Search results

This study aims to explore the different effects of inter-community group networks and intra-community group networks on group innovation.

The authors used a pooled panel dataset of 12,111 self-organizing innovation groups in 463 game product creative workshop communities from Steam support to test the hypothesis. The pooled ordinary least squares (OLS) model is used for analyzing the data.

The results show that network constraint is negatively associated with the innovation performance of online groups. The average path length of the inter-community group network negatively moderates the relationship between network constraint and group innovation, while the average path length of the intra-community group network positively moderates the relationship between network constraint and group innovation. In addition, both the network density of inter-community group networks and intra-community group networks can negatively moderate the negative relationship between network constraint and group innovation.

The findings of this study suggest that network structural characteristics of inter-community networks and intra-community networks have different effects on online groups’ product innovation, and therefore, group members should consider their inter- and intra-community connections when choosing other groups to form a collaborative innovation relationship.

What is the scope of brokerage network to be considered in thinking strategically? Given the value of bridging structural holes, is there value to being affiliated with people or organizations that bridge structural holes? The answer is “no” according to performance associations with manager networks, which raises a question about the consistency of network theory across micro to macro levels of analysis. The purpose here is to align manager evidence with corresponding macro evidence on the supplier and customer networks around four-digit manufacturing industries in the 1987 and 1992 benchmark input–output tables. In contrast to the manager evidence, about 24% of the industry-structure effect on industry performance can be attributed to structure beyond the industry's own buying and selling, to networks around the industry's suppliers and customers. However, the industry evidence is not qualitatively distinct from the manager evidence so much as it describes a more extreme business environment.

This study aims to explore the relationship between financing constraints and the innovation performance of Internet enterprises in the cross-border innovation cooperation network. The study also analyzes the moderating effect of the location of the cross-border innovation cooperation network.

The authors selected patent data, related transaction data and other data of A-share listed companies on Shanghai and Shenzhen stock exchanges from 2014 to 2019. The generalized moment estimation method of instrumental variables (IV-GMM) method was used to analyze the relationship between financing constraints and the innovation performance of Internet firms and the moderating effect of the cross-border innovation cooperation network location. The threshold value of the moderating effect of the network structure hole was calculated with the threshold model.

The empirical results show a significant inverted U-shaped relationship between financing constraints and the innovation performance in the cross-border innovation cooperation network of Internet enterprises. Network centrality positively moderates this relationship. There is a threshold for the adjustment effect of network-structural holes, and the adjustment intensity of structural holes changes before and after the threshold.

This study provides a new perspective for Internet firms in innovation cooperation networks to alleviate the negative impact of financing constraints on innovation performance. The inverted U-shaped relationship between financing constraints and the innovation performance of Internet enterprises is in two stages. The moderating range of network centrality and the structural hole besides the threshold of the moderating effect of a structural hole are detailed.

The aim of this paper, considering the two types of networks and the scope of power from structural holes, is to clarify the relationship between employees’ performance and their social network structure in Chinese small and medium enterprises from the whole-network perspective. The complicated relationship in Chinese culture requires human resource management (HRM) practitioners to be aware of the implications of social network.

An empirical research approach is used in this paper. Using the cluster sampling method, authors collected 118 employees’ characteristic data and network data by face-to-face interviews through structured questionnaire survey, and also got their performance data from the financial department, which support correlation analysis and OLS regression analysis.

First, informal network, but not formal network, has a significant impact on employees’ performance. Second, individual performance of brokerage is greater for direct than indirect contacts. Finally, broker-of-brokers will be the winner in the competition.

First, previous research focuses on egocentric network as the difficulty of data collection, while this paper analyzes a whole network based on the real social network. Second, this paper reveals the network structure mode where individuals get benefits. Third, it also uncovers the effect of relationship type on employees’ performance in Chinese SME. Finally, this paper identifies the status homophily and status crystallization phenomenon in the process of social network formation.

Digital business platforms (DBPs) such as Alibaba and Google Shopping are partnership networks that use the Internet to bring service providers (e.g. retail vendors) and customers together. One of the benefits of DBPs is network effects, in which customers can purchase from multiple providers, giving rise to a unique network. However, few studies have explored which service providers benefit from network effects and which do not.

Using the theories of transaction costs and network analysis, the authors apply network models to DBPs to understand which service providers benefit from network effects.

The authors identify three segments of service providers: (1) those with high prominence (connection to providers with high network centrality), (2) those with high network constraint (adjacent to isolated providers) and (3) those with low prominence and constraint. The authors find that segments (1) and (3) benefit from reciprocated customer exchanges, and thus benefit from network effects, while high constraint segment (2) providers do not benefit from reciprocated exchanges. Moreover, the authors find that for segments (2) and (3) future sales have a negative association with unreciprocated customer exchanges, while segment (1) has no significant association between unreciprocated exchanges and future sales.

The authors discuss implications for a multisided platform (MSP), as it decides which service providers to attract, promote and recommend. They can use this study’s results to know which segments of providers will increase network effects to make the platform more valuable.

This paper provides managers of service platforms with strategies for managing relations with their service providers.

Service platforms are an important and disruptive business model. The authors need to understand how network effects operate to create efficient platforms.

This paper extends the literature on MSPs by quantifying network effects and showing not all service providers benefit equally on an MSP from network effects. Critical insights into network effects on the MSP are provided, including different ways it can impact provider sales.

