Emotion as a Signal of Product Quality: Exploring Its Effects on Purchase Decisions In Online Customer Reviews

Two critical problems of online customer reviews is the caused information asymmetry and information overload. To reveal consumers’ information processing under this circumstance, this paper proposes a model to investigate pleasure versus displeasure embedded in reviews as a potential signal of product quality and the moderating effects of perceived empathy and perceived cognitive effort based on the signaling theory. A laboratory experiment with 120 subjects was used to empirically test the research hypotheses. The results show that pleasure and displeasure embedded in reviews influence perceived product quality, which subsequently affects purchase decisions. Additionally, pleasant online reviews were found to have a greater influence on perceived product quality compared to unpleasant online reviews when the perceived empathy and perceived cognitive effort are higher. The findings demonstrate positive effects of pleasant online customer reviews, and provide important practical implications for both sellers and consumers

Three Essays on Social Learning

Social learning broadly refers to learning through the acquisition of information from social sources. In the three essays of my dissertation, I investigate the various underlying drivers of social learning and how such learning can impact purchase decisions. In Essay 1, I investigate the link between social learning and sales of experiential products. In particular, I focus on how social capital (i.e., the propensity for people to trust and communicate with each other) moderates the level of social learning for experiential products and thus impacts aggregate sales. In Essay 2, I study how social learning operates differently across the various stages of physician prescription - trial and repeat of a new prescription drug. Given that the mechanisms of social influence varies across trial and repeat stages, the second essay further assesses who is most influential and who is most influenceable across stages. In Essay 3, I examine how consumers make purchases of experiential products and link it to their active search for information from interdependent social sources. Essay 3 assesses the impact of the pattern of similarity of preferences in individual-level social networks (homophily, i.e., the tendency of individuals to associate with similar others, and structural balance, i.e., the congruency of preference in a social network) on consumer search, learning, and purchase

Thought and Behavior Contagion in Capital Markets

Prevailing models of capital markets capture a limited form of social influence and information transmission, in which the beliefs and behavior of an investor affects others only through market price, information transmission and processing is simple (without thoughts and feelings), and there is no localization in the influence of an investor on others. In reality, individuals often process verbal arguments obtained in conversation or from media presentations, and observe the behavior of others. We review here evidence concerning how these activities cause beliefs and behaviors to spread, affect financial decisions, and affect market prices; and theoretical models of social influence and its effects on capital markets. Social influence is central to how information and investor sentiment are transmitted, so thought and behavior contagion should be incorporated into the theory of capital markets.capital markets; thought contagion; behavioral contagion; herd behavior; information cascades; social learning; investor psychology; accounting regulation; disclosure policy; behavioral finance; market efficiency; popular models; memes

Identifying the structure patterns to govern the performance of localization in regulating innovation diffusion

The macro social influence is recognized as a non-negligible ingredient in innovation propagation: more adopters in the network lead to a higher adoption tendency for the rest individuals. A recent study to incorporate such a crucial mechanism shows that sufficient intensity of macro-level social influence can cause a change from a continuous to discontinuous transition, further indicating the existence of a tricritical point. Although network localization strength determines the tricritical point, it remains unclear what network quantities govern the performance of localization in regulating innovation diffusion. To address this issue, we herein consider the model incorporating both the micro- and macro-levels social influence. We present a dynamic message-passing method to analytically treat both the outbreak threshold and recovered population, and validate the predictions through agent-based simulations. Extensive analysis on the classical synthetic networks shows that sparsely available connections, and relatively heterogeneous degree distribution, either assortative or extremely disassortative configurations are favorable for continuous transition. In such cases, the employed network can yield a strong localization effect so that the innovation is trapped in the configurations composed of the hubs with high non-backtracking centrality. We further explore the dependence of both tricritical point and localization strength on three structural quantities: network density, heterogeneity, and assortativity, which gives a clear physical picture of the joint effects of the three structure quantities on the localization strength. Finally, we conclude that the core-periphery structure, being sensitive to the change of the three structure quantities, essentially determines localization strength, and further regulates the phase transition.Comment: 23 pages, 10 figures, 1 table

Recovering the Graph Underlying Networked Dynamical Systems under Partial Observability: A Deep Learning Approach

We study the problem of graph structure identification, i.e., of recovering the graph of dependencies among time series. We model these time series data as components of the state of linear stochastic networked dynamical systems. We assume partial observability, where the state evolution of only a subset of nodes comprising the network is observed. We devise a new feature vector computed from the observed time series and prove that these features are linearly separable, i.e., there exists a hyperplane that separates the cluster of features associated with connected pairs of nodes from those associated with disconnected pairs. This renders the features amenable to train a variety of classifiers to perform causal inference. In particular, we use these features to train Convolutional Neural Networks (CNNs). The resulting causal inference mechanism outperforms state-of-the-art counterparts w.r.t. sample-complexity. The trained CNNs generalize well over structurally distinct networks (dense or sparse) and noise-level profiles. Remarkably, they also generalize well to real-world networks while trained over a synthetic network (realization of a random graph). Finally, the proposed method consistently reconstructs the graph in a pairwise manner, that is, by deciding if an edge or arrow is present or absent in each pair of nodes, from the corresponding time series of each pair. This fits the framework of large-scale systems, where observation or processing of all nodes in the network is prohibitive.Comment: Accepted at The 37th AAAI Conference on Artificial Intelligence (main track