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The purpose of this paper is to investigate different effects of three network externality factors, i.e. local network size, network strength, and total network size, on online messenger, online community, chat room and e‐mail services.

In the paper hypotheses are tested with a regression model using a survey data collected from 107 MBA students at a business school in South Korea.

The paper finds that the three network externality factors have different effects on the users' future usage intention for the four Internet services. Local network size is significant for online messenger services, local network size and network strength are significant for online community services, and total network size is significant for chat room services. For email services, none of the network externality factors are significant.

The paper shows that a total network size is an important network externality factor affecting the success of a network. However, users' satisfaction with network services and two additional network externality factors, local network size and network strength, are also important determinants. To generalize the finding, investigations into other network services in other environments and into some offline networks are necessary.

The paper shows that depending on types of networks, managers can focus on different important network externality factors in managing their networks.

The purpose of this study is to calculate the effect of different packet sizes 256, 512, 1,024 and 2,048 bytes on a large-scale hybrid network and analysis and identifies which routing protocol is best for application throughput, application delay and network link parameters for different packet sizes. As the routing protocol is used to select the optimal path to transfer data packets from source to destination. It is always important to consider the performance of the routing protocol before the final network configuration. From the literature, it has been observed that RIP (Routing Information Protocol) and OSPF (Open Shortest Path First) are the most popular routing protocols, and it has always been a challenge to select between these routing protocols, especially for hybrid networks. The efficiency of routing protocol mainly depends on resulting throughput and delay. Also, it has been observed that data packet size also plays an essential role in determining the efficiency of routing protocol.

To analyse the effect of different packet sizes using two routing protocols, routing information protocol (RIP) and open shortest path first (OSPF) on the hybrid network, require detailed planning. Designing the network for simulate and then finally analysing the results requires proper study. Each stage needs to be understood well for work accomplishment. Thus, the network’s simulation and evaluation require implementing the proposed work step by step, saving time and cost. Here, the proposed work methodology is defined in six steps or stages.

The simulation results show that both routing protocols – RIP and OSPF are equally good in terms of network throughput for all different packet sizes. However, OSPF performs better in terms of network delay than RIP routing protocol in different packet size scenarios.

In this paper, a fixed network of 125 objects and only RIP and OSPF routing protocol have been used for analysis. Therefore, in the future, a comparison of different network sizes can be considered by increasing or decreasing the number of objects in the proposed network. Furthermore, the other routing protocols can be used for performance evaluation on the same proposed network.

The analysis can be conducted by simulation of the network, enabling us to develop a network environment without restricting the selection of parameters as it minimizes cost, network deployment overhead, human resources, etc. The results are analysed, calculated and compared for each packet size on different routing protocol networks individually and the conclusion is made.

The present study increases our understanding of strong power in exchange networks by examining its incidence in complex networks for the first time and relating this incidence to characteristics of these networks. A theoretical analysis based on network exchange theory (e.g., Willer, 1999) suggests two network characteristics predicting strong power; actors with only one potential exchange partner, and the absence of triangles, that is, one's potential exchange partners are not each other's partners. Different large-scale structures such as trees, small worlds, buyer–seller, uniform, and scale-free networks are shown to differ in these two characteristics and are therefore predicted to differ with respect to the incidence of strong power. The theoretical results and those obtained by simulating networks up to size 144 show that the incidence of strong power mainly depends on the density of the network. For high density no strong power is observed in all but buyer–seller networks, whereas for low density strong power is frequent but dependent on the large-scale structure and the two aforementioned network characteristics.

The conventional starting point for the design of an electrical machine (or any low-frequency electromagnetic device) is known as “sizing”. In this process, a simple magnetic circuit is used to estimate the main geometric parameters. This does not work for many devices, particularly where eddy currents and non-linearity dominate. The purpose of this paper is to investigate an approach using a neural network trained on a large database of existing designs as a general sizing system.

The approach is based on a combination of a radial basis function neural network and a database of stored performances of electrical machines. The network is trained based on a set of typical performance requirements for a machines design problem. The resulting design is analyzed using finite elements to determine if the design performance is acceptable.

The number of neurons in the network was varied to determine the approximation and generalization capabilities. The finite element analysis showed that the network produced initial design parameters which resulted in an appropriate performance.

The research has looked at only one class of machine. Further work is needed on a range of machines to determine how effective the approach can be.

The approach can provide a good initial design and thus can reduce overall design time significantly.

The paper proposes a novel, fast and effective generalized approach to sizing low frequency electromagnetic devices.

Healthcare networks are becoming ubiquitous, yet it is unclear how hospitals with varying quality capabilities would fare by being affiliated with large healthcare networks. The purpose of this paper is to first consider the deductive configuration perspective and distinguish high and low quality hospitals by using clinical and experiential quality as two dimensions of quality capability. Next, it examines the impact of healthcare network size on operating costs of hospitals. Additionally, the paper investigates the interaction effect of hospital demand and healthcare network size on operating costs.

The paper uses a dataset that was created by combining five separate sources. Cluster analysis technique is used to classify hospitals into four groups – holistic quality leaders (high clinical and experiential quality capability), experiential quality focusers (low clinical quality capability and high experiential quality capability), clinical quality focusers (high clinical capability and low experiential quality capability), and quality laggards (low clinical and experiential quality capability). The authors test the research hypotheses by means of regression analyses after controlling for several contextual characteristics.

