19,304 research outputs found

    Highbrow Films Gather Dust: A Study of Dynamic Inconsistency and Online DVD Rentals

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    We report on a field study demonstrating systematic differences between the preferences people anticipate they will have over a series of options in the future and their subsequent revealed preferences over those options. Using a novel panel data set, we analyze the film rental and return patterns of a sample of online DVD rental customers over a period of four months. We predict and find that people are more likely to rent DVDs in one order and return them in the reverse order when should DVDs (e.g., documentaries) are rented before want DVDs (e.g., action films). This effect is sizeable in magnitude, with a 2% increase in the probability of a reversal in preferences (from a baseline rate of 12%) ensuing if the first of two sequentially rented movies has more should and fewer want characteristics than the second film. Similarly, we also predict and find that should DVDs are held significantly longer than want DVDs within-customer. Finally, we find that as the same customers gain more experience with online DVD rentals, their "dynamic inconsistency" is attenuated. We interpret our results as evidence that myopia has a meaningful impact on decisions in the field and that people learn about their myopia with experience, allowing them to curb its influence.want/should, intrapersonal conflict, dynamic inconsistency, myopia

    Cognitive coherence in the evluation of a novel single item

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    Article published in Judgement and Decision-Makin

    The Effects of Online Incentivized Reviews on Organic Review Ratings

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    As online reviews become a major factor in the consumer decision-making process, firms have started seeking ways to create and leverage reviews to help achieve their marketing objectives. One productive strategy to generate reviews is to incentivize or reward customers to write reviews. While such a strategy certainly augments the number of reviews, it naturally raises questions of how unbiased such reviews are, and how such a bias, if it exists, affects potential customers. Complicating the issue further, such incentives can be provided by either the vendor or the platform, which may affect the nature of bias. To understand the marketing value of such reviews, this research examines the effects of online incentivized reviews on subsequent organic reviews. First, we investigate whether incentivized reviews are biased compared to organic reviews. Specifically, we find that vendor – initiated incentivized reviews are more favorable whereas platform – initiated incentivized reviews are more critical. Second, we study how incentivized reviews affect future organic review ratings. The findings suggest that vendor (platform) – initiated incentivized reviews reduce (increase) the subsequent organic review ratings. Moderating effects of helpfulness of incentivized reviews and product type are significant. These findings offer important insights about the effectiveness of incentivized reviews

    Social Influence Bias in Online Ratings: A Field Experiment

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    The aim of this paper is to study the empirical phenomenon of rating bubbles, i.e. clustering on extremely positive values in e-commerce platforms and rating web sites. By means of a field experiment that exogenously manipulates prior ratings for a hotel in an important Italian tourism destination, we investigate whether consumers are influenced by prior ratings when evaluating their stay (i.e., social influence bias). Results show that positive social influence exists, and that herd behavior is asymmetric: information on prior positive ratings has a stronger influence on consumers’ rating attitude than information on prior mediocre ratings. Furthermore, we are able to exclude any brag-or-moan effect: the behavior of frequent reviewers, on average, is not statistically different from the behavior of consumers who have never posted ratings online. Yet, non-reviewers exhibit a higher influence to excellent prior ratings, thus lending support to the social influence bias interpretation. Finally, also repeat customers are affected by prior ratings, although to a lesser extent with respect to new customers

    Engaging your customers via responding to online product reviews

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    Given the tremendous impact of online reviews on consumer choice, responding to online word of mouth (WOM) has become an important channel for firms to engage the consumers. This thesis investigates how firms can proactively respond to online product reviews to engage customers and manage customer relationships. In Study One, based upon the data of hotel reviews on Tripadvisor.com, I propose that responding by firms differ in three aspects, namely frequency, speed, and the amount of information, and these metrics exert significant influence on subsequent consumes’ WOM engagement, hotel rankings, and votes of usefulness of the reviews. Moreover, in contrast to responding to positive reviews, responding to negative reviews greatly affects consumption decisions given the negativity bias among consumers. Thus, the subsequent two studies examine whether responding help to alleviate the detrimental impact of negative reviews. Drawing from the literature on crisis management, service failure recovery, Study Two posits that sellers’ responses to negative WOM can be categorized as defensive and accommodative. Further, whether accommodative or defensive responding is more effective depends upon the nature of NWOM, namely regular NWOM or product failure. Based on the results of a between-subject experiment, Study Two provides evidence for the asymmetric impact of accommodative versus defensive responding. When confronting regular NWOM, defensive response outperforms accommodative response or no response, whereas accommodative response is superior to defensive response or no response when coping with a service failure. Further, based on the attribution of negative reviews, a moderated mediation effect is found. To enhance the external validity and robustness of these findings, Study Three provides econometric evidence that the relative effectiveness of accommodative vs defensive response on subsequent consumers’ evaluation of their consumption experience. Upon analyzing the hotels’ responses on Tripadvisor.com, responding can be a double-edged sword in that it works only when seller takes the appropriate responding strategies. In particular, the higher proportion of accommodative responses (defensive responses) for product failure reviews (regular negative reviews), the higher the subsequent consumers’ satisfaction. However, responding can backfire when the proportion of defensive responses (accommodative responses) for product failure (regular negative reviews) is high. To recapitulate, this thesis identifies whether and how online responding influences consumer experiences on social media. These research findings can help firms formulate effective responding strategies to take advantage of social media’s unique ability to engage customers and improve consumer satisfaction and loyalty

    Supervised Transfer Learning for Product Information Question Answering

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    Popular e-commerce websites such as Amazon offer community question answering systems for users to pose product related questions and experienced customers may provide answers voluntarily. In this paper, we show that the large volume of existing community question answering data can be beneficial when building a system for answering questions related to product facts and specifications. Our experimental results demonstrate that the performance of a model for answering questions related to products listed in the Home Depot website can be improved by a large margin via a simple transfer learning technique from an existing large-scale Amazon community question answering dataset. Transfer learning can result in an increase of about 10% in accuracy in the experimental setting where we restrict the size of the data of the target task used for training. As an application of this work, we integrate the best performing model trained in this work into a mobile-based shopping assistant and show its usefulness.Comment: 2018 17th IEEE International Conference on Machine Learning and Application

    Extraction of aspects from Online Reviews Using a Convolution Neural Network

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    The quality of the product is measured based on the opinions gathered from product reviews expressed on a product. Opinion mining deals with extracting the features or aspects from the reviews expressed by the users. Specifically, this model uses a deep convolutional neural network with three channels of input: a semantic word embedding channel that encodes the semantic content of the word, a part of speech tagging channel for sequential labelling and domain embedding channel for domain specific embeddings which is pooled and processed with a Softmax function. This model uses three input channels for aspect extraction. Experiments are conducted on amazon review dataset. This model achieved better result
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