The impact of review valence, rating and type on review helpfulness : a text clustering and text categorization study

Dissertation presented as partial requirement for obtaining the Master`s degree in Information Management, with specialization in Marketing IntelligenceConsumers trust on online reviews to help them making their purchasing decisions. Online reviews provide consumers with clues about the quality of the products that they want to buy. Consumers rely on clues, such as, review helpfulness votes and rating to infer product quality. In this study, we perform a Text Clustering and a Text Categorization analysis to uncover the review characteristics and to predict the review rating, helpfulness votes and the product price, based on review corpus. We use a dataset with 72 878 reviews of unlocked mobile phones sold on Amazon.com to perform this analysis. The main goal of this research is to understand the impact of review valence, rating and type on helpfulness votes on Amazon, for unlocked mobile phones. This research aims, also to understand the impact of price on customer satisfaction and the relationship between customer satisfaction and ratings. Our results suggest that positive reviews that emphasize the feature level quality of the products receive more helpful votes than the positive reviews that contain mainly subjective expressions or negative reviews. Another important finding of this research is on the influence of the price of the product. The phones with high price tend to receive more positive reviews and more helpful votes. These findings have important managerial and theoretical implications. To best of our knowledge, our study is the first one to analyze the effect of the combination of valence, rating and subjectivity of the review text on helpful votes

Unveiling the features of successful ebay sellers of smartphones: a data mining sales predictive model

JEL Classification guidelines (M310); (C380).EBay is one of the largest online retailing corporations worldwide, providing numerous ways for customer feedback on registered sellers. In accordance, with the advent of Web 2.0 and online shopping, an immensity of data is collected from manifold devices. This data is often unstructured, which inevitably asks for some form of further treatment that allows classification, discovery of patterns and trends or prediction of outcomes. That treatment implies the usage of increasingly complex and combined statistical tools as the size of datasets builds up. Nowadays, datasets may extend to several exabytes, which can be transformed into knowledge using adequate methods. The aim of the present study is to evaluate and analyse which and in what way seller and product attributes such as feedback ratings and price influence sales of smartphones on eBay using data mining framework and techniques. The methods used include SVM algorithms for modelling the sales of smartphones by eBay sellers combined with 10-fold cross-validation scheme which ensured model robustness and employment of metrics MAE, RAE and NMAE for the sake of gauging prediction accuracy followed by sensitivity analysis in order to assess the influence of individual features on sales. The methods were considered effective for both modelling evaluation and knowledge extraction reaching positive results although with some discrepancies between different prediction accuracy metrics. Lastly, it was discovered that the number of items in auction, average price and the variety of products available from a given seller were the most significant attributes, i.e., the largest contributors for sales.O EBay é uma das plataformas e retalho online de maior dimensão e abarca inúmeras oportunidades de extração de dados de feedback dos consumidores sobre vários vendedores. Em concordância, o advento da Web 2.0 e das compras online está fortemente associado à geração de dados em abundância e à possibilidade da sua respetiva recolha através de variados dispositivos e plataformas. Estes dados encontram-se, frequentemente, desestruturados o que inevitavelmente revela a necessidade da sua normalização e tratamento mais aprofundado de modo a possibilitar tarefas de classificação, descoberta de padrões e tendências ou de previsão. A complexidade dos métodos estatísticos aplicados para executar essas tarefas aumenta ao mesmo tempo que a dimensão das bases de dados. Atualmente, existem bases de dados que atingem vários exabytes e que se constituem como oportunidades para extração de conhecimento dado que métodos apropriados e particularizados sejam utilizados. Pretende-se, então, com o presente estudo quantificar e analisar quais e de que modo as características de vendedores e produtos influenciam as vendas de smartphones no eBay, recorrendo ao enquadramento conceptual e técnicas de mineração de dados. Os métodos utilizados incluem máquinas de vetores de suporte (SVMs) visando a modelação das vendas de smartphones por vendedores do eBay em combinação com validação cruzada 10-fold de modo a assegurar a robustez do modelo e com recurso às métricas de avaliação de desempenho erro absoluto médio (MAE), erro absoluto relativo (RAE) e erro absoluto médio normalizado (NMAE) para garantir a precisão do modelo preditivo. Seguidamente, é implementada a análise de sensibilidade para aferir a contribuição individual de cada atributo para as vendas. Os métodos são considerados eficazes tanto na avaliação do modelo como na extração de conhecimento visto que viabilizam resultados positivos ainda que sejam verificadas discrepâncias entre as estimativas para diferentes métricas de desempenho. Finalmente, foi possível descobrir que número de itens em leilão, o preço médio e a variedade de produtos disponibilizada por cada vendedor foram os atributos mais significantes, i.e., os que mais contribuíram para as vendas

