scholarly journals Competitive Product Identification and Sales Forecast Based on Consumer Reviews

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Guoquan Zhang ◽  
Haibin Qiu
Keyword(s):  
Forecast Model ◽  
Online Reviews ◽  
Product Evaluations ◽  
Product Price ◽  
Product Review ◽  
Sales Forecast ◽  
The Neural Network ◽  
Product Competition ◽  
Product Sales ◽  
The Impact

Sellers readily obtain consumer product evaluations from online reviews in order to identify competitive products in detail and predict sales. Firstly, we collect product review data from shopping websites, social media, product communities, and other online platforms to identify product competitors with the help of word-frequency cooccurrence technology. We take mobile phones as an example to mine and analyze product competition information. Then, we calculate the product review quantity, review emotion value, product-network heat, and price statistics and establish the regression model of online product review forecasts. In addition, the neural-network model is established to suggest that the relationships among factors are linear. On the basis of analyzing and discussing the impact of product sales of the competitors, product price, the emotional value of the reviews, and product-network popularity, we construct the sales forecast model. Finally, to verify the validity of the factor analysis affecting the sales and the rationality of the established model, actual sales data are used to further analyze and verify the model, showing that the model is reasonable and effective.

scholarly journals Online Reviews and Product Sales: The Role of Review Visibility

2021 ◽  
Vol 16 (4) ◽  
pp. 638-669
Author(s):  
Miriam Alzate ◽  
Marta Arce-Urriza ◽  
Javier Cebollada
Keyword(s):  
Decision Making ◽  
Dynamic Models ◽  
Generalized Method Of Moments ◽  
Online Reviews ◽  
Consumer Decision Making ◽  
Product Sales ◽  
The Impact ◽  
The Relationship ◽  
The Empirical Analysis

When studying the impact of online reviews on product sales, previous scholars have usually assumed that every review for a product has the same probability of being viewed by consumers. However, decision-making and information processing theories underline that the accessibility of information plays a role in consumer decision-making. We incorporate the notion of review visibility to study the relationship between online reviews and product sales, which is proxied by sales rank information, studying three different cases: (1) when every online review is assumed to have the same probability of being viewed; (2) when we assume that consumers sort online reviews by the most helpful mechanism; and (3) when we assume that consumers sort online reviews by the most recent mechanism. Review non-textual and textual variables are analyzed. The empirical analysis is conducted using a panel of 119 cosmetic products over a period of nine weeks. Using the system generalized method of moments (system GMM) method for dynamic models of panel data, our findings reveal that review variables influence product sales, but the magnitude, and even the direction of the effect, vary amongst visibility cases. Overall, the characteristics of the most helpful reviews have a higher impact on sales.

Online Reviews and Product Sales: The Role of Review Visibility

2021 ◽  
Vol 16 (1) ◽  
pp. 638-669
Author(s):  
Miriam Alzate ◽  
Marta Arce-Urriza ◽  
Javier Cebollada
Keyword(s):  
Decision Making ◽  
Dynamic Models ◽  
Generalized Method Of Moments ◽  
Online Reviews ◽  
Consumer Decision Making ◽  
Product Sales ◽  
The Impact ◽  
The Relationship ◽  
The Empirical Analysis

When studying the impact of online reviews on product sales, previous scholars have usually assumed that every review for a product has the same probability of being viewed by consumers. However, decision-making and information processing theories underline that the accessibility of information plays a role in consumer decision-making. We incorporate the notion of review visibility to study the relationship between online reviews and product sales, which is proxied by sales rank information, studying three different cases: (1) When every online review is assumed to have the same probability of being viewed; (2) when we assume that consumers sort online reviews by the most helpful mechanism; and (3) when we assume that consumers sort online reviews by the most recent mechanism. Review non-textual and textual variables are analyzed. The empirical analysis is conducted using a panel of 119 cosmetic products over a period of nine weeks. Using the system generalized method of moments (system GMM) method for dynamic models of panel data, our findings reveal that review variables influence product sales, but the magnitude, and even the direction of the effect, vary amongst visibility cases. Overall, the characteristics of the most helpful reviews have a higher impact on sales.

scholarly journals Neural network approximation in forecasting economic risks

2021 ◽  
Vol 5 (S4) ◽  
Author(s):  
Kateryna I. Kotsiubivska ◽  
Olena V. Tymoshenko ◽  
Olena A. Chaikovska ◽  
Maryna S. Tolmach ◽  
Svitlana S. Khrushch
Keyword(s):  
Neural Network ◽  
Economic Development ◽  
Forecast Model ◽  
Economic Systems ◽  
Approximation Model ◽  
The Neural Network ◽  
Risk Forecasting ◽  
Methodological Approaches ◽  
The Impact ◽  
Macroeconomic Environment

The article considers methodological approaches to assessing the level of development of economic systems in the context of increasing the accuracy of forecasts in unpredictable socio-economic conditions in particular taking into account the impact of unforeseen environmental risks and disasters. The authors used methods to approximate economic criteria with the help of neural networks. Analyzing the criteria of economic development of different countries, as well as taking into account the factors of the macroeconomic environment, a neural network approximation model of risk forecasting in the economic development of the country has been developed. To date, a large number of mathematical forecasting methods are known, and experts in the world economy use appropriate risk assessment criteria, but the neural network is used when the exact type of connections between inputs and outputs is unknown, which allows us to create a more accurate and flexible forecast model. The modeling takes into account the main weights that determine the degree and the priority of the impact on each component of the economic system and characterizes the complex macroeconomic relationships to determine the aggregate indices.

