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    Title: 比較「智慧型手機主導之行動網路購物」與「電腦網路購物」兩者之相對屬性與重要性
    Relative attribute importance of smartphone driven mobile commerce compared to computer based electronic commerce
    Authors: 柯思維
    Servio Fernando Kloeth
    Contributors: 郭貞
    郭貞

    Kuo, Cheng
    Kuo, Cheng

    柯思維
    Servio Fernando Kloeth
    Keywords: 手機網路交易
    電腦網路交易
    通路價值
    線上購物
    科技接受模型
    mobile commerce
    electronic commerce
    online shopping
    technology acceptance model
    channel value
    Date: 2012
    Issue Date: 2013-06-03 18:03:34 (UTC+8)
    Abstract: 現今智慧型手機之網路通路價值與電腦相比仍較低,也使得現今使用智慧型手機網路交易的比例仍低於使用電腦網路交易的比例。本研究採用付出為結構模型及恆等性分析,研究結果顯示,智慧型手機因其有用性及易用性較電腦低,因此使用者以手機網路交易的傾向也偏低。本研究以科技接受模型發現70%至80%的使用者都是因受社會及同儕影響,而較不傾向使用手機進行網路交易。一般認為,手機的便利性相對也使手機網路交易平台的風險提高。然而,研究結果顯示,以電腦從事網路交易的風險與手機網路交易的風險相當,便利性也幾無差異。因此本研究以社會影響為探討方向,認為其為影響現代人以手機從事網路交易的重要關鍵。
    The net channel value of smartphone driven mobile-commerce measured against the alternative of computer based electronic-commerce is at this point in time still low. In an exploratory effort structural modeling and invariance analysis reveals mobile commerce is viewed with a less positive usability disposition in the light of usefulness and effortlessness. An adaptation of the Technology Acceptance model accounting for 70-80% of usage intention indicates social influences experienced from peers to engage the mobile platform is lower. Convenience and perceived risk are usually considered attributes relatively important for the m-commerce platform. However, the analysis reveals little difference of these attributes` salience compared with e-commerce, absolute scores for convenience are similar, and perceived risk seems to have marginal effects on usage in general. Social influences, experienced as lower for mobile commerce is a especially salient concept in determining usability disposition and ultimately intention to use mobile commerce, as is the salience of the usability disposition larger for mobile commerce than for electronic commerce.
    Reference: Abelson, R .P., & Levi, A. (1985). Decision making and decision theory, in the handbook of social psychology. NY: Knopf. 231-309.

    Ajzen, I., Fishbein, M., (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs, NJ: Prentice-Hall.

    Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179–211.

    Anckar, B. (2002). Rationales for consumer adoption or rejection of e-commerce: Exploring the impact of product characteristics. Proceedings of the SSGRR 2002s International Conference.

    Anckar, B., & D’Incau, D. (2002). Value creation in mobile commerce: Findings from a consumer survey. The Journal of Information Technology Theory and Application, 4 (1), 43-64.

    Balasubraman, S., Peterson, R. A., & Jarvenpaa, S. L. (2002). Exploring the implications of m-commerce for markets and marketing. Journal of the Academy of Marketing Science, 30 (4), 348-361.

    Bandura, A. (1982). Self-efficacy mechanism in human agency. American Psychologist, 37 (2), 122-147.

    Beach, L. R., & Mitchell, T. R. (1978). A contingency model for the selection of decision strategies. Academy of Management Review, 3 (3), 439-449.

    Bentler, P. M. (1992). On the fit of models to covariances and methodology to the bulletin. Psychological Bulletin, 112, 400-404.

    Bhatnagar, A., Misra, S., & Rao, H. R. (2000). On risk, convenience, and internet shopping behavior, Communications of the ACM, 43 (11), 98–114.

    Bhattacherjee, A. (2000). Acceptance of e-commerce services: The case of electronic brokerages. IEEE Transactions on Systems, Man and Cybernetics 30 (4), 411–420.

    Bouwman, H., López-Nicolás, C., & Molina-Castillo, F. J. (2012). Consumer lifestyles: Alternative adoption patterns for advanced mobile services. International Journal of Mobile Communications, 10 (2), 169-189.

    Brown, L. G. (1989). The strategic and tactical implications of convenience in consumer product marketing. Journal of Consumer Marketing, 6, 13-19.

    Bruner, G. C. II., & Kumar, A. (2005). Explaining consumer acceptance of handheld Internet devices. Journal of Business Research, 58 (5), 553-558.

