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1 – 10 of over 9000Daniel Ruiz-Equihua, Luis V. Casaló and Jaime Romero
Previous research into online reviews in the hospitality industry has focused mainly on big companies; thus, it is not yet known whether its findings apply also to small and…
Abstract
Purpose
Previous research into online reviews in the hospitality industry has focused mainly on big companies; thus, it is not yet known whether its findings apply also to small and medium enterprises (SMEs), the most abundant in the sector. Focusing on online reviews in the hospitality sector, this study aims to analyse whether firm size moderates the relationship between online review valence and customer responses.
Design/methodology/approach
This study uses a 2 (positive vs negative online review) × 2 (SME vs big company) experimental research design conducted in two hospitality settings, hotels and restaurants.
Findings
The impact of online reviews on customer responses is less intense for smaller hospitality companies.
Originality/value
This study incorporates firm size as a moderator of the relationship between online review valence and customer responses in two hospitality settings, restaurants and hotels.
研究目的
以往针对酒店业的在线评论研究主要集中在大型企业上, 因此这些研究结果是否也适用于中小企业尚不清楚, 而中小企业在该行业中最为普遍。本研究重点研究了酒店业中的在线评论, 分析了企业规模是否在在线评论极性与客户反应之间的关系中起到调节作用。
研究设计/方法
本研究采用2(正面与负面在线评论)×2(中小企业与大型企业)的实验研究设计, 并在两个实验环境下(酒店和餐饮)进行研究。
研究发现
对规模较小的酒店企业来说, 在线评论对客户反应的影响并不强烈。
研究创新性
本研究将企业规模作为餐厅和酒店行业中在线评论极性和客户反应关系的调节变量。
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Pingye Tian and Qing Yang
Online customer reviews is an important information resource for product innovation. This study aims to investigate the impact of online customer reviews on iterative innovation…
Abstract
Purpose
Online customer reviews is an important information resource for product innovation. This study aims to investigate the impact of online customer reviews on iterative innovation of software products and the moderating roles of product complexity in the process of online reviews influencing product iterative innovation.
Design/methodology/approach
To empirically test the hypotheses, this paper built a panel data of 500 software products from 2019 to 2021 and applied Poisson regression analysis.
Findings
Empirically results reveal that both sentiment and quantity of online customer reviews have positive effects on iteration innovation of software products. In addition, the authors find that product complexity negatively moderates the relationship between online reviews and iterative innovation.
Practical implications
This study suggests that firms can acquire valuable information from customers’ online reviews for product iterative innovation and improvement. However, for high-complexity products, it may be difficult for enterprises to obtain useful information for iterative innovation from online reviews. On the other hand, this study provides a reference for firms to choose more useful online reviews from the perspective of sentiment.
Originality/value
This paper provides a new finding that there is a positive relationship between online customer reviews and iterative innovation of software products. Moreover, the authors also provide a deeper understanding of how online customer reviews affects iterative innovation by examining the moderating roles of product complexity.
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Qiuli Su, Aidin Namin and Seth Ketron
This paper aims to investigate textual characteristics of customer reviews that motivate companies to respond (sentiment negativity and sentiment deviation) and how aspects of…
Abstract
Purpose
This paper aims to investigate textual characteristics of customer reviews that motivate companies to respond (sentiment negativity and sentiment deviation) and how aspects of these company responses (response intensity, length and tailoring) affect subsequent customer review quality (comprehensiveness and readability) over time.
Design/methodology/approach
Leveraging a large data set from a leading app website (Shopify), the authors combine text mining, natural language processing (NLP) and big data analysis to examine the antecedents and outcomes of online company responses to reviews.
Findings
This study finds that companies are more likely to respond to reviews with more negative sentiment and higher sentiment deviation scores. Furthermore, while longer company responses improve review comprehensiveness over time, they do not have a significant influence on review readability; meanwhile, more tailored company responses improve readability but not comprehensiveness over time. In addition, the intensity (volume) of company responses does not affect subsequent review quality in either comprehensiveness or readability.
Originality/value
This paper expands on the understanding of online company responses within the digital marketplace – specifically, apps – and provides a new and broader perspective on the motivations and effects of online company responses to customer reviews. The study also extends beyond the short-term focus of prior works and adds to literature on long-term effects of online company responses to subsequent reviews. The findings provide valuable insights for companies (especially those with apps) to enhance their online communication strategies and customer engagement.
