Customer Feedback Research Papers - Academia.edu (original) (raw)

Dealing with the ever-growing information overload in the Internet, Recommender Systems are widely used online to suggest potential customers item they may like or find useful. Collaborative Filtering is the most popular techniques for Recommender Systems which collects opinions from customers in the form of ratings on items, services or service providers. In addition to the customer rating about a service provider, there is also a good number of online customer feedback information available over the Internet as customer reviews, comments, newsgroups post, discussion forums or blogs which is collectively called user generated contents. This information can be used to generate the public reputation of the service providers'. To do this, data mining techniques, specially recently emerged opinion mining could be a useful tool. In this paper we present a state of the art review of Opinion Mining from online customer feedback. We critically evaluate the existing work and expose cutting edge area of interest in opinion mining. We also classify the approaches taken by different researchers into several categories and sub-categories. Each of those steps is analyzed with their strength and limitations in this paper.

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- Customer Feedback, Sentiment Analysis and Opinion Mining

t The aim of the study was to present a new business model of an investment recommender system using customer investment service feedback based on fuzzy neural inference solutions and customized investment services. The model designed to support the system’s process in investment companies. The type of research was qualitative and used of exploratory study and extensive library research. The model divided into two main parts using customer investment service feedback: data analysis and decision making. In this model, seven group factors proposed to implement the model of the proposed system of investment jobs through the potential investors. Machine learning use in this process and next ANFIS, which is an implementation of the neural art community uses the establishment of fuzzy logic judgment directly forward. The system act like a system consultant, studies the investor's past behavior and recommends relevant and accurate recommendations to the user for most appropriate invest...

- by Andrea Kő
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- Business, Cognitive Science, Computer Science, Management Information Systems
- by Patrick Vermeulen
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- Business, Mediation, Customer Feedback, Exporting

Our study seeks to examine the bi-directional relationship between innovation and exporting in four countries in Sub-Saharan Africa. We hypothesize that there is a positive relationship between innovation and subsequent exporting, and that this relationship is mediated by market creation. We also hypothesize that there is a positive relationship between exporting and subsequent innovation, with customer feedback mediating this relation. We analyze firm-level data from a repeated cross-sectional survey design from the 2006/07 and 2013 World Bank Enterprise Surveys and 2013 Innovation Follow-up survey. Our results show that the relation between innovation and subsequent exporting is positive and significant. However, we find a positive but non-significant relation between exporting and subsequent innovation. These relations broadly nuance a bi-directional relationship between innovation and exporting. Furthermore, we find that market creation significantly mediates about 32.5% of the ...

- by Patrick Vermeulen
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- Business, Mediation, Customer Feedback, Exporting

Purpose This study aims to propose a data-driven approach, based on open-source tools, that makes it possible to understand customer satisfaction of the accommodation offer of a whole country. Design/methodology/approach The method starts by extracting information from all hotels of Portugal available at TripAdvisor through Web scraping. Then, a support vector machine is adopted for modeling the TripAdvisor score, which is considered a proxy of customer satisfaction. Finally, knowledge extraction from the model is achieved using sensitivity analysis to unveil the influence of features on the score. Findings The model of the TripAdvisor score achieved a mean absolute percentage error of around 5 per cent, proving the value of modeling the extracted data. The number of rooms of the unit and the minimum price are the two most relevant features, showing that customers appreciate smaller and more expensive units, whereas the location of the hotel does not hold significant relevance. Orig...

- by Fernando Batista
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- Computer Science, Tourism, Analytics, Accommodation
- by Edmund Otaigbe
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- Business, Marketing, Co Creation, Customer Satisfaction
- by Paulo Rita
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- Computer Science, Sentiment Analysis, Data Science, Scopus
- by Kenton Walker
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- Business, Service Design, Accounting, Management Accounting

This article aims to (1) identify guests’ memorable experiences based on reviews posted on TripAdvisor, (2) identify the differences in memorable experiences due to hotel location, evaluation on TripAdvisor and consumer sentiment. The study used quantitative methods: text mining, topic modelling, and sentiment analysis. All reviews (n = 34,992) for all Warsaw hotels included on TripAdvisor (N = 99) were analysed. Seven topics of memorable experiences were identified via Latent Dirichlet Allocation analysis: five were very positive and two very negative. The content analysis of those topics allowed us to extract six positive factors which include: (1) view from the floor, (2) hotel staff and service, (3) breakfast and restaurant, (4) location and atmosphere, (5) pool, lounge, gym and spa, (6) price (value for money), as well as two negative factors: (1) noise at night, (2) booking and check-in. In addition, it was found that memorable hotel experiences differ depending on the locatio...

