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題名 電子化醫療服務採用：兩階段分析
E-health Service Adoption: A Two-phase Analysis
作者 林顯宗
Lin, Hsien-Tsung
貢獻者 張欣綠
Chang, Hsin-Lu
林顯宗
Lin, Hsien-Tsung
關鍵詞 E-health
Service concept
Elaboration likelihood model
Symbolic adoption
Use adoption
日期 2020
上傳時間 3-Aug-2020 17:35:36 (UTC+8)
摘要 E-health has been comprehensively discussed over the past few years. Although e-health’s potential to improve quality of care has been well-discussed in past literature, the usage of e-health is still low. Therefore, we aim to examine the items that can persuade people to try e-health services and those items that can persuade people to sustain the use of e-health. We integrate the service concept, elaboration likelihood model (ELM) and symbolic adoption model to examine users’ attitude changes across different adoption phases. Moreover, individual differences, including user involvement and expertise source, are considered moderators in this paper. Our survey respondents include two types of users: potential and experienced users. In our study, we quantify the influence of e-health items and the relationship between these items and the adoption process, distinguish which items can arouse users’ interest the most, and rationalize resource allocation when hospitals provide new e-health services.
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Anderson, S., Pearo, L. K., & Widener, S. K. (2008). Drivers of service satisfaction: Linking customer satisfaction to the service concept and customer characteristics. Journal of Service Research, 10(4), 365–381.
Angst, C. M., & Agarwal, R. (2009). Adoption of electronic health records in the presence of privacy concerns: The elaboration likelihood model and individual persuasion. MIS Quarterly, 33(2), 339–370.
Angst, C. M., Block, E. S., D’Arcy, J., & Kelley, K. (2017). When do it security investments matter? Accounting for the influence of institutional factors in the context of healthcare data breaches. MIS Quarterly, 41(3), 893–916.
Bansal, G., Zahedi, F. M., & Gefen, D. (2015). The role of privacy assurance mechanisms in building trust and the moderating role of privacy concern. European Journal of Information Systems, 24(6), 624–644.
Bhattacherjee, A., & Sanford, C. (2006). Influence processes for information technology acceptance: An elaboration likelihood model. MIS Quarterly, 30(4), 805–825.
Bigelow, B., & Arndt, M. (2005). Transformational change in health care: changing the question. Hospital Topics, 83(2), 19–26.
Bitner, M. J., & Obermiller, C. (1985). The elaboration likelihood model: Limitations and extensions in marketing. Advances in Consumer Research, 12(1), 420–425.
Bohlen, J. M., & Beal, G. M. (1957). The Diffusion Process. Special Report, Agriculture Extension Service, 18, 56–77.
Box, G. E. P. (1949). A General Distribution Theory for a Class of Likelihood Criteria. Biometrika, 36(3–4), 317–346.
Burton-Jones, A., & Volkoff, O. (2017). How can we develop contextualized theories of effective use? A demonstration in the context of community-care electronic health records. Information Systems Research, 28(3), 468–489.
Büyüköztürk, Ş., & Çokluk-Bökeoǧlu, Ö. (2008). Discriminant function analysis: Concept and application. Eurasian Journal of Educational Research, 33, 73–92.
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Chang, H. L., Szu, W. W., & Tu, Y. J. (2017). Drivers of eHealth adoption: Linking eHealth adoption to service concept. International Conference on Electronic Business (ICEB), 85–92.
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Cheung, M.-Y. C., Sia, C.-L., & Kuan, K. K. Y. (2012). Is This Review Believable? A Study of Factors Affecting the Credibility of Online Consumer Reviews from an ELM Perspective. Journal of the Association for Information Systems, 13(8), 618–635.
