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研究生:黃子芳
研究生(外文):Huang Tzu Fang
論文名稱:保險從業員對智慧型手機的接受模式之研究-以某產物保險公司為例
論文名稱(外文):An investigation on smartphone acceptance among insurance sales agent– A case of insurance companyAn investigation on smartphone acceptance among insurance sales agent – A case of insurance company
指導教授:張鐸張鐸引用關係
學位類別:碩士
校院名稱:樹德科技大學
系所名稱:資訊管理研究所
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2008
畢業學年度:96
語文別:中文
論文頁數:85
中文關鍵詞:智慧型手機科技接受模式自我效能電腦自我效能創新擴散理論
外文關鍵詞:Smart PhoneTechnology Acceptance ModelSelf-EfficacyComputer Self-EfficacyInnovation Diffusion Theory
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在資訊快速發展的時代中,行動通訊已成為人們生活中不可或缺的工具,為了做到行動辦公室的方便性,智慧型手機也因此產生。目前有許多保險公司,希望利用智慧型手機使公司對於人員的調度或是業務員可以更快速的給予客戶服務等來提升工作效率和輕鬆的可以獲得即時資訊。但在實際使用過後,卻呈現效果不彰的現象,於是本研究主要針對該公司的業務員對於將智慧型手機應用於工作的接受度做一個調查研究,並對於研究的結果提出改善現況的建議,期望可以提供後續研究者或是使用此策略的公司做為一個參考或改善的依據。

本研究主要是採用科技接受模式(Technology Acceptance Model, TAM)做為整體的研究基礎架構,配合電腦自我效能(Computer Self-Efficacy, CSE)與創新擴散理論(Innovation Diffusion Theory, IDT) 作為保險業務員接受模式之假設模式,並透過問卷調查的方式來實證研究。

經過研究後發現,認知易用、認知有用、工作特性、個人特性及組織特性均會正向的影響保險業務員對於智慧型手機的接受模式,而電腦自我效能會正向的影響認知易用和認知有用,且認知易用也會正向的影響認知有用。研究結果經驗證後均達到顯著水準。表示出此模式能有效預測使用者的接受度並給予良好的解釋能力。

關鍵詞: 智慧型手機、科技接受模式、自我效能、電腦自我效能、創新擴散理論。
With the rapid development of information, the mobile communication has been an essential tool in daily life. For the convenience of Mobile Office, the Smart Phone appears. Recently, in order to deploy staffs and provide insurance businessmen for giving consumer service immediately, many insurance companies expect to improve the work efficiency and get the instantaneous information easily through using the Smart Phones. However, no efficient effects happen after application of the Smart Phones. Therefore, this study plans to investigate the insurance businessmen’s acceptance of using the Smart Phones on work. The results will be used to modify the current situation and provide a reference or a basis to implement this strategy for the following researchers or companies.
This empirical study adopts the Technology Acceptance Model (TAM) as a research structure. Also, the Computer Self-efficacy (CSE) and Innovation Diffusion Theory (IDT) are the hypothesis models of the acceptance model among insurance businessmen proved through survey.
The results provide there are positive impacts of the perceived usefulness, perceived ease of use, working characteristics, personal characteristics, and organizational characteristics on the acceptance model of Smart Phone among insurance businessmen. Moreover, the CSE positively influences the perceived usefulness and perceived ease of use, and the perceived ease of use also positively influences the perceived usefulness. All experimental outcomes show significant effects, and this model is proved to be able to effectively predict and explain the acceptance behavior.

Keywords. Smart Phone; Technology Acceptance Model; Self-Efficacy; Computer Self-Efficacy; Innovation Diffusion Theory.
