- حسینی، ف.، عباس نژاد، ط.، و بانشی، ع. (2015). شناسایی و رتبهبندی عوامل حیاتی موفقیت سیستمهای هوشمند کسبوکار در صنعت درمان با رویکرد آمیخته (مطالعه موردی بیمارستانهای شهر بندرعباس). مطالعات مدیریت کسبوکار هوشمند، 3(11)، 47–70. https://ims.atu.ac.ir/article_1625.html
- خجسته، ن.، عابدی شربیانی، ع. ا.، و انصاری، ر. (2014). بررسی عوامل تکنولوژیک، سازمانی، فرآیندی و کسبوکار مؤثر بر پیادهسازی موفق سیستم هوشمند کسبوکار در شرکتهای خدمات اینترنتی (موردمطالعه: شرکت شاتل). تحقیقات بازاریابی نوین، 4(4)، 143–166. https://nmrj.ui.ac.ir/article_17751.html
- رضایی، ص.، میر عابدینی، س. ج.، و ابطحی، ع. (2018). عوامل مؤثر بر پیادهسازی هوشمندی کسبوکار در صنعت بانکداری ایران. مطالعات مدیریت کسبوکار هوشمند، 6(23)، 33–81. https://doi.org/10.22054/ims.2018.8851
- نوری،، ا.، و تقوا،، م. (1395). هوشمندی کسبوکار (مفاهیم و طراحی و توسعه سیستم) (1st ed.). دانشگاه علامه طباطبایی.
- Ahmad, S., Miskon, S., Alkanhal, T. A., & Tlili, I. (2020). Modeling of Business Intelligence Systems Using the Potential Determinants and Theories with the Lens of Individual, Technological, Organizational, and Environmental Contexts-A Systematic Literature Review. In Applied Sciences (Vol. 10, Issue 9). https://doi.org/10.3390/app10093208
- Anjariny, A. H., Zeki, A. M., & Hussin, H. (2012). Assessing Organizations Readiness toward Business Intelligence Systems: A Proposed Hypothesized Model. 2012 International Conference on Advanced Computer Science Applications and Technologies (ACSAT), 213–218. https://doi.org/10.1109/ACSAT.2012.57
- Arefin, M. S., Hoque, M. R., & Rasul, T. (2021). Organizational learning culture and business intelligence systems of health-care organizations in an emerging economy. Journal of Knowledge Management, 25(3), 573–594. https://doi.org/10.1108/JKM-09-2019-0517
- Avison, D., & Young, T. (2007). Time to Rethink Health Care and ICT? Communications of the ACM, 50(6), 69–74. https://doi.org/10.1145/1247001.1247008
- Ayal, M., & Seidman, A. (2009). An Empirical Investigation of the Value of Integrating Enterprise Information Systems: The Case of Medical Imaging Informatics. Journal of Management Information Systems, 26(2), 43–68. https://doi.org/10.2753/MIS0742-1222260203
- Behkami, N. A., & U. Daim, T. (2012). Research Forecasting for Health Information Technology (HIT), using technology intelligence. Technological Forecasting and Social Change, 79(3), 498–508. https://doi.org/https://doi.org/10.1016/j.techfore.2011.08.015
- Borzekowski, R. (2009). Measuring the cost impact of hospital information systems: 1987-1994. Journal of Health Economics, 28(5), 938–949. https://doi.org/10.1016/j.jhealeco.2009.06.004
- Brooks, P., El-Gayar, O., & Sarnikar, S. (2015). A framework for developing a domain specific business intelligence maturity model: Application to healthcare. International Journal of Information Management, 35(3), 337–345. https://doi.org/https://doi.org/10.1016/j.ijinfomgt.2015.01.011
- Carvalho, J. V., Rocha, Á., & Abreu, A. (2019). Maturity Assessment Methodology for HISMM - Hospital Information System Maturity Model. Journal of Medical Systems, 43(2), 35. https://doi.org/10.1007/s10916-018-1143-y
- Carvalho, J. V., Rocha, Á., Vasconcelos, J., & Abreu, A. (2019). A health data analytics maturity model for hospitals information systems. International Journal of Information Management, 46, 278–285. https://doi.org/https://doi.org/10.1016/j.ijinfomgt.2018.07.001
- Carvalho, J. V., Rocha, Á., Vasconcelos, J., & Abreu, A. (2018). Health Data Analytics: A Proposal to Measure Hospitals Information Systems Maturity BT - Trends and Advances in Information Systems and Technologies (Á. Rocha, H. Adeli, L. P. Reis, & S. Costanzo (eds.); pp. 1071–1080). Springer International Publishing.
