簡易檢索 / 詳目顯示

研究生: 劉宇豪
Liu, Yu-Hao
論文名稱: 整合科技接受模型與知覺風險探討消費者網路投保使用意願之研究
A Study on Consumers' Willingness to use Online Insurance by Integrating Technology Acceptance Model and Perceived Risk
指導教授: 胡茹萍
Hu, Ru-Ping
口試委員: 高棟梁
Kao, Tong-Liang
林倫豪
Lin, Lun-Hao
胡茹萍
Hu, Ru-Ping
口試日期: 2022/06/21
學位類別: 碩士
Master
系所名稱: 工業教育學系科技應用管理碩士在職專班
Department of Industrial Education_Continuing Education Master's Program of Technological Management
論文出版年: 2022
畢業學年度: 110
語文別: 中文
論文頁數: 165
中文關鍵詞: 網路投保財產保險科技接受模型知覺風險產品複雜度
英文關鍵詞: Online Insurance, Property Insurance, Technology Acceptance Model, Perceived Risk, Product complexity
研究方法: 調查研究
DOI URL: http://doi.org/10.6345/NTNU202201174
論文種類: 學術論文
相關次數: 點閱:161下載:4
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報
  • 本研究奠基於網路投保法規對於網路投保財產保險商品係採負面表列的開放方向,為瞭解消費者對於網路投保財產保險商品之重要影響因素,經研析採用科技接受模型為基礎理論,並加入知覺風險及產品複雜度共二項變數,探討消費者對於網路投保四種財產保險商品的使用意願,並依汽機車險、傷害險、防疫險、手機險分別說明。此外,同時檢驗產品複雜度變數對於網路投保使用意願之干擾程度。
    案經2022年第一季採取網路問卷調查,於問卷前測作業後,續於網上獲致821份資料,刪除不合格者約10.23%,以737份有效問卷作為分析基礎,接著再以SPSS驗證各觀察變項對於網路投保意願之分析,兼以AMOS執行驗證式分析,俾強化問項之信度及效度。
    本研究獲致重要結論:(一)個人背景變項下的網路投保經驗、年齡及網路平台使用偏好共三項,對知覺有用性及知覺易用性變項,皆存在顯著差異;(二)知覺易用性變項對知覺有用性變項,存在正向影響;(三)知覺易用性變項及知覺有用性變項,對於使用態度變項,皆存在正向影響;(四)知覺隱私風險變項,對於使用態度變項,假定負向影響並未成立;(五)使用態度變項對於4種產險商品之網路投保意願變項,皆成立正向影響;(六)產品複雜度變項對於4種產險商品之網路投保意願變項,皆成立負向影響;(七)在4種產險商品複雜度的干擾效果方面,只第1項(汽機車險商品)具備干擾效果,其餘第2項至第4項商品皆不成立干擾效果。
    本研究對於後續學術研究提出二點建議:(一)修正個人背景構面之分類方式;(二)注意消費管道多元化所帶入銷售競爭之排擠效應。

    This study is based on the open direction that the regulation for online insurance adopts negative list for online property insurance product. In order to explore the important factors influencing consumers on this issue, it uses a technology acceptance model as the basis of theory, and adds two variables, namely perceived risk and product complexity, to examine consumers' willingness to insure four types of property insurance products for online insurance. In addition, it also examines the degree of moderator of product complexity variables on the willingness to use online insurance.
    In the first quarter of 2022, an online survey was conducted. After the pre-testing operation, 821 responses were collected online, 10.23% of which are unqualified and therefore deleted. The remaining 737 valid questionnaires were used as the basis for analysis. This study uses SPSS to verify the analysis of each observed observation variable on the willingness to take out online insurance, and uses AMOS to perform Confirmatory Factor Analysis to strengthen the reliability and validity of these questions.
