Customer Reviews Research Articles

Information disclosing strategy and additional online customer reviews

ABSTRACT Product information asymmetry exists between firms and online customers. Online customer reviews offer product quality information and reduce information asymmetry. Many customers review products online upon receipt and again after use. Initial and additional reviews can be consistent or conflicting, which affects other customers’ perceived product value. Firms use different strategies for disclosing product featured quality information, such as exaggerating or depreciating product quality. Different strategies can lead to different customer perceptions, which can affect additional reviews. In addition, even with the same strategy, differences in the degree of information quality can affect the perceived value of customers.

Brand Reputation and Trust: The Impact on Customer Satisfaction and Loyalty for the Hewlett-Packard Brand

Reputation is shaped depending on factors such as the quality of products and services offered by a brand to its stakeholders, its reliability, and its innovative aspect in the eyes of stakeholders. The sustainability of a brand reputation depends on the brand creating a positive perception by fulfilling its social responsibilities and maintaining this perception in the long term. In this study, the brand reputation of Hewlett-Packard (HP) computers is evaluated through customer reviews. The data set in the study consists of 2012 customer reviews obtained from Hepsiburada, one of the most widely used e-commerce platforms in Turkey. Sentiment analysis and text mining artificial intelligence methods were used in the study. For sentiment analysis, the Naive Bayes method, which is one of the machine learning methods, was used, and the comments were divided into three groups as positive, negative, and neutral. In the study, 82% of the customer comments were positive, 11% were negative, and 7% were neutral. The fact that most of the comments consist of positive sentiments shows that HP Computer has a positive reputation in the eyes of stakeholders consisting of customers. Comments consisting of negative and neutral emotions show the aspects that the brand needs to improve. In the study, the text mining method emphasises the prominent features of the brand in the comments. This study makes an important contribution to the reputation assessment of brands and to ensuring sustainable brand reputation.

Analyzing Factors Influencing Customer Satisfaction with Organic Coffee in E-Commerce

In recent years, there has been a notable increase in interest in sustainable products, particularly organic goods, driven by the push for sustainable societies. However, Japan’s organic market lags behind the global trend, representing just 0.3% of total farmland in 2021. To tackle this issue, the Ministry of Agriculture, Forestry and Fisheries introduced the “Green Strategy,” with the goal of raising the organic share to 25% by 2050. The COVID-19 pandemic has brought about significant lifestyle changes, such as increased remote work and reduced outdoor activities, leading to a surge in the E-commerce market and an increased need for analyzing consumer satisfaction. To enhance Japan’s organic market, it is crucial to familiarize consumers with organic products like coffee, tea, and snacks. This study proposes a model using E-commerce customer reviews to predict consumer satisfaction. The aim is to improve services and develop new strategies based on customer preferences, promoting sustainable growth and enhancing customer satisfaction. This study delves into the impact of factors derived from latent Dirichlet allocation (LDA) on consumer satisfaction with organic coffee, utilizing three years of data from Rakuten Marketplace customer reviews (2017–2019). A total of 1444 review texts were analyzed, including 154 low-rated reviews (ratings 1–3) and 1290 high-rated reviews (ratings 4–5). To build a satisfaction prediction model using regression analysis, a survey was conducted using question items explored through factor analysis to assess the reliability of items in conveying the consistent meanings of high frequency words extracted by LDA: “taste,” “aroma,” “gift,” “sales/discounts,” “freshness,” and “decaffeinated.” The results of the regression analysis revealed that “gift,” “sales/discounts,” and “decaffeinated” significantly impact consumer satisfaction. Insights derived from these factors illuminate marketing strategies that focus on delivering happiness through products, adding value through pricing strategies, and introducing benefits and consumption methods that effectively meet customer needs. These findings underscore the potential of customer reviews to enhance consumer satisfaction and inform strategic marketing decisions, thereby contributing to the growth and success of the organic coffee market.