The results show that affiliation with large healthcare networks reduces operating costs for quality laggards, but increases these costs for experiential quality focusers and clinical quality focusers. The hypothesized positive relationship between healthcare network size and costs is not supported for holistic quality leaders. The authors find that clinical quality focusers and holistic quality leaders can complement higher utilization levels in their operations due to increased demand and healthcare network size to reduce their operating costs per day.

There has been increasing evidence suggesting that hospitals must carefully manage both clinical and experiential quality. By focusing on both clinical and experiential quality, unlike experiential quality focusers and clinical quality focusers, holistic quality leaders are not adversely affected by the size of their network. The results suggest that experiential quality focusers and clinical quality focusers should either embrace holistic quality management or restrict the size of their networks to maintain their quality level and to reduce coordination costs.

This paper aims to study the behavior of individuals in their job search. The authors analyze the impact of the size and quality of the network on the probability of finding a job through relationships and the quality of the job found through networks for men and women separately.

For this, the authors estimate a series of logit models using the Algerian employment survey (2003 and 2007). Then, they use these models to undertake predictions of the probability of finding jobs through social networks according to the size of the social networks.

The findings of this research, confirm that men and women use different methods in the job search; women use fewer connection networks in the search compared to men. There appears to exist some discrimination against women in the use of family networks; men use and find more jobs using family relationships. Overall, the jobs found through relationships are more precarious and less stable. Social networks appear to be an effective method of finding a job in Algeria but not for skilled jobs.

There are few empirical works that analyze at the same time the role of relationship networks in integration into the labor market and the quality of employment, in particular in the case of Middle East and North Africa (MENA) countries. Understanding the intermediation mechanisms can help to develop better employment policy strategies.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-08-2021-0485

The objective of this study is to highlight the questions arising in the design of district heating and cooling systems (DHCSs) in a distributed generation context and to present a model to help find cost‐effective solutions.

Literature on energy systems optimisation is reviewed and a mixed integer programming model for decentralized DHCSs design is developed and applied to two real case studies.

Distributed cooling generation partly coupled with distributed cogeneration and DH is the preferred solution in the examined areas. The optimal configurations, with special reference to network sizing and layout, significantly depend on heating demand profiles and energy prices.

Interdependencies between energy units sizing and network layout definition should be considered. Obtaining more robust and reliable network configurations should be the objective of future modelling efforts.

Despite the growth of distributed energy conversion, designers often rely on centralized concepts in order to reap economies of scale. The presented model helps in discovering less usual solutions representing the most profitable option.

Combining and comparing central and distributed production of heat and cooling under consideration of network costs.

This study aims to assess the effect of updating the Phase I data – to enhance the parameters' estimates – on the control charts' detection power designed to monitor social networks.

A dynamic version of the degree corrected stochastic block model (DCSBM) is used to model the network. Both the Shewhart and exponentially weighted moving average (EWMA) control charts are used to monitor the model parameters. A performance comparison is conducted for each chart when designed using both fixed and moving windows of networks.

Our results show that continuously updating the parameters' estimates during the monitoring phase delays the Shewhart chart's detection of networks' anomalies; as compared to the fixed window approach. While the EWMA chart performance is either indifferent or worse, based on the updating technique, as compared to the fixed window approach. Generally, the EWMA chart performs uniformly better than the Shewhart chart for all shift sizes. We recommend the use of the EWMA chart when monitoring networks modeled with the DCSBM, with sufficiently small to moderate fixed window size to estimate the unknown model parameters.

This study shows that the excessive recommendations in literature regarding the continuous updating of Phase I data during the monitoring phase to enhance the control chart performance cannot generally be extended to social network monitoring; especially when using the DCSBM. That is to say, the effect of continuously updating the parameters' estimates highly depends on the nature of the process being monitored.

Despite changes in international trade agreements and the introduction of new technologies that facilitate international business, many firms, especially SMEs, still do not diversify into international markets. In this paper, we suggest that an important factor that can influence the international diversification decision is social networks. We hypothesize that both the strength of the ties to international firms and the size of a SME’s international network influence its decision to diversify internationally. Our analysis suggests that the strength of international network ties significantly influences SME international diversification, but that the size of the international network does not. These results have important implications for researchers, managers, and public policy makers.

The purpose of this paper is to develop a theoretical model to investigate the determinants of continuance intention toward social networking sites (SNSs) by integrating the perspectives of the uses and gratifications theory, perceived interactivity and network externalities.

Data collected from 255 Facebook users in Taiwan were used to test the proposed model. The partial least squares method was used to test the measurement model and the structural model.

The findings reveal that emotional gratifications and social gratifications are the key predictors of users’ continuance intention toward SNSs. Further, the results indicate that perceived network size, perceived complementarity, machine interactivity and person interactivity influence information gratifications significantly, while perceived complementarity, machine interactivity and person interactivity exert positive effects on emotional gratifications. Finally, the results show that machine interactivity and person interactivity impact social gratifications positively, whereas perceived network size and perceived complementarity affect machine interactivity and person interactivity significantly.

This study is one of the earliest research inquiries to examine the effects of various types of gratifications on continuance intention. It is also one of the earliest studies to identify the antecedents of gratifications from social factors and technological attributes simultaneously.