Consumer Reviews Research Articles

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

<p>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.</p>

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.

Milk Quality Conceptualization: A Systematic Review of Consumers', Farmers', and Processing Experts' Views.

Milk consumption has traditionally been recognized as a fundamental element of global dietary patterns due to its perceived nutritional advantages. Nonetheless, a substantial decrease in milk consumption has been identified within diverse populations in recent times. Specifically, consumers' expectations and representations of milk quality have undergone notable transformations, contributing to the observed reduction in consumption. The objective of this systematic review was to conduct a comprehensive examination and categorization of the conceptual attributes associated with milk quality, considering the representations of citizen-consumers, farmers, and processing experts. This review was conducted following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. The titles and abstracts of 409 articles were screened, and 20 full-text articles were assessed for eligibility. The results demonstrate the existence of a dual articulation in the conceptual definition of milk quality. Farmers and processing experts exhibited a relatively similar representation of milk quality, focusing on technical indicators. In contrast, citizen-consumers held more simplistic and subjective concepts that are challenging to quantify. This study emphasized the critical need for establishing a platform for communication and knowledge exchange to foster shared representations and expectations regarding milk quality.

The Impact of Consumer’s Adaptation to the Creative Culture of Theme Parks on Review Usefulness

In the era of information overload and repetitive reviews, there has been limited exploration into the influence of consumers’ cultural adaptation to creative symbols in theme parks on the usefulness of online reviews, which is significant for enhancing tourism experiences, targeted marketing, personalized services, and informed tourism choices. This study aims to bridge this gap by examining how cultural adaptation factors interact and impact the review usefulness, and by considering the role of cultural adaptation in simplifying information during consumer decision-making processes. Additionally, the study investigates how consumers’ decision reference points, represented by advanced ticket levels, moderate their attention to attribute consistency when evaluating the review usefulness. A sample of 5929 valid consumer reviews of Disney theme parks from 2019 to 2022 on Meituan.com is analyzed using latent semantic analysis and Tobit regression to test the proposed hypotheses. We find that high symbolic creativity reviews stimulate cultural adaptation and increase attention to service attributes when evaluating review usefulness. Moreover, advanced ticket levels do not moderate the usefulness of extremely negative reviews. However, they do have a moderating effect on the usefulness of extremely positive reviews, with the direction of moderation differing based on the levels of symbolic creativity.

Which product description phrases affect sales forecasting? An explainable AI framework by integrating WaveNet neural network models with multiple regression

The rapid rise of many e-commerce platforms for individual consumers has generated a large amount of text-based data, and thus researchers have begun to experiment with text mining techniques to extract information from the large amount of textual data to assist in sales forecasting. The existing literature focuses textual data on product reviews; however, consumer reviews are not something that companies can directly control, here we argue that textual product descriptions are also important determinants of consumer choice. We construct an artificial intelligence (AI) framework that combines text mining, WaveNet neural networks, multiple regression, and SHAP model to explain the impact of product descriptions on sales forecasting. Using data from nearly 200,000 sales records obtained from a cross-border e-commerce firm, an empirical study showed that the product description presented to customers can influence sales forecasting, and about 44% of the key phrases greatly affect sales forecasting results, the sales forecasting models that added key product description phrases had improved forecasting accuracy. This paper provides explainable results of sales forecasting, which can provide guidance for firms to design product descriptions with reference to the market demand reflected by these phrases, and adding these phrases to product descriptions can help win more customers.

From Data to Human-Readable Requirements: Advancing Requirements Elicitation through Language-Transformer-Enhanced Opportunity Mining

In this research, we present an algorithm that leverages language-transformer technologies to automate the generation of product requirements, utilizing E-Shop consumer reviews as a data source. Our methodology combines classical natural language processing techniques with diverse functions derived from transformer concepts, including keyword and summary generation. To effectively capture the most critical requirements, we employ the opportunity matrix as a robust mechanism for identifying and prioritizing urgent needs. Utilizing transformer technologies, mainly through the implementation of summarization and sentiment analysis, we can extract fundamental requirements from consumer assessments. As a practical demonstration, we apply our technology to analyze the ratings of the Amazon echo dot, showcasing our algorithm’s superiority over conventional approaches by extracting human-readable problem descriptions to identify critical user needs. The results of our study exemplify the potential of transformer-enhanced opportunity mining in advancing the requirements-elicitation processes. Our approach streamlines product improvement by extracting human-readable problem descriptions from E-Shop consumer reviews, augmenting operational efficiency, and facilitating decision-making. These findings underscore the transformative impact of incorporating transformer technologies within requirements engineering, paving the way for more effective and scalable algorithms to elicit and address user needs.

