scholarly journals AGROTOURISM DEVELOPMENT OF MAPPING BRAND POSITIONING AND COMPETITVE LANDSCAPE: UGC (USER GENERATED CONTENT) APPROACH

2021 ◽  
Vol 21 (1) ◽  
pp. 65-78
Author(s):  
Annisa Firdauzi ◽  
Agustina Shinta Hartanti Wahyuningtyas ◽  
Riyanti Isaskar
Keyword(s):  
Optimal Allocation ◽  
User Generated Content ◽  
Brand Positioning ◽  
Customer Reviews ◽  
Customer Preferences ◽  
Competitive Model ◽  
Perceptual Performance ◽  
Brand Attributes ◽  
Beach Resort ◽  
Resort Hotels

Starting from 2009-2018 there has been an increase in the number of hotel resorts in Indonesia, so that the level of competition is higher and building a brand positioning agrotourism-based resort hotels can not only by creating regular marketing campaigns. The study used review data from online platforms tripadvisor.com. This study reveals the brand positioning of resort hotels and mapping the competitive landscape with the UGC approach to identifying the competing attributes of resort hotels in Bali. This study detects brand attributes using customer preferences as well as perceptual performance. Therefore, this study combines content analysis (UGC) and repertory grid (RGA) to answer research objectives. 13,784 customer reviews of the six best beach resort hotels in Bali are used to explore and visualize the competitive landscape. Sample determination techniques in this study using non-probability sampling approach. The findings of this study, identified the dominant agrotourism attributes in Bali are view and garden. This study detected that 66.67% of hotel resorts in Bali have asymmetric competitive model competition. Hotel resorts in Bali is mostly competing on PC1 which is a basic hotel offer. This research not only recommends competing for attributes to strengthen brand positioning in customers' minds but also competes with optimal allocation of hotel resort resources.

Human Resource Practice System in Malaysian Five-Star Hotels

2014 ◽  
Author(s):  
Rozila Ahmad
Keyword(s):  
Performance Appraisal ◽  
Human Resource ◽  
Hotel Industry ◽  
Human Resource Practices ◽  
Human Resource Practice ◽  
Objective Performance ◽  
Beach Resort ◽  
Recruitment And Selection ◽  
Managerial Employees ◽  
Resort Hotels

Organisations, including hotels, usually have more than one human resource practices system. Thus, this book is written to provide an understanding of the human resource practice system for managerial and non-managerial employees in the context of hotel industry. This book focuses specifically on five-star beach resort hotels in Malaysia. The human resource practices system for managerial employees includes empowerment while the recruitment and selection is more thorough. Their compensation is more attractive and their training is more rewarding. Both groups of employees are provided with a clear job description, orientation, employment security, objective performance appraisal, career development opportunity and effective communication.

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.

Customer-Driven Product Design Selection Using Web Based User-Generated Content

2011 ◽  
Author(s):  
L. Wang ◽  
B. D. Youn ◽  
S. Azarm ◽  
P. K. Kannan
Keyword(s):  
Product Design ◽  
User Generated Content ◽  
Web Based ◽  
Customer Data ◽  
Survey Techniques ◽  
Customer Preferences ◽  
Public Media ◽  
Systematic Effort ◽  
Design Selection ◽  
Customer Survey

Acquisition of the customer data for product design selection using conventional customer survey techniques can be a time-consuming and costly undertaking. The aim of this paper is to overcome this limitation by using web based User-Generated Content (UGC) as an alternative to the conventional customer survey techniques. UGC refers to various public media contents created by web users including contents in online customer reviews, blogs, and social networking interactions. So far, there has not been any systematic effort in using UGC in design selection for a customer durable product. Using UGC in product design selection is not an easy task because UGC can be freely expressed and written by customers with little constraints, structure and bounds. As a result, UGC can contain a lot of noise, variability in content and even bias induced by the customers. In order to make use of UGC, this paper develops a systematic methodology for eliciting product attributes from UGC, constructing customer preference models and using these models in design selection. To demonstrate the proposed method, design selection of a smartphone using UGC is considered as an example. It is shown in the example that the proposed method can provide a reasonable estimation of customer preferences while being useful for product design selection.

scholarly journals The Effects of Green Restaurant Attributes on Customer Satisfaction Using the Structural Topic Model on Online Customer Reviews

2020 ◽  
Vol 12 (7) ◽  
pp. 2843 ◽  
Author(s):  
Eunhye (Olivia) Park ◽  
Bongsug (Kevin) Chae ◽  
Junehee Kwon ◽  
Woo-Hyuk Kim
Keyword(s):  
Customer Satisfaction ◽  
Topic Model ◽  
Marketing Strategies ◽  
Restaurant Industry ◽  
User Generated Content ◽  
Customer Reviews ◽  
Online Customer Reviews ◽  
Longitudinal Approach ◽  
The Common ◽  
Structural Topic Modeling

Although green practice is increasingly adopted in the restaurant industry, there is still little research in terms of investigating the impacts of green practice on customer satisfaction. This study utilized user-generated content by green restaurant customers to identify various aspects of green restaurants, including perceived green restaurant practices. Our data are based on U.S. green-certified restaurants available on Yelp. Structural topic modeling was used to discover latent restaurant attributes from user-generated content. With a longitudinal approach, the changes in customers’ interest in green practices were estimated. Finally, the common restaurant attributes and green attributes were used to predict customer satisfaction. This study will contribute to marketing strategies for the restaurant industry.

