Online Review Sites Research Articles

EXAMINING EFFECTIVE ELECTRONIC WORD OF MOUTH IN SOCIAL MEDIA ON THE CUSTOMER’S PURCHASE INTENTION: A QUANTITIVELY APPROACH IN LIBYANS’ MARKETS

The purpose of this paper is to provide a comprehensive research of the effects social media developed in an essential part of marketing strategy for its ability to generate co-created value by EWOM. As a result of the advanced Technological revolution, the industries operations have grown a unique rate and creating multiple opportunities for marketing in online backgrounds. Recently, Technologies have been involved within modern marketing activities to promote their products and services and its approach is to evaluate products and services availabilities by using various platforms like social media, online review websites, blogs and discussion forums in the form of visual information. At the present time, social media has developed in an essential part of marketing strategy for its ability to generate co-created value, to interactively connect brands to consumers, to monitor brand-related discussions and sentiments, to guide consumers in the decision-making process, to activate Electronic Word of Mouth interactions, and transform consumers into brand supporters. This study highlights the role of key various digital channels by using applications of social media to be examined on the consumer buying decisions and intention with a mediating effect of the EWOM. It carried out A quantitative research approach was used to achieve objectives and examine the hypothesized research framework by using a questionnaire in the retailing sector. A total of 384 valid responses were considered for further analysis by using SSPS 21 software to conduct the key analyses. The results revealed the significant effect and role of all digital channels on the consumers buying decisions, with the moderated role of the EWOM on the effect of digital channels on consumer buying decisions. The study offers additional contributions to the existing literature and supports the assumptions to provide a better grasp about the study topic. Its originality and a unique approach for data collecting and using the schema theory of e-WOM to understand the phenomenon of social media influence on customers intention of destination services / brands.

Fuel vehicles or new energy vehicles? A study on the differentiation of vehicle consumer demand based on online reviews

PurposeThe authors hope to filter valuable information from online reviews, obtain objective and accurate information about the demands of auto consumers and help auto companies develop more reasonable production and marketing strategies for healthy and sustainable development. This paper aims to discuss the aforementioned objectives.Design/methodology/approachThe authors collected review data from online automotive forums and generated a corpus after pre-processing. Then, the authors extracted consumer demands and topics using the LDA model. Finally, the authors used a trained Word2vec tool to extend the consumer demand topics.FindingsDifferent types of vehicle consumers have the same demands, such as “Space,” “Power Performance,” and “Brand Comparison,” and distinct demands, such as “Appearance,” “Safety,” “Service,” and “New Energy Features”; consumers who buy new energy vehicles are still accustomed to comparing with the brands or models of fuel vehicles; new energy vehicles consumers pay more attention to services and service quality during the purchasing and using process.Research limitations/implicationsThe development time of new energy vehicles is relatively short, with some models being available for only one year or even six months. The smaller amount of available data may impact the applicability of topic models. The sample size, especially for new energy vehicles, needs to be increased to improve the general applicability of topic models further.Practical implicationsFirst, this measure helps online review websites improve their existing review publication mechanisms, enhance the overall quality of online review content, increase user traffic and promote the healthy development of online review websites. Second, this allows for timely adjustments in future product production and sales plans and further enhances automotive companies' ability to leverage online reviews for Internet marketing.Originality/valueThe authors have improved the accuracy and stability of the fused topic model, providing a scientific and efficient research tool for multi-dimensional topic mining of online reviews. With the help of research results, consumers can more easily understand the discussion topics and thus filter out valuable reference information. As a result, automotive companies may gain information about consumer demands and product quality feedback and thus quickly adjust production and marketing strategies to increase sales and market share.

Discordance in online commercial ratings of orthopaedic surgeons: a retrospective review of online rating scores

Background: Patients use online physician ratings to select an orthopaedic surgeon. The objectives of this paper were to investigate whether ratings are concordant among online review sites rating orthopaedic surgeons and examine physician practice characteristics associated with higher physician ratings. Methods: Orthopaedic surgeons in Illinois who accepted Medicare patients in 2015 were included in this study. Physician practice characteristics, demographics, and information regarding their Medicare volume were obtained for each surgeon. Information regarding each surgeon’s average and number of ratings was collected from Yelp, Healthgrades, Google, and Vitals.com. The authors examined concordance between sites by investigating how many physicians were given high ratings on one site (>4/5), but low ratings on another site (<2 .5/5). Finally, a multivariable regression model was developed to investigate the association between physician characteristics and online ratings. Results: Two hundred ninety-five orthopaedic surgeons were included in the study sample. The number of reviews per physician varied greatly, with some surgeons having as high as 300 and many having no reviews. Of the physicians reviewed as low-performing on one site, 65.9% were rated as high-performing on another site. Physicians were more likely to have better ratings if they graduated after 1995 (P<0.05) or performed a higher volume of Medicare services (P<0.05). Total number of reviews had a statistically significant positive correlation with average rating (r=0.26, P<0.001). Conclusions: The discordance among review sites for orthopaedic surgeons suggests that patients should exercise caution when using online reviews. As their use increases, the healthcare community should take a closer look at standardizing reviews. Level of Evidence: Level III

