scholarly journals Research on the Evaluation of Customer requirement Importance of Smart Products Based on Online Comments and Improved Quantitative Kano Model

CONVERTER ◽  
2021 ◽  
pp. 382-392
Author(s):  
Hang Liu, Zan Ren, Yingjie Li
Keyword(s):  
Customer Requirement ◽  
Kano Model ◽  
Customer Reviews ◽  
Technological Differences ◽  
Online Customer Reviews ◽  
Smart Products ◽  
Online Comments ◽  
Product Manufacturing ◽  
Product Homogeneity

With the development and popularization of smart products, the technological differences of products are decreasing, and the phenomenon of product homogeneity is becoming more and more obvious. It is necessary for the smart product manufacturing firms have the capability to analyze customer requirement deeply and adapt to the dynamically changing market quickly. Therefore, the traditional technology-oriented product development model is no longer suitable for manufacturers to obtain a competitive advantage. Based on this, this paper proposed a method to evaluate the importance of customer demands based on online comments and quantitative Kano model. First, the Python crawler tool is used to obtain online customer reviews of relevant products and the word segmentation processing is performed to obtain the product features and frequency that customers are mainly concerned about, and then the initial importance of demand can be calculated. Furthermore, use the quantitative Kano model to determine the customer satisfaction and revise the initial importance of the requirements to obtain a more reasonable ranking of the importance of user needs. Finally, a case study is carried out with the smart bracelet as an example to verify the effectiveness and feasibility of the model proposed in this paper.

scholarly journals The Effect of Ease of Shopping, Online Customer Reviews and Maslahah Perceptions on Gen Z Online Shopping Decisions (Case Study Of UINSI and UMKT Samarinda Students)

2021 ◽  
Vol 1 ◽  
pp. 179-193
Author(s):  
Rogaina Rogaina ◽  
Tikawati Tikawati
Keyword(s):  
Regression Analysis ◽  
Data Analysis ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Online Shopping ◽  
Field Research ◽  
Customer Reviews ◽  
Online Customer Reviews ◽  
Independent Variable

This study aims to examine and analyse the effect of ease of shopping, online customer reviews and perceptions of maslahah on online shopping decisions among UINSI and UMKT Samarinda students. The type of research used is field research. The sample in this study was 198 people who had shopped online. The method of collecting data is a questionnaire is distributed online using Google form. Data analysis used multiple regression analysis. The results showed that of the three variables of ease of shopping, online customer review and perception of maslahah simultaneously affect online shopping decisions. From the calculation of SPSS 23. For the Ftest, it is known that Fcount = 187.146 > Ftable 2.65 with a significance of 0.000 < 0.5. Partially, it is known that the ease of shopping, online customer reviews and the perception of maslahah have a significant effect of online shopping decisions. In addition to the Ftest and t-test, the R2 test is known to have an R square value of 0.743 which means the magnitude of the independent variable 74.3%.

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.

Application of social media analytics: a case of analyzing online hotel reviews

2017 ◽  
Vol 41 (7) ◽  
pp. 921-935 ◽  
Author(s):  
Wu He ◽  
Xin Tian ◽  
Ran Tao ◽  
Weidong Zhang ◽  
Gongjun Yan ◽  
...  
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Social Media Analytics ◽  
Social Media Data ◽  
Content Type ◽  
Customer Reviews ◽  
Analysis Techniques ◽  
Online Customer Reviews ◽  
Media Data

Purpose Online customer reviews could shed light into their experience, opinions, feelings, and concerns. To gain valuable knowledge about customers, it becomes increasingly important for businesses to collect, monitor, analyze, summarize, and visualize online customer reviews posted on social media platforms such as online forums. However, analyzing social media data is challenging due to the vast increase of social media data. The purpose of this paper is to present an approach of using natural language preprocessing, text mining and sentiment analysis techniques to analyze online customer reviews related to various hotels through a case study. Design/methodology/approach This paper presents a tested approach of using natural language preprocessing, text mining, and sentiment analysis techniques to analyze online textual content. The value of the proposed approach was demonstrated through a case study using online hotel reviews. Findings The study found that the overall review star rating correlates pretty well with the sentiment scores for both the title and the full content of the online customer review. The case study also revealed that both extremely satisfied and extremely dissatisfied hotel customers share a common interest in the five categories: food, location, rooms, service, and staff. Originality/value This study analyzed the online reviews from English-speaking hotel customers in China to understand their preferred hotel attributes, main concerns or demands. This study also provides a feasible approach and a case study as an example to help enterprises more effectively apply social media analytics in practice.

scholarly journals Significant Labels in Sentiment Analysis of Online Customer Reviews of Airlines

2020 ◽  
Vol 12 (20) ◽  
pp. 8683 ◽  
Author(s):  
Ayat Zaki Ahmed ◽  
Manuel Rodríguez-Díaz
Keyword(s):  
Sentiment Analysis ◽  
Information Service ◽  
Customer Experience ◽  
Communication Strategy ◽  
Customer Reviews ◽  
Quantitative Variable ◽  
Service Companies ◽  
Essential Tool ◽  
Online Customer Reviews ◽  
Online Comments

Sentiment analysis is becoming an essential tool for analyzing the contents of online customer reviews. This analysis involves identifying the necessary labels to determine whether a comment is positive, negative, or neutral, and the intensity with which the customer’s sentiment is expressed. Based on this information, service companies such as airlines can design and implement a communication strategy to improve their customers’ image of the company and the service received. This study proposes a methodology to identify the significant labels that represent the customers’ sentiments, based on a quantitative variable, that is, the overall rating. The key labels were identified in the comments’ titles, which usually include the words that best define the customer experience. This database was applied to more extensive online customer reviews in order to validate that the identified tags are meaningful for assessing the sentiments expressed in them. The results show that the labels elaborated from the titles are valid for analyzing the feelings in the comments, thus, simplifying the labels to be taken into account when carrying out a sentiment analysis of customers’ online comments.

