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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 feed-forward a neural network enhances organisational, process, and individual-level performance in the hospitality industry based on customer reviews.

In this paper, we aim to provide a comprehensive overview of customer feedback literature, highlighting the burgeoning role of artificial intelligence (AI). Customer feedback has long been a valuable source of customer insights for businesses and market researchers. While previously survey focused, customer feedback in the digital age has evolved to be rich, interactive, multimodal, and virtually real time. Such explosion in feedback content has also been accompanied by a rapid development of AI and machine learning technologies that enable firms to understand and take advantage of these high-velocity data sources. Yet, some of the challenges with traditional surveys remain, such as self-selection concerns of who chooses to participate and what attributes they give feedback on. In addition, these new feedback channels face other unique challenges like review manipulation and herding effects due to their public and democratic nature. Thus, while the AI toolkit has revolutionized the area of customer feedback, extracting meaningful insights requires complementing it with the appropriate social science toolkit. We begin by touching upon conventional customer feedback research and chart its evolution through the years as the nature of available data and analysis tools develop. We conclude by providing recommendations for future questions that remain to be explored in this field.

Consumers have pervasively relied on mobile reviews in digital economy. However, little knowledge exists regarding how customers adopt several mobile reviews to make purchasing decisions. With the assistance of reader-response theory, this study investigates how the consistency of product reviews, in terms of their adherence to both other reviews and the prior experience of the customer, affect perceived quality, confirmation of the customer's expectations, the customer's level of trust in the seller and the consequent purchase intention.

Based on a scenario simulation and an online experiment to collect data, the authors employed AMOS to test the proposed hypotheses using survey data collected from 314 customers in Study 1 and 420 consumers in Study 2.

The results indicate that global consistency positively and significantly contributes to confirmation, perceived quality and trust in sellers while sequential inconsistency positively and significantly influences perceived quality. Meanwhile, purchase intention is positively and significantly promoted by confirmation, perceived quality and trust in sellers, and initial valence has some moderating effects on these relationships.

This study contributes to the understanding of how customers apply product reviews to make purchasing decisions from a new angle. It also elucidates the way in which the perceived consistency of product reviews affects how reviewers are perceived and the consequent effect of these perceptions on a customer's purchase intentions.

This study aims to develop and test an integrative model that examines the effects of customization and perceived employee authenticity on customer delight, which in turn influences customers’ willingness to recommend (WTR) and willingness to pay a premium (WTPP) as outcomes in a hotel context. The moderating role of online review valence in this process is also examined.

This study adopts a 2 (customization: low vs high) × 2 (perceived employee authenticity: low vs high) × 2 (online review valence: negative vs positive) experimental design. A total of 409 US consumers were recruited and randomly assigned to a hotel check-in scenario. Partial least squares structural equation modeling was used to test the hypothesized relationships.

Findings confirmed the role of customer delight in mediating customization and employee authenticity on WTR and WTPP. In addition, perceived employee authenticity was a stronger driver of customer delight for consumers exposed to negative online reviews than for those exposed to positive reviews.

The findings provide useful guidance in designing efficient service strategies for generating a delightful customer experience. Hotel practitioners should provide customized services and manage employees in a way that helps them deliver authentic services that achieve customer delight. Understanding that customer expectations formed through online reviews play a significant role in service evaluations, hotel managers make an extra effort to monitor online reviews and manage customer expectations.

Although existing research suggests that customer delight plays an important role in positive consumer outcomes, there is still potential space to explore the theoretical mediational mechanisms underlying this effect and the moderating effect on this relationship between customer delight and consumer responses. This study contributes by testing the moderating impact of online review valence and the mediating impact of customer delight.

This research aims to examine the factors that influence customers' product return intentions and proposes that YouTube product reviews can mitigate customers' desire to return a product.

The proposed theoretical research model and hypothesized relationship were investigated using a quantitative process. This study used 302 data from Indonesian young adult respondents to examine the structural model, which was analyzed using the SmartPLS 3.2 software package.

The results show that YouTube product reviews, product fit uncertainty and customer satisfaction are the key determinants of customers' product return intention. Furthermore, the results show that the credibility of YouTube product reviews has a major impact on customers' familiarity with a product, satisfaction and the likelihood of returning goods to sellers.

In the e-commerce industry, increasing the use of YouTube product reviews will help businesses eliminate unnecessary product returns. Sellers are also encouraged to collaborate with YouTube producers to review specific products, which can benefit companies by raising brand awareness and gaining customer feedback. Furthermore, YouTube online product reviews can help consumers avoid having an unpleasant shopping experience that causes emotional reactions and lowers satisfaction.

Most research has not considered antecedents in observing the product return phenomenon; this study observes a prerequisite of consumer product returns (i.e. information asymmetry and product familiarity) and investigates the relationships between YouTube product reviews, customer satisfaction and product return intention.

The number of children using and owning mobile devices has grown significantly in the last decade. By applying a uses and gratifications approach, this paper aims to explore what customers of a tablet computer for children report about the use and expectations of these devices from an information behavior perspective.