    The study concludes that︰Ⅰ︰The three variables of personal background, namely, experience in online insurance, age, and preference for online platform use, differed significantly in the perceived usefulness variable and perceived ease of use variable. Ⅱ︰The perceived ease of use variable has a positive effect on the perceived usefulness variable. Ⅲ︰The perceived ease of use and perceived usefulness variables have a positive effect on the attitude toward using variable. Ⅳ︰Perceived privacy risk variables, assumed negative effect on attitude toward using variable not established. Ⅴ︰The positive effect of the attitude to using variable on the willingness to use online insurance for each of the four insurance products was found. Ⅵ︰The negative effect of the attitude to using variable on the willingness to use online insurance for each of the four insurance products was found. Ⅶ︰In the degree of moderator of the products complexity of the four insurance products, only the first product (Automobile and Motorcycle Insurance) had an moderator effect, while the remaining products 2 through 4 (Casualty insurance ; Epidemic prevention insurance ; Cell phone insurance ) did not have the same effect.
    This study proposes two recommendations for subsequent academic research:Ⅰ︰Revising the classification of individual background profiles;Ⅱ︰Paying attention to the sales competition crowding effect caused by the diversification of consumption channels.

    謝辭 i 摘要 ii Abstract iii 目次 v 表次 vii 圖次 x 第一章 緒論 1 第一節 研究背景與動機 1 第二節 研究目的與問題 10 第三節 研究流程 11 第四節 研究限制 12 第五節 名詞釋義 14 第二章 文獻探討 17 第一節 國內網路投保市場 17 第二節 科技接受模型 30 第三節 知覺風險 36 第四節 產品複雜度 40 第五節 消費者個人背景 43 第三章 研究設計與實施 45 第一節 研究方法及架構 45 第二節 研究假說 46 第三節 研究範圍及對象 48 第四節 研究設計 50 第五節 資料分析方法 60 第六節 正式問卷信度及效度分析 68 第七節 研究倫理 76 第四章 研究結果分析與討論 79 第一節 消費者背景結構及網路投保意向 79 第二節 消費者背景變項差異分析 95 第三節 不同研究構面及產品別對網路投保意願之相關性 118 第四節 不同研究構面對網路投保使用意願之影響程度 121 第五節 產品複雜度對網路投保使用意願之干擾情形 127 第六節 綜合討論 132 第五章 結論與建議 147 第一節 結論 147 第二節 建議 150 參考文獻 153 壹、中文部分 153 貳、外文部分 155 附錄 正式問卷 161

    壹、中文部分
    丁國章、曾相榮、潘昭儀(2010)。網路購物行為之消費者知覺風險研究。行銷評論,7(3),381-410。
    王光旭、許惠鈞(2017)。臺灣社區通網站平臺使用頻率影響因素之研究:科技接受模型的觀點。民主與治理,4(2),1-38。
    安侯建業聯合會計師事務所(2020)。2020臺灣保險業報告。取自https://home.kpmg/tw/zh/home/insights/2020/09/tw-kpmg-taiwan-insurance-report-2020.html
    吳明隆(2009)。SPSS操作與應用—問卷統計分析實務(第二版)。臺北市:五南圖書。
    吳明隆(2009)。結構方程式: AMOS的操作與應用(第二版)。臺北市:五南圖書。
    呂慧芬(2018)。保險商品比較網站之現況與監理。保險專刊,34(3),325-351。
    金融監督管理委員會保險局(2022年2月28日)。中華民國保險市場重要指標-中華民國110年12月。
    金融監督管理委員會(2016)。金融科技發展策略白皮書。取自 https://www.fsc.gov.tw/ch/home.jsp?id=517&parentpath=0,7,478
    金融監督管理委員會(2020)。金融科技發展路徑圖。取自https://www.fsc.gov.tw/ch/home.jsp?id=478&parentpath=0,7
    保險業辦理電子商務應注意事項(2021年9月9日)。
    財團法人台灣網路資訊中心(2020年10月)。2020台灣網路報告。取自https://report.twnic.tw/2020/
    財團法人資訊工業策進會產業情報研究所(2021年4月14日)。2020年網購消費者調查。取自https://mic.iii.org.tw/news.aspx?id=597
    張美瑤(2009)。網站特性、知覺價值與消費者購買意願研究-以線上保險商品為例(未出版之碩士論文)。國立成功大學,臺南市。
    張蓓琪、沈秀玲(2010)。消費者採用創新產品之影響因素分析-以電子書閱讀器爲例。高雄海洋科大學報,24,191-220。
    梁舒婷(2018)。消費者價格敏感度與產品複雜度對網路投保意願之影響(未出版之碩士論文)。朝陽科技大學,臺中市。
    許晴姿(2016)。整合科技接受模型與知覺風險理論探討影響消費者使用行動支付因素之研究(未出版之碩士論文)。國立台灣科技大學,臺北市。
    陳美君(2021年5月12日)。純網路保險有市場嗎?調查顯示:僅三成民眾線上買保單。經濟日報。取自https://money.udn.com/money/story/5613/5451660
