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on Network Economics |
| By: | Zheyin (Jane) Gu (University of Connecticut, School of Business, Marketing Department, 2100 Hillside Rd, Unit 1041, Storrs, CT, 06269.); Yunchuan Liu (University of Illinois, Urbana-Champaign, 415 Wohlers Hall, 1206 South Sixth Street, Champaign, IL, 61820, (217) 244-2749) |
| Abstract: | We model consumer social networks as information collection media and examine two major issues: first, how consumers construct product fit signals based on product feedbacks collected from their social connections to assist with their purchase decisions, and second, how a retailer can benefit from setting up a digital platform and helping consumers collect more product feedbacks on social networks. Our analysis identifies two important structure features of consumer social networks that affect the outcome of consumer social learning: social group inter-connectivity and overall social connectivity. In particular, when the consumer social network is not well-connected, characterized by low social group inter-connectivity and low overall social connectivity, with more product feedbacks collected on social networks consumers are more likely to form informative prior beliefs about which product has a good fit. In contrast, when the consumer social network is well-connected, characterized by either high social group inter-connectivity or high overall social connectivity, more product feedbacks collected on social networks are more likely to constitute uninformative product fit signals and leave consumers uncertain about which product has a good fit. Furthermore, our analysis shows that a retailer's incentive to set up a digital platform and help consumers collect more product feedbacks on social networks depends on the supplier market structure as well as the structure of consumer social networks. In particular, a big retailer that carries horizontally differentiated products offered by competing manufacturers has incentive to facilitate consumer social learning on well-connected social networks and when without retailer assistance consumers still collect product feedbacks from a good number of social connections. The big retailer's activity of facilitating consumer social learning can also enhance total channel surplus. In contrast, a small retailer that carries product(s) offered by a single manufacturer has incentive to facilitate consumer social learning only on social networks that are not well-connected and when without retailer assistance consumers only collect a small number of social feedbacks. And the total channel efficiency suffers when the small retailer withholds from facilitating consumer social learning. Our result highlights the unique motive of big retailers to embrace the digital era when internet, mobile networks, and social media have profoundly changed consumers' shopping habits as well as the unique contribution big retailers bring in channel efficiency through their efforts of facilitating consumer social learning. |
| Keywords: | Consumer Social Learning, Social Networks, Retailing, Game Theory |
| JEL: | M31 |
| Date: | 2014–09 |
| URL: | http://d.repec.org/n?u=RePEc:net:wpaper:1402&r=net |
| By: | Tarik Roukny, Co-Pierre Georg and Stefano Battiston |
| Abstract: | In this paper, we report a descriptive investigation of the structural evolution of two of the most important over-the-counter markets for liquidity in Germany: the interbank market for credit and for derivatives. We use end-of-quarter data from the German large credit register between 2002 and 2012 and characterize the underlying networks. Surprisingly, the data show little or no impact of the 2008 crisis on the structure of credit market. The derivative market however exhibits a peak of concentration in the run up to the crisis. Globally, both markets exhibit high stability for most of the networks metrics and high correlation amongst them. |
| Keywords: | financial networks, interbank market, credit default swaps, liquidity |
| JEL: | G2 G21 D85 |
| Date: | 2014 |
| URL: | http://d.repec.org/n?u=RePEc:rza:wpaper:461&r=net |
| By: | Assaf Almog; Tiziano Squartini; Diego Garlaschelli |
| Abstract: | Recent events such as the global financial crisis have renewed the interest in the topic of economic networks. One of the main channels of shock propagation among countries is the International Trade Network (ITN). Two important models for the ITN structure, the classical gravity model of trade (more popular among economists) and the fitness model (more popular among networks scientists), are both limited to the characterization of only one representation of the ITN. The gravity model satisfactorily predicts the volume of trade between connected countries, but cannot reproduce the observed missing links (i.e. the topology). On the other hand, the fitness model can successfully replicate the topology of the ITN, but cannot predict the volumes. This paper tries to make an important step forward in the unification of those two frameworks, by proposing a new GDP-driven model which can simultaneously reproduce the binary and the weighted properties of the ITN. Specifically, we adopt a maximum-entropy approach where both the degree and the strength of each node is preserved. We then identify strong nonlinear relationships between the GDP and the parameters of the model. This ultimately results in a weighted generalization of the fitness model of trade, where the GDP plays the role of a `macroeconomic fitness' shaping the binary and the weighted structure of the ITN simultaneously. Our model mathematically highlights an important asymmetry in the role of binary and weighted network properties, namely the fact that binary properties can be inferred without the knowledge of weighted ones, while the opposite is not true. |
| Date: | 2014–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1409.6649&r=net |
| By: | Zhuoxin Li (McCombs School of Business, The University of Texas at Austin, 2110 Speedway Stop B6500, Austin, Texas, 78712); Jason A. Duan (McCombs School of Business, The University of Texas at Austin, 2110 Speedway Stop B6700, Austin, Texas, 78712) |
| Abstract: | Crowdfunding is a fast emerging internet fundraising mechanism for soliciting capital from the crowd to support entrepreneurial ventures. This paper empirically investigates the dynamics of investors’ backing behaviors in the presence of network externalities and a finite time window. The proposed model captures how investors dynamically update their expectations on the prospect of a project based on its current funding status and time progress. Model estimation shows that investors are more likely to back a project that has already attracted a critical mass of funding (positive network externalities). For the same amount of achieved funding, the backing propensity declines over time (negative time effects). These two opposing forces give rise to a critical mass of funding the project must attain on time to achieve successful funding by the deadline. Counterfactual simulations show that projects may fail to attain the critical mass because of unfavorable shocks in investor visits at the early stage of the funding cycle. We derive dynamic seeding strategies for project owners to maximize the likelihood of funding success. |
| Keywords: | crowdfunding; group buying; entrepreneurship; network externality; hazards model; Bayesian inference |
| JEL: | D12 C81 L26 L86 |
| Date: | 2014–09 |
| URL: | http://d.repec.org/n?u=RePEc:net:wpaper:1409&r=net |