This paper investigates the association between team constraint and team performance. Constraint is a quantitative measure describing the pattern of connections between contacts in personal network. Team members do not leave the relationships they have with other people behind when they act as part of a team. This paper looks at one way in which the pattern of connections that team members have in their personal networks affects the performance of the team. There are 15 process improvement teams from the same company in the sample. The results from this study provide some evidence that personal networks are important to team performance, strengthening the assertion that differences in social networks do explain performance variations.

Since communication usually accounts as the foremost problem for power consumption, there are some approaches, such as topology control and network coding (NC), for diminishing the activity of sensors’ transceivers. If such approaches are employed simultaneously, then the overall performance does raise as expected. In a wireless sensor network (WSN), the linear NC has been shown to enhance the performance of network throughput and reduce delay. However, the NC condition of existing NC-aware routings may experience the issue of false-coding effect in some scenarios and usually neglect node energy, which highly affects the energy efficiency performance. The purpose of this paper is to propose a new NC scheduling in a WSN with the intention of maximizing the throughput and minimizing the energy consumption of the network.

The improved meta-heuristic algorithm called the improved mutation-based lion algorithm (IM-LA) is used to solve the problem of NC scheduling in a WSN. The main intention of implementing improved optimization is to maximize the throughput and minimize the energy consumption of the network during the transmission from the source to the destination node. The parameters like topology and time slots are taken for optimizing in order to obtain the concerned objective function. While solving the current optimization problem, it has considered a few constraints like timeshare constraint, data-flow constraint and domain constraint. Thus, the network performance is proved to be enhanced by the proposed model when compared to the conventional model.

When 20 nodes are fixed for the convergence analysis, performed in terms of multi-objective function, it is noted that during the 400th iteration, the proposed IM-LA was 10.34, 13.91 and 50% better than gray wolf algorithm (GWO), firefly algorithm (FF) and particle swarm optimization (PSO), respectively, and same as LA. Therefore, it is concluded that the proposed IM-LA performs extremely better than other conventional methods in minimizing the cost function, and hence, the optimal scheduling of nodes in a WSN in terms of the multi-objective function, i.e. minimizing energy consumption and maximizing throughput using NC has been successfully done.

This paper adopts the latest optimization algorithm called IM-LA, which is used to solve the problem of network coding scheduling in a WSN. This is the first work that utilizes IM-LA for optimal network coding in a WSN.

Dyadic multi-dimensionality informs the variation that exists within and between network ties and suggests that ties are not all the same and not all equally strategic. This chapter presents a model of dyadic evolution grounded in dyadic multi-dimensionality and framed within actor-level, dyadic-level, endogenous, and exogenous contexts. These contexts generate both strategic catalysts that motivate network action and bounded agency that may constrain such network action. Assuming the need to navigate within bounded agency, the model highlights three strategic processes that demonstrate how dyadic multi-dimensionality underlies the evolution of strategic network ties.

This paper aims to propose an automatic imaging network design to improve the efficiency and accuracy of automated construction progress monitoring. The proposed method will address two shortcomings of the previous studies, including the large number of captured images required and the incompleteness and inaccuracy of generated as-built models.

Using the proposed method, the number of required images is minimized in two stages. In the first stage, the manual photogrammetric network design is used to decrease the number of camera stations considering proper constraints. Then the image acquisition is done and the captured images are used to generate 3D points cloud model. In the second stage, a new software for automatic imaging network design is developed and used to cluster and select the optimal images automatically, using the existing dense points cloud model generated before, and the final optimum camera stations are determined. Therefore, the automated progress monitoring can be done by imaging at the selected camera stations to produce periodic progress reports.

The achieved results show that using the proposed manual and automatic imaging network design methods, the number of required images is decreased by 65 and 75 per cent, respectively. Moreover, the accuracy and completeness of points cloud reconstruction is improved and the quantity of performed work is determined with the accuracy, which is close to 100 per cent.

It is believed that the proposed method may present a novel and robust tool for automated progress monitoring using unmanned aerial vehicles and based on photogrammetry and computer vision techniques. Using the proposed method, the number of required images is minimized, and the accuracy and completeness of points cloud reconstruction is improved.

To generate the points cloud reconstruction based on close-range photogrammetry principles, more than hundreds of images must be captured and processed, which is time-consuming and labor-intensive. There has been no previous study to reduce the large number of required captured images. Moreover, lack of images in some areas leads to an incomplete or inaccurate model. This research resolves the mentioned shortcomings.

This paper aims to identify the effect of social structure variables on the purchase of virtual goods. Using field data, it also tests whether their effects on a social networking service are dynamic.

To achieve the research objectives, the authors have applied the random effects panel Tobit model with actual time-series corporate data to explain a link between network structure factors and actual behavior on social networking services.

The authors have found that various network structure variables such as in-degree, in-closeness centrality, out-closeness centrality and clustering coefficients are significant predictors of virtual item sales; while the constraint is marginally significant, out-degree is not significant. Furthermore, these variables are time-varying, and the dynamic model performs better in a model fit than the static one.

The findings will help social networking service (SNS) operators realize the importance of understanding network structure variables and personal motivations or the behavior of consumers.

This study provides implications in that it uses various and dynamic network structure variables with panel data.