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Interorganizational collaborations are crucial for delivering high-quality, integrated healthcare services. To maximize the benefits of these collaborative networks, effective governance structures and mechanisms must be in place. While previous studies have extensively examined organizational-level factors, such as partner capabilities and backgrounds, this study focuses on network-level factors, including collaboration structures and tie characteristics that shape effective network governance.

A systematic literature review (SLR) was conducted to identify and synthesize the key network-level factors influencing governance structures and mechanisms in healthcare networks.

The review identified 22 critical factors, categorized into three primary groups that impact network governance. These findings offer a robust foundation for developing context-sensitive governance models tailored to healthcare systems.

This study provides valuable insights for healthcare practitioners, policymakers and researchers by highlighting key factors that can improve interorganizational collaboration within healthcare systems. The findings contribute to both theory and practice, with the potential to enhance healthcare service delivery and patient outcomes.

This study is the first to systematically identify and categorize the network-level factors that influence governance structures and mechanisms in healthcare networks, providing a comprehensive and novel contribution to the field.

The vehicle routing problem (VRP) is critical for the successful execution of logistics activities. However, there is strong evidence that efficiently solving the VRP is often complicated and requires more powerful – and possibly intelligent – support tools. In accordance with this necessity, the present study proposes a decision support system (DSS) applicable to the VRP, which includes both initial planning and replanning phases to support the real-time operations.

The proposed DSS lies at the basis of resilience thinking to provide a capacity to absorb and withstand the impact of disruptions, where resilience is connected with the factors of preparedness, flexibility and redundancy. These factors are approached in this study through a number of operational strategies in the reactive and proactive modes. The DSS includes a multi-layer perceptron neural network to predict changes that may arise in dynamic contexts, a modified k-means clustering algorithm to group customers with both static and dynamic attributes and two mixed-integer programming models to produce primary and alternate routing plans.

The research is motivated by the operational challenges faced by a collaborative networked clinical laboratory, which seeks to enhance efficiency and productivity in the daily management of medical sample collection and delivery through the implementation of increased automation. The findings reveal that centralized planning leads to heightened vulnerability in route planning and increased costs for replanning. Furthermore, the effectiveness of resilience-enhancement strategies varies based on the source and level of uncertainty.

The contributions of this paper are as follows: incorporating resilience thinking into the operational planning of logistics services, managing the decision-making of transport and collection companies through a DSS framework to ensure proper support to real-time operations, addressing the clustered VRP in a dynamic setting and adopting forecasting approaches to cover possible sources of dynamism.

The purpose of this paper is to identify information governance (IG) requirements in the context of dynamic business networking (BN).

For the identification of IG requirements a systematic literature review is conducted. The practical significance of identified IG requirements is evaluated through a case study.

A comprehensive list of IG requirements in dynamic BN is identified. These requirements are classified in information quality, information security, and metadata domains. The conducted case study demonstrates information exchange issues in a real world dynamic BN that reflects the practical significance of the identified IG requirements.

Exploiting emerging market opportunities through a dynamic BN necessitates the realization of a comprehensive IG program within the network. Otherwise, information exchange related risks can interrupt the operations in a BN. In this research the authors concentrate on interactions between parties within a BN. The interactions with customers for the co-creation of value are not addressed directly. Although the conducted case study reflects the practical significance of the identified IG requirements clearly, more empirical study is needed for prioritizing these IG requirements.

The governor of a BN needs to balance between the value obtained from dynamic networked interactions and the risk evolving from the dynamic inter-organizational information exchange.

The comprehensive list of IG requirements that are identified in this research can be used to develop an IG program that enables high quality and secure information exchange in a dynamic BN.

This study aims to investigate the relationship between information technology and internal controls of state agencies in Iran.

The research population includes all auditors and managers working in public sector. Data collection instrument is a questionnaire designed by the researcher and administrated during March 5, 2016. The collected data are analyzed through descriptive and inferential statistics (binomial test).

The findings of the research show that there is a significant relationship between information technology and internal controls (administrative, financial and accounting controls, risk assessment, information and communication, control activities and monitoring). Moreover, the alteration of data collection methods (from traditional to modern) and the written instructions (in information technology) have a positive effect on the internal control and its subscales.

With regard to the emphasis on the development of computer application and the use of new processing facilities and the exchange of information and its specific controlling consequences, this is an innovative research.

This paper aims to predict the fire resistance of steel-reinforced concrete columns by application of the genetic algorithm.

In total, 11 effective parameters are considered including mechanical and geometrical properties of columns and loading values as input parameters and the duration of concrete resistance at elevated temperatures as the output parameter. Then, experimental data of several studies – with extensive ranges – are collected and divided into two categories.

Using the first set of the data along with the gene expression programming (GEP), the fire resistance predictive model of steel-reinforced concrete (SRC) composite columns is presented. By application of the second category, evaluation and validation of the proposed model are investigated as well, and the correspondent time-temperature diagrams are derived.

The relative error of 10% and the R coefficient of 0.9 for the predicted model are among the highlighted results of this validation. Based on the statistical errors, a fair agreement exists between the experimental data and predicted values, indicating the appropriate performance of the proposed GEP model for fire resistance prediction of SRC columns.