Peran Brand Image Situs Tripadvisor Memediasi Pengaruh Ewom terhadap Niat Beli Kamar di Kabupaten Badung

EWOM given by other consumers in a sharing review platform or community can influence consumers' purchase intentions. TripAdvisor as an online review site that provides access for its customers to write a review. On the TripAdvisor site, people who want to share their experiences are required to become regular users and have their TripAdvisor account first and it is known that 65% of respondents see hotel reviews before making a booking. Thus, high ratings and good reviews from customers can create profits for hoteliers as EWOM can help hotels attract new customers. The purpose of this study was to determine the effect of EWOM on room purchase intention in Badung Regency and the role of Tripadvisor website brand image in mediating the relationship between EWOM and room purchase intention. This research method uses a quantitative approach and Partial Least Square Structural Equation Modeling (PLS-SEM) analysis. The population determined in this study is the Indonesian population with a sample of 180 respondents in accordance with the provisions of the sample calculation. The equation models analyzed are the outer model, the inner model and hypothesis testing. Based on the results of calculations using PLS-SEM software, the results show that there is no significant effect between the effect of EWOM on room purchase intentions, there is a significant effect of brand image on EWOM, the brand image of the Tripadvisor site has an effect on room purchase intention and there is a significant influence on brand image the Tripadvisor website as a media to mediate the influence of EWOM on room purchase intentions in Badung Regency. The implication of the results of this study shows that hotels around Badung Regency must maintain professionalism in improving the quality of services and facilities provided to tourists so that they can increase the value of reviews on the Tripadvisor site.

A novel metaheuristic optimisation approach for text sentiment analysis

Automated sentiment analysis is considered an area in natural language processing research that seeks to understand a text author's mood, thoughts, and feelings. New opportunities and challenges have arisen in this field due to the popularity and accessibility of a variety of resources of ideas, such as online review websites, personal blogs, and social media. Feature selection, which can be conducted using metaheuristic algorithms, is one of the steps of sentiment analysis. It is crucial to use high-performing algorithms for feature selection. This paper applies the Horse herd Optimisation Algorithm (HOA) for feature selection in text sentiment analysis. HOA is a metaheuristic algorithm and uses six key behaviours to simulate the social performance of horses of various ages, to solve high-dimensional optimisation problems. In order to improve HOA, this paper adds another behaviour of horses to the basic algorithm; thus, the new algorithm uses seven key behaviours of horses of different ages to imitate their social performance. It is then discretised and converted to a multi-objective algorithm. The improved algorithm's performance is evaluated using 15 CEC benchmark functions, and the results are compared to the Binary Social Spider Algorithm, the Binary Grey Wolf Optimizer, and the Binary Butterfly Optimization Algorithm. The new algorithm, the Multi-objective Binary Horse herd Optimisation Algorithm (MBHOA), excels at solving high-dimensional complex problems. To evaluate the algorithm's performance in feature selection, as a practical example, it is employed in text sentiment analysis and examined on various data sets. The simulation results indicate that MBHOA has a better performance in analysing sentiment compared to similar approaches.

Opinion mining of social media reviews using fuzzy inference and intent prediction using deep learning

Sentiment Analysis (SA) or opinion mining has emerged with the quick elevation in internet users, and the increasing power of Social Medias (SM) and also online review sites. SA aids in finding what other people think and comment on. Recently, to capture the sentiment information on the feedback, numerous SA techniques are developed. The major downside of such techniques is lower accuracy and higher training time. This paper proposes a proficient SA technique in SM reviews to trounce these downsides. Originally, utilizing tokenization, lemmatization, Stop Word (SW) removal, together with URL removal, the input dataset is preprocessed. Then, from that preprocessed data, the emoticon and non-emoticon features are extracted. Subsequently, those resulting features are ranked grounded on their meaning. After that, the classification is done by utilizing Bell Shaped Gaussian Kernel Membership Function which is the Enhanced system of Adaptive Neuro Fuzzy Inference System and is called GKMFANFIS. Lastly, for identifying the user's reason behind the feedback, the Emotional Classification (EC) is performed utilizing the Modified Deep Learning Neural Networks (MDLNN). In MDLNN, Elephant Swarms Water Search Optimization (ESWSO) is applied to optimize the weight values. The proposed methodology achieves better performance and efficiency when weighed against prevailing methodologies regarding the accuracy, recall, precision, together with an F-measure. The accuracy of the proposed GKMFANFIS along with ANFIS is 92% and it shows better result when compared with the existing BERT system.