A Large Language Model and Qualitative Comparative Analysis-Based Study of Trust in E-Commerce

The primary goal of this study is to predict and analyze customer trust in e-commerce by leveraging neural computation within large language models (LLMs) alongside configurational approaches. We employ LLMs to predict trust levels based on customer reviews, applying artificial intelligence to analyze key aspects of the e-commerce experience, such as customer service, refund processes, item quality, and shipping. To extend beyond predictive performance, we integrate Qualitative Comparative Analysis (QCA) to identify the causal relationships between trust and various stages of the customer journey, including selection, delivery, and post-purchase support (recovery). This dual approach not only showcases the power of neural computation in predicting trust outcomes but also provides a deeper understanding of how specific configurations of customer experience elements contribute to either positive or negative trust. By combining machine learning techniques and QCA, this study contributes to the application of LLMs and configurational approaches, offering novel insights into the drivers of trust in e-commerce.

Examining the Mediating Influence of Credibility Linking with Influencer Marketing and Online Customer Reviews on Customer Purchase Decision

This paper focuses on the effects of influencer marketing and online customer reviews on customer purchasing decisions. Quantitative data was collected from 300 respondents and analyzed through SPSS. Process Macro was used to analyze the relations and mediation effects between the variables. The literature review indicates the increasing relevance of digital marketing and the impact of influencer marketing and online customer reviews on customer purchase decisions. Online customer reviews present ideal information as it is the social proof and influence purchasing decisions. According to the results, both influencer marketing and online customer reviews have a significant positive effect on influencing people's decisions when it comes to purchasing. This empirical research brings more light on how influencer marketing and online customer reviews along with credibility play powerful roles in structuring customer purchase decisions. It has a pragmatic impact on marketers for credibility in their digital marketing efforts to make sales.

Enhancing Accessibility in Online Shopping: A Dataset and Summarization Method for Visually Impaired Individuals

A visually impaired individual (VI) encounters numerous challenges in their daily activities, particularly in tasks reliant on visual systems such as navigation, educational pursuits, and shopping. Online shopping poses a heightened difficulty due to its reliance on visual representations of products in digital formats. The impact of visual impairment on product selection based on reviews remains inadequately investigated. This study endeavors to address two primary objectives. Firstly, we propose the creation of a dataset comprising product review videos (referred to as PVS10) tailored for visually impaired individuals. Secondly, we present a foundational summarization methodology designed to facilitate access to pertinent and informative content within extensive video collections for visually impaired individuals. Our dataset, gathered from YouTube, encompasses 10 distinct products, each associated with the top 10 review videos, totaling 100 videos of varying lengths. Utilizing the search term "review videos of PRODUCT NAME", we assembled the dataset to facilitate automated summarization processes aimed at maximizing salient information, minimizing redundant content, and preserving the overarching sentiment conveyed in the reviews. This research focuses on the challenges faced by visually impaired people in online shopping, particularly when selecting products based on customer reviews. Our study demonstrates that people with visual impairments may actively explore product reviews and only acquire the information they require.

Empowering Consumer Decision-Making: Decoding Incentive vs. Organic Reviews for Smarter Choices Through Advanced Textual Analysis

Online reviews play a crucial role in influencing seller–customer dynamics. This research evaluates the credibility and consistency of reviews based on volume, length, and content to understand the impacts of incentives on customer review behaviors, how to improve review quality, and decision-making in purchases. The data analysis reveals major factors such as costs, support, usability, and product features that may influence the impact. The analysis also highlights the indirect impact of company size, the direct impact of user experience, and the varying impacts of changing conditions over the years on the volume of incentive reviews. This study uses methodologies such as Sentence-BERT (SBERT), TF-IDF, spectral clustering, t-SNE, A/B testing, hypothesis testing, and bootstrap distribution to investigate how semantic variances in reviews could be used for personalized shopping experiences. It reveals that incentive reviews have minimal to no impact on purchasing decisions, which is consistent with the credibility and consistency analysis in terms of volume, length, and content. The negligible impact of incentive reviews on purchase decisions underscores the importance of authentic online feedback. This research clarifies how review characteristics sway consumer choices and provides strategic insights for businesses to enhance their review mechanisms and customer engagement.