Marketing insight from consumer reviews: Creating brand position through opinion mining approach

PurposeBrand managers can determine the ideas, perceptions, or experiences of consumers about their brands and their competitors through traditional marketing research techniques such as focus group discussions, surveys, and in-depth interviews. Through these methods, brands can observe the visual expression of consumer perceptions concretely by using perception maps or radar chart techniques. However, traditional methods have certain limitations. The validity of brand positioning in perception maps may be questioned since consumers' answers to survey questions can be misleading, consumers may express opinions about a product that they have not purchased yet, and other methods may divert the opinions of consumers at a specific stage.In this study, global brand positions in the minds of consumers were concretized through perception maps by analyzing product comments on e-commerce sites through the use of opinion mining/sentiment analysis, one of the text mining methods. Moreover, perception maps and radar charts were created for the selected brands by forming the dataset from consumers’ reviews about brands and products in online environments. perception map errors caused by traditional methods have been prevented by using opinion mining/sentiment analysis, which constitutes the originality of the research. Design/methodology/approachIn this study, the text mining method, which includes opinion mining and sentiment analysis techniques, has been used to create perceptual maps from consumer reviews about products on e-commerce platforms. The TF*IDF method is used to identify the features to be compared in the perceptual map, which selects the features with weighted importance among the features mentioned by consumers in their comments. In this study, a lexicon-based opinion mining technique has been used to assign sentiment scores to consumers' comments on e-commerce websites for each product and its specified features. FindingsThe findings of the study have illustrated the areas in which the brands are evaluated as “insufficient” or “successful” by consumers within the framework of the characteristics selected in the study. It has been revealed that the method used in the research can provide accurate results for brand managers to make positioning decisions and develop effective marketing strategies. Research limitations/implicationsThe research has been conducted by analyzing product reviews about selected brands on e-commerce platforms. The comments selected within the scope of the research include only those written in the Turkish language, and the evaluations have been carried out on the comments made until a specific date. Originality/valueIn this study, the analyses of the comments on the selected brands have ensured that the businesses are informed of the level of perception about their products in the market and that the research is based on a holistic approach that helps other consumers to develop a perspective on a specific product based on the comments of a consumer who shares his/her experience about the aforesaid product.

Online consumer review spam detection based reinforcement learning and neural network

Online consumer review spam detection based reinforcement learning and neural network

The Effect of Online Customer Reviews, Online Customer Ratings, and Influencers on Purchase Decisions

This study aims to determine the influence of online consumer reviews, online customer ratings, and influencers on purchase decisions among Shopee consumers. The sampling was conducted using a non-probability sampling technique with a purposive sampling approach. The study was carried out in November 2022, with a total sample size of 160 respondents. The criteria used to select respondents for this study were females who are Shopee account holders, have made fashion product purchases on the platform, and are aged between 18 to 25 years old. The data analysis technique used for this study was multiple linear regression. The results of this study indicates that: online consumer reviews have a positive effect on purchase decisions, online customer ratings also have a positive effect on purchase decisions, and Shopee influencers also have a positive effect on purchase decisions.

Customer satisfaction and loyalty with online consumer reviews: Factors affecting revisit intentions

While previous research investigated the effects of online consumer reviews on purchase behaviors, currently, there is still a lack of knowledge on the impact of the reviews' credibility, content quality and information usefulness on the customers’ satisfaction levels with them. Data were gathered from a sample of 512 participants. A partial least squares approach was utilized to evaluate the reliability and validity of the constructs and to identify the causal effects in this contribution’s structured model. The findings reveal that information usefulness is a very strong predictor of satisfaction. They also confirm highly significant indirect effects, between information quality and customer satisfaction, when information usefulness meditates this link. This study suggests that prospective customers appreciate quality reviews of consumers who have already experienced the hospitality services. It raises awareness about the usefulness of review sites as online users refer to their content before committing themselves to purchasing products and services.