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描述 碩士
國立政治大學
資訊管理學系
107356009
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0107356009
資料類型 thesis
dc.contributor.advisor 張欣綠zh_TW
dc.contributor.advisor Chang, Hsin-Luen_US
dc.contributor.author (Authors) 林顯宗zh_TW
dc.contributor.author (Authors) Lin, Hsien-Tsungen_US
dc.creator (作者) 林顯宗zh_TW
dc.creator (作者) Lin, Hsien-Tsungen_US
dc.date (日期) 2020en_US
dc.date.accessioned 3-Aug-2020 17:35:36 (UTC+8)-
dc.date.available 3-Aug-2020 17:35:36 (UTC+8)-
dc.date.issued (上傳時間) 3-Aug-2020 17:35:36 (UTC+8)-
dc.identifier (Other Identifiers) G0107356009en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/130977-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊管理學系zh_TW
dc.description (描述) 107356009zh_TW
dc.description.abstract (摘要) E-health has been comprehensively discussed over the past few years. Although e-health’s potential to improve quality of care has been well-discussed in past literature, the usage of e-health is still low. Therefore, we aim to examine the items that can persuade people to try e-health services and those items that can persuade people to sustain the use of e-health. We integrate the service concept, elaboration likelihood model (ELM) and symbolic adoption model to examine users’ attitude changes across different adoption phases. Moreover, individual differences, including user involvement and expertise source, are considered moderators in this paper. Our survey respondents include two types of users: potential and experienced users. In our study, we quantify the influence of e-health items and the relationship between these items and the adoption process, distinguish which items can arouse users’ interest the most, and rationalize resource allocation when hospitals provide new e-health services.en_US
dc.description.tableofcontents CHAPTER ONE: INTRODUCTION 1
CHAPTER TWO: CONCEPTUAL BACKGROUND 3
2.1. Service concept 3
2.2. Symbolic adoption 5
2.3. Elaboration likelihood model 7
CHAPTER THREE: RESEARCH FRAMEWORK 11
3.1. The Central and Peripheral Cues of the ELM 11
3.2. Symbolic Adoption in the Concept of the ELM 12
3.3. Use Adoption in the Concept of the ELM 13
3.4. The moderator of the ELM 14
CHAPTER FOUR: RESEARCH METHODOLOGY 17
4.1 Service introduction 17
4.2 Data Collection 22
4.3 Instrument 23
4.4 Instrument Validation 24
CHAPTER FIVE: DATA ANALYSIS AND RESULT 29
5.1 Mean Value Analysis 29
5.2 Discriminant function analysis 31
CHAPTER SIX: DISCUSSION 46
CHAPTER SEVEN: CONCLUSION 49
7.1 Summary 49
7.2 Contributions 50
7.3 Limitations 50
REFERENCE 52
APPENDIX A. QUESTIONNAIRE OF PROSPECTIVE CUSTOMERS (ENGLISH VERSION) 59
APPENDIX B. QUESTIONNAIRE OF THE FEEDBACK OF CASE (ENGLISH VERSION) 62
APPENDIX C. QUESTIONNAIRE OF PROSPECTIVE CUSTOMERS (CHINESE VERSION) 65
APPENDIX D. QUESTIONNAIRE OF THE FEEDBACK OF CASE (CHINESE VERSION) 68		zh_TW
dc.format.extent 5134796 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0107356009en_US
dc.subject (關鍵詞) E-healthen_US
dc.subject (關鍵詞) Service concepten_US
dc.subject (關鍵詞) Elaboration likelihood modelen_US
dc.subject (關鍵詞) Symbolic adoptionen_US
dc.subject (關鍵詞) Use adoptionen_US
dc.title (題名) 電子化醫療服務採用：兩階段分析zh_TW
dc.title (題名) E-health Service Adoption: A Two-phase Analysisen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) Almeida, J. P., Van Sinderen, M., Pires, L. F., & Quartel, D. (2003). A systematic approach to platform-independent design based on the service concept. 7th IEEE International Enterprise Distributed Object Computing Conference, 112–123.
Anderson, S., Pearo, L. K., & Widener, S. K. (2008). Drivers of service satisfaction: Linking customer satisfaction to the service concept and customer characteristics. Journal of Service Research, 10(4), 365–381.
Angst, C. M., & Agarwal, R. (2009). Adoption of electronic health records in the presence of privacy concerns: The elaboration likelihood model and individual persuasion. MIS Quarterly, 33(2), 339–370.
Angst, C. M., Block, E. S., D’Arcy, J., & Kelley, K. (2017). When do it security investments matter? Accounting for the influence of institutional factors in the context of healthcare data breaches. MIS Quarterly, 41(3), 893–916.
Bansal, G., Zahedi, F. M., & Gefen, D. (2015). The role of privacy assurance mechanisms in building trust and the moderating role of privacy concern. European Journal of Information Systems, 24(6), 624–644.
Bhattacherjee, A., & Sanford, C. (2006). Influence processes for information technology acceptance: An elaboration likelihood model. MIS Quarterly, 30(4), 805–825.
Bigelow, B., & Arndt, M. (2005). Transformational change in health care: changing the question. Hospital Topics, 83(2), 19–26.
Bitner, M. J., & Obermiller, C. (1985). The elaboration likelihood model: Limitations and extensions in marketing. Advances in Consumer Research, 12(1), 420–425.
Bohlen, J. M., & Beal, G. M. (1957). The Diffusion Process. Special Report, Agriculture Extension Service, 18, 56–77.
Box, G. E. P. (1949). A General Distribution Theory for a Class of Likelihood Criteria. Biometrika, 36(3–4), 317–346.
Burton-Jones, A., & Volkoff, O. (2017). How can we develop contextualized theories of effective use? A demonstration in the context of community-care electronic health records. Information Systems Research, 28(3), 468–489.
Büyüköztürk, Ş., & Çokluk-Bökeoǧlu, Ö. (2008). Discriminant function analysis: Concept and application. Eurasian Journal of Educational Research, 33, 73–92.
