跳到主要內容

臺灣博碩士論文加值系統

(18.97.9.172) 您好!臺灣時間:2025/02/16 21:22
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

我願授權國圖
: 
twitterline
研究生:鄭蕙瀞
研究生(外文):Cheng Hui Ching
論文名稱:影響顧客關係管理系統採用意圖之研究-以健保局高屏分局數位化顧客服務系統為例
論文名稱(外文):Determinants of adoption intention to eCRM -A case of eCRM in Bureau of National Health Insurance KAO-PING Branch
指導教授:閔庭祥閔庭祥引用關係傅振瑞傅振瑞引用關係
學位類別:碩士
校院名稱:國立高雄應用科技大學
系所名稱:商務經營研究所
學門:商業及管理學門
學類:一般商業學類
論文種類:學術論文
論文出版年:2006
畢業學年度:94
語文別:中文
論文頁數:108
中文關鍵詞:顧客關係管理系統科技接受模式資訊品質系統品質主觀規範電腦自我效能
外文關鍵詞:eCRMTAMSystem QualityInformation QualitySubjective NormComputer Self-efficacy
相關次數:
  • 被引用被引用:4
  • 點閱點閱:737
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:5
隨著資訊科技、網際網路、和電子商務的蓬勃發展,讓企業電子化的腳步愈來愈快,企業重要的不再是有形的產品,而是無形的服務,致使顧客意見日趨重要,惟有建立良好的顧客關係,將是企業競爭力的方法。
由於政府單位於顧客服務、申辦效率、處理問題速度等等在要求上面臨了與眾多民營企業比較的壓力。如何維繫民眾與政府提供服務之間的關係,透過顧客關係管理系統便成為極佳的途徑。但若服務人員不願意使用顧客關係管理系統,也是惘然。因此本研究以中央健保局高屏分局為例,探討高屏分局採用顧客關係管理之意圖,提供企業導入顧客關係管理系統之參考。
本研究以科技接受模式為主體,加入計劃行為理論。經由文獻探討找出科技接受模式之外生變數-資訊品質、系統品質、電腦自我效能,且加入社會因子-主觀規範,並探討外生變數透過知覺有用性與知覺易用性對行為意圖的影響。
本研究利用問卷方式進行抽樣調查,以高屏分局同仁為樣本,總計發放225份問卷,回收有效問卷169份,經AMOS5.0進行線性結構關係模式的實證分析,得到結論如下:
1.使用者對於eCRM之使用意圖,主要是受到知覺易用性的影響,知覺有用性次之。
2.主觀規範對行為意圖有顯著的影響;資訊品質、系統品質、電腦自我效能必須透過知覺有用性與知覺易用性當中介變數,才對行為意圖產生正向影響效果。
3.資訊品質對知覺有用性有顯著影響;資訊品質、系統品質、電腦自我效能對知覺易用性有顯著影響。
Following the vigorous development of the internet technology and electronic commerce, Customer Relationship Management (CRM) is getting more and more popular and important in the IT applications of enterprises. CRM should be one of the most important successful factors for profitable and competitive firms.
The government agents are getting more pressure in the customer service compared with the private companies, especially in the service efficiency and the speed of handling problems. As a result, the establishment of call center and the adoption of customer relationship management (CRM) become an urgent and most important issue. This study attempts to discover the critical factors of adoption intention for the electronic Customer Relationship Management system (eCRM.)
The model incorporates a wide variety of important factors into a theoretical framework provided by “Technology Acceptance Model (TAM)” and “Theory of Planned Behavior (TPB)”. This research selects system quality, information quality, computer self-efficacy and subjective norm as external variables. Through two intermediate variables of TAM, the perceived usefulness (PU) and perceived ease of use (PEOU), the internal and external exogenous factors are investigated to analyze the intention of users.
Data were collected from a group of staffs of the Bureau of National Health Insurance KAO-PING Branch. 225 questionnaires were distributed and 169 valid questionnaires were received. The data was analyzed using the Structural Equation Modeling by AMOS5.0 and SPSS 10.0. The main findings of this research are as following:
1.The user intention is affected and guided by PEOU; the impact of PU on adoption intention is less important than PEOU.
2.Subjective norm is positively related to intention. System quality, information quality, computer self-efficacy are positively related to intention through the influence of PU and PEOU.
3.System quality is positively related to PU. System quality, information quality, computer self-efficacy are positively related to PEOU.
