9,659 research outputs found
Rating and perceived helpfulness in a bipartite network of online product reviews
In many e-commerce platforms user communities share product information in the
form of reviews and ratings to help other consumers to make their choices. This
study develops a new theoretical framework generating a bipartite network of products
sold by Amazon.com in the category “musical instruments”, by linking products
through the reviews. We analyze product rating and perceived helpfulness of
online customer reviews and the relationship between the centrality of reviews, product
rating and the helpfulness of reviews using Clustering, regression trees, and random
forests algorithms to, respectively, classify and find patterns in 2214 reviews.
Results demonstrate: (1) that a high number of reviews do not imply a high product
rating; (2) when reviews are helpful for consumer decision-making we observe an
increase on the number of reviews; (3) a clear positive relationship between product
rating and helpfulness of the reviews; and (4) a weak relationship between the centrality
measures (betweenness and eigenvector) giving the importance of the product
in the network, and the quality measures (product rating and helpfulness of reviews)
regarding musical instruments. These results suggest that products may be central to
the network, although with low ratings and with reviews providing little helpfulness
to consumers. The findings in this study provide several important contributions for
e-commerce businesses’ improvement of the review service management to support
customers’ experiences and online customers’ decision-making.publishe
Product market relationships and cost of bank loans: evidence from strategic alliances
This paper examines the effects of strategic alliances on non-financial firms’ bank loan financing. We construct several measures to capture firms’ alliance activities using the frequency of alliance activities, the prominence of the alliance partner and the relative networking position in the overall alliance network. We find that firms with active alliance involvement experience a lower cost of debt from banks. We also document that allying with a prestigious partner (ie S&P 500 firms) can provide an endorsement effect and benefit the borrowers by reducing the price of bank loans. Moreover, a borrowing firm positioned at the centre of an alliance network enjoys a lower cost of bank loans. Finally, we find that borrowing firms with alliance experience are less likely to use collateral and covenants in their loan contracts.cost of bank loans; strategic alliances; product market relationships
Bank Networks from Text: Interrelations, Centrality and Determinants
In the wake of the still ongoing global financial crisis, bank
interdependencies have come into focus in trying to assess linkages among banks
and systemic risk. To date, such analysis has largely been based on numerical
data. By contrast, this study attempts to gain further insight into bank
interconnections by tapping into financial discourse. We present a
text-to-network process, which has its basis in co-occurrences of bank names
and can be analyzed quantitatively and visualized. To quantify bank importance,
we propose an information centrality measure to rank and assess trends of bank
centrality in discussion. For qualitative assessment of bank networks, we put
forward a visual, interactive interface for better illustrating network
structures. We illustrate the text-based approach on European Large and Complex
Banking Groups (LCBGs) during the ongoing financial crisis by quantifying bank
interrelations and centrality from discussion in 3M news articles, spanning
2007Q1 to 2014Q3.Comment: Quantitative Finance, forthcoming in 201
Three Essays on Trust Mining in Online Social Networks
This dissertation research consists of three essays on studying trust in online social networks. Trust plays a critical role in online social relationships, because of the high levels of risk and uncertainty involved. Guided by relevant social science and computational graph theories, I develop conceptual and predictive models to gain insights into trusting behaviors in online social relationships.
In the first essay, I propose a conceptual model of trust formation in online social networks. This is the first study that integrates the existing graph-based view of trust formation in social networks with socio-psychological theories of trust to provide a richer understanding of trusting behaviors in online social networks. I introduce new behavioral antecedents of trusting behaviors and redefine and integrate existing graph-based concepts to develop the proposed conceptual model. The empirical findings indicate that both socio-psychological and graph-based trust-related factors should be considered in studying trust formation in online social networks.
In the second essay, I propose a theory-based predictive model to predict trust and distrust links in online social networks. Previous trust prediction models used limited network structural data to predict future trust/distrust relationships, ignoring the underlying behavioral trust-inducing factors. I identify a comprehensive set of behavioral and structural predictors of trust/distrust links based on related theories, and then build multiple supervised classification models to predict trust/distrust links in online social networks. The empirical results confirm the superior fit and predictive performance of the proposed model over the baselines.
In the third essay, I propose a lexicon-based text mining model to mine trust related user-generated content (UGC). This is the first theory-based text mining model to examine important factors in online trusting decisions from UGC. I build domain-specific trustworthiness lexicons for online social networks based on related behavioral foundations and text mining techniques. Next, I propose a lexicon-based text mining model that automatically extracts and classifies trustworthiness characteristics from trust reviews. The empirical evaluations show the superior performance of the proposed text mining system over the baselines
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