中文摘要 --------------------------------------- i
英文摘要 --------------------------------------- ii
誌謝 --------------------------------------- iii
目錄 --------------------------------------- iv
表目錄 --------------------------------------- v
圖目錄 --------------------------------------- vi
第一章 緒論----------------------------------- 1
第一節 研究背景與動機------------------------- 1
第二節 研究目的------------------------------- 2
第三節 研究限制及預期貢獻--------------------- 3
第四節 研究流程------------------------------- 3
第五節 論文架構------------------------------- 5
第二章 文獻探討------------------------------- 7
第一節 智慧型手機----------------------------- 7
第二節 科技接受模式--------------------------- 13
第三節 自我效能與電腦自我效能----------------- 19
第四節 創新擴散理論--------------------------- 27
第三章 研究方法------------------------------- 33
第一節 研究架構------------------------------- 33
第二節 研究假設------------------------------- 34
第三節 研究方法------------------------------- 36
第四章 資料分析------------------------------- 44
第一節 敘述性統計分析------------------------- 44
第二節 信度分析------------------------------- 47
第三節 各屬性與研究變項間之差異分析----------- 49
第四節 各構面之相關性分析--------------------- 59
第五節 迴歸分析------------------------------- 61
第五章 結論與建議----------------------------- 71
第一節 研究貢獻------------------------------- 71
第二節 研究結論------------------------------- 72
第三節 研究建議------------------------------- 74
第四節 研究限制------------------------------- 75
第五節 後續研究建議--------------------------- 76
參考文獻 --------------------------------------- 77
附錄一 --------------------------------------- 82
[1]Mobile Business季刊,民93,第十一期March。
[2]朱經明,民78年,教育統計學,頁78。
[3]何文斌,民90,國小行政人員網路素養對行政網路資訊系統接受度之研究--以台南市為例,台南師範學院國民教育研究所碩士論文。
[4]吳文雄,民91,”國民中小學教師之電腦焦慮、電腦自我效能、電腦因應策略與電腦素養之相關研究”,師大學報:科學教育類,47 卷,1 期,頁 39-54。
[5]吳俊毅,民89,科技接受模型之實徵研究—從動機角度,國立中央大學資訊管理研究所碩士論文。
[6]吳萬益,民94,企業研究方法(2版)。台北市:華泰。
[7]吳肇銘,民 87,影響網站使用意向之因素研究—以入門網站為例,國立中央大學資訊管理研究所博士論文。
[8]周素華,民 82,電子通訊科技使用行為之研究─以交大學生為例,國立交通大學土木工程研究所碩士論文。
[9]麥孟生,民89,個人心理類型、自我效能及態度對電腦學習成效之影響,國力中央大學資訊管理研究所碩士論文。
[10]陳淑鳳,民90,電子化政府下國稅稽徵人員資訊科技接受行為模式之研究,國立中山大學公共事務管理研究所碩士論文。
[11]陳焜元,民 84,行政管理資訊系統使用者參與效果之研究--技術接受性模式檢證,國立政治大學公共行政學系碩士論文。
[12]陳順宇,民 93,多變量分析(3 版) 。台北市:華泰。
[13]國家傳播委員會(NCC),民96,各類電信服務用戶普及率。
[14]黃欣儀,民90,影響中小學教師網路進修使用程度相關因素之研究,國立中山大學資訊管理學系研究所碩士論文。
[15]張意珮,民92。真的很 smart 的 smartphone--談智慧型手機定義及未來趨 勢。拓墣產業研究所焦點報告,手機與行動通訊 No.16,1-6。
[16]覃業明,民89,科技接受度模型之實證研究—以國內醫療網站為例,國立成功大學資訊管理研究所碩士論文。
[17]馮炫竣,民 89,消費者使用電子銀行之行為研究--以ATM、電話銀行及網路銀行為例,元智大學管理研究所碩士論文。
[18]趙珮如,民92,醫療產業員工之資訊科技接受模式─以中南部地區為例,樹德科技大學資訊管理研究所碩士論文。
[19]劉常勇,民91,創業管理的十二堂課,台北:智勝文化事業股份有限公司。
[20]劉進福,民92,由組織情境因素探討國中行政人員對行政管理資訊系統接受度之研究,國立高雄師範大學教育學系學校行政碩士班碩士論文。
[21]謝至豪,民96,中文維基百科編輯者之系統接受度研究,世新大學資訊傳播研究所碩士論文。
[22]謝銘仁,民94,智慧型手機在物流業營業公司之接受度研究─以新竹貨運為例,國立成功大學交通管理科學研究所碩士論文。
[23]謝靜慧,民89,國民中小學教師之電腦焦慮、電腦自我效能、電腦因應策略與電腦素養之相關研究,國立中山大學教育研究所碩士論文。
[24]蕭銘宏,民95,企業發展行動商務之接受度評估─以大榮汽車貨運股份有限公司為例,國立雲林科技大學資訊管理研究所碩士論文。
[25]蘇席儀,民89,商品資訊搜尋任務暨網際網路特性之配適程度對網站接受度之影響,國立臺灣大學資訊管理研究所碩士論文。
[26]ACCESS Linux Platform From:http://alp.access-company.com/
[27]Agarwal, R., & Prasad, J., 1997. The role of innovation characteristics and perceived voluntariness in the acceptance of information technologies. Decision Science, Vol.28, No.3, pp.557-582.
[28]Bajaj, A. & S. R. Nidumolu, 1998, “A feedback model to understand information system usage”, Information and Management, Vol. 33, pp.213-224.
[29]Bandura, A., 1977, “Self-efficacy: Toward a unifying theory of behavioral change,” Psychological Review, Vol.84, No.2, pp.191-215.
[30]Bandura, A., 1982, “Self-efficacy mechanism in human agency,” American Psychologist, Vol.37, No.2, pp.122-147.
