Network Effects and Microenterprises: An Empirical Analysis of Microenterprises in China

2019 ◽  
Author(s):  
Na Zou
Keyword(s):  
Empirical Analysis ◽  
Network Effects

scholarly journals Choice or Mimetism in the Decision to Migrate? A European Illustration

2012 ◽  
Vol 12 (2) ◽  
pp. 1850257 ◽  
Author(s):  
Thierry Warin ◽  
Andrew Blakely
Keyword(s):  
Labor Market ◽  
Gravity Model ◽  
Empirical Analysis ◽  
Economic Activity ◽  
Network Effects ◽  
Herd Behavior ◽  
Market Conditions ◽  
Labor Market Conditions ◽  
Migration Flows ◽  
European Migration

This paper examines how herd behavior (mimetism) and network effects determine bilateral migration flows to thirteen EU-15 countries. Using an adapted gravity model controlling for economic activity, welfare progressivity, as well as geospatial and historic relationships, the results force us to question our explanations for migration flows. Herd behavior positively influences European migration flows, whereas network complementarities in the receiving country do not consistently predict, and may in some cases reduce, the likelihood of immigrant inflows. Moreover, economic activity, particularly labor market conditions, plays a lesser role in the migrants’ choice of destination than was previously thought. The introduction of herd behavior as a determinant of European Migration in our empirical analysis hopefully will change the paradigm for understanding migration.

Does Quality Win? Network Effects versus Quality in High-Tech Markets

2009 ◽  
Vol 46 (2) ◽  
pp. 135-149 ◽  
Author(s):  
Gerard J. Tellis ◽  
Eden Yin ◽  
Rakesh Niraj
Keyword(s):  
Empirical Analysis ◽  
Market Share ◽  
Network Effects ◽  
Historical Data ◽  
The Other ◽  
High Tech ◽  
Relative Importance ◽  
Leadership Changes ◽  
The One ◽  
Positive Effect

Researchers disagree about the critical drivers of success in and efficiency of high-tech markets. On the one hand, some researchers assert that high-tech markets are efficient with best-quality brands being dominant. On the other hand, many scholars suspect that network effects lead to perverse markets in which the dominant brands do not have the best quality. The authors develop scenarios about the relative importance of these effects and the efficiency of markets. Empirical analysis of historical data on 19 categories shows that though both quality and network effects affect market share flows, in general markets are efficient. In particular, market share leadership changes often, switches in share leadership closely follow switches in quality leadership, and the best-quality brands, not the ones that are first to enter, dominate the market. Network effects enhance the positive effect of quality.

How students’ friendship network affects their GPA ranking

2019 ◽  
Vol 33 (2) ◽  
pp. 535-553
Author(s):  
Yongli Li ◽  
Sihan Li ◽  
Chuang Wei ◽  
Jiaming Liu
Keyword(s):  
Empirical Analysis ◽  
Network Effects ◽  
Network Effect ◽  
Data Driven ◽  
Friendship Network ◽  
Probit Regression ◽  
Content Type ◽  
Data Driven Approach ◽  
The Relationship ◽  
Behaviour Data

Purpose Due to the unintentional or even the intentional mistakes arising from a survey, the purpose of this paper is to present a data-driven method for detecting students’ friendship network based on their daily behaviour data. Based on the detected friendship network, this paper further aims to explore how the considered network effects (i.e. friend numbers (FNs), structural holes (SHs) and friendship homophily) influence students’ GPA ranking. Design/methodology/approach The authors collected the campus smart card data of 8,917 sophomores registered in one Chinese university during one academic year, uncovered the inner relationship between the daily behaviour data with the friendship to infer the friendship network among students, and further adopted the ordered probit regression model to test the relationship between network effects with GPA rankings by controlling several influencing variables. Findings The data-driven approach of detecting friendship network is demonstrated to be useful and the empirical analysis illustrates that the relationship between GPA ranking and FN presents an inverted “U-shape”, richness in SHs positively affects GPA ranking, and making more friends within the same department will benefit promoting GPA ranking. Originality/value The proposed approach can be regarded as a new information technology for detecting friendship network from the real behaviour data, which is potential to be widely used in many scopes. Moreover, the findings from the designed empirical analysis also shed light on how to improve GPA rankings from the angle of network effect and further guide how many friends should be made in order to achieve the highest GPA level, which contributes to the existing literature.

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