摘要 I
ABSTRACT II
誌謝 III
目錄 IV
表目錄 VI
圖目錄 VIII
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 研究流程 3
第二章 文獻探討 5
第一節 顧客關係管理系統架構 5
一、 顧客關係管理的架構 5
二、 顧客關係管理系統的範疇 8
三、 使用顧客關係管理系統的效益 8
四、 小結 9
第二節 資訊系統成功模式探討 11
第三節 計畫行為理論 19
一、 慎思行為理論 21
二、 計畫行為理論 22
三、 主觀規範 24
四、 電腦自我效能 25
五、 小結 27
第四節 科技接受模式 29
一、 TAM 29
二、 外生變數 33
三、 修正TAM 34
四、 小結 37
第三章 研究方法 39
第一節 研究架構 39
第二節 變數的操作性定義 40
第三節 研究假說 44
第四節 問卷設計 51
第五節 研究設計與量表品質 56
第四章 資料分析 58
第一節 樣本結構分析 58
第二節 信度分析 62
第三節 效度分析 63
第四節 整體結構模型分析 76
第五節 模型實證 79
第六節 探索性研究 83
第五章 結論與建議 85
第一節 研究結論 85
第二節 管理意涵 87
第三節 研究限制 89
第四節 未來研究建議 89
附錄一 研究問卷 91
附錄二 SAMPLE CORRELATIONS 96
附錄三 各題項的平均數與標準差 97
中文文獻 98
REFERENCE LIST 100
中文文獻
1.王育民,2004,知識管理系統成功模式建構與驗證之研究,國立中山大學資訊管理學系研究所,博士論文。
2.吳明隆,涂金堂,2005,SPSS與統計應用分析,二版,五南圖書出版社。
3.吳采芳,2002,修正TAM模型在線上遊戲行為因素分析之研究,國防管理學院資源管理研究所,碩士論文。
4.吳建宏,2004,股市投資人使用券商網站意願之研究,私立義守大學資訊管理學系碩士班,碩士論文。
5.李明德、曾俊欽,2003,科技客服-客服中心的系統建置,台灣培生教育出版。
6.李鍵壕,2004,高雄市公務人員對知識管理系統之科技接受度,國立中山大學公共事務管理研究所,碩士論文。
7.邱皓政,2000,量化研究與統計分析,初版,五南書局。
8.張文彬,2002,顧客關係管理的核心活動在企業界應用過程之探討,私立中原大學企業管理研究所,碩士論文。
9.張瑞芬,張力元,2003,顧客服務管理:CRM實戰理論與實務,初版,華泰書局。
10.許由忠,2005,影響線上遊戲玩家接受遊戲之相關因素探討,國立東華大學企業管理學系,碩士論文。
11.陳文華,2000,運用資料倉儲技術於顧客關係管理,能力雜誌,第527期,1月,頁132-138。
12.陳泳成,2003,以修正後的科技接受模式探討影響「使用者自建系統接受」之因素,國立中山大學資訊管理學系研究所,碩士論文。
13.陳順宇,2005,多變量分析,四版,華泰書局。
14.陳煜鑫,2003,使用解構之期望符合論探討WWW持續使用之影響因素,國立高雄第一科技大學資訊管理所,碩士論文。
15.陳碧玉,2004,公文電子化系統效能之研究—以屏東縣政府為例,國立高雄第一科技大學資訊管理所,碩士論文。
16.葉芳枝,2003,國軍醫院主管採用顧客關係管理之意願及影響關鍵因素之研究--以國軍醫院為例,國立中正大學資訊管理學系,碩士論文。
17.董陳明,2003,企業電子郵件系統特性與規範對行為影響之研究,私立銘傳大學資訊管理學系碩士在職專班,碩士論文。
18.蘇伯方,2004,即時傳訊軟體採用模式之研究,國立中山大學傳播管理研究所,碩士論文。
19.網站:IDC商國際數據資訊,http://www.idc.com.tw/index.htm
20.網站:資策會(MIC)資訊資料服務中心http://www.cisc.iii.org.tw/
Reference List
1.Adams, D. A., Nelson, R. R., & Todd, P. A. (1992). Perceived usefulness, ease of use and usage of information technology: a replication. MIS Quarterly, 16, 227-247.
2.Agarwal, R. & Prasad, J. (1999). Are individual differences germane to the acceptance of new information technologies? Decision science, 30, 361-391.
3.Ajzen, I. &. F. M. (1980). Understanding Attitudes & Predicting Social Behavior. Understanding Attitudes & Predicting Social Behavior.Prentice-Hall, Englewood Cliffs,NJ.
