scholarly journals Featuring Mistakes: The Persuasive Impact of Purchase Mistakes in Online Reviews

2019 ◽  
Vol 84 (1) ◽  
pp. 52-65 ◽  
Author(s):  
Taly Reich ◽  
Sam J. Maglio
Keyword(s):  
Field Data ◽  
Threshold Level ◽  
Online Reviews ◽  
Consumer Reviews ◽  
Expected Performance ◽  
Promotional Materials ◽  
Feature Positive

Companies often feature positive consumer reviews on their websites and in their promotional materials in an attempt to increase sales. However, little is known about which particular positive reviews companies should leverage to optimize sales. Across four lab studies involving both hypothetical and real choices as well as field data from a retailer’s website (Sephora), the authors find that consumers are more likely to purchase a product if it is recommended by a reviewer who has (vs. has not) made a prior purchase mistake. The authors define a purchase mistake as a self-identified suboptimal decision whereby people purchase a product that subsequently fails to meet a threshold level of expected performance. This persuasive advantage emerges because consumers perceive reviewers who admit a purchase mistake as having more expertise than even reviewers whose purchase experience has not been marred by mistakes. As a result, in marketers’ attempts to increase the persuasive influence of reviews featured in their promotional materials, they may inadvertently decrease it by omitting the very information that would lead consumers to be more likely to purchase recommended products.

EXPRESS: The Upside of Negative: Social Distance in Online Reviews of Identity-Relevant Brands

2022 ◽  
pp. 002224292210747
Author(s):  
Nailya Ordabayeva ◽  
Lisa A. Cavanaugh ◽  
Darren W. Dahl
Keyword(s):  
Social Distance ◽  
Field Data ◽  
Online Reviews ◽  
Conventional Wisdom ◽  
Theory And Practice ◽  
Positive Outcomes ◽  
Marketing Theory ◽  
Brand Preference ◽  
Underlying Process

Conventional wisdom in marketing emphasizes the detrimental effects of negative online reviews for brands. An important question is whether some firms could more effectively manage negative reviews to improve brand preference and outcomes. To address this question, our research examines how customers respond to online reviews of identity-relevant brands in particular, which have been overlooked in the online reviews literature. Eight studies (field data and experiments featuring consequential and hypothetical behaviors) show that negative online reviews may not be so detrimental for identity-relevant brands, especially when those reviews originate from socially distant (but not socially close) reviewers. This occurs because a negative review of an identity-relevant brand can pose a threat to a customer’s identity, prompting the customer to strengthen their relationship with the identity-relevant brand. To document the underlying process, we show that this effect does not emerge when the review is positive or the brand is identity-irrelevant. Importantly, we identify circumstances when negative reviews can actually produce positive outcomes (higher preference) for identity-relevant brands over no reviews or even positive reviews. By demonstrating the upside of negative reviews for identity-relevant brands, our findings have important implications for marketing theory and practice.

Impact of Online Reviews on Purchasing Decisions

2016 ◽  
pp. 278-291
Author(s):  
Dhiraj Jain ◽  
Lovish Bhansali ◽  
K. Sanal Nair
Keyword(s):  
Online Reviews ◽  
Purchasing Decisions ◽  
Electronic Products ◽  
Consumer Reviews ◽  
Online Consumer Reviews ◽  
Online Sales ◽  
Purchasing Behaviour ◽  
The Impact ◽  
Informed Consumer ◽  
The Relationship

Internet has enabled today's consumer to transform himself from passive to an active and an informed consumer who can share his experiences, opinions about product or services with an infinite number of consumers around the globe. These reviews or opinions are further used by potential buyers of that particular product or service via electronic Word of Mouth (e-WOM). The study on the impact of e-WOM on online sales has gradually emerged but a number of questions still remain unanswered. The aim of this study is to assess the impact of one type of e-WOM i.e., the online consumer reviews, on purchasing decisions of electronic products. This empirical study also focuses on the relationship between reviews and purchasing behaviour. An instrument was prepared to measure the proposed constructs, with questionnaire items taken from prior studies but adapted to fit the context of e-commerce. The survey was applied to academicians in India through internet. The results show that consumer reviews have a causal impact on consumer purchasing behaviour and they have an effect on choosing the products by consumer. Finally, the results and their implications are discussed.

scholarly journals Understanding and Encouraging Online Reviewing With a Selection-Based Review System

