Consumer Reviews Research Articles

Evolutive multi-attribute decision making with online consumer reviews

In the digital age, the sheer volume of online consumer reviews imposes a cognitive burden on consumers, complicating their purchasing decisions. Many studies have integrated consumer opinions to provide consumers with clear and concise information. However, these studies often prioritize mainstream opinions, overlooking the diversity and timeliness of other important perspectives. To address this challenge, we propose an evolutive decision-making method. Firstly, we propose an attribute rating evolution algorithm to address the online reviews based on the iterative self-organizing data analysis technique and time decay. This algorithm enables real-time analysis of the diverse opinions expressed in review data. Then, taking into account consumer attribute preferences and decision-making psychology, we formulate multiple product ranking strategies to offer personalized decisions based on the evolutive opinions. Our method decreases the bias towards review quantity, ensuring that significant opinions are not overshadowed by more frequent ones. Through data experiments and an application on OpenTable.com, we demonstrate that our method can provides effective decision recommendation for consumers.

Fake Review Detection Model Based on Comment Content and Review Behavior

With the development of the Internet, services such as catering, beauty, accommodation, and entertainment can be reserved or consumed online. Therefore, consumers increasingly rely on online information to choose merchants, products, and services, with reviews becoming a crucial factor in their decision making. However, the authenticity of reviews is highly debated in the field of Internet-based process-of-life service consumption. In recent years, due to the rapid growth of these industries, the detection of fake reviews has gained increasing attention. Fake reviews seriously mislead customers and damage the authenticity of online reviews. Various fake review classifiers have been developed, taking into account the content of the reviews and the behavior involved in the reviews, such as rating, time, etc. However, there has been no research considering the credibility of reviewers and merchants as part of identifying fake reviews. In order to improve the accuracy of existing fake review classification and detection methods, this study utilizes a comment text processing module to model the content of reviews, utilizes a reviewer behavior processing module and a reviewed merchant behavior processing module to model consumer review behavior sequences that imply reviewer credibility and merchant review behavior sequences that imply merchant credibility, respectively, and finally merges the two features for fake review classification. The experimental results show that, compared to other models, the model proposed in this paper improves the classification performance by simultaneously modeling the content of reviews and the credibility of reviewers and merchants.

Pengaruh Kredibilitas Influencer dan Consumer Review Terhadap Minat Beli Produk Ms Glow pada Platform Shopee

This research aims to analyze the influence of influencer credibility and consumer reviews on buying interest. The sampling method was purposive sampling technique. The questionnaires were distributed via buying online to 100 respondents.The collected date were analysed using Partial Least Square (PLS) . The results show that both the influencer credibility and consumer review variables are positive and significant influence on buying interest in MS Glow products on the Shopee platform. Has the highest influence on buying interest.

How dark traits and regulatory focus manifest in online hotel reviews: a textual content analysis

ABSTRACT This study seeks to explore how Dark Triad of psychopathy, Machiavellianism, and narcissism interact with consumers’ regulatory focus (prevention-focus and promotion-focus) to influence online consumer reviews in the hotel industry. We conducted an empirical analysis of 1,185,940 hotel reviews from TripAdvisor.com, utilizing the textual content of online reviews and employing Language Inquiry and Word Count analytical tools. The research reveals a multifaceted link between regulatory focus and Dark Triad in online reviews. The results showed that individuals with a promotion focus tend to give higher ratings than those with a prevention focus. Dark Triad significantly influences how regulatory focus impacts online reviews. This study initiates a new academic dialogue on the interaction between regulatory focus and Dark Triad traits within online consumer behavior studies. It provides practical guidance for the hotel industry to refine their management and response strategies by acknowledging the impact of consumer personality traits on online reviews.