Analyzing user-generated content to improve customer satisfaction at local wine tourism destinations: an analysis of Yelp and TripAdvisor reviews

PurposeSocial media use has increased in recent years, and businesses are looking to capitalize on the plethora of marketing opportunities afforded by this digital shift by paying attention to user-generated content (UGC) posted on review websites. Leveraging UGC can help small businesses gain a competitive advantage over late-adopters. At the same time, there is evidence to suggest that small businesses do not have the time, resources or skill level to properly use social media to create a competitive advantage. This paper aims to explore how wine tourism businesses can analyze consumer feedback on online review websites to evaluate customer perceptions and expectations and generate more effective ways to improve customer satisfaction.Design/methodology/approachThe qualitative method of thematic analysis was used to map out consumer reviews online to assess service satisfaction and dissatisfaction. A total of 848 reviews were gathered and qualitatively analyzed from two online review websites (TripAdvisor.com and Yelp.com) using open and axial coding and thematic analysis.FindingsThe results show that wine consumers are interested in the hedonic aspects of their experience, are most often attracted to wineries as a special outing and focus on factors such as scenery and atmosphere, service quality and products in their reviews. Hence, service and sales personnel have a key opportunity to capitalize on generating better service experiences through social media analysis.Originality/valueThe present study fills a gap by providing a more in-depth, qualitative exploration of the wine consumers’ psychology and experience, including factors such as atmosphere and special occasions. Furthermore, this study uses interpretive, manual coding to pick up on nuanced themes that are often missed by using automated qualitative analysis software or by looking at frequency counts in isolation.

Heightened scrutiny: The unequal impact of online hygiene scores on restaurant reviews

The success of the public display of restaurant hygiene scores has encouraged online review sites to display these scores digitally on their platforms. By investigating 225,252 Yelp reviews toward 1,937 restaurants in Charlotte, North Carolina, we find that while displaying hygiene scores digitally can inform consumers in a way that reduces bias in reviews, it paradoxically can also promote the creation of more reviews that are biased, something we call the cognitive–discursive dilemma. Specifically, after the digital display on Yelp, reviews mentioning hygiene were more in line with scores, indicating an improvement in “accuracy” across reviews in general. Yet, the digital display also led to greater attention to hygiene, leading to lower scores for restaurants of lower social status as measured by price and cuisine type. Our findings thus call for more attention to a broader theoretical implication about the provision of “accurate information” on review sites.

Understanding reviewer characteristics in online reviews via network structural positions.

With the prevalence of online review websites, understanding online reviewer characteristics has become important, as such an understanding provides brand managers with opportunities to segment their markets, target influencers, and develop effective marketing strategies. Nonetheless, past studies have overlooked the role of network structural positions in the characteristics of online reviewers. Accordingly, using data from Yelp websites as samples, this study attempted to explore the differences in reviewer characteristics by network structural positions. The study used multiple data collection and analysis approaches, including web scraping, network analysis, and statistical analysis. The results of this study showed that compared to peripheral reviewers, core reviewers exhibited significantly more photos and brands reviewed and included a higher proportion of early reviewers. The study has significant theoretical and practical implications for researchers and brand managers who are interested in understanding online review markets.

Measuring the Monetary Value of Online Volunteer Work

Online volunteers are a crucial labor force that keeps many for-profit systems afloat (e.g. social media platforms and online review sites). Despite their substantial role in upholding highly valuable technological systems, online volunteers have no way of knowing the value of their work. This paper uses content moderation as a case study and measures its monetary value to make apparent volunteer labor’s value. Using a novel dataset of private logs generated by moderators, we use linear mixed-effect regression and estimate that Reddit moderators worked a minimum of 466 hours per day in 2020. These hours are worth 3.4 million USD based on the median hourly wage for comparable content moderation services in the U.S. We discuss how this information may inform pathways to alleviate the one-sided relationship between technology companies and online volunteers.