Unveiling user-generated content: Designing websites to best present customer reviews

2011 ◽  
Vol 54 (3) ◽  
pp. 231-240 ◽  
Author(s):  
Qianqian (Ben) Liu ◽  
Elena Karahanna ◽  
Richard T. Watson
Keyword(s):  
User Generated Content ◽  
Customer Reviews

Design Preference Centered Review Recommendation: A Similarity Learning Approach

2011 ◽  
Author(s):  
Jian Jin ◽  
Ying Liu ◽  
Ping Ji ◽  
Richard Fung
Keyword(s):  
Classification Problem ◽  
Online Reviews ◽  
Learning Approach ◽  
Similarity Learning ◽  
Customer Reviews ◽  
Customer Preferences ◽  
Review Helpfulness ◽  
Review Recommendation ◽  
Design Preference ◽  
The Relationship

The rise of e-commerce websites like Amazon and Alibaba is changing the way how designers seek information to identify customer preferences in product design. From the feedbacks posted by consumers, either positive or negative, product designers can monitor the trend of consumers’ perception with respect to their product offerings and make efforts to improve accordingly. Starting from feature extraction from review documents, existing methods in identifying helpful online reviews regard the helpfulness prediction problem as a regression or classification problem and have not considered the relationship between customer reviews. Also, these approaches only consider the online helpfulness voting ratio or a unified helpfulness rating as the gold criteria for helpfulness evaluation and neglect various personal preferences from product designers. Therefore, in this paper, the focus is on how to predict reviews’ helpfulness by taking into account the personal preferences from both reviewers and designers. We start to analyze review helpfulness from both a generic aspect and a personal preference aspect. Classification methods and the proposed review similarity learning approach are utilized to estimate from the generic angle of helpfulness, while nearest neighbourhood based methods are adopted to reflect concerns from personal perspectives. Finally, a regression algorithm is called upon to predict review helpfulness based on the inputs from both aspects. Our experimental study, using a large quantity of review data crawled from Amazon and real ratings from product designers demonstrates the effectiveness of our approach and it opens a possibility for customized helpful review routing.

Predicting popular contributors in innovation crowds: the case of My Starbucks Ideas

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Chien-Yi Hsiang ◽  
Julia Taylor Rayz
Keyword(s):  
Empirical Evidence ◽  
New Product Development ◽  
Supervised Machine Learning ◽  
User Generated Content ◽  
Machine Learning Techniques ◽  
Customer Engagement ◽  
Text Representation ◽  
Content Type ◽  
Customer Preferences ◽  
Learning Techniques

PurposeThis study aims to predict popular contributors through text representations of user-generated content in open crowds.Design/methodology/approachThree text representation approaches – count vector, Tf-Idf vector, word embedding and supervised machine learning techniques – are used to generate popular contributor predictions.FindingsThe results of the experiments demonstrate that popular contributor predictions are considered successful. The F1 scores are all higher than the baseline model. Popular contributors in open crowds can be predicted through user-generated content.Research limitations/implicationsThis research presents brand new empirical evidence drawn from text representations of user-generated content that reveals why some contributors' ideas are more viral than others in open crowds.Practical implicationsThis research suggests that companies can learn from popular contributors in ways that help them improve customer agility and better satisfy customers' needs. In addition to boosting customer engagement and triggering discussion, popular contributors' ideas provide insights into the latest trends and customer preferences. The results of this study will benefit marketing strategy, new product development, customer agility and management of information systems.Originality/valueThe paper provides new empirical evidence for popular contributor prediction in an innovation crowd through text representation approaches.

scholarly journals Automatic Identification of Product Usage Contexts from Online Customer Reviews

2019 ◽  
Vol 1 (1) ◽  
pp. 2507-2516
Author(s):  
Dedy Suryadi ◽  
Harrison Kim
Keyword(s):  
Product Design ◽  
Automatic Identification ◽  
Computing Power ◽  
Product Usage ◽  
Customer Reviews ◽  
Customer Preferences ◽  
Context Factors ◽  
Online Customer Reviews ◽  
Product Description ◽  
Usage Context

AbstractThere are three product design contexts that may significantly affect the design of a product and customer preferences towards product attributes, i.e. customer context, market context, and usage context factors. The conventional methods to gather product usage contexts may be costly and time consuming to conduct. As an alternative, this paper aims to automatically identify product usage contexts from publicly available online customer reviews. The proposed methodology consists of Preprocessing, Word Embedding, and Usage Context Clustering stages. The methodology is applied to identify usage contexts from laptop customer reviews, which results in 16 clusters of usage contexts. Furthermore, analyzing the review sentences explains the separation of “playing games” –which is more related to casual gaming, and “gaming rig” –which implies high computing power requirements. Finally, comparing customer review with manufacturer's product description may reveal a discrepancy to be investigated further by product designer, e.g. a customer suggests a laptop for basic use, although the manufacturer's description describes it for heavy use.

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