4.Ajzen, I. (1985). From Intentions to Actions: A Theory of Planned Behavior. Action-Control:From Cognition to Behavior, Heidelberg: Springer, 11-39.
5.Ajzen, I. (1991). The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes, 50, 179-211.
6.Ajzen, I. (2001). Nature and operation of attitudes. Annual Review of Psychology, 52, 27–58.
7.Ajzen, I. (2002). Perceived behavioral control, self-efficacy, locus of control, and the theory of planned behavior. Journal of Applied Social Psychology, 32, 1-20.
8.Bagozzi, R. P. & Yi, Y. (1988). On the Evaluation for Structural Equation Models. Journal of the Academy of Marketing Science, 1, 74-94.
9.Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84, 191-215.
10.Bandura, A. (1982). Self-efficacy mechanism in human agency. American Psychologist, 37, 122-147.
11.Bandura, A. (1984). Recycling misconceptions of perceived self-efficacy. Cognitive Therapy and Research, 8, 231-255.
12.Bandura, A. (1986). Social foundations of thought and action : A Social CognitiveTheory. Englewood Cliffs, NJ: Prentice-Hall..
13.Bandura, A. (1991). Social cognitive theory of self-regulation. Organizational Behavior and Human Decision Process, 50, 287.
14.Baumgartner, H. & Homburg, C. (1996). Applications of Structural Equation Modeling in Marketing and Consumer Research: A review. International Journal of Research in Marketing, 13, 139-161.
15.Brancheau, J. C. & Wetherbe, J. C. (1990). The adoption of spreadsheet technology: Testing innovation diffusion theory in the context of end-user computing. Information System Research, 1, 115-143.
16.Brown Irwin T.J. (2002) “Individual and Technological Factors Affecting Perceived Ease of Use of Web-based Learning Technologies in a Developing Country" EJISDC, 9, 5, 1-15
17.Burkhardt, M. E. & Brass, D. J. (1990). Changing patterns or patterns of change: The effects of a change in technology on social network structure and power. 35, 104.
18.Carmines, E. & McIver, J. (1981). Analyzing Models with Unobserved Variables: Analysis of Covariance Structures. Social measurement : Current Issues, Beverly Hills.
19.Chin, W. W. & Todd, P. (1995). On the Use, Usefulness, and Ease of Use of Structural Equation Modeling in MIS Research: A Note of Caution. MIS Quarterly, 19, 237-246.
20.Compeau, D. R. & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19, 189-211.
21.Compeau, D. R. & Higgins, C. A. (1999). Social cognitive theory and individual reactions to computing technology: A longitudinal study. MIS Quarterly, 23, 145-158.
22.Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user information systems: Theory and results. Doctoral dissertation, Sloan School of Management, Massachusetts Institute of Technology.
23.Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13, 319-340.
24.Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: a comparison of two theoretical models. Management Science, 35, 982-1003.
25.Delcourt, M. A. & Kinzie, M. B. (1993). Computer technologies in teacher ducation : the measurement of attitudes and self-efficacy. Journal of Research and evelopment in Education, 27, 35-41.
26.DeLone W.H. & McLean E.R. (2003). The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems, 19, 9-30.
27.DeLone, W. H. & McLean, E. R. (1992). Information System Success:The Quest for the Dependent Variable. Information System Research, 3, 60-95.
28.Fishbein Martin (1967). Readings in Attitude Theory and Measurement, New York: John Weily.
29.Fishbein, M. a. A. I. (1975). Belief, Attitude, Intention and Behavior: A Introduction to Theory and Research. Journal of Leisure Research, 27, 61-84.
30.Fornell, C. R. & D.F.Larcker (1981). Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18, 39-51.
31.Gable, G. G., Sedera, D., & Chan, T. (2003). Enterprise Systems Success: A Measurement Model. The 24th International Conference on InformationSystems, 576-591.
32.Gist, M. E., Schwoerer, C., & Rosen, B. (1989). Effects of alternative training methods on self-efficacy and performance in computer software training. Journal of Applied Psychology, 72, 307-313.
33.Grant, R. A. (1989). Building and Testing a Causal Models of an Information Technology's Impact. Proceedings of the Tenth ICIS, Boston, MA, -173.
34.Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate Data Analysis. N.Y.: Macmillan.
35.Hans van, d. H. (2003). Factors influencing the usage of Websites: The case of a generic portal in The Netherlands. Information & Management, 40, 541.