Sustainable sentiment analysis on E-commerce platforms using a weighted parallel hybrid deep learning approach for smart cities applications

Sentiment analysis (SA) of several user evaluations on e-commerce platforms can be used to increase customer happiness. This method automatically extracts and identifies subjective data from product evaluations using natural language processing (NLP) and machine learning (ML) methods. These statistics may eventually reveal information on the favourable, neutral, or negative attitudes of the consumer base. Due to its capacity to grasp the complex links between words and phrases in reviews as well as the emotions they imply, deep learning (DL) is very useful for SA tasks. A unique approach termed Weighted Parallel Hybrid Deep Learning-based Sentiment Analysis on E-Commerce Product Reviews (WPHDL-SAEPR) is introduced by the proposed system. Accurately distinguishing between distinct sentiments found in online store reviews is the aim of the WPHDL-SAEPR technique. Additional data pre-processing processes are implemented within the WPHDL-SAEPR architecture to guarantee compatibility. Words are embedded into the paper using the word2vec model, while sentiment is classified using the WPHDL model. The Restricted Boltzmann Machine (RBM) and Singular Value Decomposition (SVD) models are combined in this model. The results of the WPHDL-SAEPR approach’s simulation were assessed using a consumer review database, with the results being emphasized at each stage.

Chemosensory vocabulary in wine, perfume and food product reviews: Insights from language modeling

Chemosensory sensations are often hard to describe and quantify. Language models may facilitate a systematic understanding of sensory descriptions. We accessed consumer and expert reviews of wine, perfume, and food products (English language; about 68 million words in total) and analyzed their sensory descriptions. Using a novel data-driven method based on natural language data, we compared the three chemosensory vocabularies (wine, perfume, food) with respect to their vocabulary overlap and semantic properties, and explored their semantic spaces. The three vocabularies primarily differ with respect to domain specificity, concreteness, descriptor type preference and degree of gustatory vs. olfactory association. Wine vocabulary primarily distinguishes between white wine and red wine flavors and qualities. Food vocabulary separates drinkable and edible food products and ingredients, on the one hand, and savory and non-savory products, on the other. A salient distinction in all three vocabularies is between concrete and abstract/evaluative terms. Valence also plays a role in the semantic spaces of all three vocabularies, but valence is less prominent here than ingeneral olfactory vocabulary. Our method allows a systematic comparison of sensory descriptors in the three product domains and provides a data-driven approach to derive sensory lexicons that can be applied by sensory scientists.

A Natural Language Processing Approach for Sentiment Analysis of Hotel Reviews

Businesses rely heavily on review analysis of consumer feedback and reviews when making decisions. This research examines fifteen years' worth of hotel evaluations to learn more about the opinions of visitors and how they relate to review scores in various years and regions. After the dataset was cleansed to concentrate on reviews written in English, TextBlob was used to analyze sentiment and classify the reviews as positive, neutral, or negative. The results validate using sentiment analysis in conjunction with numerical ratings, which show a positive link between more excellent ratings and positive sentiment scores. A significant drop in sentiment scores following the 2008–2009 recession emphasizes how the global financial crisis affected the hospitality sector. Significant differences in guest feelings between cities, states, and nations are shown by geographic analysis, underscoring the necessity of region-specific approaches. Sentiment distribution and trends were revealed via intricate visualizations such as heat maps, box plots, scatter plots, and histograms. To improve guest happiness, the study uses an understanding of geographical variations. These results give hotel management a solid framework for raising customer satisfaction and loyalty, customizing marketing campaigns, and enhancing service quality. For deeper insights, future studies may investigate more sophisticated NLP techniques.

The Role Of Social Media In Building Consumer Trust In Product

This study explores the role of social media in building consumer trust in products in the digital era. Using a qualitative method, the research examines consumers' perspectives and experiences with social media as a transparent and interactive marketing platform. Based on in-depth interviews and content analysis, the study finds that consumer reviews on social media significantly influence purchasing decisions, especially those reviews that include visuals and testimonials from other users. Additionally, brand transparency and responsiveness to consumer feedback highlight the importance of openness in fostering trust. Educational content provided by brands also enhances consumers' positive perceptions, as it offers additional value beyond typical promotions. Direct interaction between consumers and brands on social media strengthens the emotional connection, creating a sense of appreciation and trust toward the brand. The findings provide practical insights for companies to strategically leverage social media in establishing sustainable relationships with consumers. By focusing on aspects of consumer review credibility, transparency, educational content, and responsive interaction, companies can increase consumer trust in their products.

