Online Consumer Reviews Research Articles

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.

Factors affecting users' impulse purchases in online group buying: online consumer reviews, countdowns and self-control

PurposeThis paper aims to investigate how online consumer reviews (OCRs), countdowns and self-control affect consumers' online impulse buying behavior in online group buying (OGB) and uncover the relationship between these factors.Design/methodology/approachBased on the stimulus-organism-response (SOR) framework, this research examines the effects of OCRs, countdowns and self-control on users' impulse purchases. First, the influence of emotions on impulse purchases in group purchasing is investigated. In addition, this study innovatively applies stress-coping theory to group buying research, with countdowns exerting temporal pressure on consumers and OCRs viewed as social pressure, to investigate in depth how countdowns and OCRs affect users' impulse purchase behavior. Finally, this study also surveys the moderating role of users' self-control in the impulse purchase process.FindingsThe results show that the perceived value of OCRs and positive emotions (PE) were positively correlated with impulsiveness (IMP) and the urge to buy impulsively (UBI), while negative emotions (NE) were negatively correlated with IMP. Countdowns (CD) had a positive effect on UBI. Self-control can indirectly affect users' impulse buying by negatively moderating the relationship between PE and UBI, PE and IMP and CD and UBI.Originality/valueThe research results can help group buying platforms and related participants understand the factors influencing users' impulse purchases in OGB and facilitate them to better design strategies to increase product sales.

Improving the service quality of cross-border e-commerce: How to understand online consumer reviews from a cultural differences perspective.

Cross-border e-commerce (CBEC) consumers come from different countries; thus, cultural differences may affect their evaluations and perceptions of service quality. This paper follows Hofstede's framework as a theoretical anchor to explore how to use online consumer reviews that reflect cultural differences to improve the service quality of CBEC. First, based on a latent Dirichlet allocation model, 14 service quality issues that consumers are concerned about in CBEC were identified. Second, a generalized ordered logistic regression model was explored to analyze the cultural influences on consumer sentiment orientation. Finally, the effect of each cultural dimension on consumer service quality perception in CBEC was evaluated by employing a binary logistic regression model. The results showed that consumers paid more attention to the service quality of logistics service, customs efficiency and tariff, shopping experience, and so on. Cultural dimensions significantly impacted consumers' emotional tendencies. Moreover, cultural dimensions had significant impacts on consumers' service quality perception (e.g., logistics service, trust in sellers, customs disputes, and cell phone performance). Still, consumers' quality perceptions of some services (e.g., cell phone functions, items as described, logistics package quality, and gifts) were less affected by cultural dimensions. Our findings not only provide new perspectives for CBEC consumer behavior studies on quality improvement but also provide practical implications for CBEC enterprises.

Fake review detection system for online E-commerce platforms: A supervised general mixed probability approach

Online consumer reviews play an important role in helping consumers judge the quality and authenticity of products on e-commerce platforms. However, the constant presence of fake reviews on these platforms has significantly impacted the operation and development of e-commerce platforms. In this study, we develop a novel supervised probabilistic method to detect fake reviews by utilizing the difference in the distribution of non-fraudulent reviews and that of fake reviews. Specifically, we first derive the univariate distributions of several unique features (linguistic, behavioral, and interrelationship features). We then integrate these distributions into two mixed distributions according to their labels to represent the overall difference between non-fraudulent reviews and fake reviews. Next, we randomly generate synthetic review data points with different labels from the above mixed distributions. Finally, we train a Multilayer Perceptron model by using these synthetic review data to obtain a classifier. We conducted several experiments to test the model using several original real-world review datasets. Numerical results indicated that the proposed supervised method outperformed some well-known sampling models and fake review detection methods, in terms of classification accuracy. Moreover, we extend the proposed method to handle the scenarios with small samples of raw review data. This study contributes to the literature by exploiting the difference in the distribution of non-fraudulent reviews and that of fraudulent reviews, which can improve the accuracy of fake review detection for online platforms.

