Volume 21, Issue 74 (1-2019)
jha 2019, 21(74): 19-34 |
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Aalikhani R, Rasouli M R, Aliahmadi A R. The impact of key factors on an appropriate network governance model in health care systems: a structural equation modeling approach. jha 2019; 21 (74) :19-34
URL: http://jha.iums.ac.ir/article-1-2775-en.html
URL: http://jha.iums.ac.ir/article-1-2775-en.html
1- faculty of industrial engineering, Iran University of Science and Technology, Tehran, Iran
2- faculty of industrial engineering, Iran University of Science and Technology, Tehran, Iran; Corresponding Author (rasouli@iust.ac.ir) ,rasouli@iust.ac.ir
2- faculty of industrial engineering, Iran University of Science and Technology, Tehran, Iran; Corresponding Author (rasouli@iust.ac.ir) ,
Abstract: (6035 Views)
Introduction: Due to the complexity of health services, which require the engagement of different parties for the provision of integrated services, inappropriate network governance models can lead to financial conflicts, lack of transparency of accountabilities for medical errors, and difficulties on multi-professional team working. Therefore, this study aimed to examine factors influencing the design of an appropriate network governance model in the healthcare system.
Methods: In this quantitative-correlational study, first factors influencing the domains of network governance were extracted from the literature. In the next step, a structural model was developed based on the hypotheses. To test the research hypotheses, structural equation modeling - partial least- squares method and Smart Pls2 software were used. The data were collected using a standard questionnaire which was distributed to two collaborative networks of diagnostic laboratories and one medical equipment supply network with 194 members. A total of 98 questionnires were collected.
Results: Opportunistic behavioral, trust, commitment, information sharing, knowledge sharing were used as key factors influencing governance model within the structural model of this research. The results showed that commitment and information sharing had the most direct impact on the network governance. Moreover, opportunistic behavior had a negative and severe effect on trust in the network, which in turn affected the governance of the network.
Conclusion: In order to design an appropriate network governance model in healthcare system, special attention should be paid to trust and commitment. These variables can also affect the governance of the network through improving information sharing. Furthermore, the governance model should be designed in such a way as to prevent the opportunistic behavior of the members.
Methods: In this quantitative-correlational study, first factors influencing the domains of network governance were extracted from the literature. In the next step, a structural model was developed based on the hypotheses. To test the research hypotheses, structural equation modeling - partial least- squares method and Smart Pls2 software were used. The data were collected using a standard questionnaire which was distributed to two collaborative networks of diagnostic laboratories and one medical equipment supply network with 194 members. A total of 98 questionnires were collected.
Results: Opportunistic behavioral, trust, commitment, information sharing, knowledge sharing were used as key factors influencing governance model within the structural model of this research. The results showed that commitment and information sharing had the most direct impact on the network governance. Moreover, opportunistic behavior had a negative and severe effect on trust in the network, which in turn affected the governance of the network.
Conclusion: In order to design an appropriate network governance model in healthcare system, special attention should be paid to trust and commitment. These variables can also affect the governance of the network through improving information sharing. Furthermore, the governance model should be designed in such a way as to prevent the opportunistic behavior of the members.
Keywords: Network Governance Model, Health Care Systems, Structural Equation Modeling, Medical diagnostic laboratory, Medical Equipment Supply Network
Type of Study: Research |
Received: 2018/07/26 | Accepted: 2019/01/23 | Published: 2019/01/23
Received: 2018/07/26 | Accepted: 2019/01/23 | Published: 2019/01/23
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