- Chen, H., Chiang, R. H. L., & Storey, V. C. (2012). Business Intelligence and Analytics: From Big Data to Big Impact. MIS Quarterly, 36(4), 1165–1188. https://doi.org/10.2307/41703503
- Christensen, C. M., Grossman, J. H., & Hwang, J. (2008). The Innovator’s Prescription: A Disruptive Solution for Health Care. McGraw Hill LLC. https://books.google.co.uk/books?id=x8KFZD_pnH4C
- Chuah, M.-H., & Wong, K.-L. (2011). A review of business intelligence and its maturity models. African Journal of Business Management, 5(9), 3424–3428. https://doi.org/10.5897/AJBM10.1564
- El-Gayar, O., & Timsina, P. (2014). Opportunities for Business Intelligence and Big Data Analytics in Evidence Based Medicine. 2014 47th Hawaii International Conference on System Sciences, 749–757. https://doi.org/10.1109/HICSS.2014.100
- Foshay, N., & Kuziemsky, C. (2014). Towards an implementation framework for business intelligence in healthcare. International Journal of Information Management, 34(1), 20–27. https://doi.org/https://doi.org/10.1016/j.ijinfomgt.2013.09.003
- Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137–144. https://doi.org/https://doi.org/10.1016/j.ijinfomgt.2014.10.007
- Gastaldi, L., Mangiaracina, R., Miragliotta, G., Perego, A., & Tumino, A. (2015). Measuring the benefits of tracking medical treatment through RFId. International Journal of Productivity and Performance Management, 64(2), 175–193. https://doi.org/10.1108/IJPPM-10-2013-0171
- Gastaldi, L., Pietrosi, A., Lessanibahri, S., Paparella, M., Scaccianoce, A., Provenzale, G., Corso, M., & Gridelli, B. (2018). Measuring the maturity of business intelligence in healthcare: Supporting the development of a roadmap toward precision medicine within ISMETT hospital. Technological Forecasting and Social Change, 128, 84–103. https://doi.org/https://doi.org/10.1016/j.techfore.2017.10.023
- Gluchowski, P., Dinter, B., & Schieder, C. (2011, August 4). Towards a Life Cycle Oriented Business Intelligence Success Model. Proceedings of the Americas Conference on Information Systems (AMCIS2011).
- Gomes, J., & Romão, M. (2018). Information System Maturity Models in Healthcare. Journal of Medical Systems, 42(12), 235. https://doi.org/10.1007/s10916-018-1097-0
- Grublješič, T., Coelho, P. S., & Jaklič, J. (2014). The Importance and Impact of Determinants Influencing Business Intelligence Systems Embeddedness. Issues in Information Systems, 15, 106–117. https://doi.org/10.48009/1_iis_2014_106-117
- Habibi, A., Sarafrazi, A., & Izadyar, S. (2014). Delphi Technique Theoretical Framework in Qualitative Research. The International Journal Of Engineering And Science (IJES), 3(4), 8–13.
- Haidzir, H., Othman, S. H., & Kutty Mammi, H. (2018). Evaluation of Business Continuity Plan Maturity Level Using Business Continuity Maturity Model. International Journal of Innovative Computing, 8(1). https://doi.org/https://doi.org/10.11113/ijic.v8n1.163
- Hanson, R. M. (2011). Good health information--an asset not a burden! Australian Health Review : A Publication of the Australian Hospital Association, 35(1), 9–13. https://doi.org/10.1071/AH09865
- Harkness, J., & Schoua-Glusberg, A. (1998). Questionnaires in translation. In J. Harkness (Ed.), Cross-cultural survey equivalence (Vol. 3, pp. 87–126). Zentrum f., Umfragen, Methoden und Analysen -ZUMA-.
- Hawking, P. (2013). Factors Critical To The Success of Business Intelligence Systems. Victoria University.
- Howson, C. (2006). The seven pillars of BI success. Intelligent Enterprise. Tata McGraw-Hill Education.
- Imhoff, C. (2004). Business intelligence–Five factors for success. Retrieved June, 22.
- Işık, M.., Yarar, O., & Söylemez Sur, D.. (2021). Measurement of the Effects of Business Intelligence Applications on Performance in Hospitals According to the Managerial Levels: A Chain Hospital Application. Journal of International Health Sciences and Management, 7(13), 97–108. https://doi.org/10.48121/jihsam.776109
- Janssen, M., & Moors, E. H. M. (2013). Caring for healthcare entrepreneurs — Towards successful entrepreneurial strategies for sustainable innovations in Dutch healthcare. Technological Forecasting and Social Change, 80(7), 1360–1374. https://doi.org/https://doi.org/10.1016/j.techfore.2012.12.003
- Li, M., & Mao, J. (2015). Hedonic or utilitarian? Exploring the impact of communication style alignment on user’s perception of virtual health advisory services. International Journal of Information Management, 35(2), 229–243. https://doi.org/https://doi.org/10.1016/j.ijinfomgt.2014.12.004
- Liaw, S.-T., Zhou, R., Ansari, S., & Gao, J. (2021). A digital health profile & maturity assessment toolkit: cocreation and testing in the Pacific Islands. Journal of the American Medical Informatics Association : JAMIA, 28(3), 494–503. https://doi.org/10.1093/jamia/ocaa255
- Lucas, F. L., Sirovich, B. E., Gallagher, P. M., Siewers, A. E., & Wennberg, D. E. (2010). Variation in cardiologists’ propensity to test and treat: is it associated with regional variation in utilization? Cardiovascular Quality and Outcomes, 3(3), 253–260. https://doi.org/10.1161/CIRCOUTCOMES.108.840009
- Mettler, T., & Vimarlund, V. (2009). Understanding business intelligence in the context of healthcare. Health Informatics Journal, 15(3), 254–264. https://doi.org/10.1177/1460458209337446
- Negash, S. (2004). Business Intelligence. Communications of the Association for Information Systems, 13, 177–195. https://doi.org/10.17705/1CAIS.01315
- Ojeda-Castro, Á., & Ramaswamy, M. (2014). Best Practices for Successful Development of Data Warehouses for Small Businesses. Issues in Information Systems, 15, 277–284.