What motivates consumers to communicate eWOM: evidence from Tunisian context

ABSTRACT Electronic word of mouth (eWOM) is increasingly important in consumer decision-making. Even though marketers don’t have complete control over eWOM, they still need to understand how it works. Few studies have investigated the antecedents of such behavior in different cultural contexts. This paper proposes a model to explore the factors motivating Tunisian customers to communicate eWOM. The empirical investigation was conducted using a sample of 624 hotel industry customers. The results of the regression analysis show that eWOM is significantly linked to customer satisfaction, which is the result of expectation confirmation. This tendency is moderated by consumers’ altruism and age. Findings also suggests that females are more likely to communicate eWOM than males do. The findings improve the understanding of eWOM in a new cultural context and help Tunisian authorities setting their marketing strategies. In addition, its findings help online review sites attracting and retaining active contributors.

Characterizing Extremely Negative Reviews of Ophthalmologists on Yelp.Com

ABSTRACT Purpose To perform a retrospective characterization of one-star reviews of ophthalmologists on Yelp.com and to increase understanding of patient complaints on Yelp.com. Methods A search was performed for reviews on Yelp.com using the keyword “ophthalmologist” for the top 8 most population-dense metropolitan areas in the United States. One-star reviews were collected and classified as procedural or nonprocedural. Complaints were also categorized as clinical, nonclinical, or both. Clinical complaints cited issues such as complications, reoperations, uncontrolled pain, misdiagnosis, unclear treatment plan, etc. Nonclinical complaints included comments such as physician bedside manner, other staff interpersonal manner, wait time, brevity of appointment time, etc. Results 5,532 total reviews were assessed, of which 477 (9%) one-star reviews were included in the study for analysis. These reviews amounted to 1,120 distinct complaints. 287 (26%) were clinical in nature and 833 (74%) were nonclinical. Technical incompetence or error (50: 4%), unsatisfactory results (46: 4%), and complications (43: 4%) represented the most common clinical complaints while office staff interpersonal manner (182: 16%), wait time (174: 16%), and physician interpersonal manner (141: 13%) were the most common nonclinical complaints. Refractive surgery was the most frequently mentioned subspecialty (89: 8%). Patients undergoing an ophthalmic procedure (surgery, injection, etc.) wrote 64 reviews that resulted in 193 (17%) complaints. Nonprocedural patients wrote 413 reviews that resulted in 927 (83%) complaints. Compared with procedural reviews, nonprocedural reviews were less likely to include a clinical complaint (rate ratio, 0.3: P < .001). Discussion The majority of one-star reviews of ophthalmologists included in this study were nonclinical. Complaints referencing a procedural episode were more likely to include a clinical component in the review. In the era of intense medical consumerism and increased physician and health care institution competition for patient acquisition and retention, the characterization of excessively negative reviews allows identification of potential areas of concern for patients that use online review sites such as Yelp.com.

Developing a Multi-Dimensional Measure of Hotel Brand Customers’ Online Engagement Behaviors to Capture Non-Transactional Value

While the historical focus of customer value research has been on transactional values, it is important to also understand the non-transactional values co-created by customer online engagement behaviors. However, online engagement research in tourism has focused almost exclusively on experience sharing behaviors via online review websites. This research is purposed to develop a multi-dimensional measure of non-transactional values created via online brand community members’ engagement behaviors. Focusing on Marriott Bonvoy Insider Brand Community members, scale development procedures were used to identify four dimensions of non-transactional value: influential-experience value, C-to-B innovation value, relational value, and functional value. The scale achieves good validity and reliability. Relationships with both antecedents (e.g., internal and external motivations) and consequences (e.g., brand attachment and brand loyalty) were examined to assess nomological validity. Tourism researchers and managers can employ this scale to diagnose the non-transactional values co-created by customer online engagement behaviors.

How to handle negative reviews of the practice

Online review and comparison sites, alongside increasing use of social media, has provided patients with many platforms to comment on the care or treatment they have received at the practice. How best to handle negative comments or reviews?

Market Segmentation of Online Reviews: A Network Analysis Approach

With the popularity of online reviews, brand managers have opportunities to segment their markets according to the reviews of their products or services by customers. Nonetheless, it has been suggested that traditional market segmentation methods are ineffective at analyzing online review data due to the complex features and large amount of this type of data; specifically, traditional methods fail to take into account the networked nature of interactive relationships among reviewers and brands across online review websites. Accordingly, this study proposes a network analysis approach for the market segmentation of online reviews. Collecting samples from Yelp via web scraping, this study demonstrates how network analysis techniques can be utilized to segment online reviewers through a four-step process. The results reveal the core and peripheral market segments, as well as the bridge segment in the core. The study contributes to offering marketing researchers and managers a new network structure analysis approach for the market segmentation of online reviews.