Unveiling the Forces driving expert Activity: The impact of information environment and peer behavior on expert reviewer engagement behavior

Online platform engagement with customers, especially those with high expertise, is crucial for companies. As these expert customer reviews directly impact a company’s brand image and sales volume, an understanding of expert reviewer engagement behavior (EREB) is critical for companies’ marketplace success. This study explores the factors that influence EREB from two key situational cues: those from the company’s information environment and from peer expert behavior. Data from 144,634 Yelp reviewers and 5,080 restaurants were analyzed. The results reveal that companies with higher aggregate ratings are more likely to encourage EREB. The impact of overall and peer expert rating inconsistency on EREB varies: Overall rating inconsistency has a positive effect, while inconsistency among expert peers has a negative effect. Additionally, EREB exhibits herding and differentiation patterns in response to changes in peer expert engagement density. This results in a U-shaped relationship between EREB and peer expert engagement density, moderated by aggregate rating, overall rating inconsistency, and peer expert rating inconsistency. This study provides practical insights for marketers looking to engage expert customers and expands on the literature on expert customer engagement behavior.

The Use of Generative Artificial Intelligence for Business Decision-Making

The purpose of this study was to develop autonomous artificial intelligence agents capable of working as a cohesive group to solve intricate decision-making processes, ultimately pushing the boundaries of artificial intelligence’s applicability in high-stakes and real-world scenarios. An autonomous multi-agent model was designed and deployed using the Analytic Hierarchy Process (AHP) decision-making framework, with autonomous AI agents, such as CrewAI and AutoGen, orchestrating multiple stages of activities performed by four distinct agents leveraging generative AI (GenAI). These agents were applied to a use case for proof of concept. The agents used a qualitative dataset to generate management recommendations based on cost and market feasibility considerations. The model was tested with publicly available customer feedback data on oatmeal cookies. It synthesized 913 customer reviews, identified common complaints, provided a summary of potential solutions, and generated a summary of market opportunities, along with relevant challenges. Although a subject matter expert's review is necessary to evaluate the practicality and relevance of the recommendations, the results demonstrated the high potential of multi-agent models in synthesizing and distilling large datasets into actionable insights, thereby augmenting decision-making processes in business contexts. The model's modular design allows for enhancements in quality, accuracy, and practicality by incorporating additional datasets and new agents. This study underscores that the integration of GenAI within a multi-agent model empowers businesses to swiftly transform vast amounts of business intelligence data into practical recommendations.

Identifying barriers of financial app usage and their impact on negative emotions, customer ratings and recommendation intention: a text-mining based approach

PurposeLimited studies in the mobile payment segment have attempted at understanding the factors that resist customers from using financial apps or mobile payment services (MPSs). This study aims at identifying the barriers from online customer reviews and examine how these barriers affect customers’ negative emotions (anger, fear, sadness), customer ratings and recommendation intentions.Design/methodology/approachThis study, divided into three phases, has adopted a text-mining based mixed-method approach on 14,043 reviews present in Google PlayStore or App Store pages about financial apps used in India.FindingsPhase 1 identified barriers like, “bad user experience”, “UPI failure”, “trust issues”, “transaction delays” from the reviews. Phase 2 found that “bad user experience” and “UPI failure” trigger both “anger” and “sadness”. “Transaction delays” and “money lost in transaction” stimulate “fear”. From the IRT stance, in Phase 3 this study has found that barriers like, “transaction error”, “UPI failure” (usage), “bad user experience” (image) and “trust issues” (tradition) have a significant negative impact on both customer ratings and recommendation intention.Originality/valueThe current study contributes to the existing literature on MPSs by identifying barriers from user generated content. Additionally, this study has also examined the impact of the barriers on customers’ negative emotions and recommendation intention.

