion Quarterly, Vol. 29, pp. 349-356.
62.Krugman, H.E. (1971), “Brain wave measure of media involvement”, Journal of Advertising Research, Vol. 11, pp. 3-10.
63.Krugman, H.E. (1982), “Memory without recall, exposure without perception”, Journal of Advertising Research, Classics 1, pp. 80-85
64.Kuo, Y.F. and Yen, S.N. (2009), “Toward an understanding of the behavior intension to use 3G mobile value-added service”, Computers in Human Behavior”, Vol. 25, pp. 103-110.
65.Lederer, A.L., Maupin, D.J., Sena, M.P., and Zhuang, Y. (2000), “The technology acceptance model and the World Wide Web”, Decision Support Systems, Vol. 29, pp. 269-282.
66.Legris, P., Ingham, J., and Collerette, P. (2003), “Why do people use information technology? A critical review of the technology acceptance mode”, Information and Management, Vol. 40, pp. 191-204.
67.Lei, P.W. and Dunbar, S.B. (2004), “Effects of score discreteness and estimating alternative model parameters on power estimation methods in structural equation modeling”, Structural Equation Modeling, Vol. 11, No. 1, pp. 20-44.
68.Liao, C. H., Tsou, C. W., and Huang, M. F. (2007), “Factors influencing the usage of 3G mobile services in Taiwan”, Online Information Review, Vol. 31, No. 6, pp. 759–774.
69.Lin, A. (2006), “The acceptance and use of a business-to-business information system”, International Journal of Information System, Vol. 26, pp. 386-400.
70.Lu, J., Yu, C.S., Liu, C. and Yao, J.E. (2003), “Technology acceptance model for wireless internet”, Internet Research: Electronic Networking Applications and Policy, Vol. 13, pp. 206–222.
71.Lu Y., Zhou T., and Wang B. (2009), “Exploring Chinese users’ acceptance of instant messaging using the theory of planned behavior, the technology acceptance model, and the flow theory”, Computer in Human Behavior, vol. 25, pp. 29-39.
72.Luarn, P. and Lin H.H. (2005), “Toward an understanding of the behavioral intention to use mobile banking”, Computers in Human Behavior, Vol. 21, pp. 873-891.
73.MacIntosh, R. (1998), “A confirmatory factor analysis of the affect balance scale in 38 nations: A research note”, Social Psychology Quarterly, Vol.61, No. 1, pp.83–91.
74.Mathieson, K. (1991), “Predicting user intentions: Comparing the technology acceptance model with theory of planned behavior”, Information Systems Research, Vol. 2, No. 3, pp. 173–191.
75.Mathieson, K., Peacock, E., and Chin, W.W. (2001), “Extending the technology acceptance model: The influence of perceived user resources”, The Data Base for Advances in Information Systems, Vol. 32, pp. 86-112.
76.McQuarrie E.F. and Munson, J.M. (1992), “A revised product involvement inventory: Improved usability and validity”, Diversity in Consumer Behavior: Advances in Consumer Research, Vol. 19, pp. 108-115.
77.Moon, J.W., Kim, Y.G. (2001), “Extending the TAM for a world-wide-web context”, Information and Management, Vol. 38, No. 4, pp. 217–230.
78.Nunnally, J. C. (1978), Psychometric Theory, 2nd edition. New York, NY: McGraw-Hill.
79.Nysveen, H., Pedersen, P.E., and Thorbjornsen, H. (2005), “Explaining intention to use mobile chat services: Moderating effects of gender”, Journal of Consumer Marketing, Vol. 22, No. 5, pp. 247-256.
80.Ophuis, P.A.M.O. and Trijp, H.C.M.V. (1995), “Perceived quality: A market driven and consumer oriented approach”, Food Quality and Preference, Vol. 6, pp. 177-183.
81.Olsson, U. H., Foss, Tron, Troye, S. V., and Howell, R. D (2000), “The performance of ML, GLS, WLS estimation in structural equation modeling under conditions of misspecification and nonnormality, Structural Equation Modeling, Vol. 7, No. 4, pp. 557-595.
82.Pedersen, P.E. and Ling, R. (2003), “Modifying adoption research for mobile internet service adoption: Cross-disciplinary interactions”, Proceedings of the 36th Hawaii International Conference on System Sciences (HICSS- 36), Big Island, Hawaii, 6–9 January. IEEE Computer Society Press, Los Alamitos, CA, USA.
83.Randall, E.S. (2002), ”Latent variable interaction modeling”, Structural Equation Modeling, Vol. 9, No. 1, pp. 40-54.
84.Priluck, R. and Till, B.D. (2004), ” The role of contingency awareness, involvement, and need for cognition in attitude formation”, Journal of the Academy of Marketing Science. Vol. 32, No. 3, pp. 329-344.