Reducing Consumer–Brand Incongruity Through Corporate Social Responsibility and Brand Trust: Exploring Negative Word‐of‐Mouth (NWOM)

ABSTRACTDrawing upon consumer–brand disidentification theory and balance theory, this study examines symbolic and ideological incongruity in consumer–brand relationships through an original conceptual model shaped by negative past experiences, brand trust, perceived corporate social responsibility (CSR), and negative word‐of‐mouth (NWOM). A preliminary study was conducted to explore the dimensions of consumers' negative past experiences by topic detection. Latent Dirichlet allocation (LDA) topic modeling was undertaken to analyze online consumer reviews (n = 6095) about a coffee chain brand. The dimensions detected in this preliminary study were included in the research model and further analyzed in the main study. The main study, a cross‐sectional consumer survey (n = 522), tested the original research model by way of partial least squares structural equation modeling (PLS‐SEM) on SmartPLS. The findings showed that negative past experiences consisted of product‐related, service‐related, and technology‐related problems and negatively influenced brand trust. It was found that brand trust and perceived CSR negatively affected symbolic and ideological incongruity, while symbolic and ideological incongruity positively influenced NWOM. The findings provide empirical evidence for balance theory by showing that the three critical domains of consumer–brand relationships (ideological, symbolic, and experiential) provide a complex cognitive model that covers personal‐symbolic and moral‐societal aspects of consumer–brand disidentification and consequent NWOM intentions. In line with consumer–brand disidentification theory, the results contribute to the literature by demonstrating the direct negative impacts of brand trust and perceived CSR on symbolic and ideological incongruity, as well as the direct positive impacts of symbolic and ideological incongruity on NWOM.

Transformation Of Conventional To Digital Marketing: A Study On Increasing Sales Through Digital Marketing

The development of digital technology continues to grow along with the times. Access to the internet is another development that affects people's lives. Similarly, internet access in economic activities also contributes to the development of small and medium enterprises. Micro, Small and Medium Enterprises (MSMEs) utilize information technology in running their business. This study aims to determine the right marketing strategy at Rene Café, the ultimate goal to be achieved is to significantly increase sales and present a good company brand image to consumers. The research method used by researchers in this study is a qualitative method. This type of research is descriptive qualitative, using the methods of observation, interview, and documentation. The results showed that Rene Café has not fully optimized using all social media, but the success of Rene Café in digital marketing through social media is working with influencers or content creators and consumer reviews. By collecting data and information related to marketing through social media and the influence of an influencer in providing information about products. This shows that reviews and influencers play an important role.

Weaving narrative threads with social psychological processes: Narrative modulations in online consumer reviews of a medical memoir

With the growing prevalence of health and illness narratives on digital platforms, research examining the social psychological processes involved in these storytelling environments remains scarce. This paper addresses this research gap by conducting a mixed-methods study of digital storytelling within the UK’s healthcare context, focusing on online consumer reviews of the medical memoir, Do no harm: stories of life, death and brain surgery (Marsh, 2014). Utilising computer-assisted text analysis methods of LIWC-22 and the Sketch Engine, linguistic cues for cognitive, affective, social and perceptual processes are identified in a corpus of online consumer reviews. A subsequent qualitative analysis, based on ‘narrative modulation’ (Huang, 2024, 2020), investigates the role of these processes in constructing and developing storylines across the user reviews. Finally, the study explores how consumer reviews in the form of ‘small stories’ challenge canonical narratives in the UK’s healthcare services. This research advances the field of narrative studies by emphasising the role of social psychological processes (Chung and Pennebaker, 2019) in modulating emerging, evolving and counter narratives in digital storytelling. The findings reveal an instrumental role of social psychological processes, as signalled by linguistic cues, in shaping narrative threads in online user reviews. This study not only develops narrative modulation as a valuable concept for narrative analysis, but also underscores its effectiveness when combined with computer-assisted text analysis tools for in-depth examinations of narrative data. Furthermore, it provides critical insights into digital storytelling in healthcare contexts, promoting knowledge transfer across narrative studies, stylistics, social psychology and medical humanities.