- by Marek Nowacki
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polarity and recency are found to have an inverse relationship with the helpfulness of eWOM. Thus, our study reports that review, hotel and reviewer characteristics impact eWOM helpfulness in different ways. This study is summarized with... more

- by Journal of Information Technology Management and +1
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- Ewom
- by Wolfgang Scholl
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- Engineering, Communication, Knowledge Management, Social Support
- by Kim Jansson
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- Business, Computer Science, Co Creation, New Product Development

Budget hotels are becoming an emerging industry for convenience and affordability, where consumer sentiments are of paramount importance. Tourism has become increasingly dependent on social media and online platforms to gather travel-related information, purchase travel products, food, lodging, etc., and share views and experiences. The user-generated data helps companies make informed decisions through predictive and behavioural analytics.
Design/Methodology/Approach: This study uses text mining, deep learning, and machine learning techniques for data collection and sentiment analysis based on 117,151 online reviews of the customers posted on the TripAdvisor website from May 2004 to May 2019 from 197 hotels of five prominent budget hotel groups spread across India using Feedforward Neural Network along with Keras package and Softmax activation function.
Findings: The word-of-mouth turns into electronic word-of-mouth through
social networking sites, with easy access to information that enables customers to pick a budget hotel. We identified 20 widely used words that most customers use in their reviews, which can help managers optimise operational efficiency by boosting consumer acceptability, satisfaction, positive experiences, and overcoming negative consumer perceptions.
Practical Implications: The analysis of the review patterns is based on real-time data, which is helpful to understand the customer’s requirements, particularly for budget hotels.
Originality/Value: We analysed TripAdvisor reviews posted over the last 16
years, excluding the Corona period due to industry crises. The findings reverberate in consonance with the performance improvement theory, which states feedforward a neural network enhances organisational, process, and individual-level performance in the hospitality industry based on customer reviews.

- by Debarshi Mukherjee's lab and +2
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- Artificial Intelligence, Deep Learning, Online Consumer Reviews, Text-mining

Purpose – Front-line employee (FLE) well-being is an under-researched field. Contrasting the prevailing view that Positive Customer Feedback (PCF) can only have ‘positive’ impacts, this study aims to answer the counterintuitive question: Could the apparently positive construct ‘Positive Customer Feedback’ have a negative impact on the well-being of front-line employees? Consequently, working within the Transformative Service Research (TSR) framework, we investigate whether PCF can negatively affect the eudaimonic and hedonic well-being dimensions of FLEs, thus decreasing their overall psychological well-being level. Design/methodology/approach – A multidisciplinary literature review was conducted, particularly in the social psychology, human resources and organizational behavior fields, to examine the potential negative impacts of PCF. Subsequently, an exploratory qualitative study consisting of seven focus groups with 45 FLEs and 22 in-depth interviews with managers working across ...

- by Jamie Burton
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- Marketing, Psychology, Social Psychology, Happiness
- by Aman Gupta
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- Business, Systems Engineering, Service Quality, Operations and Supply Chain Management
- by Aman Gupta
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- Business, Systems Engineering, Service Quality, Operations and Supply Chain Management
- by Aman Gupta
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- Business, Statistical Analysis, Aviation, Urban And Regional Planning
- by Luis Carrero Florez
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- Philosophy, Humanities, Knowledge Management, Quality Management
- by Aman Gupta
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- Business, Statistical Analysis, Aviation, Urban And Regional Planning

Nowadays, more than ever, customers have access to other consumers’ digital evaluations concerning the products or services that they have consumed. The use of online review websites, by the potential digital consumers, makes them aware of the choices they have. This, enables them to make comparisons between all the available products or services. However, the big volume of the opinionative data that is produced continuously, creates difficulties when being analyzed by stakeholders, mostly due to human’s physical or mental restrictions. In this research, web scraping combined with an aspect-level sentiment analysis using the corpus-based technique, approached methodologically the problem, by identifying not only the relevant information, but also the particular expressions and phrases that the reviewers use over the Internet. The purpose is to recommend a corpus-based, sentiment analysis web system for detecting and quantifying customers’ opinions which are written in Greek language...

