Posting Online Reviews Research Articles

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

자발적 그림자노동자의 온라인 리뷰 포스팅 동기와 행동과정 규명

자발적 그림자노동자의 온라인 리뷰 포스팅 동기와 행동과정 규명

Temporal Dimensions of Crisis and Online Hotel Reviews: A Social Support Perspective of Narcissists During the Pandemic

This study sought to investigate the impact of the COVID-19 crisis on the online reviewing behavior of individuals with narcissistic traits. We conducted a textual analysis of online reviews submitted to TripAdvisor.com for hotels in New York City, covering the period from August 1, 2019, to July 31, 2022. This time frame allowed us to examine the phases before the COVID-19 crisis, during the early stages of the pandemic, and in the later stages of the crisis. We observed that the negative correlation between narcissism and review ratings was less evident during the initial phase of the COVID-19 crisis but became more pronounced as the crisis progressed. Conversely, the positive correlation between narcissism and the perceived helpfulness of reviews was stronger in the early stages of the COVID-19 crisis compared with the later stages. This suggests that the behavior of posting online reviews may serve as a form of social support mechanism, particularly utilized by narcissistic individuals to address their vulnerabilities, which was more apparent during the early phase of the COVID-19 crisis. These findings open new avenues for research and have practical implications for professionals in the hospitality, tourism, and service industries, especially in understanding consumer behavior during crisis periods.

Analisis Sentimen Ulasan Aplikasi Info BMKG di Google Play Menggunakan TF-IDF dan Support Vector Machine

Posting online reviews has become one of the most popular ways to express opinions and sentiments towards service applications. The Meteorology, Climatology and Geophysics Agency (BMKG) Info application is an Android and iOS-based mobile application that provides information on weather, climate, air quality, and earthquakes that occur in various regions in Indonesia. The information contained in this application is very important but has a worse value than other forecasting applications. Sentiment analysis is the process of classifying text into several classes such as positive sentiment, negative or not containing both. This research aims to analyze user reviews on the BMKG Info application from the Google Play website. The benefits obtained are as consideration for developers to improve the shortcomings of the application. The classification process uses Term Frequency-Inverse Document Frequency (TF-IDF) and the Support Vector Machine (SVM) algorithm. This research successfully collected 2500 reviews from users of the BMKG Info application on the Google PlayStore website using the web scraping method. Text preprocessing of the reviews used case folding, symbolic and stopword removal, tokenization, normalization, and stemming. User ratings help in identifying the sentiment label of a review, 66% of reviews are positive while the rest are negative. The most frequently reviewed topics with sentiment value are "application", "information", "update". This research conducted three experimental scenarios based on the composition of training data and test data. Based on the prediction model, the scenario with 75%:25% split data has the highest accuracy rate of 79%.

Predicting Food Safety Violations via Social Media to Improve Public Health Surveillance

Foodborne illness outbreaks originating in food service establishments (FSEs) are a serious public health concern. There are multiple ways that FSEs can contribute to the propagation of foodborne illnesses. However, not all these risks can be detected by health authorities during official inspections for two reasons. First, health authorities have limited resources, meaning not every FSE can be inspected as often and expediently as necessary. Second, because the food safety and hygiene standards of FSEs are dynamic, the inspection results might represent a snapshot that fails to resemble the current FSE situation. Consequently, it would be helpful to have an early warning system that can allow health authorities to predict the risk levels of FSEs and proactively intervene by demanding that high-risk FSEs take appropriate countermeasures. Considering that customers who feel sick after visiting an FSE may not report their experience to health authorities but instead disclose this information by posting online reviews on social media platforms, this study leverages social media data to predict food safety violations by FSEs. The empirical data for this study derive from two sources: the FSE inspection results from the Department of Health and Mental Hygiene in New York City, which labeled each FSE according to whether it violated food safety regulations, and textual reviews posted by FSE customers on Yelp. Machine learning algorithms were employed to extract textual features from the reviews and construct predictive models. Our results demonstrate that customer reviews posted on social media platforms are valuable for predicting FSE food safety violations. Health authorities can leverage social media data to construct predictive models and deploy them to optimize resource allocation and prioritize interventions to prevent foodborne illness outbreaks originating in FSEs, improving public health. FSE managers can also deploy the models to improve their hygiene standards in response to alerts raised. By taking appropriate countermeasures earlier, they can reduce their risk of financial loss due to negative inspection results.