Analysis of customer sentiments on review sites toward selected travel companies: A big data approach

In India, the travel industry is expanding quickly, and more people are using technology to make online transactions easier. This has caused technology and trip planning to become increasingly entwined. According to studies conducted all around the world, contemporary travellers need interactive features, superior travel assistance, product details and pertinent details to help them make decisions. Because travellers evaluate their experiences based on the impression they have of a place after their journey, online travel reviews are also a good predictor of a location’s post-visit destination image (DI) (González-Rodrguez et al. 2016; Park et al. 2007). The purpose of this study is to find out customers’ experience using travel review sites that impact the travel industry. The advancements in technology have had a significant impact on the travel and tourism sector, as they have on many other service-based businesses. This research paper also examines Indian clients’ preference, usage and satisfaction level with online review sites. In this study, Airbnb, Goibibo, Oyo, Yatra, MakeMyTrip and Cleartrip customer reviews are analyzed using Wordcloud and sentiment analysis, and therefore, conclusions are made based on that. The study concludes that ill-defined situations caused by travel firms are impacting customer satisfaction levels. However, it is concluded that consumers place less satisfaction on travel sites due to various reasons.

Sentiment Analysis Using Hybrid Approach

Abstract: Most important part of information gathering is to focus on how people think. There are so many opinion resources such as online review sites and personal blogs are available. In this paper we focused on the Twitter. Twitter allow user to express his opinion on variety of entities. We performed sentiment analysis on tweets using Text Mining methods such as Lexicon and Machine Learning Approach. We performed Sentiment Analysis in two steps, first by searching the polarity words from the pool of words that are already predefined in lexicon dictionary and in Second step training the machine learning algorithm using polarities given in the first step. Keywords: Sentiment analysis, Social Media, Twitter, Lexicon Dictionary, Machine Learning Classifiers, SVM.

Dynamic Measurement and Evaluation of Hotel Customer Satisfaction through Sentiment Analysis on Online Reviews

Both researchers and practitioners have attached great importance to the measurement and evaluation of hotel customer satisfaction. However, there are several problems in the dimensions, methods and conclusions. Thus, it is urgent to standardize the theories, methods, and techniques. The purpose of this study is to propose a dynamic measurement and evaluation framework for hotel customer satisfaction through sentiment analysis on online reviews. The framework consists of five steps: (1)The corpus is obtained from online review sites; (2)From the perspective of managers, the useful texts are recognized; (3)Based on the useful texts, a three-layer index system is created; (4)The center term-based short sentence sentimental orientation (CTSSSO) algorithm is developed to compute emotional intensity, then dynamically measure the customer satisfaction; (5)The dynamic important performance competitor analysis (DIPCA) is adopted for dynamic evaluation of customer satisfaction.The feasibility of our framework was demonstrated through a case study on the online reviews of two five-star hotels.

Dynamic Measurement and Evaluation of Hotel Customer Satisfaction Through Sentiment Analysis on Online Reviews

Both researchers and practitioners have attached great importance to the measurement and evaluation of hotel customer satisfaction. However, there are several problems in the dimensions, methods and conclusions. Thus, it is urgent to standardize the theories, methods, and techniques. The purpose of this study is to propose a dynamic measurement and evaluation framework for hotel customer satisfaction through sentiment analysis on online reviews. The framework consists of five steps: (1)The corpus is obtained from online review sites; (2)From the perspective of managers, the useful texts are recognized; (3)Based on the useful texts, a three-layer index system is created; (4)The center term-based short sentence sentimental orientation (CTSSSO) algorithm is developed to compute emotional intensity, then dynamically measure the customer satisfaction; (5)The dynamic important performance competitor analysis (DIPCA) is adopted for dynamic evaluation of customer satisfaction.The feasibility of our framework was demonstrated through a case study on the online reviews of two five-star hotels.

Capitalizing on big data and revolutionary 5G technology: Extracting and visualizing ratings and reviews of global chain hotels

This paper aims to use machine learning (ML) algorithm for natural language pre-processing (NLP), text mining (TM), and sentiment analysis (SA) techniques to analyze and examine 45,500 online reviews of customers of 50 global chain hotels from different online review sites. Furthermore, the paper addresses the new business value and experiences that the revolutionary 5G technology can bring to the hotel industry. The research findings revealed that the general review star rating corresponds with the opinion (sentiment) scores for the title and the full substance of the online reviews. The case study’s contextual analysis also uncovered that both fulfilled and disappointed customers have a frequent inclination for five categories: food, stay, rooms, service, and staff. This study contributes both theoretically and practically to the multidisciplinary domains of computer science, information systems, and tourism and discovers hidden patterns in data using visual analytics techniques.