Consumer Reviews Research Articles

Segmenting and Targeting Fashion Consumers Using Social Media: A Study of Consumer Behaviour

The review of this research study segments online consumers into different consumer categories based on how they perceive and relate to the fashion business through social media. Therefore, the diversified nuances of consumer behaviour have been studied in this paper. The study focuses on two crucial stages that have a significant influence on social media usage in the world of fashion. This study categorises consumers into groups based on their perceptions and relationships they have with the fashion brands, using K-means cluster analysis. It is a ground-breaking study in the fashion world because it adopts the fashion consumer–brand relationship index and fashion consumer brand perception index with the aid of social media, laying a solid foundation for similar studies to be conducted in other fashion industry verticals. Policymakers might use the study’s results to build strategies to enhance consumer behaviour of marketers in the world of fashion.

Improving Clothing Product Quality and Reducing Waste Based on Consumer Review Using RoBERTa and BERTopic Language Model

The disposability of clothing has emerged as a critical concern, precipitating waste accumulation due to product quality degradation. Such consequences exert significant pressure on resources and challenge sustainability efforts. In response, this research focuses on empowering clothing companies to elevate product excellence by harnessing consumer feedback. Beyond insights, this research extends to sustainability by providing suggestions on refining product quality by improving material handling, gradually mitigating waste production, and cultivating longevity, therefore decreasing discarded clothes. Managing a vast influx of diverse reviews necessitates sophisticated natural language processing (NLP) techniques. Our study introduces a Robustly optimized BERT Pretraining Approach (RoBERTa) model calibrated for multilabel classification and BERTopic for topic modeling. The model adeptly distills vital themes from consumer reviews, exhibiting astounding accuracy in projecting concerns across various dimensions of clothing quality. NLP’s potential lies in endowing companies with insights into consumer review, augmented by the BERTopic to facilitate immersive exploration of harvested review topics. This research presents a thorough case for integrating machine learning to foster sustainability and waste reduction. The contribution of this research is notable for its integration of RoBERTa and BERTopic in multilabel classification tasks and topic modeling in the fashion industry. The results indicate that the RoBERTa model exhibits remarkable performance, as demonstrated by its macro-averaged F1 score of 0.87 and micro-averaged F1 score of 0.87. Likewise, BERTopic achieves a coherence score of 0.67, meaning the model can form an insightful topic.

Ranking and pricing under a cascade model of consumer review browsing

In online platforms, the reviews posted by existing consumers are playing an increasingly important role in the purchasing decisions of potential consumers. Motivated by this observation, we study the problems faced by a platform selling a single product with no capacity constraint, where the demand is explicitly influenced by the reviews presented to the consumers. More precisely, we model a consumer’s browsing of reviews for a single product as following a cascade click model, with each consumer seeing some initial number of reviews and forming a utility estimate for the product based on the reviews the consumer has read. In the first part of the paper, we consider how to rank the reviews to induce short- and long-term revenue-maximizing purchasing behaviors. In the second part, we study how to set the price of the product. We derive structural insights and bounds on both problems. We also consider the case that the parameters of the model are unknown, where we propose algorithms that learn the parameters and optimize the ranking of the reviews or the price online. We show that our algorithms have regrets O(T23).

Managing score heterogeneity between online consumer review websites

Managing score heterogeneity between online consumer review websites

Retracted: Analysis of Application Data Mining to Capture Consumer Review Data on Booking Websites

Retracted: Analysis of Application Data Mining to Capture Consumer Review Data on Booking Websites

Using natural language processing to characterize and predict homeopathic product-associated adverse events in consumer reviews: comparison to reports to FDA Adverse Event Reporting System (FAERS).

Apply natural language processing (NLP) to Amazon consumer reviews to identify adverse events (AEs) associated with unapproved over the counter (OTC) homeopathic drugs and compare findings with reports to the US Food and Drug Administration Adverse Event Reporting System (FAERS). Data were extracted from publicly available Amazon reviews and analyzed using JMP 16 Pro Text Explorer. Topic modeling identified themes. Sentiment analysis (SA) explored consumer perceptions. A machine learning model optimized prediction of AEs in reviews. Reports for the same time interval and product class were obtained from the FAERS public dashboard and analyzed. Homeopathic cough/cold products were the largest category common to both data sources (Amazon = 616, FAERS = 445) and were analyzed further. Oral symptoms and unpleasant taste were described in both datasets. Amazon reviews describing an AE had lower Amazon ratings (X2 = 224.28, P < .0001). The optimal model for predicting AEs was Neural Boosted 5-fold combining topic modeling and Amazon ratings as predictors (mean AUC = 0.927). Topic modeling and SA of Amazon reviews provided information about consumers' perceptions and opinions of homeopathic OTC cough and cold products. Amazon ratings appear to be a good indicator of the presence or absence of AEs, and identified events were similar to FAERS. Amazon reviews may complement traditional data sources to identify AEs associated with unapproved OTC homeopathic products. This study is the first to use NLP in this context and lays the groundwork for future larger scale efforts.