    Byrne, B. M., Shavelson, R. J., & Muthen, B. (1989). Testing for the equivalence of factor covariance and mean structures: The issue of partial measurement in variance. Psychological Bulletin, 105 (3), 456-466.

    Byrne, B. M. (2008). Testing for multigroup equivalence of a measuring instrument: A walk through the process. Psicothema, 20 (4), 872-882.

    Cavana, R. Y., Delanaye, B. L., & Sekaran, U. (2001). Applied business research: Qualitative and quantitative methods. Australia: John Wiley & Sons.

    Chang. M. (2012). NCC Unveils Taiwan`s 4G Telecom Schedule. Retrieved from http://www.taiwan4g.com/taiwan_4g_schedule.php

    Chang, C. C., Yan, C. F., & Tseng, J. S. (2012). Perceived convenience in an extended technology acceptance model: Mobile technology and English learning for college students. Australasian Journal of Educational Technology, 28 (5), 809-826.

    Cheon J., Lee S., Crooks S. M., & Song J. (2012). An investigation of mobile learning readiness in higher education based on the theory of planned behavior. Computers & Education, 59, 1054–1064.

    Chin, W. W., Mills, A. M., Steel, D. J., & Schwarz, A. (2012). Multi-group invariance testing: An illustrative comparison of pls permutation and covariance-based sem invariance analysis. 7th International Conference on Partial Least Squares. Retrieved from http://www.plsconference.com/Slides/PLS2012%20%28Chin,%20Mills,%20Steel,%20Schwarz%29.pdf

    CNN (2012). Fortune 500. Retrieved from http://money.cnn.com/magazines/fortune/fortune500/2012/full_list/

    Costello, A. B., & Osborne, W. (2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment Research & Evaluation, 10.

    Dallal, G. E. (2003). Why p=0.05? [Word document]. Retrieved from http://www.webpages.uidaho.edu/~brian/why_significance_is_five_percent.pdf

    Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. Management Information Systems Quarterly, 13 (3), 318–339.

    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.

    Donahue, B. H. (2006). The effect of partial measurement invariance on prediction. [Docteral dissertation]. Retrieved from University of Georgia Thesis and Dissertations.

    Doolin, B., Dillon, S., Thompson, F., & Corner, J. L. (2005). Perceived risk, internet shopping experience and online purchasing behavior: A New Zealand perspective. Journal of Global Information Management, 13 (2), 66-88.

    Elkington, K. S., Bauermeister, J. A., & Zimmerman, M. A. (2011). Do parents and peers matter? A prospective socio-ecological examination of substance use and sexual risk among African American youth. Journal of Acolescence, 34 (5), 1035-1047.

    Fogg, I. (2009). The "smartphone" is dead: Long live smart phones and smart gadgets. Forrester Research. Retrieved from http://www.forrester.com/rb/Research/smartphone_is_dead_long_live_smart_phones/q/id/53697/t/2

    Forsythe, S., Liu, C., Shannon, D., & Gardner L. C. (2009). Development of a scale to measure the perceived benefits and risks of online shopping. Journal of Interactive Marketing, 20 (2), 55-75.

    Gardner, M., & Steinberg, L. (2005). Peer influence on risk taking, risk preference, and risky decision making in adolescence and adulthood: An experimental study. Developmental Psychology, 41 (4), 625-635.

    George, J. F. (2002). Influences on the intent to make internet purchases. Internet Research: Electronic Networking Applications and Policy, 12 (2), 165-180.

    Greenspoon, P. J., & Saklofske, D. H. (1998). Confirmatory factor analysis of the multidimensional student`s life satisfaction scale. Personality and Individual Differences, 25, 965-971.

    Haenlein, M., & Kaplan, A. M. (2004). A beginner’s guide to partial least squares analysis. Understanding Statistics, 3 (4), 283–297.

    Henseler, J., Ringle, C. M., & Sinkovics, R.R. (2009). The use of partial least squares path modeling in international marketing. Advances in International Marketing, 20, 277–320.

    Hsu, M. H., Yen, C. H., Chiu, C. M., & Chang C. M. (2006). A longitudinal investigation of continued online shopping behavior: An extension of the theory of planned behavior. International journal of human-computer studies, 64, 889–904.

    Hofstede, G. (2001). Culture`s consequences: Comparing values, behaviors, institutions and organizations across nations. Thousand Oaks, CA; Sage.

    Hong, Z., & Yi, L. (2012) Research on the influence of perceived risk in consumer on-line purchasing decision. Physics Procedia, 24, 1304-1310.

