Biases in Consumer Reviews: Implications for Different Categories of Goods

Consumers frequently read online consumer reviews before purchasing products both online and offline (at stores). Yet, reviews are known to have certain biases. This paper surveys 17 types of biases that previous studies identified. The effects of these biases are intertwined and hard to isolate from one another. It is then difficult to assess the impact of each bias on how consumers rate the helpfulness of reviews. Although extant studies use different terminologies, review biases can be summarized into three basic categories: selection biases, system biases, and attribution biases. Focusing on major categories of goods, the paper then considers the overestimation of review helpfulness due to system and non-system (selection and attribution) biases. Using Amazon.com reviews on six bestselling products and the data from a survey questionnaire to 294 consumers, the paper shows the following: (1) the overestimation of review helpfulness due to non-system biases is smaller in the order of search, experience and credence goods and (2) the overestimation of review helpfulness due to system biases is more pronounced with hedonic goods than non-hedonic goods

Integrating Advertising and News about the Brand in the Online Environment: Are All Products the Same?

This research compares the effects of paid advertising (banner ad plus banner ad) and publicity (news article plus banner ad) on attitude toward the brand in the context of different product categorization approaches. The authors utilize both the elaboration likelihood model (ELM) and the economics of information theory to test the mechanism through which different electronic communication modes impact consumers\u27 attitude toward the brand for various product categories. Findings indicate that the product categorization based on the level of involvement (ELM) to be superior to the one distinguishing search from experience goods (economics of information). Including news about the brand in the online brand communication mix generates higher brand attitudes for low- and moderate-involvement products while for high-involvement products, brand attitudes become more favorable with increasing credibility of the added news message

Estimation of online reviews biases by Kalman filter

The purpose of the research is to study the presence of bias (sequential) in online reviews according to the collected data and quantify it. Estimation of bias is based on the Kalman filter. The Kalman filter belongs to a family of filters called Bayesian filters. The Kalman Filter is popular recursive estimator algorithms with no history of observations or estimates. Worth to mention there are other types of Kalman filters: Extended Kalman filter, Unscented Kalman Filter, Extended Information Filter, and Sparse Extended Information Filter. Selection of a filter and its performances depends on an application. In this study, we use the Kalman filter for linear models (LKF), which allowsoptimallinearestimation. Inthisstudy, alongsidewiththeKalmanfilterforlinear models, we use the Kalman filter for non-linear problems (NKF), and the Kalman Filter for a second-order systems (SOKF) to estimate biases in online reviews more efficiently.Declaration of Authorship ii Abstract iii Öz iv Acknowledgments v List of Figures viii 1 Introduction 1 1.1 Kalman Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Recommender Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 Search and Experience Goods . . . . . . . . . . . . . . . . . . . . . . . . . 4 2 Historical Background And Definition Of The Problem 5 2.1 Historical Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.1.1 Application of the Kalman Filter in Estimation of Biases . . . . . . 6 2.2 Definition of the Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 3 Methodology 7 3.1 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 3.1.1 Overview of the Calculation . . . . . . . . . . . . . . . . . . . . . . 7 3.1.2 Modelling the System . . . . . . . . . . . . . . . . . . . . . . . . . 8 3.1.3 Gaussian Distributions . . . . . . . . . . . . . . . . . . . . . . . . . 8 3.2 Modelling of the Kalman Filter . . . . . . . . . . . . . . . . . . . . . . . . 9 3.3 Multivariate Kalman Filter . . . . . . . . . . . . . . . . . . . . . . . . . . 11 3.3.1 Predicting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 3.3.2 Updating . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 4 Experimental Procedure 16 4.1 The Kalman Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 4.1.1 Predicting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 4.1.2 Updating . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 4.2 Non-linear Kalman Filters . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 4.3 The Second-order Kalman filter . . . . . . . . . . . . . . . . . . . . . . . . 26 5 Results 29 5.1 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 5.2 Estimation of Biases of Online Hotel Reviews by Kalman Filter: Results . 29 5.3 EstimationofBiasesofOnlineHotelReviewsbyNon-linearKalmanFilter: Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 5.4 Estimation of Biases of Online Hotel Reviews by Second-order Kalman Filter: Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 6 Discussion Of The Results 31 6.1 General Overview of the Results . . . . . . . . . . . . . . . . . . . . . . . 31 6.1.1 Estimation of Biases by Linear Kalman Filter . . . . . . . . . . . . 31 6.1.2 Estimation of Biases by Non-linear Kalman Filter . . . . . . . . . . 33 6.1.3 Estimation of Biases by Second-order Kalman Filter . . . . . . . . 34 7 Conclusion and Future Directions 36 7.1 Decision Making Based on Online Reviews . . . . . . . . . . . . . . . . . . 36 7.1.1 Evaluating Filter Performance . . . . . . . . . . . . . . . . . . . . . 36 7.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 A Dataset 38 B Estimation of Biases of Online Hotel Reviews by Kalman Filter: Results 39 C Estimation of Biases Of Online Hotel Reviews by Non-linear Kalman Filter: Results 40 D Estimation Of Biases of Online Hotel Reviews by second-order Kalman Filter: Results 42 Bibliography 4

