臺灣博碩士論文加值系統

資訊科技導入健康照護機構將帶來衝擊，並非都是帶來正向的結果。探討科技接受的相關研究結果豐富，但多從新系統的有用、易用性等角度著手，來探討影響使用者使用新系統意圖的促成因素，較無法解決新系統帶來的變革所引起使用者抗拒的問題，而這些問題並不能以改善系統設計不良而得到解決。使用者抗拒常導致額外且非預期的系統導入成本增加，造成組織資源內耗。因此，有必要瞭解影響系統使用者行為意圖的抑制因素與抗拒變革的成因。

本研究彙整相關文獻作為研究模式及問卷發展的基礎，研究模式內共計52題問項，變數包含使用意圖及其促進因子與抑制因子。量表依序經專家審訂、預試完成量表修訂與內容效度的檢測。經前測後正式將廣發問卷給全國區域級以上已經導入病歷電子化系統的醫院，共發出325份。研究對象為各醫院的醫師，研究標的系統為病歷電子化系統。問卷調查的有效樣本數297，有效回收率為91.4%，有效樣本來自15所醫學中心與3所區域醫院。填答者以醫學中心的醫師居多（72.7 %）；填答者所屬醫療專科以內科居多（40.1%）、其次為外科（26.6%）；填答者的職稱以主治醫師居多（59.6%），其次為住院醫師（25.9%）；297位受測者過去皆有使用電腦的經驗，每日使用電腦時數以4∼6小時者居多（45.1%）。經t檢定與One way ANOVA檢定，樣本之間不因所屬醫院的層級、類別、職稱、工作年資長短等等人口變項的不同而有差異。13個變數的平均數約落在2.5至3.5分之間，難以直接得知參與者對新系統的認知是傾向接受或抗拒。以變革的行為圖譜來解釋，目前填答者的認知大約落在中立點，處於抗拒與接受之間。

驗證性因素分析用以檢測假設模式與樣本之間的適配性，並進一步檢測假設模式與理論模式之間的適配性，進而驗證研究假說與研究模式。經CFA，剔除低信度與低因素負荷量的問項與因素後，萃取出10個因素，剩餘26題問項。各問項的因素負荷量皆大於0.80，信度值皆大於0.60，顯示問項具備理想品質。測量模式經矯正後，各項模式配適指標， 卡方除自由度值為1.8，AGFI趨近0.90，GFI、NFI、TLI、CFI、IFI指標值皆大於0.90且趨近於1，RMSEA為0.05，SRMR為0.03，顯示模式具備合理且良好的配適度。進一步將矯正後的模式與模式探索所產出的最佳模式進行比較，其整體配適度結果一致且良好。各因素的標準化因素負荷量皆大於0.80，平均變異萃取量皆大於0.70，組合信度皆大於0.80，顯示量表具備良好的收斂效度；兩兩因素的平均變異萃取量皆大於其相關係數的平方，顯示量表具備良好的區別效度。理論模式的驗證結果顯示，行為意圖的抑制因素包含抗拒變革及其前因，如：認知威脅、認知損失、認知不確定性與缺乏自信。而相容性變數當中的個人價值觀與行為意圖呈現負相關，意即新系統與使用者的價值觀不相容時，會降低使用意圖。另外，相容性變數當中的過去經驗與知識與抗拒變革呈現負相關，意即新系統與使用者過去的經驗或知識不相容時，會促進其抗拒變革。再者，過去學者對認知威脅與系統有用性和使用意圖之間的關係尚不清楚，而本研究證實認知威脅與系統有用性和使用意圖之間存在負面的影響關係，意即，新系統的導入若為使用者帶來威脅感，則將促使使用者對新系統的有用性認知和使用意圖降低。然而，路徑分析顯示抗拒變革與使用意圖之間不顯著，疑似存在干擾效應所導致，後續將進一步驗證之。最後，模式解釋力方面，行為意圖的R2為0.66，與過去TAM相比，提升了約30%，顯示本研究所建構的模式是可被接受的。