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Liangqiang Li, Boyan Yao, Xi Li and Yu Qian
This 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…
Abstract
Purpose
This 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/approach
To 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.
Findings
First, 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/value
First, 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.
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Studies that investigate the length of stay as a predictor of consumer post-purchase behavior are rare despite its importance in efficient hotel management. By analyzing online…
Abstract
Purpose
Studies that investigate the length of stay as a predictor of consumer post-purchase behavior are rare despite its importance in efficient hotel management. By analyzing online customer reviews, this study aims to fill this gap in the extant literature on the relationship between length of stay and customer satisfaction level.
Design/methodology/approach
The authors collected and used online review data on hotels in London for this study. A series of linear regression analyses were conducted to examine the effect of length of stay on customer satisfaction as measured by review ratings. The authors used the Mahalanobis matching approach to confirm the empirical findings.
Findings
This analysis shows that length of stay is negatively associated with customer satisfaction. Additionally, the authors find that this negative relationship is stronger in high-end hotels than in low-end hotels.
Research limitations/implications
The research findings contribute to the literature by shedding light on a new stream of research, namely, length of stay. Additionally, the research findings offer novel insights that could help hotel managers understand the trade-off between longer stays and customer satisfaction.
Originality/value
To the best of the authors’ knowledge, this is one of the first few studies to show the systematic impact of length of stay on the valence of online review ratings, as well as the moderating effect of hotel levels by analyzing customer online reviews on hotel experiences.
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Salahuddin Ahmed, Sapna Singh and Nagaraj Samala
Online brand is becoming a popular and major gateway for consumers for booking various services specifically when they travel for several purposes. The present study aims to…
Abstract
Purpose
Online brand is becoming a popular and major gateway for consumers for booking various services specifically when they travel for several purposes. The present study aims to explore whether exposure to two separate yet similar modes of communication intervene consumer's brand trust and their subsequent loyalty intention toward the brand. The study further aims to investigate whether consumer's price consciousness has any influence on association between brand trust and brand loyalty in the process of decision -making.
Design/methodology/approach
The present study follows a different approach to data collection. The data have been retrieved from online brand (Oyo) page on Facebook through Google Form application. In all, 289 useable responses were retrieved from the travelers aged between 18 and 30. Structural equation modeling using SPSS 25.0 and Amos 26.0 has been applied to examine the effects of brand communication and online reviews on brand loyalty through brand trust.
Findings
Empirical evidence supports that even after having strong brand communication, online reviews play a crucial role in consumer's brand loyalty through brand trust. The study further reveals that price consciousness acts as a significant moderator in the relationship between consumer's brand trust and brand loyalty.
Practical implications
The current research contributes to the online brand and marketing knowledge by empirically showing the pertinence of consumer–brand relationship in an online brand context through a parsimonious model by examining how the two distinct mechanisms of communication influences consumer brand trust and loyalty intention.
Originality/value
The parsimonious framework of consumer–brand relationship adds to explicating the dual marketing challenges of communication and to draw a positive consumer response (i.e. consumer brand loyalty). The study attempts to examine the impact of two distinct yet identical modes of communication which facilitate shaping consumer brand trust that reinforce the strategic value of the circumstance and equips it with solid theoretical structure within an endeavor of the strategic significance of online brand managers.
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Limei Hu, Chunqia Tan and Hepu Deng
The purpose of this paper is to develop a novel recommendation method using online reviews with emotional preferences for facilitating online purchase decisions. This leads to…
Abstract
Purpose
The purpose of this paper is to develop a novel recommendation method using online reviews with emotional preferences for facilitating online purchase decisions. This leads to better use of information-rich online reviews for providing users with personalized recommendations.
Design/methodology/approach
A novel method is developed for producing personalized recommendations in online purchase decision-making. Such a method fuses the belief structure and the Shapley function together to effectively deal with the emotional preferences in online reviews and adequately tackle the interaction existent between product criteria with the use of a modified combination rule for making better online recommendations for making online purchase decisions.