User-Generated Contents and Brand Loyalty of Skinscare Products in Port Harcourt

This paper empirically investigated the relationship between User-Generated Contents and Brand Loyalty of Skincare Products in Port Harcourt. Specifically, the objectives of the study were to determine how user authenticity and user engagement rate relate with customer satisfaction and repeat patronage of skincare products in Port Harcourt. The correlational research design was adopted and the population of the study was infinite. Topman’s formula was used to generate a sample size of 384 customers. However, only 262 respondents provided data for the study through questionnaire that was designed in the Likert 5-point scale of strongly disagree to strongly agree. Spearman Correlation (rho) was used to four hypotheses. From results of the analysis it was revealed that user authenticity and user engagement rate which are the proxies of user-generated contents positively and significantly relate with customer satisfaction and repeat patronage (i.e. measures of brand loyalty) of skincare products in Port Harcourt. Based on these findings, it was concluded that both factors have a very strong and positive impact on customer loyalty, with authenticity playing a particularly crucial role in shaping customer perceptions and trust. The strong relationship between user engagement and repeat patronage further highlights the importance of fostering active and meaningful interactions with customers. Therefore, the study recommended amongst others that skincare brands should encourage genuine customer reviews and testimonials by creating platforms where customers feel comfortable sharing their honest experiences. Authentic content resonates more with potential buyers, fostering trust and leading to higher customer satisfaction and repeat purchases.

Pengaruh Promo Big Sale, Online Customer Review, dan Online Customer Rating Terhadap Keputusan Pembelian pada Event Tanggal Kembar Shopee

The purpose of this study is to determine the impact of major discount offers, online customer reviews, and online customer ratings on purchasing decisions during the Shopee twin date event. For important sales initiatives, structural equation modeling was the data analysis method used. The results show that online customer reviews have a positive but not statistically significant impact on purchasing decisions during the Shopee twin date event, even while online customer ratings have a large impact.

Performance Comparison of Random Forest (RF) and Classification and Regression Trees (CART) for Hotel Star Rating Prediction

Purpose: This study proposes to evaluate the effectiveness of Random Forest (RF) compared to Classification and Regression Trees (CART) in prediction of hotel star ratings. The objective is to identify the algorithm that provides the most reliable and accurate classification outcomes based on diverse hotel attributes in accordance with the standard categorization of star hotel categories. This is necessary due to the important role of accurate star ratings in guiding consumer choices and enhancing competitive positioning in the hospitality industry. Method: This study conducted a comprehensive dataset about Hotel in Banyumas Regency, including location, facilities, the size of rooms, type of rooms, price of rooms, and customer reviews, subjected to training through both RF and CART algorithms. Both algorithms are evaluated using accuracy, precision, recall, and F1 score. Additionally, both algorithms due to in the same preprocessing while performing hyperparameter tuning improve the efficacy of each model. Result: The results showed that RF achieved the best overall accuracy and robustness than CART across all tests conducted. Furthermore, RF also outperformed CART in classification effectiveness among classes, including enhanced precision and recall scores across multiple stars rating categories, signifying increased generalization and consistency in classification tasks. RF classifier consistently surpassed the CART classifier in terms of both accuracy and F1-score throughout all random states and test sizes, with a highest score of 0.9932 at a random state of 100 and a test size of 0.4. The most reliable results were obtained using RF with 42 random states and a test size of 0.2, resulting in an accuracy of 0.9909, precision of 1.0, recall of 1.0, and F1 score of 1.0. Simultaneously, CART shows values of 0.9818, 1.0, 1.0, and 1.0, respectively, while maintaining the same variation. This consistent performance, regardless of fluctuations, illustrates the robustness and suitability of RF for classification tasks compared to CART. Novelty: This study offered new insights about the implementation of machine learning about hotel star rating predictions using RF and CART algorithms. Also, the novelty of the collected hotel dataset used in this study. A detailed comparative analysis was also provided, contributing to the existing literature by showing the effectiveness of RF over CART for this specific application. Future studies could explore the integration of additional machine learning methods to further enhance prediction accuracy and operational efficiency in the hospitality industry.

