Unraveling the interplay of review depth,review breadth,and review language style on review usefulness and review adoption

To examine whether long reviews are uniformly more useful to readers, this study utilizes the experimental design approach to investigate the interplay of review depth, review breadth, and review language style on readers’ perceived usefulness and adoption intention of online hotel reviews. Drawing on the results from two experiments, review depth is proven to exert a positive impact on readers’ review usefulness and adoption intention. The moderating role of review breadth is verified, and the positive effect of review depth on review usefulness is attenuated (accentuated) when review breadth is high (low). The moderating role of review language style is also confirmed, and the result shows longer reviews written in literal language are considered more useful to readers than those written in figurative language. Being one of the first studies examining the interplay of review content and review style on review usefulness, this study provides important theoretical and practical contributions.     

Comparative study of deep learning models for analyzing online restaurant reviews in the era of the COVID-19 pandemic

Online reviews remain important during the COVID-19 pandemic as they help customers make safe dining decisions. To help restaurants better understand customers’ needs and sustain their business under current circumstance, this study extracts restaurant features that are cared for by customers in current circumstance. This study also introduces deep learning methods to examine customers’ opinions about restaurant features and to detect reviews with mismatched ratings. By analyzing 112,412 restaurant reviews posted during January-June 2020 on Yelp.com, four frequently mentioned restaurant features (e.g., service, food, place, and experience) along with their associated sentiment scores were identified. Findings also show that deep learning algorithms (i.e., Bidirectional LSTM and Simple Embedding + Average Pooling) outperform traditional machine learning algorithms in sentiment classification and review rating prediction. This study strengthens the extant literature by empirically analyzing restaurant reviews posted during the COVID-19 pandemic and discovering suitable deep learning algorithms for different text mining tasks.     

Deep learning for modeling the collection rate for third-party buyers

This study evaluates a wide range of machine learning techniques such as deep learning, boosting, and support vector regression to predict the collection rate of more than 65,000 defaulted consumer credits from the telecommunications sector that were bought by a German third-party company. Weighted performance measures were defined based on the value of exposure at default for comparing collection rate models. The approach proposed in this paper is useful for a third-party company in managing the risk of a portfolio of defaulted credit that it purchases. The main finding is that one of the machine learning models we investigate, the deep learning model, performs significantly better out-of-sample than all other methods that can be used by an acquirer of defaulted credits based on weighted-performance measures. By using unweighted performance measures, deep learning and boosting perform similarly. Moreover, we find that using a training set with a larger proportion of the dataset does not improve prediction accuracy significantly when deep learning is used. The general conclusion is that deep learning is a potentially performance-enhancing tool for credit risk management.     

Conditional Effects of Touch on Purchase Behavior

Interpersonal touch has been shown to affect consumer behaviors such as compliance with a request, impulse buying, and tipping behavior. In this study, we examine if the impact of touch on purchase behavior is gender specific, and if it depends on how much the individual likes the product. Findings indicate male consumers are more likely to purchase a product at low to moderate levels of perceived tastiness when they are touched by a female server, whereas females are less likely to purchase. However, the touch encounter doesn’t matter for either gender when a consumer really likes the product.     

Dodo Lé Là: how consumers promote a local iconic brand in postcolonial creole culture

ABSTRACT

This videography shows how consumers in the Reunion Island (France) promote a local Dodo beer towards an iconic status through their identity work. An alternative approach to Holt’s theorising on iconic brands is taken on two levels. First, the videography contributes by offering a non-American, postcolonial and creole aspect of a brand myth-making, as well as the ‘promotion’ of the brand by the local consumers and multi-ethnic community. Second, the consumers’ voice in citing the brand is examined (Nakassis, 2012. American Anthropologist, 114(4), 624–638.). Based on the findings, the citing of the brand happens in two different ways: when including it into personalised identity narratives and when producing new brand tokens, thus nurturing the brand ontology further.     

The benefits and implementation of performance transparency: The why and how of letting your customers ‘see through’ your business

While some organizations swear by the benefits of transparency and are eager to learn and implement transparency practices, many managers are still reluctant or even afraid to use them. Our research reveals that only a few innovative companies have taken steps to leverage a potentially useful form of transparency: the provision of accessible and objective information to customers (e.g., sharing unbiased benchmark data, publishing unfiltered customer comments, or providing candid product reviews that may praise but also criticize the company’s products). Our study also shows that many companies remain wary and view greater calls for transparency as a challenge to be managed rather than an opportunity to be traded upon. This is partly due to limited research into the performance benefits of giving customers access to objective information, and lack of practical guidelines on how to actually implement it. This article addresses these shortcomings. First, we investigate whether performance transparency leads to customer outcomes that can be profitable for an organization and, second, we analyze the characteristics of successful transparency initiatives in a wide range of industries. Our research shows that customers exhibit more trust and are willing to pay a premium to deal with transparent businesses. Also, it uncovers seven effective strategies to leverage transparency. This article provides convincing empirical evidence for the benefits of performance transparency and the ways in which management may implement it successfully.     

In search of diners responsive to health cues: Insights from U.S. consumers

Many consumers rely on summary statistics offered by online and mobile platforms to attain nutrition information on menu items. While positive (vs. negative) summary cues garner more favorable consumer responses, this research provides a more nuanced understanding of this effect by examining the moderating role of two individual-level characteristics: health-consciousness and need for status. Two MTurk samples (Study 1: n = 142, Study 2: n = 119) were recruited in 2018. Findings from Study 1 suggest that health-conscious (vs. less health-conscious) consumers show a wider gap in perceived healthiness of a menu item in response to the valence of a health cue. This greater gap translates into more polarized attitudes toward the menu item. Findings from Study 2 indicate that consumers high (vs. low) in need for status exhibit less favorable attitudes toward a menu item with a negative health cue. Theoretical and managerial implications are discussed.     

When empathy prevents negative reviewing behavior

Previous research has found that peer-to-peer platforms have overly positive reviews. Guided by Construal Level Theory, this research investigates the relationship between social distance, empathy, and tourists’ intention to leave negative online reviews. The first study is a qualitative analysis which compares peer-to-peer settings (i.e., Airbnb) to institutional ones (i.e., Booking.com), and explores whether social closeness hinders tourists’ willingness to provide negative online reviews to express their poor experiences. The second and third study are laboratory studies which show that the mechanism behind reviewing biases is the activation of empathy.This research offers practical implications for both traditional hospitality players, on how to activate empathy, and online platforms operators, on how to increase the reliability of their reputation systems.This article also launches the Annals of Tourism Research Curated Collection on Peer-to-peer accommodation networks, a special selection of research in this field.     

The effects of consumer empowerment on risk perception and satisfaction with food consumption

This study intends to measure consumer empowerment related to food consumption and determine its effect on food risk perception and satisfaction with food consumption. Data were derived from an analysis of the “2017 Research on Food Consumption Behaviour” by the Korea Rural Economic Institute. Consumer empowerment, risk perception and satisfaction with food consumption were measured using a 5‐point Likert scale. The differences in consumer empowerment according to gender, age, educational level and monthly income were statistically significant. The factors that affected risk perception were information use, healthy eating, consumer rights, consumer responsibility and monthly income. The higher the scores for information use, healthy eating and consumer responsibility, the higher the score for satisfaction with food consumption.