Restaurant recommendation model using textual information to estimate consumer preference: evidence from an online restaurant platform

研究目的关于餐厅的文本信息, 如在线评论和食品分类, 对于消费者的购买决策产生至关重要。然而, 先前的餐厅推荐研究未能有效利这些文本信息去预测消费者喜好。本研究提出了一种新颖的餐厅推荐模型, 以有效估计消费者对多个餐厅属性的评估行为。研究方法我们从 Yelp.com 收集了来自25,369名消费者对 46,613 家餐厅的 1,206,587 条评论。利用这些数据, 我们通过结合消费者身份和在线消费者评论生成了消费者偏好向量。然后, 我们结合了餐厅身份和食品分类来生成餐厅信息向量。最后, 考虑到餐厅属性向量, 本研究调查了消费者偏好和餐厅信息向量之间的非线性交互关系。研究发现我们发现, 所提出的推荐模型相比于之前最先进的模型表现出更优秀的性能, 这表明结合消费者和餐厅的各种文本信息是预测消费者喜好的基本因素。研究创新/价值据我们所知, 这是第一项利用来自真实在线餐厅平台的文本信息开发个性化餐厅推荐模型的研究。本研究还提出了胜过最先进模型的深度学习机制。本研究的结果可以降低探索消费者行为的成本并支持有效的购买决策。

PENGARUH ELECTRONIC WORD OF MOUTH (EWOM) INSTAGRAM TERHADAP KEPUTUSAN BERKUNJUNG WISATAWAN MILENIAL DOMESTIK KE TAMANSARI, YOGYAKARTA

Electronic Word of Mouth (EWOM) is the communication media which can help shares the information there are consumer reviews of product or services, one of EWOM’s communication media is Instagram. Through Instagram tourists can find out the spread of information through reviews, comments, caption and hastags that indirectly provide benefit for potensial tourists in making decision to visit a tourist destination. The purpose of this research is to determine the influence of Electronic Word of Mouth (EWOM) on Instagram of the domestic milennial tourists visiting decisions to Tamansari Yogyakarta. This study use accidental sampling to determinant the sample. The data collected by interview, observation, shares the questionnaries from google form, literature review, documentations and netnography. The data analysis used simple linear regression analysis which had previously had been analyzed by classical assumptions. The result of this study the significance of the EWOM variable 0,01 < 0,05 and the t count was 12,808 > t table 1,981. The result of determination R Square is 60,03%. Based on this result, partially and simultaneously the Electronic Word of Mouth (EWOM) on Instagram has a positive and significant influence on the domestic millennial tourist visiting decisions to Tamansari Yogyakarta.

Online consumer reviews of a sport product: an alternative path to understanding brand associations

PurposeThis study aims to explore the use of consumer-generated online product reviews as a source of brand associations in a sport setting.Design/methodology/approachA total of 800 reviews were collected and categorized into 13 brand association dimensions derived from previous literature. Reviews were further categorized into three valence types (i.e. positive, negative and neutral) via a correspondence analysis.FindingsA correspondence analysis revealed that positive product reviews were highly linked to performance and product-related attributes, while negative reviews were related to conformance associations. Additionally, the results showed that product-related (90.8%) attributes, experiential benefits (89.1%) and functional benefits (86.6%) were the most frequently communicated brand associations. The findings of this study underline the credibility of assessing brand associations from the consumers’ experience, through online consumer reviews.Originality/valueThe findings of the current investigation contribute to existing knowledge by examining consumer-based brand equity (CBBE) in an online setting. Previous, CBBE literature suggests that brand associations are held in the minds of the brand's consumers. To date, however, few studies have examined brand associations generated by consumers and instead have relied upon brand association dimensions developed by the researcher as opposed to the consumer (Ross et al., 2006). This, study however, utilized online sport product reviews as a source of consumer derived brand associations, and, therefore, will further the knowledge as to how brand associations are identified and measured.

ANALYSIS OF THE INFLUENCE OF BRAND IMAGE, HALAL LABEL, AND CONSUMER REVIEWS OF PURCHASE DECISIONS WARDAH COSMETIC PRODUCTS

The purpose of this study was to determine the significant effect on Brand Image, Halal Label, and Consumer Reviews on Purchase Decisions for Wardah Cosmetic Products. This research is a research using a quantitative approach. The population in this study are people who use Wardah Cosmetics in South Tangerang City. The sampling technique uses simple random sampling. The data analysis technique used in this study used SPSS software. Based on statistical tests, the results of the study show that Brand Image does not have a significant direct effect on purchasing decisions, Halal Labels do not directly have a significant effect on purchasing decisions, while Consumer Reviews have a significant effect on purchasing decisions.

Influence of Review Properties in the Usefulness Analysis of Consumer Reviews: A Review-Based Recommender System for Rating Prediction

Influence of Review Properties in the Usefulness Analysis of Consumer Reviews: A Review-Based Recommender System for Rating Prediction