2019 ◽  
Vol 31 (5) ◽  
pp. 446-464
Author(s):  
Rowanne Fleck ◽  
Benjamin R Cowan ◽  
Eirini Darmanin ◽  
Yixin Wang
Keyword(s):  
Cost Benefit ◽  
Online Reviews ◽  
Review System ◽  
Personal View ◽  
Trade Off ◽  
Consumer Reviews ◽  
Online Consumer Reviews ◽  
Mobile Interface ◽  
Online Consumer ◽  
The Cost

Abstract Online consumer reviews are important for people wishing to make purchases online. However, not everyone contributes online reviews. This paper looks at consumer motivations of reviewing and rating behaviour in order to motivate the design of a mobile interface for online reviewing. An interview study found that people tend to contribute reviews and ratings based on their perception of whether they would be helpful or not to others as well as their own personal view of the usefulness of reviews and ratings when buying products. There also seems to be a cost-benefit trade-off that influences people’s decisions to review and rate: people tend to make a decision based on the perceived value of that review or rating to the community against the effort and costs of contributing. A mobile interface was designed that was intended both to reduce the cost of leaving reviews and to increase the perception of the usefulness of the reviews to others. An initial evaluation of this reviewing interface suggests that it could encourage more people to leave reviews.

Online Reviews' Trustworthiness Analysis Based on Power-Law Distribution

2013 ◽  
Vol 838-841 ◽  
pp. 3260-3267
Author(s):  
Qi Chang Yao ◽  
Xin Feng ◽  
Qi Ming Sun
Keyword(s):  
Power Law ◽  
Attitude Change ◽  
Large Scale ◽  
Online Shopping ◽  
Online Reviews ◽  
Subjective Perception ◽  
Power Exponent ◽  
Power Law Distribution ◽  
Consumer Reviews ◽  
Consumer Attention

In online shopping, studies on consumer reviews are mostly based on the Attitude Change Model. Illustrated from the perspective of perceived trustworthiness, however, it is not easy to measure and characterize the subjective perception of consumers. Starting from the inherent property of online reviews and based on the real data of 360buy which is the domestic large-scale B2C commerce website in China, this paper focuses on the interval distribution of consumer reviews and the data for statistical analysis. Research finds that the distribution of reviews interval can be depicted by the power-law function and there is a monotonically increasing relationship between power-exponent and the customers concerns with the commodity, the higher exponent, the attention consumer get. The findings give an objective basis to judge the credibility of online reviews. The relationship between power-exponent and the consumer attention has demonstrated the vital role of consumer attention in online shopping, and then the double parity between attention and exponent will effectively regulate the e-commerce market environment and promote its healthy operation. Tech Publications.

scholarly journals Pencitraan Sosial Media

2019 ◽  
Vol 7 (2) ◽  
pp. 231
Author(s):  
Intan Yulia Saragih ◽  
Gde Indra Bhaskara
Keyword(s):  
Social Media ◽  
Food Service ◽  
Secondary Data ◽  
Online Reviews ◽  
Primary Data ◽  
Consumer Reviews ◽  
Content Analysis Method ◽  
Data Source ◽  
Use Of Social Media

This research is based on widespread use of social media as an image for all people and as well as provides benefits for a company. The large number of social media users makes an American travel site provides reviews about travel related content and this includes reviews of restaurants. Hence, this study discusses the case study of TripaAdvisor's review of the five best restaurants in Bali. With various forms of benefits regarding the benefits of information from TripAdvisor, online reviews from consumers in promoting restaurants and consumer reviews in enhancing the image of a restaurant. The research method used is content analysis method to review from five restaurants, the variables were the value of Food, Service, Value and Atmosphere from these five restaurants and ranked five restaurants through an assessment of consumer reviews on TripAdvisor. The data source is through primary data and secondary data, namely through TripAdvisor. The description of this research is to look at every consumer online review on TripAdvisor, by choosing five of the best TripAdvisor restaurants in Bali and taking 50 consumer reviews from the top five TripAdvisor restaurants in Bali to see the benefits of these consumer reviews in promoting and improving the image of these five restaurants. The results of this study indicate that TripAdvisor is very useful in providing information to restaurants and with consumer reviews can be a tool for restaurant's promotion and able to enhance the image of a restaurant. Keywords : TripAdvisor, Consumer reviews, Restaurants, Bali

scholarly journals The influence of e-word-of-mouth on hotel occupancy rate

2016 ◽  
Vol 28 (9) ◽  
pp. 2035-2051 ◽  
Author(s):  
Giampaolo Viglia ◽  
Roberta Minazzi ◽  
Dimitrios Buhalis
Keyword(s):  
Critical Role ◽  
Online Reviews ◽  
Three Dimensions ◽  
Occupancy Rate ◽  
Consumer Reviews ◽  
Content Type ◽  
Online Consumer Reviews ◽  
Online Consumer ◽  
Occupancy Rates ◽  
The Impact