Peer Priming? A Large-Scale Field Experiment Studying the Impact of Popular Rankings on Demand in Mobile Retail

Consumers on mobile retail apps face significant search costs due to the small screen size of devices. One of the search aid features to improve the search convenience is to show consumers a small set of frequently used searches conducted by peer consumers on the platform as a prime cue. We refer to this feature as the popular ranking search aid (PRSA). Collaborating with Meituan, a leading services mobile app in China, we implement a large-scale field experiment to explore how PRSA affects consumer search activities and purchases. Our analyses generate three key findings. First, PRSA leads to an increase of 18.6% in page views and a 6.4% increase in purchases. Second, the change in shopping behavior emerges through a change in search behavior with more non-directed searches and fewer directed searches. Third, our mediation analysis supports that search behavior mediates the business outcomes. We offer theoretical and managerial implications

What Airbnb Reviews can Tell us? An Advanced Latent Aspect Rating Analysis Approach

There is no doubt that the rapid growth of Airbnb has changed the lodging industry and tourists’ behaviors dramatically since the advent of the sharing economy. Airbnb welcomes customers and engages them by creating and providing unique travel experiences to “live like a local” through the delivery of lodging services. With the special experiences that Airbnb customers pursue, more investigation is needed to systematically examine the Airbnb customer lodging experience. Online reviews offer a representative look at individual customers’ personal and unique lodging experiences. Moreover, the overall ratings given by customers are reflections of their experiences with a product or service. Since customers take overall ratings into account in their purchase decisions, a study that bridges the customer lodging experience and the overall rating is needed. In contrast to traditional research methods, mining customer reviews has become a useful method to study customers’ opinions about products and services. User-generated reviews are a form of evaluation generated by peers that users post on business or other (e.g., third-party) websites (Mudambi & Schuff, 2010). The main purpose of this study is to identify the weights of latent lodging experience aspects that customers consider in order to form their overall ratings based on the eight basic emotions. This study applied both aspect-based sentiment analysis and the latent aspect rating analysis (LARA) model to predict the aspect ratings and determine the latent aspect weights. Specifically, this study extracted the innovative lodging experience aspects that Airbnb customers care about most by mining a total of 248,693 customer reviews from 6,946 Airbnb accommodations. Then, the NRC Emotion Lexicon with eight emotions was employed to assess the sentiments associated with each lodging aspect. By applying latent rating regression, the predicted aspect ratings were generated. With the aspect ratings, , the aspect weights, and the predicted overall ratings were calculated. It was suggested that the overall rating be assessed based on the sentiment words of five lodging aspects: communication, experience, location, product/service, and value. It was found that, compared with the aspects of location, product/service, and value, customers expressed less joy and more surprise than they did over the aspects of communication and experience. The LRR results demonstrate that Airbnb customers care most about a listing location, followed by experience, value, communication, and product/service. The results also revealed that even listings with the same overall rating may have different predicted aspect ratings based on the different aspect weights. Finally, the LARA model demonstrated the different preferences between customers seeking expensive versus cheap accommodations. Understanding customer experience and its role in forming customer rating behavior is important. This study empirically confirms and expands the usefulness of LARA as the prediction model in deconstructing overall ratings into aspect ratings, and then further predicting aspect level weights. This study makes meaningful academic contributions to the evolving customer behavior and customer experience research. It also benefits the shared-lodging industry through its development of pragmatic methods to establish effective marketing strategies for improving customer perceptions and create personalized review filter systems

Effects of Influencers on Consumer Behaviours on Social Media: Do Audiences know their needs, or they only follow their opinion leaders?

This study aims to examine the effect of sponsored advertisements made by influencers with brands on consumer behaviour on social media. In this study, cosmetic products and beauty influencers are discussed. The opinions of consumers when they see these advertisements and their attitudes towards these advertisements are tried to be understood. It is aimed to understand whether the consumers who are social media users are positive or negative approaches to paid partnerships made by brands and influencers, the reasons that lead to shopping and whether they are shopping according to their own needs or recommendations. The prepared survey reached 104 people, and the data were analysed. Surveys were statistically analysed. The results showed that consumers who are social media users do not trust influencer advertisements one hundred percent. They try to make purchases in line with their own needs, but still cannot be completely indifferent to these advertisements. Most of the participants stated that they were looking at influencer comments when they were researching a product they needed. Reasons for respondents' following influencer were found to be high rates of beauty inspiration, product reviews, and product tutorials. It has been observed that, as age groups change, consumers follow influencer advertisements, and their trust and priority to these advertisements have changed. Also, no significant difference was found when compared with age groups, positive or negative approaches to advertised products. The majority of the participants stated that they saw these advertisements and stated that they generally approach the products they see in the advertisements positively. The majority of the participants stated that they saw these advertisements and stated that they generally approach the products they see in the advertisements positively. However, they stated that they were sceptical about how honest the influencers were about the promoted products. When these two groups were compared, a significant difference was found