36.Henry C. Lucas, Jr. and VK Spitler(1999).Summer, Decision Science
37.Hill, T., Smith, N. D., & Mann, M. F. (1987). Role of efficacy expectations in predicting the decision to use advanced technologies: the case for computers. J.Applied Psychology, 72, 307-313.
38.Hu, P. J., Chau, P. Y. K., Sheng, O. R. L., & Tam, K. Y. (1999). Examining the Technology Acceptance Model Using Physician Acceptance of Telemedicine Technology. Journal of ManagementInformation Systems, 16, 91-112.
39.Hughes, R. E. (1986). Diachronic Variability in Obsidian Procurement Patterns in Northeastern California and Southcentral Oregon. University of CaliforniaPublications in Anthropology, 17.
40.Hwang, Y. & Yi, M. Y. (2002). Predicting The Use of Web-Base Information Systems:GIntrinsic Motivation and Self-Efficacy. Proceedings of 8th AmericasConference on Information Systems, New York: AMCIS, 1076-1081.
41.Igbaria M., Zinatelli N., Cragg P., & Cavaye Angele L.M. (1997). Personal computing acceptance factors in small firms: A structural equation model. MIS Quarterly, 21, 279-305.
42.Igbaria, M. & Tan, M. (1997). The consequences of information technology acceptance on subsequent individual performance. Information & Management, 32, 113-121.
43.Igbaria, M., Guimaraes, T., & Davis, G. B. (1995). Testing the determinants of microcomputer usage via a structural equation model. Journal of Management Information Systems, 11, 87-114.
44.Igbaria, M., Iivari, J., & Maragahh, H. (1995). Why do individuals use computer technology? A Finnish case study. Information & Management, 29, 227-238.
45.Igbaria, M., Parasuraman, S., & Baroudi, J. J. (1996). A motivational model of microcomputer usage. Journal of Management Information Systems, 13, 127-143.
46.Jackson, C., Chow, S., & Leitch, R. (1997). Understanding of the Behavioral Intention to Use An Information System. Decision Sciences, 28, 357-389.
47.Jaeho, H. & Ingoo, H. (2003). Performance measure of information systems (IS) in evolving computing environments: An empirical investigation. Information & Management, 40, 243.
48.Jennex, M. E. & Olfman, L. (2003a). A Knowledge Management Success Model: An Extension of DeLone and McLean's IS Success Model. The 9th Americas Conference on Information Systems, 2529-2539.
49.Jennex, M. E. & Olfman, L. (2003b). Organizational Memory (in C. W. Holsapple). Handbook on Knowledge Management 1, Springer-Verlag, New York, 207-234.
50.Joreskog, K. G. & Sorbom, D. (1996). LISREL8: User's Reference Guide. Mooresville: ScientificSoftware.
51.Karahanna, E. & Straub, D. W. (1999). The Psychological Origins of Perceived Usefulness and Ease-of-use. Information & Management, 35, 237-250.
52.Karahanna, E., Straub, D. W. & Chervany N. L. (1999). Information technology adoption across time: A cross-sectional comparison of pre-adoption and post-adoption beliefs. MIS Quarterly, 23, 183.
53.Kim, K. K. (1989). User Satisfaction: A Synthesis of Three Different Perspectives. The Journal of Information Systems, 4, 1-12.
54.Lederer, A. L., Maupin, D. J., Sena, M. P., & Zhuang, Y. (2000). The technology acceptance model and the World Wide Web. Decision Support System, 29, 269-282.
55.Levine, T. (1997). Commitment to learning : effects of computer experience, confidence and attitude. Journal of Research on computing in Education, 16, 83-105.
56.Lin, J. C. C. & Lu, H. (2000). Towards an understanding of the behavioral intention to use a web site. International Journal of Information Management, 20, 208.
57.Lucas Jr., Henry C. and V . K. Spitler,(1999). Technology Use and Performance: A Field Study of Broker Workstations, Decision Sciences,Spring, pp. 291-311
58.Malhotra, Y. & Galletta, D. F. (1999). Extending the Technology Acceptance Model to Account for Social Influence:Theoretical Base and Empirical Validation. Proceedings of the 32nd Hawaii International Conference on System Sciences, Hawaii: IEEE.
59.Mason, R. O. (1978). Measuring Information Output: A Communication Systems Approach. Information & Management, 1, 219-234.
60.Mathieson, K. (1991). Predicting user intentions: comparing the technology acceptance model with the theory of planned behavior. Information Systems Research, 2, 173-191.