Findings
An example is presented for demonstrating the applicability of the method for facilitating online purchase. The results show that the recommendation using the proposed method can effectively improve customer satisfaction with better purchase decisions.
Research limitations/implications
The proposed method can better utilize online reviews for satisfying personalized needs of consumers. The use of such a method can optimize interface design, refine customer needs, reduce recommendation errors and provide personalized recommendations.
Originality/value
The proposed method adequately considers the characteristics of online reviews and the personalized needs of customers for providing customers with appropriate recommendations. It can help businesses better manage online reviews for improving customer satisfaction and create greater value for both businesses and customers.
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Mohammad Arief, Rita Indah Mustikowati and Yustina Chrismardani
Digitalization in marketing activities has made it easier for people to make purchase decision. This platform encourages every firm to optimize digitalization as part of its…
Abstract
Purpose
Digitalization in marketing activities has made it easier for people to make purchase decision. This platform encourages every firm to optimize digitalization as part of its marketing strategy. Optimization of attractive digital marketing involves advertising attractiveness, influencer marketing and online customer reviews. This study aims to investigate advertising attractiveness, influencer marketing and online customer reviews on purchase decision.
Design/methodology/approach
The study was conducted with a quantitative approach. A total of 120 respondents were involved in this study by using convenience sampling techniques in data collection. Multiple linear regression was used to analyze the data.
Findings
The results of the study show that influencer marketing and online customer reviews have an impact on online purchase decision. Meanwhile, advertising attractiveness does not show any influence on purchase decision.
Practical implications
Despite the start-ups have modified the website by increasing the content to make it more informative, it seems that customers are not interested in making a purchase. Therefore, notwithstanding the role of website attractiveness, the use of physical attractiveness is still considered an effective way to encourage customers to make purchasing decisions. In this way, a firm needs to make adjustments between the customers' personality, lifestyle and attitudes and endorsers.
Originality/value
This study developed previous empirical studies which a positive relationship between advertising attractiveness, influencer marketing, online customer reviews and purchase decision. The development of the model was carried out by elaborating variable indicators. In addition, the source of increasing credibility was not based on physical attractiveness, but rather emphasizes the website quality.
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Xiaoxiao Qi, Wen Chang, Anyu Liu, Jie Sun and Mengyu Fan
Wine producers and marketing professionals increasingly recognize the significance of online wine reviews. Emotions have long been acknowledged as influential in online review…
Abstract
Purpose
Wine producers and marketing professionals increasingly recognize the significance of online wine reviews. Emotions have long been acknowledged as influential in online review behaviors. However, considering the multisensory nature of the wine experience, consumers’ wine expertise also plays a substantial role. Hence, this study aims to examine the online review behaviors exhibited by wine consumers through the dual lens of wine expertise and emotionality.
Design/methodology/approach
Two studies were conducted to address the research question. Study 1 explored the relationship among expertise, emotionality and review behaviors using a panel data model, with a data set consisting of 4,600,922 reviews from Vivino.com. Study 2 used a multigroup structural equation modeling (SEM) analysis using data obtained from an online survey. Study 2 aimed to investigate the interactive impact of emotionality and expertise on online review intention mediated by customer engagement.
Findings
The findings from Study 1 demonstrated a positive correlation between emotionality and online wine reviews. In addition, expertise displayed a bell-shaped relationship with both emotionality and online wine reviews. Study 2, in turn, uncovered that novices and experts experienced a direct influence of emotionality on their review intentions. In contrast, for those classified as ordinary, the influence of emotionality on review intention occurred indirectly through the mediation of customer engagement.
Originality/value
This paper extends the current literature on online wine review by integrating the effect of emotion and expertise on online wine review behaviors, expanding the examination of Dunning–Kruger effect in the wine literature. It also adds value by introducing emotionality and the Evaluative Lexicon into the hospitality literature, extending the measurement of emotion from valence and extremity to a third dimension, emotionality, in hospitality and wine domains.
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Debarshi Mukherjee, Ranjit Debnath, Subhayan Chakraborty, Lokesh Kumar Jena and Khandakar Kamrul Hasan
Budget hotels are becoming an emerging industry for convenience and affordability, where consumer sentiments are of paramount importance. Tourism has become increasingly dependent…
Abstract
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.
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