The Influence of Consumer Product Knowledge, Trust, and Risk Perception on Purchase Intention Among Shopee Consumers: The Role of Reviews as a Moderating Variable

The development of e-commerce has changed consumer behavior. With the presence of e-commerce, many habits have changed, starting from previous shopping habits in offline stores, now switching to shopping at online stores using shopping sites and applications such as Shopee, because it makes it easier for consumers to make buying and selling transactions online. However, Shopee, which is a consumer favorite, has several problems, including some consumers who buy products at Shopee feeling disappointed because the goods purchased do not match expectations, thus affecting consumer buying intentions. Several previous studies have shown that there is a relationship between consumer trust and purchasing decisions. This study uses descriptive quantitative research using multiple linear regression analysis. The subjects of this research are Shopee consumers. The variables used in this study are consumer product knowledge, trust, risk perception and reviews as moderating variables. The results of the research show that consumer product knowledge has a positive but not significant effect on Shopee consumers’ purchasing decisions. Meanwhile, the variables of trust, risk perception and reviews have a significant positive effect on Shopee consumers’ purchase intentions. Then the review variable is not able to moderate the relationship between consumer product knowledge and purchase intention. However, reviews are able to moderate the relationship between trust, risk perception and purchase intention.

How loud is consumer voice in product deletion decisions? Retail analytic insights

This study examines the role of online consumer reviews in product deletion decisions. Building upon product portfolio management theory and the signaling theory, we integrate consumer voice, represented by online consumer review behavior into organizational voice—strategic product deletion decision-making. We empirically evaluated the relationship between online consumer reviews and organizational product deletion decisions using text mining and logistic regression models. The results indicate consumer online review characteristics are significant factors for product deletion decisions. Findings suggest that products with lower review attribute ratings, or those with textual review content less pertinent to the core performance attributes, are more likely to be deleted. The linguistic retail analytic characteristics of online reviews also provide insights for product deletion decisions. Products with reviews having higher subjectivity, shorter length, and lower readability are more likely to be deleted. Pre-purchase consumer voice—the perceived helpfulness or unhelpfulness of online reviews also impacts product deletion decisions. A general conclusion is that online reviews can provide important retail analytics for smarter retail operations planning at the strategic and tactical levels when it comes to product planning and portfolio management through product deletion.

From Consumption to Online Review Posting: Examining the Influential Factors of Conversion Rate

ABSTRACT Online reviews reflect consumer needs and generate an electronic word-of-mouth (eWOM) effect influencing businesses’ financial performance. Existing literature on online reviews largely assumes consumers have a consistent conversion rate of writing and posting online reviews after a consumption experience. Using dual-source data of consumers’ footprints and online restaurant reviews, we challenge this assumption and quantify the online review conversion rate by depicting a comprehensive pathway from consumers’ visitation to their review writing. We find that consumer overall satisfaction has a nonlinear impact on the online review conversion rate. Extremely dissatisfied consumers are more likely to post online reviews to complain than generally dissatisfied consumers. In contrast, generally satisfied consumers are more likely to post online reviews than extremely satisfied consumers. Additionally, consumer engagement in the platform stimulates their online review behavior. Further, consumers’ geographical distance from providers has a U-shaped impact on online review conversion rate. Local nearby consumers and consumers from a relatively long distance have a higher chance to post online reviews than consumers from a moderate distance. Moreover, price promotions from providers facilitate consumers’ review behavior. The findings guide businesses to prioritize reading and responding to online consumer reviews based on these influential factors. Companies thus can use more targeted actions to encourage consumers to post positive reviews and spread a positive eWOM effect to attract consumers and enhance financial performance

