Exploring the core factors of online purchase decisions by building an E-Commerce network evolution model

Making use of the results from recent infrared and radio surveys of planetary nebulae, we have selected 431 nebulae to form a sample where a number of distance-independent parameters (e.g., Tb, Td, I60μm and IRE) can be constructed. In addition, we also made use of other distance-independent parameters ne and T∗ where recent measurements are available. We have investigated the relationships among these parameters in the context of a coupled evolution model of the nebula and the central star. We find that most of the observed data in fact lie within the area covered by the model tracks, therefore lending strong support to the correctness of the model. Most interestingly, we find that the evolutionary tracks for nebulae with central stars of different core masses can be separated in a Tb-T∗ plane. This implies that the core masses and ages of the central stars can be determined completely independent of distance assumptions. The core masses and ages have been obtained for 302 central stars with previously determined central-star temperatures. We find that the mass distribution of the central stars strongly peaks at 0.6 M⊙, with 66% of the sample having masses <0.64 MM⊙. The luminosities of the central stars are then derived from their positions in the HR diagram according to their core masses and central star temperatures. If this method of mass (and luminosity) determination turns out to be accurate, we can bypass the extremely unreliable estimates for distances, and will be able to derive other physical properties of planetary nebulae.

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Dynamic Evolution Model of a Collaborative Innovation Network from the Resource Perspective and an Application Considering Different Government Behaviors

Information ◽

10.3390/info10040138 ◽

2019 ◽

Vol 10 (4) ◽

pp. 138 ◽

Cited By ~ 1

Author(s):

Wu ◽

Shao ◽

Feng

Keyword(s):

Network Evolution ◽

Evolution Model ◽

Dynamic Evolution ◽

Innovation Network ◽

Transfer Policy ◽

Collaborative Innovation ◽

Innovation Output ◽

The Government ◽

Small Change ◽

Innovative Behaviors

The evolution of a collaborative innovation network depends on the interrelationships among the innovation subjects. Every single small change affects the network topology, which leads to different evolution results. A logical relationship exists between network evolution and innovative behaviors. An accurate understanding of the characteristics of the network structure can help the innovative subjects to adopt appropriate innovative behaviors. This paper summarizes the three characteristics of collaborative innovation networks, knowledge transfer, policy environment, and periodic cooperation, and it establishes a dynamic evolution model for a resource-priority connection mechanism based on innovation resource theory. The network subjects are not randomly testing all of the potential partners, but have a strong tendency to, which is, innovation resource. The evolution process of a collaborative innovation network is simulated with three different government behaviors as experimental objects. The evolution results show that the government should adopt the policy of supporting the enterprises that recently entered the network, which can maintain the innovation vitality of the network and benefit the innovation output. The results of this study also provide a reference for decision-making by the government and enterprises.

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A continuous-time network evolution model describing 3-interactions

Communication in Statistics- Theory and Methods ◽

10.1080/03610926.2021.1985141 ◽

2021 ◽

pp. 1-20

Author(s):

István Fazekas ◽

Attila Barta ◽

Csaba Noszály ◽

Bettina Porvázsnyik

Keyword(s):

Continuous Time ◽

Network Evolution ◽

Evolution Model

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Research on the Space-Based Integrated Information Network Evolution Model Visualization Methods Based on the Super Network Theory

2017 IEEE Second International Conference on Data Science in Cyberspace (DSC) ◽

10.1109/dsc.2017.31 ◽

2017 ◽

Author(s):

Chengxiang Liu ◽

Wei Xiong

Keyword(s):

Network Theory ◽

Network Evolution ◽

Evolution Model ◽

Information Network ◽

Model Visualization ◽

Integrated Information

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PENGARUH KEAMANAN, KEPERCAYAAN, DAN PERSEPSI RISIKO TERHADAP KEPUTUSAN PEMBELIAN SECARA ONLINE PADA LAZADA ONLINE SHOP (Studi Pada Mahasiswa Fakultas Ekonomi Universitas Negeri Medan)

Jurnal PLANS : Penelitian Ilmu Manajemen dan Bisnis ◽

10.24114/plans.v11i2.9605 ◽

2016 ◽

Vol 11 (2) ◽

Author(s):

Toni Hidayat ◽

T Teviana

Keyword(s):

College Student ◽

Perceived Risk ◽

Structural Equation ◽

Purchase Decisions ◽

State University ◽

Purchasing Decisions ◽

Accounting Data ◽

Online Purchase ◽

Key Word ◽

Data Collection Technique

This research aims to identify and explain the effect of security, trust, and perceived risk in online purchase decisions at the college student of the Faculty of Economics, State University of Medan. either partially or simultaneously.This research conducted at Faculty of Economics, State University of Medan. Sample size of this research is 96 respondents. College student who become respondents came from the Departments of Management, Department of Economics, and Department of Accounting. Data collection technique used was through a questionnaire which was measure using Likert scale and statistically analyzed using multiple regression analysis with structural education Y=a+b1X1+b2X2+b3X3+e and processed with SPSS for windows 23.00.The result showed that the security (X1), trust (X2), and perceived risk (X3) simultaneously significantly influence the purchase decision (Y). This is evident from the calculated Fvalue of 18.723 and Ftable of 2,70, so the Fvalue of 18.723 > Ftable of 2,70 of the significance α = 5%. Where the influence of the variable security, trust, and perceived risk in the purchase decisions is 37,9% as indicated by the value of R square is 0.379. while the partial security has a influence on purchasing decisions with the tvalue> ttable is 2,655 > 1,985, trust has a influence on purchasing decisions with the tvalue> ttable is 3,331 > 1,985, but perceived risk has not a influence on purchasing decisions with the tvalue< ttable is -1.096 < 1,985. Structural equation of the model at can be made with Y=1,595+0,252X1+0,359X2-0,095X3+e. Key Word: Security, Trust, Perceived Risk And Online Purchase Decisions

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