Evaluating online review helpfulness based on Elaboration Likelihood Model: the moderating role of readability

It is important to understand factors affecting the perceived online review helpfulness as it helps solve the problem of information overload in online shopping. Moreover, it is also crucial to explore the factors’ relative importance in predicting review helpfulness in order to effectively detect potential helpful reviews before they exert influences. Applying Elaboration Likelihood Model (ELM), this study first investigates the effects of central cues (review subjectivity and elaborateness) and peripheral cues (reviewer rank) on review helpfulness with readability as a moderator. Second, it also explores their relative predicting power using the machine learning technique. ELM is tested in online context and the results are compared between experience and search goods. Our results provide evidence that for both types of products review subjectivity can play a more significant role when the content readability is high. Furthermore, this study reveals that the dominant predictor is varied for different product types

Predicting consumer product demands via Big Data: the roles of online promotional marketing and online reviews

This study aims to investigate the contributions of online promotional marketing and online reviews as predictors of consumer product demands. Using electronic data from Amazon.com, we attempt to predict if online review variables such as valence and volume of reviews, the number of positive and negative reviews, and online promotional marketing variables such as discounts and free deliveries, can influence the demand of electronic products in Amazon.com. A Big Data architecture was developed and Node.JS agents were deployed for scraping the Amazon.com pages using asynchronous Input/Output calls. The completed Web crawling and scraping data-sets were then preprocessed for Neural Network analysis. Our results showed that variables from both online reviews and promotional marketing strategies are important predictors of product demands. Variables in online reviews in general were better predictors as compared to online marketing promotional variables. This study provides important implications for practitioners as they can better understand how online reviews and online promotional marketing can influence product demands. Our empirical contributions include the design of a Big Data architecture that incorporate Neural Network analysis which can used as a platform for future researchers to investigate how Big Data can be used to understand and predict online consumer product demands

Predicting online product sales via online reviews, sentiments, and promotion strategies

Purpose – The purpose of this paper is to investigate if online reviews (e.g. valence and volume), online promotional strategies (e.g. free delivery and discounts) and sentiments from user reviews can help predict product sales. Design/methodology/approach – The authors designed a big data architecture and deployed Node.js agents for scraping the Amazon.com pages using asynchronous input/output calls. The completed web crawling and scraping data sets were then preprocessed for sentimental and neural network analysis. The neural network was employed to examine which variables in the study are important predictors of product sales. Findings – This study found that although online reviews, online promotional strategies and online sentiments can all predict product sales, some variables are more important predictors than others. The authors found that the interplay effects of these variables become more important variables than the individual variables themselves. For example, online volume interactions with sentiments and discounts are more important than the individual predictors of discounts, sentiments or online volume. Originality/value – This study designed big data architecture, in combination with sentimental and neural network analysis that can facilitate future business research for predicting product sales in an online environment. This study also employed a predictive analytic approach (e.g. neural network) to examine the variables, and this approach is useful for future data analysis in a big data environment where prediction can have more practical implications than significance testing. This study also examined the interplay between online reviews, sentiments and promotional strategies, which up to now have mostly been examined individually in previous studies

Spring School on Language, Music, and Cognition: Organizing Events in Time

The interdisciplinary spring school “Language, music, and cognition: Organizing events in time” was held from February 26 to March 2, 2018 at the Institute of Musicology of the University of Cologne. Language, speech, and music as events in time were explored from different perspectives including evolutionary biology, social cognition, developmental psychology, cognitive neuroscience of speech, language, and communication, as well as computational and biological approaches to language and music. There were 10 lectures, 4 workshops, and 1 student poster session. Overall, the spring school investigated language and music as neurocognitive systems and focused on a mechanistic approach exploring the neural substrates underlying musical, linguistic, social, and emotional processes and behaviors. In particular, researchers approached questions concerning cognitive processes, computational procedures, and neural mechanisms underlying the temporal organization of language and music, mainly from two perspectives: one was concerned with syntax or structural representations of language and music as neurocognitive systems (i.e., an intrapersonal perspective), while the other emphasized social interaction and emotions in their communicative function (i.e., an interpersonal perspective). The spring school not only acted as a platform for knowledge transfer and exchange but also generated a number of important research questions as challenges for future investigations