61.McGill T., Hobbs V. & Klobas J. (2003). User-developed applications and information systems success: A test of DeLone and McLean's model. Information Resources Management Journal, 16, 24-45.
62.McKinney, V., Yoon, K., & Zahedi, F. (2002). The measurement of Web-customer satisfaction: An expectation and disconfirmation approach. Information Systems Research, 13, 296.
63.Molla, A. & Licker, P. S. (2001). E-Commerce Systems Success: An Attempt to Extend and Respecify the DeLone and McLean of IS Success. Journal ofElectronic Commerce Research, 2, 131-141.
64.Moon, J. W. & Kim, Y. G. (2001). Extending the TAM for a world-wide-web context. Information & Management, 38, 217-230.
65.Murphy, C. A., Coover, D., & Owen, S. V. (1989). Development and Validation of the Computer Self-Efficacy Scale. Educational and Psychological Measurement, 49, 893-899.
66.Myers, B. L., Kappelman, L. A., & Prybutok, V. R. (1998). A Comprehensive Model for Assessing the Quality and Productivity of the Information Systems Function: Toward a Theory for Information Systems Assessment. Information Systems Success Measurement, Idea Group Publishing, Harrisburg, Pennsylvania, 91-121.
67.Nunnally, J. (1978). Psychometric theory. New York: McGraw Hill.
68.Olivier, T. & F.Shapiro (1993). Self-efficacy and computers. Journal of Computer-Based Instrument, 20, 81-85.
69.Pitt, L. F., Watson, R. T., & Kavan, C. B. (1995). Service quality: A measure of information systems effectiveness. MIS Quarterly, 19, 173-187.
70.Reisinger, Y. & Turner, L. (1999). Structural equation modeling with Lisrel : Application in tourism. Tourism Management, 20, 71-88.
71.Roldan, J. L. & Millan, A. L. (2000). Analysis of the Information Systems Success Dimensions Interdependence: An Adaptation of the DeLone & McLean's Model in the Spanish EIS field. The BITWorld 2000 Conference.
72.Seddon, P. B. & Kiew, M. Y. (1996). A Partial Test and Development of DeLone and McLean's Model of IS Success. Australian Journal of Information Systems, 4, 90-109.
73.Seddon, P. B. (1997). A respecification and extension of the DeLone and McLean model of IS success. Information Systems Research, 8, 240.
74.Sedera, D., Gable, G., & Chan, T. (2003). Measuring Enterprise System Success: A Preliminary Model. The 9th Americas Conference on Information Systems.
75.Shannon, C. E. , & Warren, W. (1949). The Mathematical Theory of Communication. University of Illinois Press, Urbana, IL..
76.Straub, D., Limayem, M., & Karahanna-Evaristo, E. (1995). Measuring system usage: Implications for IS theory testing. Management Science, 41, 1328-1342.
77.Szajna, B. (1996). Empirical evaluation of the revised technology acceptance model. Management Science, 42, 85-92.
78.Taylor, S. & Todd, P. (1995). Assessing IT usage: The role of prior experience. MIS Quarterly, 19, 561.
79.Thompson, R. L., Higgins, C. A., & Howell, J. M. (1994). Influence of experience on personal computer utilization: Testing a conceptual model. Journal of Management Information Systems, 11, 167-188.
80.Torkzadeh, G. &. K. X. (1994). Factorial validity of a computer self-efficacy scale and the Educational and Psychological Measurement. Educational and Psychological Measurement, 54, 813-821.
81.Trepper, C. (2000). Customer Care Goes End-to-End. Information Week, May 15, 55-73.
82.Venkatesh, V. & Davis, F. D. (1996). A model of the antecedents of perceived ease of use: development and test. Decision Sciences, 27, 451-481.
83.Venkatesh, V. & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: four longitudinal field studies. Management Science, 46, 186-204.
84.Venkatesh, V. & Speier, C. (1999). Computer technology training in the workplace: A longitudinal investigation of the effect of mood. Organizational Behavior and Human Decision Processes, 79, 1.
85.Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11, 342-365.
86.Venkatesh, V., Morris, M. G., & David, G. B. (2003). User acceptance of information technology: toward a unified view. MIS Quarterly, 27, 425-478.
87.Wixom, B. H. and Watson, H. J., (2001). An empirical investigation of the factors affecting data warehousing success. MIS Quarterly, 25, 17-41.
88.Xia W. & King W.R. (1996). Interdependencies between the determinants of user interaction and usage: An empirical test. in Proc.ICIS Cleveland, OH, 1-21.
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top