Promotion and Marketing Communications

This edited Promotion and Marketing Communications book is an original volume that presents a collection of chapters authored by various researchers and edited by marketing communication professionals. To survive in the competitive world, companies feel an urge to achieve a competitive advantage by applying accurate marketing communication tactics. Understanding marketing communication is an essential aspect for any field and any country. Hence, in this volume there is the latest research about marketing communication under which marketing strategies are delicately discussed. This book does not only contribute to the marketing and marketing communication intellectuals but also serves different sector company managerial positions and provides a guideline for people who want to attain a career in this field, giving them a chance to acquire the knowledge regarding consumer behavior, public relations, and digital marketing themes

Searching for yield in real assets

Three empirical chapters addressing investments in real, alternative assets are presented in this thesis. Chapter 2 focuses on fine art as an investment. In recent years, the art market has been characterized by final auction prices greatly exceeding the exante estimates published by international auction houses. We define this difference as a rarity premium and build a ‘Rarity Index’ by aggregating the premia relative to the mean. We also investigate the benefits, outside financial performance, associated with art ownership and introduce the term of ‘ownership yield’, meant to encapsulate both aesthetic yield and features of conspicuous consumption. This ownership yield may account for the large differences between the values of rarity indexes we construct for three famous families of paintings over the period 2003 to 2013. In Chapter 3, we turn our attention to residential real estate in alpha cities. We argue that relative price changes in prime property markets have greatly deviated from non-prime markets on a national level, while similarities across prime markets in different countries have increased. In order to illustrate the extent of these changes, we introduce a novel ‘luxury ratio’ and perform several statistical analyses on repeat-sales price indexes over the period 2003 to 2014. Taking the case of London, we show how the luxury ratio has evolved over the past two decades with respect to other UK cities. Results support the existence of an ownership yield in a world where high (and ultra-high) net worth individuals are growing in number and search for exclusiveness through the possession of distinctive residential property. Chapter 4 targets two types of commercial real estate: data centers and shopping complexes (companies specializing in malls, shopping centers, and outlets). First, with price indexes based on US REITs, we analyze short-term and long-term relationships between the S&P 500 and several commercial real estate categories using Engle-Granger cointegration over the period 2009 to mid-2016. We find no cointegration between data centers and the S&P 500, or retail (representing shopping complexes) and the S&P 500, indicating that both sectors are not merely an attractive investment in their own right, but also portfolio diversifiers. Second, turning to individual firms, we perform a CAPM analysis of 41 international companies. Results show that, on average, price returns from data centers surpass those of shopping complexes; moreover, US companies specializing in malls, shopping centers, and outlets outperform those of similar firms abroad. Finally, we indicate a further avenue for data centers in relation to electricity storage, and explain implications for investors

The Cost of Rankings? The Influence of College Rankings on Institutional Management.

Increasing college costs and financial consequences for students and families have created a need to collect and disseminate information about institutions to better inform students and parents as they make an important education decision. To fill this need, several media outlets started to publish college rankings for commercial purposes. Rankings have proven popular and influential as evidenced by growing sales and attention to their numerical ranking by publishers, educational observers, and higher education administrators and faculty. Despite the growing concerns that rankings intensify institutional competition for prestige, limited empirical research exists on how ranking systems affect the resource allocation behavior of universities. This dissertation explores how college rankings impact resource allocation by higher education institutions, given the unique characteristics of rankings—numerical order, arbitrary grouping, and volatility. Utilizing the U.S. News and World Report’s Best Colleges Rankings (USNWR) 1987-2009, this study examines changes in institutional expenditures, particularly in the three areas that are heavily weighted in rankings: student selectivity, financial resources, and faculty resources. Employing a differences-in-differences and differences-in-differences-in-differences approach based on the unexpected changes in the methodology of USNWR, this study demonstrates that the numerical ordering of universities encourages institutions comply with what rankings measure by increasing expenditures in all three areas. The event-study specification results indicate that expenses that are related to student selectivity are the ones that schools respond to immediately, while the effect lasts over time for financial and faculty resources. The areas of expenditures that experience significant changes differ between National Universities and National Liberal Arts Colleges. Furthermore, the arbitrary grouping of rankings serves as an important mechanism that drives institutional responses to rankings. Schools ranked near at the cut-off of the ranking groups take on a bigger increase in the expenditures in the areas that rankings directly measure. Year-to-year changes in the ranking positions encourage universities to move resources from a routinized, universal expenditure to categories that are perceived to provide more leverage to improve rankings. The study’s findings have important implications for the use of rankings/ratings systems in higher education as well as future research on the pursuit of prestige and institutional behaviors.PhDHigher EducationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113601/1/okuje_1.pd