Harnessing the power of the general public for crowdsourced business intelligence: a survey

International audienceCrowdsourced business intelligence (CrowdBI), which leverages the crowdsourced user-generated data to extract useful knowledge about business and create marketing intelligence to excel in the business environment, has become a surging research topic in recent years. Compared with the traditional business intelligence that is based on the firm-owned data and survey data, CrowdBI faces numerous unique issues, such as customer behavior analysis, brand tracking, and product improvement, demand forecasting and trend analysis, competitive intelligence, business popularity analysis and site recommendation, and urban commercial analysis. This paper first characterizes the concept model and unique features and presents a generic framework for CrowdBI. It also investigates novel application areas as well as the key challenges and techniques of CrowdBI. Furthermore, we make discussions about the future research directions of CrowdBI

The role of emotions and conflicting online reviews on consumers' purchase intentions

Drawing on dual-process theories, this paper explains how the systematic and heuristic information processing of online reviews with conflicting information can influence consumers' purchase decision making. The study adopts major assumptions of complexity and configuration theory in employing fuzzy-set qualitative comparative analysis on 680 TripAdvisor users to test the complex interrelationships between emotions and the systematic and heuristic cues used in processing reviews. The results show that the systematic and heuristic processing of online reviews can produce independent impacts on consumer decision making. Both processing routes can interact with each other to affect the domination of one route over the other. In the case of a positive–negative sequence, consumers mainly follow a heuristic processing route. In the reverse sequence, consumers' concerns about the credibility of the reviews leads them to think more deeply (systematic processing) and actively evaluate both the argumentation quality and the helpfulness of the online reviews

Determinants of online review helpfulness that steer consumer purchase decision and their willingness to give review:an extended study in a cross-cultural context

Abstract. The increased use of social media and other online platforms have enabled consumers to communicate and discuss the products and services of brands with others. Consumers’ look for information in online reviews that assist them in informed purchase decisions. Previous literature has identified factors that influence consumers in adopting those online reviews, but whether consumers are willing to provide an online review after the purchase decision is not yet been studied previously. Another gap in the literate that is addressed is to base this study on output obtained from two countries. Therefore, our study is aimed at identifying factors that contribute to a consumer purchase decision and their willingness to give a review in a cross-cultural context. Our study aimed at restaurant reviews in Finland and Pakistan. Adopting and extending the Information Acceptance Model (IACM) proposed by Erkan and Evans (2016), that is developed by integrating Information Adoption Model (IAM) and related aspects of Theory of Reasoned Action (TRA). This study examines the influence of online review helpfulness factors on consumer purchase decision, consequently influencing them to give a review to others. We also aim to identify if review adoption directly influences consumers in providing online review without purchasing the product or service. The proposed model of our study was validated through Structural Equation Modelling by using Smart Partial Least Squares software. A questionnaire was adopted from earlier studies. The questionnaire was measured on a sample size of 104 from Finland and 141 from Pakistan. This study identified review adoption leading towards consumer purchase decision, whereas, onsumers’ willingness to give is not directly linked with their adoption of information, but it is a post-purchase process. The commonalities between the two countries depict the needs of information behind seeking online review information. If the required information is being provided to the customer through online reviews, it will lead to review adoption. Generally, review positiveness, review perceived informativeness and review quality were identified most important factors in consumers review adoption that leads consumers in choosing a restaurant and try the food there. Whereas, the general attitude of consumers towards online reviews was found to be the most exciting factors identified in Pakistan output. Consumers’ perception of online reviews encourages them to read online reviews, and they think that it is always a risk to try a restaurant without referring to online reviews. Pakistani consumers find online reviews useful, providing relevant information about the restaurants that help them in choosing the best restaurant