85.Research and Markets (2007), “Taiwan - Mobile Communications - Market Overview and 3G”, available at: http://www.researchandmarkets.com/reportinfo. asp?report_id =71892
86.Petty, R.E., Cacioppo, J.T., and Schumann, D. (1983), “Central and peripheral routes to advertising effectiveness: The moderating role of involvement“, Journal of Consumer Research, Vol. 10, pp. 135-146.
87.Ristola, A., Koivumaki, T., and Kesti, M. (2005), “The effect on familiar mobile device and usage time on creating perceptions toward mobile services”, Proceeding of the International Conference on Mobile Business (ICMB’05), pp. 384-391.
88.Rothschild, M.L. (1979), “Advertising strategies for high and low involvement situations,” in Attitude Research Plays for High Srakes, J. C. Maloney and B. Silverman (eds.), Chicago: American Marketing Association: 74-93.
89.Sandoval, G. (2006), “3.5G HSPA gets on a roll”, available at: CNET News.com, http://www.finnishmobiletv.com
90.Schultz, B. (2001), “The m-commerce fallacy”, Network World, Vol. 18, No. 9, pp. 77–82.
91.Segars, A.H. (1997), “Assessing the unidimensionality of measurement: A paradigm and illustration within the context of information systems research”, Omega, Vol. 25, No. 1, pp.107-121.
92.Shin, D.H. (2007), “User acceptance of mobile Internet: Implication for convergence technologies”, Information and Organization Journal, Vol. 19, pp. 472-483.
93.Song, J., Koo, C., Kim, Y. (2007), “Investigating antecedents of behavioral intention in mobile commerce”, Journal of Internet Commerce, Vol. 6, No. 1, pp 13-34.
94.Steiger, J. H. (1990), “Structural model evaluation and modification: An interval estimate approach”, Multivariate Behavioral Research, Vol.25, pp.173–180.
95.Snowden, S., Spafford, J., Michaelides, R., and Hopkins, J. (2006), “Technology acceptance and M-commerce in an operational environment”, Journal of Enterprise Information Management, Vol. 19, No. 5, pp. 525-539.
96.Sureshchandar, G.S., Rajendran, C., and Anantharaman, R.N. (2002), “Determinants of customer-perceived service quality: A confirmatory factor analysis approach”, Journal of Services Marketing, Vol. 16, No. 1, pp. 9-34.
97.Swanson, S.R. and Davis, J.C. (2003), “The relationship of differential Loci with perceived quality and behavioral intentions”, Journal of Services Marketing, Vol. 17, No. 2, pp. 202-219.
98.Taylor, S. and Todd, P.A. (1995), “Understand information technology usage: A test of competing models”, Information Systems Research, Vol. 6, No. 2, pp. 144-176.
99.Teo, T.S.H. and Pok, S.H. (2003), “Adoption of WAP-enabled mobile phones among Internet users”, Omega, Vol. 31, pp. 483-498.
100.Venkatesh, V. and Davis F.D. (2000), “A theoretical extension of the technology acceptance model: Four longitudinal field studies”, Management Science, Vol.46 No. 2, pp.186-204.
101.Venkatesh, V., and 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, Vol. 24, No. 1, pp. 115-139.
102.Vijayasarathy, L.R. (2004), “Predicting consumer intentions to use on-line shopping: The case for an augmented technology acceptance model”, Information and Management, Vol. 41, No. 6, pp.747-762.
103.Wakefield, R.L. and Whitten, D. (2006), “Mobile computing: A user study on hedonic/utilitarian mobile device usage”, European Journal of Information Systems, Vol. 15, pp. 292-300.
104.Wang, P., Gudergan, S. and Lings, I. (2008), “The role of product involvement in e-service evaluations”, International Journal of Electronic Marketing and Retailing, Vol. 2, No. 1, pp. 59-79.
105.Wang, Y.S., Lin, H.H, and Luarn, P. (2006), “Predicting consumer intention to use mobile service”, Information Systems Journal, Vol.16, pp.157-179.
106.Weber, A. (2007), “The convergence of mobile data phones, consumer electronics, and wallets: Lessons from Japan”, Telematics and Informatics, Vol. 24, pp. 180-191.
107.Wei, R. (2006), “Motivations for using the mobile phone for mass communications and entertainment”, Telematics and Informatics, Available online 18, forthcoming.
108.Wu, J.H. and Wang, S.C. (2005), “What drives mobile commerce? An empirical evaluation of the revised technology acceptance model”, Information and Management, Vol. 42, pp. 719-729.
109.Yang, K.C.C. (2005), “Exploring factors affecting the adoption of mobile commerce in Singapore”, Telematics and Informatics, Vol. 22, pp. 257-277.
110.Zaichkowsky, J.K. (1985), “Measuring the involvement construct”, Journal of Consumer Research, Vol. 12, No. 3, pp. 341-353.
111.Zeithaml, V.A., Berry, L.L., and Parasuraman, A. (1996), “The behavioral consequences of service quality”, Journal of Marketing, Vol. 60, pp. 31-46.