Caves São Domingos : pouring the espumante’s fizz into the younger generation’s glasses

This dissertation addresses the relation between the young adult wine consumers and Caves São Domingos, a Portuguese espumante producer. Particular attention was given to the addition of insights into the sparkling wine industry and its development. The Case Study helps to understand the young adults’ market segmentation and consumer characteristics such as product knowledge, motives, purchase decision-making, and consumption behavior. In recent years Caves São Domingos has recognized the need to bring young adults closer to the sector, in order to avoid a future without consumer preferences associated with its main product – espumante. Despite some lack of interest from the young consumer, the company will try to position the brand in a way that will the ones who will assure the future's consumption. The Case allows for the analysis of the problems of a wine producer challenge by the need to modernize its products and give young consumers a drink they would purchase in a restaurant, bar, or club. Along with the Case, this Thesis includes a Literature Review that provides insights on a national and international perspective of the industry and its trends, and a Teaching Note with the analysis of the key issues and recommendations for the company’s future.Esta dissertação aborda a relação entre os jovens adultos consumidores de vinho e a Caves São Domingos, uma produtora portuguesa de espumantes. Foi dada especial atenção à adição de conhecimento sobre a indústria de vinhos espumantes e seu desenvolvimento. O Estudo de Caso ajuda a entender a segmentação de mercado dos jovens adultos e as características do consumidor, como conhecimento do produto, motivos, compra e tomada de decisão e comportamento do consumidor. Nos últimos anos a Caves São Domingos tem reorganizado a necessidade de aproximar os jovens do setor, de forma a evitar um futuro sem tendências de consumo associadas ao seu principal produto – o espumante. Apesar de algum desinteresse por parte do consumidor jovem, a empresa tentará posicionar a marca de forma a garantir o consumo do futuro. O Caso permite analisar os problemas de um produtor de vinho desafiado pela necessidade de modernizar seus produtos e oferecer aos jovens consumidores uma bebida que comprariam num restaurante, bar ou discoteca. Juntamente com o Caso, esta Tese inclui uma Revisão de Literatura que fornece informações sobre uma perspetiva nacional e internacional da indústria e das suas tendências, e uma Nota de Ensino com a análise dos principais problemas e recomendações para o futuro da empresa

Effective ways South African brands can market on instagram to influence purchase intentions: a user's perspective

A research report submitted to the Faculty of Commerce, Law and Management, University of the Witwatersrand, in partial fulfillment of the requirements for the degree of Master of Management in the field of Strategic Marketing 22 June 2018Background - Although Instagram is one of the fastest growing social network sites, research dedicated to this platform has been limited. Purpose – The purpose of this research is to investigate how South African brands could optimally utilise Instagram as a marketing channel that positively influences consumer buying behaviour. Research Methodology/Approach – A quantitative approach was used for this study. Research data was collected using an online survey, with a total of two hundred and seventeen South African Instagram users who made up the final sample. Findings – The outcomes confirmed that all hypothesised statements were significant and thus, accepted. The main findings further revealed that user perspectives played a valuable role in the co-creation process and reiterated that types of content (on Instagram) positively affected the study’s constructs; customer engagement, brand awareness and electronic-word-of-mouth. In addition, these constructs proved to positively impact consumer purchase intentions. Research limitations – The present study focuses on a niche sample which consists of South African Instagram users only. It is recommended that future researchers incorporate a global user perspective from Instagram users across the world, to determine other key factors that might positively impact consumer purchasing behaviour. Managerial implications – Marketing practitioners spend a significant amount of time in determining the driving forces that boost sales. The verified relationships between types of content, brand awareness, customer engagement and electronic-word-of-mouth reiterates the need for all stakeholders to incorporate Instagram as a powerful channel of marketing in their social media strategies.MT 201