Purpose Online consumer reviews have become increasingly important for consumer decision-making. One of the most prominent examples is the hotel industry where consumer reviews on websites, such as Bookings.com, TripAdvisor and Venere.com, play a critical role in consumers’ choice of a hotel. There have been a number of recent studies analyzing various aspects of online reviews. The purpose of this paper is to investigate their effects in terms of hotel occupancy rates. Design/methodology/approach This paper measures through regression analysis the impact of three dimensions of consumer reviews (i.e. review score, review variance and review volume) on the occupancy rates of 346 hotels located in Rome, isolating a number of other factors that might also affect demand. Findings Review score is the dimension with the highest impact. The results suggest that after controlling for other variables, a one-point increase in the review score is associated to an increase in the occupancy rate by 7.5 percentage points. Regardless the review score, the number of reviews has a positive effect, but with decreasing returns, implying that the higher the number of reviews, the lower the beneficial effect in terms of occupancy rates is. Practical implications The findings quantify the strong association of online reviews to occupancy rates suggesting the use of appropriate reputational management systems to increase hotel occupancy and therefore performance. Originality/value A major contribution of this paper is its comprehensiveness in analyzing the relation between online consumer reviews and occupancy across a heterogeneous sample of hotels.

PLATFORM COUNTERPUBLICS: GOSSIP & CONTESTED KNOWLEDGE ABOUT ONLINE LABOR PLATFORMS

2020 ◽  
Author(s):  
Julia Ticona ◽  
Ryan Tsapatsaris
Keyword(s):  
Online Reviews ◽  
Business Practices ◽  
Online Markets ◽  
Consumer Reviews ◽  
Relational Approach ◽  
Contested Knowledge ◽  
Digital Environments ◽  
Conflicting Interests ◽  
Online Labor ◽  
Critical Literature

As commercial platforms mediate large swaths of online markets for information and services, scholars have shown how users resist, or work around these opaque digital environments. From content producers to Uber drivers, digital laborers are particularly adept at appropriating and gaming platforms like YouTube, and Uber (Chen 2017; Duffy 2017; Rosenblat 2018). Often described as “multi-sided markets,” platforms bring together many different kinds of stakeholders, including consumers, workers, advertisers, and regulators (Gillespie 2010; Lingel 2020). However, investigations of working alongside algorithms have so far focused on workers’ relationship to algorithms, and neglected other stakeholders. Extending counterpublics theories (Warner 2002; Fraser 1990), we examine over 3,000 online reviews of a labor platform, Care.com, finding that both workers and clients use gossip to create a $2 that constructs a counternarrative about platform business practices. While previous studies suggest that different platform stakeholders have conflicting interests, we find that platform counterpublics draw both workers and clients together to draw boundaries demarcating acceptable platform business practices. Second, we point to the implications of platform counterpublics for the investigation of platform labor and algorithms. Consumer reviews of platforms are absent from critical literature on labor platforms. By bringing together scholarship on counterpublics with critical literature on labor platforms, this paper offers a relational approach to platforms.

Combining Anthropometric Data and Consumer Review Content to Inform Design for Human Variability

2015 ◽  
Author(s):  
Taylor Ferguson ◽  
Melissa Greene ◽  
Frank Repetti ◽  
Kemper Lewis ◽  
Sara Behdad
Keyword(s):  
Product Design ◽  
Online Reviews ◽  
Target Range ◽  
Data Sets ◽  
Anthropometric Data ◽  
Consumer Reviews ◽  
Consumer Review ◽  
Human Ear ◽  
Effective Design

In this paper, we strive to combine concepts from the field of ergonomics with techniques from design analytics in order to inform the specifications of a product, such that a designer can create products that are both universal and well-received by consumers. While the ergonomic guidelines dictated by anthropometric data can provide a broad target range for design features, consumer reviews of previous iterations of similar products can narrow the design further, within that given range, to pinpoint a more effective design. We establish a general, ergonomically-centered set of cue-phrases to identify and extract useful information from online reviews, after which this information can be coupled with anthropometric measurements to produce more accurate product dimensions and features. A new metric, the Frequency and Accuracy Summation (FAS) Number is also introduced, as a means to predict the likelihood that a cue-phrase will yield useful results. The usefulness of these cue-phrases as well as the utility of combining consumer review content with anthropometric data sets are then tested in a case study centered on the design of a new and more universal “ear bud” headphone. Using the established cue-phrases, reviews of an existing product are processed such that problem components could be targeted in the new design. The information that was gleaned from those reviews is then paired with actual human ear measurements to propose a new, hypothetical product design that is more ergonomically sound and universal.