本研究所發展的量表可供系統導入者以兼具正面與負面的角度來診斷使用者對新系統的行為反應，所得到的結果將比過往更加完整並更具可參考性，亦可輔助系統導入者提升決策成效，使得有限的資源的投入能更精準並達到應有的效果，以促成系統成功。本研究的成果，除了助於開啟抗拒變革的黑盒子外，亦增補科技接受模式與使用者抗拒系統之相關研究的完整性。

rmation system implementation has a far-reaching impact on the healthcare service providers. However, not all of its outcomes are positive. There is an abundance of studies on technology acceptance, which focus on system usefulness and ease of use as the enablers of user intention of using a new system. These research efforts tended to overlook the fact that user resistance—a by-product of change introduction—is hard to be resolved simply via technological improvement. Resistance to change often leads to an unanticipated increase of cost and a waste of resources within an organization striving for an IT upgrade. Therefore, it is crucial to understand the inhibitors of user intention and the antecedent factors of resistance to change.

This research integrates related literature to provide the basis for the research model and survey questionnaire. The initial research model includes 52 items, covering variables from behavior intention (BI), the enablers and inhibitors of BI. The instrument was first assessed by experts and then pretested to enable a further modification and the confirmation of its content validity. After the pretest, three hundred and twenty five questionnaires were distributed for the field survey in the regional or above hospitals in Taiwan, which have adopted the new generation of Computerized Physician Order Entry (CPOE) systems or the Electronic Medical Record (EMR) systems. The research subjects are practicing physicians in the survey settings and the target system is the CPOE or EMR. Two hundred and ninety seven valid questionnaires were collected from fifteen medical centers and three regional hospitals. The valid questionnaire response rate is 91.4%. The majority of the respondents are physicians from the medical centers (72.7%). Most of their specialties are internal medicines (40.1%), followed by surgery (26.6%). With regards to position, attending physicians represent the majority of the respondents (59.6%), as residents (25.9%) take the second place. All of the 297 respondents have prior experience in computer usage and 45.1% of them use computer 4 to 6 hours everyday. A t and one-way ANOVA tests show that 13 variables do not vary according to the respondents’ demographic differences. According to the descriptive statistics results, the average scores of 13 variables fall on between 2.5 and 3.5 point, making it difficult to confirm whether the respondents are resisting or accepting the new system. Moreover, the behavioral spectrum of the respondents’ reaction to change shows their perception of the system fall approximately in the middle of resistance and acceptance. In other words, from a behavioral prospective they appear to be neutral at the time of testing.

A confirmatory factor analysis is used to test the fit between the hypothetic model and the sample, as well as the fit between the hypothetic model and theoretical model, in order to finally verify the research hypotheses and research model. Through this process, items and factors with low reliability and low factor loading are deleted, leaving a final selection of 10 factors and 25 items. The factor loading of all items remained are over 0.80 and their Cronbach’s alpha coefficients are higher than 0.60. These results confirm the quality of the items. All the model fit indexes used to measure of the model after adjustment are as follow: the normed chi-square (χ2 divided by the degree of freedom ) as 1.81; GFI, NFI, TLI, CFI and IFI indexes all exceed 0.90 and close to 1; AGFI close to 0.90; RMSEA as 0.05 and SRMR as 0.03. The results suggest a reasonable and good model fit. A further comparison between the adjusted model and the optimized model also reveals a consistent and good overall fit. The standardized factor loadings of items all exceed 0.80. The average variance extracted (AVE) of constructs all exceed 0.70. The composite reliability of constructs all exceeds 0.80. All these confirm the model’s good convergent validity. The AVE estimates for any two variables is greater than the square of their correlation coefficient and that is the evidence of good discriminant validity. The testing of the theoretical model shows that the inhibitors of BI include resistance to change and its antecedent factors, including perceive threat, perceived loss, perceived uncertainty and perceived distrust. Among compatible variable, the intention to use decrease when there is a negative correlation between personal value and behavioral intention. This means when the system conflicts with the users’ own values, there are less likely to use it. Besides, a negative relationship between prior experience/knowledge and resistance to change is likely to trigger the users to resist the new system. In previous studies there was a lack of explanation of the relationships among perceived threat, perceived usefulness and BI. This study, however, confirms the existence of negative correlation between these factors. In other words, when system implementation is related to a perceived threat, its perceived usefulness and users’ intention to use would be weakened. However, the path analysis shows an insignificant correlation between RTC and BI, probably related to interference effect. It is necessary for future studies to explore the possible existence of a moderator. In the analysis of model interpretation, BI shows a 66% ( R2 as 0.66) of the interpretative power. In comparison with TAM, the interpretative power increased nearly 30% in our research model. Such result confirms the acceptability of our research model.