Can Online Consumer Reviews Identify Key Evidence Regarding Common Consumer Choices for High-Tech Pet Products?

Online consumer reviews (OCRs) significantly influence consumer purchase decisions for new products. Therefore, today’s companies actively seek practical approaches for analyzing these OCRs. This study proposes a comprehensive method for OCR analysis using topic modeling and association rule analysis to overcome the current limitations in topic interpretation and topic overlap in text mining. Meanwhile, to examine the development of the high-tech industry and customer interest in the pet care field, this study synthesizes and analyzes reviews from consumers who are using healthcare products in the pet industry. To this end, we first collected 20,820 customer reviews from Amazon.com (accessed on 2 August 2023) for high-tech pet products and categorized them into three distinct product categories. Topic modeling was then conducted on each category, revealing five key topics per category. Subsequently, association rules analysis was performed on the customer reviews associated with the most representative topic. As a result, we were able to demonstrate that ‘satisfaction’ emerged as the most crucial topic across all three categories of high-tech pet products. Satisfaction is a topic expressing consumers’ attitudes after experiencing the product, and they used words to describe their feelings in the product reviews. A diverse range of associated terms was also identified that represented the essence of each product’s corresponding representative explanations. By leveraging these approaches, we are confident that pet product companies and market players will gain valuable insights into consumer preferences and behavior.

The Effect of Online Consumer Review, Social Media Advertisement, and Influencer Endorsement on Purchase Intention

This study aims to influence Online Consumer Reviews, Social Media Advertisements, and Influencer Endorsements on Purchase Intentions of Avoskin Skincare Products. This study used a descriptive research design approach with empirical data collected through an online survey of a sample of respondents in Jabodetabek. Empirical data are then processed using the Structural Equation Modeling approach to test some hypotheses proposed. This research is an expansion model, namely by developing existing research by replacing products from previous journals and adding brand loyalty variables. The results of this study, Online Consumer Review, do not affect purchasing intentions on Avoskin skincare products. Meanwhile, Social Media Advertisement and Influencer Endorsement positively affect the intention to buy Avoskin Skincare products. Subsequent studies can be tested using samples and other variables

Disclosure in Incentivized Reviews: Does It Protect Consumers?

The well-documented rating inflation of incentivized reviews (IRs) can mislead consumers into choosing a product that they would otherwise not buy. To protect consumers from this undesirable influence, the U.S. Federal Trade Commission recommends that reviewers conspicuously disclose any material connection they may have with sellers. In theory, such disclosures safeguard consumers by motivating reviewers to be truthful and inducing consumers to discount inflated IR ratings. Our research finds, however, that IR disclosure accomplishes neither. Specifically, our empirical analysis of consumer reviews on Amazon reveals that, even with disclosure, (1) rating inflation of IRs remains, and (2) this inflation boosts sales at consumers’ expense. Finally, we propose an alternative approach to eliminate rating inflation of IRs and empirically demonstrate its effectiveness. These findings have important implications for consumers, firms, and ongoing policy discussions around IRs. This paper was accepted by Duncan Simester, marketing. Funding: S. Park gratefully acknowledges financial support from the Darla Moore School of Business Research Grant Program at the University of South Carolina. W. Shin gratefully acknowledges financial support from the Brian R. Gamache Endowed Professorship at the University of Florida. J. Xie gratefully acknowledges financial support from the JCPenney Endowed Professorship at the University of Florida. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2023.00930 .

Identifying online purchasing intention in Egypt: a fuzzy set qualitative comparative approach

PurposeThe diffusion of electronic commerce has a notable impact on the economy's prosperity. This paper embraces complexity theory principles to examine the factors affecting Internet users' acceptance and use of electronic retailers. It is essential for the sustainability of electronic retailers to understand the motivations impacting online consumer behaviour. Symmetrical and asymmetrical methods are combined to examine the relationship between perceived ease of use, perceived enjoyment, web characteristics, online consumer reviews (OCRs) and online purchase intention. Further, symmetry and differences between males and females were examined.Design/methodology/approachData collected from 425 online consumers using an online structured survey was analysed using structural equation modelling (SEM) and fuzzy set qualitative comparative analysis (fsQCA). The net effects and causal configurations of the four proposed variables and online purchase intention were examined.FindingsThe SEM findings confirmed the significance of perceived enjoyment, website characteristics and OCRs on online purchase intention. Perceived enjoyment mediated the relationship between perceived ease of use and online purchase intention. The multi-group analysis confirmed the difference in antecedent impacts between males and females. The fsQCA findings revealed that multiple recipes lead to the occurrence of online purchase intention; in addition, the recipes leading to its absence do not mirror the previous ones.Originality/valueThe present study embraces complexity theory concepts in understanding online purchase intention using fsQCA methodology; further, the role of gender in online consumer behaviour was highlighted in the result discussion.

