Effect of eWOM Valence on Online Retail Sales

Roy, Gobinda; Datta, Biplab; Basu, Rikmantra

doi:10.1177/0972150916666966

Cited by 52 publications

(40 citation statements)

References 46 publications

(64 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Users feel less confident and threatened with online stores because two-way communication does not exist, and they depend solely on the eWOM. Therefore, eWOM becomes an important guiding tool for online shoppers to shop online (Roy, Datta & Basu 2017). Kim, Ferrin and Rao (2008) examined how trust and risks influence the purchasing decisions of internet users, and it is concluded that among the variables reviewed, perceived risk and website information have a strong impact on purchase decisions.…”

Section: Relationship Between Opr and Opimentioning

confidence: 99%

Online Customer Shopping Behaviour: The Mediating Role of Online Perceived Risk

Yahya¹,

Hashim²,

Husin³

et al. 2020

Global Business Management Review (GBMR)

View full text Add to dashboard Cite

The studies of online purchase intention have been done extensively throughout the world by testing many predictors. Due to the rapid growth of technological advances affecting internet users suggest other new predictors considered as an online purchase intention element. This study suggests customer review, online store image, and perceived online risk (an element of a mediating factor) as three main variables that drive customers to purchase online. A simple convenience sampling method was used to analyse the responses from online users via the online questionnaire web surveys. Multiple regressions and Sobel tests were used in this study to test the hypotheses. The result shows that online customer review and online store image have a positive relationship with the perceived risk online as well as perceived risk itself also has a positive relationship with online purchase intention. Consequently, in terms of mediating factors, perceived risk online is seen as a mediator for both online customer review and online store image with online purchase intention. Having said that, the study hopes that the findings would help boost the online retail industry as a contributor to the country's economic development.

show abstract

Section: Relationship Between Opr and Opimentioning

confidence: 99%

Online Customer Shopping Behaviour: The Mediating Role of Online Perceived Risk

Yahya¹,

Hashim²,

Husin³

et al. 2020

Global Business Management Review (GBMR)

View full text Add to dashboard Cite

show abstract

“…In case of CE research, word-of-mouth positively influences loyalty and product recommendation (Babin, Lee, Kim, & Griffin, 2005;Cetin & Dincer, 2014) With the evolution of online social media, modern customers are now more vocal in expressing their expectations and experiences about product and service through various online communication channels (Verma, 2014). Consequently, word-of-mouth has evolved into eWOM (Hennig-Thurau, Gwinner, Walsh, & Gremler, 2004) which online shoppers use for information search, product comparison and opinion formation (Roy, Datta, & Basu, 2017) while for marketers it is a potent tool for persuasion (Alboqami et al, 2015). eWOM is defined as 'any positive or negative statement made by potential, actual or former customers about a product or company, which is made available to a multitude of people and institutions via the Internet' (Hennig-Thurau et al, 2004).…”

Section: Cognitive Experience Online Shopping (Ceos)mentioning

confidence: 99%

A Framework of Online Customer Experience: An Indian Perspective

Bhattacharya

Srivastava

2018

Global Business Review

View full text Add to dashboard Cite

With the rapid adoption of online retailing in India, retailer focus has undergone a paradigm shift. From service and relationship focus, online retailers now focus on customer experience as a strategic differentiator for sustainable competitive advantage. In this context, the aim of this article is to conceptualize and empirically test an integrated model of online customer experience (OCE) with antecedents, components and outcome variables along with the role of the moderating role of gender using structural equation modelling. Overall, results support the proposed model and confirm the relationships drawn from the literature. The study contributes to knowledge creation in the OCE domain. Implications for both research and practice are discussed along with limitations and future research directions.

show abstract

“…eWOM has been regarded as the most credible source of product information, and potential customers often pay attention to eWOM during purchase decisions [5]. Positive eWOM significantly increase sales [6,7], brand equity [8], and company's reputation [9]; whereas, negative eWOM significantly lowers purchase intentions and brand trust [10]. eWOM is more influential as compared to conventional interpersonal interactions because of high virality, reliability, and anonymity [11].…”

Section: Introductionmentioning

confidence: 99%

Role of Artificial Neural Networks Techniques in Development of Market Intelligence: A Study of Sentiment Analysis of eWOM of a Women’s Clothing Company

Nawaz

Zhao

Nawaz

et al. 2021

JTAER

View full text Add to dashboard Cite

Web 2.0 technology enables customers to share electronic word of mouth (eWOM) about their experiences. eWOM offers great market insights to the organization, and important for organization’s success. eWOM monitoring and management is one of the major contemporary challenges for the organization, because of high volume and frequency of the content. It is nearly impossible for an organization to manually monitor content generated by each user. In this paper, we propose sentiment analysis as an alternative method for analysis of emotions and behavioral intentions in real-time data. Sentiment analysis is performed on women’s e-clothing reviews collected from the Kaggle data repository. The dataset consists of 23,486 reviews, comprising ten feature variables. This study applied artificial neural network techniques to determine polarity of the data in terms of positive or negative. Sentiment analysis was performed by using two artificial neural networks, Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM), to classify the review as recommended (positive) or not recommended (negative). The proposed models have been evaluated on these performance measuring parameters: accuracy, recall, specificity, F1-score and roc-curve. The LSTM method outperformed CNN and achieved classification accuracy of 91.69%, specificity 92.81%, sensitivity 76.95%, and 56.67% F1-score. Based on results of this study, LSTM technique is highly recommended for the sentiment analysis of unstructured text-based user-generated content.

show abstract

Effect of eWOM Valence on Online Retail Sales

Cited by 52 publications

References 46 publications

Online Customer Shopping Behaviour: The Mediating Role of Online Perceived Risk

Online Customer Shopping Behaviour: The Mediating Role of Online Perceived Risk

A Framework of Online Customer Experience: An Indian Perspective

Role of Artificial Neural Networks Techniques in Development of Market Intelligence: A Study of Sentiment Analysis of eWOM of a Women’s Clothing Company

Contact Info

Product

Resources

About