- Pai, F.-Y., & Huang, K.-I. (2011). Applying the Technology Acceptance Model to the introduction of healthcare information systems. Technological Forecasting and Social Change, 78(4), 650–660. https://doi.org/https://doi.org/10.1016/j.techfore.2010.11.007
- Pereira, A., Portela, F., Santos, M. F., Machado, J., & Abelha, A. (2016). Pervasive Business Intelligence: A New Trend in Critical Healthcare. Procedia Computer Science, 98, 362–367. https://doi.org/https://doi.org/10.1016/j.procs.2016.09.055
- Popovič, A., Hackney, R., Coelho, P., & Jaklič, J. (2012). Towards business intelligence systems success: Effects of maturity and culture on analytical decision making. Decision Support Systems, 54, 729–739. https://doi.org/10.1016/j.dss.2012.08.017
- Qaseem, A., Alguire, P., Dallas, P., Feinberg, L. E., Fitzgerald, F. T., Horwitch, C., Humphrey, L., LeBlond, R., Moyer, D., Wiese, J. G., & Weinberger, S. (2012). Appropriate use of screening and diagnostic tests to foster high-value, cost-conscious care. Annals of Internal Medicine, 156(2), 147–149. https://doi.org/10.7326/0003-4819-156-2-201201170-00011
- Ranjan, Jayanthi. (2008). Hurdles and opportunities for Indian firms adopting business intelligence. Journal of Advances in Management Research, 5(1), 56–62. https://doi.org/10.1108/97279810880001267
- Ranjan, JAYANTHI. (2009). Business intelligence: Concepts, components, techniques and benefits. Journal of Theoretical and Applied Information Technology, 9(1), 60–70.
- Romanow, D., Cho, S., & Straub, D. (2012). Editor’s Comments: Riding the Wave: Past Trends and Future Directions for Health IT Research. MIS Q., 36(3), III–A18.
- Salisu, I., Bin Mohd Sappri, M., & Bin Omar, M. F. (2021). The adoption of business intelligence systems in small and medium enterprises in the healthcare sector: A systematic literature review. Cogent Business & Management, 8(1), 1935663. https://doi.org/10.1080/23311975.2021.1935663
- Tremblay, M. C., Hevner, A. R., & Berndt, D. J. (2012). Design of an information volatility measure for health care decision making. Decision Support Systems, 52(2), 331–341. https://doi.org/https://doi.org/10.1016/j.dss.2011.08.009
- Wielki, J., & Jurczyk, M. (2019). Evaluation of Healthcare Business Intelligence Using the Fuzzy TOPSIS Method. In Informatyka i zarządzanie na przełomie wieków. Metody, narzędzia, systemy. Wydawnictwo Naukowe Wydziału Zarządzania Uniwersytetu Warszawskiego. https://books.google.com/books?id=ao5wzgEACAAJ
- Williams, P. A., Lovelock, B., Cabarrus, T., & Harvey, M. (2019). Improving Digital Hospital Transformation: Development of an Outcomes-Based Infrastructure Maturity Assessment Framework. JMIR Medical Informatics, 7(1), e12465. https://doi.org/10.2196/12465.
- Wixom, B. H., & Watson, H. J. (2001). An empirical investigation of the factors affecting data warehousing success. Management Information Systems Quarterly, 25(1), 17–41.
- Yeoh, W., & Koronios, A. (2010). Critical success factors for business intelligence systems. Journal of Computer Information Systems, 50.
- Yeoh, W., Koronios, A., & Gao, J. (2007). Critical Success Factors for Implementation of Business Intelligence Systems: A Study of Engineering Asset Management Organizations BT - Decision Support for Global Enterprises (U. Kulkarni, D. J. Power, & R. Sharda (eds.); pp. 33–49). Springer US. https://doi.org/10.1007/978-0-387-48137-1_3
- Zheng, W., Wu, Y.-C. J., & Chen, L. (2018). Business intelligence for patient-centeredness: A systematic review. Telematics and Informatics, 35(4), 665–676. https://doi.org/https://doi.org/10.1016/j.tele.2017.06.015
|