Aggravated impoliteness in Chinese online negative restaurant reviews

Abstract This paper examines aggravated impoliteness in online restaurant reviews, with a dataset consisting of 92 negative reviews from the Chinese-based website Dazhongdianping. It first analyzes the usage and frequency of different types of aggravated impoliteness strategies. The topic categories of the review samples are then investigated, and the connection between these two aspects is further discussed. The variety of aggravated impoliteness strategies includes: preceding intensifiers, indignant exclamations, unpalatable questions, emoji, postpositional intensifiers, unfavorable comparisons, emphatic structure and taboo words, in decreasing order of frequency. These strategies are employed in connection with the ratings of: food, service, general experience, interpersonal judgments, price and environment. This study contributes to the impoliteness research by investigating the aggravated impoliteness in Chinese online data, and also enriches and provides insights into research on the genre of online consumer reviews.

Pengaruh Online Consumer Review Dan Program Gratis Ongkos Kirim Terhadap Keputusan Pembelian Pada Platform E-Commerce Tiktok Shop

This study aims to investigate the influence of Online Customer Review (X1) and Free Shipping Promotion (X2) on purchasing decisions in Tiktok Shop E-Commerce. A quantitative research method was employed, using a questionnaire filled out by 119 students from the Faculty of Law and Business at Duta Bangsa University, Surakarta. The results of the analysis indicate that both variables, Online Customer Review and Free Shipping Promotion, significantly influence online purchasing decisions on Tiktok Shop platform. The use of online consumer reviews and providing free shipping promotions can enhance purchasing decisions in the platform. Validity and reliability tests indicate that the questionnaire instrument used is valid and reliable. The coefficient of determination test shows that both independent variables are capable of explaining 53% of the dependent variable. The findings of this study contribute to understanding consumer behavior in the digital environment, particularly in the context of Tiktok Shop E-Commerce.

Content Analysis Of Consumer Reviews And Comments On E-Commerce

Content Analysis Of Consumer Reviews And Comments On E-Commerce

Use of Nile tilapia fish skin in treatment for burn victims

Burn injuries in the United States result in 1.1 million hospital admissions, with around 4500 deaths annually, according to the CDC. Although skin grafts have been the standard treatment since 1869, recent research suggests that tilapia skin could offer a safer and more effective alternative. This review aims to assess the strengths and knowledge gaps related to the usage of fish skin in burn patients. A systematic search was conducted on electronic databases, such as PubMed, Scopus, Web of Science, Clinicaltrials.gov, and Google Scholar, using relevant MESH keywords from 2018 to 2022. The search and analysis followed the “Preferred Reporting Items for Systematic Reviews and Meta-analysis” methodology. Data extraction utilized a template based on the Cochrane Consumers and Communication Review group’s extraction guidelines. Out of 166 studies identified, 12 relevant studies were extensively analyzed. The trials consistently demonstrated accelerated reepithelialization, improved patient compliance in managing burn-related pain, no immunologic or allergic reactions, reduced infection rates, shorter hospital stays, and decreased treatment costs when using fish skin. These findings suggest that fish skin is comparable to traditional treatments in terms of effectiveness and safety. Considering the discussed advantages, Nile tilapia fish skin emerges as a promising xenograft option for burn victims. However, further large-scale trials and studies are needed before its widespread and official implementation in burn centers. More research is required to establish the efficacy and safety of fish skin as a viable treatment modality.