Диалог с потребителем в эпоху медиатизации: ответы на отзывы о медицинских услугах в условиях Интернет-коммуникации

The article analyses online communicative interaction of the authors of reviews about provided services of the companies. The focus is on methods of consumer feedback within the framework of public communication. It can be noted that when comparing an online consumer review with its traditional paper prototype the consequences of expressing claims in the “Book of Feedback and Suggestions” and in social networks feedback differ significantly in their consequences for the reputation of the company. In this regard, the strategy of responding to consumer feedback - both positive and negative - is a topical subject matter in terms of effective communication with the consumer in the digital age. The purpose of the study is a communicative-pragmatic analysis of commentary texts on behalf of companies representing a response to consumer feedback in the context of online public communication. Reviews about doctors and medical services on the websites of the specialized network publication “Prodoctorov” and responses from a medical institution were used as materials for study. A communicative-pragmatic description of the response to a consumer feedback is based on the main strategy of the addresser, which is mainly expressed in building and preservation of the company’s image. The tactics of responding to a positive or negative assessment of a medical service are considered. Structural-semantic models of two types of responses to reviews are pointed out. The pragmatic characteristics of the response commentary to the consumer review, which bring it closer to the advertising text, and manipulative techniques in arranging feedback from the clients of medical services are noted.

Printer Product Positioning in Tokopedia E-Commerce Based on Analysis of Consumer Reviews

Proper product positioning is the key to success in attracting consumer interest. Consumer reviews on e-commerce platforms such as Tokopedia provide valuable insights into consumer perceptions of printer products and can be used to identify effective product positioning. This research aims to analyze the positioning of printer products from various brands sold at Tokopedia, based on an analysis of online consumer reviews; formulate managerial implications that printer companies can apply in managing online marketing activities based on the results of positioning from online reviews on Tokopedia. The data were obtained from search results and observations, uploaded reviews of consumers who bought printer products at the official store on Tokopedia e-commerce. Review data collection was carried out using non-probability sampling with a purposive sampling technique, then collected using data scraping techniques. Data were analyzed using descriptive analysis, content analysis and correspondence analysis using SPSS 26 software. The results showed that Canon printers had a strong positioning in terms of packaging in periods I and II; there was a change in consumer positioning towards Brother printers from period I to period II; there was a change in consumer positioning towards HP printers from period I to period II; consumers on Epson printers position Epson printers primarily in the context of good customer service by consumers. Keywords: content analysis, correspondence analysis, online customer review, product positioning

Exploring online consumer reviews of customized apparel products

PurposeVirtual try-on (VTO) technology with three-dimensional (3D) body scanning in a mobile application is a relatively new technique for selling custom-fit apparel. VTO involves scanning and measuring one's body and visualizing the fit of a garment on a 3D avatar. The purpose of this study is to explore consumers' experiences toward the custom-fit T-shirts and online mass customization (MC) services using the VTO technology found in online consumer reviews (OCRs).Design/methodology/approachA total of 297 OCRs were collected from the Amazon's Made for You site that uses VTO technology for the MC process. A qualitative content analysis, within a mixed method research process, was used to determine systematically the meanings within qualitative data with quantitative results. In the qualitative approach, combinations of two coding processes were employed, which were concept-driven (i.e., deductive/a priori) and data-driven (i.e., inductive/emergent) coding processes. In the quantitative approach, the prevalence of each coding in terms of its valence was calculated based on frequencies. Intercoder reliability reached 96 per cent.FindingsIn OCRs of customized apparel products and online MC services using VTO technology, consumers described expectations, perceived performance, dis/confirmation, dis/satisfaction, outcomes of dis/satisfaction and descriptive information. Those with expectations often expressed skepticism about the product and the MC process using VTO technology at the pre-consumption stage. In OCRs, they used four product dimensions and two service dimensions of perceived performance. Consumers had positive (negative) confirmation when the performance of the T-shirts and/or services worked better (worse) than their expectations. The OCRs also included dis/satisfaction with a product and/or service, its outcomes and descriptive information.Originality/valueThis study identified a resulting framework to identify the content in OCRs of the custom-fit T-shirts and online MC services that use VTO technology. This study extends the expectation confirmation theory by adding multiple dimensions (i.e., four product dimensions and two service dimensions) as well as more outcomes of dis/satisfaction (not limited to repurchase intentions). This study provides practical suggestions for online MC companies who are using or planning to use VTO technology on how to improve consumers' satisfaction with customized T-shirts using VTO technology.