    陳寬裕、王正華(2017)。論文統計分析實務: SPSS與AMOS的運用(第三版)。臺北市:五南圖書。
    蕭銘雄、鄭曉平(2008)。以延伸式科技接受模型探討消費者線上投保人壽保險之意願。電子商務學報,10(1),1-25。
    謝明佐(2019)。影響網路投保意願關鍵因素之研究(未出版之碩士論文)。國立成功大學,臺南市。
    藍才洋(2003)。產品價格與複雜度對線上購物決策行為之影響(未出版之碩士論文)。國立中山大學,高雄市。

    貳、外文部分
    Abramovsky A., Kochenburger P. (2016). Insurance Online: Regulation and Consumer Protection in a Cyber World. The "Dematerialized" Insurance (pp. 117-142). Switzerland︰Springer. https://doi.org/10.1007/978-3-319-28410-1_5
    Brandon-Jones, A. & Kauppi, K. (2018). Examining the antecedents of the technology acceptance model within e-procurement. International Journal of Operations & Production Management, 38(1), 22-42.
    Burnham T. A., Frels J. K. & Mahajan V. (2003). Consumer Switching Costs: A Typology, Antecedents, and Consequences. Journal of the Academy of Marketing Science, 31(2), 109-126.
    Capgemini (2016). Top 10 Trends in Insurance in 2016, 4-5. Retrieved from https://www.capgemini.com/resources/insurance-top-10-trends-2016/
    Cox, D. F. (1967). Risk handling in consumer behavior-an intensive study of two cases, Risk taking and information handling in consumer behavior (pp. 34-81). Bostion: Harvard University Press.
    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.
    Davis, Fred D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology, MIS Quarterly, 13(3), 319-340.
    Dellaert, B. G. C. & Stremersch S. (2005). Marketing Mass-Customized Products: Striking a Balance between Utility and Complexity. Journal of Marketing Research, 42(2), 219-227.
    Donaldson, Lufkin, Jenrette (2000). Insurance: The Impact of the Internet on the European Insurance industry. Switzerland: Swiss Re Economic Research & Consulting.
    Dumm, R. E. & Hoyt R. E. (2002). Insurance distribution channels: Markets in transition. https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.105.6400&rep=rep1&type=pdf
    Elkaseh A. M., Wong, K. W., & Fung C. C. (2016). Perceived Ease of Use and Perceived Usefulness of Social Media for e-Learning in Libyan Higher Education: A Structural Equation Modeling Analysis. International Journal of Information and Education Technology, 6(3), 192-199.
    Featherman, M. S. & Pavlou, P. A. (2003). Predicting e-services adoption: A perceived risk facets perspective. International Journal of Human-Computer Studies, 59(4), 451-474.
    Fishbein, M. & Ajzen, I. (1977). Belief, attitude, intention, and behavior: An Introduction to theory and research. MA: Addison-Wesley Longman Inc.
    Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39-50.
    Garven J. R. (2002). On the Implications of the Internet for Insurance Markets and Institutions. Risk Management and Insurance Review, 5(2), 105-116. https://onlinelibrary.wiley.com/doi/10.1111/1098-1616.00014
    Gebert-Persson, S., Gidhagen M., Sallis J. E., & Lundberg H. (2019). Online insurance claims: When more than trust matters. International Journal of Bank Marketing, 37(2), 579-594.
    Grover V. & Saeed, K. A. (2007). The Impact of Product, Market, and Relationship Characteristics on Interorganizational System Integration in Manufacturer-Supplier Dyads. Journal of Management Information Systems, 23(4), 185-216.
