Volume 26, Issue 3 (1-2024)
jha 2024, 26(3): 9-28 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
karimi takalo S, salmannejad M, dehmoubed sharifabadi B. Modeling and Scenario Analysis of Digital Innovation in the Healthcare Sector: A Case Study. jha 2024; 26 (3) :9-28
URL: http://jha.iums.ac.ir/article-1-4414-en.html
URL: http://jha.iums.ac.ir/article-1-4414-en.html
1- Assistant Professor, Department of Management, Faculty of Administrative Sciences and Economics, Vali-e-Asr university of Rafsanjan, Rafsanjan
2- Ph.D., Department of Industrial Management, Faculty of Economics, Management & Accounting, Yazd University, Yazd ,229.salman@gmail.com
3- Ph.D., Department of Management, Faculty of Management, University of Tehran, Tehran
2- Ph.D., Department of Industrial Management, Faculty of Economics, Management & Accounting, Yazd University, Yazd ,
3- Ph.D., Department of Management, Faculty of Management, University of Tehran, Tehran
Abstract: (1563 Views)
Introduction: Digitalization has become a crucial factor driving innovation in companies, research centers, and governmental organizations. This study aimed to identify the drivers of digital innovation, map the relationships between them, and analyze the effective routes of these drivers within governmental hospitals in Rafsanjan.
Methods: This research is pragmatic in its purpose and employs a descriptive survey for data collection, conducted in 1402. The study's population comprised experts from governmental hospitals in Rafsanjan, with a sample of 12 experts selected through the snowball method. The data's validity was confirmed using the content validity ratio index. Data analysis was performed using the fuzzy cognitive mapping method, supported by Fcmapper, Pajek, and Excel software.
Results: The study identified 12 key factors driving digital innovation in governmental hospitals. Fuzzy cognitive map analysis revealed that senior management support, with a coefficient of 1.57, exerted the most influence. Meanwhile, the organization's digital capacity, with a coefficient of 3.24, was found to have the greatest influence. Additionally, the organization's digital capacity, with a coefficient of 3.89, exhibited the highest centrality. This research contributes by identifying key digital innovation factors, establishing causal relationships among these factors, and determining the primary factors influencing others through various digital innovation scenarios within governmental hospitals.
Conclusion: Based on scenario analysis findings encompassing 12 drivers and understanding the causal relationships among them, it is recommended that hospital authorities prioritize the "needs of citizens" driver to foster digital innovation.
Methods: This research is pragmatic in its purpose and employs a descriptive survey for data collection, conducted in 1402. The study's population comprised experts from governmental hospitals in Rafsanjan, with a sample of 12 experts selected through the snowball method. The data's validity was confirmed using the content validity ratio index. Data analysis was performed using the fuzzy cognitive mapping method, supported by Fcmapper, Pajek, and Excel software.
Results: The study identified 12 key factors driving digital innovation in governmental hospitals. Fuzzy cognitive map analysis revealed that senior management support, with a coefficient of 1.57, exerted the most influence. Meanwhile, the organization's digital capacity, with a coefficient of 3.24, was found to have the greatest influence. Additionally, the organization's digital capacity, with a coefficient of 3.89, exhibited the highest centrality. This research contributes by identifying key digital innovation factors, establishing causal relationships among these factors, and determining the primary factors influencing others through various digital innovation scenarios within governmental hospitals.
Conclusion: Based on scenario analysis findings encompassing 12 drivers and understanding the causal relationships among them, it is recommended that hospital authorities prioritize the "needs of citizens" driver to foster digital innovation.
Type of Study: Research |
Subject:
Health Information Management
Received: 2023/06/16 | Accepted: 2023/09/20 | Published: 2024/02/4
Received: 2023/06/16 | Accepted: 2023/09/20 | Published: 2024/02/4
References
1. Janssen M, Van Der Voort H. Adaptive governance: Towards a stable, accountable and responsive government. Gov Info Q. 2016;33(1):1-5. doi: 10.1016/j.giq.2016.02.003. [DOI:10.1016/j.giq.2016.02.003]
2. Ansell CK, Trondal J, Øgård M, editors. Governance in turbulent times. Oxford: Oxford University Press; 2017. [DOI:10.1093/acprof:oso/9780198739517.001.0001]
3. Gallouj F, Zanfei A. Innovation in public services: Filling a gap in the literature. Struct Change Econ Dyn. 2013;27:89-97. Available from: http://hdl.handle.net/20.500.12210/681. [DOI:10.1016/j.strueco.2013.09.002]