Chaiken, S., & Maheswaran, D. (1994). Heuristic processing can bias systematic processing: Effects of source credibility, argument ambiguity, and task importance on attitude judgment. Journal of Personality and Social Psychology, 66(3), 460-473.
Chang, H. L., Shaw, M. J., Lai, F., Ko, W. J., Ho, Y. L., Chen, H. S., & Shu, C. C. (2010). U-Health: An example of a high-quality individualized healthcare service. Personalized Medicine, 7(6), 677–687.
Chang, H. L., Szu, W. W., & Tu, Y. J. (2017). Drivers of eHealth adoption: Linking eHealth adoption to service concept. International Conference on Electronic Business (ICEB), 85–92.
Chen, S. H., Wen, P. C., & Yang, C. K. (2014). Business concepts of systemic service innovations in e-Healthcare. Technovation, 34(9), 513–524.
Chen, Y., Yang, L., Zhang, M., & Yang, J. (2018). Central or peripheral? Cognition elaboration cues’ effect on users’ continuance intention of mobile health applications in the developing markets. International Journal of Medical Informatics, 116, 33–45.
Cheung, M.-Y. C., Sia, C.-L., & Kuan, K. K. Y. (2012). Is This Review Believable? A Study of Factors Affecting the Credibility of Online Consumer Reviews from an ELM Perspective. Journal of the Association for Information Systems, 13(8), 618–635.
Cho, S., Mathiassen, L., & Nilsson, A. (2008). Contextual dynamics during health information systems implementation: an event-based actor-network approach. European Journal of Information Systems, 17, 614–630.
Corchado, J. M., Bajo, J., Paz, Y. de, & Tapia, D. I. (2008). Intelligent environment for monitoring Alzheimer patients, agent technology for health care. Decision Support Systems, 44(2), 382–396.
Cortina, J. M. (1993). What Is Coefficient Alpha? An Examination of Theory and Applications. Journal of Applied Psychology, 78(1), 98–104.
Dinev, T. (2014). Why would we care about privacy? European Journal of Information Systems, 23, 97–102.
Dinoff, B. L., & Kowalski, R. M. (1999). Reducing aids risk behavior: The combined efficacy of protection motivation theory and the elaboration likelihood model. Journal of Social and Clinical Psychology, 18(2), 223–239.
Dutta-Bergman, M. J. (2004). The impact of completeness and web use motivation on the credibility of e-health information. Journal of Communication, 54(2), 253–269.
Edvardsson, B., Gustafsson, A., & Roos, I. (2005). Service portraits in service research: A critical review. International Journal of Service Industry Management, 16(1), 107–121.
Edvardsson, B., & Olsson, J. (1996). Key concepts for new service development. Service Industries Journal, 16(2), 140–164.
Farzandipour, M., Mohamadian, H., & Sohrabi, N. (2016). Intention of Continuing to use the Hospital Information System: Integrating the elaboration-likelihood, social influence and cognitive learning. Electronic Physician, 8(12), 3385–3394.
Fields, D. M., & Greco, A. J. (1991). A Model Of The Adoption Process For Revolutionary Product Innovations. JOURNAL OF MANAGERIAL ISSUES, 3(4), 528–548.
Friedman, J. H. (1989). Regularized discriminant analysis. Journal of the American Statistical Association, 84(405), 165–175.
Fu, F., & Elliott, M. (2013). The moderating effect of perceived product innovativeness and product knowledge on new product adoption: An integrated model. Journal of Marketing Theory and Practice, 21(3), 257–272.
Fynes, B., & Lally, A. M. (2008). Innovation in Services: From Service Concepts to Service Experiences. 329–333.
Goh, J. M., Gao, G. G., & Agarwal, R. (2016). The creation of social value: Can an online health community reduce rural-urban health disparities? MIS Quarterly, 40(1), 247–263.
Goldstein, S. M., Johnston, R., Duffy, J., & Raod, J. (2002). The service concept: the missing link in service design research? Journal of Operations Management, 20(2), 121–134.
Grossbart, S. L., Mittelstaedt, R. A., & DeVere, S. P. (1976). Customer Stimulation Needs and Innovative Shopping Behavior: the Case of Recycled Urban Places. ACR North American Advances.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate Data Analysis (7th ed.). Pearson Education Limited.
Hamilton, L. C. (2013). Normality Tests and Transformations. In Statistics with STATA: Version 12 (8th ed., pp. 129–132). Cengage Learning.
Hamilton, M. A., Hunter, J. E., & Boster, F. J. (1993). The Elaboration Likelihood Model as a Theory of Attitude Formation: A Mathematical Analysis. Communication Theory, 3(1), 50–65.
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dc.identifier.doi (DOI) 10.6814/NCCU202001039en_US