Multichannel business strategies and performance

[ES]El uso de Internet y las nuevas tecnologías de información está en continuo crecimiento. En el pasado año el comercio electrónico en España supuso 12.383 millones de euros, con 27.2 millones de usuarios (ONTSI, 2012). Estos nuevos instrumentos suponen una revolución en la gestión tradicional de las relaciones con los clientes. Resulta fundamental entender que las empresas tienen que atender las demandas del consumidor de manera efectiva e inmediata. Este nuevo consumidor 360º supone considerar al cliente desde una perspectiva integrada, puesto que dispone de información multicanal completa y actualizada. Las empresas necesitan por lo tanto sacar partido de sus fuentes de información internas y externas para evaluar los requerimientos de compra del consumidor y atenderlos de manera que el proceso de compra sea una experiencia plenamente satisfactoria. Las empresas (especialmente las PYMES) nunca se habían encontrado con este desafío (los consumidores tienen más acceso que nunca a información instantánea de manera gratuita), por lo que resulta fundamental atender las necesidades del cliente multicanal

Identificación de productos de búsqueda y experiencia en Long Tail a partir de opiniones en línea. Caso Ciao, UK

Phillip Nelson (1970) fue el primer autor en plantear las limitaciones de obtener información sobre la calidad de los productos y cómo afectaba el comportamiento en el mercado. A partir de este artículo, se plantea la clasificación de los productos dependiendo del momento en el que el consumidor se puede hacer una idea de la calidad de los productos en bienes de búsqueda o bienes de experiencia. A partir del concepto dado, se definen los productos de búsqueda, aquellos cuya calidad puede ser objetivamente evaluada por el consumidor antes de la compra. En el caso de los productos de experiencia, sólo después de probarlos el consumidor puede formarse una opinión sobre la calidad. Podemos concluir que el consumidor puede considerar factores que hacen al producto superior sobre otra oferta en el mercado previo a la decisión de compra en productos de búsqueda, mientras que en los productos de experiencia, sólo es posible posterior a su uso. Para la disertación de este Trabajo Fin de Máster , utilizaremos como recurso las opiniones de los usuarios disponibles en la base de datos CIAO UK en la categoría fashion, con la intención de identificar si este nicho de mercado corresponde a productos de búsqueda, productos de experiencia o a ambos.Universidad de Sevilla. Máster Universitario en Gestión Estratégica y Negocios Internacionale

Online customer experience in an emerging e-retail market

Although customer experience has attracted significant attention in marketing theorizing for over three decades, research has barely progressed beyond the traditional conceptualizations of the concept. Specifically, research on multichannel retailing experience is scarce and fragmented despite previous calls to investigate how customer experience can be optimized at different channels. Additionally, although eWOM is fast supplanting traditional WOM as a determinant of consumer behavior whilst Internet platforms have been declared the future fronts for successful customer relationship management, previous studies rarely examined how consumers process and integrate multiple online reviews especially dissatisfied eWOM. Extrapolating from the foregoing, the following research question is posed: “How can online retailers exploit the link between previous shopping experiences and perceived credibility of negative experience reviews (PCoNERs) to enhance consumer-firm relationship quality?”To answer the above research question, an experience-perception-attitude model was built on the foundations of two social cognitive psychology theories (i.e. the schema theory and the elaboration likelihood model (ELM)) and consequently tested through four scenario-based experiments mapped out into one pilot study and two main studies. The pilot study and study 1 utilized a 2 × 2 between-subject factorial design while study 2 employed 2 × 2 × 2 between-subject factorial design. Data was generated from undergraduate and postgraduate students recruited from two universities located in southern Nigeria. Exploratory factor analysis, partial least squares structural equation modelling procedure, independent sample t-test, Chi-square, one-way analysis of variance, and multivariate analysis of variance were the analytical techniques utilized.Five major contributions are made. First, the thesis developed and tested a unique experience-perception-attitude model from the perspective of two social cognitive psychology theories. The experience-perception-attitude model not only portrayed the multi-channel character of online customer experience but also advanced Verhoef et al.’s (2009) holistic and dynamic model of customer experience by demonstrating how consumer-firm relationship quality can be enhanced through a simultaneous consideration of shopping experiences emanating from both company website and social media site. Second, the thesis extends the context-specific nature of customer experience by demonstrating that emotional experience is the most important driver of PCoNERs in a recession-ridden emerging e-retailing market. Third, the study advances the eWOM literature and ELM by drawing on the ELM to demonstrate that PCoNERs have negative effect on consumer-firm relationship quality; while also demonstrating that the effects of the two thresholds of elaboration (i.e. review source credibility and review frequency) become infinitesimal if consumers are exposed to reviews with consistent valence. Fourth, the thesis adds to the experimental design technique utilized by channel integration researchers and previous panel data-based studies by drawing on the netnographic research approach to utilize naturalistic narratives as experimental scenarios. Finally, the findings offer an evidence-based guide on how e-retailers can practically engage in the systematic management of customer clues. The findings will also assist all categories of e-retailers determine the strategic position to pursue based on their resources and capabilities