The Influence of Product Quality, Brand Image, and Online Consumer Review on Purchasing Decision for The Originote Products

The Influence of Product Quality, Brand Image, and Online Consumer Review on Purchasing Decision for The Originote Products

Navigating user discontent in wearable tech: an in-depth study of Fitbit reviews

ABSTRACT In the burgeoning field of wearable technology, Fitbit has emerged as an influential entity. Even the most influential companies, however, need to understand user dissatisfaction, particularly extracted from critical reviews, in order to enhance the user experience. This study analyzes a subset of 28,664 Fitbit consumer reviews from the Google Store, all giving it a rating of 1 out of 5 points, in order to identify the reasons for user dissatisfaction. Employing thematic analysis, sentiment analysis, and topic modelling, our research reveals prominent issues impacting user perception. Key findings include significant challenges with device syncing, app functionality, and customer service, alongside a spectrum of sentiments ranging from neutral to negative. Topic modelling further distinguishes various categories of user feedback, underscoring the diversity in user experiences and expectations. These insights suggest a pressing need for improvements in technical functionality, user interface design, and customer support. This study contributes to a deeper understanding of consumer experiences in wearable technology, offering valuable guidance for enhancing product design and customer engagement strategies in this dynamic market.

Effects of bimanual vs unimanual motor actions on consumer behavior intention and attitude

PurposeThis study aims to understand how unimanual and bimanual motor actions in the form of writing a message (handwriting and typing) influences consumer behavior intention and attitude.Design/methodology/approachThree studies have been conducted to understand how the method of writing (typing or handwriting) and moderator type of message (moderator: emotional vs. rational) can influence persuasion variables.FindingsThis paper shows evidence that bimanual and unimanual motor actions activate contralateral brain hemispheres and influence consumer behavior. Specifically, how the use of these motor actions leads to a change in behavior intention and attitude. Furthermore, how bidirectional behavior and role-reversal occurs when a unimanual vs bimanual motor actions are used to create an emotional or rational message.Research limitations/implicationsThis research focuses on explaining cognitive implications of unimanual vs bimanual motor actions (typing vs handwriting) on self and specifically on consumers when they are exposed to situations of producing/creating a message. Adding further evidence to persuasion, behavior intention and attitude research in embodied cognition (e.g. Petty and Cacioppo, 1986b; Briñol and Petty, 2008), the role of haptic interfaces in online marketing and consumer behavior (e.g. Brasel and Gips, 2015). The authors did not collect any FMRI data but it can be the future scope of this work.Practical implicationsThe novel findings of bimanual and unimanual motor actions have a direct application to the current scenario of the online reviews/surveys, which have become a crucial point for e-commerce businesses and retailers that require customers to give feedback. Since, consumer emotions can influence behavioral outcomes and decision making through sensory cues (Abdolmohamad Sagha et. al, 2022; Haase Wiedmann and Labenz, 2022). From this perspective, depending on the mapping/recording of the felt emotionality or rationality by the customer, the feedback method can be oriented in a way to reduce the effect of negative reviews. Further applications are suggested for retailers, increasing prosocial or sustainable behavior and attitudes.Social implicationsMarketing and advertising research conducted in different social settings is considered important in neuropsychology and neurophysiology research (Pozharliev et al., 2017). Therefore, this research can be extended to test in different social settings and marketing contexts, for example, in the virtual reality, metaverse and gamification. Thus, this research can serve as an avenue to examine the bimanual and unimanual motor actions in a scenario where the participants can combine several motor actions, for example, talking on the phone or walking while filling consumer reviews online/offline and wearing the VR devices like Apple Vision Pro and performing unimanual or bimanual motor actions.Originality/valueMapping of human behavior has always been done dependent on the cognition aspect and limited in its scope for embodied cognition. The results can be used for more direct and impactful ways of conducting surveys, feedback and handling communications for major businesses. In addition, it can be a helpful tool for message persuasion for a new brand or increasing awareness about sustainable consumption depending on typed or handwritten inputs by a potential consumer.

