scholarly journals Reading Between the Lines: Understanding Customer Experience With Disruptive Technology Through Online Reviews

2021 ◽  
pp. 183933492199948
Author(s):  
Jeandri Robertson ◽  
Caitlin Ferreira ◽  
Jeannette Paschen
Keyword(s):  
Text Analysis ◽  
Online Reviews ◽  
Customer Experience ◽  
Disruptive Technology ◽  
Word Choice ◽  
Emotional Tone ◽  
Customer Reviews ◽  
Brand Engagement ◽  
Online Customer Reviews ◽  
Computerized Text Analysis

A customer’s experience with a brand, as evidenced in online customer reviews, has attracted multidisciplinary scholarly attention. Customer experience plays an important role as an antecedent to brand engagement, brand adoption, and eventual brand loyalty. Thus, it is important for businesses to understand their customers’ experiences so that they can make changes as necessary. The COVID-19 pandemic has brought unprecedented changes to the business landscape, forcing businesses to move online, with many utilizing enterprise video conferencing (EVC) to maintain daily operations. To ensure efficient digitization, many turned to the online reviews of others’ experiences with EVC before engaging with it themselves. This research examined how the customer experience is portrayed through emotional tone and word choice in online reviews for the EVC platform Zoom. Using computerized text analysis, key differences were found in the emotional tone and word choice for low- and high-rated reviews. The complexity and emotionality expressed in reviews have implications on the usability of the review for others. The results from this study suggest that online customer reviews with a high rating express a higher level of expertise and confidence than low-rated reviews. Given the potential dissemination and impact, digital marketers may be well advised to first and foremost respond to online reviews that are high in emotional tone.

Brand Positioning and Segmentation of Sneakers through Multi-Dimensional Customer Experience Analysis

2021 ◽  
Vol 13 (2) ◽  
pp. 335-345
Author(s):  
R. Senthilkumar ◽  
B. RubanRaja ◽  
Monisha
Keyword(s):  
Text Analysis ◽  
Customer Experience ◽  
Structured Data ◽  
Multivariate Techniques ◽  
Brand Positioning ◽  
Customer Reviews ◽  
Perceptual Mapping ◽  
Analysis Techniques ◽  
Hidden Patterns ◽  
And Cluster Analysis

A huge corpus of valuable information on customer experience is available as unstructured form in customer reviews on e-commerce websites. Multivariate data analysis techniques are effective in uncovering hidden patterns and segments in structured data. A major challenge is to convert the unstructured data into a structured form for applying multivariate techniques. In this article, we have provided a text analysis based approach coupled with multivariate techniques to uncover the sentiment of various features associated with different brands and to determine the brand positions and segments through perceptual mapping and cluster analysis.

Automated Keyword Filtering in LDA for Identifying Product Attributes from Online Reviews

2020 ◽  
pp. 1-10
Author(s):  
Junegak Joung ◽  
Harrison M. Kim
Keyword(s):  
Product Design ◽  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Online Reviews ◽  
Previous Method ◽  
Product Attributes ◽  
Customer Reviews ◽  
Online Customer Reviews ◽  
Dirichlet Allocation

Abstract Identifying product attributes from the perspective of a customer is essential to measure the satisfaction, importance, and Kano category of each product attribute for product design. This paper proposes automated keyword filtering to identify product attributes from online customer reviews based on latent Dirichlet allocation. The preprocessing for latent Dirichlet allocation is important because it affects the results of topic modeling; however, previous research performed latent Dirichlet allocation either without removing noise keywords or by manually eliminating them. The proposed method improves the preprocessing for latent Dirichlet allocation by conducting automated filtering to remove the noise keywords that are not related to the product. A case study of Android smartphones is performed to validate the proposed method. The performance of the latent Dirichlet allocation by the proposed method is compared to that of a previous method, and according to the latent Dirichlet allocation results, the former exhibits a higher performance than the latter.

The Relationship between Online Reviews, Brand Trust, and Willingness to Buy

2015 ◽  
pp. 139-161 ◽  
Author(s):  
V. Cheng ◽  
J. Rhodes ◽  
P. Lok
Keyword(s):  
Decision Making ◽  
Online Reviews ◽  
Mediating Effect ◽  
Consumer Decision Making ◽  
Brand Trust ◽  
Purchasing Decisions ◽  
Customer Reviews ◽  
Online Customer Reviews ◽  
Willingness To Buy ◽  
The Relationship

This chapter investigates how online customer reviews affect consumer decision-making (willingness to buy) during their first purchase of services or products using brand trust as a mediating variable. A brief literature review, rationale and significance, and methodology are discussed, and a conceptual framework based on the relationships between the stated variables is adopted in this empirical study to demonstrate linkages and insights. The findings demonstrate that the “reliability dimension” of brand trust had a mediating effect on online customer reviews' valence to willingness to buy, while the “intentionality dimension” of brand trust had little effect. Furthermore, the findings demonstrate that online customer reviews generated by in-group and out-group reviewers have little effect on purchasing decisions (willingness to buy). These results suggest that marketers should focus more on managing negative online customer reviews that have a damaging effect on brand trust.