- by Theodore Tsiligiridis
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With the aim of enhancing their online reputation, several hospitality businesses have started soliciting their guests to write online reviews. Available studies have not yet evaluated the effects of this strategy. To fill this knowledge gap, this study draws on the theory of psychological reactance and investigates guests’ attitudinal and behavioral reactions to received solicitations. Evidence collected from a sample of Italian travelers indicates that soliciting reviews has both benefits and drawbacks: It increases the number of reviews for the business, but it also irritates a significant share of guests. Particularly high levels of irritation arise when a business explicitly asks its guests to write positive reviews. The implications of these findings for the reputation management strategy of hospitality businesses are discussed.

- by Francesca Magno
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- Business, Communication, Business Intelligence, Consumer Behaviour in Hospitality

In the feedbacks requested from the consumers within their own consciousness, it is seen that they can exhibit a different and more positive attitude towards a product or service. However, online feedback, which is optionally added by the consumer, is evaluated as a potential tool for understanding customers correctly. It is thought that examining the consumer feedbacks to the applications in the stores that provide applications to smartphones is important for determining the strategies of both these initiatives and the competitors that will take these initiatives as an example. In this context, the aim of the study is to examine the customer comments made to the Netlix application via the Google Play Store with machine learning-based sentiment analysis. In the classiication of sentiments, support vector machines (SVM), Naïve Bayes (NB) and decision trees (DT) from machine learning algorithms were used as methods. As a result of the study, sentiments were classiied with an accuracy value of 84.4% and the highest success performance was achieved with the DT method. It is possible that the results obtained in the study and the comments made on
the Google Play Store can be used as a decision argument in determining the strategies of mobile enterprises and platforms. In addition, the path followed in this study will help digital platforms make quick decisions based on customer feedback.

- by Murat Fatih TUNA
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- Machine Learning, Netflix, Customer Feedback, Google Play
- by Caitlin Bent
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- Computer Science, Advice, Behavioural Change, Smart Metering
- by Sérgio Moro
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- Marketing, History, Social Sciences, Tourism
- by Sérgio Moro
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- Marketing, Computer Science, Sentiment Analysis, Text Mining
- by Sérgio Moro
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- Computer Science, Data Mining, Las Vegas, Knowledge Extraction

- by Paulo Rita
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- Tourism
- by Paulo Rita
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- Computer Science, Data Mining, Las Vegas, Knowledge Extraction
- by Paulo Rita
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- Marketing, Computer Science, Sentiment Analysis, Text Mining

Purpose This study aims to present a very recent literature review on tourism demand forecasting based on 50 relevant articles published between 2013 and June 2016. Design/methodology/approach For searching the literature, the 50 most relevant articles according to Google Scholar ranking were selected and collected. Then, each of the articles were scrutinized according to three main dimensions: the method or technique used for analyzing data; the location of the study; and the covered timeframe. Findings The most widely used modeling technique continues to be time series, confirming a trend identified prior to 2011. Nevertheless, artificial intelligence techniques, and most notably neural networks, are clearly becoming more used in recent years for tourism forecasting. This is a relevant subject for journals related to other social sciences, such as Economics, and also tourism data constitute an excellent source for developing novel modeling techniques. Originality/value The present...

- by Paulo Rita
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- Business, Tourism, Commercial Services

Complexity surrounding the holistic nature of customer experience has made measuring customer perceptions of interactive service experiences challenging. At the same time, advances in technology and changes in methods for collecting explicit customer feedback are generating increasing volumes of unstructured textual data, making it difficult for managers to analyze and interpret this information. Consequently, text mining, a method enabling automatic extraction of information from textual data, is gaining in popularity. However, this method has performed below expectations in terms of depth of analysis of customer experience feedback and accuracy. In this study, we advance linguistics-based text mining modeling to inform the process of developing an improved framework. The proposed framework incorporates important elements of customer experience, service methodologies, and theories such as cocreation processes, interactions, and context. This more holistic approach for analyzing fee...