What motivates customers to publish online reviews promptly? A text mining perspective

PurposeThis work aims to explore why people review their experienced online shopping in such a manner (promptness), and what is the potential relationship between the users’ review promptness and review motivation as well as reviewed contents.Design/methodology/approachTo evaluate the customers’ responses regarding their shopping experiences, in this paper, the “purchase-review” promptness is studied to explore the temporal characteristics of users’ reviewing behavior online. Then, an aspect mining method was introduced for assessment of review text. Finally, a theoretical model is proposed to analyze how the customers’ reviews were formed.FindingsFirst, the length of time elapsed between purchase and review was found to follow a power-law distribution, which characterizes an important number of human behaviors. Within online review behaviors, this meant that a high frequency population of reviewers tended to publish relatively quick reviews online. This showed that the customers’ reviewing behaviors on e-commerce websites may have been affected by extrinsic motivations, intrinsic motivations or both. Second, the proposed review-to-feature mapping technique is a feasible method for exploring reviewers’ opinions in both massive and sparse reviews. Finally, the customers’ reviewing behaviors were found to be mostly consistent with reviewers’ motivations.Originality/valueFirst, the authors propose that the “promptness” of users in posting online reviews is an important external manifestation of their motivation, product experience and service experience. Second, a semi-supervised method of review-to-aspect mapping is used to solve the data quality problem in mining information from massive text data, which vary in length, detail and quality. Finally, a huge amount of e-commerce customers’ purchase-review promptness are studied and the results indicate that not all product features are responsible for the “prompt” posting of users’ reviews, and that the platform’s strategy to encourage users to post reviews will not work in the long term.

Esports as playful consumption experiences: Examining the antecedents and consequences of game engagement

This study aims to propose and test a conceptual model identifying playful-consumption experiences (i.e., role-projection, fantasy, escapism, enjoyment, sensory experiences, emotional involvement, and arousal) as potential drivers of consumer esports videogame engagement as well as continuance intentions, electronic word-of-mouth (eWOM), and online reviews as possible outcomes. Using the esports games' context and analyzing data from 290 gamers, the study utilized PLS-SEM using SmartPLS 3.3.9 to test the structural model. The results showed that the proposed playful-consumption experiences such as enjoyment, sensory experiences, emotional involvement, and arousal positively affect consumers’ esports game engagement. Furthermore, the results indicate the positive influence of esports game engagement on continuance intentions to play esports, eWOM and post online reviews. This study extends the esports gaming literature by examining both the antecedents and consequences of esports game engagement and provides valuable theoretical and practical implications.

Exploring the reviews of Google Maps to assess the user opinions about public libraries

PurposeIn present networked society, almost all the entities are present on Google Maps. Google Maps has become the first preference for the users for searching and retrieving information about any place. Google Maps provides an opportunity for a user to post ratings and reviews for an entity, including public libraries about their facilities and services, etc. This study explores how users of public libraries in Delhi (India) use Google Maps to share their opinions on library facilities and services and to assess whether Google Maps have the potential to act as an interactive feedback system between users and the authorities.Design/methodology/approachThe Google Maps platform was used as a data source. To classify reviews into distinct categories, a content analysis was performed. Further Sentiment analysis of reviews was executed to highlight the positive, negative and neutral aspects of selected libraries. Moreover, reviews, endorsements and ratings of library products and services were also checked.FindingsThe research reveals that the culture of posting online reviews is increasing day by day and most of the reviews are posted in the English language, with Non Local Guides as the most prolific reviewers. In the reviews, the total number of topics discussed were 685, of which (70.51%) contained positive sentiments, (15.32%) were negative and (14.1%) were neutral reviews. The majority of the negative topics were discussed among the staff category, followed by facilities. Environment and children's areas received positive feedback with five-star ratings and most of them were marked helpful by other users. Users also loved technology, location and collection. The study concluded that Google Maps can be used as a new interactive feedback tool that library authorities can use to connect with users, replacing traditional suggestion boxes and online forms.Research limitations/implicationsThe research is limited to a single platform (Google Maps) and the capital city of India (Delhi) and cannot be generalized across platforms and regions. Moreover, such libraries were taken under the scanner of this study which possessed more than 50 reviews.Originality/valueThe study will help to identify the shortcomings of the Delhi public libraries through Google Maps platform and help them to devise effective strategies to improve their management in view of suggestions and complaints.