    Hooper, D., Coughlan, J., & Mullen, M. R. (2008). Structural equation modelling: Guidelines for determining model fit. Electronic Journal of Business Research Methods, 6 (1), 53-60.

    Hung, M. C., Yang, S. T., & Hsieh, T. C. (2012). An examination of the determinants of mobile shopping continuance. International Journal of Electronic Business Management, 10 (1), pp. 29-37


    International data corp (2011). Low prices to drive Taiwan’s smartphone market in 2012: IDC. Retrieved from http://focustaiwan.tw/ShowNews/WebNews_Detail.aspx?Type=aECO&ID=201112190042

    Jarvenpaa, S. L., Tractinsky. N., & Vitale, M. (2000). Consumer trust in an internet store. Information Technology and Management, 1 (1–2), 45–71.

    Javadi, M. H. M., Dolatabadi, H. R., Nourbakhsh, M., Poursaeedi, A., & Asadollahi, A. R. (2012). An analysis of factors affecting on online shopping behavior of consumers. International Journal of Marketing Studies, 4 (5), 81-98.

    Jih, W. J. (2007). Effects of consumer-perceived convenience on shopping intention in mobile commerce: An empirical study. International Journal of E-Business Research, 3 (4), 33-48

    King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information & Management, 43, 740–755.

    Kim, S. (2010). The influence of likert scale format on response result, validity, and reliability of scale - using scales measuring economic shopping orientation. Journal of the Korean Society of Clothing and Textiles, 34 (6), 913-927.

    Kim, D. J., Ferrin D. L., & Rao, H. R. (2008). A trust-based consumer decision-making model in electronic commerce: The role of trust, perceived risk, and their antecedents. Decision Support Systems, 44, 544–564.

    Kohli, A. (1989). Determinants of influence in organizational buying: a contingency approach. Journal of Marketing, 53, 50–65

    Lee, M. C. (2009). Factors influencing the adoption of internet banking: An integration of TAM and TPB with perceived risk and perceived benefit. Electronic Commerce Research and Applications, 8, 130–141.

    Lee, Y. E., & Benbasat, I. (2004). A framework for the study of customer interface design for mobile commerce. International Journal of Electronic Commerce, 8 (3), 79–102.

    Lee, J. W., Jones, P. S., Mineyama, M., & Zhang, X. E. (2002). Cultural differences in responses to a likert scale. Research in Nursing & Health, 25 (4), 295–306.

    Lewis, W., Agarwal, R., & Sambamurthy, V. (2003). Sources of influence on beliefs about information technology use: an empirical study of knowledge workers. Management Information Systems Quarterly, 27 (4), 657-678.

    Li, Y. H., & Huang, J. W. (2009). Applying theory of perceived risk and technology acceptance model in the online shopping channel. Proceedings of World Academy of Science, Engineering and Technology, 41.

    Lim, W. M., & Ting, D. H. (2012). E-shopping: an analysis of the technology acceptance model. Modern Applied Science, 6 (4), 49-62.
    Limayem, M., Khalifa, M., & Frini, A. (2000). What makes consumers buy from internet? A longitudinal study of online shopping. IEEE Transactions on Systems, Man, and Cybernetics-part A: Systems and Humans, 30 (4), 421-432.

    Lin, W. B., Wang, M. K., & Hwang, K. P. (2010). The combined model of influencing on-line consumer behavior. Expert Systems with Applications, 37, 3236–3247

    Liu, C. C. (2010). Measuring and prioritising value of mobile phone usage. International Journal of Mobile Communications, 8 (1), 42-51.

    López-Nicolás, C., Molina-Castillo, F. J., & Bouwman, F. (2008). An assessment of advanced mobile services acceptance: Contributions from TAM and diffusion theory models. Information & Management, 45, 359-364.

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

    MacCallum, R. C., Browne, M. W., & Sugawara, H. M. (1996). Power analysis and determinationa of sample size for covariance structure modeling. Psychological Methods, 1, 130-149.

    Matell, M. S., & Jacoby, J. (1971). Is there an optimal number of alternatives for likert scales items? Study I: Reliability and validity. Educational and Psychological Measurement, 31, 657−674.

    Mformobile.com (2001). Where there’s a Will there’s a Way. Mformobile M-Comment Newsletter. Retrieved from: http://www.mformobile.com

    Nielsen. (2010). Global trends in online shopping. A Nielsen global consumer report.

    Pavlou, P. A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 7 (3), 69-103.

