Mapping the electronic word-of-mouth (eWOM) research: A systematic review and bibliometric analysis

Donthu, Naveen; Kumar, Satish; Pandey, Neeraj; Pandey, Nitesh; Mishra, Akanksha

doi:10.1016/j.jbusres.2021.07.015

Cited by 289 publications

(190 citation statements)

References 130 publications

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“…To do so, this article carries out a study using a bibliometric-content analysis to identify the publication productivity and the intellectual structure of the field. Following the convention of past bibliometric studies (Donthu et al, 2021b , c ; Kumar et al, 2021a , b , c ; Lim et al, 2022b , c ) and the aims of the article, this study addresses the following research questions (RQs): RQ1. What is the publication productivity of research on AI and blockchain integration for business?…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence and Blockchain Integration in Business: Trends from a Bibliometric-Content Analysis

et al. 2022

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Artificial intelligence (AI) and blockchain are the two disruptive technologies emerging from the Fourth Industrial Revolution (IR4.0) that have introduced radical shifts in the industry. The amalgamation of AI and blockchain holds tremendous potential to create new business models enabled through digitalization. Although research on the application and convergence of AI and blockchain exists, our understanding of the utility of its integration for business remains fragmented. To address this gap, this study aims to characterize the applications and benefits of integrated AI and blockchain platforms across different verticals of business. Using bibliometric analysis, this study reveals the most influential articles on the subject based on their publications, citations, and importance in the intellectual network. Using content analysis, this study sheds light on the subject’s intellectual structure, which is underpinned by four major thematic clusters focusing on supply chains, healthcare, secure transactions, and finance and accounting. The study concludes with 10 application areas in business that can benefit from these technologies.

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Section: Introductionmentioning

confidence: 99%

Artificial Intelligence and Blockchain Integration in Business: Trends from a Bibliometric-Content Analysis

et al. 2022

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“…Thus demonstrating that a good EWOM will have a good impact on Consumer Behaviour. This means that the more word-of-mouth conversations through the internet (ewom) the higher one's online shopping behaviour will be (Bigdellou et al, 2022;Donthu et al, 2021). When viewed from the outer loading value of the three dimensions of the ewom, it can be said that the content dimension is the dimension with the highest value.…”

Section: Discussionmentioning

confidence: 99%

“…EWOM defines a medium of communication to share information about a product or service that has been consumed between consumers who do not know each other and meet before. EWOM has an influence on consumer behaviour before a consumer decides to buy a product or service (Donthu et al, 2021;Jimenez & Mendoza, 2013;Zhou et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Ecommerce Strategy Towards Shopee Consumer Behaviour In Online Shopping Through Electronic Word Of Mouth (EWOM) Variables

Astana

2021

IJSSB

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The growing use of internet-based technologies provides enterprise E-commerce with transformative benefits. The success of E-commerce is determined by the behavior of sellers and their decisions about which platform to use between application-based E-commerce or social media. Online shopping through the internet supports consumers to find information about a product or service and make purchases through direct interaction with online retailers. Word of mouth plays an essential role in consumer behavior, especially in online marketing, which supports consumers seeking more references. This study aims to analyze the effect of E-commerce and electronic word of mouth (EWOM) on consumer behavior in online shopping. The study was conducted on 75 respondents, and researchers used SmartPLS 3.0 to examine the relationship between variables in the model through SEM. From the research results obtained, internet marketing strategies directly affect EWOM and consumer behavior, while EWOM also directly affects consumer behavior. E-commerce startups have the most significant direct positive influence on online shopping consumer behavior across all channels. This result means that Shopee should give prime attention to its E-commerce strategy to increase its consumers.

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“…Travelers today increasingly book their holiday accommodation online. An increase in the preference for online booking has coincided with an increase in the power and persuasiveness of online peer reviews [1][2][3][4]. Customers consider peer reviews more independent and trustworthy and tend to rely on them more than information provided by business entities [5].…”

Section: Introductionmentioning

confidence: 99%

Predicting Online Complaining Behavior in the Hospitality Industry: Application of Big Data Analytics to Online Reviews

et al. 2022

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Purpose: This study aims to enrich the published literature on hospitality and tourism by applying big data analytics and data mining algorithms to predict travelers’ online complaint attributions to significantly different hotel classes (i.e., higher star-rating and lower star-rating). Design/methodology/approach: First, 1992 valid online complaints were manually obtained from over 350 hotels located in the UK. The textual data were converted into structured data by utilizing content analysis. Ten complaint attributes and 52 items were identified. Second, a two-step analysis approach was applied via data-mining algorithms. For this study, sensitivity analysis was conducted to identify the most important online complaint attributes, then decision tree models (i.e., the CHAID algorithm) were implemented to discover potential relationships that might exist between complaint attributes in the online complaining behavior of guests from different hotel classes. Findings: Sensitivity analysis revealed that Hotel Size is the most important online complaint attribute, while Service Encounter and Room Space emerged as the second and third most important factors in each of the four decision tree models. The CHAID analysis findings also revealed that guests at higher-star-rating hotels are most likely to leave online complaints about (i) Service Encounter, when staying at large hotels; (ii) Value for Money and Service Encounter, when staying at medium-sized hotels; (iii) Room Space and Service Encounter, when staying at small hotels. Additionally, the guests of lower-star-rating hotels are most likely to write online complaints about Cleanliness, but not Value for Money, Room Space, or Service Encounter, and to stay at small hotels. Practical implications: By utilizing new data-mining algorithms, more profound findings can be discovered and utilized to reinforce the strengths of hotel operations to meet the expectations and needs of their target guests. Originality/value: The study’s main contribution lies in the utilization of data-mining algorithms to predict online complaining behavior between different classes of hotel guests.

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Mapping the electronic word-of-mouth (eWOM) research: A systematic review and bibliometric analysis

Cited by 289 publications

References 130 publications

Artificial Intelligence and Blockchain Integration in Business: Trends from a Bibliometric-Content Analysis

Artificial Intelligence and Blockchain Integration in Business: Trends from a Bibliometric-Content Analysis

Ecommerce Strategy Towards Shopee Consumer Behaviour In Online Shopping Through Electronic Word Of Mouth (EWOM) Variables

Predicting Online Complaining Behavior in the Hospitality Industry: Application of Big Data Analytics to Online Reviews

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