Product portfolio decarbonization: Deleting hot products for a cooler supply chain

AbstractModern economic development emphasizes the reduction of carbon emissions. In rationalizing product portfolios, organizations must prioritize low‐carbon development by selecting products with minimal emissions. Eliminating high‐emission items can create opportunities for new low‐carbon products, thereby enhancing competitiveness. In this study, we propose a hybrid mixed method that integrates text mining and multi‐objective optimization algorithms, utilizing a broad range of quantitative and qualitative data sources, including online consumer reviews. We apply this method to a real case in the Chinese air‐conditioning industry for practical validation. The results indicate that for comprehensive product portfolio decarbonization, organizations should consider deleting high‐performing products (e.g., those with strong sales and market performance) to achieve full decarbonization across the entire supply chain. This approach offers valuable insights for managers aiming to simultaneously improve economic and environmental performance.

Aspect based sentiment analysis of consumer reviews using unsupervised attention neural framework

Consumer reviews posted on e-commerce platforms offer an assessment of their opinion and sentiments about various aspects of a product or service. The aspects may belong to a specific category at subsystem and component level. The irrealis often associated with an opinion term, if not handled carefully, may distort the sentiment scores. This paper introduces a clustering and negative-sampling backed feed-forward Unsupervised Attention Deep Neural Framework for Opinion-Aspect-Category-Irrealis-Sentiment (UAN-OACIS) Quintuple extraction. This framework splits the five subtasks into two stages. In the first stage, aspects and opinions are extracted using word embedding-based multi-layer Attention Deep Neural Network (DNN) models; while the second stage projects corresponding categories, irrealis, and sentiment scores by creating a hash table and a couple of algorithms. We also propose a POS-Tag-based algorithm to make an irrealis dictionary to suggest sentiment modification percentage scores; and another algorithm to modify existing sentiment-lexicon based on domain-specific words. We compare UAN-OACIS with four unsupervised and two semi-supervised approaches at subtask level, and two supervised approaches at quadruples level excluding irrealis, and have got comparable results. Since no method is available for overall comparison at quintuple level, we additionally study the impact of irrealis on sentiment scores and observe substantial improvement compared to state-of-the-art lexicons methods.

Exploring a Consumption Value Model for Islamic Halal Cosmetics

Research Originality: Halal cosmetics segmentation requires detailed analysis to enable stakeholders from the industry sector to optimize marketing and sales. The findings of this study are interesting, the market opportunity specifically for women is not only in consumption volume but also production, especially with market segmentation among millennials and Gen Z.Research Objectives: This research aims to determine the simultaneous and partial effects of halal labeling, product quality, religiosity, and online consumer reviews on purchasing decisions regarding halal cosmetic products among female and male consumersResearch Methods: This research employed an online survey method with 203 respondents. It also employed the SEM-PLS to evaluate models and validate hypotheses, and Multi-Group Analysis (MGA), which is employed to determine the gender effect's position on the hypotheses.Empirical Results: The results with the SEM-PLS method show that product quality, online consumer reviews, and religiosity satisfactorily influence purchasing decisions, this is not the case with the halal label. It seems that online consumer reviews are more suitable for male consumers. Appearance and persuasive factors are more important for male consumers who value rational factors compared to female consumers.Implications: The findings of this research are significant due to the comparison drawn between male and female consumers. Such a comparison aids practitioners and business professionals in effectively delineating market demands and preferences. When developing halal cosmetic products, manufacturers must consider not only the product's sales potential but also its efficacy and potential side effects upon usage.JEL Classification: M5, M10, M21How to Cite:Yudha, A.T.R.C., Zidna, R.R & Febriyanti, N. (2024). Exploring a Consumption Value Model for Halal Cosmetics. Etikonomi, 23(2), 465 – 480. https://doi.org/10.15408/etk.v23i2.36080.