Influence of online consumer reviews on the sales of large household appliances: a survey in Romania

Influence of online consumer reviews on the sales of large household appliances: a survey in Romania

A Novel Hybrid Lexicon Ensemble Learning Model for Sentiment Classification of Consumer Reviews

In recent past during the era of consumerism with easy accessibility to social networking world, the consumers usually give comments and opinions on daily usable ingredients, electronic goods and services offered on payments. These comments or opinions are innumerable and huge on each item, hence need the special attention for sentimental value particularly on their text parts. The present study is an attempt to perform sentiment prediction on Amazon Electronic products, gift cards and Kindle dataset. In this paper, the HLESV (Hybrid Lexicon Ensemble based Soft Voting) model is proposed by combining lexicon and ensemble approaches using optimally weighted voting to predict the sentiment, subsequently to evaluate model using various performance metrics like precision, recall, F1-score. This paper computes an additional metric namely subjectivity score along with sentiment score and proposes non-interpretive sentiment class label to evaluate the polarity of the reviews using our proposed HLESV model for sentiment classification. The accuracy score of our proposed HLESV model is evaluated to assess its effectiveness on Amazon consumer product review datasets and observed an increase of 1-6% accuracy over existing state-of-the-art ensemble methodology.

Analyzing Customers in E-Commerce Using Dempster-Shafer Method

This research explores the analysis of consumer sentiment in the context of e-commerce by applying the sophisticated Dempster-Shafer method. We started with the collection of more than 20,000 consumer reviews from various leading e-commerce platforms and continued with a detailed data pre-processing stage to obtain a clean and structured dataset. Next, we leverage the Dempster-Shafer method to represent and combine information from multiple sources, addressing uncertainty in diverse consumer opinions. The results of the sentiment analysis show that the Dempster-Shafer method achieves an accuracy of around 85%, with good evaluation metrics. Additionally, this research provides insight into the factors that influence consumers' views of products or services in the growing e-commerce context. The literature review also reveals the potential application of the Dempster-Shafer method in other aspects of e-commerce business, such as risk management and consumer trust. This research highlights the contribution of the Dempster-Shafer method in addressing uncertainty and complexity in consumer sentiment analysis, yielding a deep understanding of consumer perceptions, and enabling more accurate decision making in a dynamic e-commerce context. This research also provides a foundation for further development in consumer sentiment analysis and the application of the Dempster-Shafer method in e-commerce.

Prediction of customer review’s helpfulness based on sentences encoding using CNN-BiGRU model

&lt;p&gt;The infrastructure of smart cities is intended to save citizens’ time and effort. After COVID-19, one of such available infrastructure is electronic shopping. Online consumer reviews have a big influence on the electronic retail market. A lot of customers save time by deciding which products to buy online by evaluating the products’ quality based on user reviews. The goal of this study is to forecast if reviews based on reviews representation mining will be helpful while making online purchases. Predicting helpfulness is used in this suggested study to determine the usefulness of a review in relation to glove vector encoding of reviews text. Using an encoding-based convolution neural network and a bidirectional gated recurrent unit, the authors of this study constructed a classification model. The suggested model outperformed these baseline models and other state-of-the-art techniques in terms of classification outcomes, reaching the greatest accuracy of 95.81%. We also assessed the effectiveness of our models using the criteria of accuracy, precision, and recall. The outcomes presented in this study indicate how the proposed model has a significant influence on enhancing the producers’ or service providers’ businesses.&lt;/p&gt;

Analysis of Online Customer Review on Purchase Decisions with Customer Trust as Intervening

This internet service can be used in various fields, such as the use of the internet in the fields of economy, education, industry, telecommunications, and tourism to the field of beauty. Nowadays, the use of the internet is no stranger to finding information about beauty products. In this regard, it is necessary to discuss the influence of online customer reviews on purchase decisions through customer trust in Female Daily. This study aims to determine the effect of online consumer reviews on purchase decisions through trust as an intervening variable. The population in this study is the Indonesian people who have accessed the website Female Daily. The sample size in the study was 100 people, and the method used in this study was quantitative. The data collection method used is a questionnaire. The analytical method used in this research is path analysis. The results of the analysis in this study indicate that online customer reviews and customer trust have a significant effect on purchase decisions. Online customer reviews have a significant effect on customer trust. Customer trust can mediate online customer reviews of purchase decisions with a percentage of 84.5%.

Correlation between Social Media Utilization and the Young Generation’s Online Shopping Behavior in Eastern Indonesia

Online shopping is popular among generations Y and Z in Indonesia. This research study evaluated consumer online shopping behavior in Eastern Indonesia, a lower income and lower internet penetration region than western Indonesia. Online study questionnaires were distributed to the region's main cities or provincial capitals to collect profile data on the internet and social media use in the Indonesian society. A total of 613 respondents completed the questionnaires. Correspondence analysis was used to assess the relationship between two categorical variables. Instagram was the most popular social media followed by Facebook, Youtube, Twitter, and TikTok. Most of the respondents used more than one platform. Almost all respondents had shopped online (95.76%) after a product review from mainly consumer reviews and rarely from promotions by influencers. However, online shoppers were still wary of products that did not match their online description.