For this study, 1,185 online customer reviews published for two different versions of the Amazon Fire Tablet Kids Edition on the German Amazon website between June 16, 2019, and June 15, 2020, were analyzed. A content analysis of the reviews was conducted using different inductive coding methods.

Findings indicate that customers describe different aspects of children's use and families' expectations of tablets within their reviews. The expressed gratifications mostly relate to the aim of entertainment. Intentional information seeking activities were hardly mentioned within the reviews, but many customers emphasize learning as an important activity with the devices. Overall, the customer reviews reveal a mix of gratifications that differ from reported motivations for adults' tablet use.

The possibility of manipulated online customer reviews must be considered. It should also be viewed critically that the children's perspectives are only indirectly included in the data.

Families' expectations of tablets as a device for children have not been a focus of research to date. This study uses an innovative research design by applying a uses and gratifications approach to online customer reviews for children's tablets. The findings add to previous research on children's use and families' expectations of tablets and contribute to our understanding of children's information behavior in connection with mobile devices.

Social 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.

The 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.

The 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.

The 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.

Repeat customers play an important role in the restaurant sector. Previous studies have confirmed the positive effect of managerial responses on customer relationship management. However, the practice of managerial response strategies toward repeat customers in the restaurant sector remains unclear. This study aims to explore how social influence and the revisit intention of customers affect the responding behavior of restaurant managers.

This study collects information of 251,944 customer reviews and managerial responses from 1,272 restaurants on Yelp (a leading restaurant review website around the world) and builds four econometric models (with restaurant and month fixed effects) to test the hypotheses empirically.

The empirical results show that restaurant managers are less likely to respond to reviews posted by repeat customers (10% lower than that of new customers). This effect is moderated by customer social influence, which entails that repeat customers with great social influence are more likely to receive managerial responses. Moreover, reviews from repeat customers who have had a longer time since their last consumption are also more likely to receive managerial responses.

The results present implications for restaurant managers in business practice regarding managerial response. Managers should take advantage of platform designs and tools (i.e. customer relationship management programs to keep track of repeat customers) to locate repeat customers and avoid the potential negative effects caused by their selected response strategies.

To the best of the authors’ knowledge, this study is among the first attempts to examine empirically how restaurant managers respond to reviews generated by repeat customers in real business practice and reveals what drives such activities from the perspectives of social influence and revisit intention.

This methodological paper demonstrates how service firms can use digital technologies to quantify and predict customer evaluations of their interactions with the firm using unstructured, qualitative data. To harness the power of unstructured data and enhance the customer-firm relationship, the use of computerized text analysis is proposed.

Three empirical studies were conducted to exemplify the use of the computerized text analysis tool. A secondary data analysis of online customer reviews (n = 2,878) in a service industry was used. LIWC was used to conduct the text analysis, and thereafter SPSS was used to examine the predictive capability of the model for the evaluation of customer-firm interactions.

A lexical analysis of online customer reviews was able to predict evaluations of customer-firm interactions across the three empirical studies. The authenticity and emotional tone present in the reviews served as the best predictors of customer evaluations of their service interactions with the firm.

Computerized text analysis is an inexpensive digital tool which, to date, has been sparsely used to analyze customer-firm interactions based on customers' online reviews. From a methodological perspective, the use of this tool to gain insights from unstructured data provides the ability to gain an understanding of customers' real-time evaluations of their service interactions with a firm without collecting primary data.

This research contributes to the growing body of knowledge regarding the use of computerized lexical analysis to assess unstructured, online customer reviews to predict customers' evaluations of a service interaction. The results offer service firms an inexpensive and user-friendly methodology to assess real-time, readily available reviews, complementing traditional customer research. A tool has been used to transform unstructured data into a numerical format, quantifying customer evaluations of service interactions.

Prior hospitality studies have reviewed review trustworthiness and perceived price as predictors of restaurant selection. However, the impacts of these two factors may vary by sales promotion and customer types. This study aims to determine whether sales promotions and customer type are the key elements that facilitate behavioral intentions by moderating the linkage between perceived price and behavioral intentions as well as the linkage between online review trustworthiness and behavioral intentions.

Analysis of the responses of 533 individuals familiar with the Michelin Guide for restaurants in Seoul provided evidence supporting a sales promotion theory wherein promotions signal benefits in consumers’ minds.

The findings show that when perceived price is positive and the trustworthiness of online reviews is high, repeat customers prefer mixed coupons to price discounts. Notably, the results indicate that when the trustworthiness of online reviews is high, first-time customers also prefer mixed coupons to price discounts. Furthermore, the findings suggest that negative evaluations of perceived price increase the impact of mixed coupons by signaling to first-time customers that given restaurants’ offerings provide monetary benefits regardless of their intentions to revisit said restaurants.

The study findings provide insights that should help managers better understand various levels of promotion. Managers can design their pricing strategies to strengthen customers’ motivations to visit their restaurants – the very thing customers often seek in sales promotions.

This study provides indisputable evidence for a sales promotion theory, wherein promotions signal benefits in consumers’ minds; however, it also shows that first-time and repeat customers do not respond equally to sales promotions.