Big Data Insights into Coastal Tourism: Analyzing Customer Satisfaction at Egyptian Red Sea Dive Resorts

This research aims to explore the relationship between customer satisfaction and various extracted factors at dive resorts in the Red Sea, Egypt, utilizing a hybrid methodology of descriptive and diagnostic analytics applied to online review data. Employing techniques such as KH coder for text analysis, exploratory factor analysis (EFA), and linear regression, this study systematically identifies key elements that influence customer satisfaction. Findings reveal that activities related to diving and marine life markedly enhance guest satisfaction, underscoring the critical role these aspects play in the overall appeal of Egyptian coastal tourism. Conversely, areas such as dining and amenities were identified as needing improvement. The originality of this study lies in its application of big data analytics to dissect and understand customer feedback in a sector-specific context, providing strategic insights for the sustainable advancement of coastal tourism in Egypt. By focusing on dive resorts, this research highlights their integral role in coastal tourism and offers a model for leveraging online customer reviews to enhance service quality and promote sustainable practices within the tourism industry, contributing to the overall growth and sustainability of coastal tourism.

Pengaruh Brand Equity dan Online Customer Reviews (OCRs) terhadap Keputusan Pembelian Produk The Originote pada Aplikasi Tiktok Shop

Tiktok is the only social media that is widely used by shoppers to see product reviews before deciding to buy. One of the most searched content is beauty products. Research was conducted to determine the effect of brand equity and online customer reviews (OCRs) on purchasing decisions for The Originote products on the Tiktok Shop Application. This study focuses on The Originote skincare using quantitative methods. The sample used was Generation Z in Malang City with a multistage random sampling technique. Data analysis includes instrument testing, descriptive, classical assumption testing and hypothesis testing. The results of the study show that both brand equity and OCRs variables significantly contribute to purchasing decisions. Partially and simultaneously, both variables have a significant influence on purchasing decisions. To improve and strengthen brand equity, The Originote needs to upgrade the outer packaging so that the product is protected from impact. On the other hand, to improve online customer reviews, The Originote needs to create an SOP for admins to reply to customer reviews.

The Effect Of Content Creators And Customer Reviews On Consumer Purchase Interest In Tiktok Social Media

The currently growing TikTok social media presents an opportunity for entrepreneurs to promote products and services to consumers through digital marketing. The thing that sellers do to increase sales is to consistently create interesting and viral content. Creativity in content creation is needed whose impact will be very influential in attracting buyer interest and changing consumer perceptions. This study aims to determine and explain the effect of content creators and customer reviews on consumer buying interest in TikTok social media. Quantitative research method. Questionnaire data collection with a Likert scale. Data analysis with SmartPLS. The results show that content creators have an effect on buying interest and customer reviews have no effect on buying interest.

The Effect Of Advertising Attractiveness, Influencer Marketing, And Online Customer Review On Smartphone Purchasing Decisions For Generation Z In Surabaya

The usage of smartphones in Indonesia has continuously increased. In 2018, 56.2% of the Indonesian population used smartphones. One of the prominent market leaders is Samsung smartphones, which rank second with the largest market share in Indonesia at 20.99%. Indonesia is predominantly comprised of generation Z, accounting for approximately 27.94% of the total population. The role of digital marketing is highly significant in the decision-making process for smartphone purchases among generation Z. This study aims to determine the influence of Advertising Attractiveness, Influencer Marketing, and Online Customer Review on the purchasing decisions of smartphones by generation Z in Surabaya. This research uses a quantitative research approach. The study population consists of generation Z in Surabaya. The sampling technique employed is purposive sampling. The sample criteria include generation Z aged 15-29 years in Surabaya who have purchased a Samsung smartphone at least once, with a total sample size of 315 respondents. Data collection was conducted by distributing questionnaires to respondents through Google Forms. The research method involves multiple linear regression analysis using the SPSS program. The research results indicate that the significance value for all variables is 0.000 (< 0.05), leading to the conclusion that Advertising Attractiveness, Influencer Marketing, and Online Customer Review have a positive and significant influence on the smartphones purchasing decisions by generation Z in Surabaya.

