Does Amazon Scare Off Customers? The Effect of Negative Spotlight Reviews on Purchase Intention

Online retailers provide review systems to consumers in order to improve perceived trustworthiness and boost sales. We examine the effects of review valence and valence intensity on consumer purchase intention. Review adoption emerges as a novel, important moderating variable. We find that positive reviews have a stronger effect on consumer purchase intention than negative reviews. Moderate reviews always lead to higher purchase intention than extreme reviews, but the size of the effect is greater for extremely negative reviews than moderately negative reviews. The effect is reversed for positive reviews. Our results imply that a recent innovation in Amazon’s review system, highlighting negative reviews along with positive spotlight reviews, must be designed carefully to avoid losing customers. Choosing the wrong combination of reviews can diminish the positive effect of spotlight reviews on sales by nearly 20%

The impact of online reviews on consumer evaluations and decision making: an analysis of review volume and user-generated photos

This thesis investigates the impact of online social influence on consumer behaviour, specifically within the context of online reviews. It examines how review volume and user-generated photos affect consumer evaluations and decision-making. In Chapter 2, I introduce a novel phenomenon, the N-effect, which explores how opinion volume influences the content of online evaluations. I find that as the number of opinions increases, the content becomes more emotional and less analytical. In Chapter 3, I investigate the role of user-generated photos in shaping purchase intentions. This research demonstrates that photos can enhance review helpfulness, even when they lack diagnostic information. This effect is driven by the confidence signalled by the reviewer when posting a review with a photo, which is later assimilated by readers, leading to increased perceived helpfulness and purchase likelihood. This thesis makes several theoretical and practical contributions to the literature on human interaction with technology. Theoretically, it expands our understanding of online social influence by examining the dynamics of online opinion expression and content. I contribute to the literature on group size by demonstrating how responsibility may be lost in online contexts. Furthermore, the findings provide insights into the social influence of photos on viewers and the role of pseudo-evidence in shaping beliefs and attitudes. From a practical standpoint, this research offers valuable insights for online platform managers and marketers on interpreting and using consumer-written reviews. Overall, this thesis contributes to the existing literature on online social influence and provides insights for businesses to improve communication and interpretation with consumers by better understanding and leveraging online reviews and opinions.Open Acces

How TripAdvisor’s reviewers level of expertise influence their online rating behaviour and the usefulness of reviews

The internet has improved the buying behaviour of customers. The development of technologies has led to the dissemination of opinions on social networks where customers buy goods and services. These comments on social networks started to be a part of the purchasing process. Until a few years ago, customers used to choose their itineraries based on tourist guides or brochures. Nowadays, customers’ reviews have changed the way a destination is portrayed, enhancing the description of a product or a service to a level that not even the supplier was able to reach before. There are different types of reviewers. The aim of this study is to identify both reviews, experts and non-expert reviewers and analyse the way they write their reviews. Reviews of five hotels taken from the TripAdvisor website were used in order to conduct this study. After analyzing a great set of variables, the results show that there is not much different on the amount of positive/negative reviews written by a reviewer, however, there is a difference in the deeper meaning of a review when it is positive than when it is negative. The expert reviewer tends to be more emotional when writing positive reviews than negative reviews. Regarding the usefulness of the reviews, there is no significant difference in usefulness of a review whether is an written by an expert reviewer or by a non-expert reviewer. The results also indicate that being an expert does not influence the rating a reviewer gives to a hotel stay either. The study was conducted by using Lexalytics program to analyze a Natural Language Processing (NLP) used to classify reviews according to their polarity. With this study, a new research in study was filled. This study gives insights on the polarity of a review depending on the type of reviewer. The results of this study are also important for hotel managers in order for them to understand the type of guest in house.O desenvolvimento da tecnologia, com ênfase na internet e nos seus desenvolvimentos ao longo dos anos, melhorou o comportamento dos clientes e levou à disseminação de opiniões em redes sociais onde os clientes compram productos e serviços. Os comentários feitos a um produto ou serviço nas redes sociais começaram a fazer parte do processo da compra. Até há uns anos atrás, os clientes escolhiam os itinerários para as suas viagens com base em guias turísticos e brochuras. Recentemente, os comentários de clientes mudaram a maneira que um destino é explicado e ilustrado, melhorando, desta forma, a descrição de um produto/serviço a um nível que nem mesmo os fornecedores destes tinham alcançado ainda. Há diferentes tipos de reviewers. O objectivo deste estudo é identificar ambos tipos, expert e non-expert e analisar o estilo de reviews escrita por estes. Experts são assim denominados se tiverem escrito mais de dez reviews; por outro lado os non-expert reviewers são assim denominados se tiverem escrito menos de 10 reviews. Para este estudo, foi utilizada informação de cinco hotéis de Orlando, Florida, retirada do TripAdvisor. Depois de uma análise das variáveis, os resultados mostram que não há grande diferença no que toca ao volume de comentários positivos/negativos escritos por um utilizador. Por outro lado, existe uma diferença na emoção dada a cada comentário, entre os utilizadores. O expert reviewer tende a ser mais emocional quando escreve comentários positivos do que quando escreve comentários negativos. Relativamente a utilidade de cada comentário, não há grande diferença no que toca a ser um expert reviewer ou um non-expert a escrever um comentário. Os resultados indicam, também, que ser um expert não tem qualquer influência na avaliação que um utilizador dá a sua estadia num hotel. Este estudo foi feito com base no programa Lexalytics, com objectivo de analisar a Natural Language Processing (NLP) usada para classificar os comentários de acordo com a sua polaridade