Testing the SmartMunk's Story.ly App for the Analysis of Online Customer Reviews on Face Masks

2021 ◽  
pp. 90-116
Author(s):  
Arabela Briciu ◽  
Cristian-Laurențiu Roman ◽  
Victor-Alexandru Briciu
Keyword(s):  
Text Analysis ◽  
User Satisfaction ◽  
Product Reviews ◽  
Online Application ◽  
Customer Reviews ◽  
Customer Feedback ◽  
Specialized Software ◽  
Online Customer Reviews ◽  
Qualitative Part ◽  
Software Measures

This chapter aims to present the process of selecting and analyzing a number of reviews using a software solution (an online application) created specifically for text analysis and extracting user sentiment. This software measures the level of user satisfaction, analyzing product reviews and taking into account the qualitative part of the content generated by users. Analyzing online customer reviews with the help of specialized software can help both companies and other users. The software can also help us reach a conclusion regarding the analysis of reviews and customer feedback on products or services. This study can also be useful for customers or buyers who want to know the opinion of others about a product, having the opportunity to differentiate between positive and negative reviews.

scholarly journals Online Customer Comments and their Impact on Consumer Buying Behavior: using Social Cognitive Theory to Understand Consumer Expectations and Media Influences

2019 ◽  
Vol 8 (2S10) ◽  
pp. 887-892
Keyword(s):  
Social Cognitive Theory ◽  
Social Cognitive ◽  
Word Of Mouth ◽  
Cognitive Theory ◽  
Online Reviews ◽  
Buying Behavior ◽  
Customer Reviews ◽  
Consumer Expectations ◽  
Online Customer Reviews ◽  
Consumer Buying Behavior

Many opportunities, with the help of web-based technologies, are provided to word-of-mouth communication. The method of communication of customers and sharing the product details with others is transformed by the immense utilisation of electronic commerce shopping communities. Until recently, the area of e-commerce shopping communities where buyers participate has been underexplored in the field of academic research. The online reviews provided by the customers exert a high impact on customers’ buying decisions while shopping on e-commerce websites and thus provides significance to the concept of word of mouth. The growing amount of literature covering various domains that emphasizes customers’ reviews online can be considered as a justification of this concept. The factors that affect continual intention of buying online and the extent, reciprocity and reputation of vendor creativity affect consumer expectations. This study provides a brief insight into online customer reviews and their impact on consumer buying behavior by using social cognitive theory. A conceptual framework showcasing the various factors affecting the perceptions and attitudes of consumers in the context of online reviews will be provided in the paper. This study is the first to apply social cognitive theory on online customer reviews and to study their impact on consumer expectations.

Does the length of a review matter in perceived helpfulness? The moderating role of product experience

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Han Jia ◽  
Sumin Shin ◽  
Jinfeng Jiao
Keyword(s):  
Online Reviews ◽  
Product Type ◽  
Online Review ◽  
Content Type ◽  
Level Of Involvement ◽  
Customer Reviews ◽  
Product Experience ◽  
Online Customer Reviews ◽  
Review Helpfulness ◽  
Product Ratings

PurposeThis paper aims to offer a framework explaining how product experience (i.e. think vs feel) and product involvement (high vs low) influence the helpfulness of online reviews. It also reexamined how online consumer review dimensions help to build online review helpfulness under different contexts.Design/methodology/approachData were collected using content analysis on 1,200 online customer reviews on 12 products from four categories to measure the relationships between online review dimensions and the helpfulness of reviews. The regression analysis and analysis of variance (ANOVA) were used to test the hypotheses.FindingsThe findings indicate that the effectiveness of length of a review is moderated by product type; for think products, longer reviews yield higher helpfulness. Furthermore, the level of consistency between individual review ratings and overall product ratings is associated with review helpfulness. The length of product descriptions and product ratings is moderated by the level of involvement. For products with high involvement, longer descriptions yield higher helpfulness.Originality/valueA conceptual connection to customer interaction is proposed by online customer reviews that vary by product type. The findings provide implications for online retailers to better manage online customer reviews and increase the value of product ratings.