[31]Bandura, A., 1986, Social foundations of thought and actions: a social cognitive theory, Englewood Cliffs, Prentice Hall, NJ.
[32]Clark, J. & Guy, K., 1998, “Innovation and Competitiveness: A Review”, Technology Analysis & Strategic Management, Vol.10, No.3, pp.363-395
[33]Chau, P. Y. K., 1996. An empirical assessment of a modified technology acceptance model. Journal of Management Information Systems, Vol.13, No.2, pp.185-204.
[34]Chen, L. D., Gillenson, M. L., & Sherrell, D. L., 2002. Enticing online consumers: an extended technology acceptance perspective. Information & Management, Vol.39, No.8, pp.705-719.
[35]Compeau, D., & C. A., Higgins, 1995, “Computer Self-efficacy: Development of a measure and initial test”, MIS Quarterly, Vol. 19, pp.189-211.
[36]Compeau, D., C. A. Higgins, & S. Huff, 1999, “Social cognitive theory and individual reactions to computing technology: A longitudinal study”, MIS Quarterly, Vol.23, No.2, pp.145-158.
[37]Davis, F. D., 1993, “User acceptance of information technology: system characteristics, user perceptions and behavioral impacts”, International Journal of Man Machine Studies, Vol. 38, pp.475-487.
[38]Davis, F.D., R.P. Bagozzi, & P.R. Warshaw, 1989, “User Acceptance of Computer Technology: A Comparison of Two Theoretical Models”, Management Science, Vol.35, No.8, pp.982-1003.
[39]Faseyitan S. O. & J. Hirschbuhl, 1992, “Computers in University Instruction: What are the Significant Variables that Influence Adoption?”, Interactive Learning International, Vol. 8, pp.185-194.
[40]Fishbein, M. & I. Ajzen, 1975, Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research. Addison-Wesley, Boston, MA.
[41]Gefen, D., & M. Keil, 1998, “The impact of developer responsiveness on perceptions of usefulness and ease of use: an extension of the technology acceptance model”, The DATA BASE for advances in Information Systems, Vol. 29, No. 2, pp.35-49.
[42]Gist, M.E., C. Schwoerer, & B. Rosen, 1989, “Effects of alternative training methods on self-efficacy and performance in computer software training”, Journal of Applied Psychology, Vol.74, No.6, pp.884-891.
[43]Harrison, A. W., R. K. Rainer, W. A. Hochwarter, & K. R. Thompson, 1997, “Testing the self-efficacy performance linkage of social-cognitive theory”, Journal of Social Psychology, Vol. 137, pp.587-605.
[44]Heinssen, R. K. Jr., C. R. Glass, & L. A. Knight, 1987, “Assessing computer anxiety: Development and validation of the computer anxiety rating scales”, Computers in Human Behavior, Vol.3, pp.49-59.
[45]Henry, J. W., & Stone, R. W., 1994. A structural equation model ofend-user satisfaction with a computer-based medical information system. Information Resources Management Journal, Vol.7, No.3, pp.21-33.
[46]Hill, T., N. D. Smith, & M. F. Mann, 1987, “Role of efficacy expectations in predicting the decision to use advanced technologies: The case of computers”, Journal of Applied Psychology, Vol. 72, pp.307-313.
[47]Hong, W., Thong, J. Y. L., Wong, W. M., & Tam K. Y., 2001. Determinants of user acceptance of digital libraries: an empirical examination of individual differences and systems characteristics. Journal of Management Information Systems, Vol.18, No.3, pp.97-124.
[48]Hu, P. J. H., Clark, T. H. K., & Ma, W. W., 2003. Examining technology acceptance by school teachers: a longitudinal study. Information & Management, Vol.41, No.2, pp.227-241.
[49]Hu, P. J., Chau, P. Y. K., Liu Sheng, O. R., & Tam, K. Y., 1999. Examining the technology acceptance model using physician acceptance of telemedicine technology. Journal of Management Information Systems, Vol.16, No.2, pp.91-112.
[50]Hu, Q., Saunders, C., & Gebelt, M., 1997. Research report: diffusion of information systems “outsourcing”: a reevaluation of influence sources. Information System Research, Vol.8, No.3, pp.288-301.
[51]Igbaria, M., N. Zinatelli, P. Cragg, & A. L. M. Cavaye, 1997, “Personal computing acceptance factors in small firms: a structural equation model,” MIS Quarterly, Vol.21, No.3, pp.279-303.
[52]Karsten, K., & Roth, R. M., 1998. T he relationship of computer experience and computer self-efficacy to performance in 88 introductory computer literacy courses. Journal of Research on Computing in Education, Vol.31, No.1, pp.14-24.
