The Role of Social Networks in Online Shopping: Information Passing, Price of Trust, and Consumer Choice (1104.0942v1)

Abstract: While social interactions are critical to understanding consumer behavior, the relationship between social and commerce networks has not been explored on a large scale. We analyze Taobao, a Chinese consumer marketplace that is the world's largest e-commerce website. What sets Taobao apart from its competitors is its integrated instant messaging tool, which buyers can use to ask sellers about products or ask other buyers for advice. In our study, we focus on how an individual's commercial transactions are embedded in their social graphs. By studying triads and the directed closure process, we quantify the presence of information passing and gain insights into when different types of links form in the network. Using seller ratings and review information, we then quantify a price of trust. How much will a consumer pay for transaction with a trusted seller? We conclude by modeling this consumer choice problem: if a buyer wishes to purchase a particular product, how does (s)he decide which store to purchase it from? By analyzing the performance of various feature sets in an information retrieval setting, we demonstrate how the social graph factors into understanding consumer behavior.

• The paper investigates the role of social networks in online shopping, analyzing information passing, trust, and consumer choice prediction using Taobao data.
• The study shows information passing between buyers correlates with purchases, particularly with high message volume and temporal proximity, influencing propagation.
• The research shows the social graph is key in predicting seller choice, more than price or rating, as consumers prefer sellers with closer social ties.

Analysis of the Role of Social Networks in Online Shopping

This paper presents a compelling investigation into the intricate relationship between social networks and commerce within the field of online shopping. By examining the dataset from Taobao, the authors probe into how social interactions shape consumer decisions. The foundation of this paper lies in understanding how an individual's purchasing behavior is influenced by their embeddedness in social networks. Specifically, the authors focus on three pivotal aspects: information passing, price of trust, and consumer choice prediction.

Information Passing and Network Effects

The authors introduce the concept of information passing: a process whereby product information or purchase recommendations propagate through social connections. The paper leverages a considerable dataset from Taobao, comprising over one million users, to quantify the prevalence and effects of this phenomenon. Through directed triadic closure analysis, the paper reveals that communication—especially buyer-buyer interaction—is directly correlated with purchase behavior. Notably, the paper identifies that the likelihood of information passing is more pronounced when there is high message volume between connected individuals and follows a temporal proximity to the original purchase. Additionally, certain product categories, such as women's clothing, witness higher success in information passing.

Price of Trust in Online Transactions

In e-commerce environments, trust is often equated to seller reputation and is usually measured through ratings and reviews. The paper explores the price premium consumers are willing to pay for transacting with trusted sellers. The investigation into over 10,000 products and the associated seller ratings reveals a positive, yet super-linear correlation between seller rating and transaction price. This finding underscores the importance of seller reputations in determining consumer willingness to pay a premium. However, the elasticity observed is modest, suggesting other significant factors—such as social network influences—play a role in consumer decision-making.

Predictive Modeling of Consumer Choice

Perhaps the most significant contribution of this paper lies in its predictive model for consumer choice. The authors employ SVM-rank to predict which seller a consumer will choose when multiple sellers offer the same product. Interestingly, the paper argues that the social graph is the most crucial feature in predictive modeling, outperforming factors like product price and seller rating. The paper emphasizes that consumers usually opt for sellers with whom they share closer social ties or have frequent communication, rather than simply choosing the cheapest option. This insight aligns with Granovetter's assertion regarding the embeddedness of economic actions within social networks.

Directions for Future Research

The findings from this paper open several avenues for further exploration. While the paper provides valuable insights into the dynamics of social commerce, future research can explore personalized consumer models that account for browsing data and more complex network structures. Additionally, exploring the potential strategies for viral marketing considering social network influence can be beneficial for e-commerce stakeholders aiming to harness these effects for product promotion and sales optimization.

In conclusion, this paper enriches our understanding of the multifaceted role that social networks play in online shopping. The paper contributes to theoretical advancements in social commerce and presents practical implications for leveraging social information to influence consumer behavior.