The impact of COVID-19 on online product reviews

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Omer Cem Kutlubay ◽  
Mesut Cicek ◽  
Serdar Yayla
Keyword(s):  
Well Being ◽  
Online Reviews ◽  
Product Reviews ◽  
Product Evaluations ◽  
Content Type ◽  
Online Product Reviews ◽  
Online Product Ratings ◽  
Negative Tone ◽  
The Impact ◽  
Product Ratings

Purpose The ongoing COVID-19 pandemic has led to drastic changes in the lives of customers. Social isolation, financial difficulties, fear of being infected and many other factors have caused the psychological well-being of customers to deteriorate. By taking up the role of online reviews in the regulation of consumers’ moods, this study aims to examine the changes that have occurred in online product ratings, as well as the negative tone and word counts of product reviews during the COVID-19 pandemic. Design/methodology/approach This study examines the online reviews of 321 products in the pre-COVID, immediate COVID and extended COVID periods. This paper compares the changes that have taken place in product evaluations via various analysis of variance analyses. The authors also test the effect of COVID-related deaths on product evaluations via regression analyses. Findings The results indicate that online product ratings decreased sharply just after the outbreak of COVID-19. The study also found that the tone of reviews was found to be more negative and the length of reviews appeared to be longer in comparison to the pre-COVID-19 period. The results also revealed that the product type (experience vs search) moderated the effect of the pandemic in online reviews and the impact of COVID-19 on online product reviews diminished in the later stages of the ongoing pandemic. Practical implications Managers should be aware of the detrimental impact of pandemics on online product reviews and be more responsive to customer problems during the early stages of pandemics. Originality/value To the best of the authors’ knowledge, this is the first study that analyzes the effects of a pandemic on online product ratings and review content. As such, this study offers a timely contribution to the marketing literature.

Forecasting US movies box office performances in Turkey using machine learning algorithms

2020 ◽  
Vol 39 (5) ◽  
pp. 6579-6590
Author(s):  
Sandy Çağlıyor ◽  
Başar Öztayşi ◽  
Selime Sezgin
Keyword(s):  
Machine Learning ◽  
Global Economy ◽  
Learning Algorithms ◽  
Forecast Model ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
High Stakes ◽  
Box Office ◽  
Industry Forecast ◽  
The Impact

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

The effect of emotions, eWOM quality and online review sequence on consumer intention to follow advice obtained from digital services

2020 ◽  
Vol 31 (3) ◽  
pp. 465-487 ◽  
Author(s):  
Carla Ruiz-Mafe ◽  
Enrique Bigné-Alcañiz ◽  
Rafael Currás-Pérez
Keyword(s):  
Structural Model ◽  
Online Reviews ◽  
Moderating Effect ◽  
Online Information ◽  
Bias Effect ◽  
Content Type ◽  
Digital Services ◽  
Information Cues ◽  
Cognitive Information ◽  
The Impact

PurposeThis paper analyses the interrelationships between emotions, the cognitive information cues of online reviews and intention to follow the advice obtained from digital platforms, paying special attention to the moderating effect of the sequencing of review valence.Design/methodology/approachThe data were collected from 830 Spanish Tripadvisor users. In a two-step approach, a measurement model was estimated and a structural model analysed to test the proposed hypotheses. SmartPLS 3.0 software was used. The moderating effect of sequencing of reviews is tested.FindingsThe data analysis showed a bias effect of review sequence on the impact of online information cues and emotions on intention to follow advice obtained from Tripadvisor. When the online reviews of a restaurant begin with positive commentaries, their perceived persuasiveness is a stronger driver of the pleasure and arousal elicited by online reviews than when they begin with negative reviews. On the other hand, the perceived helpfulness of online reviews only triggers arousal when the user reads negative, followed by positive, comments. The impact of pleasure on intention to follow the advice provided in an online travel community is higher with positive-negative than with negative-positive sequences.Originality/valueWhile researchers have demonstrated the benefits of customer reviews on company sales, a largely uninvestigated issue is the interplay between emotions and cognitive information cues in the processing of online reviews. This is one of the first studies to examine the moderating effect of conflicting reviews on the impact of emotions and cognitive information cues on consumer intention to follow the advice obtained from digital services.

scholarly journals Autism Tsunami: the Impact of Rising Prevalence on the Societal Cost of Autism in the United States

2021 ◽  
Author(s):  
Mark Blaxill ◽  
Toby Rogers ◽  
Cynthia Nevison
Keyword(s):  
Forecast Model ◽  
The United States ◽  
Population Projections ◽  
Base Case ◽  
Prevention Strategies ◽  
Total Base ◽  
Cost Categories ◽  
The Cost ◽  
The Impact ◽  
First Time

AbstractThe cost of ASD in the U.S. is estimated using a forecast model that for the first time accounts for the true historical increase in ASD. Model inputs include ASD prevalence, census population projections, six cost categories, ten age brackets, inflation projections, and three future prevalence scenarios. Future ASD costs increase dramatically: total base-case costs of $223 (175–271) billion/year are estimated in 2020; $589 billion/year in 2030, $1.36 trillion/year in 2040, and $5.54 (4.29–6.78) trillion/year by 2060, with substantial potential savings through ASD prevention. Rising prevalence, the shift from child to adult-dominated costs, the transfer of costs from parents onto government, and the soaring total costs raise pressing policy questions and demand an urgent focus on prevention strategies.

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