Automated Mapping of Product Features Mined From Online Customer Reviews to Engineering Product Characteristics

2016 ◽  
Author(s):  
Sung woo Kang ◽  
Conrad S. Tucker
Keyword(s):  
New Product Development ◽  
Large Scale ◽  
Engineering Product ◽  
Automated Mapping ◽  
Customer Reviews ◽  
Pagerank Algorithm ◽  
Product Features ◽  
Online Customer Reviews ◽  
Scale Design

Until now, translating product features expressed in the market into quantifiable engineering metrics has primarily been a manual process. This manual process establishes product features from large-scale customer feedback using a product’s components from large-scale design specifications. This process exacerbates the complexity and sheer amount of information that designers must handle during the early stages of new product development. The methodology proposed in this paper automatically identifies product features by mapping terms that describe product features from technical descriptions and customer reviews. In order to discover terms related to the features expressed in the market, the authors of this work employ WordNet and the PageRank algorithm, which search for semantically similar terms in products’ technical descriptions. A case study demonstrates the methodology’s viability for matching product features that are extracted from online customer reviews to the technical descriptions that address them.

Profiling Reviewers’ Social Network Strength and Predicting the “Helpfulness” of Online Customer Reviews

2020 ◽  
pp. 101026
Author(s):  
Muhammad Bilal ◽  
Mohsen Marjani ◽  
Ibrahim Abaker Targio Hashem ◽  
Nadia Malik ◽  
Muhammad Ikram Ullah Lali ◽  
...  
Keyword(s):  
Social Network ◽  
Customer Reviews ◽  
Online Customer Reviews ◽  
Network Strength

Fraud abroad

2019 ◽  
Vol 13 (2) ◽  
pp. 249-275
Author(s):  
Jake David Hoskins ◽  
Ryan Leick
Keyword(s):  
Sharing Economy ◽  
Gdp Per Capita ◽  
Content Type ◽  
Customer Reviews ◽  
Online Customer Reviews ◽  
Level Of Economic Development ◽  
Per Capita ◽  
Using Data ◽  
Developed Nations

Purpose This study aims to investigate a sharing economy context, where vacation rental units that are owned and operated by individuals throughout the world are rented out through a common website: vrbo.com. It is posited that gross domestic product (GDP) per capita, a common indicator of the level of economic development of a nation, will impact the likelihood that prospective travelers will choose to book accommodations in the sharing economy channel (vs traditional hotels). The role of online customer reviews in this process is investigated as well, building upon a significant body of extant research which shows their level of customer decision influence. Design/methodology/approach An empirical analysis is conducted using data from the website Vacation Rentals By Owner on 1,940 rental listings across 97 countries. Findings GDP per capita serves as risk deterrent to prospective travelers, making the sharing economy an acceptable alternative to traditional hotels for the average traveler. It is also found that the total number of online customer reviews (OCR volume) is a signal of popularity to prospective travelers, while the average star rating of those online customer reviews (OCR valence) is instead a signal of accommodation quality. Originality/value This study adds to a growing agenda of research investigating the effect of online customer reviews on consumer decisions, with a particularly focus on the burgeoning sharing economy. The findings help to explain when the sharing economy may serve as a stronger disruptive threat to incumbent offerings. It also provides the following key insights for managers: sharing economy rental units in developed nations are more successful in driving booking activity, managers should look to promote volume of online customer reviews and positive online customer reviews are particularly influential for sharing economy rental booking rates in less developed nations.

EXPRESS: Hype News Diffusion and Risk of Misinformation: The Oz Effect in Healthcare

2021 ◽  
pp. 002224372110444
Author(s):  
Zijun (June) Shi ◽  
Xiao Liu ◽  
Kannan Srinivasan
Keyword(s):  
Scientific Evidence ◽  
Public Information ◽  
Research Articles ◽  
Customer Reviews ◽  
Online Customer Reviews ◽  
News Agencies ◽  
Health Products ◽  
Tv Shows ◽  
Medical Recommendations ◽  
Textual Content

Consumers' choices about health products are heavily influenced by public information, such as news articles, research articles, online customer reviews, online product discussion, and TV shows. Dr. Oz, a celebrity doctor, often makes medical recommendations with limited or marginal scientific evidence. Although reputable news agencies have traditionally acted as gatekeepers of reliable information, they face the intense pressure of “the eyeball game.” Customer reviews, despite their authenticity, may come from deceived consumers. Therefore, it remains unclear whether public information sources can correct the misleading health information. In the context of over-the-counter weight loss products, the authors carefully analyze the cascading of information post endorsement. The analysis of extensive textual content with deep-learning methods reveals that legitimate news outlets respond to Dr. Oz's endorsement by generating more news articles about the ingredient; on average, articles after the endorsement contain a higher sentiment, so news agencies seem to amplify rather than rectify the misleading endorsement. The finding highlights a serious concern: the risk of hype news diffusion. Research articles react too slowly to mitigate the problem, and online customer reviews and product discussions provide only marginal corrections. The findings underscore the importance of oversight to mitigate the risk of cascading hype news.

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