- by Mohamed Zaki
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- Marketing, Computer Science, Services Marketing and Management, Case Study Research

- by Agnis Stibe
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- Information Systems, Recognition, Social Media, Persuasive Uses of Technology

Service blueprints are usually included in listings of standard methods within service design. Still; little research has been conducted on service blueprints. The case study at hand explores how blueprints can be applied in a situation with three key actors; all with different motives and wishes. The case study is within the domain of car parking; a service which at a first glance may seem simple; but is rather complex when scrutinized. Three ways of blueprinting the situation are presented and discussed in the paper. Finally issues which arose from the blueprinting process are discussed in regard to implications for people creating blueprints.

- by Thomas Wreiner
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- Engineering, Service Design
- by Raffaele Filieri
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- Information Systems, Psychology, Cognitive Science, Computer Science

This study uses an organizational psychology lens to gain a fundamental understanding of how Agile Practices positively influence socio-psychological mechanisms that lead to a successful project. The theoretical framework of this study is based on a causal model of teamwork derived from innovation research with the major constructs of coordination capability and knowledge growth. We will investigate the impact of Agile Practices on these constructs through well-researched teamwork variables such as goal commitment and social support, and by the new constructs adaptivity and open communication. We will then determine the impact of these constructs on project performance. In addition, we will analyze the moderating effect of team autonomy on the relationship between coordination capability and project performance. The quantitative field study will be conducted in Nov 2008 and targets a sample size of 60 agile projects.

- by Chaehan So
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- Engineering, Communication, Social Support, Teamwork

In the hospitality industry, majority of the guests depend upon online reviews while choosing the hotels due to intangible services and risks associated with them. So, it is necessary to analyze the online reviews to understand the level of customers’ satisfaction and their experiences to improve the services. Moreover, consumer-generated reviews have an economic impact on the hospitality industry. The purpose of this paper is to identify the positive and negative determinants of agritourists’ experience by using text mining analysis. A total of 2566 online reviews of agri-hotels reviews were collected from 16 agri-hotels in India, which are listed on tripadvisor.com by using web-crawler developed in Python and NVivo 12, qualitative analysis software, was used to identify the determinants of agritourists’ experience. R Software was used to extract the technical features of user-generated content. The results of our analysis reveal a set of important insights about the drivers of gue...

- by Mohan Palani
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- Business

Online reviews have become increasingly popular as a way to judge the quality of various products and services. Previ- ous work has demonstrated that contradictory reporting and underlying user biases make judging the true worth of a ser- vice di-cult. In this paper, we investigate underlying factors that in∞uence user behavior when reporting feedback. We look at two sources of information besides numerical ratings: linguistic evidence from the textual comment accompanying a review, and patterns in the time sequence of reports. We flrst show that groups of users who amply discuss a certain feature are more likely to agree on a common rating for that feature. Second, we show that a user's rating partly re∞ects the difierence between true quality and prior expectation of quality as inferred from previous reviews. Both give us a less noisy way to produce rating estimates and reveal the reasons behind user bias. Our hypotheses were validated by statistical evidence from hotel review...

- by Arjun Rama Talwar
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- User Behavior
- by Jamie Burton
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- Marketing, Service, Commercial Services
- by m zaki
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- Text Mining, Value Creation, Customer Feedback

Websites like Tripadvisor, where people share their hospitality and tourism opinions generally have more than 2 million reviews and keep getting updated within minutes. They have the potential to create a great impact on new customers' sentiments. Therefore, we have developed a model for the tourism industry registered on social network platform Tripadvisor and analyzed the reviews of customers through sentiment analysis using Natural Language Processing and Machine Learning methods. We have also suggested marketing strategies to improve and strengthen the relationship of tourism companies with customers. We have extracted thousands of customer reviews about the industry on Tripadvisor using web scraping software Octoparse.

