Pradeep K, R. G. Shilpa, Chandra Sen Mazumdar
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Pradeep K, Ms. R. G. Shilpa, Chandra Sen Mazumdar
Department of Management Studies, Faculty of Management and Commerce, Ramaiah University of Applied Sciences, Gnanagangothri Campus, MSR Nagar, Bengaluru 560054, Karnataka, India.
Volume - 13,
Issue - 2,
Year - 2022
To study and analyse the Big Data BI Tools in Healthcare using TAM and to suggest the ways to improve the efficiency and effectiveness of Big Data Big Data BI tools are an important aspect relating to analysing data quicker for any organization or sector. Business Intelligence (BI) tools are application software which helps in analysing large volumes of data quicker. Once the BI tools are implemented, companies or user feel difficult to get most benefit from them due to lack of user knowledge leading to user acceptance, this led to motivation of the study. To analyse the technology acceptance factors influencing the end users of big data BI Tools, this study is conducted in a reputed hospital located in Bangalore. This study was initiated by having a study on literature reviews based on TAM models in healthcare, which gradually helped in identifying important factors influencing the acceptance and satisfaction of healthcare BI Tools users. The questionnaire was framed based on the factors identified and obtained data was analysed using IBM Statistics SPSS 25 and SMARTPLS 3 data analysis tools, Tests like reliability, factor analysis, descriptive statistics, correlation test, Regression analysis, Bootstrapping, PLS algorithm tests were done. The test depicted in five main factors such as perceived ease of use, perceived usefulness, attitude, perceived risks, intended outcomes, that helped in influencing the acceptance and satisfaction of end users using Big data BI Tools, This study also revealed that most of the responses use BI Tools on daily basis, but still failed to use most of the features of Big Data BI Tools, This enables us to know that Training and development programmes must to given to users, Management should get involve the end users in Big Data BI tools by educating them the importance of Big data BI Tools, also with the help of Information technology, user technology and perceived ease of use has to be improved.
Cite this article:
Pradeep K, R. G. Shilpa, Chandra Sen Mazumdar. Analysis of Big Data Business Intelligence Tools using Technology Acceptance Model in a Healthcare. Asian Journal of Management. 2022;13(2):110-4. doi: 10.52711/2321-5763.2022.00020
Pradeep K, R. G. Shilpa, Chandra Sen Mazumdar. Analysis of Big Data Business Intelligence Tools using Technology Acceptance Model in a Healthcare. Asian Journal of Management. 2022;13(2):110-4. doi: 10.52711/2321-5763.2022.00020 Available on: https://ajmjournal.com/AbstractView.aspx?PID=2022-13-2-2
1. MT Ghozali, Satibi, Zullies Ikawati, Lutfan Lazuardi. Exploring intention to use Asma Droid app of Indonesian Asthmatics using Technology acceptance model (TAM): A Descriptive Quantitative Study. Research J. Pharm. and Tech. 2021; 14(1):573-578.
2. C. Sriram. Health Management Information System (HMIS) in Medicare –Patients’ Experience at ESIC main Hospital and Dispensaries in Tirunelveli sub-Region. Res. J. Humanities and Social Sciences. 2018; 9(1): 49-55.
3. Gelivi Harish, J. Andrews. Effective Implementation of Data Segregation and Extraction Using Big Data in E-Health Insurance as a Service. Research J. Engineering and Tech. 6(2): April-June, 2015 page 246-249.
5. Madhumathi S, Gomathi R. Data mining in Ecommerce platforms for product managers. Research J. Engineering and Tech. 2021;12(1):01-07.
6. P. Shanmuga Sundari, M. Subaji, J. Karthikeyan. A Survey on effective similarity Search Models and Techniques for Big data Processing in Healthcare System. Research J. Pharm. and Tech. 2017; 10(8): 2677-2684.
7. Vimal Kumar Stephen. K, V. Mathivanan. Adjusting Healthcare Innovation and Software Necessities through design thinking. Research J. Pharm. and Tech 2017; 10(10):3537-3538.
8. Padmavathi Vanka, T. Sudha. Big Data Technologies: A Case Study. Research J. Science and Tech. 2017; 9(4): 639-642.
9. MT Ghozali, Satibi, Zullies Ikawati, Lutfan Lazuardi. Exploring intention to use Asma Droid app of Indonesian Asthmatics using Technology acceptance model (TAM): A Descriptive Quantitative Study. Research J. Pharm. and Tech. 2021; 14(1):573-578.
10. P. Vadivukkarasi Ramanadin, G. Muthamilselvi, Manjeet Kaur. A Comparative Study to Assess an Attitude towards Computer Application in Nursing Practice among the Staff Nurses. Asian J. Nur. Edu. and Research 3(2): April.-June 2013; Page 82-86.
11. Jaya Rani, Ajeya Jha. Impact of Age on Online Healthcare Information Search: A Study on Indian Patients. Asian J. Management 6(1): January–March, 2015 page 17-24.
12. Nilima Pandit. Information and Communication Technology and Healthcare. Int. J. Nur. Edu. and Research 1(1): Oct.- Dec., 2013; Page 09-11.
13. Kamal, S.A., Shafiq, M. and Kakria, P., Investigating acceptance of telemedicine services through an extended technology acceptance model (TAM). Technology in Society, 60, p.101212. 2020., https://doi.org/10.1016/j.techsoc.2019.101212
14. Brock, V. and Khan, H.U., Big data analytics: does organizational factor matters impact technology acceptance? Journal of Big Data, 4(1), 2017., pp.1-28. https://doi.org/10.1186/s40537-017-0081-8
15. Wang, S., Li, J. and Zhao, D., "Understanding the intention to use medical big data processing technique from the perspective of medical data analyst", Information Discovery and Delivery, Vol. 45 No. 4, pp. 194-201.2017., https://doi.org/10.1108/IDD-03-2017-0017
16. Razmak, J. and Bélanger, C. "Using the technology acceptance model to predict patient attitude toward personal health records in regional communities", Information Technology & People, Vol. 31 No. 2, pp. 306-326.,2018., https://doi.org/10.1108/ITP-07-2016-0160
17. Rahimi, B., Nadri, H., Lotfnezhad Afshar, H., & Timpka, T., A Systematic Review of the Technology Acceptance Model in Health Informatics. Applied clinical informatics, 9(3), 604–634. 2018., https://doi.org/10.1055/s-0038-1668091