Predicting the helpfulness of online customer reviews: The role of title features

2018 ◽  
Vol 62 (3) ◽  
pp. 272-287 ◽  
Author(s):  
Mina Akbarabadi ◽  
Monireh Hosseini
Keyword(s):  
Prediction Model ◽  
Real Life ◽  
Online Reviews ◽  
Machine Learning Techniques ◽  
Customer Reviews ◽  
Learning Techniques ◽  
Online Customer Reviews ◽  
Importance Analysis ◽  
Review Helpfulness

Nowadays, many people refer to online customer reviews that are available on most shopping websites to make a better purchase decision. An automated review helpfulness prediction model can help the websites to rank reviews based on their level of helpfulness. This study examines the effect of review title features on predicting the helpfulness of online reviews. Moreover, a new method is proposed to categorize action verbs in a review text. Text, reviewer, readability, and title features are the four main categories that are used in this article. We examine our proposed prediction model on two real-life Amazon datasets using machine learning techniques. The results show a promising performance of the model. However, feature importance analysis reveals the low importance of title features in the predictive model. It means that the title characteristics cannot be a powerful determinant of online review helpfulness. The results of this study can be beneficial to both buyers and website owners to have a deep insight into online reviews helpfulness.

How to prevent negative online customer reviews: the moderating roles of monetary compensation and psychological compensation

2020 ◽  
Vol 32 (10) ◽  
pp. 3115-3134 ◽  
Author(s):  
Jun Liu ◽  
HengJin Zhang ◽  
JingJing Sun ◽  
NingXin Li ◽  
Anil Bilgihan
Keyword(s):  
Service Recovery ◽  
Online Reviews ◽  
Content Type ◽  
Customer Reviews ◽  
Monetary Compensation ◽  
Beverage Industry ◽  
Online Customer Reviews ◽  
Recovery Strategies ◽  
Food And Beverage ◽  
Online Rating

Purpose This paper aims to clarify the effects of motivations on negative online customer reviews (OCRs) behavior in an integrative framework and to identify the moderating role of monetary compensation and psychological compensation in the Chinese food and beverage industry. Design/methodology/approach Data were collected from 377 consumers who posted a negative review online. Hierarchical regression analyses were used to test the research hypotheses. Findings The authors identified some characteristics of the consumers who posted negative online reviews in the Chinese food and beverage industry and found evidence that reveals the positive effects of emotional venting motivation and altruism motivation on posting negative customer online reviews. Economic motivation and self-enhancement motivation were not significantly connected to negative OCRs behaviors. Service recovery strategies can moderate the relationship between certain motivations and behaviors. The absence of psychological compensation will aggravate the influence of emotion venting motivation on consumers’ negative online reviews, while monetary compensation can restrain the influence of altruism motivation on negative online rating behavior. Research limitations/implications This paper did not explore the effect of the fairness and timeliness of service recovery on negative OCRs behavior. This paper did not consider the different restaurant types and customers' characteristics, and future research can test similar models with different and more diverse samples. Practical implications When implementing service recovery strategies, it is important to consider the psychological component of recovery. The absence of psychological compensation aggravates the influence of high levels of emotion venting motivation on consumers’ negative OCRs, leading to a lower rating, more word comments and negative photos. High levels of monetary compensation can restrain the influence of altruism motivation on negative online rating behavior. Originality/value The current paper contributes to the hospitality management literature by investigating the motivations behind consumer decisions to post negative OCRs in a food and beverage context. In addition, the moderating effect that service recovery strategies have on this relationship was also explored in depth.

Mining opinionated product features using WordNet lexicographer files

2016 ◽  
Vol 43 (6) ◽  
pp. 769-785 ◽  
Author(s):  
Saif A. Ahmad Alrababah ◽  
Keng Hoon Gan ◽  
Tien-Ping Tan
Keyword(s):  
Assessment Tool ◽  
Empirical Evaluation ◽  
Online Reviews ◽  
Product Reviews ◽  
Specific Domain ◽  
Customer Reviews ◽  
Product Features ◽  
Online Customer Reviews ◽  
Customer Perspective ◽  
Massive Number

Online customer reviews are an important assessment tool for businesses as they contain feedback that is valuable from the customer perspective. These reviews provide a significant basis on which potential customers can select the product that best meets their preferences. In online reviews, customers describe positive or negative experiences with a product or service or any part of it (i.e. features). Consumers frequently experience difficulty finding the desired product for comparison because of the massive number of online reviews. The automatic extraction of important product features is necessary to support customers in search of relevant product features. These features are the criteria that make it possible for customers to characterise different types of products. This article proposes a domain independent approach for identifying explicit opinionated features and attributes that are strongly related to a specific domain product using lexicographer files in WordNet. In our approach, N_gram analysis and the SentiStrength opinion lexicon have been employed to support the extraction of opinionated features. The empirical evaluation of the proposed system using online reviews of two popular datasets of supervised and unsupervised systems showed that our approach achieved competitive results for feature extraction from product reviews.

Sign in / Sign up

Export Citation Format

Share Document