Zomato’s race to capture the foodtech market

Learning outcomes This case allows students to critically analyse the business model of Zomato which is a multi-sided platform/in the foodtech industry. It helps students to critically analyse how firms enter into the global market to create value and maintain dominance over the local market (especially in a large market such as India). The case can also be used to introduce students to the business canvas model by analysing foodtech start-ups. The outcomes are as follows: to understand the Business Model Canvas as a tool to describe and analyse the foodtech business such as Zomato’s, based on its value proposition and the way it sells its services; to conduct a value chain analysis and analyse the business models adopted by foodtech companies; to understand how Zomato can aim at global value creation; and to design a clear growth strategy and evaluate Zomato’s options to internationalize or expand locally. Case overview/synopsis The year 2018 was an important year for Zomato as it geared up to chart new heights amidst the changing dynamics of the industry on one hand and a co-founder exiting the company on the other hand. Zomato was incepted in 2008 as a restaurant discovery platform offering users the ability to access restaurant menus and post online reviews. It provided a range of value-added services for both its restaurant partners and end customers. Its vertical integration enabled it to grow its revenues across its three lines of business, namely, dining out, delivery and sustainability. Zomato was an early internet start-up that expanded rapidly in the international markets. In the past ten years, the company both scaled and rolled back its operations with unique lessons learned in each market that paved its path for success both locally and globally. The domestic market was being dominated by a few large players sharing the market. Reports by market intelligence firms showed that Swiggy, the closest competitor was starting to dominate Zomato in India [1]. Deepinder, CEO, Zomato’s dilemma for adding value and increasing revenues by weighing options of whether the company should strengthen its presence in the domestic market, or, venture into foreign markets or serve both local and foreign markets. Complexity academic level This case is appropriate for postgraduate courses in Strategic Management or International Business. Supplementary materials Teaching notes are available for educators only. Subject code CSS 5: International Business.

Spam Reviews Detection in the Time of COVID-19 Pandemic: Background, Definitions, Methods and Literature Analysis

During the recent COVID-19 pandemic, people were forced to stay at home to protect their own and others’ lives. As a result, remote technology is being considered more in all aspects of life. One important example of this is online reviews, where the number of reviews increased promptly in the last two years according to Statista and Rize reports. People started to depend more on these reviews as a result of the mandatory physical distance employed in all countries. With no one speaking to about products and services feedback. Reading and posting online reviews becomes an important part of discussion and decision-making, especially for individuals and organizations. However, the growth of online reviews usage also provoked an increase in spam reviews. Spam reviews can be identified as fraud, malicious and fake reviews written for the purpose of profit or publicity. A number of spam detection methods have been proposed to solve this problem. As part of this study, we outline the concepts and detection methods of spam reviews, along with their implications in the environment of online reviews. The study addresses all the spam reviews detection studies for the years 2020 and 2021. In other words, we analyze and examine all works presented during the COVID-19 situation. Then, highlight the differences between the works before and after the pandemic in terms of reviews behavior and research findings. Furthermore, nine different detection approaches have been classified in order to investigate their specific advantages, limitations, and ways to improve their performance. Additionally, a literature analysis, discussion, and future directions were also presented.

Impact of the length of stay at hotels on online reviews

PurposeThe length of stay (LoS) is of major importance from the perspective of the management of tourist destinations. As tourists heavily rely on the online reviews of other travelers as a primary information source, this study aims to empirically examine how the LoS can influence the online reviews for hotels, with special emphasis on the textual review content.Design/methodology/approachThis study analyzes online review data collected from Booking.com by using the Linguistic Inquiry and Word Count program to operationalize review depth, analytical thinking and the authenticity reflected in customer reviews. Based on the analyzed data, this study used a series of regression analyses to understand the impacts of the LoS on online reviews.FindingsThe author’s analysis found that a longer stay at a hotel causes consumers to be more likely to post online reviews that not only include a numerical rating as well as written content but also lengthier and more detailed descriptions of their hotel experiences. Further analysis found that the LoS at hotels causes systematic differences in the linguistic attributes of the review content. Specifically, consumers who stay longer tend to write reviews with more analytical information, resulting in consumers perceiving the online reviews as more authentic.Research limitations/implicationsAlthough the LoS has been considered a significant issue in tourism, studies examining the impact of different lengths of stay on consumers’ post-purchase behaviors are limited. In this light, the author’s findings demonstrate how the LoS can change the linguistic attributes of online reviews. It expands the body of knowledge of the LoS in tourism.Originality/valueThis study represents the first attempt to empirically examine and reveal how the different length of stay at a hotel systemically influences consumer review-posting behaviors.