    Hair, Jr. J. F., Anderson, R. E., Tatham, R, L. & Black, W. C. (1998). Multivariate data analysis (5th ed.). NJ: Prentice Hall.
    Internet World Stats (2021). World Internet Users and 2021 Population Stats. Retrieved from https://www.internetworldstats.com/stats.htm
    Jacob, J. & Kaplan L. B. (1972). The Components of Perceived Risk (pp. 382-393). In Proceedings of the Third Annual Conference of the Association for Consumer Research, Chicago, IL: Association for Consumer Research.
    Kim, W. G. & Kim D. J. (2004). Factors affecting online hotel reservation intention between online and non-online customers, International Journal of Hospitality Management, 23(4), 381-395.
    Kotler P., Keller K. L., Ang S. H., Leong S. M., Tan C. T. (2006). Marketing Management: An Asian Perspective (4th ed., chap. 6). New Jersey: Prentice Hall.
    Lin, W. B. (2008). Investigation on the model of consumers’ perceived risk—integrated viewpoint. Expert Systems with Applications, 34(2), 977-988.
    Mallat N. (2007). Exploring consumer adoption of mobile payments–A qualitative study. The Journal of Strategic Information Systems, 16(4), 413-432.
    Mayer R. N. (2008). Online Insurance. Handbook of Consumer Finance Research (chap. 8). Springer, New York, NY. https://doi.org/10.1007/978-0-387-75734-6_8
    Méndez-Aparicio M. D., Izquierdo-Yusta A. & Jiménez-Zarco A. I. (2017). Consumer Expectations of Online Services in the Insurance Industry: An Exploratory Study of Drivers and Outcomes. Frontiers in Psychology, 8. https://doi.org/10.3389/fpsyg.2017.01254
    Park, E., Kim H., & Ohm, J. Y. (2015). Understanding driver adoption of car navigation systems using the extended technology acceptance model. Behaviour & Information Technology, 34(7), 741-751.
    Reilly M. & Holman R. H. (1977). Does Task Complexity Or Cue Intercorrelation Affect Choice of an Information Processing Strategy? An Empirical Investigation. Advances in Consumer Research, 4, 185-190.
    Rogers, E. M. (1995). Diffusion of Innovation (4th ed.). New York: Free Press.
    Schilling M. A. (2020). Strategic Management of Technological Innovationn (6th ed., chap. 3). New York: Mc Graw Hill Education.
    Stoeckli, E., Dremel, C. & Uebernickel, F. (2018). Exploring characteristics and transformational capabilities of InsurTech innovations to understand insurance value creation in a digital world. Electron Markets 28, 287–305. https://doi.org/10.1007/s12525-018-0304-7
    Swaminathan V., Elzbieta Lepkowska-White & Bharat P. R. (1999). Browsers or Buyers in Cyberspace? An Investigation of Factors Influencing Electronic Exchange, Journal of Computer-Mediated Communication, 5(2). https://doi.org/10.1111/j.1083-6101.1999.tb00335.x
    Swiss Re institute (2021, July). World insurance: the recovery gains pace, 2021(3), 37-38. Retrieved from https://www.swissre.com/dam/jcr:ca792993-80ce-49d7-9e4f-7e298e399815/swiss-re-institute-sigma-3-2021-en.pdf
    Todd P. & Benbasat I. (1999). Evaluating the Impact of DSS, Cognitive Effort, and Incentives on Strategy Selection. Information Systems Research, 10(4), 356-374.
    Wallace, L. G. & Sheetz, S. D. (2014). The adoption of software measures: A technology acceptance model (TAM) perspective. Information & Management, 51(2), 249-259.
    Wang, Wei-Tsong & Lu, Chia-Cheng (2014). Determinants of Success for Online Insurance Web Sites: The Contributions from System Characteristics, Product Complexity, and Trust.
    Journal of Organizational Computing and Electronic Commerce, 24(1), 1-35. https://doi.org/10.1080/10919392.2014.866501
    World Economic Forum (2015). The Future of Financial Services. Retrieved from https://www.weforum.org/reports/future-financial-services-2015/

    下載圖示
    QR CODE