Impact of information consistency in online reviews on consumer behavior in the e-commerce industry: a text mining approach

PurposeThe current study investigates the impact on perceived review helpfulness of the simultaneous processing of information from multiple cues with various central and peripheral cue combinations based on the elaboration likelihood model (ELM). Thus, the current study develops and tests hypotheses by analyzing real-world review data with a text mining approach in e-commerce to investigate how information consistency (rating inconsistency, review consistency and text similarity) influences perceived helpfulness. Moreover, the role of product type is examined in online consumer reviews of perceived helpfulness.Design/methodology/approachThe current study collected 61,900 online reviews, including 600 products in six categories, from Amazon.com. Additionally, 51,927 reviews were filtered that received helpfulness votes, and then text mining and negative binomial regression were applied.FindingsThe current study found that rating inconsistency and text similarity negatively affect perceived helpfulness and that review consistency positively affects perceived helpfulness. Moreover, peripheral cues (rating inconsistency) positively affect perceived helpfulness in reviews of experience goods rather than search goods. However, there is a lack of evidence to demonstrate the hypothesis that product types moderate the effectiveness of central cues (review consistency and text similarity) on perceived helpfulness.Originality/valuePrevious studies have mainly focused on numerical and textual factors to investigate the effect on perceived helpfulness. Additionally, previous studies have independently confirmed the factors that affect perceived helpfulness. The current study investigated how information consistency affects perceived helpfulness and found that various combinations of cues significantly affect perceived helpfulness. This result contributes to the review helpfulness and ELM literature by identifying the impact on perceived helpfulness from a comprehensive perspective of consumer review and information consistency.

Unmasking deception: a CNN and adaptive PSO approach to detecting fake online reviews.

Online reviews play a critical role in modern word-of-mouth communication, influencing consumers' shopping preferences and purchase decisions, and directly affecting a company's reputation and profitability. However, the credibility and authenticity of these reviews are often questioned due to the prevalence of fake online reviews that can mislead customers and harm e-commerce's credibility. These fake reviews are often difficult to identify and can lead to erroneous conclusions in user feedback analysis. This paper proposes a new approach to detect fake online reviews by combining convolutional neural network (CNN) and adaptive particle swarm optimization with natural language processing techniques. The approach uses datasets from popular online review platforms like Ott, Amazon, Yelp, TripAdvisor, and IMDb and applies feature selection techniques to select the most informative features. The paper suggests using attention mechanisms like bidirectional encoder representations from transformers and generative pre-trained transformer, as well as other techniques like Deep contextualized word representation, word2vec, GloVe, and fast Text, for feature extraction from online review datasets. The proposed method uses a multimodal approach based on a CNN architecture that combines text data to achieve a high accuracy rate of 99.4%. This outperforms traditional machine learning classifiers in terms of accuracy, recall, and F measure. The proposed approach has practical implications for consumers, manufacturers, and sellers in making informed product choices and decision-making processes, helping maintain the credibility of online consumer reviews. The proposed model shows excellent generalization abilities and outperforms conventional discrete and existing neural network benchmark models across multiple datasets. Moreover, it reduces the time complexity for both training and testing.

Integrated feature engineering based deep learning model for predicting customer’s review helpfulness

In the current market scenario, online customer reviews had a significant impact on boosting the sale of online products. Recently, there has been exponential growth in e-commerce industry owning to the online customer reviews. Over the years, researchers has observed the importance of online consumer reviews for purchasing online products. Hence, in this study, authors made an attempt to develop an efficient convolutional neural network (CNN) based classification model that aims to predict the usefulness of product reviews with higher accuracy on two different types of data sets (i.e., search product and experienced product). In our proposed study, to determine the usefulness of a review in terms of structural, linguistic, sentimental, lexical, and voting feature sets, we build a deep learning model to predict the review helpfulness as a binary classification problem. The performance of the proposed method is evaluated in terms of accuracy, precision, F1 score etc. and had been compared against the various leading machine learning (ML) state of art models viz., K-nearest neighbor (KNN), Linear regression (LR), Gaussian Naive Bays (GNB), Linear Discriminant Analysis (LDA) etc. The results demonstrate that CNN achieved better classification performance in comparison to other state of art models, with highest accuracy of 99.26% and 98.97%, precision of 99% and 99.01%, F1 score of 99% and 99.89%, AUC of 0.9999 and 0.9998, Average Precision (AP) of 0.9999 and 0.9997 and recall of 100% and 100% for two different amazon product datasets.