What’s yours is mine: exploring customer voice on Airbnb using text-mining approaches

2019 ◽  
Vol 36 (5) ◽  
pp. 655-665 ◽  
Author(s):  
Jurui Zhang
Keyword(s):  
Content Analysis ◽  
Text Mining ◽  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Local Community ◽  
Negative Emotion ◽  
Online Reviews ◽  
Sharing Economy ◽  
Consumer Reviews ◽  
Content Type

Purpose This paper aims to investigate customers’ experiences with Airbnb by text-mining customer reviews posted on the platform and comparing the extracted topics from online reviews between Airbnb and the traditional hotel industry using topic modeling. Design/methodology/approach This research uses text-mining approaches, including content analysis and topic modeling (latent Dirichlet allocation method), to examine 1,026,988 Airbnb guest reviews of 50,933 listings in seven cities in the USA. Findings The content analysis shows that negative reviews are more authentic and credible than positive reviews on Airbnb and that the occurrence of social words is positively related to positive emotion in reviews, but negatively related to negative emotion in reviews. A comparison of reviews on Airbnb and hotel reviews shows unique topics on Airbnb, namely, “late check-in”, “patio and deck view”, “food in kitchen”, “help from host”, “door lock/key”, “sleep/bed condition” and “host response”. Research limitations/implications The topic modeling result suggests that Airbnb guests want to get to know and connect with the local community; thus, help from hosts on ways they can authentically experience the local community would be beneficial. In addition, the results suggest that customers emphasize their interaction with hosts; thus, to improve customer satisfaction, Airbnb hosts should interact with guests and respond to guests’ inquiries quickly. Practical implications Hotel managers should design marketing programs that fulfill customers’ desire for authentic and local experiences. The results also suggest that peer-to-peer accommodation platforms should improve online review systems to facilitate authentic reviews and help guests have a smooth check-in process. Originality/value This study is one of the first to examine consumer reviews in detail in the sharing economy and compare topics from consumer reviews between Airbnb and hotels.

scholarly journals Distribution characteristics of star ratings in online consumer reviews

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
R. Venkatesakumar ◽  
Sudhakar Vijayakumar ◽  
S. Riasudeen ◽  
S. Madhavan ◽  
B. Rajeswari
Keyword(s):  
Online Reviews ◽  
Online Review ◽  
Online Retailing ◽  
Consumer Reviews ◽  
Content Type ◽  
Product Categories ◽  
Online Consumer Reviews ◽  
Online Retailers ◽  
Online Consumer ◽  
Analyse Data

Purpose The star rating summarises the review content and conveys the message faster than other review components. Star ratings influence helpfulness of the reviews, and extreme reviews are considered as less helpful in the decision process. However, literature has rarely addressed variations in star ratings across product categories and variations between two online retailers. In this paper, the authors have compared the distribution of star ratings across 11 products and among the retailers. Design/methodology/approach Online reviews for 11 product categories have collected, and the authors compared the distribution of star ratings across 11 products and retailers. Correspondence analysis has been applied to show the association between star ratings and product categories for the e-retail firms. Findings The Amazon site contains proportionately more number of 1-star rated reviews than Flipkart. In Amazon reviews, few product categories are closely associated with 1-star and 2-star reviews, whereas no product categories are closely associated with 1-star and 2-star reviews in Flipkart reviews. The results indicate two distinct communication strategies followed by the firms in managing online consumer reviews. Research limitations/implications The authors did not analyse data across demographic details because of access restriction policies of the websites. Practical implications Understanding the distribution of review characteristics will improve the consumer’s decision-making ability and using online review content judiciously. Social implications This study’s results show significant insights on online retailing by providing cues in using shopping sites and online review characteristics of two prominent retailers. Originality/value This paper has brought out a distinct distribution pattern of online review between Amazon and Flipkart. Amazon allows a higher degree of negative contents, whereas Flipkart allows more number of positive reviews.

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