Pengaruh Online Customer Review, Online Customer Rating, Kepercayaan Konsumen, dan Promosi terhadap Keputusan Pembelian Masyarakat Purwokerto Pengguna Tiktok Shop

This study aims to examine the effect of Online Customer Review, Online Customer Rating, consumer trust, and promotion on purchasing decisions at Tiktok Shop in the Purwokerto community. This study applies quantitative methods in order to evaluate the relevance of each variable. The sampling technique using purposive sampling. The instrument for collecting data was a five-level Likert scale questionnaire. The analysis tool uses SPSS. According to the findings of this study, it is known that, consumer confidence has a positive and significant effect on purchasing decisions, promotions have a positive and significant effect on purchasing decisions, while online customer reviews have no positive and insignificant effect on purchasing decisions, online customer ratings have a positive and insignificant effect on purchasing decisions.

Customers' sentiment on food delivery services: An Arabic text mining approach

The Covid-19 pandemic has accelerated the shift in organizations' strategies toward innovative online services. Customer reviews on platforms for online ordering and delivery are a vital source of information about how well a business is performing. Businesses that provide food delivery services (FDS) seek to leverage consumer input to locate areas where customer satisfaction could be raised. Sentiment analysis (SA) has been the subject of an enormous amount of English-language research. Despite Arabic's increasing popularity as a writing language on the Internet, not much study has been conducted on sentiment analysis of Arabic up to this point, with a limited number of publicly available resources for Arabic SA such as datasets and lexicons. The present study collects FDS-related reviews in Arabic to conduct extensive emotion mining, taking advantage of Natural Language Processing, feature selection, and Machine Learning techniques to elicit personal judgments, identify polarity, and recognize customers’ feelings in the FDS domain. To demonstrate that the proposed approach is suitable for analyzing human perceptions of FDS, we designed and carried out excessive experiments that assess the utility of each phase. Our highest categorization accuracy was 90 % using Mutual Information with the SVM classifier. The study's findings provide various managerial insights for improving their plans and service delivery, as well as revealing the main reasons for consumer complaints. It also demonstrates how future academics might harness the power of online business reviews in Arabic using a variety of text-mining approaches.

E-COMMERCE-AN INSIGHT INTO IMPACT OF ONLINE REVIEW ON CUSTOMER BUYING BEHAVIOUR

The trend of shopping in the minds of the customer is changing frequently. At present shopping practice transformed from traditional way to online shopping , which enables the customer to buy the product as per their requirement without any constraints of time , place payment method, brand and product types. Online shopping have created an huge impact on customer buying behaviour because of this 4 factors i.e on source credibility, volume, language with comprehension and relevance. Earlier the majority of the customers were depend on word of mouth method to collect information regarding product which was influencing their buying decisions. But now because of e-commerce, online shopping majorly online customer review format, played an important role in influencing customer 's buying decisions. To fill the research gap we have collected data through questionnaire from the customers those who do online shopping. For effective research outcome both primary data and secondary data is used. Primary data collected through structured questionnaire. This research paper is based on critical examination of the study which reflected the result as per the objectives . The study was mainly focused on the impact of online reviews towards customer buying decisions. through this study it is realised that majority of the public are influenced by online reviews for all types of the products and at present online reviews are creating huge impact towards customer buying behaviour . KEY WORDS: Online shopping , Online review, Word of mouth, Customer buying decisions