PENGARUH VIRAL MARKETING, ONLINE CONSUMER REVIEWS, HARGA, DAN BRAND AMBASSADOR TERHADAP KEPUTUSAN PEMBELIAN SECARA ONLINE DI TOKOPEDIA

ABSTRACTIn modern times, the internet has been considered as a necessity for many people. The use of the internet has changed consumer behavior from direct purchases in stores to online purchases. Online shopping makes it easy for consumers to shop without spending a lot of time and effort in stores. In addition to the convenience obtained in online shopping, it is important to have a marketing strategy in selling online, namely viral marketing, online consumer reviews, prices, and brand ambassadors. This study aims to determine the effect of viral marketing, online consumer reviews, prices, and brand ambassadors on online purchasing decisions at Tokopedia. The population of this research is all Tokopedia users in Indonesia. The method used is a quantitative method with data collection using purposive sampling which is distributed through a questionnaire on google form to 220 respondents. The data has been processed using multiple linear regression, validity and reliability tests, classical assumption tests, and hypothesis testing. The results show that: (1) Viral Marketing has a significant and positive influence on online purchasing decisions at Tokopedia (2) Online Consumer Reviews have a significant and positive influence on online purchasing decisions at Tokopedia (3) Price has a significant and positive influence on online purchasing decisions at Tokopedia (4) Brand Ambassadors have a significant and positive influence on online purchasing decisions at Tokopedia.Keywords: viral marketing, online consumer reviews, price, brand ambassador

Discussion of Online Reviews’ Impacts on Consumers’ Behaviors

The development of the Internet and technology has provided the public with an open space to share their thoughts without any impediments. Online consumer review has gained popularity in researching information as references before making decisions to reduce perceived risks. As many prior studies have done research on online reviews’ impacts on hospitalities and their potential customers. Thus, this study will be focusing on the influences on online retailers and their potential buyers, assessing the aspects and the extent how the reviews would impact their potential consumers’ purchase decisions. Meanwhile, the causes of the impacts and ongoing issues of online consumer review in recent years will also be discussed. The paper mainly uses the literature research method, analyzing the scholar articles mostly from recent five years. It is found that review valence and volume both have a significant impact in creating trust between potential consumers and online retailers and thus will influence people’s purchase intentions – even though review volume seems to be less effective than review valence due to various reasons. Visual contents and stimuli causing cognitive/affective responses are the two main reasons leading to such impacts. Besides, as the management system of online consumer reviews is not sound yet, ongoing issues such as fake reviews are still a tough question that required to be solved in the next few years.

Price-aware matrix factorization model for personalized recommendations

Recommendation methods aim to assist consumers in their decision-making process to find products that they are quite likely interested in. Current recommendation methods generally use online consumer reviews or ratings to predict consumers’ preferences for products. In e-commerce transactions, price plays a significant role in consumers’ purchase decisions. And each consumer's preference for product prices is specific. In this paper, we propose a novel price-aware recommendation method based on the matrix factorization model which fully considers the effect of price. We distill the price preferences of consumers from ratings and discover consumers’ real preferences for products. We further calculate the price sensitivities of consumers, not only considering the difference in price preference between a consumer and others but also focusing on the consumers’ price preferences in a specific price range. The final predicted ratings are calculated by adding the term of price effect into the matrix factorization framework. The results show that the proposed method has achieved high accuracy and performed better than several existing methods in both rating prediction and Top-N recommendation. And the products we recommend for consumers are more in line with their price preferences. Moreover, we find that, to some extent, the proposed method can solve the long-tail product recommendation problem with the consideration of the price effect.

ON LINE CONSUMER REVIEWS, SECURITY AND RISK PERCEPTION ON ON LINE PURCHASE DECISION AT LAZADA

In order to overcome difficulties in shopping online, Lazada needs to review the influence of online consumers reviews, security and perception risk to decision purchase consumer .Study this aim for analyze influence on line consumer reviews, security, and perception risk on online purchasing decisions. The problems raised in this study are not enough its popular site on line Lazada, total visitors which still a little compared to site others, and various issues related to online consumer review, security, and perception risk. Respondents in this study were Pelita Indonesia Pekanbaru students with a total of a sample of 95 people. Methods of data analysis using multiple linear regression analysis method with test t and f for testing hypothesis. Results study prove that variable on line consumer reviews, security and perception risk have influence which positive and significant to decision purchase in a manner on line in Lazada.

Discovering The Effect of Online Consumer Reviews Towards Purchasing Decisions (Study of an E-Learning Provider in Indonesia)

Discovering The Effect of Online Consumer Reviews Towards Purchasing Decisions (Study of an E-Learning Provider in Indonesia)

Do Live Streaming and Online Consumer Reviews Jointly Affect Purchase Intention?