Are Hotel Guests Altruistic? How Positive Review Disconfirmation Affects Consumers’ Online Review Behavior

The research aims to examine how positive review disconfirmation (i.e., a positive deviance between a hotel consumer’s poststay evaluation and the average review rating by prior consumers) affects subsequent consumers’ willingness to post online reviews and their own review ratings. By employing an experimental research method, this study reveals that positive review disconfirmation increases hotel guests’ willingness to post online reviews, and increases their online review ratings through the mechanism of concern for others, demonstrating an act of altruism. In addition, comparatively the positive review disconfirmation effects are stronger when the variance of prior review ratings is smaller. This study enhances the online review social influence literature, and the consumer’s altruistic motivation of posting online reviews.

How Posting Online Reviews Can Influence the Poster's Evaluations.

The current research demonstrates that posting online reviews can influence the evaluations of the individual posting the review. Across four studies, we examine the impact of individuals' naive theories about the meaning of their own posting on subsequent attitudes. In these experiments, individuals were assigned to write either positive or negative reviews about various products and services and then post them. The meaning associated with posting a review was varied to indicate either high validity (e.g., saving, extending, sharing) or low validity (e.g., deleting, hiding, archiving) with respect to their previously written reviews. When posting was associated with a high validity meaning, it increased reliance on those thoughts polarizing attitudes and behavioral intentions compared with when the posting was associated with a low validity meaning. These findings were mediated by the impact of meaning on thought confidence.

Do online reviews truly matter? A study of the characteristics of consumers involved in different online review scenarios

ABSTRACT Online reviews have become one of the most influential persuasive messages concerning decisions making. Researchers have explored different aspects of online reviews and the characteristics of reviewers using various methods; however, few studies have focused on consumers who do not post online reviews after purchasing, resulting in a gap in the research. This study aims to identify consumers’ characteristics and analyze the importance of consumers in different online review scenarios. We employed a fusion analysis framework, which used machine learning to determine the value of different types of consumers; in addition, we quantitatively estimate the relationship between variables and consumer types using econometrics modelling. This research used real consumer data from three data resources, solving problems related to the use of limited questionnaire datasets and single-source data. Through rigorous analysis, we demonstrate that lurkers are more valuable than posters because they can be easily served and create more profit; thus, researchers should consider those who do not post online reviews rather than focusing only on the influence of online reviews and posters. Our findings also provide managerial implications for precision marketing by indicating that e-marketers should employ various marketing strategies and pay attention to different types of consumers, especially lurkers.

Online Reviews of Restaurants: Expectation-Confirmation Theory

ABSTRACT Today, consumers’ engagement in the restaurant culture is defined by their involvement in posting online reviews. Drawing upon expectation-confirmation theory (ECT), this study examined whether and how consumers’ confirmations of online reviews from their actual restaurant experiences affect their subsequent participation in online reviews. The research model of this study assessed the entire decision process of a restaurant experience. First, we examined the impacts of online review features – review quality, review timeliness, visual image quality, and rating – on review adoption. Second, we investigated the impact of review adoption on the confirmation of restaurant experiences and review intentions toward online forums. Using structural equation modeling based on the data (n = 596), the results revealed that review quality and review rating significantly influenced review adoption, and both confirmations to positive and negative review intention were significant.

Mining patient opinion to evaluate the service quality in healthcare: a deep-learning approach

The emergence of social media has created several opportunities for patients to evaluate the quality of healthcare services by posting online reviews. The rich text and photographic information in the online reviews results insights into how patients’ experience with the doctor and their satisfaction with healthcare service delivery. Various studies have performed patients’ opinion analysis using textual contents. This study presents a novel multimodal approach to analyze the patients’ sentiment regarding the quality of healthcare service delivery (high vs. low). In comparison with existing studies, we consider not only the unique textual contents but photographic contents as well from the Yelp.com platform, which is more challenging due to feature extraction. We evaluate the performance of the baseline, and deep learning algorithms across textual cues, visual cues, and the fusion of both text and visual cues. Experimental results indicated that the addition of novel features increases the models’ accuracy to 15.0% across text and visual cues. Furthermore, fusing text and visual contents improve the classification accuracy by at least 12.64% instead of considering the contents alone. Preliminary comparative experiments result show that the deep learning model outperformed all other algorithms. In comparison with several other state-of-the-art methods in the biomedical domain, the proposed model could significantly enhance the performance of the classifier indicating the effectiveness and suitability of the methodology.