The questionnaire developed by this study provides change initiators a fuller picture of positive and negative user reactions to a new system. The results are more complete and provide a better reference for problem soothing and resources allocation during the process of system implementation. This study helps open the black box of resistance to change and also contributes to the studies on technology acceptance and user resistance. It adds new fuel to the research of healthcare information technology in the MIS disciple.

CHAPTER 1: INTRODUCTION 1
1.1 BACKGROUND AND MOTIVATIONS 1
1.2 RESEARCH PURPOSES AND QUESTIONS 5
1.3 RESEARCH SCOPE 7
CHAPTER 2: LITERATURE REVIEW 8
2.1 THE UNIQUENESS OF THE HEALTHCARE INDUSTRY AND ITS WORKFORCE 8
2.2 USER ACCEPTANCE 11
2.2.1 USER INTENTION RESEARCH: THE TECHNOLOGY ACCEPTANCE MODEL 11
2.2.1.1 Brief Summary 13
2.2.2 PERCEIVED USEFULNESS, COMPATIBILITY & SOCIAL FACTORS 15
2.2.2.1 Perceived Usefulness 15
2.2.2.2 Subjective norms & social factors of resisting behavior 16
2.2.2.3 Compatibility 19
2.3 USER RESISTANCE TO CHANGE 28
2.3.1 USER RESISTANCE RESEARCH ON RTC 28
2.3.2 THE NATURES OF CHANGE & THE COMPONENTS OF RESISTANCE 37
2.3.3 THE INHERENT THREAT AND LOSS OF CHANGE 41
2.3.4 THE MEASUREMENT SCALE FOR USER RESISTANCE 43
CHAPTER 3: RESEARCH METHODS & MATERIALS 46
3.1 RESEARCH MODEL AND HYPOTHESES 46
3.2 THE RESEARCH SUBJECT 53
3.3 INSTRUMENT DEVELOPMENT PROCESS 53
1.1.1 59
3.4 SURVEY SETTING AND QUESTIONNAIRE DISTRIBUTION 62
3.5 DATA ANALYSIS AND PROCESS 62
3.5.1 DESCRIPTIVE STATISTICS AND BIVARIATE ANALYSIS 62
3.5.2 FACTOR ANALYSIS 64
3.5.2.1 Exploratory Factor Analysis 64
3.5.2.2 Confirmatory Factor Analysis 65
CHAPTER 4: RESULTS 67
4.1 PRETEST 67
4.1.1 CASE HOSPITAL PROFILE 67
4.1.2 RESPONDENT CHARACTERISTICS 68
4.1.3 T & ONE-WAY ANOVA TESTS 76
4.2 SURVEY SUBJECTS & SETTING 78
4.2.1 RESPONDENT CHARACTERISTICS 78
4.2.2 T AND ONE-WAY ANOVA TEST 87
4.2.3 EXPLORATORY FACTOR ANALYSIS (EFA) 88
4.2.4 RELIABILITY ANALYSIS 90
4.2.5 VALIDITY ANALYSIS 91
4.2.5.1 Content Validity 91
4.2.5.2 Construct Validity 92
4.3 CONFIRMATORY FACTOR ANALYSIS (CFA) 97
4.3.1 MEASUREMENT MODEL ANALYSIS 97
4.3.2 STRUCTURE MODEL ANALYSIS 104
CHAPTER 5: DISCUSSION AND CONCLUSION 113
5.1 THEORETICAL MODEL EVALUATION 113
5.1.1 PRELIMINARY FIT CRITERIA 114
5.1.2 OVERALL MODEL FIT 115
5.2 DISCUSSION AND IMPLICATIONS 117
5.2.1 ACADEMIC IMPLICATIONS 119
5.2.2 PRACTICAL IMPLICATIONS 120
5.3 LIMITATIONS AND FUTURE RESEARCH 122
5.4 CONCLUSION 122
REFERENCES 124
APPENDIX: QUESTIONNAIRE 129

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