[53]Keil, M., P. M. Beranek, & B. R. Konsynski, 1995, “Usefulness and ease of use: field study evidence regarding task considerations”, Decision Support Systems, Vol. 13, pp.75-91.
[54]Kinzie, M.B., & M.A.B. Delcourt, 1991, “Computer technologies in teacher education: the measurement of attitudes and self-efficacy”, IL: American Educational Research Association, Chicago.
[55]Kwon, T. H. & Zmud, R. W., 1987. Unifying the fragmented models of information systems implementation. In R. J. Boland & R. A. Hirschheim (Eds.), Critical Issues in information Systems Research (pp. 227-251). New York: John Wiley & Sons.
[56]Levine, T., 1997, “Commitment to learning: Effects of computer experience, confidence and attitudes”, Journal of Research on Computing in Education, Vol.16, No.1, pp.83-105.
[57]Liao, S., Shao, Y. P., Wang, H., & Chen, A., 1999. The adoption of virtual banking: an empirical study. Internation of Journal of Information Management, Vol.19, pp.63-74.
[58]Lucas, H. C., & V. K. Spitler, 1999, “Technology use and performance: a field study of broker workstations”, Decision Sciences, Vol. 30, No. 2, pp.291-311.
[59]Mager, R. F., 1992, “No self-efficacy, no performance,” Training, April, pp.32-36.
[60]Mathieson, K., 1991, “Predicting User Intention: Comparing the Technology Acceptance Model with Theory of Planned Behavior”, Information Systems Research, Vol.2, No.3, pp.173-191.
[61]Mathieson, K., Peacock, E., & Chin, W. W., 2001. Extending the technology acceptance model: the influence of perceived user resources. ACM SIGMIS Database, Vol.32, No.3, pp.86-112.
[62]Moore, G. C., & Benbasat, I., 1991. Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, Vol.2, No.3, pp.192-222.
[63]Murphy, C.A., D. Coover, & S.V. Owen, 1989, “Development and validation of the computer self-efficacy scale,” Educational and Psychological Measurement, Vol.49, pp.893-899.
[64]Nokia Research Center From: http://research.nokia.com/
[65]Olivier, T., & F. Shapiro, 1993. “Self-efficacy and computers”, Journal of Computer-Based Instrument, Vol.20, No.3, pp.81-85.
[66]Rogers, E. M., 1962. Diffusion of innovations. NY: Free Press.
[67]Rogers, E. M., 1983. Diffusion of innovations. (3rd ed.). NY: Free Press.
[68]Rogers, E. M., 1995. Diffusion of innovations. (4th ed.). NY: Free Press.
[69]Shih, H. P., 2004. An empirical study on predicting user acceptance of e-shopping on the Web. Information & Management, Vol.41, No.3, pp.351-368.
[70]Symbian From: http://www.symbian.com/
[71]Szajna, B., 1996, “Empirical evaluation of the revised technology acceptance model”, Management Science, Vol. 42, No. 1, pp.85-92.
[72]Taylor, S., & P. Todd, 1995, “Assessing IT usage: the role of prior experience”, MIS Quarterly, December, pp.561-570.
[73]Torkzadeh, G., & X. Koufteros, 1994, “Factorial validity of a computer self efficacy scale and the impact of computer training”, Educational and Psychological Measurements, Vol.54, pp.813-821.
[74]Venkatesh, V. & F.D. Davis, 2000, “A theoretical extension of the technology acceptance model: four longitudinal field studies”, Management Science, Vol. 46, No. 2, pp.186-204.
[75]Venkatesh, V., & M. G. Morris, 2000, “Why do not 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.115-0139.
[76]Vijayasarathy, L. R., 2004. Predicting consumer intentions to use on-line shopping: the case for an augmented technology acceptance model. Information & Management, Vol.41, No.6, pp.747–762.
[77]Wang, Y. S., 2002. The adoption of electronic tax filing systems: an empirical study. Government Information Quarterly, Vol.20, No.4, pp.333–352.
[78]Wang, A. Y., & Newlin, M. H., 2002. Predictors of web-student performance: The role of self-efficacy and reasons for taking an on-line class. Computers in Human Behaviors, Vol.18, pp.151-163.
[79]Windows Mobile From: http:// www. microsoft. Com /Taiwan /windows mobile / default.mspx
[80]Wolfe, R. A., 1994. Organizational innovation: review, critique and suggested research directions. Journal of Management Studies, 1994(May), pp. 405-430.
[81]Wu, I. L., & Wu, K. W., in press. A hybrid technology acceptance approach for exploring e-CRM adoption in organizations. Behaviour & Information Technology.
[82]Yang, H. D., & Yoo, Y., 2004. It’s all about attitude: revisiting the technology acceptance model. Decision Support Systems, Vol.38 No.1, pp. 19– 31.
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