4. Moore M, Hartley J. Innovations in governance. In: The new public governance? 2010. p. 68-87.
5. Arduini D, Zanfei A. An overview of scholarly research on public e-services? A meta-analysis of the literature. Telecommun Policy. 2014;38(5-6):476-95. doi: 10.1016/j.telpol.2013.10.007. [DOI:10.1016/j.telpol.2013.10.007]
6. Bannister F, Connolly R. The great theory hunt: Does e-government really have a problem? Gov Info Q. 2015;32(1):1-1. doi: 10.1016/j.giq.2014.10.003. [DOI:10.1016/j.giq.2014.10.003]
7. Yoo Y, Henfridsson O, Lyytinen K. Research commentary-the new organizing logic of digital innovation: an agenda for information systems research. Inf Syst Res. 2010;21(4):724-35. doi: 10.1287/isre.1100.0322. [DOI:10.1287/isre.1100.0322]
8. Autio E, Nambisan S, Thomas LD, Wright M. Digital affordances, spatial affordances, and the genesis of entrepreneurial ecosystems. Strateg Entrep J. 2018;12(1):72-95. doi: 10.1002/sej.1266. [DOI:10.1002/sej.1266]
9. Solberg E, Traavik LE, Wong SI. Digital mindsets: Recognizing and leveraging individual beliefs for digital transformation. Calif Manage Rev. 2020;62(4):105-24. [DOI:10.1177/0008125620931839]
10. Nambisan S, Lyytinen K, Majchrzak A, Song M. Digital innovation management. MIS Q. 2017;41(1):223-38. [DOI:10.25300/MISQ/2017/41:1.03]
11. Linz C, Müller-Stewens G, Zimmermann A. Radical business model transformation: Gaining the competitive edge in a disruptive world. London: Kogan Page Publishers; 2017.
12. Rachinger M, Rauter R, Müller C, Vorraber W, Schirgi E. Digitalization and its influence on business model innovation. J Manuf Technol Manage. 2018;30(8):1143-60. [DOI:10.1108/JMTM-01-2018-0020]
13. Agostini L, Galati F, Gastaldi L. The digitalization of the innovation process: Challenges and opportunities from a management perspective. Eur J Innov Manage. 2020;23(1):1-2. doi: 10.1108/EJIM-11-2019-0330. [DOI:10.1108/EJIM-11-2019-0330]
14. Hong S, Kim SH, Kwon M. Determinants of digital innovation in the public sector. Gov Info Q. 2022;39(4):101723. doi: 10.1016/j.giq.2022.101723. [DOI:10.1016/j.giq.2022.101723]
15. OECD. The Digitalisation of Science, Technology and Innovation. Paris: OECD Publishing; 2020.
16. Cannavacciuolo L, Capaldo G, Ponsiglione C. Digital innovation and organizational changes in the healthcare sector: Multiple case studies of telemedicine project implementation. Technovation. 2023;120:102550. doi: 10.1016/j.technovation.2022.102550. [DOI:10.1016/j.technovation.2022.102550]
17. Liu Y, Dong J, Mei L, Shen R. Digital innovation and performance of manufacturing firms: An affordance perspective. Technovation. 2023;119:102458. doi: 10.1016/j.technovation.2022.102458. [DOI:10.1016/j.technovation.2022.102458]
18. Damle M, Krishnamoorthy B. Identifying critical drivers of innovation in pharmaceutical industry using TOPSIS method. MethodsX. 2022;9:101677. doi: 10.1016/j.mex.2022.101677. [DOI:10.1016/j.mex.2022.101677]
19. Khin S, Ho TC. Digital technology, digital capability and organizational performance: A mediating role of digital innovation. Int J Innov Sci. 2018;11(2):177-95. doi: 10.1108/IJIS-08-2018-0083. [DOI:10.1108/IJIS-08-2018-0083]
20. Van Looy A. A quantitative study of the link between business process management and digital innovation. In: Business Process Management Forum: BPM Forum 2017, Barcelona, Spain, September 10-15, 2017, Proceedings 15. Cham: Springer International Publishing; 2017. p. 177-92. doi: 10.1007/978-3-319-65015-9_11. [DOI:10.1007/978-3-319-65015-9_11]
21. Dohale V, Gunasekaran A, Akarte M, Verma P. An integrated Delphi-MCDM-Bayesian Network framework for production system selection. Int J Prod Econ. 2021;242:108296. doi: 10.1016/j.ijpe.2021.108296. [DOI:10.1016/j.ijpe.2021.108296]
22. Sabaghchi S, Ghazinoory S, Saghafi F. Conceptualizing and Identifying Key Dimensions of Digital Innovation in Industrial Organizations: A Grounded Theory Approach. Bus Intell Manage Stud. 2022;11(42):267-99. doi: 10.22054/ims.2023.15521 [In Persian].
23. Firk S, Gehrke Y, Hanelt A, Wolff M. Top management team characteristics and digital innovation: Exploring digital knowledge and TMT interfaces. Long Range Plann. 2022;55(3):102166. doi: 10.1016/j.lrp.2021.102166. [DOI:10.1016/j.lrp.2021.102166]
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
Site Keywords
Health, Administration, Scientific Article, Iran University of Medical Sciences,