“When you write review” matters

PurposeThis study aims to investigate how prior reviews posted by other consumers affect subsequent consumers’ evaluations and to what extent the review temporal distance can increase or reduce the social influence of prior reviews. In this study’s restaurant context, review temporal distance refers to the duration between dining time and review time of a dining experience.Design/methodology/approachThe data of paired online restaurant reservations and reviews are analyzed using Ordered Logit Model. Two robustness checks are conducted to test the stability of the main estimation results.FindingsThe empirical results demonstrate that consumers’ restaurant evaluation is socially influenced by both the prior average review rating and number of prior reviews; review temporal distance has a direct negative effect on consumers’ restaurant evaluation; and review temporal distance increases the social influence of prior reviews.Practical implicationsThis study suggests that online review matters. Both restaurants and the online review platforms should encourage consumers to share their experiences and post online reviews immediately after their consumption.Originality/valueThe study contributes to the literature on electronic word-of-mouth, social influence and psychological distance. First, the bi-directional nature of social influence on electronic word-of-mouth for experience-oriented product is documented. Second, for the first time, this study examines how review temporal distance could affect the social influence on consumers’ restaurant evaluation.

Motivations for posting online reviews in the hotel industry

AbstractSome travelers may rely on online reviews (electronic word of mouth [eWOM]) from users who have already experienced a particular hotel before making a hotel reservation. However, only a small percentage of users of eWOM contribute actively with new reviews and evaluations. The understanding of what motivates consumers to write eWOM is very important, in order that hotels can achieve more and better eWOM and, consequently, more reservations. Based on an online survey and using fuzzy‐set qualitative comparative analysis, this study identifies different causal combinations of motivations (personal, social benefit, social concern, and consumer empowerment) and demographic characteristics (gender and age) that lead to hotel eWOM. Three configurations exist: being a female older than 35 years old, combined with high social concern and high consumer empowerment; being a female older than 35 years old, combined with high social concern and high personal motivations; and being a male, combined with high social concern, high personal motivations, and high consumer empowerment.

The emotional review–reward effect: how do reviews increase impulsivity?

A growing reliance on customer reviews prompts firms to develop strategies to encourage customers to post online reviews of their products. However, little research investigates the behavioral consequences of writing a review. The act of sharing personal opinions through reviews is a rewarding experience and makes customers feel socially connected. With an application of reverse alliesthesia theory, the current study predicts that such rewarding experiences drive online reviewers to seek other rewards, such as impulsive buying. Three lab-based and two field studies demonstrate such an emotional review–reward effect: sharing emotional information in the public realm of customer reviews, rather than forming similar opinions privately, drives participants to make more impulsive buying decisions.

Toward a better fitness club: Evidence from exerciser online rating and review using latent Dirichlet allocation and support vector machine

Fitness clubs have never ceased searching for quality improvement opportunities to better serve their exercisers, whereas exercisers have been posting online ratings and reviews regarding fitness clubs. Studied together, the quantitative rating and qualitative review can provide a comprehensive depiction of exercisers’ perception of fitness clubs. However, the typological and dimensional discrepancies of online rating and review have hindered the joint study of the two data sets to fully exploit their business value. To this end, this study bridges the gap by examined 53,979 pairs of exerciser online rating and review from 100 fitness clubs in Shanghai, China. Using latent Dirichlet allocation (LDA) based text mining, we identified the 17 major topics on which the exercisers were writing. A support vector machine (SVM) classifier was then employed to establish the rating-review relations, with an accuracy rate of up to 86%. Finally, the relative impact of each topic on exerciser satisfaction was computed and compared by introducing virtual reviews. The significance of this study is that it systematically creates a standardized protocol of mining and correlating the massive structured/quantitative and unstructured/qualitative data available online, which is readily transferable to the other service and product sectors.