- by IOSR Journals
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Purpose Airbnb Experiences is a new type of service launched by Airbnb in November 2016, where users can offer travellers a wide range of activities. This study devotes attention to analysing customer feedback expressed in online reviews published in Airbnb to evaluate those experiences. Design/methodology/approach A total of 1,110 reviews were collected from 12 categories, including 111 experiences, resulting in 10 reviews per experience. First, the sentiment score was computed based on the text of the reviews. Second, 17 quantitative features encompassing user, Airbnb experience and review information were used to model the score through a support vector machine. Third, a sensitivity analysis was performed to extract knowledge on the most relevant features influencing the sentiment score. Findings Tourists writing online reviews are not only influenced by their tourist experience but also by their own online experience with the booking and online review platform. The number of rev...

- by Sérgio Moro
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- Tourism
- by Sérgio Moro
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- Tourism

- by Sérgio Moro
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- Tourism, Commercial Services

Brand choice is the biased, mindful and behavioural tendency which direct consumer's predisposition toward a brand. Brand choice is a fundamental element for all marketing strategies. Strong brand choice is the founding block for brand loyalty. Brand loyalty is one of the key drivers of top-line growth. T he committed loyal customers become evangelists for the brand. T he brands are increasingly finding it difficult to retain the customers in the era of the brand deluge. T he brand success cannot be achieved without achieving brand loyalty. T he loyal customers stay with their preferred brand as long as the brand continues to deliver its superior value proposition. Marketers and academics always interested to measure brand choice and brand loyalty. Marketing models are developed to measure both constructs. T his paper tests brand choice through the expectancy-value model and brand loyalty score is measured by using this Colombo-Morrison brand-loyalty model. T his research is carried out in the smartphone segment in India. Smartphone segment dominated the total handset market by holding a 50% market share during Q3 2018. Top five brands captured 77% share of the total smartphone market during the quarter. In the smartphone segment, Xiaomi leads the market by having a 27 % market share in Q3 of 2018. T his paper concludes by developing precise brand choice score and brand loyalty score. Keywords: Brand Choice, Brand Loyalty, Expectancy-value Colombo-Morrison model, Preference behavior model 1. Introducti on Brand choice is a fundamental element for all marketing strategies. Brand choice is the biased, mindful and behavioral tendency which direct consumer's predisposition toward a brand. Strong brand choice is the founding block for brand loyalty. Brand loyalty is one of the key drivers of top-line growth. The committed loyal customers become evangelists for the brand. The brands are increasingly finding it difficult to retain the customers in the era of the brand deluge. Marketers are interested in building brand loyalty for their brands to retain their customers. Brand loyal customers do not switch brands for offers and discounts offered by a competitor brand. Brand loyal customers are less price-sensitive. The brand success cannot be achieved without achieving brand loyalty. The loyal customers stay with their preferred brand as long as the brand continues to deliver its superior value proposition in the market. Sometimes the brand loyalty results into habitual buying brand choice and the brand will be placed in the long-term memory of the customer. Smartphone segment dominated the total handset market by holding a 50% market share during Q3 2018. Market share of 77% share held by top 5 players in the smartphone market during the quarter. In the smartphone segment, Xiaomi leads the market by having a 27 % market share in Q3 of 2018.

- by prasanna Mohan Raj
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- Consumer Behavior, Customer Loyalty, Marketing Mix Modeling, Brand Preference
- by Chong Ho Yu
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The purpose of this study is to determine and examine Internet marketing strategies that can be implemented by medical doctors/practitioners in Malaysia. Despite the increasing importance among many organizations of the Internet as a marketing tool, few studies have been conducted to examine the potential of the Internet as a marketing platform for medical doctors in the Malaysian context. The results reveal that compared to offline advertising, Internet marketing is a better mechanism and an important strategy for medical doctors. In this work, the advantages of using Internet marketing are highlighted, and implications for marketing strategy are demonstrated. We believe that Internet marketing can help Malaysian doctors improve their customer service and performance.

- by Chang Ling
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The relationship between productivity and customer satisfaction is more complex. Service Quality and Productivity Management describes that the quality and productivity are twin paths in creating value for both customers and organizations. The relationships between service quality, productivity and profitability will also be examined in detail in this book. This book is the 12th book in the Winning in Service Markets series by services marketing expert Jochen Wirtz to cover the key aspects of services marketing and management based on sound academic evidence and knowledge.