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

2016 ◽  
Vol 36 (4) ◽  
pp. 358-383 ◽  
Author(s):  
Alain Yee Loong Chong ◽  
Boying Li ◽  
Eric W.T. Ngai ◽  
Eugene Ch'ng ◽  
Filbert Lee
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Network Analysis ◽  
Online Reviews ◽  
Neural Network Analysis ◽  
Content Type ◽  
Data Architecture ◽  
Promotional Strategies ◽  
The Individual ◽  
Product Sales

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.

Understanding the determinants of online pharmacy adoption: a two-staged SEM-neural network analysis approach

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Md. Mahiuddin Sabbir ◽  
Mazharul Islam ◽  
Samir Das
Keyword(s):  
Neural Network ◽  
Network Analysis ◽  
Perceived Risk ◽  
Analysis Approach ◽  
Neural Network Analysis ◽  
Content Type ◽  
Personal Innovativeness ◽  
Utaut Model ◽  
Perceived Trust ◽  
Online Pharmacy

Purpose This study aims to understand the determinants of online pharmacy or epharmacy adoption among young consumers in Bangladesh using an extended unified theory of acceptance and use of technology (UTAUT) model. Design/methodology/approach A structured Google Docs questionnaire was sent out to 420 respondents using messenger service; 285 useable responses were finally extracted. Data were empirically validated using the two-staged structural equation model (SEM)-neural network analysis approach. Findings The robustness of the classical UTAUT model remains intact in the context of online pharmacy adoption. Among the integrated variables, while perceived trust and health literacy were found significant, perceived risk and personal innovativeness were found insignificant in determining consumers’ intention to adopt online pharmacy. The neural network analysis provided further verification of these findings derived from the SEM. Practical implications The findings of this study would facilitate in devising better strategies for entering or expanding online pharmacy business in developing countries such as Bangladesh. Originality/value The originality of the current study relates to the two-fold contributions of this study. First, while this study extended the classical UTAUT model by incorporating perceived risk, perceived trust, personal innovativeness and health literacy, the inclusion of the following two variables is fresh within the extant online pharmacy literature. Second, by using a two-staged SEM-neural network analysis approach, this study advances the past studies on e-commerce adoption in pharmaceutical settings and provides a general understanding of the customers of developing countries.

Competitor intelligence and analysis (CIA) model and online reviews: integrating big data text mining with network analysis for strategic analysis

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mehmet Ali Köseoglu ◽  
Fuad Mehraliyev ◽  
Mehmet Altin ◽  
Fevzi Okumus
Keyword(s):  
Big Data ◽  
Network Analysis ◽  
Text Mining ◽  
Hotel Industry ◽  
Online Reviews ◽  
Competitive Intelligence ◽  
Future Research ◽  
Content Type ◽  
Competitor Analysis ◽  
Competitor Intelligence

Purpose This study aims to propose a competitor intelligence and analysis (CIA) model that can be used for the analysis of a firm’s competitors. Empirically, it investigates the application of the CIA model on online reviews. This proposed model clarifies the confusion between terms such as competitive intelligence, competitor intelligence and competitor analysis and provides a more efficient process for managers. Design/methodology/approach The approach of the model integrates text mining techniques as a big data method with network analysis to form a competitor analysis. This study has considered two centrality metrics – degree centrality and betweenness centrality – to identify the functional associations among the resources elaborated by the customers of the hotels. Findings Findings show online reviews may be used as a solid source of intelligence. The intelligence maps visualized through the text-net technique is an efficient representation of tourist satisfaction and dissatisfaction with a tourism company and its competitors. Practical implications The proposed approach can be used in the hotel industry along with many others. The implications for scholars and managers and the possible directions for future research are also discussed in the study. Originality/value This study develops a new approach for competitive intelligence practices in the hotel industry and tests a new method for competitor analysis as a part of the competitive intelligence and analysis approach developed in this study.

1902: Is Steinstrasse After ESWL of Renal Stones Predictable? Artificial Neural Network Analysis

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Analysis ◽  
Renal Stones ◽  
Neural Network Analysis ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural

NEURAL NETWORK ANALYSIS OF INTERACTION OF POSTURAL CONTROL MACHANISMS UNDER ARTUFICIAL FEEDBACK

2016 ◽  
Vol 34 (2) ◽  
pp. 025-036
Author(s):  
Oleg G. Gorshkov ◽  
◽  
Irina B. Starchenko ◽  
Andrey S. Sliva ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Network Analysis ◽  
Postural Control ◽  
Neural Network Analysis

scholarly journals Artificial neural network analysis of microbial diversity in the central and southern Adriatic Sea

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Danijela Šantić ◽  
Kasia Piwosz ◽  
Frano Matić ◽  
Ana Vrdoljak Tomaš ◽  
Jasna Arapov ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Analysis ◽  
Bacterial Community ◽  
Microbial Diversity ◽  
Community Composition ◽  
Adriatic Sea ◽  
Bacterial Community Composition ◽  
Environmental Data ◽  
Large Contribution ◽  
Neural Network Analysis

AbstractBacteria are an active and diverse component of pelagic communities. The identification of main factors governing microbial diversity and spatial distribution requires advanced mathematical analyses. Here, the bacterial community composition was analysed, along with a depth profile, in the open Adriatic Sea using amplicon sequencing of bacterial 16S rRNA and the Neural gas algorithm. The performed analysis classified the sample into four best matching units representing heterogenic patterns of the bacterial community composition. The observed parameters were more differentiated by depth than by area, with temperature and identified salinity as important environmental variables. The highest diversity was observed at the deep chlorophyll maximum, while bacterial abundance and production peaked in the upper layers. The most of the identified genera belonged to Proteobacteria, with uncultured AEGEAN-169 and SAR116 lineages being dominant Alphaproteobacteria, and OM60 (NOR5) and SAR86 being dominant Gammaproteobacteria. Marine Synechococcus and Cyanobium-related species were predominant in the shallow layer, while Prochlorococcus MIT 9313 formed a higher portion below 50 m depth. Bacteroidota were represented mostly by uncultured lineages (NS4, NS5 and NS9 marine lineages). In contrast, Actinobacteriota were dominated by a candidatus genus Ca. Actinomarina. A large contribution of Nitrospinae was evident at the deepest investigated layer. Our results document that neural network analysis of environmental data may provide a novel insight into factors affecting picoplankton in the open sea environment.

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