    Pavlou, P. A., & Chai, L. (2002). What drives electronic commerce across cultures? A cross-cultural empirical investigation of the theory of planned behavior. Journal of Electronic Commerce Research, 3 (4), 240-253.

    Pi, S. M., Liao, H. L., & Chen, H. M. (2012). Factors that affect consumers’ trust and continuous adoption of online financial services. International Journal of Business and Management, 7 (9).

    Pi, S. M., & Sangruang, J. (2011). The perceived risks of online shopping in Taiwan. Social Behavior and Personality, 39 (2), 275-285.

    Preston, C. C., & Colman, A. M. (2000). Optimal number of response categories in rating scales: Reliability, validity, discriminating power, and respondent preferences. Acta Psychologica, 104, 1−15.

    Quintal, V. A., Lee, J. A., & Soutar, G. N. (2010). Risk, uncertainty and the theory of planned behavior: A tourism example. Tourism Management, 31, 797–805.

    Ramayah, T., Rouibah, K., Gopi, M., & Rangel, G. J. (2009). A decomposed theory of reasoned action to explain intention to use internet stock trading among Malaysian investors. Computers in Human Behaviour, 25(6), 1222-1230.

    Rousseau, D. M., Sitkin, S. B., Burt, R. S., & Camerer, C. (1998). Not so different after all: A cross-discipline view of trust. Academy of Management Review, 23 (3), 393-404.

    Sarker, S., & Wells, J. P. (2003). Understanding: mobile handheld device use and adoption. Communications of the ACM, 46 (12), 35–41.

    Siau, K., Sheng, H., & Nah, F. F. H. (2004). The value of mobile commerce to customers. Proceedings of the Third Annual Workshop on HCI Research in MIS.

    Smith, A. (2010). Mobile access 2010. Pew Internet & American Life Project. Retrieved from http://www.pewinternet.org/Reports/2010/Mobile-Access-2010.aspx

    Stone, M. H. (2004). Substantive scale construction. In E. V. Smith Jr. & R. M. Smith (Eds.), Introduction to rasch measurement. Maple Grove, MN: JAM Press. 201−225.

    Tan, S. J. (1999). Strategies for reducing consumers` risk aversion in internet shopping. Journal of Consumer Marketing, 16 (2), 163-180.

    Tan, F.B., Yan, L., & Urquhart, C. (2007). The effect of cultural differences on attitude, peer influence, external Influence, and self-efficacy in actual online shopping behavior. Journal of Information Science and Technology, 4 (1), 3-23.

    Tasmin, R., & Woods, P. C. (N.D.). Linking knowledge management and innovation: A structural equation modeling approach. Innovation and Knowledge Management in Business Globalization: Theory & Practice. Retrieved from http://eprints.uthm.edu.my/195/1/rosmaini_tasmin.pdf

    Tsai, C. Y., Wang, C. C., & Lu, M. T. (2011). Using the technology acceptance model to analyze ease of use of a mobile communication system. Social Behavior and Personality, 39 (1), 65-70.

    Urbach, N., Ahlemann, F. (2010). Structural equation modeling in information systems research using partial least squares. Journal of Information Technology Theory and Application, 11 (2), 5-40.

    Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F, D. (2003). Acceptance of information technology: Toward a unified view. Management Information Systems Quarterly, 27 (3), 425-478.

    Venkatesh, V., Ramesh, V., & Massey, A.P. (2003). Understanding usability in mobile commerce. Communication of the ACM, 46 (12), 53-56.

    Wang, Y. S., Lin, H. H., & Luarn, P. (2006). Predicting consumer intention to use mobile service. Information Systems Journal, 16, 157–179.

    Yang, K. (2012). Consumer technology traits in determining mobile shopping adoption: An application of the extended theory of planned behavior. Journal of Retailing and Consumer Services, 19, 484-491.

    Yoon, C., & Kim, S. (2007). Convenience and TAM in a ubiquitous computing environment: The case of wireless LAN. Electronic Commerce Research and Applications, 6, 102–112.

    Zhu, D. S., Lee, Z. C., O’Neal, G. S., & Chen, Y. H. (2011). Mr. risk! Please trust me: Trust antecedents that increase online consumer purchase intention. Journal of Internet Banking and Commerce, 16 (3).
    Description: 碩士
    國立政治大學
    國際傳播英語碩士學位學程(IMICS)
    100461018
    101
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0100461018
    Data Type: thesis
    Appears in Collections:[國際傳播英語碩士學程] 學位論文

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