Social commerce has become a mainstream online shopping phenomenon. The effects of single social-commerce modes, such as live streaming and online consumer reviews (OCRs), on consumers’ purchase intention have attracted much attention. However, the existing literature overlooks the combined impact and complementary mechanisms of multiple social business modes on purchase intention. Drawing on the previous research, we identified the characteristics of live streaming and the characteristics of OCRs. Drawing inspiration from channel complementarity theory, a dual-channel influence model is presented. We collected 448 online questionnaires from several social-commerce platforms. The data were processed via structural equation modeling. The results show that the social-commerce modes of live streaming and OCRs can jointly influence consumers’ purchase intention, sense of community, interactivity, perceived usefulness, and perceived trust as antecedents of purchase intention, with customer engagement playing a mediating role. However, emotional support had no impact on purchase intention. This study provides useful insight into the mechanisms behind how multiple social-commerce channels influence purchase intention.

Factors Influencing Intention to Use Online Consumer Reviews

Online consumer reviews have been shown to play an important role that can influence consumers' attitudes, behaviors, and purchasing decisions. This study seeks to understand factors that affect user intention to use online consumer reviews. To examine this area, this paper theorizes multiple constructs that may influence intention to use. The subjects in this study were 466 online Vietnamese consumers. The data were analyzed using structural equation modeling, and five of the seven hypotheses were found to be significant. This study found several results not found in prior studies. The construct perceived online review importance is found to be important in influencing intention to use online reviews. Both perceived usefulness and perceived credibility influenced consumers' perceptions on the importance of online reviews. The study findings contribute to the research field of online consumer reviews and provide new insights into intention to use online consumer reviews for a developing country, different from prior studies that have focused on developed countries.

The Effects of Linguistic Coordination on Perceived Quality of Consumer Reviews: A Dual Process Perspective

Online consumer reviews, as a major source of information and influence, are of great interest to marketing researchers and practitioners. This study investigates the effects of linguistic coordination on perceived review quality. Drawing on the elaboration likelihood model, the authors theorize that two types of linguistic coordination—topic matching (semantic component) and language style matching (lexical component)—have profound effects on perceived review quality. Utilizing natural language processing tools and a novel clustering technique to measure matching, empirical analyses based on an IMDb data set support the positive direct effects of both types of matching. Moreover, the authors find that there is a negative interaction between topic matching and language style matching in affecting perceived review quality. The findings contribute to the understanding of online review quality, and the application of natural language processing enriches the methodological tool kit available to researchers.

Data-driven product ranking: A hybrid ranking approach

The sudden COVID-19 epidemic has caused consumers to gradually switch to online shopping, the increasing number of online consumer reviews (OCR) on Web 2.0 sites has made it difficult for consumers and merchants to make decisions by analyzing OCR. Much of the current literature on ranking products based on OCR ignores neutral reviews in OCR, evaluates mostly given criteria and ignores consumers’ own purchasing preferences, or ranks based on star ratings alone. This study aims to propose a new decision support framework for the evaluation and selection of alternative products based on OCR. The decision support framework mainly includes three parts: 1) Data preprocessing: using Python to capture online consumer comments for data cleaning and preprocessing, and extracting key features as evaluation criteria; 2) Sentiment analysis: using Naive Bayes to analyze the sentiment of OCR, and using intuitionistic fuzzy sets to describe the emotion score; 3) Benchmark analysis: a new IFMBWM-DEA model considering the preference of decision makers is proposed to calculate the efficiency score of alternative schemes and rank them according to the efficiency score. Then, the OCR of 15 laptops crawled from JD.com platform is used to prove the usefulness and applicability of the proposed decision support framework in two aspects: on the one hand, the comparison of whether the preference of decision makers is considered, and on the other hand, the comparison with the existing ranking methods. The comparison also proves that the proposed method is more realistic, the recommendations are more scientific and the complexity of the decision is reduced.

The Effect of Rating, Online Consumer Review and Sales Promotion on Purchase Intention on the Gofood Application

This study aims to determine the effect of ratings, online consumer reviews and sales promotion on purchase intention. This research is causal research, using multiple linear regression analysis methods. The population in this study were people who had bought food through Go-food during the last 3 months in the Jabodetabek area. The sample in this study used purposive sampling method with a total of 200 respondents. The results of this study indicate that rating, online consumer review and sales promotion simultaneously affect purchase intention. Rating has no positive effect on purchase intention. Online consumer review has a positive effect on purchase intention. Sales promotion has a positive effect on purchase intention. Then rating is not a variable that has a dominant effect on purchase intention