The impact of social influence on the perceived helpfulness of online consumer reviews

Purpose This study aims to study to what extent the helpfulness votes others attach to a review affect a consumer's perceived helpfulness of that review. In addition, the purpose of this study is to investigate whether this social influence moderates the relationships among several content presentation factors and perceived helpfulness. Design/methodology/approach A choice-based conjoint experiment was carried out in which 201 respondents evaluated different reviews and chose the review they perceive as most helpful. Findings Consumers perceive reviews as more (less) helpful in the presence of clearly valenced positive (negative) helpfulness votes. In addition, helpfulness votes of others diminish the positive impact of structure and the negative impact of spelling errors. Research limitations/implications The experimental setup may limit the external validity of the study. Practical implications Providing a helpfulness button gives firms an instrument to offer content that consumers perceive as more useful and to exert some influence on the effects of content presentation factors on the review's helpfulness. Social implications Consumers tend to follow other consumers' opinions without forming their own opinion. Firms could misuse this tendency by hiring people to vote on reviews that are not necessarily helpful for consumers, but are helpful for the firm. Originality/value This study is the first to assess the extent to which social influence affects consumers' evaluation of reviews. Given that consumers use helpfulness votes to distinguish reviews, it is important to understand to what extent these votes reflect the actual helpfulness of the information in the review and to what extent they reflect previous helpfulness votes

The Valuation of User-Generated Content: A Structural, Stylistic and Semantic Analysis of Online Reviews

The ability and ease for users to create and publish content has provided vast amount of online product reviews. However, the amount of data is overwhelmingly large and unstructured, making information difficult to quantify. This creates challenge in understanding how online reviews affect consumers' purchase decisions. In my dissertation, I explore the structural, stylistic and semantic content of online reviews. Firstly, I present a measurement that quantifies sentiments with respect to a multi-point scale and conduct a systematic study on the impact of online reviews on product sales. Using the sentiment metrics generated, I estimate the weight that customers place on each segment of the review and examine how these segments affect the sales for a given product. The results empirically verified that sentiments influence sales, of which ratings alone do not capture. Secondly, I propose a method to detect online review manipulation using writing style analysis and assess how consumers respond to such manipulation. Finally, I find that societal norms have influence on posting behavior and significant differences do exist across cultures. Users should therefore exercise care in interpreting the information from online reviews. This dissertation advances our understanding on the consumer decision making process and shed insight on the relevance of online review ratings and sentiments over a sequential decision making process. Having tapped into the abundant supply of online review data, the results in this work are based on large-scale datasets which extend beyond the scale of traditional word-of-mouth research

Proceedings der 11. Internationalen Tagung Wirtschaftsinformatik (WI2013) - Band 1

The two volumes represent the proceedings of the 11th International Conference on Wirtschaftsinformatik WI2013 (Business Information Systems). They include 118 papers from ten research tracks, a general track and the Student Consortium. The selection of all submissions was subject to a double blind procedure with three reviews for each paper and an overall acceptance rate of 25 percent. The WI2013 was organized at the University of Leipzig between February 27th and March 1st, 2013 and followed the main themes Innovation, Integration and Individualization.:Track 1: Individualization and Consumerization Track 2: Integrated Systems in Manufacturing Industries Track 3: Integrated Systems in Service Industries Track 4: Innovations and Business Models Track 5: Information and Knowledge ManagementDie zweibändigen Tagungsbände zur 11. Internationalen Tagung Wirtschaftsinformatik (WI2013) enthalten 118 Forschungsbeiträge aus zehn thematischen Tracks der Wirtschaftsinformatik, einem General Track sowie einem Student Consortium. Die Selektion der Artikel erfolgte nach einem Double-Blind-Verfahren mit jeweils drei Gutachten und führte zu einer Annahmequote von 25%. Die WI2013 hat vom 27.02. - 01.03.2013 unter den Leitthemen Innovation, Integration und Individualisierung an der Universität Leipzig stattgefunden.:Track 1: Individualization and Consumerization Track 2: Integrated Systems in Manufacturing Industries Track 3: Integrated Systems in Service Industries Track 4: Innovations and Business Models Track 5: Information and Knowledge Managemen

How to monitor and generate intelligence for a DMO from online reviews

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Marketing IntelligenceSocial media and customer review websites have changed the way the tourism sector is managed. Social media has become a new source of information, due to the large amount of UGC / e-Wom generated by consumers An information that is "available" but at the same time noisy and of great volume, which makes it difficult to access and analyze. This study investigates and verifies the possibility of using data present in content reviews of a Content Web Site Review - TripAdivsor - to generate actionable information for a Destination Management Organization. With a focus on negative reviews, tourist attractions of Lisbon and using the “R code” and its packages, the study shows that with the correct technique chosen and the action of an intelligence analyst, data can be extracted and provide substrate for actions, strategy and intelligence generation – which is Social Media Intelligence. The findings prove that the flood of web 2.0 data can serve as a source of intelligence for the Destination Management Organization (DMO). By monitoring sites like TripAdvisor, a DMO can hear what tourists talk about attractions and thereby generate insights for intelligence and strategy actions. A DMO can even, analyzing this data, make your attractions more desirable, and even act in adverse situations, reducing risky situations